diff --git a/.circleci/config.yml b/.circleci/config.yml new file mode 100644 index 000000000..17b8c14c1 --- /dev/null +++ b/.circleci/config.yml @@ -0,0 +1,63 @@ +defaults: &defaults + working_directory: ~/markovmodel/PyEMMA + docker: + - image: continuumio/miniconda3 + +inst_conda_bld: &inst_conda_bld + - run: conda config --add channels conda-forge + - run: conda config --set always_yes true + - run: conda config --set quiet true + - run: conda install conda-build + +version: 2 + +jobs: + build: + <<: *defaults + parallelism: 1 + steps: + - checkout + - run: git fetch --unshallow || true + - run: apt-get install -y cpp gcc + - run: apt-get install -y libx11-6 python-dev git build-essential + - run: apt-get install -y autoconf automake gcc g++ make gfortran + - run: apt-get install -y python-tables + - run: apt-get install -y libhdf5-serial-dev + + - run: conda config --add channels conda-forge + - run: conda config --set always_yes true + - run: conda config --set quiet true + - run: conda install conda-build + - run: which pip + - run: conda install numpy; + - run: conda install numba; + - run: conda install dask; + - run: pip install tables + - run: pip install scipy==1.5.4 + - run: pip install pip --upgrade; + #- run: python setup.py install + - run: pip install coverage + - run: pip install cython + - run: pip install asciiplotlib; + - run: pip install ipfx + - run: pip install streamlit + - run: pip install sklearn + - run: pip install seaborn + - run: pip install frozendict + - run: pip install plotly + - run: pip install allensdk==0.16.3 + - run: pip install --upgrade colorama + - run: pip install -e . + - run: rm -rf /opt/conda/lib/python3.8/site-packages/sciunit + - run: git clone -b neuronunit https://github.com/russelljjarvis/jit_hub.git + - run: cd jit_hub; pip install -e .; cd ..; + - run: git clone -b neuronunit_reduced_cells https://github.com/russelljjarvis/BluePyOpt.git + - run: cd BluePyOpt; pip install -e . + - run: git clone -b dev https://github.com/russelljjarvis/sciunit.git + + - run: cd sciunit; pip install -e .; cd ..; + - run: pip install git+https://github.com/russelljjarvis/eFEL + + - run: cd neuronunit/unit_test; python -m unittest scores_unit_test.py + - run: cd neuronunit/unit_test; python rheobase_dtc_test.py + - run: cd neuronunit/unit_test; python adexp_opt.py diff --git a/.travis.yml b/.travis.yml index 4130268db..e3e022725 100755 --- a/.travis.yml +++ b/.travis.yml @@ -21,13 +21,19 @@ install: - conda info -a - pip install -U pip - pip install . + - pip install sklearn + - pip install seaborn - pip install coveralls - pip install pylmeasure # required by morphology tests + - sh build.sh + ###################################################### script: - export NC_HOME='.' # NeuroConstruct isn't used but tests need this # variable set to pass. - - sh test.sh + - cd neuronunit/unit_test; python -m unittest scores_unit_test.py; cd -; + - cd neuronunit/unit_test; python -m rheobase_dtc_test.py; cd -; + #- sh test.sh after_success: - coveralls diff --git a/README.md b/README.md index 1c5ed4e80..19f85a870 100644 --- a/README.md +++ b/README.md @@ -1,3 +1,7 @@ +### Circle CI russelljjarvis/optimization build: +[![Build Status](https://circleci.com/gh/russelljjarvis/neuronunit/tree/optimization.svg?style=svg)](https://app.circleci.com/pipelines/github/russelljjarvis/neuronunit/) +### Travis CI scidash/optimization build: +[![Travis](https://travis-ci.org/scidash/neuronunit.svg?branch=optimization)](https://travis-ci.org/scidash/neuronunit?branch=optimization) | Master | Dev | | ------------- | ------------- | | [![Travis](https://travis-ci.org/scidash/neuronunit.svg?branch=master)](https://travis-ci.org/scidash/neuronunit) | [![Travis](https://travis-ci.org/scidash/neuronunit.svg?branch=dev)](https://travis-ci.org/scidash/neuronunit) | diff --git a/build.sh b/build.sh new file mode 100644 index 000000000..fc143ff00 --- /dev/null +++ b/build.sh @@ -0,0 +1,31 @@ +apt-get install -y cpp gcc +apt-get install -y libx11-6 python-dev git build-essential +apt-get install -y autoconf automake gcc g++ make gfortran +apt-get install -y python-tables +apt-get install -y libhdf5-serial-dev +conda install numpy; +conda install numba; +conda install dask; +pip install pip --upgrade; +pip install tables +pip install scipy==1.5.4 +pip install -e . +pip install coverage +git clone -b neuronunit https://github.com/russelljjarvis/jit_hub.git +cd jit_hub; pip install -e .; cd ..; +pip install cython +pip install asciiplotlib; +git clone -b neuronunit_reduced_cells https://github.com/russelljjarvis/BluePyOpt.git +cd BluePyOpt; pip install -e . +pip install git+https://github.com/russelljjarvis/eFEL +pip install ipfx +pip install streamlit +pip install sklearn +pip install seaborn +pip install frozendict +pip install plotly +pip install --upgrade colorama +rm -rf /opt/conda/lib/python3.8/site-packages/sciunit +git clone -b dev https://github.com/russelljjarvis/sciunit.git +cd sciunit; pip install -e .; cd ..; +pip install allensdk==0.16.3 diff --git a/environment.yml b/environment.yml index a1e548ed9..4479afbe6 100644 --- a/environment.yml +++ b/environment.yml @@ -6,7 +6,15 @@ dependencies: - pip: - neo==0.4 - elephant - - scoop - - git+http://github.com/scidash/sciunit@dev#egg=sciunit-1.5.6 - - git+http://github.com/rgerkin/AllenSDK@python3.5#egg=allensdk-0.12.4.1 - - git+http://github.com/rgerkin/pyNeuroML@master#egg=pyneuroml-0.2.3 + - dask + - numba + - streamlit + - sklearn + - seaborn + - frozendict + - plotly + - asciiplotlib + - ipfx + - git+https://github.com/russelljjarvis/jit_jub@neuronunit + - git+https://github.com/russelljjarvis/BluePyOpt@neuronunit_reduced_cells + - git+https://github.com/russelljjarvis/sciunit@dev diff --git a/neuronunit/allenapi/__init__.py b/neuronunit/allenapi/__init__.py new file mode 100644 index 000000000..537314a72 --- /dev/null +++ b/neuronunit/allenapi/__init__.py @@ -0,0 +1,3 @@ +"""Allen API for NeuronUnit""" + +import warnings diff --git a/neuronunit/allenapi/aibs.py b/neuronunit/allenapi/aibs.py new file mode 100755 index 000000000..cc5d572d3 --- /dev/null +++ b/neuronunit/allenapi/aibs.py @@ -0,0 +1,238 @@ +"""NeuronUnit module for interaction with the Allen Brain Insitute +Cell Types database""" +# import logging +# logger = logging.getLogger(name) +# logging.info("test") +import matplotlib as mpl + +try: + mpl.use("agg") +except: + pass +import matplotlib.pyplot as plt +import shelve +import requests +import numpy as np +import quantities as pq +from allensdk.api.queries.cell_types_api import CellTypesApi +from allensdk.core.cell_types_cache import CellTypesCache +from allensdk.api.queries.glif_api import GlifApi +import os +import pickle +from allensdk.api.queries.biophysical_api import BiophysicalApi +from neuronunit.optimization.data_transport_container import DataTC + +# from allensdk.model.glif.glif_neuron import GlifNeuron + +from allensdk.core.cell_types_cache import CellTypesCache +from allensdk.ephys.extract_cell_features import extract_cell_features +from collections import defaultdict +from allensdk.core.nwb_data_set import NwbDataSet + +from neuronunit import models +from neo.core import AnalogSignal +import quantities as qt +from types import MethodType + +from allensdk.ephys.extract_cell_features import extract_cell_features +from collections import defaultdict +from allensdk.core.cell_types_cache import CellTypesCache + +import neo +from elephant.spike_train_generation import threshold_detection +from quantities import mV, ms +from numba import jit +import sciunit +import math +import pdb +from allensdk.ephys.extract_cell_features import extract_cell_features + + +def is_aibs_up(): + """Check whether the AIBS Cell Types Database API is working.""" + url = ( + "http://api.brain-map.org/api/v2/data/query.xml?criteria=model" + "::Specimen,rma::criteria,[id$eq320654829],rma::include," + "ephys_result(well_known_files(well_known_file_type" + "[name$eqNWBDownload]))" + ) + request = requests.get(url) + return request.status_code == 200 + + +def get_observation(dataset_id, kind, cached=True, quiet=False): + """Get an observation. + + Get an observation of kind 'kind' from the dataset with id 'dataset_id'. + optionally using the cached value retrieved previously. + """ + + db = shelve.open("aibs-cache") if cached else {} + identifier = "%d_%s" % (dataset_id, kind) + if identifier in db: + print( + "Getting %s cached data value for from AIBS dataset %s" + % (kind.title(), dataset_id) + ) + value = db[identifier] + else: + print( + "Getting %s data value for from AIBS dataset %s" + % (kind.title(), dataset_id) + ) + ct = CellTypesApi() + cmd = ct.get_cell(dataset_id) # Cell metadata + + if kind == "rheobase": + if "ephys_features" in cmd: + value = cmd["ephys_features"][0]["threshold_i_long_square"] # newer API + else: + value = cmd["ef__threshold_i_long_square"] # older API + + value = np.round(value, 2) # Round to nearest hundredth of a pA. + value *= pq.pA # Apply units. + + else: + value = cmd[kind] + + db[identifier] = value + + if cached: + db.close() + return {"value": value} + + +def get_value_dict(experiment_params, sweep_ids, kind): + """Get a dictionary of data values from the experiment. + + A candidate method for replacing 'get_observation'. + This fix is necessary due to changes in the allensdk. + Warning: Together with 'get_sp' this method may not properly + convey the meaning of 'get_observation'. + """ + + if kind == str("rheobase"): + sp = get_sp(experiment_params, sweep_ids) + value = sp["stimulus_absolute_amplitude"] + value = np.round(value, 2) # Round to nearest hundredth of a pA. + value *= pq.pA # Apply units. + return {"value": value} + + +"""Auxiliary helper functions for analysis of spiking.""" + + +def find_nearest(array, value): + array = np.asarray(array) + idx = (np.abs(array - value)).argmin() + return (array[idx], idx) + + +def inject_square_current(model, current): + if type(current) is type({}): + current = float(current["amplitude"]) + data_set = model.data_set + numbers = data_set.get_sweep_numbers() + injections = [np.max(data_set.get_sweep(sn)["stimulus"]) for sn in numbers] + sns = [sn for sn in numbers] + (nearest, idx) = find_nearest(injections, current) + index = np.asarray(numbers)[idx] + sweep_data = data_set.get_sweep(index) + temp_vm = sweep_data["response"] + injection = sweep_data["stimulus"] + sampling_rate = sweep_data["sampling_rate"] + vm = AnalogSignal(temp_vm, sampling_rate=sampling_rate * qt.Hz, units=qt.V) + model._vm = vm + return model._vm + + +def get_membrane_potential(model): + return model._vm + + +def get_spike_train(vm, threshold=0.0 * mV): + """ + Inputs: + vm: a neo.core.AnalogSignal corresponding to a membrane potential trace. + threshold: the value (in mV) above which vm has to cross for there + to be a spike. Scalar float. + + Returns: + a neo.core.SpikeTrain containing the times of spikes. + """ + spike_train = threshold_detection(vm, threshold=threshold) + return spike_train + + +def get_spike_count(model): + vm = model.get_membrane_potential() + train = get_spike_train(vm) + return len(train) + + +def appropriate_features(): + for s in sweeps: + if s["ramp"]: + print([(k, v) for k, v in s.items()]) + current = {} + current["amplitude"] = s["stimulus_absolute_amplitude"] + current["duration"] = s["stimulus_duration"] + current["delay"] = s["stimulus_start_time"] + + +def get_features(specimen_id=485909730): + data_set = ctc.get_ephys_data(specimen_id) + sweeps = ctc.get_ephys_sweeps(specimen_id) + + # group the sweeps by stimulus + sweep_numbers = defaultdict(list) + for sweep in sweeps: + sweep_numbers[sweep["stimulus_name"]].append(sweep["sweep_number"]) + + # calculate features + cell_features = extract_cell_features( + data_set, + sweep_numbers["Ramp"], + sweep_numbers["Short Square"], + sweep_numbers["Long Square"], + ) + + +def get_sweep_params(dataset_id, sweep_id): + """Get sweep parameters. + + Get those corresponding to the sweep with id 'sweep_id' from + the dataset with id 'dataset_id'. + """ + + ct = CellTypesApi() + experiment_params = ct.get_ephys_sweeps(dataset_id) + sp = None + for sp in experiment_params: + if sp["id"] == sweep_id: + sweep_num = sp["sweep_number"] + if sweep_num is None: + msg = "Sweep with ID %d not found in dataset with ID %d." + raise Exception(msg % (sweep_id, dataset_id)) + break + return sp + + +def get_sp(experiment_params, sweep_ids): + + """Get sweep parameters. + A candidate method for replacing 'get_sweep_params'. + This fix is necessary due to changes in the allensdk. + Warning: This method may not properly convey the original meaning + of 'get_sweep_params'. + """ + + sp = None + for sp in experiment_params: + for sweep_id in sweep_ids: + if sp["id"] == sweep_id: + sweep_num = sp["sweep_number"] + if sweep_num is None: + raise Exception("Sweep with ID %d not found." % sweep_id) + break + return sp diff --git a/neuronunit/allenapi/allen_data_driven.py b/neuronunit/allenapi/allen_data_driven.py new file mode 100644 index 000000000..3be83904d --- /dev/null +++ b/neuronunit/allenapi/allen_data_driven.py @@ -0,0 +1,531 @@ +from typing import Any, Dict, List, Optional, Tuple, Type, Union, Text + +import pickle +import seaborn as sns +import os +import copy +import numpy as np +from collections.abc import Iterable +import pandas as pd +import quantities as pq + +import bluepyopt as bpop +import bluepyopt.ephys as ephys +from bluepyopt.parameters import Parameter + +from sciunit.scores import RelativeDifferenceScore +from sciunit import TestSuite +from sciunit.scores import ZScore +from sciunit.scores.collections import ScoreArray + +from neuronunit.allenapi import make_allen_tests_from_id +from neuronunit.allenapi.make_allen_tests_from_id import * +from neuronunit.allenapi.make_allen_tests import AllenTest + +from neuronunit.optimization.optimization_management import inject_model_soma +from neuronunit.optimization.model_parameters import BPO_PARAMS +from neuronunit.tests import ( + RheobaseTest, + CapacitanceTest, + RestingPotentialTest, + InputResistanceTest, + TimeConstantTest, + FITest +) + + +def opt_setup( + specimen_id, + model_type, + target_num, + template_model=None, + cached=False, + fixed_current=False, + score_type=ZScore, + efel_filter_iterable=None, +): + if cached: + with open(str(specimen_id) + "later_allen_NU_tests.p", "rb") as f: + suite = pickle.load(f) + + else: + + sweep_numbers, data_set, sweeps = make_allen_tests_from_id.allen_id_to_sweeps( + specimen_id + ) + ( + vmm, + stimulus, + sn, + spike_times, + ) = make_allen_tests_from_id.get_model_parts_sweep_from_spk_cnt( + target_num, data_set, sweep_numbers, specimen_id + ) + ( + suite, + specimen_id, + ) = make_allen_tests_from_id.make_suite_known_sweep_from_static_models( + vmm, stimulus, specimen_id, efel_filter_iterable=efel_filter_iterable + ) + with open(str(specimen_id) + "later_allen_NU_tests.p", "wb") as f: + pickle.dump(suite, f) + if "vm_soma" in suite.traces.keys(): + target = StaticModel(vm=suite.traces["vm_soma"]) + target.vm_soma = suite.traces["vm_soma"] + else: + target = StaticModel(vm=suite.traces["vm15"]) + target.vm_soma = suite.traces["vm15"] + + nu_tests = suite.tests + + attrs = {k: np.mean(v) for k, v in MODEL_PARAMS[model_type].items()} + dtc = DataTC(backend=model_type, attrs=attrs) + for t in nu_tests: + if t.name == "Spikecount": + spk_count = float(t.observation["mean"]) + break + observation_range = {} + observation_range["value"] = spk_count + template_model.backend = model_type + template_model.allen = True + template_model.NU = True + if fixed_current: + uc = { + "amplitude": fixed_current, + "duration": ALLEN_DURATION, + "delay": ALLEN_DELAY, + } + target_current = None + else: + scs = SpikeCountSearch(observation_range) + target_current = scs.generate_prediction(template_model) + template_model.seeded_current = target_current["value"] + + return template_model, suite, nu_tests, target_current, spk_count + + +def wrap_setups( + specimen_id, + model_type, + target_num_spikes, + template_model=None, + fixed_current=False, + cached=False, + score_type=ZScore, + efel_filter_iterable=None, +): + """ + if os.path.isfile("325479788later_allen_NU_tests.p"): + template_model, suite, nu_tests, target_current, spk_count = opt_setup( + specimen_id, + model_type, + target_num_spikes, + template_model=template_model, + fixed_current=False, + cached=False, + score_type=score_type, + efel_filter_iterable=efel_filter_iterable + ) + else: + """ + template_model, suite, nu_tests, target_current, spk_count = opt_setup( + specimen_id, + model_type, + target_num_spikes, + template_model=template_model, + fixed_current=False, + cached=None, + score_type=score_type, + efel_filter_iterable=efel_filter_iterable, + ) + template_model.seeded_current = target_current["value"] + template_model.allen = True + template_model.seeded_current + template_model.NU = True + template_model.backend = model_type + template_model.efel_filter_iterable = efel_filter_iterable + + cell_evaluator, template_model = opt_setup_two( + model_type, + suite, + nu_tests, + target_current, + spk_count, + template_model=template_model, + score_type=score_type, + efel_filter_iterable=efel_filter_iterable, + ) + cell_evaluator.cell_model.params = BPO_PARAMS[model_type] + assert cell_evaluator.cell_model is not None + cell_evaluator.suite = None + cell_evaluator.suite = suite + return cell_evaluator, template_model, suite, target_current, spk_count + + +class NUFeatureAllenMultiSpike(object): + def __init__( + self, test, model, cnt, target, spike_obs, print_stuff=False, score_type=None + ): + self.test = test + self.model = model + self.spike_obs = spike_obs + self.cnt = cnt + self.target = target + self.score_type = score_type + self.score_array = None + + def calculate_score(self, responses): + if not "features" in responses.keys(): + return 1000.0 + features = responses["features"] + if features is None: + return 1000.0 + self.test.score_type = self.score_type + feature_name = self.test.name + if feature_name not in features.keys(): + return 1000.0 + + if features[feature_name] is None: + return 1000.0 + if type(features[self.test.name]) is type(Iterable): + features[self.test.name] = np.mean(features[self.test.name]) + + self.test.observation["mean"] = np.mean(self.test.observation["mean"]) + self.test.set_prediction(np.mean(features[self.test.name])) + if "Spikecount" == feature_name: + delta = np.abs( + features[self.test.name] - np.mean(self.test.observation["mean"]) + ) + delta += delta + delta += delta + + if np.nan == delta or delta == np.inf: + delta = 1000.0 + #print(self.test.name,'delta',delta) + + return delta + else: + if features[feature_name] is None: + return 1000.0 + + prediction = {"value": np.mean(features[self.test.name])} + score_gene = self.test.judge(responses["model"], prediction=prediction) + if score_gene is not None: + if score_gene.raw is not None: + delta = np.abs(float(score_gene.raw)) + else: + delta = 1000.0 + else: + delta = 1000.0 + #if np.nan == delta or delta == np.inf: + # delta = np.abs(float(score_gene.raw)) + if np.nan == delta or delta == np.inf: + delta = 1000.0 + #print(self.test.name,'delta',delta) + + return delta + + +def opt_setup_two( + model_type, + suite, + nu_tests, + target_current, + spk_count, + template_model=None, + score_type=ZScore, + efel_filter_iterable=None, +): + assert template_model.backend == model_type + template_model.params = BPO_PARAMS[model_type] + template_model.params_by_names(list(BPO_PARAMS[model_type].keys())) + template_model.seeded_current = target_current["value"] + template_model.spk_count = spk_count + sweep_protocols = [] + protocol = ephys.protocols.NeuronUnitAllenStepProtocol( + "multi_spiking", [None], [None] + ) + sweep_protocols.append(protocol) + onestep_protocol = ephys.protocols.SequenceProtocol( + "multi_spiking_wraper", protocols=sweep_protocols + ) + objectives = [] + spike_obs = [] + for tt in nu_tests: + if "Spikecount" == tt.name: + spike_obs.append(tt.observation) + spike_obs = sorted(spike_obs, key=lambda k: k["mean"], reverse=True) + for cnt, tt in enumerate(nu_tests): + feature_name = "%s" % (tt.name) + ft = NUFeatureAllenMultiSpike( + tt, template_model, cnt, target_current, spike_obs, score_type=score_type + ) + objective = ephys.objectives.SingletonObjective(feature_name, ft) + objectives.append(objective) + score_calc = ephys.objectivescalculators.ObjectivesCalculator(objectives) + template_model.params_by_names(BPO_PARAMS[template_model.backend].keys()) + template_model.efel_filter_iterable = efel_filter_iterable + cell_evaluator = ephys.evaluators.CellEvaluator( + cell_model=template_model, + param_names=list(BPO_PARAMS[template_model.backend].keys()), + fitness_protocols={onestep_protocol.name: onestep_protocol}, + fitness_calculator=score_calc, + sim="euler", + ) + assert cell_evaluator.cell_model is not None + return cell_evaluator, template_model + + + + +def opt_exec(MU, NGEN, mapping_funct, cell_evaluator, mutpb=0.1, cxpb=1,neuronunit=False):# was 0.625): # was 0.6 + + optimisation = bpop.optimisations.DEAPOptimisation( + evaluator=cell_evaluator, + offspring_size=MU, + eta=29, #was 35, # was 25 + map_function=map, + selector_name="IBEA", + mutpb=mutpb, + cxpb=cxpb, + neuronunit=neuronunit + ) + final_pop, hall_of_fame, logs, hist = optimisation.run(max_ngen=NGEN) + return final_pop, hall_of_fame, logs, hist + + +def opt_to_model(hall_of_fame, cell_evaluator, suite, target_current, spk_count): + best_ind = hall_of_fame[0] + cell_evaluator.evaluate_with_lists(best_ind) + model = cell_evaluator.cell_model + tests = cell_evaluator.suite.tests + scores = [] + obs_preds = [] + + for t in tests: + scores.append(t.judge(model, prediction=t.prediction)) + obs_preds.append( + (t.name, t.observation["mean"], t.prediction["mean"], scores[-1]) + ) + df = pd.DataFrame(obs_preds) + + opt = model.model_to_dtc() + opt.attrs = { + str(k): float(v) for k, v in cell_evaluator.param_dict(best_ind).items() + } + #for k,v in cell_evaluator.cell_model.attrs.items(): + # print(opt.attrs[k],'opt attrs',v,'in cell evaluator') + #assert opt.attrs[k] == v + print(best_ind,'the gene') + target = copy.copy(opt) + if "vm_soma" in suite.traces.keys(): + target.vm_soma = suite.traces["vm_soma"] + # else: # backwards compatibility + # target.vm_soma = suite.traces["vm15"] + opt.seeded_current = target_current["value"] + opt.spk_count = spk_count + #opt = opt.attrs_to_params() + + target.seeded_current = target_current["value"] + target.spk_count = spk_count + _, _, _, _, target = inject_model_soma( + target, solve_for_current=target_current["value"] + ) + _, _, _, _, opt = inject_model_soma(opt, solve_for_current=target_current["value"]) + + return opt, target, scores, obs_preds, df + + +def make_allen_hard_coded_complete(): + """ + Manually specificy 4-5 + different passive/static electrical properties + over 4 Allen specimen id's. + FITest + 623960880 + 623893177 + 471819401 + 482493761 + """ + from neuronunit.optimization.optimization_management import TSD + + ## + # 623960880 + ## + rt = RheobaseTest(observation={"mean": 70 * qt.pA, "std": 70 * qt.pA}) # yes + tc = TimeConstantTest(observation={"mean": 23.8 * qt.ms, "std": 23.8 * qt.ms}) + ir = InputResistanceTest(observation={"mean": 241 * qt.MOhm, "std": 241 * qt.MOhm}) + rp = RestingPotentialTest(observation={"mean": -65.1 * qt.mV, "std": 65.1 * qt.mV}) + + # capacitance = ((tc.observation['mean']))/((ir.observation['mean']))#*qt.pF + + # ct = CapacitanceTest(observation={'mean':capacitance,'std':capacitance}) + fislope = FITest( + observation={"value": 0.18 * (pq.Hz / pq.pA), "mean": 0.18 * (pq.Hz / pq.pA)} + ) + fislope.score_type = RelativeDifferenceScore + + allen_tests = [tc, rp, ir, rt] + for t in allen_tests: + t.score_type = RelativeDifferenceScore + allen_suite_623960880 = TestSuite(allen_tests) + allen_suite_623960880.name = ( + "http://celltypes.brain-map.org/mouse/experiment/electrophysiology/623960880" + ) + + ## + # ID 623893177 + ## + rt = RheobaseTest(observation={"mean": 190 * qt.pA, "std": 190 * qt.pA}) # yes + tc = TimeConstantTest(observation={"mean": 27.8 * qt.ms, "std": 27.8 * qt.ms}) + ir = InputResistanceTest(observation={"mean": 136 * qt.MOhm, "std": 136 * qt.MOhm}) + rp = RestingPotentialTest(observation={"mean": -77.0 * qt.mV, "std": 77.0 * qt.mV}) + + capacitance = ((tc.observation["mean"])) / ((ir.observation["mean"])) # *qt.pF + + ct = CapacitanceTest(observation={"mean": capacitance, "std": capacitance}) + + ## + fislope = FITest( + observation={"value": 0.12 * (pq.Hz / pq.pA), "mean": 0.12 * (pq.Hz / pq.pA)} + ) + fislope.score_type = RelativeDifferenceScore + ## + + allen_tests = [tc, rp, ir, rt] + for t in allen_tests: + t.score_type = RelativeDifferenceScore + allen_suite_623893177 = TestSuite(allen_tests) + allen_suite_623893177.name = ( + "http://celltypes.brain-map.org/mouse/experiment/electrophysiology/623893177" + ) + cells = {} + cells["623960880"] = TSD(allen_suite_623960880) + cells["623893177"] = TSD(allen_suite_623893177) + + ## + # 482493761 + ## + + rt = RheobaseTest(observation={"mean": 70 * qt.pA, "std": 70 * qt.pA}) # yes + tc = TimeConstantTest(observation={"mean": 24.4 * qt.ms, "std": 24.4 * qt.ms}) + ir = InputResistanceTest(observation={"mean": 132 * qt.MOhm, "std": 132 * qt.MOhm}) + rp = RestingPotentialTest(observation={"mean": -71.6 * qt.mV, "std": 71.6 * qt.mV}) + + fislope = FITest( + observation={"value": 0.09 * (pq.Hz / pq.pA), "mean": 0.09 * (pq.Hz / pq.pA)} + ) + fislope.score_type = RelativeDifferenceScore + + allen_tests = [rt, tc, rp, ir] + for t in allen_tests: + t.score_type = RelativeDifferenceScore + # allen_tests[-1].score_type = ZScore + allen_suite482493761 = TestSuite(allen_tests) + allen_suite482493761.name = ( + "http://celltypes.brain-map.org/mouse/experiment/electrophysiology/482493761" + ) + ## + # 471819401 + ## + rt = RheobaseTest(observation={"mean": 190 * qt.pA, "std": 190 * qt.pA}) # yes + tc = TimeConstantTest(observation={"mean": 13.8 * qt.ms, "std": 13.8 * qt.ms}) + ir = InputResistanceTest(observation={"mean": 132 * qt.MOhm, "std": 132 * qt.MOhm}) + rp = RestingPotentialTest(observation={"mean": -77.5 * qt.mV, "std": 77.5 * qt.mV}) + + fislope = FITest( + observation={"value": 0.18 * (pq.Hz / pq.pA), "mean": 0.18 * (pq.Hz / pq.pA)} + ) + fislope.score_type = RelativeDifferenceScore + + # F/I Curve Slope 0.18 + allen_tests = [rt, tc, rp, ir] + for t in allen_tests: + t.score_type = RelativeDifferenceScore + allen_suite471819401 = TestSuite(allen_tests) + allen_suite471819401.name = ( + "http://celltypes.brain-map.org/mouse/experiment/electrophysiology/471819401" + ) + list_of_dicts = [] + cells["471819401"] = TSD(allen_suite471819401) + cells["482493761"] = TSD(allen_suite482493761) + + for k, v in cells.items(): + observations = {} + for k1 in cells["623960880"].keys(): + vsd = TSD(v) + if k1 in vsd.keys(): + vsd[k1].observation["mean"] + + observations[k1] = np.round(vsd[k1].observation["mean"], 2) + observations["name"] = k + list_of_dicts.append(observations) + df = pd.DataFrame(list_of_dicts, index=cells.keys()) + df + + return ( + allen_suite_623960880, + allen_suite_623893177, + allen_suite471819401, + allen_suite482493761, + cells, + df, + ) + + +def make_allen_hard_coded_limited(): + # hard/hand code ephys constraints + from quantities import Hz, pA + from neuronunit.optimization.optimization_management import TSD + import pandas as pd + + rt = RheobaseTest(observation={"mean": 70 * qt.pA, "std": 70 * qt.pA}) + tc = TimeConstantTest(observation={"mean": 24.4 * qt.ms, "std": 24.4 * qt.ms}) + ir = InputResistanceTest(observation={"mean": 132 * qt.MOhm, "std": 132 * qt.MOhm}) + rp = RestingPotentialTest(observation={"mean": -71.6 * qt.mV, "std": 77.5 * qt.mV}) + + allen_tests = [rt, tc, rp, ir] + for t in allen_tests: + t.score_type = RelativeDifferenceScore + allen_suite482493761 = TestSuite(allen_tests) + allen_suite482493761.name = ( + "http://celltypes.brain-map.org/mouse/experiment/electrophysiology/482493761" + ) + rt = RheobaseTest(observation={"mean": 190 * qt.pA, "std": 190 * qt.pA}) + tc = TimeConstantTest(observation={"mean": 13.8 * qt.ms, "std": 13.8 * qt.ms}) + ir = InputResistanceTest(observation={"mean": 132 * qt.MOhm, "std": 132 * qt.MOhm}) + rp = RestingPotentialTest(observation={"mean": -77.5 * qt.mV, "std": 77.5 * qt.mV}) + fi = FITest(observation={"mean": 0.09 * Hz / pA}) #'std':77.5*qt.mV}) + + allen_tests = [rt, tc, rp, ir] # ,fi] + for t in allen_tests: + t.score_type = RelativeDifferenceScore + # allen_tests[-1].score_type = ZScore + allen_suite471819401 = TestSuite(allen_tests) + allen_suite471819401.name = ( + "http://celltypes.brain-map.org/mouse/experiment/electrophysiology/471819401" + ) + list_of_dicts = [] + cells = {} + cells["471819401"] = TSD(allen_suite471819401) + cells["482493761"] = TSD(allen_suite482493761) + + allen_tests = [rt, fi] + for t in allen_tests: + t.score_type = RelativeDifferenceScore + allen_suite3 = TestSuite(allen_tests) + + cells["fi_curve"] = TSD(allen_suite3) + + for k, v in cells.items(): + observations = {} + for k1 in cells["482493761"].keys(): + vsd = TSD(v) + if k1 in vsd.keys(): + vsd[k1].observation["mean"] + + observations[k1] = np.round(vsd[k1].observation["mean"], 2) + observations["name"] = k + list_of_dicts.append(observations) + df = pd.DataFrame(list_of_dicts) + return allen_suite471819401, allen_suite482493761, df, cells diff --git a/neuronunit/allenapi/allen_data_efel_features_opt.py b/neuronunit/allenapi/allen_data_efel_features_opt.py new file mode 100644 index 000000000..87384a44e --- /dev/null +++ b/neuronunit/allenapi/allen_data_efel_features_opt.py @@ -0,0 +1,359 @@ +from typing import Any, Dict, List, Optional, Tuple, Type, Union, Text + +import pickle +import seaborn as sns +import os +from scipy.interpolate import interp1d + +import bluepyopt as bpop +import bluepyopt.ephys as ephys +from bluepyopt.parameters import Parameter + +import matplotlib.pyplot as plt +import copy +import numpy as np +from collections.abc import Iterable +import pandas as pd + +from sciunit.scores import RelativeDifferenceScore +from sciunit import TestSuite +from sciunit.scores import ZScore +from sciunit.scores.collections import ScoreArray + +from neuronunit.allenapi import make_allen_tests_from_id +from neuronunit.allenapi.make_allen_tests_from_id import * +from neuronunit.allenapi.make_allen_tests import AllenTest +from neuronunit.optimization.optimization_management import inject_model_soma +from neuronunit.optimization.model_parameters import BPO_PARAMS + +def match_current_amp_to_model_param(spk_count, + model_type, + template_model, + fixed_current): + observation_range = {} + observation_range["value"] = spk_count + if fixed_current: + uc = { + "amplitude": fixed_current, + "duration": ALLEN_DURATION, + "delay": ALLEN_DELAY, + } + target_current = None + else: + scs = SpikeCountSearch(observation_range) + target_current = scs.generate_prediction(template_model) + return target_current + +def opt_setup( + specimen_id, + model_type, + target_num, + template_model=None, + cached=None, + fixed_current=False, + score_type=ZScore, + efel_filter_iterable=None, +): + if cached: + with open(str(specimen_id) + "later_allen_NU_tests.p", "rb") as f: + suite = pickle.load(f) + + else: + + sweep_numbers, data_set, sweeps = make_allen_tests_from_id.allen_id_to_sweeps( + specimen_id + ) + ( + vmm, + stimulus, + sn, + spike_times, + ) = make_allen_tests_from_id.get_model_parts_sweep_from_spk_cnt( + target_num, data_set, sweep_numbers, specimen_id + ) + ( + suite, + specimen_id, + ) = make_allen_tests_from_id.make_suite_known_sweep_from_static_models( + vmm, stimulus, specimen_id, efel_filter_iterable=efel_filter_iterable + ) + with open(str(specimen_id) + "later_allen_NU_tests.p", "wb") as f: + pickle.dump(suite, f) + if "vm_soma" in suite.traces.keys(): + target = StaticModel(vm=suite.traces["vm_soma"]) + target.vm_soma = suite.traces["vm_soma"] + else: + target = StaticModel(vm=suite.traces["vm15"]) + target.vm_soma = suite.traces["vm15"] + + nu_tests = suite.tests + + attrs = {k: np.mean(v) for k, v in MODEL_PARAMS[model_type].items()} + dtc = DataTC(backend=model_type, attrs=attrs) + for t in nu_tests: + if t.name == "Spikecount": + spk_count = float(t.observation["mean"]) + break + + template_model.backend = model_type + template_model.allen = True + template_model.NU = True + target_current = match_current_amp_to_model_param(spk_count, + model_type, + template_model, + fixed_current) + template_model.seeded_current = target_current["value"] + + cell_evaluator, template_model = opt_setup_two( + model_type, + suite, + nu_tests, + target_current, + spk_count, + template_model=template_model, + score_type=score_type, + efel_filter_iterable=efel_filter_iterable + ) + return suite, target_current, spk_count, cell_evaluator, template_model + + +class NUFeatureAllenMultiSpike(object): + def __init__( + self, test, model, cnt, target, spike_obs, print_stuff=False, score_type=None + ): + self.test = test + self.model = model + self.spike_obs = spike_obs + self.cnt = cnt + self.target = target + self.score_type = score_type + self.score_array = None + + def calculate_score(self, responses): + if not "features" in responses.keys(): + return 1000.0 + features = responses["features"] + if features is None: + return 1000.0 + self.test.score_type = self.score_type + feature_name = self.test.name + if feature_name not in features.keys(): + return 1000.0 + + if features[feature_name] is None: + return 1000.0 + if type(features[self.test.name]) is type(Iterable): + features[self.test.name] = np.mean(features[self.test.name]) + + self.test.observation["mean"] = np.mean(self.test.observation["mean"]) + self.test.set_prediction(np.mean(features[self.test.name])) + if "Spikecount" == feature_name: + delta = np.abs( + features[self.test.name] - np.mean(self.test.observation["mean"]) + ) + if np.nan == delta or delta == np.inf: + delta = 1000.0 + return delta + else: + if features[feature_name] is None: + return 1000.0 + + prediction = {"value": np.mean(features[self.test.name])} + score_gene = self.test.judge(responses["model"], prediction=prediction) + if score_gene is not None: + if self.test.score_type is RelativeDifferenceScore: + if score_gene.log_norm_score is not None: + delta = np.abs(float(score_gene.log_norm_score)) + else: + if score_gene.raw is not None: + delta = np.abs(float(score_gene.raw)) + else: + delta = 1000.0 + + else: + if score_gene.raw is not None: + delta = np.abs(float(score_gene.raw)) + else: + delta = 1000.0 + else: + delta = 1000.0 + if np.nan == delta or delta == np.inf: + delta = 1000.0 + return delta + + +def opt_setup_two( + model_type, + suite, + nu_tests, + target_current, + spk_count, + template_model=None, + score_type=ZScore, + efel_filter_iterable=None +): + assert template_model.backend == model_type + template_model.params = BPO_PARAMS[model_type] + template_model.params_by_names(list(BPO_PARAMS[model_type].keys())) + template_model.seeded_current = target_current["value"] + template_model.spk_count = spk_count + sweep_protocols = [] + protocol = ephys.protocols.NeuronUnitAllenStepProtocol( + "multi_spiking", [None], [None] + ) + sweep_protocols.append(protocol) + onestep_protocol = ephys.protocols.SequenceProtocol( + "multi_spiking_wraper", protocols=sweep_protocols + ) + objectives = [] + spike_obs = [] + for tt in nu_tests: + if "Spikecount" == tt.name: + spike_obs.append(tt.observation) + spike_obs = sorted(spike_obs, key=lambda k: k["mean"], reverse=True) + for cnt, tt in enumerate(nu_tests): + feature_name = "%s" % (tt.name) + ft = NUFeatureAllenMultiSpike( + tt, template_model, cnt, target_current, spike_obs, score_type=score_type + ) + objective = ephys.objectives.SingletonObjective(feature_name, ft) + objectives.append(objective) + score_calc = ephys.objectivescalculators.ObjectivesCalculator(objectives) + template_model.params_by_names(BPO_PARAMS[template_model.backend].keys()) + template_model.efel_filter_iterable = efel_filter_iterable + cell_evaluator = ephys.evaluators.CellEvaluator( + cell_model=template_model, + param_names=list(BPO_PARAMS[template_model.backend].keys()), + fitness_protocols={onestep_protocol.name: onestep_protocol}, + fitness_calculator=score_calc, + sim="euler", + ) + assert cell_evaluator.cell_model is not None + return cell_evaluator, template_model + + +""" +def multi_layered(MU, NGEN, mapping_funct, cell_evaluator2): + optimisation = bpop.optimisations.DEAPOptimisation( + evaluator=cell_evaluator2, + offspring_size=MU, + map_function=map, + selector_name="IBEA", + mutpb=0.05, + cxpb=0.6, + current_fixed=from_outer, + ) + final_pop, hall_of_fame, logs, hist = optimisation.run(max_ngen=NGEN) + return final_pop, hall_of_fame, logs, hist +""" + + +def opt_exec(MU, NGEN, mapping_funct, cell_evaluator, mutpb=0.05, cxpb=0.6): + + optimisation = bpop.optimisations.DEAPOptimisation( + evaluator=cell_evaluator, + offspring_size=MU, + eta=25, + map_function=map, + selector_name="IBEA", + mutpb=mutpb, + cxpb=cxpb, + neuronunit=True, + ) + final_pop, hall_of_fame, logs, hist = optimisation.run(max_ngen=NGEN) + return final_pop, hall_of_fame, logs, hist + + +def opt_to_model(hall_of_fame, cell_evaluator, suite, target_current, spk_count): + best_ind = hall_of_fame[0] + model = cell_evaluator.cell_model + tests = suite.tests + scores = [] + obs_preds = [] + + for t in tests: + scores.append(t.judge(model, prediction=t.prediction)) + obs_preds.append( + (t.name, t.observation["mean"], t.prediction["mean"], scores[-1]) + ) + df = pd.DataFrame(obs_preds) + + opt = model.model_to_dtc() + opt.attrs = { + str(k): float(v) for k, v in cell_evaluator.param_dict(best_ind).items() + } + target = copy.copy(opt) + if "vm_soma" in suite.traces.keys(): + target.vm_soma = suite.traces["vm_soma"] + else: # backwards compatibility + target.vm_soma = suite.traces["vm15"] + opt.seeded_current = target_current["value"] + opt.spk_count = spk_count + opt = opt.attrs_to_params() + + target.seeded_current = target_current["value"] + target.spk_count = spk_count + _, _, _, _, target = inject_model_soma( + target, solve_for_current=target_current["value"] + ) + _, _, _, _, opt = inject_model_soma(opt, solve_for_current=target_current["value"]) + + return opt, target, scores, obs_preds, df + + + +def downsample(array, npts): + interpolated = interp1d( + np.arange(len(array)), array, axis=0, fill_value="extrapolate" + ) + downsampled = interpolated(np.linspace(0, len(array), npts)) + return downsampled + + +def make_stim_waves_func(): + import allensdk.core.json_utilities as json_utilities + import pickle + + neuronal_model_id = 566302806 + # download model metadata + try: + ephys_sweeps = json_utilities.read("ephys_sweeps.json") + except: + from allensdk.api.queries.glif_api import GlifApi + + glif_api = GlifApi() + nm = glif_api.get_neuronal_models_by_id([neuronal_model_id])[0] + + from allensdk.core.cell_types_cache import CellTypesCache + + # download information about the cell + ctc = CellTypesCache() + ctc.get_ephys_data(nm["specimen_id"], file_name="stimulus.nwb") + ctc.get_ephys_sweeps(nm["specimen_id"], file_name="ephys_sweeps.json") + ephys_sweeps = json_utilities.read("ephys_sweeps.json") + + ephys_file_name = "stimulus.nwb" + sweep_numbers = [ + s["sweep_number"] for s in ephys_sweeps if s["stimulus_units"] == "Amps" + ] + stimulus = [ + s + for s in ephys_sweeps + if s["stimulus_units"] == "Amps" + if s["num_spikes"] != None + if s["stimulus_name"] != "Ramp" and s["stimulus_name"] != "Short Square" + ] + amplitudes = [s["stimulus_absolute_amplitude"] for s in stimulus] + durations = [s["stimulus_duration"] for s in stimulus] + expeceted_spikes = [s["num_spikes"] for s in stimulus] + delays = [s["stimulus_start_time"] for s in stimulus] + sn = [s["sweep_number"] for s in stimulus] + make_stim_waves = {} + for i, j in enumerate(sn): + make_stim_waves[j] = {} + make_stim_waves[j]["amplitude"] = amplitudes[i] + make_stim_waves[j]["delay"] = delays[i] + make_stim_waves[j]["durations"] = durations[i] + make_stim_waves[j]["expeceted_spikes"] = expeceted_spikes[i] + pickle.dump(make_stim_waves, open("waves.p", "wb")) + return make_stim_waves diff --git a/neuronunit/allenapi/make_allen_tests.py b/neuronunit/allenapi/make_allen_tests.py new file mode 100644 index 000000000..b741a58de --- /dev/null +++ b/neuronunit/allenapi/make_allen_tests.py @@ -0,0 +1,75 @@ +from neuronunit.tests.base import VmTest +import pickle +import numpy as np +from allensdk.core.cell_types_cache import CellTypesCache +from neuronunit.optimization.data_transport_container import DataTC + +from neuronunit.optimization.optimization_management import ( + multi_spiking_feature_extraction, +) +from sciunit.scores import RelativeDifferenceScore + + +class AllenTest(VmTest): + def __init__( + self, + observation={"mean": None, "std": None}, + name="generic_allen", + prediction={"mean": None, "std": None}, + ): + super(AllenTest, self).__init__(observation, name) + self.name = name + self.score_type = RelativeDifferenceScore + self.observation = observation + self.prediction = prediction + + aliases = "" + + def generate_prediction(self, model=None): + if self.prediction is None: + dtc = DataTC() + dtc.backed = model.backend + dtc.attrs = model.attrs + dtc.rheobase = model.rheobase + dtc.tests = [self] + dtc = multi_spiking_feature_extraction(dtc) + dtc, ephys0 = allen_wave_predictions(dtc, thirty=True) + dtc, ephys1 = allen_wave_predictions(dtc, thirty=False) + if self.name in ephys0.keys(): + feature = ephys0[self.name] + self.prediction = {} + self.prediction["value"] = feature + # self.prediction['std'] = feature + if self.name in ephys1.keys(): + feature = ephys1[self.name] + self.prediction = {} + self.prediction["value"] = feature + # self.prediction['std'] = feature + return self.prediction + # ephys1.update() + # if not len(self.prediction.keys()): + + def compute_params(self): + self.params["t_max"] = ( + self.params["delay"] + self.params["duration"] + self.params["padding"] + ) + + # @property + # def prediction(self): + # return self._prediction + + # @property + # def observation(self): + # return self._observation + + # @observation.setter + def set_observation(self, value): + self.observation = {} + self.observation["mean"] = value + self.observation["std"] = value + + # @prediction.setter + def set_prediction(self, value): + self.prediction = {} + self.prediction["mean"] = value + self.prediction["std"] = value diff --git a/neuronunit/allenapi/make_allen_tests_from_id.py b/neuronunit/allenapi/make_allen_tests_from_id.py new file mode 100644 index 000000000..e0520da3c --- /dev/null +++ b/neuronunit/allenapi/make_allen_tests_from_id.py @@ -0,0 +1,316 @@ +#!/usr/bin/env python +# coding: utf-8 +from typing import Any, Dict, List, Optional, Tuple, Type, Union, Text + +from allensdk.core.cell_types_cache import CellTypesCache +from allensdk.ephys.extract_cell_features import extract_cell_features +from allensdk.core.nwb_data_set import NwbDataSet + +from collections import defaultdict +import numpy as np +import matplotlib.pyplot as plt +import pickle + +from sciunit import TestSuite + +from neuronunit.optimization.model_parameters import MODEL_PARAMS +from neuronunit.optimization.optimization_management import efel_evaluation +from neuronunit.models import StaticModel +from neuronunit.tests.make_allen_tests import AllenTest + +from neuronunit.tests.target_spike_current import SpikeCountSearch +from neuronunit.optimization.data_transport_container import DataTC +from neuronunit.optimization.optimization_management import dtc_to_rheo +from neo.core import AnalogSignal +import quantities as qt + + +from elephant.spike_train_generation import threshold_detection + + +def allen_id_to_sweeps(specimen_id: int = -1) -> Union[defaultdict, Any, List]: + ctc = CellTypesCache(manifest_file="cell_types/manifest.json") + + specimen_id = int(specimen_id) + data_set = ctc.get_ephys_data(specimen_id) + sweeps = ctc.get_ephys_sweeps(specimen_id) + sweep_numbers = defaultdict(list) + for sweep in sweeps: + sweep_numbers[sweep["stimulus_name"]].append(sweep["sweep_number"]) + return sweep_numbers, data_set, sweeps + + +def closest(lst, K): + lst = np.asarray(lst) + idx = (np.abs(lst - K)).argmin() + return idx + + +from elephant.statistics import isi +from sklearn.linear_model import LinearRegression + + +def sweeps_build_fi_tests(data_set, sweep_numbers, specimen_id): + sweep_numbers = sweep_numbers["Square - 2s Suprathreshold"] + rheobase = -1 + above_threshold_sn = [] + currents = {} + relation_map = {} + for sn in sweep_numbers: + + spike_times = data_set.get_spike_times(sn) + + if len(spike_times) > 1: + isi_ = isi(spike_times) + rate = 1.0 / np.mean(isi_) + sweep_data = data_set.get_sweep(sn) + + current_stim = np.max(sweep_data["stimulus"]) + relation_map[rate] = current_stim + model = LinearRegression() + x = np.array(list(relation_map.keys())).reshape(-1, 1) + y = np.array(list(relation_map.values())).reshape(-1, 1) + if len(x): + model.fit(x, y) + m = model.coef_ * (qt.Hz / qt.pA) + if type(m) is type(list()): + m = m[0] + slope = m + plot = False + if plot: + c = model.intercept_ + y = supra_thresh_I * float(m) + c + plt.figure() # new figure + plt.plot(supra_thresh_I, fr) + plt.plot(supra_thresh_I, y) + plt.xlabel("Amplitude of Injecting step current (pA)") + plt.ylabel("Firing rate (Hz)") + plt.grid() + plt.show() + else: + slope = None + return slope + + +def get_rheobase(numbers, sets): + rheobase_numbers = [ + sweep_number + for sweep_number in numbers + if len(sets.get_spike_times(sweep_number)) == 1 + ] + sweeps = [sets.get_sweep(n) for n in rheobase_numbers] + temp = [ + (i, np.max(s["stimulus"])) + for i, s in zip(rheobase_numbers, sweeps) + if "stimulus" in s.keys() + ] # if np.min(s['stimulus'])>0 ] + temp = sorted(temp, key=lambda x: [1], reverse=True) + rheobase = temp[0][1] + index = temp[0][0] + return rheobase, index + + +def get_model_parts(data_set, sweep_numbers, specimen_id): + sweep_numbers = sweep_numbers["Square - 2s Suprathreshold"] + rheobase = -1 + above_threshold_sn = [] + currents = {} + # this_cnt_scheme = 0 + for sn in sweep_numbers: + sweep_data = data_set.get_sweep(sn) + + spike_times = data_set.get_spike_times(sn) + + # stimulus is a numpy array in amps + stimulus = sweep_data["stimulus"] + + if len(spike_times) == 1: + if np.max(stimulus) > rheobase and rheobase == -1: + rheobase = np.max(stimulus) + stim = rheobase + currents["rh"] = stim + sampling_rate = sweep_data["sampling_rate"] + vmrh = AnalogSignal( + [v * 1000 for v in sweep_data["response"]], + sampling_rate=sampling_rate * qt.Hz, + units=qt.mV, + ) + vmrh = vmrh[0 : int(len(vmrh) / 2.1)] + if len(spike_times) >= 1: + reponse = sweep_data["response"] + sampling_rate = sweep_data["sampling_rate"] + vmm = AnalogSignal( + [v * 1000 for v in sweep_data["response"]], + sampling_rate=sampling_rate * qt.Hz, + units=qt.mV, + ) + vmm = vmm[0 : int(len(vmm) / 2.1)] + above_threshold_sn.append((np.max(stimulus), sn, vmm)) + if rheobase == -1: + rheobase = above_threshold_sn[0][0] + vmrh = above_threshold_sn[0][2] + myNumber = 3.0 * rheobase + currents_ = [t[0] for t in above_threshold_sn] + indexvm30 = closest(currents_, myNumber) + stim = above_threshold_sn[indexvm30][0] + currents["30"] = stim + vm30 = above_threshold_sn[indexvm30][2] + myNumber = 1.5 * rheobase + currents_ = [t[0] for t in above_threshold_sn] + indexvm15 = closest(currents_, myNumber) + stim = above_threshold_sn[indexvm15][0] + currents["15"] = stim + vm15 = above_threshold_sn[indexvm15][2] + vm15.sn = None + vm15.sn = above_threshold_sn[0][1] + vm15.specimen_id = None + vm15.specimen_id = specimen_id + + del sweep_numbers + del data_set + return vm15, vm30, rheobase, currents, vmrh + + +def get_model_parts_sweep_from_spk_cnt(spk_cnt, data_set, sweep_numbers, specimen_id): + sweep_numbers = sweep_numbers["Square - 2s Suprathreshold"] + rheobase = -1 + above_threshold_sn = [] + + for sn in sweep_numbers: + spike_times = data_set.get_spike_times(sn) + if len(spike_times) >= spk_cnt: + # stimulus is a numpy array in amps + sweep_data = data_set.get_sweep(sn) + stimulus = sweep_data["stimulus"] + reponse = sweep_data["response"] + + if len(spike_times): + thresh_ = len(np.where(reponse > 0)) + + sampling_rate = sweep_data["sampling_rate"] + vmm = AnalogSignal( + [v * 1000 for v in reponse], + sampling_rate=sampling_rate * qt.Hz, + units=qt.mV, + ) + # Reduce the recording to a smaller time window when interesting things + # happen + vmm = vmm[0 : int(len(vmm) / 2.1)] + # store the sweep number in the neo AnalogSignal trace object. + vmm.sn = None + vmm.sn = sn + return vmm, stimulus, sn, spike_times + return None, None, None, None + + +def get_model_parts_sweep_from_number(sn, data_set, sweep_numbers, specimen_id): + sweep_numbers = sweep_numbers["Square - 2s Suprathreshold"] + rheobase = -1 + above_threshold_sn = [] + currents = {} + sweep_data = data_set.get_sweep(sn) + stimulus = sweep_data["stimulus"] + spike_times = data_set.get_spike_times(sn) + reponse = sweep_data["response"] + + sampling_rate = sweep_data["sampling_rate"] + vmm = AnalogSignal( + [v * 1000 for v in reponse], sampling_rate=sampling_rate * qt.Hz, units=qt.mV + ) + vmm = vmm[0 : int(len(vmm) / 2.1)] + vmm.sn = None + vmm.sn = sn + + return vmm, stimulus, sn, spike_times + + +def make_suite_known_sweep_from_static_models( + vm_soma, stimulus, specimen_id, efel_filter_iterable=None +): + sm = StaticModel(vm=vm_soma) + sm.backend = "static_model" + sm.vm_soma = vm_soma + sm = efel_evaluation( + sm, current=np.max(stimulus), efel_filter_iterable=efel_filter_iterable + ) + allen_tests = [] + if sm.efel is not None: + for k, v in sm.efel.items(): + try: + # if v is not None: + at = AllenTest(name=str(k)) + at.set_observation(v) + at = wrangle_tests(at) + allen_tests.append(at) + except: + pass + suite = TestSuite(allen_tests, name=str(specimen_id)) + suite.traces = None + suite.traces = {} + suite.traces["vm_soma"] = sm.vm_soma + + suite.model = sm + suite.stim = stimulus + return suite, specimen_id + # suite.model = None + # suite.useable = None + # suite.useable = useable + # suite.stim = None + + +def wrangle_tests(t): + if hasattr(t.observation["mean"], "units"): + t.observation["mean"] = ( + np.mean(t.observation["mean"]) * t.observation["mean"].units + ) + t.observation["std"] = ( + np.mean(t.observation["mean"]) * t.observation["mean"].units + ) + else: + t.observation["mean"] = np.mean(t.observation["mean"]) + t.observation["std"] = np.mean(t.observation["mean"]) + return t + + +def make_suite_from_static_models(vm_soma, vm30, rheobase, currents, vmrh, specimen_id): + + if vmrh is not None: + sm = StaticModel(vm=vmrh) + else: + sm = StaticModel(vm=vm30) + sm.rheobase = rheobase + sm.vm_soma = vm_soma + sm = efel_evaluation(sm, current=rheobase) + + sm = rekeyed(sm) + useable = False + sm.vmrh = vmrh + allen_tests = [] + + if sm.efel is not None: + for k, v in sm.efel.items(): + try: + at = AllenTest(name=str(k)) + at.set_observation(v) + at = wrangle_tests(at) + allen_tests.append(at) + except: + pass + + suite = TestSuite(allen_tests, name=str(specimen_id)) + suite.traces = None + suite.traces = {} + suite.traces["rh_current"] = sm.rheobase + suite.traces["vmrh"] = sm.vmrh + ## + # Not here deliberate misleading naming + ## + suite.traces["vm_soma"] = sm.vm_soma + suite.model = None + # suite.useable = None + + # suite.useable = useable + suite.model = sm + suite.stim = None + suite.stim = currents + return suite, specimen_id diff --git a/neuronunit/allenapi/neuroelectroapi.py b/neuronunit/allenapi/neuroelectroapi.py new file mode 100644 index 000000000..4bd4dd51a --- /dev/null +++ b/neuronunit/allenapi/neuroelectroapi.py @@ -0,0 +1,582 @@ +import os +import sciunit +import neuronunit +import pickle +from neuronunit import tests as _, neuroelectro +from neuronunit.tests import passive, waveform, fi +from neuronunit.tests.fi import RheobaseTestP + +from neuronunit.tests import * +import quantities as qt +from sciunit.scores import RatioScore, ZScore, RelativeDifferenceScore +import neuronunit +import pandas as pd + +import pickle + +anchor = __file__ + +import copy +import sciunit +from sciunit.suites import TestSuite +import pickle + + +import urllib.request, json + +from neuronunit import neuroelectro +from scipy import stats +import numpy as np + +import urllib.request, json + + +def id_to_frame(df_n, df_e, nxid): + ntype = str(df_n[df_n["NeuroLex ID"] == nxid]["Neuron Type"].values[0]) + pyr = df_e[df_e["NeuronType"] == ntype] + return pyr + + +def column_to_sem(df, column): + + temp = [i for i in df[column].values if not np.isnan(i)] + sem = stats.sem(temp, axis=None, ddof=0) + std = np.std(temp) # , axis=None, ddof=0) + mean = np.mean(temp) + # print(column,sem) + df = pd.DataFrame([{"sem": sem, "std": std, "mean": mean}], index=[column]) + return df + + +def cell_to_frame(df_n, df_e, nxid): + pyr = id_to_frame(df_n, df_e, nxid) + for cnt, key in enumerate(pyr.columns): + empty = pd.DataFrame() + if not key in "Species": + if cnt == 0: + df_old = column_to_sem(pyr, key) + else: + df_new = column_to_sem(pyr, key) + df_old = pd.concat([df_new, df_old]) + else: + break + return df_old.T + + +def neuroelectro_summary_observation(neuron_name, ontology): + ephysprop_name = "" + verbose = False + reference_data = neuroelectro.NeuroElectroSummary( + neuron=neuron_name, + ephysprop={"name": ontology["name"]}, + get_values=True, + cached=True, + ) + return reference_data + + +def get_obs(pipe): + with urllib.request.urlopen("https://neuroelectro.org/api/1/e/") as url: + ontologies = json.loads(url.read().decode()) + obs = [] + for p in pipe: + for l in ontologies["objects"]: + obs.append(neuroelectro_summary_observation(p, l)) + return obs + + +def update_amplitude(test, tests, score): + rheobase = score.prediction["value"] + for i in [4, 5, 6]: + tests[i].params["injected_square_current"]["amplitude"] = ( + rheobase * 1.01 + ) # I feel that 1.01 may lead to more than one spike + return + + +def substitute_parallel_for_serial(electro_tests): + for test, obs in electro_tests: + test[0] = RheobaseTestP(obs["Rheobase"]) + + return electro_tests + + +def substitute_criteria(observations_donar, observations_acceptor): + # Inputs an observation donar + # and an observation acceptor + # Many neuroelectro data sources have std 0 values + for index, oa in observations_acceptor.items(): + for k, v in oa.items(): + if k == "std" and v == 0.0: + if k in observations_donar.keys(): + oa[k] = observations_donar[index][k] + return observations_acceptor + + +""" +def substitute_parallel_for_serial(electro_tests): + for test,obs in electro_tests: + if str('Rheobase') in obs.keys(): + + test[0] = RheobaseTestP(obs['Rheobase']) + + return electro_tests +""" + + +def replace_zero_std(electro_tests): + for test, obs in electro_tests: + if str("Rheobase") in obs.keys(): + test[0] = RheobaseTestP(obs["Rheobase"]) + for k, v in obs.items(): + if v["std"] == 0: + + obs = substitute_criteria(electro_tests[1][1], obs) + + return electro_tests + + +def executable_druckman_tests(cell_id, file_name=None): + # Use neuroelectro experimental obsevations to find test + # criterion that will be used to inform scientific unit testing. + # some times observations are not sourced from neuroelectro, + # but they are imputated or borrowed from other TestSuite + # if that happens make test objections using observations external + # to this method, and provided as a method argument. + tests = [] + observations = None + test_classes = None + + dm.AP1AP1AHPDepthTest.ephysprop_name = None + dm.AP1AP1AHPDepthTest.ephysprop_name = "AHP amplitude" + dm.AP2AP1AHPDepthTest.ephysprop_name = None + dm.AP2AP1AHPDepthTest.ephysprop_name = "AHP amplitude" + dm.AP1AP1WidthHalfHeightTest.ephysprop_name = None + dm.AP1AP1WidthHalfHeightTest.ephysprop_name = "spike half-width" + dm.AP1AP1WidthPeakToTroughTest.ephysprop_name = None + dm.AP1AP1WidthPeakToTroughTest.ephysprop_name = "spike width" + # dm.IinitialAccomodationMeanTest.ephysprop_name = None + # dm.IinitialAccomodationMeanTest.ephysprop_name = 'adaptation_percent' + + test_classes = [ + fi.RheobaseTest, + dm.AP1AP1AHPDepthTest, + dm.AP2AP1AHPDepthTest, + dm.AP1AP1WidthHalfHeightTest, + dm.AP1AP1WidthPeakToTroughTest, + ] + observations = {} + for index, t in enumerate(test_classes): + obs = t.neuroelectro_summary_observation(cell_id) + + if obs is not None: + if "mean" in obs.keys(): + tests.append(t(obs)) + observations[t.ephysprop_name] = obs + + suite = sciunit.TestSuite(tests, name="vm_suite") + + if file_name is not None: + file_name = file_name + ".p" + with open(file_name, "wb") as f: + pickle.dump(tests, f) + + return tests, observations + + +def executable_tests(cell_id, file_name=None): + # Use neuroelectro experimental obsevations to find test + # criterion that will be used to inform scientific unit testing. + # some times observations are not sourced from neuroelectro, + # but they are imputated or borrowed from other TestSuite + # if that happens make test objections using observations external + # to this method, and provided as a method argument. + tests = [] + observations = None + test_classes = None + test_classes = [ + fi.RheobaseTest, + passive.InputResistanceTest, + passive.TimeConstantTest, + passive.CapacitanceTest, + passive.RestingPotentialTest, + ] # , + observations = {} + for index, t in enumerate(test_classes): + if "RheobaseTest" in t.name: + t.score_type = sciunit.scores.ZScore + if "RheobaseTestP" in t.name: + t.score_type = sciunit.scores.ZScore + + obs = t.neuroelectro_summary_observation(cell_id) + + if obs is not None: + if "mean" in obs.keys(): + tests.append(t(obs)) + observations[t.ephysprop_name] = obs + + # hooks = {tests[0]:{'f':update_amplitude}} #This is a trick to dynamically insert the method + # update amplitude at the location in sciunit thats its passed to, without any loss of generality. + suite = sciunit.TestSuite(tests, name="vm_suite") + + if file_name is not None: + file_name = file_name + ".p" + with open(file_name, "wb") as f: + pickle.dump(tests, f) + + return tests, observations + + +def get_neuron_criteria(cell_id, file_name=None): # ,observation = None): + # Use neuroelectro experimental obsevations to find test + # criterion that will be used to inform scientific unit testing. + # some times observations are not sourced from neuroelectro, + # but they are imputated or borrowed from other TestSuite + # if that happens make test objections using observations external + # to this method, and provided as a method argument. + tests = {} + observations = None + test_classes = None + test_classes = [ + fi.RheobaseTest, + passive.InputResistanceTest, + passive.TimeConstantTest, + passive.CapacitanceTest, + passive.RestingPotentialTest, + ] + observations = {} + for index, t in enumerate(test_classes): + obs = t.neuroelectro_summary_observation(cell_id) + + if obs is not None: + if "mean" in obs.keys(): + print(test_classes[index]) + print(t.name) + tt = t(obs) + tests[t.name] = tt + observations[t.ephysprop_name] = obs + # hooks = {tests[0]:{'f':update_amplitude}} #This is a trick to dynamically insert the method + # update amplitude at the location in sciunit thats its passed to, without any loss of generality. + # suite = sciunit.TestSuite(tests,name="vm_suite") + + if file_name is not None: + file_name = file_name + ".p" + with open(file_name, "wb") as f: + pickle.dump(tests, f) + + return tests, observations + + +def get_olf_cell(): + cell_constraints = {} + olf_mitral = { + "id": 129, + "name": "Olfactory bulb (main) mitral cell", + "neuron_db_id": 267, + "nlex_id": "nlx_anat_100201", + } + + # NLXANAT:100201 + + olf_mitral = { + "id": 129, + "name": "Olfactory bulb (main) mitral cell", + "neuron_db_id": 267, + "nlex_id": "nlx_anat_100201", + } + # olf_mitral['id'] = 'nlx_anat_100201' + # olf_mitral['nlex_id'] = 'nlx_anat_100201' + tests, observations = get_neuron_criteria(olf_mitral) + # import pdb + # pdb.set_trace() + cell_constraints["olf_mitral"] = tests + with open("olf.p", "wb") as f: + pickle.dump(cell_constraints, f) + + return cell_constraints + + +def get_all_cells(): + ### + # sagratio + ### + purkinje = { + "id": 18, + "name": "Cerebellum Purkinje cell", + "neuron_db_id": 271, + "nlex_id": "sao471801888", + } + # fi_basket = {"id": 65, "name": "Dentate gyrus basket cell", "neuron_db_id": None, "nlex_id": "nlx_cell_100201"} + pvis_cortex = { + "id": 111, + "name": "Neocortex pyramidal cell layer 5-6", + "neuron_db_id": 265, + "nlex_id": "nifext_50", + } + # This olfactory mitral cell does not have datum about rheobase, current injection values. + olf_mitral = { + "id": 129, + "name": "Olfactory bulb (main) mitral cell", + "neuron_db_id": 267, + "nlex_id": "nlx_anat_100201", + } + ca1_pyr = { + "id": 85, + "name": "Hippocampus CA1 pyramidal cell", + "neuron_db_id": 258, + "nlex_id": "sao830368389", + } + cell_list = [olf_mitral, ca1_pyr, purkinje, pvis_cortex] + cell_constraints = {} + for cell in cell_list: + tests, observations = get_neuron_criteria(cell) + cell_constraints[cell["name"]] = tests + with open("multicellular_constraints.p", "wb") as f: + pickle.dump(cell_constraints, f) + return cell_constraints + + +def switch_logic(xtests): + # move this logic into sciunit tests + """ + Hopefuly depreciated by future NU debugging. + """ + aTSD = neuronunit.optimisation.optimization_management.TSD() + if type(xtests) is type(aTSD): + xtests = list(xtests.values()) + if type(xtests) is type(list()): + pass + for t in xtests: + if str("RheobaseTest") == t.name: + t.active = True + t.passive = False + elif str("RheobaseTestP") == t.name: + t.active = True + t.passive = False + elif str("InjectedCurrentAPWidthTest") == t.name: + t.active = True + t.passive = False + elif str("InjectedCurrentAPAmplitudeTest") == t.name: + t.active = True + t.passive = False + elif str("InjectedCurrentAPThresholdTest") == t.name: + t.active = True + t.passive = False + elif str("RestingPotentialTest") == t.name: + t.passive = True + t.active = False + elif str("InputResistanceTest") == t.name: + t.passive = True + t.active = False + elif str("TimeConstantTest") == t.name: + t.passive = True + t.active = False + elif str("CapacitanceTest") == t.name: + t.passive = True + t.active = False + else: + t.passive = False + t.active = False + return xtests + + +def process_all_cells(): + ''' + --Synopsis: download some NeuroElectroSummary + observations and use values to construct appropriate + NeuronUnit tests, then pickle them. + TODO rename pull all cells + see alias below. + + ''' + try: + with open("processed_multicellular_constraints.p", "rb") as f: + filtered_cells = pickle.load(f) + return filtered_cells + except: + try: + cell_constraints = pickle.load( + open("multicellular_suite_constraints.p", "rb") + ) + except: + cell_constraints = get_all_cells() + + filtered_cells = {} + for key, cell in cell_constraints.items(): + filtered_cell_constraints = [] + if type(cell) is type(dict()): + for t in cell.values(): + if t.observation is not None: + if float(t.observation["std"]) == 0.0: + t.observation["std"] = t.observation["mean"] + else: + filtered_cell_constraints.append(t) + # filtered_cells[key] = TestSuite(filtered_cell_constraints) + else: + for t in cell.tests: + if t.observation is not None: + if float(t.observation["std"]) == 0.0: + t.observation["std"] = t.observation["mean"] + else: + filtered_cell_constraints.append(t) + + filtered_cells[key] = TestSuite(filtered_cell_constraints) + """ + for t in filtered_cells[key].tests: + t = switch_logic(t) + assert hasattr(t,'active') + assert hasattr(t,'passive') + for t in filtered_cells[key].tests: + assert hasattr(t,'active') + assert hasattr(t,'passive') + """ + with open("processed_multicellular_constraints.p", "wb") as f: + pickle.dump(filtered_cells, f) + return filtered_cells + +def pull_all_cells(): + filtered_cells = process_all_cells() + + + +def get_common_criteria(): + purkinje = { + "id": 18, + "name": "Cerebellum Purkinje cell", + "neuron_db_id": 271, + "nlex_id": "sao471801888", + } + # fi_basket = {"id": 65, "name": "Dentate gyrus basket cell", "neuron_db_id": None, "nlex_id": "nlx_cell_100201"} + pvis_cortex = { + "id": 111, + "name": "Neocortex pyramidal cell layer 5-6", + "neuron_db_id": 265, + "nlex_id": "nifext_50", + } + # This olfactory mitral cell does not have datum about rheobase, current injection values. + olf_mitral = { + "id": 129, + "name": "Olfactory bulb (main) mitral cell", + "neuron_db_id": 267, + "nlex_id": "nlx_anat_100201", + } + ca1_pyr = { + "id": 85, + "name": "Hippocampus CA1 pyramidal cell", + "neuron_db_id": 258, + "nlex_id": "sao830368389", + } + pipe = [olf_mitral, ca1_pyr, purkinje, pvis_cortex] + electro_tests = [] + obs_frame = {} + test_frame = {} + + try: + + anchor = os.path.dirname(anchor) + mypath = os.path.join(os.sep, anchor, "optimisation/all_tests.p") + + electro_path = str(os.getcwd()) + "all_tests.p" + + assert os.path.isfile(electro_path) == True + with open(electro_path, "rb") as f: + (obs_frame, test_frame) = pickle.load(f) + + except: + for p in pipe: + print(p) + p_observations = get_obs(p) + p_tests = p(p_observations) + obs_frame[p["name"]] = p_observations # , p_tests)) + test_frame[p["name"]] = p_tests # , p_tests)) + electro_path = str(os.getcwd()) + "all_tests.p" + with open(electro_path, "wb") as f: + pickle.dump((obs_frame, test_frame), f) + + return (obs_frame, test_frame) + + +def get_tests(backend=str("RAW")): + import neuronunit + + anchor = neuronunit.__file__ + anchor = os.path.dirname(anchor) + mypath = os.path.join(os.sep, anchor, "unit_test/pipe_tests.p") + + # get neuronunit tests + # and select out Rheobase test and input resistance test + # and less about electrophysiology of the membrane. + # We are more interested in phenomonogical properties. + electro_path = mypath + # str(os.getcwd())+'/pipe_tests.p' + def dont_use_cache(): + assert os.path.isfile(electro_path) == True + with open(electro_path, "rb") as f: + electro_tests = pickle.load(f) + + electro_tests = get_common_criteria() + electro_tests = replace_zero_std(electro_tests) + test, observation = electro_tests[0] + tests = copy.copy(electro_tests[0][0]) + suite = sciunit.TestSuite(tests) + + return tests, test, observation, suite + + +def do_use_cache(): + import neuronunit + + anchor = neuronunit.__file__ + anchor = os.path.dirname(anchor) + mypath = os.path.join(os.sep, anchor, "unit_test/pipe_tests.p") + + # get neuronunit tests + # and select out Rheobase test and input resistance test + # and less about electrophysiology of the membrane. + # We are more interested in phenomonogical properties. + electro_path = mypath + assert os.path.isfile(electro_path) == True + with open(electro_path, "rb") as f: + electro_tests = pickle.load(f) + return electro_tests + + +def remake_tests(): + # Use neuroelectro experimental obsevations to find test + # criterion that will be used to inform scientific unit testing. + # some times observations are not sourced from neuroelectro, + # but they are imputated or borrowed from other TestSuite + # if that happens make test objections using observations external + # to this method, and provided as a method argument. + tests = [] + observations = None + test_classes = None + + test_classes = [ + fi.RheobaseTest, + passive.InputResistanceTest, + passive.TimeConstantTest, + passive.CapacitanceTest, + passive.RestingPotentialTest, + ] + electro_obs = do_use_cache() + test_cell_dict = {} + for eo in electro_obs: + tests = [] + for index, t in enumerate(test_classes): + for ind_obs in eo: + test = t(ind_obs) + tests.append(test) + suite = sciunit.TestSuite(tests) + # test_cell_dict[] + + if obs is not None: + if "mean" in obs.keys(): + tests.append(t(obs)) + observations[t.ephysprop_name] = obs + + electro_tests = replace_zero_std(electro_tests) + test, observation = electro_tests[0] + tests = copy.copy(electro_tests[0][0]) + suite = sciunit.TestSuite(tests) + return tests, test, observation, suite diff --git a/neuronunit/allenapi/utils.py b/neuronunit/allenapi/utils.py new file mode 100644 index 000000000..10994ef93 --- /dev/null +++ b/neuronunit/allenapi/utils.py @@ -0,0 +1,348 @@ +import dask + + +def dask_map_function(eval_, invalid_ind): + results = [] + for x in invalid_ind: + y = dask.delayed(eval_)(x) + results.append(y) + fitnesses = dask.compute(*results) + return fitnesses + + +from sciunit.scores import ZScore + +# from sciunit import TestSuite +from sciunit.scores.collections import ScoreArray +from mpl_toolkits.mplot3d import Axes3D +import matplotlib.pyplot as plt +from neuronunit.optimization.optimization_management import ( + dtc_to_rheo, + switch_logic, + active_values, +) +from neuronunit.tests.base import AMPL, DELAY, DURATION +import quantities as pq + +PASSIVE_DURATION = 500.0 * pq.ms +PASSIVE_DELAY = 200.0 * pq.ms +import sciunit +import numpy as np +from bluepyopt.parameters import Parameter + +from neuronunit.optimization.optimization_management import TSD + +import numpy as np +import matplotlib.pyplot as plt + + +def glif_specific_modifications(tests): + + """ + Now appropriate for all tests + """ + tests = TSD(tests) + # tests.pop('RheobaseTest',None) + tests.pop("InjectedCurrentAPAmplitudeTest", None) + tests.pop("InjectedCurrentAPThresholdTest", None) + tests.pop("InjectedCurrentAPWidthTest", None) + # tests.pop('InputResistanceTest',None) + # tests.pop('CapacitanceTest',None) + # tests.pop('TimeConstantTest',None) + + return tests + + +def l5pc_specific_modifications(tests): + tests = TSD(tests) + tests.pop("InputResistanceTest", None) + tests.pop("CapacitanceTest", None) + tests.pop("TimeConstantTest", None) + # tests.pop('RestingPotentialTest',None) + + return tests + + +import streamlit as st + + +def make_evaluator( + nu_tests, + MODEL_PARAMS, + experiment=str("Neocortex pyramidal cell layer 5-6"), + model=str("ADEXP"), +): + objectives = [] + + nu_tests[0].score_type = RelativeDifferenceScore + + if "GLIF" in model: + nu_tests_ = glif_specific_modifications(nu_tests) + nu_tests = list(nu_tests_.values()) + simple_cell.name = "GLIF" + + elif "L5PC" in model: + nu_tests_ = l5pc_specific_modifications(nu_tests) + nu_tests = list(nu_tests_.values()) + simple_cell.name = "L5PC" + + else: + simple_cell.name = model + experiment + simple_cell.backend = model + simple_cell.params = {k: np.mean(v) for k, v in simple_cell.params.items()} + + lop = {} + for k, v in MODEL_PARAMS[model].items(): + p = Parameter(name=k, bounds=v, frozen=False) + lop[k] = p + + simple_cell.params = lop + + for tt in nu_tests: + feature_name = tt.name + ft = NUFeature_standard_suite(tt, simple_cell) + objective = ephys.objectives.SingletonObjective(feature_name, ft) + objectives.append(objective) + + score_calc = ephys.objectivescalculators.ObjectivesCalculator(objectives) + + sweep_protocols = [] + for protocol_name, amplitude in [("step1", 0.05)]: + protocol = ephys.protocols.SweepProtocol(protocol_name, [None], [None]) + sweep_protocols.append(protocol) + twostep_protocol = ephys.protocols.SequenceProtocol( + "twostep", protocols=sweep_protocols + ) + + cell_evaluator = ephys.evaluators.CellEvaluator( + cell_model=simple_cell, + param_names=MODEL_PARAMS[model].keys(), + fitness_protocols={twostep_protocol.name: twostep_protocol}, + fitness_calculator=score_calc, + sim="euler", + ) + simple_cell.params_by_names(MODEL_PARAMS[model].keys()) + return cell_evaluator, simple_cell, score_calc, [tt.name for tt in nu_tests] + + +def trace_explore_widget(optimal_model_params=None): + """ + move this to utils file. + Allow app user to explore model behavior around the optimal, + by panning across parameters and then viewing resulting spike shapes. + """ + + attrs = {k: np.mean(v) for k, v in MODEL_PARAMS["IZHI"].items()} + plt.clf() + cnt = 0 + slider_value = st.slider( + "parameter a", min_value=0.01, max_value=0.1, value=0.05, step=0.001 + ) + if optimal_model_params is None: + dtc = DataTC(backend="IZHI", attrs=attrs) + else: + dtc = DataTC(backend="IZHI", attrs=optimal_model_params) + dtc.attrs["a"] = slider_value + dtc = dtc_to_rheo(dtc) + temp_rh = dtc.rheobase + model = dtc.dtc_to_model() + model.attrs = model._backend.default_attrs + model.attrs.update(dtc.attrs) + + uc = {"amplitude": temp_rh, "duration": DURATION, "delay": DELAY} + model._backend.inject_square_current(uc) + vm = model.get_membrane_potential() + plt.plot(vm.times, vm.magnitude) + + cnt += 1 + st.pyplot() + + +def basic_expVar(trace1, trace2): + # https://github.com/AllenInstitute/GLIF_Teeter_et_al_2018/blob/master/query_biophys/query_biophys_expVar.py + """This is the fundamental calculation that is used in all different types of explained variation. + At a basic level, the explained variance is calculated between two traces. These traces can be PSTH's + or single spike trains that have been convolved with a kernel (in this case always a Gaussian) + Input: + trace 1 & 2: 1D numpy array containing values of the trace. (This function requires numpy array + to ensure that this is not a multidemensional list.) + Returns: + expVar: float value of explained variance + """ + + var_trace1 = np.var(trace1) + var_trace2 = np.var(trace2) + var_trace1_minus_trace2 = np.var(trace1 - trace2) + + if var_trace1_minus_trace2 == 0.0: + return 1.0 + else: + return (var_trace1 + var_trace2 - var_trace1_minus_trace2) / ( + var_trace1 + var_trace2 + ) + + +def hof_to_euclid(hof, MODEL_PARAMS, target): + lengths = {} + tv = 1 + cnt = 0 + constellation0 = hof[0] + constellation1 = hof[1] + subset = list(MODEL_PARAMS.keys()) + tg = target.dtc_to_gene(subset_params=subset) + if len(MODEL_PARAMS) == 1: + + ax = plt.subplot() + for k, v in MODEL_PARAMS.items(): + lengths[k] = np.abs(np.abs(v[1]) - np.abs(v[0])) + + x = [h[cnt] for h in hof] + y = [np.sum(h.fitness.values()) for h in hof] + ax.set_xlim(v[0], v[1]) + ax.set_xlabel(k) + tgene = tg[cnt] + yg = 0 + + ax.scatter(x, y, c="b", marker="o", label="samples") + ax.scatter(tgene, yg, c="r", marker="*", label="target") + ax.legend() + + plt.show() + + if len(MODEL_PARAMS) == 2: + + ax = plt.subplot() + for k, v in MODEL_PARAMS.items(): + lengths[k] = np.abs(np.abs(v[1]) - np.abs(v[0])) + + if cnt == 0: + tgenex = tg[cnt] + x = [h[cnt] for h in hof] + ax.set_xlim(v[0], v[1]) + ax.set_xlabel(k) + if cnt == 1: + tgeney = tg[cnt] + + y = [h[cnt] for h in hof] + ax.set_ylim(v[0], v[1]) + ax.set_ylabel(k) + cnt += 1 + + ax.scatter(x, y, c="r", marker="o", label="samples", s=5) + ax.scatter(tgenex, tgeney, c="b", marker="*", label="target", s=11) + + ax.legend() + + plt.sgow() + # print(len(MODEL_PARAMS)) + if len(MODEL_PARAMS) == 3: + fig = plt.figure() + ax = fig.add_subplot(111, projection="3d") + for k, v in MODEL_PARAMS.items(): + lengths[k] = np.abs(np.abs(v[1]) - np.abs(v[0])) + + if cnt == 0: + tgenex = tg[cnt] + + x = [h[cnt] for h in hof] + ax.set_xlim(v[0], v[1]) + ax.set_xlabel(k) + if cnt == 1: + tgeney = tg[cnt] + + y = [h[cnt] for h in hof] + ax.set_ylim(v[0], v[1]) + ax.set_ylabel(k) + if cnt == 2: + tgenez = tg[cnt] + + z = [h[cnt] for h in hof] + ax.set_zlim(v[0], v[1]) + ax.set_zlabel(k) + + cnt += 1 + ax.scatter(x, y, z, c="r", marker="o") + ax.scatter(tgenex, tgeney, tgenez, c="b", marker="*", label="target", s=11) + + plt.show() + + +def initialise_test(v, rheobase): + v = switch_logic([v]) + v = v[0] + k = v.name + if not hasattr(v, "params"): + v.params = {} + if not "injected_square_current" in v.params.keys(): + v.params["injected_square_current"] = {} + if v.passive == False and v.active == True: + keyed = v.params["injected_square_current"] + v.params = active_values(keyed, rheobase) + v.params["injected_square_current"]["delay"] = DELAY + v.params["injected_square_current"]["duration"] = DURATION + if v.passive == True and v.active == False: + + v.params["injected_square_current"]["amplitude"] = -10 * pq.pA + v.params["injected_square_current"]["delay"] = PASSIVE_DELAY + v.params["injected_square_current"]["duration"] = PASSIVE_DURATION + + if v.name in str("RestingPotentialTest"): + v.params["injected_square_current"]["delay"] = PASSIVE_DELAY + v.params["injected_square_current"]["duration"] = PASSIVE_DURATION + v.params["injected_square_current"]["amplitude"] = 0.0 * pq.pA + + return v + + +from sciunit.scores import ZScore +import bluepyopt as bpop +import bluepyopt.ephys as ephys + + +class NUFeature_standard_suite(object): + def __init__(self, test, model): + self.test = test + self.model = model + + def calculate_score(self, responses): + model = responses["model"].dtc_to_model() + model.attrs = responses["params"] + self.test = initialise_test(self.test, responses["rheobase"]) + if "RheobaseTest" in str(self.test.name): + self.test.score_type = ZScore + prediction = {"value": responses["rheobase"]} + score_gene = self.test.compute_score(self.test.observation, prediction) + # lns = np.abs(np.float(score_gene.log_norm_score)) + # return lns + else: + try: + score_gene = self.test.judge(model) + except: + # print(self.test.observation,self.test.name) + # print(score_gene,'\n\n\n') + + return 100.0 + + if not isinstance(type(score_gene), type(None)): + if not isinstance(score_gene, sciunit.scores.InsufficientDataScore): + if not isinstance(type(score_gene.log_norm_score), type(None)): + try: + + lns = np.abs(np.float(score_gene.log_norm_score)) + except: + # works 1/2 time that log_norm_score does not work + # more informative than nominal bad score 100 + lns = np.abs(np.float(score_gene.raw)) + else: + # works 1/2 time that log_norm_score does not work + # more informative than nominal bad score 100 + + lns = np.abs(np.float(score_gene.raw)) + else: + # print(prediction,self.test.observation) + # print(score_gene,'\n\n\n') + lns = 100 + if lns == np.inf: + lns = np.abs(np.float(score_gene.raw)) + # print(lns,self.test.name) + return lns diff --git a/neuronunit/examples/reduced-model-simulation.ipynb b/neuronunit/examples/reduced-model-simulation.ipynb index 92e4e9758..81865f651 100644 --- a/neuronunit/examples/reduced-model-simulation.ipynb +++ b/neuronunit/examples/reduced-model-simulation.ipynb @@ -405,7 +405,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.8.3" + "version": "3.7.4" } }, "nbformat": 4, diff --git a/neuronunit/models/backends/__init__.py b/neuronunit/models/backends/__init__.py index 535a13af8..f8adafdcc 100644 --- a/neuronunit/models/backends/__init__.py +++ b/neuronunit/models/backends/__init__.py @@ -1,14 +1,11 @@ """Neuronunit-specific model backends.""" import inspect -import warnings import sciunit.models.backends as su_backends from sciunit.utils import PLATFORM from .base import Backend -warnings.filterwarnings('ignore', message='nested set') -warnings.filterwarnings('ignore', message='mpi4py') try: from .static import StaticBackend @@ -16,12 +13,6 @@ StaticBackend = None print('Could not load StaticBackend') -try: - from .jNeuroML import jNeuroMLBackend -except ImportError: - jNeuroMLBackend = None - print('Could not load jNeuroMLBackend') - try: from .geppetto import GeppettoBackend except ImportError: @@ -29,40 +20,20 @@ print('Could not load GeppettoBackend') try: - from .neuron import NEURONBackend -except ImportError: - NEURONBackend = None - print('Could not load NEURONBackend') - -try: - from .rawpy import RAWBackend -except ImportError: - RAWBackend = None - print('Could not load RAWBackend.') - -try: - from .hhrawf import HHBackend + from .jNeuroML import jNeuroMLBackend except ImportError: - HHBackend = None - print('Could not load HHBackend.') + jNeuroMLBackend = None + print('Could not load jNeuroMLBackend') -""" -try: - from .general_pyNN import HHpyNNBackend -except ImportError: - HHpyNNBackend = None - print('Could not load HHpyNNBackend.') -except (AttributeError, IOError) as e: - if any([x in str(e) for x in ('NEURON', "'hoc.HocObject' object")]): - print('Could not load PyNNBackend due to NEURON issues: %s' % str(e)) - else: - raise e -""" +## +# Neuron support depreciated +## +#try: +# from .neuron import NEURONBackend +#except ImportError: +# NEURONBackend = None +# print('Could not load NEURONBackend') -try: - from .glif import GLIFBackend -except Exception as e: - print('Could not load GLIFBackend') available_backends = {x.replace('Backend', ''): cls for x, cls in locals().items() diff --git a/neuronunit/models/backends/brian_multi_comp_ca2_HH.py b/neuronunit/models/backends/brian_multi_comp_ca2_HH.py deleted file mode 100644 index cc6e90d4e..000000000 --- a/neuronunit/models/backends/brian_multi_comp_ca2_HH.py +++ /dev/null @@ -1,795 +0,0 @@ - -# coding: utf-8 - -# first demonstrate that the brian backend works: -# bash -# ```sudo /opt/conda/bin/conda install cython -# -# sudo /opt/conda/bin/pip install git+https://github.co -# m/brian-team/brian2.git -# -# ``` - -# In[1]: - - -''' -Hodgkin-Huxley equations (1952). -Spikes are recorded along the axon, and then velocity is calculated. -''' -from brian2 import * -from scipy import stats - -defaultclock.dt = 0.01*ms - -morpho = Cylinder(length=10*cm, diameter=2*238*um, n=1000, type='axon') - -El = 10.613*mV -ENa = 115*mV -EK = -12*mV -gl = 0.3*msiemens/cm**2 -gNa0 = 120*msiemens/cm**2 -gK = 36*msiemens/cm**2 - -# Typical equations -eqs = ''' -# The same equations for the whole neuron, but possibly different parameter values -# distributed transmembrane current -Im = gl * (El-v) + gNa * m**3 * h * (ENa-v) + gK * n**4 * (EK-v) : amp/meter**2 -I : amp (point current) # applied current -dm/dt = alpham * (1-m) - betam * m : 1 -dn/dt = alphan * (1-n) - betan * n : 1 -dh/dt = alphah * (1-h) - betah * h : 1 -alpham = (0.1/mV) * (-v+25*mV) / (exp((-v+25*mV) / (10*mV)) - 1)/ms : Hz -betam = 4 * exp(-v/(18*mV))/ms : Hz -alphah = 0.07 * exp(-v/(20*mV))/ms : Hz -betah = 1/(exp((-v+30*mV) / (10*mV)) + 1)/ms : Hz -alphan = (0.01/mV) * (-v+10*mV) / (exp((-v+10*mV) / (10*mV)) - 1)/ms : Hz -betan = 0.125*exp(-v/(80*mV))/ms : Hz -gNa : siemens/meter**2 -''' - -neuron = SpatialNeuron(morphology=morpho, model=eqs, method="exponential_euler", - refractory="m > 0.4", threshold="m > 0.5", - Cm=1*uF/cm**2, Ri=35.4*ohm*cm) -neuron.v = 0*mV -neuron.h = 1 -neuron.m = 0 -neuron.n = .5 -neuron.I = 0*amp -neuron.gNa = gNa0 -M = StateMonitor(neuron, 'v', record=True) -spikes = SpikeMonitor(neuron) - -run(50*ms, report='text') -neuron.I[0] = 1*uA # current injection at one end -run(3*ms) -neuron.I = 0*amp -run(50*ms, report='text') - -# Calculation of velocity -slope, intercept, r_value, p_value, std_err = stats.linregress(spikes.t/second, - neuron.distance[spikes.i]/meter) -print("Velocity = %.2f m/s" % slope) - -subplot(211) -for i in range(10): - plot(M.t/ms, M.v.T[:, i*100]/mV) -ylabel('v') -subplot(212) -plot(spikes.t/ms, spikes.i*neuron.length[0]/cm, '.k') -plot(spikes.t/ms, (intercept+slope*(spikes.t/second))/cm, 'r') -xlabel('Time (ms)') -ylabel('Position (cm)') -show() - - -# In[12]: - -''' -http://neuromorpho.org/neuron_info.jsp?neuron_name=tc200 -NeuroMorpho.Org ID :NMO_00881 -Notes ------ -* Completely removed the "Fast mechanism for submembranal Ca++ concentration - (cai)" -- it did not affect the results presented here -* Time constants for the I_T current are slightly different from the equations - given in the paper -- the paper calculation seems to be based on 36 degree - Celsius but the temperature that is used is 34 degrees. -* To reproduce Figure 12C, the "presence of dendritic shunt conductances" meant - setting g_L to 0.15 mS/cm^2 for the whole neuron. -* Other small discrepancies with the paper -- values from the NEURON code were - used whenever different from the values stated in the paper -''' - -from neo import AnalogSignal -import neuronunit.capabilities.spike_functions as sf -import neuronunit.capabilities as cap -cap.ReceivesCurrent -cap.ProducesActionPotentials -from types import MethodType - -from brian2 import * - -from brian2.units.constants import (zero_celsius, faraday_constant as F, - gas_constant as R) - - -#print(type(model)) -#HH_cond_exp = bind_NU_interface(model) - - - -def bind_NU_interface(): - def __init__(self): - - defaultclock.dt = 0.01*ms - - VT = -52*mV - El = -76.5*mV # from code, text says: -69.85*mV - - E_Na = 50*mV - E_K = -100*mV - C_d = 7.954 # dendritic correction factor - - T = 34*kelvin + zero_celsius # 34 degC (current-clamp experiments) - tadj_HH = 3.0**((34-36)/10.0) # temperature adjustment for Na & K (original recordings at 36 degC) - tadj_m_T = 2.5**((34-24)/10.0) - tadj_h_T = 2.5**((34-24)/10.0) - - shift_I_T = -1*mV - - gamma = F/(R*T) # R=gas constant, F=Faraday constant - Z_Ca = 2 # Valence of Calcium ions - Ca_i = 240*nM # intracellular Calcium concentration - Ca_o = 2*mM # extracellular Calcium concentration - - eqs = Equations(''' - Im = gl*(El-v) - I_Na - I_K - I_T: amp/meter**2 - I_inj : amp (point current) - gl : siemens/meter**2 - # HH-type currents for spike initiation - g_Na : siemens/meter**2 - g_K : siemens/meter**2 - I_Na = g_Na * m**3 * h * (v-E_Na) : amp/meter**2 - I_K = g_K * n**4 * (v-E_K) : amp/meter**2 - v2 = v - VT : volt # shifted membrane potential (Traub convention) - dm/dt = (0.32*(mV**-1)*(13.*mV-v2)/ - (exp((13.*mV-v2)/(4.*mV))-1.)*(1-m)-0.28*(mV**-1)*(v2-40.*mV)/ - (exp((v2-40.*mV)/(5.*mV))-1.)*m) / ms * tadj_HH: 1 - dn/dt = (0.032*(mV**-1)*(15.*mV-v2)/ - (exp((15.*mV-v2)/(5.*mV))-1.)*(1.-n)-.5*exp((10.*mV-v2)/(40.*mV))*n) / ms * tadj_HH: 1 - dh/dt = (0.128*exp((17.*mV-v2)/(18.*mV))*(1.-h)-4./(1+exp((40.*mV-v2)/(5.*mV)))*h) / ms * tadj_HH: 1 - # Low-threshold Calcium current (I_T) -- nonlinear function of voltage - I_T = P_Ca * m_T**2*h_T * G_Ca : amp/meter**2 - P_Ca : meter/second # maximum Permeability to Calcium - G_Ca = Z_Ca**2*F*v*gamma*(Ca_i - Ca_o*exp(-Z_Ca*gamma*v))/(1 - exp(-Z_Ca*gamma*v)) : coulomb/meter**3 - dm_T/dt = -(m_T - m_T_inf)/tau_m_T : 1 - dh_T/dt = -(h_T - h_T_inf)/tau_h_T : 1 - m_T_inf = 1/(1 + exp(-(v/mV + 56)/6.2)) : 1 - h_T_inf = 1/(1 + exp((v/mV + 80)/4)) : 1 - tau_m_T = (0.612 + 1.0/(exp(-(v/mV + 131)/16.7) + exp((v/mV + 15.8)/18.2))) * ms / tadj_m_T: second - tau_h_T = (int(v<-81*mV) * exp((v/mV + 466)/66.6) + - int(v>=-81*mV) * (28 + exp(-(v/mV + 21)/10.5))) * ms / tadj_h_T: second - ''') - model = SpatialNeuron(morphology=morpho, model=eqs, method="exponential_euler", - refractory="m > 0.4", threshold="m > 0.5", - Cm=1*uF/cm**2, Ri=35.4*ohm*cm) - self.model = model - - - def load_model(self): - neuron = None - self.neuron = self.model - self.M = StateMonitor(neuron, 'v', record=True) - - def init_backend(self, attrs = None, cell_name= 'HH_cond_exp', current_src_name = 'hannah', DTC = None, dt=0.01): - backend = 'HHpyNN' - self.current_src_name = current_src_name - self.cell_name = cell_name - self.adexp = True - - self.DCSource = DCSource - self.setup = setup - self.model_path = None - self.related_data = {} - self.lookup = {} - self.attrs = {} - self.neuron = neuron - self.model._backend = str('ExternalSim') - self.backend = self - self.model.attrs = {} - - #self.orig_lems_file_path = 'satisfying' - #self.model._backend.use_memory_cache = False - #self.model.unpicklable += ['h','ns','_backend'] - self.dt = dt - if type(DTC) is not type(None): - if type(DTC.attrs) is not type(None): - - self.set_attrs(**DTC.attrs) - #assert len(self.model.attrs.keys()) > 0 - - if hasattr(DTC,'current_src_name'): - self._current_src_name = DTC.current_src_name - - if hasattr(DTC,'cell_name'): - self.cell_name = DTC.cell_name - - self.load_model() - - def get_membrane_potential(self): - """Must return a neo.core.AnalogSignal. - And must destroy the hoc vectors that comprise it. - """ - - vm = AnalogSignal(volts, - units = mV, - sampling_period = self.dt *ms) - return vm - def _local_run(self): - ''' - pyNN lazy array demands a minimum population size of 3. Why is that. - ''' - results = {} - DURATION = 1000.0 - - #ctx_cells.celltype.recordable - - - if self.celltype == 'HH_cond_exp': - - self.hhcell.record('spikes','v') - - else: - self.neuron.record_v(self.hhcell, "Results/HH_cond_exp_%s.v" % str(neuron)) - - #self.neuron.record_gsyn(self.hhcell, "Results/HH_cond_exp_%s.gsyn" % str(neuron)) - self.neuron.run(DURATION) - data = self.hhcell.get_data().segments[0] - volts = data.filter(name="v")[0]#/10.0 - vm = self.M.v/mV - vm = AnalogSignal(volts, - units = mV, - sampling_period = self.dt *ms) - results['vm'] = vm - results['t'] = self.M.t/ms # self.times - results['run_number'] = results.get('run_number',0) + 1 - return results - - - - - def set_attrs(self,**attrs): - ''' - example params: - neuron.v = 0*mV - neuron.h = 1 - neuron.m = 0 - neuron.n = .5 - neuron.I = 0*amp - neuron.gNa = gNa0 - ''' - self.init_backend() - self.model.attrs.update(attrs) - for k, v in attrs.items(): - exec('self.neuron.'+str(k)+'='+str(v)) - assert type(self.model.attrs) is not type(None) - return self - - - def inject_square_current(self,current): - attrs = copy.copy(self.model.attrs) - self.init_backend() - self.set_attrs(**attrs) - c = copy.copy(current) - if 'injected_square_current' in c.keys(): - c = current['injected_square_current'] - - stop = float(c['delay'])+float(c['duration']) - duration = float(c['duration']) - start = float(c['delay']) - amplitude = float(c['amplitude'])*1000.0 #convert pico amp to micro Amp - self.neuron.I[0] = amplitude*uA # current injection at one end - - run(stop*ms) - - self.vm = self.results['vm'] - - - - - def get_APs(self,vm): - # - - vm = self.get_membrane_potential() - waveforms = sf.get_spike_waveforms(vm,threshold=-45.0*mV) - return waveforms - - def get_spike_train(self,**run_params): - - vm = self.get_membrane_potential() - - spike_train = threshold_detection(vm,threshold=-45.0*mV) - - return spike_train# self.M(self.neuron) - - def get_spike_count(self,**run_params): - vm = self.get_membrane_potential() - return len(threshold_detection(vm,threshold=-45.0*mV)) - #model.add_attribute(init_backend) - - model.init_backend = MethodType(init_backend,.model) - model.get_spike_count = MethodType(get_spike_count,self.model) - model.get_APs = MethodType(get_APs,model) - model.get_spike_train = MethodType(get_spike_train,model) - model.set_attrs = MethodType(set_attrs, model) # Bind to the score. - model.inject_square_current = MethodType(inject_square_current, model) # Bind to the score. - model.set_attrs = MethodType(set_attrs, model) # Bind to the score. - model.get_membrane_potential = MethodType(get_membrane_potential,model) - model.load_model = MethodType(load_model, model) # Bind to the score. - model._local_run = MethodType(_local_run,model) - model.init_backend(model) - - return model - - - -# In[13]: - - -HH_cond_exp = bind_NU_interface() - - -# In[ ]: - -''' -Hodgkin-Huxley equations (1952). -Spikes are recorded along the axon, and then velocity is calculated. -''' -from brian2 import * -from scipy import stats - -defaultclock.dt = 0.01*ms - -morpho = Cylinder(length=10*cm, diameter=2*238*um, n=1000, type='axon') - -El = 10.613*mV -ENa = 115*mV -EK = -12*mV -gl = 0.3*msiemens/cm**2 -gNa0 = 120*msiemens/cm**2 -gK = 36*msiemens/cm**2 - -# Typical equations -eqs = ''' -# The same equations for the whole neuron, but possibly different parameter values -# distributed transmembrane current -Im = gl * (El-v) + gNa * m**3 * h * (ENa-v) + gK * n**4 * (EK-v) : amp/meter**2 -I : amp (point current) # applied current -dm/dt = alpham * (1-m) - betam * m : 1 -dn/dt = alphan * (1-n) - betan * n : 1 -dh/dt = alphah * (1-h) - betah * h : 1 -alpham = (0.1/mV) * (-v+25*mV) / (exp((-v+25*mV) / (10*mV)) - 1)/ms : Hz -betam = 4 * exp(-v/(18*mV))/ms : Hz -alphah = 0.07 * exp(-v/(20*mV))/ms : Hz -betah = 1/(exp((-v+30*mV) / (10*mV)) + 1)/ms : Hz -alphan = (0.01/mV) * (-v+10*mV) / (exp((-v+10*mV) / (10*mV)) - 1)/ms : Hz -betan = 0.125*exp(-v/(80*mV))/ms : Hz -gNa : siemens/meter**2 -''' - -neuron = SpatialNeuron(morphology=morpho, model=eqs, method="exponential_euler", - refractory="m > 0.4", threshold="m > 0.5", - Cm=1*uF/cm**2, Ri=35.4*ohm*cm) -neuron.v = 0*mV -neuron.h = 1 -neuron.m = 0 -neuron.n = .5 -neuron.I = 0*amp -neuron.gNa = gNa0 -M = StateMonitor(neuron, 'v', record=True) -spikes = SpikeMonitor(neuron) - -#run(50*ms, report='text') -neuron.I[0] = 1*uA # current injection at one end -run(3*ms) - -#neuron.I = 0*amp -#run(50*ms, report='text') - -''' -# Calculation of velocity -slope, intercept, r_value, p_value, std_err = stats.linregress(spikes.t/second, - neuron.distance[spikes.i]/meter) -print("Velocity = %.2f m/s" % slope) - -subplot(211) -for i in range(10): - plot(M.t/ms, M.v.T[:, i*100]/mV) -ylabel('v') -subplot(212) -plot(spikes.t/ms, spikes.i*neuron.length[0]/cm, '.k') -plot(spikes.t/ms, (intercept+slope*(spikes.t/second))/cm, 'r') -xlabel('Time (ms)') -ylabel('Position (cm)') -show() -''' - - -# In[ ]: - - - - -# In[ ]: - -from pyNN.neuron import * -from pyNN.neuron import HH_cond_exp -from pyNN.neuron import EIF_cond_exp_isfa_ista -from pyNN.neuron import Izhikevich - -from pyNN import neuron -# -from pyNN.neuron import simulator as sim -from pyNN.neuron import setup as setup - -from pyNN.neuron import DCSource - - -# In[ ]: - -import io -import math -import pdb -#from numba import jit -from contextlib import redirect_stdout -import numpy as np -#from .base import * -import quantities as qt -from quantities import mV, ms, s -import matplotlib.pyplot as plt -from pyNN.neuron import * -from pyNN.neuron import HH_cond_exp -from pyNN.neuron import EIF_cond_exp_isfa_ista -from pyNN.neuron import Izhikevich -from elephant.spike_train_generation import threshold_detection - -from pyNN import neuron -# -#from pyNN.neuron import simulator as sim -from pyNN.neuron import setup as setup -#from pyNN.neuron import Izhikevich -#from pyNN.neuron import Population -from pyNN.neuron import DCSource -import numpy as np -import copy - - -from neo import AnalogSignal -import neuronunit.capabilities.spike_functions as sf -import neuronunit.capabilities as cap -cap.ReceivesCurrent -cap.ProducesActionPotentials -from types import MethodType - -def bind_NU_interface(model): - - def load_model(self): - neuron = None - from pyNN import neuron - self.hhcell = neuron.create(EIF_cond_exp_isfa_ista()) - neuron.setup(timestep=self.dt, min_delay=1.0) - - - def init_backend(self, attrs = None, cell_name= 'HH_cond_exp', current_src_name = 'hannah', DTC = None, dt=0.01): - backend = 'HHpyNN' - self.current_src_name = current_src_name - self.cell_name = cell_name - self.adexp = True - - self.DCSource = DCSource - self.setup = setup - self.model_path = None - self.related_data = {} - self.lookup = {} - self.attrs = {} - self.neuron = neuron - self.model._backend = str('ExternalSim') - self.backend = self - self.model.attrs = {} - - #self.orig_lems_file_path = 'satisfying' - #self.model._backend.use_memory_cache = False - #self.model.unpicklable += ['h','ns','_backend'] - self.dt = dt - if type(DTC) is not type(None): - if type(DTC.attrs) is not type(None): - - self.set_attrs(**DTC.attrs) - assert len(self.model.attrs.keys()) > 0 - - if hasattr(DTC,'current_src_name'): - self._current_src_name = DTC.current_src_name - - if hasattr(DTC,'cell_name'): - self.cell_name = DTC.cell_name - - self.load_model() - - def get_membrane_potential(self): - """Must return a neo.core.AnalogSignal. - And must destroy the hoc vectors that comprise it. - """ - #dt = float(copy.copy(self.neuron.dt)) - data = self.hhcell.get_data().segments[0] - volts = data.filter(name="v")[0]#/10.0 - #data_block = all_cells.get_data() - - vm = AnalogSignal(volts, - units = mV, - sampling_period = self.dt *ms) - #results['vm'] = vm - return vm#data.filter(name="v")[0] - - def _local_run(self): - ''' - pyNN lazy array demands a minimum population size of 3. Why is that. - ''' - results = {} - DURATION = 1000.0 - - #ctx_cells.celltype.recordable - - - if self.celltype == 'HH_cond_exp': - - self.hhcell.record('spikes','v') - - else: - self.neuron.record_v(self.hhcell, "Results/HH_cond_exp_%s.v" % str(neuron)) - - #self.neuron.record_gsyn(self.hhcell, "Results/HH_cond_exp_%s.gsyn" % str(neuron)) - self.neuron.run(DURATION) - data = self.hhcell.get_data().segments[0] - volts = data.filter(name="v")[0]#/10.0 - #data_block = all_cells.get_data() - - vm = AnalogSignal(volts, - units = mV, - sampling_period = self.dt *ms) - results['vm'] = vm - results['t'] = vm.times # self.times - results['run_number'] = results.get('run_number',0) + 1 - return results - - - - - def set_attrs(self,**attrs): - self.init_backend() - self.model.attrs.update(attrs) - assert type(self.model.attrs) is not type(None) - self.hhcell[0].set_parameters(**attrs) - return self - - - def inject_square_current(self,current): - attrs = copy.copy(self.model.attrs) - self.init_backend() - self.set_attrs(**attrs) - c = copy.copy(current) - if 'injected_square_current' in c.keys(): - c = current['injected_square_current'] - - stop = float(c['delay'])+float(c['duration']) - duration = float(c['duration']) - start = float(c['delay']) - amplitude = float(c['amplitude']) - electrode = self.neuron.DCSource(start=start, stop=stop, amplitude=amplitude) - - - electrode.inject_into(self.hhcell) - self.results = self._local_run() - self.vm = self.results['vm'] - - def get_APs(self,vm): - vm = self.get_membrane_potential() - waveforms = sf.get_spike_waveforms(vm,threshold=-45.0*mV) - return waveforms - - def get_spike_train(self,**run_params): - vm = self.get_membrane_potential() - - spike_train = threshold_detection(vm,threshold=-45.0*mV) - - return spike_train - - def get_spike_count(self,**run_params): - vm = self.get_membrane_potential() - return len(threshold_detection(vm,threshold=-45.0*mV)) - - model.init_backend = MethodType(init_backend,model) - model.get_spike_count = MethodType(get_spike_count,model) - model.get_APs = MethodType(get_APs,model) - model.get_spike_train = MethodType(get_spike_train,model) - model.set_attrs = MethodType(set_attrs, model) # Bind to the score. - model.inject_square_current = MethodType(inject_square_current, model) # Bind to the score. - model.set_attrs = MethodType(set_attrs, model) # Bind to the score. - model.get_membrane_potential = MethodType(get_membrane_potential,model) - model.load_model = MethodType(load_model, model) # Bind to the score. - model._local_run = MethodType(_local_run,model) - model.init_backend(model) - #model.load_model() #= MethodType(load_model, model) # Bind to the score. - - return model -HH_cond_exp = bind_NU_interface(HH_cond_exp) -#HH_cond_exp - - -# In[ ]: - -electro_tests = [] -obs_frame = {} -test_frame = {} -import os -import pickle -try: - - electro_path = str(os.getcwd())+'all_tests.p' - - assert os.path.isfile(electro_path) == True - with open(electro_path,'rb') as f: - (obs_frame,test_frame) = pickle.load(f) - -except: - for p in pipe: - p_tests, p_observations = get_neab.get_neuron_criteria(p) - obs_frame[p["name"]] = p_observations#, p_tests)) - test_frame[p["name"]] = p_tests#, p_tests)) - electro_path = str(os.getcwd())+'all_tests.p' - with open(electro_path,'wb') as f: - pickle.dump((obs_frame,test_frame),f) - - -# In[ ]: - -use_test = test_frame["Neocortex pyramidal cell layer 5-6"] -use_test[0].observation -#from neuronunit.tests import RheobaseP -from neuronunit.tests.fi import RheobaseTest# as discovery - -rtp = RheobaseTest(use_test[0].observation) -use_test[0] = rtp - - -HH_cond_exp.attrs = HH_cond_exp.simple_parameters(HH_cond_exp) -#print(HH_cond_exp.attrs) -HH_cond_exp.scaled_parameters(HH_cond_exp) -dir(HH_cond_exp) -HH_cond_exp.default_initial_values -HH_cond_exp.attrs -NGEN = 10 -MU = 10 -from neuronunit.optimization import optimization_management as om -explore_ranges = {'e_rev_Na' : (40,70), 'e_rev_K': (-90.0,-75.0), 'cm' : (0.25,1.5)} -npcl, DO = om.run_ga(explore_ranges,NGEN,use_test,free_params=explore_ranges.keys(), NSGA = True, MU = MU,model_type=None) - -#hc = - - -# In[ ]: - -#dir(HH_cond_exp) -#HH_cond_exp.get_parameters() -#hhcell[0].get_parameters() -#dir(HH_cond_exp) -HH_cond_exp.attrs = HH_cond_exp.simple_parameters(HH_cond_exp) -HH_cond_exp.celltype = HH_cond_exp -iparams = {} -iparams['injected_square_current'] = {} -#iparams['injected_square_current']['amplitude'] = 1.98156805*pq.pA -iparams['injected_square_current']['amplitude'] = 0.68156805*pq.pA - -DELAY = 100.0*pq.ms -DURATION = 1000.0*pq.ms -iparams['injected_square_current']['delay'] = DELAY -iparams['injected_square_current']['duration'] = int(DURATION) - -HH_cond_exp.inject_square_current(iparams) -print(HH_cond_exp.get_spike_count()) - -print(HH_cond_exp.vm) - -import matplotlib.pyplot as plt -plt.plot(HH_cond_exp.vm.times,HH_cond_exp.vm) - - -plt.show() -iparams['injected_square_current']['amplitude'] = 0.8598156805*pq.pA -#iparams['injected_square_current']['amplitude'] = 2000.98156805*pq.pA - -DELAY = 100.0*pq.ms -DURATION = 1000.0*pq.ms -iparams['injected_square_current']['delay'] = DELAY -iparams['injected_square_current']['duration'] = int(DURATION) - -HH_cond_exp.inject_square_current(iparams) -print(HH_cond_exp.get_spike_count()) -import matplotlib.pyplot as plt -plt.plot(HH_cond_exp.vm.times,HH_cond_exp.vm) - -plt.show() -pred = use_test[0].generate_prediction(HH_cond_exp) - - -# In[ ]: - -#dir(HH_cond_exp) - - -# In[ ]: - -print(pred) - - -# In[ ]: - -iparams['injected_square_current']['amplitude'] = pred['value'] -#iparams['injected_square_current']['amplitude'] = 2000.98156805*pq.pA - -DELAY = 100.0*pq.ms -DURATION = 1000.0*pq.ms -iparams['injected_square_current']['delay'] = DELAY -iparams['injected_square_current']['duration'] = int(DURATION) - -HH_cond_exp.inject_square_current(iparams) -print(HH_cond_exp.get_spike_count()) -import matplotlib.pyplot as plt -plt.plot(HH_cond_exp.vm.times,HH_cond_exp.vm) - - -# In[ ]: - - -import pyNN -from pyNN import neuron -from pyNN.neuron import EIF_cond_exp_isfa_ista -#neurons = pyNN.Population(N_CX, pyNN.EIF_cond_exp_isfa_ista, RS_parameters) - -cell = neuron.create(EIF_cond_exp_isfa_ista()) -#cell[0].set_parameters(**LTS_parameters) -cell[0].get_parameters() - - -# In[ ]: - - - - -# In[ ]: - - - -explore_ranges = {'E_Na' : (40,70), 'E_K': (-90.0,-75.0), 'C_m' : (0.25,1.5), 'g_K':(30,40), 'g_Na':(100,140), 'g_L':(0.1,0.5), 'E_L':(-60.0,-45)} - -attrs = { 'g_K' : 36.0, 'g_Na' : 120.0, 'g_L' : 0.3, 'C_m' : 1.0, 'E_L' : -54.387, 'E_K' : -77.0, 'E_Na' : 50.0, 'vr':-65.0 } - - -from neuronunit.optimization import optimization_management as om -print(test_frame) -MU = 12 -NGEN = 25 -cnt = 1 -#hc = { 'g_L' : 0.3, 'E_L' : -54.387, -hc = {'vr':-65.0 } - -#npcl, DO = om.run_g -npcl, DO = om.run_ga(explore_ranges,NGEN,use_test,free_params=explore_ranges.keys(), hc = hc, NSGA = True, MU = MU,model_type='HH') - - - -# In[ ]: - - - diff --git a/neuronunit/models/backends/fhn.py b/neuronunit/models/backends/fhn.py deleted file mode 100644 index e0890f3b0..000000000 --- a/neuronunit/models/backends/fhn.py +++ /dev/null @@ -1,260 +0,0 @@ -#-*- coding: utf-8 -*- -import numpy as np -from scipy import integrate as spint -from matplotlib import pyplot as plt - - - -from numba import jit - -class FNNeuron(Neuron): - """FitzHugh-Naguno neuron. - The units in this model are different from the HH ones. - Sources: - https://en.wikipedia.org/w/index.php?title=FitzHugh%E2%80%93Nagumo_model&oldid=828788626 - http://www.scholarpedia.org/article/FitzHugh-Nagumo_model - """ - # TODO: add description of the parameters - def __init__(self, I_ampl=0.85, V_0=-0.7, W_0=-0.5, a=0.7, b=0.8, - tau=12.5, neurondict=dict()): - Neuron.__init__(self, I_ampl=I_ampl, **neurondict) - - # Store intial conditions - self.V_0 = V_0 - self.W_0 = W_0 - - # Store model parameters - self.a = a - self.b = b - self.tau = tau - - # Units - self.time_unit = "" - self.V_unit = "" - self.I_unit = "" - - - def V_ddt(self, V, W, I_ext): - """Time derivative of the potential V. - """ - timederivative = V - np.power(V, 3)/3. - W + I_ext - return timederivative - - def W_ddt(self, V, W): - """Time derivative of the recovery variable W. - """ - timederivative = (V + self.a - self.b*W)/self.tau - return timederivative - - - - def _rhs(self, y, t): - - V = y[0] - W = y[1] - output = np.array((self.V_ddt(V, W, self.I_ext(t)), - self.W_ddt(V, W))) - return output - - - def solve(self, ts=None): - """Integrate the differential equations of the system. - The integration is made using an Euler algorithm and - the method I_ext() is used to modelize the external current. - Parameters - ---------- - ts : array - Times were the solution value is stored. - - Returns - ------- - Vs : array - Membrane potential at the given times. - """ - # Simulation times - if ts is None: - self.ts = np.linspace(0, 1000, 1000) - else: - self.ts = ts - - y0 = np.array((self.V_0, self.W_0)) - sol = spint.odeint(self._rhs, y0, self.ts) - # solve_ivp returns a lot of extra information about the solutions, but - # we are only interested in the values of the variables, which are stored - # in sol.y - self.Vs = sol[:,0] - self.Ws = sol[:,1] - - return Vs - - -class LinearIFNeuron(Neuron): - """Linear integrate-and-fire neuron. - Sources: - http://icwww.epfl.ch/~gerstner/SPNM/node26.html - http://www.ugr.es/~jtorres/Tema_4_redes_de_neuronas.pdf (spanish) - """ - def __init__( - self, I_ampl=10, V_0=-80, R=0.8, V_thr=-68.5, V_fire=20, - V_relax=-80, relaxtime=5, firetime=2, neurondict=dict()): - """Init method. - Parameters - ---------- - I_ampl : float - External current. - V_0 : float - Initial value of the membrane potential. - R : float - Model parameter (see references). - - V_thr : float - Voltage firing thresold. - V_fire : float - Voltaje firing value. - v_relax : float - Voltage during the relax time after the firing. - relaxtime : float - Relax time after firing - firetime : float - Fire duration. - """ - Neuron.__init__(self, I_ampl=I_ampl, **neurondict) - - # Store initial condition - self.V_0 = V_0 - - # Store parameters - self.R = R # k ohmn/cm2 - self.V_thr = V_thr # Fire threshold - self.V_fire = V_fire # Firing voltage - self.V_relax = V_relax # Firing voltage - self.relaxtime = relaxtime # Relax time after firing - self.firetime = firetime # Fire duration - - # Units - self.I_unit = "(microA/cm2)" - self.time_unit = "(ms)" - self.V_unit = "(mV)" - - - def fire_condition(self, V): - """Return True if the fire condition is satisfied. - """ - - def V_ddt(self, V, I_ext): - """Time derivative of the membrane potential. - """ - timederivative = (-(V + 65)/self.R + I_ext)/self.C - return timederivative - - - def solve(self, ts=None, timestep=0.1): - """Integrate the differential equations of the system. - - The integration is made using an Euler algorithm and - the method I_ext() is used to modelize the external current. - Parameters - ---------- - ts : array - Times were the solution value is stored. - Returns - ------- - Vs : array - Membrane potential at the given times. - """ - # Initialization - t_last = 0. # Time of the last measure - V = self.V_0 # Present voltage - - # Create array to store the measured voltages - Vs = np.zeros(ts.size, dtype=float) - - # Check the firing condition. - # _neuronstate stores the state of the neuron. - # If it is firing _neuronstate=1, if relaxing it equals 2, else - # it is 0. - self._neuronstate = int(V > self.V_thr) - if self._neuronstate == 1: - self._t_endfire = t_last + self.firetime - - for j_measure, t_measure in enumerate(ts): - # Calculate the number of steps before the next measure - nsteps = int((t_measure - t_last)/timestep) - t = t_last - - for j_step in range(nsteps): - if self._neuronstate == 0: - # Advance time step - V += self._rhs(t_last, V)*timestep - - # Check if the firing condition is met - self._neuronstate = int(V > self.V_thr) - if self._neuronstate == 1: - self._t_endfire = t + self.firetime - - # Firing - elif self._neuronstate == 1: - V = self.V_fire - - # Check if the firing has ended - if t > self._t_endfire: - self._neuronstate = 2 - self._t_endrelax = t + self.relaxtime - - # Relaxing - elif self._neuronstate == 2: - V = self.V_relax - - # Check if the relaxing time has ended - if t > self._t_endrelax: - self._neuronstate = 0 - - # Update time - t += timestep - - # Measure - Vs[j_measure] = V - t_last = t_measure - - return Vs - - - def _rhs(self, t, y): - """Right hand side of the system of equations to be solved. - This functions is necessary to use scipy.integrate. - Parameters - ---------- - y : float - Array with the present state of the variable - which time derivative is to be solved, V. - t : float - Time variable. - Returns - ------- - timederivative : float - Time derivatives of the variable. - - """ - V = y - output = self.V_ddt(V, self.I_ext(t)) - return output - - -# def solve(self, ts=None): -# """Integrate the differential equations of the system. -# -# """ -# # Simulation times -# if ts is None: -# self.ts = np.linspace(0, 1000, 1000) -# else: -# self.ts = ts -# -# y0 = self.V_0 -# sol = spint.odeint(self._rhs, y0, self.ts) -# # solve_ivp returns a lot of extra information about the solutions, but -# # we are only interested in the values of the variables, which are stored -# # in sol.y -# self.Vs = sol[:,0] -# -# return diff --git a/neuronunit/models/backends/jNeuroML.py b/neuronunit/models/backends/jNeuroML.py index 2e03bb380..e4e108a64 100644 --- a/neuronunit/models/backends/jNeuroML.py +++ b/neuronunit/models/backends/jNeuroML.py @@ -2,19 +2,20 @@ import os import io -import pathlib import tempfile from pyneuroml import pynml from sciunit.utils import redirect_stdout from .base import Backend +from elephant.spike_train_generation import threshold_detection + class jNeuroMLBackend(Backend): """Use for simulation with jNeuroML, a reference simulator for NeuroML.""" - backend = 'jNeuroML' + name = 'jNeuroML' def init_backend(self, *args, **kwargs): """Initialize the jNeuroML backend.""" @@ -37,7 +38,8 @@ def inject_square_current(self, current): """Inject a square current into the cell.""" self.model.run_params['injected_square_current'] = current self.set_run_params() # Doesn't work yet. - + self._backend_run() + self.vm def set_stop_time(self, t_stop): """Set the stop time of the simulation.""" self.model.run_params['t_stop'] = t_stop @@ -47,32 +49,22 @@ def set_time_step(self, dt): """Set the time step of the simulation.""" self.model.run_params['dt'] = dt self.set_run_params() + def get_spike_count(self): + thresh = threshold_detection(self.vm) + return len(thresh) def _backend_run(self): """Run the simulation.""" f = pynml.run_lems_with_jneuroml - try: - self.exec_in_dir = self.model.temp_dir.name - except: - self.exec_in_dir = tempfile.mkdtemp() - - path = pathlib.Path(self.exec_in_dir) - (path / 'results').mkdir(exist_ok=True) - + self.exec_in_dir = tempfile.mkdtemp() lems_path = os.path.dirname(self.model.orig_lems_file_path) with redirect_stdout(self.stdout): results = f(self.model.lems_file_path, paths_to_include=[lems_path], skip_run=self.model.skip_run, nogui=self.model.run_params['nogui'], - load_saved_data=True, - plot=False, + load_saved_data=True, plot=False, exec_in_dir=self.exec_in_dir, - exit_on_fail=False, verbose=self.model.run_params['v']) - if results is None or not results: - print(("No results returned: buffered error, warning, " - "and notice messages follow:\n")) - print(self.stdout.getvalue()) + self.vm = results['vm'] return results - diff --git a/neuronunit/models/section_extension.py b/neuronunit/models/section_extension.py deleted file mode 100644 index 04cdd6e80..000000000 --- a/neuronunit/models/section_extension.py +++ /dev/null @@ -1,43 +0,0 @@ -"""These classes are for compatibility w/ the old neuronunit.neuron module.""" -import sciunit -from neuronunit.models.backends import NEURONBackend - - -class HasSegment(sciunit.Capability): - """Model has a membrane segment of NEURON simulator.""" - - def setSegment(self, section, location=0.5): - """Set the target NEURON segment object. - - section: NEURON Section object - location: 0.0-1.0 value that refers to the location - along the section length. Defaults to 0.5 - """ - self.section = section - self.location = location - - def getSegment(self): - """Return the segment at the active section location.""" - return self.section(self.location) - - -class SingleCellModel(sciunit.Model): - def __init__(self, - neuronVar, - section, - loc=0.5, - name=None): - self._backend = NEURONBackend() - super(SingleCellModel, self).__init__() - hs = HasSegment() - hs.setSegment(section, loc) - self.reset_neuron(neuronVar) - self.section = section - self.loc = loc - self.name = name - self.tVector = self.h.Vector() - self.vVector = self.h.Vector() - self.vVector.record(hs.getSegment()._ref_v) - self.tVector.record(self.h._ref_t) - - return diff --git a/neuronunit/models/static.py b/neuronunit/models/static.py index cd26d3aff..0c170f7e1 100755 --- a/neuronunit/models/static.py +++ b/neuronunit/models/static.py @@ -5,11 +5,12 @@ import pickle import quantities as pq import sciunit +from sciunit.models import RunnableModel import sciunit.capabilities as scap from sciunit.models import RunnableModel -class StaticModel(sciunit.Model, +class StaticModel(RunnableModel, cap.ReceivesSquareCurrent, cap.ProducesActionPotentials, cap.ProducesMembranePotential): @@ -29,7 +30,7 @@ def __init__(self, vm): raise TypeError('vm must be a neo.core.AnalogSignal') self.vm = vm - + self.backend = 'static_model' def run(self, **kwargs): pass @@ -58,6 +59,7 @@ def __init__(self, *args, **kwargs): """Create an instace of a model that produces a static waveform.""" super(ExternalModel, self).__init__(*args, **kwargs) + def set_membrane_potential(self, vm): self.vm = vm @@ -66,6 +68,11 @@ def set_model_attrs(self, attrs): def get_membrane_potential(self): return self.vm + def get_APs(self, **run_params): + """Return the APs, if any, contained in the static waveform.""" + vm = self.get_membrane_potential(**run_params) + waveforms = sf.get_spike_waveforms(vm) + return waveforms class RandomVmModel(RunnableModel, cap.ProducesMembranePotential, cap.ReceivesCurrent): @@ -76,4 +83,4 @@ def get_membrane_potential(self): return vm def inject_square_current(self, current): - pass \ No newline at end of file + pass diff --git a/neuronunit/models/very_reduced_sans_lems.py b/neuronunit/models/very_reduced_sans_lems.py new file mode 100644 index 000000000..ac47f086e --- /dev/null +++ b/neuronunit/models/very_reduced_sans_lems.py @@ -0,0 +1,102 @@ +"""NeuronUnit model class for reduced neuron models.""" + +import sciunit +import neuronunit.capabilities as cap +from sciunit.models.runnable import RunnableModel + + +import numpy as np +from neo.core import AnalogSignal +import quantities as pq +from neuronunit.optimization.data_transport_container import DataTC +import copy + +import neuronunit.capabilities.spike_functions as sf +class VeryReducedModel(RunnableModel, + cap.ReceivesSquareCurrent, + cap.ProducesActionPotentials, + cap.ProducesMembranePotential): + """Base class for reduced models, not using LEMS, + and not requiring file paths this is to wrap pyNN models, Brian models, + and other self contained models+model descriptions""" + + def __init__(self,name='',backend=None, attrs={}): + """Instantiate a reduced model. + + LEMS_file_path: Path to LEMS file (an xml file). + name: Optional model name. + """ + #sciunit.Model() + + super(VeryReducedModel, self).__init__(name=name,backend=backend, attrs=attrs) + self.backend = backend + self.attrs = {} + self.run_number = 0 + self.tstop = None + self.rheobse = None + + def model_test_eval(self,tests): + """ + Take a model and some tests + Evaluate a test suite over them. + """ + from sciunit import TestSuite + if type(tests) is TestSuite: + not_suite = TSD({t.name:t for t in tests.tests}) + OM = OptMan(tests, backend = self._backend) + dtc = DataTC() + dtc.attrs = self.attrs + assert set(self._backend.attrs.keys()) in set(self.attrs.keys()) + dtc.backend = self._backend + dtc.tests = copy.copy(not_suite) + dtc = dtc_to_rheo(dtc) + if dtc.rheobase is not None: + dtc.tests = dtc.format_test() + dtc = list(map(OM.elephant_evaluation,[dtc])) + model = dtc.dtc_to_model() + model.SM = dtc.SM + model.obs_preds = dtc.obs_preds + return dtc[0], model + + def model_to_dtc(self): + dtc = DataTC() + dtc.attrs = self.attrs + try: + dtc.backend = self.get_backend() + except: + dtc.backend = self.backend + if hasattr(self,'rheobase'): + dtc.rheobase = self.rheobase + return dtc + + def inject_square_current(self, current): + #pass + vm = self._backend.inject_square_current(current) + return vm + ''' + def get_membrane_potential(self,**run_params): + #try: + # vm = self._backend.get_membrane_potential() + #except: + #vm = self.get_membrane_potential() + print(run_params) + vm = self.get_membrane_potential(**run_params) + + return vm + ''' + def get_APs(self, **run_params): + vm = self.get_membrane_potential(**run_params) + waveforms = sf.get_spike_waveforms(vm)#,width=10*ms) + return waveforms + + def get_spike_train(self, **run_params): + vm = self._backend.get_membrane_potential(**run_params) + spike_train = sf.get_spike_train(vm) + return spike_train + + def get_spike_count(self, **run_params): + train = self.get_spike_train(**run_params) + return len(train) + + def set_attrs(self,attrs): + self.attrs.update(attrs) diff --git a/neuronunit/optimization/__init__.py b/neuronunit/optimization/__init__.py index f6385ef86..abb13c08d 100644 --- a/neuronunit/optimization/__init__.py +++ b/neuronunit/optimization/__init__.py @@ -1,5 +1,4 @@ - """NeuronUnit Optimization classes""" -#from .evaluate_as_module import * -#from .nsga_parallel import * +# from .evaluate_as_module import * +# from .nsga_parallel import * diff --git a/neuronunit/optimization/algorithms.py b/neuronunit/optimization/algorithms.py index 3fb5bc677..1a5d7de45 100644 --- a/neuronunit/optimization/algorithms.py +++ b/neuronunit/optimization/algorithms.py @@ -1,5 +1,7 @@ -"""Optimisation class -Copyright (c) 2016, EPFL/Blue Brain Project +"""Optimisation class""" + +""" +Copyright (c) 2016-2020, EPFL/Blue Brain Project This file is part of BluePyOpt @@ -17,190 +19,248 @@ 51 Franklin Street, Fifth Floor, Boston, MA 02110-1301 USA. """ - +# pylint: disable=R0914, R0912 import random import logging +import shutil +import os import deap.algorithms import deap.tools import pickle -import numpy - -import copy -from neuronunit.optimization import optimization_management as om - -import pdb -import math -from bluepyopt.deapext.optimisations import tools -#from bluepyopt import tools +from tqdm.auto import tqdm +from .stoppingCriteria import MaxNGen +import streamlit as st +logger = logging.getLogger('__main__') import numpy as np +def _define_fitness(pop, obj_size): + ''' Re-instanciate the fitness of the individuals for it to matches the + evaluation function. + ''' + from .optimisations import WSListIndividual -logger = logging.getLogger('__main__') + new_pop = [] + if pop: + for ind in pop: + new_pop.append(WSListIndividual(list(ind), obj_size=obj_size)) + + return new_pop -from deap.tools import selNSGA2 def _evaluate_invalid_fitness(toolbox, population): - '''Evaluate the individuals with an invalid fitness + '''Evaluate the individuals with an invalid fitness - Returns the count of individuals with invalid fitness - ''' - invalid_ind = [ind for ind in population if not ind.fitness.valid] - invalid_pop,fitnesses = toolbox.evaluate(invalid_ind) - for ind, fit in zip(invalid_pop,fitnesses): - ind.fitness.values = fit - return invalid_pop + Returns the count of individuals with invalid fitness + ''' + invalid_ind = [ind for ind in population if not ind.fitness.valid] + fitnesses = toolbox.map(toolbox.evaluate, invalid_ind) + for ind, fit in zip(invalid_ind, fitnesses): + ind.fitness.values = fit + return len(invalid_ind) -def _update_history_and_hof(halloffame,pf, history, population,td): - '''Update the hall of fame with the generated individuals - Note: History and Hall-of-Fame behave like dictionaries - ''' +def _update_history_and_hof(halloffame, history, population): + '''Update the hall of fame with the generated individuals - if halloffame is not None: - halloffame.update(population) - if pf is not None: - try: - pf.update(population) - except: - print(population) - history.update(population) + Note: History and Hall-of-Fame behave like dictionaries + ''' + if halloffame is not None: + halloffame.update(population) - return (halloffame,pf) + history.update(population) def _record_stats(stats, logbook, gen, population, invalid_count): - '''Update the statistics with the new population''' - record = stats.compile(population) if stats is not None else {} - logbook.record(gen=gen, nevals=invalid_count, **record) - -def gene_bad(offspring): - gene_bad = False - for o in offspring: - for gene in o: - if math.isnan(gene): - gene_bad = True - return gene_bad + '''Update the statistics with the new population''' + record = stats.compile(population) if stats is not None else {} + logbook.record(gen=gen, nevals=invalid_count, **record) + +from deap import tools +def _get_offspring_time_diminishing_eta(parents, toolbox, cxpb, mutpb,gen, ngen): + '''return the offspring, use toolbox.variate if possible''' + + + BOUND_LOW = [] + BOUND_UP = [] + NDIM = len(parents[0]) + fit_dim = len(parents[0].fitness.values) + for x in range(0,len(parents[0])): + BOUND_LOW.append(toolbox.uniformparams.args[0][x]) + BOUND_UP.append(toolbox.uniformparams.args[1][x]) + ETA = int(30.0*(1-(gen/ngen))) + toolbox.register("mate", tools.cxSimulatedBinaryBounded, low=BOUND_LOW, up=BOUND_UP, eta=ETA) + toolbox.register("mutate", tools.mutPolynomialBounded, low=BOUND_LOW, up=BOUND_UP, eta=ETA, indpb=1.0/NDIM) + if (1-(gen/ngen))/2.0>0.05: + mutpb = (1-(gen/ngen))/2.0 + else: + mutpb = 0.05 + if (1-(gen/ngen))>0.4: + cxpb = (1-(gen/ngen))/1.1 + else: + cxpb = 0.4 + + if hasattr(toolbox, 'variate'): + + return toolbox.variate(parents, toolbox, cxpb, mutpb) + return deap.algorithms.varAnd(parents, toolbox, cxpb, mutpb) def _get_offspring(parents, toolbox, cxpb, mutpb): - '''return the offsprint, use toolbox.variate if possible''' - if hasattr(toolbox, 'variate'): - offspring = toolbox.variate(parents, toolbox, cxpb, mutpb) - # This suppresses errors that need to be handled - # while gene_bad(offspring) == True: - # offspring = deap.algorithms.varAnd(parents, toolbox, cxpb, mutpb) - return offspring - - -def _get_elite(halloffame, nelite): - - if nelite > 0 and halloffame is not None: - normsorted_idx = numpy.argsort([ind.fitness.norm for ind in halloffame]) - #hofst = [(sum(h.dtc.scores.values()),h.dtc) for h in halloffame ] - #ranked = sorted(hofst, key=lambda w: w[0],reverse = True) - return [halloffame[idx] for idx in normsorted_idx[:nelite]] + '''return the offspring, use toolbox.variate if possible''' + + if hasattr(toolbox, 'variate'): + return toolbox.variate(parents, toolbox, cxpb, mutpb) + return deap.algorithms.varAnd(parents, toolbox, cxpb, mutpb) + + +def _check_stopping_criteria(criteria, params): + for c in criteria: + c.check(params) + if c.criteria_met: + logger.info("Run stopped because of stopping criteria: " + c.name) + return True else: - return list() + return False + +def filter_parents(parents): + """ + --synopsis: This method does not work yet. + #Remove the genes that represent the cliff + #edge. Possibly counter productive. + """ + import copy + parentsc = copy.copy(parents) + orig_len = len(parents) + cnt = 0 + for p in parentsc: + flag=False + for fv in p.fitness.values: + if fv == 1000.0: + flag=True + if flag: + cnt += 1 + while cnt > 0: + for i, p in enumerate(parentsc): + flag=False + for fv in p.fitness.values: + if fv == 1000.0: + flag=True + if flag: + del parentsc[i] + cnt -= 1 + cnt=0 + while len(parentsc) < orig_len: + parentsc.append(parentsc[cnt]) + cnt+=1 + return parentsc + def eaAlphaMuPlusLambdaCheckpoint( - population, - toolbox, - mu, - cxpb, - mutpb, - ngen, - stats = None, - halloffame = None, - pf=None, - nelite = 3, - cp_frequency = 1, - cp_filename = None, - continue_cp = False, - selection = 'selNSGA2', - td=None): - print(halloffame,pf) - gen_vs_pop = [] - - if continue_cp: - # A file name has been given, then load the data from the file - cp = pickle.load(open(cp_filename, "r")) - population = cp["population"] - parents = cp["parents"] - start_gen = cp["generation"] - halloffame = cp["halloffame"] - logbook = cp["logbook"] - history = cp["history"] - random.setstate(cp["rndstate"]) - else: - # Start a new evolution - start_gen = 1 - parents = population[:] - gen_vs_pop.append(population) - logbook = deap.tools.Logbook() - logbook.header = ['gen', 'nevals'] + (stats.fields if stats else []) - history = deap.tools.History() - - # TODO this first loop should be not be repeated ! - invalid_ind = _evaluate_invalid_fitness(toolbox, population) - invalid_count = len(invalid_ind) - gen_vs_hof = [] - halloffame, pf = _update_history_and_hof(halloffame, pf, history, population, td) - - gen_vs_hof.append(halloffame) - _record_stats(stats, logbook, start_gen, population, invalid_count) - # Begin the generational process - for gen in range(start_gen + 1, ngen + 1): - offspring = _get_offspring(parents, toolbox, cxpb, mutpb) - - - assert len(offspring)>0 - population = parents + offspring - gen_vs_pop.append(population) - - invalid_count = _evaluate_invalid_fitness(toolbox, offspring) - halloffame, pf = _update_history_and_hof(halloffame,pf, history, population, td) - _record_stats(stats, logbook, gen, population, invalid_count) - set_ = False - - if str('selIBEA') == selection: - toolbox.register("select",tools.selIBEA) - set_ = True - if str('selNSGA') == selection: - toolbox.register("select",selNSGA2) - set_ = True - assert set_ == True - - elite = _get_elite(halloffame, nelite) - gen_vs_pop.append(copy.copy(population)) - parents = toolbox.select(population, mu) - - #elif selection == str('selIBEA'): - # from . import tools - # toolbox.register("select", tools.selIBEA) - # parents = toolbox.select(population + _get_elite(halloffame, nelite), mu) - - logger.info(logbook.stream) - - if(cp_filename and cp_frequency and - gen % cp_frequency == 0): - cp = dict(population=population, - generation=gen, - parents=parents, - halloffame=halloffame, - history=history, - logbook=logbook, - rndstate=random.getstate()) - pickle.dump(cp, open(cp_filename, "wb")) - print('Wrote checkpoint to %s', cp_filename) - logger.debug('Wrote checkpoint to %s', cp_filename) - - unique_values = [ p.dtc.attrs.values() for p in population ] - - print(len(unique_values) == len(set(unique_values))) - - #print(set(gen_vs_pop[-1][0].dtc.attrs.values()) in set(population[0].dtc.attrs.values())) - - - return population, halloffame, pf, logbook, history, gen_vs_pop + population, + toolbox, + mu, + cxpb, + mutpb, + ngen, + stats=None, + halloffame=None, + cp_frequency=1, + cp_filename=None, + continue_cp=False): + r"""This is the :math:`(~\alpha,\mu~,~\lambda)` evolutionary algorithm + + Args: + population(list of deap Individuals) + toolbox(deap Toolbox) + mu(int): Total parent population size of EA + cxpb(float): Crossover probability + mutpb(float): Mutation probability + ngen(int): Total number of generation to run + stats(deap.tools.Statistics): generation of statistics + halloffame(deap.tools.HallOfFame): hall of fame + cp_frequency(int): generations between checkpoints + cp_filename(string): path to checkpoint filename + continue_cp(bool): whether to continue + """ + + if cp_filename: + cp_filename_tmp = cp_filename + '.tmp' + + if continue_cp: + # A file name has been given, then load the data from the file + cp = pickle.load(open(cp_filename, "rb")) + population = cp["population"] + parents = cp["parents"] + start_gen = cp["generation"] + halloffame = cp["halloffame"] + logbook = cp["logbook"] + history = cp["history"] + random.setstate(cp["rndstate"]) + + # Assert that the fitness of the individuals match the evaluator + obj_size = len(population[0].fitness.wvalues) + population = _define_fitness(population, obj_size) + parents = _define_fitness(parents, obj_size) + _evaluate_invalid_fitness(toolbox, parents) + _evaluate_invalid_fitness(toolbox, population) + + else: + prog_bar = st.progress(0) + # Start a new evolution + start_gen = 1 + parents = population[:] + logbook = deap.tools.Logbook() + logbook.header = ['gen', 'nevals'] + (stats.fields if stats else []) + history = deap.tools.History() + + invalid_count = _evaluate_invalid_fitness(toolbox, population) + _update_history_and_hof(halloffame, history, population) + _record_stats(stats, logbook, start_gen, population, invalid_count) + logger.info(logbook.stream) + stopping_criteria = [MaxNGen(ngen)] + + # Begin the generational process + gen = start_gen + 1 + stopping_params = {"gen": gen} + pbar = tqdm(total=ngen) + while not(_check_stopping_criteria(stopping_criteria, stopping_params)): + #offspring = _get_offspring(parents, toolbox, cxpb, mutpb) + offspring = _get_offspring_time_diminishing_eta(parents, toolbox, cxpb, mutpb,gen,ngen) + population = parents + offspring + [halloffame[0]] + + stop = _check_stopping_criteria(stopping_criteria, stopping_params) + + invalid_count = _evaluate_invalid_fitness(toolbox, offspring) + _update_history_and_hof(halloffame, history, population) + _record_stats(stats, logbook, gen, population, invalid_count) + # Select the next generation parents + ## + # was /4 + ## + parents = toolbox.select(population, int(mu/2.5)) + logger.info(logbook.stream) + + if(cp_filename and cp_frequency and + gen % cp_frequency == 0): + cp = dict(population=population, + generation=gen, + parents=parents, + halloffame=halloffame, + history=history, + logbook=logbook, + rndstate=random.getstate()) + pickle.dump(cp, open(cp_filename_tmp, "wb")) + if os.path.isfile(cp_filename_tmp): + shutil.copy(cp_filename_tmp, cp_filename) + logger.debug('Wrote checkpoint to %s', cp_filename) + current_prog = gen / ngen + prog_bar.progress(current_prog) + gen += 1 + stopping_params["gen"] = gen + pbar.update(1) + pbar.update(1) + pbar.close() + + return population, halloffame, logbook, history diff --git a/neuronunit/optimization/bp_opt.py b/neuronunit/optimization/bp_opt.py deleted file mode 100644 index 83674a92f..000000000 --- a/neuronunit/optimization/bp_opt.py +++ /dev/null @@ -1,357 +0,0 @@ -"""Optimisation class""" - -""" -Copyright (c) 2016, EPFL/Blue Brain Project - - This file is part of BluePyOpt - - This library is free software; you can redistribute it and/or modify it under - the terms of the Lesser General Public License version 3.0 as published - by the Free Software Foundation. - - This library is distributed in the hope that it will be useful, but WITHOUT - ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS - FOR A PARTICULAR PURPOSE. See the GNU Lesser General Public License for more - details. - - You should have received a copy of the GNU Lesser General Public License - along with this library; if not, write to the Free Software Foundation, Inc., - 51 Franklin Street, Fifth Floor, Boston, MA 02110-1301 USA. -""" - -# pylint: disable=R0912, R0914 -from neuronunit.optimization import optimization_management - -import random -import logging -import functools -import numpy - -import deap -import deap.base -import deap.algorithms -import deap.tools - -from . import algorithms -from bluepyopt.deapext.optimisations import tools - -import numpy -from numba import jit -logger = logging.getLogger('__main__') - -# TODO decide which variables go in constructor, which ones go in 'run' function -# TODO abstract the algorithm by creating a class for every algorithm, that way -# settings of the algorithm can be stored in objects of these classes - -from neuronunit.optimization.optimization_management import evaluate, update_deap_pop -from neuronunit.optimization import optimization_management -import numpy as np - -class WeightedSumFitness(deap.base.Fitness): - - """Fitness that compares by weighted sum""" - - def __init__(self, values=(), obj_size=None): - self.weights = [-1.0] * obj_size if obj_size is not None else [-1] - - super(WeightedSumFitness, self).__init__(values) - - @property - def weighted_sum(self): - """Weighted sum of wvalues""" - return sum(self.wvalues) - - @property - def sum(self): - """Weighted sum of values""" - return sum(self.values) - - @property - def norm(self): - """Frobenius norm of values""" - return numpy.linalg.norm(self.values) - - def __le__(self, other): - return self.weighted_sum <= other.weighted_sum - - def __lt__(self, other): - return self.weighted_sum < other.weighted_sum - - def __deepcopy__(self, _): - """Override deepcopy""" - - cls = self.__class__ - result = cls.__new__(cls) - result.__dict__.update(self.__dict__) - return result - - -class WSListIndividual(list): - - """Individual consisting of list with weighted sum field""" - - def __init__(self, *args, **kwargs): - """Constructor""" - self.fitness = WeightedSumFitness(obj_size=kwargs['obj_size']) - self.dtc = None - self.rheobase = None - del kwargs['obj_size'] - super(WSListIndividual, self).__init__(*args, **kwargs) - - def set_fitness(self,obj_size): - self.fitness = WeightedSumFitness(obj_size=obj_size) - - -class WSFloatIndividual(float): - """Individual consisting of list with weighted sum field""" - def __init__(self, *args, **kwargs): - """Constructor""" - self.dtc = None - self.rheobase = None - super(WSFloatIndividual, self).__init__() - - def set_fitness(self,obj_size): - self.fitness = WeightedSumFitness(obj_size=obj_size) - -import bluepyopt.optimisations -class SciUnitOptimization(bluepyopt.optimisations.Optimisation): - - """DEAP Optimisation class""" - def __init__(self, error_criterion = None, evaluator = None, - selection = 'selIBEA', - benchmark = False, - seed=1, - offspring_size=15, - elite_size=3, - eta=10, - mutpb=1.0, - cxpb=1.0, - map_function=None, - backend=None, - nparams = 10, - provided_dict= {}): - """Constructor""" - - super(SciUnitOptimization, self).__init__() - self.selection = selection - self.benchmark = benchmark - - self.error_criterion = error_criterion - self.seed = seed - self.offspring_size = offspring_size - self.elite_size = elite_size - self.eta = eta - self.cxpb = cxpb - self.mutpb = mutpb - self.backend = backend - # Create a DEAP toolbox - self.toolbox = deap.base.Toolbox() - self.setnparams(nparams = nparams, provided_dict = provided_dict) - self.setup_deap() - #assert len(self.params.items()) == 3 - #assert len(self.pop.dtc.attrs.items()) == 3 - - def transdict(self,dictionaries): - from collections import OrderedDict - mps = OrderedDict() - sk = sorted(list(dictionaries.keys())) - for k in sk: - mps[k] = dictionaries[k] - tl = [ k for k in mps.keys() ] - return mps, tl - - def setnparams(self, nparams = 10, provided_dict = None): - self.params = optimization_management.create_subset(nparams = nparams,provided_dict = provided_dict) - self.nparams = len(self.params) - self.params , self.td = self.transdict(self.params) - return self.params, self.td - - - def set_evaluate(self): - if self.benchmark == True: - self.toolbox.register("evaluate", benchmarks.zdt1) - else: - self.toolbox.register("evaluate", optimization_management.evaluate) - - - def grid_sample_init(self, nparams): - from neuronunit.optimization import exhaustive_search as es - npoints = self.offspring_size ** (1.0/len(list(self.params))) - npoints = np.ceil(npoints) - nparams = len(self.params) - provided_keys = list(self.params.keys()) - dic_grid, _ = es.create_grid(npoints = npoints, provided_keys = self.params) - delta = int(np.abs(len(dic_grid) - (npoints ** len(list(self.params))))) - pop = [] - - - for dg in dic_grid: - temp = list(dg.values()) - pop.append(temp) - - for d in range(0,delta): - impute = [] - for i in range(0,len(pop[0])): - impute.append(np.mean([ p[i] for p in pop ])) - pop.append(impute) - assert len(pop) == int(npoints ** len(list(self.params))) - - return pop - - def setup_deap(self): - """Set up optimisation""" - # Set random seed - random.seed(self.seed) - - # Eta parameter of crossover / mutation parameters - # Basically defines how much they 'spread' solution around - # The lower this value, the more spread - ETA = self.eta - - # Number of parameters - # Bounds for the parameters - IND_SIZE = len(list(self.params.values())) - - OBJ_SIZE = len(self.error_criterion) - LOWER = [ np.min(self.params[v]) for v in self.td ] - UPPER = [ np.max(self.params[v]) for v in self.td ] - - if self.backend == 'glif': - for index, i in enumerate(UPPER): - if i == LOWER[index]: - LOWER[index]-=2.0 - i+=2.0 - - self.grid_init = self.grid_sample_init(self.params)#(LOWER, UPPER, self.offspring_size) - - def uniform_params(lower_list, upper_list, dimensions): - if hasattr(lower_list, '__iter__'): - other = [random.uniform(lower, upper) for lower, upper in zip(lower_list, upper_list)] - else: - other = [random.uniform(lower_list, upper_list) - for _ in range(dimensions)] - return other - # Register the 'uniform' function - self.toolbox.register("uniform_params", uniform_params, LOWER, UPPER, IND_SIZE) - - - - self.toolbox.register( - "Individual", - deap.tools.initIterate, - functools.partial(WSListIndividual, obj_size=OBJ_SIZE), - self.toolbox.uniform_params) - - # Register the population format. It is a list of individuals - self.toolbox.register( - "population", - deap.tools.initRepeat, - list, - self.toolbox.Individual) - - - # Register the evaluation function for the individuals - #@jit - def custom_code(invalid_ind, as_log=None): - if type(as_log) is not type(None): - for p in invalid_ind: - for gene in p: - gene = np.log(gene) - - if self.backend is None: - invalid_pop = update_deap_pop(invalid_ind, self.error_criterion, td = self.td) - else: - - invalid_pop = update_deap_pop(invalid_ind, self.error_criterion, td = self.td, backend = self.backend) - assert len(invalid_pop) != 0 - invalid_dtc = [ i.dtc for i in invalid_pop if hasattr(i,'dtc') ] - fitnesses = list(map(evaluate, invalid_dtc)) - return (invalid_pop,fitnesses) - - self.toolbox.register("evaluate", custom_code) - # Register the mate operator - self.toolbox.register( - "mate", - deap.tools.cxSimulatedBinaryBounded, - eta=ETA, - low=LOWER, - up=UPPER) - - # Register the mutation operator - self.toolbox.register( - "mutate", - deap.tools.mutPolynomialBounded, - eta=ETA, - low=LOWER, - up=UPPER, - indpb=0.5) - - # Register the variate operator - self.toolbox.register("variate", deap.algorithms.varAnd) - - #self.toolbox.register("select", tools.selIBEA) - - #@jit - def set_pop(self): - IND_SIZE = len(list(self.params.values())) - OBJ_SIZE = len(self.error_criterion) - if IND_SIZE == 1: - # I changed this too. - pop = [ WSFloatIndividual(g,obj_size=OBJ_SIZE) for g in self.grid_init ] - else: - pop = [ WSListIndividual(g, obj_size=OBJ_SIZE) for g in self.grid_init ] - return pop - - def run(self, - max_ngen=25, - offspring_size=None, - continue_cp=False, - cp_filename=None, - cp_frequency=0): - """Run optimisation""" - # Allow run function to override offspring_size - # TODO probably in the future this should not be an object field anymore - # keeping for backward compatibility - if offspring_size is None: - offspring_size = self.offspring_size - - pop = self.toolbox.population(n=offspring_size) - pop = self.set_pop() - hof = deap.tools.HallOfFame(offspring_size) - pf = deap.tools.ParetoFront(offspring_size) - - stats = deap.tools.Statistics(key=lambda ind: ind.fitness.sum) - stats.register("avg", numpy.mean) - stats.register("std", numpy.std) - stats.register("min", numpy.min) - stats.register("max", numpy.max) - pop, hof, pf, log, history, gen_vs_pop = algorithms.eaAlphaMuPlusLambdaCheckpoint( - pop, - self.toolbox, - offspring_size, - self.cxpb, - self.mutpb, - max_ngen, - stats=stats, - halloffame=hof, - pf=pf, - nelite=self.elite_size, - cp_frequency=cp_frequency, - continue_cp=continue_cp, - cp_filename=cp_filename, - selection = self.selection, - td = self.td) - - # insert the initial HOF value back in. - td = self.td - - attr_keys = list(hof[0].dtc.attrs.keys()) - us = {} # GA utilized_space - for key in attr_keys: - temp = [ v.dtc.attrs[key] for k,v in history.genealogy_history.items() ] - us[key] = ( np.min(temp), np.max(temp)) - self.us = us - self.results = {'pop':pop,'hof':hof,'pf':pf,'log':log,'history':history,'td':td,'gen_vs_pop':gen_vs_pop} - return self.results - #pop, hof, pf, log, history, td, gen_vs_pop - diff --git a/neuronunit/optimization/covariance_adaption_approach.py b/neuronunit/optimization/covariance_adaption_approach.py deleted file mode 100644 index c660b3b60..000000000 --- a/neuronunit/optimization/covariance_adaption_approach.py +++ /dev/null @@ -1,143 +0,0 @@ - -def active_values(keyed): - DURATION = 1000.0*pq.ms - DELAY = 100.0*pq.ms - keyed['injected_square_current'] = {} - - keyed['injected_square_current']['delay']= DELAY - keyed['injected_square_current']['duration'] = DURATION - keyed['injected_square_current']['amplitude'] = dtc.rheobase - return keyed -def passive_values(keyed): - DURATION = 500.0*pq.ms - DELAY = 200.0*pq.ms - keyed['injected_square_current'] = {} - keyed['injected_square_current']['delay']= DELAY - keyed['injected_square_current']['duration'] = DURATION - keyed['injected_square_current']['amplitude'] = -10*pq.pA - return keyed - -def format_test(dtc): - ''' - pre format the current injection dictionary based on pre computed - rheobase values of current injection. - This is much like the hooked method from the old get neab file. - ''' - dtc.vtest = {} - dtc.tests = switch_logic(dtc.tests) - - for k,v in enumerate(dtc.tests): - dtc.vtest[k] = {} - dtc.vtest[k]['injected_square_current'] = {} - - if v.active == True and v.passive == False: - #keyed = dtc.vtest[k] - #dtc.vtest[k] = active_values(keyed) - - DURATION = 1000.0*pq.ms - DELAY = 100.0*pq.ms - dtc.vtest[k]['injected_square_current']['delay']= DELAY - dtc.vtest[k]['injected_square_current']['duration'] = DURATION - dtc.vtest[k]['injected_square_current']['amplitude'] = dtc.rheobase - #''' - if v.passive == True and v.active == False: - #keyed = dtc.vtest[k] - #dtc.vtest[k] = passive_values(keyed) - #''' - DURATION = 500.0*pq.ms - DELAY = 200.0*pq.ms - dtc.vtest[k]['injected_square_current'] = {} - dtc.vtest[k]['injected_square_current']['delay']= DELAY - dtc.vtest[k]['injected_square_current']['duration'] = DURATION - dtc.vtest[k]['injected_square_current']['amplitude'] = -10*pq.pA - #''' - # not returned so actually not effective - #v.params = dtc.vest[k] - - return dtc - -from neuronunit.optimzation import optimization_management - -creator.create("FitnessMin", base.Fitness, weights=(-1.0,)) -creator.create("Individual", list, fitness=creator.FitnessMin) - -toolbox = base.Toolbox() -#toolbox.register("evaluate", benchmarks.rastrigin) -self.toolbox.register("evaluate", optimization_management.evaluate) - - -import matplotlib.pyplot as plt - -# Problem size -N = 10 -NGEN = 125 -#Then, it does not get any harder. Once a Strategy is instantiated, its generate() and update() methods are registered in the toolbox for uses in the eaGenerateUpdate() algorithm. The generate() method is set to produce the created Individual class. The random number generator from numpy is seeded because the cma module draws all its number from it. -#https://github.com/DEAP/deap/blob/master/examples/es/cma_plotting.py -pop = self.set_pop() -self.toolbox.register("evaluate", optimization_management.evaluate) - -strategy = cma.Strategy(centroid=[5.0]*N, sigma=5.0, lambda_=20*N) -toolbox.register("generate", strategy.generate, creator.Individual) -toolbox.register("update", strategy.update) - -hof = tools.HallOfFame(1) -stats = tools.Statistics(lambda ind: ind.fitness.values) -stats.register("avg", numpy.mean) -stats.register("std", numpy.std) -stats.register("min", numpy.min) -stats.register("max", numpy.max) - -for gen in range(NGEN): - # Generate a new population - population = toolbox.generate() - # Evaluate the individuals - fitnesses = toolbox.map(toolbox.evaluate, population) - for ind, fit in zip(population, fitnesses): - ind.fitness.values = fit - - # Update the strategy with the evaluated individuals - toolbox.update(population) - - # Update the hall of fame and the statistics with the - # currently evaluated population - halloffame.update(population) - record = stats.compile(population) - logbook.record(evals=len(population), gen=gen, **record) - - if verbose: - print(logbook.stream) - - # Save more data along the evolution for latter plotting - # diagD is sorted and sqrooted in the update method - sigma[gen] = strategy.sigma - axis_ratio[gen] = max(strategy.diagD)**2/min(strategy.diagD)**2 - diagD[gen, :N] = strategy.diagD**2 - fbest[gen] = halloffame[0].fitness.values - best[gen, :N] = halloffame[0] - std[gen, :N] = numpy.std(population, axis=0) - - -fitnesses = toolbox.map(toolbox.evaluate, pop) -for ind, fit in zip(population, fitnesses): - ind.fitness.values = fit - -# Update the strategy with the evaluated individuals -toolbox.update(population) - -# Update the hall of fame and the statistics with the -# currently evaluated population -halloffame.update(population) -record = stats.compile(population) -logbook.record(evals=len(population), gen=gen, **record) - -if verbose: - print(logbook.stream) - -# Save more data along the evolution for latter plotting -# diagD is sorted and sqrooted in the update method -sigma[gen] = strategy.sigma -axis_ratio[gen] = max(strategy.diagD)**2/min(strategy.diagD)**2 -diagD[gen, :N] = strategy.diagD**2 -fbest[gen] = halloffame[0].fitness.values -best[gen, :N] = halloffame[0] -std[gen, :N] = numpy.std(population, axis=0) diff --git a/neuronunit/optimization/data_transport_container.py b/neuronunit/optimization/data_transport_container.py index d96459348..e239ce7a5 100644 --- a/neuronunit/optimization/data_transport_container.py +++ b/neuronunit/optimization/data_transport_container.py @@ -1,52 +1,414 @@ import numpy as np -from numba import jit +import quantities as qt +import copy +from collections import OrderedDict +from sciunit import scores +from sciunit.scores.collections import ScoreArray +import sciunit +import pandas as pd +from jithub.models import model_classes +from typing import Any, Dict, List, Optional, Tuple, Type, Union, Text + + class DataTC(object): - ''' - Data Transport Container + """ + --Synopsis: Data Transport Container + This Object class serves as a data type for storing rheobase search + attributes and apriori model parameters, + with the distinction that unlike the LEMS model this class + can be cheaply transported across HOSTS/CPUs + """ - This Object class serves as a data type for storing rheobase search - attributes and apriori model parameters, - with the distinction that unlike the NEURON model this class - can be cheaply transported across HOSTS/CPUs - ''' - def __init__(self): - self.lookup = {} + def model_default(self): + if self.backend is not None: + if self.attrs is None: + from neuronunit.optimization.model_parameters import ( + MODEL_PARAMS, + BPO_PARAMS, + ) + + if str("MAT") in self.backend: + self.backend_ref = str("MAT") + if str("IZHI") in self.backend: + self.backend_ref = str("IZHI") + if str("ADEXP") in self.backend: + self.backend_ref = str("ADEXP") + + self.attrs = { + k: np.mean(v) for k, v in MODEL_PARAMS[self.backend_ref].items() + } + self = DataTC(backend=self.backend, attrs=self.attrs) + model = self.dtc_to_model() + self.attrs = model._backend.default_attrs + del model + else: + model = self.dtc_to_model() + self.attrs = model._backend.default_attrs + del model + return None + + def __init__(self, attrs=None, backend=None, _backend=None): self.rheobase = None - self.previous = 0 - self.run_number = 0 - self.attrs = None - self.steps = None - self.name = None - self.results = None - self.fitness = None - self.score = None - self.scores = None - - self.boolean = False + self.attrs = attrs self.initiated = False - self.delta = [] - self.evaluated = False - self.results = {} - self.searched = [] - self.searchedd = {} - self.cached_attrs = {} - self.backend = None - self.summed = None - self.constants = None - - @jit(forceobj=True) - def get_ss(self): - # get summed score - if self.scores is not None: - if len(self.scores) == 1: - self.summed = list(self.scores.values())[0] + self.backend = backend + self._backend = _backend + self.lookup = {} + if self.backend is not None: + if ( + str("MAT") in self.backend + or str("IZHI") in self.backend + or str("ADEXP") in self.backend + ): + self.jithub = True else: - self.summed = np.sum(list(self.scores.values())) + self.jithub = False + if attrs is None: + self.attrs = None + self.model_default() else: - self.summed = None - return self.summed + self.attrs = attrs + + def to_bpo_param(self, attrs: dict = {}) -> dict: + from bluepyopt.parameters import Parameter + + lop = {} + for k, v in attrs.items(): + p = Parameter(name=k, bounds=v, frozen=False) + lop[k] = p + self.param = lop + return lop + + def attrs_to_params(self): + params = self.attrs + for k, v in params.items(): + if np.round(v, 2) != 0: + params[k] = np.round(v, 2) + if k == "celltype": + params[k] = int(np.round(v, 0)) + return params + + def make_pretty(self, tests) -> pd.DataFrame: + + self.tests = tests + self.obs_preds = pd.DataFrame(columns=["observations", "predictions"]) + holding_obs = {t.name: t.observation["mean"] for t in self.tests} + grab_keys = [] + for t in self.tests: + if "value" in t.prediction.keys(): + grab_keys.append("value") + else: + grab_keys.append("mean") + holding_preds = { + t.name: t.prediction[k] + for t, k in zip(self.tests, grab_keys) + } + ## + # This step only partially undoes quantities annoyances. + ## + qt.quantity.PREFERRED = [qt.mV, qt.pA, qt.MOhm, qt.ms, qt.pF, qt.Hz / qt.pA] + + for k, v in holding_preds.items(): + if k in holding_obs.keys() and k in holding_preds: + v.rescale_preferred() + v = v.simplified + if np.round(v, 2) != 0: + v = np.round(v, 2) + + for k, v in holding_obs.items(): + if k in holding_obs.keys() and k in holding_preds: + + v.rescale_preferred() + v = v.simplified + if np.round(v, 2) != 0: + v = np.round(v, 2) + + for k, v in holding_preds.items(): + if k in holding_obs.keys() and k in holding_preds: + # units1 = holding_preds[k].units # v.units) + + units1 = holding_preds[k].rescale_preferred().units # v.units) + + holding_preds[k] = holding_preds[k].simplified + holding_preds[k] = holding_preds[k].rescale(units1) + if np.round(holding_preds[k], 2) != 0: + holding_preds[k] = np.round(holding_preds[k], 2) + + holding_obs[k] = holding_obs[k].simplified + holding_obs[k] = holding_obs[k].rescale(units1) + if np.round(holding_obs[k], 2) != 0: + holding_obs[k] = np.round(holding_obs[k], 2) + + temp_obs = pd.DataFrame([holding_obs], index=["observations"]) + temp_preds = pd.DataFrame([holding_preds], index=["predictions"]) + # like a score array but nicer reporting of test name instead of test data type. + try: + scores_ = [] + model = self.dtc_to_model() + for t in self.tests: + scores_.append(t.judge(model, prediction=t.prediction)) + + not_SA = { + t.name: np.round(score.raw, 2) for t, score in zip(self.tests, scores_) + } + temp_scores = pd.DataFrame([not_SA], index=["Z-Scores"]) + self.obs_preds = pd.concat([temp_obs, temp_preds, temp_scores]) + except: + self.obs_preds = pd.concat([temp_obs, temp_preds]) # , temp_scores]) + self.obs_preds = self.obs_preds.T + return self.obs_preds + + """ + def dtc_to_opt_man(self): + from neuronunit.optimization.optimization_management import OptMan + from collections import OrderedDict + from neuronunit.optimization.model_parameters import MODEL_PARAMS + + OM = OptMan(self.tests, self.backend) + OM.boundary_dict = MODEL_PARAMS[self.backend] + OM.backend = self.backend + OM.td = list(OrderedDict(OM.boundary_dict).keys()) + return OM + + def get_agreement(self): + self = self.self_evaluate() + OM = self.dtc_to_opt_man() + self = OM.get_agreement(self) + return self + """ def add_constant(self): if self.constants is not None: self.attrs.update(self.constants) - return #self.attrs + return # self.attrs + ''' + def format_test(self): + from neuronunit.optimisation.optimization_management import ( + switch_logic, + active_values, + passive_values, + ) + + # pre format the current injection dictionary based on pre computed + # rheobase values of current injection. + # This is much like the hooked method from the old get neab file. + self.protocols = {} + if not hasattr(self, "tests"): + self.tests = copy.copy(self.tests) + if hasattr(self.tests, "keys"): # is type(dict): + tests = [key for key in self.tests.values()] + self.tests = switch_logic(tests) # ,self.tests.use_rheobase_score) + else: + self.tests = switch_logic(self.tests) + + for v in self.tests: + k = v.name + self.protocols[k] = {} + if hasattr(v, "passive"): + if v.passive == False and v.active == True: + keyed = self.protocols[k] # .params + self.protocols[k] = active_values(keyed, self.rheobase) + + if str("APThresholdTest") in v.name and not self.threshold: + model = self.dtc_to_model() + model.attrs = model._backend.default_attrs + treshold = v.generate_prediction(model) + self.threshold = treshold + + if str("APThresholdTest") in v.name: + v.threshold = None + v.threshold = self.threshold + elif v.passive == True and v.active == False: + keyed = self.protocols[k] # .params + self.protocols[k] = passive_values(keyed) + + if v.name in str("RestingPotentialTest"): + self.protocols[k]["injected_square_current"] = {} + self.protocols[k]["injected_square_current"]["amplitude"] = 0.0 * qt.pA + keyed = v.params["injected_square_current"] + v.params["t_max"] = keyed["delay"] + keyed["duration"] + 200.0 * pq.ms + return self.tests + ''' + def dtc_to_model(self): + if ( + str("MAT") in self.backend + or str("IZHI") in self.backend + or str("ADEXP") in self.backend + ): + self.jithub = True + + else: + self.jithub = False + if self.attrs is None: + self.model_default() + if self.jithub: + if str("MAT") in self.backend: + model = model_classes.MATModel() + if str("IZHI") in self.backend: + model = model_classes.IzhiModel() + if str("ADEXP") in self.backend: + model = model_classes.ADEXPModel() + + model.set_attrs(self.attrs) + assert model.attrs == self.attrs + assert model._backend.attrs == self.attrs + #self.params = model.params = self.to_bpo_param(self.attrs) + assert len(self.attrs) + assert len(model.attrs) + return model + + else: + from neuronunit.models.very_reduced_sans_lems import VeryReducedModel + + model = VeryReducedModel(backend=self.backend, attrs=self.attrs) + + model.set_attrs(self.attrs) + if model.attrs is None: + model.attrs = self.attrs + return model + + def dtc_to_sciunit_model(self): + model = self.dtc_to_model() + sciunit_model = model._backend.as_sciunit_model() + return sciunit_model + + def dtc_to_gene(self, subset_params=None): + """ + These imports probably need to be contained to stop recursive imports + """ + from deap import base + import array + from deap import creator + + creator.create( + "FitnessMin", base.Fitness, weights=tuple(-1.0 for i in range(0, 10)) + ) + creator.create( + "Individual", array.array, typecode="d", fitness=creator.FitnessMin + ) + + # from neuronunit.optimisation.optimization_management import WSListIndividual + # print('warning translation dictionary should be used, to garuntee correct attribute order from random access dictionaries') + if "IZHI" in self.backend: + self.attrs.pop("dt", None) + self.attrs.pop("Iext", None) + if subset_params: + pre_gene = OrderedDict() + for k in subset_params: + pre_gene[k] = self.attrs[k] + else: + pre_gene = OrderedDict(self.attrs) + pre_gene = list(pre_gene.values()) + gene = creator.Individual(pre_gene) + return gene + + def judge_test(self, index=0): + model = self.dtc_to_model() + if not hasattr(self, "tests"): + print("warning dtc object does not contain NU-tests yet") + return dtc + + ts = self.tests + # this_test = ts[index] + if not hasattr(self, "preds"): + self.preds = {} + for this_test in self.tests: + this_test.setup_protocol(model) + pred = this_test.extract_features(model, this_test.get_result(model)) + pred1 = this_test.generate_prediction(model) + self.preds[this_test.name] = pred + return self.preds + + """ + def jt_ratio(self,index=0): + from sciunit import scores + model = self.dtc_to_model() + if not hasattr(self,'tests'): + print('warning dtc object does not contain NU-tests yet') + return dtc + + ts = self.tests + #this_test = ts[index] + if not hasattr(self,'preds'): + self.preds = {} + tests = self.format_test() + for this_test in self.tests: + if this_test.passive: + this_test.params['injected_square_current'] = {} + this_test.params['injected_square_current']['amplitude'] = -10*qt.pA + + this_test.params['injected_square_current']['duration'] = 1000*qt.ms + this_test.params['injected_square_current']['delay'] = 200*qt.ms + + this_test.setup_protocol(model) + pred = this_test.extract_features(model,this_test.get_result(model)) + else: + this_test.params['injected_square_current']['amplitude'] = self.rheobase + this_test.params['injected_square_current']['duration'] = 1000*qt.ms + this_test.params['injected_square_current']['delay'] = 200*qt.ms + + pred = this_test.generate_prediction(model) + #self.preds[this_test.name] = pred + ratio_type = scores.RatioScore + temp = copy.copy(this_test.score_type) + this_test.score_type = ratio_type + try: + #print(this_test.name) + self.rscores[rscores.name] = this_test.compute_score(this_test.observation,pred) + + except: + this_test.score_type = temp + #self.rscores = this_test.compute_score(model) + self.rscores[rscores.name] = this_test.compute_score(this_test.observation,pred) + + self.failed = {} + self.failed['pred'] = pred + self.failed['observation'] = this_test.observation + + return self.preds + """ + + def check_params(self): + self.judge_test() + + return self.preds + + def plot_obs(self, ow): + """ + assuming a waveform exists (observed waved-form) plot to terminal with ascii + This is useful for debugging new backends, in bash big/fast command line orientated optimization routines. + """ + + t = [float(f) for f in ow.times] + v = [float(f) for f in ow.magnitude] + fig = apl.figure() + fig.plot( + t, + v, + label=str("observation waveform from inside dtc: "), + width=100, + height=20, + ) + fig.show() + + def iap(self, tests=None): + """ + Inject and plot to terminal with ascii + This is useful for debugging new backends, in bash big/fast command line orientated optimization routines. + """ + from neuronunit.optimisation import optimization_management as om_ + + if tests is not None: + self.tests = tests + + OM = self.dtc_to_opt_man() + self = om_.dtc_to_rheo(self) + + model = self.dtc_to_model() + pms = uset_t.params + pms["injected_square_current"]["amplitude"] = self.rheobase + model.inject_square_current(pms["injected_square_current"]) + nspike = model.get_spike_train() + self.nspike = nspike + vm = model.get_membrane_potential() + return vm diff --git a/neuronunit/optimization/exhaustive_search.py b/neuronunit/optimization/exhaustive_search.py deleted file mode 100644 index 5f6a988a0..000000000 --- a/neuronunit/optimization/exhaustive_search.py +++ /dev/null @@ -1,351 +0,0 @@ - -#from neuronunit.optimization import get_neab -#tests = get_neab.tests -import pdb - -import multiprocessing -from collections import OrderedDict - -from neuronunit.optimization.model_parameters import model_params -from neuronunit.optimization import data_transport_container -from neuronunit.optimization.optimization_management import nunit_evaluation, update_deap_pop -from neuronunit.optimization.optimization_management import update_dtc_pop -import numpy as np -from collections import OrderedDict - - -import copy -from copy import deepcopy -import math - -import dask.bag as db -from sklearn.model_selection import ParameterGrid -import scipy - -import pickle -import os -import numpy as np -npartitions = multiprocessing.cpu_count() -npart = multiprocessing.cpu_count() -import shelve - -import os -from numba import jit - -class WSListIndividual(list): - """Individual consisting of list with weighted sum field""" - def __init__(self, *args, **kwargs): - """Constructor""" - self.rheobase = None - self.dtc = None - - super(WSListIndividual, self).__init__(*args, **kwargs) - - -@jit -def reduce_params(model_params,nparams): - key_list = list(model_params.keys()) - reduced_key_list = key_list[0:nparams] - subset = { k:model_params[k] for k in reduced_key_list } - return subset - -#@jit -def chunks(l, n): - # For item i in a range that is a length of l, - return [ l[:][i:i+n] for i in range(0, len(l), n) ] - - -#@jit -def build_chunk_grid(npoints, free_params, hold_constant = None, mp_in = None): - - - #grid_points, maps = create_grid(mp_in, npoints = npoints, free_params = free_params) - - temp = OrderedDict(grid_points[0]).keys() - tds = list(temp) - # old stable approach - if len(grid_points[0].keys())>1: - pops = [ WSListIndividual(g.values()) for g in grid_points ] - else: - pops = [ val for g in grid_points for val in g.values() ] - return pops,tds - - # new approach, merited? Older works better - ''' - - pops = [] - for g in grid_points: - pre_pop = list(g.values()) - pop = WSListIndividual(pre_pop) - pops.append(pop) - # divide population into chunks that reflect the number of CPUs. - # don't want lists of lengths 1 that are awkward to iterate over. - # so check if there would be a chunk of list length 1, and if so divide by a different numberself. - # that is still dependant on CPU number. - if len(pops) % npartitions != 1: - pops_ = chunks(pops,npartitions) - else: - - pops_ = chunks(pops,npartitions-2) - if len(pops_) > 1: - assert pops_[0] != pops_[1] - - return pops_, tds - ''' - -#@jit -def sample_points(iter_dict, npoints=3): - replacement = {} - for p in range(0,len(iter_dict)): - for k,v in iter_dict.items():#popitem(last=False) - if len(v) == 2: - sample_points = list(np.linspace(v[0],v[1],npoints)) - else: - v = np.array(v) - sample_points = list(np.linspace(v.max(),v.min(),npoints)) - replacement[k] = sample_points - return replacement -''' -@jit -def sample_points(iter_dict, npoints=2): - replacement = {} - for p in range(0,len(iter_dict)): - k,v = iter_dict.popitem(last=False) - v[0] = v[0]*1.0/3.0 - v[1] = v[1]*2.0/3.0 - #if len(v) == 2: - # sample_points = list(np.linspace(v[0],v[1],npoints)) - #else: - # v = np.array(v) - sample_points = list(np.linspace(v.max(),v.min(),npoints)) - replacement[k] = sample_points - return replacement -''' - -@jit -def update_dtc_grid(item_of_iter_list): - - dtc = data_transport_container.DataTC() - dtc.attrs = deepcopy(item_of_iter_list) - dtc.scores = {} - dtc.rheobase = None - dtc.evaluated = False - dtc.backend = 'NEURON' - return dtc -@jit -def create_a_map(subset): - maps = {} - for k,v in subset.items(): - maps[k] = {} - for ind,j in enumerate(subset[k]): - maps[k][j] = ind - return maps - -#@jit -#def create_grid(mp_in=None,npoints=3,free_params=None,ga=None): - ''' - Description, create a grid of evenly spaced samples in model parameters to search over. - Inputs: npoints, type: Integer: number of sample points per parameter - nparams, type: Integer: number of parameters to use, conflicts, with next argument. - nparams, iterates through a list of parameters, and assigns the nparams to use via stupid counting. - provided keys: explicitly define state the model parameters that are used to create the grid of samples, by - keying into an existing of parameters. - - This method needs the user of the method to declare a dictionary of model parameters in a path: - neuronunit.optimization.model_parameters. - - Miscallenous, once grid created by this function - has been evaluated using neuronunit it can be used for informing a more refined second pass fine grained grid - - # smaller is a dictionary thats not necessarily as big - # as the grid defined in the model_params file. Its not necessarily - # a smaller dictionary, if it is smaller it is reduced by reducing sampling - # points. - - if type(mp_in) is type(None): - from neuronunit.models.NeuroML2 import model_parameters as modelp - mp_in = OrderedDict(modelp.model_params) - - pdb.set_trace() - - whole_p_set = {} - sp = sample_points(copy.copy(mp_in), npoints=2) - whole_p_set = OrderedDict(sp) - - print(type(free_params), 'free_params') - if type(free_params) is type(dict): - subset = OrderedDict( {k:whole_p_set[k] for k in list(free_params.keys())}) - - elif len(free_params) == 1 or type(free_params) is type(str('')): - subset = OrderedDict( {free_params: whole_p_set[free_params] } ) - - else: - subset = OrderedDict( {k:whole_p_set[k] for k in free_params}) - - print('subset is wrong') - pdb.set_trace() - - maps = create_a_map(subset) - if type(ga) is not type(None): - if npoints > 1: - for k,v in subset.items(): - v[0] = v[0]*1.0/3.0 - v[1] = v[1]*2.0/3.0 - - - ''' -#@jit -def add_constant(hold_constant,pop): - hold_constant = OrderedDict(hold_constant) - for p in pop: - for k,v in hold_constant.items(): - p[k] = v - - for k in hold_constant.keys(): - td.append(k) - return pop,td - -def create_grid(mp_in = None, npoints = 3, free_params = None, ga = None): - ''' - check for overlap in parameter space. - ''' - if len(free_params)> 1: - subset = OrderedDict(free_params) - else: - subset = {free_params[0]:None} - - ndim = len(subset) - #nsteps = np.floor(float(npoints)/float(ndim)) - if type(mp_in) is not type(None): - for k,v in mp_in.items(): - if k in free_params: - subset[k] = np.linspace(np.min(free_params[k]),np.max(free_params[k]), npoints) - #import pdb; pdb.set_trace() - #subset[k] = ( np.min(free_params[k]),np.max(free_params[k]) ) - else: - subset[k] = v - # The function of maps is to map floating point sample spaces onto a monochromataic matrix indicies. - grid = list(ParameterGrid(subset)) - return grid - -@jit -def tfg2i(x, y, z): - ''' - translate_float_grid_to_index - Takes x, y, z values as lists and returns a 2D numpy array - ''' - dx = abs(np.sort(list(set(x)))[1] - np.sort(list(set(x)))[0]) - dy = abs(np.sort(list(set(y)))[1] - np.sort(list(set(y)))[0]) - i = ((x - min(x)) / dx).astype(int) # Longitudes - j = ((y - max(y)) / dy).astype(int) # Latitudes - grid = np.nan * np.empty((len(set(j)),len(set(i)))) - grid[j, i] = z # if using latitude and longitude (for WGS/West) - return grid - -def tfc2i(x, y, z,err): - ''' - translate_float_cube_to_index - - Takes x, y, z values as lists and returns a 2D numpy array - ''' - dx = abs(np.sort(list(set(x)))[1] - np.sort(list(set(x)))[0]) - dy = abs(np.sort(list(set(y)))[1] - np.sort(list(set(y)))[0]) - dz = abs(np.sort(list(set(z)))[1] - np.sort(list(set(y)))[0]) - - i = ((x - min(x)) / dx).astype(int) # Longitudes - j = ((y - max(y)) / dy).astype(int) # Latitudes - k = ((z - max(z)) / dz).astype(int) # Latitudes - - grid = np.nan * np.empty((len(set(i)),len(set(j)), len(set(k)) )) - - grid[i,j,k] = err # if using latitude and longitude (for WGS/West) - return - - - -@jit -def transdict(dictionaries): - from collections import OrderedDict - mps = OrderedDict() - sk = sorted(list(dictionaries.keys())) - for k in sk: - mps[k] = dictionaries[k] - tl = [ k for k in mps.keys() ] - return mps, tl - - -def run_rick_grid(rick_grid, tests,td): - consumable = iter(rick_grid) - grid_results = [] - results = update_deap_pop(consumable, tests, td) - #import pdb - #pdb.set_trace() - - if type(results) is not None: - grid_results.extend(results) - return grid_results - -def run_simple_grid(npoints, tests, ranges, free_params, hold_constant = None): - subset = OrderedDict() - for k,v in ranges.items(): - if k in free_params: - subset[k] = ( np.min(ranges[k]),np.max(ranges[k]) ) - # The function of maps is to map floating point sample spaces onto a monochromataic matrix indicies. - subset = OrderedDict(subset) - subset = sample_points(subset, npoints = npoints) - grid_points = list(ParameterGrid(subset)) - td = grid_points[0].keys() - if type(hold_constant) is not type(None): - grid_points = add_constant(hold_constant,grid_points) - - if len(td) > 1: - consumable = [ WSListIndividual(g.values()) for g in grid_points ] - else: - consumable = [ val for g in grid_points for val in g.values() ] - grid_results = [] - td = list(td) - if len(consumable) <= 16: - consumable = consumable - results = update_deap_pop(consumable, tests, td) - if type(results) is not None: - grid_results.extend(results) - - if len(consumable) > 16: - consumable = chunks(consumable,8) - for sub_pop in consumable: - sub_pop = sub_pop - #sub_pop = [[i] for i in sub_pop ] - #sub_pop = WSListIndividual(sub_pop) - results = update_deap_pop(sub_pop, tests, td) - if type(results) is not None: - grid_results.extend(results) - return grid_results - -def run_grid(npoints, tests, provided_keys = None, hold_constant = None, ranges=None): - subset = mp_in[provided_keys] - - consumable_ ,td = build_chunk_grid(npoints,provided_keys) - cnt = 0 - grid_results = [] - if type(hold_constant) is not type(None): - td, hc = add_constant(hold_constant,consumable_,td) - consumable = iter(consumable_) - use_cache = None - s = None - - for sub_pop in consumable: - results = update_deap_pop(sub_pop, tests, td) - if type(results) is not None: - grid_results.extend(results) - - if type(use_cache) is not type(None): - if type(s) is not type(None): - s['consumable'] = consumable - s['cnt'] = cnt - s['grid_results'] = grid_results - s['sub_pop'] = sub_pop - cnt += 1 - print('done_block_of_N_cells: ',cnt) - if type(s) is not type(None): - s.close() - return grid_results diff --git a/neuronunit/optimization/get_neab.py b/neuronunit/optimization/get_neab.py deleted file mode 100644 index b68735d8d..000000000 --- a/neuronunit/optimization/get_neab.py +++ /dev/null @@ -1,142 +0,0 @@ -import os -import sciunit -import neuronunit -from neuronunit import aibs -import pickle -from neuronunit import tests as _, neuroelectro -#from neuronunit import tests as nu_tests, neuroelectro -from neuronunit.tests import passive, waveform, fi -from neuronunit.tests.fi import RheobaseTestP - - -import urllib.request, json - -#print(obs_frame) -from neuronunit import neuroelectro - -def neuroelectro_summary_observation(neuron,ontology): - ephysprop_name = '' - verbose = False - - reference_data = neuroelectro.NeuroElectroSummary( - neuron = neuron, # Neuron type lookup using the NeuroLex ID. - ephysprop = {'name': ontology['name']}, # Ephys property name in - # NeuroElectro ontology. - ) - reference_data.get_values() # Get and verify summary data - # from neuroelectro.org. - return reference_data -import urllib.request, json - -def get_obs(pipe): - with urllib.request.urlopen("https://neuroelectro.org/api/1/e/") as url: - ontologies = json.loads(url.read().decode()) - #print(ontologies) - #with urllib.request.urlopen("https://neuroelectro.org/api/1/e/") as url: - # ontologies = json.loads(url.read().decode()) - obs = [] - for p in pipe: - for l in ontologies['objects']: - obs.append(neuroelectro_summary_observation(p,l)) - return obs - -def update_amplitude(test,tests,score): - rheobase = score.prediction['value'] - for i in [4,5,6]: - tests[i].params['injected_square_current']['amplitude'] = rheobase*1.01 # I feel that 1.01 may lead to more than one spike - return - -def substitute_parallel_for_serial(electro_tests): - for test,obs in electro_tests: - test[0] = RheobaseTestP(obs['Rheobase']) - - return electro_tests - -def substitute_criteria(observations_donar,observations_acceptor): - # Inputs an observation donar - # and an observation acceptor - # Many neuroelectro data sources have std 0 values - for index,oa in observations_acceptor.items(): - for k,v in oa.items(): - if k == 'std' and v == 0.0: - if k in observations_donar.keys(): - oa[k] = observations_donar[index][k] - return observations_acceptor - -def substitute_parallel_for_serial(electro_tests): - for test,obs in electro_tests: - if str('Rheobase') in obs.keys(): - - test[0] = RheobaseTestP(obs['Rheobase']) - - return electro_tests - -def replace_zero_std(electro_tests): - for test,obs in electro_tests: - if str('Rheobase') in obs.keys(): - test[0] = RheobaseTestP(obs['Rheobase']) - for k,v in obs.items(): - if v['std'] == 0: - #print(electro_tests[1][1],obs) - obs = substitute_criteria(electro_tests[1][1],obs) - #print(obs) - return electro_tests - -def get_neuron_criteria(cell_id,file_name = None):#,observation = None): - # Use neuroelectro experimental obsevations to find test - # criterion that will be used to inform scientific unit testing. - # some times observations are not sourced from neuroelectro, - # but they are imputated or borrowed from other TestSuite - # if that happens make test objections using observations external - # to this method, and provided as a method argument. - tests = [] - observations = None - test_classes = None - test_classes = [fi.RheobaseTest, - passive.InputResistanceTest, - passive.TimeConstantTest, - passive.CapacitanceTest, - passive.RestingPotentialTest, - waveform.InjectedCurrentAPWidthTest, - waveform.InjectedCurrentAPAmplitudeTest, - waveform.InjectedCurrentAPThresholdTest]#, - observations = {} - for index, t in enumerate(test_classes): - try: - obs = t.neuroelectro_summary_observation(cell_id) - - if obs is not None: - if 'mean' in obs.keys(): - tests.append(t(obs)) - observations[t.ephysprop_name] = obs - except: - pass - #hooks = {tests[0]:{'f':update_amplitude}} #This is a trick to dynamically insert the method - #update amplitude at the location in sciunit thats its passed to, without any loss of generality. - suite = sciunit.TestSuite(tests,name="vm_suite") - - if file_name is not None: - file_name = file_name +'.p' - with open(file_name, 'wb') as f: - pickle.dump(tests, f) - - return tests,observations - -import copy -import sciunit -def get_tests(): - # get neuronunit tests - # and select out Rheobase test and input resistance test - # and less about electrophysiology of the membrane. - # We are more interested in phenomonogical properties. - electro_path = str(os.getcwd())+'/pipe_tests.p' - assert os.path.isfile(electro_path) == True - with open(electro_path,'rb') as f: - electro_tests = pickle.load(f) - electro_tests = replace_zero_std(electro_tests) - electro_tests = substitute_parallel_for_serial(electro_tests) - test, observation = electro_tests[0] - tests = copy.copy(electro_tests[0][0]) - suite = sciunit.TestSuite(tests) - #tests_ = tests[0:2] - return tests, test, observation, suite diff --git a/neuronunit/optimization/model_parameters.py b/neuronunit/optimization/model_parameters.py index 9b21d8478..2cff2f482 100644 --- a/neuronunit/optimization/model_parameters.py +++ b/neuronunit/optimization/model_parameters.py @@ -1,194 +1,191 @@ import numpy as np import os from collections import OrderedDict -from numpy import sqrt, pi import collections import numpy as np -from neuronunit.optimization import get_neab -# http://www.physics.usyd.edu.au/teach_res/mp/mscripts/ -# ns_izh002.m -import collections -from collections import OrderedDict -import numpy as np -THIS_DIR = os.path.dirname(os.path.realpath(__file__)) +# import pickle -path_params = {} -path_params['model_path'] = os.path.realpath(os.path.join(THIS_DIR,'..','models','NeuroML2','LEMS_2007One.xml')) -# Which Parameters -# https://www.izhikevich.org/publications/spikes.htm +def to_bpo_param(attrs): + from bluepyopt.parameters import Parameter -type2007 = collections.OrderedDict([ - # C k vr vt vpeak a b c d celltype - ('RS', (100, 0.7, -60, -40, 35, 0.03, -2, -50, 100, 1)), - ('IB', (150, 1.2, -75, -45, 50, 0.01, 5, -56, 130, 2)), - ('CH', (50, 1.5, -60, -40, 25, 0.03, 1, -40, 150, 3)), - ('LTS', (100, 1.0, -56, -42, 40, 0.03, 8, -53, 20, 4)), - ('FS', (20, 1.0, -55, -40, 25, 0.2, -2, -45, -55, 5)), - ('TC', (200, 1.6, -60, -50, 35, 0.01, 15, -60, 10, 6)), - ('TC_burst', (200, 1.6, -60, -50, 35, 0.01, 15, -60, 10, 6)), - ('RTN', (40, 0.25, -65, -45, 0, 0.015, 10, -55, 50, 7)), - ('RTN_burst', (40, 0.25, -65, -45, 0, 0.015, 10, -55, 50, 7))]) + lop = {} + for k, v in attrs.items(): + temp = tuple(sorted(v)) + p = Parameter(name=k, bounds=temp[:], frozen=False) + lop[k] = p + return lop +def check_if_param_stradles_boundary(opt, model_type): + for k, v in MODEL_PARAMS[model_type].items(): + print(v, opt.attrs[k], k) -''' -temp = {k:[] for k in ['C','k','vr','vt','vpeak','a','b','c','d'] } -for i,k in enumerate(temp.keys()): +MODEL_PARAMS = {} +THIS_DIR = os.path.dirname(os.path.realpath(__file__)) +path_params = {} +path_params["model_path"] = os.path.realpath( + os.path.join(THIS_DIR, "..", "models", "NeuroML2", "LEMS_2007One.xml") +) +# Which Parameters +# https://www.izhikevich.org/publications/spikes.htm +type2007 = collections.OrderedDict( + [ + # C k vr vt vpeak a b c d celltype + ("RS", (100, 0.7, -60, -40, 35, 0.01, -2, -50, 100, 3)), + ("IB", (150, 1.2, -75, -45, 50, 0.1, 5, -56, 130, 3)), + ("TC", (200, 1.6, -60, -50, 35, 0.1, 15, -60, 10, 6)), + ("TC_burst", (200, 1.6, -60, -50, 35, 0.1, 15, -60, 10, 6)), + ("LTS", (100, 1.0, -56, -42, 40, 0.01, 8, -53, 20, 4)), + ("RTN", (40, 0.25, -65, -45, 0, 0.015, 10, -55, 50, 7)), + ("FS", (20, 1, -55, -40, 25, 0.2, -2, -45, -55, 5)), + ("CH", (50, 1.5, -60, -40, 25, 0.01, 1, -40, 150, 3)), + ] +) +temp = {k: [] for k in ["C", "k", "vr", "vt", "vPeak", "a", "b", "c", "d", "celltype"]} +for i, k in enumerate(temp.keys()): for v in type2007.values(): temp[k].append(v[i]) +explore_param = {k: (np.min(v), np.max(v)) for k, v in temp.items()} +IZHI_PARAMS = {k: sorted(v) for k, v in explore_param.items()} -explore_param = {k:(np.min(v),np.max(v)) for k,v in temp.items()} -model_params = OrderedDict(explore_param) - -''' - - +IZHI_PARAMS = OrderedDict(IZHI_PARAMS) +MODEL_PARAMS["IZHI"] = IZHI_PARAMS # Fast spiking cannot be reproduced as it requires modifications to the standard Izhi equation, # which are expressed in this mod file. # https://github.com/OpenSourceBrain/IzhikevichModel/blob/master/NEURON/izhi2007b.mod -reduced_dict = OrderedDict([(k,[]) for k in ['C','k','vr','vt','vPeak','a','b','c','d']]) - -#OrderedDict -for i,k in enumerate(reduced_dict.keys()): +""" +depricated +trans_dict = OrderedDict([(k,[]) for k in ['C','k','vr','vt','vPeak','a','b','c','d']]) +for i,k in enumerate(trans_dict.keys()): for v in type2007.values(): - reduced_dict[k].append(v[i]) - + trans_dict[k].append(v[i]) reduced_cells = OrderedDict([(k,[]) for k in ['RS','IB','LTS','TC','TC_burst']]) - for index,key in enumerate(reduced_cells.keys()): reduced_cells[key] = {} - for k,v in reduced_dict.items(): + for k,v in trans_dict.items(): reduced_cells[key][k] = v[index] - -explore_param = {k:(np.min(v),np.max(v)) for k,v in reduced_dict.items()} -model_params = OrderedDict(explore_param) - -# page 1 -# http://www.rctn.org/vs265/izhikevich-nn03.pdf - - - - -def transcribe_units(input_dic): - ''' - Move between OSB unit conventions and NEURON unit conventions. - ''' - # From OSB models - mparams = {} - mparams['a'] = 0.03 - mparams['b'] = -2 - mparams['C'] = 100 - mparams['c'] = -50 - mparams['vr'] = -60 - mparams['vt'] = -40 - mparams['vpeak'] = 35 - mparams['k'] = 0.7 - mparams['d'] = 100 - - - # FROM the MOD file. - vanilla_NRN = {} - #vanilla_NRN['v0'] = -60# (mV) - vanilla_NRN['k'] = 7.0E-4# (uS / mV) - vanilla_NRN['vr'] = -60# (mV) - vanilla_NRN['vt'] = -40# (mV) - vanilla_NRN['vpeak'] = 35# (mV) - vanilla_NRN['a'] = 0.03# (kHz) - vanilla_NRN['b'] = -0.002# (uS) - vanilla_NRN['c'] = -50# (mV) - vanilla_NRN['d'] = 0.1# (nA) - vanilla_NRN['C'] = 1.0E-4# (microfarads) - - m2m = {} - for k,v in vanilla_NRN.items(): - m2m[k] = vanilla_NRN[k]/mparams[k] - - input_dic['vpeak'] = input_dic['vPeak'] - input_dic.pop('vPeak', None) - input_dic.pop('dt', None) - for k,v in input_dic.items(): - input_dic[k] = v * m2m[k] - return input_dic - -#print(pred0,pred1) - -# General parameters - -SEED_LTS = 428577 -SEED_CONN = 193566 -SEED_GEN = 983651 - -DT = 0.1 # (ms) Time step -TSTART = 0 -TSTOP = 5000 -V_INIT = -60.0 - -# Cell parameters - -LENGTH = sqrt(20000/pi) # in um -DIAMETER = sqrt(20000/pi) # in um -AREA = 1e-8 * pi * LENGTH * DIAMETER # membrane area in cm2 -TAU = 20 # time constant in ms -CAPACITANCE = 1 # capacitance in muF/cm2 -G_L = 1e-3 * CAPACITANCE / TAU # leak conductance in S/cm2 -V_REST = -60 # resting potential - -a_RS = 0.001 -b_RS = 0.1 # full adaptation -b_RS = 0.005 # weaker adaptation -a_LTS = 0.02 -b_LTS = 0.0 -a_FS = 0.001 -b_FS = 0.0 - -TAU_W = 600 -DELTA = 2.5 - -# Spike parameters - -VTR = -50 # threshold in mV -VTOP = 40 # top voltage during spike in mV -VBOT = -60 # reset voltage in mV -REFRACTORY = 5.0/2 # refractory period in ms (correction for a bug in IF_CG4) - -# Synapse parameters - -RS_parameters = { - 'cm': 1000*AREA*CAPACITANCE, 'tau_m': TAU, 'v_rest': V_REST, - 'v_thresh': VTR, 'tau_refrac': REFRACTORY+DT, - 'v_reset': VBOT, 'v_spike': VTR+1e-6, 'a': 1000.0*a_RS, 'b': b_RS, - 'tau_w': TAU_W, 'delta_T': DELTA +""" + + +# AdExp Model paramaters +BAE1 = {} +BAE1 = {} +BAE1["cm"] = 0.281 +BAE1["v_spike"] = -40.0 +BAE1["v_reset"] = -70.6 +BAE1["v_rest"] = -70.6 +BAE1["tau_m"] = 9.3667 +BAE1["a"] = 4.0 +BAE1["b"] = 0.0805 +BAE1["delta_T"] = 2.0 +BAE1["tau_w"] = 144.0 +BAE1["v_thresh"] = -50.4 +BAE1["spike_delta"] = 30 +# general range rule: +BAE1 = { + k: (np.mean(v) - np.abs(np.mean(v) * 2.5), np.mean(v) + np.mean(v) * 2.5) + for k, v in BAE1.items() } - -LTS_parameters = RS_parameters.copy() -LTS_parameters.update({'a': 1000.0*a_LTS, 'b': b_LTS}) # 1000 is for uS --> nS -FS_parameters = RS_parameters.copy() -FS_parameters.update({'a': 1000.0*a_FS, 'b': b_FS}) -#print(model_params) -''' - -https://www.izhikevich.org/publications/izhikevich.m - -Model parameters and dimensions: -t time [ms] -C membrane capacitance [pF = pA.ms.mV-1] -v membrane potential [mV] - rate of change of membrane potential [mV.ms-1 = V.s-1] - capacitor current [pA] -vr resting membrane potential [mV] -vt instantaneous threshold potential [mV] -k constant (“1/R”) [pA.mV-1 (10-9 Ω-1)] -u recovery variable [pA] -S stimulus (synaptic: excitatory or inhibitory, external, noise) [pA] - rate of change of recovery variable [pA.ms-1] -a recovery time constant [ms-1] -b constant (“1/R”) [pA.mV-1 (10-9 Ω-1) ] -c potential reset value [mV] -d outward minus inward currents activated during the spike and affecting the after-spike behavior [pA] -vpeak spike cutoff value [mV] -+model_params['vr'] = np.linspace(-95.0,-30.0,9) - -'''; +BAE1 = {k: sorted(v) for k, v in BAE1.items()} +# specific ad hoc adjustments: +# BAE1['v_spike']=[-70.0,-20] +# BAE1['v_reset'] = [1, 983.5] +# BAE1['v_rest'] = [-100, -35] +BAE1["v_thresh"] = [-65, -15] +BAE1["spike_delta"] = [1.25, 135] +BAE1["b"] = [0.01, 20] +BAE1["a"] = [0.01, 20] +BAE1["tau_w"] = [0.05, 354] # Tau_w 0, means very low adaption +BAE1["cm"] = [1, 983.5] +BAE1["v_spike"] = [-70.0, -20] +# BAE1['v_reset'] = [1, 983.5] +BAE1["v_reset"] = [-100, -25] +BAE1["v_rest"] = [-100, -35] +BAE1["v_thresh"] = [-65, -15] +BAE1["delta_T"] = [1, 10] +BAE1["tau_m"] = [0.01, 62.78345] +for v in BAE1.values(): + assert v[1] - v[0] != 0 +MODEL_PARAMS["ADEXP"] = BAE1 + + +# Multi TimeScale Adaptive Neuron +MATNEURON = { + "vr": -65.0, + "vt": -55.0, + "a1": 10, + "a2": 2, + "b": 0.001, + "w": 5, + "R": 10, + "tm": 10, + "t1": 10, + "t2": 200, + "tv": 5, + "tref": 2, +} +MATNEURON = { + k: ( + np.mean(v) - np.abs(np.mean(v) * 0.125), + np.mean(v) + np.abs(np.mean(v)) * 0.125, + ) + for k, v in MATNEURON.items() +} +MATNEURON["b"] = [0.0000001, 0.003] +MATNEURON["R"] = [2.5, 200] +MATNEURON["vr"] = [-85, -45] +MATNEURON["vt"] = [-60, -35] +MATNEURON["w"] = [0.125, 25] +MATNEURON["tm"] = [5, 250] + +MATNEURON["tref"] = [0.5, 50] +MATNEURON["a1"] = [9, 55] +MATNEURON["a2"] = [0.5, 4] +MATNEURON["t1"] = [5, 15] +MATNEURON["t2"] = [150, 2089] +MATNEURON["tv"] = [5, 255] + +MATNEURON = {k: sorted(v) for k, v in MATNEURON.items()} +MODEL_PARAMS["MAT"] = MATNEURON + +for k, v in MATNEURON.items(): + assert v[1] - v[0] != 0 +GLIF_RANGE = { + "El_reference": [-0.08569469261169435, -0.05463626766204832], + "C": [3.5071610042390286e-13, 10 * 7.630189223327981e-10], + "init_threshold": [0.009908733642683513, 0.06939040414685865], + "th_inf": [0.009908733642683513, 0.04939040414685865], + "init_AScurrents": [0.0, 0.0], + "init_voltage": [-0.09, -0.01], + "spike_cut_length": [0.25, 94], + "El": [-0.08569469261169435, -0.05463626766204832], + "asc_tau_array": [[0.01, 0.0033333333333333335], [0.3333333333333333, 0.1]], + "R_input": [17743752.593817078, 10 * 1792774179.3647704], +} +GLIF_RANGE["th_adapt"] = [0.01, 1] # 0.1983063518904063] +GLIF_RANGE["C"] = [0, 10] +GLIF_RANGE.pop("init_AScurrents", None) +GLIF_RANGE.pop("dt", None) +GLIF_RANGE.pop("asc_tau_array", None) +GLIF_RANGE.pop("El", None) +GLIF_RANGE = {k: sorted(v) for k, v in GLIF_RANGE.items()} +MODEL_PARAMS["GLIF"] = GLIF_RANGE +BPO_PARAMS = {} +for k, v in MODEL_PARAMS.items(): + BPO_PARAMS[k] = to_bpo_param(v) + + +""" +Depricated +l5_pc_keys = ['gNaTs2_tbar_NaTs2_t.apical', 'gSKv3_1bar_SKv3_1.apical', 'gImbar_Im.apical', 'gNaTa_tbar_NaTa_t.axonal', 'gNap_Et2bar_Nap_Et2.axonal', 'gK_Pstbar_K_Pst.axonal', 'gK_Tstbar_K_Tst.axonal', 'gSK_E2bar_SK_E2.axonal', 'gSKv3_1bar_SKv3_1.axonal', 'gCa_HVAbar_Ca_HVA.axonal', 'gCa_LVAstbar_Ca_LVAst.axonal', 'gamma_CaDynamics_E2.axonal', 'decay_CaDynamics_E2.axonal', 'gNaTs2_tbar_NaTs2_t.somatic', 'gSKv3_1bar_SKv3_1.somatic', 'gSK_E2bar_SK_E2.somatic', 'gCa_HVAbar_Ca_HVA.somatic', 'gCa_LVAstbar_Ca_LVAst.somatic', 'gamma_CaDynamics_E2.somatic', 'decay_CaDynamics_E2.somatic'] +l5_pc_values = [0.0009012730575340265, 0.024287352056036934, 0.0008315987398062784, 1.7100532387472567, 0.7671786030824507, 0.47339571930108143, 0.0025715065622581644, 0.024862299158354962, 0.7754822886266044, 0.0005560440082771592, 0.0020639185209852568, 0.013376906273759268, 207.56154268835758, 0.5154365543590191, 0.2565961138691978, 0.0024100296151316754, 0.0007416593834676707, 0.006240529502225737, 0.028595343511797353, 226.7501580822364] + +L5PC = OrderedDict() +for k,v in zip(l5_pc_keys,l5_pc_values): + L5PC[k] = sorted((v-0.1*v,v+0.1*v)) + +MODEL_PARAMS['L5PC'] = L5PC +""" diff --git a/neuronunit/optimization/neuronunit_to_bpo.py b/neuronunit/optimization/neuronunit_to_bpo.py new file mode 100644 index 000000000..d4b324b73 --- /dev/null +++ b/neuronunit/optimization/neuronunit_to_bpo.py @@ -0,0 +1,10 @@ +def make_passive_protocol(): + pass +def make_zero_current_protocol(): + pass +def make_unknown_rheobase_protocol(): + pass +def make_unknown_multispike_protocol(): + pass +def neuronunit_tests_to_bpo_protocols(): + pass diff --git a/neuronunit/optimization/opt_man.py b/neuronunit/optimization/opt_man.py deleted file mode 100644 index 41463879a..000000000 --- a/neuronunit/optimization/opt_man.py +++ /dev/null @@ -1,593 +0,0 @@ -#import matplotlib # Its not that this file is responsible for doing plotting, but it calls many modules that are, such that it needs to pre-empt -# setting of an appropriate backend. -#matplotlib.use('agg') - -import numpy as np -import dask.bag as db -import pandas as pd -# Import get_neab has to happen exactly here. It has to be called only on -from neuronunit import tests -from neuronunit.optimization import get_neab -from neuronunit.models.reduced import ReducedModel -from neuronunit.optimization.model_parameters import model_params, path_params -import numpy -from neuronunit.optimization import model_parameters as modelp -from itertools import repeat - -import copy -import math - -import quantities as pq -import numpy as np -from pyneuroml import pynml - -from deap import base -from neuronunit.optimization.data_transport_container import DataTC -from neuronunit.models.interfaces import glif - - -import os -import pickle -from itertools import repeat -import neuronunit -import multiprocessing -npartitions = multiprocessing.cpu_count() -from collections import Iterable -from numba import jit -from sklearn.model_selection import ParameterGrid -# DEAP mutation strategies: -# https://deap.readthedocs.io/en/master/api/tools.html#deap.tools.mutESLogNormal -class WSListIndividual(list): - """Individual consisting of list with weighted sum field""" - def __init__(self, *args, **kwargs): - """Constructor""" - self.rheobase = None - super(WSListIndividual, self).__init__(*args, **kwargs) - -class WSFloatIndividual(float): - """Individual consisting of list with weighted sum field""" - def __init__(self, *args, **kwargs): - """Constructor""" - self.rheobase = None - super(WSFloatIndividual, self).__init__() - -@jit -def write_opt_to_nml(path,param_dict): - ''' - Write optimimal simulation parameters back to NeuroML. - ''' - orig_lems_file_path = path_params['model_path'] - more_attributes = pynml.read_lems_file(orig_lems_file_path, - include_includes=True, - debug=False) - for i in more_attributes.components: - new = {} - if str('izhikevich2007Cell') in i.type: - for k,v in i.parameters.items(): - units = v.split() - if len(units) == 2: - units = units[1] - else: - units = 'mV' - new[k] = str(param_dict[k]) + str(' ') + str(units) - i.parameters = new - fopen = open(path+'.nml','w') - more_attributes.export_to_file(fopen) - fopen.close() - return - - - -def add_constant(hold_constant,consumable_,td): - hc = list(hold_constant.values()) - for c in consumable_: - if type(c) is type(dict()): - for k,v in hold_constant.items(): - c[k] = v - for k in hold_constant.keys(): - td.append(k) - return td, consumable_ - - -# Intended to replace code in grid -def build_grid(means,stds=None,boundaries=None,k=5,exponential=True): - """Generate a list of dictionaries corresponding to grid points to search.""" - param_names = list(means.keys()) - assert (stds or boundaries) and not (stds and boundaries), "Must provide stds or boundaries, but not both" - param_names = means.keys() - if stds or boundaries: - - if exponential: - boundaries = {key:(means[key] - stds[key]*(2**k),means[key] + stds[key]*(2**k)) for key in param_names} - elif boundaries == None: - boundaries = {key:(means[key] - stds[key]*k,means[key] + stds[key]*k) for key in param_names} - else: - pass - - grids = {p:[] for p in param_names} - - ''' - What is happening below? - ''' - for p in param_names: - if exponential: - grids[p] += [means[p] - (means[p]-boundaries[p][0])/(2**kj) for kj in range(0,k+1,1)] - - grids[p].extend(means[p]) - - grids[p] += [means[p] + (boundaries[p][1]-means[p])/(2**kj) for kj in range(k,-1,-1)] - else: # if constantly proportional spacing - - grids[p] += [means[p] - (kj+1)*(means[p]-boundaries[p][0])/k for kj in range(0,k+1,1)] - - # - grids[p].extend(means[p]) - - grids[p] += [means[p] + (kj+1)*(boundaries[p][1]-means[p])/k for kj in range(k,-1,-1)] - return list(ParameterGrid(grids)) - -def build_grid_wrapper(means,stds=None,boundaries=None,k=5): - """Generate a list of dictionaries corresponding to grid points - where all but one parameter is held constant""" - grid = build_grid(means,stds=stds,boundaries=boundaries,k=k) - grid = [x for x in grid if sum(x[key]==means[key] for key in means)>=(len(means)-1)] - return grid - - -def hack_judge(test_and_models): - LEMS_MODEL_PATH = path_params['model_path'] - (test, attrs) = test_and_models - model = None - obs = test.observation - model = ReducedModel(LEMS_MODEL_PATH,name = str('vanilla'),backend = ('RAW')) - model.set_attrs(attrs) - #test.generate_prediction(model) - pred = test.generate_prediction(model) - print('gets here a') - score = test.compute_score(obs,pred) - print('gets here b') - - import pdb; pdb.set_trace() - print(score) - print('gets here c') - - #try: - # print(obs['value'],pred['value']) - #except: - # print(obs['mean'],pred['mean']) - - return score, pred - -def dtc_to_rheo(xargs): - dtc,rtest,_ = xargs - dtc.scores = {} - dtc.score = {} - print(dtc.attrs,'rtest', rtest,'failed on') - score, pred = hack_judge((rtest,dtc.attrs)) - print(score) - dtc.rheobase = pred - #if type(pred) is not type(None):#hasattr(score,'prediction'): - # dtc = score_proc(dtc,rtest,score) - return dtc - - -#@jit -def score_proc(dtc,t,score): - dtc.score[str(t)] = {} - dtc.score[str(t)]['value'] = copy.copy(score.sort_key) - if hasattr(score,'prediction'): - if type(score.prediction) is not type(None): - dtc.score[str(t)][str('prediction')] = score.prediction - dtc.score[str(t)][str('observation')] = score.observation - boolean_means = bool('mean' in score.observation.keys() and 'mean' in score.prediction.keys()) - boolean_value = bool('value' in score.observation.keys() and 'value' in score.prediction.keys()) - if boolean_means: - dtc.score[str(t)][str('agreement')] = np.abs(score.observation['mean'] - score.prediction['mean']) - if boolean_value: - dtc.score[str(t)][str('agreement')] = np.abs(score.observation['value'] - score.prediction['value']) - return dtc - -''' -Depricated. -def format_iparams(all_tests,rheobase): - - for t in all_tests[1:5]: - DURATION = 500.0*pq.ms - DELAY = 200.0*pq.ms - - #obs = t.observation - t.params = {} - t.params['injected_square_current'] = {} - t.params['injected_square_current']['delay']= DELAY - t.params['injected_square_current']['duration'] = DURATION - t.params['injected_square_current']['amplitude'] = -10*pq.pA - - for t in all_tests[-3::]: - t.params = {} - DURATION = 1000.0*pq.ms - DELAY = 100.0*pq.ms - - t.params['injected_square_current'] = {} - t.params['injected_square_current']['delay']= DELAY - t.params['injected_square_current']['duration'] = DURATION - t.params['injected_square_current']['amplitude'] = rheobase['value'] - - all_tests[0].params = all_tests[-1].params - - return all_tests -''' -@jit -def format_test(xargs): - ''' - pre format the current injection dictionary based on pre computed - rheobase values of current injection. - This is much like the hooked method from the old get neab file. - ''' - dtc,tests = xargs - - dtc.vtest = {} - for k,v in enumerate(tests): - dtc.vtest[k] = {} - dtc.vtest[k]['injected_square_current'] = {} - if k == 0 or k == 4 or k == 5 or k == 6 or k == 7: - DURATION = 1000.0*pq.ms - DELAY = 100.0*pq.ms - dtc.vtest[k]['injected_square_current']['delay']= DELAY - dtc.vtest[k]['injected_square_current']['duration'] = DURATION - dtc.vtest[k]['injected_square_current']['amplitude'] = dtc.rheobase['value'] - else: - DURATION = 500.0*pq.ms - DELAY = 200.0*pq.ms - dtc.vtest[k]['injected_square_current'] = {} - dtc.vtest[k]['injected_square_current']['delay']= DELAY - dtc.vtest[k]['injected_square_current']['duration'] = DURATION - dtc.vtest[k]['injected_square_current']['amplitude'] = -10*pq.pA - - # not returned so actually not effective - #v.params = dtc.vest[k] - - return dtc - -#from neuronunit.tests.base import AMPL, DELAY, DURATION - -def hack_judge(test_and_models): - (test, attrs) = test_and_models - model = None - obs = test.observation - LEMS_MODEL_PATH = path_params['model_path'] - - model = ReducedModel(LEMS_MODEL_PATH,name = str('vanilla'),backend = ('RAW')) - model.set_attrs(attrs) - test.generate_prediction(model) - pred = test.generate_prediction(model) - score = test.compute_score(obs,pred) - try: - print(obs['value'],pred['value']) - except: - print(obs['mean'],pred['mean']) - - return score - - -import dask.bag as db -# The rheobase has been obtained seperately and cannot be db mapped. -# Nested DB mappings dont work. -from itertools import repeat - - -def nunit_evaluation(tuple_object): - # Inputs single data transport container modules, and neuroelectro observations that - # inform test error error_criterion - # Outputs Neuron Unit evaluation scores over error criterion - dtc,tests = tuple_object - dtc = copy.copy(dtc) - dtc.model_path = path_params['model_path'] - LEMS_MODEL_PATH = path_params['model_path'] - assert dtc.rheobase is not None - - - for k,t in enumerate(tests[1::]): - - score,_ = hack_judge((t,dtc.attrs)) - #score = t.judge(model,stop_on_error = False, deep_error = True) - if type(score.sort_key) is not type(None): - dtc.scores[str(t)] = 1 - score.sort_key - else: - dtc.scores[str(t)] = 1.0 - dtc = score_proc(dtc,t,copy.copy(score)) - dtc.get_ss() # compute the sum of sciunit score components. - return dtc - - - - -def evaluate(dtc): - error_length = len(dtc.scores.keys()) - fitness = [ 1.0 for i in range(0,error_length) ] - other_fitness = [ 1.0 for i in range(0,error_length) ] - - for k,t in enumerate(dtc.scores.keys()): - fitness[k] = dtc.scores[str(t)] - if str('agreement') in dtc.score[str(t)].keys(): - other_fitness[k] = dtc.score[str(t)]['agreement'] - - return tuple(fitness,) - -def get_trans_list(param_dict): - trans_list = [] - for i,k in enumerate(list(param_dict.keys())): - trans_list.append(k) - return trans_list - - -from itertools import repeat - -def transform(xargs): - (ind,td,backend) = xargs - - # The merits of defining a function in a function - # is that it yields a semi global scoped variables. - # conisider refactoring outer function as a decorator. - dtc = DataTC() - - LEMS_MODEL_PATH = str(neuronunit.__path__[0])+str('/models/NeuroML2/LEMS_2007One.xml') - dtc.backend = 'RAW' - dtc.attrs = {} - for i,j in enumerate(ind): - dtc.attrs[str(td[i])] = j - - dtc.evaluated = False - return dtc - - -def update_dtc_pop(pop, td, backend = None): - ''' - inputs a population of genes/alleles, the population size MU, and an optional argument of a rheobase value guess - outputs a population of genes/alleles, a population of individual object shells, ie a pickleable container for gene attributes. - Rationale, not every gene value will result in a model for which rheobase is found, in which case that gene is discarded, however to - compensate for losses in gene population size, more gene samples must be tested for a successful return from a rheobase search. - If the tests return are successful these new sampled individuals are appended to the population, and then their attributes are mapped onto - corresponding virtual model objects. - ''' - - if isinstance(pop, Iterable):# and type(pop[0]) is not type(str('')): - xargs = zip(pop,repeat(td),repeat(backend)) - npart = np.min([multiprocessing.cpu_count(),len(pop)]) - bag = db.from_sequence(xargs, npartitions = npart) - dtcpop = list(bag.map(transform).compute()) - assert len(dtcpop) == len(pop) - else: - xargs = (pop,td,repeat(backend)) - # In this case pop is not really a population but an individual - # but parsimony of naming variables - # suggests not to change the variable name to reflect this. - dtcpop = [ transform(xargs) ] - assert dtcpop[0].backend is 'RAW' - print(dtcpop) - return dtcpop - -#@jit -def run_ga(model_params,npoints,test, free_params = None, hc = None): - # https://stackoverflow.com/questions/744373/circular-or-cyclic-imports-in-python - # These imports need to be defined with local scope to avoid circular importing problems - # Try to fix local imports later. - from bluepyopt.deapext.optimisations import SciUnitOptimization - ss = {} - for k in free_params: - ss[k] = model_params[k] - print(ss) - #import pdb; pdb.set_trace() - MU = 2**len(list(free_params)) - max_ngen = int(np.floor(npoints)) - selection = str('selNSGA') - DO = SciUnitOptimization(offspring_size = MU, error_criterion = test, selection = selection, boundary_dict = ss, elite_size = 2, hc = hc) - ga_out = DO.run(offspring_size = MU, max_ngen = max_ngen) - ga_out['dhof'] = [ h.dtc for h in ga_out['hof'] ] - ga_out['dbest'] = ga_out['hof'][0].dtc - - return ga_out, DO - -def rheobase_old(pop, td, rt): - ''' - Calculate rheobase for a given population pop - Ordered parameter dictionary td - and rheobase test rt - ''' - from neuronunit.optimization.exhaustive_search import update_dtc_grid - if isinstance(pop, Iterable): - dtcpop = list(update_dtc_pop(pop, td)) - xargs = iter(zip(dtcpop,repeat(rt),repeat('RAW'))) - print(xargs) - import pdb; pdb.set_trace() - dtcpop = list(map(dtc_to_rheo,xargs)) - for ind,d in zip(pop,dtcpop): - ind.rheobase = d.rheobase - return pop, dtcpop - - else: - pass - ''' - # WSFloat a DEAP BluePyOpt extensible Float type. - - pop = WSFloatIndividual(pop) - dtc = update_dtc_grid(pop, td) - - if isinstance(dtc, Iterable): - xargs = (dtc[0],rt,str('RAW')) - dtc = dtc_to_rheo(xargs) - pop.rheobase = dtc.rheobase - return pop,dtc - - else: - - xargs = (dtc,rt,str('RAW')) - dtc = dtc_to_rheo(xargs) - pop.rheobase = dtc.rheobase - - return pop,dtc - ''' - - -#@jit -def impute_check(pop,dtcpop,td,tests): - - delta = len(pop) - len(dtcpop) - # if a rheobase value cannot be found for a given set of dtc model more_attributes - # delete that model, or rather, filter it out above, and impute - # a new model from the mean of the pre-existing model attributes. - impute_pop = [] - if delta != 0: - impute = [] - impute_gene = [] # impute individual, not impute index - for i in range(0,len(pop[0])): - impute_gene.append(np.mean([ p[i] for p in pop ])) - - ind = WSListIndividual(impute_gene) - # - # newest functioning code. - # other broken transform should be modelled on this. - ## what function transform should consist of. - dtc = DataTC() - LEMS_MODEL_PATH = str(neuronunit.__path__[0])+str('/models/NeuroML2/LEMS_2007One.xml') - dtc.backend = 'RAW' - dtc.attrs = {} - for i,j in enumerate(ind): - dtc.attrs[str(td[i])] = j - - ## end function transform - - xarg = (dtc,tests[0],'RAW') - dtc = dtc_to_rheo(xarg) - ind.rheobase = dtc.rheobase - print(dtc.attrs,dtc.rheobase,'still failing') - - if type(ind.rheobase) is type(None): - - pass - pop.append(ind) - dtcpop.append(dtc) - return dtcpop,pop - - - - - -def serial_route(pop,td,tests): - #pop, dtc = rheobase_old(copy.copy(pop), td, tests[0]) - if type(dtc.rheobase) is type(None): - print('Error Score bad model') - for t in tests: - dtc.scores = {} - dtc.get_ss() - - else: - dtc = format_test((dtc,tests)) - dtc = nunit_evaluation((dtc,tests)) - print(dtc.get_ss()) - return pop, dtc - -def parallel_route(pop,dtcpop,tests,td): - #dtcpop,pop = impute_check(pop,dtcpop,td,tests) - print([type(d) for d in dtcpop]) - print([d for d in dtcpop]) - - xargs = zip(dtcpop,repeat(tests)) - print('len tests {0} tests {1}'.format(len(tests),tests)) - #for x in xargs: - - # xargs[0] = format_test(x) - dtcpop = list(map(format_test,xargs)) - npart = np.min([multiprocessing.cpu_count(),len(pop)]) - dtcbag = db.from_sequence(list(zip(dtcpop,repeat(tests))), npartitions = npart) - dtcpop = list(dtcbag.map(nunit_evaluation).compute()) - #for zipped in zip(dtcpop,repeat(tests)): - # junk = nunit_evaluation(zipped) - #import pdb; pdb.set_trace() - for i,d in enumerate(dtcpop): - if not hasattr(pop[i],'dtc'): - pop[i] = WSListIndividual(pop[i]) - pop[i].dtc = None - - pop[i].dtc = copy.copy(dtcpop[i]) - pop[i].dtc.get_ss() - invalid_dtc_not = [ i for i in pop if not hasattr(i,'dtc') ] - - return pop, dtcpop - -def test_runner(pop,td,tests): - print('get into test runner') - # NeuronUnit testing - print(pop,td,tests[0]) - import pdb; pdb.set_trace() - pop, dtcpop = rheobase_old(pop, td, tests[0]) - import pdb; pdb.set_trace() - - # parallel route: - #dtcpop = [ dtc for dtc in dtcpop if hasattr(dtc,'rheobase') ] - #dtcpop = [ dtc for dtc in dtcpop if not isinstance(dtc.rheobase,type(None)) ] - #dtcpop = [ dtc for dtc in dtcpop if dtc.rheobase!=-1.0 ] - pop,dtcpop = parallel_route(pop,dtcpop,tests,td) - for p,d in zip(pop,dtcpop): - p.dtc = d - print([p.dtc for p in pop]) - - if isinstance(pop, Iterable) and isinstance(dtcpop,Iterable): - - for p,d in zip(pop,dtcpop): - if type(p) is type(None) or type(d) is type(None): - import pdb - pdb.set_trace() - - - #serial route: - if not isinstance(pop, Iterable):# and not isinstance(dtcpop,Iterable): - pop,dtcpop = serial_route(pop,td,tests) - print('serial badness') - - print('tests, completed, now gene computations') - return pop,dtcpop - - -def update_deap_pop(pop, tests, td, backend = None): - ''' - # Inputs a population of genes (pop). - Returned neuronunit scored DataTransportContainers (dtcpop). - This method converts a population of genes to a population of Data Transport Containers, - Which act as communicatable data types for storing model attributes. - Rheobase values are found on the DTCs - DTCs for which a rheobase value of x (pA)<=0 are filtered out - DTCs are then scored by neuronunit, using neuronunit models that act in place. - ''' - pop = copy.copy(pop) - print('not even to test runner getting') - pop, dtcpop = test_runner(pop,td,tests) - for p,d in zip(pop,dtcpop): - p.dtc = d - print([p.dtc for p in pop]) - import pdb; pdb.set_trace() - if not isinstance(pop, Iterable) and not isinstance(dtcpop,Iterable): - pop.dtc = dtcpop - if type(pop.dtc.rheobase) is type(None): - pop.dtc.scores = {} - for t in tests: - pop.dtc.scores[str(t)] = 1.0 - print(pop.dtc.get_ss()) - else: - pass - - - return pop - -def create_subset(nparams = 10, boundary_dict = None): - # used by GA to find subsets in parameter space. - if type(boundary_dict) is type(None): - raise ValueError('A dictionary was not not supplied and a specific bad thing happened.') - - mp = modelp.model_params - key_list = list(mp.keys()) - reduced_key_list = key_list[0:nparams] - else: - key_list = list(boundary_dict.keys()) - reduced_key_list = key_list[0:nparams] - subset = { k:boundary_dict[k] for k in reduced_key_list } - return subset diff --git a/neuronunit/optimization/optimization_management.py b/neuronunit/optimization/optimization_management.py index c2ba015a7..4d79e48e6 100644 --- a/neuronunit/optimization/optimization_management.py +++ b/neuronunit/optimization/optimization_management.py @@ -1,679 +1,1021 @@ - - +# Its not that this file is responsible for doing plotting, +# but it calls many modules that are, such that it needs to pre-empt +import warnings + +SILENT = True +if SILENT: + warnings.filterwarnings("ignore", message="H5pyDeprecationWarning") + warnings.filterwarnings("ignore") + + +try: + import efel +except: + warnings.warn("Blue brain feature extraction not available, consider installing") + +# import time +from typing import Any, Dict, List, Optional, Tuple, Type, Union, Text +import dask +from tqdm import tqdm +import warnings +from neo import AnalogSignal +from elephant.spike_train_generation import threshold_detection import numpy as np -import dask.bag as db +import cython import pandas as pd -from neuronunit import tests -from neuronunit.models.reduced import ReducedModel -from neuronunit.optimization.model_parameters import model_params, path_params -import numpy -from neuronunit.optimization import model_parameters as modelp -from itertools import repeat - -import copy +from collections import OrderedDict +from collections.abc import Iterable import math - -import quantities as pq -import numpy as np -from pyneuroml import pynml - +import numpy +import deap +from deap import creator from deap import base -from neuronunit.optimization.data_transport_container import DataTC - - -import os -import pickle -from itertools import repeat -import neuronunit -import multiprocessing -npartitions = multiprocessing.cpu_count() -from collections import Iterable -from numba import jit -from sklearn.model_selection import ParameterGrid +import array +import copy +from frozendict import frozendict from itertools import repeat -from collections import OrderedDict - +import random +import quantities as pq +pq.quantity.PREFERRED = [pq.mV, pq.pA, pq.MOhm, pq.ms, pq.pF, pq.Hz / pq.pA] -import logging -logger = logging.getLogger('__main__') +import bluepyopt as bpop +import bluepyopt.ephys as ephys +from bluepyopt.parameters import Parameter +import sciunit +from sciunit import TestSuite +from sciunit import scores +from sciunit.scores import RelativeDifferenceScore +from sciunit.utils import config_set +config_set("PREVALIDATE", False) -from neuronunit.tests.fi import RheobaseTestP# as discovery -from neuronunit.tests.fi import RheobaseTest# as discovery +from jithub.models import model_classes -import dask.bag as db -# The rheobase has been obtained seperately and cannot be db mapped. -# Nested DB mappings dont work. -from itertools import repeat -# DEAP mutation strategies: -# https://deap.readthedocs.io/en/master/api/tools.html#deap.tools.mutESLogNormal -class WSListIndividual(list): - """Individual consisting of list with weighted sum field""" - def __init__(self, *args, **kwargs): - """Constructor""" - self.rheobase = None - super(WSListIndividual, self).__init__(*args, **kwargs) - -class WSFloatIndividual(float): - """Individual consisting of list with weighted sum field""" - def __init__(self, *args, **kwargs): - """Constructor""" - self.rheobase = None - super(WSFloatIndividual, self).__init__() - - -def mint_generic_model(backend): - LEMS_MODEL_PATH = path_params['model_path'] - return ReducedModel(LEMS_MODEL_PATH,name = str('vanilla'),backend = str(backend)) - -@jit -def write_opt_to_nml(path,param_dict): - ''' - Write optimimal simulation parameters back to NeuroML. - ''' - orig_lems_file_path = path_params['model_path'] - more_attributes = pynml.read_lems_file(orig_lems_file_path, - include_includes=True, - debug=False) +from neuronunit.optimization.data_transport_container import DataTC +from neuronunit.tests.base import AMPL, DELAY, DURATION +from neuronunit.tests.target_spike_current import ( + SpikeCountSearch, + SpikeCountRangeSearch, +) +import neuronunit.capabilities.spike_functions as sf +from neuronunit.optimization.model_parameters import MODEL_PARAMS, BPO_PARAMS +from neuronunit.tests.fi import RheobaseTest +from neuronunit.capabilities.spike_functions import get_spike_waveforms, spikes2widths +from neuronunit.tests import VmTest + +PASSIVE_DURATION = 500.0 * pq.ms +PASSIVE_DELAY = 200.0 * pq.ms +ALLEN_DURATION = 2000 * pq.ms +ALLEN_DELAY = 1000 * pq.ms + + +class TSD(dict): + """ + -- Synopsis: + Test Suite Dictionary class + A container for sciunit tests, Indexable by dictionary keys. + Contains a method called optimize. + """ + + def __init__(self, tests={}, use_rheobase_score=True): + self.DO = None + self.use_rheobase_score = use_rheobase_score + self.backend = None + if type(tests) is TestSuite: + tests = OrderedDict({t.name: t for t in tests.tests}) + if type(tests) is type(dict()): + pass + if type(tests) is type(list()): + tests = OrderedDict({t.name: t for t in tests}) + super(TSD, self).__init__() + self.update(tests) + + def display(self): + from IPython.display import display + + if hasattr(self, "ga_out"): + return display(self.ga_out["pf"][0].dtc.obs_preds) + else: + return None + + def to_pickleable_dict(self): + """ + -- Synopsis: + # A pickleable version of instance object. + # https://joblib.readthedocs.io/en/latest/ + + # This might work joblib.dump(self, filename + '.compressed', compress=True) + # somewhere in job lib there are tools for pickling more complex objects + # including simulation results. + """ + del self.ga_out.DO + del self.DO + return {k: v for k, v in self.items()} + + +@cython.boundscheck(False) +@cython.wraparound(False) +def random_p(model_type): + ranges = MODEL_PARAMS[model_type] + date_int = int(time.time()) + numpy.random.seed(date_int) + random_param1 = {} # randomly sample a point in the viable parameter space. + for k in ranges.keys(): + sample = random.uniform(ranges[k][0], ranges[k][1]) + random_param1[k] = sample + return random_param1 + + +@cython.boundscheck(False) +@cython.wraparound(False) +def process_rparam(backend, free_parameters): + random_param = random_p(backend) + random_param.pop("Iext", None) + rp = random_param + if free_parameters is not None: + reduced_parameter_set = {} + for k in free_parameters: + reduced_parameter_set[k] = rp[k] + rp = reduced_parameter_set + dsolution = DataTC(backend=backend, attrs=rp) + temp_model = dsolution.dtc_to_model() + dsolution.attrs = temp_model.default_attrs + dsolution.attrs.update(rp) + return dsolution, rp, None, random_param + + +def write_models_for_nml_db(dtc): + with open(str(list(dtc.attrs.values())) + ".csv", "w") as writeFile: + df = pd.DataFrame([dtc.attrs]) + writer = csv.writer(writeFile) + writer.writerows(df) + + +def write_opt_to_nml(path, param_dict) -> None: + """ + -- Inputs: desired file path, model parameters to encode in NeuroML2 + -- Outputs: NeuroML2 file. + -- Synopsis: Write optimimal simulation parameters back to NeuroML2. + """ + more_attributes = pynml.read_lems_file( + orig_lems_file_path, include_includes=True, debug=False + ) for i in more_attributes.components: new = {} - if str('izhikevich2007Cell') in i.type: - for k,v in i.parameters.items(): + if str("izhikevich2007Cell") in i.type: + for k, v in i.parameters.items(): units = v.split() if len(units) == 2: units = units[1] else: - units = 'mV' - new[k] = str(param_dict[k]) + str(' ') + str(units) + units = "mV" + new[k] = str(param_dict[k]) + str(" ") + str(units) i.parameters = new - fopen = open(path+'.nml','w') + fopen = open(path + ".nml", "w") more_attributes.export_to_file(fopen) fopen.close() return - -def bridge_judge(test_and_models): - # Temporarily patch sciunit judge code, which seems to be broken. - # - # - (test, dtc) = test_and_models - obs = test.observation - backend_ = dtc.backend - model = mint_generic_model(backend_) - model.set_attrs(**dtc.attrs) - pred = test.generate_prediction(model) - if pred is not None: - if hasattr(dtc,'prediction'):# is not None: - dtc.prediction[test] = pred - dtc.observation[test] = test.observation['mean'] - - else: - dtc.prediction = None - dtc.observation = None - dtc.prediction = {} - dtc.prediction[test] = pred - dtc.observation = {} - dtc.observation[test] = test.observation['mean'] - - - #dtc.prediction = pred - score = test.compute_score(obs,pred) - if not hasattr(dtc,'agreement'): - dtc.agreement = None - dtc.agreement = {} - try: - dtc.agreement[str(test)] = np.abs(test.observation['mean'] - pred['mean']) - except: - try: - dtc.agreement[str(test)] = np.abs(test.observation['value'] - pred['value']) - except: - try: - dtc.agreement[str(test)] = np.abs(test.observation['mean'] - pred['value']) - except: - pass - #print(score.norm_score) - else: - score = None - return score, dtc - -def get_rh(dtc,rtest): - place_holder = {} - place_holder['n'] = 86 - place_holder['mean'] = 10*pq.pA - place_holder['std'] = 10*pq.pA - place_holder['value'] = 10*pq.pA - rtest = RheobaseTestP(observation=place_holder,name='a Rheobase test') - dtc.rheobase = None +""" +def get_rh(dtc: DataTC, rtest_class: RheobaseTest, bind_vm: bool = False) -> DataTC: + ''' + --args: + :param object dtc: + :param object Rheobase Test Class: + :-- returns: object dtc: + -- Synopsis: This is used to recover/produce + a rheobase test class instance, + given unknown experimental observations. + ''' + place_holder = {"mean": None * pq.pA} backend_ = dtc.backend - model = mint_generic_model(backend_) - model.set_attrs(**dtc.attrs) - #model = mint_generic_model() - dtc.rheobase = rtest.generate_prediction(model)#['value'] - if dtc.rheobase is None: - dtc.rheobase = - 1.0 + rtest = RheobaseTest(observation=place_holder, name="RheobaseTest") + rtest.score_type = RelativeDifferenceScore + assert len(dtc.attrs) + model = dtc.dtc_to_model() + rtest.params["injected_square_current"] = {} + rtest.params["injected_square_current"]["delay"] = DELAY + rtest.params["injected_square_current"]["duration"] = DURATION + dtc.rheobase = rtest.generate_prediction(model)["value"] + if bind_vm: + temp_vm = model.get_membrane_potential() + dtc.vmrh = temp_vm + if np.isnan(np.min(temp_vm)): + # rheobase exists but the waveform is nuts. + # this is the fastest way to filter out a gene + dtc.rheobase = None return dtc +""" +def get_new_rtest(dtc: DataTC) -> RheobaseTest: + place_holder = {"mean": 10 * pq.pA} + f = RheobaseTest + rtest = f(observation=place_holder, name="RheobaseTest") + rtest.score_type = RelativeDifferenceScore + return rtest -def dtc_to_rheo(dtc): - # If test taking data, and objects are present (observations etc). - # Take the rheobase test and store it in the data transport container. - dtc.scores = {} - dtc.score = {} - backend_ = dtc.backend - model = mint_generic_model(backend_) - model.set_attrs(**dtc.attrs) - rtest = [ t for t in dtc.tests if str('RheobaseTestP') == t.name ] - - - if len(rtest): - rtest = rtest[0] - - dtc.rheobase = rtest.generate_prediction(model) - #print(dtc.rheobase) - if dtc.rheobase is not None and dtc.rheobase !=-1.0: - dtc.rheobase = dtc.rheobase['value'] - obs = rtest.observation - score = rtest.compute_score(obs,dtc.rheobase) - dtc.scores[str('RheobaseTestP')] = 1.0 - score.norm_score - - if dtc.score is not None: - dtc = score_proc(dtc,rtest,copy.copy(score)) - - rtest.params['injected_square_current']['amplitude'] = dtc.rheobase +def get_rtest(dtc: DataTC) -> RheobaseTest: + if not hasattr(dtc, "tests"): + rtest = get_new_rtest(dtc) + else: + if type(dtc.tests) is type(list()): + rtests = [t for t in dtc.tests if "rheo" in t.name.lower()] else: - dtc.rheobase = - 1.0 - dtc.scores[str('RheobaseTestP')] = 1.0 - - - + rtests = [v for k, v in dtc.tests.items() if "rheo" in str(k).lower()] + if len(rtests): + rtest = rtests[0] + else: + rtest = get_new_rtest(dtc) + return rtest + + +def dtc_to_rheo(dtc: DataTC, bind_vm: bool = False) -> DataTC: + """ + --Synopsis: If test taking data, and objects are present (observations etc). + Take the rheobase test and store it in the data transport container. + """ + if hasattr(dtc, "tests"): + if type(dtc.tests) is type({}) and str("RheobaseTest") in dtc.tests.keys(): + rtest = dtc.tests["RheobaseTest"] + else: + rtest = get_rtest(dtc) else: + rtest = get_rtest(dtc) + + if rtest is not None: + model = dtc.dtc_to_model() + if dtc.attrs is not None: + model.attrs = dtc.attrs + if isinstance(rtest, Iterable): + rtest = rtest[0] + dtc.rheobase = rtest.generate_prediction(model)["value"] + temp_vm = model.get_membrane_potential() + min = np.min(temp_vm) + if np.isnan(temp_vm.any()): + dtc.rheobase = None + if bind_vm: + dtc.vmrh = temp_vm + if rtest is not None: + raise Exception('rheobase test is still None despite efforts') + # rheobase does exist but lets filter out this bad gene. + return dtc + #else: # otherwise, if no observation is available, or if rheobase test score is not desired. # Just generate rheobase predictions, giving the models the freedom of rheobase # discovery without test taking. - dtc = get_rh(dtc,rtest) - return dtc - - -def score_proc(dtc,t,score): - dtc.score[str(t)] = {} - #print(score.keys()) - if hasattr(score,'norm_score'): - dtc.score[str(t)]['value'] = copy.copy(score.norm_score) - if hasattr(score,'prediction'): - if type(score.prediction) is not type(None): - dtc.score[str(t)][str('prediction')] = score.prediction - dtc.score[str(t)][str('observation')] = score.observation - boolean_means = bool('mean' in score.observation.keys() and 'mean' in score.prediction.keys()) - boolean_value = bool('value' in score.observation.keys() and 'value' in score.prediction.keys()) - if boolean_means: - dtc.score[str(t)][str('agreement')] = np.abs(score.observation['mean'] - score.prediction['mean']) - if boolean_value: - dtc.score[str(t)][str('agreement')] = np.abs(score.observation['value'] - score.prediction['value']) - dtc.agreement = dtc.score - return dtc - -def switch_logic(tests): - # move this logic into sciunit tests - for t in tests: - t.passive = None - t.active = None - active = False - passive = False - - if str('RheobaseTest') == t.name: - active = True - passive = False - elif str('RheobaseTestP') == t.name: - active = True - passive = False - elif str('InjectedCurrentAPWidthTest') == t.name: - active = True - passive = False - elif str('InjectedCurrentAPAmplitudeTest') == t.name: - active = True - passive = False - elif str('InjectedCurrentAPThresholdTest') == t.name: - active = True - passive = False - elif str('RestingPotentialTest') == t.name: - passive = True - active = False - elif str('InputResistanceTest') == t.name: - passive = True - active = False - elif str('TimeConstantTest') == t.name: - passive = True - active = False - elif str('CapacitanceTest') == t.name: - passive = True - active = False - t.passive = passive - t.active = active - return tests - -def active_values(keyed,rheobase): - DURATION = 1000.0*pq.ms - DELAY = 100.0*pq.ms - keyed['injected_square_current'] = {} - keyed['injected_square_current']['delay']= DELAY - keyed['injected_square_current']['duration'] = DURATION - if type(rheobase) is type({str('k'):str('v')}): - keyed['injected_square_current']['amplitude'] = float(rheobase['value'])*pq.pA + #dtc = get_rh(dtc, rtest, bind_vm=bind_vm) + #if bind_vm: + # dtc.vmrh = temp_vm + #return dtc + + +def basic_expVar(trace1, trace2): + """ + https://github.com/AllenInstitute/GLIF_Teeter_et_al_2018/blob/master/query_biophys/query_biophys_expVar.py + --Synopsis: This is the fundamental calculation that is used in all different types of explained variation. + At a basic level, the explained variance is calculated between two traces. These traces can be PSTH's + or single spike trains that have been convolved with a kernel (in this case always a Gaussian) + --Args: + trace 1 & 2: 1D numpy array containing values of the trace. (This function requires numpy array + to ensure that this is not a multidemensional list.) + --Returns: + expVar: float value of explained variance + """ + + var_trace1 = np.var(trace1) + var_trace2 = np.var(trace2) + var_trace1_minus_trace2 = np.var(trace1 - trace2) + # Traces are the same in variance + if var_trace1_minus_trace2 == 0.0: + return 1.0 else: - keyed['injected_square_current']['amplitude'] = rheobase - - return keyed - -def passive_values(keyed): - DURATION = 500.0*pq.ms - DELAY = 200.0*pq.ms - keyed['injected_square_current'] = {} - keyed['injected_square_current']['delay']= DELAY - keyed['injected_square_current']['duration'] = DURATION - keyed['injected_square_current']['amplitude'] = -10*pq.pA - return keyed - -def format_test(dtc): - #pre format the current injection dictionary based on pre computed - #rheobase values of current injection. - #This is much like the hooked method from the old get neab file. - dtc.vtest = {} - dtc.tests = switch_logic(dtc.tests) - - for k,v in enumerate(dtc.tests): - dtc.vtest[k] = {} - if v.passive == False and v.active == True: - keyed = dtc.vtest[k] - dtc.vtest[k] = active_values(keyed,dtc.rheobase) - - elif v.passive == True and v.active == False: - keyed = dtc.vtest[k] - dtc.vtest[k] = passive_values(keyed) - return dtc + return (var_trace1 + var_trace2 - var_trace1_minus_trace2) / ( + var_trace1 + var_trace2 + ) - -def allocate_worst(dtc,tests): - # If the model fails tests, and cannot produce model driven data - # Allocate the worst score available. - for t in tests: - dtc.scores[str(t)] = 1.0 - dtc.score[str(t)] = 1.0 +def train_length(dtc: DataTC) -> DataTC: + if not hasattr(dtc, "efel"): + dtc.efel = [{}] + vm = dtc.vm_soma + train_len = float(len(sf.get_spike_train(vm))) + dtc.efel["Spikecount"] = train_len return dtc -def nunit_evaluation_df(dtc): - # Inputs single data transport container modules, and neuroelectro observations that - # inform test error error_criterion - # Outputs Neuron Unit evaluation scores over error criterion - # same method as below but with data frame. - tests = dtc.tests - dtc = copy.copy(dtc) - dtc.model_path = path_params['model_path'] - LEMS_MODEL_PATH = path_params['model_path'] - df = pd.DataFrame(index=list(tests),columns=['observation','prediction','disagreement'])#,columns=list(reduced_cells.keys())) - if dtc.rheobase == -1.0 or type(dtc.rheobase) is type(None): - dtc = allocate_worst(tests,dtc) +def multi_spiking_feature_extraction( + dtc: DataTC, solve_for_current: bool = None, efel_filter_iterable: List = None +) -> DataTC: + """ + Perform multi spiking feature extraction + via EFEL because its fast + """ + if solve_for_current is None: + _, _, _, _, dtc = inject_model_soma(dtc) + if dtc.vm_soma is None:# or dtc.exclude is True: + return dtc + dtc = efel_evaluation(dtc, efel_filter_iterable) + dtc.vm_soma = None else: - for k,t in enumerate(tests): - if str('RheobaseTest') != t.name and str('RheobaseTestP') != t.name: - t.params = dtc.vtest[k] - score, dtc= bridge_judge((t,dtc)) - if score is not None: - if score.norm_score is not None: - dtc.scores[str(t)] = 1.0 - score.norm_score - df.iloc[k]['observation'] = t.observation['mean'] - try: - agreement = np.abs(t.observation['mean'] - pred['value']) - df.iloc[k]['prediction'] = pred['value'] - df.iloc[k]['disagreement'] = agreement - - except: - agreement = np.abs(t.observation['mean'] - pred['mean']) - df.iloc[k]['prediction'] = pred['mean'] - df.iloc[k]['disagreement'] = agreement - else: - print('gets to None score type') - # compute the sum of sciunit score components. - dtc.summed = dtc.get_ss() - dtc.df = df - return dtc + _, _, _, _, dtc = inject_model_soma(dtc, + solve_for_current=solve_for_current) + if dtc.vm_soma is None:# or dtc.exclude is True: + dtc.efel = None + return dtc + + dtc = efel_evaluation(dtc, efel_filter_iterable) + if hasattr(dtc, "efel"): + if dtc.efel is not None: + dtc = train_length(dtc) -def nunit_evaluation(dtc): - # Inputs single data transport container modules, and neuroelectro observations that - # inform test error error_criterion - # Outputs Neuron Unit evaluation scores over error criterion - tests = dtc.tests - dtc = copy.copy(dtc) - dtc.model_path = path_params['model_path'] - LEMS_MODEL_PATH = path_params['model_path'] - #df = pd.DataFrame(index=list(tests),columns=['observation','prediction','disagreement'])#,columns=list(reduced_cells.keys())) - if dtc.rheobase == -1.0 or type(dtc.rheobase) is type(None): - dtc = allocate_worst(tests,dtc) else: - for k,t in enumerate(tests): - if str('RheobaseTest') != t.name and str('RheobaseTestP') != t.name: - t.params = dtc.vtest[k] - score, dtc= bridge_judge((t,dtc)) - if score is not None: - if score.norm_score is not None: - dtc.scores[str(t)] = 1.0 - score.norm_score + dtc.efel = None - else: - print('gets to None score type') - # compute the sum of sciunit score components. - dtc.summed = dtc.get_ss() - #dtc.df = df return dtc +def constrain_ahp(vm_used: Any = object()) -> dict: + efel.reset() + efel.setThreshold(0) + trace3 = { + "T": [float(t) * 1000.0 for t in vm_used.times], + "V": [float(v) for v in vm_used.magnitude], + } + DURATION = 1100 * pq.ms + DELAY = 100 * pq.ms + trace3["stim_end"] = [float(DELAY) + float(DURATION)] + trace3["stim_start"] = [float(DELAY)] + simple_ahp_constraint_list = ["AHP_depth", "AHP_depth_abs", "AHP_depth_last"] + results = efel.getMeanFeatureValues( + [trace3], simple_ahp_constraint_list, raise_warnings=False + ) + return results + + +def exclude_non_viable_deflections(responses: dict = {}) -> float: + """ + Synopsis: reject waveforms that would otherwise score well but have + unrealistically huge AHP + """ + if responses["response"] is not None: + vm = responses["response"] + results = constrain_ahp(vm) + results = results[0] + if results["AHP_depth"] is None or np.abs(results["AHP_depth_abs"]) >= 80: + return 1000.0 + if np.abs(results["AHP_depth"]) >= 105: + return 1000.0 + if np.max(vm) >= 0: + snippets = get_spike_waveforms(vm) + widths = spikes2widths(snippets) + spike_train = threshold_detection(vm, threshold=0 * pq.mV) + if not len(spike_train): + return 1000.0 + + if (spike_train[0] + 2.5 * pq.ms) > vm.times[-1]: + too_long = True + return 1000.0 + if isinstance(widths, Iterable): + for w in widths: + if w >= 3.5 * pq.ms: + return 1000.0 + else: + width = widths + if width >= 2.0 * pq.ms: + return 1000.0 + if float(vm[-1]) == np.nan or np.isnan(vm[-1]): + return 1000.0 + if float(vm[-1]) >= 0.0: + return 1000.0 + assert vm[-1] < 0 * pq.mV + return 0 + + +class NUFeature_standard_suite(object): + def __init__(self, test, model): + self.test = test + print(self.test) + self.model = model + self.score_array = None + + def calculate_score(self, responses: dict = {}) -> float: + dtc = responses["dtc"] + model = dtc.dtc_to_model() + model.attrs = responses["params"] + self.test = initialise_test(self.test) + if self.test.active and responses["dtc"].rheobase is not None: + result = exclude_non_viable_deflections(responses) + if result != 0: + return result + self.test.prediction = self.test.generate_prediction(model) + if responses["rheobase"] is not None: + if self.test.prediction is not None: + score_gene = self.test.judge( + model, prediction=self.test.prediction, deep_error=True + ) + else: + return 1000.0 + else: + return 1000.0 + if not isinstance(type(score_gene), type(None)): + if not isinstance(score_gene, sciunit.scores.InsufficientDataScore): + try: + if not isinstance(type(score_gene.raw), type(None)): + lns = np.abs(np.float(score_gene.raw)) + else: + if not isinstance(type(score_gene.raw), type(None)): + # works 1/2 time that log_norm_score does not work + # more informative than nominal bad score 100 + lns = np.abs(np.float(score_gene.raw)) + # works 1/2 time that log_norm_score does not work + # more informative than nominal bad score 100 -def evaluate(dtc): - error_length = len(dtc.scores.keys()) - # assign worst case errors, and then over write them with situation informed errors as they become available. - fitness = [ 1.0 for i in range(0,error_length) ] - for k,t in enumerate(dtc.scores.keys()): - fitness[k] = dtc.scores[str(t)] - return tuple(fitness,) - -def get_trans_list(param_dict): - trans_list = [] - for i,k in enumerate(list(param_dict.keys())): - trans_list.append(k) - return trans_list - - - -def transform(xargs): - (ind,td,backend) = xargs - dtc = DataTC() - LEMS_MODEL_PATH = str(neuronunit.__path__[0])+str('/models/NeuroML2/LEMS_2007One.xml') - dtc.attrs = {} - for i,j in enumerate(ind): - dtc.attrs[str(td[i])] = j - dtc.evaluated = False - return dtc - - -def add_constant(hold_constant, pop, td): - hold_constant = OrderedDict(hold_constant) - for k in hold_constant.keys(): - td.append(k) - for p in pop: - for v in hold_constant.values(): - p.append(v) - return pop,td - -def update_dtc_pop(pop, td): + except: + lns = 1000 + else: + lns = 1000 + else: + lns = 1000 + if lns == np.inf or lns == np.nan: + lns = 1000 + + return lns + + +def make_evaluator( + nu_tests:Iterable, + PARAMS:Iterable, + experiment:str=str("Neocortex pyramidal cell layer 5-6"), + model_type:str=str("IZHI"), + score_type:Any=RelativeDifferenceScore, +) -> Union[Any, Any, Any, List[Any]]: + """ + --Synopsis: make a BluePyOpt genetic algorithm evaluator + ie an object that has a model attribute, + and an score calculator method. + Using these attributes the evaluator + can update parameters on the model, + and then can calculate appropriate objective + scores. The genetic algorithm can thus use this + object to evolve genes, but also the human user of the + GA can use the evaluator to find the fitness of any + particular model parameterization. + """ + + if type(nu_tests) is type(dict()): + nu_tests = list(nu_tests.values()) + if model == "IZHI": + simple_cell = model_classes.IzhiModel() + if model == "MAT": + simple_cell = model_classes.MATModel() + if model == "ADEXP": + simple_cell = model_classes.ADEXPModel() + simple_cell.params = PARAMS[model] + simple_cell.NU = True + simple_cell.name = model + experiment + objectives = [] + for tt in nu_tests: + feature_name = tt.name + tt.score_type = score_type + ft = NUFeature_standard_suite(tt, simple_cell) + objective = ephys.objectives.SingletonObjective(feature_name, ft) + objectives.append(objective) + + score_calc = ephys.objectivescalculators.ObjectivesCalculator(objectives) + + sweep_protocols = [] + protocol = ephys.protocols.NeuronUnitAllenStepProtocol("onestep", [None], [None]) + sweep_protocols.append(protocol) + onestep_protocol = ephys.protocols.SequenceProtocol( + "onestep", protocols=sweep_protocols + ) + cell_evaluator = ephys.evaluators.CellEvaluator( + cell_model=simple_cell, + param_names=list(copy.copy(BPO_PARAMS)[model].keys()), + fitness_protocols={onestep_protocol.name: onestep_protocol}, + fitness_calculator=score_calc, + sim="euler", + ) + + simple_cell.params_by_names(copy.copy(BPO_PARAMS)[model].keys()) + return (cell_evaluator, simple_cell, score_calc, [tt.name for tt in nu_tests]) + + +def get_binary_file_downloader_html(bin_file_path, file_label="File"): + """ + --Synopsis: Intended to be used with streamlit/frontend. + Allows someone to download a pickle version of + python models that are output from the optimization process. + """ + with open(bin_file_path, "rb") as f: + data = f.read() + bin_str = base64.b64encode(data).decode() + href = f'Download {file_label}' + return href + +def rescale(v): ''' - inputs a population of genes/alleles, the population size MU, and an optional argument of a rheobase value guess - outputs a population of genes/alleles, a population of individual object shells, ie a pickleable container for gene attributes. - Rationale, not every gene value will result in a model for which rheobase is found, in which case that gene is discarded, however to - compensate for losses in gene population size, more gene samples must be tested for a successful return from a rheobase search. - If the tests return are successful these new sampled individuals are appended to the population, and then their attributes are mapped onto - corresponding virtual model objects. + --Synopsis: default rescaling units are often not desirable. + this method helps circumnavigate defaults. + A constant "rescale preffered was defined as a CONSTANT at the top of this file" + --params: v is a qauntities type object (often a float multiplied by a si unit) + --returns rescaled units ''' - if pop[0].backend is not None: - _backend = pop[0].backend - if isinstance(pop, Iterable):# and type(pop[0]) is not type(str('')): - xargs = zip(pop,repeat(td),repeat(_backend)) - npart = np.min([multiprocessing.cpu_count(),len(pop)]) - bag = db.from_sequence(xargs, npartitions = npart) - dtcpop = list(bag.map(transform).compute()) - assert len(dtcpop) == len(pop) - else: - for p in pop: - p.td = td - p.backend = str(_backend) - # above replaces need for this line: - xargs = (pop,td,repeat(backend)) - # In this case pop is not really a population but an individual - # but parsimony of naming variables - # suggests not to change the variable name to reflect this. - dtcpop = [ transform(xargs) ] - assert exec('dtcpop[0].backend is '+str(_backend)+')') - return dtcpop - -def run_ga(explore_edges, max_ngen, test, free_params = None, hc = None, NSGA = None, MU = None, seed_pop = None, model_type = str('RAW')): - from bluepyopt.deapext.optimisations import SciUnitOptimization - # Inputs: - # - MODEL_PARAMS, is a dictionary of model parameter ranges (the boundaries that define regions where parameters are free to vary). - # You may not want all the parameters to be allowed to vary, so the optinal key word argument: free_params - # specifies the free_parameters, and every parameter not in that list is held constant. - # - free_params is type dictionary it takes a list of the dictionary keys for the parameters that are free to vary. - # - MU type int is the population size and - # - NGEN type int, is the number of generations to evaluate. - # MU*NGEN = total maximum number of models that could be evaluated, although DEAP is smart and - # doesn't necessarily evaluate every model in MU*NGEN - # - NSGA, type Boolean tells the optimizer to use the Pareto Front based approach (alternative is select Best). - # - string Model_type tells the Optimizer what simulator backend model combinartion to use. - # - DEAP population type list seed_pop pertains to starting the GA, with an informed guess of where to look. - # For example if you did a coarse grained grid search first, and want the first genes to look in the location - # of the coarse grained optima first. - # - use_test is a list of NU tests (a NeuronUnit tests suite), or a singular test. - # Outputs: - # - ga_out a dictionary of GA optimization results - # - stats, - # - and the pareto-front the paretofront (key 'pf') - - ss = {} - for k in free_params: - ss[k] = explore_edges[k] - if type(MU) == type(None): - MU = 2**len(list(free_params)) - # make sure that the gene population size is divisible by 4. - if NSGA == True: - selection = str('selNSGA') - else: - selection = str('selIBEA') - max_ngen = int(np.floor(max_ngen)) - DO = SciUnitOptimization(offspring_size = MU, error_criterion = test, boundary_dict = ss, backend = model_type, hc = hc,selection = selection, seed_pop= seed_pop)#, selection = selection, boundary_dict = ss, elite_size = 2, hc=hc) - - if seed_pop is not None: - # This is a re-run condition. - DO.setnparams(nparams = len(free_params), boundary_dict = ss) - - DO.seed_pop = seed_pop - DO.setup_deap() - - # This run condition should not need same arguments as above. - ga_out = DO.run(max_ngen = max_ngen)#offspring_size = MU, ) - return ga_out, DO - - -def init_pop(pop, td, tests): + v.rescale_preferred() + v = v.simplified + if np.round(v, 2) != 0: + v = np.round(v, 2) + return v + +def _opt_( + constraints, + PARAMS, + test_key, + model_value, + MU, + NGEN, + diversity, + use_streamlit=False, + score_type=RelativeDifferenceScore, +): + """ + --Synopsis: A private interface for optimizing with neuron unit ephys type tests + """ + + if type(constraints) is not type(list()): + constraints = list(constraints.values()) + cell_evaluator, simple_cell, score_calc, test_names = make_evaluator( + constraints, PARAMS, test_key, model=model_value, score_type=score_type + ) + model_type = str("_best_fit_") + str(model_value) + "_" + str(test_key) + "_.p" + mut = 0.125 + cxp = 0.6125 + optimization = bpop.optimisations.DEAPOptimisation( + evaluator=cell_evaluator, + offspring_size=MU, + map_function=map, + selector_name=diversity, + mutpb=mut, + cxpb=cxp, + neuronunit=True + ) + + final_pop, hall_of_fame, logs, hist = optimization.run(max_ngen=NGEN) + + best_ind = hall_of_fame[0] + best_ind_dict = cell_evaluator.param_dict(best_ind) + model = cell_evaluator.cell_model + cell_evaluator.param_dict(best_ind) + tests = constraints + obs_preds = [] + scores = [] - from neuronunit.optimization.exhaustive_search import update_dtc_grid - dtcpop = list(update_dtc_pop(pop, td)) - for d in dtcpop: - d.tests = tests - if hasattr(pop[0],'backend'): - d.backend = pop[0].backend - - if hasattr(pop[0],'hc'): - constant = pop[0].hc - for d in dtcpop: - if constant is not None: - if len(constant): - d.constants = constant - d.add_constant() - - return pop, dtcpop - -def obtain_rheobase(pop, td, tests): - ''' - Calculate rheobase for a given population pop - Ordered parameter dictionary td - and rheobase test rt - ''' - pop, dtcpop = init_pop(pop, td, tests) - dtcpop = list(map(dtc_to_rheo,dtcpop)) - for ind,d in zip(pop,dtcpop): - if type(d.rheobase) is not type(1.0): - ind.rheobase = d.rheobase - d.rheobase = d.rheobase + for t in tests: + scores.append(t.judge(model, prediction=t.prediction)) + if "mean" in t.observation.keys(): + t.observation["mean"] = rescale(t.observation["mean"]) + + if "mean" in t.prediction.keys(): + t.prediction["mean"] = rescale(t.prediction["mean"]) + + obs_preds.append( + (t.name, t.observation["mean"], t.prediction["mean"], scores[-1]) + ) + if "value" in t.prediction.keys(): + t.prediction["value"] = rescale(t.prediction["value"]) + + obs_preds.append( + (t.name, t.observation["mean"], t.prediction["value"], scores[-1]) + ) else: - ind.rheobase = -1.0 - d.rheobase = -1.0 - return pop, dtcpop - -def new_genes(pop,dtcpop,td): - ''' - some times genes explored will not return - un-usable simulation parameters - genes who have no rheobase score - will be discarded. - - BluePyOpt needs a stable - gene number however - - This method finds how many genes have - been discarded, and tries to build new genes - from the existing distribution of gene values, by mimicing a normal random distribution - of genes that are not deleted. - if a rheobase value cannot be found for a given set of dtc model more_attributes - delete that model, or rather, filter it out above, and make new genes based on - the statistics of remaining genes. - it's possible that they wont be good models either, so keep trying in that event. - a new model from the mean of the pre-existing model attributes. - ''' - impute_gene = [] # impute individual, not impute index - ind = WSListIndividual() - for t in td: - mean = np.mean([ d.attrs[t] for d in dtcpop ]) - std = np.std([ d.attrs[t] for d in dtcpop ]) - sample = numpy.random.normal(loc=mean, scale=2*std, size=1)[0] - ind.append(sample) - dtc = DataTC() - LEMS_MODEL_PATH = str(neuronunit.__path__[0])+str('/models/NeuroML2/LEMS_2007One.xml') - dtc.attrs = {} - for i,j in enumerate(ind): - dtc.attrs[str(td[i])] = j - dtc.backend = dtcpop[0].backend - dtc.tests = dtcpop[0].tests + obs_preds.append((t.name, t.observation, t.prediction, scores[-1])) + + df = pd.DataFrame(obs_preds) + + model.attrs = { + str(k): float(v) for k, v in cell_evaluator.param_dict(best_ind).items() + } + opt = model.model_to_dtc() + opt.attrs = { + str(k): float(v) for k, v in cell_evaluator.param_dict(best_ind).items() + } + #try: + # df = opt.make_pretty(tests=tests) + #except: + # df = pd.DataFrame(obs_preds) + + best_fit_val = best_ind.fitness.values + return ( + final_pop, + hall_of_fame, + logs, + hist, + best_ind, + best_fit_val, + opt, + obs_preds, + df, + ) + + +def public_opt( + constraints, + PARAMS, + test_key, + model_value, + MU, + NGEN, + diversity, + score_type=RelativeDifferenceScore, +): + """ + --Synopsis: A public interface for optimizing with neuron unit ephys type tests + calls _opt_ + """ + _, _, _, _, _, _, opt, obs_preds, df = _opt_( + constraints=constraints, + PARAMS=PARAMS, + test_key=test_key, + model_value=model_value, + MU=MU, + NGEN=NGEN, + diversity=diversity, + score_type=score_type, + ) + return opt, obs_preds, df + +ALLEN_DELAY = 1000.0 * pq.ms +ALLEN_DURATION = 2000.0 * pq.ms +def inject_model_soma( + dtc: DataTC, + figname=None, + solve_for_current=None, + fixed: bool = False, + final_run=False, +) -> Union[AnalogSignal, AnalogSignal, dict, Any, DataTC]: + from neuronunit.tests.target_spike_current import SpikeCountSearch + + """ + -- args: dtc: containing spike number attribute, + the number of spikes wanted. + if solve_for_current is True find the current that causes the + wanted spike number. + -- outputs: voltage at 3.0 rheobase, + voltage that causes a desired number of spikes, + current Injection Parameters, + dtc + -- Synopsis: + produce an rheobase injection value + produce an object of class Neuronunit runnable Model + with known attributes and known rheobase current injection value. + """ + if type(solve_for_current) is not type(None): + observation_range = {} + model = dtc.dtc_to_model() + model._backend.attrs = dtc.attrs + + if not fixed: + observation_range["value"] = dtc.spk_count + scs = SpikeCountSearch(observation_range) + target_current = scs.generate_prediction(model) + if type(target_current) is not type(None): + solve_for_current = target_current["value"] + dtc.solve_for_current = solve_for_current + + uc = { + "amplitude": solve_for_current, + "duration": ALLEN_DURATION, + "delay": ALLEN_DELAY, + "padding":342.85* pq.ms + } + #model = dtc.dtc_to_model() + #temp = model.attrs + model.inject_square_current(**uc) + n_spikes = model.get_spike_count() + if n_spikes != dtc.spk_count: + dtc.exclude = True + else: + dtc.exclude = False + #if hasattr(dtc, "spikes"): + # spikes = model._backend.spikes + #assert model._backend.spikes == n_spikes + #dtc.spikes = n_spikes + vm_soma = model.get_membrane_potential() + dtc.vm_soma = vm_soma + ## + # Refactor somewhere else, this simulation takes time. + # the rmp calculation somewhere else. + ## + ''' + ''' + return None, vm_soma, uc, None, dtc + +def still_more_features(instance_obj: Any,results:List,vm_used:AnalogSignal,target_vm:None) -> Any: + """ + -- Synopsis: Measure resting membrane potential and variance explained + as features, so that they can be used + as features to optimize with. + """ + if hasattr(instance_obj,"dtc_to_model"): + model = instance_obj + model = dtc.dtc_to_model() + model._backend.attrs = dtc.attrs + else: + dtc = instance_obj + uc['amplitude'] = 0*pq.pA + model.inject_square_current(**uc) + vr = model.get_membrane_potential() + vmr = np.mean(vr) + dtc.vmr = None + dtc.vmr = vmr + del model + + if target_vm is not None: + results[0]["var_expl"] = basic_expVar(vm_used,target_vm) + if vm_used[-1]>0: + spikes_ = spikes[0:-2] + spikes = spikes_ + results[0]["vr_"] = instance_obj.vmr + +def spike_time_errors(instance_obj: Any,results:List,vm_used:AnalogSignal,target_vm:None) -> Any: + """ + -- Synopsis: Generate simple errors simply based on spike times. + """ + if hasattr(instance_obj, "spikes"): + spikes = instance_obj.spikes + else: + spikes = threshold_detection(vm_used) + for index, tc in enumerate(spikes): + results[0]["spike_" + str(index)] = float(tc) + return instance_obj,results + +def efel_evaluation( + instance_obj: Any, efel_filter_iterable: Iterable = None, current: float = None +) -> Any: + """ + -- Synopsis: evaluate efel feature extraction criteria against on + reduced cell models and probably efel data. + -- Args: multiple dispatch: + This method works on both sciunit runnable models + and DataTC objects. + efel_filter_iterable: is the list of efel features to extract + it can be a list or a dictionary, if it is a list, the keys should be feature units + current is the value of current amplitude to evaluate the model features at. + """ + if isinstance(efel_filter_iterable, type(dict())): + efel_filter_list = list(efel_filter_iterable.keys()) + if isinstance(efel_filter_iterable, type(list())): + efel_filter_list = efel_filter_iterable + if "extra_tests" in efel_filter_iterable.keys(): + if "var_expl" in efel_filter_iterable["extra_tests"].keys(): + target_vm = efel_filter_iterable["extra_tests"]["var_expl"] + else: + target_vm = None + vm_used = instance_obj.vm_soma + try: + efel.reset() + except: + pass + efel.setThreshold(0) + if current is None: + if hasattr(instance_obj, "solve_for_current"): + current = instance_obj.solve_for_current + trace3 = { + "T": [float(t) * 1000.0 for t in vm_used.times], + "V": [float(v) for v in vm_used.magnitude], + "stimulus_current": [current], + } + trace3["stim_end"] = [float(ALLEN_DELAY) + float(ALLEN_DURATION)] + trace3["stim_start"] = [float(ALLEN_DELAY)] + + if np.min(vm_used.magnitude) < 0: + if not np.max(vm_used.magnitude) > 0: + vm_used_mag = [v + np.mean([0, float(np.max(v))]) * pq.mV for v in vm_used] + if not np.max(vm_used_mag) > 0: + instance_obj.efel = None + return instance_obj + + trace3["V"] = vm_used_mag + if efel_filter_iterable is None: + + default_efel_filter_iterable = { + "burst_ISI_indices": None, + "burst_mean_freq": pq.Hz, + "burst_number": None, + "single_burst_ratio": None, + "ISI_log_slope": None, + "mean_frequency": pq.Hz, + "adaptation_index2": None, + "first_isi": pq.ms, + "ISI_CV": None, + "median_isi": pq.ms, + "Spikecount": None, + "all_ISI_values": pq.ms, + "ISI_values": pq.ms, + "time_to_first_spike": pq.ms, + "time_to_last_spike": pq.ms, + "time_to_second_spike": pq.ms + } + efel_filter_list = list(default_efel_filter_iterable.keys()) + #print(len(efel_filter_list)) + results = efel.getMeanFeatureValues( + [trace3], efel_filter_list, raise_warnings=False + ) + + efel.reset() + #instance_obj = apply_units_to_efel(instance_obj, + # efel_filter_iterable) + instance_obj,results = spike_time_errors(instance_obj,results,vm_used,target_vm=target_vm) + + instance_obj.efel = None + instance_obj.efel = results[0] + try: + assert hasattr(instance_obj, "efel") + except: + raise Exception('efel object has no efel results list attribute') + return instance_obj + +def generic_nu_tests_to_bpo_protocols(multi_spiking=None): + pass + + +def apply_units_to_efel(instance_obj, efel_filter_iterable): + if isinstance(efel_filter_iterable, type(dict())): + for k, v in instance_obj.efel.items(): + units = efel_filter_iterable[k] + if units is not None and v is not None: + instance_obj.efel[k] = v * units + return instance_obj + + +def inject_and_plot_model( + dtc: DataTC, figname=None, plotly=True, verbose=False +) -> Union[Any, Any, Any]: + """ + -- Synopsis: produce rheobase injection value + produce an object of class sciunit Runnable Model + with known attributes + and known rheobase current injection value. + """ dtc = dtc_to_rheo(dtc) - ind.rheobase = dtc.rheobase - return ind,dtc + model = dtc.dtc_to_model() + uc = {"amplitude": dtc.rheobase, "duration": DURATION, "delay": DELAY} + if dtc.jithub or "NEURON" in str(dtc.backend): + vm = model._backend.inject_square_current(**uc) + else: + vm = model.inject_square_current(uc) + vm = model.get_membrane_potential() + if verbose: + if vm is not None: + print(vm[-1], vm[-1] < 0 * pq.mV) + if vm is None: + return [None, None, None] + if not plotly: + import matplotlib.pyplot as plt + + plt.clf() + plt.figure() + if dtc.backend in str("HH"): + plt.title("Conductance based model membrane potential plot") + else: + plt.title("Membrane potential plot") + plt.plot(vm.times, vm.magnitude, "k") + plt.ylabel("V (mV)") + plt.xlabel("Time (s)") + + if figname is not None: + plt.savefig(str(figname) + str(".png")) + plt.plot(vm.times, vm.magnitude) + + if plotly: + fig = px.line(x=vm.times, y=vm.magnitude, labels={"x": "t (s)", "y": "V (mV)"}) + if figname is not None: + fig.write_image(str(figname) + str(".png")) + else: + return vm, fig, dtc + return [vm, plt, dtc] + + +def switch_logic(xtests: Any = None) -> List: + try: + atsd = TSD() + except: + atsd = neuronunit.optimization.optimization_management.TSD() + + if type(xtests) is type(atsd): + xtests = list(xtests.values()) + for t in xtests: + if str("FITest") == t.name: + t.active = True + t.passive = False + + if str("RheobaseTest") == t.name: + t.active = True + t.passive = False + elif str("InjectedCurrentAPWidthTest") == t.name: + t.active = True + t.passive = False + elif str("InjectedCurrentAPAmplitudeTest") == t.name: + t.active = True + t.passive = False + elif str("InjectedCurrentAPThresholdTest") == t.name: + t.active = True + t.passive = False + elif str("RestingPotentialTest") == t.name: + t.passive = False + t.active = False + elif str("InputResistanceTest") == t.name: + t.passive = True + t.active = False + elif str("TimeConstantTest") == t.name: + t.passive = True + t.active = False + elif str("CapacitanceTest") == t.name: + t.passive = True + t.active = False + else: + t.passive = False + t.active = False + return xtests + + +def active_values(keyed: dict, rheobase, square: dict = None) -> dict: + keyed["injected_square_current"] = {} + if square is None: + if isinstance(rheobase, type(dict())): + keyed["injected_square_current"]["amplitude"] = ( + float(rheobase["value"]) * pq.pA + ) + else: + keyed["injected_square_current"]["amplitude"] = rheobase + return keyed -def serial_route(pop,td,tests): - ''' - parallel list mapping only works with an iterable collection. - Serial route is intended for single items. - ''' - if type(dtc.rheobase) is type(None): - print('Error Score bad model') - for t in tests: - dtc.scores = {} - dtc.get_ss() - else: - dtc = format_test((dtc,tests)) - dtc = nunit_evaluation((dtc,tests)) - return pop, dtc - -def filtered(pop,dtcpop): - dtcpop = [ dtc for dtc in dtcpop if dtc.rheobase!=-1.0 ] - pop = [ p for p in pop if p.rheobase!=-1.0 ] - dtcpop = [ dtc for dtc in dtcpop if dtc.rheobase is not None ] - pop = [ p for p in pop if p.rheobase is not None ] - assert len(pop) == len(dtcpop) - return (pop,dtcpop) - - -def parallel_route(pop,dtcpop,tests,td): - for d in dtcpop: - d.tests = copy.copy(tests) - dtcpop = list(map(format_test,dtcpop)) - #import pdb; pdb.set_trace() - npart = np.min([multiprocessing.cpu_count(),len(dtcpop)]) - dtcbag = db.from_sequence(dtcpop, npartitions = npart) - dtcpop = list(dtcbag.map(nunit_evaluation).compute()) - for i,d in enumerate(dtcpop): - if not hasattr(pop[i],'dtc'): - pop[i] = WSListIndividual(pop[i]) - pop[i].dtc = None - d.get_ss() - pop[i].dtc = copy.copy(d) - invalid_dtc_not = [ i for i in pop if not hasattr(i,'dtc') ] - return pop, dtcpop - -def make_up_lost(pop,dtcpop,td): - before = len(pop) - (pop,dtcpop) = filtered(pop,dtcpop) - after = len(pop) - assert after>0 - delta = before-after - if delta: - cnt = 0 - while cnt < delta: - ind,dtc = new_genes(pop,dtcpop,td) - if dtc.rheobase != -1.0: - pop.append(ind) - dtcpop.append(dtc) - cnt += 1 - return pop, dtcpop - -import dask.bag as db - -def test_runner(pop,td,tests,single_spike=True): - if single_spike: - pop, dtcpop = obtain_rheobase(pop, td, tests) - pop, dtcpop = make_up_lost(pop,dtcpop,td) - # there are many models, which have no actual rheobase current injection value. - # filter, filters out such models, - # gew genes, add genes to make up for missing values. - # delta is the number of genes to replace. +def passive_values(keyed: dict = {}) -> dict: + PASSIVE_DURATION = 500.0 * pq.ms + PASSIVE_DELAY = 200.0 * pq.ms + keyed["injected_square_current"] = {} + keyed["injected_square_current"]["delay"] = PASSIVE_DELAY + keyed["injected_square_current"]["duration"] = PASSIVE_DURATION + keyed["injected_square_current"]["amplitude"] = -10 * pq.pA + return keyed - else: - pop, dtcpop = init_pop(pop, td, tests) - pop,dtcpop = parallel_route(pop,dtcpop,tests,td) - for ind,d in zip(pop,dtcpop): - ind.dtc = d - if not hasattr(ind,'fitness'): - ind.fitness = copy.copy(pop[0].fitness) - return pop,dtcpop +def neutral_values(keyed: dict = {}) -> dict: + PASSIVE_DURATION = 500.0 * pq.ms + PASSIVE_DELAY = 200.0 * pq.ms + keyed["injected_square_current"] = {} + keyed["injected_square_current"]["delay"] = PASSIVE_DELAY + keyed["injected_square_current"]["duration"] = PASSIVE_DURATION + keyed["injected_square_current"]["amplitude"] = 0 * pq.pA + return keyed -def update_deap_pop(pop, tests, td, backend = None,hc = None): +def initialise_test(v: Any, rheobase: Any = None) -> dict: ''' - Inputs a population of genes (pop). - Returned neuronunit scored DataTransportContainers (dtcpop). - This method converts a population of genes to a population of Data Transport Containers, - Which act as communicatable data types for storing model attributes. - Rheobase values are found on the DTCs - DTCs for which a rheobase value of x (pA)<=0 are filtered out - DTCs are then scored by neuronunit, using neuronunit models that act in place. + -- Synpopsis: + Create appropriate square wave stimulus for various + Different square pulse current injection protocols. + ### + # TODO move this to BPO/ephys/protocols.py + # unify it with BPO protocol code. + # It is already a bit similar. + ### ''' - - #pop = copy.copy(pop) - if hc is not None: - pop[0].hc = None - pop[0].hc = hc - - if backend is not None: - pop[0].backend = None - pop[0].backend = backend - - pop, dtcpop = test_runner(pop,td,tests) - for p,d in zip(pop,dtcpop): - p.dtc = d - return pop - -def create_subset(nparams = 10, boundary_dict = None): - # used by GA to find subsets in parameter space. - if type(boundary_dict) is type(None): - raise ValueError('A parameter range dictionary was not supplied \ - and the program doesnt know what value to explore.') - mp = modelp.model_params - key_list = list(mp.keys()) - reduced_key_list = key_list[0:nparams] + if not isinstance(v, Iterable): + v = [v] + v = switch_logic(v) + v = v[0] + k = v.name + if not hasattr(v, "params"): + v.params = {} + if not "injected_square_current" in v.params.keys(): + v.params["injected_square_current"] = {} + if v.passive == False and v.active == True: + keyed = v.params["injected_square_current"] + v.params = active_values(keyed, rheobase) + v.params["injected_square_current"]["delay"] = DELAY + v.params["injected_square_current"]["duration"] = DURATION + if v.passive == True and v.active == False: + + v.params["injected_square_current"]["amplitude"] = -10 * pq.pA + v.params["injected_square_current"]["delay"] = PASSIVE_DELAY + v.params["injected_square_current"]["duration"] = PASSIVE_DURATION + + if v.name in str("RestingPotentialTest"): + v.params["injected_square_current"]["delay"] = PASSIVE_DELAY + v.params["injected_square_current"]["duration"] = PASSIVE_DURATION + v.params["injected_square_current"]["amplitude"] = 0.0 * pq.pA + v.params = v.params + if "delay" in v.params["injected_square_current"].keys(): + v.params["tmax"] = ( + v.params["injected_square_current"]["delay"] + + v.params["injected_square_current"]["duration"] + ) else: - key_list = list(boundary_dict.keys()) - reduced_key_list = key_list[0:nparams] - subset = { k:boundary_dict[k] for k in reduced_key_list } - return subset + v.params["tmax"] = DELAY + DURATION + return v diff --git a/neuronunit/optimization/results_analysis.py b/neuronunit/optimization/results_analysis.py deleted file mode 100644 index 363487e6c..000000000 --- a/neuronunit/optimization/results_analysis.py +++ /dev/null @@ -1,140 +0,0 @@ -import numpy as np -import pickle - -def param_distance(dtc_ga_attrs,dtc_grid_attrs,td): - distances = {} - # These imports are defined here to avoid cyclic importing, as the function provides a limited scope of the imports. - # - from neuronunit.optimization.optimization_management import model_params - from neuronunit.optimization.exhaustive_search import reduce_params - ranges = { k:model_params[k] for k,v in dtc_ga_attrs.items() } - for k,v in dtc_ga_attrs.items(): - dimension_length = np.max(ranges[k]) - np.min(ranges[k]) - solution_distance_in_1D = np.abs(float(dtc_grid_attrs[k]))-np.abs(float(v)) - try: - relative_distance = np.abs(solution_distance_in_1D/dimension_length) - except: - relative_distance = None - distances.get(k, relative_distance) - distances[k] = (relative_distance) - print('the difference between brute force candidates model parameters and the GA\'s model parameters:') - print(float(dtc_grid_attrs[k])-float(v),dtc_grid_attrs[k],v,k) - print('the relative distance scaled by the length of the parameter dimension of interest:') - print(relative_distance) - return distances - -def min_max(pop): - garanked = [ (r.dtc.attrs , sum(r.dtc.scores.values()), r.dtc) for r in pop ] - garanked = sorted(garanked, key=lambda w: w[1]) - miniga = garanked[0] - maxiga = garanked[-1] - return miniga, maxiga - - -def error_domination(dtc_ga,dtc_grid): - distances = {} - errors_ga = list(dtc_ga.scores.values()) - print(errors_ga) - me_ga = np.mean(errors_ga) - std_ga = np.std(errors_ga) - - errors_grid = list(dtc_grid.scores.values()) - print(errors_grid) - me_grid = np.mean(errors_grid) - std_grid = np.std(errors_grid) - - dom_grid = False - dom_ga = False - - for e in errors_ga: - if e <= me_ga + std_ga: - dom_ga = True - - for e in errors_grid: - if e <= me_grid + std_grid: - dom_grid= True - - return dom_grid, dom_ga - - -def make_report(grid_results, ga_out, nparams, pop = None): - from neuronunit.optimization.exhaustive_search import create_grid - grid_points = create_grid(npoints = 2,nparams = nparams) - td = list(grid_points[0][0].keys()) - - reports = {} - reports[nparams] = {} - - mini = min_max(grid_results)[0][1] - maxi = min_max(grid_results)[1][1] - if type(pop) is not type(None): - miniga = min_max(pop)[0][1] - else: - miniga = min_max(ga_out)[0][1] - - reports[nparams]['miniga'] = miniga - reports[nparams]['minigrid'] = mini - quantize_distance = list(np.linspace(mini,maxi,21)) - success = bool(miniga < quantize_distance[2]) - better = bool(miniga < quantize_distance[0]) - - print('Report: ') - print('did it work? {0} was it better {1}'.format(success,better)) - - - reports[nparams]['success'] = success - reports[nparams]['better'] = better - dtc_ga = min_max(ga_out)[0][0] - attrs_grid = min_max(grid_results)[0][0] - attrs_ga = min_max(ga_out)[0][0] - reports[nparams]['attrs_ga'] = attrs_ga - reports[nparams]['attrs_grid'] = attrs_grid - - - - reports[nparams]['p_dist'] = param_distance(attrs_ga,attrs_grid,td) - ## - # mistake here - ## - dtc_grid = dtc_ga = min_max(ga_out)[0][2] - dom_grid, dom_ga = error_domination(dtc_ga,dtc_grid) - reports[nparams]['vind_domination'] = False - # Was there vindicating domination in grid search but not GA? - if dom_grid == True and dom_ga == False: - reports[nparams]['vind_domination'] = True - elif dom_grid == False and dom_ga == False: - reports[nparams]['vind_domination'] = True - # Was there incriminating domination in GA but not the grid, or in GA and Grid - elif dom_grid == True and dom_ga == True: - reports[nparams]['inc_domination'] = False - elif dom_grid == False and dom_ga == True: - reports[nparams]['inc_domination'] = False - - - #reports[nparams]['success'] = bool(miniga < quantize_distance[2]) - dtc_ga = min_max(ga_out)[0][0] - attrs_grid = min_max(grid_results)[0][0] - attrs_ga = min_max(ga_out)[0][0] - - grid_points = create_grid(npoints = 1,nparams = nparams)#td = list(grid_points[0].keys()) - td = list(grid_points[0][0].keys()) - - reports[nparams]['p_dist'] = param_distance(attrs_ga,attrs_grid,td) - dtc_grid = dtc_ga = min_max(ga_out)[0][2] - dom_grid, dom_ga = error_domination(dtc_ga,dtc_grid) - reports[nparams]['vind_domination'] = False - # Was there vindicating domination in grid search but not GA? - if dom_grid == True and dom_ga == False: - reports[nparams]['vind_domination'] = True - elif dom_grid == False and dom_ga == False: - reports[nparams]['vind_domination'] = True - # Was there incriminating domination in GA but not the grid, or in GA and Grid - elif dom_grid == True and dom_ga == True: - reports[nparams]['inc_domination'] = False - elif dom_grid == False and dom_ga == True: - reports[nparams]['inc_domination'] = False - - - with open('reports.p','wb') as f: - pickle.dump(reports,f) - return reports diff --git a/neuronunit/optimization/rick_revised.ipynb b/neuronunit/optimization/rick_revised.ipynb deleted file mode 100644 index bc9ef24ea..000000000 --- a/neuronunit/optimization/rick_revised.ipynb +++ /dev/null @@ -1,283 +0,0 @@ -{ - "cells": [ - { - "cell_type": "code", - "execution_count": 1, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "{'injected_square_current': {'amplitude': array(-10.0) * pA, 'delay': array(200.0) * ms, 'duration': array(500.0) * ms}}\n", - "{'k': [0.000999, 0.001001]}\n", - "[] []\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/opt/conda/lib/python3.5/site-packages/sklearn/cross_validation.py:41: DeprecationWarning: This module was deprecated in version 0.18 in favor of the model_selection module into which all the refactored classes and functions are moved. Also note that the interface of the new CV iterators are different from that of this module. This module will be removed in 0.20.\n", - " \"This module will be removed in 0.20.\", DeprecationWarning)\n", - "/opt/conda/lib/python3.5/site-packages/sklearn/grid_search.py:42: DeprecationWarning: This module was deprecated in version 0.18 in favor of the model_selection module into which all the refactored classes and functions are moved. This module will be removed in 0.20.\n", - " DeprecationWarning)\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Try 1: SubMax = 41.7; SupraMin = 83.3\n", - "Try 2: SubMax = 69.5; SupraMin = 83.3\n", - "Try 3: SubMax = 78.7; SupraMin = 83.3\n", - "Z = -1.86\n", - "Try 1: SubMax = 41.7; SupraMin = 83.3\n", - "Try 2: SubMax = 69.5; SupraMin = 83.3\n", - "Try 3: SubMax = 78.7; SupraMin = 83.3\n", - "Z = -1.86\n", - "len tests 2 tests [, ]\n", - "tests, completed, now gene computations\n", - "[[0.000999], [0.001001]]\n", - "Try 1: SubMax = 41.7; SupraMin = 83.3\n", - "Try 2: SubMax = 69.5; SupraMin = 83.3\n", - "Try 3: SubMax = 78.7; SupraMin = 83.3\n", - "Z = -1.86\n", - "Try 1: SubMax = 41.7; SupraMin = 83.3\n", - "Try 2: SubMax = 69.5; SupraMin = 83.3\n", - "Try 3: SubMax = 78.7; SupraMin = 83.3\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "INFO:__main__:gen\tnevals\tavg \tstd\tmin \tmax \n", - "1 \t2 \t0.937378\t0 \t0.937378\t0.937378\n", - "2 \t2 \t0.937378\t0 \t0.937378\t0.937378\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Z = -1.86\n", - "len tests 2 tests [, ]\n", - "tests, completed, now gene computations\n", - "[[0.0010009381483599346], [0.0009992543652523919]]\n", - "Try 1: SubMax = 41.7; SupraMin = 83.3\n", - "Try 2: SubMax = 69.5; SupraMin = 83.3\n", - "Try 3: SubMax = 78.7; SupraMin = 83.3\n", - "Z = -1.86\n", - "Try 1: SubMax = 41.7; SupraMin = 83.3\n", - "Try 2: SubMax = 69.5; SupraMin = 83.3\n", - "Try 3: SubMax = 78.7; SupraMin = 83.3\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "INFO:__main__:3 \t2 \t0.937378\t0 \t0.937378\t0.937378\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Z = -1.86\n", - "len tests 2 tests [, ]\n", - "tests, completed, now gene computations\n", - "[[0.000999103642354488], [0.0009992543652523919]]\n", - "{'dhof': [, ], 'pf': , 'dbest': , 'td': ['k'], 'gen_vs_pop': [[[0.000999], [0.001001]], [[0.0010009381483599346], [0.0009992543652523919]], [[0.000999103642354488], [0.0009992543652523919]]], 'hof': , 'bd': , 'pop': [[0.000999], [0.0009992543652523919], [0.000999103642354488], [0.0009992543652523919]], 'history': , 'log': [{'gen': 1, 'std': 0.0, 'min': 0.93737798762411861, 'avg': 0.93737798762411861, 'nevals': 2, 'max': 0.93737798762411861}, {'gen': 2, 'std': 0.0, 'min': 0.93737798762411861, 'avg': 0.93737798762411861, 'nevals': 2, 'max': 0.93737798762411861}, {'gen': 3, 'std': 0.0, 'min': 0.93737798762411861, 'avg': 0.93737798762411861, 'nevals': 2, 'max': 0.93737798762411861}]}\n" - ] - } - ], - "source": [ - "\n", - "import seaborn as sns\n", - "import quantities as pq\n", - "from neuronunit.optimization.data_transport_container import DataTC\n", - "import os\n", - "import pickle\n", - "import copy\n", - "import numpy as np\n", - "import pandas as pd\n", - "import matplotlib.pyplot as plt\n", - "\n", - "from neuronunit.optimization import get_neab\n", - "from neuronunit.models.reduced import ReducedModel\n", - "from neuronunit.optimization.model_parameters import model_params, path_params\n", - "from neuronunit.optimization import model_parameters as modelp\n", - "\n", - "from neuronunit.optimization import model_parameters as modelp\n", - "mp = modelp.model_params\n", - "\n", - "mp['k'] = [1e-3-1e-6,1e-3+1e-6]\n", - "\n", - "\n", - "mp['a'] = 0.1\n", - "mp['b'] = 0.01\n", - "mp['vr'] = -65.0\n", - "#mp['vr'] = [-90.0,-60.0]\n", - "#print(mp['k'],mp['vr'])\n", - "\n", - "\n", - "free_params = ['k']\n", - "from neuronunit.optimization import optimization_management as om\n", - "mp = modelp.model_params\n", - "\n", - "import sciunit\n", - "import neuronunit.optimization as opt\n", - "tests_,all_tests, observation,suite = opt.get_neab.get_tests()\n", - "passive = [ str('RestingPotentialTest'), str('CapacitanceTest'), str('TimeConstantTest'), str('InputResistanceTest') ]\n", - "firing_tests = [ t for t in all_tests if str(t) not in passive ]\n", - "first_two = all_tests[0:2]\n", - "import pickle\n", - " \n", - "try:\n", - " ga_out = pickle.load(open('gaout.p','rb'))\n", - "except:\n", - " # 10*4 gene evaluations\n", - " ga_out, DO = om.run_ga(mp,3,first_two,free_params=free_params)\n", - " print(ga_out)\n", - " pickle.dump(ga_out,open('gaout.p','wb'))\n" - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "{'k': 0.001001}\n", - "{'RheobaseTestP': 0.9373779876241186}\n", - "{'value': array(83.34) * pA}\n" - ] - } - ], - "source": [ - "print(ga_out['hof'][0].dtc.attrs)\n", - "print(ga_out['hof'][0].dtc.scores)\n", - "print(ga_out['hof'][0].dtc.rheobase)\n", - "\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "#attrs = {'k':1e-4}#{'a': 1e-2, 'b': 1e-2, 'vpeak':40, 'k':5e-5}\n", - "attrs = ga_out['hof'][0].dtc.attrs\n", - "current = {'amplitude':-10.0*pq.pA, 'delay':200.0*pq.ms, 'duration':500.0*pq.ms}\n", - "sim_length = 1000.0 * pq.ms\n", - "\n", - "LEMS_MODEL_PATH = path_params['model_path']\n", - "from neuronunit.optimization.model_parameters import model_params, path_params\n", - "\n", - "model = ReducedModel(LEMS_MODEL_PATH,name=str('vanilla'),backend='NEURON')\n", - "model.set_attrs(attrs)\n", - "model.inject_square_current(current)\n", - "#model.get_backend().set_stop_time(sim_length)\n", - "vm = copy.copy(model.get_membrane_potential())\n", - "plt.plot(vm.times,vm*1000,label=str(attrs))\n", - "plt.ylim(-90,-50)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "dtc = ga_out['hof'][0].dtc\n", - "\n", - "LEMS_MODEL_PATH = path_params['model_path']\n", - "model = None\n", - "model = ReducedModel(LEMS_MODEL_PATH,name=str('vanilla'),backend='NEURON')\n", - "model.set_attrs(dtc.attrs)\n", - "scores = []\n", - "for t in first_two:\n", - " scores.append(t.judge(model,stop_on_error = False, deep_error = False))\n", - "print(scores)\n", - "\n", - "## Does the same thing in parallel\n", - "#from neuronunit.optimization.optimization_management import nunit_evaluation as nue\n", - "\n", - "# xargs = (dtc,first_two)\n", - "# dtc = nue(xargs)\n", - "# print(dtc.scores)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "#attrs = {'k':1e-4}#{'a': 1e-2, 'b': 1e-2, 'vpeak':40, 'k':5e-5}\n", - "attrs = ga_out['hof'][0].dtc.attrs\n", - "print(ga_out['hof'][0].dtc.rheobase)\n", - "ampl = ga_out['hof'][0].dtc.rheobase['value']\n", - "current = {'amplitude':ampl, 'delay':200.0*pq.ms, 'duration':500.0*pq.ms}\n", - "sim_length = 1000.0 * pq.ms\n", - "\n", - "LEMS_MODEL_PATH = path_params['model_path']\n", - "from neuronunit.optimization.model_parameters import model_params, path_params\n", - "\n", - "model = ReducedModel(LEMS_MODEL_PATH,name=str('vanilla'),backend='NEURON')\n", - "model.set_attrs(attrs)\n", - "model.inject_square_current(current)\n", - "#model.get_backend().set_stop_time(sim_length)\n", - "vm = copy.copy(model.get_membrane_potential())\n", - "plt.plot(vm.times,vm*1000,label=str(attrs))\n", - "plt.ylim(-90,20)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": true - }, - "outputs": [], - "source": [] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.5.2" - } - }, - "nbformat": 4, - "nbformat_minor": 2 -} diff --git a/neuronunit/optimization/test_check.ipynb b/neuronunit/optimization/test_check.ipynb deleted file mode 100644 index ce656b21d..000000000 --- a/neuronunit/optimization/test_check.ipynb +++ /dev/null @@ -1,549 +0,0 @@ -{ - "cells": [ - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "{'k': [0.000999, 0.001001], 'vr': [-90.0, -60.0]}\n", - "[] []\n", - "Try 1: SubMax = 250.0; SupraMin = None\n", - "Try 2: SubMax = 500.0; SupraMin = None\n", - "Try 3: SubMax = 500.0; SupraMin = 583.3\n", - "Try 4: SubMax = 555.6; SupraMin = 583.3\n", - "Try 5: SubMax = 574.1; SupraMin = 583.3\n", - "Try 6: SubMax = 575.6; SupraMin = 577.2\n", - "Z = 8.49\n", - "Try 1: SubMax = 41.7; SupraMin = 83.3\n", - "Try 2: SubMax = 69.5; SupraMin = 83.3\n", - "Try 3: SubMax = 78.7; SupraMin = 83.3\n", - "Z = -1.86\n", - "Try 1: SubMax = 250.0; SupraMin = None\n", - "Try 2: SubMax = 500.0; SupraMin = None\n", - "Try 3: SubMax = 500.0; SupraMin = 583.3\n", - "Try 4: SubMax = 555.6; SupraMin = 583.3\n", - "Try 5: SubMax = 574.1; SupraMin = 583.3\n", - "Try 6: SubMax = 575.6; SupraMin = 577.2\n", - "Z = 8.49\n", - "Try 1: SubMax = 41.7; SupraMin = 83.3\n", - "Try 2: SubMax = 69.5; SupraMin = 83.3\n", - "Try 3: SubMax = 78.7; SupraMin = 83.3\n", - "Z = -1.86\n", - "len tests 2 tests [, ]\n", - "tests, completed, now gene computations\n", - "[[0.000999, -90.0], [0.000999, -60.0], [0.001001, -90.0], [0.001001, -60.0]]\n", - "Try 1: SubMax = 250.0; SupraMin = None\n", - "Try 2: SubMax = 500.0; SupraMin = None\n", - "Try 3: SubMax = 500.0; SupraMin = 583.3\n", - "Try 4: SubMax = 555.6; SupraMin = 583.3\n", - "Try 5: SubMax = 574.1; SupraMin = 583.3\n", - "Try 6: SubMax = 575.6; SupraMin = 577.2\n", - "Z = 8.49\n", - "Try 1: SubMax = 41.7; SupraMin = 83.3\n", - "Try 2: SubMax = 69.5; SupraMin = 83.3\n", - "Try 3: SubMax = 78.7; SupraMin = 83.3\n", - "Z = -1.86\n", - "Try 1: SubMax = 250.0; SupraMin = None\n", - "Try 2: SubMax = 500.0; SupraMin = None\n", - "Try 3: SubMax = 500.0; SupraMin = 583.3\n", - "Try 4: SubMax = 555.6; SupraMin = 583.3\n", - "Try 5: SubMax = 560.2; SupraMin = 564.8\n", - "Z = 8.23\n", - "Try 1: SubMax = 83.3; SupraMin = 125.0\n", - "Try 2: SubMax = 111.1; SupraMin = 125.0\n", - "113.4313888888889\n", - "Try 3: SubMax = 111.1; SupraMin = 113.4\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "INFO:__main__:gen\tnevals\tavg \tstd \tmin \tmax\n", - "1 \t4 \t0.968689\t0.031311 \t0.937378\t1 \n", - "2 \t4 \t0.929754\t0.0892548\t0.781637\t1 \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Z = -1.23\n", - "len tests 2 tests [, ]\n", - "tests, completed, now gene computations\n", - "[[0.000999, -90.0], [0.00099901565782739587, -60.0], [0.001000969449092908, -89.480790933191486], [0.0010008659395419712, -63.234935502760649]]\n", - "Try 1: SubMax = 83.3; SupraMin = 125.0\n", - "Try 2: SubMax = 111.1; SupraMin = 125.0\n", - "113.4313888888889\n", - "Try 3: SubMax = 111.1; SupraMin = 113.4\n", - "Z = -1.23\n", - "Try 1: SubMax = 41.7; SupraMin = 83.3\n", - "Try 2: SubMax = 69.5; SupraMin = 83.3\n", - "Try 3: SubMax = 78.7; SupraMin = 83.3\n", - "Z = -1.86\n", - "Try 1: SubMax = 41.7; SupraMin = 83.3\n", - "Try 2: SubMax = 69.5; SupraMin = 83.3\n", - "Try 3: SubMax = 78.7; SupraMin = 83.3\n", - "Z = -1.86\n", - "Try 1: SubMax = 41.7; SupraMin = 83.3\n", - "Try 2: SubMax = 69.5; SupraMin = 83.3\n", - "Try 3: SubMax = 78.7; SupraMin = 83.3\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "INFO:__main__:3 \t4 \t0.898443\t0.0674377\t0.781637\t0.937378\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Z = -1.86\n", - "len tests 2 tests [, ]\n", - "tests, completed, now gene computations\n", - "Try 1: SubMax = 83.3; SupraMin = 125.0\n", - "Try 2: SubMax = 111.1; SupraMin = 125.0\n", - "113.4313888888889\n", - "Try 3: SubMax = 111.1; SupraMin = 113.4\n", - "Z = -1.23\n", - "Try 1: SubMax = 166.7; SupraMin = 208.3\n", - "Try 2: SubMax = 180.6; SupraMin = 187.5\n", - "185.18777777777777\n", - "Try 3: SubMax = 184.0; SupraMin = 185.2\n", - "Z = 0.27\n", - "Try 1: SubMax = 41.7; SupraMin = 83.3\n", - "Try 2: SubMax = 69.5; SupraMin = 83.3\n", - "Try 3: SubMax = 78.7; SupraMin = 83.3\n", - "Z = -1.86\n", - "Try 1: SubMax = 41.7; SupraMin = 83.3\n", - "Try 2: SubMax = 69.5; SupraMin = 83.3\n", - "Try 3: SubMax = 78.7; SupraMin = 83.3\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "INFO:__main__:4 \t4 \t0.718026\t0.2969 \t0.21571 \t0.937378\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Z = -1.86\n", - "len tests 2 tests [, ]\n", - "tests, completed, now gene computations\n", - "[[0.0010009117308337728, -63.234935502760649], [0.00099920983354791887, -69.207779017268251], [0.000999, -60.0], [0.0010009993281612753, -60.0]]\n", - "Try 1: SubMax = 166.7; SupraMin = 208.3\n", - "Try 2: SubMax = 180.6; SupraMin = 187.5\n", - "185.18777777777777\n", - "Try 3: SubMax = 184.0; SupraMin = 185.2\n", - "Z = 0.27\n", - "Try 1: SubMax = 83.3; SupraMin = 125.0\n", - "Try 2: SubMax = 111.1; SupraMin = 125.0\n", - "113.4313888888889\n", - "Try 3: SubMax = 111.1; SupraMin = 113.4\n", - "Z = -1.23\n", - "Try 1: SubMax = 83.3; SupraMin = 125.0\n", - "Try 2: SubMax = 111.1; SupraMin = 125.0\n", - "113.4313888888889\n", - "Try 3: SubMax = 111.1; SupraMin = 113.4\n", - "Z = -1.23\n", - "Try 1: SubMax = 166.7; SupraMin = 208.3\n", - "Try 2: SubMax = 173.6; SupraMin = 180.6\n", - "178.24361111111108\n", - "Try 3: SubMax = 177.1; SupraMin = 178.2\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "INFO:__main__:5 \t4 \t0.470233\t0.313991 \t0.101949\t0.781637\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Z = 0.13\n", - "len tests 2 tests [, ]\n", - "tests, completed, now gene computations\n", - "Try 1: SubMax = 166.7; SupraMin = 208.3\n", - "Try 2: SubMax = 166.7; SupraMin = 173.6\n", - "171.29944444444442\n", - "Try 3: SubMax = 170.1; SupraMin = 171.3\n", - "Z = -0.02\n", - "Try 1: SubMax = 166.7; SupraMin = 208.3\n", - "Try 2: SubMax = 180.6; SupraMin = 187.5\n", - "185.18777777777777\n", - "Try 3: SubMax = 184.0; SupraMin = 185.2\n", - "Z = 0.27\n", - "Try 1: SubMax = 166.7; SupraMin = 208.3\n", - "Try 2: SubMax = 180.6; SupraMin = 187.5\n", - "Try 3: SubMax = 185.2; SupraMin = 187.5\n", - "Z = 0.32\n", - "Try 1: SubMax = 83.3; SupraMin = 125.0\n", - "Try 2: SubMax = 111.1; SupraMin = 125.0\n", - "113.4313888888889\n", - "Try 3: SubMax = 111.1; SupraMin = 113.4\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "INFO:__main__:6 \t4 \t0.316012\t0.283774 \t0.0139506\t0.781637\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Z = -1.23\n", - "len tests 2 tests [, ]\n", - "tests, completed, now gene computations\n", - "Try 1: SubMax = 166.7; SupraMin = 208.3\n", - "187.5025\n", - "Try 2: SubMax = 180.6; SupraMin = 187.5\n", - "Try 3: SubMax = 185.2; SupraMin = 187.5\n", - "Z = 0.32\n", - "Try 1: SubMax = 166.7; SupraMin = 208.3\n" - ] - } - ], - "source": [ - "\n", - "import seaborn as sns\n", - "import quantities as pq\n", - "from neuronunit.optimization.data_transport_container import DataTC\n", - "import os\n", - "import pickle\n", - "import copy\n", - "import numpy as np\n", - "import pandas as pd\n", - "import matplotlib.pyplot as plt\n", - "\n", - "from neuronunit.optimization import get_neab\n", - "from neuronunit.models.reduced import ReducedModel\n", - "from neuronunit.optimization.model_parameters import model_params, path_params\n", - "from neuronunit.optimization import model_parameters as modelp\n", - "\n", - "mp = modelp.model_params\n", - "\n", - "mp['k'] = [1e-3-1e-6,1e-3+1e-6]\n", - "\n", - "\n", - "mp['a'] = 0.1\n", - "mp['b'] = 0.01\n", - "#mp['vr'] = -65.0\n", - "mp['vr'] = [-90.0,-60.0]\n", - "#print(mp['k'],mp['vr'])\n", - "\n", - "\n", - "free_params = ['k','vr']\n", - "from neuronunit.optimization import optimization_management as om\n", - "mp = modelp.model_params\n", - "\n", - "import sciunit\n", - "import neuronunit.optimization as opt\n", - "tests_,all_tests, observation,suite = opt.get_neab.get_tests()\n", - "passive = [ str('RestingPotentialTest'), str('CapacitanceTest'), str('TimeConstantTest'), str('InputResistanceTest') ]\n", - "firing_tests = [ t for t in all_tests if str(t) not in passive ]\n", - "first_two = all_tests[0:2]\n", - "import pickle\n", - " \n", - "try:\n", - " assert 1==2\n", - " ga_out = pickle.load(open('gaout.p','rb'))\n", - "except:\n", - " # 10*4 gene evaluations\n", - " ga_out, DO = om.run_ga(mp,10,first_two,free_params=free_params)\n", - " print(ga_out)\n", - " pickle.dump(ga_out,open('gaout.p','wb'))\n" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "{'k': 0.00099927492997407418, 'vr': -63.234935502760649}\n", - "{'RheobaseTestP': 0.7816373196015772}\n", - "{'value': array(113.4313888888889) * pA}\n", - "InputResistanceTest\n", - "input resistance score: Z = -5.29\n", - "0\n", - "score Z = -5.29\n", - "observation 457000000.0 ohm, prediction 46153398.81232915 kg*m**2/(s**3*A**2)\n", - "TimeConstantTest\n", - "0\n", - "score Z = -2.05\n", - "observation 23.186875 ms, prediction 0.00518486301876763 s\n", - "CapacitanceTest\n", - "0\n", - "score Z = 2.34\n", - "observation 6.865e-11 F, prediction 1.1233978758207026e-10 s**4*A**2/(kg*m**2)\n", - "RestingPotentialTest\n", - "0\n", - "score Z = -0.82\n", - "observation -57.9 mV, prediction -0.06323493452709672 V\n", - "InjectedCurrentAPWidthTest\n", - "> /home/jovyan/neuronunit/neuronunit/tests/waveform.py(30)generate_prediction()\n", - "-> model.inject_square_current(model.params['injected_square_current'])\n", - "(Pdb) c\n", - "1\n", - "score Z = -1.69\n", - "observation 1.505 ms, prediction 0.0006025000000000001 s\n", - "InjectedCurrentAPAmplitudeTest\n", - "1\n", - "score Z = -0.35\n", - "observation 64.0 mV, prediction 0.059506097176639176 V\n", - "InjectedCurrentAPThresholdTest\n", - "1\n", - "score Z = 3.10\n", - "observation -41.4742424242424 mV, prediction -0.024441763736844653 V\n" - ] - } - ], - "source": [ - "import pickle\n", - "ga_out = pickle.load(open('gaout.p','rb'))\n", - "\n", - "print(ga_out['hof'][0].dtc.attrs)\n", - "print(ga_out['hof'][0].dtc.scores)\n", - "print(ga_out['hof'][0].dtc.rheobase)\n", - "dtc = ga_out['bd'] \n", - "dtcpop = ga_out['dhof']\n", - "#self.results['dhof'] = [ h.dtc for h in self.results['hof'] ]\n", - "#self.results['bd'] = self.results['hof'][0].dtc\n", - "\n", - "from neuronunit.models.reduced import ReducedModel\n", - "\n", - "from neuronunit.optimization.model_parameters import model_params, path_params\n", - "LEMS_MODEL_PATH = path_params['model_path']\n", - "import neuronunit.optimization as opt\n", - "\n", - "tests_,all_tests, observation,suite = opt.get_neab.get_tests()\n", - "\n", - "model = ReducedModel(LEMS_MODEL_PATH,name = str('vanilla'),backend = ('NEURON',{'DTC':dtc}))\n", - "model.set_attrs(dtc.attrs)\n", - "params = {}\n", - "params['injected_square_current'] = {}\n", - "params['injected_square_current']['amplitude'] = ga_out['hof'][0].dtc.rheobase['value']\n", - "#['amplitude'] = dtc.vtest[k]['injected_square_current']['amplitude']\n", - "DELAY = 100.0*pq.ms\n", - "DURATION = 1000.0*pq.ms\n", - "params['injected_square_current']['delay'] = DELAY\n", - "params['injected_square_current']['duration'] = DURATION\n", - "\n", - "\n", - "cnt = 0\n", - "for t in all_tests[1::]:\n", - " print(t)\n", - " model = ReducedModel(LEMS_MODEL_PATH,name = str('vanilla'),backend = ('NEURON',{'DTC':dtc}))\n", - " model.set_attrs(dtc.attrs)\n", - " model._backend.reset_vm()\n", - " if cnt<4:\n", - " break\n", - " if cnt >3:\n", - " \n", - " model.params = params\n", - "\n", - " score = t.judge(model,stop_on_error = False, deep_error = True)\n", - " print(model.get_spike_count())\n", - " print('score {0}'.format(score))\n", - " if str('mean') in score.prediction.keys():\n", - " print('observation {0}, prediction {1}'.format(t.observation['mean'],score.prediction['mean']))\n", - " if str('value') in score.prediction.keys():\n", - " print('observation {0}, prediction {1}'.format(t.observation['mean'],score.prediction['value']))\n", - "\n", - " cnt+=1\n", - " \n", - "\n", - " " - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "{'injected_square_current': {'delay': array(100.0) * ms, 'amplitude': array(113.4313888888889) * pA, 'duration': array(1000.0) * ms}}\n" - ] - }, - { - "ename": "NameError", - "evalue": "name 'attrs' is not defined", - "output_type": "error", - "traceback": [ - "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", - "\u001b[0;31mNameError\u001b[0m Traceback (most recent call last)", - "\u001b[0;32m\u001b[0m in \u001b[0;36m\u001b[0;34m()\u001b[0m\n\u001b[1;32m 11\u001b[0m \u001b[0mmodel\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0minject_square_current\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmodel\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mparams\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'injected_square_current'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 12\u001b[0m \u001b[0mvm\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mcopy\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcopy\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmodel\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_membrane_potential\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 13\u001b[0;31m \u001b[0mplt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mplot\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mvm\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtimes\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mvm\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0;36m1000\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mlabel\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mstr\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mattrs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m", - "\u001b[0;31mNameError\u001b[0m: name 'attrs' is not defined" - ] - } - ], - "source": [ - "print(model.params)\n", - "params = {}\n", - "params['injected_square_current'] = {}\n", - "params['injected_square_current']['amplitude'] = ga_out['hof'][0].dtc.rheobase['value']\n", - "#['amplitude'] = dtc.vtest[k]['injected_square_current']['amplitude']\n", - "DELAY = 100.0*pq.ms\n", - "DURATION = 1000.0*pq.ms\n", - "params['injected_square_current']['delay'] = DELAY\n", - "params['injected_square_current']['duration'] = DURATION\n", - "model.params = params\n", - "model.inject_square_current(model.params['injected_square_current'])\n", - "vm = copy.copy(model.get_membrane_potential())\n", - "plt.plot(vm.times,vm*1000,label=str(attrs))" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - " # 10*4 gene evaluations\n", - "ga_out, DO = om.run_ga(mp,3,all_tests,free_params=free_params)\n", - "print(ga_out)\n", - "pickle.dump(ga_out,open('gaout.p','wb'))" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "#attrs = {'k':1e-4}#{'a': 1e-2, 'b': 1e-2, 'vpeak':40, 'k':5e-5}\n", - "attrs = ga_out['hof'][0].dtc.attrs\n", - "current = {'amplitude':-10.0*pq.pA, 'delay':200.0*pq.ms, 'duration':500.0*pq.ms}\n", - "sim_length = 1000.0 * pq.ms\n", - "\n", - "LEMS_MODEL_PATH = path_params['model_path']\n", - "from neuronunit.optimization.model_parameters import model_params, path_params\n", - "\n", - "model = ReducedModel(LEMS_MODEL_PATH,name=str('vanilla'),backend='NEURON')\n", - "model.set_attrs(attrs)\n", - "model.inject_square_current(current)\n", - "#model.get_backend().set_stop_time(sim_length)\n", - "vm = copy.copy(model.get_membrane_potential())\n", - "plt.plot(vm.times,vm*1000,label=str(attrs))\n", - "plt.ylim(-90,-50)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "dtc = ga_out['hof'][0].dtc\n", - "\n", - "LEMS_MODEL_PATH = path_params['model_path']\n", - "model = None\n", - "model = ReducedModel(LEMS_MODEL_PATH,name=str('vanilla'),backend='NEURON')\n", - "model.set_attrs(dtc.attrs)\n", - "scores = []\n", - "for t in first_two:\n", - " scores.append(t.judge(model,stop_on_error = False, deep_error = False))\n", - "print(scores)\n", - "\n", - "## Does the same thing in parallel\n", - "#from neuronunit.optimization.optimization_management import nunit_evaluation as nue\n", - "\n", - "# xargs = (dtc,first_two)\n", - "# dtc = nue(xargs)\n", - "# print(dtc.scores)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "#attrs = {'k':1e-4}#{'a': 1e-2, 'b': 1e-2, 'vpeak':40, 'k':5e-5}\n", - "attrs = ga_out['hof'][0].dtc.attrs\n", - "print(ga_out['hof'][0].dtc.rheobase)\n", - "ampl = ga_out['hof'][0].dtc.rheobase['value']\n", - "current = {'amplitude':ampl, 'delay':200.0*pq.ms, 'duration':500.0*pq.ms}\n", - "sim_length = 1000.0 * pq.ms\n", - "\n", - "LEMS_MODEL_PATH = path_params['model_path']\n", - "from neuronunit.optimization.model_parameters import model_params, path_params\n", - "\n", - "model = ReducedModel(LEMS_MODEL_PATH,name=str('vanilla'),backend='NEURON')\n", - "model.set_attrs(attrs)\n", - "model.inject_square_current(current)\n", - "#model.get_backend().set_stop_time(sim_length)\n", - "vm = copy.copy(model.get_membrane_potential())\n", - "plt.plot(vm.times,vm*1000,label=str(attrs))\n", - "plt.ylim(-90,20)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": true - }, - "outputs": [], - "source": [] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.5.2" - } - }, - "nbformat": 4, - "nbformat_minor": 2 -} diff --git a/neuronunit/plotting/plot_utils.py b/neuronunit/plotting/plot_utils.py new file mode 100644 index 000000000..4d4f480b0 --- /dev/null +++ b/neuronunit/plotting/plot_utils.py @@ -0,0 +1,410 @@ +import os + +def plot_as_normal(dtc): + import streamlit as st + collect_z_offset = [] + for i,t in enumerate(dtc.tests): + t.score_type = ZScore + model = dtc.dtc_to_model() + score = t.judge(model) + x1 = -1.01 + x2 = 1.0 + sigma = 1.0 + mu = 0 + x = np.arange(-sigma, sigma, 0.001) # range of x in spec + x_all = np.arange(-sigma, sigma, 0.001) + y_point = norm.pdf(mu+float(score.raw),0,1) + y2 = norm.pdf(x_all,0,1) + + y = norm.pdf(x,0,1) + y2 = norm.pdf(x_all,0,1) + + + + x_point = mu+float(score.raw) + collect_z_offset.append(score.raw) + name = t.name.split('Test')[0] + title = str(name)+str(' ')+str(t.observation['mean'].units) + + zplot(x_point,y_point,title) + st.pyplot() +def plot_as_normal_all(dtc,random): + import streamlit as st + collect_z_offset = [] + collect_z_offset_random = [] + for i,t in enumerate(dtc.tests): + #ax = axes.flat[i] + t.score_type = ZScore + model = dtc.dtc_to_model() + score = t.judge(model) + collect_z_offset.append(np.abs(float(score.raw))) + + for i,t in enumerate(random.tests): + #ax = axes.flat[i] + t.score_type = ZScore + model = dtc.dtc_to_model() + score = t.judge(model) + collect_z_offset_random.append(np.abs(float(score.raw))) + + + x1 = -1.01 + x2 = 1.0 + sigma = 1.0 + mu = 0 + x = np.arange(-sigma, sigma, 0.001) # range of x in spec + x_all = np.arange(-sigma, sigma, 0.001) + y_point = norm.pdf(mu+float(np.mean(collect_z_offset)),0,1) + y2 = norm.pdf(x_all,0,1) + + y = norm.pdf(x,0,1) + y2 = norm.pdf(x_all,0,1) + x_point = mu+float(np.mean(collect_z_offset)) + + x_point_random = mu+float(np.mean(collect_z_offset_random)) + y_point_random = norm.pdf(mu+float(np.mean(collect_z_offset_random)),0,1) + best_random = [x_point_random,y_point_random] + + zplot(x_point,y_point,'all_tests',more=best_random) + + +def zplot(x_point,y_point,title,area=0.68, two_tailed=True, align_right=False, more=None): + """Plots a z distribution with common annotations + Example: + zplot(area=0.95) + zplot(area=0.80, two_tailed=False, align_right=True) + Parameters: + area (float): The area under the standard normal distribution curve. + align (str): The area under the curve can be aligned to the center + (default) or to the left. + Returns: + None: A plot of the normal distribution with annotations showing the + area under the curve and the boundaries of the area. + """ + # create plot object + fig = plt.figure(figsize=(12, 6)) + ax = fig.subplots() + # create normal distribution + norm = scs.norm() + # create data points to plot + x = np.linspace(-5, 5, 1000) + y = norm.pdf(x) + + ax.plot(x, y) + ax.scatter(x_point,y_point,c='g',s=150,marker='o') + + if more is not None: + ax.scatter(more[0],more[1],c='b',s=150,marker='o') + + + # code to fill areas for two-tailed tests + if two_tailed: + left = norm.ppf(0.5 - area / 2) + right = norm.ppf(0.5 + area / 2) + ax.vlines(right, 0, norm.pdf(right), color='grey', linestyle='--') + ax.vlines(left, 0, norm.pdf(left), color='grey', linestyle='--') + + ax.fill_between(x, 0, y, color='grey', alpha='0.25', + where=(x > left) & (x < right)) + plt.xlabel('z') + plt.ylabel('PDF') + plt.text(left, norm.pdf(left), "z = {0:.3f}".format(left), fontsize=12, + rotation=90, va="bottom", ha="right") + plt.text(right, norm.pdf(right), "z = {0:.3f}".format(right), + fontsize=12, rotation=90, va="bottom", ha="left") + # for one-tailed tests + else: + # align the area to the right + if align_right: + left = norm.ppf(1-area) + ax.vlines(left, 0, norm.pdf(left), color='grey', linestyle='--') + ax.fill_between(x, 0, y, color='grey', alpha='0.25', + where=x > left) + plt.text(left, norm.pdf(left), "z = {0:.3f}".format(left), + fontsize=12, rotation=90, va="bottom", ha="right") + # align the area to the left + else: + right = norm.ppf(area) + ax.vlines(right, 0, norm.pdf(right), color='grey', linestyle='--') + ax.fill_between(x, 0, y, color='grey', alpha='0.25', + where=x < right) + plt.text(right, norm.pdf(right), "z = {0:.3f}".format(right), + fontsize=12, rotation=90, va="bottom", ha="left") + + # annotate the shaded area + plt.text(0, 0.1, "shaded area = {0:.3f}".format(area), fontsize=12, + ha='center') + # axis labels + plt.xlabel('z') + plt.ylabel('PDF') + plt.title(title) + plt.show() + +try: + import plotly.offline as py +except: + warnings.warn("plotly") +try: + import plotly + + plotly.io.orca.config.executable = "/usr/bin/orca" +except: + print("silently fail on plotly") +try: + import seaborn as sns +except: + warnings.warn("Seaborne plotting sub library not available, consider installing") + +def check_bin_vm_soma(target,opt): + import matplotlib + import matplotlib.pyplot as plt + import plotly.graph_objects as go + import quantities as qt + + plt.plot(target.vm_soma.times,target.vm_soma.magnitude,label='Allen Experiment') + plt.plot(opt.vm_soma.times,opt.vm_soma.magnitude,label='Optimized Model') + signal = target.vm_soma + plt.xlabel(qt.s) + plt.ylabel(signal.dimensionality) + plt.legend() + plt.show() + +def display_fitting_data(): + cells = pickle.load(open("processed_multicellular_constraints.p","rb")) + + purk = TSD(cells['Cerebellum Purkinje cell'])#.tests + purk_vr = purk["RestingPotentialTest"].observation['mean'] + + ncl5 = TSD(cells["Neocortex pyramidal cell layer 5-6"]) + ncl5.name = str("Neocortex pyramidal cell layer 5-6") + ncl5_vr = ncl5["RestingPotentialTest"].observation['mean'] + + ca1 = TSD(cells['Hippocampus CA1 pyramidal cell']) + ca1_vr = ca1["RestingPotentialTest"].observation['mean'] + + + olf = TSD(pickle.load(open("olf_tests.p","rb"))) + olf.use_rheobase_score = False + cells.pop('Olfactory bulb (main) mitral cell',None) + cells['Olfactory bulb (main) mitral cell'] = olf + + + list_of_dicts = [] + for k,v in cells.items(): + observations = {} + for k1 in ca1.keys(): + vsd = TSD(v) + if k1 in vsd.keys(): + vsd[k1].observation['mean'] + observations[k1] = float(vsd[k1].observation['mean'])##,2) + + observations[k1] = np.round(vsd[k1].observation['mean'],2) + observations['name'] = k + list_of_dicts.append(observations) + df = pd.DataFrame(list_of_dicts) + df = df.set_index('name').T + return df + +def inject_and_plot_passive_model(pre_model,second=None,figname=None,plotly=True): + model = pre_model.dtc_to_model() + uc = {'amplitude':-10*pq.pA,'duration':500*pq.ms,'delay':100*pq.ms} + model.inject_square_current(uc) + vm = model.get_membrane_potential() + + + if second is not None: + model2 = second.dtc_to_model() + uc = {'amplitude':-10*pq.pA,'duration':500*pq.ms,'delay':100*pq.ms} + model2.inject_square_current(uc) + vm2 = model2.get_membrane_potential() + if plotly and second is None: + fig = px.line(x=vm.times, y=vm.magnitude, labels={'x':'t (ms)', 'y':'V (mV)'}) + return fig + if plotly and second is not None: + import plotly.graph_objects as go + from plotly.subplots import make_subplots + # Create figure with secondary y-axis + fig = make_subplots(specs=[[{"secondary_y": True}]]) + # Add traces + fig.add_trace( + go.Scatter(x=[float(i) for i in vm.times[0:-1]], y=[float(i) for i in vm.magnitude[0:-1]], name="yaxis data"), + secondary_y=False, + ) + fig.add_trace( + go.Scatter(x=[float(i) for i in vm2.times[0:-1]], y=[float(i) for i in vm2.magnitude[0:-1]], name="yaxis2 data"), + secondary_y=True, + ) + # Add figure title + fig.update_layout( + title_text="Compare traces" + ) + # Set x-axis title + fig.update_xaxes(title_text="time (ms)") + # Set y-axes titles + fig.update_yaxes(title_text="Vm (mv) model 1", secondary_y=False) + fig.update_yaxes(title_text="Vm (mv) model 2", secondary_y=True) + return fig + if not plotly: + matplotlib.rcParams.update({'font.size': 15}) + plt.figure() + if pre_model.backend in str("HH"): + plt.title('Hodgkin-Huxley Neuron') + else: + plt.title('Membrane Potential') + plt.plot(vm.times, vm.magnitude, c='b')#+str(model.attrs['a'])) + + plt.plot(vm2.times, vm2.magnitude, c='g') + plt.ylabel('Time (sec)') + + plt.ylabel('V (mV)') + plt.legend(loc="upper left") + + if figname is not None: + plt.savefig('thesis_simulated_data_match.png') + return vm,plt + +def inject_and_not_plot_model(pre_model,known_rh=None): + + # get rheobase injection value + # get an object of class ReducedModel with known attributes and known rheobase current injection value. + model = pre_model.dtc_to_model() + + if known_rh is None: + pre_model = dtc_to_rheo(pre_model) + if type(model.rheobase) is type(dict()): + uc = {'amplitude':model.rheobase['value'],'duration':DURATION,'delay':DELAY} + else: + uc = {'amplitude':model.rheobase,'duration':DURATION,'delay':DELAY} + + else: + if type(known_rh) is type(dict()): + uc = {'amplitude':known_rh['value'],'duration':DURATION,'delay':DELAY} + else: + uc = {'amplitude':known_rh,'duration':DURATION,'delay':DELAY} + model.inject_square_current(uc) + vm = model.get_membrane_potential() + return vm + +def plotly_version(vm0,vm1,figname=None,snippets=False): + + import plotly.graph_objects as go + if snippets: + snippets1 = get_spike_waveforms(vm1) + snippets0 = get_spike_waveforms(vm0) + + import plotly.graph_objects as go + from plotly.subplots import make_subplots + + # Create figure with secondary y-axis + fig = make_subplots(specs=[[{"secondary_y": True}]]) + # Add traces + fig.add_trace( + go.Scatter(x=[float(i) for i in snippets0.times[0:-1]], y=[float(i) for i in snippets0.magnitude[0:-1]], name="yaxis data"), + secondary_y=False, + ) + + fig.add_trace( + go.Scatter(x=[float(i) for i in snippets1.times[0:-1]], y=[float(i) for i in snippets1.magnitude[0:-1]], name="yaxis2 data"), + secondary_y=True, + ) + + # Add figure title + fig.update_layout( + title_text="Double Y Axis Example" + ) + + # Set x-axis title + fig.update_xaxes(title_text="xaxis title") + + # Set y-axes titles + fig.update_yaxes(title_text="primary yaxis title", secondary_y=False) + fig.update_yaxes(title_text="secondary yaxis title", secondary_y=True) + + fig.show() + if figname is not None: + fig.write_image(str(figname)+str('.png')) + else: + fig.show() + + else: + import plotly.graph_objects as go + from plotly.subplots import make_subplots + + # Create figure with secondary y-axis + fig = make_subplots(specs=[[{"secondary_y": True}]]) + # Add traces + fig.add_trace( + go.Scatter(x=[float(i) for i in vm0.times[0:-1]], y=[float(i) for i in vm0.magnitude[0:-1]], name="yaxis data"), + secondary_y=False, + ) + + fig.add_trace( + go.Scatter(x=[float(i) for i in vm1.times[0:-1]], y=[float(i) for i in vm1.magnitude[0:-1]], name="yaxis2 data"), + secondary_y=True, + ) + + # Add figure title + + # Set x-axis title + fig.update_xaxes(title_text="xaxis title") + + # Set y-axes titles + fig.update_yaxes(title_text="Vm (mv) model 1", secondary_y=False) + fig.update_yaxes(title_text="Vm (mv) model 2", secondary_y=True) + + if figname is not None: + fig.write_image(str(figname)+str('.png')) + else: + fig.show() + +def model_trace(pre_model): + from neuronunit.tests.base import AMPL, DELAY, DURATION + + # get rheobase injection value + # get an object of class ReducedModel with known attributes and known rheobase current injection value. + pre_model = dtc_to_rheo(pre_model) + model = pre_model.dtc_to_model() + uc = {'amplitude':model.rheobase,'duration':DURATION,'delay':DELAY} + model.inject_square_current(uc) + vm = model.get_membrane_potential() + return vm +def check_binary_match(dtc0,dtc1,figname=None,snippets=True,plotly=True): + + vm0 = model_trace(dtc0) + vm1 = model_trace(dtc1) + + if plotly: + plotly_version(vm0,vm1,figname,snippets) + else: + matplotlib.rcParams.update({'font.size': 8}) + + plt.figure() + + if snippets: + plt.figure() + + snippets1 = get_spike_waveforms(vm1) + snippets0 = get_spike_waveforms(vm0) + plt.plot(snippets0.times,snippets0.magnitude,label=str('model type: '))#+label)#,label='ground truth') + plt.plot(snippets1.times,snippets1.magnitude,label=str('model type: '))#+label)#,label='ground truth') + if dtc0.backend in str("HH"): + plt.title('Check for waveform Alignment variance exp: {0}'.format(basic_expVar(snippets1, snippets0))) + else: + plt.title('membrane potential: variance exp: {0}'.format(basic_expVar(snippets1, snippets0))) + plt.ylabel('V (mV)') + plt.legend(loc="upper left") + + if figname is not None: + plt.savefig(figname) + + else: + if dtc0.backend in str("HH"): + plt.title('Check for waveform Alignment') + else: + plt.title('membrane potential plot') + plt.plot(vm0.times, vm0.magnitude,label="target") + plt.plot(vm1.times, vm1.magnitude,label="solutions") + plt.ylabel('V (mV)') + plt.legend(loc="upper left") + + if figname is not None: + plt.savefig(figname) diff --git a/neuronunit/tests/__init__.py b/neuronunit/tests/__init__.py index 33ee23383..4b3d3d3f8 100644 --- a/neuronunit/tests/__init__.py +++ b/neuronunit/tests/__init__.py @@ -2,6 +2,8 @@ from .passive import * from .waveform import * +from .dynamics import FITest + from .dynamics import * from .fi import * diff --git a/neuronunit/tests/base.py b/neuronunit/tests/base.py index 247fb883d..962570714 100644 --- a/neuronunit/tests/base.py +++ b/neuronunit/tests/base.py @@ -14,6 +14,16 @@ import neuronunit.capabilities as ncap import sciunit.capabilities as scap from neuronunit import neuroelectro +import pickle + + + +AMPL = 100.0*pq.pA +DELAY = 100.0*pq.ms +DURATION = 1000.0*pq.ms +PASSIVE_AMPL = -10.0*pq.pA +PASSIVE_DELAY = 100.0*pq.ms +PASSIVE_DURATION = 300.0*pq.ms class VmTest(ProtocolToFeaturesTest): @@ -80,7 +90,14 @@ def condition_model(self, model): model.set_run_params(t_stop=self.params['tmax']) def bind_score(self, score, model, observation, prediction): - score.related_data['vm'] = model.get_membrane_potential() + try: + score.related_data['vm'] = model.get_membrane_potential() + except: + try: + score.related_data['vm'] = model._backend.get_membrane_potential() + print(score.related_data['vm']) + except: + score.related_data['vm'] = None score.related_data['model_name'] = '%s_%s' % (model.name, self.name) def plot_vm(self, ax=None, ylim=(None, None)): diff --git a/neuronunit/tests/dynamics.py b/neuronunit/tests/dynamics.py index 29c46f2b1..922aad085 100644 --- a/neuronunit/tests/dynamics.py +++ b/neuronunit/tests/dynamics.py @@ -4,12 +4,19 @@ from elephant.statistics import isi from elephant.statistics import cv from elephant.statistics import lv +from elephant.spike_train_generation import threshold_detection + from neuronunit.capabilities.channel import * -from .base import np, pq, ncap, VmTest, scores +from .base import np, pq, ncap, VmTest, scores, AMPL, DELAY, DURATION from .waveform import InjectedCurrentAPWidthTest from .fi import RheobaseTest +import matplotlib.pyplot as plt +import numpy as np +from sklearn.linear_model import LinearRegression + + class TFRTypeTest(RheobaseTest): """Test whether a model has particular threshold firing rate dynamics, i.e. type 1 or type 2.""" @@ -138,7 +145,7 @@ def compute_score(self, observation, prediction): if prediction is None: score = scores.InsufficientDataScore(None) else: - print(observation, prediction) + #print(observation, prediction) score = self.score_type.compute(observation, prediction, key='cv') return score @@ -180,6 +187,109 @@ def compute_score(self, observation, prediction): return score + +class InjectedCurrent: + """Metaclass to mixin with InjectedCurrent tests.""" + def __init__(self,amp): + self.amp = amp + default_params = dict(VmTest.default_params) + default_params.update({'amplitude': 100*pq.pA}) + default_params.update({'delay': 100*pq.ms}) + default_params.update({'duration': 1000*pq.ms}) + self.default_params = default_params + required_capabilities = (ncap.ReceivesSquareCurrent,) + + + def get_params(self): + self.verbose = False + self.params = {} + self.params['injected_square_current'] = self.default_params + self.params['injected_square_current']['amplitude'] = \ + self.amp + return self.params + +def get_firing_rate(model, input_current): + + # inject a test current into the neuron and call it's run() function. + # get the spike times using spike_tools.get_spike_times + # from the spike times, calculate the firing rate f + IC = InjectedCurrent(amp = input_current*pq.pA) + params = IC.get_params() + if 'injected_square_current' in params.keys(): + params = params['injected_square_current'] + if 'dt' in params.keys(): + params.pop('dt',None) + if 'padding' in params.keys(): + params.pop('padding',None) + + model.inject_square_current(**params) + vm = model.get_membrane_potential() + spikes = threshold_detection(vm,threshold=0*pq.mV) + + if len(spikes): + isi_easy = isi(spikes) + rate = 1.0/np.mean(isi_easy) + #print(rate,spikes) + + if rate == np.nan or np.isnan(rate): + rate = 0 + rate = rate*pq.Hz + else: + rate = 0*pq.Hz + #print(rate,spikes) + return rate + + + +class FITest(VmTest): + name = "FITest" + ''' + file:///home/user/Downloads/CellTypes_Ephys_Overview.pdf + For all long square responses, the average firing rate and the stimulus amplitude were combined to estimate + the curve of frequency response of the cell versus stimulus intensity (“f-I curve”). The suprathreshold part of this + curve was fit to a straight line, and the slope of this line was recorded as a cell-wide feature (Figure 6C). + ''' + def generate_prediction(self,model,plot=False): + I = np.arange(-5,200,10.0) # a range of current inputs + + fr = [] + # loop over current values + supra_thresh_I = [] + for I_amp in I: + firing_rate = get_firing_rate(model, I_amp) + if firing_rate>0: + supra_thresh_I.append(I_amp) + fr.append(firing_rate) + model = LinearRegression() + x = np.array(supra_thresh_I).reshape(-1, 1) + y = np.array(fr) + if len(x): + model.fit(x, y) + m = model.coef_*(pq.Hz/pq.pA) + if type(m) is type(list()): + m = m[0] + self.prediction = {'value':m} + if plot: + c = model.intercept_ + y = supra_thresh_I*float(m) +c + plt.figure() # new figure + plt.plot(supra_thresh_I, fr) + plt.plot(supra_thresh_I, y) + plt.xlabel('Amplitude of Injecting step current (pA)') + plt.ylabel('Firing rate (Hz)') + plt.grid() + plt.show() + else: + self.prediction = None#{'value':m} + return self.prediction + def compute_score(self, observation, prediction): + """Implement sciunit.Test.score_prediction.""" + if prediction is None: + score = scores.InsufficientDataScore(None) + else: + score = self.score_type.compute(observation, prediction) + return score + class FiringRateTest(RheobaseTest): """Test whether a model exhibits the observed burstiness""" diff --git a/neuronunit/tests/fi.py b/neuronunit/tests/fi.py index 15d99ecf8..0644e2de5 100644 --- a/neuronunit/tests/fi.py +++ b/neuronunit/tests/fi.py @@ -3,38 +3,52 @@ For example, investigating firing rates and patterns as a function of input current. """ +import warnings import os import multiprocessing -import copy - -import dask.bag as db - +global cpucount +npartitions = cpucount = multiprocessing.cpu_count() +from .base import np, pq, ncap, VmTest, scores, AMPL, DELAY, DURATION +import quantities import neuronunit -from neuronunit.optimization.data_transport_container import DataTC -from neuronunit.models.reduced import ReducedModel -from .base import np, pq, ncap, VmTest, scores -N_CPUS = multiprocessing.cpu_count() +import dask.bag as db +import quantities as pq +import numpy as np +import copy +import time +import copy +import dask +from neuronunit.capabilities.spike_functions import get_spike_waveforms, spikes2amplitudes, threshold_detection + +tolerance = 0.0 class RheobaseTest(VmTest): - """Serial implementation of a binary search to test the rheobase. + """ + --Synopsis: + Serial implementation of a binary search to test the rheobase. + + Strengths: this algorithm is faster than the parallel class, present in + this file under important and limited circumstances: this serial algorithm + is faster than parallel for model backends that are able to implement + numba jit optimization. - Strengths: this algorithm is faster than the parallel class, present in - this file under important and limited circumstances: this serial algorithm - is faster than parallel for model backends that are able to implement - numba jit optimization. - Weaknesses this serial class is significantly slower, for many backend - implementations including raw NEURON, NEURON via PyNN, and possibly GLIF. """ def _extra(self): self.prediction = {} - self.high = 300*pq.pA + self.high = 900*pq.pA self.small = 0*pq.pA self.rheobase_vm = None + self.verbose = False + def __init__(self,observation=None,prediction=None,target_number_spikes=1,name="RheobaseTest"): + self._extra() + super(RheobaseTest,self).__init__(observation=observation,name=name) + self.prediction = prediction + self.target_number_spikes = target_number_spikes required_capabilities = (ncap.ReceivesSquareCurrent, ncap.ProducesSpikes) @@ -44,49 +58,97 @@ def _extra(self): "needed to evoke at least one spike.") units = pq.pA ephysprop_name = 'Rheobase' - score_type = scores.RatioScore - default_params = dict(VmTest.default_params) default_params.update({'amplitude': 100*pq.pA, - 'duration': 1000*pq.ms, + 'duration': DURATION, + 'delay': DELAY, 'tolerance': 1.0*pq.pA}) params_schema = dict(VmTest.params_schema) params_schema.update({'tolerance': {'type': 'current', 'min': 1, 'required': False}}) def condition_model(self, model): - model.set_run_params(t_stop=self.params['tmax']) + if not 't_max' in self.params: + self.params['t_max'] = 2000.0*pq.ms + else: + model.set_run_params(t_stop=self.params['t_max']) def generate_prediction(self, model): """Implement sciunit.Test.generate_prediction.""" # Method implementation guaranteed by # ProducesActionPotentials capability. + + ## + # Handle case that the + # Test constructor __init__ originated from + # a file that is older than this source code + # because it was recovered from pickle. + ## + if not hasattr(self,'target_number_spikes'): + self.target_number_spikes=1 + + self.condition_model(model) prediction = {'value': None} - try: - units = self.observation['value'].units - except KeyError: - units = self.observation['mean'].units - # begin_rh = time.time() + model.rerun = True + + if self.observation is not None: + try: + units = self.observation['value'].units + except KeyError: + units = self.observation['mean'].units + else: + units = pq.pA lookup = self.threshold_FI(model, units) - sub = np.array([x for x in lookup if lookup[x] == 0])*units - supra = np.array([x for x in lookup if lookup[x] > 0])*units - if self.verbose: + ## + # New code + ## + temp = [ v for v in lookup.values() if v>self.target_number_spikes ] + if len(temp): + too_many_spikes = np.min(temp) + else: + too_many_spikes = 0 + + spikes_one = sorted([ (k,v) for k,v in lookup.items() if v==self.target_number_spikes ]) + + if len(spikes_one)>=3: + prediction['value'] = np.abs(spikes_one[0][0]*units) + return prediction + + + single_spike_found = bool(len(spikes_one)) + sub = np.array([x for x in lookup if lookup[x]==0])*units + supra = np.array([x for x in lookup if lookup[x]>0])*units + if self.verbose>1: if len(sub): - print("Highest subthreshold current is %s" + print("Highest subthreshold current is %s" \ % (float(sub.max())*units)) else: print("No subthreshold current was tested.") if len(supra): - print("Lowest suprathreshold current is %s" + print("Lowest suprathreshold current is %s" \ % supra.min()) else: print("No suprathreshold current was tested.") - if len(sub) and len(supra): + + if len(sub) and len(supra) and single_spike_found: rheobase = supra.min() + elif too_many_spikes>=5: + rheobase = None else: rheobase = None prediction['value'] = rheobase + + if len(supra) and single_spike_found and str("BHH") in str(model._backend.name): + prediction['value'] = sorted(supra)[1] + if len(supra) and single_spike_found and str("HH") in str(model._backend.name): + prediction['value'] = sorted(supra)[0] + self.prediction = prediction + return prediction + + def extract_features(self,model): + prediction = self.generate_prediction(model) + self.prediction = prediction return prediction def threshold_FI(self, model, units, guess=None): @@ -95,21 +157,53 @@ def threshold_FI(self, model, units, guess=None): def f(ampl): if float(ampl) not in lookup: + if False: + uc = {'amplitude':ampl,'duration':DURATION,'delay':DELAY} + + model.inject_square_current(uc) + n_spikes = model._backend.get_spike_count() + assert n_spikes == model.get_spike_count() + current = self.get_injected_square_current() - current['amplitude'] = ampl - model.inject_square_current(current) - n_spikes = model.get_spike_count() + current['amplitude'] = ampl*pq.pA + if "JIT_" in model.backend: + try: + model.inject_square_current(**current) + except: + model._backend.inject_square_current(**current) + + n_spikes = model.get_spike_count() + assert n_spikes == model.get_spike_count() + + else: + + model._backend.inject_square_current(**current) + n_spikes = model._backend.get_spike_count() + + #if self.target_num_spikes == 1: + # ie this is rheobase search + #vm = model.get_membrane_potential() + #if vm[-1]>0 and n_spikes==1: + # this means current was not strong enough + # to evoke an early spike. + # the current did not come down again. + # treat this as zero spikes because a slightly higher + # spike will give a cleaner rheobase waveform. + # n_spikes = 0 + if n_spikes == self.target_num_spikes: + + self.n_spikes = n_spikes if self.verbose >= 2: - print("Injected %s current and got %d spikes" % - (ampl, n_spikes)) + print("Injected %s current and got %d spikes" % \ + (ampl,n_spikes)) lookup[float(ampl)] = n_spikes spike_counts = \ np.array([n for x, n in lookup.items() if n > 0]) if n_spikes and n_spikes <= spike_counts.min(): - self.rheobase_vm = model.get_membrane_potential() + self.rheobase_vm = model._backend.get_membrane_potential() - max_iters = 25 + max_iters = 40 # evaluate once with a current injection at 0pA high = self.high @@ -119,6 +213,7 @@ def f(ampl): i = 0 while True: + # sub means below threshold, or no spikes sub = np.array([x for x in lookup if lookup[x] == 0])*units # supra means above threshold, @@ -127,12 +222,22 @@ def f(ampl): supra = np.array([x for x in lookup if lookup[x] > 0])*units # The actual part of the Rheobase test that is # computation intensive and therefore - # a target for parellelization. + temp_ = [ v for v in lookup.values() if v==self.target_number_spikes ] if len(supra) and len(sub): delta = float(supra.min()) - float(sub.max()) - tolerance = float(self.params['tolerance'].rescale(pq.pA)) - if delta < tolerance or (str(supra.min()) == str(sub.max())): + temp = [ v for v in lookup.values() if v>self.target_number_spikes ] + if len(temp): + too_many_spikes = np.min(temp) + else: + too_many_spikes = 0 + if 'tolerance' not in self.params.keys(): + tolerance = 0.0000001*pq.pA + else: + tolerance = float(self.params['tolerance'].rescale(pq.pA)) + + if (str(supra.min()) == str(sub.max())): + break if i >= max_iters: @@ -143,7 +248,7 @@ def f(ampl): f((supra.min() + sub.max())/2) elif len(sub): - f(max(small, sub.max()*2)) + f(max(small,sub.max()*10)) elif len(supra): f(min(-small, supra.min()*2)) @@ -151,15 +256,19 @@ def f(ampl): return lookup - def compute_score(self, observation, prediction): + def compute_score(self,observation, prediction): """Implement sciunit.Test.score_prediction.""" + from sciunit.scores import BooleanScore + + if type(self.score_type) == BooleanScore: + print('warning using unusual score type') if prediction is None or \ (isinstance(prediction, dict) and prediction['value'] is None): score = scores.InsufficientDataScore(None) else: + score = super(RheobaseTest, self).\ - compute_score(observation, prediction) - # self.bind_score(score,None,observation,prediction) + compute_score(observation, prediction)#max return score def bind_score(self, score, model, observation, prediction): @@ -179,235 +288,291 @@ class RheobaseTestP(RheobaseTest): of backends. """ + + def _extra(self,target_number_spikes=1): + self.verbose = False + self.target_number_spikes = 1 + + def __init__(self,observation=None,prediction=None,target_number_spikes=1,name=""): + self._extra() + super(RheobaseTest,self).__init__(observation=observation,name=name) + self.prediction = prediction + self.target_number_spikes = target_number_spikes + + + required_capabilities = (ncap.ReceivesSquareCurrent, + ncap.ProducesSpikes) + name = "Rheobase test" description = ("A test of the rheobase, i.e. the minimum injected current " "needed to evoke at least one spike.") units = pq.pA ephysprop_name = 'Rheobase' - score_type = scores.RatioScore + get_rheobase_vm = True + default_params = dict(VmTest.default_params) + default_params.update({'amplitude': 100*pq.pA, + 'duration': DURATION, + 'delay': DELAY, + 'tolerance': 1.0*pq.pA}) + + params_schema = dict(VmTest.params_schema) + params_schema.update({'tolerance': {'type': 'current', 'min': 1, 'required': False}}) def condition_model(self, model): - model.set_run_params(t_stop=self.params['tmax']) + if not 't_max' in self.params: + self.params['t_max'] = 2000.0*pq.ms + else: + model.set_run_params(t_stop=self.params['t_max']) + + default_params = dict(VmTest.default_params) + default_params.update({'amplitude': 100*pq.pA, + 'duration': DURATION, + 'duration': DELAY, + 'tolerance': 1.0*pq.pA}) + params_schema = dict(VmTest.params_schema) + params_schema.update({'tolerance': {'type': 'current', 'min': 1, 'required': False}}) + units = pq.pA def generate_prediction(self, model): - """Generate the test prediction.""" - self.condition_model(model) + def check_fix_range(dtc): + ''' + Inputs: lookup, A dictionary of previous current injection values + used to search rheobase + Outputs: A boolean to indicate if the correct rheobase current was found + and a dictionary containing the range of values used. + If rheobase was actually found then rather returning a boolean and a dictionary, + instead logical True, and the rheobase current is returned. + given a dictionary of rheobase search values, use that + dictionary as input for a subsequent search. + ''' + + steps = [] + dtc.rheobase = None + sub, supra = get_sub_supra(dtc.lookup) + + #if 0. in supra and len(sub) == 0: + # dtc.boolean = True + # dtc.rheobase = None + # return dtc + if (len(sub) + len(supra)) == 0: + # This assertion would only be occur if there was a bug + assert sub.max() <= supra.min() + elif len(sub) and len(supra): + # Termination criterion + + steps = np.linspace(sub.max(),supra.min(),cpucount)*pq.pA + steps = steps[1:-1] + elif len(sub): + + steps = np.linspace(sub.max(),10*sub.max(),cpucount)*pq.pA + steps = steps[1:-1] + elif len(supra): + steps = np.linspace(supra.min()-100,supra.min(),cpucount)*pq.pA + steps = steps[1:-1] + + dtc.current_steps = steps + return dtc + + def get_sub_supra(lookup): + sub, supra = [], [] + for current, n_spikes in lookup.items(): + if n_spikes == 0: + sub.append(current) + elif n_spikes > 0: + supra.append(current) + + sub = np.array(sorted(list(set(sub)))) + supra = np.array(sorted(list(set(supra)))) + return sub, supra + #@dask.delayed + def check_current(dtc): + ''' + Inputs are an amplitude to test and a virtual model + output is an virtual model with an updated dictionary. + ''' + dtc.boolean = False + + model = dtc.dtc_to_model() + self.condition_model(model) + + ampl = dtc.ampl + if float(ampl) not in dtc.lookup or len(dtc.lookup) == 0: + + current = {'amplitude':ampl,'duration':DURATION,'delay':DELAY} + float(current['delay']) > 100 + current['amplitude'] = ampl + model.inject_square_current(current) + n_spikes = model.get_spike_count() + + + dtc.previous = ampl + dtc.rheobase = {} + if n_spikes == self.target_number_spikes: + dtc.lookup[float(ampl)] = self.target_number_spikes + dtc.rheobase['value'] = ampl + dtc.boolean = True + + return dtc + + dtc.lookup[float(ampl)] = n_spikes + return dtc + + def init_dtc(dtc): + ''' + Exploit memory of last model in genes. + # Exploit memory of the genes to inform searchable range. + # if this model has lineage, assume it didn't mutate that far away from it's ancestor. + # using that assumption, on first pass, consult a very narrow range, of test current injection samples: + # only slightly displaced away from the ancestors rheobase value. + + + if type(dtc.current_steps) is type(float): + dtc.current_steps = [ 0.80 * dtc.current_steps, 1.20 * dtc.current_steps ] + elif type(dtc.current_steps) is type(list): + dtc.current_steps = [ s * 1.25 for s in dtc.current_steps ] + dtc.initiated = True # logically unnecessary but included for readibility + + ''' + # check for memory and exploit it. + if dtc.initiated == True: + dtc = check_current(dtc) + if dtc.boolean: + return dtc + if dtc.initiated == False: + dtc.boolean = False + steps = np.linspace(0.0,550.0,cpucount) + steps_current = [ i*pq.pA for i in steps ] + dtc.current_steps = steps_current + dtc.initiated = True + return dtc + + def find_rheobase(self, global_dtc): + units = pq.pA + + # If this it not the first pass/ first generation + # then assume the rheobase value found before mutation still holds until proven otherwise. + # dtc = check_current(model.rheobase,dtc) + # If its not true enter a search, with ranges informed by memory + cnt = 0 + sub = np.array([0,0]); + supra = np.array([0,0]) + + big = 20 + + while global_dtc.boolean == False and cnt< big: + + if len(sub): + if sub.max() < -1.0: + pass + + + + dtc_clones = [ global_dtc for i in range(0,len(global_dtc.current_steps)) ] + for i,s in enumerate(global_dtc.current_steps): + dtc_clones[i] = copy.copy(dtc_clones[i]) + dtc_clones[i].ampl = copy.copy(global_dtc.current_steps[i]) + dtc_clones = [d for d in dtc_clones if not np.isnan(d.ampl)] + set_clones = set([ float(d.ampl) for d in dtc_clones ]) + dtc_clone = [] + + for dtc_local,sc in zip(dtc_clones,set_clones): + dtc_local = copy.copy(dtc_local) + dtc_local.ampl = sc*pq.pA + dtc_clone.append(dtc_local) + bag = db.from_sequence(dtc_clone,npartitions=8) + dtc_clone = list(bag.map(check_current).compute()) + spikes_one = sorted([ (dtc.ampl,dtc) for dtc in dtc_clone if dtc.boolean == True ]) + if len(spikes_one)>=2: + return spikes_one[0][0] + + + for d in dtc_clone: + global_dtc.lookup.update(d.lookup) + dtc = check_fix_range(global_dtc) + sub, supra = get_sub_supra(dtc.lookup) + if len(supra) and len(sub): + + delta = float(supra.min()) - float(sub.max()) + + tolerance = 0.0 + if delta < tolerance or (str(supra.min()) == str(sub.max())): + if self.verbose >= 2: + print(delta, 'a neuron, close to the edge! Multi spiking rheobase. # spikes: ',len(supra)) + too_many_spikes = np.min([ v for v in dtc.lookup.values() if v>self.target_number_spikes ]) + if too_many_spikes>10: + + dtc.rheobase = {} + dtc.rheobase['value'] = None + dtc.boolean = True + dtc.lookup[float(supra.min())] = len(supra) + + else: + if len(supra)<=10: + + dtc.rheobase = float(supra.min())*units + dtc.boolean = True + dtc.lookup[float(supra.min())] = len(supra) + else: + dtc.rheobase = float(supra.min()) + dtc.boolean = True + dtc.lookup[float(supra.min())] = len(supra)*units + #print(dtc.rheobase) + return dtc.rheobase + + + if self.verbose >= 2: + print("Try %d: SubMax = %s; SupraMin = %s" % \ + (cnt, sub.max() if len(sub) else None, + supra.min() if len(supra) else None)) + cnt += 1 + return dtc + from neuronunit.optimisation.data_transport_container import DataTC + dtc = DataTC() dtc.attrs = {} - for k, v in model.attrs.items(): + for k,v in model.attrs.items(): dtc.attrs[k] = v # this is not a perservering assignment, of value, # but rather a multi statement assertion that will be checked. + dtc.backend = model.backend dtc = init_dtc(dtc) - - if model.orig_lems_file_path: - dtc.model_path = model.orig_lems_file_path - dtc.backend = model.backend - assert os.path.isfile(dtc.model_path),\ - "%s is not a file" % dtc.model_path - prediction = {} - - rheobase = find_rheobase(self, dtc).rheobase - if rheobase is not None: - # Something like the below commented line must happen to set the - # vm trace associated with the rheobase current. One additional - # simulation may need to be run, unless we want one of the compute - # nodes to set it (when found) in either the dtc or in the calling - # instance of the test. - # self.rheobase_vm = model.get_membrane_potential() - prediction['value'] = float(rheobase) * pq.pA - if self.get_rheobase_vm: - print("Getting rheobase vm") - c = self.get_injected_square_current() - c['amplitude'] = prediction['value'] - model.inject_square_current(c) - self.rheobase_vm = model.get_membrane_potential() + temp = find_rheobase(self,dtc)#.rheobase + if type(temp) is type(pq.pA): + prediction['value'] = temp + return prediction + if type(temp) is not type(None): + if hasattr(temp,'rheobase'): + temp = temp.rheobase + if type(temp) is type(dict()): + if temp['value'] is None: + prediction['value'] = None + else: + prediction['value'] = float(temp['value'])* pq.pA + else: + prediction['value'] = temp #float(temp)* pq.pA else: - prediction = None - self.rheobase_vm = None + prediction['value'] = None + self.prediction = prediction return prediction -""" -Functions to support the parallel rheobase search. -""" - - -def check_fix_range(dtc): - """Check for the rheobase value. - - Inputs: lookup, A dictionary of previous current injection values - used to search rheobase - Outputs: A boolean to indicate if the correct rheobase current was - found and a dictionary containing the range of values used. - If rheobase was actually found then rather returning a boolean and - a dictionary, instead logical True, and the rheobase current is - returned. - given a dictionary of rheobase search values, use that - dictionary as input for a subsequent search. - """ - steps = [] - dtc.rheobase = None - sub, supra = get_sub_supra(dtc.lookup) - - if 0. in supra and len(sub) == 0: - dtc.boolean = True - dtc.rheobase = -1 - return dtc - elif (len(sub) + len(supra)) == 0: - # This assertion would only be occur if there was a bug - assert sub.max() <= supra.min() - elif len(sub) and len(supra): - # Termination criterion - steps = np.linspace(sub.max(), supra.min(), N_CPUS+1)*pq.pA - steps = steps[1:-1]*pq.pA - elif len(sub): - steps = np.linspace(sub.max(), 2*sub.max(), N_CPUS+1)*pq.pA - steps = steps[1:-1]*pq.pA - elif len(supra): - steps = np.linspace(supra.min()-100, supra.min(), - N_CPUS+1)*pq.pA - steps = steps[1:-1]*pq.pA - - dtc.current_steps = steps - return dtc - - -def get_sub_supra(lookup): - """Get subthreshold and suprathreshold current values.""" - sub, supra = [], [] - for current, n_spikes in lookup.items(): - if n_spikes == 0: # No spikes - sub.append(current) - elif n_spikes > 0: # Some spikes - supra.append(current) - - sub = np.array(sorted(list(set(sub)))) - supra = np.array(sorted(list(set(supra)))) - return sub, supra - - -def check_current(dtc): - """Check the response to the proposed current and count spikes. - - Inputs are an amplitude to test and a virtual model - output is an virtual model with an updated dictionary. - """ - dtc.boolean = False - LEMS_MODEL_PATH = str(neuronunit.__path__[0]) + \ - str('/models/NeuroML2/LEMS_2007One.xml') - dtc.model_path = LEMS_MODEL_PATH - model = ReducedModel(dtc.model_path, name='vanilla', - backend=(dtc.backend, {'DTC': dtc})) - - if dtc.backend is str('NEURON') or dtc.backend is str('jNEUROML'): - dtc.current_src_name = model._backend.current_src_name - assert dtc.current_src_name is not None - dtc.cell_name = model._backend.cell_name - - if hasattr(model._backend, 'current_src_name'): - dtc.current_src_name = model._backend.current_src_name - assert dtc.current_src_name is not None - dtc.cell_name = model._backend.cell_name - - ampl = float(dtc.ampl) - if ampl not in dtc.lookup or len(dtc.lookup) == 0: - current = RheobaseTest.get_default_injected_square_current() - uc = {'amplitude': ampl*pq.pA} - current.update(uc) - dtc.run_number += 1 - model.inject_square_current(current) - dtc.previous = ampl - n_spikes = model.get_spike_count() - dtc.lookup[float(ampl)] = n_spikes - return dtc - - -def init_dtc(dtc): - """Exploit memory of last model in genes.""" - # check for memory and exploit it. - if dtc.initiated is True: - dtc = check_current(dtc) - if dtc.boolean: - return dtc + def extract_features(self,model): + prediction = self.generate_prediction(model) + return prediction + ''' - else: - # Exploit memory of the genes to inform searchable range. + def bind_score(self, score, model, observation, prediction): + super(RheobaseTestP,self).bind_score(score, model, + observation, prediction) + def compute_score(self, observation, prediction): + """Implementation of sciunit.Test.score_prediction.""" + score = None - # if this model has lineage, assume it didn't mutate that - # far away from it's ancestor. - # using that assumption, on first pass, consult a very - # narrow range, of test current injection samples: - # only slightly displaced away from the ancestors rheobase - # value. - - if isinstance(dtc.current_steps, float): - dtc.current_steps = [0.75 * dtc.current_steps, - 1.25 * dtc.current_steps] - elif isinstance(dtc.current_steps, list): - dtc.current_steps = [s * 1.25 for s - in dtc.current_steps] - # logically unnecessary but included for readibility - dtc.initiated = True - - if dtc.initiated is False: - dtc.boolean = False - steps = np.linspace(1, 250, 7) - steps_current = [i*pq.pA for i in steps] - dtc.current_steps = steps_current - dtc.initiated = True - return dtc - - -def find_rheobase(self, dtc): - assert os.path.isfile(dtc.model_path),\ - "%s is not a file" % dtc.model_path - # If this it not the first pass/ first generation - # then assume the rheobase value found before mutation still holds - # until proven otherwise. - # dtc = check_current(model.rheobase,dtc) - # If its not true enter a search, with ranges informed by memory - cnt = 0 - sub = np.array([0, 0]) - while dtc.boolean is False and cnt < 40: - if len(sub): - if sub.max() > 1500.0: - dtc.rheobase = None - dtc.boolean = False - return dtc - dtc_clones = [copy.copy(dtc) for i - in range(0, len(dtc.current_steps))] - for i, s in enumerate(dtc.current_steps): - dtc_clones[i].ampl = dtc.current_steps[i] - dtc_clones = [d for d in dtc_clones if not np.isnan(d.ampl)] - - b0 = db.from_sequence(dtc_clones, npartitions=N_CPUS) - dtc_clone = list(b0.map(check_current).compute()) - for dtc in dtc_clone: - if dtc.boolean is True: - return dtc - - for d in dtc_clone: - dtc.lookup.update(d.lookup) - dtc = check_fix_range(dtc) - - cnt += 1 - sub, supra = get_sub_supra(dtc.lookup) - if len(supra) and len(sub): - delta = float(supra.min()) - float(sub.max()) - tolerance = self.params['tolerance'].rescale(pq.pA) - if delta < tolerance or (str(supra.min()) == - str(sub.max())): - dtc.rheobase = supra.min()*pq.pA - dtc.boolean = True - return dtc - - if self.verbose >= 2: - print("Try %d: SubMax = %s; SupraMin = %s" % - (cnt, sub.max() if len(sub) else None, - supra.min() if len(supra) else None)) - return dtc + score = super(RheobaseTestP,self).\ + compute_score(observation, prediction) + return score + ''' diff --git a/neuronunit/tests/make_allen_tests.py b/neuronunit/tests/make_allen_tests.py new file mode 100644 index 000000000..78910cc1e --- /dev/null +++ b/neuronunit/tests/make_allen_tests.py @@ -0,0 +1,35 @@ +from neuronunit.tests.base import VmTest +import pickle +import numpy as np +from allensdk.core.cell_types_cache import CellTypesCache + + +class AllenTest(VmTest): + def __init__(self, + observation={'mean': None, 'std': None}, + name='generic_allen', + prediction={'mean': None, 'std': None}, + **params): + super(AllenTest, self).__init__(observation, name, **params) + + name = '' + aliases = '' + + def compute_params(self): + self.params['t_max'] = (self.params['delay'] + + self.params['duration'] + + self.params['padding']) + + + def set_observation(self,observation): + self.observation = {} + self.observation['mean'] = observation + self.observation['std'] = observation + + + def set_prediction(self,prediction): + self.prediction = {} + self.prediction['mean'] = prediction + self.prediction['std'] = prediction +ctc = CellTypesCache(manifest_file='manifest.json') +features = ctc.get_ephys_features() diff --git a/neuronunit/tests/passive.py b/neuronunit/tests/passive.py index 5100d87fd..7b4c90445 100644 --- a/neuronunit/tests/passive.py +++ b/neuronunit/tests/passive.py @@ -6,6 +6,8 @@ DURATION = 500.0*pq.ms DELAY = 200.0*pq.ms +import warnings +warnings.filterwarnings("ignore") class TestPulseTest(VmTest): """A base class for tests that use a square test pulse.""" @@ -34,7 +36,7 @@ def condition_model(self, model): def setup_protocol(self, model): """Implement sciunit.tests.ProtocolToFeatureTest.setup_protocol.""" self.condition_model(model) - model.inject_square_current(self.params['injected_square_current']) + model.inject_square_current(**self.params['injected_square_current']) def get_result(self, model): vm = model.get_membrane_potential() diff --git a/neuronunit/tests/target_spike_current.py b/neuronunit/tests/target_spike_current.py new file mode 100644 index 000000000..27024dcd3 --- /dev/null +++ b/neuronunit/tests/target_spike_current.py @@ -0,0 +1,533 @@ +"""F/I neuronunit tests, e.g. investigating firing rates and patterns as a +function of input current""" + +import os +import multiprocessing +global cpucount +cpucount = multiprocessing.cpu_count() +from .base import np, pq, ncap, VmTest, scores, AMPL +DURATION = 2000 +DELAY = 1000 + +from neuronunit.optimization.data_transport_container import DataTC +import os +import quantities +import neuronunit + +import dask.bag as db +import quantities as pq +import numpy as np +import copy +import pdb +from numba import jit +import time +import numba +import copy + +import matplotlib as mpl +#mpl.use('Agg') +import matplotlib.pyplot as plt +from neuronunit.capabilities.spike_functions import get_spike_waveforms, spikes2amplitudes, threshold_detection +# +# When using differentiation based spike detection is used this is faster. + + +class SpikeCountSearch(VmTest): + """ + A parallel Implementation of a Binary search algorithm, + which finds a rheobase prediction. + + Strengths: this algorithm is faster than the serial class present in this file for model backends that are not able to + implement numba jit optimisation. + Failure to implement JIT happens to be typical of a signifcant number of backends + """ + def _extra(self): + self.verbose = 1 + + + required_capabilities = (ncap.ReceivesSquareCurrent, + ncap.ProducesSpikes) + params = {'injected_square_current': + {'amplitude':100.0*pq.pA, 'delay':DELAY, 'duration':DURATION}} + name = "Rheobase test" + description = ("A test of the rheobase, i.e. the minimum injected current " + "needed to evoke at least one spike.") + units = pq.pA + ephysprop_name = 'Rheobase' + score_type = scores.ZScore + + def generate_prediction(self, model): + def check_fix_range(dtc): + ''' + Inputs: lookup, A dictionary of previous current injection values + used to search rheobase + Outputs: A boolean to indicate if the correct rheobase current was found + and a dictionary containing the range of values used. + If rheobase was actually found then rather returning a boolean and a dictionary, + instead logical True, and the rheobase current is returned. + given a dictionary of rheobase search values, use that + dictionary as input for a subsequent search. + ''' + + steps = [] + dtc.rheobase = {} + dtc.rheobase['value'] = None + sub, supra = get_sub_supra(dtc.lookup) + if (len(sub) + len(supra)) == 0: + # This assertion would only be occur if there was a bug + assert sub.max() <= supra.min() + elif len(sub) and len(supra): + # Termination criterion + + steps = np.linspace(sub.max(),supra.min(),cpucount+1)*pq.pA + steps = steps[1:-1]*pq.pA + elif len(sub): + steps = np.linspace(sub.max(),2*sub.max(),cpucount+1)*pq.pA + steps = steps[1:-1]*pq.pA + elif len(supra): + steps = np.linspace(supra.min()-100,supra.min(),cpucount+1)*pq.pA + steps = steps[1:-1]*pq.pA + + dtc.current_steps = steps + return dtc + + def get_sub_supra(lookup): + sub, supra = [], [] + for current, n_spikes in lookup.items(): + if n_spikes < self.observation['value']: + sub.append(current) + elif n_spikes > self.observation['value']: + supra.append(current) + delta = n_spikes- self.observation['value'] + #print(delta,'difference \n\n\n\nn') + sub = np.array(sorted(list(set(sub)))) + supra = np.array(sorted(list(set(supra)))) + return sub, supra + + def check_current(dtc): + ''' + Inputs are an amplitude to test and a virtual model + output is an virtual model with an updated dictionary. + ''' + dtc.boolean = False + + model = dtc.dtc_to_model() + model._backend.attrs = dtc.attrs + params = {'injected_square_current': + {'amplitude':None, 'delay':DELAY, 'duration':DURATION}} + + ampl = float(dtc.ampl) + if ampl not in dtc.lookup or len(dtc.lookup) == 0: + uc = {'amplitude':ampl*pq.pA,'duration':DURATION,'delay':DELAY} + + if str("JIT_") in model.backend: + _ = model.inject_square_current(**uc) + n_spikes = model.get_spike_count() + #_ = model._backend.inject_square_current(**uc) + #n_spikes_b = model._backend.get_spike_count() + #assert n_spikes == n_spikes_b + if float(ampl) < -10.0: + dtc.rheobase = {} + dtc.rheobase['value'] = None + dtc.boolean = True + return dtc + + target_spk_count = self.observation['value'] + if n_spikes == target_spk_count: + + dtc.lookup[float(ampl)] = n_spikes + dtc.rheobase = {} + dtc.rheobase['value'] = float(ampl)*pq.pA + dtc.target_spk_count = None + dtc.target_spk_count = dtc.rheobase['value'] + dtc.boolean = True + if self.verbose >2: + print('gets here',n_spikes,target_spk_count,n_spikes == target_spk_count) + return dtc + + dtc.lookup[float(ampl)] = n_spikes + return dtc + + def init_dtc(dtc): + ''' + Exploit memory of last model in genes. + ''' + if dtc.initiated == True: + + dtc = check_current(dtc) + if dtc.boolean: + + return dtc + + else: + + if type(dtc.current_steps) is type(float): + dtc.current_steps = [ 0.75 * dtc.current_steps, 1.25 * dtc.current_steps ] + elif type(dtc.current_steps) is type(list): + dtc.current_steps = [ s * 1.25 for s in dtc.current_steps ] + dtc.initiated = True # logically unnecessary but included for readibility + if dtc.initiated == False: + dtc.boolean = False + #steps = np.linspace(-12,67,int(8)) + ### + # These values are important for who knows what reason + steps = np.linspace(-10,65,int(7)) + ### + steps_current = [ i*pq.pA for i in steps ] + dtc.current_steps = steps_current + dtc.initiated = True + return dtc + + def find_target_current(self, dtc): + # This line should not be necessary: + # a class, VeryReducedModel has been made to circumvent this. + #if hasattr(dtc,'model_path'): + # assert os.path.isfile(dtc.model_path), "%s is not a file" % dtc.model_path + # If this it not the first pass/ first generation + # then assume the rheobase value found before mutation still holds until proven otherwise. + # dtc = check_current(model.rheobase,dtc) + # If its not true enter a search, with ranges informed by memory + cnt = 0 + sub = np.array([0,0]); supra = np.array([0,0]) + ## + # + ## Important number + #big = 100 + big = 250 + while dtc.boolean == False and cnt< big: + + # negeative spiker + if len(sub): + if sub.max() < -1.0: + pass + dtc_clones = [ dtc for i in range(0,len(dtc.current_steps)) ] + for i,s in enumerate(dtc.current_steps): + dtc_clones[i] = copy.copy(dtc_clones[i]) + dtc_clones[i].ampl = copy.copy(dtc.current_steps[i]) + dtc_clones[i].backend = copy.copy(dtc.backend) + + dtc_clones = [d for d in dtc_clones if not np.isnan(d.ampl)] + set_clones = set([float(d.ampl) for d in dtc_clones ]) + dtc_clone = [] + for dtc,sc in zip(dtc_clones,set_clones): + dtc = copy.copy(dtc) + dtc.ampl = sc*pq.pA + dtc = check_current(dtc) + dtc_clone.append(dtc) + + + for dtc in dtc_clone: + if dtc.boolean == True: + return dtc + + for d in dtc_clone: + dtc.lookup.update(d.lookup) + dtc = check_fix_range(dtc) + + + sub, supra = get_sub_supra(dtc.lookup) + if len(supra) and len(sub): + delta = float(supra.min()) - float(sub.max()) + #tolerance = 0.0 + + if self.verbose >= 2: + print('not rheobase alg') + print("Try %d: SubMax = %s; SupraMin = %s" % \ + (cnt, sub.max() if len(sub) else None, + supra.min() if len(supra) else None)) + cnt += 1 + reversed = {v:k for k,v in dtc.lookup.items() } + if cnt==big: + if self.verbose >= 2: + print('counted out and thus wrong spike count') + return dtc + + dtc = DataTC() + dtc.attrs = {} + for k,v in model.attrs.items(): + dtc.attrs[k] = v + + # this is not a perservering assignment, of value, + # but rather a multi statement assertion that will be checked. + dtc.backend = model.backend + dtc = init_dtc(dtc) + prediction = {} + temp = find_target_current(self,dtc).rheobase + + if type(temp) is not type(None) and not type(dict): + prediction['value'] = float(temp)* pq.pA + elif type(temp) is not type(None) and type(dict): + if temp['value'] is not None: + prediction['value'] = float(temp['value'])* pq.pA + else: + prediction = None + else: + prediction = None + #print(prediction,'prediction') + return prediction + +class SpikeCountRangeSearch(VmTest): + """ + A parallel Implementation of a Binary search algorithm, + which finds a rheobase prediction. + + Strengths: this algorithm is faster than the serial class, present in this file for model backends that are not able to + implement numba jit optimisation, which actually happens to be typical of a signifcant number of backends + + Weaknesses this serial class is significantly slower, for many backend implementations including raw NEURON + NEURON via PyNN, and possibly GLIF. + + """ + def _extra(self): + self.verbose = 1 + + # def __init__(self,other_current=None): + # self.other_current = other_current + + + required_capabilities = (ncap.ReceivesSquareCurrent, + ncap.ProducesSpikes) + #DELAY = 100.0*pq.ms + # DURATION = 1000.0*pq.ms + params = {'injected_square_current': + {'amplitude':100.0*pq.pA, 'delay':DELAY, 'duration':DURATION}} + name = "Rheobase test" + description = ("A test of the rheobase, i.e. the minimum injected current " + "needed to evoke at least one spike.") + units = pq.pA + #tolerance # Rheobase search tolerance in `self.units`. + ephysprop_name = 'Rheobase' + score_type = scores.ZScore + + def generate_prediction(self, model): + def check_fix_range(dtc): + ''' + Inputs: lookup, A dictionary of previous current injection values + used to search rheobase + Outputs: A boolean to indicate if the correct rheobase current was found + and a dictionary containing the range of values used. + If rheobase was actually found then rather returning a boolean and a dictionary, + instead logical True, and the rheobase current is returned. + given a dictionary of rheobase search values, use that + dictionary as input for a subsequent search. + ''' + + steps = [] + dtc.rheobase = None + sub, supra = get_sub_supra(dtc.lookup) + + if (len(sub) + len(supra)) == 0: + # This assertion would only be occur if there was a bug + assert sub.max() <= supra.min() + elif len(sub) and len(supra): + # Termination criterion + + steps = np.linspace(sub.max(),supra.min(),cpucount+1)*pq.pA + steps = steps[1:-1]*pq.pA + elif len(sub): + steps = np.linspace(sub.max(),2*sub.max(),cpucount+1)*pq.pA + steps = steps[1:-1]*pq.pA + elif len(supra): + steps = np.linspace(supra.min()-100,supra.min(),cpucount+1)*pq.pA + steps = steps[1:-1]*pq.pA + + dtc.current_steps = steps + return dtc + + def get_sub_supra(lookup): + sub, supra = [], [] + for current, n_spikes in lookup.items(): + if n_spikes < self.observation['range'][0]: + sub.append(current) + elif n_spikes > self.observation['range'][1]: + supra.append(current) + + sub = np.array(sorted(list(set(sub)))) + supra = np.array(sorted(list(set(supra)))) + return sub, supra + + ''' + def check_current(dtc): + + #Inputs are an amplitude to test and a virtual model + #output is an virtual model with an updated dictionary. + + dtc.boolean = False + + model = dtc.dtc_to_model() + + params = {'injected_square_current': + {'amplitude':100.0*pq.pA, 'delay':DELAY, 'duration':DURATION}} + + ampl = float(dtc.ampl) + if ampl not in dtc.lookup or len(dtc.lookup) == 0: + uc = {'amplitude':ampl*pq.pA,'duration':DURATION,'delay':DELAY} + + dtc.run_number += 1 + model.set_attrs(dtc.attrs) + model.inject_square_current(uc) + n_spikes = model.get_spike_count() + + if float(ampl) < -1.0: + dtc.rheobase = None + dtc.boolean = True + return dtc + + #target_spk_count = self.observation['range'] + if self.observation['range'][0] <= n_spikes <= self.observation['range'][1]: + dtc.lookup[float(ampl)] = n_spikes + dtc.rheobase = {} + dtc.rheobase['value'] = float(ampl)*pq.pA + dtc.target_spk_count = None + dtc.target_spk_count = dtc.rheobase['value'] + dtc.boolean = True + if self.verbose >2: + print('gets here',n_spikes,target_spk_count,n_spikes == target_spk_count) + return dtc + + dtc.lookup[float(ampl)] = n_spikes + return dtc + ''' + def init_dtc(dtc): + ''' + Exploit memory of last model in genes. + ''' + # check for memory and exploit it. + if dtc.initiated == True: + + dtc = check_current(dtc) + if dtc.boolean: + + return dtc + + else: + # Exploit memory of the genes to inform searchable range. + + # if this model has lineage, assume it didn't mutate that far away from it's ancestor. + # using that assumption, on first pass, consult a very narrow range, of test current injection samples: + # only slightly displaced away from the ancestors rheobase value. + + + if type(dtc.current_steps) is type(float): + dtc.current_steps = [ 0.75 * dtc.current_steps, 1.25 * dtc.current_steps ] + elif type(dtc.current_steps) is type(list): + dtc.current_steps = [ s * 1.25 for s in dtc.current_steps ] + dtc.initiated = True # logically unnecessary but included for readibility + if dtc.initiated == False: + + dtc.boolean = False + + + steps = np.linspace(0,85.0,int(8)) + + steps_current = [ i*pq.pA for i in steps ] + dtc.current_steps = steps_current + dtc.initiated = True + return dtc + + def find_target_current(self, dtc): + cnt = 0 + sub = np.array([0,0]); supra = np.array([0,0]) + #if dtc.backend is 'GLIF': + # big = 100 + #else: + big = 255 + + while dtc.boolean == False and cnt< big: + + # negeative spiker + if len(sub): + if sub.max() < -1.0: + pass + #use_diff = True # differentiate the wave to look for spikes + + + #be = dtc.backend + dtc_clones = [ dtc for i in range(0,len(dtc.current_steps)) ] + for i,s in enumerate(dtc.current_steps): + dtc_clones[i] = copy.copy(dtc_clones[i]) + dtc_clones[i].ampl = copy.copy(dtc.current_steps[i]) + dtc_clones[i].backend = copy.copy(dtc.backend) + + dtc_clones = [d for d in dtc_clones if not np.isnan(d.ampl)] + #try: + # b0 = db.from_sequence(dtc_clones, npartitions=npartitions) + # dtc_clone = list(b0.map(check_current).compute()) + #except: + set_clones = set([ float(d.ampl) for d in dtc_clones ]) + dtc_clone = [] + for dtc,sc in zip(dtc_clones,set_clones): + dtc = copy.copy(dtc) + dtc.ampl = sc*pq.pA + dtc = check_current(dtc) + dtc_clone.append(dtc) + + + for dtc in dtc_clone: + if dtc.boolean == True: + return dtc + + for d in dtc_clone: + dtc.lookup.update(d.lookup) + dtc = check_fix_range(dtc) + + + sub, supra = get_sub_supra(dtc.lookup) + if len(supra) and len(sub): + delta = float(supra.min()) - float(sub.max()) + #if str("GLIF") in dtc.backend: + # tolerance = 0.0 + #else: + #tolerance = 0.0 + #tolerance = tolerance + if (str(supra.min()) == str(sub.max())): + if self.verbose >= 2: + print(delta, 'a neuron, close to the edge! Multi spiking rheobase. # spikes: ',len(supra)) + if len(supra)<100: + dtc.rheobase['value'] = float(supra.min()) + dtc.boolean = True + dtc.lookup[float(supra.min())] = len(supra) + else: + if type(dtc.rheobase) is type(None): + dtc.rheobase = {} + dtc.rheobase['value'] = None + dtc.boolean = False + dtc.lookup[float(supra.min())] = len(supra) + + return dtc + + if self.verbose >= 2: + print('not rheobase alg') + print("Try %d: SubMax = %s; SupraMin = %s" % \ + (cnt, sub.max() if len(sub) else None, + supra.min() if len(supra) else None)) + cnt += 1 + return dtc + + dtc = DataTC() + dtc.attrs = {} + for k,v in model.attrs.items(): + dtc.attrs[k] = v + + # this is not a perservering assignment, of value, + # but rather a multi statement assertion that will be checked. + dtc.backend = model.backend + dtc = init_dtc(dtc) + + prediction = {} + + temp = find_target_current(self,dtc).rheobase + if type(temp) is not type(None): + if type(temp) is not type(dict()): + prediction['value'] = float(temp)* pq.pA + elif type(temp) is type(dict()): + if type(temp['value']) is not type(None): + prediction['value'] = float(temp['value'])* pq.pA + else: + prediction['value'] = None + elif type(temp) is type(None): + prediction['value'] = None# float(temp['value'])* pq.pA + + else: + prediction = None + return prediction diff --git a/neuronunit/tests/waveform.py b/neuronunit/tests/waveform.py index 79cbc8df6..e48a95c4c 100644 --- a/neuronunit/tests/waveform.py +++ b/neuronunit/tests/waveform.py @@ -7,7 +7,7 @@ class InjectedCurrent: """Metaclass to mixin with InjectedCurrent tests.""" required_capabilities = (ncap.ReceivesSquareCurrent,) - + default_params = dict(VmTest.default_params) default_params.update({'amplitude': 100*pq.pA}) @@ -40,7 +40,7 @@ def generate_prediction(self, model): # ProducesActionPotentials capability. # if get_spike_count is zero, then widths will be None # len of None returns an exception that is not handled - model.inject_square_current(self.params['injected_square_current']) + model.inject_square_current(**self.params['injected_square_current']) widths = model.get_AP_widths() # Put prediction in a form that compute_score() can use. @@ -78,7 +78,8 @@ class InjectedCurrentAPWidthTest(InjectedCurrent, APWidthTest): "is injected into cell.") def generate_prediction(self, model): - model.inject_square_current(self.params['injected_square_current']) + print(self.params['injected_square_current']) + model.inject_square_current(**self.params['injected_square_current']) prediction = super(InjectedCurrentAPWidthTest, self).\ generate_prediction(model) @@ -105,7 +106,7 @@ def generate_prediction(self, model): """Implement sciunit.Test.generate_prediction.""" # Method implementation guaranteed by # ProducesActionPotentials capability. - model.inject_square_current(self.params['injected_square_current']) + model.inject_square_current(**self.params['injected_square_current']) heights = model.get_AP_amplitudes() - model.get_AP_thresholds() # Put prediction in a form that compute_score() can use. @@ -137,7 +138,7 @@ class InjectedCurrentAPAmplitudeTest(InjectedCurrent, APAmplitudeTest): "is injected into cell.") def generate_prediction(self, model): - model.inject_square_current(self.params['injected_square_current']) + model.inject_square_current(**self.params['injected_square_current']) prediction = super(InjectedCurrentAPAmplitudeTest, self).\ generate_prediction(model) return prediction @@ -163,7 +164,7 @@ def generate_prediction(self, model): """Implement sciunit.Test.generate_prediction.""" # Method implementation guaranteed by # ProducesActionPotentials capability. - model.inject_square_current(self.params['injected_square_current']) + model.inject_square_current(**self.params['injected_square_current']) threshes = model.get_AP_thresholds() # Put prediction in a form that compute_score() can use. diff --git a/neuronunit/unit_test/__init__.py b/neuronunit/unit_test/__init__.py index c26398f79..b9d5841b1 100755 --- a/neuronunit/unit_test/__init__.py +++ b/neuronunit/unit_test/__init__.py @@ -4,29 +4,57 @@ import matplotlib as mpl -mpl.use('Agg') # Needed for headless testing -warnings.filterwarnings('ignore') # Suppress all warning messages +mpl.use("Agg") # Needed for headless testing +warnings.filterwarnings("ignore") # Suppress all warning messages from .base import * from .import_tests import ImportTestCase from .doc_tests import DocumentationTestCase -from .resource_tests import NeuroElectroTestCase, BlueBrainTestCase,\ - AIBSTestCase -from .model_tests import ReducedModelTestCase, ExtraCapabilitiesTestCase, HasSegmentTestCase, \ - GeppettoBackendTestCase, VeryReducedModelTestCase, StaticExternalTestCase -from .observation_tests import ObservationsTestCase -from .test_tests import TestsPassiveTestCase, TestsWaveformTestCase,\ - TestsFITestCase, TestsDynamicsTestCase,\ - TestsChannelTestCase, FakeTestCase +from .resource_tests import NeuroElectroTestCase, BlueBrainTestCase, AIBSTestCase +from .model_tests import ( + ReducedModelTestCase, + ExtraCapabilitiesTestCase, + HasSegmentTestCase, + GeppettoBackendTestCase, + VeryReducedModelTestCase, + StaticExternalTestCase, +) +#from .observation_tests import ObservationsTestCase +''' +from .test_tests import ( + TestsPassiveTestCase, + TestsWaveformTestCase, + TestsFITestCase, + TestsDynamicsTestCase, + TestsChannelTestCase, + FakeTestCase, +) +''' from .misc_tests import EphysPropertiesTestCase -from .sciunit_tests import SciUnitTestCase -from .cache_tests import BackendCacheTestCase +#from .cache_tests import BackendCacheTestCase +from .opt_ephys_properties import testOptimizationEphysCase +from .scores_unit_test import testOptimizationAllenMultiSpike +#from .adexp_opt import * +''' +from .capabilities_tests import * -from .test_druckmann2013 import Model1TestCase, Model2TestCase, \ - Model3TestCase, Model4TestCase, Model5TestCase, \ - Model6TestCase, Model7TestCase, Model8TestCase, Model9TestCase, \ - Model10TestCase, Model11TestCase, OthersTestCase +from .test_druckmann2013 import ( + Model1TestCase, + Model2TestCase, + Model3TestCase, + Model4TestCase, + Model5TestCase, + Model6TestCase, + Model7TestCase, + Model8TestCase, + Model9TestCase, + Model10TestCase, + Model11TestCase, + OthersTestCase, +) +''' +#from .test_morphology import MorphologyTestCase +#from .test_optimization import DataTCTestCase +#from .sciunit_tests import SciUnitTestCase -from .test_morphology import MorphologyTestCase -from .test_optimization import DataTCTestCase -from .capabilities_tests import * \ No newline at end of file +#from .adexp_opt import * diff --git a/neuronunit/unit_test/__main__.py b/neuronunit/unit_test/__main__.py index 4ca3b97ad..85a90fb15 100644 --- a/neuronunit/unit_test/__main__.py +++ b/neuronunit/unit_test/__main__.py @@ -2,13 +2,14 @@ import sys import unittest -from . import * # Import all the tests from the unit_test module +from . import * # Import all the tests from the unit_test module + def main(): - buffer = 'buffer' in sys.argv - sys.argv = sys.argv[:1] # Args need to be removed for __main__ to work. + buffer = "buffer" in sys.argv + sys.argv = sys.argv[:1] # Args need to be removed for __main__ to work. unittest.main(buffer=buffer) -if __name__ == '__main__': + +if __name__ == "__main__": main() - \ No newline at end of file diff --git a/neuronunit/unit_test/adexp_opt.py b/neuronunit/unit_test/adexp_opt.py new file mode 100644 index 000000000..911facbb6 --- /dev/null +++ b/neuronunit/unit_test/adexp_opt.py @@ -0,0 +1,105 @@ +#!/usr/bin/env python +# coding: utf-8 +SILENT = True +import warnings +if SILENT: + warnings.filterwarnings("ignore") + +import unittest +import numpy as np +import efel +import matplotlib.pyplot as plt +import quantities as qt + +import matplotlib + +matplotlib.use("Agg") +from neuronunit.allenapi.allen_data_efel_features_opt import ( + opt_to_model, + opt_setup, + opt_exec, +) +from neuronunit.allenapi.allen_data_efel_features_opt import opt_to_model +from neuronunit.allenapi.utils import dask_map_function + +from neuronunit.optimization.model_parameters import ( + MODEL_PARAMS, + BPO_PARAMS, + to_bpo_param, +) +from neuronunit.optimization.optimization_management import inject_model_soma +from neuronunit.optimization.data_transport_container import DataTC +from jithub.models import model_classes + +from sciunit.scores import RelativeDifferenceScore + +class testOptimization(unittest.TestCase): + def setUp(self): + self.ids = [ 324257146, + 325479788, + 476053392, + 623893177, + 623960880, + 482493761, + 471819401 + ] + + def test_opt_1(self): + specimen_id = self.ids[1] + cellmodel = "ADEXP" + + if cellmodel == "IZHI": + model = model_classes.IzhiModel() + if cellmodel == "MAT": + model = model_classes.MATModel() + if cellmodel == "ADEXP": + model = model_classes.ADEXPModel() + + + target_num_spikes = 9 + + efel_filter_iterable = [ + "ISI_log_slope", + "mean_frequency", + "adaptation_index2", + "first_isi", + "ISI_CV", + "median_isi", + "Spikecount", + "all_ISI_values", + "ISI_values", + "time_to_first_spike", + "time_to_last_spike", + "time_to_second_spike", + ] + [ suite, + target_current, + spk_count, + cell_evaluator, + simple_cell ] = opt_setup(specimen_id, + cellmodel, + target_num_spikes, + template_model=model, + fixed_current=False, + cached=False, + score_type=RelativeDifferenceScore, + efel_filter_iterable=efel_filter_iterable) + + NGEN = 55 + MU = 35 + + mapping_funct = dask_map_function + final_pop, hall_of_fame, logs, hist = opt_exec( + MU, NGEN, mapping_funct, cell_evaluator, cxpb=0.4, mutpb=0.01 + ) + opt, target, scores, obs_preds, df = opt_to_model( + hall_of_fame, cell_evaluator, suite, target_current, spk_count + ) + best_ind = hall_of_fame[0] + fitnesses = cell_evaluator.evaluate_with_lists(best_ind) + assert np.sum(fitnesses) < 10.7 + self.assertGreater(10.7, np.sum(fitnesses)) + + +if __name__ == "__main__": + unittest.main() diff --git a/neuronunit/unit_test/adexp_opt_relative_difference_score.py b/neuronunit/unit_test/adexp_opt_relative_difference_score.py new file mode 100644 index 000000000..baf2bc2a8 --- /dev/null +++ b/neuronunit/unit_test/adexp_opt_relative_difference_score.py @@ -0,0 +1,136 @@ +import unittest +import matplotlib + +matplotlib.use("Agg") +from neuronunit.allenapi.allen_data_driven import opt_setup, opt_setup_two, opt_exec +from neuronunit.allenapi.allen_data_driven import opt_to_model, meta_setup +from neuronunit.allenapi.utils import dask_map_function + +# except: +# from bluepyopt.allenapi.allen_data_driven import opt_setup, opt_setup_two, opt_exec, opt_to_model +# from bluepyopt.allenapi.allen_data_driven import opt_to_model +# from bluepyopt.allenapi.utils import dask_map_function + +from neuronunit.optimization.optimization_management import check_bin_vm15 +from neuronunit.optimization.model_parameters import ( + MODEL_PARAMS, + BPO_PARAMS, + to_bpo_param, +) +from neuronunit.optimization.optimization_management import ( + dtc_to_rheo, + inject_and_plot_model, +) +import numpy as np +from neuronunit.optimization.data_transport_container import DataTC +import efel +from jithub.models import model_classes +import matplotlib.pyplot as plt +import quantities as qt +import os +from sciunit.scores import RelativeDifferenceScore, ZScore + + +class testOptimization(unittest.TestCase): + def setUp(self): + self = self + self.ids = [ + 324257146, + 325479788, + 476053392, + 623893177, + 623960880, + 482493761, + 471819401, + ] + + def test_opt_relative_diff(self): + specimen_id = self.ids[1] + model_type = "ADEXP" + + if model_type == "IZHI": + model = model_classes.IzhiModel() + if model_type == "MAT": + model = model_classes.MATModel() + if model_type == "ADEXP": + model = model_classes.ADEXPModel() + + target_num_spikes = 8 # This is the number of spikes to look for in the data + + fixed_current = 122 * qt.pA + NGEN = 15 + MU = 12 + mapping_funct = dask_map_function + cell_evaluator, simple_cell, suite, target_current, spk_count = meta_setup( + specimen_id, + model_type, + target_num_spikes, + template_model=model, + fixed_current=False, + cached=True, + score_type=RelativeDifferenceScore, + ) + final_pop, hall_of_fame, logs, hist = opt_exec( + MU, NGEN, mapping_funct, cell_evaluator + ) + opt, target = opt_to_model( + hall_of_fame, cell_evaluator, suite, target_current, spk_count + ) + best_ind = hall_of_fame[0] + fitnesses = cell_evaluator.evaluate_with_lists(best_ind) + self.assertGreater(0.7, np.sum(fitnesses)) + + def test_opt_ZScore(self): + specimen_id = self.ids[1] + model_type = "ADEXP" + + if model_type == "IZHI": + model = model_classes.IzhiModel() + if model_type == "MAT": + model = model_classes.MATModel() + if model_type == "ADEXP": + model = model_classes.ADEXPModel() + + target_num_spikes = 8 + dtc = DataTC() + dtc.backend = model_type + dtc._backend = model._backend + dtc.attrs = model.attrs + dtc.params = {k: np.mean(v) for k, v in MODEL_PARAMS[model_type].items()} + + fixed_current = 122 * qt.pA + NGEN = 15 + MU = 12 + mapping_funct = dask_map_function + cell_evaluator, simple_cell, suite, target_current, spk_count = meta_setup( + specimen_id, + model_type, + target_num_spikes, + template_model=model, + fixed_current=False, + cached=True, + score_type=ZScore, + ) + final_pop, hall_of_fame, logs, hist = opt_exec( + MU, NGEN, mapping_funct, cell_evaluator + ) + opt, target = opt_to_model( + hall_of_fame, cell_evaluator, suite, target_current, spk_count + ) + best_ind = hall_of_fame[0] + fitnesses = cell_evaluator.evaluate_with_lists(best_ind) + self.assertGreater(0.7, np.sum(fitnesses)) + + +# if __name__ == '__main__': +# unittest.main() + +tt = testOptimization() +tt.setUp() + +# + + +tt.test_opt_relative_diff() +# - + +tt.test_opt_ZScore() diff --git a/neuronunit/unit_test/agreement_df.ipynb b/neuronunit/unit_test/agreement_df.ipynb deleted file mode 100644 index 9e54acc5a..000000000 --- a/neuronunit/unit_test/agreement_df.ipynb +++ /dev/null @@ -1,9724 +0,0 @@ -{ - "cells": [ - { - "cell_type": "code", - "execution_count": 1, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Running on Python version: 3.6.4 |Anaconda, Inc.| (default, Jan 16 2018, 18:10:19) \n", - "[GCC 7.2.0]\n" - ] - } - ], - "source": [ - "import sys \n", - "print('Running on Python version: {}'.format(sys.version))\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - }, - { - "cell_type": "code", - "execution_count": 2, - "metadata": {}, - "outputs": [], - "source": [ - "import seaborn as sns\n", - "import os\n", - "%matplotlib inline\n" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Collecting prettyplotlib\n", - "\u001b[?25l Downloading 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"\u001b[?25h Stored in directory: /home/jovyan/.cache/pip/wheels/76/ad/45/9fcfb9e97ecccc850d8b2fb20b8d60992bc6d7c8d9769ee989\n", - "Successfully built prettyplotlib\n", - "Installing collected packages: brewer2mpl, prettyplotlib\n", - "Successfully installed brewer2mpl-1.4.1 prettyplotlib-0.1.7\n", - "\u001b[33mYou are using pip version 10.0.1, however version 18.0 is available.\n", - "You should consider upgrading via the 'pip install --upgrade pip' command.\u001b[0m\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/opt/conda/lib/python3.6/site-packages/sklearn/cross_validation.py:41: DeprecationWarning: This module was deprecated in version 0.18 in favor of the model_selection module into which all the refactored classes and functions are moved. Also note that the interface of the new CV iterators are different from that of this module. This module will be removed in 0.20.\n", - " \"This module will be removed in 0.20.\", DeprecationWarning)\n", - "/opt/conda/lib/python3.6/site-packages/sklearn/grid_search.py:42: DeprecationWarning: This module was deprecated in version 0.18 in favor of the model_selection module into which all the refactored classes and functions are moved. This module will be removed in 0.20.\n", - " DeprecationWarning)\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "dict_items([('a', 0.27328841763197198), ('b', -2.5511245505753264e-09), ('vr', -71.213934167873902)])\n" - ] - } - ], - "source": [ - "import pickle\n", - "try:\n", - " import prettyplotlib as ppl\n", - " from prettyplotlib import plt\n", - " from prettyplotlib import brewer2mpl\n", - "except:\n", - " try:\n", - " !pip install prettyplotlib\n", - " import prettyplotlib as ppl\n", - " from prettyplotlib import plt\n", - " from prettyplotlib import brewer2mpl\n", - " \n", - " green_purple = brewer2mpl.get_map('PRGn', 'diverging', 11).mpl_colormap\n", - "\n", - " except:\n", - " import matplotlib.plot as plt\n", - "import numpy as np\n", - "import string\n", - "\n", - "\n", - "\n", - "import matplotlib.pyplot as plt\n", - "import seaborn as sns\n", - "import pandas as pd\n", - "import numpy as np\n", - "import math as math\n", - "from pylab import rcParams\n", - "from neuronunit.optimization.results_analysis import make_report, min_max\n", - "with open('pre_grid_reports.p','rb') as f:#\n", - " grid_results = pickle.load(f)\n", - "\n", - "with open('pre_ga_reports.p','rb') as f:\n", - " package = pickle.load(f)\n", - "pop = package[0]\n", - "print(pop[0].dtc.attrs.items())\n", - "history = package[4]\n", - "gen_vs_pop = package[6]\n" - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "['a', 'b', 'vr']\n" - ] - }, - { - "data": { - "text/html": [ - "\n", - " \n", - "\n", - " \n", - " \n", - "\n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - 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\n", - " " - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "attrs_ = [ list(p.dtc.attrs.keys()) for i,p in history.genealogy_history.items() ]\n", - "attrs = attrs_[0]\n", - "print(attrs)\n", - "\n", - "scores_ = [ list(p.dtc.scores.keys()) for i,p in history.genealogy_history.items() ]\n", - "scores = scores_[0]\n", - "from collections import OrderedDict\n", - "\n", - "urlDats = []\n", - "hi = [ (i,p) for i,p in history.genealogy_history.items() ]\n", - "pops_ = [ (i,p) for i,p in enumerate(pop) ]\n", - "\n", - "sc = [ (i,p) for i,p in enumerate(grid_results) ]\n", - "\n", - "import quantities as pq\n", - "\n", - "def history_iter(mapped):\n", - " i,p = mapped\n", - " gene_contents = OrderedDict()\n", - " #gene_contents['gene_number'] = i\n", - " \n", - " attrs = list(p.dtc.attrs.keys()) \n", - " scores = list(p.dtc.scores.keys()) \n", - " for a in attrs:\n", - " gene_contents[a] = p.dtc.attrs[a] \n", - " scores0 = scores[0]\n", - " for s in scores:\n", - " gene_contents[s] = p.dtc.scores[s]\n", - " gene_contents[str('total')] = sum(p.dtc.scores.values())\n", - " for test in p.dtc.score.keys():\n", - " if 'agreement' in p.dtc.score[test]:\n", - " gene_contents['disagreement'] = p.dtc.score[test]['agreement']\n", - " if 'observation' in p.dtc.score[test].keys() and 'prediction' in p.dtc.score[test].keys():\n", - " boolean_means = bool('mean' in p.dtc.score[test]['observation'].keys() and 'mean' in p.dtc.score[test]['prediction'].keys())\n", - " boolean_value = bool('value' in p.dtc.score[test]['observation'].keys() and 'value' in p.dtc.score[test]['prediction'].keys())\n", - "\n", - " if boolean_means:\n", - " gene_contents['disagreement'] = np.abs(p.dtc.score[test]['observation']['mean'] - p.dtc.score[test]['prediction']['mean'])\n", - " if boolean_value:\n", - " gene_contents['disagreement'] = np.abs(p.dtc.score[test]['observation']['value'] - p.dtc.score[test]['prediction']['value'])\n", - " print(gene_contents['disagreement'])\n", - " \n", - " return gene_contents\n", - "\n", - "\n", - " \n", - "def process_dics(contents):\n", - " dfs = []\n", - " for gene_contents in contents:\n", - " # pandas Data frames are best data container for maths/stats, but steep learning curve.\n", - " # Other exclusion criteria. Exclude reading levels above grade 100,\n", - " # as this is most likely a problem with the metric algorithm, and or rubbish data in.\n", - "\n", - " if len(dfs) == 0:\n", - " dfs = pd.DataFrame(pd.Series(gene_contents)).T\n", - " dfs = pd.concat([ dfs, pd.DataFrame(pd.Series(gene_contents)).T ])\n", - " return dfs\n", - "\n", - "genes = list(map(history_iter,hi)) \n", - "dfg = process_dics(genes)\n", - "\n", - "grids = list(map(history_iter,sc)) \n", - "dfs = process_dics(grids)\n", - "\n", - "dfg = dfg.reset_index(drop=True)\n", - "\n", - "# Set colormap equal to seaborns light green color palette\n", - "cm = sns.light_palette(\"green\", as_cmap=True)\n", - "display(dfg.style.background_gradient(cmap=cm,subset=['total']))\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.6.4" - } - }, - "nbformat": 4, - "nbformat_minor": 2 -} diff --git a/neuronunit/unit_test/base.py b/neuronunit/unit_test/base.py index 39990f318..5e92a8432 100644 --- a/neuronunit/unit_test/base.py +++ b/neuronunit/unit_test/base.py @@ -9,14 +9,14 @@ import matplotlib as mpl -OSX = sys.platform == 'darwin' -if OSX or 'Qt' in mpl.rcParams['backend']: - mpl.use('Agg') # Avoid any problems with Macs or headless displays. +OSX = sys.platform == "darwin" +if OSX or "Qt" in mpl.rcParams["backend"]: + mpl.use("Agg") # Avoid any problems with Macs or headless displays. from sciunit.utils import NotebookTools, import_all_modules import neuronunit from neuronunit.models import ReducedModel from neuronunit import neuroelectro, bbp, aibs, tests as nu_tests -NU_BACKEND = os.environ.get('NU_BACKEND', 'jNeuroML') +NU_BACKEND = os.environ.get("NU_BACKEND", "jNeuroML") NU_HOME = neuronunit.__path__[0] diff --git a/neuronunit/unit_test/begginer_friendly.ipynb b/neuronunit/unit_test/begginer_friendly.ipynb deleted file mode 100644 index 2d2d1a151..000000000 --- a/neuronunit/unit_test/begginer_friendly.ipynb +++ /dev/null @@ -1,114 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# The Izhiketich model is instanced using some well researched parameter sets." - ] - }, - { - "cell_type": "code", - "execution_count": 1, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Collecting pip\n", - " Downloading https://files.pythonhosted.org/packages/c2/d7/90f34cb0d83a6c5631cf71dfe64cc1054598c843a92b400e55675cc2ac37/pip-18.1-py2.py3-none-any.whl (1.3MB)\n", - "\u001b[K 100% |████████████████████████████████| 1.3MB 937kB/s ta 0:00:01\n", - "\u001b[?25hInstalling collected packages: pip\n", - " Found existing installation: pip 9.0.1\n", - " Uninstalling pip-9.0.1:\n", - " Successfully uninstalled pip-9.0.1\n", - "Successfully installed pip-18.1\n", - "Requirement already up-to-date: pandas in /opt/conda/lib/python3.5/site-packages (0.23.4)\n", - "Requirement already satisfied, skipping upgrade: python-dateutil>=2.5.0 in /opt/conda/lib/python3.5/site-packages (from pandas) (2.7.5)\n", - 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"\u001b[?25hRequirement already satisfied: pytz>=2011k in /opt/conda/lib/python3.5/site-packages (from pandas==0.22) (2018.7)\n", - "Requirement already satisfied: numpy>=1.9.0 in /opt/conda/lib/python3.5/site-packages (from pandas==0.22) (1.15.4)\n", - "Requirement already satisfied: python-dateutil>=2 in /opt/conda/lib/python3.5/site-packages (from pandas==0.22) (2.7.5)\n", - "Requirement already satisfied: six>=1.5 in /opt/conda/lib/python3.5/site-packages (from python-dateutil>=2->pandas==0.22) (1.12.0)\n", - "Installing collected packages: pandas\n", - " Found existing installation: pandas 0.23.4\n", - " Uninstalling pandas-0.23.4:\n", - "\u001b[31mCould not install packages due to an EnvironmentError: [Errno 13] Permission denied: '/opt/conda/lib/python3.5/site-packages/pandas-0.23.4.dist-info/INSTALLER'\n", - "Consider using the `--user` option or check the permissions.\n", - "\u001b[0m\n" - ] - } - ], - "source": [ - "!pip install pip --upgrade\n", - "!pip install pandas --upgrade\n", - "!pip install pandas==0.22" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "electro_tests = []\n", - "obs_frame = {}\n", - "test_frame = {}\n", - "import os\n", - "import pickle\n", - "electro_path = str(os.getcwd())+'/pipe_tests.p'\n", - "with open(electro_path,'rb') as f:\n", - " contents = pickle.load(f)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": true - }, - "outputs": [], - "source": [] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": true - }, - "outputs": [], - "source": [] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.5.2" - } - }, - "nbformat": 4, - "nbformat_minor": 2 -} diff --git a/neuronunit/unit_test/begginer_friendly_intro.ipynb b/neuronunit/unit_test/begginer_friendly_intro.ipynb deleted file mode 100644 index 434669890..000000000 --- a/neuronunit/unit_test/begginer_friendly_intro.ipynb +++ /dev/null @@ -1,1395 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Assumptions, the environment for running this notebook was arrived at by building a dedicated docker container. If you docker installed - it is sufficient to perform a docker pull of the container pointed to by the link:\n", - "\n", - "https://cloud.docker.com/repository/registry-1.docker.io/russelljarvis/nuo\n", - "\n", - "You can run use dockerhub to get the appropriate file, and launch this notebook using Kitematic." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": true - }, - "outputs": [], - "source": [ - "# This is code, change cell type to markdown.\n", - "# ![alt text](plan.jpg \"Pub plan\")\n" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Import libraries\n", - "To keep the standard running version of minimal and memory efficient, not all available packages are loaded by default. In the cell below I import a mixture common python modules, and custom developed modules associated with NeuronUnit (NU) development" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "#!pip install dask distributed seaborn\n", - "#!bash after_install.sh\n", - "import numpy as np\n", - "import os\n", - "import pickle\n", - "import pandas as pd\n", - "from neuronunit.tests.fi import RheobaseTestP\n", - "from neuronunit.optimization.model_parameters import reduced_dict, reduced_cells \n", - "from neuronunit.optimization import optimization_management as om\n", - "from sciunit import scores# score_type \n", - "\n", - "from neuronunit.optimization.data_transport_container import DataTC\n", - "from neuronunit.tests.fi import RheobaseTestP# as discovery\n", - "from neuronunit.optimization.optimization_management import dtc_to_rheo, format_test, nunit_evaluation\n", - "import quantities as pq\n", - "from neuronunit.models.reduced import ReducedModel\n", - "from neuronunit.optimization.model_parameters import model_params, path_params\n", - "LEMS_MODEL_PATH = path_params['model_path']\n", - "list_to_frame = []\n", - "from neuronunit.tests.fi import RheobaseTestP\n", - "\n", - " \n", - "from IPython.display import HTML, display\n", - "import seaborn as sns\n", - "\n", - "\n", - "\n" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "collapsed": false - }, - "source": [ - "# The Izhiketich model is instanced using some well researched parameter sets.\n" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "First lets get the points in parameter space, that Izhikich himself has published about. These points are often used by the open source brain project to establish between model reproducibility. The itial motivating factor for choosing these points as constellations, of all possible parameter space subsets, is that these points where initially tuned and used as best guesses for matching real observed experimental recordings." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "explore_param = {k:(np.min(v),np.max(v)) for k,v in reduced_dict.items()}" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "collapsed": false - }, - "source": [ - "## Get the experimental Data pertaining to four different classes or neurons, that can constrain models.\n", - "Next we get some electro physiology data for four different classes of cells that are very common targets of scientific neuronal modelling. We are interested in finding out what are the most minimal, and detail reduced, low complexity model equations, that are able to satisfy " - ] - }, - { - "cell_type": "markdown", - "metadata": { - "collapsed": false - }, - "source": [ - "Below are some of the data set ID's I used to query neuroelectro.\n", - "To save time for the reader, I prepared some data earlier to save time, and saved the data as a pickle, pythons preferred serialisation format.\n", - "\n", - "The interested reader can find some methods for getting cell specific ephys data from neuroelectro in a code file (neuronunit/optimization/get_neab.py) \n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "purkinje ={\"id\": 18, \"name\": \"Cerebellum Purkinje cell\", \"neuron_db_id\": 271, \"nlex_id\": \"sao471801888\"}\n", - "fi_basket = {\"id\": 65, \"name\": \"Dentate gyrus basket cell\", \"neuron_db_id\": None, \"nlex_id\": \"nlx_cell_100201\"}\n", - "pvis_cortex = {\"id\": 111, \"name\": \"Neocortex pyramidal cell layer 5-6\", \"neuron_db_id\": 265, \"nlex_id\": \"nifext_50\"}\n", - "#does not have rheobase\n", - "olf_mitral = {\"id\": 129, \"name\": \"Olfactory bulb (main) mitral cell\", \"neuron_db_id\": 267, \"nlex_id\": \"nlx_anat_100201\"}\n", - "ca1_pyr = {\"id\": 85, \"name\": \"Hippocampus CA1 pyramidal cell\", \"neuron_db_id\": 258, \"nlex_id\": \"sao830368389\"}\n", - "pipe = [ fi_basket, ca1_pyr, purkinje, pvis_cortex]" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "electro_tests = []\n", - "obs_frame = {}\n", - "test_frame = {}\n", - "\n", - "try: \n", - "\n", - " electro_path = str(os.getcwd())+'all_tests.p'\n", - "\n", - " assert os.path.isfile(electro_path) == True\n", - " with open(electro_path,'rb') as f:\n", - " (obs_frame,test_frame) = pickle.load(f)\n", - "\n", - "except:\n", - " for p in pipe:\n", - " p_tests, p_observations = get_neab.get_neuron_criteria(p)\n", - " obs_frame[p[\"name\"]] = p_observations#, p_tests))\n", - " test_frame[p[\"name\"]] = p_tests#, p_tests))\n", - " electro_path = str(os.getcwd())+'all_tests.p'\n", - " with open(electro_path,'wb') as f:\n", - " pickle.dump((obs_frame,test_frame),f)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Cast the tabulatable data to pandas data frame\n", - "There are many among us who prefer potentially tabulatable data to be encoded in pandas data frame." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": false, - "scrolled": false - }, - "outputs": [], - "source": [ - "for k,v in test_frame.items():\n", - " if \"olf_mit\" not in k:\n", - " obs = obs_frame[k]\n", - " v[0] = RheobaseTestP(obs['Rheobase'])\n", - "df = pd.DataFrame.from_dict(obs_frame)\n", - "print(test_frame.keys())" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "In the data frame below, you can see many different cell types" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "df" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "df['Hippocampus CA1 pyramidal cell']\n", - "\n" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Tweak Izhikitich equations\n", - "with educated guesses based on information that is already encoded in the predefined experimental observations.\n", - "\n", - "In otherwords use information that is readily amenable into hardcoding into equations \n", - "\n", - "Select out the 'Neocortex pyramidal cell layer 5-6' below, as a target for optimization" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": false, - "scrolled": false - }, - "outputs": [], - "source": [ - "free_params = ['a','b','k','c','C','d','vPeak','vr']\n", - "hc_ = reduced_cells['RS']\n", - "hc_['vr'] = -65.2261863636364\n", - "hc_['vPeak'] = hc_['vr'] + 86.364525297619\n", - "explore_param['C'] = (hc_['C']-20,hc_['C']+20)\n", - "explore_param['vr'] = (hc_['vr']-5,hc_['vr']+5)\n", - "use_test = test_frame[\"Neocortex pyramidal cell layer 5-6\"]\n", - "\n", - "#for t in use_test[::-1]:\n", - "# t.score_type = scores.RatioScore\n", - "test_opt = {}\n", - "\n", - "with open('data_dump.p','wb') as f:\n", - " pickle.dump(test_opt,f)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "\n", - "use_test[0].observation\n", - "print(use_test[0].name)\n", - "\n", - "rtp = RheobaseTestP(use_test[0].observation)\n", - "use_test[0] = rtp\n", - "print(use_test[0].observation)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "reduced_cells.keys()\n", - "test_frame.keys()\n", - "test_frame.keys()\n", - "test_frame['olf_mit'].insert(0,test_frame['Cerebellum Purkinje cell'][0])\n", - "test_frame\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "test_frame" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "#!pip install neo --upgrade\n", - "\n", - "df = pd.DataFrame(index=list(test_frame.keys()),columns=list(reduced_cells.keys()))\n", - "\n", - "for k,v in reduced_cells.items():\n", - " temp = {}\n", - " temp[str(v)] = {}\n", - " dtc = DataTC()\n", - " dtc.tests = use_test\n", - " dtc.attrs = v \n", - " dtc.backend = 'RAW'\n", - " dtc.cell_name = 'vanilla'\n", - "\n", - "\n", - " for key, use_test in test_frame.items():\n", - " dtc.tests = use_test\n", - " dtc = dtc_to_rheo(dtc)\n", - " dtc = format_test(dtc)\n", - "\n", - " if dtc.rheobase is not None:\n", - " if dtc.rheobase!=-1.0:\n", - "\n", - " dtc = nunit_evaluation(dtc)\n", - "\n", - " df[k][key] = int(dtc.get_ss())\n" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "A sparse grid sampling over the parameter space, using the published and well corrobarated parameter points, from Izhikitch publications, and the Open Source brain, shows that without optimization, using off the shelf parameter sets to fit real-life biological cell data, does not work so well." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "df" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "#df\n", - "import numpy as np\n", - "import pandas as pd\n", - "import matplotlib.pyplot as plt\n", - "import seaborn as sns\n", - "from IPython.display import HTML\n", - "\n", - "\n", - "import seaborn as sns\n", - "\n", - "cm = sns.light_palette(\"green\", as_cmap=True)\n", - "#df.pivot\n", - "\n", - "#sns.heatmap(df)\n", - "s = df.style.background_gradient(cmap=cm)\n", - "#values = df.values\n", - "\n", - "#values\n", - "#pivoted = df.pivot()\n", - "#s\n", - "type(s)\n", - "type(df['RS'])\n", - "s" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": true - }, - "outputs": [], - "source": [ - "s.to_html()" - ] - }, - { - "cell_type": "code", - "execution_count": 14, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "data": { - "text/plain": [ - "RS 4.265197\n", - "IB 5.838844\n", - "LTS 5.472374\n", - "TC 5.703031\n", - "TC_burst 5.521753\n", - "dtype: float64" - ] - }, - "execution_count": 14, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "from neuronunit.tests import dm \n", - "dmtests = dm.Druckmann2013Test\n", - "d_tests = []\n", - "for d in dir(dm):\n", - " if \"Test\" in d:\n", - " exec('d_tests.append(dm.'+str(d)+')')\n", - " \n", - " \n", - "df.min() \n", - "\n", - " \n", - "#print(d_tests)\n", - "\n", - "\n", - "#from neuronunit.tests.dm import InputResistanceTest as DMInputResistanceTest\n", - "#use_test.append(DMInputResistanceTest(injection_currents=[-11.0*pq.pA,-6*pq.pA,-1*pq.pA,]))\n", - "#print(use_test)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "\n", - "\n", - "MU = 10\n", - "NGEN = 200\n", - "\n", - "import pickle\n", - "import numpy as np\n", - "print(free_params)\n", - " \n", - "index, DO = om.run_ga(explore_param,NGEN,use_test,free_params=free_params, NSGA = False, MU = MU)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "\n", - "\n", - "MU = 6\n", - "NGEN = 200\n", - "\n", - "import pickle\n", - "import numpy as np\n", - "print(free_params)\n", - " \n", - "index, DO = om.run_ga(explore_param,NGEN,use_test,free_params=free_params, NSGA = False, MU = MU)\n", - "'''\n", - "MU = 6\n", - "NGEN = 200\n", - "\n", - "import pickle\n", - "\n", - "import numpy as np\n", - "try:\n", - " with open('multi_objective.p','rb') as f:\n", - " test_opt = pickle.load(f)\n", - "except:\n", - "\n", - "for index, use_test in enumerate(test_frame.values()):\n", - "\n", - " if index % 2 == 0:\n", - " index, DO = om.run_ga(explore_param,NGEN,use_test,free_params=free_params, NSGA = False, MU = MU)\n", - " else:\n", - " index, DO = om.run_ga(explore_param,NGEN,use_test,free_params=free_params, NSGA = False, MU = MU)\n", - " #print(NSGA)\n", - "\n", - " print('can get as low as 2.70295, 2.70679')\n", - "\n", - " test_opt = {str('multi_objective')+str(index):npcl}\n", - " with open('multi_objective.p','wb') as f:\n", - " pickle.dump(test_opt,f)\n", - "\n", - "\n", - "print(np.sum(list(test_opt['multi_objective']['pf'][2].dtc.scores.values())))\n", - "print(np.sum(list(test_opt['multi_objective']['pf'][1].dtc.scores.values())))\n", - "#print(np.sum(list(test_opt['multi_objective']['hof'][0].dtc.scores.values())))\n", - "print(test_opt['multi_objective']['pf'][2].dtc.scores.items())\n", - "print(test_opt['multi_objective']['pf'][1].dtc.scores.items())\n", - "'''\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "test_opt.keys()\n", - "for value in test_opt.values():\n", - " value['stds']\n", - " value['ranges']\n", - " print(value['ranges']) \n", - " print(value['stds'])\n", - " \n", - " #fig = pl.figure()\n", - " #ax = pl.subplot(111)\n", - " #ax.bar(range(len(value.keys())), values)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": true - }, - "outputs": [], - "source": [ - "with open('data_dump.p','rb') as f:\n", - " test_opt = pickle.load(f)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "test_opt" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "#errorbar\n", - "#for \n", - "import seaborn as sns\n", - "from matplotlib.pyplot import errorbar\n", - "import matplotlib.pyplot as plt\n", - "\n", - "fig0,ax0 = plt.subplots(dim,dim,figsize=(10,10))\n", - "plt.figure(num=None, figsize=(11, 11), dpi=80, facecolor='w', edgecolor='k')\n", - "\n", - "for v in test_opt.values():\n", - " x = 0\n", - " labels = []\n", - " plt.clf()\n", - " for k_,v_ in v['ranges'].items(): \n", - " #print(k_)\n", - " value = v_\n", - "\n", - " y = np.mean(value)\n", - " err = max(value)-min(value)\n", - " errorbar(x, y, err, marker='s', mfc='red',\n", - " mec='green', ms=2, mew=4,label='in '+str(k_))\n", - " x+=1\n", - " labels.append(k_)\n", - " plt.xticks(np.arange(len(labels)), labels)\n", - " ax0[i] = plt\n", - "\n", - " #plt.title(str(v))\n", - "\n", - "plt.show()\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": true - }, - "outputs": [], - "source": [ - "\n", - "from mpl_toolkits.mplot3d import Axes3D\n", - "from sklearn.cluster import KMeans\n", - "from sklearn import datasets\n", - "import seaborn as sns; sns.set() # for plot styling\n", - "dfs.replace([np.inf, -np.inf], np.nan)\n", - "dfs = dfs.dropna()\n", - "\n", - "#X = dfs[['standard','sp','ss','info_density','gf','standard','uniqueness','info_density','penalty']]\n", - "X = dfs[['standard','sp','ss']]\n", - "\n", - "X = X.as_matrix()\n", - "#import pdb; pdb.set_trace()\n", - "\n", - "est = KMeans(n_clusters=3)\n", - "\n", - "est.fit(X)\n", - "\n", - "y_kmeans = est.predict(X)\n", - "centers = est.cluster_centers_\n", - "\n", - "fignum = 1\n", - "fig = plt.figure(fignum, figsize=(4, 3))\n", - "ax = Axes3D(fig, rect=[0, 0, .95, 1], elev=48, azim=134)\n", - "ax.scatter(X[:, 0], X[:, 1], X[:, 2], c=y_kmeans, s=50)\n", - "ax.scatter(centers[:, 0], centers[:, 1], centers[:, 2], c='black', s=200, alpha=0.5);\n", - "ax.w_xaxis.set_ticklabels([])\n", - "ax.w_yaxis.set_ticklabels([])\n", - "ax.w_zaxis.set_ticklabels([])\n", - "ax.set_xlabel('standard')\n", - "ax.set_ylabel('subjectivity')\n", - "ax.set_zlabel('sentiment polarity')\n", - "#ax.set_title(titles[fignum - 1])\n", - "#ax.dist = 12\n", - "fignum = fignum + 1\n", - "for x,i in enumerate(zip(y_kmeans,dfs['clue_words'])):\n", - " try:\n", - " print(i[0],i[1],dfs['link'][x],dfs['publication'][x],dfs['clue_links'][x],dfs['sp_norm'][x],dfs['ss_norm'][x],dfs['uniqueness'][x])\n", - " except:\n", - " print(i)\n", - "\n", - "fig.savefig('3dCluster.png')" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# the parameter 'd' only seems important\n", - "# C does not have to be too precise within a range." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "I consider the final gene populations for each of the eight tests. I compute the variance in each of the converged populations, I see that variance is low in many of the gene populations.\n", - "\n", - "When all variables are used to optomize only against one set of parameters, you expect their would be high variance in parameters, that don't matter much with respect to that error criteria (you expect redundancy of solutions).\n", - "\n", - "I compute std on errors over all the tests in order to estimate how amenable the problem is to multiobjective optimization." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "from neuronunit.models.reduced import ReducedModel\n", - "from neuronunit.optimization.model_parameters import model_params, path_params\n", - "LEMS_MODEL_PATH = path_params['model_path']\n", - "import quantities as pq\n", - "plt.figure(num=None, figsize=(11, 11), dpi=80, facecolor='w', edgecolor='k')\n", - "\n", - "for k,v in test_opt.items(): \n", - " model = ReducedModel(LEMS_MODEL_PATH, name= str('vanilla'), backend=('RAW'))\n", - " model.attrs = v['out']['pf'][1].dtc.attrs\n", - " print(str(k), v['out']['pf'][1].dtc.get_ss())#fitness)\n", - " iparams = {}\n", - " iparams['injected_square_current'] = {}\n", - " iparams['injected_square_current']['amplitude'] =v['out']['pf'][1].rheobase['value']*pq.pA\n", - " #['amplitude'] = dtc.vtest[k]['injected_square_current']['amplitude']\n", - " DELAY = 100.0*pq.ms\n", - " DURATION = 1000.0*pq.ms\n", - " iparams['injected_square_current']['delay'] = DELAY\n", - " iparams['injected_square_current']['duration'] = int(DURATION)\n", - "\n", - " model.inject_square_current(iparams)\n", - "\n", - " plt.plot(model.get_membrane_potential().times,model.get_membrane_potential(),label=str(k))\n", - " plt.legend()\n", - "plt.show()\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "'''\n", - "#print([i.fitness.values for i in test_opt['t'][0]['pop']])#.keys()\n", - "print(np.std([i[0] for i in test_opt['t'][0]['pop'][0:5]]))#.keys()\n", - "print(np.std([i[1] for i in test_opt['t'][0]['pop'][0:5]]))#.keys()\n", - "print(np.std([i[2] for i in test_opt['t'][0]['pop'][0:5]]))#.keys()\n", - "print(np.std([i[3] for i in test_opt['t'][0]['pop'][0:5]]))#.keys()\n", - "print(test_opt['t'][0]['pop'][0][0])\n", - "print(test_opt['t'][0]['pop'][0][1])\n", - "test_opt['t'][0]['pop'][0].dtc.attrs\n", - "'''" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "\n", - "#values = { k:v for v in npcl['pop'][i].dtc.attrs.items() for i in npcl['pop'] }\n", - "#print(values) \n", - "#print(stds.keys())\n", - "#stds\n", - "#dtc.variances[k] for k in dtc.attrs.keys() " - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "\n", - "DO.seed_pop = npcl['pf'][0:MU]\n", - "npcl, DO = om.run_ga(explore_param,10,reduced_tests,free_params=free_params,hc = hc, NSGA = False, MU = MU, seed_pop = DO.seed_pop)\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "attrs_here = npcl['hardened'][0][0].attrs\n", - "attrs_here.update(hc)\n", - "attrs_here\n", - "scores = npcl['hof'][0].dtc.scores\n", - "print(scores)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "#\n", - "use_test = test_frame[\"Neocortex pyramidal cell layer 5-6\"]\n", - "reduced_tests = [use_test[0], use_test[-1], use_test[len(use_test)-1]]\n", - "bigger_tests = use_test[1:-2]\n", - "bigger_tests.insert(0,use_test[0])" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "#bigger_tests = bigger_tests[-1::]\n", - "print(bigger_tests)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "DO.seed_pop = npcl['hof'][0:MU]\n", - "reduced_tests = [use_test[0], use_test[-1], use_test[len(use_test)-1]]\n", - "npcl, DO = om.run_ga(explore_param,10,bigger_tests,free_params=free_params,hc = hc, NSGA = False, MU = MU)#, seed_pop = DO.seed_pop)\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "print(npcl['hardened'][0][0].attrs)\n", - "print(npcl['hardened'][0][0].scores)\n", - "print(npcl['pf'][0].fitness.values)\n", - "print(npcl['hof'][0].dtc.scores)\n", - "\n", - "#for t in use_test:\n", - "# print(t.name)\n", - " \n", - " \n", - "pop" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# From the scores printed above, it looks like certain error criteria, are in conflict with each other.\n", - "\n", - "Tests, that are amenable to co-optimization appear to be:\n", - "* Width test\n", - "* Input resistance tets\n", - "* Resting Potential Test,\n", - "* Capicitance test.\n", - "* Time constant\n", - "\n", - "Tests/criteria that seem in compatible with the above include: \n", - "* Rheobase, \n", - "* InjectedCurrentAPThresholdTest\n", - "* InjectedCurrentAPAmplitudeTest\n", - "\n", - "Therefore a reduced set of lists is made to check if the bottom three are at least amenable to optimization togethor." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "from sklearn.cluster import KMeans\n", - "est = KMeans(n_clusters=2)\n", - "est.fit(X)\n", - "y_kmeans = est.predict(X)\n", - "\n", - "centers = est.cluster_centers_\n", - "\n", - "fig = plt.figure(fignum,figsize=(4,3))\n", - "ax = Axes3D(fig,rect=[0,0,.95,1],elav=48,azim=134)\n", - "ax.scatter(X[:,0],X[:,1],X[:,2],c=y_kmeans,s=50),\n", - "ax.scatter(centres[:,0],centres[:,1],centres[:,2],c='black',s=200,alpha=0.5)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "\n", - "print(reduced_tests)\n", - "print(bigger_tests)\n", - "\n", - "DO.seed_pop = npcl['pf'][0:MU]\n", - "npcl, DO = om.run_ga(explore_param,10,reduced_tests,free_params=free_params,hc = hc, NSGA = True, MU = 12)#, seed_pop = DO.seed_pop)\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "import pickle\n", - "import copy\n", - "import numpy as np\n", - "import pandas as pd\n", - "import matplotlib.pyplot as plt\n", - "#from neuronunit.optimization.optimization_management import wave_measure\n", - "from neuronunit.models.reduced import ReducedModel\n", - "from neuronunit.optimization.model_parameters import model_params, path_params\n", - "LEMS_MODEL_PATH = path_params['model_path']\n", - "import neuronunit.optimization as opt\n", - "import quantities as pq\n", - "fig = plt.figure()\n", - "\n", - "plt.clf()\n", - "\n", - "from neuronunit.optimization.data_transport_container import DataTC\n", - "model = ReducedModel(LEMS_MODEL_PATH,name = str('vanilla'),backend = ('RAW'))\n", - "for i in npcl['pf'][0:2]:\n", - " iparams = {}\n", - " iparams['injected_square_current'] = {}\n", - " iparams['injected_square_current']['amplitude'] =i.dtc.rheobase\n", - " model = None\n", - " model = ReducedModel(LEMS_MODEL_PATH,name = str('vanilla'),backend = ('RAW'))\n", - " model.set_attrs(i.dtc.attrs)\n", - "\n", - " #['amplitude'] = dtc.vtest[k]['injected_square_current']['amplitude']\n", - " DELAY = 100.0*pq.ms\n", - " DURATION = 1000.0*pq.ms\n", - " iparams['injected_square_current']['delay'] = DELAY\n", - " iparams['injected_square_current']['duration'] = int(DURATION)\n", - " model.inject_square_current(iparams)\n", - " n_spikes = len(model.get_spike_train())\n", - " if n_spikes:\n", - " print(n_spikes)\n", - " #print(i[0].scores['RheobaseTestP']*pq.pA)\n", - " plt.plot(model.get_membrane_potential().times,model.get_membrane_potential())#,label='ground truth')\n", - " plt.legend()\n", - "\n", - "#gca().set_axis_off()\n", - "#subplots_adjust(top = 1, bottom = 0, right = 1, left = 0, \n", - "# hspace = 0, wspace = 0)\n", - "#margins(0,0)\n", - "#gca().xaxis.set_major_locator(NullLocator())\n", - "#gca().yaxis.set_major_locator(NullLocator())\n", - "\n", - "plt.subplots_adjust(left=0.0, right=1.0, top=0.9, bottom=0.1)\n", - "fig.tight_layout()\n", - "plt.show()\n", - "\n", - "fig.savefig(\"single_trace.png\", bbox_inches = 'tight',\n", - " pad_inches = 0)\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "import pickle\n", - "import copy\n", - "import numpy as np\n", - "import pandas as pd\n", - "import matplotlib.pyplot\n", - "\n", - "\n", - "from neuronunit.models.reduced import ReducedModel\n", - "from neuronunit.optimization.model_parameters import model_params, path_params\n", - "LEMS_MODEL_PATH = path_params['model_path']\n", - "import neuronunit.optimization as opt\n", - "import quantities as pq\n", - "fig = plt.figure()\n", - "\n", - "plt.clf()\n", - "\n", - "from neuronunit.optimization.data_transport_container import DataTC\n", - "for i in npcl['hardened']:\n", - " iparams = {}\n", - " iparams['injected_square_current'] = {}\n", - " iparams['injected_square_current']['amplitude'] = i[0].rheobase\n", - " model = None\n", - " model = ReducedModel(LEMS_MODEL_PATH,name = str('vanilla'),backend = ('RAW'))\n", - " model.set_attrs(i[0].attrs)\n", - "\n", - " #['amplitude'] = dtc.vtest[k]['injected_square_current']['amplitude']\n", - " DELAY = 100.0*pq.ms\n", - " DURATION = 1000.0*pq.ms\n", - " iparams['injected_square_current']['delay'] = DELAY\n", - " iparams['injected_square_current']['duration'] = int(DURATION)\n", - " model.inject_square_current(iparams)\n", - " n_spikes = len(model.get_spike_train())\n", - " if n_spikes:\n", - " print(n_spikes)\n", - " print(i[0].scores['RheobaseTestP']*pq.pA)\n", - " plt.plot(model.get_membrane_potential().times,model.get_membrane_potential())#,label='ground truth')\n", - " plt.legend()\n", - "\n", - "#gca().set_axis_off()\n", - "#subplots_adjust(top = 1, bottom = 0, right = 1, left = 0, \n", - "# hspace = 0, wspace = 0)\n", - "#margins(0,0)\n", - "#gca().xaxis.set_major_locator(NullLocator())\n", - "#gca().yaxis.set_major_locator(NullLocator())\n", - "\n", - "plt.subplots_adjust(left=0.0, right=1.0, top=0.9, bottom=0.1)\n", - "fig.tight_layout()\n", - "plt.show()\n", - "\n", - "fig.savefig(\"single_trace.png\", bbox_inches = 'tight',\n", - " pad_inches = 0)\n", - "\n", - "\n", - "'''\n", - "hc = {}\n", - "\n", - "#free_params = ['c','k']\n", - "for k,v in explore_param.items():\n", - " if k not in free_params:\n", - " hc[k] = v\n", - "constants = npcl['hardened'][0][0].attrs\n", - "hc.update(constants) \n", - "npcl, _ = om.run_ga(explore_param,20,test_frame[\"Neocortex pyramidal cell layer 5-6\"],free_params=free_params,hc = hc, NSGA = True)\n", - "'''" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "\n", - "free_params = ['a','b','k']#vt','c','k','d']#,'vt','k','c','C']#,'C'] # this can only be odd numbers.\n", - "\n", - "##\n", - "# Use information that is available\n", - "##\n", - "hc = reduced_cells['RS']\n", - "\n", - "hc['vr'] = -65.2261863636364\n", - "\n", - "hc['vPeak'] = hc['vr'] + 86.364525297619\n", - "hc['C'] = 89.7960714285714\n", - "hc.pop('a',0)\n", - "hc.pop('b',0)\n", - "hc.pop('k',0)\n", - "hc.pop('c',0)\n", - "hc.pop('d',0)\n", - " \n", - "use_test = test_frame[\"Neocortex pyramidal cell layer 5-6\"]\n", - "DO.seed_pop = npcl['pf']\n", - "ga_out = DO.run(max_ngen = 15)\n", - "'''\n", - "hc = {}\n", - "\n", - "free_params = ['C']\n", - "\n", - "for k,v in explore_param.items():\n", - " if k not in free_params:\n", - " hc[k] = v\n", - "#,'vt','k','c','C']#,'C'] # this can only be odd numbers\n", - "constants = npcl['hardened'][0][0].attrs\n", - "hc.update(constants) \n", - "npcl, _ = om.run_ga(explore_param,20,test_frame[\"Neocortex pyramidal cell layer 5-6\"],free_params=free_params,hc = hc, NSGA = True)\n", - "'''" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "'''\n", - "import pandas\n", - " \n", - "try:\n", - " ne_raw = pandas.read_csv('article_ephys_metadata_curated.csv', delimiter='\\t')\n", - " !ls -ltr *.csv\n", - "except:\n", - " !wget https://neuroelectro.org/static/src/article_ephys_metadata_curated.csv\n", - " ne_raw = pandas.read_csv('article_ephys_metadata_curated.csv', delimiter='\\t')\n", - "\n", - "blah = ne_raw[ne_raw['NeuronName'].str.match('Hippocampus CA1 pyramidal cell')]\n", - "#ne_raw['NeuronName']\n", - "#ne_raw['cell\\ capacitance']\n", - "#blah = ne_raw[ne_raw['NeuronName'].str.match('Hippocampus CA1 pyramidal cell')]\n", - "\n", - "print([i for i in blah.columns])\n", - "#rint(blah['rheobase'])\n", - "#print(blah)\n", - "#for i in ne_raw.columns:#['NeuronName']:\n", - "# print(i)\n", - "\n", - "#ne_raw['NeuronName'][85]\n", - "#blah = ne_raw[ne_raw['TableID'].str.match('85')]\n", - "#ne_raw['n'] = 84\n", - "#here = ne_raw[ne_raw['Index']==85]\n", - "here = ne_raw[ne_raw['TableID']==18]\n", - "\n", - "print(here['rheo_raw'])\n", - "#!wget https://neuroelectro.org/apica/1/n/\n", - "'''" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "ca1 = ne_raw[ne_raw['NeuronName'].str.match('Hippocampus CA1 pyramidal cell')]\n", - "ca1['rheo']" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "\n", - " \n", - "test_frame[\"Dentate gyrus basket cell\"][0].observation['std'] = test_frame[\"Dentate gyrus basket cell\"][0].observation['mean']\n", - "for t in test_frame[\"Dentate gyrus basket cell\"]:\n", - " print(t.name)\n", - "\n", - " print(t.observation)\n", - " " - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "\n", - "'''\n", - "Inibitory Neuron\n", - "This can't pass the Rheobase test\n", - "''' " - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "\n", - "from neuronunit.optimization import optimization_management as om\n", - "import pickle\n", - "\n", - "free_params = ['a','vr','b','vt','vPeak','c','k']\n", - "for k,v in explore_param.items():\n", - " if k not in free_params:\n", - " hc[k] = v\n", - "use_test = test_frame[\"Dentate gyrus basket cell\"]\n", - "bcell, _ = om.run_ga(explore_param,20,use_test,free_params=free_params,hc = hc, NSGA = True, MU = 4)\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "\n", - " \n", - "#test_frame[\"Dentate gyrus basket cell\"][0].observation['std'] = test_frame[\"Dentate gyrus basket cell\"][0].observation['mean']\n", - "for t in test_frame[\"Hippocampus CA1 pyramidal cell\"]:\n", - " print(t.name)\n", - "\n", - " print(t.observation)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "use_test = test_frame[\"Hippocampus CA1 pyramidal cell\"]\n", - "bcell, _ = om.run_ga(explore_param,20,use_test,free_params=free_params,hc = hc, NSGA = True, MU = 10)\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "import pickle\n", - "import copy\n", - "import numpy as np\n", - "import pandas as pd\n", - "import matplotlib.pyplot as plt\n", - "\n", - "from neuronunit.models.reduced import ReducedModel\n", - "from neuronunit.optimization.model_parameters import model_params, path_params\n", - "LEMS_MODEL_PATH = path_params['model_path']\n", - "import neuronunit.optimization as opt\n", - "import quantities as pq\n", - "fig = plt.figure()\n", - "\n", - "plt.clf()\n", - "\n", - "from neuronunit.optimization.data_transport_container import DataTC\n", - "for i in bcell['hardened'][0:6]:\n", - " iparams = {}\n", - " iparams['injected_square_current'] = {}\n", - " iparams['injected_square_current']['amplitude'] =i[0].rheobase\n", - " model = None\n", - " model = ReducedModel(LEMS_MODEL_PATH,name = str('vanilla'),backend = ('RAW'))\n", - " model.set_attrs(i[0].attrs)\n", - "\n", - " #['amplitude'] = dtc.vtest[k]['injected_square_current']['amplitude']\n", - " DELAY = 100.0*pq.ms\n", - " DURATION = 1000.0*pq.ms\n", - " iparams['injected_square_current']['delay'] = DELAY\n", - " iparams['injected_square_current']['duration'] = int(DURATION)\n", - " model.inject_square_current(iparams)\n", - " n_spikes = len(model.get_spike_train())\n", - " if n_spikes:\n", - " print(n_spikes)\n", - " print(i[0].scores['RheobaseTestP']*pq.pA)\n", - " plt.plot(model.get_membrane_potential().times,model.get_membrane_potential())#,label='ground truth')\n", - " plt.legend()\n", - "\n", - "#gca().set_axis_off()\n", - "#subplots_adjust(top = 1, bottom = 0, right = 1, left = 0, \n", - "# hspace = 0, wspace = 0)\n", - "#margins(0,0)\n", - "#gca().xaxis.set_major_locator(NullLocator())\n", - "#gca().yaxis.set_major_locator(NullLocator())\n", - "\n", - "plt.subplots_adjust(left=0.0, right=1.0, top=0.9, bottom=0.1)\n", - "fig.tight_layout()\n", - "plt.show()\n", - "\n", - "fig.savefig(\"single_trace.png\", bbox_inches = 'tight',\n", - " pad_inches = 0)\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": true - }, - "outputs": [], - "source": [] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": true - }, - "outputs": [], - "source": [ - "use_test = test_frame[\"Hippocampus CA1 pyramidal cell\"]\n", - "bcell, _ = om.run_ga(explore_param,20,use_test,free_params=free_params,hc = hc, NSGA = True, MU = 10)\n" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# This cell is in markdown, but it won't be later.\n", - "Later optimize a whole heap of cells in a loop.\n", - "\n", - "try:\n", - " import pickle\n", - " with open('data_dump.p','rb') as f:\n", - " test_opt = pickle.load(f)\n", - "except:\n", - " MU = 12\n", - " NGEN = 25\n", - " cnt = 1\n", - " for t in use_test: \n", - " if cnt==len(use_test):\n", - " MU = 12\n", - " NGEN = 20\n", - "\n", - " npcl, DO = om.run_ga(explore_param,NGEN,[t],free_params=free_params, NSGA = True, MU = MU)\n", - " else:\n", - "\n", - " npcl, DO = om.run_ga(explore_param,NGEN,[t],free_params=free_params, NSGA = True, MU = MU)\n", - "\n", - " test_opt[str(t)] = {'out':npcl}\n", - "\n", - " ranges = {}\n", - " stds = npcl['pop'][0].dtc.attrs\n", - " for k in npcl['pop'][0].dtc.attrs.keys(): \n", - " stds[k] = []\n", - " ranges[k] = []\n", - "\n", - "\n", - " for i in npcl['pop'][::5]:\n", - " for k,v in i.dtc.attrs.items():\n", - " stds[k].append(v)\n", - " ranges[k].append(v)\n", - "\n", - " for k in npcl['pop'][0].dtc.attrs.keys():\n", - " ranges[k] = (np.min(ranges[k][1::]),np.max(ranges[k][1::]))\n", - "\n", - " stds[k] = np.std(stds[k][1::])\n", - " test_opt[str(t)]['stds'] = stds \n", - " test_opt[str(t)]['ranges'] = ranges \n", - "\n", - " cnt+=1\n", - " \n", - " with open('data_dump.p','wb') as f:\n", - " pickle.dump(test_opt,f)\n", - " " - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": true - }, - "outputs": [], - "source": [] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.5.2" - } - }, - "nbformat": 4, - "nbformat_minor": 2 -} diff --git a/neuronunit/unit_test/bfi.py b/neuronunit/unit_test/bfi.py deleted file mode 100644 index a71b4b92d..000000000 --- a/neuronunit/unit_test/bfi.py +++ /dev/null @@ -1,866 +0,0 @@ - -# coding: utf-8 - -# Assumptions, the environment for running this notebook was arrived at by building a dedicated docker file. -# -# https://cloud.docker.com/repository/registry-1.docker.io/russelljarvis/nuo -# -# You can run use dockerhub to get the appropriate file, and launch this notebook using Kitematic. - - -# This is code, change cell type to markdown. -# ![alt text](plan.jpg "Pub plan") - - -# # Import libraries -# To keep the standard running version of minimal and memory efficient, not all available packages are loaded by default. In the cell below I import a mixture common python modules, and custom developed modules associated with NeuronUnit (NU) development - - -#!pip install dask distributed seaborn -#!bash after_install.sh -import numpy as np -import os -import pickle -import pandas as pd -from neuronunit.tests.fi import RheobaseTestP -from neuronunit.optimization.model_parameters import reduced_dict, reduced_cells -from neuronunit.optimization import optimization_management as om -from sciunit import scores# score_type - -from neuronunit.optimization.data_transport_container import DataTC -from neuronunit.tests.fi import RheobaseTestP# as discovery -from neuronunit.optimization.optimization_management import dtc_to_rheo, format_test, nunit_evaluation -import quantities as pq -from neuronunit.models.reduced import ReducedModel -from neuronunit.optimization.model_parameters import model_params, path_params -LEMS_MODEL_PATH = path_params['model_path'] -list_to_frame = [] -from neuronunit.tests.fi import RheobaseTestP - - -# from IPython.display import HTML, display -# import seaborn as sns - - - - - -# # The Izhiketich model is instanced using some well researched parameter sets. -# - -# First lets get the points in parameter space, that Izhikich himself has published about. These points are often used by the open source brain project to establish between model reproducibility. The itial motivating factor for choosing these points as constellations, of all possible parameter space subsets, is that these points where initially tuned and used as best guesses for matching real observed experimental recordings. - -# In[ ]: - -explore_param = {k:(np.min(v),np.max(v)) for k,v in reduced_dict.items()} - - -# ## Get the experimental Data pertaining to four different classes or neurons, that can constrain models. -# Next we get some electro physiology data for four different classes of cells that are very common targets of scientific neuronal modelling. We are interested in finding out what are the most minimal, and detail reduced, low complexity model equations, that are able to satisfy - -# Below are some of the data set ID's I used to query neuroelectro. -# To save time for the reader, I prepared some data earlier to save time, and saved the data as a pickle, pythons preferred serialisation format. -# -# The interested reader can find some methods for getting cell specific ephys data from neuroelectro in a code file (neuronunit/optimization/get_neab.py) -# - -# In[ ]: - -purkinje ={"id": 18, "name": "Cerebellum Purkinje cell", "neuron_db_id": 271, "nlex_id": "sao471801888"} -fi_basket = {"id": 65, "name": "Dentate gyrus basket cell", "neuron_db_id": None, "nlex_id": "nlx_cell_100201"} -pvis_cortex = {"id": 111, "name": "Neocortex pyramidal cell layer 5-6", "neuron_db_id": 265, "nlex_id": "nifext_50"} -#does not have rheobase -olf_mitral = {"id": 129, "name": "Olfactory bulb (main) mitral cell", "neuron_db_id": 267, "nlex_id": "nlx_anat_100201"} -ca1_pyr = {"id": 85, "name": "Hippocampus CA1 pyramidal cell", "neuron_db_id": 258, "nlex_id": "sao830368389"} -pipe = [ fi_basket, ca1_pyr, purkinje, pvis_cortex] - - -# In[ ]: - -electro_tests = [] -obs_frame = {} -test_frame = {} - -try: - - electro_path = str(os.getcwd())+'all_tests.p' - - assert os.path.isfile(electro_path) == True - with open(electro_path,'rb') as f: - (obs_frame,test_frame) = pickle.load(f) - -except: - for p in pipe: - p_tests, p_observations = get_neab.get_neuron_criteria(p) - obs_frame[p["name"]] = p_observations#, p_tests)) - test_frame[p["name"]] = p_tests#, p_tests)) - electro_path = str(os.getcwd())+'all_tests.p' - with open(electro_path,'wb') as f: - pickle.dump((obs_frame,test_frame),f) - - -# # Cast the tabulatable data to pandas data frame -# There are many among us who prefer potentially tabulatable data to be encoded in pandas data frame. - -for k,v in test_frame.items(): - if "olf_mit" not in k: - obs = obs_frame[k] - v[0] = RheobaseTestP(obs['Rheobase']) -df = pd.DataFrame.from_dict(obs_frame) -print(test_frame.keys()) - - -# In the data frame below, you can see many different cell types - -df['Hippocampus CA1 pyramidal cell'] - - - -# # Tweak Izhikitich equations -# with educated guesses based on information that is already encoded in the predefined experimental observations. -# -# In otherwords use information that is readily amenable into hardcoding into equations -# -# Select out the 'Neocortex pyramidal cell layer 5-6' below, as a target for optimization - - -free_params = ['a','b','k','c','C','d','vPeak','vr'] -hc_ = reduced_cells['RS'] -hc_['vr'] = -65.2261863636364 -hc_['vPeak'] = hc_['vr'] + 86.364525297619 -explore_param['C'] = (hc_['C']-20,hc_['C']+20) -explore_param['vr'] = (hc_['vr']-5,hc_['vr']+5) -use_test = test_frame["Neocortex pyramidal cell layer 5-6"] - -#for t in use_test[::-1]: -# t.score_type = scores.RatioScore -test_opt = {} - -with open('data_dump.p','wb') as f: - pickle.dump(test_opt,f) - - -# In[ ]: - - -use_test[0].observation -print(use_test[0].name) - -rtp = RheobaseTestP(use_test[0].observation) -use_test[0] = rtp -print(use_test[0].observation) - - -reduced_cells.keys() -test_frame.keys() -test_frame.keys() -test_frame['olf_mit'].insert(0,test_frame['Cerebellum Purkinje cell'][0]) -test_frame - - - -#!pip install neo --upgrade - -df = pd.DataFrame(index=list(test_frame.keys()),columns=list(reduced_cells.keys())) - -for k,v in reduced_cells.items(): - temp = {} - temp[str(v)] = {} - dtc = DataTC() - dtc.tests = use_test - dtc.attrs = v - dtc.backend = 'RAW' - dtc.cell_name = 'vanilla' - - - for key, use_test in test_frame.items(): - dtc.tests = use_test - dtc = dtc_to_rheo(dtc) - dtc = format_test(dtc) - - if dtc.rheobase is not None: - if dtc.rheobase!=-1.0: - - dtc = nunit_evaluation(dtc) - - df[k][key] = int(dtc.get_ss()) - -# A sparse grid sampling over the parameter space, using the published and well corrobarated parameter points, from Izhikitch publications, and the Open Source brain, shows that without optimization, using off the shelf parameter sets to fit real-life biological cell data, does not work so well. - - -MU = 6 -NGEN = 150 - -for key, use_test in test_frame.items(): - ga_out, _ = om.run_ga(explore_param,NGEN,use_test,free_params=free_params, NSGA = True, MU = MU) - - test_opt = {str('multi_objective')+str(ga_out):ga_out} - with open('multi_objective.p','wb') as f: - pickle.dump(test_opt,f) -''' -MU = 6 -NGEN = 200 - -import pickle - -import numpy as np -try: - with open('multi_objective.p','rb') as f: - test_opt = pickle.load(f) -except: - -for index, use_test in enumerate(test_frame.values()): - - if index % 2 == 0: - index, DO = om.run_ga(explore_param,NGEN,use_test,free_params=free_params, NSGA = False, MU = MU) - else: - index, DO = om.run_ga(explore_param,NGEN,use_test,free_params=free_params, NSGA = False, MU = MU) - #print(NSGA) - - print('can get as low as 2.70295, 2.70679') - - test_opt = {str('multi_objective')+str(index):npcl} - with open('multi_objective.p','wb') as f: - pickle.dump(test_opt,f) - - -print(np.sum(list(test_opt['multi_objective']['pf'][2].dtc.scores.values()))) -print(np.sum(list(test_opt['multi_objective']['pf'][1].dtc.scores.values()))) -#print(np.sum(list(test_opt['multi_objective']['hof'][0].dtc.scores.values()))) -print(test_opt['multi_objective']['pf'][2].dtc.scores.items()) -print(test_opt['multi_objective']['pf'][1].dtc.scores.items()) -''' - - -# In[ ]: - - - - -# In[ ]: - -test_opt.keys() -for value in test_opt.values(): - value['stds'] - value['ranges'] - print(value['ranges']) - print(value['stds']) - - #fig = pl.figure() - #ax = pl.subplot(111) - #ax.bar(range(len(value.keys())), values) - - -# In[ ]: - -with open('data_dump.p','rb') as f: - test_opt = pickle.load(f) - - -# In[ ]: - -test_opt - - -# In[ ]: - -#errorbar -#for -import seaborn as sns -from matplotlib.pyplot import errorbar -import matplotlib.pyplot as plt - -fig0,ax0 = plt.subplots(dim,dim,figsize=(10,10)) -plt.figure(num=None, figsize=(11, 11), dpi=80, facecolor='w', edgecolor='k') - -for v in test_opt.values(): - x = 0 - labels = [] - plt.clf() - for k_,v_ in v['ranges'].items(): - #print(k_) - value = v_ - - y = np.mean(value) - err = max(value)-min(value) - errorbar(x, y, err, marker='s', mfc='red', - mec='green', ms=2, mew=4,label='in '+str(k_)) - x+=1 - labels.append(k_) - plt.xticks(np.arange(len(labels)), labels) - ax0[i] = plt - - #plt.title(str(v)) - -plt.show() - - -# In[ ]: - - -from mpl_toolkits.mplot3d import Axes3D -from sklearn.cluster import KMeans -from sklearn import datasets -import seaborn as sns; sns.set() # for plot styling -dfs.replace([np.inf, -np.inf], np.nan) -dfs = dfs.dropna() - -#X = dfs[['standard','sp','ss','info_density','gf','standard','uniqueness','info_density','penalty']] -X = dfs[['standard','sp','ss']] - -X = X.as_matrix() -#import pdb; pdb.set_trace() - -est = KMeans(n_clusters=3) - -est.fit(X) - -y_kmeans = est.predict(X) -centers = est.cluster_centers_ - -fignum = 1 -fig = plt.figure(fignum, figsize=(4, 3)) -ax = Axes3D(fig, rect=[0, 0, .95, 1], elev=48, azim=134) -ax.scatter(X[:, 0], X[:, 1], X[:, 2], c=y_kmeans, s=50) -ax.scatter(centers[:, 0], centers[:, 1], centers[:, 2], c='black', s=200, alpha=0.5); -ax.w_xaxis.set_ticklabels([]) -ax.w_yaxis.set_ticklabels([]) -ax.w_zaxis.set_ticklabels([]) -ax.set_xlabel('standard') -ax.set_ylabel('subjectivity') -ax.set_zlabel('sentiment polarity') -#ax.set_title(titles[fignum - 1]) -#ax.dist = 12 -fignum = fignum + 1 -for x,i in enumerate(zip(y_kmeans,dfs['clue_words'])): - try: - #print(i[0],i[1],dfs['link'][x],dfs['publication'][x],dfs['clue_links'][x],dfs['sp_norm'][x],dfs['ss_norm'][x],dfs['uniqueness'][x]) - except: - #print(i) - -fig.savefig('3dCluster.png') - - -# # the parameter 'd' only seems important -# # C does not have to be too precise within a range. - -# I consider the final gene populations for each of the eight tests. I compute the variance in each of the converged populations, I see that variance is low in many of the gene populations. -# -# When all variables are used to optomize only against one set of parameters, you expect their would be high variance in parameters, that don't matter much with respect to that error criteria (you expect redundancy of solutions). -# -# I compute std on errors over all the tests in order to estimate how amenable the problem is to multiobjective optimization. - -# In[ ]: - -from neuronunit.models.reduced import ReducedModel -from neuronunit.optimization.model_parameters import model_params, path_params -LEMS_MODEL_PATH = path_params['model_path'] -import quantities as pq -plt.figure(num=None, figsize=(11, 11), dpi=80, facecolor='w', edgecolor='k') - -for k,v in test_opt.items(): - model = ReducedModel(LEMS_MODEL_PATH, name= str('vanilla'), backend=('RAW')) - model.attrs = v['out']['pf'][1].dtc.attrs - print(str(k), v['out']['pf'][1].dtc.get_ss())#fitness) - iparams = {} - iparams['injected_square_current'] = {} - iparams['injected_square_current']['amplitude'] =v['out']['pf'][1].rheobase['value']*pq.pA - #['amplitude'] = dtc.vtest[k]['injected_square_current']['amplitude'] - DELAY = 100.0*pq.ms - DURATION = 1000.0*pq.ms - iparams['injected_square_current']['delay'] = DELAY - iparams['injected_square_current']['duration'] = int(DURATION) - - model.inject_square_current(iparams) - - plt.plot(model.get_membrane_potential().times,model.get_membrane_potential(),label=str(k)) - plt.legend() -plt.show() - - -# In[ ]: - -''' -#print([i.fitness.values for i in test_opt['t'][0]['pop']])#.keys() -print(np.std([i[0] for i in test_opt['t'][0]['pop'][0:5]]))#.keys() -print(np.std([i[1] for i in test_opt['t'][0]['pop'][0:5]]))#.keys() -print(np.std([i[2] for i in test_opt['t'][0]['pop'][0:5]]))#.keys() -print(np.std([i[3] for i in test_opt['t'][0]['pop'][0:5]]))#.keys() -print(test_opt['t'][0]['pop'][0][0]) -print(test_opt['t'][0]['pop'][0][1]) -test_opt['t'][0]['pop'][0].dtc.attrs -''' - - -# In[ ]: - - -#values = { k:v for v in npcl['pop'][i].dtc.attrs.items() for i in npcl['pop'] } -#print(values) -#print(stds.keys()) -#stds -#dtc.variances[k] for k in dtc.attrs.keys() - - -# In[ ]: - - -DO.seed_pop = npcl['pf'][0:MU] -npcl, DO = om.run_ga(explore_param,10,reduced_tests,free_params=free_params,hc = hc, NSGA = False, MU = MU, seed_pop = DO.seed_pop) - - -# In[ ]: - - - - -# In[ ]: - -attrs_here = npcl['hardened'][0][0].attrs -attrs_here.update(hc) -attrs_here -scores = npcl['hof'][0].dtc.scores -print(scores) - - -# In[ ]: - -# -use_test = test_frame["Neocortex pyramidal cell layer 5-6"] -reduced_tests = [use_test[0], use_test[-1], use_test[len(use_test)-1]] -bigger_tests = use_test[1:-2] -bigger_tests.insert(0,use_test[0]) - - -# In[ ]: - -#bigger_tests = bigger_tests[-1::] -print(bigger_tests) - - -# In[ ]: - -DO.seed_pop = npcl['hof'][0:MU] -reduced_tests = [use_test[0], use_test[-1], use_test[len(use_test)-1]] -npcl, DO = om.run_ga(explore_param,10,bigger_tests,free_params=free_params,hc = hc, NSGA = False, MU = MU)#, seed_pop = DO.seed_pop) - - -# In[ ]: - -print(npcl['hardened'][0][0].attrs) -print(npcl['hardened'][0][0].scores) -print(npcl['pf'][0].fitness.values) -print(npcl['hof'][0].dtc.scores) - -#for t in use_test: -# print(t.name) - - -pop - - -# # From the scores printed above, it looks like certain error criteria, are in conflict with each other. -# -# Tests, that are amenable to co-optimization appear to be: -# * Width test -# * Input resistance tets -# * Resting Potential Test, -# * Capicitance test. -# * Time constant -# -# Tests/criteria that seem in compatible with the above include: -# * Rheobase, -# * InjectedCurrentAPThresholdTest -# * InjectedCurrentAPAmplitudeTest -# -# Therefore a reduced set of lists is made to check if the bottom three are at least amenable to optimization togethor. - -# In[ ]: - -from sklearn.cluster import KMeans -est = KMeans(n_clusters=2) -est.fit(X) -y_kmeans = est.predict(X) - -centers = est.cluster_centers_ - -fig = plt.figure(fignum,figsize=(4,3)) -ax = Axes3D(fig,rect=[0,0,.95,1],elav=48,azim=134) -ax.scatter(X[:,0],X[:,1],X[:,2],c=y_kmeans,s=50), -ax.scatter(centres[:,0],centres[:,1],centres[:,2],c='black',s=200,alpha=0.5) - - -# In[ ]: - - -print(reduced_tests) -print(bigger_tests) - -DO.seed_pop = npcl['pf'][0:MU] -npcl, DO = om.run_ga(explore_param,10,reduced_tests,free_params=free_params,hc = hc, NSGA = True, MU = 12)#, seed_pop = DO.seed_pop) - - -# In[ ]: - -import pickle -import copy -import numpy as np -import pandas as pd -import matplotlib.pyplot as plt -#from neuronunit.optimization.optimization_management import wave_measure -from neuronunit.models.reduced import ReducedModel -from neuronunit.optimization.model_parameters import model_params, path_params -LEMS_MODEL_PATH = path_params['model_path'] -import neuronunit.optimization as opt -import quantities as pq -fig = plt.figure() - -plt.clf() - -from neuronunit.optimization.data_transport_container import DataTC -model = ReducedModel(LEMS_MODEL_PATH,name = str('vanilla'),backend = ('RAW')) -for i in npcl['pf'][0:2]: - iparams = {} - iparams['injected_square_current'] = {} - iparams['injected_square_current']['amplitude'] =i.dtc.rheobase - model = None - model = ReducedModel(LEMS_MODEL_PATH,name = str('vanilla'),backend = ('RAW')) - model.set_attrs(i.dtc.attrs) - - #['amplitude'] = dtc.vtest[k]['injected_square_current']['amplitude'] - DELAY = 100.0*pq.ms - DURATION = 1000.0*pq.ms - iparams['injected_square_current']['delay'] = DELAY - iparams['injected_square_current']['duration'] = int(DURATION) - model.inject_square_current(iparams) - n_spikes = len(model.get_spike_train()) - if n_spikes: - print(n_spikes) - #print(i[0].scores['RheobaseTestP']*pq.pA) - plt.plot(model.get_membrane_potential().times,model.get_membrane_potential())#,label='ground truth') - plt.legend() - -#gca().set_axis_off() -#subplots_adjust(top = 1, bottom = 0, right = 1, left = 0, -# hspace = 0, wspace = 0) -#margins(0,0) -#gca().xaxis.set_major_locator(NullLocator()) -#gca().yaxis.set_major_locator(NullLocator()) - -plt.subplots_adjust(left=0.0, right=1.0, top=0.9, bottom=0.1) -fig.tight_layout() -plt.show() - -fig.savefig("single_trace.png", bbox_inches = 'tight', - pad_inches = 0) - - -# In[ ]: - -import pickle -import copy -import numpy as np -import pandas as pd -import matplotlib.pyplot - - -from neuronunit.models.reduced import ReducedModel -from neuronunit.optimization.model_parameters import model_params, path_params -LEMS_MODEL_PATH = path_params['model_path'] -import neuronunit.optimization as opt -import quantities as pq -fig = plt.figure() - -plt.clf() - -from neuronunit.optimization.data_transport_container import DataTC -for i in npcl['hardened']: - iparams = {} - iparams['injected_square_current'] = {} - iparams['injected_square_current']['amplitude'] = i[0].rheobase - model = None - model = ReducedModel(LEMS_MODEL_PATH,name = str('vanilla'),backend = ('RAW')) - model.set_attrs(i[0].attrs) - - #['amplitude'] = dtc.vtest[k]['injected_square_current']['amplitude'] - DELAY = 100.0*pq.ms - DURATION = 1000.0*pq.ms - iparams['injected_square_current']['delay'] = DELAY - iparams['injected_square_current']['duration'] = int(DURATION) - model.inject_square_current(iparams) - n_spikes = len(model.get_spike_train()) - if n_spikes: - print(n_spikes) - print(i[0].scores['RheobaseTestP']*pq.pA) - plt.plot(model.get_membrane_potential().times,model.get_membrane_potential())#,label='ground truth') - plt.legend() - -#gca().set_axis_off() -#subplots_adjust(top = 1, bottom = 0, right = 1, left = 0, -# hspace = 0, wspace = 0) -#margins(0,0) -#gca().xaxis.set_major_locator(NullLocator()) -#gca().yaxis.set_major_locator(NullLocator()) - -plt.subplots_adjust(left=0.0, right=1.0, top=0.9, bottom=0.1) -fig.tight_layout() -plt.show() - -fig.savefig("single_trace.png", bbox_inches = 'tight', - pad_inches = 0) - - -''' -hc = {} - -#free_params = ['c','k'] -for k,v in explore_param.items(): - if k not in free_params: - hc[k] = v -constants = npcl['hardened'][0][0].attrs -hc.update(constants) -npcl, _ = om.run_ga(explore_param,20,test_frame["Neocortex pyramidal cell layer 5-6"],free_params=free_params,hc = hc, NSGA = True) -''' - - -# In[ ]: - - -free_params = ['a','b','k']#vt','c','k','d']#,'vt','k','c','C']#,'C'] # this can only be odd numbers. - -## -# Use information that is available -## -hc = reduced_cells['RS'] - -hc['vr'] = -65.2261863636364 - -hc['vPeak'] = hc['vr'] + 86.364525297619 -hc['C'] = 89.7960714285714 -hc.pop('a',0) -hc.pop('b',0) -hc.pop('k',0) -hc.pop('c',0) -hc.pop('d',0) - -use_test = test_frame["Neocortex pyramidal cell layer 5-6"] -DO.seed_pop = npcl['pf'] -ga_out = DO.run(max_ngen = 15) -''' -hc = {} - -free_params = ['C'] - -for k,v in explore_param.items(): - if k not in free_params: - hc[k] = v -#,'vt','k','c','C']#,'C'] # this can only be odd numbers -constants = npcl['hardened'][0][0].attrs -hc.update(constants) -npcl, _ = om.run_ga(explore_param,20,test_frame["Neocortex pyramidal cell layer 5-6"],free_params=free_params,hc = hc, NSGA = True) -''' - - -# In[ ]: - -''' -import pandas - -try: - ne_raw = pandas.read_csv('article_ephys_metadata_curated.csv', delimiter='\t') - !ls -ltr *.csv -except: - !wget https://neuroelectro.org/static/src/article_ephys_metadata_curated.csv - ne_raw = pandas.read_csv('article_ephys_metadata_curated.csv', delimiter='\t') - -blah = ne_raw[ne_raw['NeuronName'].str.match('Hippocampus CA1 pyramidal cell')] -#ne_raw['NeuronName'] -#ne_raw['cell\ capacitance'] -#blah = ne_raw[ne_raw['NeuronName'].str.match('Hippocampus CA1 pyramidal cell')] - -print([i for i in blah.columns]) -#rint(blah['rheobase']) -#print(blah) -#for i in ne_raw.columns:#['NeuronName']: -# print(i) - -#ne_raw['NeuronName'][85] -#blah = ne_raw[ne_raw['TableID'].str.match('85')] -#ne_raw['n'] = 84 -#here = ne_raw[ne_raw['Index']==85] -here = ne_raw[ne_raw['TableID']==18] - -print(here['rheo_raw']) -#!wget https://neuroelectro.org/apica/1/n/ -''' - - -# In[ ]: - -ca1 = ne_raw[ne_raw['NeuronName'].str.match('Hippocampus CA1 pyramidal cell')] -ca1['rheo'] - - -# In[ ]: - - - -test_frame["Dentate gyrus basket cell"][0].observation['std'] = test_frame["Dentate gyrus basket cell"][0].observation['mean'] -for t in test_frame["Dentate gyrus basket cell"]: - print(t.name) - - print(t.observation) - - - -# -# ''' -# Inibitory Neuron -# This can't pass the Rheobase test -# ''' - -# In[ ]: - - -from neuronunit.optimization import optimization_management as om -import pickle - -free_params = ['a','vr','b','vt','vPeak','c','k'] -for k,v in explore_param.items(): - if k not in free_params: - hc[k] = v -use_test = test_frame["Dentate gyrus basket cell"] -bcell, _ = om.run_ga(explore_param,20,use_test,free_params=free_params,hc = hc, NSGA = True, MU = 4) - - -# In[ ]: - - - -#test_frame["Dentate gyrus basket cell"][0].observation['std'] = test_frame["Dentate gyrus basket cell"][0].observation['mean'] -for t in test_frame["Hippocampus CA1 pyramidal cell"]: - print(t.name) - - print(t.observation) - - -# In[ ]: - -use_test = test_frame["Hippocampus CA1 pyramidal cell"] -bcell, _ = om.run_ga(explore_param,20,use_test,free_params=free_params,hc = hc, NSGA = True, MU = 10) - - -# In[ ]: - -import pickle -import copy -import numpy as np -import pandas as pd -import matplotlib.pyplot as plt - -from neuronunit.models.reduced import ReducedModel -from neuronunit.optimization.model_parameters import model_params, path_params -LEMS_MODEL_PATH = path_params['model_path'] -import neuronunit.optimization as opt -import quantities as pq -fig = plt.figure() - -plt.clf() - -from neuronunit.optimization.data_transport_container import DataTC -for i in bcell['hardened'][0:6]: - iparams = {} - iparams['injected_square_current'] = {} - iparams['injected_square_current']['amplitude'] =i[0].rheobase - model = None - model = ReducedModel(LEMS_MODEL_PATH,name = str('vanilla'),backend = ('RAW')) - model.set_attrs(i[0].attrs) - - #['amplitude'] = dtc.vtest[k]['injected_square_current']['amplitude'] - DELAY = 100.0*pq.ms - DURATION = 1000.0*pq.ms - iparams['injected_square_current']['delay'] = DELAY - iparams['injected_square_current']['duration'] = int(DURATION) - model.inject_square_current(iparams) - n_spikes = len(model.get_spike_train()) - if n_spikes: - print(n_spikes) - print(i[0].scores['RheobaseTestP']*pq.pA) - plt.plot(model.get_membrane_potential().times,model.get_membrane_potential())#,label='ground truth') - plt.legend() - -#gca().set_axis_off() -#subplots_adjust(top = 1, bottom = 0, right = 1, left = 0, -# hspace = 0, wspace = 0) -#margins(0,0) -#gca().xaxis.set_major_locator(NullLocator()) -#gca().yaxis.set_major_locator(NullLocator()) - -plt.subplots_adjust(left=0.0, right=1.0, top=0.9, bottom=0.1) -fig.tight_layout() -plt.show() - -fig.savefig("single_trace.png", bbox_inches = 'tight', - pad_inches = 0) - - -# In[ ]: - - - - -# In[ ]: - -use_test = test_frame["Hippocampus CA1 pyramidal cell"] -bcell, _ = om.run_ga(explore_param,20,use_test,free_params=free_params,hc = hc, NSGA = True, MU = 10) - - -# # This cell is in markdown, but it won't be later. -# Later optimize a whole heap of cells in a loop. -# -# try: -# import pickle -# with open('data_dump.p','rb') as f: -# test_opt = pickle.load(f) -# except: -# MU = 12 -# NGEN = 25 -# cnt = 1 -# for t in use_test: -# if cnt==len(use_test): -# MU = 12 -# NGEN = 20 -# -# npcl, DO = om.run_ga(explore_param,NGEN,[t],free_params=free_params, NSGA = True, MU = MU) -# else: -# -# npcl, DO = om.run_ga(explore_param,NGEN,[t],free_params=free_params, NSGA = True, MU = MU) -# -# test_opt[str(t)] = {'out':npcl} -# -# ranges = {} -# stds = npcl['pop'][0].dtc.attrs -# for k in npcl['pop'][0].dtc.attrs.keys(): -# stds[k] = [] -# ranges[k] = [] -# -# -# for i in npcl['pop'][::5]: -# for k,v in i.dtc.attrs.items(): -# stds[k].append(v) -# ranges[k].append(v) -# -# for k in npcl['pop'][0].dtc.attrs.keys(): -# ranges[k] = (np.min(ranges[k][1::]),np.max(ranges[k][1::])) -# -# stds[k] = np.std(stds[k][1::]) -# test_opt[str(t)]['stds'] = stds -# test_opt[str(t)]['ranges'] = ranges -# -# cnt+=1 -# -# with open('data_dump.p','wb') as f: -# pickle.dump(test_opt,f) -# - -# In[ ]: diff --git a/neuronunit/unit_test/covariance_adaption_approach.py b/neuronunit/unit_test/covariance_adaption_approach.py deleted file mode 100644 index 1813ec0eb..000000000 --- a/neuronunit/unit_test/covariance_adaption_approach.py +++ /dev/null @@ -1,26 +0,0 @@ -from neuronunit.optimzation import optimization_management - -creator.create("FitnessMin", base.Fitness, weights=(-1.0,)) -creator.create("Individual", list, fitness=creator.FitnessMin) - -toolbox = base.Toolbox() -toolbox.register("evaluate", benchmarks.rastrigin) -self.toolbox.register("evaluate", optimization_management.evaluate) - -#Then, it does not get any harder. Once a Strategy is instantiated, its generate() and update() methods are registered in the toolbox for uses in the eaGenerateUpdate() algorithm. The generate() method is set to produce the created Individual class. The random number generator from numpy is seeded because the cma module draws all its number from it. - -def main(): - numpy.random.seed(128) - - strategy = cma.Strategy(centroid=[5.0]*N, sigma=5.0, lambda_=20*N) - toolbox.register("generate", strategy.generate, creator.Individual) - toolbox.register("update", strategy.update) - - hof = tools.HallOfFame(1) - stats = tools.Statistics(lambda ind: ind.fitness.values) - stats.register("avg", numpy.mean) - stats.register("std", numpy.std) - stats.register("min", numpy.min) - stats.register("max", numpy.max) - - algorithms.eaGenerateUpdate(toolbox, ngen=250, stats=stats, halloffame=hof) diff --git a/neuronunit/unit_test/data_transport_container.py b/neuronunit/unit_test/data_transport_container.py deleted file mode 100644 index cbf7efe95..000000000 --- a/neuronunit/unit_test/data_transport_container.py +++ /dev/null @@ -1,30 +0,0 @@ - -class DataTC(object): - ''' - Data Transport Vessel - - This Object class serves as a data type for storing rheobase search - attributes and apriori model parameters, - with the distinction that unlike the NEURON model this class - can be cheaply transported across HOSTS/CPUs - ''' - def __init__(self): - self.lookup = {} - self.rheobase = None - self.previous = 0 - self.run_number = 0 - self.attrs = None - self.steps = None - self.name = None - self.results = None - self.fitness = None - self.score = None - self.boolean = False - self.initiated = False - self.delta = [] - self.evaluated = False - self.results = {} - self.searched = [] - self.searchedd = {} - self.cached_attrs = {} - self.backend = None diff --git a/neuronunit/unit_test/doc_tests.py b/neuronunit/unit_test/doc_tests.py index 65bc4d4b3..17c650cbb 100644 --- a/neuronunit/unit_test/doc_tests.py +++ b/neuronunit/unit_test/doc_tests.py @@ -2,31 +2,32 @@ from .base import * -class DocumentationTestCase(NotebookTools, - unittest.TestCase): + +class DocumentationTestCase(NotebookTools, unittest.TestCase): """Testing documentation notebooks""" - path = '../../docs' + path = "../../docs" - #@unittest.skip("Skipping chapter 1") + @unittest.skip("Skipping chapter 1") def test_chapter1(self): - self.do_notebook('chapter1') + self.do_notebook("chapter1") @unittest.skip("Skipping chapter 2") def test_chapter2(self): - self.do_notebook('chapter2') + self.do_notebook("chapter2") @unittest.skip("Skipping chapter 3") def test_chapter3(self): - self.do_notebook('chapter3') + self.do_notebook("chapter3") @unittest.skip("Skipping chapter 4") def test_chapter4(self): - self.do_notebook('chapter4') + self.do_notebook("chapter4") @unittest.skip("Skipping chapter 5") def test_chapter5(self): - self.do_notebook('chapter5') + self.do_notebook("chapter5") + -if __name__ == '__main__': +if __name__ == "__main__": unittest.main() diff --git a/neuronunit/unit_test/druckman_tests.ipynb b/neuronunit/unit_test/druckman_tests.ipynb deleted file mode 100644 index 4f6b2190b..000000000 --- a/neuronunit/unit_test/druckman_tests.ipynb +++ /dev/null @@ -1,750 +0,0 @@ -{ - "cells": [ - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [] - }, - { - "cell_type": "code", - "execution_count": 1, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Requirement already up-to-date: numpy in /opt/conda/lib/python3.5/site-packages (1.15.4)\n", - "Requirement already up-to-date: numba in /opt/conda/lib/python3.5/site-packages (0.41.0)\n", - "Requirement not upgraded as not directly required: llvmlite>=0.26.0dev0 in /opt/conda/lib/python3.5/site-packages (from numba) (0.26.0)\n", - "\u001b[31mcryptography 2.2.1 requires asn1crypto>=0.21.0, which is not installed.\u001b[0m\n", - "\u001b[31mcffi 1.11.5 requires pycparser, which is not installed.\u001b[0m\n", - "\u001b[31mallensdk 0.14.2 has requirement pandas<0.20.0,>=0.16.2, but you'll have pandas 0.23.1 which is incompatible.\u001b[0m\n", - "\u001b[33mYou are using pip version 10.0.1, however version 18.1 is available.\n", - "You should consider upgrading via the 'pip install --upgrade pip' command.\u001b[0m\n", - "Requirement already satisfied: lmfit in /opt/conda/lib/python3.5/site-packages (0.9.12)\n", - "Requirement already satisfied: numpy>=1.10 in /opt/conda/lib/python3.5/site-packages (from lmfit) (1.15.4)\n", - "Requirement already satisfied: six>1.10 in /opt/conda/lib/python3.5/site-packages (from lmfit) (1.11.0)\n", - "Requirement already satisfied: asteval>=0.9.12 in /opt/conda/lib/python3.5/site-packages (from lmfit) (0.9.13)\n", - "Requirement already satisfied: uncertainties>=3.0 in /opt/conda/lib/python3.5/site-packages (from lmfit) (3.0.3)\n", - "Requirement already satisfied: scipy>=0.17 in /opt/conda/lib/python3.5/site-packages (from lmfit) (1.1.0)\n", - "\u001b[31mcffi 1.11.5 requires pycparser, which is not installed.\u001b[0m\n", - "\u001b[31mcryptography 2.2.1 requires asn1crypto>=0.21.0, which is not installed.\u001b[0m\n", - "\u001b[31mallensdk 0.14.2 has requirement pandas<0.20.0,>=0.16.2, but you'll have pandas 0.23.1 which is incompatible.\u001b[0m\n", - "\u001b[33mYou are using pip version 10.0.1, however version 18.1 is available.\n", - "You should consider upgrading via the 'pip install --upgrade pip' command.\u001b[0m\n" - ] - } - ], - "source": [ - "!pip install numpy numba --upgrade \n", - "!pip install lmfit \n", - "\n", - "\n", - " assert np.isin\n" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "electro_tests = []\n", - "obs_frame = {}\n", - "test_frame = {}\n", - "import os\n", - "import pickle\n", - "try: \n", - "\n", - " electro_path = str(os.getcwd())+'all_tests.p'\n", - "\n", - " assert os.path.isfile(electro_path) == True\n", - " with open(electro_path,'rb') as f:\n", - " (obs_frame,test_frame) = pickle.load(f)\n", - "\n", - "except:\n", - " for p in pipe:\n", - " p_tests, p_observations = get_neab.get_neuron_criteria(p)\n", - " obs_frame[p[\"name\"]] = p_observations#, p_tests))\n", - " test_frame[p[\"name\"]] = p_tests#, p_tests))\n", - " electro_path = str(os.getcwd())+'all_tests.p'\n", - " with open(electro_path,'wb') as f:\n", - " pickle.dump((obs_frame,test_frame),f)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "import sciunit, neuronunit, quantities\n", - "from neuronunit.tests.dm import *\n", - "import time\n", - "from neuronunit.tests import dm #this is me importing the Druckman tests\n", - "from neuronunit.tests import RheobaseTestP, fi, RheobaseTest \n", - "\n", - "import matplotlib.pyplot as plt\n", - "\n", - "from neuronunit.models.reduced import ReducedModel\n", - "from neuronunit.optimization.data_transport_container import DataTC\n", - "from neuronunit.optimization.model_parameters import model_params, path_params, transcribe_units\n", - "from neuronunit.optimization.optimization_management import mint_generic_model\n", - "LEMS_MODEL_PATH = path_params['model_path']\n", - "\n", - "\n", - "model = ReducedModel(LEMS_MODEL_PATH,name = str('vanilla'),backend = ('RAW'))\n", - "dtc = DataTC()\n", - "dtc.attrs = model.attrs\n", - "dtc.ampl=0\n", - "dtc.attrs_raw = {'C':89.7960714285714, 'a':0.01, 'b':15, 'c':-60, 'd':10,\\\n", - " 'k':1.6, 'vPeak':(86.364525297619-65.2261863636364),\\\n", - " 'vr':-65.2261863636364, 'vt':-50}\n", - "\n", - "\n", - "dtc.attrs = transcribe_units(dtc.attrs_raw)\n", - "\n", - "start0 = time.time()\n", - "model = ReducedModel(LEMS_MODEL_PATH, name= str('vanilla'), backend=('NEURON', {'DTC':dtc}))\n", - "rt = RheobaseTestP(obs_frame['Dentate gyrus basket cell']['Rheobase'])\n", - "pred0 = rt.generate_prediction(model)\n", - "end0 = time.time()\n", - "print(model.attrs)\n", - "\n", - "model = ReducedModel(LEMS_MODEL_PATH, name= str('vanilla'), backend=('NEURON', {'DTC':dtc}))\n", - "start1 = time.time()\n", - "rt = fi.RheobaseTest(obs_frame['Dentate gyrus basket cell']['Rheobase'])\n", - "pred1 = rt.generate_prediction(model)\n", - "end1 = time.time()\n", - "\n", - "print('parallel Rhsearch time NEURON', end0-start0)\n", - "print('serial Rhsearch time NEURON',end1-start1)\n", - "\n", - "\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": false, - "scrolled": true - }, - "outputs": [], - "source": [ - "explore_ranges = {'E_Na' : (40,70), 'E_K': (-90.0,-75.0)}\n", - "\n", - "attrs = { 'g_K' : 36.0, 'g_Na' : 120.0, 'g_L' : 0.3, \\\n", - " 'C_m' : 1.0, 'E_L' : -54.387, 'E_K' : -77.0, 'E_Na' : 50.0, 'vr':-65.0 } \n", - "\n", - "model = ReducedModel(LEMS_MODEL_PATH,name = str('vanilla'),backend = ('HH'))\n", - "model.attrs = attrs\n", - "dtc.attrs = attrs\n", - "iparams = {}\n", - "iparams['injected_square_current'] = {}\n", - "iparams['injected_square_current']['amplitude'] = 1.98156805*pq.pA\n", - "iparams['injected_square_current']['amplitude'] = 2.98156805*pq.pA\n", - "\n", - "DELAY = 100.0*pq.ms\n", - "DURATION = 1000.0*pq.ms\n", - "iparams['injected_square_current']['delay'] = DELAY\n", - "iparams['injected_square_current']['duration'] = int(DURATION)\n", - "bf = time.time()\n", - "model.inject_square_current(iparams)\n", - "vm = model.get_membrane_potential()\n", - "af = time.time()\n", - "#volts = [v[0] for v in vm ]\n", - "print(len(vm[0]),len(vm.times))\n", - "\n", - "\n", - "bfp = time.time()\n", - "model = ReducedModel(LEMS_MODEL_PATH, name= str('vanilla'), backend=('HH', {'DTC':dtc}))\n", - "model._backend.cell_name = str('vanilla')\n", - "rt = RheobaseTestP(obs_frame['Dentate gyrus basket cell']['Rheobase'])\n", - "pred0 = rt.generate_prediction(model)\n", - "afp = time.time()\n", - "print(model.attrs)\n", - "plt.plot(vm.times,vm)\n", - "plt.show()\n", - "print('elapsed parallel: ',afp-bfp)\n", - "\n", - "\n", - "bfs = time.time()\n", - "model = ReducedModel(LEMS_MODEL_PATH, name= str('vanilla'), backend=('HH', {'DTC':dtc}))\n", - "model._backend.cell_name = str('vanilla')\n", - "\n", - "rt = RheobaseTest(obs_frame['Dentate gyrus basket cell']['Rheobase'])\n", - "pred0 = rt.generate_prediction(model)\n", - "afs = time.time()\n", - "print(model.attrs)\n", - "plt.plot(vm.times,vm)\n", - "plt.show()\n", - "print('elapsed serial: ',afs-bfs)\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "\n", - "import pdb\n", - "with open(electro_path,'rb') as f:\n", - " (obs_frame,test_frame) = pickle.load(f)\n", - "use_test = test_frame[\"Neocortex pyramidal cell layer 5-6\"]\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "use_test[0].observation\n", - "#from neuronunit.tests import RheobaseP\n", - "from neuronunit.tests.fi import RheobaseTestP# as discovery\n", - "\n", - "rtp = RheobaseTestP(use_test[0].observation)\n", - "use_test[0] = rtp" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "explore_ranges = {'E_Na' : (40.0,70.0), 'E_K': (-90.0,-75.0), 'g_K':(30,40), 'g_Na':(100.0,140.0), 'g_L':(0.1,0.5), 'E_L':(-60.0,-45.0)}\n", - "\n", - "attrs = { 'g_K' : 36.0, 'g_Na' : 120.0, 'g_L' : 0.3, \\\n", - " 'C_m' : 1.0, 'E_L' : -54.387, 'E_K' : -77.0, 'E_Na' : 50.0, 'vr':-65.0 } \n", - "\n", - " \n", - "from neuronunit.optimization import optimization_management as om\n", - "print(test_frame) \n", - "MU = 12\n", - "NGEN = 25\n", - "cnt = 1\n", - "#hc = { 'g_L' : 0.3, 'E_L' : -54.387,\n", - "hc = {'vr':-65.0, 'C_m':1.0 } \n", - "\n", - "npcl, DO = om.run_ga(explore_ranges,NGEN,use_test,free_params=explore_ranges.keys(), hc = hc, NSGA = True, MU = MU,model_type='HH')\n", - " \n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "attrs = {'cm': 0.20000000000000001,\n", - " 'e_rev_E': 0.0,\n", - " 'e_rev_I': -80.0,\n", - " 'e_rev_K': -90.0,\n", - " 'e_rev_Na': 50.0,\n", - " 'e_rev_leak': -65.0,\n", - " 'g_leak': 0.01,\n", - " 'gbar_K': 6.0,\n", - " 'gbar_Na': 20.0,\n", - " 'i_offset': 0.0,\n", - " 'tau_syn_E': 0.20000000000000001,\n", - " 'tau_syn_I': 2.0,\n", - " 'v_offset': -63.0}\n", - "\n", - "# def __init__(self, \n", - "# I_ampl=10., g_leak=0.3,\n", - "# g_K=36., g_Na=120., V_leak=-54.402, V_K=-77., V_Na=50.):\n", - "\n", - "dtc.attrs = attrs\n", - "bfp = time.time()\n", - "#model = ReducedModel(LEMS_MODEL_PATH, name= str('vanilla'), backend=('HH', {'DTC':dtc}))\n", - "model = ReducedModel(LEMS_MODEL_PATH,name = str('vanilla'),backend = ('HHpyNN', {'DTC':dtc}))\n", - "\n", - "rt0 = RheobaseTestP(obs_frame['Dentate gyrus basket cell']['Rheobase'])\n", - "pred0 = rt.generate_prediction(model)\n", - "afp = time.time()\n", - "print('elapsed parallel: ',afp-bfp)\n", - "\n", - "\n", - "bfs = time.time()\n", - "#model = ReducedModel(LEMS_MODEL_PATH, name= str('vanilla'), backend=('HH', {'DTC':dtc}))\n", - "model = ReducedModel(LEMS_MODEL_PATH,name = str('vanilla'),backend = ('HHpyNN', {'DTC':dtc}))\n", - "\n", - "rt1 = RheobaseTest(obs_frame['Dentate gyrus basket cell']['Rheobase'])\n", - "pred1 = rt.generate_prediction(model)\n", - "afs = time.time()\n", - "\n", - "\n", - "print(model.attrs)\n", - "plt.plot(vm.times,vm)\n", - "plt.show()\n", - "print('elapsed Serial: ',afs-bfs)\n", - "print(rt0,rt1)\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "attrs = { 'gK' : 36.0, 'gNa' : 120.0, 'gL' : 0.3, 'Cm' : 1.0, 'Vl' : -10.402, 'VK' : -12.0, 'VNa' : -115, 'vr':-58.402 } \n", - "\n", - "C_m = 1\n", - "V_Na = -115\n", - "V_K = 12\n", - "V_l = -10.613\n", - "g_Na = 120\n", - "g_K = 36\n", - "g_l = 0.3\n", - "\n", - "model = ReducedModel(LEMS_MODEL_PATH,name = str('vanilla'),backend = ('HH'))\n", - "model.attrs = attrs\n", - "iparams['injected_square_current']['amplitude'] = 15.6805*pq.pA\n", - "\n", - "model.inject_square_current(iparams)\n", - "vm = model.get_membrane_potential()\n", - "af = time.time()\n", - "#volts = [v[0] for v in vm ]\n", - "print(len(vm[0]),len(vm.times))\n", - "plt.plot(vm.times,vm)\n", - "\n", - "len(vm)\n", - "len(vm.times)\n", - "np.shape(vm)\n", - "print(vm[6000])\n", - "print(vm[5000])\n", - "print(vm[0])" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "start4 = time.time()\n", - "\n", - "model = ReducedModel(LEMS_MODEL_PATH, name= str('vanilla'), backend=('RAW'))\n", - "rt = fi.RheobaseTestP(obs_frame['Dentate gyrus basket cell']['Rheobase'])\n", - "pred1 = rt.generate_prediction(model)\n", - "end4 = time.time()\n", - "\n", - "\n", - "start3 = time.time()\n", - "\n", - "model = ReducedModel(LEMS_MODEL_PATH, name= str('vanilla'), backend=('RAW'))\n", - "\n", - "rt = fi.RheobaseTest(obs_frame['Dentate gyrus basket cell']['Rheobase'])\n", - "pred1 = rt.generate_prediction(model)\n", - "end3 = time.time()\n", - "\n", - "print(pred1)\n", - "ir_currents = {}\n", - "ir_currents = pred1['value']\n", - "standard = 1.5*ir_currents\n", - "standard*=1.5\n", - "strong = 3*ir_currents\n", - "print(standard,strong,ir_currents)\n", - "\n", - "\n", - "\n", - "print('parallel Rhsearch time RAW', end4-start4)\n", - "print('serial Rhsearch time RAW',end3-start3)\n", - "\n", - "print(pred)\n", - "#for i in npcl['pf'][0:2]:\n", - "iparams = {}\n", - "iparams['injected_square_current'] = {}\n", - "iparams['injected_square_current']['amplitude'] = pred1['value']\n", - "model = None\n", - "model = ReducedModel(LEMS_MODEL_PATH,name = str('vanilla'),backend = ('RAW'))\n", - "#model.set_attrs(i.dtc.attrs)\n", - "\n", - "#['amplitude'] = dtc.vtest[k]['injected_square_current']['amplitude']\n", - "DELAY = 100.0*pq.ms\n", - "DURATION = 1000.0*pq.ms\n", - "iparams['injected_square_current']['delay'] = DELAY\n", - "iparams['injected_square_current']['duration'] = int(DURATION)\n", - "model.inject_square_current(iparams)\n", - "n_spikes = len(model.get_spike_train())\n", - "\n", - "if n_spikes:\n", - " print(n_spikes)\n", - " #print(i[0].scores['RheobaseTestP']*pq.pA)\n", - " plt.plot(model.get_membrane_potential().times,model.get_membrane_potential())#,label='ground truth')\n", - " plt.legend()\n", - "print(obs_frame['Dentate gyrus basket cell']['Rheobase'])\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "#speed_up= (end1-start1)/(end0-start0)\n", - "#print(speed_up, 'speed up for NEURON')\n", - "speed_up= (end3-start3)/(end4-start4)\n", - "print(speed_up, 'speed up (slow down) for rawpy')" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "These results show that parallel rheobase is ~3.5-7 times faster for NEURON, but slower for numba jit depending on model.\n", - "\n", - "This makes sense, because numba jit evaluations are over so quickly, it rivals the time, for interprocessor communication, not so with NEURON simulations, where simulation takes a long time.\n", - "\n", - "The reason parallel is faster given interprocessor comm speed < sim evaluation time, is because in the case of binary search.\n", - "\n", - "For each sim evaluation, the search engine only narrows by 50%.\n", - "\n", - "In the parallel case, 8 simultaneous sim evaluations, are able to narrow the search interval space, by 7/8ths.\n", - "\n", - "This fast narrowing of intervals is what makes the parallel case faster than the binary case." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "\n", - "tests = [AP12AmplitudeDropTest(standard), \n", - " AP1SSAmplitudeChangeTest(standard), \n", - " AP1AmplitudeTest(standard), \n", - " AP1WidthHalfHeightTest(standard), \n", - " AP1WidthPeakToTroughTest(standard), \n", - " AP1RateOfChangePeakToTroughTest(standard), \n", - " AP1AHPDepthTest(standard), \n", - " AP2AmplitudeTest(standard), \n", - " AP2WidthHalfHeightTest(standard), \n", - " AP2WidthPeakToTroughTest(standard), \n", - " AP2RateOfChangePeakToTroughTest(standard), \n", - " AP2AHPDepthTest(standard), \n", - " AP12AmplitudeChangePercentTest(standard), \n", - " AP12HalfWidthChangePercentTest(standard), \n", - " AP12RateOfChangePeakToTroughPercentChangeTest(standard), \n", - " AP12AHPDepthPercentChangeTest(standard), \n", - " AP1DelayMeanTest(standard), \n", - " AP1DelaySDTest(standard), \n", - " AP2DelayMeanTest(standard), \n", - " AP2DelaySDTest(standard), \n", - " Burst1ISIMeanTest(standard), \n", - " Burst1ISISDTest(standard), \n", - " InitialAccommodationMeanTest(standard), \n", - " SSAccommodationMeanTest(standard), \n", - " AccommodationRateToSSTest(standard), \n", - " AccommodationAtSSMeanTest(standard), \n", - " AccommodationRateMeanAtSSTest(standard), \n", - " ISICVTest(standard), \n", - " ISIMedianTest(standard), \n", - " ISIBurstMeanChangeTest(standard), \n", - " SpikeRateStrongStimTest(strong), \n", - " AP1DelayMeanStrongStimTest(strong), \n", - " AP1DelaySDStrongStimTest(strong), \n", - " AP2DelayMeanStrongStimTest(strong), \n", - " AP2DelaySDStrongStimTest(strong), \n", - " Burst1ISISDStrongStimTest(strong),\n", - " Burst1ISIMeanStrongStimTest(strong)]\n", - "\n", - "AHP_list = [AP1AHPDepthTest(standard), \n", - " AP2AHPDepthTest(standard), \n", - " AP12AHPDepthPercentChangeTest(standard) ] \n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "print(ir_currents)\n", - "print(standard)\n", - "print(strong)\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "start2 = time.time()\n", - "\n", - "model = ReducedModel(LEMS_MODEL_PATH, name= str('vanilla'), backend=('RAW'))\n", - "\n", - " \n", - "for i, test in enumerate(tests):\n", - " mean = test.generate_prediction(model)['mean']\n", - " \n", - " \n", - "\n", - " #print(mean,test)\n", - "stop2 = time.time()\n", - "delta2 = stop2-start2\n", - "print('serial time: ',stop2-start2)\n", - "\n", - "'''\n", - "USING NEURON WOULD TAKE HALF AN HOUR\n", - "start3 = time.time()\n", - "model = ReducedModel(LEMS_MODEL_PATH, name= str('vanilla'), backend=('NEURON', {'DTC':dtc}))\n", - "model.atts = dtc.attrs\n", - "\n", - "\n", - "for i, test in enumerate(tests):\n", - " mean = test.generate_prediction(model)['mean']\n", - " #print(mean, tests)\n", - "stop3 = time.time()\n", - "print(stop3-start3)\n", - "'''" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "# can do these tests in parallel:\n", - "import dask.bag as db\n", - "import multiprocessing\n", - "npart = multiprocessing.cpu_count()\n", - "\n", - "\n", - "start5 = time.time()\n", - "bag = db.from_sequence(tests, npartitions = npart)\n", - "means = list(bag.map(takes_tests).compute()) \n", - "end5 = time.time()\n", - "#print(end5-start5)\n", - "\n", - "print(means)\n", - "print('parallel time: ',end5-start5)\n", - "print('speed up:',delta2/(end5-start5))\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "dmtests = dm.Druckmann2013Test\n", - "d_tests = []\n", - "for d in dir(dm):\n", - " if \"Test\" in d:\n", - " exec('d_tests.append(dm.'+str(d)+')')\n", - "#print()\n", - "dt = d_tests[1:-1]\n", - "print(dt)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": true - }, - "outputs": [], - "source": [ - "import quantities as pq\n", - "current_amplitude = {'mean': 106.7 * pq.pA, 'n': 1, 'std': 0.0 * pq.pA}\n", - "test = dm.AP12AmplitudeChangePercentTest(current_amplitude)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "import os\n", - "import pickle\n", - "import matplotlib.pyplot as plt\n", - "electro_path = str(os.getcwd())+'all_tests.p'\n", - "\n", - "assert os.path.isfile(electro_path) == True\n", - "with open(electro_path,'rb') as f:\n", - " (obs_frame,test_frame) = pickle.load(f)\n", - "\n", - "\n", - "rt = RheobaseTestP(obs_frame['Dentate gyrus basket cell']['Rheobase'])\n", - "\n", - "pred = rt.generate_prediction(model)\n", - "print(pred)\n", - "#for i in npcl['pf'][0:2]:\n", - "iparams = {}\n", - "iparams['injected_square_current'] = {}\n", - "iparams['injected_square_current']['amplitude'] = pred['value']\n", - "model = None\n", - "model = ReducedModel(LEMS_MODEL_PATH,name = str('vanilla'),backend = ('RAW'))\n", - "#model.set_attrs(i.dtc.attrs)\n", - "\n", - "#['amplitude'] = dtc.vtest[k]['injected_square_current']['amplitude']\n", - "DELAY = 100.0*pq.ms\n", - "DURATION = 1000.0*pq.ms\n", - "iparams['injected_square_current']['delay'] = DELAY\n", - "iparams['injected_square_current']['duration'] = int(DURATION)\n", - "model.inject_square_current(iparams)\n", - "n_spikes = len(model.get_spike_train())\n", - "\n", - "if n_spikes:\n", - " print(n_spikes)\n", - " #print(i[0].scores['RheobaseTestP']*pq.pA)\n", - " plt.plot(model.get_membrane_potential().times,model.get_membrane_potential())#,label='ground truth')\n", - " plt.legend()\n", - "print(obs_frame['Dentate gyrus basket cell']['Rheobase'])\n", - " \n", - " " - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "print(model)\n", - "help(dt[0])\n", - "dt = dt[0]()\n", - "dt[0].generate_prediction(model)\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": true - }, - "outputs": [], - "source": [ - "'''\n", - "import pdb\n", - "for d in dt:\n", - " pdb.set_trace()\n", - " #dmtO = d(pred['value'])#obs_frame['Dentate gyrus basket cell']['Rheobase'])\n", - "print(dmt0)\n", - "'''" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": true - }, - "outputs": [], - "source": [] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": true - }, - "outputs": [], - "source": [] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": true - }, - "outputs": [], - "source": [] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.5.2" - } - }, - "nbformat": 4, - "nbformat_minor": 2 -} diff --git a/neuronunit/unit_test/dynamic_bind.ipynb b/neuronunit/unit_test/dynamic_bind.ipynb deleted file mode 100644 index f7be93b01..000000000 --- a/neuronunit/unit_test/dynamic_bind.ipynb +++ /dev/null @@ -1,498 +0,0 @@ -{ - "cells": [ - { - "cell_type": "code", - "execution_count": 1, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Requirement already satisfied: lazyarray in /opt/conda/lib/python3.5/site-packages (0.3.2)\n", - "Requirement already satisfied: numpy>=1.8 in /opt/conda/lib/python3.5/site-packages (from lazyarray) (1.12.1)\n", - "\u001b[31mcryptography 2.2.1 requires asn1crypto>=0.21.0, which is not installed.\u001b[0m\n", - "\u001b[31mcffi 1.11.5 requires pycparser, which is not installed.\u001b[0m\n", - "\u001b[31mallensdk 0.14.2 has requirement pandas<0.20.0,>=0.16.2, but you'll have pandas 0.23.1 which is incompatible.\u001b[0m\n", - "\u001b[33mYou are using pip version 10.0.1, however version 18.1 is available.\n", - "You should consider upgrading via the 'pip install --upgrade pip' command.\u001b[0m\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/opt/conda/lib/python3.5/site-packages/pyNN/neuron/__init__.py:14: UserWarning: mpi4py not available\n", - " warnings.warn(\"mpi4py not available\")\n" - ] - } - ], - "source": [ - "from pyNN.neuron import *\n", - "from pyNN.neuron import HH_cond_exp\n", - "from pyNN.neuron import EIF_cond_exp_isfa_ista\n", - "from pyNN.neuron import Izhikevich\n", - "\n", - "from pyNN import neuron\n", - "#\n", - "from pyNN.neuron import simulator as sim\n", - "from pyNN.neuron import setup as setup\n", - "\n", - "from pyNN.neuron import DCSource\n", - "from types import MethodType\n" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "import io\n", - "import math\n", - "import pdb\n", - "#from numba import jit\n", - "from sciunit.utils import redirect_stdout\n", - "import numpy as np\n", - "#from .base import *\n", - "import quantities as qt\n", - "from quantities import mV, ms, s\n", - "import matplotlib.pyplot as plt\n", - "from pyNN.neuron import *\n", - "from pyNN.neuron import HH_cond_exp\n", - "from pyNN.neuron import EIF_cond_exp_isfa_ista\n", - "from pyNN.neuron import Izhikevich\n", - "from elephant.spike_train_generation import threshold_detection\n", - "\n", - "from pyNN import neuron\n", - "#\n", - "#from pyNN.neuron import simulator as sim\n", - "from pyNN.neuron import setup as setup\n", - "#from pyNN.neuron import Izhikevich\n", - "#from pyNN.neuron import Population\n", - "from pyNN.neuron import DCSource\n", - "import numpy as np\n", - "import copy\n", - "from neo import AnalogSignal\n", - "import neuronunit.capabilities.spike_functions as sf\n", - "\n", - "import neuronunit.capabilities as cap\n", - "cap.ReceivesCurrent\n", - "cap.ProducesActionPotentials\n", - "def bind_NU_interface(model):\n", - "\n", - " def load_model(self):\n", - " neuron = None\n", - " from pyNN import neuron\n", - " self.hhcell = neuron.create(EIF_cond_exp_isfa_ista())\n", - " neuron.setup(timestep=self.dt, min_delay=1.0)\n", - "\n", - "\n", - " def init_backend(self, attrs = None, cell_name= 'HH_cond_exp', current_src_name = 'hannah', DTC = None, dt=0.01):\n", - " backend = 'HHpyNN'\n", - " self.current_src_name = current_src_name\n", - " self.cell_name = cell_name\n", - " self.adexp = True\n", - "\n", - " self.DCSource = DCSource\n", - " self.setup = setup\n", - " self.model_path = None\n", - " self.related_data = {}\n", - " self.lookup = {}\n", - " self.attrs = {}\n", - " self.neuron = neuron\n", - " self.model._backend = str('ExternalSim')\n", - " self.backend = self\n", - " self.model.attrs = {}\n", - "\n", - " #self.orig_lems_file_path = 'satisfying'\n", - " #self.model._backend.use_memory_cache = False\n", - " #self.model.unpicklable += ['h','ns','_backend']\n", - " self.dt = dt\n", - " if type(DTC) is not type(None):\n", - " if type(DTC.attrs) is not type(None):\n", - "\n", - " self.set_attrs(**DTC.attrs)\n", - " assert len(self.model.attrs.keys()) > 0\n", - "\n", - " if hasattr(DTC,'current_src_name'):\n", - " self._current_src_name = DTC.current_src_name\n", - "\n", - " if hasattr(DTC,'cell_name'):\n", - " self.cell_name = DTC.cell_name\n", - " \n", - " self.load_model()\n", - "\n", - " def get_membrane_potential(self):\n", - " \"\"\"Must return a neo.core.AnalogSignal.\n", - " And must destroy the hoc vectors that comprise it.\n", - " \"\"\"\n", - " #dt = float(copy.copy(self.neuron.dt))\n", - " data = self.hhcell.get_data().segments[0]\n", - " volts = data.filter(name=\"v\")[0]#/10.0\n", - " #data_block = all_cells.get_data()\n", - "\n", - " vm = AnalogSignal(volts,\n", - " units = mV,\n", - " sampling_period = self.dt *ms)\n", - " #results['vm'] = vm\n", - " return vm#data.filter(name=\"v\")[0]\n", - "\n", - " def _local_run(self):\n", - " '''\n", - " pyNN lazy array demands a minimum population size of 3. Why is that.\n", - " '''\n", - " results = {}\n", - " DURATION = 1000.0\n", - " \n", - " #ctx_cells.celltype.recordable\n", - " \n", - " \n", - " if self.celltype == 'HH_cond_exp':\n", - "\n", - " self.hhcell.record('spikes','v')\n", - "\n", - " else:\n", - " self.neuron.record_v(self.hhcell, \"Results/HH_cond_exp_%s.v\" % str(neuron))\n", - "\n", - " #self.neuron.record_gsyn(self.hhcell, \"Results/HH_cond_exp_%s.gsyn\" % str(neuron))\n", - " self.neuron.run(DURATION)\n", - " data = self.hhcell.get_data().segments[0]\n", - " volts = data.filter(name=\"v\")[0]#/10.0\n", - " #data_block = all_cells.get_data()\n", - "\n", - " vm = AnalogSignal(volts,\n", - " units = mV,\n", - " sampling_period = self.dt *ms)\n", - " results['vm'] = vm\n", - " results['t'] = vm.times # self.times\n", - " results['run_number'] = results.get('run_number',0) + 1\n", - " return results\n", - "\n", - "\n", - "\n", - "\n", - " def set_attrs(self,**attrs):\n", - " self.init_backend()\n", - " self.model.attrs.update(attrs)\n", - " assert type(self.model.attrs) is not type(None)\n", - " self.hhcell[0].set_parameters(**attrs)\n", - " return self\n", - "\n", - "\n", - " def inject_square_current(self,current):\n", - " attrs = copy.copy(self.model.attrs)\n", - " self.init_backend()\n", - " self.set_attrs(**attrs)\n", - " c = copy.copy(current)\n", - " if 'injected_square_current' in c.keys():\n", - " c = current['injected_square_current']\n", - "\n", - " stop = float(c['delay'])+float(c['duration'])\n", - " duration = float(c['duration'])\n", - " start = float(c['delay'])\n", - " amplitude = float(c['amplitude'])\n", - " electrode = self.neuron.DCSource(start=start, stop=stop, amplitude=amplitude)\n", - "\n", - "\n", - " electrode.inject_into(self.hhcell)\n", - " self.results = self._local_run()\n", - " self.vm = self.results['vm']\n", - "\n", - " def get_APs(self,vm):\n", - " vm = self.get_membrane_potential()\n", - " waveforms = sf.get_spike_waveforms(vm,threshold=-45.0*mV)\n", - " return waveforms\n", - "\n", - " def get_spike_train(self,**run_params):\n", - " vm = self.get_membrane_potential()\n", - "\n", - " spike_train = threshold_detection(vm,threshold=-45.0*mV)\n", - "\n", - " return spike_train\n", - " \n", - " def get_spike_count(self,**run_params):\n", - " vm = self.get_membrane_potential()\n", - " return len(threshold_detection(vm,threshold=-45.0*mV))\n", - " \n", - " model.init_backend = MethodType(init_backend,model)\n", - " model.get_spike_count = MethodType(get_spike_count,model)\n", - " model.get_APs = MethodType(get_APs,model)\n", - " model.get_spike_train = MethodType(get_spike_train,model)\n", - " model.set_attrs = MethodType(set_attrs, model) # Bind to the score.\n", - " model.inject_square_current = MethodType(inject_square_current, model) # Bind to the score.\n", - " model.set_attrs = MethodType(set_attrs, model) # Bind to the score.\n", - " model.get_membrane_potential = MethodType(get_membrane_potential,model)\n", - " model.load_model = MethodType(load_model, model) # Bind to the score.\n", - " model._local_run = MethodType(_local_run,model)\n", - " model.init_backend(model)\n", - " #model.load_model() #= MethodType(load_model, model) # Bind to the score.\n", - "\n", - " return model\n", - "HH_cond_exp = bind_NU_interface(HH_cond_exp) \n", - "#HH_cond_exp" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "electro_tests = []\n", - "obs_frame = {}\n", - "test_frame = {}\n", - "import os\n", - "import pickle\n", - "try: \n", - "\n", - " electro_path = str(os.getcwd())+'all_tests.p'\n", - "\n", - " assert os.path.isfile(electro_path) == True\n", - " with open(electro_path,'rb') as f:\n", - " (obs_frame,test_frame) = pickle.load(f)\n", - "\n", - "except:\n", - " for p in pipe:\n", - " p_tests, p_observations = get_neab.get_neuron_criteria(p)\n", - " obs_frame[p[\"name\"]] = p_observations#, p_tests))\n", - " test_frame[p[\"name\"]] = p_tests#, p_tests))\n", - " electro_path = str(os.getcwd())+'all_tests.p'\n", - " with open(electro_path,'wb') as f:\n", - " pickle.dump((obs_frame,test_frame),f)" - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "metadata": { - "collapsed": true - }, - "outputs": [ - { - "ename": "NameError", - "evalue": "name 'test_frame' is not defined", - "output_type": "error", - "traceback": [ - "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", - "\u001b[0;31mNameError\u001b[0m Traceback (most recent call last)", - "\u001b[0;32m\u001b[0m in \u001b[0;36m\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0muse_test\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mtest_frame\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m\"Neocortex pyramidal cell layer 5-6\"\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 2\u001b[0m \u001b[0muse_test\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mobservation\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 3\u001b[0m \u001b[0;31m#from neuronunit.tests import RheobaseP\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 4\u001b[0m \u001b[0;32mfrom\u001b[0m \u001b[0mneuronunit\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtests\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfi\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mRheobaseTest\u001b[0m\u001b[0;31m# as discovery\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 5\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n", - "\u001b[0;31mNameError\u001b[0m: name 'test_frame' is not defined" - ] - } - ], - "source": [ - "use_test = test_frame[\"Neocortex pyramidal cell layer 5-6\"]\n", - "use_test[0].observation\n", - "#from neuronunit.tests import RheobaseP\n", - "from neuronunit.tests.fi import RheobaseTest# as discovery\n", - "\n", - "rtp = RheobaseTest(use_test[0].observation)\n", - "use_test[0] = rtp\n", - "\n", - "\n", - "HH_cond_exp.attrs = HH_cond_exp.simple_parameters(HH_cond_exp)\n", - "#print(HH_cond_exp.attrs)\n", - "HH_cond_exp.scaled_parameters(HH_cond_exp)\n", - "dir(HH_cond_exp)\n", - "HH_cond_exp.default_initial_values\n", - "HH_cond_exp.attrs\n", - "NGEN = 10\n", - "MU = 10\n", - "from neuronunit.optimization import optimization_management as om\n", - "explore_ranges = {'e_rev_Na' : (40,70), 'e_rev_K': (-90.0,-75.0), 'cm' : (0.25,1.5)}\n", - "npcl, DO = om.run_ga(explore_ranges,NGEN,use_test,free_params=explore_ranges.keys(), NSGA = True, MU = MU,model_type=None)\n", - "\n", - "#hc = " - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "#dir(HH_cond_exp)\n", - "#HH_cond_exp.get_parameters()\n", - "#hhcell[0].get_parameters()\n", - "#dir(HH_cond_exp)\n", - "HH_cond_exp.attrs = HH_cond_exp.simple_parameters(HH_cond_exp)\n", - "HH_cond_exp.celltype = HH_cond_exp\n", - "iparams = {}\n", - "iparams['injected_square_current'] = {}\n", - "#iparams['injected_square_current']['amplitude'] = 1.98156805*pq.pA\n", - "iparams['injected_square_current']['amplitude'] = 0.68156805*pq.pA\n", - "\n", - "DELAY = 100.0*pq.ms\n", - "DURATION = 1000.0*pq.ms\n", - "iparams['injected_square_current']['delay'] = DELAY\n", - "iparams['injected_square_current']['duration'] = int(DURATION)\n", - "\n", - "HH_cond_exp.inject_square_current(iparams)\n", - "print(HH_cond_exp.get_spike_count())\n", - "\n", - "print(HH_cond_exp.vm)\n", - "\n", - "import matplotlib.pyplot as plt\n", - "plt.plot(HH_cond_exp.vm.times,HH_cond_exp.vm)\n", - "\n", - "\n", - "plt.show()\n", - "iparams['injected_square_current']['amplitude'] = 0.8598156805*pq.pA\n", - "#iparams['injected_square_current']['amplitude'] = 2000.98156805*pq.pA\n", - "\n", - "DELAY = 100.0*pq.ms\n", - "DURATION = 1000.0*pq.ms\n", - "iparams['injected_square_current']['delay'] = DELAY\n", - "iparams['injected_square_current']['duration'] = int(DURATION)\n", - "\n", - "HH_cond_exp.inject_square_current(iparams)\n", - "print(HH_cond_exp.get_spike_count())\n", - "import matplotlib.pyplot as plt\n", - "plt.plot(HH_cond_exp.vm.times,HH_cond_exp.vm)\n", - "\n", - "plt.show()\n", - "pred = use_test[0].generate_prediction(HH_cond_exp)\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "#dir(HH_cond_exp)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "print(pred)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "iparams['injected_square_current']['amplitude'] = pred['value']\n", - "#iparams['injected_square_current']['amplitude'] = 2000.98156805*pq.pA\n", - "\n", - "DELAY = 100.0*pq.ms\n", - "DURATION = 1000.0*pq.ms\n", - "iparams['injected_square_current']['delay'] = DELAY\n", - "iparams['injected_square_current']['duration'] = int(DURATION)\n", - "\n", - "HH_cond_exp.inject_square_current(iparams)\n", - "print(HH_cond_exp.get_spike_count())\n", - "import matplotlib.pyplot as plt\n", - "plt.plot(HH_cond_exp.vm.times,HH_cond_exp.vm)\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "\n", - "import pyNN\n", - "from pyNN import neuron\n", - "from pyNN.neuron import EIF_cond_exp_isfa_ista\n", - "#neurons = pyNN.Population(N_CX, pyNN.EIF_cond_exp_isfa_ista, RS_parameters)\n", - "\n", - "cell = neuron.create(EIF_cond_exp_isfa_ista())\n", - "#cell[0].set_parameters(**LTS_parameters)\n", - "cell[0].get_parameters()" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": true - }, - "outputs": [], - "source": [ - "\n", - "\n", - "explore_ranges = {'E_Na' : (40,70), 'E_K': (-90.0,-75.0), 'C_m' : (0.25,1.5), 'g_K':(30,40), 'g_Na':(100,140), 'g_L':(0.1,0.5), 'E_L':(-60.0,-45)}\n", - "\n", - "attrs = { 'g_K' : 36.0, 'g_Na' : 120.0, 'g_L' : 0.3, \\\n", - " 'C_m' : 1.0, 'E_L' : -54.387, 'E_K' : -77.0, 'E_Na' : 50.0, 'vr':-65.0 } \n", - "\n", - " \n", - "from neuronunit.optimization import optimization_management as om\n", - "print(test_frame) \n", - "MU = 12\n", - "NGEN = 25\n", - "cnt = 1\n", - "#hc = { 'g_L' : 0.3, 'E_L' : -54.387,\n", - "hc = {'vr':-65.0 } \n", - "\n", - "#npcl, DO = om.run_g\n", - "npcl, DO = om.run_ga(explore_ranges,NGEN,use_test,free_params=explore_ranges.keys(), hc = hc, NSGA = True, MU = MU,model_type='HH')\n", - " " - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": true - }, - "outputs": [], - "source": [] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.5.2" - } - }, - "nbformat": 4, - "nbformat_minor": 2 -} diff --git a/neuronunit/unit_test/experiment.py b/neuronunit/unit_test/experiment.py deleted file mode 100644 index af83984d1..000000000 --- a/neuronunit/unit_test/experiment.py +++ /dev/null @@ -1,267 +0,0 @@ -import pickle - -from neuronunit.tests import np, pq, cap, VmTest, scores, AMPL, DELAY, DURATION - -from neuronunit.optimization import get_neab -import copy -import os -from neuronunit.optimization.optimization_management import run_ga -from neuronunit.optimization.model_parameters import model_params, path_params -from neuronunit.tests import np, pq, cap, VmTest, scores, AMPL, DELAY, DURATION - -import matplotlib as mpl -mpl.use('Agg') -#mpl.switch_backend('Agg') - - -electro_path = str(os.getcwd())+'/pipe_tests.p' -print(os.getcwd()) -assert os.path.isfile(electro_path) == True -with open(electro_path,'rb') as f: - electro_tests = pickle.load(f) - -electro_tests = get_neab.replace_zero_std(electro_tests) -electro_tests = get_neab.substitute_parallel_for_serial(electro_tests) -test, observation = electro_tests[0] - - -import matplotlib.pyplot as plt - - - -from neuronunit.optimization import get_neab -import copy -import os -import pickle -electro_path = str(os.getcwd())+'/pipe_tests.p' -from neuronunit import plottools -import numpy as np -ax = None -from neuronunit.optimization import exhaustive_search as es - -plot_surface = plottools.plot_surface -scatter_surface = plottools.plot_surface - -with open(electro_path,'rb') as f: - electro_tests = pickle.load(f) -from matplotlib.colors import LogNorm -from neuronunit.optimization.exhaustive_search import run_grid, reduce_params, create_grid#, mock_grid -from neuronunit.models.NeuroML2 import model_parameters as modelp - -from neuronunit.models.NeuroML2 .model_parameters import path_params - - - -electro_tests = get_neab.replace_zero_std(electro_tests) -electro_tests = get_neab.substitute_parallel_for_serial(electro_tests) -test, observation = electro_tests[0] - -import quantities as pq - - - -opt_keys = ['a','b','vr'] -nparams = len(opt_keys) -mp = modelp.model_params -observation = {'a':[np.median(mp['a']),np.std(mp['a'])], 'b':[np.median(mp['b']),np.std(mp['b'])], 'vr':[np.median(mp['vr']),np.std(mp['vr'])]} - - -tests = copy.copy(electro_tests[0][0]) -tests_ = [] -tests_ += [tests[0]] -tests_ += tests[4:7] - - -with open('ga_run.p','rb') as f: - package = pickle.load(f) -pop = package[0] -print(pop[0].dtc.attrs.items()) -history = package[4] -gen_vs_pop = package[6] -hof = package[1] - - -#import seaborn as sns -from itertools import product -import matplotlib.pyplot as plt - - - -def plot_scatter(hof,ax,keys): - z = np.array([ np.sum(list(p.dtc.scores.values())) for p in hof ]) - x = np.array([ p.dtc.attrs[str(keys[0])] for p in hof ]) - if len(keys) != 1: - y = np.array([ p.dtc.attrs[str(keys[1])] for p in hof ]) - - ax.cla() - ax.set_title(' {0} vs {1} '.format(keys[0],keys[1])) - ax.scatter(x, y, c=y, s=125)#, cmap='gray') - - #ax.scatter(x, y, z, [3 for i in x] ) - return ax - -def plot_surface(gr,ax,keys,imshow=False): - # from - # https://github.com/russelljjarvis/neuronunit/blob/dev/neuronunit/unit_test/progress_report_4thJuly.ipynb - # Not rendered - # https://github.com/russelljjarvis/neuronunit/blob/dev/neuronunit/unit_test/progress_report_.ipynb - gr = [ g for g in gr if type(g.dtc) is not type(None) ] - - gr = [ g for g in gr if type(g.dtc.scores) is not type(None) ] - ax.cla() - #gr = [ g - gr_ = [] - index = 0 - for i,g in enumerate(gr): - if type(g.dtc) is not type(None): - gr_.append(g) - else: - index = i - - z = [ np.sum(list(p.dtc.scores.values())) for p in gr ] - x = [ p.dtc.attrs[str(keys[0])] for p in gr ] - y = [ p.dtc.attrs[str(keys[1])] for p in gr ] - - # impute missings - if len(x) != 100: - delta = 100-len(x) - for i in range(0,delta): - x.append(np.mean(x)) - y.append(np.mean(y)) - z.append(np.mean(z)) - - xx = np.array(x) - yy = np.array(y) - zz = np.array(z) - - dim = len(xx) - - - N = int(np.sqrt(len(xx))) - X = xx.reshape((N, N)) - Y = yy.reshape((N, N)) - Z = zz.reshape((N, N)) - if imshow==False: - ax.pcolormesh(X, Y, Z, edgecolors='black') - else: - import seaborn as sns; sns.set() - ax = sns.heatmap(Z) - - #ax.imshow(Z) - #ax.pcolormesh(xi, yi, zi, edgecolors='black') - ax.set_title(' {0} vs {1} '.format(keys[0],keys[1])) - return ax - -def plot_line(gr,ax,key): - ax.cla() - ax.set_title(' {0} vs score'.format(key[0])) - z = np.array([ np.sum(list(p.dtc.scores.values())) for p in gr ]) - x = np.array([ p.dtc.attrs[key[0]] for p in gr ]) - - ax.plot(x,z) - ax.set_xlim(np.min(x),np.max(x)) - ax.set_ylim(np.min(z),np.max(z)) - return ax -''' -Depricated -def check_range(matrix,hof): - dim = np.shape(matrix)[0] - print(dim) - cnt = 0 - fig,ax = plt.subplots(dim,dim,figsize=(10,10)) - flat_iter = [] - newrange = {} - for i,k in enumerate(matrix): - for j,r in enumerate(k): - keys = list(r[0]) - gr = r[1] - print(line) - if i==j: - line = [ np.sum(list(g.dtc.scores.values())) for g in gr] - (newrange, range_adj) = check_line(line,newrange) - print(newrange,'newrange') - return (newrange, range_adj) -''' -def check_line(line,gr,newrange): - range_adj = False - key = list(newrange.keys())[0] - #keys = keys[0] - min_ = np.min(line) - print(min_,line[0],line[1],'diff?') - if line[0] == min_: - #print('hit') - attrs = gr[0].dtc.attrs[key] - remin = - np.abs(attrs)*10 - remax = np.abs(gr[-1].dtc.attrs[key])*10 - nr = np.linspace(remin,remax,3) - newrange[key] = nr - range_adj = True - - if line[-1] == min_: - #print('hit') - attrs = gr[-1].dtc.attrs[key] - remin = - np.abs(attrs)*10 - remax = np.abs(gr[-1].dtc.attrs[key])*10 - nr = np.linspace(remin,remax,3) - newrange[key] = nr - range_adj = True - return (newrange, range_adj) - -def mp_process(newrange): - from neuronunit.models.NeuroML2 import model_parameters as modelp - mp = copy.copy(modelp.model_params) - for k,v in newrange.items(): - if type(v) is not type(None): - mp[k] = v - return mp - -def pre_run(tests): - dim = len(hof[0].dtc.attrs.keys()) - flat_iter = [ (i,ki,j,kj) for i,ki in enumerate(hof[0].dtc.attrs.keys()) for j,kj in enumerate(hof[0].dtc.attrs.keys()) ] - cnt = 0 - for i,ki,j,kj in flat_iter: - free_param = set([ki,kj]) # construct a small-set out of the indexed keys 2. If both keys are - # are the same, this set will only contain one index - bs = set(hof[0].dtc.attrs.keys()) # construct a full set out of all of the keys available, including ones not indexed here. - diff = bs.difference(free_param) # diff is simply the key that is not indexed. - # BD is the dictionary of parameters to be held constant - # if the plot is 1D then two parameters should be held constant. - hc = {} - for d in diff: - hc[d] = hof[0].dtc.attrs[d] - - if i == j: - assert len(free_param) == len(hc) - 1 - assert len(hc) == len(free_param) + 1 - from neuronunit.models.NeuroML2 import model_parameters as modelp - mp = copy.copy(modelp.model_params) - gr = run_grid(3,tests,provided_keys = free_param ,hold_constant = hc, mp_in = mp) - # make a psuedo test, that still depends on input Parametersself. - # each test evaluates a normal PDP. - line = [ np.sum(list(g.dtc.scores.values())) for g in gr] - nr = {str(list(free_param)[0]):None} - newrange, range_adj = check_line(line,gr,nr) - while range_adj == True: - - mp = mp_process(newrange) - gr = run_grid(3,tests,provided_keys = newrange ,hold_constant = hc, mp_in = mp) - # make a psuedo test, that still depends on input Parametersself. - # each test evaluates a normal PDP. - nr = {} - line = [ np.sum(list(g.dtc.scores.values())) for g in gr] - newrange, range_adj = check_line(line,gr,newrange) - - mp = mp_process(newrange) - - with open('parameter_bf_ranges.p','wb') as f: - pickle.dump(mp,f) - import pdb; pdb.set_trace() - package = run_ga(mp,nparams*2,12,tests_,provided_keys = opt_keys)#, use_cache = True, cache_name='simple') - return package - -matrix = pre_run(tests=tests_) - - - -#plotss(matrix) -#except: diff --git a/neuronunit/unit_test/explore_ranges.py b/neuronunit/unit_test/explore_ranges.py deleted file mode 100644 index faca17c3a..000000000 --- a/neuronunit/unit_test/explore_ranges.py +++ /dev/null @@ -1,188 +0,0 @@ -import pickle -import copy -import os - -from neuronunit.tests import np, pq, cap, VmTest, scores, AMPL, DELAY, DURATION -from neuronunit.optimization import get_neab -from neuronunit.optimization.optimization_management import run_ga -from neuronunit.models.NeuroML2 import model_parameters as modelp -from neuronunit.models.NeuroML2 .model_parameters import path_params -from neuronunit.tests import np, pq, cap, VmTest, scores, AMPL, DELAY, DURATION - -#import matplotlib as mpl# -#mpl.use('Agg') -#from matplotlib.colors import LogNorm -from neuronunit.optimization.exhaustive_search import run_grid, reduce_params, create_grid, WSListIndividual -import matplotlib.pyplot as plt - -from neuronunit import plottools -plot_surface = plottools.plot_surface -scatter_surface = plottools.plot_surface - -electro_path = str(os.getcwd())+'/pipe_tests.p' -print(os.getcwd()) -assert os.path.isfile(electro_path) == True -with open(electro_path,'rb') as f: - electro_tests = pickle.load(f) - -from neuronunit.optimization import exhaustive_search as es -import quantities as pq - -from itertools import product -import matplotlib.pyplot as plt - - -def get_tests(): - from neuronunit.optimization import get_neab - electro_path = str(os.getcwd())+'/pipe_tests.p' - assert os.path.isfile(electro_path) == True - with open(electro_path,'rb') as f: - electro_tests = pickle.load(f) - test, observation = electro_tests[0] - tests = copy.copy(electro_tests[0][0]) - tests_ = [] - tests_ += [tests[0]] - tests_ += tests[4:7] - return tests_, test, observation - -tests_,test, observation = get_tests() - -ax = None - -#@jit -def check_line(line,gr,newrange,key): - # Is this a concave down shape (optima in the middle) - # Or is the most negative value on an edge? - # if it's on the edge calculate a new parameter value to explore - range_adj = False - min_ = np.min(line) - cl = [ g.dtc.attrs[key] for g in gr ] - new = None - index = None - new_param_val = None - if line[0] == min_: - attrs = gr[0].dtc.attrs[key] - # quantity may not be negative yet - # don't force it to be straight away - # avoided forcing rapid sign reversal by keeping old value as an offset - - remin = attrs - 10*np.abs(attrs) - if remin == 0.0: - remin = -1.0 - - cl.insert(0,remin) - cl = sorted(cl) - newrange[key] = cl - range_adj = True - new_param_val = cl[0] - index = 0 - if line[-1] == min_: - attrs = gr[-1].dtc.attrs[key] - # quantity might not be positve yet - # don't force it to be straight away - # avoided forcing rapid sign reversal by keeping old value as an offset - - remax = attrs + np.abs(attrs)*10 - if remax == 0.0: - remax = 1.0 - - cl.append(remax) - cl = sorted(cl) - newrange[key] = cl - range_adj = True - new_param_val = cl[-1] - index = -1 - - - return (newrange, range_adj, new_param_val, index) - -def interpolate(p0,p1): - attrs0, score0 = p0 - attrs1, score1 = p1 - scores = [score0,score1] - first = bool(scores[0] == 4.0 and scores[1] !=4.0) - second = bool(scores[1] == 4.0 and scores[0] !=4.0) - if first or second: - new = (attrs0 + attrs1)/2.0 - else: - new = None - return new - - -#from neuronunit.models.NeuroML2 import model_parameters as modelp -from neuronunit.optimization.optimization_management import nunit_evaluation, update_deap_pop -from collections import OrderedDict - -# https://stackoverflow.com/questions/33467738/numba-cell-vars-are-not-supported -# numba jit does not work on nested list iteration -#@jit -def pre_run(tests,opt_keys): - # algorithmically find the the edges of parameter ranges, via a course grained - # sampling of extreme parameter values - # to find solvable instances of Izhi-model, (models with a rheobase value). - nparams = len(opt_keys) - from neuronunit.models.NeuroML2 import model_parameters as modelp - mp = copy.copy(modelp.model_params) - mp['b'] = [ -0.5, 500.0 ] - mp['vr'] = [ -100.0, 10.0 ] - mp['a'] = [-10, 5] - cnt = 0 - fc = {} # final container - - for key in opt_keys: - cnt = 0 - print(key,mp) - gr = run_grid(3,tests,provided_keys = key, mp_in = mp) - line = [ g.dtc.get_ss() for g in gr] - nr = {key:None} - _, range_adj, new, index = check_line(line,gr,nr,key) - while range_adj == True: - # while the sampled line is not concave (when minimas are at the edges) - # sample a point to a greater extreme - gr_ = update_deap_pop(new, tests, key) - param_line = [ g.dtc.attrs[key] for g in gr ] - - temp = list(mp[key]) - inter = None - - if index == 0: - p0 = ( gr_.dtc.attrs[key], gr_.dtc.get_ss() ) - p1 = ( gr[0].dtc.attrs[key], gr[0].dtc.get_ss()) - inter = interpolate(p0,p1) - print(inter) - - gr.insert(index,gr_) - temp.insert(index,new) - elif index == -1: - p0 = ( gr_.dtc.attrs[key], gr_.dtc.get_ss() ) - - p1 = ( gr[-1].dtc.attrs[key], gr[-1].dtc.get_ss()) - inter = interpolate(p0,p1) - print(inter) - - gr.append(gr_) - temp.append(gr_) - - - mp[key] = np.array(temp) - line = [ g.dtc.get_ss() for g in gr] - - _, range_adj, new,index = check_line(line,gr,mp,key) - cnt += 1 - with open('temp_range.p','wb') as f: - pickle.dump(mp,f) - if type(inter) is not type(None): - with open('temp_inter.p','wb') as f: - pickle.dump([mp,inter],f) - - param_line = [ g.dtc.attrs[key] for g in gr ] - line = [ g.dtc.get_ss() for g in gr] - plt.clf() - plt.plot(param_line,line) - plt.savefig('check_'+str(key)+'.png') - - fc[key] = {} - fc[key]['line'] = line - fc[key]['range'] = mp - fc[key]['cnt'] = cnt - return fc, mp diff --git a/neuronunit/unit_test/failed_model2model_reproducibility.ipynb b/neuronunit/unit_test/failed_model2model_reproducibility.ipynb deleted file mode 100644 index 392d9d792..000000000 --- a/neuronunit/unit_test/failed_model2model_reproducibility.ipynb +++ /dev/null @@ -1,854 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# This notebook demonstrates: \n", - "* failure of model to model reproducibility in the Izhikitich model eminating from parameter C (capacitance)" - ] - }, - { - "cell_type": "code", - "execution_count": 1, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "import numpy as np\n", - "import time" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Set up an appropriate environment.\n", - "\n", - "* demonstrate that both backends work, before preceeding to a more specific backend versus backend results test/overlay." - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "\n", - "import copy\n", - "import numpy as np\n", - "import pandas as pd\n", - "import matplotlib.pyplot as plt\n", - "\n", - "from neuronunit.models.reduced import ReducedModel\n", - "from neuronunit.optimization.model_parameters import model_params, path_params\n", - "from neuronunit.optimization import model_parameters as modelp\n", - "\n", - "LEMS_MODEL_PATH = path_params['model_path']\n", - "import neuronunit.optimization as opt\n", - "import quantities as pq\n", - "\n" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "\n", - "# From OSB models\n", - "mparams = {}\n", - "mparams['a'] = 0.03\n", - "mparams['b'] = -2\n", - "mparams['C'] = 100\n", - "mparams['c'] = -50 \n", - "mparams['vr'] = -60\n", - "mparams['vt'] = -40\n", - "mparams['vpeak'] = 35\n", - "mparams['k'] = 0.7\n", - "mparams['d'] = 100\n", - "\n", - "\n", - "\n", - "# FROM the MOD file.\n", - "vanilla_NRN = {}\n", - "#vanilla_NRN['v0'] = -60# (mV)\n", - "vanilla_NRN['k'] = 7.0E-4# (uS / mV)\n", - "vanilla_NRN['vr'] = -60# (mV)\n", - "vanilla_NRN['vt'] = -40# (mV)\n", - "vanilla_NRN['vpeak'] = 35# (mV)\n", - "vanilla_NRN['a'] = 0.03# (kHz)\n", - "vanilla_NRN['b'] = -0.002# (uS)\n", - "vanilla_NRN['c'] = -50# (mV)\n", - "vanilla_NRN['d'] = 0.1# (nA)\n", - "vanilla_NRN['C'] = 1.0E-5# (microfarads)\n", - "\n", - "m2m = {}\n", - "for k,v in vanilla_NRN.items():\n", - " m2m[k] = vanilla_NRN[k]/mparams[k]\n", - "\n", - "\n", - "def translate(input_dic,m2m):\n", - " input_dic['vpeak'] = input_dic['vPeak']\n", - " input_dic.pop('vPeak', None) \n", - " input_dic.pop('dt', None) \n", - " for k,v in input_dic.items():\n", - " input_dic[k] = v * m2m[k]\n", - " return input_dic\n", - "\n", - "\n", - "\n", - "\n", - "# From OSB models\n", - "\n" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# A more specific Backend versus backend test/overlay.\n", - "Starting with default parameters, and agreement" - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "1e-05\n", - "1e-05\n", - "520.0 0.0\n" - ] - }, - { - "data": { - "text/plain": [ - "" - ] - }, - "execution_count": 4, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": 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- "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "\n", - "iparams = {}\n", - "iparams['injected_square_current'] = {}\n", - "iparams['injected_square_current']['amplitude'] = 53*pq.pA\n", - "DELAY = 0\n", - "DURATION = 520\n", - "iparams['injected_square_current']['delay'] = DELAY*pq.ms\n", - "iparams['injected_square_current']['duration'] = DURATION*pq.ms\n", - "\n", - "mparams['vPeak'] = 35\n", - "\n", - "model1 = None\n", - "model1 = ReducedModel(LEMS_MODEL_PATH,name = str('vanilla'),backend = ('RAW'))\n", - "model1.set_attrs(mparams)\n", - "model1.inject_square_current(iparams)\n", - "\n", - "model2 = None\n", - "model2 = ReducedModel(LEMS_MODEL_PATH,name = str('vanilla'),backend = ('NEURON'))\n", - "vanilla = translate(mparams,m2m)\n", - "model2.set_attrs(vanilla)\n", - "model2.inject_square_current(iparams)\n", - "\n", - "\n", - "plt.plot(model1.get_membrane_potential().times,model1.get_membrane_potential(),label='RAW')\n", - "plt.plot(model2.get_membrane_potential().times,model2.get_membrane_potential(),label='NEURON')\n", - "plt.legend()" - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[ 3.51250000e-06 1.32377084e-06 1.32330773e-06 ..., 2.77570696e-09\n", - " 2.77553601e-09 2.77536506e-09]\n" - ] - } - ], - "source": [ - "from numba import jit\n", - "@jit\n", - "def get_diff(vm):\n", - " differentiated = np.diff(vm)\n", - " return differentiated\n", - "\n", - "vm_ = []\n", - "for v in model2.get_membrane_potential():\n", - " vm_.append(float(v))\n", - "max_spike = get_diff(vm_)\n", - "print(max_spike)\n", - " " - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[ 3.51250000e-06 1.32377084e-06 1.32330773e-06 ..., 2.77570696e-09\n", - " 2.77553601e-09 2.77536506e-09]\n" - ] - } - ], - "source": [ - "max_spike = get_diff(vm_)\n", - "print(max_spike)\n", - " " - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "1e-05\n", - "1e-05\n", - "520.0 0.0\n" - ] - }, - { - "data": { - "text/plain": [ - "" - ] - }, - "execution_count": 7, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": 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Ou7Z7gadU9WJgIfCPSf13JvW/Pcv6MxJtS/xV5JXZiMSYfBD3R1trswVJMcg2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\nfZWqtgHvicgOYA7wUg7eSwe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- "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "\n", - "# From OSB models\n", - "mparams = {}\n", - "mparams['a'] = 0.03\n", - "mparams['b'] = -2\n", - "mparams['C'] = 100\n", - "mparams['c'] = -50 \n", - "mparams['vr'] = -60\n", - "mparams['vt'] = -40\n", - "mparams['vpeak'] = 35\n", - "mparams['k'] = 0.7\n", - "mparams['d'] = 100\n", - "\n", - "\n", - "# FROM the MOD file.\n", - "vanilla_NRN = {}\n", - "#vanilla_NRN['v0'] = -60# (mV)\n", - "vanilla_NRN['k'] = 7.0E-4# (uS / mV)\n", - "vanilla_NRN['vr'] = -60# (mV)\n", - "vanilla_NRN['vt'] = -40# (mV)\n", - "vanilla_NRN['vpeak'] = 35# (mV)\n", - "vanilla_NRN['a'] = 0.03# (kHz)\n", - "vanilla_NRN['b'] = -0.002# (uS)\n", - "vanilla_NRN['c'] = -50# (mV)\n", - "vanilla_NRN['d'] = 0.1# (nA)\n", - "vanilla_NRN['C'] = 1.0E-5# (microfarads)\n", - "\n", - "m2m = {}\n", - "for k,v in vanilla_NRN.items():\n", - " m2m[k] = vanilla_NRN[k]/mparams[k]\n", - "\n", - "\n", - "def translate(input_dic,m2m):\n", - " input_dic['vpeak'] = input_dic['vPeak']\n", - " input_dic.pop('vPeak', None) \n", - " input_dic.pop('dt', None) \n", - " for k,v in input_dic.items():\n", - " input_dic[k] = v * m2m[k]\n", - " return input_dic\n", - "\n", - "\n", - "\n", - "model1 = None\n", - "model1 = ReducedModel(LEMS_MODEL_PATH,name = str('vanilla'),backend = ('RAW'))\n", - "mparams['C'] = 100\n", - "mparams['a'] = 0.06\n", - "\n", - "#mparams['a'] = 0.03\n", - "mparams['b'] = -3\n", - "#mparams['C'] = 100\n", - "mparams['c'] = -55 \n", - "mparams['vr'] = -60\n", - "mparams['vt'] = -35\n", - "#mparams['vPeak'] = 45\n", - "\n", - "mparams['vPeak'] = mparams['vpeak'] = 45\n", - "mparams['k'] = 0.8\n", - "mparams['d'] = 150\n", - "\n", - "\n", - "\n", - "model1.set_attrs(mparams)\n", - "model1.inject_square_current(iparams)\n", - "\n", - "model2 = None\n", - "model2 = ReducedModel(LEMS_MODEL_PATH,name = str('vanilla'),backend = ('NEURON'))\n", - "#mparams['vpeak'] = 35\n", - "#mparams['C'] = 2.0E-4\n", - "td = translate(mparams,m2m)\n", - "\n", - "model2.set_attrs(td)\n", - "model2.inject_square_current(iparams)\n", - "\n", - "\n", - "\n", - "plt.plot(model1.get_membrane_potential().times,model1.get_membrane_potential(),label='RAW')\n", - "plt.plot(model2.get_membrane_potential().times,model2.get_membrane_potential(),label='NEURON')\n", - "plt.legend()\n" - ] - }, - { - "cell_type": "code", - "execution_count": 8, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[ 1.32500000e-05 1.31838000e-05 1.31180154e-05 ..., -7.98967836e-11\n", - " -7.98005828e-11 -7.97045069e-11]\n" - ] - } - ], - "source": [ - "\n", - "vm_ = []\n", - "for v in model1.get_membrane_potential():\n", - " vm_.append(float(v))\n", - "max_spike = get_diff(vm_)\n", - "print(max_spike)\n", - "\n" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## The problem is parameter C" - ] - }, - { - "cell_type": "code", - "execution_count": 9, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "1.5000000000000002e-05\n", - "1.5000000000000002e-05\n", - "1.5000000000000002e-05\n", - "520.0 0.0\n", - "1.6000000000000003e-05\n", - "1.6000000000000003e-05\n", - "1.6000000000000003e-05\n", - "520.0 0.0\n", - "1.7e-05\n", - "1.7e-05\n", - "1.7e-05\n", - "520.0 0.0\n", - "2e-05\n", - "2e-05\n", - "2e-05\n", - "520.0 0.0\n", - "2.5e-05\n", - "2.5e-05\n", - "2.5e-05\n", - "520.0 0.0\n", - "3.0000000000000004e-05\n", - "3.0000000000000004e-05\n", - "3.0000000000000004e-05\n", - "520.0 0.0\n" - ] - }, - { - "data": { - "image/png": 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ahg+PGlY7Yqi/bh+jzX0GgPTMAg41nCLNl01CUgTZGcKWF09Ly1+wWtOodWdw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\nQJkAAAAASUVORK5CYII=\n", - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "\n", - "iparams = {}\n", - "iparams['injected_square_current'] = {}\n", - "iparams['injected_square_current']['amplitude'] = 60*pq.pA\n", - "DELAY = 0\n", - "DURATION = 520\n", - "iparams['injected_square_current']['delay'] = DELAY*pq.ms\n", - "iparams['injected_square_current']['duration'] = DURATION*pq.ms\n", - "\n", - "\n", - "for C in [150,160,170,200,250,300]:\n", - " mparams['C'] = C\n", - " mparams['a'] = 0.06\n", - "\n", - " mparams['b'] = -3\n", - " mparams['c'] = -55 \n", - " mparams['vr'] = -60\n", - " mparams['vt'] = -35\n", - "\n", - " mparams['vPeak'] = 45\n", - " mparams['k'] = 0.8\n", - " mparams['d'] = 150\n", - "\n", - " model1 = None\n", - " model1 = ReducedModel(LEMS_MODEL_PATH,name = str('vanilla'),backend = ('RAW'))\n", - " model1.set_attrs(mparams)\n", - " model1.inject_square_current(iparams)\n", - "\n", - " td = translate(mparams,m2m)\n", - "\n", - " model2.set_attrs(td)\n", - " model2 = ReducedModel(LEMS_MODEL_PATH,name = str('vanilla'),backend = ('NEURON'))\n", - " model2.set_attrs(mparams)\n", - " model2.inject_square_current(iparams)\n", - "\n", - "\n", - " plt.plot(model1.get_membrane_potential().times,model1.get_membrane_potential(),label='RAW')\n", - " plt.plot(model2.get_membrane_potential().times,model2.get_membrane_potential(),label='NEURON')\n", - "plt.legend()\n", - "plt.show()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "V0 and VR in NEURON is another problem, but it's not the problem. As when they are made to be the same problems persist" - ] - }, - { - "cell_type": "code", - "execution_count": 10, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "1.5000000000000002e-05\n", - "1.5000000000000002e-05\n", - "1.5000000000000002e-05\n", - "520.0 0.0\n", - "1.6000000000000003e-05\n", - "1.6000000000000003e-05\n", - "1.6000000000000003e-05\n", - "520.0 0.0\n", - "1.7e-05\n", - "1.7e-05\n", - "1.7e-05\n", - "520.0 0.0\n", - "2e-05\n", - "2e-05\n", - "2e-05\n", - "520.0 0.0\n", - "2.5e-05\n", - "2.5e-05\n", - "2.5e-05\n", - "520.0 0.0\n", - "3.0000000000000004e-05\n", - "3.0000000000000004e-05\n", - "3.0000000000000004e-05\n", - "520.0 0.0\n" - ] - }, - { - "data": { - "image/png": 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zcLvdvPDCC0PWp6ysjObm5rjfp579N0btp61x26ayk25A2DAJhts53TOBKeke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\n", - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "iparams = {}\n", - "iparams['injected_square_current'] = {}\n", - "iparams['injected_square_current']['amplitude'] = 60*pq.pA\n", - "DELAY = 0\n", - "DURATION = 520\n", - "iparams['injected_square_current']['delay'] = DELAY*pq.ms\n", - "iparams['injected_square_current']['duration'] = DURATION*pq.ms\n", - "\n", - "\n", - "for C in [150,160,170,200,250,300]:\n", - " mparams['C'] = C\n", - " mparams['a'] = 0.06\n", - "\n", - " mparams['b'] = -3\n", - " mparams['c'] = -55 \n", - " mparams['vr'] = -70\n", - " mparams['vt'] = -35\n", - "\n", - " mparams['vPeak'] = 45\n", - " mparams['k'] = 0.8\n", - " mparams['d'] = 150\n", - "\n", - " model1 = None\n", - " model1 = ReducedModel(LEMS_MODEL_PATH,name = str('vanilla'),backend = ('RAW'))\n", - " model1.set_attrs(mparams)\n", - " model1.inject_square_current(iparams)\n", - "\n", - " td = translate(mparams,m2m)\n", - "\n", - " model2.set_attrs(td)\n", - " model2 = ReducedModel(LEMS_MODEL_PATH,name = str('vanilla'),backend = ('NEURON'))\n", - " model2.set_attrs(mparams)\n", - " model2.inject_square_current(iparams)\n", - "\n", - "\n", - " plt.plot(model1.get_membrane_potential().times,model1.get_membrane_potential(),label='RAW')\n", - " plt.plot(model2.get_membrane_potential().times,model2.get_membrane_potential(),label='NEURON')\n", - "plt.legend()\n", - "plt.show()" - ] - }, - { - "cell_type": "code", - "execution_count": 11, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2.5e-05\n", - "2.5e-05\n", - "2.5e-05\n", - "520.0 0.0\n" - ] - }, - { - "data": { - "image/png": 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hr6jrUZwM9+hQ0gGBFoZkcEQYPHROxaokdwID7NjK2QTrGNxRtncwgpQ4Vlsf\nNsYdUq4qKYrH4GS4R++VdECghSEZElTMmKHO5aVzloOb6Hldpt6mtlGIY+R85tpNSIv9qKJUJbm9\n5hKp4e3aPcaBtoUQZMY6CtVUu2FjbLLPDdE8Bgf2/ArhdbBtTZuhhSEZbJ5p+f2SepeXTllOVcv4\nTc/g6po9dMmL48o4ZQASCFlds9fcIjKTAplM25nC5uoyG8a4tkl9/i0+SyfHwqkJg6ZN0cKQDDbH\nTxvcXqSE/CyHcgwWvqQ1TR66dogjDE4ZgARGqzZRv2Lh6CxZ9TnD7m+TDWNc0+ShY04mWZlhouWk\n8dYewwGBFoZksNm41DUH3PxMh4TB5MxQSklts5cuHeLMzJ2adSYY44SCFYs2mCXbGkYCW/pc0xTF\n83PSeGuP4YBAC0My2BxKqg/uFJrp1E6X8eP1sWhw+/D5ZQKPIdB2G+dElDCYyTE4P0vOsnudog0T\nkdrmKEJqQ+4iJt7mYMP2tqtpU7QwJIPNoaS6ZiU0HTJSy2OoCcSl4wpDyADYTBwDHvRkUtVjyEhR\njyFivJz0GHQo6YBAC0MytPiiWp8R1QZCSR0y4h9naRqTM85aQ8LQBsnnVrPZepc3sSdjpF27CYyF\n7aEkkx5fOLVNUUKCTok66FDSAYIWhmSw+QsVNMB5GQ6Fkix6DHGrktoh+RwU0rj9ioUNRjYWMnBf\n2F+VZP1+q2ny0CVeKMlutMdwQKCFIRlsFoaqBvUl6uhY8tlcf0PC0B4eQxwvp6bRgCcTi7YoV7U9\nlGRPuapOPmuSxZIwCCF6CCEWCiG2BB67x7jumsA1W4QQ1wSe6yiEeE8I8b0QYr0Q4s9W+tImeOwV\nhsoGN0JALm2RfDaOoVBSO3gMhnIfsXDQYEmncgwW++zzS+pcUXIyTo2F36e3xDhAsOox3A18KqUc\nCXwa+L0FQogewH3AscAxwH1hAvKIlPJwYAIwVQhxlsX+OIu3ydbmKhvddO+YQ4ZT2whYDSWlmsdg\npF+xcHCWLAJ9tj+UZM2AB4sbIsarnRYnatIHq8IwA5gb+HkucH6Ua84AFkopK6WUVcBC4EwpZaOU\nchGAlNINrAQGWuyPs9h841c1eOjeMbtttpdIoiyxssFNVoagS168dQwOlSXGGePqRvX/6Z6fY6Jd\np6qovIjAaWhmNnyN37Y1A15er97fq1Or8fLZW0QRtV29u2paY/VW7iul3AsQeOwT5ZoBQGHY70WB\n50IIIbrZR5SDAAAgAElEQVQB56K8jqgIIW4UQhQIIQrKysosdtskHps9hgY3PfJznJtpmWy3vN5F\nz045iFgzYCkdDCXFNuDl9epv9jQjDE6tfA4bh1Rb4FYRGq/cVu06JJI68XzAkHClkBDiE+CQKC/d\nY/BvRPu2hKYTQogsYB7whJRye6xGpJRPA08DTJo0qX2mI3FKKc1Q1ehmcI+O4Gm0td0QJo13Rb2b\nXp1yY1/g5O6ccaqHyuvddM7LIi/bxA6mNn920drNsntPDIsb/1UEiht6dW4lpI6FAXUo6UAhoTBI\nKU+N9ZoQolQI0U9KuVcI0Q/YF+WyImB62O8Dgc/Dfn8a2CKlfMxQj9sTu3MMDW7GD+oGzY2JLzaD\nyWS58hjiCIOTdfBx2i6rd8UXrLjtOu/h2L5XksVxLg/zGGS4yDrmMWhhOFCweiu/DVwT+Pka4K0o\n13wEnC6E6B5IOp8eeA4hxINAV+AOi/1oG2y88aWUVDW6Vbzc45QwmGu3vN4dGZe2oV1DuGO3XV7n\niuiX4Xm0p8F8n+IR1t/w87FtcUrijIURyutV1Vv3jvuTzxKc+/zcDo2xps2xKgx/Bk4TQmwBTgv8\njhBikhDiGQApZSXwB2B54N8DUspKIcRAVDhqNLBSCLFaCHG9xf44i405hpomDx6fVDNgTyM+HDiT\n18QXVUpJeaKZuZMGII7RqmjYH+ISUSOUcXA3Ej2qaZFWgpN0v+JhcZwr6l306JhDVmZGy35ZFJyY\naGE4YLB0DqGUsgI4JcrzBcD1Yb8/BzzX6poiHPmmOoTPC9IHWR1sCSmV1Cp3/pAueeBpwpORS6bf\n5i9s0MhmdzT8lnqXF5fXH99jCBqA7Hz7q0/cDardKDP88noXU4b1NNeup1GNg92eQ8DINokOdLB7\nLDyxx8IIwSKCqO3mdAJ3vb2fX+h+y0dvopfe6JXPRgmKQZbJGHcrSmqUMPTtkgvuBjwZeba02wJ3\ngzKGwvjHXBEocYyoZAknaABy8q30LjruBsiJFDKPz091o8d8jsFdH7VdywSMtiezg/1tuxst9bm8\n3h39cwzeF3YTnDA4cV9o2hQtDEYJ3vS5nQNPWJsRldYGhSHoMQSFweYZXJIGIOjJ9OliIJTkiKFt\n3G9YwmazZXUqv9O7s1lhCGvXxjH2udRY+OwWBr9PTUYs9LmkpplDukaZcLQYCxsJTRgcuC80bYoW\nBqO46tVjTidbmiupUYauTxeVY3BnODHjbEjaABRXK89oQLc4/QkPJdmJ3x8QhsgxDvWru8lx8jTa\n31+grrZG/WC3oW0Rlkken19SUttM/25RhMGT/H1hCKfuC02bo4XBKO469ZhrkzDUNtMjP4fczIyA\nMDgUSjIpDP3jCYNTM8M4OZE9IcEyOU4xQlRWqamtBiDDbkPrthau21fXjM8vo3+OOpSkSYAWBqPY\n7DGU1jarMJLXBdLvTI7BRChpT3UzPfNz4i8ic8oAxBGc4moV4urX1YLH4IDBqq+rBSArz577IoTH\nWrguOF79o42XxdxFTELC7sC9rGlTtDAYxR0QBps8huLqJvp3zQt9mZzxGJI3AMXVTfG9BbAc5ohJ\nSHCih5K6dcwmP9dkIV2w2slmmuqVMOR0cGgsTPY5rucXrEqyGxPFDprURH+CRnEFQkk5neNfZwAp\nJbsrGxncsyM0qxi1K9OBL2q4ATBYlqiEIYFItUg+25gsDxnDaB5DU/TZbzJtOzBLdjXW0kQO2VlZ\n2DsW1sJ1+4UhWvK5wZFEfAsPVW+il9ZoYTCKy74cQ1m9i0a3j0N77BeGJieEwVUXEAZjy0WklBRX\nNyUO17jq1HqODEvLYKK3C1HHeI8RwUrUtgOzZE9THc2iI9i95XZoLMxNRPZUN9E5N4vO0U67c2gs\ncNUF+ps+y5M00dHCYJRQKCnwRbUwI9pdoWaDh/bMB5cKRTRnJjezN0RTNXToZvjysjoXDW4fQ3om\nmKUm2a5hAiJJh+BxHWos/H7JzooGNV5mkBKaw/ps4xjLpipcWda9yAiaVVI7NBZJ9nlHeQNDekWO\nV4bPpfZKcuLzc+q+0LQ5WhiMEkw+21DNsSsgDOGhpKZMm42L36/azjP+Rd1erkI5w3onmE02VyfV\nrmGCxrBV23trm2n2+BnW26QweBrVyWJ53bBzNuv1+cl21+LL7WpbmyGaqtSjyXHeXtYQdbxyvHWW\n2o2LU/eFps3RwmAUV53KL9iQWNtV2YgQMLB7h5AwNNsdSnLVAhLyjBut7WVBYUhggJtrnJtxQkTb\n28uUKA/rZXKMYrRrlaKqJjrTQEaHqCfaWiMkksmLTrPHR3FNE0OjeAw5HuWhao9BEw8tDEZpqoSO\n9hiA7WX1DOjWgdysTOeEoTl5Y7i9rJ7crIzESd4mpz2GlsbQsGAlbNfePm8vr6crDeR07mFru4C6\nL7Ly1L8k2VnRgJTRPb/coDCYEJyENNc4066mzdHCYJTGCuhocgO3VmwqqePwQwKho2b1RXVl2lzu\n2JS8MdxR3sDQXvmJD7Vvdmhm2FStvLJWSe3tZfXk52TSx+x2GA55DNvLGugqGsjvGrgv7M4PmRSy\nHUEhjecx2C3swTyODiUdEGhhMEpjJXSwPjN0eX1sL29gVEgYaiC3C9LujyKUyA1+URMbrc376hie\nKL8ALY2WnVWJMQRny756hvfpFPuoUSPtgu1Ga2tJLV1EI3md7ZkwtMCC+G4qrUMIooeSvMFQkrmk\ndkw8TerEuSTuN03qooXBKE2V0DFcGMzd+Nv2NeDzS0Yd0kU90VgRVoVjvt0IwsMyBgxqTaOHwsom\nRvfvEv9Cn1dtD9LB3kQuEDlLlhIpJeuLaxmTqF+J2gXbjda24lIy8QfCJw6PRRKsL65laK/8qIsB\nHcsxhIuv3aW7mjZHC4NRGitVKMniTb+pVH0xQ6Gkhn3QqQ/SbsPSWKEeDXo56/cqDyOhAQ5Wy9jg\nPUW2XRkhOHuqm6hp8jC6v4XYdVOleuzQ3Taj5fb6qdhXHGjXibGoUhMRE/3dUFzLmBjjleuuBoT9\nuYDg/dbRgbHQtDlaGIzg86gqHxsMwPo9teRkZex38+vLIL+P5XYjqA8cv93JWNsbipVgxTIo+9st\nTardpKgvhc6HtHjquz2qX0da8RjqS9WCvFwLbbRiy746uvkDs+TOfW1rN0RdCXRKvt2qBjd7qpti\nCnwHdyBXlhFl4ZsVQvfFIfGv06QFWhiMEJwl2zAbWrm7irEDupKdGRj6hn3QqbfldiOoK1EGINOY\nAVhfXEvfLrmJzzsIGoDONhsAKZWYtTKGG4pryMwQHNHPglGvK1VCZmOIY/2eWnqLgDCYMOBx8XnU\nDNxEu+tDAh9DGFxl9vcXkp6IaFIbLQxGqCtRjxa/UC6vj+/21DJhcCC+6/cpA+CUx5DE7G3l7irG\nDjQQd3bKY3DXq4VorcZ4VWE1I/t0ir/bayLqS203hqsKqxicE4jX221oG8oBaWqMV+6uQggYOyD6\nZ9nBVe6chwNaGA4QtDAYoTYQS+7S31IzG4prcfv8TBwcSDY3VoD0Q74DHkN9acsvaZzqk5KaZnZV\nNHLsUAMeUVAYQmJmU7K8Lig4+42Wxy9ZsavKWL/iUb/PdmO4dEclY7u61ILHUBmzTWNRHzkWRlm2\no5JRfTvTtWN0TzHP1doTsavP+1SpcZTT9zTphxYGI9TZIwwrdqmQ1MRDA8LgaLw+PCwTP4SybKdK\nzh471EDZZf0+tQFbbid7q0+ijMW2ffU0un0cO8xiOWi9uXh9LMrqXGwva2BExwYl6hmZNo9FICyT\nZLjO4/MnEFKpPAabw2pAq4mIrkpKd7QwGKG2WM0M843NwGPx5ZZyhvXOVwf0AFQXqsdugyy1G4Hf\nr4yhwVnysh0VdMrN4oh+BvZrqtvrTIy6bq96DDOG64tVDH/yEAseg6dZ5YjC+2xxjJcHhLR/ZrWz\nY2HQ4wuyvriWJo+PY2IIfHfqyJQeZxLEJpPlmtREC4MRaveqL1NmFmZnQ80eH0u3V/CDkWFho+rd\n6rHbodb7GE59iVpsZKBdKSVfbi5n8pDuZGUauB2qdkF3m/sLUL1LPXYdFJrNri6sZkSfTokT4nHb\nbT3G1mezX24uo1NuFl2a90K3wZbbi6B6l1r93bk/yfT3y81lCAHHDosupINEmfrBqc/PiXY17YIW\nBiPUFkGXfpaaWL6zEpfXz4mHtRKG7I62bbURomqnejTwRd1WVs/uykZOOcLgbK9qp/1CBkpw8nu3\nOIthXVENpxxuMcwWFJzuQ6y1E8Dvl3z6/T6mj+yJqN5lW7stqNqpBDIzufMuPt1YyvhB3ejVKbqQ\nhoTB7s/P61JetRNjoWkXtDAYoXIHdB9qqYlPN+4jJyuj5Wyuepeacdod7w0JQ+I+f7JRxbNPNmKA\nm2vVYjGnjGGrdr1+aVyw4rULts1m1+2poazOxdnDMtS5Bo6NRXL93VfXzJoEQjpYBHIXds/sqwsB\nqYXhAEILQyK8LjWz7znCdBN+v+T9dXuZflhvOuaEzQKrd6mZod1U7QKEobYXbihldL8uic95hrDZ\ntxMew86ImWznvCwmDra4dUPVTrVDqU3x7082lpIhYFrPwPkcFicMUYkikon4NCTwsf+fg8Q+mrO7\nmz4VLiYh8R1ib7uadkMLQyIqdwASeg433cTynZXsq3Nxzriwqia/H8q3Qq+R1vvYmsrt0HUgZOWE\nPRmZvNxd0ciKXVX8cKzBMFnldvUYbgDsSJZ73VBTFGrX5fUBcPzwXsbyHvGo3KHatcErk1Ly5uo9\nTB3Riy5NRerJcJG0Yyyaa1QZc5LhngWr9jCsV37cAoJDRSn1HQe0fNKOPlftUI8t+qzLVdMZLQyJ\nqNiqHiOEwfiN//aaYvKyM1q6+dU7wdsEfY4w3W5M9m2E3ofv/z2GTVywag8A508YEP2CaO0ioNeo\n+A0nS/lmkL7QWKzcrcp6Tx5lw/qOfRtajgVgdowLdlVRWNnEBRMGqHYzc8M8BpvGYt9G9ZjEfVFY\n2ciyHZVcOHFA3B1oD8sopLpT0PO1MXxZul7tvRSsKNOb6KU9loRBCNFDCLFQCLEl8Bj1JBshxDWB\na7YIIa6J8vrbQojvrPTFMco3q8ceAWFI8qZvcHl5a3UxZx/Zr+Vul/u+V4+9lQGQdn2ZfB4o3wR9\nR8e9zO+XLFhVxJRhPRlgJIwEUPod9BgGOdaPN23Bvg3qsY/q8zdbVTmopW0wQB3HWrUD+o7Z/5yF\ncX59RREdczI5Y8whyhj2HpV0gjghpevVY7DPBvobFPgZ42MLvGjYR29RS3VnBzzUfRug75FaEA4g\nrHoMdwOfSilHAp8Gfm+BEKIHcB9wLHAMcF+4gAghLgTqLfbDOfauUS6yyW2K31y9h3qXlyuOaxUa\nKAvMDHuPinyTFSq2qlLVvkfGveyLLWXsrGjkkskDjbdduiGh4JiidL3a1K3XSLbuq2PDXrXVRKLz\nghJSFhDfcGEwSVWDmzdX7+GcsQGBL91gS7sR7NugNvszmHtye/28tHQX00b0YlCP2IKdVa7utyq7\nhUFKNRZ9Iu+LpdsrWF1Ybe/f07QJVoVhBjA38PNc4Pwo15wBLJRSVkopq4CFwJkAQohOwM+BBy32\nwzn2roF+40y9VUrJf7/dxRH9ukQmUfeuURVJefbt+Ansn3FG+aKG89zXO+jbJZcfHmVwNbe7QeUY\n+jhgDEOz72ye/Xon2Zk2zTxL1qnHBGNhhJeX7abZ42fWtGHQUKHWitjQbgQl36kwksHZ9/vr9lJa\n62LWtPhJ8Kwy5ZVVd7JZGKp3qfM5Wk0YXF4/Vz67lPPnfMPMp5fw9ZZypN4mI22wKgx9pZR7AQKP\n0WrlBgCFYb8XBZ4D+APwN6DRYj+coalahSJMCsMnG/fxfUkds6YNbRn7lRIKl8HAY2zqaBiFy9Ta\niDieyIbiWr7aUs7VU4aQk2XsFsgoXglIGHC0TR0N4PdD0XLoP4HyehdvrCzi+OG97Gm7qEBtlW6x\nWqbZ4+OFb3dywshe6uS9ouXqBbvHwuuGvasNtyul5JmvtzOsd37L9TFRyN5bQJHsRXOuzWtmigrU\nY6s+761pIisjgztPO4xtZfVc+exSLvzn4lD+SJPaJLQKQohPhBDfRfk3w+DfiDb1kUKI8cAIKeUC\nQ40IcaMQokAIUVBWVmbwT1tk72r12H980m+VUvL4p5sZ3KMj549vNSuvKVLbHgxyQhiWwMBJkdtt\nh83WHl24ic55WVxxrPFVu6Joqfph0ORWr1icBZZvVqd/DZ7CnEVb8fj8nD7Gpq0Vdn8Lg4+zHPt+\naeluSmtd3HTi8P3tZmTDgImtrrQ4FnvXqLURg441dPlH60v4bk8tN504PP453VKStWcZBf7Dor1o\nrq9Bdn+r9s4K8yTrmr1UN7q5/oSh/PSUkXz1q5P44wVHUVzdxIX/WMwd81dRXu+y9nc1jpJQGKSU\np0opj4zy7y2gVAjRDyDwuC9KE0VAeMB0IFAMTAGOFkLsBL4GDhNCfB6nH09LKSdJKSf17u3AbqTR\n2Pk1iMzoM/sEbvFH60v5bk8tt540IrLksjBgZAe2NrKJ242Lq06FTwYd1+qF/UZjxa5KPtm4j5tO\nHE63jjkYJaNwiQqdhB9DakfEZ/e3AOztNp6XluzmkkmD6NclmAy3MBb1ZVC5TQlDa5IY43qXlzmL\ntjJ1RE+mjgh4MruXqMlCdljS3o7Ea+ES9Wigz16fn79+tInhvfO5MFFVWdVOMhtKKfCHeZF2JYp3\nL1X3cVgSfnt5A1kZght+MAyA3KxMLj92MJ/dOZ1bThrO++tKOGP2l3yyodSePmhsx2oo6W0gWGV0\nDfBWlGs+Ak4XQnQPJJ1PBz6SUv5TStlfSjkEmAZsllJOt9gfe9nxpTIALfIAib9QzR4fD763gcP6\nduLCiVG+tNsXQW5XOOSopNpNyO4lahvvQ6dEfdnvlzz03kZ6dcrluqlDDDebjZeMomXRDZZVdn4N\n+X3407cuEHD7qSPtMVo7v1KPg1uPRXJt/2PRViob3PzyjEDJq7sBilc6MxY7vlLlrwZ2VZ2/vJBt\nZQ3cdfqoxGs9dn4NwHK/zYUODRWqUi1sjL/YXEZlg5s+nfPoktfSa83PzeIXZxzOOz+dRu/OuVz/\nQgEPvrsBn1/nHlINq8LwZ+A0IcQW4LTA7wghJgkhngGQUlaicgnLA/8eCDyX2rgbYM8KGHJC0m/9\n95fbKapq4v5zx0R+aaWELZ/A8JMMn65mmM0fqvzC4OOjvjxv+W5W7q7mV2eOarkCOwGTM75HuOth\n5Ol29VTh88LWhZT0PYG31+7lphOH06+rwdLZRGz5WHk3FvIAm0vrePrL7Vw4cQDjBgWKB7YtUlVf\ndo+FuxF2fGGo3X11zTz84fdMGdaTM480sFPq5g/xderPJmnzKvutCwEJI08DoMnt494319ExJyvm\nfk0Aow7pzFu3TuXqKYfyzNc7uH7ucuqaPfb2TWMJS8IgpayQUp4ipRwZeKwMPF8gpbw+7LrnpJQj\nAv/+E6WdnVLK+PWVbc22ReD3wrDpSb1tc2kdTy7aytlHHcLxI6IkUUvWqYqWwJfJNqSEzR/BsJMg\nOy/i5UaPlz9/oIzJRUcnUaIKnJqxEpmVB0NPtKu3isIl0FzDk0XDGdYrn59MN7+6vAV+nxKGkaer\nsxLMNOGX/OaNdXTOy+LeH4ZV3Gz+UHl7EZ6IRXZ+pfILh52R8NIH3tmAy+PnoQuOjLugDVDbjm9b\nhHv4adh+TsLmD9VWI/1UDu6Jz7ZQWNnEYX07JSw1zs3K5IEZR/Lg+Ufy5ZZyrnx2GTVNWhxSBb3y\nORYb34G8bjBkmuG3eHx+fv7KajrnZvHAjBg6t+EtdbbDCJuFoWQt1BRGNSwS+HpLOW6vnweNGJMW\nb5acmrEC/6En2L+w7fv38Yos3qwdxUMXHGXt+M5wCpeqbSUMGNlY/POLbRTsquKeH46mR34gF+Pz\nKvEdcbL93t7376okboL77c1Ve3h37V5+evIIhvXuFPdaQIVDPQ24h9ns4XiaYeunAfHNYNmOSp76\nYhsXHz2Q7knkrq487lD+ecVENhTXcMUzS6hudNvbT40ptDBEw+eBzR/AqLOTMgCzF27muz21PHTB\nUdFdab8f1r2ivBC7z91dM19VyhxxbsRLLq+fPdXN/O7c0Qw3YkzC6FezisEZZfjGXGhXTxU+D82r\n/scn3glcdeKRTBluYxnlmnmQnQ8jzQnDil2VPLpwM+eM7cePwnNE2z6Dhn1w5I9s6mgATxOsf1N9\ndlmxQzA7yxu4Z8E6jhnSg5uNeldr5kGH7rgH/8CmzgbY9D64auHIH1HV4Ob2+asY3KMj952X/DqX\n08ccwtNXTWJzaT0/fn45TW6fvX3VJI0Whmhs+0xtZhbFyMbiw+/28o/PtzHzmEGx476FS9ROrWMv\ns6mjAXweWPsKjDoLOrY8pGXJ9gqaPT6G9uzA5cckf6jMqJJ3qJd5+EedE/0Ck1VUxSveJc9Vzsru\nZ3Ln6dHKKE3iboTvFsDoGS3OdjBKWZ2L2+atpn+3PP544VEtvas1L6t1EbEEx2xF2ffvKSM7bmbM\nSxo9Xn7y0kqyMjN47LLxxjYXbKpWbR95UWzBMdvn1S9Dl4F4B0/jZ6+sprzexd8vn0in0LYvybV7\n0uF9eOKy8awqrOa2+at0Qrqd0cIQjZUvqENjRpwa56L9N+6W0jrufGUN4wd14/54M6blz6oD0w//\noaF2DbPpfWgsh/GXt3h6R3kDN724ggyRwZThvZILIQE01zC8bCEf+I7Zf8h7C8zFrCsb3Gz76B9U\n0YWrr76e7FhGzoxt2PCmWok7PraRjdVws8fHDS8UUNHg4h+XH92yqqa+DL5/H466uNWutUEsxO9X\nzlVbYMQpdPjT+xv5vqSWxy4bb2yLdIB1r4LPFWMsLPS3uhC2fQrjLuXBDzbz+aYy7j9vDEcO6Bpo\n2lzbZx7Zj/vOGc3CDaX84d0N5vunsYwWhtbUlcCmD5SRjWYAWt30pbXNXPf8cjrkZPGvK48mNytG\nnLxmjzJaR18TdSYrrXxRF/9d7ecUVtFS3ehm1vPLEUB+Xha5Blc4t6DgP+T4GnneZz5W35pmj4/7\n//MmU73LaR53DQN7RduDyuRYSKnGos/o2EY2htHy+yV3vbqGNUXVPHbpBI4a2LXlBcueVtVIx9xg\nrm+xKF6t8gCTr4eMKJ9RoL+Lt1Xwu3NGc9Iogyfa+X3w7RwYMAn6t16IZ5El/wCRwWvidJ5fvJNZ\n04ZyxbH2nNFx7dSh/HjqUJ5fvJM3VhbZ0qYmebQwtGbVf9UW0BOuTnhpTZOHa55bRlWDm+euncQh\nXSOrgUIs/ZdaY3DMjTZ2FrV2oWgZTLk1VIFT2+zh6ueWUVTVxNNXT8LU1kNeNyz9F0XdJrNe2nMY\njdvr5ycvreS4knnIzGz6nXabLe2G2PYp7FsPx/80qVmrlJLfvvUd767dy91nHh4ZCnQ3wPJ/q5yT\n3ednLH5SeZGTrov68offlQAwY3x/rp2axOew8R21ncvU2+3d9bSpClbMZXf/s/jFwgpOPaIPvzm7\n9Rbh1vjN2Ydz7NAe/PqNdawvrrG1bY0xtDCE426EJf+C4adAr/gntjW5fVw/dznbyup56qpJjB0Y\nZ/fVuhJY/oxKWtp9+tnnf1Zx7wlXAGqb7+v+s5wNxbX888qJTB4S/WD4hKx6Aer2snpQYoE0gtfn\n57Z5q9i8aT2XZn9F5oTLoZPF85zDkRI+fxg691cxdcNvkzzw7gZeWrqbm6cP58bAat0WLH9GGcSp\nNgtZ2SZY/4byIvO6Rrz8zFfbeXftXgBumZ7ECYJ+H3z5V7VVfNywpQm+/Qd4Grh5+xSOG9qTv18+\nkUzL2+C2JCszg79fPpHuHXO46cUV1DTqMta2RgtDOCtfULH6H9yV8NKbX1pBwa4qHr1kPNNGJtj0\n7cu/qjDESb+xqaMBtn6qVlH/4C7Iyafe5eXHzy9ndWE1T86cYP68ZFedEpzBx1PYI/piuWTw+Pzc\n8b/VfLi+hOcP/ZjMzEz4wS8tt9uCje8oz2n63TFyAJFIKfnTB9/zn2928uOpQ/nlGaMi8zCNlfDl\n31SYzu7Vzp/8XlVPTftZxEvPfb2DB9/byITArrxJGd+1r6gVySffY3odR1Rq9+L95kne9U2h4+AJ\nPHvtJPtKjFvRu3Mu/7hyInurm/n1grV6Z9Y2RgtDEK8bFj+hVg0fGtsYNntUKd3K3dU8dul4zh2X\nYNvqss2w4nmYeI065MYu/D5YeJ/aunvy9VTUu5j59BIKdlUx+9LxnHWUweM6o7H4SWgog9P/YDkM\n0eRWCd131+7lkWkwouQ9OO4n0NXgqXFG8Hngk/vVSW3jrzD0Fq/Pz92vr+PpL7dz9ZRD+e05R0RP\nzn/1N5XMPvX39vUXYNdi2PQeTLsD8vdPLKSUPLpwMw+8u4EzxvTlmilDkmvX0wSfPajyCqMvsLXL\n2169B7/XzXu9r+e5aycntXreDBMHd+euM0bx/roS5i8vTPwGjW1oYQiydj7U7oET7ox5SXWjm5eX\n7gbgbxeNjXtiFqDCG+/+TFX0TI84wyj2e4yw/BkoXQen3Meeej8XP/Utm0vr+PfVR3NeNLEy2m7F\nNvj6MRhzgdqlNXGHY75S0+jhymeX8uXmMv50/mgu2vsodOyljKEhDPb5m8fVhnmnPWDoRDWvz88t\nL6/kfwWF3HbKSH5/3pjoolDyncoNjb/C4AFFBvvrdcN7d0KXAUokA/j9kt+9tZ4nPt3CJZMGMufy\niWQFE0RGP78v/gK1RUrUoyWzTfb5jbfeYOjuN1jUZQZ/u+l8OuclWN9j0wz/xhOGMW1EL37/znq2\nlNbZ0qYmMVoYQM2+v56tlvaPOCXqJeX1Li57egmldWpl5mmjDYRpVr8Mu75WBsvOeHp1oQpDDD+F\nzZJsqxsAABeMSURBVL1P56J/LqaszsWL1x/LyYdH65fBWb/fD2/fBll5cMafEl8fx5soqWnmkqe+\nZV1RDXMun8hMPoI9BXDWw1Hj6UbbbU129XZlDEfPMLTSWSJ4b91ePlpfyn3njubnpx0WXRT8Pnj7\np2r1+2kPJO5IMp7V4sfVSW1nPxJaTd7s8XH7/1bz3yW7uPEHw3j4R2MDaxWSaLdknRLJ8VcmXrFv\nsL9+v+TP765lzIrfUpPdi5NuesyAp2BfziEjQ/DoJePIz8nip/NWhTx2jbNoYQBYv0CdTnbCnVG/\nMKW1zVz29BJ2VjRwqdGjMGv3wsf3qC2wDVQ4GUZKNdtEsmT0vfzon9/i9Uv+d+MU84nmIKteUEJ2\n+gPQxXwoal1RDTPmfE1RVSP/uW4yZw3ywKcPqG1AbFw1LPDT94tfqr2hzvprwut3VTTg8fkpqWlm\n9qXjuC5elc/Sp9Quqmc9HLFo0BLlW5SQjbkADj9bPVXv4opnlvLOmmJ+debh/ObsGGGtePi8Ssg6\n9lDegg00ur3c/NIKsr99nFEZRXS96Ely8hOIugP06ZLHI5eM4/uSOv74/sY2//sHI84GCdMBKeGr\nR6HXKDg8cnVvcXUTl/97CWV1Lp6/7hiGlWxL3KbfD2/epPaTOe9Jgy69QQqehS0fsXTUL7j8tb0c\nfkgXnrlmkvFFT7Eo3wof/kbV/1sQsvfX7eXnr6ymZ34ur//keA7v3RHmXqkE95xHbS2dvCHzPToW\nL4Hz/p5wi5Gl2yu46cUVLAHOGduPARPiCHzpepWzOOxMe7e/8Lrg9VkqtHjmwwBsKqlj1tzllNW5\nmHP5RH441qQgf/EwFK+Ci5+3Rcj21jQx6/kCcktX8s/cN5FjfkTG4WdZbtcsJ43qw/XThvLM1zs4\nfngvY7vKakyjhWHzR6r2/fx/RRjwwspGZv57CTWNHl6YdSxHH9odSgy0uWQObP8cznkMetu43UPp\neuSHv2FL52O4bM04Th3dl8cuHa8Op7eC1w2v/1jtC3VB5DgYQUrJnEVbeeTjzUwc3I2nr56k9ota\n9Ed1GM+F/1aJcpvoWrmWX2S9Qt2ws+k84cq4175aUMhvFqxjUI+O5DRmMCCeiLob4dXroEM3JTh2\nrgH45PfqlLbLXobOfVn0/T5+Om8VHXIyeeX/puzf2jtZdnylKt/GX6E8EYusKazm+hcKyHTXsaj7\n02Rk9YcfPmq5Xav88szDWb6zkl+8tobR/bowuKfNmzpqQhzcoSQp4etHoetgOKpl7fv2snou/te3\n1Lu8vHzDcUoUjFC8ShmAw8+Bo6+1r6/uRnyvXEuN7MjlZddx44kjeOrKo62LAsCnAYN1/j+ga3Jb\ncoOKj//8lTU88vFmzh/fn5dvOE6Jws6vlcEadzmMvcR6P0N/sJYJy++klO6UTv9L3NXMf/pgI794\nbS3HDu3JgpunJo5+f/RrddzoBU9BJxtPCtz8sZowTL4BOepsnvt6B7PmLmdwj468fetU86LQWAlv\n3Ag9h8NZf7HczXfXFnPJU9+Smyn4eOQCOjTuhR89o4SyncnJUusbBHDLyytxeXW+wSkObmHYtVht\n0Tz1tha7qG4ureOSp5bg8fmZd8NxkdsjxKK+DOZfqU7gOu9J+2abUlL/2i1kVmzmDvdN/OqiE/j1\nWUfEP+c3spHoT294C779O0y+wdRiKJ/fzxXPLGXBqj3cdfphzL50vKptr90Lr81SJ5KdnTj+bxi/\nH968mQ6NxdzmvhV/bnSD1eDyctOLK3jqi+1ccexg/nPdZLp2TFBJs3qeKi2eers6SClZYlXiVO2E\nBf8HfcbgPuUB7n3zOx54dwOnHtGXV2+aYv5wIr8PXr9erb256DlTmwYGbwspJY9/soVbX17FUQO6\n8uHUTXTZ+hac9GsYbOwM6qgN28ygHh3568XjWLenhofe0/kGpzi4Q0mLn1Sb5YWFInaUN3D5v5eQ\nIQQv33gcI/p0jvHmVje+zwOvXqu+pD/+yEKcN/ILtfWtPzFi8xvMEZdx849v4NhhSW5RHUugSr6D\nBTepM3vPeCjpnlY0ePA3uFlfW8M/rpjI2cG1E55m+N8V4K6HqxaYM1gQ3dB+8TB8/y4bx/6Glcui\nh+l2ljdw438L2LqvnvvOHc21xw9plcyN0m5RAbxzOwz9AZx8r4nOxhhjVz3Muxykj4pznuWm51ez\nfGcVN504nF+eMSoJcY/S54W/U9uAnPck9Btnur/1Li93vbKGD9eXcOGEAfz56BpyXroXDjsLpsUu\n347dtM0HArXijDGHMGvaUJ79egfHDO3BOWMTrCXSJM3BKwzVhbDlI5j289Ch7nuqm7jymaVISUAU\nohi0aDe9lPDRb1RFzwVPq3OikyTaJnpSSt5947+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- "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "\n", - "iparams = {}\n", - "iparams['injected_square_current'] = {}\n", - "iparams['injected_square_current']['amplitude'] = 155*pq.pA\n", - "DELAY = 0\n", - "DURATION = 520\n", - "iparams['injected_square_current']['delay'] = DELAY*pq.ms\n", - "iparams['injected_square_current']['duration'] = DURATION*pq.ms\n", - "\n", - "\n", - "#for C in [250]:\n", - "mparams['C'] = 250\n", - "mparams['a'] = 0.06\n", - "\n", - "mparams['b'] = -3\n", - "mparams['c'] = -55 \n", - "mparams['vr'] = -60\n", - "mparams['vt'] = -35\n", - "\n", - "mparams['vPeak'] = 45\n", - "mparams['k'] = 0.8\n", - "mparams['d'] = 150\n", - "\n", - "model1 = None\n", - "model1 = ReducedModel(LEMS_MODEL_PATH,name = str('vanilla'),backend = ('RAW'))\n", - "model1.set_attrs(mparams)\n", - "model1.inject_square_current(iparams)\n", - "\n", - "td = translate(mparams,m2m)\n", - "\n", - "model2.set_attrs(td)\n", - "model2 = ReducedModel(LEMS_MODEL_PATH,name = str('vanilla'),backend = ('NEURON'))\n", - "model2.set_attrs(mparams)\n", - "model2.inject_square_current(iparams)\n", - "\n", - "\n", - "plt.plot(model1.get_membrane_potential().times,model1.get_membrane_potential(),label='RAW')\n", - "plt.plot(model2.get_membrane_potential().times,model2.get_membrane_potential(),label='NEURON')\n", - "plt.legend()\n", - "plt.show()" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "collapsed": true - }, - "source": [ - "# A summary of NEURON backend units (left), versus NeuroML parameters right. " - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "\n", - "NEURON units NeuroML units \n", - "\n", - "v0 = -60 (mV), vr = -60 (mV) , mparams['vr'] = -60\n", - "\n", - "k = 7.0E-4 (uS / mV), mparams['k'] = 0.7 ***different\n", - "\n", - "vt = -40 (mV)\n", - "\n", - "vpeak = 35 (mV) mparams['vPeak'] = 35\n", - "\n", - "a = 0.030000001 (kHz) [ms-1], mparams['a'] = 0.03\n", - "\n", - "b = -0.002 (uS), mparams['b'] = -2 ***different \n", - "\n", - "c = -50 (mV), mparams['c'] = -50 \n", - "\n", - "d = 0.1 (nA) --MATLAB pA mparams['d'] = 100 ******** pA\n", - "\n", - "C = 1.0E-4 (microfarads) mparams['C'] = 100 ******** co faraday)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# A dictionary in code, that these model tests where based on." - ] - }, - { - "cell_type": "code", - "execution_count": 12, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "{'vr': -60, 'vt': -40, 'a': 0.03, 'vPeak': 25, 'd': 150, 'k': 1.5, 'C': 50, 'b': 1, 'c': -40}\n", - "{'d': 0.15, 'vt': -40.0, 'a': 0.03, 'k': 0.0015, 'C': 4.9999999999999996e-05, 'b': 0.001, 'vr': -60.0, 'vpeak': 25.0, 'c': -40.0}\n", - "{'vr': -75, 'vt': -45, 'a': 0.01, 'vPeak': 50, 'd': 130, 'k': 1.2, 'C': 150, 'b': 5, 'c': -56}\n", - "{'d': 0.13, 'vt': -45.0, 'a': 0.01, 'k': 0.0012, 'C': 0.00015, 'b': 0.005, 'vr': -75.0, 'vpeak': 50.0, 'c': -56.0}\n", - "{'vr': -60, 'vt': -50, 'a': 0.01, 'vPeak': 35, 'd': 10, 'k': 1.6, 'C': 200, 'b': 15, 'c': -60}\n", - "{'d': 0.01, 'vt': -50.0, 'a': 0.01, 'k': 0.0016, 'C': 0.00019999999999999998, 'b': 0.015, 'vr': -60.0, 'vpeak': 35.0, 'c': -60.0}\n" - ] - } - ], - "source": [ - "import collections\n", - "# Fast spiking cannot be reproduced as it requires modifications to the standard Izhi equation,\n", - "# which are expressed in this mod file.\n", - "# https://github.com/OpenSourceBrain/IzhikevichModel/blob/master/NEURON/izhi2007b.mod\n", - "\n", - "from collections import OrderedDict\n", - "type2007 = collections.OrderedDict([\n", - " # C k vr vt vpeak a b c d celltype\n", - " ('RS', (100, 0.7, -60, -40, 35, 0.03, -2, -50, 100, 1)),\n", - " ('IB', (150, 1.2, -75, -45, 50, 0.01, 5, -56, 130, 2)),\n", - " ('CH', (50, 1.5, -60, -40, 25, 0.03, 1, -40, 150, 3)),\n", - " ('LTS', (100, 1.0, -56, -42, 40, 0.03, 8, -53, 20, 4)),\n", - " ('FS', (20, 1.0, -55, -40, 25, 0.2, -2, -45, -55, 5)),\n", - " ('TC', (200, 1.6, -60, -50, 35, 0.01, 15, -60, 10, 6)),\n", - " ('TC_burst', (200, 1.6, -60, -50, 35, 0.01, 15, -60, 10, 6)),\n", - " ('RTN', (40, 0.25, -65, -45, 0, 0.015, 10, -55, 50, 7)),\n", - " ('RTN_burst', (40, 0.25, -65, -45, 0, 0.015, 10, -55, 50, 7))])\n", - "\n", - "import numpy as np\n", - "param_dict = OrderedDict([(k,[]) for k in ['C','k','vr','vt','vPeak','a','b','c','d']])\n", - "#OrderedDict\n", - "for i,k in enumerate(param_dict.keys()):\n", - " for v in type2007.values():\n", - " param_dict[k].append(v[i])\n", - "\n", - "explore_param = {k:(np.min(v),np.max(v)) for k,v in param_dict.items()}\n", - "param_ranges = OrderedDict(explore_param)\n", - "\n", - "\n", - "#IB = mparams[param_dict['IB']]\n", - "IB = {}\n", - "TC = {}\n", - "CH = {}\n", - "RTN_burst = {}\n", - "for k,v in param_dict.items():\n", - " IB[k] = v[1]\n", - " CH[k] = v[2]\n", - " TC[k] = v[5]\n", - " RTN_burst[k] = v[-2]\n", - " \n", - "RTN_burstN = copy.copy(RTN_burst)\n", - "TCN = copy.copy(TC)\n", - "IBN = copy.copy(IB)\n", - "CHN = copy.copy(CH)\n", - "\n", - "\n", - "# From OSB models\n", - "mparams = {}\n", - "mparams['a'] = 0.03\n", - "mparams['b'] = -2\n", - "mparams['C'] = 100\n", - "mparams['c'] = -50 \n", - "mparams['vr'] = -60\n", - "mparams['vt'] = -40\n", - "mparams['vpeak'] = 35\n", - "mparams['k'] = 0.7\n", - "mparams['d'] = 100\n", - "\n", - "\n", - "# FROM the MOD file.\n", - "vanilla_NRN = {}\n", - "#vanilla_NRN['v0'] = -60# (mV)\n", - "vanilla_NRN['k'] = 7.0E-4# (uS / mV)\n", - "vanilla_NRN['vr'] = -60# (mV)\n", - "vanilla_NRN['vt'] = -40# (mV)\n", - "vanilla_NRN['vpeak'] = 35# (mV)\n", - "vanilla_NRN['a'] = 0.03# (kHz)\n", - "vanilla_NRN['b'] = -0.002# (uS)\n", - "vanilla_NRN['c'] = -50# (mV)\n", - "vanilla_NRN['d'] = 0.1# (nA)\n", - "vanilla_NRN['C'] = 1.0E-4# (microfarads)\n", - "\n", - "m2m = {}\n", - "for k,v in vanilla_NRN.items():\n", - " m2m[k] = vanilla_NRN[k]/mparams[k]\n", - "\n", - "\n", - "def translate(input_dic,m2m):\n", - " input_dic['vpeak'] = input_dic['vPeak']\n", - " input_dic.pop('vPeak', None) \n", - " input_dic.pop('dt', None) \n", - " for k,v in input_dic.items():\n", - " input_dic[k] = v * m2m[k]\n", - " return input_dic\n", - "IBN = translate(IBN,m2m)\n", - "CHN = translate(CHN,m2m)\n", - "TCN = translate(TCN,m2m)\n", - "print(CH)\n", - "print(CHN)\n", - "print(IB)\n", - "print(IBN)\n", - "print(TC)\n", - "print(TCN)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "The following cells follow the Backend pattern RAW, NEURON, RAW, NEURON." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": true - }, - "outputs": [], - "source": [] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": true - }, - "outputs": [], - "source": [] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.5.2" - } - }, - "nbformat": 4, - "nbformat_minor": 2 -} diff --git a/neuronunit/unit_test/get_data.ipynb b/neuronunit/unit_test/get_data.ipynb deleted file mode 100644 index 4d9262416..000000000 --- a/neuronunit/unit_test/get_data.ipynb +++ /dev/null @@ -1,1765 +0,0 @@ -{ - "cells": [ - { - "cell_type": "code", - "execution_count": 1, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Collecting lazyarray\n", - " Downloading https://files.pythonhosted.org/packages/a0/c5/4cac8d8749bea1e675aa97232e7eaa1e340b5bd56a041268eb48c8dc523c/lazyarray-0.3.2.tar.gz\n", - "Requirement already satisfied: numpy>=1.8 in /opt/conda/lib/python3.5/site-packages (from lazyarray) (1.12.1)\n", - "Building wheels for collected packages: lazyarray\n", - " Running setup.py bdist_wheel for lazyarray ... \u001b[?25l-\b \b\\\b \bdone\n", - "\u001b[?25h Stored in directory: /home/jovyan/.cache/pip/wheels/b6/04/41/5fb855bf80313b5526e43e293153a1f8b7bce477a5b2d0f346\n", - "Successfully built lazyarray\n", - "\u001b[31mcffi 1.11.5 requires pycparser, which is not installed.\u001b[0m\n", - "\u001b[31mcryptography 2.2.1 requires asn1crypto>=0.21.0, which is not installed.\u001b[0m\n", - "\u001b[31mallensdk 0.14.2 has requirement pandas<0.20.0,>=0.16.2, but you'll have pandas 0.23.1 which is incompatible.\u001b[0m\n", - "Installing collected packages: lazyarray\n", - "Successfully installed lazyarray-0.3.2\n", - "\u001b[33mYou are using pip version 10.0.1, however version 18.1 is available.\n", - "You should consider upgrading via the 'pip install --upgrade pip' command.\u001b[0m\n" - ] - } - ], - "source": [ - "!pip install lazyarray\n", - "import numpy as np\n" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "from neuronunit.optimization.model_parameters import reduced_dict, reduced_cells \n", - "explore_param = {k:(np.min(v),np.max(v)) for k,v in reduced_dict.items()}\n", - "#print(ontologies) \n", - " " - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "\n", - "\n", - "electro_path = 'pipe_tests.p'\n", - "purkinje = { 'nlex_id':'sao471801888'}#'NLXWIKI:sao471801888'} # purkinje\n", - "fi_basket = {'nlex_id':'100201'}\n", - "#pvis_cortex = {'nlex_id':'nifext_50'} # Layer V pyramidal cell\n", - "olf_mitral = { 'nlex_id':'nifext_120'}\n", - "ca1_pyr = { 'nlex_id':'830368389'}\n", - "pipe = [ fi_basket, olf_mitral, ca1_pyr, purkinje ]\n", - "\n", - "\n", - "\n", - "\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": true - }, - "outputs": [], - "source": [] - }, - { - "cell_type": "code", - "execution_count": 4, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "purkinje ={\"id\": 18, \"name\": \"Cerebellum Purkinje cell\", \"neuron_db_id\": 271, \"nlex_id\": \"sao471801888\"}\n", - "fi_basket = {\"id\": 65, \"name\": \"Dentate gyrus basket cell\", \"neuron_db_id\": None, \"nlex_id\": \"nlx_cell_100201\"}\n", - "pvis_cortex = {\"id\": 111, \"name\": \"Neocortex pyramidal cell layer 5-6\", \"neuron_db_id\": 265, \"nlex_id\": \"nifext_50\"}\n", - "#does not have rheobase\n", - "#olf_mitral = {\"id\": 129, \"name\": \"Olfactory bulb (main) mitral cell\", \"neuron_db_id\": 267, \"nlex_id\": \"nlx_anat_100201\"}\n", - "ca1_pyr = {\"id\": 85, \"name\": \"Hippocampus CA1 pyramidal cell\", \"neuron_db_id\": 258, \"nlex_id\": \"sao830368389\"}\n", - "pipe = [ fi_basket, ca1_pyr, purkinje, pvis_cortex]" - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "electro_tests = []\n", - "electro_frame = {}\n", - "\n", - "#p_tests, p_observations = get_neab.get_neuron_criteria(olf_mitral)\n", - "#electro_frame[p[\"name\"]] = p_observations#, p_tests))\n" - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "electro_tests = []\n", - "obs_frame = {}\n", - "test_frame = {}\n", - "import os\n", - "import pickle\n", - "try: \n", - "\n", - " electro_path = str(os.getcwd())+'all_tests.p'\n", - "\n", - " assert os.path.isfile(electro_path) == True\n", - " with open(electro_path,'rb') as f:\n", - " (obs_frame,test_frame) = pickle.load(f)\n", - "\n", - "except:\n", - " for p in pipe:\n", - " p_tests, p_observations = get_neab.get_neuron_criteria(p)\n", - " obs_frame[p[\"name\"]] = p_observations#, p_tests))\n", - " test_frame[p[\"name\"]] = p_tests#, p_tests))\n", - " electro_path = str(os.getcwd())+'all_tests.p'\n", - " with open(electro_path,'wb') as f:\n", - " pickle.dump((obs_frame,test_frame),f)\n", - "\n", - "# print(test_frame)\n", - " " - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "metadata": { - "collapsed": false, - "scrolled": false - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "dict_keys(['olf_mit', 'Hippocampus CA1 pyramidal cell', 'Neocortex pyramidal cell layer 5-6', 'Dentate gyrus basket cell', 'Cerebellum Purkinje cell'])\n" - ] - }, - { - "data": { - "text/plain": [ - "Cell Capacitance {'mean': 89.7960714285714 pF, 'n': 14, 'std': ...\n", - "Input Resistance {'mean': 107.080327644332 Mohm, 'n': 113, 'std...\n", - "Membrane Time Constant {'mean': 24.5021946169772 ms, 'n': 46, 'std': ...\n", - "Resting membrane potential {'mean': -65.2261863636364 mV, 'n': 110, 'std'...\n", - "Rheobase {'mean': 189.24 pA, 'n': 17, 'std': 287.163664...\n", - "Spike Amplitude {'mean': 86.364525297619 mV, 'n': 64, 'std': 1...\n", - "Spike Half-Width {'mean': 1.31895278450363 ms, 'n': 59, 'std': ...\n", - "Spike Threshold {'mean': -47.5985714285714 mV, 'n': 70, 'std':...\n", - "Name: Hippocampus CA1 pyramidal cell, dtype: object" - ] - }, - "execution_count": 7, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "import pandas as pd\n", - "from neuronunit.tests.fi import RheobaseTestP\n", - "#obs_frame.pop(\"olf_mit\", 0)\n", - "#test_frame.pop(\"olf_mit\", 0)\n", - "\n", - "for k,v in test_frame.items():\n", - " if \"olf_mit\" not in k:\n", - " obs = obs_frame[k]\n", - " v[0] = RheobaseTestP(obs['Rheobase'])\n", - "\n", - "\n", - "df = pd.DataFrame.from_dict(obs_frame)\n", - "\n", - "print(test_frame.keys())\n", - "df['Hippocampus CA1 pyramidal cell']" - ] - }, - { - "cell_type": "code", - "execution_count": 8, - "metadata": { - "collapsed": false, - "scrolled": false - }, - "outputs": [], - "source": [ - "##### \n", - "from neuronunit.optimization import optimization_management as om\n", - "import pickle\n", - "\n", - "\n", - "free_params = ['a','b','k','c','C','d','vPeak','vr']#vt','c','k','d']#,'vt','k','c','C']#,'C'] # this can only be odd numbers.\n", - "\n", - "##\n", - "# Use information that is available\n", - "##\n", - "hc_ = reduced_cells['RS']\n", - "\n", - "hc_['vr'] = -65.2261863636364\n", - "\n", - "hc_['vPeak'] = hc_['vr'] + 86.364525297619\n", - "\n", - "explore_param['C'] = (hc_['C']-20,hc_['C']+20)\n", - "explore_param['vr'] = (hc_['vr']-5,hc_['vr']+5)\n", - "#hc = {}\n", - "#hc['C'] = 89.7960714285714\n", - "\n", - "\n", - "use_test = test_frame[\"Neocortex pyramidal cell layer 5-6\"]\n", - "\n", - "#from sciunit import scores# score_type \n", - "\n", - "#for t in use_test[::-1]:\n", - "# t.score_type = scores.RatioScore\n", - "#print(use_test)\n", - "#reduced_tests = [use_test[0], use_test[-2], use_test[len(use_test)-1]]\n", - "#bigger_tests = use_test[1:-2]\n", - "#bigger_tests.insert(0,use_test[0])\n", - "\n", - "test_opt = {}\n", - "import pickle\n", - "\n", - "with open('data_dump.p','wb') as f:\n", - " pickle.dump(test_opt,f)\n", - "\n", - "\n", - " \n" - ] - }, - { - "cell_type": "code", - "execution_count": 9, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "RheobaseTestP\n", - "{'mean': array(213.849583333333) * pA, 'n': 32, 'std': array(170.452454715608) * pA}\n" - ] - } - ], - "source": [ - "#print(reduced_cells.keys())\n", - "#print(test_frame.keys())\n", - "use_test[0].observation\n", - "#from neuronunit.tests import RheobaseP\n", - "from neuronunit.tests.fi import RheobaseTestP# as discovery\n", - "#print(use_test[1].observation)\n", - "#print(use_test[3].name)\n", - "print(use_test[0].name)\n", - "\n", - "rtp = RheobaseTestP(use_test[0].observation)\n", - "use_test[0] = rtp\n", - "print(use_test[0].observation)" - ] - }, - { - "cell_type": "code", - "execution_count": 10, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "data": { - "text/plain": [ - "\"\\ntry:\\n import pickle\\n with open('data_dump.p','rb') as f:\\n test_opt = pickle.load(f)\\nexcept:\\n MU = 12\\n NGEN = 25\\n cnt = 1\\n for t in use_test: \\n if cnt==len(use_test):\\n MU = 12\\n NGEN = 20\\n\\n npcl, DO = om.run_ga(explore_param,NGEN,[t],free_params=free_params, NSGA = True, MU = MU)\\n else:\\n\\n npcl, DO = om.run_ga(explore_param,NGEN,[t],free_params=free_params, NSGA = True, MU = MU)\\n\\n test_opt[str(t)] = {'out':npcl}\\n\\n ranges = {}\\n stds = npcl['pop'][0].dtc.attrs\\n for k in npcl['pop'][0].dtc.attrs.keys(): \\n stds[k] = []\\n ranges[k] = []\\n\\n\\n for i in npcl['pop'][::5]:\\n for k,v in i.dtc.attrs.items():\\n stds[k].append(v)\\n ranges[k].append(v)\\n\\n for k in npcl['pop'][0].dtc.attrs.keys():\\n ranges[k] = (np.min(ranges[k][1::]),np.max(ranges[k][1::]))\\n\\n stds[k] = np.std(stds[k][1::])\\n test_opt[str(t)]['stds'] = stds \\n test_opt[str(t)]['ranges'] = ranges \\n\\n cnt+=1\\n \\n with open('data_dump.p','wb') as f:\\n pickle.dump(test_opt,f)\\n\"" - ] - }, - "execution_count": 10, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "'''\n", - "try:\n", - " import pickle\n", - " with open('data_dump.p','rb') as f:\n", - " test_opt = pickle.load(f)\n", - "except:\n", - " MU = 12\n", - " NGEN = 25\n", - " cnt = 1\n", - " for t in use_test: \n", - " if cnt==len(use_test):\n", - " MU = 12\n", - " NGEN = 20\n", - "\n", - " npcl, DO = om.run_ga(explore_param,NGEN,[t],free_params=free_params, NSGA = True, MU = MU)\n", - " else:\n", - "\n", - " npcl, DO = om.run_ga(explore_param,NGEN,[t],free_params=free_params, NSGA = True, MU = MU)\n", - "\n", - " test_opt[str(t)] = {'out':npcl}\n", - "\n", - " ranges = {}\n", - " stds = npcl['pop'][0].dtc.attrs\n", - " for k in npcl['pop'][0].dtc.attrs.keys(): \n", - " stds[k] = []\n", - " ranges[k] = []\n", - "\n", - "\n", - " for i in npcl['pop'][::5]:\n", - " for k,v in i.dtc.attrs.items():\n", - " stds[k].append(v)\n", - " ranges[k].append(v)\n", - "\n", - " for k in npcl['pop'][0].dtc.attrs.keys():\n", - " ranges[k] = (np.min(ranges[k][1::]),np.max(ranges[k][1::]))\n", - "\n", - " stds[k] = np.std(stds[k][1::])\n", - " test_opt[str(t)]['stds'] = stds \n", - " test_opt[str(t)]['ranges'] = ranges \n", - "\n", - " cnt+=1\n", - " \n", - " with open('data_dump.p','wb') as f:\n", - " pickle.dump(test_opt,f)\n", - "''' " - ] - }, - { - "cell_type": "code", - "execution_count": 11, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "data": { - "text/plain": [ - "{'Cerebellum Purkinje cell': [,\n", - " ,\n", - " ,\n", - " ,\n", - " ,\n", - " ,\n", - " ],\n", - " 'Dentate gyrus basket cell': [,\n", - " ,\n", - " ,\n", - " ,\n", - " ,\n", - " ,\n", - " ],\n", - " 'Hippocampus CA1 pyramidal cell': [,\n", - " ,\n", - " ,\n", - " ,\n", - " ,\n", - " ,\n", - " ,\n", - " ],\n", - " 'Neocortex pyramidal cell layer 5-6': [,\n", - " ,\n", - " ,\n", - " ,\n", - " ,\n", - " ,\n", - " ,\n", - " ],\n", - " 'olf_mit': [,\n", - " ,\n", - " ,\n", - " ,\n", - " ,\n", - " ,\n", - " ,\n", - " ]}" - ] - }, - "execution_count": 11, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "reduced_cells.keys()\n", - "test_frame.keys()\n", - "test_frame.keys()\n", - "test_frame['olf_mit'].insert(0,test_frame['Cerebellum Purkinje cell'][0])\n", - "test_frame\n", - "#pd.DataFrame(index=test_frame.keys(),columns=reduced_cells.keys())\n" - ] - }, - { - "cell_type": "code", - "execution_count": 12, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/opt/conda/lib/python3.5/site-packages/sciunit/scores/complete.py:73: RuntimeWarning: divide by zero encountered in true_divide\n", - " value = (p_value - o_mean)/o_std\n", - "/home/jovyan/neuronunit/neuronunit/tests/passive.py:82: RuntimeWarning: overflow encountered in exp\n", - " vm_fit[offset:,0] = a * np.exp(-t[offset:]/b) + c\n", - "/home/jovyan/neuronunit/neuronunit/tests/passive.py:82: RuntimeWarning: overflow encountered in multiply\n", - " vm_fit[offset:,0] = a * np.exp(-t[offset:]/b) + c\n" - 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" - ], - "text/plain": [ - " RS IB LTS TC \\\n", - "olf_mit 7.12322 7.37872 6.83968 6.79105 \n", - "Hippocampus CA1 pyramidal cell 4.2652 5.84565 5.70527 5.74633 \n", - "Neocortex pyramidal cell layer 5-6 4.66967 5.83884 6.21074 6.36435 \n", - "Dentate gyrus basket cell 5.33575 6.44702 6.03769 6.27535 \n", - "Cerebellum Purkinje cell 6.04342 5.95173 5.47237 5.69825 \n", - "\n", - " TC_burst \n", - "olf_mit 6.68538 \n", - "Hippocampus CA1 pyramidal cell 6.06554 \n", - "Neocortex pyramidal cell layer 5-6 6.5461 \n", - "Dentate gyrus basket cell 6.38901 \n", - "Cerebellum Purkinje cell 5.53656 " - ] - }, - "execution_count": 12, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "\n", - "from neuronunit.optimization.data_transport_container import DataTC\n", - "from neuronunit.tests.fi import RheobaseTestP# as discovery\n", - "from neuronunit.optimization.optimization_management import dtc_to_rheo, format_test, nunit_evaluation\n", - "import quantities as pq\n", - "from neuronunit.models.reduced import ReducedModel\n", - "from neuronunit.optimization.model_parameters import model_params, path_params\n", - "LEMS_MODEL_PATH = path_params['model_path']\n", - "list_to_frame = []\n", - "import pdb\n", - "\n", - "df = pd.DataFrame(index=test_frame.keys(),columns=reduced_cells.keys())\n", - "\n", - "for k,v in reduced_cells.items():\n", - " temp = {}\n", - " temp[str(v)] = {}\n", - " dtc = DataTC()\n", - " dtc.tests = use_test\n", - " dtc.attrs = v \n", - " dtc.backend = 'RAW'\n", - " dtc.cell_name = 'vanilla'\n", - "\n", - "\n", - " for key, use_test in test_frame.items():\n", - " dtc.tests = use_test\n", - " dtc = dtc_to_rheo(dtc)\n", - " dtc = format_test(dtc)\n", - "\n", - " if dtc.rheobase is not None:\n", - " if dtc.rheobase!=-1.0:\n", - "\n", - " \n", - " dtc = nunit_evaluation(dtc)\n", - " df[k][key] = dtc.get_ss()\n", - " #temp[str(v)][str(key)] = dtc.get_ss()\n", - "\n", - "# list_to_frame.append(temp)\n", - "#df = pd.DataFrame(list_to_frame)\n", - "df\n", - " #bridge_judge\n" - ] - }, - { - "cell_type": "code", - "execution_count": 13, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "data": { - "text/html": [ - "
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\n", - "
" - ], - "text/plain": [ - " RS IB LTS TC \\\n", - "olf_mit 7.12322 7.37872 6.83968 6.79105 \n", - "Hippocampus CA1 pyramidal cell 4.2652 5.84565 5.70527 5.74633 \n", - "Neocortex pyramidal cell layer 5-6 4.66967 5.83884 6.21074 6.36435 \n", - "Dentate gyrus basket cell 5.33575 6.44702 6.03769 6.27535 \n", - "Cerebellum Purkinje cell 6.04342 5.95173 5.47237 5.69825 \n", - "\n", - " TC_burst \n", - "olf_mit 6.68538 \n", - "Hippocampus CA1 pyramidal cell 6.06554 \n", - "Neocortex pyramidal cell layer 5-6 6.5461 \n", - "Dentate gyrus basket cell 6.38901 \n", - "Cerebellum Purkinje cell 5.53656 " - ] - }, - "execution_count": 13, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "df" - ] - }, - { - "cell_type": "code", - "execution_count": 14, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "from neuronunit.tests import dm \n", - "dmtests = dm.Druckmann2013Test\n", - "d_tests = []\n", - "for d in dir(dm):\n", - " if \"Test\" in d:\n", - " exec('d_tests.append(dm.'+str(d)+')')\n", - "\n", - " \n", - "#print(d_tests)\n", - "\n", - "\n", - "#from neuronunit.tests.dm import InputResistanceTest as DMInputResistanceTest\n", - "#use_test.append(DMInputResistanceTest(injection_currents=[-11.0*pq.pA,-6*pq.pA,-1*pq.pA,]))\n", - "#print(use_test)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "['a', 'b', 'k', 'c', 'C', 'd', 'vPeak', 'vr']\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/home/jovyan/neuronunit/neuronunit/tests/passive.py:82: RuntimeWarning: overflow encountered in exp\n", - " vm_fit[offset:,0] = a * np.exp(-t[offset:]/b) + c\n", - "/home/jovyan/neuronunit/neuronunit/tests/passive.py:82: RuntimeWarning: overflow encountered in multiply\n", - " vm_fit[offset:,0] = a * np.exp(-t[offset:]/b) + c\n", - "INFO:__main__:gen\tnevals\tavg \tstd \tmin \tmax \n", - "1 \t6 \t5.63008\t0.555441\t4.81908\t6.24896\n", - "2 \t5 \t5.61101\t0.582143\t4.81122\t6.24476\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "got here a a a \n", - "\n", - "\n", - "\n", - "\n", - " \n", - "got here a a a \n", - "\n", - "\n", - "\n", - "\n", - " \n", - "got here a a a \n", - "\n", - "\n", - "\n", - "\n", - " \n", - "got here a a a \n", - "\n", - "\n", - "\n", - "\n", - " \n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "INFO:__main__:3 \t6 \t5.71771\t0.287229\t5.40427\t6.09923\n" - ] - } - ], - "source": [ - "\n", - "\n", - "MU = 6\n", - "NGEN = 200\n", - "\n", - "import pickle\n", - "import numpy as np\n", - "print(free_params)\n", - " \n", - "index, DO = om.run_ga(explore_param,NGEN,use_test,free_params=free_params, NSGA = False, MU = MU)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "\n", - "\n", - "MU = 6\n", - "NGEN = 200\n", - "\n", - "import pickle\n", - "import numpy as np\n", - "print(free_params)\n", - " \n", - "index, DO = om.run_ga(explore_param,NGEN,use_test,free_params=free_params, NSGA = False, MU = MU)\n", - "'''\n", - "MU = 6\n", - "NGEN = 200\n", - "\n", - "import pickle\n", - "\n", - "import numpy as np\n", - "try:\n", - " with open('multi_objective.p','rb') as f:\n", - " test_opt = pickle.load(f)\n", - "except:\n", - "\n", - "for index, use_test in enumerate(test_frame.values()):\n", - "\n", - " if index % 2 == 0:\n", - " index, DO = om.run_ga(explore_param,NGEN,use_test,free_params=free_params, NSGA = False, MU = MU)\n", - " else:\n", - " index, DO = om.run_ga(explore_param,NGEN,use_test,free_params=free_params, NSGA = False, MU = MU)\n", - " #print(NSGA)\n", - "\n", - " print('can get as low as 2.70295, 2.70679')\n", - "\n", - " test_opt = {str('multi_objective')+str(index):npcl}\n", - " with open('multi_objective.p','wb') as f:\n", - " pickle.dump(test_opt,f)\n", - "\n", - "\n", - "print(np.sum(list(test_opt['multi_objective']['pf'][2].dtc.scores.values())))\n", - "print(np.sum(list(test_opt['multi_objective']['pf'][1].dtc.scores.values())))\n", - "#print(np.sum(list(test_opt['multi_objective']['hof'][0].dtc.scores.values())))\n", - "print(test_opt['multi_objective']['pf'][2].dtc.scores.items())\n", - "print(test_opt['multi_objective']['pf'][1].dtc.scores.items())\n", - "'''\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "test_opt.keys()\n", - "for value in test_opt.values():\n", - " value['stds']\n", - " value['ranges']\n", - " print(value['ranges']) \n", - " print(value['stds'])\n", - " \n", - " #fig = pl.figure()\n", - " #ax = pl.subplot(111)\n", - " #ax.bar(range(len(value.keys())), values)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": true - }, - "outputs": [], - "source": [ - "with open('data_dump.p','rb') as f:\n", - " test_opt = pickle.load(f)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "test_opt" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "#errorbar\n", - "#for \n", - "import seaborn as sns\n", - "from matplotlib.pyplot import errorbar\n", - "import matplotlib.pyplot as plt\n", - "\n", - "fig0,ax0 = plt.subplots(dim,dim,figsize=(10,10))\n", - "plt.figure(num=None, figsize=(11, 11), dpi=80, facecolor='w', edgecolor='k')\n", - "\n", - "for v in test_opt.values():\n", - " x = 0\n", - " labels = []\n", - " plt.clf()\n", - " for k_,v_ in v['ranges'].items(): \n", - " #print(k_)\n", - " value = v_\n", - "\n", - " y = np.mean(value)\n", - " err = max(value)-min(value)\n", - " errorbar(x, y, err, marker='s', mfc='red',\n", - " mec='green', ms=2, mew=4,label='in '+str(k_))\n", - " x+=1\n", - " labels.append(k_)\n", - " plt.xticks(np.arange(len(labels)), labels)\n", - " ax0[i] = plt\n", - "\n", - " #plt.title(str(v))\n", - "\n", - "plt.show()\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": true - }, - "outputs": [], - "source": [ - "\n", - "from mpl_toolkits.mplot3d import Axes3D\n", - "from sklearn.cluster import KMeans\n", - "from sklearn import datasets\n", - "import seaborn as sns; sns.set() # for plot styling\n", - "dfs.replace([np.inf, -np.inf], np.nan)\n", - "dfs = dfs.dropna()\n", - "\n", - "#X = dfs[['standard','sp','ss','info_density','gf','standard','uniqueness','info_density','penalty']]\n", - "X = dfs[['standard','sp','ss']]\n", - "\n", - "X = X.as_matrix()\n", - "#import pdb; pdb.set_trace()\n", - "\n", - "est = KMeans(n_clusters=3)\n", - "\n", - "est.fit(X)\n", - "\n", - "y_kmeans = est.predict(X)\n", - "centers = est.cluster_centers_\n", - "\n", - "fignum = 1\n", - "fig = plt.figure(fignum, figsize=(4, 3))\n", - "ax = Axes3D(fig, rect=[0, 0, .95, 1], elev=48, azim=134)\n", - "ax.scatter(X[:, 0], X[:, 1], X[:, 2], c=y_kmeans, s=50)\n", - "ax.scatter(centers[:, 0], centers[:, 1], centers[:, 2], c='black', s=200, alpha=0.5);\n", - "ax.w_xaxis.set_ticklabels([])\n", - "ax.w_yaxis.set_ticklabels([])\n", - "ax.w_zaxis.set_ticklabels([])\n", - "ax.set_xlabel('standard')\n", - "ax.set_ylabel('subjectivity')\n", - "ax.set_zlabel('sentiment polarity')\n", - "#ax.set_title(titles[fignum - 1])\n", - "#ax.dist = 12\n", - "fignum = fignum + 1\n", - "for x,i in enumerate(zip(y_kmeans,dfs['clue_words'])):\n", - " try:\n", - " print(i[0],i[1],dfs['link'][x],dfs['publication'][x],dfs['clue_links'][x],dfs['sp_norm'][x],dfs['ss_norm'][x],dfs['uniqueness'][x])\n", - " except:\n", - " print(i)\n", - "\n", - "fig.savefig('3dCluster.png')" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# the parameter 'd' only seems important\n", - "# C does not have to be too precise within a range." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "I consider the final gene populations for each of the eight tests. I compute the variance in each of the converged populations, I see that variance is low in many of the gene populations.\n", - "\n", - "When all variables are used to optomize only against one set of parameters, you expect their would be high variance in parameters, that don't matter much with respect to that error criteria (you expect redundancy of solutions).\n", - "\n", - "I compute std on errors over all the tests in order to estimate how amenable the problem is to multiobjective optimization." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "from neuronunit.models.reduced import ReducedModel\n", - "from neuronunit.optimization.model_parameters import model_params, path_params\n", - "LEMS_MODEL_PATH = path_params['model_path']\n", - "import quantities as pq\n", - "plt.figure(num=None, figsize=(11, 11), dpi=80, facecolor='w', edgecolor='k')\n", - "\n", - "for k,v in test_opt.items(): \n", - " model = ReducedModel(LEMS_MODEL_PATH, name= str('vanilla'), backend=('RAW'))\n", - " model.attrs = v['out']['pf'][1].dtc.attrs\n", - " print(str(k), v['out']['pf'][1].dtc.get_ss())#fitness)\n", - " iparams = {}\n", - " iparams['injected_square_current'] = {}\n", - " iparams['injected_square_current']['amplitude'] =v['out']['pf'][1].rheobase['value']*pq.pA\n", - " #['amplitude'] = dtc.vtest[k]['injected_square_current']['amplitude']\n", - " DELAY = 100.0*pq.ms\n", - " DURATION = 1000.0*pq.ms\n", - " iparams['injected_square_current']['delay'] = DELAY\n", - " iparams['injected_square_current']['duration'] = int(DURATION)\n", - "\n", - " model.inject_square_current(iparams)\n", - "\n", - " plt.plot(model.get_membrane_potential().times,model.get_membrane_potential(),label=str(k))\n", - " plt.legend()\n", - "plt.show()\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "'''\n", - "#print([i.fitness.values for i in test_opt['t'][0]['pop']])#.keys()\n", - "print(np.std([i[0] for i in test_opt['t'][0]['pop'][0:5]]))#.keys()\n", - "print(np.std([i[1] for i in test_opt['t'][0]['pop'][0:5]]))#.keys()\n", - "print(np.std([i[2] for i in test_opt['t'][0]['pop'][0:5]]))#.keys()\n", - "print(np.std([i[3] for i in test_opt['t'][0]['pop'][0:5]]))#.keys()\n", - "print(test_opt['t'][0]['pop'][0][0])\n", - "print(test_opt['t'][0]['pop'][0][1])\n", - "test_opt['t'][0]['pop'][0].dtc.attrs\n", - "'''" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "\n", - "#values = { k:v for v in npcl['pop'][i].dtc.attrs.items() for i in npcl['pop'] }\n", - "#print(values) \n", - "#print(stds.keys())\n", - "#stds\n", - "#dtc.variances[k] for k in dtc.attrs.keys() " - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "\n", - "DO.seed_pop = npcl['pf'][0:MU]\n", - "npcl, DO = om.run_ga(explore_param,10,reduced_tests,free_params=free_params,hc = hc, NSGA = False, MU = MU, seed_pop = DO.seed_pop)\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "attrs_here = npcl['hardened'][0][0].attrs\n", - "attrs_here.update(hc)\n", - "attrs_here\n", - "scores = npcl['hof'][0].dtc.scores\n", - "print(scores)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "#\n", - "use_test = test_frame[\"Neocortex pyramidal cell layer 5-6\"]\n", - "reduced_tests = [use_test[0], use_test[-1], use_test[len(use_test)-1]]\n", - "bigger_tests = use_test[1:-2]\n", - "bigger_tests.insert(0,use_test[0])" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "#bigger_tests = bigger_tests[-1::]\n", - "print(bigger_tests)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "DO.seed_pop = npcl['hof'][0:MU]\n", - "reduced_tests = [use_test[0], use_test[-1], use_test[len(use_test)-1]]\n", - "npcl, DO = om.run_ga(explore_param,10,bigger_tests,free_params=free_params,hc = hc, NSGA = False, MU = MU)#, seed_pop = DO.seed_pop)\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "print(npcl['hardened'][0][0].attrs)\n", - "print(npcl['hardened'][0][0].scores)\n", - "print(npcl['pf'][0].fitness.values)\n", - "print(npcl['hof'][0].dtc.scores)\n", - "\n", - "#for t in use_test:\n", - "# print(t.name)\n", - " \n", - " \n", - "pop" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# From the scores printed above, it looks like certain error criteria, are in conflict with each other.\n", - "\n", - "Tests, that are amenable to co-optimization appear to be:\n", - "* Width test\n", - "* Input resistance tets\n", - "* Resting Potential Test,\n", - "* Capicitance test.\n", - "* Time constant\n", - "\n", - "Tests/criteria that seem in compatible with the above include: \n", - "* Rheobase, \n", - "* InjectedCurrentAPThresholdTest\n", - "* InjectedCurrentAPAmplitudeTest\n", - "\n", - "Therefore a reduced set of lists is made to check if the bottom three are at least amenable to optimization togethor." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "from sklearn.cluster import KMeans\n", - "est = KMeans(n_clusters=2)\n", - "est.fit(X)\n", - "y_kmeans = est.predict(X)\n", - "\n", - "centers = est.cluster_centers_\n", - "\n", - "fig = plt.figure(fignum,figsize=(4,3))\n", - "ax = Axes3D(fig,rect=[0,0,.95,1],elav=48,azim=134)\n", - "ax.scatter(X[:,0],X[:,1],X[:,2],c=y_kmeans,s=50),\n", - "ax.scatter(centres[:,0],centres[:,1],centres[:,2],c='black',s=200,alpha=0.5)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "\n", - "print(reduced_tests)\n", - "print(bigger_tests)\n", - "\n", - "DO.seed_pop = npcl['pf'][0:MU]\n", - "npcl, DO = om.run_ga(explore_param,10,reduced_tests,free_params=free_params,hc = hc, NSGA = True, MU = 12)#, seed_pop = DO.seed_pop)\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "import pickle\n", - "import copy\n", - "import numpy as np\n", - "import pandas as pd\n", - "import matplotlib.pyplot as plt\n", - "#from neuronunit.optimization.optimization_management import wave_measure\n", - "from neuronunit.models.reduced import ReducedModel\n", - "from neuronunit.optimization.model_parameters import model_params, path_params\n", - "LEMS_MODEL_PATH = path_params['model_path']\n", - "import neuronunit.optimization as opt\n", - "import quantities as pq\n", - "fig = plt.figure()\n", - "\n", - "plt.clf()\n", - "\n", - "from neuronunit.optimization.data_transport_container import DataTC\n", - "model = ReducedModel(LEMS_MODEL_PATH,name = str('vanilla'),backend = ('RAW'))\n", - "for i in npcl['pf'][0:2]:\n", - " iparams = {}\n", - " iparams['injected_square_current'] = {}\n", - " iparams['injected_square_current']['amplitude'] =i.dtc.rheobase\n", - " model = None\n", - " model = ReducedModel(LEMS_MODEL_PATH,name = str('vanilla'),backend = ('RAW'))\n", - " model.set_attrs(i.dtc.attrs)\n", - "\n", - " #['amplitude'] = dtc.vtest[k]['injected_square_current']['amplitude']\n", - " DELAY = 100.0*pq.ms\n", - " DURATION = 1000.0*pq.ms\n", - " iparams['injected_square_current']['delay'] = DELAY\n", - " iparams['injected_square_current']['duration'] = int(DURATION)\n", - " model.inject_square_current(iparams)\n", - " n_spikes = len(model.get_spike_train())\n", - " if n_spikes:\n", - " print(n_spikes)\n", - " #print(i[0].scores['RheobaseTestP']*pq.pA)\n", - " plt.plot(model.get_membrane_potential().times,model.get_membrane_potential())#,label='ground truth')\n", - " plt.legend()\n", - "\n", - "#gca().set_axis_off()\n", - "#subplots_adjust(top = 1, bottom = 0, right = 1, left = 0, \n", - "# hspace = 0, wspace = 0)\n", - "#margins(0,0)\n", - "#gca().xaxis.set_major_locator(NullLocator())\n", - "#gca().yaxis.set_major_locator(NullLocator())\n", - "\n", - "plt.subplots_adjust(left=0.0, right=1.0, top=0.9, bottom=0.1)\n", - "fig.tight_layout()\n", - "plt.show()\n", - "\n", - "fig.savefig(\"single_trace.png\", bbox_inches = 'tight',\n", - " pad_inches = 0)\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "import pickle\n", - "import copy\n", - "import numpy as np\n", - "import pandas as pd\n", - "import matplotlib.pyplot\n", - "\n", - "\n", - "from neuronunit.models.reduced import ReducedModel\n", - "from neuronunit.optimization.model_parameters import model_params, path_params\n", - "LEMS_MODEL_PATH = path_params['model_path']\n", - "import neuronunit.optimization as opt\n", - "import quantities as pq\n", - "fig = plt.figure()\n", - "\n", - "plt.clf()\n", - "\n", - "from neuronunit.optimization.data_transport_container import DataTC\n", - "for i in npcl['hardened']:\n", - " iparams = {}\n", - " iparams['injected_square_current'] = {}\n", - " iparams['injected_square_current']['amplitude'] = i[0].rheobase\n", - " model = None\n", - " model = ReducedModel(LEMS_MODEL_PATH,name = str('vanilla'),backend = ('RAW'))\n", - " model.set_attrs(i[0].attrs)\n", - "\n", - " #['amplitude'] = dtc.vtest[k]['injected_square_current']['amplitude']\n", - " DELAY = 100.0*pq.ms\n", - " DURATION = 1000.0*pq.ms\n", - " iparams['injected_square_current']['delay'] = DELAY\n", - " iparams['injected_square_current']['duration'] = int(DURATION)\n", - " model.inject_square_current(iparams)\n", - " n_spikes = len(model.get_spike_train())\n", - " if n_spikes:\n", - " print(n_spikes)\n", - " print(i[0].scores['RheobaseTestP']*pq.pA)\n", - " plt.plot(model.get_membrane_potential().times,model.get_membrane_potential())#,label='ground truth')\n", - " plt.legend()\n", - "\n", - "#gca().set_axis_off()\n", - "#subplots_adjust(top = 1, bottom = 0, right = 1, left = 0, \n", - "# hspace = 0, wspace = 0)\n", - "#margins(0,0)\n", - "#gca().xaxis.set_major_locator(NullLocator())\n", - "#gca().yaxis.set_major_locator(NullLocator())\n", - "\n", - "plt.subplots_adjust(left=0.0, right=1.0, top=0.9, bottom=0.1)\n", - "fig.tight_layout()\n", - "plt.show()\n", - "\n", - "fig.savefig(\"single_trace.png\", bbox_inches = 'tight',\n", - " pad_inches = 0)\n", - "\n", - "\n", - "'''\n", - "hc = {}\n", - "\n", - "#free_params = ['c','k']\n", - "for k,v in explore_param.items():\n", - " if k not in free_params:\n", - " hc[k] = v\n", - "constants = npcl['hardened'][0][0].attrs\n", - "hc.update(constants) \n", - "npcl, _ = om.run_ga(explore_param,20,test_frame[\"Neocortex pyramidal cell layer 5-6\"],free_params=free_params,hc = hc, NSGA = True)\n", - "'''" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "\n", - "free_params = ['a','b','k']#vt','c','k','d']#,'vt','k','c','C']#,'C'] # this can only be odd numbers.\n", - "\n", - "##\n", - "# Use information that is available\n", - "##\n", - "hc = reduced_cells['RS']\n", - "\n", - "hc['vr'] = -65.2261863636364\n", - "\n", - "hc['vPeak'] = hc['vr'] + 86.364525297619\n", - "hc['C'] = 89.7960714285714\n", - "hc.pop('a',0)\n", - "hc.pop('b',0)\n", - "hc.pop('k',0)\n", - "hc.pop('c',0)\n", - "hc.pop('d',0)\n", - " \n", - "use_test = test_frame[\"Neocortex pyramidal cell layer 5-6\"]\n", - "DO.seed_pop = npcl['pf']\n", - "ga_out = DO.run(max_ngen = 15)\n", - "'''\n", - "hc = {}\n", - "\n", - "free_params = ['C']\n", - "\n", - "for k,v in explore_param.items():\n", - " if k not in free_params:\n", - " hc[k] = v\n", - "#,'vt','k','c','C']#,'C'] # this can only be odd numbers\n", - "constants = npcl['hardened'][0][0].attrs\n", - "hc.update(constants) \n", - "npcl, _ = om.run_ga(explore_param,20,test_frame[\"Neocortex pyramidal cell layer 5-6\"],free_params=free_params,hc = hc, NSGA = True)\n", - "'''" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "'''\n", - "import pandas\n", - " \n", - "try:\n", - " ne_raw = pandas.read_csv('article_ephys_metadata_curated.csv', delimiter='\\t')\n", - " !ls -ltr *.csv\n", - "except:\n", - " !wget https://neuroelectro.org/static/src/article_ephys_metadata_curated.csv\n", - " ne_raw = pandas.read_csv('article_ephys_metadata_curated.csv', delimiter='\\t')\n", - "\n", - "blah = ne_raw[ne_raw['NeuronName'].str.match('Hippocampus CA1 pyramidal cell')]\n", - "#ne_raw['NeuronName']\n", - "#ne_raw['cell\\ capacitance']\n", - "#blah = ne_raw[ne_raw['NeuronName'].str.match('Hippocampus CA1 pyramidal cell')]\n", - "\n", - "print([i for i in blah.columns])\n", - "#rint(blah['rheobase'])\n", - "#print(blah)\n", - "#for i in ne_raw.columns:#['NeuronName']:\n", - "# print(i)\n", - "\n", - "#ne_raw['NeuronName'][85]\n", - "#blah = ne_raw[ne_raw['TableID'].str.match('85')]\n", - "#ne_raw['n'] = 84\n", - "#here = ne_raw[ne_raw['Index']==85]\n", - "here = ne_raw[ne_raw['TableID']==18]\n", - "\n", - "print(here['rheo_raw'])\n", - "#!wget https://neuroelectro.org/apica/1/n/\n", - "'''" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "ca1 = ne_raw[ne_raw['NeuronName'].str.match('Hippocampus CA1 pyramidal cell')]\n", - "ca1['rheo']" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "\n", - " \n", - "test_frame[\"Dentate gyrus basket cell\"][0].observation['std'] = test_frame[\"Dentate gyrus basket cell\"][0].observation['mean']\n", - "for t in test_frame[\"Dentate gyrus basket cell\"]:\n", - " print(t.name)\n", - "\n", - " print(t.observation)\n", - " " - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "\n", - "'''\n", - "Inibitory Neuron\n", - "This can't pass the Rheobase test\n", - "''' " - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "\n", - "from neuronunit.optimization import optimization_management as om\n", - "import pickle\n", - "\n", - "free_params = ['a','vr','b','vt','vPeak','c','k']\n", - "for k,v in explore_param.items():\n", - " if k not in free_params:\n", - " hc[k] = v\n", - "use_test = test_frame[\"Dentate gyrus basket cell\"]\n", - "bcell, _ = om.run_ga(explore_param,20,use_test,free_params=free_params,hc = hc, NSGA = True, MU = 4)\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "\n", - " \n", - "#test_frame[\"Dentate gyrus basket cell\"][0].observation['std'] = test_frame[\"Dentate gyrus basket cell\"][0].observation['mean']\n", - "for t in test_frame[\"Hippocampus CA1 pyramidal cell\"]:\n", - " print(t.name)\n", - "\n", - " print(t.observation)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "use_test = test_frame[\"Hippocampus CA1 pyramidal cell\"]\n", - "bcell, _ = om.run_ga(explore_param,20,use_test,free_params=free_params,hc = hc, NSGA = True, MU = 10)\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "import pickle\n", - "import copy\n", - "import numpy as np\n", - "import pandas as pd\n", - "import matplotlib.pyplot as plt\n", - "\n", - "from neuronunit.models.reduced import ReducedModel\n", - "from neuronunit.optimization.model_parameters import model_params, path_params\n", - "LEMS_MODEL_PATH = path_params['model_path']\n", - "import neuronunit.optimization as opt\n", - "import quantities as pq\n", - "fig = plt.figure()\n", - "\n", - "plt.clf()\n", - "\n", - "from neuronunit.optimization.data_transport_container import DataTC\n", - "for i in bcell['hardened'][0:6]:\n", - " iparams = {}\n", - " iparams['injected_square_current'] = {}\n", - " iparams['injected_square_current']['amplitude'] =i[0].rheobase\n", - " model = None\n", - " model = ReducedModel(LEMS_MODEL_PATH,name = str('vanilla'),backend = ('RAW'))\n", - " model.set_attrs(i[0].attrs)\n", - "\n", - " #['amplitude'] = dtc.vtest[k]['injected_square_current']['amplitude']\n", - " DELAY = 100.0*pq.ms\n", - " DURATION = 1000.0*pq.ms\n", - " iparams['injected_square_current']['delay'] = DELAY\n", - " iparams['injected_square_current']['duration'] = int(DURATION)\n", - " model.inject_square_current(iparams)\n", - " n_spikes = len(model.get_spike_train())\n", - " if n_spikes:\n", - " print(n_spikes)\n", - " print(i[0].scores['RheobaseTestP']*pq.pA)\n", - " plt.plot(model.get_membrane_potential().times,model.get_membrane_potential())#,label='ground truth')\n", - " plt.legend()\n", - "\n", - "#gca().set_axis_off()\n", - "#subplots_adjust(top = 1, bottom = 0, right = 1, left = 0, \n", - "# hspace = 0, wspace = 0)\n", - "#margins(0,0)\n", - "#gca().xaxis.set_major_locator(NullLocator())\n", - "#gca().yaxis.set_major_locator(NullLocator())\n", - "\n", - "plt.subplots_adjust(left=0.0, right=1.0, top=0.9, bottom=0.1)\n", - "fig.tight_layout()\n", - "plt.show()\n", - "\n", - "fig.savefig(\"single_trace.png\", bbox_inches = 'tight',\n", - " pad_inches = 0)\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": true - }, - "outputs": [], - "source": [] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": true - }, - "outputs": [], - "source": [ - "use_test = test_frame[\"Hippocampus CA1 pyramidal cell\"]\n", - "bcell, _ = om.run_ga(explore_param,20,use_test,free_params=free_params,hc = hc, NSGA = True, MU = 10)\n" - ] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.5.2" - } - }, - "nbformat": 4, - "nbformat_minor": 2 -} diff --git a/neuronunit/unit_test/grid_entry_point.py b/neuronunit/unit_test/grid_entry_point.py deleted file mode 100644 index be5dcc630..000000000 --- a/neuronunit/unit_test/grid_entry_point.py +++ /dev/null @@ -1,113 +0,0 @@ - -import pickle -import os - -from neuronunit.optimization import exhaustive_search -from neuronunit.optimization import get_neab -from neuronunit.optimization import optimization_management - -import dask.bag as db -from neuronunit.optimization import get_neab -from sklearn.grid_search import ParameterGrid -import scipy -import multiprocessing -multiprocessing.cpu_count() -import pdb -electro_path = str(os.getcwd())+'/pipe_tests.p' -assert os.path.isfile(electro_path) == True -with open(electro_path,'rb') as f: - electro_tests = pickle.load(f) - -electro_tests = get_neab.replace_zero_std(electro_tests) -electro_tests = get_neab.substitute_parallel_for_serial(electro_tests) -purkinje = { 'nlex_id':'sao471801888'}#'NLXWIKI:sao471801888'} # purkinje -fi_basket = {'nlex_id':'100201'} -pvis_cortex = {'nlex_id':'nifext_50'} # Layer V pyramidal cell -olf_mitral = { 'nlex_id':'nifext_120'} -ca1_pyr = { 'nlex_id':'830368389'} - -pipe = [ fi_basket, pvis_cortex, olf_mitral, ca1_pyr, purkinje ] - - -class WSListIndividual(list): - """Individual consisting of list with weighted sum field""" - def __init__(self, *args, **kwargs): - """Constructor""" - self.rheobase = None - super(WSListIndividual, self).__init__(*args, **kwargs) - - -nparams = 2 - -grid_points = exhaustive_search.create_grid(npoints = 5,nparams = nparams) -tds = [ list(g.keys()) for g in grid_points ] -td = tds[0] - - -pops = [] -for g in grid_points: - pre_pop = list(g.values()) - pop = [ WSListIndividual(pre_pop) ] - pops.extend(pop) - - -def chunks(l, n): - # For item i in a range that is a length of l, - ch = [] - for i in range(0, len(l), n): - # Create an index range for l of n items: - ch.append(l[:][i:i+n]) - return ch - -npartitions = multiprocessing.cpu_count() -# divide population into chunks that reflect the number of CPUs. -pops_ = chunks(pops,npartitions) - -## -# Create a -# Consumble iterator. -# That promotes memory friendly lazy evaluation. -## - -consumble = [(sub_pop, test, observation ) for test, observation in electro_tests for sub_pop in pops_ ] -try: - with open('grid_cell'+str(pipe[cnt])+'.p','rb') as f: - pipes = pickle.load(f) - with open('iterator_state.p','rb') as f: - consumble_ = [(sub_pop, test, observation ) for test, observation in electro_tests for sub_pop in pops_ ][cnt] - sub_pop, test, observation, cnt = pickle.load(f) - if len(consumble_) < len(consumble) and len(consumble_) !=0 : - consumble = iter(consumble_) -except: - consumble = iter(consumble) - -cnt = 0 -pipes = {} -results = [] - -for sub_pop, test, observation in consumble: - print('{0}, out of {1}'.format(cnt,len(pops_))) - results.append(optimization_management.update_exhaust_pop(sub_pop, test, td)) - with open('grid_cell_results.p','wb') as f: - pickle.dump(results,f) - with open('iterator_state.p','wb') as f: - pickle.dump([sub_pop, test, observation, cnt],f) - cnt += 1 - print('done cell: ',cnt) -print('done all') - -from neuronunit.optimization import get_neab -from neuronunit.optimization.model_parameters import model_params -from bluepyopt.deapext.optimisations import DEAPOptimisation -from neuronunit.optimization.optimization_management import write_opt_to_nml -from neuronunit.optimization import optimization_management -from neuronunit.optimization import optimization_management as om -key_list = list(model_params.keys()) -reduced_key_list = key_list[0:nparams] -subset = { k:smaller[k] for k in reduced_key_list } -DO = DEAPOptimisation(error_criterion = test, selection = str('selIBEA'), provided_dict = model_params, elite_size = 3) -package = DO.run(offspring_size = MU, max_ngen = 6, cp_frequency=1,cp_filename=str(dic_key)+'.p') -pop, hof_py, pf, log, history, td_py, gen_vs_hof = package - -with open('all_ga_cell.p','wb') as f: - pickle.dump(package,f) diff --git a/neuronunit/unit_test/grid_vs_ga.py b/neuronunit/unit_test/grid_vs_ga.py deleted file mode 100644 index d044e0621..000000000 --- a/neuronunit/unit_test/grid_vs_ga.py +++ /dev/null @@ -1,102 +0,0 @@ -import timeit -import numpy as np -import matplotlib -matplotlib.use('Agg') - -import matplotlib.pyplot as plt -import seaborn as sns -import pandas as pd -import numpy as np -import math as math -from pylab import rcParams -from neuronunit.optimization.optimization_management import run_ga -from neuronunit.optimization.exhaustive_search import run_grid, reduce_params, create_grid -from neuronunit.optimization.model_parameters import model_params - - -import os -import pickle -from neuronunit.optimization.results_analysis import min_max, error_domination, param_distance - - -from neuronunit.optimization.results_analysis import make_report -from neuronunit.optimization import get_neab -import time - -import copy -reports = {} - - -electro_path = str(os.getcwd())+'/pipe_tests.p' -print(os.getcwd()) -assert os.path.isfile(electro_path) == True -with open(electro_path,'rb') as f: - electro_tests = pickle.load(f) - -electro_tests = get_neab.replace_zero_std(electro_tests) -electro_tests = get_neab.substitute_parallel_for_serial(electro_tests) -test, observation = electro_tests[0] - -npoints = 6 -tests = copy.copy(electro_tests[0][0][0:2]) - - -start_time = timeit.default_timer() -flat_iter = [ (tests[0:2], observation) for tests[0:2], observation in electro_tests ] #for s in se - - - -import nbformat -import os -from nbconvert.preprocessors import ExecutePreprocessor -import shutil -def run_and_save(opt_keys,tests): - nparams = len(opt_keys) - - it = timeit.default_timer() - grid_results = run_grid(nparams,npoints,tests,provided_keys = opt_keys) - ft = timeit.default_timer() - elapsed_grid = ft - it - with open('dim_{0}_errs{1}_grid.p','wb') as f:# - pickle.dump([grid_results,opt_keys,tests,elapsed_grid],f) - - it = timeit.default_timer() - ga_out = run_ga(model_params,nparams,npoints,tests,provided_keys = opt_keys, use_cache = True, cache_name='simple') - ft = timeit.default_timer() - elapsed_ga = ft - it - with open('dim_{0}_errs{1}_ga.p'.format(len(opt_keys),len(tests)),'wb') as f: - pickle.dump([ga_out,opt_keys,tests,elapsed_ga],f) - file_name ='dim_{0}_errs{1}_ga.ipynb'.format(len(opt_keys),len(tests)) - - os.system("ipython nbconvert --to html --execute agreement.ipynb") - - os.system("cp agreement_df.ipynb "+file_name) - os.system("ipython nbconvert --to html --execute "+file_name) - - #shutil.chown('dim_{0}_errs{1}_ga.p', user='jovyan', group='user') - - with open('dim_{0}_errs{1}_ga.p'.format(len(opt_keys),len(tests)),'wb') as f: - pickle.dump([ga_out,opt_keys,tests,elapsed_ga],f) - file_name ='dim_{0}_errs{1}_ga.ipynb'.format(len(opt_keys),len(tests)) - - - os.system("cp agreement_df.ipynb "+file_name) - os.system("ipython nbconvert --to html --execute "+file_name) - - return ga_out - -import grid_vs_ga_h_constant - - -pipe_results = {} -dic_key = 0# TODO dic_key key, must be replaced with a cell names later -for test, observation in flat_iter: - opt_keys = [str('a'),str('vr'),str('b')]# ,str('c')]#,str('C')] - ga_out = run_and_save(opt_keys,test) - pipe_results[dic_key] = ga_out - dic_key += 1 - - -with open('agreement_df.p','wb') as f: - pickle.dump(pipe_results,p) - diff --git a/neuronunit/unit_test/grid_vs_ga_function.py b/neuronunit/unit_test/grid_vs_ga_function.py deleted file mode 100644 index 22ffb1ad5..000000000 --- a/neuronunit/unit_test/grid_vs_ga_function.py +++ /dev/null @@ -1,89 +0,0 @@ -import timeit -import numpy as np -import matplotlib -matplotlib.use('Agg') - -import matplotlib.pyplot as plt -import seaborn as sns -import pandas as pd -import numpy as np -import math as math -from pylab import rcParams -from neuronunit.optimization.optimization_management import run_ga -from neuronunit.optimization.exhaustive_search import run_grid, reduce_params, create_grid -from neuronunit.optimization.model_parameters import model_params - - -import os -import pickle -from neuronunit.optimization.results_analysis import min_max, error_domination, param_distance - - -from neuronunit.optimization.results_analysis import make_report -from neuronunit.optimization import get_neab -import time - -import nbformat -import os -from nbconvert.preprocessors import ExecutePreprocessor - -import copy -reports = {} - -electro_path = str(os.getcwd())+'/pipe_tests.p' -print(os.getcwd()) -assert os.path.isfile(electro_path) == True -with open(electro_path,'rb') as f: - electro_tests = pickle.load(f) - -electro_tests = get_neab.replace_zero_std(electro_tests) -electro_tests = get_neab.substitute_parallel_for_serial(electro_tests) -test, observation = electro_tests[0] - -npoints = 6 -tests = copy.copy(electro_tests[0][0][0:2]) - - -with open('pre_ga_reports.p','rb') as f: - package = pickle.load(f) - - -start_time = timeit.default_timer() -flat_iter = [ (test[0:2], observation) for test, observation in electro_tests ] #for s in sel ] - - - -import shutil - -def run_and_save(opt_keys,tests): - nparams = len(opt_keys) - - it = timeit.default_timer() - grid_results = run_grid(npoints,tests,provided_keys = opt_keys) - ft = timeit.default_timer() - elapsed_grid = ft - it - with open('dim_{0}_errs{1}_grid.p','wb') as f:# - pickle.dump([grid_results,opt_keys,tests,elapsed_grid],f) - - it = timeit.default_timer() - ga_out = run_ga(model_params,nparams,npoints,tests,provided_keys = opt_keys, use_cache = True, cache_name='simple') - ft = timeit.default_timer() - elapsed_ga = ft - it - - with open('dim_{0}_errs{1}_ga.p'.format(len(opt_keys),len(tests)),'wb') as f: - pickle.dump([ga_out,opt_keys,tests,elapsed_ga],f) - file_name ='dim_{0}_errs{1}_ga.ipynb'.format(len(opt_keys),len(tests)) - - shutil.chown(file_name, user='jovyan', group=None) - - return ga_out - - -for (s,test, observation) in flat_iter: - opt_keys = [str('a'),str('vr'),str('b')] - ga_out = run_and_save(opt_keys,test) - pipe_results[dic_key] = ga_out - - -_ = run_and_save(opt_keys,tests) -import grid_vs_ga_h_constant diff --git a/neuronunit/unit_test/grid_vs_ga_h_constant.py b/neuronunit/unit_test/grid_vs_ga_h_constant.py deleted file mode 100644 index 62a2e2ff6..000000000 --- a/neuronunit/unit_test/grid_vs_ga_h_constant.py +++ /dev/null @@ -1,171 +0,0 @@ -import timeit -import numpy as np -import matplotlib -import shelve -matplotlib.use('Agg') - -#import matplotlib.pyplot as plt -import seaborn as sns -import pandas as pd -import numpy as np -import math as math -from pylab import rcParams -from neuronunit.optimization.optimization_management import run_ga -from neuronunit.optimization.exhaustive_search import run_grid, reduce_params, create_grid -from neuronunit.optimization.model_parameters import model_params - - -import os -import pickle -from neuronunit.optimization.results_analysis import min_max, error_domination, param_distance - - -from neuronunit.optimization.results_analysis import make_report -from neuronunit.optimization import get_neab - -import copy -import time - -from neuronunit.optimization import exhaustive_search as es -import numpy as np - -reports = {} - - -electro_path = str(os.getcwd())+'/pipe_tests.p' -print(os.getcwd()) -assert os.path.isfile(electro_path) == True -with open(electro_path,'rb') as f: - electro_tests = pickle.load(f) - -electro_tests = get_neab.replace_zero_std(electro_tests) -electro_tests = get_neab.substitute_parallel_for_serial(electro_tests) -test, observation = electro_tests[0] - - -tests = copy.copy(electro_tests[0][0][0:2]) - -#tests = copy.copy(electro_tests[0][0]) - - - -with open('dim_3_errs8_ga.p','rb') as f: - package = pickle.load(f) - - - -# timing performance profiling - -import time -start_time = timeit.default_timer() - -# plotting tools -try: - from prettyplotlib import plt -except: - import matplotlib.pyplot as plt - -# import NU tests - -from neuronunit.optimization import get_neab -import copy - -electro_path = str(os.getcwd())+'/pipe_tests.p' -print(os.getcwd()) -assert os.path.isfile(electro_path) == True -with open(electro_path,'rb') as f: - electro_tests = pickle.load(f) - -electro_tests = get_neab.replace_zero_std(electro_tests) -electro_tests = get_neab.substitute_parallel_for_serial(electro_tests) -test, observation = electro_tests[0] - -npoints = 10 - -opt_keys = [str('a'),str('C'),str('b')] -nparams = len(opt_keys) -ga_out = run_ga(model_params,nparams,npoints,tests,provided_keys = opt_keys, use_cache = True, cache_name='simple') -''' -fname = 'dim_{0}_errs{1}_ga.p'.format(len(opt_keys),len(tests)) -with open(fname,'wb') as f: - pickle.dump([ga_out,opt_keys,tests,elapsed_ga],f) - - -with open(fname,'rb') as f: - package = pickle.load(f) - -''' - -pop = ga_out[0] -ga_keys = list(pop[0].dtc.attrs) - -grid_results = {} -hof = package[1] - -with shelve.open('hcg.p') as db: - db['grid_results'] = None - -import os -assert os.path.isfile('hcg.p') is True - - - - -for i in range(len(ga_keys)): - for j in range(len(ga_keys)): - if i!=j: - provided_keys = [ga_keys[j],ga_keys[i]] - ss = set(provided_keys) - bs = set(attrs_list) - # this finds the parameter that you do not have the explicit index for: - diff = bs.difference(ss) - bd = {} - for i in range(0,len(diff)): - bd[list(diff)[i]] = hof[0].dtc.attrs[list(diff)[i]] - provided_keys.append(attrs_list[i]) - provided_keys.append(attrs_list[j]) - gr = run_grid(1,1,tests,provided_keys = provided_keys ,hold_constant = bd) - assert gr[0] != gr[-1] - key = str(attrs_list[i])+str(attrs_list[j]) - grid_results[key] = gr - - -for i in range(len(ga_keys)): - for j in range(len(ga_keys)): - - if i == j: - provided_keys = [ ga_keys[i] ] - ss = set(provided_keys) - bs = set(ga_keys) - # this finds the parameter that you do not have the explicit index for: - diff = bs.difference(ss) - bd = {} - for i in range(0,len(diff)): - bd[list(diff)[i]] = hof[0].dtc.attrs[list(diff)[i]] - - provided_keys.append(attrs_list[i]) - assert len(bd) > len(provided_keys) - assert len(bd) == 2 and len(provided_keys) == 1 - - gr = run_grid(10,tests,provided_keys = provided_keys ,hold_constant = bd, use_cache = True, cache_name='complex') - - # do a 1D exhaustive search where everything except parameter i is held constant. - - if i>j: - - provided_keys = [ga_keys[j],ga_keys[i]] - ss = set(provided_keys) - bs = set(attrs_list) - # this finds the parameter that you do not have the explicit index for: - diff = bs.difference(ss) - bd = {} - for i in range(0,len(diff)): - bd[list(diff)[i]] = hof[0].dtc.attrs[list(diff)[i]] - - provided_keys.append(attrs_list[i]) - provided_keys.append(attrs_list[j]) - gr = run_grid(2,10,tests,provided_keys = provided_keys ,hold_constant = bd, use_cache = True, cache_name='complex') - key = str(attrs_list[i])+str(attrs_list[j]) - grid_results[key] = gr - with shelve.open('hcg.p') as db: - db['grid_results'] = grid_results diff --git a/neuronunit/unit_test/held_constant_grid.p b/neuronunit/unit_test/held_constant_grid.p deleted file mode 100644 index d81bf6a4d..000000000 Binary files a/neuronunit/unit_test/held_constant_grid.p and /dev/null differ diff --git a/neuronunit/unit_test/import_tests.py b/neuronunit/unit_test/import_tests.py index ca7370738..1a1588f9b 100644 --- a/neuronunit/unit_test/import_tests.py +++ b/neuronunit/unit_test/import_tests.py @@ -8,13 +8,10 @@ class ImportTestCase(unittest.TestCase): def test_import_everything(self): import neuronunit - # Recursively import all submodules - import_all_modules(neuronunit, - skip=['neuroconstruct','optimization', - 'backends','unit_test'], - verbose=True) + # Recursively import all submodules + import_all_modules(neuronunit, skip=["neuroconstruct"], verbose=True) -if __name__ == '__main__': +if __name__ == "__main__": unittest.main() diff --git a/neuronunit/unit_test/izhi_opt.py b/neuronunit/unit_test/izhi_opt.py new file mode 100644 index 000000000..3142a6512 --- /dev/null +++ b/neuronunit/unit_test/izhi_opt.py @@ -0,0 +1,100 @@ +#!/usr/bin/env python +# coding: utf-8 +SILENT = True +import warnings +if SILENT: + warnings.filterwarnings("ignore") + +import unittest +import numpy as np +import efel +import matplotlib.pyplot as plt +import quantities as qt + +from neuronunit.allenapi.allen_data_efel_features_opt import ( + opt_to_model, + opt_setup, + opt_exec, +) +from neuronunit.allenapi.allen_data_efel_features_opt import opt_to_model +from neuronunit.allenapi.utils import dask_map_function + +from neuronunit.optimization.model_parameters import ( + MODEL_PARAMS, + BPO_PARAMS, + to_bpo_param, +) +from neuronunit.optimization.optimization_management import inject_model_soma +from neuronunit.optimization.data_transport_container import DataTC +from jithub.models import model_classes + +from sciunit.scores import RelativeDifferenceScore + +class testOptimization(unittest.TestCase): + def setUp(self): + self.ids = [ 324257146, + 325479788, + 476053392, + 623893177, + 623960880, + 482493761, + 471819401 + ] + + def test_opt_1(self): + specimen_id = self.ids[1] + cellmodel = "IZHI" + + if cellmodel == "IZHI": + model = model_classes.IzhiModel() + if cellmodel == "MAT": + model = model_classes.MATModel() + if cellmodel == "ADEXP": + model = model_classes.ADEXPModel() + + + target_num_spikes = 9 + + efel_filter_iterable = [ + "ISI_log_slope", + "mean_frequency", + "adaptation_index2", + "first_isi", + "ISI_CV", + "median_isi", + "Spikecount", + "all_ISI_values", + "ISI_values", + "time_to_first_spike", + "time_to_last_spike", + "time_to_second_spike", + ] + [ suite, + target_current, + spk_count, + cell_evaluator, + simple_cell ] = opt_setup(specimen_id, + cellmodel, + target_num_spikes, + template_model=model, + fixed_current=False, + cached=False, + score_type=RelativeDifferenceScore) + + NGEN = 165 + MU = 55 + + mapping_funct = dask_map_function + final_pop, hall_of_fame, logs, hist = opt_exec( + MU, NGEN, mapping_funct, cell_evaluator, cxpb=0.4, mutpb=0.01 + ) + opt, target, scores, obs_preds, df = opt_to_model( + hall_of_fame, cell_evaluator, suite, target_current, spk_count + ) + best_ind = hall_of_fame[0] + fitnesses = cell_evaluator.evaluate_with_lists(best_ind) + assert np.sum(fitnesses) < 10.5 + self.assertGreater(10.5, np.sum(fitnesses)) + +if __name__ == "__main__": + unittest.main() diff --git a/neuronunit/unit_test/launch_tests.py b/neuronunit/unit_test/launch_tests.py deleted file mode 100644 index 5d27d8821..000000000 --- a/neuronunit/unit_test/launch_tests.py +++ /dev/null @@ -1,14 +0,0 @@ -import subprocess - -from scoop import futures, _control, utils, shared -from scoop._types import FutureQueue -from scoop.broker.structs import BrokerInfo - -from .base import * - -#def multiworker_set(self): -global subprocesses -worker = subprocess.Popen([sys.executable, "-m", "scoop.bootstrap.__main__", - "--brokerHostname", "127.0.0.1", "--taskPort", "5555", - "--metaPort", "5556", "test_optimization.py"]) -subprocesses.append(worker) diff --git a/neuronunit/unit_test/list_of_cells.py b/neuronunit/unit_test/list_of_cells.py deleted file mode 100644 index d8144a2f8..000000000 --- a/neuronunit/unit_test/list_of_cells.py +++ /dev/null @@ -1,150 +0,0 @@ - -import os -import pickle -from dask import distributed -import pickle -import pandas as pd -import timeit - -from neuronunit.optimization import get_neab -from neuronunit.optimization.model_parameters import model_params -from bluepyopt.deapext.optimisations import SciUnitOptimization -from neuronunit.optimization.optimization_management import write_opt_to_nml -from neuronunit.optimization import optimization_management -from neuronunit.optimization import optimization_management as om - -import numpy as np -import copy - - -electro_path = 'pipe_tests.p' -purkinje = { 'nlex_id':'sao471801888'}#'NLXWIKI:sao471801888'} # purkinje -#fi_basket = {'nlex_id':'100201'} -purkinje ={"id": 17, "name": "Cerebellum Lugaro cell", "neuron_db_id": null, "nlex_id": "nifext_133"}, {"id": 18, "name": "Cerebellum Purkinje cell", "neuron_db_id": 271, "nlex_id": "sao471801888"} -fi_basket = {"id": 65, "name": "Dentate gyrus basket cell", "neuron_db_id": null, "nlex_id": "nlx_cell_100201"} -#pvis_cortex = {'nlex_id':'nifext_50'} # Layer V pyramidal cell -pvis_cortex = {"id": 111, "name": "Neocortex pyramidal cell layer 5-6", "neuron_db_id": 265, "nlex_id": "nifext_50"} -#olf_mitral = { 'nlex_id':'nifext_120'} -olf_mitral = {"id": 129, "name": "Olfactory bulb (main) mitral cell", "neuron_db_id": 267, "nlex_id": "nlx_anat_100201"} -#ca1_pyr = { 'nlex_id':'830368389'} -ca1_pyr = {"id": 85, "name": "Hippocampus CA1 pyramidal cell", "neuron_db_id": 258, "nlex_id": "sao830368389"} -pipe = [ fi_basket, olf_mitral, ca1_pyr, purkinje ] - - -electro_path = 'pipe_tests.p' -try: - assert os.path.isfile(electro_path) == True - with open(electro_path,'rb') as f: - electro_tests = pickle.load(f) - electro_tests = get_neab.replace_zero_std(electro_tests) - electro_tests = get_neab.substitute_parallel_for_serial(electro_tests) - -except: - electro_tests = [] - for p in pipe: - p_tests, p_observations = get_neab.get_neuron_criteria(p) - - electro_tests.append((p_tests, p_observations)) - electro_tests = get_neab.replace_zero_std(electro_tests) - electro_tests = get_neab.substitute_parallel_for_serial(electro_tests) - with open('pipe_tests.p','wb') as f: - pickle.dump(electro_tests,f) - -#MU = 4; NGEN = 3; CXPB = 0.9 -USE_CACHED_GA = False - -cnt = 0 -pipe_results = {} -## -# TODO move to unit testing -## - -start_time = timeit.default_timer() -#sel = [str('selNSGA2'),str('selIBEA'),str('')] -flat_iter = [ (test, observation) for test, observation in electro_tests ]#for s in sel ] -print(flat_iter) -from neuronunit.optimization import optimization_management as om - -# http://www.physics.usyd.edu.au/teach_res/mp/mscripts/ -# ns_izh002.m -import collections -from collections import OrderedDict - -# Fast spiking cannot be reproduced as it requires modifications to the standard Izhi equation, -# which are expressed in this mod file. -# https://github.com/OpenSourceBrain/IzhikevichModel/blob/master/NEURON/izhi2007b.mod - -reduced2007 = collections.OrderedDict([ - # C k vr vt vpeak a b c d celltype - ('RS', (100, 0.7, -60, -40, 35, 0.03, -2, -50, 100, 1)), - ('IB', (150, 1.2, -75, -45, 50, 0.01, 5, -56, 130, 2)), - ('LTS', (100, 1.0, -56, -42, 40, 0.03, 8, -53, 20, 4)), - ('TC', (200, 1.6, -60, -50, 35, 0.01, 15, -60, 10, 6)), - ('TC_burst', (200, 1.6, -60, -50, 35, 0.01, 15, -60, 10, 6)), - ('RTN', (40, 0.25, -65, -45, 0, 0.015, 10, -55, 50, 7)), - ('RTN_burst', (40, 0.25, -65, -45, 0, 0.015, 10, -55, 50, 7))]) - -import numpy as np -reduced_dict = OrderedDict([(k,[]) for k in ['C','k','vr','vt','vPeak','a','b','c','d']]) - -#OrderedDict -for i,k in enumerate(reduced_dict.keys()): - for v in reduced2007.values(): - reduced_dict[k].append(v[i]) - -explore_param = {k:(np.min(v),np.max(v)) for k,v in reduced_dict.items()} -cnt = 0 -for (test, observation) in flat_iter: - dic_key = str(list(pipe[cnt].values())[0]) - init_time = timeit.default_timer() - free_params = ['a','b','vr','k','vt','d'] - hc = ['C','c'] - #DO = SciUnitOptimization(error_criterion = test, selection = sel, provided_dict = model_params, elite_size = 3) - start_time = timeit.default_timer() - - #ga_out, DO = om.run_ga(explore_param,5,TC_tests,free_params=free_params,hc = hc, NSGA = True, MU = 8) - ga_out, DO = om.run_ga(explore_param,17,test,free_params=free_params,hc = hc, NSGA = True) - elapsed = timeit.default_timer() - start_time - ga_out['time_length'] = elapsed - #package = DO.run(offspring_size = MU, max_ngen = 6, cp_frequency=1,cp_filename=str(dic_key)+'.p') - #pop, hof_py, pf, log, history, td_py, gen_vs_hof = package - with open('dump_all_cells'+str(pipe[cnt])+str(cnt),'wb') as f: - pickle.dump(ga_out,f) - cnt += 1 - - - print('entire duration', elapsed) - - ''' - model_to_write = pipe_results[dic_key]['gen_vs_hof'][-1].dtc.attrs - - optimization_management.write_opt_to_nml(file_name,model_to_write) - - - - - times_list = list(pipe_results.values()) - ts = [ t['duration']/60.0 for t in times_list ] - mean_time = np.mean(ts) - total_time = np.sum(ts) - ''' -# return pipe_results -#pipe_results = main_proc(flat_iter) - -''' -pipe_results[dic_key] = {} -pipe_results[dic_key]['duration'] = finished_time - init_time -pipe_results[dic_key]['pop'] = copy.copy(pop) -pipe_results[dic_key]['sel'] = sel -pipe_results[dic_key]['hof'] = copy.copy(hof[::-1]) -pipe_results[dic_key]['pf'] = copy.copy(pf[::-1]) -pipe_results[dic_key]['log'] = copy.copy(log) -pipe_results[dic_key]['history'] = copy.copy(history) -pipe_results[dic_key]['td_py'] = copy.copy(td_py) -pipe_results[dic_key]['gen_vs_hof'] = copy.copy(gen_vs_hof) -pipe_results[dic_key]['sum_ranked_hof'] = [sum(i.dtc.scores.values()) for i in pipe_results[dic_key]['gen_vs_hof'][1:-1]] -pipe_results[dic_key]['componentsh'] = [list(i.dtc.scores.values()) for i in pipe_results[dic_key]['gen_vs_hof'][1:-1]] -pipe_results[dic_key]['componentsp'] = [list(i.dtc.scores.values()) for i in pipe_results[dic_key]['pf'][1:-1]] -file_name = str('nlex_id_')+dic_key -''' -exit diff --git a/neuronunit/unit_test/m2m_test.ipynb b/neuronunit/unit_test/m2m_test.ipynb deleted file mode 100644 index 5b54b0c60..000000000 --- a/neuronunit/unit_test/m2m_test.ipynb +++ /dev/null @@ -1,192 +0,0 @@ -{ - "cells": [ - { - "cell_type": "code", - "execution_count": 1, - "metadata": {}, - "outputs": [], - "source": [ - "%matplotlib inline\n", - "import os\n", - "import matplotlib.pyplot as plt\n", - "import sciunit\n", - "import sciunit.scores\n", - "import neuronunit\n", - "from neuronunit.models.reduced import ReducedModel\n", - "from neuronunit.capabilities import ProducesSpikes" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "metadata": {}, - "outputs": [], - "source": [ - "# Assumes imported neuronunit is from source, e.g. pip install -e\n", - "path = os.path.join(neuronunit.__path__[0],'models/NeuroML2/LEMS_2007One.xml')\n", - "# Instantiate three identical models\n", - "models = [ReducedModel(path, name='Izhikevich', backend='jNeuroML') for i in range(3)]" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "metadata": {}, - "outputs": [], - "source": [ - "# Let the first two models remain identical but change the third one\n", - "models[2].set_attrs({'izhikevich2007Cell':{'a':'0.04 per_ms'}})\n", - "models[2].name = 'Izhikevich_new'" - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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+duxaRvnO2JoNp2Bx0q+vrBjdhGEx2IwxaBWD3dWrerKZA1VH2RaDpPtfHYnZ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\n1YxrTNtLVMJLx1/CygkrMbztWUiUYNrFtzLb7+j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- "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "for model in models:\n", - " plt.plot(model.get_membrane_potential(),label=model)\n", - "plt.legend();" - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "metadata": {}, - "outputs": [], - "source": [ - "# A new M2M test which will compare the equality of spike counts across models\n", - "class MyTest(sciunit.TestM2M):\n", - " required_capabilities = (ProducesSpikes,)\n", - " score_type = sciunit.scores.BooleanScore\n", - " def generate_prediction(self,model):\n", - " count = model.get_spike_count()\n", - " return count\n", - " def compute_score(self, prediction1, prediction2):\n", - " return self.score_type(prediction1==prediction2)" - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "metadata": {}, - "outputs": [], - "source": [ - "test = MyTest()" - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "metadata": {}, - "outputs": [], - "source": [ - "s = test.judge(models)" - ] - }, - { - "cell_type": "code", - "execution_count": 8, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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" - ], - "text/plain": [ - " Izhikevich Izhikevich Izhikevich_new\n", - "Izhikevich Pass Pass Fail\n", - "Izhikevich Pass Pass Fail\n", - "Izhikevich_new Fail Fail Pass" - ] - }, - "execution_count": 8, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "# Should show fail when comparing the third model to the other two\n", - "s" - ] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.6.3" - } - }, - "nbformat": 4, - "nbformat_minor": 2 -} diff --git a/neuronunit/unit_test/misc_nb.py b/neuronunit/unit_test/misc_nb.py new file mode 100644 index 000000000..ef4b08266 --- /dev/null +++ b/neuronunit/unit_test/misc_nb.py @@ -0,0 +1,14 @@ +"""Tests of NeuronUnit documentation notebooks""" + +from .base import * + + +class DocumentationTestCase(NotebookTools, unittest.TestCase): + """Testing documentation notebooks""" + + def test_chapter1(self): + self.do_notebook("relative_diff_unit_test") + + +if __name__ == "__main__": + unittest.main() diff --git a/neuronunit/unit_test/model2model_reproducibility.ipynb b/neuronunit/unit_test/model2model_reproducibility.ipynb deleted file mode 100644 index de2f8c2dd..000000000 --- a/neuronunit/unit_test/model2model_reproducibility.ipynb +++ /dev/null @@ -1,1369 +0,0 @@ -{ - "cells": [ - { - "cell_type": "code", - "execution_count": 1, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "import numpy as np\n", - "import time" - ] - }, - { - "cell_type": "code", - "execution_count": 15, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "{'k': 0.001, 'vr': 1.0, 'vpeak': 1.0, 'd': 0.001, 'b': 0.001, 'a': 1.0, 'C': 1e-06, 'vt': 1.0, 'c': 1.0}\n" - ] - } - ], - "source": [ - "\n", - "import copy\n", - "import numpy as np\n", - "import pandas as pd\n", - "import matplotlib.pyplot as plt\n", - "\n", - "from neuronunit.models.reduced import ReducedModel\n", - "from neuronunit.optimization.model_parameters import model_params, path_params\n", - "from neuronunit.optimization import model_parameters as modelp\n", - "\n", - "from neuronunit.optimization.model_parameters import model_params, path_params\n", - "LEMS_MODEL_PATH = path_params['model_path']\n", - "import neuronunit.optimization as opt\n", - "import quantities as pq\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "# From OSB models\n", - "mparams = {}\n", - "mparams['a'] = 0.03\n", - "mparams['b'] = -2\n", - "mparams['C'] = 100\n", - "mparams['c'] = -50 \n", - "mparams['vr'] = -60\n", - "mparams['vt'] = -40\n", - "mparams['vpeak'] = 35\n", - "mparams['k'] = 0.7\n", - "mparams['d'] = 100\n", - "\n", - "'''\n", - "# FROM the MOD file.\n", - "vanilla_NRN = {}\n", - "#vanilla_NRN['v0'] = -60# (mV)\n", - "vanilla_NRN['k'] = 7.0E-4# (uS / mV)\n", - "vanilla_NRN['vr'] = -60# (mV)\n", - "vanilla_NRN['vt'] = -40# (mV)\n", - "vanilla_NRN['vpeak'] = 35# (mV)\n", - "vanilla_NRN['a'] = 0.03# (kHz)\n", - "vanilla_NRN['b'] = -0.002# (uS)\n", - "vanilla_NRN['c'] = -50# (mV)\n", - "vanilla_NRN['d'] = 0.1# (nA)\n", - "vanilla_NRN['C'] = 1.0E-4# (microfarads)\n", - "\n", - "m2m = {}\n", - "for k,v in vanilla_NRN.items():\n", - " m2m[k] = vanilla_NRN[k]/mparams[k]\n", - "\n", - "print(m2m)\n", - "mparams['vPeak'] = 35\n", - "\n", - "'''" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# compile and discard a model, to initialize the JIT\n", - "and discard, this is to purge a space and make model speed comparison fair.\n" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "model = None\n", - "model = ReducedModel(LEMS_MODEL_PATH,name = str('vanilla'),backend = ('RAW'))\n", - "model.set_attrs(mparams)\n", - "iparams = {}\n", - "iparams['injected_square_current'] = {}\n", - "iparams['injected_square_current']['amplitude'] = 10*pq.pA\n", - "#['amplitude'] = dtc.vtest[k]['injected_square_current']['amplitude']\n", - "DELAY = 0# 100.0*pq.ms\n", - "DURATION = 520*pq.ms\n", - "iparams['injected_square_current']['delay'] = DELAY\n", - "iparams['injected_square_current']['duration'] = int(DURATION)\n", - "\n", - "model.inject_square_current(iparams)\n", - "model = None" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Vanilla parameters model to model check (succeeds).\n", - "# RAW is significantly faster" - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2\n", - "3\n", - "5\n", - "7\n", - "mean simulation time: 0.0014051198959350586. Total time: 0.005620479583740234\n" - ] - }, - { - "data": { - "image/png": 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CiDFSyq1CiDFAQ4rNaoFDYv4eD7wX8/f9wBop5e2GatxPSOfO6BOLwaEeW6wr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\nkFnGFGuzFZxRnMGsFbNICE/gzxc/x94CK1uyS9iSU8qug+U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- "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "times = []\n", - "\n", - "for current in [60,70,85,100]:\n", - " # compile a model and discard\n", - "\n", - " model = None\n", - " model = ReducedModel(LEMS_MODEL_PATH,name = str('vanilla'),backend = ('RAW'))\n", - " model.set_attrs(mparams)\n", - " iparams = {}\n", - " iparams['injected_square_current'] = {}\n", - " iparams['injected_square_current']['amplitude'] = current*pq.pA\n", - " #['amplitude'] = dtc.vtest[k]['injected_square_current']['amplitude']\n", - " DELAY = 0# 100.0*pq.ms\n", - " DURATION = 520*pq.ms\n", - " iparams['injected_square_current']['delay'] = DELAY\n", - " iparams['injected_square_current']['duration'] = int(DURATION)\n", - "\n", - " model.inject_square_current(iparams)\n", - " print(model.get_spike_count())\n", - " plt.plot(model.results['vm'].times,model.results['vm'])\n", - " \n", - " times.append(model.results['sim_time'])\n", - "print('mean simulation time: {0}. Total time: {1}'.format(np.mean(times),np.sum(times)))\n", - "\n" - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2.5e-05 s\n" - ] - } - ], - "source": [ - "dt = model.results['vm'].times[1] - model.results['vm'].times[0]\n", - "print(dt)" - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Sim time of 0.001 s took 0.0025\n", - "Sim time of 0.0022 s took 0.0061\n", - "Sim time of 0.0046 s took 0.009\n", - "Sim time of 0.01 s took 0.019\n", - "Sim time of 0.022 s took 0.045\n", - "Sim time of 0.046 s took 0.085\n", - "Sim time of 0.1 s took 0.19\n", - "Sim time of 0.22 s took 0.4\n", - "Sim time of 0.46 s took 0.49\n", - "Sim time of 1 s took 1\n" - ] - }, - { - "data": { - "image/png": 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KInz1Nzj4OzTqbfX+1hauyk1KkjgOAcvya1ZlndnoydNxlXKVojrsnUzLLr2k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\nHEoppZyiiUMppZRT/h9etFTaBIbFggAAAABJRU5ErkJggg==\n", - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "import time\n", - "sim_times = np.logspace(-3,0,10)*pq.s\n", - "wall_times = np.zeros(len(sim_times))\n", - "for i,sim_time in enumerate(sim_times):\n", - " model = ReducedModel(LEMS_MODEL_PATH,name=str('vanilla'),backend='NEURON')\n", - " model.inject_square_current({'amplitude':200*pq.pA, 'duration':sim_time/2, 'delay':100*pq.ms})\n", - " model._backend.set_stop_time(sim_time)\n", - " model._backend.set_time_step(0.025*pq.ms)\n", - " start = time.time()\n", - " result = model._backend.local_run()\n", - " finish = time.time()\n", - " wall_times[i] = finish - start\n", - " print(\"Sim time of %.2g s took %.2g\" % (sim_time,wall_times[i]))\n", - "plt.plot(sim_times,wall_times,'-o',label='NEURON')\n", - "plt.plot(sim_times,sim_times,'--',label='Real-time')\n", - "plt.xlabel('Simulation time')\n", - "plt.ylabel('Wall time')\n", - "plt.xscale('log')\n", - "plt.yscale('log')\n", - "plt.legend()\n", - "plt.plot();" - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Sim time of 0.001 s took 0.014\n", - "Sim time of 0.0022 s took 0.001\n", - "Sim time of 0.0046 s took 0.0012\n", - "Sim time of 0.01 s took 0.001\n", - "Sim time of 0.022 s took 0.00092\n", - "Sim time of 0.046 s took 0.00092\n", - "Sim time of 0.1 s took 0.0011\n", - "Sim time of 0.22 s took 0.001\n", - "Sim time of 0.46 s took 0.00093\n", - "Sim time of 1 s took 0.00093\n" - ] - }, - { - "data": { - "image/png": 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- "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "import time\n", - "sim_times = np.logspace(-3,0,10)*pq.s\n", - "wall_times = np.zeros(len(sim_times))\n", - "for i,sim_time in enumerate(sim_times):\n", - " model = ReducedModel(LEMS_MODEL_PATH,name=str('vanilla'),backend='RAW')\n", - " model.set_attrs(mparams)\n", - " model.inject_square_current({'amplitude':200*pq.pA, 'duration':sim_time/2, 'delay':100*pq.ms})\n", - " model._backend.set_stop_time(sim_time)\n", - " #model._backend.set_time_step(0.025*pq.ms)\n", - " start = time.time()\n", - " result = model._backend.local_run()\n", - " finish = time.time()\n", - " wall_times[i] = finish - start\n", - " print(\"Sim time of %.2g s took %.2g\" % (sim_time,wall_times[i]))\n", - "plt.plot(sim_times,wall_times,'-o',label='NEURON')\n", - "plt.plot(sim_times,sim_times,'--',label='Real-time')\n", - "plt.xlabel('Simulation time')\n", - "plt.ylabel('Wall time')\n", - "plt.xscale('log')\n", - "plt.yscale('log')\n", - "plt.legend()\n", - "plt.plot();" - ] - }, - { - "cell_type": "code", - "execution_count": 25, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2\n", - "0.3918569087982178 simulation\n", - "3\n", - "0.38318800926208496 simulation\n", - "5\n", - "0.39646315574645996 simulation\n", - "7\n", - "0.37295007705688477 simulation\n", - "mean simulation time: 0.38611453771591187. Total time: 1.5444581508636475\n", - "0.3869502544403076 finish\n", - "0.3789191246032715 finish\n", - "0.3838818073272705 finish\n", - "0.38811588287353516 finish\n", - "without init NEURON simulation time: 0.3844667673110962. Total time: 1.5378670692443848\n" - ] - }, - { - "data": { - "image/png": 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BTDaTHgB/V1K4L8we7zkQMdFT17chLYHh9E+ifl/BeNeWKdNVFfqhz2QlRWYZm6idxHnJ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\n0mbDb+xYQubNazQpEbZi/vfnR1hStJ1/5BUx0lZJsjMG08C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- "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "\n", - "import time\n", - "times = []\n", - "times_without_init = []\n", - "\n", - "model = ReducedModel(LEMS_MODEL_PATH,name = str('vanilla'),backend = ('NEURON'))\n", - "\n", - "\n", - "#model.set_attrs(mparams)\n", - "for current in [60,70,85,100]:\n", - " iparams = {}\n", - " iparams['injected_square_current'] = {}\n", - " iparams['injected_square_current']['amplitude'] = current*pq.pA\n", - " DELAY = 0# 100.0*pq.ms\n", - " DURATION = 520\n", - " iparams['injected_square_current']['delay'] = DELAY*pq.ms\n", - " iparams['injected_square_current']['duration'] = DURATION*pq.ms\n", - "\n", - " model.inject_square_current(iparams)\n", - " \n", - " print(model.get_spike_count())\n", - " plt.plot(model.get_membrane_potential().times,model.get_membrane_potential())\n", - " print(model.results['sim_time'], 'simulation')\n", - " times.append(model.results['sim_time'])\n", - "print('mean simulation time: {0}. Total time: {1}'.format(np.mean(times),np.sum(times)))\n", - "\n", - "\n", - "for current in [60,70,85,100]:\n", - " iparams = {}\n", - " iparams['injected_square_current'] = {}\n", - " iparams['injected_square_current']['amplitude'] = current*pq.pA\n", - " DELAY = 0# 100.0*pq.ms\n", - " DURATION = 520\n", - " iparams['injected_square_current']['delay'] = DELAY*pq.ms\n", - " iparams['injected_square_current']['duration'] = DURATION*pq.ms\n", - " model.inject_square_current(iparams)\n", - "\n", - " #model.inject_square_current(iparams)\n", - " #model.inject_square_current(iparams)\n", - "\n", - " start = time.time()\n", - " model._backend.h('run()')\n", - " stop = time.time()\n", - " finish = stop-start\n", - " print(finish,'finish')\n", - " times_without_init.append(finish)\n", - "print('without init NEURON simulation time: {0}. Total time: {1}'.format(np.mean(times_without_init),np.sum(times_without_init)))\n" - ] - }, - { - "cell_type": "code", - "execution_count": 9, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "\n", - "#model._backend.h('psection()')" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "\n", - "NEURON units NeuroML units \n", - "\n", - "v0 = -60 (mV), vr = -60 (mV) , mparams['vr'] = -60\n", - "\n", - "k = 7.0E-4 (uS / mV), mparams['k'] = 0.7 ***different\n", - "\n", - "vt = -40 (mV)\n", - "\n", - "vpeak = 35 (mV) mparams['vPeak'] = 35\n", - "\n", - "a = 0.030000001 (kHz) [ms-1], mparams['a'] = 0.03\n", - "\n", - "b = -0.002 (uS), mparams['b'] = -2\n", - "\n", - "c = -50 (mV), mparams['c'] = -50 \n", - "\n", - "d = 0.1 (nA) --MATLAB pA mparams['d'] = 100 ******** pA\n", - "\n", - "C = 1.0E-4 (microfarads) mparams['C'] = 100 ******** co faraday)" - ] - }, - { - "cell_type": "code", - "execution_count": 29, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "{'vPeak': 25, 'C': 50, 'vr': -60, 'b': 1, 'c': -40, 'd': 150, 'a': 0.03, 'k': 1.5, 'vt': -40}\n", - "{'C': 4.9999999999999996e-05, 'c': -40.0, 'vpeak': 25.0, 'd': 0.15, 'a': 0.03, 'vt': -40.0, 'k': 0.0015, 'vr': -60.0, 'b': 0.001}\n", - "{'vPeak': 50, 'C': 150, 'vr': -75, 'b': 5, 'c': -56, 'd': 130, 'a': 0.01, 'k': 1.2, 'vt': -45}\n", - "{'C': 0.00015, 'c': -56.0, 'vpeak': 50.0, 'd': 0.13, 'a': 0.01, 'vt': -45.0, 'k': 0.0012, 'vr': -75.0, 'b': 0.005}\n", - "{'vPeak': 35, 'C': 200, 'vr': -60, 'b': 15, 'c': -60, 'd': 10, 'a': 0.01, 'k': 1.6, 'vt': -50}\n", - "{'C': 0.00019999999999999998, 'c': -60.0, 'vpeak': 35.0, 'd': 0.01, 'a': 0.01, 'vt': -50.0, 'k': 0.0016, 'vr': -60.0, 'b': 0.015}\n" - ] - } - ], - "source": [ - "import collections\n", - "# Fast spiking cannot be reproduced as it requires modifications to the standard Izhi equation,\n", - "# which are expressed in this mod file.\n", - "# https://github.com/OpenSourceBrain/IzhikevichModel/blob/master/NEURON/izhi2007b.mod\n", - "\n", - "from collections import OrderedDict\n", - "type2007 = collections.OrderedDict([\n", - " # C k vr vt vpeak a b c d celltype\n", - " ('RS', (100, 0.7, -60, -40, 35, 0.03, -2, -50, 100, 1)),\n", - " ('IB', (150, 1.2, -75, -45, 50, 0.01, 5, -56, 130, 2)),\n", - " ('CH', (50, 1.5, -60, -40, 25, 0.03, 1, -40, 150, 3)),\n", - " ('LTS', (100, 1.0, -56, -42, 40, 0.03, 8, -53, 20, 4)),\n", - " ('FS', (20, 1.0, -55, -40, 25, 0.2, -2, -45, -55, 5)),\n", - " ('TC', (200, 1.6, -60, -50, 35, 0.01, 15, -60, 10, 6)),\n", - " ('TC_burst', (200, 1.6, -60, -50, 35, 0.01, 15, -60, 10, 6)),\n", - " ('RTN', (40, 0.25, -65, -45, 0, 0.015, 10, -55, 50, 7)),\n", - " ('RTN_burst', (40, 0.25, -65, -45, 0, 0.015, 10, -55, 50, 7))])\n", - "\n", - "import numpy as np\n", - "param_dict = OrderedDict([(k,[]) for k in ['C','k','vr','vt','vPeak','a','b','c','d']])\n", - "#OrderedDict\n", - "for i,k in enumerate(param_dict.keys()):\n", - " for v in type2007.values():\n", - " param_dict[k].append(v[i])\n", - "\n", - "explore_param = {k:(np.min(v),np.max(v)) for k,v in param_dict.items()}\n", - "param_ranges = OrderedDict(explore_param)\n", - "\n", - "\n", - "#IB = mparams[param_dict['IB']]\n", - "IB = {}\n", - "TC = {}\n", - "CH = {}\n", - "RTN_burst = {}\n", - "for k,v in param_dict.items():\n", - " IB[k] = v[1]\n", - " CH[k] = v[2]\n", - " TC[k] = v[5]\n", - " RTN_burst[k] = v[-2]\n", - " \n", - "RTN_burstN = copy.copy(RTN_burst)\n", - "TCN = copy.copy(TC)\n", - "IBN = copy.copy(IB)\n", - "CHN = copy.copy(CH)\n", - "\n", - "\n", - "# From OSB models\n", - "mparams = {}\n", - "mparams['a'] = 0.03\n", - "mparams['b'] = -2\n", - "mparams['C'] = 100\n", - "mparams['c'] = -50 \n", - "mparams['vr'] = -60\n", - "mparams['vt'] = -40\n", - "mparams['vpeak'] = 35\n", - "mparams['k'] = 0.7\n", - "mparams['d'] = 100\n", - "\n", - "\n", - "# FROM the MOD file.\n", - "vanilla_NRN = {}\n", - "#vanilla_NRN['v0'] = -60# (mV)\n", - "vanilla_NRN['k'] = 7.0E-4# (uS / mV)\n", - "vanilla_NRN['vr'] = -60# (mV)\n", - "vanilla_NRN['vt'] = -40# (mV)\n", - "vanilla_NRN['vpeak'] = 35# (mV)\n", - "vanilla_NRN['a'] = 0.03# (kHz)\n", - "vanilla_NRN['b'] = -0.002# (uS)\n", - "vanilla_NRN['c'] = -50# (mV)\n", - "vanilla_NRN['d'] = 0.1# (nA)\n", - "vanilla_NRN['C'] = 1.0E-4# (microfarads)\n", - "\n", - "m2m = {}\n", - "for k,v in vanilla_NRN.items():\n", - " m2m[k] = vanilla_NRN[k]/mparams[k]\n", - "\n", - "\n", - "def translate(input_dic,m2m):\n", - " input_dic['vpeak'] = input_dic['vPeak']\n", - " input_dic.pop('vPeak', None) \n", - " input_dic.pop('dt', None) \n", - " for k,v in input_dic.items():\n", - " input_dic[k] = v * m2m[k]\n", - " return input_dic\n", - "IBN = translate(IBN,m2m)\n", - "CHN = translate(CHN,m2m)\n", - "TCN = translate(TCN,m2m)\n", - "print(CH)\n", - "print(CHN)\n", - "print(IB)\n", - "print(IBN)\n", - "print(TC)\n", - "print(TCN)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "The following cells follow the Backend pattern RAW, NEURON, RAW, NEURON." - ] - }, - { - "cell_type": "code", - "execution_count": 30, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "0\n", - "0.0008738040924072266 simulation\n", - "1\n", - "0.0007090568542480469 simulation\n", - "3\n", - "0.0006754398345947266 simulation\n", - "5\n", - "0.0006630420684814453 simulation\n", - "mean simulation time: 0.0007303357124328613. Total time: 0.0029213428497314453\n" - ] - }, - { - "data": { - "image/png": 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tBmd2Q5gJaTUE8t9Pa2/DWV9f9DYdw8bZ2op7QvkqhHb07WB61fSy3U+hGE8o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\nMf6xruw3cnog2ARSkIp5CW3TqDp6CsH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- "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "tstop = 100*pq.ms\n", - "IinRange = [73.2,100,200,400]\n", - "\n", - "model = ReducedModel(LEMS_MODEL_PATH,name = str('vanilla'),backend = ('RAW'))\n", - "\n", - "\n", - "model.set_attrs(TC)\n", - "#print(FS)\n", - "times = []\n", - "for current in IinRange:\n", - " iparams = {}\n", - " iparams['injected_square_current'] = {}\n", - " iparams['injected_square_current']['amplitude'] = current*pq.pA\n", - " #['amplitude'] = dtc.vtest[k]['injected_square_current']['amplitude']\n", - " DELAY = 0\n", - " #DURATION = tstop*pq.ms\n", - " iparams['injected_square_current']['delay'] = 0\n", - " iparams['injected_square_current']['duration'] = 100*pq.ms\n", - "\n", - " model.inject_square_current(iparams)\n", - " print(model.get_spike_count())\n", - " plt.plot(model.get_membrane_potential().times,model.get_membrane_potential())\n", - " print(model.results['sim_time'], 'simulation')\n", - " times.append(model.results['sim_time'])\n", - "print('mean simulation time: {0}. Total time: {1}'.format(np.mean(times),np.sum(times))) \n" - ] - }, - { - "cell_type": "code", - "execution_count": 31, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "{'C': 0.00019999999999999998, 'c': -60.0, 'vpeak': 35.0, 'd': 0.01, 'a': 0.01, 'vt': -50.0, 'k': 0.0016, 'vr': -60.0, 'b': 0.015}\n", - "1\n", - "0.13012313842773438 simulation\n", - "{'C': 0.00019999999999999998, 'c': -60.0, 'vpeak': 35.0, 'd': 0.01, 'a': 0.01, 'vt': -50.0, 'k': 0.0016, 'vr': -60.0, 'b': 0.015}\n", - "2\n", - "0.12086725234985352 simulation\n", - "{'C': 0.00019999999999999998, 'c': -60.0, 'vpeak': 35.0, 'd': 0.01, 'a': 0.01, 'vt': -50.0, 'k': 0.0016, 'vr': -60.0, 'b': 0.015}\n", - "5\n", - "0.1221153736114502 simulation\n", - "{'C': 0.00019999999999999998, 'c': -60.0, 'vpeak': 35.0, 'd': 0.01, 'a': 0.01, 'vt': -50.0, 'k': 0.0016, 'vr': -60.0, 'b': 0.015}\n", - "10\n", - "0.1243281364440918 simulation\n", - "mean simulation time: 0.12435847520828247. Total time: 0.4974339008331299\n", - "{'vPeak': 35, 'C': 200, 'vr': -60, 'b': 15, 'c': -60, 'd': 10, 'a': 0.01, 'k': 1.6, 'vt': -50}\n" - ] - }, - { - "data": { - "image/png": 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YI6U8JoSYA/SYXNYBXFHw/TxgXcH33wf2Sym/7jCP72evZfXq1SctYJsziFYr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\n", - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "tstop = 100*pq.ms\n", - "IinRange = [73.2,100,200,400]\n", - "\n", - "\n", - "times = []\n", - "for current in IinRange:\n", - " model = None\n", - " model = ReducedModel(LEMS_MODEL_PATH,name = str('vanilla'),backend = ('NEURON'))\n", - "\n", - " model.set_attrs(TCN)\n", - " print(TCN)\n", - "\n", - " iparams = {}\n", - " iparams['injected_square_current'] = {}\n", - " iparams['injected_square_current']['amplitude'] = current*pq.pA\n", - "\n", - " iparams['injected_square_current']['delay'] = 0*pq.ms\n", - " iparams['injected_square_current']['duration'] = 100*pq.ms\n", - "\n", - " model.inject_square_current(iparams)\n", - " print(model.get_spike_count())\n", - " plt.plot(model.get_membrane_potential().times,model.get_membrane_potential())\n", - " print(model.results['sim_time'], 'simulation')\n", - " times.append(model.results['sim_time'])\n", - "print('mean simulation time: {0}. Total time: {1}'.format(np.mean(times),np.sum(times))) \n", - "#plt.plot(model.results['vm'].times,model.results['vm'])\n", - " \n", - "print(TC)" - ] - }, - { - "cell_type": "code", - "execution_count": 32, - "metadata": { - "collapsed": true - }, - "outputs": [], - "source": [ - " # CH\n", - "tstop = 210\n", - "IinRange = [200,300,400,600]" - ] - }, - { - "cell_type": "code", - "execution_count": 33, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "{'vPeak': 0, 'C': 40, 'vr': -65, 'b': 10, 'c': -55, 'd': 50, 'a': 0.015, 'k': 0.25, 'vt': -45}\n", - "{'vPeak': 0, 'C': 40, 'vr': -65, 'b': 10, 'c': -55, 'd': 50, 'a': 0.015, 'k': 0.25, 'vt': -45}\n", - "0\n", - "0.0013985633850097656 simulation\n", - "0\n", - "0.0013499259948730469 simulation\n", - "0\n", - "0.001348257064819336 simulation\n" - ] - }, - { - "data": { - "image/png": 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- "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "IinRange = [30,50,90]\n", - "tstop=720\n", - "print(RTN_burst)\n", - "model = ReducedModel(LEMS_MODEL_PATH,name = str('vanilla'),backend = ('RAW'))\n", - "model.set_attrs(RTN_burst)\n", - "print(RTN_burst)\n", - "times = []\n", - "for current in IinRange:\n", - " iparams = {}\n", - " iparams['injected_square_current'] = {}\n", - " iparams['injected_square_current']['amplitude'] = current*pq.pA\n", - " #['amplitude'] = dtc.vtest[k]['injected_square_current']['amplitude']\n", - " DELAY = 0\n", - " #DURATION = tstop*pq.ms\n", - " iparams['injected_square_current']['delay'] = 0\n", - " iparams['injected_square_current']['duration'] = tstop*pq.ms\n", - "\n", - " model.inject_square_current(iparams)\n", - " print(model.get_spike_count())\n", - " plt.plot(model.get_membrane_potential().times,model.get_membrane_potential())\n", - " times.append(model.results['sim_time'])\n", - "\n", - " print(model.results['sim_time'], 'simulation') " - ] - }, - { - "cell_type": "code", - "execution_count": 34, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "9\n", - "11\n", - "19\n", - "20\n", - "mean simulation time: 0.0008618831634521484. Total time: 0.0034475326538085938\n" - ] - }, - { - "data": { - "image/png": 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clSRA2s7nV8THAN7MZ/e1QT4Gn9O5Yh5DDeGqRozfgGse1KYkjTWKdUkML+eyLtyU5H1r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- "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "tstop = 210\n", - "IinRange = [290,370,500,550]\n", - "model = ReducedModel(LEMS_MODEL_PATH,name = str('vanilla'),backend = ('RAW'))\n", - "model.set_attrs(CH)\n", - "times = []\n", - "for current in IinRange:\n", - " iparams = {}\n", - " iparams['injected_square_current'] = {}\n", - " iparams['injected_square_current']['amplitude'] = current*pq.pA\n", - " #['amplitude'] = dtc.vtest[k]['injected_square_current']['amplitude']\n", - " DELAY = 0\n", - " DURATION = tstop*pq.ms\n", - " iparams['injected_square_current']['delay'] = DELAY\n", - " iparams['injected_square_current']['duration'] = int(DURATION)\n", - "\n", - " model.inject_square_current(iparams)\n", - " print(model.get_spike_count())\n", - " plt.plot(model.get_membrane_potential().times,model.get_membrane_potential())\n", - " times.append(model.results['sim_time'])\n", - "\n", - "print('mean simulation time: {0}. Total time: {1}'.format(np.mean(times),np.sum(times))) \n", - " " - ] - }, - { - "cell_type": "code", - "execution_count": 35, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "7\n", - "0.1985156536102295 simulation\n", - "11\n", - "0.19085168838500977 simulation\n", - "19\n", - "0.19084787368774414 simulation\n", - "21\n", - "0.1939389705657959 simulation\n", - "mean simulation time: 0.0008618831634521484. Total time: 0.0034475326538085938\n" - ] - }, - { - "data": { - "image/png": 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pv1FDDMcNDTEopOoY6mYlxWqMLcYgg89pY6b2rSD76F2qtqtgRPSoW2sh+Xp0oSG2SUl1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- "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "tstop = 210\n", - "IinRange = [290,370,500,550]\n", - "\n", - "for current in IinRange:\n", - " iparams = {}\n", - " iparams['injected_square_current'] = {}\n", - " iparams['injected_square_current']['amplitude'] = current*pq.pA\n", - " #['amplitude'] = dtc.vtest[k]['injected_square_current']['amplitude']\n", - " DELAY = 0*pq.ms\n", - " DURATION = tstop*pq.ms\n", - " iparams['injected_square_current']['delay'] = DELAY\n", - " iparams['injected_square_current']['duration'] = DURATION\n", - " model = None\n", - " model = ReducedModel(LEMS_MODEL_PATH,name = str('vanilla'),backend = ('NEURON'))\n", - " model.set_attrs(CHN)\n", - " model.inject_square_current(iparams)\n", - " print(model.get_spike_count())\n", - " plt.plot(model.get_membrane_potential().times,model.get_membrane_potential())\n", - " print(model.results['sim_time'], 'simulation') \n", - "print('mean simulation time: {0}. Total time: {1}'.format(np.mean(times),np.sum(times))) \n", - " " - ] - }, - { - "cell_type": "code", - "execution_count": 36, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "6\n" - ] - }, - { - "data": { - "text/plain": [ - "[]" - ] - }, - "execution_count": 36, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": 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Ug8G7x3Hf8IwHw9GeIe7+zV6+/cQ+ugZGOHtVHf/z3Sfzrg2LZv3kO5HRDQb7\nh6n36mgmRaOOB19r5Y5Hmnlqb2xA+RO/GxtQnurWHjMh1mII45zLWatFZLIKLhheP9zN0try0dW6\nkxUPhtbuweksVpLdR3v5xqPN/PCZg4xEoly8YRHXX7iGs1dNfrB8tsTDoKNvZoNhKBzhJ8+9ye2P\nNLOrtTcnA8qTUVFaRNTBUDg67etHRGZK/n2TZtjuo32sXVg95fcvq4utiG3pHJiuIo3asa+Dr//a\nG1AuCnH12cu57vzVrGmcenlnS33V2EZ66xdN/3+/e3CE7zy5n7se38OR7iE2LIkNKF9+2hKKZ3lA\neTLiN+sZGI4oGCQwCioYnHM0H+3lminslBlXX1VKZWkRBzr7p61MD77ayr/8ejc79nVSWxEbUP7Q\nuU00zpv5LpnpkthimE5Huge56/E9/McT++kZCnP+uga+cM1bOH9dQyC6Zkbv4jYSYfIbmovkRkEF\nw5HuIfqGI6xtnNyNaBKZGSvqKjnQkV2LwTnHIzvb+OL9r/N8SxfL5lfw1+/ZwPs2rcibAeXJGN1I\nb5pWP795bICvPbyLe55uIRyNcvnpS/nEhWumfcuKmTZ2FzctcpPgCN4ZKAvxefZNDVMPBoAVCypo\nyaLF8ERzO/9w/+s8vbeTZfMr+PxVp/P7Zy2b9Tn206mussTbYDC7FsPBYwN87Ve7uGf7AQCu2biC\nT164lpX1szMDbLrpLm4SRAUVDIe7Y1f5S2qz2zlzxYJKHt/VTjTqCE1idtAz+zv54v1v8NiuNhbO\nK+OWLafwvk0rKCsOft9zcVGI+qoyDndNbVC+pbOfr/5qNz/YEQuE929awac2r2PZNO1ymiujXUkK\nBgmQrILBzBYA3wOagL3A+5xz41Y5mdlW4LPe01udc3d7r5cCXwE2A1HgM865H2ZTpuM53BXr5sh2\nf5wNS2oYGInQ3NbHugwGsl862MWXHniDB19rZUFVKZ+9/GT+4G2r5txg5NrGKna29kzqPUd7hvjn\nh3byH0/uJ2TGtZtW8qnNa6dt2+tcq0gYfBYJimxbDDcBDzrnbjOzm7znf5F4gBcenwM2Ag7YYWbb\nvAD5DNDqnDvBzELAjM7HPNI9yLyy4qynNZ62PNbP/dLBruMGw84jPXzpl29w74uHqSkv5s8vOZEP\nn9dEVR5Oq5wOJyyax4+fO5jRnP3eoTB3PNLMHY82MxSOcu2mFdzwjnVZt+byTXy8SC0GCZJsz1Bb\niF3tA9w21jdHAAAKoklEQVQNPExKMACXAA845zoAzOwB4FLgO8BHgZMAnHNRoC3L8hzX4a5BFk3D\nbprrGqupKi3iyT0dXHnmsnG/39vWxz89uJMfP3eQypIiPn3Rej52/mpqK2Zm2+t8sX5RNT2DYQ51\nDaa94h8OR/nOU/v58oM7ae8b5vLTlvCnF58QiCm5UzHWlaTBZwmObINhkXPuEIBz7pCZLfQ5Zhlw\nIOF5C7DMzOJLj28xs83AbuAG59yRLMuU1uHuQRZPw6rY4qIQbz9pIfe/fJj/fcUplBbHBo1bOvv5\nykO7+P6OFkqKjOsvXMMnLlw75cV0QXPWytiEzCea23nvWcuTfuec494XD/P5+15jX3s/b1uzgDsv\nO3nKW5MEhcYYppdzjqFwlKGRKCPRKOGIYyQSJRx1RKJRRiIu9lo0SiTq/S7iCEfjP2OvRaKxx845\nog6i3k/nHJHo2ONo4u+j44+NOkckOv5Y5/2MlRkczvvJ6GvEX/NeTDwm8TVGX4u9+Pmr3zJ6zpkp\nEwaDmf0S8Ntx7jMZ/g2/PgXn/e3lwOPOuRvN7EbgC8AH05TjeuB6gJUrV2b4p5MtqiljVX12M5Li\n3r9pBT994RB/e++rvOOkhWx7/k1+/OxBQmZ88G2r+MO3r835xnazbcOSGuqrSnnwtdakYNixr4Nb\nf/Yqz+4/xkmL5/HNj2xi8wmNgViHkK34zZG6BkZyXJLZNRKJcqx/hK6BEfqGwvQNhen1/sUeR5Je\n6x8OMzQSZTAc8f85EokFQjia6/+1USGDkFnsX2jssVnspBcK2ejJzyz2OPaRN+81Rl+zca+NfTfM\nxo4xGwucmTRhMDjn3pnud2Z2xMyWeK2FJUCrz2EtjHU3QSwMHgbagX7gR97r3wc+dpxy3A7cDrBx\n48Yp1czXP7hxKm/zdcH6Rq7dtIJv/WYv3/rNXspLQvzB21Zx/YVr5szA6WSFQsaVZy7jW7/Zy+O7\n2jDgrsf38stXj7BwXhmfv+p0rjp7ed7t8zSTSotDVJcV09mfX/eqmKxI1NHeO8SR7iGOdA/S2jNE\na88gnX3DdPaP0Nk/zDHvZ1f/CD1DE3edhQyqvDG/ytIiykti/8qKQ9RUlFBWHBp9Hv9ZFv/p/SsK\nhSguMkqKjOJQiOKQUVwUe604FHutpMh7LWTe67HXQmYUhbwTu8VOxqMn+5AlnfjNe1wUGjt2Lsu2\nK2kbsBW4zfv5E59j7gP+1sziCz8vBv7SOefM7P8RC42HgIuAV7Isz6z6P+89jQ+cs5KewTBnrpw/\nZweVJ+NTm9fyi5cO81+/8SQA8ytLuPFdJ3DdBasDuXBvOsyvLOFYf363GAaGI7R09nOgs5/97f0c\n6Bxgf0c/h7sGae0Z5GjPEFGfy7Ga8mLqqkqZX1lKfXUp6xZWU1tRQl1lKXVVJdRWlFBdVjwaAPGf\n1WXFlJeE5vwJNqiy/abeBtxjZh8D9gPXAJjZRuCTzrnrnHMdZnYL8LT3npvjA9HEBqq/bWb/CBwF\nPpJleWaVmU15l9a5qqG6jHs/fQH3v3KYmooSLljfULCBEFdXWZoXLQbnHEd7h9h1pJc3jvSws7WX\nna297Gnr42jKzafKS0KsqKtk6fwKTl4yj0U15SycV8bCmnIW1ZSzqKaMhuqyQC/KlPSy+sY659qJ\nXemnvr4duC7h+V3AXT7H7QMuzKYMkn9qK0uy2o9qrplfWULnLLcYolHH3vY+XjzYxYstXbxwsIvX\nD/ckjXXUlBdzwqJ5bD6hkZULKllZX8nyukpWLqikobpUV/MFrLAv5URmwfzKUvZ3TM+mi+n0DoXZ\nsa+Tp/a088y+Y7x0sGu0n7+sOMSGpTW8+7QlnLComhMWzWP9wmoa55Xp5C++FAwiM6yusmTad53t\nGwrz293tPNHczlN7O3jpYOzOhMUh45SlNVx55jJOW1bLactrWbewWl0+MikKBpEZtri2nJ7B2LTM\nqa66d86xq7WXh18/ysNvtPL0nk6GI1HKikOcuXI+N7xjPW9dvYAzV84v+DEdyZ4+QSIzbEVdbGfY\nls5+Tlpck/H7nHO80NLFz148xM9fOjS61fsJi6r58O80sfmERs5uqpsTmzBKflEwiMyw+L3BWzoG\nJgwG5xwvv9nNtuff5N4XD9HSOUBxyDh/fQOf/N21bD5xYeB3nJX8p2AQmWGrvGDYdbSXd+J/39OO\nvmF+/OxBvr+jhVcPdVNSZJy/roE/uWg9F29YTG3l3N5nS/KLgkFkhtVVldJUX8mOfck70kejjsd3\nt/Gdp/bzwCtHGIk43rK8lluvPJX3nL5UYSA5o2AQmQXnrm3gJ88dpNObnfTDZ1r4v0/uZ09bH3WV\nJXzo3Cau2bh8UmMQIjNFwSAyCz507iq+v/0Am7/wML1DYSJRx9mr6viTi9Zz2WmLNYAseUXBIDIL\nTl5Swzc/sokfP/smS+eX8+7TlnDyErUOJD8pGERmyQXrG7lgfWOuiyEyIS2HFBGRJAoGERFJomAQ\nEZEkCgYREUmiYBARkSQKBhERSaJgEBGRJAoGERFJYs65XJdh0szsKLBvim9vANqmsThzleopM6qn\nzKmuMjOT9bTKOTfhKstABkM2zGy7c25jrsuR71RPmVE9ZU51lZl8qCd1JYmISBIFg4iIJCnEYLg9\n1wUICNVTZlRPmVNdZSbn9VRwYwwiInJ8hdhiEBGR45izwWBml5rZ62a2y8xu8vl9mZl9z/v9k2bW\nNPulzL0M6ulCM3vGzMJmdnUuypgPMqinG83sFTN7wcweNLNVuShnrmVQT580sxfN7Dkze8zMNuSi\nnPlgorpKOO5qM3NmNnszlZxzc+4fUATsBtYApcDzwIaUY/4Q+Ffv8bXA93Jd7jytpybgdODfgKtz\nXeY8rqe3A5Xe40/p85S2nmoSHl8B/CLX5c7XuvKOmwc8AjwBbJyt8s3VFsM5wC7nXLNzbhj4LrAl\n5ZgtwN3e4x8AF5mZzWIZ88GE9eSc2+ucewGI5qKAeSKTevqVc67fe/oEsHyWy5gPMqmn7oSnVUCh\nDnJmco4CuAX4PDA4m4Wbq8GwDDiQ8LzFe833GOdcGOgC6meldPkjk3qSydfTx4Cfz2iJ8lNG9WRm\nf2Rmu4md8D49S2XLNxPWlZmdCaxwzv10NgsGczcY/K78U69MMjlmrlMdZCbjejKzPwA2An8/oyXK\nTxnVk3Puq865tcBfAJ+d8VLlp+PWlZmFgC8BfzprJUowV4OhBViR8Hw58Ga6Y8ysGKgFOmaldPkj\nk3qSDOvJzN4JfAa4wjk3NEtlyyeT/Tx9F7hyRkuUvyaqq3nAqcDDZrYXeBuwbbYGoOdqMDwNrDez\n1WZWSmxweVvKMduArd7jq4GHnDfaU0AyqSfJoJ68Zv/XiYVCaw7KmA8yqaf1CU8vB3bOYvnyyXHr\nyjnX5ZxrcM41OeeaiI1bXeGc2z4bhZuTweCNGdwA3Ae8CtzjnHvZzG42syu8w+4E6s1sF3AjkHa6\n2FyVST2Z2SYzawGuAb5uZi/nrsS5keHn6e+BauD73lTMggvYDOvpBjN72cyeI/a925rmPzenZVhX\nOaOVzyIikmROthhERGTqFAwiIpJEwSAiIkkUDCIikkTBICIiSRQMIiKSRMEgIiJJFAwiIpLk/wOQ\nR+RAQYCMPQAAAABJRU5ErkJggg==\n", - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "iparams = {}\n", - "iparams['injected_square_current'] = {}\n", - "iparams['injected_square_current']['amplitude'] = 200*pq.pA\n", - "#['amplitude'] = dtc.vtest[k]['injected_square_current']['amplitude']\n", - "DELAY = 0*pq.ms\n", - "DURATION = tstop*pq.ms\n", - "iparams['injected_square_current']['delay'] = DELAY\n", - "iparams['injected_square_current']['duration'] = DURATION\n", - "\n", - "model = ReducedModel(LEMS_MODEL_PATH,name = str('vanilla'),backend = ('RAW'))\n", - "#model.set_attrs(CHN)\n", - "model.set_attrs(CH)\n", - "\n", - "model.inject_square_current(iparams)\n", - "print(model.get_spike_count())\n", - "plt.plot(model.get_membrane_potential().times,model.get_membrane_potential())\n", - "\n" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "collapsed": false - }, - "source": [ - "Ignore the rest of cells\n" - ] - }, - { - "cell_type": "code", - "execution_count": 37, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "['__class__', '__delattr__', '__dict__', '__dir__', '__doc__', '__eq__', '__format__', '__ge__', '__getattribute__', '__gt__', '__hash__', '__init__', '__le__', '__lt__', '__module__', '__ne__', '__new__', '__reduce__', '__reduce_ex__', '__repr__', '__setattr__', '__sizeof__', '__str__', '__subclasshook__', '__weakref__', '_cell_name', '_local_run', 'attrs', 'backend', 'check_run_params', 'current_src_name', 'f', 'get_disk_cache', 'get_membrane_potential', 'get_memory_cache', 'init_backend', 'init_cache', 'init_disk_cache', 'init_memory_cache', 'inject_square_current', 'load_model', 'local_run', 'memory_cache', 'model', 'save_results', 'set_attrs', 'set_disk_cache', 'set_memory_cache', 'set_run_params', 'set_stop_time', 'sim_time', 'temp_attrs', 'use_disk_cache', 'use_memory_cache', 'vM']\n" - ] - }, - { - "ename": "NameError", - "evalue": "name 'first_two' is not defined", - "output_type": "error", - "traceback": [ - "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", - "\u001b[0;31mNameError\u001b[0m Traceback (most recent call last)", - "\u001b[0;32m\u001b[0m in \u001b[0;36m\u001b[0;34m()\u001b[0m\n\u001b[1;32m 25\u001b[0m \u001b[0;31m#print(first_two)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 26\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 27\u001b[0;31m \u001b[0mscore\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mfirst_two\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mjudge\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmodel\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mstop_on_error\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;32mFalse\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdeep_error\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;32mTrue\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 28\u001b[0m \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mscore\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 29\u001b[0m \u001b[0mstuff\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mfirst_two\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mgenerate_prediction\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmodel\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", - "\u001b[0;31mNameError\u001b[0m: name 'first_two' is not defined" - ] - } - ], - "source": [ - "\n", - "model = ReducedModel(LEMS_MODEL_PATH,name = str('vanilla'),backend = ('RAW'))\n", - "model.set_attrs(mparams)\n", - "\n", - "import seaborn as sns\n", - "from neuronunit.optimization.data_transport_container import DataTC\n", - "import os\n", - "print(dir(model._backend))#.attrs)\n", - "\n", - "\n", - "\n", - "\n", - "params = {}\n", - "params['injected_square_current'] = {}\n", - "params['injected_square_current']['amplitude'] = 52*pq.pA\n", - "DELAY = 100.0*pq.ms\n", - "DURATION = 1000.0*pq.ms\n", - "params['injected_square_current']['delay'] = DELAY\n", - "params['injected_square_current']['duration'] = DURATION\n", - "\n", - "#first_two[0].observation['mean'] = 50*pq.pA\n", - "\n", - "#first_two[1].observation['mean'] = 50*pq.ohm*1e6\n", - "\n", - "#print(first_two)\n", - "\n", - "score = first_two[1].judge(model,stop_on_error = False, deep_error = True)\n", - "print(score)\n", - "stuff = first_two[1].generate_prediction(model)\n", - "print(stuff)\n", - "print(model.get_spike_count(),'npsikes')\n", - "print(np.mean(model.get_membrane_potential()),'mean membrane potential')\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "score = first_two[1].judge(model,stop_on_error = False, deep_error = True)\n", - "print(score.prediction)\n", - "print(model.get_spike_count())" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "score = first_two[0].judge(model,stop_on_error = False, deep_error = True)\n", - "print(score.prediction)\n", - "#print(model.get_spike_count())" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "\n", - "model = None\n", - "model = ReducedModel(LEMS_MODEL_PATH,name = str('vanilla'),backend = ('RAW'))#,{'DTC':dtc}))\n", - "model.set_attrs(mparams)\n", - "\n", - "prediction = all_tests[0].generate_prediction(model)\n", - "print(prediction)\n", - "\n", - "prediction1 = all_tests[1].generate_prediction(model)\n", - "print(prediction1)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "\n", - "cnt = 0\n", - "scores = []\n", - "tests_,all_tests, observation,suite = opt.get_neab.get_tests()\n", - "#passive = [ str('RestingPotentialTest'), str('CapacitanceTest'), str('TimeConstantTest'), str('InputResistanceTest') ]\n", - "#firing_tests = [ t for t in all_tests if str(t) not in passive ]\n", - "\n", - "print(first_two)\n", - "\n", - "for t in all_tests:\n", - " score = t.judge(model,stop_on_error = False, deep_error = True)\n", - " \n", - " if cnt==0:\n", - " model.rheobase = score.prediction['value']\n", - " \n", - " params = {}\n", - " params['injected_square_current'] = {}\n", - " params['injected_square_current']['amplitude'] = score.prediction['value']\n", - " DELAY = 100.0*pq.ms\n", - " DURATION = 1000.0*pq.ms\n", - " params['injected_square_current']['delay'] = DELAY\n", - " params['injected_square_current']['duration'] = DURATION\n", - " model.params = params\n", - " scores.append(score)\n", - " print(score)\n", - " print('score {0}'.format(score))\n", - " if str('mean') in score.prediction.keys():\n", - " print('observation {0}, prediction {1}'.format(t.observation['mean'],score.prediction['mean']))\n", - " if str('value') in score.prediction.keys():\n", - " print('observation {0}, prediction {1}'.format(t.observation['mean'],score.prediction['value']))\n", - " cnt+=1\n", - "#http://www.physics" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "import pandas as pd\n", - "import numpy as np\n", - "k_grid = np.logspace(-1.5,1,10)\n", - "#k_grid = np.logspace(-2,-1,10)\n", - "#k_grid = np.logspace(-1,1,10)\n", - "\n", - "\n", - "print(k_grid)\n", - "\n", - "params = {}\n", - "params['a'] = 0.03\n", - "params['b'] = -2\n", - "params['C'] = 100\n", - "params['c'] = -50 \n", - "params['vr'] = -60\n", - "params['vt'] = -40\n", - "params['vPeak'] = 35\n", - "params['k'] = 0.7\n", - "params['d'] = 100\n", - "params['dt'] = 0.025\n", - "params['tMax'] = 1000.0;# % max time [ms]\n", - "first_two = all_tests[0:2]\n", - "list_dics = [] \n", - "fig = plt.figure()\n", - "import copy\n", - "import pdb\n", - "tests_ = all_tests[0:2]\n", - "for k in k_grid:\n", - " LEMS_MODEL_PATH = path_params['model_path']\n", - " model = None\n", - " model = ReducedModel(LEMS_MODEL_PATH,name=str('vanilla'),backend='NEURON')\n", - " params['k'] = k\n", - " model.set_attrs(params)\n", - "\n", - " scores = []\n", - " for index,t in enumerate(first_two):\n", - " print(\"k=%.2g; Test=%s\" % (k,t))\n", - " score = t.judge(model, stop_on_error=True, deep_error=False)\n", - " print(score.prediction,'rheobase why not?')\n", - " scores.append(score)\n", - "\n", - " for s in scores:\n", - " agreement = {}\n", - " agreement['k'] = k\n", - " print('bad k', k)\n", - " agreement['test'] = s.test\n", - " try:\n", - " agreement['observation'] = s.observation['mean'].rescale(s.test.units)\n", - " agreement['prediction'] = s.prediction['value'].rescale(s.test.units)\n", - " agreement['agreement'] = float(agreement['observation'])/float(agreement['prediction'])\n", - " except Exception as e:\n", - " print('error score skip because %s' % e)\n", - " agreement['agreement'] = None\n", - " list_dics.append(agreement)\n", - "\n", - "\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "#pd.set_option('display.precision', 3)\n", - "df = pd.DataFrame(list_dics)\n", - "dfg = df.reset_index(drop=True)\n", - "#dfg = dfg.dropna()\n", - "#dfg\n", - "#how='all'\n", - "#dfg = df.reset_index(drop=nan)\n", - "\n", - "\n", - "import seaborn as sns\n", - "cm = sns.light_palette(\"green\", as_cmap=True)\n", - "#display(dfg.style.background_gradient(cmap=cm,subset=['agreement']))\n", - "#df\n", - "#ax = df[df['test'].index % 2 == 0].plot(x='k',y='agreement',label='Rheobase')\n", - "\n", - "ax = df[df['test'].index % 2 == 0].plot(x='k',y='agreement',label='Rheobase')\n", - "df[df['test'].index % 2 == 1].plot(x='k',y='agreement',label='InputRes', ax=ax)\n", - "plt.plot(k_grid,np.ones(k_grid.shape),'--')\n", - "#plt.xscale('log')\n", - "#plt.yscale('log')\n", - "\n", - "plt.xscale('log')\n", - "plt.yscale('log')" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "list_dics" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "print(k_grid)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": true - }, - "outputs": [], - "source": [ - "k_grid = np.logspace(-1,0,10)\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "print(k_grid)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "\n", - "import seaborn as sns\n", - "cm = sns.light_palette(\"green\", as_cmap=True)\n", - "#display(dfg.style.background_gradient(cmap=cm,subset=['agreement']))\n", - "#df\n", - "\n", - "ax = df[df['test'].index % 2 == 0].plot(x='k',y='agreement',label='Rheobase')\n", - "#df[df['test'].index % 2 == 1].plot(x='k',y='agreement',label='InputRes', ax=ax)\n", - "plt.plot(k_grid,np.ones(k_grid.shape),'--')\n", - "plt.xscale('log')\n", - "plt.yscale('log')" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "ax = df[df['test'].index % 2 == 0].plot(x='k',y='agreement',label='Rheobase')\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "# tests that do well, 4th last to second last.\n", - "# rheobase.\n", - "ft_ = all_tests[-4:-2]\n", - "ft_.insert(0,all_tests[0]) \n", - "print(ft_)\n", - "free_params = ['k','b']\n", - "from neuronunit.optimization import optimization_management as om\n", - "ga_out, DO = om.run_ga(mp,3,ft_,free_params=free_params)\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "type2007.keys()" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "for k, v in type2007.items():\n", - " print(v)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "param_dict" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": true - }, - "outputs": [], - "source": [] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.5.2" - } - }, - "nbformat": 4, - "nbformat_minor": 2 -} diff --git a/neuronunit/unit_test/model_parameters.py b/neuronunit/unit_test/model_parameters.py deleted file mode 100644 index 6ff7cbb62..000000000 --- a/neuronunit/unit_test/model_parameters.py +++ /dev/null @@ -1,34 +0,0 @@ -import numpy as np - - -model_params={} - -model_params['vr'] = np.linspace(-75.0,-50.0,9) - - -model_params['a'] = np.linspace(0.0,0.945,9) -model_params['b'] = np.linspace(-5*10E-10,-0.25*10E-9,9) -model_params['vpeak'] =np.linspace(30.0,40.0,9) - - -model_params['k'] = np.linspace(7.0E-4-+7.0E-5,7.0E-4+70E-5,9) -model_params['C'] = np.linspace(1.00000005E-4-1.00000005E-5,1.00000005E-4+1.00000005E-5,9) -model_params['c'] = np.linspace(-55,-60,9) -model_params['d'] = np.linspace(0.050,0.2,9) -model_params['v0'] = np.linspace(-75.0,-45.0,9) -model_params['vt'] = np.linspace(-50.0,-30.0,9) - -''' -+model_params['vr'] = np.linspace(-95.0,-30.0,9) - -+model_params['a'] = np.linspace(0.0,0.945,9) -+model_params['b'] = np.linspace(-3.5*10E-10,-0.5*10E-9,9) -+model_params['vpeak'] =np.linspace(0.0,80.0,9) - -+model_params['k'] = np.linspace(7.0E-4-+7.0E-5,7.0E-4+70E-5,9) -+model_params['C'] = np.linspace(1.00000005E-4-1.00000005E-5,1.00000005E-4+1.00000005E-5,9) -+model_params['c'] = np.linspace(-55,-60,9) -+model_params['d'] = np.linspace(0.050,0.2,9) -+model_params['v0'] = np.linspace(-85.0,-15.0,9) -+model_params['vt'] = np.linspace(-70.0,0.0,9) -''' diff --git a/neuronunit/unit_test/model_tests.py b/neuronunit/unit_test/model_tests.py index 2e6ae781a..418d0b9ff 100644 --- a/neuronunit/unit_test/model_tests.py +++ b/neuronunit/unit_test/model_tests.py @@ -50,7 +50,8 @@ def test_geppetto_backend(self): class HasSegmentTestCase(unittest.TestCase): """Testing HasSegment and SingleCellModel""" - + @unittest.skip + # this test does not really work def test_(self): from neuronunit.models.section_extension import HasSegment hs = HasSegment() @@ -64,7 +65,7 @@ def section(location): class VeryReducedModelTestCase(unittest.TestCase): def setUp(self): from sciunit.models.backends import Backend - + class MyBackend(Backend): def _backend_run(self) -> str: return sum(self.run_params.items()) @@ -84,9 +85,9 @@ def inject_square_current(*args, **kwargs): def test_very_reduced_using_lems(self): from neuronunit.models.reduced import VeryReducedModel - + vrm = VeryReducedModel(name="test very redueced model", backend="My", attrs={}) - + vrm.rerun = False vrm.run_defaults = {"param3": 3} vrm.set_default_run_params(param1=1) @@ -106,7 +107,7 @@ def test_very_reduced_not_using_lems(self): class MyVRM(VeryReducedModel): def run(self): self.results = { - 'vm': [0.01*pq.V, 0.05*pq.V, 0*pq.V], + 'vm': [0.01*pq.V, 0.05*pq.V, 0*pq.V], 't': [0.1*pq.s, 0.2*pq.s, 0.3*pq.s] } vrm = MyVRM(name="test very redueced model", backend="My", attrs={}) diff --git a/neuronunit/unit_test/morphology/pyramidalCell.swc b/neuronunit/unit_test/morphology/pyramidalCell.swc deleted file mode 100644 index 909f348f0..000000000 --- a/neuronunit/unit_test/morphology/pyramidalCell.swc +++ /dev/null @@ -1,4454 +0,0 @@ -# Sources: -# http://neuromorpho.org/neuron_info.jsp?neuron_name=VD110330-IDC -# http://neuromorpho.org/dableFiles/markram/CNG%20version/VD110330-IDC.CNG.swc -# -# Original file VD110330-IDC.swc edited using StdSwc version 1.31 on 11/29/15. -# Irregularities and fixes documented in VD110330-IDC.swc.std. See StdSwc1.31.doc for more information. -# -# Neurolucida to SWC conversion from L-Measure. 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-174.98 -18.3 0.085 1851 -1853 2 269.38 -171.87 -17.44 0.085 1852 -1854 2 274.18 -172.16 -19.65 0.085 1853 -1855 2 279.07 -174.04 -19.65 0.085 1854 -1856 2 283.88 -172.26 -19.65 0.085 1855 -1857 2 290.19 -171.94 -19.65 0.085 1856 -1858 2 295.76 -171.84 -24.48 0.085 1857 -1859 2 304.05 -172.04 -28.74 0.085 1858 -1860 2 316.57 -172.95 -28.74 0.085 1859 -1861 2 326.75 -170.16 -28.74 0.085 1860 -1862 2 345.76 -168.34 -31.8 0.085 1861 -1863 2 356.39 -164.06 -34.71 0.085 1862 -1864 2 367.08 -161.37 -36.42 0.085 1863 -1865 2 381.5 -157.21 -34.57 0.085 1864 -1866 2 387.35 -156.48 -37.41 0.085 1865 -1867 2 393.57 -146.41 -37.27 0.085 1866 -1868 2 402.13 -143.0 -38.05 0.085 1867 -1869 2 410.14 -138.8 -40.47 0.085 1868 -1870 2 421.92 -133.57 -47.01 0.085 1869 -1871 2 427.77 -130.75 -47.01 0.085 1870 -1872 2 427.97 -130.61 -47.01 0.085 1871 -1873 2 437.31 -124.72 -49.21 0.085 1872 -1874 2 442.34 -123.66 -51.2 0.085 1873 -1875 2 442.34 -123.66 -50.63 0.085 1874 -1876 2 458.46 -120.68 -56.24 0.085 1875 -1877 2 474.38 -114.86 -64.63 0.085 1876 -1878 2 478.62 -108.29 -67.61 0.085 1877 -1879 2 21.64 -149.73 -8.35 0.085 1730 -1880 2 28.77 -150.59 -8.35 0.085 1879 -1881 2 40.02 -150.78 -3.37 0.085 1880 -1882 2 45.15 -148.17 1.95 0.085 1881 -1883 2 53.44 -144.37 1.95 0.085 1882 -1884 2 69.38 -131.27 12.53 0.085 1883 -1885 2 72.0 -127.02 12.53 0.085 1884 -1886 4 0.65 13.26 -2.24 2.395 1 -1887 4 0.95 17.05 -2.24 2.395 1886 -1888 4 1.86 21.4 -2.24 2.055 1887 -1889 4 1.99 26.25 -2.24 2.055 1888 -1890 4 1.81 30.28 -2.24 1.71 1889 -1891 4 2.37 36.61 -2.24 1.71 1890 -1892 4 3.15 42.22 -2.24 1.71 1891 -1893 4 3.56 45.62 2.44 1.455 1892 -1894 4 4.07 48.66 2.09 1.455 1893 -1895 4 3.76 52.89 2.09 1.455 1894 -1896 4 5.91 58.44 2.09 1.455 1895 -1897 4 6.5 58.85 -0.39 1.455 1896 -1898 4 5.64 61.95 -0.39 1.37 1897 -1899 4 5.54 65.44 -0.39 1.195 1898 -1900 4 6.57 69.44 -0.39 1.025 1899 -1901 4 8.76 76.19 -0.39 1.025 1900 -1902 4 6.04 80.6 -0.39 1.025 1901 -1903 4 5.44 84.18 -2.1 1.025 1902 -1904 4 6.02 88.6 0.52 1.025 1903 -1905 4 5.31 93.58 0.52 1.025 1904 -1906 4 6.26 101.25 0.52 1.025 1905 -1907 4 7.83 105.33 0.52 1.025 1906 -1908 4 6.86 110.87 2.09 1.025 1907 -1909 4 5.37 117.38 2.09 1.025 1908 -1910 4 4.2 120.72 1.8 1.025 1909 -1911 4 4.23 120.89 1.8 1.025 1910 -1912 4 5.12 126.12 1.8 1.025 1911 -1913 4 5.04 126.65 1.8 1.025 1912 -1914 4 5.33 131.47 1.8 1.025 1913 -1915 4 2.97 138.01 2.87 0.94 1914 -1916 4 2.85 142.38 2.87 0.94 1915 -1917 4 2.68 142.41 2.87 0.94 1916 -1918 4 1.01 149.99 1.09 0.94 1917 -1919 4 1.74 154.21 1.09 0.94 1918 -1920 4 2.25 157.25 0.6 0.94 1919 -1921 4 2.65 157.53 0.6 0.94 1920 -1922 4 4.24 161.78 0.6 0.94 1921 -1923 4 2.6 166.41 0.6 0.94 1922 -1924 4 -1.11 172.08 0.6 0.94 1923 -1925 4 0.68 179.6 -2.31 0.94 1924 -1926 4 0.91 179.9 -2.31 0.94 1925 -1927 4 1.03 180.58 -2.31 0.94 1926 -1928 4 1.94 185.98 -2.31 0.94 1927 -1929 4 3.09 190.65 -2.31 0.94 1928 -1930 4 4.19 193.07 -2.31 0.94 1929 -1931 4 3.75 195.58 -2.31 0.94 1930 -1932 4 1.56 202.93 -3.59 0.94 1931 -1933 4 0.18 202.98 -3.59 0.94 1932 -1934 4 0.34 205.91 -0.89 0.94 1933 -1935 4 3.81 208.98 -2.95 0.94 1934 -1936 4 5.57 211.12 -5.65 0.94 1935 -1937 4 5.59 211.28 -5.65 0.94 1936 -1938 4 5.12 214.66 -5.65 0.94 1937 -1939 4 4.35 224.36 -5.65 0.94 1938 -1940 4 3.11 225.26 -8.7 0.94 1939 -1941 4 2.32 229.74 -7.78 0.94 1940 -1942 4 1.72 236.44 -7.78 0.94 1941 -1943 4 4.94 242.15 -8.06 0.94 1942 -1944 4 7.09 243.53 -8.06 0.94 1943 -1945 4 4.84 248.6 -7.21 0.94 1944 -1946 4 4.25 252.35 -9.84 0.94 1945 -1947 4 5.1 254.29 -9.84 0.94 1946 -1948 4 5.98 257.78 -9.13 0.94 1947 -1949 4 4.95 260.91 -9.13 0.77 1948 -1950 4 2.99 263.68 -9.13 0.77 1949 -1951 4 1.85 267.17 -9.13 0.685 1950 -1952 4 1.41 269.68 -9.13 0.685 1951 -1953 4 1.5 270.19 -9.13 0.685 1952 -1954 4 2.67 273.99 -9.13 0.685 1953 -1955 4 2.7 274.16 -9.13 0.685 1954 -1956 4 2.56 276.44 -9.13 0.685 1955 -1957 4 1.84 277.25 -13.18 0.685 1956 -1958 4 0.3 280.48 -10.91 0.685 1957 -1959 4 1.83 283.35 -10.91 0.685 1958 -1960 4 1.97 286.28 -10.91 0.685 1959 -1961 4 -0.05 291.65 -10.91 0.685 1960 -1962 4 -0.22 296.73 -10.91 0.685 1961 -1963 4 0.12 300.84 -10.91 0.685 1962 -1964 4 -4.3 305.42 -10.91 0.685 1963 -1965 4 -3.93 305.53 -10.91 0.685 1964 -1966 4 -3.9 305.7 -10.91 0.685 1965 -1967 4 -4.84 310.38 -10.91 0.685 1966 -1968 4 -4.75 310.89 -10.91 0.685 1967 -1969 4 -4.86 311.25 -10.91 0.685 1968 -1970 4 -4.04 314.06 -10.91 0.685 1969 -1971 4 -2.51 318.92 -12.47 0.685 1970 -1972 4 -1.95 323.17 -12.47 0.685 1971 -1973 4 -1.89 323.51 -12.47 0.685 1972 -1974 4 -2.64 329.37 -9.63 0.685 1973 -1975 4 -2.76 332.7 -9.63 0.685 1974 -1976 4 -2.74 332.87 -9.63 0.685 1975 -1977 4 -0.51 335.79 -9.63 0.685 1976 -1978 4 2.2 339.5 -9.63 0.685 1977 -1979 4 2.26 339.84 -9.63 0.685 1978 -1980 4 3.38 345.39 -9.63 0.685 1979 -1981 4 3.43 345.72 -9.63 0.685 1980 -1982 4 4.92 350.33 -9.63 0.685 1981 -1983 4 1.75 353.14 -9.63 0.685 1982 -1984 4 1.78 353.31 -9.63 0.685 1983 -1985 4 -0.2 357.98 -9.63 0.685 1984 -1986 4 -0.47 358.38 -9.63 0.685 1985 -1987 4 -2.61 364.13 -9.63 0.685 1986 -1988 4 -2.9 364.53 -9.63 0.685 1987 -1989 4 -4.22 371.01 -9.63 0.685 1988 -1990 4 -3.97 371.49 -9.63 0.685 1989 -1991 4 -3.43 374.7 -9.63 0.685 1990 -1992 4 -0.41 380.96 -9.27 0.685 1991 -1993 4 0.5 386.37 -9.27 0.685 1992 -1994 4 -2.79 393.53 -9.27 0.685 1993 -1995 4 -2.62 393.5 -9.27 0.685 1994 -1996 4 -5.08 398.44 -9.27 0.685 1995 -1997 4 -4.99 398.95 -9.27 0.685 1996 -1998 4 -1.78 406.57 -9.27 0.685 1997 -1999 4 2.09 413.03 -9.27 0.685 1998 -2000 4 2.12 413.2 -9.27 0.685 1999 -2001 4 1.25 417.34 -4.72 0.515 2000 -2002 4 0.31 419.94 -4.72 0.43 2001 -2003 4 -0.48 423.38 -6.57 0.43 2002 -2004 4 -0.59 425.83 -8.21 0.43 2003 -2005 4 1.05 429.37 -8.21 0.43 2004 -2006 4 1.11 429.71 -8.21 0.43 2005 -2007 4 1.53 431.21 -8.21 0.43 2006 -2008 4 1.59 431.55 -8.21 0.43 2007 -2009 4 1.76 431.52 -8.21 0.43 2008 -2010 4 2.22 433.62 -8.28 0.43 2009 -2011 4 2.28 433.96 -8.28 0.43 2010 -2012 4 3.1 437.82 -8.28 0.43 2011 -2013 4 3.62 440.86 -8.28 0.43 2012 -2014 4 3.73 441.53 -8.28 0.43 2013 -2015 4 5.32 443.7 -8.28 0.43 2014 -2016 4 5.59 447.3 -8.28 0.255 2015 -2017 4 3.74 451.79 -11.62 0.255 2016 -2018 4 3.78 454.04 -11.62 0.255 2017 -2019 4 2.86 456.8 -11.62 0.515 2018 -2020 4 3.3 459.34 -11.62 0.43 2019 -2021 4 4.8 462.04 -11.62 0.43 2020 -2022 4 5.51 464.17 -11.62 0.43 2021 -2023 4 3.24 467.17 -11.62 0.43 2022 -2024 4 0.78 471.06 -13.11 0.43 2023 -2025 4 1.51 477.37 -13.11 0.43 2024 -2026 4 0.95 480.24 -13.11 0.34 2025 -2027 4 1.51 483.46 -13.11 0.34 2026 -2028 4 1.53 483.62 -13.11 0.34 2027 -2029 4 2.22 486.63 -13.11 0.34 2028 -2030 4 3.04 489.45 -13.11 0.34 2029 -2031 4 3.72 492.48 -13.11 0.34 2030 -2032 4 3.75 492.65 -13.11 0.34 2031 -2033 4 3.39 494.62 -13.11 0.34 2032 -2034 4 0.42 496.51 -13.11 0.34 2033 -2035 4 0.28 496.71 -13.11 0.34 2034 -2036 4 -1.26 498.89 -14.82 0.34 2035 -2037 4 -1.74 503.14 -15.88 0.34 2036 -2038 4 -0.1 507.73 -16.38 0.34 2037 -2039 4 2.89 511.05 -17.8 0.34 2038 -2040 4 2.37 514.08 -14.74 0.34 2039 -2041 4 -0.28 516.8 -14.74 0.34 2040 -2042 4 -2.24 521.65 -14.74 0.34 2041 -2043 4 -2.15 522.16 -14.74 0.34 2042 -2044 4 -0.76 524.18 -14.74 0.34 2043 -2045 4 -0.52 529.7 -14.74 0.34 2044 -2046 4 -0.22 532.43 -14.74 0.34 2045 -2047 4 -1.25 536.6 -14.74 0.34 2046 -2048 4 0.1 540.54 -14.74 0.34 2047 -2049 4 0.03 543.16 -14.74 0.34 2048 -2050 4 -0.07 543.53 -14.74 0.34 2049 -2051 4 -3.18 544.58 -14.74 0.34 2050 -2052 4 -6.45 546.87 -14.74 0.34 2051 -2053 4 -6.39 547.21 -14.74 0.34 2052 -2054 4 -6.49 547.58 -14.74 0.34 2053 -2055 4 -6.45 550.87 -14.74 0.34 2054 -2056 4 -5.35 556.38 -14.67 0.34 2055 -2057 4 -3.73 559.76 -14.67 0.34 2056 -2058 4 -3.7 559.92 -14.67 0.34 2057 -2059 4 -2.04 562.59 -14.67 0.34 2058 -2060 4 -4.4 564.03 -14.67 0.255 2059 -2061 4 -6.67 564.94 -14.67 0.34 2060 -2062 4 -9.53 567.51 -14.67 0.34 2061 -2063 4 -9.65 571.88 -14.67 0.34 2062 -2064 4 -9.54 572.56 -14.67 0.34 2063 -2065 4 -8.57 576.22 -14.67 0.34 2064 -2066 4 -8.42 579.15 -14.67 0.34 2065 -2067 4 -8.39 579.32 -14.67 0.34 2066 -2068 4 -10.17 584.14 -17.16 0.34 2067 -2069 4 -12.43 588.18 -17.16 0.34 2068 -2070 4 -12.6 588.21 -17.16 0.34 2069 -2071 4 -12.62 590.12 -17.16 0.34 2070 -2072 4 -12.53 590.63 -17.16 0.34 2071 -2073 4 -10.73 593.1 -17.16 0.34 2072 -2074 4 -8.75 594.5 -17.16 0.34 2073 -2075 4 -8.72 594.67 -17.16 0.34 2074 -2076 4 -11.21 598.39 -20.07 0.34 2075 -2077 4 -11.18 598.56 -20.07 0.34 2076 -2078 4 -13.93 601.81 -20.07 0.34 2077 -2079 4 -13.81 602.49 -20.07 0.34 2078 -2080 4 -13.98 602.52 -20.07 0.34 2079 -2081 4 -14.59 605.05 -20.07 0.34 2080 -2082 4 -11.76 610.48 -20.07 0.34 2081 -2083 4 -12.85 615.72 -22.99 0.34 2082 -2084 4 -15.2 620.29 -22.99 0.34 2083 -2085 4 -15.14 620.63 -22.99 0.34 2084 -2086 4 -15.06 628.26 -22.99 0.34 2085 -2087 4 -15.51 633.73 -22.99 0.34 2086 -2088 4 -15.34 633.7 -22.99 0.34 2087 -2089 4 -14.11 637.84 -22.99 0.34 2088 -2090 4 -14.08 638.01 -22.99 0.34 2089 -2091 4 -13.94 639.89 -22.99 0.34 2090 -2092 4 -14.08 640.09 -22.99 0.34 2091 -2093 4 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66.2 7.2 0.17 4416 -4418 4 42.28 68.47 7.2 0.17 4417 -4419 4 44.0 69.39 9.41 0.17 4418 -4420 4 46.54 70.0 9.41 0.17 4419 -4421 4 51.29 72.49 9.41 0.17 4420 -4422 4 54.61 73.66 9.41 0.17 4421 -4423 4 58.58 79.6 13.67 0.255 4422 -4424 4 61.16 79.5 13.67 0.255 4423 -4425 4 62.54 78.4 13.67 0.17 4424 -4426 4 64.06 79.19 13.67 0.17 4425 -4427 4 65.91 78.87 13.67 0.17 4426 -4428 4 68.57 81.19 12.53 0.17 4427 -4429 4 69.97 81.3 12.53 0.34 4428 -4430 4 72.74 84.31 12.53 0.17 4429 -4431 4 75.92 84.64 12.53 0.17 4430 -4432 4 76.03 84.27 14.38 0.17 4431 -4433 4 79.31 87.19 14.38 0.17 4432 -4434 4 80.12 87.92 14.38 0.685 4433 -4435 4 80.75 89.55 14.38 0.085 4434 -4436 4 82.81 90.42 14.38 0.17 4435 -4437 4 85.28 89.65 14.38 0.17 4436 -4438 4 86.92 92.15 14.38 0.17 4437 -4439 4 89.9 93.38 14.38 0.17 4438 -4440 4 92.12 94.21 14.38 0.17 4439 -4441 4 93.43 96.78 14.38 0.17 4440 -4442 4 94.12 98.74 14.38 0.17 4441 diff --git a/neuronunit/unit_test/neuroconstruct_tests.py b/neuronunit/unit_test/neuroconstruct_tests.py deleted file mode 100644 index 454c8a335..000000000 --- a/neuronunit/unit_test/neuroconstruct_tests.py +++ /dev/null @@ -1,16 +0,0 @@ -"""Unit tests for the showcase features of NeuronUnit""" - -from .base import * - -class DocumentationTestCase(NotebookTools,unittest.TestCase): - """Testing documentation notebooks""" - - path = '../../docs' - - @unittest.skip("Skipping chapter 4") - def test_chapter4(self): - self.do_notebook('chapter4') - - -if __name__ == '__main__': - unittest.main() diff --git a/neuronunit/unit_test/old/progress_report_.ipynb b/neuronunit/unit_test/old/progress_report_.ipynb deleted file mode 100644 index 7f8e95e6e..000000000 --- a/neuronunit/unit_test/old/progress_report_.ipynb +++ /dev/null @@ -1,7819 +0,0 @@ -{ - "cells": [ - { - "cell_type": "code", - "execution_count": 1, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "\n", - "
\n", - " \n", - " Loading BokehJS ...\n", - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "application/javascript": [ - "\n", - "(function(root) {\n", - " function now() {\n", - " return new Date();\n", - " }\n", - "\n", - " var force = true;\n", - "\n", - " if (typeof (root._bokeh_onload_callbacks) === \"undefined\" || force === true) {\n", - " root._bokeh_onload_callbacks = [];\n", - " root._bokeh_is_loading = undefined;\n", - " }\n", - "\n", - " var JS_MIME_TYPE = 'application/javascript';\n", - " var HTML_MIME_TYPE = 'text/html';\n", - " var EXEC_MIME_TYPE = 'application/vnd.bokehjs_exec.v0+json';\n", - " var CLASS_NAME = 'output_bokeh rendered_html';\n", - "\n", - " /**\n", - " * Render data to the DOM node\n", - " */\n", - " function render(props, node) {\n", - " var script = document.createElement(\"script\");\n", - " node.appendChild(script);\n", - " }\n", - "\n", - " /**\n", - " * Handle when an output is cleared or removed\n", - " */\n", - " function handleClearOutput(event, handle) {\n", - " var cell = handle.cell;\n", - "\n", - " var id = cell.output_area._bokeh_element_id;\n", - " var server_id = cell.output_area._bokeh_server_id;\n", - " // Clean up Bokeh references\n", - " if (id !== undefined) {\n", - " Bokeh.index[id].model.document.clear();\n", - " delete Bokeh.index[id];\n", - " }\n", - "\n", - " if (server_id !== undefined) {\n", - " // Clean up Bokeh references\n", - " var cmd = \"from bokeh.io.state import curstate; print(curstate().uuid_to_server['\" + server_id + \"'].get_sessions()[0].document.roots[0]._id)\";\n", - " cell.notebook.kernel.execute(cmd, {\n", - " iopub: {\n", - " output: function(msg) {\n", - " var element_id = msg.content.text.trim();\n", - " Bokeh.index[element_id].model.document.clear();\n", - " delete Bokeh.index[element_id];\n", - " }\n", - " }\n", - " });\n", - " // Destroy server and session\n", - " var cmd = \"import bokeh.io.notebook as ion; ion.destroy_server('\" + server_id + \"')\";\n", - " cell.notebook.kernel.execute(cmd);\n", - " }\n", - " }\n", - "\n", - " /**\n", - " * Handle when a new output is added\n", - " */\n", - " function handleAddOutput(event, handle) {\n", - " var output_area = handle.output_area;\n", - " var output = handle.output;\n", - "\n", - " // limit handleAddOutput to display_data with EXEC_MIME_TYPE content only\n", - " if ((output.output_type != \"display_data\") || (!output.data.hasOwnProperty(EXEC_MIME_TYPE))) {\n", - " return\n", - " }\n", - "\n", - " var toinsert = output_area.element.find(\".\" + CLASS_NAME.split(' ')[0]);\n", - "\n", - " if (output.metadata[EXEC_MIME_TYPE][\"id\"] !== undefined) {\n", - " toinsert[toinsert.length - 1].firstChild.textContent = output.data[JS_MIME_TYPE];\n", - " // store reference to embed id on output_area\n", - " output_area._bokeh_element_id = output.metadata[EXEC_MIME_TYPE][\"id\"];\n", - " }\n", - " if (output.metadata[EXEC_MIME_TYPE][\"server_id\"] !== undefined) {\n", - " var bk_div = document.createElement(\"div\");\n", - " bk_div.innerHTML = output.data[HTML_MIME_TYPE];\n", - " var script_attrs = bk_div.children[0].attributes;\n", - " for (var i = 0; i < script_attrs.length; i++) {\n", - " toinsert[toinsert.length - 1].firstChild.setAttribute(script_attrs[i].name, script_attrs[i].value);\n", - " }\n", - " // store reference to server id on output_area\n", - " output_area._bokeh_server_id = output.metadata[EXEC_MIME_TYPE][\"server_id\"];\n", - " }\n", - " }\n", - "\n", - " function register_renderer(events, OutputArea) {\n", - "\n", - " function append_mime(data, metadata, element) {\n", - " // create a DOM node to render to\n", - " var toinsert = this.create_output_subarea(\n", - " metadata,\n", - " CLASS_NAME,\n", - " EXEC_MIME_TYPE\n", - " );\n", - " this.keyboard_manager.register_events(toinsert);\n", - " // Render to node\n", - " var props = {data: data, metadata: metadata[EXEC_MIME_TYPE]};\n", - " render(props, toinsert[toinsert.length - 1]);\n", - " element.append(toinsert);\n", - " return toinsert\n", - " }\n", - "\n", - " /* Handle when an output is cleared or removed */\n", - " events.on('clear_output.CodeCell', handleClearOutput);\n", - " events.on('delete.Cell', handleClearOutput);\n", - "\n", - " /* Handle when a new output is added */\n", - " events.on('output_added.OutputArea', handleAddOutput);\n", - "\n", - " /**\n", - " * Register the mime type and append_mime function with output_area\n", - " */\n", - " OutputArea.prototype.register_mime_type(EXEC_MIME_TYPE, append_mime, {\n", - " /* Is output safe? */\n", - " safe: true,\n", - " /* Index of renderer in `output_area.display_order` */\n", - " index: 0\n", - " });\n", - " }\n", - "\n", - " // register the mime type if in Jupyter Notebook environment and previously unregistered\n", - " if (root.Jupyter !== undefined) {\n", - " var events = require('base/js/events');\n", - " var OutputArea = require('notebook/js/outputarea').OutputArea;\n", - "\n", - " if (OutputArea.prototype.mime_types().indexOf(EXEC_MIME_TYPE) == -1) {\n", - " register_renderer(events, OutputArea);\n", - " }\n", - " }\n", - "\n", - " \n", - " if (typeof (root._bokeh_timeout) === \"undefined\" || force === true) {\n", - " root._bokeh_timeout = Date.now() + 5000;\n", - " root._bokeh_failed_load = false;\n", - " }\n", - "\n", - " var NB_LOAD_WARNING = {'data': {'text/html':\n", - " \"
\\n\"+\n", - " \"

\\n\"+\n", - " \"BokehJS does not appear to have successfully loaded. If loading BokehJS from CDN, this \\n\"+\n", - " \"may be due to a slow or bad network connection. Possible fixes:\\n\"+\n", - " \"

\\n\"+\n", - " \"
    \\n\"+\n", - " \"
  • re-rerun `output_notebook()` to attempt to load from CDN again, or
    • \\n\"+\n", - " \"
  • use INLINE resources instead, as so:
    • \\n\"+\n", - " \"
\\n\"+\n", - " \"\\n\"+\n", - " \"from bokeh.resources import INLINE\\n\"+\n", - " \"output_notebook(resources=INLINE)\\n\"+\n", - " \"\\n\"+\n", - " \"
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\\n\"+\n \"

\\n\"+\n \"BokehJS does not appear to have successfully loaded. If loading BokehJS from CDN, this \\n\"+\n \"may be due to a slow or bad network connection. Possible fixes:\\n\"+\n \"

\\n\"+\n \"
    \\n\"+\n \"
  • re-rerun `output_notebook()` to attempt to load from CDN again, or
    • \\n\"+\n \"
  • use INLINE resources instead, as so:
    • \\n\"+\n \"
\\n\"+\n \"\\n\"+\n \"from bokeh.resources import INLINE\\n\"+\n \"output_notebook(resources=INLINE)\\n\"+\n \"\\n\"+\n \"
\"}};\n\n function display_loaded() {\n var el = document.getElementById(\"ebccf65e-cbe4-4468-9dc8-673a09756d9d\");\n if (el != null) {\n el.textContent = \"BokehJS is loading...\";\n }\n if (root.Bokeh !== undefined) {\n if (el != null) {\n el.textContent = \"BokehJS \" + root.Bokeh.version + \" successfully loaded.\";\n }\n } else if (Date.now() < root._bokeh_timeout) {\n setTimeout(display_loaded, 100)\n }\n }\n\n\n function run_callbacks() {\n try {\n root._bokeh_onload_callbacks.forEach(function(callback) { callback() });\n }\n finally {\n delete root._bokeh_onload_callbacks\n }\n console.info(\"Bokeh: all callbacks have finished\");\n }\n\n function load_libs(js_urls, callback) {\n root._bokeh_onload_callbacks.push(callback);\n if (root._bokeh_is_loading > 0) {\n console.log(\"Bokeh: BokehJS is being loaded, scheduling callback at\", now());\n return null;\n }\n if (js_urls == null || js_urls.length === 0) {\n run_callbacks();\n return null;\n }\n console.log(\"Bokeh: BokehJS not loaded, scheduling load and callback at\", now());\n root._bokeh_is_loading = js_urls.length;\n for (var i = 0; i < js_urls.length; i++) {\n var url = js_urls[i];\n var s = document.createElement('script');\n s.src = url;\n s.async = false;\n s.onreadystatechange = s.onload = function() {\n root._bokeh_is_loading--;\n if (root._bokeh_is_loading === 0) {\n console.log(\"Bokeh: all BokehJS libraries loaded\");\n run_callbacks()\n }\n };\n s.onerror = function() {\n console.warn(\"failed to load library \" + url);\n };\n console.log(\"Bokeh: injecting script tag for BokehJS library: \", url);\n document.getElementsByTagName(\"head\")[0].appendChild(s);\n }\n };var element = document.getElementById(\"ebccf65e-cbe4-4468-9dc8-673a09756d9d\");\n if (element == null) {\n console.log(\"Bokeh: ERROR: autoload.js configured with elementid 'ebccf65e-cbe4-4468-9dc8-673a09756d9d' but no matching script tag was found. \")\n return false;\n }\n\n var js_urls = [\"https://cdn.pydata.org/bokeh/release/bokeh-0.12.15.min.js\", \"https://cdn.pydata.org/bokeh/release/bokeh-widgets-0.12.15.min.js\", \"https://cdn.pydata.org/bokeh/release/bokeh-tables-0.12.15.min.js\", \"https://cdn.pydata.org/bokeh/release/bokeh-gl-0.12.15.min.js\"];\n\n var inline_js = [\n function(Bokeh) {\n Bokeh.set_log_level(\"info\");\n },\n \n function(Bokeh) {\n \n },\n function(Bokeh) {\n console.log(\"Bokeh: injecting CSS: https://cdn.pydata.org/bokeh/release/bokeh-0.12.15.min.css\");\n Bokeh.embed.inject_css(\"https://cdn.pydata.org/bokeh/release/bokeh-0.12.15.min.css\");\n console.log(\"Bokeh: injecting CSS: https://cdn.pydata.org/bokeh/release/bokeh-widgets-0.12.15.min.css\");\n Bokeh.embed.inject_css(\"https://cdn.pydata.org/bokeh/release/bokeh-widgets-0.12.15.min.css\");\n console.log(\"Bokeh: injecting CSS: https://cdn.pydata.org/bokeh/release/bokeh-tables-0.12.15.min.css\");\n Bokeh.embed.inject_css(\"https://cdn.pydata.org/bokeh/release/bokeh-tables-0.12.15.min.css\");\n }\n ];\n\n function run_inline_js() {\n \n if ((root.Bokeh !== undefined) || (force === true)) {\n for (var i = 0; i < inline_js.length; i++) {\n inline_js[i].call(root, root.Bokeh);\n }if (force === true) {\n display_loaded();\n }} else if (Date.now() < root._bokeh_timeout) {\n setTimeout(run_inline_js, 100);\n } else if (!root._bokeh_failed_load) {\n console.log(\"Bokeh: BokehJS failed to load within specified timeout.\");\n root._bokeh_failed_load = true;\n } else if (force !== true) {\n var cell = $(document.getElementById(\"ebccf65e-cbe4-4468-9dc8-673a09756d9d\")).parents('.cell').data().cell;\n cell.output_area.append_execute_result(NB_LOAD_WARNING)\n }\n\n }\n\n if (root._bokeh_is_loading === 0) {\n console.log(\"Bokeh: BokehJS loaded, going straight to plotting\");\n run_inline_js();\n } else {\n load_libs(js_urls, function() {\n console.log(\"Bokeh: BokehJS plotting callback run at\", now());\n run_inline_js();\n });\n }\n}(window));" - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "text/plain": [ - "256" - ] - }, - "execution_count": 1, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "\n", - "from bokeh.plotting import figure, output_file, show, ColumnDataSource\n", - "from bokeh.models import HoverTool\n", - "from bokeh.plotting import figure, output_file, show\n", - "%matplotlib inline\n", - "from bokeh.plotting import show, output_notebook\n", - "from bokeh.models import ColumnDataSource, OpenURL, TapTool\n", - "import os\n", - "output_notebook()\n", - "os.system('jupyter trust Visualisation_search_terms_reading_levelGS.ipynb')" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Progress Report and Documentation\n" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "What I have been working on. \n", - "1. Updating Docker-stacks dockerfile build instructions in order to make a HPC ready Dockerbuild.\n", - "2. Updating the aforementioned stack, in order to perform regular maintance and to fix build problems due to \n", - "updates of upstream software sources.\n", - "3. Debugging and visualisation of the BluePyOpt GA algorithm.\n" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Other work\n", - "1. The people at BluePyOpt (Werner) agreed to make a scidash branch, they want me to make use extensibility and inheritence. Such that my scidash derivation of BluePyOpt inherits from and extends as much as possible the parent class of their elitist branch at BPO.\n" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Resolving Docker Build issues. \n", - "\n", - "* Issue 1 Upgrading pip to pip10, breaks NU installation.\n", - "Work around: re-write setup.py to exclude problems associated with process dependency links.\n", - "\n", - " \n", - "* Issue 2 \n", - "Developing in BPO and NU simultaneously requires a dockerfile at to build from a location at one location down in the directory hierarchy: There is a need to install both packages using the pip -e paradigm (file changes during session at developer locations, effect modules that are in sys.path):\n", - "** ADD neuronunit neuronunit\n", - "** ADD BluePyOpt BluePyOpt\n", - "To this end scidash opt was created.\n", - "https://github.com/russelljjarvis/scidashopt\n" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/opt/conda/lib/python3.6/site-packages/sklearn/cross_validation.py:41: DeprecationWarning: This module was deprecated in version 0.18 in favor of the model_selection module into which all the refactored classes and functions are moved. Also note that the interface of the new CV iterators are different from that of this module. This module will be removed in 0.20.\n", - " \"This module will be removed in 0.20.\", DeprecationWarning)\n", - "/opt/conda/lib/python3.6/site-packages/sklearn/grid_search.py:42: DeprecationWarning: This module was deprecated in version 0.18 in favor of the model_selection module into which all the refactored classes and functions are moved. This module will be removed in 0.20.\n", - " DeprecationWarning)\n" - ] - } - ], - "source": [ - "#!pip install radon\n", - "#!pip install bokeh\n", - "\n", - "from neuronunit.optimization import optimization_management\n", - "from neuronunit.optimization import exhaustive_search\n", - "#from neuronunit.unit_test import test_complexity\n", - "#from neuronunit.unit_test.test_complexity import rank_all_sub_module_functions\n", - "\n", - "#ranks = test_complexity.rank_all_sub_module_functions(optimization_management)\n", - "#ranks.extend(test_complexity.rank_all_sub_module_functions(exhaustive_search))\n", - "#print(ranks)\n", - "\n", - "\n" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [] - }, - { - "cell_type": "code", - "execution_count": 3, - "metadata": {}, - "outputs": [], - "source": [ - "from sklearn.grid_search import ParameterGrid\n", - "#dir()" - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "metadata": {}, - "outputs": [], - "source": [ - "import bokeh\n", - "import numpy as np\n", - "import matplotlib" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# 1D (Resting Membrane potential), 2 error result" - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "/home/jovyan/neuronunit/neuronunit/unit_test\n" - ] - } - ], - "source": [ - "\n", - "\n", - "import matplotlib.pyplot as plt\n", - "import seaborn as sns\n", - "import pandas as pd\n", - "import numpy as np\n", - "import math as math\n", - "from pylab import rcParams\n", - "from neuronunit.optimization.results_analysis import make_report\n", - "\n", - "from neuronunit.optimization.optimization_management import run_ga\n", - "from neuronunit.optimization.exhaustive_search import run_grid, reduce_params, create_grid\n", - "from neuronunit.optimization.model_parameters import model_params\n", - "import os\n", - "import pickle\n", - "from neuronunit.optimization import get_neab\n", - "reports = {}\n", - "npoints = 10\n", - "\n", - "\n", - "electro_path = str(os.getcwd())+'/pipe_tests.p'\n", - "print(os.getcwd())\n", - "assert os.path.isfile(electro_path) == True\n", - "with open(electro_path,'rb') as f:\n", - " electro_tests = pickle.load(f)\n", - "\n", - "electro_tests = get_neab.replace_zero_std(electro_tests)\n", - "electro_tests = get_neab.substitute_parallel_for_serial(electro_tests)\n", - "test, observation = electro_tests[0]\n", - "\n", - "reports = {}\n", - "\n", - "\n", - "'''\n", - "with open('pre_grid_reports.p','rb') as f:\n", - " [grid_results,nparams] = pickle.load(f)\n", - "opt_keys = list(grid_results[0].dtc.attrs.keys())\n", - "nparams = 2\n", - "tests = [electro_tests[0][0][0],electro_tests[0][0][3]]\n", - "ga_out = run_ga(model_params,nparams,npoints,tests,provided_keys = opt_keys)\n", - "'''\n", - "with open('pre_grid_reports.p','rb') as f:\n", - " package = pickle.load(f)\n", - "grid_results = package[0] \n", - " #[grid_results,nparams] = pickle.load(f)\n", - "opt_keys = list(grid_results[0].dtc.attrs.keys())\n", - "\n", - "with open('pre_ga_reports.p','rb') as f:\n", - " ga_out = pickle.load(f)\n", - "\n" - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "metadata": {}, - "outputs": [], - "source": [ - "\n", - "from neuronunit.optimization.exhaustive_search import create_grid\n", - "gp = create_grid(npoints = 10,nparams = 2)\n", - "\n", - " " - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "\n", - "# Found tough parameters for which the GA is not able to perform particularily # well. Suspect b's error surface is not concave.\n", - "# Explore 1D cross section.\n" - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "'\\nmapped_params = np.matrix(sqr,sqr)\\nfor i in range(0,sqr):\\n for j in range(0,sqr):\\n #params[i,j] = 0\\n params[0,i] = 0\\n params[1,j] = 0\\n print(next(iter_grid))\\n'" - ] - }, - "execution_count": 7, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "\n", - "import neuronunit\n", - "from neuronunit import optimization\n", - "from neuronunit.optimization.exhaustive_search import create_grid\n", - "grid = create_grid(npoints=10,nparams=2)#,provided_keys=None)\n", - "sqr = int(np.sqrt(len(grid)))\n", - "iter_grid = iter(grid)\n", - "'''\n", - "mapped_params = np.matrix(sqr,sqr)\n", - "for i in range(0,sqr):\n", - " for j in range(0,sqr):\n", - " #params[i,j] = 0\n", - " params[0,i] = 0\n", - " params[1,j] = 0\n", - " print(next(iter_grid))\n", - "''' " - ] - }, - { - "cell_type": "code", - "execution_count": 8, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "\n", - "
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"p = figure(plot_width=400, plot_height=400, tools=[\"hover\"],title=\"scores model paramaters\")\n", - "\n", - "#print(dir(source))\n", - "#print(type(source))\n", - "\n", - "p.line(x='x', y='y', line_width=2, color='#ebbd5b', source=source)\n", - "\n", - "#p.circle('x', 'y', size=8, source=source)\n", - "\n", - "show(p)\n", - "df" - ] - }, - { - "cell_type": "code", - "execution_count": 9, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[[0.44989926848619399, -74.747370143933693], [0.44053440553209633, -74.747370143933693], [0.29747197817950066, -74.747370143933693], [0.44523705619343629, -73.463653077131326], [0.37280404232601283, -73.463653077131326], [0.44523705619343629, -75.358651366209045], [0.44523705619343629, -75.412257924114144], [0.11383611435048496, -72.103578472278485], [0.61164296604089941, -71.858293828363003], [0.61042517991290857, -71.858293828363003]]\n", - "Report: \n", - "did it work? True was it better True\n", - "the difference between brute force candidates model parameters and the GA's model parameters:\n", - "-0.344899268486194 0.105 0.449899268486 a\n", - "the relative distance scaled by the length of the parameter dimension of interest:\n", - "0.364972770885\n", - "the difference between brute force candidates model parameters and the GA's model parameters:\n", - "1.414036810600365 -73.3333333333 -74.7473701439 vr\n", - "the relative distance scaled by the length of the parameter dimension of interest:\n", - "0.0202005258657\n", - "[0.10565115634842881, 0.9999986241180363]\n", - "[0.10565115634842881, 0.9999986241180363]\n", - "the difference between brute force candidates model parameters and the GA's model parameters:\n", - "-0.344899268486194 0.105 0.449899268486 a\n", - "the relative distance scaled by the length of the parameter dimension of interest:\n", - "0.364972770885\n", - "the difference between brute force candidates model parameters and the GA's model parameters:\n", - "1.414036810600365 -73.3333333333 -74.7473701439 vr\n", - "the relative distance scaled by the length of the parameter dimension of interest:\n", - "0.0202005258657\n", - "[0.10565115634842881, 0.9999986241180363]\n", - "[0.10565115634842881, 0.9999986241180363]\n", - "100 100\n" - ] - }, - { - "data": { - "image/png": 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\n", 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" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "#nparams = 1\n", - "#pop = ga_out[0]\n", - "\n", - "with open('pre_ga_reports.p','rb') as f:\n", - " package = pickle.load(f)\n", - "pop = package[0][0]\n", - "history = package[0][4]\n", - "genes_vs_gen = package[0][6]\n", - "for p in genes_vs_gen:\n", - " print(p)\n", - "with open('pre_grid_reports.p','rb') as f:\n", - " [grid_results,nparams] = pickle.load(f)\n", - " \n", - "new_report = make_report(grid_results,pop, nparams)\n", - "\n", - "reports.update(new_report)\n", - "\n", - "with open('reports.p','rb') as f:\n", - " reports = pickle.load(f) \n", - " \n", - "#reports = reports[2] \n", - "#print(reports.keys())\n", - "#grid_results = reports['grid_results']\n", - "#ga_out = reports['ga_out']\n", - "\n", - "plt.clf()\n", - "print(len(grid_results),len([ sum(g.dtc.scores.values()) for g in grid_results ]))\n", - "plt.scatter([ sum(g.dtc.attrs.values()) for g in grid_results ],[ sum(g.dtc.scores.values()) for g in grid_results ],label='exhaustive search' )\n", - "\n", - "plt.scatter([ sum(g.dtc.attrs.values()) for g in pop ],[ sum(g.dtc.scores.values()) for g in pop ],label='ga search' )\n", - "#plt.scatter(grid_results,[ sum(g.dtc.scores.values()) for g in grid_results ])\n", - "plt.xlabel('vr (mV)')\n", - "plt.ylabel('error sum')\n", - "\n", - "plt.legend()\n", - "plt.show()\n" - ] - }, - { - "cell_type": "code", - "execution_count": 10, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "\n", - "
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Application\",\"version\":\"0.12.15\"}};\n", - " var render_items = [{\"docid\":\"f3891873-12f5-44d0-aab5-c5ed5593a7bf\",\"elementid\":\"23973d9d-5d39-49be-bb90-4e0c03d1e3d4\",\"modelid\":\"97cff111-4e4f-4694-a83c-c7c1d11a24b1\"}];\n", - " root.Bokeh.embed.embed_items_notebook(docs_json, render_items);\n", - "\n", - " }\n", - " if (root.Bokeh !== undefined) {\n", - " embed_document(root);\n", - " } else {\n", - " var attempts = 0;\n", - " var timer = setInterval(function(root) {\n", - " if (root.Bokeh !== undefined) {\n", - " embed_document(root);\n", - " clearInterval(timer);\n", - " }\n", - " attempts++;\n", - " if (attempts > 100) {\n", - " console.log(\"Bokeh: ERROR: Unable to run BokehJS code because BokehJS library is missing\")\n", - " clearInterval(timer);\n", - " }\n", - " }, 10, root)\n", - " }\n", - "})(window);" - ], - "application/vnd.bokehjs_exec.v0+json": "" - }, - "metadata": { - "application/vnd.bokehjs_exec.v0+json": { - "id": "97cff111-4e4f-4694-a83c-c7c1d11a24b1" - } - }, - "output_type": "display_data" - }, - { - "data": { - "text/plain": [ - "dict_keys([2])" - ] - }, - "execution_count": 11, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "from bokeh.plotting import figure, output_file, show\n", - "\n", - "p = figure(plot_width=500, plot_height=500, tools=[\"hover\"],title=\"scores model paramaters\")\n", - "\n", - "p.circle([ list(g.dtc.attrs.values())[0] for g in grid_results ], [ sum(g.dtc.scores.values()) for g in grid_results ], size=6, color=\"navy\", alpha=0.5)#, source=source)\n", - "\n", - "show(p)\n", - "reports.keys()" - ] - }, - { - "cell_type": "code", - "execution_count": 12, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "" - ] - }, - "execution_count": 12, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "\n", - "plt.clf()\n", - "plt.scatter([i for i in range(0,len(grid_results))],[ sum(g.dtc.scores.values()) for g in grid_results ] )\n", - "\n", - "plt.clf()\n", - "\n", - "plt.scatter([i for i in range(0,len(pop))],[ sum(g.dtc.scores.values()) for g in pop ] ,label='ga pop')\n", - "plt.scatter([i for i in range(0,len(grid_results))],[ sum(g.dtc.scores.values()) for g in grid_results ] ,label='grid labels')\n", - "plt.ylabel('score')\n", - "plt.xlabel('gene number')\n", - "plt.legend()\n" - ] - }, - { - "cell_type": "code", - "execution_count": 13, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "{'vr': {-50.0: 0, -57.777777777777779: 1, -65.555555555555557: 2, -73.333333333333329: 3, -81.111111111111114: 4, -88.888888888888886: 5, -96.666666666666657: 6, -104.44444444444444: 7, -112.22222222222223: 8, -120.0: 9}, 'a': {0.94499999999999995: 0, 0.83999999999999997: 1, 0.73499999999999999: 2, 0.62999999999999989: 3, 0.52499999999999991: 4, 0.41999999999999993: 5, 0.31499999999999995: 6, 0.20999999999999996: 7, 0.10499999999999998: 8, 0.0: 9}}\n" - ] - } - ], - "source": [ - "from neuronunit.optimization.exhaustive_search import run_grid, reduce_params, create_grid\n", - "grid_points,maps = exhaustive_search.create_grid(npoints=10,nparams=2,provided_keys=[str('vr'),str('a')])\n", - "print(maps)" - ] - }, - { - "cell_type": "code", - "execution_count": 14, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[[ 1.96355343 1.99442179 1.91243408 1.15163227 1.92332722 1.99997004\n", - " 1.99999984 1.99999989 1.99999992 1.99999994]\n", - " [ 1.96388688 1.99442244 1.91243411 1.15163227 1.92332722 1.99997006\n", - " 1.99999984 1.99999989 1.99999992 1.99999994]\n", - " [ 1.96431167 1.99442328 1.91243413 1.15163227 1.92334053 1.99997006\n", - " 1.99999984 1.99999989 1.99999992 1.99999994]\n", - " [ 1.94670884 1.9944244 1.91243417 1.15163228 1.92335385 1.99997008\n", - " 1.99999984 1.99999989 1.99999992 1.99999994]\n", - " [ 1.94807168 1.99442596 1.91243423 1.15146517 1.9234337 1.99997008\n", - " 1.99999984 1.99999989 1.99999992 1.99999994]\n", - " [ 1.95004963 1.99442831 1.91243431 1.15129806 1.9234337 1.9999701\n", - " 1.99999984 1.99999989 1.99999992 1.99999994]\n", - " [ 1.95317553 1.99443223 1.91239488 1.15113095 1.9234869 1.99997014\n", - " 1.99999984 1.99999989 1.99999992 1.99999994]\n", - " [ 1.95882714 1.99411404 1.91211778 1.15046242 1.9235932 1.99997027\n", - " 1.99999984 1.99999989 1.99999993 1.99999994]\n", - " [ 1.97172175 1.99414577 1.9105201 1.14845618 1.9239114 1.99997053\n", - " 1.99999984 1.9999999 1.99999993 1.99999994]\n", - " [ 1.99377512 1.93739769 1.67400071 1.55211295 1.99674934 1.99999954\n", - " 1.99999986 1.99999991 1.99999993 1.99999995]]\n" - ] - } - ], - "source": [ - "grid = np.zeros((10,10))\n", - "\n", - "for i in grid_results:\n", - " xy = []\n", - " for k,v in i.dtc.attrs.items():\n", - " xy.append(maps[k][v])\n", - " grid[xy[0],xy[1]] = sum(i.dtc.scores.values())\n", - "print(grid) \n", - "\n", - "\n", - "#Works too.\n", - "\n", - "from pylab import figure, cm\n", - "from matplotlib.colors import LogNorm\n" - ] - }, - { - "cell_type": "code", - "execution_count": 15, - "metadata": {}, - "outputs": [], - "source": [ - "\n", - "\n", - "#scat = ax.scatter(x, y, c=z, s=200)\n", - "#ax.margins(0.05)" - ] - }, - { - "cell_type": "code", - "execution_count": 16, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "\"\\nplt.clf()\\nfig, ax = plt.subplots(1)\\nplt.xlabel('a')\\nplt.ylabel('vr')\\nplt.imshow(grid.T)\\n\"" - ] - }, - "execution_count": 16, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "'''\n", - "plt.clf()\n", - "fig, ax = plt.subplots(1)\n", - "plt.xlabel('a')\n", - "plt.ylabel('vr')\n", - "plt.imshow(grid.T)\n", - "'''\n" - ] - }, - { - "cell_type": "code", - "execution_count": 17, - "metadata": {}, - "outputs": [], - "source": [ - "genes_vs_gen = ga_out[-0]\n", - "\n", - "\n", - "\n" - ] - }, - { - "cell_type": "code", - "execution_count": 18, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "()\n" - ] - } - ], - "source": [ - "new_list = genes_vs_gen[1:-0]\n", - "print(new_list)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "![alt text](rick_style_guide.png \"Ricks table guide\")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Vr, and a\n", - "Why are the scores only for vt?\n", - "why does one of the parameters not matter in the grid but it does matter in the GA?" - ] - }, - { - "cell_type": "code", - "execution_count": 19, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "['__class__', '__delattr__', '__dict__', '__dir__', '__doc__', '__eq__', '__format__', '__ge__', '__getattribute__', '__gt__', '__hash__', '__init__', '__init_subclass__', '__le__', '__lt__', '__module__', '__ne__', '__new__', '__reduce__', '__reduce_ex__', '__repr__', '__setattr__', '__sizeof__', '__str__', '__subclasshook__', '__weakref__', 'decorator', 'genealogy_history', 'genealogy_index', 'genealogy_tree', 'getGenealogy', 'update']\n", - "190\n", - "{1: (), 2: (), 3: (), 4: (), 5: (), 6: (), 7: (), 8: (), 9: (), 10: (), 11: (1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10), 12: (1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10), 13: (1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10), 14: (1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10), 15: (1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10), 16: (1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10), 17: (1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10), 18: (1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10), 19: (1, 2, 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gene_numberavrRheobaseTestPInputResistanceTesttotalInputResistanceTestsort_key
01.00.126974-60.6796380.9839630.9999571.9839204.299538e-05
01.00.126974-60.6796380.9839630.9999571.9839204.299538e-05
02.00.721767-102.1451681.0000001.0000002.0000001.195153e-07
03.00.468186-88.5356250.9999501.0000001.9999502.917181e-07
04.00.615755-64.7893650.9333870.9999891.9333761.074079e-05
05.00.088697-118.0156771.0000001.0000002.0000005.899161e-08
06.00.789798-89.7063050.9999921.0000001.9999912.664842e-07
07.00.720355-119.8525761.0000001.0000002.0000005.774974e-08
08.00.420891-69.4921980.6803330.9999971.6803303.488229e-06
09.00.216180-53.8310510.9983400.9986201.9969601.380044e-03
010.00.851849-117.8587011.0000001.0000002.0000006.172400e-08
011.00.126974-60.6796380.9839630.9999571.9839204.299538e-05
012.00.721767-102.1451681.0000001.0000002.0000001.195153e-07
013.00.468186-88.5356250.9999501.0000001.9999502.917181e-07
014.00.615755-64.7893650.9333870.9999891.9333761.074079e-05
015.00.088697-118.0156771.0000001.0000002.0000005.899161e-08
016.00.789798-89.7063050.9999921.0000001.9999912.664842e-07
017.00.720355-119.8525761.0000001.0000002.0000005.774974e-08
018.00.420891-69.4921980.6803330.9999971.6803303.488229e-06
019.00.216180-53.8310510.9983400.9986201.9969601.380044e-03
020.00.851849-117.8587011.0000001.0000002.0000006.172400e-08
021.00.126974-60.6796380.9839630.9999571.9839204.299538e-05
022.00.721767-102.1451681.0000001.0000002.0000001.195153e-07
023.00.614628-64.9317530.9298300.9999901.9298201.036786e-05
024.00.543769-94.8165701.0000001.0000002.0000001.834464e-07
025.00.764160-114.3730931.0000001.0000002.0000006.982181e-08
026.00.116792-93.2281051.0000001.0000002.0000001.953815e-07
027.00.718567-119.9983141.0000001.0000002.0000005.748033e-08
028.00.420891-58.1488670.9937750.9998801.9936551.203823e-04
029.00.199332-53.8310510.9983400.9986511.9969911.349209e-03
........................
0161.00.249079-50.0699470.9993040.9639631.9632663.603711e-02
0162.00.449899-74.7473700.1056510.9999991.1056501.375882e-06
0163.00.611643-71.8582940.3921030.9999981.3921012.230879e-06
0164.00.307624-51.8696980.9989680.9920561.9910247.944420e-03
0165.00.312221-50.6168520.9992680.9854251.9846931.457468e-02
0166.00.229706-52.8086400.9987500.9972911.9960412.708979e-03
0167.00.494663-63.6257040.9561560.9999851.9561411.501540e-05
0168.00.113836-72.1035780.3527740.9999981.3527721.924845e-06
0169.00.385448-58.5895150.9928470.9999071.9927549.268173e-05
0170.00.782982-65.4078970.9170100.9999911.9170019.126549e-06
0171.00.249079-50.0699470.9993040.9639631.9632663.603711e-02
0172.00.449899-74.7473700.1056510.9999991.1056501.375882e-06
0173.00.312221-50.6523060.9992310.9806681.9798991.933191e-02
0174.00.698006-69.3959770.6898840.9999961.6898813.587675e-06
0175.00.611643-71.8582940.3921030.9999981.3921012.230879e-06
0176.00.238410-51.3936240.9990640.9877881.9868521.221234e-02
0177.00.113836-72.1035780.3527740.9999981.3527721.924845e-06
0178.00.445237-73.4636530.1283430.9999981.1283421.685156e-06
0179.00.308842-51.8696980.9989680.9920481.9910177.951763e-03
0180.00.494663-65.6606030.9091510.9999921.9091438.457112e-06
0181.00.258444-50.0699470.9993040.9635451.9628493.645494e-02
0182.00.440534-74.7473700.1056510.9999991.1056501.375089e-06
0183.00.736838-70.5543210.5671940.9999971.5671912.852175e-06
0184.00.406219-50.7604360.9992310.9844571.9836881.554266e-02
0185.00.611643-71.8582940.3921030.9999981.3921012.230879e-06
0186.00.270201-51.3755070.9990640.9866931.9857571.330690e-02
0187.00.113836-69.2048320.7057730.9999971.7057703.304276e-06
0188.00.372804-73.4636530.1283430.9999981.1283421.675480e-06
0189.00.397432-65.8506780.9028720.9999921.9028647.975929e-06
0190.00.494663-51.7174290.9990640.9937181.9927826.282020e-03
\n", - "

191 rows × 7 columns

\n", - "
" - ], - "text/plain": [ - " gene_number a vr RheobaseTestP InputResistanceTest \\\n", - "0 1.0 0.126974 -60.679638 0.983963 0.999957 \n", - "0 1.0 0.126974 -60.679638 0.983963 0.999957 \n", - "0 2.0 0.721767 -102.145168 1.000000 1.000000 \n", - "0 3.0 0.468186 -88.535625 0.999950 1.000000 \n", - "0 4.0 0.615755 -64.789365 0.933387 0.999989 \n", - "0 5.0 0.088697 -118.015677 1.000000 1.000000 \n", - "0 6.0 0.789798 -89.706305 0.999992 1.000000 \n", - "0 7.0 0.720355 -119.852576 1.000000 1.000000 \n", - "0 8.0 0.420891 -69.492198 0.680333 0.999997 \n", - "0 9.0 0.216180 -53.831051 0.998340 0.998620 \n", - "0 10.0 0.851849 -117.858701 1.000000 1.000000 \n", - "0 11.0 0.126974 -60.679638 0.983963 0.999957 \n", - "0 12.0 0.721767 -102.145168 1.000000 1.000000 \n", - "0 13.0 0.468186 -88.535625 0.999950 1.000000 \n", - "0 14.0 0.615755 -64.789365 0.933387 0.999989 \n", - "0 15.0 0.088697 -118.015677 1.000000 1.000000 \n", - "0 16.0 0.789798 -89.706305 0.999992 1.000000 \n", - "0 17.0 0.720355 -119.852576 1.000000 1.000000 \n", - "0 18.0 0.420891 -69.492198 0.680333 0.999997 \n", - "0 19.0 0.216180 -53.831051 0.998340 0.998620 \n", - "0 20.0 0.851849 -117.858701 1.000000 1.000000 \n", - "0 21.0 0.126974 -60.679638 0.983963 0.999957 \n", - "0 22.0 0.721767 -102.145168 1.000000 1.000000 \n", - "0 23.0 0.614628 -64.931753 0.929830 0.999990 \n", - "0 24.0 0.543769 -94.816570 1.000000 1.000000 \n", - "0 25.0 0.764160 -114.373093 1.000000 1.000000 \n", - "0 26.0 0.116792 -93.228105 1.000000 1.000000 \n", - "0 27.0 0.718567 -119.998314 1.000000 1.000000 \n", - "0 28.0 0.420891 -58.148867 0.993775 0.999880 \n", - "0 29.0 0.199332 -53.831051 0.998340 0.998651 \n", - ".. ... ... ... ... ... \n", - "0 161.0 0.249079 -50.069947 0.999304 0.963963 \n", - "0 162.0 0.449899 -74.747370 0.105651 0.999999 \n", - "0 163.0 0.611643 -71.858294 0.392103 0.999998 \n", - "0 164.0 0.307624 -51.869698 0.998968 0.992056 \n", - "0 165.0 0.312221 -50.616852 0.999268 0.985425 \n", - "0 166.0 0.229706 -52.808640 0.998750 0.997291 \n", - "0 167.0 0.494663 -63.625704 0.956156 0.999985 \n", - "0 168.0 0.113836 -72.103578 0.352774 0.999998 \n", - "0 169.0 0.385448 -58.589515 0.992847 0.999907 \n", - "0 170.0 0.782982 -65.407897 0.917010 0.999991 \n", - "0 171.0 0.249079 -50.069947 0.999304 0.963963 \n", - "0 172.0 0.449899 -74.747370 0.105651 0.999999 \n", - "0 173.0 0.312221 -50.652306 0.999231 0.980668 \n", - "0 174.0 0.698006 -69.395977 0.689884 0.999996 \n", - "0 175.0 0.611643 -71.858294 0.392103 0.999998 \n", - "0 176.0 0.238410 -51.393624 0.999064 0.987788 \n", - "0 177.0 0.113836 -72.103578 0.352774 0.999998 \n", - "0 178.0 0.445237 -73.463653 0.128343 0.999998 \n", - "0 179.0 0.308842 -51.869698 0.998968 0.992048 \n", - "0 180.0 0.494663 -65.660603 0.909151 0.999992 \n", - "0 181.0 0.258444 -50.069947 0.999304 0.963545 \n", - "0 182.0 0.440534 -74.747370 0.105651 0.999999 \n", - "0 183.0 0.736838 -70.554321 0.567194 0.999997 \n", - "0 184.0 0.406219 -50.760436 0.999231 0.984457 \n", - "0 185.0 0.611643 -71.858294 0.392103 0.999998 \n", - "0 186.0 0.270201 -51.375507 0.999064 0.986693 \n", - "0 187.0 0.113836 -69.204832 0.705773 0.999997 \n", - "0 188.0 0.372804 -73.463653 0.128343 0.999998 \n", - "0 189.0 0.397432 -65.850678 0.902872 0.999992 \n", - "0 190.0 0.494663 -51.717429 0.999064 0.993718 \n", - "\n", - " total InputResistanceTestsort_key \n", - "0 1.983920 4.299538e-05 \n", - "0 1.983920 4.299538e-05 \n", - "0 2.000000 1.195153e-07 \n", - "0 1.999950 2.917181e-07 \n", - "0 1.933376 1.074079e-05 \n", - "0 2.000000 5.899161e-08 \n", - "0 1.999991 2.664842e-07 \n", - "0 2.000000 5.774974e-08 \n", - "0 1.680330 3.488229e-06 \n", - "0 1.996960 1.380044e-03 \n", - "0 2.000000 6.172400e-08 \n", - "0 1.983920 4.299538e-05 \n", - "0 2.000000 1.195153e-07 \n", - "0 1.999950 2.917181e-07 \n", - "0 1.933376 1.074079e-05 \n", - "0 2.000000 5.899161e-08 \n", - "0 1.999991 2.664842e-07 \n", - "0 2.000000 5.774974e-08 \n", - "0 1.680330 3.488229e-06 \n", - "0 1.996960 1.380044e-03 \n", - "0 2.000000 6.172400e-08 \n", - "0 1.983920 4.299538e-05 \n", - "0 2.000000 1.195153e-07 \n", - "0 1.929820 1.036786e-05 \n", - "0 2.000000 1.834464e-07 \n", - "0 2.000000 6.982181e-08 \n", - "0 2.000000 1.953815e-07 \n", - "0 2.000000 5.748033e-08 \n", - "0 1.993655 1.203823e-04 \n", - "0 1.996991 1.349209e-03 \n", - ".. ... ... \n", - "0 1.963266 3.603711e-02 \n", - "0 1.105650 1.375882e-06 \n", - "0 1.392101 2.230879e-06 \n", - "0 1.991024 7.944420e-03 \n", - "0 1.984693 1.457468e-02 \n", - "0 1.996041 2.708979e-03 \n", - "0 1.956141 1.501540e-05 \n", - "0 1.352772 1.924845e-06 \n", - "0 1.992754 9.268173e-05 \n", - "0 1.917001 9.126549e-06 \n", - "0 1.963266 3.603711e-02 \n", - "0 1.105650 1.375882e-06 \n", - "0 1.979899 1.933191e-02 \n", - "0 1.689881 3.587675e-06 \n", - "0 1.392101 2.230879e-06 \n", - "0 1.986852 1.221234e-02 \n", - "0 1.352772 1.924845e-06 \n", - "0 1.128342 1.685156e-06 \n", - "0 1.991017 7.951763e-03 \n", - "0 1.909143 8.457112e-06 \n", - "0 1.962849 3.645494e-02 \n", - "0 1.105650 1.375089e-06 \n", - "0 1.567191 2.852175e-06 \n", - "0 1.983688 1.554266e-02 \n", - "0 1.392101 2.230879e-06 \n", - "0 1.985757 1.330690e-02 \n", - "0 1.705770 3.304276e-06 \n", - "0 1.128342 1.675480e-06 \n", - "0 1.902864 7.975929e-06 \n", - "0 1.992782 6.282020e-03 \n", - "\n", - "[191 rows x 7 columns]" - ] - }, - "execution_count": 29, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "attrs_ = [ list(p.dtc.attrs.keys()) for i,p in history.genealogy_history.items() ]\n", - "attrs = attrs_[0]\n", - "print(attrs)\n", - "\n", - "scores_ = [ list(p.dtc.scores.keys()) for i,p in history.genealogy_history.items() ]\n", - "scores = scores_[0]\n", - "from collections import OrderedDict\n", - "\n", - "urlDats = []\n", - "hi = [ (i,p) for i,p in history.genealogy_history.items() ]\n", - "sc = [ (i,p) for i,p in enumerate(grid_results) ]\n", - "#print(hi)\n", - "\n", - "def history_iter(mapped):\n", - " i,p = mapped\n", - " urlDat = OrderedDict()\n", - " urlDat['gene_number'] = i\n", - " \n", - " #attrs_ = [ \n", - " attrs = list(p.dtc.attrs.keys()) # for i,p in history.genealogy_history.items() ]\n", - " scores = list(p.dtc.scores.keys()) # for i,p in history.genealogy_history.items() ]\n", - " #scores = scores_[0]\n", - " for a in attrs:\n", - " urlDat[a] = p.dtc.attrs[a] \n", - " scores0 = scores[0]\n", - " for s in scores:\n", - " urlDat[s] = p.dtc.scores[s]\n", - " urlDat[str('total')] = sum(p.dtc.scores.values())\n", - " for k,v in p.dtc.score.items():\n", - " #for value in :\n", - " urlDat[str(k)+str('sort_key')] = v['value'] #p.dtc.score['value']\n", - "\n", - " return urlDat\n", - " \n", - "def process_dics(urlDats):\n", - " dfs = []\n", - " for urlDat in urlDats:\n", - " # pandas Data frames are best data container for maths/stats, but steep learning curve.\n", - " # Other exclusion criteria. Exclude reading levels above grade 100,\n", - " # as this is most likely a problem with the metric algorithm, and or rubbish data in.\n", - " # TODO: speed everything up, by performing exclusion criteri above not here.\n", - " if len(dfs) == 0:\n", - " dfs = pd.DataFrame(pd.Series(urlDat)).T\n", - " dfs = pd.concat([ dfs, pd.DataFrame(pd.Series(urlDat)).T ])\n", - " return dfs\n", - "\n", - "genes = list(map(history_iter,hi)) \n", - "print(urlDats)\n", - "dfg = process_dics(genes)\n", - "\n", - "grids = list(map(history_iter,sc)) \n", - "dfs = process_dics(grids)\n", - "\n", - "dfg" - ] - }, - { - "cell_type": "code", - "execution_count": 30, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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gene_numberavrRheobaseTestPInputResistanceTesttotalInputResistanceTestsort_key
01.00.126974-60.6796380.9839630.9999571.9839204.299538e-05
01.00.126974-60.6796380.9839630.9999571.9839204.299538e-05
02.00.721767-102.1451681.0000001.0000002.0000001.195153e-07
03.00.468186-88.5356250.9999501.0000001.9999502.917181e-07
04.00.615755-64.7893650.9333870.9999891.9333761.074079e-05
05.00.088697-118.0156771.0000001.0000002.0000005.899161e-08
06.00.789798-89.7063050.9999921.0000001.9999912.664842e-07
07.00.720355-119.8525761.0000001.0000002.0000005.774974e-08
08.00.420891-69.4921980.6803330.9999971.6803303.488229e-06
09.00.216180-53.8310510.9983400.9986201.9969601.380044e-03
010.00.851849-117.8587011.0000001.0000002.0000006.172400e-08
011.00.126974-60.6796380.9839630.9999571.9839204.299538e-05
012.00.721767-102.1451681.0000001.0000002.0000001.195153e-07
013.00.468186-88.5356250.9999501.0000001.9999502.917181e-07
014.00.615755-64.7893650.9333870.9999891.9333761.074079e-05
015.00.088697-118.0156771.0000001.0000002.0000005.899161e-08
016.00.789798-89.7063050.9999921.0000001.9999912.664842e-07
017.00.720355-119.8525761.0000001.0000002.0000005.774974e-08
018.00.420891-69.4921980.6803330.9999971.6803303.488229e-06
019.00.216180-53.8310510.9983400.9986201.9969601.380044e-03
020.00.851849-117.8587011.0000001.0000002.0000006.172400e-08
021.00.126974-60.6796380.9839630.9999571.9839204.299538e-05
022.00.721767-102.1451681.0000001.0000002.0000001.195153e-07
023.00.614628-64.9317530.9298300.9999901.9298201.036786e-05
024.00.543769-94.8165701.0000001.0000002.0000001.834464e-07
025.00.764160-114.3730931.0000001.0000002.0000006.982181e-08
026.00.116792-93.2281051.0000001.0000002.0000001.953815e-07
027.00.718567-119.9983141.0000001.0000002.0000005.748033e-08
028.00.420891-58.1488670.9937750.9998801.9936551.203823e-04
029.00.199332-53.8310510.9983400.9986511.9969911.349209e-03
........................
0161.00.249079-50.0699470.9993040.9639631.9632663.603711e-02
0162.00.449899-74.7473700.1056510.9999991.1056501.375882e-06
0163.00.611643-71.8582940.3921030.9999981.3921012.230879e-06
0164.00.307624-51.8696980.9989680.9920561.9910247.944420e-03
0165.00.312221-50.6168520.9992680.9854251.9846931.457468e-02
0166.00.229706-52.8086400.9987500.9972911.9960412.708979e-03
0167.00.494663-63.6257040.9561560.9999851.9561411.501540e-05
0168.00.113836-72.1035780.3527740.9999981.3527721.924845e-06
0169.00.385448-58.5895150.9928470.9999071.9927549.268173e-05
0170.00.782982-65.4078970.9170100.9999911.9170019.126549e-06
0171.00.249079-50.0699470.9993040.9639631.9632663.603711e-02
0172.00.449899-74.7473700.1056510.9999991.1056501.375882e-06
0173.00.312221-50.6523060.9992310.9806681.9798991.933191e-02
0174.00.698006-69.3959770.6898840.9999961.6898813.587675e-06
0175.00.611643-71.8582940.3921030.9999981.3921012.230879e-06
0176.00.238410-51.3936240.9990640.9877881.9868521.221234e-02
0177.00.113836-72.1035780.3527740.9999981.3527721.924845e-06
0178.00.445237-73.4636530.1283430.9999981.1283421.685156e-06
0179.00.308842-51.8696980.9989680.9920481.9910177.951763e-03
0180.00.494663-65.6606030.9091510.9999921.9091438.457112e-06
0181.00.258444-50.0699470.9993040.9635451.9628493.645494e-02
0182.00.440534-74.7473700.1056510.9999991.1056501.375089e-06
0183.00.736838-70.5543210.5671940.9999971.5671912.852175e-06
0184.00.406219-50.7604360.9992310.9844571.9836881.554266e-02
0185.00.611643-71.8582940.3921030.9999981.3921012.230879e-06
0186.00.270201-51.3755070.9990640.9866931.9857571.330690e-02
0187.00.113836-69.2048320.7057730.9999971.7057703.304276e-06
0188.00.372804-73.4636530.1283430.9999981.1283421.675480e-06
0189.00.397432-65.8506780.9028720.9999921.9028647.975929e-06
0190.00.494663-51.7174290.9990640.9937181.9927826.282020e-03
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191 rows × 7 columns

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0.720355 -119.852576 1.000000 1.000000 \n", - "0 18.0 0.420891 -69.492198 0.680333 0.999997 \n", - "0 19.0 0.216180 -53.831051 0.998340 0.998620 \n", - "0 20.0 0.851849 -117.858701 1.000000 1.000000 \n", - "0 21.0 0.126974 -60.679638 0.983963 0.999957 \n", - "0 22.0 0.721767 -102.145168 1.000000 1.000000 \n", - "0 23.0 0.614628 -64.931753 0.929830 0.999990 \n", - "0 24.0 0.543769 -94.816570 1.000000 1.000000 \n", - "0 25.0 0.764160 -114.373093 1.000000 1.000000 \n", - "0 26.0 0.116792 -93.228105 1.000000 1.000000 \n", - "0 27.0 0.718567 -119.998314 1.000000 1.000000 \n", - "0 28.0 0.420891 -58.148867 0.993775 0.999880 \n", - "0 29.0 0.199332 -53.831051 0.998340 0.998651 \n", - ".. ... ... ... ... ... \n", - "0 161.0 0.249079 -50.069947 0.999304 0.963963 \n", - "0 162.0 0.449899 -74.747370 0.105651 0.999999 \n", - "0 163.0 0.611643 -71.858294 0.392103 0.999998 \n", - "0 164.0 0.307624 -51.869698 0.998968 0.992056 \n", - "0 165.0 0.312221 -50.616852 0.999268 0.985425 \n", - "0 166.0 0.229706 -52.808640 0.998750 0.997291 \n", - "0 167.0 0.494663 -63.625704 0.956156 0.999985 \n", - "0 168.0 0.113836 -72.103578 0.352774 0.999998 \n", - "0 169.0 0.385448 -58.589515 0.992847 0.999907 \n", - "0 170.0 0.782982 -65.407897 0.917010 0.999991 \n", - "0 171.0 0.249079 -50.069947 0.999304 0.963963 \n", - "0 172.0 0.449899 -74.747370 0.105651 0.999999 \n", - "0 173.0 0.312221 -50.652306 0.999231 0.980668 \n", - "0 174.0 0.698006 -69.395977 0.689884 0.999996 \n", - "0 175.0 0.611643 -71.858294 0.392103 0.999998 \n", - "0 176.0 0.238410 -51.393624 0.999064 0.987788 \n", - "0 177.0 0.113836 -72.103578 0.352774 0.999998 \n", - "0 178.0 0.445237 -73.463653 0.128343 0.999998 \n", - "0 179.0 0.308842 -51.869698 0.998968 0.992048 \n", - "0 180.0 0.494663 -65.660603 0.909151 0.999992 \n", - "0 181.0 0.258444 -50.069947 0.999304 0.963545 \n", - "0 182.0 0.440534 -74.747370 0.105651 0.999999 \n", - "0 183.0 0.736838 -70.554321 0.567194 0.999997 \n", - "0 184.0 0.406219 -50.760436 0.999231 0.984457 \n", - "0 185.0 0.611643 -71.858294 0.392103 0.999998 \n", - "0 186.0 0.270201 -51.375507 0.999064 0.986693 \n", - "0 187.0 0.113836 -69.204832 0.705773 0.999997 \n", - "0 188.0 0.372804 -73.463653 0.128343 0.999998 \n", - "0 189.0 0.397432 -65.850678 0.902872 0.999992 \n", - "0 190.0 0.494663 -51.717429 0.999064 0.993718 \n", - "\n", - " total InputResistanceTestsort_key \n", - "0 1.983920 4.299538e-05 \n", - "0 1.983920 4.299538e-05 \n", - "0 2.000000 1.195153e-07 \n", - "0 1.999950 2.917181e-07 \n", - "0 1.933376 1.074079e-05 \n", - "0 2.000000 5.899161e-08 \n", - "0 1.999991 2.664842e-07 \n", - "0 2.000000 5.774974e-08 \n", - "0 1.680330 3.488229e-06 \n", - "0 1.996960 1.380044e-03 \n", - "0 2.000000 6.172400e-08 \n", - "0 1.983920 4.299538e-05 \n", - "0 2.000000 1.195153e-07 \n", - "0 1.999950 2.917181e-07 \n", - "0 1.933376 1.074079e-05 \n", - "0 2.000000 5.899161e-08 \n", - "0 1.999991 2.664842e-07 \n", - "0 2.000000 5.774974e-08 \n", - "0 1.680330 3.488229e-06 \n", - "0 1.996960 1.380044e-03 \n", - "0 2.000000 6.172400e-08 \n", - "0 1.983920 4.299538e-05 \n", - "0 2.000000 1.195153e-07 \n", - "0 1.929820 1.036786e-05 \n", - "0 2.000000 1.834464e-07 \n", - "0 2.000000 6.982181e-08 \n", - "0 2.000000 1.953815e-07 \n", - "0 2.000000 5.748033e-08 \n", - "0 1.993655 1.203823e-04 \n", - "0 1.996991 1.349209e-03 \n", - ".. ... ... \n", - "0 1.963266 3.603711e-02 \n", - "0 1.105650 1.375882e-06 \n", - "0 1.392101 2.230879e-06 \n", - "0 1.991024 7.944420e-03 \n", - "0 1.984693 1.457468e-02 \n", - "0 1.996041 2.708979e-03 \n", - "0 1.956141 1.501540e-05 \n", - "0 1.352772 1.924845e-06 \n", - "0 1.992754 9.268173e-05 \n", - "0 1.917001 9.126549e-06 \n", - "0 1.963266 3.603711e-02 \n", - "0 1.105650 1.375882e-06 \n", - "0 1.979899 1.933191e-02 \n", - "0 1.689881 3.587675e-06 \n", - "0 1.392101 2.230879e-06 \n", - "0 1.986852 1.221234e-02 \n", - "0 1.352772 1.924845e-06 \n", - "0 1.128342 1.685156e-06 \n", - "0 1.991017 7.951763e-03 \n", - "0 1.909143 8.457112e-06 \n", - "0 1.962849 3.645494e-02 \n", - "0 1.105650 1.375089e-06 \n", - "0 1.567191 2.852175e-06 \n", - "0 1.983688 1.554266e-02 \n", - "0 1.392101 2.230879e-06 \n", - "0 1.985757 1.330690e-02 \n", - "0 1.705770 3.304276e-06 \n", - "0 1.128342 1.675480e-06 \n", - "0 1.902864 7.975929e-06 \n", - "0 1.992782 6.282020e-03 \n", - "\n", - "[191 rows x 7 columns]" - ] - }, - "execution_count": 30, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "dfg" - ] - }, - { - "cell_type": "code", - "execution_count": 26, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "\n", - "\n", - "with open('pre_ga_reports.p','rb') as f:\n", - " package = pickle.load(f)\n", - "log = package[0][3] \n", - "%matplotlib inline\n", - "import matplotlib.pyplot as plt\n", - "import numpy as np\n", - "\n", - "plt.style.use('ggplot')\n", - "fig, axes = plt.subplots(figsize=(10, 10), facecolor='white')\n", - "#log = package[3]\n", - "gen_numbers = [ i for i in range(0,len(log.select('gen'))) ]\n", - "\n", - "mean = np.array([ np.sum(i) for i in log.select('avg')])\n", - "std = np.array([ np.sum(i) for i in log.select('std')])\n", - "minimum = np.array([ np.sum(i) for i in log.select('min')])\n", - "\n", - "stdminus = mean - std\n", - "stdplus = mean + std\n", - "\n", - "axes.plot(\n", - " gen_numbers,\n", - " mean,\n", - " color='black',\n", - " linewidth=2,\n", - " label='population average')\n", - "\n", - "axes.plot(\n", - " gen_numbers,\n", - " minimum,\n", - " color='black',\n", - " linewidth=2,\n", - " label='population minimum')\n", - "axes.fill_between(gen_numbers, stdminus, stdplus)\n", - "\n", - "plt.xlabel('generation')\n", - "plt.ylabel('error')\n", - "\n", - "\n", - "plt.legend()\n", - "plt.show()" - ] - }, - { - "cell_type": "code", - "execution_count": 21, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "\"\\n\\nwith open('pre_ga_reports.p','rb') as f:\\n package = pickle.load(f)\\n \\nfiltered = package[0][3] \\nprint(filtered,len(filtered))\\nfor i, gen in enumerate(filtered[1:-1]): \\n print(gen['nevals'][i])\\n pop = [g.dtc.scores for g in gen['nevals'][i] ]\\n \\n other_points = []\\n labels = []\\n for p in pop:\\n xy = []\\n for k,v in p.dtc.attrs.items():\\n xy.append(v)\\n labels.append(k)\\n other_points.append(xy)\\n print(other_points)\\n\\n \\n zi, yi, xi = np.histogram2d(yy, xx, bins=(10,10), weights=zz, normed=False)\\n counts, _, _ = np.histogram2d(yy, xx, bins=(10,10))\\n\\n zi = zi / counts\\n zi = np.ma.masked_invalid(zi)\\n\\n fig, ax = plt.subplots()\\n \\n scat = ax.pcolormesh(xi, yi, zi, edgecolors='black')\\n \\n fig.colorbar(scat)\\n ax.margins(0.05)\\n #scat = ax.plot(other_points[0], other_points[1])#, c=z, s=200)\\n for xy in other_points:\\n print(xy)\\n ax.plot(xy[0], xy[1],'ro') \\n #fig.colorbar(scat)\\n ax.margins(0.05)\\n plt.xlabel(labels[0])\\n plt.ylabel(labels[1])\\n #plt.savefig(str('movie')+str(i)+str('.png'))\\n plt.show()\\n\"" - ] - }, - "execution_count": 21, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "'''\n", - "\n", - "with open('pre_ga_reports.p','rb') as f:\n", - " package = pickle.load(f)\n", - " \n", - "filtered = package[0][3] \n", - "print(filtered,len(filtered))\n", - "for i, gen in enumerate(filtered[1:-1]): \n", - " print(gen['nevals'][i])\n", - " pop = [g.dtc.scores for g in gen['nevals'][i] ]\n", - " \n", - " other_points = []\n", - " labels = []\n", - " for p in pop:\n", - " xy = []\n", - " for k,v in p.dtc.attrs.items():\n", - " xy.append(v)\n", - " labels.append(k)\n", - " other_points.append(xy)\n", - " print(other_points)\n", - "\n", - " \n", - " zi, yi, xi = np.histogram2d(yy, xx, bins=(10,10), weights=zz, normed=False)\n", - " counts, _, _ = np.histogram2d(yy, xx, bins=(10,10))\n", - "\n", - " zi = zi / counts\n", - " zi = np.ma.masked_invalid(zi)\n", - "\n", - " fig, ax = plt.subplots()\n", - " \n", - " scat = ax.pcolormesh(xi, yi, zi, edgecolors='black')\n", - " \n", - " fig.colorbar(scat)\n", - " ax.margins(0.05)\n", - " #scat = ax.plot(other_points[0], other_points[1])#, c=z, s=200)\n", - " for xy in other_points:\n", - " print(xy)\n", - " ax.plot(xy[0], xy[1],'ro') \n", - " #fig.colorbar(scat)\n", - " ax.margins(0.05)\n", - " plt.xlabel(labels[0])\n", - " plt.ylabel(labels[1])\n", - " #plt.savefig(str('movie')+str(i)+str('.png'))\n", - " plt.show()\n", - "'''\n", - " " - ] - }, - { - "cell_type": "code", - "execution_count": 22, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "{'gen': 2, 'nevals': [[0.12697421068621914, -60.679638414393715], [0.72176701493290019, -102.14516819824048], [0.61462826595305742, -64.931753126744383], [0.54376905020588917, -94.816569557667421], [0.76415978332674273, -114.37309331161231], [0.11679183226242559, -93.228104576636866], [0.71856702469829248, -119.99831429472441], [0.42089089838178734, -58.148867377477316], [0.19933180254319893, -53.831051311225437], [0.86697081035421841, -117.85870118765125]], 'avg': 1.974945575543233, 'std': 0.070560055948416675, 'min': 1.6803295300912127, 'max': 1.9999999425196724}\n", - "[0.12697421068621914, -60.679638414393715]\n" - ] - }, - { - "data": { - "text/plain": [ - "
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\n", 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\n", 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\n", 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\n", 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\n", 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\n", 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\n", 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\n", 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\n", 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\n", 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\n", 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J29LlI1mqV0jvnYhY3GaXtwFImgv8crptiJ/c9QPMAHa3OP8AMJCeo+TP0s1sQskv46wFPgHcPs4+34iIX+nkpKVr3pF0Qvrfo4D/CqxOizYAyyQdI2k2MAe4v5hampkdTuTXeyciNpPMY5Kr0iV9YLmk7wLfIbmT/zOAiNgB3AE8AnwFuCYihgurpZnZWJ216U8dbYZOlxVHEPFcSQ9J+ltJ87McULqXsyLiNuC2FmWrgFX9rZGZWQeyN+/s63Ji9P8DnBoR/yLpIuAukhaQcZUu6fdCbvNfliSOY1UnjmNVL1bX+vQUMSL2N3y+R9InJU2NiH3jHVfG5h0zs8rqV5dNSSdJUvp5IUk+f7bdcbW40x/e0/YbT1dG70J6HcexqhPHsaoVK9dvEjnd6UtaBywiafsfAm4EpgBExGrgMmClpEPAvwLLIqJt9FokfTOzvoj8xtWJiOVtyj9B0qWzI076ZmZ5KvmbQU76ZmY5KvvroE76ZmZ5ctI3M6uJgkfQzMJJ38wsJ8LNO2ZmteKkb2ZWJ076ZmY14qRvZlYTBc+KlYWTvplZnpz0zczqI69hGHrFSd/MLEdu3jEzqwu/nGVmVjNO+mZm9eA3cs3MakYj5c76ni7RzCwv0cHShqQ1kvZK2t5mvzdJGpZ0WZYqOumbmeUoxzly1wJLxo0lTQI+BmzMWr9aNO/kOv9lCeI4VnXiOFb1YnUtp9adiNgsaVab3d4H/DXwpqznLeROX9LlknZIGpG0YEzZ9ZIGJT0m6cKG7UvSbYOSrut/rc3M2svxTn/8ONJ04BJgdSfHFXWnvx24FPh040ZJ84BlwHzgZGCTpLlp8Z8AvwgMAd+StCEiHskSbHjPnLzq3dToXUiv4zhWdeI4VrVi5fpNIntCnyppa8P6QEQMdBDpj4DfjYhhSZkPKiTpR8SjAE0quhRYHxEHgF2SBoGFadlgRDyRHrc+3TdT0jcz64voaBiGfRGxoP1uLS0A1qd5dCpwkaRDEXHXeAeVrU1/OrClYX0o3Qbw9Jjtb251EkkrgBW5187MbBz97KcfEbN/HFdaC3ypXcKHHiZ9SZuAk5oU3RARd7c6rMm2oPmzh5b/tOlXpIG0HuXuNGtmE0vkk3IkrQMWkTQDDQE3AlOSENFRO36jniX9iFh8BIcNATMb1mcAu9PPrbabmZVGXreZEbG8g32vyrpv2frpbwCWSTpG0mxgDnA/8C1gjqTZko4medi7ocB6mk0s1z2DZuxE03aiGTvhumeKrlE15fhyVq8U0qYv6RLgfwCvBb4saVtEXBgROyTdQfKA9hBwTUQMp8dcS/ICwiRgTUTsKKLuZhPOdc+gz+7/SdvqMPDZ/UleuvnEwqpVVR5Pv4mI+CLwxRZlq4BVTbbfA9zT46qZ1Y4+t/+wh2kC+Nx+wkm/Y076ZlZuwx1ut9aC3B7k9oqTvlndTaJ5gp/U74pMDGXvL1i2B7lm1mdxxXGHPVeMdLsdAT/INbNSu/nEJAd9bn9yxz8pTfhuz++YJ1Exs2q4+UQ/tM1DROknUXHSNzPLU7lzvpO+mVme3LxjZlYXAbh5x8ysRsqd8530zczy5OYdM7Mace+dEvBk245VVBzHql6srhT84lUWtUj6Zmb9kLycVe6sX4uk78m2HavfcRyrWrFy/SZR8lE2PfaOmVmOFJFpaXseaY2kvZK2tyhfKulhSdskbZX081nq56RvZpaXfGfOWgssGaf8XuDMiDgLeDfwmSwnrUXzjplZf+Q39k5EbJY0a5zyf2lY/Sky/ilx0jczy1P2B7lTJW1tWB+IiIFOQqVTz/434ATgl7Mc46RvZpaX6Gi6xH0RsaCrcOnUs5J+AbgJWNzuGLfpm5nlKSLbkmvI2AycJmlqu32d9M3M8tSnmbMk/YwkpZ9/DjgaeLbdcW7eMTPLkUby6agvaR2wiKTtfwi4EZgCEBGrgbcDV0o6CPwr8M6I9l8hCkn6ki4HPgq8EVgYEVvT7a8B7gTeBKyNiGsbjjmbpAvTy4F7gN/OcoFmZn0T5PZyVkQsb1P+MeBjnZ63qOad7cClwOYx238EfBj4YJNjPgWsAOaky3j9V83M+k5kezGryKEaCkn6EfFoRDzWZPsLEfEPJMn/xyRNA46LiPvSu/vbgV/rT23NzDpQwIPcTlSlTX86MNSwPpRua0rSCpJvBWZm/VXyVueeJX1Jm4CTmhTdEBF3d3q6Jtta/sumLzgMpPUo90/AzCaOHNv0e6VnST8i2r4k0IEhYEbD+gxgd47nNzPLRV69d3qlEv30I2IP8ANJ56T9Uq8EOv22YGbWYxnb8+v2IFfSJWm/03OBL0va2FD2JPDfgaskDUmalxatJBlFbhB4HPjb/tbazKyNoPRJv5AHuaPjRbQom9Vi+1bg9B5Wy8yse+Vu3alM7x0zs0rwdIlmZnXipF+8XOe/LEEcx6pOHMeqXqyuRMBwudt3apH0zcz6xnf6xRveM6en5x+9C+l1HMeqThzHqlasXL9JOOmbmdVEADnNkdsrTvpmZrkJCLfpm5nVQ1D6B7mVGIbBzKwycnojV9IaSXslbW9R/u8lPZwu/yjpzCzVc9I3M8tTfsMwrGX8yaJ2AedFxBnATaQjC7fj5h0zs9zkN65ORGyWNGuc8n9sWN3CS0cibslJ38wsLwEUM7Ty1WQchNJJ38wsT9nv9KdK2tqwPpBOANURSeeTJP2fz7K/k76ZWW46GoZhX0Qs6CaapDNIhpz/pYh4NssxTvpmZnkJiD7105d0CvAF4Dci4rtZj3PSNzPLU05v5EpaBywiaQYaAm4EpgBExGrgI8BrgE8mEwpyKMs3Byd9M7M85dd7Z3mb8vcA7+n0vE76ZmZ5iSiq905mTvpmZnnyKJtmZnURxPBw0ZUYl5O+mVlePLSymVnNlHxo5UIGXJN0uaQdkkYkLWjY/ouSHpD07fS/b20oOzvdPijpj5X2UTIzK4sAYiQyLUUpapTN7cClwOYx2/cBvxoRPwv8JvDnDWWfAlYAc9JlvNHnzMz6L9JJVLIsBSmkeSciHgUYe7MeEQ82rO4AXibpGODVwHERcV963O3Ar5FxgKFc578sQRzHqk4cx6perG75Qe6RezvwYEQckDQdGGooGwKmtzpQ0gqSbwUAB0i+WUxUU0m+IU1kvsbqq8L1ndrtCX7A9zduijunZty9kH+PniV9SZuAk5oU3RARd7c5dj7wMeBto5ua7NayUSwdqW4gPdfWbgc1KrOJfn3ga5wIJvr1jYqI0jc79yzpR8TiIzlO0gzgi8CVEfF4unmIl04QMAPY3V0Nzczqp1TTJUp6JfBl4PqI+Obo9ojYA/xA0jlpr50rgXG/LZiZ2eGK6rJ5STpq3LnAlyVtTIuuBX4G+LCkbelyQlq2kmTc6EHgcTI+xCXjvJEVNtGvD3yNE8FEv77KUJR8nAgzM8tPqZp3zMyst5z0zcxqZEIkfUlLJD2WDtFwXZPyYyR9Pi3/35Jm9b+W3clwjb8j6RFJD0u6V1LXfY77rd01Nux3maRoHMKjCrJcn6R3pD/HHZL+st917FaG39NTJH1V0oPp7+pFRdSz1iKi0gswieTB7uuAo4GHgHlj9vmPwOr08zLg80XXuwfXeD7wivTzyol4jel+x5IM37EFWFB0vXP+Gc4BHgRela6fUHS9e3CNA8DK9PM84Mmi6123ZSLc6S8EBiPiiYh4EVgPLB2zz1Lgs+nnO4ELKjZgW9trjIivRsQP09UtvPS9hirI8nMEuAm4BfhRPyuXgyzX917gTyLi+wARsbfPdexWlmsM4Lj08/H4fZu+mwhJfzrwdMN6syEafrxPRBwCnieZULgqslxjo6vJ3qW1LNpeo6R/A8yMiC/1s2I5yfIznAvMlfRNSVsklf7tzjGyXONHgSvSLtv3AO/rT9VsVJnH3skqyxANHQ3jUEKZ6y/pCmABcF5Pa5S/ca9R0lHAHwJX9atCOcvyM5xM0sSziOSb2jcknR4R/6/HdctLlmtcDqyNiFslnQv8eXqN5R6EfgKZCHf6Q8DMhvVmQzT8eB9Jk0m+Vj7Xl9rlI8s1ImkxcANwcUQc6FPd8tLuGo8FTge+JulJ4BxgQ4Ue5mb9Pb07Ig5GxC7gMZI/AlWR5RqvBu4AiGTU3JeRDMZmfTIRkv63gDmSZks6muRB7YYx+2wgGZ8f4DLgf0X6JKki2l5j2vTxaZKEX7W2YGhzjRHxfERMjYhZETGL5LnFxRGxtZjqdizL7+ldJA/kkTSVpLnnib7WsjtZrvEp4AIASW8kSfr/t6+1rLnKJ/20jf5aYCPwKHBHROyQ9PuSLk53+1PgNZIGgd8BWnYHLKOM1/hx4KeBv0qHrxj7P1upZbzGysp4fRuBZyU9AnwV+FBEPFtMjTuX8Ro/ALxX0kPAOuCqit2AVZ6HYTAzq5HK3+mbmVl2TvpmZjXipG9mViNO+mZmNeKkb2ZWI076ZmY14qRvZlYjTvpWK5LukvRAOl79iqLrY9ZvfjnLakXSqyPiOUkvJxk24LwqvfVq1q2JMMqmWSd+S9Il6eeZJAOaOelbbTjpW21IWgQsBs6NiB9K+hrJgF9mteE2fauT44Hvpwn/DSTDM5vVipO+1clXgMmSHiaZdnFLwfUx6zs/yDUzqxHf6ZuZ1YiTvplZjTjpm5nViJO+mVmNOOmbmdWIk76ZWY046ZuZ1cj/B1sXKg34ZJEhAAAAAElFTkSuQmCC\n", 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\n", 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gY0QcjIg9wCCwsID6mZm1VvKXs8qY9HcCo8M2Xw3MTD9PB55u2G8o3WZmVg4Zx90p8mFvIUlf0hZJO5ssS4F3Aqsk3U8ywucLo4c1OVXTfzpJKyRtl7S9N1dgZtZCTnf6ktZJ2i+p6RSIkhZJek7SjnT5cJbqFdJ7JyIWt9nlTQCS5gK/mG4b4sd3/QAzgL0tzj8ADKTnKPmzdDObUPLLOOuBTwK3jbPP1yPilzo5aemadySdkv73OOC/AWvTok3AMkknSJoNzAHuK6aWZmZHE/n13omIrSTzmOSqdEkfWC7p28CjJHfyfwYQEbuA24GHgS8DqyJiuLBampmN1Vmb/tTRZuh0WXEMES+U9KCkv5U0P8sBpXs5KyJuBW5tUbYGWNPfGpmZdSB7886BLidG/z/AGRHxr5IuA+4kaQEZV+mSfi/kNv9lSeI4VnXiOFb1YnWtT08RI+J7DZ/vlvQpSVMj4sB4x5WxecfMrLL61WVT0qmSlH5eSJLPn2l3XC3u9If3tf3G05XRu5Bex3Gs6sRxrGrFyvWbRE53+pI2AItI2v6HgBuBKQARsRa4Clgp6TDwb8CyiGgbvRZJ38ysLyK/cXUiYnmb8k+SdOnsiJO+mVmeSv5mkJO+mVmOyv46qJO+mVmenPTNzGqi4BE0s3DSNzPLiXDzjplZrTjpm5nViZO+mVmNOOmbmdVEwbNiZeGkb2aWJyd9M7P6yGsYhl5x0jczy5Gbd8zM6sIvZ5mZ1YyTvplZPfiNXDOzmtFIubO+p0s0M8tLdLC0IWmdpP2SdrbZ73WShiVdlaWKTvpmZjnKcY7c9cCScWNJk4CPAZuz1q8WzTu5zn9ZgjiOVZ04jlW9WF3LqXUnIrZKmtVmt/cAfw28Lut5C7nTl3S1pF2SRiQtGFO2WtKgpN2SLm3YviTdNijpg/2vtZlZezne6Y8fR5oOXAGs7eS4ou70dwJXAp9p3ChpHrAMmA+cBmyRNDct/hPg54Eh4JuSNkXEw1mCDe+bk1e9mxq9C+l1HMeqThzHqlasXL9JZE/oUyVtb1gfiIiBDiL9EfA7ETEsKfNBhST9iHgEoElFlwIbI+IgsEfSILAwLRuMiCfS4zam+2ZK+mZmfREdDcNwICIWtN+tpQXAxjSPTgUuk3Q4Iu4c76CytelPB7Y1rA+l2wCeHrP99a1OImkFsCL32pmZjaOf/fQjYvaP4krrgS+2S/jQw6QvaQtwapOiGyLirlaHNdkWNH/20PKfNv2KNJDWo9ydZs1sYol8Uo6kDcAikmagIeBGYEoSIjpqx2/Us6QfEYuP4bAhYGbD+gxgb/q51XYzs9LI6zYzIpZ3sO+1WfctWz/9TcAySSdImg3MAe4DvgnMkTRb0vEkD3s3FVhPM7Oj5fhyVq8U0qYv6QrgE8ArgS9J2hERl0bELkm3kzygPQysiojh9JjrSV5AmASsi4hdRdTdzGw8Hk+/iYj4AvCFFmVrgDVNtt8N3N3jqpmZdcVJ38ysLoLcHuT2ipO+mVmOyt5f0EnfzCxPTvpmZvXgSVTMzOokovSTqDjpm5nlqdw530nfzCxPbt4xM6uLANy8Y2ZWI+XO+U76ZmZ5cvOOmVmNuPdOCXiybccqKo5jVS9WVwoeQTOLWiR9M7N+SF7OKnfWr0XS92TbjtXvOI5VrVi5fpMo+SibZZtExcys0hSRaWl7HmmdpP2SdrYoXyrpIUk7JG2X9LNZ6uekb2aWl3xnzloPLBmn/B7g3Ig4D3gn8NksJ61F846ZWX/kN/ZORGyVNGuc8n9tWP0JMv4pcdI3M8tT9ge5UyVtb1gfiIiBTkKlU8/+PnAK8ItZjnHSNzPLS3Q0XeKBiFjQVbh06llJPwfcBCxud4zb9M3M8hSRbck1ZGwFzpQ0td2+TvpmZnnK70HuuCT9lCSln38GOB54pt1xbt4xM8uRRvLpqC9pA7CIpO1/CLgRmAIQEWuBNwPXSDoE/Bvw1oj2XyEKSfqSrgY+ArwWWBgR29PtrwDuAF4HrI+I6xuOOZ+kC9OLgbuB38pygWZmfRPk9nJWRCxvU/4x4GOdnreo5p2dwJXA1jHbfwh8CHh/k2M+DawA5qTLeP1Xzcz6TmR7MavIoRoKSfoR8UhE7G6y/fmI+AeS5P8jkqYBJ0XEvend/W3Ar/SntmZmHSjgQW4nqtKmPx0YalgfSrc1JWkFybcCM7P+Knmrc8+SvqQtwKlNim6IiLs6PV2TbS3/ZdMXHAbSepT7J2BmE0eObfq90rOkHxFtXxLowBAwo2F9BrA3x/ObmeUir947vVKJfvoRsQ/4vqQL0n6p1wCdflswM+uxjO35dXuQK+mKtN/phcCXJG1uKHsS+O/AtZKGJM1Li1aSjCI3CDwO/G1/a21m1kZQ+qRfyIPc0fEiWpTNarF9O3B2D6tlZta9crfuVKb3jplZJXi6RDOzOnHSL16u81+WII5jVSeOY1UvVlciYLjc7Tu1SPpmZn3jO/3iDe+b09Pzj96F9DqOY1UnjmNVK1au3ySc9M3MaiKAnObI7RUnfTOz3ASE2/TNzOohKP2D3EoMw2BmVhk5vZEraZ2k/ZJ2tih/m6SH0uUfJZ2bpXpO+mZmecpvGIb1jD9Z1B7goog4B7iJdGThdty8Y2aWm/zG1YmIrZJmjVP+jw2r2zhyJOKWnPTNzPISQDFDK19HxkEonfTNzPKU/U5/qqTtDesD6QRQHZF0MUnS/9ks+zvpm5nlpqNhGA5ExIJuokk6h2TI+V+IiGeyHOOkb2aWl4DoUz99SacDnwd+LSK+nfU4J30zszzl9EaupA3AIpJmoCHgRmAKQESsBT4MvAL4VDKhIIezfHNw0jczy1N+vXeWtyl/F/CuTs/rpG9mlpeIonrvZOakb2aWJ4+yaWZWF0EMDxddiXE56ZuZ5cVDK5uZ1UzJh1YuZMA1SVdL2iVpRNKChu0/L+l+Sd9K//vGhrLz0+2Dkv5YaR8lM7OyCCBGItNSlKJG2dwJXAlsHbP9APDLEfHTwK8Df9FQ9mlgBTAnXcYbfc7MrP8inUQly1KQQpp3IuIRgLE36xHxQMPqLuBFkk4AXg6cFBH3psfdBvwKGQcYynX+yxLEcazqxHGs6sXqlh/kHrs3Aw9ExEFJ04GhhrIhYHqrAyWtIPlWAHCQ5JvFRDWV5BvSROZrrL4qXN8Z3Z7g+3x385a4Y2rG3Qv59+hZ0pe0BTi1SdENEXFXm2PnAx8D3jS6qcluLRvF0pHqBtJzbe92UKMym+jXB77GiWCiX9+oiCh9s3PPkn5ELD6W4yTNAL4AXBMRj6ebhzhygoAZwN7uamhmVj+lmi5R0kuBLwGrI+Ibo9sjYh/wfUkXpL12rgHG/bZgZmZHK6rL5hXpqHEXAl+StDktuh74KeBDknakyylp2UqScaMHgcfJ+BCXjPNGVthEvz7wNU4EE/36KkNR8nEizMwsP6Vq3jEzs95y0jczq5EJkfQlLZG0Ox2i4YNNyk+Q9Lm0/H9LmtX/WnYnwzX+tqSHJT0k6R5JXfc57rd219iw31WSonEIjyrIcn2S3pL+HHdJ+st+17FbGX5PT5f0FUkPpL+rlxVRz1qLiEovwCSSB7uvBo4HHgTmjdnnN4C16edlwOeKrncPrvFi4CXp55UT8RrT/U4kGb5jG7Cg6Hrn/DOcAzwAvCxdP6XoevfgGgeAlennecCTRde7bstEuNNfCAxGxBMR8QKwEVg6Zp+lwJ+nn+8ALqnYgG1trzEivhIRP0hXt3Hkew1VkOXnCHATcDPww35WLgdZru/dwJ9ExHcBImJ/n+vYrSzXGMBJ6eeT8fs2fTcRkv504OmG9WZDNPxon4g4DDxHMqFwVWS5xkbXkb1La1m0vUZJ/w6YGRFf7GfFcpLlZzgXmCvpG5K2SSr9251jZLnGjwBvT7ts3w28pz9Vs1FlHnsnqyxDNHQ0jEMJZa6/pLcDC4CLelqj/I17jZKOA/4QuLZfFcpZlp/hZJImnkUk39S+LunsiPh/Pa5bXrJc43JgfUTcIulC4C/Sayz3IPQTyES40x8CZjasNxui4Uf7SJpM8rXy2b7ULh9ZrhFJi4EbgMsj4mCf6paXdtd4InA28FVJTwIXAJsq9DA36+/pXRFxKCL2ALtJ/ghURZZrvA64HSCSUXNfRDIYm/XJREj63wTmSJot6XiSB7WbxuyziWR8foCrgP8V6ZOkimh7jWnTx2dIEn7V2oKhzTVGxHMRMTUiZkXELJLnFpdHxPZiqtuxLL+nd5I8kEfSVJLmnif6WsvuZLnGp4BLACS9liTp/9++1rLmKp/00zb664HNwCPA7RGxS9LvSbo83e1PgVdIGgR+G2jZHbCMMl7jx4GfBP4qHb5i7P9spZbxGisr4/VtBp6R9DDwFeADEfFMMTXuXMZrfB/wbkkPAhuAayt2A1Z5HobBzKxGKn+nb2Zm2Tnpm5nViJO+mVmNOOmbmdWIk76ZWY046ZuZ1YiTvplZjTjpW61IulPS/el49SuKro9Zv/nlLKsVSS+PiGclvZhk2ICLqvTWq1m3JsIom2ad+E1JV6SfZ5IMaOakb7XhpG+1IWkRsBi4MCJ+IOmrJAN+mdWG2/StTk4Gvpsm/NeQDM9sVitO+lYnXwYmS3qIZNrFbQXXx6zv/CDXzKxGfKdvZlYjTvpmZjXipG9mViNO+mZmNeKkb2ZWI076ZmY14qRvZlYj/x/PvhzsmnfZ0wAAAABJRU5ErkJggg==\n", 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\n", 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\n", 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\n", 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\n", 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\n", 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\n", 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rJX85q4xJfycwOmzzFcDM9PN04OmG/YbSbWZm5ZBx3J0iH/YWkvQlbZG0s8myFHgXsErS/SQjfL44eliTUzX9p5O0QtJ2Sdt7cwVmZi3kdKcvaZ2kfZKaToEoaZGk5yTtSJcPZ6leIb13ImJxm13eCiBpLvBL6bYhfnzXDzAD2NPi/APAQHqOkj9LN7NxJb+Msx74OHDbGPt8PSJ+uZOTlq55R9LU9L8TgP8GrE2LNgHLJB0vaTYwB7ivmFqamR1N5Nd7JyK2ksxjkqvSJX1guaTvAN8muZP/nwARsQu4HXgY+DKwKiKGC6ulmdmROmvTnzLaDJ0uK44h4nmSHpT0N5LmZzmgdC9nRcStwK0tytYAa/pbIzOzDmRv3tnf5cTo/wc4LSL+VdLFwJ0kLSBjKl3S74Xc5r8sSRzHqk4cx6perK716SliRHy/4fPdkj4haUpE7B/ruDI275iZVVa/umxKOlmS0s8LSfL5M+2Oq8Wd/vDett94ujJ6F9LrOI5VnTiOVa1YuX6TyOlOX9IGYBFJ2/8QcAMwGSAi1gKXAyslHQL+DVgWEW2j1yLpm5n1ReQ3rk5ELG9T/nGSLp0dcdI3M8tTyd8MctI3M8tR2V8HddI3M8uTk76ZWU0UPIJmFk76ZmY5EW7eMTOrFSd9M7M6cdI3M6sRJ30zs5ooeFasLJz0zczy5KRvZlYfeQ3D0CtO+mZmOXLzjplZXfjlLDOzmnHSNzOrB7+Ra2ZWMxopd9b3dIlmZnmJDpY2JK2TtE/Szjb7vVHSsKTLs1TRSd/MLEc5zpG7HlgyZixpIvBRYHPW+tWieSfX+S9LEMexqhPHsaoXq2s5te5ExFZJs9rs9l7gr4A3Zj1vIXf6kq6QtEvSiKQFR5StljQo6VFJFzVsX5JuG5R0Xf9rbWbWXo53+mPHkaYDlwJrOzmuqDv9ncBlwKcaN0qaBywD5gOnAFskzU2L/wT4BWAI+KakTRHxcJZgw3vn5FXvpkbvQnodx7GqE8exqhUr128S2RP6FEnbG9YHImKgg0h/CPxORAxLynxQIUk/Ih4BaFLRpcDGiDgA7JY0CCxMywYj4on0uI3pvpmSvplZX0RHwzDsj4gF7XdraQGwMc2jU4CLJR2KiDvHOqhsbfrTgW0N60PpNoCnj9j+plYnkbQCWJF77czMxtDPfvoRMftHcaX1wBfbJXzoYdKXtAU4uUnR9RFxV6vDmmwLmj97aPlPm35FGkjrUe5Os2Y2vkQ+KUfSBmARSTPQEHADMDkJER214zfqWdKPiMXHcNgQMLNhfQawJ/3caruZWWnkdZsZEcs72PfqrPuWrZ/+JmCZpOMlzQbmAPcB3wTmSJot6TiSh72bCqynmdnRcnw5q1cKadOXdCnwx8CrgS9J2hERF0XELkm3kzygPQSsiojh9JhrSV5AmAisi4hdRdTdzGwsHk+/iYj4AvCFFmVrgDVNtt8N3N3jqpmZdcVJ38ysLoLcHuT2ipO+mVmOyt5f0EnfzCxPTvpmZvXgSVTMzOokovSTqDjpm5nlqdw530nfzCxPbt4xM6uLANy8Y2ZWI+XO+U76ZmZ5cvOOmVmNuPdOCXiybccqKo5jVS9WVwoeQTOLWiR9M7N+SF7OKnfWr0XS92TbjtXvOI5VrVi5fpMo+SibZZtExcys0hSRaWl7HmmdpH2SdrYoXyrpIUk7JG2X9HNZ6uekb2aWl3xnzloPLBmj/B7grIg4G3gX8OksJ61F846ZWX/kN/ZORGyVNGuM8n9tWP0JMv4pcdI3M8tT9ge5UyRtb1gfiIiBTkKlU8/+d2Aq8EtZjnHSNzPLS3Q0XeL+iFjQVbh06llJPw/cCCxud4zb9M3M8hSRbck1ZGwFTpc0pd2+TvpmZnnK70HumCT9lCSln38WOA54pt1xbt4xM8uRRvLpqC9pA7CIpO1/CLgBmAwQEWuBtwFXSToI/Bvwjoj2XyEKSfqSrgA+ArwBWBgR29PtrwLuAN4IrI+IaxuOOYekC9NLgbuB38pygWZmfRPk9nJWRCxvU/5R4KOdnreo5p2dwGXA1iO2/xD4EPCBJsd8ElgBzEmXsfqvmpn1ncj2YlaRQzUUkvQj4pGIeLTJ9hci4u9Jkv+PSJoGnBgR96Z397cBv9qf2pqZdaCAB7mdqEqb/nRgqGF9KN3WlKQVJN8KzMz6q+Stzj1L+pK2ACc3Kbo+Iu7q9HRNtrX8l01fcBhI61Hun4CZjR85tun3Ss+SfkS0fUmgA0PAjIb1GcCeHM9vZpaLvHrv9Eol+ulHxF7geUnnpv1SrwI6/bZgZtZjGdvz6/YgV9Klab/T84AvSdrcUPYk8D+AqyUNSZqXFq0kGUVuEHgc+Jv+1trMrI2g9Em/kAe5o+NFtCib1WL7duCMHlbLzKx75W7dqUzvHTOzSvB0iWZmdeKkX7xc578sQRzHqk4cx6perK5EwHC523dqkfTNzPrGd/rFG947p6fnH70L6XUcx6pOHMeqVqxcv0k46ZuZ1UQAOc2R2ytO+mZmuQkIt+mbmdVDUPoHuZUYhsHMrDJyeiNX0jpJ+yTtbFH+HyQ9lC7/IOmsLNVz0jczy1N+wzCsZ+zJonYD50fEmcCNpCMLt+PmHTOz3OQ3rk5EbJU0a4zyf2hY3cbhIxG35KRvZpaXAIoZWvkaMg5C6aRvZpan7Hf6UyRtb1gfSCeA6oikC0iS/s9l2d9J38wsNx0Nw7A/IhZ0E03SmSRDzv9iRDyT5RgnfTOzvAREn/rpSzoV+Dzw6xHxnazHOembmeUppzdyJW0AFpE0Aw0BNwCTASJiLfBh4FXAJ5IJBTmU5ZuDk76ZWZ7y672zvE35u4F3d3peJ30zs7xEFNV7JzMnfTOzPHmUTTOzughieLjoSozJSd/MLC8eWtnMrGZKPrRyIQOuSbpC0i5JI5IWNGz/BUn3S/pW+t+3NJSdk24flPRHSvsomZmVRQAxEpmWohQ1yuZO4DJg6xHb9wO/EhE/DfwG8OcNZZ8EVgBz0mWs0efMzPov0klUsiwFKaR5JyIeATjyZj0iHmhY3QW8RNLxwCuBEyPi3vS424BfJeMAQ7nOf1mCOI5VnTiOVb1Y3fKD3GP3NuCBiDggaTow1FA2BExvdaCkFSTfCgAOkHyzGK+mkHxDGs98jdVXhes7rdsTPM/3Nm+JO6Zk3L2Qf4+eJX1JW4CTmxRdHxF3tTl2PvBR4K2jm5rs1rJRLB2pbiA91/ZuBzUqs/F+feBrHA/G+/WNiojSNzv3LOlHxOJjOU7SDOALwFUR8Xi6eYjDJwiYAezproZmZvVTqukSJb0c+BKwOiK+Mbo9IvYCz0s6N+21cxUw5rcFMzM7WlFdNi9NR407D/iSpM1p0bXATwEfkrQjXaamZStJxo0eBB4n40NcMs4bWWHj/frA1zgejPfrqwxFyceJMDOz/JSqecfMzHrLSd/MrEbGRdKXtETSo+kQDdc1KT9e0ufS8n+UNKv/texOhmv8bUkPS3pI0j2Suu5z3G/trrFhv8slReMQHlWQ5fokvT39Oe6S9Bf9rmO3MvyenirpK5IeSH9XLy6inrUWEZVegIkkD3ZfCxwHPAjMO2Kf/wSsTT8vAz5XdL17cI0XAC9LP68cj9eY7ncCyfAd24AFRdc755/hHOAB4BXp+tSi692DaxwAVqaf5wFPFl3vui3j4U5/ITAYEU9ExIvARmDpEfssBT6Tfr4DuLBiA7a1vcaI+EpE/CBd3cbh7zVUQZafI8CNwM3AD/tZuRxkub73AH8SEd8DiIh9fa5jt7JcYwAnpp9Pwu/b9N14SPrTgacb1psN0fCjfSLiEPAcyYTCVZHlGhtdQ/YurWXR9hol/QwwMyK+2M+K5STLz3AuMFfSNyRtk1T6tzuPkOUaPwJcmXbZvht4b3+qZqPKPPZOVlmGaOhoGIcSylx/SVcCC4Dze1qj/I15jZImAH8AXN2vCuUsy89wEkkTzyKSb2pfl3RGRPy/HtctL1mucTmwPiJukXQe8OfpNZZ7EPpxZDzc6Q8BMxvWmw3R8KN9JE0i+Vr5bF9ql48s14ikxcD1wCURcaBPdctLu2s8ATgD+KqkJ4FzgU0Vepib9ff0rog4GBG7gUdJ/ghURZZrvAa4HSCSUXNfQjIYm/XJeEj63wTmSJot6TiSB7WbjthnE8n4/ACXA/870idJFdH2GtOmj0+RJPyqtQVDm2uMiOciYkpEzIqIWSTPLS6JiO3FVLdjWX5P7yR5II+kKSTNPU/0tZbdyXKNTwEXAkh6A0nS/799rWXNVT7pp2301wKbgUeA2yNil6Tfk3RJutufAa+SNAj8NtCyO2AZZbzGjwE/CfxlOnzFkf+zlVrGa6ysjNe3GXhG0sPAV4APRsQzxdS4cxmv8f3AeyQ9CGwArq7YDVjleRgGM7MaqfydvpmZZeekb2ZWI076ZmY14qRvZlYjTvpmZjXipG9mViNO+mZmNeKkb7Ui6U5J96fj1a8ouj5m/eaXs6xWJL0yIp6V9FKSYQPOr9Jbr2bdGg+jbJp14jclXZp+nkkyoJmTvtWGk77VhqRFwGLgvIj4gaSvkgz4ZVYbbtO3OjkJ+F6a8F9PMjyzWa046VudfBmYJOkhkmkXtxVcH7O+84NcM7Ma8Z2+mVmNOOmbmdWIk76ZWY046ZuZ1YiTvplZjTjpm5nViJO+mVmN/H9YTCMGdy5JEgAAAABJRU5ErkJggg==\n", 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\n", 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\n", 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\n", 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\n", 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\n", 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\n", 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\n", 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\n", 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\n", 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\n", 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\n", 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\n", 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GpL8DGBu2+SpgRvp5GvB0w37D6TYzs3LIOO5OkQ97C0n6kjZL2tFkWQK8E1gp6X6SET6fHzusyama/tNJWi5pm6RtvbkCM7MWcrrTl7RW0l5JTadAlLRQ0n5J29Plw1mqV0jvnYhY1GaXNwNImgP8YrptmB/f9QNMB3a3OP8gMJieo+TP0s1sQskv46wDPgHcNs4+X4+IX+rkpKVr3pF0cvrfY4D/CaxJizYCSyUdJ2kWMBu4r5hampkdSeTXeycitpDMY5Kr0iV9YJmk7wDfJrmT/98AEbETuB14GPgysDIiRgqrpZnZ4Tpr0x8Ya4ZOl+VHEfECSQ9K+mtJ87IcULqXsyLiVuDWFmWrgdX9rZGZWQeyN+/s63Ji9P8LnB4R/ybpUuBOkhaQcZUu6fdCbvNfliSOY1UnjmNVL1bX+vQUMSK+3/D5bkmflDQQEfvGO66MzTtmZpXVry6bkk6RpPTzApJ8/ky742pxpz+yp+03nq6M3YX0Oo5jVSeOY1UrVq7fJHK605e0HlhI0vY/DNwITAGIiDXAlcAKSYeAfweWRkTb6LVI+mZmfRH5jasTEcvalH+CpEtnR5z0zczyVPI3g5z0zcxyVPbXQZ30zczy5KRvZlYTBY+gmYWTvplZToSbd8zMasVJ38ysTpz0zcxqxEnfzKwmCp4VKwsnfTOzPDnpm5nVR17DMPSKk76ZWY7cvGNmVhd+OcvMrGac9M3M6sFv5JqZ1YxGy531PV2imVleooOlDUlrJe2VtKPNfq+XNCLpyixVdNI3M8tRjnPkrgMWjxtLmgR8DNiUtX61aN7Jdf7LEsRxrOrEcazqxepaTq07EbFF0sw2u70H+Evg9VnPW8idvqSrJO2UNCpp/mFlqyQNSXpU0iUN2xen24Ykfaj/tTYzay/HO/3x40jTgMuBNZ0cV9Sd/g7gCuDTjRslzQWWAvOAU4HNkuakxX8C/DwwDHxT0saIeDhLsJE9s/Oqd1NjdyG9juNY1YnjWNWKles3iewJfUDStob1wYgY7CDSHwK/FREjkjIfVEjSj4hHAJpUdAmwISIOALskDQEL0rKhiHgiPW5Dum+mpG9m1hfR0TAM+yJifvvdWpoPbEjz6ABwqaRDEXHneAeVrU1/GrC1YX043Qbw9GHb39DqJJKWA8tzr52Z2Tj62U8/Ima9EFdaB3yxXcKHHiZ9SZuBU5oUXR8Rd7U6rMm2oPmzh5b/tOlXpMG0HuXuNGtmE0vkk3IkrQcWkjQDDQM3AlOSENFRO36jniX9iFh0FIcNAzMa1qcDu9PPrbabmZVGXreZEbGsg32vybpv2frpbwSWSjpO0ixgNnAf8E1gtqRZko4ledi7scB6mpkdKceXs3qlkDZ9SZcDfwy8CviSpO0RcUlE7JR0O8kD2kPAyogYSY+5juQFhEnA2ojYWUTdzczG4/H0m4iILwBfaFG2GljdZPvdwN09rpqZWVec9M3M6iLI7UFurzjpm5nlqOz9BZ30zczy5KRvZlYPnkTFzKxOIko/iYqTvplZnsqd8530zczy5OYdM7O6CMDNO2ZmNVLunO+kb2aWJzfvmJnViHvvlIAn23asouI4VvVidaXgETSzqEXSNzPrh+TlrHJn/VokfU+27Vj9juNY1YqV6zeJko+yWbZJVMzMKk0RmZa255HWStoraUeL8iWSHpK0XdI2ST+bpX5O+mZmecl35qx1wOJxyu8BzomIc4F3Ap/JctJaNO+YmfVHfmPvRMQWSTPHKf+3htWfIOOfEid9M7M8ZX+QOyBpW8P6YEQMdhIqnXr2d4GTgV/McoyTvplZXqKj6RL3RcT8rsKlU89K+jngJmBRu2Pcpm9mlqeIbEuuIWMLcIakgXb7OumbmeUpvwe545L0U5KUfv4Z4FjgmXbHuXnHzCxHGs2no76k9cBCkrb/YeBGYApARKwB3gJcLekg8O/A2yLaf4UoJOlLugr4CPA6YEFEbEu3vxK4A3g9sC4irms45jySLkwvBe4GfiPLBZqZ9U2Q28tZEbGsTfnHgI91et6imnd2AFcAWw7b/iPgBuADTY75FLAcmJ0u4/VfNTPrO5Htxawih2ooJOlHxCMR8WiT7c9FxN+TJP8XSJoKnBAR96Z397cBv9Kf2pqZdaCAB7mdqEqb/jRguGF9ON3WlKTlJN8KzMz6q+Stzj1L+pI2A6c0Kbo+Iu7q9HRNtrX8l01fcBhM61Hun4CZTRw5tun3Ss+SfkS0fUmgA8PA9Ib16cDuHM9vZpaLvHrv9Eol+ulHxB7gB5LOT/ulXg10+m3BzKzHMrbn1+1BrqTL036nFwBfkrSpoexJ4PeBayQNS5qbFq0gGUVuCHgc+Ov+1trMrI2g9Em/kAe5Y+NFtCib2WL7NuCsHlbLzKx75W7dqUzvHTOzSvB0iWZmdeKkX7xc578sQRzHqk4cx6perK5EwEi523dqkfTNzPrGd/rFG9kzu6fnH7sL6XUcx6pOHMeqVqxcv0k46ZuZ1UQAOc2R2ytO+mZmuQkIt+mbmdVDUPoHuZUYhsHMrDJyeiNX0lpJeyXtaFH+nyU9lC7/IOmcLNVz0jczy1N+wzCsY/zJonYBF0bE2cBNpCMLt+PmHTOz3OQ3rk5EbJE0c5zyf2hY3cqLRyJuyUnfzCwvARQztPK1ZByE0knfzCxP2e/0ByRta1gfTCeA6oiki0iS/s9m2d9J38wsNx0Nw7AvIuZ3E03S2SRDzv9CRDyT5RgnfTOzvAREn/rpSzoN+DzwaxHxnazHOembmeUppzdyJa0HFpI0Aw0DNwJTACJiDfBh4JXAJ5MJBTmU5ZuDk76ZWZ7y672zrE35u4B3dXpeJ30zs7xEFNV7JzMnfTOzPHmUTTOzughiZKToSozLSd/MLC8eWtnMrGZKPrRyIQOuSbpK0k5Jo5LmN2z/eUn3S/pW+t83NZSdl24fkvRHSvsomZmVRQAxGpmWohQ1yuYO4Apgy2Hb9wG/HBE/Dfw68GcNZZ8ClgOz02W80efMzPov0klUsiwFKaR5JyIeATj8Zj0iHmhY3Qm8RNJxwCuAEyLi3vS424BfIeMAQ7nOf1mCOI5VnTiOVb1Y3fKD3KP3FuCBiDggaRow3FA2DExrdaCk5STfCgAOkHyzmKgGSL4hTWS+xuqrwvWd3u0JfsD3Nm2OOwYy7l7Iv0fPkr6kzcApTYquj4i72hw7D/gY8OaxTU12a9kolo5UN5iea1u3gxqV2US/PvA1TgQT/frGRETpm517lvQjYtHRHCdpOvAF4OqIeDzdPMyLJwiYDuzuroZmZvVTqukSJZ0EfAlYFRHfGNseEXuAH0g6P+21czUw7rcFMzM7UlFdNi9PR427APiSpE1p0XXATwE3SNqeLienZStIxo0eAh4n40NcMs4bWWET/frA1zgRTPTrqwxFyceJMDOz/JSqecfMzHrLSd/MrEYmRNKXtFjSo+kQDR9qUn6cpM+l5f8oaWb/a9mdDNf4m5IelvSQpHskdd3nuN/aXWPDfldKisYhPKogy/VJemv6c9wp6c/7XcduZfg9PU3SVyQ9kP6uXlpEPWstIiq9AJNIHuy+BjgWeBCYe9g+/xVYk35eCnyu6Hr34BovAl6Wfl4xEa8x3e94kuE7tgLzi653zj/D2cADwMvT9ZOLrncPrnEQWJF+ngs8WXS967ZMhDv9BcBQRDwREc8DG4Alh+2zBPg/6ec7gIsrNmBb22uMiK9ExA/T1a28+L2GKsjycwS4CbgZ+FE/K5eDLNf3buBPIuJ7ABGxt8917FaWawzghPTzifh9m76bCEl/GvB0w3qzIRpe2CciDgH7SSYUroos19joWrJ3aS2Lttco6T8AMyLii/2sWE6y/AznAHMkfUPSVkmlf7vzMFmu8SPA29Mu23cD7+lP1WxMmcfeySrLEA0dDeNQQpnrL+ntwHzgwp7WKH/jXqOkY4A/AK7pV4VyluVnOJmkiWchyTe1r0s6KyL+tcd1y0uWa1wGrIuIWyRdAPxZeo3lHoR+ApkId/rDwIyG9WZDNLywj6TJJF8rn+1L7fKR5RqRtAi4HrgsIg70qW55aXeNxwNnAV+V9CRwPrCxQg9zs/6e3hURByNiF/AoyR+BqshyjdcCtwNEMmruS0gGY7M+mQhJ/5vAbEmzJB1L8qB242H7bCQZnx/gSuDvIn2SVBFtrzFt+vg0ScKvWlswtLnGiNgfEQMRMTMiZpI8t7gsIrYVU92OZfk9vZPkgTySBkiae57oay27k+UanwIuBpD0OpKk///6Wsuaq3zST9vorwM2AY8At0fETkm/I+mydLc/BV4paQj4TaBld8AyyniNHwd+EviLdPiKw/9nK7WM11hZGa9vE/CMpIeBrwAfjIhniqlx5zJe4/uBd0t6EFgPXFOxG7DK8zAMZmY1Uvk7fTMzy85J38ysRpz0zcxqxEnfzKxGnPTNzGrESd/MrEac9M3MasRJ32pF0p2S7k/Hq19edH3M+s0vZ1mtSHpFRDwr6aUkwwZcWKW3Xs26NRFG2TTrxHslXZ5+nkEyoJmTvtWGk77VhqSFwCLggoj4oaSvkgz4ZVYbbtO3OjkR+F6a8F9LMjyzWa046VudfBmYLOkhkmkXtxZcH7O+84NcM7Ma8Z2+mVmNOOmbmdWIk76ZWY046ZuZ1YiTvplZjTjpm5nViJO+mVmN/H+0Bi6G19sqYQAAAABJRU5ErkJggg==\n", 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\n", 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\n", 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\n", 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\n", 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\n", 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\n", 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\n", 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\n", 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\n", 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\n", 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\n", 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\n", 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\n", 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\n", 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\n", 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\n", 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\n", 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\n", 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\n", 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\n", 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\n", 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\n", 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\n", 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RcTAi9gCDwMIC6mdm1ljJX84qY9LfBYwO23wlMDP9PB14uma/oXSbmVk5ZBx3p8iHvYUkfUlbJO2qsywF3g2skrSDZITPF0cPq3Oquv90klZI2i5pe2euwMysgZzu9CWtk7RfUt0pECUtkvScpJ3p8uEs1Suk905ELG6yy9sAJM0Ffj7dNsRLd/0AM4C9Dc4/AAyk5yj5s3Qz6yv5ZZz1wCeA28fZ5+sR8QutnLR0zTuSTkn/OwH4b8DatGgTsEzS8ZJmA3OA+4uppZnZ0UR+vXciYivJPCa5Kl3SB5ZL+g7wbZI7+T8GiIjdwB3Aw8CXgVURMVxYLc3MxmqtTX/KaDN0uqw4hojnS3pQ0l9Lmp/lgNK9nBURtwG3NShbA6zpbo3MzFqQvXnnQJsTo/9f4PSI+GdJlwB3kbSAjKt0Sb8Tcpv/siRxHKt34jhW78VqW5eeIkbE92s+3yPpk5KmRMSB8Y4rY/OOmVnP6laXTUmnSlL6eSFJPn+m2XGVuNMf3tf0G09bRu9COh3HsXonjmP1Vqxcv0nkdKcvaQOwiKTtfwi4CZgMEBFrgSuAlZIOA/8CLIuIptErkfTNzLoi8htXJyKWNyn/BEmXzpY46ZuZ5ankbwY56ZuZ5ajsr4M66ZuZ5clJ38ysIgoeQTMLJ30zs5wIN++YmVWKk76ZWZU46ZuZVYiTvplZRRQ8K1YWTvpmZnly0jczq468hmHoFCd9M7McuXnHzKwq/HKWmVnFOOmbmVWD38g1M6sYjZQ763u6RDOzvEQLSxOS1knaL2lXk/3eJGlY0hVZquikb2aWoxznyF0PLBk3ljQR+BiwOWv9KtG8k+v8lyWI41i9E8exei9W23Jq3YmIrZJmNdnteuAvgTdlPW8hd/qSrpS0W9KIpAVjylZLGpT0qKSLa7YvSbcNSrqh+7U2M2suxzv98eNI04HLgLWtHFfUnf4u4HLg07UbJc0DlgHzgWnAFklz0+I/BH4WGAK+KWlTRDycJdjwvjl51buu0buQTsdxrN6J41i9FSvXbxLZE/oUSdtr1gciYqCFSL8H/GZEDEvKfFAhST8iHgGoU9GlwMaIOAjskTQILEzLBiPiifS4jem+mZK+mVlXREvDMByIiAXNd2toAbAxzaNTgEskHY6Iu8Y7qGxt+tOBbTXrQ+k2gKfHbH9zo5NIWgGsyL12Zmbj6GY//YiY/aO40nrgi80SPnQw6UvaApxap+jGiLi70WF1tgX1nz00/KdNvyINpPUod6dZM+svkU/KkbQBWETSDDQE3ARMTkJES+34tTqW9CNi8TEcNgTMrFmfAexNPzfabmZWGnndZkbE8hb2vSbrvmXrp78JWCbpeEmzgTnA/cA3gTmSZks6juRh76YC62lmdrQcX87qlELa9CVdBvwB8BrgS5J2RsTFEbFb0h0kD2gPA6siYjg95jqSFxAmAusiYncRdTczG4/H068jIr4AfKFB2RpgTZ3t9wD3dLhqZmZtcdI3M6uKILcHuZ3ipG9mlqOy9xd00jczy5OTvplZNXgSFTOzKoko/SQqTvpmZnkqd8530jczy5Obd8zMqiIAN++YmVVIuXO+k76ZWZ7cvGNmViHuvVMCnmzbsYqK41i9F6stBY+gmUUlkr6ZWTckL2eVO+tXIul7sm3H6nYcx+qtWLl+kyj5KJtlm0TFzKynKSLT0vQ80jpJ+yXtalC+VNJDknZK2i7pp7PUz0nfzCwv+c6ctR5YMk75vcDZEXEO8G7gM1lOWonmHTOz7shv7J2I2Cpp1jjl/1yz+mNk/FPipG9mlqfsD3KnSNpesz4QEQOthEqnnv0fwCnAz2c5xknfzCwv0dJ0iQciYkFb4dKpZyX9DHAzsLjZMW7TNzPLU0S2JdeQsRU4Q9KUZvs66ZuZ5Sm/B7njkvQTkpR+/ingOOCZZse5ecfMLEcayaejvqQNwCKStv8h4CZgMkBErAXeDlwt6RDwL8A7I5p/hSgk6Uu6EvgI8EZgYURsT7e/GrgTeBOwPiKuqznmXJIuTC8H7gF+PcsFmpl1TZDby1kRsbxJ+ceAj7V63qKad3YBlwNbx2z/IfAh4AN1jvkUsAKYky7j9V81M+s6ke3FrCKHaigk6UfEIxHxaJ3tL0TE35Ek/x+RNBU4MSLuS+/ubwd+qTu1NTNrQQEPclvRK23604GhmvWhdFtdklaQfCswM+uukrc6dyzpS9oCnFqn6MaIuLvV09XZ1vBfNn3BYSCtR7l/AmbWP3Js0++UjiX9iGj6kkALhoAZNeszgL05nt/MLBd59d7plJ7opx8R+4DnJZ2X9ku9Gmj124KZWYdlbM+v2oNcSZel/U7PB74kaXNN2ZPA/wSukTQkaV5atJJkFLlB4HHgr7tbazOzJoLSJ/1CHuSOjhfRoGxWg+3bgTM7WC0zs/aVu3WnZ3rvmJn1BE+XaGZWJU76xct1/ssSxHGs3onjWL0Xqy0RMFzu9p1KJH0zs67xnX7xhvfN6ej5R+9COh3HsXonjmP1Vqxcv0k46ZuZVUQAOc2R2ylO+mZmuQkIt+mbmVVDUPoHuT0xDIOZWc/I6Y1cSesk7Ze0q0H5v5f0ULr8vaSzs1TPSd/MLE/5DcOwnvEni9oDXBARZwE3k44s3Iybd8zMcpPfuDoRsVXSrHHK/75mdRtHjkTckJO+mVleAihmaOVryTgIpZO+mVmest/pT5G0vWZ9IJ0AqiWSLiRJ+j+dZX8nfTOz3LQ0DMOBiFjQTjRJZ5EMOf9zEfFMlmOc9M3M8hIQXeqnL+k04PPAr0TEd7Ie56RvZpannN7IlbQBWETSDDQE3ARMBoiItcCHgVcDn0wmFORwlm8OTvpmZnnKr/fO8ibl7wHe0+p5nfTNzPISUVTvncyc9M3M8uRRNs3MqiKI4eGiKzEuJ30zs7x4aGUzs4op+dDKhQy4JulKSbsljUhaULP9ZyXtkPSt9L9vrSk7N90+KOn3lfZRMjMriwBiJDItRSlqlM1dwOXA1jHbDwC/GBE/Cfwq8Cc1ZZ8CVgBz0mW80efMzLov0klUsiwFKaR5JyIeARh7sx4RD9Ss7gZeJul44FXAiRFxX3rc7cAvkXGAoVznvyxBHMfqnTiO1Xux2uUHucfu7cADEXFQ0nRgqKZsCJje6EBJK0i+FQAcJPlm0a+mkHxD6me+xt7XC9d3ersneJ7vbd4Sd07JuHsh/x4dS/qStgCn1im6MSLubnLsfOBjwNtGN9XZrWGjWDpS3UB6ru3tDmpUZv1+feBr7Af9fn2jIqL0zc4dS/oRsfhYjpM0A/gCcHVEPJ5uHuLICQJmAHvbq6GZWfWUarpESa8EvgSsjohvjG6PiH3A85LOS3vtXA2M+23BzMyOVlSXzcvSUePOB74kaXNadB3wE8CHJO1Ml1PSspUk40YPAo+T8SEuGeeN7GH9fn3ga+wH/X59PUNR8nEizMwsP6Vq3jEzs85y0jczq5C+SPqSlkh6NB2i4YY65cdL+lxa/n8kzep+LduT4Rp/Q9LDkh6SdK+ktvscd1uza6zZ7wpJUTuERy/Icn2S3pH+HHdL+vNu17FdGX5PT5P0FUkPpL+rlxRRz0qLiJ5egIkkD3ZfBxwHPAjMG7PPfwLWpp+XAZ8rut4duMYLgVekn1f24zWm+51AMnzHNmBB0fXO+Wc4B3gAODldP6XoenfgGgeAlennecCTRde7aks/3OkvBAYj4omIeBHYCCwds89S4LPp5zuBi3pswLam1xgRX4mIH6Sr2zjyvYZekOXnCHAzcAvww25WLgdZru+9wB9GxPcAImJ/l+vYrizXGMCJ6eeT8Ps2XdcPSX868HTNer0hGn60T0QcBp4jmVC4V2S5xlrXkr1La1k0vUZJ/waYGRFf7GbFcpLlZzgXmCvpG5K2SSr9251jZLnGjwBXpV227wGu707VbFSZx97JKssQDS0N41BCmesv6SpgAXBBR2uUv3GvUdIE4HeBa7pVoZxl+RlOImniWUTyTe3rks6MiP/f4brlJcs1LgfWR8Stks4H/iS9xnIPQt9H+uFOfwiYWbNeb4iGH+0jaRLJ18pnu1K7fGS5RiQtBm4ELo2Ig12qW16aXeMJwJnAVyU9CZwHbOqhh7lZf0/vjohDEbEHeJTkj0CvyHKN1wJ3AEQyau7LSAZjsy7ph6T/TWCOpNmSjiN5ULtpzD6bSMbnB7gC+N+RPknqEU2vMW36+DRJwu+1tmBoco0R8VxETImIWRExi+S5xaURsb2Y6rYsy+/pXSQP5JE0haS554mu1rI9Wa7xKeAiAElvJEn6/6+rtay4nk/6aRv9dcBm4BHgjojYLem3JV2a7vZHwKslDQK/ATTsDlhGGa/x48CPA3+RDl8x9n+2Ust4jT0r4/VtBp6R9DDwFeCDEfFMMTVuXcZrfD/wXkkPAhuAa3rsBqzneRgGM7MK6fk7fTMzy85J38ysQpz0zcwqxEnfzKxCnPTNzCrESd/MrEKc9M3MKsRJ3ypF0l2SdqTj1a8ouj5m3eaXs6xSJL0qIp6V9HKSYQMu6KW3Xs3a1Q+jbJq14tckXZZ+nkkyoJmTvlWGk75VhqRFwGLg/Ij4gaSvkgz4ZVYZbtO3KjkJ+F6a8N9AMjyzWaU46VuVfBmYJOkhkmkXtxVcH7Ou84NcM7MK8Z2+mVmFOOmbmVWIk76ZWYU46ZuZVYiTvplZhTjpm5lViJO+mVmF/CvJT0QHRe2UJgAAAABJRU5ErkJggg==\n", 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gY0QcjIg9wCCwsID6mZm1VvKXs8qY9HcCo8M2Xw3MTD9PB55u2G8o3WZmVg4Zx90p8mFvIUlf0hZJO5ssS4F3Aqsk3U8ywucLo4c1OVXTfzpJKyRtl7S9N1dgZtZCTnf6ktZJ2i+p6RSIkhZJek7SjnT5cJbqFdJ7JyIWt9nlTQCS5gK/mG4b4sd3/QAzgL0tzj8ADKTnKPmzdDObUPLLOOuBTwK3jbPP1yPilzo5aemadySdkv73OOC/AWvTok3AMkknSJoNzAHuK6aWZmZHE/n13omIrSTzmOSqdEkfWC7p28CjJHfyfwYQEbuA24GHgS8DqyJiuLBampmN1Vmb/tTRZuh0WXEMES+U9KCkv5U0P8sBpXs5KyJuBW5tUbYGWNPfGpmZdSB7886BLidG/z/AGRHxr5IuA+4kaQEZV+mSfi/kNv9lSeI4VnXiOFb1YnWtT08RI+J7DZ/vlvQpSVMj4sB4x5WxecfMrLL61WVT0qmSlH5eSJLPn2l3XC3u9If3tf3G05XRu5Bex3Gs6sRxrGrFyvWbRE53+pI2AItI2v6HgBuBKQARsRa4Clgp6TDwb8CyiGgbvRZJ38ysLyK/cXUiYnmb8k+SdOnsiJO+mVmeSv5mkJO+mVmOyv46qJO+mVmenPTNzGqi4BE0s3DSNzPLiXDzjplZrTjpm5nViZO+mVmNOOmbmdVEwbNiZeGkb2aWJyd9M7P6yGsYhl5x0jczy5Gbd8zM6sIvZ5mZ1YyTvplZPfiNXDOzmtFIubO+p0s0M8tLdLC0IWmdpP2SdrbZ73WShiVdlaWKTvpmZjnKcY7c9cCScWNJk4CPAZuz1q8WzTu5zn9ZgjiOVZ04jlW9WF3LqXUnIrZKmtVmt/cAfw28Lut5C7nTl3S1pF2SRiQtGFO2WtKgpN2SLm3YviTdNijpg/2vtZlZezne6Y8fR5oOXAGs7eS4ou70dwJXAp9p3ChpHrAMmA+cBmyRNDct/hPg54Eh4JuSNkXEw1mCDe+bk1e9mxq9C+l1HMeqThzHqlasXL9JZE/oUyVtb1gfiIiBDiL9EfA7ETEsKfNBhST9iHgEoElFlwIbI+IgsEfSILAwLRuMiCfS4zam+2ZK+mZmfREdDcNwICIWtN+tpQXAxjSPTgUuk3Q4Iu4c76CytelPB7Y1rA+l2wCeHrP99a1OImkFsCL32pmZjaOf/fQjYvaP4krrgS+2S/jQw6QvaQtwapOiGyLirlaHNdkWNH/20PKfNv2KNJDWo9ydZs1sYol8Uo6kDcAikmagIeBGYEoSIjpqx2/Us6QfEYuP4bAhYGbD+gxgb/q51XYzs9LI6zYzIpZ3sO+1WfctWz/9TcAySSdImg3MAe4DvgnMkTRb0vEkD3s3FVhPM7Oj5fhyVq8U0qYv6QrgE8ArgS9J2hERl0bELkm3kzygPQysiojh9JjrSV5AmASsi4hdRdTdzGw8Hk+/iYj4AvCFFmVrgDVNtt8N3N3jqpmZdcVJ38ysLoLcHuT2ipO+mVmOyt5f0EnfzCxPTvpmZvXgSVTMzOokovSTqDjpm5nlqdw530nfzCxPbt4xM6uLANy8Y2ZWI+XO+U76ZmZ5cvOOmVmNuPdOCXiybccqKo5jVS9WVwoeQTOLWiR9M7N+SF7OKnfWr0XS92TbjtXvOI5VrVi5fpMo+SibZZtExcys0hSRaWl7HmmdpP2SdrYoXyrpIUk7JG2X9LNZ6uekb2aWl3xnzloPLBmn/B7g3Ig4D3gn8NksJ61F846ZWX/kN/ZORGyVNGuc8n9tWP0JMv4pcdI3M8tT9ge5UyVtb1gfiIiBTkKlU8/+PnAK8ItZjnHSNzPLS3Q0XeKBiFjQVbh06llJPwfcBCxud4zb9M3M8hSRbck1ZGwFzpQ0td2+TvpmZnnK70HuuCT9lCSln38GOB54pt1xbt4xM8uRRvLpqC9pA7CIpO1/CLgRmAIQEWuBNwPXSDoE/Bvw1oj2XyEKSfqSrgY+ArwWWBgR29PtrwDuAF4HrI+I6xuOOZ+kC9OLgbuB38pygWZmfRPk9nJWRCxvU/4x4GOdnreo5p2dwJXA1jHbfwh8CHh/k2M+DawA5qTLeP1Xzcz6TmR7MavIoRoKSfoR8UhE7G6y/fmI+AeS5P8jkqYBJ0XEvend/W3Ar/SntmZmHSjgQW4nqtKmPx0YalgfSrc1JWkFybcCM7P+Knmrc8+SvqQtwKlNim6IiLs6PV2TbS3/ZdMXHAbSepT7J2BmE0eObfq90rOkHxFtXxLowBAwo2F9BrA3x/ObmeUir947vVKJfvoRsQ/4vqQL0n6p1wCdflswM+uxjO35dXuQK+mKtN/phcCXJG1uKHsS+O/AtZKGJM1Li1aSjCI3CDwO/G1/a21m1kZQ+qRfyIPc0fEiWpTNarF9O3B2D6tlZta9crfuVKb3jplZJXi6RDOzOnHSL16u81+WII5jVSeOY1UvVlciYLjc7Tu1SPpmZn3jO/3iDe+b09Pzj96F9DqOY1UnjmNVK1au3ySc9M3MaiKAnObI7RUnfTOz3ASE2/TNzOohKP2D3EoMw2BmVhk5vZEraZ2k/ZJ2tih/m6SH0uUfJZ2bpXpO+mZmecpvGIb1jD9Z1B7goog4B7iJdGThdty8Y2aWm/zG1YmIrZJmjVP+jw2r2zhyJOKWnPTNzPISQDFDK19HxkEonfTNzPKU/U5/qqTtDesD6QRQHZF0MUnS/9ks+zvpm5nlpqNhGA5ExIJuokk6h2TI+V+IiGeyHOOkb2aWl4DoUz99SacDnwd+LSK+nfU4J30zszzl9EaupA3AIpJmoCHgRmAKQESsBT4MvAL4VDKhIIezfHNw0jczy1N+vXeWtyl/F/CuTs/rpG9mlpeIonrvZOakb2aWJ4+yaWZWF0EMDxddiXE56ZuZ5cVDK5uZ1UzJh1YuZMA1SVdL2iVpRNKChu0/L+l+Sd9K//vGhrLz0+2Dkv5YaR8lM7OyCCBGItNSlKJG2dwJXAlsHbP9APDLEfHTwK8Df9FQ9mlgBTAnXcYbfc7MrP8inUQly1KQQpp3IuIRgLE36xHxQMPqLuBFkk4AXg6cFBH3psfdBvwKGQcYynX+yxLEcazqxHGs6sXqlh/kHrs3Aw9ExEFJ04GhhrIhYHqrAyWtIPlWAHCQ5JvFRDWV5BvSROZrrL4qXN8Z3Z7g+3x385a4Y2rG3Qv59+hZ0pe0BTi1SdENEXFXm2PnAx8D3jS6qcluLRvF0pHqBtJzbe92UKMym+jXB77GiWCiX9+oiCh9s3PPkn5ELD6W4yTNAL4AXBMRj6ebhzhygoAZwN7uamhmVj+lmi5R0kuBLwGrI+Ibo9sjYh/wfUkXpL12rgHG/bZgZmZHK6rL5hXpqHEXAl+StDktuh74KeBDknakyylp2UqScaMHgcfJ+BCXjPNGVthEvz7wNU4EE/36KkNR8nEizMwsP6Vq3jEzs95y0jczq5EJkfQlLZG0Ox2i4YNNyk+Q9Lm0/H9LmtX/WnYnwzX+tqSHJT0k6R5JXfc57rd219iw31WSonEIjyrIcn2S3pL+HHdJ+st+17FbGX5PT5f0FUkPpL+rlxVRz1qLiEovwCSSB7uvBo4HHgTmjdnnN4C16edlwOeKrncPrvFi4CXp55UT8RrT/U4kGb5jG7Cg6Hrn/DOcAzwAvCxdP6XoevfgGgeAlennecCTRde7bstEuNNfCAxGxBMR8QKwEVg6Zp+lwJ+nn+8ALqnYgG1trzEivhIRP0hXt3Hkew1VkOXnCHATcDPww35WLgdZru/dwJ9ExHcBImJ/n+vYrSzXGMBJ6eeT8fs2fTcRkv504OmG9WZDNPxon4g4DDxHMqFwVWS5xkbXkb1La1m0vUZJ/w6YGRFf7GfFcpLlZzgXmCvpG5K2SSr9251jZLnGjwBvT7ts3w28pz9Vs1FlHnsnqyxDNHQ0jEMJZa6/pLcDC4CLelqj/I17jZKOA/4QuLZfFcpZlp/hZJImnkUk39S+LunsiPh/Pa5bXrJc43JgfUTcIulC4C/Sayz3IPQTyES40x8CZjasNxui4Uf7SJpM8rXy2b7ULh9ZrhFJi4EbgMsj4mCf6paXdtd4InA28FVJTwIXAJsq9DA36+/pXRFxKCL2ALtJ/ghURZZrvA64HSCSUXNfRDIYm/XJREj63wTmSJot6XiSB7WbxuyziWR8foCrgP8V6ZOkimh7jWnTx2dIEn7V2oKhzTVGxHMRMTUiZkXELJLnFpdHxPZiqtuxLL+nd5I8kEfSVJLmnif6WsvuZLnGp4BLACS9liTp/9++1rLmKp/00zb664HNwCPA7RGxS9LvSbo83e1PgVdIGgR+G2jZHbCMMl7jx4GfBP4qHb5i7P9spZbxGisr4/VtBp6R9DDwFeADEfFMMTXuXMZrfB/wbkkPAhuAayt2A1Z5HobBzKxGKn+nb2Zm2Tnpm5nViJO+mVmNOOmbmdWIk76ZWY046ZuZ1YiTvplZjTjpW61IulPS/el49SuKro9Zv/nlLKsVSS+PiGclvZhk2ICLqvTWq1m3JsIom2ad+E1JV6SfZ5IMaOakb7XhpG+1IWkRsBi4MCJ+IOmrJAN+mdWG2/StTk4Gvpsm/NeQDM9sVitO+lYnXwYmS3qIZNrFbQXXx6zv/CDXzKxGfKdvZlYjTvpmZjXipG9mViNO+mZmNeKkb2ZWI076ZmY14qRvZlYj/x/PvhzsmnfZ0wAAAABJRU5ErkJggg==\n", 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\n", 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\n", 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\n", 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\n", 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\n", 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\n", 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\n", 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\n", 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\n", 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\n", 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\n", 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\n", 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\n", 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\n", 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\n", 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\n", 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\n", 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\n", 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sMib9HcDYsM1XATPSz9OApxv2G063mZmVQ8Zxd4p82FtI0pe0WdKOJssS4J3ASkn3k4zw+fzYYU1O1fSfTtJySdskbevNFZiZtZDTnb6ktZL2Smo6BaKkhZL2S9qeLh/OUr1Ceu9ExKI2u7wZQNIc4BfTbcP8+K4fYDqwu8X5B4HB9Bwlf5ZuZhNKfhlnHfAJ4LZx9vl6RPxSJyctXfOOpJPT/x4D/E9gTVq0EVgq6ThJs4DZwH3F1NLM7Egiv947EbGFZB6TXJUu6QPLJH0H+DbJnfz/BoiIncDtwMPAl4GVETFSWC3NzA7XWZv+wFgzdLosP4qIF0h6UNJfS5qX5YDSvZwVEbcCt7YoWw2s7m+NzMw6kL15Z1+XE6P/X+D0iPg3SZcCd5K0gIyrdEm/F3Kb/7IkcRyrOnEcq3qxutanp4gR8f2Gz3dL+qSkgYjYN95xZWzeMTOrrH512ZR0iiSlnxeQ5PNn2h1Xizv9kT1tv/F0ZewupNdxHKs6cRyrWrFy/SaR052+pPXAQpK2/2HgRmAKQESsAa4EVkg6BPw7sDQi2kavRdI3M+uLyG9cnYhY1qb8EyRdOjvipG9mlqeSvxnkpG9mlqOyvw7qpG9mlicnfTOzmih4BM0snPTNzHIi3LxjZlYrTvpmZnXipG9mViNO+mZmNVHwrFhZOOmbmeXJSd/MrD7yGoahV5z0zcxy5OYdM7O68MtZZmY146RvZlYPfiPXzKxmNFrurO/pEs3M8hIdLG1IWitpr6QdbfZ7vaQRSVdmqaKTvplZjnKcI3cdsHjcWNIk4GPApqz1q0XzTq7zX5YgjmNVJ45jVS9W13Jq3YmILZJmttntPcBfAq/Pet5C7vQlXSVpp6RRSfMPK1slaUjSo5Iuadi+ON02JOlD/a+1mVl7Od7pjx9HmgZcDqzp5Lii7vR3AFcAn27cKGkusBSYB5wKbJY0Jy3+E+DngWHgm5I2RsTDWYKN7JmdV72bGrsL6XUcx6pOHMeqVqxcv0lkT+gDkrY1rA9GxGAHkf4Q+K2IGJGU+aBCkn5EPALQpKJLgA0RcQDYJWkIWJCWDUXEE+lxG9J9MyV9M7O+iI6GYdgXEfPb79bSfGBDmkcHgEslHYqIO8c7qGxt+tOArQ3rw+k2gKcP2/6GVieRtBxYnnvtzMzG0c9++hEx64W40jrgi+0SPvQw6UvaDJzSpOj6iLir1WFNtgXNnz20/KdNvyINpvUod6dZM5tYIp+UI2k9sJCkGWgYuBGYkoSIjtrxG/Us6UfEoqM4bBiY0bA+Hdidfm613cysNPK6zYyIZR3se03WfcvWT38jsFTScZJmAbOB+4BvArMlzZJ0LMnD3o0F1tPM7Eg5vpzVK4W06Uu6HPhj4FXAlyRtj4hLImKnpNtJHtAeAlZGxEh6zHUkLyBMAtZGxM4i6m5mNh6Pp99ERHwB+EKLstXA6ibb7wbu7nHVzMy64qRvZlYXQW4PcnvFSd/MLEdl7y/opG9mlicnfTOzevAkKmZmdRJR+klUnPTNzPJU7pzvpG9mlic375iZ1UUAbt4xM6uRcud8J30zszy5ecfMrEbce6cEPNm2YxUVx7GqF6srBY+gmUUtkr6ZWT8kL2eVO+vXIul7sm3H6nccx6pWrFy/SZR8lM2yTaJiZlZpisi0tD2PtFbSXkk7WpQvkfSQpO2Stkn62Sz1c9I3M8tLvjNnrQMWj1N+D3BORJwLvBP4TJaT1qJ5x8ysP/IbeycitkiaOU75vzWs/gQZ/5Q46ZuZ5Sn7g9wBSdsa1gcjYrCTUOnUs78LnAz8YpZjnPTNzPISHU2XuC8i5ncVLp16VtLPATcBi9od4zZ9M7M8RWRbcg0ZW4AzJA2029dJ38wsT/k9yB2XpJ+SpPTzzwDHAs+0O87NO2ZmOdJoPh31Ja0HFpK0/Q8DNwJTACJiDfAW4GpJB4F/B94W0f4rRCFJX9JVwEeA1wELImJbuv2VwB3A64F1EXFdwzHnkXRheilwN/AbWS7QzKxvgtxezoqIZW3KPwZ8rNPzFtW8swO4Athy2PYfATcAH2hyzKeA5cDsdBmv/6qZWd+JbC9mFTlUQyFJPyIeiYhHm2x/LiL+niT5v0DSVOCEiLg3vbu/DfiV/tTWzKwDBTzI7URV2vSnAcMN68PptqYkLSf5VmBm1l8lb3XuWdKXtBk4pUnR9RFxV6ena7Kt5b9s+oLDYFqPcv8EzGziyLFNv1d6lvQjou1LAh0YBqY3rE8Hdud4fjOzXOTVe6dXKtFPPyL2AD+QdH7aL/VqoNNvC2ZmPZaxPb9uD3IlXZ72O70A+JKkTQ1lTwK/D1wjaVjS3LRoBckockPA48Bf97fWZmZtBKVP+oU8yB0bL6JF2cwW27cBZ/WwWmZm3St3605leu+YmVWCp0s0M6sTJ/3i5Tr/ZQniOFZ14jhW9WJ1JQJGyt2+U4ukb2bWN77TL97Intk9Pf/YXUiv4zhWdeI4VrVi5fpNwknfzKwmAshpjtxecdI3M8tNQLhN38ysHoLSP8itxDAMZmaVkdMbuZLWStoraUeL8v8s6aF0+QdJ52SpnpO+mVme8huGYR3jTxa1C7gwIs4GbiIdWbgdN++YmeUmv3F1ImKLpJnjlP9Dw+pWXjwScUtO+mZmeQmgmKGVryXjIJRO+mZmecp+pz8gaVvD+mA6AVRHJF1EkvR/Nsv+TvpmZrnpaBiGfRExv5toks4mGXL+FyLimSzHOOmbmeUlIPrUT1/SacDngV+LiO9kPc5J38wsTzm9kStpPbCQpBloGLgRmAIQEWuADwOvBD6ZTCjIoSzfHJz0zczylF/vnWVtyt8FvKvT8zrpm5nlJaKo3juZOembmeXJo2yamdVFECMjRVdiXE76ZmZ58dDKZmY1U/KhlQsZcE3SVZJ2ShqVNL9h+89Lul/St9L/vqmh7Lx0+5CkP1LaR8nMrCwCiNHItBSlqFE2dwBXAFsO274P+OWI+Gng14E/ayj7FLAcmJ0u440+Z2bWf5FOopJlKUghzTsR8QjA4TfrEfFAw+pO4CWSjgNeAZwQEfemx90G/AoZBxjKdf7LEsRxrOrEcazqxeqWH+QevbcAD0TEAUnTgOGGsmFgWqsDJS0n+VYAcIDkm8VENUDyDWki8zVWXxWu7/RuT/ADvrdpc9wxkHH3Qv49epb0JW0GTmlSdH1E3NXm2HnAx4A3j21qslvLRrF0pLrB9Fzbuh3UqMwm+vWBr3EimOjXNyYiSt/s3LOkHxGLjuY4SdOBLwBXR8Tj6eZhXjxBwHRgd3c1NDOrn1JNlyjpJOBLwKqI+MbY9ojYA/xA0vlpr52rgXG/LZiZ2ZGK6rJ5eTpq3AXAlyRtSouuA34KuEHS9nQ5OS1bQTJu9BDwOBkf4pJx3sgKm+jXB77GiWCiX19lKEo+ToSZmeWnVM07ZmbWW076ZmY1MiGSvqTFkh5Nh2j4UJPy4yR9Li3/R0kz+1/L7mS4xt+U9LCkhyTdI6nrPsf91u4aG/a7UlI0DuFRBVmuT9Jb05/jTkl/3u86divD7+lpkr4i6YH0d/XSIupZaxFR6QWYRPJg9zXAscCDwNzD9vmvwJr081Lgc0XXuwfXeBHwsvTziol4jel+x5MM37EVmF90vXP+Gc4GHgBenq6fXHS9e3CNg8CK9PNc4Mmi6123ZSLc6S8AhiLiiYh4HtgALDlsnyXA/0k/3wFcXLEB29peY0R8JSJ+mK5u5cXvNVRBlp8jwE3AzcCP+lm5HGS5vncDfxIR3wOIiL19rmO3slxjACekn0/E79v03URI+tOApxvWmw3R8MI+EXEI2E8yoXBVZLnGRteSvUtrWbS9Rkn/AZgREV/sZ8VykuVnOAeYI+kbkrZKKv3bnYfJco0fAd6edtm+G3hPf6pmY8o89k5WWYZo6GgYhxLKXH9JbwfmAxf2tEb5G/caJR0D/AFwTb8qlLMsP8PJJE08C0m+qX1d0lkR8a89rlteslzjMmBdRNwi6QLgz9JrLPcg9BPIRLjTHwZmNKw3G6LhhX0kTSb5WvlsX2qXjyzXiKRFwPXAZRFxoE91y0u7azweOAv4qqQngfOBjRV6mJv19/SuiDgYEbuAR0n+CFRFlmu8FrgdIJJRc19CMhib9clESPrfBGZLmiXpWJIHtRsP22cjyfj8AFcCfxfpk6SKaHuNadPHp0kSftXagqHNNUbE/ogYiIiZETGT5LnFZRGxrZjqdizL7+mdJA/kkTRA0tzzRF9r2Z0s1/gUcDGApNeRJP3/19da1lzlk37aRn8dsAl4BLg9InZK+h1Jl6W7/SnwSklDwG8CLbsDllHGa/w48JPAX6TDVxz+P1upZbzGysp4fZuAZyQ9DHwF+GBEPFNMjTuX8RrfD7xb0oPAeuCait2AVZ6HYTAzq5HK3+mbmVl2TvpmZjXipG9mViNO+mZmNeKkb2ZWI076ZmY14qRvZlYjTvpWK5LulHR/Ol798qLrY9ZvfjnLakXSKyLiWUkvJRk24MIqvfVq1q2JMMqmWSfeK+ny9PMMkgHNnPStNpz0rTYkLQQWARdExA8lfZVkwC+z2nCbvtXJicD30oT/WpLhmc1qxUnf6uTLwGRJD5FMu7i14PqY9Z0f5JqZ1Yjv9M3MasRJ38ysRpz0zcxqxEnfzKxGnPTNzGrESd/MrEac9M3MauT/A5KSLobzGMv7AAAAAElFTkSuQmCC\n", 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\n", 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\n", 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\n", 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\n", 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\n", 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\n", 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\n", 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\n", 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\n", 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\n", 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\n", 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\n", 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I2B8RO4EhYFEB5TMza63kL2eVMelvB8aGbb4UmJ1+ngk80XDccLrNzKwcMo67U+TD3kKSvqRNkrY3WZYBbwdWSbqHZITP58ZOa3Kppv90klZK2ippa2/uwMyshZxq+pLWStojqekUiJIWS3pK0rZ0+VCW4hXSeycilrQ55A0AkuYDv5xuG+YntX6AWcCuFtcfBAbTa5T8WbqZTSr5ZZx1wCeAmyY45hsR8SudXLR0zTuSjkv/exjwP4E16a4NwHJJR0iaC8wD7i6mlGZmBxP59d6JiM0k85jkqnRJH1gh6bvAd0hq8n8FEBE7gFuAB4CvAKsiYqSwUpqZjddZm/7AWDN0uqw8hIhnS7pP0t9JWpDlhNK9nBURNwI3tti3Gljd3xKZmXUge/PO3i4nRv+/wEkR8e+SLgBuI2kBmVDpkn4v5Db/ZUniOFZ14jhW9WJ1rU9PESPihw2f75D0SUkDEbF3ovPK2LxjZlZZ/eqyKel4SUo/LyLJ50+2O68WNf2R3W2/8XRlrBbS6ziOVZ04jlWtWLl+k8ippi9pPbCYpO1/GLgWmAYQEWuAS4ArJR0A/gNYHhFto9ci6ZuZ9UXkN65ORKxos/8TJF06O+Kkb2aWp5K/GeSkb2aWo7K/Duqkb2aWJyd9M7OaKHgEzSyc9M3MciLcvGNmVitO+mZmdeKkb2ZWI076ZmY1UfCsWFk46ZuZ5clJ38ysPvIahqFXnPTNzHLk5h0zs7rwy1lmZjXjpG9mVg9+I9fMrGY0Wu6s7+kSzczyEh0sbUhaK2mPpO1tjnuNpBFJl2QpopO+mVmOcpwjdx2wdMJY0hTgY8DGrOWrRfNOrvNfliCOY1UnjmNVL1bXcmrdiYjNkua0OexdwN8Ar8l63UJq+pIulbRD0qikheP2XS1pSNJDks5v2L403TYk6f39L7WZWXs51vQnjiPNBC4C1nRyXlE1/e3AxcCnGzdKOgVYDiwATgA2SZqf7v5z4BeBYeBbkjZExANZgo3snpdXuZsaq4X0Oo5jVSeOY1UrVq7fJLIn9AFJWxvWByNisINIfwL8bkSMSMp8UiFJPyIeBGhS0GXAzRGxH9gpaQhYlO4biohH0/NuTo/NlPTNzPoiOhqGYW9ELGx/WEsLgZvTPDoAXCDpQETcNtFJZWvTnwlsaVgfTrcBPDFu+2tbXUTSSmBl7qUzM5tAP/vpR8TcH8eV1gFfapfwoYdJX9Im4Pgmu66JiNtbndZkW9D82UPLf9r0K9JgWo5yd5o1s8kl8kk5ktYDi0magYaBa4FpSYjoqB2/Uc+SfkQsOYTThoHZDeuzgF3p51bbzcxKI69qZkSs6ODYy7MeW7Z++huA5ZKOkDQXmAfcDXwLmCdprqTDSR72biiwnGZmB8vx5axeKaRNX9JFwJ8BLwO+LGlbRJwfETsk3ULygPYAsCoiRtJzriJ5AWEKsDYidhRRdjOziXg8/SYi4ovAF1vsWw2sbrL9DuCOHhfNzKwrTvpmZnUR5PYgt1ec9M3MclT2/oJO+mZmeXLSNzOrB0+iYmZWJxGln0TFSd/MLE/lzvlO+mZmeXLzjplZXQTg5h0zsxopd8530jczy5Obd8zMasS9d0rAk207VlFxHKt6sbpS8AiaWdQi6ZuZ9UPycla5s34tkr4n23asfsdxrGrFyvWbRMlH2SzbJCpmZpWmiExL2+tIayXtkbS9xf5lku6XtE3SVkk/n6V8TvpmZnnJd+asdcDSCfbfCZweEWcAbwc+k+WitWjeMTPrj/zG3omIzZLmTLD/3xtWf4qMf0qc9M3M8pT9Qe6ApK0N64MRMdhJqHTq2T8AjgN+Ocs5TvpmZnmJjqZL3BsRC7sKl049K+kXgOuAJe3OcZu+mVmeIrItuYaMzcDJkgbaHeukb2aWp/we5E5I0s9IUvr554DDgSfbnefmHTOzHGk0n476ktYDi0na/oeBa4FpABGxBngjcJmk54H/AN4c0f4rRCFJX9KlwIeBVwOLImJruv2lwK3Aa4B1EXFVwzlnknRhehFwB/DbWW7QzKxvgtxezoqIFW32fwz4WKfXLap5ZztwMbB53PZngQ8C721yzqeAlcC8dJmo/6qZWd+JbC9mFTlUQyFJPyIejIiHmmx/JiL+iST5/5ikGcBREXFXWru/Cfi1/pTWzKwDBTzI7URV2vRnAsMN68PptqYkrST5VmBm1l8lb3XuWdKXtAk4vsmuayLi9k4v12Rby3/Z9AWHwbQc5f4JmNnkkWObfq/0LOlHRNuXBDowDMxqWJ8F7Mrx+mZmucir906vVKKffkTsBp6WdFbaL/UyoNNvC2ZmPZaxPb9uD3IlXZT2Oz0b+LKkjQ37HgP+CLhc0rCkU9JdV5KMIjcEPAL8XX9LbWbWRlD6pF/Ig9yx8SJa7JvTYvtW4NQeFsvMrHvlbt2pTO8dM7NK8HSJZmZ14qRfvFznvyxBHMeqThzHql6srkTASLnbd2qR9M3M+sY1/eKN7J7X0+uP1UJ6HcexqhPHsaoVK9dvEk76ZmY1EUBOc+T2ipO+mVluAsJt+mZm9RCU/kFuJYZhMDOrjJzeyJW0VtIeSdtb7P91Sfenyz9LOj1L8Zz0zczylN8wDOuYeLKoncA5EXEacB3pyMLtuHnHzCw3+Y2rExGbJc2ZYP8/N6xu4YUjEbfkpG9mlpcAihla+QoyDkLppG9mlqfsNf0BSVsb1gfTCaA6IulckqT/81mOd9I3M8tNR8Mw7I2Ihd1Ek3QayZDzvxQRT2Y5x0nfzCwvAdGnfvqSTgS+APxGRHw363lO+mZmecrpjVxJ64HFJM1Aw8C1wDSAiFgDfAh4KfDJZEJBDmT55uCkb2aWp/x676xos/8dwDs6va6TvplZXiKK6r2TmZO+mVmePMqmmVldBDEyUnQhJuSkb2aWFw+tbGZWMyUfWrmQAdckXSpph6RRSQsbtv+ipHskfTv97+sb9p2Zbh+S9KdK+yiZmZVFADEamZaiFDXK5nbgYmDzuO17gV+NiJ8FfhP4bMO+TwErgXnpMtHoc2Zm/RfpJCpZloIU0rwTEQ8CjK+sR8S9Das7gCMlHQEcCxwVEXel590E/BoZBxjKdf7LEsRxrOrEcazqxeqWH+QeujcC90bEfkkzgeGGfcPAzFYnSlpJ8q0AYD/JN4vJaoDkG9Jk5nusvirc30ndXuBpvr9xU9w6kPHwQv49epb0JW0Cjm+y65qIuL3NuQuAjwFvGNvU5LCWjWLpSHWD6bW2djuoUZlN9vsD3+NkMNnvb0xElL7ZuWdJPyKWHMp5kmYBXwQui4hH0s3DvHCCgFnAru5KaGZWP6WaLlHSMcCXgasj4ptj2yNiN/C0pLPSXjuXARN+WzAzs4MV1WXzonTUuLOBL0vamO66CvgZ4IOStqXLcem+K0nGjR4CHiHjQ1wyzhtZYZP9/sD3OBlM9vurDEXJx4kwM7P8lKp5x8zMestJ38ysRiZF0pe0VNJD6RAN72+y/whJn0/3/x9Jc/pfyu5kuMffkfSApPsl3Smp6z7H/dbuHhuOu0RSNA7hUQVZ7k/Sm9Kf4w5Jn+t3GbuV4ff0RElflXRv+rt6QRHlrLWIqPQCTCF5sPsK4HDgPuCUccf8N2BN+nk58Pmiy92DezwXeHH6+crJeI/pcdNJhu/YAiwsutw5/wznAfcCL0nXjyu63D24x0HgyvTzKcBjRZe7bstkqOkvAoYi4tGIeA64GVg27phlwP9KP98KnFexAdva3mNEfDUifpSubuGF7zVUQZafI8B1wPXAs/0sXA6y3N87gT+PiO8DRMSePpexW1nuMYCj0s9H4/dt+m4yJP2ZwBMN682GaPjxMRFxAHiKZELhqshyj42uIHuX1rJoe4+S/hMwOyK+1M+C5STLz3A+MF/SNyVtkVT6tzvHyXKPHwbemnbZvgN4V3+KZmPKPPZOVlmGaOhoGIcSylx+SW8FFgLn9LRE+ZvwHiUdBvwxcHm/CpSzLD/DqSRNPItJvql9Q9KpEfGDHpctL1nucQWwLiJukHQ28Nn0Hss9CP0kMhlq+sPA7Ib1ZkM0/PgYSVNJvlbu60vp8pHlHpG0BLgGuDAi9vepbHlpd4/TgVOBr0l6DDgL2FChh7lZf09vj4jnI2In8BDJH4GqyHKPVwC3AEQyau6RJIOxWZ9MhqT/LWCepLmSDid5ULth3DEbSMbnB7gE+MdInyRVRNt7TJs+Pk2S8KvWFgxt7jEinoqIgYiYExFzSJ5bXBgRW4spbsey/J7eRvJAHkkDJM09j/a1lN3Jco+PA+cBSHo1SdL/f30tZc1VPumnbfRXARuBB4FbImKHpN+XdGF62F8CL5U0BPwO0LI7YBllvMePAz8N/HU6fMX4/9lKLeM9VlbG+9sIPCnpAeCrwPsi4sliSty5jPf4HuCdku4D1gOXV6wCVnkehsHMrEYqX9M3M7PsnPTNzGrESd/MrEac9M3MasRJ38ysRpz0zcxqxEnfzKxGnPStViTdJumedLz6lUWXx6zf/HKW1YqkYyNin6QXkQwbcE6V3no169ZkGGXTrBO/Jemi9PNskgHNnPStNpz0rTYkLQaWAGdHxI8kfY1kwC+z2nCbvtXJ0cD304T/KpLhmc1qxUnf6uQrwFRJ95NMu7il4PKY9Z0f5JqZ1Yhr+mZmNeKkb2ZWI076ZmY14qRvZlYjTvpmZjXipG9mViNO+mZmNfL/ATkhSBDFHwj5AAAAAElFTkSuQmCC\n", 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\n", 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\n", 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\n", 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\n", 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\n", 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\n", 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\n", 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\n", 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\n", 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\n", 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\n", 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zG0BVTPonAhvzz/8AvCX/vBRYFxF7ImI7MAwsLKF+ZmatVfzlrCom/a3A+LDNlwCz8s8zgCcb9hvJt5mZVUPBcXfKfNhbStKXtEHS1ibLUuCdwJWS7iUb4fO58cOanKrpj07SCkmbJW3uzRWYmbWQ6E5f0mpJuyQ1nQJR0iJJz0jaki8fKlK9UnrvRMTiNru8CUDSPOCX8m0j/OSuH2AmsKPF+YeAofwcFX+WbmZTSrqMswb4OHDzJPt8IyJ+uZOTVq55R9LR+X8PAv4nsCovWg8sk3S4pDnAXOCecmppZrY/ka73TkRsJJvHJKnKJX1guaTvAt8hu5P/S4CI2AbcAjwIfAW4MiJGS6ulmdlEnbXpTxtvhs6XFQcQ8SxJ90v6O0nzixxQuZezIuIm4KYWZSuBlf2tkZlZB4o37+zucmL0fwaOj4h/l3Q+cBtZC8ikKpf0eyHZ/JcVieNYgxPHsQYvVtf69BQxIn7Q8PkOSZ+UNC0idk92XBWbd8zMBla/umxKOkaS8s8LyfL5U+2Oq8Wd/ujOtt94ujJ+F9LrOI41OHEca7BiJf0mkehOX9JaYBFZ2/8IcB1wKEBErAIuBq6QtA/4D2BZRLSNXoukb2bWF5FuXJ2IWN6m/ONkXTo74qRvZpZSxd8MctI3M0uo6q+DOumbmaXkpG9mVhMlj6BZhJO+mVkiws07Zma14qRvZlYnTvpmZjXipG9mVhMlz4pVhJO+mVlKTvpmZvWRahiGXnHSNzNLyM07ZmZ14ZezzMxqxknfzKwe/EaumVnNaKzaWd/TJZqZpRIdLG1IWi1pl6StbfZ7naRRSRcXqaKTvplZQgnnyF0DLJk0lnQw8FHgzqL1q0XzTtL5LysQx7EGJ45jDV6sriVq3YmIjZJmt9ntPcDfAq8ret5S7vQlXSJpm6QxSQsmlF0taVjSw5LOa9i+JN82LOmD/a+1mVl7Ce/0J48jzQAuBFZ1clxZd/pbgYuATzdulHQSsAyYDxwLbJA0Ly/+BPALwAjwLUnrI+LBIsFGd85NVe+mxu9Ceh3HsQYnjmMNVqyk3ySKJ/RpkjY3rA9FxFAHkf4E+N2IGJVU+KBSkn5EPATQpKJLgXURsQfYLmkYWJiXDUfEY/lx6/J9CyV9M7O+iI6GYdgdEQva79bSAmBdnkenAedL2hcRt012UNXa9GcAmxrWR/JtAE9O2P76VieRtAJYkbx2ZmaT6Gc//YiY83xcaQ3wpXYJH3qY9CVtAI5pUnRNRNze6rAm24Lmzx5a/mjzr0hDeT2q3WnWzKaWSJNyJK0FFpE1A40A1wGHZiGio3b8Rj1L+hGx+AAOGwFmNazPBHbkn1ttNzOrjFS3mRGxvIN9Lyu6b9X66a8Hlkk6XNIcYC5wD/AtYK6kOZIOI3vYu77EepqZ7S/hy1m9UkqbvqQLgT8DXgl8WdKWiDgvIrZJuoXsAe0+4MqIGM2PuYrsBYSDgdURsa2MupuZTcbj6TcREV8AvtCibCWwssn2O4A7elw1M7OuOOmbmdVFkOxBbq846ZuZJVT1/oJO+mZmKTnpm5nVgydRMTOrk4jKT6LipG9mllK1c76TvplZSm7eMTOriwDcvGNmViPVzvlO+mZmKbl5x8ysRtx7pwI82bZjlRXHsQYvVldKHkGziFokfTOzfshezqp21q9F0vdk247V7ziONVixkn6TqPgom1WbRMXMbKApotDS9jzSakm7JG1tUb5U0gOStkjaLOnnitTPSd/MLJW0M2etAZZMUn4XcGpEnAa8E/hMkZPWonnHzKw/0o29ExEbJc2epPzfG1Z/ioJ/Spz0zcxSKv4gd5qkzQ3rQxEx1EmofOrZPwCOBn6pyDFO+mZmqURH0yXujogFXYXLp56V9PPA9cDidse4Td/MLKWIYkvSkLEROEHStHb7OumbmaWU7kHupCT9jCTln08HDgOeanecm3fMzBLSWJqO+pLWAovI2v5HgOuAQwEiYhXwFuBSSXuB/wDeFtH+K0QpSV/SJcCHgdcCCyNic779FcCtwOuANRFxVcMxZ5B1YXoxcAfwW0Uu0Mysb4JkL2dFxPI25R8FPtrpectq3tkKXARsnLD9x8C1wPubHPMpYAUwN18m679qZtZ3otiLWWUO1VBK0o+IhyLi4Sbbn42IfyRL/s+TNB04MiLuzu/ubwZ+pT+1NTPrQAkPcjsxKG36M4CRhvWRfFtTklaQfSswM+uvirc69yzpS9oAHNOk6JqIuL3T0zXZ1vInm7/gMJTXo9r/AmY2dSRs0++VniX9iGj7kkAHRoCZDeszgR0Jz29mlkSq3ju9MhD99CNiJ/BDSWfm/VIvBTr9tmBm1mMF2/Pr9iBX0oV5v9OzgC9LurOh7HHgj4DLJI1IOikvuoJsFLlh4FHg7/pbazOzNoLKJ/1SHuSOjxfRomx2i+2bgZN7WC0zs+5Vu3VnYHrvmJkNBE+XaGZWJ0765Us6/2UF4jjW4MRxrMGL1ZUIGK12+04tkr6ZWd/4Tr98ozvn9vT843chvY7jWIMTx7EGK1bSbxJO+mZmNRFAojlye8VJ38wsmYBwm76ZWT0ElX+QOxDDMJiZDYxEb+RKWi1pl6StLcr/q6QH8uWfJJ1apHpO+mZmKaUbhmENk08WtR04OyJOAa4nH1m4HTfvmJklk25cnYjYKGn2JOX/1LC6iReORNySk76ZWSoBlDO08uUUHITSSd/MLKXid/rTJG1uWB/KJ4DqiKRzyJL+zxXZ30nfzCyZjoZh2B0RC7qJJukUsiHnfzEinipyjJO+mVkqAdGnfvqSjgM+D/xaRHy36HFO+mZmKSV6I1fSWmARWTPQCHAdcChARKwCPgS8AvhkNqEg+4p8c3DSNzNLKV3vneVtyt8FvKvT8zrpm5mlElFW753CnPTNzFLyKJtmZnURxOho2ZWYlJO+mVkqHlrZzKxmKj60cikDrkm6RNI2SWOSFjRs/wVJ90r6dv7fNzaUnZFvH5b0p8r7KJmZVUUAMRaFlrKUNcrmVuAiYOOE7buBN0fEzwK/DvxVQ9mngBXA3HyZbPQ5M7P+i3wSlSJLSUpp3omIhwAm3qxHxH0Nq9uAF0k6HHg5cGRE3J0fdzPwKxQcYCjp/JcViONYgxPHsQYvVrf8IPfAvQW4LyL2SJoBjDSUjQAzWh0oaQXZtwKAPWTfLKaqaWTfkKYyX+PgG4TrO77bE/yQ79+5IW6dVnD3Un4ePUv6kjYAxzQpuiYibm9z7Hzgo8Cbxjc12a1lo1g+Ut1Qfq7N3Q5qVGVT/frA1zgVTPXrGxcRlW927lnSj4jFB3KcpJnAF4BLI+LRfPMIL5wgYCawo7sampnVT6WmS5T0UuDLwNUR8c3x7RGxE/ihpDPzXjuXApN+WzAzs/2V1WXzwnzUuLOAL0u6My+6CvgZ4FpJW/Ll6LzsCrJxo4eBRyn4EJeC80YOsKl+feBrnAqm+vUNDEXFx4kwM7N0KtW8Y2ZmveWkb2ZWI1Mi6UtaIunhfIiGDzYpP1zS5/Ly/yNpdv9r2Z0C1/g7kh6U9ICkuyR13ee439pdY8N+F0uKxiE8BkGR65P01vzfcZukv+53HbtV4Pf0OElflXRf/rt6fhn1rLWIGOgFOJjswe6rgcOA+4GTJuzzG8Cq/PMy4HNl17sH13gO8JL88xVT8Rrz/Y4gG75jE7Cg7Hon/jecC9wHvCxfP7rsevfgGoeAK/LPJwGPl13vui1T4U5/ITAcEY9FxHPAOmDphH2WAv8r/3wrcO6ADdjW9hoj4qsR8aN8dRMvfK9hEBT5dwS4HrgB+HE/K5dAket7N/CJiPg+QETs6nMdu1XkGgM4Mv98FH7fpu+mQtKfATzZsN5siIbn94mIfcAzZBMKD4oi19jocop3aa2Kttco6T8BsyLiS/2sWCJF/g3nAfMkfVPSJkmVf7tzgiLX+GHg7XmX7TuA9/SnajauymPvFFVkiIaOhnGooML1l/R2YAFwdk9rlN6k1yjpIOCPgcv6VaHEivwbHkLWxLOI7JvaNySdHBH/r8d1S6XINS4H1kTEjZLOAv4qv8ZqD0I/hUyFO/0RYFbDerMhGp7fR9IhZF8rn+5L7dIoco1IWgxcA1wQEXv6VLdU2l3jEcDJwNckPQ6cCawfoIe5RX9Pb4+IvRGxHXiY7I/AoChyjZcDtwBENmrui8gGY7M+mQpJ/1vAXElzJB1G9qB2/YR91pONzw9wMfC/I3+SNCDaXmPe9PFpsoQ/aG3B0OYaI+KZiJgWEbMjYjbZc4sLImJzOdXtWJHf09vIHsgjaRpZc89jfa1ld4pc4xPAuQCSXkuW9P9vX2tZcwOf9PM2+quAO4GHgFsiYpuk35d0Qb7bXwCvkDQM/A7QsjtgFRW8xo8BPw38TT58xcT/2Sqt4DUOrILXdyfwlKQHga8CH4iIp8qpcecKXuP7gHdLuh9YC1w2YDdgA8/DMJiZ1cjA3+mbmVlxTvpmZjXipG9mViNO+mZmNeKkb2ZWI076ZmY14qRvZlYjTvpWK5Juk3RvPl79irLrY9ZvfjnLakXSyyPiaUkvJhs24OxBeuvVrFtTYZRNs078pqQL88+zyAY0c9K32nDSt9qQtAhYDJwVET+S9DWyAb/MasNt+lYnRwHfzxP+a8iGZzarFSd9q5OvAIdIeoBs2sVNJdfHrO/8INfMrEZ8p29mViNO+mZmNeKkb2ZWI076ZmY14qRvZlYjTvpmZjXipG9mViP/H052SyFHBgaFAAAAAElFTkSuQmCC\n", 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\n", 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\n", 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\n", 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\n", 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h47g7RT7sLSTpS9oiaWeTZRnwLmC1pPtIRvh8YeywJqdq+lcnaaWk7ZK29+YKzMxayOlOX9I6SfskNZ0CUdJiSc9J2pEuH8lSvUJ670TEkja7vBlA0jzgV9Jtw/z0rh9gJrCnxfkHgcH0HCV/lm5mk0p+GWc98Eng1gn2+UZE/GonJy1d846kk9M/jwH+K7A2LdoELJd0nKQ5wFzg3mJqaWZ2JJFf752I2Eoyj0muSpf0gRWSHgW+Q3In/z8BImIXcBvwEPAVYHVEjBRWSzOz8Tpr0x8Ya4ZOl5VHEfF8SQ9I+mtJC7IcULqXsyLiFuCWFmVrgDX9rZGZWQeyN+/s73Ji9H8ATo+If5V0CXAHSQvIhEqX9Hsht/kvSxLHsaoTx7GqF6trfXqKGBHfb/h8l6RPSRqIiP0THVfG5h0zs8rqV5dNSadIUvp5EUk+f6bdcbW40x/Z2/YbT1fG7kJ6HcexqhPHsaoVK9dvEjnd6UvaACwmafsfBm4ApgFExFrgCmCVpEPAj4DlEdE2ei2SvplZX0R+4+pExIo25Z8k6dLZESd9M7M8lfzNICd9M7Mclf11UCd9M7M8OembmdVEwSNoZuGkb2aWE+HmHTOzWnHSNzOrEyd9M7MacdI3M6uJgmfFysJJ38wsT076Zmb1kdcwDL3ipG9mliM375iZ1YVfzjIzqxknfTOzevAbuWZmNaPRcmd9T5doZpaX6GBpQ9I6Sfsk7Wyz3+sljUi6IksVnfTNzHKU4xy564GlE8aSpgAfBzZnrV8tmndynf+yBHEcqzpxHKt6sbqWU+tORGyVNLvNbu8F/hJ4fdbzFnKnL+lKSbskjUpaOK7sOklDkh6RdHHD9qXptiFJH+p/rc3M2svxTn/iONIM4DJgbSfHFXWnvxO4HPhM40ZJ84HlwALgVGCLpHlp8Z8CvwQMA9+StCkiHsoSbGTv3Lzq3dTYXUiv4zhWdeI4VrVi5fpNIntCH5C0vWF9MCIGO4j0x8DvRsSIpMwHFZL0I+JhgCYVXQZsjIgDwG5JQ8CitGwoIp5Ij9uY7psp6ZuZ9UV0NAzD/ohY2H63lhYCG9M8OgBcIulQRNwx0UFla9OfAWxrWB9OtwE8PW77G1qdRNJKYGXutTMzm0A/++lHxJyfxJXWA19ql/Chh0lf0hbglCZF10fEna0Oa7ItaP7soeVfbfoVaTCtR7k7zZrZ5BL5pBxJG4DFJM1Aw8ANwLQkRHTUjt+oZ0k/IpYcxWHDwKyG9ZnAnvRzq+1mZqWR121mRKzoYN+rs+5btn76m4Dlko6TNAeYC9wLfAuYK2mOpGNJHvZuKrCeZmZHyvHlrF4ppE1f0mXAfwdeAXxZ0o6IuDgidkm6jeQB7SFgdUSMpMdcS/ICwhRgXUTsKqLuZmYT8Xj6TUTEF4EvtihbA6xpsv0u4K4eV83MrCtO+mZmdRHk9iC3V5z0zcxyVPb+gk76ZmZ5ctI3M6sHT6JiZlYnEaWfRMVJ38wsT+XO+U76ZmZ5cvOOmVldBODmHTOzGil3znfSNzPLk5t3zMxqxL13SsCTbTtWUXEcq3qxulLwCJpZ1CLpm5n1Q/JyVrmzfi2Svifbdqx+x3GsasXK9ZtEyUfZLNskKmZmlaaITEvb80jrJO2TtLNF+TJJD0raIWm7pF/IUj8nfTOzvOQ7c9Z6YOkE5XcDZ0fEOcC7gM9mOWktmnfMzPojv7F3ImKrpNkTlP9rw+rPkPFXiZO+mVmesj/IHZC0vWF9MCIGOwmVTj37B8DJwK9kOcZJ38wsL9HRdIn7I2JhV+HSqWcl/SJwI7Ck3TFu0zczy1NEtiXXkLEVOEPSQLt9nfTNzPKU34PcCUn6OUlKP78OOBZ4pt1xbt4xM8uRRvPpqC9pA7CYpO1/GLgBmAYQEWuBtwBXSToI/Ah4W0T7rxCFJH1JVwIfBV4LLIqI7en2lwO3A68H1kfEtQ3HnEvShenFwF3Ab2e5QDOzvglyezkrIla0Kf848PFOz1tU885O4HJg67jtPwY+DHygyTGfBlYCc9Nlov6rZmZ9J7K9mFXkUA2FJP2IeDgiHmmy/fmI+DuS5P8TkqYDJ0TEPend/a3Ar/entmZmHSjgQW4nqtKmPwMYblgfTrc1JWklybcCM7P+Knmrc8+SvqQtwClNiq6PiDs7PV2TbS3/ZtMXHAbTepT7X8DMJo8c2/R7pWdJPyLaviTQgWFgZsP6TGBPjuc3M8tFXr13eqUS/fQjYi/wA0nnpf1SrwI6/bZgZtZjGdvz6/YgV9Jlab/T84EvS9rcUPYk8N+AqyUNS5qfFq0iGUVuCHgc+Ov+1trMrI2g9Em/kAe5Y+NFtCib3WL7duDMHlbLzKx75W7dqUzvHTOzSvB0iWZmdeKkX7xc578sQRzHqk4cx6perK5EwEi523dqkfTNzPrGd/rFG9k7t6fnH7sL6XUcx6pOHMeqVqxcv0k46ZuZ1UQAOc2R2ytO+mZmuQkIt+mbmdVDUPoHuZUYhsHMrDJyeiNX0jpJ+yTtbFH+7yU9mC5/L+nsLNVz0jczy1N+wzCsZ+LJonYDF0TEWcCNpCMLt+PmHTOz3OQ3rk5EbJU0e4Lyv29Y3cbhIxG35KRvZpaXAIoZWvkaMg5C6aRvZpan7Hf6A5K2N6wPphNAdUTShSRJ/xey7O+kb2aWm46GYdgfEQu7iSbpLJIh5385Ip7JcoyTvplZXgKiT/30JZ0GfAH4jYh4NOtxTvpmZnnK6Y1cSRuAxSTNQMPADcA0gIhYC3wEeDnwqWRCQQ5l+ebgpG9mlqf8eu+saFP+buDdnZ7XSd/MLC8RRfXeycxJ38wsTx5l08ysLoIYGSm6EhNy0jczy4uHVjYzq5mSD61cyIBrkq6UtEvSqKSFDdt/SdJ9kr6d/vmmhrJz0+1Dkv5EaR8lM7OyCCBGI9NSlKJG2dwJXA5sHbd9P/BrEfHzwG8Cf95Q9mlgJTA3XSYafc7MrP8inUQly1KQQpp3IuJhgPE36xFxf8PqLuBFko4DXgacEBH3pMfdCvw6GQcYynX+yxLEcazqxHGs6sXqlh/kHr23APdHxAFJM4DhhrJhYEarAyWtJPlWAHCA5JvFZDVA8g1pMvM1Vl8Vru/0bk/wA763eUvcPpBx90L+PnqW9CVtAU5pUnR9RNzZ5tgFwMeBN49tarJby0axdKS6wfRc27sd1KjMJvv1ga9xMpjs1zcmIkrf7NyzpB8RS47mOEkzgS8CV0XE4+nmYQ6fIGAmsKe7GpqZ1U+ppkuUdBLwZeC6iPjm2PaI2Av8QNJ5aa+dq4AJvy2YmdmRiuqyeVk6atz5wJclbU6LrgV+DviwpB3pcnJatopk3Ogh4HEyPsQl47yRFTbZrw98jZPBZL++ylCUfJwIMzPLT6mad8zMrLec9M3MamRSJH1JSyU9kg7R8KEm5cdJ+nxa/n8lze5/LbuT4Rp/R9JDkh6UdLekrvsc91u7a2zY7wpJ0TiERxVkuT5Jb03/HXdJ+ly/69itDD+np0n6qqT705/VS4qoZ61FRKUXYArJg91XAccCDwDzx+3zH4G16eflwOeLrncPrvFC4CXp51WT8RrT/Y4nGb5jG7Cw6Hrn/G84F7gfeGm6fnLR9e7BNQ4Cq9LP84Eni6533ZbJcKe/CBiKiCci4gVgI7Bs3D7LgP+Vfr4duKhiA7a1vcaI+GpE/DBd3cbh7zVUQZZ/R4AbgZuAH/ezcjnIcn3vAf40Ir4HEBH7+lzHbmW5xgBOSD+fiN+36bvJkPRnAE83rDcbouEn+0TEIeA5kgmFqyLLNTa6huxdWsui7TVK+jfArIj4Uj8rlpMs/4bzgHmSvilpm6TSv905TpZr/CjwjrTL9l3Ae/tTNRtT5rF3ssoyRENHwziUUOb6S3oHsBC4oKc1yt+E1yjpGOCPgKv7VaGcZfk3nErSxLOY5JvaNySdGRH/0uO65SXLNa4A1kfEzZLOB/48vcZyD0I/iUyGO/1hYFbDerMhGn6yj6SpJF8rn+1L7fKR5RqRtAS4Hrg0Ig70qW55aXeNxwNnAl+T9CRwHrCpQg9zs/6c3hkRByNiN/AIyS+BqshyjdcAtwFEMmrui0gGY7M+mQxJ/1vAXElzJB1L8qB207h9NpGMzw9wBfB/In2SVBFtrzFt+vgMScKvWlswtLnGiHguIgYiYnZEzCZ5bnFpRGwvprody/JzegfJA3kkDZA09zzR11p2J8s1PgVcBCDptSRJ///1tZY1V/mkn7bRXwtsBh4GbouIXZJ+X9Kl6W5/Brxc0hDwO0DL7oBllPEaPwH8LPAX6fAV4/+zlVrGa6ysjNe3GXhG0kPAV4EPRsQzxdS4cxmv8f3AeyQ9AGwArq7YDVjleRgGM7MaqfydvpmZZeekb2ZWI076ZmY14qRvZlYjTvpmZjXipG9mViNO+mZmNeKkb7Ui6Q5J96Xj1a8suj5m/eaXs6xWJL0sIp6V9GKSYQMuqNJbr2bdmgyjbJp14rckXZZ+nkUyoJmTvtWGk77VhqTFwBLg/Ij4oaSvkQz4ZVYbbtO3OjkR+F6a8F9DMjyzWa046VudfAWYKulBkmkXtxVcH7O+84NcM7Ma8Z2+mVmNOOmbmdWIk76ZWY046ZuZ1YiTvplZjTjpm5nViJO+mVmN/H/D5xTdGiUoRwAAAABJRU5ErkJggg==\n", 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MSX87MDFs8yXA7PTzTODJhv1G021mZuWQcdydIh/2FpL0JW2StL3Jshx4F7BG0r0kI3w+N3FYk1M1/aeTtErSVklbe3MFZmYt5HSnL2mdpD2Smk6BKGmJpGckbUuXj2SpXiG9dyJiaZtd3gwgaT7wK+m2UX561w8wC9jV4vzDwHB6jpI/SzezgZJfxlkPfBK4aYp9vhERv9rJSUvXvCPpmPS/04D/CtyYFm0EVkg6QtJcYB5wTzG1NDM7mMiv905EbCaZxyRXpUv6wEpJ3wO+S3In/2cAEbEDuAV4EPgKsCYixgqrpZnZZJ216Q9NNEOny6pDiHiWpPsl/Y2khVkOKN3LWRFxPXB9i7K1wNr+1sjMrAPZm3f2djkx+reBEyPiXySdD9xG0gIypdIl/V7Ibf7LksRxrOrEcazqxepan54iRsQPGz7fIelTkoYiYu9Ux5WxecfMrLL61WVT0rGSlH5eTJLPn2p3XC3u9Md2t/3G05WJu5Bex3Gs6sRxrGrFyvWbRE53+pI2AEtI2v5HgauBGQARcSNwMbBa0n7gX4EVEdE2ei2SvplZX0R+4+pExMo25Z8k6dLZESd9M7M8lfzNICd9M7Mclf11UCd9M7M8OembmdVEwSNoZuGkb2aWE+HmHTOzWnHSNzOrEyd9M7MacdI3M6uJgmfFysJJ38wsT076Zmb1kdcwDL3ipG9mliM375iZ1YVfzjIzqxknfTOzevAbuWZmNaPxcmd9T5doZpaX6GBpQ9I6SXskbW+z3+sljUm6OEsVnfTNzHKU4xy564FlU8aSpgMfB+7MWr9aNO/kOv9lCeI4VnXiOFb1YnUtp9adiNgsaU6b3d4L/DXw+qznLeROX9IlknZIGpe0aFLZlZJGJD0s6byG7cvSbSOSPtT/WpuZtZfjnf7UcaSZwIXAjZ0cV9Sd/nbgIuAzjRslLQBWAAuB44FNkuanxTcAvwSMAt+StDEiHswSbGz3vLzq3dTEXUiv4zhWdeI4VrVi5fpNIntCH5K0tWF9OCKGO4j0x8DvRcSYpMwHFZL0I+IhgCYVXQ7cHBH7gJ2SRoDFadlIRDyWHndzum+mpG9m1hfR0TAMeyNiUfvdWloE3Jzm0SHgfEn7I+K2qQ4qW5v+TGBLw/poug3gyUnb39DqJJJWAatyr52Z2RT62U8/Iua+EFdaD3ypXcKHHiZ9SZuAY5sUXRURt7c6rMm2oPmzh5b/tOlXpOG0HuXuNGtmgyXySTmSNgBLSJqBRoGrgRlJiOioHb9Rz5J+RCw9hMNGgdkN67OAXennVtvNzEojr9vMiFjZwb6XZd23bP30NwIrJB0haS4wD7gH+BYwT9JcSYeTPOzdWGA9zcwOluPLWb1SSJu+pAuB/wG8EviypG0RcV5E7JB0C8kD2v3AmogYS4+5guQFhOnAuojYUUTdzcym4vH0m4iILwBfaFG2FljbZPsdwB09rpqZWVec9M3M6iLI7UFurzjpm5nlqOz9BZ30zczy5KRvZlYPnkTFzKxOIko/iYqTvplZnsqd8530zczy5OYdM7O6CMDNO2ZmNVLunO+kb2aWJzfvmJnViHvvlIAn23asouI4VvVidaXgETSzqEXSNzPrh+TlrHJn/VokfU+27Vj9juNY1YqV6zeJko+yWbZJVMzMKk0RmZa255HWSdojaXuL8uWSHpC0TdJWSb+QpX5O+mZmecl35qz1wLIpyu8CTouI04F3AZ/NctJaNO+YmfVHfmPvRMRmSXOmKP+XhtWfIeOfEid9M7M8ZX+QOyRpa8P6cEQMdxIqnXr2vwHHAL+S5RgnfTOzvERH0yXujYhFXYVLp56V9IvANcDSdse4Td/MLE8R2ZZcQ8Zm4CRJQ+32ddI3M8tTfg9ypyTp5yQp/fw64HDgqXbHuXnHzCxHGs+no76kDcASkrb/UeBqYAZARNwIvBW4VNLzwL8Cb49o/xWikKQv6RLgo8BrgcURsTXd/grgVuD1wPqIuKLhmDNIujC9GLgD+J0sF2hm1jdBbi9nRcTKNuUfBz7e6XmLat7ZDlwEbJ60/SfAh4EPNDnm08AqYF66TNV/1cys70S2F7OKHKqhkKQfEQ9FxMNNtj8bEX9PkvxfIOk44KiIuDu9u78J+LX+1NbMrAMFPMjtRFXa9GcCow3ro+m2piStIvlWYGbWXyVvde5Z0pe0CTi2SdFVEXF7p6drsq3lv2z6gsNwWo9y/wTMbHDk2KbfKz1L+hHR9iWBDowCsxrWZwG7cjy/mVku8uq90yuV6KcfEbuBH0k6M+2XeinQ6bcFM7Mey9ieX7cHuZIuTPudngV8WdKdDWWPA/8duEzSqKQFadFqklHkRoBHgb/pb63NzNoISp/0C3mQOzFeRIuyOS22bwVO6WG1zMy6V+7Wncr03jEzqwRPl2hmVidO+sXLdf7LEsRxrOrEcazqxepKBIyVu32nFknfzKxvfKdfvLHd83p6/om7kF7HcazqxHGsasXK9ZuEk76ZWU0EkNMcub3ipG9mlpuAcJu+mVk9BKV/kFuJYRjMzCojpzdyJa2TtEfS9hbl/17SA+nyD5JOy1I9J30zszzlNwzDeqaeLGoncHZEnApcQzqycDtu3jEzy01+4+pExGZJc6Yo/4eG1S0cOBJxS076ZmZ5CaCYoZUvJ+MglE76ZmZ5yn6nPyRpa8P6cDoBVEcknUOS9H8hy/5O+mZmueloGIa9EbGom2iSTiUZcv6XI+KpLMc46ZuZ5SUg+tRPX9IJwOeB34iI72U9zknfzCxPOb2RK2kDsISkGWgUuBqYARARNwIfAV4BfCqZUJD9Wb45OOmbmeUpv947K9uUvxt4d6fnddI3M8tLRFG9dzJz0jczy5NH2TQzq4sgxsaKrsSUnPTNzPLioZXNzGqm5EMrFzLgmqRLJO2QNC5pUcP2X5J0r6TvpP99U0PZGen2EUl/orSPkplZWQQQ45FpKUpRo2xuBy4CNk/avhd4S0T8PPCbwF80lH0aWAXMS5epRp8zM+u/SCdRybIUpJDmnYh4CGDyzXpE3NewugN4kaQjgJcDR0XE3elxNwG/RsYBhnKd/7IEcRyrOnEcq3qxuuUHuYfurcB9EbFP0kxgtKFsFJjZ6kBJq0i+FQDsI/lmMaiGSL4hDTJfY/VV4fpO7PYEP+IHd26KW4cy7l7Iv0fPkr6kTcCxTYquiojb2xy7EPg48OaJTU12a9kolo5UN5yea2u3gxqV2aBfH/gaB8GgX9+EiCh9s3PPkn5ELD2U4yTNAr4AXBoRj6abRzlwgoBZwK7uamhmVj+lmi5R0kuBLwNXRsQ3J7ZHxG7gR5LOTHvtXApM+W3BzMwOVlSXzQvTUePOAr4s6c606Arg54APS9qWLsekZatJxo0eAR4l40NcMs4bWWGDfn3gaxwEg359laEo+TgRZmaWn1I175iZWW856ZuZ1chAJH1JyyQ9nA7R8KEm5UdI+lxa/n8kzel/LbuT4Rp/V9KDkh6QdJekrvsc91u7a2zY72JJ0TiERxVkuT5Jb0t/jjsk/WW/69itDL+nJ0j6qqT70t/V84uoZ61FRKUXYDrJg91XA4cD9wMLJu3zH4Eb088rgM8VXe8eXOM5wEvSz6sH8RrT/Y4kGb5jC7Co6Hrn/DOcB9wHvCxdP6boevfgGoeB1ennBcDjRde7bssg3OkvBkYi4rGIeA64GVg+aZ/lwJ+nn28Fzq3YgG1trzEivhoRP05Xt3Dgew1VkOXnCHANcC3wk35WLgdZru89wA0R8QOAiNjT5zp2K8s1BnBU+vlo/L5N3w1C0p8JPNmw3myIhhf2iYj9wDMkEwpXRZZrbHQ52bu0lkXba5T0b4DZEfGlflYsJ1l+hvOB+ZK+KWmLpNK/3TlJlmv8KPCOtMv2HcB7+1M1m1DmsXeyyjJEQ0fDOJRQ5vpLegewCDi7pzXK35TXKGka8EfAZf2qUM6y/AwPI2niWULyTe0bkk6JiP/X47rlJcs1rgTWR8R1ks4C/iK9xnIPQj9ABuFOfxSY3bDebIiGF/aRdBjJ18qn+1K7fGS5RiQtBa4CLoiIfX2qW17aXeORwCnA1yQ9DpwJbKzQw9ysv6e3R8TzEbETeJjkj0BVZLnGy4FbACIZNfdFJIOxWZ8MQtL/FjBP0lxJh5M8qN04aZ+NJOPzA1wM/O9InyRVRNtrTJs+PkOS8KvWFgxtrjEinomIoYiYExFzSJ5bXBARW4upbsey/J7eRvJAHklDJM09j/W1lt3Jco1PAOcCSHotSdL/v32tZc1VPumnbfRXAHcCDwG3RMQOSX8g6YJ0tz8FXiFpBPhdoGV3wDLKeI2fAH4W+Kt0+IrJ/7OVWsZrrKyM13cn8JSkB4GvAh+MiKeKqXHnMl7j+4H3SLof2ABcVrEbsMrzMAxmZjVS+Tt9MzPLzknfzKxGnPTNzGrESd/MrEac9M3MasRJ38ysRpz0zcxqxEnfakXSbZLuTcerX1V0fcz6zS9nWa1IenlEPC3pxSTDBpxdpbdezbo1CKNsmnXityVdmH6eTTKgmZO+1YaTvtWGpCXAUuCsiPixpK+RDPhlVhtu07c6ORr4QZrwX0MyPLNZrTjpW518BThM0gMk0y5uKbg+Zn3nB7lmZjXiO30zsxpx0jczqxEnfTOzGnHSNzOrESd9M7MacdI3M6sRJ30zsxr5/2tAMSZ8PZaCAAAAAElFTkSuQmCC\n", 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\n", 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\n", 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\n", 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WGZP+TmB02OZrgJnp+nTgmYb9htJtZmblkHHcnSIf9haS9CVtkbSzybIUeBewStIDJCN8vjB6WJNTNf2nk7RC0nZJ23tzBWZmLeR0py9pnaT9kppOgShpkaSDknaky0ezVK+Q3jsRsbjNLm8FkDQX+OV02xA/uesHmAHsbXH+AWAgPUfJn6Wb2YSSX8ZZD3wKuH2cfb4ZEb/SyUlL17wj6dT0vycA/xlYmxZtApZJOknSbGAOcH8xtTQzO5bIr/dORGwlmcckV6VL+sBySd8FvkNyJ/8/ACJiF3AH8CjwVWBVRAwXVkszs7E6a9OfOtoMnS4rjiPiRZIekvTXkuZnOaB0L2dFxG3AbS3K1gBr+lsjM7MOZG/eOdDlxOj/BzgzIv5Z0uXAXSQtIOMqXdLvhdzmvyxJHMeqThzHql6srvXpKWJE/KBh/R5Jn5Y0NSIOjHdcGZt3zMwqq19dNiWdJknp+kKSfP5su+Nqcac/vK/tN56ujN6F9DqOY1UnjmNVK1au3yRyutOXtAFYRNL2PwTcBEwBiIi1wNXASklHgH8BlkVE2+i1SPpmZn0R+Y2rExHL25R/iqRLZ0ec9M3M8lTyN4Oc9M3MclT210Gd9M3M8uSkb2ZWEwWPoJmFk76ZWU6Em3fMzGrFSd/MrE6c9M3MasRJ38ysJgqeFSsLJ30zszw56ZuZ1UdewzD0ipO+mVmO3LxjZlYXfjnLzKxmnPTNzOrBb+SamdWMRsqd9T1doplZXqKDpQ1J6yTtl7SzzX5vlDQs6eosVXTSNzPLUY5z5K4HlowbS5oEfALYnLV+tWjeyXX+yxLEcazqxHGs6sXqWk6tOxGxVdKsNru9F/hL4I1Zz1vInb6kayTtkjQiacGYstWSBiXtlnRZw/Yl6bZBSR/uf63NzNrL8U5//DjSdOBKYG0nxxV1p78TuAr4bONGSfOAZcB84HRgi6S5afGfAL8IDAHflrQpIh7NEmx435y86t3U6F1Ir+M4VnXiOFa1YuX6TSJ7Qp8qaXvD54GIGOgg0h8BvxsRw5IyH1RI0o+IxwCaVHQpsDEiDgF7JA0CC9OywYh4Mj1uY7pvpqRvZtYX0dEwDAciYkH73VpaAGxM8+hU4HJJRyLirvEOKlub/nRgW8PnoXQbwDNjtr+p1UkkrQBW5F47M7Nx9LOffkTMfjGutB74cruEDz1M+pK2AKc1KboxIu5udViTbUHzZw8t/2nTr0gDaT3K3WnWzCaWyCflSNoALCJpBhoCbgKmJCGio3b8Rj1L+hGx+DgOGwJmNnyeAexN11ttNzMrjbxuMyNieQf7Xpd137L1098ELJN0kqTZwBzgfuDbwBxJsyWdSPKwd1OB9TQzO1aOL2f1SiFt+pKuBP4b8BrgK5J2RMRlEbFL0h0kD2iPAKsiYjg95gaSFxAmAesiYlcRdTczG4/H028iIr4EfKlF2RpgTZPt9wD39LhqZmZdcdI3M6uLILcHub3ipG9mlqOy9xd00jczy5OTvplZPXgSFTOzOoko/SQqTvpmZnkqd8530jczy5Obd8zM6iIAN++YmdVIuXO+k76ZWZ7cvGNmViPuvVMCnmzbsYqK41jVi9WVgkfQzKIWSd/MrB+Sl7PKnfVrkfQ92bZj9TuOY1UrVq7fJEo+ymbZJlExM6s0RWRa2p5HWidpv6SdLcqXSnpY0g5J2yX9fJb6OembmeUl35mz1gNLxim/FzgvIs4H3gV8LstJa9G8Y2bWH/mNvRMRWyXNGqf8nxs+/hQZ/5Q46ZuZ5Sn7g9ypkrY3fB6IiIFOQqVTz/4X4FTgl7Mc46RvZpaX6Gi6xAMRsaCrcOnUs5J+AbgZWNzuGLfpm5nlKSLbkmvI2AqcJWlqu32d9M3M8pTfg9xxSfoZSUrXfw44EXi23XFu3jEzy5FG8umoL2kDsIik7X8IuAmYAhARa4G3AddKOgz8C/DrEe2/QhSS9CVdA3wMeAOwMCK2p9tfDdwJvBFYHxE3NBxzAUkXppcC9wDvy3KBZmZ9E+T2clZELG9T/gngE52et6jmnZ3AVcDWMdt/DHwE+GCTYz4DrADmpMt4/VfNzPpOZHsxq8ihGgpJ+hHxWETsbrL9+Yj4O5Lk/yJJ04CTI+K+9O7+duDX+lNbM7MOFPAgtxNVadOfDgw1fB5KtzUlaQXJtwIzs/4qeatzz5K+pC3AaU2KboyIuzs9XZNtLf9l0xccBtJ6lPsnYGYTR45t+r3Ss6QfEW1fEujAEDCj4fMMYG+O5zczy0VevXd6pRL99CNiH/BDSRem/VKvBTr9tmBm1mMZ2/Pr9iBX0pVpv9OLgK9I2txQ9hTwX4HrJA1JmpcWrSQZRW4QeAL46/7W2sysjaD0Sb+QB7mj40W0KJvVYvt24JweVsvMrHvlbt2pTO8dM7NK8HSJZmZ14qRfvFznvyxBHMeqThzHql6srkTAcLnbd2qR9M3M+sZ3+sUb3jenp+cfvQvpdRzHqk4cx6pWrFy/STjpm5nVRAA5zZHbK076Zma5CQi36ZuZ1UNQ+ge5lRiGwcysMnJ6I1fSOkn7Je1sUf5vJT2cLn8v6bws1XPSNzPLU37DMKxn/Mmi9gAXR8S5wM2kIwu34+YdM7Pc5DeuTkRslTRrnPK/b/i4jaNHIm7JSd/MLC8BFDO08vVkHITSSd/MLE/Z7/SnStre8HkgnQCqI5IuIUn6P59lfyd9M7PcdDQMw4GIWNBNNEnnkgw5/0sR8WyWY5z0zczyEhB96qcv6Qzgi8BvRsR3sx7npG9mlqec3siVtAFYRNIMNATcBEwBiIi1wEeBVwOfTiYU5EiWbw5O+mZmecqv987yNuXvBt7d6Xmd9M3M8hJRVO+dzJz0zczy5FE2zczqIojh4aIrMS4nfTOzvHhoZTOzmin50MqFDLgm6RpJuySNSFrQsP0XJT0g6ZH0v29pKLsg3T4o6Y+V9lEyMyuLAGIkMi1FKWqUzZ3AVcDWMdsPAL8aET8L/BbwZw1lnwFWAHPSZbzR58zM+i/SSVSyLAUppHknIh4DGHuzHhEPNnzcBbxE0knAq4CTI+K+9LjbgV8j4wBDuc5/WYI4jlWdOI5VvVjd8oPc4/c24MGIOCRpOjDUUDYETG91oKQVJN8KAA6RfLOYqKaSfEOayHyN1VeF6zuz2xP8kO9v3hJ3Ts24eyH/Hj1L+pK2AKc1KboxIu5uc+x84BPAW0c3NdmtZaNYOlLdQHqu7d0OalRmE/36wNc4EUz06xsVEaVvdu5Z0o+IxcdznKQZwJeAayPiiXTzEEdPEDAD2NtdDc3M6qdU0yVKegXwFWB1RHxrdHtE7AN+KOnCtNfOtcC43xbMzOxYRXXZvDIdNe4i4CuSNqdFNwA/A3xE0o50OTUtW0kybvQg8AQZH+KScd7ICpvo1we+xolgol9fZShKPk6EmZnlp1TNO2Zm1ltO+mZmNTIhkr6kJZJ2p0M0fLhJ+UmSvpCW/29Js/pfy+5kuMbfkfSopIcl3Sup6z7H/dbuGhv2u1pSNA7hUQVZrk/S29Of4y5Jf97vOnYrw+/pGZK+JunB9Hf18iLqWWsRUekFmETyYPd1wInAQ8C8Mfv8e2Btur4M+ELR9e7BNV4CvCxdXzkRrzHd7+Ukw3dsAxYUXe+cf4ZzgAeBV6afTy263j24xgFgZbo+D3iq6HrXbZkId/oLgcGIeDIiXgA2AkvH7LMU+J/p+p3ApRUbsK3tNUbE1yLiR+nHbRz9XkMVZPk5AtwM3AL8uJ+Vy0GW63sP8CcR8X2AiNjf5zp2K8s1BnByun4Kft+m7yZC0p8OPNPwudkQDS/uExFHgIMkEwpXRZZrbHQ92bu0lkXba5T0r4CZEfHlflYsJ1l+hnOBuZK+JWmbpNK/3TlGlmv8GPCOtMv2PcB7+1M1G1XmsXeyyjJEQ0fDOJRQ5vpLegewALi4pzXK37jXKOkE4A+B6/pVoZxl+RlOJmniWUTyTe2bks6JiP/X47rlJcs1LgfWR8Stki4C/iy9xnIPQj+BTIQ7/SFgZsPnZkM0vLiPpMkkXyuf60vt8pHlGpG0GLgRuCIiDvWpbnlpd40vB84Bvi7pKeBCYFOFHuZm/T29OyIOR8QeYDfJH4GqyHKN1wN3AEQyau5LSAZjsz6ZCEn/28AcSbMlnUjyoHbTmH02kYzPD3A18L8ifZJUEW2vMW36+CxJwq9aWzC0ucaIOBgRUyNiVkTMInlucUVEbC+muh3L8nt6F8kDeSRNJWnuebKvtexOlmt8GrgUQNIbSJL+/+1rLWuu8kk/baO/AdgMPAbcERG7JP2+pCvS3f4UeLWkQeB3gJbdAcso4zV+Evhp4C/S4SvG/s9WahmvsbIyXt9m4FlJjwJfAz4UEc8WU+POZbzGDwDvkfQQsAG4rmI3YJXnYRjMzGqk8nf6ZmaWnZO+mVmNOOmbmdWIk76ZWY046ZuZ1YiTvplZjTjpm5nViJO+1YqkuyQ9kI5Xv6Lo+pj1m1/OslqR9KqIeE7SS0mGDbi4Sm+9mnVrIoyyadaJ35Z0Zbo+k2RAMyd9qw0nfasNSYuAxcBFEfEjSV8nGfDLrDbcpm91cgrw/TThv55keGazWnHStzr5KjBZ0sMk0y5uK7g+Zn3nB7lmZjXiO30zsxpx0jczqxEnfTOzGnHSNzOrESd9M7MacdI3M6sRJ30zsxr5/984Kz0lgvpGAAAAAElFTkSuQmCC\n", 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\n", 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\n", 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\n", 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\n", 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\n", 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\n", 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\n", 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\n", 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\n", 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\n", 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Am9PPy4CNEbE/InYBQ8CiAspnZtZayV/OKmPS3wGMDdt8GTA7/TwTeKJhv+F0nZlZOWQcd6fIh72FJH1JmyXtaLIsA94JrJZ0D8kIn8+PHdbkVE3/6SStlLRN0rbeXIGZWQs51fQlrZO0V1LTKRAlLZb0jKTt6fKRLMUrpPdORCxps8ubACTNB34tXTfMz2r9ALOA3S3OPwgMpuco+bN0M5tU8ss464FPAjdNsM83I+LXOzlp6Zp3JB2X/vcI4L8Aa9NNm4Dlko6SNBeYB9xdTCnNzA4l8uu9ExFbSOYxyVXpkj6wQtL3gO+S1OT/B0BE7ARuBh4AvgqsjoiRwkppZjZeZ236A2PN0Omy8jAiniPpPkl/K2lBlgNK93JWRNwI3Nhi2xpgTX9LZGbWgezNO/u6nBj9/wInRcS/SroQuJWkBWRCpUv6vZDb/JclieNY1YnjWNWL1bU+PUWMiB82fL5d0qckDUTEvomOK2PzjplZZfWry6ak4yUp/byIJJ8/2e64WtT0R/a0vePpylgtpNdxHKs6cRyrWrFyvZPIqaYvaQOwmKTtfxi4FpgGEBFrgUuBVZIOAs8ByyOibfRaJH0zs76I/MbViYgVbbZ/kqRLZ0ec9M3M8lTyN4Oc9M3MclT210Gd9M3M8uSkb2ZWEwWPoJmFk76ZWU6Em3fMzGrFSd/MrE6c9M3MasRJ38ysJgqeFSsLJ30zszw56ZuZ1UdewzD0ipO+mVmO3LxjZlYXfjnLzKxmnPTNzOrBb+SamdWMRsud9T1doplZXqKDpQ1J6yTtlbSjzX6vkzQi6dIsRXTSNzPLUY5z5K4Hlk4YS5oCfBy4I2v5atG8k+v8lyWI41jVieNY1YvVtZxadyJii6Q5bXZ7D/DXwOuynreQmr6kyyTtlDQqaeG4bVdLGpL0kKQLGtYvTdcNSfpg/0ttZtZejjX9ieNIM4GLgbWdHFdUTX8HcAnwmcaVkk4FlgMLgBOAzZLmp5v/AvgVYBj4tqRNEfFAlmAje+blVe6mxmohvY7jWNWJ41jVipXrnUT2hD4gaVvD98GIGOwg0p8Bvx8RI5IyH1RI0o+IBwGaFHQZsDEi9gO7JA0Bi9JtQxHxaHrcxnTfTEnfzKwvoqNhGPZFxML2u7W0ENiY5tEB4EJJByPi1okOKlub/kxga8P34XQdwBPj1r++1UkkrQRW5l46M7MJ9LOffkTM/WlcaT3w5XYJH3qY9CVtBo5vsumaiLit1WFN1gXNnz20/KdNb5EG03KUu9OsmU0ukU/KkbQBWEzSDDQMXAtMS0JER+34jXqW9CNiyWEcNgzMbvg+C9idfm613sysNPKqZkbEig72vSLrvmXrp78JWC7pKElzgXnA3cC3gXmS5ko6kuRh76YCy2lmdqgcX87qlULa9CVdDPw34BXAVyRtj4gLImKnpJtJHtAeBFZHxEh6zFUkLyBMAdZFxM4iym5mNhGPp99ERHwJ+FKLbWuANU3W3w7c3uOimZl1xUnfzKwugtwe5PaKk76ZWY7K3l/QSd/MLE9O+mZm9eBJVMzM6iSi9JOoOOmbmeWp3DnfSd/MLE9u3jEzq4sA3LxjZlYj5c75TvpmZnly846ZWY24904JeLJtxyoqjmNVL1ZXCh5BM4taJH0zs35IXs4qd9avRdL3ZNuO1e84jlWtWLneSZR8lM2yTaJiZlZpisi0tD2PtE7SXkk7WmxfJul+SdslbZP0S1nK56RvZpaXfGfOWg8snWD7ncAZEXEm8E7gs1lOWovmHTOz/shv7J2I2CJpzgTb/7Xh68+R8U+Jk76ZWZ6yP8gdkLSt4ftgRAx2EiqdevaPgeOAX8tyjJO+mVleoqPpEvdFxMKuwqVTz0r6ZeA6YEm7Y9ymb2aWp4hsS64hYwtwsqSBdvs66ZuZ5Sm/B7kTknSKJKWffxE4Eniy3XFu3jEzy5FG8+moL2kDsJik7X8YuBaYBhARa4E3A5dLOgA8B7w1ov0tRCFJX9JlwEeB1wKLImJbuv7lwC3A64D1EXFVwzFnkXRhejFwO/C7WS7QzKxvgtxezoqIFW22fxz4eKfnLap5ZwdwCbBl3PqfAB8G3t/kmE8DK4F56TJR/1Uzs74T2V7MKnKohkKSfkQ8GBEPNVn/bET8A0ny/ylJM4BjIuKutHZ/E/Cb/SmtmVkHCniQ24mqtOnPBIYbvg+n65qStJLkrsDMrL9K3urcs6QvaTNwfJNN10TEbZ2ersm6lv+y6QsOg2k5yv0TMLPJI8c2/V7pWdKPiLYvCXRgGJjV8H0WsDvH85uZ5SKv3ju9Uol++hGxB/iRpLPTfqmXA53eLZiZ9VjG9vy6PciVdHHa7/Qc4CuS7mjY9hjwX4ErJA1LOjXdtIpkFLkh4BHgb/tbajOzNoLSJ/1CHuSOjRfRYtucFuu3Aaf1sFhmZt0rd+tOZXrvmJlVgqdLNDOrEyf94uU6/2UJ4jhWdeI4VvVidSUCRsrdvlOLpG9m1jeu6RdvZM+8np5/rBbS6ziOVZ04jlWtWLneSTjpm5nVRAA5zZHbK076Zma5CQi36ZuZ1UNQ+ge5lRiGwcysMnJ6I1fSOkl7Je1osf3fS7o/Xf5R0hlZiuekb2aWp/yGYVjPxJNF7QLOjYjTgetIRxZux807Zma5yW9cnYjYImnOBNv/seHrVl44EnFLTvpmZnkJoJihla8k4yCUTvpmZnnKXtMfkLSt4ftgOgFURySdR5L0fynL/k76Zma56WgYhn0RsbCbaJJOJxly/lcj4sksxzjpm5nlJSD61E9f0onAF4HfiojvZT3OSd/MLE85vZEraQOwmKQZaBi4FpgGEBFrgY8ALwc+lUwoyMEsdw5O+mZmecqv986KNtvfBbyr0/M66ZuZ5SWiqN47mTnpm5nlyaNsmpnVRRAjI0UXYkJO+mZmefHQymZmNVPyoZULGXBN0mWSdkoalbSwYf2vSLpH0nfS/76xYdtZ6fohSX+utI+SmVlZBBCjkWkpSlGjbO4ALgG2jFu/D/iNiPgF4LeBzzVs+zSwEpiXLhONPmdm1n+RTqKSZSlIIc07EfEgwPjKekTc2/B1J/AiSUcBLwOOiYi70uNuAn6TjAMM5Tr/ZQniOFZ14jhW9WJ1yw9yD9+bgXsjYr+kmcBww7ZhYGarAyWtJLkrANhPcmcxWQ2Q3CFNZr7G6qvC9Z3U7Ql+xA/u2By3DGTcvZB/j54lfUmbgeObbLomIm5rc+wC4OPAm8ZWNdmtZaNYOlLdYHqubd0OalRmk/36wNc4GUz26xsTEaVvdu5Z0o+IJYdznKRZwJeAyyPikXT1MC+cIGAWsLu7EpqZ1U+ppkuUNB34CnB1RHxrbH1E7AF+JOnstNfO5cCEdwtmZnaoorpsXpyOGncO8BVJd6SbrgJOAT4saXu6HJduW0UybvQQ8AgZH+KScd7ICpvs1we+xslgsl9fZShKPk6EmZnlp1TNO2Zm1ltO+mZmNTIpkr6kpZIeSodo+GCT7UdJ+kK6/f9ImtP/UnYnwzX+nqQHJN0v6U5JXfc57rd219iw36WSonEIjyrIcn2S3pL+HHdK+ny/y9itDL+nJ0r6mqR709/VC4soZ61FRKUXYArJg91XAUcC9wGnjtvnPwJr08/LgS8UXe4eXON5wEvSz6sm4zWm+x1NMnzHVmBh0eXO+Wc4D7gXeGn6/biiy92DaxwEVqWfTwUeK7rcdVsmQ01/ETAUEY9GxPPARmDZuH2WAf8z/XwLcH7FBmxre40R8bWI+HH6dSsvfK+hCrL8HAGuA64HftLPwuUgy/W9G/iLiPgBQETs7XMZu5XlGgM4Jv18LH7fpu8mQ9KfCTzR8L3ZEA0/3SciDgLPkEwoXBVZrrHRlWTv0loWba9R0r8BZkfEl/tZsJxk+RnOB+ZL+pakrZJK/3bnOFmu8aPA29Mu27cD7+lP0WxMmcfeySrLEA0dDeNQQpnLL+ntwELg3J6WKH8TXqOkI4A/Ba7oV4FyluVnOJWkiWcxyZ3aNyWdFhFP97hseclyjSuA9RFxg6RzgM+l11juQegnkclQ0x8GZjd8bzZEw0/3kTSV5Lbyqb6ULh9ZrhFJS4BrgIsiYn+fypaXdtd4NHAa8HVJjwFnA5sq9DA36+/pbRFxICJ2AQ+R/BGoiizXeCVwM0Ako+a+iGQwNuuTyZD0vw3MkzRX0pEkD2o3jdtnE8n4/ACXAv870idJFdH2GtOmj8+QJPyqtQVDm2uMiGciYiAi5kTEHJLnFhdFxLZiituxLL+nt5I8kEfSAElzz6N9LWV3slzj48D5AJJeS5L0/19fS1lzlU/6aRv9VcAdwIPAzRGxU9IfSroo3e0vgZdLGgJ+D2jZHbCMMl7jJ4CfB/4qHb5i/P9spZbxGisr4/XdATwp6QHga8AHIuLJYkrcuYzX+D7g3ZLuAzYAV1SsAlZ5HobBzKxGKl/TNzOz7Jz0zcxqxEnfzKxGnPTNzGrESd/MrEac9M3MasRJ38ysRpz0rVYk3SrpnnS8+pVFl8es3/xyltWKpJdFxFOSXkwybMC5VXrr1axbk2GUTbNO/I6ki9PPs0kGNHPSt9pw0rfakLQYWAKcExE/lvR1kgG/zGrDbfpWJ8cCP0gT/mtIhmc2qxUnfauTrwJTJd1PMu3i1oLLY9Z3fpBrZlYjrumbmdWIk76ZWY046ZuZ1YiTvplZjTjpm5nViJO+mVmNOOmbmdXI/wcBZTVwbxMA7gAAAABJRU5ErkJggg==\n", 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\n", 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\n", 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\n", 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\n", 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\n", 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\n", 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PAjbkn/8OeGv+eQmwNiJ2R8Q2YBhYUEL9zMxaq/jLWVVM+luA8WGbLwFm5p+nA0817DeSbzMzq4aC4+6U+bC3lKQvab2kLU2WJcC7gCsl3Uc2wufz44c1OVXTfzpJyyVtkrSpN1dgZtZCojt9Sask7ZTUdApESQslPStpc758uEj1Sum9ExGL2uzyZgBJc4FfybeN8NO7foAZwPYW5x8ChvJzVPxZuplNKukyzmrgE8AtE+zzzYj41U5OWrnmHUnH5P89BPjvwMq8aB2wVNIRkmYDc4B7y6mlmdmBRLreOxGxgWwek6Qql/SBZZK+B3yX7E7+zwEiYitwK/AQ8FXgyogYLa2WZmb766xNf+p4M3S+LD+IiGdJekDS30iaV+SAyr2cFRE3ATe1KFsBrOhvjczMOlC8eWdXlxOj/xNwQkT8m6TzgdvJWkAmVLmk3wvJ5r+sSBzHGpw4jjV4sbrWp6eIEfHDhs93SvqkpKkRsWui46rYvGNmNrD61WVT0rGSlH9eQJbPn253XC3u9Ed3tP3G05Xxu5Bex3GswYnjWIMVK+k3iUR3+pLWAAvJ2v5HgOuAwwAiYiVwMXCFpL3AvwNLI6Jt9FokfTOzvoh04+pExLI25Z8g69LZESd9M7OUKv5mkJO+mVlCVX8d1EnfzCwlJ30zs5ooeQTNIpz0zcwSEW7eMTOrFSd9M7M6cdI3M6sRJ30zs5ooeVasIpz0zcxSctI3M6uPVMMw9IqTvplZQm7eMTOrC7+cZWZWM076Zmb14DdyzcxqRmPVzvqeLtHMLJXoYGlD0ipJOyVtabPf6yWNSrq4SBWd9M3MEko4R+5qYPGEsaQpwMeAu4rWrxbNO0nnv6xAHMcanDiONXixupaodSciNkia1Wa39wKfB15f9Lyl3OlLukTSVkljkubvV3a1pGFJj0g6r2H74nzbsKQP9b/WZmbtJbzTnziONB24EFjZyXFl3elvAS4CPt24UdLJwFJgHnAcsF7S3Lz4ZuCXgBHg25LWRcRDRYKN7piTqt5Njd+F9DqOYw1OHMcarFhJv0kUT+hTJW1qWB+KiKEOIv0J8PsRMSqp8EGlJP2IeBigSUWXAGsjYjewTdIwsCAvG46Ix/Pj1ub7Fkr6ZmZ9ER0Nw7ArIua3362l+cDaPI9OBc6XtDcibp/ooKq16U8HNjasj+TbAJ7ab/sbWp1E0nJgefLamZlNoJ/99CNi9gtxpdXAl9slfOhh0pe0Hji2SdE1EXFHq8OabAuaP3to+U+bf0UayutR7U6zZja5RJqUI2kNsJCsGWgEuA44LAsRHbXjN+pZ0o+IRQdx2Agws2F9BrA9/9xqu5lZZaS6zYyIZR3se1nRfavWT38dsFTSEZJmA3OAe4FvA3MkzZZ0ONnD3nUl1tPM7EAJX87qlVLa9CVdCPwv4JXAVyRtjojzImKrpFvJHtDuBa6MiNH8mKvIXkCYAqyKiK1l1N3MbCIeT7+JiPgi8MUWZSuAFU223wnc2eOqmZl1xUnfzKwugmQPcnvFSd/MLKGq9xd00jczS8lJ38ysHjyJiplZnURUfhIVJ30zs5SqnfOd9M3MUnLzjplZXQTg5h0zsxqpds530jczS8nNO2ZmNeLeOxXgybYdq6w4jjV4sbpS8giaRdQi6ZuZ9UP2cla1s34tkr4n23asfsdxrMGKlfSbRMVH2azaJCpmZgNNEYWWtueRVknaKWlLi/Ilkh6UtFnSJkm/UKR+TvpmZqmknTlrNbB4gvK7gdMi4nTgXcBnipy0Fs07Zmb9kW7snYjYIGnWBOX/1rD6MxT8U+Kkb2aWUvEHuVMlbWpYH4qIoU5C5VPP/g/gGOBXihzjpG9mlkp0NF3iroiY31W4fOpZSb8IXA8saneM2/TNzFKKKLYkDRkbgBMlTW23r5O+mVlK6R7kTkjSz0lS/vl1wOHA0+2Oc/OOmVlCGkvTUV/SGmAhWdv/CHAdcBhARKwE3gpcKmkP8O/A2yPaf4UoJelLugT4CPBaYEFEbMq3vwK4DXg9sDoirmo45gyyLkwvBu4EfqfIBZqZ9U2Q7OWsiFjWpvxjwMc6PW9ZzTtbgIuADftt/wlwLfCBJsd8ClgOzMmXifqvmpn1nSj2YlaZQzWUkvQj4uGIeKTJ9uci4u/Jkv8LJE0DjoqIe/K7+1uAX+tPbc3MOlDCg9xODEqb/nRgpGF9JN/WlKTlZN8KzMz6q+Ktzj1L+pLWA8c2KbomIu7o9HRNtrX8l81fcBjK61Htn4CZTR4J2/R7pWdJPyLaviTQgRFgRsP6DGB7wvObmSWRqvdOrwxEP/2I2AH8SNKZeb/US4FOvy2YmfVYwfb8uj3IlXRh3u/0LOArku5qKHsC+J/AZZJGJJ2cF11BNorcMPAY8Df9rbWZWRtB5ZN+KQ9yx8eLaFE2q8X2TcApPayWmVn3qt26MzC9d8zMBoKnSzQzqxMn/fIlnf+yAnEca3DiONbgxepKBIxWu32nFknfzKxvfKdfvtEdc3p6/vG7kF7HcazBieNYgxUr6TcJJ30zs5oIINEcub3ipG9mlkxAuE3fzKwegso/yB2IYRjMzAZGojdyJa2StFPSlhbl/1nSg/nyD5JOK1I9J30zs5TSDcOwmokni9oGnB0RpwLXk48s3I6bd8zMkkk3rk5EbJA0a4Lyf2hY3ci+IxG35KRvZpZKAOUMrXw5BQehdNI3M0up+J3+VEmbGtaH8gmgOiLpHLKk/wtF9nfSNzNLpqNhGHZFxPxuokk6lWzI+V+OiKeLHOOkb2aWSkD0qZ++pOOBLwC/ERHfK3qck76ZWUqJ3siVtAZYSNYMNAJcBxwGEBErgQ8DrwA+mU0oyN4i3xyc9M3MUkrXe2dZm/J3A+/u9LxO+mZmqUSU1XunMCd9M7OUPMqmmVldBDE6WnYlJuSkb2aWiodWNjOrmYoPrVzKgGuSLpG0VdKYpPkN239J0n2SvpP/900NZWfk24cl/anyPkpmZlURQIxFoaUsZY2yuQW4CNiw3/ZdwFsi4ueB3wT+sqHsU8ByYE6+TDT6nJlZ/0U+iUqRpSSlNO9ExMMA+9+sR8T9DatbgRdJOgJ4OXBURNyTH3cL8GsUHGAo6fyXFYjjWIMTx7EGL1a3/CD34L0VuD8idkuaDow0lI0A01sdKGk52bcCgN1k3ywmq6lk35AmM1/j4BuE6zuh2xP8iB/ctT5um1pw91L+PXqW9CWtB45tUnRNRNzR5th5wMeAN49varJby0axfKS6ofxcm7od1KjKJvv1ga9xMpjs1zcuIirf7NyzpB8Riw7mOEkzgC8Cl0bEY/nmEfadIGAGsL27GpqZ1U+lpkuU9FLgK8DVEfGt8e0RsQP4kaQz8147lwITflswM7MDldVl88J81LizgK9Iuisvugr4OeBaSZvz5Zi87AqycaOHgcco+BCXgvNGDrDJfn3ga5wMJvv1DQxFxceJMDOzdCrVvGNmZr3lpG9mViOTIulLWizpkXyIhg81KT9C0ufy8n+UNKv/texOgWv8PUkPSXpQ0t2Suu5z3G/trrFhv4slReMQHoOgyPVJelv+c9wq6a/6XcduFfg9PV7S1yTdn/+unl9GPWstIgZ6AaaQPdh9NXA48ABw8n77/BawMv+8FPhc2fXuwTWeA7wk/3zFZLzGfL8jyYbv2AjML7veiX+Gc4D7gZfl68eUXe8eXOMQcEX++WTgibLrXbdlMtzpLwCGI+LxiHgeWAss2W+fJcBf5J9vA84dsAHb2l5jRHwtIn6cr25k3/caBkGRnyPA9cANwE/6WbkEilzfe4CbI+IHABGxs8917FaRawzgqPzz0fh9m76bDEl/OvBUw3qzIRpe2Cci9gLPkk0oPCiKXGOjyynepbUq2l6jpP8AzIyIL/ezYokU+RnOBeZK+pakjZIq/3bnfopc40eAd+Rdtu8E3tufqtm4Ko+9U1SRIRo6GsahggrXX9I7gPnA2T2tUXoTXqOkQ4A/Bi7rV4USK/IzPJSsiWch2Te1b0o6JSL+X4/rlkqRa1wGrI6IGyWdBfxlfo3VHoR+EpkMd/ojwMyG9WZDNLywj6RDyb5WPtOX2qVR5BqRtAi4BrggInb3qW6ptLvGI4FTgK9LegI4E1g3QA9zi/6e3hEReyJiG/AI2R+BQVHkGi8HbgWIbNTcF5ENxmZ9MhmS/reBOZJmSzqc7EHtuv32WUc2Pj/AxcD/ifxJ0oBoe41508enyRL+oLUFQ5trjIhnI2JqRMyKiFlkzy0uiIhN5VS3Y0V+T28neyCPpKlkzT2P97WW3SlyjU8C5wJIei1Z0v+/fa1lzQ180s/b6K8C7gIeBm6NiK2S/lDSBflufwa8QtIw8HtAy+6AVVTwGj8O/Czw1/nwFfv/z1ZpBa9xYBW8vruApyU9BHwN+GBEPF1OjTtX8BrfD7xH0gPAGuCyAbsBG3gehsHMrEYG/k7fzMyKc9I3M6sRJ30zsxpx0jczqxEnfTOzGnHSNzOrESd9M7MacdK3WpF0u6T78vHql5ddH7N+88tZViuSXh4Rz0h6MdmwAWcP0luvZt2aDKNsmnXityVdmH+eSTagmZO+1YaTvtWGpIXAIuCsiPixpK+TDfhlVhtu07c6ORr4QZ7wX0M2PLNZrTjpW518FThU0oNk0y5uLLk+Zn3nB7lmZjXiO30zsxpx0jczqxEnfTOzGnHSNzOrESd9M7MacdI3M6sRJ30zsxr5/4eSM6HJG4R+AAAAAElFTkSuQmCC\n", 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\n", 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\n", 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\n", 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\n", 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\n", 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\n", 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PM4CnGvYbTreZmZVDxnF3inzYW0jSl7RF0s4myzLgHcBqSfeRjPD5/NhhTU7V9J9O0kpJ2yVt780VmJm1kNOdvqR1kvZJajoFoqTFkp6VtCNdPpSleoX03omIJW12eSOApHnAr6TbhvnpXT/ATGBPi/MPAoPpOUr+LN3MJpX8Ms564OPArRPs8/WI+NVOTlq65h1JJ6f/PQb4r8DatGgTsFzScZLmAHOBe4uppZnZkUR+vXciYivJPCa5Kl3SB1ZI+g7wbZI7+T8HiIhdwG3AQ8CXgdURMVJYLc3MxuusTX9grBk6XVYeRcTzJT0g6W8kLchyQOlezoqIW4BbWpStAdb0t0ZmZh3I3ryzv8uJ0f8vcHpE/KukS4A7SFpAJlS6pN8Luc1/WZI4jlWdOI5VvVhd69NTxIj4fsPnuyR9QtJAROyf6LgyNu+YmVVWv7psSjpFktLPi0jy+dPtjqvFnf7I3rbfeLoydhfS6ziOVZ04jlWtWLl+k8jpTl/SBmAxSdv/MHADMA0gItYCVwCrJB0CfgQsj4i20WuR9M3M+iLyG1cnIla0Kf84SZfOjjjpm5nlqeRvBjnpm5nlqOyvgzrpm5nlyUnfzKwmCh5BMwsnfTOznAg375iZ1YqTvplZnTjpm5nViJO+mVlNFDwrVhZO+mZmeXLSNzOrj7yGYegVJ30zsxy5ecfMrC78cpaZWc046ZuZ1YPfyDUzqxmNljvre7pEM7O8RAdLG5LWSdonaWeb/V4raUTSFVmq6KRvZpajHOfIXQ8snTCWNAX4KLA5a/1q0byT6/yXJYjjWNWJ41jVi9W1nFp3ImKrpNltdns38FfAa7Oet5A7fUlXStolaVTSwnFl10kakvSIpIsbti9Ntw1J+kD/a21m1l6Od/oTx5FmAJcBazs5rqg7/Z3A5cCnGjdKmg8sBxYApwJbJM1Li/8U+CVgGPimpE0R8VCWYCN75+ZV76bG7kJ6HcexqhPHsaoVK9dvEtkT+oCk7Q3rgxEx2EGkPwZ+NyJGJGU+qJCkHxEPAzSp6DJgY0QcAHZLGgIWpWVDEfF4etzGdN9MSd/MrC+io2EY9kfEwva7tbQQ2Jjm0QHgEkmHIuKOiQ4qW5v+DGBbw/pwug3gqXHbX9fqJJJWAitzr52Z2QT62U8/Iub8JK60Hvhiu4QPPUz6krYApzQpuj4i7mx1WJNtQfNnDy3/adOvSINpPcrdadbMJpfIJ+VI2gAsJmkGGgZuAKYlIaKjdvxGPUv6EbHkKA4bBmY1rM8E9qSfW203MyuNvG4zI2JFB/tenXXfsvXT3wQsl3ScpDnAXOBe4JvAXElzJB1L8rB3U4H1NDM7Uo4vZ/VKIW36ki4D/gfwMuBLknZExMURsUvSbSQPaA8BqyNiJD3mWpIXEKYA6yJiVxF1NzObiMfTbyIivgB8oUXZGmBNk+13AXf1uGpmZl1x0jczq4sgtwe5veKkb2aWo7L3F3TSNzPLk5O+mVk9eBIVM7M6iSj9JCpO+mZmeSp3znfSNzPLk5t3zMzqIgA375iZ1Ui5c76TvplZnty8Y2ZWI+69UwKebNuxiorjWNWL1ZWCR9DMohZJ38ysH5KXs8qd9WuR9D3ZtmP1O45jVStWrt8kSj7KZtkmUTEzqzRFZFrankdaJ2mfpJ0typdJelDSDknbJf1Clvo56ZuZ5SXfmbPWA0snKL8bODsizgHeAXw6y0lr0bxjZtYf+Y29ExFbJc2eoPxfG1Z/hox/Spz0zczylP1B7oCk7Q3rgxEx2EmodOrZ/wacDPxKlmOc9M3M8hIdTZe4PyIWdhUunXpW0i8CNwJL2h3jNn0zszxFZFtyDRlbgTMkDbTb10nfzCxP+T3InZCkV0pS+vnngWOBp9sd5+YdM7McaTSfjvqSNgCLSdr+h4EbgGkAEbEWeBNwlaSDwI+At0S0/wpRSNKXdCXwYeA1wKKI2J5ufylwO/BaYH1EXNtwzLkkXZheCNwF/HaWCzQz65sgt5ezImJFm/KPAh/t9LxFNe/sBC4Hto7b/mPgg8D7mhzzSWAlMDddJuq/ambWdyLbi1lFDtVQSNKPiIcj4pEm25+LiL8nSf4/IWk6cEJE3JPe3d8K/Hp/amtm1oECHuR2oipt+jOA4Yb14XRbU5JWknwrMDPrr5K3Ovcs6UvaApzSpOj6iLiz09M12dbyXzZ9wWEwrUe5fwJmNnnk2KbfKz1L+hHR9iWBDgwDMxvWZwJ7cjy/mVku8uq90yuV6KcfEXuBH0g6L+2XehXQ6bcFM7Mey9ieX7cHuZIuS/udng98SdLmhrIngP8OXC1pWNL8tGgVyShyQ8BjwN/0t9ZmZm0EpU/6hTzIHRsvokXZ7BbbtwNn9rBaZmbdK3frTmV675iZVYKnSzQzqxMn/eLlOv9lCeI4VnXiOFb1YnUlAkbK3b5Ti6RvZtY3vtMv3sjeuT09/9hdSK/jOFZ14jhWtWLl+k3CSd/MrCYCyGmO3F5x0jczy01AuE3fzKwegtI/yK3EMAxmZpWR0xu5ktZJ2idpZ4vyfy/pwXT5B0lnZ6mek76ZWZ7yG4ZhPRNPFrUbuCAizgJuJB1ZuB0375iZ5Sa/cXUiYquk2ROU/0PD6jYOH4m4JSd9M7O8BFDM0MrXkHEQSid9M7M8Zb/TH5C0vWF9MJ0AqiOSLiRJ+r+QZX8nfTOz3HQ0DMP+iFjYTTRJZ5EMOf/LEfF0lmOc9M3M8hIQfeqnL+k04PPAb0TEd7Ie56RvZpannN7IlbQBWEzSDDQM3ABMA4iItcCHgJcCn0gmFORQlm8OTvpmZnnKr/fOijbl7wTe2el5nfTNzPISUVTvncyc9M3M8uRRNs3M6iKIkZGiKzEhJ30zs7x4aGUzs5op+dDKhQy4JulKSbskjUpa2LD9lyTdJ+lb6X/f0FB2brp9SNKfKO2jZGZWFgHEaGRailLUKJs7gcuBreO27wd+LSJ+DvhN4DMNZZ8EVgJz02Wi0efMzPov0klUsiwFKaR5JyIeBhh/sx4R9zes7gJeIOk44CXACRFxT3rcrcCvk3GAoVznvyxBHMeqThzHql6sbvlB7tF7E3B/RByQNAMYbigbBma0OlDSSpJvBQAHSL5ZTFYDJN+QJjNfY/VV4fpO7/YEP+B7m7fE7QMZdy/k36NnSV/SFuCUJkXXR8SdbY5dAHwUeOPYpia7tWwUS0eqG0zPtb3bQY3KbLJfH/gaJ4PJfn1jIqL0zc49S/oRseRojpM0E/gCcFVEPJZuHubwCQJmAnu6q6GZWf2UarpESScBXwKui4hvjG2PiL3ADySdl/bauQqY8NuCmZkdqagum5elo8adD3xJ0ua06FrglcAHJe1Il5PTslUk40YPAY+R8SEuGeeNrLDJfn3ga5wMJvv1VYai5ONEmJlZfkrVvGNmZr3lpG9mViOTIulLWirpkXSIhg80KT9O0ufS8v8jaXb/a9mdDNf4O5IekvSgpLsldd3nuN/aXWPDfldIisYhPKogy/VJenP6c9wl6bP9rmO3MvyenibpK5LuT39XLyminrUWEZVegCkkD3ZfARwLPADMH7fPfwTWpp+XA58rut49uMYLgReln1dNxmtM9zueZPiObcDCouud889wLnA/8OJ0/eSi692DaxwEVqWf5wNPFF3vui2T4U5/ETAUEY9HxPPARmDZuH2WAX+Rfr4duKhiA7a1vcaI+EpE/DBd3cbh7zVUQZafI8CNwE3Aj/tZuRxkub53AX8aEd8DiIh9fa5jt7JcYwAnpJ9PxO/b9N1kSPozgKca1psN0fCTfSLiEPAsyYTCVZHlGhtdQ/YurWXR9hol/RtgVkR8sZ8Vy0mWn+E8YJ6kb0jaJqn0b3eOk+UaPwy8Le2yfRfw7v5UzcaUeeydrLIM0dDRMA4llLn+kt4GLAQu6GmN8jfhNUo6Bvgj4Op+VShnWX6GU0maeBaTfFP7uqQzI+Jfely3vGS5xhXA+oi4WdL5wGfSayz3IPSTyGS40x8GZjWsNxui4Sf7SJpK8rXymb7ULh9ZrhFJS4DrgUsj4kCf6paXdtd4PHAm8FVJTwDnAZsq9DA36+/pnRFxMCJ2A4+Q/BGoiizXeA1wG0Ako+a+gGQwNuuTyZD0vwnMlTRH0rEkD2o3jdtnE8n4/ABXAP870idJFdH2GtOmj0+RJPyqtQVDm2uMiGcjYiAiZkfEbJLnFpdGxPZiqtuxLL+nd5A8kEfSAElzz+N9rWV3slzjk8BFAJJeQ5L0/19fa1lzlU/6aRv9tcBm4GHgtojYJen3JV2a7vZnwEslDQG/A7TsDlhGGa/xY8DPAn+ZDl8x/n+2Ust4jZWV8fo2A09Legj4CvD+iHi6mBp3LuM1vhd4l6QHgA3A1RW7Aas8D8NgZlYjlb/TNzOz7Jz0zcxqxEnfzKxGnPTNzGrESd/MrEac9M3MasRJ38ysRpz0rVYk3SHpvnS8+pVF18es3/xyltWKpJdExDOSXkgybMAFVXrr1axbk2GUTbNO/Jaky9LPs0gGNHPSt9pw0rfakLQYWAKcHxE/lPRVkgG/zGrDbfpWJycC30sT/qtJhmc2qxUnfauTLwNTJT1IMu3itoLrY9Z3fpBrZlYjvtM3M6sRJ30zsxpx0jczqxEnfTOzGnHSNzOrESd9M7MacdI3M6uR/w8dVBeSZB0vHAAAAABJRU5ErkJggg==\n", 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\n", 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\n", 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\n", 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\n", 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\n", 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\n", 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\n", 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\n", 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\n", 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Uz8ystZK/nFXGpL8bGBu2+XJgdvp5JvBEw3HD6TYzs3LIOO5OkQ97C0n6krZJ2t1kWQ68E1gj6R6SET6fHTutyaWa/tNJWiVpp6SdvbkDM7MWcqrpS9og6YCkplMgSloi6WlJu9Llw1mKV0jvnYhY2uaQNwFImg/8WrptmJ/V+gFmAftaXH8QGEyvUfJn6WY2qeSXcTYCnwRunuCYb0bEr3dy0dI170g6Mf3vFOC/AOvTXVuAFZKOlTQXmAfcXUwpzcyOJPLrvRMR20nmMclV6ZI+sFLS94DvktTk/ztAROwBbgEeAL4KrImIkcJKaWY2Xmdt+gNjzdDpsuooIp4r6T5Jfy1pYZYTSvdyVkTcCNzYYt86YF1/S2Rm1oHszTsHu5wY/f8Ap0bEP0u6CLiNpAVkQqVL+r2Q2/yXJYnjWNWJ41jVi9W1Pj1FjIgfNny+Q9KnJA1ExMGJzitj846ZWWX1q8umpJMkKf28mCSfP9nuvFrU9Ef2t/3G05WxWkiv4zhWdeI4VrVi5fpNIqeavqRNwBKStv9h4FpgOkBErAcuA1ZLOgz8C7AiItpGr0XSNzPri8hvXJ2IWNlm/ydJunR2xEnfzCxPJX8zyEnfzCxHZX8d1EnfzCxPTvpmZjVR8AiaWTjpm5nlRLh5x8ysVpz0zczqxEnfzKxGnPTNzGqi4FmxsnDSNzPLk5O+mVl95DUMQ6846ZuZ5cjNO2ZmdeGXs8zMasZJ38ysHvxGrplZzWi03Fnf0yWameUlOljakLRB0gFJu9sc9zpJI5Iuy1JEJ30zsxzlOEfuRmDZhLGkqcDHgK1Zy1eL5p1c578sQRzHqk4cx6perK7l1LoTEdslzWlz2HuAzwOvy3rdQmr6ki6XtEfSqKRF4/atlTQk6SFJFzZsX5ZuG5L0wf6X2sysvRxr+hPHkWYClwDrOzmvqJr+buBS4DONGyUtAFYAC4GTgW2S5qe7bwJ+BRgGvi1pS0Q8kCXYyP55eZW7qbFaSK/jOFZ14jhWtWLl+k0ie0IfkLSzYX0wIgY7iPSnwO9HxIikzCcVkvQj4kGAJgVdDmyOiEPAXklDwOJ031BEPJqetzk9NlPSNzPri+hoGIaDEbGo/WEtLQI2p3l0ALhI0uGIuG2ik8rWpj8T2NGwPpxuA3hi3PbXt7qIpFXAqtxLZ2Y2gX7204+IuT+NK20Evtwu4UMPk76kbcBJTXZdExG3tzqtybag+bOHlv+06VekwbQc5e40a2aTS+STciRtApaQNAMNA9cC05MQ0VE7fqOeJf2IWHoUpw0DsxvWZwH70s+ttpuZlUZe1cyIWNnBsVdmPbZs/fS3ACskHStpLjAPuBv4NjBP0lxJx5A87N1SYDnNzI6U48tZvVJIm76kS4BPAC8HviJpV0RcGBF7JN1C8oD2MLAmIkbSc64meQFhKrAhIvYUUXYzs4l4PP0mIuKLwBdb7FsHrGuy/Q7gjh4XzcysK076ZmZ1EeT2ILdXnPTNzHJU9v6CTvpmZnly0jczqwdPomJmVicRpZ9ExUnfzCxP5c75TvpmZnly846ZWV0E4OYdM7MaKXfOd9I3M8uTm3fMzGrEvXdKwJNtO1ZRcRyrerG6UvAImlnUIumbmfVD8nJWubN+LZK+J9t2rH7Hcaxqxcr1m0TJR9ks2yQqZmaVpohMS9vrSBskHZC0u8X+5ZLul7RL0k5Jv5SlfE76ZmZ5yXfmrI3Asgn23wmcGRFnAe8EPpvlorVo3jEz64/8xt6JiO2S5kyw/58bVn+OjH9KnPTNzPKU/UHugKSdDeuDETHYSah06tk/Ak4Efi3LOU76ZmZ5iY6mSzwYEYu6CpdOPSvpl4HrgKXtznGbvplZniKyLbmGjO3AaZIG2h3rpG9mlqf8HuROSNIvSFL6+bXAMcCT7c5z846ZWY40mk9HfUmbgCUkbf/DwLXAdICIWA+8BbhC0nPAvwBvi2j/FaKQpC/pcuAjwGuAxRGxM93+MuBW4HXAxoi4uuGcs0m6ML0QuAP43Sw3aGbWN0FuL2dFxMo2+z8GfKzT6xbVvLMbuBTYPm77T4APAe9vcs6ngVXAvHSZqP+qmVnfiWwvZhU5VEMhST8iHoyIh5psfyYi/o4k+f+UpBnA8RFxV1q7vxn4jf6U1sysAwU8yO1EVdr0ZwLDDevD6bamJK0i+VZgZtZfJW917lnSl7QNOKnJrmsi4vZOL9dkW8t/2fQFh8G0HOX+CZjZ5JFjm36v9CzpR0TblwQ6MAzMalifBezL8fpmZrnIq/dOr1Sin35E7Ad+JOmctF/qFUCn3xbMzHosY3t+3R7kSrok7Xd6LvAVSVsb9j0G/FfgSknDkhaku1aTjCI3BDwC/HV/S21m1kZQ+qRfyIPcsfEiWuyb02L7TuD0HhbLzKx75W7dqUzvHTOzSvB0iWZmdeKkX7xc578sQRzHqk4cx6perK5EwEi523dqkfTNzPrGNf3ijeyf19Prj9VCeh3HsaoTx7GqFSvXbxJO+mZmNRFATnPk9oqTvplZbgLCbfpmZvUQlP5BbiWGYTAzq4yc3siVtEHSAUm7W+z/t5LuT5e/l3RmluI56ZuZ5Sm/YRg2MvFkUXuB8yLiDOA60pGF23HzjplZbvIbVycitkuaM8H+v29Y3cHzRyJuyUnfzCwvARQztPJVZByE0knfzCxP2Wv6A5J2NqwPphNAdUTS+SRJ/5eyHO+kb2aWm46GYTgYEYu6iSbpDJIh5381Ip7Mco6TvplZXgKiT/30JZ0CfAH4rYj4XtbznPTNzPKU0xu5kjYBS0iagYaBa4HpABGxHvgw8DLgU8mEghzO8s3BSd/MLE/59d5Z2Wb/u4B3dXpdJ30zs7xEFNV7JzMnfTOzPHmUTTOzughiZKToQkzISd/MLC8eWtnMrGZKPrRyIQOuSbpc0h5Jo5IWNWz/FUn3SPpO+t83Nuw7O90+JOnPlPZRMjMriwBiNDItRSlqlM3dwKXA9nHbDwJvjohfBH4b+IuGfZ8GVgHz0mWi0efMzPov0klUsiwFKaR5JyIeBBhfWY+IextW9wAvkHQs8FLg+Ii4Kz3vZuA3yDjAUK7zX5YgjmNVJ45jVS9Wt/wg9+i9Bbg3Ig5JmgkMN+wbBma2OlHSKpJvBQCHSL5ZTFYDJN+QJjPfY/VV4f5O7fYCP+IHW7fFrQMZDy/k36NnSV/SNuCkJruuiYjb25y7EPgY8KaxTU0Oa9kolo5UN5hea2e3gxqV2WS/P/A9TgaT/f7GRETpm517lvQjYunRnCdpFvBF4IqIeCTdPMzzJwiYBezrroRmZvVTqukSJb0Y+AqwNiK+NbY9IvYDP5J0Ttpr5wpgwm8LZmZ2pKK6bF6Sjhp3LvAVSVvTXVcDvwB8SNKudDkx3beaZNzoIeARMj7EJeO8kRU22e8PfI+TwWS/v8pQlHycCDMzy0+pmnfMzKy3nPTNzGpkUiR9ScskPZQO0fDBJvuPlfS5dP//ljSn/6XsToZ7/D1JD0i6X9Kdkrruc9xv7e6x4bjLJEXjEB5VkOX+JL01/TnukfSX/S5jtzL8np4i6WuS7k1/Vy8qopy1FhGVXoCpJA92XwkcA9wHLBh3zH8A1qefVwCfK7rcPbjH84EXpZ9XT8Z7TI87jmT4jh3AoqLLnfPPcB5wL/CSdP3Eosvdg3scBFannxcAjxVd7rotk6GmvxgYiohHI+JZYDOwfNwxy4H/kX6+FbigYgO2tb3HiPhaRPw4Xd3B899rqIIsP0eA64DrgZ/0s3A5yHJ/7wZuiogfAETEgT6XsVtZ7jGA49PPJ+D3bfpuMiT9mcATDevNhmj46TERcRh4mmRC4arIco+NriJ7l9ayaHuPkv4VMDsivtzPguUky89wPjBf0rck7ZBU+rc7x8lyjx8B3p522b4DeE9/imZjyjz2TlZZhmjoaBiHEspcfklvBxYB5/W0RPmb8B4lTQH+BLiyXwXKWZaf4TSSJp4lJN/Uvinp9Ij4fz0uW16y3ONKYGNE3CDpXOAv0nss9yD0k8hkqOkPA7Mb1psN0fDTYyRNI/la+VRfSpePLPeIpKXANcDFEXGoT2XLS7t7PA44Hfi6pMeAc4AtFXqYm/X39PaIeC4i9gIPkfwRqIos93gVcAtAJKPmvoBkMDbrk8mQ9L8NzJM0V9IxJA9qt4w7ZgvJ+PwAlwH/K9InSRXR9h7Tpo/PkCT8qrUFQ5t7jIinI2IgIuZExByS5xYXR8TOYorbsSy/p7eRPJBH0gBJc8+jfS1ld7Lc4+PABQCSXkOS9P9vX0tZc5VP+mkb/dXAVuBB4JaI2CPpDyVdnB7258DLJA0Bvwe07A5YRhnv8ePAzwN/lQ5fMf5/tlLLeI+VlfH+tgJPSnoA+BrwgYh4spgSdy7jPb4PeLek+4BNwJUVq4BVnodhMDOrkcrX9M3MLDsnfTOzGnHSNzOrESd9M7MacdI3M6sRJ30zsxpx0jczqxEnfasVSbdJuicdr35V0eUx6ze/nGW1IumlEfGUpBeSDBtwXpXeejXr1mQYZdOsE78j6ZL082ySAc2c9K02nPStNiQtAZYC50bEjyV9nWTAL7PacJu+1ckJwA/ShP9qkuGZzWrFSd/q5KvANEn3k0y7uKPg8pj1nR/kmpnViGv6ZmY14qRvZlYjTvpmZjXipG9mViNO+mZmNeKkb2ZWI076ZmY18v8B3wArTNbDtWsAAAAASUVORK5CYII=\n", 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\n", 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\n", 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\n", 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\n", 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\n", 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\n", 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\n", 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\n", 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\n", 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\n", 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\n", 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GgEUF1M/MrLWSv5xVxqS/HRgbtvlSYHb6eSbwRMN+w+k2M7NyyDjuTpEPewtJ+pI2SdreZFkGvB1YJekekhE+nxs7rMmpmv7TSVopaaukrb25AjOzFnK605e0VtIeSU2nQJS0WNJTkraly4eyVK+Q3jsRsaTNLm8AkDQf+OV02zA/uesHmAXsanH+QWAwPUfJn6Wb2aSSX8ZZB3wCuGmCfb4REb/SyUlL17wj6bj0v4cB/xNYkxZtAJZLOkLSXGAecHcxtTQzO5jIr/dORGwmmcckV6VL+sAKSd8FvkNyJ/9XABGxA7gFeAD4CrAqIkYKq6WZ2XidtekPjDVDp8vKQ4h4tqT7JP2dpAVZDijdy1kRcSNwY4uy1cDq/tbIzKwD2Zt39nY5Mfr/BU6KiH+XdAFwG0kLyIRKl/R7Ibf5L0sSx7GqE8exqhera316ihgRP2z4fIekT0oaiIi9Ex1XxuYdM7PK6leXTUnHS1L6eRFJPn+y3XG1uNMf2d32G09Xxu5Ceh3HsaoTx7GqFSvXbxI53elLWg8sJmn7HwauBaYBRMQa4BLgSkkHgP8AlkdE2+i1SPpmZn0R+Y2rExEr2pR/gqRLZ0ec9M3M8lTyN4Oc9M3MclT210Gd9M3M8uSkb2ZWEwWPoJmFk76ZWU6Em3fMzGrFSd/MrE6c9M3MasRJ38ysJgqeFSsLJ30zszw56ZuZ1UdewzD0ipO+mVmO3LxjZlYXfjnLzKxmnPTNzOrBb+SamdWMRsud9T1doplZXqKDpQ1JayXtkbS9zX6vkTQi6ZIsVXTSNzPLUY5z5K4Dlk4YS5oCfAzYmLV+tWjeyXX+yxLEcazqxHGs6sXqWk6tOxGxWdKcNru9C/gb4DVZz1vInb6kSyXtkDQqaeG4sqslDUl6SNL5DduXptuGJL2//7U2M2svxzv9ieNIM4GLgDWdHFfUnf524GLg040bJZ0CLAcWACcAmyTNT4v/HPhFYBj4lqQNEfFAlmAju+flVe+mxu5Ceh3HsaoTx7GqFSvXbxLZE/qApK0N64MRMdhBpD8BfjciRiRlPqiQpB8RDwI0qegy4OaI2A/slDQELErLhiLi0fS4m9N9MyV9M7O+iI6GYdgbEQvb79bSQuDmNI8OABdIOhARt010UNna9GcCWxrWh9NtAE+M2/7aVieRtBJYmXvtzMwm0M9++hEx98dxpXXAl9olfOhh0pe0CTi+SdE1EXF7q8OabAuaP3to+U+bfkUaTOtR7k6zZja5RD4pR9J6YDFJM9AwcC0wLQkRHbXjN+pZ0o+IJYdw2DAwu2F9FrAr/dxqu5lZaeR1mxkRKzrY9/Ks+5atn/4GYLmkIyTNBeYBdwPfAuZJmivpcJKHvRsKrKeZ2cFyfDmrVwpp05d0EfBnwMuAL0vaFhHnR8QOSbeQPKA9AKyKiJH0mKtIXkCYAqyNiB1F1N3MbCIeT7+JiPgi8MUWZauB1U223wHc0eOqmZl1xUnfzKwugtwe5PaKk76ZWY7K3l/QSd/MLE9O+mZm9eBJVMzM6iSi9JOoOOmbmeWp3DnfSd/MLE9u3jEzq4sA3LxjZlYj5c75TvpmZnly846ZWY24904JeLJtxyoqjmNVL1ZXCh5BM4taJH0zs35IXs4qd9avRdL3ZNuO1e84jlWtWLl+kyj5KJtlm0TFzKzSFJFpaXseaa2kPZK2tyhfJul+SdskbZX081nq56RvZpaXfGfOWgcsnaD8TuD0iDgDeDvwmSwnrUXzjplZf+Q39k5EbJY0Z4Lyf29Y/Sky/ilx0jczy1P2B7kDkrY2rA9GxGAnodKpZ/8AOA745SzHOOmbmeUlOpoucW9ELOwqXDr1rKRfAK4DlrQ7xm36ZmZ5isi25BoyNgMnSxpot6+TvplZnvJ7kDshST8jSennnwMOB55sd5ybd8zMcqTRfDrqS1oPLCZp+x8GrgWmAUTEGuCNwGWSngf+A3hzRPuvEIUkfUmXAh8GXg0sioit6faXArcCrwHWRcRVDcecSdKF6UXAHcBvZ7lAM7O+CXJ7OSsiVrQp/xjwsU7PW1TzznbgYmDzuO3PAh8E3tvkmE8BK4F56TJR/1Uzs74T2V7MKnKohkKSfkQ8GBEPNdn+TET8E0ny/zFJM4CjIuKu9O7+JuDX+lNbM7MOFPAgtxNVadOfCQw3rA+n25qStJLkW4GZWX+VvNW5Z0lf0ibg+CZF10TE7Z2ersm2lv+y6QsOg2k9yv0TMLPJI8c2/V7pWdKPiLYvCXRgGJjVsD4L2JXj+c3McpFX751eqUQ//YjYDTwt6ay0X+plQKffFszMeixje37dHuRKuijtd3o28GVJGxvKHgP+CLhc0rCkU9KiK0lGkRsCHgH+rr+1NjNrIyh90i/kQe7YeBEtyua02L4VOLWH1TIz6165W3cq03vHzKwSPF2imVmdOOkXL9f5L0sQx7GqE8exqherKxEwUu72nVokfTOzvvGdfvFGds/r6fnH7kJ6HcexqhPHsaoVK9dvEk76ZmY1EUBOc+T2ipO+mVluAsJt+mZm9RCU/kFuJYZhMDOrjJzeyJW0VtIeSdtblP+6pPvT5Z8lnZ6lek76ZmZ5ym8YhnVMPFnUTuCciDgNuI50ZOF23LxjZpab/MbViYjNkuZMUP7PDatbeOFIxC056ZuZ5SWAYoZWvoKMg1A66ZuZ5Sn7nf6ApK0N64PpBFAdkXQuSdL/+Sz7O+mbmeWmo2EY9kbEwm6iSTqNZMj5X4qIJ7Mc46RvZpaXgOhTP31JJwJfAH4jIr6b9TgnfTOzPOX0Rq6k9cBikmagYeBaYBpARKwBPgS8FPhkMqEgB7J8c3DSNzPLU369d1a0KX8H8I5Oz+ukb2aWl4iieu9k5qRvZpYnj7JpZlYXQYyMFF2JCTnpm5nlxUMrm5nVTMmHVi5kwDVJl0raIWlU0sKG7b8o6R5J307/+/qGsjPT7UOS/lRpHyUzs7IIIEYj01KUokbZ3A5cDGwet30v8KsR8bPAbwKfbSj7FLASmJcuE40+Z2bWf5FOopJlKUghzTsR8SDA+Jv1iLi3YXUHcKSkI4BjgaMi4q70uJuAXyPjAEO5zn9ZgjiOVZ04jlW9WN3yg9xD90bg3ojYL2kmMNxQNgzMbHWgpJUk3woA9pN8s5isBki+IU1mvsbqq8L1ndTtCZ7m+xs3xa0DGXcv5N+jZ0lf0ibg+CZF10TE7W2OXQB8DHjD2KYmu7VsFEtHqhtMz7W120GNymyyXx/4GieDyX59YyKi9M3OPUv6EbHkUI6TNAv4InBZRDySbh7mhRMEzAJ2dVdDM7P6KdV0iZKOAb4MXB0R3xzbHhG7gaclnZX22rkMmPDbgpmZHayoLpsXpaPGnQ18WdLGtOgq4GeAD0rali7HpWVXkowbPQQ8QsaHuGScN7LCJvv1ga9xMpjs11cZipKPE2FmZvkpVfOOmZn1lpO+mVmNTIqkL2mppIfSIRre36T8CEmfT8v/j6Q5/a9ldzJc4+9IekDS/ZLulNR1n+N+a3eNDftdIikah/CogizXJ+lN6c9xh6TP9buO3crwe3qipK9Kujf9Xb2giHrWWkRUegGmkDzYfQVwOHAfcMq4ff4bsCb9vBz4fNH17sE1ngu8OP185WS8xnS/6STDd2wBFhZd75x/hvOAe4GXpOvHFV3vHlzjIHBl+vkU4LGi6123ZTLc6S8ChiLi0Yh4DrgZWDZun2XA/0o/3wqcV7EB29peY0R8NSJ+lK5u4YXvNVRBlp8jwHXA9cCz/axcDrJc3zuBP4+I7wNExJ4+17FbWa4xgKPSz0fj9236bjIk/ZnAEw3rzYZo+PE+EXEAeIpkQuGqyHKNja4ge5fWsmh7jZL+EzA7Ir7Uz4rlJMvPcD4wX9I3JW2RVPq3O8fJco0fBt6adtm+A3hXf6pmY8o89k5WWYZo6GgYhxLKXH9JbwUWAuf0tEb5m/AaJR0G/DFweb8qlLMsP8OpJE08i0m+qX1D0qkR8YMe1y0vWa5xBbAuIm6QdDbw2fQayz0I/SQyGe70h4HZDevNhmj48T6SppJ8rdzXl9rlI8s1ImkJcA1wYUTs71Pd8tLuGqcDpwJfk/QYcBawoUIPc7P+nt4eEc9HxE7gIZI/AlWR5RqvAG4BiGTU3CNJBmOzPpkMSf9bwDxJcyUdTvKgdsO4fTaQjM8PcAnwj5E+SaqItteYNn18miThV60tGNpcY0Q8FREDETEnIuaQPLe4MCK2FlPdjmX5Pb2N5IE8kgZImnse7Wstu5PlGh8HzgOQ9GqSpP//+lrLmqt80k/b6K8CNgIPArdExA5Jvy/pwnS3vwReKmkI+B2gZXfAMsp4jR8Hfhr463T4ivH/s5VaxmusrIzXtxF4UtIDwFeB90XEk8XUuHMZr/E9wDsl3QesBy6v2A1Y5XkYBjOzGqn8nb6ZmWXnpG9mViNO+mZmNeKkb2ZWI076ZmY14qRvZlYjTvpmZjXipG+1Iuk2Sfek49WvLLo+Zv3ml7OsViQdGxH7JL2IZNiAc6r01qtZtybDKJtmnfgtSReln2eTDGjmpG+14aRvtSFpMbAEODsifiTpayQDfpnVhtv0rU6OBr6fJvxXkQzPbFYrTvpWJ18Bpkq6n2TaxS0F18es7/wg18ysRnynb2ZWI076ZmY14qRvZlYjTvpmZjXipG9mViNO+mZmNeKkb2ZWI/8fthNIIoRV+2EAAAAASUVORK5CYII=\n", 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\n", 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\n", 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\n", 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\n", 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zMyuHjOPuFPmwt5CkL2mTpO1NlsXA24EVku4lGeHzubHDmpyq6Y9O0jJJWyVt7c0VmJm1kNOdvqQ1kvZIajoFoqQFkp6WtC1dPpileoX03omIhW12eQOApDnAL6XbhvnJXT/AdGBXi/MPAoPpOUr+LN3MJpX8Ms5a4OPALRPs87WI+OVOTlq65h1JJ6b/PQL4X8DqtGgDsETS0ZJmAbOBe4qppZnZoUR+vXciYjPJPCa5Kl3SB5ZK+jbwLZI7+b8EiIgdwK3Ag8CXgBURMVJYLc3MxuusTX9grBk6XZYdRsTzJd0v6e8kzc1yQOlezoqIm4GbW5StAlb1t0ZmZh3I3ryzt8uJ0f8FOC0i/l3SJcDtJC0gEypd0u+F3Oa/LEkcx6pOHMeqXqyu9ekpYkT8oOHznZI+IWkgIvZOdFwZm3fMzCqrX102JZ0kSenn+ST5/Kl2x9XiTn9kd9tvPF0ZuwvpdRzHqk4cx6pWrFy/SeR0py9pHbCApO1/GLgemAoQEauBK4Dlkp4H/gNYEhFto9ci6ZuZ9UXkN65ORCxtU/5xki6dHXHSNzPLU8nfDHLSNzPLUdlfB3XSNzPLk5O+mVlNFDyCZhZO+mZmORFu3jEzqxUnfTOzOnHSNzOrESd9M7OaKHhWrCyc9M3M8uSkb2ZWH3kNw9ArTvpmZjly846ZWV345Swzs5px0jczqwe/kWtmVjMaLXfW93SJZmZ5iQ6WNiStkbRH0vY2+71G0oikK7JU0UnfzCxHOc6RuxZYNGEsaQrwUWBj1vrVonkn1/kvSxDHsaoTx7GqF6trObXuRMRmSTPb7PYu4G+A12Q9byF3+pKulLRD0qikeePKVkoakvSwpIsbti9Ktw1Jen//a21m1l6Od/oTx5GmAZcBqzs5rqg7/e3A5cCnGjdKOgNYAswFTgE2SZqTFv8Z8AvAMPANSRsi4sEswUZ2z86r3k2N3YX0Oo5jVSeOY1UrVq7fJLIn9AFJWxvWByNisINIfwz8TkSMSMp8UCFJPyIeAmhS0cXA+ojYD+yUNATMT8uGIuKx9Lj16b6Zkr6ZWV9ER8Mw7I2Iee13a2kesD7NowPAJZKej4jbJzqobG3604AtDevD6TaAJ8dtf22rk0haBizLvXZmZhPoZz/9iJj147jSWuAL7RI+9DDpS9oEnNSk6LqIuKPVYU22Bc2fPbT80aZfkQbTepS706yZTS6RT8qRtA5YQNIMNAxcD0xNQkRH7fiNepb0I2LhYRw2DMxoWJ8O7Eo/t9puZlYaed1mRsTSDva9Ouu+ZeunvwFYIuloSbOA2cA9wDeA2ZJmSTqK5GHvhgLraWZ2qBxfzuqVQtr0JV0G/CnwUuCLkrZFxMURsUPSrSQPaJ8HVkTESHrMtSQvIEwB1kTEjiLqbmY2EY+n30REfB74fIuyVcCqJtvvBO7scdXMzLripG9mVhdBbg9ye8VJ38wsR2XvL+ikb2aWJyd9M7N68CQqZmZ1ElH6SVSc9M3M8lTunO+kb2aWJzfvmJnVRQBu3jEzq5Fy53wnfTOzPLl5x8ysRtx7pwQ82bZjFRXHsaoXqysFj6CZRS2SvplZPyQvZ5U769ci6XuybcfqdxzHqlasXL9JlHyUzbJNomJmVmmKyLS0PY+0RtIeSdtblC+W9ICkbZK2Svq5LPVz0jczy0u+M2etBRZNUH4XcHZEnAO8Hfh0lpPWonnHzKw/8ht7JyI2S5o5Qfm/N6z+FBn/lDjpm5nlKfuD3AFJWxvWByNisJNQ6dSzvw+cCPxSlmOc9M3M8hIdTZe4NyLmdRUunXpW0s8DNwAL2x3jNn0zszxFZFtyDRmbgdMlDbTb10nfzCxP+T3InZCkn5Gk9POrgaOAp9od5+YdM7McaTSfjvqS1gELSNr+h4HrgakAEbEaeCNwlaQDwH8Ab45o/xWikKQv6UrgQ8CrgPkRsTXd/hLgNuA1wNqIuLbhmHNJujC9ALgT+K0sF2hm1jdBbi9nRcTSNuUfBT7a6XmLat7ZDlwObB63/VngA8B7mxzzSWAZMDtdJuq/ambWdyLbi1lFDtVQSNKPiIci4uEm25+JiH8iSf4/Julk4LiIuDu9u78F+NX+1NbMrAMFPMjtRFXa9KcBww3rw+m2piQtI/lWYGbWXyVvde5Z0pe0CTipSdF1EXFHp6drsq3lTzZ9wWEwrUe5/wXMbPLIsU2/V3qW9COi7UsCHRgGpjesTwd25Xh+M7Nc5NV7p1cq0U8/InYDP5R0Xtov9Sqg028LZmY9lrE9v24PciVdlvY7PR/4oqSNDWWPA38IXC1pWNIZadFyklHkhoBHgb/rb63NzNoISp/0C3mQOzZeRIuymS22bwXO7GG1zMy6V+7Wncr03jEzqwRPl2hmVidO+sXLdf7LEsRxrOrEcazqxepKBIyUu32nFknfzKxvfKdfvJHds3t6/rG7kF7HcazqxHGsasXK9ZuEk76ZWU0EkNMcub3ipG9mlpuAcJu+mVk9BKV/kFuJYRjMzCojpzdyJa2RtEfS9hblvybpgXT5Z0lnZ6mek76ZWZ7yG4ZhLRNPFrUTuCAizgJuIB1ZuB0375iZ5Sa/cXUiYrOkmROU/3PD6hYOHom4JSd9M7O8BFDM0MrXkHEQSid9M7M8Zb/TH5C0tWF9MJ0AqiOSLiRJ+j+XZX8nfTOz3HQ0DMPeiJjXTTRJZ5EMOf+LEfFUlmOc9M3M8hIQfeqnL+lU4HPAr0fEt7Me56RvZpannN7IlbQOWEDSDDQMXA9MBYiI1cAHgZcAn0gmFOT5LN8cnPTNzPKUX++dpW3K3wG8o9PzOumbmeUloqjeO5k56ZuZ5cmjbJqZ1UUQIyNFV2JCTvpmZnnx0MpmZjVT8qGVCxlwTdKVknZIGpU0r2H7L0i6V9I30/++vqHs3HT7kKQ/UdpHycysLAKI0ci0FKWoUTa3A5cDm8dt3wv8SkT8LPAbwF81lH0SWAbMTpeJRp8zM+u/SCdRybIUpJDmnYh4CGD8zXpE3NewugM4RtLRwIuB4yLi7vS4W4BfJeMAQ7nOf1mCOI5VnTiOVb1Y3fKD3MP3RuC+iNgvaRow3FA2DExrdaCkZSTfCgD2k3yzmKwGSL4hTWa+xuqrwvWd1u0Jfsj3N26K2wYy7l7Iz6NnSV/SJuCkJkXXRcQdbY6dC3wUeMPYpia7tWwUS0eqG0zPtbXbQY3KbLJfH/gaJ4PJfn1jIqL0zc49S/oRsfBwjpM0Hfg8cFVEPJpuHubgCQKmA7u6q6GZWf2UarpESScAXwRWRsTXx7ZHxG7gh5LOS3vtXAVM+G3BzMwOVVSXzcvSUePOB74oaWNadC3wM8AHJG1LlxPTsuUk40YPAY+S8SEuGeeNrLDJfn3ga5wMJvv1VYai5ONEmJlZfkrVvGNmZr3lpG9mViOTIulLWiTp4XSIhvc3KT9a0mfT8v8raWb/a9mdDNf425IelPSApLskdd3nuN/aXWPDfldIisYhPKogy/VJelP677hD0mf6XcduZfg9PVXSlyXdl/6uXlJEPWstIiq9AFNIHuy+HDgKuB84Y9w+/w1YnX5eAny26Hr34BovBF6Yfl4+Ga8x3e9YkuE7tgDziq53zv+Gs4H7gBel6ycWXe8eXOMgsDz9fAbweNH1rtsyGe705wNDEfFYRDwHrAcWj9tnMfC/08+3ARdVbMC2ttcYEV+OiB+lq1s4+L2GKsjy7whwA3Aj8Gw/K5eDLNf3TuDPIuL7ABGxp8917FaWawzguPTz8fh9m76bDEl/GvBkw3qzIRp+vE9EPA88TTKhcFVkucZG15C9S2tZtL1GSf8JmBERX+hnxXKS5d9wDjBH0tclbZFU+rc7x8lyjR8C3pp22b4TeFd/qmZjyjz2TlZZhmjoaBiHEspcf0lvBeYBF/S0Rvmb8BolHQH8EXB1vyqUsyz/hkeSNPEsIPmm9jVJZ0bEv/W4bnnJco1LgbURcZOk84G/Sq+x3IPQTyKT4U5/GJjRsN5siIYf7yPpSJKvlfv6Urt8ZLlGJC0ErgMujYj9fapbXtpd47HAmcBXJD0OnAdsqNDD3Ky/p3dExIGI2Ak8TPJHoCqyXOM1wK0AkYyaewzJYGzWJ5Mh6X8DmC1plqSjSB7Ubhi3zwaS8fkBrgD+MdInSRXR9hrTpo9PkST8qrUFQ5trjIinI2IgImZGxEyS5xaXRsTWYqrbsSy/p7eTPJBH0gBJc89jfa1ld7Jc4xPARQCSXkWS9P9fX2tZc5VP+mkb/bXARuAh4NaI2CHp9yRdmu72F8BLJA0Bvw207A5YRhmv8WPATwN/nQ5fMf5/tlLLeI2VlfH6NgJPSXoQ+DLwvoh4qpgady7jNb4HeKek+4F1wNUVuwGrPA/DYGZWI5W/0zczs+yc9M3MasRJ38ysRpz0zcxqxEnfzKxGnPTNzGrESd/MrEac9K1WJN0u6d50vPplRdfHrN/8cpbViqQXR8Q+SS8gGTbggiq99WrWrckwyqZZJ35T0mXp5xkkA5o56VttOOlbbUhaACwEzo+IH0n6CsmAX2a14TZ9q5Pjge+nCf+VJMMzm9WKk77VyZeAIyU9QDLt4paC62PWd36Qa2ZWI77TNzOrESd9M7MacdI3M6sRJ30zsxpx0jczqxEnfTOzGnHSNzOrkf8PoqEV1ZXi9jEAAAAASUVORK5CYII=\n", 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\n", 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\n", 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\n", 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\n", 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\n", 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\n", 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\n", 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\n", 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mZlYOGcfdKfJhbyFJX9ImSdubLIuBtwMrJN1LMsLnc2OHNTlV0x+dpGWStkra2psrMDNrIac7fUlrJO2R1HQKREkLJD0taVu6fDBL9QrpvRMRC9vs8gYASXOAX0q3DfOTu36A6cCuFucfBAbTc5T8WbqZTSr5ZZy1wMeBWybY52sR8cudnLR0zTuSTkz/ewTwv4DVadEGYImkoyXNAmYD9xRTSzOzQ4n8eu9ExGaSeUxyVbqkDyyV9G3gWyR38n8JEBE7gFuBB4EvASsiYqSwWpqZjddZm/7AWDN0uiw7jIjnS7pf0t9JmpvlgNK9nBURNwM3tyhbBazqb43MzDqQvXlnb5cTo/8LcFpE/LukS4DbSVpAJlS6pN8Luc1/WZI4jlWdOI5VvVhd69NTxIj4QcPnOyV9QtJAROyd6LgyNu+YmVVWv7psSjpJktLP80ny+VPtjqvFnf7I7rbfeLoydhfS6ziOVZ04jlWtWLl+k8jpTl/SOmABSdv/MHA9MBUgIlYDVwDLJT0P/AewJCLaRq9F0jcz64vIb1ydiFjapvzjJF06O+Kkb2aWp5K/GeSkb2aWo7K/Duqkb2aWJyd9M7OaKHgEzSyc9M3MciLcvGNmVitO+mZmdeKkb2ZWI076ZmY1UfCsWFk46ZuZ5clJ38ysPvIahqFXnPTNzHLk5h0zs7rwy1lmZjXjpG9mVg9+I9fMrGY0Wu6s7+kSzczyEh0sbUhaI2mPpO1t9nuNpBFJV2SpopO+mVmOcpwjdy2waMJY0hTgo8DGrPWrRfNOrvNfliCOY1UnjmNVL1bXcmrdiYjNkma22e1dwN8Ar8l63kLu9CVdKWmHpFFJ88aVrZQ0JOlhSRc3bF+UbhuS9P7+19rMrL0c7/QnjiNNAy4DVndyXFF3+tuBy4FPNW6UdAawBJgLnAJskjQnLf4z4BeAYeAbkjZExINZgo3snp1XvZsauwvpdRzHqk4cx6pWrFy/SWRP6AOStjasD0bEYAeR/hj4nYgYkZT5oEKSfkQ8BNCkoouB9RGxH9gpaQiYn5YNRcRj6XHr030zJX0zs76IjoZh2BsR89rv1tI8YH2aRweASyQ9HxG3T3RQ2dr0pwFbGtaH020AT47b/tpWJ5G0DFiWe+3MzCbQz376ETHrx3GltcAX2iV86GHSl7QJOKlJ0XURcUerw5psC5o/e2j5o02/Ig2m9Sh3p1kzm1win5QjaR2wgKQZaBi4HpiahIiO2vEb9SzpR8TCwzhsGJjRsD4d2JV+brXdzKw08rrNjIilHex7ddZ9y9ZPfwOwRNLRkmYBs4F7gG8AsyXNknQUycPeDQXW08zsUDm+nNUrhbTpS7oM+FPgpcAXJW2LiIsjYoekW0ke0D4PrIiIkfSYa0leQJgCrImIHUXU3cxsIh5Pv4mI+Dzw+RZlq4BVTbbfCdzZ46qZmXXFSd/MrC6C3B7k9oqTvplZjsreX9BJ38wsT076Zmb14ElUzMzqJKL0k6g46ZuZ5ancOd9J38wsT27eMTOriwDcvGNmViPlzvlO+mZmeXLzjplZjbj3Tgl4sm3HKiqOY1UvVlcKHkEzi1okfTOzfkhezip31q9F0vdk247V7ziOVa1YuX6TKPkom2WbRMXMrNIUkWlpex5pjaQ9kra3KF8s6QFJ2yRtlfRzWernpG9mlpd8Z85aCyyaoPwu4OyIOAd4O/DpLCetRfOOmVl/5Df2TkRsljRzgvJ/b1j9KTL+KXHSNzPLU/YHuQOStjasD0bEYCeh0qlnfx84EfilLMc46ZuZ5SU6mi5xb0TM6ypcOvWspJ8HbgAWtjvGbfpmZnmKyLbkGjI2A6dLGmi3r5O+mVme8nuQOyFJPyNJ6edXA0cBT7U7zs07ZmY50mg+HfUlrQMWkLT9DwPXA1MBImI18EbgKkkHgP8A3hzR/itEIUlf0pXAh4BXAfMjYmu6/SXAbcBrgLURcW3DMeeSdGF6AXAn8FtZLtDMrG+C3F7Oioilbco/Cny00/MW1byzHbgc2Dxu+7PAB4D3Njnmk8AyYHa6TNR/1cys70S2F7OKHKqhkKQfEQ9FxMNNtj8TEf9Ekvx/TNLJwHERcXd6d38L8Kv9qa2ZWQcKeJDbiaq06U8DhhvWh9NtTUlaRvKtwMysv0re6tyzpC9pE3BSk6LrIuKOTk/XZFvLn2z6gsNgWo9y/wuY2eSRY5t+r/Qs6UdE25cEOjAMTG9Ynw7syvH8Zma5yKv3Tq9Uop9+ROwGfijpvLRf6lVAp98WzMx6LGN7ft0e5Eq6LO13ej7wRUkbG8oeB/4QuFrSsKQz0qLlJKPIDQGPAn/X31qbmbURlD7pF/Igd2y8iBZlM1ts3wqc2cNqmZl1r9ytO5XpvWNmVgmeLtHMrE6c9IuX6/yXJYjjWNWJ41jVi9WVCBgpd/tOLZK+mVnf+E6/eCO7Z/f0/GN3Ib2O41jVieNY1YqV6zcJJ30zs5oIIKc5cnvFSd/MLDcB4TZ9M7N6CEr/ILcSwzCYmVVGTm/kSlojaY+k7S3Kf03SA+nyz5LOzlI9J30zszzlNwzDWiaeLGoncEFEnAXcQDqycDtu3jEzy01+4+pExGZJMyco/+eG1S0cPBJxS076ZmZ5CaCYoZWvIeMglE76ZmZ5yn6nPyBpa8P6YDoBVEckXUiS9H8uy/5O+mZmueloGIa9ETGvm2iSziIZcv4XI+KpLMc46ZuZ5SUg+tRPX9KpwOeAX4+Ib2c9zknfzCxPOb2RK2kdsICkGWgYuB6YChARq4EPAi8BPpFMKMjzWb45OOmbmeUpv947S9uUvwN4R6fnddI3M8tLRFG9dzJz0jczy5NH2TQzq4sgRkaKrsSEnPTNzPLioZXNzGqm5EMrFzLgmqQrJe2QNCppXsP2X5B0r6Rvpv99fUPZuen2IUl/orSPkplZWQQQo5FpKUpRo2xuBy4HNo/bvhf4lYj4WeA3gL9qKPsksAyYnS4TjT5nZtZ/kU6ikmUpSCHNOxHxEMD4m/WIuK9hdQdwjKSjgRcDx0XE3elxtwC/SsYBhnKd/7IEcRyrOnEcq3qxuuUHuYfvjcB9EbFf0jRguKFsGJjW6kBJy0i+FQDsJ/lmMVkNkHxDmsx8jdVXhes7rdsT/JDvb9wUtw1k3L2Qn0fPkr6kTcBJTYqui4g72hw7F/go8IaxTU12a9kolo5UN5iea2u3gxqV2WS/PvA1TgaT/frGRETpm517lvQjYuHhHCdpOvB54KqIeDTdPMzBEwRMB3Z1V0Mzs/op1XSJkk4AvgisjIivj22PiN3ADyWdl/bauQqY8NuCmZkdqqgum5elo8adD3xR0sa06FrgZ4APSNqWLiemZctJxo0eAh4l40NcMs4bWWGT/frA1zgZTPbrqwxFyceJMDOz/JSqecfMzHrLSd/MrEYmRdKXtEjSw+kQDe9vUn60pM+m5f9X0sz+17I7Ga7xtyU9KOkBSXdJ6rrPcb+1u8aG/a6QFI1DeFRBluuT9Kb033GHpM/0u47dyvB7eqqkL0u6L/1dvaSIetZaRFR6AaaQPNh9OXAUcD9wxrh9/huwOv28BPhs0fXuwTVeCLww/bx8Ml5jut+xJMN3bAHmFV3vnP8NZwP3AS9K108sut49uMZBYHn6+Qzg8aLrXbdlMtzpzweGIuKxiHgOWA8sHrfPYuB/p59vAy6q2IBtba8xIr4cET9KV7dw8HsNVZDl3xHgBuBG4Nl+Vi4HWa7vncCfRcT3ASJiT5/r2K0s1xjAcenn4/H7Nn03GZL+NODJhvVmQzT8eJ+IeB54mmRC4arIco2NriF7l9ayaHuNkv4TMCMivtDPiuUky7/hHGCOpK9L2iKp9G93jpPlGj8EvDXtsn0n8K7+VM3GlHnsnayyDNHQ0TAOJZS5/pLeCswDLuhpjfI34TVKOgL4I+DqflUoZ1n+DY8kaeJZQPJN7WuSzoyIf+tx3fKS5RqXAmsj4iZJ5wN/lV5juQehn0Qmw53+MDCjYb3ZEA0/3kfSkSRfK/f1pXb5yHKNSFoIXAdcGhH7+1S3vLS7xmOBM4GvSHocOA/YUKGHuVl/T++IiAMRsRN4mOSPQFVkucZrgFsBIhk19xiSwdisTyZD0v8GMFvSLElHkTyo3TBunw0k4/MDXAH8Y6RPkiqi7TWmTR+fIkn4VWsLhjbXGBFPR8RARMyMiJkkzy0ujYitxVS3Y1l+T28neSCPpAGS5p7H+lrL7mS5xieAiwAkvYok6f+/vtay5iqf9NM2+muBjcBDwK0RsUPS70m6NN3tL4CXSBoCfhto2R2wjDJe48eAnwb+Oh2+Yvz/bKWW8RorK+P1bQSekvQg8GXgfRHxVDE17lzGa3wP8E5J9wPrgKsrdgNWeR6GwcysRip/p29mZtk56ZuZ1YiTvplZjTjpm5nViJO+mVmNOOmbmdWIk76ZWY046VutSLpd0r3pePXLiq6PWb/55SyrFUkvjoh9kl5AMmzABVV669WsW5NhlE2zTvympMvSzzNIBjRz0rfacNK32pC0AFgInB8RP5L0FZIBv8xqw236VifHA99PE/4rSYZnNqsVJ32rky8BR0p6gGTaxS0F18es7/wg18ysRnynb2ZWI076ZmY14qRvZlYjTvpmZjXipG9mViNO+mZmNeKkb2ZWI/8fk04V1W0RGAwAAAAASUVORK5CYII=\n", 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\n", 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SPxHYmH/+B+At+eelwLqI2BMR24FhYGEJ5TMza63iL2dVMelvBcaHbb4EmJV/ngE82XDcSL7NzKwaCo67U+bD3lKSvqQNkrY2WZYC7wSulHQv2Qifz42f1uRSTf/pJK2QtFnS5t7cgZlZC4lq+pJWS9olqekUiJIWSXpG0pZ8+VCR4pXSeyciFrc55E0AkuYBv5RvG+EntX6AmcCOFtcfAobya1T8WbqZTSnpMs4a4OPAzZMc842I+OVOLlq55h1JR+f/PQj4n8CqfNd6YJmkwyXNAeYC95RTSjOz/Yl0vXciYiPZPCZJVS7pA8slfRf4DllN/i8BImIbcAvwIPAV4MqIGC2tlGZmE3XWpj9tvBk6X1YcQMSzJN0v6e8kzS9yQuVezoqIm4CbWuxbCazsb4nMzDpQvHlnd5cTo/8zcHxE/Luk84HbyFpAJlW5pN8Lyea/rEgcxxqcOI41eLG61qeniBHxg4bPd0j6pKRpEbF7svOq2LxjZjaw+tVlU9IxkpR/XkiWz59qd14tavqjO9t+4+nKeC2k13Eca3DiONZgxUr6TSJRTV/SWmARWdv/CHAdcChARKwCLgaukLQP+A9gWUS0jV6LpG9m1heRblydiFjeZv/Hybp0dsRJ38wspYq/GeSkb2aWUNVfB3XSNzNLyUnfzKwmSh5BswgnfTOzRISbd8zMasVJ38ysTpz0zcxqxEnfzKwmSp4VqwgnfTOzlJz0zczqI9UwDL3ipG9mlpCbd8zM6sIvZ5mZ1YyTvplZPfiNXDOzmtFYtbO+p0s0M0slOljakLRa0i5JW9sc9zpJo5IuLlJEJ30zs4QSzpG7BlgyaSzpYOCjwJ1Fy1eL5p2k819WII5jDU4cxxq8WF1L1LoTERslzW5z2HuAvwVeV/S6pdT0JV0iaZukMUkLJuy7WtKwpIclndewfUm+bVjSB/tfajOz9hLW9CePI80ALgRWdXJeWTX9rcBFwKcbN0o6CVgGzAeOBTZImpfv/gTwC8AI8C1J6yPiwSLBRnfOTVXupsZrIb2O41iDE8exBitW0m8SxRP6NEmbG9aHImKog0h/AvxuRIxKKnxSKUk/Ih4CaFLQpcC6iNgDbJc0DCzM9w1HxGP5eevyYwslfTOzvoiOhmHYHREL2h/W0gJgXZ5HpwHnS9oXEbdNdlLV2vRnAJsa1kfybQBPTtj++lYXkbQCWJG8dGZmk+hnP/2ImPN8XGkN8KV2CR96mPQlbQCOabLrmoi4vdVpTbYFzZ89tPynzb8iDeXlqHanWTObWiJNypG0FlhE1gw0AlwHHJqFiI7a8Rv1LOlHxOIDOG0EmNWwPhPYkX9utd3MrDJSVTMjYnkHx15W9Niq9dNfDyyTdLikOcBc4B7gW8BcSXMkHUb2sHd9ieU0M9tfwpezeqWUNn1JFwJ/BrwS+LKkLRFxXkRsk3QL2QPafcCVETGan3MV2QsIBwOrI2JbGWU3M5uMx9NvIiK+AHyhxb6VwMom2+8A7uhx0czMuuKkb2ZWF0GyB7m94qRvZpZQ1fsLOumbmaXkpG9mVg+eRMXMrE4iKj+JipO+mVlK1c75TvpmZim5ecfMrC4CcPOOmVmNVDvnO+mbmaXk5h0zsxpx750K8GTbjlVWHMcavFhdKXkEzSJqkfTNzPohezmr2lm/Fknfk207Vr/jONZgxUr6TaLio2xWbRIVM7OBpohCS9vrSKsl7ZK0tcX+pZIekLRF0mZJP1ekfE76ZmappJ05aw2wZJL9dwGnRsRpwDuBzxS5aC2ad8zM+iPd2DsRsVHS7En2/3vD6k9R8E+Jk76ZWUrFH+ROk7S5YX0oIoY6CZVPPfsHwNHALxU5x0nfzCyV6Gi6xN0RsaCrcPnUs5J+HrgeWNzuHLfpm5mlFFFsSRoyNgInSJrW7lgnfTOzlNI9yJ2UpJ+RpPzz6cBhwFPtznPzjplZQhpL01Ff0lpgEVnb/whwHXAoQESsAt4CXCppL/AfwNsi2n+FKCXpS7oE+DDwWmBhRGzOt78CuBV4HbAmIq5qOOcMsi5MLwbuAH6ryA2amfVNkOzlrIhY3mb/R4GPdnrdspp3tgIXARsnbP8xcC3w/ibnfApYAczNl8n6r5qZ9Z0o9mJWmUM1lJL0I+KhiHi4yfZnI+IfyZL/8yRNB46MiLvz2v3NwK/0p7RmZh0o4UFuJwalTX8GMNKwPpJva0rSCrJvBWZm/VXxVueeJX1JG4Bjmuy6JiJu7/RyTba1/JfNX3AYystR7Z+AmU0dCdv0e6VnST8i2r4k0IERYGbD+kxgR8Lrm5klkar3Tq8MRD/9iNgJ/FDSmXm/1EuBTr8tmJn1WMH2/Lo9yJV0Yd7v9Czgy5LubNj3OPBHwGWSRiSdlO+6gmwUuWHgUeDv+ltqM7M2gson/VIe5I6PF9Fi3+wW2zcDJ/ewWGZm3at2687A9N4xMxsIni7RzKxOnPTLl3T+ywrEcazBieNYgxerKxEwWu32nVokfTOzvnFNv3yjO+f29PrjtZBex3GswYnjWIMVK+k3CSd9M7OaCCDRHLm94qRvZpZMQLhN38ysHoLKP8gdiGEYzMwGRqI3ciWtlrRL0tYW+/+rpAfy5Z8knVqkeE76ZmYppRuGYQ2TTxa1HTg7Ik4BricfWbgdN++YmSWTblydiNgoafYk+/+pYXUTLxyJuCUnfTOzVAIoZ2jlyyk4CKWTvplZSsVr+tMkbW5YH8ongOqIpHPIkv7PFTneSd/MLJmOhmHYHRELuokm6RSyIed/MSKeKnKOk76ZWSoB0ad++pKOAz4P/FpEfLfoeU76ZmYpJXojV9JaYBFZM9AIcB1wKEBErAI+BLwC+GQ2oSD7inxzcNI3M0spXe+d5W32vwt4V6fXddI3M0sloqzeO4U56ZuZpeRRNs3M6iKI0dGyCzEpJ30zs1Q8tLKZWc1UfGjlUgZck3SJpG2SxiQtaNj+C5LulfTt/L9vbNh3Rr59WNKfKu+jZGZWFQHEWBRaylLWKJtbgYuAjRO27wbeHBE/C/w68FcN+z4FrADm5stko8+ZmfVf5JOoFFlKUkrzTkQ8BDCxsh4R9zWsbgNeJOlw4OXAkRFxd37ezcCvUHCAoaTzX1YgjmMNThzHGrxY3fKD3AP3FuC+iNgjaQYw0rBvBJjR6kRJK8i+FQDsIftmMVVNI/uGNJX5HgffINzf8d1e4Id8/84Nceu0goeX8u/Rs6QvaQNwTJNd10TE7W3OnQ98FHjT+KYmh7VsFMtHqhvKr7W520GNqmyq3x/4HqeCqX5/4yKi8s3OPUv6EbH4QM6TNBP4AnBpRDyabx7hhRMEzAR2dFdCM7P6qdR0iZJeCnwZuDoivjm+PSJ2Aj+UdGbea+dSYNJvC2Zmtr+yumxemI8adxbwZUl35ruuAn4GuFbSlnw5Ot93Bdm40cPAoxR8iEvBeSMH2FS/P/A9TgVT/f4GhqLi40SYmVk6lWreMTOz3nLSNzOrkSmR9CUtkfRwPkTDB5vsP1zS5/L9/0fS7P6XsjsF7vF3JD0o6QFJd0nqus9xv7W7x4bjLpYUjUN4DIIi9yfprfnPcZukv+53GbtV4Pf0OElflXRf/rt6fhnlrLWIGOgFOJjswe6rgcOA+4GTJhzzG8Cq/PMy4HNll7sH93gO8JL88xVT8R7z444gG75jE7Cg7HIn/hnOBe4DXpavH112uXtwj0PAFfnnk4DHyy533ZapUNNfCAxHxGMR8RywDlg64ZilwP/KP98KnDtgA7a1vceI+GpE/Chf3cQL32sYBEV+jgDXAzcAP+5n4RIocn/vBj4REd8HiIhdfS5jt4rcYwBH5p+Pwu/b9N1USPozgCcb1psN0fD8MRGxD3iGbELhQVHkHhtdTvEurVXR9h4l/SdgVkR8qZ8FS6TIz3AeME/SNyVtklT5tzsnKHKPHwbennfZvgN4T3+KZuOqPPZOUUWGaOhoGIcKKlx+SW8HFgBn97RE6U16j5IOAv4YuKxfBUqsyM/wELImnkVk39S+IenkiPh/PS5bKkXucTmwJiJulHQW8Ff5PVZ7EPopZCrU9EeAWQ3rzYZoeP4YSYeQfa18ui+lS6PIPSJpMXANcEFE7OlT2VJpd49HACcDX5P0OHAmsH6AHuYW/T29PSL2RsR24GGyPwKDosg9Xg7cAhDZqLkvIhuMzfpkKiT9bwFzJc2RdBjZg9r1E45ZTzY+P8DFwP+O/EnSgGh7j3nTx6fJEv6gtQVDm3uMiGciYlpEzI6I2WTPLS6IiM3lFLdjRX5PbyN7II+kaWTNPY/1tZTdKXKPTwDnAkh6LVnS/799LWXNDXzSz9vorwLuBB4CbomIbZJ+X9IF+WF/AbxC0jDwO0DL7oBVVPAePwb8NPA3+fAVE/9nq7SC9ziwCt7fncBTkh4Evgp8ICKeKqfEnSt4j+8D3i3pfmAtcNmAVcAGnodhMDOrkYGv6ZuZWXFO+mZmNeKkb2ZWI076ZmY14qRvZlYjTvpmZjXipG9mViNO+lYrkm6TdG8+Xv2Ksstj1m9+OctqRdLLI+JpSS8mGzbg7EF669WsW1NhlE2zTvympAvzz7PIBjRz0rfacNK32pC0CFgMnBURP5L0NbIBv8xqw236VidHAd/PE/5ryIZnNqsVJ32rk68Ah0h6gGzaxU0ll8es7/wg18ysRlzTNzOrESd9M7MacdI3M6sRJ30zsxpx0jczqxEnfTOzGnHSNzOrkf8PYwBLIa4aZe4AAAAASUVORK5CYII=\n", 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\n", 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\n", 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WZmYVVMak/0pgc/r5n4A3pp8XA+sjYn9E7AQGgfkF1M/MrLWSv5xVxqS/HRgdtvlyYEb6eRrwRMN2Q+k6M7NyyDjuTpEPewtJ+pI2SdreZFkMvANYIekekhE+nxvdrcmhmv7TSVomaaukrb05AzOzFnK60pe0RtIeSU2nQJS0QNJTkraly4ezVK+Q3jsRsbDNJm8AkDQH+OV03RA/ueoHmA7sanH8AWAgPUbJn6Wb2YSSX8ZZC3wCuGmcbb4eEb/SyUFL17wj6fj0v4cB/xNYnRZtAJZIOkLSLGA2cHcxtTQzO5jIr/dORGwmmcckV6VL+sBSSd8Bvk1yJf/XABGxA7gFeAD4MrAiIoYLq6WZ2VidtelPHW2GTpdlhxDxXEn3SfoHSXOz7FC6l7Mi4kbgxhZlq4BV/a2RmVkHsjfv7O1yYvT/C5wSEf8u6SLgNpIWkHGVLun3Qm7zX5YkjmNVJ45jVS9W1/r0FDEiftjw+Q5Jn5Q0NSL2jrdfGZt3zMwqq19dNiWdIEnp5/kk+fzJdvvV4kp/eHfbO56ujF6F9DqOY1UnjmNVK1audxI5XelLWgcsIGn7HwKuBaYARMRq4DJguaQDwH8ASyKibfRaJH0zs76I/MbViYilbco/QdKlsyNO+mZmeSr5m0FO+mZmOSr766BO+mZmeXLSNzOriYJH0MzCSd/MLCfCzTtmZrXipG9mVidO+mZmNeKkb2ZWEwXPipWFk76ZWZ6c9M3M6iOvYRh6xUnfzCxHbt4xM6sLv5xlZlYzTvpmZvXgN3LNzGpGI+XO+p4u0cwsL9HB0oakNZL2SNreZrvXSBqWdFmWKjrpm5nlKMc5ctcCi8aNJU0CPgZszFq/WjTv5Dr/ZQniOFZ14jhW9WJ1LafWnYjYLGlmm83eDfwd8Jqsxy3kSl/S5ZJ2SBqRNG9M2UpJg5IeknRhw/pF6bpBSR/of63NzNrL8Up//DjSNOASYHUn+xV1pb8duBT4dONKSacBS4C5wEnAJklz0uK/AH4RGAK+KWlDRDyQJdjw7tl51bup0auQXsdxrOrEcaxqxcr1TiJ7Qp8qaWvD94GIGOgg0p8CvxsRw5Iy71RI0o+IBwGaVHQxsD4i9gM7JQ0C89OywYh4NN1vfbptpqRvZtYX0dEwDHsjYl77zVqaB6xP8+hU4CJJByLitvF2Klub/jRgS8P3oXQdwBNj1r+21UEkLQOW5V47M7Nx9LOffkTM+nFcaS3wxXYJH3qY9CVtAk5oUnRNRNzearcm64Lmzx5a/tOmt0gDaT3K3WnWzCaWyCflSFoHLCBpBhoCrgWmJCGio3b8Rj1L+hGx8BB2GwJmNHyfDuxKP7dab2ZWGnldZkbE0g62vTLrtmXrp78BWCLpCEmzgNnA3cA3gdmSZkk6nORh74YC62lmdrAcX87qlULa9CVdAvw58DLgS5K2RcSFEbFD0i0kD2gPACsiYjjd52qSFxAmAWsiYkcRdTczG4/H028iIr4AfKFF2SpgVZP1dwB39LhqZmZdcdI3M6uLILcHub3ipG9mlqOy9xd00jczy5OTvplZPXgSFTOzOoko/SQqTvpmZnkqd8530jczy5Obd8zM6iIAN++YmdVIuXO+k76ZWZ7cvGNmViPuvVMCnmzbsYqK41jVi9WVgkfQzKIWSd/MrB+Sl7PKnfVrkfQ92bZj9TuOY1UrVq53EiUfZbNsk6iYmVWaIjItbY8jrZG0R9L2FuWLJd0vaZukrZJ+Pkv9nPTNzPKS78xZa4FF45TfCZwZEWcB7wA+k+WgtWjeMTPrj/zG3omIzZJmjlP+7w1ff4qMf0qc9M3M8pT9Qe5USVsbvg9ExEAnodKpZ/8QOB745Sz7OOmbmeUlOpoucW9EzOsqXDr1rKRfAK4DFrbbx236ZmZ5isi25BoyNgOnSprablsnfTOzPOX3IHdckn5GktLPPwccDjzZbj8375iZ5Ugj+XTUl7QOWEDS9j8EXAtMAYiI1cAbgSskPQ/8B/DmiPa3EIUkfUmXAx8BXg3Mj4it6fqXArcCrwHWRsTVDfucTdKF6UXAHcBvZTlBM7O+CXJ7OSsilrYp/xjwsU6PW1TzznbgUmDzmPXPAh8C3tdkn08By4DZ6TJe/1Uzs74T2V7MKnKohkKSfkQ8GBEPNVn/TET8M0ny/zFJJwJHR8Rd6dX9TcCv9ae2ZmYdKOBBbieq0qY/DRhq+D6UrmtK0jKSuwIzs/4qeatzz5K+pE3ACU2KromI2zs9XJN1Lf9l0xccBtJ6lPsnYGYTR45t+r3Ss6QfEW1fEujAEDC94ft0YFeOxzczy0VevXd6pRL99CNiN/C0pHPSfqlXAJ3eLZiZ9VjG9vy6PciVdEna7/Rc4EuSNjaUPQb8MXClpCFJp6VFy0lGkRsEHgH+ob+1NjNrIyh90i/kQe7oeBEtyma2WL8VOL2H1TIz6165W3cq03vHzKwSPF2imVmdOOkXL9f5L0sQx7GqE8exqherKxEwXO72nVokfTOzvvGVfvGGd8/u6fFHr0J6HcexqhPHsaoVK9c7CSd9M7OaCCCnOXJ7xUnfzCw3AeE2fTOzeghK/yC3EsMwmJlVRk5v5EpaI2mPpO0tyv+rpPvT5V8knZmlek76ZmZ5ym8YhrWMP1nUTuC8iDgDuI50ZOF23LxjZpab/MbViYjNkmaOU/4vDV+38MKRiFty0jczy0sAxQytfBUZB6F00jczy1P2K/2pkrY2fB9IJ4DqiKTzSZL+z2fZ3knfzCw3HQ3DsDci5nUTTdIZJEPO/1JEPJllHyd9M7O8BESf+ulLOhn4PPC2iPhO1v2c9M3M8pTTG7mS1gELSJqBhoBrgSkAEbEa+DDwUuCTyYSCHMhy5+Ckb2aWp/x67yxtU/5O4J2dHtdJ38wsLxFF9d7JzEnfzCxPHmXTzKwughgeLroS43LSNzPLi4dWNjOrmZIPrVzIgGuSLpe0Q9KIpHkN639R0j2SvpX+9/UNZWen6wcl/ZnSPkpmZmURQIxEpqUoRY2yuR24FNg8Zv1e4Fcj4meBXwdubij7FLAMmJ0u440+Z2bWf5FOopJlKUghzTsR8SDA2Iv1iLi34esO4EhJRwDHAUdHxF3pfjcBv0bGAYZynf+yBHEcqzpxHKt6sbrlB7mH7o3AvRGxX9I0YKihbAiY1mpHSctI7goA9pPcWUxUU0nukCYyn2P1VeH8Tun2AE/z/Y2b4tapGTcv5N+jZ0lf0ibghCZF10TE7W32nQt8DHjD6Komm7VsFEtHqhtIj7W120GNymyinx/4HCeCiX5+oyKi9M3OPUv6EbHwUPaTNB34AnBFRDySrh7ihRMETAd2dVdDM7P6KdV0iZKOBb4ErIyIb4yuj4jdwNOSzkl77VwBjHu3YGZmByuqy+Yl6ahx5wJfkrQxLboa+BngQ5K2pcvxadlyknGjB4FHyPgQl4zzRlbYRD8/8DlOBBP9/CpDUfJxIszMLD+lat4xM7PectI3M6uRCZH0JS2S9FA6RMMHmpQfIelzafn/kTSz/7XsToZz/B1JD0i6X9Kdkrruc9xv7c6xYbvLJEXjEB5VkOX8JL0p/TnukPTZftexWxl+T0+W9BVJ96a/qxcVUc9ai4hKL8Akkge7rwAOB+4DThuzzW8Aq9PPS4DPFV3vHpzj+cCL08/LJ+I5ptsdRTJ8xxZgXtH1zvlnOBu4F3hJ+v34ouvdg3McAJann08DHiu63nVbJsKV/nxgMCIejYjngPXA4jHbLAb+Jv18K3BBxQZsa3uOEfGViPhR+nULL3yvoQqy/BwBrgOuB57tZ+VykOX83gX8RUR8HyAi9vS5jt3Kco4BHJ1+Pga/b9N3EyHpTwOeaPjebIiGH28TEQeAp0gmFK6KLOfY6Cqyd2kti7bnKOk/ATMi4ov9rFhOsvwM5wBzJH1D0hZJpX+7c4ws5/gR4K1pl+07gHf3p2o2qsxj72SVZYiGjoZxKKHM9Zf0VmAecF5Pa5S/cc9R0mHAnwBX9qtCOcvyM5xM0sSzgORO7euSTo+IH/S4bnnJco5LgbURcYOkc4Gb03Ms9yD0E8hEuNIfAmY0fG82RMOPt5E0meS2cl9fapePLOeIpIXANcDFEbG/T3XLS7tzPAo4HfiqpMeAc4ANFXqYm/X39PaIeD4idgIPkfwRqIos53gVcAtAJKPmHkkyGJv1yURI+t8EZkuaJelwkge1G8Zss4FkfH6Ay4D/HemTpIpoe45p08enSRJ+1dqCoc05RsRTETE1ImZGxEyS5xYXR8TWYqrbsSy/p7eRPJBH0lSS5p5H+1rL7mQ5x8eBCwAkvZok6f+/vtay5iqf9NM2+quBjcCDwC0RsUPS70u6ON3sr4CXShoEfgdo2R2wjDKe48eBnwb+Nh2+Yuz/bKWW8RwrK+P5bQSelPQA8BXg/RHxZDE17lzGc3wv8C5J9wHrgCsrdgFWeR6GwcysRip/pW9mZtk56ZuZ1YiTvplZjTjpm5nViJO+mVmNOOmbmdWIk76ZWY046VutSLpN0j3pePXLiq6PWb/55SyrFUnHRcQ+SS8iGTbgvCq99WrWrYkwyqZZJ35T0iXp5xkkA5o56VttOOlbbUhaACwEzo2IH0n6KsmAX2a14TZ9q5NjgO+nCf9VJMMzm9WKk77VyZeByZLuJ5l2cUvB9THrOz/INTOrEV/pm5nViJO+mVmNOOmbmdWIk76ZWY046ZuZ1YiTvplZjTjpm5nVyP8Hk6xFYEFDD70AAAAASUVORK5CYII=\n", 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\n", 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\n", 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PS4GNEXEwInYDg8DCAupnZtZayV/OKmPS3wGMDtt8NTAr/TwDeKZhv6F0m5lZOWQcd6fIh72FJH1JmyXtaLIsBd4JrJL0IMkIny+MHtbkVE3/6SStkLRN0rbeXIGZWQs53elLWidpn6SmUyBKWiTpgKTt6fKhLNUrpPdORCxus8ubACTNA34x3TbEj+/6AWYCe1qcfwAYSM9R8mfpZjah5Jdx1gMfB24fZ5+vRcQvdXLS0jXvSDo5/e8k4H8Aa9OiTcAyScdLmgPMBR4oppZmZkcT+fXeiYgtJPOY5Kp0SR9YLunbwLdI7uT/F0BE7ATuAB4FvgSsiojhwmppZjZWZ23600abodNlxTFEvFDSw5L+RtL8LAeU7uWsiLgNuK1F2RpgTX9rZGbWgezNO/u7nBj9n4DTI+LfJF0G3EXSAjKu0iX9Xsht/suSxHGs6sRxrOrF6lqfniJGxPcaPt8j6ROSpkXE/vGOK2PzjplZZfWry6akUyQp/byQJJ8/2+64WtzpD+9t+42nK6N3Ib2O41jVieNY1YqV6zeJnO70JW0AFpG0/Q8BNwFTASJiLXAVsFLSYeDfgWUR0TZ6LZK+mVlfRH7j6kTE8jblHyfp0tkRJ30zszyV/M0gJ30zsxyV/XVQJ30zszw56ZuZ1UTBI2hm4aRvZpYT4eYdM7NacdI3M6sTJ30zsxpx0jczq4mCZ8XKwknfzCxPTvpmZvWR1zAMveKkb2aWIzfvmJnVhV/OMjOrGSd9M7N68Bu5ZmY1o5FyZ31Pl2hmlpfoYGlD0jpJ+yTtaLPf6yQNS7oqSxWd9M3McpTjHLnrgSXjxpImAx8F7s1av1o07+Q6/2UJ4jhWdeI4VvVidS2n1p2I2CJpdpvd3gP8FfC6rOct5E5f0tWSdkoakbRgTNlqSYOSdkm6tGH7knTboKQP9r/WZmbt5XinP34caQZwBbC2k+OKutPfAVwJfKpxo6SzgGXAfOBUYLOkeWnxnwI/DwwB35C0KSIezRJseO/cvOrd1OhdSK/jOFZ14jhWtWLl+k0ie0KfJmlbw/pARAx0EOmPgN+OiGFJmQ8qJOlHxGMATSq6FNgYEQeB3ZIGgYVp2WBEPJketzHdN1PSNzPri+hoGIb9EbGg/W4tLQA2pnl0GnCZpMMRcdd4B5WtTX8GsLVhfSjdBvDMmO2vb3USSSuAFbnXzsxsHP3spx8Rc34UV1oPfKFdwoceJn1Jm4FTmhTdEBF3tzqsybag+bOHlv+06VekgbQe5e40a2YTS+STciRtABaRNAMNATcBU5MQ0VE7fqOeJf2IWHwMhw0BsxrWZwJ70s+ttpuZlUZet5kRsbyDfa/Num/Z+ulvApZJOl7SHGAu8ADwDWCupDmSjiN52LupwHqamR0tx5ezeqWQNn1JVwB/ArwS+KKk7RFxaUTslHQHyQPaw8CqiBhOj7me5AWEycC6iNhZRN3NzMbj8fSbiIjPA59vUbYGWNNk+z3APT2umplZV5z0zczqIsjtQW6vOOmbmeWo7P0FnfTNzPLkpG9mVg+eRMXMrE4iSj+JipO+mVmeyp3znfTNzPLk5h0zs7oIwM07ZmY1Uu6c76RvZpYnN++YmdWIe++UgCfbdqyi4jhW9WJ1peARNLOoRdI3M+uH5OWscmf9WiR9T7btWP2O41jVipXrN4mSj7JZtklUzMwqTRGZlrbnkdZJ2idpR4vypZIekbRd0jZJP5ulfk76ZmZ5yXfmrPXAknHK7wPOjYjzgHcCn85y0lo075iZ9Ud+Y+9ExBZJs8cp/7eG1Z8g458SJ30zszxlf5A7TdK2hvWBiBjoJFQ69ezvAScDv5jlGCd9M7O8REfTJe6PiAVdhUunnpX0c8DNwOJ2x7hN38wsTxHZllxDxhbgDEnT2u3rpG9mlqf8HuSOS9JPSVL6+WeA44Bn2x3n5h0zsxxpJJ+O+pI2AItI2v6HgJuAqQARsRZ4M3CNpEPAvwNvjWj/FaKQpC/pauDDwGuBhRGxLd3+CuBO4HXA+oi4vuGY80m6ML0YuAf4zSwXaGbWN0FuL2dFxPI25R8FPtrpeYtq3tkBXAlsGbP9h8CNwPubHPNJYAUwN13G679qZtZ3ItuLWUUO1VBI0o+IxyJiV5Ptz0fE35Mk/x+RNB04MSLuT+/ubwd+pT+1NTPrQAEPcjtRlTb9GcBQw/pQuq0pSStIvhWYmfVXyVude5b0JW0GTmlSdENE3N3p6Zpsa/kvm77gMJDWo9w/ATObOHJs0++VniX9iGj7kkAHhoCZDeszgT05nt/MLBd59d7plUr004+IvcD3JV2Q9ku9Buj024KZWY9lbM+v24NcSVek/U4vBL4o6d6GsqeAPwCulTQk6ay0aCXJKHKDwBPA3/S31mZmbQSlT/qFPMgdHS+iRdnsFtu3AWf3sFpmZt0rd+tOZXrvmJlVgqdLNDOrEyf94uU6/2UJ4jhWdeI4VvVidSUChsvdvlOLpG9m1je+0y/e8N65PT3/6F1Ir+M4VnXiOFa1YuX6TcJJ38ysJgLIaY7cXnHSNzPLTUC4Td/MrB6C0j/IrcQwDGZmlZHTG7mS1knaJ2lHi/K3SXokXf5B0rlZquekb2aWp/yGYVjP+JNF7QYuiohzgJtJRxZux807Zma5yW9cnYjYImn2OOX/0LC6lSNHIm7JSd/MLC8BFDO08nVkHITSSd/MLE/Z7/SnSdrWsD6QTgDVEUkXkyT9n82yv5O+mVluOhqGYX9ELOgmmqRzSIac/4WIeDbLMU76ZmZ5CYg+9dOXdBrwOeDXIuLbWY9z0jczy1NOb+RK2gAsImkGGgJuAqYCRMRa4EPAK4BPJBMKcjjLNwcnfTOzPOXXe2d5m/J3Ae/q9LxO+mZmeYkoqvdOZk76ZmZ58iibZmZ1EcTwcNGVGJeTvplZXjy0splZzZR8aOVCBlyTdLWknZJGJC1o2P7zkh6U9M30v29sKDs/3T4o6Y+V9lEyMyuLAGIkMi1FKWqUzR3AlcCWMdv3A78cET8N/Drw5w1lnwRWAHPTZbzR58zM+i/SSVSyLAUppHknIh4DGHuzHhEPNazuBF4k6Xjg5cCJEXF/etztwK+QcYChXOe/LEEcx6pOHMeqXqxu+UHusXsz8FBEHJQ0AxhqKBsCZrQ6UNIKkm8FAAdJvllMVNNIviFNZL7G6qvC9Z3e7Qm+z3fv3Rx3Tsu4eyH/Hj1L+pI2A6c0KbohIu5uc+x84KPAm0Y3NdmtZaNYOlLdQHqubd0OalRmE/36wNc4EUz06xsVEaVvdu5Z0o+IxcdynKSZwOeBayLiiXTzEEdOEDAT2NNdDc3M6qdU0yVKeinwRWB1RHx9dHtE7AW+L+mCtNfONcC43xbMzOxoRXXZvCIdNe5C4IuS7k2Lrgd+CrhR0vZ0OTktW0kybvQg8AQZH+KScd7ICpvo1we+xolgol9fZShKPk6EmZnlp1TNO2Zm1ltO+mZmNTIhkr6kJZJ2pUM0fLBJ+fGSPpuW/6Ok2f2vZXcyXONvSXpU0iOS7pPUdZ/jfmt3jQ37XSUpGofwqIIs1yfpLenPcaekv+h3HbuV4ff0NElflvRQ+rt6WRH1rLWIqPQCTCZ5sPtq4DjgYeCsMfv8F2Bt+nkZ8Nmi692Da7wYeEn6eeVEvMZ0vxNIhu/YCiwout45/wznAg8BL0vXTy663j24xgFgZfr5LOCpoutdt2Ui3OkvBAYj4smIeAHYCCwds89S4H+nn+8ELqnYgG1trzEivhwRP0hXt3Lkew1VkOXnCHAzcAvww35WLgdZru/dwJ9GxHcBImJfn+vYrSzXGMCJ6eeT8Ps2fTcRkv4M4JmG9WZDNPxon4g4DBwgmVC4KrJcY6PryN6ltSzaXqOk/wDMiogv9LNiOcnyM5wHzJP0dUlbJZX+7c4xslzjh4G3p1227wHe05+q2agyj72TVZYhGjoaxqGEMtdf0tuBBcBFPa1R/sa9RkmTgD8Eru1XhXKW5Wc4haSJZxHJN7WvSTo7Iv5fj+uWlyzXuBxYHxG3SroQ+PP0Gss9CP0EMhHu9IeAWQ3rzYZo+NE+kqaQfK18ri+1y0eWa0TSYuAG4PKIONinuuWl3TWeAJwNfEXSU8AFwKYKPczN+nt6d0QciojdwC6SPwJVkeUarwPuAIhk1NwXkQzGZn0yEZL+N4C5kuZIOo7kQe2mMftsIhmfH+Aq4P9E+iSpItpeY9r08SmShF+1tmBoc40RcSAipkXE7IiYTfLc4vKI2FZMdTuW5ff0LpIH8kiaRtLc82Rfa9mdLNf4NHAJgKTXkiT9/9vXWtZc5ZN+2kZ/PXAv8BhwR0TslPS7ki5Pd/sz4BWSBoHfAlp2ByyjjNf4MeAngb9Mh68Y+z9bqWW8xsrKeH33As9KehT4MvCBiHi2mBp3LuM1vg94t6SHgQ3AtRW7Aas8D8NgZlYjlb/TNzOz7Jz0zcxqxEnfzKxGnPTNzGrESd/MrEac9M3MasRJ38ysRpz0rVYk3SXpwXS8+hVF18es3/xyltWKpJdHxHOSXkwybMBFVXrr1axbE2GUTbNO/IakK9LPs0gGNHPSt9pw0rfakLQIWAxcGBE/kPQVkgG/zGrDbfpWJycB300T/mtIhmc2qxUnfauTLwFTJD1CMu3i1oLrY9Z3fpBrZlYjvtM3M6sRJ30zsxpx0jczqxEnfTOzGnHSNzOrESd9M7MacdI3M6uR/w9RHCjXRKT4xgAAAABJRU5ErkJggg==\n", 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "plt.clf()\n", - "fig, ax = plt.subplots(1)\n", - "\n", - "zz = [ np.sum(list(i.dtc.scores.values())) for i in grid_results]\n", - "yy = [ i.dtc.attrs['vr'] for i in grid_results]\n", - "xx = [ i.dtc.attrs['a'] for i in grid_results]\n", - "from neuronunit.optimization import exhaustive_search\n", - "\n", - "\n", - "pop = package[0][0]\n", - "history = package[0][4]\n", - "genes_vs_gen = package[0][-6]\n", - "print(genes_vs_gen[1])\n", - "#gen_versus_pop = ga_out[-6]\n", - "\n", - "for i,p in history.genealogy_history.items(): \n", - "\n", - " \n", - " other_points = []\n", - " labels = []\n", - " #xy = []\n", - " for k,v in p.dtc.attrs.items():\n", - " labels.append(k)\n", - " #xy.append()\n", - " other_points.append(v)\n", - " \n", - " print(other_points) \n", - " \n", - " #grid = exhaustive_search.make_grid(xx,yy,zz)\n", - " #x,y,z = exhaustive_search.remap_point(xx,yy,zz,other_points)\n", - " \n", - " \n", - " zi, yi, xi = np.histogram2d(yy, xx, bins=(10,10), weights=zz, normed=False)\n", - " counts, _, _ = np.histogram2d(yy, xx, bins=(10,10))\n", - "\n", - " zi = zi / counts\n", - " zi = np.ma.masked_invalid(zi)\n", - "\n", - " fig, ax = plt.subplots()\n", - " \n", - " scat = ax.pcolormesh(xi, yi, zi, edgecolors='black')\n", - " \n", - " fig.colorbar(scat)\n", - " ax.margins(0.05)\n", - " #scat = ax.plot(other_points[0], other_points[1])#, c=z, s=200)\n", - " for p in other_points:\n", - " ax.plot(other_points[0], other_points[1],'ro') \n", - " #fig.colorbar(scat)\n", - " ax.margins(0.05)\n", - " plt.xlabel(labels[0])\n", - " plt.ylabel(labels[1])\n", - " plt.savefig(str('movie')+str(i)+str('.png'))\n", - " plt.show()\n" - ] - }, - { - "cell_type": "code", - "execution_count": 23, - "metadata": {}, - "outputs": [], - "source": [ - "# !sudo apt-get install gifsicle ffmpeg" - ] - }, - { - "cell_type": "code", - "execution_count": 24, - "metadata": {}, - "outputs": [], - "source": [ - "# !convert -delay 0.5 -loop 0 movie*.png gene-evolution.gif\n", - "# !gifsicle --delay=1 --loop movie*.png > anim.gif\n", - "#genes_vs_" - ] - }, - { - "cell_type": "code", - "execution_count": 28, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "'\\nimport pandas as pd\\nlog.header\\ndf = pd.DataFrame(index=log.select(\"gen\"),columns = log.header)\\nlog.select\\nprint(log.select(\"gen\"))\\nseq = pd.Series(data=log.select(\"gen\", \"nevals\", \"avg\",\"std\",\"min\"))\\nscore_ = [ list(p.dtc.scores.keys()) for p in pop ]\\nscore = score_[0]\\nattrs_ = [ list(p.dtc.attrs.keys()) for p in pop ]\\nattrs = attrs_[0]\\nfor a in attrs:\\n df_header.append(a)# = [ attrs[0], attrs[1], score[0] ]#, score[1] ]\\nfor s in score:\\n df_header.append(s)\\nprint(attrs)\\n#score2 = [ list(p.dtc.scores.values())[1] for p in pop ]\\nprint(score1)\\n\\nattrs_ = [ list(p.dtc.attrs.values()) for p in pop ]\\n\\nfor i,p in enumerate(pop):\\n df1 = pd.DataFrame(p.dtc.attrs.values())\\n \\n df2 = pd.DataFrame(columns = df_header)#,columns = ga_out.header)\\n # df2.index[i] = str(\\'gen\\')+str(i)\\ndf2\\ndf1\\n'" - ] - }, - "execution_count": 28, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "matrices = log\n", - "\n", - "'''\n", - "import pandas as pd\n", - "log.header\n", - "df = pd.DataFrame(index=log.select(\"gen\"),columns = log.header)\n", - "log.select\n", - "print(log.select(\"gen\"))\n", - "seq = pd.Series(data=log.select(\"gen\", \"nevals\", \"avg\",\"std\",\"min\"))\n", - "score_ = [ list(p.dtc.scores.keys()) for p in pop ]\n", - "score = score_[0]\n", - "attrs_ = [ list(p.dtc.attrs.keys()) for p in pop ]\n", - "attrs = attrs_[0]\n", - "for a in attrs:\n", - " df_header.append(a)# = [ attrs[0], attrs[1], score[0] ]#, score[1] ]\n", - "for s in score:\n", - " df_header.append(s)\n", - "print(attrs)\n", - "#score2 = [ list(p.dtc.scores.values())[1] for p in pop ]\n", - "print(score1)\n", - "\n", - "attrs_ = [ list(p.dtc.attrs.values()) for p in pop ]\n", - "\n", - "for i,p in enumerate(pop):\n", - " df1 = pd.DataFrame(p.dtc.attrs.values())\n", - " \n", - " df2 = pd.DataFrame(columns = df_header)#,columns = ga_out.header)\n", - " # df2.index[i] = str('gen')+str(i)\n", - "df2\n", - "df1\n", - "'''\n", - "# screen shot of richards computer shows pcolor.\n", - "#p.color" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "'''\n", - "nparams = 1\n", - "import numpy as np\n", - "import matplotlib\n", - "matplotlib.use('Agg')\n", - "\n", - "import matplotlib.pyplot as plt\n", - "import seaborn as sns\n", - "import pandas as pd\n", - "import numpy as np\n", - "import math as math\n", - "from pylab import rcParams\n", - "from neuronunit.optimization import exhaustive_search\n", - "from neuronunit.optimization.exhaustive_search import run_grid\n", - "\n", - "reports = {}\n", - "npoints = 10\n", - "\n", - "nparams = 1\n", - "\n", - "ga_out = run_ga(model_params,nparams)\n", - "grid_results = run_grid(npoints,nparams)\n", - "\n", - "plt.clf()\n", - "plt.scatter(grid_results,[ sum(g.dtc.scores.values()) for g in grid_results ] )\n", - "plt.show()\n", - "\n", - "miniga = min_max(ga_out[0])[0][1]\n", - "\n", - "plt.clf()\n", - "plt.scatter(ga_out[0],[ sum(g.dtc.scores.values()) for g in ga_out[0] ] )\n", - "plt.scatter(grid_results,[ sum(g.dtc.scores.values()) for g in grid_results ] )\n", - "plt.scatter(miniga,int(len(grid_results)/2))\n", - "plt.show()\n", - "\n", - "\n", - "plt.clf()\n", - "plt.scatter(ga_out[0],[ sum(g.dtc.score.values()) for g in ga_out[0] ] )\n", - "plt.scatter(grid_results,[ sum(g.dtc.score.values()) for g in grid_results ] )\n", - "plt.show()\n", - "\n", - "plt.clf()\n", - "for j in [ list(g.dtc.scores.values()) for g in grid_results ]:\n", - " plt.scatter([i for i in range(0,len(j))] ,j)\n", - "plt.show()\n", - "\n", - "mini = min_max(grid_results)[0][1]\n", - "maxi = min_max(grid_results)[1][1]\n", - "quantize_distance = list(np.linspace(mini,maxi,21))\n", - "worked = bool(miniga < quantize_distance[2])\n", - "print('Report: ')\n", - "print('did it work? {0}'.format(worked))\n", - "reports[nparams] = {}\n", - "reports[nparams]['success'] = bool(miniga < quantize_distance[2])\n", - "dtc_ga = min_max(ga_out[0])[0][0]\n", - "attrs_grid = min_max(grid_results)[0][0]\n", - "attrs_ga = min_max(ga_out[0])[0][0]\n", - "\n", - "grid_points = exhaustive_search.create_grid(npoints = 1,nparams = nparams)#td = list(grid_points[0].keys())\n", - "td = list(grid_points[0].keys())\n", - "\n", - "reports[nparams]['p_dist'] = param_distance(attrs_ga,attrs_grid,td)\n", - "dtc_grid = dtc_ga = min_max(ga_out[0])[0][2]\n", - "dom_grid, dom_ga = error_domination(dtc_ga,dtc_grid)\n", - "\n", - "# Was there vindicating domination in grid search but not GA?\n", - "if dom_grid == True and dom_ga == False:\n", - " reports[nparams]['vind_domination'] = True\n", - "elif dom_grid == False and dom_ga == False:\n", - " reports[nparams]['vind_domination'] = True\n", - "# Was there incriminating domination in GA but not the grid, or in GA and Grid\n", - "elif dom_grid == True and dom_ga == True:\n", - " reports[nparams]['inc_domination'] = False\n", - "elif dom_grid == False and dom_ga == True:\n", - " reports[nparams]['inc_domination'] = False\n", - "''' " - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "\n", - "'''\n", - "with open('grid_cell_results.p','rb') as f:\n", - " results = pickle.load(f)\n", - " \n", - "\n", - "with open('all_ga_cell.p','rb') as f:\n", - " package = pickle.load(f)\n", - "\n", - "'''\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "'''\n", - "garanked = [ (r.dtc.attrs , sum(r.dtc.scores.values())) for r in package[0] ]\n", - "garanked = sorted(garanked, key=lambda w: w[1]) \n", - "miniga = garanked[0][1]\n", - "maxiga = garanked[-1][1]\n", - "results\n", - "'''" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "'''\n", - "results[0]\n", - "gridranked = [ (r.dtc.attrs , sum(r.dtc.scores.values())) for r in results[0] ]\n", - "gridranked = sorted(ranked, key=lambda w: w[1]) \n", - "mini = gridranked[0][1]\n", - "maxi = gridranked[-1][1]\n", - "\n", - "\n", - "print(mini,maxi)\n", - "quantize_distance = list(np.linspace(mini,maxi,10))\n", - "\n", - "# check that the nsga error is in the bottom 1/5th of the entire error range.\n", - "print('Report: ')\n", - "print(\"Success\" if bool(miniga < quantize_distance[0]) else \"Failure\")\n", - "print(\"The nsga error %f is in the bottom 1/5th of the entire error range\" % miniga)\n", - "print(\"Minimum = %f; 20th percentile = %f; Maximum = %f\" % (mini,quantize_distance[0],maxi))\n", - "'''" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "'''\n", - "with open('all_ga_cell.p','rb') as f:\n", - " results = pickle.load(f)\n", - "print(results[3])\n", - "'''\n" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## What works \n", - "\n", - "Plotting of candidate solutions in 2D error surface slices, and using this as a means to validate GA candidate solutions, see below.\n", - "\n", - "Presenting tabular data as pandas Data frames. see below.\n", - "\n" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "\n" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "\n", - "This motivated me to stop convenient, but lazy reading and writing of attributes from disk, which could otherwise be stored\n", - "in the Data Transport Container. \n", - "* I updated the file models/backends/neuron.py in two crucial places to stop unnecessary file reads.\n", - "\n", - "\n", - "\n" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## What will work with more time\n", - "\n", - "Grid Search on HPC over docker seems to mostly work.\n", - "\n", - "A docker container was made especially to run on a Spike server administratored by Renate. To this end, I have created a dedicated [branch](https://github.com/russelljjarvis/docker-stacks-returned/tree/hpc), with a different entrypoint. \n", - "The docker container builds The container has an entrypoint which runs an exhaustive search simulation. \n", - "\n", - "\n", - "## The command: \n", - "```\n", - "nohup docker run -v /home/rjarvis/git/neuronunit:/home/jovyan/neuronunit scidash/neuronunit-optimization```\n", - "is sufficient to run the file\n", - "```/home/rjarvis/git/neuronunit/neuronunit/unit_test/grid_entry_point.py``` with python.\n", - "\n", - "On the neurospike server many core are available ~50 I have used 36 in dask bags function \n", - "`db.from_sequence(items,npartitions = 36)`. This occurs in two places in the exhaustive (grid) search algorithm. In the find rheobase algorithm, and the neuronounit score algorithm. \n", - "\n", - "Once the NU algorithm has run, files from the spike server are aggregated in a directory accessible to this script, using the command:\n", - "\n", - "```alias cnspike='cd /Users/rjjarvis/Dropbox\\ \\(ASU\\)/capsule/neuronunit/neuronunit/unit_test/grid_server; scp -P 2200 rjarvis@129.219.30.18:/home/rjarvis/git/neuronunit/neuronunit/unit_test/*.p .'```\n", - "\n", - "\n", - "Previously when running this file, it ran without error, however, because I forgot to mount a volume, pickle files generated in this session where not stored. I have since executed the command using a mounted volume, and I am now waiting for an output from the command.\n", - "\n", - "It is expected that this will write four different files, with the prefix=grid_cell, and suffix=neurolex ID\n", - "At the location neuronunit/neuronunit/unit_test on the spike server.\n", - "\n", - "\n", - "This file runs without error\n", - "As confirmed by running \n", - "```tail $HOME/nohup.out```, on the spike server, however the expected pickle files are not present there.\n", - "\n", - "I expect this is because on the first run, I failed to define a mount point, to mount a volume in the docker run command, such that results would write to a location on the HOST OS upon exiting. \n", - "\n", - "\n", - "I am currently waiting for the output of this job.\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "\n", - "# Assumptions about this code:\n", - "The NB was launched with a command that mounts two volumes inside a docker container. \n", - "In the future invocation of this script will be simplified greatly. NU is from a specific fork and branch -b results https://github.com/russelljjarvis/neuronunit \n", - "BluePyOpt is also from a specific fork and branch: -b elitism https://github.com/russelljjarvis/BluePyOpt\n", - "\n", - "Below BASH code for Ubuntu host:\n", - "\n", - "``` bash\n", - "cd ~/git/neuronunit; sudo docker run -it -v `pwd`:/home/jovyan/neuronunit -v ~/git/BluePyOpt:/home/jovyan/BluePyOpt neuronunit-optimization /bin/bash'\n", - "```\n", - "\n", - "## Parallel Environment.\n", - "Parallelisation module: dask distributed." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "import os\n", - "os.system('jupyter trust test_ga_versus_grid.ipynb'); #suppress the untrusted notebook warning.\n", - "import deap\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "grid_results = reports['grid_results']\n", - "ga_out = reports['ga_out'][3]\n", - "reports.keys()" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "\n", - "\n", - "import pandas as pd\n", - "ga_out.header\n", - "df = pd.DataFrame(index=ga_out.select(\"gen\"),columns = ga_out.header)\n", - "ga_out.select\n", - "print(type(ga_out))\n", - "print(dir(ga_out))\n", - "print(ga_out.select(\"gen\"))\n", - "df\n", - "\n", - "print(ga_out)\n", - "seq = pd.Series(data=ga_out.select(\"gen\", \"nevals\", \"avg\",\"std\",\"min\"))\n", - "print(seq)\n", - "df2 = pd.DataFrame(data = seq)#,columns = ga_out.header)\n", - "\n", - "'''\n", - "import pandas as pd\n", - "for index, val in enumerate(pipe_results.values()):\n", - " if index == 0:\n", - " sci = pd.DataFrame(list(val['pop'][0].dtc.scores.values())).T\n", - " else: \n", - " sci = sci.append(pd.DataFrame(list(val['pop'][0].dtc.scores.values())).T)\n", - " \n", - "sci\n", - "'''\n", - "#param_names = list(pipe_results[list(pipe_results.keys())[0]]['pop'][0].dtc.attrs.keys())\n", - "#df = pd.DataFrame(index=pipe_results.keys(),columns=param_names)\n", - "#print(ga_out[4])\n", - "#print(ga_out[5])\n", - "\n", - "#ga_keys = list(ga_out.keys())\n", - "#grid_keys = list(grid_results.keys())\n", - "df2" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "import pandas as pd\n", - "\n", - "cell_names = list(pipe_results.keys())\n", - "param_names = list(pipe_results[list(pipe_results.keys())[0]]['pop'][0].dtc.attrs.keys())\n", - "df = pd.DataFrame(index=pipe_results.keys(),columns=param_names)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "import pandas as pd\n", - "for index, val in enumerate(pipe_results.values()):\n", - " if index == 0:\n", - " sci = pd.DataFrame(list(val['pop'][0].dtc.scores.values())).T\n", - " else: \n", - " sci = sci.append(pd.DataFrame(list(val['pop'][0].dtc.scores.values())).T)\n", - " \n", - "sci" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "import pandas as pd\n", - "for index, val in enumerate(pipe_results.values()):\n", - " if index == 0:\n", - " attrs = pd.DataFrame(list(val['pop'][0].dtc.attrs.values())).T\n", - " else: \n", - " attrs = attrs.append(pd.DataFrame(list(val['pop'][0].dtc.attrs.values())).T)\n", - " \n", - "attrs.columns = val['pop'][0].dtc.attrs.keys() \n", - "#print(attrs)\n", - "attrs" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "import pandas as pd\n", - "\n", - "\n", - "for index, val in enumerate(pipe_results.values()):\n", - " if index == 0:\n", - " #,columns=['Dice number','value'],index=[1,2,3,4])\n", - " rheobase = pd.DataFrame([i.dtc.rheobase for i in val['pop']]).T\n", - " else: \n", - " rheobase = rheobase.append(pd.DataFrame([i.dtc.rheobase for i in val['pop']]).T)\n", - " \n", - "rheobase\n", - "\n", - "names = [ str('generation: ')+str(i) for i in range(0,len(rheobase)) ]\n", - "\n", - "rheobase\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "scrolled": false - }, - "outputs": [], - "source": [ - "import matplotlib\n", - "%matplotlib inline\n" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "################\n", - "# GA parameters:\n", - "about $10^{3}=30$ models will be made, excluding rheobase search.\n", - "################\n", - "\n", - "\n", - "# Choice of selection criteria is important. \n", - "Here we use BluepyOpts IBEA, such that it can be compared to NSGA2.\n", - "\n", - "https://link.springer.com/article/10.1007/s00500-005-0027-5\n", - "\n", - "\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "MU = 6; NGEN = 6; CXPB = 0.9\n", - "USE_CACHED_GA = False" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "################\n", - "# Grid search parameters:\n", - "$ 2^{10}=1024 $ models, will be made excluding rheobase search\n", - "################" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "An oppurtunity to improve grid search, by increasing resolution of search intervals given a first pass:" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "from neuronunit.plottools import plot_surface\n" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Below two error surface slices from the hypervolume are plotted.\n", - "The data that is plotted consists of the error as experienced by the GA.\n", - "Note: the GA performs an incomplete, and efficient sampling of the parameter space, and therefore sample points are irregularly spaced. Polygon interpolation is used to visualize error gradients. Existing plotting code from the package BluePyOpt has been extended for this purpose.\n", - "Light blue dots indicate local minima's of error experienced by the NSGA algrorithm.\n", - "\n", - "\n", - "Mostly these plots show that a low error solution was found in each 2D plane, however occasionally the plots show, that a non optimal solution was arrived at." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "for index, val in enumerate(pipe_results.values()):\n", - " td = val['td_py']\n", - " history = val['history']\n", - "\n", - " plot_surface('a','b',td,history)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "\n", - "for index, val in enumerate(pipe_results.values()):\n", - " td = val['td_py']\n", - " history = val['history']\n", - "\n", - " plot_surface('v0','vt',td,history)\n", - " " - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "list(pipe_results.keys())\n", - "for k in pipe_results.keys():\n", - " print(pipe_results[k]['hranked'])\n", - "pipe_results['100201']['hranked']" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "I am currently writing code that should enable the plotting of HOF values versus generation." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "\n", - "dtcs = list(filter(lambda d: hasattr(d,'dtc'), pipe_results['100201']['hranked']))\n", - "dtcs = [d.dtc for d in dtcs ]\n", - "fitness = list(filter(lambda f: hasattr(f,'fitness'), pipe_results['100201']['hranked']))\n", - "fit_v_gen = [np.sum(f.fitness.values) for f in fitness ]\n", - "\n", - "fit_v_gen\n", - "\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "\n", - "print(pipe_results.keys())\n", - "\n", - "scores = list(filter(lambda dtc: hasattr(dtc,'score'), dtcs))\n", - "\n", - "print(scores)\n", - "import pickle\n", - "dominate\n", - "with open('protected/dominated_by_rheobase.p','rb') as f:\n", - " pipe_results = pickle.load(f)\n", - "%matplotlib inline\n", - "import matplotlib.pyplot as plt\n", - "import numpy as np\n", - "\n", - "plt.style.use('ggplot')\n", - "fig, axes = plt.subplots(figsize=(10, 10), facecolor='white')\n", - "val = list(pipe_results.values())[0]\n", - "log = val['log']\n", - "gen_numbers = [ i for i in range(0,len(log.select('gen'))) ]\n", - "print(gen_numbers)\n", - "\n", - "\n", - "hof = val['hof_py']\n", - "mean = np.array([i for i in log.select('avg')])\n", - "std = np.array([ i for i in log.select('std')])\n", - "minimum = np.array([ i for i in log.select('min')])\n", - "best_line = [None,None]\n", - "best_line +=val['hranked']\n", - "\n", - "\n", - "stdminus = mean - std\n", - "stdplus = mean + std\n", - "\n", - "axes.plot(\n", - " gen_numbers,\n", - " mean,\n", - " color='black',\n", - " linewidth=2,\n", - " label='population average')\n", - "\n", - "\n", - "axes.plot(\n", - " gen_numbers,\n", - " best_line,\n", - " color='black',\n", - " linewidth=2,\n", - " label='population average')\n", - "\n", - "\n", - "\n", - "\n", - "axes.plot(\n", - " gen_numbers,\n", - " minimum,\n", - " color='black',\n", - " linewidth=2,\n", - " label='population minimum')\n", - "axes.fill_between(gen_numbers, stdminus, stdplus)\n", - " \n", - " " - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "For some reason the global minimum solution is not converged on, as shown by the evolution of errors below:" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "\n", - "best_line = None\n", - " \n", - "for k in pipe_results.keys():\n", - " fig, axes = plt.subplots(figsize=(10, 10), facecolor='white')\n", - "\n", - " historiesc = [list(h.dtc.scores.values()) for h in pipe_results[k]['history'].genealogy_history.values() ]\n", - "\n", - " historiest = [(sum(h.dtc.scores.values()),h.dtc) for h in pipe_results[k]['history'].genealogy_history.values() ]\n", - " ranked = sorted(historiest, key=lambda w: w[0],reverse = True) \n", - " pipe_results[k]['abs_min'] = ranked[0][1]\n", - " \n", - " historiess = [sum(h.dtc.scores.values()) for h in pipe_results[k]['history'].genealogy_history.values() ]\n", - " min_line = np.min(historiess) \n", - " axes.plot([i for i in range(0,len(historiess)) ],\n", - " historiess,\n", - " color='black',\n", - " linewidth=2,\n", - " label='population average')\n", - " \n", - "\n", - " axes.plot(\n", - " [i for i in range(0, len(historiess)) ],\n", - " [min_line for i in range(0, len(historiess)) ],\n", - " color='black',\n", - " linewidth=2,\n", - " label='population average')\n", - "\n", - "\n" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "For some reason, the GA population does not converge to the absolute minimum, although it does sample it.\n", - "Perhaps the absolute minimum is a highly dominated solution, which is a testable hypthosis.\n", - "\n", - "None the less because the GA samples the absolute minimum, this value can be corroborated with the GA." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "\n", - "\n", - "\n", - "print(pipe_results[k]['abs_min'].scores)\n", - "print(sum(pipe_results[k]['abs_min'].scores.values()))\n", - "\n", - "print(pipe_results[k]['abs_min'].attrs)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "plt.style.use('ggplot')\n", - "fig, axes = plt.subplots(figsize=(10, 10), facecolor='white')\n", - "val = list(pipe_results.values())[1]\n", - "log = val['log']\n", - "hof = val['hof_py']\n", - "mean = np.array([ np.sum(i) for i in log.select('avg')])\n", - "std = np.array([ np.sum(i) for i in log.select('std')])\n", - "gen_numbers = [ i for i in range(0,len(log.select('gen'))) ]\n", - "\n", - "\n", - "print(len(mean),len(std))\n", - "minimum = np.array([ np.sqrt(np.mean(np.square(i))) for i in log.select('min')])\n", - "\n", - "historiess = [sum(h.dtc.scores.values()) for h in pipe_results[k]['history'].genealogy_history.values() ]\n", - "min_value = np.min(historiess) \n", - "\n", - "\n", - "stdminus = mean - std\n", - "stdplus = mean + std\n", - "print\n", - "\n", - "\n", - "axes.plot(\n", - " gen_numbers,\n", - " mean,\n", - " color='black',\n", - " linewidth=2,\n", - " label='population mean')\n", - "\n", - "\n", - "\n", - "axes.fill_between(gen_numbers, stdminus, stdplus,label='+- std deviation')\n", - "plt.xlabel('generation')\n", - "plt.ylabel('error')\n", - "\n", - "\n", - "plt.legend()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "\n", - "\n", - "\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "best_line = None\n", - "for k in pipe_results.keys():\n", - " plt.style.use('ggplot')\n", - " fig, axes = plt.subplots(figsize=(10, 10), facecolor='white')\n", - " val = pipe_results[k]\n", - " log = val['log']\n", - " gen_numbers = [ i for i in range(0,len(log.select('gen'))) ]\n", - " historiess = [sum(h.dtc.scores.values()) for h in pipe_results[k]['history'].genealogy_history.values() ]\n", - " min_value = np.min(historiess) \n", - " \n", - "\n", - " axes.plot([i for i in range(0,len(pipe_results[k]['componentsh'] )) ],\n", - " pipe_results[k]['componentsh'] ,\n", - " linewidth=2,\n", - " label='population average')\n", - " \n", - "\n", - " axes.plot(\n", - " [i for i in range(0,len(pipe_results[k]['componentsh'] )) ],\n", - " [min_value for i in range(0,len(pipe_results[k]['componentsh'] )) ],\n", - " color='black',\n", - " linewidth=2,\n", - " label='population average')\n", - " \n", - " for i in pipe_results[k]['componentsh']:\n", - " print(sum(i),min_value)\n", - " if sum(i) == min_value:\n", - " print('yes')\n", - "\n", - " \n", - "\n", - "\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "##\n", - "# Rational, want to find out how dominated the best solution is:\n", - "##\n", - "best_line = None\n", - "for k in pipe_results.keys():\n", - " plt.style.use('ggplot')\n", - " fig, axes = plt.subplots(figsize=(10, 10), facecolor='white')\n", - " val = pipe_results[k]\n", - " log = val['log']\n", - " \n", - " historiest = [(sum(h.dtc.scores.values()),h.dtc) for h in pipe_results[k]['history'].genealogy_history.values() ]\n", - " ranked = sorted(historiest, key=lambda w: w[0],reverse = True) \n", - " pipe_results[k]['abs_min'] = ranked[0][1]\n", - " min_value = ranked[0][0]\n", - " \n", - " axis = [ i for i in range(0,len(ranked[0][1].scores.values())) ]\n", - " plt.bar(axis,list(ranked[0][1].scores.values()),tick_label=list(ranked[0][1].scores.keys()))\n", - " fig.autofmt_xdate()\n", - " plt.title('neuroelectro_cell_{0}_{1}'.format(str(k),str('solution components')))\n", - " plt.legend('left')\n", - "\n", - "\n", - " \n", - " \n", - " " - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "for index, val in enumerate(pipe_results.values()):\n", - "\n", - " print(val['gen_vs_hof'])" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Comment on plot\n", - "The plot shows the mean error value of the population as the GA evolves it's population. The red interval at any instant is the standard deviation of the error. The fact that the mean GA error is able to have a net upwards trajectory, after experiencing a temporary downwards trajectory, demonstrates that the GA retains a drive to explore, and is resiliant against being stuck in a local minima. Also in the above plot population variance in error stays remarkably constant, in this way BluePyOpts selection criteria SELIBEA contrasts with DEAPs native selection strategy NSGA2" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Comment on plot\n", - "There is good agreement between traces produced by the best candidate found by Genetic Algorithm, and exhaustive grid search." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Quantize distance between minimimum error and maximum error.\n", - "This step will allow the GA's performance to be located within or below the range of error found by grid search.\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "The code below reports on the differences between between attributes of best models found via grid versus attributes of best models found via GA search:\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "'''\n", - "import pandas as pd\n", - "\n", - "from neuronunit.optimization import evaluate_as_module as eam\n", - "NSGAO = NSGA(0.85)\n", - "NSGAO.setnparams(nparams=nparams,provided_keys=provided_keys)\n", - "#td = eam.get_trans_dict(NSGAO.subset)\n", - "#print(td)\n", - "td = { v:k for k,v in enumerate(td) }\n", - "from neuronunit.optimization import model_parameters as modelp\n", - "mp = modelp.model_params\n", - "#minimaga = pareto_dtc[0]\n", - "for k,v in minimagr_dtc.attrs.items():\n", - " #hvgrid = np.linspace(np.min(mp[k]),np.max(mp[k]),10)\n", - " dimension_length = np.max(mp[k]) - np.min(mp[k])\n", - " solution_distance_in_1D = np.abs(float(hof[0][td[k]]))-np.abs(float(v))\n", - " \n", - " #solution_distance_in_1D = np.abs(float(minimaga.attrs[k]))-np.abs(float(v))\n", - " relative_distance = dimension_length/solution_distance_in_1D\n", - " print('the difference between brute force candidates model parameters and the GA\\'s model parameters:')\n", - " print(float(hof[0][td[k]])-float(v),hof[0][td[k]],v,k)\n", - " print('the relative distance scaled by the length of the parameter dimension of interest:')\n", - " print(relative_distance)\n", - "\n", - "''' \n", - " " - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "\n", - "print('the difference between the bf error and the GA\\'s error:')\n", - "print('grid search:')\n", - "from numpy import square, mean, sqrt\n", - "rmsg = sqrt(mean(square(list(minimagr_dtc.scores.values()))))\n", - "print(rmsg)\n", - "print('ga:')\n", - "rmsga = sqrt(mean(square(list(dtc_pop[0].scores.values()))))\n", - "print(rmsga)\n", - "print('Hall of Fame front')\n", - "print(sqrt(mean(square(list(hof[0].fitness.values)))))\n", - "print(miniga)\n" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "If any time is left over, may as well compute a more accurate grid, to better quantify GA performance in the future." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "from neuronunit.optimization import get_neab\n", - "#fi_basket = {'nlex_id':'NLXCELL:100201'}\n", - "neuron = {'nlex_id': 'nifext_50'} \n", - "\n", - "error_criterion, inh_observations = get_neab.get_neuron_criteria(fi_basket)\n", - "print(error_criterion)\n", - "\n", - "from bluepyopt.deapext.optimisations import DEAPOptimisation\n", - "\n", - "DO = DEAPOptimisation(error_criterion=error_criterion)\n", - "DO.setnparams(nparams = nparams, provided_keys = provided_keys)\n", - "pop, hof, log, history, td, gen_vs_hof = DO.run(offspring_size = MU, max_ngen = NGEN, cp_frequency=4,cp_filename='checkpointedGA.p')\n", - "with open('ga_dump.p','wb') as f:\n", - " pickle.dump([pop, log, history, hof, td],f)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# Layer V pyramidal cell\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.6.4" - } - }, - "nbformat": 4, - "nbformat_minor": 2 -} diff --git a/neuronunit/unit_test/old/progress_report_10thJuly-Copy1.ipynb b/neuronunit/unit_test/old/progress_report_10thJuly-Copy1.ipynb deleted file mode 100644 index 3a9125461..000000000 --- a/neuronunit/unit_test/old/progress_report_10thJuly-Copy1.ipynb +++ /dev/null @@ -1,24590 +0,0 @@ -{ - "cells": [ - { - "cell_type": "code", - "execution_count": 1, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Running on Python version: 3.6.4 |Anaconda, Inc.| (default, Jan 16 2018, 18:10:19) \n", - "[GCC 7.2.0]\n" - ] - } - ], - "source": [ - "import sys \n", - "print('Running on Python version: {}'.format(sys.version))\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - }, - { - "cell_type": "code", - "execution_count": 2, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "256" - ] - }, - "execution_count": 2, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "\n", - "#output_notebook()\n", - "import os\n", - "%matplotlib inline\n", - "os.system('jupyter trust Visualisation_search_terms_reading_levelGS.ipynb')" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Rick Gerkin [3:16 PM]\n", - "Here is the list of ideas/suggestions I promised:\n", - "- Reduce the amount of stuff in the progress report. You can have a different one for each week, or just copy them as needed, and remove all the stuff that isn't pertinent to the current work. You can always bring it back in later. If rendering figures, just render the ones you need to show the point. \n", - "\n", - "\n", - "- Show some raw values in the table, e.g. the input resistance for a candidate model. You don't need to show the test observation values, except maybe outside the table on a separate line (since they are the same for every gene using the same test). \n", - "\n", - "- Try scaling up to three parameters - you can reduce the grid sampling to, say 6x6x6 (from 10x10 for 2 parameters), that way it isn't too much more computationally complex. \n", - "\n", - "- Then you can show the corresponding figures and tables for this three parameter case, show all of the 2D cross-sections through the global optimum. Or maybe you can think of other ways for showing 3D. \n", - "- Make sure the code isn't getting too unwieldy. Condense and refactor and clean up regularly." - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/opt/conda/lib/python3.6/site-packages/h5py/__init__.py:36: FutureWarning: Conversion of the second argument of issubdtype from `float` to `np.floating` is deprecated. In future, it will be treated as `np.float64 == np.dtype(float).type`.\n", - " from ._conv import register_converters as _register_converters\n", - "/opt/conda/lib/python3.6/site-packages/sklearn/cross_validation.py:41: DeprecationWarning: This module was deprecated in version 0.18 in favor of the model_selection module into which all the refactored classes and functions are moved. Also note that the interface of the new CV iterators are different from that of this module. This module will be removed in 0.20.\n", - " \"This module will be removed in 0.20.\", DeprecationWarning)\n", - "/opt/conda/lib/python3.6/site-packages/sklearn/grid_search.py:42: DeprecationWarning: This module was deprecated in version 0.18 in favor of the model_selection module into which all the refactored classes and functions are moved. This module will be removed in 0.20.\n", - " DeprecationWarning)\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "dict_items([('a', 0.273288417631972), ('b', -2.5511245505753264e-09), ('vr', -71.2139341678739)])\n" - ] - } - ], - "source": [ - "import pickle\n", - "#!pip install prettyplotlib\n", - "import prettyplotlib as ppl\n", - "from prettyplotlib import plt\n", - "from prettyplotlib import brewer2mpl\n", - "import numpy as np\n", - "import string\n", - "\n", - "green_purple = brewer2mpl.get_map('PRGn', 'diverging', 11).mpl_colormap\n", - "\n", - "\n", - "import matplotlib.pyplot as plt\n", - "import seaborn as sns\n", - "import pandas as pd\n", - "import numpy as np\n", - "import math as math\n", - "from pylab import rcParams\n", - "from neuronunit.optimization.results_analysis import make_report, min_max\n", - "with open('pre_grid_reports.p','rb') as f:#\n", - " grid_results = pickle.load(f)\n", - "\n", - "with open('pre_ga_reports.p','rb') as f:\n", - " package = pickle.load(f)\n", - "pop = package[0]\n", - "print(pop[0].dtc.attrs.items())\n", - "history = package[4]\n", - "gen_vs_pop = package[6]\n" - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "['a', 'b', 'vr']\n" - ] - } - ], - "source": [ - "attrs_ = [ list(p.dtc.attrs.keys()) for i,p in history.genealogy_history.items() ]\n", - "attrs = attrs_[0]\n", - "print(attrs)\n", - "\n", - "scores_ = [ list(p.dtc.scores.keys()) for i,p in history.genealogy_history.items() ]\n", - "scores = scores_[0]\n", - "from collections import OrderedDict\n", - "\n", - "urlDats = []\n", - "hi = [ (i,p) for i,p in history.genealogy_history.items() ]\n", - "pops_ = [ (i,p) for i,p in enumerate(pop) ]\n", - "\n", - "sc = [ (i,p) for i,p in enumerate(grid_results) ]\n", - "\n", - "import quantities as pq\n", - "\n", - "def history_iter(mapped):\n", - " i,p = mapped\n", - " gene_contents = OrderedDict()\n", - " gene_contents['gene_number'] = i\n", - " \n", - " attrs = list(p.dtc.attrs.keys()) \n", - " scores = list(p.dtc.scores.keys()) \n", - " for a in attrs:\n", - " gene_contents[a] = p.dtc.attrs[a] \n", - " scores0 = scores[0]\n", - " for s in scores:\n", - " gene_contents[s] = p.dtc.scores[s]\n", - " gene_contents[str('total')] = sum(p.dtc.scores.values())\n", - " for test in p.dtc.score.keys():\n", - " if 'prediction' in p.dtc.score[test]:\n", - " \n", - " \n", - " \n", - " gene_contents['observation'] = p.dtc.score[test]['observation']['mean']\n", - " try:\n", - " pass\n", - " #gene_contents['prediction'] = p.dtc.score[test]['prediction']['value']\n", - " #x = pq.Quantity(gene_contents['prediction'])\n", - " \n", - " #print(x.simplified)\n", - " \n", - " except:\n", - " gene_contents['prediction'] = p.dtc.score[test]['prediction']['mean']\n", - " x = pq.Quantity(gene_contents['prediction'])\n", - " print(x.simplified)\n", - " \n", - " gene_contents['disagreement'] = np.abs(float(gene_contents['observation'])) - np.abs(float(gene_contents['prediction']))#p.dtc.score[test]['agreement']\n", - " return gene_contents\n", - "\n", - "\n", - " \n", - "def process_dics(contents):\n", - " dfs = []\n", - " for gene_contents in contents:\n", - " # pandas Data frames are best data container for maths/stats, but steep learning curve.\n", - " # Other exclusion criteria. Exclude reading levels above grade 100,\n", - " # as this is most likely a problem with the metric algorithm, and or rubbish data in.\n", - "\n", - " if len(dfs) == 0:\n", - " dfs = pd.DataFrame(pd.Series(gene_contents)).T\n", - " dfs = pd.concat([ dfs, pd.DataFrame(pd.Series(gene_contents)).T ])\n", - " return dfs\n", - "\n", - "genes = list(map(history_iter,hi)) \n", - "dfg = process_dics(genes)\n", - "\n", - "grids = list(map(history_iter,sc)) \n", - "dfs = process_dics(grids)\n", - "\n", - "\n", - "#dfg.set_index('gene_number', inplace=True)\n", - "#dfg = dfg.drop(dfg.index[0])\n", - "dfg = dfg.reset_index()\n", - "\n", - "def highlight_min(s):\n", - " '''\n", - " highlight the maximum in a Series yellow.\n", - " '''\n", - " is_min = s == s.min()\n", - " return ['background-color: yellow' if v else '' for v in is_min]\n", - "\n", - "\n", - "def highlight_col(x):\n", - " #copy df to new - original data are not changed\n", - " df = x.copy()\n", - "\n", - " mask = df['total'] == df['total'].argmin()\n", - " #if mask:\n", - " print(df['total'].argmin())\n", - " df.loc[mask, :] = 'background-color: yellow'\n", - " df.loc[~mask,:] = 'background-color: \"\"'\n", - " return df,x \n", - "\n", - "\n", - "\n", - "\n", - "#colors,dfg = highlight_col(dfg)\n", - "\n", - "#dfg" - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "\n", - " \n", - "\n", - " \n", - " \n", - "\n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - 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\n", - " " - ], - "text/plain": [ - "" - ] - }, - "execution_count": 5, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "\n", - "\n", - "def highlight_col(x):\n", - " df = x.copy()\n", - " mask = df['total'] == df['total'].min()\n", - " df.loc[mask, :] = 'background-color: yellow'\n", - " df.loc[~mask,:] = 'background-color: \"\"'\n", - " return df \n", - "\n", - "dfg.style.apply(highlight_col, axis=None)" - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "6 5 1\n", - "(6, 5, 1)\n" - ] - } - ], - "source": [ - "import numpy as np\n", - "import matplotlib.pyplot as plt\n", - "from matplotlib.widgets import Slider, Button, RadioButtons\n", - "import scipy.ndimage as ndi\n", - "import neuronunit.optimization.exhaustive_search as es\n", - "\n", - "data = np.zeros((6, 6, 6))\n", - "#data = ndi.filters.gaussian_filter(data, sigma=1)\n", - "\n", - "axis = [ [ str('vr'), str('a'), str('b') ], [ str('vr'), str('b'), str('a')], [ str('b'), str('a'), str('vr') ] ]\n", - "\n", - "gen_vs_pop = package[6] \n", - "\n", - "k = axis[0]\n", - "ee = [ np.sum(list(i.dtc.scores.values())) for i in grid_results ]\n", - "zz = [ i.dtc.attrs[k[2]] for i in grid_results ]\n", - "yy = [ i.dtc.attrs[k[1]] for i in grid_results ]\n", - "xx = [ i.dtc.attrs[k[0]] for i in grid_results ]\n", - "hvc = es.tfc2i(xx,yy,zz,ee)\n" - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "'\\ncontents = [] \\ndtcpop = [ p.dtc for p in pop ]\\ndtcpop.extend([ p.dtc for p in gen_vs_pop[0] ])\\nfor d in dtcpop:\\n contents.append(proc_xargs(d))\\n\\ncontents.append(proc_xargs(mingr[2]))\\ncontents.append(proc_xargs(minga[2]))\\n\\nrealign(contents) \\n'" - ] - }, - "execution_count": 7, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "#from neuronunit imp\n", - "from neuronunit.models.reduced import ReducedModel\n", - "from neuronunit.optimization.model_parameters import model_params, path_params\n", - "import quantities as pq\n", - "\n", - "super_pop = []\n", - "\n", - "for gp in gen_vs_pop:\n", - " super_pop.extend(gp)\n", - "minga,maxga = min_max(super_pop)\n", - "mingr,maxgr = min_max(grid_results)\n", - "abs_min = np.min((mingr[1],minga[1]))\n", - "abs_max = np.max((maxgr[1],maxga[1]))\n", - "v = list(np.linspace(abs_min, abs_max, 15, endpoint=True))\n", - "\n", - "def proc_xargs(dtc,dtcpop = None):\n", - " model = ReducedModel(path_params['model_path'],name=str('vanilla'),backend='NEURON')\n", - " xargs = {}\n", - " xargs['injected_square_current'] = {}\n", - " xargs['injected_square_current']['duration'] = 1000 * pq.ms\n", - " xargs['injected_square_current']['amplitude'] = dtc.rheobase['value']\n", - " xargs['injected_square_current']['delay'] = 250 * pq.ms # + 150\n", - " model.set_attrs(dtc.attrs)\n", - " model.inject_square_current(xargs)\n", - " v_m = model.get_membrane_potential()\n", - " ts = model.results['t'] # time signal\n", - " return v_m,ts,model\n", - "\n", - "def realign(contents):\n", - " plt.clf()\n", - "\n", - " for c in contents: \n", - " v_m,ts,model = c\n", - " try:\n", - " spt = float(model.get_spike_train())\n", - " #thr = float(model.get_threshold())\n", - "\n", - " new_time = [ t-spt for t in ts ] \n", - " plt.plot(new_time,v_m)\n", - " plt.xlim(-0.03,0.03)\n", - " except:\n", - " print(model.get_spike_train())\n", - " \n", - " plt.show() \n", - " return _\n", - "\n", - "'''\n", - "contents = [] \n", - "dtcpop = [ p.dtc for p in pop ]\n", - "dtcpop.extend([ p.dtc for p in gen_vs_pop[0] ])\n", - "for d in dtcpop:\n", - " contents.append(proc_xargs(d))\n", - "\n", - "contents.append(proc_xargs(mingr[2]))\n", - "contents.append(proc_xargs(minga[2]))\n", - "\n", - "realign(contents) \n", - "'''" - ] - }, - { - "cell_type": "code", - "execution_count": 8, - "metadata": {}, - "outputs": [], - "source": [ - "from collections import OrderedDict\n", - "zz = [ list(i.dtc.scores.items()) for i in grid_results ]\n", - "xx = [ list(i.dtc.scores.items()) for i in pop ]\n", - "\n", - "def sample_points(iter_dict, npoints=2):\n", - " replacement = {}\n", - " for p in range(0,len(iter_dict)):\n", - " k,v = iter_dict.popitem(last=False)\n", - " sample_points = list(np.linspace(v.max(),v.min(),npoints))\n", - " replacement[k] = sample_points\n", - " return replacement\n", - "\n", - "from neuronunit.optimization import model_parameters as modelp\n", - "mp = OrderedDict(modelp.model_params)\n", - "smaller = {}\n", - "smaller = OrderedDict(sample_points(mp, npoints=2))\n", - "ranges = OrderedDict( {k:smaller[k] for k in smaller})\n", - " \n", - "lims = []\n", - "for k,v in pop[0].dtc.attrs.items():\n", - " lims.append([np.min(ranges[k]), np.max(ranges[k])])\n" - ] - }, - { - "cell_type": "code", - "execution_count": 9, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[0.315, -8.333333333333334e-10, -13.333333333333334]\n", - "[0.315, -8.333333333333334e-10, -66.66666666666667]\n", - "[0.315, -3.3333333333333334e-09, -13.333333333333334]\n", - "[0.315, -3.3333333333333334e-09, -66.66666666666667]\n", - "[0.013333333333333334, -8.333333333333334e-10, -13.333333333333334]\n", - "[0.013333333333333334, -8.333333333333334e-10, -66.66666666666667]\n", - "[0.013333333333333334, -3.3333333333333334e-09, -13.333333333333334]\n", - "[0.013333333333333334, -3.3333333333333334e-09, -66.66666666666667]\n" - ] - }, - { - "data": { - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "image/png": 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\n", 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\n", 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\n", 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6C05a/kDqhJAPJ/G6c+GYTGDAZ7awGEVYZ5EgpSQQCODz+QgGgwghMBqNGI1GcnJyMBgMGAwGXeR0dNJMOgd8ZhPL4Rp1FhgpJaFQiI6ODkpKSigoKFDjOeFwmFAohM/nU4VMEThF7IQQusjp6GhIusbHZBu6oOloSjgcJhAIEA6HcbvdWCwWRkdHCQaDlJSUkJeXF3W8lHJqGKPXqz4mhMBkMmE0GjGZTKolp5McerssHd1C09GZB1JKgsGg6loUQhAMBmlpaaGgoIDc3Fz6+/vx+/2YzWaKi4spLi6mqKgoqnYr8lx+v1+11AwGgypykZacjo7O3OgWmo5OAijuxWAwiJQSIQThcJiOjg5GRkZYt24dNTU1BINBDAYDUko8Hg+Tk5OMjIxw6tQpwuEwBQUFqsgVFhZGWWVK4lJkj0Z4S+QUodPjcTo6sdEtNB2dOYh0LwohMBgMjIyM0NraSlVVFWvWrIlqwAtT7kSLxYLFYlG7dITDYVwuF5OTk/T19eF0OjEYDBQVFakiZzabo+qqlFY3Sg2fHo/T0YmPbqHp6MQh0r0IUyLl8/loampCCMHevXvJz8+nra2NRMpCFPEqKipSu4kEg0EcDgeTk5O0t7fj8XjIyclRBa64uDjheJwSi9PjcTo6Sxtd0HQSJpZ7UUpJZ2cnAwMDbNmyhfLycvV45flkMJlMlJaWRs1Z8/l8qsj19fUlHI8LhUIEAgGam5upr6/HYDCoIqfH43SWA7qFpqMTQSz34sTEBM3NzVRUVHDgwIEolyC8JWhaiUVeXh55eXmqaMaLxxUWFqoiV1BQoFplHo9Htc6Udk0KejxOZ6mzHDb75XCNOikQy70YCAQ4efIkfr+f3bt3Y7FYYr42FQstEeLF45xOJ5OTk/T09OByuVSXZiAQwOPxzBqPi0w80eNxOksFAeQks9sHtV5JetEFTScmsdyLAL29vXR3d7Nx40ZWrlw56wafbkGLhcFgUK0zBSUeNzw8rMbjcnNzo+Jxubm5UefR43E6SwkhwKQLms5yJJZ7cXJykqamJqxWKwcOHJgRq4pFJgQtFko8Li8vj507dwJT8bjJyUnsdjs9PT0EAoF5xeMUIVeKwJdSPC7g95MzTeB1FjdCQI5x7uMWO7qg6ahIKXE6nYRCIfLy8tTi6NbWVpxOJ9u2bYsa2jkX2SJoscjLy6OiooKKigpg/vE45TWwdOJxzuFhjn7964w1N5NXUsK+G26gat+i6UurMwtJW2iLjGVwiTpzobjXAoEAAwMDCCGoqalhYGCAjo4O1q1bR319fVKbcrYK2nTmE49TLLi56uOmx+OmuyqzTeSO3HknE+3tlGzYgM9m40+33cZ777uP4urqTC9NJ0WSjqEtMpbBJerMhtIRPxQKqe5Fr9fLq6++isViYf/+/eTkJJfwazAYCIfDGq944YgXj5ucnFQtufnE43w+Hz6fj+HhYbUjynRXZaZEzu/1MtbSQunGjQDkl5biHR/HduqULmhLAQHoLkedpUqs7MVwOMzw8DBOp5Pdu3djtVpTfp/FLGixMJlMlJWVUVZWpj4WKx5nsVii4nGRVpzdbsdisahJNwqZjMfl5OWRa7HgdzjILSoiHAohw2HyI+oAdXSyHV3QliHTkz6EEAwPD9Pa2kpRURE1NTWaiFm2udTSRax4nNvtZnJykqGhIbVjihKPU1yRkRmSims2XlPmdMfjhBDs/cIXOPqNb+AeHkaGQtScdx6Vp5NodBaGffv2pcdHn2QzR4vFcuZsazp27NiolLIihZVpii5oy4hY7kW3201zczMmk4l9+/YxMTGBy+XS5P2yOSkknQghKCgooKCggNWrVwPR8TiXy0VTU9OMVl75+fkzkk4WMh639h3voKS2FtupU+SWlLBqz56Uz6kzP1599dW0nHdfnkhqt6+vr591TUKIrhSWpTm6oC0DItPN4S2hOXXqFENDQ2zdupUVK1ZEPacFWncKWcxExuMmJydZu3YteXl5aiuvoaEhvF4veXl5UU2Z54rHKcRyVSbzuZfU1lJSW5vy9epkIctgt18Gl7i8ieVeHBsb4+TJk6xatYqDBw9Gub7SIWjLhuF+TD/4e8RQC7J0DcGPfQ2qt804TBH5nJyclONxyvmyKR6nk4XoSSE6i5lYAzf9fj/Nzc2EQiH27NmD2Wye8TotMxOXm6CZvv1ZTIOtkF8CPccxPPAp/Dc9D8XR8cjZrNZE43GRVpzFYpkzHieljHJVLqb6OB0NWCYD0ZbBJS4v4rWs6u7upre3l82bN1NZWRn39bqFliT9XRgH26CsBgwCKEGM9iKaf4vc/5GoQ+fzmcSKx4VCITUe19XVhcvlwmg0zhmPA2bE4/x+P/n5+ZjNZgQw+utf4xsdpWDDBsrf8Y7UPhOd7EEXNJ3FRqyWVTabjaamJlasWMHBgwdnuKumo1toSWLKQQAEg5CbA2EJ4RDSlBfz8FQsI6PRSElJCSUlJepjgUAgKh7n8XjIz8+PclXm5ubOKALv7u6moqKCYDBI56234njxRcjNRYRCrP70p9n4uc/pTZmXCrrLUWcxEK8jfmtrK263m507d1JYWJjQuXQLLUlKcwmevQpTXwsETDCYQ3h1Hex634xD05EoMz0eJ6VU43ETExN0dXURDAZnxOOklJhMJrzNzTj+9CfMW7YAEPR46P/Odyj+i78gP6IAXG/KvEhJ1kJbZH++uqAtYhT3Ynd3N6tXr1Y3yb6+Prq6utiwYQPbtm2b1+apC1oSBAII/3eQF15I8LU1iJE2wmesJLT/8ZgN9BYi81MIQX5+Pvn5+aqLWUqJy+WKisd5PJ6pxJPOTmSEZW4ymwlKicHpRBQVxa2PUxJO9KQTnWxAF7RFSqR7sbOzkzVr1uBwOGhsbKS4uDjpllVauxwDgQCvv/46brdbdYGVlJRQXFycUMf+xcEoUk5A7nrkwTVIIBxshzx/zKMzVcoghKCwsDDKWj9x4gTl5eW4pMRjMuFpbobiYgyTk+Rv2ED4dIF9vHhcJIqw6UknWUiyFlpg7kOyiaWyoywbYrkXAZqbm7Hb7dTX10f1HpwvWllV4XCYwcFBRkZG2LVrFwUFBQQCAex2O2NjY3R0dMTtZr/4sGCAqfiZyQRBDwIDktiDT7PNai0uLmbVqlVU/td/0fvv/45vYADD7t3kfvKTtLW1qfVxkUknyuBTBaU+LhQK4fP51O+lPiQ1i0gmhqYLmk46iOyIH3mHPzQ0hMvloqamhq1bt6a8WWhhodntdhobGykqKmLlypWUlZURCARUF9jKlSuB6O4Z3d3duFwuTCZTlBWXlxc7qSKryCkhHHgHIvwCIiiQSKTh3ZBTEvcl2bKph8Nh9SaiaNs26h96aMYx8eJxBQUFqsAVFhbGrI/Th6RmCXqWo062ECt7UWmfpHSWqKqq0mSTTMVCU2anORwOdu7cSSgUoqenJ+7xkd0zqk93dA8EAmphcV9fH36/H7PZrApcrMLirMByHjKwHokNsELOhriHZlP3lEhBi8dc8biBgQGcTueM+rhYFnesIakGgyHKVanH49KALmg6mSZeR/yOjg5GRkaoq6ujtLSUV155JeOp9kqSQW1tLXV1dQghsNvt8z5PTk4OK1asUFtxxSosBtSNs6SkZKqGKhs2wFlELJJsErRk1xIZj6uqqgJi18cpFrfyLy8vb9YhqS0tLWzZsmXRDknNWnRB08kUsdyLQghGRkZobW2lqqqKAwcOqJuClokc8z2X1+ulsbERk8nEWWedFdV70GAwpBwvildYrNRctbe3R80kUyy5ZGe4LQTZFENLxEJLlHj1ccr8uIGBAXw+H/n5+VGWXGQ8zuFwqN+bWE2Z9XhcCmShY0NrdEHLMuIN3Gxqapoa8bF3L/n5+VGvyUQxtJSSrq4u+vv72bJlC+Xl5XGP0xqj0YjVao0acePz+bDb7VExHiXhpKSkhIKCAs3XkQrZshErbux0Ecvi9nq9OByOmPG4cDhMOByeMx4npYxyVerxuDnQLTSdhSReR/zOzk4GBgbYvHmz2t9vOgttoU1OTtLY2EhZWRkHDhyIG9NayG77eXl5VFZWqjGecDisxnh6enpwuVy43W5aW1ujEk4yISzZ5HIEFlQEhBCYzWbMZnPU70pxKwcCAV577TWEEBQVFamWnB6PSxFd0HQWilgd8ScmJmhubqaiomJW0YCFs9CCwSBtbW3Y7Xa2b99OUVFR0ufSAr/bjaOnh5Dfj6WyksLT2ZMw9ZkoG+KaNWsAOHr0KGVlZVHuL7PZHBXjWaiEk6zcZEMeCHWDDIFpFRjL5n6NBhgMBgoLC7FYLPT397Nv374ot3JnZydut3ve8bjI8+vxuOWBLmgZZHpHfIPBgN/v5+TJk/j9fnbv3o3FEruWKZKFEDRlovXatWsTLg9Ip6AFvV6GXn8dhMBgNDJ64gQyHKbodJwt3nqmu788Hg+Tk5OMjIzQ3t4elalXUlKCxWJZHptfyIPB/xsETsCE9L1BOO88MC7cMOLIeF4st7Lf71dFbmBgAK/XG3VDUlRUFLM+biGHpGYt+vgYnXQRy70I0NPTQ3d3Nxs3bmTlypUJ/4FpLWiReL1empubAdi3b9+86sLSKWheu52Q30/BaTeswWjE0dMzq6DFWp/FYsFisbBq1SrgrUw9u91OR0cHbrebnJwc1U0Za+jmkiDUjcADxqnyCREah0DzggpaKBSa1ULOzc2NGY+bnJxUi/VDoVDC9XHKkFSXy8X111/PD3/4w7ReX0bRXY466SAcDjMyMkJxcbHqXpycnKSpqQmr1cqBAwfm3RJKS0FTkFLS09NDT08PW7ZsiRu/m41IQdM6bmQwGIg8m5QSNDh/rEy9yKGb3d3dUUkMJSUlFBYWLv5kBOkn+hbeBPjiHJwe5ptxGRmPiyzWV2Kn/f39OJ1ONR4Xb36cEr9b8iyD3X4ZXGJ2EOlebGho4JxzzlELkZ1OJ9u2bZszJhUPrQUtFApx9OjRpAVWIVLQtHbn5JaUYDSb8UxMgBCE/X4qd+3S9D0UYg3ddLlcavG30+mMKhJX5pEtBheWakGb1iB9byBCNsBESNrAuH1B1zKXhZYIsWKnkfE4xepW4nHj4+N4vd6Ep1HMxV133cWXv/xlRkZGKC8vR0rJ9ddfzzPPPIPFYuHRRx9l7969mrzXvEify3GdEGIYGJZS7pjxtkKUAN8B1jKlN3dJKR9Jy0rQBS3txBu4OTAwQEdHB+vWraO+vj6lzU8rQQuFQmrvvt27d6fUExLS63I05eayau9eXAMDhEIhLGVl5Futc79QAyKLipVNMxgMqvVWQ0NDeL3eqHlkWd+M2Vg2FTMLNAJhML4NchMrFNcKLWviIokXj5ucnOS5557jBz/4AR0dHVxyySXs37+fSy65hLq6unm/T09PD8899xxr165VH3v22WdpbW2ltbWVI0eOcN1113HkyBFNrmtepM/lOApcATwW5/nPAY1Syg8JISqAk0KI70opY3fuTpEs/gtb/MRqWeV0OvF4PIyPj88oRE4WLQRtdHSUkydPUl1drc7MSpV0ZzmacnMpqa1N2/nng8lkmjGPTInvjI6ORjVj9vv9OJ1OCgoKMm7FRb2/cdXUvwyhhYWWKLm5uZSXl3Pdddexc+dOfvazn/HlL3+Zo0eP4nK5kjrnF7/4Rb7xjW9w8cUXq489/fTTfOpTn0IIwcGDB7HZbAwMDKhNAhaM9AmaExif5XkJFImpL1rh6WODaVkJuqClhXgtq9rb2xkfH8disVBXV6fZHbty/mTw+Xw0NzcTDoc588wzyc/Pp6+vT7N1ZVNXjIUkXnzH4XAwPj6utobKyclRLbiSkpIFTTjJtt9Nuiy0uXC5XBQVFbFhwwY2bEjOKv3Zz37GmjVr2L17d9TjfX191NTUqD9XV1fT19e38IIGSe32IyMj7Nu3T/35mmuu4ZprrpnPKe4Ffgb0A0XAx6WU2gb8I9AFTUNiuReFEGrKe3V1NQcOHODYsWOEQiHNBC0ZC01KSW9vL93d3WzevFktctWS5SxosTAYDKpobd8+FZ9SXF92u53e3t6pYZsWi5pVGStLTysyJSDxCIVCGVmP0+lMKH596NAhBgdQjDiHAAAgAElEQVQHZzx+++23c8cdd/Cb3/xmxnOxvv8ZscqTjKFVVFTw6quvpvLOFwDHgfcAG4HnhBAvSinTkoWjC5pGxHIvut1umpubMZlMUSnvRqNR0yQOg8GgWoOJ4HQ61fEuqSR9zIUuaHOjuL6U1mFKM2a73c7AwAAOhwMhRFQsTqtmzNnWsSRWy6uFwOVyJZQU8vzzz8d8/M0336Sjo0O1znp7e9m7dy9Hjx6luro6auJEb2+v2sx5mfBXwL/IqY2gTQjRAdQBR9PxZrqgpUgs96KUklOnTjE0NMTWrVvVuhkFg8FAKBTSbA2JNgEOhUKcOnWKsbEx6uvro1LTdbKDyGbMysYXDAbVLL22tjY8Ho8m0791C20Kp9M54290PuzcuZPh4WH153Xr1vHqq69SXl7ORRddxL333stll13GkSNHKCkpyYy7MXN1aN3AecCLQoiVwFbgVLreTBe0JInXEX9sbIyTJ0+yatUqDh48GPMPNB0W2lznU9ZVVVXF/v3759w4tLh71y00bTCZTJSWllJaWgpED9yMnP5dUFCgClwi07+zTdDC4XBGMkGdTifr1q1Ly7nf//7388wzz7Bp0yYsFguPPJK2jPXZSZ+grQdeAsqFEL3AV4EcACnlA8BtwKNCiDdPr+JmKeVoWlaCLmhJobTSiXQvKskVoVCIPXv2YDab474+HRZaPEHz+/00NzcTDAbnXJeCVk2Fs8mdtZSINXAzcvp3T08PTqdzzunf2eZyDIVCGZlQnqjLMVE6OzvV/xdCcN9992l27pRIjze3Q0q5L96TUsp+4L1peecY6II2D2K5FwG6urro7e1NOLlC60LoWOeTUtLf309nZ2fSrbSy6e5dZ3YiC7sV5pr+nW3jVjKZ5ailoGUleusrnUjC4TB+vz/KvWiz2WhqamLFihUcPHgw4YC20WhMq4XmcrlobGykoKCA/fv3z3vYpZauQmVuWk9PT1S7qKKioqzaTJcic03/ttlsajPsbJj+nSlBSzTLcVGjC5oOxB646ff7aW1txe12s3Pnznnf3aXLQguHw5w6dYqRkRHq6+ujOiPMB60EzeFw4Ha78fv9nHnmmeoQTqXHnmJVKL0TM+FuWk5Mn/49OTlJb28vK1euzIrp3wtZWB2JUoe2pNEFbXmj1JR1dXVhsVjUgHxvby9dXV1s2LCBbdu2JXU3m46kEI/Hw8svv8zq1as5cOBASne6qQquIqyjo6Pk5+ezefNm/H5/zOw9u92uNpL1+/1qDdaSafo7jWxKklGSMOYz/VupjUvH7yWTWY5L3uUI+viY5UpkTZnf78doNOJ0OmlqaqKoqCgpN14kWiaF+P1+2tvbcTgcHDhwIKH5aXORioWmuGFXrVrF/v37efnll+MeazKZYrrEpjf9VSyFpWDFZZOgxUsKmW36d29vLy6XK8q61mr6d6bq0JxOpyat3rIa3UJbfsQauCmEoK+vj1AoRH19vSZffKPRGDVRN9m1Kg2O16xZg5RSEzGD5Cy0yMkBu3btoqCgYN7vG6sGS0lsOPX887R+61uEAwGqP/IRNn74wylZcSG/n4Z776X/hRfILSpix9/9HZX798/7PMmQLZmFicasYnWwV34vWk7/zlQMTWkkrbP40QWN+AM3BwcH6erqwmq1cuaZZ2q2EaXq0nO73TQ2NmI2m9m/fz+hUIixsTFN1gbzt9CUxsZr166lrq4u5ueU7GeXk5ODv6WFP99wA2G/H4SgtaUFs9mM821vi7Li4qWnx+LN//gPOn74Q/KsVlwOBy/fcAPvfOQRSjZvTmqdiZJNqfJK2UkyxEo4SXX6d6ZiaMrN65JGt9CWB9NbVgkhcLlcNDU1kZeXx8aNGwkEAppuQsnG0MLhMJ2dnQwODlJfXx9VaKtlTC5RQYuscVMaG6eDEw89RMjvx3jazRsOBun97nf5yFVXAdHp6Uo/xLkGcPY99xx5ZWUY8/Iwms24BwYYPnJkWQmalFKzjVyL6d+ZsNCklFnlBk4regxt6RKvI35HRwcjIyPU1dVRWlrK8PAwXq9X0/dOJoY2MTFBc3MzlZWVMzqQLERdWyRSSgYHBzl16tS8a9ySIta5Ix6LZS0oAzh7e3vVImOfz8fIyAglJSWYzGaCLhdEWHPGBIrOUyWbNs90C8h8p3+n6oZPhWy5yUgbuoW2NInXEX90dJSWlhaqqqqisgS1rhlTzpmoAAUCAVpaWnC73XFjU1oL2mwWmtfrpbGxkZycHM3muc3FjquvpuvXvybs8wFgyMlh9+c/H/f4WAM4A4EAr776Kg6Hg97eXkzvfS8TDz+McWICgxAU1dZSfehQ2q9FWV82kIrLMVlmm/7t9/t57bXXFnT6d6bcnAuOLmhLj1gd8b1eL83NzQDs3bt3htssHYKWiIUmpWRoaIj29nbWr18/a4mA1j0T43Ue6enpoaenh61bt6rd4RMlFVfb6oMHufDJJzl+zz2E/X52XHkl6z/0oXmdIycnB5PJpM67kmecQe/evfS8+CIBo5GcvXs50d4elVGZDrFeqi7HZIm8+ejr6+Oss86aMf3b4/HMSDjRquejy+VKKoFp0ZHk+JjFxrIQtHgd8Ts7OxkYGGDz5s3qHeN00iVos1lUbrebpqYmcnNzE7KCtN4gpwuky+WioaGB4uLitI6bmY01557LmnPP1ex8QghqzjmHmnPOUR+LNZtMafhbUlKSUMPfucgmQctUM+B4KJ/LfKZ/RzZjTuZzVSaHL3l0C23xE8+9qMSjKioqOHDgwKwuh3S5HGOdMxwO09XVxcDAAHV1deof9EKjxBOVJJShoaGUOo8sFqbPJlPqr+x2e1TD38iMyvlacdkkaNm0ltmIN/1bSThJZfr3sugSorCkd/spluwlxnIvKn3r/H4/u3fvTqhua6EsNLvdTmNjIxUVFXHHziwUBoMBl8tFW1sblZWVKXceUTbNxbKBKkTWXylEWnFKUoPSRSMRKy6bPoNsakA9X5d5rGbMyUz/1i20pcWSu8R4HfF7enro7u6ed1ZeupNCgsEgLS0tOJ3OpPpCao1S0xYIBNizZ48m61E28WzK8EuW2ay47u7uKEtBseQiu8pk02eQTYKmRYLKfKd/SymZnJzM+N+cjnYsGUGLN3BzcnKSpqYmrFZrUvGfdCaFDA0N0dbWRm1tLfX19Rm/c1eGgObl5bF27VpN/tCFCCF9TRiCA0jykaatYFg6bYYirbjq6mpgylKw2+0zrLh0JZokS7ZZi1pnG841/fvf/u3f+N3vfkdOTg633XYbBw8e5Oyzz5739/7WW2/loYceUuPwd9xxB+9///sBuPPOO3n44YcxGo3cc889XHDBBZpeY8LoSSGLB6Uj/p///Ge2b9+utpZqa2vD4XCwbdu2pP3k6fiD9/l8OJ1OhoaGFiz1fTYCgQAnT57E5/OxZ88eBgcHNTt3fk4fUnqBUpAeDMHjhE37wbB0Ww3l5uZGpaZHWnH9/f3Y7XZef/31qIzKhepoH0m2WWgLkT4fOf37P//zP/n+979PS0sLdXV1/OpXv6K8vJw9e/bM+7xf/OIXufHGG6Mea2xs5Mknn6ShoYH+/n4OHTpES0tLZsoEdJfj4kGZHu31egkGgwwNDdHR0cG6devitmLKBMpssP7+fnJzc9m1a1eml6RaievXr2f16tVqvFGrurY80zjILWAApAWkF3ACS1fQphNpxVmtVrq7u9m0aRN2ux2bzTbDilNicen+3mZD2r5Cpjrtu91uqqur+djHPsbHPvYxTc/99NNPc9lll5GXl8f69evZtGkTR48e5eyzz9b0fRJmSez2s7MkLjHyjuf48eMUFhZmheUTyeTkJI2NjZSVlXHgwAGOHDmi6fmVzMRENwWfz0djYyMGg2HGZ6VtvCtvSsTElICFRZh4X7ugaCVofA3IJSf4dozMr9ZtMaB8rrGsuFhZe5EZlVpbcZkorI5HpqxFh8OhFt+nwr333stjjz3Gvn37uPvuuyktLaWvr4+DBw+qx1RXV9PX15fyeyWF7nJcPIRCIVpaWnA4HNTX16v+8mwgGAzS1taG3W5n+/btaUsRVqyquTYFKSV9fX10dXWxefNmdUTI9HNpFTf0BNYjpQukCwiCqALDzPT/oOENPHlfRxIGJAHTr7F4/3lJilosEYmVtae0iYqcSxbZ7DdVKy6bXI6Z6tjhdrsTynI8dOhQTFf87bffznXXXcctt9yCEIJbbrmFL33pS3z729+OeVOYsRsI3eW4eOju7iYvL4+VK1diTlM/vmQC6MPDw7S2trJ27Vq2bt2a1i9zIm5CpUu/xWKZNUFGSwtNimJCxo0I4UZKA0KUxjzOa3oKZB4Gpp4Piz4Cpj9gDH5Ek3VkC/P5Hk1vExVpxXV2duJ2u1OaLp1NSSGZHO6ZyE3m888/n9D5rr76aj74wQ8CUxZZT0+P+lxvb2/mbrZ1QVs8bNiwgWAwyMmTJzXPSIS3Mh0TzZD0er00NTUhhGDfvn0LMpRyNkGLjN1FdumPh5aCJoRACjPCUABSxm40DCC8QORmbAB8mqwhm0hFRCKtuJqaGiB6unRnZ2dUB425RrZkk4WWyeGeqXpNBgYGWL16NQA/+clP2LFjBwAXXXQRV1xxBTfccAP9/f20trayf4Fm7sVEdzkuLkwmk1p/piWJClpkv8MtW7bEbacVebxWd8jxRMjhcNDQ0KDG7hLZNLRMClHWpUz+jvf+puA78ec+DhJgquu6KbRPkzVkE1pbRbGmSytp6crIltzc3KiMSuV7nG2Clom1uFyulMtTbrrpJo4fP44QgnXr1vHggw8CsH37di699FK2bduGyWTivvvuy1wjZN1CW3yko2Ys0fM6HA4aGxsTrnebr9U3F9NFKBwO097eztjY2Lxjd1oXQXd2dqoDSJXWUVarNcpFlhf6IPglQdMfgDzy/Zdikls0W0O2kO7C6shhp9OtuPHx8Sgrzuv14vF4yM3NzbjrMVMxNC1aXz3++ONxnzt8+DCHDx9O6fw6ibMkBC2yqWk6LbRYhEIh2tramJiYYNu2bVFB/dlI5wyziYkJmpqaWL16Nfv375/3na9Wa1MSGiwWC2eddRYwVfM23UVWVFR0ehM+RIH5QxnfXNNNJka2xLLiJiYm6O7uxuv1qlacYsktdNPiTFloWrgcFwW6hbb4MBqN+P3+tJw3lqCNjIzQ0tJCdXU1Bw4cmNdGlezU6ngYDAYCgQCNjY24XC7OOOOMhHpVxiJVC02xDsfHxykpKaG6uloVyVjp6g6HA7vdTltbmzoqRLHiioqKssYtpgXZkIihWHF5eXlqIwKlm/3Y2JjazT4yo3K2WJwWhEKhjJTZLJvmxLqgLT4WykLz+Xw0NzcTDoc588wzZ8xQSwQtU+OVNZ04cYINGzak3EZLqWlLBqXJ8qpVq9i/fz9vvPHGrMdHusjWrl2LlBKPx6P232tpaYk6JtvaR82XbBA0hUirKD8/n/z8fNWKC4VCakblqVOn8Hg85OXlRWVUamnFZcpC8/v9C5K0lRXoSSGLA2WDSHcMTUpJb28v3d3dcWu4EkUrt57f76e5uRmXy8XmzZvVbKtUMBgM87bQQqEQ7e3tTExMRDVZnq+1J4TAYrFgsVjUawkEAjO6qC90Vw2tyCZBm20tRqNR/XwVvF4vdrudsbExTp06hZQywl1cgtlsTvrals3k6EyhW2iLj3RaaC6Xi87OToqKijQZcpmq+EopGRgYoKOjg02bNpGfn6/ZHfN8Rchms9HY2EhVVRX79++P2tS0SDDJyclhxYoVrFixAohdj6VYD0qySbZujtnUbX++4qNYccpMslAopGZUtre343a7yc/PVy24+VhxmbDQsul3kXZ0QVt8pEPQlHEqbrebM844I+qONRVSsdA8Hg+NjY3k5eWxf/9+cnJycDgcmsXkEl2bkhBjt9vZvXt3zI4L6RgbE6seS7EeRkZGaG9vB4ga4ZKMWzhdZJOFlgpGoxGr1aoOfp0+WVqx4iKHbsaz4jJpoWXL7yPtZOc9nqYsKUHT2uWojFOxWCzU1tZqJmaQ3FqVOrfe3l62bt2qWiyQntqx2VAyKdesWcOWLVvibgoLNQdtuvWgjAmx2WwMDAzg9/vxer309PRgtVrnHMSZLrLJ5ag1sSZLK1ac3W6fYcWVlJRQVFSEyWTKiIUWCAQyMuUgI+gW2uJB67R9JS4VDAbZs2cPNpsNj8eT8nkjma8AOZ1OGhoaKCkpiVkgrbWgxTuX0jfT6XQmlEmZqcGekWNCYEpIjhw5gsFgoKenB6fTmfbmv7HIJkFbiHXEs+IirWll/JNyk5FKLG4+LJtp1aAL2mJDi7EnkY17IydbOxyOtEytTuSc4XCYjo4OhoeH2bZtW1wrUUtBi5cUMj4+TnNzM9XV1QmP5cmWSdVCCIxGI2vWrFG7qyuDOGPXxKWW5BCPbBK0TBBpxa1atQqYukl6/fXX1UbeXq+X/Pz8qIzKdLgjl00N2jJiyQgapHbH6XK5aGxspKCgQI1LKaRravVcAqSkwFdWVnLgwIFZXTJalgFMF6FgMEhra2vCVtls58omptfEKanqNptNrYmzWCxR7rFU3WLZ9Flky1qMRiMGg4G1a9eSk5MT04oDom428vPzU74xcDqdmkxlXxToFtryIBwOc+rUKUZGRqivr1ddI5GkQ9BmK6wOhUK0trYyOTkZlQI/G6nUjk0nUmyVOGJNTU1Sw1IVQdPSKgkHg7xx992MvvYapfX17LzpJnKTLCKPZHqqerpq4rLBQssWMVOIbE4cz4pTSjeGh4ejCvCVpJP5WnEul2v5uBwBqSeFLB6SsQQUF9rq1atntYDSZaEFAoEZj4+NjdHc3ExNTc28Rs7MWTsWOAnOVxDGbqAOWXQxiNjfcEUcGxsbcbvd7NmzJ+mxPFpbaFJKnvvYxxj+05+QoRADL7xA3+9+xwdfeAGDxm6p2WribDYbPT09UZOmrVbrnB01ssXlmE3TqmHuz8VoNM6IiXo8HiYnJxkeHo7KbFVclXNZccvJ5SgFhJbMbh+fJXmJc2VM+f1+Wlpa8Hq9CbnQFsJCCwQCnDx5Ep/Px969e+ctILO6MN0vg+t+cuTPITB1HWHvGYSsz4LJAOHJ0ycpA4MJm82G3W6nqqpKk64jWgqavbOT4T/9CYTAcNo9NdnayvCf/sSqt79ds/eJx2w1cUp3+7y8vKhkk0jLIZsELRvWEcl81hN5s5GIFae4jCN/F8vN5agL2iLEZDLFHRYYWYy8YcMGVq1aldAfUbosNOWcg4ODtLe3z2tNsc6nCpp7HMOp74EwEl5/GQRfwCT/ePrIQsCPgQak717CgXNABoAwQWnhZHc+Hk+IgoICqqurU77OVDbNYDBIyOnEmJ+P6XQdWdjni5qpJoQAIQj4MjM7bb41cX6/Pytad2XT6BitiGfF2e12hoaGaGtrA6Z+F62trXR3d2vicvyv//ov7r33XkwmEx/4wAf4xje+AcCdd97Jww8/jNFo5J577uGCCy5I+b2SRQoIGpfW7zsWS0bQpqfuT0/BVqY1m83mGUkfc5EuQfP7/bz++usYjUbOOuuslDY6VdAmTpLzy3MQQS8gkce/SuD8ywEnoJxfTP3zNIDlHDCtwGaz0dvxKiuqzqaybg9HjhxJ/SKJju3Nxyrwjo4y/PTTBB0OEIKy887DumMHpVu2UFhTg6OrSz1nfnk5lQcParJeLYhVE6dYDkNDQ4RCIWw2W1Trrkx0ycgmQUtHTC+Wy1ipT/zpT3/KL3/5S4aGhjh69Chnn302V155JWVlZfN6jxdeeIGnn36aN954g7y8PIaHhwFobGzkySefpKGhgf7+fg4dOkRLS0vGiselEIQWeIJCJlhyVzhdfJS096GhoYSmNSdyzlSRUjI2NsbQ0BC7du2acxBoIiiCZvr9JxFBDxhyQEpEwIHpz88jdxcjGALMQAgwIHPrCIWho62NgD/Ilq315FpKkVmw0Y3+6lfIUAjLunUE3W7Gn3uO/FWryC8v54Jf/IL/+8IXsDU1UbRuHQfvuYe8LHYdmUwmysrKKCsrw2QyYTQaKS4uxm63x6yJixzCmS7C4XDWuRwXAqU+8aabbiI3N5fa2lrOPvtsXnrppaQ+j/vvv5+vfOUraoNjpb/r008/zWWXXUZeXh7r169n06ZNqnBmilCWtoPTkiUnaJHF1RMTEzQ3N1NZWcnBgweTviPV8g/f7XbT0NBAbm4u5eXlmogZvCVowjPIlAXGlGtOghh3Ecy5DRH4KoIxII+Q6ROMej7AYPcLVK7cSnlFGSJsB9MKTa83mRhaMBjEPzaGpbYWAJPFgl8IgpOTUF6OZeVKzn/qKc3WuJAollFhYSGFhYUZq4nLJpdjpuJ5yrTqzZs3s3nz5qTO0dLSwosvvsjhw4fJz8/nrrvu4qyzzqKvr4+DEV6D6upq+vr6tFr6vJEIQsug99WSEbTIjvs+n4+Ghgbcbje7du3KitTccDhMV1cXAwMD1NfXk5ubS2trq2bnVwRNlmxGjByFCBEJl+6C0k8SlJ8A/yCBYB5N7b2EQiG2bfsQeYYhQIJ5D5hmli2kQjKCZjKZMJWU4J+YILe0lKDXiwRMCQ5PzWbibd6xauKmz4nTsiYum5JCMiWuLpcroYG8hw4dYnBwcMbjt99+O8FgkImJCV5++WVeeeUVLr30UrWH5XSy5fNeyiwZQYO3gsCDg4Ns2bKFbdu2ZcWXaHJyksbGRlasWKFaih6PJy0Tq4Pv/hE5P99/2lIDWbiW0DtOj4gXgmG7kdbWpmkJKBs0W8d0ks1yrHzf+xh6+mncHR1gNFL2rneRX16ehhUuLIkKSayWUVrWxGWThZapxsQOhyOhLMfnn38+7nP3338/f/EXf4EQQp0OPzo6SnV1NT09Pepxvb29VFVVabLuZJAIgovMQhNClAI1UsrZhypGsGQEze128+abb+L3+9mwYUNGvzwKyoyw8fFxtm/fHlXzonVcTk0KsZQT+FgbTDSAwQSl9cBb/SnD4TD79u1bsKGGyQpa/qpVrPn0pwnabJgKCzFlcYxsPiSb/BCvJs5ut6uxOKUmzmq1zjllOpsELZMWWqpp+x/+8If53e9+x7ve9S5aWlrw+/2Ul5dz0UUXccUVV3DDDTfQ399Pa2sr+/fv12jlyRFaBNu9EOL3wEVMadNxYEQI8Qcp5Q2JvD77rzBBQqEQ69evx+Vypa0LwnzcNEo3+qqqKg4cODDjdVr2XpxxPoMBVuxUn1NSljdu3KjW7CwUQgi184nP51OtjkRiQqb8fEwLvN6FQCuvQU5ODuXl5ZSftlznUxOXTS7HTFloTqczIZfjbHz2s5/ls5/9LDt27CA3N5f/+Z//QQjB9u3bufTSS9m2bRsmk4n77rsvozP6FlEMrURKOSmEuAp4REr5VSHE8rPQiouLMZvN+Hw+zTvjw1sW1VzZZ8FgkJaWFnV+Wryi7dlaXyVDLIH0+/00NjYihEi5LCBZPB4PAwMDrF+/nhUrVuBwONQxIkpMyGq1UlhYmDUWQzpJp5BMr4mTUuLz+bDZbDNq4pSRLdnAYrbQcnNz+c53vhPzucOHD3P48OGUzq8Vi0jQTEKI1cClwLw/vCUjaArpqBmLPO9sgjY8PExrayvr1q2bs8OG1h00pguaUqy9adMmtR5qIVGGfw4PD7NmzRqqq6sJBAJYrVZ1s1ViQn19fTgcDkwmk+ouW4jU9UywkJaREIL8/HxWrVqlWuZKTdzg4CA2m42jR49SUFCQ0Zq4TFloWgjaYmKRCNo/Ab8G/k9K+YoQYgOQcPbcktkxtJ6JNp3ZhNLv99PU1ISUckHjU5EoAunz+WhqasJgMGTMKrPb7TQ0NFBVVcX69etj/j5ixYSU1PXx8XE6OzvVaceKFad+rqF+CA6CKADjejBmvvNGomS6KbBSExcKhTCbzaxbtw6Xy5XxmrhMWGihUGjZDPhcLEkhUsofAD+I+PkU8JFEX79kBE0h3RZaJJGttDJlCSkIIfD7/bz66qts3rxZLfBcSMLhsJoEs3v3bgoKCujv7094E4+Vuq40AlamTpeVjFBReAqzZQX5eRIZ6kHmvhsWUdFoNsSulMJqIcSMmjifzxd1YxEOh9UbC61Gt0QSr1VdOsn0jcVCM+VyzP7tXgixBbgfWCml3CGE2AVcJKX850Ren/1XOE8WykLzeDw0NjaSl5c371ZaWuPz+WhsbCQYDHLOOedotpb5uMccDgcNDQ1UVlayf/9+9XWpuFZj9ebzj38Hp2cFA8N+fF4fBfk9yHwrRdbNmswrSzfZkowxW+urvLw8Kisr1ZuiyJq41tZWzWviIkfHLDTZ8LtYKNLkclwnhBgGhqWUO2IdIIR4F/AfQA4wKqV85yznewj4MvAggJTyDSHEE8DyErTIwup0WmhSSrq7u+nr66Ourm7evd+0JNJC3LJlCx6PRzMxS3SOmZRSbS22Y8cOtTTB29XFyI9+hMPpxHjeeXC660eqazJb8rBYyqmsNCKlJODtZNxjUmuzImealZSUZJ1LKVsEbT5uvtlq4vr7+3E6nWo7L6vVSnFx8bxc3bqFtqgZBa4AHov1pBDCCnwTeJ+UslsIMZfryCKlPDrtbyRhC2XJCJpCOi00p9NJW1sbVquVAwcOaHJXmewG5/V6aWxsJDc3V7UQ09F5ZLaNxuVyceLECcrKyqLmybkaGjj+znci/f6p6/vP/2TN//0fpnXrUl5XWGxEhE9iCJUgcJOTW0pF4TYqVk1toEptls1mo6urK8pdlg1ZfdkkaMmuY66auO7uboLBYFTrrrlq4hbaQvP5fOSfnuCwHEhjlqMTGJ/l+SuAH0spuwGklMNznG9UCLERkABCiI8CA4kuZkkJmhAiLRZaOBzGZrPh8XjYvXu3OtE4VZShnPPZWKSU9Pf309nZyTEqrOcAACAASURBVNatW9UaJK2ZzVUopaSrq4v+/n62b98+4/PoOHyYsMultt+SwSCd//iPbHos5k3c/Mg5AxkyEwr3A2WQszMqKWR6bZbiLrPZbHi93qisPqvVSkFBwYILTDYImpRSUxFJpSYuHA4veEar0+nMipZ4C4WETCWFbAFyThdMFwH/KaWcbSP4HPDfQJ0Qog/oAD6R6JstKUED7TcLm81GU1MTubm5rF+/XjMxg7dmoiXqbvF6vTQ0NJCfn8+BAwfSugnEK/xWmisXFxfHtVIDIyNRvSSRkuDIiDYLMxrBuA3YluDhb7nLRkZG2Ldvn5rV19XVhcvlmnUgp9Zki6sr3ZmFsWrilDlxkROmS0pK8Pl8C+66dzgci2ZatTZlDcklhSh/MwrXXHMN11xzzXxOYQLOBM5jatTHS0KIl6WULTNWKIQB2CelPCSEKAAMUkrHfN9MJwbBYJDW1lYcDge7du1iYmIiLVOrE0kdllLS19dHV1cXdXV16rTkdDLdQpNS0tvbS09Pz5xjeFZcfDHuhgbCXu/UA3l5lF18cbqXnBCxsvoiN9q2traoHolWq1XTONxScDkmgxACs9mM2WyeURPX0dFBX18fvb29UTVxhYWFaVujy+XKGgvt5ptvpra2lr/5m78B4NZbb6WoqIhf/OIXrF69muPHj9PY2JjSeyTrcqyoqODVV19N5a17mUoEcQEuIcT/AruBGYImpQwLIT4PPHX6+HmzpARNq2Ll0dFRTp48ydq1a6mrq0MIweTkJH6/X4NVvkUi7a88Hg8NDQ1YLJaErDKtNszItSmWoTIcda411Nx4I4GhIQYfeQQJ5H/sY6z5whcIBAIprysdxBrIqcThenp6CIVCajzIarWmlLaeLYKWDQM+lZq40dFRKisrKSkpUa3n7u5uXC6XWhOnJJto5ZVwuVxZY6Fddtll/N3f/Z0qaE899RRf+cpXOHr0KCdOnGD9+vWavE9yMbSUb+KfBu4VQpiYmjB8APj3WY5/TghxI/B9QBU1KeVscTqVJSVokSSzcfj9fk6ePEkgEODMM8+MChqnIzY3W/urSIto69atCVllighp4TJTJk339/fT0dExL8tQGAxsvPtuNt59N2NjY4yMjGTFJp4oJpOJFStWqNcbDoej0ta9Xm9U2675dNfIFkHLxubEs9XEjY2N0dHRoVlNXKKd9heCPXv2MDw8TH9/PyMjI5SWlrJ27Vr279+vmZglnxQy5563HngJKBdC9AJfZSo9HynlA1LKJiHEr4A3gDDwLSnliVnO99nT//1c1PITHAmyJAUt0b6LClJKhoaGaG9vnzZWZeY5tUSJoU1HiVMVFhYmZBFFnk8rQQuHwzQ1NWE2m1OK12nd4isTRLog165di5QSt9sd1V0jNzc3ypKI9zvIls8iW4QVZo8RzVUT5/V6MZvN866Jy7a2Vx/96Ef54Q9/yODgIJdddhmApi7RNHYK6ZBS7pvtACnlvwL/msjJpJQpKfiSErTp7a8S2YSV9HeTyTRrq6h0CVqkhSalpKenh97e3jnjVImcL1kGBweZmJhg48aNrEsx1X4pCNp0hBAUFBRQUFCgjilSLInIJsCKwE2fVZYNQpKNFloixKuJs9lsUTVxc9UiZpPLEabcjldffTWjo6P84Q9/4OTJk5q/xyLpFJIDXAe84/RDvwcelFImFK/I/itMAqPROGctmuLS6+7uTij9PV0uR+WcilVWVFSUdI1bquIRCATU4HNFRcW8BTUda1osTLcklIQHm81Gb28vgUCAoqIiPB6PalVkUtiySdBSyeKLrIlTbi6m18SFQiEKCwujauIcDkfKo2O0ZPv27TgcDtasWcPq1avTImiLhPuZcll+8/TPf3n6sasSefGSFDSTyTSr+LhcLhobGyksLEzYnZZOl2NXVxd9fX1JWWXTz5eshTYyMkJLS4s6M62xsVETa2+5CNp0lIQHJR1dqctqamqiu7ub1tbWjI7PySaXo9biOldN3Je+9CXcbjdbt26lvr6effv2xR3zFI+Pf/zjqujYbDasVivHjx8H4M477+Thhx/GaDRyzz33cMEFFyR0zjfffFP9/3e96128613vmteaZmMRjY85S0q5O+Ln3wkh/pzoi5eUoM3VcT8cDtPZ2cnQ0BD19fWq2yIR0iFoyuDLiooKTTqPJCNowWCQ5uZm/H5/1KQArYRouQradJS6rPz8fOrq6sjNzVVdZcr4nHRl9MUi2yy0hayJe/bZZ/nKV75CQUEB3//+97nxxhv57W9/Oy8X5Pe//331/7/0pS+p9amNjY08+eSTNDQ00N/fz6FDh9SWbJlkEQlaSAixUUrZDnB6fEzCG++SEjSFWOIzOTlJY2Mj5eXlUW2aUjlnsiidNgYHB1mzZg2bN2/W5LzzFbSxsTGam5tZt24dVVVVUXfsSpZjquiCFptYrjJlfI6S0aeMz1HicFqOJcomCw1YUHFV3usDH/gA7373u1M6l5SSp556it/97ncAPP3001x22WXk5eWxfv16Nm3axNGjRzn77LNTXneqLIbxMUw1Jn5BCHEKEEAt8FeJvnhJClqkhaYMmrTZbGzfvj3pQLBWguZ0OmloaKC0tJT169druqkkKmihUIiWlhZcLteM8oTIc2ltoWXTBpopZhOS2cbn9Pf34/f71ViQ1WqdtT/iXGSThZYJtMpyfPHFF1m5cqV6U9rX18fBgwfV56urq+nr60v5fVJlsYyPkVL+VgixGdjKlKA1Syl9ib4++69wHkR23A8Gg4yPj9Pc3ExVVVXUSJNUzp0sUko6OzsZHBxk27ZtlJSU0NfXp2mxcSKCNjExQVNTEzU1NWrReCx0l2N6mI9lFGt8jtPpxGazqf0R8/PzVYGbzxiXbBK0THw/nE7nnDe3hw4dYnBwcMbjt99+Oxef7nzzve99j8svv1x9Lta1ZMON3GJxOQohPgd8V0r5xumfS4UQV0opvznHS4ElJmgKQgh6e3vJyclhz549mM3mjK5Hscqmd6U3Go14lfZQGjCboIXDYVpbW7Hb7ZxxxhlzBsG1KgFQBG1oaIiRkRF188307yRTpOLqE0JQVFREUVFRVH9EZQBq5PgcJQ4Xr21XtrkcF5pEBO3555+f9flgMMiPf/xjjh07pj5WXV1NT0+P+nNvb6/qUs40i0HQgKullPcpP0gpJ4QQV/NW1uOsLDlBGx4e5tSpUxQVFbFnz56Mp0YrSSjbt2+fkSaslWjMdT673U5jYyOrV6/mrLPOSugz0cqyUtpIAaxevRqHw0FLSws+n4+CggK1pigV99liQkshieyPOH2My8TExIxp00rbLsguCy0Tv3ct6tCef/556urqqK6uVh+76KKLuOKKK7jhhhvo7++ntbWV/fv3p7rclEljYbXWGIQQQp7efIQQRqZaZiXEkhI0t9vNwMAAW7duxWazZXSDVCY4z5aEMlvrq2SYLmjhcJhTp04xNjbGzp075xUz0CIpZHx8XJ3ZtmvXLoLBIGVlZdTW1qruMyWV2uVyYTabVYFb6DT2pcJs43NOnjyp3kj4fD41jpTpm75M4Ha7552qP50nn3wyyt0IU/Vkl156Kdu2bcNkMnHfffdlPMMRFk8MDfg18JQQ4gGmWl5dC/wq0RcviitMlIKCAnbv3o3dbmd0dDQt7zHXHXY4HKajo4ORkZE5k1Ditb5KlkhBUwS1srKSs846a97ikEpSiOLenJycZMeOHbS3t894/0j3WXV1dVTHh97eXrWdlJLhl+6xLgvFQrv6YnXWcLlc2Gw2tQGwMj5HicMt5OecieGeoM08uEcffTTm44cPH+bw4cMpnXsZczNwDVPdQgT/n703D2+jvL7Hz8iSvO+Ol3hPHNuxnc1LEgqlEEjZ2pQWSmlIQgghgQIJWwttCmULFJoWyg5lL5C04QcNH6DZoMC3pUlwSInl3YlXebck25K16/394bzDSJbskTSSRrLO8/C0drSMZGnO3HvPPQc4COBlvncOKUKj8MXOGPdxXe0HjY+PQ6FQID09HcuXL5+RRHzRcrRarWhvb0d/fz8qKio8bqt4WqHR9yArKwvV1dUwnUmt5vN8jjJ2o9EIjUbDxrpw50OJiYl+D4UUAoGeXVEDYLlcjvLycgBg53ADAwNobW1lvSvp+yxkfI4jAtH6nK0ipWCYoRFCbABeAPACwzApAHIIIbNzD22mxWpv4YrQaGtveHjYLRIRmnjNZjN6e3uRlZXl0a4dF+5Wj1wVJ/c98GYWFxkZiYyMDDbWxWw2Q6PRQK1Wo729HQDYPa2kpCSXPpxig9hmhVFRUcjMzGRzysxmM7suQK2jhIrPcYQw4ZWeQWx/B18iiFSOnwFYg0lu+h+AIYZhPieE3MHn/iFFaBS+JjQuxsbGUF9fj4yMDF5VGRdCVWiEEHR1dUGpVCI9PV2QRW13iEiv10OhUCAxMXEKkQop25fJZHZ7Wo5+iRaLxW4RebYqKflgur+JTCZzGp+j0Wicxud4M4cLRIXm73BTMSBYCA1AIiFkjGGYzQBeI4T8jmGYk3zvHHKExjCMz1uOwOSX4tSpU1CpVG4LLpw9nqegZBIfH4/58+cLttfGh2wJIejt7UVHRwfKysqc+lByCU3odpszv0R64nWmpJytrSZvwY3PAcDG59AAVHficxwRiApNr9d7LQgJRgSJylHKMEwWgKsAuD2IDDlCA3zXSqAERGXwmZmZXi1se1OhEUKgVCrR1dXFmhr39/fDaOS9VD8tZqqsTCYT6uvrIZPJpjV49qdTCPfEy1VScheR6+rqAmYILCZ4azJA43Mcgzj5xOdwEYgKTUzhnv5CEKkcH8Sk0vHfhJCvzng5tvK9c1C8QrFAIpGgs7MTBoMBixcv9jqAz9MKzWAwoL6+HtHR0XYBoEKKTKZ7LOrMX1RUxM63XCGQTiGOi8jHjh3D/PnznSopA6HwCyXwjc/hLtYzDBOQCk1s4Z7+QLC0HAkhewHs5fx8GsAVfO8fcoTmqxOoRqNBf38/5syZ47WNFoW7x0oIQV9fH9rb251muAlJaM6OzWq1orm5GXq93s6Z393HCRScKSkdFX6hoKScCf74e7iKz9FoNDh16hS7ByaVSllFrb8qNa1WO+sIDQgOlaO3CL1vKwdCzGyoufHo6CiysrKQmJgoqNMDX9AWn1QqxfLly51KqX1JaBqNBg0NDcjNzcXChQt5H7vYh+/OFH4ajQYqlYpVUvJpnQUTArE6wI1woceg1+vR1dWF8fFx1NbW+i0+Z7a2HINkhuYVQpbQZtoZ4wNq5JudnY3i4mJWwuxvDAwMoK2tDQsWLGBbOs7gi5Yj121kyZIlXrdZxY7plJTd3d12SkqulVQwQQy2V7RaTkhIQFxcHHJycvwWnzMbW46zBSFHaFzHfU8JjQZvjo+P2xn5+ko96QpmsxmNjY2w2WyoqamZsToQukIzmUz46quvkJaW5pHbSCjAlZJSrVbbWUkFkydloJe7ueB+R/0VnzMbCU3sohCGYabdMyOE/InP44j3FXoJuovm7lUdjZzJyclBSUmJ3RcmIiICJpNJ6EN1Ciq8mDdvHms8OxOE3GsbHBzE8PAwqqurWbl2GM4l7HQ2dPr0aXY2xPWkFAt5UIihQqOY7lgc43NsNhtr2+VNfA4fp/1QhMhnaPQPUgKgBsAHZ37+IYAv+D5IyBMaX1gsFrS2tkKr1bqMnPFHhWaxWNDU1ASTycRbeEEhBKFRBaVUKsWcOXPCZDYDnEW6UE9K7o6WmHbhxERo7qgcJRKJW/E5rkQ9Wq2WrQJnC8SuciSEPAAADMMcBFBJCBk/8/P94KgeZ0LIEZpjy5EPaFU2U+ilLwiNq/BSqVRobGxEQUEB5s6d6/aVvbeE1t/fj1OnTqGkpASRkZGsKCIM/phJSTkxMYGvv/46oEpKMbUcvSFXd+Jz6HsdFRUFnU6HefPmCfkyggJiJjQO8gBw22AmAAV87xxyhEbBp0KzWCxoaWnBxMQEryBQXxCaRCKB2WzG6dOnodPpUFlZ6bFlk6eExp3VUQWlTqcLWLRHqIGrpBwbG8OiRYsCqqQUU4UmtNv+dPE5/f39uO222yCRSKBWq1FWVoaysjK334v//e9/uPHGG2EwGCCVSvHcc89h+fLlIIRg+/bt+PjjjxETE4PXX38dlZWVgr02bxBEKse/AjjGMMz7mIyP+TGAN/neOWQJbSbyGRkZQVNTE/Lz83nL0H1BaDabDbW1tTNWh3zgCaHRqrCwsNAuWVdM+2OhhpmUlNQM2FdKSjFVaFar1afk6hifc+jQIdxyyy2QyWR45JFHcOrUKXz55ZdukeqvfvUr/O53v8Mll1yCjz/+GL/61a/w2Wef4Z///CdaW1vR2tqKo0eP4qabbsLRo0d99dLcgthFIRSEkJ0Mw/wTwHfP/Oo6QsgJvvcX/yt0EzM57lssFjQ3N8NgMKCqqsqtk4WQhGaz2dDW1sZWh1RF5w3cITRuZpmzqlDoaJswXMOZkpISnC+UlGKq0HxNaI6g7d0NGzZg2bJlHj0GwzAYGxsDMJkGTy8E9+3bhw0bNoBhGKxcuZKd7fEVdfkaQdJyBIAYAGOEkNcYhpnDMEwhIYTX/CPkCI0iIiJiilHv8PAwmpubPZ5RCUVo1KE/MzMTqampgrWY+FZVjpllzt6HcIUWOEgkkimhnEIqKcVEaIEI+PRW5fjkk0/ioosuwl133QWbzYYvv/wSAKBUKpGbm8veLicnB0qlUhSEJnZRCAXDML8DUI1JteNrAGQA3gJwNp/7hyyhSaVS6PV6AJMzoubmZphMJrerMi68JTRubhp16K+rqxN0d2w6cDPLZkoI8DTg09XzKpVKtLe3IyYmBsnJyUhKSkJsbKxoWl/+gKcXCO4qKRMSEqYlrNnUcnQGPntoF154Ifr7+6f8fufOnfjkk0/wxBNP4IorrsDf//53XH/99Th8+LDTv69Y3ucgwo8BLAPwNQAQQnoZhuF99RFyhObYcqT7XIWFhcjKyvLqA+YNoWm1WigUCtYLkn6JIyIi/NLa0+v1qKurQ1JSEq/wT4lEIkiFZjKZoNfroVarUVlZCZPJBI1Gg46ODuh0OkRHR7MEJ8adLTHCW0/KcIWmZS24XOHw4cMu/23Dhg3485//DAD46U9/is2bNwOYrMi6u7vZ2/X09NjNpQOJIBKFmAghhGEYAgAMw7hlTRRyhEZBl4PdMdKdCZ6cbLlVUXl5+ZQvkrvJ0J48f29vLzo7O9mYGT4QokKjwhuZTIaKigoYDAZERUUhKysL2dnZdpVGV1cXdDodIiMj7QhOLCdeIeDLysjRk5LaSDlTUprNZtFcOASCXA0Gg1fng7lz5+Lzzz/Heeedh08//ZQN1F2zZg2eeeYZXH311Th69CgSExNF0W6kCAZRCIC/MwzzIoAkhmFuALAJwF/43jkoXqG7GBwcRHNzM+RyOZYuXRqwL69Op4NCoUBycrLLqsiX4gtuZhk3ZoYPvKnQuIKTqqoqfP3117BarWxcCAD2fyMjI5GZmYm5c+faLcoqlUqMj4+zrbTk5GTeThBihT9bfY42Ulwl5eDgICwWC4xGoyg8KQPx/fTmc/SXv/wF27dvh8ViQVRUFF566SUAwKWXXoqPP/4YRUVFiImJwWuvvSbU4XqNYJmhEUJ2MQyzGsAYJudo9xFCDvG9f8gRmtVqxdDQEBYvXoxTp04F5MtCCEF3dzd6enpQVlbGDvedwVfuI7TVOpOhsSt4+r7R1mpGRgaqqqpACEF8fDyOHj3KEhNtgVHzY+BbgpPL5cjIyGCvbLlOEM3NzZDJZHazomDKLwukyIarpIyOjobJZEJiYqKdklIIn0SxQ4iLinPOOQfHjx+f8nuGYfDss8969di+QrAQGgAQQg4xDHMUZ/iJYZgUQoiKz31DjtCkUinKy8thNpvdsr4SCnq9HgqFAnFxcVixYsWMJ1yhKzRCCOrr62E0GgVrtfJ93p6eHnR3d6OiogLx8fEsYS1cuBA2m411cKCBj4mJiSzByWQypwQnk8mQnp7OttKMRiNbZbS1tdntGSUmJoqe4MRAEoSQKftZwehJ6SnEJIrxJ4KB0BiG2YrJ1Go9ABsABpML1rysXUKO0IDJk4YvfRedfSGoko/OqvjulQl5nBqNBjqdDnl5ecjJyfHbl5a2NuVyOVasWAEA7MUEwzDs38Nx14oSnFKphNlsRkJCAktwcrl8CsERQliPSZqUTUUmw8PDOHXqlJ3kXWwBnWI5kTqbWzlTUk5MTGB0dJRVUkZGRtrllQVj+zcQCdliQBCJQu4CUE4IGfbkzuL5tgsMoVR6jnAWS2M0GlFfX4/IyEisWLHC7VmVt4Rms9lw6tQpqFQqxMTEIDs7228nTrrbt2DBAsyZM4clIUpkriCRSKY4qY+NjUGtVqOvrw9Go9GO4KKiomC1WqcQXEREBNLS0ti2qrOATrEYA4uJ0GY6qTMMg9jYWMTGxk5RUvb397NKSnrh4MnFQyD+HlqtNuQz/ZwhWJxCAJwCMOHpnYPiFYoJjoTW19eH06dPo7i42CMHb2cL4O7AcR3g2LFjflGO2Ww2tLS0QKvVorq6GnK5nCWZmcjMGbiVVWFhoV3uWEtLCwwGA+Lj4+0IzmazgRACm81mR3Cpqal2YghaCer1etTW1rJVRlJSktPkb19BLIRGCPHo8+GOkpKPJ2Ug3g+tVjvrstAogqHlCODXAL48M0Mz0l8SQrbxuXNIEpovXS4ooZlMJjQ0NEAikbCGvp7A0wqNKzwpLy9nY178YVml1WpRV1eHrKwsFBcXgxDCthiFIlJu7lhBQQEIISzBtbW1Qa/XIy4ujpX4OyM4+jjJyclITU2FWq3G0qVL2UqQ+iZyCc7XxsBiIDShLnicKSlHR0fZNqXVakVCQgL7/joqKcW6VB1GQPEigE8B1GFyhuYWQpLQuBD6KjAiIgKDg4NQKpUoKipiZzmewhNCo5llMTExU4QnviQ0SqJKpRIVFRWIi4vj3WL0FgzDICEhAQkJCcjPz2dFDGq1GqdOncLExARiY2NZgouOjmbJzWazwWw2s8eZlJTEzvJoKjIVq3CNgZOTkwUV1QS65UlB3wehIZVKkZqaitTUVPZ56KoAzfiLi4tjLx4kEklAlqpnI6EFkcrRQgiZNr16OoQ0odGTu1BfGjqfMRgMqKmpEeRq3l2nEG5mGY3I4MJXhGY0GqFQKBATE4Ply5cDmCr88Ce4Ioa8vDwQQqDT6aBSqexUepSU2tvbkZOTM6WCYxiGVVsC9ifhhoYGmM1mO4LzZl8r2FuO7mI6T8pTp05Bp9PBarWiu7vbb0rK2UxoQSIK+RfDMFsA/B/sW46zU7YPTLW/EoLQqPghNjYWBQUFgrWm+BIazSwjhEzb4hTSg5GC7rQVFxcjLS3Nb1WZO2AYBnFxcYiLi7MjuI6ODgwNDUEul0OtVgOYnPPExsbakRuX4GggZEFBATvLc9zXoifq6Oho3u+BWAgtUNZXjkrK8fFxtLe3QyKR+E1JOVsJDQgap5C1Z/7315zfzW7ZPoUQknjHuBk6GxAKfFqO1ELKMbPM1eMJ1doihKCxsRETExOCCD/8CavVio6ODgDAd7/7XURERGBiYgJqtRpdXV3QarWsj2RiYiLi4uJcElx8fDwSExORn58Pm83GVhncWR6faBexEJqYjiMyMhLZ2dnIzs4G4FpJSQnO2zUMb532gxXB0nIkhBR6c/+QJjQ+qdXTgYZf5ufno6yszCf7bdO1CK1WK1pbWzE+Ps47JUColuP4+Dh0Oh1ycnJQUlLiE+GHrzA+Po76+nrk5eXZXQBQGTptPVLT5J6eHoyPjyMqKsqO4IDJk67VarV77Y6VoONCMje7jJsoIKYZmhj+hs5EIa6UlCMjIzh9+jQAeKVSna0VWrAQGsMwEQAuA1AADj8RQv7E5/4hSWj0BOIp+XCJxDH8UmhCc/V4NLNs7ty5KCkp4X1F7S2hEULQ2dmJvr4+xMbGsibC9OQjhit7V6BuJX19fVi0aNG0+0Zcx3paHVCC6+3tZX0kuUbJ9Dm4fpTcfS26kKzT6ewSBajjhq8VlHwhFkLjcxyulJTcdO/plJSO0Ol07N97tiFIZmj/B8CAsMpxKjyp0KgYgFYmjidwX1do1J1/YGBgxswyPo/nDqjwIzY2FitWrMCxY8dYZ3Kxk5nZbEZDQwPkcjmqqqo8mptGR0cjOjrabpGYLnqPjY1BJpOxFVxiYiIIIU4JjhKlYyVIH+fkyZN2llL+JhextBw9ce1wVFJarVZ2xulMSek445ytsv0gWqzOIYQs9vTOQfEKPYU7hGaz2dDW1ga1Wo0lS5a4vLqPiIiAyWQS7Bi5BDkxMQGFQoGkpCS7zDR34CmhDQ4OorW1FSUlJUhNTYXNZsPcuXNRV1cHAKzKLzk5WVR2UgDYk1lhYaHXaxRc0KgbR6Nk+l45GiUDsFNQUoKLjIxEVlYW4uPjoVQqUVBQwLY6ueGc/koUEEuFJsQemqMnpc1mYytkus5BK+SRkRGMjY15NUP75ptvcOONN0Kr1aKgoABvv/02+7d/9NFH8corryAiIgJPPfUULrroIq9em5AIlpYjgH8yDPN9QshBT+4srjOTQHC35Tg2Nob6+npkZmZi+fLl0169+qJCs1qtdj6QfDPLXD2eO4RmtVpZ0UtNTQ1kMhkr/Jg7dy6ys7NhsVig0WigVqtZRwhKbklJSQEjONoeHRoawpIlS+xaw76A43yHGiUPDQ3ZGSUnJyfbERz9e9ALIblczkbmAN8SJW11UqKkjyM0+fhLtj8TfBHuKZFInHpSqtVqPPbYY/jf//6Hr7/+GidOnMC5556L73znO249/ubNm7Fr1y5873vfw6uvvoo//OEPeOihh9DQ0IA9e/agvr4evb29uPDCC9HS0jIr/Vs/YgAAIABJREFUfSO9xBEA7zMMIwFgxhlzYkLI9ImsZxCShEYhlUqntZWy2Wxob2/H0NAQ7/ae0IRmNpvZlpS7mWXO4A6hjY2NQaFQIDc3F6WlpS6FH1KpFGlpaezem8VigVqtZne+AP8TnMlkYlMNqqqqAnKCjoyMREZGhp1RslqtZgmO61dJ/TYXLFjAO1GAmz4tZKKArxarPTkOX39WuDPO3bt3Y/PmzbjuuuswPDyMjz76yG1Ca25uxrnnngsAWL16NS666CI89NBD2LdvH66++mpERkaisLAQRUVFOHbsGM466yxfvCyPECQV2h8BnAWgjnigogppQouIiIDBYHD6b9QDMS0tza32npCERve7aKKzEOBDaNw53eLFixEbG8u2yvjMyqjjPR3Ucw2BT58+befGkZSUJPhVqkqlYg2RnS2XBwo0y40SnNlshlqtxunTpzE+Po7Y2FhoNBp2141atDkSnK8TBcTUcvRXvBGFTqdDSUkJLr74Yo/uX1FRgQ8++AA/+tGPsHfvXnR3dwMAlEolVq5cyd4uJycHSqVSkGMWAp62HANAga0AFJ6QGRCihOa4WM0FV8VXXl7Otob4QsjdNppZ5iws0FPMRGgGgwF1dXVISEjA8uXLp1Rlnly5y2QypwQ3PDw8pVLxhuBoRa3RaLBs2bKApizzRW9vLxISElBVVQWr1WpH/ADsWot0yd6R4GZKFKBuJ5TkZiI4sYhCAkGsOp1uxhnahRdeiP7+/im/37lzJ1599VVs27YNDz74INasWcMqV52df8XwHlMQeKZyDACh9QH4jGGYf8LeKWT2yvYpHMmHK7pYsWKFR18mbwmNqijz8vJ8EvMy3aL2wMAA2traUFpaipSUFJ85fjgjOLVaPYXgUlJSeLfQDAYDFAoFkpOTUVlZKaqThTOMjY2hoaHBTqgikUimSNDpbLKjowM2m80u1dsVwUkkkimJAhqNBhqNBp2dnSCETLurJRZCC0Q2GZ89tMOHD0/77wcPTuoVWlpa8NFHHwGYrMhotQYAPT09M5og+Bc+UzkWMAwzCGCQEOKyzcQwTA0m52M/I4S8O83jtZ/5T37mP7cQ0oRGKzSuM31ZWRmriPIEnhIaN7Ns6dKliImJ8fgYpoNEIpkyN7RYLGhqaoLZbJ4i/PCH4wedEdEKg7bQqFowIiKCreCcERydSZWUlPAOTg0UaNCrUqmccRfOcTZJKzi1Wo3Ozk7YbDa7VG+pVOo09JSGp3JnnHRXq6uri30cbi6cGAgtEBWa2Wz2qs05ODiI9PR02Gw2PPzww7jxxhsBAGvWrMHatWtxxx13oLe3F62traznqRjgQ5XjMCbtqt50dYMzy9KPATgw04MRQh7w5mBCktC4KkeTyYTjx4+zu1XeXhF6Qmh0Xpeenu5SRSnUScax5Tg6OmrnmiEGxw+5XD6F4NRq9RSCS0pKwtDQEPR6PaqqqkSzmOwKVqsVjY2NYBgG1dXVbn/WaJYbd8eKZrl1d3fDYrHYERy1InNGcDQyh/s4Go0GPT09mJiYQHNzM0tw/p5jUQSiQvP2O7Z79248++yzAICf/OQnuO666wAA5eXluOqqq1BWVgapVIpnn31WdApHHxGaFsBMxsG3Avj/ANTM9GAMw/wLkx1SOxBCVvE5mJAkNIqhoSGo1WpUVlayX25v4Q6hEULQ1dXFxq24mtcJmQpAH4sQgvb2dgwODmLJkiWIiYlxS/jhTziKKUwmE/r7+6FQKNhF5e7ubrZFKQZBgyN0Oh0UCgVycnIEc6KglZezqBulUgmz2WyX6h0ZGcmSmmPoKTcy59ixY8jIyGBdUWiiADdbzh/wd4VGF+G9+exv374d27dvd/pvO3bswI4dOzx+bF/CU7f9oaEhVFdXsz9v2bIFW7Zs4X1/hmGyAfwYwCrwIDQAd3H+fxSAKwDwdscISUIjhODEiROQyWSIjY0VjMwA/ld4dObDpzKkJCkEoTEMA5PJxCYzCyX88CdUKhV6e3uxdOlSJCYmshVcf38/mpubWccOeiIPNMENDAygvb0d5eXlPjW+5bZmgW+jbqgLiclksiMmZwRHQRMF6OPQ8FSu2waX4HzxmQlEhTZb4alTSMacOaitrfXmqZ8EcDchxMrnM0QIcVTI/YdhmM/5PllIEhrDMCgpKUFMTAy+/PJLvz8/zSwrLS3lRaZCZphRp/Jly5ax+09ii3pxBbrkbbFYUFVVxYoZHCs4o9HolOBSUlJ8sojsCjabDS0tLTAajXbH6y9w5fuFhYV2BNfU1ASj0WhXwcnlcpw6dQoxMTF2iQJ0GZmmnnMTBVpaWmAwGFg7qeTkZLcic6aDvys0k8kk+rZ1CKIawJ4zn5c0AJcyDGMhhPzD2Y0ZhuEOySUAqgBk8n2ykCQ0AGyasj/BN7PMEUKtAjQ2NkKv1yMjIwPJyclBE/UCTM4Z6+vr2SiR6Y43MjJyimMHbZ81NTVBJpMhJSXFZ04bwKSRMZ2LumMe7Us4Izhu5TU6Ooro6Gjk5ubCYrEgKirKLtWbG5kTFxeHhISEKYkC3HRwSnDTReZMB39XaFqtdlqRTqgjEIvV3DgYhmFeB/ChKzI7g+OYnKExmGw1tgO4nu/zhSyh+Rs0s2zevHms9x9feFuh0VWA/Px8xMTEoKenJ+DCD74ghKC3txfd3d0et+wcCY6aClOCo675KSkpgnglDg8Po7W1FaWlpV7ZlPkaEomEbcn29/ejoqICUVFRUKvVdllutIKLjo52mQkXGxtrZydF/RLb29vtEgXcSZ72t9pytkbHAD5VORYC+C+ANIZhegD8DoAMAAghL7j7YOE8NB7wxReHPiaNmtFqtbwzyxzBN7Xa2TGcPn0aw8PDWLp0KaKjo2EwGDA+Po7jx4+7vevlb9CqUiKRoKamRrBjdGYqzM09k8vlbAXnDsHR93t0dDQoVJcA0NfXh66uLrsVgoSEBOTn57OVF3UzoZUXJbiYmBiXBBcTE4O4uDg2UWBiYoJdE6DhqZTg4uPjXX7//ElofJaqQxUEDKw2n5wD2gkh1TPfDCCEbJzpNgzD/BTAfkLIOMMwvwVQCeBhQsjXfJ4jZAmNflGovZCQXxzaIpyYmEB9fb3bmWWO4JNa7Qi9Xo+6ujokJyejpqaGFX5IpVIsX76c9VscHBxk7bUCMWdyBbp4nJ+f73ZF6y6cEZxKpUJPTw/GxsbYYE/aonT2d6TekQkJCVi2bJkoWozTwWazobW1lZ3vOXMPoWnc8fHxbGtRp9Oxi9608qIEFxsbaxeZw+0C0Ogdmp+n1+vZNQGtVovIyEg7ggvE529WtxwJYLGI76LWCe4lhOxlGOYcABcB2AXgeQAr+Nw5ZAmNgi5XC3k1LZFIcPr0aahUKo8yy5w9njuE1tfXh9OnT7NL4s6EH47LzI5zJm6V4uok7gvQJff+/v4ZF499haioKMydO5d1cuAmV3MJjrYoR0dH0djYKDrvSFeguXapqakoLi7m/bels7O4uLgpTvXcyouqHx0JDvg2MocmE3DfY8dEAWrC7K8LrFndciQMrJagON3TE+FlAJ4nhOxjGOZ+vncOilfoDYQmtImJCYyNjSE2NtbjzDJH8G05WiwWNDQ0AADrzM9X+OFszsStUuiJKiUlhfcMxF2YzWbU19cjKioK1dXVAa8SKRyDPSnBdXV1YWRkBDabDbm5uZDL5aJx2XCF0dFRNDQ0oLi42Ot1Fa5TvWNYaXd3N8bHx1nyp7MzVwRHkwlolWw0GnH8+HH09/ejtbUVUqnULlvOFy1yrVY7e1uOhIE1OCo0JcMwLwK4EMBjDMNEYlLtyAshT2hCueNTS6Ouri4kJiYiNzdXsBMyH0JTq9WsN2BWVhZsNptXwg9ulUJPVCqVCh0dHdBqtYiJiWGXej1VsXFBQzjnzZvHVo1iRXR0NKRSKYaGhpCeno68vDx2PjQ+Pu4X8vcE1HKLzlOFBjeNm7YW6XxSqVRibGwMkZGRLMHFx8dP2YGjBBcREQGpVIrS0lIA9nZoNFuO64oiBMHN5goNBMFCaFcBuBjALkKIhmGYLAC/5HvnkCW06Rz33YXJZEJ9fT1kMhmWL1+O5uZmn4R8OgP1gFSr1Vi2bBmio6MFd/zgnqjolTidpbS1tWFiYgJxcXEswblzsqRRNcPDw34J4RQC4+PjqK+vR0FBAVvRxsbG2s2H6JyJkj+dwQWC4Gw2G5qammCz2VBVVeU3ARDDMC6rW9papApT+t5QglOpVGxeISU4Z4kCIyMjbCSRo6+lu9DpdLOW0AhhYDGLn9AIIRMA3uP83IdJB35eCFlCo4iIiPCK0Ki/4IIFC9gvmy9Sq51VaBMTE6irq0NaWhpqamqmVGW+OnE6m6VotVqoVCo0NTXBYDAgISGBncG5UnYajUbU19cjPj4+YCGc7kKpVKKnp8flfM9ZlULnTI4El5KSgtjYWJ8SHI0DysjIQG5ubsCrRUeC465QjI2NQSaTgWEYGI1GLFmyBBEREU4z4RwTBcxmM+trSVPTp0sUcAatViv67kAY3iHkCY3OmdwFN7OspqbGbgYnNKFFRETAaGSjf9jdrM7OTpSVlSExMTGgjh9cNVx+fj67sKtSqdDQ0ACTycRePaekpEAul2NkZAQtLS2CzHL8AavViqamJhBC3DIWdjZnogR3+vRpVilIyV9IgqML02Leh+MqTG02G+rr62E0GpGYmIiTJ09CKpWyFRydb1GC484rJRKJW4kCzmbms7lCAxjYrCF/ug9dQuM67rtboc2UWebLCs1sNqOhoQESiQTLly9nn0tMjh90YTcxMZF1pBgdHYVKpUJ3dzcmJibAMAzmz5/vdoBqIECNhfm4lMwEVwRHQz11Oh276+UpwVGl6MDAQNAEnZpMJpw8eRJz5sxBXl4e+5odkxa4YbBUfesqE266RAGr1cr6VVJfy1lNaARAcMzQvELIEhqFOxUad141XWaZLyo0q9UKlUqFxsZGzJ8/HxkZGV4LP/wFenKJjo6GWq1GdnY2kpOToVar8b///Q+EENbtnU+isj9BjYXLysp8Qr5cguO6bHArOEpwfAQ4NKJGIpEETRuXziSLioqmrD04S1pwTDvnpnrTiz9nkTncRAGaTKDRaKBUKrFt2zZERUVhzpw5KC0tRU5ODq9j37t3L+6//340Njbi2LFjds7zjz76KF555RVERETgqaeewkUXXQQA2L9/P7Zv3w6r1YrNmzfjnnvu8fo99BqECRNaKEAqldq181yBm1lWU1Mz7UlFaEIDJq2zNBoNKisrERUVJdqoF1cYHBxkDZlp+4tePdMlb1qlMAwTcBcTunhMs9b8ZSzsbD7pKMCJjY1lW5RcgqPL9HPnzuV9Qg40BgcHcfr0ad47h45ZeVQcQj87AOxSvacjONoGLywsxOHDh7Fx40ao1Wps3rwZUqkUH3744YzHU1FRgffeew9bt261+31DQwP27NmD+vp69Pb24sILL0RLSwsA4Oabb8ahQ4eQk5ODmpoarFmzBmVlZW69b4KDALCI/zziLUKW0Lgtx+nIh2aW9fb2ory8nNdVOg0OFQI6nQ4tLS2s/ZO/hB9CwZEYnM0upFIp5syZYzfg57aZ6BzFXy4mVEgxZ84ctxaPfQFXBKdSqewUpnK5HMPDwygrKxPtvIwLmsen0Wi8umCQyWR2nx2LxcKmenPFIVz1o7PQU5lMBpvNhjvuuIN1ReGDhQsXOv39vn37cPXVVyMyMhKFhYUoKirCsWPHAABFRUWYN28eAODqq6/Gvn37Ak9ogBupYsGLkCU0iulk+zSzLC4ujp1X8YEQFRp3r62wsJBd4A2WqBdgUoWpUCiQmZnpFjEE0sWEilXEKqTgElxeXh57wTA0NISYmBg0NTWxmWdCRrkICavVivr6ekRGRmLp0qWCXqBIpVKkpaVNEYeo1Wp0dnay4hBKcDKZDFarFd3d3fjqq6/sLPG8gVKpxMqVK9mfc3JyoFQqAQC5ubl2vz969KhXzyUICMKEFgpwRT7UPopvZhmfx+QLx702g8GAjo4OqFQqUQRW8kFfXx+rwvR29uQPFxNqLEzbupGRkV4dsz9AiUEul+M73/kOJBKJ3QpFS0sL9Ho9S3ApKSk+C+PkC4PBgJMnT7ICG19DKpUiNTXVThxCK7ju7m788Y9/BCEE33zzDV577TU7sqG48MIL0d/fP+X3O3fuxI9+9COnz+uswqMCFme/DzjChBbccLVYTVWEDMO4lVnGhTeERmNmioqKkJ6eDpvNBrlcjvz8fPT396OlpYWtUFJTU0XlRAF8K2+32Wyorq72icBDaBcTrrFwZWWlqN5PV6A7iLm5uexOFzB1hYIQYpd5ZjAYEB8fz1a4/lxkp7Zbgax+IyIiWIIjhODyyy/Hyy+/jMsvvxxPPPEE7r//fnzxxRd287zDhw+7/Tw5OTno7u5mf+7p6WH/Tq5+H1AQAOZAH4TvEbKEBkx++bmE5k1mGReeEBptHY2NjaGqqgqRkZF2wg9uheJ4AndHBedLuBPCKRS8dTHRaDRobGxEUVERO4cRO6jCj0/1yzAMEhIS7CJh6I4gdwme26L0Bfr6+tDd3e0z2y13YbPZ8MQTT+CLL77Ahx9+yBKsUKnVa9aswdq1a3HHHXegt7cXra2tWL58OQghaG1tRXt7O7Kzs7Fnzx688847Xj9fGPwQ0oQGfLuH1tjYCJ1O53FmmeNjukNoVEGZkZGB6urqGYUfNIaDOlFQkQAVX1CXjpSUFL+0zui8T6lUory8PKC7PHxdTJKTk9kFZ7GcZGcCFVKo1WpUVlZ6dOLlElxBQcGU1Gqj0TilRentMdMLi8rKSlGsZJhMJmzfvh0ymQwfffSR3fvo7nv6/vvv49Zbb8XQ0BAuu+wyLF26FAcOHEB5eTmuuuoqlJWVQSqV4tlnn2Vn8M888wwuuugiWK1WbNq0CeXl5YK+Po9A8K2PfQiD4av2OQO3bhxo0J2Wo0ePoqSkxG6h0xsYjUbU1dXZ7aQ4AyEEPT096O7uRkVFBeLj470WfnBdOlQqFcxmM7t/k5ycLLj8nDr8S6VSlJSUiDIolAubzcYaIVutVtbFnetiIkZYLBYoFArExMSgqKjIZ3NULsGpVCqYTCa7FqU7BGexWFBXV4eEhATMmzdPFK1clUqFDRs24JJLLsGdd94ZFPNof4AprQZeqnX7flV3VKO21vX9GIY5zjfg0x8I/OWUD9He3o7+/n5ER0cjPz9fsMflU6HRuU1kZCRWrFjBGhB76/jh6NJBHRJoixIAe/JOSkryioBoDhjXpFfsoGsQ8+bNQ2Zmpp2LCXWQSExM9NkFgKfHXFdX55f3mfv54VZwXBszbovSFcHRGV9+fr5oPhttbW3YuHEjfvvb3+InP/lJoA9HXAiLQoIfiYmJyMvLw5EjRwR93JkIbXh4GM3NzViwYAHmzJnDthgZhhH8ijEiIoJtPwLfLqLSOQz33/nueNHdvIGBASxevNilY4rY0Nvbi+7ubrslXq6VEmCvguvs7Ay4iwldPK6oqAhIK9eZjdnY2BjUajXq6+thNpvtjKgjIyOhUqnQ3NzMe2/TH/j3v/+Nu+66C6+88gpqamoCfTjiQ5jQgh+pqam8gjPdhavqymazoaWlBVqtFtXV1ZDL5X53/HBcRDUajVCpVFAqlWhsbERUVBRLcM4UlHSlICYmRlQhnNPBHWNhrgoO+HZR198uJoQQnDp1CuPj4351KpkJ1GoqKSnJjuDoZ0in04EQgvnz54ti9YEQgt27d+Mvf/kLPvroI6ey/DAQJrRQAHeJ0maz+fTkrNVqUVdXh6ysLBQXF4MQIgrHj8jISNbtHABrlNve3s76CNIVAYPBwK4UBIsiUKfTob6+HnPnzvVIeem4qOvoYuJJhTsTzGYzFAoF4uPjsXTpUlHMnlyBElxCQgKMRiPkcjnmzp2L0dFRKBQKmM1muxauP2eUNpsNO3fuhEKhwOHDh2dtGjUvhAktdECl+774slHnc6VSybaNxOz44SiB12q1GBkZwddffw2j0Yi0tDRYLBYYjUZRXIFPB18YCzu6mJhMJqhUKjsXE65Nl7t/X2rUGwzJ3RQmkwl1dXVITU1Ffn4+GIZhK1zHpAWLxcKKcHxJcHq9HjfddBOysrLw/vvvi0JdKXqECS004AszYWCynUeVacuXLxdM+OEvMAzDZpdlZGSgoKCAlcDX1dXBarXaKSjFctLwp7GwXC6f1sVkphYuF/39/ejo6OBt1CsG0JWT+fPnO63anc0oqRVVd3c3K8IRkuAGBwexfv16XH311fjFL34h+u+ZKBBerA5+uHILEQIWiwW1tbUoLi5GWlqaT4UfvsLw8DBaW1vtQjjp/GTevHmsgIK2KMXgkh9oY2FnLiaOadWOLiY2mw1tbW3Q6/U+c1fxBYaGhnDq1Cm3BCuOIiVXBOepyrShoQGbN2/GI488gksvvdTt1xRGaCM4vlleQsgKzWq1oqWlBWazGdXV1YiOjg66qBea+zY+Pj6tr6GjgII7X2ppaYFMJrObL/n6tYvNWJjrYkKX4OmMki4bx8TEQKfTIS0tDYsXLw6KzwchhPUW9bYCdkVwKpWKNRPmtiine65PP/0UO3bswF//+lcsXrzY42OalZgli9WzgtCEqtDGx8fZZGN6AheD8MMd6PV6KBQKpKWlYdmyZW4ds+N8yZmJMD15eZLE7ArBYizsGOZJhRPJycnQ6XQ4cuSInQRejEnTVqsVDQ0NkMlkWLZsmc/XTBzXKCjB0TUKmUwGQgheffVV/O1vf8OBAwdEs/cWVAiLQoIffDPRZgIhBJ2dnejr62PbLyMjI+jp6UF6erqgJ29fgoZwLly4EElJSV4/nmP7jVYnp06dmtFjkS+4xsK+OMH6CnQnbtmyZewen7MlZjpfEoOLCW3nZmVl+S1A1LELwG1zHzt2DL/73e/Y9Zt3333XJ2T2y1/+Ev/3f/8HuVyO+fPn47XXXnP6/SgoKEB8fDwiIiIglUqnddAQHWYJoYW09ZXNZoPZbEZHRwdkMplHcRbU5iouLo6d2VitVhgMBgwPD0OlUmFiYsLv/oruwGq1orW1FUajEWVlZX7ZeeKa5FKLpYSEBKSmpvIWBwSjsTDdRTSbzSgrK5t2zshVCKrV6oC6mFCn/JKSErZ6CjS0Wi02b96M+Ph45OTk4N///jcqKyvx9NNPC/o8Bw8exKpVqyCVSnH33XcDAB577LEptysoKEBtbS274hFMYPKrgR0eWF+9FLa+Eh08rdDoLlJJSQl7lWi1WsEwDKKjo5Gbm8sa5NLlU4VCwUqX6ck7kP6HdE8rMzMTJSUlfqsknZnk0pN3V1eXXWvJUUFJVyH6+/uDxlgY+PbiZ86cObzea7G4mPT19aGrq0tU73Vvby/WrVuHLVu24LrrrmPfS18YJXz/+99n///KlSvx7rvvCv4cosAsqNBCmtC4Kke9Xs/7flarFc3NzTAYDKipqWFTb13J8RmGmeKvqNFoMDIyglOnTvlkOZcPent70dXVJeielqfgnrznz59v59BBFZRUPalUKiGXy4PGqQT4tpr0psLxt4sJdSvRarWoqqoSjfrym2++wdatW/Hkk09i1apVdv/m68/Dq6++ip/97GdO/41hGHz/+98HwzDYunUrtmzZ4tNjERSzpOUojk+wj+GOKGRsbAwKhQK5ubkoLS21c/zgu1vmeGKiy7nUfio6Ohqpqak+yzezWCxoamoCANHKxB0dOkwmE3p7e6FQKBAREYHY2Fh0dXUhJSUF8fHxop1R0kSFvr4+wSscvi4mycnJbiedU3f/2NhYLFmyRDTv78cff4xHHnkEf//731FaWirY4/JJpd65cyekUimuueYap4/xn//8B3PnzsXg4CBWr16N0tJSnHvuuYIdo08RJrTQAZ+WI5UqU0Pe2NhYwRw/uMu5dHdpZGSElXYLOX+jThR5eXnIysoSzYlqJgwPD2NgYADV1dWIi4tjFZRdXV0YHx93O6XaH6AekgBQVVXl89ayKxeTvr4+NDc327mYxMfHuyQ4vV6PkydPsp8RMcBms+H555/Hxx9/jIMHDwo+p5oplfqNN97Ahx9+iE8++cTlZ4smT6enp+PHP/4xjh07FlyEFl6sDg3MVKFRdVdCQgKWL1/OCj984fjB3V0Scv5GK4Xe3t6AObd7AtretVqtdm0vRwUlDTmlFwE0w0uIkEpPQD8zmZmZyMnJCQjBeuJiQp3yy8rKkJiY6Pdjdgaz2Yxf/vKX0Ov12L9/v99FVfv378djjz2Gzz//3GWyhE6ng81mQ3x8PHQ6HQ4ePIj77rvPr8cZxswIaULjI9sfGBhAW1sbSktLkZKSYif88Mf8Roj5m9lsRmNjI2Qy2Yxu82LCxMQEFAoFKxN3RQrclOq8vDy7iwAacULVgSkpKT5XB1JSEGr9QSjM5GJC9ybFMFOlGB0dxcaNG/Hd734Xv/nNbwIyM73llltgNBqxevVqAJPCkBdeeAG9vb3YvHkzPv74YwwMDODHP/4xgMl27dq1a3HxxRf7/Vg9xixZrA5p2T4w2ZahV9PchGk6ZzKbzSgvL59R+BEo0LbSyMgIu7zMnb+NjY2hsbERhYWFyMjICPTh8gbNAVu4cKHXlQJNqabyd0IIK0ARUmVKc+IGBwexaNEiUS5GO4PNZmNFTikpKdBoNILtCXqDzs5OrF+/HnfeeSeuvvpq0XznQhFMdjWw1QPZ/gdh2b7o4FihjY6OsnMmejXrrvDDX3A1f2ttbcXo6CiAyf0YMVUK04H6Gup0OsGMhSUSiZ37hMVigVqttqtyk5OTkZqa6rHKlDpoSKVSVFVVBY360mw24+TJk0hJSUFpaSkYhkF+fj6btKBSqdDU1ASDwcAmVfujjXvs2DFs27YNzz//PM4++2yfPlcYCItCQgmU0AghaG9o255QAAAgAElEQVRvx+DgIJYsWYKYmBhRR704gs7fpFIphoeHkZmZifT0dGg0GtHtvzmDwWCAQqFAamqqT3PApFKpXcgpNwKGT8ipI/R6Perq6pCdne3Rcn6gMJ1TPsMwiI+PR3x8PPLz8526mHDFSkK5mBBC8N577+Gpp57Cvn37UFhYKMjjhjEDwoQWGqBEZbPZUFtbi8TERJ8LP3wJOr/humckJyez8ze1Ws3aTwVq/80ZqLFwIJwoHMUTer0eKpWKnS3RkFPaeuN+Fuhxi0lEwQfDw8Noa2vjLRCSSCR2s1y6CK9Wq9HT0yOIi4nNZsOuXbvw3//+F4cOHQqaroIz0MBgev4QPcIqx9BBf38/JiYmUFZWhuTkZL8LP4QA16B32bJlTltCERERU3a7/Ln/5uq429vboVarRWMsHB0dzVZbXAUlnTPR1ptOp8PY2BiqqqoC7rPIF9R3dGRkBJWVlR4fN3cRnkYJcV3y3XUxMRqN2LZtG2JjY/Hhhx/61dZLaNx3332QyWQ455xzcNZZZwXHLDUsCnGKoBOFKBQK6HQ66HQ6fOc73wnKqsxgMKC+vp7NKfPkuLnzN3/5T5pMJtTX1yMuLg7z588PiosHm80GtVqN5uZmNuWca9El5hOxzWZDQ0MDIiIiUFJS4tP3m+tiotFopnUxGRkZwYYNG/DDH/4Qt912W1B8DpzBarVi48aNIITgiiuuwFtvvYWqqiqsW7cOeXl5gT68acFkVAM/80AU8mVYFCIqFBYWQiKR4MiRI9Dr9ZDJZEFFZkNDQ2hra/O6Veer/TdXoGa3wWQsDEy2I9va2lBYWIisrCw79/eOjg4AYE/cSUlJoplTGo1GnDx5EpmZmcjNzfX5883kYsIwDP7xj39g4cKFeO655/DAAw/g8ssv98mx3Hvvvdi3bx8kEgnS09Px+uuvs0vQXLzxxht4+OGHAQC//e1vce2117r1PH19fRgcHGSd+SMiIrBt2zZIJBJs2rSJXXgXJWbJDC3kKzSz2Qyz2cwuHVPPQG8Ub/4AVw1YXl7u85YXd/6mVqs9nr9xjYUXLVokGrNbPqAJzeXl5YiPj3d6G3rippWJTCZjCS5Qn6exsTHU19eLyil/YmICf/zjH/H+++8DAPLz83HxxRfj9ttvF/y5xsbG2L26p556Cg0NDXjhhRfsbqNSqVBdPVltMAyDqqoqHD9+3K2gWK1Wi23btuHcc8/Fxo0bMTAwgBtuuAEFBQVYu3YtVq5cyc7WxAYmvRq40oMK7Vi4QhMV1q9fjwULFmDVqlWoqqpiW0pU8UYtlVJTU126BPgbNIRzzpw5WLBggV+qSSHmbxaLhQ2H9IcVlFCg88nR0dEZVwkc7aeMRqPd+xQVFcW+T/7IyRsYGEBHRwer2hUDqJLxs88+w+HDh5GTkwOlUomTJ0/65Pm4S+I6nc7pe37gwAGsXr2aJfzVq1dj//79+PnPf877eaKjo3H22Wfjn//8J/bv34+Wlhb8+te/Rl1dHV577TWsXLlSlGQGICwKCRU8+eSTOHz4MF577TVs27YNRUVFWLVqFS644AKUlpayc6WWlhbo9XokJiayJ6RAzEsGBgbQ3t4uyMKxN3DXf5JKxPPz80XjD8gHZrMZCoUCcXFxbid4A0BkZCSysrKQlZXFvk/UHV+n0/lseZmSMBWtiMWA2maz4aGHHkJTUxMOHTrEKix9vfKwY8cOvPnmm0hMTMS//vWvKf+uVCrtWrGUZPnCbDZDJpNh7dq1WL16NU6cOIGMjAysXLkSCxcuxDvvvCPI6/AZZokoRBzfAh8iMzMT69atw7p169ih+YEDB3DHHXegv78fK1aswKpVq/C9730PcXFxGB0dxcjICDo7O/3anrRarWhpaYHJZBJs4VgozDR/0+v1sFgsmD9/vrjnCA6gJCyUywr3fcrJyXG6vMy16PK0jWy1WqFQKBATE+PTfT53odfrsXXrVuTl5eG9994TtEKfyS1/586d2LlzJx599FE888wzeOCBB+xu52y0wvd9s1qt7PexubkZS5cuZUUgXV1duPXWW3H++ee7+5L8i/AMzSmCboY2HQwGA/7zn//gwIED+PzzzyGXy3H++edj1apVqKysBCGEtZ0aHR31mexdp9NBoVBg7ty5ATO69QTUWNhsNiMzM5PdWxLT/psr0ErYn0bONpsNY2NjGBkZYROqXYWcugJd8s7JyXEqfAgUBgYGsH79elxzzTW48cYbA/YZ7uzsxGWXXQaFQmH3+927d+Ozzz7Diy++CADYunUrzjvvvBlbjlarlSXmjRs3wmg04q233gIw+b1dvXo1Lr744ikEKjYwqdXARR7M0FqCa4Y2qwmNC0IIBgcHcejQIRw8eBAnTpywa0/m5eXBYDBgZGQEIyMjgrQnCSFsWvB0QgQxYjpj4Zn8JwNJ2IQQO7FNICthrhDHUfqelJQ05UKAhoiKzRS5oaEB119/PR577LGAGPa2trZiwYIFAICnn34an3/++ZTUaZVKhaqqKnz99dcAgMrKShw/fnxaEQ0VeGg0GqxduxY5OTl46aWX7G7T19cXFC12JqUauMADQmsPE1pIgNuePHTokNP2JL3aVqlUIISw7Uk+YYvcEM7S0lLRzED4wB1jYUIIJiYmoFKp/Lb/5gomkwkKhQKJiYke7/P5EmazmX2fRkdHIZPJ2PdpfHwcvb29WLx4sagWeQ8fPoz77rsPb731FioqKgJyDFdccQWam5shkUiQn5+PF154AdnZ2aitrcULL7yAl19+GcBkGvUjjzwCYHLmdt1117GP4crxo66uDlu3bsUll1yCe++9F4B91RYsTiFMcjVwvgeE1hUmtJAE3/YkvdqmajeqnuR+6LkhnGJqG80E7ipBRUWFx1Upnb+pVCq/+U+OjY2hoaHBqa+hWEE7Ap2dnTAajWz15i8F5XQghOCVV17B3r178e677wZV0oMjuFJ7vV6PyMhISCQStLS0YO3atbj55ptZ8hOrLH8mMEnVwLkeEFpvmNBCHjO1J/Pz86e4clAxgF6vx9DQEMrLyxEbGxvol8IbXGPhgoICwU6mQu2/TQfa1q2oqAiq99xsNqOurg5JSUkoKChgFZT0MxWokFOLxYIdO3ZgcHAQr7/+elDtGk6Hhx9+mF292LJlC5YtW4auri4UFRUBCJ5qzBmYxGrgbA8IbTBMaLMOfNqTXV1daG9vh0Qisave+LQnAw3qc+iPxV0h5282mw2tra0wGo0oKysLqrauTqdDXV0d5s2b51Q5Sghh3fFVKhVMJpOdebCvFvG1Wi2uv/56LFmyBA8++KDoP7szwWKxQCqVYseOHTh9+jSefvppXHnllZg7dy7efvtt1tg82F+nDwltBIANwCAhZErPmWGYawDcfeZHLYCbCCHfuH0gPBEmNB/AsT1pNBqhVqtx++23Y9OmTSCEsHldo6OjiIyMdNmeDCQIIejo6MDIyAgqKir8PrvxZv5mNBpRV1eHtLQ05Ofni+Y95QOad+eOUIi649P3ymazsS1KoVq5SqUS11xzDW6++WZs2LAhqN5TLoaGhvD3v/8dN998M/u7J554Atdddx0efPBBdHR04J133oFcLg96IqNgEqqBFR4QmnpGQmsGsBbAmy4I7TsAGgkhaoZhLgFwPyFkhdsHwhNhQvMhbDYbHn/8cfzjH//Az372M5w4ccJle5JWJfSkTauSQLm8i9FYmO/8jfpIFhcXIzU1NcBHzR/UNmxwcBCLFy/26m9PQ07pTJcbgupJV+DEiRO46aab8NRTT+G8887z+LgCjZaWFlx66aXQaDT4xS9+gQcffBAA8JOf/ARffPEFbrnlFtx///0AgJdeegmZmZlYs2ZNAI9YGDAJ1UC1B4Q2NnPLEcCVAD50RmgOt00GoCCE+GzDPnh6MEGK1NRU/L//9/9YAQWf5W6qnuzu7gYhhE1bdibl9gUoIYhtUZphGLvMLmf5b1KpFBMTE1i6dKlorKD4wGazobGxEQzDoLKy0uu/s6uQ076+PjQ1NSEyMpIluPj4+GmrrQ8//BC///3v8e6776K4uNir4wo0rFYrNm/ejGuuuQZXXnklsrOzsXXrVjz55JM455xzsGDBAhBCsGvXLrz99tvYu3dvoA9ZGHi4WD00NITq6m9HZFu2bMGWLVs8PYrrAfzT0zvzQbhCCzBmUk8CsFNP0vakL5RuhBD09PSgr68PFRUVQUcI9fX1MBgMiI6Oxvj4uKj236aDyWTCyZMnkZ6ejtzcXL8cJ1dgotVqWU9T7ntls9nwzDPP4ODBg9i7d29QVbvTgZoZf/rpp7jtttvw2GOP4ZJLLsGXX36J2267DQsXLsTg4CDeeOMNUV3QeQMmrhpY7EGFZhKmQmMY5nwAzwE4hxAy4vaB8ESY0EQEPupJ7nK3kO1JaiwslUpRUlISNMbCwORFQV1dHTIyMlhCENP+23SgKxwLFiwIGGFwQ05VKhU+/fRTHDlyBFarFfHx8fjrX//qs/eLb/RLREQEFi1aBADIy8vDBx98IMjzv/XWW3j88cdx+PBhpKen44svvkBNTU3IKDcpmNhqoMwDQiPeExrDMIsBvA/gEkJIi9sH4QbChCZizKSejI+Pt1vupkIAd9uT1NMw2PbiAECtVqOpqQmlpaXTRoFw528jIyOs7ZSv99+mA11QX7RokajWCYaGhrB161ZIJBJ22fu2227D+vXrBX8uPtEvABAXFwetViv48wOTs7Inn3wSZrMZv/nNb+wWrkMFTEw1UOoBoUm8IzSGYfIAfApgAyHkS7cPwE2EZ2gihkQiQUVFBSoqKnDnnXfatSf/9Kc/OW1P0pDFlpYWyOVyVj3pqj3Z19eHzs5Ov3oaCgEqoBgYGMCyZctmVGDymb/5y3+SEIL29nZoNBrRGVF3dHRg/fr1uPvuu/HTn/4UDMPAYDBgdHTUJ8/HJ/rF1ygsLER7ezsef/zxkCQzFp6YE8/c9CkE8F8AaQzD9AD4HQAZABBCXgBwH4BUAM+d+dtafLm3Fq7QghTutCdVKhV0Oh3i4+NZgouIiEBzczMsFkvQ7WhZrVY0NjZCIpGgtLRUEPLxl/+k1WpFfX09IiMjUVxcLKq53pEjR3DbbbfhpZdewsqVK/32vI7RL86cXKRSKZYuXQqpVIp77rlHsPRrk8mEq666Ctdffz1++MMfCvKYYgQTXQ0UelChxYQXq8MIAPi2J1UqFYaGhqDVapGYmIiCggIkJSUFzcyMus3TZAJfwFfzN4PBgJMnT/o8G8xdEEKwd+9ePPvss9i7dy8KCgoEffyZol8oHn30URgMBqfO9b29vZg7dy5Onz6NVatW4ZNPPsH8+fMFOT6adRbKCBOac4QJLUjgSj0pk8nw1Vdf4bnnnoPVasXIyAg0Gg2v9mSgQYNY/e02L8T8TaxO+XRX8quvvsKePXsCGirrKvrFERs3bsQPfvADXHnllX46suAHE1UN5HpAaIlhQgtDZCCEoLe3FzfeeCMUCgWSk5ORn59v1540Go2sepLbnhSDIpAQgs7OTgwPD2PRokUBPx53/Sd7e3vR09ODRYsWiUo9ZzAYcOuttyIxMRF//vOfA1Kl8Il+UavViImJQWRkJIaHh3HWWWdh3759KCsr8/vxBiuYyGog2wNCSwkuQguewUkYHoNhGOzevZs9EQCYdrk7Pj4e4+PjGBkZgUKhgMVisVNP+rM9SWdOcrlckIVjIRAREYG0tDSkpaUB+Hb+plQq0djYyM7fkpOToVQqYTAYUFVVJaq27vDwMDZs2IDLL78c27dvD1hFfs8990yJfgFgF/3S2NjIqi5tNhvuueeeMJm5i3BitVOEK7QgxXRO4TMtdzMMw86T1Go15HI5m/0WFxfns5PhxMQE6urqkJubGzTrBHT+NjQ0hM7OTgBgW7liqHYBoLm5GZs2bcIDDzwQErZOYcwMRl4NpHlQoc0NrgotTGhh2IGPepK2J6nLBI0xSU1NFeyEPTw8jLa2NpSVldlJu4MBExMTOHnyJAoLC5Geni6q/bcvvvgCd999N15//XUsW7bMr88dRuDAyKqBJA8ILT9MaGGEEPioJ2l7khoGe9OepDtaarUaixYtCpg5s6egwpXy8nKnROyP/DdnIITgr3/9K958803s3btXVCrLMHwPRloNJHhAaPPChBZGCINPe5JG42g0GkilUrblNlN70mKxQKFQICYmBkVFRaKYl/EF9cEcGBhwS7jij/03q9WKBx98EG1tbXjrrbdE5UoShn/ARFQDsR4QWnGY0MKYJeDbnqQnbK1Wi7i4OPaEzXX3oPZbBQUFyMzMDOCrch82mw1NTU0ghGDhwoUeE7Ev9t8mJiawdetWFBYW4rHHHhOVMCUM/4GRVANRHhBaWZjQQh779+/H9u3b2SiKe+65x+7f//SnP+Hll19mIzxeffVV5OfnB+ho/Qdn7cmVK1fi/PPPZ9uTWq2WXQ+geWYREREYGhpCRUUF70BLscBkMrFBonl5eYKnH3gzf+vv78e6detw7bXXYsuWLaLcLQzDPwgTmnPMekKzWq0oLi7GoUOHkJOTg5qaGuzevdtORvyvf/0LK1asQExMDJ5//nl89tln+Nvf/hbAow4MZmpPWq1WHDp0CElJSZBKpZDJZKy4ZKaMLjGAVpVFRUWshN+XcGf+plAocMMNN+APf/gDvv/97/v82MIQNximGpB6QGhLwoQW0vjvf/+L+++/HwcOHAAwadcDAL/+9a+d3v7EiRO45ZZb8J///MdvxyhGOLYna2trYTKZsGTJEjz44IMoKCiAyWRixSXj4+Mu25NigBic8h3nb4ODg2hsbERGRgb+8pe/4O2330Z5ebnPj2PXrl345S9/iaGhIafE/sYbb+Dhhx8GAPz2t7/Ftdde6/NjClacca/3AaqqwLhPaCDMBIDGaW5RQggRTVslvFjtJpRKJXJzc9mfc3JycPToUZe3f+WVV3DJJZf449BEDYZhkJGRgXXr1mHx4sXYuHEjbrzxRpjNZtx5553TticbGhpgMpmQnJzMViSBmgURQtDR0QG1Wh1wp3y5XI7MzExkZmaybiofffQR3njjDcTFxeGPf/wjrrzySlx66aU+O4bu7m4cOnQIeXl5Tv9dpVLhgQceQG1tLRiGQVVVFdasWTNt1M9shq+qHYapJh6WI43THRPDeMKSvkOY0NyEs4rWVWvsrbfeQm1tLT7//HNfH1ZQYWxsDO+88w5KS0sBgFc0DsMw0Gg0GBkZwenTpyGVSv3enrRarWhoaIBMJsPSpUtFpcK0Wq149tlnYbVa0dzcDJlMhuPHj0OtVvv0eW+//XY8/vjjdibDXBw4cACrV69GSkoKAGD16tXYv38/fv7zn8/42B0dHfjBD34wo7djGGFQhAnNTeTk5KC7u5v9uaenx6mLxeHDh7Fz5058/vnnonCHEBPOOeecKb+LiorCBRdcgAsuuMCuPfnqq6/i1ltvtVNPLl++nG23dXV1YXx8HLGxsex6gC/akzQVOysry2cu/55ifHwcmzZtQlVVFZ5++mmWaJcvX+7T5/3ggw+QnZ2NJUuWuLyNs46GUqn06XGFMXsRJjQ3UVNTg9bWVrS3tyM7Oxt79uzBO++8Y3ebEydOYOvWrdi/fz/S09MDdKTBC257ct26dXbqSeo96aw9qVKp2PYkVw3obdbb6OgoGhoaZkzFDgR6enpwzTXXYNu2bVi3bp3glep00S+PPPIIDh48OO393eloOIPFYsG1116LEydOoLi4GG+++SZiYmJ43z8Mn+OlQB8AF2FRiAf4+OOPcdttt8FqtWLTpk3YsWMH7rvvPlRXV2PNmjW48MIL2at5AMjLy8MHH3wQ4KMOHfBZ7tZoNOw+V0REBCsuSUhIcOuE2tfXh66uLixevFhUTvkAcPz4cdx88814+umn8b3vfc+vz11XV4cLLriAJRfaqTh27JjdHuHu3bvx2Wef4cUXXwQAbN26Feeddx7vlmNhYSH+/e9/4+yzz8amTZtQVlaGu+66yzcvShzwSe+cYaoIcMSDe8pFpWKcCWFCCyOowWe522w2s7tv3PZkSkqKS5IihKCtrQ0TExMoLy8XVaI3IQQffPABdu3ahT179rDxK4FEQUEBamtrp6gcVSoVqqqq8PXXXwMAKisrcfz4cXamNh06Ojpw7rnnoqurCwDw6aef4qmnnsI//vEP4V+AeOAjQqskgCdK65igIjTxTLXDcBv79+9HSUkJioqK8Pvf/97l7d59910wDDPtPkmwgtuefPPNN/HNN9/goYcegslkwh133IGzzz4b99xzD2pra5GXl4eVK1di3rx5sFgsaGxsxJEjR9DU1IShoSFYLJP5GhaLBd988w0YhsHixYtFRWY2mw1//vOf8corr+DQoUOiIDNH1NbWYvPmzQCAlJQU3HvvvaipqUFNTQ3uu+8+XmRG4VhNi303MYzAIlyhBSn4LHgDk4KByy67DCaTCc888wyqq4PmYksQzNSelEgkrHpSrVaDEAKj0YicnBwUFhaK6gRKSRoAXnjhhaAzbnYXtOX45Zdf4qyzzsINN9yA0tJS3HnnnYE+NF/CRxXaMgJ4orZOPA7gJIAfABgkhFRMfWyGAfBnAJcCmACwkRDytTfH6ynCFVqQ4tixYygqKsK8efMgl8tx9dVXs+GdXNx777341a9+JbrFZH+Bqicff/xxHDlyBO+++y6Ki4vx6quv4uyzz8b69evx/vvvQyaTYXBwEMePH0dubi70ej2OHDmCb775Bj09PdDr9QF9HWq1GldccQVKSkrw8ssvhzyZUSxcuBBvvPEGFi9eDJVKhZtuuinQhxSkoAmf7v4HAHgdwMXTPPglABac+W8LgOeFPnq+EE8vJQy3wGfB+8SJE+ju7sYPfvAD7Nq1y9+HKDpMp5786U9/itHRUVx88cXIzc1l1ZM6nQ4qlQpNTU0wGo1ISkpil7v91Ypsb2/Hhg0b8Otf/xpXXHGFqKpGX6KgoAANDQ2BPowQAQFg9uyehHzBMEzBNDf5EYA3yWS77wjDMEkMw2QRQvo8ekIvECa0IMVMcmibzYbbb78dr7/+uh+PKrggkUhQUVGBN998E1VVVXjqqadw/Phxl8vd3PZkR0cHGIaxU0/6YtH6yy+/xB133IGXX37Z53tlYYQyPCc0HsgG0M35uefM78KEFgY/zLTgPT4+DoVCgfPOOw/ApPP6mjVr8MEHH8y6OdpMuOGGG1BUVASGYXgvd9fU1MBsNkOlUkGpVKKxsRExMTHscre3En9CCP72t7/hxRdfxIcffujSWiqMMPjDMvNNPIOzlkFA9BZhUUiQwmKxoLi4GJ988gmys7NRU1ODd955x6UZ7XnnnYddu3aFycxD8InGmZiYYNcDjEYjEhMT2eVudzwfbTYbfv/73+PEiRPYs2dP0EXqhOEVfCQKWUSA9zy4Z/FxQkj1mZbjhy5EIS8C+IwQsvvMz80Azgu3HMPgDalUimeeeQYXXXQRu+BdXl5ut+AdhnCg7cmKigpe3pO0PalSqdj2JPWenK49aTAY8Itf/AJz5szBvn37RLUyEEYwg4pCfIIPANzCMMweACsAjAaCzIBwhRZGGF6D73K3Wq3GyMgIRkdHERMTwxIcddsYGhrC+vXrceWVV+LWW2/1ufhjptiXiIgILFq0CEDY7caP8FGFVk6A3R7cc8lxAK0AzgPw/7d3P6FN3nEcx9+PtRaCB4t6WAWZOhFTm7RaoaJC24OWHtqOYVFBcCoO7S5Ske4QFqQiKO7QeBh2Fj1FPMpoM6lQD8HCDIo6DQbsaCNzElPqnxG09dlBG1prNRrz5M/zeUGhefIEvrcvv9/v+/t+FwH/Aj8DxQCmaf76tmz/NG8qIf8DvjdNMyuXXpXQRL6wT9mejMfjHDt2DMMwCIfDdHZ2sn379ozHODIywr59+wiHw4RCofcmtPnz5/P8+fOMxyLT5FxCU6cQsZ1UupZcvHgRp9NJeXk5O3futDhC60xuT7a3txMIBBgcHGTbtm2EQiGam5tpaGjA5/Px6NEj1qxZw/79+4nH49TX1+Pz+di0aRO3bt3KaIyTY1/scgVA0rqHlje0QS9pm5iYoK2tbVrXkqampmldSyKRCMePHycYDFJaWsrjx4+zGLG1PjQaZ+/evcyZM4erV68mq1Tj8XhGRw6lMvYF3pznVVdXM3fuXDo6OmhpaclYTJJpGS3bzxlKaJK2qV1LgGTXkqkJrbu7m7a2tuT4FbuO1Xn3cverV6949uzZtP6Gn9LrcDbpjn0BGB4epqysjAcPHlBfX09FRQUrVqxIOzbJhowWheQMJTRJWypdS+7fvw/Axo0bmZiYwOv10tDwoW469lBcXPxFEti7+vv73/v89u3bDA0NJVdn0WiUtWvXzhj7AiRXjMuXL6e2tpYbN24ooeUtrdBEUpLKEMfx8XEikQgDAwNEo1E2b97MnTt3WLBggVVhClBRUTFtu3e2sS+jo6M4HA5KSkqIxWIEg0GOHDlidbjyxdhjhaaiEEnbx7qWTL7T3NxMcXExy5YtY9WqVUQiEatDlQ+YOvbl3r17VFdX43a7qauro6OjY8YkB8knkyu0T/3LLyrbl7Sl0rUkEAjg9/s5f/48sViMqqoqbt68ycKFC7MYuUjOyVDZ/koTfvmMXzblVdm+thwlbal0Ldm6dSuXL1/G6XRSVFTEyZMnlcxELGOPMzSt0EREckeGVmjfmHDiM375XV6t0HSGZhMtLS2sW7eO8vJyzpw5k+1wRMRS9jhDU0KziZ6eHkKhENevX6erq4snT55kO6SM+ljnkuHhYerq6qiqqsLlctHb25uFKEWsYo9OIUpoNtHV1YXb7aampoaRkZGCrjCc7FzS19fH3bt38fv9MyYfd3Z20tramhzRcvDgwSxFK2IFe6zQVBRiAwMDA/T393uuFTUAAAJcSURBVHPt2jUcDge1tbUkEolsh5UxqXQuMQyDp0+fAjA2NjbjmoFIYbHHPTQlNBsYGxujtLQUh8NBOBxmcHAw2yFlVCqdS7xeL1u2bMHn8/HixYtZO2u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\n", 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\n", 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HslUI+UgKx70fjsokGnzmCotRCOssEqSUhMNhgsEgkUgEIQRGoxGj0UheXh4GgwGDwaALOR2dDJPJBp+5xHK4Rp0FRkpJNBqlra2N4uJi8vPzNX+OoihEo1GCwaAmyFQBpwo7IYQu5HR00kim2sfkGrpA00kriqIQDodRFAWfz4fdbmd4eJhIJEJxcTEWiyVufynlRDPGQEB7TwiByWTCaDRiMpk0TU4nNfRyWTq6hqajMweklEQiEc20KIQgEonQ1NREfn4+ZrOZ3t5eQqEQNpuNoqIiioqKKCwsjMvdih0rFAppmprBYNCEXKwmp6OjMzu6hqajkwSqeTESiSClRAiBoii0tbUxNDREbW0t1dXVRCIRDAYDUkr8fj8ul4uhoSFOnTqFoijk5+drQq6goCBOK1MDl2JrNMJ7Qk4VdLo/TkcnMbqGpqMzC7HmRSEEBoOBoaEhmpubqaysZM2aNXEFeGHCnGi327Hb7VqVDkVR8Hq9uFwuenp68Hg8GAwGCgsLNSFns9ni8qrUUjdqDp/uj9PRmR5dQ9PRmYZY8yJMCKlgMEhDQwNCCPbs2YPVaqWlpYVk0kJU4VVYWKhVE4lEIrjdblwuF62trfj9fvLy8jQBV1RUlLQ/TvXF6f44HZ2ljS7QdJImkXlRSkl7ezt9fX1s3ryZ0tJSbX91eyqYTCZWrFgR12ctGAxqQq6npydpf1w0GiUcDtPY2EhdXR0Gg0ETcro/Tmc5oGtoOjoxJDIvjo2N0djYSFlZGQcOHIgzCcJ7Ai1dwsJisWCxWDShOZ0/rqCgQBNy+fn5mlbm9/s17Uwt16Si++N0ljrLYbFfDteoMw8SmRfD4TAnT54kFAqxa9cu7HZ7wmPno6Elw3T+OI/Hg8vloqurC6/Xq5k0w+Ewfr9/Rn9cbOCJ7o/TWSoIIC+V1T6S7plkFl2g6SQkkXkRoLu7m87OTjZs2MCqVatmXOAzLdASYTAYNO1MRfXHDQ4Oav44s9kc548zm81x4+j+OJ2lhBBg0gWaznIkkXnR5XLR0NCAw+HgwIEDU3xViciGQEuE6o+zWCycccYZwIQ/zuVy4XQ66erqIhwOz8kfpwpyNQl8KfnjAoHAnHrv6eQ+QkCecfb9Fju6QNPRkFLi8XiIRqNYLBYtObq5uRmPx8O2bdvimnbORq4ItERYLBbKysooKysD5u6PU4+BpeOPG+/v543vfIexlhbMhYWc9Y1vsGb//mxPSycNpKyhLTKWwSXqzIZqXguHw/T19SGEoLq6mr6+Ptra2qitraWuri6lRTlXBdpk5uKPUzW42fLjJvvjJpsqc03IvfGd7zB26hQlmzfjHxnhf+64gwt+8AMctbXZnprOPEnZh7bIWAaXqDMTakX8aDSqmRcDgQBHjhzBbrezf/9+8vJSC/g1GAwoipLmGS8c0/njXC6XpsnNxR8XDAYJBoMMDg5qFVEmmyqzJeQCgQAjTU2Ubt0KgG3lSryDg4y1tuoCbSkgAN3kqLNUSRS9qCgKg4ODeDwedu3ahcPhmPd5FrNAS4TJZKKkpISSkhLtvUT+OLvdHuePi9XinE4ndrtdC7pRyaY/zmq1YrbZCDidWIuLCYdCCCYEm47OYkEXaMuQyUEfQggGBwdpbm6msLCQ6urqtAizXDOpZYpE/jifz4fL5WJgYECrmKL641RTZGyEpGqana4o80L443Z/9asc/t738Pb3o0QiVH/4w1SceWZGzqWTmH379mXGRp9iMUe73b53pjkdPXp0WEpZNo+ZpRVdoC0jEpkXfT4fjY2NmEwm9u3bx9jYGF6vNy3ny+WgkEwihCA/P5/8/HxWr14NxPvjvF4vDQ0NU0p5Wa3WKUEnC+mPW3fuuazYsIGx1lbMhYV6QEgWOHLkSEbG3WcRKa32dXV1M85JCNExj2mlHV2gLQNiw83hPUFz6tQpBgYG2LJlCytPm5bSKYTSXSlkMRPrj3O5XKxduxaLxaKV8hoYGCAQCGCxWOKKMs/mj1NJZKpM5b47amt1n9lSZRms9svgEpc3icyLIyMjnDx5koqKCg4ePBhn+sqEQFs2dLRh+NlXEUNtyOJylC98Dzbum7KbKuTz8vLm7Y9Tx8slf5xODqIHhegsZhI13AyFQjQ2NhKNRtm9ezc2m23KcemMTFxuAs3w4GcwjXRBnhX6TqJ8/zIit70OK0vj9ptJa03WHxerxdnt9ln9cVLKOFPlYsqP00kDy6Qh2jK4xOXFdCWrOjs76e7uZtOmTZSXl097vK6hpUjrSYwjPVBUDobTVUTGhxBHnkZeeF3crnO5J4n8cdFoVPPHdXR04PV6MRqNs/rjgCn+uIDLBSMjmAFLeTmB3l4UtxtrRQWF27bN547o5BK6QNNZbCQqWTU+Pk5DQwMrV67k4MGDU8xVk9E1tBQxGAAJ4RBYLBP/lwrSMlULhvlFgBqNRoqLiykuLtbeC4fDcf44v9+P1WqNM1Wazea4zz8aDHLqpZdw2O3kr1hB249+RKCvD3NJCUZFofSzn6Xqoov0osxLBd3kqLMYmK4ifnNzMz6fjzPOOIOCgoKkxtI1tBSpWoGyrwzjaCcETchBgVKyBt7/hSm7ZiJQZrI/Tkqp+ePGxsbo6OggEonE+eOsoRAyEMCydi3S4yHY0YG1shJrTQ0hv5+BZ57BetZZWCwWzUypF2VepKSqoS2yn68u0BYxqnmxs7OT1atXa4tkT08PHR0drF+/nm3bts1p8dQFWgqEw4jQv8LnLiP6yiuIkQ6U7aUoH3wmYQG9hYj8FEJgtVqxWq2aiVlKidfr1bQ4Z1cXwfFxZF8fNqeTSDiMBcBgwJyfTxQw+v0Iq3Xa/Dg14EQPOtHJBXSBtkiJNS+2t7ezZs0a3G439fX1FBUVpVyyKt0mx3A4zFtvvYXP59NMYMXFxRQVFSVVsX9xMIyUY2BZBx/7EyQgI61gSWzjyVYqgxCCgoICTVtXNmzg+NAQhUIQMJvxhcN4u7shGsXi8WBfv56ozUYeTOuPi0UVbHrQSQ6SqoYWnn2XXGKprCjLhkTmRYDGxkacTid1dXVxtQfnSrq0KkVR6O/vZ2hoiJ07d5Kfn084HMbpdDIyMkJbW9u01ewXH3YMAJHIhEYW8SMwIEnc+DRXtFaDyYRhyxYc+flYDQaqzjiDsd/8hsDAALK2FvGhD9Hc3Kzlx8UGnaiNT1XU/LhoNEowGNS+l3qT1BwiFR+aLtB0MkFsRfzYJ/yBgQG8Xi/V1dVs2bJl3otFOjQ0p9NJfX09hYWFrFq1ipKSEsLhsGYCW7VqFRBfPaOzsxOv14vJZIrT4iwWy7zmsiDkFaOEP4hQXkFEBBKJNHwE8oqnPSRnFnWTCXtNjdb/rOh0v7hYpvPH5efnawKuoKAgYX6c3iQ1R9CjHHVyhUTRi2r5JLWyRGVlZVoWyfloaGrvNLfbzRlnnEE0GqWrq2va/WOrZ1RVVQET0XpqYnFPTw+hUAibzaYJuESJxTmB/VxkeB2SccABeeun3TWXqqcoijKrQJnNH9fX14fH45mSH5dI407UJNVgMMSZKnV/XAbQBZpOtpmuIn5bWxtDQ0Ns3bqVFStW8MYbb2Q91F5N+q2pqWHr1q0IIXA6nXMeJy8vj5UrV2qluBIlFgPawllcXIzNZsuNBXAGIRZLLgm0VOcS64+rrKwEEufHqRq3+rJYLDM2SW1qamLz5s2LtklqzqILNJ1skci8KIRgaGiI5uZmKisrOXDggLYopDOQY65jBQIB6uvrMZlMnHXWWXG1Bw0Gw7z9RdMlFqs5V62trXE9yVRNLtUebgtBrvjQIDkNLVmmy49T+8f19fURDAaxWq1xmlysP87tdmvfm0RFmXV/3DzIQcNGutEFWo4xXcPNhoYGhBDs2bNH83eoZCMZWkpJR0cHvb29bN68mdLS0mn3SzdGoxGHwxHX4iYYDOJ0OuN8PGrASXFxMfn5+Wmfx3zIlYVYNWNnikQadyAQwO12J/THKYqCoiiz+uOklHGmSt0fNwu6hqazkExXEb+9vZ2+vj42bdqk1febzEJraC6Xi/r6ekpKSjhw4MC0Pq2FrLZvsVgoLy/XfDyKomg+nq6uLrxeLz6fj+bm5riAk2wIllwyOQILKgSEENhsNmw2W9xnpZqVw+Ewb775JkIICgsLNU1O98fNE12g6SwUiSrij42N0djYSFlZ2YxCAxZOQ4tEIrS0tOB0Otm+fTuFhYUpj5UOQj4f7q4uoqEQ9vJyCk5HT8LEPVEXxDVr1gBw+PBhSkpK4sxfNpstzsezUAEnObnIRv0Q7QQZBVMFGEtmPyYNGAwGCgoKsNvt9Pb2sm/fvjizcnt7Oz6fb87+uNjxdX/c8kAXaFlkckV8g8FAKBTi5MmThEIhdu3ahd2eOJcploUQaGpH67Vr1yadHpBJgRYJBBh46y0QAoPRyPC77yIVhcLTfrbp5jPZ/OX3+3G5XAwNDdHa2hoXqVdcXIzdbl8ei1/UjyH0GwQewIQMHkexnAvGhWtGHOvPS2RWDoVCmpDr6+sjEAjEPZAUFhYmzI9byCapOYvePkYnUyQyLwJ0dXXR2dnJhg0bWLVqVdI/sHQLtFgCgQCNjY0A7Nu3b055YZkUaAGnk2goRP5pM6zBaMTd1TWjQEs0P7vdjt1up6KiAngvUs/pdNLW1obP5yMvL08zUyZqurkkiHYi8INxIn1CREch3LigAi0ajc6oIZvN5oT+OJfLpSXrR6PRpPPj1CapXq+X66+/nmeeeSaj15dVdJOjTiZQFIWhoSGKioo086LL5aKhoQGHw8GBAwfmXBIqnQJNRUpJV1cXXV1dbN68eVr/3UzECrR0+40MBgOxo0kpIQ3jJ4rUi2262dnZGRfEUFxcTEFBweIPRpAh4h/hTUBwmp0zw1wjLmP9cbHJ+qrvtLe3F4/Ho/njpusfp/rvljzLYLVfBpeYG8SaF0+cOME555yjJSJ7PB62bds2q09qOtIt0KLRKIcPH05ZwKrECrR0m3PMxcUYbTb8Y2MgBEooRPnOnWk9h0qippter1dL/vZ4PHFJ4mo/ssVgwtI0aNMaZPA4IjoOmIjKcTBuX9C5zKahJUMi32msP07VulV/3OjoKIFAIOluFLNxzz338M1vfpOhoSFKS0uRUnL99dfzwgsvYLfbefTRR9mzZ09azjUnMmdyrBVCDAKDUsodU04rRDHwr8BaJuTNPVLKn2ZkJugCLeNM13Czr6+PtrY2amtrqaurm9fily6BFo1GaWlpIRAIsGvXrnnVhITMmhxNZjMVe/bg7esjGo1iLynBGuNvySSxScXqohmJRLR8q4GBAQKBQFw/spwvxmwsmfCZhesBBYzvA3NyieLpIp05cbFM549zuVy89NJLPP3007S1tXHJJZewf/9+LrnkErZu3Trn83R1dfHSSy+xdu1a7b0XX3yR5uZmmpubOXToENdddx2HDh1Ky3XNicyZHIeBK4DHptn+NaBeSvlJIUQZcFII8TMpZWia/edFDv/CFj+JSlZ5PB78fj+jo6NTEpFTJR0CbXh4mJMnT1JVVaX1zJovmY5yNJnNFNfUZGz8uWAymab0I1P9O8PDw3HFmEOhEB6Ph/z8/KxrcXHnN1ZMvLJEOjS0ZDGbzZSWlnLddddxxhln8Pzzz/PNb36Tw4cP4/V6UxrzG9/4Bt/97nf51Kc+pb333HPPceWVVyKE4ODBg4yPj9PX16cVCVgwMifQPMDoDNslUCgmvmgFp/eNZGQm6AItI0xXsqq1tZXR0VHsdjtbt25N2xO7On4qBINBGhsbURSFvXv3YrVa6enpSdu8UhFoEgWfcJMnrZhZBMWJEzCdf8ftdjM6OqqVhsrLy9M0uOLi4gUNOMmliiWQOQ1tNrxeL4WFhaxfv57161PTSp9//nnWrFnDrl274t7v6emhurpa+7uqqoqenp6FF2iQ0mo/NDTEvn37tL+vvfZarr322rkMcR/wPNALFAKfk1Km1+Efgy7Q0kgi86IQQgt5r6qq4sCBAxw9epRoNJo2gZaKhialpLu7m87OTjZt2qQluaaTVATauBjmZdvj+IQbAewOfpQdkfenfW7ZwGAwaEJr+/YJ/5Rq+nI6nXR3dxMOh7FvKJTWAAAgAElEQVTb7VpUZaIovXSRLQEyHdFoNCvz8Xg8SfmvzzvvPPr7+6e8f+edd3LXXXfxm9/8Zsq2RN//rGjlKfrQysrKOHLkyHzOfCFwDPgosAF4SQjxeyllRqJwdIGWJhKZF30+H42NjZhMpriQd6PRmNYgDoPBoGmDyeDxeLT2LvMJ+piNVATaq9Yn8QoXZixEifKW5beUK2spV9bOfvAiRDV9qaXD1GLMTqeTvr4+3G43Qog4X1y6ijHnWsWSRCWvFgKv15tUUMjLL7+c8P133nmHtrY2TTvr7u5mz549HD58mKqqqriOE93d3Vox52XCnwLfkRMLQYsQog3YChzOxMl0gTZPEpkXpZScOnWKgYEBtmzZouXNqBgMBqLRaNrmkGwR4Gg0yqlTpxgZGaGuri4uND0XkCg4DcPknTYzGjESJsqwoWfJCrTJxBZjVhe+SCSiRem1tLTg9/vT0v1b19Am8Hg8U36jc+GMM85gcHBQ+7u2tpYjR45QWlrKxRdfzH333cdll13GoUOHKC4uzo65MXt5aJ3AucDvhRCrgC3AqUydTBdoKTJdRfyRkRFOnjxJRUUFBw8eTPgDzYSGNtt46rwqKyvZv3//rAtHOp7e56qhCQxYZT4hEcBEHgoTx+bLhYlezFVMJhMrVqxgxYoVQHzDzdju3/n5+ZqAS6b7d64JNEVRshIJ6vF4qK2tzcjYH//4x3nhhRfYuHEjdrudn/40YxHrM5M5gbYOeA0oFUJ0A7cBeQBSygeBO4BHhRDvnJ7FzVLK4YzMBF2gpYRaSifWvKgGV0SjUXbv3o3NZpv2+ExoaNMJtFAoRGNjI5FIZNZ5qaSrqHAqx78/8GlesT1FlAgSherIFtZG5x5CvZRJ1HAztvt3V1cXHo9n1u7fuWZyjEajWelQnqzJMVna29u1/wshuP/++9M29rzIjDW3TUq5b7qNUspe4IKMnDkBukCbA4nMiwAdHR10d3cnHVyR7kToRONJKent7aW9vT3lUlrZeHpfo2ziYt9XGTH0YJMFrFJqEOTOopurxCZ2q8zW/TvX2q1kM8oxnQItJ9FLX+nEoigKoVAozrw4Pj5OQ0MDK1eu5ODBg0k7tI1GY0Y1NK/XS319Pfn5+ezfv3/OzS7TmT+m9k3r6uqKKxdVWFg47eJVJFdQFF2RlvMvZ2br/j0+Pq4Vw86F7t/ZEmjJRjkuanSBpgOJG26GQiGam5vx+XycccYZc366y5SGpigKp06dYmhoiLq6urjKCHMhXQLN7Xbj8/kIhULs3btXa8Kp1thTtQq1dmI2zE3Licndv10uF93d3axatSonun8vZGJ1LGoe2pJGF2jLGzWnrKOjA7vdrjnku7u76ejoYP369Wzbti2lp9lMBIX4/X5ef/11Vq9ezYEDB+b1pDtfgasK1uHhYaxWK5s2bSIUCiWM3nM6nVoh2VAopOVgLZmiv5PIpWRmNQhjLt2/1dy4THwu2YxyXPImR9DbxyxXYnPKQqEQRqMRj8dDQ0MDhYWFKZnxYklnUEgoFKK1tRW3282BAweS6p82G/PR0FQzbEVFBfv37+f111+fdl+TyZTQJDa56K+qKSwFLS6XBNp0QSEzdf/u7u7G6/XGadfp6v6drTw0j8eTllJvOY2uoS0/EjXcFELQ09NDNBqlrq4uLV98o9EY11E31bmqBY7XrFmDlDItwgxS09BiOwfs3LmT/Pz8OZ83UQ6WGtjQ+J//Sesjj6CEw1R++tPUXXXVvLQ4RVHwdnXhHxzEYLFQVFuLeYEWtVyJLEzWZ5Wogr36uaSz+3e2fGhqIWmdxY8u0Ji+4WZ/fz8dHR04HA727t2btoVoviY9n89HfX09NpuN/fv3E41GGRkZScvcYO4amlrYeO3atWzdujXhfUr13uXl5eE5coT6224DRQEhaL/vPqwWC2Wf/GScFjddeHoivF1duFtbMRUVEfH5GDl2jLJ9+zCl6aFgOnIpVF5NO0mFRAEn8+3+nS0fmvrwuqTRNbTlweSSVUIIvF4vDQ0NWCwWNmzYQDgcTusilKoPTVEU2tvb6e/vp66uLi7RNp0+uWQFWmyOm1rYOFW6X3yRkTffJH/tWtZffjmGmATbt3/4Q2Q0St5prS/q99PzzDN88KabgPjwdLUe4mwNOH09PeQ5HBjMZoxAcGSEkNu9rASalDJtC3k6un9nQ0OTUuaUGTij6D60pct0FfHb2toYGhpi69atrFixgsHBQQKBQFrPnYoPbWxsjMbGRsrLy6dUIFmIvLZYpJT09/dz6tSpOee4JeLYHXfQcP/9RH0+jHY7bf/+75z3/POI09coZlnkEmkLagPO7u5uLck4GAwyNDREcXExIi8PGfMZSEWZ9TzpIJcWz0wLkLl2/56vGX4+5MpDRsbQNbSlyXQV8YeHh2lqaqKysjIuSjDdOWPqmMkKoHA4TFNTEz6fb1rfVLoF2kwaWiAQoL6+nry8vLT0c4t4vZz4p39Cnl7Moj4fI0ePMvi//8uq909U2d953XX0HzpE2OudEDpSsv2aa2ac/+QGnOFwmCNHjuB2u+nu7sbvdCK7uynIz8een09RWRmWFTPnvoVcLnwDAwiDAduqVZhTjIzLlcVzPibHVJmp+3coFOLNN99c0O7f2TJzLji6QFt6JKqIHwgEaGxsBGDPnj1TzGaZEGjJaGhSSgYGBmhtbWXdunUzpgiku5HmdJVHurq66OrqYsuWLVp1+GSR7jGE1wV5Zigug9MmxYjPhzAYiJ29MBoJud7rLlFzwQV85MEHOX7//SjhMNuvuoq6K6+c0/nz8vIwmUxavyspJc6BAYZ6evD6/YyEw/QcPx4XURkrrEMuF8NHj2K0WJCKgq+nh5V79sxZqC1Vk2OqxD589PT0cNZZZ03p/u33+6cEnKSr5qPX600pgGnRkWL7mMXGshBo01XEb29vp6+vj02bNmlPjJPJlECbSaPy+Xw0NDRgNpuT0oLSvUBOFpBer5cTJ05QVFSUWruZsUEM9f+D0dkFkSBK5XaUHR8BkwlLaSmF69bhamlBqi1wpKTsrLPihth48cVsvPji+V6ahhACR0UFjor3OjQn6k2mFvylvx+jxaJFQgbHx/EPDCxqgZatYsDTod6XuXT/ji3GnMp9VTuHL3l0DW3xM515UfVHlZWVceDAgRlNDpkyOSYaU1EUOjo66OvrY+vWrdoPeqFR/YlqEMrAwMC8Ko/Qfhzz8acw+McmNLGm/yJYvBK5bg9CCM59/nn+8KUvMXrsGPbVq3nfj3+MdZoHjEwyuTeZmn/ldDoZ6O/HNTCApbCQgsJCLIqCKYWWI7kk0HJpLjMxXfdvNeBkPt2/l0WVEJUlvdpPsGQvMZF5Ua1bFwqF2LVrV1J5WwuloTmdTurr6ykrK5u27cxCYTAY8Hq9tLS0UF5ePu/KI8buY+AbRSmunCgz7OzH9PbzhNftAcBeUcEFL7yQnsmnkdj8q/KCAoaPHUMxGPC53bhdLtwjI5w6fFirolFcXDxr25ZcEiK51D5mribzRMWYU+n+rWtoS4sld4nTVcTv6uqis7NzzlF5mQ4KiUQiNDU14fF4UqoLmW7UnLZwOMzu3bvTMh9psSCiUYhGQCqQZ0UooTTMduEwOxyU7tmDr6+PgtWrqV29GnNBQZwW19nZGacpqBF+sVVlllOU41xIR4DKXLt/SylxuVxZ/83ppI8lI9Cma7jpcrloaGjA4XCk5P/JZFDIwMAALS0t1NTUUFdXl5Un98DoKCG/H5PFgl8ITp48icViYe3atWn5oQsRRdlUi+ldPzLYh8wrQRgguvGcNMx+YTEXFU2pJhKrxVVVVQETmoLT6YwLTVf9PfONCk0nuaYtpjvacLbu39/73vf47W9/S15eHnfccQcHDx7k7LPPnvP3/vbbb+fhhx/W/PB33XUXH//4xwG4++67eeSRRzAajXz/+9/nwgsvTOs1Jo0eFLJ4UCviv/3222zfvl0rLdXS0oLb7Wbbtm0p28kz8YMPBoN4PB4GBgbSEvqeKs6ODsaam5FC0NHWhrGkhH0XXUR/f3/azmHN60FZvZLwBddgfPMFRDRApO5SojsuSts5cg2z2RwXmh6rxfX29uJ0OnnrrbfiIioXqqJ9LLmmoS1E+Hxs9+9//ud/5t///d9pampi69at/PrXv6a0tJTdu3fPedxvfOMb3HjjjXHv1dfX8+STT3LixAl6e3s577zzaGpqyk6agG5yXDyo3aMDgQCRSISBgQHa2tqora2dthRTNlB7g/X29mI2m9m5c2fW5qJEIjhPncJ3ulbl6tpaCgHj6VDudOW1WUyjIDejbFhJdP1OhBxFMW0HwzJ4XDxNrBbncDjo7Oxk48aNOJ1OxsfHp2hxqi8u09/bXAjbV8lWpX2fz0dVVRWf/exn+exnP5vWsZ977jkuu+wyLBYL69atY+PGjRw+fJizzz47redJmiWx2s/MkrjE2CeeY8eOUVBQkFXNJxEul4v6+npKSko4cOAAhw4dSuv4amRisouC3++nubkZS0kJO3bswGQy4R8ehtO+jPT5eiwgAyAm8vsUoTDd184nfkvQ+ApIE9bo5djYnKY55A7qfU2kxSWK2outUZluLS4bidXTkS1t0e12a8n38+G+++7jscceY9++fdx7772sWLGCnp4eDh48qO1TVVVFT0/PvM+VErrJcfEQjUZpamrC7XZTV1en2ctzgUgkQktLC06nk+3bt2csRFjVqmZbFKSU9PT00NHRQdWWLVgVBaJRAi4X5qIiTHY7hvHxtPkN/eF1SOkF6QUiICrBMDX832f4BUHLneokCeS9CoGHZhVqkUCAdx58kNGGBkp37mTHtddizIL5bi4kEiKJovbUMlGxfclii/3OV4vLJZNjtip2+Hy+pKIczzvvvISm+DvvvJPrrruOW2+9FSEEt956K3/1V3/FT37yk4QPhVl7gNBNjouHzs5OLBYLq1atwmazZeQcqTjQBwcHaW5uZu3atWzZsiWjX+ZkzIRqlX673T4Rig+Mt7URHB3FXlFB8fr1WsucdGloUhQRNW5ACB9SGhAicXmpoOkRkGYMTGxX6Cdg/Dds0dveGwuJNP4Bafo1YIDQJ3jxc3cxcOQISjhM+69+Rf9rr3HB44/njOYxmbl8jyaXiYrV4trb2/H5fPPqLp1LQSHZbO6ZzEPmyy+/nNR411xzDZ/4xCeACY2sq6tL29bd3Z29h21doC0e1q9fTyQS4eTJk2mPSIT3Ih2TjZAMBAI0NDQghGDfvn0L0pRyJoEW67uLrdIPULJp05T90ynQhBBIYUMY8kFKmG4BFSHivo5CgIgvCi2NryPzHkLKFYAkFLqbSPgIAEazGSkl3a++iruzk6KamrTMP93MR4jEanHV1dVAfHfp9vb2uAoas7VsySUNLZvNPedrNenr62P16tUAPPvss+zYsQOAiy++mCuuuIIbbriB3t5empub2b9//7znnDK6yXFxYTKZtPyzdJKsQIutd7h58+Zpy2nF7p+uJ+TphJDb7ebEiROa7y6ZRSOdQSHqvNTO39OeP/IByHsWBQFMFCq2RM+L20Ua/wdJIYKJBUiGTZSfAyNvx5zPYEAJ5W6OW7q1okTdpdWwdLVli9lsjouoVL/HuSbQsjEXr9c77/SUm266iWPHjiGEoLa2loceegiA7du3c+mll7Jt2zZMJhP3339/9goh6xra4iMTOWPJjut2u6mvr086322uWt9sTBZCiqLQ2trKyMjInH136S523N7erjUgNZlMFBcX43A44kxkjui3GAcw/Q5kAZbIldjlufEDSTuqsAOwOOwYTUXI6BhSCARQtHEjRacLEOcimU6sjm12OlmLGx0djdPiAoEAfr8fs9mcddNjtnxo6Sh99fjjj0+77ZZbbuGWW26Z1/g6ybMkBFpsUdNMamiJiEajtLS0MDY2xrZt2+Kc+jORyR5mY2NjNDQ0sHr1avbv3z/nJ990zU0NaLDb7Zx1uthwOByeYiIrLCw8vQhfj832rWkXV0Pkj4gajyFFNyARooidX34a98l7GW1spHTnTt53990YcrwdSDZatiTS4sbGxujs7CQQCGhanKrJLXTR4mxpaOkwOS4KdA1t8WE0GgllwNw0nUAbGhqiqamJqqoqDhw4MKeFKtWu1dNhMBgIh8PU19fj9Xo588wzk6pVmYj5amiqdjg6OkpxcTFVVVWakEwUru52u3E6nbS0tGitQlQtrrCwUFvohKzBGPw20vAmYEAo+7CXlnH+T3+a8lwXmlwIxFC1OIvFohUiUKvZj4yMaNXsYyMqZ/LFpYNoNJqVNJtlU5xYF2iLj4XS0ILBII2NjSiKwt69e6f0UEuGVLpWz0QwGOTdd99l/fr18y6jpea0pYJaZLmiooL9+/dz/PjxGfePNZGtXbsWKSV+v1+rv9fU1BS3T3HxSszmP0ppbimhBAAfYAZDGupa5oBAU4nViqxWK1arVdPiotGoFlF56tQp/H4/FoslLqIynVpctjS0UCi0IEFbOUFuGy7SwpIQaOoCkWkfmpSS7u5uOjs72bRpk/bjT4V0mfVCoRCNjY14vV42bdqkRVvNB4PBMGcNLRqN0traytjYWFyR5blqe0II7HY7drtdu5ZwODylivqCVNVQRhHKO0gkIEFZD6b5RU/mkkCbaS5Go1G7vyqBQACn08nIyAinTp1CShljLi7GZrOlfG3LpnN0ttA1tMVHJjU0r9dLe3s7hYWFqTW5TDDmfISvlJK+vj7a2trYuHEjVqs1bU/McxVC4+Pj1NfXU1lZyf79++MWtXQEmOTl5bFy5UpWnu4/ligfS9Ue1GCTeS+OioJQTiDIRxisoCgotIFSBobUTLmQW9X25yp8VC1O7UkWjUa1iMrW1lZ8Ph9Wq1XT4OaixWVDQ8ulzyLj6AJt8ZEJgaa2U/H5fJx55plxT6zzYT4amt/vp76+HovFwv79+8nLy8PtdqfNJ5fs3NSAGKfTya5duxJWXEh3xKQ6v8n5WKr2MDQ0RGtrK0BcC5e5m4WViZfBqp4UFAGEgNQFGmSxWsQk5vu5GI1GHA6H1vh1cmdpVYuLbbo5nRaXTQ0tVz6PjLMMFOAlJdDSbXIcGRnh5MmT2O12ampq0ibMILW5qnlu3d3dbNmyRdNYIDO5YzOhRlKuWbOGzZs3T7soZEKgJWKy9qC2CRkfH6evr49QKEQgEKCrqwuHwzFrI04MJqRSjFDGJ0p1KV4ERuQ8hVkumRzTTaLO0qoW53Q6p2hxxcXFFBYWYjKZsqKhhcPhrHQ5yAq6hrZ4SHfYvuqXikQi7N69m/Hxcfx+/7zHjWWuAsjj8XDixAmKi4sTJkinW6BNN5ZaN9Pj8SQVSblQAm0ysW1CYEKQHDp0CIPBQFdXFx6PZ/biv4atKEojBmUIiQ1p2AmG+UXi5ZJAW4h5TKfFxWrTavsn9SFjPr64ubBsulWDLtAWG0KIeS/qsYV7Yztbu93ujHStTmZMRVFoa2tjcHCQbdu2TaslplOgTRcUMjo6SmNjI1VVVUm35cmWQEs0D6PRyJo1a7Tq6mojzsQ5cafNY6YzJ+5rmrSHXBJo2SBWi6uoqAAmHpLeeustrZB3IBDAarXGRVRmwhy5bHLQlhFLRqDB/J44vV4v9fX15Ofna34plUx1rZ5NAKkh8OXl5RPFhGdYVNOZBjBZCEUiEZqbm5PWymYaK5eYnBOnhqqPj49rOXF2uz3OPDZfs1gu3YtcmYvRaMRgMLB27Vry8vISanFA3MOG1Wqd94OBx+NJS1f2RYGuoS0PFEXh1KlTDA0NUVdXp5lGYsmEQJspsToajdLc3IzL5YoLgZ+J+eSOTSZW2Kp+xOrq6pSapaoCLd1ayXhTE56eHvIrKijctCktEZ6TQ9Vnz4krTikZOBc0tFwRZiqxxYmn0+LU1I3BwcG4BHw16GSuWpzX610+JkdA6kEhi4dUNAHVhLZ69eoZNaBMaWjhcHjK+yMjIzQ2NlJdXT2nljOz5o75j4P7NTBGwLwXCg9Ou6sqHOvr6/H5fOzevTvltjyZ0NC6f/tbho8dw2S1Mvr22xSdOkXtH6U/2XqmnLjx8XG6urriOk07HI5ZK2rkiskxl7pVw+z3xWg0TvGJ+v1+XC4Xg4ODcZGtqqlyNi1uOZkcpYDoklntp2dJXuJsEVOhUIimpiYCgUBSJrSF0NDC4TAnT54kGAyyZ8+eOQuQGU2Yo8/A+N0g2wEJtmLwfxaK/g7MgOI6PUgJGEyMj4/jdDqprKxMS9WRdAq0gMfD2Lvvkr92LcbTWpmrqYnAgQNYS0vTdp7pmCknTq1ub7FY4oJNYjWHXBJouTCPWOYyn9iHjWS0ONVkHPtZLDeToy7QFiEmk2naZoGxycjr16+noqIiqR9RpjQ0dcz+/n5aW1vnNKdE42kCLRQCX+fE/80l4HwY5AgY8wELBL1g+C0o7wP/SpBhQCEi7ZzstOL3R8nPz6eqqmre1zmfRTMSiRD1eDBarZhi8sgkaMIsdt9sMNecuFAolJWahZPJpdYx6WI6Lc7pdDIwMEBLSwsw8Vk0NzfT2dmZFpPjD37wA+677z5MJhN/9Ed/xHe/+10A7r77bh555BGMRiPf//73ufDCC+d9rlSRAiLGpfV5J2LJCLTJofuTQ7DVbs02m21K0MdsZEqghUIh3nrrLYxGI2eddda8FjpNoIU8cOIH4D0t0GylUOQBUxSwAKYJiQDgqQf7B8C0kvHxcbrbjrCy8mzKt+7m0KFD871EIN63NxetIDA8zOBzzxFxu0EISs49F8eOHVgLCiiorsbd3o6lpISwy4W9snJBtLNkSZQTp2oOAwMDRKNRxsfH40p3ZaNKRi4JtEz49BKZjNX8xJ///Of86le/YmBggMOHD3P22Wdz9dVXU1JSMqdzvPLKKzz33HMcP34ci8XC4OAgAPX19Tz55JOcOHGC3t5ezjvvPJqamrKWPC6FILrAHRSywZK7wsnCRw17HxgYmNKtOdUx54uUkpGREQYGBti5c+esjUCTQRNoHb+cEGZFpztRj70D5EExYPIAFjAogAnMlUQVaGtpIRyKsHlLHWb7CmQOLHTDv/41MhrFXltLxOdj9KWXsFZUYC0tZe1FF9H/2msEhoYoqKqidP/+BW93MhdMJhMlJSWUlJRgMpkwGo0UFRXhdDoT5sTFNuHMFIqi5JzJcSFQ8xNvuukmzGYzNTU1nH322bz22msp3Y8HHniAb33rW1qBY7W+63PPPcdll12GxWJh3bp1bNy4UROc2SK6DGpl5u4qkCKxydVjY2M0NjZSXl7OwYMHU34iTecP3+fzceLECcxmM6WlpWkRZhAj0Hy9YIrpyWZeCbISpANCb4DJD9a1kHctI/499Hf+lvJVWygtK0EoTjCtTOv1puJDi0QihEZGsNdMFAI22e2EhCDickFpKSarlaqPfCRtc1xIVM2ooKCAgoKCueXEpfFzySWTY7b8eWq36k2bNrFp06aUxmhqauL3v/89t9xyC1arlXvuuYezzjqLnp4eDh58L/CqqqqKnp6edE19zkgE0WVQ+2rJCLTYivvBYJATJ07g8/nYuXNnToTmKopCR0cHfX191NXVYTabaW5uTtv4mkArWg/OerCvgkgEIm5YfQFU/TWEhiGkEBYraejoIBqNsG3bJ7EYBpgIFtkNpqlpC/MhFYFmMpkwFRcTGhvDvGIFkUAACZiSbJ6ay0y3eCfKiZvcJy6dOXG5FBSSLeHq9XqTash73nnn0d/fP+X9O++8k0gkwtjYGK+//jpvvPEGl156qVbDcjK5cr+XMktGoMF7TuD+/n42b97Mtm3bcuJL5HK5qK+vZ+XKlZqm6Pf7M9Oxes3HITACI29ObCg/CJUfBpMZTAUMegZpbn53UgDK+rTNYzKpRjmWf+xjDDz3HL62NjAaKfnwh3PKT5YqyQqSRCWj0pkTl0saWrYKE7vd7qSiHF9++eVptz3wwAP8n//zfxBCaN3hh4eHqaqqoqurS9uvu7ubysrKtMw7FSSCyCLT0IQQK4BqKeXMTRVjWDICzefz8c477xAKhVi/fn1Wvzwqao+w0dFRtm/fHpfzkm6/nCbQzGaouxpCn5vYYJ74war1KRVFYd++fQvW1DBVgWatqGDNF79IZHwcU0EBpiUSXp1q8MN0OXFOp1Pzxak5cQ6HY9Yu07kk0LKpoc03bP/Tn/40v/3tb/nwhz9MU1MToVCI0tJSLr74Yq644gpuuOEGent7aW5uZv/+/WmaeWpEF8FyL4R4FbiYCdl0DBgSQvxOSnlDMsfn/hUmSTQaZd26dXi93oxVQZiLmUatRl9ZWcmBAwemHJfO2osJxzO/90NVQ5Y3bNig5ewsFEIIrfJJMBjUtI5kfEImqxXTAs93IUiX1SAvL4/S0lJKT2uuc8mJyyWTY7Y0NI/Hk5TJcSa+9KUv8aUvfYkdO3ZgNpv5l3/5F4QQbN++nUsvvZRt27ZhMpm4//77s9rAdBH50IqllC4hxJeBn0opbxNCLD8NraioCJvNRjAYTHtlfHhPo5ot+iwSidDU1KT1T5suaXum0lepkEhAhkIh6uvrEULMOy0gVfx+P319faxbt46VK1fidru1NiKqT8jhcFBQUJAzGkMmyaQgmZwTJ6UkGAwyPj4+JSdObdmSCyxmDc1sNvOv//qvCbfdcsst3HLLLfMaP10sIoFmEkKsBi4F5nzzloxAU8lEzljsuDMJtMHBQZqbm6mtrZ21wka6K2hMFmhqsvbGjRu1fKiFRG3+OTg4yJo1a6iqqiIcDuNwOLTFVvUJ9fT04Ha7MZlMmrlsIULXs8FCakZCCKxWKxUVFZpmrubE9ff3Mz4+zuHDh8nPz89qTly2NLR0CLTFxCIRaH8H/P/A/0gp3xBCrAeSjp5bMitGunuiTWYmQRkKhWhoaJzmc24AACAASURBVEBKuaD+qVhUARkMBmloaMBgMGRNK3M6nZw4cYLKykrWrVuX8PNI5BNSQ9dHR0dpb2/Xuh2rWpx2X6O9EOkHkQ/GdWDMfuWNZMl2UWA1Jy4ajWKz2aitrcXr9WY9Jy4bGlo0Gl02DT4XS1CIlPJp4OmYv08Bf5zs8UtGoKlkWkOLJbaUVrY0IRUhBKFQiCNHjrBp0yYtwXMhURRFC4LZtWsX+fn59Pb2Jr2IJwpdVwsBq12nS4qHKCs4hc2+EqtFIqNdSPNHYBEljeaC70pNrBZCTMmJCwaDcQ8WiqJoDxbpat0Sy3Sl6jJJth8sFpoJk2PuL/dCiM3AA8AqKeUOIcRO4GIp5d8nc3zuX+EcWSgNze/3U19fj8VimXMprXQTDAapr68nEolwzjnnpG0uczGPud1uTpw4QXl5Ofv379eOm49pNVFtvtDov+Lxr6RvMEQwECTf2oW0Oih0bEpLv7JMkyvBGDOVvrJYLJSXl2sPRbE5cc3NzWnPiYttHbPQ5MJnsVBkyORYK4QYBAallDsS7SCE+DDwT0AeMCyl/NAM4z0MfBN4CEBKeVwI8QSwvARabGJ1JjU0KSWdnZ309PSwdevWOdd+SyexGuLmzZvx+/1pE2bJ9jGTUmqlxXbs2KGlJkQ8HgK9vQSHhggn6DGX6pxsdgt2eynl5UaklIQD7Yz6TVpuVmxPs+Li4pwzKeWKQJuLmW+mnLje3l48Ho9WzsvhcFBUVDQnU7euoS1qhoErgMcSbRRCOIAfAh+TUnYKIWYzHdmllIcn/UaS1lCWjEBTyaSG5vF4aGlpweFwcODAgbQ8Vaa6wAUCAerr6zGbzZqGmInKIzMtNF6vl3fffZeSkpK4fnKR8XH6n3mG6Pg4XrebkNlM5OtfhzT4FhWxAaGcxBAtRuAjz7yCsoJtlFVMLKBqbtb4+DgdHR1x5rJciOrLJYGW6jxmy4nr7OwkEonEle6aLSduoTW0YDCINaaDw1Ing1GOHmB0hu1XAP8ppewEkFIOzjLesBBiA6dLqAshPgP0JTuZJSXQhBAZ0dAURWF8fBy/38+uXbu0jsbzRW3KOZeFRUpJb28v7e3tbNmyRctBSjczmQqllHR0dNDb28v27dun3A/Xm2+ieDzYamsJu1wEW1txvfkmhekozJp3JjJqI6r0AiWQd0ZcUMjk3CzVXDY+Pk4gEIiL6nM4HOTn5y+4gMkFgSalTKsQmU9OnKIoCx7R6vF4cqIk3kIhIVtBIZuBvNMJ04XAP0spE2pzp/ka8CNgqxCiB2gDPp/syZaUQIP0Lxbj4+M0NDRgNptZt25d2oQZvNcTLVlzSyAQ4MSJE1itVg4cOJDRRWC6xG+1uHJRUdG0WmrU48EYm39nsSA9nvRMzGgE4zZgW5K7v2cuGxoaYt++fVpUX0dHB16vd8aGnOkmV0xdmY4sTJQTp/aJi+0wXVxcTDAYXHDTvdvtXjTdqtOT1pBaUIj6m1G59tprufbaa+cyhAnYC5wL2IDXhBCvSymbpsxQCAOwT0p5nhAiHzBIKd1zPZlOAiKRCM3Nzbjdbnbu3MnY2FhGulYnEzospaSnp4eOjg62bt2qdUvOJJM1NCkl3d3ddHV1zdqGx7xxI77GRgx2O9FAAOnxYEuxmnm6SRTVF7vQtrS0xNVIdDgcafXDLQWTYyoIIbDZbNhstik5cW1tbfT09NDd3R2XE1dQUJCxOXq93pzR0G6++WZqamr46le/CsDtt99OYWEhv/zlL1m9ejXHjh2jvr5+XudI1eRYVlbGkSNH5nPqbiYCQbyAVwjx38AuYIpAk1IqQoivA0+d3n/OLCmBlq5k5eHhYU6ePMnatWvZunUrQghcLhehUCgNs3yPZMpf+f1+Tpw4gd1uT0orS9eCGTs3VTNUm6PONofibduQwSDuw4eRgQCms88mf/PmtN+/ybgiMBoWVJgl1jn8dhM15FT9cF1dXUSjUc0f5HA45hW2nisCLRcafKo5ccPDw5SXl1NcXKxpz52dnXi9Xi0nTg02SZdVwuv15oyGdtlll/GXf/mXmkB76qmn+Na3vsXhw4d59913WbduXVrOk5oPbd4P8c8B9wkhTIAZOAD8fzPs/5IQ4kbg3wFNqEkpZ/LTaSwpgRZLKgtHKBTi5MmThMNh9u7dG+c0zoRvbqbyV7Ea0ZYtW5LSylQhlA6Tmdppure3l7a2tjlrho7du3Hs3s3IyAhDQ0Pzns9s/HzQxHfaJ3xpdqPkB1uCbC9ILQjEZDKxcuVK7XoVRYkLWw8EAnFlu+ZSXSNXBFouFieeKSduZGSEtra2tOXEJVtpfyHYvXs3g4OD9Pb2MjQ0xIoVK1i7di379+9PmzBLPShk1jVvHfAaUCqE6AZuYyI8Hynlg1LKBiHEr4HjgAL8WEr57gzjfen0v1+Lm36SLUGWpEBLtu6iipSSgYEBWltbJ7VVmTpmOlF9aJNR/VQFBQVJaUSx46VLoCmKQkNDAzabbV7+unSX+EpEh19wd7uZfIPEbAB3BL7RZOHXu/0Y0iA7Yk2Qa9euRUqJz+eLq65hNpvjNInpPoNc8aHlimCFmX1Es+XEBQIBbDbbnHPicq3s1Wc+8xmeeeYZ+vv7ueyyywDSahLNYKWQNinlvpl2kFL+I/CPyQwmpZyXBF9SAm1y+atkFmE1/N1kMs1YKipTAi1WQ5NS0tXVRXd396x+qmTGS5X+/n7GxsbYsGEDtbW18xprIQRaZ8CAATCfXscKTTASFjgjsCIDaWhCCPLz88nPz9faFKmaRGwRYFXATe5VlguCJBc1tGSYLidufHw8LidutlzEXDI5woTZ8ZprrmF4eJjf/e53nDx5Mu3nWCSVQvKA64APnn7rVeAhKWU4meNz/wpTwGg0zpqLppr0Ojs7kwp/z5TJUR1T1coKCwtTznGbr/AIh8Oa87msrGzOAjUTc0qGSotCVEJYgTwDeKOQb4SiBfx2T9Yk1ICH8fFxuru7CYfDFBYW4vf7Na0im4ItlwTafKL4YnPi1IeLyTlx0WiUgoKCuJw4t9s979Yx6WT79u243W7WrFnD6tWrMyLQFgkPMGGy/OHpv//k9HtfTubgJSnQ/h977x3fVn1+j5+r6b3jFc/EiR3bGV5JaCmFfJKyvk1poUDDaIA0oQUSCrTQpmUHPrRQKGWVTQtlhB80/bATKNCWkpAQiOW9h7wtybZk7fv+/WHelytZsq+kK+lK9nm9eBEPSVeydM99nuc85ygUijnJx2QyobGxEQkJCYLbacFsOfb09ECr1fpVlbnfn78V2ujoKFpbW7nMtMbGRlGqvVAQ2vI4gt35NjzYp4KCBeQMcN9KC+RhLISo4IHK0eleVlNTE3p7e9HW1hbW+BwptRzFJtf5duKuv/56TE9Po7S0FKtWrUJtba3XmCdvuOCCCzjSMRgMSElJwRdffAEAuPvuu/HUU09BLpfjwQcfxOmnny7oPuvr67l/n3rqqTj11FN9Oqa5EEHxMXWEkLW8rz9gGOZLoTeOKkKbz3GfZVl0d3djeHgYq1at4toWQhAMQqPBl0uWLBHFecQfQnM4HGhubobNZnNJChCLiEJBaABwca4D/5PuxJidQb6aRYq0HK+4vayYmBiUlZVBpVJxrTIanxMsRZ8nSK1CC+VO3Ntvv42bbroJ8fHxePnll3HDDTfg/fff96kF+fLLL3P/vv7667n91MbGRrz00ktoaGjAwMAANm/ezFmyhRMRRGhOhmGWE0I6AOCr+BjBJ96oIjQKT+QzOTmJxsZGZGRkuNg0BXKf/oI6bQwNDWHp0qVYIdKOlq+ENj4+jubmZhQVFSE3N9flip2qHANFqAgNAHLUBDlqaYgu5oOnVhmNz6GKPhqfQ+dwYsYSSalCAxBScqWPdfbZZ+O0004L6L4IIXjllVfwwQcfAAAOHDiACy+8EGq1GsXFxSgpKcGRI0dwkhguOQEiEuJjMGNM/E+GYToBMAAKAVwm9MZRSWj8Co0GTRoMBlRUVPg9CBaL0IxGIxoaGpCamori4mJRTypCCc3pdKK1tRUmk2nWegL/vsSu0KR0Ag0X5iKSueJzBgYGYLPZuFlQSkrKnP6I80FKFVo4IJbK8V//+heysrK4i1KtVouNGzdyP8/Ly4NWqw34cQJFpMTHEELeZxhmBYBSzBBaMyHEKvT20n+GPoDvuO9wOKDT6dDc3Izc3FyXSJNA7ttfEELQ3d2NoaEhlJeXIzk5GVqtFna7IPGOIAghNL1ej6amJuTn53NL454QzJaj2Qbc0K7CQb0CKgbYk2/DT/LFN5SWInypjDzF5xiNRhgMBs4fMSYmhiM4X2JcpERo4VhlMBqN817cbt68GUNDQ7O+v2/fPnzve98DALz44ov40Y9+xP3M03ORwoVcpLQcGYa5CsALhJATX32dyjDMFYSQR+a5KYAoIzQKhmHQ398PpVKJqqoqxMbGhvV4aFXm7kovl8thsVhEe5y5CI1lWbS1tWFiYgLr1q2bdwjuzzyOZYFjWgYmK7A2hyA1/mtCGx4exujoKJKTk3HX2FIc0CsRLwcsTuCObjUy1QTfyxQ/9kdqCKTVxzAMEhMTkZiY6OKPSANQ+fE5dA7nzbZLai3HUEMIoR06dGjOnzscDrz22ms4duwY9728vDz09fVxX/f393Mt5XAjEggNwE8IIQ/TLwgheoZhfoKvVY9zIuoIbWRkBJ2dnUhMTERVVVXYpdFUhFJRUTFLJizW3th89zcxMYHGxkbk5OSgrq5O0Gvia4XmcAIXvaTA4T4GMgZIUAOv/MiO3NgZGykAyMnJwdTUFN4fB+TEBkIAhUwGCyvHu6OyRULzEXx/RPcYF71ePyttmtp2AdKq0MLxGRVjD+3QoUMoKytDXl4e972tW7di27ZtuO666zAwMIC2tjasX78+0MMNGEFcrBYbMoZhGPLVyYdhGDlmLLMEIaoIbXp6GoODgygtLYXBYAgrmdEE57lEKHNZX/kDd0JjWRadnZ0YHx/H6tWrfZoZ+CoKef64DJ/0MkhSAwwDTJiBaw4Q3FJ+AiqVCmvWrIHD4UBaWhoyhlWYtsqhkLFg2Zn/LCMDOOEc5JZmQy1jjxbMFZ/T0tICq9WK+Ph4WK1Wbo4U7ou+cGB6etpnqb47XnrpJZd2IzCzT3b++eejvLwcCoUCDz/8cNgVjkDkzNAAvAvgFYZhHsOM5dWVAN4ReuOIeIZCER8fj7Vr12JiYgJjY2NBeYz5rrBZlkVXVxdGR0fnFaF4s77yF3xCo4SamZmJuro6n8nBV1FIl44BITNkBgBy2NA9zqKyshIdHR0uj//bZTbsbIrFlFMGQIaMGII71i5BKuK5RWRqJ0UVfsGOdQkVQt3q8+SsYTKZYDAYOANgGp9D53ChfJ3DEe4JiJMH9+yzz3r8/t69e7F3796A7nsB40YAOzHjFsIAeA/Ak0JvHFWERhGMnTH+/XrbD5qamoJGo0FmZibWr18/L4kEo+XodDrR1dWFoaEhVFZW+t1W8bVCW5NDIGMAh5OF3WqD2alCYTKDJ4/JkEdiUc373S0ZLF5bO413xxVQMQQXZTuQFQMArjJ2q9UKg8HAxbrw50PJyckhD4UUA+GeXVEDYJVKhYqKCgDg5nDDw8Noa2vjvCvp6yxmfI47wtH6lIqfZqgRCTM0QggL4DEAjzEMkwYgjxCyMPfQ5lusDhTeCI229sbGxnwiEbGJ1263Y2BgADk5OX7t2vHha/X4/QonDjZM4kBrAuRyNWw2GcaNBE8cVoE4SrF0GYP1S7/+/aokgqqkuRWearUaWVlZXKyL3W6HwWCAXq9HV1cXAHB7WikpKV59OKUGqYkxYmJikJ2dzeWU2e12bl2AWkeJFZ/jDnHCK/2D1P4OwUQEqRw/BLAVM9z0BYBRhmE+IoRcJ+T2UUVoFMEmND4mJyfR0NCArKwsQVUZH2JVaIQQ9Pb2QqvVIjMzU5RFbV9EIWazGRqNBtdVJ+P2s0rw5GEWf/mMwZKEmWMbnSC4/0MFXrxI8DqJRyiVSpc9LXe/RIfD4bKIHG51q5Qx199WqVR6jM8xGAwe43MCmcOFo0ILdbipFBAphAYgmRAyyTDMDgDPEEJuYRjmhNAbRx2hMQwT9JYjMPOh6OjogE6n81lw4en+/AUlk8TERCxfvly0vTYhZEsIwcDAALq7u1FeXs7tS6kUDPjnCzlDMG0Tv93myS+RnnhbW1s5AQSt4BZqqylQ8ONzAHDxOTQA1Zf4HHeEo0Izm80BC0IiERGiclQwDJMD4HwAPg8io47QgOC1EigBURl8dnZ2QAvbgVRohBBotVr09vZypsZDQ0OwWgOrgijmq9BsNhsaGhqgVCpnGTxvXsniuc9kMFoBuQywOGXYWhn8q2L+ibewsNDjInJ9fX3YDIGlhEBNBmh8jnsQp5D4HD7CUaFJKdwzVIgglePtmFE6/psQ8tlXXo5tQm8cEc9QKpDJZOjp6YHFYsGaNWsCDuDzt0KzWCxoaGhAbGysSwComCKTue6LOvOXlJRw8y0+1i0l+NMPHLj/IzmmbcCmrH7sOqkAQegCzwn3ReQjR45g+fLlHpWU4VD4RROExudQkqPxOeGo0KQW7hkKRErLkRCyH8B+3tedAM4VevuoI7RgmeEaDAYMDQ1hyZIlAdtoUfh6rIQQDA4Ooqury2OGm5iE5unYnE4nWlpaYDabXZz5PeHUEoJTSxwghOC//9VCJisQ5bgCgSdDYHeFXzQoKedDKFqv3uJzDAYDOjo6uD0whULBKWpDVakZjcYFR2hAZKgcA0X0fVp5EGNmQ82NJyYmkJOTg+TkZFGdHoSCtvgUCgXWr1/vUUodTEIzGAxobGxEfn4+Vq1aJfjYpT5896TwMxgM0Ol0nJJSSOsskhCO1QF+hAs9BrPZjN7eXkxNTeHo0aMhi89ZqC3HCJmhBYSoJbT5dsaEgBr5Ll26FCtXruQkzKHG8PAw2tvbsWLFCq6l4wnBaDny3UbWrl0bcJtV6phLSdnX1+eipORbSUUSpGB7RavlpKQkJCQkIC8vL2TxOQux5bhQEHWExnfc95fQaPDm1NSUi5FvsNST3mC329HU1ASWZVFXVzdvdSB2hWaz2fDZZ58hIyPDL7eRaIA3JaVer3exkqIEF0ikS6gQ7uVuPvif0VDF5yxEQpO6KIRhmDn3zAghfxByP9J9hgGC7qL5elVHI2fy8vJQWlrq8oGRy+Ww2WxiH6pHUOHFsmXLOOPZ+SDmXtvIyAjGxsZQW1vLybUX4VnCTmdDnZ2d3GyI70kpFfKgkEKFRjHXsbjH57Asy9l2BRKfI8RpPxoh8Rka/YOUAqgD8I+vvv4ugI+F3knUE5pQOBwOtLW1wWg0eo2cCUWF5nA40NzcDJvNNq/wwh1iEBpVUCoUCixZsmSRzOaBp0gXs9k8a0dLSrtwUiI0X1SOMpnMp/gcb6Ieo9HIVYELBVJXORJCbgMAhmHeA1BNCJn66utbwVM9zoeoIzT3lqMQ0KpsvtDLYBAaX+Gl0+nQ1NSEoqIi5Obm+nxlHyihDQ0NoaOjA6WlpVCr1ZwoYhHCMZ+Scnp6Gp9//nlYlZRSajkGQq6+xOfQ1zomJgYmkwnLli0T82lEBKRMaDwUAOC3wWwAioTeOOoIjUJIheZwONDa2orp6WlBQaDBIDSZTAa73Y7Ozk6YTCZUV1f7bdnkL6HxZ3VUQWkymcIW7RFt4CspJycnsXr16rAqKaVUoYnttj9XfM7Q0BCuvfZayGQy6PV6lJeXo7y83OfX4osvvsCVV14Ji8UChUKBRx55BOvXrwchBHv27MFbb72FuLg4PPvss6iurp7/DkOACFI5/hXAEYZhXsdMfMz3AfxF6I2jltDmI5/x8XE0NzejsLBQsAw9GITGsiyOHj06b3UoBP4QGq0Ki4uLXZJ1g7XPt4j5lZTUDDhYSkopVWhOpzOo5Ooen3Pw4EFcffXVUCqVuOuuu9DR0YFPPvnEJ1L95S9/iVtuuQVnnnkm3nrrLfzyl7/Ehx9+iLfffhttbW1oa2vD4cOH8dOf/hSHDx8O1lPzCVIXhVAQQvYxDPM2gG999a3LCCHHhd5e+s/QR8znuO9wONDS0gKLxYKamhqfThZiEhrLsmhvb+eqQ6qiCwS+EBrLsmhra8Pk5KTHqlDsaJtFeIcnJSUluGAoKaVUoQWb0NxB27uXXnopqqqq/LoPhmEwOTkJYCYNnl4IHjhwAJdeeikYhsHGjRu52Z5QUVewESEtRwCIAzBJCHmGYZglDMMUE0IEzT+ijtAo5HL5LKPesbExtLS0+D2jEovQqEN/dnY20tPTRWsxCa2qaG5bTk4OamtrPb4OixVa+CCTyWaFcoqppJQSoYUj4DNQleMDDzyA008/HTfccANYlsUnn3wCANBqtcjPz+d+Ly8vD1qtVhKEJnVRCAXDMLcAqMWM2vEZAEoAzwP4ppDbRy2hKRQKmM1mADMzopaWFthsNp+rMj4CJTR+bhp16K+vrxd1d2wuEELQ3d2NoaGheRMCfA34nO9xtVoturq6EBcXh9TUVKSkpCA+Pl4yra9QwN8LBF+VlElJSXMS1kJqOXqCkD20zZs3Y2hoaNb39+3bh/fffx/3338/zj33XLzyyiu44oorcOjQIY9/X6m8zhGE7wOoAvA5ABBCBhiGEXz1EXWE5t5ypPtcxcXFyMnJCegNFgihGY1GaDQazguSfojlcnlIWntmsxn19fVISUkRFP4pk8lEqdBsNhvMZjP0ej2qq6ths9lgMBjQ3d0Nk8mE2NhYjuCkuLMlRQTqSblYoRk5Cy5vOHTokNefXXrppfjjH/8IAPjhD3+IHTt2AJipyPr6+rjf6+/vd5lLhxMRJAqxEUIIwzAEABiG8cmaKOoIjYIuBwsx0hUKf062/KqooqJi1gfJ12Rofx5/YGAAPT09XMyMEIhRoVHhjVKpRGVlJSwWC2JiYpCTk4OlS5e6VBq9vb0wmUxQq9UuBCeVE68YCGZl5O5JSW2kPCkp7Xa7ZC4cwkGuFosloPNBbm4uPvroI5x66qn44IMPuEDdrVu34qGHHsKFF16Iw4cPIzk5WRLtRopIEIUAeIVhmD8DSGEY5icALgfwhNAbR8Qz9BUjIyNoaWmBSqXCunXrwvbhNZlM0Gg0SE1N9VoVBVN8wc8s48fMCEEgFRpfcFJTU4PPP/8cTqeTiwsBwP1frVYjOzsbubm5LouyWq0WU1NTXCstNTVVsBOEVBHKVp+7jRRfSTkyMgKHwwGr1SoJT8pwfD4DeR898cQT2LNnDxwOB2JiYvD4448DAM466yy89dZbKCkpQVxcHJ555hmxDjdgRMoMjRByL8MwWwBMYmaOdjMh5KDQ20cdoTmdToyOjmLNmjXo6OgIy4eFEIK+vj709/ejvLycG+57QrDcR2irdT5DY2/w93WjrdWsrCzU1NSAEILExEQcPnyYIybaAqPmx8DXBKdSqZCVlcVd2fKdIFpaWqBUKl1mRZGUXxZOkQ1fSRkbGwubzYbk5GQXJaUYPolShxgXFSeffDKOHTs26/sMw+Dhhx8O6L6DhUghNAAghBxkGOYwvuInhmHSCCE6IbeNOkJTKBSoqKiA3W73yfpKLJjNZmg0GiQkJGDDhg3znnDFrtAIIWhoaIDVahWt1Sr0cfv7+9HX14fKykokJiZyhLVq1SqwLMs5ONDAx+TkZI7glEqlR4JTKpXIzMzkWmlWq5WrMtrb2132jJKTkyVPcFIgCULIrP2sSPSk9BdSEsWEEpFAaAzD7MJMarUZAAuAwcyCtSBrl6gjNGDmpBFM30VPHwiq5KOzKqF7ZWIep8FggMlkQkFBAfLy8kL2oaWtTZVKhQ0bNgAAdzHBMAz393DftaIEp9VqYbfbkZSUxBGcSqWaRXCEEM5jkiZlU5HJ2NgYOjo6XCTvUgvolMqJ1NPcypOScnp6GhMTE5ySUq1Wu+SVRWL7NxwJ2VJABIlCbgBQQQgZ8+fG0vm0iwyxVHru8BRLY7Va0dDQALVajQ0bNvg8qwqU0FiWRUdHB3Q6HeLi4rB06dKQnTjpbt+KFSuwZMkSjoQokXmDTCab5aQ+OTkJvV6PwcFBWK1WF4KLiYmB0+mcRXByuRwZGRlcW9VTQKdUjIGlRGjzndQZhkF8fDzi4+NnKSmHhoY4JSW9cPDn4iEcfw+j0Rj1mX6eEClOIQA6AEz7e+OIeIZSgjuhDQ4OorOzEytXrvTLwdvTArgvcF8HOHLkSEiUYyzLorW1FUajEbW1tVCpVBzJzEdmnsCvrIqLi11yx1pbW2GxWJCYmOhCcCzLghAClmVdCC49Pd1FDEErQbPZjKNHj3JVRkpKisfk72BBKoRGCPHr/eGLklKIJ2U4Xg+j0bjgstAoIqHlCOBXAD75aoZmpd8khOwWcuOoJLRgulxQQrPZbGhsbIRMJuMMff2BvxUaX3hSUVHBxbyEwrLKaDSivr4eOTk5WLlyJQghXItRLCLl544VFRWBEMIRXHt7O8xmMxISEjiJvyeCo/eTmpqK9PR06PV6rFu3jqsEqW8in+CCbQwsBUIT64LHk5JyYmKCa1M6nU4kJSVxr6+7klKqS9WLCCv+DOADAPWYmaH5hKgkND7EvgqUy+UYGRmBVqtFSUkJN8vxF/4QGs0si4uLmyU8CSahURLVarWorKxEQkKC4BZjoGAYBklJSUhKSkJhYSEnYtDr9ejo6MD09DTi4+M5gouNjeXIjWVZ2O127jhTUlK4WR5NRaZiFb4xcGpqqqiimnC3PCno6yA2FAoF0tPTkZ6ezj0OXRWgGX8JCQncxYNMJgvLUvVCJLQIUjk6CCFzplfPhagmNHpyF+tDQ+czFosFdXV1olzN++oUws8soxEZfASL0KxWKzQaDeLi4rB+/XoAs4UfoQRfxFBQUABCCEwmE3Q6cv88IgAAIABJREFUnYtKj5JSV1cX8vLyZlVwDMNwakvA9STc2NgIu93uQnCB7GtFesvRV8zlSdnR0QGTyQSn04m+vr6QKSkXMqFFiCjknwzD7ATwf3BtOS5M2T4w2/5KDEKj4of4+HgUFRWJ1poSSmg0s4wQMmeLU0wPRgq607Zy5UpkZGSErCrzBQzDICEhAQkJCS4E193djdHRUahUKuj1egAzc574+HgXcuMTHA2ELCoq4mZ57vta9EQdGxsr+DWQCqGFy/rKXUk5NTWFrq4uyGSykCkpFyqhARHjFLLtq///ive9hS3bpxBDEu8eN0NnA2JBSMuRWki5Z5Z5uz+xWluEEDQ1NWF6eloU4Uco4XQ60d3dDQD41re+Bblcjunpaej1evT29sJoNHI+ksnJyUhISPBKcImJiUhOTkZhYSFYluWqDP4sT0i0i1QITUrHoVarsXTpUixduhSAdyUlJbhA1zACddqPVERKy5EQUhzI7aOa0ISkVs8FGn5ZWFiI8vLyoOy3zdUidDqdaGtrw9TUlOCUALFajlNTUzCZTMjLy0NpaWlQhB/BwtTUFBoaGlBQUOByAUBl6LT1SE2T+/v7MTU1hZiYGBeCA2ZOuk6n0+W5u1eC7gvJ/OwyfqKAlGZoUvgbehKFeFNSjo+Po7OzEwACUqku1AotUgiNYRg5gLMBFIHHT4SQPwi5fVQSGj2B+Es+fCJxD78Um9C83R/NLMvNzUVpaangK+pACY0Qgp6eHgwODiI+Pp4zEaYnHylc2XsDdSsZHBzE6tWr59w34jvW0+qAEtzAwADnI8k3SqaPwfej5O9r0YVkk8nkkihAHTeCraAUCqkQmpDj8Kak5Kd7z6WkdIfJZOL+3gsNETJD+z8AFiyqHGfDnwqNigFoZeJ+Ag92hUbd+YeHh+fNLBNyf76ACj/i4+OxYcMGHDlyhHMmlzqZ2e12NDY2QqVSoaamxq+5aWxsLGJjY10Wiemi9+TkJJRKJVfBJScngxDikeAoUbpXgvR+Tpw44WIpFWpykUrL0R/XDnclpdPp5GacnpSU7jPOhSrbj6DF6jxCyBp/bxwRz9Bf+EJoLMuivb0der0ea9eu9Xp1L5fLYbPZRDtGPkFOT09Do9EgJSXFJTPNF/hLaCMjI2hra0NpaSnS09PBsixyc3NRX18PAJzKLzU1VVJ2UgC4k1lxcXHAaxR80Kgbd6Nk+lq5GyUDcFFQUoJTq9XIyclBYmIitFotioqKuFYnP5wzVIkCUqnQxNhDc/ekZFmWq5DpOgetkMfHxzE5ORnQDO3LL7/ElVdeCaPRiKKiIrzwwgvc3/7uu+/GU089BblcjgcffBCnn356QM9NTERKyxHA2wzDfIcQ8p4/N5bWmUkk+NpynJycRENDA7Kzs7F+/fo5r16DUaE5nU4XH0ihmWXe7s8XQnM6nZzopa6uDkqlkhN+5ObmYunSpXA4HDAYDNDr9ZwjBCW3lJSUsBEcbY+Ojo5i7dq1Lq3hYMB9vkONkkdHR12MklNTU10Ijv496IWQSqXiInOAr4mStjopUdL7EZt8QiXbnw/BCPeUyWQePSn1ej3uuecefPHFF/j8889x/PhxnHLKKfjGN77h0/3v2LED9957L7797W/j6aefxu9//3vccccdaGxsxEsvvYSGhgYMDAxg8+bNaG1tXZC+kQHiUwCvMwwjA2DHV+bEhJC5E1m/QlQSGoVCoZjTVoplWXR1dWF0dFRwe09sQrPb7VxLytfMMk/whdAmJyeh0WiQn5+PsrIyr8IPhUKBjIwMbu/N4XBAr9dzO19A6AnOZrNxqQY1NTVhOUGr1WpkZWW5GCXr9XqO4Ph+ldRvc8WKFYITBfjp02ImCgRrsdqf4wj2e4U/43zxxRexY8cOXHbZZRgbG8Obb77pM6G1tLTglFNOAQBs2bIFp59+Ou644w4cOHAAF154IdRqNYqLi1FSUoIjR47gpJNOCsbT8gsRUqHdB+AkAPXEDxVVVBOaXC6HxWLx+DPqgZiRkeFTe09MQqP7XTTRWQwIITT+nG7NmjWIj4/nWmVCZmXU8Z4O6vmGwJ2dnS5uHCkpKaJfpep0Os4Q2dNyebhAs9wowdntduj1enR2dmJqagrx8fEwGAzcrhu1aHMnuGAnCkip5RiqeCMKk8mE0tJSnHHGGX7dvrKyEv/4xz/wve99D/v370dfXx8AQKvVYuPGjdzv5eXlQavVinLMYsDflmMYKLANgMYfMgOilNDcF6v54Kv4KioquNaQUIi520YzyzyFBfqL+QjNYrGgvr4eSUlJWL9+/ayqzJ8rd6VS6ZHgxsbGZlUqgRAcragNBgOqqqrCmrIsFAMDA0hKSkJNTQ2cTqcL8QNwaS3SJXt3gpsvUYC6nVCSm4/gpCIKCQexmkymeWdomzdvxtDQ0Kzv79u3D08//TR2796N22+/HVu3buWUq57Ov1J4jSkI/FM5hoHQBgF8yDDM23B1Clm4sn0Kd/Lhiy42bNjg14cpUEKjKsqCgoKgxLzMtag9PDyM9vZ2lJWVIS0tLWiOH54ITq/XzyK4tLQ0wS00i8UCjUaD1NRUVFdXS+pk4QmTk5NobGx0EarIZLJZEnQ6m+zu7gbLsi6p3t4ITiaTzUoUMBgMMBgM6OnpASFkzl0tqRBaOLLJhOyhHTp0aM6fv/fejF6htbUVb775JoCZioxWawDQ398/rwlCaBE0lWMRwzAjAEYIIV7bTAzD1GFmPnYBIeTVOe6v66v/VF/95xOimtBohcZ3pi8vL+cUUf7AX0LjZ5atW7cOcXFxfh/DXJDJZLPmhg6HA83NzbDb7bOEH6Fw/KAzIlph0BYaVQvK5XKugvNEcHQmVVpaKjg4NVygQa9arXbeXTj32SSt4PR6PXp6esCyrEuqt0Kh8Bh6SsNT+TNOuqvV29vL3Q8/F04KhBaOCs1utwfU5hwZGUFmZiZYlsWdd96JK6+8EgCwdetWbNu2Dddddx0GBgbQ1tbGeZ5KAUFUOY5hxq7qL95+4atl6XsAvDvfnRFCbgvkYKKS0PgqR5vNhmPHjnG7VYFeEfpDaHRel5mZ6VVFKdZJxr3lODEx4eKaIQXHD5VKNYvg9Hr9LIJLSUnB6OgozGYzampqJLOY7A1OpxNNTU1gGAa1tbU+v9dolht/x4pmufX19cHhcLgQHLUi80RwNDKHfz8GgwH9/f2Ynp5GS0sLR3ChnmNRhKNCC/Qz9uKLL+Lhhx8GAPzgBz/AZZddBgCoqKjA+eefj/LycigUCjz88MOSUzgGidCMAOYzDr4GwP8HoG6+O2MY5p+Y6ZC6gBCyScjBRCWhUYyOjkKv16O6upr7cAcKXwiNEILe3l4ubsXbvE7MVAB6X4QQdHV1YWRkBGvXrkVcXJxPwo9Qwl1MYbPZMDQ0BI1Gwy0q9/X1cS1KKQga3GEymaDRaJCXlyeaEwWtvDxF3Wi1WtjtdpdUb7VazZGae+gpPzLnyJEjyMrK4lxRaKIAP1suFAh1hUYX4QN57+/Zswd79uzx+LO9e/di7969ft93MOGv2/7o6Chqa2u5r3fu3ImdO3cKvj3DMEsBfB/AJgggNAA38P4dA+BcAILdMaKS0AghOH78OJRKJeLj40UjM0D4FR6d+QipDClJikFoDMPAZrNxycxiCT9CCZ1Oh4GBAaxbtw7JyclcBTc0NISWlhbOsYOeyMNNcMPDw+jq6kJFRUVQjW/5rVng66gb6kJis9lciMkTwVHQRAF6PzQ8le+2wSe4YLxnwlGhLVT46xSStWQJjh49GshDPwDgRkKIU8h7iBDirpD7D8MwHwl9sKgkNIZhUFpairi4OHzyySchf3yaWVZWViaITMXMMKNO5VVVVdz+k9SiXryBLnk7HA7U1NRwYgb3Cs5qtXokuLS0tKAsInsDy7JobW2F1Wp1Od5QgS/fLy4udiG45uZmWK1WlwpOpVKho6MDcXFxLokCdBmZpp7zEwVaW1thsVg4O6nU1FSfInPmQqgrNJvNJvm2dRSiFsBLX71fMgCcxTCMgxDyd0+/zDAMf0guA1ADIFvog0UloQHg0pRDCaGZZe4QaxWgqakJZrMZWVlZSE1NjZioF2BmztjQ0MBFicx1vGq1epZjB22fNTc3Q6lUIi0tLWhOG8CMkTGdi/piHh1MeCI4fuU1MTGB2NhY5Ofnw+FwICYmxiXVmx+Zk5CQgKSkpFmJAvx0cEpwc0XmzIVQV2hGo3FOkU60IxyL1fw4GIZhngXwhjcy+wrHMDNDYzDTauwCcIXQx4taQgs1aGbZsmXLOO8/oQi0QqOrAIWFhYiLi0N/f3/YhR9CQQjBwMAA+vr6/G7ZuRMcNRWmBEdd89PS0kTxShwbG0NbWxvKysoCsikLNmQyGdeSHRoaQmVlJWJiYqDX612y3GgFFxsb6zUTLj4+3sVOivoldnV1uSQK+JI8HWq15UKNjgGCqnIsBvBfABkMw/QDuAWAEgAIIY/5emeLeWgCEIwPDr1PGjVjNBoFZ5a5Q2hqtadj6OzsxNjYGNatW4fY2FhYLBZMTU3h2LFjPu96hRq0qpTJZKirqxPtGD2ZCvNzz1QqFVfB+UJw9PWemJiICNUlAAwODqK3t9dlhSApKQmFhYVc5UXdTGjlRQkuLi7OK8HFxcUhISGBSxSYnp7m1gRoeColuMTERK+fv1ASmpCl6mgFAQMnG5RzQBchpHb+XwMIIdvn+x2GYX4I4B1CyBTDML8BUA3gTkLI50IeI2oJjX5QqL2QmB8c2iKcnp5GQ0ODz5ll7hCSWu0Os9mM+vp6pKamoq6ujhN+KBQKrF+/nvNbHBkZ4ey1wjFn8ga6eFxYWOhzResrPBGcTqdDf38/JicnuWBP2qL09Hek3pFJSUmoqqqSRItxLrAsi7a2Nm6+58k9hKZxJyYmcq1Fk8nELXrTyosSXHx8vEtkDr8LQKN3aH6e2Wzm1gSMRiPUarULwYXj/begW44EcDikd1HrAb8lhOxnGOZkAKcDuBfAowA2CLlx1BIaBV2uFvNqWiaTobOzEzqdzq/MMk/35wuhDQ4OorOzk1sS9yT8cF9mdp8z8asUbyfxYIAuuQ8NDc27eBwsxMTEIDc3l3Ny4CdX8wmOtignJibQ1NQkOe9Ib6C5dunp6Vi5cqXgvy2dnSUkJMxyqudXXlT96E5wwNeROTSZgP8auycKUBPmUF1gLeiWI2HgdETE6Z6eCM8G8Cgh5ADDMLcKvXFEPMNAIDahTU9PY3JyEvHx8X5nlrlDaMvR4XCgsbERADhnfqHCD09zJn6VQk9UaWlpgmcgvsJut6OhoQExMTGora0Ne5VI4R7sSQmut7cX4+PjYFkW+fn5UKlUknHZ8IaJiQk0NjZi5cqVAa+r8J3q3cNK+/r6MDU1xZE/nZ15IziaTECrZKvVimPHjmFoaAhtbW1QKBQu2XLBaJEbjcaF23IkDJyRUaFpGYb5M4DNAO5hGEaNGbWjIEQ9oYnljk8tjXp7e5GcnIz8/HzRTshCCE2v13PegDk5OWBZNiDhB79KoScqnU6H7u5uGI1GxMXFcUu9/qrY+KAhnMuWLeOqRqkiNjYWCoUCo6OjyMzMREFBATcfmpqaCgn5+wNquUXnqWKDn8ZNW4t0PqnVajE5OQm1Ws0RXGJi4qwdOEpwcrkcCoUCZWVlAFzt0Gi2HN8VRQyCW8gVGggihdDOB3AGgHsJIQaGYXIA/ELojaOW0OZy3PcVNpsNDQ0NUCqVWL9+PVpaWoIS8ukJ1ANSr9ejqqoKsbGxojt+8E9U9EqczlLa29sxPT2NhIQEjuB8OVnSqJqxsbGQhHCKgampKTQ0NKCoqIiraOPj413mQ3TORMmfzuDCQXAsy6K5uRksy6KmpiZkAiCGYbxWt7S1SBWm9LWhBKfT6bi8QkpwnhIFxsfHuUgid19LX2EymRYsoRHCwGGXPqERQqYBvMb7ehAzDvyCELWERiGXywMiNOovuGLFCu7DFozUak8V2vT0NOrr65GRkYG6urpZVVmwTpyeZilGoxE6nQ7Nzc2wWCxISkriZnDelJ1WqxUNDQ1ITEwMWwinr9Bqtejv7/c63/NUpdA5kzvBpaWlIT4+PqgER+OAsrKykJ+fH/Zq0Z3g+CsUk5OTUCqVYBgGVqsVa9euhVwu95gJ554oYLfbOV9Lmpo+V6KAJxiNRsl3BxYRGKKe0OicyVfwM8vq6upcZnBiE5pcLofVykX/cLtZPT09KC8vR3JyclgdP/hquMLCQm5hV6fTobGxETabjbt6TktLg0qlwvj4OFpbW0WZ5YQCTqcTzc3NIIT4ZCzsac5ECa6zs5NTClLyF5Pg6MK0lPfh+ApTlmXR0NAAq9WK5ORknDhxAgqFgqvg6HyLEhx/XimTyXxKFPA0M1/IFRrAgHVG/ek+egmN77jva4U2X2ZZMCs0u92OxsZGyGQyrF+/nnssKTl+0IXd5ORkzpFiYmICOp0OfX19mJ6eBsMwWL58uc8BquEANRYW4lIyH7wRHA31NJlM3K6XvwRHlaLDw8MRE3Rqs9lw4sQJLFmyBAUFBdxzdk9a4IfBUvWtt0y4uRIFnE4n51dJfS0XNKERAJExQwsIUUtoFL5UaPx51VyZZcGo0JxOJ3Q6HZqamrB8+XJkZWUFLPwIFejJJTY2Fnq9HkuXLkVqair0ej2++OILEEI4t3chicqhBDUWLi8vDwr58gmO77LBr+AowQkR4NCIGplMFjFtXDqTLCkpmbX24ClpwT3tnJ/qTS/+PEXm8BMFaDKBwWCAVqvF7t27ERMTgyVLlqCsrAx5eXmCjn3//v249dZb0dTUhCNHjrg4z99999146qmnIJfL8eCDD+L0008HALzzzjvYs2cPnE4nduzYgZtuuing1zBgEGaR0KIBCoXCpZ3nDfzMsrq6ujlPKmITGjBjnWUwGFBdXY2YmBjJRr14w8jICGfITNtf9OqZLnnTKoVhmLC7mNDFY5q1FipjYU/zSXcBTnx8PNei5BMcXabPzc0VfEION0ZGRtDZ2Sl459A9K4+KQ+h7B4BLqvdcBEfb4MXFxTh06BC2b98OvV6PHTt2QKFQ4I033pj3eCorK/Haa69h165dLt9vbGzESy+9hIaGBgwMDGDz5s1obW0FAFx11VU4ePAg8vLyUFdXh61bt6K8vNyn1010EAAO6Z9HAkXUEhq/5TgX+dDMsoGBAVRUVAi6SqfBoWLAZDKhtbWVs38KlfBDLLgTg6fZhUKhwJIlS1wG/Pw2E52jhMrFhAoplixZ4tPicTDgjeB0Op2LwlSlUmFsbAzl5eWSnZfxQfP4DAZDQBcMSqXS5b3jcDi4VG++OISvfvQUeqpUKsGyLK677jrOFUUIVq1a5fH7Bw4cwIUXXgi1Wo3i4mKUlJTgyJEjAICSkhIsW7YMAHDhhRfiwIED4Sc0wIdUschF1BIaxVyyfZpZlpCQwM2rhECMCo2/11ZcXMwt8EZK1Aswo8LUaDTIzs72iRjC6WJCxSpSFVLwCa6goIC7YBgdHUVcXByam5u5zDMxo1zEhNPpRENDA9RqNdatWyfqBYpCoUBGRsYscYher0dPTw8nDqEEp1Qq4XQ60dfXh88++8zFEi8QaLVabNy4kfs6Ly8PWq0WAJCfn+/y/cOHDwf0WKKAYJHQogHeyIfaRwnNLBNyn0LhvtdmsVjQ3d0NnU4nicBKIRgcHORUmIHOnkLhYkKNhWlbV61WB3TMoQAlBpVKhW984xuQyWQuKxStra0wm80cwaWlpQUtjFMoLBYLTpw4wQlsgg2FQoH09HQXcQit4Pr6+nDfffeBEIIvv/wSzzzzjAvZUGzevBlDQ0Ozvr9v3z5873vf8/i4nio8KmDx9P2wY5HQIhveFqupipBhGJ8yy/gIhNBozExJSQkyMzPBsixUKhUKCwsxNDSE1tZWrkJJT0+XlBMF8LW8nWVZ1NbWBkXgIbaLCd9YuLq6WlKvpzfQHcT8/HxupwuYvUJBCHHJPLNYLEhMTOQq3FAuslPbrXBWv3K5nCM4QgjOOeccPPnkkzjnnHNw//3349Zbb8XHH3/sMs87dOiQz4+Tl5eHvr4+7uv+/n7u7+Tt+2EFAWAP90EEH1FLaMDMh59PaIFklvHhD6HR1tHk5CRqamqgVqtdhB/8CsX9BO6LCi6Y8CWEUywE6mJiMBjQ1NSEkpISbg4jdVCFn5Dql2EYJCUluUTC0B1B/hI8v0UZDAwODqKvry9otlu+gmVZ3H///fj444/xxhtvcAQrVmr11q1bsW3bNlx33XUYGBhAW1sb1q9fD0II2tra0NXVhaVLl+Kll17C3/72t4AfbxHCENWEBny9h9bU1ASTyeR3Zpn7ffpCaFRBmZWVhdra2nmFHzSGgzpRUJEAFV9Ql460tLSQtM7ovE+r1aKioiKsuzxCXUxSU1O5BWepnGTnAxVS6PV6VFdX+3Xi5RNcUVHRrNRqq9U6q0UZ6DHTC4vq6mpJrGTYbDbs2bMHSqUSb775psvr6Otr+vrrr+Oaa67B6Ogozj77bKxbtw7vvvsuKioqcP7556O8vBwKhQIPP/wwN4N/6KGHcPrpp8PpdOLyyy9HRUWFqM/PLxB87WMfxWCEqn2+gk+/HG7QnZbDhw+jtLTUZaEzEFitVtTX17vspHgCIQT9/f3o6+tDZWUlEhMTAxZ+8F06dDod7HY7t3+TmpoquvycOvwrFAqUlpZKMiiUD5ZlOSNkp9PJubjzXUykCIfDAY1Gg7i4OJSUlARtjsonOJ1OB5vN5tKi9IXgHA4H6uvrkZSUhGXLlkmilavT6XDppZfizDPPxPXXXx8R8+hQgCmrBR4/6vPtaq6rxdGj3m/HMMwxoQGfoUD4L6eCiK6uLgwNDSE2NhaFhYWi3a+QCo3ObdRqNTZs2MAZEAfq+OHu0kEdEmiLEgB38k5JSQmIgGgOGN+kV+qgaxDLli1Ddna2i4sJdZBITk4O2gWAv8dcX18fkteZ//7hV3B8GzN+i9IbwdEZX2FhoWTeG+3t7di+fTt+85vf4Ac/+EG4D0daWBSFRD6Sk5NRUFCATz/9VNT7nY/QxsbG0NLSghUrVmDJkiVci5FhGNGvGOVyOdd+BL5eRKVzGP7Phe540d284eFhrFmzxqtjitQwMDCAvr4+lyVevpUS4KqC6+npCbuLCV08rqysDEsr15ON2eTkJPR6PRoaGmC3212MqNVqNXQ6HVpaWgTvbYYC//73v3HDDTfgqaeeQl1dXbgPR3pYJLTIR3p6uqDgTF/hrbpiWRatra0wGo2ora2FSqUKueOH+yKq1WqFTqeDVqtFU1MTYmJiOILzpKCkKwVxcXGSCuGcC74YC/NVcMDXi7qhdjEhhKCjowNTU1MhdSqZD9RqKiUlxYXg6HvIZDKBEILly5dLYvWBEIIXX3wRTzzxBN58802PsvxFYJHQogH8JUqWZYN6cjYajaivr0dOTg5WrlwJQogkHD/UajXndg6AM8rt6urifATpioDFYuFWCiJFEWgymdDQ0IDc3Fy/lJfui7ruLib+VLjzwW63Q6PRIDExEevWrZPE7MkbKMElJSXBarVCpVIhNzcXExMT0Gg0sNvtLi3cUM4oWZbFvn37oNFocOjQoQWbRi0Ii4QWPaDS/WB82KjzuVar5dpGUnb8cJfAG41GjI+P4/PPP4fVakVGRgYcDgesVqskrsDnQjCMhd1dTGw2G3Q6nYuLCd+my9e/LzXqjYTkbgqbzYb6+nqkp6ejsLAQDMNwFa570oLD4eBEOMEkOLPZjJ/+9KfIycnB66+/Lgl1peSxSGjRgWCYCQMz7TyqTFu/fr1owo9QgWEYLrssKysLRUVFnAS+vr4eTqfTRUEplZNGKI2FVSrVnC4m87Vw+RgaGkJ3d7dgo14pgK6cLF++3GPV7mlGSa2o+vr6OBGOmAQ3MjKCSy65BBdeeCF+9rOfSf5zJgksLlZHPry5hYgBh8OBo0ePYuXKlcjIyAiq8CNYGBsbQ1tbm0sIJ52fLFu2jBNQ0BalFFzyw20s7MnFxD2t2t3FhGVZtLe3w2w2B81dJRgYHR1FR0eHT4IVd5GSN4LzV2Xa2NiIHTt24K677sJZZ53l83NaRHQjMj5ZAULMCs3pdKK1tRV2ux21tbWIjY2NuKgXmvs2NTU1p6+hu4CCP19qbW2FUql0mS8F+7lLzViY72JCl+DpjJIuG8fFxcFkMiEjIwNr1qyJiPcHIYTzFg20AvZGcDqdjjMT5rco53qsDz74AHv37sVf//pXrFmzxu9jWpBYIIvVC4LQxKrQpqamuGRjegKXgvDDF5jNZmg0GmRkZKCqqsqnY3afL3kyEaYnL3+SmL0hUoyF3cM8qXAiNTUVJpMJn376qYsEXopJ006nE42NjVAqlaiqqgr6mon7GgUlOLpGoVQqQQjB008/jZdffhnvvvuuZPbeIgqLopDIh9BMtPlACEFPTw8GBwe59sv4+Dj6+/uRmZkp6sk7mKAhnKtWrUJKSkrA9+fefqPVSUdHx7wei0LBNxYOxgk2WKA7cVVVVdwen6clZjpfkoKLCW3n5uTkhCxA1L0LwG9zHzlyBLfccgu3fvPqq68Ghcx+8Ytf4P/+7/+gUqmwfPlyPPPMMx4/H0VFRUhMTIRcLodCoZjTQUNyWCCEFtXWVyzLwm63o7u7G0ql0q84C2pzlZCQwM1snE4nLBYLxsbGoNPpMD09HXJ/RV/gdDrR1tYGq9WK8vLykOw88U1yqcVSUlIS0tPTBYsDItFYmO4i2u12lJeXzzln5CsE9Xp9WF1MqFN+aWkpVz2FG0ajETt27EBiYiLy8vLw73//G9XV1fjTn/4k6uO899572LRpExQKBW688UYAwD333DPr94qKinD06FFuxSOSwBTWAnv9sL56fNH6SnLwt0Kju0ilpaXcVaKNEiV9AAAgAElEQVTT6QTDMIiNjUV+fj5nkEuXTzUaDSddpifvcPof0j2t7OxslJaWhqyS9GSSS0/evb29Lq0ldwUlXYUYGhqKGGNh4OuLnyVLlgh6raXiYjI4OIje3l5JvdYDAwO4+OKLsXPnTlx22WXcaxkMo4TvfOc73L83btyIV199VfTHkAQWQIUW1YTGVzmazWbBt3M6nWhpaYHFYkFdXR2XeutNjs8wzCx/RYPBgPHxcXR0dARlOVcIBgYG0NvbK+qelr/gn7yXL1/u4tBBFZRUPanVaqFSqSLGqQT4upoMpMIJtYsJdSsxGo2oqamRjPryyy+/xK5du/DAAw9g06ZNLj8L9vvh6aefxgUXXODxZwzD4Dvf+Q4YhsGuXbuwc+fOoB6LqFggLUdpvIODDF9EIZOTk9BoNMjPz0dZWZmL44fQ3TL3ExNdzqX2U7GxsUhPTw9avpnD4UBzczMASFYm7u7QYbPZMDAwAI1GA7lcjvj4ePT29iItLQ2JiYmSnVHSRIXBwUHRKxyhLiapqak+J51Td//4+HisXbtWMq/vW2+9hbvuuguvvPIKysrKRLtfIanU+/btg0KhwEUXXeTxPv7zn/8gNzcXIyMj2LJlC8rKynDKKaeIdoxBxSKhRQ+EtBypVJka8sbHx4vm+MFfzqW7S+Pj45y0W8z5G3WiKCgoQE5OjmROVPNhbGwMw8PDqK2tRUJCAqeg7O3txdTUlM8p1aEA9ZAEgJqamqC3lr25mAwODqKlpcXFxSQxMdErwZnNZpw4cYJ7j0gBLMvi0UcfxVtvvYX33ntP9DnVfKnUzz33HN544w28//77Xt9bNHk6MzMT3//+93HkyJHIIrTFxerowHwVGlV3JSUlYf369ZzwIxiOH/zdJTHnb7RSGBgYCJtzuz+g7V2n0+nS9nJXUNKQU3oRQDO8xAip9Af0PZOdnY28vLywEKw/LibUKb+8vBzJyckhP2ZPsNvt+MUvfgGz2Yx33nkn5KKqd955B/fccw8++ugjr8kSJpMJLMsiMTERJpMJ7733Hm6++eaQHuci5kdUE5oQ2f7w8DDa29tRVlaGtLQ0F+FHKOY3Yszf7HY7mpqaoFQq53WblxKmp6eh0Wg4mbg3UuCnVBcUFLhcBNCIE6oOTEtLC7o6kJKCWOsPYmE+FxO6NymFmSrFxMQEtm/fjm9961v49a9/HZaZ6dVXXw2r1YotW7YAmBGGPPbYYxgYGMCOHTvw1ltvYXh4GN///vcBzLRrt23bhjPOOCPkx+o3FshidVTL9oGZtgy9muYnTNM5k91uR0VFxbzCj3CBtpXGx8e55WX+/G1ychJNTU0oLi5GVlZWuA9XMGgO2KpVqwKuFGhKNZW/E0I4AYqYKlOaEzcyMoLVq1dLcjHaE1iW5UROaWlpMBgMou0JBoKenh5ccskluP7663HhhRdK5jMXjWCW1gK7/JDt/2NRti85uFdoExMT3JyJXs36KvwIFbzN39ra2jAxMQFgZj9GSpXCXKC+hiaTSTRjYZlM5uI+4XA4oNfrXarc1NRUpKen+60ypQ4aCoUCNTU1EaO+tNvtOHHiBNLS0lBWVgaGYVBYWMglLeh0OjQ3N8NisXBJ1aFo4x45cgS7d+/Go48+im9+85tBfaxFYFEUEk2ghEYIQVdXF0ZGRrB27VrExcVJOurFHXT+plAoMDY2huzsbGRmZsJgMEhu/80TLBYLNBoN0tPTg5oDplAoXEJO+REwQkJO3WE2m1FfX4+lS5f6tZwfLszllM8wDBITE5GYmIjCwkKPLiZ8sZJYLiaEELz22mt48MEHceDAARQXF4tyv4uYB4uEFh2gRMWyLI4ePYrk5OSgCz+CCTq/4btnpKamcvM3vV7P2U+Fa//NE6ixcDicKNzFE2azGTqdjpst0ZBT2nrjvxfocUtJRCEEY2NjaG9vFywQkslkLrNcugiv1+vR398viosJy7K499578d///hcHDx6MmK6CJ9DAYHr+kDwWVY7Rg6GhIUxPT6O8vBypqakhF36IAb5Bb1VVlceWkFwun7XbFcr9N2/H3dXVBb1eLxlj4djYWK7a4iso6ZyJtt5MJhMmJydRU1MTdp9FoaC+o+Pj46iurvb7uPmL8DRKiO+S76uLidVqxe7duxEfH4833ngjpLZeYuPmm2+GUqnEySefjJNOOikyZqmLohCPiDhRiEajgclkgslkwje+8Y2IrMosFgsaGhq4nDJ/jps/fwuV/6TNZkNDQwMSEhKwfPnyiLh4YFkWer0eLS0tXMo536JLyidilmXR2NgIuVyO0tLSoL7efBcTg8Ewp4vJ+Pg4Lr30Unz3u9/FtddeGxHvA09wOp3Yvn07CCE499xz8fzzz6OmpgYXX3wxCgoKwn14c4LJqgUu8EMU8smiKERSKC4uhkwmw6effgqz2QylUhlRZDY6Oor29vaAW3XB2n/zBmp2G0nGwsBMO7K9vR3FxcXIyclxcX/v7u4GAO7EnZKSIpk5pdVqxYkTJ5CdnY38/PygP958LiYMw+Dvf/87Vq1ahUceeQS33XYbzjnnnKAcy29/+1scOHAAMpkMmZmZePbZZ7klaD6ee+453HnnnQCA3/zmN/jxj3/s0+MMDg5iZGSEc+aXy+XYvXs3ZDIZLr/8cm7hXZJYIDO0qK/Q7HY77HY7t3RMPQMDUbyFAnw1YEVFRdBbXvz5m16v93v+xjcWXr16tWTMboWAJjRXVFQgMTHR4+/QEzetTJRKJUdw4Xo/TU5OoqGhQVJO+dPT07jvvvvw+uuvAwAKCwtxxhln4Oc//7nojzU5Ocnt1T344INobGzEY4895vI7Op0OtbUz1QbDMKipqcGxY8d8Coo1Go3YvXs3TjnlFGzfvh3Dw8P4yU9+gqKiImzbtg0bN27kZmtSA5NZC5znR4V2ZLFCkxQuueQSrFixAps2bUJNTQ3XUqKKN2qplJ6e7tUlINSgIZxLlizBihUrQlJNijF/czgcXDhkKKygxAKdT05MTMy7SuBuP2W1Wl1ep5iYGO51CkVO3vDwMLq7uznVrhRAlYwffvghDh06hLy8PGi1Wpw4cSIoj8dfEjeZTB5f83fffRdbtmzhCH/Lli1455138KMf/Ujw48TGxuKb3/wm3n77bbzzzjtobW3Fr371K9TX1+OZZ57Bxo0bJUlmABZFIdGCBx54AIcOHcIzzzyD3bt3o6SkBJs2bcL//M//oKysjJsrtba2wmw2Izk5mTshhWNeMjw8jK6uLlEWjgOBr/6TVCJeWFgoGX9AIbDb7dBoNEhISPA5wRsA1Go1cnJykJOTw71O1B3fZDIFbXmZkjAVrUjFgJplWdxxxx1obm7GwYMHOYVlsFce9u7di7/85S9ITk7GP//5z1k/12q1Lq1YSrJCYbfboVQqsW3bNmzZsgXHjx9HVlYWNm7ciFWrVuFvf/ubKM8jaFggohBpfAqCiOzsbFx88cW4+OKLuaH5u+++i+uuuw5DQ0PYsGEDNm3ahG9/+9tISEjAxMQExsfH0dPTE9L2pNPpRGtrK2w2m2gLx2Jhvvmb2WyGw+HA8uXLpT1HcAMlYbFcVvivU15ensflZb5Fl79tZKfTCY1Gg7i4uKDu8/kKs9mMXbt2oaCgAK+99pqoFfp8bvn79u3Dvn37cPfdd+Ohhx7Cbbfd5vJ7nkYrQl83p9PJfR5bWlqwbt06TgTS29uLa665BqeddpqvTym0WJyheUTEzdDmgsViwX/+8x+8++67+Oijj6BSqXDaaadh06ZNqK6uBiGEs52amJgImuzdZDJBo9EgNzc3bEa3/oAaC9vtdmRnZ3N7S1Laf/MGWgmH0siZZVlMTk5ifHycS6j2FnLqDXTJOy8vz6PwIVwYHh7GJZdcgosuughXXnll2N7DPT09OPvss6HRaFy+/+KLL+LDDz/En//8ZwDArl27cOqpp87bcnQ6nRwxb9++HVarFc8//zyAmc/tli1bcMYZZ8wiUKmBSa8FTvdjhtYaWTO0BU1ofBBCMDIygoMHD+K9997D8ePHXdqTBQUFsFgsGB8fx/j4uCjtSUIIlxY8lxBBipjLWHg+/8lwEjYhxEVsE85KmC/EcZe+p6SkzLoQoCGiUjNFbmxsxBVXXIF77rknLIa9bW1tWLFiBQDgT3/6Ez766KNZqdM6nQ41NTX4/PPPAQDV1dU4duzYnCIaKvAwGAzYtm0b8vLy8Pjjj7v8zuDgYES02Jm0WuB//CC0rkVCiwrw25MHDx702J6kV9s6nQ6EEK49KSRskR/CWVZWJpkZiBD4YixMCMH09DR0Ol3I9t+8wWazQaPRIDk52e99vmDCbrdzr9PExASUSiX3Ok1NTWFgYABr1qyR1CLvoUOHcPPNN+P5559HZWVlWI7h3HPPRUtLC2QyGQoLC/HYY49h6dKlOHr0KB577DE8+eSTAGbSqO+66y4AMzO3yy67jLsPb44f9fX12LVrF84880z89re/BeBatUWKUwiTWguc5geh9S4SWlRCaHuSXm1TtRtVT/Lf9PwQTim1jeYDf5WgsrLS76qUzt90Ol3I/CcnJyfR2Njo0ddQqqAdgZ6eHlitVq56C5WCci4QQvDUU09h//79ePXVVyMq6cEdfKm92WyGWq2GTCZDa2srtm3bhquuuoojP6nK8ucDk1ILnOIHoQ0sElrUY772ZGFh4SxXDioGMJvNGB0dRUVFBeLj48P9VASDbyxcVFQk2slUrP23uUDbupWVlRH1mtvtdtTX1yMlJQVFRUWcgpK+p8IVcupwOLB3716MjIzg2Wefjahdw7lw5513cqsXO3fuRFVVFXp7e1FSUgIgcqoxT2CSa4Fv+kFoI4uEtuAgpD3Z29uLrq4uyGQyl+pNSHsy3KA+h6FY3BVz/sayLNra2mC1WlFeXh5RbV2TyYT6+nosW7bMo3KUEMK54+t0OthsNhfz4GAt4huNRlxxxRVYu3Ytbr/9dsm/d+eDw+GAQqHA3r170dnZiT/96U8477zzkJubixdeeIEzNo/05xlEQhsHwAIYIYTM6jkzDHMRgBu/+tII4KeEkC99PhCBWCS0IMC9PWm1WqHX6/Hzn/8cl19+OQghXF7XxMQE1Gq11/ZkOEEIQXd3N8bHx1FZWRny2U0g8zer1Yr6+npkZGSgsLBQMq+pENC8O1+EQtQdn75WLMtyLUqxWrlarRYXXXQRrrrqKlx66aUR9ZryMTo6ildeeQVXXXUV9737778fl112GW6//XZ0d3fjb3/7G1QqVcQTGQWTVAts8IPQ9PMSWguAbQD+4oXQvgGgiRCiZxjmTAC3EkI2+HwgArFIaEEEy7L43e9+h7///e+44IILcPz4ca/tSVqV0JM2rUrC5fIuRWNhofM36iO5cuVKpKenh/mohYPaho2MjGDNmjUB/e1pyCmd6fJDUP3pChw/fhw//elP8eCDD+LUU0/1+7jCjdbWVpx11lkwGAz42c9+httvvx0A8IMf/AAff/wxrr76atx6660AgMcffxzZ2dnYunVrGI9YHDBJtUCtH4Q2OX/LEcB5AN7wRGhuv5sKQEMICdqGfeT0YCIU6enp+Ne//sUJKIQsd1P1ZF9fHwghXNqyJyl3MEAJQWqL0gzDuGR2ecp/UygUmJ6exrp16yRjBSUELMuiqakJDMOguro64L+zt5DTwcFBNDc3Q61WcwSXmJg4Z7X1xhtv4H//93/x6quvYuXKlQEdV7jhdDqxY8cOXHTRRTjvvPOwdOlS7Nq1Cw888ABOPvlkrFixAoQQ3HvvvXjhhRewf//+cB+yOPBzsXp0dBS1tV+PyHbu3ImdO3f6exRXAHjb3xsLwWKFFmbMp54E4KKepO3JYCjdCCHo7+/H4OAgKisrI44QGhoaYLFYEBsbi6mpKUntv80Fm82GEydOIDMzE/n5+SE5Tr7AxGg0cp6m/NeKZVk89NBDeO+997B///6IqnbnAjUz/uCDD3DttdfinnvuwZlnnolPPvkE1157LVatWoWRkRE899xzkrqgCwRMQi2wxo8KzSZOhcYwzGkAHgFwMiFk3OcDEYhFQpMQhKgn+cvdYrYnqbGwQqFAaWlpxBgLAzMXBfX19cjKyuIIQUr7b3OBrnCsWLEibITBDznV6XT44IMP8Omnn8LpdCIxMRF//etfg/Z6CY1+kcvlWL16NQCgoKAA//jHP0R5/Oeffx6/+93vcOjQIWRmZuLjjz9GXV1d1Cg3KZj4WqDcD0IjgRMawzBrALwO4ExCSKvPB+EDFglNwphPPZmYmOiy3E2FAL62J6mnYaTtxQGAXq9Hc3MzysrK5owC4c/fxsfHOdupYO+/zQW6oL569WpJrROMjo5i165dkMlk3LL3tddei0suuUT0xxIS/QIACQkJMBqNoj8+MDMre+CBB2C32/HrX//aZeE6WsDE1QJlfhCaLDBCYximAMAHAC4lhHzi8wH4iMUZmoQhk8lQWVmJyspKXH/99S7tyT/84Q8e25M0ZLG1tRUqlYpTT3prTw4ODqKnpyeknoZigAoohoeHUVVVNa8CU8j8LVT+k4QQdHV1wWAwSM6Iuru7G5dccgluvPFG/PCHPwTDMLBYLJiYmAjK4wmJfgk2iouL0dXVhd/97ndRSWYc/DEnnr/pUwzgvwAyGIbpB3ALACUAEEIeA3AzgHQAj3z1t3UEc29tsUKLUPjSntTpdDCZTEhMTOQITi6Xo6WlBQ6HI+J2tJxOJ5qamiCTyVBWViYK+YTKf9LpdKKhoQFqtRorV66U1Fzv008/xbXXXovHH38cGzduDNnjuke/eHJyUSgUWLduHRQKBW666SbR0q9tNhvOP/98XHHFFfjud78ryn1KEUxsLVDsR4UWt7hYvYgwQGh7UqfTYXR0FEajEcnJySgqKkJKSkrEzMyo2zxNJggGgjV/s1gsOHHiRNCzwXwFIQT79+/Hww8/jP3796OoqEjU+58v+oXi7rvvhsVi8ehcPzAwgNzcXHR2dmLTpk14//33sXz5clGOj2adRTMWCc0zFgktQuBNPalUKvHZZ5/hkUcegdPpxPj4OAwGg6D2ZLhBg1hD7TYvxvxNqk75dFfys88+w0svvRTWUFlv0S/u2L59O/7f//t/OO+880J0ZJEPJqYWyPeD0JIXCW0REgMhBAMDA7jyyiuh0WiQmpqKwsJCl/ak1Wrl1JP89qQUFIGEEPT09GBsbAyrV68O+/H46j85MDCA/v5+rF69WlLqOYvFgmuuuQbJycn44x//GJYqRUj0i16vR1xcHNRqNcbGxnDSSSfhwIEDKC8vD/nxRioYdS2w1A9CS4ssQoucwcki/AbDMHjxxRe5EwGAOZe7ExMTMTU1hfHxcWg0GjgcDhf1ZCjbk3TmpFKpRFk4FgNyuRwZGRnIyMgA8PX8TavVoqmpiZu/paamQqvVwmKxoKamRlJt3bGxMVx66aU455xzsGfPnrBV5DfddNOs6BcALtEvTU1NnOqSZVncdNNNi2TmKxYTqz1isUKLUMzlFD7fcjfDMNw8Sa/XQ6VScdlvCQkJQTsZTk9Po76+Hvn5+RGzTkDnb6Ojo+jp6QEArpUrhWoXAFpaWnD55Zfjtttuiwpbp0XMD0ZVC2T4UaHlRlaFtkhoi3CBEPUkbU9SlwkaY5Keni7aCXtsbAzt7e0oLy93kXZHAqanp3HixAkUFxcjMzNTUvtvH3/8MW688UY8++yzqKqqCuljLyJ8YJS1QIofhFa4SGiLiCIIUU/S9iQ1DA6kPUl3tPR6PVavXh02c2Z/QYUrFRUVHok4FPlvnkAIwV//+lf85S9/wf79+yWlslxE8MEoaoEkPwht2SKhLSKKIaQ9SaNxDAYDFAoF13Kbrz3pcDig0WgQFxeHkpISSczLhIL6YA4PD/skXAnF/pvT6cTtt9+O9vZ2PP/885JyJVlEaMDIa4F4Pwht5SKhLWKBQGh7kp6wjUYjEhISuBM2392D2m8VFRUhOzs7jM/Kd7Asi+bmZhBCsGrVKr+JOBj7b9PT09i1axeKi4txzz33SEqYsojQgZHVAjF+EFr5IqFFPd555x3s2bOHi6K46aabXH7+hz/8AU8++SQX4fH000+jsLAwTEcbOnhqT27cuBGnnXYa1540Go3cegDNM5PL5RgdHUVlZaXgQEupwGazcUGiBQUFoqcfBDJ/GxoawsUXX4wf//jH2LlzpyR3CxcRGiwSmmcseEJzOp1YuXIlDh48iLy8PNTV1eHFF190kRH/85//xIYNGxAXF4dHH30UH374IV5++eUwHnV4MF970ul04uDBg0hJSYFCoYBSqeTEJfNldEkBtKosKSnhJPzBhC/zN41Gg5/85Cf4/e9/j+985ztBP7ZFSBsMUwso/CC0tYuEFtX473//i1tvvRXvvvsugBm7HgD41a9+5fH3jx8/jquvvhr/+c9/QnaMUoR7e/Lo0aOw2WxYu3Ytbr/9dhQVFcFms3HikqmpKa/tSSlACk757vO3kZERNDU1ISsrC0888QReeOEFVFRUBP047r33XvziF7/A6OioR2J/7rnncOeddwIAfvOb3+DHP/5x0I8pUvGVe30QUFMDxndCA2GmATTN8RulhBDJtFUWF6t9hFarRX5+Pvd1Xl4eDh8+7PX3n3rqKZx55pmhODRJg2EYZGVl4eKLL8aaNWuwfft2XHnllbDb7bj++uvnbE82NjbCZrMhNTWVq0jCNQsihKC7uxt6vT7sTvkqlQrZ2dnIzs7m3FTefPNNPPfcc0hISMB9992H8847D2eddVbQjqGvrw8HDx5EQUGBx5/rdDrcdtttOHr0KBiGQU1NDbZu3Tpn1M9CRrCqHYapJX6WI01zHRPD+MOSwcMiofkITxWtt9bY888/j6NHj+L/b+/uY6qu2weOv788aDu1UrptM5QJESjPCvjDYYWSyc8ces90WlL9SHPKbQtxjtbwZqXljO4toN2ZypQ/gJr7VczhybDBvTDmjxOR3kDRhMFhdiscRFJ5OPD9/aHnDOTp8HCeONdrY/Mcvl/OxT9cfq7v9bk+5eXl1g7Lqdy6dYuCggIWL14MYNHROIqicPPmTdrb27l69SoeHh42L0/29/dTW1uLp6cnERERDtWF2d/fz6effkp/fz+//vornp6e6HQ6Ojo6rPq5qampHD16dMiQ4cG+/fZb1qxZg5eXFwBr1qxBq9Wybdu2cX92U1MT69evH3e2oxAmktAmaMGCBbS0tJhf6/X6EadYlJaWcvjwYcrLyx1iOoQjWbly5bD3HnroIeLj44mPjx9SnszLy2Pv3r1DuieXL19uLrc1NzfT1dXFww8/bN4eYI3ypOlU7Pnz51ttyv9kdXV1kZycTGRkJDk5OeZEu3z5cqt+bnFxMd7e3oSHh496zUgVjdbWVqvGJVyXJLQJio6OpqGhgcbGRry9vSkqKqKgoGDINdXV1ezatQutVssTTzxhp0id1+Dy5Pbt24d0T5pmT45UnjQYDOby5OBuwKme9dbZ2Ultbe24p2Lbg16v55VXXuGtt95i+/bt075SHevolw8++IDz58+Pef9EKhojMRqNvPbaa1RXVxMQEEB+fj4ajcbi+4XVfW7vAAaTppBJKCkp4e2336a/v5/k5GTeffddDh48SFRUFImJiTz//PPm/80D+Pj4UFxcbOeoZw5LNnffvHnTvJ/L3d3d3Fzy6KOPTugP6rVr12hubiYsLMyhJuUD6HQ6UlJSyMnJ4bnnnrPpZ1++fJn4+HhzcjFVKi5dujRkH2FhYSFlZWUcO3YMgF27dhEXF2dxydHX15cffviB2NhYkpOTCQoKYv/+/db5pRyDVWrnihKpQuUk7pzlUF2M45GEJpyaJZu7+/r6zHvfBpcnvby8Rk1Sqqry+++/c+fOHYKDgx3qRG9VVSkuLiYrK4uioiLz8Sv2tGjRIqqqqoZ1ORoMBiIjI/npp58AWLZsGTqdzvxMbSxNTU08++yzNDc3A/D999+TnZ3N119/Pf2/gOOwUkJbpsJkOq01TpXQHOeptpgwrVZLYGAg/v7+HDlyZNTrzpw5g6IoY+4ncVaDy5P5+fnU1NTw/vvv09vby759+4iNjSU9PZ2qqip8fHyIiYnBz88Po9FIXV0dlZWV1NfXc+PGDYzGe+drGI1GampqUBSFsLAwh0pmAwMDfPLJJ5w8eZLvvvvOIZLZg6qqqtixYwcAXl5eZGRkEB0dTXR0NAcPHrQomZk8uJp29L2Jwr5kheakLNngDfcaBl588UV6e3vJzc0lKspp/rM1LcYrT7q5uZm7Jzs6OlBVlZ6eHhYsWICvr69D/QE1JWmAzz77zOkGN0+UqeR48eJFVqxYwc6dO1m8eDFpaWn2Ds2arLRCW6rCZLqtH9MBvwDrgeuqqoYM/9mKAnwCrAPuAK+rqvrTVOKdLFmhOalLly7h7++Pn58fs2bNYuvWrebDOwfLyMjgwIEDDrcx2VZM3ZNHjx6lsrKSM2fOEBAQQF5eHrGxsSQlJfHVV1/h6enJ9evX0el0LFy4kLt371JZWUlNTQ16vZ67d+/a9ffo6Ohg06ZNBAYGcuLEiRmfzEyWLFnC6dOnCQsLw2AwsHv3bnuH5KRMJ3xO9AuAU0DCGD/8v4Gn73+9CfxzuqO3lOPUUsSEWLLBu7q6mpaWFtavX09WVpatQ3Q4Y3VPbt68mc7OThISEli4cKG5e/L27dsYDAbq6+vp6elhzpw55s3dtipFNjY28uqrr/LOO++wadMmh1o1WtOiRYuora21dxgzhAr0Te5OVf2XoiiLxrhkA5Cv3iv3VSqKMkdRlPmqql6b1AdOgSQ0JzVeO/TAwACpqamcOnXKhlE5Fzc3N0JCQsjPzycyMpLs7Gx0Ot2om7sHlyebmppQFGVI96Q1NlpfvHiRffv2ceLECavvKxMz2eQTmgW8gZZBr/X33wVR/LwAAAcrSURBVJOEJiwz3gbvrq4urly5QlxcHHBv8npiYiLFxcUu9xxtPDt37sTf3x9FUSze3B0dHU1fXx8Gg4HW1lbq6urQaDTmzd1TbfFXVZUvvviCY8eOcfbs2VFHSwlhOeP4l0zOSCUDu/RbSFOIkzIajQQEBHDhwgW8vb2Jjo6moKBg1GG0cXFxZGVlSTKbJEuOxrlz5455e0BPTw+PPfaYeXP3RGY+DgwMcOTIEaqrqykqKnK6I3XElFipKSRUhf+dxJ0BOlVVo+6XHM+O0hRyDChTVbXw/utfgTgpOQqLeXh4kJuby9q1a80bvIODg4ds8BbTx1SeDAkJsWj2pKk8aTAYzOVJ0+zJscqT3d3d7Nmzh3nz5vHNN9841JYB4cxMTSFWUQz8TVGUIuC/gE57JDOQFZoQU2bp5u6Ojg7a29vp7OxEo9GYE5xp2saNGzdISkripZdeYu/evVZv/hjv2Bd3d3dCQ0MBmXZjQ1ZaoQWrUDiJO8N1QAMQB/wF+A/wd8ATQFXVz+637edyrxPyDvA/qqraZdOrJDQhptlEypMGg4HDhw+jKAr19fUcOnSIrVu3Wj3GlpYWduzYQX19PTqdbsSE9sgjj/Dnn39aPRYxhMMlNJkUIlyOJVNLvvzyS4KCgggODubll1+2cYS2YypPpqWlodVqqaysZPPmzeh0OjZs2EBCQgI5OTn88ccfhISE8Oabb2IwGFi9ejU5OTmsXLmSX375xaoxmo59cZUtAGJK+9CchhToxZT19/eTkpIyZGpJYmLikKklDQ0NfPjhh1RUVDB37lyuX79ux4hta6yjcd544w3c3NwoLy83d6kaDAarHjlkybEvcO95XlRUFB4eHqSnp7Nx40arxSSszapt+w5DEpqYssFTSwDz1JLBCe348eOkpKSYj19x1WN1Htzc3dfXR1dX15D5hhOZdTiaqR77AtDc3MyTTz7J1atXWb16NaGhoTz11FNTjk3Yg1WbQhyGJDQxZZZMLfntt98AiI2Npb+/n8zMTBISxpqm4xo8PT2nJYE9qLS0dMT3L1++TGNjo3l1ptfrWbZs2bBjXwDzitHPz4+4uDiqq6sloTktWaEJYRFLDnE0Go00NDRQVlaGXq/nmWee4cqVK8yZM8dWYQogNDR0SLl3tGNfOjo60Gg0zJ49m7a2NioqKjhw4ICtwxXTxjVWaNIUIqZsvKklpms2bNiAp6cnvr6+BAYG0tDQYOtQxRgGH/tSV1dHVFQU4eHhrFq1ivT09GEnOQhnYlqhTfTLuUjbvpgyS6aWaLVaCgsLOX36NG1tbSxdupSff/6Zxx9/3I6RC+FwrNS2/7QK/5jEnYlO1bYvJUcxZZZMLVm7di3nz58nKCgId3d3PvroI0lmQtiMazxDkxWaEEI4Diut0PxVODqJOzc51QpNnqG5iI0bNxIZGUlwcDCff/65vcMRQtiUazxDk4TmIvLy8tDpdFRVVZGdnU17e7u9Q7Kq8SaXNDc3s2rVKpYuXUpYWBglJSV2iFIIW3GNSSGS0FxEdnY24eHhxMTE0NLSMqM7DE2TS86dO0dtbS2FhYXDTj4+dOgQW7ZsMR/RsmfPHjtFK4QtuMYKTZpCXEBZWRmlpaX8+OOPaDQa4uLi6O7utndYVmPJ5BJFUbh16xYAnZ2dw7YZCDGzuMY+NEloLqCzs5O5c+ei0Wior6+nsrLS3iFZlSWTSzIzM3nhhRfIycnh9u3bo07WEGJmcI0uRyk5uoCEhASMRiNhYWFkZGQQExNj75CsypLJJYWFhbz++uvo9XpKSkpISkpiYGDAViFOSmZmJt7e3kRERBARETHqcz9LTj4QrsY1nqHJCs0FzJ49m3Pnztk7DJuxZHLJyZMn0Wq1AKxYsYLu7m7a2tocfmhyamoq+/fvH/X7lpx8IFyRrNCEcErR0dE0NDTQ2NhIb28vRUVFJCYmDrnGx8eHCxcuAPfGPHV3dzNv3jx7hDutBj8/nDVrlvn5oXB1rrFCk4QmZpzBk0uWLFnCli1bzJNLiouLAfj44485fvw44eHhbNu2jVOnTjnFYZe5ubmEhYWRnJxMR0fHsO+P9PywtbXVliEKYTdSchQz0rp161i3bt2Q99577z3zv4OCgqioqLB1WOMa6xyz3bt3k5GRgaIoZGRkkJaWRl5e3pDrLHl+KFyRa5QcJzr6SgjhABRFWQScVVU15IH3VwCZqqquvf/6HQBVVT+0dYzCcSiKogX+Mu6Fw7Wpquo0BxfKCk0IJ6EoynxVVa/df/lX4MoIl/0f8LSiKL5AK7AVeNlGIQoH5UxJaSokoQnhPI4qihLBvfpRE7ALQFGUJ4ETqqquU1XVqCjK34BvAXcgT1XVf9srYCFsSUqOQgghZgTpchRCCDEjSEITQggxI0hCE0IIMSNIQhNCCDEjSEITQggxI0hCE0IIMSNIQhNCCDEj/D9z9HBLkfVTNwAAAABJRU5ErkJggg==\n", 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\n", 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\n", 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\n", 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\n", 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\n", 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\n", 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\n", 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\n", 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\n", 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\n", 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\n", 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\n", 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\n", 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\n", 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\n", 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" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "from mpl_toolkits.mplot3d import Axes3D\n", - "import matplotlib.pyplot as plt\n", - "import numpy as np\n", - "from matplotlib import cm\n", - "\n", - "from matplotlib import animation, rc\n", - "from IPython.display import HTML\n", - "\n", - "#rc('animation', html='html5')\n", - "#options = {'c1':0.5, 'c2':0.3, 'w':0.9}\n", - "\n", - "\n", - "#moviewriter = animation.MovieWriter(...)\n", - "#moviewriter.setup(fig=fig, 'my_movie.ext', dpi=100)\n", - "\n", - "\n", - "super_set = []\n", - "length = len(gen_vs_pop)\n", - "\n", - "#frame_number = [i for i, pop in enumerate(gen_vs_pop)] \n", - "for i, pop in enumerate(gen_vs_pop):\n", - "#def to_movie(frame_number):\n", - " other_points = []\n", - " pf_points = []\n", - " hof_points = [] \n", - " labels = []\n", - " for p in pop:\n", - " print(p)\n", - " xyz = []\n", - " for k,v in p.dtc.attrs.items():\n", - " xyz.append(v)\n", - " labels.append(k)\n", - " other_points.append(xyz)\n", - "\n", - " #lims = []\n", - " #for k,v in pop[0].dtc.attrs.items():\n", - " # lims.append((np.min(ranges[k]), np.max(ranges[k])))\n", - "\n", - " fig = plt.figure()\n", - " #ax = fig.add_subplot(111, projection='3d')\n", - " fig, ax = plt.subplots(1, 1)#, figsize=figsize)\n", - " ax = Axes3D(fig)\n", - " \n", - "\n", - " ax.set_xlim(lims[0])\n", - " ax.set_ylim(lims[1])\n", - " ax.set_zlim(lims[2])\n", - "\n", - " # Set plot title\n", - " #labels=('x-axis', 'y-axis', 'z-axis')#,\n", - " interval=80#,\n", - " title='Particle Movement in 3D space'#,\n", - " title_fontsize=\"large\"#,\n", - " text_fontsize=\"medium\"\n", - " ax.set_title(title, fontsize=title_fontsize)\n", - "\n", - "\n", - "\n", - " # Set plot axes labels\n", - "\n", - " # Set plot limits\n", - "\n", - " errors = [ np.sum(list(p.dtc.scores.values())) for p in pop ]\n", - " xx = [ i[0] for i in other_points ]\n", - " yy = [ i[1] for i in other_points ]\n", - " zz = [ i[2] for i in other_points ]\n", - " if len(super_set) !=0 :\n", - " for ss in super_set:\n", - " ops, ers = ss\n", - " p = ax.scatter3D([i[0] for i in ops], [ i[1] for i in ops], [i[2] for i in ops], alpha=0.0925, c=ers, cmap='jet', marker='o', vmin=abs_min, vmax=abs_max)\n", - " #ax.set_autoscalez_on(False)\n", - "\n", - " p = ax.scatter3D(xx, yy, zz, c=errors, cmap='jet', marker='o', vmin=abs_min, vmax=abs_max)\n", - " #print(xx,yy,zz)\n", - " #print([l for l in lims[0]], [l for l in lims[1]], [l for l in lims[2]])\n", - " #p = ax.scatter3D([l for l in lims[0]], [l for l in lims[1]], [l for l in lims[2]], c=[2.0,2.0], marker='*', vmin=abs_min, vmax=abs_max)\n", - " #ax.set_autoscalez_on(False)\n", - "\n", - " cb = fig.colorbar(p)\n", - " cb.set_label('summed scores')\n", - "\n", - " zz = [ np.sum(list(i.dtc.scores.values())) for i in grid_results ]\n", - " zz_sorted = sorted([( np.sum(list(i.dtc.scores.values())), index) for index,i in enumerate(grid_results) ])\n", - " gbest = zz_sorted[0]\n", - " gworst = zz_sorted[-1]\n", - "\n", - " gworst_grid_attrs = grid_results[gworst[1]].dtc.attrs\n", - " gbest_grid_attrs = grid_results[gbest[1]].dtc.attrs\n", - "\n", - " ax.set_xlabel(str(labels[0]))\n", - " ax.set_ylabel(str(labels[1]))\n", - " ax.set_zlabel(str(labels[2]))\n", - "\n", - " plt.savefig('particle_cube_'+str(float(i/length))+str('.png'))\n", - " super_set.append((other_points,errors)) \n", - " plt.show()\n", - "\n", - " #return ax\n", - "\n", - "\n", - "frame_number = [i for i, pop in enumerate(gen_vs_pop)] \n", - "#fig = plt.figure()\n", - "\n", - "#_ = list(map(to_movie,frame_number))\n", - "\n", - "#anim = animation.FuncAnimation(fig, func=to_movie,\n", - "# frames=frame_number,\n", - "# interval=30)\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - }, - { - "cell_type": "code", - "execution_count": 10, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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Tz9h9L0W7C7zbtu1+fcEFXTt8qap6/cAJ3mycaZSEGtYynQuYt2u+//F5bL/t64CY8qb37CqTtLsQOvWsV9L/+DxuuO0RFrG7IL5yv705ZX7z7Sq2h6CULpBZuERjltNapu9xC3aky7utXb2/YfuaL5D0PO5h+5rLdy0f6ULoDbc9xCJWDtr3gie2ccPqPUYHKd/ChYS0a2LhwlLDqeqNTm7Bm+Vw8arlPCvY8lGYftfu5VuOgL98bku+0ZpaaFfv3zUWzY6mlUNa6fTsgH5ovhC6/bZvAWJRixFVGkm+UhYuhJUrB8e6cmWyfMWK4uMJiP6x7ycp6VPAKSS/6CeAcyOi7Q2nbsGb5fCtjx7Fju1i3efgJz9OptWrYO2n4KqPHFXgMAUAC4GtDB7SKaDnr7u7K7a4ELq7ZT8+xDB9KmPlyqRTfcEt+qCwFvxnI2JORBwO3AhcPNIb3II3y+F3v3oRO7b3csTHdw5qwQM8jz/yBj4NwEq+wr48tWvd0/RyKid0NZYfsYpFTNs1fwsLOIFboXf7rjr8sBdCd7Xsq+nWWyEWLeR4hu8wX16LvphhCCLi6abZaew5OOMe3II3y+moTyTJvbl2LWAeD/EjjucJprMvTw1atw/9XD+K0T6Gq/c/TS+Lhhx/ESv5EcdnG4+mf29u4diW+165X7Hj2bTSe0KS3Bs/WyYF3j3VQQt+hqS1TVNHw6VK+rSkh4F3kaEF7wRvltO+d7dOOo0kO4M/t6xt7zOKJvMFzNuV5BvTWqazD/0tj7GIlYNa7cOZesYcTj/rUVbut/egfZfdi6ZRcXnzwMrsib1o0dGdrJsjYm7T1Ne8K0krJN3XYjoFICKWRsSBwLeBER+E5BKN2TjT3CWyYRXLu7LvqnWJvOUWOPVUkksLLQQdtOjHUpeusUZE1osH/wosBz7RbiMneDOrrF1DG7S523XYJL9gwdgE1SKAgnrRzI6IxtNTTgb+faT3uERjllOrujgkieenvJo/8fxh6+ZjGUNV6ud5bd0KP+5Z0PIcD1uTX7Cg0C6TBfWi+UxarlkHLCJ50l5bTvBmObWqizdq45/gZZzJG3ma3kHrut2L5gLmccuQ45ddP++WbdvguP4VKG2RD5cnA3Zn0iKTO8WMJhkR74iIw9Kukm+PiEdGeo9LNGZd0Kou3qzbXSJbaRxh6lnlPX91TDWS9tBB5augouPBO8Gb2bgyXHovLe2XNFJkFi7RmNn4smDPenyky8sSA5FpKpoTvJmNLyt21+MbVPBF1WYR0L9zINNUNJdozGz8KSmZD2egoiUaJ3gzs1zKKb9k4QRvZpZDBAw4wZuZ1VNVW/C+yGpWsmLHjLeuq/BFVid4s5Kt50Kgl/VcVHYoNgpBMBDZpqK5RGNWktX8gObbc/rZh9XcCATzeXtpcVmHwiUaMxtiDkvp4a8Megg2f2UOHx+0nUs41ecbnVqQdIKkByRtkHRhi/VTJH0nXf8LSQcVH6XZ2JjOOrTrCdmNf/xTmM69g7ZzCafagqQXTZapaKWVaCT1Al8Cjgc2AXdIWhYRv27a7H3AkxHxCkmLgUuBdxYfrdnY6Od5DB5FRbvKNI353du6hFNJFS7RqKxBciTNAy6JiLek8xcBRMQ/NW1zc7rNGkmTgMeBF0WboCVV80ybtXQccAPJM5RFktj/Arwd6G2z7idlBJtLFQfkknRnRMzNs4/XHHJY/OsXv5dp28NPfFXu43WizIusM4GHm+Y3AUcPt01E7JT0FLAvsLl5o/TBtR09vNasGlYB20mSeCMBPsfuBN5unVWBb3RqrdXonkPPUpZtSB9c2wdpC37hzPzRdduKdGz+qsXmuDozFnGtnAa9f4aXXg4PnQ/902DBzJHXFRFbN6wY8bkU415VSzRlJvhNQPPjZmYBjw6zzaa0RPN8YEsx4ZkVZMErdr8++Irs66waKtyCL7MXzR3AbEkHS5oMLAaWDdlmGfCe9PUZwKp29Xczs6IF2bpIltHKL60Fn9bUzwduJrmadGVErJf0SWBtRCwDvg58S9IGkpb74rLiNTNrKR2qoIpKvZM1Im4Cbhqy7OKm19uAM4uOy8ysE1UtLHioAjOzHBo3OlWRE7yZWR7hB36YmdWWW/BmZjUUFR6qwAnezCyPCHbu7C87ipac4M3Mcgjcgjczq62BAfeDNzOrn4DodwvezKx2gnAL3syslio82JgTvJlZDhFBf0V70fih22ZmORU5mqSkj0kKSTNG2tYteDOznIqqwUs6kOQ51n/Isr1b8GZmOTQe2Zdl6oLPAxfQ4sl2rbgFb2aWS0fllxmS1jbN96WPHB2RpJOBRyLiHqnV00z35ARvZpZDBJ0MVbA5IuYOt1LSCmD/FquWAh8HFnUSmxO8mVkuQXSpBh8RC1stl/Ra4GCg0XqfBdwl6aiIeHy4/TnBm5nlUUA/+Ii4F9ivMS/p98DciNjc7n1O8GZmOUW/72Q1M6udKOFO1og4KMt2TvBmZrl0rQtk1znBm5nlEEFlhypwgjczy6V7vWi6zQnezCyHMmrwWTnBm5nl5F40ZmZ15Ba8mVldhVvwzSRNB74DHAT8HjgrIp5ssV0/cG86+4eIOLmoGM3MsogB6H+umr1oyhou+EJgZUTMBlam8638NSIOTycndzOroGwP++jWAz86UVaCPwX4Rvr6G8CpJcVhZpZLBAz0D2SailZWDf6/RMRjABHxmKT9htlu73Ts5J3AZyLi+kx7X/FId6IcC1WNzXF1pqpxQbVjq6UJWIMfYVzjrF4SEY9KehmwStK9EbGxxbGWAEtGGaqZ2egFRP8E60Uz3LjGAJL+KOmAtPV+APDEMPt4NP3/byWtBl4H7JHg0yei9KX7DhbO7MJP0GWNVlXVYnNcnalqXFDd2Gr+F0UE9O/wRdZmy4D3pK/fA9wwdANJL5Q0JX09A3gD8OvCIjQzyyKSEk2WqWhl1eA/A1wr6X0kTwc/E0DSXOADEfF+4NXAFZIGSL6IPhMRTvBmVjkDE61E005E/AlY0GL5WuD96eufA68tODQzs84EHmzMzKyOgiilC2QWTvBmZnlMxF40ZmYTQURUtheNE7yZWU4T7kYnM7MJwSUaM7OaCl9kNTOrpcDdJM3M6il8o5OZWT1FMFDRB344wZuZ5RDAgEs0ZmZ1FAyEE7yZWe1EuAVvZlZTwcCAa/BmZrUTATv6d5YdRktO8GZmuYRLNGZmdRRQ2YusZT2yz8ysHiKpwWeZ8pB0iaRHJN2dTieN9B634M3Mcii4H/znI+JzWTd2gjczy8X94M3MaikCdu7M3ItmhqS1TfN9EdHXweHOl3QOsBb4aEQ82W5jJ3gzs1yC/shcX98cEXOHWylpBbB/i1VLga8AnyKpCn0K+Gfg79sdzAnezCyHbt7JGhELs2wn6WvAjSNt5wRvZpZLMf3gJR0QEY+ls6cB9430Hid4M7McCuwHf5mkw9ND/h44b6Q3OMGbmeURxbTgI+Ldnb7HCd7MLIcg2Nm/o+wwWnKCNzPLw8MFm5nVUwD9Fb3RqZSxaCSdKWm9pAFJ7fqEniDpAUkbJF1YZIxmZtkkNfgsU9HKGmzsPuB04LbhNpDUC3wJOBE4FDhb0qHFhGdmlk0UNNjYaJRSoomI+wEktdvsKGBDRPw23fYa4BTg1yMeYMUj+YMcK1WNzXF1pqpxQbVjq6EAdvqBHx2bCTzcNL8JOLrVhpKWAEvS2e1kuAFgApgBbC47iArweajIORihQVeEVufhpXl3+heeunk1y2dk3LzQ38OYJfh2YypExA1ZdtFiWbTaMB2spy897tp2Yz1MFD4PCZ8Hn4OGsToPEXFCt/fZLWOW4LOOqdDGJuDApvlZwKM592lmNmFU+YlOdwCzJR0saTKwGFhWckxmZuNGWd0kT5O0CZgHLJd0c7r8xZJuAoiIncD5wM3A/cC1EbE+w+47GVu5znweEj4PPgcNE+48KKJlWdvMzMa5KpdozMwsByd4M7OaGtcJfrghDyQdL+lOSfem/z+uad2R6fINkr6oCnTOzaPdsA+SLkp/zgckvaVpea2HgJB0uKTbJd0taa2ko9LlSn/nGyStk3RE2bGONUkfSn/X6yVd1rS85WejriR9TFJImpHOT4zPQkSM2wl4NfBKYDUwt2n564AXp68PAx5pWvdLkou7An4InFj2zzFG5+BQ4B5gCnAwsBHoTaeNwMuAyek2h5b9c3T5nNzS+L0CJwGrm17/MP3dHwP8ouxYx/g8vBlYAUxJ5/dr99koO94xPA8HknTWeAiYMZE+C+O6BR8R90fEAy2W/yoiGn3m1wN7S5oi6QBgn4hYE8lv+ZvAqQWG3HXDnQOSYR2uiYjtEfE7YAPJ8A+7hoCIiOeAxhAQdRLAPunr57P7/olTgG9G4nbgBelnoq4+CHwmIrYDRMQT6fLhPht19XngAgbfKDkhPgvjOsFn9A7gV+mHfCbJDVQNm9JlddRqqIeZbZbXyT8Cn5X0MPA54KJ0+UT42ZsdAvytpF9I+omk16fLJ8x5kHQyyV/w9wxZNSHOQZXHogHyDXkg6TXApcCixqIWm1W+n+goz8FwP2urL/XKn4Oh2p0TYAHwkYj4nqSzgK8DCxmnv/92RjgPk4AXkpQgXg9cK+ll1Ow8jHAOPs7uf/+D3tZi2bg9B8OpfIKPUQ55IGkW8H3gnIjYmC7eRDLkQcO4GP5glOeg3VAP434IiHbnRNI3gQ+ns98F/iV9XbvhL0Y4Dx8ErkvLkb+UNEAy4FatzsNw50DSa0muMdyT9qWYBdyVXnSv1TkYTi1LNJJeACwHLoqIf2ssj4jHgGckHZP2njkHyDLw2Xi0DFicXns4GJhNcoF5IgwB8ShwbPr6OOA36etlwDlpD4pjgKfSz0RdXU/y8yPpEJKL6psZ/rNRKxFxb0TsFxEHRcRBJEn9iIh4nInyWSj7Km+eCTiN5Je2HfgjcHO6/H8AzwJ3N02NHgRzSYYT3ghcTno373idhjsH6bql6c/5AE29hUh6EDyYrlta9s8wBufkjcCdJD1FfgEcmS4XyUNkNgL30tTrqI4TSUL/v+nn/S7guJE+G3WegN+zuxfNhPgseKgCM7OaqmWJxszMnODNzGrLCd7MrKac4M3MasoJ3sysppzgzcxqygneJiRJvWXHYDbWKj9UgdloSLoUeCgivpzOXwI8A7wNeAw4nGTYXLPacgve6uoa4J1N82cB/0EyLO7SiHByt9pzC95qKSJ+JWk/SS8GXgQ8CfwB+GUkY6Cb1Z4TvNXZ/wPOIBlK9pp02bPlhWNWLCd4q7NrgK+RDJF7LMmjDc0mDNfgrbYiYj3wPJIn+tRvKFizEXg0STOzmnIL3sysppzgzcxqygnezKymnODNzGrKCd7MrKac4M3MasoJ3syspv4/BNr72VJELGkAAAAASUVORK5CYII=\n", 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jImJmo/2STgGOBWZEne90EbEu/fmYpCuA6UDNQG+Wl+oAC8kv/wBPAWwS5Mv7t2JwROcrB/Wy5UyqW4Yjn3plROfoeTNnEkuXblzVjBmwZElOhRHK3v4+uarVYX5aSQUgItZK+grwMPAisKiqqbvs/ZLeQXKP89MR0fCvaS5NN5JmAf8AHBcR6+ukmShpy/JrkgrKqu6V0qz3VNbcy0tl0B8TZs6EpUuHXAOWLk2256QPZVqAJyJioGKZX5mPpK2B44HdgB2BiZL+oup0/wVMTTuzLAH+rXn58nEhsCVJc8ytki4CkLSjpGvSNNsBv5C0ErgJWBARbna0Ma1ezX0sqazJZ9neDdnCfKZwOxN4ICIej4hXgcuBP61MEBFPRsTL6eo/Awc1yzSvXjd71Nm+DjgmfX0/cEA3y2U2ErWaTiLdDvAs/Zs030S6vZ0CNjnH4knj23oO25TSf23yMHCIpAkkTTczgCEdTCTtEBGPpqvHAXc0y7QXet2YjWqncyjLmUTAxmV5eiMW4ARm8Sz9Q/Y/28YbsWWLKvIvB/m8b8R2Qr2Qmts3G0Gf+jMtzUTEr4EfATeTdK3sA+ZLOkfScWmyT0hanbZ2fAL4y2b5eggEszYoB/V62hXUG317mJW21fdaz662mzGDSNvoy4L0hmxO2tnrJiLOBs6u2nxWxf4zafyQ6SZcozcbRZp9exgTlizZJKjn2etGiL6M//LiGr3ZKDOmgno9uXWlrK3Xn8R3oDcza1HGHjW5caA3M2tJW3vddIQDvZlZCwSZetTkyYHezKxFfa7Rm5kVmdxGb2ZWdO51Y2ZWYB0YprjtHOjNzFoi+ts8blG7OdCbmbVAMJzx6HPhQG9m1hL3ozczK7xeD/TKMjO7pC2AvwHeTjKO0i+Ab1fOTN5LJBVzunmzUSxLrOk2SSsiYqCVPA486MD4xS9/kSntxNdNbPl8I5G1Rv9d4Dng/6XrHwT+HTipE4UyMxtNer1GnzXQ7x0RlbM9/Xc66P2ISfoiydyIJeAx4C/Lk4FXpTsF+Fy6+n8ioun8iADM3KmV4rXfkrVA740VXlqYzCk8cfZeOZdkqBcuuxuAEy6dk3NJNnXlnEsBOJz35FySoZaxAOjdz1hhBUkU62FZbxXfIumQ8oqkg4FftnjuCyJi/4iYBvyEioH1K84ziWQA/oOB6cDZ6eS5ZmY9IyLbkpeGNXpJt5H8vdoMOFnSw+n6rsDtrZw4Ip6tWJ2Y5lvt3cDiiHgqLc9iYBbw/VbObWbWVqXeu/9QqVnTzbGdPLmkLwEnA88A76qRZCeg8nvfmnRbrbzmAnPbXUYzs4aiN280V2oY6CPioVYyl7QE2L7GrnkRcVVEzAPmSToT+DibzpNY6w5HzSsaEfOB+el5e/uqm1mx9HgbfUf70UfEzIxJLwUWsGmgXwMcXrG+M7Cs5YKZmbVRr9foc3tuV9KeFavHAXfWSHYdcJSkrdObsEel28zMekNADEamJS95Phl7rqS9Sb70PAScBiBpADgtIk6NiKfSbpi/SY85p3xj1sysFwTR8zX63AJ9RLy/zvblwKkV65cAl3SrXGZmw9XOOC/p0yQxMIDbgA9VjkIgaXOSh1gPAp4EPhARDzbKs7eHXDMz63VB0r0yy9KEpJ2ATwADEbEv0A/Mrkr2V8DTEbEH8DXgvGb5OtCbmbWozQ9MjQNeJ2kcMAGoHjHgeKA8QsCPgBlqMsWVA72ZWSsCYrCUaQEmS1pesQx59ici1gJfAR4GHgWeiYhFVWfc+HxRRGwgeQ5pm0ZF9DDFZmYtCIZVW3+i0eiVae/C44HdgD8AP5T0FxHxH5XJ6hSjLtfozcxakrF9PtswCTOBByLi8Yh4Fbgc+NOqNGuAXQDS5p3XAw17I7pGb9YG53MDAxW/a8uZxOkcmmOJrJva2OvmYeAQSROAF4EZwPKqNFcDpwA3ACcC10eT/p2u0Zu1qBzkBRuXAZ7ifG7IuWTWFelYN1mWpllF/JrkBuvNJF0r+4D5ks6RdFya7DvANpLuBf4XcEazfB3ozVpUDvKVysHe2mPxYhg3Dq6/Pt0wcyYhbVyYmXW0lQ4pZVwyiIizI+JNEbFvRPzPiHg5Is6KiKvT/S9FxEkRsUdETI+I+5vl6UBvZj3vAx+AwUE48USSoL506ZBvUCxdml+wH16vm1w40Jv1gKfYn2VcyVPsl3dReoqULE8/DQuZyZNPi1i6tGbaWLr0tQO6HPR7feIRB3qzFi1n0iZ92yLdntVqzgD6Wc2Z7SzaqLdoEUyYkAT5o3itFl/LkO1drOEn3Svb00bfKer1wXhGwuPRW7ctJBlatWwRyVRozZWo3y26WPWwkcaaSZPgyadHOP12k3NKWtGoX3sWB+w7LRb9Z/UzTbVt/5btWj7fSLh7pVkbZAvqtcwEriKZTVMkAf554M/aUq4iWL8+7xI0kXNtPYviBvqZNWcczM+StQD0zdol54IMVVqYzNQ4cfZeOZdkqBcuuxuAEy6dk3NJNnXlnEsBOJz3tCW/n1NiECg/3NhPcBh/BMPMfxkLgN79jI3USy9Rv72mR0SPzxlbrO+GZqNQic3p5zl251/o5zlKbJ53kXrPjBk174MwY0ay1DmmGyJgcEMp05KX4tbozUaJd/La1AxTuDzHkvSwJUtQ2q2yTDNmwJIlyUrVPir3dUHJTTdmZm3QKHB3MahvKtx0U4ukL0r6raRbJS2StGOddINpmlslXd3tcpqNVqXH30Zp4f2Unqg93k6z/ZZdBJRKkWnJS15t9BdExP4RMQ34CXBWnXQvRsS0dDmuThozq7byW0A/3HrRyPbbsEQpMi15yaXpJiKerVidSJOxlM0sqxKlhRVdVDZsTWnhw0DQN2tXSgsfYkgXlqr9NgLpzdhelluvG0lfkvQI8D+oX6PfIp2F5UZJJ3SxeGaj1Ezoe4HX6k4Bfc/DQDrt6EFzGu+3YQuCUmRb8tKxQC9piaRVNZbjASJiXkTsAnwP+HidbKakT5HNAf5R0h83ON/c8vRcbX8zZqPG9dD3Svo6DSx9r9I3ORkyue+Nv2y430Yger/ppmOBPiJmpsNsVi9XVSW9FCr6lw3NY136835gGfDWBuebHxEDeTxebNZTBl8H4/4Ae30p+Tn4uuHtt2Hr9UCfSxu9pD0j4p509TjgzhpptgbWR8TLkiYDbwPO72IxzUalvndXPOW8+/xh77fhCci1R00WefWjP1fS3iQjOj0EnAYgaQA4LSJOBd4M/JOkEsk3j3Mj4vacymtmVlv0/hAIefW6qddUsxw4NX39K/Dg3GbW64LBHCcVycJPxpqZtaD8wFQvc6A3M2tRrzfdePRKsw7r9jSBeQ5vMJxzbzLh92jlIRDMrOvTBOY5vMEwzj1kwu9RLMjWtXLMda80GwuW8V9UDjcwyFYs4ydAcHhHZpBKpyXckK52cXiDRkMrVNcnVTWJyNNPv7atx0f7ra2NQyCkvRF/ULFpd+CsiPjHijSHk0xL9kC66fKIOKdRvg70Zm1yPjcwwFMb1xdxBLMot0sk0wT28RL70vB3sgU1piXsewEO/KvMOVx70+858qlXNq4vnjSeo6dv1/zAg+bALRdDacKm5656Vn3RIjjhhKFTBE6cCFeP4vFp2zWVYETcBUwDkNQPrAWuqJH05xFxbNZ83XRj1gblIC/YuBzFMhZyJJU1XfEqk7itQ6W4Hng5fT384Q3KQb7yPRz51Ctce9Pvmx47nKEVjjwSNq+aRGv8eDjiiEzF7DnlB6Y60EY/A7gvIh5qtYzq9UltR0JS8d6U9bS00WQTScNFiSQAvwyMByZ0sCTrgReAc4EzSGr32c7X+D20fu7KWLPFFjBhApxxBpx7blK7f+mlTCdpK0krWh025U277xMXf+nSTGkPm/PWzOeTdAlwc0RcWLX9cODHwBpgHfCZiFjdKC833Zh1xUvA1l04T2VQ/2oXzjeyc1cG9dNP70xpumkYtfXJVQMvzo+ITcahkDSeZHiYWnfwbwZ2jYjnJR0DXAns2eikxQ30M3fKuwRDLVkLQN+sXXIuyFClhY8AMHH2Xk1SdtcLl90NwAmXzsm5JJu6ck5Sezuc91RsXdDkqNdXpW+/ZWkZRvwZSz8LtbTyuS01yLcIYnhDIDyRsUZ/NEltfpN2s8r5PCLiGknfkjQ5Ip6ol5nb6M3aYDmTNpk9J4BFzKDc4r2Mn6Q9cXrT4knja76HxZPG51Gc0SOCDRsGMy3D8EHg+7V2SNpeSvopSZpOEsefbJSZA71ZG5zOoRuDfXlZzLuYxeI0RdDHi+zPZzc5ttsPVNVz9PTtNgb7je8ha6+bMSxo7zDFkiYARwKXV2w7TdJp6eqJwCpJK4FvALOjyc3W4jbdmHXZ6Qx9GvTnlG/QJb+D9XrcVD5QdRj5NlU5qI9MqdS+Qc0iYj2wTdW2iypeXwhcWH1cIw70Zh1SYnP6eY5d+REPcSIlhvYp7P4DVdYRATHY2x39HOjNOuSdFROnTXntW/hG+zOPVXyeElvQnQeqrBOCaGuNvhMc6M1yMonfIl4FtqBZ8471MA9TbGaNNGvesd4XEQwOr0dN1+Xe60bSZyRFOi9srf2nSLonXU7pdvnMstjtrY/z+cULOOSke/j84gVMOeDxTMe9k/dzGHOYwuUcxpwhzT02evT66JW5BnpJu5B0I3q4zv5JwNnAwcB04Ox00nCznnLi2TejfjjytLtRP3zgf9+cd5Gsi0qlUqYlL3k33XwNOJ1kyL1a3g0sjoinACQtBmZR50ECs2476/oFQ4bdLb+e8PoNnP3fC4iAc47o7BOxlq/RMJVgbjV6SccBayNiZYNkOwGVz0+vSbeZ9YR//7vpvLy+f+M46pU/X17fz79+enp+hbMuGeMTj0haAmxfY9c84LPAUc2yqLGt5tWSNBeYO6wCmrXogVveyOCG5GNaHewHXxUPr3xjTiWzbolguMMbdF1HA31EzKy1XdJ+wG7AynTIhp2BmyVNj4jfVSRdAxxesb4zsKzOueYD89P8e/t7lBXKZpuXePHZcWw2YQP9/RAlePmFcWy2RW/3rbZ2CcL96DcVEbcB25bXJT0IDNQYfe064MsVN2CPovawnWa5+fKso/MuguXJbfTDJ2lA0sUA6U3YLwK/SZdzyjdmzcx6RQyWMi15ybvXDQARMbXi9XLg1Ir1S4BLciiWmVlTo6HXTU8EejOz0WtE88F2lQO9mVkLIuj5IRCKG+jTqft6Ta9Oq1aeuq/XlKft60XLmk4fmI9e/YwVl3vdmJkVmtvo8+TJwTPx5ODDV3ty8Py1PDl4h4yFbxh59qjJoriB3sysG1yjNzMrunCN3sysyKIEg6+4142ZWYHlOzJlFg70ZmYtiIBSjzfd9NxYN2Zmo0u0bawbSXtLurVieVbSp6rSSNI3JN0r6beSDmyWr2v0ZmatCIjB9jTdRMRdwDQASf3AWuCKqmRHA3umy8HAt9OfdTnQm5m1IAIGX+3IzdgZwH0R8VDV9uOB70ZEADdKeoOkHSLi0XoZOdCbmbUihtW9crKk5RXr89NJk2qZTe35setNsepAb2bWKaXsTTdPRMRAs0SSxgPHUXuipcxTrJY50JuZtSLoxKBmRwM3R8Tva+xbA1SOc7EzsK5RZu51Y2bWgiAoDZYyLcPwQWo32wBcDZyc9r45BHimUfs8uEZvZtaaNva6AZA0ATgS+OuKbacBRMRFwDXAMcC9wHrgQ83yzDXQS/oMcAHwxhoTgyNpELgtXX04Io7rZvnMzJqJiLb2uomI9cA2VdsuqngdwMeGk2dugV7SLiR/tR5ukOzFiJjWpSKZmY1Irw9qlmcb/deA02lyt9jMrKelTTdZlrzkUqOXdBywNiJWSrV6Cm20RdrndANwbkRc2SDPucDc9pbUzKyJiJ4f66ZjgV7SEmD7GrvmAZ8FjsqQzZSIWCdpd+B6SbdFxH21EqYPHcxPz+1vCWbWFUFHule2VccCfUTMrLVd0n7AbkC5Nr8zcLOk6RHxu6o81qU/75e0DHgrUDPQm5nlIob1wFQulNzAzbEA0oPAQHWvG0lbA+sj4mVJk4EbgOMj4vYMefb2VTcbg/KONbVIWpHlSdVGdnjdTnHKH/+cWwf2AAAHwElEQVRNprTnrf5cy+cbiZ7qRy9pADgtIk4F3gz8k6QSyU3jc7MEeTOzbgqgNFabbrKKiKkVr5cDp6avfwXsN+KMZ+7UatHaa8laAPpm7dIkYXeVFiZjI02cvVfOJRnqhcvuBuCES+fkXJJNXTnnUgAO5z05l2SoZSwAevczVlxBKRzozcwKK8I1ejOzggtKJU8ObmZWWBHw6uCGvIvRkAO9mVlLwk03ZmZFFuCbsWZmhRZuozczKzT3ozczKzz3ozczK7QI2LDBvW7MzAosGAy30ZuZFZafjDUzKzz3ozczKzT3ozczK7pwjd7MrNCCYMPgq3kXo6G+vAtgZjaqpTdjsyxZSHqDpB9JulPSHZIOrdp/uKRnJN2aLmc1y9M1ejOzFgQw2N42+q8DCyPiREnjgQk10vw8Io7NmmEuNXpJX5C0tuIv0jF10s2SdJekeyWd0e1ympk1F22r0UvaCngH8B2AiHglIv7QagnzbLr5WkRMS5drqndK6ge+CRwN7AN8UNI+3S6kmVkjkQ5qlmUBJktaXrHMrcpud+Bx4F8k3SLpYkkTa5z2UEkrJV0r6S3Nyqg8ZmaX9AXg+Yj4SoM0hwJfiIh3p+tnAkTE/82Qf+9NN282xuURa5qRtCIiBlrJY0u9IQZ4e6a0y1jQ8HySBoAbgbdFxK8lfR14NiI+X5FmK6AUEc+nrSFfj4g9G503zzb6j0s6GVgO/F1EPF21fyegclbhNcDB9TJL/zKW/zq+DKxqY1lHo8nAE3kXogf4OvTINZCUdxFqXYddW830eZ65bhkLJmdM3uz/YQ2wJiJ+na7/CBjSbB0Rz1a8vkbStyRNjoi6eXcs0EtaAmxfY9c84NvAF0nuY3wR+Crw4eosahxbt0oQEfOB+em5l7f6V3q08zVI+Dr4GpR16jpExKw25vU7SY9I2jsi7gJmALdXppG0PfD7iAhJ00ma4J9slG/HAn1EzMySTtI/Az+psWsNsEvF+s7AujYUzcysl/0t8L20x839wIcknQYQERcBJwIflbQBeBGYHU3axXJpupG0Q0Q8mq6+l9rNLL8B9pS0G7AWmA3M6VIRzcxyERG3AtXfPC6q2H8hcOFw8syrjf58SdNImmIeBP4aQNKOwMURcUxEbJD0ceA6oB+4JCJWZ8x/fgfKPNr4GiR8HXwNysbsdcil142ZmXWPh0AwMys4B3ozs4Ib1YFe0kmSVksqpQ8alLcfKWmFpNvSn0dU7Dso3X6vpG+oBzr3tqredUj3nZm+17skvbtie2GHl5A0TdKN6fAay9MuaCjxjfQ9/1bSgXmXtdMk/W36/7xa0vkV22t+LopK0mckhaTJ6frY+ixExKhdgDcDewPLgIGK7W8Fdkxf7wusrdh3E3AoST/9a4Gj834fHbwO+wArgc2B3YD7SG5s96evdwfGp2n2yft9tPF6LCr/vwLHAMsqXl+b/t8fAvw677J2+Dq8C1gCbJ6ub9voc5F3eTt4HXYh6dTxEDB5LH4WRnWNPiLuiOShgurtt0REuc/9amALSZtL2gHYKiJuiOR/+7vACV0sckfUuw7A8cBlEfFyRDwA3AtMT5d7I+L+iHgFuCxNWxQBbJW+fj2vPX9xPPDdSNwIvCH9TBTVR4FzI+JlgIh4LN1e73NRVF8DTmfoA5dj6rMwqgN9Ru8Hbkk/7DuRPIhVtibdVlS1hpHYqcH2ovgUcIGkR4CvAGem24v+vqvtBRwm6deSfirpT9LtY+Y6SDqO5Bv9yqpdY+YawCgYj77RUAoRcVWTY98CnAccVd5UI9mo6F86wutQ7/3W+gM/Kq5DWZMhNmYAn46IH0v6c5IhX2cyiv//62lyHcYBW5M0TfwJ8J+Sdqdg16HJNfgsr/3+DzmsxrZRew2a6flAHxmHUqgmaWfgCuDkiLgv3byGZCiFslEzrMIIr0OjYSRG9fASja6HpO8Cn0xXfwhcnL4u3LAaTa7DR4HL02bKmySVSAb2KtR1qHcNJO1Hcg9iZdrnYmfg5vTmfKGuQTOFbLqR9AZgAXBmRPyyvD2SYReek3RI2tvmZKDht4JR7mpgdnp/YjdgT5Kb0RuHl0jH05idpi2KdcA709dHAPekr68GTk57XBwCPBOvDcVRRFeSvH8k7UVy4/0J6n8uCiUibouIbSNiakRMJQnuB0bE7xhrn4W87wa3spCMk7OGZFji3wPXpds/B7wA3FqxlHscDJCMrXMfyXgRyvt9dOo6pPvmpe/1Lip6GJH0Org73Tcv7/fQ5uvxdmAFSc+SXwMHpdtFMpnNfcBtVPRQKuJCEtj/I/283wwc0exzUeSFZLiVcq+bMfVZ8BAIZmYFV8imGzMze40DvZlZwTnQm5kVnAO9mVnBOdCbmRWcA72ZWcE50NuYJKk/7zKYdUvPD4FgNhKSzgMeiohvpetfAJ4DjgUeBaaRDNdrVniu0VtRXQZ8oGL9z4HHSYbjnRcRDvI2ZrhGb4UUEbdI2lbSjsAbgaeBh4GbIhmD3WzMcKC3IvsRcCLJELaXpdteyK84ZvlwoLciuwz4Z5Khed9JMt2i2ZjjNnorrIhYDWxJMsNQcYegNWvCo1eamRWca/RmZgXnQG9mVnAO9GZmBedAb2ZWcA70ZmYF50BvZlZwDvRmZgX3/wEpDnayBEJNkwAAAABJRU5ErkJggg==\n", 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swHgqCDRu3tVYlnAfH2VdT8qL6CqDt7vUMqgSCKKFqcHhR1Exg0j8stSRO3NF8lQQaDYVDCIGnTF2uWVQpWsoWnjKTs+538CZwDgbOIvXPHkSWMTIGuBzfCkJBCPmsBNu46g/upVLz3sB/+cri0oft2Mf/bo59dGv5es0DzJv5+kjO9Mo4xmjYflygIM7vzbJ9GX6aC+la2jEHHnarQC88d23lj6mG330izmbMR7hiTEFM8YjLOb9sx536eOpn8w0TrCDJuMZo+OsLs5H7NMFZT2TFsGIOOeqVTQ/LU+CD3xrFTac+4bZu3m60Ud/Ax/mydNOxSS/wg38txlbBJcCvzXtqKn/K4P6KzvjGcOv60+VNHj7cM8fTYtgRKz5xxc86VfH1Pql571gxmOmBna7oTG1tHmW0dSU0x1bBB9lHVexaocgQNP2IAaBGA0f+Uh38zPD3yJIIBgR67606ElBYOp1tnGCqYHdbpjPDYzzYLHVqMA4D+4w5XR618oouYpVbKu6EtHWmTM912unuTFGUGYZUAkEI0RqnPxvWbcAe+Ym8Fq+zlq+wXaeDoit7NGyj/5enjGn8ifZjXEe5Nf4n4zzIJPstkOaVl0rw2Q983f4ruCJ8YIxSDAYKg8+0I1chr1FkDGCEdJuLGDKYs7mJv4rk8U9hRbws5ZdNM/kgTnN/Hkdv/P4+v58tXS9mw3qIPGU93HYk2YNtfre8utq8E2dlKVbN3We2fBfWZxAUEPzuQHxGI2by83+D3iMRziQc3tep6larOO5nM2BPS+vE1PjF1fl2QMxZbjHihMI6mqqG+e5fHnWdOKxvj2LYBceGJqL0DJdNB43ArOGEghqqrkbZz3zd+i7N7CG17Xs5+9EP8vqleYB7+mmHlbVnSH4GBZD3jOU7sxodHVMDYJOLeuZz0d42pMCxrCV1SvtgkB+XdVLY/po7jUUI2Cq3/uJ5/v2bh5/P8vqt/yHqqnh7hnKv9uIiI4M+K/9MtI1FDFHra4lGPRpr9FbnnSpZVAlEETM0UzjHLktRj3ZsH3bZKllUKVrKGIn5KQfzSaHvGsogSAioiOD3e1TRgJBREQHbJhMIIiIqLdhbxFksLhmdubh8hExixEYLE4gqJnmh8tHROeMmXS5ZVCla6gm6vRw+Yi+cn+6hiTNB74AHAD8GHib7ftbpPsocCyNH/prgPe6zRVvaRHUxM4+XL633gA8lm6qqMSaNQAHH9yNvPp0QdmZwJW2FwFXFttPIulVwKuBxcCBwCuA17XLuNJAIOloSbdI2iSp1YfaTdIXive/L+mA/tdyNDzxDAKYCgb9vMV0a18k3VRRlbe/vTv5mMasoTJLh5YCFxTrFwDHzVCd3YFdgd2AXYC722VcWdeQpHHgPOAoYAK4VtJK2zc3JTsFuN/28yWdCPwV0KW/vvppfgbBT3hrZbd9TjdVVGmmR7jutLl1DS2QtL5pe4XtFSWPfbbtLQC2t0jaa4eq2OskfQvYQuM/2cdtb2yXcZVjBIcAm2zfDiDpIhoRrzkQLAU+WKx/Gfi4JLXr71rbxydHDVdZu7MduB1oxFhY27OyZnMkcAnwVBr/Vg08BPw2a/l2z0rt599VDK7LL4fjjoOHH+5WjmZ7+QfTbLW9ZKY3JV0B7N3irbPLZC7p+cCLgX2LXWskvdb21bMdV2Ug2Ae4s2l7Ajh0pjS2t0l6AHgmsLU5kaRlwLLeVTW66yrgFzQCwVRM/yX0MAhETDnqKNhtt+4Fgm5eUGb7yJnek3S3pIVFa2AhcE+LZMcD19h+qDjmm8ArgYENBDM922OuaSiaVisAJJkj9+m8du1ccVfjNWXtnCufCr4XWA7zzobtT4UjelRmFd9fDLSHH4Y994T7798y0Y38+nRB2UrgZGB58XpJizR3AP9J0kdonD9fB/xtu4yrDAQTwH5N2/sCm2dIMyFpHvAM4L7+VC966ojnP3HSPPyiausStfPoo41XaXPbgdS2+neLieXAFyWdQuOEfwKApCXAabZPpdGF/gbgxkbNuNT219tlXGUguBZYJOl5wF3AibDDk8unIuA64K3AVe3GByIi+sl9uumc7XuBI1rsXw+cWqxvB/5wrnlXFgiKPv/TgctoPOv7fNsbJJ0LrLe9Evg08DlJm2i0BE6sqr4RES0Vt5gYZpVeWWx7NbB62r5zmtYfpWj+REQMqmHvqMgtJiIiOjB1QdkwSyCIiOiE82CaiIjaS4sgIqLG3Ke7j/ZSAkFERCdstm3bXnUtOpJAEBHRAZMWQURE7U1O5jqCiIj6Mnh7WgQREbVlnBZBRESt9e+mcz2TQBAR0QHbbM+soYiIesusoYiImssYQUREjXXzUZVVSSCIiOhIbjoXEVFrNrnFREREvRlnjCAiosYyRhAREd6eFkFERG1l1lBERO05gSAios5scouJiIh6y6yhiIhaG4UxgrGqKxARMey8fbLU0glJJ0jaIGlS0pJZ0h0t6RZJmySdWSbvBIKIiE4ULYIyS4duAt4CXD1TAknjwHnAm4CXACdJekm7jNM1FBHREfflOgLbGwEkzZbsEGCT7duLtBcBS4GbZzuokkAgaT7wBeAA4MfA22zf3yLdduDGYvMO22/uVx0jIsrwJGz/5cDMGtoHuLNpewI4tN1BVbUIzgSutL286MM6E/iLFukesX1Qf6sWETEXc7r76AJJ65u2V9heMbUh6Qpg7xbHnW37khL5t2outK1cVYFgKXB4sX4BsJbWgSAiYqDZMFm+a2ir7RkHem0f2WF1JoD9mrb3BTa3O6iqQPBs21sAbG+RtNcM6XYvouc2YLntr5XK/Yq7ulPLlDVaZfXzM0WN9GeMoKRrgUWSngfcBZwI/G67g3oWCGZr4swhm/1tb5b0a8BVkm60/W8tyloGLNvJqkZE7DyDt/f+OgJJxwP/ADwLWCXpettvlPQc4FO2j7G9TdLpwGXAOHC+7Q3t8u5ZIJitiSPpbkkLi9bAQuCeGfLYXLzeLmkt8DJgh0BQ9LGtKPI2R+7ThU/QxtSvy5Q1+GVV8ZmiNmzY/ljvB4ttXwxc3GL/ZuCYpu3VwOq55F3VdQQrgZOL9ZOBHQZBJO0pabdifQHwatpMgYqI6Du7LxeU9VJVYwTLgS9KOgW4AzgBoLha7jTbpwIvBv5J0iSNgLXcdgJBRAycyT50DfVSJYHA9r3AES32rwdOLda/B7y0z1WLiJgbk5vORUTUmfFcpo8OpASCiIhO9GnWUC8lEEREdMB2X2YN9VICQUREhwZ5RlAZCQQREZ1I11BERM05g8UREbVmMn00IqLenAvKIiLqzWZycB5Ms1MSCCIiOmBgMl1DERF1ZiadQBARUVt2WgQRETVnJiczRhARUVs2PLZ9W9XV6EgCQURER5yuoYiIOjNksDgiotacMYKIiFrLdQQREbWX6wgiImrNhm3bMmsoIqLGzHZnjCAiorZyZXFERO3lOoKIiFobhesIxqquQETEUHOjRVBm6YSkEyRtkDQpackMafaT9C1JG4u07y2Td1oEEREdMGbb9sf6UdRNwFuAf5olzTbgz2z/QNKvAtdJWmP75tkyTiCIiOhEnwaLbW8EkDRbmi3AlmL9QUkbgX2ABIKIiF4xsL38GMECSeubtlfYXtH9WoGkA4CXAd9vl7aSQCDpBOCDwIuBQ2yvnyHd0cDfAePAp2wv71slIyJKmdOsoa22W/bvA0i6Ati7xVtn276kbCGSngZ8BfhT2z9vl76qFkHbvi5J48B5wFHABHCtpJXt+roiIvrJXbzpnO0jO81D0i40gsC/2P5qmWMqCQRl+rqAQ4BNtm8v0l4ELKVNXxcAV9zVeSXLSlnDU1Y/P1PUhoFtA/JgGjVOqp8GNtr+67LHDfIYwT7AnU3bE8ChrRJKWgYsKzZ/QaPFUWcLgK1VV6JClX/+Nj9yeq3yzz8Ayn4Hz+20oId44LK1rFpQMvlO/71IOh74B+BZwCpJ19t+o6Tn0Og6PwZ4NfBO4EZJ1xeHvt/26tny7lkg6EJfV6v/SW6VsBhsWVGUu362Prg6qPt3kM9f788P/f0ObB/dp3IuBi5usX8zcEyx/l1anztn1bNA0IW+rglgv6btfYHNHeYZERHTDPKVxdcCiyQ9T9KuwInAyorrFBExcioJBJKOlzQBHEajr+uyYv9zJK0GsL0NOB24DNgIfNH2hhLZ92RO7pCp+3eQzx/5DuZAdstu94iIqIlB7hqKiIg+SCCIiKi5kQwEkj4o6S5J1xfLMVXXqSqSzpBkSWXnOY8ESR+SdEPx9395Mde6NiR9TNKPiu/gYkl7VF2nfipzy+Z4wkgGgsLf2D6oWGa9mGJUSdqPxi067qi6LhX4mO3Ftg8CvgGcU3WF+mwNcKDtxcCtwFkV16ffpm5jc3XVFRkGoxwIAv4GeB8zXIg3yqbdaOup1Ow7sH15MfMO4Boa1+HUhu2Ntm+puh7DYpQDwelFs/h8SXtWXZl+k/Rm4C7bP6y6LlWR9GFJdwLvoH4tgmZ/AHyz6krE4Bra6aOz3cKCxi+grTR+BX4IWGj7D/pYvb5o8x28H/gt2w9I+jGwxPZI3X+m7G1MJJ0F7G77A32rXB+U+fySzgaWAG/xsP5nn0HJz78WOGOmW91Hw9AGgrKKhzN8w/aBFVelbyS9FLgSeLjYNXV7jkNs/9/KKlYRSc8FVtXp3wCApJOB04AjbD/cLv0oSiAoZ5DvPrrTJC0sHtkGcDw1uxup7RuBvaa2R7VFMBtJi2zfVmy+GfhRlfXpt+KhTn8BvK6uQSDKG8kWgaTPAQfR6Br6MfCHTYGhdmoaCL4CvBCYBH4CnGa7Ng8kkLQJ2A24t9h1je3TKqxSX027ZfPPgOttv7HaWg2ukQwEERFR3ijPGoqIiBISCCIiai6BICKi5hIIIiJqLoEgIgZOcUeAeyR1Zeq3pL+SdFOxvL0beY6SBIIYaZIO6NbJJPrqM0BXHgov6Vjg5TSmlB8K/Lmkp3cj71GRQBARA8f21cB9zfsk/bqkSyVdJ+k7kl5UMruXAN+2vc32/wN+SJeCzKhIIIg6mCfpguImhF+W9JSqKxQ7ZQXwHtsHA2cAnyh53A+BN0l6SvFcjtcD+/WojkNpJG8xETHNC4FTbP9vSecD7wb+e8V1ijmQ9DTgVcCXJE3t3q147y3AuS0Ou8v2G21fLukVwPeAnwLrgG0t0tdWriyOkVbcdPBq2/sX228A/sT2cVXWK9prvmFk0ad/i+2FXcj3fwGfr+sDq1pJ11DUwfRfO/n1M2SKBw39u6QTANTwm2WOlTQu6ZnF+mJgMXB5zyo7hBIIog72l3RYsX4S8N0qKxPtSbqQRhfOCyVNSDqFxgOGTpH0Q2ADsLRkdrsA35F0M41xhv/Y9PS2IF1DMeKK7oXVNJ5d+yrgNuCduTVzxBMSCCIiai5dQxERNZdAEBFRcwkEERE1l0AQEVFzCQQRETWXQBARUXMJBBERNff/AXsSLNRuEgy8AAAAAElFTkSuQmCC\n", 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" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "axis = [ [ str('vr'), str('a'), str('b') ], [ str('vr'), str('b'), str('a')], [ str('b'), str('a'), str('vr') ] ]\n", - "\n", - "gen_vs_pop = package[6] \n", - "pop = gen_vs_pop[-1]\n", - "\n", - "for k in axis: \n", - " \n", - " zz = [ np.sum(list(i.dtc.scores.values())) for i in grid_results ]\n", - " yy = [ i.dtc.attrs[k[1]] for i in grid_results ]\n", - " xx = [ i.dtc.attrs[k[0]] for i in grid_results ]\n", - "\n", - " last_frame = len(gen_vs_pop)\n", - " other_points = []\n", - " pf_points = []\n", - " hof_points = []\n", - " labels = []\n", - " \n", - " pf = package[2]\n", - " hof = package[1]\n", - " \n", - " for p in pop:\n", - " xy = []\n", - " for key in k:\n", - " v = p.dtc.attrs[key]\n", - " xy.append(v)\n", - " labels.append(key)\n", - " other_points.append(xy)\n", - " \n", - " \n", - " for p in pf:\n", - " xy = []\n", - " for key in k:\n", - " v = p.dtc.attrs[key]\n", - " xy.append(v)\n", - " labels.append(key)\n", - " pf_points.append(xy) \n", - " \n", - " for p in hof:\n", - " xy = []\n", - " for key in k:\n", - " v = p.dtc.attrs[key]\n", - " xy.append(v)\n", - " labels.append(key)\n", - " hof_points.append(xy) \n", - "\n", - " \n", - " zi, yi, xi = np.histogram2d(yy, xx, bins=(6,6), weights=zz, normed=False)\n", - " counts, _, _ = np.histogram2d(yy, xx, bins=(6,6))\n", - " #binned , _, _ = np.histogram(zce, bins=10)\n", - "\n", - " zi = zi / counts\n", - " zi = np.ma.masked_invalid(zi)\n", - " fig, ax = plt.subplots()\n", - " #scat = ppl.pcolormesh(fig, ax, xi, yi, zi, edgecolors='black', cmap=green_purple)\n", - " scat = ax.pcolormesh(xi, yi, zi, edgecolors='black', cmap=green_purple)\n", - " \n", - "\n", - "\n", - " fig.colorbar(scat)\n", - " ax.margins(0.05)\n", - "\n", - " #if i == last_frame-1:\n", - " for xy in hof_points:\n", - " ax.plot(xy[0], xy[1],'y*',label ='hall of fame') \n", - " for xy in pf_points:\n", - " ax.plot(xy[0], xy[1],'b*',label ='pareto front') \n", - " #legend = ax.legend([rect(\"r\"), rect(\"g\"), rect(\"b\")], [\"gene population\",\"pareto front\",\"hall of fame\"])\n", - "\n", - "\n", - " for xy in other_points:\n", - " ax.plot(xy[0], xy[1],'ro',label ='gene population') \n", - " ax.margins(0.05)\n", - "\n", - " plt.xlabel(labels[0])\n", - " plt.ylabel(labels[1])\n", - " plt.show()\n" - ] - }, - { - "cell_type": "code", - "execution_count": 11, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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\n", 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\n", 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G9eTqlPdxxE6jaHp9buk1Db+pv3KlOzdXP1hfV7Jus71yphclXQ3s3eOl1bYvsb0aWC3pHOBM4AOzNZYEP4L25GbE43QmFZv9B3OMRziY8xc8pqkoNvB8VnPwgrdXxdT5gWsy93tMGdA5VttHldz088BakuCjl6lyyvP5u1m3E4/XNhf8bjy0aC6OyrDIeEJ9o2iW2/5hsXo88IO59kmCH1Hd5ZSN7LlLbdzAVbyyZx29ijrbWijdJ4qnm7q5z2BOXcdiUdMY9wskHUjnR+zHwBlz7ZByYfA+jnji5OHUspE9+TBP3+kXwWJra6HMldzTaxotnWGSCz8Xje032j64GCr5Bttb59onP4sBPFlXfvL+pQs3Dr3OtuqW/1AjKvPBR0S0UEMzRZaREk1En3qNhR/24Z2xsDzpUkvdkuAj+jTTeYRMrzCabNgxMVlqqVtKNBHzkGQe3SaHtESTBB8RUUkz5ZcykuAjIiqwYTIJPiKinYa1B5+TrCNmPjfFjohZDPFJ1iT4EdN9U+yIqM6YSZdb6pYSzYgYpZtiR9TKKdFEw+Z7U+yF9Wrg8ZSLohFXXQVw6KGDOFYudOpB0jGS7pC0WdIuN9KStIekLxav/4OkA+qPsh2enAMeppJ8nVMB9/YlUi6Kprz5zYM5jumMoimz1K2xEo2kceBC4GhgC3C9pEtt39a12enAg7ZfKOlU4M+AAX1bRk/3HPA/5k2NTc+bclE0aaZbWc7bEJdo1NQkOZKOAM6z/bpi/RwA2x/u2uaKYpsNkpYAPwWe41mCljScn3R0eTVwCZ37BotOH+gXwBuA7zQY1+AM6+RT0SnNnHgibN8OsBJ7Y6WU/xsvOtif//hXSm17yLEvvmG2W/YNWpMlmn2Au7vWtxTP9dzG9gTwELDX9ANJWjV1E9sFijUG6hrgl8XjqUT4GG1J7jHcjj4a9hjgH69TFzqlRLOzme6Z0O822F4DrIGiB3/U9N8TC+DqYq79tDU/33oa+H7gAliyGnY8DV6zQG028fnFUNu+HZ79bHjwwXu3DOJ4w1qiaTLBbwH261rfF7hnhm22FCWaZ0JxO/tY3F7zwieT4ZFfaDaWGDmPPtr5Kt3zz5UPNsRTFTRZorkeWC7pBZJ2B04FLp22zaXA24vHbwKuma3+HhFRN1NuiGQTvfzGevC2JySdCVxB5x7FF9neJOl8YKPtS4G/BP5a0mY6PfdTm4o3IqKnYqqCYdTolay21wHrpj13btfjR4FT6o4rIqIfw1pYyFQFEREVTF3oNIyS4CMiqnBu+BER0VrpwUdEtJCHeKqCJPiIiCpsJiZ2NB1FT0nwEREVmPTgIyJaa3Iy4+AjItrH4B3pwUdEtI5xevAREa00xJONJcFHRFRgmx1DOoomN92OiKioztkkJZ0lyZKWzrVtevARERXVVYOXtB+d+1j/pMz26cFHRFRQ8y37Pga8jx53tuslPfiIiEr6Kr8snXbv6DXFLUfnJOl4YKvtm6Ry9wlPgo+IqMCmn6kKttleOdOLkq4G9u7x0mrg/cBr+4ktCT4iohLjAdXgbR/V63lJLwFeAEz13vcFvi/pMNs/nel4SfAREVXUMA7e9i3Ac6fWJd0FrLS9bbb9kuAjIiryjlzJGhHROm7gSlbbB5TZLgk+IqKSgQ2BHLgk+IiICmyGdqqCJPiIiEoGN4pm0JLgIyIqaKIGX1YSfERERRlFExHRRunBR0S0ldOD7yZpT+CLwAHAXcC/s/1gj+12ALcUqz+xfXxdMUZElOFJ2PHYcI6iaWq64LOBb9leDnyrWO/lEduHFEuSe0QMoXI3+xjUDT/60VSCPwH4bPH4s8CJDcUREVGJDZM7JkstdWuqBv+vbd8LYPteSc+dYbunFHMnTwAX2P56qaNfvXUwUaatdrVV53uKETKCNfg55jUua3/b90j6VeAaSbfY/sceba0CVs0z1IiI+TN4x4iNoplpXmMASf8saVnRe18G3DfDMe4pvv5I0nrgt4BdEnxxR5Q1xbHNUfsM4B3MYao3mLaGv60m3lOMDBt2PJ6TrN0uBd5ePH47cMn0DSQ9W9IexeOlwCuA22qLMCKiDHdKNGWWujVVg78A+JKk0+ncHfwUAEkrgTNsvxv4deB/SZqk84voAttJ8BExdCZHrUQzG9v3A6/p8fxG4N3F4+8BL6k5tIiI/phMNhYR0UbGjQyBLCMJPiKiilEcRRMRMQpsD+0omiT4iIiKRu5Cp4iIkZASTURESzknWSMiWslkmGRERDs5FzpFRLSTzeSQ3vAjCT4iogIDkynRRES0kZl0EnxEROvY6cFHRLSUmZxMDT4ionVseHzHRNNh9JQEHxFRiVOiiYhoI8PQnmRt6pZ9ERHt4E4NvsxShaTzJG2VdGOxHDfXPunBR0RUUPM4+I/Z/vOyGyfBR0RUknHwERGtZMPEROlRNEslbexaX2N7TR/NnSnpbcBG4I9tPzjbxknwERGVmB0uXV/fZnvlTC9KuhrYu8dLq4FPAR+kUxX6IPDfgHfN1lgSfEREBYO8ktX2UWW2k/QXwGVzbZcEHxFRST3j4CUts31vsXoScOtc+yTBR0RUUOM4+I9IOqRo8i7g9+baIQk+IqIK19ODt/3WfvdJgo+IqMCYiR2PNx1GT0nwERFVZLrgiIh2MrBjSC90amQuGkmnSNokaVLSbGNCj5F0h6TNks6uM8aIiHI6NfgyS92ammzsVuBk4NqZNpA0DlwIHAscBJwm6aB6wouIKMc1TTY2H42UaGzfDiBpts0OAzbb/lGx7ReAE4Db5mzg6q3VgywrbS2etup8TzEyDEzkhh992we4u2t9C3B4rw0lrQJWFau/pMQFAC23FNjWdBANavz9z9F5WWiNv/8hUPYzeH7Vhn7BQ1esZ+3SkpvX+n1ZsAQ/25wKti8pc4gez7nXhsVkPWuKdjfONtfDKBj1zyDvf7TfP9T7Gdg+po525mPBEnzZORVmsQXYr2t9X+CeiseMiBgZw3xHp+uB5ZJeIGl34FTg0oZjiohYNJoaJnmSpC3AEcBaSVcUzz9P0joA2xPAmcAVwO3Al2xvKnH4fuZWbqtR/wzy/iOfASC7Z1k7IiIWuWEu0URERAVJ8BERLdXKBC/pPElbJd1YLMc1HVNTJJ0lyZLKjtNtBUkflHRz8f2/UtLzmo6pTpI+KukHxWfwNUnPajqmOpWdDqXtWpngCx+zfUixrGs6mCZI2g84GvhJ07E04KO2V9g+hM6tzc5tOqCaXQUcbHsFcCdwTsPx1G3O6VBGQZsTfMDHgPcxwwVibWb7512rT2PEPgPbVxYj0QCuo3MdyciwfbvtO5qOo2ltTvBnFn+eXiTp2U0HUzdJxwNbbd/UdCxNkfQhSXcDb2H0evDd3gV8s+kgon6LdpjkbFMh0OmxbKPTa/sgsMz2u2oMrxZzfAbvB15r+yFJdwErbbdqfpKy02FIOgd4iu0P1BZcDcq8f0mrgZXAyV6s/9lnUPL9rwfOsr2xztiGxaJN8GVJOgC4zPbBDYdSG0kvAb4FbC+emprm4TDbP20ssIZIej6wdpR+BgAkvR04A3iN7e1zbd9Go57gh3k2yXmTtMz2vcXqSYzY7JK2bwGeO7Xe1h78bCQtt/3DYvV44AdNxlM3SccAfwK8clSTe7S0By/pr4FD6JRo7gJ+ryvhj5wRTfBfAQ4EJoEfA2fYHpkJ4SVtBvYA7i+eus72GQ2GVCtJJwH/E3gO8DPgRtuvazaq+rUywUdERLtH0UREjLQk+IiIlkqCj4hoqST4iIiWSoKPiKFTXIF+n6SBDHGW9GeSbi2WNw/imItBEny0mqQDBpUkolafAQZyM2tJrwdeSmfo9OHAf5b0jEEce9glwUfE0LF9LfBA93OSfk3S5ZJukPRdSS8uebiDgO/YnrD9/4CbGNAvj2GXBB+jYImkzxaTz/2dpKc2HVDMyxrgPbYPBc4CPllyv5uAYyU9tbgvwquA/RYoxqHSyqkKIqY5EDjd9v+RdBHwB8CfNxxT9EHS04GXA1+WNPX0HsVrJwPn99htq+3X2b5S0m8D3wP+BdgATPTYvnVyJWu0WjHZ3LW29y/WXw38ke0Tm4wr5tY9UWBRM7/D9rIBHPfzwN+Mwo2AUqKJUTC9F5NezSJT3MDlnySdAqCO3yyzr6RxSXsVj1cAK4ArFyzYIZIEH6Ngf0lHFI9PA/6+yWBibpIuplNKOVDSFkmn07lxy+mSbgI2ASeUPNxuwHcl3Uanjv8fuu521Wop0USrFX/mr6Nzb86XAz8E3popdGMUJMFHRLRUSjQRES2VBB8R0VJJ8BERLZUEHxHRUknwEREtlQQfEdFSSfARES31/wFsAmktnSgVdAAAAABJRU5ErkJggg==\n", 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" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "\n", - "gen_vs_pop = package[6] \n", - "pop = gen_vs_pop[-1]\n", - "for k in axis: \n", - " zz = [ np.sum(list(i.dtc.scores.values())) for i in grid_results ]\n", - " zz_sorted = sorted([( np.sum(list(i.dtc.scores.values())), index) for index,i in enumerate(grid_results) ])\n", - " gbest = zz_sorted[0]\n", - " gworst = zz_sorted[-1]\n", - " assert gbest!=gworst\n", - " gworst_grid_attrs = grid_results[gworst[1]].dtc.attrs\n", - " gbest_grid_attrs = grid_results[gbest[1]].dtc.attrs\n", - " gba = gbest_grid_attrs[k[2]] \n", - " zz = [ np.sum(list(i.dtc.attrs.values())) for i in grid_results ]\n", - " zce = [ np.sum(list(i.dtc.scores.values())) for i in grid_results if i.dtc.attrs[k[2]] == gba]\n", - " yy = [ i.dtc.attrs[k[1]] for i in grid_results if i.dtc.attrs[k[2]] == gba ]\n", - " xx = [ i.dtc.attrs[k[0]] for i in grid_results if i.dtc.attrs[k[2]] == gba ]\n", - " last_frame = len(gen_vs_pop)\n", - " other_points = []\n", - " pf_points = []\n", - " hof_points = []\n", - " labels = []\n", - " \n", - " pf = package[2]\n", - " hof = package[1]\n", - " \n", - " for p in pop:\n", - " xy = []\n", - " for key in k:\n", - " v = p.dtc.attrs[key]\n", - " xy.append(v)\n", - " labels.append(key)\n", - " other_points.append(xy)\n", - " \n", - " \n", - " for p in pf:\n", - " xy = []\n", - " for key in k:\n", - " v = p.dtc.attrs[key]\n", - " xy.append(v)\n", - " labels.append(key)\n", - " pf_points.append(xy) \n", - " \n", - " for p in hof:\n", - " xy = []\n", - " for key in k:\n", - " v = p.dtc.attrs[key]\n", - " xy.append(v)\n", - " labels.append(key)\n", - " hof_points.append(xy) \n", - "\n", - " \n", - " zi, yi, xi = np.histogram2d(yy, xx, bins=(6,6), weights=zce, normed=False)\n", - " counts, _, _ = np.histogram2d(yy, xx, bins=(6,6))\n", - "\n", - " zi = zi / counts\n", - " zi = np.ma.masked_invalid(zi)\n", - " fig, ax = plt.subplots()\n", - " scat = ax.pcolormesh(xi, yi, zi, edgecolors='black', cmap=green_purple)\n", - "\n", - " fig.colorbar(scat)\n", - " ax.margins(0.05)\n", - "\n", - " #if i == last_frame-1:\n", - " for xy in hof_points:\n", - " ax.plot(xy[0], xy[1],'y*',label ='hall of fame') \n", - " for xy in pf_points:\n", - " ax.plot(xy[0], xy[1],'b*',label ='pareto front') \n", - " #legend = ax.legend([rect(\"r\"), rect(\"g\"), rect(\"b\")], [\"gene population\",\"pareto front\",\"hall of fame\"])\n", - "\n", - "\n", - " for xy in other_points:\n", - " ax.plot(xy[0], xy[1],'ro',label ='gene population') \n", - " ax.margins(0.05)\n", - "\n", - " plt.xlabel(labels[0])\n", - " plt.ylabel(labels[1])\n", - " plt.show()\n", - "\n", - " \n", - " " - ] - }, - { - "cell_type": "code", - "execution_count": 12, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Report: \n", - "did it work? True was it better True\n", - "the difference between brute force candidates model parameters and the GA's model parameters:\n", - "-0.017819912248960484 0.20000000000000018 0.21781991224896066 a\n", - "the relative distance scaled by the length of the parameter dimension of interest:\n", - "0.0192647699988762\n", - "the difference between brute force candidates model parameters and the GA's model parameters:\n", - "-2.4122636718780694e-09 -5e-09 -2.5877363281219307e-09 b\n", - "the relative distance scaled by the length of the parameter dimension of interest:\n", - "0.9649054687512277\n", - "the difference between brute force candidates model parameters and the GA's model parameters:\n", - "-20.633595623251388 -64.0 -43.36640437674861 vr\n", - "the relative distance scaled by the length of the parameter dimension of interest:\n", - "0.34389326038752316\n", - "[0.9996337918357708, 0.018053990681726084]\n", - "[0.9996337918357708, 0.018053990681726084]\n", - "the difference between brute force candidates model parameters and the GA's model parameters:\n", - "-0.017819912248960484 0.20000000000000018 0.21781991224896066 a\n", - "the relative distance scaled by the length of the parameter dimension of interest:\n", - "0.0192647699988762\n", - "the difference between brute force candidates model parameters and the GA's model parameters:\n", - "-2.4122636718780694e-09 -5e-09 -2.5877363281219307e-09 b\n", - "the relative distance scaled by the length of the parameter dimension of interest:\n", - "0.9649054687512277\n", - "the difference between brute force candidates model parameters and the GA's model parameters:\n", - "-20.633595623251388 -64.0 -43.36640437674861 vr\n", - "the relative distance scaled by the length of the parameter dimension of interest:\n", - "0.34389326038752316\n", - "[0.9996337918357708, 0.018053990681726084]\n", - "[0.9996337918357708, 0.018053990681726084]\n", - "{3: {'miniga': 1.017687782517497, 'minigrid': 1.9999994712005862, 'success': True, 'better': True, 'attrs_ga': {'a': 0.21781991224896066, 'b': -2.5877363281219307e-09, 'vr': -43.36640437674861}, 'attrs_grid': {'a': 0.20000000000000018, 'b': -5e-09, 'vr': -64.0}, 'p_dist': {'a': 0.0192647699988762, 'b': 0.9649054687512277, 'vr': 0.34389326038752316}, 'vind_domination': False, 'inc_domination': False}}\n" - ] - } - ], - "source": [ - "nparams=3\n", - "new_report = make_report(grid_results,pop, nparams)\n", - "from neuronunit.optimization import exhaustive_search as es\n", - "print(new_report)" - ] - }, - { - "cell_type": "code", - "execution_count": 37, - "metadata": {}, - "outputs": [], - "source": [ - "import numpy as np\n", - "import matplotlib\n", - "matplotlib.rcParams.update({'font.size':16})\n", - "import matplotlib.pyplot as plt\n", - "\n", - "def plot_surface(fig_trip,ax,model_param0,model_param1,history):\n", - " '''\n", - "\n", - " Move this method back to plottools\n", - " Inputs should be keys, that are parameters see new function definition below\n", - " '''\n", - " td = list(history.genealogy_history[1].dtc.attrs.keys())\n", - " x = [ i for i,j in enumerate(td) if str(model_param0) == j ][0]\n", - " y = [ i for i,j in enumerate(td) if str(model_param1) == j ][0]\n", - " \n", - " all_inds = history.genealogy_history.values()\n", - " sums = np.array([np.sum(ind.fitness.values) for ind in all_inds])\n", - "\n", - " xs = np.array([ind[x] for ind in all_inds])\n", - " ys = np.array([ind[y] for ind in all_inds])\n", - " min_ys = ys[np.where(sums == np.min(sums))]\n", - " min_xs = xs[np.where(sums == np.min(sums))]\n", - "\n", - " #fig_trip, ax_trip = plt.subplots(1, figsize=(10, 5), facecolor='white')\n", - " trip_axis = ax.tripcolor(xs,ys,sums,20,norm=matplotlib.colors.LogNorm())\n", - " plot_axis = ax.plot(list(min_xs), list(min_ys), 'o', color='lightblue',label='global minima')\n", - " #fig_trip.colorbar(trip_axis, label='Sum of Objective Errors ')\n", - " if type(td) is not type(None):\n", - " ax_trip.set_xlabel('Parameter '+str((td[x])))\n", - " ax_trip.set_ylabel('Parameter '+str((td[y])))\n", - " plot_axis = ax_trip.plot(list(min_xs), list(min_ys), 'o', color='lightblue')\n", - " #fig_trip.tight_layout()\n", - " return ax_trip,plot_axis\n", - " #plt.show()" - ] - }, - { - "cell_type": "code", - "execution_count": 38, - "metadata": {}, - "outputs": [], - "source": [] - }, - { - "cell_type": "code", - "execution_count": 39, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "/home/jovyan/neuronunit/neuronunit/unit_test\n" - ] - }, - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "from prettyplotlib import plt\n", - "from neuronunit.optimization import get_neab\n", - "import copy\n", - "\n", - "electro_path = str(os.getcwd())+'/pipe_tests.p'\n", - "print(os.getcwd())\n", - "assert os.path.isfile(electro_path) == True\n", - "with open(electro_path,'rb') as f:\n", - " electro_tests = pickle.load(f)\n", - "\n", - "electro_tests = get_neab.replace_zero_std(electro_tests)\n", - "electro_tests = get_neab.substitute_parallel_for_serial(electro_tests)\n", - "test, observation = electro_tests[0]\n", - "\n", - "npoints = 6\n", - "tests = copy.copy(electro_tests[0][0][0:2])\n", - "import numpy as np\n", - "ax = None\n", - "from neuronunit.optimization import exhaustive_search as es\n", - "\n", - "\n", - "fig,ax = plt.subplots(3,3,figsize=(10,10))\n", - "attrs_list = list(pop[0].dtc.attrs)\n", - "\n", - "\n", - "with open('pre_ga_reports.p','rb') as f:\n", - " package = pickle.load(f)\n", - "\n", - "grid_results = {}\n", - "hof = package[1]\n", - "for i in range(len(attrs_list)):\n", - " for j in range(len(attrs_list)):\n", - " if ij:\n", - " ax[i,j].set_title('Param {0} vs Param {1}'.format(attrs_list[i],attrs_list[j]))\n", - " ss = set([attrs_list[j],attrs_list[i]])\n", - " bs = set(attrs_list)\n", - " diff = bs.difference(ss)\n", - " bd = {}\n", - " bd[list(diff)[0]] = hof[0].dtc.attrs[list(diff)[0]]\n", - " \n", - " \n", - " ax_trip,plot_axis = plot_surface(fig,ax[i,j],attrs_list[j],attrs_list[i],history)\n", - " ax[i,j].plot_axis = plot_axis\n", - " provided_keys = []\n", - " provided_keys.append(attrs_list[i])\n", - " provided_keys.append(attrs_list[j])\n", - " gr = es.run_grid(2,10,tests,provided_keys = provided_keys ,hold_constant = bd)\n", - " grid_results[str(attrs_list[i])+str(attrs_list[j])] = gr\n", - " \n", - " \n", - " #print(best)\n", - "\n", - " # here access the GA's optimum for that parameter\n", - " #ax[i,j].pcolormesh(Z) \n", - "\n", - " \n" - ] - }, - { - "cell_type": "code", - "execution_count": 40, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "Text(0,0,'Projections using the best value for other parameters')" - ] - }, - "execution_count": 40, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": 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\n", 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" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "from prettyplotlib import plt\n", - "import numpy as np\n", - "fig,ax = plt.subplots(4,4,figsize=(10,10))\n", - "attrs_list = list(pop[0].dtc.attrs)\n", - "for i in range(4):\n", - " for j in range(4):\n", - " if ij:\n", - " ax[i,j].set_title('Param %d vs Param %d' % (i,j))\n", - " # Z = 2D grid search with all params except i and j held constant at optimum\n", - " Z = np.random.rand(25,25)\n", - " ax[i,j].pcolormesh(Z)\n", - " else: # i==j\n", - " #z = 1D grid search with all params except i held contant at optimum\n", - " z = np.random.rand(25)\n", - " ax[i,i].plot(z)\n", - "plt.tight_layout()\n", - "plt.text(0,0,'Projections using the best value for other parameters')" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "So imagine 6 2D plots. The first three are: XY at Z=z, XZ at Y=y, and YZ at X=x, where x, y, z \n", - "is the location of the global minimum. The second three are XY with the minimum taken across Z, \n", - "XZ with the minumum taken across Y, YZ, with the minimum taken across X.\n", - "With these 6 I think you would get a pretty good idea of the contours around the global minimum." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "\n", - "\n", - "with open('pre_ga_reports.p','rb') as f:\n", - " package = pickle.load(f)\n", - "log = package[3] \n", - "%matplotlib inline\n", - "import matplotlib.pyplot as plt\n", - "import numpy as np\n", - "\n", - "plt.style.use('ggplot')\n", - "fig, axes = plt.subplots(figsize=(10, 10), facecolor='white')\n", - "gen_numbers = [ i for i in range(0,len(log.select('gen'))) ]\n", - "\n", - "mean = np.array([ np.sum(i) for i in log.select('avg')])\n", - "std = np.array([ np.sum(i) for i in log.select('std')])\n", - "minimum = np.array([ np.sum(i) for i in log.select('min')])\n", - "#minimum = np.array([ np.sum(i) for i in log.select('min')])\n", - "worst = np.max([ sum(g.dtc.scores.values()) for g in grid_results ])\n", - "#grid_min = np.min([ sum(g.dtc.scores.values()) for g in grid_results ])\n", - "gwl = [ worst for i in range(0,len(log.select('gen'))) ]\n", - "grid_min = np.min([ sum(g.dtc.scores.values()) for g in grid_results ])\n", - "gml = [ grid_min for i in range(0,len(log.select('gen'))) ]\n", - "\n", - "if new_report[nparams]['success'] == True:\n", - " axes.plot(gen_numbers,\n", - " gml,\n", - " color='blue',\n", - " linewidth=2,\n", - " label='exhaustive search best')\n", - "\n", - " axes.plot(gen_numbers,\n", - " gwl,\n", - " color='yellow',\n", - " linewidth=2,\n", - " label='exhaustive search worst')\n", - " \n", - "\n", - "\n", - " stdminus = mean - std\n", - " stdplus = mean + std\n", - "\n", - " axes.plot(\n", - " gen_numbers,\n", - " minimum,\n", - " color='black',\n", - " linewidth=2,\n", - " label='population minimum')\n", - " axes.fill_between(gen_numbers, stdminus, stdplus)\n", - "\n", - "\n", - "axes.plot(\n", - " gen_numbers,\n", - " mean,\n", - " color='black',\n", - " linewidth=2,\n", - " label='population average')\n", - " \n", - "\n", - "\n", - "\n", - "plt.xlabel('generation')\n", - "plt.ylabel('error')\n", - "\n", - "\n", - "plt.legend()\n", - "plt.show()\n", - "print(worst,grid_min)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "\n", - "\n", - "\n" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "\n", - "#fig.savefig('pcolormesh_prettyplotlib_labels_other_cmap_diverging.png')\n" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "\n", - "\n", - "\n" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "import bokeh\n", - "import numpy as np\n", - "import matplotlib" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "\n", - "\n", - "from neuronunit.optimization.optimization_management import run_ga\n", - "from neuronunit.optimization.exhaustive_search import run_grid, reduce_params, create_grid\n", - "from neuronunit.optimization.model_parameters import model_params\n", - "import os\n", - "import pickle\n", - "from neuronunit.optimization import get_neab\n", - "reports = {}\n", - "npoints = 10\n", - "\n", - "\n", - "\n", - "with open('pre_grid_reports.p','rb') as f:\n", - " grid_results = pickle.load(f)\n", - "opt_keys = list(grid_results[0].dtc.attrs.keys())\n", - "\n", - "with open('pre_ga_reports.p','rb') as f:\n", - " ga_out = pickle.load(f)\n", - "\n", - "pop = ga_out[0]\n", - "genes_vs_gen = ga_out[-1]" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "\n", - "from neuronunit.optimization.exhaustive_search import create_grid\n", - "gp = create_grid(npoints = 6,nparams = 3)\n", - "\n", - " " - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "\n", - "Found tough parameters for which the GA is not able to perform particularily # well. Suspect b's error surface is not concave.\n", - "Explore 1D cross section.\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "\n", - "plt.clf()\n", - "\n", - "plt.scatter([g.dtc.attrs['a'] for g in pop ],[ sum(g.dtc.scores.values()) for g in pop ] ,label='ga pop')\n", - "plt.scatter([g.dtc.attrs['a'] for g in grid_results ],[ sum(g.dtc.scores.values()) for g in grid_results ] ,label='grid evaluations')\n", - "plt.ylabel('score')\n", - "plt.xlabel('gene attribute a')\n", - "plt.legend()\n", - "plt.show()\n", - "\n", - "\n", - "plt.scatter([g.dtc.attrs['vr'] for g in pop ],[ sum(g.dtc.scores.values()) for g in pop ] ,label='ga pop')\n", - "plt.scatter([g.dtc.attrs['vr'] for g in grid_results ],[ sum(g.dtc.scores.values()) for g in grid_results ] ,label='grid evaluations')\n", - "plt.ylabel('score')\n", - "plt.xlabel('gene attribute vr')\n", - "plt.legend()\n", - "plt.show()\n", - "\n", - "\n", - "\n", - "plt.scatter([g.dtc.attrs['b'] for g in pop ],[ sum(g.dtc.scores.values()) for g in pop ] ,label='ga pop')\n", - "plt.scatter([g.dtc.attrs['b'] for g in grid_results ],[ sum(g.dtc.scores.values()) for g in grid_results ] ,label='grid evaluations')\n", - "plt.ylabel('score')\n", - "plt.xlabel('gene attribute b')\n", - "plt.legend()\n", - "plt.show()\n", - "\n", - "\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "\n", - "nparams = 3\n", - "opt_keys = [str('a'),str('vr'),str('b')]\n", - "\n", - "\n", - "#grid_results = run_grid(nparams,npoints,tests,provided_keys = opt_keys)\n", - "from neuronunit.optimization import exhaustive_search #import run_grid, reduce_params, create_grid\n", - "\n", - "from neuronunit.optimization.exhaustive_search import run_grid, reduce_params, create_grid\n", - "grid_points,maps = exhaustive_search.create_grid(npoints=6,nparams=3,provided_keys=opt_keys)\n", - "print(maps)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "def blah():\n", - " '''\n", - " This works but I changed the grid\n", - " '''\n", - " grid = np.zeros((6,6,6))\n", - " import matplotlib.pyplot as plt\n", - " print(maps)\n", - " for i in grid_results:\n", - " xyz = []\n", - " for k,v in i.dtc.attrs.items():\n", - " print(k,v)\n", - " #print(maps[v][k])\n", - " xyz.append(maps[k][v])\n", - " grid[xyz[0],xyz[1],xyz[2]] = sum(i.dtc.scores.values())\n", - "\n", - " for i in range(0,6):\n", - " flat = grid[i,:,:]\n", - " plt.imshow(flat)\n", - " plt.show()\n", - "\n", - " for i in range(0,6):\n", - " flat = grid[:,i,:]\n", - " plt.imshow(flat)\n", - " plt.show()\n", - "\n", - " for i in range(0,6):\n", - " flat = grid[:,:,i]\n", - " plt.imshow(flat)\n", - " plt.show()\n", - " \n", - " \n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "\n", - "\n", - "\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "#new_list = genes_vs_gen[1:-1]\n", - "#print(new_list)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "#![alt text](rick_style_guide.png \"Ricks table guide\")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "dfg\n", - "\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "\n", - " " - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "\n" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "\n", - "\n" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "scrolled": false - }, - "outputs": [], - "source": [ - "\n" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "\n", - "\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "\n", - " " - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "I am currently writing code that should enable the plotting of HOF values versus generation." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "\n", - " " - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "For some reason the global minimum solution is not converged on, as shown by the evolution of errors below:" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "\n", - "\n" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "For some reason, the GA population does not converge to the absolute minimum, although it does sample it.\n", - "Perhaps the absolute minimum is a highly dominated solution, which is a testable hypthosis.\n", - "\n", - "None the less because the GA samples the absolute minimum, this value can be corroborated with the GA." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "\n", - "\n", - "\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "\n", - "\n", - " \n", - "\n", - "\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "\n", - " \n", - " " - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Quantize distance between minimimum error and maximum error.\n", - "This step will allow the GA's performance to be located within or below the range of error found by grid search.\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "The code below reports on the differences between between attributes of best models found via grid versus attributes of best models found via GA search:\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "\n", - "\n", - " " - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.6.4" - } - }, - "nbformat": 4, - "nbformat_minor": 2 -} diff --git a/neuronunit/unit_test/openworm_tests.py b/neuronunit/unit_test/openworm_tests.py deleted file mode 100644 index 5d819fdd0..000000000 --- a/neuronunit/unit_test/openworm_tests.py +++ /dev/null @@ -1,16 +0,0 @@ -"""Unit tests for the showcase features of NeuronUnit""" - -from .base import * - -class DocumentationTestCase(NotebookTools,unittest.TestCase): - """Testing documentation notebooks""" - - path = '../../docs' - - #@unittest.skip("Skipping chapter 2") - def test_chapter2(self): - self.do_notebook('chapter2') - - -if __name__ == '__main__': - unittest.main() diff --git a/neuronunit/unit_test/opt_ephys_properties.py b/neuronunit/unit_test/opt_ephys_properties.py new file mode 100644 index 000000000..8446d73d0 --- /dev/null +++ b/neuronunit/unit_test/opt_ephys_properties.py @@ -0,0 +1,169 @@ +#!/usr/bin/env python +# coding: utf-8 +import pandas as pd +import matplotlib.pyplot as plt +import pickle +import glob +import os +import scipy +import quantities as qt +import unittest + +from neuronunit.optimization.optimization_management import _opt_ +from neuronunit.optimization.optimization_management import TSD +from neuronunit.optimization.model_parameters import MODEL_PARAMS, BPO_PARAMS +from neuronunit.allenapi.allen_data_driven import make_allen_hard_coded_limited as make_allen_hard_coded +from neuronunit.allenapi import neuroelectroapi + +from sciunit.scores import RelativeDifferenceScore, ZScore +from sciunit import TestSuite +from sciunit.utils import config_set, config_get +config_set('PREVALIDATE', False) +import warnings +warnings.filterwarnings("ignore") + + +class testOptimizationEphysCase(unittest.TestCase): + def setUp(self): + _,_,_,a_cells = make_allen_hard_coded() + self.MU = 10 + self.NGEN = 10 + self.a_cells = a_cells + if os.path.exists("processed_multicellular_constraints.p"): + with open("processed_multicellular_constraints.p","rb") as f: + experimental_constraints = pickle.load(f) + else: + experimental_constraints = neuroelectroapi.process_all_cells() + + + NC = TSD(experimental_constraints["Neocortex pyramidal cell layer 5-6"]) + NC.pop("InjectedCurrentAPWidthTest",None) + NC.pop("InjectedCurrentAPAmplitudeTest",None) + NC.pop("InjectedCurrentAPThresholdTest",None) + self.NC = NC + CA1 = TSD(experimental_constraints["Hippocampus CA1 pyramidal cell"]) + CA1.pop("InjectedCurrentAPWidthTest",None) + CA1.pop("InjectedCurrentAPAmplitudeTest",None) + CA1.pop("InjectedCurrentAPThresholdTest",None) + self.CA1 = CA1 + def test_allen_good_agreement_opt(self): + [final_pop, + hall_of_fame, + logs, + hist, + best_ind, + best_fit_val, + opt, + obs_preds, + df] = _opt_( + self.a_cells['471819401'], + BPO_PARAMS, + '471819401', + "ADEXP", + self.MU, + self.NGEN, + "IBEA", + use_streamlit=False, + score_type=RelativeDifferenceScore + ) + def test_allen_fi_curve_opt(self): + [final_pop, + hall_of_fame, + logs, + hist, + best_ind, + best_fit_val, + opt, + obs_preds, + df] = _opt_( + self.a_cells['fi_curve'], + BPO_PARAMS, + 'fi_curve', + "ADEXP", + self.MU, + self.NGEN, + "IBEA", + use_streamlit=False, + score_type=RelativeDifferenceScore + ) + def test_neuro_electro_adexp_opt(self): + self.MU = 10 + self.NGEN = 10 + [final_pop, + hall_of_fame, + logs, + hist, + best_ind, + best_fit_val, + opt, + obs_preds, + df] = _opt_( + self.NC, + BPO_PARAMS, + "Neocortex pyramidal cell layer 5-6", + "ADEXP", + self.MU, + self.NGEN, + "IBEA", + use_streamlit=False, + score_type=ZScore + ) + + + ''' + Rick, some of these bellow are unit tests + that cannot pass without changes to sciunit complete + ''' + @unittest.skip + def test_neuro_electro_adexp_opt_ca1(self): + self.MU = 35 + self.NGEN = 10 + final_pop, hall_of_fame, logs, hist,best_ind,best_fit_val,opt = _opt_( + self.CA1, + BPO_PARAMS, + "Hippocampus CA1 pyramidal cell", + "ADEXP", + self.MU, + self.NGEN, + "IBEA", + score_type=ZScore + ) + + @unittest.skip + def test_neuro_electro_izhi_opt_pyr(self): + self.MU = 100 + self.NGEN = 1 + [final_pop, + hall_of_fame, + logs, + hist, + best_ind, + best_fit_val, + opt, + obs_preds, + df] = _opt_( + self.NC, + BPO_PARAMS, + "Neocortex pyramidal cell layer 5-6", + "IZHI", + self.MU, + self.NGEN, + "IBEA", + score_type=ZScore + ) + old_result = np.sum(best_fit_val) + self.NGEN = 35 + + final_pop, hall_of_fame, logs, hist,best_ind,best_fit_val,opt = _opt_( + self.NC, + BPO_PARAMS, + "Neocortex pyramidal cell layer 5-6", + "IZHI", + self.MU, + self.NGEN, + "IBEA", + use_streamlit=False, + score_type=ZScore + ) + new_result = np.sum(best_fit_val) + assert new_result1: - t.params = dtc.vtest[k] - score = t.judge(model,stop_on_error = False, deep_error = True) - scores.append(score.norm_score,score) - return scores - -def exhaustive_search(self): - iter_list = create_list() - scores = list(dview.map(parallel_method,iter_list).get()) - #score_parameter_pairs = zip(scores,iter_list) - - #print(iter_list) - - -from neuronunit import tests -#from deap import hypervolume - - -#test_0_run_exhaust() - -os.system('ipcluster start -n 8 --profile=default & sleep 5;') -import ipyparallel as ipp -rc = ipp.Client(profile='default') -rc[:].use_cloudpickle() -dview = rc[:] - -class ReducedModelTestCase(unittest.TestCase): - """Test instantiation of the reduced model""" - - """Testing model optimization""" - - - def setUp(self): - #import sys - #sys.path.append('../') - #import neuronunit - - from neuronunit.models.reduced import ReducedModel - #self.ReducedModel = ReducedModel - #path = ReducedModelTestCase().path - #self.model = self.ReducedModel(path, backend='NEURON') - self.predictions = None - self.predictionp = None - self.score_p = None - self.score_s = None - self.timed_p = None - self.timed_s = None - self.attrs_list = None - - def run_test(self, cls): - observation = self.get_observation(cls) - test = cls(observation=observation) - score = test.judge(self.model) - score.summarize() - return score.score - - - def nrn_backend_works(self): - from neuronunit.optimization import get_neab - get_neab.LEMS_MODEL_PATH = '/home/jovyan/neuronunit/neuronunit/optimization/NeuroML2/LEMS_2007One.xml' - - #from neuronunit.models import backends - from neuronunit.models.reduced import ReducedModel - model = ReducedModel(get_neab.LEMS_MODEL_PATH,name=str('vanilla'),backend='NEURON') - #import pdb; pdb.set_trace() - #score = get_neab.tests[0].judge(model,stop_on_error = False, deep_error = True) - def check_parallel_path_consistency(): - ''' - import paths and test for consistency - ''' - from neuronunit import models - return models.__file__ - - # def test_0check_paths(self): - - # path_serial = check_paths() - # paths_parallel = dview.apply_async(check_parallel_path_consistency).get_dict() - # self.assertEqual(path_serial, paths_parallel[0]) - @unittest.skip("This times out") - - def test_1_run_opt(self): - from neuronunit.optimization import nsga_parallel - with open('opt_run_data.p','rb') as handle: - attrs = pickle.load(handle) - self.attrs_list = attrs - - - #for i in iter_list - def test_3check_paths(self): - self.nrn_backend_works() - - - - def test_4rheobase_setup(self): - from neuronunit.tests.fi import RheobaseTest, RheobaseTestP - from neuronunit.optimization import get_neab - from neuronunit.models import backends - from neuronunit.models.reduced import ReducedModel - import time - from neuronunit import aibs - import os - dataset_id = 354190013 # Internal ID that AIBS uses for a particular Scnn1a-Tg2-Cre - # Primary visual area, layer 5 neuron. - observation = aibs.get_observation(dataset_id,'rheobase') - #os.system('ipcluster start -n 8 --profile=default & sleep 25;') - - rt = RheobaseTest(observation = observation) - rtp = RheobaseTestP(observation = observation) - - get_neab.LEMS_MODEL_PATH = '/home/jovyan/neuronunit/neuronunit/optimization/NeuroML2/LEMS_2007One.xml' - model = ReducedModel(get_neab.LEMS_MODEL_PATH,name=str('vanilla'),backend='NEURON') - ticks = time.time() - self.score_p = rtp.judge(model,stop_on_error = False, deep_error = True) - self.predictionp = self.score_p.prediction - - self.score_p = self.score_p.norm_score - tickp = time.time() - self.timed_p = tickp - ticks - self.score_s = rt.judge(model,stop_on_error = False, deep_error = True) - self.predictions = self.score_s.prediction - - self.score_s = self.score_s.norm_score - self.timed_s = time.time() - tickp - - - print(' serial score {0} parallel score {1}'.format(self.score_s,self.score_p)) - print(' serial time {0} parallel time {1}'.format(self.timed_s,self.timed_p)) - - def test_5rheobase_check(self): - self.assertEqual(int(self.score_s*1000), int(self.score_p*1000)) - - def test_6rheobase_check(self): - self.assertGreater(self.timed_s,self.timed_p) - - def test_7rheobase_check(self): - self.assertEqual(int(self.predictionp['value']), int(self.predictions['value'])) - # return self.score_s, self.score_p, self.timed_s, self.timed_p, self.predictionp, self.predictions - - # difference_progress, fitnesses, pf, logbook, pop, dtcpop, stats = nsga_parallel.main(MU=12, NGEN=4, CXPB=0.9) - - - - -if __name__ == '__main__': - #a = ReducedModelTestCase() - #a.test_0_run_exhaust() - unittest.main() diff --git a/neuronunit/unit_test/parse_glif.py b/neuronunit/unit_test/parse_glif.py deleted file mode 100644 index 4ddfbd315..000000000 --- a/neuronunit/unit_test/parse_glif.py +++ /dev/null @@ -1,185 +0,0 @@ -usage=''' -Provenance: This file is originally from -https://github.com/vrhaynes/AllenInstituteNeuroML -https://github.com/OpenSourceBrain/AllenInstituteNeuroML -It is authored by pgleeson@github.com, vrhaynes@github.com and russelljjarvis@github.com - -This file can be used to generate LEMS components for each of a number of GLIF models - -Usage: - - python parse_glif.py -all - -''' - -import sys -import os -import json - -from pyneuroml import pynml - -def generate_lems(glif_package, curr_pA=None, show_plot=False): - if curr_pA == None: - curr_pA = 10 - glif_dir = os.getcwd() - - model_metadata,neuron_config,ephys_sweeps = glif_package - - template_cell = ''' - - <%s %s/> - - - ''' - - type = '???' - print(model_metadata['name']) - if '(LIF)' in model_metadata['name']: - type = 'glifCell' - if '(LIF-ASC)' in model_metadata['name']: - type = 'glifAscCell' - if '(LIF-R)' in model_metadata['name']: - type = 'glifRCell' - if '(LIF-R-ASC)' in model_metadata['name']: - type = 'glifRAscCell' - if '(LIF-R-ASC-A)' in model_metadata['name']: - type = 'glifRAscATCell' - - cell_id = 'GLIF_%s'%glif_dir - - #model_metadata['name'] - - attributes = "" - - attributes +=' id="%s"'%cell_id - attributes +='\n C="%s F"'%neuron_config["C"] - attributes +='\n leakReversal="%s V"'%neuron_config["El"] - attributes +='\n reset="%s V"'%neuron_config["El"] - attributes +='\n thresh="%s V"'%( float(neuron_config["th_inf"]) * float(neuron_config["coeffs"]["th_inf"])) - attributes +='\n leakConductance="%s S"'%(1/float(neuron_config["R_input"])) - - if 'Asc' in type: - attributes +='\n tau1="%s s"'%neuron_config["asc_tau_array"][0] - attributes +='\n tau2="%s s"'%neuron_config["asc_tau_array"][1] - attributes +='\n amp1="%s A"'% ( float(neuron_config["asc_amp_array"][0]) * float(neuron_config["coeffs"]["asc_amp_array"][0]) ) - attributes +='\n amp2="%s A"'% ( float(neuron_config["asc_amp_array"][1]) * float(neuron_config["coeffs"]["asc_amp_array"][1]) ) - - if 'glifR' in type: - attributes +='\n bs="%s per_s"'%neuron_config["threshold_dynamics_method"]["params"]["b_spike"] - attributes +='\n deltaThresh="%s V"'%neuron_config["threshold_dynamics_method"]["params"]["a_spike"] - attributes +='\n fv="%s"'%neuron_config["voltage_reset_method"]["params"]["a"] - attributes +='\n deltaV="%s V"'%neuron_config["voltage_reset_method"]["params"]["b"] - - if 'glifRAscATCell' in type: - attributes +='\n bv="%s per_s"'%neuron_config["threshold_dynamics_method"]["params"]["b_voltage"] - attributes +='\n a="%s per_s"'%neuron_config["threshold_dynamics_method"]["params"]["a_voltage"] - - - file_contents = template_cell%(type, attributes) - - print(file_contents) - - #cell_file_name = '%s.xml'%(cell_id) - cell_file_name = '{0}{1}.xml'.format(os.getcwd(),str(model_metadata['name'])) - cell_file = open(cell_file_name,'w') - cell_file.write(file_contents) - cell_file.close() - return cell_file_name - - ''' - import opencortex.build as oc - - - nml_doc, network = oc.generate_network("Test_%s"%glif_dir) - - pop = oc.add_single_cell_population(network, - 'pop_%s'%glif_dir, - cell_id) - - - pg = oc.add_pulse_generator(nml_doc, - id="pg0", - delay="100ms", - duration="1000ms", - amplitude="%s pA"%curr_pA) - - - oc.add_inputs_to_population(network, - "Stim0", - pop, - pg.id, - all_cells=True) - - - - nml_file_name = '%s.net.nml'%network.id - oc.save_network(nml_doc, nml_file_name, validate=True) - - - thresh = 'thresh' - if 'glifR' in type: - thresh = 'threshTotal' - - lems_file_name = oc.generate_lems_simulation(nml_doc, - network, - nml_file_name, - include_extra_lems_files = [cell_file_name,'../GLIFs.xml'], - duration = 1200, - dt = 0.01, - gen_saves_for_quantities = {'thresh.dat':['pop_%s/0/GLIF_%s/%s'%(glif_dir,glif_dir,thresh)]}, - gen_plots_for_quantities = {'Threshold':['pop_%s/0/GLIF_%s/%s'%(glif_dir,glif_dir,thresh)]}) - - results = pynml.run_lems_with_jneuroml(lems_file_name, - nogui=True, - load_saved_data=True) - - print("Ran simulation; results reloaded for: %s"%results.keys()) - - info = "Model %s; %spA stimulation"%(glif_dir,curr_pA) - - times = [results['t']] - vs = [results['pop_%s/0/GLIF_%s/v'%(glif_dir,glif_dir)]] - labels = ['LEMS - jNeuroML'] - - original_model_v = 'original.v.dat' - if os.path.isfile(original_model_v): - data, indices = pynml.reload_standard_dat_file(original_model_v) - times.append(data['t']) - vs.append(data[0]) - labels.append('Allen SDK') - - - pynml.generate_plot(times, - vs, - "Membrane potential; %s"%info, - xaxis = "Time (s)", - yaxis = "Voltage (V)", - labels = labels, - grid = True, - show_plot_already=False, - save_figure_to='Comparison_%ipA.png'%(curr_pA)) - - times = [results['t']] - vs = [results['pop_%s/0/GLIF_%s/%s'%(glif_dir,glif_dir,thresh)]] - labels = ['LEMS - jNeuroML'] - - original_model_th = 'original.thresh.dat' - if os.path.isfile(original_model_th): - data, indeces = pynml.reload_standard_dat_file(original_model_th) - times.append(data['t']) - vs.append(data[0]) - labels.append('Allen SDK') - - - pynml.generate_plot(times, - vs, - "Threshold; %s"%info, - xaxis = "Time (s)", - yaxis = "Voltage (V)", - labels = labels, - grid = True, - show_plot_already=show_plot, - save_figure_to='Comparison_Threshold_%ipA.png'%(curr_pA)) - - readme = - ''' diff --git a/neuronunit/unit_test/pipe_entry_point.py b/neuronunit/unit_test/pipe_entry_point.py deleted file mode 100644 index ff95587cd..000000000 --- a/neuronunit/unit_test/pipe_entry_point.py +++ /dev/null @@ -1,142 +0,0 @@ - -import os -import pickle -from dask import distributed -import pickle -import pandas as pd -import timeit - -from neuronunit.optimization import get_neab -from neuronunit.optimization.model_parameters import model_params -from bluepyopt.deapext.optimisations import SciUnitOptimization -from neuronunit.optimization.optimization_management import write_opt_to_nml -from neuronunit.optimization import optimization_management -from neuronunit.optimization import optimization_management as om - -import numpy as np -import copy - - -electro_path = 'pipe_tests.p' -purkinje = { 'nlex_id':'sao471801888'}#'NLXWIKI:sao471801888'} # purkinje -fi_basket = {'nlex_id':'100201'} -#pvis_cortex = {'nlex_id':'nifext_50'} # Layer V pyramidal cell -olf_mitral = { 'nlex_id':'nifext_120'} -ca1_pyr = { 'nlex_id':'830368389'} -pipe = [ fi_basket, olf_mitral, ca1_pyr, purkinje ] - - -electro_path = 'pipe_tests.p' -try: - assert os.path.isfile(electro_path) == True - with open(electro_path,'rb') as f: - electro_tests = pickle.load(f) - electro_tests = get_neab.replace_zero_std(electro_tests) - electro_tests = get_neab.substitute_parallel_for_serial(electro_tests) - -except: - electro_tests = [] - for p in pipe: - p_tests, p_observations = get_neab.get_neuron_criteria(p) - electro_tests.append((p_tests, p_observations)) - electro_tests = get_neab.replace_zero_std(electro_tests) - electro_tests = get_neab.substitute_parallel_for_serial(electro_tests) - with open('pipe_tests.p','wb') as f: - pickle.dump(electro_tests,f) - -#MU = 4; NGEN = 3; CXPB = 0.9 -USE_CACHED_GA = False - -cnt = 0 -pipe_results = {} -## -# TODO move to unit testing -## - -start_time = timeit.default_timer() -#sel = [str('selNSGA2'),str('selIBEA'),str('')] -flat_iter = [ (test, observation) for test, observation in electro_tests ]#for s in sel ] -print(flat_iter) -from neuronunit.optimization import optimization_management as om - -# http://www.physics.usyd.edu.au/teach_res/mp/mscripts/ -# ns_izh002.m -import collections -from collections import OrderedDict - -# Fast spiking cannot be reproduced as it requires modifications to the standard Izhi equation, -# which are expressed in this mod file. -# https://github.com/OpenSourceBrain/IzhikevichModel/blob/master/NEURON/izhi2007b.mod - -reduced2007 = collections.OrderedDict([ - # C k vr vt vpeak a b c d celltype - ('RS', (100, 0.7, -60, -40, 35, 0.03, -2, -50, 100, 1)), - ('IB', (150, 1.2, -75, -45, 50, 0.01, 5, -56, 130, 2)), - ('LTS', (100, 1.0, -56, -42, 40, 0.03, 8, -53, 20, 4)), - ('TC', (200, 1.6, -60, -50, 35, 0.01, 15, -60, 10, 6)), - ('TC_burst', (200, 1.6, -60, -50, 35, 0.01, 15, -60, 10, 6)), - ('RTN', (40, 0.25, -65, -45, 0, 0.015, 10, -55, 50, 7)), - ('RTN_burst', (40, 0.25, -65, -45, 0, 0.015, 10, -55, 50, 7))]) - -import numpy as np -reduced_dict = OrderedDict([(k,[]) for k in ['C','k','vr','vt','vPeak','a','b','c','d']]) - -#OrderedDict -for i,k in enumerate(reduced_dict.keys()): - for v in reduced2007.values(): - reduced_dict[k].append(v[i]) - -explore_param = {k:(np.min(v),np.max(v)) for k,v in reduced_dict.items()} -cnt = 0 -for (test, observation) in flat_iter: - dic_key = str(list(pipe[cnt].values())[0]) - init_time = timeit.default_timer() - free_params = ['a','b','vr','k','vt','d'] - hc = ['C','c'] - #DO = SciUnitOptimization(error_criterion = test, selection = sel, provided_dict = model_params, elite_size = 3) - start_time = timeit.default_timer() - - #ga_out, DO = om.run_ga(explore_param,5,TC_tests,free_params=free_params,hc = hc, NSGA = True, MU = 8) - ga_out, DO = om.run_ga(explore_param,17,test,free_params=free_params,hc = hc, NSGA = True) - elapsed = timeit.default_timer() - start_time - ga_out['time_length'] = elapsed - #package = DO.run(offspring_size = MU, max_ngen = 6, cp_frequency=1,cp_filename=str(dic_key)+'.p') - #pop, hof_py, pf, log, history, td_py, gen_vs_hof = package - with open('dump_all_cells'+str(pipe[cnt])+str(cnt),'wb') as f: - pickle.dump(ga_out,f) - cnt += 1 - - - print('entire duration', elapsed) - - ''' - model_to_write = pipe_results[dic_key]['gen_vs_hof'][-1].dtc.attrs - - optimization_management.write_opt_to_nml(file_name,model_to_write) - - - - - times_list = list(pipe_results.values()) - ts = [ t['duration']/60.0 for t in times_list ] - mean_time = np.mean(ts) - total_time = np.sum(ts) - ''' -# return pipe_results -#pipe_results = main_proc(flat_iter) -''' -pipe_results[dic_key] = {} -pipe_results[dic_key]['duration'] = finished_time - init_time -pipe_results[dic_key]['pop'] = copy.copy(pop) -pipe_results[dic_key]['sel'] = sel -pipe_results[dic_key]['hof'] = copy.copy(hof[::-1]) -pipe_results[dic_key]['pf'] = copy.copy(pf[::-1]) -pipe_results[dic_key]['log'] = copy.copy(log) -pipe_results[dic_key]['history'] = copy.copy(history) -pipe_results[dic_key]['td_py'] = copy.copy(td_py) -pipe_results[dic_key]['gen_vs_hof'] = copy.copy(gen_vs_hof) -pipe_results[dic_key]['sum_ranked_hof'] = [sum(i.dtc.scores.values()) for i in pipe_results[dic_key]['gen_vs_hof'][1:-1]] -pipe_results[dic_key]['componentsh'] = [list(i.dtc.scores.values()) for i in pipe_results[dic_key]['gen_vs_hof'][1:-1]] -pipe_results[dic_key]['componentsp'] = [list(i.dtc.scores.values()) for i in pipe_results[dic_key]['pf'][1:-1]] -file_name = str('nlex_id_')+dic_key -''' diff --git a/neuronunit/unit_test/pipe_tests.p b/neuronunit/unit_test/pipe_tests.p deleted file mode 100644 index ced6f0f5e..000000000 Binary files a/neuronunit/unit_test/pipe_tests.p and /dev/null differ diff --git a/neuronunit/unit_test/post_install.sh b/neuronunit/unit_test/post_install.sh deleted file mode 100644 index 29cec0f85..000000000 --- a/neuronunit/unit_test/post_install.sh +++ /dev/null @@ -1,7 +0,0 @@ -cd $HOME -sudo /opt/conda/bin/pip install -e BluePyOpt -sudo /opt/conda/bin/pip install -e neuronunit -sudo rm -r /opt/conda/lib/python3.5/site-packages/PyLEMS-0.4.9-py3.5.egg -sudo /opt/conda/bin/pip install git+https://github.com/NeuroML/pyNeuroML -cd $HOME/neuronunit/neuronunit/unit_test -ipython -i grid_entry_point.py diff --git a/neuronunit/unit_test/prJuly.py b/neuronunit/unit_test/prJuly.py deleted file mode 100644 index 3aed6a982..000000000 --- a/neuronunit/unit_test/prJuly.py +++ /dev/null @@ -1,352 +0,0 @@ -import pickle - -from neuronunit.tests import np, pq, cap, VmTest, scores, AMPL, DELAY, DURATION - -from neuronunit.optimization import get_neab -import copy -import os -from neuronunit.optimization.optimization_management import run_ga -from neuronunit.optimization.model_parameters import model_params, path_params -from neuronunit.tests import np, pq, cap, VmTest, scores, AMPL, DELAY, DURATION - -import matplotlib as mpl -mpl.use('Agg') -#mpl.switch_backend('Agg') - - -electro_path = str(os.getcwd())+'/pipe_tests.p' -print(os.getcwd()) -assert os.path.isfile(electro_path) == True -with open(electro_path,'rb') as f: - electro_tests = pickle.load(f) - -electro_tests = get_neab.replace_zero_std(electro_tests) -electro_tests = get_neab.substitute_parallel_for_serial(electro_tests) -test, observation = electro_tests[0] - - -import matplotlib.pyplot as plt - - - -from neuronunit.optimization import get_neab -import copy -import os -import pickle -electro_path = str(os.getcwd())+'/pipe_tests.p' -from neuronunit import plottools -import numpy as np -ax = None -from neuronunit.optimization import exhaustive_search as es - -plot_surface = plottools.plot_surface -scatter_surface = plottools.plot_surface - -with open(electro_path,'rb') as f: - electro_tests = pickle.load(f) -from matplotlib.colors import LogNorm -from neuronunit.optimization.exhaustive_search import run_grid, reduce_params, create_grid#, mock_grid -from neuronunit.optimization import model_parameters as modelp -from neuronunit.optimization.model_parameters import path_params -mp = modelp.model_params - - - -electro_tests = get_neab.replace_zero_std(electro_tests) -electro_tests = get_neab.substitute_parallel_for_serial(electro_tests) -test, observation = electro_tests[0] - -import quantities as pq - -from neuronunit.optimization import model_parameters as modelp -mp = modelp.model_params - - - -opt_keys = ['a','b','vr'] -nparams = len(opt_keys) - -observation = {'a':[np.median(mp['a']),np.std(mp['a'])], 'b':[np.median(mp['b']),np.std(mp['b'])], 'vr':[np.median(mp['vr']),np.std(mp['vr'])]} - - -tests = copy.copy(electro_tests[0][0]) -tests_ = [] -tests_ += [tests[0]] -tests_ += tests[4:7] - -try: - assert 1 == 2 - with open('ga_run.p','rb') as f: - package = pickle.load(f) - pop = package[0] - print(pop[0].dtc.attrs.items()) - history = package[4] - gen_vs_pop = package[6] - hof = package[1] -except: - print(mp) - - nparams = len(opt_keys) - package = run_ga(mp,nparams*2,12,tests_,provided_keys = opt_keys)#, use_cache = True, cache_name='simple') - pop = package[0] - history = package[4] - gen_vs_pop = package[6] - hof = package[1] - - with open('ga_run.p','wb') as f: - pickle.dump(package,f) - - grid_results = {} - -#import seaborn as sns -from itertools import product -import matplotlib.pyplot as plt - - - -def plot_scatter(hof,ax,keys): - z = np.array([ np.sum(list(p.dtc.scores.values())) for p in hof ]) - x = np.array([ p.dtc.attrs[str(keys[0])] for p in hof ]) - if len(keys) != 1: - y = np.array([ p.dtc.attrs[str(keys[1])] for p in hof ]) - - ax.cla() - ax.set_title(' {0} vs {1} '.format(keys[0],keys[1])) - ax.scatter(x, y, c=y, s=125)#, cmap='gray') - - #ax.scatter(x, y, z, [3 for i in x] ) - return ax - -def plot_surface(gr,ax,keys,imshow=False): - # from - # https://github.com/russelljjarvis/neuronunit/blob/dev/neuronunit/unit_test/progress_report_4thJuly.ipynb - # Not rendered - # https://github.com/russelljjarvis/neuronunit/blob/dev/neuronunit/unit_test/progress_report_.ipynb - gr = [ g for g in gr if type(g.dtc) is not type(None) ] - - gr = [ g for g in gr if type(g.dtc.scores) is not type(None) ] - ax.cla() - #gr = [ g - gr_ = [] - index = 0 - for i,g in enumerate(gr): - if type(g.dtc) is not type(None): - gr_.append(g) - else: - index = i - - z = [ np.sum(list(p.dtc.scores.values())) for p in gr ] - x = [ p.dtc.attrs[str(keys[0])] for p in gr ] - y = [ p.dtc.attrs[str(keys[1])] for p in gr ] - - if len(x) != 100: - delta = 100-len(x) - for i in range(0,delta): - x.append(np.mean(x)) - y.append(np.mean(y)) - z.append(np.mean(z)) - - xx = np.array(x) - yy = np.array(y) - zz = np.array(z) - - dim = len(xx) - - - N = int(np.sqrt(len(xx))) - X = xx.reshape((N, N)) - Y = yy.reshape((N, N)) - Z = zz.reshape((N, N)) - if imshow==False: - ax.pcolormesh(X, Y, Z, edgecolors='black') - else: - import seaborn as sns; sns.set() - ax = sns.heatmap(Z) - - #ax.imshow(Z) - #ax.pcolormesh(xi, yi, zi, edgecolors='black') - ax.set_title(' {0} vs {1} '.format(keys[0],keys[1])) - return ax - -def plot_line(gr,ax,key): - ax.cla() - ax.set_title(' {0} vs score'.format(key[0])) - z = np.array([ np.sum(list(p.dtc.scores.values())) for p in gr ]) - x = np.array([ p.dtc.attrs[key[0]] for p in gr ]) - - ax.plot(x,z) - ax.set_xlim(np.min(x),np.max(x)) - ax.set_ylim(np.min(z),np.max(z)) - return ax -''' -Depricated -def check_range(matrix,hof): - dim = np.shape(matrix)[0] - print(dim) - cnt = 0 - fig,ax = plt.subplots(dim,dim,figsize=(10,10)) - flat_iter = [] - newrange = {} - for i,k in enumerate(matrix): - for j,r in enumerate(k): - keys = list(r[0]) - gr = r[1] - print(line) - if i==j: - line = [ np.sum(list(g.dtc.scores.values())) for g in gr] - (newrange, range_adj) = check_line(line,newrange) - print(newrange,'newrange') - return (newrange, range_adj) -''' -def check_line(line,newrange): - range_adj = False - keys = keys[0] - min_ = np.min(line) - print(min_,line[0],line[1],'diff?') - if line[0] == min_: - #print('hit') - attrs = gr[0].dtc.attrs[keys] - remin = - np.abs(attrs)*10 - remax = np.abs(gr[-1].dtc.attrs[keys])*10 - nr = np.linspace(remin,remax,10) - newrange[keys] = nr - range_adj = True - - if line[-1] == min_: - #print('hit') - attrs = gr[-1].dtc.attrs[keys] - remin = - np.abs(attrs)*10 - remax = np.abs(gr[-1].dtc.attrs[keys])*10 - nr = np.linspace(remin,remax,10) - newrange[keys] = nr - range_adj = True - return (newrange, range_adj) - -def grids(hof,tests): - dim = len(hof[0].dtc.attrs.keys()) - flat_iter = [ (i,ki,j,kj) for i,ki in enumerate(hof[0].dtc.attrs.keys()) for j,kj in enumerate(hof[0].dtc.attrs.keys()) ] - matrix = [[0 for x in range(dim)] for y in range(dim)] - plt.clf() - fig,ax = plt.subplots(dim,dim,figsize=(10,10)) - #fig1,ax1 = plt.subplots(dim,dim,figsize=(10,10)) - - cnt = 0 - for i,ki,j,kj in flat_iter: - free_param = set([ki,kj]) # construct a small-set out of the indexed keys 2. If both keys are - # are the same, this set will only contain one index - bs = set(hof[0].dtc.attrs.keys()) # construct a full set out of all of the keys available, including ones not indexed here. - diff = bs.difference(free_param) # diff is simply the key that is not indexed. - # BD is the dictionary of parameters to be held constant - # if the plot is 1D then two parameters should be held constant. - hc = {} - for d in diff: - hc[d] = hof[0].dtc.attrs[d] - - if i == j: - assert len(free_param) == len(hc) - 1 - assert len(hc) == len(free_param) + 1 - gr = run_grid(3,tests,provided_keys = free_param ,hold_constant = hc) - # make a psuedo test, that still depends on input Parametersself. - # each test evaluates a normal PDP. - line = [ np.sum(list(g.dtc.scores.values())) for g in gr] - newrange, range_adj = check_range(line,hof) - while range_adj == True: - gr = run_grid(3,tests,provided_keys = free_param ,hold_constant = hc, nr=newrange) - # make a psuedo test, that still depends on input Parametersself. - # each test evaluates a normal PDP. - line = [ np.sum(list(g.dtc.scores.values())) for g in gr] - newrange, range_adj = check_range(matrix,hof) - - matrix[i][j] = ( free_param,gr ) - - fp = list(copy.copy(free_param)) - ax[i,j] = plot_line(gr,ax[i,j],fp) - if i >j: - assert len(free_param) == len(hc) + 1 - assert len(hc) == len(free_param) - 1 - # what I want to do, I want to plot grid lines not a heat map. - # I want to plot bd.attrs is cross hairs, - # I want the range of the plot shown to be bigger than than the grid lines. - - gr = run_grid(10,tests,provided_keys = free_param ,hold_constant = hc) - - fp = list(copy.copy(free_param)) - #if len(gr) == 100: - ax[i,j] = plot_surface(gr,ax[i,j],fp,imshow=False) - - matrix[i][j] = ( free_param,gr ) - - if i < j: - matrix[i][j] = ( free_param,gr ) - - fp = list(copy.copy(free_param)) - if len(fp) == 2: - ax[i,j] = plot_scatter(hof,ax[i,j],fp) - - plt.savefig(str('cross_section_and_surfaces.png')) - return matrix - -matrix = grids(hof,tests=tests_) -with open('surfaces.p','wb') as f: - pickle.dump(matrix,f) - - -import matplotlib.pyplot as plt - -from neuronunit.models.reduced import ReducedModel -from neuronunit.optimization.model_parameters import model_params, path_params -from neuronunit.tests import np, pq, cap, VmTest, scores, AMPL, DELAY, DURATION - -def plot_vm(hof,ax,key): - ax.cla() - ax.set_title(' {0} vs $V_{M}$'.format(key[0])) - best_dtc = hof[0].dtc - best_rh = hof[0].dtc.rheobase - neuron = None - model = ReducedModel(path_params['model_path'],name = str('regular_spiking'),backend =('NEURON',{'DTC':best_dtc})) - params = {'injected_square_current': - {'amplitude': best_rh, 'delay':DELAY, 'duration':DURATION}} - results = modelrs.inject_square_current(params) - vm = model.get_membrane_potential() - times = vm.times - ax.plot(times,vm) - #ax.xlabel('ms') - #ax.ylabel('mV') - #ax.set_xlim(np.min(x),np.max(x)) - #ax.set_ylim(np.min(z),np.max(z)) - return ax - - -def plotss(matrix,hof): - dim = np.shape(matrix)[0] - print(dim) - cnt = 0 - fig,ax = plt.subplots(dim,dim,figsize=(10,10)) - flat_iter = [] - for i,k in enumerate(matrix): - for j,r in enumerate(k): - - keys = list(r[0]) - gr = r[1] - - if i==j: - ax[i,j] = plot_vm(hof,ax[i,j],keys) - if i>j: - ax[i,j] = plot_surface(gr,ax[i,j],keys,imshow=False) - if i < j: - ax[i,j] = plot_scatter(hof,ax[i,j],keys) - - - print(i,j) - plt.savefig(str('surface_and_vm.png')) - return None - -with open('surfaces.p','rb') as f: - matrix = pickle.load(f) - - - - - -#plotss(matrix) -#except: diff --git a/neuronunit/unit_test/progress_report_30thJuly.ipynb b/neuronunit/unit_test/progress_report_30thJuly.ipynb deleted file mode 100644 index 5637104d9..000000000 --- a/neuronunit/unit_test/progress_report_30thJuly.ipynb +++ /dev/null @@ -1,2271 +0,0 @@ -{ - "cells": [ - { - "cell_type": "code", - "execution_count": 1, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Running on Python version: 3.6.4 |Anaconda, Inc.| (default, Jan 16 2018, 18:10:19) \n", - "[GCC 7.2.0]\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/opt/conda/lib/python3.6/site-packages/sklearn/cross_validation.py:41: DeprecationWarning: This module was deprecated in version 0.18 in favor of the model_selection module into which all the refactored classes and functions are moved. Also note that the interface of the new CV iterators are different from that of this module. This module will be removed in 0.20.\n", - " \"This module will be removed in 0.20.\", DeprecationWarning)\n", - "/opt/conda/lib/python3.6/site-packages/sklearn/grid_search.py:42: DeprecationWarning: This module was deprecated in version 0.18 in favor of the model_selection module into which all the refactored classes and functions are moved. This module will be removed in 0.20.\n", - " DeprecationWarning)\n" - ] - } - ], - "source": [ - "import sys \n", - "\n", - "import os\n", - "%matplotlib inline\n", - "os.system('jupyter trust progress_report_10thJuly.ipynb')\n", - "print('Running on Python version: {}'.format(sys.version))\n", - "import pickle\n", - "hc = pickle.load(open('held_constant_grid.p','rb'))\n", - "\n", - "\n", - "\n" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "/home/jovyan/neuronunit/neuronunit/unit_test\n" - ] - } - ], - "source": [ - "from neuronunit.optimization import get_neab\n", - "import copy\n", - "\n", - "electro_path = str(os.getcwd())+'/pipe_tests.p'\n", - "print(os.getcwd())\n", - "assert os.path.isfile(electro_path) == True\n", - "with open(electro_path,'rb') as f:\n", - " electro_tests = pickle.load(f)\n", - "\n", - "electro_tests = get_neab.replace_zero_std(electro_tests)\n", - "electro_tests = get_neab.substitute_parallel_for_serial(electro_tests)\n", - "test, observation = electro_tests[0]\n" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "dict_keys(['vrb', 'ba', 'vra'])\n", - "[-13.333333333333334, -8.3333333333333335e-10, 0.21781991224896066]\n", - "0.217819912249\n", - "-2.58773632812e-09\n", - "-43.3664043767\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "0.217819912249\n", - "-2.58773632812e-09\n", - "-43.3664043767\n", - "['b', 'a', 'a']\n" - ] - } - ], - "source": [ - "\n", - "\n", - "print(hc.keys())\n", - "\n", - "print(hc['vrb'][0])\n", - "print(hc['vrb'][0][-1])\n", - "print(hc['vra'][0][-1])\n", - "print(hc['ba'][0][-1])\n", - "\n", - "print('\\n\\n\\n\\n')\n", - " \n", - " \n", - "print(hc['vrb'][-1][-1]) \n", - "print(hc['vra'][-1][-1])\n", - "print(hc['ba'][-1][-1])\n", - "\n", - "\n", - "print([h[-1] for h in hc])\n" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "#All I can tell is that changing VR does not do anythingg" - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "dict_items([('a', 0.27328841763197198), ('b', -2.5511245505753264e-09), ('vr', -71.213934167873902)])\n", - "{'b': -2.5877363281219307e-09}\n", - "vra\n", - "{'vr': -43.366404376748612}\n", - "{'b': -2.5877363281219307e-09}\n", - "{'vr': -43.366404376748612}\n", - "{'a': 0.21781991224896066}\n", - "ba\n", - "{'a': 0.21781991224896066}\n", - "{'b': -2.5877363281219307e-09}\n", - "{'a': 0.21781991224896066}\n", - "{'b': -2.5877363281219307e-09}\n", - "vra\n", - "dict_keys(['vrb', 'ba', 'vra'])\n" - ] - }, - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "try:\n", - " from prettyplotlib import plt\n", - "except: \n", - " import matplotlib.pyplot as plt\n", - "\n", - "from neuronunit.optimization import get_neab\n", - "import copy\n", - "import os\n", - "import pickle\n", - "electro_path = str(os.getcwd())+'/pipe_tests.p'\n", - "from neuronunit import plottools\n", - "import numpy as np\n", - "ax = None\n", - "from neuronunit.optimization import exhaustive_search as es\n", - "\n", - "plot_surface = plottools.plot_surface\n", - "scatter_surface = plottools.plot_surface\n", - "\n", - "with open(electro_path,'rb') as f:\n", - " electro_tests = pickle.load(f)\n", - "with open('pre_ga_reports.p','rb') as f:\n", - " package = pickle.load(f)\n", - " \n", - "with open('pre_grid_reports.p','rb') as f:#\n", - " grid_results = pickle.load(f)\n", - "\n", - "with open('pre_ga_reports.p','rb') as f:\n", - " package = pickle.load(f)\n", - "pop = package[0]\n", - "print(pop[0].dtc.attrs.items())\n", - "history = package[4]\n", - "gen_vs_pop = package[6]\n", - " \n", - "pop = package[0]\n", - "electro_tests = get_neab.replace_zero_std(electro_tests)\n", - "electro_tests = get_neab.substitute_parallel_for_serial(electro_tests)\n", - "test, observation = electro_tests[0]\n", - "\n", - "npoints = 6\n", - "tests = copy.copy(electro_tests[0][0][0:2])\n", - "\n", - "fig,ax = plt.subplots(3,3,figsize=(10,10))\n", - "attrs_list = list(pop[0].dtc.attrs.keys())\n", - "\n", - " \n", - "hc['vra'] \n", - "hckeys = list(hc.keys())\n", - "with open('pre_ga_reports.p','rb') as f:\n", - " package = pickle.load(f)\n", - "\n", - "grid_results = {}\n", - "hof = package[1]\n", - "from matplotlib.colors import LogNorm\n", - "\n", - "\n", - "from neuronunit.optimization import model_parameters as modelp\n", - "mp = modelp.model_params\n", - "\n", - "for i,ki in enumerate(pop[0].dtc.attrs.keys()):\n", - " for j,kj in enumerate(pop[0].dtc.attrs.keys()):\n", - "\n", - " ax[i,j].set_title(' {0} vs {1}'.format(str(ki),str(kj)))\n", - " ss = set([ki,kj])\n", - " bs = set(attrs_list)\n", - " diff = bs.difference(ss)\n", - " bd = {}\n", - " bd[list(diff)[0]] = hof[0].dtc.attrs[list(diff)[0]]\n", - " print(bd)\n", - " for k,v in hc.items():\n", - " if str(ki) in k and str(kj) in k:\n", - " key = k\n", - " \n", - " tuples = np.array([ (np.sum(list(p.dtc.scores.values())), p.dtc.attrs[str(ki)],p.dtc.attrs[str(ji)]) for p in hc[key] ]) \n", - " \n", - " z = np.array([ np.sum(list(p.dtc.scores.values())) for p in hc[key] ])\n", - " # x = np.array([ p.dtc.attrs[str(ki)] for p in hc[key] ])\n", - " # y = np.array([ p.dtc.attrs[str(kj)] for p in hc[key] ])\n", - "\n", - " N = int(np.sqrt(len(x)))\n", - " x = x.reshape((N, N))\n", - " y = y.reshape((N, N))\n", - " z = z.reshape((N, N))\n", - " \n", - " #plt.pcolor(X, Y, Z1)\n", - "\n", - " ax[i,j].pcolormesh(x, y, z)#, norm=LogNorm(vmin=z.min(), vmax=z.max()), cmap='PuBu_r')\n", - " if i==j:\n", - " \n", - " ax[i,j].set_title(' {0} vs score'.format(attrs_list[i],attrs_list[j]))\n", - "\n", - " print(key)\n", - " z = np.array([ np.sum(list(p.dtc.scores.values())) for p in hc[key] ])\n", - " x = [i for i in range(0,len(z))]\n", - " ax[i,j].plot(x,z)\n", - " \n", - " ax[i,j].set_xlim(np.min(x),np.max(x))\n", - " ax[i,j].set_ylim(np.min(z),np.max(z))\n", - " \n", - " \n", - " #if 'vr' in key:\n", - " #print(z,np.mean(z),np.std(z))\n", - " \n", - "print(hc.keys())\n", - " " - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "#from matplotlib.colors import LogNorm\n", - "plt.clf()\n", - "fig,ax = plt.subplots(3,3,figsize=(10,10))\n", - "\n", - "visited = []\n", - "from neuronunit.optimization.exhaustive_search import run_grid, reduce_params, create_grid, mock_grid\n", - "one_d = {}\n", - "two_d = {}\n", - "cnt = 0\n", - "\n", - "#from neuronunit.optimization.model_parameters import model_params\n", - " \n", - "from neuronunit.optimization import model_parameters as modelp\n", - "mp = modelp.model_params\n", - "for i,ki in enumerate(pop[0].dtc.attrs.keys()):\n", - " for j,kj in enumerate(pop[0].dtc.attrs.keys()):\n", - "\n", - " \n", - "\n", - " free_param = set([ki,kj]) # construct a small-set out of the indexed keys 2.\n", - " fp = { k:None for k in list(free_param) }\n", - "\n", - " bs = set(attrs_list) # construct a full set out of all of the keys available 3. \n", - "\n", - " diff = bs.difference(free_param) # diff is simply the key that is not indexed.\n", - " visited.append(diff)\n", - "\n", - " # BD is the dictionary of parameters to be held constant\n", - " # if the plot is 1D then two parameters should be held constant.\n", - "\n", - " bd = {}\n", - " for d in diff:\n", - " bd[d] = hof[0].dtc.attrs[d]\n", - "\n", - " for k,v in hc.items():\n", - " if str(ki) in k and str(kj) in k:\n", - " key = k\n", - " \n", - " if ki == kj:\n", - " #pass\n", - " gr = run_grid(10,tests,provided_keys = fp ,hold_constant = bd)#, use_cache = True, cache_name='complex')\n", - " one_d[str(fp.keys())] = gr\n", - " ax[i,j].plt()\n", - " if ki != kj:\n", - " \n", - " \n", - " reduced = { k:mp[k] for k in fp.keys() }\n", - " gr = None\n", - " gr = run_grid(10,tests,provided_keys = reduced ,hold_constant = bd)\n", - " if str('kj') not in two_d[ki].keys():\n", - " two_d[ki] = {}\n", - " two_d[ki][kj] = gr\n", - " else:\n", - " two_d[ki][kj] = gr\n", - " \n", - " print(key)\n", - " z = np.array([ np.sum(list(p.dtc.scores.values())) for p in hc[key] ])\n", - "\n", - " \n", - " x = [ p.dtc.attrs[ki] for p in hc[key] ]\n", - " y = [ p.dtc.attrs[kj] for p in hc[key] ]\n", - " \n", - " N = int(np.sqrt(len(x)))\n", - " x = np.array(x).reshape((N, N))\n", - " y = np.array(y).reshape((N, N))\n", - " z = np.array(z).reshape((N, N))\n", - " print(len(x),len(y),len(z), np.shape(z))\n", - " ax[i,j].pcolormesh(x, y, z)\n", - " \n", - "with open('surfaces.p','wb') as f: \n", - " pickle.dump(f,[two_d,one_d,ax])\n", - "\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "one_d" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "plt.clf()\n", - "fig,ax = plt.subplots(3,3,figsize=(10,10))\n", - "\n", - "for i in range(len(attrs_list)):\n", - " for j in range(len(attrs_list)):\n", - " if i>j:\n", - " ax[i,j].set_title(' {0} vs {1}'.format(attrs_list[i],attrs_list[j]))\n", - " ss = set([attrs_list[j],attrs_list[i]])\n", - " bs = set(attrs_list)\n", - " diff = bs.difference(ss)\n", - " bd = {}\n", - " bd[list(diff)[0]] = hof[0].dtc.attrs[list(diff)[0]]\n", - " \n", - " \n", - " #ax_trip,plot_axis = plot_surface(fig,ax[i,j],attrs_list[j],attrs_list[i],history)\n", - " #ax[i,j].plot_axis = plot_axis\n", - " \n", - " for k,v in hc.items():\n", - " if str(attrs_list[j]) in k and str(attrs_list[i]) in k:\n", - " key = k\n", - " z = np.array([ np.sum(list(p.dtc.scores.values())) for p in hc[key] ])\n", - " x = np.array([ p.dtc.attrs[str(attrs_list[i])] for p in hc[key] ])\n", - " y = np.array([ p.dtc.attrs[str(attrs_list[j])] for p in hc[key] ])\n", - " \n", - " print(x[0],'x')\n", - " print(y[0],'y')\n", - " print(z[0],'z')\n", - " \n", - " for k,l in enumerate(x):\n", - " if k!=0:\n", - " diff = l - y[i-1]\n", - " #print(diff)\n", - " \n", - " N = int(np.sqrt(len(x)))\n", - " x = x.reshape((N, N))\n", - " y = y.reshape((N, N))\n", - " z = z.reshape((N, N))\n", - " if i == j:\n", - " for k,v in hc.items():\n", - " if str(attrs_list[j]) in k and str(attrs_list[i]) in k:\n", - " key = k\n", - " ax[i,j].set_title(' {0} vs score'.format(attrs_list[i]))\n", - " #y = [ g.dtc.attrs[attrs_list[j]] for g in pop ]\n", - " x = np.array([ p.dtc.attrs[str(attrs_list[j])] for p in hc[key] ])\n", - " y = np.array([ np.sum(list(p.dtc.scores.values())) for p in hc[key] ])\n", - " ax[i,j].plot(x,y)\n", - "\n", - "\n", - " " - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "plt.clf()\n", - "fig,ax = plt.subplots(3,3,figsize=(10,10))\n", - "\n", - "for i in range(len(attrs_list)):\n", - " for j in range(len(attrs_list)):\n", - " if ij:\n", - " ax[i,j].set_title(' {0} vs {1}'.format(attrs_list[i],attrs_list[j]))\n", - " ss = set([attrs_list[j],attrs_list[i]])\n", - " bs = set(attrs_list)\n", - " diff = bs.difference(ss)\n", - " bd = {}\n", - " bd[list(diff)[0]] = hof[0].dtc.attrs[list(diff)[0]]\n", - " \n", - " \n", - " #ax_trip,plot_axis = plot_surface(fig,ax[i,j],attrs_list[j],attrs_list[i],history)\n", - " #ax[i,j].plot_axis = plot_axis\n", - " \n", - " for k,v in hc.items():\n", - " if str(attrs_list[j]) in k and str(attrs_list[i]) in k:\n", - " key = k\n", - " z = np.array([ np.sum(list(p.dtc.scores.values())) for p in hc[key] ])\n", - " x = np.array([ p.dtc.attrs[str(attrs_list[j])] for p in hc[key] ])\n", - " y = np.array([ p.dtc.attrs[str(attrs_list[i])] for p in hc[key] ])\n", - " \n", - " \n", - " \n", - " N = int(np.sqrt(len(x)))\n", - " x = x.reshape((N, N))\n", - " y = y.reshape((N, N))\n", - " z = z.reshape((N, N))\n", - " \n", - "\n", - " ax[i,j].pcolormesh(x, y, z)\n", - "\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "fig,ax = plt.subplots(3,3,figsize=(10,10))\n", - "\n", - "for i in range(len(attrs_list)):\n", - " for j in range(len(attrs_list)):\n", - " if ij:\n", - " ax[i,j].set_title(' {0} vs {1}'.format(attrs_list[i],attrs_list[j]))\n", - " ss = set([attrs_list[j],attrs_list[i]])\n", - " bs = set(attrs_list)\n", - " diff = bs.difference(ss)\n", - " bd = {}\n", - " bd[list(diff)[0]] = hof[0].dtc.attrs[list(diff)[0]]\n", - " \n", - " \n", - " #ax_trip,plot_axis = plot_surface(fig,ax[i,j],attrs_list[j],attrs_list[i],history)\n", - " #ax[i,j].plot_axis = plot_axis\n", - "\n", - "\n", - " ax[i,j].scatter(x, y, c=z)\n", - " ax[i,j].set_xlim(np.min(x),np.max(x))\n", - " ax[i,j].set_ylim(np.min(y),np.max(y))\n" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "import pickle\n", - "!pip install prettyplotlib\n", - "import prettyplotlib as ppl\n", - "from prettyplotlib import plt\n", - "from prettyplotlib import brewer2mpl\n", - "import numpy as np\n", - "import string\n", - "\n", - "green_purple = brewer2mpl.get_map('PRGn', 'diverging', 11).mpl_colormap\n", - "\n", - "\n", - "import matplotlib.pyplot as plt\n", - "import seaborn as sns\n", - "import pandas as pd\n", - "import numpy as np\n", - "import math as math\n", - "from pylab import rcParams\n", - "from neuronunit.optimization.results_analysis import make_report, min_max\n", - "with open('pre_grid_reports.p','rb') as f:#\n", - " grid_results = pickle.load(f)\n", - "\n", - "with open('pre_ga_reports.p','rb') as f:\n", - " package = pickle.load(f)\n", - "pop = package[0]\n", - "print(pop[0].dtc.attrs.items())\n", - "history = package[4]\n", - "gen_vs_pop = package[6]\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "attrs_ = [ list(p.dtc.attrs.keys()) for i,p in history.genealogy_history.items() ]\n", - "attrs = attrs_[0]\n", - "print(attrs)\n", - "\n", - "scores_ = [ list(p.dtc.scores.keys()) for i,p in history.genealogy_history.items() ]\n", - "scores = scores_[0]\n", - "from collections import OrderedDict\n", - "\n", - "urlDats = []\n", - "hi = [ (i,p) for i,p in history.genealogy_history.items() ]\n", - "pops_ = [ (i,p) for i,p in enumerate(pop) ]\n", - "\n", - "sc = [ (i,p) for i,p in enumerate(grid_results) ]\n", - "\n", - "import quantities as pq\n", - "\n", - "def history_iter(mapped):\n", - " i,p = mapped\n", - " gene_contents = OrderedDict()\n", - " gene_contents['gene_number'] = i\n", - " \n", - " attrs = list(p.dtc.attrs.keys()) \n", - " scores = list(p.dtc.scores.keys()) \n", - " for a in attrs:\n", - " gene_contents[a] = p.dtc.attrs[a] \n", - " scores0 = scores[0]\n", - " for s in scores:\n", - " gene_contents[s] = p.dtc.scores[s]\n", - " gene_contents[str('total')] = sum(p.dtc.scores.values())\n", - " for test in p.dtc.score.keys():\n", - " if 'prediction' in p.dtc.score[test]:\n", - " \n", - " \n", - " \n", - " gene_contents['observation'] = p.dtc.score[test]['observation']['mean']\n", - " try:\n", - " pass\n", - " #gene_contents['prediction'] = p.dtc.score[test]['prediction']['value']\n", - " #x = pq.Quantity(gene_contents['prediction'])\n", - " \n", - " #print(x.simplified)\n", - " \n", - " except:\n", - " gene_contents['prediction'] = p.dtc.score[test]['prediction']['mean']\n", - " x = pq.Quantity(gene_contents['prediction'])\n", - " print(x.simplified)\n", - " \n", - " gene_contents['disagreement'] = np.abs(float(gene_contents['observation'])) - np.abs(float(gene_contents['prediction']))#p.dtc.score[test]['agreement']\n", - " return gene_contents\n", - "\n", - "\n", - " \n", - "def process_dics(contents):\n", - " dfs = []\n", - " for gene_contents in contents:\n", - " # pandas Data frames are best data container for maths/stats, but steep learning curve.\n", - " # Other exclusion criteria. Exclude reading levels above grade 100,\n", - " # as this is most likely a problem with the metric algorithm, and or rubbish data in.\n", - "\n", - " if len(dfs) == 0:\n", - " dfs = pd.DataFrame(pd.Series(gene_contents)).T\n", - " dfs = pd.concat([ dfs, pd.DataFrame(pd.Series(gene_contents)).T ])\n", - " return dfs\n", - "\n", - "genes = list(map(history_iter,hi)) \n", - "dfg = process_dics(genes)\n", - "\n", - "grids = list(map(history_iter,sc)) \n", - "dfs = process_dics(grids)\n", - "\n", - "\n", - "dfg.set_index('gene_number', inplace=True)\n", - "dfg = dfg.drop(dfg.index[0])\n", - "#dfg = dfg.reset_index()\n", - "\n", - "def highlight_min(s):\n", - " '''\n", - " highlight the maximum in a Series yellow.\n", - " '''\n", - " is_min = s == s.min()\n", - " return ['background-color: yellow' if v else '' for v in is_min]\n", - "\n", - "\n", - "def highlight_col(x):\n", - " #copy df to new - original data are not changed\n", - " df = x.copy()\n", - "\n", - " mask = df['total'] == df['total'].argmin()\n", - " #if mask:\n", - " print(df['total'].argmin())\n", - " df.loc[mask, :] = 'background-color: yellow'\n", - " df.loc[~mask,:] = 'background-color: \"\"'\n", - " return df,x \n", - "\n", - "\n", - "\n", - "\n", - "#colors,dfg = highlight_col(dfg)\n", - "\n", - "#dfg" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "\n", - "\n", - "def highlight_col(x):\n", - " df = x.copy()\n", - " mask = df['total'] == df['total'].min()\n", - " df.loc[mask, :] = 'background-color: yellow'\n", - " df.loc[~mask,:] = 'background-color: \"\"'\n", - " return df \n", - "\n", - "dfg = dfg.style.apply(highlight_col, axis=None)\n", - "#dfg = dfg.style.background_gradient(cmap='viridis')\n", - "\n", - "'''\n", - "import seaborn as sns\n", - "\n", - "cm = sns.light_palette(\"green\", as_cmap=True)\n", - "\n", - "s = dfg.style.background_gradient(cmap=cm)\n", - "s\n", - "\n", - "\n", - "'''\n", - "\n", - "#dfg = dfg.style.bar(subset=['total'], color=['#d65f5f', '#5fba7d'])\n", - "dfg \n", - "\n", - "#s = dfg.style.background_gradient(cmap=cm)\n", - "#s" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "import numpy as np\n", - "import matplotlib.pyplot as plt\n", - "from matplotlib.widgets import Slider, Button, RadioButtons\n", - "import scipy.ndimage as ndi\n", - "import neuronunit.optimization.exhaustive_search as es\n", - "\n", - "data = np.zeros((6, 6, 6))\n", - "#data = ndi.filters.gaussian_filter(data, sigma=1)\n", - "\n", - "axis = [ [ str('vr'), str('a'), str('b') ], [ str('vr'), str('b'), str('a')], [ str('b'), str('a'), str('vr') ] ]\n", - "\n", - "gen_vs_pop = package[6] \n", - "\n", - "k = axis[0]\n", - "ee = [ np.sum(list(i.dtc.scores.values())) for i in grid_results ]\n", - "zz = [ i.dtc.attrs[k[2]] for i in grid_results ]\n", - "yy = [ i.dtc.attrs[k[1]] for i in grid_results ]\n", - "xx = [ i.dtc.attrs[k[0]] for i in grid_results ]\n", - "hvc = es.tfc2i(xx,yy,zz,ee)\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "#from neuronunit imp\n", - "from neuronunit.models.reduced import ReducedModel\n", - "from neuronunit.optimization.model_parameters import model_params, path_params\n", - "import quantities as pq\n", - "\n", - "super_pop = []\n", - "\n", - "for gp in gen_vs_pop:\n", - " super_pop.extend(gp)\n", - "minga,maxga = min_max(super_pop)\n", - "mingr,maxgr = min_max(grid_results)\n", - "abs_min = np.min((mingr[1],minga[1]))\n", - "abs_max = np.max((maxgr[1],maxga[1]))\n", - "v = list(np.linspace(abs_min, abs_max, 15, endpoint=True))\n", - "\n", - "def proc_xargs(dtc,dtcpop = None):\n", - " model = ReducedModel(path_params['model_path'],name=str('vanilla'),backend='NEURON')\n", - " xargs = {}\n", - " xargs['injected_square_current'] = {}\n", - " xargs['injected_square_current']['duration'] = 1000 * pq.ms\n", - " xargs['injected_square_current']['amplitude'] = dtc.rheobase['value']\n", - " xargs['injected_square_current']['delay'] = 250 * pq.ms # + 150\n", - " model.set_attrs(dtc.attrs)\n", - " model.inject_square_current(xargs)\n", - " v_m = model.get_membrane_potential()\n", - " ts = model.results['t'] # time signal\n", - " return v_m,ts,model\n", - "\n", - "def realign(contents):\n", - " plt.clf()\n", - "\n", - " for c in contents: \n", - " v_m,ts,model = c\n", - " try:\n", - " spt = float(model.get_spike_train())\n", - " #thr = float(model.get_threshold())\n", - "\n", - " new_time = [ t-spt for t in ts ] \n", - " plt.plot(new_time,v_m)\n", - " plt.xlim(-0.03,0.03)\n", - " except:\n", - " print(model.get_spike_train())\n", - " \n", - " plt.show() \n", - " return _\n", - "\n", - "'''\n", - "contents = [] \n", - "dtcpop = [ p.dtc for p in pop ]\n", - "dtcpop.extend([ p.dtc for p in gen_vs_pop[0] ])\n", - "for d in dtcpop:\n", - " contents.append(proc_xargs(d))\n", - "\n", - "contents.append(proc_xargs(mingr[2]))\n", - "contents.append(proc_xargs(minga[2]))\n", - "\n", - "realign(contents) \n", - "'''" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "from collections import OrderedDict\n", - "zz = [ list(i.dtc.scores.items()) for i in grid_results ]\n", - "xx = [ list(i.dtc.scores.items()) for i in pop ]\n", - "\n", - "def sample_points(iter_dict, npoints=2):\n", - " replacement = {}\n", - " for p in range(0,len(iter_dict)):\n", - " k,v = iter_dict.popitem(last=False)\n", - " sample_points = list(np.linspace(v.max(),v.min(),npoints))\n", - " replacement[k] = sample_points\n", - " return replacement\n", - "\n", - "from neuronunit.optimization import model_parameters as modelp\n", - "mp = OrderedDict(modelp.model_params)\n", - "smaller = {}\n", - "smaller = OrderedDict(sample_points(mp, npoints=2))\n", - "ranges = OrderedDict( {k:smaller[k] for k in smaller})\n", - " \n", - "lims = []\n", - "for k,v in pop[0].dtc.attrs.items():\n", - " lims.append([np.min(ranges[k]), np.max(ranges[k])])\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "from mpl_toolkits.mplot3d import Axes3D\n", - "import matplotlib.pyplot as plt\n", - "import numpy as np\n", - "from matplotlib import cm\n", - "\n", - "from matplotlib import animation, rc\n", - "from IPython.display import HTML\n", - "\n", - "#rc('animation', html='html5')\n", - "#options = {'c1':0.5, 'c2':0.3, 'w':0.9}\n", - "\n", - "\n", - "#moviewriter = animation.MovieWriter(...)\n", - "#moviewriter.setup(fig=fig, 'my_movie.ext', dpi=100)\n", - "\n", - "\n", - "super_set = []\n", - "length = len(gen_vs_pop)\n", - "\n", - "#frame_number = [i for i, pop in enumerate(gen_vs_pop)] \n", - "for i, pop in enumerate(gen_vs_pop):\n", - "#def to_movie(frame_number):\n", - " other_points = []\n", - " pf_points = []\n", - " hof_points = [] \n", - " labels = []\n", - " for p in pop:\n", - " #print(p)\n", - " xyz = []\n", - " for k,v in p.dtc.attrs.items():\n", - " xyz.append(v)\n", - " labels.append(k)\n", - " other_points.append(xyz)\n", - "\n", - " #lims = []\n", - " #for k,v in pop[0].dtc.attrs.items():\n", - " # lims.append((np.min(ranges[k]), np.max(ranges[k])))\n", - "\n", - " fig = plt.figure()\n", - " #ax = fig.add_subplot(111, projection='3d')\n", - " fig, ax = plt.subplots(1, 1)#, figsize=figsize)\n", - " ax = Axes3D(fig)\n", - " \n", - "\n", - " ax.set_xlim(lims[0])\n", - " ax.set_ylim(lims[1])\n", - " ax.set_zlim(lims[2])\n", - "\n", - " # Set plot title\n", - " #labels=('x-axis', 'y-axis', 'z-axis')#,\n", - " interval=80#,\n", - " title='Particle Movement in 3D space'#,\n", - " title_fontsize=\"large\"#,\n", - " text_fontsize=\"medium\"\n", - " ax.set_title(title, fontsize=title_fontsize)\n", - "\n", - "\n", - "\n", - " # Set plot axes labels\n", - "\n", - " # Set plot limits\n", - "\n", - " errors = [ np.sum(list(p.dtc.scores.values())) for p in pop ]\n", - " xx = [ i[0] for i in other_points ]\n", - " yy = [ i[1] for i in other_points ]\n", - " zz = [ i[2] for i in other_points ]\n", - " if len(super_set) !=0 :\n", - " for ss in super_set:\n", - " ops, ers = ss\n", - " p = ax.scatter3D([i[0] for i in ops], [ i[1] for i in ops], [i[2] for i in ops], alpha=0.0925, c=ers, cmap='jet', marker='o', vmin=abs_min, vmax=abs_max)\n", - " #ax.set_autoscalez_on(False)\n", - "\n", - " p = ax.scatter3D(xx, yy, zz, c=errors, cmap='jet', marker='o', vmin=abs_min, vmax=abs_max)\n", - " #print(xx,yy,zz)\n", - " #print([l for l in lims[0]], [l for l in lims[1]], [l for l in lims[2]])\n", - " #p = ax.scatter3D([l for l in lims[0]], [l for l in lims[1]], [l for l in lims[2]], c=[2.0,2.0], marker='*', vmin=abs_min, vmax=abs_max)\n", - " #ax.set_autoscalez_on(False)\n", - "\n", - " cb = fig.colorbar(p)\n", - " cb.set_label('summed scores')\n", - "\n", - " zz = [ np.sum(list(i.dtc.scores.values())) for i in grid_results ]\n", - " zz_sorted = sorted([( np.sum(list(i.dtc.scores.values())), index) for index,i in enumerate(grid_results) ])\n", - " gbest = zz_sorted[0]\n", - " gworst = zz_sorted[-1]\n", - "\n", - " gworst_grid_attrs = grid_results[gworst[1]].dtc.attrs\n", - " gbest_grid_attrs = grid_results[gbest[1]].dtc.attrs\n", - "\n", - " ax.set_xlabel(str(labels[0]))\n", - " ax.set_ylabel(str(labels[1]))\n", - " ax.set_zlabel(str(labels[2]))\n", - "\n", - " plt.savefig('particle_cube_'+str(float(i/length))+str('.png'))\n", - " super_set.append((other_points,errors)) \n", - " plt.show()\n", - "\n", - " #return ax\n", - "\n", - "\n", - "frame_number = [i for i, pop in enumerate(gen_vs_pop)] \n", - "#fig = plt.figure()\n", - "\n", - "#_ = list(map(to_movie,frame_number))\n", - "\n", - "#anim = animation.FuncAnimation(fig, func=to_movie,\n", - "# frames=frame_number,\n", - "# interval=30)\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "axis = [ [ str('vr'), str('a'), str('b') ], [ str('vr'), str('b'), str('a')], [ str('b'), str('a'), str('vr') ] ]\n", - "\n", - "gen_vs_pop = package[6] \n", - "pop = gen_vs_pop[-1]\n", - "\n", - "for k in axis: \n", - " \n", - " zz = [ np.sum(list(i.dtc.scores.values())) for i in grid_results ]\n", - " yy = [ i.dtc.attrs[k[1]] for i in grid_results ]\n", - " xx = [ i.dtc.attrs[k[0]] for i in grid_results ]\n", - "\n", - " last_frame = len(gen_vs_pop)\n", - " other_points = []\n", - " pf_points = []\n", - " hof_points = []\n", - " labels = []\n", - " \n", - " pf = package[2]\n", - " hof = package[1]\n", - " \n", - " for p in pop:\n", - " xy = []\n", - " for key in k:\n", - " v = p.dtc.attrs[key]\n", - " xy.append(v)\n", - " labels.append(key)\n", - " other_points.append(xy)\n", - " \n", - " \n", - " for p in pf:\n", - " xy = []\n", - " for key in k:\n", - " v = p.dtc.attrs[key]\n", - " xy.append(v)\n", - " labels.append(key)\n", - " pf_points.append(xy) \n", - " \n", - " for p in hof:\n", - " xy = []\n", - " for key in k:\n", - " v = p.dtc.attrs[key]\n", - " xy.append(v)\n", - " labels.append(key)\n", - " hof_points.append(xy) \n", - "\n", - " \n", - " zi, yi, xi = np.histogram2d(yy, xx, bins=(6,6), weights=zz, normed=False)\n", - " counts, _, _ = np.histogram2d(yy, xx, bins=(6,6))\n", - " #binned , _, _ = np.histogram(zce, bins=10)\n", - "\n", - " zi = zi / counts\n", - " zi = np.ma.masked_invalid(zi)\n", - " fig, ax = plt.subplots()\n", - " #scat = ppl.pcolormesh(fig, ax, xi, yi, zi, edgecolors='black', cmap=green_purple)\n", - " scat = ax.pcolormesh(xi, yi, zi, edgecolors='black', cmap=green_purple)\n", - " \n", - "\n", - "\n", - " fig.colorbar(scat)\n", - " ax.margins(0.05)\n", - "\n", - " #if i == last_frame-1:\n", - " for xy in hof_points:\n", - " ax.plot(xy[0], xy[1],'y*',label ='hall of fame') \n", - " for xy in pf_points:\n", - " ax.plot(xy[0], xy[1],'b*',label ='pareto front') \n", - " #legend = ax.legend([rect(\"r\"), rect(\"g\"), rect(\"b\")], [\"gene population\",\"pareto front\",\"hall of fame\"])\n", - "\n", - "\n", - " for xy in other_points:\n", - " ax.plot(xy[0], xy[1],'ro',label ='gene population') \n", - " ax.margins(0.05)\n", - "\n", - " plt.xlabel(labels[0])\n", - " plt.ylabel(labels[1])\n", - " plt.show()\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "\n", - "gen_vs_pop = package[6] \n", - "pop = gen_vs_pop[-1]\n", - "for k in axis: \n", - " zz = [ np.sum(list(i.dtc.scores.values())) for i in grid_results ]\n", - " zz_sorted = sorted([( np.sum(list(i.dtc.scores.values())), index) for index,i in enumerate(grid_results) ])\n", - " gbest = zz_sorted[0]\n", - " gworst = zz_sorted[-1]\n", - " assert gbest!=gworst\n", - " gworst_grid_attrs = grid_results[gworst[1]].dtc.attrs\n", - " gbest_grid_attrs = grid_results[gbest[1]].dtc.attrs\n", - " gba = gbest_grid_attrs[k[2]] \n", - " zz = [ np.sum(list(i.dtc.attrs.values())) for i in grid_results ]\n", - " zce = [ np.sum(list(i.dtc.scores.values())) for i in grid_results if i.dtc.attrs[k[2]] == gba]\n", - " yy = [ i.dtc.attrs[k[1]] for i in grid_results if i.dtc.attrs[k[2]] == gba ]\n", - " xx = [ i.dtc.attrs[k[0]] for i in grid_results if i.dtc.attrs[k[2]] == gba ]\n", - " last_frame = len(gen_vs_pop)\n", - " other_points = []\n", - " pf_points = []\n", - " hof_points = []\n", - " labels = []\n", - " \n", - " pf = package[2]\n", - " hof = package[1]\n", - " \n", - " for p in pop:\n", - " xy = []\n", - " for key in k:\n", - " v = p.dtc.attrs[key]\n", - " xy.append(v)\n", - " labels.append(key)\n", - " other_points.append(xy)\n", - " \n", - " \n", - " for p in pf:\n", - " xy = []\n", - " for key in k:\n", - " v = p.dtc.attrs[key]\n", - " xy.append(v)\n", - " labels.append(key)\n", - " pf_points.append(xy) \n", - " \n", - " for p in hof:\n", - " xy = []\n", - " for key in k:\n", - " v = p.dtc.attrs[key]\n", - " xy.append(v)\n", - " labels.append(key)\n", - " hof_points.append(xy) \n", - "\n", - " \n", - " zi, yi, xi = np.histogram2d(yy, xx, bins=(6,6), weights=zce, normed=False)\n", - " counts, _, _ = np.histogram2d(yy, xx, bins=(6,6))\n", - "\n", - " zi = zi / counts\n", - " zi = np.ma.masked_invalid(zi)\n", - " fig, ax = plt.subplots()\n", - " scat = ax.pcolormesh(xi, yi, zi, edgecolors='black', cmap=green_purple)\n", - "\n", - " fig.colorbar(scat)\n", - " ax.margins(0.05)\n", - "\n", - " #if i == last_frame-1:\n", - " for xy in hof_points:\n", - " ax.plot(xy[0], xy[1],'y*',label ='hall of fame') \n", - " for xy in pf_points:\n", - " ax.plot(xy[0], xy[1],'b*',label ='pareto front') \n", - " #legend = ax.legend([rect(\"r\"), rect(\"g\"), rect(\"b\")], [\"gene population\",\"pareto front\",\"hall of fame\"])\n", - "\n", - "\n", - " for xy in other_points:\n", - " ax.plot(xy[0], xy[1],'ro',label ='gene population') \n", - " ax.margins(0.05)\n", - "\n", - " plt.xlabel(labels[0])\n", - " plt.ylabel(labels[1])\n", - " plt.show()\n", - "\n", - " \n", - " " - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "nparams=3\n", - "new_report = make_report(grid_results,pop, nparams)\n", - "from neuronunit.optimization import exhaustive_search as es\n", - "print(new_report)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "from prettyplotlib import plt\n", - "import numpy as np\n", - "fig,ax = plt.subplots(4,4,figsize=(10,10))\n", - "attrs_list = list(pop[0].dtc.attrs)\n", - "for i in range(4):\n", - " for j in range(4):\n", - " if ij:\n", - " ax[i,j].set_title('Param %d vs Param %d' % (i,j))\n", - " # Z = 2D grid search with all params except i and j held constant at optimum\n", - " Z = np.random.rand(25,25)\n", - " ax[i,j].pcolormesh(Z)\n", - " else: # i==j\n", - " #z = 1D grid search with all params except i held contant at optimum\n", - " z = np.random.rand(25)\n", - " ax[i,i].plot(z)\n", - "plt.tight_layout()\n", - "plt.text(0,0,'Projections using the best value for other parameters')" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "So imagine 6 2D plots. The first three are: XY at Z=z, XZ at Y=y, and YZ at X=x, where x, y, z \n", - "is the location of the global minimum. The second three are XY with the minimum taken across Z, \n", - "XZ with the minumum taken across Y, YZ, with the minimum taken across X.\n", - "With these 6 I think you would get a pretty good idea of the contours around the global minimum." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "\n", - "\n", - "with open('pre_ga_reports.p','rb') as f:\n", - " package = pickle.load(f)\n", - "log = package[3] \n", - "%matplotlib inline\n", - "import matplotlib.pyplot as plt\n", - "import numpy as np\n", - "\n", - "plt.style.use('ggplot')\n", - "fig, axes = plt.subplots(figsize=(10, 10), facecolor='white')\n", - "gen_numbers = [ i for i in range(0,len(log.select('gen'))) ]\n", - "\n", - "mean = np.array([ np.sum(i) for i in log.select('avg')])\n", - "std = np.array([ np.sum(i) for i in log.select('std')])\n", - "minimum = np.array([ np.sum(i) for i in log.select('min')])\n", - "#minimum = np.array([ np.sum(i) for i in log.select('min')])\n", - "worst = np.max([ sum(g.dtc.scores.values()) for g in grid_results ])\n", - "#grid_min = np.min([ sum(g.dtc.scores.values()) for g in grid_results ])\n", - "gwl = [ worst for i in range(0,len(log.select('gen'))) ]\n", - "grid_min = np.min([ sum(g.dtc.scores.values()) for g in grid_results ])\n", - "gml = [ grid_min for i in range(0,len(log.select('gen'))) ]\n", - "\n", - "if new_report[nparams]['success'] == True:\n", - " axes.plot(gen_numbers,\n", - " gml,\n", - " color='blue',\n", - " linewidth=2,\n", - " label='exhaustive search best')\n", - "\n", - " axes.plot(gen_numbers,\n", - " gwl,\n", - " color='yellow',\n", - " linewidth=2,\n", - " label='exhaustive search worst')\n", - " \n", - "\n", - "\n", - " stdminus = mean - std\n", - " stdplus = mean + std\n", - "\n", - " axes.plot(\n", - " gen_numbers,\n", - " minimum,\n", - " color='black',\n", - " linewidth=2,\n", - " label='population minimum')\n", - " axes.fill_between(gen_numbers, stdminus, stdplus)\n", - "\n", - "\n", - "axes.plot(\n", - " gen_numbers,\n", - " mean,\n", - " color='black',\n", - " linewidth=2,\n", - " label='population average')\n", - " \n", - "\n", - "\n", - "\n", - "plt.xlabel('generation')\n", - "plt.ylabel('error')\n", - "\n", - "\n", - "plt.legend()\n", - "plt.show()\n", - "print(worst,grid_min)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "\n", - "\n", - "\n" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "\n", - "#fig.savefig('pcolormesh_prettyplotlib_labels_other_cmap_diverging.png')\n" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "\n", - "\n", - "\n" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "import bokeh\n", - "import numpy as np\n", - "import matplotlib" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "\n", - "\n", - "from neuronunit.optimization.optimization_management import run_ga\n", - "from neuronunit.optimization.exhaustive_search import run_grid, reduce_params, create_grid\n", - "from neuronunit.optimization.model_parameters import model_params\n", - "import os\n", - "import pickle\n", - "from neuronunit.optimization import get_neab\n", - "reports = {}\n", - "npoints = 10\n", - "\n", - "\n", - "\n", - "with open('pre_grid_reports.p','rb') as f:\n", - " grid_results = pickle.load(f)\n", - "opt_keys = list(grid_results[0].dtc.attrs.keys())\n", - "\n", - "with open('pre_ga_reports.p','rb') as f:\n", - " ga_out = pickle.load(f)\n", - "\n", - "pop = ga_out[0]\n", - "genes_vs_gen = ga_out[-1]" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "\n", - "from neuronunit.optimization.exhaustive_search import create_grid\n", - "gp = create_grid(npoints = 6,nparams = 3)\n", - "\n", - " " - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "\n", - "Found tough parameters for which the GA is not able to perform particularily # well. Suspect b's error surface is not concave.\n", - "Explore 1D cross section.\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "\n", - "plt.clf()\n", - "\n", - "plt.scatter([g.dtc.attrs['a'] for g in pop ],[ sum(g.dtc.scores.values()) for g in pop ] ,label='ga pop')\n", - "plt.scatter([g.dtc.attrs['a'] for g in grid_results ],[ sum(g.dtc.scores.values()) for g in grid_results ] ,label='grid evaluations')\n", - "plt.ylabel('score')\n", - "plt.xlabel('gene attribute a')\n", - "plt.legend()\n", - "plt.show()\n", - "\n", - "\n", - "plt.scatter([g.dtc.attrs['vr'] for g in pop ],[ sum(g.dtc.scores.values()) for g in pop ] ,label='ga pop')\n", - "plt.scatter([g.dtc.attrs['vr'] for g in grid_results ],[ sum(g.dtc.scores.values()) for g in grid_results ] ,label='grid evaluations')\n", - "plt.ylabel('score')\n", - "plt.xlabel('gene attribute vr')\n", - "plt.legend()\n", - "plt.show()\n", - "\n", - "\n", - "\n", - "plt.scatter([g.dtc.attrs['b'] for g in pop ],[ sum(g.dtc.scores.values()) for g in pop ] ,label='ga pop')\n", - "plt.scatter([g.dtc.attrs['b'] for g in grid_results ],[ sum(g.dtc.scores.values()) for g in grid_results ] ,label='grid evaluations')\n", - "plt.ylabel('score')\n", - "plt.xlabel('gene attribute b')\n", - "plt.legend()\n", - "plt.show()\n", - "\n", - "\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "\n", - "nparams = 3\n", - "opt_keys = [str('a'),str('vr'),str('b')]\n", - "\n", - "\n", - "#grid_results = run_grid(nparams,npoints,tests,provided_keys = opt_keys)\n", - "from neuronunit.optimization import exhaustive_search #import run_grid, reduce_params, create_grid\n", - "\n", - "from neuronunit.optimization.exhaustive_search import run_grid, reduce_params, create_grid\n", - "grid_points,maps = exhaustive_search.create_grid(npoints=6,nparams=3,provided_keys=opt_keys)\n", - "print(maps)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "def blah():\n", - " '''\n", - " This works but I changed the grid\n", - " '''\n", - " grid = np.zeros((6,6,6))\n", - " import matplotlib.pyplot as plt\n", - " print(maps)\n", - " for i in grid_results:\n", - " xyz = []\n", - " for k,v in i.dtc.attrs.items():\n", - " print(k,v)\n", - " #print(maps[v][k])\n", - " xyz.append(maps[k][v])\n", - " grid[xyz[0],xyz[1],xyz[2]] = sum(i.dtc.scores.values())\n", - "\n", - " for i in range(0,6):\n", - " flat = grid[i,:,:]\n", - " plt.imshow(flat)\n", - " plt.show()\n", - "\n", - " for i in range(0,6):\n", - " flat = grid[:,i,:]\n", - " plt.imshow(flat)\n", - " plt.show()\n", - "\n", - " for i in range(0,6):\n", - " flat = grid[:,:,i]\n", - " plt.imshow(flat)\n", - " plt.show()\n", - " \n", - " \n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "\n", - "\n", - "\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "#new_list = genes_vs_gen[1:-1]\n", - "#print(new_list)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "#![alt text](rick_style_guide.png \"Ricks table guide\")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "dfg\n", - "\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "\n", - " " - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "\n" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "\n", - "\n" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "scrolled": false - }, - "outputs": [], - "source": [ - "\n" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "\n", - "\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "\n", - " " - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "I am currently writing code that should enable the plotting of HOF values versus generation." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "\n", - " " - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "For some reason the global minimum solution is not converged on, as shown by the evolution of errors below:" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "\n", - "\n" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "For some reason, the GA population does not converge to the absolute minimum, although it does sample it.\n", - "Perhaps the absolute minimum is a highly dominated solution, which is a testable hypthosis.\n", - "\n", - "None the less because the GA samples the absolute minimum, this value can be corroborated with the GA." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "\n", - "\n", - "\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "\n", - "\n", - " \n", - "\n", - "\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "\n", - " \n", - " " - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Quantize distance between minimimum error and maximum error.\n", - "This step will allow the GA's performance to be located within or below the range of error found by grid search.\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "The code below reports on the differences between between attributes of best models found via grid versus attributes of best models found via GA search:\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "\n", - "\n", - " " - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.6.4" - } - }, - "nbformat": 4, - "nbformat_minor": 2 -} diff --git a/neuronunit/unit_test/relative_diff_unit_test.ipynb b/neuronunit/unit_test/relative_diff_unit_test.ipynb new file mode 100644 index 000000000..215f34035 --- /dev/null +++ b/neuronunit/unit_test/relative_diff_unit_test.ipynb @@ -0,0 +1,457 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/home/rudolph/anaconda3/lib/python3.8/site-packages/cerberus/validator.py:1607: UserWarning: No validation schema is defined for the arguments of rule 'not_zero_obs_zscore'\n", + " warn(\n" + ] + } + ], + "source": [ + "import unittest\n", + "import matplotlib\n", + "from neuronunit.allenapi.allen_data_driven import opt_setup, opt_setup_two, opt_exec\n", + "from neuronunit.allenapi.allen_data_driven import opt_to_model,wrap_setups\n", + "from neuronunit.allenapi.utils import dask_map_function" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": { + "lines_to_next_cell": 1 + }, + "outputs": [], + "source": [ + "from neuronunit.optimization.optimization_management import check_bin_vm_soma\n", + "from neuronunit.optimization.model_parameters import MODEL_PARAMS, BPO_PARAMS, to_bpo_param\n", + "from neuronunit.optimization.optimization_management import dtc_to_rheo,inject_and_plot_model\n", + "import numpy as np\n", + "from neuronunit.optimization.data_transport_container import DataTC\n", + "import efel\n", + "from jithub.models import model_classes\n", + "import matplotlib.pyplot as plt\n", + "import quantities as qt\n", + "import os\n", + "from sciunit.scores import RelativeDifferenceScore,ZScore\n", + "import copy" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [], + "source": [ + "\n", + "\n", + "class testOptimization(unittest.TestCase):\n", + " def setUp(self):\n", + " self = self\n", + " self.ids = [ 324257146,\n", + " 325479788,\n", + " 476053392,\n", + " 623893177,\n", + " 623960880,\n", + " 482493761,\n", + " 471819401\n", + " ]\n", + " self.specimen_id = self.ids[1]\n", + " def optimize_job(self,model_type,score_type=ZScore):\n", + "\n", + " target_num_spikes = 8\n", + " dtc = DataTC()\n", + " dtc.backend = model_type\n", + " model = dtc.dtc_to_model()\n", + " model.params = copy.copy(BPO_PARAMS[model_type])\n", + " fixed_current = 122 *qt.pA\n", + " if model_type is \"ADEXP\":\n", + " NGEN = 100\n", + " MU = 20\n", + " else:\n", + " NGEN = 100\n", + " MU = 100\n", + " \n", + " mapping_funct = dask_map_function\n", + " cell_evaluator,simple_cell,suite,target_current,spk_count = wrap_setups(\n", + " self.specimen_id,\n", + " model_type,\n", + " target_num_spikes,\n", + " template_model=copy.copy(model),\n", + " fixed_current=False,\n", + " cached=False,\n", + " score_type=score_type\n", + " )\n", + " del model\n", + "\n", + " final_pop, hall_of_fame, logs, hist = opt_exec(MU,NGEN,mapping_funct,cell_evaluator)\n", + " opt,target = opt_to_model(hall_of_fame,cell_evaluator,suite, target_current, spk_count)\n", + " best_ind = hall_of_fame[0]\n", + " fitnesses = cell_evaluator.evaluate_with_lists(best_ind)\n", + " target.vm_soma = suite.traces['vm15']\n", + " check_bin_vm_soma(target,opt)\n", + " \n", + " return np.sum(fitnesses)\n", + " def test_opt_relative_diff(self):\n", + " model_type = \"ADEXP\"\n", + " sum_fit = self.optimize_job(model_type,score_type=RelativeDifferenceScore)\n", + " print('met fitness goals')\n", + " #assert sum_fit<9.0\n", + " def test_opt_ZScore(self):\n", + " model_type = \"ADEXP\"\n", + " sum_fit = self.optimize_job(model_type,score_type=ZScore)\n", + " print('met fitness goals')\n", + " #assert sum_fit<0.7\n", + "\n", + " def test_opt_relative_diff_izhi(self):\n", + " model_type = \"IZHI\"\n", + " self.optimize_job(model_type,score_type=RelativeDifferenceScore)\n", + "\n", + " def test_opt_ZScore_izhi(self):\n", + " model_type = \"IZHI\"\n", + " self.optimize_job(model_type,score_type=ZScore)\n" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [], + "source": [ + "tt = testOptimization()\n", + "tt.setUp()\n" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\t8.80877\t4.17984\t5.12873\t34.8842\n", + "2021-01-18 14:26:26.848 INFO __main__: 93 \t65 \t11.034 \t4.97103\t5.12873\t36.6708\n", + "2021-01-18 14:27:01.845 INFO __main__: 94 \t55 \t336.73 \t2568.58\t5.12873\t22000 \n", + "2021-01-18 14:27:49.030 INFO __main__: 95 \t68 \t81.7458\t799.355\t5.12873\t9018.44\n", + "2021-01-18 14:28:31.106 INFO __main__: 96 \t61 \t183.321\t1951.35\t5.12873\t22000 \n", + "2021-01-18 14:29:15.279 INFO __main__: 97 \t63 \t143.549\t1509.42\t5.12873\t17019.3\n", + "2021-01-18 14:29:58.776 INFO __main__: 98 \t64 \t8.43222\t2.64746\t5.12873\t18.2819\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "2021-01-18 14:30:39.997 INFO __main__: 99 \t62 \t11.1948\t5.03033\t5.12873\t35.336 \n", + "2021-01-18 14:31:28.115 INFO __main__: 100\t68 \t186.757\t1951.04\t5.12873\t22000 \n", + "2021-01-18 14:31:28.118 INFO __main__: Run stopped because of stopping criteria: Max ngen\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + 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\n", 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" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "tt.test_opt_relative_diff_izhi()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "IzhiModel IZHI\n", + "IzhiModel IZHI\n", + "[, , , , , , , , , ]\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "2021-01-18 14:33:14.765 INFO __main__: gen\tnevals\tavg \tstd \tmin \tmax \n", + "1 \t100 \t7002.21\t9141.09\t12.6378\t22000\n" + ] + }, + { + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "a723d076dcf14b4bbddab1733161c580", + "version_major": 2, + "version_minor": 0 + }, + "text/plain": [ + "HBox(children=(HTML(value=''), FloatProgress(value=0.0), HTML(value='')))" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "2021-01-18 14:34:40.136 INFO __main__: 2 \t72 \t7251.13\t9270.58\t12.6378\t22000\n", + "2021-01-18 14:35:15.905 INFO __main__: 3 \t72 \t896.45 \t3267.05\t12.6378\t22000\n", + "2021-01-18 14:36:08.222 INFO __main__: 4 \t67 \t1926.02\t5554.26\t12.252 \t22000\n" + ] + } + ], + "source": [ + "\n", + "tt.test_opt_ZScore_izhi()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Z score works" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "\n", + "tt.test_opt_ZScore()\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Relative DIfference Also Works Now" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "\n", + "\n", + "tt.test_opt_relative_diff()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "jupytext": { + "cell_metadata_filter": "-all", + "notebook_metadata_filter": "-all", + "text_representation": { + "extension": ".py", + "format_name": "light" + } + }, + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.8.5" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/neuronunit/unit_test/rheobase_dtc_test.py b/neuronunit/unit_test/rheobase_dtc_test.py new file mode 100644 index 000000000..e90fe0cd5 --- /dev/null +++ b/neuronunit/unit_test/rheobase_dtc_test.py @@ -0,0 +1,44 @@ +import unittest +#!/usr/bin/env python +# coding: utf-8 +import matplotlib + +import numpy as np +from neuronunit.optimization.model_parameters import MODEL_PARAMS, BPO_PARAMS, to_bpo_param +from neuronunit.optimization.optimization_management import dtc_to_rheo,inject_and_plot_model +from neuronunit.optimization.data_transport_container import DataTC +from jithub.models import model_classes +import matplotlib.pyplot as plt +import quantities as qt + +class testOptimization(unittest.TestCase): + def setUp(self): + self = self + + def test_opt_1(self): + cellmodel = "ADEXP" + + if cellmodel == "IZHI": + model = model_classes.IzhiModel() + if cellmodel == "MAT": + model = model_classes.MATModel() + if cellmodel == "ADEXP": + model = model_classes.ADEXPModel() + + dtc = DataTC() + dtc.backend = cellmodel + dtc._backend = model._backend + dtc.attrs = model.attrs + dtc.params = {k:np.mean(v) for k,v in MODEL_PARAMS[cellmodel].items()} + other_params = BPO_PARAMS[cellmodel] + dtc = dtc_to_rheo(dtc) + assert dtc.rheobase is not None + self.assertIsNotNone(dtc.rheobase) + vm, plt, dtc = inject_and_plot_model(dtc, plotly=False) + self.assertIsNotNone(vm) + model = dtc.dtc_to_model() + self.assertIsNotNone(model) + + +if __name__ == "__main__": + unittest.main() diff --git a/neuronunit/unit_test/rick_revised.ipynb b/neuronunit/unit_test/rick_revised.ipynb deleted file mode 100644 index bc9ef24ea..000000000 --- a/neuronunit/unit_test/rick_revised.ipynb +++ /dev/null @@ -1,283 +0,0 @@ -{ - "cells": [ - { - "cell_type": "code", - "execution_count": 1, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "{'injected_square_current': {'amplitude': array(-10.0) * pA, 'delay': array(200.0) * ms, 'duration': array(500.0) * ms}}\n", - "{'k': [0.000999, 0.001001]}\n", - "[] []\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/opt/conda/lib/python3.5/site-packages/sklearn/cross_validation.py:41: DeprecationWarning: This module was deprecated in version 0.18 in favor of the model_selection module into which all the refactored classes and functions are moved. Also note that the interface of the new CV iterators are different from that of this module. This module will be removed in 0.20.\n", - " \"This module will be removed in 0.20.\", DeprecationWarning)\n", - "/opt/conda/lib/python3.5/site-packages/sklearn/grid_search.py:42: DeprecationWarning: This module was deprecated in version 0.18 in favor of the model_selection module into which all the refactored classes and functions are moved. This module will be removed in 0.20.\n", - " DeprecationWarning)\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Try 1: SubMax = 41.7; SupraMin = 83.3\n", - "Try 2: SubMax = 69.5; SupraMin = 83.3\n", - "Try 3: SubMax = 78.7; SupraMin = 83.3\n", - "Z = -1.86\n", - "Try 1: SubMax = 41.7; SupraMin = 83.3\n", - "Try 2: SubMax = 69.5; SupraMin = 83.3\n", - "Try 3: SubMax = 78.7; SupraMin = 83.3\n", - "Z = -1.86\n", - "len tests 2 tests [, ]\n", - "tests, completed, now gene computations\n", - "[[0.000999], [0.001001]]\n", - "Try 1: SubMax = 41.7; SupraMin = 83.3\n", - "Try 2: SubMax = 69.5; SupraMin = 83.3\n", - "Try 3: SubMax = 78.7; SupraMin = 83.3\n", - "Z = -1.86\n", - "Try 1: SubMax = 41.7; SupraMin = 83.3\n", - "Try 2: SubMax = 69.5; SupraMin = 83.3\n", - "Try 3: SubMax = 78.7; SupraMin = 83.3\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "INFO:__main__:gen\tnevals\tavg \tstd\tmin \tmax \n", - "1 \t2 \t0.937378\t0 \t0.937378\t0.937378\n", - "2 \t2 \t0.937378\t0 \t0.937378\t0.937378\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Z = -1.86\n", - "len tests 2 tests [, ]\n", - "tests, completed, now gene computations\n", - "[[0.0010009381483599346], [0.0009992543652523919]]\n", - "Try 1: SubMax = 41.7; SupraMin = 83.3\n", - "Try 2: SubMax = 69.5; SupraMin = 83.3\n", - "Try 3: SubMax = 78.7; SupraMin = 83.3\n", - "Z = -1.86\n", - "Try 1: SubMax = 41.7; SupraMin = 83.3\n", - "Try 2: SubMax = 69.5; SupraMin = 83.3\n", - "Try 3: SubMax = 78.7; SupraMin = 83.3\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "INFO:__main__:3 \t2 \t0.937378\t0 \t0.937378\t0.937378\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Z = -1.86\n", - "len tests 2 tests [, ]\n", - "tests, completed, now gene computations\n", - "[[0.000999103642354488], [0.0009992543652523919]]\n", - "{'dhof': [, ], 'pf': , 'dbest': , 'td': ['k'], 'gen_vs_pop': [[[0.000999], [0.001001]], [[0.0010009381483599346], [0.0009992543652523919]], [[0.000999103642354488], [0.0009992543652523919]]], 'hof': , 'bd': , 'pop': [[0.000999], [0.0009992543652523919], [0.000999103642354488], [0.0009992543652523919]], 'history': , 'log': [{'gen': 1, 'std': 0.0, 'min': 0.93737798762411861, 'avg': 0.93737798762411861, 'nevals': 2, 'max': 0.93737798762411861}, {'gen': 2, 'std': 0.0, 'min': 0.93737798762411861, 'avg': 0.93737798762411861, 'nevals': 2, 'max': 0.93737798762411861}, {'gen': 3, 'std': 0.0, 'min': 0.93737798762411861, 'avg': 0.93737798762411861, 'nevals': 2, 'max': 0.93737798762411861}]}\n" - ] - } - ], - "source": [ - "\n", - "import seaborn as sns\n", - "import quantities as pq\n", - "from neuronunit.optimization.data_transport_container import DataTC\n", - "import os\n", - "import pickle\n", - "import copy\n", - "import numpy as np\n", - "import pandas as pd\n", - "import matplotlib.pyplot as plt\n", - "\n", - "from neuronunit.optimization import get_neab\n", - "from neuronunit.models.reduced import ReducedModel\n", - "from neuronunit.optimization.model_parameters import model_params, path_params\n", - "from neuronunit.optimization import model_parameters as modelp\n", - "\n", - "from neuronunit.optimization import model_parameters as modelp\n", - "mp = modelp.model_params\n", - "\n", - "mp['k'] = [1e-3-1e-6,1e-3+1e-6]\n", - "\n", - "\n", - "mp['a'] = 0.1\n", - "mp['b'] = 0.01\n", - "mp['vr'] = -65.0\n", - "#mp['vr'] = [-90.0,-60.0]\n", - "#print(mp['k'],mp['vr'])\n", - "\n", - "\n", - "free_params = ['k']\n", - "from neuronunit.optimization import optimization_management as om\n", - "mp = modelp.model_params\n", - "\n", - "import sciunit\n", - "import neuronunit.optimization as opt\n", - "tests_,all_tests, observation,suite = opt.get_neab.get_tests()\n", - "passive = [ str('RestingPotentialTest'), str('CapacitanceTest'), str('TimeConstantTest'), str('InputResistanceTest') ]\n", - "firing_tests = [ t for t in all_tests if str(t) not in passive ]\n", - "first_two = all_tests[0:2]\n", - "import pickle\n", - " \n", - "try:\n", - " ga_out = pickle.load(open('gaout.p','rb'))\n", - "except:\n", - " # 10*4 gene evaluations\n", - " ga_out, DO = om.run_ga(mp,3,first_two,free_params=free_params)\n", - " print(ga_out)\n", - " pickle.dump(ga_out,open('gaout.p','wb'))\n" - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "{'k': 0.001001}\n", - "{'RheobaseTestP': 0.9373779876241186}\n", - "{'value': array(83.34) * pA}\n" - ] - } - ], - "source": [ - "print(ga_out['hof'][0].dtc.attrs)\n", - "print(ga_out['hof'][0].dtc.scores)\n", - "print(ga_out['hof'][0].dtc.rheobase)\n", - "\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "#attrs = {'k':1e-4}#{'a': 1e-2, 'b': 1e-2, 'vpeak':40, 'k':5e-5}\n", - "attrs = ga_out['hof'][0].dtc.attrs\n", - "current = {'amplitude':-10.0*pq.pA, 'delay':200.0*pq.ms, 'duration':500.0*pq.ms}\n", - "sim_length = 1000.0 * pq.ms\n", - "\n", - "LEMS_MODEL_PATH = path_params['model_path']\n", - "from neuronunit.optimization.model_parameters import model_params, path_params\n", - "\n", - "model = ReducedModel(LEMS_MODEL_PATH,name=str('vanilla'),backend='NEURON')\n", - "model.set_attrs(attrs)\n", - "model.inject_square_current(current)\n", - "#model.get_backend().set_stop_time(sim_length)\n", - "vm = copy.copy(model.get_membrane_potential())\n", - "plt.plot(vm.times,vm*1000,label=str(attrs))\n", - "plt.ylim(-90,-50)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "dtc = ga_out['hof'][0].dtc\n", - "\n", - "LEMS_MODEL_PATH = path_params['model_path']\n", - "model = None\n", - "model = ReducedModel(LEMS_MODEL_PATH,name=str('vanilla'),backend='NEURON')\n", - "model.set_attrs(dtc.attrs)\n", - "scores = []\n", - "for t in first_two:\n", - " scores.append(t.judge(model,stop_on_error = False, deep_error = False))\n", - "print(scores)\n", - "\n", - "## Does the same thing in parallel\n", - "#from neuronunit.optimization.optimization_management import nunit_evaluation as nue\n", - "\n", - "# xargs = (dtc,first_two)\n", - "# dtc = nue(xargs)\n", - "# print(dtc.scores)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "#attrs = {'k':1e-4}#{'a': 1e-2, 'b': 1e-2, 'vpeak':40, 'k':5e-5}\n", - "attrs = ga_out['hof'][0].dtc.attrs\n", - "print(ga_out['hof'][0].dtc.rheobase)\n", - "ampl = ga_out['hof'][0].dtc.rheobase['value']\n", - "current = {'amplitude':ampl, 'delay':200.0*pq.ms, 'duration':500.0*pq.ms}\n", - "sim_length = 1000.0 * pq.ms\n", - "\n", - "LEMS_MODEL_PATH = path_params['model_path']\n", - "from neuronunit.optimization.model_parameters import model_params, path_params\n", - "\n", - "model = ReducedModel(LEMS_MODEL_PATH,name=str('vanilla'),backend='NEURON')\n", - "model.set_attrs(attrs)\n", - "model.inject_square_current(current)\n", - "#model.get_backend().set_stop_time(sim_length)\n", - "vm = copy.copy(model.get_membrane_potential())\n", - "plt.plot(vm.times,vm*1000,label=str(attrs))\n", - "plt.ylim(-90,20)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": true - }, - "outputs": [], - "source": [] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.5.2" - } - }, - "nbformat": 4, - "nbformat_minor": 2 -} diff --git a/neuronunit/unit_test/round_trip_tests.ipynb b/neuronunit/unit_test/round_trip_tests.ipynb deleted file mode 100644 index 661200864..000000000 --- a/neuronunit/unit_test/round_trip_tests.ipynb +++ /dev/null @@ -1,992 +0,0 @@ -{ - "cells": [ - { - "cell_type": "code", - "execution_count": 1, - "metadata": {}, - "outputs": [], - "source": [ - "import sys\n", - "import os\n", - "nu_russell = os.path.realpath(os.path.join(os.getcwd(),'..','..'))\n", - "sys.path.insert(0,nu_russell)\n", - "import neuronunit\n", - "assert 'neuronunit-russell' in neuronunit.__file__" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "OrderedDict([('RS', {'b': -2, 'k': 0.7, 'vt': -40, 'vPeak': 35, 'c': -50, 'vr': -60, 'C': 100, 'a': 0.03, 'd': 100}), ('IB', {'b': 5, 'k': 1.2, 'vt': -45, 'vPeak': 50, 'c': -56, 'vr': -75, 'C': 150, 'a': 0.01, 'd': 130}), ('LTS', {'b': 8, 'k': 1.0, 'vt': -42, 'vPeak': 40, 'c': -53, 'vr': -56, 'C': 100, 'a': 0.03, 'd': 20}), ('TC', {'b': 15, 'k': 1.6, 'vt': -50, 'vPeak': 35, 'c': -60, 'vr': -60, 'C': 200, 'a': 0.01, 'd': 10}), ('TC_burst', {'b': 15, 'k': 1.6, 'vt': -50, 'vPeak': 35, 'c': -60, 'vr': -60, 'C': 200, 'a': 0.01, 'd': 10})])\n" - ] - } - ], - "source": [ - "# http://www.physics.usyd.edu.au/teach_res/mp/mscripts/\n", - "# ns_izh002.m\n", - "import collections\n", - "from collections import OrderedDict\n", - "import numpy as np\n", - "\n", - "# Fast spiking cannot be reproduced as it requires modifications to the standard Izhi equation,\n", - "# which are expressed in this mod file.\n", - "# https://github.com/OpenSourceBrain/IzhikevichModel/blob/master/NEURON/izhi2007b.mod\n", - "\n", - "reduced2007 = collections.OrderedDict([\n", - " # C k vr vt vpeak a b c d celltype\n", - " ('RS', (100, 0.7, -60, -40, 35, 0.03, -2, -50, 100, 1)),\n", - " ('IB', (150, 1.2, -75, -45, 50, 0.01, 5, -56, 130, 2)),\n", - " ('LTS', (100, 1.0, -56, -42, 40, 0.03, 8, -53, 20, 4)),\n", - " ('TC', (200, 1.6, -60, -50, 35, 0.01, 15, -60, 10, 6)),\n", - " ('TC_burst', (200, 1.6, -60, -50, 35, 0.01, 15, -60, 10, 6))])\n", - "\n", - - "type2007 = collections.OrderedDict([\n", - " # C k vr vt vpeak a b c d celltype\n", - " ('RS', (100, 0.7, -60, -40, 35, 0.03, -2, -50, 100, 1)),\n", - " ('IB', (150, 1.2, -75, -45, 50, 0.01, 5, -56, 130, 2)),\n", - " ('LTS', (100, 1.0, -56, -42, 40, 0.03, 8, -53, 20, 4)),\n", - " ('TC', (200, 1.6, -60, -50, 35, 0.01, 15, -60, 10, 6)),\n", - " ('TC_burst', (200, 1.6, -60, -50, 35, 0.01, 15, -60, 10, 6)),\n", - " ('RTN', (40, 0.25, -65, -45, 0, 0.015, 10, -55, 50, 7)),\n", - " ('RTN_burst', (40, 0.25, -65, -45, 0, 0.015, 10, -55, 50, 7)),\n", - " ('CH', (50, 1.5, -60, -40, 25, 0.03, 1, -40, 150, 3)),\n", - " ('FS', (20, 1.0, -55, -40, 25, 0.2, -2, -45, -55, 5))])\n", - "\n", - "import numpy as np\n", - "reduced_dict = OrderedDict([(k,[]) for k in ['C','k','vr','vt','vPeak','a','b','c','d']])\n", - "\n", - "#OrderedDict\n", - "for i,k in enumerate(reduced_dict.keys()):\n", - " for v in type2007.values():\n", - " reduced_dict[k].append(v[i])\n", - "\n", - "explore_param = {k:(np.min(v),np.max(v)) for k,v in reduced_dict.items()}\n", - "param_ranges = OrderedDict(explore_param)\n", - "\n", - "#IB = mparams[param_dict['IB']]\n", - "RS = {}\n", - "IB = {}\n", - "TC = {}\n", - "CH = {}\n", - "RTN_burst = {}\n", - "cells = OrderedDict([(k,[]) for k in ['RS','IB','CH','LTS','FS','TC','TC_burst','RTN','RTN_busrt']])\n", - "reduced_cells = OrderedDict([(k,[]) for k in ['RS','IB','LTS','TC','TC_burst']])\n", - "\n", - "for index,key in enumerate(reduced_cells.keys()):\n", - " reduced_cells[key] = {}\n", - " for k,v in reduced_dict.items():\n", - " reduced_cells[key][k] = v[index]\n", - "\n", - "print(reduced_cells)\n", - "cells = reduced_cells" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "{'b': 15, 'k': 1.6, 'vt': -50, 'vPeak': 35, 'c': -60, 'vr': -60, 'C': 200, 'a': 0.01, 'd': 10}\n", - "200.0\n", - "1.5707957651346172\n", - "\n", - "500.0\n" - ] - } - ], - "source": [ - "model = None\n", - "from neuronunit.models.reduced import ReducedModel\n", - "from neuronunit.optimization import get_neab\n", - "\n", - "from neuronunit.optimization.model_parameters import model_params, path_params\n", - "LEMS_MODEL_PATH = path_params['model_path']\n", - "model = ReducedModel(LEMS_MODEL_PATH,name = str('vanilla'),backend = ('NEURON'))\n", - "print(cells['TC'])\n", - "model.set_attrs(cells['TC'])\n", - "#dir(model._backend)\n", - "dir(model._backend.h)\n", - "#loc = \n", - "#for sec in \n", - "dir(model._backend.h.m_RS_RS_pop[0])#:\n", - "\n", - "for sec in model._backend.h.m_RS_RS_pop[0].Section(0.5):\n", - " sec.cm = (50/31.831)\n", - " print(sec.cm)\n", - " print(sec.area)\n", - " print(sec.diam)\n", - "\n", - "sec = model._backend.h.Section(model._backend.h.m_RS_RS_pop[0]) \n", - "sec.L, sec.diam = 6.3, 5 \n", - "\n", - "model = ReducedModel(LEMS_MODEL_PATH,name = str('vanilla'),backend = ('RAW'))\n", - "model.set_attrs(cells['TC'])\n", - "\n" - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "{'value': array(76.21875) * pA}\n" - ] - } - ], - "source": [ - "tests_,all_tests, observation,suite = get_neab.get_tests()\n", - "\n", - "rheobase = all_tests[0].generate_prediction(model)\n", - "print(rheobase)" - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "data": { - "image/png": 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393uBewHKy8sH5OflIw0tjBxcTEGB5bsUEenHug0Gd7+oq31mdsDMJrn7PjObBBxM02wPcEGH9TLgWWABUG5mO4I6xpvZs+5+AZLW4WMtjNL4gohELNtDSZVA2yyj5cCv07R5ErjEzEYHg86XAE+6+/9x95PdfTpwPvCWQuH4DrzbyPjhpfkuQ0T6uWyD4XbgYjPbAlwUrGNm5Wb2IwB3P0RqLOHF4OvWYJucoP21jUwapWAQkWhlNYrp7jXAhWm2rwO+0GH9PuC+4zzPDmBONrX0d8mks7+2kYkjS6mua8p3OSLSj+nM55g4dKyZ5tYkk0akegyarioiUVEwxMSuQ6kZvZNHD8HQrCQRiY6CISa2HkidHD5r/LA8VyIi/Z2CISa2HKyjpKiAKWOG5LsUEennFAwx8cb+OmaOG0ahTm4TkYgpGGKgNem8vOsIc6eOyncpIjIAKBhi4PV971LflOCcGWPyXYqIDAAKhhh45o3UlUbOO2VsnisRkYFAwRADj2/aR/m00UwIzmEwDTOISIQUDH3c+p2HeGN/HUvPPjnfpYjIAKFg6OO+//RWRg0p5op5ZfkuRUQGCAVDH/afm/fzzJvVXPvhmQwZpJvziEjvUDD0UbsPHeP6X77CGROHc835M/JdjogMIAqGPmjPkQY+e98LuMMPPjOP4kL9N4lI79HxiT5mw+4j/O2D66lrSvDTz53D9JOGpm3nuryqiEREwdBHtCadH/x+G9976i0mjCil4ovnMWfyyLRtNVtVRKKkYOgD9tc28rWHNvCn7TX85fsm8U+Xv4+RQ3RvZxHJDwVDnv3n5v1c/8tXaGpJ8t0rzuIT5WWYzmATkTxSMORJY0sr33n8dR54fidzJo/gfy+by8xxuteCiOSfgiEPthyo4+8qXuaN/XV84fwZXL/kDAYVaeaRiPQNCoZeVrlxL9c/spGhg4r4yefm85HTx+e7JBGREAVDL0kmnbtXb+Ge1VuYP300K//mA4wPLorXE5qsKiJRUTD0gtakc8MvX+GR9VV8Yl4Zt10+h5Kiwh4/n8amRSRKCoaIJVqTfOPhjTy6YS9/f+EsvnrRLM06EpE+TcEQIXfnW5WbeXTDXv774tP58kdOzXdJIiLd0lSYCP3oj2/z87W7uPbDMxUKIhIbCoaIrNtxiH/+zetcOmci1y8+Pd/liIhkTMEQgbrGFr760AYmjx7Md688i4ICjSmISHxojCEC96zewp4jDTxy7QKGl0ZzzaO+eHHVxpZW3m1s4d2GFmobErzb0MLR5gQtrUmaE0maW52WRJLm1iStyfe+gbaxeOtwecD3tqWW2/a1bzfrsC9Y7/AYQvutvR0dn6/jY8l8tlcm7SzDSx3mah5CphMaMmmV8fuQwbNl/lwZtMn4vTp+w2ElRSw6dawmgRyHgiHH3n7nKD9ds4NPzpvCvGljInmNTP/o5FpzIsnOmqNsPVjP1oP17DnSwN7aRvbXNrCvtpG6xkRe6hI5UT/93Hwu0MmlXVIw5Ni/rt5CcWEB34j5uEKiNclbB+p5efdhXt51hI27j/D2O0dJdPikf9KwEk4eVcr0sUNZcMpYxo8oZcTgYkaUFjFycDEjBhczdFARg4oKGFRUQHGhUVJYSHGRURgcXkvX82nb5jju753M5+54234P72+7P0Xbfk81CK2/97zBc/l7r+cZnjKYSU8tk2fK9H4amT1XRk+V0bPl6vvL/Lly+L5n0CbpzlX//jyPbdynYDiOrILBzMYADwHTgR3AJ939cJp2y4FvBqu3ufv9wfZBwPeBC4AkcJO7/zKbmvJp75EGKjfu5eoF0xg3vCTf5ZwQd2dHzTH+uKWaP7xVzZ+21XC0uRWAMUMHcfaUUVw8ewKzJgzj1HHDmTl+qO5DLbG0+MyJPLl5P02J7E407c+y/c2+EVjt7reb2Y3B+g0dGwThcTNQTurDxnozqwwC5CbgoLufZmYFQDTHXnrJz9fuIunO5xfF4x7N7s4rVbU8vmkfv311P7sOHQNg6pghfHzuZM6ZMYa5U0YzZcxgHY+VfuOvzprEI+ureOaNgyyZMynf5fRJ2QbDUlKf9gHuB56lUzAAi4Gn3P0QgJk9BSwBKoDPA2cAuHsSeCfLevLG3Xl0wx7OnzWOKWOG5Luc49peXc9DL+7m8U37qDrcQHGhcf6pJ/HFD53Ch2adxLSx6W8nKtIfnH/qSUweNZifPLdDwdCFbINhgrvvC5b3AxPStJkM7O6wXgVMNrNRwfq3zewCYBtwnbsfyLKmLv3g99uormsKHXPueNw6fMwaaG/z5/vajmEng8c3NLdSdbiBr110WlTlZ6WlNclTrx3gwed3smZbDUUFxgdnncTfXziLS2ZP1B3jZMAoKizgc4umc9vjr7N+52HmTRvdq6/fmnSONSdoSiRpbGkN/dvUkqQx0UpTS5JEMjV7L9HqJJJJEkmnNel8+txp7WN0Uek2GMzsd8DENLtu6rji7m5mJzKJsggoA9a4+9fN7OvAvwBXd1HHCmAFwNSpU0/gZd7zxKZ9bDtYT4F1mqqYdtpix+1Q0L7vvWmTnadCzps2msVz0r1VuTV4UCFHGpqpPdbS7R/0fbUNVKzdxaoXd3OwronJowbzjUtO45PzpzB+eM+v7ioSZ8vOmcq//3E7//PRV3n0y4uyuh9KXWML+2sbOVjXRHVdEwfrGqmua+Kd+mZqG1LTt+saE9Q1tvBuY4L6puxm732yfAqFBdGOjVimsyPSPtjsTeACd99nZpOAZ9399E5trgra/Ldg/YekDjmtAuqB4e6eNLMpwG/d/czuXre8vNzXrVvX47rj7rW97/LRe/7Ily6YyQ1Lzviz/cmk88et7/Dg8ztZ/foBHPjwaeP4zLnT+MgZ4yP/tCESB799dT/XPriey95/Mv/ryrMoLe76j21tQwvbquvZXn2UXTVH2XnoGDtrjrHr0DEOHW3+s/YlRQWMG17CqCHFDC8pZsTgIkaUFjO8NLU8dFARpcUFlBQXUlJUQElRYWq9qJCS4gIGFaZm8hUVGEUFBRQVWmq5sIDRQ4p7POZnZuvdvby7dtkeSqoElgO3B//+Ok2bJ4F/MrO2/tolwD8EPYzHSI1RPA1cCLyWZT0DwuyTR/DXH5jMD3+/jb+YNILLzkodJ91Zc4z/+8peVr24m6rDDYwZOogVH5rJp8+d2ufHPUR625I5E7lhyRnc8ds32Lj7CEvPPjn1e+JQXd/E3iMNbKuuZ1v1UarrmtofV2Bw8qjBTBs7hMVnTmTa2CGcPGow44eXMC74Gl5SFOsJG9n2GMYCvwCmAjtJTVc9ZGblwLXu/oWg3eeB/xE87Dvu/pNg+zTgAWAUUA18zt13dfe6A73HAFDflOCzP17LS7uOMH54Ca1Jpyb45LJw5lg+NX8KS+ZM1HQ8kW78/q1q/nX1Fl7adZgOp+kwcnAxM8cNZea4YZw6fhgzxw3jlHFDKRs9JLa34s20x5BVMOSLgiGlKdHKoy/v4YW3D1NcaJx58gguOH28egciPdDQ3ErN0dTklJOGlTB4UP/7UNVbh5Ikj0qKCvnU/Kl8an7PBuNF5D2DBxVSNkgfqkBXVxURkU4UDCIiEqJgEBGREAWDiIiEKBhERCREwSAiIiEKBhERCVEwiIhISCzPfDazalKX4OiJk4jvfR9Ue++La92g2vOlL9c+zd3HddcolsGQDTNbl8kp4X2Rau99ca0bVHu+xLn2NjqUJCIiIQoGEREJGYjBcG++C8iCau99ca0bVHu+xLl2YACOMYiIyPENxB6DiIgcR78NBjNbYmZvmtlWM7sxzf4SM3so2L/WzKb3fpV/LoO6v25mr5nZK2a2OrgLXp/QXe0d2l1hZh7c6a9PyKR2M/tk8N5vNrOf93aNXcngZ2aqmT1jZi8HPzcfzUednZnZfWZ20Mxe7WK/mdk9wff1ipl9oLdr7EoGtX86qHmTma0xs/f3do1Zcfd+9wUUAtuAU4BBwEZgdqc2fwv8IFheBjwUk7o/AgwJlr/UF+rOtPag3XDgD8DzQHm+6z6B930W8DIwOlgfn++6T6D2e4EvBcuzgR35rjuo5UPAB4BXu9j/UeA3gAHnAWvzXfMJ1L6ww8/KpX2p9ky++muP4Rxgq7tvd/dmYBWwtFObpcD9wfIjwIWW/7t3d1u3uz/j7seC1eeBsl6usSuZvOcA3wbuABp7s7huZFL7F4GV7n4YwN0P9nKNXcmkdgdGBMsjgb29WF+X3P0PwKHjNFkK/MxTngdGmdmk3qnu+Lqr3d3XtP2s0Ld+TzPSX4NhMrC7w3pVsC1tG3dPALXA2F6prmuZ1N3RNaQ+UfUF3dYeHAqY4u6P92ZhGcjkfT8NOM3MnjOz581sSa9Vd3yZ1H4L8BkzqwKeAP6ud0rL2on+PvRVfen3NCO653NMmdlngHLgw/muJRNmVgDcBfzXPJfSU0WkDiddQOrT3x/M7H3ufiSvVWXmKuCn7n6nmS0AHjCzOe6ezHdh/Z2ZfYRUMJyf71pORH/tMewBpnRYLwu2pW1jZkWkutg1vVJd1zKpGzO7CLgJ+Ji7N/VSbd3prvbhwBzgWTPbQeqYcWUfGYDO5H2vAirdvcXd3wbeIhUU+ZZJ7dcAvwBw9z8BpaSu59PXZfT70FeZ2VnAj4Cl7p7vvy0npL8Gw4vALDObYWaDSA0uV3ZqUwksD5avBJ72YKQoj7qt28zmAj8kFQp95Tg3dFO7u9e6+0nuPt3dp5M67voxd1+Xn3JDMvl5eZRUbwEzO4nUoaXtvVlkFzKpfRdwIYCZ/QWpYKju1Sp7phL4bDA76Tyg1t335buoTJjZVOBXwNXu/la+6zlh+R79juqL1IyGt0jN2Lgp2HYrqT9GkPrleBjYCrwAnJLvmjOs+3fAAWBD8FWZ75ozrb1T22fpI7OSMnzfjdShsNeATcCyfNd8ArXPBp4jNWNpA3BJvmsO6qoA9gEtpHpk1wDXAtd2eM9XBt/Xpj7289Jd7T8CDnf4PV2X75pP5EtnPouISEh/PZQkIiI9pGAQEZEQBYOIiIQoGEREJETBICIiIQoGEREJUTCIiEiIgkFEREL+P3qlD8yaMQy4AAAAAElFTkSuQmCC\n", - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "import pickle\n", - "import copy\n", - "import numpy as np\n", - "import pandas as pd\n", - "import matplotlib.pyplot as plt\n", - "\n", - "from neuronunit.models.reduced import ReducedModel\n", - "from neuronunit.optimization.model_parameters import model_params, path_params\n", - "LEMS_MODEL_PATH = path_params['model_path']\n", - "import neuronunit.optimization as opt\n", - "import quantities as pq\n", - "\n", - "from neuronunit.optimization.data_transport_container import DataTC\n", - "model = ReducedModel(LEMS_MODEL_PATH,name = str('vanilla'),backend = ('RAW'))\n", - "model.set_attrs(cells['TC'])\n", - "\n", - "iparams = {}\n", - "iparams['injected_square_current'] = {}\n", - "iparams['injected_square_current']['amplitude'] =75.36800000000001*pq.pA\n", - "#['amplitude'] = dtc.vtest[k]['injected_square_current']['amplitude']\n", - "DELAY = 100.0*pq.ms\n", - "DURATION = 1000.0*pq.ms\n", - "iparams['injected_square_current']['delay'] = DELAY\n", - "iparams['injected_square_current']['duration'] = int(DURATION)\n", - "\n", - "model.inject_square_current(iparams)\n", - "\n", - "plt.plot(model.get_membrane_potential().times,model.get_membrane_potential(),label='RAW')\n", - "plt.legend()\n", - "plt.show()" - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "cnt = 0\n", - "scores = []\n", - "tests_,all_tests, observation,suite = opt.get_neab.get_tests()" - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[array(76.21875) * pA, array(76.21875) * pA, array(76.21875) * pA]\n", - "[array(76.21875) * pA, array(-10.) * pA, array(-10.) * pA, array(-10.) * pA, array(-10.) * pA]\n" - ] - } - ], - "source": [ - "def format_iparams(all_tests,rheobase):\n", - "\n", - " for t in all_tests[1:5]:\n", - " DURATION = 500.0*pq.ms\n", - " DELAY = 200.0*pq.ms\n", - "\n", - " obs = t.observation\n", - " t.params = {}\n", - " t.params['injected_square_current'] = {}\n", - " t.params['injected_square_current']['delay']= DELAY\n", - " t.params['injected_square_current']['duration'] = DURATION\n", - " t.params['injected_square_current']['amplitude'] = -10*pq.pA\n", - " \n", - " \n", - " for t in all_tests[-3::]: \n", - " t.params = {}\n", - " DURATION = 1000.0*pq.ms\n", - " DELAY = 100.0*pq.ms\n", - "\n", - " t.params['injected_square_current'] = {}\n", - " t.params['injected_square_current']['delay']= DELAY\n", - " t.params['injected_square_current']['duration'] = DURATION\n", - " t.params['injected_square_current']['amplitude'] = rheobase['value']\n", - " \n", - " all_tests[0].params = all_tests[-1].params\n", - " \n", - " return all_tests\n", - "\n", - "pt = format_iparams(all_tests,rheobase)\n", - "print([t.params['injected_square_current']['amplitude'] for t in pt[-3::] ])\n", - "print([t.params['injected_square_current']['amplitude'] for t in pt[0:5] ])" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "##\n", - "# * Get predictions from models.\n", - "## * Fake NeuroElectro Observations\n", - "## * Do roundtrip testing\n", - "##" - ] - }, - { - "cell_type": "code", - "execution_count": 8, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "injected current seen: {'delay': array(200.0) * ms, 'amplitude': array(-10.0) * pA, 'duration': array(500.0) * ms}\n", - "injected current seen: {'delay': array(200.0) * ms, 'amplitude': array(-10.0) * pA, 'duration': array(500.0) * ms}\n", - "injected current seen: {'delay': array(200.0) * ms, 'amplitude': array(-10.0) * pA, 'duration': array(500.0) * ms}\n", - "[{'value': array(75.53061224489795) * pA}, {'value': array(31739282.99824782) * kg*m**2/(s**3*A**2)}, {'value': array(0.0034631849083750876) * s}, {'value': array(1.0911352057216523e-10) * s**4*A**2/(kg*m**2)}, {'std': array(0.00019622743842309925) * V, 'mean': array(-0.060317405613048956) * V}, {'std': array(0.0) * s, 'mean': array(0.0006500000000000001) * s, 'n': 1}, {'std': array(0.0) * V, 'mean': array(0.056127191359642024) * V, 'n': 1}, {'std': array(0.0) * V, 'mean': array(-0.02112719135964202) * V, 'n': 1}]\n" - ] - } - ], - "source": [ - "predictions = []\n", - "import dask.bag as db\n", - "# The rheobase has been obtained seperately and cannot be db mapped.\n", - "# Nested DB mappings dont work.\n", - "ptbag = db.from_sequence(pt[1::])\n", - "\n", - "def obtain_predictions(t): \n", - " model = None\n", - " model = ReducedModel(LEMS_MODEL_PATH,name = str('vanilla'),backend = ('RAW'))\n", - " model.set_attrs(cells['TC'])\n", - " return t.generate_prediction(model)\n", - "predictions = list(ptbag.map(obtain_predictions).compute())\n", - "predictions.insert(0,rheobase)\n", - "print(predictions) " - ] - }, - { - "cell_type": "code", - "execution_count": 9, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[{'value': array(76.21875) * pA}, {'value': array(31739282.99824782) * kg*m**2/(s**3*A**2)}, {'value': array(0.00346318) * s}, {'value': array(1.09113521e-10) * s**4*A**2/(kg*m**2)}, {'std': array(0.00019623) * V, 'value': array(-0.06031741) * V}, {'std': array(0.) * s, 'value': array(0.00055) * s, 'n': 1}, {'std': array(0.) * V, 'value': array(0.05065779) * V, 'n': 1}, {'std': array(0.) * V, 'value': array(-0.01565779) * V, 'n': 1}]\n" - ] - } - ], - "source": [ - "# having both means and values in dictionary makes it very irritating to iterate over.\n", - "# It's more harmless to demote means to values, than to elevate values to means.\n", - "# Simply swap key names: means, for values.\n", - "for p in predictions:\n", - " if 'mean' in p.keys():\n", - " p['value'] = p.pop('mean')\n", - "print(predictions)" - ] - }, - { - "cell_type": "code", - "execution_count": 10, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "76.21875 pA\n", - "31.73928299824782 Mohm\n", - "3.4631849083750876 ms\n", - "109.11352057216521 pF\n", - "-60.317405613048955 mV\n", - "0.55 ms\n", - "50.65778903053697 mV\n", - "-15.657789030536964 mV\n" - ] - } - ], - "source": [ - "# make some new tests based on internally generated data \n", - "# as opposed to experimental data.\n", - "\n", - "TC_tests = copy.copy(all_tests)\n", - "for ind,t in enumerate(TC_tests):\n", - " if 'mean' in t.observation.keys():\n", - " t.observation['value'] = t.observation.pop('mean')\n", - " pred = predictions[ind]['value']\n", - " try:\n", - " pred = pred.rescale(t.units)\n", - " t.observation['value'] = pred\n", - " except: \n", - " t.observation['value'] = pred\n", - " t.observation['mean'] = t.observation['value']\n", - " \n", - " print(t.observation['value'])\n", - " \n", - "pickle.dump(TC_tests,open('thalamo_cortical_tests.p','wb')) " - ] - }, - { - "cell_type": "code", - "execution_count": 11, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/Users/rgerkin/Dropbox/miniconda3/lib/python3.5/site-packages/sklearn/cross_validation.py:41: DeprecationWarning: This module was deprecated in version 0.18 in favor of the model_selection module into which all the refactored classes and functions are moved. Also note that the interface of the new CV iterators are different from that of this module. This module will be removed in 0.20.\n", - " \"This module will be removed in 0.20.\", DeprecationWarning)\n", - "/Users/rgerkin/Dropbox/miniconda3/lib/python3.5/site-packages/sklearn/grid_search.py:42: DeprecationWarning: This module was deprecated in version 0.18 in favor of the model_selection module into which all the refactored classes and functions are moved. This module will be removed in 0.20.\n", - " DeprecationWarning)\n", - "INFO:__main__:gen\tnevals\tavg \tstd \tmin \tmax \n", - "1 \t8 \t4.41708\t0.968631\t2.97143\t5.84005\n", - "2 \t8 \t4.45406\t1.12392 \t2.26595\t5.47306\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Could not update this population [[0.01, -0.4116027206952335, -56.24766913409749], [0.01, -2, -73.75233086590251], [0.01, 13.919119365130545, -75], [0.011765562374505122, 15, -55], [0.2, -2, -75], [0.1569308785782718, -1.2118742936905633, -55], [0.18228274808717254, 15, -75], [0.2, 15, -55.0]]\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "INFO:__main__:3 \t8 \t3.73223\t1.24518 \t1.84382\t5.04707\n", - "/Users/rgerkin/Dropbox/dev/scidash/neuronunit-russell/neuronunit/tests/passive.py:83: RuntimeWarning: overflow encountered in exp\n", - " vm_fit[offset:,0] = a * np.exp(-t[offset:]/b) + c\n", - "/Users/rgerkin/Dropbox/dev/scidash/neuronunit-russell/neuronunit/tests/passive.py:83: RuntimeWarning: overflow encountered in multiply\n", - " vm_fit[offset:,0] = a * np.exp(-t[offset:]/b) + c\n", - "INFO:__main__:4 \t8 \t3.6366 \t1.07453 \t2.00279\t5.76905\n", - "INFO:__main__:5 \t8 \t3.21516\t1.46924 \t1.27557\t5.87427\n" - ] - } - ], - "source": [ - "from neuronunit.optimization import optimization_management as om\n", - "free_params = ['a','b','vr'] # this can only be odd numbers.\n", - "\n", - "hc = {}\n", - "for k,v in cells['TC'].items():\n", - " if k not in free_params:\n", - " hc[k] = v\n", - "#print(hc)\n", - "import pickle\n", - "TC_tests = pickle.load(open('thalamo_cortical_tests.p','rb')) \n", - " #run_ga(model_params, max_ngen, test, free_params = None, hc = None)\n", - "ga_out, DO = om.run_ga(explore_param,5,TC_tests,free_params=free_params,hc = hc)" - ] - }, - { - "cell_type": "code", - "execution_count": 12, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "{'k': 1.6, 'vt': -50, 'vr': -57.0603409987644, 'C': 200, 'vPeak': 35, 'c': -60, 'b': 13.742629850689024, 'a': 0.010004723957151636, 'd': 10}\n", - "{'k': 1.6, 'vt': -50, 'vr': -60, 'C': 200, 'vPeak': 35, 'c': -60, 'b': 15, 'a': 0.01, 'd': 10}\n" - ] - }, - { - "data": { - "text/plain": [ - "" - ] - }, - "execution_count": 12, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": 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DgQeUUpt9r+YnKjEILTUgQWg18rzYR1DaGLTWnwCfnHDsgUbvq4Brm/ncH4A/BMMGNyIVIEFoGXGw7UdGPjuAhJIEwX9kYSv7EWFwgB91Qe50QfAXEQb7EGFwELnRBaFlxMG2HxEGB5BQkiD4j4z7sR8RBkeQXkmC4C8N9Sh5YGxDhMFBpF+2IPiPPC32IcLgABJKEgT/CckpwA1HhMEB6m9zqQEJgv+Ih20fIgwOIve5IPiPPC72IcLgAOIaC4L/yONiPyIMDtAQSpIqkCC0SMPcYvK82IYIg4NIv2xBaBkZx2A/IgwOIK6xILQe8RjsQ4TBAWTAjnNI+477kL/MfkQYHER0wX6kkHEfsn6J/YgwOIDUWp1DfnkXI8pgGyIMDiIDduxHRNl9yF9mPyIMDiKyYD9SxriPH2cKkCfGLkQYHMANNSBTa9aGZsts6rurii7YhgiDA7hhwI6pBagWn8G1hPDjYhwiDA4gA3acw1TBMxkRc/sRYXCA+sIpLIR1QR5FIVRoqEiFsottGCIMDuD90WUIWaSNQQg1RBfsQ4TBAerLprAQvtNNLT8lLOE+5B+zHxEGB6ivjYeuLJhbszY1XybjhufFNEQYHKChjSGEGxlMrVmbmSuzkWnq7UeEwQG8LqgBmVqzNrXt5MwglJ8YsxBhcIAfa0Byo9uNyIL7EC23HxEGB/C6YCSnqQ+jqfkyGQkl2Y8IgxO4IZRkat3a0GwZje95CeVefKYhwuAAXhcM2DG1Zm2s4BmMN/SH/RiHCIMDuGHhEVOLT6+pGTMYN8wtZhpBEQal1Ayl1G6lVKZSan4z56OVUkt859cqpZIanfuN7/hupdQlwbAn1HFDbdzU3jum5stkvF5rK3OL2UfAwqCUCgeeAy4FRgA3KKVGnJDsDqBIaz0YeBJ43PfZEcD1wEhgBvBX3/WMRmqtziE/vfvwipjbTjA8hnFAptZ6n9a6BngLmH1CmtnAIt/7pcBUZQXYZwNvaa2rtdb7gUzf9YzGDbXW0LewbbjgpxcExwmGMPQHchrt5/qONZtGa10HFAM9/PyscbihcHKDjW1BGp/dh3gM9uOaxmel1Dyl1Aal1Ib8/HynzQkIVxROLjCxTZiaL4OR0Kv9BEMYDgIDGu0n+I41m0YpFQF0AQr8/CwAWusFWut0rXV6fHx8EMx2Djfc6K4QrzZgZq7MRjwG+wmGMKwHUpRSyUqpKKzG5GUnpFkGzPW9vwZYrq1A+zLgel+vpWQgBVgXBJtCGle0MYS+iW3C1HwZjfxnthMR6AW01nVKqXuAz4BwYKHWertS6mFgg9Z6GfB3YLFSKhMoxBIPfOneBnYAdcAvtNaeQG0KddxQOLnAxDZhqidkMuIx2E/AwgCgtf4E+OSEYw80el8FXHuKz/4P8D/BsMMtuOE2d4NX0xYMzZbRuCH0ahquaXw2CTfUgELfwrZhar5MRrw8+xFhcAAX6AJeQ6tppnpCJlM/8lmwDxEGB3CDx2CoLrhClIWmiJjbjwiDA7jhPneDeAlnBnIn2o8Ig9AspgqDodkyGlPvxVBGhMEB3HCju8DENuExNWMGY2pYM5QRYXCAgMommwo2N4hXWzA1XyYjbQz2I8LgAAEVTtqeLhqm1tLqCxlZ9MU9iC7YjwiDAwR0n9smDGY+jfWCJ+sHuwdT78VQRoTBAQJyjW0SBlPd9/pCJkx0wTWY6r2GMiIMDhBYG4OEkgLB460PJYkyuAVTKymhjAiDA7ijjcHMWHz9Tx9uWsYMRnTBfkQYHCCwNgabeiX59Me0WLyEktyHtDHYjwiDAwQ0D5HNHoNpBag0PrsPU8OaoYwIgwO4oVdSfSXNtFi8qSEyk5HZVe1HhMEB3ND47DHVY/BVP8NMy5jBiMdgPyIMDhBYd1V7Rz6b1kjrlcZn1yG9kuxHhMEBAqoB2TyOwbRY/I+hJLPyZTKiC/YjwuAAAcVMbR7HYFr5aWqjuslIryT7EWFwADe0MZgaize1G67JSBuD/YgwOEC9MLTpfrfZYzCtABWPwb1I7yT7EGFwgIDWBLC9jcGWr7MNaWMQhJYRYXAAjysGuFlb0wrQek0OkztfEE6JPB6uw97uqqZ6DKaFyAQhmIgwuA3bp8QwqwCt99ZMy5f09ReCiQiD27CpANDGNj5bW9M8oYDCk4JwAiIMbkOm3Q4IUwfu1YkwCEFEhMFtSHfVgDA3XyIMQvAQYXAbMu12QHgM9YQklCQEExEGt2H3yGfDStD6UFK4YYonwiAEExEGtyFzJQWE6b2tBCEYiDC4Da/Hlq+pD7lEGDYS7Me5kpy1I9iIMAjBJKCnXinVXSn1hVIqw7ftdop0c31pMpRSc33HYpVSHyuldimltiulHgvEljMGu4TBV4KaFnIxdUqMgKZZEYQTCLQ6OB/4SmudAnzl22+CUqo78CAwHhgHPNhIQP6stR4GjAEmKaUuDdAe89H2CEOdx+cxhJtVgBrbqC4egxBEAhWG2cAi3/tFwE+bSXMJ8IXWulBrXQR8AczQWldorb8G0FrXAJuAhADtMR9vnS1fU1/QmOYx1Pf3jwg3K0QmwiAEk0Cfjt5a68O+90eA3s2k6Q/kNNrP9R1rQCnVFZiJ5XUIp8MmYWgoQA0TBo/h+RKEYBDRUgKl1JdAn2ZO/bbxjtZaK6VafXcqpSKAN4FntNb7TpNuHjAPIDExsbVfYw7iMQRErcfMfIkwCMGkRWHQWl98qnNKqTylVF+t9WGlVF/gaDPJDgJTGu0nACsa7S8AMrTWT7VgxwJfWtLT08/cp8CmxucfPQbTQi5Wo3qkaaEkaXwWgkigT8cyYK7v/Vzgg2bSfAZMV0p18zU6T/cdQyn1B6ALcF+Adpw52OYxmNkrqc5QT0g8BiGYBCoMjwHTlFIZwMW+fZRS6UqpvwForQuBR4D1vtfDWutCpVQCVjhqBLBJKbVZKXVngPaYj+0eg1kFaENvK8PyJcIgBJMWQ0mnQ2tdAExt5vgG4M5G+wuBhSekyQXMejrtwC6PwdBYfL3ghRmWLxEGIZiYFWg9E7C5V5JpwlAfIjMrVyIMQnARYXAbDaGk9i3a6gsawwYIG7tugQiDEExEGNxGvccQFt6uX2NqAVrfxmAa0itJCCYiDG6jfkqMsICah1qkPuRiGqbWrE3Nl+AMIgw2E/Ci7Q0eQ/sKg7Eegwieq/Aamq9QR4TBZgIucOvbGNo5lGRqQWNqKMnUfNUaKuShjgiDzQT8ANd7DEraGNqCqfmq9ZhZgNYaKnihjgiDzdQE+gDb1PjsMfSBNNUTCvi+ClFq6szMV6gjwmAzAdfsvPY0PptaszY1X6YWoKZ6QqGOCIPNBC2U1M7CYOoDWWdovkwNuZgqeKGOCIPNBFzgemqtbTuHkkx9IE31GEwVclPzFeqIMNhM4MJQbW1V+/51psas639/0+ShXshNG6luqicU6ogw2EzAN3q9x9DOmOoxVNeama96ITdtnQlT78NQx6y7yAUE7DHU+TyGdp4CwdQHsrrOnmnL7ab+vooyTRgM9VxDHbPuIhcQtFBSO1Nt6ANpakFTf1+ZF0oy8/8KdUQYbCbgxs+6muAY0gLGegymhpIM/b9EGJxBhMFmagN9gO3yGAwNuVQbW4Ca2UgrwuAMIgw2Uyseg6MYmy9DC1BT/69QR4TBZqprA6yJe0QYAsFUTyhgTzREqTHUEwp1RBhspjJgYbAnlGRqDdTUUFLA91WIUlVjZr5CHREGm6kK9AFuCCVJd9W2YGq+Ar6vQhRTBS/UEWGwmapAe8XY5TEYWIDWebzGTolRYWjN2tR8hToiDDYTcA3IhsbnmjozC1BTw2Ngbs3a1HyFOiIMNhN4KKkyOIachoqaunb/DicwufZZaWjeKg29F0MdEQabCbgGVFMeHENOQ7mhhUx5tbmFjKmiJx6DM4gw2EzAI29rKoJjyGkwtQAtMzRfYG4BaqrghToiDDYTkMuvNdSUBc+YU2CqMJRXm1vImBpKMrW3VagjwmAzVYEMsKqtpKGbajvOrmpqLa3c0Hi11vpHj8GwPgOm3ouhjgiDzQRUa61t/zASmOwxmJmv6jovHgN7kYG5/1moI8JgM6VVASy0Y0MYCX6spUWEmTWHc30h0yGyfZdFtZviSuueio4w73EurqwlMtys+9ANmHcnhTilVXWEt7XAre+RFBETPIOaoV68OkZHtOv32E2Zz1szLV/1wtClQ6TDlgSf4spaI/MV6ogw2ExpdS1xMW0smOqFIapj8AxqhqIKq6DpFmvWA1niK0A7RZvpMZhYgBZX1tLZwHyFOgEJg1Kqu1LqC6VUhm/b7RTp5vrSZCil5jZzfplSalsgtriFksq6tgtD5XFrG9M1eAY1Q1FFDR2jwo1bP7ioooausZFt99hClOIKM4Whus5DVa3XuHy5gUCf/PnAV1rrFOAr334TlFLdgQeB8cA44MHGAqKUugqwJ3juMFpryqrr6BzTxhu9ssjadmhWf4PG8YpaunWMatfvcILC8hq6x5qXL1M9BlPz5QYCFYbZwCLf+0XAT5tJcwnwhda6UGtdBHwBzABQSnUC7gf+EKAdrqCixoPHqwPwGAqtbWwP2rNfYlFFDd0MLEALy2uMFDxTC9ASQ/PlBgIVht5a68O+90eA3s2k6Q/kNNrP9R0DeAT4f0CL/TCVUvOUUhuUUhvy8/MDMNk5SqusXjFxbfUYKgoBBTFdgmdUMxRV1NLVsPYF8HkMBgrDsbJqIsKUcbH4/FJrwsgeHaMdtuTMo0VhUEp9qZTa1sxrduN0WmtNK6qxSqk04Cyt9Xv+pNdaL9Bap2ut0+Pj4/39mpCioNyaMrvN4YzKIujQFcLat/G0sLzayALU8oTMKjwB8kqq6RUXTZgyq+3kaGkVAH26iDDYTYsxDa31xac6p5TKU0r11VofVkr1BY42k+wgMKXRfgKwAjgPSFdKZfns6KWUWqG1noKh5JdawhAf18YbvbKw3dsXvF5NXnE1fbrEsONQSbt+l53Uebzkl1bTu3P7dvV1grySKnoZmi/AyP8s1Ak0lLQMqO9lNBf4oJk0nwHTlVLdfI3O04HPtNbPa637aa2TgMnAHpNFAeBYmeUa9+zUxtp46RHo1CeIFp1MQXkNNR4v/bp0aNfvsZsjJVV4NfTrala+wCpAe3c2r1Z9pLia2KhwOkaZNe7EDQQqDI8B05RSGcDFvn2UUulKqb8BaK0LsdoS1vteD/uOnXEcK7M8hp5t9RhKDkKX/i2nC4DDxdZ6D327mFVLO3Tcqn32N1AYjpRUGVmrzvPly7AImSsISIq11gXA1GaObwDubLS/EFh4mutkAaMCscUN5JcGUAPyeqHkEHTuB2XNReyCQ30BalrN+uBxq3+DafkqLK+htKqOxO6xDf+dKWQVlJPYPdZpM85IzBrBFOIcLa1ue/tCRQF4aqCzz2Nop96q+45ZQ0oSe5j1QB4oqEAp8zyGffnW/zUovn1Hw9uN1pr9x8pJ7mlWvtyCCION5BRWMKBbGwvcoixr22UA0H6+dWZeGX27xLR9EF6IsievlIHdY+kQZdZ0GPuOWdOkDOrZyWFLgkteSTUVNR7OMkzw3IIIg41kF1YwoK2ucf4uaxs/NHgGNcOeo6UM7mVWIQOw60gpQ3rH/XjAkFmqM/JKiYoII6GbWZ7QnrxSAM6KN+9edAMiDDZRWlVLYXlN22Om+busWVW7JQXVrsZU13nIyCtjaOMC1ADKq+vIOlbOsD5WvpRBrZmbc44zql9nIgyb12pzznGUglEJ7TuYU2ges+6mECa70Gr8bLMw5G2HnintOrht28ESquu8pCe171gJu9l4oAivhnOSujttSlCp9XjZmlvMmESz/i+A77OLGBzfybiQplsQYbCJjDyrkfCsXm2ImXo9kLsBEs4NslVNWZ9l9SJON6wAXbu/gPAwRfpAswrQ77OPW0JuWL5q6rxsyCoyroLiJkQYbGLH4RKiwsPaFjM98gPUlELixOAb1oivduYxrE8cPTuZNVjqyx1HGZvY1bgFer7cmUdkuGJySk+nTQkq6/YXUlpdx9RhzU29JtiBCINN7DxcwpA+ndq2xkHGF9Y2aXKjg8FtPT1aUsWGA0VcOqpvUK/rNBlxJOEAAAAaJ0lEQVR5pezOK+Xy0Wbly+PVfPLDYSYM6tH2SRlDlI+2HiImMoxJg80SPDchwmADWmu2HSxmRN/ObbvAjvdhwATo7Cvc2qHxdMn6HLSGK842qwBdvOYAkeGKy1LNytfKPUfJLark+nMTnTYlqBRX1vLB5kPMPru/cV2L3YQIgw1kHC2jqKKWc9sSu89eC3nbYPQ1wTfMR2WNh1fXHGDK0HijugfmlVSxZH0OV47pT684c6aM0FrzzFeZ9Okcw/SRZoVb/vavfVTWerj5vIFOm3JGI8JgA2v2FQAwYVCP1n/4X3+2ZlRNuzHIVv3I8ysyyS+t5hcXDm6373CChz/agQbj8vXOpoNszjnO/dOHGLX8ataxcl761z5mnt2PUf2lm6qTmHNXhTDfZRbQv2uH1g9u2/UxZHwOk+6DqPYZAbopu4jnV+5ldlq/tnk0Icoba7P5eOthfnnhYAb2MGf0bObRUh78YBvjkrpz9dgEp80JGpU1Hn755vdER4Tzm0uHOW3OGY8IQztTVevhm4x8pgxt5eJCRVnwwS+g10g47xftYtuBgnLufm0jfbrE8PAsc+Yw/GjrIR74YBs/GRLPvxvkLWQdK2fO39bRISqcp29IIzzMjIF6VbUe5i3ewLZDxfz52rONm+jQjZjVfy8EWbknn4oaT+t6+xzPgcVXgdbws8UQHvxeJzsPl3Dby+upqfOy6PZxdDFgZTOPV/PCyr38+fPdpA/sxl9uHGNM4fltxjHueXMTCnhz3gT6GrJexsHjlfx88UZ+OFjMH69JZdoIs9pM3IoIQzvzzx8O0zU2kvGD/AzT5G6Et2+B6lKYsxR6nNV8Ot227qpaa5asz+HBZdvpGhvJG3dNYFifNvaWCiH25JXy+/e3sXZ/IVek9uVP15xtRK+W4spa/vTZLl5fm01Kr068eHO6ETOOerya19ce4E+f7gbgb7ekc7GIQsggwtCOFJXX8M9tR7g2PaHlRkKvB1Y9BV8/CnF94dYPoe/Zp0jctlpw5tFSfvf+NtbsK2TiWT145oYxrh/MdqDAarB8Y202cTGR/PHqVK5NT3D9fEgVNXW8tuYAL67cR1FFDbdNTOY/pw9x/SA9r1fz+Y48nv4qg52HSzg/pSd/+Okoo9qBTMDdd1mI8/aGHKrrvNxyXtLpE5YcgnfnQda/YMRPYeZTQV3bedvBYl78Zh8fbz1EXEwkj145muvPHUCYS8MsHq9mxe6jvLr6ACv35BMRprh5wkDuu3gI3Tq2cdnUECG/tJol67N5eVUWBeU1nJ/Sk/+aMcz1vXSqaj188sNhFnyzj11HSknu2ZFnbhjDzNS+rhdxExFhaCc8Xs3iNQcYn9y96XTPJ7LrY6uRua4aZj0LY+YEZQCb1prVewt4fuVe/pVxjE7REdx1/iDmXTCIHi71EgrLa3h7Qw6vrTlAblElveKi+V9TU7hxfKKrl7bUWrNufyGvrc3m022HqfVoLhgSz/+amsI5Lp8H6UBBOW+szebtDTkUVdRyVnxHnvzZ2cxM7WfcjLAmIcLQTqzYbY1M/c2lw5s9H00N/b/7HexcbIWMrv67NXtqgHi8mk+3HeHFb/ayNbeYnp2i+d8zhnLT+IF06eC+BmatNVtyi3l1dRYfbT1MTZ2X8cnd+c2lw5k+srer+/GXVtXy3vcHeW3NAfbkldE5JoKbJyRx04REVw80rPN4Wb7rKK+tzeabPfmEhymmj+jNnAkDmXhWD/EQXIAIQzvx6uoD9O4c3ezI1NjiDN6P+j3xO3PgvHtg6oMQEVgIpKrWwzubcnnpm31kFVSQ1COWR68czVVj+xMT6b5G2KpaD8u2HGLx6gP8cLCYjlHh/Cx9ADefN/D0HpgL2HawmNfXZvPB5oNU1HhITejCH69OZebZ/VzdYH7UN9L8zXXZHCquonfnaO67OIXrz02kTxf3enRnIiIM7UDWsXJW7snnvotTTq7R/rCU9M9/QZGKYu/0Vzhr4pUBfVdxZS2vrTnAy6uyOFZWTWpCF/5601guGdnHlV01DxSU89qaA7y9IZfiylpSenXikdkj+emY/q6eLK6yxsOHWw/x+tpstuQcJyYyjFln92POhIGkJnR12rw24/VqVu09xutrsvliZx4er2by4J48MHMEU4e726M7kxFhaAdeW3OAiDDFjeMaTXDm9cLKx2Dl45T2TOfS3Nt4acCFbf4Oj9fLHz/ZyetrsymrruP8lJ7c/ZM0znOhq+71albuyWfR6ixW7sknTClmjOzDnAkDmTCou+vy05g9eaW8sTabdzblUlpVR0qvTjw0cwRXjk1wZWivnoKyav6xMZc312VzoKCCbrGR3Dk5mevHJRrRnfZMR4QhyFTVevjHxlwuGdWHXvUNol4PLPslbH4d0uawOWU++Yu3tun62QUVHM0uon9pFS/9ax+Xje7Lz39ylit7rZRU1fKPDbksXp1FVkEF8XHR3HuR+xuT6zxePt1+hFe/O8C6rEKiwsO4dHQfbho/kHOTurla6DbnHOflVfv55w9HqPF4GZfcnfunDWHGqD5ER7g3DCY0RYQhyHy45RDFlbXMGe+bHdJTB+/fDT+8DT+ZD1Pmo3fnt/q6WcfKefqrDJZtOcRjEeUMjo5g+S+nkOTC2llGXimLVmfx7iYrxn7OwG7cP30oM0b2ISrCvaGH4spalqzPZtF3Bzh4vJKBPWL5P5cN45pzBtDdxd1o6zxePtuex9+/3cem7OPExURw04REbhyXSIrL23uE5hFhCDKvrc1mcK9OTBjU3Rqd/MmvLFGY+gCc/5+tvt7RkiqeWZ7BW+tyiAhX3DYxicsr+xKbk0FXF4mC1pr1WUU8vyKTr3fnExVhxdhvnZjkSm+nMVnHynnluyze3pBDRY2HCYO689CskUwd1su1Y0XA8uiWrMvhle+yGoTuoZkjuCZ9AJ1cPtBOOD3y7waR7YeK2ZJznAdnjrDCBd/9BTa+bM2O2kpRqKr18MLKvby4ch+1Hi83jEvklxcNtsJTH7jHZfd6NV/tOsrzKzLZlH2cHh2j+M9pQ7hpwkBX16IBdh0p4bmv9/LR1kNEhClmnd2f2ycnMbKfu4WuoKyahav28+p3ByitrmN8cnce9DUmu7FDQz31Yd5v9uTz+NWprr//2hMRhiDyweZDRIYrrhzTH7LXwBcPwPBZVnfUVrByTz4PfLCNAwUVXD66L7++ZKjrQkZer+bDrYd4dnkmGUfLSOjWgYdnj+Tacwa4uksmwNbc4zy7PJPPd+TRMSqcf7vgLG6flPRjm5JLOVpSxYJv9vH62myq6jxcNrovd7u0/aoxlTUe3liXzYJv9pJXUg1A2oBs49bpCCYiDEHC69V8tOUQF6TE0zWsCt69C7oMgNnPQZh/cfOKmjoe+WgHb67LYVB8R16/c7zr1r3VWrNidz6Pf7qLXUdKGdo7jqevT+Py0X1dP9J1c85xnvxiDyv35NM5JoL7Lk7h1olJdI11d80zr6SK577O5K31OXi8mtln9+PfLxzM4F7uHWQHlofw6uosXly5j4LyGiYM6s6T16Xx9FcZvLU+m7t/cparQ33tyRklDM+v2Et+aTUa3TA5qdYajdUcUH+8YV/r5o9j7ehGn6+o8XCouIpfzxgKKx+3ps6+/TOI8W/m0syjpcxbvJH9x8r5+U/O4j+mpZy+l0cbZ1dtTzbtPczzn21iX85BhnT28OCULozvX02Y9zBsqQZPDXhqrek/tKfRJ30PZ5PeOo2PqUbn1AnHTtzSzOcabYEZNXvoU9wBvt9x6s83Iq+kik9+OMzW3GISoyP4W1ovJg3uQYfI45DxffM/ht89j/xI5+e1RhYepJQC+KHUr2tV1Hj4YkceK/YcxeOFhwZ1Z9rw3sTHVcHRfXA0GHb5+Tuc5lrxucXMCNtL5/0lUNKy5+zR8N3eAj7aeojjFbXc3DeOyy/oR0ovL9Qeg+QqblzegX9lHuMnQ1q5TkoQ0VpTXeelpKqWkso6qmo9VNd5qa7zbWut9zV1XqrrvNR5vNR5NbdNSm73kN4ZJQyfbjvM3vxy61ZV1i0bFqZQgFL1W+uk8p23to33rT9EqZPPpQ/sxvQ+5fDhi9acR4nj/bJrzb4C5r26gaiIMF6/YzwTW/QSHKrl1NVA4T44thvy90BxNhQfpLowF09xLmO95bwEEA1UA2ucMbMlfgVQBXzgX/rewG0AUVi1gV2+V4hxNXC1At7xL30sMBuYHYa1ZFe27xVipAIvRAFf+Zc+HDjf9yIKKAC+/vH8RODKuN/x1JfduCClZ9C7D1fWeMgurCCnsIKjpdUcLa0ir6Sa/NIq8stqKKmspaSyltKqOmo83lZff86EgYSHtW849owShg/umdz+X/Luv0F4lNULyQ/W7S9k7sJ1DOgey8u3ntv65T/bC08dgzz7mVycBe//HQ5uhIJM8Nb9mCQ2nkPeHuwoj+NY2PkMShrE2GHJRHfqDjFdIaaLtSRpRLT1m4RHWVN/hEdBmO/Wa/B8GnlAjY9p/eO5+vdNtviRpul1bnxpDWf1jOWR2SNP/g6gtLKO19Ye4J1NuWgNs8f04+YJA+kWGxU8T83v6/iRznet51Zk8tm2Iyw7xX1e5/Hw+Y48Xl19gPyyasYnd+P2SYOahoz8sstP24N0rTX7Cnho2XaeuT7tlNOhbM45zsJV+8jIKyOxeyxzJyZxXnODI7UXXpnJL3tu5aK9I1i25RCz0/r7YefJlFTVsutwKdsPFbP7SClZBeVkHavgSEnVSWl7dIwiPi6a+LhoErvH0jkmgriYSDp3iKBzTCRxMRF0iAwnOjKc6Igw3yucqIb3YUSEhxERroi2oUt3QMKglOoOLAGSgCzgOq11UTPp5gK/8+3+QWu9yHc8CngWmAJ4gd9qrf2s74QgxbmwbSmcexd06tVi8qxj5fz+/W3079aBJfMmODvrqdaWN7B3OWR+BVn/4sWaMigHynpA/3QYehnED6Ok0yCe3xbG39cfBQ23nDeQf79wsGt6eRwNi6dbZBx0G9jkeHWdh8WrD/CX5VmUVNVyZdoY/mPakNAR6xYoiKljP+EQP6TJca01y3cd5bF/7iLjaDljEwfz6A3DGD+oh0OWto7ywjx26VKqegyHPk2nD9l9pJT/+8+drNhdTL8u/fmPq4dw1diE04dahl9B8s4POXfAHfzu/W0M69OZoX1OPx6jvLqOrbnFbM45ztbc42w/VEJ2YUXD+e4do0ju2ZGJg3uQ1KMjST07MqBbB3p3jqFnp2jXjc8J1GOYD3yltX5MKTXft/9fjRP4xONBIB2rerBRKbXMJyC/BY5qrYcopcIAd69Gv+Flq0Yy4W6/kt+3ZDNdYyN59fZxzoiC1nBoE2x/D3Z+aK0zDdAtCVKv49Ht3ajsPYZH5l4BSlFWXcfL3+5nwbv7KK+p4+qxCdw3bQj9Xb5Gb30Pqj99tpvcokouGBLP/BnDGNHP/Svbbc09zqOf7GTNvkKSe3bkhTnncMnI3q4efQ1Wu88Tn+/hHxtz6BgdwW8uHcbciUn+TRg58irU5td5/tx8Lv+yG9e+8B33XTyE6SN706NjNOU1dRwoKCcjr4wtucV8n13EnrxSvD7nJqlHLKP7d+Fn5w5gRN/OjOzXmfi4aNf/po0JVBhmY9X2ARYBKzhBGIBLgC+01oUASqkvgBnAm8DtwDAArbUXOBagPc6htTWQbdCFJ9VET8efrjmbhG4210iPZcKmRbDjfTieDWGRcNaFMPGXcNZF0H0QAF/vWUlKZCeKq+p4a102L6zcS1FFLdNG9ObXlwx1/Syn9VOUP/d1JjsOlzC8b2cW3zGa81Oca5AMFptzjvPXr60utT06RvHw7JHcMC7R9ZPaHS6uZME3+3hzXTYer+bWicn88qLBrVugadAU6DKAnttfYenP/8Gvl27h4Y928PBHO05K2jkmgrTEbkwf2YcxiV1JS+jq+sWg/CFQYeittT7se38Eq53uRPoDOY32c4H+Sql6n/ARpdQUYC9wj9Y6L0CbTs23T0HZURriytrLaePRJ205+Xj9NWorrUJ2yv9p0YzSaitO37NTtH2Ln3tqrUWBNvwd9n9jxfjPusiapmPYZc2uGOfxaj754QjLdx2lqtbLBUPiuX/aENIGuHc2UACv1nz8w2HW7i/kWFk1g+I78sR1ZzM7rb+rB3CBdW9Nf3Jlw/oO905N4a7zk109M209NyxYQ2WtB6UUP03rz71TB7dtSdDwCBj/c/j8twwo38abd01g5+FSvs8poriyltjIcAZ0j2Vwr04M6BYb/C6tXg/UlFu98+oqrW2tb1tX9ePLU2ul9daBt9a39UD67eB047NS6kugTzOnftt4R2utlVKtaZmLABKA77TW9yul7gf+DNx8CjvmAfMAEhMTm0vSMjveh2MZoMJo6JbUYvfH+u5HYScca+azA8bD8CtaNGN0/y7079qBBbec07Z8RMZCZZH1amkJ0OKDsPEV2PQqlB2xxlZc9DsYcwvEnV6U0pO6UV3n5cJh8Vx/bqLrBzrVM2ZAN0oq6xiX3I3Zaf252OUjeusZ0a8zXTpE0t3nIVw1NsGIqSsG9oild+dounaI4pKRvbk2fUDg7T7nzIXVz8LH96PuXM6Ifp3bHjqsKrGW5y07YlU8S49AWR6U50PlcagqhuoSK11VMdScpjuxP4y5ud2FQekAelkopXYDU7TWh5VSfYEVWuuhJ6S5wZfm33z7L2KFnN4CyoA4rbVXKTUA+FRrPbKl701PT9cbNmxos92u58gP8MJkmPwfcPFDJ5/3emHfcli/EPb80/JsBl8M594BKdPb/aYSBFew80NYMgdGXQ2z/wqRpxm5XnncqlQWZEDhfija/+O2ouDk9BExVgeUDt0gurPVQy+mqzWuqaG3XgxEdrC2EdEQ0cG3jWnUey/Sel7DIy0vPywSYru3eflfpdRGrXV6S+kCrU4sA+YCj/m2zfUM/wx4VClVX7WdDvzG52F8iNVGsRyYCpwc5BNOps9oOPsGWPU09B5l3dhg9Sra/q7lHRzPhtgeMPFeSL/NalAWBOFHhs+0KlZfPmR1xx59rfWcaG3V+EsOWmJwbI+1X48Kgy4J0C3Zuka3ZGs/rg906m0JQnTnoKzd7hSBegw9gLeBROAAVnfVQqVUOvBzrfWdvnS3A/XB9//RWr/sOz4QWAx0BfKB27TWLQ6xOeM9BoDqUlh8FeSug059rPhjha/tPvkCGDvXumkjHOwCKwhuIPNLWPkn61nSjQacxXSFnkOsV7xv2yMFuiYGvBSvU/jrMQQkDE4hwuCjrhq2LoEDq60GtT6pkDJNvANBaAs1FVblSmvoGA9R7hi/0hrsCiUJThIRDWNvsV6CIARGVCxEtbFji2G4u1OzIAiCEHREGARBEIQmiDAIgiAITRBhEARBEJogwiAIgiA0QYRBEARBaIIIgyAIgtAEEQZBEAShCa4c+ayUyseagqMt9MS96z6I7fbjVrtBbHeKULZ9oNa6xQVHXCkMgaCU2uDPkPBQRGy3H7faDWK7U7jZ9noklCQIgiA0QYRBEARBaMKZKAwLnDYgAMR2+3Gr3SC2O4WbbQfOwDYGQRAE4fSciR6DIAiCcBqMFQal1Ayl1G6lVKZSan4z56OVUkt859cqpZLst/Jk/LD7fqXUDqXUVqXUV75V8EKClmxvlO5qpZT2rfQXEvhju1LqOt9vv10p9YbdNp4KP+6ZRKXU10qp7333zWVO2HkiSqmFSqmjSqltpzivlFLP+PK1VSk11m4bT4Uftt/ks/kHpdR3Sqmz7bYxILTWxr2AcGAvMAiIArYAI05I8+/AC7731wNLXGL3hUCs7/3doWC3v7b70sUB3wBrgHSn7W7F754CfA908+33ctruVti+ALjb934EkOW03T5bLgDGAttOcf4y4J+AAiYAa522uRW2T2x0r1waSrb78zLVYxgHZGqt92mta4C3gNknpJkNLPK9XwpMVcrx1btbtFtr/bXWusK3uwZIsNnGU+HPbw7wCPA4UGWncS3gj+13Ac9prYsAtNZHbbbxVPhjuwY6+953AQ7ZaN8p0Vp/AxSeJsls4FVtsQboqpTqa491p6cl27XW39XfK4TWc+oXpgpDfyCn0X6u71izabTWdUAx0MMW606NP3Y35g6sGlUo0KLtvlDAAK31x3Ya5gf+/O5DgCFKqVVKqTVKqRm2WXd6/LH9IWCOUioX+AT4pT2mBUxrn4dQJZSeU7+QNZ9dilJqDpAO/MRpW/xBKRUGPAHc6rApbSUCK5w0Bav2941SarTW+rijVvnHDcArWuv/p5Q6D1islBqltfY6bZjpKKUuxBKGyU7b0hpM9RgOAgMa7Sf4jjWbRikVgeViF9hi3anxx26UUhcDvwVmaa2rbbKtJVqyPQ4YBaxQSmVhxYyXhUgDtD+/ey6wTGtdq7XeD+zBEgqn8cf2O4C3AbTWq4EYrPl8Qh2/nodQRSmVCvwNmK21drpsaRWmCsN6IEUplayUisJqXF52QpplwFzf+2uA5drXUuQgLdqtlBoDvIglCqES54YWbNdaF2ute2qtk7TWSVhx11la6w3OmNsEf+6X97G8BZRSPbFCS/vsNPIU+GN7NjAVQCk1HEsY8m21sm0sA27x9U6aABRrrQ87bZQ/KKUSgXeBm7XWe5y2p9U43frdXi+sHg17sHps/NZ37GGswgish+MfQCawDhjktM1+2v0lkAds9r2WOW2zv7afkHYFIdIryc/fXWGFwnYAPwDXO21zK2wfAazC6rG0GZjutM0+u94EDgO1WB7ZHcDPgZ83+s2f8+XrhxC7X1qy/W9AUaPndIPTNrfmJSOfBUEQhCaYGkoSBEEQ2ogIgyAIgtAEEQZBEAShCSIMgiAIQhNEGARBEIQmiDAIgiAITRBhEARBEJogwiAIgiA04f8DNlMs6WQioTkAAAAASUVORK5CYII=\n", - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "#print(ga_out['dhof'][3].attrs)\n", - "#print(cells['TC'])\n", - "model1 = None\n", - "model1 = ReducedModel(LEMS_MODEL_PATH,name = str('vanilla'),backend = ('RAW'))\n", - "model1.set_attrs(cells['TC'])\n", - "model1.attrs.update(ga_out['dhof'][0].attrs)\n", - "#model1.attrs.update({'vr':-60,'a':0.01,'b':15})\n", - "print(model1.attrs)\n", - "rheobase = ga_out['dhof'][0].rheobase\n", - "iparams['amplitude'] = rheobase\n", - "model1.inject_square_current(iparams)\n", - "model2 = None\n", - "model2 = ReducedModel(LEMS_MODEL_PATH,name = str('vanilla'),backend = ('RAW'))\n", - "model2.set_attrs(cells['TC'])\n", - "print(model2.attrs)\n", - "model2.inject_square_current(iparams)\n", - "plt.plot(model1.get_membrane_potential().times,model1.get_membrane_potential(),label='optimizer result')\n", - "plt.plot(model2.get_membrane_potential().times,model2.get_membrane_potential(),label='ground truth')\n", - "plt.legend()" - ] - }, - { - "cell_type": "code", - "execution_count": 13, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "" - ] - }, - "execution_count": 13, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": 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" - ], - "text/plain": [ - " 0 1 2 3 4 5 6 7\n", - "InputResistanceTest 0.15 0.18 0.18 0.04 0.31 0.35 0.18 0.12\n", - "TimeConstantTest 0.06 0.07 0.07 0.16 0.19 0.13 0.05 0.03\n", - "InjectedCurrentAPThresholdTest 0.04 0.01 0.17 0.27 0.19 0.13 0.25 0.74\n", - "InjectedCurrentAPAmplitudeTest 0.02 0.00 0.07 0.12 0.08 0.06 0.11 0.37\n", - "RheobaseTestP 0.45 0.62 0.55 0.17 0.00 0.52 0.62 0.45\n", - "CapacitanceTest 0.19 0.41 0.28 0.58 0.57 0.37 0.51 0.27\n", - "InjectedCurrentAPWidthTest 0.04 0.04 0.07 0.03 0.13 0.04 0.00 0.22\n", - "RestingPotentialTest 0.34 0.53 0.45 0.49 0.53 0.51 0.55 0.34" - ] - }, - "execution_count": 14, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "df = pd.DataFrame(index=ga_out['dhof'][0].scores.keys(),columns=range(len(ga_out['dhof'])))\n", - "for i,dhof in enumerate(ga_out['dhof']):\n", - " df[i] = np.array(list(dhof.scores.values())).round(2)\n", - "df" - ] - }, - { - "cell_type": "code", - "execution_count": 15, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "=== Model vanilla achieved score Z = -0.54 on test 'RheobaseTestP'. ===\n" - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "text/plain": [ - "0.586530056203717" - ] - }, - "execution_count": 15, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "score = tests_[0].judge(model1)\n", - "score.summarize()\n", - "score.norm_score" - ] - }, - { - "cell_type": "code", - "execution_count": 16, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Try 1: SubMax = 42.5; SupraMin = 84.0\n", - "Try 2: SubMax = 72.1; SupraMin = 78.1\n", - "Try 3: SubMax = 74.7; SupraMin = 75.5\n", - "Try 1: SubMax = 42.5; SupraMin = 84.0\n", - "Try 2: SubMax = 72.1; SupraMin = 78.1\n", - "Try 3: SubMax = 74.7; SupraMin = 75.5\n", - "75.53061224489795 pA 75.53061224489795 pA\n", - "injected current seen: {'delay': array(200.0) * ms, 'amplitude': array(-10.0) * pA, 'duration': array(500.0) * ms}\n", - "injected current seen: {'delay': array(200.0) * ms, 'amplitude': array(-10.0) * pA, 'duration': array(500.0) * ms}\n", - "input resistance score: Z = 0.00\n", - "31.73928299824782 Mohm 31739282.99824782 kg*m**2/(s**3*A**2)\n", - "injected current seen: {'delay': array(200.0) * ms, 'amplitude': array(-10.0) * pA, 'duration': array(500.0) * ms}\n", - "injected current seen: {'delay': array(200.0) * ms, 'amplitude': array(-10.0) * pA, 'duration': array(500.0) * ms}\n", - "3.4631849083750876 ms 0.0034631849083750876 s\n", - "injected current seen: {'delay': array(200.0) * ms, 'amplitude': array(-10.0) * pA, 'duration': array(500.0) * ms}\n", - "injected current seen: {'delay': array(200.0) * ms, 'amplitude': array(-10.0) * pA, 'duration': array(500.0) * ms}\n", - "1.0911352057216523e-10 s**4*A**2/(kg*m**2) 1.0911352057216523e-10 s**4*A**2/(kg*m**2)\n", - "-60.317405613048955 mV -0.060317405613048956 V\n", - "0.55 ms 0.00055 s\n", - "50.65778903053697 mV 0.05065778903053697 V\n", - "-15.657789030536964 mV -0.015657789030536964 V\n", - "1.0\n", - "Z = 0.00\n" - ] - } - ], - "source": [ - "def hack_judge(test_and_models):\n", - " (test, attrs) = test_and_models\n", - " model = None\n", - " obs = test.observation\n", - " model = ReducedModel(LEMS_MODEL_PATH,name = str('vanilla'),backend = ('RAW'))\n", - " model.set_attrs(attrs)\n", - " test.generate_prediction(model)\n", - " pred = test.generate_prediction(model)\n", - " score = test.compute_score(obs,pred)\n", - " try:\n", - " print(obs['value'],pred['value'])\n", - " except:\n", - " print(obs['mean'],pred['mean'])\n", - " \n", - " return score\n", - "\n", - "scores = []\n", - "for i,t in enumerate(TC_tests):\n", - " test_and_models = (t,cells['TC'])\n", - " score = hack_judge(test_and_models)\n", - " scores.append(score)\n", - "print(scores[0].norm_score) \n", - "print(scores[0]) \n", - "\n" - ] - }, - { - "cell_type": "code", - "execution_count": 17, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[1.0, 1.0, 1.0, 0.9999999999999994, 1.0, 1.0, 1.0, 1.0]\n", - "[0.0, 0.0, 0.0, -6.930132477823624e-16, 0.0, 0.0, 0.0, 0.0]\n", - "0.55 ms 0.00055 s\n", - "Z = 0.00\n" - ] - } - ], - "source": [ - "print([s.norm_score for s in scores])\n", - "print([s.score for s in scores])\n", - "\n", - "score = hack_judge((TC_tests[-3],cells['TC']))\n", - "print(score)" - ] - }, - { - "cell_type": "code", - "execution_count": 14, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Try 1: SubMax = 42.5; SupraMin = 84.0\n", - "Try 2: SubMax = 48.4; SupraMin = 54.4\n", - "Try 3: SubMax = 51.0; SupraMin = 51.8\n", - "Try 1: SubMax = 42.5; SupraMin = 84.0\n", - "Try 2: SubMax = 48.4; SupraMin = 54.4\n", - "Try 3: SubMax = 51.0; SupraMin = 51.8\n", - "75.53061224489795 pA 51.81632653061225 pA\n", - "injected current seen: {'delay': array(200.0) * ms, 'amplitude': array(-10.0) * pA, 'duration': array(500.0) * ms}\n", - "injected current seen: {'delay': array(200.0) * ms, 'amplitude': array(-10.0) * pA, 'duration': array(500.0) * ms}\n", - "input resistance score: Z = 0.62\n", - "31.73928299824782 Mohm 79634016.52239381 kg*m**2/(s**3*A**2)\n", - "injected current seen: {'delay': array(200.0) * ms, 'amplitude': array(-10.0) * pA, 'duration': array(500.0) * ms}\n", - "injected current seen: {'delay': array(200.0) * ms, 'amplitude': array(-10.0) * pA, 'duration': array(500.0) * ms}\n", - "3.4631849083750876 ms 0.009810667396493434 s\n", - "injected current seen: {'delay': array(200.0) * ms, 'amplitude': array(-10.0) * pA, 'duration': array(500.0) * ms}\n", - "injected current seen: {'delay': array(200.0) * ms, 'amplitude': array(-10.0) * pA, 'duration': array(500.0) * ms}\n", - "1.0911352057216523e-10 s**4*A**2/(kg*m**2) 1.231969430266598e-10 s**4*A**2/(kg*m**2)\n", - "-60.317405613048955 mV -0.06075346627665097 V\n", - "0.55 ms 0.000771875 s\n", - "50.65778903053697 mV 0.05343030234908487 V\n", - "-15.657789030536964 mV -0.018430302349084875 V\n", - "0.6244989741265031\n", - "Z = -0.49\n", - "[0.6244989741265031, 0.5372765850958718, 0.4696921520174828, 0.45016300791078434, 0.946777506200236, 0.6779755333175013, 0.8278401818038221, 0.6136943079479662]\n" - ] - } - ], - "source": [ - "scores = []\n", - "for t in TC_tests:\n", - " test_and_models = (t,cells['RS'])\n", - " score = hack_judge(test_and_models)\n", - " scores.append(score)\n", - "print(scores[0].norm_score) \n", - "print(scores[0])\n", - "print([s.norm_score for s in scores])\n" - ] - }, - { - "cell_type": "code", - "execution_count": 15, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "injected current seen: {'delay': array(200.0) * ms, 'amplitude': array(-10.0) * pA, 'duration': array(500.0) * ms}\n", - "injected current seen: {'delay': array(200.0) * ms, 'amplitude': array(-10.0) * pA, 'duration': array(500.0) * ms}\n", - "injected current seen: {'delay': array(200.0) * ms, 'amplitude': array(-10.0) * pA, 'duration': array(500.0) * ms}\n", - "injected current seen: {'delay': array(200.0) * ms, 'amplitude': array(-10.0) * pA, 'duration': array(500.0) * ms}\n", - "injected current seen: {'delay': array(200.0) * ms, 'amplitude': array(-10.0) * pA, 'duration': array(500.0) * ms}\n", - "injected current seen: {'delay': array(200.0) * ms, 'amplitude': array(-10.0) * pA, 'duration': array(500.0) * ms}\n", - "input resistance score: Z = 0.62\n", - "31.73928299824782 Mohm 79634016.52239381 kg*m**2/(s**3*A**2)\n", - "3.4631849083750876 ms 0.009810667396493434 s\n", - "1.0911352057216523e-10 s**4*A**2/(kg*m**2) 1.231969430266598e-10 s**4*A**2/(kg*m**2)\n", - "-21.127191359642023 mV -0.018576602637434048 V\n", - "0.6500000000000001 ms 0.0007750000000000001 s\n", - "56.127191359642026 mV 0.05357660263743405 V\n", - "-60.317405613048955 mV -0.06075346627665097 V\n", - "[{'value': array(75.53061224489795) * pA}, , , , , , , ]\n" - ] - } - ], - "source": [ - "import dask.bag as db\n", - "# The rheobase has been obtained seperately and cannot be db mapped.\n", - "# Nested DB mappings dont work.\n", - "from itertools import repeat\n", - "test_a_models = zip(TC_tests[1::],repeat(cells['RS']))\n", - "tc_bag = db.from_sequence(test_a_models)\n", - "\n", - "scores = list(tc_bag.map(hack_judge).compute())\n", - "scores.insert(0,rheobase)\n", - "print(scores) " - ] - }, - { - "cell_type": "code", - "execution_count": 16, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Try 1: SubMax = 42.5; SupraMin = 84.0\n", - "Try 2: SubMax = 72.1; SupraMin = 78.1\n", - "Try 3: SubMax = 74.7; SupraMin = 75.5\n", - "{'value': array(75.53061224489795) * pA}\n" - ] - } - ], - "source": [ - "score = TC_tests[0].judge(model,stop_on_error = False, deep_error = True)\n", - "print(score.prediction)\n", - "#print(model.get_spike_count())" - ] - }, - { - "cell_type": "code", - "execution_count": 17, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "ename": "ImportError", - "evalue": "cannot import name 'ReducedModel'", - "output_type": "error", - "traceback": [ - "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", - "\u001b[0;31mImportError\u001b[0m Traceback (most recent call last)", - "\u001b[0;32m\u001b[0m in \u001b[0;36m\u001b[0;34m()\u001b[0m\n\u001b[1;32m 1\u001b[0m \u001b[0;32mfrom\u001b[0m \u001b[0mneuronunit\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0moptimization\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0moptimization_management\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0mopt\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 2\u001b[0;31m \u001b[0;32mfrom\u001b[0m \u001b[0mneuronunit\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmodels\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mReducedModel\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m", - "\u001b[0;31mImportError\u001b[0m: cannot import name 'ReducedModel'" - ] - } - ], - "source": [ - "from neuronunit.optimization import optimization_management as opt\n", - "from neuronunit.models import ReducedModel" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [] - }, - { - "cell_type": "raw", - "metadata": { - "collapsed": true - }, - "source": [] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.5.2" - } - }, - "nbformat": 4, - "nbformat_minor": 2 -} diff --git a/neuronunit/unit_test/round_trip_tests_five_param_two.ipynb b/neuronunit/unit_test/round_trip_tests_five_param_two.ipynb deleted file mode 100644 index e11b25435..000000000 --- a/neuronunit/unit_test/round_trip_tests_five_param_two.ipynb +++ /dev/null @@ -1,2976 +0,0 @@ -{ - "cells": [ - { - "cell_type": "code", - "execution_count": 1, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "import sys\n", - "import os\n", - "nu_russell = os.path.realpath(os.path.join(os.getcwd(),'..','..'))\n", - "sys.path.insert(0,nu_russell)\n", - "import neuronunit\n", - "import dask.bag as db\n", - "from neuronunit.optimization import optimization_management as om\n", - "import pickle\n", - "\n" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "OrderedDict([('RS', {'c': -50, 'vt': -40, 'a': 0.03, 'b': -2, 'd': 100, 'C': 100, 'k': 0.7, 'vr': -60, 'vPeak': 35}), ('IB', {'c': -56, 'vt': -45, 'a': 0.01, 'b': 5, 'd': 130, 'C': 150, 'k': 1.2, 'vr': -75, 'vPeak': 50}), ('LTS', {'c': -53, 'vt': -42, 'a': 0.03, 'b': 8, 'd': 20, 'C': 100, 'k': 1.0, 'vr': -56, 'vPeak': 40}), ('TC', {'c': -60, 'vt': -50, 'a': 0.01, 'b': 15, 'd': 10, 'C': 200, 'k': 1.6, 'vr': -60, 'vPeak': 35}), ('TC_burst', {'c': -60, 'vt': -50, 'a': 0.01, 'b': 15, 'd': 10, 'C': 200, 'k': 1.6, 'vr': -60, 'vPeak': 35})])\n" - ] - } - ], - "source": [ - "# http://www.physics.usyd.edu.au/teach_res/mp/mscripts/\n", - "# ns_izh002.m\n", - "import collections\n", - "from collections import OrderedDict\n", - "\n", - "# Fast spiking cannot be reproduced as it requires modifications to the standard Izhi equation,\n", - "# which are expressed in this mod file.\n", - "# https://github.com/OpenSourceBrain/IzhikevichModel/blob/master/NEURON/izhi2007b.mod\n", - "\n", - "reduced2007 = collections.OrderedDict([\n", - " # C k vr vt vpeak a b c d celltype\n", - " ('RS', (100, 0.7, -60, -40, 35, 0.03, -2, -50, 100, 1)),\n", - " ('IB', (150, 1.2, -75, -45, 50, 0.01, 5, -56, 130, 2)),\n", - " ('LTS', (100, 1.0, -56, -42, 40, 0.03, 8, -53, 20, 4)),\n", - " ('TC', (200, 1.6, -60, -50, 35, 0.01, 15, -60, 10, 6)),\n", - " ('TC_burst', (200, 1.6, -60, -50, 35, 0.01, 15, -60, 10, 6)),\n", - " ('RTN', (40, 0.25, -65, -45, 0, 0.015, 10, -55, 50, 7)),\n", - " ('RTN_burst', (40, 0.25, -65, -45, 0, 0.015, 10, -55, 50, 7))])\n", - "\n", - "import numpy as np\n", - "reduced_dict = OrderedDict([(k,[]) for k in ['C','k','vr','vt','vPeak','a','b','c','d']])\n", - "\n", - "#OrderedDict\n", - "for i,k in enumerate(reduced_dict.keys()):\n", - " for v in reduced2007.values():\n", - " reduced_dict[k].append(v[i])\n", - "\n", - "explore_param = {k:(np.min(v),np.max(v)) for k,v in reduced_dict.items()}\n", - "param_ranges = OrderedDict(explore_param)\n", - "\n", - "#IB = mparams[param_dict['IB']]\n", - "RS = {}\n", - "IB = {}\n", - "TC = {}\n", - "CH = {}\n", - "RTN_burst = {}\n", - "cells = OrderedDict([(k,[]) for k in ['RS','IB','CH','LTS','FS','TC','TC_burst','RTN','RTN_busrt']])\n", - "reduced_cells = OrderedDict([(k,[]) for k in ['RS','IB','LTS','TC','TC_burst']])\n", - "\n", - "for index,key in enumerate(reduced_cells.keys()):\n", - " reduced_cells[key] = {}\n", - " for k,v in reduced_dict.items():\n", - " reduced_cells[key][k] = v[index]\n", - "\n", - "print(reduced_cells)\n", - "cells = reduced_cells" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "model = None\n", - "from neuronunit.models.reduced import ReducedModel\n", - "from neuronunit.optimization import get_neab\n", - "\n", - "from neuronunit.optimization.model_parameters import model_params, path_params\n", - "LEMS_MODEL_PATH = path_params['model_path']\n", - "\n", - "\n", - "model = ReducedModel(LEMS_MODEL_PATH,name = str('vanilla'),backend = ('RAW'))\n", - "model.set_attrs(cells['TC'])\n", - "\n" - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "{'value': array(74.921875) * pA}\n" - ] - } - ], - "source": [ - "tests_,all_tests, observation,suite = get_neab.get_tests()\n", - "#tests_,all_tests, observation,suite = opt.get_neab.get_tests()\n", - "rheobase = all_tests[0].generate_prediction(model)\n", - "print(rheobase)" - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[, , , , , , , ]\n" - ] - } - ], - "source": [ - "print(all_tests)" - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "data": { - "image/png": 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mdjLJQ0s7erTK1NKpfTdwMYCZ/RXJYKjs0Sq7ZxXwmWB20gVAtbvvz3VR6TCz\nCcB/ANe5+xu5rueE5Xr0O6ovkjMa3iA5Y+PmYN1tJP8YQfKX4yFgG/A8cFqua06z7j8CbwEbgq9V\nua453do7tH2aXjIrKc333YAfAa8CLwOLc13zCdQ+A3iW5IylDcBlua45qKsc2A80kewdXA/cANzQ\n7j1fHnxfL/eyn5euar8HONzu93Rdrms+kS+d+SwiIiF99VCSiIh0k4JBRERCFAwiIhKiYBARkRAF\ng4iIhCgYREQkRMEgIiIhCgYREQn5/1TiGs2Gil+hAAAAAElFTkSuQmCC\n", - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "import pickle\n", - "import copy\n", - "import numpy as np\n", - "import pandas as pd\n", - "import matplotlib.pyplot as plt\n", - "\n", - "from neuronunit.models.reduced import ReducedModel\n", - "from neuronunit.optimization.model_parameters import model_params, path_params\n", - "LEMS_MODEL_PATH = path_params['model_path']\n", - "import neuronunit.optimization as opt\n", - "import quantities as pq\n", - "\n", - "from neuronunit.optimization.data_transport_container import DataTC\n", - "model = ReducedModel(LEMS_MODEL_PATH,name = str('vanilla'),backend = ('RAW'))\n", - "model.set_attrs(cells['TC'])\n", - "\n", - "iparams = {}\n", - "iparams['injected_square_current'] = {}\n", - "iparams['injected_square_current']['amplitude'] =75.36800000000001*pq.pA\n", - "#['amplitude'] = dtc.vtest[k]['injected_square_current']['amplitude']\n", - "DELAY = 100.0*pq.ms\n", - "DURATION = 1000.0*pq.ms\n", - "iparams['injected_square_current']['delay'] = DELAY\n", - "iparams['injected_square_current']['duration'] = int(DURATION)\n", - "\n", - "model.inject_square_current(iparams)\n", - "\n", - "plt.plot(model.get_membrane_potential().times,model.get_membrane_potential(),label='RAW')\n", - "plt.legend()\n", - "plt.show()" - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "cnt = 0\n", - "scores = []\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [] - }, - { - "cell_type": "code", - "execution_count": 8, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[array(74.921875) * pA, array(74.921875) * pA, array(74.921875) * pA]\n", - "[array(74.921875) * pA, array(-10.0) * pA, array(-10.0) * pA, array(-10.0) * pA, array(-10.0) * pA]\n" - ] - } - ], - "source": [ - "def format_iparams(all_tests,rheobase):\n", - "\n", - " for t in all_tests[1:5]:\n", - " DURATION = 500.0*pq.ms\n", - " DELAY = 200.0*pq.ms\n", - "\n", - " obs = t.observation\n", - " t.params = {}\n", - " t.params['injected_square_current'] = {}\n", - " t.params['injected_square_current']['delay']= DELAY\n", - " t.params['injected_square_current']['duration'] = DURATION\n", - " t.params['injected_square_current']['amplitude'] = -10*pq.pA\n", - " \n", - " \n", - " for t in all_tests[-3::]: \n", - " t.params = {}\n", - " DURATION = 1000.0*pq.ms\n", - " DELAY = 100.0*pq.ms\n", - "\n", - " t.params['injected_square_current'] = {}\n", - " t.params['injected_square_current']['delay']= DELAY\n", - " t.params['injected_square_current']['duration'] = DURATION\n", - " t.params['injected_square_current']['amplitude'] = rheobase['value']\n", - " \n", - " all_tests[0].params = all_tests[-1].params\n", - " \n", - " return all_tests\n", - "\n", - "pt = format_iparams(all_tests,rheobase)\n", - "print([t.params['injected_square_current']['amplitude'] for t in pt[-3::] ])\n", - "print([t.params['injected_square_current']['amplitude'] for t in pt[0:5] ])" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "\n", - "# * Get predictions from models.\n", - "## * Fake NeuroElectro Observations\n", - "## * Do roundtrip testing\n" - ] - }, - { - "cell_type": "code", - "execution_count": 9, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[{'value': array(74.921875) * pA}, {'value': array(31739282.99824782) * kg*m**2/(s**3*A**2)}, {'value': array(0.0034631849083750876) * s}, {'value': array(1.0911352057216523e-10) * s**4*A**2/(kg*m**2)}, {'mean': array(-0.060317405613048956) * V, 'std': array(0.00019622743842309925) * V}, {'mean': array(0.000675) * s, 'n': 1, 'std': array(0.0) * s}, {'mean': array(0.05802236247240052) * V, 'n': 1, 'std': array(0.0) * V}, {'mean': array(-0.02302236247240052) * V, 'n': 1, 'std': array(0.0) * V}]\n" - ] - } - ], - "source": [ - "predictions = []\n", - "\n", - "# The rheobase has been obtained seperately and cannot be db mapped.\n", - "# Nested DB mappings dont work (daemons cannot spawn daemonic processes).\n", - "ptbag = db.from_sequence(pt[1::])\n", - "\n", - "def obtain_predictions(t): \n", - " model = None\n", - " model = ReducedModel(LEMS_MODEL_PATH,name = str('vanilla'),backend = ('RAW'))\n", - " model.set_attrs(cells['TC'])\n", - " return t.generate_prediction(model)\n", - "predictions = list(ptbag.map(obtain_predictions).compute())\n", - "predictions.insert(0,rheobase)\n", - "print(predictions) " - ] - }, - { - "cell_type": "code", - "execution_count": 10, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[{'value': array(74.921875) * pA}, {'value': array(31739282.99824782) * kg*m**2/(s**3*A**2)}, {'value': array(0.0034631849083750876) * s}, {'value': array(1.0911352057216523e-10) * s**4*A**2/(kg*m**2)}, {'std': array(0.00019622743842309925) * V, 'value': array(-0.060317405613048956) * V}, {'n': 1, 'std': array(0.0) * s, 'value': array(0.000675) * s}, {'n': 1, 'std': array(0.0) * V, 'value': array(0.05802236247240052) * V}, {'n': 1, 'std': array(0.0) * V, 'value': array(-0.02302236247240052) * V}]\n" - ] - } - ], - "source": [ - "# having both means and values in dictionary makes it very irritating to iterate over.\n", - "# It's more harmless to demote means to values, than to elevate values to means.\n", - "# Simply swap key names: means, for values.\n", - "for p in predictions:\n", - " if 'mean' in p.keys():\n", - " p['value'] = p.pop('mean')\n", - "print(predictions)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Make some new tests based on internally generated data \n", - " as opposed to experimental data." - ] - }, - { - "cell_type": "code", - "execution_count": 11, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "\n", - "\n", - "TC_tests = copy.copy(all_tests)\n", - "for ind,t in enumerate(TC_tests):\n", - " if 'mean' in t.observation.keys():\n", - " t.observation['value'] = t.observation.pop('mean')\n", - " pred = predictions[ind]['value']\n", - " try:\n", - " pred = pred.rescale(t.units)\n", - " t.observation['value'] = pred\n", - " except: \n", - " t.observation['value'] = pred\n", - " t.observation['mean'] = t.observation['value']\n", - " #t.observation['std'] = 0.0\n", - " \n", - " if float(t.observation['std']) == 0.0:\n", - " print('got here')\n", - " t.observation['std'] = 5.0*t.observation['mean'].units\n", - " \n", - "pickle.dump(TC_tests,open('thalamo_cortical_tests.p','wb')) \n", - "\n", - "\n", - "\n", - "\n" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "collapsed": false - }, - "source": [ - "\n", - "## Call Genetic Algorithm optimizer\n" - ] - }, - { - "cell_type": "code", - "execution_count": 12, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "from neuronunit.optimization import optimization_management as om\n", - "free_params = ['a','b','vr','vt','k'] # this can only be odd numbers.\n", - "#2**3\n", - "hc = {}\n", - "for k,v in cells['TC'].items():\n", - " if k not in free_params:\n", - " hc[k] = v\n", - "#print(hc)\n", - "import pickle\n", - "TC_tests = pickle.load(open('thalamo_cortical_tests.p','rb')) \n", - " #run_ga(model_params, max_ngen, test, free_params = None, hc = None)\n", - " \n", - "#ga_out, DO = om.run_ga(explore_param,10,TC_tests,free_params=free_params,hc = hc, NSGA = False, MU = 10)\n", - " \n", - "#ga_out_nsga, _ = om.run_ga(explore_param,1,TC_tests,free_params=free_params,hc = hc, NSGA = True)" - ] - }, - { - "cell_type": "code", - "execution_count": 13, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "from neuronunit.optimization import optimization_management as om\n", - "free_params = ['a','b','vr','vt','k'] # this can only be odd numbers.\n", - "#2**3\n", - "hc = {}\n", - "for k,v in cells['TC'].items():\n", - " if k not in free_params:\n", - " hc[k] = v\n", - "#print(hc)\n", - "import pickle\n", - "try:\n", - " #assert 1==2\n", - " ga_out_nsga = pickle.load(open('chatter_ga_out_nsga.p','rb'))\n", - " \n", - "except:\n", - " TC_tests = pickle.load(open('thalamo_cortical_tests.p','rb')) \n", - " ga_out_nsga, _ = om.run_ga(explore_param,25,TC_tests,free_params=free_params,hc = hc, NSGA = True)\n", - " pickle.dump(ga_out_nsga,open('chatter_ga_out_nsga.p','wb'))\n", - "\n", - " " - ] - }, - { - "cell_type": "code", - "execution_count": 14, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "pickle.dump(ga_out_nsga,open('chatter_ga_out_nsga.p','wb'))\n", - "\n" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [] - }, - { - "cell_type": "code", - "execution_count": 15, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "data": { - "image/png": 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v2663hwk3pTCZFG4m9cdjZfzfZOKy5zLyCknMyKddSDX+cUsLejYJclrC2Xny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\nrXnj9zdYcnwJb3Z/k1kHZ5F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pQsgLAGYBuJ9S+p+iCJItmvh1CvHE7/RVJSLJ3STkXQeZrjR6xJqaCYugU43o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\n8ZtnYdw3p9aUDoL8PTSY37fKU8QGKoH/xANr4yjgGHcG9Gl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- "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "\n", - "for item in ['avg','max','min']:\n", - " plt.plot([x[item] for x in ga_out_nsga['log']],label=item)\n", - "plt.legend()\n", - "plt.show()\n", - "\n", - "history = ga_out_nsga['history']\n", - "hof = ga_out_nsga['hof']\n", - "temp = [ v.dtc for k,v in history.genealogy_history.items() ]\n", - "temp = [ i for i in temp if type(i) is not type(None)]\n", - "temp = [ i for i in temp if len(list(i.attrs.values())) ]\n", - "true_history = [ (v, np.sum(list(v.scores.values()))) for v in temp ]\n", - "plt.plot([i for i,j in enumerate(true_history)],[ i[1] for i in true_history ])\n" - ] - }, - { - "cell_type": "code", - "execution_count": 16, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "0.643622317713 \n" - ] - } - ], - "source": [ - "true_history = ga_out_nsga['hardened']\n", - "true_mins = sorted(true_history, key=lambda h: h[1])\n", - "\n", - "print(true_mins[0][1], true_mins[0][0])\n", - "try:\n", - " if true_mins[0][1] < np.sum(list(hof[0].dtc.scores.values())):\n", - " #print('history unreliable')\n", - " hof = [i[0] for i in true_mins]\n", - " best = hof[0]\n", - " best_attrs = best.attrs\n", - " else:\n", - " best = ga_out_nsga['dhof'][0]\n", - " best_attrs = ga_out_nsga['dhof'][0].attrs\n", - " ga_out_nsga['dhof'][0].scores\n", - "except:\n", - " best = ga_out_nsga['dhof'][0]\n", - " best_attrs = ga_out_nsga['dhof'][0].attrs\n", - " ga_out_nsga['dhof'][0].scores\n", - "#true_mins " - ] - }, - { - "cell_type": "code", - "execution_count": 17, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "best = true_mins[0][0]\n", - "best_attrs = true_mins[0][0].attrs" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [] - }, - { - "cell_type": "code", - "execution_count": 18, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "72.9765625 pA\n", - "best 0.643622317713\n" - ] - } - ], - "source": [ - "print(best.rheobase)\n", - "\n", - "print('best',best.get_ss())" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "collapsed": false - }, - "source": [ - "# with five parameters, and 20 generations a really good fit can be found" - ] - }, - { - "cell_type": "code", - "execution_count": 57, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "{'a': 0.01, 'b': 15, 'C': 200, 'k': 1.6, 'vr': -60, 'vPeak': 35, 'c': -60, 'vt': -50, 'd': 10}\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/opt/conda/lib/python3.5/site-packages/matplotlib/font_manager.py:1297: UserWarning: findfont: Font family ['normal'] not found. Falling back to DejaVu Sans\n", - " (prop.get_family(), self.defaultFamily[fontext]))\n" - ] - }, - { - "data": { - "image/png": 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- "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "100.0 ms\n", - "dict_keys(['amplitude', 'delay', 'duration'])\n", - "52000 52200\n" - ] - }, - { - "data": { - "text/plain": [ - "[]" - ] - }, - "execution_count": 57, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": 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- "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "fig = plt.figure()\n", - "\n", - "model1 = ReducedModel(LEMS_MODEL_PATH,name = str('vanilla'),backend = ('RAW'))\n", - "model1.attrs.update(best_attrs)\n", - "rheobase = best.rheobase\n", - "iparams['injected_square_current']['amplitude'] = rheobase\n", - "model1.inject_square_current(iparams) # this one is rheobase firing failure.\n", - "\n", - "model2 = ReducedModel(LEMS_MODEL_PATH,name = str('vanilla'),backend = ('RAW'))\n", - "model2.set_attrs(cells['TC'])\n", - "model2.attrs.update(cells['TC'])\n", - "\n", - "\n", - "iparams['injected_square_current']['amplitude'] =75.36800000000001*pq.pA\n", - "model2.inject_square_current(iparams) # this one is rheobase success.\n", - "\n", - "print(model2.attrs)\n", - "\n", - "times = model1.get_membrane_potential().times\n", - "plt.plot(times,model1.get_membrane_potential(),label='optimizer result')\n", - "plt.plot(times,model2.get_membrane_potential(),label='ground truth') #fires\n", - "plt.legend()\n", - " #fires\n", - "plt.show()\n", - "delay = iparams['injected_square_current']['delay']\n", - "print(delay)\n", - "dur = iparams['injected_square_current']['duration']\n", - "print(iparams['injected_square_current'].keys())\n", - "width = int(delay/len(times))+int(dur/len(times))\n", - "current1 = [75.36800000000001 for i in times]\n", - "#current0 = [0 \n", - "\n", - "first_int = len(times)/delay\n", - "second_int = len(times)/dur\n", - "\n", - "for i in range(0,int(first_int)):\n", - " current1[i] = 0\n", - " \n", - "\n", - "for i in range(int(second_int),len(times)):\n", - " current1[i] = 0\n", - " \n", - " \n", - "#current0.extend(current1)\n", - "#current2 = [0 for i in range(0,int(100)) ]\n", - "#current0.extend(current2)\n", - "\n", - "print(len(times),len(current0))\n", - "plt.plot(times,current1,label='current')\n", - "\n", - "\n", - "#plt.show(bbox_inches='tight')\n", - "#plt.tight_layout()\n", - "#plt.subplots_adjust(left=0.2,right=0.8)\n", - "#fig.tight_layout()" - ] - }, - { - "cell_type": "code", - "execution_count": 20, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "0.643622317713\n" - ] - }, - { - "data": { - "text/plain": [ - "(,\n", - " 0.64362231771345146)" - ] - }, - "execution_count": 20, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "abs_max = true_mins[-1][1]\n", - "abs_min = true_mins[0][1]\n", - "pop = ga_out_nsga['pop']\n", - "print(ga_out_nsga['hof'][0].dtc.get_ss())\n", - "ga_out_nsga['hardened'][0]" - ] - }, - { - "cell_type": "code", - "execution_count": 21, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "GrayCode(5)\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/opt/conda/lib/python3.5/site-packages/matplotlib/font_manager.py:1297: UserWarning: findfont: Font family ['normal'] not found. 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6ZyhSStUvJ4TAYDBgs9moOlGOnC8xGkx925xeCAAhEAYDAkm8MLFs2TIcDgcd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\n8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c1qzBvXv3MmPGDNLS0gD0rVGLxaKfb4ihwcnOaCpZZgifwbAgpSQYDOqVPXY6\nTZiQtLW14fV6mFpQwG7FQ0CqmI7uVlosKm6PBa/XhKZDHiFZHOy10jIzM8nMzNSr3auqisvloqur\ni0OHDtHd3Y2iKLoQ5uTkkJ7eGxGqiZ1mfQaDQbxery54mhAa/kIDgxMfQ/gMjjuqquL3+1FVVReP\n6rYuHC092HNtFBSMAQTTZYAvhY/0ELewQBIMKpjM0IOKCcG3A5Hb24SKnEYgEKCrq4uuri6am5vp\n6enB6/VSV1dHXl4eOTk5/crAaWIYWsmmr78w1JI0MBiNGBafgUECaD62QCCAEAJFUeju7qasrAyV\nIuyFk8jJOCYei2QGh1Uf7YognaPNI6UARdJtAlVIfuDLZBKxh6+bzWby8/P1mp4Ae/bswWaz4XA4\nOHToEH6/X/cXaj7Dvu2CovkLQ6NIDX+hwWhjtAjGaHkdBiOYvtuaQghUVaW6upqWlhZmzZqFPS2f\nR+vCLSYLJi7yWPlU9NBoE/QEFITVj9vmx+5W+aFq51tq/zqk8WIymRgzZoye/yWl1P2FTU1NVFZW\nxuwv9Hg8fPnll3p3DU0MNUE0/IUGJyoCsCSqGIFkzmToGMJnkFL6bmsKIWhpaaGiooIJEyZQWlqK\noijkSklxuqTRKyhKO/Z8CyYWu8BmzWZ3T5BvjndzjjML81etLFw4LfqFh0As/kKXy4XJZOrnL9T8\nhNq/Ukp8Pp/hLzQ44RECEq79bQifwclA321NIQRer5eysjKEECxatCiswoYQ8MNTVH51yEStR1Bo\nkdiO7i62B810ec1cn6uyOjMdv19hX5LzTwfLZ43kL/T7/TidTt0y9Hg8WK1WvF4vbW1tZGdnD+ov\n7I1YVXRfoeEvNBipCAEW0+DnnQgYwmeQVKSUeDwe3G43NpsNIQRSSmpra2loaGDGjBkUFERuC5Zv\ngfuLg3zWJXinXaHOAz6/iYmKh2snBphuDaTEOkp0PIvFEuYvlFLS09PDl19+SUdHB3V1dTH7C7Wt\n4FB/YWiiveEvNBhuhmTxjTBGycswGAlo25oOh4P6+nrmzp1LR0cH5eXlFBYWUlpa2m/R70umCS7I\nk3wzN4hPgqvLRcPhZmZnFqD1NNXEdKQhhCAtLQ2LxcJpp50GRPcXZmdn62Jos9ki+gv9fj8ul4v6\n+nqmTp1q+AsNhpUh+fhGGKPkZRgMJ6HbmtBrrQQCAfbu3YvP52PBggXYbLa4xhQC0gR4lWMiJ6XU\nLb6RKHysmia8AAAgAElEQVSRiOQvDAaDur+wtraW7u5uzGZzmBhq/kIhBB6PB0VRdH+hz+fTxzf8\nhQbHDUFvkdxRgCF8BgmjbdEFAgFdlACam5tpbW1l7ty5jBs3bkgLcajIhY7jR6VS6aRTeFBQmKBm\nMk7aEAmWKDyeQmoymbDb7WEd4SP5C9PS0sjIyMDn8xEIBLBYwrOoouUX9t0iNfyFBgbhGMJnkBB9\nozUVRaGrq4uysjJsNhv5+fkUFRUN+Tqa8GmiJ5GUmdt5b5aLLOvXR4/1fhkdq9q41D+FfBlfW5qR\nYCFF8hd6vV5aWlpob29n7969BAIBMjMzdaswKysrJn+hlmxv+AtPHhYvXpz8b3JDKNZps9nOiDan\nXbt2tUopC4cws7gxhM8gLvpuawohCAQCVFRU4HK5mD17NiaTiQMHDiTlen23NfeaWnnPUofZAQUy\n49i8kHQoHl5KO8h3vDPJlWmRhks5ybIchRCkp6eTn59PV1cXc+bMQUpJd3c3XV1dNDY24nQ6+/kL\no+UX+v1+/H4/UkoOHz5McXGx4S8cxezcuTPpYy5OEwkrRklJSdQ5CSFqhzCthDCEzyAmtG01bfHU\nFsmGhgaqq6uZMmUKJSUlCCHo6elJqgDoyeEE2GKpJ1dNo10NX6QFArtMo114+NR8hBX+U5Ny/URI\npoCE3mshBFlZWWRlZemPR/MXhkaRav5C6LXUW1tbmTx5ckR/Yd+UCkMMDcIYJYoxSl6GQSrRgipC\ntzVdLpe+rblkyZIw/5PW6icZhC68FaZOgqhYRXQPe65M46Cpk/P9fjJjrCw4koNlQoUvEtH8hVo9\n0sbGRt1fqFmEWu5g3+uoqhrWzDeSv9AInjmJMYJbDE4GIm1rqqpKVVUV7e3tlJSUhC24GskUktCx\nGhQ3FgYO1FCOhrd0KF4y1dFSUjc+LBYLY8aMYcyYMcAxf2FXVxednZ309PSwffv2mP2F2t8fDH/h\nSc0oasg3Sl6GQbJRVRWfzxeWQtDc3ExFRQUTJ06ktLQ06oKXbItPE754YhNHy1I8mMUXC5q/MD09\nndzcXNxuNwsWLND9hQ0NDbhcLgC9iW+ovzB0LkC/4twulwu73U5aWprhLxzNGMJnMFrRGsMGg0F9\nW9PtdlNeXo7ZbGbx4sV6w9ZopMLi6+npIc3Rg3usjxyTNer5QXoFd4waX2Rnsuabii3TVPgMQ/2F\nEyZMAAb3F+bk5JCWltYveKa6upoZM2YQDAb16xj+wlGKsdVpMJqI1EFBW9QaGxuZOXOmvnU2GMkU\nPlVVcbvdfP7555wyroCAu5nD3m6EP0BHRwdWqxWrNQ2zuXfLrUN4mRcoID2Ot/ZIKYEWiWQL6UAW\n5GD+woaGBjweDxkZGWGRpFJKPYleu0ZffyEQcYvUEMMTiKFYfCPMhW4InwGqqnLkyBG9CasQgra2\nNg4cOEBRURFLly6NKwk6WcLncDjYv38/UkpKS0sJBAJ825TL6/avcTd3kp6Rgd/no9PRSSDgp8em\nYBdpzOxOw5/l75fwfSKSjK3OoYwXyV/o8Xjo6uqivb2d2tpanE4nBw4cwG63J+QvDA2gMYTQ4Hhg\nCN9JTGjwSkNDg94toby8nGAwyOmnn05GRsYgo/RnqItXIBDg4MGDdHd3M3v2bMrLy3XhPU3NZbVv\nGi/J3XRnSMiwILBgAaZ7s1jYkoWno4svvz6MqqpkZmbqC3Jfn9WJwHALX1+EEGRkZJCRkcG4ceMA\n2LFjB5MnT8blctHQ0IDT6UQIEbEeaeg8INxfqLVzMvILRyhDsfj8yZzI0DGE7yQk0ramEIL6+nra\n2tqYPn06Y8eOHZZ5NTU1UVVVpecFqqraz3qcotpZfjCTibkz6FJ8KFIwXmaSLa1QQO8PkXvo9fVZ\nhTafPRnQUlKSjSZyof5CrQRbdXU1brc7Jn8hQGdnJ7W1tZSUlADoW6MWi8XwFw43ifr4DOEzGE4i\nNYbt7OykpaWFgoICli5dOmgHhVTQ09NDWVkZFouFM888U+9jF23bVCCYILOYEOz3kE60HnqhPiuv\n10sgEMBsNhMIBMjJyRmW1x+NkWbxRaPvmCaTidzcXHJzc/VjPp9PF0Pt3qenp4cl21ssFj2oSmvm\nq6oqwWDQaOY73KQuqjNTCLELqJJSfjslV+iDIXwnCZFy8vx+PxUVFbjdbgoLC5kwYcJxX/RVVaWu\nro4jR45EDKBJdnJ5JJ/VV199hcViobm5maqqqrAyYHa7Xe8rGAvHMxgl0fGGa7vXarVG9Re2tbVR\nXV1NMBjEarUSDAZxOBxR/YWRinOH+guNZr4pIHXCNw6oBwJCCEVKmZxcqAEwhG+UE62DwpEjR6ip\nqWHq1KnMnj2bioqKpOXexYoWvFJQUBC1V1+qv8VrC2ZBQQHZ2dnAsbB+h8Ohb9NZLBZdCLUgoIHG\nHKmkyuJLhEj+QlVVaWhooLm5mSNHjuByuQb1F8KxL3ah+YVa/0LNKjSCZ4bIEISvpaWFxYsX67/f\neuut3HrrraEjPwA8CJwCHBrCLGPCEL5RTKQOCk6nk7KyMrKzs8NKjSUz6XwwtKLWTqeTefPmhdWe\nHC5CLbVIYf0+nw+Hw4HD4eDQoUN6Z3VNCLOzs1NiYZwoW53JQlEUfftz2rRpQGR/ofZFJNRfqL3H\n4djfU6tF6nA4UFWVgoKCsChSw18YJwluCBUWFg5UOLsVeBioo9fySzmG8I1CIm1rBoNBKisrcTgc\nlJSUhPm9tHOOR4CH1oW8uLiYWbNmjYhFJ5Y5WK1WCgsLKSzs7Z4S2imhoaGBgwcP6k1nvV4vbreb\njIyMpLy+k0n4oPcLW+iXiGj+Qs1XW19fj9frJSMjI0wMNXGDXh+yNrbhL0yQ1G11OqSUiwc/LXkY\nwjeKiLatqUVKTp48mZkzZ0b8UKfa4tOCV8xmc1jwyolKpMongUCA9vZ2Ojs7qaysDCsOrf3Em1s4\n0n2GqaCv8EXCarVSUFBAQUFvCK9W3cfpdIb5C7OyssjJycHtdpOVlRW1OHe0Zr6GvzAEo2SZwUgj\n0rZmd3c3ZWVlpKWlDSo2qRK+wYJXRhNms1kPhpk/f35Ycej29nZqamrizi082bY6ITbh64sQApvN\nhs1mC/MXalZ5Z2cnbW1teqEGzWcYzV/Yt5mvFmX6ptLEVtGBimCaKYMfmKeTLoxl9ETD+Iud4ETr\noFBdXU1LSwuzZs0iLy9v0HFSIXydnZ2Ul5czZsyYqMErI4FUbfOGFofW8iK1xVjzFQ6UW5gKkp3H\nl4r7lojwRUJRFL3otsfjITc3F7vdjtPpxOl0xuUv3Ewz/6R20hrIQpKJQCKA/xD7uc4CP7XOGf3+\nQqMtkcFwE6kxrBCClpYWKioqmDBhAqWlpTEvIIqi6AntQyUQCODxeDhw4ABz585NWvDKaFhUQhdj\njUi5hZq/SisWniySnc6QCgtSVVXM5uQuTcFgUN++zMvLC/syGMlfaLPZdKvwixwvPzF5Cao2soQH\n7eVKKfFJE//ps+IK7OGLtf/Apk2bRsX7NCLGVqfBcCKlpK2tjfT0dN0H4fF4KCsrQwjBokWL4rYa\nkmXxacErZrOZRYsWpbRe5omwbRcLkXILe3p6dCF0u920trYmnFsYyomwdaqqatJ3BwayIqP5C7u6\numhtbWW93YtfzSVT6S24LY/2fASwChWBj5dlBhnerqTOeUQyShTD8NieQGgtgzweD1VVVXg8Hr2D\nwu7du5k0aRILFy5MaKtsqMLX09PD7t27aWpq4swzz0zpdp1GKprdjgQ0f1VRURFFRUVMnjyZRYsW\nMX78eAKBANXV1ezYsYM9e/ZQVVVFa2urHrY/GMkWqmRtS6Z6TM3ii4XQ+981YwyNIg+b4qPX1BMg\nJaqUSAkSiZkgAalgvXtlwvf25ZdfZtKkSbz99ttArxV6+eWXc/7557Njx46Exkw62lZnIj8jjFGi\n36Ofvo1hTSYTDoeDr776isLCwiH70BIVPikltbW1/YJXUh0lOpItvVTMLVm5hSer8CU65jbFgVAt\nKNotE73/EfSKHhz9AiaD+E6bzMUXX8ySJUu49NJLOfvss2O+zpo1a3jjjTf0399991127NjBtGnT\nEioUnxKMrU6D40WkxrA+n4/Ozk66u7tZsGABNpttyNdJRKgcDgdlZWURg1dSYUWFLtojzUrTOJ7p\nB7HmFoYGbqQiuCXZQh8MBofV4osLIVCEQFEVLFYrzz33HDt27KCra2jbnn6/n7POOotly5axceNG\n5s6dm6QJDwFD+AxSTaQOCgCHDh2irq4Om81GcXFxUkQP4hO+0Mor0YJXjofFl0yRGYkiqhGrsETL\nLdSqnlRWVuJ0OjGbzfj9/oRzC0MZSdbZQCQqfKWqnd/Tgyo5ZvVFQEWQXllH0SUrueKKK+K+zsaN\nG/nggw8oKyvjkUce4fXXX+fxxx9nw4YNPPPMM3GPlzJGiWKMkpcxuojUQaGrq4uysjJyc3MpLS2l\nuro6qcISq1DFWnllpFpkkRjJ26ZDvYd9oxgPHTqEqqpkZGTouYWhid52uz2uvoWpCm4ZKWK6mDFM\nUvZzWM0mU4T7USW9RpBPKpgJYn/hE7jkzoTmt2rVKlatWhV27JNPPklorJRhpDMYpILQnDxN8DTr\nyuVyMXv2bD0MPtkW1WDjaVGjJpOJxYsXk5aWNuB4yRa+jo4OKisrycjIwG63o6rqcS+qPRykIv3A\narUyduzYAXMLNZ/iYLmFI0mkBmIoAv1TCrlbdONWrWQIH6HD+KSCT1pZ4aynM2gspycKxl9qBBCt\n1FhDQwPV1dV6U9bQD26yhU9LfI80Ny14ZcaMGXrI92Aka36BQICenh6qqqo47bTT9JwrLYo0NJhj\nJPTSG+lWbiQhjTe3MPRenyjCNxTOoZDHheQfRQetaiZIgQQUVNJFgBtNPSzdH+SVEVBsPaUYPj6D\nZBGp1JjL5aKsrAybzRa11JjJZCIYHKALa5woitJv0R4oeGUwkmHxNTc3U1FRgclk4owzztC/GOTn\n5+NwOJg5cyZSShwOh95LD3q7gWtiGGuh6GQK1kgOHol1vIFyC1taWvS+hWlpaQSDQbq7uxPOLezL\nSBM+gGWMZRljeVNpYKviQAUyGzq4t/BMMqSZd51fh31xGJUYwmcwVKKVGquqqqK9vZ1Zs2aFVaPv\ni6IoSRc+zUKLJXglnvHixev1UlZWBsDixYvZvXt3xPO0kmAZGRkUFRUBvUEMmqVSWVlJT0+PbqnY\n7Xays7P7VQUZ6T6+kSCkobUwQ+/1kSNHaGlpSahvYTRSIXzJuocrGM8KdTwAO+p2kDGu973U3d09\n+oUPDB+fQWJE2tYUQujWzcSJEyktLR30g2oymWJOWo4FTai0eQy1bVAiFp+Ukvr6empra5k+fbru\ng4o0h2jjm0ymsGAOrcu3w+EIs1S07Tq73T7ityeTSTKF1GQy6T5XrXdeMvoWJlv4jsfft7u7m8zM\nzJRfZ1gxLD6DRHCqjdQFP6ZF+RLVEiSTcRS6l9D4lYrFbI0paEQj2T4+zXfW0NAQ1zyiEa/wud1u\n9u3bR2ZmJqWlpWFWWaSxYh0/tMt3qKXidDpxOBxUVVXR2dmJw+Ggq6srrJfbSGCkWHwDjRcqUonk\nFvbdjk52QM/x2Dp1Op1hxQVGJYbwGcSDlJKa4IccFK8iTRKzzEAgaPaWU88X5C+YxgLTbZiJXWyS\n5ePTglfq6+uxWCwsWLBgyGNC7MKsqiq1tbU0NDRQUlISsZNENJFL9Jt838amFRUVeq+21tZWvv76\na6SUYb7CWP1XI71/3vGu3BJLbmHfvoXJvoepihINpbu7m4kTJyb1GiMOQ/gMYkHb1qx0fEpF1n9h\nM+dhojd52OXuxmq1kWnLxysa2ac+y/zg90PK3w5MMiy+rq4u9u/fT35+PkuXLmXbtm1DGi+UWCwy\n7foFBQUsXbo06uIUzeJLFoqiYLVaGTNmjN7LLRgM4nK5cDgcfP311/T09OiLsyaG0azCZPsMR7Lw\nJTJe39zCvn0Le3p62L59e8K5hX1JRdWWvmN2d3cnrQvJiMbw8RkMhBatGVSD1Gd8gAwoCJOC0+U8\nak3kYDL1fpCt5NChVOJSD5MtJ8U0/lAsvkAgQGVlJQ6Hgzlz5qTEKT+QMAeDQSorK+ns7Iz5+olu\ndSZKpNqYmq9Q6/Ctqmq/jgnJZqRbP8kYr2/fws7OTs4444yw3MLu7m4URQlLXdH65g3G8RA+l8s1\n+oNbDIvPIBp9ozU9SitepZWg00SXtwubzdYv0k2z8hqVnWQHYxO+RC0+LXhl8uTJzJw5M2URjdGE\nqa2tjQMHDnDKKaewZMmSmK6vpVqEnjscJcu0xTm0w7fmK9SiGk0mE4FAgPb29qT4CkeChTYQqfKf\nJZJbGClid6hzlEhqhMoWs596RSVDwllBMyXBwMlp8Y0SDOFLEtEaw7a7GvBYvAiZjj3XHnXRUTDT\nQ1vM14tX+LTKK4qiJCV4ZTD6CpPf7+fAgQN4vV5OP/30uCvOp7L3XqLjahZIqFXY0dFBVVVVmFWY\nlZWln5esXLdEGelCOhCx5hZqVnhOTg6ZmZkJW3weJP9m9bDLFMAEpElBUJHsNgXIyAmyJisNrXT0\nSZHOYFh8BqFE66BQUVGBQzaSPi+NgGoacIFQCWIh9nDoWLc6pZTU1dVRX18fV+WVoaIJs5SSpqYm\nqqqqmDp1KkVFRXEvlJqIhorpSK0FmpaWRnp6OtOnTweOWYVdXV26VWi1WsN8hQMViR7pQjWcqQfR\ncgtdLhddXV3U1NTgdrsB9MClWHMLJZKnrB52mfwUSeWY7132/tsug2yYnMM8EeQUacLpdI5+4QPD\nx2cQOQkdoL6+npqaGqZOnUpJ0Sw+VT7DLzoHHW+cuijma8di8YUGrwy1X1+8CCHw+Xzs2bMHs9kc\ntQJNrGMdbx9fovSdU6hVOGlS7za21+vF4XDQ0dFBTU1NmFWoWSnae+lkE76hjhfqm9Xu95EjR+jo\n6IiaW5iVldXvs1GlqOwxBcJFL4RMVcUl4HWLj7/1ZRgW3wnGKHkZx59IHRScTidlZWVkZ2ezZMkS\n/Zv85MAF7DW9iERFRGh678NFhswnT06P+foDWXzHI3hlIKSUdHZ20tbWxrx584ZsZR4P4TueJcvS\n0tL6FYnWIkg1K8VisWC32+np6dG/WCWDk01I4Zi/cPLkyUD/3EKn09kvt/B/7b2f1GhR1lKV5Krw\nf6YA36N3yzUVwU0jCkP4Tl4ibWuGCk1JSQk5OTlhz5nI2Rxo2Y63+AgmLJixIRAE8ROgG4vMYV7g\nbyKKYjSiWXxDDV4Z6kLmcrnYv38/iqJQXFyclK3VVFt3w12yTFEUfcENtQq7urpoa2ujpqaGmpqa\nMF9hqFUYDyNdqFRVTfrORN85DpZbWFVVxe5JWfhtVlyKGYvFgsViCRtDSolZKHgBh1BTMu8Rh9GW\n6OQjWmPYxsZGqqqqBhQaRSjYapYy45Qc6kybcYpaQGAmg+LgRZyino2V+KyyvtdJRvBKqC8tXlRV\npbq6mubmZmbPno3b7aanpyfucQaa12DHRhNpaWkUFhbS0dHB2LFjycnJ0X1XtbW1dHd361ah5i+M\npaFsKnxyI1lIodfvN1h0bd/cwsnWbioJYPYF8Hm9dHd3I1UVs8WMxWIlGAhgMptRAdPofRuGY1h8\nJxeRtjW7u7spKysjLS0tJv+VQFAo51EYmEcQLyoBzGTEZeVFQgteOXz4MDNmzNBLRSWCZkXGu/B0\ndnZSVlbG2LFjKS0tRVEUenp6kiZMJ6Pw9SXUKtQqhGh1MTs7O6mrqyMQCIT5CrOysvqJ0kgXqlQJ\nX7z+5aVBC/usKnlHU1jg6JffQACf34/P56M74Md35AgP/eI/ADh8+DATJ06M+/6+/PLL3HPPPTz9\n9NN861vfAuDdd9/lqquuYs+ePcyaNSuu8VLKKFGMUfIyUoOqSr5sDlDvVMkww8ICQbqp17JpaWlh\n1qxZEUtsDYaJNExxlCeLRjAYZNu2bXrllaFutcSbIhHaxWH+/PlhRXqTWUs02SXLIpGssY5nybK+\ndTFDG8rW1dXpVmFoBOlIF75gMJiS/n7xfjZKgxZelF66kWQe9fMJITBbLJgtFgLBINJm5WYyCVx4\nIf/36af84Ac/4PDhw1x33XX83d/9XczXWrNmDW+88Yb+e3NzM6+99hqlpaVxzTnlGBbf6EZKyZ/2\nH+TxPWk0ubMxCUGako2QcG5mPXcusOiWzXCg+RQ9Hg8LFy5MWvBKPGLV0tLCwYMHmTx5csQuDsm0\nyKJ1Z0gWI7nEWDzjhSZ9h1qFXV1duhg6nU4OHDhAbm7ukEuBwci3ICGxyi2ZCO72ZfBoWg89SPKl\nQDkqgN1IWiyC8zyCy2x5uC+6iN///vds2rQJKSUul2tI8926dSv79u1j7969bNiwgUceeWRI4xn0\nxxC+PnR017Duq/d5ce9VKEoAa7oLKcCDE8Vj4gPXFByV8P+mBOK22ZKxSGjBK5MmTcJmsyU1YjMW\n4fP5fJSXlxMMBjnjjDP0baC+JFv4VFWls7M3JSQ7O/uk2eoc6mu0Wq0UFBToQUY7d+5k0qRJOJ1O\nDh06hMvlwmw2h/kK49kWPBG2OhMdc65q5h89Nl61+NhjCqAgUYF8KVhZ72JlTiGmbIHL5dKrtggh\n4v5Mbty4kQ8++ICysjIeeeQRNm/ezOrVq1m2bBk33nhj3PNOGUZwy+hDSkmH6zD/2fFv/OmrH5Nm\n8mA2qYCEo2uPmh7EGjjCruZT+GO5ws1zYt/K09IPEi1hpQWvCCH04JXDhw8n9Rv3QMInpaShoYHq\n6mqmTZumJwwnMla8qKpKZWUliqJgMplwuVz4/X7cbrfeWy/RHMGRTioqo2hdJzRCrcLDhw/j9/vJ\nzMwM8xVGE47RavFpTJEmfuTLwIFKh5BYERRJwb52N9a83s9yqPAlwqpVq1i1alW/41u2bEl4zJRg\nbHWOPgKBADvbn+bTxjORqsBsCc2RO7rHLyU+s0qG7GbD/kxuKFExxfgZTVT4Bgpe0cQlWWHU0cSq\np6eH/fv3k5aWFpafOBDJssgaGxtpamqiuLiY4uJiPcCooqICk8mkb+GFBnYkEu4/Uq3H45F319cq\n1PLcNCHUrELNIgy1CkezxReKHQV7yFsk9LM8VOE7oRglijFKXkYyCHDA3k7VzhmYFC1huO+C0/t7\nUOmgy5fJIRdMySEmEummoFVeycvLixi8oo2ZKuELLXc2c+ZMvUZiImPFi9frZf/+/ZhMJoqKisjL\nywsb02w2k5WVFRbYEZoE3t3dTVpamr5QH882QiOdwV5vaJ7bKaecAvTWWtWa9YZahW63Ww+iSYZg\njTSLL5YxnU7nySF8w7jVKYT4BpAvpXzj6O9jgCeBucA7wH1SypgXWEP4jtLR8zUeswlVVRBC0l/0\njhFQJGYJwTjW9XiELxAI6J3BB6q8kuwu7KHjOZ1OXXQTKXeWqMUXuqU6ffp0xo4dy4EDB5BSho3X\nd/xISeBaG6HQ5rKhVkt6enrSRW+kN6JNFIvFEtEq3Lt3Lw0NDVRVVYWVZ9PaBsXLiSJ8ofM8aToz\nDO9W53rgA0ALf30MWAG8D/wt4AAejnUwQ/iO0tnRAWNgfG4DzZ1FWM3Ry0QF1d43fFHsNaVjFj4t\nWnLSpEnMmDFjwEUvWV3YNbQqNBUVFbS1tTF79ux+VWhiJRHh83g87Nu3r9+WaqLpDH3bCAWDQd2X\n1dzcrJeZ0vrqJWvRHelRoslAswotFgslJSUoiqK3DQr1FWo1Me12+4C+Qo2RutXZl9AvJIbwHRdK\ngEcAhBAWYA1wt5TyD0KIu4HvYwhf/EyduASb479YdNouvqxdiJQQbb3x+zK4aoZK5uCuLh1FUQYU\nKY/HQ3l5OcCA0ZJ9x0ymxef1eikvL2fSpEksWbJkSItFPHOTUlJfX09tbS0zZ87sV+YsWQnsJpOp\nX+fvnp4evv76azo7O9m1a1eY1RJvhGMqGKm+R41QAYjUNkiriVlfX4/L5dItc+3+9rUKVVUdcg/D\nSHNMZerRSdGEVmP4ojqzgK6j/78EyOSY9bcbmBzPYIbwHUURZkpcY3EVNjF74ld8dWgeGdYeFHFs\n4ZFS0uPPYJw1je/Pi8/SMpvNEYVPSsmhQ4c4dOhQ3JVXkmXxBQIBDh48SGdnpx5EMlRiFSa3282+\nffvIzMyktLQ04qIXOlZor8NkzNFms+lRoUVFRRGtlmTUyEyUkbLVORDR5hepJmZoM9kjR47g8/nC\nOiUEAoG4ezUORqq/PLhcLr3g+KhmeC2+emAB8DFwKfCVlLL56GN5gDuewQzhO4oQgjPybqW6+yEu\nPusNzKZAmOWnqgJhkoy3uvjTxbmcEufORiSR0vxoubm5CVVeSYbFp+UFTpkyhfT09JgiNpMxt9DA\nmVmzZpGfnx/13OPZnaGv1RJaDaVvjUztJ9kWymgmklXodrtxOBwcOXKEtrY2zGYznZ2duhjGsvtx\nPOn7ZeSk2eocXv4L+IUQYhm9vr11IY8tAiriGcz4xIaQbs3nwq47+KTtCb511iaWztvKVzXz6XDm\nk272siKviVtmro05hSGU0K3O0OCV2bNnJ7xNMtj26UB4vV7KysoA9LxArTdcMhhImLq7u9m3bx92\nuz2mwJlUC99AFlWkaihaP7329nb9nmm5cSaTKemCPNItvqEghCAzM5PMzEwmTJhARUWFHsHrcDho\naGjA6/WSkZER5isczk4IfX2GJ81W5/BafA8CHmApvYEu/xry2ALgv+MZzBC+PuTYJjD10A18Mz+L\n6sD/cskpX2NTHEy3X0GaxT7o86OhbXXGE7wyGCaTKW6hCvWnaVGTGqmurymlpKamhoaGBmbPnk1u\nbm7CYw0nffvpBYNBnE4nDoeDtrY2XC4XX375pb5QZ2dnJ7xQj3bh64uqqrpFre0CaFZhaP+80Che\nzYvbzpUAACAASURBVFd4vO5T33zck8biG0bhO5qq8M9RHlsZ73iG8PVBE6i8jOnkZcTeGHYwVFXl\n0KFDZGVlxRy8MhjxCt9g/jQtqjMZ9BVRbVtXK6gdT7DBSO/OYDKZyM3NJTc3l7y8POrr6ykuLsbh\ncNDU1ERFRcWgQR0DcbIJX9/3RqhVOH78eKB310TzxTY2NoZZhTk5OfqXjVREdAYCgbAvMieN8EGq\nglsmCCE+B/ZJKb870IlCiPnAecAY4D+klI1CiNOAJimlM9YLGsJ3FG1xMZlMSe94fejQIaqrq7Hb\n7SxcuDBpY8e61amqKrW1tTQ0NFBSUhK1o4SiKElvJRTap2/OnDkJpUdEE75kRrQmuwO7zWbDZrNF\nXKi1oA6tLNhAxaJHirgfL2IVKrPZTH5+fphV2NPTE/ZlQwuuCQQC9PT0JC1vs29eoMvlSjjt54Qi\ndRafpFdSu6NeWog04I/A6qMzkcBfgEbgUeAg8NNYL2gIXwhaR/VkLaihwSslJSW0t7cnZVyNWOaq\nVX8pKCgY1NJK5mvX8rq2bdsW1qcvEYbSIDfW8VNNpIVaC5rRikWHBs3k5ORgsVhOyq3ORN4n0b5s\ntLW10dHRwcGDB/F4PHoU71C2oPsK30lj8Q1B+FpaWli8eLH++6233sqtt96q/dpIb4pCmxDiZ1LK\nlghD/DNwIfA94D2gKeSxt4AfYghf4iTrG2FlZSWdnZ2UlJSQk5NDZ2dnUi0U6LVOvV7voHMYqPpL\nKMkSPlVVqaqqwuPxcNZZZw15URjpW519ieU9FKksmNZYtqOjQw+a8Xq9NDU1kZeXh81mG9L7MxVV\nZZJNMmvPms1mPTBp7ty5/azCyspKgDBfYSxWYSSL76QIboGEtzoLCwvZuXNntIeLgG30piq0Rjnn\nO8DPpZQvCiH6zqIamBLPfAzhSzLRgleSvYUK0YWqra2NAwcOcMopp7BkyZK4+rkNVfg6OzvZv38/\n48ePx2azJeWb8IkkfEOZU6TGsjt37iQQCPD111/jdrtJT08PswrjEYnjUfB6qKSy6HUkqzBSNZ9Q\nX2Gke9xX+Dwez4hLuUgJqdvqbJBSLh7knDFAWZTHFIivS5whfCEMZTEdrPJKssuLRRrT7/dz4MAB\nvF4vp59+etyJwEMRvmAwSEVFBV1dXSxYsIDMzEwaGhoSGisSkeY1EjuwJxOtDdOkSZP0NAmt/mhz\nczNVVVUA/eqPRiMVLYRGuvANVqczWjWfrq6usHuspatoSfZ9xxyuptTHleFNZ6gGzgL+N8JjS4AD\n8QxmCF8EtMCJWN7MoZVX+qYHhJJI6sFgaEIlpaSpqYmqqiqmTp1KUVFRQgtSosLX3t5OeXk5EydO\nZObMmcelVuXxyuMbSQghyMjIICMjQ++HGCm6MVp9zFRYfKmoqzmc/f1CrULtHmtWYVdXF5WVlXR1\ndWG1Wjl8+DDt7e1D2pp9+eWXueeee3j66af51re+xZdffskPf/hDGhoaePPNN5k5c2bCY48yngP+\nXghRA7x69JgUQiwH7qE3zy9mDOGLgGZJDfaB0YJXtETsgSp4pGKr02Qy4fP52LNnD2azmTPPPHNI\ntSXjFT6t1Jnb7WbhwoXYbLaErz0Q0bY6TwYGE6tIQTNaJZT6+nqcTqfeYT0zMzOplm0qUgUguX/b\nZHRm6GsV1tTUYDKZqK2t5bXXXqOuro7S0lIWL17M6tWrufDCC2Mee82aNbzxxhv677Nnz2br1q2s\nWbOGurq6kSV8w2vxPUpvovrzwH8ePbYVSAf+JKV8Ip7BDOELQfvAmc1mAoFA1PJdWuBIR0dHzB0M\nkr3VKaWkubmZlpYWFixY0K+wcyLEI3ytra0cOHCA4uJiSkpKUipEmvD5/X49Mm+k+viSTbxWWt9K\nKHCsw3p7ezvd3d1s375d37qz2+0JB82cCBGnqer2kJWVxTnnnENpaSkXXnghW7ZsYdeuXUMe22w2\n86tf/YoJEyZw0UUXJWG2yUUOU8Gcowns1wghngIuAcYCbcDbUsoP4x3PEL4IRCsoDceCVyZOnEhp\naWnMH/xkLhAul4v9+/eTkZFBXl5eUkQPYhO+UD9ishLxB0MIoRc1tlqt+Hw+zGYziqLoTUCHcn+T\nmRM4EsVA67CelZWFx+Nh7ty5etPe6upquru7SU9P132FAzXtDSVVFl8ySXUTWi2VwWazce6558Y9\n1saNG/nggw8oKyvjkUceYf369dx3332cffbZvP7661x55ZVJnftQkAKCw6AYQggrvT33PpBSfkxv\n9OeQMIQvApG2JbXallLK47bg9yU0GXz27NlYrVa93mYyGEz4tILWp556KuPHjz8uC7zP56OmpoZA\nIKDnAQkhOHz4MG1tbdTV1dHd3Y3Vag0rHD2ctRyTTbLusybKsTTthcHD/E9W4QsEAvoXg6F2X1+1\nahWrVq0KO+b3+4c0v5QxTMInpfQJIdbTa+klBUP4Qgjd6tQsvliDV1JNZ2cnZWVlYcngXq83ZR3Y\nQ/H5fJSXl6Oqql7Q+nig5VoVFhYihNCtPS0AwefzMW3aNOBY4ei+C3dubm7cJcJGKwNZowM17W1q\natK3mEPrj45E67YvqejvF8niOxmQAgKJVOhPDmXAVOCjZAxmCF8ENIsvnuCVVKF1RHc6ncyfP5/M\nzGNt31PRgb2v8DU2NlJVVcW0adP0KLdUEyq0Z555pp7UHUpfH1/fwtGh0Y719fWD9tUz/IXhRArz\nD20f5HK59HvW0tKSlKa9qfgbHK+tzpMBKQTB4WvB9Q/Ab4QQu6SUe4c6mCF8EVAURW9CGmvwSqzE\ns/ho/sTJkycza9asfs9Ldgf20PG8Xi/79+/HZDINKVo0XqtA204NFdpogSwDLZSRoh01v1ZNTQ1u\nt5u0tDTsdnu/avtDYSQL6FAstEhBMy0tLXq3hGQ07U2FBRlLdHYiY2rCN9StzhON4PC5EO6jtwv7\nnqMpDQ301uvUkFLK82MdzBC+EIQQtLS0UFdXR25uLosWLUrqB1Gz0AZbZDWLJxgMDuhPTGZRaTgW\n5HHkyBGqq6uHvLWrCWks37j9fj9lZWUEg8F+26nJSGcQQvTrq+fxeOjs7KShoQG3201ra2vY9mii\nYj9St/+SLSwm0/9n782jJDurK9/fF0NG5Bg51JhZs2rMGlRzIWGBsKBdvWgsCxtjPIAxvWQ1NMty\ns+BZtF93LT97uTG07aax3LJNS8/2o9tTSxgzSSADRoBUJSFV5TzPc2ZEZGTMEfd7f0R9t25Exhz3\nVmapYq/FQpVZ9cWQkXffc84+e9upq6vjwIEDQPbQXuPstZBoxioFppUV39ra2l1DfBJB0qJ4hiKQ\nBHrMOqxKfAasrKwwNTXFgQMHSCQSpl/ACi2xSymZnZ1ldHT0trYWFaLRKOFwmJWVFS5evFhxGnux\nKweqss21fG+VZZnb7WbHjh1IKUkkEuzcuRO/34/f70+rYBQRVuqVudEwe+E8k6jyhfYuLy8zMjKC\nlDJNNFNbW6u/p1aTlJlQzzkYDN49Pp0bCCnlg2aeVyU+A1pbWzlz5gwLCwv4/X7Tz1ezw2yVRDgc\npqenB5fLZQrplAJjOG1NTQ0nTpww5dxCrVi1GhGLxfKKZm6HV6eUEofDQVtbG21tbUDqQqzao8or\n05gE3tTUtOlVjUaY7YpSTAWZL7R3aGiIcDisi2bMig0ywmrl6d3U6pQIEhtX8ZmKKvEZkLnAbjay\niVGklIyPjzMzM8PRo0f1mdTtQmY47SuvvGLa2fnISS3AF7MaYXWVlev8TNm/8sr0+XzMzc3pAbMe\nj0evCjczrPDqLJVUjKG96jmpxISFhQV8Ph+vvvpq2aG9mbCq4lMIhUJ6usbdgOQGUoYQYifwCeDt\nQCupBfbvAH8opZwr5awq8WVBvgX2SpDZ6lSq0ZaWFi5dunRbd8+klExMTDA9PW0Z4WYjvkQiQX9/\nP5FIpOh9yM2SzmD0ylTu/vF4XG+PTkxMEI1GsdlszM7OrmvlbTQ2A/FlwuiN2dDQgN1u59ChQ/p7\nqhS5xYT2ZoPZxJf5mavO+G4PhBCHSS2utwAvAUOk4ox+A/igEOIBKeVgsedViS8LrPDVhFQFkUgk\n9Ly65eVlU1SjpV7QgsEg3d3d+pqGVYSb2epcXl6mr6+Pffv20d7eXrLiz4jNYlnmdDrZsmWL7p6z\ntLTE3Nwc8Xic4eFhQqHQuv23jWqPbvZYIkWkmS1nFdrr8/l0w4Jsob35zjQLma/5bsri22Bxy2eA\nVeCSlHJMfVEIsRd4/ub331vsYVXiM8DqVqfD4cDv99Pf38/OnTu5ePGiKXfMxV6ApJSMjY0xOztL\nZ2en3m6yCuq5KTPrcDhcluuNUb2qXqcVMz4zYLPZcLvd7NmzRz83HA7j8/n0/Te73a63R/NdtM3G\nZqz4ijnPGNprFM2srq6mhfYa/UdVpW11zNHdtMcHbCTxvQN4zEh6AFLKcSHEFeDJUg6rEl8WWJGd\nl0gkWFpaQtM0zpw5Y1qSQalJEq2trbzlLW+5LVWHzWbD6/UyPj5esZm1ldWd1QbbqpVnNI3OTFrP\nzNSz4jndKRVfMXC5XOtCe5VoRlXatbW1RKNRfD5fyaG9uWC0K4PNVfGNjY2xf/9+PvShD/HMM8+Y\nfv4Gi1tqgECO7wVufr9oVIkvA0II04lPLWU3NDTQ2tpqanxPIeWk0d/z+PHjBduqZt0lJxIJ/H4/\noVCIs2fPlhyKm/mcNmursxxkJq0blY6Dg4OEw2F9ppVMJk2rWu7Uiq8YKJGREhgpIdKPf/xjFhYW\nGBoaQgiR5udajmgmW8W3WYjPaqRanRtGGa8DHxdCfF1KqV/wROoD/dGb3y8aVeLLArMuDsrYWgjB\n+fPnWVxctDyF3YjV1VW6u7vT/D0LQRFpJRc0r9dLb28vTqeTo0ePVkR6cPvWGTYK2ZSOyh4sFotx\n7dq1tJmWx+Mpy2lmMxFVrvPMmjcrIZLT6dQz7Yw2djMzM8RisZyhvblQbXVuWMX3O8A/Ab1CiL8h\n5dyyA3gfcAh4dymHVYnPAhj34ozuJzabzXTn9WwVnxLPrKyscPLkyZJ+MSuxQUsmk7qv6OnTpxkb\nGzOFUIwkZ6xaNmMCu1mvV9mDTU9Pc+HCBWKxGD6fj5WVlbT2qFqjKGZu+mZqdZaDbDZ2ymlmamqK\ntbU1PbRX/S9z/ppJfJup1flmhpTyG0KIfwP8LvAfScXiSuBV4N9IKZ8v5bwq8WWg0koicy/OeGfu\ncDgIBoNmPE0dmRWfz+ejp6dHF8+UemEql/hUlbdr1y6OHDmCEMK0qsx4jlHcsllhxXOrqalZtwiu\nqpe5uTmi0Wia5D9bRuFmJyorFJj5YBTNqF08NX9VCtJEIpEmmkkkEmnEF4/HN33yh6ZpPP744/z3\n//7feeSRR/jSl75UVqzaBqs6kVJ+A/iGEKKO1FqDV0oZKuesKvHlQSkXCk3TGB8fZ3Z2lmPHjumu\n9kZYIZpRRKWqrdXVVe699960FIdyzisWKo3e7/dz+vTptPmlWSba2ZSrd/KMzwxkS09Q1UuujMLN\nXvElk8mKEx6MKIdIM+evRvee0dFR/H4/drudpaUl5ufnKyLqv//7v+c3f/M3+fM//3MuX75MMBjk\n3e9+N2tra3zxi1/k3nvvLftshUgkwi//8i/zD//wD3zsYx/j85//fNnPWcKGiVuEEE6gRkoZvEl2\nIcP36oGYlLLodlqV+HJALbEXM0tZXV2lp6eHLVu25FVMmp2mAKkLoMrqM1Zb5aIUQlHVZUdHB4cP\nHy7KY7OS5xSPx1lZWcHj8ZhKfG8GEs1WvWRmFMZiMX3uZUZG4Wav+MxYXs9075mamiKRSLCwsMCX\nv/xlpqameMtb3sLFixf5mZ/5GR566KGiz/65n/s5/umf/kn/8wsvvMCJEyd48MEHefrpp/njP/7j\nip77ysoKDz/8MC+99JKe7F4ZNlTc8heAE/jFLN97CogBv1bsYVXiy4C6eKsl9nzEp6odn8/HiRMn\nCs7SzN4PTCQSLC8vA6yrtspFMeSsaRpDQ0N4vd681aVZ6RFCCKLRKK+88goej4exsTGSySSJRIL5\n+Xmam5s3fbvJKryQmOX3QzATa8Rti/PT9X7+Y80uXMKxzidzfHyceDxOMBjUxR319fX6nLDUGKE7\ngfisSHtwu91cvHiRCxcu8MADD/Diiy9y7do1U29qK62kx8fHuXz5MsPDw/zVX/0Vv/RLv1Txc9rg\nVuc7gE/m+N4/Ap8t5bAq8eVAIZJaXl6mv7+fjo6OomdphdIZSoHyulT2WWatSBQiPr/fX/QMUa1G\nVAKjxdn999+PzWZDCEE4HKarq4tQKMTs7CyxWOxNlaQA+WdUXi3MA944k4GDpP5a6gL/3wJJ/sSW\n4Df2fomft2/nQPw8NaRUtaoqVKkf+TIKlSNKvorpTpgZWpn2EIvFcLlc1NfX8/a3Fx0Fp+PZZ5/l\n29/+Nr29vXzmM5/hK1/5Cn/0R3/ED3/4Q774xS+W/Rz7+/u57777CAaDfP3rXy+pCi2E8omv4hHP\nNmAhx/cWge2lHFYlvhzINY9TiQLRaJQzZ86UJNW32WwVz/iMj3/u3Dnm5uYsC6M1ohylaKUVnxLM\ndHR0EAwGcbvdxGIxIHVj4nA42L9/v/78MpMUlFS9ubm5oFTdrFbn7WiZJqXGpRWN+bUdSJn+mqRm\nJ67Z+MPRX0Ec+q8cbPgabw//Ku3JI+uIJV9Godp9U62+bBmFd0LFZyXxBQKBsmfpAI888giPPPJI\n2te++93vVvT8AAYGBlhZWeH06dOcPXu24vMUKqv4Kia+BeAk8M9ZvneSlGF10agSXwZy2ZZJKZmf\nn2d4eDhnblwhVNrqVIvwxkQDK1PYFdQ+4I4dO0pSipZb8RlbqadPn8blcjE3l26+njmXy5akoKzC\nlFTd6XTqF3CPx6NfwMyuDM06L1dF9T8Tk8yvHUPKXBchgabZ+f/mfoFP7/ldvlP7ND8V+lhRFZrK\nKFRVoTIiyJZRaPZqzmYnUiBt7r9ZDarf8573cOTIET796U/z0EMP8fzzz+tespVgg51b/gn4v4UQ\n35FSXldfFEKcJLXe8Gwph1WJLweMFV8kEqGnpweHw8GFCxfKVp6VS1IqkV3TtHW5dXa73dQLkPE5\naprGyMgIS0tLJe8DQnmikUAgQFdXVxrJappW8vuWzSosU+wB4PF4LBEdWYn/ttqyrtJbD8Hs6m5W\nkx48dj+v13yd/fKdZd2s5cooDAaDXL9+PW0JvJKMwjuh4jOuM2xm15YnnniC2tpafvM3f5N3vOMd\nfOtb32L79pK6gVmxgeKW/wS8C3hVCHEVmAI6gIvAKPDbpRxWJb4ccDgcxONxJiYmmJyc5MiRIxXf\nNZXT+pubm2N4eDhnIrtVFZ8ioO3bt5dtpl3Kc5NSMjo6yvz8PCdOnCh4QSmHVDPFHqqamZ2d1Xe3\n1M5Wc3OzZZ6ZxSJXhbYYbSa1v5sfQkgm47tosvuZcwzRbr8PISrLDDRW1ktLSxw/fpxkMpk1o1D9\nr9gbxTul4lPEt7a2VlGr02o8/vjjuN1uPvrRj/L2t7+dF198Ub8JvNMgpVwSQlwA/gMpAjwNLAG/\nB/yRlLKk5PAq8WVAXWiSySTDw8O63Vc5FlGVIBqN0tPTg91uz1tlmr0bKIRgdnaWSCRSFAEVOqsY\n4guFQty4cYPW1tas1mpWWZapakZKSW1tLfv37ycQCODz+RgYGCAajVJXV6fbiZWqeqwUuYhPUNzr\nloCDJAKBXTqIOtZM3+Oz2WzU1NTkzCicnJwkmUymCY9yZRSaLUaxesa3WVudRjz22GO43W4+8pGP\n8La3vY0XX3xRTw4pFZtggd1HqvL7T5WeVSW+DCgRx8zMDFu3buXo0aO39fGllMzOzjI6Oppmd5YL\nZlZ8gUCAqakpmpqaTIlMKmTRJqVkcnKSqampvDFJuS7WZluWGSuVvXv3puXAjY+PEwwGcblc+gU8\nW1vvdohbOhtmuBY7CAXanUJI7nENIZFoQsOWtN8WFWZmRmG25IRsGYVmrx9YTXybudVpxK/+6q/i\ncrn44Ac/qJPfgQMHSj5ng4NobYBNSpkwfO2ngBPAi1LKH5dyXpX4MqAsxY4cOcLq6uptfexIJEJ3\ndzcul4uLFy8WldNmRsWnaRpjY2PMz8+zc+dOamtrTbkA5avKIpEIXV1d1NXVlRWGezsqr8wcOOX4\nr2zCBgcH07L1VDKA1eKW36m38+4V8tZ9Qmh0tr6BUySRgFO6cUU8iKbb79ySLTkhHA7rZtEqozAc\nDuP1emlpaTElo1DTNNM7NarKhdSN4maq+Pbt25fz9+0DH/gAH/jAByp+jA0Ut/wvIAp8EEAI8Ri3\nMvjiQoh3Sym/VexhVeLLQFNTE263m+XlZdPtxRQyLxhGU+tSZ4mVVnxra2t0d3fT1tbGpUuXmJmZ\nMe115yK+2dlZRkZGOHr0qC6aKOfs2w3l+F9bW6vPWzOz9WKxGE6nU/fNrGSxPhex/IR9G+/Z2sNX\nFrMrO4XQqHWt8cFt/y8SiQMnx2PvALk5FKxG4ZFqj8ZiMV599VVWV1eZnJxMC5Ytd96aTCYtNTa4\n25IZNjiW6C2A0Xrmk6TcXD4B/BkpZWeV+CqFVSnsaoldVTj5TK1LOa9UqDT2ubm5tJw+MxMkMkk5\nFovR09ODzWYruqK9XSi3RZnp7Tg/P8/y8jLBYJDp6ek0+X9zc3PO+VYu5Pq7f12/l/9g6+Yvlw8T\nT6beRwFIKdjbPMK/a/88dbYwDpxsTxzkaPwB+rX+2xJAXA5qampwOBwcPHgQyJ5RqOatxUYIWZ32\nEAwGTVFK3inY4BnfNmAaQAhxENgPfEFKGRBCPA18qZTDqsSXA8qyzGwYZxkTExNMT09z9OhRPSql\n3PNKQTAYpKurK6uYxAy3FeNZilAWFxcZGBjg4MGDm+5iYWYVZLPZdKEM3JL/+3y+tPlWMRfwQmT8\nh7X7+WxHlL9IDnE9GSbumONMw/dosUXQhIZTNnI89g6Oxt+GDZvpTitWIl9GoXEvM19GoRUzPiPu\nBHGL2dhA4lsFVHvoQWDJsM+XBEqKm6gSXwaMC+xWtDodDgeBQIDh4WE8Hk9Z8y0jSl0ZGB8fZ2Zm\nhuPHj+szl8zzzBJo2Gw2EokEXV1dxGKxdTuIdwOM8n+4Nd/Ktljf3NycZhNWDFHZhY1fd+y9+Zt8\nlHDkHEHhxYELj7YVwS1SvZOILxPGjEIlyVdt5syMQtUeNZv4Mn/P7sZW5wbO+H4A/JYQIgE8DnzN\n8L2DpPb6ikaV+HLAilanuuj19vZy4sSJnCrGUlCsuCUYDNLd3Y3H47ltCRJra2vMzs5y5MgROjo6\n7tiLbikoJgMu22K9z+djcXGR4eFhhBC632ipP4ta2UitzK40vJOJLxsy28zGjML+/n78fj+RSIQt\nW7bkzCgsBZnrFndbCO0Gz/g+BXyVlCH1CHDF8L33Az8s5bAq8WWBEML0/TglIpFScvz4cVNIDwrP\n+KSUeks138qAghnEp7IBV1ZW2Lp1q+4DuVlhdixRqRdXl8vF9u3b9RawWqxfWloiEAhw9epVGhsb\n0/bgysGbjfgykZlReP36dTo6OohEInpGodG2rqmpqaSZemZM2d3Y6twoSCkHgcNCiDYpZaYv528A\nc1n+WU5UiS8HzLpAaJrG6OgoCwsLHD9+nOnpadPjS3KdFwqF6OrqKqmlWinxra6u0tXVRXt7O52d\nnUxNldSBqIJbi/W1tbXE43E6OztZXV3F5/MxPz+flrZeymL9m534MiGlpLGxkba2tpwZhUCaCXe+\nVnxm+vqdssdnJjZygR0gC+khpbxR6jlV4rMQytxZub/YbDbm5+ctz+0yLobnSoPPhXKJTxH84uIi\np06doqGhgdXV1Ts+4HUzwGazrRN6qMX6sbExPblC/R21EJ4JM4nP7J+rpmmmk3K2hfhstnWqPVoo\nozBzZri2tqbPbu8GbLRzi5moEl8WVNr6yhfhY0Y0UT6onLqGhoayhDPlEJ9xF9Do+GKF+bNVFctm\nJOiclmU5Fut9Ph+zs7MMDAzoqkij4tFMctnsWXxQXBCtw+GgtbVVV1XnyyhUWZAKwWCwLK/O559/\nnieeeIJdu3bx3HPPMTg4yMMPP4zD4eAP//APede73lXymbcDVhKfEOKLwHEp5VsseYAMVImvAEr9\nBff5fHmDWq1ak5BSMjU1xeTkZMXrEaWoRNX8MJtK1OzZ2erqKiMjI/odudmWZWbAzNdb7GfPuFhv\nXAg3Kh4VOS4vL9Pa2lqxuvZOMJQGSj4zX0bh/Pw8gUCA7u5uXnjhBWw2GysrKyWv5zz55JM89dRT\nXLlyhTfeeAOPx0MgEMBms236ebhFqs5W4Dpw3IrDs6FKfHmgBC7FDMCVoGN1dZV77703552guvM2\nE5qm8eqrr1JfX8/FixcrsmkqlvhUZdnY2JizsjSL+KSURKNRent7OXDgAJFIhPn5eUKhEK+99pre\n4jNm7G0kNsMcLZvi8dq1a4RCIebm5ojH42nOKKUu1t8JFZ9ZUBmFQgiam5s5deoUNpuNV155hV/6\npV9ieXmZX/7lX+YTn/hEyWcLIZicnOSd73wnFy5c4Jvf/CbHjh2z4FVUjkpUnYuLi5w/f17/86OP\nPsqjjz6q/tgEvBc4KoS4T0pZkkKzHFSJLwsyw2gLEcnKygp9fX10dHRw5MiRvBcEM51RlNVZKBTi\n2LFjZdt/GVGI+KSUzMzMMDY2xrFjx/JWlma0OsPhMDdu3EBKyYULF/Q28Y4dO1hdXeXEiRP4/X6W\nl5cZGRnRVwEUEW4md5hSYSa52O127HY7+/fv138uuRbrVWJ9vse+Uyo+M6FmfE1NTTz88MN87nOf\n41vf+haJRILl5eIDwB977DEeffRROjo6uHLlCr/3e7/H97//fV599VX+/M//3MJXUBkqaXVuJEMW\n8gAAIABJREFU3bqVa9eu5fr2GPAh4H/fDtKDKvHlRaGVhkQiwcDAAKFQiNOnT1NXV1fwTIfDoRth\nVwJlaF1bW0t9fX3Zrc1M5COrWCxGd3c3TqezKHu1Sio+Y0pFZ2cnvb29+kK88YKcWdmoVQCVqKBp\nmk6Ezc3NWeOdzG7JmgWzqyrjedkW65UzyuTkJGtra9TU1OjvnXGxHu5e4lOfeePnxeFwlNTuvHz5\nMpcvX0772tDQkDlP0mJYNeOTUo6R8uPUIYT4YIln/GWxf7dKfHmQb4l9aWmJ/v5+9u7dy7Fjx4q+\nQJXrralgrLiUyfPLL79cdEu2EHIR3/z8PENDQ0VFJRU6qxDi8Tjd3d3Y7fY0gi2GnDITw9VSs8/n\n070z1U6cavGZic1IoEbk+pxmc0ZRi/ULCwsMDQ3pKQvqBmIztzqt+DkkEgnc7pQzlvIOvZuwAc4t\nz6x7CimILF8DqBJfJVC/0Nkqvng8Tn9/P9FolHPnzum/CMWiElVnJBKhp6eHmpqaNEIwUz2ZeVY8\nHqevr49EIpE3EDcbyqmklpeX6evrW5c4X25VlrnUrLLhjGGzNTU1aJpGMBikrq5uU8zoYOP37jIX\n6+PxuH4TsbKyQjgcpr+/Py1BoVxYQXxWrEfcKenrbxLsN/z3LlJG1F8F/jcwD2wHPgD865v/XzSq\nxJcHmRXfwsICg4OD7N+/n507d5b1i1WOFZqx7ZcttshMlxnja1IkVO7rLYWQk8kkAwMDBIPBrDcU\nRuKr5KKWLWx2bm6OmZkZRkZGSp51ZYOZF9zNQsKQCphV1fS2bduYnJxk+/bt+Hw+/WYw1w5cIZhN\nfLcjhPZuc2253ZZlUspx9d9CiP9GagZojCbqB74nhPgMKUuzR4o9u0p8eaCMqmOxGH19fWiaVrHR\ncqmtzmg0Sk9PD06nM2eUj9n7clJKent7c5JQsSi2SlOL/u3t7Rw9ejTn7lopZ5byHFWL7+jRo/qs\ny+fz6TZXKnU933K4FdjMbVPlW5m5WG/cgQsGg9TW1uoVYb737k6YGRqJLxAI3HWuLbChzi0PAV/I\n8b0XgH9XymFV4ssCY6tzZWWFkZGRda23clFKq1MFth4+fFgXb2SDmRWf3+8nGAyye/funCRULAr9\nW2MmYOaif7azshGB2S0tIxEqmyuVpjAzM0MgEMiZpmA2NrrVmQ/ZiCVzB864WG9MWs9crM91XiWw\nuuK7G1udG+zcEgXOkz1s9gIQK+WwKvHlQDQaZXp6WpfRlzLbyodi4o6UetJutxcV2GpGxWd0m6mt\nrWXPnj0VnVcIag9Q+YgWuugJIdDCYeTkJHJ5CeF04lxaRGoawuLdvczlcOX3uLi4qIs+FBGa6cqz\nmYmvmOdWaLF+dHQUSHllSilN+x2D4lxbyjnT2OqsVny3FX8LXBFCJIG/49aM7+eB/wx8sZTDqsSX\nBWtra1y7do2tW7dis9lM/YUsVPHNzc0xPDxcsnqykgvu2toaXV1dbN26lQsXLvCjH/2o7LOKwczM\nDKOjowX3ABWklLhGhtFeexWkhqxxgdTwDA6hrQUQP/lORAU7jKW2TzP9HuPxOH6/H6/Xy+LiIpqm\nEY1G9crGzM/PZkG5FVq29ZPV1VUmJyf19y8ziaIc8s+MEDIDmRXf3UZ8G5zH9wmgEfh94L8Yvi5J\niV5Kcg+oEl8WKJ9LdVdvJnIFvcZiMXp6erDZbCVXmOWuSKhg2tnZWY4fP2654W48HqenpwchRFF7\ngApaVxd1r79Gcl8jInADW2IFELicDSRXWxBf+ydsP/0wwmNO1FOpcDqdbNmyhS1btlBfX08sFqOx\nsRGfz8fk5CTJZFJPAGhubi56RnynV3zFQHllBoNBfR9OLdYPDQ0RiUSoq6vT54TFio2saHUayb5c\nn847GRuZxyelDAO/IoT4f0jt++0AZoGXpZQDpZ5XJb4sEELgcDhMz+TLBbUjd/DgwZJ9/6C8GV9m\nZJHVgo1cawqFIMNhki/9M3X2N7AtBsDRgGZvACR1yUnsy4toNScQP96DePAd1r2AEmCz2dKMj5PJ\npL5CMTs7qxOjIkK3271pCS4XrBKjGBfr9+zZkyY2Mi7WGzP1shGcVQvx6ue0trZmWqbmnYSNTme4\nSXIlE10mqsSXB1aksBsRi8Xo7e2teI5YqrH09PQ04+PjdHZ25owsMuuOXkpJX18fa2trZSlEtYlx\ntPl/xs0y0r0PIWz69mrC1oJWU489dp3k67WICxcRZd6FW6mgzFQ/Gu3CBgcH9apG/R21BrCZK77b\npcLMJjaKRCL4/f6si/XKps6Kis+ItbU1du/ebdn5mxEbHUskhKgHPgK8jZSx9a9LKQeFEL8AvC6l\n7Cv2rCrxZUG+BXazoHYCzVCLFkt80WiU7u5uXC5X3lajOq/SC0cgECAYDBblYZoLiZHrkJwhZm9j\nXYNQCITNgXQ2I/yvw9oalEF8t5tcslU1mfl6tbW1OJ1OPWh4s9l5baRJtdvtxu12py3W+/1+/H6/\nblNnt9txu91EIpGKFutz4W7c49tICCF2A98htcjeB5wgNfMDeAfwTuDfFntelfjywIqKLx6PEw6H\nmZ6erngnUKEYglaimUKrEVA58RnXFGpra9m3b19Z5wDI5R5SDkV5LrKOBoRcILkygqOMVrGZKKdy\nzJavFw6HmZycxO/3c+3aNb29p1YoSiVCK4JjN8venXHGCqnW8vDwMJFIZN1ifXNzc1nuPJnv392o\n6txgcct/JbXScAiYIX194bvAlVIOqxJfDgghTK/4FhcXGRgYoKamhpMnT5rirQn5Ex/i8XjJ7dRK\n1iMikQg3btygqamJS5cuVawQlQ1A0oZw3qwykEgk4iYRSikRmgZ2B7Lye4hNASEEdXV1tLS04HK5\n2LdvX1om3ODgYFr7tJg4JisNr82AmURqt9v18Njt27enLdaPjo4SCoX0xHqPx1OUKUG29PW7seLb\nKHEL8C7gUSnlhBAi88M+DXSUcliV+PIglwKzVCh/z1gsxvnz57l+/bqpTiu5CFoZaR84cEDfoyoG\n5RKfWrjPXFOo5CIpduyGGoGIJpB1IG8eo0kNTWpITUIwiGytR9R58h+WB2a7wZgB4/umMuFUW1zt\nwxUbx7SZiUqdZ+ZMzvj8Ci3WK1MCYxJF5k1pJvHdja3ODZ7x1QCBHN/zACVlvVWJz2Io8jH6XRaz\nxF4KMolKxSWFw+GyBCWl7rWpqhJYt3BfqUjDvu9+Eu3P4RwOEVhYxF5fh8PhIByNUOuowR4MIWtt\naDt2Y2u9h1gsht1uRwhR9IV5swpI8r1vpcYx2Wy2TU98t8u5Jddivc/n028kAN1dRhkTZFZ8Vq//\nbDZsMPFdB34W+EaW7/1r4NVSDqsSXw5U6gmZSCTo6+vLmuJgdgvVeJ7X66W3t5fdu3eXFJdkRCkV\n38rKip6Mnq2qrDSTz7b1MMmOk9TY+3CvughNzxKNxXDYbMRElMi2FtzNUcSBy9Q7UykLUko0Tbv1\nHkejaMEgdocDe3Oz5U4vG4FCcUyxWIx4PM7c3FzFSQpgfgVpttNKqRVkTU1NmimBWqxX7180GtX3\nXkOhUNlenc8//zxPPPEEu3bt4rnnniMej/Pe976XQCDA5z73OS5cuFDymbcTG0h8nwX+/uZn7ks3\nv9YphHiYlNLzp0s5rEp8RaDUX3K1s7Zv3z7a29vX/dtKnVYyoc4bGBjA5/MVHYqb77xCxKdpGoOD\ng6yuruatKsslPiklyWQSKSXO+z5O9J9/j0CkF+2eNpqb20FKwE8y6sfX9jYWHYcIXb1KfX09LS0t\nNDc344xECF69SqgvpXKWgK22loZz56g7eRL7JndUqahSzohjUrPXaDSqCz4aGhrScglLeaw7oeKr\n5Dy1WK9a9l6vl6mpKRYWFrhy5QrDw8N85CMf4YEHHuAnf/InOX78eFHnPvnkkzz11FNcuXKFN954\ng6mpKa5evco999xjejak2dhIcYuU8v8IIT5KyrXl125++S9JtT//vZQyWyWYE1XiK4BSFI7FthjN\nrvgikQjz8/McOHCACxcuVHwnXoj4AoEAXV1d7Ny5k/Pnz+d9vLxnySUgROpjuA2EQ6/W1L+x2Wz4\nEg76Pf+Gg9vvw7PwEgTnwWZDazuG8/Bl2vecp51b6QBer5fha9cIf+MbuJxO6nftor6hAXdNDclo\nlMB3v0tkchLPT/0UwuHQq0QzsFkTFYQQ1NTUsHfvXj2OSe0SDg8PEwqF0iKFCjmkbPaZodl7fJqm\nUVdXx8mTJ/nqV7/KW9/6Vq5cucL3v/99/uVf/qVo4jNCCEE8Hue+++7jwQcf5Nlnn+XEiROmPWez\nsZHOLQBSyv8hhPgr4D5gG7AM/EBKmWv2lxNV4ssB9UutVhoK/RKpll8xiexmzfiklIyOjjI7O0tT\nUxP79+8v/I+KQC6yMlqcnThxoqhWT9aKTxsHvg/MATZStZgLqZ1H4wyaJvR/Nzg4SCAQ4MxbHrh5\nI/ELaPEYNocTMt5jJWJoqKvD/eKLyMOHSbjdBINBFhcWUqGzLhf1TU04Bwao3b0b16lTTE1N4XK5\ndGWsmg+WeyG2QtxSKTRNSzvLKPjYvXt3yXFMm73is5JI1c/l2LFjdHZ2lnTOY489xqOPPkpHRwdX\nrlzhL//yL/nc5z7H008/zTPPPGPa87UKm8C5JUj2hIaSUCW+AihEUolEgsHBQYLBIGfPni2qXWFG\nqzMUCnHjxg1aW1s5c+YMfX1FmxYURDbii0QidHV16T6mxV5U1p2lDQBfBtkC4pbzhSbDaNp3gFmE\n+NeEQqll+23btnHmzJm0i7bNmb9FGZueJun3U7NrFw5Sisi2tjaQkmgsRnBtDZ/Nxtw//APR1VVa\ntmyho6MDh8ORNh9UP6NKibBcmEl8Usq8zz/TISWX8tGYQrGZE9PNrviynVfO8718+TKXL19O+9pL\nL71U0XO7WyCEcJCq9nYD69ppUsr/WexZVeIrALvdnnOJ3SgkKSW7rpJWp5SSyclJpqam6OzspLm5\nmVgsZqlKVC2/Hz16VBdPFIu0ik8Gga+D3AEitXQnkTfJpoaUOUMf8wsNjI7W0tnZWZZyLjo2hshm\nDCAELpcLV00NCEF0fp5j7e0kPR6mp6cJBAK43W5aWlr0dh+QRoSKQDaCCCtBqcSSTfkYjUbx+Xws\nLCywtLREMBiktbVVb49Wupe6mcUyRuLbjE46twMbqeoUQpwFniXl3JLtgyKBKvFVCmOrM5NUlJBk\nbW2NM2fOlDyULpf4IpEI3d3d1NbWcunSJf0X0ewEdnWecfm9mFzAbFCWWwDIQUBLI73UfC3VtUwm\nJbOzUez2H3Lhwqew28v7eMp4HJEr6TuZZGp6GpsQ7N27l9aWFmp27kwLnfV6vczMzLC6uorL5dLF\nMooIlfBG3RBZRYKFqrRSz6qUWFwuF9u3b2f79u3E43H27NlDLBbD6/Xq2XrGFYpyPi9mwcz3DlKd\nHSUYuxuTGWDDnVv+B7AG/Awpy7KSgmczUSW+Asis+FSVt2vXrrITyu12O9FotKR/o5bDs1VdZotl\nbDYbgUCA4eFh9u/fT3t7e0Vn3ZrxDYNsRApV5cmbf0ewthZkZmaGbdt24GleA8LcsuIrDY62NrTu\n7nVfD4VCTE9Ps3XLFjweD/GZGewZS8iqylGvORKJ4PV6mZ2dZXV1FafTqROhClBVRAipnUZjTFQl\nF1+zW51mO7fU1NTQ1NSkW4UZVwAqiWMyC1a1Tu/G9HWFDRS3dAI/L6X8mhmHVYmvAJS4JZlMMjQ0\nhN/vr3hdwOFwEAqFivq7xpy+XFWX2eq6paUlIpEI58+fr1hinU58CSRCJz0hUlsJc3NzhEJh9u3b\nR02NE8kaqc5FeXAfPMjq976HTCZTO3tSsri0xOrqKnv37KHG5SK+tIT7nnuwFxDouN1udu7cqbf7\nFBHOzc0xMDCgz708Hk9KRLO4SGdnp06EyWQybZl+o1pkVqgwM8/LXAF4M8Ux3e0htLDhC+wDgGl3\nG1XiywFjQkMgEGB0dJSOjg4OHz5syrpAMRWa8vYsN6evVKytrXHjxg3cbjd79uwxZa/I2OpMyu1I\nbRqoQwiIxeJMTU3S2NjI/v37br6vMVLuROXfWNgbGmi4eJHASy9h27GDqdlZ3G43Bw4cQAhBcm0N\nGYvRcOlSyWdnEmE0GmV5eZn+/n7i8Tj19fUsLCzoZAi3ZoRwiwjV//IR4Wau+IqZcxWKYwqHw/ou\noZqfblYizCS+u82uDDac+D4NfEYI8bKUcqLSw6rElweq+gkGg5w7d8609kah1qRyfVHenla3iKSU\nTExMMDMzw/HjxwkEAjlNr0uFIr5kMommHcEmXgE0vN5VlpeX6OjoyKieF0gJtyr7aDZcvIjf52P8\nm99k67ZtNDY3k1xZQQuFsNXX0/ZzP4ezQEpFMYjFYkxMTOjONWrmtbS0xPDwMDabTV8kb2xs1N8P\n/Wbg5udAWYoZyWQzE185M7R8cUyxWIxXXnklLZew2LT124FqqzOFDVxg/4YQ4kFgUAgxAHjX/xX5\n9mLPqxJfDoRCIa5du0Z9fT3t7e2mftDzrUgY9wE7Ojos/8VXawr19fVcvHgRu93O2tqaaWIZIQTR\naPRma2wrieQplha/SSKxgwMHDmRIxJeQeICTFT2mpmkMDw8T2LaNez/9abSJCRKLi2C3477nHlx7\n9iAqVCBKKZmammJ2dpaTJ0/qn4+amhpdAAKpmZ+RCIUQNDc309LSQlNTk656zUaEZi7DW7EnV+ln\nU8Ux1dbWMj8/z9mzZwmHw2lp6yploZQ4JitMBKqtzo1dYBdC/BbwKWARWAUqEjVUiS8H3G43p06d\nIhwOs7y8bOrZ2VqdyWRSX9Yudh+wUqg1hSNHjugCBfX8Kr14qIt5W1sbY2NjjIyM4Ha7CQRqOHLk\nfnbtnABmARcQR5IEdgLvppJWfigUoru7m61bt97a/zOhsjNCqV2dTifnzp3Luy/mdDrTPCDj8Tg+\nn4+VlRXdDNlIhEoYo+ZjbrebeDyetSIsBZs5lkiRsopjqqurSxMX+Xw+5ubmio5jsiJ93Xjm3ZjM\nABve6nwceIqUPVnFSr4q8eWA3W6nvr6eeDxuegp7ZqtzdXWVrq4u2tvby04qh+IvRolEgt7eXpLJ\nZNaMvkrXI4xL4K2trbS0tDAyMsLS0hLbtu1gcrKBsbEWtmzx0+IRNDS14XQeBLaTN3C2AObm5hgb\nG+PYsWP6fM1s+P1+ent72bdvnx4RVAqcTue6VAWfz4fX62VsbAxN02hsbMTv99PW1sa2bdtyVoSl\nEKEV8zOziS8bssUx+Xw+lpaWcsYxWbFnl0l8d2PFt8GoA/7ODNKDKvEVRL4F9nKhWp2apjE6Osri\n4iKnTp2q6C6yWE9Rr9dLT09PWkxSrrPKgXpd6hzVSm1ra+PSpUv642maht/vT1U+Yz7i8TGam336\nPKyYwFzjY/b19ZFMJjl37pwl+2PKOGBubo5Tp05VpOo1wuFwpKWHLy4u0tfXR3NzM36/n9deew2P\nx6Mv1TtueosaiVBKWTCKaTMLR0ohqmwpCj6fT49jklJSV1dHIpEgFouV9DkqBPX+BQKBsm563gzY\nwIrv66SG/y+acViV+HIg06vTTNhsNmKxGFevXqWtrY2LFy9WfIeqWmS5iE/TNIaGhvD5fAVbqeUQ\nn9FcWikWZ2dnGR8f5+jRo7qyz/gYxvSAZDKJ3+/H6/UyOTlJIpHQW4D5iDAQCNDd3c3u3buzJmGY\ngXg8Tk9PDy6Xi/Pnz1u2rD46OorX6+XixYu6oCnb+2IkQlXhFCLCNwvxZSLzxiGZTDI/P69/LhKJ\nxLoVikoRCoWqrc4SYQJd/jHwzM3P8DdYL25BSjlS7GFV4ssDIYTpy+GqcgiFQly6dMm0lpyaG2ar\ndtbW1ujq6mLbtm1FpTeUSnzGCCEhhF6BAZw/f74oKyu73b5uB8x4wU8mk3g8Hr116nA49ArMKC4x\nG6q1eeDAAb3KMBuxWIzu7m4aGxs5c+ZMGgnke1+mp6eJx+M0NTXpS/U1NTVpVbf6uZjdrjcTZrYm\n7XY7DQ0NeDwejh49iqZp+i6hGXFMcDeLW8pXdZpAfMrQ9P8BfqfSh6kSXwGYWfGFw2G6urpobGyk\nvr7e1DlUNrIy+nqeOHGiaN/LYokva4SQz0dfXx979+7NGkxbLHJd8FdWVhgbGyMYDOJ2u9m/f7+p\n7SwFlUSxuLjIvffea5nYSL1f99xzjz73y4fM90XTNFZXV1lZWWFmZoZ4PE5jYyOtra14PB5cLheR\nSIS5uTl27Nihr6lUKpYxE1YmKdhsNj1JvVAcU3NzM/X19euIMFPodbfu8bGxsUS/RiWuFhmoEl8B\nmKVwnJmZ0YUXra2t/OAHPzDpGaaQWZlGo1G6urqoq6tL8/UsBsUQn1HAoi5aIyMjrKysWEIU6oIv\nhGBxcZFjx47hdDr1ilDTNJqbm3XT5ErmfKoCq6+v59y5c5a1NicmJlhYWKjo/bLZbOuWxFdXV3Wb\ntXA4TCwWY+fOnXqlbLxhKVcskwmxtoxt+jrCOwl2B9r2I2g7jkFN4VloqWnpxZyX63Xki2MaHx/X\nb6iMu4SZO4tVVecGPLaUz5h5XpX48qDc9HAj1EXU6XRy6dKlih3sc8FIVvPz8wwNDa1bUyjnrGzI\nJmDp7u6mpaWFs2fPWkIUSgjk9Xo5c+aMPqtRvqXJZFJfExgbG0NKqc8ISyFCn89Hb28vBw8eLKoC\nKwfGmaHZxGokwpqaGqampjh48CDhcJiBgQEikQiNjY36jNDtdldGhJqGvf9F7MP/AjY70tUAUsMx\nPwDdXydx73vR2vNn1lmZpFAI+eKYVGKHw+HQjQlqamoIBoNlpYY8//zzPPHEE+zatYvnnnsOIQTP\nP/88Dz/8MD/+8Y85evRoyWfebmx0Hp9ZqBKfhVAEdOjQoazzITMFB3a7nVgsRldXF/F4POuaQrHI\nF0SbKWBRKwTZBCxmQSlDW1paOHfuXNb3zG6309bWphOhcU1AJQcYxTKZNyBSSsbGxlhaWuL06dOW\ntTZXV1d1Va1VNnRqxiql5Pz58zoJ7Nu3DyklgUAAr9fL0NCQbhumiLC2tjYrERozCY03g/ah72Ef\n/A6ydQ/Ybl0UZV0LxMM4Xv1fxF2/hmzLHZK8mdLXs8Ux+f1+BgYGGBkZ4bHHHiMcDvOFL3yBy5cv\nc//99xc973vyySd56qmnuHLlCm+88Qbt7e0899xzXCrDOm8jsMHpDAghLgPvI3seX9W5xQqUQlLx\neJy+vj4SiUROAip2/aBYxGIxent7ueeeeypWN+aaF2YTsEgpLVshAFhYWNCzAJUCtBhkqv2yEaEi\nwbq6Ovr6+mhsbLS0tTk9Pc3MzIylYpxwOMyNGzfYuXMnu3btWvc5EELotmHGmZfX62VkZESfeSki\nrKurWxfOqz4bWjhAzeB3ka2700hPh7MWWdeMo+d54g/8es7nvNnT19VOb2dnJ1evXuXtb387999/\nPy+88AJf/epX+fznP1/ymUIIvv/979Pd3c2NGzd4+umn+cxnPmPac7YCG+zc8ingv5BybhmiGktk\nPdRdbjFksry8TF9fX949Obg1k6uU+JQ9l8/n48CBA3quXCUwEl82AYtSOlYqYMkH5WSjUiIqJdZs\nRKhy9xYXF6mtraWxsZGVlRWam5tNbUknk0l6e3ux2WwFnV4qweLiIsPDwyUt8BtnXkb/TLVQHwwG\nqaur04nQ5XLR19eXeh9ne0kmE2gaCKlhEwJhy/i81zYjViYQgQVkY3ZVrBUVn5mCp8zfU03T+Nmf\n/Vne9773lXTOY489xqOPPkpHRwdXrlzh2Wef5b3vfS8PPvggH/7wh017vm9S/Huqzi23B8aEhkQi\nkfeXSYXTKkPrQvtC+fw6i0UwGOTGjRts27aNXbt2mXaxVsSXTcAyOjrK0tKSqUvcmVhbW6O7u7ti\nJ5t8sNvtrK6uEovFeOtb34rdbk+zElOemkosUy5Zqdei9gytgJSSkZER/H4/Z8+ereiir/wzGxoa\n0sQfXq+X4eFhvF4vDQ0NOJ1OkgtzuJxuhM2eChWWGjIhb55jw3bz5yaEDSKrcJuIz+rWabkjisuX\nL3P58uV1X//Od75TydO7rdjAGV8TVeeW2wu10pDrguL3++nu7i4pnLbYaKJsMK4pHD9+HI/Hw9jY\nmGm7WipBwJgwHo1G6e7uxuPxWNoOnJmZYWpqis7OTst2pZTi1ePxpIlxjFZiylNzaWmJoaGhtIX7\nYolQLfCfOHHCMhWgmus2NTXd8iY1EUr8sba2RiwW080WvF4v8yt+3NOTaE1J6urqqK2rTS3eS3Qi\nBJDJJEkpIEc4r6ZpplfYZlbVxvOsMMC+U7DBXp3fBN5C1bnl9iHXErtqMyoJfylzm3IX49VFu7a2\nNm1NwZj6XQlUledyubh69SrNzc0IIVheXi55zlYKlPGzw+FIE2SYjeXlZQYGBjh8+PC6JHsjMj01\njSkLRiJU+3LG55tMJunv7yeRSBS9wF8O/H4/PT09lipQpZQMDQ2xtraWNsutr69H1L8Te3SYWON2\nQsEQXq+PaDSCw+Ggvq4ed20tboeAGhfSsxMtRybhnVDxGX+G6nnfbZAIkpolv5ctQogXSQlUHsrx\nd/498KwQQgLPU3VusQ75bMuUG8rWrVu5cOFCyb9o5RDfwsICg4ODHD58eN2FzmazVZyhZxSwnD59\nWl/FiEQi2O12BgcH09p/Zl3QKzV+LgaapqW1A0vNOMyWsuD1evWfid1up6WlhdraWiYnJ2lvb88q\nLjEDRqHMvffea1nLOR6Pc+PGDTweD6dPn16/2N3cAS27qFlbpqZ5m67qjcVjhEIh/H4f/uUx1jou\nIeaXaG5uprGxcd3cOBqNUltbuy6tvlxYWfElEgnLbso2PSQkEpa8dh+wlVQYZ55HJwB+wz25AAAg\nAElEQVT8HvC7Of5O1bnFTBiNqpWjx+zsLMePHy9rn0edWSzxqWDafGsKdrudSCRS1nPJJ2DZs2eP\nLtJRghDjHCxX1VPs46oVAivdUdSeYXNzM2fPnjWFjDKJMBaLMTY2xsDAADU1NczPzxOLxXRRiFkX\nS6MdnJVCGeV1mddRRggSZ9+H4wf/E3xT0Lgd7E5qnDXU1CZoTniRZ95B8MTP4F1dY3Z2lv7+fpxO\np94y9nq9BINB9u7dW3EChYIVxKdulO7mEFopBclEeZSxuLjI+fPn9T8/+uijPProo/rRwGlgQAhh\nzzHHewa4H/gjoI+qqtN6KCFKKBTSZ0OXLl2q6M60WOLz+Xz09PSwZ8+evMG05SYqZBOwjI2N6YkR\nxmrC4XBkbf8tLCwwMDCgX9BaW1sLhoaqmaGVKwQAS0tLDA4OcuTIEd3my2yo5fpwOMxb3/pWnE6n\nvvCs3huHw5F2k1DO61Wfv/b2dktDimdnZ5mYmChu7aK+lcQDj2IbfRnb6MugJUBqUOtBO/XTaHvO\n4bY72FnfqCuAo9Eoy8vL9PT0kEgkaGhoYG5uTk+ph1ufSyidCM1udSYSCf33IBAI3JU+naCIr7wb\niq1bt3Lt2rVc3+4AXgb684hXHiSl6HymrCeQgSrx5YFR1bm4uMjIyAjHjh0zZc5ViPiM88PTp08X\nbGeV0zrNdGBRZNTU1FQUGWVWPdFoVF8R6Ovro6amRjeVVmnjcIuMCs3ZKoGewh4IcO7cOUv8PCFV\nTd64cYOtW7dy+PBh/TVmJrErIpybm0urelRFWOi9LmdVoVRomqavkJw7d674Vra7Ce3Yu9AOvQ0i\nARACalsgx2vSNI2pqSn2799PR0dH2k3C4OBgmpBIfW6MHYlEIqETYDYitLLVebfalQEgKZv4CmBa\nSnm+wN9ZAubNesAq8RVANBplenoau93OxYsXTZtr5SOqYDBIV1cXW7ZsKXp+WErFl82BRS2KV1IZ\nuVyutNDQSCTCysoKU1NTrK6u4na7dbItZ85WLIwZgFYoHRUUgRcj+skkQnWTkEmEmdWylJLh4WFW\nV1crXlXIh1gsxo0bN2htbU0j8JLgcEFD/p+p1+ulr6+PY8eO6TPBbDcJmYpaY0q9+qznqggrqfiS\n4RBrL/418Zf+GkdoBrDhdm4j/hMfRL7n3xIMBu/qVmcivmHzzc8DHxVCfFNKWbGKr0p8eRAMBrl2\n7Rpbt27Fbrebqs6z2+1Eo9G0r0kpmZqaYnJyUl9TKOW8YhMVjA4smqbR399PPB43ZVHcCLfbTXt7\nO+3t7frOoYqBee2116irq9OTBurq6kwhqMXFRYaGhixVoBrJqNxqMvMmIbNadjqdeDwelpeXLSdw\npQ61sgIH9Bgpo9dqNmSGzarVEjVbhlv2c6paNlqsRaPRNOVosSSYCPjw/ddfwOkdxOaoJeneBkhq\n1uapefH3Wer+Jr4zH7lrW50bjBbgBNAjhHiB9apOKaX8z8UeViW+PKivr+fixYu61ZWZcDgchEIh\n/c+qzehyucqqLAvtBWYTsCjfSCtDXOHWPpuxTWd0CBkaGtLDPRURlip0UUG7ykDAqspIrZM0Nzeb\nSkaZRLi4uEhvby8NDQ0sLS2xurqqV4SNjY2mzbCmp6eZnp621J9U0zT6+vr0Sr/UNmS21RKVuq7s\n5zweDw0NDUxNTbFnzx49qR6KT6n3/ulHcfoGSdTvQGBD/WTjziZsNU5q5n9My4t/QkPbA+W9EXc8\nBFpywyjjPxr++3CW70ugSnxmQAiB0+m0LIVdEVW+NYVSzstV8eUSsCwsLFjqG6nUqMosOXMXKtMh\nZG1tjZWVFfr6+ohEIjQ1NekzwnwVgso53Lp1K4cOHbKMwFWbzsrKSFX9s7OzXLhwQSejSCSC1+tl\namqKQCCQNj8thwiNZGSlOjQajeruQrt37zZNUWskwkQiwczMDIODg7hcLj2OSVWESpyWjwijk4M4\nZ66SdG9DsP69tNkcJF1b2OG/gWfLfRW/hjsSErBmxlf4oaU0Vf1WJb4iYAXx2e124vE43d3dRKPR\nitIU1HnZKr5MAYsx7fv8+fOWqSlVNWlch8gHo2ekkrYHAgFWVlbo6ekhFovh8Xj0qke9V2o2aZwZ\nmQ21drG8vFywTVcJlKenEGIdGbndbnbu3KkrI43z00AggMvl0gUhhYhQCXK2b99uGhllw+1qoS4t\nLTE7O8vFixepq6vTI6q8Xi8TExMkEom01qjT6dTJT90QBl/6P9ikhshmti0lQgAOF0ImOBAcs+y1\nbGpIsWHEZzaqxJcHRlWnWXZgCuFwmPn5eY4cOWKKND1zxpdPwGJ1xaJmOZVUk8bk7P3796Npmp7A\nPjU1RSKR0ANCrV7i7urqor6+3rKsQbi1qtDR0VGU0bhxfgqpz5OqCJWQyNgaVZ8vVbVaud4Bt1Yi\nrPzZKI9SNWtVHYXMiKpkMonf79dDixOJhH4T5fF4qKmpQQaWAUHKkUz5jd58nNSf1IPSYDP3WnDH\nQAKJN4djTZX4CkAIYWrFpxxEFhYW8Hg87Nq1y5Rzja3TbAKWgYEBYrGYpfMvVU3W1dWZXk0aJe6h\nUEh3FHE4HHR3d+vBs2a6yqgl/gMHDmTNUzQLCwsLjIyM0NnZWbYhgsqQyyTCiYkJAoEAbrcbm81G\nKBSydJ4npWRwcJBwOFzaSkSJSCaTunVfNlcZI+x2uz47BvSbKK/Xy/T0NPF4nJaEoE3TAJky1U4Z\njpKUWuo/b1Z9miYRDdbdMGx6mNv4KhpCiNQPJw+klFXnFjNhVsWnLthtbW2cPn2a3t5eE55dCiot\nPrO1GQgE6Onp0SsJq9paKysr9Pf3W+obCalw39HR0XX7bGa6yhirVisdZdSuYaYPphkwEmEikaCr\nq4tYLEZDQwOvv/46tbW1+vvT0NBgyudCWZw1Nzdz6tQpyz5rkUiE69evs2vXrrISL4w3UZD6OSw1\nOtF6/o5wKIgUNuw2u24D6HK7EAKS0TBroQgTrZs/Kd0SSDaM+IDfYT3xtQH/CnCRcnYpGlXiKwLl\nuqIoKG/FiYkJOjs7aW5uJpFImN4+jcfjzM/P09raisPhYHx8nPn5eY4fP27Z0q3RA9Pq+Zexas0k\niXyuMoODg2nOKflcZRKJBD09PTidTktnoGpvrqWlpWDFUglUMK3yDoXU51FVhOPj4wQCAWpra3Wx\nTDlEqLxrra6OfT4fvb29ps50bTYb246fY3HfT9Aw8RJa3U5i8TixWAybEMzPzRONhGiribDQeo6P\nfPxxUx73jsMGEp+U8kq2rwsh7MBXAH8p51WJrwAqdWJX7b+ampq0NYVKYokyoaq8zs5OlpaWdPus\nuro6Dh48aNmMJRwO093dTWtrq2kemNkQDAbp7u7OmSqeDeW4yih/SivNsuHWxfvQoUN6MK4VUFV4\nJkkIIairq6Ouro6Ojg6dCFdWVhgbG2NtbU0Pn21tbU0lMeR5z1Wr1sr4JUCPrMrXqpWaBmX+zrZ8\n9E9Z+ewvIBZvIDQH9Q1bEAKcDYKQjDGm7eKL3m18+swZnnnmGc6cOVPpS7qzIIHKfPBNh5QyKYR4\nEvgC8MfF/rsq8VmIxcVFBgYGOHTo0Lq7YJXqXgkyBSytra0kk0mWl5fp7OwEbq1KqAt9ptihXMzP\nz+sWblapKeHWDmAl8y8o7CoDqSrx6NGjlkb8qFUFq+ds4+PjLC0tFeWQYyTCXbt2pYXPjoyM6Cns\n6kZBEaExANfsVm3m61G7nmfPnl03N9QiESJXrxL82tdITk+Dw4HrzBnq3/UunCUEGdvqGlj++T9A\ne/nLbB36Nva1ScLhCF2zEY78yu/yng99kp++ecN6N+fybUK4gJIGr1XiswAqjy0cDnP+/HlLrLmy\nCViMogIlYFE2UOpCr8QO5bqmGFuOZju9ZD5Of38/yWTSkkw7pYrcvn07vb29JBIJdu3axfz8PMPD\nw6a7yiSTSXp6erDb7ZbuzSWTSb3DUK4KVYXP1tfXpxGhmp8Gg0Fqa2sJh8M0NTVx+vRpy1rCaj7Z\n0NCQdW6Y9Pvxfu5zxMfGsLe0YN+9GzSN6PXrRF95hfr3vIeG972v4M8vkUjogqn9H/40Uj7BF77w\nBb75zW/yd3/3d2mV+d0cS8QGCVqFEHuyfLmGlJvLfwFyOmBnQ5X4CkD9whTrAajS2Hfv3s2xY8dM\nb/9lVnk2m421tTW6u7tpb2/nSI47XKP8PZtrSmNjo36hzzWnMz6OVVlzxsexWpCjPFGVSEIIoS/T\nm+kqox5HOeRYBSWeMvtxjES4e/dugsEgb7zxBk1NTSSTSV5++WXq6+v11qhZ9nPhcJjr16/ru6CZ\nkFLif/JJElNTOPfuvfUNux3H9u3IRIK1L38Ze3s7dT/xEwUfZ+/evezYsYN4PM4nPvEJ4vE43/jG\nNyzzlL0jsXHiljGyqzoFMAx8rJTDqsRXJNRKQ65VANX2WVxcLDmNvVhkc2CZmJjQswGLna9kc01Z\nXV1lZWWF7u5u4vE4Ho9Hv9A7HA7d2spKoQyk5jgTExMcP37cUk/Eubk5xsbGsj6Oma4yav5l9etR\nhs6VtoQLQSXYnzhxQn+cbDcK9fX1+vtTDhGqfcPOzs6cnrWJsTFiPT3Y92QrBkA4HNi3biX47LPU\n3n8/IstNq1qyV4/j8/n40Ic+xIMPPsgTTzxhWSV7R2JjVZ2/xnriiwDjwNU8cUZZUSW+IpFvpUHd\nabe2tnLx4sWSfllUq7IQMtcUlOuL2pmrpP0ihFi3LK5MgcfHxwmFQrhcLu655x7L5lLKtQSwpLWp\nYNxpLPZxynGVsXJVwQgpJaOjo3i9XkvTG6SUTExMsLi4uG5umO1GIRgMsrKyklYxq/dHGZXngrrJ\nKqQSjly9Cg5H3rNsDQ0kJidJTE6mV4Wkbn7Gx8f1eevY2Bi/8iu/wic/+Une//73W9ZpuGOxsarO\nZ8w8r0p8BaA+/A6Hg6R/GlvfD7GNfx+SMbTmPcy3voXBUAOdx0+WLPJQaxL5SCubA4uKw7FKFWiz\n2WhtbcVms7G0tMThw4dxOp2srKwwOjqathBcKHC2GCgp/J49eyxtBSpPz+3bt+dsCReDQq4y8Xg8\ntRTd0sLx48ctIz01/6qrq+PMmTOWVSfqpsRmsxU1NzQS4Z49e/SK2ev1Mjg4mNY6bmlp0YlQSsnA\nwADRaLSoOWjS50MUQ/Q2G9JgCK9uFpQox+Fw8KMf/YjHH3+cp556ivvuu0u9OAthA4lPpFwFbFLK\nhOFrP0VqxveilPLHpZxXJb4i4Vm8Sv2r/webww7uZpIIAgM/pFF+l7fuv4SsLV3arKrIXL/g2QQs\nRnWbVbMH5U25tLSUtsCt1I6xWIyVlRVmZmbo7e3F7XbrRFjKDpjab5yenrZcCm9lkKtxIdrn8+mJ\nF8lkkjfeeMMSVxl1s2D16oXy9dyxYwe7d+8u6wxjxWwkwpWVFQYGBgiHw9TX1xMMBmltbeXEiRNF\nkbi9tRUtFiMfPUopIZlE3Bw9aJpGT08PDoeDe++9FyEEf/u3f8uf/Mmf8I//+I/s27evrNd4V2Bj\nW53/C4gCHwQQQjwGPHnze3EhxLullN8q9rAq8RUBMfs6Owb+mmTTTpwNHsKRCKt+P00t7dS63YjF\nHrTvf5bkT165ZfBXBJRrfCbyCVh27txZflBoEYhEInR3d+PxeHKmsNfU1KStBmTugKn5Tr62ViKR\n0KuISlu1+WBsOVrdCpycnGR+fp4zZ86ktYQTiURanlwlrjJw+/bm1L6h2dmGma3jYDDI66+/jsfj\nIRQK8fLLL+tiK1URZoP74kXWvvzlvOMCGQhg37EDx+7dxGIxrl+/rptza5rGZz7zGV555RVeeOEF\nS9dy3jTYOOJ7C/B/Gf78SeAvgE8Af0YqtqhKfGZBCIHt9b8GZz2azYnX5yOZSLBl6xbsN53cZVMH\nYuY1WBmBtnuKPjvbEns2Acvk5CQzMzN0dnZaKpBQIa6lGhjX1tbqCkzjfKe/vz9NCNLa2orL5dIX\nxffu3ZtVrWcWjEnsVrqjKBJ3OBxZbxYcDgdbtmzR29KZrjLFto5VAG4gELB0bgi3J6cPbi3Znzx5\nMk0so2aoRjGRqqrV83Hs3o3r3nuJdXVhz6L+lYkEyeVlmj/+cX0Of/DgQbZs2UIkEuHjH/84TU1N\nfOUrX7H0vXzTYGMX2LcB0wBCiIPAfuALUsqAEOJp4EulHFYlvkJYnUEsDRB3NhD2+2lsbKS5uZm0\nXzEhQNiwjbyIVgLxGQUz2YJi4/E4PT09uN1uy6siNXup1MQ6c76jaRqrq6t4vV66uroIBoNIKTlw\n4IClriVKfWh1CkE5qwqZrjLG1rFylTHaqwkhdB9Mj8djKYlnGppbubOm/FCziWWamppoampi3759\nuphIKT2NRNj8q7+K/NM/Jdbfj83jwdbYCJpGcnkZ4nEa3/9+ggcPMnTjhl4hLy0t8cEPfpCHH36Y\nxx9/vCpiuTOwSsqbE+BBYElKef3mn5NASV6JVeIrABn2EQpHCEs7dXV1uVtLdjciMFvS2Yr4sglY\n1IXbatNnZQe2fft2S1qoNpuN5uZm6uvrCQQCtLW1sW3bNnw+Hz/+cWoerS7yzc3NFV9o1VqJz+ez\ndA4KtwyzK11VyGwdZ7rKOJ1OQqEQe/fuZc+ePZZdqJV/aFtbW0Xin0JQ5JpIJIpKZDeKidYR4dwc\nsXe8g6ZDh6h/7TWc8/PY3W5q77uPuoceYt7pZHZ0lDNnzuByuRgYGODDH/4wV65c4eGHH7bk9b1p\nsYEL7MAPgN8SQiSAx4GvGb53EJgq5bAq8RVAOAFIDU9TE4l83ppaHOkuTTRht9t1s2o1p1CxLmom\nZeWFW+3MWb37pUJpjUKMTDPpxcVFBgcHcTqdadZqpSgVY7EYXV1dNDU1WeodahQZWdFyNJoNzMzM\nMD4+zq5du1hdXeVHP/qR6a4ygN5+Vq1AqxCPx7l+/Tqtra1lk2suIly5dIk5r5dYLEZTUxNRnw9A\nJ9fvfe97fOpTn+KZZ57h7NmzZr80HX/xF3/Bxz72Mfx+P5/97Gd57rnneOSRR/jt3/5tyx7ztmBj\nxS2fAr4K/CMwAlwxfO/9wA9LOaxKfAVQ334MbeteIsFVpMxzgUvGkPveVvS5Ukrsdjuzs7PY7XZ9\nsK+qr0OHDlk6k+rr6wOs3ZkzxvucOnUqq1l2NjNpY7WjUgMKmSXfLuPnaDRKV1cXra2tuirQCqj2\ncyQS4cKFC/rPyGgfZoarDNyqXCsJDi4GwWCQGzdumJ7gkLleEo/Hef311/WfzQMPPEBrayuTk5P8\nzd/8jaWk19fXx/j4uD67/rM/+zPGxsbYt29flfgqeWgpB4HDQog2KeVyxrd/A5gr5bwq8RWCzUby\n5PtxfPezRBw5ZkXBRfDsRu64t6gj1ZpCe3s7S0tLTE1Ncf36dZLJJLt377a0tamqL6t35tR80uVy\nlRTv43K52LlzJzt37kxLDVAekZkXeWXIvLi4aGksEtxyE7EywR5utRxbW1vXtZ8z7cMqcZW5nWIZ\n5SxjtYONyurbvXs3O3fuJJlM8tBDD9Hd3c0v/uIv8lu/9VskEgm+/e1vm3bT8qUvfYkvfSmlrbh0\n6RI/+MEPmJ+f5+mnn6443WVTYWMrvtRTWE96SClvlHqO2ACX8TvK1lxKSSwaJfa9P0brepaG1h1Q\n25oStMQjiNAisq6VxL/6fWjqKHiWUcCiRAu9vb04nU527dqly97D4XCabVilMnzlvKHy+ay8s1fJ\n5fv379dNss2A8SKv3qNkMkl9fT2dnZ2WkZ567xYWFjh58qSl5KostMqtXI2uMt6bbb9MVxlI3Zh0\ndXXR2NjIPffcY9nFWVX9CwsLnDp1yrJ1Ekjd1HV3d+vrF6FQiF//9V9n3759/MEf/IE+SyzGc7dS\n7Nu3j76+Pv7/9s48PKr66uOfO1tWQjYISVjCTggJS+IKKIrWpeJGS/uiUEHBChYRfWtdKFh4Qa0g\njwhWRAGpaKWlWpFN0NRCqMgihEDYQkggCSSZyUaSSebOff/Ae5kJZJvMTULy+zyPj5rJzL13ksy5\n55zv+Z7XX3+dL774ggcffJA//vGPuh5Tb6QeSfBCo7ygNRI/TGLv3qs/V5KkfYqiJDXl3BqLCHwN\nwG63c7GsjNw9n9O/6jBSfhoggSUA54AHcPa7C/zqnnO6moBFlXL37t37itKPqoZUP+SdTifBwcGE\nhYU1WgSi7gT09/enb9++uv3Ru2ZfgwYN0lUGr37IRUREoCgKNpsNWZa1QfGQkBCvlHDVxbQWi4V+\n/frp+oHpOszvrR2Krq4y6nsUEBCAzWajV69eREfXfbPW1GOnp6ejKAqxsbG6vncXLlzQyrX+/v7k\n5eXx6KOPMnHiRJ588sm2k3W1IFL3JHjew8D3UesKfKLU2QAkScJkNlMUNAA56VGorgCnA8wB0IA/\n5poOLOp+sZKSklrLc6oaMjg4mF69euFwOLDZbFrJyGQyuc1+1faHrQZXvdWhrsG1tsF3b+C6027w\n4MFuAUKWZWw2m2at5jooHhwc3OhzUk0DatsO4C2cTifHjh3D4XB4fYTA1VUGLgWI48ePExYWRk5O\nDufOnfO6qwygDYt36tRJVyWqerNltVoZNmwYZrOZw4cPM2XKFN544w3uuusuXY7bLmkFpU5vIQJf\nA3FzWTH/lMk4ZaSM/Uj7NyMV5oCPH0rsSJxxIyEw9KoOLOr4QOfOnRulPDSZTHTq1EkLXjVFIKrS\nLywsTOt9qUtC9e59qcKSq2Wu3kQdFK9tp53RaLxiUNxqtXL+/HmOHz/uphit62YBvDeqUB+qJVjn\nzp2bJUAUFhZy/fXXayVHb7vKwGU7Nb0VompGCWg7Abdt28bcuXNZt24dcXFxuh27XdIKN7B7igh8\nDeQKl5XyEoz/fA3p7DEUH3/wDYSLxRj+/TGGXX/DMeZZ5J5D3eyUzp07R3Z2tlfGB2qKQMrLy7XZ\nv/Lycs0kedCgQc3i6am3w4f6YdoYtxez2UxERMQVy3izs7NrXcbrqqbUW/Ch3jDoPWSvLsE1m81X\nmFk31FUmJCSEjh071ps1q56oetupqWMR4eHhdP9pLdGKFSvYsGED27Zt0/UGTHDtIwJfA6ipzJKr\nL2L86mWc1tNInXojudjkKn6BKJUXMf7zdRz/Mx8pso+bpdV1113ndTcMV6Wfj48PGRkZ9O7dW1MH\nyrJMSEiIR/3B2lBn5jp06KBraRMuzRtmZ2c3+cO05jLemmMB/v7+lJWV0blz56tu+/YWruVavbPx\niooKUlNTNUu5+qjNVSY3N5djx465ucq4zlm6ZpR63zCUl5dz6NAhbSzC4XDw4osvUlhYyNatW3W9\nAWvXtOwAu1cR4pYGUF1djdPpZPeur0gYeAS5+p8Yis6imMwY7GYsGV3xyYtBUiwokg+KomAoPo8z\nZggXbpnMsWPH6NWrl1cVjjWRZVnrE8XGxrp98Kj9QavVSlFRUYP7g7Whyvr17huq1yTLMrGxsbrN\nGwLabr3Q0FAqKys1NaSa7XhLjSjLslaeGzBggK6WYOrPKTY21msGzGrWbLPZKCkpwdfXl5CQEGw2\nGz4+PgwYMEDXmyD1muLi4ggKCqK0tJRJkyaRlJTE3LlzxeJYHZGik+BJD8Ut/xLilmsSh/0sXbv+\nGYfJjrHQjqHMD0xGFONF7HGpyJ3SCPxPIBCK5N8LZ2AYFYeSyep8PUOHXa/rXb0qwlDv6msGssb2\nB2t1uv9pj5nVatU9U1F7oVFRUVe9Jm/hqkRNSkrSrslVDZmVlYXT6dQynZCQEI8Clpp9RUZG0rVr\nV12VhqpxgLd/TjWz5uLiYg4fPozZbNb6ld52lVHJycnh7Nmz2jWdPXuWRx55hN/97ndMmDBBKDf1\nRohb2heSJFF+cTZGnzIMzp4Yqk6A5ERyliA5QSky4ujkpDy+Cv+DZVD8A2XVEZikjgzp1xN0nC87\ne/YsOTk5xMXFNbgMWFd/sLb5QbvdTlpammYHpuedtSos0dtKzeFwkJaWho+PzxXlWlc1ZO/evbWs\nubCwkFOnTmE0Gt1EIPW9H6q61pvZ19VQBR9Op7NBPphNoaysjKNHjxIbG0tYWJgurjJwedD+4sWL\nmqhp3759TJ8+naVLl3Lrrbd6+coEV0UEvvZF9cVDKOZjOMtCcPopGCUDBkpRMIBkQFLAUCJR1bsK\ncyo4KowEmfPA5EuVRZ+gpzqjWCyWJm1ucO0Pum5TUDNCWZbx9fWlpKSEAQMG6FrabE5hSWPFMjWz\n5qv1vlw9RtXsQx1+z8/P19171W63k5qaqvsIAVy+OUlISNDMELzpKqMiyzJpaWn4+fmRkJAAwBdf\nfMGbb77JP/7xD/r27avbNQpqIFSd7YuqqhSQQJIMoCgoviaocIJkuvTLIIGkSCg4qQqtIEAJhqpS\n8CuDQO9bW6lqQD36hq7zgz179uTkyZMUFBQQFhZGRkYGWVlZTeoP1kZFRQWHDx+mc+fOui7aBcjL\nyyMzM7NJYpnalvGeOXOGsrIy/P39CQkJoaCgAD8/P92zZNXxRW87NbXcXVRUVO/NSc2Fs66uMkeO\nHNHMpGtzJ7Lb7Rw6dEgrdzudTt5++2127NjB119/rasSVnAV2pC4RQS+BqA4y8AoIRkkFKcCih1F\nMoLiBIMBxangVJxISJj9Lr2lkqygBPqBvbheV5cGn4dLj03v8QF1E3twcDA33nijFog87Q/WRUFB\nASdOnGiWMuDx48ex2+1eN+euuYy3sLBQs6Kz2+0cPXq0UZlOY8jNzSUrK+uKgX5vo2ZfPj4+2txc\nY6hpJq32UW02m1ZdUMvsZrOZY8eO0a9fP0JDQ6mqqmLWrFkoisLmzZt1tT4T1IEodbYfTKZo7D/N\n48lOGbPiRPHvgFRehuJwoCBd2q0nyRhKZaTKMpQufVAC/EG2e+Uc1EDUsWNH3ZJk7DEAACAASURB\nVLMHNRBdbb7Mk/5gbTidTjIyMigpKWnyAtz6ULexh4eH67prDi4twT1x4gQJCQl07NjRLdNJS0uj\nurrazVrN05KuusKqoqKCxMREXVWvqvlzVFQUXbt29cpruvZRe/XqhSzLFBcXc/bsWS1Tfu+99+ja\ntSvr1q3jnnvu4fe//73uyk3XtUITJ07k4MGDTJkyheeff17X47Z6RI+vfWH2/xmULcVihoryCnAo\nmGUHTos/Jrkao1yNYqzGUA7Gik44e8egBAQjXcwHn6aLM/Lz8zl58qTug85Op9PNrb++QNSQ/mBt\n84Pqep+QkBCGDh2qayBShSV6v39qRm6z2dzev5qZjvoBr5ZGFUVp9DJedSN7cHCwrjOHcLmMqpo/\n64XRaKSsrIzq6mpGjBiBwWDg6NGjvP/++5SUlPDVV19RVlbGCy+8oJubTs21QuvWrWPLli188skn\nuhxP0DKIwNcA5rz6FoMHWLjxtrMEBPTCWmImTHGAyY9qg4FqixlDhyp8jvRF6RULSEhlucgxt4LZ\n89KTKvZQF542R0YUFhbmcSBqqL+oyWQiOzubAQMG6B6Izpw5Q0FBge7jF6pC1M/P7wp3lJqobijq\ntVdXV1NUVKS9T66PBwUFXfFaqjDH23vtrkZeXh5nzpzRvbSulqFlWdbev5SUFFasWMH777/PDTfc\ngM1m47vvvvP6z7GutUK33HILixYt4uOPP/bqMa9J2pC4RQywN4DS0lK+/XYHlUXziO57AoPBRJTF\nhJ8ZDP5mkIBjXXFkR+OoduBDFX5GGcddi7F07u3RMdU5ti5dutCtWzdd7+jVjFLvO/rKykqOHz+O\nzWbDbDYTEBDQ5P5gbaiqV19fX103UsDlBauuG+abgtpHtVqt2pC4GgjLy8s5ffq07pZgrrv64uPj\ndS2jqtlrSEgIMTExAHz22We8++67rF+/nh49euh27NpQ1woNHToUk8nEyJEjWb58ebOfR2tCCk+C\n+z0cYD/UugbYReBrIGfPnuX+++9nzkvjGDr4LEXFKViKzyCfrqTydEc6BPQgsnMn/AxVVEs+5PSf\nTI4cqnlmhoWFNWjwWVEUTayg9xyb0+nk5MmTXLx4kbi4OF0zSnV7Q2BgoLb/Te0Penv/oJoReSsQ\n1cWFCxfIyMjQ1cxanY3Lzs6moqKC0NBQwsPDtSFxbyPLMocPH8bf358+ffroetNVUVHBoUOHiImJ\nISIiAqfTyWuvvcb+/fv59NNPdf39FzQOKSwJfu5h4DtSZ+A7B1wAziiK8pDnZ9hwROBrIE6nk9zc\nXHe/Q6eMnPMjOd9+wPkT+zhxJoeUPF/8Yu9k5Oh7uPnmm7FYLBQVFVFYWIjNZtPKfWFhYW7zXnB5\n+4DBYKB///663mWr4wOdOnWiR48eun64FRUVkZ6eTu/evWudA1T7g2ogVJ1SGusvmpuby5kzZ5o1\nIxo0aJCuM4euZdTevXtrgdBqtbrNxoWGhjZ5TlB1l+natStRUVFeuoKro47lDBw4kI4dO1JZWcm0\nadMIDw9nyZIluv7+CxqPFJYEd3kW+Lrv6uH2tz916lSmTp166XUlaR8wGkhVFKW7F061XkTg8zKl\npaUkJyezdetWUlJSCA0N5fbbb2f06NHExcVpq3IKCwspLS0lICCAsLAwLBYLp06datT2AU+5cOEC\np06d0n18QN2+ff78+UYvpm2sv6jaI6qqqmLgwIG6l+aaY3s5XMr2UlNTa90JWHNhcVOW8aqBSO/f\nC7jUO8zKyiI+Ph4/Pz/y8/OZMGECY8eOZcaMGcJ+rBUihSbBaA8zvtN1Znw/ArnAG4qiJHt8go1A\nBD4dUV07tm3bxrZt2zh69CgJCQncfvvt3H777XTu3JnS0lIOHjyILMv4+PgQFhbm1Q3irqhimYqK\nCuLi4nTNUtQem4+Pj1c2l6t9L/WGwXV+UJIkbfhdb8eS0tJS0tLSmkVYoo6JqBlRQ5BlWduvZ7PZ\nALQbhrr266k+mGog0gt1T2RJSYnWO0xPT2fy5MnMnz+f++67T7djC5qGFJIEt3kY+LLqDHz5gB04\nBfxMUZQqj0+ygYjA14zIssy+ffvYtm0b27dvx2az4XA4uO666/jzn/+Mr6+vW1nUaDRqgbCpLinl\n5eUcPnyYiIiIZgsOevXYXOcH8/LyKC0tJTQ0lMjIyCb3B+tCdXyJj4/XbLr0QM2UL1y4QHx8fJPK\nl+p+PTVzrrmMF9B8NQcNGqSrt6e6F9BisWjuPMnJybz44ousWbOGIUOG6HZsQdORgpPgFg8DX44Q\nt7TbwOfK9u3bmTVrFg8//DAFBQWNKos21vBX9VWMjY1tcObgCYqicO7cOc6dO8egQYN0Dw6ZmZkU\nFhYyaNAgKisrm9wfrA1VBKQGBz3LqLIsa31ePVb8uK4VKi4uprq6msDAQPr160dgYKBuN0RVVVUc\nOnSIiIgIzcfzo48+Yu3atfz973/XvZ8oaDpSxyQY7mHguyACnwh8wKeffsqoUaO0jKghZVE1yyks\nLKSqqorg4GBNLXq1D2NZlt36XnqWNtUPbEmSdN8zV11dTVpamqY6rBkcvLl/UF3mGxISQs+ePXXN\nlNW1PuoIi55UVFRw8OBBIiMjMRgMWK1Wr21TqImqsu3Tpw/h4eHIsszcuXM5ffo0a9eu1fUGSeA9\nROBrGiLwNYCaZdGysjJGjBjB6NGjNbVocXGxVhY1GAxazysoKIjy8nLS0tKaZfebug+wW7duut+5\nq2XUnj17Ntigu67+YF3zgyUlJaSlpdG3b1/Cw8O9eRlXoApL9J6lhMvLXGv2DhVF0azVrFYrdru9\nySMmqn2bqrK9ePEiU6dOpW/fvixcuFDXGySBd5GCkuAGDwOfTQQ+Efg8oD61qMPh0Ep9VqsVh8NB\nt27diI6O1lWsoI4P6DnHppKTk0N2dnaTyqiu/cG65gfVY8XHx+tq/Axo5WG9hSWAtr8xISGhXgcU\n12W8agm5MYrRs2fPkpuby+DBg7FYLOTl5fHII48wefJknnjiCaHcvMaQgpIgycPAVyICnwh8TaS2\nsuiIESPYsmUL9957Lw888ICm7rPb7doHlmoZ1lTUMmp1dbXu4wNOp5Njx47pcqyrzQ8qioLRaNQ+\nsPXCdQQjLi5O1+xHVfTa7XaPj+VwONwUo2qVoeYyXkVR3ErsRqOR1NRUpk6dyqJFi7jjjju8fXmC\nZkDqkARDPQx85SLwicDnZWRZ5h//+AfPPfcc3bp1w263M3LkSK0s6uPj46YWlSSJsLAwrSza2Dtv\nVSHaHGVUte+liiL0PJa6/83f3x+j0UhxcXGT+oN1ofYOw8LCdDcQcDW09mafsqqqSuulFhcXY7FY\n6NixI1arVdtcL0kSW7Zs4U9/+hPr1q1j4MCBXjl2bbhuVpBlmZtvvpl77rmH1157TdfjtgekwCRI\n8DDwVbWuwCesEdoADoeDFStW8NVXX5GQkOBWFp0zZ45bWTQpKQmHw3HFTj01ENZXalMtuvRWiMLl\nObbmGKhWe2w1Nzio/cHs7OxG9QfrQu1T1uVk4y1UH9HG9EQbisViISIiQnvd4uJiUlNTsVgsHDhw\ngBkzZhAZGcnJkyfZunWr149fk5qbFWbPns3DDz9MRUWFrsdtNwiT6iYhMj4dUH7aF3i1r9enFq2o\nqNDUoqqgQVWLqkpQV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MRVVVAoEA7UGV1pDAaVKobm2hqa2ROiWDtqAVIwLVEcBm8xCS4aoLCmAVBups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\nlv/6r//id7/7XViP23uzQn5+P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qn8K+jz/B4XDg8XhwuVy43W40TcOFQhuSMOCWAl3XiUajKcFTFAXDMLDb7aYY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\ncCNSHKZC5PXt2LEDu92O1+vluuuu46qrr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Jp1Peji1btjBp0iQcDgeQTIlQVRWbzZYub4mhxZGOFeOzsDhITFPSHDZoDCVo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\nUhIFBQVs3rwZrVbLzp07GT16NO+8885dX+f48eOMG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6WIrXUin2WDDZZTA+2sKe2l3E9XICnhAWIJYGnghJPY7L9KCRwtANUBD0uBlT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\n0WDDxd+X6/u/pvjCpYrzeBrAbkO+nsa5KznkWN3pM2AIehcXM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wiXZ0okwTOlNsaOtgyrZaTL+HYChEhbeYglABhmEQRKMDIQ6ERGFZFslkMivw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Os96rpOIpEgFouxvr6OZVkUCgUnstnL/SMG/yNxz18iKTySexEAkoz2FxT9DziS/fcozflw7hmt\nRmi1xk+SySTz8/PO9at2omgl61BtIdQOVEd8Txvx7bMf3+eAEPC/A/+y7HVJqemlKfWAA+KrAVvn\n0n6qbyfqGb0Wi0XGxsZQFKXpCLPVEQnbmHZ5eZlLly51VHBXIHDrXfx4/g1cho9nn32uYmOSWBRI\n4iaIuzoVmczASpSiP4lxfxQrvUbCfBfFCFEc6sYrJQwNIVyCUnpzpxSUG8hQypy0R07F5XLR19dH\nX18fgUCAYrFIKBQiHo8zPz+PaZqOA0BXV1fDNWLDNYXo+wskBmxz2RNIdHTlAUnXn9Clf6Yt76VR\ntKtr0tbKzGQyzjycPVh///598vk8fr/fqRM22mzUiVRnOdm3qtP5fsZ++vFJKXPAZ4QQ/5zSvN9h\nYBn4oZRystn1DoivBoQQaJrWdk++erBn5M6cOdO07h+0VuOrtizqdMOG3SF69OxZvAMSkzwCD6UN\n3MCgiIsIbjJbKdEtmCbmwhLu8TfRi1nwBcDnR5hBtIcPyHzvNawzL+H3/xxiIAQobCeJWmgf8VVD\nUZQK4WPTNJ0RiuXlZYcYbSL0er01N/NC6L+AqEV6NgSSIkn3HxPRf7lmt2yn0KlmlPLB+uPHj1c0\nG5UP1pd76tUiuE4NxNufUzqdbpun5vsJ++3OsEVyTRNdNQ6Ibwd0woW9HMVikfHx8T3XEZsVll5c\nXGRubo7h4eG6lkXteqKXUjIxMUE6nXY6RIvkyZEgTwqJRMNLhEN0yR5i4o3KBbIF8j/6Du7UHK7j\nAyDSSAKoVhCC/ahdAYpzf4rqN/FGfgYpsugZHbNYQCgKrkAA1VdNLLWtnDqF6u7HcrmwqakpJ6qx\nf8ceAzC972HXRHeCJZKYIoom+3b8vXbicXVh1mo2yufzJBKJmoP1tkxdJyK+cqTTaY4d216L/knG\nftsSCSECwGeBj1DqUvsfpZRTQoi/A7wjpZxodK0D4quBnQbY2wV7JrAd3aKNEl+hUGB0dBSPx7Oj\n5Ji93l43jlQqRSaT2aZh6saLGy9hBrA3doFAEsBFBJ0ELiKAhbX4Bqz8GO1QAEERiwJuqSDUH+ML\nulD9HqTSQ276e3DqHHruIWiHEd5ukBbFVBpFKPj6elC9HtA0hJCUd3Y+7lm5WlFNtb+ez+cjcN1E\n7PoRCBqLcNuL/RSp9nq9eL3eisH6RCJBIpFwZOpUVcXr9ZLP5/c0WF8PT+Mc335CCHEM+EtKg+wT\nwGVKNT+AvwF8DPgfGl3vgPh2QCciPl3XyeVyLC4u7nkm0EYjBG03zew2GgF7Jz4pJbOzs6ysrODz\n+Th58mTN3xNVm7ZAodv6IJvKd8mmZ1Cj72BO/DmWmgFVYCgqHtmPKnyoWgqXaw0pD6F6u8gvjGPO\nLhE8cwisNFLxgfSh5rOY8SSZhwsEDg+g+N3IriAi6GnIGqiV994savnr5XI5lrInEZF3nDUFogbH\nSYR0o8qd026dMI59UubuymusUEotT09Pk8/ntw3Wd3V1taTOU339nsauzn1ubvnXlOoTZym1bZeP\nL/xX4HYzix0QXx0IIdoe8a2vrzM5OYnb7ebZZ59ti7Ym7Oz4oOt60+nUvYxH5PN57t69Szgc5ubN\nm013iCpFN+obSRDfRzl3l7xhoGCi5DOohoniCyEUiWV5kdINrGDqXopFi3BAA9ENSgFRWEVuekAo\nqKEAsuBCt4p4FAHrJjK/CX19T5Qyig0hBH6/n0DyF8mHx0EpYjNe9QYs0AgWPonY5avcScHrdqCd\nRKqqqmMee+jQoYrB+gcPHpDNZh3H+kgk0pAoQS339acx4tuv5hbgZ4CXpZQPhdiWB1mkvE27ARwQ\n3w6o14HZLGx9z2KxyPXr13nvvffaqrRSj6BtIe3Tp087c1SNoFXiswfuq+2KGt0kpZTE//LPMNNv\nEzwWQcmdxrTWofCQgtRAUzGsJdSebqxiD4apAFn0ZBw0BeHyUeraPARr74IrAp4gUET1mBTTeTxd\nJxCBADKVQnq9iK2n9nZGRO0iBJG7DOlnEeG7SErk92htCVJB6iFm71xm1nxrRzumJ5mo7PXaWZMr\nP7/dButtUYJyJ4rqh9Jq4nsaU537XONzA6k6P4sATXm9HRBfh2GTT7neZSND7M2gmqhsu6RcLteS\n5Fizc212VAlsG7i312pk09VXV8mvvofvVA9C3USYLjwD/eQeLqAaCvmcjqbriECMbFrg9fgRuoFe\nSKNEuhHdhynqRdSiidAjCN8AbH1RhXCDNJGWG6EAHg/EosgWtDgfGyQoi5/D7/sjMq5vbb1UBBQE\nbtzyDAP6v+L0lb5d7ZgURXniie9xKbfUG6yPx+OOryPgqMvYwgTVEV8nx3+eROwz8b0HfAr4Zo2f\n/RzwVjOLHRBfHezVo80wDCYmJmq6OLQ7hVq+XiwWY3x8nGPHjjVll1SOZiK+aDTqOKPXiiqbuY6Z\neyO4IjpYIbBUJCZadxjR04WSSBJwBShm0ujRFC63l3xCYqgFihtF3Oc+hpLJIb0eZDaLJQTSVED6\nwLRA1zHzeSwpSwMPmoYsFqCDXbvtgJAu+gv/K93Fv0da+xamsowiwwSMn8ZtnXF+bzc7pmKxiK7r\nrKys7NlJAdofQbZbaaXZCNLtdleIEtiD9fb1KxQKztxrNpttWavz9ddf5/Of/zxHjx7la1/7Grqu\n88lPfpJUKsUXvvAFXnzxxabXfJzYR+L7V8D/s3XP/dHWa8NCiE9Q6vT8+WYWOyC+BtDsl9yeWTt5\n8iSDg4Pb/navSivVsNebnJwkHo83bIq703q7EZ9lWUxNTZFMJneMKpshPmNzHRF2lUivEIawhZTg\nvXiG3LtjZGJRpNtDMBxAcflwZwVG1kA/cZUNEWb1zbfwej0EAwHCEQ+qISiuzlNcW0caBlIIlEQC\n17FjuA8dKn2FGzw3aZqYmQxWJoO0LBSfDzUQQGlDc1LdY5bdd5rso0v/5Yb/ttqOya69FgoFp+Ej\nGAxW+BI2c4+/HyK+vaxnD9bbKftYLMbCwgJra2vcvn2b6elpPvvZz/JTP/VT/PRP/zSXLl1qaN0v\nfelLvPrqq9y+fZt3332XhYUF3nzzTZ555pm2e0O2G/vZ3CKl/C9CiL9PSbXlN7Ze/kNK6c9/KKWs\nFQnWxQHx7YJmOhwbTTG2O+LL5/Osrq5y+vRpXnzxxT0/ie9GfKlUipGREY4cOcL169d3PF7dtaQF\nVoySfqYG9CLcbqRhYWkmVt6HVvCjuHMUCxqpoSOEBvoRa+tYmxsIJYE4dITImZ9n4PDHSC8towiV\nfKFAZm6KxcU8mZVRXG43wf5+PG4XkSODCEWhMDODGd3Ec+wYimVhWdaO5Gxms+irqyBlSRpNCMxk\nEjMaRY1E0MqaZJ5URwUhBG63mxMnTjh2TPYs4fT0NNlstsJSaDeFlCe9ZtjuOT7LsvD7/Tz77LN8\n4xvf4MMf/jC3b9/mjTfe4Lvf/W7DxFcOIQS6rvPBD36Ql156ia9+9atcvny5befcbuyncguAlPLf\nCSH+I/BBYADYBL4vpaxX+6uLA+KrA+dJe2ukYbcvkZ3ya8SRvV01PiklDx48YHl5mXA4zKlTp3b/\nowZQj6zKJc4uX77cUKpnW8QnJVhzlFL2cUo1OAvw4j2nkXyriOtIASF8GJtnyfl/hGXlODR0BFV1\nYR45hJVaI3D2DKo8heDDIAT+/l4yy6u4RQH3yTP4/3oMzp1DBxKxKHFgY24Oj9dDKBDEs7iEq7cP\nRVVZePAAj8fjdMYKIRwVEatQoLi8jOLzIcruAbHV/GCmUqAouLZSjPbftwPtJBfLsirWKm/4OHbs\nWNN2TE96xNdJIrU/l4sXLzI8PNzUOq+88govv/wyQ0ND3L59mz/8wz/kC1/4Al/+8pd57bXX2na+\nncIToNySobZDQ1M4IL5dsBtJGYbB1NQUmUyGF154oaF0RTtSndlslrt379LT08PVq1eZmGhYtGBX\n1CK+fD7PyMiIo2Pa6KaybS1rCvgh0EVpFrUEkxyuIwuIQBKpC3RTYWVllVDXeXqHCqi+NaTMIY11\nPAMnULT/FsyjYKXByqFqEDjSi55RyK3E0YtFXJkMnu5uhs6cxbX1ueTzeVKxGNFcltm//msKiQTd\nvb0MDQ2haRpSSizLwjTNkhfj2hooCkodAlICAYxEAjUcRtnFRaNZtJP4pJQ7fmbVCin1Oh/LXSie\nZMf0dkd8tdZr5Xxv3brFrVu3Kl773ve+t6dze1oghNAoRXvHYLsqu5TyDxpd64D4doGqqnWH2Msb\nSZrxrttLqlNKyfz8PAsLCwwPD9PV1UWxWOxol6g9/H7hwgWneaJRVER8ZgK4Q8lNpHTrScBCYlke\nVN8zhG9mWPzGJBk5y8DgOXzebmQaCmsRzMIKnmPP4va9hMKxUrCohilFjKC6FVQ/yLSJOHkKV28v\nqmWVRuB0HaTEa1l4e3uR3d2kpqcZPn0aw+1mcXGRVCqF1+ulu7u75CDu9UI+j+L3l66vaToEoigK\noqxd3spkUJ5g7cZmiaVW52OhUCAej7O2tsbGxgaZTIaenh4nPbrXudQnuVmmnPg6pQP6pGM/uzqF\nEC8AX6X0tFzrRpHAAfHtFeWpzmpSsRtJ0uk0V69ebboo3Srx5fN5RkdH8fl83Lx50/kittuB3V6v\nfPi9EV/AWhBClJ3bDKV79hHpmVI6m7JhGCwlBOKFswxtnsDYfAu9GAcJWrgL/6Wfx9U1jFzLVZ9x\n5X9KieJyofT2gqZBLlciPgGmO8yDxUWEonD+4kXCvb1o4XCF6WwsFmNpaYnExgbuZJKuw4eJhMOO\nGr+UEtM0kfYDkZQIXUdp42dgH6ddG2w7IiqPx8OhQ4c4dOgQuq5z/PhxisUisVjM8dYrH6Fo5X5p\nF9p57aCU2bEbxp5GZwbYd+WWfwekgf+OkmRZU8az1Tggvl1QHfHZUd7Ro0dbdihXVZVCodDU39jD\n4bWirnY3yyiKQiqVYnp6mlOnTjE4OLintR7V+JaAcFmUJ7d+R5BMpph/OM/gkaN0P1MAPo6Zu4XM\nJ0BTUEJdKLiRhoEhHu54TOHzIU0T3G5QFNiqRabTaR5MT3NkcJC+vj70jQ2UKiUbO8oZHBzEKhZJ\n3r9PyjBYXV0llU7j0jQiXV1EwmGCoRBIiVksYm2Roa7rFTZRe9l8253qbLdyi9vtJhwOO1Jh5SMA\ne7Fjahc6lTp9Gt3Xbexjc8sw8ItSyj9tx2IHxLcL7OYW0zS5f/8+iURiz+MCmqaRzWYb+t1yn756\nUVe7u+s2NjbI5/Ncv359zy3WlcQnkSgO6dlp0MWFRTKZDGfOPIPHW5IhAwvV54Oq4wtNQwkGS6nF\nOuem+f0U/f4KX4OlxUVisRhnz53D6/ViJJO4BgZQdphnEy4X3mAQn6JwaGu+q1AoEN9yBpiemcGl\naYRcLrrPniW3usr6+jrDw8POg4hpmk6zjH099gOd6MKsXq96BKBVO6YnEU+7CS3s+wD7JDsbbTaF\nA+Krg3KHhlQqxYMHDxgaGuLcuXNtGRdoJEKztT1b9elrFul0mrt37+L1ejl+/Hhb5orKU50m3VjW\nHDCAEIJiLs+DuVkikQhnz53bEo3OAkFKCkW1ofT2InUdK51GlHVbSsNA5vNoXV14n3uOwswMejjM\nzIMH+P1+hi9dKp3PVurTc+LErueudXejr66ibslTeTweDg0MOESYT6WIJ5NMzc2h6zqBQIC1tTWn\n7gU4zTLwiAjt/+1EhE9yxNdInWs3O6ZcLufMEppb9dMnlQirie9pkyuDfSe+/xn4HSHED6WUO6d8\nGsAB8e0AO/rJZDJcu3atbemN3VKTtuqLre3Z6RSRlJKHDx+ytLTEpUuXSKVSdUWvm4VNfKZpYlnP\nIMQUINncjLK2usrxEycIBsuvaxR4kW11u/I1VRX1yBGsdBorFkNupY2FpqEcOoQSDOITgngsxuz3\nv8/g8eN09fRgplLIQgHF6yXwwgtoDWxeWiiELBQw4nGE2+2kRqVlYeXz6LrOYpDddaQAACAASURB\nVD7vKNfYNa+NjQ2mp6dRFMUZJA+FQs71cB4Gtu4DW1KsnEyeZOJrpYa2kx1TsVjkRz/6UYUvYaNu\n648DB6nOEvZxgP2bQoiXgCkhxCQQ2/4r8qONrndAfHWQzWa5c+cOgUCAwcHBtt7oO41IlM8DDg0N\ndfyLb48pBAIBbty4gaqqpNPptjXLCCEoFApbqbFeDOM0y4v/FVMOcO78eTTN/iJJYBXoA3afRxSK\nUhohCIVg61xFWdfd/fv3SQFXPvMZRDqNmUwiVBVXTw9qT09Tm7arrw/F78eIxzHtFLUQrOZyrCaT\nPHflinN/uN1upwEESjqm5UQohKCrq4vu7m7C4bCT7q1FhO0chu/EnNxe703bjsnn87G6usoLL7xA\nLpercFu3XRZs8ehG3kMnRAQOUp37O8AuhPgnwD8G1oEksKemhgPiqwOv18tzzz1HLpdjc3OzrWvX\nSnWapsnU1BSpVKrhecC9wh5TOH/+vNOgYJ/fXjcPezPv7e1ldnaWmZkZvF4vqZTOM8+8wPHBOLBM\nyRDWvhbHgOvslOashhACyuarstkso6Oj9Pf3c/Xq1dLPIxEYasq1ZBtUvx/V70duNbCM37uH2+3m\n+tbDQj24XK4KDUhd14nH40SjUUcMuZwI7cYYuz7m9XrRdb1mRNgMnmRbIpuUbTsmv9/vNFTZs4Qr\nKytMTU1VpE8jkUjNa98J9/XyNZ9GZwbY91TnbwKvUpIn23Mn3wHx1YGqqgQCAXRdb7sLe3WqM5lM\nMjIywuDgYIVTebNodDMyDIPx8XFM06zp0bfX8YjyIfCenh66u7uZmZlhY2ODgYEjLC+nefjQQ29P\nke4eD+FwL273MUpD7a1jZWWF2dlZLl686NTX2o1kOs34+DgnT57k8OHDTf+9y+Wiv7/fMQM2DIN4\nPE4sFmN2dhbLsgiFQiQSCXp7exkYGKgbETZEhNIsuVKYxbb7tLeb+GrB6/Vy+PBh51rbLgobGxvM\nzMwghNhmx9SJObtq4nsaI759hh/4k3aQHhwQ367YaYC9VdipTsuyePDgAevr6zz33HN7eopsVFM0\nFosxNjZWYZNUb61WYL8vex07ldrb28vNmzed41mWRSKRIBqNMjcXR9cnnMinu7u7IcPc8mNOTExg\nmibXrl3ryPyYLRywsrLCc889t6eu3nJomlbhHr6+vs7EROlaJBIJ3n77bSKRiDNUr2nathqhlBJV\nVSuJ0NKhmEAYKSQSNRfFpeeh2A+uEB2xn28RzRBVLReFeDzu2DFJKfH7/RiGQbFYbOo+2g32vZtK\npVp66PlJwD5GfH9GSbXlO+1Y7ID46qBaq7OdUBSFYrHIm2++SW9vLzdu3NjzE6qdIqtHfHbdKx6P\n75pKbYX47KjErv0IIVheXmZubo4LFy44nX3lxyh3DzBNk0QiQSwWY35+HsMwGiLCVCrF6Ogox44d\nq+mE0Q7ous7Y2Bgej4fr1693ZCTB1l2NxWLcuHHDaWiqdV3KidCOcCqI0CygFVdLn4PmLzXUqDmE\nYiIK60grD57+J4b89hKhVT84mKZZmrncui8Mw9g2QrFXZLPZg1Rnk2gDXf5b4LWt7/c32d7cgpRy\nptHFDohvBwgh2j4cbkcO2WyWmzdvti0lZ9cNa0U76XSakZERBgYGGnJvaJb4HCWTrVSrHYEBXL9+\nvSEpK1VVt82AlW/4pmkSiUSc1KmmaU4E9uyzz3asyy6RSDh+g3aU0W4Ui0VGR0cJhUJcvXq1ggR2\nui6Li4vouk44HKa7u5uuri7cLhdWfhFLChBuTMNwPh+ECq4gQk8hFS+4nwwj1XamJlVVJRgMEolE\nuHDhApZlObOE7bBjgqe5uaX1rs42EJ8taPrPgX+218McEN8uaGfEl8vlGBkZIRQKEQgE2lqHqkVW\n5bqely9fbtgxulHiK4/y7L+Lx+NMTExw4sSJmsa0jaLehh+NRpmdnSWTyeD1ejl16lRb01k2bCeK\n9fV1nn/++Y41G9nX65lnnnHqfjuh+rpYlkUymSQajbK0tIRZSNPjzRHuHSQU0vB4POTzedbW1hgY\nGCiNqUgNJb8JagClzU0graCTTgqKojhO6rvZMXV1dREIBLYRYXWj19M6x8f+2hL9BiXubQsOiG8X\ntKvDcWlpyWm86Onp4fvf/36bzrCE6si0UCgwMjKC3++v0PXcDSbLmJ43cfeOk1Hu4ZLDuOQZRFWn\nZXkDi71pzczMEI1GO0IU9oYvhGB9fZ2LFy/icrmciNCyLLq6uhzR5L3U+ewILBAIcO3atY6lNh8+\nfMja2tqerpeiKJVD4rl10rFlEukCq6ur5LdmDQcGBpwaoZQqspjCNPKYlttZZy9do5g6FDJgbEnx\nuf3gDpQk43ZBs27pjaxX733sZMc0NzfnPFCVzxJWzywedHXuw7GlfK2d6x0Q3w5oxj28HuxN1OVy\ncfPmzT0r2NdDeZS2urrK/fv3t40p7ARJgZzyp+hiFMslQM1isUBOmSSPH7/5C2iURgJqNbCMjo7S\n3d3NCy+80BGisBuBYrEYV69edWo1tm6paZrOmMDs7CxSSqdG2AwRxuNxxsfHOXPmTEMRWCsorxm2\nm1gVIQiHI4S7PbhcLpaXlzl16hS5XI6ZmZlHqb6gRkAbwBvwV0TuTXeNAmRiiFwMEKBogCyRoLKJ\nDA2USHAHdNJJYTfsZMdkO3ZomuYIE7jdbjKZTMPZk3K8/vrrfP7zn+fo0aN87WtfQwjB66+/zic+\n8Ql+/OMfc+HChabXfNzYbz++duGA+DoIm4DOnj1bsz7UzlkoVVUpFouMjIyg63rNMYWdUCK9MVR5\nFImJZSyj0I0iu7FIkVX/b/zGbyCs7ooGFnuEoFYDS7tgd4Z2d3dz7dq1mtdMVVV6e3sdIiwfE7Cd\nA8qbZaofQKSUzM7OsrGxwZUrVzqW2kwmk05XbUdk6BQ3lhlj6v4sAM8//7xDKseOHQMgnUqRjC5z\nf2aWXL5IMBh0mmV8Pl9NIiw35614GMzGEdloKbor/1w0D1gmIrmMjAyBq35TyZPkvl7LjimRSDA5\nOcnMzAyvvPIKuVyOL37xi9y6dYsPfehDDdf7vvSlL/Hqq69y+/Zt3n33XQYHB/na177GzZs3WzrX\nx419dmdACHEL+AVq+/EdKLd0As2QlK7rTExMYBhGXQJqdPygURSLRcbHx3nmmWea7m40Wd4ivSEE\nYlukqxDCkFny/ACP9bMVDSxSyo6NEAAlMegtL0C7A1SaZklvU1FQ64wVVHf71SJCmwT9fj8TExOE\nQqGOpjYXFxdZWlrqaDNOThdMjdyl79AJBusM7Qf9boLhCwx6B5yaVywWY2Zmxql52UTo9/sr0trl\n0b5l6KjZ2HbSs6GooLgR2SgyUt/h40l3X7dneoeHh3nzzTf56Ec/yoc+9CG+/e1v841vfIPf/d3f\nbXpNIQRvvPEGo6Oj3L17ly9/+cv8zu/8TtvOuRPYZ+WWfwz8S0rKLfc5sCXqPOyn3EbIZHNzk4mJ\niR3n5OBRTW6vxGdZFtPT08TjcU6fPu34yjWDongPIV2IrRHnEvGVNjcJpX/LbnTxHl7lp0km8o6s\n2l4aWHaCrWRju0S4XC7MbJb0nTskvvtdrGwWpMQ9OEj4Ix8hcOmSI1lWC7WI0PbdW19fx+fzEQqF\niEajdHV1tTUlbZom4+PjKIrCtWvX2q4qYmN9fZ3p6WmGz10j7CkZ724jJEsHTHCVovPymle5fqY9\nUJ/JZPD7/Q4RejweJiYm6Ovrw8ylsAwdKbRSNCiU7YSjuaGQLtUA1doPR52I+NrZ8FT9PbUsi099\n6lP8wi/8QlPrvPLKK7z88ssMDQ1x+/ZtvvrVr/LJT36Sl156iV//9V9v2/n+hOIfcqDc8nhQ7tBg\nGMaOXybbnNYWtN5tXmgnvc5GkclkuHv3LgMDAxw9erTlzdoU6wgeRU52xGeTXslCSAMhmX04xsaq\n0dYh7mqk02lGR0crlGyMRILVP/gDiuvruPv7cdkpzUSC9f/0n8hdv07fpz61I/mVQ1VVkskkxWKR\nD3/4w6iqWiElZmtq2s0yrZKV/V7sOcNOQErJzMwMiUSCF154AbfLBXocCtEt4ts6d6mDcIFvCNTa\n97KtnxkMBiuaP2KxGNPT08RiMYLBIC6Xi2IuQ0BzI1WtNDJhmRhmqQPaJkIABQVM47ERX6dTp62W\nKG7dusWtW7e2vf6Xf/mXezm9x4p9rPGFOVBuebywRxrqEV8ikWB0dLQpc9pGrYlqoXxM4dKlS0Qi\nEWZnZ1teT+BBlmm+lhwEJJb1qL5jGAZr0VWEITqaDlxaWmJhYYHh4eGK2snGV76CkUjgPX684m+0\nSAQ1FCJ95w7uw4eJfOQjux7H7niNRCIVzTjlUmK2pubGxgb379+vGLhvlAjtAf7Lly93rAvQruuG\nw+FH2qQA7m7QQmCkt6I8QAuA6mtqcN1u/kin0xSLRUdsIRaLsbA0T2FzCVcgQqQrQiQcIRAIlB6a\ntogQSilRaZqOQ331vWNZVtsj7HZG1eXrdUIA+/2Cfdbq/BbwAQ6UWx4f6g2x22lGu4W/mbpNq4Px\n9qbt8/kqxhTKXb+bhUteIqdMocguJ8rTNI25uYfOgG82v85A3xn6eq+0dIzdoOs64+PjaJrG9evX\nKzauwtISuZkZvHX884Si4B4cJPFXf0Xogx9E2aHeuLm5yeTkJOfOndvmZF+Oak3NcpeFciLs6enZ\nJpZsmib37t3DMIyGB/hbQSKRYGxsrH4HqqKBe28NR1JK7t+/TzqdrqjlBgIBONyPjPZTQCMeK5nN\nptNp3B53yU0hFCYY8CM1F5bmqetJ+FgiPssAa8tZQ/GC0pwkXvlnaJ/30waJwLQ6QnzdQojvUGpQ\n+Zt1fucfAl8VQkjgdQ6UWzqHnWTLbDWU/v5+Xnzxxaa/uK0Q39raGlNTU5w7d27bRqcoSsseei75\nDHl8mKTACgCSo0ePYpoGy8sr6HoBtz/N/NQAUdekk/5r14Zuq6PUE37OTU7umsJUvF70tTWKS0s1\nCdKyrIp0YLMeh7VcFmKxmPOZqKpKd3c3Pp+P+fl5BgcHOXr0aEc2yPJGmeeff75jKWdd17l79y6R\nSIQrV65sfy+aB+EO4DULHD5ymMNHSp9dPp8nEU+wsrpCLr6OCPYTPlxyVQiFQtuEDwqFAj6fb5tb\nfauoiPisImTnUQqLWFvRmiIElmcI/McaIsDy9QzD6FiN9omHBMPoyHuPA/3A2s5HJwX8NvAv6vzO\ngXJLO1EuVG0reiwvL3Pp0qWW5nnsNRslPtuYdqcxBVVVyefzLZ0L0o1X/xQZ5Y9ApFHpI5/TWVlZ\noafPTSDkwm39PGrX3yAeq6yD1Yt6Gjps2QjBTkPcVjaLaIRkFQVZ3N7sZc8ZdnV18cILL7SFjKqJ\nsFgsMjs7y+TkJG63m9XVVYrFotMU0q7NslwOrpONMrbW5a6KMqF+SCyV5vZcPlAUvF4v3n6NQ10B\nOH+BvCtMLF6KCO/du4fL5XJSxrFYjEwmw4kTJ1p3oKiCQ1RGHiX5DsgiaF1OzRFpoRQWobiOFb4C\n2s71eNM0nQelp9mEVkqBabRGGevr61y/ft3575dffpmXX37ZWRq4AkwKIdQ6dbzXgA8B/wcwwUFX\nZ+dhN6Jks1mnNnTz5s09PZk2SnzxeJyxsTGOHz++ozFtq44KjuWNeZiA8usU1R+ymf4+2WyGI8f6\n8Gh9uMwP4ZIXEZqomf5bW1tjcnLS2dB6enp2NQ0tFAqOPuVuNUOtpwfZSDRrmqhVtbSNjQ2mpqY4\nf/68I/PVbtjD9blcjg9/+MOlxo+tgWf72miaVvGQ0Mq9Y99/g4ODHTUpXl5e5uHDh42NXagadA1B\nLgn5OOjy0evhQ+AJ4hWCI2VzcYVCgc3NTcbGxjAMg2AwyMrKiuNSD2X3Jc0ToZPqzE6DNMBV9bkL\npfSaHofMfYhc3nE9wzCcqDqVSj2VOp1gE19rD1r9/f3cuXOn3o+HgB8C93ZoXnmJUkfnay2dQBUO\niG8HlHd1rq+vMzMzw8WLF515sr1gN+Irrx9euXJl13RWK6nTagWWQiHI2OhhwpFPc/qZQyi4EUbY\nGXOoRnXUUygUnBGBiYkJ3G63Iyptu43DIzLarc5mw3/hAtGvfx1pmnVTnkYyievwYVxbqVL7+qVS\nKa5du9YRPU8oRZN3796lv7+fc+fOOe+x2ondJsKVlZWKqMeOCHfbzO1RhU56DVqW5YyQXLt2rfFU\ntqJCoBt8kZL/H6L0Wh1itiyLhYUFTp06xdDQUMVDwtTUVEUjkX3flKdGDcNwCLAWEZqmiSoLiOI6\nuHdQLnJ1IYobSCMLWv3v14EJ7RYkLRPfLliUUl7f5Xc2gNV2HfCA+HZBoVBgcXERVVW5ceNG2+pa\nOxFVJpNhZGSEvr6+huuHzUR8tSyE7EHxvURGHo+nwjQ0n88TjUZZWFggmUzi9Xodsm2mzqZ1dRH6\n0IdIvvEGnuPHEdVdgYUCxuYmA7/2awghKjwAKzod2wybwMuH6+uhmgjth4RqIqyOlqWUTE9Pk0wm\nS6MKHSLwYrHI3bt36enpqSDwpqAowM73aiwWY2JigosXLzpKP7UeEqo7astd6u17vV5EaFkWCjlE\nA5rGAoE0Mw7xmYUCham3yf/oW7AyC4qK8HQhP/LzcO2jZDKZpzrVaej7Vt/8XeDvCyG+Je0h4z3g\ngPh2QCaT4c6dO/T396Oqalu781RVpVAoVLwmpWRhYYH5+XlnTKGZ9Rp1VCi3ELIsi3v37qHrujMo\n3i54vV4GBwcZHBx0Zg7tLtG3334bv9/vOA34/f4dN9ueW7ew8nkyb72F8HhQw2GwLIx4HKSk72//\nbQIXL7K+vs79+/cbIqNWUU5GrUaT1Q8J1dGyy+UiEomwubnZcQK3u0MbjcBbhW0jVa61WgvVZrP2\naIldW4ZH8nN2tFwusVYoFLZUZiRCWo9qe/WwtY+ahQLJ//d3kdPvQrAL5dDJ0s9n72F8/f8iPvMu\n8dC5pzbVuc/oBi4DY0KIb7O9q1NKKf9po4sdEN8OCAQC3Lhxw5G6aic0TSObzTr/bde8PB5PS5Hl\nbnOBtSyEbN3ITpq4wqN5tvI0XblCyP379x1zT5sIqxtdhKbR96lPEf7AB0j96EcU5ucRHg9d164R\neP551K6uCgGBTkVG9jhJV1dXW8momgjX19cZHx8nGAyysbFBMpl0IsJQKNS29v/FxUUWFxc7qk9q\nWRYTExNOpN9sQ06t0RLbdd2Wn4tEIgSDQRYWFjh+/Dia5gVZklgzqXKpLyNCCwuUUuYh9ad/gPXg\nLurxixXHN0N9iFAE696PUMQswWBnfBmffAgsc98o438p+/e5Gj+XwAHxtQNCCFwuV8dc2G2i2mlM\noZn16kV8tSyEZmdnWVtb66hupN2NKqXcNs9WSyEknU4TjUaZmJggn88TDoedGqHX60UIgefoUTxH\nj1YcJ5fL8c5bb9Hf38/Zs2c7RuB2mq6TkZEd9S8vL/Piiy86ZJTP50tD4wsLpFKpivppK0RYTkad\n7A4tFAqOutCxY8fa1lFbToSGYbC0tMTU1BQej4fl5WVyuS4OaZKAWkDzBDGtR/VshwiFgRBe0MIY\n0XXM8R+iHj69/YBS4vK6YOAkoXd+QPDkz+z5PbwvIYHO1Ph2P7SUbVXMOCC+BtAJ4lNVFV3XGR0d\npVAoNO2mUGu9WhFfdQNLudv39evXO6LAAo9cCI4fP76jZqmNcs1Iu7U9lUoRjUYZGxujWCwSiUSc\nqMe+VnZtsrxm1G7YYxebm5u7pun2AlvTUwixjYy8Xi9HjhxxOiPL66epVAqPx+M0hOxGhHZDzqFD\nh9pGRrXwuFKoGxsbLC8vc+PGDfx+/yOLqvUUa/fvULB8hCLdhMMRgsEALs2FaeSgmEAPPQemSX7y\n7VJNsMYDgJSy1Anq8WEV8hyW6Y69lycaUuwb8bUbB8S3A8q7Oveqq1mNXC7H6uoq58+fb0trenWN\nb6cGlk5HLHYtZy/RZLlz9qlTp7Asy3FgX1hYwDAMxyC000PcIyMjBAKBjnkNwqNRhaGhoYaExsvr\np1C6n+yI0G4kKk+N2veXHbV2crwDHo1EdPKzsTVK7VqrnVGosKgqnkTGx8ikYySTq6wvJbGMAv5g\nN75DVwi5unG7XOTSSSwUFGk5NT/EowYj5996kZD7Kd02JWD8ZCjWPKWfYOMQQrQ14rMVRNbW1ohE\nIhw9ehSJxKK4pZcpUHAhmtTEK0+d1mpgmZycpFgsdrT+ZUeTfr+/7dFkeYt7Npt1FEU0TWN0dNQx\nnm2nqoytKHP69OmafortwtraGjMzMwwPD7csiGB7yFUT4cOHD0mlUni9XhRFIZvNdrSeJ6VkamqK\nXC7X3EhEkzBN05Huq6kqY8Pdh+j7EMGuOEE9AYClBEhkVWKJFPNjY+i6TiSVJZjLo0oQqrZFfhLd\nMLEsWZI8EwrFooEr1JlxkvcF2pv4ahhCiNIHsgOklAfKLe1EuyI+e8Pu7e3lypUrjI+PY5BDJ4HE\nQKAgsQCBRgAXYcQu7eE2bEeF6tRmKpVibGzMiSQ6ldaKRqPcu3evo87lUDL3ffDgwbZ5NttmqB2q\nMuVR606KMnuFPWtYrYPZDpQToWEYjIyMUCyWTGffeecdfD6fc32CwWBb7gtb4qyrq4vnnnuuY/da\nPp/nvffe4+jRo405XihaaZ5va6ZPAbp90N1buk8tyyI60EvqrT9jdXkZqSi43W5cmptUOkVfbw8I\nhXwqwf35JbxaZ1LqTzwk+0Z8wD9jO/H1An8L8FBSdmkYB8TXAFpVRbFhays+fPiQ4eFhurq6MAwD\nXWYosomKB8GjKEwiMcki0XHT2zD56brO6uoqPT09WyLTc6yurnLp0qWODd2Wa2B2uv5VHrVWk4Sm\naXVVZaampiqUU3ZSlTEMg7GxMVwuV0droPbcXHd3984Ryx6Ry+W4e/euox0KpfvRjgjn5uZIpVL4\nfD6nWaYVIrS1azsdHceiUSZ+/GPODQ4SNk2M5WWUcBjh822b72wUiqLQd/osrpsfI/jeX6EOXSAZ\njxNPxnFpbr77ve8R3VilKxsj/OGP86v/4B+0+V29T7CPxCelvF3rdSGECvx/QKKZ9Q6IbxfsVYnd\nTv+53e6KMQWhSEw1hYp3G7EJBCpeDHIY5HCxc53MjvKGh4fZ2Nhw5LP8fj9nzpzpWI0ll8sxOjpK\nT09P2zQwayGTyTA6OsqRI0caFn5uRVXG1qesJ5bdLsTjccbHxzl79qxjjNsJ2FF4deOPEAK/34/f\n72doaMghwmg0yuzsLOl02jGf7enpIRAI7HjN7VRtJ+2XABZnZ1l67z0unTmDLxgsabPqOsbyMsLj\nQTt8GLGHqDn4c79GIhMnffcH5FA4euI8QlNRY/28szxD6tiz/MVinn979SqvvfYaV69ebeO7ex9A\nAq3p4HcMUkpTCPEl4IvAv2307w6Ir4NYX19ncnKSs2fPbnsKtpQiUlo7RnMqHgxSaPhryoZVN7D0\n9PRgmiabm5sMDw8Dj0Yl7I2+utmhVayurjoSbp3qpoRHM4B7qX/B7qoyUIoSL1y40LFUbfmoQqfr\nbHNzc2xsbDSkkFNOhEePHq0wn52ZmXFc2O0HBZsIyw1w252qrX4/9+/dI/vgAZeffRZX+XVTVXC7\nsWIxcj/+MWpPD0JRUEIh1HAY0YQLh3C5WLv2CYqREwxtTCOXHzC/MM+fvXOPT/+Lf8/wx3+BfwRO\n/fwATww8QFOdWgfE1wHYfmy5XI7r16/X3HgsCrBLLbZEihYSE1H1UdVqYClvKrAbWGwZKHujt5sd\nmlFNqX5vdsqx3Uov1ce5d+8epml2xNPO7oo8dOhQqdZqGBw9epTV1VWmp6dbvj71YJomY2NjqKra\n0bk50zSdDEOrXai2+WwgEKggQrt+mslk8Pl85HI5wuEwV65c6VhK2K5PBi2LC+fOoVY9LEjLwtzY\nQGazYBgIRUH4fFiZDFYyidrbi9rAg5lhGE7D1PmP/xJSSr74xS/yrdll/uTbYxWR+dNsS0R7m9sb\nhhDieI2X3ZTUXP4lUFcBuxYOiG8X2Bteo4aZthv7sWPHuHjx4p43TFlVz62O8hRFIZ1OMzo6yuDg\nIOfPn695zPL291qqKaFQyNno69Xpyo/TKa+58uN0uiHH1kS1mySEEM4wfTOqMo0ex1bI6RTs5ql2\nH6ecCI8dO0Ymk+Hdd98lHA5jmiY//OEPCQQCTmq0HQ8KUEqlv/feexw/fpy+YhFqPPyY0SiyUEAE\nAmAYWMkkmt+P8HpLD4cbGwi3G2WHdL99nBMnTnD48GF0Xedzn/scuq7zzW9+s2nvxp9o7F9zyyy1\nuzoFMA00VXg9IL4GYY801BsFsNM+6+vrDbmxK3hA7Pz4JCmlQu3RhloKLA8fPnS8ARutr9RSTUkm\nk0SjUUZHR0vt3ZGIs9FrmuZIW3WyUQZgaWmJhw8fcunSpY5qIq6srDA7O1vzOK2oytSDXf/q9Pux\nBZ33mhLeDbaD/eXLl53j1HpQCAQCzvVphQjteUP7/egPHqBUEZDUdWQmg7BJbavmZ0MIAR4PZjRa\nl/jsIfvh4WEikQjxeJxf/dVf5aWXXuLzn/98xyLZ9yX2t6vzN9hOfHlgDnhzBzujmjggvgax00iD\n/aTd09PDjRs3GvqyaJQ2S0uaKKJ26sSiiEYIgdg2pmCrvtgzc3tJvwghtg2L26LAc3NzZLNZPB4P\nzzzzTMfqUrZqCdCR1KaN8pnGRo/TiqpMJ0cVyiGl5MGDB8RisY66N0gpefjwIevr69vqhrUeFDKZ\nDNFotCJitq+PLVReD/ZDVkWXsBBIy6ro3LRyuUrrI8vacoh4BOFyYWUyyGIRUXVtVlZWmJubc+qt\ns7OzfOYzn+G3fuu3+KVf+qWOZRret9jfrs7X2rneAfHtAvvm1zQNo5gHW3XhOwAAIABJREFUClDM\ngLSQqoulzQxzS6vOmELD66KimCF0K4db9W9rcjEpoOBClX5HZ9DuMLXtcDrVFagoCj09PSiKwsbG\nBufOncPlchGNRnnw4AGqqjrR4G6Gs43AboU/fvx4R1OBuVyOkZERDh06VDcl3Ah2U5XRdR1d1+nu\n7ubSpUsdIz27/uX3+7l69WrHohP7oURRlIbqhuVEePz4cSdijsViTE1NVaSOu7u7HSKUUjI5OUmh\nUNhWB1XCYaxkElH24CUNo5LoikWUemo0Zc0o9sOC3ZSjaRo/+MEP+M3f/E1effVVPvjBD7Z2oX7S\nsY/EJ4RQAEVKaZS99rOUanzfkVL+uJn1DoivQWhWERmdg0gIVA+6YTI5cheXCjcuPo/WQnrJRQDV\njGCp2a20pr0RSxT8uGQIy5QVDSz2E3QzfnbNwtam3NjYqBjgtrsdi8Ui0WiUpaUlxsfH8Xq9DhE2\nMwNmzzcuLi52vBW+k0au5aoy8XjccbwwTZN33323I6oy9sNCp0cvbF3Pw4cPc+zYsZbWKI+Yy4kw\nGo0yOTlJLpcjEAiQyWTo6enh8uXL28hVDYex4vEKM2KhacitLIg0DBCifi1vaz3LshgbG0PTNJ5/\n/nmEEHzlK1/h937v9/j617/OyZMnW3qPTwX2N9X5x0AB+BUAIcQrwJe2fqYLIT4upfzzRhc7IL5G\noOfxGlEMjoArwObmJjMzM5w8dZL+vn7QM5DdgCbtSjRNQ5huvISxKAKlL7GQLqQlMGs0sBw5cqR1\no9AGkM/nGR0dJRKJcO3atZpP9263u2I0oHoGzK7v7JTWMgzDiSL2mqrdCeUpx06nAufn51ldXeXq\n1asVKWHDMCr85PaiKgOPb27Onjdst7dhdeo4k8nwzjvvEIlEyGb///bOPD6q+ur/7zt7JslkJZBA\nSNiXkIAQFVkqij5qKy7F2r6wWLUVK7QWtY+7lT5Yt4r1EZdH1ILaopXqz9aKCKhxw0pBJAsJBEIW\nSEJIZrJntnu/vz+GO0wCIdtcEsN9v16+lEwy996J3HPPOZ/zOa18/fXXQbFVMCM0mzElJ+M/5qwi\n2WwY7HbkujpEWxuSEBiHDQsGRRXh8yFZrUhmM16vl9zc3KA5t6IoPP7442zfvp0tW7ZoOpYzaOi/\nwDcTuDvkz/8NvAzcCawhsLZID3zhQpIkaHNhNNnwK4FSjNvtZurUqcdvouZI8DSCLRZM3b+xqv6a\ngYH1QPZ2MgFLRUUFlZWVTJ48WVOBhLrEtacGxhEREUEFZmh/Z+/eve2EIPHx8Vit1uCgeFpaWnDb\ngBaEbmLX0h1FDeImk+mkDwsmk4nExMRgWbqjq0x3S8fqAtympiZN+4Zwevb0wfEh+8zMzHZiGbWH\nGiomiouLIzYxEYvfj9LQgESgh4ckYUpKOmF4XSgKwuPBFLIIeezYsSQmJuJ2u/n1r3+Nw+Hgvffe\n0/SzHDT07wB7EnAYQJKkscAo4FkhRJMkSWuB9T15Mz3wdYXsB38rPmGg8sABRo4c2cnON0Og99eD\nwBcqmDnZolifz8eePXuw2WyaZ0Vq76WvJtYd+zuKotDY2IjL5SI/P5+WlhaEEIwePVpT1xJVfaj1\nFoLejCp0dJUJLR2rrjKh9mqSJAV9MGNiYjQN4h0NzbWcWVP9UE8mlnE4HDgcDtLT04NiIpfLxd4j\nRwKBMDo6sIV90iQsjY0oLS2BPp7ZDEIgPB5QFIxJSbja2iguLg5myLW1tVx//fVceeWVLF++XBex\nfDdoJODNCTAPqBVC5B77swz0yCtRD3xdIBSZsrJyamsbGDp0aOcrYwxGUHr2OKQGvpOtEFJv3Fqb\nPqt2YEOHDtWkhGowGIiNjSUyMpKmpiYSEhJISkqivr6eXbsC/Wj1Jh8bG9vnG606VlJfX69pHxSO\nG2b3dVShY+m4o6uM2WymtbWVtLQ0Ro4cqdmNWvUPTUhI6JP4pyvU4Or3+7u1kT1UTNQxEBbt3YvX\n4yHaYiHWZMJhtWKLiMDgcGCMjuZwTQ1VVVWcddZZWK1W9u3bx4033siKFSu48sorNbm+QUs/DrAD\n24B7JEnyA8uBjSGvjQUO9eTN9MDXBa1tbQhFZtSoUbg97s6/UYhA8OsBRqMRv9/fzoFFXeui9qS0\nvHGrM3Naz36pS2lDhRgdzaSPHj1KcXExZrO5nbVaT5SKXq+X/Px8HA6Hpt6hoSIjLUqOoWYDlZWV\nlJWVMWLECBobG/n3v/8ddlcZIFh+VkuBWuHz+cjNzSU+Pr7XwbWzQOh0Oil2ufDW1+NwOPCUlwME\ng+tnn33GXXfdxbp165g+fXq4Ly3Iyy+/zLJly2hoaOCPf/wj7777LldffTUPPPCAZsc8LfSvuOUu\n4H3gn0AJsCLktR8DX/XkzfTA1wVRMXGkj5lInbMW2X+Kxx3hD/T6uokQAqPRSFVVFUajMdjYV7Ov\nk5dTw4Pf76eoqAjQdmYudL1PVlbWSc2yT2YmHZrtqFsDujJLPl3Gzx6Ph/z8fOLj44OqQC1Qy89u\nt5uzzz47+DsKtQ8Lh6sMHM9c+7I4uDuofbZwb3DoOF7i8/n49ttvg7+buXPnEh8fT0VFBX/72980\nDXpFRUWUlZUFe9dr1qyhtLSU9PR0PfD15dBCFAPjJUlKEELUdXj5N0B1T95PD3zdwR6Hsa4aubNl\ntD43mCJQzGY4tlfvVObTqs9mSkoKtbW1HDp0iNzcXGRZJjU1VdPSppp9aT0zp/YnrVZrj9b7WK1W\nkpOTSU5Obrc1QPWI7HiTVw2Zjx49qulaJDjuJqLlBns4XnKMj48/ofzc0T6sL64yp1MsozrLaO1g\no+7qS01NJTk5GVmWmT9/PgUFBSxatIh77rkHv9/PRx99FLaHlvXr17N+fUBbce6557Jt2zaOHDnC\n2rVr+7zdZUDRvxlf4BRODHoIIfJ6+j5SP7iMf6dszYUQeL1emmrKqdyfx4RJU8B0rPyoyAi5Db9R\nwRflQDYCyCjImLBhxo6JCAwdLMdUAYsqWigsLMRsNjNixIig7L2tra2dbVhfZfiq84a6n0/LJ3t1\nc/moUaOCJtnhIPQmr35GsiwTGRnJ5MmTNQt66mdXU1NDZmampsFVtdDqbeYaWvZzuVwndZWBwINJ\nfn4+0dHRjBkzRrObs5r119TUkJWVpdk4CQQe6goKCoLjF62trdxyyy2kp6fzxBNPBHuJ3fHc7Svp\n6ekUFRXx+OOP849//IOrrrqK3/3ud5oeU2uktGy4u0de0EFm/DmbHTtO/rOSJO0UQmT35dx6ih74\nuoHH46GlpYX9RXlMHZcGvlYAhMGIx27CbwbJYEPGg58WBH6UYwPpFhxYcWAWUQiFdgIWVco9ZsyY\nE9cWHVNDqjd5RVGIjY0lISGhxyIQdSeg3W5n3Lhxmv2lD82+pkyZoqkMXr3JDR06FCEELpcLWZaD\ng+JxcXFhKeGqi2ktFgvjx4/X9IYZOswfrh2Koa4y6mcUGRmJy+Vi9OjRnYu1wnTsoqIihBBMmjRJ\n08+upqYmWK612+1UV1fz05/+lOuvv55bbrll8GRd/Yg0Mht+28vA99rACnx6qbMbSJKEyWTCJ0zg\nSA4IWYTAZ3DjpwETEXhpwkcLRizBbeoyPmS8+EQLPtmNRcRhkAyB/WL799PY2NhpeU5VQ8bGxjJ6\n9Gj8fj8ulytYMjKZTO1mvzr7i60GV63VoaHBtbPB93AQutNu6tSp7QKELMu4XK6gtVrooHhsbGyP\nz0k1DRg5cqSm84aKorB37178fn/YRwhCXWUgECD27dtHQkIClZWVHD58OOyuMkBwWHzIkCGaKlHV\nhy2n08n06dMxm83k5+dz880388QTT3DJJZdoctwzkgFQ6gwXeuDrJiaT6bhJtSQhJPApTRjb3Ciu\nA8j+GkwGG0QngCMejGaMwoRXacUgIhB4MRk8uFsEBQUFJCUl9Uh5aDKZGDJkSDB4dRSBqEq/hISE\nYO9LXRKqde9LFZacLHMNJ+qgeGc77YxG4wmD4k6nkyNHjrBv3752itFTPSxA+EYVukK1BEtKSjot\nAaKuro5zzjknWHIMt6sMHLdT01ohqmaUQHAn4ObNm1mxYgXr168nIyNDs2OfkQzADey9RQ983UR1\nWVERfg+iag9SUwt+iwwWI5IiQ00Z1B5CpIxFtkUhMKBIXkxYOVRdwpHS5rCMD3QUgbS2tgZn/1pb\nW4MmyVOmTDktnp5aO3yoN9OeuL2YzWaGDh16wjLeioqKTpfxhqopey348LkxlG5DaqlDWKNR0mdC\nxIl2WOoDg9ZD9uoSXLPZfIKZdXddZeLi4oiJiekya1Y9UbW2U1PHIhITExk5MrCjdM2aNbzzzjts\n3rxZ0wcwne8+euDrBh2VWX7Zi1zyBbL/CCbHKBSpEYMEYACzBcXjQZQVItImY7BZ8Pn9HCiuwGCW\nyT57JiZjeNVzoUo/q9VKSUkJY8aMCaoDZVkmLi6uV/3BzlBn5qKjozUtbUJg3rCioqLPN9OOy3g7\njgXY7Xaam5tJSkoiKyur59mXomD8+hXM/34ZZC8o8rHZTgl/5pX4L7wbzLZ25Vqts/G2tjby8vKC\nlnJd0ZmrTFVVFXv37m3nKhM6ZxmaUWqtEG1tbSU3Nzc4FuH3+7n33nupq6vjww8/1PQB7IymfwfY\nw4oe+HqA8DfTevgDxNEtSDUH8cVFIJoj8UWNxRg5CZPRgiJAGC1IJi/GhhrqfNFUHKhh1IixxCVG\nY0QbCyhZloN9ouzs7OCNpy/9wc5QZf1a9w3Va5JlObg+Jlx0HAtQd+vFx8fT1NTE119/HVTVxsXF\ndb6A2ONBNDdBmxvTl89i3PdPlKgYJFvIBgjFj2n32xicpbQtfIGi4gMAmluCqb+nSZMm9dqAuStX\nGZvNRlxcHC6XC6vVqul6JDh+TRkZGTgcDpqamrjxxhvJzs5m9erV+uJYrdF7fGcWfo+TeO/bKPUm\njG4rRnMqFoz4/Q1Irs/wtRRC5Fwkox3JGomw2KnZX8DR+OFkTjoHq9XS5Xxfb1FFGOpTfcdA1tP+\nYGeBUN1j5nQ6Nc9UVCu1lJSUk15TuAhVomZnZwevKVQNWV5ejqIowUwnLi4Og8GAqD0KjU1gMiLV\nFmPM+yeKMRKaBIZoBcl87HdtMCEi4qDsP5S/9784zlnEiBEjNFUaqsYB4f49dcyaGxoayM/Px2w2\nB/uV4XaVUamsrOTQoUPBazp06BDXXXcdv/71r1m8eLGu3NQaXdxyZiFJEp6K9ZgNzRitZ2OorwLJ\nj7mlFb/NiIHh+EU1vuZ8rI4ZeBuPUFHjIirSQsboKVitdvy0YSO8Q89qyayyspKMjIxulwFP1R/s\nbH7Q4/FQUFAQtAPT8slaFZZobaXm9/spKCjAarWeUK4NVUOOGTMmmDXX1dVx4MABzE2NxJtMxCSn\n4LDbMe95F8kggc2CUARKowdDjBXJFHhPvyzj9QvG1nyCPOKu9pvDw4gq+FAUpVs+mH2hubmZwsJC\nJk2aREJCgiauMnB80L6lpSWYJe/cuZNly5axevVqzj///DBfmc5J0QPfmYWvuRzJW4giJaMIgWSQ\nkLytGC1mrD4TbrOMJBKQjWXUNaTiamwleUgsUcZYjKYo/LgxEYmB8A3vqs4oFoulT5sbQkt+odsU\n1IxQlmVsNhuNjY1MnDhR09JmWIQl3aSnYpnQrFn4fHgOHKDB56Om5ggHDhxgavE2zJgxyDJGoxFh\nMKC0+TFGW/B6Pfh8fuyOeAxNh5F9rWAJv4GAx+MhLy9P8xECOP5wkpWVFTRDCKerjIosyxQUFBAR\nEUFWVhYA//jHP3jyySd5++23GTdunGbXqNMBXdV5ZqG0HEQgYTRICEUgImwoTg/CYsUgg10x4sNK\nXVs9HtMhRo04C5PXi8FoRDFKmInCTGTIhvW+oaoBR48eHVZnFGg/Pzhq1Cj2799PbW0tCQkJlJSU\nUF5e3qf+YGe0tbWRn59PUlKSpot2AaqrqyktLe21WEa0tGCxWkiKi2VI0hAUfLAnArffHdjQ4XZj\nEhJGt0QbXowmI5GR9mPWDRIIJezXpDq+aG2nppa76+vru3w46bhwNtRVZs+ePXi93na7Gjv2UT0e\nD7m5ucFyt6IoPPPMM3z00Uds2bJFUyWszknQxS1nFrK/BSQTGA3Ish+jSUKYLEg+L1iseNxeXPUu\nYqNtpESPxiAnIlqdkJKOQUlAMoQncwntsWk9PqBuYo+NjWXmzJnBQNTb/uCpqK2tpbi4uE8ijO6g\nrsPxeDx9M+f2+cBkREHBQwMKfszDJ2A69A3KsZk/2S3jr2nGoFiQZT9tbW7M+DBEJoIlvDL/qqoq\nysvLTxjoDzdq9mW1WoNzcz2ho5m02kd1uVzB6oJaZjebzezdu5fx48cTHx+P1+vljjvuQAjBBx98\noKn1mc4p0EudZw5mSyI+xY/BEIHP78MsCUgaCkdraD5Sg1soDElMxIAfg0fCIDdC0liwx0OYsjw1\nEMXExGjeY1MD0cnmy3rTH+wMRVEoKSmhsbGxzwtwu0Ldxp6YmNj3XXNGA0JR8FCPQGDCChMXYCjf\nhcEn8MsKbsWDLdZOpN2KwSgh+2VESwMlo67m8Ndft7NW621JV11h1dbWFnbVa0dU8+eUlBRGjBgR\nlvcM7aOOHj0aWZZpaGjg0KFD1NbWEhERwYsvvsiIESNYv349l112GXfddZfmys3QtULXX389u3fv\n5uabb+a3v/2tpscd8Og9vjMLKXYyVJmJtplw1rlw+puxmcDtV4iOjmKIEYTbBcKAYVgGxA0FezT4\n20Dq+1/So0ePsn//fs0HnRVFaefW31Ug6k5/sLP5QXW9T1xcHGeddZampU3Vti1cn59kj8RXX4Wi\nBj1AJE9GSTsbZf/n+IwRRFoi8EcaUCxGDD4Zk68RJSWD1CvuJMVoDSpGy8rKEEL0eBmvupE9Nja2\ndzOHPUAto6rmz1phNBppbm7G5/MxZ84cDAYDhYWFvPTSSzQ2NvL+++/T3NzM3XffrZmbTse1QuvX\nr2fTpk288cYbmhxPp3/QA183WPH7xxhqLmP+2X6GjpjJ7m/LSIy0EBkTT7PPS6viJcruw5JwFZbh\n4wJJnq8NrDF9Cnyq2ENdeGqxWEDxgNJGoNhuBqM9UIbtI2pGlJCQ0OtA1F1/UZPJREVFBRMnTtQ0\nkKujCrW1teGV9VutyBYFg0fhWNxDVhT2Jv+A4R6Iq94OLQ0YYoz4ZCNmt4I8+nv4vv8HsNgxQjAr\nhkAQq6+vD35OqluK2kftmOGowpxw77U7GdXV1ZSVlWleWlfL0LIsB2cBt23bxpo1a3jppZc499xz\ncblcfPbZZ2EfoznVWqHvfe97rFq1ir/+9a9hPeZ3kkEkbtG3M3SDpqYmPvnkE/b/+08Y3V9js0Uy\nasRY0kcOJyHBhiz8NMvnUq9MoKWlhcgIK/GxUThGZGCL7N2TqTrHNmzYMFJTU5GQwXcEFPexYCqB\nkAOyeGMsGON6LZFXM0qtn+jdbjf79u3D5XJhNpuJjIzsc3+wM1TVq81mC/tGCoGg1XcIY1UDyDKt\nQqG45ADDU1JIjI6BhqNIDYUYzG3I9igihl+GiEvt9vurfVSn0xkcElcDYWtrKwcPHtTcEix0V19m\nZqamZVQ1e42LiyM9PR2At956ixdeeIENGzaQlpam2bE7Q10rdNZZZ2EymZg7dy7PP//8aT+PgYSU\nmA1X9HI7Q+7A2s6gB75ucujQIa644gruu/0GsifA/j2fcOjAHvIKnBhjpjHtnIuZPWsWiXHRtLg9\n1Hks1NY3BT0zExISiIuL67KMJYQIihWCc2xCBt/hwFYIg7XjD4BoCQQ+U8/UfIqisH//flpaWsjI\nyNC0x6Zub4iKigruf1P7g+HeP6hmROnp6UHHkXDTwhEMshFnWTmVewK7B6Mi7GCzQmws2CMQKAgU\n7PTNqFmdjauoqKCtrY34+HgSExODQ+LhRpZl8vPzsdvtjB07VtMyaltbG7m5uaSnpzN06FAUReGx\nxx7jm2++4c0339R0jlOnZ0gJ2fCDXga+PacMfIeBGqBMCHF178+w++iBr5soikJVVdUJfoeyu4Vv\nt3/O5zkf8dnnX1DT6Obs877HhfMvYtasWVgsFurr66mrq8PlcgXLfQkJCURHR7f3AD22fcBgMDBh\nwoTjT9l+F8guMHRykxMCRCtYRoLUPaGEOj4wZMgQ0tLSNL251dfXU1RUxJgxYzqdA1T7g2ogVJ1S\neuovWlVVRVlZmeYZkVs0UFK+l9YmLxMnjMdkOHZ+Iefpx4OFKCycYmZPUUD2BH6HkiGw5LjD70Id\ntI+IiGDMmDHBQOh0OtvNxsXHx/fZkFz19hwxYgQpKSl9eq+uUMdyJk+eTExMDG63m6VLl5KYmMjT\nTz+taZap03OkhGy4pHeBb+SXae3+7i9ZsoQlS5YE3leSdgLzgTwhxMgwnGqX6IEvzDQ1NZGTk8OH\nH37Itm3biI+P58ILL2T+/PlkZGQEV+XU1dXR1NREZGQkCQkJWCwWDhw4cOJAtRDgKwWsp+4XKq2B\nkqep655ZTU0NBw4c0Hx8QN2+feTIkR4vplX7g06nk/r6+i79RdUekdfrZfLkydqX5gq+xRqvMGrk\nWAwn8V8VKMh4sTPkpK8jBLQ1QJuT4F8JARhMEJkA1kDQbm1tJS8vr9OdgB0XFvdlGa8aiLT+/wIC\nvcPy8nIyMzOJiIjg6NGjLF68mIULF3Lbbbfp9mMDECk+G+b3MuM7eMqM71ugCnhCCJHT6xPsAXrg\n0xAhBOXl5WzevJnNmzdTWFhIVlYWF154IRdeeCFJSUk0NTWxe/duZFnGarWSkJDQ/qYl/OAt7zzb\nCx7MB5jA0rkLiSqWaWtrIyMjQ1NnFLXHZrVaw7K5XO17qQ8MofODkiQFh9+1dixpamqioKCA0aNH\nE5cUiYcmDJgwhOjEZHwIZKzEYuYkQgwhoPkoeBrBYm//QKPI4G2DyETq2mT27dsXzIi6gyzLwf16\nLpcLOC6kOdV+PdUHUw1EWqHuiWxsbAz2DouKirjpppt4+OGHufzyyzU7tk7fkOKy4YJeBr7yUwa+\no4AHOAD8lxDC2+uT7CZ64DuNyLLMzp072bx5M1u3bsXlcuH3+zn77LP54x//iM1ma1cWNRqNJMTH\nkOhoJdox9NQjgcILWMFycieX1tZW8vPzGTp06GkLDlr12ELnB6urq2lqaiI+Pp7k5OQ+9wdPher4\nkpmZGbTpkvHipRk5KHcTmLBhJhIjnTxYeFuhoRJsnZRiFUFV2X4q3WamTD2rT+VLdb+emjl3XMYL\nBH01p0yZoqm3p7oX0GKxBN15cnJyuPfee3n11VeZNm2aZsfW6TtSbDZ8r5eBr1IXt5yxgS+UrVu3\ncscdd/DDH/6Q2traU5ZFG2v30NLSTITdQVxcHLGxsSdKupUWMCaB6UQVqeqrOGnSpG5nDr1BCMHh\nw4c5fPgwU6ZMCQYHrY5VWlpKXV0dU6ZMwe1297k/2BmqCEgNDicrHyrIgOjeBo6GykAmbzwxQCty\nICs3Kh5GZ0zHEBXeDebqWiGXy0VDQwM+n4+oqCjGjx9PVFSUZg9EXq+X3Nxchg4dGvTxfO2113j9\n9df5+9//rnk/UafvSDHZMLuXga9mYAU+vXvcT9TW1rJ58+ZgRhRaFn3yySfblUXnzzuXcWNl3B4T\nTpeT4uJifD4fDsexQBgTjdFgCMz0hSDLcrDvpbXpsyzLFBYWIklSn0yzu4PP56OgoAC73R50sbHZ\nbMTGxrbbpBCO/YPqMt+4uDimTp3a6c+etI93MhQF/O5AibMDHreHwqJCkoYkkZI8NmCAEGbUtUJx\ncXHs3r2b9PR0DAYDJSUlYdum0BFVZTt27FgSExORZZkVK1Zw8OBBtmzZoukDko7OydAzvgFK+7Lo\nFszUc/6caZxz3gVkZ5+DxWKhsbGReucRGhuc+A1DiI1PISEhAYfDQWtrKwUFBSQnJ2u++03dB5ia\nmqr5k7taRh01alS3DbpP1R881fxgY2MjBQUFjBs3jsTEMGVeigLOUrC2D3yNDY0UFxczduxYYmJj\nAr0+oUBs9+f/uou6zLVj71AIETSRdjqdeDyePo+Y1NXVUVxcHFTZtrS0sGTJEsaNG8ejjz6q6QOS\nTniRHNlwbi8zPtfAyvj0wPcdoamxkc8/28hXn23k2107iI2L4bzzzmPmrAsYP+k8/IopWOpzOp34\n/X5SU1MZPny4pmIFdXwgIyNDMxsplcrKSioqKvpURg3tD55qflA9VmZmZvhn5eoPAQKMgQy8uqqa\n6upqJk6ciC3iWAnb1waWaAhzqVPd35iVldWlA0roMl61hNwTxeihQ4eoqqpi6tSpWCwWqqurue66\n67jpppv4xS9+oSs3v2NIjmzI7mXga9QDnx74+oIQCMVDeUUZW7Z8zIebPw6WRefMmcOmTZv4/ve/\nz5VXXhlU93k8nuANS7UM6ytqGdXn82k+PqAoCnv37tXkWCebHxRCYDQagzfssONphqZqhDmSAwcO\n4PP5mDBhAgbjsd6gEOBtCWR7pr7N5amoil6Px0NGRkavMi2/399OMWowGNopRlXlrhCi3WiJ0Wgk\nLy+PJUuWsGrVKi666KKwXJPO6UWKzoazehn4WvXApwe+MCPLMm+//TZ33nknqampeDwe5s6dy/z5\n85k1axZWq7WdWlSSJBISEoJl0Z4+easK0dNRRnW73eTl5QVFEVoeS93/ZrfbMRqNNDQ09Kk/2ClC\n4KsrZ2/BbmISh5GaOvK4YlcI8LSAPS4wzxcGQg2tR40aFbbP0Ov1BhWjDQ0NWCwWYmJicDqdwc31\nkiSxadMm/ud//of169czefLksBy7M0I3K8iyzKxZs7jssst47LHHND3umYAUlQ1ZvQx83oEV+HRx\nyyDA7/ezZs0a3n//fbKystoN0T/00EPt1KLZ2dn4/f4TduqpgbADTFY8AAAciElEQVSrsmhNTQ0l\nJSWaK0SB4Lqj0zFQrQ5vd9zgoPYHKyoqetQfPBVNzc0UFB9mXPp4EuzmQHYnHVtQKxkgMhEiwvPZ\ntrS0kJeX16OeaHexWCwMHTo0+L4NDQ3k5eVhsVjYtWsXt912G8nJyezfv58PP/ww7MfvSMfNCg8+\n+CA//OEPaWsLv0jojEQ3qe4TesanAUKIk96EuzNE39bWRl1dHXV1dUFBg+otqipBQ309p0yZoqlC\nVF2463K5mDJlSp9tuLo6ltqL6mp4u7v9wVOhjpYEZwFlf0DlKRQwGMEUAWEy1FYfHKZMmaJ5/1UV\nAqkPKV6vl9tvv52SkhJiY2M5ePAgV155JStXrgzrcTtuVsjJyWHbtm089dRTrFmzBr/fHwz+unq0\nb0j2bJjYy4zPMLAyPj3wnWF0HKJvbm5mzpw5J5RFVUGDJEk4HA5cLhdDhw4lPT1d03KjOqoQGRnJ\nmDFjNF06KssyRUVFAEycOLHHfa+e+IuGbjvQ+sEBoKKigurqarKysjR9cIBAMC8tLSUrK4uIiAga\nGxu58cYbOffcc/nd736HwWBAlmWqq6tP8LrVAnWzgs1mY926dRQVFemlzjAg2bNhbC8Dn0UPfHrg\nG0B05S363nvvYbfbSUxMxO12tyv1hVvtGGoHpvWeOXWjeDj7lJ35i8bExFBaWorD4Qj2vbRCFQLJ\nsszkyZM1fXBQTQRcLheZmZmYzWYqKir46U9/yvLly1m0aJGu3BxESBHZMKqXgc+uB75+D3ybN2/m\n3nvvZcSIEbz77ru0trbygx/8gObmZl555RW+/PJLHnzwQb788ktSU1PbvbZr1y6effZZZsyYwYsv\nvtjflxJWQsuiH374IV999RUOh4NbbrmFK664ottl0d4QjlGF7qJuZNd6/6DH46GqqorS0lKMRmNw\ni4IW+wchkC3n5uaSkJCg+cYNRVHabRIxGAzs2LGDX/3qVzz33HPMnTtXs2Pr9A+DKfCdkeKW559/\nnhdffJEVK1awe/duSktLmTJlCvPmzWPt2rU8/fTTvPXWWwBs2bKl3Ws5OTls3bqVCy64gPr6es1F\nF6cTSZJIS0vjxz/+MRs2bODnP/85l112GR999BE33XTTScuiDQ0N1NXVUVpaiiRJwRv7yTaHnwxF\nUSgqKkKWZc0dX9TAXlNTE96N7J3Q1NREdXU1M2bMICoqKtgf3LdvX1j3D8Jxd5RTrX4KF2qATUxM\nZOTIwBaZd999l1WrVvHOO+8wduxYTY+v008MInHLGRn4Qun4V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Sl8sFpEMiVFVF1/VsfUcYOhzrODY+B4eDxLYluyMmG6JJIjZUWibNKZP1VfXU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e98p+mU7HpoRHxzc3PcvHmzKaukRmtJbHIskGEZBQ1tRUrLxiTJJF5CdLAH25AMDQ2hqmoVwTZD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\nIskSnkwPnkwPc5iDqqh0d3dzqO4QkWgEJyo/vvNOFq9Yw89//nPuvvtuP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GjBghmUxmr9r8yle+Ij6fr/Bsinzye1522WV96p9yyikCyO9+97uicsdxxOfzyYknnlgo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\niA/OMfE999xzfOITn2DTpk08/fTTRKNR/vAP/5DZ2Vn+7d/+jV/84hfce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X4cNYnG1pl1rLYLkvgxNzCdtdDA01MtSXzZyklYug2ZKqo0mNpt1nYG9PsHFT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xXIOAYWNoh7RrYKsgtmWwvNNk/YYVBEMW4XiE4cE6KmJxLMsijCaF0ANUi8J1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\n9+7da3qqQ6lUYnBwkPb29n2Tez0uOo5PqKIoTSV3KSWTk/ZNX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01R4VbCqeCdaq0RXQkh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/i/TxKcP+NEPcX87DZz/D84T2jM3mnM1fssLogEFHJsOqZBPiRLFMG8PzcO0Q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722ruvce++97eNbmUwRWabl83my2eyaJ9s77jeQywrLRUWqR7vQJA6egYdDLFnFJRxYsiSLSQyF\nRjaIIYAmSTRcPNVoJYwEuPiYQRy9OE4iWV2zlZdIBMyXBW3XHa1omOU+R2j6AH/inOcbqkKeOHHd\nxMdFIegxn2aszIWMScqukNAaiD9OPA4T43XE9InFUiRSMcT0SVpCIjXGaP4wGgaO67CwVKVaWeab\n37xCEATtqcuoEo+wHYS4GfF8U01TMr7eRYYAtAjLkCyOWmLR+Hv2uu/b0jGubOtupUp+5plneOaZ\nZ7p+9nd/93dbOj6AS5cusby8zGOPPcYTTzyx5fUibK1C3DIhzgMPA/9Pj9seJhz27xs7hDggelWI\nS0tLXLx4kf379w/sxQlQ8sLRfGvFOcTWUuAvoCRAlEZSh+uNFWsHTYo1OPfNr7Jv37724zcajaEO\nrvQixHq9zquvvkoul1sVmrweBh3QidDZkn3ooYfIZrPMzc1t+FjRXk+UTNFsNtsBwJOTk+i6vmFY\nbTYdmqbXG5CId/vKuh40bYODEzpJlQhPhp0tzcAmVX6Ox5r/N/r1NI3kQ8jIMzTVLpo2mH6cWDOG\n72iYScGjtqqCFAIc8UhoKZTT2XJtEJ5U4twKFnhZ1clrirgy8fEJVIASC0vZJERRw+UVLcu7tCX8\noI4uSZQSklaDZjBCXDOxzCaBZmMFY2itU4RlWuRGRjgwMkLMDAkgski7ePEitm2TTCbJ5/PYtj3o\nW7suNkuwBeOFtga3E51kZUiOgvECE+7T64ZzbwTf99ufmyAItjXLcbP43u/9Xh588EF+9md/lne/\n+9184QtfaE8nbwV32anmz4GfV0r9rYi8HP1QKfUwoQzjuUEW2yHEPtGpWYoI0XXd9sng8ccfJ5HY\nYNpgDVgqtN4W6T7RBkqnrMbJyxxNSeKL3kWavlPj2rUpCs3UqjT5tSrZzaKTEEWEmzdvcuvWrb72\nSFdiMy3TRqPBq6++SjabHYh8eyEej7Nnzx727NkD0NbyFYtFrl+/3q5+4vF4+zXUNNgzLhRKUK4q\naInXUYJpKPbvERKtIqmTDPXK35K4/kOI+GhSgyI4xb9i7urvUUt+EGPip9CVTq0R+qnO6Ukm0hqi\n1+/IKwQUOr6fZlyPrXjtaoCJEPCSN4unFAllIAiiAhQ6Spn4uknMa6LhsmAmKTuKjPJBeXieT9xw\nEd3ADkzSuoQ2c9x5X2sOZOPhQBGsnxFZLpcpl8skEomuC43NvmebJcSmdhtNVssIOtM4NAx8fFxV\nJiabJ4fOCjFq078e8fGPf5xEIsFHP/pR3vnOd/I3f/M37USbreAuDtX8AvAe4CWl1FeBW8B+4DRw\nDfi5QRbbIcQBYRgGtm0zOzvLlStXOHz4MHv37t3SFWHGgINanbKXJreiOGnoWSRQZGSJwGvyjnEB\nz6dQLHDl6jS773mIpx4+vOrxhy1tiAZhImLKZDKbJqZBjk1EuH37NtevX+f48eOMjo4O/HgbYaWW\nL6p+5ufnKZVK7aGdaB8yn022J0ANA2JWb3LXai+SmPoBlDTaFCkCy/U0jqcYl9/DXW7i7Pl5CGDC\ngoov3C7H2JuOYeoeQoArGoEY7DYEw+82YKA9bRqwqNtYQSgDCT2KpDXRbOBpcTRdSEkF3XcpuxlM\nfJAamtYkZuTIj1jUnUXqjRw5tRdRKeoSuiDlk8LoOuf4zkq8XC5z4MABLMuiVCqxuLjI1atXAbrM\ny1eK29fCZgkxHKDp8d6IdI0khW5OW2vxdhJipVJ53RIiwEc+8hHi8Tg/9mM/xtvf/naef/75N6wR\nuYgsKqVOAT9FSIyPAYvArwC/JiKlQdbbIcQBEQQBt2/fJp/Pc+rUqS2N+UdQCr4rNsd/8CfIGLLK\nQ7OpZSh5KWzd5lvGK5y/MkndDjj51HvWrEqHXSEqpVheXmZ6enpTVeHKtfqpEG3b5uzZs1iWxZkz\nZ4ZiwtwPourHsiw8z+P48eNtzdnVq1ep1+tta698Po9ppHuesOPTP4OSRtfPmn6cipshY1VBwCz+\nIe7Yf4tl7cU0FLt0qAYQBIKvhc83qwkpXTAAW1vpYxsjlF7FMCSsdlABoa1jxwdJWTh6AiUOcVVk\nd7rBwSCFrTxKehpNS5PNLJAXi5j9ODhjSBBqDlPx0LmnX3QOlezevZvdrcgMz/Pabdbp6Wkcx+ka\neIqSKFZis4SY8U9QN6dAuskpPL5wPZ8GpuS2PFTjeV778/lGsG378Ic/TDwe50d/9Ef59m//dp5/\n/nnuueeeTa31OhDmFwkrxV/Y6lo7hDgAbty4wdTUFKlUikceeWSoaz8VK1Me8fj8gkHOENKtd0YE\nCi40A52fnGhw7ex5Dh06xLF9+9atSodZITYaDa5du4amaZw+fXrLxNTPsUUV+AMPPMD4eO8Ejk5s\n555Np0H0wYMH29ZexWKR27dvU6lUME2zfWLP5XIY3lW05vlVaxXtHJYetkL9QKMZJLDn/gh//CfI\n5YTFJUhZChzF/o4hKxGo1WDXmKyYdE0Byyg07pcsF1lCREPhcocQwyxMpQxqsouy5BiLzWOobNhO\nbdYxlEY2OEpSnkA3s/RwmOsbaxGYYRiMjo62q3wRoVqtUiqVmJqaol6v98yI3Cwh5r0nmDf/igCn\na39QaOWGIviqwrj7zFArxDeCSw3Aj/zIjxCLxfjgBz/YJsUjR44MvM5dDgjWAE3kTpK2Uuq7gIeA\n50Xk64Ost0OIfcJxHBqNBo888gg3btwY+vq6rvFTh2weyin+j5sac7ZCU+ALHE95vNu/zNFKiRNP\nPtmXvdIwKsTOduXevXu7roK3gvUqRNd1OX/+PEEQtCvwqAEIqi+bue1Gp7VX1GqybburPTimvswJ\nc3WQl+3HMDWHQiNH2c4RiIYXzOCYCqWBlRDcRpgt6KbDE7fjhgbuo6Nhkn3XsWAi5IECj2ijfNFf\noOgb5HWNQLyWTlUR2c2Xg4B71G52qXchLON5deqlOooJ0vr9eDHQt/gW9zthqZRalUQRDTzNz89z\n+fJllFJ4nkcqlSIejw/UkTHIsM/5fm5bf4gmcXRSYUByIKB5uGqRtH+cEe/Mpp9rhNdzy/TQoUNr\nft8+8IEP8IEPfGDLj3EXh2r+A2ADHwRQSn2YOxmIrlLqvSLyN/0utkOIfSIWi/Hggw9Sr9eH2oqM\nYBgGge/xvj0B3zMRcK0ODV8RlBeoXL/IkSNH2LPnSN+V0FYrxGazydmzZ0kkEpw5c4ZSqcTCwsKm\n1+vEWoS4uLjIxYvhc927dy8+PnVq2DTb9zExiZHoaZK9Heh3+Gdle5DiIuqmYvUWlrBY30XdTZE0\naygFriUY8bAKrNuKWFqIyR172lxGSKdCr9XeCNvXGb3Ee9Vu/jSYZcnXyBsCOGjEcIMGRfGIqzzf\nrT2ALnEWm2M0avdSKC6TiMcpNRTLdSFpKcbTgrHJc9xWZBcrB548z+Ob3/wm9Xqds2fPtgN/o2o8\nmUyu+53I+0+g22nmzc/T1GZRaHi6DeiMu9/BLu9d7UnaraCTEN8ILdNh4i7HP70F6LTa+RlC95qf\nBv4t4aTpDiEOG/0K8zeLqDUEYfTQQdPh/OXziMim9io71xsEIsL09DRTU1McO3as7d047IDgzrV8\n3+fixYvU63WebFXAHh5VyghgYLYnN308KpRIkiS+TnbkMLClNmzmLWisduOxNIfFxi7Gk6F7iU+C\nhnUajbCqSsWhUIFDe7qNxNc9ThQwipDlSW2UmGT5QnCTRV8hmo8mghckuJcx3qcfJqdbzNWa2I0k\neTNBxRDiJiSscLWmC7Nlxd7cxr6lvTBMHaJhGJimyaFDh4jFYgRB0G6zXrt2jVqt1jXNmslkVg16\nZYIHSNv3Y6tZPFVmcaGI3tzN7v2Hh3KMsJoQ3wgt02HhLu8h7gZuAyil7gMOA58RkYpS6neAPxhk\nsR1CHABKqW0jRE3T8H0fEWF2dparV69y3333bXokOlpvEERVYSwWWzXEslntYC90VojFYpFz585x\n4MABjh8/Ht5GQJVKK8Ox+4umY6ChU6eO3qoSBcGXJoGyAQG9ieCvme34mkDP4+Xeh1F8rrWfF8Lz\nDQzlEkjoVwswVTpGY+5VTNMinUrjGzl8L8Gq1N4VCFvJdURVAA/QUJLhIf0BHtZPMOUvMxPU0JTP\nXl0nryVQCpqe4NSz5K142EJc0eKMm6HMoh/f0p7HNWTrNt/32wQbxTBls9n2fm7UZp2dnWVycrLL\n4DyXy2FZFgpFXPaC7KVQv7ktA1rRc65Wq2+qChHuah5iGYguHd8BLHboEUOB7gDYIcQBsVWXlbWg\n6zqNRoOrV69iGAanT5/uKRDvF4Pak83MzHDt2jUefPDBnmLdYT7viKwvXQqddVZqKB1cAnwseo/l\nKxQ6OjYNUB62zOGoBppqfZzNGraaxZAsBnfvSr2571dJ1f4/cGdRLU2h51vsS02z0NyNaWgEB/5n\njqZOAFCtN1ku1dDsWb75cpXlMa8r/LfrJK48AjVDSIQW4bRpgKgiQhFNJjikj3KI1gBLtA8rYNd1\n4uqOWrJXRRc3oNBQZOLCoNz2Wlq3KaVIJBIkEol2VFNnRuTNmzfbGZHRa9lPWs1WUK1WhyJ4f6Pg\nLgvzvwz8j0opD/gI8J87bruPUJfYN3YI8XWAaGpxfn6eEydO9DVVOSzYts25c+cwTXNdEh5my7Re\nr7O8vEw+n28763gIDXwqeFSpEhCQwSOJjtmjUtIxcKhDvEwgoJO4I4gPLDTiuKoEwqZJccueq8Yo\ntfv/nvjtn8EofQ4fA9FSpIwqWirPfPbnqRmPoRxBlCIWj3NiNEbcHKXpwL6RcFCnUCgwNTWFiLTs\n0jJoxjJwENXVNtaBBIJHoObQZC+qVUWrjowV21cYnU5mofK/C7oGDTfUIOoDEuLdNvdeKyOyVCpx\n5coVisUiiUQC27bbbdZhEni1WuXw4eG1Y1/vuMt7iP8D8HlCI++rwCc6bvsB4CuDLLZDiANg2LFF\nEEoazp07h+M43Hfffa8ZGXa2ZvuRNgyDEKOMxOnpaTKZTHvE2yVgvlVBWWjEAB9FnYAaASPoJHpc\ngfqEuYAKPUww6DgRh1VkAk+VQ3uyu3UFa4zSvPdZKoXrVKa/QOzQvZTUUczMfYwDeV9apuHS1vrZ\nLiRjgmVZjI+Pt9+byDCgWJrBdut84+vnSKVSbU/RKNkjFKS7CDUgBzQIVJkwCUcDlUEkRaStWIvA\nlNqoabs2hi2D2cp6nS1UCD09oz2+6elpqtVqX/Z9a2HlOeHNNlRzNyEik8ADSqkxEVnpW/qTwOwg\n6+0Q4l2CiHDr1i1u3LjBsWPHqFQq29KK7QXHcTh79uxArdmtEmLke5rP53nyySd55ZVXAAgQFnDQ\nUZgtSYWOgY9DDI0AoYCPgdZVKQZ4+DRBwtbXyouVOyd5hU8Tg7s7Bi/6CGX9bRzac4zC0h2Zn6lD\nJ1eLgOMJu3skZ0WGAZkRm0I5zSMPPxqauJcrTE1N0Wg0wgGTbI50NkUytYym1VvtWotwOyUgEy+x\nXC9gqt0gqZ6E6HjhXuJdzuXdFohI21gharM6jrPKvq+zzbpejJiIdFWYb0ZCvJvCfIAeZIiIvDLo\nOjuEuAlEAyabbbNEI+TpdLo9vDJsM+61EAne77///jsSgT6wWUKMiP/mzZtthxvXddvk1cBHoE2G\nACZWW2qhhWMf1PHJdXxcXWwsLHS1/hdRoSE9pj0HOf5hIm7BSApK9XCqs/MjFARQt8Pb4+tscYm4\nKPQuw4B9+/e1B0xKpRKzM3M0/avowSiZzB5y2Szp1gRmwkwg+PjMo7N3TULcm35tLtBea/QKHO5V\njUdt1ihGLDIvj2LEou//yvXeKML8YeFuO9UMEzuEOABWSi8G3ZjvTGtYaX82zNDclY+plMJxHM6d\nO4emaZuScWyGEDu1jJ0ON51r1QkwVojtjZba0MPBwMJqtU+zrXRIHx8NRYxYWBmuOK6oWrxzkt9c\nu2273G9GM6BrQqEW+qFG0DQYywi5DYpZEb2tUeyEIiBheSTGFBNjMQI1jufdT5RbMRoAACAASURB\nVKXqsLS0xNT1qdBSLZ4HI0tJT5BNlrrSOTwfml7oXZrcvrmTu4p+LmZ1XSefz7fjzESEer1OqVTi\n1q1bVKtVTNMkl8uRSCS6PiublV184Qtf4OMf/zgHDhzgT//0T5mcnOT9738/hmHw6U9/mve85z0D\nr/laYDsJUSn1LHBSRAYPltwEdghxEzAMY2BJQ7VabQfo9jLFNgyDWq02zMNsE8/i4iKXL1/esoxj\nEEKcmZnh6tWrPadWO9ubftuAuhtJktQJK0GFho/CwwMEDY00o/gsIQQ0mk1u3riBZVnkcndOYAAB\nfphX+DqCUpBPQzYp2B7t4OGY0V+LUkkKlNP9w6CB8hYIozFMAooo0UhoS8TzGXaNHaHpaswtCsvF\nKtVilUJ5marrYDdGcIIiE4FJOhVnd0bIvL5esqGiV4W4EZRSpFIpUqlU250oarMuLCxQLpd5/vnn\n+f3f/32KxSKl0kCe0gD81m/9Fr/927/NJz7xCb75zW+Sy+WoVCpomtZ28nm9YpumTEeBl4GT27F4\nL+wQ4ibQGQG1EYIgYGpqirm5OU6cONHe2O+15rBbpkopXn75ZZRSWzYi73egqLMSXWt/snMtHYWP\noK8gRYVGijQecWya+PgY6C2XmlZWoKTx/BoXzp/n0KHD+L5HoVBo71dmckmy2Rxj6XGs18bYZl2s\nTiSJxPADQhLtwRmFCYGN8uZAxUGFA0YAijFEJcCvYjs608VdWAbs25MDcnge1Jo1vvy1OjdnFeXi\nFFmrSmkkzuhIuHe2luH2GxmbIcReiNqslmW1Q6kdx+GXfumX+Lmf+zlmZ2f5zu/8Tn71V3914LWV\nUty8eZPv+I7v4NSpU/zVX/0Vx48f3/Ixbwe2MmW6sLDAU0891f73hz70IT70oQ9F/8wC3wccU0q9\nVUQGmhjdDHYIcQAM6lZTqVQ4e/YsY2NjnDlzZt02zWaE9Othfn6ecrnMgw8+uGkX+070c1JcWFjg\n0qVLHD16tG29tdFaGXQWcLuM2KITephWZ+CRYAKDTMfH1XEcXj17CdfWePixo8TMNIhi165dVKoV\n7n/gMI1GjeoyTF99hSAI2gMS+Xy+r4uDYU4VD3UvUhSBO0pYX7sQlFDKRJQCmoCgyTio8HUULcXy\nYgXLyGEa4Sttu7BQBk0p8imNiYldZDIHSCUFp9nA8ZaYmpqiVqsRj8fbr9uwJQobYTvCdoetk4wI\nNh6P8/TTT/PLv/zLfP7zn0cpxexs/0OOH/7wh/nQhz7E/v37+cQnPsGv/Mqv8A//8A+89NJLfPaz\nnx3a8Q4bW2mZjo+P87WvfW2tm6eAHwb+8LUgQ9ghxE1hI+PsIAi4evUqi4uLnDx5sq/9hGE54ETm\n2L7vMzo6+poIhD3P4+LFizSbTZ566qm+c+4glFlYKGx8NGyalLGpEyY0GMTIYpAm2RLpi++zeOsW\nly+c5+gDD3LTdbHUKAH10J1GQBkupp4kk9/Pnnz4EW9LFloJFa7rtrP5oinCNxTERJN9iFQguI1o\nMUBQkmlpE30CmQcFtqPwfZ2YVSdoVYYLZbAMB10lUKpJPKawHRjLKSwjSSBJTtxzAKXuOMHMzMxw\n6dKltkShl2HAsIeQhk1esHoqdKvoVXFqmoZSaqCcwaeffpqnn36662eXL18eyjFuN7ZrD1FEpgj9\nSttQSn1wwDX+z37vu0OIm8B65FUqlTh37hwTExOcPn267y/eMFqmUYUWmWO//PLL22JE3olCocD5\n8+e55557OHHixMBX8xqKUUxuM0+BJXQ0Yi3yswmos8AEFUQO4i5XuPrSf8FuNnniwQcwJWB+fg59\nYQ/a7t2I8lEKNHsMI8h1mTavTHiPxNrFYpGLFy9i2/Yq0+jXK6KBIYWOkgTInnBfsfM+6KAshCaO\nl2g5hYefhZoTDuDougf+KCIFUKHkw/PDCdeaHf7JJFY7wUR7Z8vLy1y7dg24E/ybyWTuqij/bqCT\nEF8r6dTrCXfBqeZ3Vx1CCNXjZwA7hLgdiL7ovSpE3/e5cuUKhUKBhx9+eGAd0lZapq7rcuHCBTzP\n66rQhukusxJBEDA5OUmpVFplvTbwWlQxWWacJA1Uy5UTRtCIkyWgya3Zr7L45Wn2HjrC/fv3t98L\nSaSQagUFqPHdKF1H20CKAd1i7XvvvbedzdcZAhyPx2k0GlQqFdLp9OtmL61rglYpekRqoFBoMkag\nlkA1WvfQEISaY2OZAQRjQDxMjFfdyRyWAeW6IpNYvXYviUJklRZNYJ4/f75dRa6cwhwEbzRCjPB6\n+az8I0WnDdABQgPvzwN/CMwBE8AHgO9u/b9v7BDiJrCyQoyqpH379rWtyLa6Zr+IIpMOHz7M3r17\nux57GJmIvVAulzl79ix79+7l1KlTW/ryCwENChhYmBir8ytEmLk+S+HGWU4+/q2MpEbwqBBghzcb\nDYK4BfUG1GuQybZ+bbAr9c5svsg0ulQqceHCBW7cuEGtViMWi921vbS1YYaDNeKB6v46Kww0GSem\nN4BFAk0hYhN4aTQzCa3QXBFBwksKDL017KTA6fOjo+t6O/j3wIEDvPLKK+zfv59isciVK1eo1+tr\navg2whuFEKM96WEN7LyR8Fpbt4nI9ejvSql/Q7jH2BkBdRH4e6XUpwit3Z7pd+0dQtwEDMPAcZy2\nQXW1Wt1ylTRoNed5HhcuXMBxnHZk0lbX3AgiwpUrV1hYWNhUFdwLAR4OTYwe0ohms8nly5fJKbjv\n6IN48QqOF6ZYtK3YjCaOWsSIj6AVCpDJrjkMI/gENFqWZk7LGyeHYnWkQxQCHI/HOXnyZFeqwvT0\nNJVKBdM02wQZpbuvhWH6e66sEEUfQXnzoK9+PxQacd3A1A7ge/sxjbB6jKQe0XqOp8gk72Qg+kLb\nSm4QBEGAruvtRIpo/ZUavlAik9vwtduuPcRhwvO89nf/zZh0AXfVqebdwGfWuO2vgX8xyGI7hDgA\nOlumlUqFF154gQMHDnDs2LEtn+wGSZNYWlriwoULHDp0iH379q352MOsEGu1GvV6nSAIBtob3RiC\nh4/R2f4XYX5hgemZaY4cOUKmXsfWyrjU0OgOCdQkBoGBF6tg2DG01lTiytfSp06gFoAyigYQahQD\nAjQZQ+cwrJGuAb1TFWzb7kp31zStTZCr0imGiJXkKnqMQOKoYBGlUijVInjxQZooZTA6tpvpxbAr\nmokL5boi0Xq6tgu6phjpmP1yPJjID04cvYi/l4Yveu0WFhbar13noE4k13ktkzM2i86qsFqtkkrd\nXZvA1xp32anGBp6idwjwKcDp8fM1sUOIA8LzPG7evEm5XObMmTMkEpsIjNvCY1+6dIlGo7FmVdiJ\nYRly37x5k1u3bhGPxzl69OhQTyiqbc4WuqW4nsvVK1fQNJ2HH3o4HDZq1PGpY7aijLp+XykIBK0R\n4JamMQu7Uc0mnRYwAU18NY9GuZVNmCaK1RWEQJVALqDzIJ3xaRvJLmKxGBMTE22zgyh2qFAotIdN\nopP8duwphc+riIiDMhUEJspfQvk6ukqjMBFtFPQMcaWzf1xYKIWvdsNWOB6YmmBoAXvGaFeHthf+\nPbEJ7Wa/hLPytfM8r+0leuPGDXzfJ5vNYprmULsc202Ib7Zw4Ah3kRD/E/AJpZQP/F/c2UP8p8C/\nBJ4dZLEdQhwAjuPwwgsvMDExgWEYrykZRlXhvffe2w7S3Qhb1TY2m01effVVUqkUZ86c4Wtf+9rQ\nTygaJnFS2NjUi02mpq5x8OA9jI3dqQS9hIZftcnoq1vSWrMJ01cRtYAYywT1GZKVBnIrhtzzACoe\nJ1BFNJwWGXavEdJihkDV0WQGxSE2a/W2MnbI87y21GNxcRHbthGRdhU5iDylEyKC0h08NY/CQlOt\n56QlEG0EMWoIcXQZR3UMGMUsODAuLXlFwFxRoeuQTTiYuobrh5WhrsG+EdmUsfdmJQ2GYTA2NtZ+\n3yMv0ZmZGYrFIi+++CKpVKpdQW7WMGA79vh2KsS7mof400AG+NfAJzt+LoTDNj89yGI7hDgALMvi\n9OnTuK7LxYsXX5PHjKrCer3OE088MRAJb1bK0RkYfOzYsfZJajv2JJVSJPxRLt36B5xawIkTJ7Cs\nO0QhCF7CxyjFiWHSaXsa1GvEKq/iaJM4U7fxfQ/NiKGZCjc9Scx4L2r/kwSxJrqqslZ4dlgz6QiN\nVjt1OJILwzDawyYjIyPMz88zMTHR1vQ5jkMmk+nSQvZzkg/ER5llFAdWxVopFKg00grP0smu+v2Y\nFf7Jp4VaEy6jaHrhnuF4VkjFNp9yMSwhfeQl6nke8XicQ4cOUavVKBaLbcOARCLRrsD7HXJ6LQjx\nzbaHeDfzEEWkAfxzpdQvEeoV9wAzwAsicmnQ9XYIcQAopdotnO3S93XuwSwvL3PhwgUOHjzYd1XY\nCV3XsW17oN9ZLxpqmIQYtSMjN5/xg/eQOA4olwAdUAguPh6GkSGZTaAtNJF4DMwAnwb+8pdg9i+x\nZ5oYyRzsag3IzM3T/NJX4b4ZUnETtT9yzVnvhBm2bRkiIa5E5x4jhOQRST2iRIVIC5nP50kmkz3f\nc1ENwtPQeif2GIGqoknmTnDyCugaZJOwO2NzeDf0km8Miu1wgYlE7ul0mnQ6zYEDB7qGnDoNA6IK\ncq093NdiD3GnZfrao0V+AxPgSuwQ4oBQSg3NVWYlOiu6aHr18ccf33RrdlACm5+fZ3Jyck0T8GET\nYuTmE02sejjUKdGghCDoWGTYjUUcL7uIaA5B6SpiN6DZpH7uPyOVEsaxCTQjBk2FLiZaNosW1/Bu\nTdG0/nfM0X+BY1QoNW9R9iYJNBsrkSZnHSfNfVikCcmg9aUWAWwUHiLbF8mladr/z96bB9l13Xd+\nn3PvffvaO7obALEDBEASIEFCUpxIXkaiJrK1TOy4aiwr1siMMlbVqCqObSWuGtpJpqySxplKeeSR\n7URLUlK5JA8ZuTySKcsjuSxZJCVKBLoBNIBGL+h9eft+7z0nf9y+F6+7Xy/v9XtsyuhvFUigl3OX\n9979nt/2/XrdmEePHkUp5UVBExMTFItFb1whmUx6s5BSVdelQhveX3SkquEM47+hLfFvyGB+oyan\n7QQDXLm+gwix/dhv+ychRAT4F8B/hSMI/t8rpe4IIX4Z+LFS6tZu1zogxBbQqYF3TdNIpVLcuXOn\nLd2ruz1Pd4TDNM1tRcDbdd3lcplCoUAymVzXsWrgJ04fMXpxCEp40Y1NACu6iBbpRzMF5uJ3sTJL\nqN6kQw66DSEF5Qy+QBEjGEX4uqlOjFM5/w+krNewfEE0Xy86Ycx8hQXxXYI9rzNo/Cx+vRtNKTQ5\niSa/gVBZdCkZiOXRzBBSOwV6Z/2QGkVB5XKZTCbD/fv3KRQKBAIBgtEqlmmjpES8yWb09rMrdCvB\ngHq5Pr/f742B7EUwoB71ddOHkRD3E0KII8C3cQb0bwEXcWqKAD8N/Bzwkd2ud0CILaATHYO2bVMu\nlxkfH9/zTKOL3YxdpFIpbt68ueMIB+ydEOtrk+FwmBMnTjTe/a8NidfDwIdEIc0sqnib6sx/RPny\nGEGBJXT8dhThE2gBC6o2SIHmC1HOFEnf/Q7GY0eI2hqmAbYAvQpGVkfOplgZ+AsGxM/hj8wgwjbo\nh1AMImtFjPI0evbP0dQRLN+7INwL/jemmcqdhQyHw964QqVSYX7pHrnCIq9fu4auObqibqSp6c79\nVKzV8tQbS5idIMRWx1fqBQPctWZmZlhdXeXu3bvrTH/rI/C9oFgsMjw8vKc1ftKwz001/xZn9OI0\nMMf6MYvvAM83s9gBITaJdjoguMhkMty4cQOfz8eFCxfapqO5HYHZts2dO3fI5/O7btbZCyGapsno\n6Ci6rnP16lVef/31ptZSMgdLS9iVbyND89iqiqZqKFXCoIxmmEAI6TcAH2AipZ9ipYrwCXTVRcW/\ngmktorIBUAJiChGykJUS1eDfEF40UPoxCC+iVbIIXSCVhjKOIsQyuvl97NXLEO2G2OYRkDcCwWCQ\n7uQgVWuJkyfPY9VssjknTTg1PYUQgngsTizpJxEdwme8sYT4RqVMW4GmaQSDQbq6ujh27JgXKdZH\n4H6/36tD7iS20AjFYvGh6zIF9q2pBvgnwHNKqWmxuY4wCzS1OzkgxH1EvR7oE088wcTERFtTsVtF\niNlsltHRUYaHhzl79uyuH2CtEqI7MlJvC9XMxkKhMJdGkeXvYETCCHEcGdSwxRSiFkAZGlLk0EJV\npN2FsiVSWahcAemvQrCfsgCp4gTSZQQVVEBHyBpSM6iUDbKhBXoX+2DuJYRfofuiCAxiwkb0hFGR\nJLoxjtSfhmIaZfghtPvUWDs3UQIdYSdQqozhD60b9bAtm0x+mXy2wMz4BEpNbqqjdRJv9kH6+npf\nvWCAG9U1EltoJBiwFR7Gppp9riH6gfwW30sAZjOLHRBiixBC7OnD6pLS0NCQpwfabu3RjQTm2lKt\nrq7yxBNPNL2TbTY6dqPQQqGwSUhgO3JVKBRVJAUkVWSlQi37A/QuHxphUEV83cNUwkGQVaplA5+p\n0AM5amUNTY9hSI2KXcCOBqn1J1FK4iv7EfkoMqojqiZSRAAfmpWiUCohMwuoriSiVITeIMKKoM8t\noL0+hnz8DComEWIJ5RtCFDOoJgjRvX/tgFIKZBidXiQZFHJNi1QhDOhJHqI/mUQ8YmyyvbIsa9Oo\nRzvRHmuladC+BeI6Pf05dHEReC/sYUbUxU5NNVuJLWSzWaamppBSEo/HPZL0+/3rXteHcTB/nwnx\nGvDPgG80+N67gR82s9gBITaJjY4XzX74pZSMj4+TSqU2kZJhGG0lxHqCLRQKjIyM0NfXx9NPP93S\nQ6uZCDGfzzMyMsLQ0FDDKHRrvVGJzSo2JbQ14WqZmQL/CkoEkMpEA7RwCHHoNNrCLcJJH1bewi6Z\niGoZq2BQ9mWpLtnUjhwF28Qw/YiahRKAtFFaCNsWCMtGrKwgjlepDsQwqjWEkih/GU3XscIBMHS0\nsUnUk70gaqD7oFoAy0QJDWXbIATaDtFDu+CmJXUiaCqEoobCWmtBCiCatL2qVCrMzc3t2ZnCXb/1\n31cgvoLQXwSlAwlQJv7Q90F/BdQ7Qf4q24/P7Hx+zdQkN4otuIIB7v2rVCqYpsnMzAwrKyueM0qz\neOmll/jEJz7B4cOHefHFFzFNkw984APk83k+/elP8/TTTze95huJfSTETwFfXXvPfWnta+eFEO/F\n6Tz9hWYWOyDEFuGOXuyUQqmH6xJx6NChhq4Ye1WW2Qh3vcnJSebn57lw4YInuNzqejsRolKKyclJ\nFhYWthUA34oQbdJIKujURa+VMoYeRSqBLSsorYZQESLnn6RYrVJcnESEaoQTPkRZw14FuxCAvieo\nxgXVxTkCtRi+gJ+grhCWjpkqolIFlDTBXCJkC0p2jYSho9kamBrSX0MFaqhQD1omiyqWIKxAB2la\nmEsL1DJ5ymNjVO/eBaUInjhB7G1vw9/Xh9BNUFUc415ndrAdqK/TOdJ3u4/yGtlevfzyy559WalU\n8hRhkslk04owe0pxir8G7S9ADeM+mqSsgepCoKPE10HEQP2z1tZn74P57qyjO0taLpe5efMmlUqF\n3/u932N0dJTf/M3f5F3vehdvf/vbd01kn/nMZ/jsZz/L888/z+uvv87MzAyvvvoqJ0+efEMVsVrB\nfjbVKKX+oxDiX+Ko1Hx47ctfxEmjfkwp1Shy3BIHhNgimklv1qcqtyOJdqdM3bmseDzOM888s+f5\nq50IsVwuMzIyQjwe5+rVq9s+GButpTCRKo8mLSCDkx7rdiIyS4EZQullbGljaDa2UmQHjhDp6cVY\nnceaWUCvGmjdjxI5818wcPgZogs3mQp8A4MALJYoFyvU8iWE1DHiQTS9iC8QIYzCXM1TsnSCCR86\nwrF80EEaFpquoChR4S4nhTs3TylnkvraX6JKJUQohCYE5Tt3yP7nb9H1M0+S/Lm3oa9lADSVwk8B\n1GEQb54HnJuqP3LkiGd75TaaTE1NUSwWCQaD6zoxt3tdW2+qqSG0r+LIUD54LCkl1zwfdQRDoH0N\nZT8LtNa40okaZyAQ4NSpU7zwwgu85z3v4d/8m3/DzZs3+da3vtVSZCeEwDRN3vrWt/KOd7yDF154\ngYsXL7btnNuN/VSqAVBK/QchxP8DvBXoB1aB7ymltqotbokDQmwS7od9t8P5bupwYGBgx1Rlu1Km\nSilmZ2eZmpoiEAhw9uzZPa8J2xPi/Pw89+7d49FHH/Xa3LdDQ0cKewL4B6DEg1qRhh7vw5zzowVs\nNDuJFDkyhWXKpRIDw4fwGRG0Ixep9d7F338JXTuMJpwxhUTPcRK58+Ssa4hegW/axpcIYwd1qlYO\n3bCJjPspdyuCwQLWso1pKHz+KKVCDaF0LKOKFsph5S9jd+uY87PU0jlWv/qXGMkk+trcG4ChLFRh\ngczffg80P13v/lk0nw9FGEkZYc2hjKE9kWK7raTqsbHRpF4RZnZ2dkfbq5YJR9xYk85b/95Rqr72\n6gNhgrgG6q3NH4P2S7dtXK9UKvHYY481TYQf/ehHee655xgeHub555/ni1/8Ip/+9Kf53Oc+x+c/\n//m2nW+n8CZQqinS2PGiKRwQYovYibyklExMTLC8vMzFixd3VWjXNA3TbKopahOq1Sqjo6P4/X6u\nXr3KK6+8sqf16tGIEE3T5MaNGwghNkm9NbWWPY0zX2sAg96XFVVkeAbdKGNbFkr6WVzK4Td6GRro\nAq2CUAK7PI8R7UYLvg2hYmDlQZYwdEFv8hy+aj/F1WlK1VfQSxL8YXp6ThARcQz/BKYGWsrANhbJ\nlvJUV6oII0E06UcLFlH3BlE9ZzFzFcxSgZW/fxWCQfRYzH1qAzhaqKEIhi9I/h9+QOTpywQHnQYN\nIQykKbDTt7HKTspNC4Uw4nG0Nje3tAvN2l5ZltViBJZFNEgpK6XWS88phZM9aA2dJsRardaSaPuz\nzz7Ls88+u+5r3/3ud/d8fg8DhBAGTnR4hAaCxUqp/3u3ax0QYovQdX3LCNHT5+zra8o70DAMSqVS\ny+e0uLjI3bt3OXPmjKfW0U5srHG64xQnTpzwHpa7xboI0a4A3we6vO8rFBKJVDpCG0QfrFG4M0e2\n/DqJ/mHiwSFQBlQ1pLmIFugD3oLGIYebfBFAAooIA1jhGQKyi1h/Ar0rhiFToGyUz8Ls8hOdKxPQ\nBqj6+ijXJghpYTQjQHEhRHFFx5g3sE6Bb2kFQwSxFxYwBgcfXINSgO04amh+hA6yVKX441GPEO1c\nnqoNiAoiHAMthKxUqOZyGIkERm/vriK/jpkN7xKNOjEzmQypVIqlpSWWl5fJZDIeSe5ukxRwNhUb\nOLHx+bUeXXdyjONhxX52mQohngRewFGqafRGVsABIXYK9SnTjRGilJLJyUkWFxd3HRXWo9WmGtM0\nuXnzJlLKbaXX9go3gpVScvv2bfL5/K58GRvBHVtxcB8w0ehHkkKhsLG9wEtKWFxVWJEI/cn3o9Vu\nIGv3AYEwAuhdV9D8TyAXzA2fCFcSTidhH2HVzmL7LURPBNMXhtoMQpUJ9Z4jMHObjF1ACYuocQqt\ncha/COEz08T1eaxnf45UzwUWp+bJ3boJ6TSheJxQIOBdv1JybWREgZRITcNMp7BtG7tYQqWzaP0D\nCCUcARlNQwQCEAhg5/Og6/h2kW5uJyG2gyB8Pp8nmSaEIJFIoGmaN/Duehtua3ulHnXEErBYX0Os\nv9a1eqJqvZ7WyQjR3Rx1QsnqzYx9Vqr5D0ABeB+OdFtThsAbcUCILWJjhFgoFBgdHaWnp2fHhpLt\n1myWEFdWVhgbG2spSmsWmqZRLpd5+eWXGRwcbGqov9FaD+pXc0DI6SREx1JlBH6EgEqpwszMLN3d\n3Rw6HALOotn/BEwbodngj6ETQEmJEpNb6nsa+OkWx8jll0D0IZSBbgyh6yuYXUXuDRbonZ8nXjMw\ni0W08CoUDVTYgJ/9VXxH38qA0OmO91FEsPjaa9hrEX0qk0ETglBIJxSQjl6mpoGSiLXXtLK0jPT7\nnfNUko2aGiIcxspkMBIJxBsYcbRbWUYphWEYdHV1bfI23N72KoFSP43QvrXWZeqck8I9PwXMo+Rb\ngN6Wz6/TKdOHFfvYVHMe+CWl1H9qx2IHhNgiDMOgXC6vGzPY61hDM001tm0zNjZGuVxuOUprBkop\nVlZWWF5e5sqVK3sePl5PiM5guUQhZAxN5JBUSS3nSaezHDl8GH/YB8yhEcPQ42zckApNQ+vqQqZS\niC0EB7RgEH+0G92MoQknil5eyrGSm+bwmbcSeSKCNT+HyKbRTp/E7oqgBo6gG4dxD6jH4xhdXRiR\nCH4hiNbNp1WqJSqVFdIrabRACCNXIHx0mPTSEgszsxx/9Jwzp4eNbQkQpmdtJDQNoRSyXEbfYY5t\nv1Om26FRxLlxVGGj7VWlUiESidDV9Q76Bqbw+ccQ9ADRtRRqEcQqSp4C+eFNx9zr+e0Ftm17c421\nWq3jSkBvRuzzYP5tWm05boADQmwS9YP55XKZV155he7u7pajwnrsNmXqap8ePnx4Vz6Je33oVSoV\nrl+/jmEYDA4OtkWJoz5lqujDVjMoFUMTBpYZYXp6nkBIceLUsEN2SDR60Ng6CtaSSajVkPk8IhhE\nrD2olG2jymW0UIjAU09RuXULkUgwff8+AKdOPoMWKGKbeWRYEH76v0TG4ghC6HSv8x3UAgH8w8ME\nT52iNDKCCAbRDANd14mEY6iAIhGvYRVtzGiEeUNQuztOQNfJZLMoVSYa7UY3giilHqSNbRtp24hq\nFREOd2i0obNrwe4IZzvbq9u33o9u/AMDh75PKDSPz1cFcRJl/wtQP8Ve6oe7Pb9m4BoYg5Mlehh1\nTPeZEP9n4JNCiJeVUtN7XeyAEFuAGy0tLi7y1FNPkUgk2rLuTilTKSV3ssMVCwAAIABJREFU794l\nnU7v2hHDbV5p9aHnjlO4VlSLi4strdPovFyjZSmHEeJHaJpFJl1kdnaO4eFhkl1Jr8dCMA88iiNd\nuPWaWn8/hMOoTAbpNijpOlp/P1o0iqFpFItF7n7rW/T199M7OAh5G7msIBgicu4xfLFeJ2VL42YQ\nX28v3f/0n2IuLVGdncXX3Y0WCKAAJX2oQgapKYo/83YOHR5mqKeP4tR9yqpELl9gcqaGYoV4LEYi\nmSQei6HpOkoI1NqmyH0fuBFku01tXXQiZdrsepttry5RLn+I9Oos9+7dw+dLEgiESCQW6erqIhaL\n7el+tPN6H3YvRBf7OJj/DSHEO4A7QojbQHrzj6i373a9A0JsEqZp8uqrrxIKhejr62sbGcL2KdP6\necZGKjdbwTUdbvYB4jbqKKW8cYp0Ot1Wg+BarbYm9RVDyossLXydQjHG6TOn8fsdMnJa8VdxXOzP\n7GpdPRaDWMyRVAOneWVtYzA1NcViJsOFX/5lfJUKdibjmD53d2N0d6PtQtZLaBqhkycZ/vjHSX3j\nGxR/+EPMVMp5TXw+7NMXyZ4e5PzVR4nGoihl4gtI/IFDJHt7OIqOZdvk1jQyZ+7fx5aSuM9HbzxO\ncm1YXkrp3e96gtybPNp6vBndKRzbqwjh8BlmZrI8/fTT3izk/Pw8t2/fXpeGTcRjOJwkQLyxj7SN\nhPiw6ZjC/g7mCyF+B/gtYBnI4Thit4wDQmwSPp+P8+fPI4Tgzp07bV27Ucq0vka5l87VZvQbXY/E\njY066+t+rcFNEyYSCcbHx5mdnSUUCpHP5zl65BKnTqcRYgnnrSnX/vQDV2m2VCA2zIeNjo4SDoe5\ncuXKg4f2HhqRfD09DPzzf479vvdhrqwgbZvJVArT5+Py+fNr1ktO56vR2425kkL3OedkbPDqM/N5\nCkqRL5W4PzeHZVkkk0m6urpIJBIYhuFF1LlcjkAgQK1WQ9f1PUWQb4YIcTcIBoMcOnTIc0up1Wpk\n0sukl24zd28JISAWixGL9xLtOoov0L6N6naoJ8SH1fppn1OmHwc+iyPTtmdVkwNCbBJueqdarbZV\nZg02E06pVGJkZIRkMvmGdK66dlS5XK6hR2I7DILdqCcWi3Hp0iXu37/PzMwM/f39pNIl5uaDdHWZ\ndHUJEolegsHDQE/LxwRnXvL27ducOnWqI/OZeiRCVQhGRkY4dOgQR44cqSOFtWacRABlSaxsFmEY\naGujB9I0UdUqRjRK/8AAA2uvsev2nk6nuX//vudSUSwWCYfDHD582Pn9ughSKbWZIGUFrEWEvQKA\n0pLgGwARBmUhreoe/SPWo901uq3gNxQDyRoDyUEQxzAti3w+Tzazyvzst6nKCJHE0Y7bXh1EiPuO\nMPCVdpAhHBBiS3D1H3cj3dYKlFLMzMxw//59Hn30Uc+poBXslsTclOyhQ4e4cuVKw13+rsS9sXAM\nrBUOGQQROOlK96HtajXeuHGDYDDI1atXvYeK68aQTqeZnU1Rq90hFlugu7ubrq6uprppXWcRl+Bb\nURDZDRYWFpicnOT8+fNbdhkLIfD19qJFIljZLHaphACE349vcBAtHF53zze6vWezWa5fv048Hqda\nrfLaa68Rj8e9CDIQCKzVY+sIsjqHT91FCAOhhwGBsGZQ5VGQCZQxhChXCNpLUDsMvjiIvZFZOwlx\ny2yEkghzAYTP+YOTufHulzqFtHJkShHSuQIzMzNYlkU8Hsc0TSqVStu6sg9qiA72MUL8Oo5Kzd+2\nY7EDQmwR7RbidiGl5LXXXiMUCvHMM880lepsBLcWtRXcutr8/PyOKdntPQxtFKs4M7IKb45M6UiZ\nBBn2IpftIrZ6N4Zjx455BJlKpbhx4wa1Ws0jgu0I0hUa7+3t5cknn+xIGs8dfbEsi6eeempXiix6\nKIQeCu16iFspxdzcHDMzM1y+fNlLyUkpyeVypNNp5ufnqVar6wgyqOVA3UGKJFLo2BKQNrpZRdg6\nQlsCIigtiRIGoroKdhkVHNgTKb4hogGyDMoGbQtSEwJND9EdF3T1nvDWymQyrK6uerZX0WjUiyBb\ntb06IMS9pUzbQKP/Dvj82mv3DTY31aCUurfbxQ4IsUV04gE7Pz9PqVTi3Llznv/aXrHdKEelUmFk\nZIRoNLorN4ytCNEhw3mcetmDGooTFdZQah4hBkBFPcPg3UZs9QR5/PjxdUSwkSC7u7sJBAIsLi4y\nMTHBuXPnvNm3dqNYLDI6Osrg4CCHDx9uqbNyJ9i2zc2bNxFCcOXKlXWvT71+qHtf3Mj69u3b+Gs/\nJBiJE08YRKMxAn4/mCmktJF6AJQPapPY5nkQOviiYBWhloHAzmo5W6GdEeKWa9mFnZtnND/IokOc\nQkfTNKLRKJFIhCeeeAKllDcLOT4+TqlYJObT6fZpJIIBQpEIROIQiYFv63Rr/Tk+jObA4Gx/W+0y\nbQMhuoKv/yvw+3s9zAEhtoBmneN3Qq1W48aNG2iaRiQS8RQ+2oGtSGxhYYHx8XHOnTu36+NtTYg5\noOaRYX2tEDSEiFApzzA6kmVgYIjTp0/vSeFmIxHkcjlSqRTXr1+nUChgGAbHjx/vmI/c/Pw8U1NT\nXLhwoWMPwGKxyMjICIcPH2Z4eHjHn18XWR/pRpYyVGohMtkM09NT1MolEv4ikXgfkahBwO/HMmFl\n6Q7ByLAjKq98aJUUGDE0vTWz4zdGVk7uLopV3n8A1hl6CyGcJpxYjCNDQ5BapJxeJVuucH8lRWly\nmpCuEY9FiB45QWRgcEuid6+3UCh0pEb95se+2j99mHYZjXJAiPuO5eVlbt++zcmTJzl06BDf//73\nkVK2TQ5qY2rXsixu3LiBlLIpdwqFidLyoKewWQLCaDh1Kafb2SGfejJ0HxQLC4ssLk7w6PmrxKJD\nbbkuFy5B6rrO8vIyJ0+eJBqNkk6nGRkZwTRNEomEV4PcS3OFbdvcunULKSVXrlzZczp7K7g1yZYJ\nV9XQNEk4EiHsdj3WCpRz8xTKJrNzM1QqFbDTBKKn6enpwTAMlFKoWhnbLDspVvDS3M1EfesIUdpQ\nLYJZdv7tC0EgAtrO7+8tCVEEQOa8+mFDKInz3nzw+1t+rjIrCLNGuKefMI7XilKKarVKNp1m6dYI\n6fFx9EhsS9srcDYxBynTN/jYSn2+nesdEOIe0eqO2LIsxsbGqFQqXLlyxUsfugTWLkKsj+rccYrj\nx48zNLQ7YnJcJzIosghNoKg55MgKEg2dLhyXB21T44xlWdy5cxufz89jj13eHHUoBWbV+b+mb5ua\n2vL81hqQ5ubmuHDhgvdAchuRpJRks1lSqRQzMzOYpumNMzRDkK5W7eHDhxkaGupIylxKydjYGKZp\n7pFwDeqJAAABoXCYUCyIP+BnbnaewcEhKjLB9PQ05XKZcDhMMuYn0ttFOBbzNjf1QgFNEWQljygs\nO3/X1l77WhGKK6hoHwS3J/stCVGPgJ3a/tiqgtIT6yLJ+gjRg1lDlPIQXn8uQgiCwSDBwUEGentA\n16nEe9bZXum6TrVaZXV1lUAg0HLK9KWXXuITn/gEhw8f5sUXX0QIwUsvvcR73/tefvSjH3Hu3Lmm\n13yjsd9+iO3CASG2gHr5tmZn/ADS6TQ3b97k6NGj3kyji3aZBLvQdR3TNLl9+zaZTKbhOMV2kOSQ\nZBBE0IQAZaypuPhRWNgsIpQJMuhFhZqmkU6nGb83zrFHjtHb24uqF6FXCgpZKGScCAIFaA4hJnog\nsLvzcztV/X7/phqbC03TPPKDxuMMOxHk3Nwc9+/fX0e47Ua5XOb69esNxjZagBYFNJAWaGvvTc0A\nJZmbnaVUKnHmzGl0kYfgOQ6tmRWXSiWyq3NMTc9SKE0QDoe9Jp1IJLKJIJVSHjluIppKAfKLjg3X\nuu/5QUpEbhGFgODW93NLQtQCKD2BsPMOOW76xRogwFjf8dswQiwXQd/h8+vzQ7lIQNfW2V7VajV+\n+MMfsrq6ykc+8hHPAqtUKvFTP/VTuzLKBvjMZz7DZz/7WZ5//nlef/11hoaGePHFF7l69equfn+/\nsc9uFwghngV+kcZ+iAdKNW8UDMPAsqxdE6I755fNZreUXmvVAmormKbJ1NQUR48e5emnn27qQauw\nkaQRRNabtK5BYCBVAMtOoakImtDXhAQmKBSKPHbxsbrGGQsIOmSYWYZiziE+ve79a5mwPAvdhyC8\nPfGk02nPi9F9QAGeOs1WjhEbxxk2EqRt216KNRaLMT4+DrAl4bYDS0tL3Lt3j0cffbQ9ykeaAb5j\nYN7FETUASwqmxscJR7s5deo0yCzofSAebD7CQR/hw8cYDB9GKUWpVCKdTjM9PU2hUCAUCnkEGY1G\nnRTrWlbAHUFSSjmarIUVNF94Axm656eBP4woraICEc9ceSO2LR0YPU4vs51zmoLQAQXKAmGgfEOb\nGm8aZl5sa1f1SNfSa/1laAQCAc6cOcPf/d3f8cEPfpC3v/3tvPLKK3z5y1/my1/+8o7rboQQgr//\n+79ndHSU69ev87nPfY5PfvKTTa/zRmKflWp+C/gDHKWauxzYP+0fXELcDXK5nNeVuB0xtWucQynF\n9PQ0c3NzDAwMcPz48abXkDhaoI3I0I0WlNKRyocmCpTLfsbGxujt7eXixYveNSrk2jphqJQcMmxE\neIbPSZ2mlyAQbLhzV0oxMTFBKpXi0qVLhNZGGOxCASuTQZkmAJrfj55IoEej224CtiLIhYUFRkZG\n8Pv99PX1sbq6SldX165rrruBq01bLBZ3PbaxaxhHnC5La55iSTA+Mc/R4dMkA2Wwl0FPOKTpQtlO\nZBV2GniEEEQiESKRyJq+qKJcLpNOp5mZmSGfzxMMBunq6iKZTBIOh7l9+zbxeBy7WkQzq9iagVAK\nbY1w1kV7mg5mBawq+BqPTzRMcboQGvj6nLSoLIA0na/pEYfkG7zmDSNO3VirN24PgaMzu/H86gm2\nWq3y7ne/m6NHj+64Xj0++tGP8txzzzE8PMzzzz/PCy+8wAc+8AHe8Y538Gu/9mtNrfUQ4mMcKNXs\nLzamTLeDlJKJiQmWl5d57LHHdky5tYMQ3XGKSCTCqVOnqFarLa5kIhq8RR6QoWPYqhFlaXGJhfl5\nTp26SCz2IFXlDOpXgAHHNSKfBv824xaaBignlRVdHy1VKhVGR0dJJBI8+eSTjrKPlNQWF7GLRfRg\nEG0t6pamSW1xEb1Uwt/X19AjsfHhNUqlEvl8nmeeeYZQKEQmkyGdTjM1NYWUcl2KtVUSc1+jnp4e\nLl261P6apKahfGeZW6yRXf4x504P4g8EwDRAhkHrXYt4qqBMJ8oKDa6P2Ovg6IuGCYfDXterS5DT\n09MsLy8TCoUYHBykUiwSMwyU5mQMbOm8n21pO1ZXCCe1LnDSultgVyMcmh+03aUmG0aIoQhkd6hH\nmjVng2asf603rteqUs2zzz7Ls88+u+nr3/72t5tea7+wjzXEOAdKNW8O7BQhuq3z3d3dPPPMM7tq\nRNgrIbrjFGfPnqW3t5elpSVKrutD01hLQ9XBe8Ct2dnblsXtu7fRVT+PP/42NL2IolT3ewZwCI0I\n2LbzcAnt4NLh8zuRZB0hLi8vc/fuXc6ePbuuNmOlUshSCWPDRkPz+dB8Pux8Hssw8O1itMSyLG7e\nvImu6+tSpD09PevMbusJUinlEWQymdwVQbrCBOfOnduTCtF2eDDD6OPc5Q+ii7X3VMgAlDNzKNc2\nSkYY9FDTA/mhUIhKpUKxWPRq0+l0moXFOSYXJ9DCcZKJ5LoUq1QSqSQosC0LbBth2w31WDvhXbiJ\nEH1+VDiKqJYb166lBLOK6to8+rJxvQMt033BXwNv4UCpZv+xFXkppTyNzgsXLjRVF2qVEN2HuW3b\nPP30015zyF70RzWC2GvCD25UGI3GeO2HPyQWi+P3+1heXuT4iaP09zzupJWwcNL4rnRboGHKdVsI\n4aWxpJTcvn2bSqXCU089ta7pRdk2Vi7nRYUNryESwc5mMZLJbZ3oc7kcN27c4JFHHlknaL4Ruq6v\nI0jLsjyCnJycXEeQXV1d6+rLSinu3bvnNTd1SkquVCpx/fr1DTOMG67d37qRNTx4jy8uLnL58mVP\nMWhwcJDB/j4YSlJVOtlsjsWlRcbHxzF8Bol4gkQyQTTsNNvYeqCho4drD9ZOQpRSbq73Swuifqjl\noFBxRkIM34MOaNtGdfc3JMuNhCilbG/a+ycECoEtO0KIXUKIv8VpjPnZLX7mY8ALQggFvMSBUs0b\nDze91ShCrE9X1mt07hatEKKr2nLs2LFNIwF7IURBAAggVQUpDVCK02dOI22bO3fvsrS0hD8omby3\nSnp5zJv18/m2IChNA113IsXt7otlQjjuqcEcOnSIs2fPbkorykoFdhh7EUJgS4msVtEbEKc7tjE/\nP89jjz3W9A7fMAx6e3s9ZaF6gpyYmACcEZBoNMrc3JyX7u3E2AY8aNDZTld1r3CjT03TeOqppxrX\n5QIxAtUC/QP99A84jT21Wo1sJsvy8jITqVFEME50UKerq8s713rT5Eql4rx+W0SQrZy3twmRFpTv\no1VmkcpEGRqaqqKqfjD7QQ+jwnGIRLccB6onRLfB6KGEAsvqCCFmgD5gafujkwf+d+B/2+JnDpRq\n3gjUC3wrpZifn/ckw1pVm2nWneLu3btkMhkuX77csGt1LylYpRTILmw1D9RABKmWq4yNjdHdk+DM\nmQtoIoayk+SyjlrMxjTiuihJCIh1OU0zW3WRKoWybeazeabn7mz/YN/lA0gI0fBn68c2nnrqqbZ0\nkTYiyPv37zM2Nobf7yeVSqGU8lKs7Rrud4XMC4VC+xt06uCOhwwNDXmOGw0R7XEaXSoF8IdA050G\npd5u+uIhOHmCWrCHdDbrpcPrR2RKpRLLy8tcuHBhywiyWYL0Ik5pIXLXEFYefAk0txvVLxF2AVQK\nmTgKxvb1/kYp2E5tdN7MUEpgW629j5eXl7ly5Yr37+eee47nnnvOWxq4BNwWQuhb1Ak/D7wN+D+A\nWxx0me4fDMOgWq16XnuGYXD16tU9PeTcYd+dUCgUGBkZob+/f9uu1VYjRK9xRuroYhBJgaWlu8zN\nzXHy5EnisS4ggUYEoYt1nZpulJRKpZiYmEAI4T3okvE4uj8IjWo2SmEX8ozNL6Ji3TsPp+v6rklx\nY/t/Npv1RArqxzbaCTf6XFlZ4S1veQvBYBDTNL17c+/ePYQQJJNJuru7PcWdZlGr1bh+/TrJZLIz\nDTprcGuf58+f37kMoOkQH4RKDsoZJ/0IzusQ7YVgHL+mMRAMevffNE1SqRR37tyhXC4TiURYWFjw\nIkj3vdwqQXoEVp52yNC/YdMqNGd20S4iCjdRyad3XM99fz600SEuIba2mezr6+MHP/jBVt8eBl4G\nxrZpmnkHTofp51s6gQ04IMQWUJ8yXVpaYm5ujtOnT9Pf37/ntQ3D2LYJpr4+efHixR3TYq1EiJsV\nZ+DmzfvoepAnLr4Tw/A17D6tv4b6KMk0TdLpNCsrK47Ch4A+XdEV8hOLx53anm1TKBS5ObfE4XMX\nGNyFko4WDCIMAyXlll2kyrYRuo62VuNy79/CwgKPP/54w6i6HTBN0zMkrk8r+nw++vr6PM1LlyBX\nVlYYHx9fv3nYBUFmMhlu3rzJ6dOn2yYIvxGuI8rKykpztU9Ng3ASQokH3aSaseXcIcDs7Cz9/f0c\nP3583cbq3j2nDORmHlzptI0E2dATcg1SSnSh0Cpz4NuG0PUIqrbsCJ37txaHr48QK5VKx7Rz3/RQ\ntEyIO2BWKXVlh59ZARbbdcADQmwRlmUxPT1NqVTiLW95S9sMSLcbzK9Wq4yMjBAKhXZdn2wmQtyo\nQ+oqzoyNje0pkvKJNAOxOQZiCk4cpap6SKfTzC0tkr87hU/XQDcoSXji6tt2XccTQmB0d2MuLaFF\nIpsiI6UUslzGNzDgeTCOjo4SDAa5cuVKx4xs3QadEydO7LhJakSQ9ZuH+jRiPUHW1z7decxOwNW+\nDQQC3qhL0xACdhALdzMeJ0+e9O7FxnuzsYGpfgQmkUjsSJC2baNRRikLsYNbhiZ8SCu7jhBLN39A\n9dVvoubugrQxg12Iyz+N3f0uCoXCQ9lhCk6EaJn71mX6fwL/Ugjx10rtYqB0BxwQYgswTZOXX36Z\ngYEBAoFAW924t4roFhcXuXv3LmfOnGlKUX8nP0QXG6NCpRTj4+Oeqk5LhqrWEnrmq2iVG+B1mip0\n/wkCXb/EwMDjVKtVrl27hq7rxP1+rl27RjAY9FKw0R0G64143Ok2TaWcmci110LWaqAURm8vRixG\nJpPxlG3aEck3Qj1JtRp9+nw++vv7vXN0CdKts+m6TiKRIJvNEgqF2lb7bAR3bOjo0aPbdt7uFW4j\n0MWLF7ed021Un3VVhtwZ0UQi4RGkz+fzTJNd5R0pY0ipEEp6ggENITRHrGANmRf+GHvk79EiCbS+\nI2D4EFN30f72S+Smr5F98j0PpfXTmwBdwEXghhDim2zuMlVKqX+928UOCLEF+P1+nnnmGarVqift\n1S5s1DK1LItbt25hmua6cYrdYicpuPVWTU7UVS6XGR0d3Zu5rrWEb+kPQZko35EHM25KgbWAsfyH\nLBsf4ua90qZ0X7lcJpVKMTk5SaFQIBwOex2skQaRoK+rCz0axc7nkWXHVcFY+5owDCYnJ1leXuaJ\nJ57oeCRlGEZbSWojQWYyGS9LUCgU+NGPfuRFkG6U1A4sLy8zPj7e0W5Vdwwll8u11AhkGMamGdGN\nOrWuJdbc3Bzd3d2EwwnIrOmxsqEGWU+Qdg0Mh+Dy3/oL5Oh30YdOrxvOV9EkWmQYNTtOLf3nD22E\nCAJp7xuV/C91fz/T4PsKOCDETsPdfe5Wum23qCcwVwT8kUceadlhYVuX+w0pUiGE5/W3V11NI/OV\nNTI8tP4bQqD0HjKrE5i1P+HJy/+WwIboMxQKMTw8zPDwsLezT6VSjpFrqUQ0GvUMgV2nc83nQ9sg\nplyr1Rj98Y+JRCKNxwPaBDfdt9MM417hRlJPPPGEF43UajXS6TRLS0vcvn0bwzC8e5NIJJq+Zpek\nstksTz75ZFuzH/WwLMursbarEaiRDN/S0hJ37tzB5/OxurqKaZr0+XTiIo8/1O0IBax9BjyCRCKE\nBr4u7GqV2mvfROs+vEmpRiqFEDrawCPooz+g12ivtdlPDBTQmRrizodWqq0f6gNCbBFCiKa0THcL\nd5Tjzp07nl7nXho/tnrQNLJqunXrlufOvqdxAHMRUbnpRIYbYJkmi4uLRCLdDPVmscQcihPbnr+r\nqXnkyBHP6dx1hi+Xy8TjcY8E3NSuK/596tSpjpq2uk4YO6X79oLtRir8fv8mB4Z0Os3CwgJjY2P4\nfL51EeR2BOnWWCORCJcvX+5Yt6orHNDpVGw2m2VycpJLly4Rj8c9K7DsqiQz8T1qto9gLEkyniQS\njRLw+5F2DWopzNBZsBWlOz8Cs4IIb47+pJRougDDh23VOCrKHbuWNzWU2DdCbDcOCHEPaJcQdz2q\n1SqZTIauri6eeeaZtj+UGjXOuPW1Y8eOcejQoZ0X2QHCmgPEJimwQj5POpOmr6/fIa5aDmHOoAJb\nE+Kmteuczo8ePYpSilwu54kTVKtVL8reTRduq3DNgpVSHXXCcBupurq6dhVJbSTIarXakCC7u7u9\nUQZ4EOV2cgwFHoxuXLhwoWOvDeDVcuu7YtdZgT1yFJW9RqmQJp9fZXppAqtWJhyOEux5nFiin6DP\nh16tYCnpKCe5PRtCAAKpFPra62FZkph/zz0dP5lQgPWPY/7ygBD3gHaSVf04RTAY5OTJkwBIaihs\nQKDhcwSy93CMjY0z9+7dI51Ot7W+JpSiXq1NScnyyjJSSoaHhtE88mg8MN/UsYTw6kRDQ0Ncv34d\nv99POBzmzp07niHwAxWdvQ+suwo6Q0NDDA8PdyySckcqzpw507LQQyAQ4NChQ95GxyXIubk5bt26\nhc/nw+/3k8vleOyxxzpaL3RFwDdK8LUTrtSfaZo8+eSTW29UfHFE99uIxDNEzDSHlEISIm8GSWfz\nzK/JBUZWl0lWagSVswF23q/OptIyLceCStmY1SpGb2uv0T8KtDdRtmsIISQbBZc3QCl1oFTTabiE\n0iqUs60CDGrV2rpxipdffhlJDZMsNrU1jVDng6gTwU+8aWJ0u+3cc3edI7q7u1tvp9/q2ox+bzdd\nrVZZWlokkUgQj8U3zKBJlK890UgqlWJsbGxTg47baLGjis4usbi4yMTERMebTVyd0HaPVNQTpFKK\nW7dukc1mSSQSnmqPe2/qI8i9wJV603W97e+1epimyfXr1+nq6moo9bcJmgb+bucPoAEJINHVw7Fj\nx1BKkVk5RfbVv2J5dgplBJ0NhC9AoVggmXTuT61cZWp6mpHkpY5c15se7qNsf/D7bCbEHuCdQABH\nyWbXOCDENxhVcY+C+M+UtR8BErMSYGXqBEcfeQ8DvY5nodJMqiwj8GGw/mFoU6FCjSC9uyJFV2Nx\nYWGB7u5u/H6/1zhz7tw5ksmtB49bhfIfRvqOUkhPki3qDAwM4N9o+WTnUXoXKnB6b8eqE8yuF5l2\nsbHRYlsVnW0G4d3Io1qtdlQazRVpd7tVO0UetZqzCUskEly9etUjj0qlsi6C9Pv9XnQdi8WaPp9K\npcL169cZHBzcXuptjygUCoz84Acc6+mhxzCw5+cRsZgzn9riPRRC0NXXh/4z/w3Rb/85YuAopWqN\n1dUV/D4/r7/+OvdnZkiU01jHHudTf/x/tfmqfkKwj4SolHq+0deFEDrwl0C2mfUOCLENcFOQOyGn\nvUROexHwockeMpkckjIDj44ixTym9a8w6Ef5Cmj4GxKeTgCbChYFfGzfBeqmSB999FGWl5c9IQG/\n38/Jkyc7NjdlmiY35x/nqHGD4YEhhG8zGQp7Fav3Y2tu563Bra+x9Yx1AAAgAElEQVQlEgkuX768\nq4f1jio6ur6pS7NcLnsyebuKPFqEO/d35MgRhnah1NMq8vk8o6Oj64bgXQSDQcexYq3ZpVKpkEql\nPFPgQCDgbSB2Ikg35dtJmyuAlcVF7r78MmcfeYRoIuF4Qdo2cmkJqesYQ0OIPaRo429/L9n8Cvnv\n/hVFUzF07DR6IECwmqd27wbZ/hP8nezhjy9f5pOf/CTvfve723h1PwFQgLnfJ7EeSilbCPEZ4I+A\nf7fb3zsgxBax0SR4p9RbRYyS015Apx+zKllNrxKNRolEukGATZoV4zP0Wb8JQm4b/WkEMCliEG34\ncxsbZ9zxiZWVFU6dOkUgEPDm/OojqHakyNzuzpMnnyKaPIdIfR5Rm8RJSAlAovQEVu/HUMFHWz6O\n25yxl/oabJ7z29ilCQ7xnjx5suXRl93ATcVeuHChowPe8/PzTE9P79rZIxgMMjQ05BG0awo8MzND\nLpcjGAx6G4hYLObdn5mZGebm5hpG7e2CUorpqSlWRkd5/Px5/PVdvroOPh8yl6Py4x+jJZMIw0CL\nRtHicU/Kb7fHSV34GdIywtHcNNy/zeK9Jf7qez/i2d/6A6784n/HxwIBlFJt7zo/wJ4QAHbnHr2G\nA0LcI9xB+p0IMad9HaEi5HMlKpUKPb09635HpwuLBUpiBBDbRp3CqyramwhxY+MMwL1791hdXV2n\nnuJGBrVajVQqxdzcHDdv3mxKJWbjcd0GHfchqABr4F8jauOI2n1AoXyDqMDZliNDKeW6Obl2ewq6\nXZp9fX2eUs/w8DDZbJb79++3fH+2gutYUiqVOpqKlVJy584dz1ey1bGaUChEKBTaRJDT09Pk83mC\nwSCWZXn1wna5eWyElNLp8i2VuHjmDPqGkRcF2CsrqEIBZZqOTVgohCyXkbkcWjKJ3tOz4+snpfSs\nri6955cQQvCVr3yFf/+Nf89XvvZDjh075v2sEOKh9EN0bvb+HFoIcbTBl/046jV/AGypHN4IB4S4\nR7hzg9s9mC3SlORdcisGwWDIiUYafg79lMQP0MRJR4i4iVb+RoozbuNMV1fXlvUov9+/rgtxo0pM\nJBLxCMAdgt8I1wPSPc66nxEaKnB6z7XC+uO4jUCditbqRx02Xk+zKjq7OU53dzdPPPFEx67HdcPo\n7u7mzJkzbT1OPUFWq1Vef/11AoEAmqbx6quvEgqFvAiyHRsIeHA9PT09DCcSjr/mBtjpNKpYREQi\nICUyl0NPJBCBAAQCyEwG4fOhbyM+YZom165do6+vz5uB/eQnP8mrr77KN7/5zY7U339isX+B8SSN\nu0wFMA78RjOLHRBii9jOJLgeSilmF+6RixRIJo/jD2xdyxD4QZTRdYEtN3uteWuu9Zy60WEjxZmF\nhQXPm7GZ+s1GlZhiseh1cFYqFeLxuEeQgUCApaUlxsfHO18nWlnhzp07nD171muQ6QTca93qOFup\n6Ny7d49isdhQRacR3NTyXlO+OyGbzXLjxo2OH8etS546dcqrzyqlvAhyamqKfD5PKBTyNhCtEORG\nEXBzago2RGVKSsjnEWvduULTUGt6pu7xRDiMTKfR4vGG51AsFrl+/bp3nEqlwsc+9jESiQRf+9rX\nHs5IcCvsb5fph9lMiBVgCnh1G9uohjggxD1iu+H8arXK6OgogbBF1+EEhtj+Q6SoYqgedN2PbVtb\nOnVLahhEEOibUqS2bTM2NoZt21y5cmVPH1whBNFolGg0ytGjR5FSekPw169fp1AoYBgGJ06c6LhK\nSz6f7+j8mlKKiYkJ0un0rlOxrajouPN4S0tLHa2vgWOlNDs721E3DICFhQWmpqY21SWFEITDYcLh\nsLeBaBRhu006OxGkq6+6ThVI10HKdX6XqlJxLMHW1lJKwdpG0Ts3TUPaNqpS8YjTRSqV8sQDYrEY\nKysrfPCDH+R973sfH//4xx9KE+Btsb9dpp9v53oHhLhHGIaBVatCJQ/VnDN/p/tYzte4fW+a02fO\n0N/fzzKvUGMCna2jKIVJVL0NQxYwZYkggU01QkkNEBgqii3tdYozrumtK4m11w+uSRkbE4MABk4a\nLJlM4vP5WF5e5vjx40SjUa8LEfAe/q2a3dbD7e7s7e3tqJSYa/Aci8V23a3aCLtR0bFtm3A4zMWL\nFztGhm59TUrZUTcMpRR3796lWCzuqi5ZT5CHDx/2ImzXzmmrFLTrx7i6urpJX1WLx7GXlxF1x1Yb\nCbJaRd+qUWnDLPHc3Byzs7NcvnyZQCDA2NgYH/7wh/m93/s9fuEXfqGFu/QQYB8JUQihAZpSyqr7\n2rtwaoh/q5T6UTPrHRBii/BSpliQnQZfL+h+bKkYH7uOWS3x9KOP4V9LU8Xlu1nS/xBBFY3N0YfF\nCn51mIA6gyFuoptJJDaKKgINd5RfJ4BfJZE2KPVgFzwxMcHKykpbTG/TYpop4xUy2n1Hp1AI+u3T\nHLGukJs1uX///rpuSDcVV29V5Aoqu+nVZmfYXLujTqdi3dGATmie1qvo9Pb2MjIywqFDh9A0jRs3\nbnRERced+xsYGODIkSMd20SYpsnIyAixWKzl+md9hF1PkPUp6HA4TLVaJRAIcOnSpU3krkUiyNVV\nlGki1u6f0DSP6JRtI6RE26qjdu096ZJ7qVTyFG6+853v8Nu//dt84Qtf4PLly01f30OD/U2Zfhmo\nAr8KIIT4KPCZte+ZQoj/Win1N7td7IAQ9wLbJFBZxTL84I+Qz+cZGxtjaGiIwcGLCKsChSWIHSLA\nKbrtD5HW/18kCo0kAh1JGUURg0P02v8DAh1d11G2QZAuFOaadBugdJA6dl3jjJuWTSQSbRnkvq//\nkLvGd9CVj6BKIhBIJVkUtxmvvsah2lt5+ul3NIw6No4wVKtVL3rM5XJe/ai7u3vLBhS3G9Ltuuxk\nitRNXXY6pbjVSEW7VXTcumSn66xufa3duqcbU9DVapUf//jHXiT9yiuvEIlEvCxEOBxGrM0ZWrOz\nSNNEBIOItZ+X5TJCKbS+vk1ziMq2EbqOCASwbZuRkRHC4TCPP/44AF/84hf5whe+wNe//nWGh4fb\ndo3/aLF/hPgW4Lfr/v0/AX8G/I/An+DYQx0Q4huCag7dMKhKmJyaJJVKcf78+QcRmi8E1TxYFfCF\niKir+K1jFLXvUdZeRVLGUP1E5S8SUo97kaNbl3TGK5wPcqPGmcXFRe7du9e2B2BGzHLX+A5BFUer\ne2vYNZtixiQci1E+ewOrdhWdnWuGgUDAG/Kurx/VN6DUd7C6KdK+vr62d0PWwzRNzwW+k2owG0cd\nNkaAW6nopNPpplR0XGPihYWFjtcl3Tpep+cl8/k8IyMj65qB3CavdDrtRXNuF3Syp4egbSOzWUdL\n1+9H2DZGf/9mMlQKVS6j9/dTq9V4/fXXOXz4MENDQ9i2ze///u9z9+5d/uZv/uYh9jhsAvs7mN8P\nzAIIIU4Bx4E/UkrlhRCfA77UzGIHhNgiBEA5i4XBzMw0Q0NDXLp0abMLt+5zaos+JwLxMUBSvp+k\nfP+Wa29s1NnYOCOlZGxsDMuy9tw4U4/7+g/QlPGADJXT1VepVOju7kI3DMqkWdBv8Ij9TFNrN6of\nFQoFUqkUt27dolAoYFkWR48e7egAfC6X48aNGx13dahWq1y/fp3e3t5dk3srKjquTqimaR0ld6UU\nk5POpq+TPonwwPfx8ccf39Sk4zZ5uU1Mbhf0+OSk45UZiZBMJkmeOkWwXEblcs7nxu8HpVC1GlgW\nWlcXJSEYee01b0NZKpV47rnnOHHiBF/96lc7Vns9QFuRw9EuBXgHsKKUurb2bxtoand4QIgtQkmb\nhcV57s8uk0wm1w3oroNmgN1cPsElxEZWTe4D3ZX3ahdx2Fis6vcIKmcuS9qSdDqNz+dzHtBrh/ER\nZkFrnhA3wm1AiUQilNdc7o8cOUI2m+XatWvYtr2uvrbXAW+lFLOzs8zNze1apaVVtCt1uZOKjq7r\nlMtlBgcHOXnyZMfI0LZtp1s6ENhT09FOcEk3nU7vSqRgYxd0fZfvvXv3HIL0+UgYBgm/n1AwiBaL\nocfjrORyjI+OeqS7sLDAr/zKr/ChD32I55577qCTtBns42A+8D3gd4QQFvBx4D/Vfe8UMNPMYgeE\n2CJM06JYKHD27FmWlpa2/kElQWsugnOH/TcqzkxOTrK0tNSRB7rEAuWYTFUrVbK5LIl4gkBwfQOQ\nQMMS1bYcs1QqeY0mrkaoGx3Ztk06nd4kwu12sDbzUN4YRXWy63Jqaorl5eWOpC7rvQ5XV1e5desW\nw8PDVCoVXn755bar6IDT6Xv9+nUvpdgp2LbNjRs38Pl8TqalBdJt1OXrEuREKkU5myVaLKLm56lU\nKl6kOzIywq//+q/zqU99ine+850duDoHf/Znf8Zv/MZvkM1m+dSnPsWLL77I+9//fn73d3+3Y8d8\nQ7C/TTW/BfwV8DXgHvB83ff+W+AfmlnsgBBbRCAU4uT5SxQzq9ubBEsTwrsfiFZKoWkai4uLhEIh\nkskktVqNGzduEIvFuHLlSkd26Dp+NAwyuTR2TdLb04umbz6OTY2I2vuA98LCApOTkzz66KOe1uq6\n89H1TenDVCrF4uIit2/fXtfBGt9iuBqclO/o6GjHBbMty/KiqE6nLqemplhZWeHKlSvr5iXdGq07\nBL8XFR14IFJw/vz5hq9Ru1CtVrl27VrbHTE2EqTbPFOpVPD7/bz//U7ZYnx8nD/90z/tKBneunWL\nqakpTzT9T/7kT5icnOTYsWMHhLiXQyt1BzgjhOhRSq1u+Pa/AhaaWe+AEPeCQAJDW8G2tqgo2zUQ\nBtLwAxYCbW2EojHcFGlvby9CCJaWlrz2fLc5pVOpnEqpgjmdRA7fpyc2tIW0HFiiyrDVuu+bbdue\ngWsz2p0+n2+dE7zrwnD//v11D3+v+1AIz+Zq3SB3B+CqpzzyyCPeA68TcFOXfr+/oa9gu1R03sgm\nnVwux+joaMc7Y12vxO7ubq+T9Od//uf52te+xkc+8hH+6I/+iN/5nd/he9/7Xts6jr/0pS/xpS85\nPR1Xr17le9/7HouLi3zuc5/zGuP+UWB/I0TnFDaTIUqp682uc0CIe4Dwh9Dig6jaGJgVMAKOAa6S\nYFWwhEUtFkNqqygkCgsNP34iGITWdXLWN8640VEqlSIej3P8+HHy+bz3YIvFYt7Dvx0PK5c4Hrvw\n04yF/j9qlPCzeZaxInKEZQ+98mRLx3Gd5t1IYC8PhHoXhvqHvzsoLqXE5/N1nAz///bePL6pOt//\nf55sTdo03aAsLbRl39qyVBEBRcFR7+gs4vV6GXH5KS44etVx7uDCQ/zq6HUdZ1DcRVxwQR13UVFw\nRBRBGFq6AWXpwtI2TdqkWU/O+f0RTmyBbmlOW+p5Ph4+HtKQnE9izfu83+/X+/VWMl21r+PxeCgq\nKup0ptsZF50T/R5FTLNluf2N8zHgyJEj7N+/n/z8/G7PzraH1+ulsLCQnJwc0tPTEUWRJUuW4HA4\nWLt2bav3HsvMfsGCBSxYsCDy56VLl5Kdnc1VV11FbW0tBQUFLFq0KGbX61X6yZIPoTtb32NMnzlI\nZwkEAoRCIX78/ltOyx8fHrEAEHT4440EjKDXmwkRJIgbCRGZEDISFlIwYsUkJyJLQqtxCsUXMjMz\nk4yMjFaBQ/lis9vtNDQ0EAgEIuKT1NTULolPRFGkvLwcSZIYP348BoOBJuEwRcb3CeJFTxw69IQI\nEhICJEhp5AUvwkzX5fZK0FVbrq8EDuWzcDgcBAIBkpKSIuXDWCgkW45UTJw4UbWtDvCzScHEiROx\n2Wwxec2WLjrK71FCQgJNTU0MGTKEnJwc1TIYZTNKU1MTubm5qn52ivGCUvZ1uVxcddVVFBQUsGzZ\nMtVK278khOEFcHuXlkpEmPZKAVu3nvi5giD8JMtyQXfO1lW0DLGb6HQ6JMEI1nRIGAiyjCgECAgO\nDJgR8RGgET0m9ITLgxIhgvgRZB3BkJ84OQXd0XVIBw4c4MiRI20KZ1r2RbKzsyPD3Xa7nf379yMI\nQiQ4KtL8E6EE3WNt3mzyYKYHrqJOt5tD+mJEfFhJJ0PMI1XKapXVdoZQKBSxESsoKFD1y0+R67fs\nS+bk5CBJUmQAvrKyEkmSIqXDlJSULmdBytaNroxURENLf9VYmxS0dNHJzs7G4XBENqM4HA5qa2tj\n7qIDRPp4FouFyZMnq1o2PHToEFVVVZGyb3V1NX/4wx+46aabWLhwYf8pWfY2faBkGiu0gNgNFJ/F\nFj8AQSCAB73Xh+yoJBiowSCYICENklLBGIdO1hGQvIRkMzJBRMELARPFxcVYrdYuCWeOHe5WxCeK\nND8uLo7U1FTS0tIiAbaqqorDhw+3GXQNxDFEmsQQaVK3Ph9F0KL0tdT6AlKyNa/Xe8K+pE6niwy4\njxw5ElEUcTgc2O12KioqTjjf1xYdbcOIFcFgkOLiYuLj41UddYCwf2dVVRXTpk2L9M+OddFRbiKi\nddGB8I1EYWGh6opVJQN1uVyRnYw//fQTN954I8uXL+fMM89U7dq/SHp3MD+maAExxsiSiHSkBIPD\niWiUEEwCgiyAvRrsVciDc5CsaYCekOAjDhu1DdVUlTsYO2Zst1f0HCs+aekOowy/JyQkMGnSJFX7\nNgcPHqSyslL1Eqni3dkVdxuDwcDAgQOPW5J86NAhysvLMZlMrTxYWxpM19fXRy80keWw0Eo6qko2\nxIHu+OxUEelkZ2dH9lSqQXtLgzvjoqNkkJ0xclfWUI0fP17VPYLK+IbJZCI/Px+ADz74gEceeYR3\n332X0aO7v5dTo/+iBcQYocwLBit/IuTajWAbiWTwIQghQA+mJAgGkat3Iw3TISQkhrel792LL+hi\n6rTTMJtiH6AU5aHFYqGsrIysrCxkWaasrKxb/ce2EEWRsrIyANVLpMqOxO4agLe1JPnAgQO43W4s\nFgs+nw+r1cqUKVOiE5oEPNBcD6FgWMErE64omJPAkhIxmVbKvmqLdBTVZXJycqduJKJx0VFQSpdq\ne8YqNmyKaEuSJP7xj3/w1VdfsW7dOlUz+l80vTuYH1O0gNgNIstGZRFPzRfgXI9Qs4tgsgmpOZ5Q\nwhh0iaPQ6a1IgIweIc6CvuEwjYKO/XsqGTogh+whGZgEdaywJEli7969NDY2MnXq1Ehm053+Y1u0\n7EuqXRKrqKigqalJFQPwluMLLpeLwsJCkpKSEEWRH3/8MbIkOSUlpc1MUQ4EkJvd4PGC6AWxCSE5\nFSGuRYlalsHrBDGAbE2nYt++yN5HNRfQKhnoiBEjIi44XaUjFx1lTrS5uZlgMBgpXaqF8p5Gjx5N\nWloagUCAW2+9FUEQ+Oyzz1S1mtOg3/QQNZVpNxBFEb+vkYpvlzFkoBuDJxFDs4+A1YRfakQOHSFk\nsGKMPwPBZEOIs4IujobKCqrjkxk7+jSs8YnIhDAzEKGt4b8o8Xq9FBcXk5qa2inVoNJ/bGhooLGx\n8bj+Y3tza8oy2okTJ6qa2fj9fnbu3ElycjIjRoxQXZRx7ByjJEm4XK7I53TsCieDwYDsaIAGBxj0\n4cyvqQZkHSDAgDR0x3w+oruR4gO1WAcOVf099VQGqmzEUL5f1HDRUbDb7ezevTvynhwOB5dffjnn\nnHMO//u//6spSVVGGFoA10SpMv1UU5n2KwIHPyJOfxDBOBWd3gmyD4O3maBJQNYNIUg9Qd924oxn\nIrrqOFjrRGcUmDByPPHxSYj4MJEU82BYW1tLRUVFl8qJJ+o/2u32ducfRVGM2KIVFBSoOremeIS2\n3ICgBpIksWvXLgKBwHFlX51OF1Fn5uTkHCc+EZqaSEUmeehQbPHJ6EVPeKu7KT68uLa2Dlmvj2xp\nb3Y3U162i6zMDNJGjAiXUVVAEZo0NjaqnoEqW0taGhXE2kVHoaqqiiNHjkRs2Pbu3csVV1zBkiVL\nuPjiizUlaU+gqUw1AEJiM7JnM5JuCCFJwijoQWxGpzNhCprwG0PodWmI8iGczVXUNYRIT7OSZEjC\nYExGxI8eE4auGbK3f6ajTjDKl3l3vvgsFguZmZmttlPY7XZKSkoic2uNjY1kZ2fH1HLrWBTTZ7vd\nrrpziiLSSU9Pj/irtkdL8YkcChGoqMAVDGBvcLBv/wHigo0kWxOwpaSFM7I4M3KDEyHDQu2RWmpq\nahg3YSLxesJiG33s/5dUbOUsFgtTpkxRNUgoNy3H2r3FykVHQZbliOOR4tqzadMmbrvtNl544QVO\nPbV75vMaXUBTmWoASI3lIIvo9fFIsgRxBggGkMzx6CQZS0BPKKDD4QvgFg6QnTkDkySjD4jIRiNG\n4jFibdfOrSsoYw5Dhw7tthPMsbScf8zKyqKyspKamhpSU1M5ePAghw4d6lb/sS2UzewJCQkntCuL\nJcpIRdQiHZ8Po0FPWtIA0gYMIISIz7GPxkY7B+0H8B0IYjKYSTIacdXXE9LryMvLQ2/Qg9+DGl0D\nxahAmTdtE68dfem7GHauRvDUgTEecfSvCU26DDl1VKeupZTNO7ppidZFR0EURYqKikhKSmLMmDEA\nvPnmmzzzzDN8/PHHDB8+vFPn1YgRmqhGAwBJREaHTq9HCoWQjHpkSzyCz4dgNhMMijQ47FjNegak\nZGKUByG76pDTh6OTUxFilBm27OFNmDBB1TGHYDBIaWkpRqOR6dOnR0qkHc0/RhOcGxsbKS0t7Zb4\nozO0zECnTp3ayjC7S68TDIJOR4gAXuoJEkBvCZEsWEhOT0FGxuf2U1V4AFkGyWRk165dJNsSSbYl\nYNHF9n9Hu93Orl27OnS4EezlxH10FfibkA0WZHMKyCKG0ncxlP2T4Jn3ERr7m7bf99Fsze/3R7VN\n5ERbKpQ+rVKNUIRMLdXSgwcPRpIkHnzwQbZv3866deti5uSj0UW0kqmGLi4ZHeFxi0AwSLzegJw2\nEKG+Hk9tLS5JIjU1DYMQQi+bERqdCGnDwTYY5Nhkb0qAMhgMqvfwlAB1ovm4aPqPbSHLcsQ8ID8/\nX1WpfssB+O5moJJOJijZ8eJFRkJPHJJJj+hzoZcFfM0CB6oOkJGdQvqYyejiLDQ3N+OsO8juag/N\nlc7IF39qamr0gVmWqayspK6urmMVbsCN6eOrkQMeMLfIigUjsjkZQgGM3yxFSs5CHpR/3NOVDF7J\n1mJRlRAEAZvNhs1mIzs7G0mSaGpq4uDBgxw+fJi4uDjeeustbDYbX3/9NUOHDuXDDz9UVcUKrdc3\nXX755ezYsYNFixZx++23q3rdPo/WQ9QAwJqDJCRiNok4nUGa6puJN4bwiRLx8QmkG/QQaAZZREjJ\ngcGjwZoankk7wUB2V3E6nZSVlam+/b1lgMrLy+vUQH9H/ce25h9FUYzsxVNr1ZWCMiYSi89PJkTQ\n4kGUfYAOI0eDuM4AloHYD+7C7gwwMnscRgKIcRImARJMAgnDc8iwDUUivP2hoaGBoqKiqJYkK7sf\n9Xp9pwK8fs9nCP4m5Lg21jvpTchBD4btLxA8b3mrh5RybHZ2tqq/fzqdDr/fj8vlYsaMGZhMJurq\n6njqqac4cuQINTU1LFmyhNtuu021cZ9j1zetXr2atWvX8sYbb6hyPY3eQQuI3WDFU8/QWFnGb8/0\nMyjjVPZW1OMT3SQPGIwvGKBGFrEmNmO0ziUp51TQCSD6wRQf/qKMEqXEV19f/3MGJQVA8hAu5htB\nbwGh+0rCYDBISUkJcXFxUQeozvqvms1mKisrycnJUdWhBcIjFZWVlTFbtiziRTLqkKxx6DzNEB/O\n7iRkqo7YkcQ4snOSMTW7ITUFMeTEFALirJAwAHQ6dEBycnJkpCQUCuF0OiNLkoFWfdpjqwGKIKgr\newX1JW8jCx38LsbZ0B/4hmDQA8bwzZDSb42l4fiJUH7XnU5nxE2ntLSUhx9+mPvvv58LLrgAt9vN\nd999F3OxVXvrm8444wwee+wxXn/99Zhe86SkH4lqtDnEbuDz+fj2228pXP8kev864kxGhg8ezYis\nDAakJ4Ig4hEn4RCm4WpyYTGbSE20YBs2kXhbdM4qfr+f4uJibDYbI0aMQCfIINaD5CZsgaKDozYA\n6G2gTwMhuixLKZGqnYEGg0H27NnDkSNHMJlMWCyWbvcf20KSJMrLywkGg0yYMCEmZTYZGR+1CBjw\nheoRDjsQfAECeoG9lZWkJCeTnpqK5HOhj0/ElJpBSCcSr89E0Hd+YFxxh2loaMDpdLZakqysbeqq\nICjutbPDVYwOziH4m/At+Bysg1tVC6It63YGSZIoKSnBYDAwZswYdDod69ev58477+SVV16JWLP1\nJNnZ2ZSVlTFlyhQMBgOzZ89mxYoVPX6OvoQwoAB+E+UcYmHfmkPUAmI3aW5uZt68eVz2n+fxu7OG\nUlHyBZUVJZSXHcGrG8/Egl9x+swZZA5Kx+f3U+83Ut/YjM/na1U27Mx4hGJVFpnDkyUIHgI5ALoT\n9NmkZtBZwZDepfk2xbezrq6OSZMmqdrDU7ZhyLLM+PHj0ev1kf5jQ0NDTPc/KhnUoEGDGDZsWMwC\nrUwIL7XoMeOlAUEC95E6qgt3kjlkKIkJVogzIadYkeMtxAmpSARIoHs3GcqS5OrqalwuF8nJyQwc\nOJDU1NRO30iY3rkYwbEXTO1kybKE4G/Cc+X37NpXjSiKkf9WahEIBCgsLCQ9PT0itFm1ahWvv/46\na9asUdUJSaNrCGkF8OsoA2JJuwGxBqgFDsiy/PvoT9h5tIAYA6qrq48rUUkBH6U7tvCvrz/n2283\nsu9QPfkFMzh77jmcccYZWK3WSNmwoaEhUjZMS0vDZrO1Kk0qJswej4eJEyf+LJIQXRA6Eg56bSE1\ngzEDdJ0LJIFAICIyGT16tKo9vObmZnbu3BnZfnCiL/CWisPu7H9UFJdqmEu3DIgBmqmp3Ye9rpEx\no0YTZzh6o6PXIyGiQ4+eePSYMdNOqVGWw+V1WQpn+Mry6bdDkwkAACAASURBVBYoBgLBYJDx48fj\n9/sjn5PH48FqtUY+p7ZuavQlb2PceH/bPUQAvxMxczZbBl9DSkoK2dnZqs4yKi43I0eOZODAgYRC\nIe655x4OHDjAq6++qqopvUbXEdIK4NzoAuLw77IiJvsA1157Lddee234dQXhJ2AuUCTLco/M0mgB\nsYfw+Xxs3LiRzz//nG+++Ya4uDjOOusszj77bKZOnYosyzQ0NGC322lsbMRisZCWlkZ8fDx79uxh\n8ODBDB8+vPUXUaAakNvvFUr+cPZo7HhsQVmmOmrUqFa/pGqgbJrv6jaMlv1Hh8PRof+q0oNqaGhg\n0qRJqpX4fNgRpSAVu/cimlzkZI/EcMx/FxHf0blTPRbSIvsxj3+xJvA0hAf1FSNwnT5sAm62hVeM\nBQIUFRWRlpZGVlbWcQHq2BsJv98fWZKcmpr6802V30XcG+dDwA1xJ/jvEAogBzxsG/m/pOefq+r4\nC4R7k8qoSGJiIs3NzSxatIixY8fywAMPqJqVakSHkFoAc6PMEPe1myH+GzgEPCzL8oaoD9gFtIDY\nC8iyTG1tLV9++SVffPEF27dvZ/To0Zx99tmcffbZDB8+HK/Xy7///W8CgQAmk4mUlBTS0tJ+Lq/K\nMgT2ga6Du2VZAoJgymr3PIpIR+0SaUtbtFj08NrzXzWZTJSUlBAfH8+oUaNUzXbdXidFe35gcNow\nBg5NJUATAvpI0JORjtr02TCTjJE2SpTNDeBtAKOltRJZlsLD+/HJuKQ4iouLGTVqVGT7REe0XJLc\n0NCAJEmRTDtNqiX+00UQCM8hoo8LK6MDbkISlGZezqA5N6g63wrhwf6DBw9GepOHDh3isssu4+qr\nr+bqq6/WbNj6KEJKAZwVZUCsbDcg1gF+oAL4lSzLgagP2Um0gNgHkCSJ4uJiPv/8c9atW0dNTQ1G\no5HBgwfz/PPPk5SURFNTU6S8KssyqSkppCe5SUwajKBr54tCDgESmIad8OFAIMDOnTtJTExk5MiR\nqgYNr9dLUVERgwcPjmkP79hr2O12amtrcTgc2Gw2MjIyut1/bA9FcTlqQibxSXr0mJCRCOJFxI+M\niEQICwOxkIqeNgQsoh+cVeF+3ok+G1mmvmY/+xwiE/Kndksdq+w3jAh0go1kuzczsOYzDAEHsjEe\nR/ps9tlmM3rGBapui5BlmT179uD1epk4cSJ6vZ7CwkKuu+46HnvsMebNm6fatTW6j5BcAGdEGRAP\naqKatugzB+lNtm3bxjXXXMM555yDJEntllcb63fjdtURZ7aRnJxMSkoK8ZZ4WvmESx7Qp4DheOWh\n8kU+evToTmca0VJXV0dFRQXjx49v5XGpBsoG+IkTJyJJUrf7j23RcgA+NzeXuLg4gngQcSMjIiMc\nHdA3YcD682xiW7jrwopP4wkCtwz79u3D63IyZmI+hpTYikqU9U0NDQ04HQ6CoojJZGLcuHEkJSWp\nlp2FQqGINd/IkSMj65ruv/9+Vq9ezfjx41W5rkbsEJIKYGaUAbFWC4ht0WcO0pt88sknjB49OuLR\n2F55de5ZMxk+RIfPr8fhdOJ0On/2gUxJJTnZhkEvhrPDFv0sZfOBw+Fg0qRJqpplS5JERUUFbre7\ntSBIpWuVlZURCoWYMGHCcf2mrvYf2yMUClFcXIzJZIqMBCjIyMgR6w4BXWfHfRv2g8F03JiMGAwv\nXbZarWRnZUHQCwNGdPqsXUFRdyYnJ2OxWHA4HK22U6SmphIfHx+TAOnz+SgsLIyIqmRZ5plnnuHD\nDz/knXfeUb2PrREbtICoDn3mIH2ZY8urzY3VnDlzEtNPO5PpM2aSEJ+Ay+3C0VBHk7OWgJxCUmom\naWlpJCUltbLaGjFihKolUp/Px86dO0lLS1NdmRhNOTba/Y+KQ8uwYcNiK/9v2AcGc6tyqcfjoay0\njOHDhzNg4NEs3u9pPyCKfgh4w31HvfGoEUTHYhRlye6xvUlZlmlubqahoQGHw4HH4+n2KExTUxPF\nxcWRuclgMMhf/vIX3G43L7zwgqo3aRqxRbAVwPQoA6JDC4ht0WcOcjLh8/nYtPELNv3rY7Zt/QGj\n0cRpp03ntBlnMnHyGchC+C7fbrdjt9sJBAIMGTKE4cOHx+xO/0QoYw5jx44lNTVVlWsce63ujlR0\nZv5RmQU9dr1RTHDVgugNj1gQfl8H9h9g7NixJFiP9gtDAdAZwXaCzRWh4NHX8AFCONOUQuEAG58K\n8W1/NkpJuzOLg2VZjljMKUuSWypYO5qpVZYUKzaATU1NXHnllcyYMYOlS5dqC31PMgRbARREGRCb\ntIDYFn3mICcrcshPbe1hvvpqPWu/+DpSXp0zZw7bt29n2LBhXH/99RFfUY/Hg81mi6hXY1HOVMqx\nTqdT1TEH5Vr79u3D4XCQm5sb03LsieYfdTodoVCI/Px8dWbhgj5orAFTAlVVVTgcDsaPH4/R1CLA\n+JvDwdB0zPVDYvi5EAmoLd5M+HnWAccFRcWEwW63R/0ZSpIUEeg4HA5kWW7lwaqUrlteKy8vD6PR\nSGVlJZdddhm33norCxYs0JSkJyFCYgFMiTIgerSA2BZ95iD9BUmSWL9+Pddffz1JSUmIosj06dOZ\nO3duxBzgWPWqMt6RnJzc5Tt1RbFqs9kiAgm1UEq/VqtVdXWsKIrs3LkTQRCwWCw4nc5u9R/bI+Q8\nzJ7inzBYkhgxcuTPCmJZhqAHTFawnsB5qNkO3sbjA6WCLIdN5VOzIkuIFWs0vV7P2LFjY/YeRFFs\nZTGn1+tJTk7G5XJhMpkYP348Op2OLVu2cNNNN/HUU08xe/bsmFy7PVpuqwiFQpx++umcf/75/N//\n/Z/q1+7PCNYCyIsyIAb6VkDUzL37MYIgsHz5cp5//nnmzJnTyhzg0UcfPU69CmHlaW1tLbt27SIu\nLi6SPXZkBaZsSu8JxWpTUxMlJSWq70mEn11Tjl15pcb+R6/XS1H5AYYNymJIcjyIHkA4OkuqA0ty\nuPR57GtLUjgYnkidqiAI4X/8bohPxu/3U1hYGOm5xhKDwcDAgQMjopjm5mYKCwvR6XQ4HA5uueUW\nRo4cydatW3n//fcjAjI1OXZbxdKlS7nooovwer2qX7vfo5l7q0KfOUh/QpblNi3R2jMHyMrKwufz\nRXqPbZVXW/qe5ubmqi6GqKmpobq6mkmTJsVkS0V7KL2uzrjpdNd/VRmBifRBpVC4FyiFwoIYg7lt\nYUwoCI6qtrNDBdEPBjMuIZ6dO3f+7ImrIooAKScnh/T0dCRJ4r777uNf//oXmZmZlJeXM336dJ5/\n/vmYX/vYbRUbNmxg06ZNPP744zz33HOIohi54VH7d6k/I8QXwLgoM0Rd38oQtYCoARyvXj18+HCr\n8mpiYmKr8qokSSQlJdHY2IjNZjtu9CDWhEIhysvLkSRJdWNpWZapqKjA5XIxadKkThmvH/t8pf9o\nt9sJBoNtzj/Kskx1dTWHDx+O/oYiFISGSojr4Etd9FPndFNR64rZ2qv2UKoGyooov9/PLbfcgtFo\nZMWKFZhMpsj7j3WW2hbKtgqz2czLL79MWVmZVjLtJkJ8AYyKMiCatIDYFn3mIBode69+9913uFwu\nhg4dGrGXS0tLU2VlkzJSoez564nepOLcE4trtTX/mJyczOHDhwEYN25c9EFelsFRGd6x2VYWKUNV\nRSl20cTEKad2Och3lUOHDlFVVUVeXh5ms5mGhgYuv/xyzj//fP70pz9pStJ+hGApgJwoA2K8FhDb\notcP8sUXX3DHHXeQmZnJ+++/j8fj4de//jVut5sXX3yR7777jqVLl/Ldd98xbNiwVo9t376dJ598\nkmnTpvHss8/29luJKS3Lq59//jnr16/HaDRy9dVXc9FFF7UqryolQ5vN1spTNFpUHXM4BpfLRXFx\nseq9yWAwyJEjR6ioqIgsT+72/kdvE7hrwwuHj0GSJMpLSzAb9GRNmY0uBjsg20LJrt1uN5MmTcJg\nMLBnzx6uvPJK7r77bi666CLVrq3RO/SngKiJalqwYsUKnn32WZYtW8aOHTvYv38/kyZNYs6cOaxc\nuZInnniCt99+G4Avv/yy1WMbNmxg3bp1nHXWWTidzpivGOpNBEFg0KBBXHrppXzyySf8+te/ZtGi\nRWzYsIHbbrutzfJqQ0MDhYWFSJJESkpKJCvqTCakjG80NjYybdo0VR1u4OftGz1RSvR4PFRVVZGb\nm0tqamqk/7h3797o9z+aE8MONn5XuJd41O3G7/dTVlTIoPQ0Bo+dAioGQ8W9x2w2k5+fjyAIbNy4\nkdtvv50XX3yRU045RbVra/Qi/UhUowXENjj2Lr29u/aWj/WhjDvmGAwGbr75ZmbMmAFAQUEBt99+\ne6fUqw6Hg7q6Onbv3t1heVXZyZiYmMiUKVNULZFKksSePXvweDwUFBR029+0IxSf1cmTJ0e2ilgs\nFjIzM8nMzGzVfywuLm63/9gKQYDE9PAMotcJcgiXy0XF7j1kj51AcsbI4+cTY4iiWlXK2rIss3r1\nap5//nk++eSTHusRavQCMhDq7UPEBq1k2oK1a9dy5513kpGRgcFg4NVXX21VFv3hhx+46667mDx5\nMh988EGrx7Zt28ZTTz3F1KlTVVHMnSx0Rr3q9/sj6lUlI1LUq36/n5KSkshyWDVRdgqmpKSQk5PT\nI4FX2ejQ2cAblf+qJHH4YBVVlZVMysvHYm1nEXEMUCzfRo8eTVpaGpIk8de//pWdO3eyevVq1ddG\nafQugqkABkdZMk3vWyVTLSBqqEpn1Ksul4v6+noOHTqE3+9n8ODBDBo0qNPl1WhQvDR7Ym4yGAxS\nVFREcnJytwNvR/6rQEQhm5ubq3rGW19fz549eyKlZq/Xyw033MCQIUN47LHHVL++Ru8jmApgQJQB\ncagWENuizxwkFnRFoBMIBFi8eDGHDh3i008/5cEHH6SsrIyZM2fy2GOP9fZbiSknUq/OmjWLn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uIByIEolGqQ1VUhZLYJomETRphDSQEIXjOORyuaKmqLXG8zyCwSCWZWEYhq9F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BNJIIXUCZKBzHwbKs/EhRKYWI4Pf7MU0TXde9UaSHRxGOpBHikXIeHkcYriu0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\nphsY7BZa3znOsJMgXdclkUjQ19dHLBZjbm4OYFdTS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BIdv2efpHK5HHfu3Olp9qMVjuM0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fOQ+ffQzPE1ozNhtyNv+XFYYGDNoyGRpow7OEgFYoUXiuAUpw0Jh4mMpDK4es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\nGEV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llbHotreS0wZWwCJqxQhYQap0kL9YHRwkcbJYJEThOA6FQqE8U9Ra43kewWAQ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\nu+X7/Pjjjww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6+Ow5rudSl0zSmPGosBRLgqvJZEyUJxjaxTJARGFpF6UUjifEw+202LVsSwpb\n164hYoYJxcJUByooicaJWCYZ7eGJSTeaclG4rott24WZolIKESEYDGJZFoZh+LNIH58BOJRmiIfK\nefgcYnie0Ja22Za1+X8ZYUTAYIu9jQ7VheuEiVkZlHi4XghRCheNgYcSTdBMooI2Iddk3PgjMbOa\nzlQXLV1tNG7bgeM6WNEwFaE43dFS4tEolmEWZoV5rcnatWupqakpxC9qrQuzSMMw0Fr7QtLnE49v\nQ/TxOUB4ntCSdKjrtkmI0OBlac54rG1sZWdwLaHDO5lsbsBQLgoQNK12JTu9SjAMBAOFB2SoDLgo\nrTBCFmWhMqTcIy5R8BwS6STpriTbm3fQtaYDS2ni8TglJSWUlJQQieQ8VA3DwDCMQpxSNpstWtVg\nIFWrLyR9fD6e+ALR56DB8zwaOrKsSbuIAaZpk+jspGFHC5RalFdvJmZuIyUBkm4EjYehhJpAI6Ve\nOw3uWEDjIhgiHBFy6VY2KXHxEBxSpFUCrYWuaAY7mkXVWmgqKXNKCSWEbEeS+vp6kskktm3jOA7Z\nbJaSkhKCweKFWkQEz/PIZDKFlQ3yQjQ/i8yrWn0h6XOo4s8QfXyGEBHBcRw6Uw7b04K2IOGmqK/f\nygbLg5FVxAPvEjETZL0YnvYwtIfralBCSiIEdZphbGGjfTgaTakhmJbHdlws5aJJkSSFRqGUByg0\nAcJoHFI0WTa6PMKwsiqOksMJoKmrqyMSidDV1cW2bdvIZDKEQqHCLDIej/dL0O6KR8Z1SDs2Jh/Y\nI3urWn17pM+hxqEiSA6V8/D5GCIiuK6L4ziICB1ZRUYL2xu3s6m9CX1YLaOjERqyaUqD7+PaNWBu\nw1AZRCsMHEQMHE/hqSBhlSBkJFHEOTxoEsLCAFwcdpLCQiHYCBoLTZwAcVxsTMIogkpoYycGBsMk\ngDY0kdIS4mUlHIbCFMikM3R2dtLa2kp9fT2u6xKLxYiUxFGlEYhF0NpAKTBRxD1FBI3jOCxfvryw\nTJNvj/Q5VFCAta+SxBnKnuw/vkD0+UjwPA/btvE8LycIlKa5vZN3t27CqghTceQ4tAEJJcTMVgQP\nVxs42Qqs8A5MwENhGOB4glJCQHtUOgnKZAygiKHpwiFNggCKNC4hDJI9ET4xhAgaF69nG4BDmjTN\nQJvl4ekstrIRBEMpYmGLmnBNYeFY13NoTraxIdNC987t2A1JTDGIhyqIRkvpiMepDkWo0LmZpG+P\n9DnUUApUFvDIAAAgAElEQVT2OZe6LxB9Psnk1aP5JX6UUjiOw+o1a9m402XMxLF0hxTbsYkojcal\n1BQyCFlP0ERw0pWYoXY0WUwUQd1ju/OChDojBMtMygkTJUwHNgkSeLobVyVxsQh55UQkTAQP0Bho\nsnhksQmqIB2SolNpPARTBLOnFkC7snFEKMPCwyNldJIoEYZTQ6A6twhz1k3Tlewk25kgsbmdtdk0\n1Z6Bk8nQ3Nzs2yN9DimUAsv4qHsxNPgC0edDIS8I29vbicVihUF927ZtbNy4kTFjxlByxHA6RWgl\nCdh4ygGyRADRHp4Itqvx3BiSVWScbsqCFhbgiQnShXbChDCIEUcjaGMjpXoTqkfoaaWwdCOOhEg7\nRxMllu8hHh4CdKgMcQmQDDgkjAyQRClNSCwCGOwkQy7QI00aFxGLgNK42GTI4po2VjyALslS6VUy\nllLcrMvWFStJJBI0NDSQzWYJh8NFnq19lyzqrVLO49sjfQ429muGeJBxiJyGz8FMXj2YSqVYs2YN\nJ554IolEgrq6OuLxONOnT8eyLBJZ6E56GKobmw7CWHhAF0FQGlN3oCjFMg1CKkjaTlMRiOAJpCWL\noU2kq5yaqmpiRNhprMExdmBJDAONSwYluTRurkrTar1H0D4ekyCCh4FJJza2ylkaDU8REIOAsnBx\naVRtpMhgYdAsHiGdRkspIRUmS5oMKZQyMQmAys0qU7oLEVCBGMoyGTt2bOGapFKpInuk53lEo9GC\ngIzFYkUZc/LxkY7jkMlkWLt2LRMmTChStfr2SJ8Pm/2yIR5kHCKn4XMw0lc9ahgGnuexZs0a2tvb\nmTRpEiUlJYX6UQuCVidVXor3UTjiIlphYyLOcQSN5bhKY6hIToXpBrAkgu1mCBpp4jIZyQQxCeKo\nJN3GVgJSQgYHcDHQZHARNIogWTLsNBqocsfgAQGC7JCdlKkKgmhcctlqHDxaaSeDg6E0ljJx8Qhi\n0kKKnaQowyCogqhembE0BiIuCkWKbqRXdkKlFJFIhEgkwrBhw4CcXbW7u5vOzk4aGhro6upCKVXw\naM3HR+YFXjKZxDByuirfHunzkaEAX2Xq4zM4nueRzWZzi272DMQtLS10dnYyYsQIjjrqqH4DdFa1\nEwlvpSYdojrr8L5yCYrGRtHNMKrUVMqCf8b1UmTcKIKDLQmChkOpHIfSR5N012Nh0qzX4knO01Nj\n4ODiFASSYGJgEaFT7wC3ikpKyJJFY2FIgAiaVE//Okhg4xBWQQTBxkXjoTGIK5PtdKFQ1Pa45RSh\nJJelVwme3rUHgda55ADxeJzDDjsMyM0GE4kEiUSiEB9pWRbxeLwQIxkMBguCEYrtkel0unCdDcMo\npKHz7ZE+Pv3xBaLPkCIi2LaN67o9g61m4/ZuXnlnE65h4WSqObZkJCKK3mOxTYIkjVg6SEXE4tig\nUGor2sTDUBkyVhpHDyckFdh6C4bqJuMIFfpYSt1RpJVFShxSpoclkFWd2Bh45ILy0ygEkxguLi4K\nFxeN4OIiZARCooirUixyoRKtgINDEpeAynmJKhQOQrBnJhgENIoMgo2DRV87INho4gI7dyMQB8I0\nTcrLyykvLy+UZbNZ2tvbaWxspK6urmCP7B0fOZA90vM80ul0oSxvj8zPJv2k5oc+06ZNG/pFFPYj\nmWkkEjlhsD69/fbbLSJSvR8922t8gegzJOQdQGzbBnKDbSLp8szrO9jSmWD06BFUxaO8+94almxy\nqQgqPjNKEwspPBxsOgETjSaJR9RQHKNN2sSllSAZ0nQLCFGi7kSCVLG5ZT2jKo/DkyxdkiGjHXKr\nI1pECNFNFo0iiwdASAKEMbFyQRykcfFUhnKvhDDlRLGoEA+HD2ZOjvYQBM0HgsLBo0YiKNWNAUSB\ndgFbuUUC0cPFUwamaGIoRA/NWBQIBKisrGTLli1MmTKlyB7Z3NzM+++/v1t7ZP6e5WeZSim2bNnC\nqFGjihx2fKedQ4sVK1YMeZvTgmqfJcmkSZMG7ZNSatN+dGuf8AWiz37TN6ZQKcW2bS088foOwjUV\nnDx1HFrlVHolpseImKK5S3h1s8uZRxqgk+TmXkaPqBIUClMpapRJlQhpNA4u3V6UJp0likaAFC6C\nEFcQwaTV1WjPoMKtJWu2YRLCAyJiEMUkRgBBUICLjZIIxzAaB8EQF1N5pHvaRdHjd/qBQLDxsFCE\nMHEkhK2SxICM8kgJaJUL03DxsMUhSowqybn0KG/oZl+9VdF7ao/Mq2TzQjIcDhfNCpuamhg1alSR\nLRIoCEbfacdnUA4RSXKInIbPR0FvpxmlFFpr0uk0q1evpmGnSeWYsYyqCA64b3VM0ZDw2JLwGFGa\nRWFiAim6yWDQgUcARQAIKkUEE1c8UgjDCKOVRdAVyjARcWhXOQWoNjM0q26GudUEzLWY4qEwiGAg\ngAY0GkFIqSRj7SkE8iVKAVliaJSArUB7Oacam9xM0URRIgEMNJoQiJBVNmHJUE0MJS5ZXCygSmLE\nyDnaOOJhuh9exsdd2SM7OzvZsGEDqVQKy7IKAjIvZAeyR7quSyaT8e2RPv3xnWp8PskMpB4VETZt\n2sS2bdsYP348jWYFlcFdqwjLLMXaFo8Rpbl6GUy2k0te0Q24QBc5IVZBbrZmYxNXEbIkCZFGk6JV\nJUgrjxAW4axBmRdAK5fDnIk0mnUYYvXMPnP+LS4OKZWgwhtOjTcKetoOiSZEkA7SRJVJha0JekES\nOFgIgZ42ouSEvEIRJIKIQQaXMIoAGlNCmARRPWpWBxtDWQdshrinDGSPzGRyqeg6OztJp9O8+eab\n+2yP9BdZ/oRyCC2IeIichs+HxUDq0Z07d7J69Wqqq6uZMWMGShl0N7iURnbdVshUdNgAIZJ00kqQ\nODFsOnEBFyGMgQvsIEsUGxdI9wRSBDyPDtrJ6hQuJopAj5AyCWES9Q4jYofYYbxPp25HofDQWGJx\nuHM0I9wjC7ZBV4QIBoGevxNkcHXOnmi6JWzVbUQIUS3hntCPnuuBhy0eY2UUppIeV53cDNTFRkSw\nCBAhWhSSsb/si0AciGAwSHV1NdXV1bS1tTFt2rQB7ZGxWKwgJKPR6ID2yL6LLKdSKcLhMOFw2LdH\nHsr4AtHnk8ZAKdds22bNmjVks1mOP/54IpFIr/ogucxog+IKOTUnIdroIISHgYVJGYoUraRJ4mIg\naBwSxPEII2jKMNmG4DlCd2uSTNTDDOUGW4+cBsfAICoVTLJradEJbMlQSYS4lGP00vFkcAkqjSW5\n/UMEsDyL9V6QpBukWmKMdS0SupsOsnSKQVwgol1AUStllBHriVm0yZIFPHIh+iGMA/SaHQjhMpg9\nsquri87OTrZs2UJ3d/du7ZEiQkNDA9XVxU6CflLzQxRfZerzSaDvihT5wWvr1q1s3ryZcePGUVtb\n229QG10O7yeEmujgg11HShg9IjdrU5Sg6AQCaAwixAgRIY1NFw4egqdMYpjEJaeQTEqWDWvWU15e\nQWpnmtbsOpwMbNm6FSMcRMdjBEIBXOVS7ZXRhUuIIEIuT6lLzj4YxKBcrKIZXEIUGYLUikm5CgEh\nKiROF0nSYpMCKjyL4TqEiYn0KHR1j2rVK8wSHRS6yEt1qO7Lh4XWuiD48gxkjwwEAoV6eZtkXvDl\n+zxYUnPfHvkxZn9miB/eY7xH+ALRZ1A8z6OpqYlAIFDIkNLZ2UldXR1lZWXMmDGjn30pz5E1mnWd\nHrYjWGb/wS1rg2MIY0sMbIQAYSwMHBIIdqFeCEWUUrpxcDCJKYMdyRRr69fjicOkSZMRz6NSCe2q\nm7XrGzi8ohy6OmnYupVUOoUENbWBSg6LVBIusbCDOYEYEEUZwR5F6wd9zIjDdpVGGe0kDYcwgcJM\nr4QYJQo8gSygxcNWnbiqGxeHjEriSRZFAIsS8kunBiRclKlmfxkqlWnv9vaGXdkjOzo62LJlC4lE\nglQqRUVFxR7bI0UErbVvj/T5SPAFok8/eqtHm5ubKS0tJRgMsm7dOhKJBEcffTTxeHyXbZRGFDMP\nM3h9s0c0IJQFFUqD58HOhJBSwomjNeUhRRJwcDHwEKzcsk4YGIR68owCdOB5QtOWjbS07GTYuPG0\nb6tDtIF4gieKAGEiuFRGA6h4NWVU53S3WYd4u0F3R4LtWxrIODaRaJSyeBxdUooVj6O0ynmekqRd\npXHEIJf1VOMqh246CEiIIBEUCq3AEaFLtRNQaQQLW6VRmARUOBfwL51YlKEJkVVJXDPzIdy9fWMo\nBGxveyTAu+++y7Bhw7Btm+bmZjZs2ICI7JM9sreQ9O2RBxn7M0O0d1/lw8QXiD4FBlKPaq1pa2tj\n/fr1jB49mokTJ+7xQDS6UhMNKt5r8djRKQjCTttkcrkws0pTG9E0OYptXppG1cVY0yOoDFQh1Vo3\nAliEaE10smFTA8eUVvCZqcfjKYOGjWDj4AEhBaVK0Z2FSgni9dgDXVxKrFKC1RbJ6koiysMRj2Qy\nRWtXF81N23HXrsVQinBZiEC5RSBajg4E0Er3REeaGJhkVRotBoGeFG0eSRzShAiRJYWHm0vsDSgM\ntAri0ElAgpgE8EwbF2dIbIoHYoY41AJGRIhGo4TDYYYPHw70t0d2dXVhGEZRvtaB7JGQm4G++eab\nTJ06FfDtkQcV+2pD9AWiz8FIX+9RrTVdXV1s27aNUCjEiSeeSCAQ2Ot2q2OK02IGKUdwPHiro53T\njjBZn9UsalPU2bnovgwVBLVwRiTDqZEslsoF6bfYLazfspOd2Bw5fjSRYJQmPEqBchdqNYjhodHY\neBiugYHG7Fn4V+Wyk9KqXNJ4BFEElEEkGqMiGiVTW4OBoswWmpPb6O5MsqN5M422jeW5uRmMUkSj\nMUwrQEalsKQntlJ1YWAhCFmV7ifoVMFWmcYk53Bkk/nECETP8/rN/gazR+ZDP5qamkilUgSDwSJ7\nZCAQKDyX/iLLBxkHzss0qpR6G9ggIl84IEfogy8QP+EM5D3qeR4bNmygra2NmpoaYrHYPgnD3oR7\n7IghA1amFPd0GoSVMMJIoZXGw6PNg+cSIeqzJl8p7WZ7axNrm7dTM6KWE0rHEVZJgig8oA2XlNbE\nidBGAg8bA4UpJp4IXs9S3KUSpQshjUekj2OLRhFGkcGj3XIpKS2hsrSKwwS2eELLls0opWjv6GRb\nwzY8EQJRi4pQDfFYCURtgirSYxv0UPQPvNeYeGSBCEo0rrIPOkcCOHAzxD1p0zRNKioqqKioKJT1\ntUfatk0oFCrkcY3H40UJBPLH8xdZ/gg4cAKxFmgAHKWUFhHvgBylF75A/ISSHzxs2y4MXEopmpqa\nWLduHSNHjmTGjBls3boVzxu65zCjTR7q1FRoIazzK1DkQtgrtBBVHu8kFTTt4KhoigkTJhI3XCox\nEaKkSGJgEkKTNBUuiqhnkVE2BgE8Q7DFISYRguQ8R7uUQ3gXMYBBNB3YGAgWYCgoU4odpkl5OERF\nRSWQm/F0pjpwOzzqt23HtDeTkCCxeBSjzKM0Uk4wNFBmng+un5KhGYiHWoANNJv7KNvsa48UEXbu\n3MmGDRtobGxk/fr1A9ojldKkFWR7LnkAIejkzAAeLiAopbGMILZh4ioDyzAIGoqQ77ezb+yHQGxu\nbmbatGmFv6+++mquvvrq3i3fAtwKHAZs2Y9e7hG+QPwE0ndFCq01qVSKuro6TNNk2rRpBIO5gV1r\nXchIMxSstkrIeDDM7J8nVAlkOjuIprJsLB/F3w7vpkRpPLJoBJMwBiYZUrjYoBzayVIlIcoow8Bg\nazpIuRfDMnKztXRPyjW1m7AHhcLuNXUrVRBB6BaIC1gqdy2CkShGOM6oYSZRXYbnQFdXN63JHdS3\nvI+TcQgGQ8RiUaLRGJGYiaVz6kFPOQUb4/7ycVGZDlWbSilCoRCRSIQJEyYAkHY9Gru72JroJrF9\nO93dSVxtURqNURqPEYtGsYIhtLYpM7qwlEPGS7GTBM1ZBRKBlIXXpRhWdRgRy2BEyCRs+vbIvWYf\nbYjV1dW7SjjeAtwObCY3Uzzg+ALxE8RA6lERob6+nh07djBhwgQqKyuL9snXGSrWBuJEVV4QKvL6\nw3Qmzc62nUSjUcbUVrPBVaTdDGVmLsIvPzSZWJhYeHhEM2EsSghDIc5Pq337zLcwANUjPHNLU5WJ\n9AhiSAoIQgA4TJlElMImhmN1Ul5eTrw8SlolMCRAJp2hq7uLlrZW0tvakUwJkUgcJ+uS7XawIkMv\nfPaXA6UyHcpZZ+8ZZ4cntCqFjsU5IhYnLcPY5oHruhjJbiSRYHtzE0m7AyPmYYRiDC9xkJjQQYSI\nIWidoDOTJaVtPB0hY5eyLmszwnQxlIMywDIDBM0QprZ8ITkYB05l2iEi03ZfbejwBeInhL4L9iql\naG1tZc2aNQwbNoyZM2cOOHhprYdUZZpPbAY9TieeR9vOFjzXpbq6uhCnpsgpGvMCSvexz2k0SnTP\nzM8rOq/eAtxA0WciOgiaqISxVYpAIVcphICqnsuSJUtYooR6GjMJ45HCI4NBAFNCOCpNMBwgGK6g\nvDKKyXjEDdCZ7KSjpYuN9ZsKi/yWlJRQWlpacBrZGz4OM0QY2mw6ruvmnL08oYXcsltK5SJr2kQR\nU2BYJqmSEuIlJYzULmnVipOB7clG6tPtZJoF7TUTCgYJR0K4ksXwInSpnZQZFojFNpWk2sjZeruz\nHspWKLEwiWIaFgHTImxYvj0yj5+6zefjwkDq0Uwmw5o1a3AchylTphSlXOtLb4EoBUG27wPAYZKl\nzssl6+7u6qats5WSshixSIy81HIkJ8ii2sMli0V4QJWnq3IrYWilC0Kwr0C0UARUblFfc5B+52eg\ncUJkRMiQRisDQfDEw8HBE5cgIYJ8YCNUGASkgiwdeCqNiUaJQUZ1IwIGUQQLw7AYHjuc7UYrkydP\nBnKL/HZ0dBQ5jUQikYKAjMfju51dfRwE4lDieR5Ka3aS+1DJdzdLLhdtUOdT70EnuQy5GgMrqImE\nNOIegVWlKVEuqWyWVDJFZ0c7drKbREcXZnwLlVYtVqSSqliAoKGxRWgSh+06DZIl5MSIOxBHU45F\nSBlobbIq2827kkAMxeFWmJMDJcQC0Y/mQvnsM75APEQZaEUKgE2bNrF161bGjx9PTU3NbtvJapfX\nYinuMdbToFwUMN6z+LyU8Wkp2+uUZFO8BH9xXBramghaFiOHHY6rbTxcdI/assnVnBxOoXUKgzAW\nAw8stoKYfKB27X2evSkVgybl9KxSU7zdQ0ghVEouXCNClABBMpJGlIunXEwxCRLFHMCLVGEQpAJP\nHAQbwSMsuudIqidtW38DSyAQ6Oc0kl+/cPv27axduxalVFHoQTgcLpzfUKdu+7gIRFdrHCDSq6uO\n8IF0JPcMKBG6JUVchbBJ9nzc9OgmtCIcDBIOBvE8B1XiUVkymiZ7C16HS9OOHXQl2zEMTUt5KYFo\niKpIFDOgsLRHmhBpPDLYNCXSPOo2sM3MopRGHA+dVvwnwt+Y5VxZMeHQV7X6yz/5HMwMtCJFe3s7\nq1evpqKigpkzZ/ZzWR+IBBkWlnewTilKlUcFGg9Yr21+TDMneZ181zkcU+3ZC+95HtGudsa59ayr\nGcuwsIVWOfWnh4OLTaurCSiH2dE0IcpIY5GfOboipEVwyS3Ua3qKMJq06+F6YPTI5r7CIoSmRkxa\nlUumZ4FfoMeJRlEuBtFeb7SJiUmMqFNCyAkRJbbbc9OY7M/rpJQiFosRi8UYMWIE8EG+0I6ODpqa\nmkin04X4PNM0h1QoflwEInqA57b4m6gHwSOnzXCw0T33fIBqKK0RwyVkhKkMVlNeHaVGu2x0M8TS\n3dCdYkdbA5lsBitoUBasIRyN8VfXZLG1DbE8apSJpUxEIOO4dGfTPBvdSbrtPd644Q7uueeefonO\nDxl8lanPwYiI0N7envOGDAZRSuE4DmvXriWZTDJ58mRisd0P7nl+qrdTrz0qU0IskBuIDKBCFFnH\n4xUvg9HawLyuSoIRh3gZREMBTIL9Zo5tbW2sXr0arTXfGlfNC4bJS8mc8jWowBWLLBY1hnBdmcPh\nRi4mrR2XDnHpFKFL6BnkcqES7U6Q/9fiEczkUqkJiqbuAIdlhFCo+FxCaIaL6rH45ZS/MTQh9KCq\n1KF2KNpb+uYLFZFCfF5zczM7d+7krbfeIhqNFlStA6VC2xOG2gHmQOB5HqZW/YRaAHKGxCKBnlty\n2sPFATrI0Kk1lpdbszJMLguR6zmYVhgXJyc0xUADGTzSlkeNFUHH46geh6t0NonTpWluS/B71UGi\n1KXGMfFMhWMoDNNA4xExTcQzeFkl2JpoIRwOf1iX6aPhEJEkh8hpfLLpnXJty5YtlJeXU11dzbZt\n29i4cSNjxozh6KOP3qsZwEaS/FlnqHYMbPkg7EI8IZMGz9GUmS7LKzuZowQvG6Bjh1Ba1k15mUmQ\nGAEiZLNZ1q5dSyaTYcqUKWzYsAFLK74U9zg74vFmWtHgKMIKpgaFiQHB6OUpWo5J2su52FQCplIE\n0SSTimyXSbencANCNaA1KKXZ3qEwLCjrYxrVKKIYgyhgD37yoQehUIhAIIBlWRx55JF0d3cXbJFd\nXV2Yplmkag31/ToYgKEMkdgVeSepfcHzPII6v34lhTUpLZVL3ZcVCKj8cSBImEZa2YlHh7iUGDZZ\npdmGR1hpqiWAh42pKhFy2Y4cMYnqJC06iUgWWylQGkOCmAQxAwFKKkrYrlyygW7KlEHQNfBswbaz\npNIO4nooQ4Nt0m1mSZ80aZ8F4lNPPcW3vvUtHnjgAf7mb/6GbDbLvHnzSCQS3HXXXZx44on71O6Q\n4qtMfQ4W+qpHDcMgmUzy1ltvEY/HmT59OpbV3/a1O17THQhgKEW2V3k2A+KCYQmGcuhSsMqEEyVA\n0BISO4WQqfCiHWzfsZ2t7+8oWiKqt5NOlQGzo/l17AcmLUK3KGr0B49q1oHWLghaHjEDEh6kyHkd\nmlqIWEJLtyIcEIKH6BPeO9dsPB4vSrZu23YhFdr27dvJZDKEw+EiITlQlpcDJRBtPJK4dCsXAUwU\nMTEIYfSz6e4Kz/Mw/j977x6j53me+f3u53lP33nOw6FInY+WZMdyLDlJN/Dai2ziInGUGmjSbhGk\nLVAsisVuihb5Y1s02HYX6zZI/wmKXWRbB02QtuskTlMVdbyrOPXayfqgyHYkUUdSJIfDIef4nd/T\n89z94/2+4ZAiKQ5FWbQ0lyCSM9/M+72n77nf+3BdlzG0gYtAY19SOG9g3UOmVWm9KbArsIMjwtKW\nJm3pYYM6W2VI7h3rpg/OAAEj7xhojVT6BMGQTCt3EyNV37EgpSQnoEHpYc3v4CkJJUACRxwY0Aio\nkaUZXpU0zdja3MDdvcSnPvUpnnzyST71qU/xMz/zMzd8zJ/73Od45pln9r7+yle+wre//W3uu+++\n2yfrPCyZHuK9xtU4hc45tra29rKx/XqRB8UWZfXQJ7I3Xeq94gqoOO/lJI4JA1MFOEGoJXBhPaXX\nf51aM+BHn/w4cbhvMvOAZciuV8Ir1sxhWvULjam2FQG7Oh3DrziEgUB/DPH1TTneFu9lyfR6uF4A\nC8OQ+fn5PU6pqjIej/dKrVPXiamYdqfTueUZ4vS8jSjZkaocGU/KlB6lKyV9HAsaXbNkfSW89wRB\nQMsIhVd2gUCrkmkgMC/Khq/K6k4957SkzSKJ6TIvMZkIJV1mgoiBC+n7mK7PGJmSBh2atk872CaX\nmF3vyCWgoyEFhoyAkjGhBjQVkDHAZHBqsv9SwkQy0FpDZ6bDne0ap9ZW+aM/+iOee+45ut3uOzqv\nRVHwYz/2Y3zyk5/kS1/60t7U8nuKw4B4iPcK1zLsvXDhAm+88QaNRoOjR4++o2AIMEdA5QfPXgLn\nyukTuYI4UIPgafqqjOXVs7a2xtraNp/4xD3MLtYnyqOXMNVKvVGk8JbZzn4KUTAJrlQBc6jTNlIV\nJJMABpmw0Lr5gHa7D5ncKESEer1OvV7nyJEjQMXpm7pOnDp1il6vt/ezN8uN3A9VxRlhW0qSijW6\n95pBSLDkeLYlZ1Gjy8qoipKhFJN7J8IQUT3wTRWU5oxQV6WnMKh+iZrAowGECquSsWJ8pV8kHTwN\nYm1QMCRjQGiUuigXk4K7TJ2FUMnMiBIhQFkQzys+Y0tSlrRFgBJTwzNiiBLZJjCk1BIjAZeIroqS\nY6mhCoXxuBNnmP+FeX7qp37qwOfxS1/6Es8++ywnTpzg85//PH/yJ3/Cb/zGb/CFL3yB3/md37mp\na/Ou4H0SSd4nh/HBwNUcKUajESdOnCCKIj7+8Y9z8eJFnHPv+L1+wnf4Q9PD4y9VNC8juAsZEKrw\neJHQ7/c4efIU8/MLPPrYo7TbIYLbE9mewhhzoKzrqgOEVIF5GvyuDFzV925LDe1bhnda4rTW0ul0\n6HQ6HD9+nM3NTXZ2dmi32++IG7l//9JQCJDLguF+RBjGODJ0T+wgw7MtJQ7dG8vyOCxCqo7mvvdP\nREgE3kIekup3WlKJQOQobtJBjmnS4Agex0g8cZ7TanmsKJE2iMSQM0amoVFLxrLLrM5jiSllQGiF\nmThgNrN0w5I24CZc1up+LQFP6krCEOzXvgv/8AAXZx+efvppnn766cu+941vfOPmNvZu4bCHeIgf\nJK7lSHHq1Ck2NjZ4+OGH9yYRb5X26H3UedzHfE9S6lNC/kQVBCr+XjeAT/Ysq6+/SVHkPPTQQyRJ\nwnDkMWYayi5fDA9aMm0I9BT2d0tCC6lz5KFjI93h7PoFmhIS1xt7mXPhID546/SHCre6xBkEwTvi\nRu5H6R1FaIjehqcaIIxxJBgyPBtSECFv+b0SZcs6ZszVj1lRSgocUxEJR4rSxVfCC9MHJK0CcQtL\nisf4DGMntBkBi6VGk4F6jgMXtMaYnJpaInGMtdJPqgfwo9ksX/MbXDQFbSzBpN9YCqRakoWGT7uI\nnRAzqgEAACAASURBVNG7btJwiFuEw4B4G+Nq5VERYXNzk1dffZWVlRWeeuqpy57ab6XU2n/lj/Lf\nmLOciFOEkkZg8KnSN0pu4MGdnPu/s8XMsePML8xXnK9SSWIhDKXiCl4hZn3Q/WuJsOsrPVMjVf/J\nNjM2ezn9Yszo3Fnmjx1jloKdwZjeeMArr76C2gb3HkmIyxadTmdPEu6guJ17iLd6e1cGtutxI3u9\nHq+//vpl3Mjp/2EY4lW5Ruy6/D0QSqm8DXekJESuOmgTIBjn6QfKIo6MjEyySSAsASWc5KMAnpSz\npCxLh2R/+iJUpVr1jMURaUpiJl4oqlV2qTBUQ+HBlspOXuMb3YiLaYOaifh4S/mxGU+rEdMczfMN\n3WXDekQ9XqE0QsMqP2s7fHo34NnGD+tc8w3isId4iHcbVyuPpmnKyy+/DMATTzxx1XF6a+0tKZkC\ndIj4p+44//zUc7zyUJPzxuFqcKQrPHKqz4cLz32PP4611W3kVUkz5ehKgKfEYLFXdAAPmiFGIiyY\nalgiQRlJRj/dZfWVNUofcNf99zIfRLTwuEaHNBuzUjvGbCMm0V22t7d588038d7TbDb3yoT1ev1t\nM6zbuYf4Xtk/XY8bOT3XzjniWkLmc/qDPo36tbmRHiVWQ45SqlK7jji7ccrYFmzJLgZwQF8GjLTy\n1EyImNcOAQEqEYmMGTPE0CCnKsd6lACDEWVLc+qZJ7DBZDQmwOPZKQ0brpqm/outFn++sYDRig6k\nGnFiN+T3z3n+vWNbPF5v8J/Q4kw5YFVSMIrsrvHJ5Ye5O1lkffDGZRPA70scBsRDvFu4liPF6dOn\nWVtb44EHHriu4sWtFuOuS8RTF5Vfvf9+nHecPHmK117dZGn5XlorBic56oUiE1SFxUVDmJR4lDqz\nb+Gc3cz+dYzB4lkrC767+gZulPP4R+7l5VdXqeVCJJWepVdD6iwSFxxbaGHNEsvLVYfJe7+X2Zw6\ndYrRaEQURZcJbN8MPeX9gpsNsPu5kVMpQO89W1tbbK2e4sz5NfLBCGvtHj2k1Wzt+UY6oI7FoW8r\nwF5QEgQ5jphUchweJwUNqQrqGQXrukOHDkYNd9LhDdllF0+DOhaDRchw9DUnxuKdrXzHENQ1uJB3\nOVeG7Djh+Z02X9ucZzYsSKyCWgr1jCkovPC/nZnnF+8+w8eahmVb41HbpBOUvLy6xnLYYSiO/nB4\nIDGMH1oc9hAPcStxLcPenZ0dXn75ZRYXF3nqqafeVnLtygxRUVKgiyejWnMaVCot8QE4YDs7O5w4\ncYIjR47w0z/9JHkOu92SYTHC2THNGaVetwQhWBIiGtir3F43q/4y2tzkzJsvc++dd3D07nuxIvha\nwR0dh5OAslSshZVaSb2VY8zl/UtjzGVDJFC5sne7XXZ2di7LIqdB8lZLo91K3M5uF8YY6vU683Gd\n+QceIEFwRclgMKDf73PhwgXyPMfUY2YaLWr1GaJ2621Xo0xSGjLLQDJq2Mkd7ZgUVEmIyE3Olh+S\nSI0IS4eEHhljSqwaFCUQw1HqqFpesxEFOVombI4i+rRpBQO2S+Evt2dpBAXgKJzFi1BoCCYnthWF\n5P9bP8KPPHiWvhTkZR1HicsirAlwCr3hgMZhyfSHBu+Tw/jhhtOCYbFL5gYggjUB2cjyzefO4ouS\nn/jRx5hp31jZZX8GVrLDNiOGNIloMi2wDoFdddRSoV5UATaOIImvUL+i4j2Nx2PeeOONy5wxkgSO\nJBGVJ3lnb5r0WmLWUxw0IGZZxokTJwB47Ec+go1CUMOq9zzbqZHFJXEADzp4QgMiK3itSmNvR/qO\n45ilpaXLMpvBYEC32+X06dPs7u5irSVN09sui7ydAyJU5zISy6KGbEkBoaEzO0NndgaPkqvixym2\nO2ZzY5PtkyfZjpT5WrPKItstarX63v3o8TgcKiCkKBk5AxSPx1CJ+TUqXVxxpBSUCDUiGmqIaVAC\nQzxOLQ7IcaQ24owv0bGjYSwiMWUR8HovIFOhhqIaMHQB3ggiimiIUhKJYzuNOD8OubOekts+XbeI\nuqjiyOIZ9PuHJdMfIrxPDuOHE6rKwG2zU5ypRrUlYHdU8ntftDzz5RVc8QjWRDTqyr//mZxf/ncD\nOk2DRyknfvMWIdg3kWetxda/Rz/4F2TyEoqlCaj/CfC/CHofpLC7BWdLZdF66hicr6Y3lxeqwKiq\nnD9/nlOnThEEAR/72MeuuWAK8pZe4bVwoyVTVd2Tnps6c2yT0veeL2vOt23OsBWxYJSheP5V4Pmq\nOD5WD/lxvTl5MGPM3mAIwLlz5yjLknq9zs7ODqdPn6Ysy8t6kY1G4z3pNb4bAfFWaplOtxdjWNaI\nEY6xeNzEhmuBkKgWY2ozcGQFgAs+pTsckvUHnDlzlvF4TBgEtFot6u0GqZZYOyAgB4kxxOiE8KCk\neEZAh5gaPRxWHDFmwqcVhhMyRSwVTUJFmEkNQd5m2wwRMaQakiFsjGqE3oAJyXxI6jy5qwa7nLcY\nsdRQSoQzW3WO2RgbCSlCjuwd53jwASiZHgbEQ7xTeO+50D3D6d3vc3zlPgJp8OaFHf7+f32E8xdm\nmJuFWsdjKMnSgN/+lzFf+brnt/77jLjjmFIaKhd3Qw2LwzFKfof23b9PJh0K5ifB0iHm62D+gmz0\n65y/8HHiWJiNIUeZm+hL5qXn9Lpntj3i9BuvUK/VefLJJ/n2t799yxbfGyHmj0YjXnzxRRqNBk89\n9dTehKhRy5/6Md8KSlYw7BZKswYBhpYKY688O5uwkFseukm9zCtxJRVhmkX2ej1Onz7NcDi8zOy3\n0+ncNlnkQfBuZIjT7VmEFgFvp5Ewb2J829BsN1lhBUEo8oJuv8tWv0ea9jh/8jy9sE2z0SJshmgt\nYzqHozhyukQcoyRBCQlRRAwjrTJClZxNMsY46t4yjEp6LmDRL2BNitGUnRxiSrwK6ixOHU4UVcEV\nFbVCjVIYyNWw40POdENa9ZK5miNHyFDmNGB02EP8ocJhQPwBY2rYm7uUodli1M+RZcPrZ1/jf/jt\ne9janueOIx4RpfrPkcSWJBZOrwu//luW3/yHlzKgsS95w3fZ9iNWzEssx18kGzQIkiZuUkyqKMMz\nqKYQ/COS2u9gzTwg5ChZVcQiNWNOb5znu6/u8NSTd7DUWcQe0O/w7XA9nqT3ntOnT3P+/HkeeeSR\nvUnGKbad5a+CkkXkktzXvkW2JkLdKX9RM3zmBkqmN7v/0yzy2LFjwOVmv2fOnLksi5w6UNxq3I4l\nU93zllAKnyE3wrvYhwBhUUN6OEYyeeiLLM35OZbmFilPvMHx+x8hzxzj0Yjt3XW6W12MWkytxrgZ\nMo4LIruJ0xDRWY5KjTltsc2IsYwYM0BxlVmXKKa2RWo2GckcDa3RKNucdxlHAodXweOq8rt48sIS\nGp3IzwmlKl6UsfF0w5ytVOkFhiSEuhrqGPr9/h5l5X2LwwzxEAfFfsNeR0lfNslNn1GW8v0Xvocm\nR3n51btYmHMTArzgvOK9YtSDgU7b89x3Q86d9xxbUTbLgtfLLVR3qZFRi75IqZXkVe4KMJZMHBaD\nYPAa4xlgG/8Pbvx3sBgcngEjxv0uZ0+eZX5ugSMP/AjNmicjo6BAxd+yBfhaPcRer8dLL73E/Pw8\nn/jEJ95SvisVXhaHiCVQpUSrzGCyKcXjBZpO6CGcxPHAO7y9b1Rm7kqzX+/9ngPFmTNnGA6HBEFA\nlmVsbm6+Y1m0/ft3q3CjtIurQfHkjChkzPSCpLZLEQ0oSAl5e7eNKQKEOQLaOpk8ZToy44k8xCZG\nayVRXWgQssIKa7rLmSLD5znsphT5OkYb5PUBL8Tz3Bc6BhZKhsQYAo2oTXiNmJC6pBS6Q9/MMiMF\nJhgwZ5vMWmW7NERW8a5iOArV59OKYyTKnYtvsPLwn7GLYbdYYnVwN3fPjCnIEEJGo9EHI0N8n+Aw\nIP4AMJ0eLX3BWIY4KdgZrXPy3ElyHPc/9iG+/NVZVCsLo9KBc4AIIh5VT+EFUYMrlW9+11BfzHgz\n71GzF0gkRNWQmJNk2QxFljIwGWIMSRhjAiqStArqGzSTr3Ju/Au0qJMWAy6eOYmkjocefIikljAa\ngSuFJIooKSjC4l0LiM45Xn/9dXZ3d3n00UevOYDggb46AoQaAQUlHihxk0EeIVFLpoYx0Bd9z7Tb\n9jtQTLPINE15/vnnL8sipz6G017kQQLS7TK1qiip9HDkWC5pkooLsGJJ6VKR5g/mzBDsqwJU6qCe\nXAxoTCojCgY0qJNSsiGGdhQRRDG22QGEMJ8hS1MudjNe5A1sLjQjyyjpECSWURSQK6TUiEMhHStZ\nsENpI9ppnXYIn5rp8y832wy8Yp0ixiPG4QAXZCw3Nvjb9z9DRIagLEYX8XMvc6r+If6CC3zaw2Aw\nOByq+SHC++Qwbk/s5xSqKCPp43zJ6uk1Lo7OcuzuOzl3Zp3Y1hhmHq+6Fwyna6MKYKqnU6NQOOj2\nhfNlRmIuTsjEEYOdXVwoIIKxEFhDgeKKEucs9ajaoGIRk4IqZ7bOcW7jLB9euIPFexf2LYiXFEQD\nQjRQCl8Qm/jKQzww9g/VbG1t8corr3DHHXfw5JNPXndBNkAoBq9MnBNCEm9JNCAm4lJPVbDcfjf2\n1L/wvvvuAy7PIvf7GE7LrJ1O57pZ5O1SMq1skbK3ZoGqGLEEEpHRx2p03enja6HEsysZuToyCyqW\nkDZdTSkkY4eMEEubRiXRRkGNNlE0g48KOm14k5jdEoKyhk9T0t1tdidle5N7zg9L6tbjfIgYJaDJ\njOR8qDHm02XGc4MOF8oIS45Ti40GfHjleR49+tdIAAUxChV5n5L7a9/n667B3VnEcDz4YGSIhz3E\nQ1wLV5Nc6zPm5PYGZ06vcWz5CI/e/VGc6bLKGoKwPCuI8TivEx3QCqJVZoiAihJYg0SOwmfUTImT\nmGHXMxgmiLGIr3o4Rqrf9cZh1JIXnig0iGTk+VFef/V18rrhow88wHzQuGL/Ido3FyJGqoDIOw+I\nIkJZlrzwwgtkWcZHP/rRG/J1CwTuV8uzXDKZnRrv7M86nYFAlbu9fVui943g3ZJuu5qPYZ7n9Ho9\nut0uq6urFEWxl0W2222azea75mp/MwFRUQoZXXXC2HuPGLMnpVaSEh3Qmtnh2ZIMAWoSEhQxFkeT\nECMtcrWclS1mCTBY6pO80tLcu/QhjjlqnDU5i1FIEIfYTosISzkcs7O1BVnKuXyMH47p+hjxGRs2\nZK4e0WyM+Xi9wKWefpGTBzt89NE/xQWTz+XkwbEqp4InwGvJjH2J18wxhvoBKJkeZoiHuBaulFzz\nIryZjvjeme8RYnngsUcxQcQWOcYPQTyK58efSAlDT5ZDLZ6UisQjakENiKcolCgQPvShAl9kaCI4\nD4M+RHHE1ugTLNW/wXiiHxpQfVDVKK4EDRTvRjx/4gnm7jjGXNPsWzoqjFNo1JX9g5J2ktVV+pTv\nLMrs7u6ytrbGI488wsrKyoEW4fut5U4fct4ULE8KarrPX7gEeoHwVKq0m+88cLwb/oDXQxRFLCws\nsLCwsPc70yxydXV1L4tst9t7dJBbuX8HD4gVOzC8yoOS94qZjH8aAkoKDto1HVJOpqgn6YdPqO5o\nj1WhJoInpEaMRVByoIbsLWuKIUDV0RalNTEkDif3/MgIYRSxsLjI0BcYHM0iZ9grWeuXvFr0CTNH\nfzxHx6S0taR+9ARxmDH2tcoFbWqFOCmqiCglEW3pcsKcIwvyd2zFdtvjMCAe4kpUmo4lWVFiBYJA\nKL3nW2fPsr5xgXseWKTTWKI7rH6+XovIwyYDDXHkxInl6Z+7wO/9H0cJA48NHKIG8REouFLpdgP+\ng8+W1GuQqRJpwLDwqBcwwkb/0yzWvkVgU5iUsAJ8Nb7uHKP0IspRVuY+S93UCDWrSrJUgSXNql7j\n/OylxXtH4cudJf4Rhp08JRb4W8byiybgkQNkK2macuLECcqyZHl5+aYm7+oCv+Tr/DM/4KwpCY2Q\noHiFvio94zheOD41tHAbPpQfNODsF9e+4447gEooYRogu90u6+vrNBqNvTLrzWaRt0sJFqoeeu6U\nrimpWcPeoLMaIp2lkF08DoNi8YxIaWJRIpQaSuVIYTAYTRgwZFGhJTBC9hSbMqeU1nART46hI5Z7\n4ojuUkE4E3BmELKTlWwbIdchNh8Shbs4NahWJP3p35XynO59TxVS2yctsw9GhnhYMj0ETAjk247v\nni05u6OEIoSxcDTeZWf7ZTp3rvCjH36C504NePn1gFEaIALGeO6/a5a8nEW1BpLxuZ9dZ3ML/vWf\nHUVdSBwICqSpgIb8zb+R8dnPFBSFJTfVc7N4ocCBQFLOcPbif8xi67cI2cBphJEAnw8w6onlHprh\nP2bhaI3NXWUwjFFx9PFYLK2mMtNWpsYQpzz8/VLY6Cwxp8KCgFP4snP8qXP8lzbgc8H1OXeqyurq\nKmfOnOGhhx4iCALOnTt3U+fa41ixY/6ed3zVeZ61nr5RElPS8fDzZcyxnV1q4fvk03kVhGHIwsIC\n4/GYIAg4cuTIXhZ57tw5BoPBnkzdNEhOTXWvh5sJYFNVIsXvlUanmLrbQ3Xdohsotxcl7PShPxa8\nKjthpT/Trivt+sSCjIBQ52lRZ8AGR0hYlwJLYxIGc6rctSCkTYOATJUHtImjpENIiVBQWVSph56W\ntCWiSYARy5ARcSTM13PG44S7Zj1pFlJrxViJ9kZ9JieuMskwiphLD5ICxOrI0+HhUM0PEd4nh/He\noMgdf/Fan6+/npOEjnZi8d5y9s0NXiuhdfyjfLIT8pVvCSfXExZmHXOdSme09PD6m5ZzF+7kiQdm\nuWfWY+Mx/+nPNfjbj8L/9Sy8dqbqgz3ymPI3n8qJOwV/9VrGzKIwOxOzU5bEkpDriEhzwnxEOWzx\nnVd/iSMPbLDY/CuiYkSk95AOnyae/XeQsEYQwOxCwR0zDdRF9OhRC5TQXlrUxgr/RSkMVZn1SkyV\nsQTAHFCo8huu5G5j+FFz9QA0GAx46aWXaLfbewT7brd74L6cohT0KBkiCDPG8PMoj++cp2zNsGTu\nolMaIms5y81ppV7zvW9j+6ep3u3VsshpL3JtbY08z/eMfq+VRd6MUo0ghFonk/5byqZe/US+bDqc\ndX3qRV7A2lbVE27E4AUyK4QeekNhmCrTVLHypajT0qPkss053aAvGa19w1UxHSDgggxp+TottYx0\nyAUtcQQYFYYOegEEWBrAvLYwahDZImOMVcuRRkmOMDCOyHlGxQJWVjGWvZKpsG8ITkEocRg2vvYC\n+ShndXWV+++//8APHH/wB3/Ar/7qr/Lbv/3b/PRP/zQAX/nKV/jsZz/L888/z8MPP3yg7b2reJ9E\nkvfJYfxgoaoMxxnfeuMCX3st4PiskkSGne0L7OxuMjNzlOMzx9gJDL//VSHI4d6jETkDph/qwMDK\nImxsCv/q30b83Z/x1AIwLsTV1/i5X+7xrTziVDrDThnzb0vLY0aQcUi66yjnLbvlEsuNcyxaT9Ar\n0aBgHCb0aVK+XmdsjjOzdA/WtPEDT7Q8hMmzfIsajSCCAGLaDBlM1B+rD+2fecOuBsyLZbxv6nSK\nUARR5QuufEtAnJoXX7x4kQ996EN0Op29127G7aIKhgMMyWWSbLEmzJSetuxgZRawNy0efjX8sNo/\nhWHI/Pw88/Pzez87Nfrdn0XuV9fZryxzEITElIwpKQj2D9dMvAVLcmJtvO2E6YUdwRqIJiuSnVBp\nvCi1WOiPPKPs8mqEJUS0xaNEnJEuPVUSCfATWk6pOS0i7qPFpmugJMyaHVRGpKKM/YiUmCVfo06L\nlgkRPDFzeM7j1BIEOZFRJCk45jzCImecJQoyKivjy2e3RJSInC1/jE+0lRd3Bvzar/0ab7zxBp/+\n9Kf5zd/8zRs+t5/73Od45pln9r6+ePEif/zHf8xTTz11w9v4geAwQ/xgQlU5P9zhubVzrPd7vHKu\nQSQLXBxB/8wqC60a99zzMCJKWmyT+zlOnrV8+B4wGAIqXp+tip1ARbbf6MOpiymLSxn98ALfiS1f\n3HyQEoPXSq3RiOEZZ/k7rYynImFJLTNxyGbWo9XYon9hF1NPyIabzHUH3H/sbqSzyBgYDHOOLNQI\niy3GcUxMeNniFRLSYabiHE5Eur/ihRhlSke+WoxpA99RT1eVzmQx7Xa7vPTSSywtLb3FvBgOLu7t\nKa8aDKfbkonTeckQy8zedTrEJVzN6HeaRfZ6PdbW1uj1emRZxtzcHJ1Oh1ardUMZo2Co6QwZA0pJ\n975faEZiYhJtXZODODX3HefKyAmdOGR/eGn4gK4psGoJrSctY9SzJ9VW4inFcUTbLPkWZ+lxil0y\nUkIMc5rQFGHbZzht0JJZAt/BU9CihPEm48iQ+VmcCBHgEKwGhJJQkODUEKAT66iMUEoWh4+w2fo+\n1jgykqqPiCI4InIGvsOn8odYfrTD/zL6Xf7w2T8EqorJO8HXv/51XnzxRf76r/+aL3zhC3z+859/\nR9s7xFtxGBBvEON8zP998tt8r7uLcwVlnvPK7gpJdJZ4GHDX/H1oo4EXsGJJQkd/awzaoJsKRwFb\n6WRQkE0a/+Ctp4GwuinU54Z8rRfwf2b30bAevCPTSb/a5DgT8Ls+oUVOa+jpLCipabKiCStzKc+9\nfJFmo4kPjtCPFknGCYEajs2nLM60CIoMcTW8DemSEmAIJk/ughASEU5mAXc1JaByD6+GBN4aZIwI\nFqWH0igrgn2v1+Pxxx8naiaUlJMHgUu32UEDoiOd7N1Vshepek3DfkZWbLHYfG+Etm8EtwuRfoor\ns8gXXniBI0eOkOc558+f59VXX70si2y321c1pIYqKCa08drATyZDw6JBzc9cNRgqSkbGWFJA6Tkl\nDw19C7GvEWmMIMQaUFNPKg6vDkTIy8qZpcCTUdLRCIthiwG55Nwns5fE7gVy9azKiCMmwOocKQZL\njBCTuj6BWEI8oQaVNRRCKAENagyiguEAxII6wVPi1NIuHyDvRuzWX6EZ9SnETmooBjdc4sd7j7A8\nM8Osn0XzS9fpoL3EL33pSzz77LOcOHGCz3/+83z1q1/lF37hF/jkJz/Jr/zKrxxoW+8qDodqPjhQ\nVYbpkN/7/pc5lZcsNmYJAsP6SNHCE9dCygi2ynPUy7vZFOhEAQkBsaao1Kk4wFPeXDAZI69CYuAa\n1AIY65DSlfx+/wGaxhMZJZeKf2el+rBZHPiS389iPubGqDgSVb576iQdtrnvJ+4jd4ZXXj+LC4cs\nNOFYI6QV2uoJV4KqtzPJ+0YUtK9yJ2cF1AvhzEQFtfCCLwVM5YgxhVfFCZRb23zzlVe48847ueOh\nY2xLl4xNDFUPqUmDGTokRAcumXryfWP0b7k4rK+vV1ZDNeHsa11cocRxTBiGNzxQcj3cykB2u0xx\nXgv1ep2FhYW9LLIsy71e5Pnz58myjFqtdlkvcr8/p5nkUQA4izVXv25jxqSSEhIiCNZB4AWrSmbG\nePXUfB1BaPmIUEo2GeNCJRWPoiRqaZHQl4wxOVsyoi4x5srhHjUkGjGyY5bLghkiRigOSMqSIy5i\nUyrLsEyFmkBdhQviqFvDyFgy9Sxi8JpQSo5oxPHyEY53l+mVKXF7iziEWV2md3FMq9mgo20aRfua\n5+BG8PTTT/P000+/5ft//ud/ftPbfFdwWDL94MA5x0vbp1gtR7RlgUA9W90tCg1oxBGRcSQCu27E\nbLqOLdsUYjBBk1YExoGJwaOYqawVgqPizc0g5GVOp+X4zgAyDaiZqmzp9/OcEFSUWDy7pXISCxvb\nnNp6HTNT4/jCAj7OiRHaKxlLc0oSKRsT6e5F2pO+TrVghFhSClrEl2VfWQGru/BTUcCJKMcC1oAR\nxflqn+LJXdNFeWCny+7aOh/72MfoJ0PW2SAioknFkfN4xowZMOQoRzAH7vHtEyzdh52dHVZXV5md\nneOBBx8gd0Nq9y6ztrrOcDhkOBzuDZTcrETa7Zptvhu4WoANgoC5uTnm5ub2fmY0GtHr9Th//jz9\nfv8ysfPpA8hUA/Zq57mgICMj2sdKjMLJvY4QaEhuUkIfERAgCDUN6eQh7SxmWWIirXqMHqXHmG26\nIDnVuFpV99g7rul7+ICeGXPEx3vG2H3naBpL4gNetwUFQozgRJkjpC+epCG4QUjgDD6o4SSlQUrD\nG8qyzYO1DjU7i7qQgIQsPcdCfYE5XWTUT9//5sBTvE8iyfvkMN49GGP4690zBISM0yFFVlJvNwgL\nx7jdZXvYoplUfcGLxZB2MAfFiEKUeq3JTENYnGwr27ewR0AzF7YHhiwsWJpVnhtEeJG9PklVhrnc\n+V1EcHj+ev088/Uhyw8cY9sPEBlSo4EgGGNIyYgkJNKIXUo6PiMwMZiqdzgNgvu3rgoXetVgw0+K\n4XcRdtBqAdEqMDoPWQmZyxm6kl8Wy0c/+lGGjNilR53aZU/pBkNCVT69wEVWzNKBMsSAhIwhTHqe\nU+3TNE05escdkwXYI9iqAGwt9Xqd48ePT45JrymRdmjXdLDtiQiNRoNGo8HKSuVhuD+LXF9f38si\nsyzb61vuzyIzsj3C/hRJxETQvvrbeENhUgJ/ib+XZko7ouIlMp08HuHosSM7JES4ideGJSCkjmAR\nqopInZAh6WXv65zDWsssEWnpKMOUde0zppzcwY6HjKXVcAxTIUtDHC22TI2dwLHQbBJFAQ0PM9Xs\nKtujbWbm5kmoc6G/8f6nXMB7WjIVkR8H5lT1mcnX88BvAY8Bfwr8mqq6G93eYUB8G6gq5/vb+BEY\nEzA/N0PmdvHFiIVmycZuHZWcZlgw1gIwqI1weZ+tcZu/8ZhhbRWiHGaiqkyajuDiBaHbh5PnGjx6\n75i1NWFkKgcHVXBaWUB5D8gkQ1Qlywq8elaW5zm+Ms/rnCYwhlrQwZcZoyBgEAiZFgwZEEhJYrAv\nfwAAIABJREFU08/Qdz2S2qXRb78nf3YJWQm5g2YIIPyTIuLXwpwNWxHg8UrqlN0yJzLCf27q3N9I\n2M2hH/aIJHxLyWqKgICcgqGMDpQhGiIEi6dkZ6vLyZMnOX78OA8++CBra2uVTB45gTar83RFBno1\nWsLUrml3d5fTp0/jnKPVau0FyHq9vkdpuF0HdG4XIv3VssjxeMyJEyfY2dlhbW0NEblkvDwL9bhx\n2WimMTDXVjZ3oR4LRiyFFHvdx6KEolRm9xkqZgzJZUSLBkgft69XrZRkDAhoYARamlRNAin3aCAA\n6iuqSak5c7LLMgXnKSkm7yB0AYexLVoNw3pduKghMyaiJQkL2iTDs2aUrsY8rAEmDwhtlf0OPyhe\niO9tyfSfAs8C03Hc/xH4DPCvgb8LdIH/7kY3dhgQ3waDwYAiz5nrzNEd5lCOMXjiSLDZkI8vvsjq\nZhs/9gSNEm8sG5qwm1seWtziZx/rsHNvxDPfhPVUUD/EzH+L5U/9G443ejxeWlq9j4A+zPIgQbxD\nohJfCjJRvFCqp9nClUQmIImafGw+Zyw7pHmNIy3o25BcR/hygFBSSlWSHfuctOxRRHOYCBYmx1Xg\nqO9zJwBI8yoLnOIYhn9WxHxxp8//24rY0hKbF/xkEfIftRo8FFZi2+t5yVBSjobXlxILsAxlfKAg\nIxhM0eKlk9+iLDyPf/hxkngy3CEeJwUBKzBZPm8kiF3Nrqnf79PtVgF3NBqRJAnWWqy1e5nEO8Ht\nGlineCf2T/shItTrdeI45t5776Ver1OW5d75XTu1RjEuqdfqtFpN2q02jWaTZq16763upGphYVyA\n9xAEsDxTkI2nD3OOQkYEk3L/sm+wZcaVQAWV/6enQMjo0KFmDKsuI5CCddmkaocLAzPCScFAdlky\nEZFENGWIY4zDYWUGR4YqdInZMI5ZlIQYiFCUGEMMdMXzGiXOuT1BguFw+MEomb63AfER4PMAIhIC\nnwP+gar+ryLyD4D/jMOAeOvQbre5c/4IO/1dKMFLDfWeJjlWegyThKMrQ9ZKT5m2KAtlxV7kifva\nHJv3nDcJUTTHJx9uc2LrHOm9/zNBMsRqQiOsoeGIovkNVL6JOfG3qBdH2NE6s0lZjaSUMMpKRByN\nWsyQiMdrjhmbs1ZkzMcJ7dDQpyCvtXClh0FJkBeENsEG84wbHXpBjXV2CbC0qCFAQkA+mXY1+/7c\njxbCj28PmX1hlWOLsxy/8zipsxyZvG4EGhYulJAbiK4bNwQxN+YxOMXFixd57bXXuOfe+5k70sBL\nOpk8BUQxRZOQDuWELnIzWd1U2WXKl1RV0jTl7Nmz9Ho9nn/+eYDLeHvXmri8Hm6HjO5Wby9Tz7Zz\nbPqSXCERWLYB5T5eYxAEzM7OMjs7ywILlDiKNKff67OxscGpN0+hQL3RotFoEdVqhNKkHim1uCqp\nbmx4iknAzkmpQlq1/VnqDH1B3UQTUzAQIsxkatTblJHZQVwHxRKp4PGsxwUm2uQxk9CcPPCIOgpJ\niWQqfZjgydiWmDkMIYIjnfzr0vnqYNhSR6mXHp76/f4Ho2QK7+WUaRPoTf79JNDgUrb4V8CdB9nY\nYUC8ATwxew/P9P+SxBgKHxC6jMhnlFGLWEtyM6Dl4cEZ5dGVkEYc4+gR02Zjq8aa2yZOxiQ/8luE\nmmF9B+9hlClpEWOJSGqOhYf+lE8/1+CP3FNcTEPawRi0IKkFCBG9MqJlDb80t4PThHvqET4WNide\ndLnU6IfKdjPHmJConkzGyiveoyFknS4hATUSNsgrjuNkGtRHlnQYUpsERu89p988zan1bRbm5rnr\n7rurEyKVsMAUVgyBCl3nmZukmFf7fDhKatK4oYCV5zknTpxAVfn4xz++Z4VUsTKrRS/yntKXXJWS\n8Q4gItRqNWZmZgjDkHvuueeyLGd9fZ00TS8b1rlZDVGHJyUnlSqgB2qoERFir3tct0NA7HnHq0Ve\nBTMxNARKlFNlwWkr3Cu8hXQRE1MwoFarUavVmF9aou+E7RyGwwFbwxGj3TVa/ZL5OKEzeQgpy3Iv\n0HiKy4j+CQEtYnqaTiZNJ5mk5uRkvMkuHUm4wybkXsgUwDCXlSxGY3YCYVaTCV2jwF/R4xyJMNYx\ns1IFtxKloW99qAuMsGVk7zwelkx/IDgHfAT4N8DPAC+o6sXJa7PA6CAbOwyIN4APdY7xgm3witlm\nWUaEZcFQLVYDBl7ZNjFHA8sTyw2CMMIFAUGWMcoNqc8YyZhh6y8h6CF5EzUF3hvywlTC3FaBBDEj\nHjr6Gp95aYW/mqtx1i3RiCKsGjwBn54r+HsrKfORZyFI6JGzA4xRlBE7dPFASIDRkLrEjBhT4GkR\nApYaSorH40kmjKwpisDTCzMiF1H2B7z22mssLS+xdOwuyMcApEUlrRXsi3iCEFHntOtRTpZACzQx\nJPu6ih6lLddfIHRCpTh58iT3338/y8vLl71eDUpUb27EvCW4vltKNfuznOl+jkajPQ3Rfr9/2bBO\nu92+rp8hQEpOX7Jq+5NjKsWzy5hEA5oke4v7u42DBsSx97xS5NREiPf9nkWIxXLKK6+7go9ogt33\nekhITExKChpyobA4FeqB0phpMjeTYPwK4hOiIiUc7HLx4kW2trYAqnL2rKXRrlNPKt6pwbBME6uG\nXVIUxYqQk5JTEGnIMWkRiCG07JlQndecpp1ljNKjYJaoEuYnxFXOocD0npvwhvEYLPYqk88G8PZS\nQBwMPiBeiO8t/nfgn4jIJ6l6h//tvteeAF47yMYOA+LbQCa+Lp9ZuJ/R7ncY6RirPXplzCC3aABH\nkoQP1ebZGeY0bUEtjEl8zOY4Q+s7xKMRbu7beAeiBWhJ5gzYipHotVLdkKJOe/n75F95ik+bJtFK\nH1cLWGpYjkXC8VbJ3bWQUgSDEBHQpMACa/QBQ0xIQQZaET0iIuqEGDx1IkKUXQrumkyk7kcohqWk\n4JsvvUqjn/LYY49Sq9XYOruDVyWvkhhm9rUKPcqOOHKaGB1gKImIKFG6eMYos1hSxjRpkFxH03Lq\niBEEAU8++eTbTn9eLfj9oAZh9k9cTnl7+/0Mz5w5Q1mWNJvNPbrH/v3KKelJWj287LsOBksAZFKC\nprSvpfLyHmeIG67EwmXBcD8S9WQi9Lxj1l6+zNSoYdRwssgptSAyipucg0QTYokRKwylxsxiwsrK\nEVZXV/f6k1uDDU6efoNi5EiShFarRbvVYq7ZZC6oM2TMUHdoSk6mdmKxto2jjqHOVIhcfXXMiRh2\nyJnREIPQ0pi+ZBR4BI+j8h0tJnvZoHbV7L0EAn/pGg8Ggw9GyfS9zRB/HUiBT1AN2PxP+177CPDF\ng2zsMCDeCMTSiBMe90s8ePRBXlyt0UWpJ2OkYZDEEFJSpsqg16Ihnsw0cDqEtCCUiDLIoYxQtdXC\n6B2BzShdjKpSOI9mJWHd07mrSaNj6UQFaMaDM5aGxmyNLXXvWG4KIoaEGik5NRwNQrYZ4TEUBrx3\n1MhoENKhyZiSjJwCQ+0al31zc5OTp05y18oxZu6+D+ciRjnghN0RHDWw1LycnN/Dk6LUfETDHgEu\nMmZcWe8g9KrwzB20WWD+qu+rqqytrfHmm2/y4IMP7g27vO1luUbwe68GWK70M/TeMxgM9gJkv9/n\nu9/9Lp1OB5mNqbeamODqASUiIJWShnrsNSZ3bzVP8ka351W56B1NuXaJWFWpG8tF99aAKAj4hNgn\ntM3ERBvZ4x1OkQjseKGliveeOI6ZnZ2lM9thWeYq4+uskqDb2trizdOn8ZQ0ZhxJO2Y2uYOs7ggk\nBBSvQ5QCS6cKimowEqA4nFbia0YDECVU2KHPUMZ4cgrapATM0qKkxCHkMsZolfVGVJ/rdnmplDoc\nDvfuhfc13sOAOKFU/ONrvPbzB93eYUC8AUgQQdgAl6PaYjm6g3tbFnTMtl5gXJZEXgGHCwxb4zma\nzT6BHzE27Wqu3MeYsMC5gLIUvFgsJc4U+ELxpSOOLSqWdlKnlVjK1BLWPKIwkpQwithOLQtxDYmE\ngJAGDTznWSQmwpMzIf47xwwJ9YktagGkjIhpkVwxEJDlGa+9VlUWPvKRj2CjEKNKo4DCQTbjKYuS\nlZnLz4tDGYonUmHk4Y44IuQoORlDRnhclQ1QZ2HiZ34lxuMxL730EkmS7Dli3PB1uUaGeCvxToLr\nftL68vIyL774Ig8//DDb3R1WdzdIz5xBVWk1W7TbLVrtNkkc76kxCJBS0riKte57ObVa2SxVCkrX\ngqoSGLM3tHUlRp6JWe+1qwBWIPVKoTBynoEJSJ0gWBJaWNslSQKWkiWWlpaqxoG/wGDUI+15zq2v\nsWq7RGFCp96kXm9Qa3jEhtiJYWagTQo2EQlAISRinfOk5IQSMkcDaNCgycuMSBlS1wYNmUWoxMdH\npKwrHPcJw32H+4GZMoV3a6jmqIh8F3hRVf/D6/2giHwY+ElgHvjnqrouIvcDF1S1f6NveBgQbxT1\nOcQX7A4hiluIW0el+qj0bZdIA0iWsPUZskxw+TkIWogxZJREg0cpO98mMDEFIFoZofoyRSQhiANs\n2Gdr/VE6HV9xsRxExmPFApYRI5pBk9E4YW6yRobEdOgwxtGY6MK0NCYvLE06CEKBJ8BM3M0DapNF\nSFU5v36e1dVV7r3n3r2n2RIP8v+z9+YxlmV3nefnnLvft8aSGRm5VGVl7fte5WoWYwzqYu9CNRJu\nGECDZFlGrbG71XJDM6g16pbsbg2rB2kwFswwzUxLZjwDHmAMLqBtY9ku46Uqs5yVa0QusUe8/d31\n/OaP+97LiMiIyIjMLLJM5ddKlePd98699y3ne36/8/t9vxC4RVFEXlEsN1I6KYR2UVkKkA7Mefu5\nou6CV4iu4hPgr0v19TAkg4q/Idb7JD7wwAMjTc29YEiI60nw7ex2oZTC9332+fuxKeMpG5MXLR+t\ndovls+eIoj5BEFCpVgkrJdySBXprQrxVSjqF3HuR6tc7XIIRsLe5xitmTteAwGIOl8ViAj0yeOrh\nk2aasu4QWlEhEk6HxGrhV6qMl6u4Bx0OSJe5rIPqxjRbTS7PdxAySvZB8jwn7mfkfkAJQZHRJyWS\nGEcXvxhQONSx0UyTcp6IFJsQm3zgvpgYTR2omYjeur6lTqdT9F7+Y8dbFyEKBdV2tz21Uh7wvwM/\nyVDLBP4UmAf+I/Am8G92e8LbhLhb2D49Z5I06eGyjKRrKBSecvBNQuSl+E4IkuOREGPjhT79fk4i\nOVPtx2hWv4nyY6zUJY5TUtG4tkWcAiSgFHbvu1DaJs4T0lxxsAJFLFY0FdesMlFsXVH1AAJsShTV\nqiltWqqJMdngVQaN4GIo4RDiYGPT7/c5efIkYRjy1FNPYa9La2UI5XVLPq01dZUy4cFaUnzjhCJx\nn2iYcqF6jW/SeooyxvDqq69SLpf3HBWux63cQ7wRrLcM0pamVq9Rqw8sskToRxHtVpuFxUUuNmcK\n6bJ1yjqu695aQlSKSW2xanIqO6RNI4S7ra0jQAfIhGvaBq8ZRdmCwGT4lh4RcAD4uLRMlUy18LTC\nIcASi1hczkuCkBLgkTsJ1TGf+kA8IM97JN2AZrPJ+UuzdJKUQ7lPXF2lP9GhHJQRbUBsFB5G5SSS\nEqqMx6ixIoUvjMKhJHA3DiHQzfuId+UzeccU1dwAIS4tLfHMM8+M/n7/+9/P+9///uGf8xStFCtK\nqX8rIktbDPEfgB8A/lvgL4GFdcf+HPggtwnx5mI4yRrtIo4F9jjGLSPJJbQ0mXB9lp2Mfn4BO18g\n9e5F6QpOKYLcodSqYTkB9fmXWJz8LyivQ2iViVONVjk6aOEGFovnf4wkGcdSGWnkEVRyxsPCgknh\nMVa0qbNeh0ijqVNijQ778SljcV5S5umTkhJi4Q/84Q4yQWgCTl2aZW1hiXvvvZd6bWMeVAZaHuEm\nQhQxjLlQcyDKi7RZiiK0oHKNeXkYDYgIMzMz9Ho9Hn744VHF5o1+Lt9psLGwlUXOFvuDg5aPIAio\nT40zJiGSmlGxzsWLF0nTlDzPcV13VNzzD02O+y2bpTwjFXC2OHVEIU9Y3cY8OrRA8itybVuhn0Om\noaZheSvhACUY1aclNkfEpUfOsuTkWINI0hQShnmFy7rDpE4JlAat8EouJlDccfc97JcKQWRYai6x\n2OiyNtvEGAjDgLACYSXA+A4lAmxsLN2nYjKqbEyHqhxS98rNvGPaLuC6U6b79u3j1Vdf3e7wAeDL\nFC0Vy9s8533Ar4jIHymlNl/FOeDoXq7nNiHuAb6dIOQYdwzRPShNYcw0Kk+ZICNyFG3VpasUNTej\nphTT1UnWEpsziz0uX44Z3/8TlI9dJqp9Eztsk6UWSzP3UNbfR9adJk41cSS4XszdB20qdkhKsd8S\nokhzKLsbJ5EaIat0ycmo4HAXVbxOlyNTE+QYUjJ8SpRamhMnvoGeqvPoU0/ib1LiF4Q+OZVB7eMQ\n6x0qtCrSpsXzi1zG+hL1zShqXSHtdPnW8RPU63VKpdINkyFsndK82ST5VhFuIA5N1R+0l199HwkZ\nDlbRjuFYG6yajDG89tprAMzMzNDtdkfuHsN/1xt17xah1txtu5zJUhRCSVvYChIRulJ0it7reNum\nTG0FY5bQyBUhV5NiJtASqNkDndMtCDElRakcMTZ9gSVxUKpHOBpLAwYfRWCqNCVFWxFCjiUWYRpy\nROrF4s+HSX8fucoJDwcYI/S6PdqdNgsXVlk1bTzLoVQqQ8kmDz2MVcIGfKxiOyA3aEsjA1nE2ynT\nG8aciDxzjedMAG9sc0xz7STEBtwmxD3At7pUfItuKvheA/BAa0QX77kPeHmJUuRyeKyCSRdISIh6\nM5RMzPOP30VmB5Ddjd16jn6zQZ4cZv6NOfK7p7G6miiymZpOODxlYTkxXRR1SoRFCQ55pqnXNl6X\nh8MdTHCRFRJyjAaT52gKsgpyh/j0Gicbszz68COElTJrJPQpmto1CjOIDWs4lDd9LbazbFIoxtAs\nYgjhqp45QeiYnO7sJS7NLfDQQw9Rq9VGPWU3A5uv6+26h7g5xenjkIuhq+KBZ2RBjTmGHIONRVW2\nblHRWmPbNgcOHBgVbcRxTLPZZHV1lfPnz2OM2aDPGgTBTY8ix22bQGuW8pwlk5EPUqBHLYsszQiv\nIVQwZgEIa7kqqjULDXlSKWay/bYQ6WGTvVxFiLGKRwVjbQEoCFgwo9YKG4tIRdSpoMSmmluUVR2V\nlkiy+Q2ZEGvdN1hrRblSolwp3t+mioiimKU4YjlucGmhjZvM4Ych5XKJyVIVN0uwrSsKOu+YCPHW\ntl2cA14AXtni2HPAyb0MdpsQdwE1kOK3bQjdnF7cJ04VnrPxByoC3cRhotTHtvezunyB2fPfYPzw\nHRy56xBaWUCCiMIxhqRaxkidxYUZ7r2jaM1IJGMtAd/OsAf9g2U0mRjiVHPIsyGHuQVI0qKAtVKC\nUuhxzNlPiz7zepWEvIgw1gwX3jjDkUNHeOi5B0eT4iQeCYaYHAGK3RJry0hvJw/DEhaTwCoGheAM\nXp8idLodLp94k6NjEzzy/PMbJrSbsQf2DxEhvpUo4eGKTX+gVFPMKxYV8XBx9tSU73ke+/cX1ZZQ\naN8OlXVOnz5Nv9/f4GVYqVRuWJ8VINCaO7TmDpwN6c+ZXVy7UjBuQ8US2jnEpiDCulXIAXYM9AfP\nNcag1dULrqHsQ1sUJaVAKhjVBLzBUk+NnmuT00PYR4U4z64iWHdgkJ2SXlX96ojFom8R+mWO4LJv\n3xTKKLrdNt1uhzPzs8StHvUIvr76dc6dO0cURdfdh/ipT32KD3/4w3ziE5/gxRdf5Fvf+hYf/OAH\nmZub48/+7M+4//77r2vcf4T434BfVkqdB/6vwWOilHoP8GGKPsVd4zYh7hZKYVkWioyD9YzFlkU3\n0mhdmOhmRqEVTJZjAqvP668dx3UzHnvsPTg6xpgMo4qVsIVFqn1wQzylqVQypsYVC3QRcmp9h1bP\npitFWUyCjUfAIT8gb8PlGByn+CcCqw1YWYOpfTbjpQol5dJtLtJ6fYE4jnn6yacJgqsbvF007i5q\n/a5l6lvBwkfTGzTiG2NYPj9Lb2mZ5x96+KpJYavq0OvB260P8XrgYOEQUBVGqbbd4Frvn2VZ1Ot1\n6vX66Pn9fp9ms8nCwgKnTp0atYXUarU96ctuh+v9OJ0BMW5GoMHkBcsaY9CbCFyhyEUGPa/DNpAQ\nBIxqj6zThBxDEU3mZgJtOeR5vOWCYEzGWGAOrQo9miGyQc1pSh9LQhbpoawYr6KoVwKq+Mw1bfYt\nu0g75XOf+xyzs7O88MILPPHEE/zQD/0QL7/88q7fk5dffpnPfOYzo78feughvvCFL/Dyyy8zOzv7\n9iLEWxsh/keKBvw/BH5v8NgXKBJ2/6eI/PZeBrtNiHuAsmvkaY+w4nJoPCVOFf1EYaRoVg+djIW5\ns5xbTTh67+PUxzwsplGi0XkPBvZQWCGiM9RAZs8oQ48uVUJiUpwgI/RSklwRS0JInzvtMt1liyiB\n8qbWpjCEPIe5RThyEFaWl2k0Ghw6dIjp6ekbJp7duNw7KGpY0Gxy4sQJpqameOLZ57bU97xZUdxw\nnCzL6Pf7I9umm4V/6GjzZmuybhh7oPIShuGWXoZRFPGVr3xlz2bKc11FI1b4tnBnRdDq5i1IHAVV\nBR0pIt6rIjpxWZE+B5RiFTVqA7EI0VKIcqfEBOLiUCEX94r92TbuHgEB4zLJqlpBoXCwAcUyEV2J\nWFZqkKotyoa0EqooDmATpIqknPDEXQ/x8Y9/nO/5nu/h7/7u7/jWt751w9sEtm3za7/2axw8eJAf\n/MEfvKGx3grILRL3HjTm/5RS6n8G/imwH1gB/kJE/nav490mxF1g+CPSbh2Td0FKoLp4joPnFD/+\nTqfD8TdPMTFW4Ymn/gnYNooUGFQF2BujJD2q54TUTgf6iEURRYBgtEE05CjKVOjFMe1uQKW89aRp\nWYCJ+fwXTrF/MicMw5Gk2HbIyEiISAcOcBYOPh72pnTRbggxz3POnDnD2toajz766I57J8PxbjRl\np5QiiiK++tWv4rouURThui5ZltFsNqlUKjfF0ujtiJsRYa/3MlxeXuaZZ565ykx5fbFOtVodyel9\na0Xzn0/avNm0sFTRczjmGV6+O+MHD6c3bWEyoSEzQlcUudKjCSsWiMSmphW+NlRF0xQ16n4dVmYX\n8XcJjUWfK/ZnO1l6VakSmIAuHXqqT4+UNhaJ3o9PSpXOQDCxkJqPsVnCopxrAq2JVJNAxlBK4Xke\nzz777J7v+9Of/jSf+9zneOONN/jYxz7GRz/6UT7ykY/wXd/1XfzJn/wJP/7jP77nMd8qiIL8FjCJ\nUsql8Dz8nIh8nqIa9YZwmxD3AG0HJLhoA0aloD2MgfPnzhH1W9x77AjB2DGwXAxdFOPbrvptHBSa\njBSjc7ToUXOaRqGxMOQDdVKHtX4KdgZc3dclIsxdnuPSpUtMH7qHhx6q89WvfmnHe4no0adPoftR\nfA0yUlrE+PgEhKNrv9bktra2xhtvvMHBgwd57rnnduW8fqNRRJZlnD9/nlarxXPPPTeqqlxdXeXc\nuXNcvnyZTqeDZVnUajXq9fqGCf1W4Fa7U+w0Fuxspry2tjYq1vl2Ns1/nj9CzTdMB4rhmqObwu+8\n7nK6oXjXTYp2tYIDGsbzGKUUXVMU3pSUsM/SOKpES3VwMRixyURhKSEbNCeVJSy2KAaFOqXBZW0V\nca6Hg0OdMapSZ07FaBUP+nN7KMINovhD2+GLllBTNoIhk+SGvuMvvfQSL7300obH0jS97vHeUtwi\nQhSRRCn1UYrI8KbgNiHuAbZtk+GivElUarOy/A0uXlxi+sBh7jr2NMqtItoB+mg8NNtvqCs0HgEt\n1tBKY0yOta45vvCjSAkpSkqzFJS+Okrrdru8+eabVMoVnnrqKaKkaNrfDiLQziK69PC0i7NOf2vo\nOB5RtAP47Gz4m+c5b775Jp1OhyeeeIIw3Pn5Q+wm4twJQwIeap4GQUCapiMlmCAIePDBB4GtJ/TN\nvoY7EcvNSv9d9zgiYPqQtVAmBizEroLkN5UQtxtrs5nyYlf49684jNkxKu6y3M2wbAvXcXFdl8Ml\nh89esKhU6uw9LtoaSkEghiP2evIeHrWoSYWYlFxHzJlin74mLr5yUGLRo1hrTusrcnO7zVAkCBnQ\nBjxyFDlq07TpoEgQIjFgFcvZXtK+Ls/M70SIgsy6ZZmYN4BjwH+9GYPdJsRdYDhZWJZFmqb0E+HE\niVVc9yAPPPkYjqNA6cFPJ0NRRVMflX5vB4+QkBQcIZFkpFgp5IDCp4o1iAi1DXl05bXGGGZnZ1lZ\nXuGuB6fJJhaZV18m74fs18e2PF87gpWu0KRfjCsKzyncK4J1gZODS58ID3/be1hZWeHkyZMcPnyY\nBx54YE+T8/VGiOsJ+MknnxylaXcae/OEvr768tSpU0RRRBiGW/oa3gjh5HkhzQfgXO+vTHJUPA8m\nBuWAcgGDypZx83kwx9hjm9XWp1lHiCk9Ljhf5Lzz10S6iSU2+7PHOJb+ADVzJ39z2UHbNmOhhZFi\nASQmJ00ToqhP2m6RpzafjcZ4cX6Bev36zJS3w1YfiYUmxCPEY1IV6dUmEEnRLz6moKyK3schdkqZ\nrkeKIUNwZLDJodgy9jUI2uTkdrHn+I5puQBEqcF93xL8KvCbSqmvichrNzrYbULcAyzL4vLly1y+\nfJn7779/1CgtxDDQPSzWizv/0AwZOX0MKRZCIB4qVeiByaBDgIW7oew+DA2d5sCJu9Xm5Mk3qUzU\nqHzXEmdK/xWlBTEKFQpN969J7gjIeAZ7QLMrHVjtgetkBI7BFhsQMqOYb8C+CpQH85aiMAxOB1ZO\n65FlGSdPnqTf7/Pkk09uWb16LVwPIQ6jwvUE3O129yzdtlX15dDX8OLFixv2zSzL2nPljAd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Pssyh947AnFGk7AZgklTS7OrOL5Ezz66H2MWyEg2Ozjy7liTF1NMmrIBhRSWCdyIRHB3cPE0W63\nOX78OJZlcc8999zwCnorQrxekrRtIUnUqEVBq+KHvn6sLIO1VmF1owZs2c5iKpFLeMCiXBLKIYS+\n0IsgTQDlYHsRkufMqx4W4LKuj1NBhqGhEuq4xDQxEpGrGCwIqjZBtc7+wzW0eEjfpdloDcTNv0aS\n2Cg1hutWqVQq+L7D5GSxMLq8BpvXLCKwuAa9CFZaxd8drXCluB+dVfBmfxjr/A9zecHwPc8Idx26\n8nrfhiNlIc4hMcV742rwbVl3jn+YlOn1wvM8LMsa9bZuZaa8WVnnWvuNeZ5viDTfSYQItzRl+j8C\nnwHeUEr9FwqlmgPAfwPcC/zIXga7TYh7xG6sixSKGkXt7+YOupXlNc6dO8fUkaMcO7qP/cqm0W9B\nNr+RDLMlTNbg4qzPxQZUvRbjQQLk7A/nWGpNcunSFPceq1OqONwbHKPktbAHk63WFk5uk0iCJ22Q\nLjmK1QVNc2mFo3eN45QUrhIcJtGEA/eNrdd6Sm10RVBK2F29bTGhnT9/noWFBR555BGWl5dvqh/i\ntR7bDapVmJ+/InnmWOBaQj4YKs/h0mWFHwilgWWCYIizPlXfYWGpyEGXy0XVaTmEYbCfolmImlhb\nOLED2AMlmB4ZlnToSEycjhGZ4jwlS6haBkcn6NBwIJji4sWLPPvss6RpOhIvbzTO02hk9HoVSpU6\n/XSM0LvSmpCksLQas9To0e0bsIUodeiZHr7XxYsX8CkTpyHGKNZixVwbgqZQD8F3hu9xQYzbyTO8\nnSLEnbBeo3g7M+W5uTna7fYGM+VarYa3SRsvy7INc8LbKWX6jxki8hdKqR8F/j3wb7lSvfg14EdF\n5LN7Ge82Ie4Sm1svrrVirKCJMHQRPMAkKadOnUKA+x5/DN/1mEKjUTi2s7F61fTANFlcK7O81uPB\n0hJzukxXlfEwaFViX71HI17lwnLCC/v3s8/16dIeWEYVrRxWpvGlQyRtkkgxc+EsY/5+Hnr4GUQL\nlkBVQIs9qp64R8NrOXhXpQvVyEWjK8LEYE/xWuh0Orz++utMTk7y/PPPo7VmZWXlphHizUIQFGnH\nKCpaLgDqgXAp0xhTtCckmXBo4sprolQIPCl0Sm1heU0TBDmbvxoZQk6GVrCdHZiDpkNENxPy3Kei\nILQKLdTIQDu3GbMUVadPTnLldY5zVYN7u91mZW2V85dPcWa2QxiElPwKeZ4R+msEviJwqpSCOfwg\npq6g2akzs1pFO6tMhmsEso+S7zJZK/ZSL63BdF0Id7Ej83YgREOKIaLQGLax8Da4VOz0/dvOTLnZ\nbI7k59I03aBWJCLfcYRojOFDH/oQv/3bv81LL73EH/3RH12XCtUtrjJFRP4C+AulVEjRfrEmIr3r\nGes2Ie4Rw9aLa+kgKhT70ARiODk/x8zlSxw9epTJiQmqKCrrFCMtyyLPc4yBXgprrTWakceJMzkH\n/SWM8TigDR2JaStTNEkrg++D22rjdXzU+BgBY/RZRchRWiGS4EvC/OU2890ljh6+h3o4gRo4urnK\nQUsCsgaq+OH/mKP5am62SHtdibxWDXzA27nFQUQ4f/488/PzPPzww1TXdYffqP3TTrheklQKDhwQ\n5ucV3W6xB+fZULZTlhqwugJ33gG2A0lWOFl4riIMiwWp1sWCod9XlMsbJ9uMHC3Xvq7VPCE3FuOW\nwaGISgE8C1wxrOUaW7lou7P9fWjwaop99RBnX4VWx0JLm4WlM7RaKxy/XEZbKdTO0M5zfNdnX2WB\nwLNZ6msc506iBOJ8mcnxfVQCG62K6tGFpuKOCeFaTj+3MmVaLD0aGNVnKFqgBka+lpSxBm1He4Vt\n2xuKdYZqRY1Gg9nZWVqtFsYYXnvtNU6dOkW/f/3SbZ/61Kf48Ic/zCc+8QlefPFFut0uP/IjP0Kn\n0+GTn/wkjz/++HWNux5RFPEzP/Mz/PEf/zG/+Iu/yG/91m9df2Eb3LKiGqWUA7gi0h2QYG/dsRKQ\niMiujSRvE+IesZfm/H6vz7ePHycslfjhx5/Ctm0s2OBiAQVBJJlwbg3m+zmOxKRZSJR2WLOF5a7i\nQNCk7vSpIuRolGTYNFi1jrK6skZtrI6lXEImSeignJxutMjiicvUawf53nu+B2tQoq8G/yv+8EC6\nIBkomycteMaCVzM4qK8UTihV7EfOG+EOS/FeZ/tJpdvt8vrrrzM+Pj6KCtfj5sqrFfs3KysrVKvV\nXQk2bwfbhoMHhX4fmk1FlilCR3jgqGE2AG1regl4DoyXBN9RxLgDyTMb25JCOm/T1pGQY0kIWKP2\nhc3IBDoiTGhD0vNYa2qStCBZxy4KaLxAaOQOgdXdUq5NEGLWyFQPB5+xwKbXEzKTYGwLb/9+9o0b\nquEM/rLhzTMHSZIIiQPi1EE7s6RRQmrdT70Wc+RIA62KyNPSxcTXjaF6DYOTmx0h7rZnUDCkamVQ\nFbv5Ig2xWkJLE5sJRO1ecWorrDdTPnz4MCsrK6ysrJDnOa+88gqnT5/m+7//+3n66ad58cUX+cmf\n3HUrHC+//DKf+cxnRn//5V/+JY888gjf933fx+///u/zG7/xGzd07aurq/zET/wEX/ziF/noRz/K\nRz7ykRsaj1tbVPN7FK7p/3yLY/8LRaH+f7fbwW4T4h6xm+Z8YwwzMzPMzc3x4IMPjhqJt4PWNpc7\nQpakuK6haqCTQ1n3CG2LXDVY6AkqDBhzzWA6tQBB6RZiXJAUlIvGxjVV2nOGqN/msUefplKe3Pmm\nFDBwtrCU4pd8zW9Ghs9nAkpwgKYulE+f0MXx8hYRgIgwMzPD5cuXefjhh6nVatvcr75p7t95nvOV\nr3yFcrk8EomO45i5uTnq9fqe5b20LnoRSyWhUsmBlIk69PpQDq8mIYeAPo2BEPrVJJCT4uHRxaBz\nl4wMZ4tK8H4OJjc0VwNM18F3C0FwKIp5llc0gS/444ZE1FVRjpBgWCJTF7HwmTWL/I17kdVDMUms\nKUvIoaVJSt4Z+mlAvWbz+ANd+j2bbs8jamhcDZlcpBf1mCiPEeWGZsehUqqglca1oBMpqsHOi5lb\nFSEa+ggpel0yXxByumSq8MfK6RSLMb9FrJZxZeyadm27wdA94+jRo3z84x/nu7/7u/niF7/IN77x\nDZaXl294/CFu9H2dmZnhxRdf5MyZM/zhH/4hP/3TP33D13SLU6bvAf71Nsf+BPhPexnsNiHuEuv3\nEHcixFarxYkTJ5iYmOBd73rXNX/IhpiuNInsVSx3Hk8LedrAsmTgsBihlcHTDs1UU7ENti6qRg1l\n0twj8NcGkccVLdAgCDh08E4qpV1UuhU52NGfgVL8m8DiZ4zwhdSwKBAlPb4bzQtjW5ekd7tdjh8/\nTr1ev+Z934wIcdjUH0URL7zwAo7jjBquv/KVr5AkySh1VSqVRs3uQ2eK3UJEihYGS21p3WTh4EmF\nWLVJck3dKyYGwZCToLAoS50WTRAHG4eMBAt7Q6TYJyVrBuheQDXceH22XVTB9vqK/prh8IGN+zyF\nfPwcKV2EkN/Vr9Iota58rB60yn1ax1a440Kfu9MGgV3F1i5Rz+LSAvTTEmMlKFUq1Ke6jNlH6cdr\nzF2+xNlejG3blMpVqpUq+yvlHXv3btUeYq7aox7fITI65HTReCgURimMybHwETIStYwrkzdMiuuj\n2OGCwPM83vWud+15rE9/+tN87nOf44033uBjH/sYf/qnf8qv//qv86UvfYlPfvKT132NJ0+e5IUX\nXqDb7fLnf/7nvPe9773usTbj+glxt6V522I/sLjNsSVgai+D3SbEPWK437cZQ9++RqPBww8/vKv9\ng5weGSu0Y4u+8fFUkeYxuoIbzFGtBSS9NrZfxdIQZYaeKKpiUOTkEiI51KuG3EScPT3L2toajz76\nKN1ul057GbhGakhSwAN1dfXjYa34qcEEfy7r41neVWQiIszOznLp0iUeeuihUaXeTtit4s92GBbq\n7Nu3jzAMKZVKJEkyGtuyLO68887R9W12phhKptXrdarV6raTrVqXLq5VhdWGorRFutDBh8wmI8EL\nO4VqEBauVHHwUGiqxkMALSGalEz1EXIMhpQcx/hYjX0EYWfbtGoQGBZbgp680qtaLJsWAJdMdfmk\n9SoNv1Xoka6/l8FwF+70CZeaHEtXMdl+/BCmp/qImzHuBQRlD3FyQlcIShOM1+9CY5OkCUsrHTrt\nVb75zTMYY0ZVl8NIfIi3ghCv1TwvmMFmgrPusawgQ+WNImqFRWZitNJoXHLigaD+jRXAbE7r3kiU\n/NJLL/HSSy9teOxv//Zvb+j6AN58801WV1d54okneOqpp254vCFuLEK8YUJcBB4F/nqLY49SFPvv\nGrcJcY/YKkJcWVnh5MmTHDp0aNdanIXT4QoalzQvBJWHqihG19F5xL4Dy1w+nUJqsJ1i5zHNwdIR\niamx0nU5PNklySy+9bUvMTX14Oj8/X6f3DhAABIV0l9XXYQAEehDVx/bhK0KYXq9Hq+//jq1Wu0q\n0+SdMIzk9or1KdlHHnmEarXKwsLCNc813OsZOlNEUTQyAD516hSWZV3TrLZahm6vSJ0G/kbx6zSD\nKLY5MmURqqCYDNfTkQhWHlGPezhZhxiFtkJio4hicHIfL/LIE40TChndqyJIwZBIRqBLqGR9yrVP\nMan4vGmWaJRaO5sCK8Wpeo27V1bJdRdlXNB9vLBPRyzE6RO4KZnu45t9I39E13GpjY1xeGwMzykI\nYCiRdvLkSeI4JgxD6vU6cRzv+JnsFbsj2KtvOicqWoQ2HcvzK21TGodcdbGlfF3FNlfGzEffG2PM\nW+rleL34sR/7Me6//35++Zd/mfe+97189rOfHVUn3whusVLNZ4D/QSn1NyLyreGDSqlHKdowPr2X\nwW4T4i6xvmdpSIhpmo4mgyeffJIguEa1wTrk9AalLRa2BrO+ClFpMmsKz0s4fGSJxYsNepEPGjJH\nsZzUSfIKBye65PEs5y7aPPro04ThkdEQo0hWT4GZB+kA/hU7IYmAFNQkqI3qOFthPSGKCBcuXODi\nxYu72iPdjOtJmfb7fV5//XWq1eqeyHcr+L7PgQMHOHDgAMCol6/RaDAzMzOKfnzfH2UDtIYD+4S1\nJrQ6hUSeQLHHaisOHRCCwZpjw8RqElQ2j84aePQYyxJSk7HYi+in4zhWFUspuv1CT3XBCpkqa8Tq\nXWmvkCKyyfMy+yxv03vXBRwEw9/ZF3Z1/8ZRLAjsUw1iy6KrQHsKpV1isfGcVVqySih3DLUY6CZQ\n9YuCItjZI7LVatFqtQpVnXULjev9zHZDiAqFEg+j0lGUaEg3tFoUyMC46EGprEJjMBR76Nf/nVof\nIQ7T9G9H/NIv/RJBEPDhD3+Y97znPfzVX/0VU1N7yipuiVtYVPOrwA8CX1NKfRW4CBwCngPOAb+y\nl8FuE+IeYds2cRwzPz/PmTNnuOuuu5ienr72ilAMmGikzm10GzVIU9Z8sERITKH6AYCyyOwjWBMR\n026ZdmONdt8idDz2VcBVl5hfnGfy0APcdXgcrTZao48ITNmgDxaEKI2iohRAlUEd2Dpy3ALDQpgh\nMVUqlesmpr20XYgIly5dYmZmhgcffJDx3VjA7xGbe/mG0c/i4iLNZnNUtDPch6xXw1EFqG2D525D\n7pKi0kuDz6CMqCaifFZ7mjiGMXsVsTRYFTAw5UI7Fy61PKbLHo6VFZWTojFis98W7Hyz4e/w3Ia2\nE+0cHY5eIixaAWHSpq81LhGhG1KtJURJgzSaJFATdFQXyapo41APhfEd5vj1kXir1eLw4cO4rkuz\n2WR5eZmzZ88CbBAv39zcvh12m4K1qZDKYuElOWi5kELht7htTFGdmzs3vdF/PSG22+23LSECfOhD\nH8L3fT74wQ/y7ne/m1deeeU7VohcRJaVUs8C/5KCGJ8AloH/APy6iDT3Mt5tQtwjjDFcunSJer3O\ns88+e+0yfxHImpCucmXyAtQSOONgjRE4ipLK6WeC46h1k5qFUEeCMlgHuCtMuNQ09TsAACAASURB\nVMPtMXP+LGux4f7H340XOBSVxRuj0w17nUqDqgLVgpDZxvBuByilWF1d5fLly9cVFW4eazcRYhzH\nHD9+HNd1ef7552+KCPNuMIx+XNclyzIefPDBUc/Z2bNn6fV6I2mver2OY5e3nmCzNUCDcoGiqjZK\nFe1IU/EFJETla4gV4roWjq2YtKBjwBgh18X9VrVQsgQbiPVmc2GPovXKQ8vuJ/kxv8Ok0qQ6o6UD\nfLdEOVyjHmrs5ClIJ0hNhOO1OejWca3df1/WF5Xs37+f/fv3F29Hlo3SrJcvXyZJkg0FT0Mnis3Y\nLSFqPCyq5ANNZ41HRgTYg/3aBEfqRHmGpYfFTxka54aLarIsG30/vxNk2z7wgQ/g+z6/8Au/wPd+\n7/fyyiuvcMcdd1zXWG+DxvwGRaT4qzc61m1C3ANmZ2c5f/48pVKJxx57bHcvSlcga4AONxi5avqY\ndBllDJY7yXQpxcfQiC3KTiEdhuREWZ0otRkLUvy4zTffPMuRI0e458AUqAyIUExfVYSxbRS2jZns\nTuj3+5w7dw6tNc8999wNE9NuIsRhBH7fffexb9+1ndffyj2b9QLRR44cGUl7NRoNLl26RLvdxnGc\n0cReq9WwtKBMG9TGSKHZV7gD3c9cikjRpBHaLlGrCcsrUHIVJIpDpfX6oNDtwuSEbKp0LQGrKDR3\nJvv4ttNDbaFFux6iFNPKJQ1KCA7VLCZQQolpXI7iOAGWUyQRc0lQkrOXqWI7ArNtm/Hx8VGULyJ0\nOh2azSbnz5+n1+tt6RG5lyIdmxpKbHLVQolBVEKOQePhyCQaF2Mao/EMKY7ceNZhfYT4naBSA/Dz\nP//zeJ7Hz/7sz45I8dixY3se5xYbBGtAi0i27rF/CjwCvCIiX9/LeLcJcZdIkoR+v89jjz3G7Ozs\njs/t9KDZhZITUXfXipTYJmgq5FYMWRusMr6tuK9maGYWl3rQTkHrmNAa51DJoXnxyzSkwWOP3Yvr\nOwyjAsUhthJR264adi9Yn66cnp7esAq+EewUIaZpyhtvvIExZhSBj1JdqEG/363FemmvYaopjuMN\n6UFFwmQlolI7QKV65fOPc42jhEZX0eppxPiIzkErlAY3ENJ+4S2YlotAPkkL8+HxcaG2yQ9Y4SDU\ngTV+ILuHk8zseO0iYCchgXkCrA6eJHR6MX0zRqAPYRyDsbtYUhqMb8jJcfYwVey2wlIpdZUTxbDg\naXFxkdOnT6OUIssySqUSvu/vSnjBooSWECHDMnUStYaFP9pbzE2OsiCnjy0lrF2JEO6Mt3PK9OjR\no9v+3t73vvfxvve974bPcQuLav4PIAZ+FkAp9QGueCCmSqkfEZG/2u1gtwlxl/A8j/vvv59er7ct\n0ZycUfyv/6/mb/5eoxXkGTx+92F+/of7vOvhZMNzFW5hGqw76LyJZdsgGdNlh+kyRHmMUi6NpQ7n\nT57h2LF7mTowiVIpRerVRrH9Hsxu9+mMKZRZ0rQwwg0CjeMooiji+PHjBEHA888/T7PZZGlpaS9v\n2bbYjhCXl5c5efIkx44dY3p6mpycHl1iotFzHBw8gi1Fst8K7Lb456r0YNKlu/ptGu02ly5fIk3S\ngfPIEsaqYfAI3ULjQ7RA4f1LL1Z4ZcGTK8F8rSKUS1csnq5Gkb6esJo81buHr4WntsyIi4ASi5fi\nx5iwc+bSKr2oRKfTwnNdurGiE2lcF8Y9QVuCoPdcfXkjbRebC56yLOOb3/wmvV6P48ePjwx/h9F4\nGIZbkq8aGK5pHCzxSVWbfPA9Sk0PbSkcqWMR3lB16RDrCfE7IWV6M3GL7Z/eBayX2vnXFOo1/wr4\nXYpK09uEeLNxrcb8z39d8Uu/YwPCRFWwLDBZyhszPh/6rYAP/LM2P/9D3SvjobBljExBbtawrAxj\nYgy66KcyilMnFhDRm/Yqd0cEw1TTTmg0clZXCwFprYvGc2MyOp1F2u3zPPzwAyPtxpupP7p5rDzP\nOXnyJL1ej6effhrf98nI6NAaUL8zmrRyMtr/P3tvHiRnet/3fZ7nPfvungMzAwywwAK7C2AX3Au7\nEC1KpCxSWldRViLFil2J7ZTj2tCpVKwqKyozUVVW0ZHYoVRJRcWIpXLEoiNbiVMinVgHSZNhFIrk\nLndJ7S6OxQCDa07M0ff1Xs+TP97uRs8FTM80FmR2vlV7oHvwzPu+/fbzfX/X90uFJElcHtwdux/s\nJw1rWgnyhVHyIw4ISbVWZW5ujrbyuXRzhaTRwHVdskmDRO4xEqk4qkq5UKrB8cmNQuL3PU4EMIIm\nyyfVCIlGhm+5F1FGvxqQxg6y/Lz3PBNGlpX2Mn5gkjct2kZsEuxYsbKLHwnW6jCWkQhhYwz49D/M\nOUTTNLEsi+PHj+M4DkqpXpr15s2bNBqNDd2smUxmS6OXxMbRscckKIwgQCiJucWLZu/YTIg/DCnT\nYeER1xAPAQsAQohTwAngd7TWNSHE7wP/YpDFDghxAAghtiXEu0X4r/5nk6SrSfZlX6SAQkYRRPC7\n/zrDuccDXnzqXqQokFh6BK0MpC6jIhOhk6zdrXJzdo5Tp07tuSVaSnnflGmpFLG2FlsZyY63n+e1\nuXr1Kko5nD59nkLhXkiy19nB7dAfIZbLZS5fvsz09DRnzpyJ30NRp4ZAYm76ohmYSAyaNDE6Dwca\nTaTbKOEBGox2LHD+CAv9CIk2coiwCEY6lj6zbLIjU5yUFllX4bdr1Bt1lpbXabUXsCybdCpNZOaI\nwgTbzdb1I04lN9GiRizAIBE6w0/qD/Px1o/xlr7GLbmCCZxWhzgkbJSoEIaaqHmIglsmRG2amwyw\nZRIvatPw0oy41pbP4EEYtnRbFEU9gu3aMGWz2V49t5tmXV5e5tq1a0gpN4x7dB8muzOVKpQPpUGr\ne871ev0DFSHCI/VDrALdR8ePAWt984jxgO4AOCDEAbGdysr/9f9KghBGNkt3CgN0hGVKLEPzB19N\nbSBEALSPMHIYQuM3XS7O3sI0TV5++eVtB8R3i/ttSEGgWV/XpNOiR04rK3e5c+cOJ0+eZGRklEZD\nU6spcjljx/PeK7pkPTMzQ6lU4rnnniOZvBft+QQoIuwdUsLx9KaBRwtEiKfv4osWsjtjaTXwxDKm\nzmLuU4FkXzBy8ahNVO9090IYSqZyEWtVhW1ZjE0+yZiIz7PebFOsNJDeMm+/U6c4GvY6WXO53MZN\nXIQosURMhDZxt6lCizKaMlJP8KJ4ghf1EwBooVCEhBiUWxYJTIzIom6sEsgWCWmhCUF4aJ3AkEnq\n7TTTduJBvLwF76d0mxCCRCJBIpHoWTV150orlQpzc3OEYUgmk+ldy9241ewH9Xp9KAPvPyx4xIP5\n3wL+sRAiBH4R+JO+904RzyXuGgeEOAT86bckmdQ2ZCHiuiDEkeK3Lzr4Adj9PKd8tDVOq7XEysoK\nZ8+e3VVX5X5QryukjMnQ9z1mZmYwTYvnn38e04wPznWhXIZsNn7aH2bKtNlsUiwWyefzPWWdEE2L\niBohdeooFBlCkhhY2+zIBiY+TXCrKA0GiXtRjorrs4GogGbPpLhvzVUh0dYERBUEC0jdBN0ibUWY\nhQzldoFGYMfTckLguC5nRxxca4S2D4cLXs/899atW2itO3JpGaRZBI4iNqSNDSCBJkSJu0g91dP2\nFEgMbKQ+hK/WMGSIoZNkwmnW/BpR0icQTUR4CIM8SZ0k8F2EFgMT4qO0f4KdPSIrlQqzs7OUy2US\niQSe5/XSrMMk8Hq9zokTJ4a23g86HnEN8ZeBPyYW8r4BvNb33r8PfHuQxQ4IcQDstEE22mBu94Ak\nZKwRqgOkiOXZ2r7AtnRnUL9BO3K49PYVfN/n1KlTD50MITbBNU3NysoKt2/f5vHHT/ZqhV0YhqDV\nUkSdQfBhEGLXI3FxcZFMJtNr8Q5QrHRUWeLpMYgQNFE0UBQwSGzzBBoR+wIKjNjxrm8jjqPIBKGo\nYujko0ufCglmgdAQ+CJiJDVOOzRxXZPJJASR7oiG63jUBvACSDoa27YZHx/v3RNdwYByZQkvaPKX\n379MKpXqaYp2nT0EJpoATQPIAS2UqBLPq0qESsbKSEYLicIKEmTaU2SsKQTxULtEokQ82r6n0x7y\nGMx+1utPoUKs6dmt8S0uLlKv13cl37cTNu8JH7SmmkcJrfU14EkhxKjWerNu6T8ElgdZ74AQh4Aj\n43BjYVPk14WwAIHvBzimJmXWIIpVThZXPG4t3OX0mTPUarWhegTeD77vc/nyDK5rbogKtxx63/z+\nfgmxq3uaz+d58cUXeffddwFQaFbxMTo9gRBHfxE+DhKFpkSE2Rmf7kIRxl2DuuPxuOlh5R45CiLa\nQ22g2BOEBOmQTTtU1+klgy2DDYphWoMfag5t45zVFQzIFDxK1TQfOvcsjUaDWrXGrVu3aLVacYNJ\nNkc6myKZKiJlE4FPnFZ1AUXWrVNsKgx1CK2TqLaPTBcw+1LUfgiuFUvWaR2PgSgd18UdY2Bdhx8o\naK17wgrdNKvv+1vk+/rTrPezEdNab4gwP4iE+CgH8wG2IUO01u8Ous4BIe4B3QaT7pfgFz4e8drv\nmeS2e5oWgDApNS3+1sdbGMlJmq02l67Mks5kufAjP4JpmrRarYfmIt+P5eVlrl69wejoKY4d27nO\nEQQa2xYYHYWSvRKi1pr5+Xnm5uZ6CjdBEPTIq0WEhh4ZAljYvVGLuOlf0yQi13e7BnjY2Bji/l9E\ngUSzd+/FYT+kuDYUUlBpQsLeaCelFDS9+H33PiUurQMExgbBgMNHDvcaTCqVCstLd2lHNzDUCJnM\nJLlslnSnAzNhJdBERKxgMLVtitMPYSqtqXuw3hKE6l7jjSEh52pyzg8nMW5nOLxdNN5Ns3ZtxLri\n5V0bse73f/N6PyyD+cPCo1aqGSYOCHEAbB696BbmP/q85ughzeIqjG2jaFapQ9KBn/+4wa35tW3l\nz4ZpmtuP7mYXR4WXkVLyYz/2IouLBkrpXofpZrTbmsnJPmWdPRBi/yxjv8JN/1pNFOamYXuzM20Y\n4mNiY3fSp1niTTkiQiJwiO2o9Kbj6kaL9zb5ve3aD0v9ZiQDhtSUGnGmoAspYTSjyT0gmNXa2FZw\nSKBI2CGJUcHEqIMS44ThE9TqPuvr69y6fSuWVHPzYGapGAmyycqGLtMwgnYI+WSsrbvWgKQFrtkX\nnWsotgSR0ow+3MmXh4Ld1CQNwyCfz/fszLTWNJtNKpUK8/Pz1Ot1LMsil8uRSGz0CN3r2MVXvvIV\nPv3pTzM9Pc2XvvQlrl27xs/+7M9imia//du/zSc+8YmB13w/8DAJUQjxz4CntdaDG0vuAQeEuAeY\nprlhpMF14H/6L0L+898ymVsWWKbGsTu2QL4gm9L8xqtV5m+8Qz6f31YU2zRNGo3G5l+1L3SJZ21t\njevXr28Y45iYUCwtKVwXLKtvs1OaZlOTyQjSabFlrd1iaWmJGzdu8NRTT23puOtPb0ad0e/NSJKk\nSRwJCiQRgpAQ0EgkaUaIWEejaLXbzN25g23b5HL3NjAARRT7FfZBEXWUb0BiDGUwexAIAfk0ZJMa\nL6RnPOyYWw2It/37OgViU7eyaiHCVWJrDAtFGaElCbmOm88wNvo47UByd01TLNepl+uUqkXqgY/X\nKuCrMhPKIp1yOZTROBbMVQRpe2sUKAWkLCi3BUlLk3h/NBKGhu0ixAdBCEEqlSKVSvXUibpp1tXV\nVarVKl//+tf5gz/4A8rlMpXKQJrSAHz2s5/lc5/7HK+99hpvv/02uVyOWq2GlLKn5PODiofUZToC\nvAM8/TAW3w4HhLgH9FtAdTExAv/rr4Z86x3BH31DcnddkE3BJz8S8Pj4TWrlZc6ePdsr7G+35rBT\npkII3nnnHYQQW4TI02nJ9LRgfV1Rr6u+WqFgdFSQz8sNT7277bjsj0R3Gh3pX8uItVowNpGSQJIi\nTYiLR5uICBOjo1LT8QrUacKowXtXrnD8+AmiKKRUKvXqlZlckmw2x2h6HNuKh/p9WgSiDVoAGiEk\njk5g9XepPiRsjjiljNOmA0Mneo0zAguUhwjvxs4lIm4wAhCMokWCtt9k1bO5XR7DMTW50TyHDuUI\nA1ivNfj6dwJK12zy8wtMpqscGbcwUwVkIk/KSrJThG0ZUPH4QBDiduimWW3b7plS+77Pr/3ar/Er\nv/IrLC8v81M/9VP803/6TwdeWwjB3NwcH//4x3nppZf48pe/zJkzZ/Z9zA8D++kyXV1d5fz5870/\nv/rqq7z66qvdP2aBnwNOCyE+rLUeqGN0LzggxAHwILUay4SPvqD56AvxhlSr1bh06RKOOcrZCxfu\nm6Z50CD9oFhZWaFarfLUU0/tqGKfSAimpw08L+50FCKWB9sujbqb9OHq6iozMzOcPHmyJ721HfrX\nymCwSrBBf6e7ocej1CYhCSYwyXRvV63xvRYXL18l8CTnnjuJY6VBC8bGxqjVazzx5AlarQb1Iize\neJdQB7hjBrlsjkJmFNvuDvUr2qJBpENcMhtIcd9jF/3nNMxapBaoYIQ4vg5AVRDCQgsBtAGN1OMo\nIlY9g0qQp1hs4VoBhmFSDiV3alBa06QsyWgGDk3kSSQmMU1NMWhhNMuo0hx3Zps4rkMumyOby5JO\n3audOQY0/G56enin14+HYbY77DnJLsG6rssrr7zCr//6r/PHf/zHCCFYXt59k+OnPvUpXn31VY4c\nOcJrr73Gb/zGb/DNb36Tt956i9/7vd8b2vEOG/tJmY6Pj/Pmm2/u9PYt4O8Cf/h+kCEcEOKe8CDh\nbKUUN27cYG1tjaeffnpX9YSdSHZQdMWxoyhiZGRkVwPCjrO/DScMQ65evUq73eb8+fO79rmDeMzC\nRuARIfFoU8WjSey1YOKQxSRNEgdCH10rUroxw+3Z6zz2+EnmAhtbZVH4sTqNBmEGWEaSTP4Ik3kT\njaaq1qjXGtSrda4uXiUMA9LpDLlclmw2B66HoS1sdm/y/EihLaQ+jNY1UAto6QAaoTOd2cSINa9E\nLTKwtcZCYBstlMgQBZrFVYEwI0adBFIGuLYgDGEsJ/DCJAu1JOdPTmFbGs/zKFcqvLeyTqW5QF5E\njKfT5HI5pJuhv0122E1IwyYv2NoVul9sF3FKGWdYBvEZfOWVV3jllVc2vHb9+vWhHOPDxsOqIWqt\nbxHrlfYghPg7A67xhd3+7AEh7gH3I69KpcLly5eZmJjg5Zdf3vUXbxgp026E1hXHfuedd4YadW6H\nUqnElStXOHbsGGfPnh34aV4iGMFigRVKrGMgcTrt/x6KJqtMUEO3RgluzTJ3+TLtQHH21JNYhsHa\nwgxm2kU8cQ7t2AgB0hvFVLmeVFdEgDBgJD/CSD62+ulqYlarVWZnr9MI2lgZl7HkYUayeQqJH9wu\nwW7DkMBA6AToybiu2AdfGVRCl7TZou4lOkrh8b2wXo+bepJOQMUfQ+tq7+9FEaQcMKqwXIWjo4Jv\niBR/kMwz70qMzu//MVXnr5YXSZXnqCa8nvFvJpN5pEP5jwL9hPh+jU79IOERKNV8fsshxBDbvAZw\nQIgPA90v+nYRYhRFzM7OUiqVOHfu3MBzSHtJmWo0PopK0Obq9evoMOS58y+Qcdzemg9rlEMpxbVr\n16hUKluk1wZeizoWRcZJ0kJ0VDmhgMQli4pqLN78GqW31jh07ATHp6buuaBn8mgvQl6/jHjyWYTt\nIDeNYoT4W2yjupqYyWya5PQ4vo6ot6o0yh6L87O0W20Khotu+dRqNdLp9EP1XBwEGzpoRVwL7SLo\nNOlUAomh80ARRKvXQqSBYsMnbSuEKtBUDpGKrbX6MZ6GxRL886TNv/VNTKFJEadGI+D/lhm+VXiS\n33u8zfPuPam0bgfmlStXejN8m7swB8EPGyF28YNyr/z/FP0yQNPEAt5/DPwhcBeYAP4W8Nc6/901\nDghxD9gcIXajpMOHD/ekyPa75oMQoVnH525pnduzN3ns6DTjhw5RE9DGZxR7KJ6I26FarXLp0iWm\npqZ46aWX9vXl1yhalDCxsTA3+VcoQu2zNHuV2sIlnnruw4w4I4Sijuoo22jTQyUdjEYDiisweTR+\nvX9In60bPsTXsNTp1nSFiZlMk0zmMDBRWrFaK7N09SZ37tyh0WjgOE6vFX/Ycl97h4XAot2OKNYs\nWm2JEFDyJIEQjOXHsK0WUERJgdYeYZgGK4FWsdSBIn7K1xq6+3rSgjdck6/4Jhk0/WVlQ0AaTVvD\nL1Zdvp5UPePf6elp3n33XY4cOUK5XGZ2dpZms7njDN+D8MNCiN2GtWE17Pww4f2WbtNa90w/hRD/\nI3GNsd8C6irw50KIf0Is7fbv7nbtA0LcA0zTxPf9nkB1vV7fd5Q0SDSn0KyETWZmr6P9kBfOfQi3\nr27no1jDAzk8hwqISWZ2dpbV1dU9RcHbQRHi08bcNBoR4lHzity+fZNCrciREydo2KuYQQ1HZzo/\nr9GWh2+UMDMZxMo8TB7d0gxjYBLgIYlQtIEG4FNHokjikCQeVxAI3XFVEJK8m2YhbXP69BmkFj1X\nhcXFRWq1GpZl9Qiy6+5+v2s3rKhhc4RY80a4u1LCcWzSyfi8fVNTaQvWiha5VIQppojCKSyzk+IK\nYkk+4qtIEAoyyXsShJ6CryctXKFj0tTxI0X3qhoCsgbUFXy1ZfIzyfhhTimFYRg9R4ru8W6e4YtH\nZHIPvHYPq4Y4TIRh2PvufxCdLuCRKtX8JPA7O7z3VeAfDLLYASEOgP6Uaa1W4/XXX2d6eprTp0/v\ne7MbxE1iqbjGmzdnOHXkKJMTE1t+t42kRURoDa9ztdFo0Gw2UUoNVBt9MDQhEWZfBBdoj6XiTdbu\nrvPYscfJLERE5XV0o4EVFAhyLWThCBILqS2IFKFYwqy0EdVJzLCMDtt0nV9MbBSrhKKKpA60USia\nRFgoFAU0E5g6vSG1KhAILWgRkRbWFlcFz/M2uLtLKXsEucWdYojoJ8QwhLtlFydhYYoiWicRwiUp\nFRWhSTkelZpFfnyESj0mtMmsZrksSBsxsalAIw1Btm8fn/UFdVeQlTETah3/g4jnEPvvuP+zZfQI\ncTvi326Gr3vtVldXe9eu39mjO67zfjpn7BX9UWG9XieVesQyge8zHrFSjQecZ3sT4JcAf5vXd8QB\nIQ6IMAyZm5ujWq1y4cIFEon3rysxDENmZmZYChs8/8w50s7OVl82kra5/whRa83c3Bzz8/O4rsvJ\nkyeHuqGInjhbrJYShD4zdy5iCMmZp57GWlnH+bO/IKitYcgEVuRi6Qg1NgLnP4IZ1RCNAKE1odfC\nrCzjercxGiaYp8EdAQIs6gSsdeYdU53fFyGFQOkK0Mbh3MZjEyCUJmT7a+g4DhMTEz2xg67tUKlU\n4ubNmwC9Tf5h1JQUbcrtKqEIcRwItYmISohIYosUCcPF01msRArfk0yOatZrgqStaXmCZqSZcMAQ\nikOFe9FhvQ3YYMl74xQdWdgtMIC66hd22B3hbL52YRj2tETv3LlDFEVks1ksyxpqluNhE+IHzRy4\ni0dIiP878JoQIgL+FfdqiL8A/NfAPxtksQNCHAC+7/P6668zMTGBaZrvKxmur6/z3nvv8dhjj5E7\ncgJ3O+2uPhgIlGRfEWK73ebixYukUikuXLjAm2++OfQNRWLhksLDo1luc+vOLIeO5RjLTyCXVkh8\n8cuEjkPoprHlOOjOLVutIr/8J5hHxyBMITNXEMcWMcx/y6EE2JXHMDlGpH4SlRrFwURom0AI6FBc\n1FH0FCKFpQWCNeAom3f+3VLZZtuhMAxjd4pymbW1NTzPQ2vdiyIHGU/ph9YaYfiEYoVGM4FrJ+Jj\nFAm0mUebTTQOBWOUVd/Ej8BrwdiI5vCoppYCw41YvivJ2dBwA0wpafnQ9OOB+79yWBGUBUpvrB9u\nRoDgmLlRVH0v94dpmoyOjvZcV7paoktLS5TLZd544w1SqVQvgkylUnt6yHgYNb6DCPGR+iH+IyAD\n/LfAf9f3uiZutvlHgyx2QIgDwLZtXn75ZYIg4OrVq+/L7+xGhc1mkxdeeIFEIsEybRTc9xZUaCzD\nQAV7E+ReWlri5s2bnD59urdJDbtrtZteS0QjzMx/E7+hePL0Ewg7AKVwv/YXqFyKIGEjl5uYEb07\nVmeTqPoSmdlvY5yrIKJlwopFWHHQBoRWBdkMMWSR0LoA9gQOOSwNIQERIabQmNrAxkLjo2kjaEPf\nLGIkNc4evyamafaaTQqFAisrK0xMTFAul1laWsL3fTKZTI8g7+eo0A+lI4RVRTAdPyD0/RXRi4Bb\nSKPJpJuhEcCSJ2iEsTpO2tUcyYAxpVitwPwtSdUD19Q8OakZz8b1xfPNiLd8SXaHQ9I67gb+heQ9\nDd5hDdJ3tUTDMMR1XY4fP06j0aBcLnPr1i0ajUbs7NGJwHfb5PR+EOIHrYb4KP0QtdYt4G8LIX6N\neF5xElgCXtdazwy63gEhDgAhRC+F87Dm+/prMMVikffee4+jR49y5syZ3utJDOqEGPehxABFSlhE\n0WDD/r7vc+nSJUzT3CK9NkxC7Da+dNV8xo8eI3EGtAhQBJjLa6hKiXB6HImFnZ9GrpbQUQiWRFMn\nSsxhL19E3Iloj6fRiVjgWld8gvocYqKG2fwrmInvEpkfQcspJGDjAA6jOqIiVCdlKzrdqPcIMUIj\ntcBmOBFxf40R7s1ClsvlnqNCOp3u/UwymdyWXLRoEW9DBo6tabQkprG5/myjaGCKNElDcCyjOZqJ\nG2T6lzwyCmenarx0emv9+h9mff7uWgJPazZrN2gNFS14wY54wb53TzwMFZjukHs6nSadTjM9Pd1z\n9ug+XMzMzPRItOtpuF0N9/2oIR6kTN9/dMhvYALcjANCHBBCiKGpymxG/3B+t3v1+eef35KaTWJQ\nI9xWAxTijVwBKUw8tfua8srKCteuXdsgAt6PYRNiV82n27Ea4lNnnRJzEYpZXgAAIABJREFUWKUS\nQpq45DExUak6Wjro+jKEVXSrTBRcR9o+etJGHI3AV6AjRDZAhxZhcQ2ib2HkPoZqv0spaNIKl9Ey\nwkykSNhTWDJPW1iYKGTflzrQIaGISPpiW/HxoVwDCZlsgnTW5eixadCiFwXdvHmTRqPRG1fI5/O9\nWUilPURn1jKTVlQbWzd4gdEZTYloeyZjhfunPrfD87bitwptfrnsUlUaS8QRodfpOP2QFfE7I+0N\nBDvMblrYmcCEEFuanLpi28VisVfD7QoG5PN5bNs+iBAfAh61/ZMQIgX8x8CPEwuC/yda62tCiL8J\n/KXW+r3drnVAiHvAwxp4l1JSLBa5du3afbtXTSSj2KzjIxHYcT9k3D4fe8dTwKIuDVq7OM4wDHnv\nvfcIgmCLCPjm4xvGebdaLer1Ovl8fkPHqolNnqlOBNfAIke3U1RjohJVhDtKFOTw5+eRVhMnqwka\nEtrxNZCRQCsDYSv0FIQry/i1VYLKJarUMZ0x0AZ+rUqLVdzRLI79FJ5lo7RGaA+YxyUiE2nKcgWi\nGpC8N6S3T2gUEXWUqHXmGDRagEGaVDpNOj3di4JarRblcpm5uTnq9TqO4+CmPcIgQiuFY0syKUWt\nIUhtU9Jue7E+bToJkYJmGD83CMA1IfGAU/pEIuKrdpM/apr8ScvE03DcVPyHqZAPO9EWkn2UXaHb\neRp2G3UWFhYIggDbtntjIPsRDOhHf930g0iIjxJCiKPAN4gH9N8DniGuKQL8BPBx4O/vdr0DQtwD\nHkbHYBRFtFotZmdndzXT6GJwCIcGEc1eTAgJDNKY8ejFLgbzi8UiV65c4fjx4xw+fPi+57ZfQuyv\nTSaTSR5//PFtNzuXHO2RUZQK6DY3SgwUGr9VpL14E6qXcCd8WIUwpRAeGJYGQ4GKILDAAZX2qS/O\n4h4/REqbREZIKCSGF2JUAtTSAsH4KqPmM8hMEpEyMGQ6FhVvV7Ara4jVt+KGFecoJAtgWQh/AaFa\naGGgrSkwdrcJaiICsQ74CBxEpzlKo1GigaKFpccRmAghSCaTJJPJ3rhCu91maeUG1fpd3n7nHQxp\nxFqsukC9kUMaAkNCpDRKSzKO5FBBUw9h3YuH783OGEXZj8cufHX/+3nM0LyaCXg182C/zodBiHsd\nXzEMo1fD7a41Pz/P+vo6169f32D62x+B7weNRoMjR47sa40fNjzipprfIh69eAJYZOOYxf8DvDbI\nYgeEOCCG6YDQRblc5vLly1iWxdNPP73rAX8LSR5JFrOnxdKf3rsfgUVRxLVr16jVar1mnQdhP4QY\nBAGXLl3CMAwuXLjA22+/veNaEgN38ixq5DuE1RJk0yhdI1pbxV96B9OuQNLDqIKa0BgjIShJFMi4\nRRIDhEa3QaUURiUkisZoOC1UVERXXNCgswoZ+Kh2mSh5lcTdUbQrEdkGor6CjHyQPsLJgQjBX0FU\n3kaKGbBkPMgPgEAlThNlfhTkzqMwABFVIEBuEhGP41sXhUcoSlh6fNu/77ouI/kpvHCFkyfPEvoR\nlWqFamWVcvEmXmCSSOTIFUwmRyfIJQVVH1ZagpTJloguULAaOPgR2EPY04adMg1VhJTWjuWBQSCl\nxHVdCoUCx48f70WK/RG4bdu9OuSDxBa2Q6PR+MB1mQKPrKkG+ATwqtb6jhBi84e1AAz0dHJAiI8Q\n/Xqgzz77LDdv3twT4exU49pJuq1SqXDp0iWOHDnCU089tesNbK+E2B0Z6beFetCDhRQm8if/OsYf\n/SFah/iqhLp7BdsyQefRzSo6Uad1xsVGoUWANCUq9hCOfQFVbHsoHYtSNEGos5jlGugmOCZS+YSG\nQasFtVSbTOgibn8bLXxE8jDSdHAaqwh/Hm3mMaKr0J7Bb7qEZhpMG2kamIkEZusqZlgmLPwMyK0p\nZ611nCoVDSQ7k6bEQdG853W4DQQGIsqhdQvTTmwY9YjCiHJtlVqlxvUrTSJ1i7o9xng+g5PPYlsb\nj82SYKAptmFyCPv4sCLEEEWDiFUjIGlbCNHGQpLRJu4+opH+el+/YEA3qttObGE7wYCd8EFsqnnE\nNUQbqO3wXg54cFqjDweEuEcIIfb15e+S0uHDh3t6oMPWHt1MYF1bqvX1dZ599tmBn2QHjY67UWi9\nXufFF1/Ede8Rwf3IVaPReKhDJurnPob42jdQl96ERg3LTaGUjzeRxv+RBiqpiQKBNCMQGqUEEgsh\nJNoK0JGkbY3SVEmcdgKjOoJKm0jPQ5HDEBaRqtForaDbDjqdQpRbqEyECh3acyXm3vtzQtkgN34d\nnXqCxNgUlhmhXQcdKfxandBycZMLyPZVVPLctuclZNe490EPIOK+hKi1BpXEYAxFGY1Cd4TVhAmj\n+UkO5fOIx0wqrYgbaw3ajQpX7y4RhRGpdKoXAbmOiy01jVAQKo25Ty4bhrVSgEeRIkKXyKjrpFWe\nZOATGAXWhU0ai9wO1+ZBeFBTzU5iC5VKhdu3b6OUIpvN9kjStu0ND5QfxMH8R0yI7wA/D/zZNu/9\nNeCtQRY7IMQBsdnxYtAvv1KK2dlZisXiFlIyTXOohNhPsPV6nYsXLzI+Ps5LL720p01rkAixVqtx\n8eJFDh8+vG0UuhO5xg0n6wRBBdUIUMokOvcE4UiBYM0lTOZRWUGQrELSQ0YVIsOEKMAwAqQRQSQR\nVgAtjdcqsN44gem6EPoordBBSGgkQUsIIkSlBtk67ZyP1Wqj3QDVXmLpG39JaWYVd/owbrZI5LXx\nSsu0F4vknnwcO5EFIZCmQeS1aWPhWm+hEk937Ja2nt1OVBibIqu488UQvZrwtj/bSUsapJA60Zmh\nDDtpVwfR97WOhMFILoc7mgNA6Y7tVaXK7OwsvufjeR6rKysUZIpCen+NJvudQ9SUKOs7WFERUxuI\nyMOQTaS6iROtYskJ6tYoDnJPkeKgNcnNYgtdwYByudzzAA2CgPn5edbW1nrOKIPiK1/5Cp/+9KeZ\nnp7mS1/6EkEQ8HM/93PUajU+85nP8NJLLw285vuJR0iI/z3wf3TuuX/Ree2sEOJniTtP//ogix0Q\n4h7RHb14UAqlH12XiMnJyW1dMfZiAXU/dNe7desWS0tLPP300z3B5b2u9yBC1Fpz69YtlpeX7ysA\nvhMhhrqIV75LtN5CGAYYBqpeITIk7ZyLztsYDuh2HumO4d8NEfUGVl5giTgdKLXEW7JoG4dYXj5O\nOVrFLUIilSBhaAQ20UoVtV5DaY+IeZKBSyNVImfnEIHJ0jdu4jV8nMMOVi6NIa+DdLGzKVRoUL54\nhZyZQAuDsFgkXF0lCgNyx5Lopy7gTJ9CGAFoD9AEVFjKLdEy1kiQ4Wh0BBMTTYCqLCKvvIP1zmXw\nfLRjwrmfgLM/Ctn8tte4e+/EQyH3ScGKjcZwUkiymSzZTJZp4k7W733/e0RRxM0bN7jWbvQUYfL5\n/MCKMPtLmdbw9DIirGGQRAubiArSyKBlAkETodZwAkXNmtoTIe537KI769idJW21Wly5coV2u82v\n/uqvcunSJX7pl36Jn/7pn+ajH/3oronss5/9LJ/73Od47bXXePvtt5mfn+e73/0uJ0+efF8VsfaC\nR9lUo7X+IyHEf0qsUvP3Oi9/gTiN+p9prbeLHHfEASHuEYOkN/tTlfcjiWGnTLtzWdlslpdffnnf\n81cPIsRWq8XFixfJZrNcuHDhvhvjdmtpArzyAuH6GkZKdjowc0g7iWHb6IbCbzSwTAvLTeGtHsbP\n1jBaNmrVx2u10C2NmTpM4tRhss7ThKuTMDmPbaaJlitUazW8eg2pTKyMi5EskrASuLZJuLZMM1hH\nhYLWWp3k4QKNUpNQtbGEQiKJEAgp0X5A9b2rmGGICkKk40CkaC8tU3v3X5I4dYz8xy5AJsm3Em9z\nKX0TrRTClEgkAsm54DTP3dC4/+ZrCC1RoyNgGeigifG9byL/8vuon/k76CPH9vyZuQZEXVXubSCE\nQAiDI0emOJaeRHCv0eT27ds0Gg1c193QiXm/z3XvTTUaKOFHYVwT7zzcRDrqrCfQJED6WKpFUzeI\nhDNwo83D6IJ1HIdTp07xxS9+kU9+8pP85m/+JleuXOFrX/vaniI7IQRBEPDhD3+Yj33sY3zxi1/k\nmWeeGdoxDxuPUqkGQGv9u0KIfw58GDgErAPf0lrvVFvcEQeEOCC6X/bdDud3U4cTExMPTFUOK2Wq\ntWZhYYHbt2/jOA5PPfXUvteE+xPi0tISN27c4MyZM7029/thuwgxjK6jGl/FLEQIJFpohBKI/Cii\nksFN1fGqJsqIqJkV2q2AvHMWmV+HXJWwVsFKjpMYOYouP4lY/xBjj+VpBwrPX8KccDEXa6QyeZQL\nLX+VSIT4ZReVichiEDU9qrOrWOkMjaaHFBaRaNJuWlg5D61ANRtI26bx3lVyTz6BVSjE10e3wcxh\nJCTt24sU//z7fPvfEyzaq8hIxiMP2kCLWFrvSvO7HP7TGY5mn0QnEsTV0xBpFmDSQjUqGP/mXxL+\nB/8A0vci+0FIxzXBlgIvAmeb5yGtNW0tydndwf2NjSb9ijALCwsPtL3aO+F4QAi6DuJe1kUrhei1\nxkoEEQqFCOpou8DulWZjDHswf/N6zWaTc+fODUyEn/rUp3j11Vc5cuQIr732Gl/4whf4zGc+w+//\n/u/z+c9/fmjH+7DwA6BU02B7x4uBcECIe8SDyEspxc2bN1ldXeWZZ57ZVaFdSkkQDNQUtQWe53Hp\n0iVs2+bChQu88cYb+1qvH9sRYhAEXL58GSHEFqm3gdaK7hA2vgJESBU3NMRbnYdMLyFyEY7nEbSS\nrFxbJTV+hENTRyHyoTyJV72NVKMkU38DWXwc05TIQwIhDUb141S9cbz1Mo32IqbZxnA1o4emcdwE\n0mnjmw2suk9LV1m7W8UYrSNS06QzeaRUePURsmIVFfioMMCvNeMDNK17NT2zjt88inBSmK7JbGqO\nRcPB1FanUUjEba8qQmjNxLtLFCmRtlvkO19FqZNI4uhIpzJQvYt87xLq/If3/LlNJDWLTUE7jEmx\ny6VKQyMEVyhy22sxbKsIcz/bqzAM90iIEWiFpTWeuCdKqJVG9tVjNfG4i9ERpRj4tzxkQvR9f0+i\n7a+88gqvvPLKhtf+4i/+Yt/H90GAEMIkjg6Pwtb6gdb6f9ntWgeEuEcYhrFjhNjT5xwfH8g70DRN\nms3mno/p7t27XL9+nSeffLKn1jFMbK5xdscpHn/88d5muVtsiBCjNvAddJRByO4tqdFaoTAQTGBN\nBNSutAnUHIXpDAk7T9i0IIjQepVEahIn9QnswoW+bTL2gk8xQZicx1Zp3MkQs2BgqGUQEq0VoaVI\nlR1cx6GZLiDSK1iGg+MYtOo+LT9ArSlUMs/o6DraKaAaDURH0UfrCMOq4TdTRGocYVjoIGDmJdBB\niHDsmBCV6tTzYjPiyXeWaY4kWIjukmukMNP53qA+xOMV4UgG6903NxDioGlJ24AjKU3Fg2pwz+bX\nEIIRWzFqBwPJum3XiVkulykWi6ysrLC6ukq5XO6R5O4ekuIpWgujoykbY2vEqQjQuNreEyE+DK3V\nYUvB/bDhUXaZCiFeAL5IrFSz3Q2hgQNCfFjoT5lujhCVUty6dYu7d+/uOirsx16baoIg4MqVKyil\n7iu9tl90I1ilFDMzM9RqtS3jFLtFd2wlxhwQII0ckW4AGqWjntiA0rBS1IT5PCPJ55C1d7DGKh1e\ncTBTP4KR/wiqam/6RnQl4Qxy0VHWowqRHSJGLZR1DPxlhK6TiMZxXcV6Ywml60ydeYrKHQ93ZAS3\n7ZALKoSj45QrksbiHJn0TaRYxcznkHYFy7JoVkZptY7hpuLINyCkNgJmqMABrRUo1ZN/E4DVCmjl\nE9QtH4oKJVoY6U2jMJYF5caGl/ZSp7MkjCWg4GjCjhapJTVRpDCN/RGEZVk9yTQhBLlcDillb+C9\n6214f9srF4TAlBmSqkpLxqLqWum+h4RO762ChJHb07E+zAix+4D3MJSsfpDxiJVqfheoA/8OsXTb\nQIbAm3FAiHvE5gixXq9z6dIlRkdHH9hQcr81ByXEtbU1rl69uqcobVBIKWm1Wrz++utMTU0NNNS/\n3Vr3aoiLQALTSuJFEqXb8Swh0G57LC4tUcgXmJh0CVtn0K0fxx0bi+f63ByGlYkH32u3OjWnrdfe\nxGZEHKdaWwESCJXAMA9hGEUC0eLW+hx5e5SRZA4/WKOZuou/XsYuTCBGn8Gxpxgd82gtpGmtjePN\nfJvMuSeo3DKoFgUYKfKHNNL0sV0HYRqgFXQ0ZnWkNtpMAH7KwfAilCsRjo1qtZDJRO/4NQrh+ej0\n3jb/7WDIjbZhw1aW0VpjmiaFQmGLt+H9ba8kkAfpkY40aEFTKDyh0FJ3Koct0A4FkpjG3mb9HnbK\n9IOKR9hUcxb4Ba31nwxjsQNC3CNM06TVam0YM9jvWMMgTTVRFHH16lVardaeo7RBoLVmbW2N1dVV\nzp8/v+/h442EGA+WC9vCMHNEfhVt+xSLdSqVKkcOH8Z2TOAuqmWSGn8MM7eRJIQQyEIBVSwidhAc\nkK6LnR7BCJJIEYFwKK7WWa0tMH3kGRKJJKpax3RGOPrSjzL35W/S8jK46RFAYFhg2B4EZZKPncaM\nsiRHDjM+mUIJQbPVoNFcYGV5HZFMYpdcwlEXEUVEG0Z0NBqDpeeOcORb1zHGjvRSyDoIEL0IKsQo\nt1Af+atbPothkdj74U6xeVRhs+1Vu93ujHrkyBccklaKjKrhCgcn1BhopG5hawtb5xHWYdii0rX3\n49sPoijqzTX6vv/QsjM/yHjEg/kzwNC08g4IcUD0D+a3Wi3eeOMNRkZG9hwV9mO3KdOu9un09PQG\nn8SdsN9Nr91u8+6772KaJlNTU0NR4uhPmWrGifQ8WmewC2M0VyLmZmZxMyaPHZ+KmyqiEB0mcLMn\nMXd46JD5PPg+qlZDuC6is1HpKEK3WshEAufFF2lfeQeRbTO/tAjAyePPI9wWUdBAqSbJDz2JmZY8\n9rf/IxozdUrf+0u81VsIAnJnnuTwT36EYHGB+ltvYSSbGGYCA4dcJgduyFghIFQmle83ufwJUEEs\nUh4phUQjpYFAsnp6gqk3b/PYSoLIjuLPKQjj2qSIENU6wsmgnjiz4Tx/2AhxM6SUZLNZstksx44d\nQ2t9z/bqRp1Wq0Qm3SSfjUhEZXKqjSCPNqbAyG3oQn0YxzcIugbGEGeJPog6po+YEP9L4J8IIV7X\nWt/Z72IHhLgHdKOlu3fv8uKLL5LLDSel9aCUqVKK69evUyqVduWIAfeaV/a66XXHKbpWVHfv3t3T\nOtsdV9doWakjCPF9pAwpNxrMF0tMTZ8iZUiU1zG1tUoYmfOY6SM7nosQAnnoECST6HIZ1W1QMgzk\noUPIdBpTShqNBje+8SUOjaYpTB6DeoRaU+A4pE6fxko7CHKYyWmc5wUjHzqK8isIKxP/bqXxCwXC\nYpXGezOYmQqGO40WFjpy0F6NSGqy+glyUYWG28II4lYRrRR+GI9X6JTB0k/9GM/+b4sYlUUiJ4FK\nWohGG1muQ2IM72f+JriJIVkUb8XDSJkOut525r+x7VWRtWqS775tYjshuVydQsEik8nsi9SGeb4f\ndC/ELh7hYP6fCSE+BlwTQswApa0/oj+62/UOCHFABEHAd7/7XRKJBOPj40MjQ7h/yrR/nnE7lZud\n0DUdHnQD6TbqaK174xSlUmmoBsG+73ekvjIo9Qwry39KvZHhydOnsW0LrTQ6CkGUkOY08AIPmjsT\nQmBkMpDJoLvXsuO4rrXm9u3b3C2XOfM3/j5Wc4movICQYBamMEcySNMC0iDG4pqf9kHXkXZfVCoF\n9uQEY598BffEYzQvXsRfXQWrgLRt/MfOsTYiefr5Uzwb2nw1eoM7xhJaxJ2UEgkIjq5PcWrxOO8+\ntYy7NMv4wk0yGFjjR9A/8QrhiSdRjgtR1LsvuuMqP8wR4oPQb3u1sLDESy+91JuFXFpaYmZmZkMa\nNpfNdHqVBIj3d0vbTIgfNB1TeLSD+UKIfwz8MrAKVIF9DXIfEOKAsCyLs2fPIoTg2rVrQ117u5Rp\nf41yP52rg+g3dj0SNzfqbKz77Q1aa5RS5HI5ZmdnWVhYIJFIUKvVOHb0OU49UUKIFcBESIWQilh8\n4gLblQri+T6/owUKAgvZEX4Wm+bDLl26RDKZ5Pz58/GmXRiBw6eBFuiQ2DYqsXFTVXXYIT6TiQTZ\n8y+Qef5ZomYRpae4NrdIqDUvnD2LZUog4GeCE6zXl7keXsJPQCpKcqL5GKkoCceB4ycJah+irjWL\nne7MsB2SX1qmUCiQy+UwTbMXUVerVRzHwfd9DMOII+M9ktAPQoS4G7iuy+TkZM8txfd9yqVVSisz\nLN5YQQjIZDJksmOkC8ewnOE9qN4P/YT4QbV+esQp018EPkcs07ZvVZMDQhwQ3fSO53lDlVmDrYTT\nbDa5ePEi+Xz+felc7dpRVavVbT0Sh2EQrJRCKUUmk+G5555jbm6O+fl5Dh06RLHUZHHJpVAIKBQE\nudwYrjsNjG5/vHiElNDc6/bVaCQuFoWeyPX6+jozMzOcOnVq63ymMIgjwp2OOmInQry3hEFoSN69\ncpWJyaMcPXq0jxTijWIk/RgvrKcJFyoI04yl3ojri9rzMNNpDk1MMNH5jLtu76VSibm5OcIwJJPJ\n0Gg0SCaTTE9Px3+/83lEUVyD3EKQqg3hXUS0Fl8fmQdrAkQSdIgKvX26DG7EsGt0O8E2NRN5n4n8\nFIjjBGFIrVajUl5naeEbeCpFKnesF0U+rGaXgwjxkSMJ/KthkCEcEOKe0LVq2o10216gtWZ+fp65\nuTnOnDlDoSMNthfslsS6KdnJyUlePP8CVblMmTICSUZPkGJkd+LehMQyXJqYDFwEcbqyu2l3tRov\nX76M67pcuHCht6kopajVapRKJRYWivj+NTKZZUZGRigUCr0GBoVHwArxtNpG4lb4BKxgqDFuzN7u\nEfxeFERiMrx/VLy6ssrC/DWeeuqjZHPbk7cQAmtsDJlKEVYqRM1mXBu1baypKWQyuSGy2uz2XqlU\nePfdd8lms3iex/e+9z2y2WwvgnQcp1OP7SNIbxFLX0cIE2EkAYEI59GtS6ByaPMwotXGjVbAnwYr\nu4NLx+4xTELcMRuhFSJYjptrOg02lmXdu176FCqsUm6mKFXrzM/PE4Yh2WyWIAhot9tD68o+qCHG\neIQR4p8Sq9R8fRiLHRDiHjFsIe4ulFJ873vfI5FI8PLLLw+U6twO3RriTujW1ZaWlnjmmWcIskXe\nML5Aiwr3iECQ19M8ZnzkPh6GEZp14hnZe2LSWhsolQeV7EUu94vYuoasuVyO48eP9wiyWCxy+fLl\neIYtmyE3FpLLjeA6W6+PxKbZrjLz3jcZy53ghRde2HsaT6YgKm/7looU12evE4Uez5x7AdPdngz7\nYSQSGInEroe4tdYsLi4yPz/P888/30vJKaWoVquUSiWWlpbwPG8DQbqyCvoaSuRRwiBSgIowAg8R\nGQi5AqTQMo8WJsJbh6iFdif2RYrDTJnuSK6qBToCuQOpCYE0EoxkBYWxx3trlctl1tfXuXr1Kp7n\nkU6nexFkIrE326sDQtxfynQINPo/AJ/vfHZ/xtamGrTWN3a72AEh7hEPo06ytLREs9nk9OnTPf+1\n/eJ+oxztdpuLFy+STqd5+eWXKZl3eNf411i4pLgn0K3RVMUSFxNfxDae3rJOTIZLQIDoq/PFUaGP\n1ksIMQE63TMM3m3E1k+QJ06ciDe26irF+g1WZkr4gU8mnYln2PIFbNtmdW2VO3fucPKJo4xlju7C\nkPc+EC7ggm53/j9Gs9lk5uoMhybGmZqcQhiHBlt2F/dPFEVcuXIFIQTnz5/fMADerx/avS7dyHpm\nZgbbfws3lSWbM0mnMzi2DUERpSKU4YC2wL9FFJyN08ZWGsIG+GVwHizOvhOGGSHuuFZUf3DzjLRB\nNWLiFAZSStLpNKlUimeffRatdW8WcnZ2lmajQcYyGLEkOdchkUpBKgupDFg7p1v7j/GDaA4M8ePv\nXrtMh0CIXcHXXwP+m/3+mgNC3AMGdY5/EHzf5/Lly0gpSaVSPYWPYWCnNOfy8jKzs7OcPn2a0dFR\nFCHvGV/GJom1SR9XIEiQoyGLlA5dAn58w/uaKuD3yLC/VggSIVK0W/NculhhYuIwTzzxxL4UbrL5\nJKn8MeS0Gxve1mqUymWWrlyh0WhgmibHjh3DcRw00QbD3D3BmIBoOd5ghcPKSpH5+TmeevIEqbQd\nd6TKB4/ADIJGo8HFixeZnp7myJEjD/z5DZH10RFUs0zbT1CulLlz5zZ+q0nObpDKjpNKmzi2TRjA\n2so13NSRWFReW8h2EcwM0tjbrN/7EiGidhfF6t6/ADYYegsh4iacTIajhw9D8S6t0jqVVpu5tSLN\nW3dIGJJsJkX66OOkJqZ2JPru+dbr9YeiIfyDj0dq//T3eFBNYwAcEOIjxurqKjMzM5w8eZLJyUm+\n853voJQamhzU5tRuGIZcvnwZpdQGd4p1cYuA9obIsB8ahaMTlLN3aHADl0NI4rpU3O0c1/H6ybC7\nUSwv3+Xu3ZucOXuBTPrwUM6rCykk2WwOaRisr69z/PhxUqkU5UqZa9fmCBoL5DJjvRrknporhAnG\nYaKgyuy1N9HK59kPPYFh5UFmN0SOw8Dy8jK3bt3i6aef3lvEoX2kVCRTKZLdrke/Tqu6RL0VsLA4\nT7vdhqiEk36C0dFRTNOMlXL8FlHQilOs0EtzDxL1bSBEFYHXgKAV/9lKgJMC+eD7e0dCFA6o6v0H\n9Duyef0NUTt+r8priMAnOXqIJDBFfB97nkelVGLlvYuUZmcxUpkdba8gfog5SJm+z79b688Pc70D\nQtwn9vpEHIYhV69epd1uc/78+V76sEtgwyLE/gixO05x4sQJDh8YYvdXAAAgAElEQVTeSExVsYTY\nppsyfvTy0PixI4LW1MUqtpYoJAYFoONfuKlxJgxDrl2bwbJszp17fmvUoTUEXvxfadw3NbXhnPj/\n2Hvz4Ejy+tr388vMylpVVSot3ZK61ep9nd5GzAzYbMNiiIux8bXvi4dZjIEx8cDxbD8/M/azw3P9\nuDYYE/eFMRgb3sXgYLAfOIwHcw3DALaBuRcGmJmW1C11t7pbUmuXqlT7ksvv/ZHK7FKptFdND4xO\nxERMS6XMqlIpT36/3/M9R8fyciMk01PTzMzOcPz4ccIhhwCisSiSTjS7k0w6TzKZ5Pbt2xiGQTwe\np7W1dUsEmcsXGBoaYd++k3R3dTm/8wa3zW3bZmRkBMMw6O/v38H8WGOVMlZAMBQi2BJA9+tMTU7T\n1dVNyY4xPj5OsVgkFAoRb9EJt7cSamnxbm6sqj3ILRFkKYvIzTv/ryz/7it5yC8gIx0QWJ/s1yRE\nNQxWcv1zyxJSja2oJKsrRA9GBVHIQmjlcxFCEAgECHR1sae9DVSVUrRtReyVqqqUy2UWFxfx+/3b\nbpk+/vjj/O7v/i779u3jS1/6EkIIHn/8cX7u536Op59+mhMnTmz5mM817nYeYqOwS4jbQLV921Z3\n/ABSqRRXrlyht7fX22l00aiQYBeqqmIYBlevXmVpaanuOgWwInJnJSpIKoDmPU9n1y+MxMRiFiEN\nsANeVagoCqlUitEbo/Qd6KO9vX35GO7JJOTSkFtyKggkoDiEGGsD/+rnVw2BjkDDMItcu3oDn0/n\n3LlzqFVVh6SCQghV0Wlt1T2lbr11ho0IcmpqiomJCU6fPt20CqBYLDIwMMDevXtr1ja2ASUCKGCb\n4MZpKRpIm6nJSQqFAseOHUUVWQicYK9w3u9CoUB6cYqx8UlyhZuEQiFPpBMOh1cRpJTSI8dVRFPK\nQXYWfGFY8T0dbBuRmXXyIQNrv59rEqLiR6oxhJV1yHHVD1YAAdpKi7+6FWIxD+oGf78+HYp5/Kqy\nIvaqUqnwwx/+kMXFRd71rnd5EViFQoGf/umf3lRQNsDHP/5x/uqv/opHHnmEZ599lu7ubr70pS9x\n//33b+rn7zbuctoFQojXAb9E/TzEXaea5wqapmGa5qYJ0d3zS6fTa1qvbTcCai0YhsHY2Bi9vb28\n6EUvWvNC2yL3uuE6Hpyl9zKgOasTOLlBYemYNAs0bOnHtJIoMowi1GUjgZvkcnnuOXNPlXDGxBGn\nSFiah3zGIT616vNrGjA/CYm9EFr7QikQ5FIKI7eepnf/QTrb75gHSMvCpoyiamis9jytXWeoJUjL\nsojFYiQSCVpaWhgdHQVYJWppJObm5rhx4wYnT55sjPORooGvD4zrOKYGYNqCsdFRQpEER44cBTsN\naodjRLCMUMBHaF8fXSHHPq1QKJBKpRgfHyeXyxEMBj2CjEQiTot1uSvgriBJKbEtC5FbQPGFasjQ\nfX4K6CFEYRHpD69Zaa87OtDaHC2zlVneJVUB6RgsCA3p614lvKnbebHMTc0jJdKJ71rxMhT8fj/H\njh3j3//933nrW9/Ky1/+cr7//e/z+c9/ns9//vMbHrcWQgi+853vMDQ0xMDAAJ/+9Kf50Ic+tOXj\nPJe4y041vwN8EMep5jq78U93Dy4hbgaZTIahoSG6urrWJaZGrXNIKRkfH2dqaoo9e/Zw8ODBdR/f\nLg+ioWNSRqOaxPBUmiWy+HOtBMJ32mlSqtjShyJyFIs6IyMjtLe3c+bMGe81ukQrCEGp4JBhPcLT\nfE7rNDUH/kDdO3cpJTdv3iSZTHL29MvwBcuYMo+dK2ItZZBGxVnM19uwYkXUSGTdamstgpyZmWFw\ncBBd1+no6GBxcZHW1tZNht1uDq43bT6f5957723osdH2OyIgc5p8QTB6c5renqPE/UWw5h2TbF/f\nncdLy6msQo6ARwhBOBwmHA6v8BdNpVLcvn2bbDZLIBCgtbWVeDxOKBTi6tWrRKNRrHIexShjKRpC\n3km8X1HtKSoYJTDL4Ks/g63b4nQhFPB1OG1ROwe24XxNDS+7Da3+ndetOFVted64PgQCWfOztQRb\nLpd5/etfT29v74bHq8Z73vMeHnroIXp6enjkkUf4x3/8R37hF36BV7ziFbzjHe/Y0rFegHgfu041\ndxe1LdP1YNs2N2/eZH5+nnvuuWfDllsjCNFdpwiHwxw5coRyubzhz6j4OG69hkH1n/Ejl5WmjkrU\nqRPzKGjEZvchO10ydDL+FCLMzc4xMz3NkSNnaGm5U5k5i/olYA8CFbIp0NdZt1CWF+GLeajJASyV\nSgwNDRGLxbh48aLj7GPbWLO3IW/gCyRQQgEEKrZhUJmdRS0U0Ds66mYk1j+9QqFQIJvNct999xEM\nBllaWiKVSjE2NoZt2ytarNslMfd31NbWxvnz5xu/xqMoSN9xpmYrpOef4cTRLnS/HwwN7BAo7csV\nTxmk4VRZwa6VFXsVqv1FXdWrS5Dj4+PMz88TDAbp6uqilM/TomlIxekYWLbzebZsCyEEAuG01gVO\nW3cNbGqFQ9FB2Vxrsm6FGAxDeoN5pFFxbtC0lb/r2uNt16nmda97Ha973etWff1f//Vft3ysu4W7\nOEOMsutU8/zARhWiK51PJBLcd999mxIi7JQQ3XWK48eP097eztzcHAU39WEDdMqj3GO9kavqN8iT\nRGIgMRCoRGQ7J6yXcrl8zbnASQlCYJkmV69fRZWdnD37EhQ1j6TAHSW0BuxFIQyW5VxcghusKPh0\np5KsIsT5+XmuX7/O8ePHV8xmzGQSCga+yMq9TcXnQ/H5sLJZTE3Dt4lVFtM0uXLlCqqqrmiRtrW1\nrQi7rSZIKaVHkPF4fFME6RoTnDhxYkcuROvhzg6jjxMX3ooqlj9TQQ2Qzs6hvXyjpIVADW55IT8Y\nDFIqlcjn895sOpVKMTM7xa3ZmyihKPFYfEWL1ZY2trRBgmWaYFkIy6rrx9qM7MJVhOjTkaEIolys\nP7u2bTDKyNbVqy+1x9v1Mr0r+BrwALtONXcfa5GXlNLz6Dx9+vSW5kLbJUT3Ym5ZFi960Ys8cchW\n/Uc75GHazD6WxG3yYg5YosXeR1gmkLYkEmnhRz/8IS0tUXTdx/z8LAcP9dLZdnZ5zmjitPFd6zb/\n1hfjhfDaWLZtc/XqVUqlEvfee+8K0Yu0LMxMBmWdGCwlHMZKp9Hi8RVm37XIZDJcvnyZAwcOrDA0\nr4WqqisI0jRNjyBv3bq1giBbW1tXzJellNy4ccMTN23PSm5jFAoFBgYGanYYa167vv0ga7jzGZ+d\nneXChQueFVpXVxddnR3QHacsVdLpDLNzs4yOjqL5NGLRGLF4jEjIEdtYqn+F3Rw4n1k3HqyRhGjb\n9up5v21CRIdKBnIlZyVE891RQFsWMtFZlyxrCdG27ca2vX9MIBFYdlMIsVUI8U0cYcyr1njM+4B/\nFEJI4HF2nWqee7jtrXoVYnW7stqjc7PYDiGmUikuX75MX18f3d3dK9pv2zHkVlBJyAMk5AFMppHS\ndI4hJUePHcW2LK5dv87c3Bx6wObWjUVS8yPerp/PtwZBKQqoqlMprve+mAaEouTzeYaGhti7dy/H\njx9f1Va0SyXYYO1FCIFl29jlMmod4nR9Y6enp7nnnnu2fIevaRrt7e2es1A1Qd68eROA1tZWIpEI\nU1NTXru3GU5HcEegc+rUKaJrBCnvFG71qSgK9957b/25nL8FfzlH555OOvc4wp5KpUJ6Kc38/Dw3\nk0OIQJRIl0pra6v3XN3ZNDh/S0IIrDUqyO08b+8mxDahOIFSmsSWBlJTUGQZWdbB6AQ1hAxFIRxZ\ncx2omhBdgdELEhJMsymEuAR0AHPrn50s8F+AD6zxmF2nmucC1QbfUkqmp6e5efOm5/6y3WNuJZ3i\n+vXrLC0tceHChbqq1Z20YKWUYLdiyWmgAiJAuVhmZGSERFuMY8dOo4gWpBUnk86QTCZXtRFXVElC\nQEurI5pZS0UqJdKymE5nGZ+6tv6FfZMXICFE3ce6BuO6rnPvvfc2REVajyAnJiYYGRlB13WSySRS\nSq/FulOvWhe2bTM6Okoul2u8QKcK7npId3e3l7hRF5E2R+hSyoEeBEV1BErtCTqiQTh8iEqgjVQ6\n7bXDFUXxPjOFQoH5+XlOnz69ZgW5VYL0Kk7bRGQuIcws+GIorhpVtxFWDmQSO9YL2vrz/not2Gbd\n6DyfIaXAMrf3OZ6fn6e/v9/790MPPcRDDz3kHRo4D1wVQqhrzAn/BngJ8F+BYXZVpncPmqZRLpe9\nrD1N07j//vt3dJFzl303Qi6XY3BwkM7OznVVq9uNbPJUpLaKKrqwyTE3d52pqSkOHz5MtKUViKEQ\nRqhihVLTrZKSySQ3b95ECOFd6OLRKKoegHozGymxcllGpmeRLYmNl9NVddOkWCv/T6fTnkmBu1fW\naLjV58LCAg888ACBQADDMLz35saNGwghiMfjJBIJ4vH4tki5UqkwMDBAPB5vjkBnGe7s89SpUxuP\nARQVol1QykBxyWk/gvN7iLRDIIquKOwJBLz33zAMkskk165do1gsEg6HmZmZ8SpI97O8XYL0CKw4\n7pChXnPTKhRnd9HKI3JXkPEXbXg89/P5gq0OcQlxezeTHR0d/OAHP1jr2z3A94CRdUQzr8BRmP7N\ntp5ADXYJcRuobpnOzc0xNTXF0aNH6ezcmsFzPWiatq4Ipno+eebMmQ3bYtupEFc7zsCVKxOoaoBz\nZ16LpvnW9QetrZIMwyCVSrGwsOA4fAjoUCWtQZ2WaNSZ7VkWuVyeK1Nz7Dtxmq7ujS3elEAAoWlI\n215TRSotC6GqKMszLvf9m5mZ4ezZs3Wr6kbAMAwvkLi6rejz+ejo6PA8L12CXFhYYHR0dOXNwyYI\ncmlpiStXrnD06NGGGcLXwk1EWVhY2NrsU1EgFIdg7I6aVNHWdfiZnJyks7OTgwcPrrixunHDGQO5\nnQfXOq2WIOtmQi7Dtm1UIVFKU+Bbh9DVMLIy7xid6/E1H1ZdIZZKpbqGFy8ISLZNiBtgUkrZv8Fj\nFoDZRp1wlxC3CdM0GR8fp1Ao8MADDzQsgHS9xfxyuczg4CDBYHDT88mtVIi1PqSu48zIyMiOKimf\nJuhsj9DZHgFxiIohSaVSTM3Nkr0+hk9VQNUo2HDu/pdseo4nhEBLJDDm5lDC4VWVkZQSu1jEt2eP\nl8E4NDREIBCgv7+/aUG2rkDn0KFDG94k1SPI6puH6jZiNUFWzz7Pnz/ftIux633r9/u9VZctQwjY\nwCzc7XgcPnzYey9q35taAVP1CkwsFtuQIC3LQqGIlCZig7QMRfiwzfQKQixc+QHlp76OnLoOtoUR\naEVceCVW4mfI5XIvSIUpOBWiadw1lemfA/+bEOJrUm5ioXQD7BLiNmAYBt/73vfYs2cPfr+/oWnc\na1V0s7OzXL9+nWPHjm3JUX+jPEQXtVWhlJLR0VHPVWdbgarSBDPpLE57LjgCXYTZ05Fgz549lMtl\nLl26hKqqRHWdS5cuEQgEvBZsZIPFei0addSmyaSzE7n8u7ArFZASrb0draWFpaUlhoeHN0VS20U1\nSW23+vT5fHR2dnrP0SVId86mqiqxWIx0Ok0wGGzY7LMe3LWh3t7edZW3O4UrBDpz5sy6e7r15rOu\ny5C7IxqLxTyC9Pl8Xmiy67xj2y3YtkRI2zMMqAuhOGYFy1j6x7/EGvwOSjiG0rEfNB9i7DrKNx8l\nM36J9MU3vCCjn54HaAXOAJeFEF9ntcpUSin/cLMH2yXEbUDXde677z7K5bJn7dUo1HqZmqbJ8PAw\nhmGsWKfYLDayglsZ1eRUXcVikaGhIdrb27eviJQmGJPLxt01xGCXwZhkMRvg6rVbq9p9xWKRZDLJ\nrVu3yOVyhEIhT8EarlMJ+lpbUSMRrGwWu+ikKmjLXxOaxq1bt5ifn+fcuXNNr6Q0TWsoSdUS5NLS\nktclyOVyPP30014F6VZJjcD8/Dyjo6NNVau6ayiZTGZbQiBN01btiNb61LqRWFNTUyQSCUKhGCwt\n+7FSM4OsJkirAppDcNlv/AP20HdRu4+uWM6XkThKuAc5OUol9fcv2AoRBLZ116jk/6r6/2N1vi+B\nXUJsNty7z81at20W1QTmmoAfOHBg1TrFVo63Zsp9TYtUCMH09DRjY2N1fTUlNiYGEhuBgoqGspai\n2VxcJsPVlaVEZ+zWVbL5IhcvvnLVTCoYDNLT00NPT493Z59MJp0g10KBSCRCa2sriUTCSzpXfD6U\nGjPlSqXC0DPPEA6H668HNAhuu2+jHcadwq2kzp0751UjlUqFVCrF3NwcV69eRdM0772JxWJbfs0u\nSaXTaS5evNjQ7kc1TNP0ZqyNEgLVs+Gbm5vj2rVr+Hw+FhcXMQyDDp9KVGTRgwnHKGD5b8AjSGyE\nUMDXilUuU/nR11ES+1Y51dhSIoSKsucA6tAPaNcaG232YwMJNGeGuPGppWzoH/UuIW4TQogteZlu\nFu4qx7Vr10gmk2uagG8Wa11o6kU1DQ8Pe+nsKxbKkVQoUqGAjVxetHdUdT78BIisjI6ShtMmVVbf\nMZdKJYaHh2lra+PMqZ51I+3c5+96au7fv99LOneT4YvFItFo1CMBt7WbSqUYHh7myJEjTQ1tdZMw\nNmr37QTrrVTour4qgSGVSjEzM8PIyAg+n29FBbkeQboz1nA4zIULF5qmVnWNA5rdik2n09y6dYvz\n588TjUaxbZt0Ok160Wbp5pNULB+BljjxaJxwJIJf17GtClSSGMHjYEkK154Go4QIrf4s27aNogrQ\nfFhmhV5RbNpreV5DirtGiI3GLiHuAI0y4q5GuVxmaWmJ1tZW7rvvvoZflOoJZ9z5Wl9fH3v37l39\nnMhToYCKH7XGdcagjI1NiNgdRxpp1D33/Pw8ExMTHD161Klw7PzyYzfv2FKddN7b24uUkkwm45kT\nlMtlr8rejAp3u7Asi+HhYaSUTU3CcIVUra2tm6qkagmyXC7XJchEIuGtMsCdKreZayhwZ3Xj9OnT\nTfvdAN4st1oVWy1Q4kAvMn2JQi5FNrvI+NxNzEqRUChCoO0sLbFOAj4farmEKW3HOcnVbAgBCGwp\nUZd/H6Zp06LvWNPx4wkJmD8Z+5e7hLgDNIKsJJIMJSxpk7o9x+zENIFAgMOHDwNgU0FiAQIFn2OQ\nvd1z1RHO3Lhxg1QqteZ8zcKkTBFtDQs2DT8GZQzK6KuiyBy4sVe2bXP27Nma3cKd7W8JIbw5UXd3\nNwMDA+i6TigU4tq1a14g8B0XnZ0vrLsOOt3d3fT09DStknJXKo4dO7Ztowe/38/evXu9Gx2XIKem\nphgeHsbn86HrOplMhnvuuaep80LXBLzWgq+RcK3+DMPg4sWLa9+o+KKIxEsIR5cIGyn2SolNkKwR\nIJXOMr1sFxhenCdeqhCQzg2ws/fq3FSahulEUEkLo1xGa9/e7+gnAo1tlG0aQgibDS4iUspdp5pm\nwyWU7cLCZkCZ5PvKOClZoJDLoXaoPLD/CMpTi9hUMEhjUbmTRYhEJYxOdMvE6Krt3OfuJkckEol1\n5fQGJZbzCdY8topGhUIVId75WOVzeUaujtDd3c3ePXtZdZiNeqabRDKZZGRkZJVAxxVabOiis0nM\nzs5y8+bNpotNXJ/QRq9UVBOklJLh4WHS6TSxWMxz7XHfm+oKcidwrd5UVd3+6sYmYBgGAwMDtLa2\n1rX6WwVFAT3h/AcoQAyItbbR19eHlJKlhSOkn/oK85NjSC3g3ED4/OTyOeJx5/2pFMuMjY8zGD/f\nlNf1vIfkrhEi8EesJsQ24LU4rae/2crBdgnxOYbEoEKaL6tXuCbS+CoSK12moyWBL6DzLFMUTqQ5\nwTjtRNFYeTG0KFGiQoD2TZGi67E4MzNDIpFA13VPOHPixAni8bUXjwFMKigbfEwUVEzKntgGRUcS\nYHryFnNzS5w4foJQuGYOKg2HDMXODK6rDbOrTaZd1Aot1nXRWWcR3q08yuVyU63RXJN2V63aLPKo\nVCoMDg4Si8W4//77PfIolUorKkhd173quqWlZcvPp1QqMTAwQFdX1/pWbztELpdj8Ac/oK+tjTZN\nw5qeRrS0OPup23wPhRC0dnSgPviLRP717xF7eimUKywuLqD7dJ599lkmbt8mVkxh9p3lw3/5/zb4\nVf2Y4C4SopTykXpfF0KowJeB9FaOt0uIDYDbgtwIFnksFviBssg1kUFLG9jSJtHWgqJIFASthMgq\nC3xZG+Md5uo7ThU/FiVMcvhY3z7LbZGePHmS+fl5z0hA13UOHz68yb2pOwKadc9V9RjDMLh8ZYoW\nPcfZsydQajP2pAGyAr6edV1LNoI7X4vFYly4cGFTF+sNXXRUdZVKs1gsejZ5m6o8tgl372///v10\nb8KpZ7vIZrMMDQ2tWIJ3EQgEnMSKZbFLqVQimUx6ocB+v9+7gdiIIN2WbzNjrgAWZme5/r3vcfzA\nASKxmJMFaVnYc3PYqorW3Y3YQYs2+vKfI51dIPvdr5A3JN19R1H9fgLlLJUbl0l3HuLf7Tb+8sIF\nPvShD/H617++ga/uxwASqC8buGuQUlpCiI8DfwH8P5v9uV1C3CZqQ4I3ar3ZlLFYwMbP9+wZzHSe\nUCDsrA0gkNjYlBDoBEyFDBUmRIYDcjXpKfgxyKMRqVsl1gpn3PWJhYUFjhw5gt/v9/b8qiuoei0y\nDZ0KxbXXK3DmjBo+BIqn7jx8+DCd7TEw58HKVWXtSacy9PXUXcnYLFxxxk7ma7B6z69WpQkO8R4+\nfHjbqy+bgduKPX36dFMXvKenpxkfH990skcgEKC7u9sjaDcU+Pbt22QyGQKBgHcD0dLS4r0/t2/f\nZmpqqm7V3ihIKRkfG2NhaIizp06hV6t8VRV8PuxMhtIzz6DE4whNQ4lEUKJRz8pvs+dJnn6QlB2m\nNzMOE1eZvTHHV558mtf9zgfp/6Vf4X1+P1LKhqvOd7Ej+IHNpUcvY5cQdwh3kX5jQswgbZVnZkZJ\ntebojraiqXd+RqBgYyKXb7UUBNdEsi4hCm+qaK0ixFrhDMCNGzdYXFxc4Z7iVgaVSoVkMsnU1BRX\nrlxZ5RKjiwBl8nfaoXUgMdFljNEbo6RSqZUXQX2fs4jvhtEqutMm3Sax2La9Yk+u0ZmCrkqzo6PD\nc+rp6ekhnU4zMTGxJRedzcBNLCkUCk1txbrCJjdXcrsG9MFgkGAwuIogx8fHyWazBAIBTNP05oWN\nSvOohW3bjsq3UODMsWOoNSsvErAWFpC5HNIwnJiwYBC7WMTOZFDicdS2tg1/f7Zte1FX59/wnxBC\n8IUvfIGPffVjfOGxH9LX1+c9VgjxgsxDdN7su3NqIURvnS/rOO41HwTWdA6vh11C3CHcvcH1LswS\ni1xxkatXxrG6QkRbWtDqvPUCxSE5IVAklLfQmK/nOOMKZ1pbW9ecR+m6vkKFWOsSEw6HaWkPEUr4\nCAWiqFUE7C7q22XBMwOXSLQmuPfee1dfZBS/898O4WZNukKgZlVr1asOta9nqy46mzlPIpHg3Llz\nTXs9bhpGIpHg2LFjDT1PNUGWy2WeffZZ/H4/iqLw1FNPEQwGvQqyETcQcOf1tLW10ROLOfmaNbBS\nKWQ+jwiHwbaxMxnUWAzh94Pfj720hPD5UNdJ7TAMg0uXLtHR0eHtwH7oQx/iqaee4utf//qG8/cX\nFO5eYXyL+jMdAYwC793KwXYJcZtYLyS4GlJKJm6PMzU/zLGjZ7FadL7LANJbcF91ZIQAA5tW6qsL\n5fLEzq0O6znOzMzMeNmMW5nf1LrE5PN5kskk4yPTFKyrhFvCxOMxYvE4AV+A7HyR8eu3OXniZHPn\nRAsLXLt2jePHj3sCmWbAVauudZ61XHRu3LhBPp+v66JTD25react342QTqe5fPly08/jziWPHDni\nzWellF4FOTY2RjabJRgMejcQ2yHIWhNwY2wMaqoyaduQzSKW1blCUZDLfqbu+UQohJ1KoUSjdZ9D\nPp9nYGDAO0+pVOJ973sfsViMxx577IVZCa6Fu6sy/VVWE2IJGAOeWic2qi52CXGHWG85v1wuMzQ0\nhN/v49zZc/iWvRH3EiZJiTC1f1TSITnhLP2etOtfwGwqaIQRqKtapJZlMTIygmVZ9Pf37+gPVwhB\nJBIhEonQ29uLZVssZZKkllLcnJggnyni03wcOnSo6S4t2Wy2qftrUkpu3rxJKpXadCt2Oy467j7e\n3NxcU+dr4EQpTU5ONjUNA2BmZoaxsbFVc0khBKFQiFAo5N1A1KuwXZHORgTp+quucAVSVbDtFXmX\nslRyIsGWjyWlhOUbRe+5KQq2ZSFLJY84XSSTSc88oKWlhYWFBd761rfy8z//8/zGb/zGCzIEeF3c\nXZXp3zTyeLuEuENomoZZKUMpC+WM42ah+pjPVrh6Y5yjx47R2dmJyQI2FRR0Xmrt4/9TR/ChoK+Y\nAUpAI+9TOGuEiaurL/42FUCgyQiWba1wnHFDb11LrJ3+4Urs5UrUaeeqikpbvIOAL0RybolDBx0i\ndFWIgHfx327YbTVcdWd7e3tTrcTcgOeWlpZNq1XrYTMuOpZlEQqFOHPmTNPI0J2v2bbd1DQMKSXX\nr18nn89vai5ZTZD79u3zKmw3zmmtFrSbx7i4uLjKX1WJRrHm5xHVVoO1BFkuo64lVKrZJZ6ammJy\ncpILFy7g9/sZGRnhV3/1V/nP//k/88Y3vnEb79ILAHeREIUQCqBIKc2qr/0Mzgzxm1LKp7dyvF1C\n3Ca8likmpMfB1w6qjmVLRkcGMMoFXnTyHvTlNpVCFJtpJCr7ZZQ3WIf5qnqDvDDwSxWBRRmQokxv\nNsorfCewYxaS8rKYxWmTqvjRZRzbAinv3AXfvHmThYWFhoTeWhhUKGBS9r7mI4RP+pmZmmNiYmKF\nGtJtxVVHFbmGyq4AZas7bG7cUbMl++5qQDM8T6tddNrb2w7SPbEAACAASURBVBkcHGTv3r0oisLl\ny5eb4qLj7v3t2bOH/fv3N+0mwjAMBgcHaWlp2fb8s7rCribI6hZ0KBSiXC7j9/s5f/78KnJXwmHs\nxUWkYSCW3z+hKB7RSctC2DbKWora5c+kS+6FQsFzuPm3f/s33v/+9/OZz3yGCxcubPn1vWBwd1um\nnwfKwNsAhBDvAT6+/D1DCPEfpJRPbPZgu4S4E1gG/tIipqaDHiabzTIy4riydHWdQZglyM1By14U\noaPSgckCAoVjMkaPeQ/DyjxXRRJL6By1Ozgv95Oav426RydAKxJj2boNkCrYKlaVcMZty8ZisYYs\nclcoUiaDQEVb9hiVSEpmhoHRUQJWlBe96EV1q47aFYZyuexVj5lMxpsfJRKJNQUorhrSVV02s0Xq\nti6b3VJca6Wi0S467lyy2XNWd77WaN/T2hZ0uVzmmWee8Srp73//+4TDYa8LEQqFEMt7hubkJLZh\nIAIBxPLj7WIRISVKR8eqPURpWQhVRfj9WJbF4OAgoVCIs2fPAvDZz36Wz3zmM/zLv/wLPT09DXuN\nP7G4e4T4APD+qn//n8CngP8D+GuceKhdQnxOUM6gahplG26N3SKZTHLq1Kk7FZovCOUsmCXwBVEJ\nodCNRQGbHCHgXrub+ziOIOCJbLKqs8rhrFc4f8j1hDOzs7PcuHGjYRdAC4MyGVT0FSsWuWyWq9eu\nsW9/D20diXVt3Krh9/u9Je/q+VG1AMUlyGAw6LVIOzo6Gq6GrIZhGF4KfDPdYGpXHWorwLVcdFKp\n1JZcdNxg4pmZmabPJd05XrP3JbPZLIODgyvEQK7IK5VKedVcOBx2WvRtbQQsCzudRkgJuo6wLLTO\nztVkKCWyWETt7KRSqfDss8+yb98+uru7sSyLP/qjP+L69es88cQTL+CMwy3g7i7mdwKTAEKII8BB\n4C+klFkhxKeBR7dysF1C3CYEQDGNicbt2+N0d3dz/vz51Sncqs+ZLfqWFW9oaESBtX0wa4U6tcIZ\n27YZGRnBNM0dC2eqYVBEoHpkKJHcnrjN4uIip06eIhgMYlLGpIy+hgJ2LdSbH+VyOZLJJMPDw+Ry\nOUzTpLe3t6kL8JlMhsuXLzc91aFcLjMwMEB7e/umyX07LjquT6iiKE0ldyklt245N33NzEmEO7mP\nZ8+eXSXScUVerojJVUGP3rrlZGWGw8TjceJHjhAoFpGZjPN3o+sgJbJSAdNEaW2lIASDP/qRd0NZ\nKBR46KGHOHToEF/84hebNnvdRUORwfEuBXgFsCClvLT8bwvWSBxYA7uEuE1I22JmdpqJyXni8fiK\nBd0VUDSwttZPcAmxXlSTe0F37b0aRRwSiUEJdbkirVQqjIyMEImEOXvurEf0ChompS0TYi1cAUo4\nHKa4nHK/f/9+0uk0ly5dwrKsFfO1nS54SymZnJxkampq0y4t20WjWpcbueioqkqxWKSrq4vDhw83\njQwty1pWS/t3JDraCC7pplKpTZkU1Kqgq1W+N27ccAjS5yOmacR0nWAggNLSghqNspDJMDo05JHu\nzMwMb3nLW3j729/OQw89tKsk3Qru4mI+8CTwsBDCBH4D+O9V3zsC3N7KwXYJcZswDJN8Lsfx48eZ\nm5tb+4HSBmVrFZy77F/rOHPr1i3m5uaadEF3RAgCsWx8fYNDhw6vErS4NnONQKFQ8IQmrkeoWx1Z\nlkUqlVplwu0qWLdyUa6topqpuhwbG2N+fr4prcvqrMPFxUWGh4fp6emhVCrxve99r+EuOuAofQcG\nBryWYrNgWRaXL1/G5/M5nZZtkG49la9LkDeTSYrpNJF8Hjk9TalU8irdwcFB3v3ud/PhD3+Y1772\ntU14dQ4+9alP8d73vpd0Os2HP/xhvvSlL/GmN72J3//932/aOZ8T3F1Rze8AXwEeA24Aj1R9738B\n/sdWDrZLiNuEPxjk8Knz5JcW1w8Jtg0IbX4hWkqJoijMzs4SDAaJx+NUKhUuX75MS0sL/f39TblD\nFyhIKRm9eY1CvsQ995yt2xazsdf1Nd0sZmZmuHXrFidPnvS8Vquhquqq9mEymWR2dparV6+uULBG\n11iuBmeRe2hoqOmG2aZpelVUs1uXY2NjLCws0N/fv2Jf0p3RukvwO3HRgTsmBadOnar7O2oUyuUy\nly5dangiRi1BuuKZUqmEruu86U1vAmB0dJRPfvKTTSXD4eFhxsbGPNP0v/7rv+bWrVv09fXtEuJO\nTi3lNeCYEKJNSrlY8+3/HZjZyvF2CXEn8MfQlAUsc42JslUBoWFrOmAiUNb0A4U7wpn29naEEMzN\nzXnyfFecUntRs5EYy/kXADoKfjSUdc5TD4VCgcvD14h1hjhz6MyawhmJic72l/Aty/ICXLfi3enz\n+VYkwbspDBMTEysu/p76UAgv5mrFIncT4LqnHDhwwLvgNQNu61LX9bq5go1y0XkuRTqZTIahoaGm\nK2PdrMREIuEpSX/2Z3+Wxx57jHe96138xV/8BQ8//DBPPvlkwxTHjz76KI8+6mg67r//fp588klm\nZ2f59Kc/7QnjfiJwdytE5ymsJkOklANbPc4uIe4AQg+iRLuQlREwSqAtm1ZLG8wSpjCptLRgK4vL\nS+4mCjo6YTSCK3IGq4UzbnWUTCaJRqMcPHiQbDbrXdhaWlqctlgiSjngNDDVZQorI8lRJoKfIJsT\nPnj5iCdPo8WsZdPw1R8NkwoqPm/OuFW4SfNuJbCTC0J1CkP1xd9dFLdtG5/P13QydCvdZp+nUCgw\nMDCw6Up3My467ufIddGBKtNsKddPnG8AZmdnuXXrFufOndvx7ux6KBaLXLp0iYMHDzomGabJww8/\nTCqV4qtf/eqK197Iyv7Nb34zb37zm71//8Ef/AF9fX284x3vYG5ujv7+ft797nc37Hx3FT8hIR9i\nJ6nvDcbz5olsFpVKBcuy+P7/+DYPnDvprFgACIVyyEfFB6oawMLAILecZmEhsQnSio8IumxB2mLF\nOoXrC7lv3z56enpWEId7YZtJzjORm8MuG7RG48Rb48TjrWiqio2kgkmMIIFV9nB3YJomIyMj2LbN\nyZMnneQOTIosYWMvG3kLj8xVdALEt1x9wh3SbbZc3yWORCKBpmmkUikqlQqxWMxrHzZCIVm9UnH6\n9OmmpTrAHZOC06dPE42urU7eCqpddJLJJJVKhXA4TCaToauri4MHDzatgnFDnTOZDPfcc09T3zvX\neMFt+2azWd7xjnfQ39/PI4880rTW9gsJorcffntLoRIe7v1sPz/4Qf2fFUL8UErZv5PntlXsVog7\nhKIo2MIHkU4Id4CUmKJCRaTQCGBSokIaFR11mZzs5Y0/IRUMq4xftqII5058bGyM2dnZNYUz7lzE\naFFpYw/YkM1kSKZSTExMIBDEW1uJxWOIKPiFVrf96ZJurc2bikaYxLJbTRGQKPjwEUXFt+kdRBeW\nZXk2Yv39/U29+Lly/eq55MGDB7Ft21uAHx8fx7Ztr3XY2tq65SrITd3YykrFdlDtr9pok4JqF52+\nvj5SqZSXjJJKpZibm2u4iw7gzfGCwSDnz59vattwenqaiYkJr+17+/ZtfvmXf5lf//Vf561vfetP\nTsvybuN50DJtFHYJcQdwfRarvgBCUKGAWiwhU+MYlUk0oUO4DWIJ8PlRpELFLmLJABIDUxShojM0\nNEQkEtlQOGNgYWLixwcKzt7VchSNYZosLaVYmJtn8eZ1WkWIPfF22traPIKdmJhgZmZmbdJFQcPv\nOdVsF66gxZ1rNesC5FZrxWKx7lxSURRvwf3w4cOYpkkqlWJxcZHR0dG6+31rYaM0jEbBMAyGhoYI\nhUJNXXUAx79zYmKCe++915uf1brouDcR23XRAedG4tKlS01XrLoVaDab9TIZf/jDH/Le976Xj370\no7z85S9v2rlfkLi7i/kNxS4hNhjSNrFnL6OlljB9NkIXCClg8TYsTiD3HsSOtAEqlijhJ8pc8jYT\nIymOHzu+qYieO5bbq+HTNDraO+ho76AXE60oKSad+aO7/B4Ohzlz5kxT5zZTU1OMj483vUXqendu\nxd1G0zQ6OjpWhSRPT08zMjKCrusrPFirDaYXFha2LzSR0hFa2cuqZM0Pyurq1BXp9PX1eTmVzcB6\nocGbcdFxK8jNGLm7MVQnT55sao6gu76h6zrnzp0D4J/+6Z/48Ic/zD/8wz9w9OjRpp17Fz/+2CXE\nBsHdFzTGf4iVvYaIHsbWSghhASroMTAM5O1r2PsVRLjFSUu/cYOSkeXivQ8Q0BtPUKFgkERPlGAw\nyPDwMAcOHEBKyfDwMJVKxbuouTO3ncI0TYaHhwGa3iJ1MxJ3agC+Vkjy2NgYuVyOYDBIqVQiEolw\n4cKF7QlNKgXIL4BlOPcyEqejEIhBsNUzmXbbvs0W6biqy3g8vqkbie246LhwW5fN9ox1bdhc0ZZt\n2/z5n/853/jGN3jiiSeaWtG/oHF3F/Mbil1C3AG8sFFpUph8HJa+hZi8ihHXsfMhrPAxlJYjKGoE\nG5CoCH8QNTlDWijcuj5Od/tB+rp60MXm50O+Kmu1jWZ6qi24fuM66XSaixcvepVNX1+f1xZbXFzk\n1q1bCCE8ctyodVgP1XPJZrfERkdHyWQyTTEAr15fyGazXLp0iVgshmmafP/73ycajXqztbUqRVmp\nIPM5KBTBLIKZQcQTCH9Vi1pKKC6BWUFGOhm9edPLfWxmAK1bgR46dMhzwdkqNnLRcfdE8/k8hmF4\nrctmwX1NR48epa2tjUqlwm/+5m8ihOBf/uVfmmo1twt+YmaIuyrTHcA0TcqlNKPffoSujhxaoQUt\nX6IS0SnbaaQ1i6VF8IVehtCjCH8EFD/J8VFuh+IcP/oAkVALEosAHVsSrGQoUcasyVO8AwMLs2Rw\ne3CURCKxKdWgu/yeTCZJp9P4/X4SiYQ3f1xvb80Noz19+nRTK5tyuczg4CDxeJxDhw41XZRRu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3sXOIeD+8bJuEOLUrqlkLz5sncjfxox/9iHe961285jWvwbbtddur6YVr5LLz+ANR4vE4ra2t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\nGfj7+9vfx59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axxeTPjRVovA8D8dxij1FpRQiQigUwrIsDMMIepEBAWXYn3qI+8t1BOxn+L7Q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\nOBnV6lNPPcX8+fOprKx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TRv3L7zCl5L93s+yhgQ0xIGDncX2PVak0W/M+1ZbmebaRdQxC5AkbeWxbo0QR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\nefz5z38+LeuTKisr2bhxIy+//DK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ajRriEWOoZoi7wkD2SMdx6O7upru7e0B7ZNEmWRR8xT4PZo/0F1n+\nBLM3M8ShDwLZK3yB6DMonufR1tZWChjXWpNKpVi5ciXRaJTp06f3i3cr0lCtWd7qkXWEkLnd4CaC\n7SpyShhXWbAZWoQIUINNCo/iYrtCCEWUSnLYgMbSwuZsis1NW9COYuS4EcSVSauCMCa57izxgIH2\nHHpSSfL5AOgUyVAlc3NBwiEXJ1RwojFFUUGAILpMGObFYYvKgpEkZ5gMI8Kx7nAMPpzpeVDIiCqC\nrVK4KoWLQ540Dnm0MjEljiIIpAhIZMjCLgq38KMTiANhmiZVVVVlmYb62iObmpro7u6mq6uLZDK5\nU3tk30WW+9ojfacdn48SXyD69KOverS9vZ1gMEg4HGbdunVs27aNSZMmUVFRscM2YiHFzINMXvvA\nIaihKqRQGkQU23o80iiOOUhTG9Jkei1ubkFMlex+BiFMgoAiQgedniK3tZlkcguJkWNItmYJ6SjK\nyzIG4T0sQiQJWw4BHUKHq/FQtIvH4T1hxrR30djVRM6xCUciVMbjGIkKrHi8NIvJkKZTZbFFY2Gg\nxMDDJaOSmBIiRASFQivwREjRRVClAQtbZREMgiQKyclJYWJgECav0rhWdof37JNOX3skwKpVq6iq\nqkJESvZIz/P6xUcOZo8squfhw6TmfnzkfsjezBDtnVf5KPEFok+J7RfsLX6pd3V1sWHDBg466CDG\njx+/ywPRwdWKWMhkZavHpi4BhG2OxaQEzBimGR7RbLEVm5wsLUaS8YYQMkwUBg4uNimCCBZhct0p\nlm/dyoRwnM+Mn0pSZWjf6iCAKWGOUQ5RlWcFYbpVHaYyAA3icjwRzgtHcMZW0q08bPHIZrJsS6Vo\na2vGXbcWQyBUEcSqMgnHqtGBguArxEeagImjcuREESLSe4U5HNVDmDA2OTzsXgFOIapSBXFJYkig\n4OBj2Lg4ve3t/W811AJhTzxXd9ZeJBIhHo8zfPhwYHB7ZF9VazgcLhOSxX75iyzvx+ypRcAXiD77\nI8UVKVzXLXmPptNpPvjgA7TWTJs2jWAwuNvtVkcUn20wyDhC3oG/pLr47GjFWtfg51s1K3J5wkaW\ntApjGnBKSJgd9wgZhcwuHU4n6z5YR5udZezBIwkEozQDCcJU5QwqtUcOB43J6RJkygedhMeFsC0T\nE4/RYjFKwnThkVYeQRQBZRCNRKmKRMgPq0OjqHKgNb2ZdHcPmzZuYovtEvAcQqEwAcskGo1hWIVZ\nYEBCaDSe6kb3xjTmVQa93etU8DYFlywmMUCTJ0OY+N7/YDCkg/9QC0MoCL/tZ387skcmk0nWrVtH\nJpPpZ4/s++wVF1n27ZH7CfvOyzSqlFoGrBORL+6TM2yHLxA/5QzkPSoirF+/nubmZmprawkGg3sk\nDPsSNhVhE6KW5u204tZtJgE8GgI9KGUgKDpFeCatWZOH66pd2rpaWdm8mdrhNRxTdTgRlcHAIIVH\nB0KPEaRa1eJICks8Asog4lgc6grKLGSjqZAIGSCtPCJsNzijCKHI49FhesQTcaoTNYwUqBFo3fwB\nXt6mK9nN5s1bcF2XQNSiJlRHPFaJRPOEjTCCIHgYBPpdt8LqtYnGUJ7GVfaQOBLsCwG2L2acu9Lm\nQPbIXC5XEpJNTU3kcjnC4TD5fJ5t27YRj8d3yR5ZbN+3R+5D9p1ArAeaAEcppUXE29kBe4svED/F\neJ5HPp8vDVxKKdrb21m9ejUjRoxg5syZNDc3k8lkhuyceaW5q8MioYUK08bQLp4EUEClgoiC9/KK\n/1jfzJGhFOPHTyBuetSiEaJkSFOBphqTFs+jDgtPwmQkh0kI1xRscamQCCEsFIpu5RDaQdhDAE1S\nbEwlWIBWUCGw1bSIWIGy5ZlS2SS5LocNW5sx7I10e1YhT2iVS2WkikAwuIPBdseJCnaHoVaZFuNK\nh5KBZoi7SvEjrGiPFBFSqRTLly+ntbWV9evXD2iPVEqTU5DrHToDSsAp2CO93oxDSiksI4RjmDjK\nwDIMgoYiqAqL3frsJnshEFtbW5k2bVrp7yuvvJIrr7yyb8s3ATcDo4BNe9HLXcIXiJ9CBlKPZrNZ\nVq9ejed5HH300YTDhaj64hqGQ8VyI0G3C3WB3gyhZRMdRb4nTSydYV1iGF8dVUOloRFsFEKAMEZp\nnfo8CodtkqdeQiQkgYlJcy5ElRcl3GvLyyN4ImhV8CzN45BVdmEBYBQhsQhiopUihxDt7UlcQVQ8\n0trEFrBUr80qHCYYijOmPkBcV+C5Qqo7TXummfVt63GyDsFQiFg0SjQaIxyzCBoFFamnXEzpP4vc\nU4ZaZTrUmWWGUmgrpUrOXePHjwcg63o096Ro6u5h1datZNIZHGUSj0apiMWIRWMEQiG0dqgwUlgq\nj+Pl2UaKFtvFy4Yx8mHy2xxG1h9MyDIYFTSJWL49crfZQxtiXV3djhKOtwG3Au9TmCnuc3yB+Cli\noBUpRISNGzfS1NTE+PHjqasrX21lyAWijhNSBSkofCgP83mbrq4uQuEwo+pq2Cge7Z5QbRRzihYG\nJxMLEwsPj1g+TEAS9M2WqlBl6kRBQIGLR5fK9K5nWPAgFSCj8mTIY0mwN0GbV4gbVFAlQtRzMYG0\n0JtDVTFSmUQAlxiO2UVlVSXxqihp1YUpQfK5HN2pFB2d28hs7USycSKROLbjkEs5BKJ7Lyj2hQPM\n/jRD3Fl73Z7QqhQ6FufgWJycDKfJK9Qxenqgu5vm9jbSdhc6YmOFK6mP2xBz6VJRQlpjmD1020m6\ndRpPx3HsCtbkbUaaHqayUQZYZoCAGcTSAV9IDsa+U5l2ici0nVcbOnyB+ClhoBUpOjs7WblyJbW1\ntcycObPf8kAw9ALRVholRYFoIiJ0JrtwHJfKqioss9AHTSFEo9QPyuMdNRotBkhBqJYE4nYDloHC\nQ0iqgk0p0OeRV3yYlzSpMoz0AjjKxuLDIP2gCMN6x3Qbm6CECPfaIg3CuGRwyWEQxJIIjsoQCAWo\nCVVTXRPFYBx4IbrTXXS1pdi08QN6enpKi/xWVFT0cxrZFYZagH0SBKLrugVnLxFagAgF9TZApwcR\npbBMg0wiTjQRZ7T2yKl2nLzQ3NPK+tw28u0OuO2EAxbhSBhRNoYXIKU7SCgDLUGaVJp6o2DrTecF\nyXtoAhgSwTQCBC2LkGFhGqZvjwQ/dZvPJ4ftF+xVSmHbNu+99x6ZTIYjjzySaDQ66PF9BWIxsHxv\n7GAHk6MRVZidZXKkUykqKyyCFdWlVh2vYG2r0B4uDiZB9AA6GVEKLYLu05/irLdIIYhDyOIQG8Dp\npXA99M4aLYJikiOLUhoUpZykLi5BCREi3Oc4TUCqsEniqgwWGiUWeZXGEw+DGEoCmFozLDqaJqOd\nyZMnA/2D2PP5PJFIpGyR34E+UMr6vZ8LxH1h59Ras00gyIfCMC+FLdIre8NKkRIhTAaFxgoYhIJQ\n5R0MNZoq7ZLN22QyaTq7usj1dJNenUXHNlEVqEUiCay4JmwYaM+kUwxaVR5H8oTdGAlXEUVTjUFY\nWWhtsjafYZl04SnF8GCQzwUqiQUGf6989k98gXiAMljKtaamJjZu3MjYsWMZPnz4Tgcs0cLqivU8\nZ/yFFl2YZdV7IU7wDucYmTKgoNoRM3UP/+O5NLdtI6ANqmsOJmCk0OTxMAFNuwdHB12iZg5NgMAg\nYQq2qUhIIVawJKy3E4gAASkka3NVYaX7sutDSItQiYGtHCokhkWAnGRxVWGVRC0GEaJlmWqKKAwC\nVOFJHMHGwiMsmsLcVBVqSP/XbPsgdhEhnU6TTCZpbm5m7dq1AP3yhRZ/r0+CynRfCERPa3JAtE+z\nDvTzhlFAWjLElUWebMEDVXSvOkERCgYIBQuGbDuaZVjVWDbZ6+npydLTnif9QRJTC61VMXQ4wvBg\njHgghKE9soTJIDjKpTOV58F8Ex9YeURpRHnovOJ+NvB5s4pvVI0/8JMI+Ms/+ezPDLQiRXd3NytW\nrCCRSJRWpNgZGdI8ElvI2miWuNJUSWFm16YyPGq+zV+8NXzZOYsw4V164T3PI5TqZGpPI8tqxjIu\nZKE1OFKBliyGztDl5jCUwd9W2lRTQQ8mwWK6NBEyIr2Juj20pwiLwnYLicT1IF0wlaIWky488khJ\nrLkIHlCJJo5BDgdBMDExiRFzEgDEdiFuUPcG7+8pSimi0SjRaJQRI0YA5esXrl+/nkwmQyAQKKlY\nh1Io7qtUcEMtENF6YP1EX705BZW701soePSuBd3/sN7nJq0zmEGL2uAwnMoodYZLk2sTyaXQ6Qyb\nu5sg5RK0TCoDwwhH46zA4BljC9mgSy0WljIRgbzjksqmWRjfRvu2v9L0g3/jhhtuYNy4cUN2L/Yr\nfJWpz/6IiJBMJhERgr3u/67rsnbtWrq6upg0aRKJRGKX2/tv/RwbdJ7anE2kj6qwShTpjGJZMk86\n/SKz7ZMJRmzilRANWZgUAtf70tXVxYoVKwD4h0Or+K02ebpbIQ6EFXiEyUiYGsPjpjqHiVZx7UGX\nLvFIiku3FHKIFmx/mm4nyLsdiqjbu/KgguaeANVZ6LPwBlogoKBeTHII2d75ZBhFBI3Rd4a5nfp1\nKO2nu8tA6xfmcjm6urpob2+ns7OTN954g2g0WrJFDpQKbVf4uHOj7gqe52EMcG0FRXi5RPSgN+m6\niw0kydOtzcJHFIpwb/5az7VBG+RVDgsDpCBwHSX0mMIwK4KOxanFQ6Ex8h5uStPemeJZ6aKj2mFY\n1sAzNY5WGJaBgRAyLZRn8qbqoSfbSSAwdN7F+yUHiCQ5QC7j001f9WhTUxPhcJgRI0bQ3NzMunXr\naGhoYMKECbs14LXTxnJjG3WOh9PHuUU8SLVZ5DMm1cE8Gyu30JP7ADcXo2srJCqgqtIgpGIEiOI4\nDu+99x49PT1MmTKlkPlGwd9Ve5wT91jco2m0IaRgekSYFhIs/aH+pRKTnLh0iaIGMFRhmaZcVpFN\nmaQ8QSzFMMDQCqWgOakIhqGq14QTJkAnaUIYvYNhfxw8QmLt98s0BYNBhg0bRiQSQUSYOHFiv1Ro\nWuuyLC+hUGinv/2+iEMcCNmLWEzP8whqTQBKoTAAZm/8ak4gqIrngQgRWmihQwlJcYkYOUQbNIlH\nWClqJYinbZSO9X4QaVzPIqgzdJBGsHHQFBXjLgaWFSRelaBFe6SNFBVaEzQNJC/YTp5MzkG83msU\nyKgMnZ+ZQDy+Z9mJHn/8cb797W9z33338Td/8zfk83nmzJlDd3c3d955J8cdd9wetTuk+CpTn/2F\n7dWjhmGQyWRYtmwZoVCI4447bo++Tt9Vq3EBUxk4fRIOpjst8nmDYNRGG3nSKN5nM0dZUwhb0NMl\nBE2FxLppaW1h45rNjPn/2XuzWMmy60zvW3vvM8V8xxwqs7KyBlbWxCJZHFsNqtj0IHbTpkqm24bh\nFxlt2IZfJL/0iwEbMNAyYcENu20DFhtNGQa6bUjdpKRCy6JEkZI4qatEUiWycqycM2/mnSPiRMSZ\n9t5+OHFv3sy6ycrMyprvny95px1xIk7stdda//r/hx7atoja2eNbDODv935+BlZ4T+qEBbnRhykt\nrAwhDj0t7clFGDlPBwi0kISO9TEkIcQBBGgMmhJbZwG3voZT86aE3Z073o3YyuhuJ4W2RdjZElZI\nkuSmILmbystb5XBf4RlRMRKLp+6utryeTpXeeXDcItV0geuA8Tdah7MKlhxkfqqhKrApnnUPAdCW\nDm3pE6iY1SqgdJZrakhQCWEUkDlL5poUasyCHjHxIRqDEoXHUfmc3FtiDM7DcjXEBZZYNGhLlCgg\nAJ+QZ/n253J9eZXRYocvfvGLfPKTn+Szn/0sv/zLv3zH1/zlL3+ZF198cfvrb37zm7z00ks88sgj\n27PC7zj2SqZ7eKexm+Sac461tTXSNOXDH/7wTVJYd4ux5Cg/7cF4j/dgSyFLNVHDIlLVx3ClyFRe\n16iAOIaV6xmjc6+hI89zn/gkcXjjg3u3YxxD71Fycy9qlNV9H63q5xZ5GABbxWARCJQwyDxxUJdB\nuz5hU8bkVJipQ6EHKupNuusTzG5M1rdAIu2thjGG2dlZZmdngfoasixjMBiwurq67TrRarW2Rz/e\nqgxxgmVNKjR1mVttj8FUDLHM+4CAOwvEWwGxpQTrPKvUQTECtMC8eFZcTbJRHpZ8RSyzJJIyLxWF\naEqG9EzAyAYMXcSKK4mVQ/s2XVPQ1teoxDB0lkw0Ha8pvSFHU5KT+4qeS3EqhSlpauteAotsEbGU\not1p81Ar4rWNZf7gD/6AH/3oRywtLb2p17MsSz7zmc/w/PPP8/Wvf52nn376Ta13X7AXEPfwTmE3\n9qiIsLKywunTp2m1Whw+fPhNBUOAhg9wCN4J1inKAvKxwllwzmMCC17hPcSunqFz3nH92jUuXVrh\n4584ysL+Joqbg99uLNCfh2x6wt+JcQ6hYTs9MFL/nvU31o8CSHNhoeURqccqZnyTgpKJlBRT497E\nh8QE6F025XdzT+1uen4iQpIkJEnCvn37gKkMXZrS7/e5cOECg8Fgu998r7ORt6ISWJ/K5u2kwtQa\nspoKx5qULPrwpp97PAWeYnrvBCgi5Ka5xq4SYu8ZeBjWf0Qk8ISB0MNVyVhQFT1CjLRxJES+ScmI\nnCGB8jTEsxaWPChtkrBCy5AShwJmpOKsq1hTwoJvoHEYYhLJGQt41cT7lJQSTUC1HRwdTgo0Id5D\nLo7qtcv0vtjj85///F2/hl//+tf51re+xfHjx/nKV77C7//+7/Obv/mbfO1rX+O3f/u37/3Nud94\nn0SS98llfDCwG3t0Mplw4sQJRISPfexjbG5uMhqN3vRjHbPHeJHTTKwH71FbrBU8ZeVRytckAjxH\n3RHSUcrZ187Sm+nx1FNPMdMLEeo5wp2424C4G5yvb1yZbpJbiZ3cuv4tD1NvxCHxfZRPe6fwZkkw\nO/uMAJubmywtLdHr9d7UbOROZKZme96GF4pBMcaS40imb2KOY13qvH3riOKwaITMWxr6xvEoEmFB\nYOHWhQXA0hKFxlPisSigSUSLJvtxODKxhKWj27Bo0eQYEhoUTFBYnBicy+irIbHr0KRJQUqoDM0o\noTkxpFEdCr3UnxOLUEodVCeuQhlQf/wy8mv39l698MILvPDCCzd973vf+949rfWWYa+HuIe3E7dz\npDh//jxLS0t86EMf2p5nu1/KMmq0wMNhlzNxn25RRxatPN4LStVjDusCTxcd1s6tMhqNefTRR2k0\nGozGDqXq4HnrNnC3z68psO5vvlGjAEalIzcV6+WApasrxM4QNpqURYl3nsqC0e9vseb7PZhvjHlT\nsymGOvcAACAASURBVJE74bwj1xC+QY8wRBhhSdDkOFakJEAIb8nYLZ4VqXjA7J61ejwVtUZt/bUj\nx5NOi+JbT9H7unTbRjMBlMsR1aLlG1gZYYGIJimWHhmWiNQZYh/jRBh5y6qfMCbmo8zwUrXCiilp\ne0041cutgMxZysDzOdXh0uW1O38j9vCOYi8gvouxm2GviLC+vs7JkydZXFzk05/+9E1kCK011to3\n9bilhUEOX+Df5ffM73HOWFriaMZQScAEsFoxP4TZV1s0DzZ56OhRBMFaTxgIQQAVFeaWGb67zRBb\nIqz5LYHu6XhEkrNWlAzKEcMrI2YPH2ZWLJvpmHQ8ZPDaAK+bPLSYsObrzTsI7o0w827tIb4dg/l3\nOxu5VWoNggDna8bLGzFKFYKbzghuSoVBMLv8jUbQ1jE09X1VUJBLPg2EFR5H3Y2sPwvWZ1z2GftU\nh3jnNidQ4tnwFRkWTUYiIQpN7COcaHIKlsnxKHQZkFvHWZtjracVlHSlpCt9FltdmuN9vFysshZa\nxDu8h1IgCByfVz3+M7vAHzQar7ue9xX2eoh7eKuxmyNFnuecPHmSsix59tlnaezyQbsfGWJW1plV\nQotfGf8H/NHlF1l7fMKaqch6JY21hMOrbWYGizx17BFacR1snPdMMs++RV0rdqDRt2qQ3uXzMyIs\niue6gwhPJgXDvM/Sa1coS8NDjx5mLmrQ8Q7f6HCxLJjTC8x2mjTos7GxwYULF6iqilarRbfbpdvt\n0mw23zDDer/0EO/nerebjRwMBje91kkjoapK+sMB7ebtZyMdHu1rT8rSexK5PcFGOU+uS9akj6Em\nzwxlxNiPQBQNImZ9t9arVSGxZIwY0aJVZ214HA6Nwgis+YIks2hdB0SDoaRkWHr6ViFOM6g8Eyf1\nCt7gC8OlMsEHFXNmyIKZ4z/iMNeLIRdNjigHw1U+2j7Cg8F+hqurtHYOxb4fsRcQ9/BWYTdHCoCL\nFy9y6dIlHn30URYXF2+7ed2PDHGn6ktIzIFrD/HvHfo41lkuX77MmdOrzM0eJlgQvM6pnKLMBedh\nbk4TNx0OR4MZ5JbS1730ENtKocWzVJX89ZWz5MMxTzx1lNfOLxMWQqTrU7m3ikmlcUnBg/MzaLXA\nvsW6w7RFIhkMBly4cIHRaLTtyr4VJO81i3w/4M2wTKMoYmFhYdspxTlHv99n+bWTXF6+Rjkc1+zQ\nVmt7RGRLOKLE0yHAAm+QTFL6EmU0jiZjKWqFWSloSgNByCi4xho938N5xRE6nJEBffo0aGAwaBQl\nljUqAi9YV7cAKkpGzrFcpFwuDRtWs2krCoFZLTSVxilL6iLWpG4d9KsMG13DSJeeNDiiW8wYy/l8\njcWZDmMc4Sh9/wdE2Osh7uH+YzdHin6/z/Hjx5mZmbkjybVbMzCPJwMGODLqPScB2qjbmuYGpiau\n7MRwMOTU6VPMzs7y/PMfoaoU19YrdDnCqgmNjqfV0gSBQxER0ULvcnttqefcLSbr65w/8yqHD+/n\n8FNPokTQUcFc14Jx2MqjFRxsWNrtElE3K5fsJJEcOnQIqAW2+/0+/X6fixcv3pRFdjqdd225FN6a\nDPF+zSEqpWg0GsyEMfsfeZQAwVeWdJgyTIesrKyQ5zkqDum22sSNHlGnjbzBeaSQgoY0SSUjmYY2\n6mlZmBKmSkrWGBFIgwTDLDEbZBQ4Kl9L8ykRDvgYEE5KQKlyxtayMY6YMEdi1gi9p7IKLZCVOZvE\nOGMY2hhlhgSicISU1uHCgrEuKMsGWIctQ0SHRMrTH43e/wFxL0Pcw/2E9SWjcpPcpiBgVIgtFcdP\nXmCSTnj2yafp3aHk2s4M0WFZp2KIEKCJp7+TAa8sOV78Y82Pf1TfAs897vnVX3I8fdQTT+8K78FZ\nS5ZlnHntDMeOHdt2xvACBxYDDnd7IF3clE0qqJ8r+K2UuqtAU5YlJ06coCgKnnz2GUwcYr3lvNvk\nO01LoXKaoWYxcDwtBm3qPqPD35bduIUwDF+X2YxGo+0A2e/3t91BtoLku0WC691u/+ScIxDNvA9Y\nkxJvhPZMl85MFwcU3uKyAtOfsL62zsb586wFjpmkSbvVpr1N2Jmuh6eQkqYI+BwvGcVUTcZO+cOK\nLYKPI6esnS7QHPAxIU0sMMZResGiKLBURJxlgp8EtJTgJMJXM4zLERPvSUqH9QlXig5ehWAcqBYj\nPaZNRW4VVWCJECozZKNcpCxCvGia4lgfDvcC4nsI75PLeG/Ce8/IbrJeXqi5aaIpXMmpC6ucPDWh\nkxxmpvUYL521HD2YcmS2QWC2mGxuKjYlNw02K6VwPseywgYDUhxNNNAG2ngf8r//y4p//scKH5SE\nWUmQx3z9zxS/9+eKL3/O8Y/+gWU2gVOXVrl++RxKKT7ykY9sb5jOw6SEA+0tFqe8rld4O9xpydR7\nvy09t+XMsSEjTvIqf15tckkiyu6YdrBCoWdZL/dzEs2BWPEc7p7EwXaqvhw6dIjr168zGo3odDo3\nZZFb2qFbvci3SuHl7cRbFWBDFIs+JMMyEkc1FVefIySKY1TchX37AVj1BeujIfkg5fLly4zHY4zW\ntNttmp0WRVXhdYqRAiRBEeEQBI0jwzJG6BDSYECJFU0wnYRVCCOEEgin5CwvQrsEKRKGYlGqJPdQ\n+oBxEdIUi/cBG2WLtQyGTmGMUFQRlY1ZVI4iLOgUmv3NGB1BKcKmUyTak4iQpXsZ4nsJ75PLeO/B\nOcfaaInT11/mwUOPEEiDtdE6f/bSEsWkzZEji7TigIAM8h4nLlQsrQ459mhEFW6VHGXq4q5I6skp\nMr1OFl9ixCJDIhrb2doIyzr/y7c2+b/+6CniJEMFHhpQbQhJ3EC7Fr/zZ55mkvOFJ39KZmMeeeJZ\nTrz6U+xUVbuo6oC4rwWtWxjwmw7+9URx2tZl2c9Gnk+G/iYXijsh1WRZxquvvkoQBNvSc5aS1+T7\nfKeoWGGeBbHkXhP6EJEhLuyTlI9z2gTECp5B7ktbQ2t90yjCVhY5GAy4dOkSaZpijNnOILvd7tuS\nRb4XMsStg4JGaGJovsE5qCcBVbuNa7c4QM1QLYuSQTpgPR2Q5n3U+cuMww6tZouwGeEaBUp5tpwr\nC/oEHMDSwOLqw6IIEw8ZFicla2RMsERek0YlsVMsuDZKMpTP2SwUzUqxiociJseRa0dlDW4zoQJ0\nVDFWjsJpklyRVzGNpKKdWLSytI2njWY8Gt2zjul7Cns9xD3cC7ZmCvMqY8wq42GGcpqzl85y/Eqf\nJHyUR46E07JfReUzorBkYSbgYjomv+z46MO1Vn/hYc05XrN9Nt0YNj1X/nqDv/r+Y3RnhaPPTPi7\nH42Zm6llil91/5rv/uXzNBpjROu67qlAAg/BGkG8TnfS4F98D770+RmePLiIVJqLpxzWeZQSZpI6\nEAY7PgDOwz9JFb810ji2tEHht8dwQMP/0at4fJpA/rwM0XvPpUuXuHTpEo8//vh2EAJYkuNcsQP6\n8hANKQhEkU/3b00MviAPzhBzkCUVs+5L9smbC0y7PdfdtEO3zH77/T6XL1+mLMv74kDx8/BuDIh+\nW4jBU7ocuZ0f121gEOZ9wBDLWGw9YhNqGrMzzM/M406dZ//Dj1NaTzbO2Bhco7+xgTiDjmMmzZBJ\nXBCYVbyPwM+xT2LmfZt1MlJJmUzLrIZaUF5Hq2TSYmgCmj4hsR2uuoLABmRlThjkOKtIKEknHeKg\nINYa5xXOW0LlWEITRhmj3OFCaJqKhihaXpOmHwBSzV6GuIe7xU7JtcqXjGWdXA+ZlAWv/PSv6S7M\n0uweoxPXG6ezwjhTVFWJsRN0oGg0YGPTkmWCDeC1quJSuUbEBidfKfjZmSGRH1PuC7mwZnn1uw2+\n/aOML3wq4rO/eI4fn9SooGLu4Cqba/O4SoN4ku4Y5S1FIWjxYFv89dmQwwcLxJT0GiUPdBzG7L6p\n/+NU8VsjRVs8RnZeM1yz8J+sG353ruKoub2lUpqm/OxnP6PX6/HpT3/6JkUUS8VVOUHqD4CHUDEt\nGN+AYMiZEOoJlWtyWTwLd9BHvB/Yzex3qxd5+fLl7Swyz3NWVlbetizybvBmAqLHUzKmkDFb8kAT\nNaSKhpRMCHb1F9kdBmEGQ8frqZ/hdAYRT2AtkY4gcISJosE+9nOAq36dS1WBLQukn1Hm18G3qBoj\n1uJZjpqSzChyxsSA8QnxtILgfUhT5Vi7TipdZiQg1mOEFl1lWCkngDCsFAPv6Yc5XoSOyknI8d1l\nXDThLIY4ChlW8+zvjMn8BKFDmqbbmrJ7ePdjLyC+Ddhij1auYiIpVpWsZ9c5d+UsBRmPPnWU/kTh\nVxRaw2gMowko0YjJsVRs5hUyMVSVZTV1bLY9K8WAnrnOK68Y/uwHLfYvbDJY7VJmFa3FNZJqk3R9\ngX/1RzAINhipDuNJk1ZrzKSVkQ1jgqhEcovNNCjwusBaWF4XAgIqKqqwuG2Z84qFfzpSdKQWWN4J\nEWgLbDj4n1PNP+nZ15FqnHOcO3eO5eVlnnzySbrd7useY0KfEofDoKSmT1hqPfFq2i8Uap87p8eU\nVT18XfeO3n6ICK1Wi1artZ1FlmXJyy+/zHA4vCmL3Cqz3m0W+VaUOO9Glm37eeDJGFBJjiHcHsRX\nNkNhyBjgcYQ072rdOgjeuL6ciokXxCeM6WMZ0KRBTsWKGFoBBEGIaXRxeKJyljzLWB7mnHTnoIB2\nqJnEPXRkiCOD9Z5UEh40MWnl8DqlUAENG9PSUIri3ChmORixVnkkymjqMSjFJBzTbKwRJgMiEQSP\nGEfmNtn0wktqmYarD0ZHjhy569f1PYW9DHEPd4Kdkmsex0SGWF9x9cISS4PzHDx6gMv5dRLTZNUP\nqXzOZBIxGsOWrrJTHnFCYDyBU1xP4fIQqqSgqVewleJPfhDjKotNLcpAoBVOB5i4JF5PcQa++Y1j\nPP8f/gSmljneC6aRY0yJ6+vt8pajRClIpo9vMBBA4QpCXp/V/O5E1ZY+P2dv7gh8K1OsTUUGtgLi\n5uYmx48fZ9++fXzqU5+6bUDwU8FlNaVQCLXkV+Qh8LItD2anRHzlPPp1E5D3hvs1ehEEAcYYHn74\n4e11t7LInT6GO3uRbySu/W4Yu7AUVJIRbHOYd6wnGiMROSOMj38u+/j26zs2pSD3lkwrvNYktFn1\nEwopSMnRCF061J3EkoQWUTCDC0rabbhCyoq1mLKBz3Ly/jr9slafUHnF8hCCoMDairYqCGjQlYo4\nGPHjKGC5jKASevFa3a80A/a3V1GU5C4C7VBe05AShWXcG3FGrnNIAtLJB4BlCns9xD3cHrs5Uowo\nOddf5fzZKxxYWODJpz6C0wPgKgBNE5H7AiYBUVhvTH7qDy/ENSsOi2jFauZpu4zEFBw/n1BlDl8C\n2qCcw2mHckJZhSRzGeVag3QM4i1aW8rSYK0nmMsICofrBWAFW2jIQSn428/cyAiVKEpX7Hqtf13I\nG95EWsB4zzkrPKoUVVVx/PjxbZuqrVGO2yGiiRLHguSc8AmFr/s/W+Fgy1LI4nAuIRQ44AX1JgPG\nW6lUc7sscqsXefXq1W1x7S1G684s8u2QbrsTFDLalWHsfK1nu/WvIrvrLNHiWZMcgEQMQRFSj+UH\nKNpkXnFF1umhp5pIevqvtV01CHHMEXJeCubDgDAK0N0WIRqb5axeW0aVBavpiCqbMPQWXM4KIc0m\nXI0M7TBChWAKj/iS+dYVAmWxLsRIiRJLMtVfNcphvWeVK1yXWVKZvP8D4l6GuIfb4dbhei/CxXzC\nTy6+gljPI08+gQkj1ikRNwapKSi9hkGrinFeEAX1adupDFO10DZGSU7pLCFtgtjjXAFe2Bg48oki\njCEt23TUAKs0OIW2Hm0tulFhy5BxKjz2+Gu89MpHMfMOLR5lHc4JKI9u5EyqDh99zPHwwRvXpETj\n/O4lUyOvM5XYFV7qm21zc5Pl5WUef/xxjh07dkebcEiDWf8glbnG/rzBZQICypv+tta0FKyd5WBV\nsSDvvVs7CALm5uaYm5sDbmSRW+4TW1nklmjAG4k03A3uJSDWQmgVhtdnsn4Hy1ShqaQg9HcXECdU\nOCDayvVdPUzvpzOvbTFTU6aAYOprAREyDdAeVxsy+YCm5PTQ0yBZX2chChMGzM/PM3E9wNFyYwaD\nkuv9kuteqGKhXFe0/ISwKjHREGMslQ0QD4GyeBGUeLSrM00tmoKMs1whN/n7n2W6FxD3cCu89+R5\nRV5WdUZkBOscL1+5zOVr1zjy6AwLvf2sD6DIoJmEFEGTIRGVz1FieHBR85PTJa0oQAU5ygdIMYt3\nAlj6acDRec1G4PDWExjNdA4ZpaAoY6xMMDoHicCBKRyyaVA2ROyYX/jCWU6vPMbq+TlUy6EDB14o\nC02exRyY9/zD/3wEbHkcwtholqzQd3XpskMtuh2K8Iuh588zRZpPJd8UJIFnJ/+mrGtZZMdfYaXI\nmJ2d3VaLuVMccs+wqa/yVHCdcXmQZUK0n9Cc+uZtkqGrwxx08NHc3thE38PYmUUePFifULayyCtX\nrjAej1ldXd22aOp2u7Tb7Xsqfb4VrNWbrUbuPKO1Fgrr2VAViZYdjWAh9LOUsongKcmI8aTk9DB4\nQjyNaWXFT/fpmIEfM4/QEsgQcuo9PHOOSmtWceRSj4YcCDSdOUfUifjhOGCpLHFRhXETYpVSVsU0\nnApaecoiIIgszgtKKkxoya0GNDlDMjv+YATEvZLpHqD+4C9tOP7mSsm5VU8gQhAJB5Mh/bVXaRxc\n4OMf/hg/vpjyJz8wTHKD9yDKcfTgLJXMYF0D0TnzszmHDlSsr3qqzSYy6mKtJS8cLm+y/4hicd5S\nFZpUAgTP0YOgIwuRQ1X1eX2ueYVmo6SaBHgfsjnoEufwzJGc1tyE//IffIMf/umH+eH3nyIdthHl\nMMryb33uLP/15x6n0a4oCRCvuOYdQx1wwCmaIjjvGQCb3jNrPeNTitUmdVDe3vOEVgTzzXoGcbOy\n/J2Nyxzdv0iz2eS1116769c5ocsT9nOc0t/mOfkbLlQznCDhuoQEruIBe4DP+P3M5WtE/v4Fw3eb\nfNtWFlkUBVVVcejQIcbjMf1+n6WlJU6dOrWdRW71I+M4fsN17yUgbqkSOezr+oPOedRUqNthCfwb\nM00rC5spDMb1fbYeeCKv6SSeTnPrMTWBn0XRYMQKc1RckZJ4qkRTZ4keR0lAmxaGHM+DvolQ0alr\nC5SAqywNV4/0dwiIEJQPyKQgCCwLcU6Qh4TNApNbdCS4wuMLBcbjxCNeyDNNEuREzdpH0UsTwRKK\nJ8vGeyXT9xDeJ5fxzqAqLS+fTfnTkwWhtnQTwTnD5fOrnCwsnQef5bO9kG//lXDqSszcrGWmUw/V\nWwcXrymuLj3EE4dbVAPLZrGGzYXH2i0u9TU5nlhrHpht0m0LG1XFT07nhAug2hHrVjgwKxzYlzHM\nMrouI3Y5o2ETm+S0wwHVqEL3S44szvHU4n/Mpv02vvVTfvFL3+Ozv/RnpOs9rMCjnYd40vzbCIoC\nz8SXDH1ITknXRehpYFAiJIB1nv/1J4qXXxU+c8jzl0dqsoupD+ekOeQWwrDgoC/5jccWmY8CxuPx\nPblxlAxRlDxu/xYj2eQhs8yx6hqmnOHh7kdo+ASjNUsoKl+98YJ3gPeC28VOi6atLLKqqu1e5NLS\nEnmekyTJTb3IWxml95ohBr5BJoNdAmJdMvXTidpbSTe3oqzg6prgHTRC8CJMdE2cGkyESQlbt40g\nGBLa/iCFbHDFL9OXnA4hdSYqhHRQhCwzJiGm5wMKP2LJV1RotFdMHKwbIfCKHtD0EVoZPDk5GQte\nSBolGtCBJalKlDHE7bymeVkDSiPaMhuXCFB4wZYllSo59Rc/ZbKZsrS0xP79++86c//d3/1dfv3X\nf52vfvWr/NIv/RIA3/zmN/nSl77Ej3/8Y44dO3ZX672leJ9EkvfJZby98N4zmhS8cu46f3xKc6jn\niUPF5uYm6+vX6XT3cfCBwwwCw7/8nuCG8MjhkJwRU193tIKFGfjuSw3+0/+2wYPtZUwYsrHcpt0Z\n8oUvrPLkkyPKyrMxmeXa5BCRauByQ7FeEPcKzvkmB8Ir/L1PK/7gGzkSjJgEoEJLdslgR7P0i1n0\nfMIXX7hOZBbZZz/LQfsZCnUJowvChSZd9yjlVA5Opp2hARUZijlpMiJ8XRA7ty788JzwwIKnlQrx\nBc/LB4XRlF/hxJFa+Du55589GjEz3Qvuxe2iZEhJH0WCIHR9gy4HYXwZpSGSMbUytL6n9d+L+HkB\nzBjD7Ozs9vzbTqPfpaUlhsPhtsDAVpC8V7cLQ4whpyJH7xi78N4jSqjIiXwL9QZbzUpfEA/xtB0p\n1IGqwpGEiknuSSe3WIlhUL7JMfazRMoASyQGCKlwFH5Ei4BjvsOmbeJImFVrWMbkCnI3phRDx8Yk\n0qatShSemDab5ARec1gqroWeOK5o24qWV3jl0CanIsIrCCtBIWgUiCILPQvEHN7/GL+9+hK/8Ru/\nwZkzZ/jEJz7BV7/61Tt+bb/85S/z4osvbn+9vLzMN77xDT71qU/d8RpvC/YyxA8u1kYjXrp+gSub\nA05cCRC3j5WJMLh0hdlGwNGjxxCBstokL2c5e1Hx2D5BUBhCKvJ6c/DC7/2p8NOzbRqtMZWCKLLs\nO3KZsLHJ908plirDoaMVpVklTkaYyX5mOj1aRlgkZDGc4Wo1oBv8K7749Aqnrj7KuSsPo/oOl2n6\nusnTz67w5Cc8D+1v0rVDlr2hKQ0Ouo8R7xijiKdGq7XSSEXpQ3o0CdGIKNwt9hd/ekaIRXDG40rP\noVR44JTnmim5VqTEJmAubzCnQ3qP3XC3uNuA5bDT7DDhVrNZEQFfb0aVpNvkjg9CQLwb7Gb0u5VF\nDgYDrl27tv3/NE3pdDq02+07mksUhNh3yBlRyYQtj5GKjECHRL5NyO4GuVvmvnnpGeSKXmzY6VDS\n9AGbOsM4RRh4stLgLKjp07I4CrHs8z32+y5XGXKWTSYMCVDMEtFCMbQ5hU9oS5fQtadC9CU63yRz\nlsLNkCtFgqWA6Sc1okLzhDOktmTdaAKEWFUYaaBZQUmFI6TrDWr6yS4oaXnhF9RRFp7o8U83/29+\n58XfQUQYDAZv6n387ne/y89+9jP+5m/+hq997Wt85StfeVPr7eH12AuId4iszPij83/Fv1lfw9oK\nW+Sc2jyI1ldpjAIenDkKzVZtRiOKwAjZ+piqbDGsrQ0xRDXjjpzjFx2nLiqa3QzlHZNcM784RvkU\n2gH5KOTEqx7TCdjX9oTJmCBcZiwFTbufSSrM7HOw/ocsDH5IZ77PY+o8Mq/YGHexccxCtEJDJoTt\nv8+BuecJqgrlmpQ6YEiOQWOmpS6hHsQPCIAKRURl67JS6gzjAsbT60gUnFhRdGJq+qjUrMJ+f4Bk\nGU/NzhIahY8qLm8q8gqmHsJ3bRBsybaf363YCq7jcclo0mex3b7vGeK7Nbi+WbumW7PIEydOMDMz\ng3OO69evc/r0aURk2zKr2+0Sx/GuWaSgiGnjfINavttjygGJat42GGZkTGQCQGo9eSCkWghcTOTj\nmg3qNQ0XMJIK8Q5RiqKCWEOJo8DRdgFGFOuMmFBwVLrb9zRA6R3nVcasDDBulgw1zWRDsmqEVgqj\nIHaC6ADxGYFoYkJGoSXPKz6q4UKhmOiKFAV+P2HVYr+/RKRKSqmVk2JyHpiEHBsfZmFmlo7v4LIb\n90/nDh1rtvD1r3+db33rWxw/fpyvfOUrfPvb3+ZXfuVXeP755/nVX/3Vu1rrLcUeqeaDg62Zwn/+\nyh9yYlww3+gQBoaVrMSWlnYUYCNYtleJikOIMvTCmgoe+QynmpQOtk6+OTmrssI3f7BImkYkVOhc\naBxZRZxFWpYib2C0p1Swuqp56IAl1gYrJXE5oe8zmq6JL6/TSP+SlWoGHU1oHdWMiojGsGBsAqog\nYcZs0i7/XwyfQ6NpeM36VNJsQkn7lju5xOIqxWCkWMmh8PDaJCIphSMN6DUg8zD2EAMJnmyS09/Y\npNlssu9wG2msQpCDhygUliVgkS4x8V0HrHqY4jYD+96zsrLC9WvXiFuaK6/1qQpPEATbPbMtI9p7\nwbu5hwj3//klSUKn07kpixwOh7XZ7/Iyk8nkpl7krVmkQm/3EsVqtNp9exkzJpOMgGBaGoXACcp7\ncjXBe0fsatPflgsxImz4MT4UsimDNPKaNgEDPAUVazIikbAuW+6A94rYh+Qy4eB0RGQyHdyIreMo\niiHUAyS+fs4hE0pqhutQB+S+4ikM7bJNIWNC32XW7yNgno2iIGkNSIyjTZfxsCIMAjp0mPHzuOre\njZdfeOEFXnjhhdd9/zvf+c49rfeWYa9k+sGBc45Xl1/jbJ7S9F1ChPX+CrlVtRuFcsTKk1YjsmqF\ncNKjEAHTohmCqcBrGJPxqvoJfbVJVQlXrx1GNyqqiSOIC4LA0epusp4naO9ABBPA6gqgPGNfopXH\nqphxtYFvJRRL38Jbx1rYJY5zSDRBE8Jmziw5xsGqWcCWa8yPT0L4GCGGLpo+tcpHg3oTcVNnuaoS\nJhsxRgk28JQeeqHHaE9ewcoQFtvwxD7HX172dNIhjbRkYWEe0xpBewXKCIoG4xz2xR6rJ1zhOgdY\nJFKv70e+EXYLn4PBgIsXL9JqtXjmmWfI7ZDk6D6Wr62xvr7OZDLh2rVrZFl2k13TWyG0/U7g7RjM\nN8YwMzPDzMzM9u9MJhP6/T7Xr1/nzJkzALtmkTvdLnaioropGAIYXeve1sExpFA5gQ8xvi4rxD6g\nmwc0s4BFiYg92/cs4lgjxUqGw1Obod3Y1rZ8MGIfsalGPOBiounjTipLEiR0nOGkKim80PYNhjKh\nBYxRtJuwmcbo0lIFhkhaNKnoOI+t2nwoFhphF+8UmpgqX2LeLDDr5zE2fF/ca3eE90kkeZ9cDSb0\nfgAAIABJREFUxlsHEeGVzbMYDEWRs1GMiDsNTGVZyPqsDDu0khIjAdeLlE4yh5QTrFjCuMN8D3pS\n8pL+ARMpAQ1O15/UAWTeECYpyuSYqETZFj7ziAERj/OWihzwaK/waCY25cLaSY5VF8labca6WZ+E\nq9oDbuwVQ9EoBUFesazb+GyNA9HjoCMSBINik4wch8ZP5wsjVoaGUAuBhrzyKOXRovDOERnISrg2\nhKO9Tf6/44ZeFLGwMIMEObTWIW+wNTjWz+FLT3liiaiouMYyh+TAXW3mmpiKdPtrZy1nz51jOBxu\nK7yAQ7xBxGCMIY5jHnroIeAGoWRLIm04HGKM2d7A341C23eCd8LtQkRoNBo0Go3tLNJau81o3ZlF\nlmVJEAT0er2bssiCAoW6qQQehaBNPfOqBJTT5JJtB0SAovS0QqEZ3AgwFRM8Q9ZknYgAS0bFBI3B\n0EShEfF4D00JGVPgcKjp/WmdRWvNDCEPWkceTFh2KSNVIkTAmAeU8FQT8kmDMusAhk1VcUpX7Gu1\nMaGh5YQOlhAhHQ/pNWZJaNIf9d//IxfwjpZMReRvAbPe+xenX88B/xvwNPBHwD/03tufs8RN2AuI\nd4DLww3K4QTRHebnehR2gC9TZpoVK/0Yn5e0Q0fqa9UKa0J8mTLIOnz6mOb7105RTsa0mzUFvSgN\nOi3JJiEm0PgE8okjzzUqsLgctKnwgSWZrf2/a9cGz+pySuFHPHbkMYJskZVyA20coa2orGNFJxTG\nkOsQrxRBlBNPRvzhGny+2eW56aZnULSJmKcx3Z6EvISyhFZcl0W7CBM8uRKoLGnpWZ9UnLq0RLdh\n+dyjR/jLMxGtCGY6fcSGgMI6WE7hqX2eDx/w08cz5BSMZHJXAVERIhgcFYPNlDNnznDgwAEeeeQR\nVlZWyCYTLAXGt3bvb+0yllCWJf1+n36/z6VLl6iqilardZPp79ZIw7u1h3i/ca8BVmv9uiwyyzJO\nnjy5HSSBbUar70Izae7kziACs23P8oaQRKClVrbxeAShrCArPLPtG5WFgjGZpLRoYGSEBYLpdlb7\nIqYENIDaMth5wYufZpH1e+qcQ5RgfUFPNthHyYpYakXSEshQ3iKqhW7mLDcKrhPQIuSACpj3TSo8\nV5VnjZAnnUEKQ6DrA1aapm8oS/i+wDtbMv0fgW8BW3Tc/wn4u8CfAP8V0Af+hztdbC8gvgGyLKMo\ncmZ7PQZpCdUEwRKFgilGPLdwnMvLXVwO3aTAEbBmIzaygIdn1vn8U03OZt9h5QePMt4IsYWj2Ax5\n+LkzrJ6fRcSjK4sJStZXmrQeyAkXLMVIsKnh0DFLnhui0rE0XmMubnP4gaPMNTqMoqfIrl9ijj7j\nqMFaoREcKIc1tTFqZkOKIOB08yG+s675P1twOKx7hU2Cm3ouhb0hLuJ87TgwTz1bdaGE5eV1huur\nHJ07yJMH2zSNsBg7/uI0ZOUYlTbqz4aCX3zY8cUnPGbHyTEgIJXxXb3+gqCrDifOv8xkPOGpp58i\nibeGvB1WcjQxMrUYupMgFgTB60x/0zSl3+9z/vx5xuMxYRgShnV5t6qq+yqTdj/wbvRDhPr1T5KE\nJEk4cOAAnU5nO4scDAYsXbhGPslpxAmtdptOu02r1aIRaxZmPGv9Olx5BUEFztbZ475eyWBzS4De\nkskIQ4QgzLsGa2pEid0ur9ds6ZwuPRpacaUqEClZkXWs1PnpUMZ0VYeUVeaVJpImLcaUjKko0dLF\nTVdNfcSyVPTEExEiPsLjp2xWGOI4JRXe2u2MeDQafXAyxHfu4/EE8BUAEQmALwO/5r3/ZyLya8B/\nwV5AvH9IkoRHFg5ypb9a635KA+89LUq09BlHMQ8cGLBUKUaTJpW17NcrPPtwmwcXCi5EOdqWfOiJ\n8yxfmufqiTZBlHH4iQH9S12qcUjYycnyEJ9MkJFnEGvC2NKeLXhgv2UztTiV8tCDMwSxJnINKmtZ\nN0foBJqWH7Ch21TzJU2dQenRLgcrVD7iJ/Gn2Aj3o4sB/8+m5r9ZjPFAREBBfeqemvZsX/eWt6tC\nsIVlcHWNfb0OH3n4MTKnMYAo+MVjjn//cce/GToalaMdwqPznubr9Z6nItx3XL0AYHV1lZMnT/Lg\nkcN86JEeTsbbzFOUQ9kWITNUU33Le8nqtpRdOp0Ohw8fBuqD0NWrV1lbW+MnP/kJ3vubyqy3Y1y+\nXXi3BMTSezZsyXVnKTxEAvuUoZg6m8DNWeQCC+QU2LxiMBiytrbG+Qvncd7TaLZpNtuESYNAN2iG\nEIeeJIKNjRs9yYp8W7wbYIaE1OU0VDhVP2WqWGoJAK9yJsEGzrYARejr/uP1sKSKVnlGN2jr6cgO\njpycUOqZSk2E+IwNieiSECFYcrQEiL/xerVRrHmL93b78JSm6ftftm0L7xzLtAVszbN8EmhyI1v8\nEfDg3Sy2FxDvAM/2HuHi5lUUJZWbI3AFoR1RBR1ib7FJRtM6nu46ntlnaCcRjj6xP8DSWsTYB5SB\notXu88DDGSbyOKcwX/wZr3z7GJPNmMmp/SwcXcYZT9wqySTCTRx/8VLJ3/6FjH3dWdJJg41+m7Eq\nmPiAblthHniB8eC3iNoDRDQD1STXMUqoByvEUuQGlymMC/hBNiD1QkearFFQTRmnHo/TmhxDE11P\nKDrHpatXOXt1nW6rw4MPTe+tHLSitpIC2gE8PquJVMlCtFW2uqkqBoDFEu1iIbUbyrLk5MmTFEXB\nc889ty0/5mlPtSohdJqsGmxLhN1PxHFMr9fDWstjjz22a69spxPFG2mI3i5IWxwZJZnUMy3GKxJC\ngqkQ9c9b7377Id4tASR1llNlQQU0RdEUqPCctyWXFBwQuDUchIRkkhHFEYtxzNzCAsNKuFbC1dGY\n/nhCnq4wOyg4ZAIWuvVBpSzL7ednqZAdO3BCQI+YDT+hIdG2KbT1loqKS2ySSMSDukHhFLkHEGYK\nyz6TMzCaOZ8QoNDTLHPna1+IZsiE2enVODyht697fwIlXFNsZ4h7JdO3BVeAZ4G/AL4A/NR7vzz9\n2QxwVyWpvYD4BhARHuvs57GozY+D6/RkjClKRlOzmdQ51r1mUSV8fMEQqhRrc3Q5IasijPSoYo/W\nI4xoaFnEeMbDBlWm+dBnzjDJQtKlNh7N4Pp+stWKuQ8toZuCcxF//u0mf+/fiThsZpjrRojOiJQw\nVLBWNimbcyQuYyPs4bwQS4ZMz8d9P8MD8VnWy1c5xSfIC0PqPUos8XQiawulcaQmJ3AhpBMunD6D\nml3gwKEjTEb1IaysIAogMDB20FOgRUhoctGt46elSw00USTbFAaosCzyegPgW7G8vMzp06c5evQo\nBw4cuGnjl6l1LLBrILyffb+dj7tbr2yLcblTQ3QrQN4JWSejZCDZDZUToBLHBmMiDB0f73j13lrc\nbYDNveNkWRCJ0N75OiFEornsPWespec9ZsfPDYbYx2SSIT5gqdCsuLq/N9tuMNsOUH6B3MZkVUkx\n2WR1dZW1tTWccxRFQTxjaHZimkl7+znP00R5xToTwKNEKMixvkShOCztej5Ys21CtexKWiahFNjw\nOYskIBUBARa3/Z7U7pp2+n+HYGr/z1tuswAo5MbBIk3TD0bJ9J3FvwD+kYg8T907/O92/OxjwOm7\nWWwvIN4BBM8X9x1j7eoyo+YEzQbDKmRYVHgNi0mDJ7WhP8xothRJFJNYxZXCo+OrHE1D0vgqhW0R\n2BKw5JkiaGa4LKI5kzLz4CZL332MICpZujZPWu4jaBVoDdkg5McNz+HPZ3RCBRIQuzaBF+bcDzle\nWF5rPkLgC0IqSuJtrf9AeRp2yEfj73Ni8At4rxhJySKt7ZP0FgJR7G84fnDiNaL1Ec89/jhF0uTl\na0MyVwdDa6HTgpGDtoKO8myIIyNGvEYoiYiweAY4xnhm0WSMaZLcpI5zK4qi4Pjx43jv+fjHP/6G\nBrnw+szr7SLC7Ma43OlnePnyZcqy3CbrbFk2baHEMpBs2sW98T6oqVhCgSUlp8PuotjvdMl0zdaZ\nVHSbvwmdwwps2IoFc3P9vEED5RXnypxl6/DKEwkoFIFPCCWibYRUItJokWP7Ful0rpHnOb1ej/XB\nChcun6cYWcIwpD3tRfbabXomYcKE1PdpS06G0JYQzzqWBorG9lyrdw4lighFX0rmp4IALWLSurC7\nVTvBYSixCNAgRvH60aECCO2Ng9QHpmT6zmaI/z2QAZ+mJtj84x0/exb4nbtZbC8g3glULV/2tN7H\n4w88wckrDdYyiMKUsGWwcUFU5FhrGKRtGmIpgphKCqR0/P/svXuQXdd15vfbe5/3ffYbL4IEKb5J\n8QWRVCR7JEtyOLFHDl1M1YyljCv5Q/HYSWwlNaVUJpWkkkxKcqpkRa4kM6VR2TVOOZMqeZTYKslj\nRX5SlinaepAEQJB4dgPod/d93/PcK3+c240G0AC6AVCQh/hYKFbfc+++55577v72Wvtb67u3mOQ1\ns8TQMaTWQ+UKMyhohmssZJMoC8snDzBou1Smu4TTgqtzlAHtWYJmwutHfT70Qc1irpiulj9TT3yc\n/C9o6ozlpMIwDChUgSkrtHAQQpsQiaWil/DcJR7S43jigroyqbm+vsaJkye5a3ovzQOPQeERFDAF\nvJVYEoFmHSou1HVpENVTlgEWr/A4oGfQrDBkiB6J6wfkJAgHqDLF1FUjnoWFBU6ePMl9993Hnj17\ndvS1bEd+t1MZermfobWWfr9Pu91mdnaWwWDA97//fRqNBmosoFKvoK8i1vEwxGSb6dN3GrslxIUi\no6Kufl4iEBnNks2Z2sZAWNsAigBX5USUrhEG55I0ZKShL4qVQnCsxXGccnHRqDGpxtBosjin0+2w\ntr7O7NwchWRETUtQc2mGe0lDhVYOIFjpI6QYmig0hRW0DrGkIGABIw6iBFcUA3p01XBU9tSgj8sU\nNSwFiQiZGqIEPFw8DLkV6tnFPfI7opp3HqOSin96lWP//m7Hu0OIO4F2wItQNkWrKhPOfg5OKJCY\nVrFEN1kjIASbYX1Yzyaoez1c2ybWe/CM4eHkACejDosDS289IDYegR0SqQGzb95PZ22cajVBtIuR\nskmwTTXWWFQBNohx3Jx2YqinmsAVqjRxih7WqzCZDujleWlPrzSKAk9yPFtsWqdGusv7ojGCyyae\nNEs5eeIkeZ7x+GOP4wZ+KSLJIc1BMki6Ce+bvNTiziJ0lcVHMbCwz/fw2ENKQp8BloKIEEXEJBHO\nNntiSZJw9OhRjDG8733v21VN4I+C/G5m/I0G2rVajX379jEYDHjkkUdYb68z21nm3Lk5rLVUK9Wy\nf2i9RhgEmxdZKUUi2baEeDsjRCtCgcK5xtOtWBylSa5y/WJbqpc97W5DlyW0Ki3FehZ0Yckdl7wo\ni94iamjTwg8cpoIppqamABjKEt1Bi2GnYGFpiQuqg3ZdGlGVqFIhqgiYAQ5VEMFVFWIGo1Q8eIQs\nyHmGKsFRDmNUgYg6dd5kwDl6VCUiUhMoylTvgJhFgX0EDLcEjr1eb8eLu7/1eGfWbPuUUj8AjojI\nJ671RKXUe4GfBCaAfy4iC0qp9wCLItLd6RveIcQdQCmFBOMoyVnvWdyggcrnESVUxKMzcqgnnEGH\nTSQ1ZNki6CpaaWJbUCmaPO8cZN5zeDPr0bEaSQrefLXG2sJeaocSpIC4V8XxC6TQWDtSZlrwjGFI\nh6YJSIfTRG6IwaDUPqr5BRJbJZCEVTxCG2PEbpY/pyg8En6i5rNPaSJVdgnZaH125uwZ7r77bqan\nplFKUSAoVfYfDVzQheKcSenlUHEuKlAzoBDIc0XdBV9v9LUMywXCCAMsMfaSiV1EmJ+f5/Tp0zzw\nwAObE9quv5d3MEK81SpSpRS+7zM5PYUmwlcuthhFkZ02Z06fIY6HBEFIrV6jUqtiqnW2C6pvJyFq\nVSYdc+GqpCgiiC5NpLfDyCXsuvOoEmhZGFpNUzuMjx7v45PlY1R0j4qJKcgZMiTRawTVJmOVOt5e\nl33EnMvaqEFKt9NhYaGPJSNy9pDnOckwowgiAnIUGQNS+iS4Sm2mRR0auBj2ozhBRkZCOJLfZMBQ\nNFWEcVLm1MV7r9/v30mZ3hw2bpH+Vd9aKR/4P4GfH52JAH8ALAC/DrwF/Fc7fcM7hLhTOD5Dd4o4\njqmyjGSrKFG4WEIShoEQmhAkxyNmKB5B1WPQFxJlaeZVjKvZO+GRtsc5fqrF2jBk33jOmTmLVjHG\nwHBYx3Es2mTglBv1iTK8/5kCVwVMqRlUVtnMeIr3EkHxeRpFH0+GeCg6SmMFLIpCKepqANzPQzSY\ncn185TAcxhw99jaO4/Lex58kCC5GZgVCsLUEQ2tqKmPKh/WsJEGARITEwIwPDecyg/QtGAlSN2Gt\n5Xvf+x6+7/Pss8/iuleLEa6NH7eU6U6hUJsXSxtNrV6jVh9NnCIM45hup8vSyjJzp84Q5u4lYp2d\n7K3uFrsl2BntsFBkNK7miCHCUODQVfqZbjScuV4RTg9KFasUhFqVYhYgBAI8urZBoToEWuNgMTJG\nIgFnJMWSEuIjrk/QDGiOBFHWDkh6IZ12l9kLs3SSlJnMp6h3GE72CKIArQFxUPiIsiSS46uM91Jj\nRSwZGRqPQOAeXKpAYjPyLR/3XSOquQlCXF5e5vDhw5t/f+pTn+JTn/rUxp8LlKUUq0qpfyIiy9sM\n8U+BjwL/IfBNYHHLsW8Av8wdQnxnoLwQ6xpwGlgvgnQeZZcYx7DiGoYsYbIVcu8eRCIkHKAyRbVb\nttDWqkq7f55M1rn37r3U4ojJbsJr53pUG136SRNXEpQYsthF+UI6FFxX89QDPjUboJW5hFzE+QCV\n+LdJnTb7TJem7bOsqrRVgDGKqu0TMmSZj9KUOod0ldeOn+f8yVUOHrybRrPJwjLUqlCvlerRHEu0\nRfyitUbE0vSg7kJclCv8HEVooHadedQCzigiPXfuHIPBgIceemizMP6Gv4+/pYRo0LgYcizO5eHf\nlsL2xvQ4Y0SoXDbFOhcuXCBNU6y1WGtxXZdKpXJLembuhhAnjWHB5iQi2wprYhFcpRkz208xgYJQ\nlanTqwlpk1G6dL+jyK1FX/4+SrBqQEs0d0vIEMuK5BSUdbLOqKbQKSos6j7jOqOiyoYVQS1AAsX+\ne9/DU1IjSiyr7VUWOj1W57pYC2EUEFUhqoUQeIQqwMXF6JiqzWlwKdnpAopAbXbYedfsIcINp0yn\npqb467/+66sd3gO8QllSsXKV5/wD4L8Rkd9V6opN7dPAPbs5nzuEuANsTBSBSaFIKaJxlB4ilRyK\nGdTgAuPWkhpDW6f0lUPNj2kA+2p1WqnhxHKL+dYCtcjlwN676KUpFbNAZxjwvvYa33trH308jGNK\nqxs/QwoHPYz4pU9YHEeIRJMXinBL2rIQA+azjEX/GNQKDelTzTtkRY74LpkELBa/SkXdz3024Dvf\nOoIdr/L44YeJjMdwaFldLTh+vPQ2v+te4T17fbzw0ghxoyG3VhBt3jWKAWXt2Xb7g8BocoBiMOSv\njxylWq1SrVY3bYdu9nt5J8nvnRy/Ih4tNUCjrlD7QqlCdZXBFQMOVxj+Hj16FKUUc3Nz9Ho9XPdi\nFFmv12846t4pAq15wPV4K0uJraWiDY6CTKAvpePgg56PexWSNQpmHGE9VQwEosueVgh0C3ANTBiY\nt6X901bk5KAKlHUYCqyKiyhLuDmWRrD4QGSbtCTBMTEiFlcUbuZzl9TL3W1fMTE9QUpKZX+EtcJw\nMKDb7bF0bo0128XVLrVqFV1xySKDmCoGCCi9Em1RoI3erMG9ozK9acyLyOHrPGcCOHaVYxrYVTrl\nDiHuAoETUw0sg1QIgzbgg9FItAfSNTwTMGkt0UBzoF6B/AKJJGTpW1Qc4ZmHHid3KogSGk5G1lNM\nmjrJTJ/3PmT4y+/BD2ZL8/dmRfPB98U8/0TBwUoVOzSsxTl5Ypisl2KXbNRq7cDYNI7/z5grvkmS\n/htcLhDHltz7WbrqgzSDJuZcmx+cPs899zzI1HSNtk45sxBz/lyB0QY/FPIczh51SE5b4nszDh0q\nJ9VreRiOYVgk31SVboUg9MQyODfPubnzPPzww4yNjfHKK6/cEqLZSlgbi5Yf1wjx8nPycKhKQF8l\nqFGz9bJ21FJgcTDUJdh2LKUUrusyPT1No1HWdaZpSrvdZn19nTNnzpRinS39WaMouuV7onVteMz1\nWS0KFmxOIWWK/y7jkGUFletErU0H7hfh7UyxOooYXSCzZQlDaISmUYRq+8YBqUpHqmVFT4QcF0+p\nUa1g+VwHQ6JSGnggIbXCUNU1PDtON56jskXSYzCbwmutFZVqhUq1wh5m6BIzSFNW4iGrSRu71MGL\nlwiikKhaZbJSo1oIRpvNX8GdPcQfCU4D7wf+eJtjzwLHdzPYHULcBRxdEPmKLEuJU0vgjX6gJgRv\nDEnb9GPNWDTEM1O01gacn32T6K49PHLoAZTSQIwVcMWS1x1yNUFn7TyHHlJ86Jkcay2rqeC5BUYr\nHApqaGwgWDR1z+BaWF8rV9kVHzIPfBNyyP336DofYSVrceL8m7x37+M0WwVzb5xienIv77n/GaqV\nkrbSC4b1Ew7T4wblaLQoPG1IHEWjbjl5ssBx4K673GsSYohmEsMqFoXgjqaDHKE3HHDh6Fvsr9Z5\n7rnnNjt4bNgD7cSR/Vr425YyvZyQIjxcKcsrhipDUYqoauLj4W4bOW7gCoL1PKamLqotrbWbXoan\nTp1iMBgQBMElUeTNXn8oI8X9WrMft7RwGp3y3A5fP+FCzQgrBSwVkCqIDNxtyn3G9dFtJ9sQosWO\nZGNCH1V2NpU6VrdA/FHsXZ6QIHgU9MQyLeMUxZX3n4tLJBGJSq/oqOTi0vVzIr+OLx5TUzNoqxgM\nevT6PU4vnSPpDqgNLG8M3uDYsWMMh8MbJsSvfOUrfPrTn+ZLX/oSL7zwAq+99hq//Mu/zPz8PF//\n+td58MEHb2jcfwvxL4H/Wil1BvjXo8dEKfVh4NOUdYo7xh1C3AWM8ZAiZc+YZbmr6ccapUArobBV\nFBHjzTZV1eGtt04iyuG+934U10kRC1aBFo0WjTUuqVcKXKr1gumJISuUraaasUdrYChyhSUjxiMg\n5GAUIgNFf1Aa9bpOWe+13oa1dZiedBirOtR1wEprluGxVXq9Hk++90m0qXB+oZywrLXMzlnGqw4u\nmlEbUKDsT5oXmokJOH1a2LvXYsy1SaaKIUAzGBXiiwirs+fozi9w+OFHNqOYzfe4RaR1tXF+XAlx\nO7gYXAw1CTb3nnaC64lgtnbN2Xh+HMebredOnjwJsNmfdbcelVA2gB/CqBVa2cc0lIvp/J3C07BP\nw77LsryFQIvyHrciVxCiRpNLgVIaJaOKI0KwYFV31OJPIRQIDlo5KDuBxiWzybYLgjoNFmQeR5ky\nYtw4l9G/lD6agGUGKJPiVRWNakCdgIWew9i8wss9Xn75Zd58801++qd/mieeeIKPfexjfPKTn9zx\nNXnppZf42te+tvn3I488wssvv8xLL73E7Ozsjxch3t4I8dcpC/B/B/gXo8depvQv/1ci8pu7GewO\nIe4AmxOPW8fmAxxTZ28zI8kMw1SVEZ+ByCtYWVzmjaU+d7/nCcYnIgpyjFShGJYFfWgwAYVWMPL5\ns6LosU6DGRIscZAS+jlJrkglI6TPQadCvObQHZYCmK2IQrAWFpbBcaDbWaXb7XLXXXfx0EMPoZRi\nGF9cvXe7kCRCpbJ9hCACxmiyrKDVgsnJ689wDoo6BtXtcvToUcbHx3nvs89tK/a4VsS5G2wQYlEU\nDAaDW54W/FFHmzslw8uRIvx/TsbLJiNT8GhheDHzaGxRq2x1odiojcvzfFOsE8cxr7zyyo7NlAcC\nyxYSW9YKQrmYMgqmb1G3OaOgqYSWjAyHL/tuPfFoSZ8ZpekoVRbWA4YQLT5CSkqMLx4uNUS8zb3u\noii2/WwhAVMyxYpaHqWyHTSaZWIGkrCqFEOVY+igcEEpamj2oKmmmjxKuW/vA3zxi1/kgx/8IK+8\n8gpHjhzh/PnzN3UtHMfh85//PPv27eNjH/vYTY31TkBuU3PvUWH+31dK/W/AvwtMA6vAH4rIn+12\nvDuEuAtor0Ger6DFwSqL7wq+W84Gg36fY0dOUK95PPn0B1CuCySAA8qAcymLKTI2ihFyp5RtGu0Q\nAQEuVlnEBYumSoMkS2j3QmqV7WcbrUGrjL/49ttMjSdEUcSBAwc2jxtdEh2UnocKhVUFmU4YmpRU\nQFsHbMD4SBmolCLPt3u3K2Gt5fTp0ywtLfHoo49Sr9ev+txbGSGmacqrr76K67okSbL5/3a7fd2G\n23+bsREhfttk/BdBn0zJZhfjP3LgN/wh/ygJ+E+y4KpE6zjOplhnZWWFw4cPb2umvDXN6nkeQ4Gz\nOWQJxJkadT0CRymqnnDeg+QWNVxvasis0BPI9cVZNxFIxdDQikgXKDGsiiIaHVdoBA+DQzQyCx4A\nYxtitC02TZejShXf+vTpMVBDhuR0lCZTk7hYKnTI8Sg7mwo5wjIODdGExiVRHYxMoJTC8zyefvpp\nnn766V197q9+9at861vf4tixY3zuc5/js5/9LJ/5zGf4wAc+wO///u/z8Y9/fPcX8x2CKChuA5Mo\npTxKz8NvichfUKpRbwp3CHEH2LSxcQLSYgwKCzoHlWNFMzc7S6+zxn333EU4dggcH2GIoo4mx5Ki\nL9uTKNtUaXJylGsxWwQUpfLQYCnQOBgclns5/axAxRqjwXdKEoRyclxaXGJ2dpbpmUM89tgkr776\nl5e8n+dBFECSlq8rnCFtL6ZrNRSm7NOhcpKozaIXsEdKm6ud8Emn0+HIkSNMT0/z3HPbR4VbcSsi\nxA0C7na7PPfcc5uqyk6nw/Hjx69ouN1sNmk0Gu+4+vJHBRHhh57wn4Y9Uko53caPuaBLQum4AAAg\nAElEQVRcav0zv7TJ+qVs+36ol2M7M+U0TTejyDNzp0mJaYUzZGaCyVqFeuWiDVZuYS1WVAqhZZxL\n9hRvFHoUcY5nMa7W9Ee3TaiESaNwqdBTfRxyEEMmGkcJ+ajCMZIQB0M+Wn9VR+dzPXcPF5cmYzSk\nyYJKcIlpA3XaQDgi3BLFiBgvaOE+XTZOzCS+qUXfiy++yIsvvnjJY1mW3fB47yhuEyGKSKqU+ixl\nZHhLcIcQdwHHcUiKABXuQ2c+q2s/YG52nj1T+zj4+NMor4FoD4hRuGjqKDIyFuEyQlQoAiI6tFAG\npPAuqeURLAUZvq2zNlCcX1Xkg5w4Hyk/FYyF4DDkrbfeIggDnnrqKZLM4WpcMzEGcxcg9xI6Xh+b\n+NSMQSkQC2niMNl0MG7ChUQIVUizefXrYa3lxIkTrK+v8/jjj++45upmI8R2u82RI0eYmpqi2WxS\nqVRI0xSlFEEQ4Ps+Dz30EFBOIu12e7OfaFEUVKvVTYIMw/CqadZbmTK94XFEwMaQt1E2ATTi1EBy\nPlsvSNi+BExRdhL6P/yYX8h86jfomuF5Hna6z6kDf8o55zip9TmzfoA98Tj9hUPoMwGu51GtVKnU\nqlQrFfqpoVcYEsqNnJuFUuAVOQdchVKy+VgJQ0OqxGRYnXDBWpRAXTwC5aLFbEbOe/XFzjrXihC3\nIkXIYZQgzSmlbaMMyug5DooUIbYFhfEwGArS0XneWmXvjyNEQW5uWybmGHAv8Oe3YrA7hLgLOI5D\nv98nToU331wDpnngiYfxfQNKIxTAkHJXoQkIxehHlLCEQ3PDaRAoV6E+GgjIigJ3pG6R0erWtzXa\nPY9hBtUQhqkiGlXV2EJ4/cQiSfcCTz5yD7XxCkJKkeeI2j4KygRsKMyvJbTw6V9QDJsQagg9aDQh\n8EHEpR3H1CcDPG/7G73VanH06FH27t3Ls88+u6sf/o0SjbWWkydPsra2xhNPPIExhiNHjlxzbNd1\nmZyc3GwCsFV9efLkyc29x536Gu4URQH5qA2L61w8t11BClS6VO4/KweUB1hUvs6ys8SymkRfxQ0D\nLk7Y/4+b8g+znVGTIBSkZKqPpeC0+SHfC/6ojLiUYpAaRKBVOU/r0ApP7P8p9sT3kPR7tNZbnD93\njsxCN7OcX1xkX6N+S8yUN1LE2w2jy0Z4RPhMKKEvijZlWlUDTVVGhu6W115tD/Fy5FgyymYKIiBq\n+/vWIigr5KNm7Vme/VuTjbgeRCmKqzSp/xHgvwX+V6XU34jI6zc72B1C3AW01qyvr7O0tHRJ/00h\n5WITKg/Q5LTJ6Y0mpTJqTDiPwsOlSkGKYPGo4ucNdFHakwI4BDj4DDLFIIPIA9GWeL08Puj3efvE\nSaKozvSh92BrMYluUxQgPqSekLvDS+qxWn1Y7oHvF+zZVyBVl/lTGSvrFh0pJmuKwIc8sfR60ByH\nmbuFxJY9SjdQFAVvv/02nU6HJ5544oYMUG8kZbqRlt2zZ88mAadpuuuyi+3Ul8PhkFarxYULF+j1\nehhjNv0Md3ueeQ6tDnT7ZccSkbL3ZyVUFLvMEqt0GWwCZus11qAc5sKIPfECs94+4jhAupSGzT7o\nmqBG/JcAr5uiDBevA8GSqDY5CRqXjl7le8G/oVD5qMRdM0xChrFHqlxA8bJ8l+fzPUxGE+wfm8DV\nZbP4V988SX8Q89biAnEc35JFx05I1VWKpqJcjl4jZbu7sh/BoPFw6YjCVZd2GMoRMoQws7h+udUx\n7KXvDnPgEYpbUMJzg/gMUAW+Pyq9mOfSTpEiIn9np4PdIcQdQClFt9vl+PHjiAjPP/88zpYVkbos\nHZqxTkEfw0VBgyFAmCRhgSGruExgqJSpU6cgk1UCmugtjRXag3KvsKAgcAyRp3nr+Czr7Q7T0+9B\nRQVr8YDkvM9kTeEYOLivLG62JqMvbSqqSZopVgZQDSBXGVa6VKoJjz1R0OoISxciLqxWaHqGakVx\n73s0tXEX5GLfUoDV1VWOHz/OgQMHePDBB2941b+bCNFay6lTp1hZWdk2LbsxzsUIYnfR51Zfw637\nZu12m+XlZdbW1nj11Vc3yxMajcZVI548h/OLZTP2MLg4gVsL7R6stDyKAnY0d9gEKfoos30aWqFJ\nhy7+7JCBH6I8KXvb9iBraXRDMJNliORd53JsXK+EDjkZzijqfNP9S+xm6QJIDnHfRUQRuAmlZ3zG\ngvoBjfjDLA9gMixZyDgu9999N6G5GxHZFOtsLDp2a6Z8I7jW7bnTlKmLxpUyuvQxNKnQo0c6+s0L\n5e+tgWGlsDTKJqgMu9m7pm2boCh+BBZlV0EBHL1Vg90hxB1gw7j2vvvuY3Fx8RIyvBx2ZO1qNvVu\nF1EwRFAjH3l/M30aSANdOAxYJGQGg4+1pbrOc/KykH5NmDv7fTK7n+bYo3hRjqn28LOALIP+oJyE\nN+DikxZDfCeiE/s4GlApqEWU7kJeBa1oNoRmI2WYDvDUJNPN8rxzLEVW0nme58RxzKlTp3jqqacI\nw52JNK6GnUaI3W6XN954g5mZGZ599tkroortOtXcCmwUuW98zgcffHAzzfr2229fEfFslCcsr5fn\nEF12ebQuBU15rlhrwdTEDk6i6KOu8fO8azWjXUREfo9O1EA2VJ0OGATbKT3eoynhg8W1U3ciAtqS\nk+Bs2fWbc97cTBG6RUoyCBg3LSRz0Dn4qiBVHvPOKR43HyYtoJUoXCOE2M3MwnZinWuZKTcaDSqV\nyju6/3Y9Uc0GPDSBMoyLZVlBnQhXEgplYdSGwiIo0VgRGlrwpMKwv/bu6FJzmyEiH7qV490hxB3A\n8zyeffZZ4ji+bj1RQR+11eYIIQZ6FKR08HEJgZwuhrCsdTIGP4sICElo4TI2cqoQVGY4d2qedJBy\n8IFHObEeMlSwlg4Jh5pKDrUADuwFx8DyOvh+STpKNCl9OomLcmO6+hwWjWAoKCi/fgX4hK5Lmq9R\nrnddRAQXl87aCifeOo7jODz55JO3ZF/kelHchoJ0eXmZxx577KoTyzvdqWZjGGMMzWaT5khhtDXi\nOXfuHL1eD5THIJ1kz3QVx9SuWDSJCL5n6Q4UYw3hulsuNisL+64Ctap577jmbaPQYikue66uCnlH\n4TThI/n1CVFMNuqkNHoMoVDlnrZfxJgsp501CcIEL0tJdEhmDYGkZFISl6uhlUOUWfab7JoF+tcy\nUz579iz9fh/P8zYJ8lbXgxZFseN7uSkOQ2VZJ6OLpkYTpIsQk4/qHxHDVDykrut4VOn1eu+alKmg\nyG9fhHhLcYcQd4CNlarjOBTFtQ1rhGxzZZ8gLI9UapoUQUjRtIEKGVOlzSpGG6y1RNRw6eMSobXL\n2dVV3jh9mnvuvovhPdP8zaqicGF1XfH9kzmnF32k0DwzLXzi/ZaDM5YCaA9y8FIS22WIpWUyApPh\nqAItLgZF5nRpFwENHQAKlMYqjTCgICJOHLqn3qJTDHnmmWd4/fXXb6nP4NUixG63u6kg3S4qvHyc\nW02IRQH9PrRaik7HMD/v0RwH7UNiyyYMvqtoBBX27LkY8ay1Uk6c7rG+vs7c3BwiQqVaoVFvUKvX\nMNqUheUW0ozrEyKGS7dCLiLPYRgb/lHh8t8B8+piQ2lGr7KAq4X/caWCV792pFUUQm4hTzVqVM6j\nUHjiU9DHl4S+VEBpjLJE3gBSRaE1sfh41mEopeumZ2EMi3e9PO1l2GqmvFE/u1FPurq6ymAw2Exd\nb6Svr6UQvh52mjKFMm26VzxcUZxQGcsIUAMV4NiCBoaD4rDQCQl1WX/7rrF+GqG4jVSilNoL/JfA\n3wHGKQvz/xT4vIgs7GasO4S4QyilMMaQ76hSvdxkX0DwAB9FQUGBQVOKLTqAhzAGaGMoRooLhaZI\nMo4cfZO0cHj40SfoBC4XCnAT+MbLhr86qqk1ndJdAPiTOcVfnRZ++nDOz76/y/IwIQsyunZAQYJ2\nXLKijWuqI0VexB5HcV4GdKxQVxF5AY5xiGmxulSwdmqRw/ceYHrvg1ilyPXFc7xZlHZSl06Y1lrO\nnDnD4uLiNaPCrbjVrduyDObnFVkGYQhRVC5m3p7XUAj79kCtVtbbLXYUvgt76oLR4Loek5MTVMIy\n4imKgm63S7fbZWlpiSRJSLOUhcUFqlGFMLhOStCpQNJhu2b91pYCmPEi439OJ/ic7/Oqk+GOIrVC\nCQes4T9PQ55WzuialA3hRUrC85yS/DtdWFmFlZWI8/MWR5c2YLUaHEqfYs78KRZTbsipcp+yEgxQ\nKIZphEfC3fkhGsUAwUWUYm9g6bRvPt3p+z7T09NMTU3RarV4+umnN1PXJ06cYDgcljZZW8Q6OyW5\nnaZMN+Ci2YvPlHj0KOhh0eITYmig0GguFLL5/u8apwtu7x6iUuoByoL8MeDbwAlK26hfBf6hUuon\nROTtnY53hxB3gZ2sRjUhOR06oz78F22RyibEUJZVVHDpoqgiGKPJsxwRYWFxgQun3+b+ex/lbHua\nL/2x5s0M9lXgwgX43tuayANHfLxggLEuqpEhGbx8ZMB4veCnHg1JlceC6jNJjckgYKHXIjU9fJrl\nLqaEHHQc1mxML09JckXDsZw7MU84LPjJw08Qey5zFlDCiudxtrDss5baSLxyM9dxK2n1ej2OHDnC\nxMTEjgr7rzbOxmM3AhFYXCxfu7GwT3NFK3HYXwFQrK0Ivgd+ULbqG6aw1FXsbQjOZQHd5WnWjX1o\nFJw/P8uZ071LUoKNRuPSyVwHZZmFTcrw9JLPCMoKWnImzBi/ngSspJbXdVmOfkgM91lDXJTk1+5D\nq6/IC0GrMsr1HIi7pdhnaQ1OzY9haxA6irGeMNGH+/c+x1Lwp8RaY3R5fwrld18JBwR+TJFVeLj/\nMBW3T+g1SAuLJzsradgpNshru9T1cDik3W6zsLDA22+/jdb6EgHU1cyUdxMhboWDoonD1cpzN+6/\nd1OEeJtFNZ8DOsBzInJm40Gl1N3AH42O//xOB7tDiLcYhpCENl0s0VbXeTyK0YxpyfEYwwJDLEYb\nenGPH/zwB1RqHgfv+wC/+i9DziwpEgeyCN4eCrPnNb4DgSckSUBY6aGiFDKNdQtCk/GH3w34qSdy\nHKUQyUgIqDuWyHUZZCmOm2yqCD1cJrWikIDhoM3g7GkeO7SXxuTjLNiyWKQy2giKAG0tS1IWK0/e\nBCFuiGpEhDNnzrCwsHDddm/bYTvyu1FCjGNIU9i67dOJFa6+2K3HcRWdrjA10p2EHvQSIcnB9y3i\npgwYoExZvO3YCC1u6bqgFFYMB+/aw4E9M1hyBmmPdrvF4to8J0+dRKGo1+ubTQN8fwaVzIPtgwrK\nFoAiuDrBc4YkagLHlCczKZoPF5epnTMY5JB2FKEPgXfx2lyYh9dPgtGKaiDUA0U98hlmKXNLHu2u\n8KDb5Lnpj/Pt4OsYXeC6OXnuYozFYHC04QP27zHuR6AyUgHfUfjFtRuP7xZXi+a2KoT37t0LlAKw\njUYM58+fJ8uyS/qzbpgp7zZC3C3eVebAcDsJ8cPAL20lQwAROauU+u+B/303g90hxB1ip3tTCgfF\nGJZVBG9zP1HhojBkdHFpoPHQCKm1rK6usra2xiOP349x9vMf/UbIagemG9BzIXNgPVZoBXlarvan\nqg5JGlJptsgGIZ6XojJNmsLJRctMI8exER4eA2WZjCqsDhO6SYKvQowuU2/DrGD9/DH2+C7PPP0Q\njjPFOVt605ktk5pWCoNQUYqWCJEI0U2UXQyHQ7773e8yPj6+q6jwnUK3e+m+Xl5AnJeKyQ34Pgz6\nimJcNksnHK3oJClRtUVt3LKy4hH6DmKERLfQGLyiSV5AlimaYxlt1WbIEOVrwumA2nRY+klmHnGn\nuHQyjwLG6z6NKCEKPZTSiKnhNxqkRY3oKrV2SVK6qySFonaZ4DnP4cwFRZopqtWytEcp0GmD0GsR\njiW02y4nzimebdzDR+wvcMZ9k9PuWyymEZ643G8f42D+OAEhkJLagERgfyQMu7eWbHZDXo7jXCLW\nEZFNsc5WM+UkSTZLaN6JAvper8fdd999y8e9EZw5c4ZDhw7xi7/4i/z2b//2LR//NotqPKB7lWNd\nLm8Rdh3cIcQbwPWsd1wiyrLlPgVDynSpQlHDGZGkYOn3+hw/cYoZ32dipk6zPs2/+uMaSy3FzCgn\noxGMSdEIrtZY8UgSSIIyPSvrijDq43t9VKrww4R+ZvFsBLmPiyLBorTLeMUhCoaYvE5RaFprK6yv\nzPHgwUPsn5gGcga2nD3NZZ9PaY21JTl4QMsKkdk9IYoI6+vrdLtdnnzyySusoW4XiuJib1gorY1E\nttuPFLIM4qQ8nkmO9VtUMNQDDzNRip4kKdOmmcoYFm10XqE+OWAtXMCKxVUuAiRiMUpTE4W4CdXx\nCuPjh7AU5BLT63fodLqcXEwY9jr4vk+jqTCu0GiUTRQ8ryTrjc8Rx2WtoxcpnG2mg/4QllqKqTFI\ncoizUQ0nGp02QWfUqz2WOim9LGLCyXgy/RhP83dJFCzHhiRXWA0DoLAFrlNlfyQEDvRvcfR1M9Gc\nUopqtUq1WmX//v1Amb7+4Q9/SLfbZX5+HmsttVptM4q8EbHO5fdJv99/V6lMb6Oo5gfAf6aU+oaI\nbIocVPkF/vLo+I5xhxB3CWMMRVFcsxbRReEToPBxsZS1o2oULVqyosuZc2+y2u/yzAP34uWK+XMt\nXBnj//oLh3oEIISmy5jbYlUV5NZyd5Qi4rDcHqOz3sCtC7YIGA4DKpmiYVOG1iUAXDogjAqrAckx\nIrisoqXL+blVHDPGY4/dz7gTUerEZujL5W3IS2itsaP7zVOKvkjpUbeLiaPf73PkyBGUUtxzzz0/\nNmQI4LplinEDWl3p65fnsNZSiBbU6GA/TwmHmppyqFaFSgihL8RJGaWhHBw/wWHIbBaXjfrUloJR\nBQWWtspoiE+i+ljJKFTp1xXUDEGtweT+Og4+MvRptzqkacq5c6+RZQalmrhuk1qtShB4jI9DVIFz\nq+Ve51aIwEoLur2SNAUYKIUaCXIUGqyPZD7pOvRWc/Yc8FHSB+XgGzhQKYgLRZoLMMR1qvjG26wS\nud6Ccbe41elNz/NwHId7770X13W3becXhuHmXuROzJQv/8z9fv9dI6qB25oy/R+ArwHHlFL/N2Wn\nmj3AfwDcD/zMbga7Q4g7xOWlF9ciRIAGsAxURg5tG1hf73Dy5Ekm9u7nqYf3sEcZBr0YyQYkqWK9\nV0aHdWeVqrfO2rrP0CjGKn1UHOO4BXsr51npT5AnE6SVJuK76CzChBm27/LMfRmDtgN5GcEY6aIk\nxqJorRjW5le4+1ATv+rgKIvHFJqQMilqtzUL2q6YfqdaThFhdnaW8+fP88gjj9Dtdn/sTHyrVWi3\nBd8ffc8GAhey4mK3mQuL4LlQqZSPCUKcD2gEDourmsJaGvVyzzEKLxboF2iWshZaZbjb/OTMqDFa\nnwxP+vREkWQTxLZ8n1ALDUfQOkGFBTPBNOfPn+fJJ59ERGi326yvt2i1ztJqZfT7Vaq1JsNsjMi/\nGO1kOSytZiyu9egNLLhCnLsMbYKrB4TJIiFV0izAWsPaQLjQ0UStacaiNSKvVXZyRhNoW/a9pQlq\n7JK87a0msHdiv2+rqGa3ZsobqdarjQdl+dC7iRBvF0TkD5VSPwv8T8A/4aJ68W+AnxWRP9rNeHcI\ncZfYKL24mnptAxU0MZYugg9IlnPixEmyPOP+xx/D931mKG2XHJOXJDtasTtqSNVdp9WtkKcD7q8t\nsRJUKMIKg57g6Ar1cEDh+fSdhLTYA5lPX2l+8tGUsapi2FUYazDSx5UOg9Tn7Owp6u4kjz52GNGC\nFqiLYMSUgg0gUNCRK2+MrYRYiODAjvwTBoMBb7zxBo1Gg+eeew5jDP1+/8fOysb3IQwVw2FZcgFQ\nC4TcaqwtyxOSBEbaDQDiVAgCReSX6eS1dU0lKq6oMcwpW+Zp2OwtezkcNANiVvIMW9SpKohG+5ep\nhflU0zA+dWdITmnrtFEKdHmBe6/XY621xuz825yc7ROGIdWwRlEIlWCVMIAoHCMK5gn1kDGtabWq\nzK2PgbPGVAQRU1SCgOmmUIjmQneSmWqDqj+kzHgYUGHZdPwy/DgQYmkANaSsyHQw+Jd0/rHWXtPl\n5FpmyvPz8yRJcolYxxhzySL5b4OoxlrLr/3ar/Gbv/mbvPjii/zu7/7uFUS/E9xmlSki8ofAHyql\nIsryi3URGVznZdviDiHuEo7j7KgWUaGYQOOL5cTyEqfmZjl48CBTk5M0lKaKHjV+2pKGNfDEPcLc\naos18Wjllj3RKpn1qA2Eg5WUc6os7NaBQgqh2otJnDXWkhkenmjwSz+zjEWjjSYthkyI0FtMmW2f\n5a799zFenRpZT23Y4xRgV8GUxdA1pWhZ4fIwUSuNjAgxBqbUtRWdIsLc3Bznzp3j4YcfZmxs7OK1\n+RE70e8ESsHMjLCwoOj3yxSq7yhqbspaF5aX4K79o9RqUUZbrquoRQIIWpdODP2BolG/9LMVZBi5\n/k9tNc/Jrcu4kbIfrS1TnK4GTwvtQmOUj3H6V71+Siu8Okw0Qt47UWG96+CoPkvLJ2i3Vji2UKFQ\nGdRP08sLfC9gqrZM6BYsxRrf3MMw1STFMs3GNLWKi6PBsbDYdfE954o07OW4nSlTwZKzjlUX9+6h\nbFhhJMKMyo5gd4rkrWbKcKlY59y5c3Q6HfI859ixY7z22msMBoMbjhC/8pWv8OlPf5ovfelLvPDC\nC/T7fX7mZ36GXq/Hl7/8ZZ544okbGncr4jjmk5/8JL/3e7/Hr/zKr/DFL37xhhcxArdNVKOUcgFP\nRPojEhxsOVYBUhHZ8er7DiHuEJsmwSPy2gmSOOHto0dxXZcXHn8Sz/MwlAbAW2GMIckKzrbg6ady\nZv9swMBGGHooP6cvDnWvzZgMmTCKlZ4iUDmwysnWvYxPdHjp7zV48f2KyJsgo0+iM4p4nQvH52mE\nM3zgPT+BawLK9Vz5n7UwTDVF3gOd4vsenqtoaGFdhOqWz621IrMFSzLAQ1FRV19JDgYDjhw5Qq1W\n24wKt+JWGARvhbWWtbU1qtXqdSP3a8EY2LdPGA6h3YbBQAgc4cEDlihQaEcxSMt9uamalClVQgpi\nNC6OI8TxlYRoKTC2NJUtTZ+vnDwKga61NJWQDjzW25o0G91zGhoNSxAJrdyhYhKsbB/hJLTIVB8X\nn2bk0h8KeZFQGIU3NcXEmKIWHWNxNefEif0kaYpkPlmuUc45conJ04epN+DAoXUcPQ2UgiOjoR9D\n8zpakdsVIQpCptaA5ApbLMGSqjWUdDGMIfrmMhSXi3U2lMG+7/Od73yH119/nY9//OMcPnyYj370\no/zCL/zCjsd+6aWX+NrXvrb59ze/+U0ee+wxPvShD/Fbv/VbfOELX7ipc19bW+Pnfu7n+Pa3v81n\nP/tZPvOZz9zUeNxeUc2/oOw3ud0F/udACvzHOx3sDiHuEjuJEDeio7m5OR566KHNdNbVoJRmqa8x\nccYz91lWFyzf/AFU8xhtoea3KJSwEoeQCh9/uuDwfUJnaOnlq8zUPe57xNI3LgUKVxqYhfOk610e\nf+8TNGozW98NKEUV622wBRijyAqLFahVYWJMobTQEkCEmIRXxuY4VVvCcy2RiRkj4t8pHuMpez9m\ntOIWEc6dO8fc3NwVUeGln/fWRYhFUfDd736XSqXC7OwsWZYRxzEXLlyg2WzuWjGoFERR+a/ZFNI0\nZWoChglUwivP2cUnZ0CZmtNlN5ctsOSlslgpTO5TkG9LiPEoGuytu9iuT+BDNHq/ooDVNU3QF6IJ\nIZYrP4+QYVkm5zwOHsvFOn8jK1xoZKy3FTVc9qQRYXiWOHNp1EMef3hIv6cZxh7DdU1oLAmLxPmQ\nRjFJbgtaXZd6tYFWGs9AN1E0K9f+7kTkNhFijBBfQYY5fXLVAwRR3VI97HVI1DKujG0a/t4MiqLA\n8zzuvfdevvCFL/ChD32IP//zP+fYsWPMzs7e9PgbuNnI++zZs7zwwgucPHmS3/md3+ETn/jETZ/T\nbU6Zfhj4x1c59vvA/7Kbwe4Q4i5xvQhxo+tKs9nk+eefv646zZLSLzpk/jrKXSAw8PMfXGNmrODr\nfyKk2RC0JS4C6hXhhWdyHj0gKMmoVyLSfkg1WsVD8HHodrscPXqUIAgYm7mLRvXKtE27A2ut0oFB\n+WVphzsitcEQ8kyxd08ZKZ6XLv/a+SbdZp+GQMV4CA49hnzd/BVv6rP8/fwjZMOUI0eOUKlUePbZ\nZ68pOtquddtusVHUPxwOef755/F9f7NH6ne/+13yPL/EAHij2H3DmWI372NMGT1uZ92kcfClTqK6\nxLmiXi93CgVLQYrCUJdxYulhxeCIT6ZSHJxL9hOHkpJ1XaRboVm5PIMAlUiIY1hbLdizzy9jfLUh\n7kmAC2QMQAX8v/EJjrttUqXKetNGTlLJWUg1jw3W2YcQuRUcHRAPDAsrQj+tMlbV+FHIxMyAMa9O\nnLZZXLrAmZNn0UZTqdaJKnX21KrXrN2z1l5XdLYb7LSrTK5KX/tLHqNHTg9Nec0sDkKGFn8UNa7g\ny9QlDflvxTkWRUEURRw+fJjDhw/vaqyvfvWrfOtb3+LYsWN87nOf4w/+4A/4jd/4Db7zne/w5S9/\n+YbP8fjx47z//e+n3+/zjW98g4985CM3PNbluHFC3Fm27RqYBpaucmwZmLnKsW1xhxB3iK0q0+0i\nxA3fvuXlZR555JEdlRRYYjKW6MQOQ4nwdLmytbrOBx4/zz3NKivzPayp4bkZvg8z1VJEpbBYVcHm\nMNm0FAw5e3KepaUlHn30UbIsY3npPHBpajLPYb0FlZBRsJiPOq6WE0kYQK9f/qvW4Gvet4AeTas3\nrYAUChcHB8MpNc/vd/+cmdc8Hn744c09lutdy5tJmW4IdZrNJpVKhVqttinS2XFLlH0AACAASURB\nVGjxdfDgQWB7Z4qNlmnNZvOakvqN71wpGGsIK6uKypWuXjj4UBhSSfHDHvnI5MuTOi4+Ck1dSsLW\nRLhiKFSMJae0ly5w8HDWp6mGvUuMnbfCD4TFLpjsYgQkCLAIuBQK/iA+yWtel4o4jGuPXApE5Thi\nScKYtys+Qa9HxaxjsymCCPZNJ2QqYyKKCEMX8YTQL/CjJuONQzh4pFnKylqXeNDitddObdbubSw0\ntnpE3r6UaXZJdCgU5OoiGUIparKSAKBxsSTk9HC5uRKgrYQoIje14HvxxRd58cUXL3nsz/7sz27q\n/ADeeust1tbWePLJJ3n66adverwN3FyEeNOEuAQ8DvzJNscep2z0vWPcIcRdwnEc0jS95LFWq8Wx\nY8eYmZnZcdcVoSBjBY1PYQ0WhTMKDKyuo4shU9OrZJ0cbQocz5DkpaBDmwEFVVY6PvunhhTK5fvf\n+yvGxx/afP9Wq0VhDagqyABUOZP3ByNXIVWehWZIwZ6NB4CSFNfbsFZbZF318MUjV/nl3Aoi2CTn\n9eoZ/u5z/4DQ2ZlC7UZTphsp2dnZWR555BHGxsZY+f/Ze/cgya77vu9zzrmvfve8Z3f2iV1gF4sH\nAZILiKIlUiUqgcp6xErKCVOKrEQphFGlYr1iG6rERpUtKakgSlzlosywLKnkxHGkxGJSVhxRCm1K\nIkMZJEWA2Ad2Fvua3Z33TL9u932ekz9ud2/P7Mzs9MwsFwz2WzUFbE/P6dv3dp/v/b2+35WVB77W\nZi++MAyp1WosLy9z9erVftt9b3PfKvop5jMXDL/TjawHgrgkgU5oMTOhKMlc1lSyuU6sI8pRGydp\nEiCQKk+sIQgFdurihR5pLLFykNJCojbZiGliE+NRQISDddIOWR9rgeW0zkXVpojAk5lUoBEpxiiU\nANeA0iHvOhWO6jVS2c5mDulQKES0jA1ugGsnaBlimyJWdyrVsR3K1VFmTo7i2Sf74uU9j8hOp9Pv\nuuz5RR4UhmuquXfuUwKyiv3Ga6E1KJmdW4FDKtpYprRtB/BusFUU+zD9HPeCH/3RH+XMmTP88i//\nMj/4gz/IF7/4RcbHx/e97iNWqvnnwH8lhPhXxpi3ew8KIZ4jG8P4/WEWe0yIQ2LQ8SJJEmZnZ2m1\nWjz//PNDKVOkdBAYBAops04t05PhEpJETeI4CTPHllm+Vafd9DBSkCrBalwh0lUOjXQQ8U1mb0ie\nfvoFisWT/fX7jStiAsxCZqWORxhY2BZkjQcRKaMYNqZVlcqUWC6LG6Qm7crO9aIRyNRaEqI4wnNd\nUmWYT9Z4whze1XvfS1NNEARcuHCBXC7Hyy+/vK+UnOu6TE1NMTWVZVN6ZrW1Wo1bt26Rpinlcpl8\nPt9Pj0sJUxOGWh1qzXsbncFgKcGhSdOPHjdswCZGxIvIpIlHm2oSEacxK50OnWgEy6qihKDdgU4T\n7pBjpigxVpuUqPciCCRJWmLCdjfdTHQAC4PhYtoktAwjwsruXUQKRoGARCqcFBwB6zbMt2BK1oik\nha8kJmewhKKtXRynRs2sMp0e6b+KH0HRy2TeYGePyHq9ztraGnfu3NkgXr7Xa7bbFKwyeVIRIvpu\n9sl9ZGhIQDt98hKIrmh5um9CdJzu6x5wl+1B4rXXXiOXy/HzP//z/MAP/AB//Md/3P8e7AePsKnm\nbwM/BHxDCPEmcBuYAV4CrgP/5TCLPSbEXWLzYP7y8jJXrlzh+PHjnD179sFfAKMz5wKTgpAY2SLr\nGIayB5YxRKnGtXo244rEOoIaaTNt5fDrdeptRcFxmSiCp+aZX7zDyKEn+dC5SYTYeKfXr3UKBfIQ\nGB/MOuDTk+tOmcRsakAYREdEXc8OyOpiYIwmCEKEzISVBYKUmIjd2GLdO5fDRIjz8/Ncu3aNM2fO\nHMgd7WZsNqvtRT+rq6u0Wq1+004vgjx2uEAUC4zJbh5cx2ypJ4pJEPFdMAIt8mhckB41P08nEIw4\ndYySoCoIYNID3xjm6g6Hyw62SrIxAiOJjWJcgZ1srmNqerPIC4SoVIJ1z1dFCkFiFKlwiBUUdBMh\nUlrSoRRHtKXGFSGeZVOtJgTJGlEwRl6M0xIddBJhaYeyZxgrbq2bChsj8Xa7zejoKMVisU+ON27c\nQGvdH26vVqu7nnnbbYQoKZDgY7DvI8LsbOjsfKUu4r719kdggxFiGIb76nZ+2Pi5n/s5PM/jZ3/2\nZ/nEJz7Bl770pX725LsNxpgVIcR54BfIiPEFYAX4FeC/N8bUh1nvMSEOCa01i4uLtNttPvKRj+zu\nSx3XIV4FND0nVyPWEFYZrDE8JShbJtMgVXJg0xEYRjF5By1nODYTcsLtcOfWDe42Qp56/vvI5V0g\nRLAxRbUhChMSRAko4eQ0azVB3tp+A0iSzB5oXJQGosLsS99OEhzXxVIqu7vGYASUdiDWzdhthNi3\nTAJeeumlHRs5DvKuvBf95HK5fvTfarWo1+vcuHED3/f7PnzVahXHLm1wm+8jbXQNCD3oDtMHsaAe\nSEqeAZNHpOsYVcCxLTwXPMAVoFNDKrOvZ1EaCiqL7trRPQWcDB7Qomc0ZlBoEZPdb/TqZmCERSws\nOozgigaTbo1x1yUUGj/0sFWOYr5OFYOKnkfFE4S6g+02mXFGcIbQre0RmOM4TExMMDExkZ2ONO0P\nt7/77rsEQbDhRqNYLG55DXdPiC6WqZCKOuBm5QgTZN83UoyJUFSJU1Cq2xmNBiER5uCaalqt1vte\nx/Qzn/kMnufxMz/zM3z/938/X/rSl/p192HxPhjMr5FFin97v2s9JsRdwhjD3bt3uXr1Kq7r8uKL\nL+7uD6M1SFZBFmBg05QmQSdrCKOR9gSHiykKTS2EgpUREkYTJCWCSFHORRSTDt++OsuhQ4f40JnD\nWUqMDjB9X5fcdt2wxYJkdT2ro2y3x3QCmJ6E59NT/Kn8NqlJCeIQDeRyWQEtwSAxpKSUTZ7DZveR\n224ixF4EfurUqb5ayHZ42EP+QogNbu49aa9arcbdu3dptVp9Eu2nB5VEpPVMzWUArUBgd8+7NoIw\nUqRxgLCKlMuwvGwouAIdCWYK9yJPY6DdhpERsylCzNGbLT0lx3lX3kIaD01AdvclUVKTaoXRNm2R\n0NJ5Sl5ILIoYY1PSEY7RFJjE5iSOXUDZWTyVmgBMAuzeEWK7sQulFCMjI/1xnMHh9lu3buH7/gaP\nyHK5jGVZw7ldUEYYi1Q0wKQYGaFNihAuViaVgdEtpOgRYoRlKltGlMNgUM7xu8Uc+Kd/+qdxXZef\n+qmf6pPiE088MfQ6j9ggWALSGJMMPPZvAs8CXzLG/MUw6z0mxF2id3f74osvMju7swGzMV33BCJk\nsgby/lyTFHm0bEPqgyri2pLj5QQfhzttWA8AGVG0yswUPZp3vs5qsswzz5zCy+cAH3AQHL4vOoTt\nozDLgslxWFjKmmcGSzNaZ2RYKkKxAJgSR1vjvOvcxrUcLL1RzDtFE4uUTyYvDrWh7NRlmiRJP3r4\n6Ec/iuu6WRTaTQ3KTXWeR6F6Myjt1fPhi6Konx68fv06gpixYptSZZpy5Z7PYycWKGFo+IL1dldI\nWyYIlaWkLc8QhxDEEBWzYfg4yT5P1bKhuskyMnNOGQVW+bg6wpfTOZoYysomJukLylvCxsiUWiqo\nyAJe+gNo5VMyIR0/op0UKFgnsGyNtjpIU6QXYSbooTx0dpJF23weNztRhGFIvV5nZWWFa9eu9dez\nLItCobCrVKQijzJ5NDGWHiEUayjjIbuknqYpUgk0AcJ4qC2+P8MiSZL3bYR44sSJbb8jn/70p/n0\npz+979d4hE01/wsQAj8FIIT4DPc8EGMhxF82xvzxbhd7TIi7hG3bnD17ljiOtx3MjxNotDKHcq1B\nJG3yls1IVeBt2lEkDtLkSUUdlTRQUoJJmS7BdBGCNEIIQWO1w7V3vs3x48c5PPMSQkR0FU8RbL85\n7DQvWSpm0eHqGjRahjCAONZYCiYnJFMTgjjO5gpP5aZJzgluywXiJEF1PzIJKVJIvjd5gRk9XP1h\nuznE9fV1Ll26xLFjxzh37hxGaDq0Cej0n2Nh4ZHD7m7RgxvvQTczDEO296UHkwB/7RK1hs/8wjxR\nGJHqlMhaIqKKJkfeASk0RhrIvH8JIoFTMFi612AF5aKhWMhsnrZGNjIgWeMvc4T/g5uspZKcTHGk\nRuIQmQ6BiXFlkR/lCSbsiPlY0G5P0/R9pAQ/FDQDiW3DmGewlNlTo8l+xi5c12VycpLJyUwhJ0kS\nLly4QBAEXLp0iSiKNqRZC4XCttdcYmc/xiOmkTmIAAltpNIoU8aisO/oEDamTL8bdEwPEo/Y/ul7\ngEGpnf+CTL3mF4H/kazT9DEhPgz0xJS3IpowgjvL2SaWc7ubWdAmTFzm5g0T+RbVfJg1uTh5sFwU\n5cz+x6ygVEyiQzQOhhSpBdfeXSWK9KZa5e4u2YOIoZCHJE5pz4MwhpwjsCxoNVMW5ldoNN7j9NnT\nTE0WeUaf42r7Fl+L3yIZBYnkCT3Dc+lpqpTokBCj+8P9uzm2QaLRWjM7O0u9XueFF17IujtJaNLE\noLEGmiRSUprU8ciT595muHnNR93pp5RLpTpKpWqDULTbba6+dxVhEi5eWyYnfTzPpVxQ5MvHyRWz\n4825sN6CI+OGo5O7e63s3FQxFHlRTeCmJf4Ft1gzPsKkKJMQa5dJxvg35BGOqiLL4QJBZDNi2URW\nFvm7dnarFaew1BRMlTNja2fIbeIgz71lWdi2zdGjRykUChhjNtRz2+125hE5kGa9TyoQG5cxjEmz\n71ZosHUem4Mjrc01xO+GlOlB4RHXECeBOwBCiNPASeAfGGOaQojfAv7JMIs9JsQhsVV0ozXMr4Bt\n0R1puPcLN67hxB2Wlyy8yQTP1hDUwfYQhQksWUVpELTQiUKSZ3XJ5+rsDU6efIJDhw49lI29VktZ\nXjaUy6J/Nx/HEVeuXCGKJE8++WGqYzZ1QgSCGT3Bi7ee4NnSs1uulwxBiIPp3EajwYULF5ienub8\n+fMZsWFo0SRzkNwYFikUEklAB7ubAtNGdwesO4BBWCHaJChx8E7ou4YQGDmCSFdAFJBSYls2IxOH\nOGUcSl5KEvq0/DrzS2t0bt7FsmxKxRKpVSap5HiQn0iWRg6Y9Vtc9RMMipO5PE8Xz/GMeJ6r6RJ3\nkyYJMU/IHFWpSMUaUaqJ2lOMOOukIs0+z/36dowj8wRJSDPIM5pzsIZsODnowfw0Tfvrba7nAv16\n7tLSElevXkUI0SfIarXaH4cQ3dlOnUosdbBb32ZC/CBFiPBI/RAbQE8b85PAysA8YkrWdbZrPCbE\nA0AnhESDN5jBNAaiDoQ1hFfFUYL1wOZQvptujUNoLUF+BKHK2NIiCXJcfHsOrTUf/ej5h9a6nSSG\n1VVDoSCQXaPb5eVlbty4zokTJ5mYmMD3DY1Giq5Cx0BNK1a1op5AQcFgk+qwdN2rIb733nssLy/z\n3HPPbdhAYiI0up8Wve/vESgsOrQRShOyRCoiBDK7ebDbhCxgU86aLPaYEtt3fVKVMKaD0H7XRxDC\nRHK4krDSSrEsxdjUU4x1hdL9IGC11kaEy1y40KQ+H1GtbiMYIFJm/WX+h6uGd1suqckh0dgq5Fiu\nzX9+coTnKpOcJgszTdcHIiHrcnW1g6MtfLVEogIUXjajR4DGxZIeraDAUTc/9AX+TmuZep7H9PT0\nBqum3jzknTt3iOOYYrHYP4+DBHtQGHzPH8SU6SOsIX4V+FtCiAT4OeD/GvjdabK5xF3jMSEOge02\nyIbf7QodRBplAiK2CwZc2+AH8l53p+1C2IZoHcpnaLdXuH37NmfPnn1gV+V+0W53G1RkViucnZ3F\nGMMLL7yAbWcklMvB6pqglQNLCFIEoYaaFqynUFaGEZWlhg2ghtg1gyCg0WgwMjLCSy+9hJSSFEOb\ntKs86ZOQUiKhgOrbZA1CoYhokzo1jDmCRe6eSolxELikogWGfcty7RlCgDWJSesYs4CkA9onbxmm\nKgUa0QR+7GYTngJs2+PsMZeCN4IfwJHRiEYj29hv3rw5MMdX5g4Bf+fbinbsUbB6PVsKYxTXWimv\nXVrn750d44Vq9sHMbiJspJkkTpexVIA0HsXkKGudDroUZFqoehzLjOCSx048hJYMu9fttqlmmPWG\nITDLsrb0iKzX61y/fp1arYbjOCRJQqVSoVQq7Uordbf4oJkDP+Ia4t8A/oBMyPsa8PrA7/5d4P8d\nZrHHhLhHDNZJthxhCBpge1lkkNRAZeKhfT41BmRK1Opw4fplgjDk+PHjD50MIWvftyxYWVnh+vVr\nHD9+ot/E0H9/QrAaC/KpxHM0xpIonZLrEmAtzabeypbG6rYvPAiDLiCe5/Hkk08CWbp1mYgUcLrK\nkwpBgMZHM4oit5VlEm2EFAhjbYhiMlIEiUcqWihTOBBHgz1BCLCqaMsmwme0OIUfWniuhZeHJDWk\n3c+P3TUEDmPIuwbHsRkfH++LEfQEA9Zri/zP/mHWE0lBhZmhlFIgsxnWnKWIteLvXYn43fMKIQIM\ndYyIAIkyHkmqQAUIUuzEI9eeoerOdFWJsm5eH+iN+A+D94NB8CCklJTLZcrlMkePHuXatWuZFZtS\nLCwsMDs725fv6/0423cwPRC+73/XDrp/t8EYMws8JYQYM8Zs1i3968DCMOs9JsQ9oFcD691VOnbm\nE2cN7tk6BmGDckFIdNRApBHSBJBmxLi8njB3c4GjL3yCOEn35CIfxPDlK4Ivz0rCGE5PGn7kec3R\nrvPSVg0OaRpz+fJVlEo3RIWDaOiM6EeFTZMoiwR1V9xbQAFYS8FSmmnhPTAtGQQB77zzTt8N4+tf\n/3p2mjCsECMQ5LqkqrBISHCQaAyrpEwicAZIV5OS0kZq+773mEXyvWYTgaaDZG937Ac20iEESJdS\nwaXWEbhdmT5LbfrckHUrj5fvX6I363hXpSzciBnNOxijSdOUMIrRRiMRKCWR0sJPG3yr0eBD1Q7g\nkpVTDHmnQ5CkKD2BMQV0qJFOCWug3BKn4HSPzRiI0syzUZDJt8kdLvejNAjeDYwx5HI5xsbG+mMz\ncRz306xzc3MkSUKpVOrXIYexEXtcQ/zOYwsyxBjz7WHXeUyIQ2Cz40WPEEv5bNRi07Pp312rIgFF\nqiMBwokJw5h3r1zH8XJ86EMvYE1OsbC4SBAE/b/uuaVLub1c1jdvCf7mP1P4ocBSBingq9ck//hr\nin/rBc33ljYSN2S1wosXZymXT3Ly5NYahtrAemQo2IKclfXo1WREIFLCrjq9EQaMxEs93M07+gCM\nMczPz3P9+vW+N+SgG0CEJsaQH/hC2diE3VEL2Z2089EbCDElwsFBCbUjaQksNHszg30YzUyuDaNF\nw3pLkHc3Zha0hnYI5bwht0OAcrmlSXUWDQohs4adbocoOiPIJEnJyWX+dLbNyKSiUi5nqUHLIu94\nrHc0KcsorC0JLExgumzwI1htC2LdNZYW2eex6hkq7tafzYOuIcLBXouthLhte2M0rrXui5cP2oj1\nIshSqdR/j5tTxL7vfwBTpo+WEA8KjwlxD9g8euG5UPCgHWROCADYBQjWQeaIEhBSUCra3F1Y4c6d\nO5w6dYrRSh6sHAyMc4QhNBrZD2QbTqUC5TIM9lRcXhD89f/VwrUNkxsc2g1aw//+F5LbE8d4+XxG\niHEcc/nyZZIk4S/9pQ+zsGCTpga1hSSXBoLQMDGRee7ZKMbxKMaSQrez00ZghMRsYVbbQxRls4yW\nZW2QXtuweZDeVyNUWNg4xERYONgIOmgqGASGmIiYEBeZyXJtIsSNtd6uVt77CKMlUNJQ80V249N9\nXAgYLRmqhe1vggC0VigR3fe4IsVRHfKqhU2I4y4yXn2KQsFnfX2dm7duAuB5FYRTYV0UKBXWM3m+\n7uulOss6lD1DomG5DTkb3IEuKm1grS2IU8N4fudjfT9iNxHnYAoVss9Yp9OhXq9vUCfaSnJur001\nX/ziF3nttdc4cuQIX/jCF5idneXHf/zHsSyLX//1X+eHfuiHhl7zO4GHSYhCiH8EPGOM+Z6H8gKb\n8JgQ94CtPBGnRmFxDVqdXhqsgI7XiGKNbSlGCj4X3rlMPp/nwx/+cKalGPlQzL5wSimaTcPcXFbf\ny3c3GmMycqzX4fBh6I0jfvbLEjAUt2hElRImS4Yv3xrn1qqmYq3w7rvvcvLkyf4Yx9SU4c6dFNcF\nxxnY7LSh5Rs8T1Ao3ts0BAJbCwoDH5nQbC//trS0xOzsLKdPn95RTT8liwI3I9dVD4kIEWRNN01C\nYjqAoECRkBah06YRNVm5voJj25QHNjDINCytTTqrGt0dWaBrs/Sd39ErhSwSDOJ7NWjX2v58DuJE\noYCj/HvuKIBLwIhcQqKJjU3ObmBSyTOFZSaqHuNjJwhjyeIqrNV8Wist1ut3aIYhYTjK0cOKVHgU\n8x7jRcg5cKsuKDj3p0elgIIDjVCQtw2FvZfbHgl2azg8CCEyMft8Pn+fOtHq6iq+7/OVr3yFz3/+\n8zSbTWq12tCp489+9rN87nOf4/XXX+ett96iUqnQbDaRUvZHTN6veEhdpqPA28AzD2PxrfCYEIfA\nZseLQUgJh8Yz26RmG6JYYVcmGE3mWastcXVujdOnT1MdqUIaZx2mhfGs8QZIU8XSkuDQoY2bohBZ\nx2eSwPw8HDsGyz58/aZkvLh9qlBJMEbw219a4a+cXbhPiDyfFxw9qlhZ0bRaur+xCgGTYwIvL9Fi\nZ52SGBjf9IQkSbh8+TJxHPel13aCBYSY+7pUMyvdIg4eIQFNGmigTAUbB4EgYZQousjlGxc4eeg0\njrap12o0W00uXrxAuVKiXCkwVhxFOVndMaRNLMJuWGYQQuKYHA7elqosD1MWTgh2TI1uhxcrDnkD\nkQmxhYtFxJicJzQuBgtB2p0vnOC5EUUYt1lp1rm+PoVjGyqjFcYnyhxPYaXR4k+/kfCt91xuzS8w\nll/n6KRNrjSCcUcoVPJsF2G7CtYDQcH5zkrn7RcHVZPsqRP1bMKefPJJOp0Ov/Zrv8Ybb7zBL/3S\nL/Hxj3+c3/iN3xh6bSEEc3NzfOpTn+L8+fP84R/+IU8//fS+j/lhYD9dpsvLy3z0ox/t//vVV1/l\n1Vdf7f2zDPwEcFYI8TFjzFAdo3vBY0LcAwY9ETfDc+/NI/o+XHx7nlFX8OKzZ7KoMGyB5UFlJlOs\nofdcC2PSbSMEy4IwzNacrwssuXNjQxTFSBOx0Cnz4otbD/d7nuDIEUUUGXr87rogpcBOYSGB0jav\nEZtsFjE38Pu1tTUuXbrEiRMnOHz48K7ujgtY+IQb6oOGtJvolFhYBFiU8RjpaU4aQxKHXJm9ThxI\nTp85gpNzKOoyY2NjdDodjh4/TBwHtNYUF25cJNER7piiXC5TLY3idYnaoAmFT0JMfpNJ7EHWrQ6S\nWCXw79gN/ifygE9FNkmMBUhsGRAZw3J7ktfOtFkLHepJlfVaiGdFKGVTTyR3mrC2YijYFtW8YWS0\njOdNk8sZakFAu10jXbvN7ettbMemUq5QKpcoFe+NKNgK/DhLoe70WXy/YS8R4m7WcxyHT33qU7zx\nxhv83u/9HuVymfn5+V2v85nPfIZXX32VmZkZXn/9dX7lV36FP/uzP+Mb3/gGn//85w/seA8a+0mZ\nTkxM9BvstsAN4K8B//Q7QYbwmBD3hK0ixEEYY7hx4wYLCws8/fRzmYlqmmSeiEKAul9Bpd1WWNbO\nnoKum6VPHZVtQtu9dqvlZ800lkO1JB64sQ+mTHsoSqgoaKTgiXuD+MZkqdIUmLGzjTBNU2ZnZ2k2\nm3z4wx8ml9u9FVQ2MSgJSFFEBNQJ8enptXqUaWOYxIUkRvh1mrfe49bV9zg0cxhlHJy0CGhCGgiT\nAxVhS49q5ShTFRtz0tDQq/gtn1ajxdWFq8RxRLFY7PvzGTckwsY9AKHn7XCQBHvObvN3T43x+qUI\nmKdpbDAJUVRCiTx/86mAE/k2tVjhGIPUAtfpoIWNSQ3zK4JUaaqugxQax87qmSVPkDg57tRzfOjU\nNHkHgjCgUW+wtLLCe9dvYElJuVyiUq4gvTKma0L83YKHRYg9tNvtvsbqMOMXr7zyCq+88sqGx65e\nvXpgx/kw8bBqiMaYG2R6pX0IIX5qyDV+Z7fPfUyIQ6C3oe0UITabTS5evMjo6Cgvv/zyvdTMDlJR\nWTepQuvtSRayVGocw+kpg2tlnYDuwLJRFOP7LXK5HJ7rUVsK+d6TAXu5zELAhMoiwLU0I8EOEt9A\nScGIBEdCvV7nwoULzMzMcObMmaE3fYFgFJu7rLDMChKB2xUtD9G0WcIlREXTcP0Gdy6/SzuMefLk\nSRzbpvXeLI4wyDPPoHMuDmVktILS1b67gSYBaRipjDBSGeHo0Sxt5vs+9UaDa9eu0QrbWCWX8fwM\no5URRnPv3y7BXm3qe0bhDz4G31wa4UKriAHOlDQvjySAxfVOnrzqEES5TEOXrBFnrQVCGEpORCMa\ny9R0AAQkaabFqwQs1uHkBOB4MObhjk3hYYiTBOM3Wa7VaDXu0LjZySLvrhLMQeKgh/x7ax60tNwg\nIW7u7P7/Ox6BUs1v33cIGcQWjwE8JsSHCcuy7psZ1Fpz/fp1lpaWeOaZZyiXtxgk2wZCgG1Lonhn\nQkySzPHAteGvfiTlN78qmRrRGCfG7/gYkVIaraKMRTOAgqt56WiHbGpweAiRkV9RZinS2ybkpJNt\nllprrl69xsrKCs8///y+5q40bRxWGccjQBKTpQVHkHiUqKVtFm/+Ka1vrlA5dJjThw/3G2F0uQKx\nwbp6kfjM80jHQQprQ4oyJtpgWwVZF2GpVCJfKpKfGScymlbQoF2PWLxzjXa7Q0V5pEGHRqNBsVg8\n8FGCvWKwWUMJyUujCS+NZzdoSZIlI1qJQJgqQqxi6GAQgIMBVpsROdegsXx3jQAAIABJREFUTJW2\n9khMtzN34DUmSzC3JhgdNawbsIUg32VN17IJK6PE+VFeekozYt8z/p2fn8f3fS5evNif4cvn83sm\ntYMmrx4e1hjHd9qK7AOKkwP/f4RMwPsPgH8KLAJTwKeBH+7+d9d4TIh7gFJqw8xgT6B6cnJyY1Q4\nBEZHJWGw83OiCLpjUvzkx1L+9XLEWwspduhTzLl4xTxaxqz6CdJ4/I3vX8CW+9dDFSJza7e7psCt\nls8777zDxMREX3ptrzAYAuqARR57U8JSk5iY9bkbdG5f5PhzH2HUGSERLUx3ttCokDQ/Qhq0sVfW\n4fDWHa1bdZJqDOsiQmPwhMLKFcnnSqhpG200650WN779Lrdv36bVauE4DtVqlWq1Srlcfn8QpLBA\nuIRhwnrTwe9k84m1SBIYwUR1HNsOEayipQQCoqSAZ+cxJuvo0SYzItZkdUHIRi2khNsxjFtiQ41Q\nChAJ5BS0pGBU3jP+Ncbw5ptvcvToUWq1GtevX8f3fXK5XP/cDXNz8bAI8SCxVQr2UTqtfKfxnZZu\nM8bc7P2/EOLvk9UYBy2g3gX+RAjx35BJu/2V3a79mBCHwObB/J5A9dra2n0C1cMic53ISG8r1agg\nyEYucrmMRJqyw6efvc4oBb6+fIROrAgSSLXixRMJP/lym4lGRJoe3CUWQnDjxg3m5+d55plnDiQ9\nZkjo4GNt8nZMiWjFa9y4cZ1CY5Xxw9N08mu0Yh9HF1BdVRVjBcSqhih65BaXMIfO3Kc5K1FoNJK0\n6yTvAxE+kpQcbi+CFgZhss1XCknVK6ALFk+dO4uF7Lsq9OS+esoxvVThTmmyg4wcNrfzt+NR5hdX\nsW0o5rPX0ZYmDBRrNYtSPsWWkyTxYRxbYClBFA/q7xriRJDP3zOMjhLwiobJXGYabZJ7urXGQNEx\njOQgIBN/L3QPp5cu7DlSHD16dMMM3507d2g2m9i23Y8gy+Vy321+Mx4GIR50FJemaf/4H7Xt2KPC\nIxzM/0HgH2zzuz8C/tNhFntMiHuAUpm/3de+9jUOHTrESy+9tO8vgVIwNhZhTNZJanfv0NM0qxvm\ncjA1lW1KC+tr/Ovr73Ji6hB/6ycOEWvN9VVDksJUyTBehA6aWhty2zjTD4tOp4Pv+3Q6HV5++eUD\nqZEYYzAi82EYnEXUxCzUbrJwZ4ljx05QlgJfrxAFTWRgEVttpJ2Zvwpjo5MI24qgtYqoX8NK1jHJ\nJHT97jILqZBELHYVOtsYDB1SJKY78j+NZYrIgS92pokqCEgpIu9zVYiiiFqt1nd379kObelOwcOJ\nGtIUFtYdHM/DFusYk0cID1cZhNAU3IBG06I6NkrYEUTAVNkwtyKwVJb+TpOM5EYG7ucaIYxMwoQH\n2s3mJROTPd9R9yJJy0DL3EvKb0UI283w1Wo1VldXuXbtGsCGc9fTEv1uiBCTJOmPNPUUbT5IeMRK\nNSHwUbY2AT5Pr3C+SzwmxCGRpilzc3Osr6/z8ssvUyjsrT7XQxRnc4uNDiy3XI7bkM9lUqjGZJ2l\nk5NZdKh1yuXLs9wJ6jz39NNUctlrOwrOTG6863WQhLYgfUBd8kEwxnD37l1u3LhBoVDg1KlTB0KG\nvShOCIlE9J0q0jTlyty30anh7Nlz2KlBLNex4xWUzCOTzDQ4tGs4lcPIpInXcXGFQ9JpQqOB3V5B\ntl3wJHijCGIsWoSsojBAAU023qGEQNPAmA45nr3vGKU2RGx9U+E4zn3u7rVajVqtxs2bNzHG9JtN\nNpPjftAjHU1IPawRGYPtaWLjIPQ6IgFbFCkql44p4+bzdALF9JhhrSFwrax5ZqFpmHIzPdrJ0XvR\noR9m4hKj3b4iKSC/zbykZGPH824JbKtz12g0qNVqG7REc7kcWusDi7weRo3vg+6FCI80Qvxd4HUh\nRAr8HvdqiH8V+DvAPxpmsceEOATSNOXP//zPmZycpFqt7psMWx1YXM82HM+BnKNxHGhH2YZ0ePye\n4XCtVuPixYvMzMxw7sxxLLHzpqO60mrJAzpXd0IYhly4cAHHcXj55Zd5++23+8a++0VPIN2SFi4l\n2jQJmwnXrl9l7EiByZFDiDhB3V0kLuVIm4aSNY5rW5nOTNzGnV8gV2tjyRicFUypA0mAG/qIpQZC\nNTDiaVJP4SARxiISksyXS5D5p6cICjiAZJUs1tl4bne7DVuWdZ87RW+TX11dpd1uc/ny5X6adVAo\nYRgYYxAqJhXL+B0bx3K7XaI5jCpjVIBBUbXGiUObSEMaGpSE6TFDMQTlGeYXBWULAidCKUkQgR+B\nJeGF45oVJTao4WyFlGwsZ/DY9hLRWZbF6Ogoo6OjwD0t0cXFRVqtFm+++Sb5fL4fQW6WS9stHkYH\n6AedEB+xH+IvAiXg14D/euBxQ9Zs84vDLPaYEIeAZVmcP38eYwxvvfXWvtYKI1hY67a4d/cPIQQC\nTcHLnCvmV+HwmOa992ap1+t86EMfolAosET4wPUNBkspdPrg526FxcVFrl69ypNPPtm/ix90ut8v\nBut8nq5y5e47tOohT515EuFGmQXRyhrGkqQjOaxGDifSSEsigcT2EPVl3KUbJNNrdFZukSwb5M0C\nRjlE7jLeOkgCGJ9BWOBQwTKChARNgotGoXCwyeLAAEWbXqoVIBVkmqk9xDG0W4hWIwuNbAtTHgEv\nd5/umlL3mk0mJia4efMmhw4dolar8e677xKGYd+4dhhHBYNBOk3ABmNvIKyseSiHoYOUbaa8Mu0E\n5kNoJVmKM2fDh6YNH5k2LNdh/ragFQpyjuH0lGGiktUXk9TQ0GKD+MJmxMZQHNgLD2pMoqcl2osM\nT58+nY3J1OvcunUL3/dxXbefZt2tp+HDSME+JsRH54dojOkA/4EQ4u+SzStOA/PAnxtjrgy73mNC\nHBKO43TdBHYeon8QGl3NUzXw3ewRjpQS14b55SZXL1/giePTnD9/vr/RFFDUiLf0COwh6jpImO0m\n+LdBHMdcunQJrTXnz5/f4Au3bwf5AUgpuyICLd555x1Gpg9z5JwiFSEJMTLS6KBJWnSQFHDHn0LN\nLWNUE1wbLeqk4Xuk9a+StprIUoW05IHukC6tEiwsYY7cIH/iU6iKAFUFUUUCDg7gMIpm3aTd89r7\nCekRohHZI17va9L2ESuLWcjketl/0wSxvAiugxmfvpd33AK9GmOlUuH48eP991+r1fqOCoVCoU+Q\nveHuzdAmAFIECs8x1JsS29p8XVw0PpYokleSowU4Usq6SQc7RmfG4Nx0kxfOGpTauEZJQl1nIzf2\nFhzX1pmS0WCE+DBm/KSUCCEoFosUi0VmZmYA+k1Oi4uL/SanQU/DrdLUBz2Uv3nNDyIhwvvC/ukK\nMDQBbsZjQtwDepv5XqE1NHzIb5qI6DleSCm5ceMGy6t1nj33PCeObizSeygECekWGqCQjROkGErS\npj5ERLe6usrly5c3iIAP4iAjRIC5uTkWFxd59tlnKZfLJES0WKfGLdI4RAmbHBO4uCSVNbRdgvVr\n0F7BtNfwb30DYVbhXAmTM9lJTW2ESjAY0voc7Rv/HLv6Y8ipSXy9Tie8i5YJllcg703hyDECY2MJ\ng5VVxABITEKMphBlNU6iELG8kEWCgxuqZWc/YYBYXcRMHt4yx7jl50VAseRRKE1x5OhhMJJ2u92v\nQfq+j+d5940raDqI7gZUyBvWG9yX2hRINBpICEKHatlsuPnafGxbEa8t4LBlWEgEUZcUewnnxBiK\nXfGG3ay1V+xEsJubnHqehr3zp7Xu13Cr1Squ6z4mxIeAR23/JIQoAD8DfD+ZIPh/YoyZFUL8e8C3\njDGXd7vWY0J8BOjtjZv3DaUUjUaDW7duMTExwYdffAG5hUikQjCGzQoREtGVus6eF6FJMYxiE8qd\nJeZ6SNOUK1eu4Pv+fSLggzgoQgzDkEajgeu6GzpWLRyqTOHikFJD4SG6/YsajyQ/j8xPo8NxOpe/\nQhwtIiYdaILQEZZto9JMZFUoDQVFunCbaPU9gvZfkOSPYuWmEcYi8X3WzAXc0RI59yyh7aINCGKE\nuYNDQsVIluUyJHWo+RnxbbeZuh60fQiDjDS3QI8oDJoUHy2a3Q+DwQhQFMgXShQKM8zMzGCM6UdB\ng+MKxYomThJ0qnFsSaWoqTUFhdxmUsxS81JBuZBleP0You4lzFnZLOFOJOYKOGoZOgaaOotLCyJL\nk3pb/MlBR4jDrLfZ03Cwhjs/P08URbiuS5Ik+L6/L8GAQQwS4gfNC/FRQwhxFPhXZAP6l4Fnoe8G\n/gPAp4D/eLfrPSbEIXEQacOe6e/gXb3Wmna7zY0bNzh37hyFQoEwBnubvcBFMYmLT4pPSi+yyaEo\nYuEgiXdBYL1mnSNHjnD27NkdN4iDIMRebbJQKPDEE09sebfuUaFjd9AmRJG5LVg4pGhaC7dofvMr\n6Pk3cct1ZMciFCkqKUDBYHkxtFNk6iFsQeIlNG69h/vsEfKOwsiIWHrIKMKtx+i7dwjHlxl1nkEW\ncojyDEqUMsuosI3dXEEufwMWa1A4iWEkkzIzIVlLiQDhZAPyygK/tS0hQkaGiVjFECJwEd3mKINB\nizaaDraZRGAhhCCXy5HL5frjCmEYsrB8k7h+m7e//TZSSsqlCkKM0GxVsGyJkpmvoTaCgq2YGjO0\nU1hpZ00yVvezV4+yMYpIy52vu8jmDAsyO9KdcNDmwPsh2MEabm+thYUF5ufnuXbtGp1Oh1wu169D\n7lWNaPAY9+qF+N2MR9xU89+R1TmeBO6yccziy8Drwyz2mBAfAYSASh4a7aypptlqcuXdK0glOfXE\nqX73apzAZHX7dWwkVSRlrL4N7uA832Yj40EMigr0mnUehP0QYs8WKkkSzp8/z6VLl7a9sRBIcs40\nUa5FEjYwrkMcL9P8sz8huPol7BEfXYqRRiNkBzcXgdQkQY5I2RitwEqgY5F4GkECYZl2oY2miam7\nkIIpgciHpGGNxLYprYxi6hZipINoLYFJECIEqwRuA8waYr0GOTC5HBiB6EbwRhRAFrOhvh2Q0sQQ\nIjd5NGYxvochJhZrOGZyy793XZfx0cM027c5c+ZZ0lhTb9Rp1Fdp1m4QRArPq1Cu2kyPT1ItSFoR\nLHQERet+V4pEw3LsEKaZndN+cdDao4lO0VJsWx4YBlJms6SVSoXTp0/3BQM2R+C9TtYHiS0Movee\nm81mP0L9IOFRNdUAPwS8aoy5JYTYfLHuADPDLPaYEPcIIcS+7l7LBVhraa5eu0W9tsqZs2dYXFjs\nE04QZaMY3i788rYy2IXtCazZbPLOO+8wNTU1lKjAXglxfX2dS5cucfz48b4t1IPWEkjcsRM483Po\nENpf/ROS2X+JU8ohTZ4oWkWoFrFjY6UaqX1kPiUOSiitAEGSGrRrEFaOprAQFHBrqwjdxnguQndI\nhSKMBQ3TpJwcQSx+DXMnQuSmwC7gtVcQ8V3QIcbOrIXD+ZAYD7w80lLYxSJOzoekDYVjO5wJjRYt\nBNvL6QlsjOmgiZBsffEFEpIyhgBlu4yNjTE2NsZJIE1S6s0Vmo0m195tEyc3aLvjjFZLuJUyrrPx\ntS0JljCsBoLDhf03TB1UyjTF0CJhWUUopbBFgIWkaBS5fZg6D6Y3BwUDeq4UYRhSr9c3iC0MCpc7\nW8lIDcD3fU6cOLGnY/tuxSOuITpAc5vfVYB4m99ticeEOCQ2y7c96AuyHYJOk7vXLmHsaZ46+yKe\nKxFihSBM8YNMwHt6dOcZsAdhc4Q4aEv17LPPDl3rGJYQe1Ho+vo6L7zwwgYFjwelnjUh2mmiD0mS\nazeJrnwJ4VhYqYchQKgcUcWGNCGNBNggdUiUWjhxHtfxSGRMKhRRpUpooBDkkfUUU7QQYQQUUcIh\nTdu0g1WSzhyyVESsLaMnDELk0c2E9pV5tG6TBoskZgY7P4Ztx+B5aJ0SrjeI2xb5HDC29XsyxoBI\nu6o8DyAMIdAm3JYQjTEIk0OZcbSoYYi64t0GYcHoyCQT1TOIYxbNMOW95TaxX2d2aZE4ijPbq0qZ\nSrmC67rYwtBJIEozkYf94CCaahJCVlkHs0oxvY4j8+RjTaxGWBcOETYVrD2R4oOaalzXvU8woF6v\nU6/XmZubI01TSqVSP826ud7+QawhPmJCfBv4t4H/e4vf/TDwjWEWe0yIe8ReCXHQFePDLzxLLl+i\n1cnUajQKSJkezZzU93ujPUhg7Xabd955h2q1umcB8mEI0fd9vv3tbzM5OblhZORBaxkMKWskSY2k\nFWGClMalt0kn2lntT7kYR6GNhbabyHYDoVRmUqsjLBOTJCmhbqHbCXE6TpQUsUIHmcscHUySYJSH\n0RKRaGjW0eU6UXUSpxNjvAhhlohqHcKWjx4DE0TE9XVwJ4jiFqLootIYIRXKliTNJp0kR/5I0CW/\n+zfr7XjCEJPgowkQRiKEjdjBl7FHOoo80uQwhBiyVK3ERXBv3CBFUSmXyY+WOcpRtMlsrxr1Bteu\nXyMMQsIwZGlpkTIFxsv7azTZb4RoqFPjFna8ioWkriOUEkh9A1cvYosp2vY4NjkKe9i+hj0+y7L6\nEXjv75vNJrVajdnZWTqdDmEYcvv2bVZWVmg2m3uqIX7xi1/ktdde48iRI3zhC18gjmN+4id+gmaz\nyRtvvMH58+eHXvM7iUdIiP8t8L91P7P/pPvYOSHEj5N1nv7YMIs9JsQ9Yqf63HbozdyNj49vIKVq\nMfsxgUGImMLeBEy2PMYkSZibm2Nubo6nn36632CwF+yGEI0xzM3Ncfv27R0FwLeLEFPqBLV5kpUO\nQkqEZZGsLGFSiTaGxItQrkC0LYQzQtLW6HYL4YmMSCTY0oK1lMg5Rq1zjObSPDlHQ5DgSY1yHfRK\ni3S5iSYgNfPkQ0WruMaIM4JMHcLVhMSso6oJynMI1tuIchmZZNqrwdIylnBJmz7h7dt07s6TGCg+\ndYzyyz+Gc/I0tgfoENBgIlKREpNgSFAoICYWi8TJddL2OqoZZN0wQqFLzyFzz6Dsna9Xr/a47TUT\n97qas39LSsUSpWKp38n6zb/4JtoYbt28ybVOs68I02s0GYYg90eIPpFZgKSOwsMIF22aCFnEyBKC\nNkKvkYsNTXua/B5Sp4NC3HtBTzCg97mO45hvfetbaK351V/9Vd5++20uXbrEj/zIj/B93/d9fPzj\nH9/Vup/97Gf53Oc+x+uvv85bb73F7du3efPNNzl16tRQhtuPAo+yqcYY88+EED9LplLzH3Uf/h2y\nNOp/ZozZKnLcFo8JcUhsTpnuBoOpyp28EpVS9/ks7gdRFNFut2k2m7z00kv72gjgwYQYhiHvvPMO\nuVzugQLgWxGiISGoz5GsLKEKstuBWUa6RUxsQeyQdiKwFcp1MX6BTs7HtixkEBBcbyHiFLtcwn3q\nHLb3JOJqijq8SqE0TrJSx6+1CPwmMlbYZQ8rX8OVDp5roVeXaEfr2LkcoV3EqRRA+CTGz+YiSy4U\nxzBtSO/eJqj5tN+dxWiQ5TJCCjpXrtC59Jt4T04z8WOfwhkbIyTCFyukzjqBtjEyyiyUWEZEt7GW\n2ijjgFPEuKB1A924RlRvYk98BMvb2GAzTFrSU73+4+2vgxSKw4emOVaYQop7jSZzc3O0Wq2+IszI\nyAilUmlHwttfynSNKE2QBujalhmtu58DgSEPMkCmHYzukEgPe0hCfBhjIY7jcOzYMX73d3+Xn/zJ\nn+QXfuEXuH37Nn/0R3+0a0IchBCCOI752Mc+xic/+Ul+//d/n2efffbBf/iI8CiVagCMMf9QCPGP\ngY8Bk8Aq8FVjzHa1xW3xmBD3iN1GiK1WiwsXLjA6OvrAVOVmn8X9YGFhgffeew/HcTh37tyBrCml\n3Jawl5aWmJ2d5amnnmJiYmJXa20m1zS9iW7+MXY1zBzehQatKDxjUf9/JO4oBDUHITSp1yFMQgr2\nBKKcYFyfVFhY009SnDiNaR5BtWaYfH4aI2cJwjmsCRfrdod8rogeU7TDVRIZI1seTZ1QNorEjzDN\nOtapozQaBoVDZJpExLipB0IRd3zS0ijrX30Td2QEt5q1AuskRjkeIlVEc/Ms/59/Qunf/2HSnEBS\nRKY+no6JSEmsNp1kkfxSE9sqgWVh0BgSLDmByEvSqINcvoiaLiPse1HgMKTjKMhb7NhFGiCo2L3B\n/fsbTXqzkPPz81y5cmVH26u9E04IxAjjY+S9lK82oPrrZXVSI0EmNXCGz3Yc9GD+5vV83+epp57i\nE5/4xFDrfOYzn+HVV19lZmaG119/nd/5nd/hjTfe4Ld+67f47d/+7QM73oeF94FSjc/WjhdD4TEh\n7hEPihCNMdy8eZP5+XnOnTu3K+/AvaRhNyOOYy5evAjASy+9xJtvvrmv9QaxFYn1xiniOL5P6m0n\n3BchpndJgn8BBAhzaGDcLcI+toY9k0PEdWKnTOOOj1MoUqxWkVGKCMFfaeFUDjM+89eQ9gmsikE6\nAqEk4+YE9WiEcK2B35lHyhQrbxgfn8EtuEgnIrJbOEsRsWiyvOIj87NI9yQjY9MopZFpGWnliVt1\ndLtO6/Y6Jo6xisUsipESIWO0sbCcPHZVEizeRd++xsiTZ4mIEEJB7EFrnjBcxLRvE4YCNWpjKadb\nFywg8TL3DydGx010ewVVObLn6zaRM9z1BZ0kixj7s68GOmlm/FzdwUd6GNurJEn26OyRzXRaOiFQ\n9w4m7Uvr9SDJYpJo2+7qHV+lqwR1UNhMiK1Wa09NNa+88gqvvPLKhse+8pWv7Pv4PggQQlhk0eFR\nuL9+YIz5zd2u9ZgQh0Tvy9mrz20F3/e5cOHC0A0s+yXE5eVlrly5wqlTp/qb10GiN2rSQ2+o/9ix\nY8zMzAyVKttAriYBvobRpYHGE4Mxma0vjFP8npdY/+K/RIo75CfL2HEB7VskayGJWaUwNUXl7H+I\nN/3JDc6KYMhxiCh/G0uv4ky+gF21UNwFoTA6JXEMuXWPvOMQFkcxjVmKdhm3aNNaC2gH0f/H3pvH\nxpbd952fc7faF+7Le+TbN759abUcy4jsQWLBASLLwDixoPFYhi14xjIcaGBPnJEwQjwGYtjAREEg\njwVjZNiGgkHgqJ2xZUtKBCeR7HF3q1v9yPe483HfWVWsveree878cauKVWRxJ/u11PyiG91kFc+t\ne6vqfO9v+34RK6uE8xl8ykYXPpiexRdsQcoiAj+aKGPbLj4jCMJASQcn5kM9n4CrN1FKIXMFykvr\nSJVBC1oYZT+ulBTXsgT9Ecy2DjStMhKAAHSUT0OlV6GOEA+bljQ1OBdSpEqQtr0oC0ATghafot0s\n75hP3AvbrZvqJdNWV1cBjxiqUeTBbpI8orM0E4Wkqi3r3WzUvziFg8RfETA8LE7a7WI7IVaH/d9P\neJldpkKIR8BX8ZRqmn0gFHBGiKcNw9gpi6aUYnZ2loWFBQYGBojH95iqb4KjEqLjOIyOjlIsFnny\n5Ak+3x63+8dAVcO1fqh/+zjFQdEQIcp5II9OHEdtUiVDVymvKQRIFEKUHjzAGtQJpN/EjK2DEqi4\nj0DPPyB0759CNrjtG+HdiBjoxNw+UmQp+WycVgvpv4Aor4Ao4pdtBPwuycIq5WKCjt5+tMh5Al1h\nAjkLsfEGsr9MIt9FMm/j5DYoF4v4/Bv4y0UI36BUCqNkEKELpJIeIYZ8sL6JdFxksYhMbiLaO9AI\noOlpNAyEL4jUgIxCalm0tsZMgtKEF8rVda4epU5naNAegBafwqlcdlPzru9hyLAZ6iXTDMPA7/dj\nWRapVIr5+Xkcx2nQFG0uDWgBOpoIE5Qp8po3dKKkQquzOlO4KCUIaK1Heq2nnTIF3vOGxieNl6xU\n838BWeAn8aTbDmUIvB1nhHhEGIZBqbRlrVQda4jFYkd2lD8KIVaH3vv7+xkYGDgVV/YqNE2jVCrx\nxhtv0N7efqih/u1oTJmuAgEMX5CCNJCqiIaJJqBUsllcWiQSjnD+6gWc849wE/8DgYAPNIXRcR49\n1AoInMIMynURTa69gUlcXETLLqG0EEIa6EYXur6BqwrMrs8TNuJ0Bi9hpzYQJriZNLrmoEU1zL4P\n015wyMysUlJhkqVVlIqQTpVxlp6TMW4TabfQyzaWZXnpUemC6cdFUdpIelZRSgE6KAul6ygpQdPQ\ngn7cbB49FkYY3utXeESo6cHjDaTWQddo2LpO2jBXKYWu6029DZPJ5B62VxoQA71ASGooBQWhKOEi\nNXCRuBQRykeLCmDozRvT9sNpuHFUv+unYT78/YKX2FQzAPy0UuprJ7HYGSEeEvUpU9d1G8YMjjvW\ncBhClFIyPu75JO4VpR1XUacKpRTr6+usrq7y+PHjA9VE94ImBMopgJMFcihho3QNw2rBKadQVplU\nKkcikeJcby9+v4liCVUyCZ27htXkOmstLci1NcQuc2DCNDFDLejlEJpmg/CTWs+zsrlAb88tQuEI\nMpNFMyMEbl6hlFjDST7F8l8DoWMEXMI9JqpUInAhhkzbhFs7MOMF2kKtFNwgxcIKibUkWH5UtkTk\nx36EbDLF0uwc569ermyaAuGGcXx+tPQmQvOupQDcQgkjEvSG7RUYxQAi1pj+PklHiXfDnWL7qMLu\ntlcx4i1+Qr4yEZkmoPzM2wpDgKHyhJSBpeIIs9fTjj0C3o0I8TRvSt+LeMmD+WPA8Zza63BGiEeE\nYRgUi0XefPNNIpHIkaPCehyUEKvSa93d3U2H3utR77F4VJRKJZ49e4YQgo6OjmOTIW4B01lE4EIZ\nlLRx1QJKgBmN4qzbLEy8QAsqLl7q9VJmUiLsAL5gP+YuqWgtGoVSCZlOI3w+RKW5Q7kuqlhEM02C\nH/gA+aF3kKrA4toyju1y5dJDRKCA62SQdpbgnavocZ1g9BqiNE657MfJrwFlfK0B/NHLBAIWyf/v\nOWaghK6ZaCqJHu5GuYp4pICUBtl8hvWgxdrUJKamkU5nQNmEQhHrHt1dAAAgAElEQVQsoxU3mMPO\nJAkWirim8LwryzZSllFaEeEEsOhBBBubNN7LhHiQ9YQQRCIRIpEIfX19KKW2bK+mNykUUoSCOVqi\nSfxOirhbRKcVpXeBFgftaOpQcLqEeNINO98veMmE+C+A3xZC/J1Sava4i50R4hFQjZbW1tZ49OjR\nsaLCeuxHiFWVm7W1Ne7evXsgRYzqmkedQawfp/D5fMzMzBxpnRrcAhQXELqJKy1c/CgugRhHo0gu\nk2FmPU1H70Wiuo4seaJkQk+hR+5hhC/tuuEKIdA6OhChEDKZRObzXu1N19Ha2tAiEQxdJ5vLMfGf\nX6O91Udvdz/kXGRCgWkRuP4QKx5BEESIEEY4jBXwo5RECK/2pRREdQN7I0fm2QxGwEUL+XG0IkqZ\nqGIS5ToUnjyku/s87V1x3Lk1CqpELpdhfqmEqxIEYjrRQC/hwirYRZTj4FJClcMYpRgmF1HdF5Da\nvmJvR8a76V+4G4QQhEIhQqHQNturBOvpMNkhC8MsE4/niMdNolH9yKR20ilTx3Fq2Zn3o9NFFS9x\nMP+vhBAfBsaFEGNAcudT1IFnYM4I8ZCQUvLd734Xn8/XYC1zEthr8D2XyzE0NERraysf+MAHDvyl\nPqogd7VRp1Qq1cYpstns8eyflILyKuh+hDCxywXvDlsLotQd1hL/iUxacPXKTXzhLpRUKNcBUgij\nGyFepXkj2RaEEIhQCC0UQrlujRCrNcv5+XkW1te59dM/j5Vbxk3NAQo93onVEUczDCAMoh0MGyVc\nUFmEqNdhBbM9QsePPyZwoZPs03corpeQ5TRYFu6le6x3+Bh4dYBoLEKeItmAg1+PEWi7SBsaSipK\nqRKFRJ6ZTR+ivE7ILhD39xP1XcBqP4fyBZFCgOvW0vO6rp+oo8RpRIjHJZwt26tzzM8v8sorr1Aq\nlWpdrBMTE+i6Xhv1iEUjGIbXlbpfKvWk7anqI8SjyrZ9v+NlDuYLIf458OvAGpDGm985Ms4I8ZDQ\ndZ0bN27g8/l45513TnTtZhvTQaXQdoOmaYdu1Nnc3OTZs2c7ximO7YcoCyAdXGEQCoVYXVvle9/7\nHn6/n1wuR1f3LS5dSCKYB6kQmkJoDtCON2bUvJFCUq5peQqMmih2fXONbdsMDw9jGAZPnjzxNrF4\nC/ReB4qgbEADEQBRmaPTdVToCiL/Dvh21mh1v0ns/iViVy2K0R9FGjeZWl6nUC7z+M4dLNOTZwsB\nZqyH4toaaH50oaMrHS2qVU7pMnYuR862yZgmi4kU9sp4TR0mFothmiZSSqSUbG5uAt48oF4h+6Nu\n8qcRIZ5GDc3n89HV1UVXVxfgvZ+p5Bqb61MsvVgClJeGjbYTae3H9B2uw/uo2G4O/P4lxJeWMv1n\nwO/jybQdb4ibM0I8EqLRKI7jHHuIfj/kCkXefDqMDIS5cO9VsrqO4Xgu5wdtla9GFAeBlJKpqSk2\nNjaaNuoclxCVW0JKhRKKYDDI7YHbLK8sMzc3R1tbG9lMkbe+Z9ESzRBqjRBtOUcg0ItHiDs3fEkZ\nhySqzuFFARomBq1oFZHrVCrFyMgIly5dqm2oNQgd5fpB+WqRZMMxonfQc0+95h9j22anFKK4hIzd\nRoU6GRqZpLWtl5u3b9et420UVtSPsMFJJhGmQPN5r03aNrJUwgiF6OjspLOyuVaJL5lMsrCwgG17\nLhWFQgHLsrh161bteUBDBNlAkLIIzhpCroOSKC0OZheIICgH5ZaO6TLYiJNOSe4GU1d0xsp0xjpA\n9OG4LplMhs3UBstL/5WSGyAY7a91sp7WKNIZIb50BIF/fxJkCGeEeGSchHv8blBKMb2wxHenlrh0\n+QpdrXE0Aa6CpRJYAnr83sD1fjgoIebzeQYHB2lra+OVV15BaRIHb6xEQ0fDOJi4Nw6eDFdlvAA/\nAi9d6bquF/kZAsd1GB8fR9M0Hj16tFUTUpDdXCGRDzAykqNYnCQSWaW1tZWWlpba0LOkjM1qJSJs\nHISW2NisYKhOZqcXWF9f5/79+zsGpt18HieVQlbk8oQQ6PE4RiSCqNZcrXbc7o+gr/03KCyCHgBh\nINwCKBcZGWBN3WB2+DmXr/wILa3bCLcOZlsbeiiEk07jZrNebdSy8PX0oAUCiDoi0TStISWfyWR4\n+vQp4XAY13X57ne/SzQarUWQfr+/RopSSu9al1cw5JiXRtbDgEA486jCM5ARlHEeSmX87jKUk2DG\nQBy/G/nUCVEphL0M6LUGG8Mw6q7XVVx7k3Q+SDKTZ3FxEdu2iUQi2LZNoVDA7/efSCR7ljL18BIj\nxL/ESx996yQWOyPEI+K0WqullLz59vdYJ8jjB/cJWFtvkebZ/lF0YakI5wP7R4r7pUyVUiwsLDA7\nO8vAwACReIgimzh1862eZooPQ/PvSoieDmcC2Kz7K1BKQ8o4yBAIC00oNtObTE5M0tfXR0fndt1T\nRTgSIdx5gX6ho5Qik8mQSCQYGRmhWCwSjUaJtrvEYjEC/p2qIBomxVKJodH/Six4kcePH+/YpO2N\nDS9asyz0SiSspMRJJHDTaazeXjTTBC0IVhtu7z9FFOcR+UlwbVS4DTdwiRcLm+TzS9y58xAzsDsZ\n1l6b34/l90Nn54HTlcvLy0xPT3Pv3r2aLFj9bN/Y2BjFYpFIJLJFkEYeoUaRIooUBq4ElEQvlxHK\nRIgNUAGU0Q2aD1FOgpNDBXo8Hdkj4rRSpg1QBcD13ptdoBshWqKSePul2uvKZrMkEgnGx8cpFouV\nUQ8vggwGj2Z7VU+I2Wz2fUmIx0mZngCN/mvgDyvv3V+xs6kGpdTUQRc7I8QjYD9z26NidXWVfD5P\n+8VrtLV0Edjl0+LXIet6OpShfd7BvaK6crnMs2fPsCzLG7I3FHmSCHTMbZKALjZFLY2rdsrVeWS4\nhCcSEapZ8njRio10lxBaJ0KLMDO3wOZmhoHbt5srlsgi6JHaplx1LI9Go1y8eBEpJelMgo3sBJMT\nSUrlEuFQ2Jthi7fg8/nY2NhgevoFl66eoyN2YYchr5PN4iSTaKFQwyYoNA09FEKWStjLy1jnzyOE\nBSIM5FDBC6jgBcATvB4ZHqGtrZXbA5cR2v5kuB37bcBSSsbGxiiXyzx58qShU7h+tu/ixYu1G4dk\nMsnExARa6W3CQR/hmEYkHMXns1DFDZQsIzUfqCjYc7gy4kWFRgicApQS4N9fnH2v13xSEeKu3zE3\nz75bqTDAzXm1YWGiaRrhcBifz8e9e/dQSpHL5UilUrx48YJcNkvY1Gk1BLGAn2AoBMEIhCJg7Z5u\n3Z4yfb+ZA4OXCzpql+kJEGJV8PU3gX953MOcEeJ7APUC2dFoFD3Shn+fPcUSsGnvT4i7jXJUdU+v\nXbtW06TMkailR3esg4kSJRy9sOMxRRbPM2Hr7th13QoRC4QWplxaZGgwTXtLB3fv9DakB7f+qOAR\n4R4egJqmEYkFCcTOo50LeIa32SzJVIqxsTEymQyaptHf31+JHh2gUWzaTaW8OcVdCEnz+XBzOWSx\niB4IgNYOrgMyC8LPxsYm09PTXLvWTzQaANEG+slGBoVCgaGhIbq6urhx48aBZvuqNw4X+tpQ+QSF\ncoh02nN6LxZyxIwcoVgbobCJ32fhOoLVlTF8vh7PxUTpiGISYcTQjKPN+p1kk87u5HqwUkVV6ada\nJK1fTwhBOBwmHA5zvrcXNlYoJtfZLJRYWE+Qm5nDp0E0FCLSd5Fwz/mmr+UsQgRerv3Tz1NnBXBc\nnBHiMXASKjCJRILh4WEuXrxIb28vb373LWzXRdunfdwQYB/gY7A9QnRdt6nuqYuNi70jMqxC4SBE\nCXwb2CyhEanU7jS8LIWXuqzWsKqpMyEEK6srLC5Mcu36I+KxC55pbmkN3CwNYxR6BMxW0A7+sdSE\nRiQSRdcNNjY26OvrIxaLktrcZGJygnJ2gWi4vVZfsnQdWS7X0qS7Qeg6MpfzCFHooPcgnQwvJt+i\nXMpx/95VDKsFtBiIE3J0rmBtbY3JyUlu3bp1NBEE6SDElo1Td3cPOAVKqXnSBZulpUUKxQLKTmOF\n+ujp7cQwPFk5aQtcu4CrvPel2qBz0M/4ju+DdKGUA7tyI2X4wBcGff/3eNfvlrCA3N5/XCFmVZf+\n3XUoP7WBKBUItHUSAKq6QMVSkXRqk/WJEcanXiBCkaa2V9UbgGw2W7PMej/hZXaZKqX+8CTXOyPE\nI6DeJPio6hSu6zI+Pk4mk+HRo0e1hg/T8BwY6rScm/+98khxP9RHiNVxir6+Pm7dutVwJ+9i7+o+\n7pJGsYlCIDQXlxJQQiLQaQUcBP5a40w1SpDSZXx8AoB79x6i6ZWoQ/NB4Dy4JSjnKrOCgaajDc0g\nMBpuCVdWlplfWOD69etEwl7KKhqNIWnHkF1k0nmSySTPnj2jnM8Tzudp6e0lHovt7sSwLS2eLxQZ\nGhqlu/sKl6+d967dCdfKqqLp2WyWx48fH9FGieaNMULhCwboiLQSCASYnZ3jXG83JVqZn58nn88T\nCASIhy3C7TGC/ihKqdp7Wn1fq+S422e+oammmEVk1wAFVY/Dch5yG6hQGwT3Ho3YlRD1EDgbe18D\nVUJp0YZ6aNPvqmMjcmkI7ozs/D4//i4/ne1tIF1Krd07bK9KpRLr6+v4/X7y+fyRIsRvfOMb/MZv\n/Abnz5/ntddeQwjBN77xDT760Y/y9ttvc/PmzUOv+W7jZfshnhTOCPEYqFpAHXbjqhLTuXPndqTD\ndF0nJFyKkl1riABlBZ0HOKymaTiOw+TkJGtra9y/f59Q6ODSfy4ZJCkEQc9/TukITDQsFC4uKwhV\nBhmoRaKappFOpxkfH+d833m6OrsaRiNQCvIZyCTBcUAoUCmwLIi0QmDv16dhoWFiuwUmJzzlnAf3\n76PXRR2SMoIAumYRj1vE43EuXbqEa9usP3tGulBgeWkJ23GIRiLE4vEGglSuW+s0XVlZ4cWLF0eP\n2A6AYrHI0NAQbW1tPHjw4HhpRxEGfCDLWzJnQgOlWFleYnMzzfXr1zC0LPhv0iW8653P50knFpmd\nXyAzNk0gEKg16YTD4QaCrFqf1ZNj1Q1FCAHlPCK9DFYQtPoPsgVSQm7d+71/95rb7hGiidLjCHfT\nI8ftUDYgwWh8r5paP5UK+9/Y6AaqXMQS7LC9evPNN0mlUnz6059maWmJZ8+eoes6P/IjP3Igo2yA\nL37xi/z+7/8+n//853nnnXfo7e3ltdde49VXXz3Q379svGS3C4QQHwH+e5r7IZ4p1bxb2M8keDuq\nc37r6+vcu3ev6d2krusEccgoLwrUm3xXy9KrEm+vH9pI8tgUKzUWPxoF12ZxZpbe3t49FW50TLan\n4hUSySaCQEP0qFH17NORyo8rNxBuAK2y+c7MzJBKpbjd0DhjQ7XGuLkB2RT4Ao0NC44N64vQ2gWh\nvd0MCmmD4ak3ONdznu6uvq3XLCVSldB0DZOdEYhumrT09hIrFLjQ349Uikw6TWpzs4Ego5ZFW3s7\nE8PDlMvl40Vs+2BjY4OxsTFu3LhRc4g4FjQNzAtQHgXN25RdV2f2xSymL8D1a9dBpUGPgdgilKDf\nItjTT3eoD4VXx0wmk8zPz5PJZPD7/TWCjEQiDQTpHWNr7ENk1hCmfxsZbr0+YYa8SNEKea+3CfYs\nRxitKEC4qUpEbADK89YUOsrs3RJYqKBpytRxmr/GbRBonjNJ/UswDAzD4OrVq/zVX/0Vn/70p3n4\n8CHvvPMOf/RHf8RXv/rVQ9/YCCH49re/zbNnzxgcHOTLX/4yv/3bv32oNd5tvGSlml8H/hWeUs0E\nZ/ZP7z62O14cBNlslqGhITo6OvYmJl1HUy69AVis3Lz6NWpziCXpVe16t41cpCmTqdynGXjRwIuV\nBV4sz9Db2crVa1d3TYkCnjcfOhK3RniqMk8oKl2aLg6aNGqPexughqt8GFqeUkkxOjpKLBbj7t27\ntXNUFaNeQdi7I8+mmqaoMExvc0qte2Rp7CSgqufkysoKd25/CCtk46gcKl/CSW6iyiUEPkyjFa2l\ngB4K7WjgMeJxSpkMStfRDGPLiaFCkKnlZTbyecbffLPm9ZdIJLwa5IHMbg8GpRRTU1OkUikePXp0\nssPjRg/IDLgL5Asak1OLnO++SEuwBDLhRVbm9boX44JbhGAvCO+TUq1Bnjt3DtgiyMXFRdLpdE2+\nMB6PEwqFmJycJBgM4pbyaHYJ1woiXAeB2Kmmo2lgS3CKXhTZBHsSohBgtqGMqFeLlmWPGPWQV9Nt\nkjZuup5heHXOfaCQO4h7+3qlUokf/dEf5f79+/uuV49f+qVf4lOf+hTnzp3j85//PF/96lf5qZ/6\nKT784Q/zyU9+8lBrvQ/xac6Uat4bOEiEqJRiZmaGxcXFA0mvVUk2oEN/EDKO100q8aLFVhPChmf4\nWkUWmwx2xUNcYNs2o6OjmIbBrUtXWM+myeEQZvcIRyAIECNHAvCiQIXdQIYKie74alGAUhKEQBcR\nNtZWWZh/zpUrd4hGt85R4QIFoAOBAdl12CvS0jSvz6aQhUhjt2l1TCQYDPLkyZNais5dXUBlypi+\nOFowgEBHOQ7llRX0UAirq6tx6N2ysHp7sZeXkaWS54pR0QxVrottGKQ1jSdPnhAKhWqKMXNzc7iu\nW5NUOw5BlstlhoaGiEajPHr06ORn9zQN/DdZWXBZW/wu1y934Av4wTYqM6FtXuqSUiWqEhDoBmP3\nOq6nLxqoNY4Ui8Waks7q6iqWZdHT00MxlyOi6ShNRyqJVBIUuNL1Gq2qBCmE1727Cw7kbi9MMA6m\nJ9w0QvQFvBT+nn/oIEwLZTa+19vXy2azRxq7+MhHPsJHPvKRHb//67/+60Ov9bLwEmuIUc6Uat4b\n2C9CrLbOH8Yeqn5NU4NWy/t3tyYbhSKHg69ChhsbG0xNTXH50iXa2ttJpVIYriKLTRDDqwPudmxM\nQrRSJFNJvNooSojKZKKPCEiBW93EhEC6LhNT47ilOHfv/D10M49q6AA0gC40wt5JFPMQ2Kd5xvTt\nIMREIsHo6ChXr15tqM04ySQqU8QMtzUsIQwDIxzGzeex19exKnWf2rkGAmj9/bj5PG42C1KifD6m\nFhdxNY1XXnmlNvdXb3bruu4OgozH4zWCPEhaNZVKMTw8zLVr12hvb9/3+UeBlJLR0VFs22Xg4ccr\nN1ASApXUopv3IkIA3e/NIR5Spcbv9xMMBpmZmamVAFKpFMurS0wvT6EFIsSiMWLxGOGQlxFQSm0R\npF1GuS6a6zbVYz1pO6XmEaKJCkUR+Uzz2rVSqFIB2rp3PLSdEN+v0m0vWcv068AHOVOqeXmo7zJt\nFiEqpVhcXGR6eppbt24dqi60G8nuFkCUkbgodFcxNjlJsVjk/v37tchF13WQXtKyjMS/zwe3Sore\nGEYAFwedCJryJODi8ThvvfUW4XAEv9/H2toK/f09dHc+9OosuDRKt/m2UrVHEDOo1l03Nzd5+PBh\nwzC/khI3ldpzhEILBHAzGVRr65YcWwVC1zEiEYxIhGw2W2t0qhc033F9trnBu65LKpUimUwyMzNT\nu0bNCLKaLVhbW9txLieJYrHI4OAgXV1d9PX1NT8XLQLm8YbIFxYWWFhY4N69ezXd2+7ubro7O6An\nRlnppNMZ1tfXeTH1Ak3XagQZCUdA15DWVjNW9XOvadqJGVvXo2mEKCWETZQtEdl18EW9VL1SYJe9\nCDbe3jTF34wQo9G9a98/iFAIXHkqhNgihPgWXmPMf7fLcz4NfFUIoYBvcKZU8/JQHbuoR9VM17Is\nXn311UP7EOq67g1JHxAKyGYyTI9O0NPTw/Vr1xrYUxOi9hrVIeZXdUx0TFzySFVGSm+M4tLly1yU\nksnJKZaXV/AHFbMzSVIbozW9UWuXmpDX7GF6m8xec2iODYFQLcJub29vmlaUpRJqn02z+jeyWETf\n5e59cXGR2dlZbt++feiUl67rtLW10dbmRajbCVIpRSwWIxqNsrS0RCgUaiold1KoNujcvHnzRK3J\n6lGNPh3H4fHjxztJRtMhEMPKp2jvaKe9w4uC7bLN5uYmGxsbzI6Poiw/oR7vBiMSidRmZqsEWSwW\na007x3H0qKKBwKQDxQW0wjxSlb1GH6OALAUQ5Q4wgqhAeE+lmu2EWCqVTk1E/D0NBY5zKoSYAjqA\n1b2PTgb4LeD/2OU5Z0o17wZ0Xadc3mpqWllZYWJiguvXrx+45brZmsWK2PR+UErx4sUUU+k17t+6\n5clNbYOmachKZLZXU82ux3BbcNUSkAPNT7lUZnR0lGg0yJMP3EYXIZBtpDfTJBKJ/dOI4TgkVpo3\n1VThOqxmCkyNTHLz5k3i8V3m1fYb1qxCiB0dguBtaMPDwwA7pNGOimYEubCwwNjYWC2jMDExUWtG\nOanOVaUU09PTJBKJk2/QqUOpVGJwcJCOjg76+/t3r30GWrwIq5QFMwCajmmZtLe10h4NwKWL2MF2\nkptp1tfXmZycBKhdl3K5zOLiIgMDA7tGkIclSCml9x5LB5F5jrA9QXNNVGrePtCcDKh1ZOwBGHtH\ne7sO+r/PoJTAdY723VlbW+PJkye1nz/1qU/xqU99qrY08AAYE0Lou9QJ/xD4e8D/CYxw1mX67mN7\nyrTqtSelrJnpHhUH7VzN5/MMDQ0Ra4nz8N49rF3UXTRdx3EdBGAewne91jgjNXTRgxI51tenmJuf\n5dLly8SjrUAUjTBCEw3ODNUoKZFI1KKk6uMtsRi6FYBiAbYLcyuFzGUZX1ymFIztO+rQVP6t2blI\nuSNdWk2R9vX1nZq6SDV1vry8zCuvvEIwGMRxnFoE+eLFC4CGm4ejkLJt27Vmo4cPH55a9FmtfR5o\nPETTINrlEWIhCXZp6/ehNvBHMDWdzk5/w1xfTVs0lyMYDLKyskJLS4snaVhxbtnX8moX1AisuOCR\nodW280lGBNwCWnoIGf/griMhDeuxpbt66sLm70F4hHi0G4OOjg7efPPN3R4+B/wdMLpH08yH8TpM\n//BIL2AbzgjxGNB1nWw2y+uvv87ly5fp6ek5kTX3c6eo1icHBgZoaWkhU9dluh2aplESkiAG+gEj\nxO2KM64Lo6PzuK7Gvdv/ANO0oNLEs9s51EdJjuOQTCZJJBJMTk6ioWg3FK1+i0g06rnUS0k+X2B4\nbpGua7e4fv78/vqdPh/CMLwh+l3u1JWUaLqOVqnXVa9f1XD5tJogHMdheHgYXdcb0oqGYdDe3l5r\npmlGkFVyjMfj+xJkJpPh2bNnzb0eTwhKKebn51laWjpc7VPTIBD1hu+li6dYY+wa1QshWFhYoKWl\nhVdeeaUh/Tw9Pd1Qn61Kpx2GIKWUaIBWmN+7fqoHUOUcOCmwdid+13VPJKvwfQ/FkQlxHywopZ7s\n85x1YOWkDnj2bh4RrusyNzfH5uYmP/RDP3RiDRJ7EWK5XOb58+fout5Qnwxj4CLJ4WCg1SJBB0lZ\nU+hlSXSPkYsqtuuQaprG5uYmw8PD9Pf309PTc6Q7YEOHjrYQHW0h4CK2K0gmkyytrTA2OYuhCTTT\nImdL7jz54IEbE4QQGG1t2MvLaMHgjohRKYXM5zHa2xEVxZ7h4WG0ykjFaaW7qtFn9ZrthWYEWb15\nqMqDVUlgO0EuLi4yNzfH3bt3D6U+dBi4rsvIyAhA83rhQSDEvtqlVT/O+mu2/do0a2CqjsDEYjFM\n06wTld9JkK7roosSUjloYu8sjtAsVDnZQIjFiacUvvtN5MwIwrEpW2F48GO4nZ2ehPwpCTe816GU\nwLFfWur43wD/sxDi60qpYxvUnhHiESCl5PXXX6ejowMp5Yl2C+5GiOvr67Wxg+2RgEAQw8KPQQ6H\nAl7nqx+ddi1AsKT2rR/WG8tW76xfvHhRU9UJ7iOG3XxRB5yENyBeB1ME6Gxvo7OzE9u2GRwcxFGK\nWNTP8+fPsSyr1qATjUb3JGEjHIbOTuz1dRDC8zDEc6LHdTHa2jDj8VokdeHChROJ5HdDlaSOGn0a\nhkFHR0etBl1NI1bHaaoEmclk0HX9VIm9UCgwODhIT08P5w8QsR8V1Uag27dv73kz1Kw+Wz8C4zhO\nA0FallUjRdd1yefzqBZ/Rc1Iou05ZiKgbn9N//n/jf32t9ADYfS2c14n6vwU5l9/hc0Xb+P8+M+/\nL0cu3gNoAe4Az4UQ32Rnl6lSSv3vB13sjBCPgOpGJKVkcHDwxNeuJ0TXdRkbGyOfz/P48eNdyVcg\n8KPjR0dh1X7n/bP7RlYfFUIlxVrplI1Go0fviFQu2Ivef0WwMU0mS2AvkMwFGRl9weXLlxtIvlgs\n1hp0MpkMgUCgRpDhcHjHxmxEo954RTaLzOdrv9MjEYRpMjc3x9LS0qlHUqOjo7iuy+PHj08slWaa\nZgNBZjIZBgcHsSwL27Z56623GlKsJ0WO1bnPW7du7d7UdExUVYfW1tZ4/PjxoWvvzUZg0ul0TSyg\naqcWi8VYWVkhFAoRjXegEuNI28at02D1hALqXTrKoHs3gdm/+Uuct7+F3nu1QT1JhlsQbd2o1Vky\nf/al9zEhCqT70qjkf6v7/+tNHlfAGSGeNqob0kGl2w6KekJMp9MMDQ1x/vx5bt68eeA79IN2kzaz\nalpdXWVycrJp44RC4uIgcRFo6GzJuO2Am/AixGau5sLH7MwUm5sJHtz/MQLbok+/309vby+9vb0o\npSgUCiQSCaanp8lms4RCIVpaWmhtba05nWumidbSAnWjBrZtMzw4iGEYR0/3HQDVBqfe3t49ZxiP\ni2okNTAwUCMp27ZJJpOsr697xsCaVrs29RZFB0U9SZ1mt2q1w1fXdR49enQijUC6rjc0d0kpWVtb\nY3R0FMMwKJfLjE+6dPh9RH1pfKEOT0mn8h1wqXSxAkLJmlFy+fWvIVp7dkgJKim9bPD562hDb9Br\nvg9HLqDiEPzS7J9OtIPsjBCPgcNomR5mTcdxmJqaYnV1dQK09pYAACAASURBVFcR8ONip1VTVdnE\n5smTJzvqIWXylCggkRXCVSgUFn58hBtd6ZULbgbEti5SvLb9oclnFDtLBC8ohn2D9HKRLtXZlMjr\nff3Onz9fczqvOsPn83kikUiNBKo2Wul0mufPn3Px4kW6u3eqjJwUqk4YAwMDpzaU7Y3XvCCZTO6I\npEzTbHBgKJfLpFIpVldXGR8fbyCJ/QjSdV2eP3+OaZonRlLNUCwWefr0Kb29vZw/f/5UjgFeND01\nNcXdu3dpaWlBSulFkBuwOfd3lMozBCKtxKIxQpEwPstCuTaqtIEduAyuoDQ5iJtJYfbtDD68rIp3\nPR3p0H+8jv/vXyjx0gjxpHFGiMfAaUQCpVKJbDZLW1vbniLgh4FCsSjmGNOG2BCrKAWdbg/XxAAd\noptMOsPz589rIwjbz6tEjiI5DKyKK8YWHMpINgkSq+meosrAzhnB9Y11/rbwOtk7BXRDx5I2BS3J\noPGCmIzwYedDxNXeWq/1Tud9fX0opchmsyQSCUZGRigWi7X50Nu3b5/qcPr4+DjFYvFUnTBs22Zo\naIhwOHygkQrLsnYQZDKZbEqQ8Xi8tl61qeU0x1Bga3TjNIUDAJaWlpidneXBgwe1myRN02oGv1y4\ngEo9JZ9NkMmuM7c+g1PKEwiG8LUMEI314jcMSuUCQmhePbFaUxQC8PwytYodTUlptLw/e2oqEeIP\nxrjJGSEeEWKbgexxIJFIpVheWmbmxTQ+n4/r169XHrNROHiJUAPtkG+Zi8PspVFmjecYysBUPlCw\naMwxb8zQttpFZLR91/qaxK2Qoa9pBKdj4VDEpoRFfUS4dW2UVExOTTIanSR3qUhURNDQ0CmjqyBF\nFSQlMrxmfp2/X/4wLUQIYmEdQGBCCEEkEiESidDb28uzZ88QQtDe3s7U1BS2bROLxepUdI7vVlFV\n0Ons7OT69eunliKtRrmXL1+uEdxhYVkWXV1dtRptPUFWxQJ8Ph+bm5unegMBMD8/z+Li4qnK1iml\nGkyWd63lGmFE6wcJRdOE7A26lUKJIBnbIrmZY2JigkKhQDi9SqxYBKm8tSrfeSUltm17PyoXt1iE\n0Ono0n5f4OAueCcKIYRnpbMHlFJnSjXfDyhiU8CmYBcZmxhH13TuvnqfoTfeQVLGZrPiTu9tuALQ\nCGARQxxQjegt7W9Jx5L0yHMIVdm4BQScIKnNJPORF7z66nlCqnmziU0RgbaPdZRFiTwm/kojjwls\nRR5jo2MEe0Pk+gpEVF16VUlKmoaNxE+AnMjxwpgg4twnSZ4QFmEOVpepjodsn8eTUrK5uXkwFZ0D\nYH19nfHx8VNtNgFPJ3R+fv7EG4HqCVIpxcTEBOvr67S0tHgOKabZkGI9iQzFvlJvJwTHcWoCBffv\n39//RkXTwIp7/+J9v6JANN7OhQsXatmH9df/I+sLsyjTj2maWKaPXD5LOBTGMg1QktnpKYa63qeE\nqHhphAj8S3YSYhvwDwEfnpLNgXFGiCeAmkv4QZ6LjUuONAny2OTTOWbH1+jvu0ZHZzdlXDK+IllW\nsPBh0NhwIilTZA0/HfuSYp4cU9oYvrKfYr6Iz+erScNlMhlP/cOnM8Ig18q3MZp8HBzs3RtnKtDQ\ncLBRVMY7hIESQdaWZ5lfXOf69etMxWbwbFa9DVYoFxsoCxO98rugCjKjz3LXuYMPkyxlNARBdo/q\nqk0gq6ur3L9/v5Yeq722SpPJdhWdeq3RmopOS8uum3VVZDydTh+pI/KgqHarSilPdaSiSh6BQIBX\nX321RnylUolkMsny8nKNIOtHYA5LkOVymcHBQdra2rhw4cKpRdP5fJ6hN9/kXCxGp9+Ps7iIFo16\n86lHvIbV7AM/9lMU/vOfoHf1UyzbrK2vYRoWIyPPmXrxgrZymlz7Rf7Nv/sPJ3xW3yd4iYSolPp8\ns98LIXTg/wU2D7PeGSEeEdvl2w4SabjkcNmghCLrKpZnVygW8ty624dlgcDBwgAzTwZJezPlGSxc\nSpRJ42Pv9NaCmEUqSSwap1gskkolcRwHITQikQimaaKhURR51rRleuTJNDg4jsPI6BI+Uty/ewvd\nDLChpbCU93ETykWXZTatKLrYOkePLBUFUcBS3u1ADpsAZtMItd4f8aDjIXup6FTn/LZ3aZZKJYaG\nhmhpaeHhw4entqm/W3N/uVyOwcHBpg1HPp/Pc6yo/L5UKpFIJFhcXGRkZATLsmo3D/sRZHX28+rV\nq6dmcwWQ3Nhg9Dvf4WpfH7F43Iv8pMRdXUVqGnpPT02p6CiI/PA/QqU3yHz7z8kUi3RdvI7lDxCR\nBTLjg+RbzvF6+ApfevyYz372s/z0T//0CZ7d9wEUcHA/gncFSilXCPFF4N8C//qgf3dGiMdEtdN0\nP0KUlHFZR+BnPZdicmqC7vZOLl+87NUjKWOzgU4rhlC4EmxNNq2jeaSYQxFtGiVWxylyWgb0LQWN\nUqlEOBzBMHRKpTK5XM7zxQ25bBTW6Ar07tjgDCxK5PaMEiUuGjoaW8o2Fy5coKfrNjhr4OawtBKa\nVsRQEikMMlYrrqbvWFWxNTaiIVBIHCTmtmdWmzOuXLly5PoaNB+Er6+xVa/bpUuXTpWk3q1U7Orq\nKlNTUwd29vD5fPT09NTEDKqmwNsJst6xAmB5ebnmk3gkUYcDYmFhgcW33+bO9ev466+briNMEzeT\nwXnnHbR4HGGaaMEgWiyG8PsP9V5u3vlRVkomF7JziLkxEvOTvPZf3uTDn/4XfOh//BX+J59nnH0Y\np5oznDp8wMG99zgjxGNjN0/E7ZCkUUpndm6eyfQSA1duNGwUAgtJHocsuqaDUpRwmhKiRxgCicN2\nSqkfp/BrAVCQy2UpFIrE4jHMyiyVz+fdMUvpknKTpDcyvL74ek0lprrBmcJHiRwKudVFug0uNn4V\nYXpmujYqUjs36xzIEh1ikxn9HdBiuMLEFQpBo9KSg4OpzIZ6pvesugadiqvD+vp6QwfhSaE6xtDR\n0cH09DRra2tcvnyZTCbD66+/figVnYNAKcXU1BSpVOpUU7HVZpNMJnOsrli/39+UIOfn58lkMliW\n5YnCK8XDhw9P9XzGx8cpplLcvXEDYxu5K8Dd2EBlMijHASnRgkFUqYSzsIAWjaJ3dOz7/imlGBsb\nw7ZtHv3jf4KmaXzta1/jt37rt/jK//Ntbt26VXuuEOLUzvc9DQWc7PTZgSGE6G/yawtPveZfAbsq\nhzfDGSEeEdUv0kFmERWSfDHB2MgMwUiY2wN3CGg7NyQNA4c8mqYjXYnUD97Ful1xRghBW7GLLFmC\nMkxbW1vTL7/SIKAFeKX/g5j9Vk0lZnZ2lkwmQzAYJNIWItBmEg5E0Os+Mt6gvg22xrOhYcKhME+e\nPNmZRtN89HOb180JikrUEqD1Z6cqqdI7zp1tJK/QKhFjuVxmaGiISCRyqp6C1VRsOLzzfA6rorMX\nqiMVkUikqd/jSaH+OA8ePDjR49QTpG3bvPPOOxiGgWEYvPXWW/h8voYU60kc23EcBgcHiUaj3Lpw\nAZrYpbmbm8hMBi0UAim9/4/HPUF4nw+ZToNpYuzRVVs9TiwWq3V9f/GLX+TP//zP+cY3vnFki7cf\nSLy8ppppmneZCmAS+OXDLHZGiMfEQSLExcUFZpeec+3KbaLxOOsUthpQGqAjkGi6wHFdjD3smryO\nU+/ta6Y4s7a2xsTEBBceX2Ej2txfU6HIiyy3nPuYlcaV7Sox+XyeRCLB0sQKGXuMUCRILBYnFo8R\nsPzkEkVejM5x/dr1PetEfnx8yP4gf21+B59ysfBVzl7hIsmTp122cc29UvsbiUJDw0AjmUwyMjLC\n1atXT3UjSqVSjIyMcOXKlabHOYqKTjOcxEjFQVCt4532cap1ye1dvoVCoRZBptNp/H5/Q4r1sARZ\nnZe8cOEC3d3d2PPzoO/MkqjNTbRK9kBomjciJWXtuSIUQiWTqFisqY1YtZ7b39/vHce2+bVf+zUK\nhQJf//rX359GwLvh5XaZ/jw7CbEIzABv7GEb1RRnhHhM7EWItm3z/PlzhFDcv38P04jUNEfLuDvq\nYuCi40PXTGxZxrfL2yMpoxFAoDdVnKkfGBcW/Df1Tda1ZSzlx6qMMRQpYIsy/e4lbrsPmh5HCEEo\nFCIUCtHX14dUks1MikRyg5mFBXLpAprQuHz5MrHY3gP1ABdlP/+wbPKG8T1S2iayQoYGOteda9x2\nB2qdrgovZRxTPl68eEEikTj1+bW5uTlWVlaadqs2w0FUdMLhcC0FXV1zfn6ehYWFU6+vLS8vMz09\nfaoaruCZvE5OTjatSwYCAQKBQG3Yv3oDUc1ABAKBWgS5H0FW9VXrRcCFrqO21e1UsYhQquZlWJsX\nrlu7+l1RxSJi23uwubnJ8+fPa/Xczc1Nfu7nfo4f/uEf5rOf/eypZSa+b/Fyu0z/8CTXOyPEI6Ih\nZerYUMxAOeP5vmkGibzL8MQ0l69coaenB4cEkjwCP0FMiri4yNrIAXjNKSYtQBjDddCQsC1KlJRR\nKEwVwZVug1VT1Xaot7eX6zeuoyqv8e/bP86cNs2oPsim5onBt8tObjh36ZF9jbJrdVCV6p1XsdTQ\nhEZLtBW/GSCxmqK/r594PE4ikeB73/seSini8XgthdhsZKBX9fCP7W6SIkVO5Ckol6AK48eHhoZE\n4eAiUVglGH42RDQaPVUpseqNi8/nO1Yq9iAqOlJKLMvi7t27p0aGUsoaIT958uTUPPuq9dxEIsGj\nR48OVD8LBAKcO3eOc+fO1SLsZDLZQJDNUtBVP8bt+qpaNIqztISoP7ZStc8tAOUyook9GEJ4UWMd\nVlZWmJ6ertWnZ2dn+cQnPsFnPvMZfuZnfuZ9aQC8L14iIQohNEBTSjl1v/txvBrit5RSbx9mvTNC\nPCYM4UJyCfxdoFtI4MXYMLlMiie37+Fr89JuOhFcsihcDHRa8LFJCbviYago4aLjIgioINFyCwqF\nQ77SzOJ9yXUsTBVHuQKlZO0LOjs7y9LSErduDyDCflYo1fIIBoJOeZEL8kplFXYlQfCaZMoUcNiq\nzZj4MQmytrzO9PQ0t27dqkWFVRHw6gjDxsYGk5OTNZmwtra2hhZ9gaBVtdCqvPqNjUsem3KlMh/A\nJLeRZnRsguvXr9dGJE4Dp2mwW6+i09nZydOnT2lvb8cwDIaHh09FRac699fS0nKw4fQjwnVdnj17\nhs/nO5CkXDPUR9j1BFmfgg4EAti2ja7rPHz4cAe5i0AAYZqocnmLFIVoUJRRjoPRJP0toCGKnJ6e\nrjU3GYbB66+/zq/8yq/we7/3e3zoQx869Pm9b/ByU6b/DigBPwsghPgl4IuVx2whxD9SSv2ngy52\nRojHgetgFdcpSQVWiGwuy+jIKJ2dnVy+fgvhlCCzDLFzCGFi0onDGgqBgUkLPmxs8uQR+AjRRoAA\nOfzgGgToqkSElTS40kHqlcYZL0Vq23ZtFu/hk8ckdRsXBwut1oziIElSpoxBbJeZvipsihRJV9ws\nLAQChaIsCwy/GETkA7t2KW4fYSiXyw0zbNUGi7a2toa7fxOdWCV9XN91eZpuC0opr2V/cfFdSynW\n30RcunTpxFV0qnXJ0577q9bXzp8/f6K6p9tT0OVymXfeecebl9U03njjDYLBYK0GGQqFEJqG0dOD\ns7iIzOe9cQq/38uOFAooKdHb2xHbPkdKSpQQCL8fKSXPnz/HMIzaTcSf/umf8oUvfIE/+7M/4/Ll\nyyd2jj+weHmE+EHgf637+deAPwD+F+BLePZQZ4R42hBCQCmDoevklGRubo7V1VVu3LxBOFRxpzD9\nUM6CXQAriIYfkx5c8khyCCQ+LIK0IvDXxhrqO1e1SrNLs8aZ6uzatWvXaG9vZ50SCoGf7bOEXmNK\nDgcTQYjmG63EoUgaHbNhxCKXyzE6Okpvbzftl9vR9zRW3YJlWQ1D3s0aUOrra1Ufxng8fqpdl1Xr\nISHEqUqJVUcd0ul005TiYVR04vH4nqnPqjHxadclq3W8gYGBA9WNj4pqk059M1C1ySuZTDI1NUUu\nlyMYDNLa2ko8HiegFDKVQiiF5vejSiWM7u6mQ/kqn0dvb8eudJJ2dHTQ39+PlJLf/d3f5Tvf+Q7f\n/OY3T1Xb9QcGL3cwvxNYABBCXAUuAf9WKZURQnwZ+MphFjsjxOOgmMIVJsvLc3R1dfHw0cOdLty6\nBcVNsLxNSmBgeIqJuy67fZSjWeNMtUZUjaLsyui/f4+31IdOFocgRnPlF4p4Cc1KGgnF0uISyyvL\n3Lx5k1AwhEsZhyIWh4+ottePcrkciUSCsbExMpkMjuNw/vz5Ux2Ar9ZZT9vVoToiEo1GD6xucxQV\nHSklY2NjlMvlU5V6U0oxPz/P8vLyqTY3wZbv4507dxqadOqbvKpNTNUu6Bezs+RyOUKhEPFYjPil\nSwRKJVQ6jSyVEJVoW9k22DZaPE7BMBh6661aR3GpVOJXf/VXCQQC/MVf/MWpOZic4USRxtMuBfgw\nsK6Uelr52QUO9UE9I8SjQkpWV5eZmvOcuK9cudL8eUIHebjbJ13XK0r6jVFhtXHm+fPndHd3Nzgt\nFHH3NQbWEBXvDIXZ5LkOhdqcoePYjI6O4vP5eXD/PpqmV9YwKB+REOtRbUAJBoOUSiVc16W/v59s\nNsvQ0BC2bTc06JzE5rS0tMTMzMyBVVqOiqpaz26jGwfFXio64+PjaJpGsVikvb2d27dvnxoZSikZ\nHh4GONX5z2qn7+rq6oFECrZ3Qdd3+U5PT3sEaRjEDIOYZREMBNDCYbSuLlL5PGNDQ7XPQiKR4Gd/\n9mf5iZ/4CT7zmc+cdZIeBi9xMB/4G+CfCyEc4J8BX6t77Cowf5jFzgjxiHClJL2Z4fbAALNzc7s/\nUUmPFA+BqgB3fVQIMDc3x+LiIgMDAzs2dAn7EqL3PLGrV4o3G+nJr01MjHPh4kXa27bXokTlaMdH\nsVhkaGiItra2Woq0o6ODS5cu4bpurb42PT0NUIuO4vH4oTZ/13VraiOn3XVZrUueRuqy3gw4lUrx\n7Nkzenp6KJfLvPHGGyeuogOebN3Tp0/p6uqir6/v1CJ3KSUjIyMopY7cUdysy7dKkDOJBPmVFcK5\nHGJ1lWw2y4MHD/D7/YyPj/PJT36Sz33uc3zsYx87hbPz8Ad/8Af88i//Mpubm/zO7/wOr732Gh/7\n2Mf47Gc/e2rHfFfwcptqfh34C+A/AlPA5+se+yfA3x5msTNCPCIM0+TanUcUMxt7K9W4ZQgdfCBa\nKYWmaWxsbBAOh2lpacFxHJ4/f47f7981LWZAg8TZbvAG+nc7uMb07BSbqQx37txt2tCikIf2ZGyG\nqnDAbkaxuq7X6ouwFR2tra0xPj5esylqbW3dU2Q6n88zNDR06oLZrusyMjICcOp1ySrpPnr0qGFe\nsqqiUx2CP46KDmzN4924caP2PpwGTssRYztBViPdTCaD3+/nE5/4BLlcjqmpKb7whS/wkz/5kydy\n3GYYGRlhZmamJnn3pS99ienpaS5evHhGiMc5tFLjwHUhRJtSamPbw78KLB9mvTNCPA78MfTcBo5T\nav64a4Omoyw/CqfiK7j7nW81RRqPx+nr62N9fZ2RkRHK5TJdXV2cO3dux8YvK4ZSNg45CihMfFi1\nDtN62Eh8lQab7SgWiwyPjBFqs7h77+7OWmjteDYBji4+Xa1/5nK5Q2l31kdHsOXCsLCwwPDwMH6/\nn7a2tq3uQyFqQtb13Z2ngSrp9vb2cu7cuZdGuielogNek878/Pyp6MXWo5oiP256eT+4rsvQ0BCh\nUIiBgQEAPvrRj/Inf/In/MIv/AJ//Md/zOc//3m+9a1vnViH7le+8hW+8hWvp+PVV1/lb/7mb1hZ\nWeHLX/5yrTHuBwIvN0L0XsJOMkQpNXjYdc4I8TgwLPRoL6L8FOwiGL7KDJQEu4gjypSjMaS2gUIh\nsdGxsAhh4Eer6/asb5zRNI329nZSqRTBYJB79+6RzWaZmZkhk8kQCoW80YXWGHYAJBINDQuNJAVM\nyoTxEajzEfRUYRQtTbwFV1dXmZyc5MbNG1gtqjLmsZMQXWx0TPRdulT3Q9VpvqOjg2vXrh1rQ6h3\nYajf/Kempshms7XrWK9qchpoNlJxGigWiwwODtLd3X2gSPeoKjpVpaNSqXSqkS5sXbs7d+4QDodP\n7TjFYpGnT5/WxkSklPzmb/4mw8PD/OVf/mXt2NVa/Unh4x//OB//+MdrP3/uc5/j4sWLfPKTn2R1\ndZUnT57wi7/4iyd2vJeKl0yIJwVRkzV6+XjPvJCDwrZtpJT87bf/Cz/0YABKGUCB0Cj7TUo+hf7/\nt/fm8VHV9/7/88w+yWQyWUggISQQkC0BJAEXQHBp1W/1qldr/XFdr3Wp9breLtr6kKqPurQuFbXF\n1uJWrGJbq15FQYutYlFcSEIIS1YSsk4ySWafM+fz++MwQxISkkwyJMTzfDx8qBkm5zOTMO/zfr9f\n79dbbyWMTIhuFORD7i8KFpIxkYhRJIMi9Rqn8Hg87Nq1i8zMTKZNm9brL2nkg625vZU6dzNhfwhH\nUjKOFAfJDgd+g4SHEEFkHFiwYEJGQYdECibMPeziwuFw1OZt3rx56qYCZHx0EUZGhz46hyiQ0WPE\nQvKgC4P7IxJ0473eKBI47HY7ZrOZjo4O/H5/dAA+NTV1VAbge45UFBQUxHXLQWTUYaDyciz0dNFp\nb2/H7/djs9lwu92kp6czc+bMuGUwQghqa2txOp0UFhbG9b3r6upi165d0ffO5/Nxww03MHXqVH79\n61/HrZ/8TUKaVgz/O6ylElGKXixmx47+nytJ0hdCiOKRnG24aL8No4DQGcE2CRLTQQjCUoig1IEB\nMzJ+gnSixxTNrBTCyPjRCQOhcAsmkYpeUn8UEZ/L/oQzcLgvErLpcDAJnZDo7uqmw+Wiob4BgcDu\ncGBOseGzSyRIJhyHAqG+Rxk1EnSnTJnC7Nmzox9+OgwkkEKYICH8h3qGeowkHZpPHN6HZCTj8Pl8\nFBcXx1XKHpHr9wwceXl5KIpCV1dXtL8WGYCP9NeG+6EYGalITk6O68JgIQR1dXW0tLSM+qhDTxed\n3Nxcuru7KSkpITk5me7ubrZv3z7qLjpwWLEqSVLMDjdDJVIyX7hwIQkJCTQ3N3PFFVewevVqfvCD\nH0yckuVYMw5KpqOFFhBHE0kCSSKEFykQgM56ZO8BDJIREtPAngImCzqhI6gECYkwOiTCkhclZGX3\n7t2YTKZB58lkFEKEVfNvCZKTk9VyXW4uIVmms9OFq9VFW3UtLqxkOtKj7jBweIh7oPEDCQkDZgyM\nzCUm0lvLzMzsNSIy2kTcbTo7O/vtS+p0OhwOBw6HgxkzZkQH4Nvb26muru413+dwOI76IR0RmkTM\nEOJFOByOuqfEc9QBegeOnuXD0XTRAfVGoqSkhIyMjLgqVntmoBFXpfLycq699loeeughzj333Lhc\n9xvL2A7mjypaQBwBfUuZkiQhFBm5ZTeGdieyQQEzSEICZz04axGZM1CS0gE9iuTHhANnZxM15U5m\n5s8c0ooeBdW6rT+MBgPpaemkp6UzDRm9X+BvV/uPXV1dyLKMxWKJe9+mubmZ6urquPfWIgpFh8MR\n8wB8KBSivb2dlpYW9u7di9FojJZXI+MLkcH0xsbGkQlN5IBqAA9qz1l35I1PxBotYmIQLyI3El1d\nXUfY8Q3FRSeSZQ/mogOHPWNnzZoVV2/ayPgGEM1At2zZwj333MPLL79MYWFh3K6tcfyjBcRRIOIs\nYzAYkOt3EnbtRbJPRzGGQAoCBjAlQyiMaNiHMkVCl5yMIgTVNTV0eztYdOJJJFiGFqDUvt7gH/wC\nMFsspGbZSUxMpLy8PNqTjAgnevbWRqOcGZn5CwaDI9rMPhQiOxJHagBuNBrJzMyMmnv3XQJssVgI\nBoNYLBYWL14cW98p5AOPUw2I0e3IEliSICE1GhgjZd94W6PJshxVXQ5laXAsLjoRIhlovD1jQ6EQ\nJSUl0fENUGf/Nm7cyHvvvRe1ENQYZcZ2MH9U0QLiKKCXFLwNH2Jwfwz1uwg5jCheC2FrPlLyLPR6\nx6GcTkKyJKHvaMBtMlFVWUtmcg7z5s3FIh1ZnpRctej2/B/6+u2gyIj02chzL0CfWahurumxTb5/\nBEaho6a2htbW1mgvBSA3NzdaFnM6ndTV1SGEiAbHwUqH/dGzLxnPmb9ISaytrS0uNmI9xxc8Hg8l\nJSXYbDYUReHzzz8nKSkp+j4NdG0RCiHcbvB6QfaD3ImUnIJk7hEQhFCFWOEgwjaZmro62tvbhzWO\nEguRJbvTpk2LzsUNl6O56ESybIfDQSAQwOfzxf3myOv1UlJSEvU+lWWZn//85zQ1NfH+++/HdXRE\ngwnTQ9RUpiNAlmUCQR9Vn9xHRnIbpnAiho4AQZsZP53o5FZCBgtG66lIxnQkqw0kMx0Hq6gz2jhh\n1inYbcmHZvsyDs8oCoG+9FUMXzwHkoSwJAM6pGAXhEOEc1fQufJOPHox4BLhEGHkYIiDZdUkJSWR\nn58/aICLfKi1t7fjcrmizid9t1P0R8QWbd68eXEdcwiFQtHMZubMmcekt9bzNQkh6O7ujqozI1l2\nWlpaVHyiuFzgdIJOAr0BuhoOmfvoID0VXZ++bdjbxe7aZozJGcyaNSuur8npdLJv3764/5wiZV9Z\nltHpdHFx0YkQqRRERmzcbjfXXnstCxYs4P7779ds2OKMlFUM349RZfqOpjKdMEiSRLDxbay6KoRx\nETrZAyKAwe9Fb5IQukwEbYQ8X2NOPYuwu42DrZ1IksL8uUtJsKUgE8CIrdfAvq5yC4YdzyJsk0Hf\nY1bRaFGDZc1HJJmTCC27kQAyRvTRTFEgCBKm09VJS0Udc2bNHnI5se/we6R02HOwOzL8HrnjDofD\n7NmzB1mW42qLBoc9QntuQIgHiqJQWVmJ2+0+IrORqfpkOwAAIABJREFUJAm73Y7dbo8qWCPik7q6\nOujuJlUJ48jKwp7swKAEQC+BNRGhKNDWhqLToTtUOvR6vezZvY/szHQyZs2K7ucbbSKK1dbW1iEv\n842VQCAQNSqYOnUqMPouOhEaGxs5cOBAtFLQ0NDA5Zdfzo033sjVV1+tKUmPBZrKVANADgcR3f9G\nIQslHFYzgrAXnd6EOWTCbwyj16UT0jXR1V1Lc4dgUkoCDlMqBl0yMgH0GDHSw/NSKBi+/CPCmtor\nGEaRJERSNsZ9m7Av+i98iSn4CBI65GYqhKCx6gByp5/iE4tGtE+wr/OJx+PB6XRSUVFBIBAgISGB\nrq4ucnJyjpiXHE0ips9NTU1xX2/Uc8HuUHprPcUnM6ZPJ1RZSXcwQIerk5raOoyhLlITLSSlppNk\nSwKLFTo6EAkJtDvbqa2tZfbsOSSaAEUG3egHqsi6K71eH7NP6FCJzP31tXsbTRcdODwHGrlp0ev1\nfPXVV/zgBz/gN7/5DaeffnrcXqNGHzSVqQaA0rkHFD96owMhFLCYEXIIYUlAUgTWoJ5wSIffH6JL\nqSJ32nLMQo/e50dYzRiwYuqTHUot5UheJyLpKL0dnQ6EwFjzL3TzLyEBI2EUvF4fFbt2kzEpg9wT\n541qgOrpC5mbm0t9fT01NTWkpqbS0tJCc3PziPqPAxHxcTUajXF3TnG5XOzevTv2kQq/H4NeIjU9\nndT0dBTC+F21dHW20NRxgJq6IAadGYfJjMflwh9WWLBgAQajAYKe0X9BHDYqiPR1B0QoEPRCwKX6\n70o6MNvBlNT/jVk/NDc3U1NT06tX3R+xuuhECIfD7Nq1C4vFEl3o+/bbb/Pggw+yceNGZs+ePaTz\naowSmqhGA0ASIYQkodODEhYoRpAsViS/D8liVZV47U4sFh3pSZMxiimIrjaEIxedkoqkO7LRL/k6\n1A+jwdDpwd2sPgeJ1sYWamtrjxhzEEKA348IqH6rktkMFkvMwTLipymE4OSTT46WSPuKKobTfxyI\niFQ/Nzc3ZvHHUOiZgY5opCIcBiTCBPHhJEQAvTWEHQv2SakIFALeIPUltchhGWGxsHfvXhzJdlJs\nFiwO/TBtD45OJMAP6nCjyOBuUsU/ejMYrIAAvwv8HZCQCeaBFdB9xzeGWzbvb0tFxEWnoqICv9+P\n3W6P+tTu2bMnGuAVReGpp55i06ZNbN68Oa6zoRpHQSuZakjmZCTCSJKeYCiIMJshLR2prQNfWxud\ngSCpk9Ix6mX02JA6XUiObEidDAN99BkTVPXhYChhMNujAUpRlCN6eMLvR7S0gByK9qaEooDBAJMy\nkIb5wR9ZrhvxhOwZ5GLpPx6NiHlAvKX6kQF4vV4/4gxU6AQB0Y6fAGHCGDCjmPSEfB70QiLk11Nb\nU8vknGQmn3AiOksiXq8XV0sD+xvcuOs/G5KCdSjU19dz8ODBwVW4Qqg3VooMpp5BT1J/F5UweJrV\nLNFwZPm9Z7Y2lBLzUOjroqMoCt3d3TQ1NUXNK958801MJhM7duxAkiQ2bdo0ovbAUOi5vunKK69k\n586dXHfddfzv//5vXK877tF6iBoABls+QkrHYvDR7g5zoMNJglEmKAvMZiuZtiQIBoAQUkY+ZM2B\nhBSQvQNmgUrmfITeBHIQDAP0kw4FzO7UBZR8/nlUPt/LKCAQQDQ0gNmE1CegCFlGHDwIWVlDCopC\niOj2g/nz5w9poH+w/uNA8489M9B4boAHdUykrKyMnJwcsrKyRvS9BAoBsxeZAEKASTr0nksGsE7C\n1bSHFqePGXnzMUohQmYFswQJJh0JOblkJWcjJF3UYm7Xrl3RJcmR3tpRxxZkGTxulK4uqqoqCep0\nFBUtQT9YUJX96j+mAW46dHrQGcDfCbbeQqa+ptnxQqfTRSsQJ510ElarFY/Hw+OPP05DQwNpaWn8\n5Cc/4dZbb2X69OlxOUPf9U0bNmxg06ZNvPLKK3G5nsbYoAXEEfDHP/6R2tJdXHi6mymTF1PX1Ym3\nw0nqpMkEdRL1QsaW2IXRdjqO/FMOiW6CaklKP0CwM1gIz78Yw9cvIezZqh1cHyR3M10JuZQ1+g9n\nUCIEsge1mK9HtHSAyYjUz4eoZDAgzALR2gqDWGjJsszu3bvR6XQxB6i+/ceB5h8TEhKoq6tj6tSp\ncV2jBIdHKgayrxsuMn6EQSCSE5G6O4nopBQEjc4Ogj4T06cnY/a6kRx2ZMWFKSQhmWyqB65OLZdG\nbPj6LkmOvE/9Lkl2d0NrC0FZpqKqipSUFPIzJyM1N0JSEqRN6vf3CICgWw14R0NvgpAbRHr0Ri5i\nYRdvs3Yg6ucaUcfW1NTwwAMP8KMf/Yjvfe97+P1+tm3bNuqzhkdb33Taaafx6KOP8qc//WlUr3lc\nMoFENdoc4ggIBoN88sknfP2PZ5C6N2E0SmRNyWd2Tg6TsuwghfGHZ+FUluL2+DGZ9KTZrNinzSPB\nnjbwB344hOGjB9DX/AthtqviBkmCkBfhceIiiYPFP2VGwVL0OgnkNlC6ia7/DQVQGhuQEjNAlzpg\nNio8HqSsbKQBsohID2/atGlxzQBCoRDV1dUcPHgQk8mExWIZcf9xIHqOVBQUFIzasLiPFiR0+JUO\npOZ2JF8AWa+nur4OW2IikydNQvi96CxmTKk5yPowifpsJP3Qy3w93WFcLpfqHpOYQFrADwmJ7Kus\nZPr0vN7LfD1uSE6B1AFGb7ob1XLpYMKZkBeSp4HOQGNjI3V1dSxYsCCuA++KorB3715kWWbevHno\ndDq2b9/Orbfeyrp16zjllFPidu2ByMvLo6KighNPPBGDwcCKFSt45plnjvk5xhNSejH8R4xziCXj\naw5RC4gjJBgM8u1vf5v/d9YpXHHBfKrK3qJ6/2727m2kKzyL+YvPYtmy5eROnUwgEKTNp8PpDuD1\nerHb7dG+2hFzYUoYXdU/0Jf9GV1HLUgSQZ2VStsS7Cf9F+nZ09XSaagJhA90PVR9Xi+itQUSdIAZ\ndBn9ZgjC40HKyETqUwKNbGVvaGigoKAgrj28yIdeIBBg3rx5GI3GaP/R6XTG1H8ciMh8XGpqKnl5\neaMWaAUKPprRY8FPB0JR8LY6qdtZRvakDJKTHWA0Ihw2hM2MRZpEGD8JPc0YYnw9nbvLaWo8SKfX\nh92eRFpaGg5HyqElyai/I14v5OSqveO+eFrVYBfpD0b+FvZ8a4QA2YdIzmV/ZRUej4eCgoK4zpzK\nskxpaWk0WwZ4/fXXeeqpp9i4cSN5eXlxu7bG8JDSiuE7MQbE8qMGxAagBagVQlwU+wmHjhYQR4H6\n+vojJO1K0M/+8q/454fv89E//8m+2kYKi07m9DPOYuXKldjtdrq7u3E6nbS3tw9smyYEir+L2ppq\n2j1B5hcsOCySULwQOgi6Pj09nw/R0oKUkIAQbtBPBunIQCLcbqTMyb0CYmTMwWAwMHv27Lj28CIL\ngzMzMwfcftCz/9jTGWa4/qsRxeVIfU/7QyAOBUQzIXwcbKumtamdmTNmYo0IPfT6QyuaBSbs6DBi\n4SilRiFU31OhqDczevMRQ/siEKB228e4BcyZMwdZDtHR3kGHy4XX6yUhIYEUhwOH2YQleyqSvR9v\n1JBPddIx6CDYCUoIEOoNlikJ9BaQA8iSkbLqFmw2G/n5+XEtZ/t8PkpKSsjNzWXy5MkoisKvfvUr\ntm/fzp///Oe4l2g1hoeUVgxnxxYQp32SG7X/A7j++uu5/vrr1e8rSV8AZwKlQohpo3DUQdEC4jEi\nGAyybds23nvvPbZu3YokSaxcuZIzzzyTJUuWAKoFldPppLOzE7PZHC0ZVlZWkpaWxvTp03t/EAUP\nAjJIfbJLWUY0NCAlJIAIISSdGhT7INwepJwcpEPZaVdXF+Xl5eTl5cXdCLm1tZX9+/cPuwfVs//Y\n0dExqP9qZKSiubmZgoKCuJX4ArQjiyDVlbV4JSf5uTMw+cMQCKrBzWJGTtBj0NmQ0GMlDT0D9JH9\n3eBtV0uZ0R+3BNZUsCarK8ZCIXZ9+QWpfh85c+YeUQAQQhUNuVwddDY349EbSMyeGn2voopMJQxt\nOyHUDcYk0B26wQgHABl0NgKyxM6adnLyZsZ1/AUO9yYj5uaBQICbb74Zu93Ok08+GVc/VI3YkFKL\n4cwYM8Tqo2aIXwONwCNCiK0xH3AYaAFxDBBC4HQ62bJlC++//z6ff/4506dP5/TTT+fMM88kPz8f\nv9/P119/jc/nw2QykZKScmR5NVClZn79ZVatbeD3qXOHkh+h732DJQIBMBjRHVKBRubw5s+fH/cS\naaSHN3/+/BFbiB3Nf9VisVBRURHNduPp0OINdFOybxtpyZPJSrUhO+uQFIFkMKlLlWUZWfKjT8/D\nnJiNiQGUul4XeNrAZFUVnnJQzRTDYVCCkDgJjz6J0rIypmdnkSmHYLCfl8+LYnfQJelob2+no6Mj\nqmBNT1RwJIAhHFCDoN6simyEAiE/7s5mKpolZp14Wly3bwA0NTVRW1sb7U22tbVx5ZVXcsEFF3Db\nbbdpNmzjFCmlGE6PMSDWHTUgtgIBoBL4thAiGPMhh4gWEMcBkT7ae++9x+bNm6mpqSEhIQGr1cqL\nL75IWlpar/KqoiikpKSQYfdgT85A0vfzQR8OI5oa1Q9SiwSGwwFR+P0ASFnZyEB5eTlms5lZs2bF\ntUTq9/spKysjLS1tVHt4fa/R3t5Oc3Mz7e3t2Gw2pk6dOuL+49GIlGNnzM7CZpUxNrWjWM3I+iBh\ngiiEUZCxhJOx+CzoM3MhoZ8gJgfBdQBMCWpA8jhV9xgkVRglwnS1NLHPbeCE4tNUdWx9ndobPNrP\nzeOB7BzocfMRDodxtbfS3bQLV3cIhEJKkgWHVcKeYEHSG2h2eWlqdTFn3lzMqbMGVqqOECEE1dXV\ndHZ2UlhYiMFgYO/evVxzzTWsWbOGCy64IC7X1RgdJEcxnBZjQDw4vkQ12tjFOECn0zFnzhzmzJnD\nt771La6++mqWLVuG2WzmsssuO6K8KkmS6grT2kp1zXb0Rpsqx09RRxeQAL0eafIUREcTwiNA51Yv\nJiRISkJKSaHL62X37t1Mnz49ugswXkT2/PX1uBxtLBYLer2eQCDAkiVL0Ol0Q5p/jJXIAPyiRYuw\nWiyED+4jZDEh9KDDhA4jEgaMJGDQW8EiQ3sbWBOODDCB7qgtH10th4blDwVOAQcPNtDV4WZhXg4G\nfUh9viMFWltgoNlQnw8Sbb2CIRzab5icQJp1OugTkGWZTlcnra4OKls6CAaDGI1G8mfOxmiQQAmo\n/cRRRlGUaM964cKF6HQ6/vnPf/LjH/+Y559/nsWLF4/6NTVGmQk0mK9liOOMDz74gIyMjOhm76OV\nV886YwUzckwEgno6XC5ch8QUNpsaIFMcDoyGEEI3BeRDH75GI+j11NXVRftq8TTLjtz9d3R0UFBQ\nEFc3EUVRol6Y8+fPPyLgDbf/ONi1Ig5Bc+fOVTPrgB8aGyAxERH9hJCQ6JO9eTwwJRvMfQKM64Ca\nCfrd4HOpmSIgwgr7KysxGY3q4lvZB9Y0SM9VxyWcbdDpArMJjIcCnyyrphAmC2RO7j+DDLog0HHI\nqi3yNJmKigqsVis2m039nepqRW/LwZE+JWqfNhrZfTAYpKSkJCqqEkLw0ksv8cILL/D666+TnZ09\n4mtoxB8puRiWxZghtoyvDFELiMcZfcur7a1VrFw2n6UnncbJpyzHnmTH7XHT4Wylq7MZfziJ5NTc\n6Ie+oijs2rULq9Ua9917wWCQsrIy7HY7M2bMiOu1YhmpiHX/Y8Qw+wh1rNcDLU2D9/S8HkjPVDO3\nnrgOABJ0Nal9PJ2egN/P/v37yczIJD3jkBovdCggJqZA4qFs2+uFrk7w+w6pU8Ngt0NSMlhsaj/y\niDegGwItoFfP6/P5qKioICcnp5cnqJA9eEUKHV1e2tvb8Xg8RzXfHgoej4fS0lJmzpxJeno64XCY\n++67j3379vHyyy8PyQ1JY3wg2YvhpBgDYocWEAdi3BzkeCIYDPLvbVv45KO3+eLzT0GCpUtP4pRT\nV7LgxJVIBltUvep0OgkEAmRmZpKbmztqd/r9MeLNEcMgsiB2pCMVQ5l/jFyrX8PsoQZEjwcyJh/Z\nR3S3gr8L3G1gSqCrs5OamlryZ8wgMelQgAiHAAls6WrQTO6h+gzL0NWszhXq9NG+o6pQTYEER+8y\nrSKDtw50VlydnVRWVjJ79uzewUiR1X5mYk70uRHz7YFGYQYTSkWWFBcUFGCz2fB6vdxwww3k5eXx\nyCOPxLWPrTH6SPZiKI4xIHZpAXEgxs1BjldEOIDT2cqHH25l0/sfRsurK1euZP/+/VitVu688068\nXi9OpxOPx0NSUlL0Q380ypmRRbQtLS1xHXPoe63CwsIRmWH39717zj/6/X4MBgPBYJAFCxb0b/cm\ny3CgFhL76Q/2xOOFqdOOHJSXA9BeA+4Oml3dtDnbmDVzFqaeP5egR/UU1Rt7B0QlDJ0NoChg7PM+\nCKE+LyEFEvvcMPidNNdV0OjsZt68eb2DmRAQ9oBlMhgHzth6Lknu6OggHA7jcDiiC4B7DvDX19fT\n2NjIggULMJvNNDU1cfnll3PllVdyww03aErS4xApqRhOjDEgerWAOBDj5iATBUVR2L59O9///vcx\nGo0IIViyZAlnnHEGK1euJDk5me7u7mhWFA6Ho+Mdvbwyh0goFKK8vByLxRL3cmzEQMBkMnHCCSfE\n9VqRjRiyLEf7agP2H9tawOtWRTP94feBJREmZfT7sNLdSt1n7xHWm8ifOUf1v4VDrkQ+td+XlHEo\nME4Cq1193OsCr3Ngk24h1MwxZVrUpk1RFPbu2YPib2H29MnoDZZDc4hC9dxFAXMamIY3CB8Oh6Ol\n6I6OjugSZa/XixCCgoIC9Ho9ZWVlXHfddTzyyCOcffbZw7pGLPTcVhEOhzn11FM599xzeeihh+J+\n7YmMZCuGBTEGxOD4CoiaynQCo9PpWLt2LY888gjf+c53epkDrF27dkD1altbG/v27RvWTsPIUP+x\nUKxG1lBFtnzEE5/PR2lpKVlZWb3ciAbc/+hwYDMYkTwesFoPu8soCvj9qtpzAF/RQCBAye5qsiaf\nQK7Bc3grihDq7aLFpg7ni7CagZojClShinCMR8nGJQmQ1KH/xFRCoRClpaWkpKSQN+c0JCUAoS5V\nsANgtKuD+j1N6JXQoWvrQDIOmAXr9XrS09OjpXK/38/OnTsRQhAOhzn33HPJz8/n008/5fXXX2fB\nggVD+VGMiL7bKu655x7+8z//E5/PF/drT3g0c++4MG4OMpEQQgxoiTaYOUAgEIj2HiPl1UiAjJRX\nhRDRMli8FaugDm/X1NSM2paKoxEZFYm4phyNXv3Hrk7sQLpRjyM5GbPFDEiQ7FBFLv1k3hGHltmz\nZ5OakqKOXXhaVI9RnUEdmNfr1R6iHISkTDVAgto77KiLqlIHJBwCnQGPwU5paSkzZsyI7q88+vN8\nEOwApUfwkAxgTFWD5iDvS0lJCTk5OUyZMgUhBI8++ijvvvsu06ZNo6Kigrlz5/LKK6+Merm077aK\nrVu3sm3bNh577DGeffZZZFmOinviaUYx0ZESimFOjBmibnxliFpA1ACOVK/W1dWxdOnSXuXVnkIK\nWZajX7NarcyZMyeuYghFUdi3bx9+vz9qAh4vhBDU1NTgdDopLCwcdm81uvG9rY2O1lYCwQD2lFRS\n09P7nX+M7JosLCw83HMVAnydauanhNXZUoEaIBPTVCebCEoY2muHEBCDdHS52dPYNfQbipAbAk1q\npqjr2V8Mq9mkKQVM/We8XV1d7Nq1KypAkmWZu+66C6fTyfr167FarVGXpGnTjolVZXRbhcVi4fnn\nn6eiokIrmY4QKaEYZsYYEE1aQByIcXMQjcG9V7/++msaGhqYNm1adIg7LS0tLiubAoEApaWlpKen\nk5ubG1fhhSzL0Q3wo9UHHWj+0eFw0NbWRiAQiPbV+nmy2s8TQlWODrQ0uqMeEAOvcRLQWLOPJo/C\n/OJThmaZp8jgq1MH8vtbIRYV3WSBvncwbmlpobq6msLCQhISEuju7ua///u/KSoqYs2aNXHt+Woc\nWyRrMUyPMSAmaAFxIMb8IO+//z533XUXU6dO5Y033sDr9fKd73wHt9vNc889xyeffMI999zDJ598\nQk5OTq/HvvrqK5566imKiopYt27dWL+UUaVveXXz5s0AXHXVVVx66aXR8mrPkQWbzRYNkCNRr0bG\nHOLtcAPqbFxZWVnce5OhUIi2tjb279+Poii93quYbyYCHuhqBPORalChCPbtrUAKB8lfcgY6wxCz\n66ALQu1gOErmqQRAMqtKVNTfldraWtrb2yksLMRoNFJfX8/ll1/OzTffzBVXXKEpSScYEykgaqKa\nHjzzzDOsW7eONWvWsHPnTmpqaigoKGDVqlWsX7+eJ554gtdeew2AzZs393ps69atbNmyhdNPPx2X\nyzWhVtRIkkR6ejrf+973+PDDD1mxYgV33nknH3/8MXfffXe/5dXIyEJZWRmyLONwOEhLSyMlJWVI\npdXIB2traysnnnjiqI5U9EdrayuVlZXMmzcPu90e12sFAgFqa2uZM2cOkyZNivYfa2pqYt//aEoA\nix18XYeNwVGD7+6ynaQ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thJD2UdpHkUOJ4KsorpcjZqQ4oayFuBJqUERRIJBVKUK+xbj8ZxgjMUylMU0T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SEUIIqhFJDmNW0xAF7aZw5MgRnE7ngMIZ6NkXcaQScRiYQh4o2rRwl9tNXX0doiBiz0jDkmbH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\nCJQFqgo+F5itA49iEgSwJONra+T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porUelKotIGAfYcS//A5SSv57N9seGNgQAwJ2Htf3eDOdYWvBp9bSPM82co7G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G9eXubcwmXuO/kAG41pvrIMflilRK1fsFCEiIbZpNIZinJ8cxXmV3NmahsEXkwYpuR2SBKk1SYn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\na1dSczslxEEUGscxzz///Lq5u9vdWJi+Poz22ugDi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cs7jn7XvpZABXUITKCOFiHCTiJl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\nSSQS2O127r777lPWVs6AoV6VJIm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H6kyXT6R8agkxRupJEUeJpiBFwn6ItNQwCgNDaUzTxDCMyt9AQAbspwz7jX2Y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\n81+VUubM1Hfv3r26z0+PQXbU3kd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GRnKEfYOEVDIaHUNpGIaBrusYhoGmaQNStQUEHCEM+5ffcUrJfx1g22MDH2JA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\n5W0EtYJhGFy6dAmXy1XzYGnLshg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EfYOE1DAaHUNpGIaBrusYhoGmaQOmagsIOEQY9i+/o5SS/9rLtkcEPsSAgN3H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QLAIdUGVq9Sak50k4XcNXgIE3Xk6B2jxOv3Ix0VOaWkKDUVBSXVrJQlJKvDxmu3E1Et9Ym0BUVF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\nUPqqsVnBYjZQZ7CgTkjEXwm2qDgyLuhZkrae9ds+Z/D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- "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "from mpl_toolkits.mplot3d import Axes3D\n", - "import matplotlib.pyplot as plt\n", - "import numpy as np\n", - "from matplotlib import cm\n", - "\n", - "from matplotlib import animation, rc\n", - "from IPython.display import HTML\n", - "font = {'family' : 'normal',\n", - " 'weight' : 'bold',\n", - " 'size' : 16}\n", - "import matplotlib\n", - "matplotlib.rc('font', **font)\n", - "plt.rc('xtick', labelsize=5) # fontsize of the tick labels\n", - "plt.rc('ytick', labelsize=5)\n", - "super_set = []\n", - "gen_vs_pop = ga_out_nsga['gen_vs_pop']\n", - "\n", - "length = len(gen_vs_pop)\n", - "\n", - "lims = []\n", - "for k,v in pop[0].dtc.attrs.items():\n", - " lims.append([np.min(explore_param[k]), np.max(explore_param[k])])\n", - "\n", - "x = 0\n", - "y = 1\n", - "z = 2\n", - "plt.clf()\n", - "from collections import OrderedDict\n", - "\n", - "\n", - "from sympy.combinatorics.graycode import GrayCode\n", - "a = GrayCode(5)\n", - "print(a)\n", - "pre_empt = list(a.generate_gray())\n", - "\n", - "for i, pop in enumerate(gen_vs_pop):\n", - " other_points = []\n", - " pf_points = []\n", - " hof_points = [] \n", - " labels = []\n", - " od = OrderedDict(pop[0].dtc.attrs)\n", - " for p in pop:\n", - " \n", - " xyz = []\n", - " for k in od.keys():\n", - " v = p.dtc.attrs[k]\n", - " xyz.append(v)\n", - " labels.append(k)\n", - " other_points.append(xyz)\n", - " best_xyz = []\n", - " \n", - " #for k,v in values(): \n", - " for k in od.keys():\n", - " v = ga_out_nsga['hof'][0].dtc.attrs[k]\n", - " best_xyz.append(v)\n", - " best_error = ga_out_nsga['hof'][0].dtc.get_ss()\n", - "\n", - " fig = plt.figure()\n", - " fig, ax = plt.subplots(1, 1)#, figsize=figsize)\n", - " ax = Axes3D(fig)\n", - " \n", - "\n", - " ax.set_xlim(lims[x])\n", - " ax.set_ylim(lims[y])\n", - " ax.set_zlim(lims[z])\n", - " \n", - " title='Particle Movement in 3D space, frame:' +str(i)#,\n", - " title_fontsize=\"large\"#,\n", - " text_fontsize=\"medium\"\n", - " ax.set_title(title, fontsize=title_fontsize)\n", - "\n", - "\n", - " errors = [ p.dtc.get_ss() for p in pop ]\n", - " xx = [ i[x] for i in other_points ]\n", - " yy = [ i[y] for i in other_points ]\n", - " zz = [ i[z] for i in other_points ]\n", - " if len(super_set) !=0 :\n", - " for ss in super_set:\n", - " ops, ers = ss\n", - " p0 = ax.scatter3D([i[0] for i in ops], [ i[1] for i in ops], [i[2] for i in ops], s=100, alpha=0.0925, c=ers, cmap='jet', marker='o', vmin=abs_min, vmax=abs_max)\n", - "\n", - " p1 = ax.scatter3D(xx, yy, zz, c=errors, cmap='jet', marker='o', s=100, vmin=abs_min, vmax=abs_max)\n", - " if i == length:\n", - " p2 = ax.scatter3D(best_xyz[x], best_xyz[y], best_xyz[z], c=best_error, cmap='jet', marker='o', s=100, vmin=abs_min, vmax=abs_max)\n", - "\n", - " cb = fig.colorbar(p1)\n", - " cb.set_label('summed scores')\n", - "\n", - " ax.set_xlabel(str(labels[x]))\n", - " ax.set_ylabel(str(labels[y]))\n", - " ax.set_zlabel(str(labels[z]))\n", - " for item in ([ax.xaxis.label, ax.yaxis.label, ax.zaxis.label]):\n", - " item.set_fontsize(20)\n", - " \n", - " #for item in ([ax.get_xticklabels() + ax.get_yticklabels() + ax.get_zticklabels()]):\n", - " # item.set_fontsize(10) \n", - " plt.savefig(str(i)+str('.png'))\n", - " super_set.append((other_points,errors)) \n", - " plt.show()\n", - " \n", - "# ls -v *.png >> sorted.txt \n", - "# convert -delay 100 -loop 0 @sorted.txt animation.mp4 \n", - " " - ] - }, - { - "cell_type": "code", - "execution_count": 22, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "GrayCode(5)\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/opt/conda/lib/python3.5/site-packages/matplotlib/font_manager.py:1297: UserWarning: findfont: Font family ['normal'] not found. Falling back to DejaVu Sans\n", - " (prop.get_family(), self.defaultFamily[fontext]))\n" - ] - }, - { - "data": { - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "image/png": 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zxaCpPCXCEbwAicxVzYQhu0Zvby+KiooCigszAahUKiGTySCTySRhKHHWM5FK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\ngpEOCl+dED4hfE7Gs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8Y6Q1FyFykFI5fqwVAOlrwW+y4xu3YNAcGrFMpIqihFSyCfcXqpqhjs5oRdf4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\ndhYvXszBgwe57LLLAHjuuee47bbbwrHkY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MP//88yUgn3nmmajtmqZJm80mV65cGd62e/duCchvf/vbMY992WWXGdqaW35+782l24eW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CuKScUSqVarIsLl5WVCoZDloNq9PONLJpNVkc68HSARZLSqEF+7EOJv2RlQ+Xt5vuYngBeFEBL4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\n0WgkJSWFzs5OAFV0IonL5WL37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SSy6RFosl9tuU8tPP8/rrr+81/pxzzpGA/OMf/5iwPRKJSIvFIleuXBnbtnPnTgnIb37z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PEGJ0+eJBaL8eDBg6IJFKXwd7QmZlxZloTGAek2H0oNJCFhIIH/JRPEXYNWJ2wnvnoK/fc6dvGM\nr5V955adhRKxl0t8aigjkUg4jvkpNdWZn2Je6Qad31JVIun29nZu3LixTaZQ6Bfd5/PlOOyrVIH5\n+XlGR0fx+Xym32Kh8yqVbHD27Nkcv9N6wuVy4fP5WFtbo6+vjyNHjpgV4fz8PNls1vS+zDdErhbq\n/NLtdnP9+nXH7eJyiTD/4UNV1GtrayUNtcuBk3BewzAIBAI0Nzfn3Hc4HDYTKMoVlzfg4kczQf7K\nE+e2O0XKpZH2uci6dM7oPr5bazarwVrASnz50obHCbvs1fnnwNPsO7fsHCptP6pf4FpdK98Eupqn\nTmvFpyzYVOJ6NQMsKmdOnc3ZTTBa3VmUVq0WG7ETLC8vb2tt5vtAqogcKxGqe6/Ufkud9ao2ci2Q\nT4TWSKPR0VG8Xi9tbW2Ew2Gam5urSqIvBrtwXmUtZ7VeU5mE6qHJmkChugfj4+NFg2obcPEhrYX3\naU2Mbq6zsZnkdINOQKwhXOskjE4aZW3s5PLzMes92LVXIRHoRWQkVaBdCPF1tgZU/k6B7/k48GUh\nhAS+xr5zS/1QTkKDFercS1V5b731luP0drvhFikl09PTrKyslJQplAuXy4Wmabz99ttIKc0BFqcO\nLKVg3ZStE4yTk5NsbGyY043ZbBafz1f3DUVVzJlMpujUpsvlMqsm9TpFhA8ePEDTNHNwo1wiXFxc\nZHZ21kyirxfyHz7W19cZHh6moaGBcDjMm2++ad53teeyxSClNI3cr1+/bpqc22USqgSK7u5uU1ye\nTqeJRCJmUK3VZUUNVgVw0WosEe68zaLXi9I4ezMxDoU36Ek04RUt6E1PYDRfBpfzhytrxadpWl1z\n//Y0JGhaXd57GOgCVopfnSjwi8AvFPiefeeWWqKU/ZiagMxmsznuLpX4deZXfCpNoVyZQrmIRCLE\n43HTMcYwjJo4sBSDGuWPRCKk02mTeDY2Nrh3715Bm7JaIR6PMzQ0tM3zshwo70ur04mVCHVdz6kI\nrYSqyDabzdpKM+oJVdlevXrVtNlStl2Li4vmuWytiVC1zq1DOoB5TqxQLJzX4/HkJFAol5W1tTUz\ngcJ7KMNy1xv49CaaZCdIg87lV2gLvY0uNCKuAB16L97o6+BuJHv4FkbjWUfvRdd1sxvxOIfQSinQ\ntcp+dldXVxkYGDD/fOvWLW7dumUuDVwFxoUQ7gLneJ8D/hbwb4BR9qc6649iBLaxscHIyAh9fX30\n9PRsE6NX6sJizforJ5oIyjt0t2oJGxsbTdKrlwOLFeoBQUqZQwBWu698m7JaidJVtVUru7NCRLix\nscHs7KyZotDU1MT8/Dw9PT2OybYaKKu1RCKxrbLNt+2ythgVESo7s0pCbtXwjJ2/aD6chPPmJ1Ck\ns0m+7f4jZMRDIp0mHZunLzlIc2KEdONhhNtNlhRhT4ZO4zjoMbzz/zeZ3p9GBo6X/X40TTOHj6LR\n6GPp0wmK+Cqr+Lq6urh9+3ahLx8BXgXGigyvvJetic7PVXQDedgnviIo1upUQmeVvG23KVdKfIZh\ncOfOHdxud9nRRIWy8qxIJBLcvXuXzs5Obty4wSuvvEI2mzWfwuu5KW9ubjI8PFz0bMvOrzPfnaW1\ntbWoFi8fyrNU07S6Vlt2RDg7O8vk5CR+v5+FhQUSiQQdHR0FY4FqhUwmw927d+no6OCJJ54o+e/q\n9/uLEqHH48mpCAsRoZLurKysVHxm6yScN+pdxuNz0ZxpQzQK2nySw+MTxNztZBMJkBK3x0PWt0aL\ncRCfuxn0JJ71/0b2yMfNa+gY/P/tnXl8VPW5/99nlux7IIEASdiXJKwBrVjFoparF0VrtaUWUepS\nbXu1t/dntbSit4u9V3t7rbUubbXaolVbEPUKiIqttYobhGxkJQkEQjIz2ZPZzvf3R/geZ4ZJMnsi\nnHfL6yVhZs73zEzOc57n+zyfT5uhky6lHwOQpaYywa2AsCMMyboJrUQQcuAbhaNCiNJRHtMBtEXq\ngHrgCwDfANbd3a2VzEpLS4e9sIQS+Do6Oujr6wvYBcLzWCOpY7S2ttLY2OjVwJKens6+ffsiKvXl\ni7wYtrW1UVJSErTxrac6i+csXkVFhVfnZVZW1in7bH19fZSXl2vltlhmW9K775xzziEuLk7TgLRa\nrRw+fBghhLZHGMlAKN3Jw+mQ9Q2EcmTlxIkT1NTU+A2EbrdbU9qJ5DjKSIGwmxMoqgGX04XZbCa1\nux6j0UhiYjKJoHUm21UHbbZWDPZ44uPiSO37GFfGMczJkzli6OADcy2DOAGBqtoYUFpwG21MVDtI\nFA7c+Vn0qheTxuc1H8kzESEUXM4x2998CLhVUZRdQoiw5bT0wBcAnv51jY2NtLe3U1JSMuqdXzDW\nRJ5jCklJSUEFPfi0ROqb0TidTiorK1EURZsllKWkefPmAXhlVXa73SurCqfTUna4JiUlUVpaGvbF\n0N8sngwm0lpH7rMNDg7S2tpKUVFRTEtTwwlM+2pAulwuOjs7sdlsNDY2AmjlxUAd0z3xnAtcvHhx\nRG9gfEdWPANhbW0tMHTeubm5zJo1K6r+iJ6B0Gww09/fj8thHHJS6LbiVuIQqkAAigImkxmUeLIn\nTiTOnYLdbsfZZ6G++kOakpI4PLuNNFWQbE7A5LLRZjhKv8mIiwT6jdOYaT+B2dROq+k5Wg0txPdM\nOWNLnWNMJlAMVCqK8jqndnUKIcQ9gb6YHvhGwHOAfXBwkA8++ICsrKyAm0wCbW7xHVMIdhhdrtH3\nWFKua8aMGUyaNGnYBhbfrEoOdh85ckTrXvTXtDEScp9n1qxZw7rUh4u/zkvZMepwOLQSo1x7tJtK\n5DkHIjAtGzc8jWJtNpvm6wdo5zZaIJTZlqIoIc0FBotnIJQydlOnTsXhcPDhhx96fS4ZGRlRWY+q\nqpxo6MY1yUHe5BlDXcimeAyK1O0UJ/+vIhAYXHGgQEJCPAYlhekFRj7JKCddHcToduFw9NFtGGDA\nlIpJNWJS3DgUE8fMmeQ7BklQBAPKP2mYuYSU98/QUicKqnvMQsYPPP7bX3eSAPTAFymEEFgsFtra\n2li2bFlQclZSPHqk1x5uTCFYdQjPuTzp5Waz2Vi6dCkJCQkBN7BIn7aMjAxmzJhxStOGEEK7qGVm\nZp5yUZPHlnufsZzNGxgYoKGhQdtHdLvdp2RVI609VOSsmsViYcmSJSHJWZlMJi97HKfTSWdnpxYI\nFUXxygjl2mX3ZF5eXkzLuTLDPH78uPYdkzgcDjo7O+no6KCuri7igdBut1NWVkbupBko6SdwCwcm\n4ulPLSTNWj4UBFFAAQcDJIkMTEocQgiEexC3oZv65L/hYBoZhjQUg4Li7qUjLoU44QZlAIeagFGo\n9BjiGMSICYUUZy/GvMMkZk0K9+1j586d/Od//ic2m42///3vMRNuCAsBRGePb/RDCxHRMoIe+EbA\n5XLx8ccfYzKZNC3KYBhpj09esDIyMk7JIANpVPF3LFVVtQaWCRMmsHz58lOyvGAvjL5NGzIzsVgs\n1NfXYzAYvMqilZWVTJgwgaVLl8Z0yLe1tZWWlhaKioq0GwjfrEoGE2k6qiiKl85oKBdkp9NJeXk5\nycnJER0MN5vNpwRCm83mFUzi4+Pp6uqiqKgopm70qqpSVVUFwNKlS0953+Li4rxk7fyt3XN/M5hM\nXO6vyz3MVJeRQ+a3iFdVSJ6KKy4Vo7MXlzkZl+LAiIl0dcqnaxw4TudklR5jDgaMIFSEcOHAgarE\ngQAFFbPBiZsknBiwG8wkuAbp6eknLvEEtUYHVVVVYYlIXHjhhaxZs4bly5djsVg+I4FPGbPAF2n0\nwDcCJpOJWbNmYTabOXToUNDP9xf45JhCU1MT8+fP9zumIJ8XzEVUURSOHz9OR0eH1kYeDQsh38xE\n7vc0NjZqyiAwdIGKpJPAcMgRCcDLYcAfwwWTUAW3/QlMRwuz2awFEyEEdXV1dHR0kJ2dTW1trZZV\nhRPEA0GKW0+aNCng8QzPtcOn77un63kgZV05kyh1ZAGyxDTmOy+k0fgevQYrDflnU9i4C4O9m7i4\nPDJFASbiwN2P4mjDkZrD4AQ7RkPCyd8LA6hu3B4OfwIDBly4UTmZOGKOMxOfmY3LZcecHsePfvQj\nampqeOeddwLe89u6dStbt24FYPXq1TQ3N3P11VczZ05wc4VjhgBcp4dijR74RkCWl+x2e9AKLHDq\nHp/D4aCyshKj0agppfhDBr5A99OcTqc2l7dixQqvBpZoyysZjUYsFgtms5nzzjsPt9t9ipOAzBhD\nVeQfjp6eHi8XiWDxvSBLOyBfwW1f8edgBKYjjdPppKKiguTkZM466yxtTbK86BvERxtBCAbZMRqu\nSa6/QOhZ1gVOKY02NDTQ3d3tV20nQ+Sx2LWOHqUdu7kXCs4hveNj4rsPMuRiAxiTcedeTX92L4ph\nH1lCRRiMKAIwGP1IfiiACgjiVQcIAwacODGzbN5Svnn9xqCrMuvXr2f9+vUA/PnPf+ZHP/oRy5Yt\n4/zzz2fFihXBvo1jQ/CXwYigKMrQhzECQghduSWShDKWIJ8nA6Z0U5g5c6bWJh6J41ksFqqrq0lN\nTdUG6KOtwCLp6emhsrKSqVOnasc2m82aXJanRNnhw4c11QtPrc5QrZ6OHj3K0aNHIyr/FRcX59XG\nLzUvPYO41LyMj4+PSSOJJzLQT58+/ZSuX9/yom/nZbj2UUeOHKG1tTXiHaPgPxOXgbC+vp6BgQES\nExOZMWPGsK+hYCBN5ILIBfNMmLwCZ04vitMGihERlwuKEQyvIhBMQJAkBINAAgbiDSkowj3UDTr0\ngtgxk+ToI8l40vdPdXCkJ528qkH4XHgGstdccw3XXHNNyM8fEwRjFviA+zg18GUDFwPxDCm7BIwe\n+AIgULdyX2Tgq6qq0hQ0Aml8CMSM1nOAftmyZZqQciwUWDwzHs89NV883calR1tfX5/Wednf36/N\nEAbqku7rzB7NwDOc5qU0I5VD4uH4ywWKVJ4JdBZyuBEEz2w2EJkyVVU5dOgQbrc7ZoFeBsLU1FQ6\nOzuZOXMmCQkJmsktENgMpDEFYfT+bprV6QwY92IASlUX7xjMqAiSzFmkOo/SbYrHJASDxIFbZZro\nwWg0oDh7OOI2kty5gG9ef3OU34FxyhgGPiHEFn8/VxTFCLwMdAXzenrgG4VwSoX9/f1YrVYmTJgQ\n1Eb4aBlfX1+fZiJaWlqqDaPX1tbS0tKi3dlHo5VclmsTEhKCvhAqikJKSgopKSmnKLN4msPKQOg7\nkC4znmCc2SOFDDxLliwhJSXFbzablJSkrT3UbNYXVVWpqanBbreHpTzjGwj9yZTJ7420BJLBfeLE\nieTn58e0WUmWVT09A2VG6DkDKcUA5PzmaGIAZjEHg0hFpY9skvm86mS/wUSnIR7MeahqF70GhRRX\nP1OdnSSJQQaFg3anguo6h+8tvs1jN/AMQwDOsV6EN0IIt6IojwAPA78M9Hl64IsCckyhra2NpKQk\nCgoC1wWE4QOfzLRk92JaWprWwCK7Q2WZSHbQeXZlBmMM649Iz+b5U2bxnCF0u93anX1/fz8nTpwI\nWv0lXIYTmB4um7XZbFo2G67gtgw8EyZMYO7cuRENPMPJlEkrI0VRGBwcpLCwcFiH9mjR2trKkSNH\nhi2r+puB9AyE8vdBZrSegVDBRKprPV3mJxDCRSZpXKA66UKhB4We/m6MThu9CZn0GqHNmUBlucLF\nBddz8bRVGIju9oFOSMQDQW0664EvwviOKbz//vtBv4Y/M1qpghIXFzdiA4vvfondbtfMVauqqkJq\nNpGKNZ2dnSHPqQWCvxlCi8VCXV0dTqeThIQEbSA9WoPRnsjPMhA3B89sNhKC2zabjerq6rAbSQLF\nMxC2trbS3NxMYWEhfX197Nu3j7i4uKBc3kNBCEFtbS0DAwNBVRN8A6Hn/KanYPingXAW6c5b6DNu\nx2VoBaGQJATOThsp7gVMS/sRBpHOw796mF27dvHCCy9or31GI4CI+J8Hj6Io+c5in4UAACAASURB\nVH5+HMeQmsv9wLAK2P7QA98oyIudoigjamFK13WphxmIm8Jw+GZ8soFl1qxZ5OTkaLN5vhYv/oiP\nj/fap5Ll18bGRk13MCsri+zsbL8X44GBASoqKsjKyor5bF5fXx8NDQ2a8oy/bDacho2RaG9vp66u\nLiCHAX+MJLhdVVXlJQ2XlZWlDfoLIWhpaaGtrS2qNxn+kGVVh8PB8uXLvQKPb6NPfHy8FsQjEQhd\nLhcHDx4kLS2NhQsXhvU9ky4OKdlmsgxGVLeKs9NIt6VbC4Tp6elkZq0nNasfl2inrraeyROWMDln\nHk6nk9v//XacTic7d+6MqQjDuGfsmlsO47+rUwHqgduCeTE98AWIdGjwZzjqOaYQqJvCSMjAJy9E\nvb29LFu2jPj4+LAthHzLc729vV7uB3KvJCsrC5vNRkNDg9c+SyyQF//jx497lTZ9s1nfhg3PrCQt\nLS2k90cqz8j3PFSndV+GK+vabDbKy8txOp2kpaXR19en7Z9GuyvXE+nokJ2d7bes6tvoMzAwcErH\nq8yogn3vpehCYWFh0Bq1/higk0bT37AaGlBQECYgV5A5cTrF7vOIc6V9qujTMCQKP3FiEX976wCL\nFsGdd97JqlWruOuuu2L6GYx7xrar8wZODXyDQBPwwQh2Rn7RA1+ADLfvFsyYQjDHGhgY4P3332fy\n5MnMnTs3bAUWf3hmJQUFBdrFuKOjg5qaGtxut5ZpuVyumBioSlHtQMYFfBs2fLOSxMTEoLouhxOY\njgaeZd3p06fT29vLgQMHSE5Oxm6388EHHwxrbBtppBrK7NmzAy7pJSYmkpiYqM1PykDY0tJCT0+P\nVlKXGeFw76XcN5Z71uEyoNg4YH4eN3YSRSbKyT05gUqnoZkDhudYyNVkZ2dr869nnXUWg4ODPPbY\nY9xzzz2YTCbmzJnDjh07WLduXdhrOm0Y267OpyL5enrgG4XhPPlk00NfX9+oYwrBDLoKITRVi6VL\nl5KamhoVBRZ/GAwGTCYTFotFKy9Knc7GxkYvia+MjIyI3w1LJRR/c2qB4JmVeHZd+pZ1/TWbBCMw\nHWlkWbWkpES7+PsztvXUGY3UTcixY8dobm72UkMJBc9AKIRgcHAQq9VKc3MzPT09JCYmat8deRMi\ns/pIarrWmfag4iRReG81KBhIFBkMKt3UGHeTWrOC7q6hUSCTyUR5eTkfffQRf/7zn1m8eDHvv/++\n5jyhc5IxDHyKohgAgxDC5fGzLzK0x/emEOKTYF5PD3wB4pnxyTvkvLy8UccU5AxgIBv1DoeD8vJy\nhBDk5eWRmpoaMwUWz6Fwz9k8TysdT4mvmpoaL2WTUEuL8tjSsy9SSigjzRB6NpvIjtGurq6Y76kJ\nITRBb9+yqj+NVH+C26E2+kiHdtlIEslsXlEUEhMTNR9EeRMiuy57e3u1bYN58+ZFrJzcr1joUo6S\nKIa/cYlTUzjWV4eB6SxatAJFUXj++ef59a9/zY4dOygsLARg1apVrFq1KiLrOm0Y21Lns4Ad2ACg\nKMotwCMn/82pKMqlQog9gb6YHvgCxGQy4XQ6tTGFQPz45PM8HZyHo729nZqaGmbPno3BYKCjoyPg\nBpZwkeXFuLi4EYfCfaWm5F29LG/J0mJWVhbJyckBBUIpwZWYmBgRz77h8DdDKC11VFXFZDLR0NAw\n7AxhpHE6nVozx5IlS0Z9r4YT3PZ1QAhEq1PeYGVkZITdSBIInjchEydOpKysjOzsbJKSkmhubtZm\nIOX6A/3u+NKjDBl0Dzdn51aHuoRNqSZyZiWDC37+85+zb98+Xn/99ZjuY39mGbvAdzZwp8ff/wP4\nLfDvwOMM2RbpgS9SyF9AqWCRk5MTsB8ffKreMtyF1NOAtrS0lLi4OPr6+ujo6KCnp0fruIyWMohs\nm58xY0bQ5cWEhATy8vK08pbsGK2vrw9IlaWzs1M7drRFnn3p7u7WbjRkp6yvqa2nDVAk99i6u7up\nrKxk5syZIc9Djia47euSLr+vUgQgnGOHSm9vL+Xl5V7Hltl4f3+/1kzV19eniQFkZmYGHAgFw6sd\nOV1OrBbLkHh7Yh/2ATs333IzaWlpvPzyy1HdQz1tGNsB9hzgKICiKLOA6cDDQogeRVGeBLYG82J6\n4BsF6aZw/PhxpkyZErSS+kgqLD09PZSXl5OXl8fcuXMRQuByuYiPj+fss8/WMipPncvs7Gxtjyrc\n82psbMRqtUZEf1FRFJKTk0lOTtbm2HxVWTzVNY4dO0Z7ezuLFi2KuPbjSAwnMO3P1FbqRUaitCiR\n9kmRHsT3zcblQPqxY8e0jlez2Ux3dzeLFi2KmL5poLS3t1NfX+9XW9Xzu+MZCKVotdyflZ/PcIHQ\nd19PMjg4SFdXJ1lZ2ZjNZjodnfzgjvv4fOml3H777TEd0dEJmW6GtDkBVgEdQoiyk393A0HtUeiB\nbxS6urq0Zo9QGM6aqLm5mdbWVu1C4K+BxXefxHP0QM6BZWdnk5mZGVRpbnBwkIqKCjIyMiLqIeeJ\nv/b9rq4u2tvbqaysxGAwMGnSJPr6+oiLi4uJBqTU+TQajaN2jMpZMM/9zXBmCGXFwOVyRV1jFLwH\n0oUQVFdX09XVRVpaGuXl5cTHx3sNpEfr4i+EoKmpSWvWCuR76u8mSqriyGqCP7HzNDGZBJGGkwHM\nDN1M9fb10t/Xz4QJE4ecRLpP8O4/3mPD2ttYd/kVUTnn05YxHGAH3gW+ryiKC7gd+D+Pf5sFHAnm\nxRQhRnR6iAYxP2A4CCFwOBwcO3aMgYGBoANgdXU1EydO1C6gsmU+KSmJOXPmaCotwTaweJbmrFar\n5ow+WkZy4sQJ6uvrx6R7Ueovzpo1i8zMTC2jstlsUR1Gh6EyW0VFBdOmTQvJwsgXOUNotVrp6uoi\nLi5OuxD7NvpID7vc3NyYy39Jo9y0tDRmzJihHVt2vNpsNq/92UgKbrvdbqqqqrTxgEh9pp6NSjab\nzUsezjChl/q0ncSLJHo77bjdbrKyslAUhaNtzVTUf8JFubfy+Xn/GpG1nEko00vh3qAEUjSWPVTK\nhx/6f66iKB8JIUpHPLaizAZeZSjINQAXCiEOn/y3N4EmIcT1ga5HD3yjIANfe3s7Npst6FJnbW0t\n6enp5OTkaPsvc+bMYcKECREdU5DO6PJi4NtxKd0cBgcHWbBgQdSbNzwRQnD48GE6OjooLi72W9qU\ngcRisdDd3a0Fkkjsb0qB6UhaGPkiy9JWq9UrkBiNxhFNh6OJ3FMbbQ/Vc4/NarV6lRZDFdy22+2U\nlZUxadIkpk2bFu6pjIinPJzVasVmPEzPtDKM8YK4uHgSE5I4crSZhtpmvrroLopyVkZ1PacrSmEp\n/CjEwPdIeIHP47HZQgiLz89KgONCiPZA16OXOgMkVE8+k8mkKbsMDg6yfPlyzGZzxMcUfJ3RpUbn\nkSNH6OzsxOFwkJ2drTnKxwrZQZiamjqiGonvMHokfPyGE5iOBv4aferq6rDZbMTFxdHS0kJfX1/I\ngtXBcuLECRoaGgIK9v722MIR3JbjPrGqKngKMeTk5HDggJ1ptiIGk9v440uP0tR8mPb6Qa5dcytJ\ns/KCNpDVOcnYjjMMLcEn6J382cFgX0cPfAHiO8AeKE6nk6amJmbMmMG8efO0BhYIz/JoNOQej1Rj\nKSoqwm63a3sk/nQiI43sGA3FzcF3f1OWtqSI8WjrD0ZgOtK43W7q6uqIj4/nvPPOQ1GUU2YI09LS\ntEASzOzgAN0cNu2nw9CCIhRy1RkUuBcRd3JPSwgxolt5IAwnuO07A+lPcPv48eM0NTWFPRAfCjLg\nzps37+R85hSO/k1hRv75/HDz1/jb3/7GHXfcwbPPPhvThqrTijEOfJFCL3UGgN1up6+vj9raWhYv\nXhzQc+SmflNTE5MmTWLOnDkxU2CBoYAr91fmzp3rteenqqrWcWm1WnG5XFrrfiRUQTw7RouLiyM+\nFO67fqfT6bX+zs7OsASmw0F6JY7kGSiE0OyXbDabV8frcI1KAkGV6e9UmvYiEB6zagIFA8uc/8pU\newnl5eUkJycza9asqDasSJ1Rq9WKw+EgNTVVM0JetGhRTOTtPDlx4gSNjY2UlJSQlJTE8ePHufba\na9mwYQM333yznuFFACW/FL4XYqnz6ciUOiOFHvgCwOFwaA0KpaWjfz6Dg4OUl5eTkpJCWloa/f39\nFBYWxkSBBT6djyssLAxIP9Szdd9ms6EoipZNBdtoYrfbqaio0JopYiHyK+W9LBYLx44dw+12M3ny\nZCZOnBgT+yJJW1sbjY2NFBUVkZqaGvDzPBuVbDab1wyhvBE5ZPwHZebXMRGPAe/zcePCJexkHlxE\nSdo5EdOMDRSn08mBAwcQQmAwGHA6naMG8kghbzCtVislJSWYzWbKy8u58cYb+a//+i+++MUvRu3Y\nZxrKtFK4I8TAt3V8BT691BkgUoFlNNra2qirq2Pu3LlkZ2djsVhobGzEbDYPa/0TKTybSIKZj/Nt\n3ZeNJseOHaO6uloTHM7Ozh5xmFjqXc6ZM0d7rVhgNBpJTk6msbGRadOmMXXqVGw2G+3t7dTW1vp1\nF48kvo4OwZYXR5shVA0uDq/YhcllxmA04CtMIpzgcLnoX1BPriu2osqypDx16lStW9YzkLe0tOB2\nu6MiuK2qKtXV1QAsXrwYg8HA7t272bJlC1u3bqWoqCgix5Hs3buXjRs3UlhYyAsvvMC3v/1tGhoa\n6O3tpbKyEoCnnnqK+++/nylTpvDGG29E9Phjzjh0YA8VPfAFiD9zWE9cLheHDh3SvMxkA0taWhpF\nRUVe1j/ybj4rKytiFwE5m5eenh62pY2/RhOLxaINE6ekpGiBMCEhQdtXirZR7XD4E5j2Heb2NeOV\nQgChymNJpJ1PJB0dfG9E6sVHNBlBdQr67f2AgslkxGg04Xa7cLvdJCek4jQM0K42kaNOD3sNgSDH\nU3xtq4YL5NIYVgjhZQwbSlnU6XRSVlbGhAkTyM8f8ih9/PHH+etf/8ru3bujpgRkNpsxGo0YDAae\ne+45Hn74YWw2m/bvUmIwLS0Nu92ue/mNU/TAFwCjlSe7urq0GTHZjOFpIeRr/SPLck1NTQBaEAzV\n8UCq+0cr00pMTGTq1KleHn4Wi4XKykrsdru2x1ZSUjImYxIWi2XEgDucGa9vIA9WEaerq4vKysqg\n7HxCod/ciWJUiDcOnZ9Qh75fg4MDCAFGowGX04Uwu+nFRg7RD3xS0DyQGx3fQB6u4HZ/fz9lZWXa\nmIbL5eKuu+7CYrGwa9euiFZVtm7dytatQ2pY5557LrW1taxdu5Y333yTL3/5yzzxxBPs2rVLe/w1\n11zDddddR0lJCWVlZSxfvjxiaxlzxnaAPaLogS8M5IW3ra1N62IbrYHF925Yaiy2tbVRU1NDXFyc\ndpEYLRuRs3n9/f0RNU0dCc/W8dTUVA4dOkR+fj4ul0vb58nMzCQ7O3tUseRwkIPZKSkpQavPjGTG\n6+uM7rfR5KSTRWtra0Tk3kbD6PNrKhA4nQ7i4+Mxmc0IVcXlcuN0uamrqaOn71MxgHAzWl+EENTU\n1GC320dVvxmOcAS3Zaew9O/r6enh+uuvp7S0lF/96lcRL2OvX7+e9evXA/Dyyy+zcuVKenp6WLly\nJW+++Sbz5s1j0qRJ7N+/nx07djBp0iR+//vfk5KSEvFS67hA7+oMmc9cc4vT6URVVd59913OOecc\n4FM1jrS0NGbPno2iKBGZzZNlRTlIPJzQc19fHxUVFdqAcCy71lRVpaGhga6uLoqLi73KOTKQW61W\nOjs7tUH67OzsiEljSd++aIhby/EP2THq22iiKArV1dUIIZg/f35MGmcsSgtvxT+JiXjcLjf2QTuJ\niQkYPI4tELgY5F8G/w36zF7D6MHM4I2EvNlIT09n+vTpUfvOyT1mm82mfYcyMzNRVRWLxcKiRYtI\nSEjgyJEjfO1rX+Pb3/42X//61/XOzSij5JXCN0Jsbvk/vbnlM4f8hVIUBVVVNdmvefPmkZWVhaqq\nuN3uiFgI+ZYVe3p6sFgsVFRUaCVFRVGwWq0Rc60OBrmXmJmZydKlS0+52AxnXSQNSZOSkryEtoO5\nWA0nMB1JPJ3RZ8yY4dVoImcgMzIyKCgoiNmFNktMJUXNotN9AuE0DA3wG7yP7WSAyepsksmAZLyG\n0aWqiecMoQzkge7HyjGNUE2Cg8F3j3lwcJDq6mp6enowmUxs2LCB3Nxc3n77bR577DFWr14d1fXo\nnGQcDLBHCj3jCwCXa6iB4P333ycxMRFVVSkqKtI6PWM1pmC32zl48CAOh0NzS/eUJYv26EBHRwe1\ntbUhK3J4DqJbrVbtIiwD4UilWk+Bad+5xFhgsVg0GyO3261pdEbKjHck3G43H9W9S+PcN1ESBHEk\noDD0WauoOBkgSWRwof0bJDDyGIW/GcjRRg/kuRcXFwc1phEJ3G635tc4a9YsYKhz8umnn2batGnU\n1taSn5/PX/7yl5juL5+JKJNK4eshZnxv6RnfZ5Kuri56enrIycmhoKAgZgosnsevqqryGox2OBxY\nLBatWzExMVELIqHoKw6HbNfv6ekJay/R1wzWs6woPfBkSS4zM1MLbpEWmA4GOYxvs9lYunSpVtb1\nzEY8zXilj1ykPoNPxwVmskAposy9h1ZjtRb4QDDdtZQS14UkMLrNkcFgID09XStX+hs98PQhPHbs\nGG1tbV7nHiuk3mdeXh5TpkxBVVUeeugh3njjDV577TXt5qulpUUPerHgNGpu0TO+AGhububw4cOa\nyrxsYolFwJMDuu3t7RQVFQ1b3pP6kHJ/MJhsaiTkMH52djaFhYVRPV+32+0ltG00GjGbzfT29rJw\n4cKYZxvSHT4pKYlZs2aNmlF7+shZrdaAzHhHQmZavgo0g/TRY+gAIF3N0eTKIoEs7UoxAIC8vLyo\nNyv5Ig1z58yZQ1ZWFg6Hg+9+97sIIXjsscf0QDcGKLmlcE2IGd+74yvj0wNfAAwMDABQWVlJTk6O\n1uQQi9JmRUUFKSkpAV14PZHZlAyEqqp6jU0EcgGTYxJS+zCWSEub/v5+kpKSvBwPotGt6It0Nghn\nT8vTjFdKewWiaCL9Gtvb2ykpKYl5piVnEydMmMDkyZO1Pc6uri7N2T2a5XVpWivNem02Gxs2bOCi\niy7i//2//xfxY1osFr70pS9hMBh46qmn+MlPfsKHH37It7/9bTZu3AjA7t27ueuuu5g6dSrbt28/\nIxtplJxSuCrEwLdvfAU+vdQZAGazGYfDwcSJEzl8+DB1dXVkZGRoJrDRuAuWd/uhzuZ5NmnMnDlT\nsy3yVDOR2aBvt6WqqtTV1dHX1xezMQlPPAWmi4qKUBQFIYTm2CCbTMLJpkbi+PHjHD58OGwbo+HM\neGWzj6qqp5R2ZcA3Go1RMwkeCRnwZ82apY0b+HN2b21tpbq6WrOPipShrQz40rS2sbGRDRs28P3v\nf5+rrroqKgHn2Wef5fDhwxQUFGA2m3n33Xd56623uPDCC7XA98gjj/DYY4+xZcsWDhw4ELBmr874\nRA98AbB582YGBgZYvXo1K1euJC4uTvOOq6+vx2QyDRtEgkUGnd7e3ojuq/jaFsm9qaamJs32Jzs7\nm6SkJOrq6pg4caI2phFLZJbpW95TFOWU+TuZTXl2vIYji+XpWRgNGyPPGU7PmxH5PYKhwDJp0qSg\nM/xIIK2MZKblD09ndxi6SbHZbF5du/IzCCYrV1WVmpoa3G43S5YswWAw8O677/Ld736XJ554grPO\nOiti5wneg+mrV69m3bohqbc9e/Zojxlu7WditgecVpJleqkzAHp6enjrrbfYvXs37777LllZWXzh\nC19g9erVFBUV4XA4sFqtWCwWent7SUlJ0QJhMJlIf38/5eXlWgNNrH7BZMt7U1MTJ06cID4+Xpu9\ni6S24kh46l0WFRUFnWVKoWpZVlQUxUsNZLQgIjtmY7GX6Q+bzUZVVRWTJk1icHAw4ma8IyGFGGw2\nmyb0HOrryD1Om80WsCqO0+nUZN8KCwsBeP755/nNb37DCy+8QEFBQainFhDNzc1ceeWVCCF46aWX\nuPfee/n444+57bbbyMvLo7m5mfz8fO6++26mTJnCjh07zsjgp0wohctCLHWWja9Spx74gkTuv+ze\nvZvdu3dTVVXFwoUL+cIXvsAXvvAFcnJy6Ovrw2KxYLFYcDqdmpLJSGVR6RI+f/78mFvpyExnYGCA\nBQsWYDKZtAYH6dbgqcYS6UzEbrdTXl5OVlZWxIKOHKS3WCx0dXWNGESk5mSsxbVh6PvU0tJCW1sb\nJSUlXjdKsrRrtVq9zHgjaWbrdruprKzEbDYzZ86ciH62nqo4VquVwcHBU3wUBwYGKCsro7CwkNzc\nXFRV5f777+fjjz/mueeei/mcqs7wKNmlcGmIga9yxMB3FDgBNAkhrgh9hYGjB74wcbvdfPTRR+ze\nvZs9e/bQ29vLueeey+rVqznnnHOIi4vzCiJy9k4qmbjdbi8lkFj7mA0MDGhZZn5+vt+LqdPp1DLa\nrq6uiIo8+xOYjgaytCuzchlE5M9LSkpibk4qP3uA+fPnjxh0hpuBDMdMWKoPSbPeaOM7Qzg4OIjT\n6WTKlCmkp6eTlpbGrbfeyoQJE/jlL38Z898FnZFRskvhi6EFvvx/FHiZUd90003cdNNNQ6+rKB8B\nq4GDQoj8CCx1VPTAF2F6enrYu3cvu3bt8lsW9QwinZ2dOJ1OcnJymDlzZsxdDeSeTrBZpixnWSwW\nrclEBsJAL8CeAtPRMKsd7djd3d1UV1fjcDgwmUykp6drWXksmnlk0Jk0aVJIDvGjmfGOVq6UAtvR\nvuEYDunUXlBQQENDA9/97nfp6Ohg3rx5/Pu//zvnnXdezMdXdEZGySqF1SFmfI0jZnz7gWPAfwkh\n9oa8wCDQA18UGa4sesEFF1BZWUl8fDy33HKLFkgcDodXl1+07nhlI4HdbmfBggVh7eF5uolbLBZt\nAHqk0q6nwPTMmTNj3sTR39+vecjJwWjf0Q9Z2o2Gka0srYYzJnJU6eSluHc4bmhAoKKSwuyeBSxp\nycbe3gUM73ggg87ChQtjnuVKC6vu7m5KSkowmUxUV1dzww03cPfdd5Oens6bb75Jf38/v/rVr2K6\nNp2RUTJL4YIQA1/ziIGvHbAD9cDFQghHyIsMED3wxRC3282ePXv4t3/7N8xmMwkJCaxcuXLYsqi0\nc4mkHJZsoImWuLUcQpcZre85dHd3R01gOhCG6xr1RHZbSqFtT2m41NTUsAL1kSNHaG1tZeHChSFn\nua+Z9vGJ+Q0EYFRUYOiXSggDPWSwafArFDhSThELz8zMpL+/H6fTqQWdWCL3E+Pi4pgzZw6KorB3\n717uuusu/vCHP+gjAuMcJaMUzgsx8LXqzS1nbOATQrBu3TpuvvlmLrnkkqDKoj09PdrIQbC+cZK2\ntjYaGxtj2kAjTWBlNuV2uykoKGDSpElREZkeDplpSEeJYMqZ8hysVivd3d0hScNJt3BVVcNydfjQ\nWM3uuG0YTgY8X9wo9Ios7h64gVQ+PUcpMi1/3yO5TxsIDoeDsrIycnNzmTZtGkIInn76aZ555hle\nfPHFmEvR6QSPkl4KK0MMfCf0wHfGBj5AE7T29/PRukWlJJnFYsHhcHiVFEe6e3e73dTU1OBwOMIu\nbYaCFJg2GAxMmzZNy2oHBwe1vbVIutH7IkurqampzJw5M6yL/HCyZCPtccpRiZycnLCz7J8n/gqh\ndI/4GFUozHJeytWuRQBa52R+fj6TJ0/2EgPwtC4KxYw3EHyH4t1uN1u2bKGxsZFnnnlm2JnBcPBV\nY7n22ms5evQoP/nJT/jKV74CwJYtW9i+fTtFRUX86U9/ivgaTjdOp8Cnt03FmJGGYgsKCrjxxhu5\n8cYbvbpFb7jhBr/dotLJvbGxEYPBoHWLepZFpW+f7NyL9fyRP4HptLQ0TaTaU8nE08Q2VDd6X7q7\nu6msrIxYaVVRFJKTk0lOTtYyF7nHWV5ero2vyH3avr4+KisrIzIqcUJpx00Po74riuAT88dc7Vqk\nGbd6lnb9iQF4mvHKG5KRzHgDxWKxUFtbq6ng9PX1cdNNNzF79mxeeOGFqGl/+qqxvPnmmzz66KMc\nPHhQC3wGgwG32x2VwHtaog+wh8UZnfGFymhlUZfLpZUTu7u7SU5Oxmg0aqW9sZiHkrOJgUp/+ZrY\nhjvA3draSktLC8XFxTG7uHn697W1teFwOMjLyyM3NzfsGchyQxXb47djGqbM6UmvSOObdVdy9OjR\noPcTRzPjDXRvUHonLlq0iLi4OI4fP87XvvY1brjhBr7xjW9E/CbMV42lqakJgGXLlpGfn8/PfvYz\nXnjhBa1bdHBwUJPuq62t9Wq31zkVJa0USkPM+LrHV8anB77PIKOVRZOSknj++ec1BRTPVvesrKyo\nNzXI0qrT6dQG4kNBluMsFotWjgtEEUdVVQ4dOhT28UNFds06HA7mzJmjZbVdXV2aKk5WVlbQwbze\n0MCz8X8OKPD1uDO55oNzKSoqCjur8gzmnoIGw6niCCG8SutGo5GDBw9y00038eCDD3LhhReGtZ5A\n8FVjmT17NsXFxaxdu5YVK1bQ3NzMsWPHePnll5k8efIZq8YSDEpqKSwJMfD164FPD3wRxrMsumPH\nDpqamjj33HO57rrrWLlyJfHx8acosWRnZ59SFo0EngLTkSytynKcpyKOvz1OOR830kB+NJHOBtnZ\n2X5l53zVWILZW3Pg4MHEB1FGCXxuYSC5ewH/ZrosKucvm678mfEmJSVRUVFBamoqM2bMQFEUdu7c\nyX333cfWrVtZsGBBxNejExuUlFJYGGLgc4yvwKfv8Z0GGI1GVqxYQWtrK9u2bWPbtm1YLBZ27drF\nli1bvMqipaWluFwuzfy1u7ubpKQkLRCG09gQyKhAqCiKQmpqKqmpqRQWq5zHFgAAGiBJREFUFnpl\nIY2NjSiKQmJiIp2dncyfPz/m0mMwtJ9YUVHB7NmzNWcDXxITE5kyZQpTpkzx2lurqqrysi3y1+wT\nRxwz3POpN1ZiUEa6f1RYH7caRUQn6JvNZnJzc08x4z18+DAdHR2kpKTQ0tJCd3c37733Hjt27GD3\n7t1jMr6iE0H0Pb6w0DO+KFFXV0dubq6X4kUg3aIDAwNaJmW3271UTALptAxXYDpc5KjC8ePHSUtL\no7e3V/Puk44T0c78jh07RnNzMyUlJSGPaXg2+1itVq3Zx3MI3YGDRxKeoF/pOiX4CQEqBj7nuJQv\nuBdG4rQCRgb9efPmERcXx3PPPceTTz5JU1MTa9as4eKLL2bt2rVjohKjExmUpFKYF2LGZxhfGZ8e\n+M4wRtMWlWVRT5cDz25R3/2caAhMB4PL5fIaijYYDH5HDqSuZXZ2dkQDs6eVUVFRUUT3Ez1ti+QQ\nelZWFqnZqezNfo86UzluBAIFAyomkcW/OL5IiTo9YmsIhLa2Ng4fPqwpwXR3d3P99ddz1llnsXnz\nZsrKynjjjTe49NJL9VLnZxglqRRmhRj44vTApwe+cUQg3aKe+znSby07O5vBwcGYCEwPR19fH+Xl\n5V6jEv6QupaeQ/SRkCRzOByUl5eTkZHB9OnTox70BwcHtUDY09NDXFIcHfEWSDCyIn8pmYbQ5M9C\nxZ+dUUtLC9deey23334769ev1xtGTiOUxFKYHmLgS9IDnx74ximBlkU7OjpoaWnRXOlzc3Nj5tsn\nkQLbRUVFQYsZe0qS2Ww2LZOSjhmBXKx7enqoqKhg5syZY9IGb7fb2b9/PwkJCdpAerhuDcGgqqom\nSjB37lwMBgMffvgh3/rWt/j1r3/N5z//+ageXyf26IEvPPTA9xnBX1m0tLSUjz76iPXr17Np0yav\nTGq0smgk8NxPLC4ujkiwlc0ZVqtVk4aT5+Gv2ef48eMcPnx4RKfyaCKD7uzZs7UmHs+s1maz4XK5\nQpq9CwSn00lZWRkTJkwgP3/IReall17iwQcf5M9//jOzZs2K2LE88VVjufjii5k0aRKbNm3i61//\nOgC7d+/mrrvuYurUqWzfvl3POCOIklAK00IMfOnjK/DpXZ06wyK7RVesWMHmzZvZu3cvN9xwA4sX\nL+aZZ57h1Vdf9eoWdbvdWK1WWltbqaqq0jQtZQAJ9yIkRwUyMjJYvHhxxC5qCQkJ5OXlkZeXp/ne\nWSwWTcXEM4A0NTXR19dHaWnpmPjFyUzXN+gaDAbS09O1blrZ9WqxWGhoaDhF2SfUmxKp+SkzXVVV\n+eUvf8nbb7/Nnj17QnabCARfNRa5n+gpzvDII4/w2GOPsWXLFg4cOKALX0cSAbjHehGRQQ98OgEh\nhOCPf/wje/bsYcaMGV5l0QceeOCUsui8efO0mbWamhoGBgbC0uWU/nGzZs2KamlRURRSUlJISUmh\noKAAVVXp7Oyko6ODyspKjEYjkydPpru7O2KyaoHguZ+2bNmyUd8/6YohM0KHw4HNZtNuSkIRqZaf\npSwvOxwObr/9dkwmE6+++mpUunl91VjWrVsHwJ49e3jvvfd45ZVXePzxx7n88stPea6e7UUYAbjG\nehGRQS91RhB/ZZbdu3dz+eWX88knnzBv3ryxXmLUCKRb1LNVH9AaTEaT8pJWPsXFxTF1dJBIkeUZ\nM2aQmZnp1ewjlViys7Oj5nLgaecze/bsiARbz67XQFRxWltbOXLkCIsWLSI+Ph6r1cqGDRtYs2YN\n3/ve92JyA+CpxrJt2zbWr1/PiRMn2Lx5Mzk5OTQ3N5Ofn8/dd9/NlClTdDWWCKPElcKEEEudeeOr\n1KkHvgiybt06Nm/ezJYtW/jxj39MXl4eW7ZsobKykkcfffS0Dny+jNYtKsuiFouFrq4uv1Y/breb\n6upqhBBhWfmEgywtDqc3KmcgZQBJTU3VGkwi4SovlWjy8vKYMmVK2K/nD09VHGmILMu7GRkZNDU1\n0d/fT3FxMUajkfr6ejZu3MgPfvADrrzyyqisSWf8oZhLISPEwFcwvgKfXuqMEoqi8M4771BRUcHB\ngwd58skn+fnPfz7Wy4oZqamprF27lrVr145aFp0/f74WQGpraxkYGCA5OZmenh6mTJniV/or2ggh\nqK+vp6enZ8TSYmJiIlOnTtVcDmSDSUVFxSlODcHuCcrybjhO7YHgq4rjW941GAw4nU5ee+01kpKS\nuPvuu/nd737H8uXLo7YmnXHIabTHp2d8EWTnzp1amcVkMrFt2zYAVq1adcZlfCMxWll0165dAMye\nPZv+/n6EEFo5MVyHg0CIlH+fZ4OJzWbDaDRq2eBoDSZSCUYOhccau91OWVkZeXl55OTk8N577/GL\nX/yCDz/8kOLiYi699FLWrl3L/PnzY742nbFBMZZCcogZ35zxlfHpgU9nzJFl0Z07d/LSSy8RHx/P\n+vXr+dd//VetLCoHt+W+mmzciLQcmexanD59uqZFGSkcDoeXdZQUA/As7wohqKuro6+vj+Li4jHp\nHO3p6aG8vFwTJlBVlZ/+9KccPHiQrVu30tXVxRtvvEFKSgpf+tKXYr4+nbFBMZRCQoiBb4Ee+PTA\np3MKLpeLa665hoKCAm699Vbeeustv0P0ubm5XnZF/f39ETNNbW9vp76+PqSh+GCRsmoyEA4MDJCa\nmkpvby+ZmZnMmTMHt+Ki0bifg6a36TZ0AJCjFlLiPJ+p6jwUIl/+le+BHJcYGBjgm9/8JpMnT+bB\nBx8ck0CsMz7QA1946IFPxy/79+8/Ze4qkG7R7u5uLYDIsuhwXnH+EELQ2NhIZ2enJr0Va/r7+/nk\nk09ISUnB4XDgMtppWvJ3BhO6EQY3yknfdRUVIyYKXMWc5/wKhtH92ANC7sN2dHSwcOFCzGYzJ06c\n4Otf/zrXXHMNt912W8T3WX0H0jds2IDVamXq1Kn83//9HwBbtmxh+/btFBUV8ac//Smix9cJDkUp\nBVOIgW/R+Ap8+u3bZwTfUQmn08mVV15JT08PDzzwwGnRaOBv2Nh3iN6zW/See+7x6hZdtmyZVhZt\na2ujpqZm1LKoy+WioqKCxMREFi9eHLO5PE+sViuHDh2iuLhYG0B/zfwYA8ZOVFWACxRFRVEUDIoR\noag0mco5IPawxHVx2MeXxr2qqrJkyRIMBgNVVVVs2rSJn/70p1xyySVhH8MfvgPpe/fu5Xvf+x6L\nFi3SHmMwGHC73WOikPNZRlGUjyL/qsuWhdrc8tFHH/UrilI1zD/PDXlJIaJnfJ8RfEcljhw5wqZN\nm5g5cyaPP/44xcXFY73EmDOatmhubi6Dg4Oa5ZJ0aZBjEy6Xi4MHD5Kfn8/kyZPH5ByOHDnCsWPH\nKCkp0cYfOpU2Xkr45UnfBeXkuQ6dr/yDAiZMfKnr+6Qkpo10iBFxOp0cPHiQzMxMzV3jzTff5Ac/\n+AHPPPMMCxdG1t7IdyC9qakJgGXLlnHVVVexePFiysrKNK3RwcFBzGYz2dnZ1NbW+hUv+Oc//8k5\n55zDFVdcwV//+le/x50/fz4NDQ0cO3aMsrIyLrjgAu655x4uueQS7r33Xv75z39is9lobGyksLAw\nouc8RkS8Dq4opQJCy/hg+KxOUZQP9YxPZ1QURcHpdPK5z32OVatWsW3btjMy8CmKQkFBATfeeCM3\n3nijV1n0hhtuOKUsmpCQoJVFGxoaGBgYIDc3l/j4eFRVjWm2p6oqNTU1OJ1Oli5d6jWjWG/8BBXV\nq4ypKFKJxCMQqoIPjr1N4omJXrJqgZZqBwYGKCsro7CwkNzcXIQQ/P73v+e5555j586dUbkZWL9+\nPevXrwe8B9K/973v8eyzz7Ju3Tri4+PZuXMnzc3NHDt2jJdffpnPf/7zw5r7fu5zn2Pu3Lm88sor\nWCyWU0yI9+3bR3V1NV/60pe8XET++c9/8rOf/Yxzzz2XG264gY6Ojph7SeqMDXrG9xnBd1Ti6aef\nZs2aNfT29vLUU0+xZMmSsV7iuMPfEP0FF1zA4cOHiY+PZ8uWLZouZ2dnZ0xUWMB/luXJ383PU2v6\nAOOo96UGVjquZIZzidfYhBQLz8rKGnb8o7Ozk6qqKhYsWEB6ejput5sf/vCHHD16lD/84Q9jopAT\nDj/72c+4++67+dWvfsW3vvUtr3+77bbbeOSRR9ixYwdr165l7969XHDBBQA8+uij3HzzzWOx5Gij\nZ3wjrUYPfDpnAkIIqqur2bBhAw6HA1VVKSkp8VsWlSos0TCvleMSM2bMICcnx+9jPjTt5KD5rVEb\nVxQMXGC/lmmq9yyd0+n0UsXx1eU8fvw4LS0tLFy4kISEBHp7e/nGN75BUVERP/7xj8dEISdcjhw5\nQkFBAUuXLuWDDz7Qfu5wOJg8eTImk4mjR49iMpm0wLd48WI++eSTMVx1VPksBb6bhBCPh7G0oNFL\nnTpnBIqi8Mgjj3DbbbexcePGoMqiZWVlqKrqZV4bSlm0o6ODurq6UcclZrqXUGH+GwJ12JEFgYoB\nE5PVUy2AzGYzubm52hyi1OWsr6+ns7NTywhPnDiByWTi2muv5aabbuL666//zGpbTp06ldWrV/P6\n669TWVmpOb2//PLLWK1W7rjjjlNGMVasWDEWS/0MIwBn5F81xkEPiFAvtM5nnt27d7Ns2TIuv/xy\nhBDU1NQwf/58SkpKeP3118d6eRHhoYceYuPGjcCn3aLSbumtt95i9erV7Nq1i4suuojLL7+cJ598\nkr6+PpYuXcrSpUvJyMjgxIkT7Nu3j48//pimpiZ6e3sZrWoihKCpqYmmpiaWLl066oxgpsglRy0Y\neq6fAslQ04uRIufnMTH6fl5SUhKTJ0/GYDCQm5vLwoULaWho4Ctf+Qpnn3225mDf398/6muNZ+Rn\n+4c//EH7mfzv66677pTHT5o0KSbrOn2Q9gyh/Blf6IFPB/jUx8ztdnPgwAHMZjM9PT10dXUxderU\nsV5eRBgpm5Haog8//DAfffQRv/vd75g4cSIPPPAAZ599Nrfccguvv/46GRkZnHXWWcyfPx+TyURD\nQwPvvfce5eXlHDt2DLvd7vW6qqpSWVlJX18fS5YsCbhk+gX710lXJ6JgQMWNOPk/N0MzffmuIua6\nltNuaMCqNOMe4eLicDj4+OOPyczMZN68eWRkZGAymTT3kE2bNrFnzx4efPDBwN7IccoVV1xBWloa\nf/zjH3G73bS3t/Paa6+xaNEirxEJyWc1u9UJH73UqXMKiqLQ0tLChRdeyPLly9m1a9cZpckYareo\n1Wrl4MGDuN1uMjMzSU9Pp7m5mdzcXKZNmxbUhTaeJNbav0O98WMOmt+mR7GgoJCrFlLoKqLNWMar\nifdhECZQBAZhZI7rfOa7LsLokQVKSyXp1i6E4JFHHuHVV1/l9ddf1zol16xZE/H3MdYkJiZy9dVX\n89vf/pY9e/ZQVVWFy+Xym+3phEJkSp2KolwF/A9woxBip6IoycCrQAqwSQhxIOyDjLYGvblFB07t\nGv3JT37CZZddRmJiIk888QRnn332WC9x3DCa5ZKqqhw8eJCuri7NKVw2yaSkpISUaciZvjbDIf4W\n/zhunBgwfjrnhwooZKn5XGD/FkbMmtuFtFRyOp38x3/8BwMDA/z2t7/VZuVOJ/7xj39w7rnnsn79\neqqqqjh48CBHjx71aiSSzS333HMPW7ZsGbvFRpcoNLcsFvBmiM/O9mpuURTlKeC5k4FvHXAhsBc4\nVwhxe9iLHQU949MBhu74fe/66+rqxmg145vRLJcmT55MQ0MDW7dupbi4GLvdjsViobGxUfPsk12W\ngQYfBQUXdt6J+y0qrlNGHRQMCARWQzMVpl1kNi6ira2NpUuXEhcXR1dXFxs3buTcc8/lBz/4QVRm\nFnfu3Mn9999PZWUlDz/8MIcOHWL79u1cccUVbN68GYADBw6wadMmUlJSePXVVyOuyLJy5UpmzZrF\nCy+8gNPpZO3atcN2z+oES3SaW/wcJOroe3w6OmHgWRZ9/vnn+fKXv0xfXx9f/epXufPOOzn//PO5\n7777qK6uZvbs2Zx99tnk5+czODhIeXk577//PjU1NVgsFtzukfWgmowfnRxs9z9uMJT9CaqVN7F2\nWbQ9xebmZtauXcvGjRvZvHlz1Ab116xZw+uvv05WVhaXXHIJjz/+OPv27eOxxx7THvPkk0/y/e9/\nn+Li4qg1TV133XU4nU7tv3UihQx8ofz5FEVRrgBWA/cqivIWsAcoAe4C/kAM0DM+HZ0IYbVaAfj7\n3/+uzcKNpC26ZMkShBDYbDY6Ojqora3V5Ln8lUVbjPtRFSeGEX5t3S6BQRFMLcnAKIzs27eP73zn\nO/zmN79h5cqVET9nTwmyCy+8kMmTJ3PRRRdpax+prBut5pLNmzdrGaY/Vq1aNWonrs5whN+hKYTY\nBmzz+fH5Yb9wEOh7fDox4cUXX+SOO+7giSeeYM2aNfT19XHppZfS29vL7373O79dd6cbgWiL2u12\nbfi8t7eX1NRUbX/wnbTf0GFo9JvxCQFutwuDwYBRMXOe/Wb+/uIB/vd//5fnn3+eGTNmxOQczz//\nfB599FHmz5/Pvffey0svvcS6deu47LLL2LFjB5dddplXqTMlJSUm6zoDicIeX4kA/1qoozMn5g4M\nI6EHPp2YsXHjRr7yla+wZs0atm/fzp49e1i1ahXvvPMOv/zlL8d6eTFnNMslqaoiRbaPFb5DT14d\nBkwYDJ9e14QQuN3uoSzz5I9b/ieHd3bt47nnniMzM3OMzlBnDIlC4CsW8EKIz14wrgKfXurUGXPO\n1HmqYCyXlixZQqEykT38AlW4UV0ACooyFPiMRiOKouAWLtor7Bw5dIJXXnllTLwFdU5XYtLcEhP0\njE8nJmzbto3vfOc75OXlkZSUxMsvv+xV6vTnxXcmM1xZ9IL/nIypsBvFYMBhd2gBz263owoV1aXS\n9dw07r7pZ2PiLagzbohCxlck4NkQn71oXGV8euDT0fkMoJVF39jJwIr95C6Lx2Q2Yo4zD5U4VYXe\n7j7qnjBR/cZxLBYLP/zhD1m3bt1YL11nbIhC4Fsg4I8hPnuZHvhifUAdndMFq9XK1Vdfzb9cex65\n5wtOOA9jOW7l4780c+/GR1ix5HPAkNdef3//Kd50OmcMUQh88wU8FeKzzx5XgU/f49MZlcOHDzN9\n+nSuu+46nnrqqbFezhmNoijceeedXHTRRdrPxGxBx/wOL3fyxMREEhMTx2KJOqctUqQ6svhKmEX8\nAH7QNwF0TgtefPFFpk2bxs6dQ783ZWVlnHvuucycOZNDhw6N8eoiR2ZmplfQg6Fg6Bn0IsnOnTtZ\ntWoVOTk5PP/881x99dXMnTuXBx54QHvMU089xbx58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9hVVVVdjtdux2u5Fsb9AvEIA5VYkR7M6ZdB1D8Bn0KImYNRPR+FwuF/X19WRl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MrsmFeXwtraGtPT05w7d66mARYrIkwkEgSDQebn54lEInlEmMlkLIXw9UApIgyHw2QyGUM+YU6g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\nq0IyMzOx2+2sXbs25dZjMLGf2NTUxMqVK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XGgwLBGDJRGKE+97kYGMIPoNBpXtOXqKHfiqCr62tjba2NtxuN1lZWUNOO9TP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\nbKSyspL77ruPXbt26Q8ATqdT37/t6OiI2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XkyrZpPoLDc3QxGSkYmp8JiZ9IDF4JdHkKITIuK/ckSATfyHECqqb/kKTkcxI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\nlZDyPrmitAhIruKD2FWdoijHMhVfBktCURQCgQBvf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jbN++HY/HQ2FhIXv27MHn86EogsHZjRSpm3DYWlBt2Rg5c9Cd00HJ6s9bSwkz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\n++67j4cffjgrNwMHDx7k4MGDQKwg/XOf+xz33nsvV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WdSPBSAtD25ca78UzkZrC0MTk+MQUfCZHlHhmzVS0vP4UMh3KXrzql+DPiRF6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\n3c0TTzzB5cuX645MqxHf2toaw8PDdHd3u9ZBCwsLTes6XV5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hisoO9eDYsWN84AMfoL+/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8nJSXcmTxF4syJZL1Sn8MGDB4ssq9bzJNR1nb179xbVy2KxmFsvK+dAsREhaSFgvUeAZotUv2DE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\nDAYJBAK0tLSsuaGwrut0d3fjcDg4//zzF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rZiUbKSWKopgmUstfaHE8YGl8Fha9wPCxRSIRU8AYf4NF8CWiO2FoaKzGvmhh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kqW/Tl51ZYvNWOxHeKrACFEAankcjnGxsYc4epKCin1EN/Kygpzc3MMDAxw6tSpmo+rFvGZpsmN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\njo+P093dnZJmegsqtXo5tXo54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HHVJdqyPy5RubxcZ8kkk+zsbGMtmkiiSDIB06mAPB/s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\nX/nKV8LUonv27DHWohMTE3R1dRlr0cLCwqh2A03TO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9TnO9aFOr5Y+06G/0F0HSX47D4jhE6s0YvhXgfx+kjk0IQII6ED39aoTIOCYC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\nnnuOxx9/nL/5m7/hHe94x7rvLU2HBYYAYtt9EYVBa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Cyx9pcUwgAHu2kkTrf8jhxBKIFkeEdMyjiqL0KxADgQB1dXV4vV7y8vIybrA7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1NMkILc29KLP+S8PZ/PlxpBGi2Iit1QsXvZ9Z47d05rj6/2QJEqQRqNxpiJHnKI8Llz5/D7/czN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\nZWFhQTO768MWsg1FUSgqKqKoqIht27ahqiper1cLR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FC0nLH2kxEhkpgmSknIfFSYgpPDRNi+QURguKZAWiue9gr60/c8b7I831m/7I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PAAAg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\nkiRKS0spLS1lx44dxprY4/HQ399vfN0jjzxCfX09n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YEysoTpRA7Imw6ngOxvpVVSUUCtHY2IjFYmHQoEGmP9JkQCAAW3clidr1kOOJ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\nzz3H9773Pb70pS/xtre97YrvLU+HRYYA4ht9EcVBe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F0efzUVVVhcfjITMzM+UGu/2NKXLmOQaDQVRVtfyRFgMGISDtWuqWIFocyXRl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\nZWlpiZGRkR3r7xN5rI2NjTQ2NiaIjLYreWZ4eJhQK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PtBiKDBVBMlTOw+ILiCk8NE2L5RTGC4pMBaK5b3+vrTdzJvsjzfWb/siGhgac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sXFX+QNlpSUxBCk1+vlzJkzLC0t6QQpSRLnz59n9+7dugYxV4gnSFi+KEejUUZHR7HZbPocUpIk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\nQtx6cRFioWW6zpAkieeff57e3l56enr43ve+x9/8z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1wtraWgoKCix/pIVFEgwmH6JlF7I4rjD9hKFQiD179uD3+7v28UmJLfQ2UmTH\nC8NYlHQEIdRI8uW0zOOZmqEp/Ex/pN/vp6WlBZ/PR0tLC4FAgEgkgq7rSXefsLCwOP6wNESL44aO\n5tEe/XhGI+gHQXTfOcEQ6SjaFnRn51ZaydKVqdUwDEKhULSzQaw/0grasTgZGEwaoiUQLY45sdGj\ncET4CCF60Lg0QECPwsYGsnMrnp7W1BPd+SPNczHHxZpaLX+kxWBjsAiSwXIeFicgscJDStlJwPQo\nEDU/orkSRa8C1Y3hHgb2rE4CUshWDOXULibpTF+EVcdzMNevaRrhcJiGhgZUVSU7O9vyR1oMCgRg\n760k0XoecjSxBKLFMSEZ86jpt+u8cxCl+hmUlo0YoTZ0rRpF8WBrKcVwDsHIngmqu32slEAY3dGr\n7mN9R0CbqKVWLSEkWmh1B8kMjSZLZhIOG53yIzumflhC0uJ4RwjodS11SyBanMx0ZR5NhBCiU3sg\npIFy6PeI1s9p8GcQCIwkL72FsA6aJlCDVdC2hnD2mTidaTjUQxiOWUg1udZKsevsKxGC7HZ8QJNS\ngUBBwUZbWgs+717aHLuZHPkqLpkRPZ7lj7Q4EREC7GrP404ELIFocVQwb/iRSCSheTQRiUymIrCL\nSP0Galo8pKcrFAwfh6Fl44hsBQJImYYMt0DgC/yRTA60TaA2NANvxl4yMjLIyMjA6XT2eNy+YqBT\n7HibZqUatxyCOJwPHdYkiqIQcDWxw/EGp4WuwI7L8kdanLD0SUM8zhgkp2FxPCOlpK2tjQMHDlBY\nWJh0FZiOAjEUCtG04484dZ1hwwqO9LxTMtGc5yKMWhT9EMKZhgs34pSHcaujyItEaG5uprm5mQMH\nDhCJRHC73VEBmZGREVcPtT9oUvbjUw7hkdlRYRg9LwQumYlf1FOr7ma4nri3Z0/+SPP6xJpaLX+k\nxdGmTz7E44xBchoWxyOx5lFN06irq0u6KzwcEYhSSioqKqioqGB2pg9P2lhQOn51FaSSj67kt+8b\nrkCKPBACh8NBbm4uubm50XX5/X6am5upqamhtLQUKSXp6elkZmai63pnU22KHLRtx4azkzCMxSHT\nqbT9gwJ9erfjTEwhF+tzNYWkWa+1pKSE8ePHW/5Ii6OHACyTqYVF1xhGe8CIaR612Wwp++WEEPj9\nfjZt2kRWVhbz5s3DWfEGyB488eZxROJfqRCCtLQ00tLSKCgoAEDXdVpbW2lubiYQCLBt2zbsdnuc\nFul0OpNld0q+AAAgAElEQVQWKn6lHrt0dTvGhhO/aMBAQ+1lJpe5HlPDNav7mP5IszWWOcYqam5h\n0TWWQLToV6SU0aotsZpMzzmF8WiaRm1tLeFwmBkzZuD1etvnT5+F0vgOUk3rcl+hN2I4x4HSva8w\nFlVVyczMJDMzk4aGBiZPnoyiKFFT66FDhwgGg51MrbYunCdCqki61zLl4Xr2Sj8/XndXRCAYDMaN\nM82sVlHzk4PZs2f3fymlPhQz9Xg8s7pa05YtW+qklHl9WFnKWALRol/oriMFENeqqad5qqurKS0t\nJS0tjVNOOSUqDAGMzHNQGt8GIwyKI8EEBkJvRs+7rtfnYq7bbreTk5NDTk5OdG3BYBCfz0d9fT1l\nZWUYhkFaWhoZGRlkZmaSlpaGoijk6uM4aNuOR3ZYY8xPPyxaGWKMQhyFCopdCclYfyQcqdcaW7bO\n0iIHD5s3b+73OWc7Ra8lyZQpU7pckxBiXx+W1SssgWjRZ3rsSEFyGqLf76eoqAiHw8GcOXOoqqrq\nPMgxDD3vWtSaZ5C2bFAzjryn+1G0GjTvuRiexIEqyZJorUII3G43brebYcOGAe3nbppaKyoqaGtr\nQ1EUXDluQmOC2JQgDtUV33RHgMRAJ8xw7fQ+rbMvdCxqHuuPjMUUjFbQjkWXDBJJMkhOw+JYkKhh\nb1d0dwM1DIOysjKqq6uZPHky2dnZ0X0SaZVyyJfRbUNQ6l+FUAVC0u43tGUQyb0WPeO8rot99zOK\nokTNpyaaptHc3EykppGDWZuQQRU7HpwOJ5qmYdgiSOFnhHYGmcYpR2WdydDRHwlHTK26rhMKhdi7\ndy/jxo2z/JEWR7CCaixOZnoyj6ZCQ0MDxcXFDBs2jPnz58cJ1a4EIoD0noGePgMZqkALNoLiQjpH\ndRlIkyp9Scy32WxkZ2eTzfmMUsazP20TLbIGTfMTDPgRLS7SSsfSGsngQMYBMjIy8Hq9x6X/ruNn\n6/P5oubvjv5Iq8nyScogaog4SE7D4mhhmkc3b97MrFmzei0IQ6EQu3btIhKJMGPGDDweT6cxPZpZ\nhQDnSAyR36/aSX/OlW2MZkh4FEHhIyKCHKqvJss+jLzxefj9fnw+HwcPHqS1tRUhBF6vN+qPdLvd\nx6XWlUyTZWjXnhNV2rEYZFgC0eJko2PJtdhAjFTnOXDgAPv372f8+PEMHTq029Jtg6G/oEDgllm4\nJTREggh7u3k5PT2d9PR0Tjml3WyqaRotLS00NzdTWlpKIBDA4XDERbU6HAkCiY4DuvJHWk2WTxIs\nk6nFyUBXHSlMc2YqZjFd19m0aROZmZnMmzevy5QFk2MpEI/FcW02G0OGDGHIkCHRbaFQiObmZnw+\nHxUVFUQiETweT1RAer3efq+y0x905Y80myx3LGpu+SNPYPqiIR5nz7uWQLToku46UnTZiSIBmqZR\nUlJCMBiMyynsiWMlEI+nm7HT6SQvL4+8vPZ0rNgqO9XV1ZSUlCCljJpadV2PPrgcbySTH3no0CFy\nc3Nxu92WP9LiqGMJRItOJNORwtQQu9NOpJTU1NRQUlLCqFGj8Hg8SQtDSF7oHo83/67oq4DvqcpO\nOBzms88+w2azRX2RyRQ0PxprT0TH71ZjYyPZ2dmWP/JEoi8aYqTnIUcTSyBaROmpYW8sPSXamzmF\ndrudOXPm4HA4qKioSGk9yWiIzc3NlJWVRZPj+6tQ90Bqpv19I4+tslNVVcWcOXMIh8PRKjuVlZWE\nw+G4Kjter7dHk3VHjobmaT5kxWqFlj/yBKC3PzlLIFocjyTTsDeWrgSiYRiUl5dTVVUVl1PYG7pL\nuzDNsD6fj8LCQkKhELW1tdFC3bHRmh6PJ6Ub5WC4qSYqaB4IBGhubqa2tpa9e/diGAbp6elRIWlW\n2ekK87sxkCTyS6fqj7SKmh9lBi7KNE0IsQUolVJ+fUCO0AFLIJ7kxNYeheRzChOZM82cwvz8/E45\nhbHHS/YG1ZWGWFNTw549exg1ahQTJ06MmnbN6jGmCdHn81FWVobf78dut0fNh8dztOZAIYTA4/Hg\n8Xi6rLLT2tqKqqpxUa0ulyv6eUkpU/blaUi+UCKst4VpEgbZUuEczck0w4aaoMNHsoFa3fkjrSbL\nR5mBE4j5QCWgCSEUKWXfWtAkgSUQT1JiG/Z++umnnHnmmSlrUab2Fg6H2bVrV7QQd6KcQnOfvgjE\nYDBIcXExALNnz8bpdEY1hdg5Y02IJrHRmvv370fTNNLS0qJCMj09/aQL3EhUZScS0zuyurqaYDCI\ny+XC6/Xi8XhSMiXXCp2HnS1UCwMbAruEMkVnoxrmFKlyd8hLjoy/5qlGLseSTJNlTdPw+XwMGzbM\n8kf2F30QiLW1tcyePTv6980338zNN98cO/N9wP3AKUBqPpdeYAnEk5COHSnMbancFBRFQdd1Dhw4\nwL59+xg3bhz5+d0nyKcaNZqoH+LEiROjEZepkChas62tDZ/PR2VlJa2trVEBEQwGCQaDKZtak+F4\nz6vsqqB5c3MzjY2NtLa2smnTpjhTa6KHiTYMHnS24BMGQ2WMg+nw6VcJnZXOZv49mIk7RlPsbz9l\nRyEZCoWorq4mJyfH8kf2J730Iebl5XVXcLwOWAHsp11THHAsgXgS0VX0qKqqKT+Za5rGF198QU5O\nTlI5hXDE75hs0IsQglAoFNcPMdVAkO7mTpQYb9749+3bR0lJSTQQJTMzs1eBKF0deyAYqChQs6B5\nRkYGkUiEadOm0dbWFg3YMR8mTL9tRkYGH3sFdcJgmEz8WedKlSqhs1ENcZ7efd/I/sT8/ln+yH5k\n4EymPinl7J6H9R+WQDxJ6NiwtzetmaBdaJSWltLU1MT48eMZOXJk0mtIRUPUdZ2KigoaGxuZPXt2\nnFkvEf2hWZg1SNPT0xk1ahRpaWnRdk+JAnbMQJTj5SY50FGg5vym8PN6vV1W2XlhXBpSVWlVbdjt\ndmx2G6oSLxzTpeBt27ERiLFY/sg+YpVuszhR6KphbyzJCkQzEXzkyJEMGzasS19hVyR7nLq6Onbv\n3k1OTg65ubndCkPzhtSf2lGsr7Nju6fYnL/y8vJowE5szt+xCtgZaHNsd1GmsVV2JBLD3UiedqTf\nYltbG1JKVFt7VRq73Y7TZqNG0Qd0zR3Rdb1PQTux/khzXKyp1fJHnthYAnGQkkpHip4EVSAQoKio\nCJvNFg1m2b17d9JapUlPgiscDlNcXIyu68ycORNN09i7d29KxxhoYgN2TO04UXm12KbBsT62gRZa\nA60hJiVMENgBVBWXasPlbNcAJe0CMhKJEAwECegRwqrCrrLaTsE9A0WyAjERHX9D5mcZ22TZNL8C\npKennxz+SKv9k8XxTDINe2NJJqdw0qRJ0UCL7vbpzXGklFRWVrJv3z7Gjx9Pfn4+AG1tbSkf41jQ\nVcBORx9bRkYGgUAgGq05EAE7R8NkmgxzdAcb1TB5MT5EgcBus2O32cENIaFzQcROfr4eLbDg9/v5\n/PPP41I/+qPKjkkqPuyeMK9Fx6LmDQ0NNDQ0MHbsWAC+973v8dhjj/UqGOyEwDKZWhyPdGzYm0pO\nYUfB09jYSHFxMUOHDk2YU9gbgZhIQ2xtbWXnzp14vd5OQTMnanHv2ICd4cOHA0cCdsrLy6msrGT/\n/v1xlWMyMjL6HLBzNARistrVhZqLT9UwEST2BPmGESQS+IruJivLRlZWFlJKNm/ezLRp0/q9yo6J\nrusDWgxdCIGu69Fi5QB79+7F7XYP2DGPCwaJJBkkp3FyY5pHA4EA27dvZ+bMmSmnUMTmFO7evZtg\nMMjpp5/epZ8wleLeJrECzjAM9u7dS21tLVOnTo3LGUw0/mgyEELFDNhpbGwkKyuL7OzsaMBOXV0d\ne/fuRUpJenp61BeZasDOsfQhdmScYeMbETfP2wN4pCADgUAgkTQj8QvJdWEPo+WRW5Ap0BNV2TEL\nmtfU1ESDm3pzrfqS55gsHYVuMBgc3ALRMplaHC/EmkcVRYkriJwsvc0p7K3J1KxoU1BQwLx587q8\nQR1LDXGgSRSwYxhGNFJz3759tLW1pRywc7yYTAEu1twUGCqv24PsVTRUQAfGGzaWhN1MN+xx47sS\nVj0VNDevlVnQPNbU2nG9pvY2kCTSQgd10QfLZGpxrEmUU2jmE6aKpmkUFRX1KqcwFQzDoKSkBKDb\nijYmx1JDHKjjdjevoiidKuyYRbo79kM0BaTX643zXw2kQOxNLdMZhoMZIQd1QqcNSTpKp+o0sfMn\nKzgSVSOKrbJz6NChqGYWKyT704fYFbFCNzbIZtBiCUSLY0UqHSl6wswprK+vp7CwkMLCwqT3NbXR\nZNd86NAhamtrGTVqFOPHj09qzYNVQ0zl80pkPjQDdg4ePEhraytCCDIyMnC73RiGMWCCsTe1TE1y\npUpuD2P6as7srspOfX09ZWVlBAIB0tLSCIfDZGZm9ljQvDck0hAHdZQpDBpJMkhO4+Qg1Y4U3WEW\nyB45ciQjR45MOZIvWR+i3+9n586duFwuhg0bRnZ2dp+Le3fkaHRhOF7oLmCnoaGBtrY2Nm3a1O8B\nO3B0NND+FE6xZmkzcnn37t2kpaVFywG2tbXF1XTtWNC8N8QKxL6keZwwWD5Ei6NJMg17kyUQCFBc\nXIyiKNGcwvLy8l5FjHa3T2zKxpQpUxgyZAi7du1K6Tg9CUQpJfv27WP//v1RP1NsmbW+5JudSJqp\nGbDjcrkIBAKceuqpnTSj2FZPpmaU6ndooANSjkbAS2ylIRPzgSK2oLnT6YwTkqn4HWMFYltbG+np\n6f1+HhYDgyUQj2P60zxqGAb79u3j0KFDA5pTCNDU1ERRUVGnlI1UI1O7O0ZLSws7d+5kyJAhzJ07\nF2gX9j6fj0OHDrF79+64J//MzMyEQRaDDfM70lEzMls9+Xy+uCCU2JZYPVkJjkcfZaok0tjMBwqz\nd6eUMlpswaxra3ZHiU396Ep4xwrE1tZW0tLSBvScjjmWD9FioOlP86iZU5iXl9dlTqHZDzFZEgmr\nSCTCnj17aGtr47TTTut0I0hV8CbS1AzDiPo9p06dGi04bRhGNBKxoynR5/NRVVVFKBTC7XbHBaQM\ndIBFRwZSqHQ3d6JWT7EBOwcOHOgUsGNWWjkaa4f+TZrvimRMmEIIXC4XLpeLoUOHRtdmpn7E+m5j\n69qa3VF0XY+aqFtbW/F6vQN6TsccSyBaDBSmebS0tJScnBy8Xm/KNyHzxhWbU5hIQJmkEiATu48p\n3KSUVFdXU1paSmFhIVOmTEm45r6aIhsbGykqKqKgoIC5c+ceia4kAMoepGgGXGCMRpCb8Mnf1CKr\nqqrYs2dPNCDFFAKm/+hEMpmapCqwusr3M7XslpaW6PUx22L1Z9WYjhwNDbG3QldRlIS+WzNNZu/e\nvQQCAex2O36/n6amJjweT59Npi+//DJ33nknTz31FKeccgrnnHMOf/nLX/jqV7/a6zkHBMuHaNGf\nxDbsNaPjzIozqaCqKpqmUVNTQ3l5OWPHjmXYsGHdztNbk6kpYMw6p3PmzOk2Ty5VQWOuWdM0du3a\nRSAQiEvXkBgYrEfa3gOpgVABA1SJNKaAfimC9Lj5zK7xZj5brP+oqqqKYDCIYRjRYujHQovsLX0V\n4rH5fh1v+mbHD03TqKuri3uI6K+WXH2JYk2W/qxUE1vQ3CQUCrFlyxZaWlpYsWIF69atw+Fw8Ktf\n/Yq5c+cya9aslIriX3nllbz55ptIKfnNb37D4sWL+2Xt/YqlIVr0J4k6UthstpTNmND+BLx161a8\nXi9z585NKhigNwIRwOfz8fnnn3fySfbncSKRCBs3bqSwsJCpU6fGCXZDvIfBGjAKQNogWiLMAKUE\nxB+R2vUIuq4SkkiLLCoqwm63R7t7AHG+yL5GIcayM+IDFIapdrKVvrdB6m8NK/amrygKdrudrKys\nfg/YgaMTVDPQx3A6ndjtdsaPH88TTzzBG2+8wZo1a8jNzeXPf/4zmqaxcOHClOf97LPPKC4ujvoz\njysN0RKIFv1Bdx0pUvXr6bpOaWkpbW1tnH766SkVEk41od/n87Fjxw6klJx55pkpNfxNVosJhUIU\nFRURiUSYP39+J1OdpAZDfISQoxBwuDKmiQKyAMQ+ULaCcVaSZ0a0dNiQIUOiQjJWS6qpqSEQCMT5\nIjMyMpK+BlJKWowgT7XtZr3RhF+EMQwIBB04hMJ4xcn1aSM4xzWk58kSzH00fHwDEbBjznM0BOLR\nDKwKBAIUFhaybNkyli1blvL+r732Gh9++CFFRUW89dZb3HXXXVx99dUDsNI+YAlEi77SU0cKVVWT\nFohmTuGIESPIycnpVZ/CZI6laRp79uyhpaWFCRMmcPDgwZTMT8kcJ7bzxYQJE/D7/QlvpobYgpB2\nRLQgWKLJ8kH5BGnMPzyud3Q0jZkmbVNAlpaWAuD1eqNCwO12J7zxHoz4eVipImAoYGjo2FFUHa+3\nGUXC7oCbnwRLmR70cL9nDEMdydfAPFbdLroL2Glubo4L2IltidXxu3M0BOLRbujbVx/ikiVLWLJk\nSfTvZ555ph9WNQCcGF6FHrEE4lEmmYa9kJxADAaDFBUVoSgKs2bNwuVy4fP5+jWFwsQUuqNGjWLy\n5MkEg8E+FfdORFtbGzt37iQtLS1aQs40WXZEUg701D/PBdQDbUmMTZ6uGgebAqCkpCSqRcbmRb6r\nVfOrU+pxOiUudDRdJV224lA0DGxg08nIaqXV76SIIdzv38t/OqYlva7jqf1TqgE7GRkZgzKJvaWl\nJeqPHbRYGqJFqqTSsBfaBWI4HE74nmEY7N+/n4MHDzJx4sToTcfcrz9SKExMoSuEiCbym+vvL8Gb\nKIm/Z9oNpT0Pk5Cg/VBPpCrsVVXtUossr6nm+fo9bMjUcXlsBHUVWyhIuj2IqhqEDBfoAmE4MFwR\n0t0h3HoVu8OCNaE6Fjp7KnrWuzWnSl80uO4CdsyHiObmZux2O+FwuFcJ8clwtDuC+P1+KzH/BMIS\niEeBVBv2QteCzUx6z8vLY968eQmr6veHoDJLW1VUVDBx4sROPsn+6ofo8/nYuXNnlzmSXc4lJyLF\n/wHd5Xi1gcwCUkuM7g8ty9Qi2+wqH6RF2E0NdqEhkdiDIVQ7NJOGLl0oaKiqBAFqwIYuBS41hMve\nxN/C1UkLxERrr9fh/aCNWl2QocCXXRojbfGfQV2ToLSyfb/xIyQ5mYmFRn9roB1N0eXl5aiqitPp\npKGhIVpBKS0tLa7N0/GsRXaMYrXyEE8sBslpHJ9IKfH5fGiaRnp6ekr+i47+tkgkwu7du/H7/d3m\nFPaHhmhWgcnKyuqy+0VfBa+u65SUlNDU1MT06dNTfopWmIEuPkTKSHu6RcIn/zowLkNwbG6gjbKF\nVyM1bHc2Eo6EUYXEpYSxezVCWhouVUOhlbDmQBM2bGoYHQcyoiIRpNlbORRqS/p4sQIrIuE/fA5e\n8tvRJWi0u3keanZwjlPnkSFBWusFT/yvg/X/UFEPXyLdgHNnatz2LxGG5cgu5x8IpJS4XC7y8vLi\nEuLNNk/79++Pa/NkCslkcyMHev3QrvV2FIgnhYZo+RAtuiK25FpDQwPhcDjlp0RTsJmdIsrKyhg7\ndmyn1IOO9EVQmUKqsbExWgWmP49jaoj19fUUFxczcuRIJk6c2KublCAbYSzGUP4KDAVib4oacBDk\nBDBmpDw39M20FiFMjahli17LLsOBX21DETpue4R0GSQi7aAo2EQEkHjsAcK6jTAObEoETVcJ6g7S\nHAGCkRa2bduWVN6fecOXEu5sdPJ+wIZLgCvmecCQsC6ocsVBN+JR8DcLsjNknEBcu8XG57tUnvpJ\nkFPyjlyHY1HLNDZgZ8SIEUB8wE5lZSXhcDgasNNdBaKj4aPsqCGeFLVMLQ3Rois6mkdtNhuBQCDl\neVRVJRgMsnnzZtLS0pLOKeythhgKhdiwYQMjRoxg3rx5PQqp3lRz0XWdmpoaWlpamDlzZp+7iKt8\nCQwPOm+DqCHqKxQq6F8C43wEqfug+qJFNISbqKKeRsPH7ogNvx4AxY9LCaOiYRgKIeFCVTSktCGE\ngQ441AhSU9EUUA0NA5WQYedUj2DSpEmd8v7MkmGZmZnRkmGmQNwQVvkwaMMjoOOpKAI8wD9bVHLH\nSybujX+oURXIGyKp9wl+/rSD390Tir4Xq2FJCbubFHY0KGgGjEiXzB6q4+iDppCswO0qYMcszm1W\nIIq9RmZrrKNRGi72gaWtra3bB8tBgSUQLTrSVUeK3ggos3t9Q0MDs2fPjmuC2hOpam5mvl8gEODs\ns8/G5UouOTwVoSGlpKqqKtp6Z+bMmf1mulKZgaFPQRrlIPyAHeQIBKmlnvSVA80aXzQ0sVdrwkeQ\nJnuQNoeOPz2MU21BA4QU6IYdVHASISDtqBgIAQYKThFEU1wICVIYGFLhbJsXl6NzXU0zL7KsrAy/\n34/D4Yg+MP1Ps4ouQXQhWzQNtAg0zRHIvYlDjrIzJNtLVcoOCsYMl9HjCiHY1yL4+WYnpT4F/fBD\nkSoEaXbJHaeF+crI1AtKmPP3RoOLDdgxKxDFRv2Wlpbi9/ujATv19fUDErBjHvekM5la7Z8sTHrq\nSJGqQKytrWX37t3k5+czZMiQlIRhKseLzfcbP348fr8/aWGYCsFgkJ07d2K325k6dSq1tbX96scx\nDIN95RXoOmRmFvRrKbFk+Wd1mM8bmxF2H2G7xAiGsLl0giEDv2HDnatjCAVdKEgEulRR1QB26UcP\n2xBoIMBQFFRDR0oFoQicBPmSY0yn4ymK0qlbfCgUoqKigqamJja1RhDohBWBoqgoioKiCEzRF4qA\n0CDsAd0OtgRlbIVo1wL/WaIyZnj7Q56UkqqgjTs3ugjpMNRtxGigkoAGKzY7iRghLh7duypL/WXS\nTBT129jYSFlZWTRgR9d10tPT+zVgJ5HJ1AqqOXEYJKdxbEimI4XNZotqjd0Rm94wa9YsFEXhn//8\nZ8prSqZQd2trKzt37sTr9UaDZszk8v4iNkp10qRJ5Obm9ipH0pwrkRBtampi586dDB06FLfbTV1d\nHXv37gWOJMn3ptRa0o2J0SltDvH3Rh9D3BGqI5KmthAqBjabxC1UWmQQoYOq6ug2B6pmYA+HcAWb\nyCBCCBcadiLShqaDS9ixKWEiDgdftmXhTjJC1uzfZ7PZcDvdSAmqNDAMAy0SwZAGAoGiKuiGDVB7\nzEaREjQ99m/Jfxd78EdgqKfz9XHbQBEGj25zsGB4gLQUFbCBrCJjui88Hg8TJkyIHs8M2KmoqKC1\ntTUasBPbMiwVOgrEcDg8IA+axx2DRJIMktM4uqTSsLcnjS02p3DChAnR9AZN03pVy7S745mtk+rq\n6pg6dWrK2meytLa2smPHDrKyspg/f370BtEbv2Osb8xE13X27NlDc3Mzp59+Ok6nE03T4pLkTZPi\nnj17CAQC0bZGXVVJSZUW/NQoPj5tMNCdQco0gwO6n1bVIE1RkBEbmhIiTUTQDAVFiaDoGnmBCsKH\nbOghieIBtyOEdNjQ7Q4UVUM0BnEFAoyqruGyMedgm5T8Ddm8TrMdOu8HbaQrKoqiRn/l8rCAVISB\noSo4m3SCTX7sNhWbzZ4ghQdGDD3yANMQVtlY6yAvgTA0carQFIK1lSqLC1P7/g50ce+OPsREFXYi\nkUi0JdbBgwcJhUJxLbF6KvbeUSCeiF1TUsbSEE9OOnakSCaNojsBZeYU5ubmdsop7I3vEbr2ITY0\nNFBcXExBQQHz5s0bkBuPYRjs3buX2trahAK3L101TMzzGDFiBJMmTUII0UkDV1WVrKwssrKygPi2\nT2aVFPNmaArJVDSBQ/j4XFTQElbYE3Fgd2ocCNuwqzoOGUG1h9B1QUAV2HCD1oxbtDGkuhJ3oBW1\nTdA0ZCS6rmMPhBGBIIpTw6ZqFJSVkLm5mi85qkkv2o4x+quoSWoY5nfy+vQIHwZtGLI9iMZECAVV\nVUhzQ0NYULBN4HY6iWgawUAATddRhMBmtxPRbWSm25g9+cjnVRlwoghQe1DiVAE7GlIXiAMdxZpM\npwu73U5OTk60WH2yATvmfUDX9WjHl5NCGA4yLIGYJL1t2JtIsMXmFHaVg9db01HH/MVwOMyuXbsI\nh8NxrZP6G1O45+fndylw+6Ihmi2ggsEgZ5xxRkoRqt21fTI1gXA4HE0AD4fDCQV3hAifUMNW5QAI\nFQ1JmRrEUAxwOBkmJSJsQ9pDGCKEwxFA2gJkRaoY2lKBPeLHF8jA4bKTHaomoKShG3Z0m0JaQxMj\n95aR31xLwb4vyFZ01DH5BLe/R9qcS5M6T/PaznIYLPFEeNVvxwnYYr5KhgQ/cJpNJ/C5QHPZcLmO\n3AbaG+Hq+Fol3zh3J1u21EX9bMmY/o9c86SHxh37aKd19ERXATumBcIM2DFN1oFAIFoU3ty/L2Zg\nsx/igw8+yB/+8AcqKyt59tlnWbBgQa/n7HesoJqTB/NmvH//fux2O0OHDk3pCx4rEGNzCseMGdNj\nTmFvMDtXdMxf7KknYm8xC363trZ2WzAAei8Qa2tr2bt3L4WFhQwfPrxfziNR26e2tjZ8Ph8+n4/6\n+noqKyujGmRGZgYf2+vYrtThEYJ0XLSpIYTuwKVrtCqSOsUgxzAICjDSW8hSDpEmm8CQZDQ3IYbo\nOBtaCZJGOOwiy6hHapDmb2Jk5X4ySw4yLNSIQ4YINdgR42yE96yHJAWieb2EgJ9nhclXJf/d6iAo\nQZNHNLslHo3lBSHWfEvlkeedtPjbzaNSgiFVnDaVh74X4sL5YzGMwuh1yaGR1rY27GENh92O3W7D\nZrMfDtg5gi5hRs6xDapJRH/1QuxogYD2GAAzNaa1tZXnnnuOzZs3o+s6n3/+OdOnT+9VsJfZDzE7\nO5v169fzwx/+kF27dh1/AnGQSJJBchoDhxlBahgG4XA45ZuxOb61tZWioiI8Hk/SOYW9QVXVaJNS\ntybcYgMAACAASURBVNs9oMcyexWOHj2ayZMn93htUjWZhsNhWltbOXjwYFwd1b4SQuOA0oRfRHCg\nMtzIxIsz2hHd7MyQlZVFQ2Mj1bUNbNxfwmfDg2S4JRGXg7A9TEQ1SPcIWkIqbkXBbzjwu9pQRZAc\n9QBDOYjUINSkk9HaSJrRRrBG4nCFMHCQXt9EVk0T7jofjpYIhpQ4tPYKrQoCKVREJHnBEutrVQV8\nPyPCzekRPgqp1OmCdAUWODVyDsuExWfpnDvTz4ef2fhnSbsgmjlJ57yZOmmHFXBFUfB6vXi9Xqqq\nqlg0wcXaShWPLUwkouH3B5BSYjvsh9SEHadq4+zhx59AHMj5Xa721JjGxkaGDRvGjBkz+Oijj1i+\nfDmPPfYY27dv59prr+XOO+/s1fyqqvLSSy9RUVHBQw891M+r7wcGiSQZJKcxcLSHrLc37A2FQj3v\n0AFd1wkGg2zfvp0pU6bEPVX2N4ZhUFlZSUNDA7NmzUqySPYRki1tFQ6HKSoqQtM05s6dm7T5MhUN\nsaqqitLSUpxOJ9OmTetWGCb7kGIg+Vw9wD9th9CFAVIiBQgEY/UczoqMwYkNIQQRTaO+JUhj2EDJ\nSKc+3Y9NV9ADoBEmHGiiVYZR1RDhoBe7TcfuVGhS7YxwNpAj6qHNIBR0kx6uw+UP4FFD2IIhCEnS\ng004K3y4W4M4Ww4XfFdBqqC3SjxZXoSho2SOSOrcIPHn51HgYnfXwindDZct0LgsCYVDSsl3p0XY\nVm+jJewkx+OIpmfoukZTQKclHOG6oTsp+mdLNBAlMzMz6lfrjo5RplVhwYvVNor8Ci4FLszWuGCI\njr2XMq1j0vxAYJZuczqdTJo0icLCwmjLpt5EWJv9ED/99FN2797NWWedxapVq7j55pv7eeV94Bia\nTIUQXwKypZRvHv47B3gCOBV4F7hHSpn005klEJPEZrPh9/tT2qeuro7du3cjhOh1IEuyQqqxsZHi\n4uJo7lWqwtA0tXZnUpJScvDgQcrLyxk/fjzBYDAl7TMZDTEUCrFz505UVWXOnDl88cUX/RKcIJF8\naitnh62KbMONKhXzDQwke9V6WkSIReEpaLpBtd+H1ytwOe34Igo1NgeqTYFIiFCbwJ2TjdcVwQja\nUO0RqqtthEJBDG8Yh1pHpEEnghObPYyTIIpmgA72dIVQrUQYEXDaEC0gEQgkhgRpgNFkkDW9AEN1\n4Jh+YfLneBRqdeanwZMLgjz8uYPP6xTMriMCO7npdh44LcyXCiZEy6v5fL5oP8TYIt3p6emdfg+m\nv02XsKLcwR+r7e1mXNqF7up6Gx4V/ntSgDkZqQuXo9FvMdYs27FsW2+O3bEf4nHJsTWZrgQ+BN48\n/PfDwCLgA+DfAB+wItnJLIGYJKqqJh1UEAwGKS4uBmDmzJls3bq1Vz8GU4B0J6Q6Fv222+1s27at\nV8fqzsfi9/vZuXNnnBl2//79KT31dqchxgrb2O4avfE7JqJOtFFkqybH8KB0SMBTEGQbbmqUFkrU\nOlr9rWi6isfppCbUfoN2qiAVcDs8IAJozYH2hwGhkJUlyMgQNDQ5aWlpISPUgopOptGGaoRQ7BKv\n0obiB2GPoLtthBpUXIrefrdHIB0GRggiBw2yRmThsIM26hzSx52a0nkejea3BWmSX58doqJVsKNB\nQTcEw9MMTs81olGticqrmTl/Bw4ciMv561hk4P+z995hkqR3nefnjYj0lZVZvrrad0/7HtMz0z0z\nkpA5ZBgJFmF2geUB3a64kRDigIOVhgMGPRLCSOL2WFhW4hGchAwgbpG0gkOLhMwIRhrTY6vaVFV3\nme7yLrPSZ5j3/siO6MiszKqILNczXd/nmWdmKjPfiEjzfuPnvt8PjAT53EwATVRqm24sG/DTFyP8\n3ekCp2P+SHGjaohrHcOOQnO53Ko19VcMtpcQTwB/ACCECAA/DvyylPIvhBC/DLyLHULcONgbjKZp\nntzex8fHuX79+opNvZm7U7shp96PWErJzMwMV65c4cCBA06Djl3v9ItG0ZuUkrGxMSYnJzl+/HhV\nB53fmmCj5xcKBS5cuEA4HF7hrrFRhHhJnUWVygoyvAlB1ArwpDpMh2UQjcWYN3KUZYyYIui0glyl\nRBiVMEkycglhSMKyTLlUQrFMWiOSjsw0e9UURshEK5XRidCq5QnFFIxFHRHR0DoUVE1FuVqibEjA\nwMwrBOYM2uMxOo4cxNjzfbS8/dd9XeNWRIhu7G2R7G3xlo2yRxXi8Ti7d+8GKjdz6XTaIclcLsc/\nvzjEZzJ3oSkSpc61hBTIm/Dh0SB/daro63y3OkLMZDKvfJUaG9vXZdoCLN/473NUvN7saPFZYJ+f\nxXYI0SPWihDT6TQXL16ko6OjahgdbpJpM4S4GoEEg0HOnj1bVZ/ZyPnFTCbDwMAAHR0dG+K9WEtu\ntprN9evXOXbsmDP7VXuMjSDEKXWZqKz/dS9LSUGWKIgCKXKIqIIVtEhpFgERRDE72S3jXJVZDGES\nkBoRQyWcvk5CzlDQQyxrLWQDGl2zKeLdJjPhKIZmkjByJLQySluSoJlCTxvoSoB42CTSYWLNGyh5\ni2BKkjj6KqInX4V69odpOXK372vcCnumjUQgEKiKIp966im+GrkDmQFpmZQNC4RAEQKhKJV/C0FE\ngSczKhMlwe6Q93PaigjRXQe9LZwuYLsjxAngbuA7wMNAv5Ry9sZjbVSmjDxjhxA9opEEm67rDA0N\nkcvlOH36dN0fgE2mfrs9a8nNsizGxsaYmppqSCDrmV90exVeuXKFxcVFTp061fAu12/05j63XC7H\nwMAAra2tdcm22WM0PPaNWpQbRUuyUDBZKhcoKHlQJKYmiJRUIkFQzRCKsJgJT9JT2sPRcoKB0CJJ\nM0Xb4lVi+QKiNUI4YBFW0/SmwrQWAlCK0aMsYqolIjKHMCWEQpg9XeQSJvH0PO2pZbDCtB4+Qvur\nfwWx93Wk02nm0mlGUlnUZ5+tSid6aUp5uQ+CCyEY0iMIRSFw4+ZRAtK6IUEnpUP6iqLQv1BgV2/Q\n843mVkSI7rnD20LYe/vxV8DvCiFeT6V2+Nuux+4FhvwstkOIa8BpY68hJ9vBwZ6PO3HiRNPybY3g\nfp3tLF9P1WYjYBOirQTT19e3pg3UahGipJIKBA3hSlNKKRkZGWFqaoqTJ0+u2XVrp5vXi91Wkkvq\nDCGpYSKZzhUYTxcpSwND1cFUUAICTVGZnA8ipaC7yyCAhmYEWNRmOaT30pITzOSHyOhZiskoWjSE\nkBp3IDhsmmiTIyxF42jhKfSSxUJYJWMqmEgIW8RFmt5SnpZwL4kHfpnkiX9baS8FYrEYfX19wM10\nYjqd5tq1a47JtE2QsVhsxWez2RHiZqdjpZQElQoJOseESnToIjJdSkpS8s18lkL/JHt13ZPq0FZE\niG5ks9nbI2W6vRHiB4Ai8CCVBpv/7HrsbuBv/Sy2Q4ge4a4h5nI5Lly44HmmcD1pTHvEIZPJNOUs\n7wfDw8NYluVZ0aYeIVrMYCqPYyjfAcoIGUaVr0e1Xk0uEyafz2MYBg8++KCnu/WNihCPm90MaFPM\nllNcy6WYSZmYQmBScZgIBkANCgJjbZRMi2tzMcJxCzVcppUwZSVNWZlid2qOHl2wmNZoa9WgqNMr\noU0kEEFBOdFBaDxHTj2IFp+kz0xQFiWscppINgvLWWKZIySP/wrhfQ80PN/adKJlWc6A/NjYGLlc\njkAg4JCA3ZSylTXEjYSdanxjm8G3U/VJS6dSLNKpRGFfOZDkf6hJTlmS9y1n0VIpR3XINgxOJBLE\n43Hnu7rZEaIbuVzO+fxe0dhGQrwxUvHhBo+93e96O4ToAfYmY1kWw8PDzM3N+ZopbJYQi8UiAwMD\nHDp0yNPge7OYmZlhdnaWvXv3cuTIEc/HqSVEQwygq38KUiJkN4IAkjK6+J8s5b/I9atvJRzuc9wG\nvMALIebzebLZLMlksmEEEDVUgtk0z2mLFJZBKIKAJlFVgY5GQVVRUxbB/DIqAkuYTM13Et2to4s8\nARYxTQsKy5RRORCJENXD6BJaw0WkzCOsGOquHsSLF4mleyCdQGkLEEguoqodiHCUa8OSUOu9BHff\n7/k9gOoBeds5vlQqkU6nHTujQqFAS0sLpmmu0Ni81WELe/9Qp8EHRkPoFlXzhjqwSEV6TkiI9ZQJ\nqJVo8kVF8Egyzl8nk9xz4/uSz+cd7drBwUEURaFYLLK4uEh7e/umOFDU3hzeNl2msFlNNX1CiOeB\nASnlT6/2RCHEXcBrgQ7gE1LKaSHEHcCMlDLj9YA7hOgR8/Pz5HI5NE3zPVPolxBtK6h8Ps8dd9zh\ndOVtNOzjqKrKrl27aG9vb9omyWIWXflvCJlAuCyLyiWLuXloiUc5ce+/8OJ3f9DXOa41qjE6OsrU\n1BTxeJyRkREAWltbSSaTTvpsfDnF18YfZ0i/Ti4Qp5SMIAMaUggUaaJpJnrGJD0WpyCgO5AnHNQx\njRZkbhFDTaGoEjNfQjEX6UsaqDmVsozQqkkUQqAUkFYENRGDg33IuQUUNYkWOIYaaEPqOmYmQyk3\ngfaqMygbkLoLhUJ0d3c7xsFXrlxB0zR0XXc0NiORiBMptba2ritluJk1Sjt6a1HhT44U+fnBMEUL\nQqKii5riJhlqYYv2vQWgEpxEgALwa5h85Yawgq0/aqegDcPg/PnzFItFLl++TKlUIhKJbKgDSu2I\nVDabrXLSeMVi8yJESYVqcw0PLUQI+Czwo9hDsfAVYBr4CDAIPOr1gDuE6AH9/f1OGubAgQO+X+/V\nE7F2bCObzTad4lmtniSl5Pr164yPjzvjIUNDQ005UTiNOMq/gLAQskKGlrRYWlyiWCrS3dVNMBjE\nEteJJgaAt3g+RiNCtC2m2traOHfuHIZhoChKlWj31fEJvj2/yIW5EhG5wCy7WRxNIpKC8B06miqR\nhsAoCcLhImq7QUnXSAWjdJaX6emcpT2pENEEphmjXSRpQ0NEllDy08TUIAGtEmlIAFFCyAhqZ5Jg\nTxLzmgKGgZlKoWgagUOHoLUVZZME1qFSh3TP/hWLRdLpNLOzs47npbve5jVS2uz6pDud+aZ2k8+d\nLPDbIyGGCgoWYMqKfEFLV5n2/QWUmp0rBIwAA1Jyqs55apqGoigcPHjQ+U7ZLha2A4oQourmwa+P\nZq0Szm3TVLMOQpybm+P++29mSx555BG3Cs80lVGKBSHEb0gp5+os8WHgjcDPAF8DZlyP/SPwHnYI\ncWNxxx13EAqFeOKJJ5raGLxEiJlMhgsXLlR5CBYKhXWNUNS747W7O93mwPY5rosQxXcQsrIRF4tF\n5ufnicfj9HX0OU01QnbQ0ubP9LjeqMbo6CjT09OOxZT7vG3RbiMc4ctDC1wchRZrmSVaMcIh1DZJ\nLhhDHzUIagaKAmpQp2wEEUjUoKAkDIq6hqJkUUKddEY1WmUrfZEEipHHCrcg2mJY2QWk1nfD2kEB\nYVWUb0QZJdRF4ORRQrv2V2RWVLVyLem0r+v3C/d3UwhBJBIhEok4XpGGYThODdPT03X9/urdhG0l\nIQI80Grx1bsLDOUFf1oQ/J1ikWgxVhChDQGYwLNITjWYNXVfQz0XC/tmanl5menpaYrF4opa5GpR\npC3bZuO2GbuAplOmXV1dPPPMM40e7gWepDJSMd/gOT8F/KaU8vNCiNqzGAEO+DmfHUL0gEgk4hBM\nM3qIqxGiaZoMDw+TSqU4efJkVVdarZWTn+PVEqJlWYyMjDA7O1u3/tnMsWxClFhIUUCabSwtzlPW\ny/T09NRpNgqiaP7k79xziHZU2N7e3jBtvZAx+ddrM/zj4BUmR/NEgjkSWhqzU6MnPE/ejDBR6COT\nSiIjOpYFGAGEIQmYBrolKKaClGJlovECpigRIEyXbCUcCEIsjlU0MaIdUB5FFpcgmARNIqXA1FMI\nw0Souwn27EXU2UA3i1i8kJamaVXSfu562+TkpJOVcDfrBIPBhmtLCS/oCvOmIKlIzgStNf0S66FR\nw8uRqORoxCCEZK1ciaRCis2ingOK7aM5MzPD8PAwgGMqbEfYbi/EnZTphmJKSrlWsb0DuNjgMYVK\n8sAzdgjRB9ZDiOVyecXf5+bmGBwcZM+ePZw7d27FhtPodWvBJjebkOyRje7u7oZE0qx5r2VZCBSK\nBY3FxVFa4110dHZUjVrcRBFT93fHbI9djIyMVEWFtZAS/nnwMl8emKGlbQhlSbKnxUKNCiyhYqhB\nsFQMM0BvbIaoKJHKtqK0mujZIKKooYYtCEmyC4KezjJSzdNqKew222gRN8i9tRW1VEKUBLRKKGYx\nyikoFhHFFjQ9jrL3AQIdB1E2yWWkEZqJ4urV29yu8bYOaTQapVwuk8lkbkQ9gi/kNT66HGTREihU\nCCkq4L3xMm+I6hRFRUbkgKg2Kq6H1TpAjyEIsXr9UlLZzI42VCLyfyPSyEfTjrBnZ2cpFApOLVJR\nlBWztrdFhLi9YxcjwEPAN+o8dg647GexHUL0ALd8m2EYvm2IaiPEUqnEpUuXsCyL++67r2EdZ72q\nM4ZhMDw8zPLy8pojG81EiLZU3EsvvUSw5Ri7D71EQGl8RyzFIpmlM8hu7xu3rdXa29vbkMxnsot8\neuCfuTqWJ5KYJxRYxmjZR0t7BgToiwEUU2LJip1E2QjSElomXwyjSh0zEMAsqRimQnE2RGtApy1k\n0GeodI+HGC/MEYyO0hLtoCUep6W9DS2XJ5QzMdUeAi0CqQvUxH6Utj2IsHfz4o3ERjW91LrGW5ZF\nKpVicHCQ8fFxcrkcn289xH+P7kUKCAiwbnycCxb8ZjpIX1Hh3ngJCbQr8GMKvEptbBy8GiG+FkGE\nSmdFo1sMHWinMoy2magXYRcKBZaXl5mZmSGTyTA4OMiXvvQlCoUCc3NzvpvVbNjmwH/2Z3/Gxz/+\nca5fv87v/d7v8eY3exd8vw3wl8D/KYQYBf7uxt+kEOINwK9QmVP0jB1C9IH1DNgbhlHVzHLkyBGn\nO3C11zUzlK6qKgsLC4yPj7N3716OHTvmyatQ13Vfx8nlcszOznL8+HF6ev89JeWDSJYRrCRFSQoh\nYxSXT3iKZCzLYnR0lJmZGfbu3cvhw4frPm+plOIzg99geXoCNaqidpmUFyJgKKALLKFASKE1kMUo\nKRW7omCQMDoB1UCTBkY0j2kGKBkKQSHobpd0CXhN8jjtPacAKOoL5HITLC/PMzFZRloW8ZBJJB6l\nJbabSPQASmB7iNCNzUjHKopCLBYjEolw6tQpni0rfGk2gpASRVqYlgQJZUVBVxQUCVMlDT1sciBk\nkpXw3wzJLPAjDXac1QhRE4IPSYVfwUJnJSnqVIKUD6PU1T+FzeuQdUeRQgiSySRnzpxB0zR++7d/\nm0cffZSrV6/ypje9iT/8wz/0tbZtDvzCCy9gmiaf+MQn+J3f+Z1bjxC3N0L8CJUB/M8An7zxt38B\nwsBfSyn/2M9iO4ToA167Reu9rlgs8vTTT69oZlkNzRBwuVwmlUpRKBRWjT5r4SdlWi6XuXDhAoVC\ngX379jnppJD5S5TU/4IU1xCynUr6vogUSwjZQtD8JaQ1ueaAdDabpb+/n46ODvbt27fqNXzr2nkK\nmXmWCxryhMDKqgSFiaJahIM65bKKalYMitQwiEULLJAtCpFoERE2CIZLTC73UCq3cKonRzRmcjIo\nSWq9znHCgQ5CyRaSyRySHJZpkM/lSWV7mRkrUSi86DSnuIfBa7GZzSmbubb7M/tEJogOhJSbRngG\nYEhQ5Q2NIgueTEnaI2mCwRC7g0G+aAruUeBgnY9+re/EW4TCRyX85g1StAsJISo730dQeJ1o/Pqt\nFPaOxWL8wA/8AB/+8If54he/CEAqldqQY9yqc6Vym8S9bwzm/6QQ4r9SaV/vBhaAr0opv+13vR1C\n9AA/jhe1ME2T69evs7i4yP3331+3/tUIfgjRLSUXjUY5ePCgr+FjL4QopWRqaoqRkRGOHDmCYRhV\npskKhwibH8QQT2Eq3waWgASa+RNo8n4EcRRluuHdujsqPHXqFK2trYyOjjZ8fraUZzh/HXNJYnWA\n1EFaGuHIMkKYBCgigxGKuoJlKAhNokZM4qUcei5AaTlEqyiStyLkSx3sbs3RFS2wR1M5HDyFImJg\nlUCppMgFIVRCSBlHpUAieReJ9ijsu5k6S91QS8lms6iqWtWc4lfL1i+2imy/WVRXbBxleUNmTVSq\nxwJYJIKiZigU8pTTKTJqgM9LnXeqckVDSq05cD28TSh8vxR8DcnzN2qK9yF4I4Kgh4zDVhBirVOL\nfU1+/UnhpjnwxYsX6e7u5pFHHuF3f/d3N+x8NwpSgLkNTCKECFLxPPxnKeV3qHSjrgs7hOgDfjwR\nARYWFrh8+TKdnZ3OoLgfeK3r2e4XoVCIc+fOcfXqVd8porXSs7Zqjn2MQCDA9PRKchO0EpBvJGC+\nse46q80V9vf3O1qt9ua12mD+fHGeUkFHN0yCHWVyuRglUyUQKtMSTyEKIGIWSkAiTIlZkghdouQh\nlCsSF8uUTY2lmW6kFeNQMMMJ9RD7wyEiIQsZ6EUY82BmKzs8FXt4ITRkYA8oN1Ok7tRZPT3S8fFx\nTNNE13VmZ2fp6uracCWZrSJEE1a0rpiwsgtUQCQao+WGWkvSshjRdYoLk8zMzDhjDYlEAtM0PZ17\nWAh+CMEP+Tz/rfJCtPsL5A0h8vXgZWEODLBNhCilLAshfh8/g81rYIcQfcBrhFgul7l8+TLlcpkz\nZ86gqmpTpr1rRYj2IP/ExESV+0UzqdbV/BDtuuexY8eqtBmbEd6uPU69qNCN1aXbJFgSSYhAoISC\niiUhr8fZ1TZHaiGJLIPUJAGKREsGhqpQzIVJZ1rpCC6iFE2C5TC7wwEe6DjM2QOdBORkxbpeBJCB\nXVQWsZN0GohQ4+4QF+rpkT7//PMYhlGlJJNIJEgmkw3TrF6xFUoyAPs1i0Fdwe2/UXtkCUSErBrB\nUIWCGgw54hbusYb5+XkKhQKLi4vOTORqQt3rOf/Ngpt0S6XSpsjD3YqQAgx16zRia3AROAQ8vhGL\n7RCiB7gdL1ZrPHG7vh8+fJienh6HNJqpPa4WtbkH+WvdL5odoagl0Xw+z8DAALFYrG7ds5njuAnO\n9lusjQobPb8W7aE2ApqgYIZoUcsogTylYpxlpZUOZZ7dXdeYyfVg5DWkBeWIitQFLdFl+vomMFCh\n0ErnQorT+1XO3bmb7oRkZgaqPi4RrPyzTiiKgqZp9PX1EYlEqgjBrblZOwPoB1sRIb67Red9qRBS\n3rwvUKmQoHN0AXdFq38ry8ABcfOzdEfVlmUhpaSnp8cZ+ZiYmEDXdWKxWJXLRzPEtlURov0buW1U\nagApBKbPUbQNxGPAHwkhzkspX1rvYjuE6AOaplEoFOo+tpoDRrOODfUiPcuyuHLlCvPz83UjKljf\nkD1UNr+xsTEmJyc5ceJEw/rHeoj3ypUrzM7ONrwGG0KIhtcSD8fpi+9iJrpAdj5EsjNLzgR1yaRg\nhkgElzgWuojUVKYivRRaY4RLOVq7MmAFUZY76GjT6ds/zx0xi7B5DUXpdd6DzYJbLaV2zs2eAUyl\nUlW2T3bK3e5mrIetSpn+m6jBn2aDXNEF2g1SDAooSFBkZQQjqsDJiO56PWSl5Aca7Di2uHftyIeU\n0nH5uHbtGtlslkAgUCU/56U2uxURolup5ua85u0BcwtttWrwfirjrs/dGL2YojphIaWUr/O62A4h\n+kAjgrIVYI4fP16XPJrdpGpTkktLS1y8eJFdu3atKjC+Hhk2Ww2mXuRZ7zV+icMwDF544QV6eno8\niaSvdgyB4LX7HuT65Fe4lomgtGXZbwyjBSWmpZKTYbR0idbUMgcTWUqFAN2ZeYLhKDLQRzxRoC1u\nEoz0EspKlGwKOtZu7thM1JsBzGazpNNpRkZGyOVyhMNhhwzcgt1b1WUaFvDfO/P87EKEC7pC6cbH\nI6mQYVKVvC1ZJHzjo5USJiScUuDOBh+3ZVl1O6+FELS0tNDS0uKI3JfLZdLpNKlUyqnNur0i6900\nbFWEaB/jdnK6kAjMTbK78AATuLBRi+0QogfUDubbsAnK6+be7HENw2BwcJBcLufJq1BV1aruT6/I\nZDK89NJLDdVg6p2fV+K1bxzS6TSnTp1ytDW9HGM10u2JtvG6E8d56slvUHhxCWWfhdViEVlcJpAr\noRkGhUCYQKHEXS+9RDIkUJNdBLpnEL0aSugupBJCUiZgBcD0rwy0mVAUxZEK27t37wrB7uHhYec5\nxWLREaHfaNSSbYcKf99V4Dld4a9yASZNQUKRqEGDtGaSRpCXoEuQQnK/Cu/UQGtiML8WwWCQrq4u\nurq6nNe6bxry+TyhUKhKqHura4i5XO72MAfeZkgpX7+R6+0Qog/YEaKtnpLP5z2b6TYLXdd58skn\n2b9/PydOnPAUAfhNZS4vL9Pf34+U0hexez2OXSvs6uqiu7ubSMT7APtahJhfmMIa+jqvEV8lmzGZ\nfzFBSQtCREEJK2jlIvvmxukpz9EiJQFDwzAKBNOScvtxlEQnOiUCUkEJJtYQCNsYrCeKayTYvby8\nzMLCAleuXFkRMcVisXVHjvXGIoSAe4MW9wZv3nxJCdclnLckaQltAu5XoG+Nr9R6CKv2pgFwbhrm\n5+e5evUquq4TCAScrlYvThY5E/41o/IPKY1FQ9CmSh5OGnxfq0lLnYDITYi3U8pUIir1+FcAdgjR\nB1RVJZvN8tRTT3Hw4EFOnjzpa6Pxk9IqlUpcvHgRXdd58MEHfXXbeSUq2/B4aWmJEydOMDQ05GtT\nWus4dlQ4NzfHqVOniMfjDAwM+CLrRoRon3tq5nEOBh+nXF6ir5zh1OwgZMFSVFRFElJLyJJFERUz\npKKoAYS0yJvtaBmFcluWoBkkpCaQoRgItemarxdsxrq2KHU0GuXo0aMEg0Gn7jY2NkYulyMYmDNY\nQwAAIABJREFUDFY16/hNH9o1vrUgBOwVsNcnt210BBcOhwmHw/T09AAwMTFBLpejVCoxNDREoVBY\n4fLhfk9GioLfuhYiZQpaVElIwKwh+K8zQT47L/ng3hKHw9Wfpfsabhsd0xswt5FKhBC7gF8FXkdF\nwW8B+Bbwf0kpp/2stUOIHiCEcDouS6USr371q313/3kVBpdSMjExwdjYGEeOHHHSP80cazWkUiku\nXLjArl27OHfuHFLKdY9QuJHJZOjv76e7u5tz5845G4XfumM9clpeXmZgYIDenm7uaBummC+gpU0S\nc8tIJIYqEUETBFhSEFAhrEuyCxamMFHbSxS1ApG5FKFeSUyPILv6kJFWULdWkHsjYd9wKYpCPB4n\nHo+zZ88eYGXEBP58Ebfa/mmjIaWkpaXFmRF1d/hOT087N4Otra2IeBu/me7DENAXvPndCwGtqmTJ\ngN+4FuJPDxZpr/k52+9RNpu9bVKm21lDFEIcpTKQ3wb8KzBMxTbql4CfFUJ8n5RyyOt6O4ToAZZl\nMTAwwMGDBxkaGvJNhuCNEOuNOVy5csX3ZrEaURmGwdDQEJlMhrvvvruq8N8MIdaSlWVZXL16lfn5\neU6fPr1iU/Abfbmfb6+9sLDAnXfeSTgoyPQPgBEmNpdCIrAQoNy4DlnRTCkHJZoliSLRyxaKIlFK\nFsWFArMXUqhtIaLhLNGESvxGanAzu0w3C6uRVm3E5DZSnpqaolwuV403tLS0VK3lRUlmPdhsQqxt\n2mnkZLG8vMzfzkimcgU6rAIpTSMYCBAIBtE0DSEEbRpMlQXfSGv8eEf9caodQtwy/AGViZ4HpJSj\n9h+FEPuBf7rx+I96XWyHED1AURQnihocHGxqjdWiNsuyGBsbY2pqasWYg01ufjaLRseylXP27NnD\n8ePHV5jJ+kVtU40dudVGhW74rW/a5OSuQ549exZFUTAy42iGiVIERZpYioKQFjhmRFQG4xQFM2QR\nKErIltDKEbRwJ5Geo/Seew2FSIy0jDE5M0/2yiiWZREIBIjH4yQSCd92X9sFPyRez/uv3niDLRpg\nGMamE9Zmru+ly9R+T763FGaPBjE1imEYlMs6+XwOXTdQFEEgECSiBfnSQoAf76i/Vi6Xc6LR2wHb\nSIhvAN7tJkMAKeWYEOIDwJ/6Wezl8Ut/BaARSdkk0tnZyYMPPrhiU2jGg7GWdHRd5/Lly5RKJc6c\nOeOrqcXLcdaKCt1oJvpKpVIsLi6uXDsQRbUUAiUJisAKKGhFy1ZYqzhbSCr/YUikoqAoQYh1IZO9\nyPvegNh/mogWJiIEdt/r5OQkS0tLLC0tMTpaIUg7vZhMJtetQLKZkdZ6RnxqxxtKpRLpdJqFhQXm\n5+exLIt8Pl+VZt2oa9mKCNHr+vO6oCdQkRnQtACaFgCizjq6XqZUKjOe13nyqWeJ32hgMk3TidI3\nIkL82Mc+xmc+8xk0TeOpp55qemxkdHSUgwcP8o53vINPfepT6zqnetjmppogkGnwWObG456xQ4ge\nsd40Wq3sm2malaaQVGpVEmlGhs39mtnZWYaGhjhw4AB9fX0buhkrikK5XObJJ5+kp6enYVRY+xqv\nEWIul+Py5csoilK3+1ULd1IO30FIP49lKZTbFMS8haIbWDpIDVAEomiAVNF00LUYBlGCp1+DOHw/\naCtrhpqmOQLpUPms7PSifWNhpxeTyaSvLs7NTMVudJ0vFArR3d3tdAbbdTjbQb5YLK5IszZLapud\nkvUzh9iiSnQJoTqnoygKoVAYEQjTY8HZO+4nm82SSqXQdZ2nnnqKD33oQwSDQUd1ZzXhidUQDAaZ\nm5vjxIkTmz5DuR5UUqbbRiXPA78ohPhHKaWzsYjKl+k9Nx73jB1CbALNbDxuYfCFhQUuXbrEnj17\nOHfu3KprNatLag/AW5bF/fffv2GakDbsqLBQKPDQQw957qjzcmPhVsrZv38/y8vLjb3yet+OXHgB\nlAhWOo/ZGYKSilzSEUULBQsRFoh5IGdhRlvQ7vtBtId+sC4Z2ufohqqqK0xhc7kcqVTK6eIMhUJ1\nh+W3EputVFPPHDefz5NOp7l+/TrZbBZN05wbhdbWVs8OH5Zlbep75idCfGPC4EuLGrtWiS0WDcHb\n2gynESccDrO0tMTdd9/NH//xH/Prv/7rPPfcczz88MMAfOc73/F9s/Diiy/y2c9+lkcffZSlpaWm\nHDO2CtuYMv0g8PfARSHE31BRqukF/i1wBHibn8V2CNEn7AjH74/XHpbv7++nVCpx7733ekpd+lWd\nkVIyPz9POp3mzjvv9DwA7wd2mrenp4doNOqrvXytYf58Pk9/fz+JRIIHHnjAuftuhGDv68kvvYDW\n/WdErhcwZgysIJVEiQayaCFmg6g5iYx1EHj3nxC461U+rrb+NdjpRXcXZyqVqhqWX48maTPYaq9F\nIQSxWIxYLObUy2wVGXe62a7FJhKJhg4ft1KE+HDS5CtLAfKmJFrnJQWrkoV/a/JmQ41btu3gwYOE\nQiF+67d+ixMnTlAul5uKnO+44w7e85730NfX13SU+UqHlPKrQogfBH4H+A0qXQMSOA/8oJTyn/ys\nt0OIHlHrieiHEO076evXr3P06FF27drl+cfvJ0IsFotcvHgRRVGIRqMbToa2juri4iJ33nknLS0t\nTE/7GvNpOHYhpeTatWtcv369qrHIS0QZOf6/UyzF0IyPo6UmkUW98v6Wo4hCCBHQEIf6MP7X/0Jg\n30lP5+k3tRkOh+nt7a0alrflxWxN0ng8TrFYpFgsEggEblmz13rwGmHVqsiYpunc1AwPD1fN/7mN\nlL3OOW72+UNl1OL9fSV+bzLEsgmdAYlWKUOzoAsk8J/6yuwN3fyO1O4J7hpiszdDjz76KI8++mhT\nr22ES5cu8eijj/L44487PQWPPfYYb37zm5tec5u7TJFSfhX4qhAiSmX8YklKmW9mrR1C9Ak79en1\nS14sFrlw4QKlUon9+/f77jzzQoju2cWjR4/S1dXFE0884es47rXqbdTpdJoLFy7Q29u7Zpp3NdSL\nEAuFQtW4iXtj8Vq7VU78DHQ8hPriF5Bz4zC/hAhKZFcIse8k8nX/ASWxy/M5rheapq3QJM1kMqRS\nKUZGRhwvQFu0ez31NxtbHSF6gdsk2V7Hnv9zGynb1k/JZHJTjJT93sQ+FLf4o/1Fvryk8c1lDbPS\nt8XrW01+uF1fMZRfu/6tKN02MjLCQw89xOnTp3nXu97F1NQUf/M3f8PDDz/M5z//eX7iJ36iqXUl\nbFtTjRAiAASllLkbJJh3PRYDylLKxhZFNdghRJ/w6oloRzzXrl3j2LFjlEqlVa2jGmEt5wqbTCKR\nSF2LJr/Hqk0H14sK1wP39bjtso4fP+6Qhxt+mplkzxHk//J+xOIYIjsHQoHELqzEbthkHcu1YKdQ\nw+EwJ0+eRNO0FfU395hDa2trU5/lrUaItWjk8PH000+zvLzMtWvXPIl1+0UzXawHw5Jf3qXzCz06\neQsiCgQbLFFLiPl8/pYT93788cf5tV/7NT760Y86f3vve9/LQw89xLvf/W4efvjhJlOz29pU80kg\nAPz7Oo99AigD/9HrYjuE6BPu5phGyGazXLhwgdbWVoek7K68Zo5XjxDdKcbjx48782TrgV2vtH/Y\nGxUVumETXKlUYmBggGAwuCqR++7uVTVk12Fk1+F1n+tmolH9LZVKsbCwUKUmY0eRG90Y5QebWeML\nBAJomsbhw4edY2UyGdLpNFevXq0yUrbTrH5r+OtxuwgokFiDS2vX3+wmoWaQSCR47LHHqv52//33\n89M//dN8+tOf5otf/CLveMc7fK+7zSnTNwD/qcFj/wP4aIPH6mKHED2ikeOFG24rqFrHCC9EWg/1\nCDGXyzEwMOAQ7kb98OzoTVVVR+N0I6LC2mPY5q92enc1bIdqzHYp1QSDQWfMAW6Oe6RSKSYnJymX\ny07klEwmNyRy8orNrvG54W5Iso9tNy25Zdb8NC1ttvScmxA3+1jN4t57762bxn3961/Ppz/9aZ57\n7rmmCBHW02Xqr4O+DrqB2QaPzQE9fhbbIUSfaBSxpVIpLl68SHd3d92ZuWbGJ+zXlcsVSyK3os3J\nkydJJpPNXUQD2GR19erVDY0KbZTLZcbGxjBNc4WJ8mrntBY5WZZFKpWipaVlU+pPGwk/m2XtuIdl\nWc64h21z5PZGfDnNOPqB2+Gj1kjZTjnrur7qzcJmn3tthHgrkqIt21cLuxEsnU43te76IsR1E+Is\ncCfwzTqP3UlF6NszdgjRJ2ojRLc26F133dWwbrAeQjRN05Eu6+joqKtoUw9+fpS2p9yVK1dWaJxu\nBGZmZhgeHqarqwshhGfiWitay2az9Pf3Ew6HKRQKjqpMMpkkmUxua5pxo+EW7a71Rpyeniafz3P+\n/HknxerVTd4LtsJP0A/8GilvdsRvmqbzXm/2CEmzmJmZqft3u1PciwdqPWyzUs3fA78lhPiWlPJF\n+49CiDupjGF80c9iO4ToEfYX3J36nJubY3BwkH379q3QBq1Fs4QIMD8/z9zcHCdPnvRc9LaJxMsP\nM51OMzAwgKZpnDx50hcZrnUcXde5ePEilmVx9uxZZ0bNz/r15hbt4X07WrZlxNxpRlu0uqWlxSEJ\nL2nGl4u4d603on1Tlk6nHesnew7Qvv5m5dZuxYjHjXpGyqVSyZkNzefzPPPMM1Vp1o28WXJHiIVC\n4ZZrqAF49tlnyWQyK9Km3/rWtwA4c+ZM02tvY1PNY8CbgPNCiKeB68Bu4BwwAvymn8V2CNEn7O7A\nF198EdM0ue+++zxpWzZDiOl0msHBQQKBgCdZtHrHW+01bvm4u+++m9HRUd9EYBNWvTqmfcNw6NAh\nJ9XVrLi3G4VCgf7+fuLxuPO+2GnlemlGO3KwGzTsObhkMrkh4w63EgKBAJ2dnXR2dgLV4x5DQ0PO\nuIf7+r0Q3a040rEahBBVs6HLy8vcc889Tpp1YmICXdebluCrRa058K1IiOl0mg9+8INVXabPPPMM\nn/vc50gkEvzIj/zINp5dc5BSzgshzgL/BxVivAeYBz4M/Gcppa888A4h+oCU0vkxnTx50tfg+2rN\nOLVwE9WRI0eYn5/3vWmvRTy2H2JfX59TK/RLVu7juAnRMAwuXbpEuVxeIRu3Hvsn95jGiRMnqpwa\nVju/2sihUCiQSqWqxh3sFOtmK4JsdeRZr0Eln887ggHZbNaTefBmpky3QthbCFHX4cO+WapnpOxn\n9MUwDOe5t6o58Gtf+1o++clP8uSTT/LqV7/amUO0LItPfOITTX/3b4HB/BSVSPGxtZ67FnYI0SPK\n5TLnz59HURS6u7t9q8B4jRAXFxe5dOkSu3fv5ty5c+RyOWZnGzVR+T+eTbbpdHpFrXA9hGjD1mlt\nJCbeLCGWy2UGBgYIBAJ1xzS83tm75+DscQc7tTY3N8fw8LCzQc/Pz29oHc7vuW4G3OMeta4WbvNg\nmxSSySTBYHBTI8Tt8FpczsLYpAok2N9300jZ/i74NVJ2R4i3qhfiwYMH+fjHP86jjz7Kxz/+cUdC\n8rHHHuMtb3lL0+tus0GwAihSSsP1t7cAp4FvSCmf87PeDiF6RCAQ4PDhw2iaxsjIiO/Xr/WDNwyD\nwcFB8vk899xzD9FoxW6m2dpjPXJzR4Vnz55dcU7NEqKUsur8V0sjN5MyLZfLPP300xw5csQZSdhI\nhEIhenp6nC68ubk5pqamqupwG2n/dKvB7WoBN41yU6mUk1rUdZ3Z2Vk6Ozs3fNxjs0c63GQ1vyT4\niy8G+Np3VewrkMCbHjL5D2/X6Wqv/i64a9LT09OUSqUqhaFYLFY1rgS3Xsr0wIEDVTehX/7ylzf8\nGNvYVPNXQAn4WQAhxLu56YGoCyHeJqX8utfFdgjRIxRFoa2tjXw+33RzTCPMz89z+fJl9u/fz4kT\nJ6o2m/V2p8LqUWHta/wSohCCpaUlRkZG2Lt374rzr/d8rxGiYRhcvHiRcrnMa1/72i0RyIZKejsS\niTiD4naXbyqVclLBsVjMSbNu5TzgVqA2tWhZFs8++6zjcGIPytukYOuRNou1at3rhR0hziwIfvHD\nYeZT0Nkm0W7s4YYJX/0XladeUvmT3yjS23nz+9nI6aTWSNlWHUokEhuSMv3e977HL/zCL3D48GG+\n8IUvrGutzcY22z89CLzf9f//iYp6za8Cf0al03SHEDcaXgbz/ULXdS5duoSu6w2jqrWk2xrBjsTs\nqHD37t0cPXp01Y3bb/RmCzePjo5WRbVezmstLC4ucvHiRQ4cOMDy8vKWkaENN2mrquqQn/2YLVhd\nSxCvxEYdRVFQVZU9e/YQCARW6JFmMpkq26dEIuFLdm6rIsSP/EWQpWWqCA9AUyt/m1sU/P6fB/m/\n319quFYjI+Xz58+TTqd53/vex4svvkh7ezuf+9znePWrX83+/ft93zB97GMfo1wuo2naLTfyUott\nriF2AxMAQog7gIPAn0gpM0KI/wf4vJ/FdgjRB+zC/EYQoj2Xd+jQIXp7exv+YJqNEIUQjI6Oouu6\nL7LyeqzaUQ0v69vntVqEaJqmM9dpW2SNjY2tue5G1ri8jGXUzgPajToTExNkMhmnUae2UeVWH19o\nBPd519MjrWf75DXNvBVNNdMLYZ67pNDT3vi719kmefGywtikYH+f9zp3KBQiEAhw5MgRPvWpT/HJ\nT36Sq1evcu3aNX7xF3+Rj3zkI5w4ccLXOeu6zoc+9CE+8IEPMDExwd69e329fquxjYS4DNgiyK8H\n5l3ziCbgq76xQ4g+4UU5pRGEEBQKBS5fvowQgrNnz64Z+TSzeS4tLTE9PU13dzf33HOP5zW8EKJb\n7Nse1fCD1fwQbZ/FXbt2cezYsZcNcTRq1HE3qgghSCQSTj3u5SYYsBZp1bN9skccLl++TKlUajji\nsNmEaJomVyZakLLiY9gIQoCU8NKQyv6+5m96S6USd999Nz/3cz/X9Brvete7eP/738++ffucSPRW\nxTYP5j8BPCqEMIBfBv4/12N3UJlL9IwdQtwi2I0n58+f5+jRo5vSHOKOrnbt2kUymfRFKoqirOrI\nkclk6O/vr5J1W8vwt94xam8obA3Yubm5DddObQYbMZhfr1HFVpTp7++vGph/OTTq+I1sG9XeUqmU\nM+IQCoWc6Hmzu0xNS0F6+JpKCfo6E0DZbJaDBw+ua423vvWtvPWtb13fiWwRtrmG+D7gH6gIeV8F\nPuB67CeA7/pZbIcQfaDZjbJYLDIwMOAM8m9GS/bS0hIXL15kz549HDt2zNEM9YNG9T03YZ0+fbrq\n/P3WHWufn8vl6O/vp6Ojw7f4QC1u5XSk7Y8YDocdRRC7e9EdQbm7F2+1a1nP+bhrb/aIgy07NzMz\nw/LyMtls1pdgt1eYpklvB6iqbabeGKoq2NXp7zdeuyfcqnOIr0RIKYeAo0KIDillrW7pLwG+HMx3\nCHETIaXk+vXrjI+Pc/z4ca5du7bhm5xpmgwODpLNZqtqhc0049QjN1srtLOzsy5hNas847avOnXq\nVNM6ivaaLxe5NRuKotRt1Emn04yOjpLL5Xx1cm72tW/G+uFwmHA4jKqqtLS0sG/fPkd2zu2LaL8H\nkUikqd+PaZrcdVQnHpMUihBpEIwXihCPSe475e93U9sUdDsS4nYO5gPUIUOklC/5XWeHEJuAnSZc\nbYPK5/MMDAzQ0tLiDJJPTU01PbJRL/pxR4W1Wqqqqvo2JHaTm5SS0dFRpqenOXXqVEMVi2YI0TRN\nzp8/TywW21D7qo3CZpJroyjW3aizZ8+eKssju5NzIwyENxI6OhmxjCUsojJKVDY3e2f/luwo2i3Y\nbfsiDg8PUygUqmYAvXbzWpZFKKjy3p8q8zufCKEoFqGa4LNUhqWMwm/8byUCPt/WWqeLW3Uwf7Ow\n3Uo1G4kdQvSB2tGLeikdW3R6cnKSEydOODUUWP+Qvf2jaxQVuqGqKqVS4/bxerDnEO00ZltbW10r\nq9pz80MeMzMzZDIZ7rvvPmfjWy8Mw2B8fJxwOOwoq7zcUc/yyDYQrlWUSSaTxOPxTU2xutfWKTOk\nXWJcHcUSEiFBCkmX2c0x4xSt0l+03+jm0i07t2/fvobdvGvdJFiWhaZpvPEhk1K5xB99NsjSsiAY\nqHxvy7pAUyW/+rMl3vQq/79PwzCqCDGXy+0Q4gZBCPHnwCkp5YObcoAa7BBiE9A0rS6xZbNZBgYG\nHCKpjXzWO2SvquqqUaEbzaRMhRCk02leeOEFz36LXptqyuUyFy5cQFEUYrHYhpGhPWfZ3d1NNpvl\n+vXrmKb5smpY8YpaA2G7USedTjM+Pk42m+XSpUtVow4bTZI6ZZ4M/ivLSpqY1YIqb46TLCqLPBH8\nNg+UX0ObbPe8ptcu03rdvPa4x8LCQpXUmh1FhkIhTNN0unrf9jqT77uvwDee1HjuUuWYZ45bvOEB\ng4THLKdBiYwygSUMAjKKMFtXEOLtljLdpC7TduBF4NRmLF4PO4TYBNwWUHCz6WR2dpaTJ082rIet\nhxB1XefKlSurRoVu+E1lFgoFRyTgVa96lec0ppfj2K4Xhw8fpre3lyeeeMLzeTWCe/zjnnvuQdM0\nJx1pWdaKhhW7FpVMJtesRW12PXKjSMqdYjQMg+eff55du3aRSqUYHBxcddShWQxpl0graRJW9Xdc\nIIjJGCWKPBd8iteX3oyCtwap9WiZNhr3sFPN5XIZKaVz/dFolNYWwdu/3+Dt3+/vWCY617XvMqu9\nhHQZ28rWAIHEISQnEAgymcymi8TfSlhPl+nc3Bz333+/8/+PPPIIjzzyiP2/rcCPAseFEA9JKX11\njDaDHUL0gXpqNfbsXHd395rpxWYJ0TAMnn32WQ4cOLCm76LfY9mNP9euXePAgQPMzs76qumtRoiG\nYTiEVOt6sR7kcjleeuklurq6nPEP2/7JPid3w4rbaf7KlSvk8/kq6bVbsaOzGbhTjPv3768adbAb\ndey0sp1m9drVK6VER2dcHaXFahz9hAizLNLMK7N0W71ILEoih8QiKKOorBRKtyxrwwTU6417XLhw\nASEEIyMj5PN5QqGQE0G2trZ6q0NiMBT8B9LKGGHZhuKKiHLWMqldzzCldtJn3kexWHzFZCW8YD0p\n066uLp555plGD48C7wD+eivIEHYIsSnYEdvg4CBLS0ueZ+f8qtwYhsHQ0BC5XI5Tp075ml30ErnZ\n4yCRSIRz586h67rjnu3nOPWad+zUrj1YvBGE4yZvP52p9ZzmbQuksbExstlslfTay6lb1Ua9Zp3a\nUQd3o87U1BSDg4OOLJ1NpKs16mSUNJaQTpq0ERSpMKtMI8Uy0+oFdFGonA8KncYd9JrHCXGzAWcz\nB/Ntdamenh4SiYTzHtjjHkNDQ84NlP0e1CPnefUSKWWUqOxEUP0+K1aAoJ7gevRfabcOVf52C0ut\nbQY2q4YopRylolfqQAjxsz7X+Euvz90hxCZgu8Dv37/fiVC8QFXVqkhmNdg2UHv37qW7u9v3HfRq\nEaKUkqmpKUZGRjh27JhjJmuaZlPi3m4CsSyLoaEh0um0Z8k4L3CT93o7U2stkNxEMTExQTqdxjAM\nRkZGnGaNW60TthZeZjAbNeqk02kWFxcdF5da6yf7tRaVBpq1IZhWL5NDJyJbid6oJ1qYzGlDLKlj\nHNffSFhW0opboVRjf37u98C2cNN1vaoW665BJxIJQuEQU9ozhGR8BRkCSGmhCA3QmVUubNp13KrY\nBqWaT604hQpEnb8B7BDiZsA0TS5evMji4iJ79+7lwIEDvl7vJY3ptlE6c+YMkUiEy5cvb9iQfalU\n4sKFC2iaxrlz56qIdr1+iG4lm3r2Us3CMAyeeeYZjh075tSKNhK1RJHNZhkZGSEajTI7O8vw8HBV\nGtavePVWoFlRgtoaXD3rp3g8jq7riLyClbCQyLrEYCMrFmjFIG5V628qqERlG0WRYVj7Dqf0tyIQ\nmy7uvRbhBgIBOjs7nRtDe9wjlUpVMjR6itydE8ToIBhSCAYCVRpwllV57wMySlodBbbX8/I2gFsG\naA8VAe9/AP4amAF6gJ8CHr7xb8+4tX7Vtziy2ayTgvJLHLA2Idrmuvv27auyUWrGlqnesaanp7ly\n5UpDX8FmjmN3s169epWZmZkVSjbrgWEYVY0+kUhkQ9b1AlVVq3zxdF0nlUqtiKRsktxoE+HtQj3r\nJzuCnBicoNhdJtM6QauaIBwOEQgEq6hRR6cklukyDzQ8RljGySkLZMUccdm9pRGiF7hrsQAlmeXZ\n4HnIC7LZLLquoygKoVCIYDB4Y6xDBQSW9HesVwK2WrpNSumo/Qsh/ohKjdFtAXUZeFwI8QdUpN1+\nxOvaO4ToA3YDxvT0NPl83vfrGxGiOyq0HR68vG41uCM3e+RhLUFxv7qkUCGKyclJ+vr61mwq8gN7\nnGLfvn2O59xq2OjO0Nq1AoHAikgqnU6TSqUcVRW73T+ZTG65ePdmydYpikJrayvhcJi77rqL/ezj\ncfWf0YtlSukSul5GVbUKOYQ1cpFlOmSUEKtfv0AlpUwRN7eGENezflBECSktKC0aLbQ4a5ZLJYrF\nAvl8AUVRyKcXyAyFN2zk4p3vfCcDAwN873vf25D1NhPbOJj//cCfNHjsa8DP+1lshxB9wB2xNWMB\nVW9+0Y4K65kD22iGEO3XzM7OMjQ05Iw8rAY/G6rd4GLX2Y4ePerr/BrBNqFdWFhwapCTk5Nb2uji\n5X2oVVVxmwhPTU1RLpdXzEJuZhptM3Vc3WMRCZK8xnoDz7U8RSFeJCJDmKZFUS+SLZdpmQxjxfMs\niEUikYo0Wz0yEiiYlJ31Nztluq6aMwq7jHsZD3yHqKzcEKmqSiQaJRKNAoJgKIBZyvDEP1V+b/fc\ncw9nzpzh53/+5zl37pzvY37mM5/hrrvuYmBgoOnz3ipss1JNCbif+ibAZwFvTRs3sEMO1W1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Lmsz2nzjR12qrkX8ALvZMO6La1A4FgUCDFDZFohGo1GfD4fL774IlarddOUZ7LXpSKy11uk7dmz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\nbW0tU1NT7Nu3jzvvvJM9e/YU3GdyDMXSDbsybJnuv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VVcnNzbX9kTYnBQJwpCtJ9N6HHE1sgWhzTEjGPKooSq8C0e/3U11dTXZ2NllZ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\nzz/P9773Pb761a/yG7/xGxteW6wO8wwBRHd6EflBs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EKvpIKVEUJWJqtfyRFscCloZoYTGAmD48TdMigsd8DRWBmIhkQtLUcM33ooWk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\nHC1cnc5modmEWEuXqZt6dhu+/N/JrVu3yJaNfLS2tnoPx+W/nTcSIQK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+/M//vCn72cSm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wFIeSTCliuvJGS14vP5GBgYIBQKGZVJdXV1VgQplbbT09McOHAgq03nqQgSMH6n8jOhaVpCRdHS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\nhcgtjcVihqXgyJEjBSNcu91OY2MjjY2Nxt5BORczK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9dAGp/2sISIPhxHARJMPlOgw+h+gaVigU6hE9CvQqEEX7XpS6N7B7ykAogETL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g8++CAPPvhgXq61VdjGoZqDwAeEEP+Uj4sVCTFLGFumuVSIi4uLjI+Pc+jQIX3UPRNkMuwiDbkr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\n272I/KBIiEVsGqdPn+bBBx/klltuSWivPvLII/zpn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S0sL48eOJxWIJieVG0I4hJC1/pIWF5UO0sBgy4s2jO3fuZNq0aX2WuBsKDAGZ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iguLqavry/tylx8n/j/RAQpyzKNjY26ibUxAipyL7C0tDTpn11Ozb2J5c4UXplJ6tYNSlBJAtvF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\nVU3T9Avy4uKi3l41RiqpqsrIyAg+n29TIpPgVfFOV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GDfHmUUVRkm5vNNT0ZGo1TTNS0UdKiaIoEVOr7Y+0+SJga4g2NoOI5cPTdT0i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\nEna5qcYPJCo8l7j7fNXYJ0QP2M5GVkpShmEwNjZGKpXalETs93n5Mdu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YXV3l/e9/PxMT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\nQrZu3aq3iefm5ujr69O/76c//SnV1dV86EMf4qtf/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0/ZEmJl9+TIFo0mdoHzTTlwRLR6ZWXdcTKvq090camqSJyamKqSGamPQg8UEz\n8aZLIUTG/f5OBpn4I6GtQL3pjzQ5lTlVBMmpch4mX0LizaOQLGAURenTAjEV7f2RxvoNf2RNTQ1u\ntxu3220WETA5JRCAtauSROvJlXQfUyCanBQyMY9moiH6fD4aGhrIy8uLNWjtS7TPjwwEAuTk5CCl\nTKiyY4wx/ZEmXzaEgC67+U2BaHI60948mu6hL4RIag9koGka5eXl+Hw+CgsLOXjwIH6/H0VRyMvL\nw+124/F4cDgcfUqoGOkj7QV3e3+klBJFUVJW2jEx6UsIAVa183FfBkyBaHJCMMyjmqZlnFOYSkOU\nUsYaz5aUlDB27NiYcIU2Qenz+fD5fNTV1dHa2orD4SAvLy/2SteY+UTQkTacyh9pmJTj/ZHxBQRM\nf6TJyaZbGmIf4xQ5DZO+jK7rNDU10djYyJAhQzLWctoLxEAgwK5du3A4HMycORObzZakQVosFvr3\n7x9rFCylJBQK4fP5aGxspKqqCk3TcLlcMQHpdrv7pObVkT8yEokkNN41gnZMf6TJyaBbPsQ+xily\nGiZ9kXjzaDgcpqmpiaFDh2a8vyEQdV2nsrKSuro6xo8fT79+/ZKOk87s6nA4cDgcDBgwAGgT0IFA\nAJ/Px+HDh2lpaUEIgdvtJi8vD13X0855suioXqvhjwyHw+zdu5fRo0eb/kiTE4cATJOpiUlqUplH\nVVXNOmJUCEFTUxM7d+5k0KBBzJ49O6Uml+2DXlEUcnNzyc3NZfDgwUCbqbW5uRmfz0coFGLr1q1Y\nrVY8Hk9Mk7TZbFkd50TQXtD5fL5Y0fP2+ZHtTa19USs2MTmZmALRpEdJ1ZECss8pDIVC1NfX09zc\nzLRp08jJyUk5rqe0HovFQr9+/ejXrx91dXXMnDkzZmr1er1UV1cTiURwOp0JptZ4bS0TToTmmak/\n0igiYPojTx9mzJjR83lM3Shm6nQ6p3e0pu3btx+TUhZ1Y2VZYwpEkx4hXUcK4++OIkbbz1NdXU11\ndTUul4sRI0Z0KAx7G7vdTlFREUVFRbG1GabW2tpa9u7di5QyZmrNy8vD5XL1SaFi+iNNALZt29bj\nc86wiy5LkgkTJnS4JiFEVTeW1SVMgWjSLTLtSJFJkr3P56O0tJT8/Hxmz55NRUVFn0rMF0Lgcrlw\nuVwMGjQIgGg0SktLCz6fj/379xMIBLBYLOTl5cXMrXa7/SSvPJlM/JEGZpNlk045RSTJKXIaJicD\n4+HZ3jy6Skj1AAAgAElEQVSainQm0/icwokTJ+J2u2P7ZKJVnkxUVcXj8eDxeBg2bBjQVpXGSP04\ndOgQ4XCYnJwc8vLyCIVCRKPRk7zq1KSr1xoKhQgGg1RWVjJq1KiUqR+mkDxNMYNqTE5nOjOPpiK+\nu338PPE5hePHj08ys/YlDTFTbDYbhYWFFBYWAm3n2drais/no7W1lfLy8lhgT7yptbtBLr1xrdo3\nY/Z6vQghYkFT8eNMf+RpyinUEPEUOQ2TE4XRoX779u1Mnz4944deqpzC0tJSbDZbLKews31OJD0Z\n/CKEwOl04nQ6aWpqYvDgweTm5sZMrdXV1bS0tKCqakIBgWyr7PT2tYovsxe/LuO4HRU1N/2Rpzim\nQDQ53ZBSEolEiEajCCESHnyZYGiIRk5hbW0t48ePjyXQp+LLqiFmglFiLi8vL7YtEonQ3NyM1+ul\ntra2S1V2elPgGOXkOjpm+6Ad0x95GmGaTE1OBzrrSJEpQggikQibNm1i4MCBzJkzp1MT4aksEFNh\ntVqTquwEg8FYAfP9+/cTjUYTTK3xBc17O6Ujm/k780eGQqHY9lSmVlNIfonojobYx77epkA06ZCe\natgbDocpKysjGAxy7rnn4nQ6M9rvVDGZtp83U4QQ5OTkkJOTQ3FxMdD2mfj9/ljATktLC4qi4Ha7\ncblcvVplR9f1bvk50+VHGv7I6upqhg4dis1mSwjcMQWkyYnAFIgmSXTUsLcr8xw8eJADBw4wevRo\nmpubMxaGcPIE4olInO8qhvBzu90MGTIE+KLKTmNjI8FgkK1bt2Kz2RJMrT1RZac3BG17IXn06FHO\nOOOMBH+klDLB1Gr6I/sY3dEQI50POZGYAtEkRk+ZRwGam5vZtWsXHo+H2bNnY7FYqKioyGqOTHIX\n6+vrOXjwYCw5vq8W6u5NjCo7ubm5eL1epk2bFquy09TUxIEDB9A0DafTGcuNzM3N7ZNVdoDY55eq\nyXJ7f6RRs9X0R55kuupDNAWiSV+kp8yjmqaxd+9evF5vQk5hV0iXh2iYYTVNY+jQoQQCAY4cOcKe\nPXsQQsQ0o672RPwy+i7jBVaqKjuGqfXIkSM0NzcnFDTPy8vD6XSmvU7dNZl2lY6KCOi6TjQaNZss\nn2x6L8rUJYTYDlRIKa/plSO0wxSIpznx0aOQuVZomLKMsVJK6urq2Lt3L2eccQbjxo3r9oMolUCU\nUnL48GH279/PqFGjGDBgAJFIhPz8/KRC3V6vN6knoqEhWdI0cDPWXRMWvNugcigk8FhgXn6UCU6d\nvvp8NT4PbwAONyhYVElJkcRmaTun9gXNo9ForKB5ZWUlgUAAq9WaYGqNr7LTlzqApAvaaV/U3DCz\nmk2We4neE4jFwCFAE0IoUsper9JhCsTTlPhgho8++ohzzjknq4edIaxUVaW1tZXS0lIsFgszZszo\nsVJl7X2Ifr+fXbt24XK5mDVrFlarNaUmF1+oGxKjNevr66msrETX9Vi0psfjSahBGpGCBw/Yea3B\nhk6bNSgKrK2xMt6ps2pUiIG2rmmQvSlUDtYLHv+whM9ediKEREqB3Sr5xjka35obwdnuY1FVlfz8\nfPLz82Pb0lXZsVgsfVpz7khIxvsjjQjXwsLCmBZpBu10k24IxKNHjzJjxozY37fccgu33HJL/Mz3\nAvcBQ4DqbqwyI0yBeBrS3jwKXWuhpGkaVVVV1NTUMG7cOAoKCnp0nYZAjO+HOGHChIQHeKbzpIrW\nbGlpwev1UlVVhd/vP16D1MN/BYfz+TErA2wSJe6ySAm7Awq3lDl4YkIr/dKnBJ5QKmoF3/sfNw0+\nG0OKJKoCIAlFYM16Kx/tUfndd4LkOtLP01GVHa/Xy7Fjx/B6vWzdupXc3NyYtt2TBc17WuC2L2re\n2tpKQ0NDQv4nfOGPNIN2ukgXfYhFRUXpCo4fA1YCB2jTFHsdUyCeRnSl5FpHRKNRtm3blnFOYVcQ\nQhAIBGK5ix31Q+wKqRLjw+Ewf68N8rHMIz/UTEtIoqgqlrgHZYEVaiKCF+qsfHdI34gI0HW451k7\n4Yikv1M7LgzbsFthgEey+5DC79+28tNF2a05vsqO0Wh51KhRMVNr4o+JRFNrXxQoRsSq6Y/sQXrP\nZOqVUs7ofFjPYQrE04BUDXu7k1O4e/duWltbmTp1atpKM90hEolw8OBBWltbmTFjRlbpGl3FZrPx\nTiQXRURwu3NBCPTj1y0UDsf8rDbFwjOHLHwzP4DbmXPSH5IfVyocaVTo79IIBJLXIgT0c0te/8TK\n9y+OdKoldkR8EXejoLlBJBKJmVqPHDlCMBiMmVqNVzq/LZwYH2U0Gk2Krs3GH5mq0s5pj1m6zeTL\nQkcNe7NFSsmhQ4eoqqpi1KhRRCIRHI4uPlk7OU5NTQ379u2joKCA/Pz8EyIMDUoDKg7aHoKCL0xp\nhvtNSokWjVIflnxSXoUj5MsqYKc3fHAf7VHR5fGiHx3IE6sKUR12VivMHtO12ISOSrdBW5WdgoKC\nmNm8I7+ty+VKWWUHTkwUa6bHSFdEIL7JsqIoKTt/mHw5MQXiKUq25tF0v86NnMK8vLxYTmFtbW2X\nWjOlO45R8NtutzNz5ky8Xi9NTU1ZH6M7qIAUAklq2SKEwKJasFgFZ06aQH+LjOX8pQrYSdXJoqcf\nmK1h0WYm7VTYSkJa14+dbem2jqrseL1eDh48iN/vj5mu3W73Cfnhk0pDzJT2/siOmixrmoaqqrhc\nrtPDH2m2fzLpq2TasDceo/B2+weFpmlUVFTQ1NTEhAkTEvxtqdo5dUb7VA0DXdepqqriyJEjCQW/\n0+Uhtp+3pzjPo7GnKb0GEdBhkE3S/3g6g+FbGzBgAPBFwI7P5+PAgQMJPrZgMEg4HMblcvXYmkuK\ndIzL1NGVkBKkFAz0dF1D7a5JM77KjoGmaTFTa01NDS0tLezYsSOrgubZoOt6p6ZbgBZ0NlnCHBM6\nLimYHrUxWCabWiE5P/Lo0aNIKWPpLddeey3r1q07dYWiaTI16Yu070iRqfkplUCsq6ujvLycYcOG\nMXbs2KQvc3cEYjxNTU2UlpZSVFSUFJxzMkq3LS7SWFsFEQn2FM8vKaElKrhjaLjDfMT4gJ2hQ4cC\nX6Qz1NfXs2/fPqLRKE6nMzbO7XZ3WXP56hSNx960oaX5OJqDcEahzphBXU/l6g2TpsViiRU0b21t\nZc+ePYwbNw6fz0djYyNVVVVompZkau3qtepMQ4wiecoa4FVrEA2JTtvzXsHP2VEbPw7l4iF9I2xd\n12O1WA1XwynPKSJJTpHTOL0xzKONjY0cPHiQiRMnZvVrNF64ZZpTmElZtfbECzhN09izZw9+v58p\nU6ak1JhOhkAscUi+YTvMq9oo3Co4456dER0ao4I5eVEuL9A6niQFRjpDbW0tw4cPx+l0EggE8Pl8\n1NbWUl5eHqscY/gic3IyC9gpyIVvzo2wdr2C25o8PhiGUFhw56WhbhUVOBHdNFRVTalxG9eqq1V2\nDNIJdYnkMZuftyxB8qTAEif4JJKP1TB353j5TasHVxqh2FHgzimLaTI16SsYDXuNgAejIHc2qKqK\npmkcPnyYI0eOZJRTmKk5Mx5D8NbW1rJ3715KSkqYMGFCh+s9WcW9F9rqmDxoEP9d66QhImJ2SIuA\n64sj3DI4grWLipJxPkIIXC4XLpeLQYMGAW0PUsN8WFFRQSAQyLgf4ne/GsHXEuXFTVaCEuw2kDqE\nowKbCv+yNMSsLgbTGMTnrfYGHQkrRVFSVtnx+Xw0Nzezb98+WltbsVqtsR8THRU0T6ch7lWivGMJ\nki8FSjvjs0CQLwXVIso6S5BrtI79nfHH6EvVfXoN02RqcrJJZR5VVbVLgS6RSIRPPvmEQYMGZZxT\n2BWTqa7r/OMf/8BmszFz5sxOOzBkKhB746HztX4RLh/Qyj/8CvURgVOBs3KjMY1RIjkoNI4pUSxS\nMEq34kyjNcTT0VpVVU2qsGME7LTvhxifFN8W/g/fu7CFOcPqKW0cx65DChYFzhsf5aIpGnk5mZ13\nFEkEiR2BaCcU0kWZ9gTZmGTbXysgdq28Xi/V1dVEIpEks3S6Y/zF0gqQJAzjcUnBq9YgV2k5qB2M\nixeIwWCwV6Kx+xSmQDQ5WaTrSKGqaixXLhPC4XDMbDlp0qSYiSoTshGIUkqqqqpobm5m4sSJsV/5\nnXEy2z9J2ValZmpu8jnuVsI8ZfNRJbSYCBTABZqTayK5GQvGTNaRLmCnurqalpaWWMCOqqoMyA0x\nf2b2BQOqlBB/tXjZpragAzYEczU3CzQPRbJNK+1tbae7PspUBc3jzdJ79+4lEAigaRr9+/dPqrKz\nS9XIkenPz47AK3S8QtK/g7HxArGlpaVHA6j6LKeIJDlFTuP0oLOOFJkKqfY5hUDW/fIy9SH6fD52\n7doVC5zIpuxapgIxVYRsb/GpEuS39iasCAqkEtOiNCTvWgLsUcPcG+xPTg8JxfakqrBjJMXX1tbS\n1NTEli1byMnJiWmRnQXsfKQ286TtKALIkyoqggiSv1l8bLQ0c0doIGP0nJNmMu0K5eVhnn22hS1b\nQkgJM2YUce21I7DZyhgwYAChUCipyk5knBvdqoBIfy91dke2F4jd6fjypcD0IZqcSNo37O0olSIT\nDbG5uZnS0lLcbncsp7CxsbFLEaPp9olvAzVp0iTcbjeffvppVsfpTCBGo1EqKio4cuRILO/LqKDS\nPvevJ2hFZ7Xdi1OKJIFnQVAkVaqUCK9aW/hmJC/lHL2h8RpJ8UIIbDYbo0aNStKMgFgQisfjiQXs\nVCkhnrQdJVcq2OLOqU3gWwgQZbW9hpWtw/qUyRTaIn7rGgURDQrzJQ5b2/X97W+9PPFEM7oucTja\n5nv55RZefdXPvHlWHn7Yg8PxRbCYEQE82tfKe26BFo6iKErKZPsgknypkJ9Gk9Q0LZbacdpoiKcI\npkDsw2Rbci2dAIlGo+zdu5fGxkYmTJiQUHarK/7AdPscPXqUPXv2MGzYsIQ2UNlGpqYTug0NDZSV\nlTF48GDmzJmTEInY3pRoCMlMteCOruNWNUgIiTvNz+F+usq7llYWR3JxdKAl9paW1VnAjtESq6Ki\ngtbWVmw2G38dm4OmglVxpExidKJSj8YmSwtj+ojJVIvCaxst/O87VuoaQRGgqrDwPA2ONbJmjQ+3\nW0GNK+rqcKjouuTtt6384Q9+7rzzC4FoRABfJzQ2O5pw2gVCl2hRjUgkQjAYRCJRFBW/3cKSgA0U\nSULl9zjiNUS/309ubm43r0wfx/QhmvQ2XSm51tHDqjdyClPtEwwGKSsrA2D69OlJwQTZHieVAI1E\nIuzZsydWS9XpdBIOhxOSvocMGRIb6/V68fl8HDx4kEgkEtMiU5UO64xP1VCnXxgrgmZ0Dikao/Ts\nzNA9QbqAnfatnvzBVlbnHsAZjOKLeNGlxGKxYLVYsFitMS3HKRU+sDQzqg+YTMMR+OV/2dn0uUKe\nCwYcj6mJaPDS3yzsKXUzyNWCqiZbShRFkJMjWbvWz4035uN2Jx5rhLSwOOLgFWsQlyKwKTY4HtQb\nRdIkowxvjTK+vIbtLRUJ5uu8vLxYI+p4X2tzc/PpYTI9RSTJKXIapw6GebS0tJThw4djs9m6/BBq\nbW2lrKwMRVE6zSnsikA0AnuklFRXV1NdXc3YsWNjQQ3tyTZIpv15G+a/4cOHZ5RrabVak1oZtS8d\npqpqQh3SdL0co5AUeZly3cfHnmiyDXoRDhsWqxW35YsQVE3T0CIRWltb0TQNRQiwWWi0WohEerfW\naCYC8ek3LWz6XGVggUzIqbRawCKjRKXC0ZxinNrhlJ+UokiiUVi/vpVFi5JNmTdGXPSTKs/aAnjR\n0SGm538lmsMPRC7OCW1adyQSiWndtbW1sUbUoVCIhoYG7HY7fr+/WwLxxRdf5Ec/+hGPP/44Q4YM\n4fzzz+e5557ja1/7Wpfn7BVMH6JJT9LePOr3+7scxBBfCi2TnMKuCkQpZazOqcfjifkkO6IrAlHX\ndUKhELt27epUsGcyn5HPFq9FGqH6hhYZiUQ4fPgwhYWFCVrkCN3Kx2oo7TGiSHQBA/QT/4TIViDa\nUbAg0JBYjosPo5ODodtLKfFqIVwhnYaGBhoaGhJ8kd2psNOezgRiKAz/9zcr/fMSheEX70uErhFR\nrbQKB04ZTBojhCASkdTUpC6sIBAs0XJYqDn4WA3TJCR2CdN0G/1k4tqsVmssWAy+KGj+ySef0NDQ\nwL333hsLKCsoKGDWrFlMnTo1qwC2q6++mtdeew0pJf/+7//OwoULM973hGFqiCY9SSrzqMViySqF\nwkDTNDZv3kxRURGzZ8/O6GHVFYEopaSuro7a2lomTpyY1HC1p47T2trKtm3b0mqe3SFVl4ZPP/0U\nRVE4dOgQLS0tsXZH4/u5kUN0oigd5qB5hc7Zmp38Dn4yx/8gCOph9kYDKAgGqnb6K93LV8s2YEdF\ncJ7m5n2Lj/4y9aNACEHUpnI5AykoEBQXF2Oz2WJa0d69e5FSJpgOM60a057OooXLqhTCYcjrICde\nVQ03qKRFOFMKxLbAIHC50muidgTnRLP74WUUNLdarYwZM4bnnnuO1atX4/f7sdls/OEPf+AnP/kJ\nEydOzGpegK1bt1JWVhYrZdenNERTIJr0BOk6UiiK0qWcwlAoxPTp07OKbMs2ob++vp7y8nKcTicz\nZszIqgNCpg9tv9/P559/jqZpzJ07N6OCzD2BEAKLxUJxcTE5OW2mxPhef9MirWwstpGvCRwWKxZr\nm0YlEPiEjhXBNZH0JrIjsoXH/bvYoQcIo6NJC/6QDSeCKaqT5a4hzLB70s6Rbv3Z8BUtj79bmmlF\nT5kq4hNR8rAwPeriwPEoU6NhcPuAHZ/PF6saY7PZEkzRmRTo7kwgBsN0XL0c6N9PaRsgQe9goJSg\nqoK5c3snWb79/R0MBpk4cSLXXnstt9xyS9bzvfLKK/z1r3+ltLSU119/nZ/85CcsW7asp5bbM5gC\n0aQ7ZBI9mmmSvZSSw4cPs3//fkaOHInP54s9yDMl3h+YjnA4TFlZGZqmMWbMmFhNyWyO05ng1XWd\n/fv3U1tby5gxY9i3b98JE4YG7QV3vBb5IyRnWJr5k6OZxmgUqYXQwgFA0F8qfM+XQ/8cDRyp1/x3\nrYa/6IdxqEGcIopFtxG1WLHbdPSolW0hjX+0hpgRyuE+xwjctsy1lK4kzhdLG7eGBvKf9hrq0XDJ\nNjNqGJ1WIclD5YfBQThRO5w/VcBOKBTC6/UmFOiOb4mVKqCpvYugpQXWrbPwwgtWmpoEuQU6DRZB\nv1xJKrlptysUFSnU1IOV1PdzIKAwf76dkpKe66ART/vScN2NMl28eDGLFy+O/b127druLK/3MH2I\nJl0h0+jRTARiS0sLu3btIjc3l1mzZmG1Wjlw4EDW+VydCar4RP7Ro0dTXFxMQ0MDXq8342NA5xqi\n1+tl165dMXOvrusnpVJNOhQE39DyuFhzsckSpNqmYZeCM8MWhja10uL1sftALaFQKJYc7/F4yM3N\n5bHAP9g4oZEiSwSJJBKx4bE2YxURpKLQancSdHhp9OfwqSjkwdABHrCNyXhtXa0kM17P4V+Cw/hI\nbeZDSzN+oZMvLVwZ8TBTc+E8/rTL5r6y2+0MGDAgocKO3++PRfy2tLQkBTTFz//55wo3fMeB329U\nYYKDh1Ua8wU1R2DmtCipfveNHmOjsSWKrGsmZJfYj7csCYclfr9OcbHOqlXpferdob1APG0S808R\nSXKKnEbfJ9uGvenMmEZCekNDQ1JOoSFIs9Gq0glEv9/Prl27cLlcCUEzXSmr1tFxjBzJpqYmJk+e\nnPCLuq8JRAMPKpdocWZpAfR3UtT/C1+kkRe56/Ahfu9oIDCgFociaQzmISKQaw3RGnXSIi1YFA2r\nGsZqURjg8uOPRNgSVtkVamaiPbMHandKq+VLC5dq/bhU69fhmO7M31FajGGKPnLkSCy4ad++IDf9\n0yjCYYjvGWyzgRKV1EUEWz9WOXd2lPjbXJfQ4FP47rIoA/VcnnyymebmtvvN6RR873tupk49RkFB\n76kzPa0hmpxYTIHYy3SlYS90rCEaSe9Dhw5l9uzZvZZTqOs6+/bt4+jRo0yYMCGp5FpP9UOsr6+n\nrKwsZY7kya5l2t05XC4Xx2wqzzmb8VoacVpaaQ71g7DE4QzTigOkCiKKjCqEInZUXSdqkeTaWglI\nH+uitUyk9wXiyZi/fUBTWVkZ/fr143eP5tPSInE4QgSD8njxchVVUchBUBiBYwj2VAqGDWmrOesP\ntjVAvnCmxs+XR7BZ87jpJjd1dW3foaIilWg0TFlZ79r2UglEU0P88nCKnEbfREpJfX09qqqSk5OT\ntRkzXiAGg0FKS0sRQqRMejfItsC3cax44WZUgRk0aBCzZ8/usCVPd8q9hcNhdu/eTTgc5uyzz+7Q\n75mJYDpZgrMzjuiNrI3UcTDnKE69hXDUjhACpzuA1C1YFB2kjqZbiFoUrHqYsGZDiwosSpRc1UtF\nsPlkn0aM3mgQHI+UkpwcJ6+91h+XC6xWC9D2gzLmapA6FiHoF1XBJhgyVRLVFcadobPofI3xw/VY\nSoaiCOqkjT99buGYX1DkVJiW6+KsXjuDxLJtcJok5oPpQzTpmHjzaG1tLS6XC6ez4/5pqVBVNeZr\nPHDgAIcPH2bs2LGxJPOO6I6GGIlE2L17N8FgMFYFpjeOc+TIEfbt28fIkSMZOHBg2n6IX0bChDgi\nanldq6NMV2nV/eQIDV04cah+VB3CQkFFByGxqNHjQtGCqkUJk0Mw6sBpC9Lsr+cf+/8R80Wmy/v7\nsmmI7WnLOVUIBOALGSJQFBVFUWNPKyl1REQnWiv5/tc2oWkaLpcLt9VDc3NbwE59QOWfXnBQWqcQ\njrYl1+tSRchJPFcleGxJEE8vBJqaGuKXm1PkNPoO7YNmLBZLl3oUqqpKU1MTmzdvprCwMOOcwq5o\niEIIWlpa2LJlS6dCKn6fbLUyowmxx+PJqB/iyaQrGqdEUh+uZ7t2jM+Fj21BG8dEBKsATXGAJhCq\ngi4kIJBSRQiJDlhEhKguiFgsRMMCRQgiUnBevovRntFJhbqNaE2Px5NQMqy3K8n0tkDMyVGO/x86\nOhUhFIRQyMuTnH322QkBO4cOHaKuMcBdH55NXaskxxrFZVWOWyckmib5aL+F65/N4cVvt2Lr4Sdg\ne4HY2tqa9Y/hLx2mQDRpT6qGvdA1ARWJRGKlxWbMmJFVTmG2mlsgEKCsrIxgMMi5556bUb5Ytscx\nSrsdOHCAoqIiJk2alPH6TgZdeejvb9T4R0MDn8hG9skIUbtO2NpCTm4Ym/CjKQI9aiOMwKGGsBBB\nk1ZU2p4nUgishPHLtoen1AV6VLBAzcdpa8v7GzhwIPBFt3iv10tdXR2tra3k5OQgpcTlcqXtCt8d\nToTAtVoVLpgf5b33VdLFooTDsPSatgC19gE7H2620hCx4bTryDj/vWFLtSk6u2oV3tpt4euTUles\n6Srtr/2JbE120jDbP5kYpGvYC20CMRRKX+4rfq4jR45QWVnJgAEDYh0LsiFTAWzk+9XU1DBy5EiO\nHDmSsTCEzAViS0sLO3fuJD8/n9GjR2eU75gNmqZRUVGBqqo9XkosE3Qdth4OUhbwEVR9VKkSe8RP\nRERoalUI6TZUd5vgk0JD0y0Eo1Hy1BY8kWPkac0IIQkIF35rLq3SiUOEsKg6ObrGOMfQpGO27xZv\nlAzbt28fzc3NfPLJJ0Dqdk/dobdMplLC3gOC598ZiGWjk34lOrpUiUZJmW8YibRtv+661PfS/7fZ\niipAVZQENVOLRtGjUXQ9SliL8vDbrYxRK2LXqKsVduKJj/Dui37tXsHUEE2g84a90FYb0u/3dzpX\nS0sLpaWluFwuZs2aRSAQ4ODBg1mvKRNB1dTURGlpKUVFRcyZMyemkfbkceKjVCdOnIjH46Gmpibj\nHweZYETcDhkyBCEEtbW1lJeXx7oQGH63rtQ+zeRhFiXKZw2tfOL3ke+I8HlQI6wHUZQoPl0lEnUQ\nbomgqQ78VoFQBFY0iv11nB36BzmRABFpJWoX6IqVJs1NVBFEnYJANIcr7cOw0vnajZJhbrcbm83G\nwIEDE9o97d27N6ZFxtcgzbbgQW8IxKZm+OXv7HxSquIPFOPOtRGNCtRCqK0S5OVKXK425U7XIRBo\nk3G//lWI4cOTP6NIFGqaBa4Uv+0ExPU5hLpwPoMGDcLr9VJZWUkgEMBqtcbyIvPy8rI260ej0YT7\nLdOo8i89p4gkOUVO48SSacNe6Fxji0aj7Nu3j2PHjsUEB2Rfui2T42maxp49e/D7/UyZMiWmffZ0\nP8TGxkZKS0sZOHBgQpRqT0WDGhVzotEoM2bMiK3FKCWmaVrMpHj48GHC4TBOpzMhST6d6S+TB1gj\nPo7iZ3OjjuoIUqaFKZdhQgro0TwIR7FbAihoKKqGRSrIVsEZhyqY3LqTXNlEMM+FUCQEVEIqqFaN\nmXIjx5r70a/cz4XTr0EZnLnWHi+w2lePMbRIr9fL0aNHqaioSKhBmokW2dMCMRiCOx90UH5AoTBf\nYlMj5OW1RboU9pNU5QkChwV+P1itEI3C+edHufUHYc4+O/W910GLwiQkoCrE7gmDUCgUu3cOHDgQ\nC9gxrlFn9068ybQrsQNfSkwN8fQk24a9kF5AHT16lPLycgYPHpyU3tAV3yOkFlRSSmpra6moqGD4\n8OFMmDAhYd09lVMYL3DPOuusJHNvV47TnpqaGioqKhg1alTMp9beDGuxWJK6EMS3fTKaBxsPw2w1\ngTIOs1VU4w1a2S0t5Gh2DgcFwiohGsUhNCJCJyhtbW6rYJTBTYcoPlDLqJbdeHMKELZ8CEYJO61I\ni8Cq6zjcDQytK+eMijCjmjREVQ3itnkZryudwDK0yJycnARfpFGDtKKigkAggMPhSKge05tl8/62\nWSr5ZFEAACAASURBVKW8SqGwn9G9oi3YqG29UDJacqwA7lgWYs5knfx8Sbt02CRUBaYO1vnsiEJO\nu98S0pgYCGpw8bhk/6HdbqeoqChWSN5oPO31ehOKvafqgwiJAjEQCGTt8jA5uZgCMUO60rAXUgu2\n+Ea6Z599dsqcwu4IRKMaDrRFuZWWlmKxWDqM7OyqQIzHMF+WlJQkCdz4fbqqIRotoFRVzTpCNVXb\np3A4nKQJ5Obm4vF4Yp9ze5oI8LJezmfKMbSoij8YodZuQ1qDSKFiD0IonIMjpxlFathsIWx6gME1\nVRS2HGJwqBZ7TgQrYfK0eqJSITcgCPa3Y4+2MLT8AHZHC/28tXha8rFIleDuTTjGzcnoPLPV4FJp\nkUYN0vr6eiorK9F1HbfbjcfjiZXS6ykt8Zl1Vhz21K2coE125Tgkf95oZdnXkztXdMR3zwlzxysO\n4uJo2ji+dl2CRYF/mt25P1tRlKR7J94CUVNTQzAYjJmj490jLS0t3apSY/RCfOCBB/jDH/7AoUOH\neOKJJ5g3L/MfSScEM6jm9MEwj5aXl1NQUIDH48n6oWMItvicwjFjxqRtZ9RVgaiqKuFwGCklVVVV\nHD58uNOeiN15wIVCIcrKypBSdtqrMD4xP1OMOqr79+/v0RZQNpstoXmwruu0tLTg9Xpj5eria5Gq\nbiv/E93HPqWZXOzIsBW71JCtOVhliFa7JKRqRDU7QQVyXK3kcoyipjoUIuT7mnBaA9i0EP1kA3al\nFYcaREhJbpUPO024a5tw2oMIVUdr9iFc/dD2boEsBGJ3EELgcDhwOBwUFxcDbRqPcV3C4TBbt27F\nbrf3iBa5/5BCf0/6NTtz2sYlCbc0XDI2ytfGa7xRZsEiwBL3sI5EAQnXT48wc1jXrBWpLBCGOToY\nDLJnzx7+9Kc/UVFREesXOn78+KwjdI1eiP3792fjxo389Kc/Zffu3X1TIJ4ikuQUOY3ew/AVxvsM\ns8FisaBpGl6vl9LSUgoKCjLKKeyqeVFVVQKBAJs3b874WF1BSkk4HGbbtm2xgt+dYTQVzpTW1lZa\nW1tpamrqtPlwd4k3g/n9fgYPHozdbudYfQMHD9fxoVbH58MiDLBCSFgQwoLdrmO1gR4VKFGFiA0s\nVg1p0eivHKEw0kBOqx+9VcfT3IzI0ckNN2HVIrgI0K/1GNbWII7GILpdEpVga4WIHWRUQSg29Cyj\ncns6gMOI3vV4PNTW1jJz5kyCwSA+ny9BizS062yiNS2Wtvqj6cSE1NvGZXNaigKPXBFibJHOf2+y\ntQlBQNPBZZf8aH6I5TN6Lt0i3hxdU1PDxIkTmTRpEi+++CJPP/00K1eupLS0lHvuuYelS5dmPb+q\nqrzwwgtUV1fz4IMP9ti6e5RTRJKcIqfRe7TVUVRigi1bdF2nubmZPXv2cOaZZ2ZsQslWeECb8D50\n6BBer5fp06f3WoWMQCDArl27iEajnHfeeRmna2RqMjXyFg8ePIjNZus0b7EnfJPxCCEIRTR8YR2v\nBtLtpkpvwBmEsKbTqoDV4icY0rE7/HibcxAigtAgYpMMVI+SEw0Q9imIYA45IS9RTcEe8ePxNxFW\nHLjCXqy+VqxhDSUiQYWoBXQLyBBY7bnoUYnIH5Lxunu7koyBoUXGd7IwfJFGtGYm/RDnTIny4Scq\nBfkd3xNNzYJzpnbBUqLA7XMjfHdOhK3VKt4gBBqqmT8+h6KCjguYdxfDh2iz2RgxYgTTp0/nscce\nA7IPsjF6IX700Ufs2bOH8847jzVr1nSpr2KvchJNpkKIc4H+UsrXjv9dAKwGzgTe4v9n782jI7nv\ncu/Pr6p619KSRhqNZjSLx7Noxh6PZ7ONsxKTxIGXkLAkXC7JuSx2CFwgJLzxuYDJwRDgJnDhvCxJ\nCLyEhBBIbnJ5k5CQfbXjZeyxPZJGMxqNZtMudbd6r+33/tFT5VKrW+pqqSWNrYfDiUet/lV1q7ue\n+n5/3+d54L1Sypo/QJuEWCP8tjAdTeHIyAiqqvoK0q0HU1NTbls3FAo1hAyllIyOjjI+Pk5fXx+D\ng4Orrl3MZrP09/fT0tLCXXfdxeOPP17Tea0mDMvi6uwcsaYmQqEgk2lIhAURVcPUdWxbINrChENg\nBhSEopOYU0GaKIEiHcUZInoaRRfEyBIT87SZc0TMHIatEZQGIfKl0ki88P+KDYTAGoX2Xd1I0UTk\nyGtqPu+1IsRyKIriVoe9vb3AC3mIc3NzjI6OLqgiW1paiMVi/MzrDb77tFdvuPDcLbtUQb71/vq1\nq0EN7t1T+t4ODaUJao0dcvH6vZYnXfhtmZZnIW5YrG/L9E+ArwNfuPHvDwBvAL4G/AqQAh6pdbFN\nQlwGzgXGT4Xo7EFFo1HuuusunnzyyYZdqLwDOidOnKBQKHD16tVVP878/DwDAwMrasMuVSF6yfbQ\noUML0jXW8kIvkczkMrS2bCGoBZkqCgq2RUCApgjQwmh2FisLaotARRBpkoQCBRIZm1BhioicR7Fs\n2knSos+jiCKqpkNWISOa2C6voyo2qmo7B8XSQA2AMQua1YJmg3nkjWjtte+ZbiQheKU8RGcvcnR0\nlFwuh6YFuO/krXzp+1uItyx8fr4A81nBW15vcKxvdar/Rjn4eOH9rL5kop/WlxD7gD8FEEIEgJ8C\nflNK+Q9CiN8EHmSTEFcfmqah6/qSv+PVFFaKTFpNOG3Fq1evLhg20XV9VduHTvZiIpHg8OHDK6o8\nq1WI6XSa/v5+Ojo6uPvuuxfcSTsk2mhCtKQkjclEcY7LSobeYISiGUQSJaootFoqiZBORAYRZgRh\nZ1GlQtguYBUsbAltqs7WwlX2xK+TtqKYVoBsIE68MEOxpRkxlaZDJCkKhYChEVSyqAGQTaBokL0K\nkWyE9n37MQ/+BLHX/Dffr2OjisC9e7TeKnLHjnm2dVzjU//ZRiqjkM0XUFSNthbBb/83izffZ/na\nP1wKjU7rKMdLIhzYwfpNmTYB8zf++xQQ44Vq8Wlgp5/FNgmxRqiqumSFODMzw/nz5ytqCleCSmTg\nTK7F4/FFwyb1TqdWOpYTA9XT08OpU6cqXmz9kFV5heh1szl8+DAtLS2LnlPrXmq9pCmRjGQMzpsJ\nsoEkikgxH9a5HpynqBVoFSHazU52ZDuYjY2jCIugAK2Qpy05TsBKUbDCJLRWpKHQMZ2jNaRjRIME\nAnmiWp6uxBwhO0VgSwIlaVMsRIiRpzApUGZLwzT6PMT3vZzIK16FduKNxHbu9/9aGnjj0Ijqs1RF\ndvIr/xV+4adN/s+XL9DWsRuFNF2tUxTyWc6cecE5prW11VeLvhxrUSF6kclk3Ar5RY31rRCvA3cA\n3wXuB85KKaduPNYG5PwstkmIy2C5lqnTspRSVtUUQn0XK4cMvKLfixcvMjc3x6FDh6oSSD0VovM8\nJ3ZqaGiIYrHInXfeWTWr0G/15j23VCrFwMAAW7duXfIGopFZh6dnEnz58ixX9Fns0DyWEiBgCJS8\nwjYLlECI+ahJUhsjZPYQTzSRaE3QaU7SnJgiaFgQ0RAhG1NLs+dqkqZZ0NoE25UEqDlCQR2tw0CL\nBJFqnEwUWmeniGSLWLEwanMPW175LpTbfqKUaJFKMTKRJDD7jLsv19raWtOEbaMJsZHVpxA2B3YV\nOHo0DISBFzoeqVSKZDK5SC/q7EXWevNpWVbD8xy979FLpmW6vvgX4P1CiFdR2jv8fc9jx4ALfhbb\nJMQaUV55SSm5cuUK165dW1Yf5yWbeo6pKIpbgW7fvp277rqrbqu45c5xZmaG4eFh9uzZw7Zt25a8\nCDrPqfUi4+gQh4aGSCaT3H777cteMOrRLi6HpFHkb5/uZ3BykrxqEY5lkGkFXQuRD0UpGFHGCipb\nZZ62YggjYtPbnOBwupvpzChGaIp8CHLxALYMIGxBb6bA7SmDppFh9NatRINTZFFIRiQ5NUImEkPt\nsGjJTNMcNhGF3Wx92W/ReuhNpX4ppUinHTtKZt7OUMrM3AyPZ55iomMaERZssTs4Jo/QE1z8t2kk\naa1F9FOlz1EwGFzkHOO4Dl25coVsNksgEFgw0VrNtGGp7+BqvHflr+El0zJd3wrxfUABuJvSgM3/\n8jx2B/BpP4ttEmKN8FaIjqawvb2du+++e1mic0jKLyEqiuI6zViWtWQF6n1OPQQipeS5554jEAjU\n7ATj91jOnX5nZ2fVFmw5VrNCtLH46NBFvjl6kbmshqVoKJaCpQRQmkoVRKSQQrVyWNkWMs0hBCZm\nMUxUneVAdJ7d40kSSUlBt2jqkshCkl4ZYIuIEOhoRSoh7OE8OdGN6EgSExqGVUSSIWLkYC5LXJ6k\n9fhvE9x6W9VzDYVCWN2Sr+z5DgYGJiYSmJKzDMrzdEy1cWz0dtqa4y4RNHKoZi2in2pZ3xv15Nw8\nVHIdisVibnXtVJHlpDc0NMvHP36Wr3zlEoWCxZYtEX72Zw/x5jcfoKOjcldkKXiTLuAlVCGuIyHe\nkFT8UZXHfsLvepuEWCM0TcMwDAYGBshkMr40hfVUbY6N1rPPPsv+/ftrEr6Df5KSUnLt2jXS6TR9\nfX2uPdVqHsvxOM1kMsRiMXbv3l3zMWohxEwmQ7FYpLW1tepFdb6Q5c/OPUb/TIGQSLK93UQN2VhB\njTniZLJRYloOK2KiWRa5WUkwVKC1U2IoFnk7DUUIUSSmBDgQg2ZFwzCKtMVMFNtAqGH03T1Yg8NE\nmm8hMtFCoFtDxCYJxDoQVjOXR2yiJ9+4JBkCzIoEn4p8FkMYqDf+r/SGlPY9E90phtsv8/Kr3a5Z\ndz6fx7IsOjs73ZSP1arqGt0yXUkFWsl1yAkMvnr1qutdWygUmJmZobW1lc9/foT3v/8xLEvS3Bwk\nGg1QKJj89V8/zSc+0c9HP3o/+/a1+zoP0zQX3PS+ZAgRGjVU0yOEOAP0Syl/bqlfFEIcAV4BdAAf\nllJOCCFuBSallOlaD7hJiMvAuSBPT0+TTCbZvn17Va/OavBLiJlMxhW+33bbbW7uXa3nWysczV9z\nczPt7e2+p2JrGXiZmZlhaGiIXbt2sX//fp588klfx1iKEC3LYnh4mEQiQSQSYXh4eIEmrrW1lUAw\nwODMJJ8a+Tr9802E5DwUNJJ6HGFIlIiFGQkRjElS6TbaA9OomonaaZBMxdjZMUnUMjDQyM1n2MI8\nO9tsyGmghGiNBEpptcEiWCG03d3IXApmU0gzjq33ogR2YsfjKKqKHksSPHT7sq/7u6HHMDBfIELv\ne4JACsmV0HXoUdjfXRrAOXv2LO3t7eTzeTdqKxqNLoh8qrfKWwtCXK0KtDwwGHBdlebn5/nqVwd4\n5JF+YrEAsVgAIUqvLRTSCIU00ukiDz74Zb7whZ8mGq19iKe8C5TJZCru87/o0LgKUVKi2qr5eUKI\nEPAJ4M03zkQCnwcmgP8JnAceqvWAm4S4DKSUPP3004RCIWKxmK8KykGtGsbyDMHr16835CJk2zaX\nLl1iamrKlYc899xzdRl8V3uOYRicO3cOwzA4fvw44XAYKaXvtl41QkwmkwwMDNDT08OJEycwTRNF\nUTAMg1QqRSqV4smhi3x2Kk3SzGFYzUwXu9miBSmqAQIhk5AwmCcOhiQcLBIMG2QLcdpaZonl8xAL\nUlQ12sMGttWKqUaIRwrYgQxhEjQrQbRoBHPexLZ0VGGiCI3Q3q1AB+awgZAKoY4O1M5OAjt3Yl+5\ngrpMPmOeAhe1SyhU/9sLBLa0OB04w48WX+u+V06LEEqfXSepYWxszE1q8N4w1GqS3mjJQqNbssFg\nkEAgwN69e/nABy4QiUSIxTQsy7ohVbIQQqCqKpGISiJR4GtfG+XHf3xfzccoJ8SXTIW4AkKcnp7m\nxIkT7r8feOABrxPPBCUpxawQ4neklNMVlvgj4D7g54GvApOex74EvJNNQlw9CCE4fPgw4XCYRx99\ntK41aqkQHYnDtm3b3KnLiYmJuiUU1eBMd3Z1dS2Y7lRVddUyEScnJxkeHuaWW26hu7vbJfV6yL2c\nEJ2qMJVKuRFT3vcoEAiwZcsWzplF/m4ii2lbbCHNuLmV5miW5qYMptmBYYQQQkGYEkWRmMUASgiK\npoZtCwLohLQCObuJcCRHc7GJriaNVjlBMdBEtDmNZiYg3InW0oxVmMfKF1BssE2JEooSvfMQkQOH\nUYJBUNXS6x8bW/Y1p5R5FKlgi2X+HgKm1Bn3n+U3DkIIYrEYsViMnp4eoHSj4uy3Xbt2DcMwFuy3\nNTU1rVheUw8aPQEKpdeQShV5/PExWlpCKIpAUVQcJYcT71aKeCvykY88yt69el1ZiPASGqqBulum\nnZ2dPPXUU9Ue7gYepySpmKnyOz8L/K6U8pNCiPKzuATs9nM+m4RYAyKRyIoGFpYiRF3XOX/+PMVi\nkaNHjxKNRt3H6g0JrgTTNBkeHmZ+fr7idGc9xyonRF3XGRgYQAjhO6JpqWOYssCsuMZc7irXro7R\n09THif0nUMTii9P1pM7/GT3HN85dQ6LQKpJEpE6sRSekzhOJFSgUcuhKECMSROg2lq2iKSaCUhSR\n1G0MJUQwZCM0HWGGCeda6OgANduJWpwg1NQGqXHsQgYRjKI0x1CDKjKbRoZ7CLUdI7y7D1GHIbmK\niqQG7SWgyoXXgOVIKxAI0NHR4aafSCldBxnv1KY3KzIQCDR8yrTRFaJD6JmMjqoKlApJwkIINE27\nMRijImWYXbt2LcpC9Ooiveku5YRYKBSWHYJ7UaBxLdNxKeWJZX6nAxis8pgCLN2OKcMmIdaAlU46\nViJEx+v00qVLiyqppZ5XD5x9vN7eXg4cOFDxwlbPdKrzHO9r2bdv36qKkfMtlxiKfItcJoNRtIjv\ni5PTnmCIC+y2XkeE0oXdlvCZ557gK8PTxFtGiCsxQpqFCGiYaCiaRbEYQTVt2iNzzKtxCqpAiRro\nMowwbExDIFQTaQhkk0AVOhE1SiTXQ4staItK0oGdxGcNQobAam7DNEys4gzoBUSuFaHtIrTrZQTa\ntyPqFIF32G1oaBQpoiyRBaFKhf3mXvff9VRxQoiKU5upVIpEIuH6kIZCIQzDIJvN1pxm4QeNbsk6\n6zc3BzFNiW3LiqTowDBstmyJLspC9FbYY2Nj6LpONBp1czTL9aJr6Yyzblhf2cUl4B7gGxUeOwUM\n+VlskxB9wNkz8/shLyc2Jy0iEolw6tSpqu4bK3WdcfbxTNN09/Gqod6Q4GKx6O6xLvVa6sGUOMN8\n2zPYExHiLZ10b3khi1InzXn1cxywfpKryQwfe/7bTIwXaeuaJhrIkwq2E4ymkbaNnAsQsXMkrQ50\nK0gwoNMcmUe3NBRhodgWUoBAEpI5DCuMJQNEcgaK0cL1ecnO9vNcn2qluyVCuGc3SiaOyERRAmFk\n0MK2W1C79qF07EAEqlfGtdxYKSgc1+/gseCTSCERFfYSbWxUVI6YLySBrFZbs5L2b2JigrGxMTfN\nIhQKLagiV+oA02hCdKq3lpYQP/RD23nssevE49W/D5Yl+amfOrDo55UqbGefdnJyEl3X+fa3v81j\njz2Goihcv37dvdHwCycg+CMf+Qgf+tCHuHbtGn/8x3/Ma1/72rrWe5Hin4D/IYQYBT5742dSCPFq\n4F2UdIo1Y5MQfcArlPf7PNM0sW2b0dFRJiYm6OvrW3Z6tJ59PcD9Il6+fJm9e/eydevWZS+U9cg1\nstksU1NTHD582B15Xy0UrDQD+S9hzofo2tJJU9PCvZggzRSYY1D/Ov9+Pk9xYgptiyDUZZGbjWAb\nEtsQSARKi0WbSCFRKJphDBkiaBdQJAQxCKkFMkYLdlBFKJJ8MUKsaLA91syO5lt4zW7JtmAYvTBJ\nPpNmaKKU3t4cFkSDbTQ19xKO7kSotbWIayGtu4zjXNKuMKFMLiBFiXTJ8PXF1xCVL+jlHEK0kVxU\nr3EmcJ6sKNBmN3PC6GO7XV+4sqIo7rTq/v37XUlQKpVyJR+A61Xa2tpKOBz2Rc5rQYjO+r/0S3fw\n2GPX0XWLYHAxkc/PF2lrC3PffbuXXde7T2sYBuFwmEOHDtHU1MSjjz7KL//yL3P9+nXe9ra38Z73\nvMfXOTsBwc8++yyWZfHhD3+YP/zDP9x4hLi+FeL/pCTA/zjw0Rs/+x4lu6NPSSn/Hz+LbRJiDSi3\nb/NbBWmaRjKZ5PHHH6erq2uRgXU1ODZqfpDP58lms8zOzvqq2PwQYi6Xo7+/H9M02b9//6qTYSKR\n4Nnx/0S9RaUpGkNVK39MQ8R5tvgkSq6TeTWA6Ib8XAhzPoglA9ixALYh0FQDEYS2ziSJVAuFYgxL\nBUsXFJUQQa1AgNJAjBaWGJrK3mie123r5VBMoUezkWyFWBy7I42kiGUWyGYMkpmtXL+YRtfP1DSc\nUis0NN6afxPfDT7GmcBZ9+c2NltkBz9cfDm7rcW+xXPKPP8c+wpZkUfHBAFCCp4JnGe71cl/zb+e\niL9tFWBh9SmEcDMRHX2sZVluK3Fqaop8Pu+2EmuRfKxFy9SpYo8d6+b3f/9lPPLI98lmDZqagqiq\nQNctCgWT1tYQH/nI64lE/H3PnSo0Ho/zYz/2Y/zFX/wFX/rSl7Btm/n5+eUXqAEb1bxdrpO59w1h\n/luFEH8NvA7oAmaBL0spv+13vU1C9AFN03y3MA3DcDfkT5w44Y7E1wI/LVPHSu769evEYjH279+/\n6lmFUkouX77M2NgYfX19zM3NreoX1LIsLly4wPz8PNuPtZIPtTOf06HKgEmmmCNr6IhiEXqC5CZi\nhC2L5rZZioUgimIhRYBCPoZimMTa8rRFU5jZNAJBOJxDUwroagyZihE1DBRdsK0lyW3hXRyIaGxV\nCkDwRoUWRiWMlEVUYRBs30HblpD73mSzWddz0xlOicdfcJKpxY/UCw2NV+sv5+X6PVxXxzExaZUt\nbLE7Kv5+XtP5h/gXyYsiUrzwnkkhMTC5qk7yD9HP847cmyrqG5fCcu1YVVVpa2tzux5SSvL5PKlU\nivHxcc6fP+8mXjgk6R1IsW3b9/vjB+WE+xM/sZ/Dh7fwL/8ywJe+NEImY9LVFeXBB4/yxjfup63N\n/zCMd6jGK7lQFKWu5BsnIHhwcJCuri4eeOAB3v/+9/tep9GQohRfttYQQgQpZR5+XUr5XUrTqCvC\nJiH6wHKJF15IKZmcnOTixYt0dnbe0D35CyetdfKzPP3i+eef991qXa49m8lk6O/vd4+hqirJZHLV\n7MISiQSDg4Ps2LGDAwcOcFX9Fjln6rPKIfJmHtuSGMLGEkGkGYCoQThg0lTIYJohzIBAtS2sQgCz\nWECoNh25DEWCqNJAqILZ2VZmtTa6W+bZFyiwQ8uyJ56hI7wP1Uwh7cyClqUghAzsAOWFC7oQwh3A\nKPcjnZ2dZWRkBChNHk5OThKPx2ueQNTQ2GX1Lvt7Qz1jFIW+gAy9sITNjJJkULvMbeYtNR3bgd8p\nUyEE0WiUaDTKtm3bgNKks6MRvX79+gLJRz6fb6iIvZJ14r597Tz88Mt4+OGXrcoxTNN0ST2dTq9Y\ng3gzBQSvByFKKXUhxJ9QqgxXBZuEWAO8LdNaCCqfzzMwMOD6gubz+bpCe5erEG3b5uLFi8zOzi5I\nv6hnGEdRlIpk7xXxHzp0iNbWVvex1TDetizLtXXzyk5a5E5mlX5KAqfKF3gbE9sSZAphZAsoAQtL\nqOhWkNZImoJhY0mFghpDVQ2snEosVMBUNexZQWcxgYhYKJZFr5hkZ0s7h3cphMxpWvQQitCQwR1g\nF5E4resAUqmt5VgekmtZFk888QSFQsFNE/G2FZfTuS0FG5uRnimsZbSLujD5XvCMb0JcDVmEpmmL\nBlIco+5kMsnMzAxjY2MLnYZWaUhrLXSO1SrEFzukAFNdt2naQeAW4DursdgmIfrAchWibdtcuXKF\nsbExDhw44H7xdV2vubIsP141wnEqqm3btnHq1KkFX/Z6JRTlJDo/P09/fz+dnZ0VI5pWYiQuhFhQ\nFR48eHBBBdIid6LKCFJNI2Vlo2U1VCSbaGcu1Ua0Owdhga1rZOwmmtQ0LaE5hKkTlkVMRcFUgrTY\nKULBPNGOPNJWKOoRYoVpdnQZtN6q0tEcITsbIFDIo1o6aJEblaD/fbdF56uqaJrmerk6E4rJZJJr\n166RyWQWaABrjX0CKKBjK7X9LWZEyve5N0KY762qC4UC8Xic5ubmRXFPzc3N7vtRr+SjnrQZv3jp\nEqLAamC7exk8DPylEOK0lPL5lS62SYg+sJQFm5OA0dHR4bYUvc+rRz5RqdJzjLKz2ewiIb+DlYrs\nbdt2PUJvu+22qm4b9RxHCOGaBJRXhQvWJsAe+7U8rf4zhpJBEnXbljYWBTFHu7qTcMFEsQRG1iCy\nJU1Bj0FOkjcjtJBkK5MohiQh2shqEbZa4wQ7DGRaBT3A1lCW9jaLtpCkaDQhlF3YBIkWVbQGpkc4\n74Uzoej13CzXADrTm06btRIhqCg1ifmBJbWN1bAWQy9CiIqSD8c4wCv58MY91XLTsBbhwN5jrEbL\n9GaCtYbBy2V4L9AEPHNDejHOwpaSlFK+stbFNgmxBjgXoGoE5QyCVEvAqFdPWP68qakpLly4wK5d\nu5Y0GF+JDZvjEepUnsvlIfqtfJ22YW9v76KqsBzNspfO2VeT3zJIXkyDVKAkpGCLfYht9t0YO8cY\nvvgtxhMRot1puvJjaIBUNIp2EGFYRIt5usLj5OdD3JIbJru1BVuGaVIs2mIqVrAFsiah4jy2pRA2\nVJrsGI2y8F8K5YRgWRbpdJpUKsWFCxeqTm+GCNKUD5OOFZZcX5GCfTXsR5aj0dZtUsqKhOUM4rS0\ntNDbWzrvQqFQyoqcmWFkZAQp5YJhnUo3DY0mdFhIiJlM5iVDiBKBtQ7flRuwgIHVWmyTEH1A0zTy\n+bz7b4egdu7cueTFfSWhvZZlUSwWGRwsuROdOHFiwXTeah1PSsnMzMwCj9Dl4GcP0blxKBQKd3dt\nYAAAIABJREFU3HXXXTV7PIbNTtpSO4lHg+gig0AhKjvRKA2k7OvYxb0H4nztmXGC53KotwgIWIRS\naRTdJGiZFMMhRFZw27PP0hzP0DWfwWqPITqb0YMd2AoI2yaUt7BMjS3ZVtSmFlDX33bLGeN3phQr\nGXarqkprayt71U76bxvHENVvUlQUXqYf8X0ea+FlWuv6lSQf5TcNkUhkwU2D3woxhc13tQJf0/LM\nC5smqfBqM8wrrDAdS2gMnNeQzWZfOj6m6wgp5atWc71NQvQBh2gKhQKDg4MIIWoiqFpikqo9L5fL\n8dRTT/myRPO7tzc7O8vg4CChUIiTJ0/WfGGq9TiOcXlvby8tLS3Lvl9eOLZ5ETqIyMVyg9TEMOHM\nU9ynnWX2ShvZdJBcaxSzKQgRDTVRYNv1qwSKFl3hJBFhEUxJFFMnFdtKSb9r0pYTtHTspNXoZca6\nghXpAGX1XHdWC9UMu1OpFN3nWpjYlma6bR5LXfx3CUiNV+hH2Wb7141u5LSLSjcNjuRjcnKSCxcu\nuBOtmqYtknyUY0Qx+NNQiiySZgnNUmAg+WwgyxcCOd5TbKXPXtqE4aXUMpUIzPWrEFcVm4RYA7wt\n00QiwdTUFPv373fbWo2AV/x+7733+tJo+QnuHRoaolAo0NfXx8TEhK8qYLnjOPuduVyOO++8k0gk\nwuTkpC+yruYja5om586dozD/NXqiT6OEJtkippm/0kROBBDNKpowCFgmasEk1pxDC9pEprOYNGOb\nUeJJk3BQoUNvoU2NoahbsfUcRrQLLdC4v+1qw0n4CIfC/GroZ/iG8RSPiuexpQ0SbCRBS+Ou6QMc\nZx92s39yu5nyECtJPoaHh1FVlWw2u8iD1Dvhm8LmT0MpbCnp8uy1qkBYqmSx+UAoxZ8U2ulaolLM\n5XJ1RcXdrLDWkUqEENuAdwOvBNopCfO/Bfy5lHLCz1qbhFgj0uk0Q0ND2LbN3Xff3TARsW3bXL58\nmfHxcfr6+hgcHPR9rFpaptPT05w/f57du3fT09NDNptdtfgnWFgVevc7/VbLlQhxdnaWc+fOsWfn\nDpCDTBV0lBx0a1O0hOaYz0UxcjGssEIoUKCZNDINwYl5LENBjRnIEIQz0N4kiBYkYssW7FgHsus2\nTJlBW5Qkc3NAQeE+4xSvMo5zRZ2gIHSarSjt2Sbmc/OMp14QyfvJRbyZCLEaWltbaW9vBxZKPpwJ\nX03TeGZ3G6mtIbpFgEqzRzEUprD4hpbnrcYLFWD5Z3RzD3FtIITYT0mQ3wZ8HximFBv1G8DbhBAv\nl1JeqHW9TUKsAbquMzg4yN69exkfH28YGToyhy1bttRs71YJTlBuJei6zrlz57Asa0G7t16pRvmF\noFJV6IXf5BDv7ztr5/N5jh8/jlYcY3ZmBGkHEXoBBIQVg1AoCVoKaQowBYo0Sv6lRUm0mCPYFCBj\nBNATgrRoQ7b3Emm+lVj8CE3hOIicr/dhI0JD5RbLU6FEIRaNuRWTk9qQTCa5evUqpmnS1NTkOuuU\nyxvWKo2iket79xC9kg/vhO/fhWcJ6gapYh4pJZqmEdACBAIaqqYhgLhU+LpWWESI3vfrpZSFuM5D\nNX8KzAN3SSlHnR8KIXYBX7nx+JtrXWyTEGuAk+RQLBbrEtg7qHaX7YTeJpPJJWUOtaJa3JTjnLN3\n7166u7sXPaeetAvvc5zKbakpWL/E6xCio1ncuXOnu7YxnyBoSDTdwApphHIWtjBAguJMXguJFVKR\nhiRgGoTykoDRRJu6g8iOI8jjryEXjJCwwly5NkY2ewHbtonFYgSDwVVJctiIKE9tKJc3ZLNZwuGw\nW0H6GXqpB2tp7l0NwWCQQkijTQZQIwJJ6SbMMAyyuZy7hqYFSIVVCqZBWAu463s/Jy8lHSKwnoT4\nauAdXjIEkFJeFkK8D/gbP4ttEqIPrCSOyXlueXU5OzvL0NAQ27dvX1bmUCvKSccZAlJVtWpw70q0\ni96q8NixY4uqQi/8VohSSiYmJpicnFxccQZihGyFJkxmoyG0jEFQV9CFhdPvkoAeDBGZm0dVQChR\naNkGW7YjT/4I9BwgqoWJAk49NTIygmEYC5IcWltb3eppNYKPNxrK5Q1SSlfeMDU1xfT0NNPT0yST\nyYpepCvFRiBEgCapYCBRKYU4BDSNgKbBjc+dZdvkDR3FMDn77HNI26a5udmtqF8IIl55hfjBD36Q\nj3/842iaxhNPPFHXjdno6Ch79uzh7W9/O//4j/+4ovOphnUeqgkC6SqPpW88XjM2CbFGCCFWRIiO\nON8hRMMwXPuuSq1FL/zu3zjnKaV0Y6CWGwKqt2Waz+d5/PHHl9VG1nOcZDLJpUuXaGlp4ejRo4vW\nFi23gNZDa+E6ZiZNansz1vUsSrGIoUvsaBCpKMSmk0SzBqGcggg3YSkxwsd/GHbcDhUukpqmEY1G\n3Sra68F57do1DMNw3VPi8TiRSGTDphDUCyEEkUiESCRCd3e3K+1wPGwdL9KmpiaXIGOxWN3vg1+v\n1HrWr4VQXm2G+VwgS7jKwIyqKOTDQe43w5w4vtOVfExPT5PNZvnGN77Bn//5n2PbNoODg+zbt2/J\n7/ZSCAaDTE9P09fXt6G7FKWW6bpRyRngvwshviSldC8sovRheueNx2vGJiH6wEq+sI7tWzAYdFuX\nt9xyC93d3cumCFSqLJeCoijous7p06eJRCLcddddyz7fLyGapsnFixfJZrPcc889NRtV11Ihep1y\ndu3aVfWGQFOD6FveQNj6e+JmkMiVSdJdTZixEHLGIJTMEraLBIIW2kwAdV5Cb5zAK96CcuTVFcmw\n0jmWe3A67cVkMsnFixfJ5XK+oo5uRjj7aW1tbe5gim3b7mDK5cuXyWazBIPBBcM6fi7kjdY51nIu\nr7TCfDGQI4tNrMJUTf5G9fgjZonkHMmHs32wb98+enp6+PVf/3W+/OUv82d/9mfs3LmTz3zmM77P\n+bnnnuMTn/gEDz30EIlEYtn81PXEOrZM/wD4AjAohPhXSk413cBPA/uAH/Wz2CYhrhFUVSWXyzE0\nNISmaVVbl5We5ze4d2pqipmZGe6880734rUc/FyMnL3Cnp4eTNOsmQyd4yz1epzBou7ubk6dOsXk\n5CTZbLbq7wd3vBF9/hzBPV8n8FSS6MUUZlhiWyai2cKeLaJebkFLKojenYTe9Teouw9XXa8WeNuL\nsFD3NjY2RjqdRtM0t8Xqx5N0o6LSTYmiKDQ3N9Pc3OwmfJS7yACLXGQqodEVdq0t2Q6p8u5iKx8I\npchh0SoVgpR0iElRIsP/rjfTIxf+PR3CVRSFvr4+TNPkb//2bwmHw+i6Xtc533rrrbzzne+kp6en\noUkgNzOklF8WQvwY8IfA71DqdEvgNPBjUsqv+Fnv5v6WriH87n154biLDA4OcujQIV+Bun7atNls\nlv7+fiKRCO3t7TWTYa3w6haPHz+OEILZ2Vlfa1STXdi2zcjICDMzM9x+++3uQMJy77umhbAP/jbW\nlX3YfAZ16BnUXB4BiEwzZHsRzc1w1w+hvfW9KA2Y/Kuke6vmSWoYBoVCwddNxEZArYRS7iJjmqYb\nHDw+Po6u66sapFwr/OxRHrKD/Emhna9reb6h5UlgE0RwnxnmPjPCdrn4sllegRqG4e6x1rvn/NBD\nD/HQQw/V9dxKOHfuHA899BDf+c533K2ahx9+mNe+9rUrWnedp0yRUn4Z+LIQIkpJfpGQUtY1Kr5J\niHXAz55eJpNhYGAAy7I4cOCA73T5WgjRq108dOgQ4XCY/v5+X8dZDk5V6OgWhRAYhrHiyVQovUdn\nz56ls7NzUXJHLTcigUAY2ftmxM6fxDh4DvPKAAyPoFgq1tFutDtfhrrNn3/nSnMeK3mSzs/PMz09\nzblz51xicKrIley/rQXq1SFqmrbg5syr//MGKReLRebm5uoKUq4Vfs5/q1T5L0YTP2vEMCldKB1z\n+UrwEuJqZYSuJi5dusQ999zDbbfdxoMPPsj4+Dj/+q//yv33388nP/lJ3vKWt9S9toR1G6oRQgSA\noJQye4MEc57HYoAupaysQauATUL0ifLhmGoozxGcnp6u63jLTX+m02n6+/vp6OhwtYu6rq84p9BB\neVXorWzqsaTzPkdKyejoKBMTExw+fLhiW8hvZR7oOEig4yDceePfvs7uhWOuNpxE+VAoxNGjR11i\nSCaT7v5bKBRyB3Wam5s31CDFag29VNL/FYtFTp8+7QYp12LWvVYQiJo+Q5X2KDfSDc53vvMd3vOe\n9/CBD3zA/dmv/dqvcc899/COd7yD+++/fwVt2XUdqvkopa/5f6nw2IcBHfiFWhfbJMQa4bVv8yZj\nV0IymWRwcJCtW7e6OYKJRGLVIqBgYYvRGw681HP8YmZmhqGhoQVVoRf1BAQ7BJfNZjl79izt7e0V\nsxbLf//FBi8xePffksmk67/p102mkViNgOBqCIVCaJrGvn37gBeqaceLtFAoLGqz1mM910hYlrWh\n5Titra08/PDDC3524sQJfu7nfo6PfexjfO5zn+Ptb397XWuvc8v01cBvV3ns/wM+UOWxitgkRJ9Y\nKhPRSXRIp9McOXJkQWLEcuHC1VCJ3FKpFAMDA2zdunVRixHqD+514PiEFovFRVVh+XH8XmiEEO6U\n7eHDh2ltbV3299eDENfjmOFwmO7ublfuUe4mY1nWgrDctZR7NNK6rbz6dKppZ6rSm/DhtVlzqumW\nlhYCgfU1YvdWiLqur6pGczVw7NixirrIV73qVXzsYx/jmWeeqZsQYSVTpiu+ce8Cpqo8Ng1s9bPY\nJiH6RLXqy/EGrRYFpaoqxWKxruM55OZ1tPEOnpRjJReuco/T1bwI5nI5rl+/TjQaXRSiXA21kK5p\nmiSTyVVzldkora5KbjJOzNHw8LCbjejsQzaSxBspnF+u+qyU8FFpaKnazUKjfVhhcThwLfFpawln\nyKkczs1XKpWqe+2VVYgrJsQp4HbgmxUeu52S0XfN2CTEGuF8ocorRMfn1LbtJauplYYEO2bZq+lo\n44Uz/XjlypUlX0c9kFJy7do1rl69ypYtW3wR13IVYjKZpL+/n6amphe9q4y3hbpz585FlVMul+OZ\nZ55ZUDmt1oBKoytEv2S7VJCy92bB0YWuBSE67/VGNPaenJys+POJiVIYxHKdmqWwzk41XwB+Twjx\nLSnlc84PhRC3U5JhfM7PYpuE6BNeF5jx8XEuXbrErbfeWvUOrPx5fuGQiRCCo0ePEo1G6z31qnCq\nwkAgwO23376qJFIoFDh79izRaJRTp04xNjbm29y7UvvXEe8nk0mOHj2KpmkIIVxXmfI2o0OQ6zmg\nsdoor5yy2SyHDh0ilUq5AypQ0gE6r7/eVt5GI8RyVMtETCaTjI+Pk81mefrppxfsya5mm9U0Tfcm\nbyOGAz/99NOk0+lF5/Wtb30LgDvvvHNF66/jUM3DwI8Ap4UQTwLXKLkwngIuAb/rZ7FNQvQJTdPI\n5XKcPn2acDjMqVOnavpi1UOIMzMzXL58mXg8zh133NGQqnBoaAhd1zl+/Dhnz55dtbablJKxsTFG\nR0c5ePCg2/bz65laqUJMp9OcPXuW7u5uTp48CeCKnyu5yqTTaZLJJOfPn6dYLLoDGvF4vKLcoZ5h\nofWG8x6FQiG6urrcMGnLslzbuXK7tXg8vijVohoaaa3WiHasVxva0dGBaZr09fW578WVK1ewLGuB\n9Vyt70UleFummUxmw7VMU6kUf/AHf7BgyvSpp57in//5n2ltbeVNb3rTOp5d/ZBSzgghTgK/RYkY\njwIzwB8B/0tK6asXvEmINcK5MDvVx5EjR3wJ35caximHYRicO3cOwzDYu3cvuq6v+sXIqQr37NnD\ntm3bEEKseBjHQbFYpL+/n2AwuMg2biXxT16ZhjcVZKn1vG1GxwbOkTuMjo6SzWaJRCIL5A43I6pV\ncKqqLtABOnZrjk9sLpdzUy2c11+JnBo5ZbpWxt5OkLKjBfbuyY6MjJDL5dzPgtNqrbW1X06IG61l\n+opXvIKPfvSjPP7449x7772uDtG2bT784Q+vyAlnAwjzk5QqxYeX+93lsEmINcKpCoPBIL29vb5d\nYGqtECcnJxkeHnZ9TmdmZigUCnWdc6WLpEO2pmkuyEOElU+nQmlP4uLFi1XNxOslxFwux9mzZ4nH\n40vKNGpZzyt3cFIdksmka7tm2zbhcJhoNNpQofhqotb31Gu35k21cFqL58+fd428va3Fjd4yXQrV\nki68N0vAgvdiYmLCl/TFS4gbsWW6Z88ePvShD/HQQw/xoQ99iGKxyLFjx3j44Yd53etet6K11zkg\nWAEUKaXp+dnrgNuAb0gpn/Gz3sb/pm8QaJrGoUOHXK9Gv1iOEIvFIoODgwghFvic1rv36JCb9w63\nUlVY6Tn1QNd1BgYGUBRlyTZyPcfIZrOcOXOGvr6+VTc49qY6OLZrV69eJZPJLNiHcy6I8Xh8ww7q\n1ENYlV6/YRhuJ+Ty5cvYtk2hUGBycrIh+7BrHQ5cDdXeC0cT6SSdVGo5eyvodDq9YSrE3bt3L7hZ\n+vd///eGHGcdh2r+BSgCbwMQQryDFzIQDSHEj0opv1brYpuEWCNCoZBrV7aaAnvvcM6+ffvcvZ/l\nnlfr8VRVXbIqLH9OPYToVLW1DBf5qRCdmwTDMLj33nvXrFLTNI2mpiZ6e0t2b44fZzKZ5Nq1a5im\n6Q7qxOPxDTGos5oVXHlr0bIsnnjiCYrFIufPn3eF8s6gzkr9SNeiQqxXjlNrkHKxWCSRSBCLxVYl\nHPgHP/gBv/qrv8revXv5t3/7txWt1Wisc/zT3cB7Pf/+bUruNe8GPkJp0nSTEBsFP3uBXlQitkKh\nQH9/P6FQqGpVVU9wr/M827aZmpriwoULNUVN+a3eDMMgn88zNjZWc3pHrcdwWq+7d+9mcnJyXduW\n5X6c3kGdCxcukM/nXYKoNqjTaDSypamqKpqmsWvXrgX7sKlUyq2mndgnR+7hh4DWIgtxtQi3WpDy\nM888w9TUFO9+97sZGxvj1ltvpauri3vvvdetNv3ggx/8ILquo2law28YVop13kPsAq4DCCFuBfYA\nfyWlTAsh/l/gk34W2yREn6jXccb7hXekFFeuXFkwgVntePUQohCCgYEBhBBLVoVe+CFfp/0aDAY5\nfPhwzW3E5SpEwzBcXefJkyeRUrpaqbXCcudYae+pki+pQ5BrkY+4ls461fxIk8kk09PTvvWg67WH\nuBpw2qyBQIADBw7whS98gYcffph4PE5/fz8f+chH+PSnP+1b52cYBo888gjve9/7uH79utut2KhY\nR0KcB5wL6KuAGY8e0QJ8Cao3CbFGeIX5K/EJzeVyrpC8luDeetqYU1NTzM3NsXfvXnbv3l3z3Xct\n1Ztj6+aVavjBUpIGJ1HjlltuWRCjtNElEJV8SZ18xPJBFdM0l/XCXcl5rBdCoRBbt25dFPvktJkN\nw3CdZOLx+CInmY2wh7iS9b3QdZ17772X++67r+41H3zwQd773veyc+dO96Zjo2KdhfmPAg8JIUzg\nN4H/8Dx2KyVdYs3YJESfqLdlKqWkWCz6Hg7xU5E61ZVlWXR1ddHe3u7rIrkcIVaKgPKr2atkxWZZ\nFufPnyebzS5yyblZzb2d4QzHGsuxGhsbG+PMmTPAC4L51RjUWQt7Mj+o1GZ29t4uXrxILpdznWS8\novZGYCV7iLWgnHAzmcyKp0zf8IY38IY3vGGlp7YmWOc9xP8b+CIlI+8R4H2ex94CPOZnsU1C9AEh\nRF0tzEwmQ39/P1JKTp065as6qHXPzdkr3Lt3L93d3S4x+kG1Y3kJ69ixY0QiEd/nV+33U6kU/f39\n7Nixo6IH7IvF3NuxGhsdHeXEiROuYD6ZTLqCea+jjl/j7o1GiOWotPfmVNHT09MUCgVmZ2fd19/a\n2rpqVbRt21XXkhIyBpg2tARBraNQLY+D24g6xBcrpJQXgP1CiA4pZblv6W8AvvZbNgnRJ/xcdMoz\nER1Zgh8sZ26t6zrnzp1z99ycSqOeYZxK5JZIJBgcHKxKWH4J0SE427a5ePEic3Nz3HHHHVWdPdaD\nENeCWCoJ5su9OL2OOstNcjaaEFf7b+B1knH2+LZs2bLIsNtrO1evv26lCtGy4ZuXVT7VH2AkqSCA\nWFDykwcN3rjfJO7jUOUV7kbUITYa6ynMB6hAhkgpn/e7ziYhNgjz8/P09/fT1dXlCsmddutq6djK\nq0Iv6tl79JKbk6yRSqWW9FCthxB1XeeJJ56gs7OTkydPLpt08GKoEJdDNePuZDLpJst7A4RbWloW\nvG83Y1vZgVPBVTLsdjSAExMTNdnuVVvf+15ZNvzh94J887JGU8Bma9RGCCiY8I/PBviPYY2/fG2R\n7qba3tNyws1kMityfrnZsN5ONauJTUL0gVouzpZlcfHiRRKJxKKIptUK7nUSNqSUVeUO9RCiqqru\nXld/fz89PT2cPHlyWalGrRdjR3OZTCY5efJkTReNWt5zXde5cuWKK31YaattI7QevcbdzlBFeYCw\nM6gTj8cJBAINO+9GV5/Vpkwr5SJWmub12s5V2issJ6x/HdD45mWVbbESEToIa7CtSTKbF/zut0L8\n3Y8WqOVll6+fzWY3nJdpI9FIQhRC/D1wWEp5d0MOUIZNQqwTlS4STnuxp6enYkTTahCiI4KvVBV6\nUa9+cWpqisnJSY4cOVLTPkitQzX5fJ6zZ88SiURoa2ur+Q56uQvx9PQ0Q0ND9PT0uBdKKeWqDqxs\nFFQKEE4mkyQSCebm5igUCpw/f37FyRblWC9CLEelad7ym4RKVmve9Q0LPjUQoD0sq5Jde1gykhSc\nnVa4vWv5z3Y5IS61Z/liRYOmTNuB54DDjVi8El5af7VVgkNszofeNE0uXLhAJpNZsr24EkIsFouc\nO3duyarQC7+tzHQ6zcWLF12TgNWSanhTL/r6+ohGo/T399d8XtVgmiZDQ0MUi0VOnDixsCXmGVhx\nnGW8BLmaWY/riUAg4LYYM5kMly9fprOzc8GgTlNTk/u6/Q7qONgohFgJlW4SHLmHE/9lmibhcBhN\n0xhOx8gZ0LVEipoQIIDvXFF9E+LN3LquFyuZMp2enubEiRPuvx944AEeeOAB558twJuBg0KIe6SU\nviZG68EmIfpAeUiwpmmuFGHnzp0Vh068qJcQTdPkqaeeWrYqLD9WsVhc9ve8gz+7d+8ml8utmlRD\n13X6+/sJBAKu5rJYLK74ouG0dHt7e91KwTAM9/FqAyuJRMIl0aWI4maUejik5W0xOlKHZDK5QOrg\nvO5aLdduJieZSlZrZ86ccffET0+GyOf2kxegBQKlHM0K62gKzBdre82VhnY2Qtt9rbCSlmlnZydP\nPfVUtYdHgbcDn1oLMoRNQqwLDtkMDw+7rvFeKUI1+NUwOnuFjgepn3DgWipERw7S0dHBXXfdRTKZ\nJJPJ1HwM5ziVyMMZ+Cn3Z12Jgbht24yMjDA7O7tgMlVKuWQVU8lZppwovNZrNxsZOqg0AexIHYAF\ngzpeyzWnxVrNcm0thPONWl9RFBRFobe3l2AwSHhW8E/jQaBIIZ/HNE3EjWioQCBA4EbQtGHDtqba\nPqfebtHN+tlZKRq1hyilHKXkV+pCCPE2n2v8U62/u0mIdcAwDJ599lluvfXWiqkR1eCnQnS8PG+9\n9VZM0/R9wVjqWFJKLl++zPj4OIcOHXKJoh6yKt9DNE3TJfFKrd16q69cLsfzzz9PR0fHspOptZxz\neQSSQxSXL18mlUq5F9K1sl5bKWppay41qDM1NcXw8LD7mtcq+gnWJu3CWX9fu2RHqyBZCNMcKbXO\nbSkxdB1d18lms0gJBSvC0eZZisXYsnuxTksWSnvlfm5cXwxYB6eaf1x0CiWICj8D2CTERsAwDJ5/\n/nny+TwHDhxYNtmhHLUQohOj5I2BGh8fXzWRvZMr2NrauihXsB5C9D5nbm6OwcHBqvFS9RxDSomu\n65w5c4ZDhw4Rj8d9nV8tKCeK6elp5ubmCAaDbkZiIBBwK0i/5tVrgXpJq9IeXHn0UzQaRdd1isXi\nqg3qeNHolqzXy1QIeOBOg9/7doiQahNUQRGCUChEKBRCShjPCl7Vm6FVpBgcvIKu6xUjn7zre8OB\nX0oTpuuEPZ7/3kHJwPuLwKeASWAr8LPA/Tf+t2ZsEqIPJBIJtm7duugLUSuWI0RvVegl23olFN5j\nSSm5evUq165dq0os9RKi42+aTqeXbR/7qRCdPUjLsmpy+FmtSkYIQSAQYNu2ba6nqmNeXV5JOdXU\nek8VrtZrrxT9ND09TSaTYXBw0CUH53XX+13wYi3SHLzrv3ynxa+f1Pmb0wFsCS1BiSIgowtMCXdv\nt/m9l6tEAnvc83PkHpcuXSKXyxEOh12C9ArzVyP66WbDWlu3SSkvO/8thPhLSnuM3gioIeA7Qog/\npWTt9qZa194kRB/YunUrpmmSz+frzijUdX3Rz73hupXajPUM43jJzZE8OIbi1aqbegixWCwyNjbG\n7t27OXDgQE1tu1rgpGns27ePfD6/JOE4nqqN3L8pN692KqlEIsGlS5eA2tMdGoFGvXZVVWlubqap\nqYnDhw8v8CQdGRlZMKjjZCP6JbdG71FWwpsPmpzssfjiBY3vXVUxbTi13eLNB02ObrVRPB9TRVEW\ntdidoPDx8XGmpqZIp9MMDw8zMzNT0zzBcvjFX/xF+vv7+cEPfrDitdYC6yjMfw3wV1Ue+yrwK34W\n2yREH3Au5vVGQFVKynCqwkrhwA7qJUTTNLl27RqXL1+mr6/PnbqsBj+VqDOdOjExwbZt29i9e7ev\n86sGy7IYGhqiUCi4sVXDw8OrsnatqIVcyyspb7qDM+7vlXo0otVY6bwbAW8FV82T1JG4ZDIZ3+3l\nRsYzLYXeFsk7jhu847ix/C974EQ+OebthmGwa9cuUqkUX/3qV3n00Uc5ceIEJ06c4F3p5n3gAAAg\nAElEQVTvehcHDhzwtf7HP/5xjhw5sirypLXAOjvVFIETVA4BPgksrkCWwCYh1gFHPuAXXiJ10uCr\nVYVe1COyN02TRCJBMBisKWbKOU4thJjNZjl79iwdHR3s3bu3YtVbD7xyir6+vptqdL083cGyLDdE\neHx83G01GoZBLperWxNYDY0cfFlqba8naU9PD1C5veytnsuDsNejQlxNWJZFOBzmvvvuw7Ztdu/e\nzSOPPMLp06frsnD72te+xujoKOfOneOxxx7jnnvuacBZry7WkRD/DXifEMICPs0Le4g/A/w+8Pd+\nFtskxDpQr57Qed74+DgjIyNLVoX1Hs+xRxsZGSEUCnH4cO0mD8sRr3cf8vDhw7S2tjIxMbHivEKn\n2pyZmVnS6PtmgqqqbpUEL2gCE4nEAvNu53dq9eWshvUixEqo1l5OpVLuoE5LS4tLko3eQ2y0FKJ8\nqKa5uZlwOMy9995b13of+9jHGB0d5a1vfetNQYbrnIf4bqAZ+GPgTzw/l5SGbd7tZ7FNQvSBcmG+\nX1iWxczMDLZtc+rUqUV3ytVQKyEWi0UGBgbQNI0TJ07w7LPP+jq/pSrEQqHA2bNnicViC/YhV6Ir\nhOpyCguDy8rTDKhfIfmyCa5p/5vt9u0csu6jTe6o+3i1YrUvok6rMRgMcuTIkQW+nKOjo+6ghlc0\n74ckGnnRXylhVRrUcUy7z507RzabZXBwsOoU51qcu2XDRE5g2bAlIonW9tVcdIzV8jHdvXv3TbN/\nuJ55iFLKPPDzQohHKOkVu4Fx4HEp5Xm/620SYh3wWyFKKZmYmGB4eJhQKMSRI0d8H2850infi7Rt\nuy5NYSWMjY1x6dIlDh486DqAOKiXEKWUXL9+nStXriyaei2Q5j8DHyAr5jApggYFdC4pP2BUeZI7\nrP+L26zX+z5mrViLVm25L2elvThHNF8p3WItz3u1q89y0+7HH3+c3t5eksmkO6gTiUTqvjnwYjlC\nLFrwhYsanz6vMa8LBKAIeP1uk585YNIZre1Gw3l/MplMQ6RBGx3rnXZxg/x8E2A5NgmxDvipEL1V\n27FjxxgYGPB9PFVVF1iTeeG42QCL8hBXWjV4p1+rVbT1THc6dlqBQGCRnEIi+VrgL0iLaWxMBAJJ\nSXErsbGxeFb9PM2yi132sRW9vo2ESntxldItvAS54H1rYMu00TrBSkYJzs3B9evXF+hAHcOAWnWg\nlWzVHBRM+N3vhXh2WqE9LNl6g/wMCz4/ovHd6yoffGWRHc21f74zmQy9vb01//6LAesd/ySEiAG/\nCLyCkiH4g1LKC0KItwJnpJTnal1rkxB9wDtlulyF6FSFIyMj7N+/n87OTmzbrqvVWu14jjTBj8dp\nrXDWLtdElsNvhTg9PU0ul2P//v0V90+nxAXmxaRLhuUQCCx0nlE/y077TgTCNQRwLpptbW0rdpfZ\nCBZc5aJ5J5prdnaWkZERAJcgTdNsaIW4lkMv1QZ1UqkUMzMzjIyMIIRwydGJv6qEpSrEjw0EeG5G\nYXvTwuSLgArbYpKZvOCRx0J86Edqi4GCl6YOcT0hhOgFvkVJoH8OuI3SniLAq4H7gF+qdb1NQqwD\ny1WI3qrQW1nVW7WVD7t47dEcacJqQUpJf3+/myKx3Nq1EqIjp3CsraoNE51Xv4OJXpEMvciJJAl5\njZkLeVKpFLfffjtSSlcbdv78eTRNWyCer/WivlGnW8sDdE3TdF1lpqensSyLYrG46lKPRlu31YJQ\nKERXV5f7ufG+dkfm0tzcvCjRpFqFmDPgixc1OiPVY6A6wpLLaUH/rMJtWyp/xsu/z9lslubm5oq/\n+2LFOg/V/Bkl6cU+YIyFMotvA+/zs9gmIfqEEKJqxeZMeF66dMmtClcD3uM56RpL2aPVi0QiQTab\nZdeuXWzfvr2mtWshxHI5xWOPPVb1IpsVs+A2SStDIBBS4czQk+wOHuX48ePouo5t23R1dbkVVfn4\nv7fl6KfttlGhaZqb7BCLxcjn88TjcZLJJGNjYxiGsYgk6vm8NLplWg+8rx0WylycRJNYLEY4HMay\nrEWft3NzCqaE4BIfAefXnxivTojlhJvJZF6SFeJ6DdUAPwI8IKW8IoQo/2teB7b7WWyTEOtApUqv\nWCy6UUd+JkhrgaNfHBgYIJ/Pc/z48VXN9LNtmwsXLpBKpYhGo26kUi1YKiBYSsnIyAjT09ML5BTO\nvmOli2xINi3FhaV1kZi2yS29+9gV3YNt227ihWVZ7gVQ0zQ6Ozvdlq+u6ySTSWZmZrh48eKGs19b\nKcqHVZzYq2Qyyfnz512SaI3HSbd08LQSY04qxIXk7pDFrWrlaqmRLdPVqj7LZS5Oosn4+DiZTIYn\nnnjCHdRpbW0lb9Q2+KIKyJrVz6+cEF+KLdN13kMMAukqj7UCvlwXbu4rwAZAo6pCLzKZDFNTUxw8\neNC3YH25C046nebs2bN0d3dz8uTJJau3SqjWBnbkFO3t7Zw6dWrBBXWpQZy99r1MKOdK06WVXs+N\n/w0pEXZE+tzWdSAQcMnZIUaHqB2CVFWVLVu2uG03J3F+bm7OtV9z9qPqDXJeL1T6m3ljr3bt2oWU\nkqlMjr+YV3l6TkWaWcKKgGCQzwYCHA4p/FazTruy/NqrhUZVn0IImppjtFsSLSLZteMgxXyAVCrF\n2NgYE1NXSaf7iNmSYDDofn7KYUnY0VR9m6NShViPGP9mxjoT4nPATwJfrvDY/cBpP4ttEqJPeC/m\nhUKBgYEBgsGgr6qw1guMZVlu5dba2uqrcoMX2pnVMu4uXbrE5OQkt912m7vv4RCcH0L0VohLySlq\nOa8d9u0EZBhDFMFDmtJzOhohDpuvQ1rS9TH1rg24a3sJUkqJbdsu2SmKQkdHx4I9OWeqM5FI8NRT\nT7lDG0sNbmwE1PI3MxH8lWzjYkShT5EIwLRM9GKRQjbPU2nBb86Y/A8twbZ4qzuY1MiW6WqI8k0M\nJtVrXFEvkhdpVFR6ZI4WhplqTjEW1jhv/wcZcZBdbT/Mnd19HDwIX8wFmM5ayGLRzQENBgIEbhCk\nREEAr9hR/eaoEiG+1PYQYV1lFx8APnPj8/nJGz87JIR4I6XJ0x/3s9gmIdYBJ5Lo9OnTvqtCZz9w\nufZcMplkYGCA7du3s2fPHp577jnf51mNeLzVW6UIKD/ekl5CdNIpKskpvFiqQpSZPMe+d4jHX/sE\nVsDCVmWpLLzx66oMsMM+wj79lYvIsNr5wWKCdHSa5QTZ3t7uhsXecsstzM/Pk0gk3MENL0GutYH3\nUqiFEJ82FAYMhV5Pa1TTNDRNIxqL0Q5c1iXPmRLGxzh//jyqqqIoCk1NTUtKGFZy3ishxLzI8nTg\nu+SUDGE7QkRGiSiPU1DPcVGPcd3YQ1FvIWQLItool/hb/vfUL/Ib7Xt4x1GL3/1+iKZIkObm0rkY\nho5hGGSyOaaLQe7fnsRIGeRprbgHW/6ebOYhrvGxpfysEOKdlFxqfuHGj/+JUhv116SUlSrHqtgk\nRJ9wHFts265rL285QrRtm+HhYRKJhLvv5uyL+YVzLKeykVJy7do1rl69umoRUM7ve9MplrOjq3YM\nadvMfeUrRNMKr/jmfVzYdY5re69gaxKhQFOymd7vt3Hw4OtQt9f30a2FIJ00EyEE8Xh8gT+pQ5DX\nrl1bFwPvleDzBY0mhYr7hA7aNcF3A538zLZS20/XdYaHh8lkMjzzzDOu3MF5zSvdd621QpQShi3B\nuFWq2m7VbLaqJmcC36coCrTYpX3ToBglLC4yWOjBwqYzdo2U3A2iGcvuQBE57mj7J35n4nf4yx6V\n957U+fPTQRIFiAZAiBA5wsgQvPWAwc/fopGezyzcg73x+p3vppcQb3Zf1nqwnk41AFLKDwkhPg7c\nA3QBs8CjUspqe4tVsUmIPjE2NsbOnTvrbiMtpWH8/9l78/A4z/re+/PMqhmtI1m7Lcm2bMuWd8sJ\nJEAB50BarpwkZTsNkJK0uFwF2hT68iY5ealbSkv7nl7lLW0g9FyQkhIC4UAatuAmIbQlkOAstrVZ\nkiVrsfaZ0Uiafea53z/k+8kzo5nR7DLxfLkgxNbMc8+i+/v8fvfv+/0uLy/T19dHU1MT1113nfb8\n2brB6B8XCATo6+vDZrOlrN6yCfD1er1MTEykLQFJViGGZmYILyxgaW1laXIR8ytW3tJ+gpmlaRrr\nmrAbbUQ8HvzBc9ha82PfpidI6dU6PT3N3r1717VYJRnoh1YkQeqnOh0OR8zofzGQzkY8ETVQpaSW\n/VQoMKWu5QKalDWph91u185e4wOEhRAxNwWZVs3pEOJA2MBDXjOTqgJXBDlCCHZZghx1hGk3yBal\noMzQy1K0gohQMCtGVGHAZnXiD639TFjYqTEtUWk5y3Or13FLe4QjjX5+OmHiFzNrMVBdtVFu7ojQ\nUS2Aamodr53BxtvtyezMubm5vJ0d/tVf/RXf+c53aG9v53vf+15enrPQuAqcarwkTrzICCVCzBA7\nd+4kGo1qFUKmSESIqqoyOjrK4uIiBw4cWDellu35jbyWtHXbs2eP5ieZDJkQopRTGAwGjh49mvY6\nk02m+i9eRDWbGR0dxWI2s/tKbE6FUsHkxUnsdjvldjvW8XGqvV6MeTQBD4fD2nnw8ePHk1aQ+mpd\nURSNDOTPxo/+yzBdh8NRUIJMp2VqAjb6ZKXgRb+96Z87kS+pJMipqSkikUgMQW70mjcixN6wgT9f\nsWBToNUgUK4QuirgXDTCeecb+JhjgDpTEAU/RmWJ2Ugd8hnVqBmLeZVAOIoQa68qEK1gb/mrfMd1\nA7dUR6gtg3fvjvDu3alNMxLZ7U1MTLC6usrPfvYzPv/5z7OyssKf/dmf8eY3v5k3vvGNWU2cfvrT\nn+ZjH/sYBw4cyPix1yIURTGxVh1uA9Z94YQQX033uUqEmCVyyUTUP25lZYW+vj7q6+vXTWPmAxcu\nXMhICpIOIcbLKc6ePZsRaSebTHVNT3N5cpLWzk6qqqvXpBSqSn1DA/X19fj9flZWV3HPzDD1/PNU\nt7RoMoNcQlndbjeDg4Ps2LFjnSvPRi1WvcxDBsnKqU6ZcLG0tMTw8DB+v59gMMjly5e1NedrWCUd\nQrzOEuG5oIlmY/Iq0akqHDWrMW3VVM9tNBpjYq9S3RTU1NSse82pCDEi4O9XzVQogqq4HzEoUG3w\nsxAx84PVDn635gIKKqAQFgrmK5cQVzQ8ClHEFZpXMWAzhJgN5/beS02yw+Hgfe97H+9+97t585vf\nzJEjR/jxj3/M2NgYJ0+ezPh5vV4vv/M7v8PXv/71nNZXLGzmlKmiKEeB77HmVJPoAxVAiRALBX3i\nRS4VohCCS5cuMTs7S3d3d95HtRcXF1lYWKC9vZ3Ozs60H7cRIaaSU6SL+JZpNBplaGiIldVVOrZu\nxVZdvbYGIUAOzigK9vJybHY7jkiEuje+EV80qpFZIBCgsrKS2tratAlSTtq6XC4OHz6c1mNSEaSc\nZpU3PHIYpaKiQmuzv/jii6iqysWLF/H5fHmNgNoI77BGeSZoJiyERhh6RAX4gXeVxd7oZXI8kEjq\nIW8KEr3mVANc58IGllQDW02Jv48GjFQbfVwIOnBHrTiMERAGjFfIb23F4sr/vnYNq+JnOrIHSx7e\n6kgkog3ReL1eqquruf3227n99tuzfs57772X8+fPc+rUKU6fPn1VDW8lwiY71XwZWAVuY826Ladw\n1hIhZolsK0Sj0YjX6+XixYsJpzxzhd4irampKWPn/WTJGunIKdKFnnSlDrKlpYXtv/mbLD75JKps\nSSZ4X6JLS1i2bcNcUUE1UF1dTUdHh1aZxBOkw+GgtrZ23YSgPFOtqanh6NGjWX8GiQgSXtM+ypsm\n+U+j0Uhra6tmYq0/k/J6vdjt9piUh3SJKJ0KcYdJcIctzCM+Ew6DoFJ5bcBmVQWnMPDfrWEOxBFQ\nLoMiiYy7fT6fdgbp8XhQFIXx8XFqampiPGiHIgYMKc48y9VKAgYfCjATseMwBgmIThrMw8yEazEr\nYDSECUcqEMKEz2BmxlKB1+JgVOlgZ+sY00o1zaJsQ6vAZIjPQsxH9NOXvvQlvvSlL+X8PMXEJg7V\n7APeJ4T4UT6erESIWSKbTEQhBMvLy6ysrHD48GGqq6szfnyqTU9KNaRF2sjISMbDOIkqxHTlFOlC\nniGOj48zPT3N/v3718b6IxHMjY2EFxexJJhUVUMhoqur1Jw4kXDdsjLp6OhACMHKygoul2sdQaqq\nyuTkJF1dXVqrL1+Qm7n8p3wvI5EIly5dwmazaW1WvYm1PJOSZCHPptKNQUpXO3pbWYRGg+BbfhPT\nqoKBtRqq1iD4mC3E263RdVOo+RTmK4pCeXk55eXltLa2srCwgMvlwmKxMD09HZNssVLRglCqSFZ8\nWLFhwoSqRBBXhKpBdS+VhjHcipcINgxKlEBoC+OWai7ZajErAcKRcgKKDVPVIg8aFrk+UstvhZu1\nmjITJAoHvtawycL8ISBvwwQlQswQmSRe6OHz+ejr6wPWwj8zJcNUcg3ZgpOtP9nCyXSNsN5IPBM5\nRboQQjA4OEhNTQ3XXXcdgHZzUXPiBEunTxOcmsJUXY3BbkdEo0TcbohEqDlxAksayR5y4KWqqkoj\nSI/Hw/DwMD6fD7PZzOXLl/H5fNTW1ub1PE8Pg8FAMBikt7eXmpoa9u/fj6IompOOvoJUFIWysjKa\nm5tpbW1FCEEgENBkHqurq1itVo0g9dVUuqSlKHCDNcobLVHGowqrQsGmCLYbBYYkDy9kor0QAqvV\nSnNzM83NzcBrHrTVngWWDGBRQ1gtFixWKxaLBcOVhRpQqIs2MS+WsVtmCQMmygmqb6fO/Aw+MYd7\ntYE5UyUT9koq1WVWww5mgtvpssJWxYoqBL8wObEJIzdFkqe6JEM8IV5rtm2w6YR4P/A3iqK8IISY\nyPXJSoSYJUwmU9KMQj302r+9e/dqgxWZIlkrc3V1ld7eXhoaGmKkGpCdXEM+Rt96zWeixvz8PHNz\nc2zfvp3t27drwylSZG+oqKD2llsITk7iPXeOiNMJJhPl3d3Ydu/GlGWr1ufzMTQ0RHNzs+b4s7q6\nisvlYmhoCL/fT0VFhTakk6/UdqfTydDQEHv27ImpRvUEI99vfaizJEqLxUJTU1NMRqKUechqyuFw\n4Pf7M2rXKQp0mHSOBylQaOu2eLK1Wq00NjZyWwP8wF2GmSimcIhgMMjKyjKw9r5YLFY8JitvtFRz\ng9jFBMOsGDwYhZXm6O9jDhjod/dzvqWJSMjKQmg7doONozaVuis7nwGFWmHhP82L3BCpw57hlliI\nlumvIzZRmP+UoihvBYYVRRkC3Ot/RPxGus9XIsQsYTQaCQQCKX9GivjtdrvWagyFQnmRawghGB8f\nZ2ZmJsZ6LdVj0oHBYGB1dZXR0VG2bt2asXdqMkiCDQQCNDc3a84ncrONIXKzGduOHdh27Mj5ukII\npqenmZqaYt++fTHvkzzb0g9/uN1uRkZG8Pl8ORGklNIsLy9z9OjRlDcUBoMhIUHKYR14jSDNZjON\njY3rqqmFhQU8Hg9TU1OazKOqqiovzjKb5WVqUeAPy0P8zaqVOouB6isSDlUVBIMhFsNRAr5l3rA0\njL/cQmf1EWocNZRZr0zem2B+WWFqm4UqUYbZDlbD+htEEwaiCC4YVzkSzeyGq9Qy3VxhvqIo9wKf\nBhaAZSAnE+ISIWYI/ZRpsjNEuQlfunSJrq4uLaIGsiMpiG1l+nw+ent7qa6uTjmUYzAY0qpi9et2\nuVx4vV6OHTuW0d1uqk1zeXmZ3t5etm7dSldXFyMjI8zPz2M2m6msrCzYZhsOhxkYGMBkMtHT05OS\nHPTDH21tbQkJsry8XBvSSUWQcmDH4XBw5MiRjF9fIoLU/xfQAoFlosfq6qrWRpUEKSOvpJlAtpFX\nhWyZbvTcb7Cq3EeQL/ksTEUUhCJAKCiWMraWCe6pCNHevk+TegwODBIKhTSpx4oSxmywkE4j06Nk\nPqCoJ8RrMekCNr1leg/wEGs2bTk78pcIMUskk13IGCiLxcL111+/7swvW0KUU61TU1OMj4+zb98+\nzTEl1WM2qmIlJMkajUba29szbL+tySjUcBj3yAiesTGEEFS2trJqszHvdnPw4EHKy8tRVVUbppCT\nlZJoHA5H3qQHS0tLDA4O0tHRoeUjZoJEBOn1enG5XOsIUr/uxcVFhoeH17VIc0E6BBkMBrXNOVGi\nh9PpjEmaz8R6rZAVokwhSYXrrCpHLQHOhg1MRg0YgV0mlT0mqZeMjX7S6z9Vf4iV5SjmqP9Km9Wy\n9prjXo8AykTmm3o8IV6LFeImww48ng8yhBIhZo1EsouZmRlGR0dTGn5nS4iwJrKvqKhISLSJkK7I\nfnp6mvHxce2MMxTK7E7ZYDDgHBri4pNPEgkEMNtsRCIRXvnBD7Da7Rz/4AexXfkzWDsj2rZtW4z0\nwO12Mzo6GkOQG1ViyV7PpUuXWFxc5NChQzkJ9vXQu5ToCVKue3V1VSOOvXv35iRL2Qh6gpStaFVV\nqampSRh5pU/0SGS9tlGiRy6EKFBRSF4BRqPRtHR2JgWOWVSObei1s/b+yIGqtpdnGayyYwsrRENh\nVldXiUQiGI1GjSCNZhOKAp1qdtWdfG9WVlayuvl6PWATK8Qfs+ZS82w+nqxEiBkiUcs0FArR39+P\nwWDY0BEmG0H/3Nwc8/PztLW1ZSSy34h8E8kpgsFgxoM4obk5Bn70IyoaGylvaGB5eZl5j4et+/dT\nZrEw/MQTCKDh4MF1G6ueaOIJUl+JScF9KoKU1XlVVRXHjh0rqMmyft319fWcP3+eqqoq7HY7k5OT\nDA4OYrfbNWIvhOje7/dz/vx5bVBIPn+iClJPkLW1tZr1WiQS0QhyYmICVVXXJXpk2jIVeBGG8wjT\nL1k71rGgRA+jqEdRROykcqHNsK1RhZ5oLS9anNSZ7djLy0EIItEooVAIr8/HkiFEq9fEqmcGU00N\nVVVVWa1Jnjtfa8ilZZoHGv0C8PCV7/5TrB+qQQgxmu6TlQgxC0jLpmg0yvz8PMPDw3R2dq6z/UqE\nTAT98gxMVVW2bt2acTsmVYUo5RTx646XXWwEIQSuX/6Sxi1bMJaVcXl6mmgkQsf27WuG2aqKvbGR\n8X//d7bs24dxA/u4RASZbNhFL5eQZ2b5bFWmA3ndrq4urYWtF6C7XC6t8pUE6XA4MhLdp7ruvn37\n1kl4ErVYU4UmOxwO7Zw73ps0eoU4FhcXqaur23DaWCjzqOZ/BbwgtqDQhiCCML6KMJ5BidyGQd0f\ns7ZCp0O8M9zInOJj2OTCJsJYFQWTYsNgKiNcbmCHWsMHRDPh4Cpzc3MMDw9jNL7Whq2qqkqrI3Pt\nDtVkP2WaB0L8+ZV/fhb4i1wvUyLELCFF9tPT0xw/fjxte6V0W6ZOp5PBwUG2b99Oc3Mz4+PjGVeW\niaQaG8kpMpVqeGdmCLvdRJubGR0bo/bKhi+E0BxnzDYb/oUFli9dwrFrV0avIdWwi5RLyE113759\nRUsrlzFdcgAp/vPXC9D1BOl2u7l06ZImupeVb7oEKTWnq6urCa+bCJmGJsdHXp05cwa/309/fz/h\ncDipebcgiGp+bG3ohW2vvReYQDQhCCJM30OEa1FEi7aWQhOi13CRHuXnVKhW+gwOFhQjsEKZgLeH\nt/Om8A5sViM0VWgtz1AohMfj0c5eAe01V1dXrwUIx/nxXqs6RDY3/ulu0tEOpYkSIWaBhYUFLly4\ngMlk4vDhwxk9diNClL6ecqOVG06mlVuix8h0ilRyikwJMeDxEL5SKW/btg2LxYJQVVQhMOieXzEY\n8LvdpB4D2hh6gqyrq6O3t5e6ujosFgtjY2P4/f4Yy7Z8nSHq4ff76e3tpb6+nl27dqUpiH+NIPWu\nNPEEKSvIRNO3UuDvcDg4fPhw1hVmpqHJRqOR7du3a98NvUWenOh0OBzU1E1jtixhoC3xe4AVgRVh\neBElept27UKK/kNV04ybz1AmHBwTFo6ofgIoCMCMj6hhAlWpBhHb3bFYLNTX12tnr/Gt5Wg0SmVl\nJZFIhGAwiNVqzcuU6enTp7nvvvvYunUrTzzxREG9bfOFzZwyFUI8nM/nKxFiFlhZWaGnp4eXXnop\n48em+oLrCaurqyvmZ41GY0YSCniN3PTpFAcPHkz5S5sJIQYCAfr6+0EIWltaMJtMWlVoiH+dQmDI\nU9q6EIKZmRnNV1VfFSazbMvE9DsV5ufnGR0dpaurK6fBmUQE6ff7cbvdTExMsLKyEkOQ4XA4ocA/\nH0hFkB6Ph3A4rMk89Ike8mflROe86zS4lzEZp7Hb7dhsdsxmc+x3XtQhjH2I6G+iYC0oIUbUEMHm\nIWrFNoysVdIGwK4VFDZCRLls/jm7Qren9DM1mUzU1dXFtJZdLhdut5tXXnmFj3/849hsNp555hkc\nDgft7e1ZkdmDDz7IQw89xKlTpzh79mzGN9ybhc3OQ8wXSoSYBXbu3JlVYG8ySBG30+nk0KFDCSUP\nmUgo9I8JhUL86le/wuFwpJVOkS4hahmLPT24nnsOVSeyTwQpw8gVkUiEgYEBDAYDPT096852Elm2\nyRBfSZBVVVUxpt/pQFVVLcLp2LFjaUVpZQK9r6m0bfP7/bhcLvr7+/H5fFRXV7O8vIzJZCqoflN+\nR2ZnZ5mamuLw4cPacE18BSnPfKuqqoiaHQhRTShowO/3s7CwQCgUwmq1XiFI21qLVxFAGApMiB7G\nwRTRyDARzKIcn7JAQHFiE6mzQvUwGo3aWfeBAwf4+c9/zu23387y8jJ/9Ed/RHl5Od/85jdzWv+v\nQ3UIm552gaIoNwPvJXEeYsmpppjIVaMlrdfq6+s5fvx40s0hmXVbqnXNzc2xvLxMT0/PhppFiY0I\nMRKJMDg4SCQS0SZT6/bvZ/zVV3G0t1NxJaJJXyH6FhaobGujPMeRdI/Hw8DAAPJ1asgAACAASURB\nVO3t7ZpTy0aQurtEqRj9/f2EQqGYCjIRQcoWaUNDA7t37y7KRiWT2BcWFqitreX48eOEQiFcLpdW\nQZaVlcW0WPNFLKqqMjg4SDQajTE02DA0WSlHMcxjtdZqaxNCEAqF8Pl8OJ1OgkEf9kovkeV5HA5S\nxj/lCr/iQtlAW6igoGAgpKxkRIgQq0G0Wq0Eg0HuvfdezUs4G3z0ox/l5MmTtLa2cvDgwayfp5jY\nZKeaTwOfZ82pZoRS/NPmQQbdZrNByrTty5cv093dvaHZdyb6RSmnMJlMmqYvXaQiRNnSbWtro6Wl\nBSEEkUiEnve/n75QCNflyyyFQszNz2NQFOxlZRj8fsqrqth1yy1pryEe0qZOtnxz2XDiUzEkQcpK\nLBQKaRWkw+HA4/EwNjbG3r17MzZkzwWS/Hfs2KGJ7G02G62trbReqbRli3Vqaorl5eW8EGQgEOD8\n+fM0Njaybdu2pOfMkGBIR/QglG+jqmutZHHlHNlisWC1WteSRpgl5D+Ce9XK+Pg4brebCxcuZDxc\nlA7UqIKS1lsgSJwtmxp6QoS137t0Ow7JcPPNN3PzzTfn9BzXGD5Oyalmc6FPvIhEIhkHeAohOHPm\nDJWVlVx//fVp2WmlS4iLi4tcuHCBzs5OGhoa+MUvfpHR2hIRojyDlGJ3u92uVQYGgwFrZSUHPvxh\nLj//PHMvvYSqKKjRKH6PB2NbG8HOTvrGxqj1eDSPzXQ3a6ktrKysLIi2UE+Q0mx8eXkZl8vF8PAw\n0WiU+vp6/H4/ZWVleTM5TwZpBj8zM7Mh+dtsNmw2m2b8HU+QkoRqa2vTIkiXy8WFCxcyNhbQIq/Y\nQ9TYiGpwo4gtCCFiwpPBj8EYxmq8kebmBlpbW3n55ZfZsWMHq6urTE5Opkz0yBS28JYNCVGgIgC7\nmthIIxXiCbGQjj5XOzbxDLGKklPN1QEpzk+XEOUwiM/nyzhOaaMpUzmd6vP5ckqniG/NSvG3w+Hg\n+PHjWlUo1yQ3ALPdTsdNN7H1TW8i4HIhhKDM4cB8ZUOXKQ2XL19mcHAQi8VCbW2ttlkn2khkUsTu\n3btj/GALCYPBgMViYXFxkY6ODlpbW7UK8vLly4TDYc0b1OFw5JUg5fmo0Wjk2LFjGfuOpkuQ8Tcl\nsgJfXFzc0Ig8FRTMGCN3gOlfEcoUBmoAG6oaQlHcqAIIvQ81UgdENV1kWVkZFRUVMeteWlrSEj0s\nFkuMJjBdgjSHHJhNVYSUVSwi8SBZUFmiRt2BOYtIPT0hxkswriVsspfpT4A3UHKq2Xxk28aUv9j5\nula8eXYud6n6ClFa0e3bt0+zBdNHNSWCqayMiisbmx4y50+e/SWaqNS70UitXS4bdDaYm5tb1yKN\n98n0eDwauUuClGvPtFsgIc+SZTs6H4gnSH101ODgIGazmerqapaWlqioqODo0aM5V+AKDoyRj6Aq\nFxDGXwIuDAYbivobmNSDKKYaVIOqWezpLehkJqTFYlmX6OF2u5mdnWVoaEgLEJa/R8luHIQKDudx\nwnXnCChLWEW1NkmqEiWoeLCIclrDb8zqtcbnk6b6vXg9Q6AQVQtCiA5FUZ5lbTBmfSr4Gj4OfE9R\nFAGcpuRUU3ykk3ihh3SzkVXhq6++mrZbjUSioRohBGNjY8zPz28op0gXBoOBSCTCuXPnEEJogzPJ\nopqyhX6z1k9UjoyM4HK5tGnLcDiMxWIp+EYjK+xQKJRyitRgMGhVlnycJMjJyUkikYgWv5QuQc7M\nzDA+Ps7+/fsLKuyOvylxOp309/djs9lYWlrilVde0dZdXV2dNTkqlGEUhyByKOHfCyE0A/xjx45p\n5vCJMiFlokdDQ4Mmmg8Gg3g8Hubn57VED71oXhJkNBqlTK1le+g2pk0vsGycQBGvfY8c0V00R45n\nVR3K55fXkv6o1yQERCIFee1LQD0wn/rqrACfA/4yyc+UnGqKgY1s2OREZjgcjnGzycbPNL5ClOkU\n6cop0oXH48Hr9WoOOaqqxrRICwEpOfB4PASDQY2QXC4XFy9eTGrXli94vV76+vrWeYKmA+kNqnd2\n0RNkNBqNqSD1RCtJOBwOJ5SQFBKyEj58+LBmNxYIBFhaWmJmZoYLFy5o4cO5EqQesgWvHw4CNI2j\nRKrQZJPJFJPoIV1lFhcXtUSPmpoazZquTDjYEb6ZUGSFoOIBFMrUWszEalI9ROg3+rhg9BFBUC/M\nHIpWsE21YkwwdBONRrXuxbUcDiyEQjSS3Xd3YWGBnp4e7d9PnjzJyZMntacGDgNDiqIYk5wTPgzc\nAPw9MEhpynTzkIrYXC4XAwMDdHR00NLSsk5kn63rjD5rMZ0IKEjvsF+vhbTb7RoZysGZQlZo8sZB\nCBFDDHrbs3i7tnyJ7WV1li/bt2QE6XK5GB8f11IpysvLuXz5Mi0tLRmTcC6QlnM+n29dJVxWVkZT\nU1NMJSZblZIgpa1bNuHDcmgnkf9qPDIJTU6W6DE1NaXJPV7LhGxMWP0PGnz82OxCIKgUJgwoTBpC\nDBsWaFfLuCVcR1lcakckEtGGnlZWVq5JH1OQhJhdhVhfX8+ZM2eS/XUr8AJwIcXQzFtZmzB9OKsF\nxKFEiFkgVctUCrhlUnqizTpbQlRVlbNnz2I0GtOOgJLtqFQbrs/n4/z589TV1XH8+HF+8YtfEA6H\ntbv2Qm7Wy8vL9Pf3pzw7S+RnGu9GU1VVlVJLGA/p6RqJRApanSUiyPHxcUZGRrBarUxPT+Pz+ait\nrU0av5QvhEIhzp8/T21tLYcOHdrwc7VarSkJ0mQyxVSQSc/yrkiM5ufnsz4TziQ02WAwUFtbSyAQ\nwGAw0NDQsC7ySkprqqurmbcKfmB24hBmrDrSswoDYGLCEOTHZhe3h2N1iqVw4CsQZE2IG+CyEKJn\ng59ZBObydcESIeaAeGJbXl7WWm89PT1JN5xsCHFxcRGv15t2qob+WqncQKanpxkbG4sZnKmurubF\nF1/Mq+VZPOQmOTc3x4EDBzIOJNa70ei1hH19fTGToLW1tevO8bxeL729vVrbrpjVmcxOvOGGG7BY\nLJpHpsvl4tKlSwghtDPIfBLk0tISAwMDOU3sxhNkKBTC7XYzPz/P0NBQQoKMRqOas1A+ZTPpEKTf\n79fiwpIlekxOTvLTFkGg0krYbMdkK8NojN0W64WJfoMLm8mDVYEqtYwdau06QryWW6aR8Kadn/4D\n8IeKovxECJGzfViJEHOAPj9wbGyMhYUFDhw4sOGdYiYRUHo5hd1uz4gM4bVWa3wFFA6H6e/vR1EU\nTQspW1JdXV0AMVVYMBiMqcJymfyUE7d2u52enp6cN8lEWkJJMjLCSJ7jBQIBpqen6e7uLmqLK5kx\nd7xHZiQSYWlpCbfbzdjYGIDWpkw34V4Pva7x8OHDeb2xkdOg8jupJ8jh4WFg7XU3NjbS2dlZ0FQL\nPUHKCdbV1VXa2toSRl7J99RDhGctMzT4owT9AVaWl9fOBsvKsJWVYbQbmbEssKz4OatYaBNlTBg8\nnGcWS52fJuNaV+NabpluMhzAfqBfUZR/Z/2UqRBC/Fm6T1YixCygF+YHAgF+9atfUVtbm/ZwS7pD\nNfFyikxF9nKN8deStmU7duygqakp6eBMfBUmBetTU1PaNGWiYZFUkOdInZ2d2plPvpFoElROsEpv\nzenpaW3thR5mka85HWNuOTCiD/B1u91ariKgvbaNCFJWZ4qiZKVrzBR6gnS73QwMDLB161ZCoRBn\nzpyJ+VxqamoKsh5VVenv78doNMbISJKFJi8rIRQz2MrKsNvWzgOFUAkGgqwGvVwOT6OGVBSDhZBB\npQwjFWYrAsFwuYtf2qd5J5XXcPQTgIIa3TQq+Z+6/787wd8LoESIhYYQAqfTydzcHMeOHcvI1kua\nbqd67mRyikzdMPS6Qpml53a7OXr0KGVlZWkPzsicvJqaGnbs2LFuWEQIoW12Dodj3WYnry3PVoup\nLfT7/YyOjmrnlNFodF0Vlmrt2UJWKk6nkyNHjmRl62UymWJiiMLhMEtLSxpByolKWUHKtctpzpaW\nlqK2hWVFOjs7q33HJEKhEEtLSywuLjIyMpJ3ggwGg5w7d46mpia2bdsW83fJQpPL1CgqAqEKBFEE\nAoNiwFpmZaXci9VQRplqZVWNYAhEWVxaJBqJYrFYsAUFExVuZsVK3s4Qn3rqKT772c/idrv5z//8\nz6IZUuQEARTmDHHjSwuR17ZDiRCzQCQS4eWXX8ZkMlFbW5uxx2WqM0S5kdXU1KyrONMZkEl0LVVV\ntcGZLVu2cPz48XVVYaYbZvywiKxknE4nFy9e1AYbZHu1v7+fLVu2cPTo0aKKl6enp5mcnKS7u1vb\nsOKrMEkyMgxWnjnJzzabjTocDtPb20t5eXleBO8SZrN5HUG63e4YkrFarXg8Hrq7u/MeFZUKqqoy\nMDAAwNGjR9e9bxaLhYaGBk0ukWjt+vPTTCp3eX6f7hmp/DwajXbqDVYCioodI0JVEVcI0m10Y46a\nQQFhNNBmq6CmzARijdz9c358nlX+9PG/YuGxl2lvb2dgYCAnc4ybbrqJm2++mePHj+N0On9NCFHZ\nNELMN0qEmAVMJhOdnZ2YzWYuXLiQ8eMTEaKUU4yPj7N3796Ecgr5uEw2V0VRmJ2dZXFxURt3T8dx\nJlPEVzLyPGlsbExzQoG1jSufyQzJIKUcQExiQyIkIxl5FmY0GmMIcqO1JzLmLhTMZrNGMkIIRkZG\nWFxcpK6ujuHhYa0Ky4Xc04E0BW9qakpbRqJfO7z2vutT6tNpD0tNpfTZzQQKCm9Uq/mRYYodkQE6\nos9hV+fxKnYWTG8mSgseyrGrCpURBZW13xuzxYzRaKSxrpG2kx/k7LiVhYUFPvOZzzA0NMR//dd/\npX2m+Oijj/Loo48CcOLECSYmJnjf+97H7t2JOoBXIQQQeX049JQIMQvINlUwGMzYcQbWnyGGQiHt\n3EM6wySCJMR0z+vC4bCmK7zuuutiBmcKbTNlNBpxOp2YzWbe8pa3EI1G1yUzyAoznwkHsDbgoE/l\nyBTxG7WMXdLr8fQ+rPphjnSNufONcDhMX18f5eXlXH/99dqaZJsyntw3kkpkAjnBmmt4cSKC1LeH\ngXUt1tHRUZaXl3PKqNwb9eMIfZlVMUVEqcGrNBBBUMY8leooZsMBtrEbo9GgGZZHw1GEKoiqKhbM\nmM1mbr31Vt7znvdk3MW54447uOOOOwD41re+xWc+8xmOHTvGb/zGb3Dddddl9ZqKjsy3wbxAURQV\nSGkkK8QGGWA6lAgxB2Qjn5CPk0Qq0yl27typjbPn43pOp5PBwUEqKys1Y4BCO85IrKys0N/fz9at\nW7Vrm81mzTZMb9UmpwHLy8tjvEyzjdS6fPkyly9fzqsNmsViiZEbSE9QPblLT1Cr1VqUARY95A3A\n9u3b100hx7cp4ydBM61+4zE1NcX09HTeJ1ghceUuCfLixYv4/X5sNhs7duzI/iIiijny/9EmvLgN\nu5hRQiwrERQU7KqCUBzsiQ6AoR6UelDW3sP5+XnqttThNUToWK7gu9/9Ljt37gRyC/Z9//vfz/vf\n//7sX89mQLBphAj8BesJsQ54B2BlzckmbZQIMQekmy4fD0mIAwMDmmNIOgMX6YQE640Bjh07phlQ\nF8NxRl8h6c/s4qFPh9+6dStCCLxerzYJ6vP5NA1kuqn28v00GAwbtkhzRTJPUBkSK8Xv+c73SwTp\ntJOuljOZVEJf/aZj16aqKhcuXCAajRbtBkASZGVlJUtLS+zcuZOysjKWlpZiJCqZaDgVMYAiJhFK\nGzUCaoTpymgN1IsQpy2VKIRQxAVUpR6vz4fL5aSxqRHMRoI+F//P73yG+++/nw9/+MOFfQOuVmwi\nIQohTiX6c0VRjMD3AU8mz1cixCyRS8vR5/PhcrnYsmVLRgfwG1WIXq9XC3ft6enRRPbDw8NMTk5q\nlUAhRt5l27esrCzjDVJRFCoqKqioqFjnRKMP7ZUEGS+0lxVSe3u7RlLFgiSkI0eOUFFRkbD6tdvt\n2tqzrX7joaoqQ0NDBIPBnJx24gkykV2b/N7I6CVJ+vX19bS1tRV1SEq2Z/WZjbKC1Gs4pcmB1J8m\nI0iD+l8IYieepW9phxqgO7JKn6maatVFYHmOldUIzc3N+I1RLrsv86M/epC/+Z9/ztve9rYCv/Kr\nGAIIb/YiYiGEiCqK8iDwj8AX0n1ciRCLCCmnmJubw263097entHjkxGirMzkNGVVVZU2OCOnVWW7\nSU706adEM8mYS4R8awsTOdHoNZDRaFSrBHw+H/Pz8xm73eSKZMbcyapft9utVb+5GpVLQtqyZQt7\n9uzJKyEls2uTkVGKohAIBOjo6GDbtm1FnxiemppK2p5NpOHUE6T8fZAVsNlsRhFuIHEHQgFujHjY\nooZ4UQ2zIJapbmlhyRDE3TfFjz7zv3nkb7/Mnj17CviqS8gBViCjQ+0SIRYJ8XKKF154IePnSBQS\nLF1fLBZLysGZ+POYYDCohd4ODAxkNeQiHXqWlpay1tmlg0QaSKfTycjICOFwmLKyMk1oXyjBtx7y\ns0wnHUNf/ebDqNztdjM4OJjzAEu60BPk9PQ0ExMTdHR04PV6efHFF7FYLBq5F2p6WAjB8PAwfr8/\no+5DPEHq9afSaL1za4jqSg8Wa+K1C1XFMTfG7XY3fsfvEw5X8o3//TDPPvEUP3r8ce25r2kIIC95\n9ZlDUZS2BH9sYc295vNAUufwRCgRYpaQm6CiKCm9QoUQzMzMaH6h6aRTJEN8hSgHZzo7O2loaNC0\nhfFROolgtVpjzsFkG3dsbEzzZaytraWuri7hJu33++nr66O2trbo2kKv18vo6KjmtJOo+s1lUCQV\nFhYWGBkZSSuxIRFSGZUPDAzEWOTV1tZqBgZCCCYnJ5mbmyvozUciyPZsKBTi+PHjMYQUP2BktVo1\ncs8HQUYiEc6fP09VVRUHDx7M6XsmUzH0nqbe5RDR8BeYW5pDCIHVasVms2G1WhFCMD8/j6NGxWY/\nhilSz6c+9SnC4TBPPfVUUc0lrnps3lDNJRJPmSrAReBjmTxZiRBzhEy8SBQEq5dTpJtOkQqSEOUG\ntbq6yrFjx7BarTlHNcW3+VZXV2PSJORZTG1tLW63m9HR0ZhznGJAksLs7GxMizS++o0fFNFXMVVV\nVVm9P9JpR77n6QT/poNk7WG3201vby/hcJiqqiq8Xq92PlvoKWE9ZEJGXV1dwvZs/ICR3+9fN4Er\nW5SZvvfSTKKjoyNjD990YDQaqaq5AVPkKSoqF1FpIBAIaLmQ4XCIcrsZ58IoAeW3+eNPvoe3vvWt\n3HfffUX9DK56bO6U6d2sJ8QAMA78KkVsVEKUCDFHJDvXy0ROkcm1/H4/L7zwAs3NzezZsydnx5lE\n0Fcx7e3t2ia9uLjI0NAQ0WhUq8wikUhRgm2lGXk6sob4QZH4KsZms2U0BZrMmLsQ0LeHt2/fzurq\nKmfPnqW8vJxgMMivfvWrpIHD+YZ0f9m1a1farUGbzYbNZtP0n5IgJycnWVlZ0VrzsoJM9l7Kc2l5\nJl4wKEYipnswRf5fDGISW1ktqmom4F9ha4uBaNTHN77t4Itf/lNMJhO7d+/mySef5Lbbbivcmn7d\nsLlTpg/n8/lKhJglkmUiymELr9e7oZwiEwGvEEJz8Th69CiVlZUFcZxJBIPBgMlkwul0am1K6WM6\nNjYWY3VWU1OT97tn6fySSGeXDvRVjH4KNL49nGjIJRNj7nxDtmcPHDigkUKiwGG9D2u+bk5mZmaY\nmJjIyv1FDz1BCiEIBAK4XC4mJiZYWVnBZrNp3x15cyK7AEXzvFXqiZj+DCX6AqtL30JEZ2luakAY\nb+CVcw4eeezzfOtb3+Lw4cO88MILWpJHCVewiYSoKIoBMAghIro/eydrZ4jPCiFeyeT5SoSYI/QV\noryjbmlp2VBOITWM6QwIhEIhent7EULQ0tJCZWVl0Rxn9GJ3vbZQfxajtzobGhqKcXLJtkUpry0z\nE/Pl/JJKA6kfcpETrB6Pp+hndkIIzQg9vj2byEM2kVF5tgNGqqoyMjKiDbDks/pXFAWbzablUMqb\nEzkFurq6qh0/dHV15a0tnQ5UYaN/sAGT6U/YvWsXYYOBxx9/nH/6p7/nySefpKOjA4C3vvWtvPWt\nby3aun4tsLkt028CQeBOAEVRPgo8eOXvwoqivEsI8XS6T1YixBxhMpkIh8OanCKdPET5OH3AaDIs\nLCwwNDTErl27MBgMLC4upj04kytkm9JisaQUu8dbbskqQLbJZIuytraW8vLytAhSWpHZbLa8ZCYm\nQyINpIwuUlUVk8nE6OhoUg1kvhEOh7UhkiNHjmz4XiUzKo9PlEjHy1TeeNXU1OQ8wJIO9Dcn9fX1\nnDt3jrq6Oux2OxMTE5qGU64/3e9OpgiFQpw7d47Gxka2bduGqqr8zd/8DS+++CL//u//XtRz8l9b\nbB4hvgH4v3X//n8B/xv4FPAV1uKhSoRYaMhfTOnY0dDQkHYeIrzmVpNsg9UHA/f09GCxWPB6vSwu\nLrKysqJNgBbKCUWO9+/YsSPjNmVZWRktLS1am0xOsF68eDEtF5qlpSXt2oU2x47H8vKydgMiJ3fj\nw4b1cUv5PMNbXl6mv7+fnTt3Zq3n3MioPD7VXn5fpblBLtfOFqurq/T29sZcW1bvPp9PG+Lyer2a\nyYHD4cgLQUozi87OTrZs2UIgEOATn/gEVVVVfP/73y/oGe3rBpsrzG8ALgMoitIJbAf+UQixoijK\n14BHM3myEiFmCZlOMTs7S2tra8bO9KlcZ1ZWVujt7aWlpYU9e/YghCASiWC1WnnDG96gVWB6H9C6\nujrtDCzX1zU2NobL5cqLP6WiKJSXl1NeXq7p8OJdaPRuIjMzMywsLHDo0KG8e2OmQjJj7kRhw9JP\nMx8tSgkZU5Vvg4H46l0K7WdmZrQJXLPZzPLyMocOHSp6yO3CwgIXL15M6D2r/+7oCVKafcvzX/n5\nZEqQTqeT4eFh7dqLi4vceeed3Hrrrdxzzz1FlRKVkDWWWfMuBXgrsCiEOHfl36Mkc11IghIhZgmP\nx6MNmWSDZBFQExMTTE9Pa7+kiQZn4s9h9BIJqWOrq6vD4XBk1OILBAL09fVRU1OT1ww/PRLJDDwe\nDwsLC/T392MwGGhqasLr9WKxWIrikSl9UI1G44YTrPFatlw1kLLDEIlECu7BCrFCeyEEg4ODeDwe\nqqqq6O3txWq1xgjtC0UKQgjGx8e1IbF0vqeJbq6kC5DsPqRrEi9vfo4cOYLVamVoaIi77rqLU6dO\nceutt+b75b6+sYnCfOB54F5FUSLAPcCPdH/XCUxl8mSKECmTM4qJq2Yh6UAIQSgUYmZmBr/fnzEx\nDg4OUl9fr22scrTfbreze/duzZUm08EZfYvP5XJpSfYbVTDz8/NcvHhxU6YppT9lZ2cnDodDq8Dc\nbndBRfaw1q7r6+tj27ZtWUVFxUNqIF0uFx6PB4vFom3Q8QNGMkNQnl0VsyKRAcZVVVXs2LFDu7ac\nwHW73THnv/k0Ko9GowwMDGgyhnx9pvoBKbfbHWOTJwkSYHh4mEAgQHd3N0ajkf/4j//g05/+NA8/\n/DBHjx7Ny1quJSjbe+DPMzKE0XDsH3o4cybxYxVFeUkI0ZPy2oqyC/gha+Q3CtwkhLh05e+eBcaF\nEHelu54SIWYJSYgLCwu43e6MW6bDw8NUV1fT0NCgne/s3r2bLVu25FVOIZPs5SYRPwEq0zECgQD7\n9u0r6mSfEIJLly6xuLjI/v37E7ZIJcE4nU6Wl5c1gsnH+ak05s5nVFQ8ZHvb5XLFEIzRaEwZBl1I\nyDO7jc5o9Wd4LpcrpkWZrVF5MBjk3LlzNDU1sW3btlxfSkrobfJcLhc+n49IJEJlZSV1dXW0trby\njW98g3/5l3/hO9/5Dq2trQVdz+sVSkcPfCZLQnwwN0LU/WydEMIZ92cHgFkhxEK66ym1THNEtpmI\nJpNJc7IJBAIcP34cs9mcdzlFfJK99DCdmppiaWmJUChEXV0dnZ2dRR0gkBONlZWVKd1X4kX2+chR\nTGbMXQgkGjAaGRnB7XZjsViYnJzE6/VmbfSdKebn5xkdHU3rJiDRGV4uRuVSllSsLoTeYKKhoYGz\nZ8/S1NSE0Wjk3nvv5eWXX0YIwX333UcoFMo42LeEK9hc2cXaEuLI8Mqfnc/0eUqEmCPihfnpIhwO\nMz4+zo4dO+jq6tIGZyC3aKmNIM+QpPtMd3c3wWBQO4NJ5KOZb8gJ1mzSMeLPT2WLTJo/b7T+TIy5\n841oNMrIyAhWq5W3vOUtKIqyTgNZVVWlEUw+tY9CiJzT5ZMZlcdrOBMZlc/OzjI+Pp6z0D8bSCLu\n6urS9KVGo5F3v/vdfOADH+A//uM/+JM/+RO++c1vFnWQ63WFTSbEfKHUMs0BwWAQr9fL8PAwhw8f\nTusxcphgfHycpqYmdu/eXTTHGVgjYnl+s2fPnpgzRVVVtQlQl8tFJBLRJAb5cEHRT7Du378/72L3\n+PWHw+GY9S8tLeVkzJ0L5Hh/qsxGIYQWc+V2u2MmcDMdkNIjEonQ29tLeXk5nZ2dBR2UkT6sLpeL\nUChEZWWlFlB96NChotj86TE/P8/Y2BgHDhzAbrczOzvLBz/4Qe68807+4A/+oFQR5gFKWw/8aZYt\n06/np2WaL5QqxBygKEpGFWIgEKC3t5eKigp27dqFz+crmuMMvKbv6+joSOivajAYqK6uprq6mu3b\nt6+TGCiKolVfmQ64BINB+vr6qKqqKtgEa6L1y2ngwcFBotEozc3NRCKRtEwR8oW5uTnGxsbo7u6m\nsrIy6c8pihKzfv2A1OTkZIwGMt0bFEnEyT7zfEK//o6ODsLhMGfPnkUIgcFg4MyZM3kh+HQgbzxd\nLhdHjx7FbDbT29vLRz7yEf72b/+Wd77znQW79jWHq6Blmi+UCDFHSMeZl04ehQAAIABJREFUjTA3\nN8fIyAh79uyhrq4Op9PJ2NgYZrM5acRSvqAfXslE3xcvMZADLjMzMwwODmpGzXV1dSk1YNIPdPfu\n3dpzFQNGo5Hy8nLGxsbYtm0bW7duxe12s7CwwPDwcMI0+HwiPiEj0zZlrhpIqfHbiIgLAdma3rp1\nqza9m4jgC2FUrqoqg4ODABw+fBiDwcDp06c5deoUjz76KN3d3Xm5jsRzzz3Hhz/8YTo6Onj88cf5\nxCc+wejoKKurq/T39wPw8MMP8/nPf57W1laeeeaZvF5/07G5wvy8okSIOSJRaK8ekUiECxcuaFly\ncnCmqqqK7u7umIglefdfW1ubt81Bagurq6tzjg5KNODidDo1kXRFRYVGkGVlZdq5VaEDhJMhkTF3\nvEg9PiRZGhzk6oIiY5PymZCRSAPpdrtjNJCSXKTsI12NXz4hZTTx8WDJCF4G9gohYhLts2mvhsNh\nzp07x5YtW2hrW8uO/cpXvsJ3v/tdTp8+XTDnI7PZjNFoxGAw8Nhjj/GP//iPuN1u7e+l1WJVVRXB\nYLCUpXiVonSGmAPC4TCqqvL8889zww03rPt7j8ejadzkEIiqqsD6wRl59+x0OnG5XAAaOWabICHT\nEopRmckBC7n+YDConeEV26hZVsROpzOjs0rpgiIlBpLgM3UA8ng89Pf3ZxSblA+EQiEWFxe5ePEi\nqqrGWOQVUmSvhzSCP3jwYMY3QHqjckkmmbgA+Xw+zp07p8lJIpEI9913H06nk6997Wt57cI8+uij\nPPromivYm970Ju69915uueUW7rzzTt773vdy6NAhfvKTn2ht6kAgQFlZGQcOHOCrX/0qx48fz9ta\nNhtKaw98LMszxO+WzhBf95Ab8tzcnDZVt9HgTPzds7z7n5ubY2hoCIvFolUHG1UvUlvo8/nyGmab\nCvoR98rKSi5cuEBbWxuRSEQ7R3I4HNTV1W1oMp0LpOC8oqIi47PKVCHJ8Un2id5TmQwyPT2dF9u7\nTBGJRJicnKSzs5Pm5uZ1UUuFNMoWQjA0NEQwGNzQ7ScZcjEql5PLMj9xZWWFu+66i56eHr74xS/m\nvR1+xx13cMcddwDw/e9/nxtvvJGVlRVuvPFGnn32Wbq6umhqauLVV1/lySefpKmpia9+9atUVFTk\nvWV7VeB1coZYqhBzQKIKUbqPVFVVsWvXLhRFycvgjGxPyuolmUG21+ulr69PEz4Xc4pOVVVGR0fx\neDzs378/pi0kCd7lcrG0tKQZBNTV1eWtepG5iYUwBZcyFVlBxg+4KIrC4OAgQgj27t1btIEdicXF\nRYaHh5MG6iYS2WeiIUwFeRMih4EK9Z2TZ9hut1v7DjkcDlRVxel0cujQIcrKypiamuIDH/gAn/jE\nJ/jQhz5UmiQtMJSWHvj9LCvEH11dFWKJEHOAnFb8xS9+wfXXX6/Zn3V1dVFbW1swOYU0yJYEKVuT\niqLgcrkKnzKeAPKs0uFwpLUpxju42O32GIPyTN4vvTH3/v37i6Jz0w+4OJ1OfD4fNTU1tLe343A4\nCh7NJaH3BD1w4EDa3YB4Fxe9htPhcKTd7pRTrNmGN+eCQCDA4OAgKysrmEwm/v7v/57GxkZ+9rOf\n8dBDD3HixImirudahdLcA3dlSYinS4SYDFfNQtKFJMQXXngBm82Gqqp0d3drk6fFklMEg0HOnz9P\nKBTS0u319myF3pxldZKtA4leYK/fnCVBptrk9cbc8brKYsDpdGpxUdFoVBtmyVdIcipEo1H6+vqw\nWCw5e4Im0nBuJJGQr33//v1Fn2KVr91ms9HZ2QmsTXJ+/etfZ9u2bQwPD9PW1sb/+T//p+hDRdca\nlKYe+FCWhPjTq4sQS2eIOcLj8bCyskJDQwPt7e1Fc5zRX39gYCBG8B0KhXA6ndr0pM1m08glG//J\nZJCygpWVlZzOKuNDevXtSZlBKFt7DodDI718G3NnAmky4Ha7OXr0qNYellVSfEiyzPHL12eQSNaQ\nC+I1nKk0kDKma25uLua1FwvSD7WlpYXW1lZUVeUf/uEfeOaZZ/jxj3+s3ZRNTk6WyLAY2Ny0i7yi\nVCHmgImJCS5duqS59svhmWIQoWyVLSws0N3dnbRNKM+OZHs1k+orFaTJQF1dHR0dHQV9vdFoNMag\n3Gg0YjabWV1d5eDBg0WvTsLhMH19fdjtdjo7OzeszPQ5ftJkeqOQ5FSQlVkxHXdki9jpdDIzMwNA\nS0tLwYek4iGDjHfv3k1tbS2hUIhPfvKTCCF46KGHSgS4CVAae+D9WVaIz19dFWKJEHOA3+8HoL+/\nn4aGBm24ohgt0r6+PioqKtLakPWQ1ZckSFVVY+Qd6WxsUs4hvSGLCRkd5PP5sNvtMQkShZiejIdM\nisjlzEwfkiwtztJxcJF5mQsLCxw4cKDolZnUVm7ZsoXm5mbtDNXj8WAymQpqcgCvGQ3IEGW3282d\nd97Jf/tv/41Pf/rTeb+m0+nk3e9+NwaDgYcffpjPfe5znDlzhk984hN8+MMfBuD06dPcd999bN26\nlSeeeOKaHOBRGnrgPVkS4otXFyGWWqY5wGw2EwqFqK+v59KlS4yMjFBTU6OF8xbirllWB9lqCw0G\nAzU1NdTU1LBz504tHkrv3iKrx/jpT1VVGRkZwev1Fk3OoYfemLu7uxtFURBCaAkY0qA8l+orFWZn\nZ7l06VLOcVHJQpKlREJV1XUtYnkjYDQaC2Z9lwryRqCzs1OTRcSbHLjdbqanpxkcHNRiuvKlgZQ3\nAtJoYGxsjDvvvJN7772X97znPQUhom9+85tcunSJ9vZ2zGYzzz//PD/96U+56aabNEJ88MEHeeih\nhzh16hRnz55N29O4hKsTJULMAQ888AB+v58TJ05w4403YrFYtOy+ixcvYjKZkpJLppBktLq6mtdz\nm/h4KHn2NT4+rsUr1dXVYbfbGRkZob6+XpOTFBOyKo1vEyqKsk4/KKuvvr6+dQbf2TgA6TMjCxEX\npdeg6m9S5PcI1ginqakp445APiAjo2RllggyRUUK0f1+P263O0YDKT+DTKp4VVUZGhoiGo1y5MgR\nDAYDzz//PJ/85Cf553/+Z66//vq8vU6IFdyfOHGC2267DYCnn35a+5lka78Wq0PgdWXdVmqZ5oCV\nlRV++tOfcvr0aZ5//nlqa2t5+9vfzokTJ+ju7iYUCmlj+aurq1RUVGgEmUnl4vP56O3t1QZ3ivWL\nJ0fzx8fHmZ+fx2q1atrBfHpPpoLeD7S7uzvjqlQafMv2pKIoMe4nG5GLnOAtxllpIrjdbgYGBmhq\naiIQCOQ9JDkVpMGE2+3mwIEDWX/e+jNUt9udtgtQOBzW7O86OjoA+Pa3v82XvvQlHn/8cdrb27N9\naWlhYmKC3/7t30YIwb/927/x53/+57z88st87GMfo6WlhYmJCdra2rj//vtpbW3lySefvCZJUdnS\nA/89y5bpuaurZVoixDxBnu+cPn2a06dPMzAwwMGDB3n729/O29/+dhoaGvB6vTidTpxOJ+FwWHNu\nSdVelanue/fuLXpkkayM/H4/+/btw2QyaYMVbrdbIxc5WJHvyiUYDNLb20ttbW3eyEgaBDidTjwe\nT0pykZ6cxTYlh7Xv0+TkJHNzcxw4cCDmBkq2iF0uV0xIcj5DhqPRKP39/ZjN5pwlHfHQuwC5XC4C\ngcC6HEu/38+5c+fo6OigsbERVVX5/Oc/z8svv8xjjz1WdJ1tCcmh1PXAu7IkxP6UhHgZmAfGhRC3\nZ7/C9FEixAIhGo3y0ksvcfr0aZ5++mlWV1d505vexIkTJ7jhhhuwWCwx5CK1g9K5JRqNxjifFDtH\nzu/3a1VpW1tbwk02HA5rFbDH48mrOXYiY+5CQLaIZRUvyUX++YEDB4puwSY/e4C9e/emJKNkGs5c\nQp6l25IMUS404jWQgUCAcDhMa2sr1dXVVFVV8Yd/+Ids2bKFL3zhC0X/XSghNZS6HnhndoTY9vP2\nmJDwkydPcvLkybXnVZSXgBPAeSFEWx6WuiFKhFgkrKys8Nxzz/GTn/wkYXtVTy5LS0uEw2EaGhrY\nuXNn0VMi5JlRplWpbItJ55bKykqNINPdmLM15s4HZMDt4OAgoVAIk8lEdXW1VsUXY4hIklFTUxNb\nt27N+KZio5Dkjdqe0pi80DciyTA7O8v4+Djt7e2Mjo7yyU9+ksXFRbq6uvjUpz7FW97ylqLLbEpI\nDaW2B05kWSGOpawQXwVmgL8VQjyX9QIzQIkQNwHJ2qtve9vb6O/vx2q18tGPflQjmFAoFDN1WKg7\nZDnAEAwG2bdvX05nhPr0d6fTqQm7U7WI9cbcO3fuLPrwiM/n08TuUvAdL1GRLeJ0JSqZQLZo8yln\niT9DheQJEpKMDh48WPSqWEaFLS8vc+DAAUwmE4ODg9x9993cf//9VFdX8+yzz+Lz+fjiF79Y1LWV\nkBqKowfeliUhTqQkxAUgCFwE3iGECGW9yDRRIsSrANFolKeffpo//uM/xmw2U1ZWxo033pi0vSpz\n8fJpCyYHdwplCi7F9bICjn8Ny8vLBTPmTgfJplj1kNOf0qBcb5FXWVmZE4FPTU0xPT2dVWxSJkhk\nsu5wOPD5fITDYY2Migl5Xikt6BRF4bnnnuO+++7jX/7lX0pShqscSk0PvCVLQpwuDdUkw1WzkGJD\nCMFtt93GH/zBH/Bbv/VbGbVXV1ZWNGlEprl9EnNzc4yNjRV1cEeG88rqKxqN0t7eTlNTU1HMuSVk\nZSITOjJpi8rX4HK5WF5ezsoiT6a7q6q6KSkZ0pxb7gP5PAdOB6FQiHPnztHY2Mi2bdsQQvD1r3+d\nRx55hO985ztFt+QrIXMo1T1wY5aEOF8ixGS4ahayGZBG4In+fKPpVWnN5nQ6CYVCMa3JVHf70WiU\noaEhQqFQzi3SbCCNuQ0GA9u2bdOq4EAgoJ3d1dbWFmxdskVbWVnJzp07c9r8k9mzpTpDlZKOhoaG\nokd1AdokZ1tbG83NzTEmB7mGJKeDeLF/NBrl1KlTjI2N8cgjjyTVPOaCePeZD37wg1y+fJnPfe5z\n/I//8T8AOHXqFE888QTd3d184xvfyPsaXm94PRFiaVzrKkEqsW97ezsf+chH+MhHPhIzvXr33Xcn\nnF71eDw4nU7GxsYwGAza9Kq+vSpzE+UkYbE340TG3FVVVZq5t965RR8unI52MB0sLy/T39+ftxat\noiiUl5dTXl6uVTryDLW3t1eT2chzYK/XS39//6ZIOuC1QF19iziRyYE+JFneqKQKSU4XTqeT4eFh\nzfXH6/Vy8uRJdu3axeOPP16wSjnefebZZ5/ly1/+MufPn9cI0WAwEI1GC0LIr0uUhPkFwVWzkF8n\nbNRejUQiWltyeXmZ8vJyjEaj1iLcDD2X1Fama4EWf+6VqzB9enqayclJ9u/fX7RNT5+fODc3RygU\noqWlhcbGxoJoOFPh8uXLXL58OePzyo1CktM9e5TZlYcOHcJisTA7O8sHPvAB7r77bn7/938/7zdn\n8e4z4+PjABw7doy2tjb++q//mscff1ybXg0EApqF4fDwcIwsoIT1UKp6oCfLCnH56qoQS4T4OsJG\n7VW73c63v/1tzfFFP5JfW1tb8GEK2aINh8Oa0D8byLae0+nU2nrpOACpqsqFCxdyvn62kFO8oVCI\n3bt3a1Wwx+PRXIBqa2sL5j4jjRaCwSDd3d05V2F6ktcbNSRzARJCxLTojUYj58+f5+TJk/zd3/0d\nN910U07rSQfx7jO7du1i//793HLLLVx33XVMTEwwMzPD97//fZqbm69Z95lMoFT2wJEsCdFXIsRk\nuGoW8nqBvr365JNPMj4+zpve9CZ+93d/lxtvvBGr1brOeaaurm5dezUf0Btz57NFK9t6egegRGeo\nUt+XymigkJBJEXV1dTH2e2FCXDD2MqCcwx/xYV2xU3epmUZa8np2J23Qampq2L59e0Fevxz2ShSS\nbLfb6evro7Kykh07dqAoCk899RR/8Rd/waOPPsq+ffvyvp4SigOlogcOZkmIoRIhJsNVs5DXG554\n4gk++9nP8sUvfhGn05myvSorr+XlZex2u0aQuWzK6Uga8oVEVYvNZmNpaYm9e/duynnd8vIyfX19\n7Nq1S0uKAJg0jPF962MIBOErhzAKCkaMNAe3cfjSjXgWPTHxUNkMGclJ0mJLWqTbz8LCAouLi1RU\nVDA/P8/WrVv55S9/yZNPPsnjjz++KTKbEvIHpbwH9mVJiKJEiMlw1Szk9YaRkREaGxtjHD7SmV71\n+/1a5RUMBmNcW9LZlHM15s4VUlIxOztLVVUVq6urWnaiTPAodKU4MzPDxMQEBw4ciJGTzCszfLvs\nq0SJoBDXWkRgwMC26HZuC30gZsjI5XJpQ0bpZFjK4ZXu7u5NcXiRNwNdXV1YLBYee+wxvva1rzE+\nPs7NN9/MO97xDm655ZZNccUpIT9Q7D3QlSUhGkqEmAxXzUKuRWzkvSrbq/rUCP30avx5USGMuTNB\nJBKJEXsbDIaE0gjp+1lXV5dXwtZHRnV3d687r/yu5REmjaPryFBCIDBi5L2Bu2gUsVo8fTyUFNfL\n6lG2uqU5+Pz8PAcPHtyUJPm5uTkuXbqkOd8sLy9z1113cf311/PAAw9w7tw5nnnmGd71rneVWqa/\nxlDsPdCZJSFaSoSYDFfNQkpIb3pVf14k8+7q6uoIBAJFMeZOBq/XS29vb4ykIxGk76feHCAf1myh\nUIje3t6k53VeVvmq7QuoqCgkv1EQCLoiB3hnOLXRfyAQ0AhyZWUFm81GKBTCarWyf//+oov9E8VG\nTU5O8sEPfpB77rmHO+64ozSo8jqCYuuB7VkSor1EiMlw1SykhFik215dXFxkcnKSUChEfX09jY2N\nRctNlJDG5Nm0CPXWbG63W6u8ZAJJOpv4ysoKfX197Ny5M+m4/oxhku9Z/5UIkZTPpaLSoDZzR/Bk\n2q8hGAzy6quvUlZWpgntc02/yASqqmpmC3v27MFgMHDmzBk+/vGP80//9E+8+c1vLuj1Syg+SoRY\nGFw1CykhNRK1V3t6enjppZe44447+L3f+72Yymuj9mo+oD+v3L9/f15IWA6FuFwuzSJPvo5EQ0az\ns7NcunQpZbI8wIIye+X8MJr6NaHSqrbx3uBdaa1XkvGuXbu04SF9Fex2u4lEIllpB9NBOBzm3Llz\nbNmyhba2tbSef/u3f+Pv/u7v+Na3vkVnZ2ferqVHvPvMO97xDpqamvi93/s9PvShDwFw+vRp7rvv\nPrZu3coTTzxRqlDzCKWsB7ZlSYjVVxchlpxqSsgYRqOR6667juuuu44HHniA5557jrvvvpvDhw/z\nyCOP8MMf/lBrr/b09BCNRnG5XExPTzMwMKB5fkpiy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DU0B29EdKKQmFQnGBO7Y/0sZm8GALRJsBQ0fzqKIoSbc36m+6MrUahmFV9JFS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\ntLTEY489lpNMy2j3mXe9610APPHEE/ziF7/gRz/6E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1BaaA7OiPlFISCoViAncsf6SFRf/BEogWxwwdzaOKoqTc3qivSWRqNQwjUtFH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\nh7nvvvt49dVXueWWW2hvb+eZZ57hhRde0NurnZ2de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- "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "from mpl_toolkits.mplot3d import Axes3D\n", - "import matplotlib.pyplot as plt\n", - "import numpy as np\n", - "from matplotlib import cm\n", - "\n", - "from matplotlib import animation, rc\n", - "from IPython.display import HTML\n", - "\n", - "from matplotlib import animation, rc\n", - "from IPython.display import HTML\n", - "font = {'family' : 'normal',\n", - " 'weight' : 'bold',\n", - " 'size' : 16}\n", - "import matplotlib\n", - "matplotlib.rc('font', **font)\n", - "plt.rc('xtick', labelsize=5) # fontsize of the tick labels\n", - "plt.rc('ytick', labelsize=5)\n", - "\n", - "\n", - "super_set = []\n", - "gen_vs_pop = ga_out_nsga['gen_vs_pop']\n", - "\n", - "length = len(gen_vs_pop)\n", - "\n", - "lims = []\n", - "for k,v in pop[0].dtc.attrs.items():\n", - " lims.append([np.min(explore_param[k]), np.max(explore_param[k])])\n", - "\n", - "x = 2\n", - "y = 3\n", - "z = 4\n", - "plt.clf()\n", - "from collections import OrderedDict\n", - "\n", - "\n", - "from sympy.combinatorics.graycode import GrayCode\n", - "a = GrayCode(5)\n", - "print(a)\n", - "pre_empt = list(a.generate_gray())\n", - "\n", - "for i, pop in enumerate(gen_vs_pop):\n", - " other_points = []\n", - " pf_points = []\n", - " hof_points = [] \n", - " labels = []\n", - " od = OrderedDict(pop[0].dtc.attrs)\n", - " for p in pop:\n", - " \n", - " xyz = []\n", - " for k in od.keys():\n", - " v = p.dtc.attrs[k]\n", - " xyz.append(v)\n", - " labels.append(k)\n", - " other_points.append(xyz)\n", - " best_xyz = []\n", - " \n", - " #for k,v in values(): \n", - " for k in od.keys():\n", - " v = ga_out_nsga['hof'][0].dtc.attrs[k]\n", - " best_xyz.append(v)\n", - " best_error = ga_out_nsga['hof'][0].dtc.get_ss()\n", - "\n", - " fig = plt.figure()\n", - " fig, ax = plt.subplots(1, 1)#, figsize=figsize)\n", - " ax = Axes3D(fig)\n", - " \n", - "\n", - " ax.set_xlim(lims[x])\n", - " ax.set_ylim(lims[y])\n", - " ax.set_zlim(lims[z])\n", - " \n", - " title='Particle Movement in 3D space, frame:' +str(i)#,\n", - " title_fontsize=\"large\"#,\n", - " text_fontsize=\"medium\"\n", - " ax.set_title(title, fontsize=title_fontsize)\n", - "\n", - "\n", - " errors = [ p.dtc.get_ss() for p in pop ]\n", - " xx = [ i[x] for i in other_points ]\n", - " yy = [ i[y] for i in other_points ]\n", - " zz = [ i[z] for i in other_points ]\n", - " if len(super_set) !=0 :\n", - " for ss in super_set:\n", - " ops, ers = ss\n", - " print('gets here')\n", - " p0 = ax.scatter3D([i[0] for i in ops], [ i[1] for i in ops], [i[2] for i in ops], s=100, alpha=0.0925, c=ers, cmap='jet', marker='o', vmin=abs_min, vmax=abs_max)\n", - "\n", - " p1 = ax.scatter3D(xx, yy, zz, c=errors, s=100, cmap='jet', marker='o', vmin=abs_min, vmax=abs_max)\n", - " if i == length:\n", - " p2 = ax.scatter3D(best_xyz[x], best_xyz[y], best_xyz[z], c=best_error, cmap='jet', marker='o', s=100, vmin=abs_min, vmax=abs_max)\n", - "\n", - " cb = fig.colorbar(p1)\n", - " cb.set_label('summed scores')\n", - "\n", - " ax.set_xlabel(str(labels[x]))\n", - " ax.set_ylabel(str(labels[y]))\n", - " ax.set_zlabel(str(labels[z]))\n", - " for item in ([ax.xaxis.label, ax.yaxis.label, ax.zaxis.label]):\n", - " item.set_fontsize(20)\n", - " \n", - " plt.savefig(str(i)+str('.png'))\n", - " super_set.append((other_points,errors)) \n", - " plt.show()\n", - " " - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [] - }, - { - "cell_type": "code", - "execution_count": 23, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "data": { - "text/plain": [ - "dict_keys(['hof', 'dhof', 'gen_vs_pop', 'pf', 'history', 'pop', 'td', 'bd', 'dbest', 'hardened', 'log'])" - ] - }, - "execution_count": 23, - "metadata": {}, - "output_type": "execute_result" - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/opt/conda/lib/python3.5/site-packages/matplotlib/font_manager.py:1297: UserWarning: findfont: Font family ['normal'] not found. Falling back to DejaVu Sans\n", - " (prop.get_family(), self.defaultFamily[fontext]))\n" - ] - }, - { - "data": { - "image/png": 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o0KH4BwToM4QsJshKpF+/fixfvpywsDCcnZ1p2bIlC+bPp3PopaOUGzRocK3mq4zsYLRn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\n+vJ39y8OY6PZyI7YHaw6s4o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- "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "for item in ['avg','max','min']:\n", - " plt.plot([x[item] for x in ga_out_nsga['log']],label=item)\n", - "plt.legend()\n", - "\n", - "ga_out_nsga.keys()\n", - "\n", - "#ga_out['gen_vs_pop']" - ] - }, - { - "cell_type": "code", - "execution_count": 24, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "data": { - "text/plain": [ - "" - ] - }, - "execution_count": 24, - "metadata": {}, - "output_type": "execute_result" - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/opt/conda/lib/python3.5/site-packages/matplotlib/font_manager.py:1297: UserWarning: findfont: Font family ['normal'] not found. Falling back to DejaVu Sans\n", - " (prop.get_family(), self.defaultFamily[fontext]))\n" - ] - }, - { - "data": { - "image/png": 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t7Wr0jsOGoCi3gB3zF+G0WBj6xrRanTl00fX0rAFKLuSgK8zDFhaJXy3f1Vnb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\nvn///q7PX3jhBUpKSkhOTubLL7+syW/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zaFW4eG4+3qxtF73Ko9PlnfIBJgAoFAwWg4ZSKAmydaQj9zwRhzfE4v51szHL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\nwhg1uaRQJq0D55xvBLBx1Ju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- "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "plt.plot([x['std'] for x in ga_out_nsga['log']],label=item)\n", - "plt.legend()" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [] - }, - { - "cell_type": "code", - "execution_count": 26, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "InjectedCurrentAPAmplitudeTest 0.988486736759166\n", - "InputResistanceTest 0.8682789264939796\n", - "TimeConstantTest 0.9477166124616718\n", - "InjectedCurrentAPWidthTest 0.9254488796223879\n", - "RestingPotentialTest 0.8693821053291544\n", - "InjectedCurrentAPThresholdTest 0.9732789481058128\n", - "CapacitanceTest 0.8163224375616873\n" - ] - }, - { - "ename": "NameError", - "evalue": "name 'dfs' is not defined", - "output_type": "error", - "traceback": [ - "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", - "\u001b[0;31mNameError\u001b[0m Traceback (most recent call last)", - "\u001b[0;32m\u001b[0m in \u001b[0;36m\u001b[0;34m()\u001b[0m\n\u001b[1;32m 7\u001b[0m \u001b[0magreement\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mk\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mv\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 8\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 9\u001b[0;31m \u001b[0mdfs\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mappend\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0magreement\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 10\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 11\u001b[0m \u001b[0mdf\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mpd\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mDataFrame\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdfs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", - "\u001b[0;31mNameError\u001b[0m: name 'dfs' is not defined" - ] - } - ], - "source": [ - "#df = df.T\n", - "#dfs = []\n", - "for i,dhof in enumerate(true_history):\n", - " agreement = {}\n", - " for k,v in dhof[0].score.items():\n", - " print(k,v['value'])\n", - " agreement[k] = v\n", - "\n", - " dfs.append(agreement)\n", - " \n", - "df = pd.DataFrame(dfs)\n", - "df = df[0:3]\n", - " \n", - "#f, ax = plt.subplots(figsize=(9, 6))\n", - "#sns.heatmap(df, annot=True, linewidths=.5, ax=ax, vmin=0, vmax=1) \n", - "df" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "#df = pd.DataFrame(index=best.scores.keys(),columns=best.scores.keys())\n", - "#df = df.T\n", - "dfs = []\n", - "for i,dhof in enumerate(true_history):\n", - " new = {}\n", - " #print(dir(dhof[0].score))\n", - " for k,v in dhof[0].scores.items():\n", - " if 'Injected' in str(k):\n", - " #print(k,v)\n", - " #print(v)#.prediction\n", - " #v#.observation\n", - " k = k[15::]\n", - " k = k[0:-4]\n", - " new[k] = v\n", - " #if np.nan(dhof[0].scores)\n", - "\n", - " dfs.append(new)\n", - " \n", - "df = pd.DataFrame(dfs)\n", - "df = df[0:3]\n", - "\n", - "\n", - "\n", - "f, ax = plt.subplots(figsize=(9, 6))\n", - "sns.heatmap(df, annot=True, linewidths=.5, ax=ax, vmin=0, vmax=1) " - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "#df = pd.DataFrame(index=best.scores.keys(),columns=best.scores.keys())\n", - "#df = df.T\n", - "dfs = []\n", - "for i,dhof in enumerate(gen_vs_pop[0]):\n", - " new = {}\n", - " for k,v in dhof.dtc.scores.items():\n", - " if 'Injected' in str(k):\n", - " k = k[15::]\n", - " k = k[0:-4]\n", - " new[k] = v\n", - " #if np.nan(dhof[0].scores)\n", - "\n", - " dfs.append(new)\n", - " \n", - " \n", - "from IPython.display import HTML, display\n", - "import seaborn as sns\n", - "\n", - "df = pd.DataFrame(dfs)\n", - "df = df[0:3]\n", - "\n", - "\n", - "#dfg = df.reset_index(drop=True)\n", - "\n", - "\n", - "f, ax = plt.subplots(figsize=(9, 6))\n", - "sns.heatmap(df, annot=True, linewidths=.5, ax=ax, vmin=0, vmax=1)\n", - "\n", - "# Set colormap equal to seaborns light green color palette\n", - "#cm = sns.light_palette(\"green\", as_cmap=True)\n", - "#display(dfg.style.background_gradient(cmap=cm))#,subset=['total']))" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "import matplotlib.pyplot as plt\n", - "import seaborn as sns\n", - "sns.set()\n", - "\n", - "# Load the example flights dataset and conver to long-form\n", - "flights_long = sns.load_dataset(\"flights\")\n", - "flights = flights_long.pivot(\"month\", \"year\", \"passengers\")\n", - "\n", - "# Draw a heatmap with the numeric values in each cell\n", - "f, ax = plt.subplots(figsize=(9, 6))\n", - "sns.heatmap(flights, annot=True, fmt=\"d\", linewidths=.5, ax=ax)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "#df = pd.DataFrame(index=best.scores.keys(),columns=best.scores.keys())\n", - "df = df.T\n", - "dfs = []\n", - "for i,dhof in enumerate(true_history):\n", - " new = {}\n", - " for k,v in dhof[0].scores.items():\n", - " if 'Injected' in str(k):\n", - " k = k[15::]\n", - " k = k[0:-4]\n", - " new[k] = v\n", - " #if np.nan(dhof[0].scores)\n", - "\n", - " dfs.append(new)\n", - " \n", - " \n", - "from IPython.display import HTML, display\n", - "import seaborn as sns\n", - "\n", - "df = pd.DataFrame(dfs)\n", - "df = df[-4::]\n", - "\n", - "\n", - "dfg = df.reset_index(drop=True)\n", - "\n", - "# Set colormap equal to seaborns light green color palette\n", - "cm = sns.light_palette(\"green\", as_cmap=True)\n", - "display(dfg.style.background_gradient(cmap=cm))#,subset=['total']))" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "\n", - "f, ax = plt.subplots(figsize=(9, 6))\n", - "sns.heatmap(dfg, annot=True, linewidths=.5, ax=ax)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "score = tests_[0].judge(model1)\n", - "score.summarize()\n", - "score.sort_key" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "from neuronunit.optimization import optimization_management as om\n", - "free_params = ['a','b','vr'] # this can only be odd numbers.\n", - "2**3\n", - "hc = {}\n", - "for k,v in cells['TC'].items():\n", - " if k not in free_params:\n", - " hc[k] = v\n", - "#print(hc)\n", - "import pickle\n", - "TC_tests = pickle.load(open('thalamo_cortical_tests.p','rb')) \n", - " #run_ga(model_params, max_ngen, test, free_params = None, hc = None)\n", - " \n", - "#ga_out, DO = om.run_ga(explore_param,10,TC_tests,free_params=free_params,hc = hc, NSGA = False, MU = 10)\n", - " \n", - "ga_out_sbest, _ = om.run_ga(explore_param,20,TC_tests,free_params=free_params,hc = hc, NSGA = False)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "for item in ['avg','max','min']:\n", - " plt.plot([x[item] for x in ga_out_sbest['log']],label=item)\n", - "plt.legend()\n", - "\n", - "\n", - "best = ga_out['dhof'][0]\n", - "best_attrs = ga_out_sbest['dhof'][0].attrs\n", - "print('best nsga',np.sum(list(ga_out_nsga['dhof'][0].scores.values())))\n", - "print('best BPO select best: ',np.sum(list(ga_out_sbest['dhof'][0].scores.values())))" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "plt.plot([x['std'] for x in ga_out_sbest['log']],label=item)\n", - "plt.legend()" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "def hack_judge(test_and_models):\n", - " (test, attrs) = test_and_models\n", - " model = None\n", - " obs = test.observation\n", - " model = ReducedModel(LEMS_MODEL_PATH,name = str('vanilla'),backend = ('RAW'))\n", - " model.set_attrs(attrs)\n", - " test.generate_prediction(model)\n", - " pred = test.generate_prediction(model)\n", - " score = test.compute_score(obs,pred)\n", - " try:\n", - " print(obs['value'],pred['value'])\n", - " except:\n", - " print(obs['mean'],pred['mean'])\n", - " \n", - " return score\n", - "\n", - "scores = []\n", - "for i,t in enumerate(TC_tests):\n", - " test_and_models = (t,cells['TC'])\n", - " score = hack_judge(test_and_models)\n", - " scores.append(score)\n", - "print(scores[0].norm_score) \n", - "print(scores[0]) \n", - "\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "print([s.norm_score for s in scores])\n", - "print([s.score for s in scores])\n", - "\n", - "score = hack_judge((TC_tests[-3],cells['TC']))\n", - "print(score)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "scores = []\n", - "for t in TC_tests:\n", - " test_and_models = (t,cells['RS'])\n", - " score = hack_judge(test_and_models)\n", - " scores.append(score)\n", - "print(scores[0].norm_score) \n", - "print(scores[0])\n", - "print([s.norm_score for s in scores])\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "import dask.bag as db\n", - "# The rheobase has been obtained seperately and cannot be db mapped.\n", - "# Nested DB mappings dont work.\n", - "from itertools import repeat\n", - "test_a_models = zip(TC_tests[1::],repeat(cells['RS']))\n", - "tc_bag = db.from_sequence(test_a_models)\n", - "\n", - "scores = list(tc_bag.map(hack_judge).compute())\n", - "scores.insert(0,rheobase)\n", - "print(scores) " - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "score = TC_tests[0].judge(model,stop_on_error = False, deep_error = True)\n", - "print(score.prediction)\n", - "#print(model.get_spike_count())" - ] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.5.2" - } - }, - "nbformat": 4, - "nbformat_minor": 2 -} diff --git a/neuronunit/unit_test/rtt.ipynb b/neuronunit/unit_test/rtt.ipynb deleted file mode 100644 index 5f5ab003e..000000000 --- a/neuronunit/unit_test/rtt.ipynb +++ /dev/null @@ -1,969 +0,0 @@ -{ - "cells": [ - { - "cell_type": "code", - "execution_count": 1, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "OrderedDict([('RS', {'d': 100, 'a': 0.03, 'b': -2, 'k': 0.7, 'c': -50, 'C': 100, 'vPeak': 35, 'vr': -60, 'vt': -40}), ('IB', {'d': 130, 'a': 0.01, 'b': 5, 'k': 1.2, 'c': -56, 'C': 150, 'vPeak': 50, 'vr': -75, 'vt': -45}), ('LTS', {'d': 20, 'a': 0.03, 'b': 8, 'k': 1.0, 'c': -53, 'C': 100, 'vPeak': 40, 'vr': -56, 'vt': -42}), ('TC', {'d': 10, 'a': 0.01, 'b': 15, 'k': 1.6, 'c': -60, 'C': 200, 'vPeak': 35, 'vr': -60, 'vt': -50}), ('TC_burst', {'d': 10, 'a': 0.01, 'b': 15, 'k': 1.6, 'c': -60, 'C': 200, 'vPeak': 35, 'vr': -60, 'vt': -50})])\n" - ] - } - ], - "source": [ - "# http://www.physics.usyd.edu.au/teach_res/mp/mscripts/\n", - "# ns_izh002.m\n", - "import collections\n", - "from collections import OrderedDict\n", - "import numpy as np\n", - "\n", - "# Fast spiking cannot be reproduced as it requires modifications to the standard Izhi equation,\n", - "# which are expressed in this mod file.\n", - "# https://github.com/OpenSourceBrain/IzhikevichModel/blob/master/NEURON/izhi2007b.mod\n", - "\n", - "\n", - "reduced2007 = collections.OrderedDict([\n", - " # C k vr vt vpeak a b c d celltype\n", - " ('RS', (100, 0.7, -60, -40, 35, 0.03, -2, -50, 100, 1)),\n", - " ('IB', (150, 1.2, -75, -45, 50, 0.01, 5, -56, 130, 2)),\n", - " ('LTS', (100, 1.0, -56, -42, 40, 0.03, 8, -53, 20, 4)),\n", - " ('TC', (200, 1.6, -60, -50, 35, 0.01, 15, -60, 10, 6)),\n", - " ('TC_burst', (200, 1.6, -60, -50, 35, 0.01, 15, -60, 10, 6))])\n", - "\n", - "\n", - "type2007 = collections.OrderedDict([\n", - " # C k vr vt vpeak a b c d celltype\n", - " ('RS', (100, 0.7, -60, -40, 35, 0.03, -2, -50, 100, 1)),\n", - " ('IB', (150, 1.2, -75, -45, 50, 0.01, 5, -56, 130, 2)),\n", - " ('LTS', (100, 1.0, -56, -42, 40, 0.03, 8, -53, 20, 4)),\n", - " ('TC', (200, 1.6, -60, -50, 35, 0.01, 15, -60, 10, 6)),\n", - " ('TC_burst', (200, 1.6, -60, -50, 35, 0.01, 15, -60, 10, 6)),\n", - " ('RTN', (40, 0.25, -65, -45, 0, 0.015, 10, -55, 50, 7)),\n", - " ('RTN_burst', (40, 0.25, -65, -45, 0, 0.015, 10, -55, 50, 7)),\n", - " ('CH', (50, 1.5, -60, -40, 25, 0.03, 1, -40, 150, 3)),\n", - " ('FS', (20, 1.0, -55, -40, 25, 0.2, -2, -45, -55, 5))])\n", - "\n", - "reduced_dict = OrderedDict([(k,[]) for k in ['C','k','vr','vt','vPeak','a','b','c','d']])\n", - "\n", - "#OrderedDict\n", - "for i,k in enumerate(reduced_dict.keys()):\n", - " for v in type2007.values():\n", - " reduced_dict[k].append(v[i])\n", - "\n", - "explore_param = {k:(np.min(v),np.max(v)) for k,v in reduced_dict.items()}\n", - "param_ranges = OrderedDict(explore_param)\n", - "\n", - "\n", - "RS = {}\n", - "IB = {}\n", - "TC = {}\n", - "CH = {}\n", - "RTN_burst = {}\n", - "cells = OrderedDict([(k,[]) for k in ['RS','IB','CH','LTS','FS','TC','TC_burst','RTN','RTN_busrt']])\n", - "reduced_cells = OrderedDict([(k,[]) for k in ['RS','IB','LTS','TC','TC_burst']])\n", - "\n", - "for index,key in enumerate(reduced_cells.keys()):\n", - " reduced_cells[key] = {}\n", - " for k,v in reduced_dict.items():\n", - " reduced_cells[key][k] = v[index]\n", - "\n", - "print(reduced_cells)\n", - "cells = reduced_cells" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "model = None\n", - "from neuronunit.models.reduced import ReducedModel\n", - "from neuronunit.optimization import get_neab\n", - "\n", - "from neuronunit.optimization.model_parameters import model_params, path_params\n", - "LEMS_MODEL_PATH = path_params['model_path']\n", - "\n", - "model = ReducedModel(LEMS_MODEL_PATH,name = str('vanilla'),backend = ('RAW'))\n", - "model.set_attrs(cells['TC'])\n", - "\n" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "{'value': array(75.53061224489795) * pA}\n" - ] - } - ], - "source": [ - "\n", - "tests_,all_tests, observation,suite = get_neab.get_tests()\n", - "\n", - "rheobase = all_tests[0].generate_prediction(model)\n", - "print(rheobase)" - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "data": { - "image/png": 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mdjLJQ0s7erTK1NKpfTdwMYCZ/RXJYKjs0Sq7ZxXwmWB20gVAtbvvz3VR6TCz\nCcB/ANe5+xu5rueE5Xr0O6ovkjMa3iA5Y+PmYN1tJP8YQfKX4yFgG/A8cFqua06z7j8CbwEbgq9V\nua453do7tH2aXjIrKc333YAfAa8CLwOLc13zCdQ+A3iW5IylDcBlua45qKsc2A80kewdXA/cANzQ\n7j1fHnxfL/eyn5euar8HONzu93Rdrms+kS+d+SwiIiF99VCSiIh0k4JBRERCFAwiIhKiYBARkRAF\ng4iIhCgYREQkRMEgIiIhCgYREQn5/1TiGs2Gil+hAAAAAElFTkSuQmCC\n", - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "\n", - "\n", - "import pickle\n", - "import copy\n", - "import numpy as np\n", - "import pandas as pd\n", - "import matplotlib.pyplot as plt\n", - "\n", - "from neuronunit.models.reduced import ReducedModel\n", - "from neuronunit.optimization.model_parameters import model_params, path_params\n", - "LEMS_MODEL_PATH = path_params['model_path']\n", - "import neuronunit.optimization as opt\n", - "import quantities as pq\n", - "\n", - "from neuronunit.optimization.data_transport_container import DataTC\n", - "model = ReducedModel(LEMS_MODEL_PATH,name = str('vanilla'),backend = ('RAW'))\n", - "model.set_attrs(cells['TC'])\n", - "\n", - "iparams = {}\n", - "iparams['injected_square_current'] = {}\n", - "iparams['injected_square_current']['amplitude'] =75.36800000000001*pq.pA\n", - "#['amplitude'] = dtc.vtest[k]['injected_square_current']['amplitude']\n", - "DELAY = 100.0*pq.ms\n", - "DURATION = 1000.0*pq.ms\n", - "iparams['injected_square_current']['delay'] = DELAY\n", - "iparams['injected_square_current']['duration'] = int(DURATION)\n", - "\n", - "model.inject_square_current(iparams)\n", - "\n", - "plt.plot(model.get_membrane_potential().times,model.get_membrane_potential(),label='RAW')\n", - "plt.legend()\n", - "plt.show()\n" - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "\n", - "cnt = 0\n", - "scores = []\n", - "tests_,all_tests, observation,suite = opt.get_neab.get_tests()\n" - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[array(75.53061224489795) * pA, array(75.53061224489795) * pA, array(75.53061224489795) * pA]\n", - "[array(75.53061224489795) * pA, array(-10.0) * pA, array(-10.0) * pA, array(-10.0) * pA, array(-10.0) * pA]\n" - ] - } - ], - "source": [ - "\n", - "\n", - "\n", - "def format_iparams(all_tests,rheobase):\n", - "\n", - " for t in all_tests[1:5]:\n", - " DURATION = 500.0*pq.ms\n", - " DELAY = 200.0*pq.ms\n", - "\n", - " obs = t.observation\n", - " t.params = {}\n", - " t.params['injected_square_current'] = {}\n", - " t.params['injected_square_current']['delay']= DELAY\n", - " t.params['injected_square_current']['duration'] = DURATION\n", - " t.params['injected_square_current']['amplitude'] = -10*pq.pA\n", - " \n", - " \n", - " for t in all_tests[-3::]: \n", - " t.params = {}\n", - " DURATION = 1000.0*pq.ms\n", - " DELAY = 100.0*pq.ms\n", - "\n", - " t.params['injected_square_current'] = {}\n", - " t.params['injected_square_current']['delay']= DELAY\n", - " t.params['injected_square_current']['duration'] = DURATION\n", - " t.params['injected_square_current']['amplitude'] = rheobase['value']\n", - " \n", - " all_tests[0].params = all_tests[-1].params\n", - " \n", - " return all_tests\n", - "\n", - "pt = format_iparams(all_tests,rheobase)\n", - "print([t.params['injected_square_current']['amplitude'] for t in pt[-3::] ])\n", - "print([t.params['injected_square_current']['amplitude'] for t in pt[0:5] ])\n", - "\n" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "##\n", - "# * Get predictions from models.\n", - "## * Fake NeuroElectro Observations\n", - "## * Do roundtrip testing\n", - "##" - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "injected current seen: {'duration': array(500.0) * ms, 'delay': array(200.0) * ms, 'amplitude': array(-10.0) * pA}\n", - "injected current seen: {'duration': array(500.0) * ms, 'delay': array(200.0) * ms, 'amplitude': array(-10.0) * pA}\n", - "injected current seen: {'duration': array(500.0) * ms, 'delay': array(200.0) * ms, 'amplitude': array(-10.0) * pA}\n", - "[{'value': array(75.53061224489795) * pA}, {'value': array(31739282.99824782) * kg*m**2/(s**3*A**2)}, {'value': array(0.0034631849083750876) * s}, {'value': array(1.0911352057216523e-10) * s**4*A**2/(kg*m**2)}, {'std': array(0.00019622743842309925) * V, 'mean': array(-0.060317405613048956) * V}, {'std': array(0.0) * s, 'n': 1, 'mean': array(0.0006500000000000001) * s}, {'std': array(0.0) * V, 'n': 1, 'mean': array(0.056127191359642024) * V}, {'std': array(0.0) * V, 'n': 1, 'mean': array(-0.02112719135964202) * V}]\n" - ] - } - ], - "source": [ - "predictions = []\n", - "import dask.bag as db\n", - "# The rheobase has been obtained seperately and cannot be db mapped.\n", - "# Nested DB mappings dont work.\n", - "ptbag = db.from_sequence(pt[1::])\n", - "\n", - "def obtain_predictions(t): \n", - " model = None\n", - " model = ReducedModel(LEMS_MODEL_PATH,name = str('vanilla'),backend = ('RAW'))\n", - " model.set_attrs(cells['TC'])\n", - " return t.generate_prediction(model)\n", - "predictions = list(ptbag.map(obtain_predictions).compute())\n", - "predictions.insert(0,rheobase)\n", - "print(predictions) \n" - ] - }, - { - "cell_type": "code", - "execution_count": 8, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[{'value': array(75.53061224489795) * pA}, {'value': array(31739282.99824782) * kg*m**2/(s**3*A**2)}, {'value': array(0.0034631849083750876) * s}, {'value': array(1.0911352057216523e-10) * s**4*A**2/(kg*m**2)}, {'std': array(0.00019622743842309925) * V, 'value': array(-0.060317405613048956) * V}, {'std': array(0.0) * s, 'n': 1, 'value': array(0.0006500000000000001) * s}, {'std': array(0.0) * V, 'n': 1, 'value': array(0.056127191359642024) * V}, {'std': array(0.0) * V, 'n': 1, 'value': array(-0.02112719135964202) * V}]\n" - ] - } - ], - "source": [ - "# having both means and values in dictionary makes it very irritating to iterate over.\n", - "# It's more harmless to demote means to values, than to elevate values to means.\n", - "# Simply swap key names: means, for values.\n", - "for p in predictions:\n", - " if 'mean' in p.keys():\n", - " p['value'] = p.pop('mean')\n", - "print(predictions)\n" - ] - }, - { - "cell_type": "code", - "execution_count": 9, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "75.53061224489795 pA\n", - "31.73928299824782 Mohm\n", - "3.4631849083750876 ms\n", - "1.0911352057216523e-10 s**4*A**2/(kg*m**2)\n", - "-60.317405613048955 mV\n", - "0.6500000000000001 ms\n", - "56.127191359642026 mV\n", - "-21.127191359642023 mV\n" - ] - } - ], - "source": [ - "# make some new tests based on internally generated data \n", - "# as opposed to experimental data.\n", - "\n", - "\n", - "TC_tests = copy.copy(all_tests)\n", - "for ind,t in enumerate(TC_tests):\n", - " if 'mean' in t.observation.keys():\n", - " t.observation['value'] = t.observation.pop('mean')\n", - " pred = predictions[ind]['value']\n", - " try:\n", - " pred = pred.rescale(t.units)\n", - " t.observation['value'] = pred\n", - " except: \n", - " t.observation['value'] = pred\n", - " t.observation['mean'] = t.observation['value']\n", - " \n", - " print(t.observation['value'])\n", - " \n", - "pickle.dump(TC_tests,open('thalamo_cortical_tests.p','wb')) \n", - " \n", - "\n", - "\n" - ] - }, - { - "cell_type": "code", - "execution_count": 10, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "{'d': 10, 'k': 1.6, 'c': -60, 'C': 200, 'vPeak': 35, 'vt': -50}\n", - "not even to test runner getting\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/opt/conda/lib/python3.5/site-packages/sklearn/cross_validation.py:41: DeprecationWarning: This module was deprecated in version 0.18 in favor of the model_selection module into which all the refactored classes and functions are moved. Also note that the interface of the new CV iterators are different from that of this module. This module will be removed in 0.20.\n", - " \"This module will be removed in 0.20.\", DeprecationWarning)\n", - "/opt/conda/lib/python3.5/site-packages/sklearn/grid_search.py:42: DeprecationWarning: This module was deprecated in version 0.18 in favor of the model_selection module into which all the refactored classes and functions are moved. This module will be removed in 0.20.\n", - " DeprecationWarning)\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[array(227.1326530612245) * pA, array(6.928571428571429) * pA, array(319.9708454810496) * pA, array(32.33673469387755) * pA, array(226.28571428571428) * pA, array(6.081632653061225) * pA, array(473.7609329446064) * pA, array(83.15306122448979) * pA]\n", - "injected current seen: {'duration': array(1000.0) * ms, 'delay': array(100.0) * ms, 'amplitude': array(227.1326530612245) * pA}\n", - "injected current seen: {'duration': array(1000.0) * ms, 'delay': array(100.0) * ms, 'amplitude': array(6.928571428571429) * pA}\n", - "injected current seen: {'duration': array(1000.0) * ms, 'delay': array(100.0) * ms, 'amplitude': array(32.33673469387755) * pA}\n", - "injected current seen: {'duration': array(1000.0) * ms, 'delay': array(100.0) * ms, 'amplitude': array(319.9708454810496) * pA}\n", - "input resistance score: Z = 3.30\n", - "input resistance score: Z = 0.47\n", - "injected current seen: {'duration': array(500.0) * ms, 'delay': array(200.0) * ms, 'amplitude': array(-10.0) * pA}\n", - "injected current seen: {'duration': array(500.0) * ms, 'delay': array(200.0) * ms, 'amplitude': array(-10.0) * pA}\n", - "input resistance score: Z = -0.11\n", - "injected current seen: {'duration': array(500.0) * ms, 'delay': array(200.0) * ms, 'amplitude': array(-10.0) * pA}\n", - "input resistance score: Z = 0.22\n", - "injected current seen: {'duration': array(1000.0) * ms, 'delay': array(100.0) * ms, 'amplitude': array(226.28571428571428) * pA}\n", - "injected current seen: {'duration': array(500.0) * ms, 'delay': array(200.0) * ms, 'amplitude': array(-10.0) * pA}\n", - "injected current seen: {'duration': array(500.0) * ms, 'delay': array(200.0) * ms, 'amplitude': array(-10.0) * pA}\n", - "input resistance score: Z = 1.07\n", - "injected current seen: {'duration': array(1000.0) * ms, 'delay': array(100.0) * ms, 'amplitude': array(473.7609329446064) * pA}\n", - "injected current seen: {'duration': array(500.0) * ms, 'delay': array(200.0) * ms, 'amplitude': array(-10.0) * pA}\n", - "injected current seen: {'duration': array(1000.0) * ms, 'delay': array(100.0) * ms, 'amplitude': array(6.081632653061225) * pA}\n", - "injected current seen: {'duration': array(500.0) * ms, 'delay': array(200.0) * ms, 'amplitude': array(-10.0) * pA}\n", - "injected current seen: {'duration': array(500.0) * ms, 'delay': array(200.0) * ms, 'amplitude': array(-10.0) * pA}\n", - "input resistance score: Z = 0.06\n", - "injected current seen: {'duration': array(500.0) * ms, 'delay': array(200.0) * ms, 'amplitude': array(-10.0) * pA}\n", - "injected current seen: {'duration': array(500.0) * ms, 'delay': array(200.0) * ms, 'amplitude': array(-10.0) * pA}\n", - "injected current seen: {'duration': array(1000.0) * ms, 'delay': array(100.0) * ms, 'amplitude': array(83.15306122448979) * pA}\n", - "input resistance score: Z = 3.26\n", - "injected current seen: {'duration': array(500.0) * ms, 'delay': array(200.0) * ms, 'amplitude': array(-10.0) * pA}\n", - "input resistance score: Z = 0.65\n", - "injected current seen: {'duration': array(500.0) * ms, 'delay': array(200.0) * ms, 'amplitude': array(-10.0) * pA}\n", - "injected current seen: {'duration': array(500.0) * ms, 'delay': array(200.0) * ms, 'amplitude': array(-10.0) * pA}\n", - "injected current seen: {'duration': array(500.0) * ms, 'delay': array(200.0) * ms, 'amplitude': array(-10.0) * pA}\n", - "injected current seen: {'duration': array(500.0) * ms, 'delay': array(200.0) * ms, 'amplitude': array(-10.0) * pA}\n", - "injected current seen: {'duration': array(500.0) * ms, 'delay': array(200.0) * ms, 'amplitude': array(-10.0) * pA}\n", - "[, , , , , , , ]\n", - "tests, completed, now gene computations\n", - "not even to test runner getting\n", - "[array(227.1326530612245) * pA, array(260.2040816326531) * pA, array(67.90816326530613) * pA, array(219.51020408163265) * pA, array(98.39795918367346) * pA, array(420.5539358600583) * pA]\n", - "injected current seen: {'duration': array(1000.0) * ms, 'delay': array(100.0) * ms, 'amplitude': array(227.1326530612245) * pA}\n", - "injected current seen: {'duration': array(1000.0) * ms, 'delay': array(100.0) * ms, 'amplitude': array(260.2040816326531) * pA}\n", - "injected current seen: {'duration': array(1000.0) * ms, 'delay': array(100.0) * ms, 'amplitude': array(67.90816326530613) * pA}\n", - "injected current seen: {'duration': array(1000.0) * ms, 'delay': array(100.0) * ms, 'amplitude': array(98.39795918367346) * pA}\n", - "injected current seen: {'duration': array(1000.0) * ms, 'delay': array(100.0) * ms, 'amplitude': array(219.51020408163265) * pA}\n", - "injected current seen: {'duration': array(1000.0) * ms, 'delay': array(100.0) * ms, 'amplitude': array(420.5539358600583) * pA}\n", - "input resistance score: Z = -0.09\n", - "injected current seen: {'duration': array(500.0) * ms, 'delay': array(200.0) * ms, 'amplitude': array(-10.0) * pA}\n", - "input resistance score: Z = 0.47\n", - "injected current seen: {'duration': array(500.0) * ms, 'delay': array(200.0) * ms, 'amplitude': array(-10.0) * pA}\n", - "injected current seen: {'duration': array(500.0) * ms, 'delay': array(200.0) * ms, 'amplitude': array(-10.0) * pA}\n", - "input resistance score: Z = 1.86\n", - "input resistance score: Z = 1.22\n", - "injected current seen: {'duration': array(500.0) * ms, 'delay': array(200.0) * ms, 'amplitude': array(-10.0) * pA}\n", - "injected current seen: {'duration': array(500.0) * ms, 'delay': array(200.0) * ms, 'amplitude': array(-10.0) * pA}\n", - "input resistance score: Z = 0.16\n", - "injected current seen: {'duration': array(500.0) * ms, 'delay': array(200.0) * ms, 'amplitude': array(-10.0) * pA}\n", - "injected current seen: {'duration': array(500.0) * ms, 'delay': array(200.0) * ms, 'amplitude': array(-10.0) * pA}\n", - "injected current seen: {'duration': array(500.0) * ms, 'delay': array(200.0) * ms, 'amplitude': array(-10.0) * pA}\n", - "input resistance score: Z = 0.08\n", - "injected current seen: {'duration': array(500.0) * ms, 'delay': array(200.0) * ms, 'amplitude': array(-10.0) * pA}\n", - "injected current seen: {'duration': array(500.0) * ms, 'delay': array(200.0) * ms, 'amplitude': array(-10.0) * pA}\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/home/jovyan/neuronunit/neuronunit/tests/passive.py:83: RuntimeWarning: overflow encountered in exp\n", - " vm_fit[offset:,0] = a * np.exp(-t[offset:]/b) + c\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "injected current seen: {'duration': array(500.0) * ms, 'delay': array(200.0) * ms, 'amplitude': array(-10.0) * pA}\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/home/jovyan/neuronunit/neuronunit/tests/passive.py:83: RuntimeWarning: overflow encountered in multiply\n", - " vm_fit[offset:,0] = a * np.exp(-t[offset:]/b) + c\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "injected current seen: {'duration': array(500.0) * ms, 'delay': array(200.0) * ms, 'amplitude': array(-10.0) * pA}\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "INFO:__main__:gen\tnevals\tavg \tstd \tmin \tmax \n", - "1 \t8 \t3.89399\t0.918618\t2.38982\t5.21792\n", - "2 \t6 \t3.82178\t1.18981 \t1.16878\t5.21792\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[, , , , , ]\n", - "tests, completed, now gene computations\n", - "[[0.01, -2.0, -75.0], [0.01, -2, -55], [0.01, 15.0, -72.162359315819316], [0.019842752401015011, 15, -57.837640684180677], [0.20000000000000001, -2, -74.652924436008334], [0.20000000000000001, -2, -55], [0.20000000000000001, 15, -56.279875816303409], [0.20000000000000001, 13.919119365130545, -73.720124183696583]]\n", - "not even to test runner getting\n", - "[array(93.31632653061224) * pA, array(397.23032069970844) * pA, array(416.1807580174927) * pA]\n", - "injected current seen: {'duration': array(1000.0) * ms, 'delay': array(100.0) * ms, 'amplitude': array(93.31632653061224) * pA}\n", - "injected current seen: {'duration': array(1000.0) * ms, 'delay': array(100.0) * ms, 'amplitude': array(397.23032069970844) * pA}\n", - "injected current seen: {'duration': array(1000.0) * ms, 'delay': array(100.0) * ms, 'amplitude': array(416.1807580174927) * pA}\n", - "input resistance score: Z = 1.25\n", - "injected current seen: {'duration': array(500.0) * ms, 'delay': array(200.0) * ms, 'amplitude': array(-10.0) * pA}\n", - "input resistance score: Z = 0.10\n", - "injected current seen: {'duration': array(500.0) * ms, 'delay': array(200.0) * ms, 'amplitude': array(-10.0) * pA}\n", - "input resistance score: Z = 0.68\n", - "injected current seen: {'duration': array(500.0) * ms, 'delay': array(200.0) * ms, 'amplitude': array(-10.0) * pA}\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/home/jovyan/neuronunit/neuronunit/tests/passive.py:83: RuntimeWarning: overflow encountered in exp\n", - " vm_fit[offset:,0] = a * np.exp(-t[offset:]/b) + c\n", - "/home/jovyan/neuronunit/neuronunit/tests/passive.py:83: RuntimeWarning: overflow encountered in multiply\n", - " vm_fit[offset:,0] = a * np.exp(-t[offset:]/b) + c\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "injected current seen: {'duration': array(500.0) * ms, 'delay': array(200.0) * ms, 'amplitude': array(-10.0) * pA}\n", - "injected current seen: {'duration': array(500.0) * ms, 'delay': array(200.0) * ms, 'amplitude': array(-10.0) * pA}\n", - "injected current seen: {'duration': array(500.0) * ms, 'delay': array(200.0) * ms, 'amplitude': array(-10.0) * pA}\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "INFO:__main__:3 \t3 \t3.44631\t0.539488\t2.54048\t3.93815\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[, , ]\n", - "tests, completed, now gene computations\n", - "not even to test runner getting\n", - "[array(93.31632653061224) * pA, array(93.31632653061224) * pA, array(83.15306122448979) * pA, array(101.78571428571428) * pA]\n", - "injected current seen: {'duration': array(1000.0) * ms, 'delay': array(100.0) * ms, 'amplitude': array(93.31632653061224) * pA}\n", - "injected current seen: {'duration': array(1000.0) * ms, 'delay': array(100.0) * ms, 'amplitude': array(93.31632653061224) * pA}\n", - "injected current seen: {'duration': array(1000.0) * ms, 'delay': array(100.0) * ms, 'amplitude': array(83.15306122448979) * pA}\n", - "injected current seen: {'duration': array(1000.0) * ms, 'delay': array(100.0) * ms, 'amplitude': array(101.78571428571428) * pA}\n", - "input resistance score: Z = 0.65\n", - "input resistance score: Z = 0.55\n", - "injected current seen: {'duration': array(500.0) * ms, 'delay': array(200.0) * ms, 'amplitude': array(-10.0) * pA}\n", - "injected current seen: {'duration': array(500.0) * ms, 'delay': array(200.0) * ms, 'amplitude': array(-10.0) * pA}\n", - "input resistance score: Z = 1.25\n", - "input resistance score: Z = 0.19\n", - "injected current seen: {'duration': array(500.0) * ms, 'delay': array(200.0) * ms, 'amplitude': array(-10.0) * pA}\n", - "injected current seen: {'duration': array(500.0) * ms, 'delay': array(200.0) * ms, 'amplitude': array(-10.0) * pA}\n", - "injected current seen: {'duration': array(500.0) * ms, 'delay': array(200.0) * ms, 'amplitude': array(-10.0) * pA}\n", - "injected current seen: {'duration': array(500.0) * ms, 'delay': array(200.0) * ms, 'amplitude': array(-10.0) * pA}\n", - "injected current seen: {'duration': array(500.0) * ms, 'delay': array(200.0) * ms, 'amplitude': array(-10.0) * pA}\n", - "injected current seen: {'duration': array(500.0) * ms, 'delay': array(200.0) * ms, 'amplitude': array(-10.0) * pA}\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "INFO:__main__:4 \t4 \t2.52815\t0.260893\t2.24113\t2.94118\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[, , , ]\n", - "tests, completed, now gene computations\n", - "[[0.17283487762003827, 13.821996424655216, -56.602532543393217], [0.20000000000000001, 13.821996424655216, -56.602532543393217], [0.20000000000000001, 15.0, -55.0], [0.20000000000000001, 13.002091326490282, -57.788628990542293]]\n", - "not even to test runner getting\n", - "[array(83.15306122448979) * pA, array(95.01020408163265) * pA, array(93.31632653061224) * pA, array(135.66326530612244) * pA]\n", - "injected current seen: {'duration': array(1000.0) * ms, 'delay': array(100.0) * ms, 'amplitude': array(83.15306122448979) * pA}\n", - "injected current seen: {'duration': array(1000.0) * ms, 'delay': array(100.0) * ms, 'amplitude': array(95.01020408163265) * pA}\n", - "injected current seen: {'duration': array(1000.0) * ms, 'delay': array(100.0) * ms, 'amplitude': array(93.31632653061224) * pA}\n", - "injected current seen: {'duration': array(1000.0) * ms, 'delay': array(100.0) * ms, 'amplitude': array(135.66326530612244) * pA}\n", - "input resistance score: Z = 0.65\n", - "injected current seen: {'duration': array(500.0) * ms, 'delay': array(200.0) * ms, 'amplitude': array(-10.0) * pA}\n", - "input resistance score: Z = 0.61\n", - "injected current seen: {'duration': array(500.0) * ms, 'delay': array(200.0) * ms, 'amplitude': array(-10.0) * pA}\n", - "input resistance score: Z = 0.08\n", - "injected current seen: {'duration': array(500.0) * ms, 'delay': array(200.0) * ms, 'amplitude': array(-10.0) * pA}\n", - "injected current seen: {'duration': array(500.0) * ms, 'delay': array(200.0) * ms, 'amplitude': array(-10.0) * pA}\n", - "input resistance score: Z = 0.64\n", - "injected current seen: {'duration': array(500.0) * ms, 'delay': array(200.0) * ms, 'amplitude': array(-10.0) * pA}\n", - "injected current seen: {'duration': array(500.0) * ms, 'delay': array(200.0) * ms, 'amplitude': array(-10.0) * pA}\n", - "injected current seen: {'duration': array(500.0) * ms, 'delay': array(200.0) * ms, 'amplitude': array(-10.0) * pA}\n", - "injected current seen: {'duration': array(500.0) * ms, 'delay': array(200.0) * ms, 'amplitude': array(-10.0) * pA}\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "INFO:__main__:5 \t4 \t2.47542\t0.34641 \t2.07671\t3.03096\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[, , , ]\n", - "tests, completed, now gene computations\n", - "[[0.20000000000000001, 15.0, -55], [0.20000000000000001, 13.05819718288449, -57.193440475374892], [0.17283487762003827, 13.813518137336468, -56.602532543393217], [0.20000000000000001, 11.735821400014721, -61.046405670263503]]\n" - ] - } - ], - "source": [ - "from neuronunit.optimization import optimization_management as om\n", - "free_params = ['a','b','vr']#,'k','vt'] # this can only be odd numbers.\n", - "\n", - "#\n", - "hc = {}\n", - "for k,v in cells['TC'].items():\n", - " if k not in free_params:\n", - " hc[k] = v\n", - "print(hc)\n", - "import pickle\n", - "TC_tests = pickle.load(open('thalamo_cortical_tests.p','rb')) \n", - "ga_out, DO = om.run_ga(explore_param,5,TC_tests,free_params=free_params,hc = hc)" - ] - }, - { - "cell_type": "code", - "execution_count": 11, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "{'a': 0.019842752401015011, 'b': 15, 'vr': -57.837640684180677}\n", - "{'d': 10, 'a': 0.01, 'b': 15, 'k': 1.6, 'c': -60, 'C': 200, 'vPeak': 35, 'vr': -60, 'vt': -50}\n" - ] - }, - { - "data": { - "text/plain": [ - "" - ] - }, - "execution_count": 11, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": 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Pa++zWvvDSx19TNV6w+etvT/S8fUda7Ue1BV37fO2apHO724YQvf2kS80FSWG\nhNtzqIyZiwsYk3ci/bq3hb/8xEsKo+6BC/85qc/97rodPPzGOtZtO0hul9b8+7f6cfXgnAabhjLT\n0+jZqRU9O7VieJ8u1duLyyrYuL2I9dsOsm7bQTZs95YfbNhF0eGKBuNpnZVOy6wMWrdIp1VWBq2y\n0mmbnUH3dtm0apFOq6x0WmdlVO9rmZVOepoRco6Q8z7sQiHv47ky5Ag5R2UIf+nCtnnlI28/sr92\nOq6dnyPdF+OoMkcVieKYCK9NQ88d6bvD0fE3/AUjUpGISdDf6GqVqUqO4cfULkOtMrWPrbmtjjK1\n9tcfU839NY6rI+6qZcg53lqznZeWF/KDEachkSkxJNhzn2ymuKySH43qAx89ActmerOjJjEpbN1X\nws9fXc2bq7eT26U1j373LMad1SPupp9WWRmc1bMDZ/XscNS+qm/9ZRUhyipCmEF2ZjotMtLIzkwn\nKz2NtCQ1PYnE49u/XcxfP9umxFAPJYYEKq8M8cxHX3Fhny6cfng1vH0P9BsHo+5N2nO+uKyQe+at\notI5/vWKvtw6PJesjOSPQm7lf9MXaWquOrM7D76+ji17iunZKYqbZTVDuo4hgRas2c62A6VMyu8C\nL90O7XvC+N9CWuJf5pBz/GTuCv75+f9hQI/2vP2Ti7lzxKmNkhREmrIrB3q3yP3rqm9SHElw6Stf\nAr2y4mu6tm3Bxd/M8KbOnvQmZLeL+vhoB6AWl1VyqOgw81Z8zU8uPZ0pl5yWtBFDMSkvhdL9ULrP\nW5bsg7IiqCyHysNQWeY9rjgMrjLswKqeQatjm4Xts1rbai+JcFzYsr59UQ8KiKJcSs4VzdNFc64o\nn6+JnqtnVhvyerTj5eVbuf3CUzSyLQIlhlqcczjnfUg75/yl15kV3pFVtR7yyxQfruTd9TuZkmek\nffJ7OPtG6HVuVM8Zyxtz046DrFm7g6Ehx+xJQ7mwT9eYf8djVlEGe76AXeth5wbYvxn2fw0Htno/\nh/c3XiwicfjpOb/j5g/bs6RgL0NzO6U6nMBpVonhW49/yMYdReB/sFeNfKn68E+EGw6/AOlZ3iik\nBFv21V5unb2E+xx0bpNF92QmhcoK2LkWCpdA4VL4ehns3gShsNFIrbtB+x7ePSNyL4Q2J0DLDpBd\n9dPeu8I7o4X3mqRnQYa/TPPfetUvfPiwl7Btzh3ZV/W4xpIoykRznijfAIl6o0R9nijKNfq5Evla\nNfK5XAjdWN3PAAAK80lEQVRmjWV46Qe0b3k10z/4/JgSQ2XIsWVPMRu2H6Rg9yF2HDjM9oOH2Xmw\nlKLD3sCMEn9IdnllqMZIu6ovlOHXDkU7VNgwVt43muzMCLM0J1BcicHMOgFzgd5AAXCtc25vhHIT\ngX/3Vx9wzs32t2cBTwAjgBBwl3PuxXhiqs+3B/Vg16HDpJk3ONDMe6G9pbch0nbzL7CKuN1fB+iZ\nvodOC+bDObdDmwgzqcZhwZrtTHnuU7q3y+bSk08gc8vahJ4f57zawOcLYdM7UPCh1wwE0Koz9MiH\nM66Crn2h6+nQ5XTdh1qapn5jyFj7F+4Y/iMefvtLPtiwk4tOr/9L1qHDFSwp2MMnX+5hScEeVhbu\nr3H9TcvMdLq1a0HXNi3o1jabllnptMr0hmRnpqdVX4xp5l3AWfUZhFl10mtoqHDV/sZoNo63xjAN\neMc595CZTfPX/zW8gJ887gXy8X63ZWY2308gdwE7nHOnm1kakNQ6XaSb5CTUO89630jOuzOhp527\nZDP/9vIqBpzUjhk3n0ObdxKUO52DrZ/C6pdh7avefaYBOvaGvGuh1wWQk++tqx1WjhcDroYVz3Jr\ntw0837UDP5m7gjmTz6PPCW1rFPtiZxHvrt/Ju+t28PGXuymvdGSkGQN7tOeGc0+mb/e29DmhDad0\nbUO77Izjqq8i3sQwHu/bPsBs4D1qJQbgcuBt59weADN7G7gCeA6YBPQFcM6FgF1xxpM6znkXsp0y\nEjqenKBTOp5YuIn/fHsDF53eld/dMJjWLRLQ+rdrE3w6G9a8Avs2Q1omnDoSLvghnHoJdEpyAhVJ\npVNGQPueZC19iqdumst3f/8RY36ziMsHdOeEdi34Zn8pyzfv4+t9JQCc1q0NtwzL5aI+XRl8codm\nMUw73t/wBOfcNwDOuW/MLFL7SQ9gS9h6IdDDzKqumvqFmY0APgemOOe2xxlT3RY9BkU7qG5XdqEj\nj6NacvT2qnOUl3gfsiP+LSGhVlSGuHf+ap79eDPfObsHD/9DXnxDUSvLYd1rsPQP8OUHXhv/qZd4\n03T0vUo3B5LmIz0Dzr0D3rqLU0vX8NqPLuSxBRtYuG4H+0vK6dKmBWf1bM8/XnwKI8/olvhrHUKV\nUHbIG51XUeIty/1lRemRn8pyr2yoAkLl/rIS8idBWor7GMxsAdA9wq67onyOSPUr5z93DrDYOfdT\nM/sp8B/A9+uIYzIwGaBXr15RPnUta16BXRvB0rywzA+v3uGL/rL6GKv72J7nQr8xMYdlQKusdFYW\n7sM5x86iw0x9fiUfbNjJHRefyv+9/IyaVxFntoKSvd5PQx/o+7+GZbPg0/+Gom3etRWX/DucfRO0\nPSHmWEWOC0MmejMTvPZTTrhtIf/v6rxjP1fpAW9UXtE274vnwW1QtB0O7fSGbJfuh8MHvHKl+6Hs\nYHyxn/39pCcGizT5VtQHm60HRvi1hROB95xzZ9Qqc71f5h/99d/jNTnNAYqAts65kJn1BN5wzg1o\n6Hnz8/Pd0qVLjznuIPrde5/z8BvrGHBSO77aXUxZZYj7xw3guqERkuC2z+C/hsPwn8Cl9x29PxSC\nLxbCkhmw4a9ezea0S+GcW6HP6KS/qUSahLWvwtwbYeA/wPgnIbOeSfVK9nlfKndvhD1fwt4vjyyL\ndx9dPiPbG4DSsiO0aOeN0Mvu4F3XVD1aLxsyW3rLjBaQ0dJfZoeN3sv0/l/TM71aflomtOp0zH1+\nZrbMOZffULl4m5LmAxOBh/zlvAhl3gQeNLOqr7ajgZ8555yZvYrXR7EQGAWsiTOeJuuOi08hM914\nY9U2rhjYnTsuPpXTurWJXLj7mXDW9bD413DCQO+NDd6ootUvebWDfZu90UQX/Ajyb/E6kEXkiH5j\nvS9WC+7zhmOfeY33f+Kc943/wNdeMti1wVuvYmnQPgc65nrn6Jjrrbft7g3ZbtPNSwZNuDM63hpD\nZ+DPQC9gM3CNc26PmeUDdzjnbvPLTQKqGt9/6Zyb6W8/GXgG6ADsBG5xzm1u6HmPxxpDzA4fhGeu\nhsJPoE13r/2x2O+7z70IBk/03rRh930QkQg2LYD3/7/3v+TCpoDP7uANy+5y+pEh2p37QIde3jf6\nJijaGkNciSFVlBh8FYdh5Vz46iOvQ617nnerUNUORGJXVux9uXIOWneFrONvgr3GakqSVMpoAYNv\n8n5EJD5ZrSDrGAe2HGc0FaeIiNSgxCAiIjUoMYiISA1KDCIiUoMSg4iI1KDEICIiNSgxiIhIDUoM\nIiJSQ5O88tnMdgJfHePhXWi6931Q7I2vqcYNij1Vghz7yc65Bu8J3CQTQzzMbGk0l4QHkWJvfE01\nblDsqdKUY6+ipiQREalBiUFERGpojolheqoDiINib3xNNW5Q7KnSlGMHmmEfg4iI1K851hhERKQe\nx21iMLMrzGy9mW0ys2kR9rcws7n+/o/NrHfjR3m0KOL+qZmtMbOVZvaOfxe8QGgo9rByE8zM+Xf6\nC4RoYjeza/3XfrWZ/amxY6xLFO+ZXmb2rpkt9983V6UiztrMbIaZ7TCzVXXsNzN73P+9VprZ4MaO\nsS5RxH6DH/NKM/ubmZ3V2DHGxTl33P0A6cDnwClAFvA/QP9aZX4A/Jf/+DpgbhOJeyTQyn98ZxDi\njjZ2v1xb4APg70B+quOO4XXvAywHOvrr3VIddwyxTwfu9B/3BwpSHbcfy0XAYGBVHfuvAv4KGHAe\n8HGqY44h9gvC3itXBin2aH6O1xrDUGCTc+4L51wZMAcYX6vMeGC2//gFYJRZyu/e3WDczrl3nXPF\n/urfgZxGjrEu0bzmAL8AfgWUNmZwDYgm9tuB3zrn9gI453Y0cox1iSZ2B7TzH7cHtjZifHVyzn0A\n7KmnyHjgv53n70AHMzuxcaKrX0OxO+f+VvVeIVj/p1E5XhNDD2BL2Hqhvy1iGedcBbAf6Nwo0dUt\nmrjD3Yr3jSoIGozdzM4Gejrn/tKYgUUhmtf9dOB0M1tsZn83sysaLbr6RRP7fcCNZlYIvA78sHFC\ni1us/w9BFaT/06gcr/d8jvTNv/bwq2jKNLaoYzKzG4F84OKkRhS9emM3szTgUeDmxgooBtG87hl4\nzUkj8L79fWhmA51z+5IcW0Oiif16YJZz7j/N7HzgGT/2UPLDi0sQ/0djYmYj8RLD8FTHEovjtcZQ\nCPQMW8/h6OpzdRkzy8CrYtdXrW0M0cSNmV0K3AWMc84dbqTYGtJQ7G2BgcB7ZlaA12Y8PyAd0NG+\nX+Y558qdc18C6/ESRapFE/utwJ8BnHMfAdl48/kEXVT/D0FlZnnA08B459zuVMcTi+M1MSwB+phZ\nrpll4XUuz69VZj4w0X88AVjo/J6iFGowbr855vd4SSEo7dzQQOzOuf3OuS7Oud7Oud547a7jnHNL\nUxNuDdG8X17B6/jHzLrgNS190ahRRhZN7JuBUQBm1g8vMexs1CiPzXzgJn900nnAfufcN6kOKhpm\n1gt4Cfi+c25DquOJWap7v5P1gzeiYQPeiI27/G33430YgffP8TywCfgEOCXVMUcZ9wJgO7DC/5mf\n6pijjb1W2fcIyKikKF93Ax4B1gCfAdelOuYYYu8PLMYbsbQCGJ3qmP24ngO+Acrxage3AncAd4S9\n5r/1f6/PAvZ+aSj2p4G9Yf+nS1Mdcyw/uvJZRERqOF6bkkRE5BgpMYiISA1KDCIiUoMSg4iI1KDE\nICIiNSgxiIhIDUoMIiJSgxKDiIjU8L8OmMUOgIMTNQAAAABJRU5ErkJggg==\n", - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "print(ga_out['dhof'][0].attrs)\n", - "print(cells['TC'])\n", - "\n", - "\n", - "model1 = None\n", - "model1 = ReducedModel(LEMS_MODEL_PATH,name = str('vanilla'),backend = ('RAW'))\n", - "model1.set_attrs(ga_out['dhof'][0].attrs)\n", - "model1.inject_square_current(iparams)\n", - "\n", - "model2 = None\n", - "model2 = ReducedModel(LEMS_MODEL_PATH,name = str('vanilla'),backend = ('RAW'))\n", - "#vanilla = translate(mparams,m2m)\n", - "model2.set_attrs(cells['TC'])\n", - "model2.inject_square_current(iparams)\n", - "\n", - "\n", - "plt.plot(model1.get_membrane_potential().times,model1.get_membrane_potential(),label='optimizer result')\n", - "plt.plot(model2.get_membrane_potential().times,model2.get_membrane_potential(),label='ground truth')\n", - "plt.legend()" - ] - }, - { - "cell_type": "code", - "execution_count": 12, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "75.53061224489795 pA 75.53061224489795 pA\n", - "injected current seen: {'duration': array(500.0) * ms, 'delay': array(200.0) * ms, 'amplitude': array(-10.0) * pA}\n", - "injected current seen: {'duration': array(500.0) * ms, 'delay': array(200.0) * ms, 'amplitude': array(-10.0) * pA}\n", - "input resistance score: Z = 0.00\n", - "31.73928299824782 Mohm 31739282.99824782 kg*m**2/(s**3*A**2)\n", - "injected current seen: {'duration': array(500.0) * ms, 'delay': array(200.0) * ms, 'amplitude': array(-10.0) * pA}\n", - "injected current seen: {'duration': array(500.0) * ms, 'delay': array(200.0) * ms, 'amplitude': array(-10.0) * pA}\n", - "3.4631849083750876 ms 0.0034631849083750876 s\n", - "injected current seen: {'duration': array(500.0) * ms, 'delay': array(200.0) * ms, 'amplitude': array(-10.0) * pA}\n", - "injected current seen: {'duration': array(500.0) * ms, 'delay': array(200.0) * ms, 'amplitude': array(-10.0) * pA}\n", - "1.0911352057216523e-10 s**4*A**2/(kg*m**2) 1.0911352057216523e-10 s**4*A**2/(kg*m**2)\n", - "-60.317405613048955 mV -0.060317405613048956 V\n", - "0.6500000000000001 ms 0.0006500000000000001 s\n", - "56.127191359642026 mV 0.056127191359642024 V\n", - "-21.127191359642023 mV -0.02112719135964202 V\n", - "1.0\n", - "Z = 0.00\n" - ] - } - ], - "source": [ - "def hack_judge(test_and_models):\n", - " (test, attrs) = test_and_models\n", - " model = None\n", - " obs = test.observation\n", - " model = ReducedModel(LEMS_MODEL_PATH,name = str('vanilla'),backend = ('RAW'))\n", - " model.set_attrs(attrs)\n", - " test.generate_prediction(model)\n", - " pred = test.generate_prediction(model)\n", - " score = test.compute_score(obs,pred)\n", - " try:\n", - " print(obs['value'],pred['value'])\n", - " except:\n", - " print(obs['mean'],pred['mean'])\n", - " \n", - " return score\n", - "\n", - "scores = []\n", - "for i,t in enumerate(TC_tests):\n", - " test_and_models = (t,cells['TC'])\n", - " score = hack_judge(test_and_models)\n", - " scores.append(score)\n", - "print(scores[0].sort_key) \n", - "print(scores[0]) \n", - "\n" - ] - }, - { - "cell_type": "code", - "execution_count": 13, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "data": { - "text/plain": [ - "{'CapacitanceTest': 0.3941135778347633,\n", - " 'InjectedCurrentAPAmplitudeTest': 0.056286812623258964,\n", - " 'InjectedCurrentAPThresholdTest': 0.1301810981679412,\n", - " 'InjectedCurrentAPWidthTest': 0.07455112037761191,\n", - " 'InputResistanceTest': 0.12468893958387861,\n", - " 'RestingPotentialTest': 0.2550425415991806,\n", - " 'RheobaseTestP': 0.1269862366342951,\n", - " 'TimeConstantTest': 0.006927779205798235}" - ] - }, - "execution_count": 13, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "\n", - "ga_out.keys()\n", - "ga_out['dhof'][0].scores\n" - ] - }, - { - "cell_type": "code", - "execution_count": 14, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[1.0, 1.0, 1.0, 1.0, 1.0, 0.9999999999999999, 1.0, 0.9999999999999996]\n", - "[0.0, 0.0, 0.0, 0.0, 0.0, -2.029026769368064e-16, 0.0, 6.317017647578838e-16]\n", - "0.6500000000000001 ms 0.0006500000000000001 s\n", - "Z = -0.00\n" - ] - } - ], - "source": [ - "print([s.sort_key for s in scores])\n", - "print([s.score for s in scores])\n", - "\n", - "score = hack_judge((TC_tests[-3],cells['TC']))\n", - "print(score)" - ] - }, - { - "cell_type": "code", - "execution_count": 15, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "75.53061224489795 pA 51.81632653061225 pA\n", - "injected current seen: {'duration': array(500.0) * ms, 'delay': array(200.0) * ms, 'amplitude': array(-10.0) * pA}\n", - "injected current seen: {'duration': array(500.0) * ms, 'delay': array(200.0) * ms, 'amplitude': array(-10.0) * pA}\n", - "input resistance score: Z = 0.62\n", - "31.73928299824782 Mohm 79634016.52239381 kg*m**2/(s**3*A**2)\n", - "injected current seen: {'duration': array(500.0) * ms, 'delay': array(200.0) * ms, 'amplitude': array(-10.0) * pA}\n", - "injected current seen: {'duration': array(500.0) * ms, 'delay': array(200.0) * ms, 'amplitude': array(-10.0) * pA}\n", - "3.4631849083750876 ms 0.009810667396493434 s\n", - "injected current seen: {'duration': array(500.0) * ms, 'delay': array(200.0) * ms, 'amplitude': array(-10.0) * pA}\n", - "injected current seen: {'duration': array(500.0) * ms, 'delay': array(200.0) * ms, 'amplitude': array(-10.0) * pA}\n", - "1.0911352057216523e-10 s**4*A**2/(kg*m**2) 1.231969430266598e-10 s**4*A**2/(kg*m**2)\n", - "-60.317405613048955 mV -0.06075346627665097 V\n", - "0.6500000000000001 ms 0.0007750000000000001 s\n", - "56.127191359642026 mV 0.05357660263743405 V\n", - "-21.127191359642023 mV -0.018576602637434048 V\n", - "0.6190101302868543\n", - "Z = -0.50\n", - "[0.6190101302868543, 0.5372765850958718, 0.4696921520174828, 0.45016300791078434, 0.946777506200236, 0.81503865531969, 0.841429780079807, 0.6423611420888804]\n" - ] - } - ], - "source": [ - "scores = []\n", - "for t in TC_tests:\n", - " test_and_models = (t,cells['RS'])\n", - " score = hack_judge(test_and_models)\n", - " scores.append(score)\n", - "print(scores[0].sort_key) \n", - "print(scores[0])\n", - "print([s.sort_key for s in scores])\n" - ] - }, - { - "cell_type": "code", - "execution_count": 16, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "injected current seen: {'duration': array(500.0) * ms, 'delay': array(200.0) * ms, 'amplitude': array(-10.0) * pA}\n", - "injected current seen: {'duration': array(500.0) * ms, 'delay': array(200.0) * ms, 'amplitude': array(-10.0) * pA}\n", - "injected current seen: {'duration': array(500.0) * ms, 'delay': array(200.0) * ms, 'amplitude': array(-10.0) * pA}\n", - "injected current seen: {'duration': array(500.0) * ms, 'delay': array(200.0) * ms, 'amplitude': array(-10.0) * pA}\n", - "injected current seen: {'duration': array(500.0) * ms, 'delay': array(200.0) * ms, 'amplitude': array(-10.0) * pA}\n", - "input resistance score: Z = 0.62\n", - "31.73928299824782 Mohm 79634016.52239381 kg*m**2/(s**3*A**2)\n", - "3.4631849083750876 ms 0.009810667396493434 s\n", - "injected current seen: {'duration': array(500.0) * ms, 'delay': array(200.0) * ms, 'amplitude': array(-10.0) * pA}\n", - "-21.127191359642023 mV -0.018576602637434048 V\n", - "0.6500000000000001 ms 0.0007750000000000001 s\n", - "56.127191359642026 mV 0.05357660263743405 V\n", - "1.0911352057216523e-10 s**4*A**2/(kg*m**2) 1.231969430266598e-10 s**4*A**2/(kg*m**2)\n", - "-60.317405613048955 mV -0.06075346627665097 V\n", - "[{'value': array(75.53061224489795) * pA}, , , , , , , ]\n" - ] - } - ], - "source": [ - "import dask.bag as db\n", - "# The rheobase has been obtained seperately and cannot be db mapped.\n", - "# Nested DB mappings dont work.\n", - "from itertools import repeat\n", - "test_a_models = zip(TC_tests[1::],repeat(cells['RS']))\n", - "tc_bag = db.from_sequence(test_a_models)\n", - "\n", - "scores = list(tc_bag.map(hack_judge).compute())\n", - "scores.insert(0,rheobase)\n", - "print(scores) " - ] - }, - { - "cell_type": "code", - "execution_count": 17, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "{'value': array(75.53061224489795) * pA}\n" - ] - } - ], - "source": [ - "score = TC_tests[0].judge(model,stop_on_error = False, deep_error = True)\n", - "print(score.prediction)\n", - "#print(model.get_spike_count())" - ] - }, - { - "cell_type": "code", - "execution_count": 18, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "ename": "ImportError", - "evalue": "cannot import name 'ReducedModel'", - "output_type": "error", - "traceback": [ - "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", - "\u001b[0;31mImportError\u001b[0m Traceback (most recent call last)", - "\u001b[0;32m\u001b[0m in \u001b[0;36m\u001b[0;34m()\u001b[0m\n\u001b[1;32m 1\u001b[0m \u001b[0;32mfrom\u001b[0m \u001b[0mneuronunit\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0moptimization\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0moptimization_management\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0mopt\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 2\u001b[0;31m \u001b[0;32mfrom\u001b[0m \u001b[0mneuronunit\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmodels\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mReducedModel\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m", - "\u001b[0;31mImportError\u001b[0m: cannot import name 'ReducedModel'" - ] - } - ], - "source": [ - "from neuronunit.optimization import optimization_management as opt\n", - "from neuronunit.models import ReducedModel" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [] - }, - { - "cell_type": "raw", - "metadata": { - "collapsed": true - }, - "source": [] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.5.2" - } - }, - "nbformat": 4, - "nbformat_minor": 2 -} diff --git a/neuronunit/unit_test/run_ref_range.py b/neuronunit/unit_test/run_ref_range.py deleted file mode 100644 index c5ae4fdca..000000000 --- a/neuronunit/unit_test/run_ref_range.py +++ /dev/null @@ -1,449 +0,0 @@ -import matplotlib as mpl -mpl.use('Agg') -import matplotlib.pyplot as plt -# verify that backend is appropriate before compute job: -plt.clf() - -import copy -import os - -import pickle -from neuronunit.tests import np, pq, cap, VmTest, scores, AMPL, DELAY, DURATION -import matplotlib.pyplot as plt -from matplotlib.colors import LogNorm -from neuronunit.optimization.exhaustive_search import run_grid, reduce_params, create_grid, run_simple_grid -#from neuronunit.optimization import exhaustive_search as es - -from neuronunit.optimization.optimization_management import run_ga -from neuronunit.tests import np, pq, cap, VmTest, scores, AMPL, DELAY, DURATION -import matplotlib.pyplot as plt -from neuronunit.models.reduced import ReducedModel -from itertools import product - -import quantities as pq -from numba import jit -from neuronunit import plottools -ax = None -import sys -import pickle - - - -from neuronunit.optimization import get_neab - -grid_results = {} - -def plot_scatter(history,ax,keys,constant): - pop = [ v for v in history.genealogy_history.values() ] - z = np.array([ p.dtc.get_ss() for p in pop ]) - x = np.array([ p.dtc.attrs[str(keys[0])] for p in pop ]) - y = np.array([ p.dtc.attrs[str(keys[1])] for p in pop ]) - ax.cla() - ax.set_title('held constant: '+str(constant)) - ax.scatter(x, y, c=y, s=125)#, cmap='gray') - ax.set_xlabel('free: '+str(keys[0])) - ax.set_ylabel('free: '+str(keys[1])) - return ax - -def plot_surface(gr,ax,keys,constant,imshow=True): - # from https://github.com/russelljjarvis/neuronunit/blob/dev/neuronunit/unit_test/progress_report_4thJuly.ipynb - # Not rendered https://github.com/russelljjarvis/neuronunit/blob/dev/neuronunit/unit_test/progress_report_.ipynb - gr = [ g for g in gr if type(g.dtc) is not type(None) ] - gr = [ g for g in gr if type(g.dtc.scores) is not type(None) ] - ax.cla() - gr_ = [] - index = 0 - for i,g in enumerate(gr): - if type(g.dtc) is not type(None): - gr_.append(g) - else: - index = i - - xx = np.array([ p.dtc.attrs[str(keys[0])] for p in gr ]) - yy = np.array([ p.dtc.attrs[str(keys[1])] for p in gr ]) - zz = np.array([ p.dtc.get_ss() for p in gr ]) - dim = len(xx) - N = int(np.sqrt(len(xx))) - X = xx.reshape((N, N)) - Y = yy.reshape((N, N)) - Z = zz.reshape((N, N)) - if imshow==True: - img = ax.pcolormesh(X, Y, Z, edgecolors='black') - #ax.colorbar() - - else: - - - import seaborn as sns; - sns.set() - - current_palette = sns.color_palette() - sns.palplot(current_palette) - - #df = pd.DataFrame(Z, columns=xx) - - img = sns.heatmap(Z)#,cm=current_palette) - #ax.colorbar() - - ax.set_title(' {0} vs {1} '.format(keys[0],keys[1])) - return ax, img - -def plot_line_ss(gr,ax,free,hof,constant): - ax.cla() - - ax.set_title(' {0} vs score'.format(free)) - z = np.array([ p.dtc.get_ss() for p in gr ]) - print(str(free)) - print(free) - x = np.array([ p.dtc.attrs[str(free)] for p in gr ]) - - y = hof[0].dtc.attrs[free] - i = hof[0].dtc.get_ss() - #ax.hold(True) - ax.scatter(x,z) - ax.scatter(y,i) - ax.plot(x,z) - - ax.set_xlabel(str(key[0])) - ax.set_ylabel(str('Sum of Errors')) - return ax - -def plot_agreement(ax,gr,key,hof): - dtcpop = [ g.dtc for g in gr ] - for dtc in dtcpop: - if hasattr(score,'prediction'): - if type(score.prediction) is not type(None): - dtc.score[str(t)][str('prediction')] = score.prediction - dtc.score[str(t)][str('observation')] = score.observation - boolean_means = bool('mean' in score.observation.keys() and 'mean' in score.prediction.keys()) - boolean_value = bool('value' in score.observation.keys() and 'value' in score.prediction.keys()) - - if boolean_means: - dtc.score[str(t)][str('agreement')] = np.abs(score.observation['mean'] - score.prediction['mean']) - - if boolean_value: - dtc.score[str(t)][str('agreement')] = np.abs(score.observation['value'] - score.prediction['value']) - - ss = hof[0].dtc.score - #for v in ss: - if str('agreement') in ss.keys(): - ax.plot( [ v['agreement'] for v in list(ss.values()) ], [ i for i in range(0,len(ss.values())) ] ) - ax.plot( [ v['prediction'] for v in list(ss.values()) ], [ i for i in range(0,len(ss.values())) ] ) - ax.plot( [ v['observation'] for v in list(ss.values()) ], [ i for i in range(0,len(ss.values())) ] ) - return ax - -from collections import OrderedDict - -def grids(hof,tests,ranges,us,history): - ''' - Obtain using the best candidate Gene (HOF, NU-tests, and expanded parameter ranges found via - exploring extreme edge cases of parameters - - plot a error surfaces, and cross sections, about the optima in a 3by3 subplot matrix. - - where, i and j are indexs to the 3 by 3 (9 element) subplot matrix, - and `k`-dim-0 is the parameter(s) that were free to vary (this can be two free in the case for ij: - #assert len(free_param) == len(hc) + 1 - #assert len(hc) == len(free_param) - 1 - cpparams['freei'] = (np.min(ranges[freei]), np.max(ranges[freei])) - cpparams['freej'] = (np.min(ranges[freej]), np.max(ranges[freej])) - free_params = [freei, freej] - gr = run_simple_grid(10, tests, ranges, free_params, hold_constant = hc) - #gr = run_grid(10,tests,, hold_constant = hc, mp_in = params) - fp = list(copy.copy(free_param)) - #ax0[i,j],img = plot_surface(gr,ax0[i,j],fp,hc,imshow=True) - ax0[i,j],img = plot_surface(gr,ax0[i,j],fp,hc,imshow=True) - - #plt.colorbar(img, ax = ax0[i,j]) - #ax1[i,j] = plot_surface(gr,ax1[i,j],fp,imshow=False) - - if i < j: - free_param = list(copy.copy(list(free_param))) - #if len(free_param) == 2: - - ax0[i,j] = plot_scatter(history,ax0[i,j],free_param,hc) - #ax0[i,j] = ps(fig0,ax1[i,j],freei,freej,history) - - cpparams['freei'] = (np.min(ranges[freei]), np.max(ranges[freei])) - cpparams['freej'] = (np.min(ranges[freej]), np.max(ranges[freej])) - gr = hof - - limits_used = (us[str(freei)],us[str(freej)]) - scores = [ g.dtc.get_ss() for g in gr ] - params_ = [ g.dtc.attrs for g in gr ] - - # To Pandas: - # https://stackoverflow.com/questions/28056171/how-to-build-and-fill-pandas-dataframe-from-for-loop#28058264 - temp.append({'i':i,'j':j,'free_param':free_param,'hold_constant':hc,'param_boundaries':cpparams,'scores':scores,'params':params_,'ga_used':limits_used,'grid':gr}) - #print(temp) - #intermediate = pd.DataFrame(temp)blah - with open('intermediate.p','wb') as f: - pickle.dump(temp,f) - - #df = pd.DataFrame(temp) - plt.savefig(str('cross_section_and_surfaces.png')) - return temp - - -# http://www.physics.usyd.edu.au/teach_res/mp/mscripts/ -# ns_izh002.m -import collections -from collections import OrderedDict - -# Fast spiking cannot be reproduced as it requires modifications to the standard Izhi equation, -# which are expressed in this mod file. -# https://github.com/OpenSourceBrain/IzhikevichModel/blob/master/NEURON/izhi2007b.mod - -reduced2007 = collections.OrderedDict([ - # C k vr vt vpeak a b c d celltype - ('RS', (100, 0.7, -60, -40, 35, 0.03, -2, -50, 100, 1)), - ('IB', (150, 1.2, -75, -45, 50, 0.01, 5, -56, 130, 2)), - ('LTS', (100, 1.0, -56, -42, 40, 0.03, 8, -53, 20, 4)), - ('TC', (200, 1.6, -60, -50, 35, 0.01, 15, -60, 10, 6)), - ('TC_burst', (200, 1.6, -60, -50, 35, 0.01, 15, -60, 10, 6)), - ('RTN', (40, 0.25, -65, -45, 0, 0.015, 10, -55, 50, 7)), - ('RTN_burst', (40, 0.25, -65, -45, 0, 0.015, 10, -55, 50, 7))]) - -import numpy as np -reduced_dict = OrderedDict([(k,[]) for k in ['C','k','vr','vt','vPeak','a','b','c','d']]) - -#OrderedDict -for i,k in enumerate(reduced_dict.keys()): - for v in reduced2007.values(): - reduced_dict[k].append(v[i]) - -explore_param = {k:(np.min(v),np.max(v)) for k,v in reduced_dict.items()} - -opt_keys = [str('vr'),str('a'),str('b')] -nparams = len(opt_keys) - - -## -# find an optima copy and paste reverse search here. -## - -#IB = mparams[param_dict['IB']] -RS = {} -IB = {} -TC = {} -CH = {} -RTN_burst = {} -cells = OrderedDict([(k,[]) for k in ['RS','IB','CH','LTS','FS','TC','TC_burst','RTN','RTN_busrt']]) -reduced_cells = OrderedDict([(k,[]) for k in ['RS','IB','LTS','TC','TC_burst']]) - -for index,key in enumerate(reduced_cells.keys()): - reduced_cells[key] = {} - for k,v in reduced_dict.items(): - reduced_cells[key][k] = v[index] - -print(reduced_cells) -cells = reduced_cells -from neuronunit.optimization import optimization_management as om -free_params = ['a','b','vr','k'] # this can only be odd numbers. -#2**3 -hc = {} -for k,v in cells['TC'].items(): - if k not in free_params: - hc[k] = v -#print(hc) -TC_tests = pickle.load(open('thalamo_cortical_tests.p','rb')) - #run_ga(model_params, max_ngen, test, free_params = None, hc = None) - -#ga_out, DO = om.run_ga(explore_param,10,TC_tests,free_params=free_params,hc = hc, NSGA = False, MU = 10) -try: - #assert 1==2 - ga_out_nsga = pickle.load(open('contents.p','rb')) -except: - - ga_out_nsga, _ = om.run_ga(explore_param,25,TC_tests,free_params=free_params,hc = hc, NSGA = True) - pickle.dump(ga_out_nsga, open('contents.p','wb')) - -hof = ga_out_nsga['hof'] -history = ga_out_nsga['history'] - - -attr_keys = list(hof[0].dtc.attrs.keys()) -us = {} # GA utilized_space -for key in attr_keys: - temp = [ v.dtc.attrs[key] for k,v in history.genealogy_history.items() ] - us[key] = ( np.min(temp), np.max(temp)) - - - - -if 1==2: - with open('surfaces.p','rb') as f: - temp = pickle.load(f) - from neuronunit.plottools import plot_surface as ps - #get_justas_plot(history) - - plt.clf() - dim = len(hof[0].dtc.attrs.keys()) - fig0,ax0 = plt.subplots(dim,dim,figsize=(10,10)) - list_axis = [] - for t in temp: - fp = t['free_param'] - i = t['i'] - j = t['j'] - scores = t['scores'] - params = t['params'] - gr = t['grid'] - hc = t['hold_constant'] - #print(t.keys()) - if i==j: - #ax0[i,j] = plot_surface(gr,ax0[i,j],fp,imshow=False) - #ax0[i,j] = plot_line_ss(gr,ax0[i,j],fp,hof) - ax0[i,j] = plot_line_ss(gr,ax0[i,j],fp,hof,hc) - - if i < j: - #ax0[i,j] ,plot_axis = ps(fig0,ax0[i,j],fp[0],fp[1],history) - ax0[i,j] = plot_scatter(history,ax0[i,j],fp,hc) - - if i > j: - - ax0[i,j],img = plot_surface(gr,ax0[i,j],fp,hc,imshow=True) - list_axis.append(ax0[i,j]) - - #plt.colorbar(img)#,ax = ax0[i,j]) - - plt.savefig(str('cross_section_and_surfaces_new.png')) - -else: - - temp = grids(hof,TC_tests,explore_param,us,history) - with open('surfaces.p','wb') as f: - pickle.dump(temp,f) - - -def get_justas_plot(history): - - # try: - import plotly.plotly as py - from plotly.offline import download_plotlyjs, init_notebook_mode, plot#, iplot - import plotly.graph_objs as go - import cufflinks as cf - cf.go_offline() - gr = [ v for v in history.genealogy_history.values() ] - gr = [ g for g in gr if type(g.dtc) is not type(None) ] - gr = [ g for g in gr if type(g.dtc.scores) is not type(None) ] - keys = list(gr[0].dtc.attrs.keys()) - xx = np.array([ p.dtc.attrs[str(keys[0])] for p in gr ]) - yy = np.array([ p.dtc.attrs[str(keys[1])] for p in gr ]) - zz = np.array([ p.dtc.attrs[str(keys[2])] for p in gr ]) - ee = np.array([ np.sum(list(p.dtc.scores.values())) for p in gr ]) - #pdb.set_trace() - # z_data = np.array((xx,yy,zz,ee)) - list_of_dicts = [] - for x,y,z,e in zip(list(xx),list(yy),list(zz),list(ee)): - list_of_dicts.append({ keys[0]:x,keys[1]:y,keys[2]:z,str('error'):e}) - - z_data = pd.DataFrame(list_of_dicts) - data = [ - go.Surface( - z=z_data.as_matrix() - ) - ] - - - - layout = go.Layout( - width=1000, - height=1000, - autosize=False, - title='Sciunit Errors', - scene=dict( - xaxis=dict( - title=str(keys[0]), - - #gridcolor='rgb(255, 255, 255)', - #zerolinecolor='rgb(255, 255, 255)', - #showbackground=True, - #backgroundcolor='rgb(230, 230,230)' - ), - yaxis=dict( - title=str(keys[1]), - - #gridcolor='rgb(255, 255, 255)', - #zerolinecolor='rgb(255, 255, 255)', - #showbackground=True, - #backgroundcolor='rgb(230, 230,230)' - ), - zaxis=dict( - title=str(keys[2]), - - #gridcolor='rgb(255, 255, 255)', - #zerolinecolor='rgb(255, 255, 255)', - #showbackground=True, - #backgroundcolor='rgb(230, 230,230)' - ), - aspectratio = dict( x=1, y=1, z=0.7 ), - aspectmode = 'manual' - ),margin=dict( - l=65, - r=50, - b=65, - t=90 - ) - ) - - fig = go.Figure(data=data, layout=layout)#,xTitle=str(keys[0]),yTitle=str(keys[1]),title='SciUnitOptimization') - plot(fig, filename='sciunit-score-3d-surface.html') - - - -sys.exit() diff --git a/neuronunit/unit_test/scores_unit_test.py b/neuronunit/unit_test/scores_unit_test.py new file mode 100644 index 000000000..28fd5d4bf --- /dev/null +++ b/neuronunit/unit_test/scores_unit_test.py @@ -0,0 +1,94 @@ +#!/usr/bin/env python +# coding: utf-8 + +import unittest +import matplotlib +matplotlib.use('Agg') +SILENT = True +import warnings +if SILENT: + warnings.filterwarnings("ignore") + +from neuronunit.allenapi.allen_data_driven import opt_setup, opt_setup_two, opt_exec +from neuronunit.allenapi.allen_data_driven import opt_to_model,wrap_setups +from neuronunit.allenapi.utils import dask_map_function +from neuronunit.optimization.model_parameters import MODEL_PARAMS, BPO_PARAMS, to_bpo_param +from neuronunit.optimization.optimization_management import dtc_to_rheo,inject_and_plot_model +import numpy as np +from neuronunit.optimization.data_transport_container import DataTC +from jithub.models import model_classes +import matplotlib.pyplot as plt +import quantities as qt +import os + +from sciunit.scores import RelativeDifferenceScore,ZScore +from sciunit.utils import config_set, config_get +config_set('PREVALIDATE', False) +assert config_get('PREVALIDATE') is False + + +class testOptimizationAllenMultiSpike(unittest.TestCase): + def setUp(self): + self = self + #In principle any index into data should work + # but '1' is chosen here. Robust tests would use any index. + self.ids = [ 324257146, + 325479788, + 476053392, + 623893177, + 623960880, + 482493761, + 471819401 + ] + self.specimen_id = self.ids[1] + def optimize_job(self,model_type,score_type=ZScore): + find_sweep_with_n_spikes = 8 + dtc = DataTC() + dtc.backend = model_type + model = dtc.dtc_to_model() + model.params = BPO_PARAMS[model_type] + fixed_current = 122 *qt.pA + if model_type == "ADEXP": + NGEN = 55 + MU = 16 + else: + NGEN = 45 + MU = 100 + + mapping_funct = dask_map_function + cell_evaluator,simple_cell,suite,target_current,spk_count = wrap_setups( + self.specimen_id, + model_type, + find_sweep_with_n_spikes, + template_model=model, + fixed_current=False, + cached=False, + score_type=score_type + ) + final_pop, hall_of_fame, logs, hist = opt_exec(MU,NGEN,mapping_funct,cell_evaluator) + opt, target, scores, obs_preds, df = opt_to_model(hall_of_fame,cell_evaluator,suite, target_current, spk_count) + best_ind = hall_of_fame[0] + fitnesses = cell_evaluator.evaluate_with_lists(best_ind) + target.vm_soma = suite.traces['vm_soma'] + return np.sum(fitnesses) + def test_opt_relative_diff(self): + model_type = "ADEXP" + sum_fit = self.optimize_job(model_type,score_type=RelativeDifferenceScore) + assert sum_fit<42.0 + # this is just to speed up CI tests to avoid timeout. + @unittest.skip + def test_opt_ZScore(self): + model_type = "ADEXP" + sum_fit = self.optimize_job(model_type,score_type=ZScore) + assert sum_fit<2.1 + @unittest.skip + def test_opt_relative_diff_izhi(self): + model_type = "IZHI" + self.optimize_job(model_type,score_type=RelativeDifferenceScore) + assert sum_fit<32.0 + # this is just to speed up CI tests to avoid timeout. + @unittest.skip + def test_opt_ZScore_izhi(self): + model_type = "IZHI" + self.optimize_job(model_type,score_type=ZScore) + assert sum_fit<2.1 diff --git a/neuronunit/unit_test/scores_unit_test.py.orig b/neuronunit/unit_test/scores_unit_test.py.orig new file mode 100644 index 000000000..261365f3a --- /dev/null +++ b/neuronunit/unit_test/scores_unit_test.py.orig @@ -0,0 +1,89 @@ +#!/usr/bin/env python +# coding: utf-8 + +import unittest +import matplotlib +matplotlib.use('Agg') +<<<<<<< HEAD +======= + +>>>>>>> bc9859eedf2195e6f81fc149cc5cc8c34009ac5b +from neuronunit.allenapi.allen_data_driven import opt_setup, opt_setup_two, opt_exec +from neuronunit.allenapi.allen_data_driven import opt_to_model,wrap_setups +from neuronunit.allenapi.utils import dask_map_function +from neuronunit.optimization.optimization_management import check_bin_vm_soma +from neuronunit.optimization.model_parameters import MODEL_PARAMS, BPO_PARAMS, to_bpo_param +from neuronunit.optimization.optimization_management import dtc_to_rheo,inject_and_plot_model +import numpy as np +from neuronunit.optimization.data_transport_container import DataTC +import efel +from jithub.models import model_classes +import matplotlib.pyplot as plt +import quantities as qt +import os +from sciunit.scores import RelativeDifferenceScore,ZScore +import copy + + + +class testOptimization(unittest.TestCase): + def setUp(self): + self = self + self.ids = [ 324257146, + 325479788, + 476053392, + 623893177, + 623960880, + 482493761, + 471819401 + ] + self.specimen_id = self.ids[1] + def optimize_job(self,model_type,score_type=ZScore): + find_sweep_with_n_spikes = 8 + dtc = DataTC() + dtc.backend = model_type + model = dtc.dtc_to_model() + model.params = BPO_PARAMS[model_type] + fixed_current = 122 *qt.pA + if model_type == "ADEXP": + NGEN = 100 + MU = 20 + else: + NGEN = 100 + MU = 100 + + mapping_funct = dask_map_function + cell_evaluator,simple_cell,suite,target_current,spk_count = wrap_setups( + self.specimen_id, + model_type, + find_sweep_with_n_spikes, + template_model=copy.copy(model), + fixed_current=False, + cached=False, + score_type=score_type + ) + final_pop, hall_of_fame, logs, hist = opt_exec(MU,NGEN,mapping_funct,cell_evaluator) + opt,target = opt_to_model(hall_of_fame,cell_evaluator,suite, target_current, spk_count) + best_ind = hall_of_fame[0] + fitnesses = cell_evaluator.evaluate_with_lists(best_ind) + target.vm_soma = suite.traces['vm15'] + check_bin_vm_soma(target,opt) + return np.sum(fitnesses) + def test_opt_relative_diff(self): + model_type = "ADEXP" + sum_fit = self.optimize_job(model_type,score_type=RelativeDifferenceScore) + assert sum_fit<9.0 + def test_opt_ZScore(self): + model_type = "ADEXP" + sum_fit = self.optimize_job(model_type,score_type=ZScore) + assert sum_fit<0.7 + + def test_opt_relative_diff_izhi(self): + model_type = "IZHI" + self.optimize_job(model_type,score_type=RelativeDifferenceScore) + assert sum_fit<9.0 + + def test_opt_ZScore_izhi(self): + model_type = "IZHI" + self.optimize_job(model_type,score_type=ZScore) + assert sum_fit<0.7 diff --git a/neuronunit/unit_test/serialization_test.ipynb b/neuronunit/unit_test/serialization_test.ipynb deleted file mode 100644 index 94d782b66..000000000 --- a/neuronunit/unit_test/serialization_test.ipynb +++ /dev/null @@ -1,966 +0,0 @@ -{ - "cells": [ - { - "cell_type": "code", - "execution_count": 1, - "metadata": {}, - "outputs": [], - "source": [ - "%matplotlib inline\n", - "import os\n", - "from pprint import pprint\n", - "import matplotlib.pyplot as plt\n", - "import sciunit\n", - "import sciunit.scores\n", - "import neuronunit\n", - "from neuronunit.models.reduced import ReducedModel\n", - "from neuronunit.capabilities import ProducesSpikes" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "metadata": {}, - "outputs": [], - "source": [ - "# Assumes imported neuronunit is from source, e.g. pip install -e\n", - "path = os.path.join(neuronunit.__path__[0],'models/NeuroML2/LEMS_2007One.xml')\n", - "# Instantiate three identical models\n", - "models = [ReducedModel(path, name='Izhikevich', backend='jNeuroML') for i in range(2)]" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "metadata": {}, - "outputs": [], - "source": [ - "# Change the parameters of the second model\n", - "models[1].set_attrs(izhikevich2007Cell={'a': '0.04 per_ms'})\n", - "models[1].name = 'Izhikevich_new'" - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "pyNeuroML >>> Reloading data specified in LEMS file: /var/folders/_j/vg2m860n23d_9ty1h2z9_2880000gn/T/tmpw4nxbjl4/Izhikevich.xml (/private/var/folders/_j/vg2m860n23d_9ty1h2z9_2880000gn/T/tmpw4nxbjl4/Izhikevich.xml), base_dir: /var/folders/_j/vg2m860n23d_9ty1h2z9_2880000gn/T/tmp1hr8cang, cwd: /Users/rgerkin/Dropbox/dev/scidash/neuronunit/neuronunit/unit_test\n", - "pyNeuroML >>> Reloading data specified in LEMS file: /var/folders/_j/vg2m860n23d_9ty1h2z9_2880000gn/T/tmpddw73r6n/Izhikevich.xml (/private/var/folders/_j/vg2m860n23d_9ty1h2z9_2880000gn/T/tmpddw73r6n/Izhikevich.xml), base_dir: /var/folders/_j/vg2m860n23d_9ty1h2z9_2880000gn/T/tmp8bwyo0o8, cwd: /Users/rgerkin/Dropbox/dev/scidash/neuronunit/neuronunit/unit_test\n" - ] - }, - { - "data": { - "image/png": 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\n", - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "for model in models:\n", - " plt.plot(model.get_membrane_potential(),label=model)\n", - "plt.legend();" - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "metadata": {}, - "outputs": [], - "source": [ - "# A new M2M test which will compare the equality of spike counts across models\n", - "class MyTest(sciunit.Test):\n", - " required_capabilities = (ProducesSpikes,)\n", - " score_type = sciunit.scores.ZScore\n", - " def generate_prediction(self,model):\n", - " count = model.get_spike_count()\n", - " return count" - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "metadata": {}, - "outputs": [], - "source": [ - "test = MyTest(observation={'mean':8, 'std':3, 'url':'http://somewhere.thathasdata.com'})" - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "Executing test MyTest on model Izhikevich... " - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "text/html": [ - "Score is Z = -0.33\n" - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "text/html": [ - "Executing test MyTest on model Izhikevich_new... " - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "text/html": [ - "Score is Z = 0.00\n" - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "s = test.judge(models)" - ] - }, - { - "cell_type": "code", - "execution_count": 8, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "
MyTest
IzhikevichZ = -0.33
Izhikevich_newZ = 0.00
\n", - "
" - ], - "text/plain": [ - " MyTest\n", - "Izhikevich Z = -0.33\n", - "Izhikevich_new Z = 0.00" - ] - }, - "execution_count": 8, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "# The score matrix\n", - "s" - ] - }, - { - "cell_type": "code", - "execution_count": 9, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "{'description': None,\n", - " 'name': 'MyTest',\n", - " 'observation': {'mean': 8,\n", - " 'std': 3,\n", - " 'url': 'http://somewhere.thathasdata.com'},\n", - " 'unpicklable': [],\n", - " 'verbose': 1}\n" - ] - } - ], - "source": [ - "x = test.json(string=False)\n", - "pprint(x)" - ] - }, - { - "cell_type": "code", - "execution_count": 10, - "metadata": {}, - "outputs": [], - "source": [ - "def check_url(x):\n", - " if not x.json(add_props=True,string=False).get('url'):\n", - " print(\"Model has no associated url; please set the url attribute of the model\")\n", - "check_url(models[0])" - ] - }, - { - "cell_type": "code", - "execution_count": 11, - "metadata": {}, - "outputs": [], - "source": [ - "# How do you want your GitHub URLs? \n", - "lems_url = (\"https://github.com/scidash/neuronunit/blob/master/neuronunit/\"\n", - " \"models/NeuroML2/LEMS_2007One.xml\")\n", - "models[0]._url = lems_url\n", - "models[1]._url = lems_url" - ] - }, - { - "cell_type": "code", - "execution_count": 12, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "'https://github.com/scidash/neuronunit/blob/master/neuronunit/models/NeuroML2/LEMS_2007One.xml'" - ] - }, - "execution_count": 12, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "models[0].url" - ] - }, - { - "cell_type": "code", - "execution_count": 13, - "metadata": {}, - "outputs": [], - "source": [ - "check_url(models[0])" - ] - }, - { - "cell_type": "code", - "execution_count": 14, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "{'attrs': {},\n", - " 'backend': 'jNeuroML',\n", - " 'name': 'Izhikevich',\n", - " 'run_params': {'default_java_max_memory': '400M', 'nogui': True, 'v': False}}\n" - ] - } - ], - "source": [ - "# Has fewer attributes because most of them came from the LEMS file so those are assumed\n", - "# to still be the values in memory. If we need to extract these then I need to find a\n", - "# way to read them from the file; however, there may be no general way to do so for all\n", - "# LEMS files, and certainly not for all possible models. \n", - "x = models[0].json(string=False)\n", - "pprint(x)" - ] - }, - { - "cell_type": "code", - "execution_count": 15, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "{'attrs': {'izhikevich2007Cell': {'a': '0.04 per_ms'}},\n", - " 'backend': 'jNeuroML',\n", - " 'name': 'Izhikevich_new',\n", - " 'run_params': {'default_java_max_memory': '400M', 'nogui': True, 'v': False}}\n" - ] - } - ], - "source": [ - "# Has more attributes because some of the original attributes from the LEMS file \n", - "# were replaced with new ones in this session\n", - "x = models[1].json(string=False)\n", - "pprint(x)" - ] - }, - { - "cell_type": "code", - "execution_count": 16, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "{'model': {'attrs': {},\n", - " 'backend': 'jNeuroML',\n", - " 'name': 'Izhikevich',\n", - " 'run_params': {'default_java_max_memory': '400M',\n", - " 'nogui': True,\n", - " 'v': False}},\n", - " 'observation': {'mean': 8,\n", - " 'std': 3,\n", - " 'url': 'http://somewhere.thathasdata.com'},\n", - " 'prediction': 7,\n", - " 'related_data': {},\n", - " 'score': -0.3333333333333333,\n", - " 'test': {'description': None,\n", - " 'name': 'MyTest',\n", - " 'observation': {'mean': 8,\n", - " 'std': 3,\n", - " 'url': 'http://somewhere.thathasdata.com'},\n", - " 'unpicklable': [],\n", - " 'verbose': 1},\n", - " 'unpicklable': []}\n" - ] - } - ], - "source": [ - "# Select one score from the score matrix\n", - "score = s['Izhikevich','MyTest']\n", - "x = score.json(string=False)\n", - "pprint(x)" - ] - }, - { - "cell_type": "code", - "execution_count": 17, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "{'_class': {'name': 'ZScore', 'url': 'http://github.com/scidash/sciunit'},\n", - " '_id': 4574572840,\n", - " 'hash': '370f8d0e9b6a87ae568560b0cedb4cfc567e0e097ff2dce7fe123281',\n", - " 'model': {'_class': {'name': 'ReducedModel',\n", - " 'url': 'https://github.com/scidash/neuronunit/blob/master/neuronunit/models/NeuroML2/LEMS_2007One.xml'},\n", - " '_id': 4564190320,\n", - " 'attrs': {},\n", - " 'backend': 'jNeuroML',\n", - " 'capabilities': ['Runnable',\n", - " 'ReceivesSquareCurrent',\n", - " 'ProducesActionPotentials',\n", - " 'ProducesSpikes',\n", - " 'ProducesMembranePotential'],\n", - " 'hash': '04433628b02110a59660c84bc268c68fab32cd033f4da5edaf7a18ac',\n", - " 'name': 'Izhikevich',\n", - " 'remote_url': 'https://github.com/scidash/neuronunit',\n", - " 'run_params': {'default_java_max_memory': '400M',\n", - " 'nogui': True,\n", - " 'v': False},\n", - " 'url': 'https://github.com/scidash/neuronunit/blob/master/neuronunit/models/NeuroML2/LEMS_2007One.xml',\n", - " 'version': '52528e5f1d49c5e607a9e567a493b7a634658200*'},\n", - " 'observation': {'mean': 8,\n", - " 'std': 3,\n", - " 'url': 'http://somewhere.thathasdata.com'},\n", - " 'prediction': 7,\n", - " 'raw': '-0.3333',\n", - " 'related_data': {},\n", - " 'remote_url': 'http://github.com/scidash/sciunit',\n", - " 'score': -0.3333333333333333,\n", - " 'score_type': 'ZScore',\n", - " 'norm_score': 0.7388826803635273,\n", - " 'summary': \"=== Model Izhikevich achieved score Z = -0.33 on test 'MyTest'. \"\n", - " '===',\n", - " 'test': {'_class': {'name': 'MyTest', 'url': ''},\n", - " '_id': 4574574576,\n", - " 'description': None,\n", - " 'hash': 'a525227bebc09871767c6e3bf0414556820727f7095437f2c58356ab',\n", - " 'name': 'MyTest',\n", - " 'observation': {'mean': 8,\n", - " 'std': 3,\n", - " 'url': 'http://somewhere.thathasdata.com'},\n", - " 'remote_url': None,\n", - " 'unpicklable': [],\n", - " 'url': None,\n", - " 'verbose': 1,\n", - " 'version': None},\n", - " 'unpicklable': [],\n", - " 'url': 'http://github.com/scidash/sciunit',\n", - " 'version': 'eb5469103f510c12b4897180db7551ccbae7cc0f*'}\n" - ] - } - ], - "source": [ - "# Add additional properties to the JSON output\n", - "x = score.json(add_props=True,string=False)\n", - "pprint(x)" - ] - }, - { - "cell_type": "code", - "execution_count": 18, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "{'_class': {'name': 'ZScore', 'url': 'http://github.com/scidash/sciunit'},\n", - " '_id': 4574572840,\n", - " 'hash': '370f8d0e9b6a87ae568560b0cedb4cfc567e0e097ff2dce7fe123281',\n", - " 'model': {'_class': {'name': 'ReducedModel',\n", - " 'url': 'https://github.com/scidash/neuronunit/blob/master/neuronunit/models/NeuroML2/LEMS_2007One.xml'},\n", - " '_id': 4564190320,\n", - " 'attrs': {},\n", - " 'backend': 'jNeuroML',\n", - " 'capabilities': ['Runnable',\n", - " 'ReceivesSquareCurrent',\n", - " 'ProducesActionPotentials',\n", - " 'ProducesSpikes',\n", - " 'ProducesMembranePotential'],\n", - " 'hash': '04433628b02110a59660c84bc268c68fab32cd033f4da5edaf7a18ac',\n", - " 'name': 'Izhikevich',\n", - " 'remote_url': 'https://github.com/scidash/neuronunit',\n", - " 'run_params': {'default_java_max_memory': '400M',\n", - " 'nogui': True,\n", - " 'v': False},\n", - " 'url': 'https://github.com/scidash/neuronunit/blob/master/neuronunit/models/NeuroML2/LEMS_2007One.xml',\n", - " 'version': '52528e5f1d49c5e607a9e567a493b7a634658200*'},\n", - " 'observation': {'mean': 8,\n", - " 'std': 3,\n", - " 'url': 'http://somewhere.thathasdata.com'},\n", - " 'prediction': 7,\n", - " 'raw': '-0.3333',\n", - " 'related_data': {},\n", - " 'remote_url': 'http://github.com/scidash/sciunit',\n", - " 'score': -0.3333333333333333,\n", - " 'score_type': 'ZScore',\n", - " 'norm_score': 0.7388826803635273,\n", - " 'summary': \"=== Model Izhikevich achieved score Z = -0.33 on test 'MyTest'. \"\n", - " '===',\n", - " 'test': {'_class': {'name': 'MyTest', 'url': ''},\n", - " '_id': 4574574576,\n", - " 'description': None,\n", - " 'hash': 'a525227bebc09871767c6e3bf0414556820727f7095437f2c58356ab',\n", - " 'name': 'MyTest',\n", - " 'observation': {'mean': 8,\n", - " 'std': 3,\n", - " 'url': 'http://somewhere.thathasdata.com'},\n", - " 'remote_url': None,\n", - " 'unpicklable': [],\n", - " 'url': None,\n", - " 'verbose': 1,\n", - " 'version': None},\n", - " 'unpicklable': [],\n", - " 'url': 'http://github.com/scidash/sciunit',\n", - " 'version': 'eb5469103f510c12b4897180db7551ccbae7cc0f*'}\n" - ] - } - ], - "source": [ - "# Nicer formatting for the output\n", - "x = score.json(add_props=True,string=False)\n", - "pprint(x)" - ] - }, - { - "cell_type": "code", - "execution_count": 19, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "{'_class': {'name': 'TestSuite', 'url': 'http://github.com/scidash/sciunit'},\n", - " '_id': 4646233592,\n", - " 'hash': '2a162712298f803f30e097fef52c44f42104feede9500e6a76299098',\n", - " 'hooks': None,\n", - " 'include_models': [],\n", - " 'name': 'My Suite',\n", - " 'remote_url': 'http://github.com/scidash/sciunit',\n", - " 'skip_models': [],\n", - " 'tests': [{'_class': {'name': 'MyTest', 'url': ''},\n", - " '_id': 4574574576,\n", - " 'description': None,\n", - " 'hash': 'a525227bebc09871767c6e3bf0414556820727f7095437f2c58356ab',\n", - " 'name': 'MyTest',\n", - " 'observation': {'mean': 8,\n", - " 'std': 3,\n", - " 'url': 'http://somewhere.thathasdata.com'},\n", - " 'remote_url': None,\n", - " 'unpicklable': [],\n", - " 'url': None,\n", - " 'verbose': 1,\n", - " 'version': None},\n", - " {'_class': {'name': 'MyTest', 'url': ''},\n", - " '_id': 4646233984,\n", - " 'description': None,\n", - " 'hash': '1a7b9374f4ee0c3e01680316dacf2ecaed63cb27af2f1e3973d9ad5a',\n", - " 'name': 'Second Test',\n", - " 'observation': {'mean': 7,\n", - " 'std': 3.8,\n", - " 'url': 'http://somewhereelse.thathasdata.com'},\n", - " 'remote_url': None,\n", - " 'unpicklable': [],\n", - " 'url': None,\n", - " 'verbose': 1,\n", - " 'version': None}],\n", - " 'unpicklable': [],\n", - " 'url': 'http://github.com/scidash/sciunit',\n", - " 'version': 'eb5469103f510c12b4897180db7551ccbae7cc0f*',\n", - " 'weights': [0.5, 0.5],\n", - " 'weights_': []}\n" - ] - } - ], - "source": [ - "test2 = MyTest(observation={'mean':7, 'std':3.8, 'url':'http://somewhereelse.thathasdata.com'},name='Second Test')\n", - "suite = sciunit.TestSuite([test,test2],name='My Suite')\n", - "x = suite.json(add_props=True,string=False)\n", - "pprint(x)" - ] - }, - { - "cell_type": "code", - "execution_count": 20, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "Executing test MyTest on model Izhikevich... " - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "text/html": [ - "Score is Z = -0.33\n" - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "text/html": [ - "Executing test Second Test on model Izhikevich... " - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "text/html": [ - "Score is Z = 0.00\n" - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "text/html": [ - "Executing test MyTest on model Izhikevich_new... " - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "text/html": [ - "Score is Z = 0.00\n" - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "text/html": [ - "Executing test Second Test on model Izhikevich_new... " - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "text/html": [ - "Score is Z = 0.26\n" - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "score_matrix = suite.judge(models)" - ] - }, - { - "cell_type": "code", - "execution_count": 21, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "{'models': [{'_class': {'name': 'ReducedModel',\n", - " 'url': 'https://github.com/scidash/neuronunit/blob/master/neuronunit/models/NeuroML2/LEMS_2007One.xml'},\n", - " '_id': 4564190320,\n", - " 'attrs': {},\n", - " 'backend': 'jNeuroML',\n", - " 'capabilities': ['Runnable',\n", - " 'ReceivesSquareCurrent',\n", - " 'ProducesActionPotentials',\n", - " 'ProducesSpikes',\n", - " 'ProducesMembranePotential'],\n", - " 'hash': '04433628b02110a59660c84bc268c68fab32cd033f4da5edaf7a18ac',\n", - " 'name': 'Izhikevich',\n", - " 'remote_url': 'https://github.com/scidash/neuronunit',\n", - " 'run_params': {'default_java_max_memory': '400M',\n", - " 'nogui': True,\n", - " 'v': False},\n", - " 'url': 'https://github.com/scidash/neuronunit/blob/master/neuronunit/models/NeuroML2/LEMS_2007One.xml',\n", - " 'version': '52528e5f1d49c5e607a9e567a493b7a634658200*'},\n", - " {'_class': {'name': 'ReducedModel',\n", - " 'url': 'https://github.com/scidash/neuronunit/blob/master/neuronunit/models/NeuroML2/LEMS_2007One.xml'},\n", - " '_id': 4301722344,\n", - " 'attrs': {'izhikevich2007Cell': {'a': '0.04 per_ms'}},\n", - " 'backend': 'jNeuroML',\n", - " 'capabilities': ['Runnable',\n", - " 'ReceivesSquareCurrent',\n", - " 'ProducesActionPotentials',\n", - " 'ProducesSpikes',\n", - " 'ProducesMembranePotential'],\n", - " 'hash': '640c67dede0892f857f37332a7dbb9e4fd6bb6f21a48f7b22c059fab',\n", - " 'name': 'Izhikevich_new',\n", - " 'remote_url': 'https://github.com/scidash/neuronunit',\n", - " 'run_params': {'default_java_max_memory': '400M',\n", - " 'nogui': True,\n", - " 'v': False},\n", - " 'url': 'https://github.com/scidash/neuronunit/blob/master/neuronunit/models/NeuroML2/LEMS_2007One.xml',\n", - " 'version': '52528e5f1d49c5e607a9e567a493b7a634658200*'}],\n", - " 'scores': [[{'_class': {'name': 'ZScore',\n", - " 'url': 'http://github.com/scidash/sciunit'},\n", - " '_id': 4410766840,\n", - " 'hash': '370f8d0e9b6a87ae568560b0cedb4cfc567e0e097ff2dce7fe123281',\n", - " 'model': {'_class': {'name': 'ReducedModel',\n", - " 'url': 'https://github.com/scidash/neuronunit/blob/master/neuronunit/models/NeuroML2/LEMS_2007One.xml'},\n", - " '_id': 4564190320,\n", - " 'attrs': {},\n", - " 'backend': 'jNeuroML',\n", - " 'capabilities': ['Runnable',\n", - " 'ReceivesSquareCurrent',\n", - " 'ProducesActionPotentials',\n", - " 'ProducesSpikes',\n", - " 'ProducesMembranePotential'],\n", - " 'hash': '04433628b02110a59660c84bc268c68fab32cd033f4da5edaf7a18ac',\n", - " 'name': 'Izhikevich',\n", - " 'remote_url': 'https://github.com/scidash/neuronunit',\n", - " 'run_params': {'default_java_max_memory': '400M',\n", - " 'nogui': True,\n", - " 'v': False},\n", - " 'url': 'https://github.com/scidash/neuronunit/blob/master/neuronunit/models/NeuroML2/LEMS_2007One.xml',\n", - " 'version': '52528e5f1d49c5e607a9e567a493b7a634658200*'},\n", - " 'observation': {'mean': 8,\n", - " 'std': 3,\n", - " 'url': 'http://somewhere.thathasdata.com'},\n", - " 'prediction': 7,\n", - " 'raw': '-0.3333',\n", - " 'related_data': {},\n", - " 'remote_url': 'http://github.com/scidash/sciunit',\n", - " 'score': -0.3333333333333333,\n", - " 'score_type': 'ZScore',\n", - " 'norm_score': 0.7388826803635273,\n", - " 'summary': '=== Model Izhikevich achieved score Z = -0.33 on '\n", - " \"test 'MyTest'. ===\",\n", - " 'test': {'_class': {'name': 'MyTest', 'url': ''},\n", - " '_id': 4574574576,\n", - " 'description': None,\n", - " 'hash': 'a525227bebc09871767c6e3bf0414556820727f7095437f2c58356ab',\n", - " 'name': 'MyTest',\n", - " 'observation': {'mean': 8,\n", - " 'std': 3,\n", - " 'url': 'http://somewhere.thathasdata.com'},\n", - " 'remote_url': None,\n", - " 'unpicklable': [],\n", - " 'url': None,\n", - " 'verbose': 1,\n", - " 'version': None},\n", - " 'unpicklable': [],\n", - " 'url': 'http://github.com/scidash/sciunit',\n", - " 'version': 'eb5469103f510c12b4897180db7551ccbae7cc0f*'},\n", - " {'_class': {'name': 'ZScore',\n", - " 'url': 'http://github.com/scidash/sciunit'},\n", - " '_id': 4646232304,\n", - " 'hash': '66ea475bc3bc615f2629b2f42f4d4266f65b1ea572a2a303b1836b1a',\n", - " 'model': {'_class': {'name': 'ReducedModel',\n", - " 'url': 'https://github.com/scidash/neuronunit/blob/master/neuronunit/models/NeuroML2/LEMS_2007One.xml'},\n", - " '_id': 4564190320,\n", - " 'attrs': {},\n", - " 'backend': 'jNeuroML',\n", - " 'capabilities': ['Runnable',\n", - " 'ReceivesSquareCurrent',\n", - " 'ProducesActionPotentials',\n", - " 'ProducesSpikes',\n", - " 'ProducesMembranePotential'],\n", - " 'hash': '04433628b02110a59660c84bc268c68fab32cd033f4da5edaf7a18ac',\n", - " 'name': 'Izhikevich',\n", - " 'remote_url': 'https://github.com/scidash/neuronunit',\n", - " 'run_params': {'default_java_max_memory': '400M',\n", - " 'nogui': True,\n", - " 'v': False},\n", - " 'url': 'https://github.com/scidash/neuronunit/blob/master/neuronunit/models/NeuroML2/LEMS_2007One.xml',\n", - " 'version': '52528e5f1d49c5e607a9e567a493b7a634658200*'},\n", - " 'observation': {'mean': 7,\n", - " 'std': 3.8,\n", - " 'url': 'http://somewhereelse.thathasdata.com'},\n", - " 'prediction': 7,\n", - " 'raw': '0',\n", - " 'related_data': {},\n", - " 'remote_url': 'http://github.com/scidash/sciunit',\n", - " 'score': 0.0,\n", - " 'score_type': 'ZScore',\n", - " 'norm_score': 1.0,\n", - " 'summary': '=== Model Izhikevich achieved score Z = 0.00 on test '\n", - " \"'Second Test'. ===\",\n", - " 'test': {'_class': {'name': 'MyTest', 'url': ''},\n", - " '_id': 4646233984,\n", - " 'description': None,\n", - " 'hash': '1a7b9374f4ee0c3e01680316dacf2ecaed63cb27af2f1e3973d9ad5a',\n", - " 'name': 'Second Test',\n", - " 'observation': {'mean': 7,\n", - " 'std': 3.8,\n", - " 'url': 'http://somewhereelse.thathasdata.com'},\n", - " 'remote_url': None,\n", - " 'unpicklable': [],\n", - " 'url': None,\n", - " 'verbose': 1,\n", - " 'version': None},\n", - " 'unpicklable': [],\n", - " 'url': 'http://github.com/scidash/sciunit',\n", - " 'version': 'eb5469103f510c12b4897180db7551ccbae7cc0f*'}],\n", - " [{'_class': {'name': 'ZScore',\n", - " 'url': 'http://github.com/scidash/sciunit'},\n", - " '_id': 4646232584,\n", - " 'hash': '9baf52a5280631a2372af96162c57a447582445fc293d899f6791855',\n", - " 'model': {'_class': {'name': 'ReducedModel',\n", - " 'url': 'https://github.com/scidash/neuronunit/blob/master/neuronunit/models/NeuroML2/LEMS_2007One.xml'},\n", - " '_id': 4301722344,\n", - " 'attrs': {'izhikevich2007Cell': {'a': '0.04 per_ms'}},\n", - " 'backend': 'jNeuroML',\n", - " 'capabilities': ['Runnable',\n", - " 'ReceivesSquareCurrent',\n", - " 'ProducesActionPotentials',\n", - " 'ProducesSpikes',\n", - " 'ProducesMembranePotential'],\n", - " 'hash': '640c67dede0892f857f37332a7dbb9e4fd6bb6f21a48f7b22c059fab',\n", - " 'name': 'Izhikevich_new',\n", - " 'remote_url': 'https://github.com/scidash/neuronunit',\n", - " 'run_params': {'default_java_max_memory': '400M',\n", - " 'nogui': True,\n", - " 'v': False},\n", - " 'url': 'https://github.com/scidash/neuronunit/blob/master/neuronunit/models/NeuroML2/LEMS_2007One.xml',\n", - " 'version': '52528e5f1d49c5e607a9e567a493b7a634658200*'},\n", - " 'observation': {'mean': 8,\n", - " 'std': 3,\n", - " 'url': 'http://somewhere.thathasdata.com'},\n", - " 'prediction': 8,\n", - " 'raw': '0',\n", - " 'related_data': {},\n", - " 'remote_url': 'http://github.com/scidash/sciunit',\n", - " 'score': 0.0,\n", - " 'score_type': 'ZScore',\n", - " 'norm_score': 1.0,\n", - " 'summary': '=== Model Izhikevich_new achieved score Z = 0.00 on '\n", - " \"test 'MyTest'. ===\",\n", - " 'test': {'_class': {'name': 'MyTest', 'url': ''},\n", - " '_id': 4574574576,\n", - " 'description': None,\n", - " 'hash': 'a525227bebc09871767c6e3bf0414556820727f7095437f2c58356ab',\n", - " 'name': 'MyTest',\n", - " 'observation': {'mean': 8,\n", - " 'std': 3,\n", - " 'url': 'http://somewhere.thathasdata.com'},\n", - " 'remote_url': None,\n", - " 'unpicklable': [],\n", - " 'url': None,\n", - " 'verbose': 1,\n", - " 'version': None},\n", - " 'unpicklable': [],\n", - " 'url': 'http://github.com/scidash/sciunit',\n", - " 'version': 'eb5469103f510c12b4897180db7551ccbae7cc0f*'},\n", - " {'_class': {'name': 'ZScore',\n", - " 'url': 'http://github.com/scidash/sciunit'},\n", - " '_id': 4646234152,\n", - " 'hash': '39695c4093b1fe19fd7a13770c0823ddfeee9c2a62f12454b894003b',\n", - " 'model': {'_class': {'name': 'ReducedModel',\n", - " 'url': 'https://github.com/scidash/neuronunit/blob/master/neuronunit/models/NeuroML2/LEMS_2007One.xml'},\n", - " '_id': 4301722344,\n", - " 'attrs': {'izhikevich2007Cell': {'a': '0.04 per_ms'}},\n", - " 'backend': 'jNeuroML',\n", - " 'capabilities': ['Runnable',\n", - " 'ReceivesSquareCurrent',\n", - " 'ProducesActionPotentials',\n", - " 'ProducesSpikes',\n", - " 'ProducesMembranePotential'],\n", - " 'hash': '640c67dede0892f857f37332a7dbb9e4fd6bb6f21a48f7b22c059fab',\n", - " 'name': 'Izhikevich_new',\n", - " 'remote_url': 'https://github.com/scidash/neuronunit',\n", - " 'run_params': {'default_java_max_memory': '400M',\n", - " 'nogui': True,\n", - " 'v': False},\n", - " 'url': 'https://github.com/scidash/neuronunit/blob/master/neuronunit/models/NeuroML2/LEMS_2007One.xml',\n", - " 'version': '52528e5f1d49c5e607a9e567a493b7a634658200*'},\n", - " 'observation': {'mean': 7,\n", - " 'std': 3.8,\n", - " 'url': 'http://somewhereelse.thathasdata.com'},\n", - " 'prediction': 8,\n", - " 'raw': '0.2632',\n", - " 'related_data': {},\n", - " 'remote_url': 'http://github.com/scidash/sciunit',\n", - " 'score': 0.2631578947368421,\n", - " 'score_type': 'ZScore',\n", - " 'norm_score': 0.7924288824046,\n", - " 'summary': '=== Model Izhikevich_new achieved score Z = 0.26 on '\n", - " \"test 'Second Test'. ===\",\n", - " 'test': {'_class': {'name': 'MyTest', 'url': ''},\n", - " '_id': 4646233984,\n", - " 'description': None,\n", - " 'hash': '1a7b9374f4ee0c3e01680316dacf2ecaed63cb27af2f1e3973d9ad5a',\n", - " 'name': 'Second Test',\n", - " 'observation': {'mean': 7,\n", - " 'std': 3.8,\n", - " 'url': 'http://somewhereelse.thathasdata.com'},\n", - " 'remote_url': None,\n", - " 'unpicklable': [],\n", - " 'url': None,\n", - " 'verbose': 1,\n", - " 'version': None},\n", - " 'unpicklable': [],\n", - " 'url': 'http://github.com/scidash/sciunit',\n", - " 'version': 'eb5469103f510c12b4897180db7551ccbae7cc0f*'}]],\n", - " 'tests': [{'_class': {'name': 'MyTest', 'url': ''},\n", - " '_id': 4574574576,\n", - " 'description': None,\n", - " 'hash': 'a525227bebc09871767c6e3bf0414556820727f7095437f2c58356ab',\n", - " 'name': 'MyTest',\n", - " 'observation': {'mean': 8,\n", - " 'std': 3,\n", - " 'url': 'http://somewhere.thathasdata.com'},\n", - " 'remote_url': None,\n", - " 'unpicklable': [],\n", - " 'url': None,\n", - " 'verbose': 1,\n", - " 'version': None},\n", - " {'_class': {'name': 'MyTest', 'url': ''},\n", - " '_id': 4646233984,\n", - " 'description': None,\n", - " 'hash': '1a7b9374f4ee0c3e01680316dacf2ecaed63cb27af2f1e3973d9ad5a',\n", - " 'name': 'Second Test',\n", - " 'observation': {'mean': 7,\n", - " 'std': 3.8,\n", - " 'url': 'http://somewhereelse.thathasdata.com'},\n", - " 'remote_url': None,\n", - " 'unpicklable': [],\n", - " 'url': None,\n", - " 'verbose': 1,\n", - " 'version': None}]}\n" - ] - } - ], - "source": [ - "x = score_matrix.json(add_props=True,string=False)\n", - "pprint(x)" - ] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.5.2" - } - }, - "nbformat": 4, - "nbformat_minor": 2 -} diff --git a/neuronunit/unit_test/showcase_tests.py b/neuronunit/unit_test/showcase_tests.py deleted file mode 100644 index ad20f2849..000000000 --- a/neuronunit/unit_test/showcase_tests.py +++ /dev/null @@ -1,16 +0,0 @@ -"""Unit tests for the showcase features of NeuronUnit""" - -from .base import * - -class DocumentationTestCase(NotebookTools,unittest.TestCase): - """Testing documentation notebooks""" - - path = '../../docs' - - #@unittest.skip("Skipping chapter 3") - def test_chapter3(self): - self.do_notebook('chapter3') - - -if __name__ == '__main__': - unittest.main() diff --git a/neuronunit/unit_test/sub_pr_july.py b/neuronunit/unit_test/sub_pr_july.py deleted file mode 100644 index 3c47390ab..000000000 --- a/neuronunit/unit_test/sub_pr_july.py +++ /dev/null @@ -1,333 +0,0 @@ -import pickle -from neuronunit.tests import np, pq, cap, VmTest, scores, AMPL, DELAY, DURATION - - -from neuronunit.optimization import get_neab -import copy -import os -from neuronunit.optimization.optimization_management import run_ga - -import matplotlib as mpl -mpl.use('agg') - - -electro_path = str(os.getcwd())+'/pipe_tests.p' -print(os.getcwd()) -assert os.path.isfile(electro_path) == True -with open(electro_path,'rb') as f: - electro_tests = pickle.load(f) - -electro_tests = get_neab.replace_zero_std(electro_tests) -electro_tests = get_neab.substitute_parallel_for_serial(electro_tests) -test, observation = electro_tests[0] - - -import matplotlib.pyplot as plt - - - -from neuronunit.optimization import get_neab -import copy -import os -import pickle -electro_path = str(os.getcwd())+'/pipe_tests.p' -from neuronunit import plottools -import numpy as np -ax = None -from neuronunit.optimization import exhaustive_search as es - -with open(electro_path,'rb') as f: - electro_tests = pickle.load(f) -from matplotlib.colors import LogNorm -from neuronunit.optimization.exhaustive_search import run_grid, reduce_params, create_grid#, mock_grid -from neuronunit.optimization import model_parameters as modelp -from neuronunit.optimization.model_parameters import path_params -mp = modelp.model_params - - - -electro_tests = get_neab.replace_zero_std(electro_tests) -electro_tests = get_neab.substitute_parallel_for_serial(electro_tests) -test, observation = electro_tests[0] -import quantities as pq - -from neuronunit.optimization import model_parameters as modelp -mp = modelp.model_params - - - -opt_keys = ['a','b','vr'] -nparams = len(opt_keys) - -observation = {'a':[np.median(mp['a']),np.std(mp['a'])], 'b':[np.median(mp['b']),np.std(mp['b'])], 'vr':[np.median(mp['vr']),np.std(mp['vr'])]} - -tests = copy.copy(electro_tests[0][0]) -tests_ = [] -tests_ += [tests[0]] -tests_ += tests[4:7] -#import pdb -#pdb.set_trace() -try: - #assert 1 == 2 - with open('ga_run.p','rb') as f: - package = pickle.load(f) - pop = package[0] - print(pop[0].dtc.attrs.items()) - history = package[4] - gen_vs_pop = package[6] - hof = package[1] -except: - print(mp) - nparams = len(opt_keys) - package = run_ga(mp,nparams*2,12,tests_,provided_keys = opt_keys)#, use_cache = True, cache_name='simple') - pop = package[0] - history = package[4] - gen_vs_pop = package[6] - hof = package[1] - - with open('ga_run.p','wb') as f: - pickle.dump(package,f) - - grid_results = {} - -#import seaborn as sns -from itertools import product -import matplotlib.pyplot as plt - - - -def plot_scatter(hof,ax,keys): - z = np.array([ np.sum(list(p.dtc.scores.values())) for p in hof ]) - x = np.array([ p.dtc.attrs[str(keys[0])] for p in hof ]) - if len(keys) != 1: - y = np.array([ p.dtc.attrs[str(keys[1])] for p in hof ]) - - ax.cla() - ax.set_title(' {0} vs {1} '.format(keys[0],keys[1])) - ax.scatter(x, y, c=y, s=125)#, cmap='gray') - - #ax.scatter(x, y, z, [3 for i in x] ) - return ax - -def plot_surface(gr,ax,keys,imshow=False): - # from - # https://github.com/russelljjarvis/neuronunit/blob/dev/neuronunit/unit_test/progress_report_4thJuly.ipynb - # Not rendered - # https://github.com/russelljjarvis/neuronunit/blob/dev/neuronunit/unit_test/progress_report_.ipynb - gr = [ g for g in gr if type(g.dtc) is not type(None) ] - - gr = [ g for g in gr if type(g.dtc.scores) is not type(None) ] - ax.cla() - #gr = [ g - gr_ = [] - index = 0 - for i,g in enumerate(gr): - if type(g.dtc) is not type(None): - gr_.append(g) - else: - index = i - - z = [ np.sum(list(p.dtc.scores.values())) for p in gr ] - x = [ p.dtc.attrs[str(keys[0])] for p in gr ] - y = [ p.dtc.attrs[str(keys[1])] for p in gr ] - - if len(x) != 100: - delta = 100-len(x) - for i in range(0,delta): - x.append(np.mean(x)) - y.append(np.mean(y)) - z.append(np.mean(z)) - - xx = np.array(x) - yy = np.array(y) - zz = np.array(z) - - dim = len(xx) - - - N = int(np.sqrt(len(xx))) - X = xx.reshape((N, N)) - Y = yy.reshape((N, N)) - Z = zz.reshape((N, N)) - if imshow==False: - ax.pcolormesh(X, Y, Z, edgecolors='black') - else: - import seaborn as sns; sns.set() - ax = sns.heatmap(Z) - - #ax.imshow(Z) - #ax.pcolormesh(xi, yi, zi, edgecolors='black') - ax.set_title(' {0} vs {1} '.format(keys[0],keys[1])) - return ax - -def plot_line(gr,ax,key): - ax.cla() - ax.set_title(' {0} vs score'.format(key[0])) - z = np.array([ np.sum(list(p.dtc.scores.values())) for p in gr ]) - x = np.array([ p.dtc.attrs[key[0]] for p in gr ]) - - ax.plot(x,z) - ax.set_xlim(np.min(x),np.max(x)) - ax.set_ylim(np.min(z),np.max(z)) - return ax - -def grids(hof,tests): - dim = len(hof[0].dtc.attrs.keys()) - flat_iter = [ (i,ki,j,kj) for i,ki in enumerate(hof[0].dtc.attrs.keys()) for j,kj in enumerate(hof[0].dtc.attrs.keys()) ] - matrix = [[0 for x in range(dim)] for y in range(dim)] - plt.clf() - fig,ax = plt.subplots(dim,dim,figsize=(10,10)) - #fig1,ax1 = plt.subplots(dim,dim,figsize=(10,10)) - - cnt = 0 - for i,ki,j,kj in flat_iter: - free_param = set([ki,kj]) # construct a small-set out of the indexed keys 2. If both keys are - # are the same, this set will only contain one index - bs = set(hof[0].dtc.attrs.keys()) # construct a full set out of all of the keys available, including ones not indexed here. - diff = bs.difference(free_param) # diff is simply the key that is not indexed. - # BD is the dictionary of parameters to be held constant - # if the plot is 1D then two parameters should be held constant. - hc = {} - for d in diff: - hc[d] = hof[0].dtc.attrs[d] - - if i == j: - assert len(free_param) == len(hc) - 1 - assert len(hc) == len(free_param) + 1 - gr = run_grid(10,tests,provided_keys = free_param ,hold_constant = hc) - # make a psuedo test, that still depends on input Parametersself. - # each test evaluates a normal PDP. - - - - - matrix[i][j] = ( free_param,gr ) - - fp = list(copy.copy(free_param)) - ax[i,j] = plot_line(gr,ax[i,j],fp) - if i >j: - assert len(free_param) == len(hc) + 1 - assert len(hc) == len(free_param) - 1 - # what I want to do, I want to plot grid lines not a heat map. - # I want to plot bd.attrs is cross hairs, - # I want the range of the plot shown to be bigger than than the grid lines. - - gr = run_grid(10,tests,provided_keys = free_param ,hold_constant = hc) - - fp = list(copy.copy(free_param)) - #if len(gr) == 100: - ax[i,j] = plot_surface(gr,ax[i,j],fp,imshow=False) - - matrix[i][j] = ( free_param,gr ) - - if i < j: - matrix[i][j] = ( free_param,gr ) - - fp = list(copy.copy(free_param)) - if len(fp) == 2: - ax[i,j] = plot_scatter(hof,ax[i,j],fp) - - plt.savefig(str('first_surfaces.png')) - return matrix - -import matplotlib.pyplot as plt -from neuronunit.models.reduced import ReducedModel - -def plot_vm(hof,ax,key): - ax.cla() - best_dtc = hof[1].dtc - best_rh = hof[1].dtc.rheobase - print( [ h.dtc.rheobase for h in hof ]) - - neuron = None - model = ReducedModel(path_params['model_path'],name = str('regular_spiking'),backend =('NEURON',{'DTC':best_dtc})) - params = {'injected_square_current': - {'amplitude': best_rh['value'], 'delay':DELAY, 'duration':DURATION}} - model.set_attrs(hof[0].dtc.attrs) - results = model.inject_square_current(params) - print(model.attrs,best_dtc.attrs,best_rh) - vm = model.get_membrane_potential() - times = vm.times - ax.plot(times,vm) - #ax.xlabel('ms') - #ax.ylabel('mV') - #ax.set_xlim(np.min(x),np.max(x)) - #ax.set_ylim(np.min(z),np.max(z)) - return ax - - -def plotss(matrix,hof): - dim = np.shape(matrix)[0] - print(dim) - cnt = 0 - fig,ax = plt.subplots(dim,dim,figsize=(10,10)) - flat_iter = [] - for i,k in enumerate(matrix): - for j,r in enumerate(k): - - keys = list(r[0]) - gr = r[1] - - if i==j: - ax[i,j] = plot_vm(hof,ax[i,j],keys) - if i>j: - ax[i,j] = plot_surface(gr,ax[i,j],keys,imshow=False) - if i < j: - ax[i,j] = plot_scatter(hof,ax[i,j],keys) - - - print(i,j) - plt.savefig(str('other_surfaces.png')) - return None - - -def evidence(matrix,hof): - dim = np.shape(matrix)[0] - print(dim) - cnt = 0 - fig,ax = plt.subplots(dim,dim,figsize=(10,10)) - flat_iter = [] - newrange = {} - for i,k in enumerate(matrix): - for j,r in enumerate(k): - - keys = list(r[0]) - gr = r[1] - line = [ np.sum(list(g.dtc.scores.values())) for g in gr] - print(line) - if i==j: - keys = keys[0] - min_ = np.min(line) - print(min_,line[0],line[1],'diff?') - if line[0] == min_: - print('hit') - attrs = gr[0].dtc.attrs[keys] - remin = - np.abs(attrs)*10 - remax = np.abs(gr[-1].dtc.attrs[keys])*10 - nr = np.linspace(remin,remax,10) - newrange[keys] = nr - - if line[-1] == min_: - print('hit') - attrs = gr[-1].dtc.attrs[keys] - remin = - np.abs(attrs)*10 - remax = np.abs(gr[-1].dtc.attrs[keys])*10 - nr = np.linspace(remin,remax,10) - newrange[keys] = nr - - print(newrange,'newrange') - return newrange -#matrix = grids(hof,tests=tests_) -#surfaces.p -#with open('surfaces.p','wb') as f: -# pickle.dump(matrix,f) -with open('surfaces.p','rb') as f: - matrix = pickle.load(f) -import pdb - -newrange = evidence(matrix,hof) -#_ = plotss(matrix,hof) -#except: -print('finished') -print(newrange) -exit diff --git a/neuronunit/unit_test/test_auxillary.py b/neuronunit/unit_test/test_auxillary.py deleted file mode 100644 index 9c34dac81..000000000 --- a/neuronunit/unit_test/test_auxillary.py +++ /dev/null @@ -1,344 +0,0 @@ -"""Tests of NeuronUnit test classes""" -import unittest -import os -import sys -#from sciunit.utils import NotebookTools#,import_all_modules -import dask -from dask import bag -from base import * - -def grid_points(): - npoints = 2 - nparams = 10 - from neuronunit.optimization.model_parameters import model_params - provided_keys = list(model_params.keys()) - USE_CACHED_GS = False - electro_path = 'pipe_tests.p' - import pickle - assert os.path.isfile(electro_path) == True - with open(electro_path,'rb') as f: - electro_tests = pickle.load(f) - from neuronunit.optimization import exhaustive_search - grid_points = exhaustive_search.create_grid(npoints = npoints,provided_keys=['a','b','vr']) - import dask.bag as db - b0 = db.from_sequence(grid_points[0:2], npartitions=8) - es = exhaustive_search.update_dtc_grid - dtcpop = list(b0.map(es).compute()) - assert dtcpop is not None - return dtcpop - -def test_01a_compute_score(dtcpop, tests): - from neuronunit.optimization import get_neab - from neuronunit.optimization.optimization_management import dtc_to_rheo - from neuronunit.optimization.optimization_management import nunit_evaluation - from neuronunit.optimization.optimization_management import format_test - from itertools import repeat - #dtcpop = grid_points() - rheobase_test = tests[0][0][0] - - xargs = list(zip(dtcpop,repeat(rheobase_test),repeat('NEURON'))) - dtclist = list(map(dtc_to_rheo,xargs)) - - #dtclist = list(map(dtc_to_rheo,dtcpop)) - for d in dtclist: - assert len(list(d.attrs.values())) > 0 - import dask.bag as db - b0 = db.from_sequence(dtclist, npartitions=8) - dtclist = list(db.map(format_test,b0).compute()) - - b0 = db.from_sequence(dtclist, npartitions=8) - dtclist = list(db.map(nunit_evaluation,b0).compute()) - return dtclist - -class testOptimizationBackend(NotebookTools,unittest.TestCase): - - def setUp(self): - self.predictions = None - self.predictionp = None - self.score_p = None - self.score_s = None - self.grid_points = grid_points() - dtcpop = self.grid_points - import pickle - electro_path = 'pipe_tests.p' - assert os.path.isfile(electro_path) == True - with open(electro_path,'rb') as f: - self.electro_tests = pickle.load(f) - #self.electro_tests = get_neab.replace_zero_std(self.electro_tests) - - self.test_01a_compute_score = test_01a_compute_score - self.dtcpop = test_01a_compute_score(dtcpop,self.electro_tests) - self.dtc = self.dtcpop[0] - self.rheobase = self.dtc.rheobase - from neuronunit.models.reduced import ReducedModel - from neuronunit.optimization import get_neab - self.standard_model = ReducedModel(get_neab.LEMS_MODEL_PATH, backend='RAW') - self.model = ReducedModel(get_neab.LEMS_MODEL_PATH, backend='RAW') - - def check_dif(pipe_old,pipe_new): - bool = False - for key, value in pipe_results.items(): - if value != pipe_new[key]: - bool = True - print(value,pipe_new[key]) - - return bool - - def get_observation(self, cls): - print(cls.__name__) - neuron = {'nlex_id': 'nifext_50'} # Layer V pyramidal cell - return cls.neuroelectro_summary_observation(neuron) - - def run_test(self, cls, pred =None): - observation = self.get_observation(cls) - test = cls(observation=observation) - score = test.judge(self.standard_model, stop_on_error = True, deep_error = True) - return score - - # Get experimental electro physology bservations for a dentate gyrus baskett cell - # An inhibitory neuron - @unittest.skip("Not fully developed yet") - def test_get_rate_CV(self): - # Dictionary of observations, in this case two ephys properties from one paper - from neuronunit.tests import dynamics - import quantities as pq - doi = 'doi:10.1113/jphysiol.2010.200683' - observations={doi:{'ap_amplitude':{'mean':45.1*pq.mV, - 'sem':0.7*pq.mV, - 'n':25}, - 'ap_width':{'mean':19.7*pq.ms, - 'sem':1.0*pq.ms, - 'n':25}}} - - # Instantiate two tests based on these properties - ap_width_test = APWidthTest(observation=observations[doi]['ap_width']) - ap_amplitude_test = APAmplitudeTest(observation=observations[doi]['ap_amplitude']) - from neuronunit import tests as nu_tests, neuroelectro - from neuronunit.tests import passive, waveform, fi - cholinergic = {'neuron':'115'} - observation = {} - observation[doi] = {} - observation[doi]['isi'] = 598.0*pq.ms - observation[doi]['mean'] = 598.0*pq.ms - observation[doi]['std'] = 37.0*pq.ms - isi_test = dynamics.ISITest(observation=observation[doi]) - observation = {} - observation[doi] = {} - observation[doi]['isi'] = 16.1 - observation[doi]['mean'] = 16.1*pq.ms - observation[doi]['std'] = 2.1*pq.ms - - #@unittest.skip("Not fully developed yet") - def test_get_inhibitory_neuron(self): - from neuronunit import tests as nu_tests, neuroelectro - from neuronunit.tests import passive, waveform, fi - fi_basket = {'nlex_id':'NLXCELL:100201'} - #observation = cls.neuroelectro_summary_observation(fi_basket) - test_class_params = [(fi.RheobaseTest,None), - (passive.InputResistanceTest,None), - (passive.TimeConstantTest,None), - (passive.CapacitanceTest,None), - (passive.RestingPotentialTest,None), - (waveform.InjectedCurrentAPWidthTest,None), - (waveform.InjectedCurrentAPAmplitudeTest,None), - (waveform.InjectedCurrentAPThresholdTest,None)]#, - inh_observations = [] - for cls,params in test_class_params: - inh_observations.append(cls.neuroelectro_summary_observation(fi_basket)) - self.inh_observations = inh_observations - return inh_observations - - def test_rheobase(self): - from neuronunit.models.reduced import ReducedModel - from neuronunit.optimization import get_neab - import copy - import unittest - dtc = copy.copy(self.dtc) - self.assertNotEqual(self.dtc,None) - dtc.scores = {} - size = len([ v for v in dtc.attrs.values()]) - assert size > 0 - self.assertGreater(size,0) - model = ReducedModel(get_neab.LEMS_MODEL_PATH, name= str('vanilla'), - backend=('NEURON', {'DTC':dtc})) - temp = [v for v in model.attrs.values()] - assert len(temp) > 0 - self.assertGreater(len(temp), 0) - rbt = get_neab.tests[0] - scoreN = rbt.judge(model, stop_on_error = False, deep_error = True) - import copy - dtc.scores[str(rbt)] = copy.copy(scoreN.norm_score) - assert scoreN.norm_score is not None - self.assertTrue(scoreN.norm_score is not None) - dtc.rheobase = copy.copy(scoreN.prediction) - return dtc - - def test_rheobase_on_list(self): - from neuronunit.optimization import exhaustive_search - grid_points = self.grid_points - second_point = grid_points[int(len(grid_points)/2)] - three_points = [grid_points[0], second_point, grid_points[-1]] - self.assertEqual(len(three_points),3) - dtcpop = list(map(exhaustive_search.update_dtc_grid, three_points)) - for d in self.dtcpop: - assert len(list(d.attrs.values())) > 0 - dtcpop = self.test_01a_compute_score(self.dtcpop) - self.dtcpop = dtcpop - return dtcpop - - def test_neuron_set_attrs(self): - from neuronunit.models.reduced import ReducedModel - from neuronunit.optimization import get_neab - self.assertNotEqual(self.dtcpop,None) - dtc = self.dtcpop[0] - self.model = ReducedModel(get_neab.LEMS_MODEL_PATH, - backend=('NEURON',{'DTC':dtc})) - temp = [ v for v in self.model.attrs.values() ] - assert len(temp) > 0 - self.AssertGreater(temp,0) - old_ = self.model.attrs.items() - assert self.model.attrs.keys() in old_ - assert self.model.attrs.values() in old_ - - def test_rheobase_serial(self): - from neuronunit.optimization import data_transport_container - from neuronunit.tests.fi import RheobaseTest as T - score = self.run_test(T) - self.rheobase = score.prediction - self.assertNotEqual(self.rheobase,None) - self.dtc.attrs = self.model.attrs - - - def test_inputresistance(self): - from neuronunit.tests.passive import InputResistanceTest as T - score = self.run_test(T) - print(score) - print(score.norm_score) - self.assertTrue(-0.6 < float(score.norm_score) < -0.5) - - def test_restingpotential(self): - from neuronunit.tests.passive import RestingPotentialTest as T - score = self.run_test(T) - self.assertTrue(1.2 < score < 1.3) - - def test_capacitance(self): - from neuronunit.tests.passive import CapacitanceTest as T - score = self.run_test(T) - self.assertTrue(-0.15 < score < -0.05) - - def test_timeconstant(self): - from neuronunit.tests.passive import TimeConstantTest as T - score = self.run_test(T) - self.assertTrue(-1.45 < score < -1.35) - - - - def test_ap_width(self): - from neuronunit.models.reduced import ReducedModel - from neuronunit.optimization import get_neab - from neuronunit.tests.waveform import InjectedCurrentAPWidthTest as T - - #self.update_amplitude(T) - score = self.run_test(T,pred=self.rheobase) - self.assertTrue(-0.6 < score < -0.5) - - def test_ap_amplitude(self): - from neuronunit.models.reduced import ReducedModel - from neuronunit.optimization import get_neab - from neuronunit.tests.waveform import InjectedCurrentAPAmplitudeTest as T - #from neuronunit.optimization.optimization_management import format_test - #from neuronunit.optimization import data_transport_container - #dtc = data_transport_container.DataTC() - #dtc.rheobase = self.rheobase - #def run_test(self, cls, pred =None): - #dtc = format_test(dtc) - #self.model = ReducedModel(get_neab.LEMS_MODEL_PATH, backend=('NEURON',{'DTC':dtc})) - - score = self.run_test(T,pred=self.rheobase) - self.assertTrue(-1.7 < score < -1.6) - - def test_ap_threshold(self): - from neuronunit.models.reduced import ReducedModel - from neuronunit.optimization import get_neab - from neuronunit.tests.waveform import InjectedCurrentAPThresholdTest as T - from neuronunit.optimization.optimization_management import format_test - from neuronunit.optimization import data_transport_container - dtc = data_transport_container.DataTC() - dtc.rheobase = self.rheobase - dtc = format_test(dtc) - self.model = ReducedModel(get_neab.LEMS_MODEL_PATH, backend=('NEURON',{'DTC':dtc})) - #score = self.run_test(T) - score = self.run_test(T,pred=self.rheobase) - - - - - def test_rheobase_single_value_parallel_and_serial_comparison(self): - from neuronunit.tests.fi import RheobaseTest, RheobaseTestP - from neuronunit.optimization import get_neab - from neuronunit.models.reduced import ReducedModel - from neuronunit import aibs - import os - dataset_id = 354190013 # Internal ID that AIBS uses for a particular Scnn1a-Tg2-Cre - # Primary visual area, layer 5 neuron. - observation = aibs.get_observation(dataset_id,'rheobase') - rt = RheobaseTest(observation = observation) - rtp = RheobaseTestP(observation = observation) - model = ReducedModel(get_neab.LEMS_MODEL_PATH, backend=('NEURON')) - - #model = ReducedModel(get_neab.LEMS_MODEL_PATH,name=str('vanilla'),backend='NEURON') - self.score_p = rtp.judge(model,stop_on_error = False, deep_error = True) - self.predictionp = self.score_p.prediction - self.score_p = self.score_p.norm_score - #model = ReducedModel(get_neab.LEMS_MODEL_PATH,name=str('vanilla'),backend='NEURON') - - serial_model = ReducedModel(get_neab.LEMS_MODEL_PATH,name=str('vanilla'),backend='NEURON') - self.score_s = rt.judge(serial_model,stop_on_error = False, deep_error = True) - self.predictions = self.score_s.prediction - self.score_s = self.score_s.norm_score - import numpy as np - check_less_thresh = float(np.abs(self.predictionp['value'] - self.predictions['value'])) - self.assertLessEqual(check_less_thresh, 2.0) - - def test_backend_inheritance(self): - from neuronunit.models.reduced import ReducedModel - from neuronunit.optimization import get_neab - print(get_neab.LEMS_MODEL_PATH) - model = ReducedModel(get_neab.LEMS_MODEL_PATH, backend='NEURON') - ma = list(dir(model)) - if 'get_spike_train' in ma and 'rheobase' in ma: - return True - else: - return False - - """Testing notebooks""" - @unittest.skip("takes too long") - def test_parallelnb_15(self): - ''' - Lastly test the notebook - ''' - self.do_notebook('test_ga_versus_grid') - - @unittest.skip("Not implemented") - def test_subset(self): - from neuronunit.optimization import create_subset - create_subset(5) - - @unittest.skip("Not implemented") - def test_update_deap_pop(self): - from neuronunit.optimization import update_deap_pop - - @unittest.skip("Not implemented") - def test_dtc_to_rheo(self): - from neuronunit.optimization import dtc_to_rheo - dtc_to_rheo(dtc) - - @unittest.skip("Not implemented") - def test_evaluate(self,dtc): - from neuronunit.optimization_management import evaluate - assert dtc.scores is not None - evauate(dtc) - - -if __name__ == '__main__': - unittest.main() diff --git a/neuronunit/unit_test/test_auxillary_unit.py b/neuronunit/unit_test/test_auxillary_unit.py deleted file mode 100644 index 2079dc9d3..000000000 --- a/neuronunit/unit_test/test_auxillary_unit.py +++ /dev/null @@ -1,19 +0,0 @@ -"""Unit tests for the core of NeuronUnit""" - -# Run with: -# python -m unittest -bv unit_test.core_tests.py -# coverage run --source . core_tests.py - -#from .base import * - -from test_optimization import testOptimizationBackend -tob = testOptimizationBackend() -#tob.main() -from test_backends import TestBackend -tbe = TestBackend() -#tbe.main() - - - -if __name__ == '__main__': - unittest.main() diff --git a/neuronunit/unit_test/test_complexity.py b/neuronunit/unit_test/test_complexity.py deleted file mode 100644 index ac9fef69e..000000000 --- a/neuronunit/unit_test/test_complexity.py +++ /dev/null @@ -1,57 +0,0 @@ - -import inspect -import types -import pandas as pd -import inspect, radon, pprint -from radon.complexity import cc_rank, cc_visit -pp = pprint.PrettyPrinter(indent=4) - -def ccomplexity_rater(other_function): - ''' - This function calculates the radian cyclomatic complexity of other functions. - Radian complexity is used as a proxy for cognitive complexity, ie how hard is a code block to understand. - Inputs: Other Python functions. - Outputs: A positive integer value that is located in the interval 1-41. The scalar is used in conjunction - with a printed legend. - - The program first uses introspection to convert other_function to a string representation of the - source code that the function was originally expressed in. - Subsequently another module radon that calculates cognitive complexity is called. - Dependencies: If the radon module is not installed consider executing ```pip install radon``` - From: http://radon.readthedocs.io/en/latest/api.html - https://www.guru99.com/cyclomatic-complexity.html - - ''' - - f_source_code = "".join(inspect.getsourcelines(other_function)[0]) - results = radon.complexity.cc_visit(f_source_code) - ranking = radon.complexity.sorted_results(results) - ranking_guide = ''' - 1 - 5 A (low risk - simple block) - ... - 41+ F (very high risk - error-prone, unstable block) - ''' - actual_value = ranking[0][-1] - if actual_value > 10: - pp.pprint('Consider rewriting your code it might be hard for you and others to understand, and therefore maintain') - pp.pprint('cognitive complexity of function {0} is: {1}'.format(other_function,actual_value)) - pp.pprint(ranking_guide) - - return (actual_value, other_function) - -def is_function(object): - return isinstance(object, types.FunctionType) - -def rank_all_sub_module_functions(provided_module): - sc_objects = [v for k,v in inspect.getmembers(provided_module) ] - ranks = [] - for sc in sc_objects: - if is_function(sc): - ranks.append(ccomplexity_rater(sc)) - return ranks -#from neuronunit.optimization import optimization_management -#from neuronunit.optimization import exhaustive_search -#ranks = rank_all_sub_module_functions(optimization_management) -#ranks.extend(rank_all_sub_module_functions(exhaustive_search)) - -#print(ranks) diff --git a/neuronunit/unit_test/test_ga_versus_grid.ipynb b/neuronunit/unit_test/test_ga_versus_grid.ipynb deleted file mode 100644 index 10f055680..000000000 --- a/neuronunit/unit_test/test_ga_versus_grid.ipynb +++ /dev/null @@ -1,3052 +0,0 @@ -{ - "cells": [ - { - "cell_type": "code", - "execution_count": 1, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Uninstalling sciunit-0.2:\n", - " Successfully uninstalled sciunit-0.2\n", - "\u001b[33mYou are using pip version 9.0.1, however version 10.0.1 is available.\n", - "You should consider upgrading via the 'pip install --upgrade pip' command.\u001b[0m\n", - "Collecting git+https://github.com/scidash/sciunit@dev\n", - " Cloning https://github.com/scidash/sciunit (to dev) to /tmp/pip-sj_l8_58-build\n", - "Requirement already satisfied: cypy>=0.2 in /opt/conda/lib/python3.5/site-packages (from sciunit==0.2)\n", - "Requirement already satisfied: quantities==0.12.1 in /opt/conda/lib/python3.5/site-packages (from sciunit==0.2)\n", - "Requirement already satisfied: pandas>=0.18 in /opt/conda/lib/python3.5/site-packages (from sciunit==0.2)\n", - "Requirement already satisfied: ipython in /opt/conda/lib/python3.5/site-packages (from sciunit==0.2)\n", - "Requirement already satisfied: matplotlib in /opt/conda/lib/python3.5/site-packages (from sciunit==0.2)\n", - "Requirement already satisfied: bs4 in /opt/conda/lib/python3.5/site-packages (from sciunit==0.2)\n", - "Requirement already satisfied: lxml in /opt/conda/lib/python3.5/site-packages (from sciunit==0.2)\n", - "Requirement already satisfied: nbconvert in /opt/conda/lib/python3.5/site-packages (from sciunit==0.2)\n", - "Requirement already satisfied: ipykernel in /opt/conda/lib/python3.5/site-packages (from sciunit==0.2)\n", - "Requirement already satisfied: nbformat in /opt/conda/lib/python3.5/site-packages (from sciunit==0.2)\n", - "Requirement already satisfied: gitpython in /opt/conda/lib/python3.5/site-packages (from sciunit==0.2)\n", - "Requirement already satisfied: numpy>=1.9.0 in /opt/conda/lib/python3.5/site-packages (from pandas>=0.18->sciunit==0.2)\n", - "Requirement already satisfied: python-dateutil>=2 in /opt/conda/lib/python3.5/site-packages (from pandas>=0.18->sciunit==0.2)\n", - "Requirement already satisfied: pytz>=2011k in /opt/conda/lib/python3.5/site-packages (from pandas>=0.18->sciunit==0.2)\n", - "Requirement already satisfied: prompt-toolkit<2.0.0,>=1.0.4 in /opt/conda/lib/python3.5/site-packages (from ipython->sciunit==0.2)\n", - "Requirement already satisfied: simplegeneric>0.8 in /opt/conda/lib/python3.5/site-packages (from ipython->sciunit==0.2)\n", - "Requirement already satisfied: pexpect; 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python_version >= \"3.4\" or python_version == \"2.7\" and extra == \"test\"->jupyter-client->ipykernel->sciunit==0.2)\n", - "Requirement already satisfied: py>=1.5.0 in /opt/conda/lib/python3.5/site-packages (from pytest; python_version >= \"3.4\" or python_version == \"2.7\" and extra == \"test\"->jupyter-client->ipykernel->sciunit==0.2)\n", - "Requirement already satisfied: attrs>=17.2.0 in /opt/conda/lib/python3.5/site-packages (from pytest; python_version >= \"3.4\" or python_version == \"2.7\" and extra == \"test\"->jupyter-client->ipykernel->sciunit==0.2)\n", - "Installing collected packages: sciunit\n", - " Running setup.py install for sciunit ... \u001b[?25l-\b \b\\\b \b|\b \bdone\n", - "\u001b[?25hSuccessfully installed sciunit-0.2\n", - "\u001b[33mYou are using pip version 9.0.1, however version 10.0.1 is available.\n", - "You should consider upgrading via the 'pip install --upgrade pip' command.\u001b[0m\n", - "fatal: destination path 'AllenInstituteNeuroML' already exists and is not an empty directory.\n", - "Collecting git+https://github.com/OpenSourceBrain/OpenCortex@dev\n", - " Cloning https://github.com/OpenSourceBrain/OpenCortex (to dev) to /tmp/pip-ec1wpzwa-build\n", - "\u001b[33m Could not find a tag or branch 'dev', assuming commit.\u001b[0m\n", - "error: pathspec 'dev' did not match any file(s) known to git.\n", - "\u001b[31mCommand \"git checkout -q dev\" failed with error code 1 in /tmp/pip-ec1wpzwa-build\u001b[0m\n", - "\u001b[33mYou are using pip version 9.0.1, however version 10.0.1 is available.\n", - "You should consider upgrading via the 'pip install --upgrade pip' command.\u001b[0m\n" - ] - } - ], - "source": [ - "!bash sciunit_fix.sh" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "\n", - "# Assumptions about this code:\n", - "The NB was launched with a command that mounts two volumes inside a docker container. \n", - "In the future invocation of this script will be simplified greatly. NU is from a specific fork and branch -b results https://github.com/russelljjarvis/neuronunit \n", - "BluePyOpt is also from a specific fork and branch: -b elitism https://github.com/russelljjarvis/BluePyOpt\n", - "\n", - "Below BASH code for Ubuntu host:\n", - "\n", - "``` bash\n", - "cd ~/git/neuronunit; sudo docker run -it -v `pwd`:/home/jovyan/neuronunit -v ~/git/BluePyOpt:/home/jovyan/BluePyOpt neuronunit-optimization /bin/bash'\n", - "```\n", - "\n", - "## Parallel Environment.\n", - "Parallelisation module: dask distributed." - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "import os\n", - "os.system('jupyter trust test_ga_versus_grid.ipynb'); #suppress the untrusted notebook warning.\n", - "\n" - ] - }, - { - "cell_type": "code", - "execution_count": 16, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "data": { - "text/plain": [ - "['830368389', 'nifext_50', 'nifext_120', 'sao471801888', '100201']" - ] - }, - "execution_count": 16, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [] - }, - { - "cell_type": "code", - "execution_count": 25, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "data": { - "text/plain": [ - "{'C': 0.00010821973620985332,\n", - " 'a': 0.75726110392930013,\n", - " 'b': -4.1604158856079779e-09,\n", - " 'c': -58.179186826126831,\n", - " 'd': 0.090007907214554594,\n", - " 'k': 0.00075848843197318333,\n", - " 'v0': -73.598616612150579,\n", - " 'vpeak': 35.068721342349846,\n", - " 'vr': -61.286196326503116,\n", - " 'vt': -44.178156326585992}" - ] - }, - "execution_count": 25, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [] - }, - { - "cell_type": "code", - "execution_count": 26, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "data": { - "text/html": [ - "
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" 0.13106532444949723,\n", - " -55.587604995840714,\n", - " -48.521790797261744,\n", - " -3.7367904485509988e-09,\n", - " -60.274943550436205,\n", - " 0.00068575105305734548,\n", - " 0.22938927499819892,\n", - " 38.246247890176505,\n", - " -42.395421073426895]],\n", - " 'td_py': ['C',\n", - " 'd',\n", - " 'c',\n", - " 'v0',\n", - " 'b',\n", - " 'vr',\n", - " 'k',\n", - " 'a',\n", - " 'vpeak',\n", - " 'vt']}}\n" - ] - } - ], - "source": [ - "import pickle\n", - "with open('dump_all_cells','rb') as f:\n", - " pipe_results = pickle.load(f)\n", - "import pprint\n", - "pprint.pprint(pipe_results)" - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "data": { - "text/html": [ - "
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Polygon interpolation is used to visualize error gradients. Existing plotting code from the package BluePyOpt has been extended for this purpose.\n", - "Light blue dots indicate local minima's of error experienced by the NSGA algrorithm." - ] - }, - { - "cell_type": "code", - "execution_count": 10, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "data": { - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "image/png": 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\n", 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\n", 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\n", - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "for index, val in enumerate(pipe_results.values()):\n", - " td = val['td_py']\n", - " history = val['history']\n", - "\n", - " plot_surface('a','b',td,history)" - ] - }, - { - "cell_type": "code", - "execution_count": 11, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "data": { - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "image/png": 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\n", 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\n", 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\n", 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\n", 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48WKzr79//z7Wr1+Pp59+GkFBwn4AXbt2DSEhIcjPz0edOnUwcuRITJw4EZ6enlJ9GyQhS0ELOC9qlTadNYdT21K2xC1gW+ACjl+qwMAlklYJlyJIasyYMRg0aBCKiorQu3dvhIeHQ6Mx/I+HOnXqCD6easJ2wYIFGDNmDADAy8sL8+bNw5AhQ8y+ftOmTfjf//6HoUOHCjp+ixYt0Lp1azRu3BgFBQXYuHEjUlNT8eeff+Lzzz83+Z6MjAxkZGQAAHJz7bughYRj0D5ialqrtlvpOnJtrTm2xi1gf+ACjolcrsMlsp9WCxRzYiup+Ph4AMAHH3yADz80/fO9uLhY8PEcHrZZWVlITEy0+rr4+Hjs2bNH/+vnn38ecXFxuH79OrZs2YIxY8bA09MTo0ePNvn+ZcuW4bHHHkOPHj0EnVf53Q969OiBgIAApKenY8qUKahXr57Re5KTk5GcnAwA+s2EST5KDFq1TGddkeR3IbMjbgHbAxdwXuQycInE41IEaS1ZskTS4zk8bNu3b49Tp05ZfZ2fn5/Br8teHJaUlIR79+5h4sSJGDFiBLy8vAxee/nyZWRlZWHMmDF2LUgeMGAA0tPTcejQIZNhS46jpHW0SolZKdbWOns5ghKmtWXZG7eAfYELOHapAgOXSJzSNbZciiAloX+yLpTDw9bPzw/R0dF2HycmJgbLli3D1atXERERYfBcZmYmiouLJfubVX6tBzkOg5ZsYekmDdZIEbeAdIELyB+5DFwi4Yp55zFZaLVanDx5Enl5eQgKCkKjRo1s6i/VrLEtb+/evQgICMBjjz1m9Nzy5cvRrFkztGjRwq7PWLlyJTQaDdq0aWPXcUg8pSw7UGrMSrkTgrOmtlJMa6VejqAjVdwC9gcu4LjI5TpcIst0N2ggaX3++ef497//bXC90mOPPYZZs2Zh5MiRoo6l+LBdtGgRDh48iISEBERERODGjRtYt24dNmzYgLS0NHh7exu8/siRI/j111/x/vvvmz1mly5dkJOTg+zsbABATk4OBg8ejBdeeAFRUVF48OABNm7ciKVLl2L06NGoW7eurN8jPcKgJaWQMm6B0sC1J251HLVUgVNcIlO4FEFqK1euRHJyMrp06YJBgwYhLCwMV65c0T/u5+cnattWxYdt06ZNsXnzZkycOBF5eXkICQlBw4YNsXXrVpN3pFi2bBkqVKiAF1980ewxi4uLUVRUpP91pUqVEBQUhLlz5+Lq1avw8PBAdHQ0PvroI7zyyiuyfF9kSAlBq5aYtTSttXVHBEdPbZW2ttYcOeIWsG96q+PoKS4Dl6hUCZciSOrdd9/Fiy++iBUrVhg8PnToUAwePBhz584VFbYarVbr/HuMuoCYmBgcOnTI2aehOgxa8eQIWwCKD9tKna+Zfc7UcgRb19iaImXcGhxXgsAtT+5JLgOXlMBZP3NDGoag1/Jeot93/OXjbAQzfH19sXnzZnTr1s3oue3bt6N37964f/++4OMpfmJLrsnZQau2mNVxx7uMKYHUk1v9cSWc4OrIvVSB63DJ3XEpgrQqVaqEixcvmnzu4sWLqFSpkqjjMWzJoawFLSBf1Ko1Zh3F2Vt/KZ1ccQvIG7iA/JHLwCUiW3Xv3h1vvPEG6tevjw4dOugfP3DgAP7973+je/fuoo7HsCWHYNDaj9Na55MzbgF5AheQP3IZuOQuSvex5RpbKb377rs4ePAgOnXqhBo1aiA8PBxXrlzBxYsXERUVhXfffVfU8Ri2JCtnBa2rxKwYUtxKV86prZxRa2rbL3v2srVE7rgF5AtcQN6lCgxccge8eExaYWFhOHbsGL744gv88MMPyMvLQ2RkJOLj4zFs2DCjG3ZZw7AlWTBopeUK01pX4oi4BRwTuID0kct1uOSquI+ttB4+fIhvvvkGzZo1w6uvvopXX33V7mMybElSzghaV41ZZ5Fjaqv2JQimOCpuAXkDF3BM5DJwyVXw4jHpeHl5oX///vj2229Ru3ZtSY7JsCVJCAlaQLqodaeY5bRWGLnuQmaJI+MWkD9wAfmWKjBwySVoucZWanXq1MG1a+a3cxSLYUt2YdCSNa44rS3L0XELODZwAWkjl4FLaqYF19hKbfLkyZg9ezY6d+6M0FD7hxMMW7KJI4PWnWNW6LRWigvHyuLWX+I4I24BxwQuIE/kch0uqRUnttL67rvvkJeXh9q1ayMuLg7h4eHQaB79PdZoNFi2bJng4zFsSRQGLYnh6GmtM5Yj6DgrbgHHBS4gz1IFTnFJLXjxmPR++OEHeHl5ITQ0FKdPn8bp06cNni8buUIwbEkQRwUtY/YRZ6+t5dRWPGfGLVAauI6IW0DeKS4Dl5SMYSutc+fOSXo8hi1ZJDRoAdujljHrmlx9ba05SohbwDHTWx2pp7gMXFIq3qBBWoWFhZgyZQoGDhyINm3aSHJMhi2ZxKB1LmdPa3XUOLUtuxxBrps0WOPsuAWcG7iANJHLdbhErs3b2xuLFi1Cnz59JDsmw5YMyB20jFnrlBK19nDXaW1ZSohbwDmBC8gXuQxccjbuiiCtli1b4vjx4+jYsaMkx2PYkp6c62gZtPKRekcEko5S4hZwXuAC0i5VYOCSU2m5xlZq77//PgYMGIBatWqhZ8+eoi8WK49hS7IFLWNWPCVOa8UuR+C01pCS4hZQRuAC9kculymQM3BXBOn169cPt27dwrPPPqvfHaH8dl85OTmCj8ewdWNyLTtg0JKzOXPbL1OUFreAcwMXkCdyGbjkCAxbaXXp0sXuKW1ZDFs3JEfQMmbtp8RprY7QqS2nteYpMW4B5wcuIN1SBQYuyY27Ikhv6dKlkh6PYetGpA5axiyROEqNW0BZgQvYF7kMXJKTlmGraB7OPgGSX/05H4paR2staoOO3WTUSszWaa0jLxzzbFjf4vNKm9aGNsl19imYVCtA2f/u1ArJ00euM3nWvKv/slXdDz7QfxFJpQQa0V9k6LnnnkN2drbBYx988AFycw1/3z5+/DiaNWsm6tgMWxcmZdDqYpZBS2qze6+43xQdQelxCygncAHYHbgAGLgkCe3fuyKI/SJDmzZtQl7eo99fiouLMWnSJFy4cMHgdffu3cOJEydEHVvQUgRPT08cOHAAsbGxRs8dPnwYsbGxKC4uFvXBJB8xSw4Ay8sOGLLyU/LaWqGUNq0l6ShhiYKOFEsVuEyB7MWlCPLQam272VN5gsLW0ocVFxdLejUb2U6qoGXMkjlquxOZ0nZHKEvJ621NUVLgAvZHLgOXbMMJrNJZDNuSkhJ91JaUlKCkpMTg+fv372Pbtm0ICQmR7wzJKimCljHrHJzWuje1xS1QGrhKiVsde3ZV4H64RNI6ffo0hg4dimvXrsHf3x+fffYZYmJiHPb5ZsN2+vTpmDFjBoDSzXGfeOIJswd55ZVXpD8zsopBS86gtqmt0qk1bgHlTG91OMUlR+BSBMteeuklDB06FKNGjcLOnTvx4osv4rfffjP6031Tf9ovxQoAs2HbqVMnAKXLEGbMmIGRI0ciIiLC4DU+Pj5o1KgRnn76abtPhISzN2gZs8pg77RWCbfSVcO0Vqm7I5SlxrgFlBu4gH2Ry8Alc+S889iePXvw1FNPGT1epUoV5Ofny/KZFy9exNy5c3Ho0CH8/PPPuH//Ps6ePYvIyEiTr79w4QJee+017Ny5E1qtFgkJCUhPT0fNmjUBALm5uTh48CC++eYbAEBiYiK0Wi0OHz5sNLXt1asXvL29DR7r0aMHvLy89L8uLCwU/T2ZDdv4+HjEx8cDKC3oUaNGoXr16qI/gKRVf5bwqNX8vXKkIJj/dUnSU0PUqola4xZQduACti9VYOCSEW3pzghy+uijj9CmTRv9rytUkO+WA9nZ2Vi3bh1at26NDh06YMeOHWZfe+/ePXTu3Bk+Pj5YtmwZNBoN/v3vf+Opp57CL7/8An9/f5w/fx7h4eEGcRoZGYnz588bhO3QoUNl+54E/d2qWrWqUVWT4wmJWk2J8WMFwYDvjUe//quL7T98qu9SxvY/aqf2tbXW9rQl26g5bgH1BC4gLnK5DpfKsmVfWjHvaNiwIeLi4gS//vLlyzh79izatzceNty7dw979+5F9+7dTb63Y8eOuHr1KgDg888/txi2n332Gc6cOYPff/8dUVFRAIBmzZqhXr16WLRoEcaL+HdjyZIlgl8rlqCwnThxIiZNmoSuXbtiyJAhePbZZ+Hj4yPbSZF4poIWALSepf9bPm5txShWjsLqlZx9Cqqxe28zPBX/i7NPQxC1xy2g/MAF7I9cBq570sK2NbZy/rlpeno6PvnkE2zbtg1PPvmk/vH79++jV69eOHHiBLKzsxEQEGD0Xg8P4bcz2LJlC+Li4vRRCwC1a9fGE088gc2bN2P8+PGoWbMmLl++jIcPH+qntufOndMvVXAEQd/RhQsX8M477+DSpUt44YUXUK1aNYwaNQrff/+93OdnZM2aNdBoNEbrfXU+++wzREdHw8fHBw0aNMDChQsFH3vfvn1o3749KlasiLCwMIwfPx7379+X6tQlpyl59GWKLmp1CoLlPydL/uoSZNOXq5FqWut1/b7JL0e5F1Fi9KVUJdUeOPsURFHDDRyEUNJNHiyx5QYQvOGDuxJ/c4YSrQa5ubmIiYnRf2VkZJj9hBdffBGenp4IDg7GwIEDcf78eYtnNHv2bCQlJaF79+7Yv38/gEdR+/PPP2PHjh0mo1asEydOoEmTJkaPN27cGCdPngQAhIaGIjY2FkuXLgUA/Vrc1q1b2/35Qgma2IaFhWHChAmYMGECjh8/jhUrVmD16tVYvHgxatWqhUGDBmHmzJlynyvy8/ORkpKCsLAwk89/9tlnGD16NFJTU5GQkIBdu3bhlVdegVarxcsvv2zx2L/88gsSExPRrVs3bN26FWfPnsWkSZNw6dIlrF27Vo5vxybmIlYoqSa3jsQpcSltmLBt9UzF7cOQilKfjknl49bvonNvbng1tzIAoFnkJeQWBCDU945Tz0cMV5jc6qhhggvYNsXlBNf92LLGNjQ0FIcOHbL4mipVqmDChAmIj49H5cqVcfToUcyZMwft2rXD0aNH8dhjj5l8X4UKFbB69Wq88MIL6N69OzZu3Ih33nkHx44dw65du0TfktacvLw8BAYa/54UFBSEmzcf/cf4woULMXToULz33nvw8/PDypUrHXq/A9Erkps2bYp3330Xc+fOxTfffIOXXnoJc+bMcUjYTp48Gc2bN0d4eDiysrIMnisqKsKbb76JwYMHY/bs2QCAp556Cn/99Rfeeust/POf/zRYzFze1KlTERERgfXr1+tf5+3tjaFDh2LKlClo1aqVfN+YQBoR/zKVn9aWpca4tZXSoljMtNZayGpKSqAV+MdIzopdZ4Rul/DfseqXNiafyy0onVqoJXBdKW4B9QQuIP6CM67DdR9ybffVsmVLtGzZUv/r+Ph4dOzYEbGxsfjoo48wa9Yss++tUKEC1qxZg/79+yMhIQGBgYH47rvv0Lx5c1nO1ZJ69erh//2//+fwz9Wx6VK7vXv3IjMzExs2bMCtW7ccsvHu/v37kZmZiV9++cXkP9wDBw4gNzcXgwYNMnh88ODBWLJkCfbt22dyGw0AePjwIb799luMHz/eIH779++PUaNGYfPmzYoIW6EsRa2OO8WtrRwZxUKnsVJyRuyaWq4gVex2Cf8dAMxGbVlqClxXi1tAnYELcIpLpdNaR+5j26pVK9SvXx8//fST1dcWFRXhzp078PDwwMOHD3Hv3j1JzyUwMNBgMqtjbpLrLILD9rfffsOKFSuwcuVKnD9/HjVr1sS//vUvDB48GA0aNJDzHPHw4UMkJydj0qRJBouWyzpx4gQAGK3/aNy4MQDg5MmTZsP29OnTKCgoMHqvr68v6tatq1874kwNZorbu1YIxq18TEVxpe5XUPnv/+6SKmTFTG2FUELsig1dMUFbnloC1xXjFlBX4ALiI5eB65qccUtda3+UX1BQgN69e+PIkSM4ePAg3nnnHSQlJWH79u1o166dJOfQuHFjfWuVdfLkSTRq1EiSz5CCoLBt3bo1jh07hkqVKqFv374YPHiwfo9bR5g7dy4ePHiA1NRUs6/R9OvxAAAgAElEQVTJyyv9DbL8fzUEBQUZPC/mvbr3m3tvRkaGfgF4bq6DNoHXoPSyTDOETGvLYtzK43arAv1fV6/2aGNtZ0xm7eXo2BUy1dXFLGBb0JanhsB11bgF1Be4gLilCgxcstWhQ4fw+++/o2/fvmZf8+DBA/Tp0wc//fQTdu3ahRYtWmDt2rV4/vnn9XErZvswc5555hlMnDgRZ86cQZ06dQCU7niwf/9+pKWl2XXsu3fvYvHixfj+++9x48YNZGRkoF69elizZg1atGiB6OhowccSFLbh4eGYPHkynn32Wfj6+tp84gCQlZWFxMREq6+Lj4/Hnj17kJ2djdmzZ2Pjxo12f7bUkpOTkZycDAAOvQ+yOWKjVodxK1zZYBWibNQCwMWupT+4I3ao+6K20tg1v2ZdavciSlAJ0gdteUoPXFeOW0DdgQtYj1yuw3UNct2g4cUXX0Tt2rXRqlUrVK1aFUePHsU777yDGjVqYOzYsWbf9+abb+LHH39EVlYWWrRoAQDw8vLSx22vXr1w9uxZszsjbNiwAQBw+PBhAMC2bdsQGhqK0NBQgyHmqFGjMH/+fDz77LOYNWsWNBoN3nrrLTz++OMYPXq0zd/3hQsX0KlTJ1y8eBHR0dH49ddfcfv2bQDA7t27kZWVhc8//1zw8QSF7datW207WxPat2+PU6dOWX2dn58fAGDs2LHo3Lkz4uLi9LeUKywshFarRX5+Pnx8fFCxYkX9tPXmzZsIDw/XH0c3bdVNbk0p+97y8vLy9MsZXJm7x63YYBWifNQCQJWuV3BrRxgudg2SLG6lXo4gREF1f9T8tlj0+3J62n6e9iw5EEvJgevqcQuUBq6a4lbHlshl4KqPXGtsmzRpgtWrV+Pjjz/GvXv3EBYWhueeew7Tp09HSIj5P+178803MXToUDRt2tTgcV3cHjp0yOJ2X/369TP49SuvvALg0YBRx9/fH9999x1ee+01DB48GFqtFl26dEF6erpd24lNmDABPj4++OOPP1CjRg2DG4LFx8dj+vTpoo4n333azPDz8xM1Uj558iRycnJMLhMIDAzEuHHjkJ6ero/PEydOGIStbn2spfUfdevWhY+Pj9HakYKCApw5c8boH7rTmViOYOu01pXJEatCmApaU9Q6vS2oLu62pGXV+tr2/epWVZY/aMvTBS6grMh1l7gF1DW9LUvoUgUGrrpooZEtbFNTUy0uuTQnMDDQ7MVbXl5eVtfYakWMoGvWrIkvv/xS1PlZs3PnTmRkZKBWrVooLjYcmNSoUQOXLl0SdTyHh61Ya9asQUGBYaCkpaXh8OHDWL9+vf5GDe3atUNISAhWrlyJhIQE/WszMzMRFBSEJ554wuxneHt7IykpCevWrcO0adP092XesGEDHjx4gGeeeUaG70w6UkWtWqa2zgpWIYRErW5qqyPF9NZRU1t7otYWfz1Zutyh2L8EgPgJsZSUNsV1h7gFXCdwAcuRy8BVD5lWIritwsJCVKpk+k6at27d0jeZUIoPW1MLnpcuXQofHx906tRJ/5iXlxdmzpyJV155BTVq1EBCQgK+++47fPHFF/j4448NRtsjR47EsmXLUFRUpH9s2rRpiIuLQ//+/fGvf/0L586dw6RJk9C3b1+H3jHD2ZwZt0oOVmuETml15IhbuTkqanUxq1RKClx3iVtA/YELCItcrsNVOAdv9+UOmjVrhi+//BJJSUlGz23btk10gyk+bMV46aWXoNFo8P777+O9995DzZo1MX/+fP16EZ3i4mKjcXeLFi2wY8cOTJkyBT179kSVKlUwZMgQzJkzx5HfgnB/L0eQYwmClHGr5lgVSmzUmqPkpQmOiFpzQVs6rbVds0hxf4wllFIC153iFnCNwAWELVXgFFehOLKVlG6ICAADBw4EULqMdPPmzVi8eDG2bNki6ngarZXFFYWFhfj000/RpUsXk/cIplIxMTFWb5dnD1P72Mq5rjao9TXc22769n2AewSrEPZGbdmpbVm2xq0cyxHkjFoh01ld2GqDCm36DLnCtjxnB647xW1Zag/csqytx2XgPiL3z1xzfOvWwONpL4t+X5W5m5xyvmqxcOFCvP7667h9+7Z+zW+lSpXw3nvv6XefEsrqxNbb2xuvv/46tm/fbtvZkuoEtb4GgPFqiVRTWnOUMr2VI2rFLDWwd1rrSM6e4Lrb5FbHVSa4gPWlCpzgKoNc2325s5deegmDBw/GgQMHcO3aNQQHB6N9+/Zm195aImgpQsOGDXHmzBl07NhR9AeQeuiCFgD+WXsfZuf2cOLZKJeUUVt+rW15YtfeSnkRmdRRa+/aWU2et81TW0dyZuC6a9wCrhW4gOWlClyH6zxacI2t1G7cuIHg4GD4+/sbXPxvK0E/AWfMmIGZM2fi+PHjdn8gSUPqZQjlo5ZMk3tSa4pueutIUkXtX0966b/EUtO01pTcggCD7cIcpVaA8X7c7qRWSJ4+cl2BZ827+i9T6n7wgUHoksy0ALQa8V9kVnh4OHr37o0vv/wShYX2Dy8ETWznzp2LO3fuoGXLloiMjER4eLjBfYs1Gg327t1r98mQc5iK2tk/clpblpxBa21qC4hbmmDv1NbeqFX6rgaO5owJrjtPbnVcbYILWF6qwGUKjsOlCNKaNWsWMjMz0a9fP1StWhX9+/fH4MGDLW7Taomgn36enp5o1KgROnTogMcffxwVKlSAp6en/svDwXc9IulwUmudM6a05sg9vbUnam2dzJqi9mmtKboJrqOmuO4+udVxtQmujrkpLie4pDaTJ0/GL7/8giNHjmD48OH46quv0LFjR9StWxfTpk1Ddna2qOMJmtiWvaUauYayQQswak1xZNAKmdrqyHVhmS1Ry+msbRw1xeXk9hFXnOAC5qe4XIcrI05sZdGiRQu0aNEC7733HrKyspCZmYn3338fs2bNMrjvgDUutY8tCcOotU5JU1pzLF1YJnY5gpiolTtmXXFaa44jApdxa8hVAxewHrkMXCnId0tdKuXh4QF/f39UrFgRXl5euHfvnrj3C33hpUuXMH78eMTExKB27dr49ddfAQDp6en473//K+6syS72XDjGqLXOWVFbpesV0e+RYmmC0KiVcqkBGZJ7iQKXJRhzxeUJZZlaqsBlChLR2vBFVv355594++23UbduXXTs2BHbtm3D6NGjRW9cIGhie+LECXTo0AGenp5o164djh49qr9yLScnBz/++CNWrVol/rsghxIate564ZgaprSmmFuaIGRqay1qHR2yUk9rHXVzBqnIOcHl5NaYK09vdUxNcTnBtQNvqSu5+fPnIzMzEz/99BP8/f3xj3/8A5999hmeeuopg40KhBIUthMmTEDDhg2xfft2+Pr6wtvbW/9c+/btMWXKFNEfTI5TPmgBTmrLU0rUillrW57YPW/NRS2nss4nV+Aybk1zh8AFjCOX63BtxAmspF577TUkJCRgxYoV6NOnDypWrGjX8QSF7b59+7B69WoEBASguLjY4Llq1arhyhXxf4RKjsGlB5YpJWjLckTcmopaZwetO62tFUqOwGXcmucugQsY3wCCU1wxOLGV0sWLF1GtWjXJjicobC1t53X9+nW765rkwai1TIlRK4WySxNMLUcoG7XOjlkSRurAZdxa5o6BCxhOcRm4FnBiKykpoxYQGLaxsbFYsmQJevXqZfTcunXrbN5El8QTeuEYo9YypUetPVNbHVPT24Lq/oqMWU5rhZEycBm31rlT4AKGU1wGrgUMW7t17twZn3zyCaKjo9G5c2eLr9VoNNi1a5fgYwsK27feegsJCQno2rUrBg4cCI1Gg6ysLMybNw8bN27E999/L/gDSV5cT2uZ0oNWarrpbY2sfJzpX9XJZ0NSKbuDgj2Ry7gVxl0DF+B+uEZ0t9Qlu2jL3L6tpKTE4kViWpG3ehMUtvHx8di0aRNSUlIwYsQIAMDrr7+OyMhIbNq0CW3bthX1oSQPKaLWlXdEUFvUSjG11VFy1HJaax97p7iMW+HcLXAB05Hr7oHLW+rab/fu3fq/lvomYIL3se3Zsyf+/PNP/PHHH9i3bx9OnTqFM2fOoHv37pKeENmGk1rL1Ba1RGLZsxcu97kVx1Vv02uNbm9c7odLUlq+fDlu3Lhh8rm8vDwsX75c1PEEhe2MGTPw119/AQCioqLQvn17NGjQAABw+fJlzJgxQ9SHkrQYteZVr5av6qi15aYNaiJ2WqvJ87b+Ijdna+AybsVz98Ctv2GmewYub9AgqeHDh+P06dMmnzt79iyGDx8u6niCwnb69Om4ePGiyef++usvTJ8+XdSHkm3KXzgW1Poao9YCNQctkb1sCVzGrW3cNXAB6APXrSJXqxH/RWZZWkN79+5dVKggaNWsnqBXW/rQmzdvwsfHR9SHkjgNZn5o9JipoAUYtYDrBa0Ua2198oAHClsWKPfaWrXddUwuct7NjAy54xrcsnSRW3ze36XX4Wo4gbXbsWPHcOTIEf2vv/rqK/z6668Gr7l//z7WrFmDevXqiTq22bDds2cPvvvuO/2vFy1ahK1btxp96Ndff43GjRuL+lCyj1xR6woXjrla1BJJRWjg8mIy+zFwSwMXAP7o+5aTz0ZiXFogic2bN+v/tF+j0WD27NkmXxccHIzFixeLOrbZsN27dy9mzZql/9AlS5YYvcbb2xuNGjXCRx99JOpDyXac1JrnylEr5Q4JSsCdEJxHSOAybqVRKyTPbeNWp/6GmS4Wt1xaIIWUlBQMGzYMWq0WderUwX/+8x+0bNnS4DU+Pj6oVq2axa3ATDEbtlOnTsXUqVMBlN557ODBg4iNjbXh9EkqjFrTXDloieRiLXAZt8Itilpj8vGDBTWBCAefjMIMjPqvs09BepzY2q1KlSqoUqUKgNILxKpXrw4vL2luHiRojW1JCacrzhYYy6g1xZ2i1lWmtpzWKoulmz24c9yai1USxiWDVodhK6njx4/jq6++wquvvmr03IIFC1C7dm306CF8qaTgS83u3r2LxYsX4/vvv8eNGzeQkZGBevXqYc2aNWjRogWio6MFfyiJ49HkfyYfd+eodaegLcueuFXiBWSkLKamuK4Ut46M1YMFNR32WUrj0lELMGwlNnPmTDz33HMmn7t//z5mzpwpfdheuHABnTp1wsWLFxEdHY1ff/0Vt2/fBlB694isrCx8/vnngj+UxOtR4wS+ufToIj1GLakRp7XqUD5wlRy3SpysMmpdGG+pK7nffvsNrVq1MvlcixYt9Nd7CSUobCdMmAAfHx/88ccfqFGjBry9H22SHh8fz31sHUyOqFXLjgiMWtdZkkDKVzZwHR23SgxWIdw1al0+aMvgdl/SKikpwZ07ptf63759Gw8fPhR1PEFhu3PnTmRkZKBWrVooLi42eK5GjRq4dIn7RTqKu05qGbTqx2mteukC1964VWuskmXuFLUkvebNm2PlypXo06eP0XMrV65Es2bNRB1PUNgWFhaiUqVKJp+7deuW6LtCkG0YtaSjtqkto9Y15BYEwK/CQ9wrenT1MmP1EXec1rpl1HJiK6kJEybgH//4B/r164dRo0YhIiICly5dQkZGBjZu3Ij169eLOp6gW+o2a9YMX375pcnntm3bhtatW4v6UHusWbMGGo0GERGGe6hcvnwZqampiImJQdWqVREaGoouXbrg+++/F3TcadOmQaPRGH317t1bjm/DJv0Dcpx9Cg5VvVo+o1ZiPm5y10/edUxeYRVvY1HUGkZtGYxaItv06dMH8+bNw/bt29G9e3c0bdoU3bp1w/bt2/HRRx+ZvbDMHEGj1kmTJqFv374AgIEDBwIATp48ic2bN2Px4sXYsmWLyG/DNvn5+UhJSUFYmPGk6vDhw1i7di2GDx+OuLg4FBYW4pNPPkGnTp2wZcsWPP3004I+Y9++ffD09NT/OiiIl5E7A4PWOrVMbTmtdR2VvQqcfQqK5G5R6+5ByzW20hszZgyGDRuG/fv3Iy8vDyEhIWjfvj0CAgKsv7kcQWH73HPP4ZNPPsHrr7+OL774AgAwZMgQVKpUCfPnz0dSUpLoD7bF5MmT0bx5c4SHhyMrK8vguSeffBJ//PGHwbKIbt26oXHjxnj33XcFh23btm0VvbSif0AO1t2pJekxlXbhGKOWSHkYtQQwagFwVwSZVKpUSZKeFLQUAQBeeuklXLp0Cdu3b0dmZia2bduGixcvIjk52e6TEGL//v3IzMzEggULTD5ftWpVoyCtUKECWrRowYvbVIJLD8Sr0vWKs0/BIjmmtZo8b+svIslU9ipg1FrgTtNaRi3+3u7Lhi+y6NKlSxg/fjxiYmJQp04d/PrrrwCA9PR0/Pe/4v5/J2o06e/vj4SEBFEfIIWHDx8iOTkZkyZNQlRUlOD3FRYW4sCBA6KuqHv88cdx7do1RERE4IUXXsC0adNQsWJFW05bNnJMbZ2NQUukLIxZ6xi1boqhKqkTJ06gQ4cO8PT0RLt27XD06FEUFhYCAHJycvDjjz9i1apVgo8nOGyLiopw4MABXLhwAQUFxr/hjRgxQvCHijV37lw8ePAAqampot43bdo0XLx4EStXrrT62qioKKSlpaFly5bQaDTYsWMHPvzwQxw5cgQ7d+40+Z6MjAxkZGQAAHJzc0WdGz3CqLWP2LW2jroDGdfWqhej1jp3iVoGrTGusZXWhAkT0LBhQ2zfvh2+vr4G90po3749pkyZIup4gsL2yJEj6NOnDy5evAit1vifqEajERy2WVlZSExMtPq6+Ph47NmzB9nZ2Zg9ezY2btwIX19fQZ8BAKtWrUJaWhreeustdOjQwerrBw0aZPDrxMREREREICUlBVlZWSYn1cnJyfqlGDExMYLPzVY+mgp4oC0C4BpTWwatdNRyIRkpm9CgnV7ja5nPhJSAUWsGw1ZS+/btw+rVqxEQEGB0r4Rq1arhyhVxS+4Ehe1LL72EgIAAbNq0CdHR0QY1LVb79u1x6tQpq6/z8/MDAIwdOxadO3dGXFwc8vNLQ6iwsBBarRb5+fnw8fExWirw1VdfYdiwYRg5cqRdd0UbMGAAUlJS8NNPPzllCUZZU4J/dernS41R69o4rVUXTmjFcYdpLaPWAoatpDw8zF/udf36ddHLQQWF7cmTJ7Fu3Tr06GH/1fN+fn6Ijo4W/PqTJ08iJycHgYHGd7sJDAzEuHHjkJ6ern9s165d6NevH/r06YNFixbZfb5A6URaiaSY2jp6RwQGrXw4tSWxGLTiuXrUMmgt02i5FEFqsbGxWLJkCXr16mX03Lp16/DEE0+IOp6gsK1fvz7u3r0r6sBSWbNmjdGa3rS0NBw+fBjr1683uFHDgQMH8Oyzz6JLly7IzMy0+F8BQujW5sbGxtp1HCrFqHUPnNaqA6NWPEYtkfTeeustJCQkoGvXrhg4cCA0Gg2ysrIwb948bNy4UfCNtnQEhe2cOXMwZcoUtG3bFjVrOvZf7Li4OKPHli5dCh8fH3Tq1En/2G+//YaePXsiJCQEkyZNwuHDh80eZ+TIkVi2bBmKior0j7Vs2RJDhgxBgwYNoNFosHPnTnz88cdISkpC586dpf/GJKKWtbaMWscQOrV11AVkzsC7jlnGoCVTGLUicB9bScXHx2PTpk1ISUnRX6/1+uuvIzIyEps2bULbtm1FHU9Q2CYlJWHPnj2oV68e6tevb7QsQKPRYO/evaI+WGoHDx7EzZs3cfPmTTz11FNGz5e96K24uNhogXKDBg0wf/58XL58GSUlJahTpw7efvttTJ48WfZzd2UMWvfCaa1yMWjt48rTWkatSFyKILmePXuiZ8+eyM7OxrVr1xAcHIwGDRrYdCxBYZuWloZ3330XoaGhqFy5ssEtZ51h6dKlRo8NGzYMw4YNE/z+8sdYs0a99zxX6tSWUescXGtL5TFq7eOqUcugtQ3X2MonKipK1P0KTBEUtunp6Rg9ejTmz5/v9Kgl6ch14RiD1j1xWqs8DFr7MWrJCMPWbsuXL0fPnj0RHByM5cuXW3ytRqNBcHAwWrZsifDwcKvHFhS29+7dQ79+/Ri1CqaUqS2jVhk4tXVvcgYt97BVP0atHbgrgiSGDRuGgwcPIjg4WPCftvv4+ODzzz/Hiy++aPF1gsK2e/fuOHDggKIvonJ1fxSV3l7uodYDUaJuhOw4jFp1kfICMk5rlYNTWum44rSWUSsBhq3dzp49q5++nj171urrb926hf/7v//DtGnTpAnblJQUfVEnJSWZ3FO2Tp06Qg5FEsgugsm4ddbUlkGrTJzauhcGrbRcLWoZtBJi2NqtVq1aJv/aklGjRuGbb76x+jpBYavbHPett97C22+/bfI15XcZIHmZi1tHY9SSM6a1mjxvaIMKHf65SsSglR6jlizhUgR5ZGdn48cff8SlS5dQo0YNtG3bFnXr1tU/36FDB1y/ft3qcQSl0RdffKHYu2+RIUdObRm1yseprWtj1JI1jFpSuoKCArzyyitYsWKFwZDU09MTQ4cOxYIFC+Dj4yP4eILCVujCXnIse6a29uyIwKBVFznjlmtrnYNBKx9XmdYyaGXEia2kJk6ciJUrV2L69Ol44YUXUK1aNVy9ehWrV6/GjBkz4Ofnh48++kjw8ey75yw5XXaR8WP9A3Jk+zxGrWvxyXP2GZAYlb0KGLUyYtSSVX/viiD2i8xbs2YNpk6dijfeeAN16tSBv78/6tSpgzfffBNvv/02Vq1aJep4gud9165dw+rVq/H777+joMDwN1aNRoPFixeL+mCSjiPW2zJo1U2Oqa0Sp7WufDtdJQStK2/1xaglco4HDx4gNjbW5HNt27ZFYaG46ykE5dDvv/+Odu3aoaioCHfv3kVISAjy8vJQXFyMwMBAVKlSRdSHkvTKx62Ua20ZtUTOo4SgJXVg1DoIJ7CSSkhIwI4dO5CQkGD03I4dO0RvNSsobCdNmoQ2bdpg06ZN8Pf3x7Zt29CsWTMsX74cU6dOxcaNG0V9KMlDjskto9Z1SDm1VeK01tUwaB1H7dNaBq2DMWztdubMGf1fjx8/HoMHD8bdu3fRr18//RrbdevW4ZtvvkFmZqaoYwvKoJ9++gkLFy7UX5VWUlKCChUqYMSIEcjNzUVKSgp2794t6oNJHmXj1tzUVsiFYwxaIudh1DoOo5bE0IBrZqUQFRVlsNuWVqvFp59+ioULFxo8BgDx8fGitpQVFLZ37txBUFAQPDw8UKVKFYN9xNq0aYOZM2cK/kCy3UOtsGv97J3cMmpdl6mprdg7kHFaKx8GrWMxaskmDFu7LVmyRLZjC8qfyMhIXLlyBQDQoEEDrF+/HklJSQCArVu3omrVqrKdINlHzFpbBi2RczBoSQwGrRNxlwNJDB06VLZjCxoBJiYmYufOnQBK10IsWbIEDRo0QOPGjTFv3jyMGDFCthMk25jaBswSRq37qNL1is3v5bRWeoxa51DrtJZRqwBaG77IoqtXr+LQoUM4dOgQrl69atexBE1s33nnHTx48AAA0L9/f1SsWBFr167FvXv3MG7cOIwaNcqukyB56JYkWJvaMmqJHI9B6zyMWrILQ1UyK1asQFpaGn777TeDx6OjozFlyhQMGTJE9DGthm1xcTF+++03VK9eXf9Yr1690KtXL9EfRo5nab0tg9Z92bJDAqe10lBr0LrKHraMWrIXlyJI4+WXX8aiRYvQqFEjTJ48GZGRkQCAs2fPYuvWrRg+fDj279+PRYsWiTqu1bDVaDSIiYnB119/ja5du9p08uRc2UWPpra6HREYtVSWtQvIGLXSUGvUkvMwaBWIYWu31atXIyMjA+np6Rg7dqzR82lpaZg3bx4mTJiATp06YcCAAYKPbXWNrYeHBx5//HHcvXtX3FmTopRdc8uoJcC+tbZKpOS7jvFWuMqgtmkto1aBbFlfyxA2snDhQowcOdJk1OqMGzcOw4YNw6effirq2IIuHhs9ejTS09NF39aMlKWVbw6jlgwIiVulTms1ed7OPgWrGLTKwaglUo6ff/4Zzz33nNXX9e3bFz///LOoYwu6eOz27ds4ffo06tSpg6SkJISHhxtsrKvRaDB9+nRRH0ziCN3D1pqZ9TbhdklFu47x7ulukpwLkStj0CoHo5akxDW29nv48CF8fX2tvs7X1xdFReK2eRIUtnPmzNH/9RdffGH0PMNWfh9e7orh1X6Q5FiVPO7bFbeT626X5DzKYzA7h6ULyZQ6rVUyBi3ZikGrEgxbu9WrVw8//PADOnXqZPF133//PaKiokQdW1DYlpTwh5ursTdu5SBHMDOWhRN7BzIyxKBVJrVMaxm16sGJrf2ef/55pKWloWfPnmjVqpXJ1xw6dAjvv/8+UlNTRR3bjhuvktopMW6lxlgWxtTUltNa4Ri1ysSoJVkwbO322muvYePGjXjiiScwatQo9OrVS7/d17lz57BlyxZ8/vnnaNKkCVJSUkQdm2Hr5twhbqXGWCYddwlaNe5hq4aoZdCqEHc5kISvry+ysrIwduxYfPrpp1iwYIHB8xqNBgMHDsTHH38saC1uWYLDNiMjA59++il+//13/V3IyiouLhb1wSTekqsdJFtnWxbj1vmUsG65StcruHaodGrLaa1l7hK0JB9GrTpp/v4i+1WuXBlLly7FO++8gz179uDChQsAgIiICHTq1MngxmBiCArb5cuXY8yYMRg6dCh+/vlnjBgxAg8fPsSWLVsQGhqKF1980aYPJ+Vg3LomscE87tyLCKpxC/nZgTKdkfoxapVP6dNaRq3KcWIrqfDwcFE3YLBG0B5S6enpSE1N1W+S+8orr2DZsmU4c+YMKlasiODgYMlOiJynksd9Z58CKcCt34JQNeqm/otKcU9adWDUktw0WvFf5DiCwvbPP/9Ex44d4eHhAQ8PD/2NGgIDA/Hmm29i3rx5sp4kOQ7j1n198deTJh9XQ+TKeWiP7ngAACAASURBVNcxBq16KDlqB0b9l1HrKnjnMUUTFLYVK1ZESUkJNBoNwsLCcObMGf1zAQEB+Ouvv2Q7wfLWrFkDjUaDiIgIo+c6deoEjUZj9JWeni7o2Pv27UP79u1RsWJFhIWFYfz48bh/3/1Cj3Hrnn4+VUv/17d+M73vlxoiV0oMWpICg9bFMGwVTdAa26ZNmyI7OxsJCQno0KED5syZg9q1a6NChQqYNm0aoqOj5T5PAEB+fj5SUlIQFmZ6M3kAaNasGRYtWmTwmG4LCUt++eUXJCYmolu3bti6dSvOnj2LSZMm4dKlS1i7dq29p646XHPrXspOa4srFcPztqfV9+ji1hXX4zJo1Uep01pGLZFjCQrb5ORk/ZR25syZSEhIwJNPlv4grFSpEjZt2iTfGZYxefJkNG/eHOHh4cjKyjL5mkqVKiEuLk70sadOnYqIiAisX78eXl5eAABvb28MHToUU6ZMMbuBsCtj3JIQZae3ao9cBq0xNWz1xaglh+GaWUm0atUKK1asQOPGjTFjxgz885//tHkXhPIELUV4/vnn9Xd+iIqKwokTJ7B9+3Zs3LgR2dnZVm+JJoX9+/cjMzPTaK8zKTx8+BDffvst+vfvr49aAOjfvz+8vb2xefNmyT9TqIYbDW9VvORqB4d+PpcluL4v/nrSYBmCjrnlCJaodakC19GqlxKjlutpXRyXItjt+PHjuHPnDgBg+vTpuHjxomTHFjSxvX79OgICAvSb5Pr7+yMhIUGyk7Dm4cOHSE5OxqRJk6zeM/jo0aOoUqUK7t27h4YNG2LcuHEYOXKkxfecPn0aBQUFaNKkicHjvr6+qFu3Lk6ePGn392CvfX/WxZP1Tjvlszm5dV3mLhjTFANa66sRLFLLJJdBS1Ji0Lo+TmztV716dWzatAnVqlWDVqvFlStXcP78ebOvr1lT+H/Amp3YFhcXY9q0aQgMDES1atVQuXJl/OMf/0B+fr64s5fA3Llz8eDBA6v3C+7YsSPS09OxZcsWbNiwAfXq1cM///lPzJo1y+L78vLyAJTu8lBeUFCQ/vnyMjIyEBMTg5iYGOTm5gr8btSJk1v3UlS19IYrtkxtTZFriqvJ87b5vZzSqp/SprWMWjfBia3dRo8ejblz56Ju3brQaDTo06cPateubfZLDLMT24ULF2LGjBno1KkT2rRpgzNnzmDjxo2oXLkylixZYvM3k5WVhcTERKuvi4+Px549e5CdnY3Zs2dj48aNVm+rNmPGDINfP/vss+jTpw9mz56NlJQUBAQE2HzepiQnJyM5ORkAEBMTI+mxyyq4a/sPbylxcutadNNaU8sQ5KKEKS5j1jUoKWoZtO6FE1v7vfHGG0hMTMTJkycxfPhwpKamok6dOpIc22zYfvbZZxg1apTBDgOLFi3Cq6++ikWLFsHb27bYat++PU6dOmX1dX5+fgCAsWPHonPnzoiLi9NPiwsLC6HVapGfnw8fHx9UrGg+tgYMGIBNmzbh+PHjaNeuncnX6Ca1N28aT5Ty8vLQuHFjq+frLhi37kOK5QiWOCNyGbWugVFLTsMJrGTatGmDNm3aYOnSpRg8eLBkO2yZDdszZ87g//7v/wwee/755/Hyyy8jJycH9erVs+kD/fz8RJ38yZMnkZOTY3KZQGBgIMaNGydon1qNxvzdnevWrQsfHx+cOHHC4PGCggKcOXMG/fr1E3y+7oBxq35CprVFVUu3/br1WxCqRJtejiMVuSOXQUtyYNS6KYatpHbv3i3p8cyusb1z5w4qV65s8FilSpUAALdv35b0JCxZs2YNdu/ebfDVrVs3hISEYPfu3Xj11Vctvn/lypWoWLEimjZtavY13t7eSEpKwrp161BUVKR/fMOGDXjw4AGeeeYZyb4fKTh6ZwRTuOaW5CJ2Pa6lu45xHa3rUcq0llHrnjTgLXXlcPz4cfTt2xehoaGoUKECQkND0b9/fxw/flz0sSzuinDp0iWDu4wVFxfrH69atarBa6VaG1GeqT1ply5dCh8fH4Ntxn744QekpaXhueeeQ2RkJG7duoVly5Zhy5YtSEtLg7+/v/61I0eOxLJlywwidtq0aYiLi0P//v3xr3/9C+fOncOkSZPQt29ftG7dWpbvTe10ccvprbqY2wnBFLmXI1hi7xSXQWs/pe1hq4SoZdASJ7bS+umnnxAfH4+KFSvimWeeQVhYGK5cuYKvvvoKX3/9Nb7//ntRHWYxbPv27Wvy8d69exs9poteZwkPD0dJSQnefvttXL9+HV5eXmjWrBlWrVqFAQMGGLy2uLjY6HxbtGiBHTt2YMqUKejZsyeqVKmCIUOGYM6cOY78NlSJSxPUo2zUCrlozJHLESwRE7kMWtfEqCWl0GhZtlJKTU1FkyZNsGvXLv3KAKB0dUBCQgJSU1OxY8cOwcczG7b27Hwgt6VLlxo9FhUVhW3btgl+v6ljdOzYEQcOHLDz7NwT45YcxVzkMmhJToxaInkcPHgQK1asMIhaoHT565QpUzB06FBRxzMbtmIPRMS4VTYxSxDK0i1HcPbU1pSqUTfhodEioMIDZ58KycjZ01pGLelxVwTJWbq4X8jz5Qm6pS6RULyoTB0cuXetXPJv+SH/Vum2gHeKfJx8NiQXZ0Ytb41LpvDiMWm1bdsWc+bMMdqY4O7du5g7d67Ja60sEXRLXVKeJVc7YHi1H5x9GiZxcqs8tk5rgUfrbJVCF7Pl3Sny4eTWxTg7aolMYqhKas6cOejUqRNq1aqFp59+GuHh4bhy5Qq++eYb3Lt3D3v27BF1PIYtyYJx61qcvRzBXMyWp5vcMnDJHoxassSWCSxb2LzY2FgcPHgQM2bMwPbt25GXl4egoCA89dRTeOuttyxu12oKw1ZF9v1ZF0/WO+3s0xCMcasM5ae1almGIDRmTeH0Vv2cMa1l0JIgrFTJNWvWDBs2bJDkWAxbkhXj1rnsWYJQlqOWI9gTs+VxemsfZ+5hy6glxeKaWcVj2KrMob8e1/91avU7AIArxQHOOh1BGLfKYc+0Vq7lCFLGrCmc3qoLo5YUj2GraAxbFfH1LzT5eJin8gOXcet4Uk1r5SB3zJbHuCVzGLUkhu6WuqRcDFsXovTAZdyqW9nlCLZMbR0ds+VxaYLyOXJay6Alm/HOY4rGfWxdkC5wlYj73DqGqWmt1BeN3TkWjDvHgq2+rux+s0rAPW+ViVFLasF9bJWNE1sXpeTpLSe36qVbZ1tW2bgNaHEDAHDzpj80Hsr93ZzTW2Vh1JJq8M5jisewVakvYzIEvU6pgcu4lY+z1tY+rFWAmzf99b/Wloi7DaIzQphrb90Lo5ZImU6dOoUNGzbgwoULKCgoMHhOo9Fg2bJlgo/FsHUTSgxcxq30zEWtVMsQym/7pSku/d/COgVm3iGcs0KYcWvMkVt9OWJay6AlKWlKnH0GrmX58uUYMWIENBoNHnvsMXh7exs8r9GI+9nAsHUzYZ53GLdkF13MShm1thAbwpZwaYJzMGpJlbgUQVIzZ87Es88+i8WLF6Nq1ap2H49h64aUNr1l3ErDGUsQpIxar3O+ot/j0fA2Xqj2I9ZcjZXkHDi9dRxGLakVLwaT1pUrV7Bw4UJJohZg2Lo1JQUu41Y+Uu+GUPGyBvfDtSaj1pY4tdfUX3pherOvAECSwOX0Vv0YtCQbLbjdl8SeeOIJnDp1Cl26dJHkeAxbUkzgMm5t56hpbaVTpb9lVLyswYNCx0dsWR4Nbxs9xumtOsg5rWXUktw4sZXW/Pnz8dxzzyE4OBhdu3ZFYGCg0Ws8PITvTsuwJT0lrL9l3JIQpqJW54VqPwKQbnrLuJUWo5ZUj2ErqYiICLRs2RKDBg0y+bxGo0FRUZHg4zFsyYASpreMW3EsTWulXIagm9Y6m6WoLUuqwOXSBOkwaknteEtd6Y0aNQpr165F7969ER0dbbQrgljK+ElFiuPswNXdoYyBa5mjlyA4m9CoLUvKwGXcKg+DlhxKq+UaW4lt3rwZ7733HsaNGyfJ8XhLXRUSenMGKYR53nHqLXp5C17bSTWtVUrU2uuFaj/qI9dWd4p8XP6WvHLtYSvHtJZRS87AW+pKy9/fH40aNZLseAxblfD1L3Tq5zNulcdZdxhzFkvT2qm/9BJ8HHvjFoDLx63UGLXkUrQ2fJFZw4cPx6pVqyQ7nmuMYsghnLk8getunUMp01pbliBYIsXyBC5NEEbqqGXQErmWWrVqYfXq1UhMTERSUpLJXRFGjBgh+HjK+KlFquKswGXcPmJtWivFMgRXjdqy7A1cXljmWIxaUgIuLZDWyy+/DADIycnBrl27jJ7XaDQMW3IMZ2wPxrh1DKVEraNIEbiMW2NSTmsZtaQIWgAlLFspnT17VtLjuddPL5KcM6a37h63cq+tVVLUyjmtNcWewOX01hCjllwWu1ZStWpJe3dMXjxGknD07gnuekGZkKiV+ha6zuLoqC3LngvMeGGZdFE7MOq/jFpSHO6KoGzKGc2QS3DkBNfdJ7dyUMq01paonfpLL0xv9pVk58DprXMxaEmxuI+tpGrXrg2NRmPxNWfOnBF8PNVNbNesWQONRoOIiAiDx/fs2QONRmP26+DBgxaPO23aNJPv6927t5zfjmjDG1j+PpTCUdNbd5rcussSBGdOak2xZ/9btU1vpdjDVoppLaOWlIwTW2nFx8cbfTVp0gT/+9//UFJSgk6dOok6njJ+kgmUn5+PlJQUhIWFGT3XqlUrHDhwwOjxkSNHIi8vD23atBH0Gfv27YOnp6f+10FBQbafsJtz1PSWk9tHbF2GoJSoVTJbJ7judGEZo5ZcHvelldzSpUtNPp6fn4+kpCQkJCSIOp6qfppNnjwZzZs3R3h4OLKysgyeq1y5MuLi4gwey8nJwalTpzBhwgSDWLWkbdu2qFBBVX9bFM8RgevqcesuN2NQ2rTWFFsC1x2WJtgbtQxaUgMNAA2XIjhE1apVMWnSJLz55psYOHCg4PepZinC/v37kZmZiQULFgh+z4oVK6DVajF06FAZz4yEkvsCM3dalmCK2qe1aojasmxZnqC2pQmOwqglVSmx4Yts4uvri4sXL4p6jyrC9uHDh0hOTsakSZMQFRUl+H3Lly9Hq1at0KRJE8Hvefzxx+Hp6YlatWphypQpuH/fvWNJDoxbceSc1rpa1Iq5ta4UbFl/e6fIx+UC155pLaOW1Eaj1Yr+InGKiopw7NgxTJs2DY0bNxb1XmX8VLNi7ty5ePDgAVJTUwW/58CBA/jzzz8xb948Qa+PiopCWloaWrZsCY1Ggx07duDDDz/EkSNHsHPnTpPvycjIQEZGBgAgNzdX8LmRvMsTXGlZgjtErSuwdXmCKyxNsDVqGbREBAAeHh5md0WoXLkyvv5a3EWtDv/JlpWVhcTERKuvi4+Px549e5CdnY3Zs2dj48aN8PX1Ffw5y5Ytg5eXl+B1GYMGDTL4dWJiIiIiIpCSkoKsrCyTi5eTk5ORnJwMAIiJiRF8bmL5+hfKdmxnkytwXSluhVDz3rVqW4JgidjAVXvcMmrJ7fDiMcm9/fbbRmHr6+uLWrVqoXv37qhSpYqo4zk8bNu3b49Tp05ZfZ2fnx8AYOzYsejcuTPi4uKQn58PACgsLIRWq0V+fj58fHxQsaJhwDx48ADr1q1Dz549ERISYvO5DhgwACkpKfjpp59EX5VH4sgRuGqPW3eY1rpS1JYlJnCVcmGZFFt9CcGoJXXTch9biU2bNk3S4zn8p5ufnx+io6MFv/7kyZPIyclBYGCg0XOBgYEYN24c0tPTDR7fsmULbt68KdlFY9Y2DibphHneYdzKjFHrOC9U+9Flp7e2TGsZteQKuC+tvG7duoU///wTYWFhRvcsEEIZP+EsWLNmDQoKCgweS0tLw+HDh7F+/XqT3/SyZcsQEhKCnj172vXZK1euBADExoq/85Ac1HJzBntJPb1VY9yKmdaKWYbAqHU8NU5vrREbtQxacimc2Npt+/bt2L17N9LS0gwenz17Nv5/e3cfF1WVwA38N8qbICIKiaYIisEjaFrYglqAgoAGYou6JkglvkSrLW1KLrlhm1naixu4YbSJivi+vuULiVquhs9muWVghSJahskKpojAgOf5w4eJy8wwMzBvzPy+n898PnLuuXfOPXt2+e3h3HNfffVVNDY2AgCmT5+ODRs26LQNq3n8lmtD671pgXub+drb26t8G8W1a9dQUFCAZ599Fra2tiqvOXv2bKxfv17RcQAwcuRIzJo1C76+vpDJZDh8+DAyMzMRFRWFcePG6e1+Oupx529M3QSj0WfA7YzhVt/MJdQamr5frasvugZccw23DLVk1QQg4/ZdHZadna301/DDhw9j6dKlGDZsGJKTk3Hu3DmsXbsWDz/8MP785z9rfW2L+023adMmNDY2trkMoampCU1NTZIyX19fZGVloaKiAnfv3sWgQYPw17/+FYsXLzZ0k0kDfQXczhJuLf1lDNY0W6uKtgHXnMOtthhqySJxxrbDzpw5g6VLl0rK1q1bBwcHBxQUFEjeMJufn2/5wVbd69cAIDU1FampqRrPb32NLVu26KFlZEj6WH/bvM+tuQZcXUOttssQzGW21tpDbUvaBFxzW5qgy2wtQy1ZLObaDrt27RoGDx4sKTt8+DDGjh0rCbWTJk3Cxo0bdbq2efy2I9KStc3e6gNDrXnT5gEzc5i91TbUMtCSpeMLFzrO2dkZt2/fVvxcWlqK69evKy0/7dGjh9Jf2DXpFG8eI2pNH6/nNbe3lBlittZcQi21TZs3mJnyjWUMtUQtCKH7hyT8/PywZ88exc979uyBTCbDhAkTJPUuXryIPn366HRt/tajTq2jM7jWNHNrSsaerTXXB8g00XZ5gr5nb/Wxhy1DLVkFAYAPj3VYamoqnnjiCVRVVaFPnz7Izc3FsGHDMGbMGEm9AwcO4MEHH9Tp2pyxJYvQkdlbc5i5NcQDY+YyW8slCLrTNINrzJlbTbO1T/r8X4ZashoyCMiE7h+SiouLw+rVq/HFF19gw4YNCAoKwvbt2yU7JVy9ehWFhYWYOHGiTtc2j998pJG17GHbER2Zve1sM7ealiEw1FqGtmZwjfFgmTahloiopQsXLiApKQnXrl2Dk5MTcnJyEBgYqFRv4cKFWLhwodrreHh44H//+5/O388ZW7I47V1/a6qZW33P1ppLqCX9McXsLUMtkRpcY9um+fPnIykpCT/88ANWrlyJmTNnQhixDxhsyWK1J+AaO9xa8p61nK3Vr7aWJxj7wTKGWrJqRgq2UVFRkMlkePnll/V8A7/56aefsGDBAgQHB8PR0REymQzl5eVq6//444+Ij4+Hi4sLevTogSeeeAKXL19WHK+srMSpU6fw1FNPAQAiIiIghMCXX35psHtojcGWLJ65h1tdtbUMwVxmaxlqDUdTwNUHdbO1XE9LVq/54TFdPzravHkzvv76a700uS3nz5/Htm3b4OrqikcffbTNurW1tRg3bhy+++47rF+/Hhs3bkRpaSnCwsIUW3ddvnwZffv2lbz51cvLSxJ+DY3BlqyCrrO3xgi3lroEwVxC7SvfxJi6CQalLuB2NNy2FWqJCAZ/eKy6uhqpqal45513tKpfUVGBzz//XOWx2tpaHDx4UO25jz32GH755RccOHAAU6dObfN7cnJyUFZWht27dyMuLg6TJ0/G3r17cenSJaxdu1arthoDg60Z+z+7lpm6CRZHl4Br7jO3LTHUWi9VAbe9SxMYaom0YOClCGlpaQgICMCMGTO0qr969WpERkbixIkTkvI7d+4gJiYGTz/9NGpqVP/e69JF+xi4d+9eBAUFwcfHR1Hm7e2NMWPGKPak9fT0REVFBeRyuaJOeXk5PD21f2thRzHYklXSNuAaKty2d7ZW1TIEcwm1ZFrtnb3VtIctQy1RS+0ItToE2xMnTmDDhg1Ys2aN1ucsX74cUVFRiI6OxsmTJwH8Fmq//vprfPLJJ+jevWNv6wSA4uJiBAQEKJX7+/ujpKQEAODu7o5HHnkEubm5AO69JlcIgYcffrjD368t/kYkq+bRtUbj9mCdbSswU+Fsremp2h5M223BWs/WMtASqSDQrofBKisrJVtezZ07F3PnzpXUaWhowLx58/Diiy/C19dX62vb2Nhg8+bN+MMf/oDo6Gjs2rULK1aswH//+18cOXIEw4cP17m9qlRVVcHV1VWpvFevXqiurlb8nJ2djaSkJKxatQqOjo7YtGmTZH9aQ2OwJaunzf63+gy3ljhby1BrXtQFXHXhlqGWSAfteBjM3d0dp0+fbrPOypUrcefOHaSnp+t8fRsbG2zZsgXTpk1DeHg4XF1dcfToUZ3f2qUPQ4YMUbvm1xjM47citYkvZzAOTQFXH+FWnw+MMdSSJq0Drqpwy1BLpBtDvEns8uXLWL58OT788EPU19ejvv63/57W19fjxo0bcHZ2RteuXdVeo7GxETU1NejSpQvkcjlqa2v12kZXV1fJzGwzdTO5psI1tkSttLX+1lweKDOXUGvuLH1nBG21fMBM3YNl3MqLSEsGWGNbVlaGuro6JCQkwNXVVfEBgLfeeguurq44e/as2vPr6uoQFxeHr776CqdOnUJERASioqJQVFSkt9v29/dHcXGxUnlJSQmGDh2qt+/pKAZbIjX0HW47Mlur6RW6psLZ2s6l5QNmNY32iO597xclAy2RaY0YMQLHjh1T+gBAQkICjh07JtmNoKX6+npMmTIFX3zxBQoLCzFq1Chs3boV4eHhiIqKwqlT+vmrb2xsLE6dOoWysjJFWXl5OU6ePInY2Fi9fIc+cNqHqA3qlieY8oEyU87WNoz9Lch6964CAFy63stUzSEtTe3+a4t/H1b8e3uNC6b7fGGKJhF1TgLAXf0vRejZsydCQ0NVHhs4cKDaYwCQnp6O//znPygsLMSIESMAALa2tti6dSumT5+OmJgYXLx4Ue3OCDt27AAAxdvBDh48CHd3d7i7uyMkJERRb86cOcjKysLkyZPx2muvQSaTYenSpRgwYADmzZvXjrs2DAZbIi2oCri6hFt9ra01VqhtGWA1Gfj/A25LDLvG1zK8qtLF4welsumGagyRxWr/K3INJT09HUlJSRg2bJikvDncnj59us3tvlq/mCElJQUAEBISgk8//VRR7uTkhKNHjyI1NRWJiYkQQmD8+PFYvXq1XrYT0xcG207kcedvTN0Eq9c64GoTbjsaapuXIRgq1OoSYrXVOuwy6HacpuDaTFWAJSI9MmKwFVp8V8v1uK3Z2toiODi4w9/RzNPTEzt37tS6vikw2Jqxutt2cHBqMHUzSIWW+98aY1mCvkJte0PsooDDSmW/63ZR8e8Xy+LbPN+Us7qvfBODZcP3GeW7OkLb4AowvBKZlJnN2JIUg60ZY6g1by1nb5sfKGsdcPW5vZcudAmwqkKrrt4atEPtMXWh1xqXMDC8EnVyBlpjS/rDYEvUQa0Drj5nb78+N1DjbG1bIVYfobWjdAm9nT3s6hJcAYZXos5HAKIdb2ggo2GwNXN8OUPn0XJ7sFt3u+lltrZ1qG0ZYs0htHZUW6EXuBd8zS3sMrwSWTkuRTBrDLZEeqZu/1tNRvb8UbnsmR/R30452FkLtcF3kPJsr77Crq7BtRkDLJEV4FIEs8dga8bOTXkFALCyJFpRNthG+Y1BZH62/eqnMqi2x08N0sBmzUG3JaXQO0j644tl8SrDbnuDK8DwSkTgjK2ZY7DtBBYPPahT/fqKQZorkUEt6S199eGK68PU1NQdg6523hq0QxF2/W0dtT6P4ZWI2sRga9YYbC2Qfd8yzZW0xJCsH62DbjN9BN7WQRdg2AXaDrMMr0RElonBltrEkGxY6gIv0LHQa02zuuoCLMMrEemf+b15jKQYbMloGJJ1o89ZXksJuqpCLAMsERmNAHCX232Zs04RbL28vHDp0iWl8l27diEuLk5SlpOTg7fffhsXL16El5cXUlNTMX/+fK2+58SJE1i8eDHOnDkDFxcXPPnkk1i+fDm6dTPsW6VId9YckvUxy2vuQbd1gGV4JSKzwRlbs9Ypgi0AREZGIiMjQ1Lm6+sr+TknJwfz5s3DkiVLEB4ejiNHjiAlJQVCCDz77LNtXv+bb75BREQEIiMj8fHHH+PixYtYtGgRrly5gq1bt+r7dsiMWFJIbu8sr6mCbssAy/BKRJ0Cg61Z6zTB1s3NDUFBQWqPNzY2Ij09HYmJiVi+fDkAICwsDD///DOWLl2K5ORk2Nraqj3/lVdeQf/+/bF9+3ZFPTs7OyQlJSEtLQ0PPfSQfm+ILJK5hmRdZ3n1HXSbAyzDKxF1boL72Jq5ThNsNSkqKkJlZSUSEhIk5YmJiVi3bh1OnDiBsLAwlefK5XIcOnQIL7zwgiT8Tps2DXPmzMGePXsYbMnojBWStZnl1Tbo+ts6MrwSkeUSgOArdc1apwm2+/btg6OjI5qamjBy5Ei89NJLkvW1xcXFAICAgADJef7+/gCAkpIStcH2woULqKurUzrXwcEBgwcPRklJiT5vhcjo2hOSM/qqLq+vGKTX0E1E1KlwxtasdTF1A7QRExODzMxMFBQUYNOmTXBwcMCUKVOQl5enqFNVdW8GydXVVXJur169JMdVUXdu8/nqzv3ggw8QGBiIwMBAVFZW6nZTRJ0UQy0RWTUhdP+Q0Rg92BYWFkImk2n8hIaGKs7JzMzErFmz8OijjyI+Ph5HjhxBYGAglixZYuzmS8ydOxenT5/G6dOn4e7ubtK2EBERkYEJcW+7L10/ZDRGX4owevRonDt3TmM9R0f1bw3q2rUrpk6dirS0NFRUVKBv376K2dbq6mr07fvb31CbZ1ubZ25VaXlua1VVVYrlDERERGTlOANr1owebB0dHeHn56e368lkMgC/raUtLi6WBNvm9bFDhw5Ve43Bhy/PcwAAEb5JREFUgwfD3t5esU63WV1dHcrKyjB16lS9tZeIiIiIDKNTrLFtrbGxEVu3boWnpyc8PDwAAMHBwXBzc8OmTZskdfPy8tCrVy+MGTNG7fXs7OwQFRWFbdu2obGxUVG+Y8cO1NfXIzY21jA3QkRERJ2KuHtX5w8Zj9nvirB582bs2bMHEydOxIABA/DLL79gzZo1+Oqrr7B582ZFPVtbW/ztb39DSkoK7r//foSHh+Po0aP46KOPkJmZCTs7O0Xd2bNnY/369ZIQm5GRgaCgIEybNg3PPfccysvLsWjRIsTHx+Phhx826j0TERGROeLDYObO7IOtt7c3rl27hkWLFqGqqgpOTk4IDAzEoUOHEBkZKak7f/58yGQyvP3221i1ahU8PT2RlZWFlJQUSb2mpiY0NTVJykaMGIFPPvkEaWlpmDRpElxcXDBr1iy8/vrrBr9HIiIi6gQEuN2XmZMJwf/roQ+BgYE4ffq0qZtBRERk8Uz1O9elS28E2UXpfN71gO+ZEYzE7GdsiYiIiMyBACA4Y2vWGGyJiIiItCEEwFfqmjUGWyIiIiItccbWvDHYEhEREWmLM7ZmjcFWT8rLyxEYGKjXa1ZWVvJVvRqwjzRjH2nGPtKMfaQd9pNm+uij8vJy/TRGR6MjH8H//ndR5/Pc3NwM0BpShbsimDHutKAZ+0gz9pFm7CPN2EfaYT9pxj4iQ+qUbx4jIiIiImqNwZaIiIiILELXjIyMDFM3gtTj63w1Yx9pxj7SjH2kGftIO+wnzdhHZChcY0tEREREFoFLEYiIiIjIIjDYEhEREZFFYLA1M7m5uZDJZGo/V69eVdQNDQ1VWWf16tUmvAPD06WPACAnJwd+fn6wt7eHr68vsrOzTdRy47ty5QqeeeYZeHh4wN7eHt7e3liyZImkjrWOo2ba9BFgvePIy8tL5fjYvXu3pJ61jyNt+wmw3rHU0pYtWyCTydC/f3+lY9Y+lqhj+IIGMzNp0iQUFRVJyoQQiImJwaBBg+Dh4SE5Nnz4cKxdu1ZS5uXlZehmmpQufZSTk4N58+ZhyZIlCA8Px5EjR5CSkgIhBJ599lljN92oysvLMWbMGHh7e+O9995Dnz59UF5ejvPnzyvVtcZxBGjfR9Y8jgAgMjISrZ8z9vX1VapnreOomTb9ZO1jCQBu3LiBP/3pT0q/z1qy9rFEHSDI7B0/flwAEFlZWZLykJAQMWbMGBO1yryo6iO5XC7c3d3FrFmzJHWffvpp0bt3b9HQ0GDsZhpVZGSkGDVqlMb7tOZxpE0fWfs4GjhwoJg5c6bGetY8joTQrp+sfSw1mzNnjpgwYYJISkoS999/v9Jxax9L1DFcitAJrF+/HnZ2dpgxY4apm2K2VPVRUVERKisrkZCQIKmbmJiI69ev48SJE8ZuptFcuHABBQUFWLBgAWxtbU3dHLOkbR9Z8zgi/eJYAk6ePIm8vDysWbPG1E0hC8Vga+bu3LmD7du34/HHH0evXr2Ujp85cwYuLi6wtbXF8OHD8c9//tMErTQtdX1UXFwMAAgICJDU9/f3BwCUlJQYr5FGdvLkSQBAt27dEBERAXt7e7i6umLWrFm4fv26Un1rHEfa9pE1j6Nm+/btg6OjI+zt7REUFKRy3ShgneOoJU39ZO1jSS6XY+7cuVi0aBF8fHzarGvtY4naj8HWzO3evRs3b95EUlKS0rHHHnsMq1evxt69e7Fjxw4MGTIEycnJeO2110zQUtNR10dVVVUAAFdXV0l5c/htPm6Jfv75ZwDAM888gwceeAAHDx7Em2++if379yMyMhJ3795V1LXWcaRtH1nzOAKAmJgYZGZmoqCgAJs2bYKDgwOmTJmCvLw8ST1rHUfNtOknax9Lb775Jurr61U+nNmStY8l6iBTr4WwdIcPHxYANH5CQkJUnh8ZGSnuu+8+IZfLtfq+uLg44eDgIG7duqXHuzAsQ/XR8uXLBQBx584dSblcLhcAxKuvvmqoW9I7Xfuo+d5jYmIk19myZYsAIA4cONDm91nDONK2j6x5HKnS2NgoAgMDRf/+/TV+X2ccR0IYrp+seSyVlpYKBwcHcfDgQcU11K2xVaWzjiUyPu6KYGCjR4/GuXPnNNZzdHRUKquoqEBhYSEWLFgAGxvt/qOaMWMGdu/ejbNnzyI4OFjn9pqCofqoeVakuroaffv2VZQ3z4qoWtphrnTto969ewMAIiIiJMcnTJgA4N6f+aKjo9VexxrGkbZ9ZM3jSJWuXbti6tSpSEtLQ0VFhaRPWuuM4wgwXD9Z81hauHAhxo0bh6CgINy4cQMA0NDQACEEbty4AXt7e3Tr1k3tdTrrWCLjY7A1MEdHR/j5+bXr3Ly8PDQ1NalchqCJTCZr13eagqH6qHndWnFxseSXSPM6tqFDh7brO01B1z5qvnd1unTRbhWSJY8jbfvImseRJtqOj840jgDD9ZM1j6WSkhJcunRJaRkGcG8S4vnnn9dqn9rONpbIBEw9ZUzqBQQEiOHDh+t0TmxsrOjWrZuoqakxUKvMS1t91NDQINzc3MRTTz0lKZ89e7bo1auXqK+vN0YTTUIulwsPDw8xadIkSXl+fr4AIAoLC9s83xrGkbZ9ZM3jSBW5XC4eeugh4enpqbGuNYwjdVT1kzWPpaKiInHs2DHJJzIyUri5uYljx46J0tLSNs+35rFEumGwNVNffvmlACDefvttlcePHz8uJk6cKD788ENRWFgodu7cKWJjYwUA8cYbbxi5taahqY+EEOL9998XMplMpKeni2PHjomlS5cKmUymtCewJcrNzRUAxLx580RBQYFYs2aN6NmzpwgNDRV3794VQnAcadNHQljvOMrPzxfTp08X69evF0ePHhWbN28WY8eOFQDE5s2bFfWsfRxp209CWO9YUkXVGltrH0vUcQy2ZmrhwoXCxsZGXL16VeXx0tJSERUVJfr16yfs7OyEk5OTCA4OFvn5+UZuqelo6qNm2dnZYsiQIcLOzk74+PiINWvWGKmFprdhwwbh7+8v7OzshIeHh/jjH/8oefiC40hzHzWzxnFUVFQkwsLCxH333SdsbGyEi4uLGD9+vDh06JCknrWPI237qZk1jiVVVAVbax9L1HEyIYQwzSIIIiIiIiL94T62RERERGQRGGyJiIiIyCIw2BIRERGRRWCwJSIiIiKLwGBLRERERBaBwZaIiIiILAKDLRFpJTc3FzKZTPFxdnbGgw8+iKysLDQ2Npq6eQaTkZGBo0ePmroZSqqrq5GcnAw3Nzc4OTkhPDwcZ8+eNXWziIhMisGWiHSyfft2FBUVYefOnXjkkUewYMECvPrqq6ZulsEsW7bM7IKtEAIxMTE4dOgQMjMzsXPnTsjlcoSFheGnn34ydfOIiEzGxtQNIKLOZcSIEfDx8QEATJgwAefPn8ff//73DofbpqYmCCFgY2P5/7NUX18Pe3v7dp+/d+9enDx5EkePHkVYWBgAIDg4GN7e3li5ciXee+89fTWViKhT4YwtEXXIqFGjcPPmTVy7dg0AsGXLFowbNw7u7u7o3r07Ro4cifXr1yudJ5PJkJ6ejjfeeAPe3t6ws7PD2bNnUVdXh9TUVAQEBKB79+7w8PBATEwMvvvuO8n5zUsjPv/8c0ybNg3Ozs7o06cPVqxYAQA4dOgQRo4cCScnJ4waNQpffvmlUhv+9a9/ISgoCI6OjujZsyemTp2Ky5cvS9oIAMuXL1cswcjIyFAc/+yzzzB+/Hg4OzvDyckJkZGR+PbbbyXfERoairFjx2Lfvn0YOXIk7O3t8Y9//ENlX06aNAkPPfSQUnlFRQVsbGzw7rvvArgXbPv166cItQDg4uKCmJgY7NmzR+W1iYisAYMtEXXIxYsX0bVrV3Tv3h0AUFZWhvj4eGzatAm7d+9GTEwMkpOTkZ2drXRubm4u9u/fj7feegv79+9Hv379UF9fj1u3buHll1/G/v378f7776Ourg7BwcG4evWq0jWSkpIwbNgw7Nq1C3FxcfjLX/6CtLQ0LFq0CGlpadi6dStu376NuLg4NDQ0KM7Lzs7G73//ewwdOhQ7duzA2rVr8e233yIkJAS3bt0CABQVFQEAnnrqKRQVFaGoqAjJyckAgP3792P8+PHo3r078vLykJ+fj1u3buHRRx/Fjz/+KGnjDz/8gIULF2LBggUoKCjA+PHjVfZlYmIizpw5g5KSEkl5fn4+AODJJ58EABQXFyMgIEDpfH9/f1y+fBk1NTUqr09EZPEEEZEW1q1bJwCI7777TsjlclFVVSWys7NFly5dxOTJk1We09TUJORyuUhOThbDhw+XHAMg+vbtK2pra9v83sbGRnH79m3RvXt38c477yi1Z9myZYoyuVwu3N3dhY2NjSgrK1OU79mzRwAQn376qRBCiFu3bokePXqIp59+WvJdZWVlwtbWVrz77ruSdqanpyu1a/DgwWLcuHGSsl9//VX07t1bPP/884qykJAQIZPJxJkzZ9q8TyGEqK2tFT169BAvvfSSpPzBBx8U0dHRip+HDBkipk+frnR+Tk6OACAuX76s8buIiCwRZ2yJSCd+fn6wtbVFr169kJKSgpkzZ+Kjjz5SHC8tLcWMGTNw//33w9bWFra2tvjwww/x/fffK10rKioK3bp1Uyrftm0bfve736Fnz56wsbGBk5MTampqVF4jOjpa8W8bGxv4+PjggQcegLe3t6TNABQzqUVFRbh58yZmzpyJxsZGxWfAgAHw8/PD8ePH2+yD0tJSXLhwQel8R0dHBAcHK53v5eWFESNGtHlNAOjWrZtitlsIAQA4e/Ysvv76ayQmJmo8n4jI2ln+UxpEpFe7du1C//794ezsjIEDB8LBwUFxrKamBhEREXB0dMQbb7yBwYMHw87ODu+//74k/Dbr27evUtm+ffswffp0JCUl4ZVXXoGbmxu6dOmCiRMnoq6uTqm+q6ur5Gc7OzuVZQAU5zevBw4PD1d5j63Pb635/NmzZ2P27NlKxz09PSU/q7pPdRITE/HRRx/h008/RVhYGDZu3AhnZ2fExcVJ2lddXa10blVVlVbtJyKyVAy2RKSTgIAAxa4IrRUVFeHSpUv497//jbFjxyrK1e1z2/xwVktbtmyBj48PcnNzFWVyuVwR2vShd+/eAO6t8fX391c67uzsrNX5K1asUBmOm4N0M1X3qU5ISAg8PT2Rl5eHkJAQ5OfnIz4+XjKz7e/vj08++UTp3JKSEnh6eirWOxMRWRsGWyLSm9raWgCAra2toqy6ulqnJ/Vra2uVtvzauHEjmpqa9NNIAKNHj4azszPOnz+PpKSkNuva2dnhzp07kjJfX194eXmhuLgYL730kt7aBdwLwQkJCcjKysKUKVNw5coVpWUIsbGxWLduHT777DOEhIQAAG7evIl9+/YpHjAjIrJGDLZEpDejR49Gjx498Nxzz2HZsmW4ffs2XnvtNbi5ueHXX3/V6hpRUVHYvXs3UlNT8fjjj+P06dPIzMxEz5499dbOHj16YNWqVXjuuedQWVmJ6OhouLi44MqVK/jss88QGhqqCIhDhw7F/v37ERUVBVdXV/Tr1w/9+vXDmjVrMHnyZDQ0NGDatGlwc3PDL7/8gs8//xyenp544YUX2t2+xMREvP7665g/fz48PT0RGhoqOR4bG4vg4GAkJCRg1apVcHV1xYoVKyCEwOLFizvSNUREnRofHiMivXF3d8euXbvQ1NSE+Ph4LFmyBMnJyUhISND6GnPmzEF6ejq2bt2KmJgYHDhwAPv27YOLi4te2zpv3jzs3bsX33//PRITEzFx4kRkZGSgsbFR8qBXVlYWnJycEBMTg1GjRuGDDz4AAEycOBHHjx/H7du3kZycjMjISCxevBhXr15FcHBwh9rm5+eHwMBAXLlyBTNnzlRaytClSxd8/PHHiIiIQEpKCqZMmYKuXbvi2LFjGDBgQIe+m4ioM5OJ5kdviYiIiIg6Mc7YEhEREZFFYLAlIiIiIovAYEtEREREFoHBloiIiIgsAoMtEREREVkEBlsiIiIisggMtkRERERkERhsiYiIiMgi/D/+gRnz9SwqkAAAAABJRU5ErkJggg==\n", - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "\n", - "for index, val in enumerate(pipe_results.values()):\n", - " td = val['td_py']\n", - " history = val['history']\n", - "\n", - " plot_surface('v0','vt',td,history)\n", - " " - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "list(pipe_results.keys())" - ] - }, - { - "cell_type": "code", - "execution_count": 13, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[, [9.2687289516612265e-05, 0.1771150605405849, -56.181126905116926, -67.347929227817346, -3.7614122822701475e-09, -63.762723380281543, 0.0011317265889965273, 0.74534356682305991, 30.93859586774235, -49.433050469559873], [9.2687289516612265e-05, 0.1771150605405849, -56.181126905116926, -67.347929227817346, -3.7614122822701475e-09, -63.762723380281543, 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0.11091496456405203, -55.414069346592591, -66.991959724596512, -4.6205412058307926e-09, -67.197631637629087, 0.00064779434661928453, 0.2094986246280181, 33.351868911896275, -39.9816752790273]]\n", - "[, [9.2687289516612265e-05, 0.1771150605405849, -56.181126905116926, -67.347929227817346, -3.7614122822701475e-09, -63.762723380281543, 0.0011317265889965273, 0.74534356682305991, 30.93859586774235, -49.433050469559873], [9.2687289516612265e-05, 0.1771150605405849, -56.181126905116926, -67.347929227817346, -3.7614122822701475e-09, -63.762723380281543, 0.0011317265889965273, 0.74534356682305991, 30.93859586774235, -49.433050469559873], [9.4606336849949391e-05, 0.15248466310239464, -56.156943494627598, -74.668501416010756, -3.7614122822701475e-09, -64.741400725138448, 0.00094517855553909592, 0.73066788201343646, 30.300786161369402, -49.433050469559873], [0.00010958754810432006, 0.15651773411011438, -58.953288827285164, -45.312293137196598, -3.6042771233666399e-09, -62.613872673060229, 0.00065658488921653308, 0.46892767133483032, 38.01742108692784, -41.713720013984513], [0.00010675206264677055, 0.12313972355394168, -56.175295344457368, -70.095447060770127, -3.7448953395780954e-09, -60.681651092360475, 0.00082405789026522742, 0.89328080729845649, 38.976537722975358, -45.839073336068488], [0.00010675206264677055, 0.12313972355394168, -56.175295344457368, -70.095447060770127, -3.7448953395780954e-09, -60.681651092360475, 0.00082405789026522742, 0.89328080729845649, 38.976537722975358, -45.839073336068488], [0.00010675206264677055, 0.12313972355394168, -56.175295344457368, -70.095447060770127, -3.7448953395780954e-09, -60.681651092360475, 0.00082405789026522742, 0.89328080729845649, 38.976537722975358, -45.839073336068488]]\n", - "[, [9.2687289516612265e-05, 0.1771150605405849, -56.181126905116926, -67.347929227817346, -3.7614122822701475e-09, -63.762723380281543, 0.0011317265889965273, 0.74534356682305991, 30.93859586774235, -49.433050469559873], [9.2687289516612265e-05, 0.1771150605405849, -56.181126905116926, -67.347929227817346, -3.7614122822701475e-09, -63.762723380281543, 0.0011317265889965273, 0.74534356682305991, 30.93859586774235, -49.433050469559873], [9.468083086294944e-05, 0.14628583769403886, -58.906094813311555, -61.211896027867994, -3.9908029246304575e-09, -74.462757368352271, 0.0012749350412601811, 0.49732947018412965, 36.096150656906033, -45.520971457838755], [9.0480745623745093e-05, 0.1312118709190245, -55.158955205746949, -63.512879103162547, -4.4585015071734669e-09, -67.033477012863159, 0.00073902175535281152, 0.25496543756911638, 34.378875936505722, -40.083755172362984], [9.0508921745315079e-05, 0.11116775219452474, -55.245813366169401, -63.563872869353631, -4.6205412058307926e-09, -64.053591664928547, 0.00095451429898716603, 0.41736290692153133, 31.89507370082211, -46.414984003131437], [9.0508921745315079e-05, 0.11091496456405203, -55.414069346592591, -66.991959724596512, -4.6205412058307926e-09, -67.197631637629087, 0.00064779434661928453, 0.2094986246280181, 33.351868911896275, -39.9816752790273], [9.0508921745315079e-05, 0.11091496456405203, -55.414069346592591, -66.991959724596512, -4.6205412058307926e-09, -67.197631637629087, 0.00064779434661928453, 0.2094986246280181, 33.351868911896275, -39.9816752790273]]\n", - "[, [9.2687289516612265e-05, 0.1771150605405849, -56.181126905116926, -67.347929227817346, -3.7614122822701475e-09, -63.762723380281543, 0.0011317265889965273, 0.74534356682305991, 30.93859586774235, -49.433050469559873], [9.2687289516612265e-05, 0.1771150605405849, -56.181126905116926, -67.347929227817346, -3.7614122822701475e-09, -63.762723380281543, 0.0011317265889965273, 0.74534356682305991, 30.93859586774235, -49.433050469559873], [9.3476035339857326e-05, 0.1789919793192935, -56.03030085957198, -61.661721658257065, -3.196288981041829e-09, -59.2094043232931, 0.0013982235783757042, 0.2200458235293889, 37.510679826031414, -36.593888671718581], [9.5840506468531072e-05, 0.13740206056336696, -59.259293176395225, -45.172463034412203, -3.8744479461807054e-09, -60.274943550436205, 0.00065658488921653308, 0.022349947563652578, 37.97404247554303, -41.830059576622077], [9.5840506468531072e-05, 0.13740206056336696, -59.259293176395225, -45.172463034412203, -3.8744479461807054e-09, -60.274943550436205, 0.00065658488921653308, 0.022349947563652578, 37.97404247554303, -41.830059576622077], [9.5840506468531072e-05, 0.13740206056336696, -59.259293176395225, -45.172463034412203, -3.8744479461807054e-09, -60.274943550436205, 0.00065658488921653308, 0.022349947563652578, 37.97404247554303, -41.830059576622077], [9.6067375022026861e-05, 0.13813709092153392, -55.318407491653602, -64.772753611836052, -3.9220712917606887e-09, -60.222938971170777, 0.00065658488921653308, 0.22033715302017445, 37.97404247554303, -42.145058423783325]]\n", - "[, [9.2687289516612265e-05, 0.1771150605405849, -56.181126905116926, -67.347929227817346, -3.7614122822701475e-09, -63.762723380281543, 0.0011317265889965273, 0.74534356682305991, 30.93859586774235, -49.433050469559873], [9.2687289516612265e-05, 0.1771150605405849, -56.181126905116926, -67.347929227817346, -3.7614122822701475e-09, -63.762723380281543, 0.0011317265889965273, 0.74534356682305991, 30.93859586774235, -49.433050469559873], [9.468083086294944e-05, 0.14628583769403886, -58.906094813311555, -61.211896027867994, -3.9908029246304575e-09, -74.462757368352271, 0.0012749350412601811, 0.49732947018412965, 36.096150656906033, -45.520971457838755], [9.0480745623745093e-05, 0.1312118709190245, -55.158955205746949, -63.512879103162547, -4.4585015071734669e-09, -67.033477012863159, 0.00073902175535281152, 0.25496543756911638, 34.378875936505722, -40.083755172362984], [9.0508921745315079e-05, 0.11116775219452474, -55.245813366169401, -63.563872869353631, -4.6205412058307926e-09, -64.053591664928547, 0.00095451429898716603, 0.41736290692153133, 31.89507370082211, -46.414984003131437], [9.0508921745315079e-05, 0.11091496456405203, -55.414069346592591, -66.991959724596512, -4.6205412058307926e-09, -67.197631637629087, 0.00064779434661928453, 0.2094986246280181, 33.351868911896275, -39.9816752790273], [9.0508921745315079e-05, 0.11091496456405203, -55.414069346592591, -66.991959724596512, -4.6205412058307926e-09, -67.197631637629087, 0.00064779434661928453, 0.2094986246280181, 33.351868911896275, -39.9816752790273]]\n" - ] - } - ], - "source": [ - "for index, val in enumerate(pipe_results.values()):\n", - " '''\n", - " log = val['log']\n", - " gen_numbers =[ i for i in range(0,len(log.select('gen'))) ]\n", - " hof = val['hof_py']\n", - " mean = np.array([ np.sqrt(np.mean(np.square(i))) for i in log.select('avg')])\n", - " std = np.array([ np.sqrt(np.mean(np.square(i))) for i in log.select('std')])\n", - " minimum = np.array([ np.sqrt(np.mean(np.square(i))) for i in log.select('min')])\n", - " best_line = np.array([ np.sqrt(np.mean(np.square(list(p.fitness.values)))) for p in hof])\n", - " blg = [ best_line[h] for i, h in enumerate(gen_numbers) ]\n", - "\n", - " '''\n", - " print(val['gen_vs_hof'])" - ] - }, - { - "cell_type": "code", - "execution_count": 32, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "ename": "ValueError", - "evalue": "operands could not be broadcast together with shapes (10,) (100,) ", - "output_type": "error", - "traceback": [ - "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", - "\u001b[0;31mValueError\u001b[0m Traceback (most recent call last)", - "\u001b[0;32m\u001b[0m in \u001b[0;36m\u001b[0;34m()\u001b[0m\n\u001b[1;32m 20\u001b[0m '''\n\u001b[1;32m 21\u001b[0m \u001b[0mmean\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmean\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mval\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'gen_vs_hof'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 22\u001b[0;31m \u001b[0mstd\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mstd\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mval\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'gen_vs_hof'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 23\u001b[0m \u001b[0;31m# print(gvh)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 24\u001b[0m \u001b[0;31m#import pdb\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", - "\u001b[0;32m/opt/conda/lib/python3.5/site-packages/numpy/core/fromnumeric.py\u001b[0m in \u001b[0;36mstd\u001b[0;34m(a, axis, dtype, out, ddof, keepdims)\u001b[0m\n\u001b[1;32m 3025\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 3026\u001b[0m return _methods._std(a, axis=axis, dtype=dtype, out=out, ddof=ddof,\n\u001b[0;32m-> 3027\u001b[0;31m **kwargs)\n\u001b[0m\u001b[1;32m 3028\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 3029\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n", - "\u001b[0;32m/opt/conda/lib/python3.5/site-packages/numpy/core/_methods.py\u001b[0m in \u001b[0;36m_std\u001b[0;34m(a, axis, dtype, out, ddof, keepdims)\u001b[0m\n\u001b[1;32m 133\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0m_std\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0ma\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0maxis\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mNone\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdtype\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mNone\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mout\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mNone\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mddof\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mkeepdims\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mFalse\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 134\u001b[0m ret = _var(a, axis=axis, dtype=dtype, out=out, ddof=ddof,\n\u001b[0;32m--> 135\u001b[0;31m keepdims=keepdims)\n\u001b[0m\u001b[1;32m 136\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 137\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0misinstance\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mret\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmu\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mndarray\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", - "\u001b[0;32m/opt/conda/lib/python3.5/site-packages/numpy/core/_methods.py\u001b[0m in \u001b[0;36m_var\u001b[0;34m(a, axis, dtype, out, ddof, keepdims)\u001b[0m\n\u001b[1;32m 110\u001b[0m \u001b[0;31m# Note that x may not be inexact and that we need it to be an array,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 111\u001b[0m \u001b[0;31m# not a scalar.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 112\u001b[0;31m \u001b[0mx\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0masanyarray\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0marr\u001b[0m \u001b[0;34m-\u001b[0m \u001b[0marrmean\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 113\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0missubclass\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0marr\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdtype\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtype\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mnt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcomplexfloating\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 114\u001b[0m \u001b[0mx\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mum\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmultiply\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mum\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mconjugate\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mout\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mreal\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", - "\u001b[0;31mValueError\u001b[0m: operands could not be broadcast together with shapes (10,) (100,) " - ] - }, - { - "data": { - "image/png": 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\n", - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "\n", - "%matplotlib inline\n", - "import matplotlib.pyplot as plt\n", - "import numpy as np\n", - "\n", - "plt.style.use('ggplot')\n", - "fig, axes = plt.subplots(figsize=(10, 10), facecolor='white')\n", - "\n", - "for index, val in enumerate(pipe_results.values()):\n", - " '''\n", - " log = val['log']\n", - " gen_numbers =[ i for i in range(0,len(log.select('gen'))) ]\n", - " hof = val['hof_py']\n", - " mean = np.array([ np.sqrt(np.mean(np.square(i))) for i in log.select('avg')])\n", - " std = np.array([ np.sqrt(np.mean(np.square(i))) for i in log.select('std')])\n", - " minimum = np.array([ np.sqrt(np.mean(np.square(i))) for i in log.select('min')])\n", - " best_line = np.array([ np.sqrt(np.mean(np.square(list(p.fitness.values)))) for p in hof])\n", - " blg = [ best_line[h] for i, h in enumerate(gen_numbers) ]\n", - "\n", - " '''\n", - " mean = np.mean(val['gen_vs_hof'])\n", - " std = np.std(val['gen_vs_hof'])\n", - " # print(gvh)\n", - " #import pdb\n", - " #pdb.set_trace()\n", - " '''\n", - " stdminus = mean - std\n", - " stdplus = mean + std\n", - " try:\n", - " assert len(gen_numbers) == len(stdminus) == len(stdplus)\n", - " except:\n", - " pass\n", - "\n", - " axes.plot(\n", - " gen_numbers,\n", - " mean,\n", - " color='black',\n", - " linewidth=2,\n", - " label='population average')\n", - " axes.fill_between(gen_numbers, stdminus, stdplus)\n", - " '''\n", - " axes.plot([i for i in range(0,len(gvh))], gvh,'y--', linewidth=2, label='grid search error')\n", - " #axes.plot(gen_numbers, bl, 'go', linewidth=2, label='hall of fame error')\n", - "\n", - " #axes.plot(gen_numbers, stdminus, label='std variation lower limit')\n", - " #axes.plot(gen_numbers, stdplus, label='std variation upper limit')\n", - "\n", - " #axes.set_xlim(np.min(gen_numbers) - 1, np.max(gen_numbers) + 1)\n", - " #axes.set_xlabel('Generations')\n", - " #axes.set_ylabel('Sum of objectives')\n", - " #axes.legend()\n", - " #fig.tight_layout()\n", - " #fig.show()\n" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Comment on plot\n", - "The plot shows the mean error value of the population as the GA evolves it's population. The red interval at any instant is the standard deviation of the error. The fact that the mean GA error is able to have a net upwards trajectory, after experiencing a temporary downwards trajectory, demonstrates that the GA retains a drive to explore, and is resiliant against being stuck in a local minima. Also in the above plot population variance in error stays remarkably constant, in this way BluePyOpts selection criteria SELIBEA contrasts with DEAPs native selection strategy NSGA2" - ] - }, - { - "cell_type": "code", - "execution_count": 33, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/opt/conda/lib/python3.5/site-packages/sklearn/cross_validation.py:44: DeprecationWarning: This module was deprecated in version 0.18 in favor of the model_selection module into which all the refactored classes and functions are moved. Also note that the interface of the new CV iterators are different from that of this module. This module will be removed in 0.20.\n", - " \"This module will be removed in 0.20.\", DeprecationWarning)\n", - "/opt/conda/lib/python3.5/site-packages/sklearn/grid_search.py:43: DeprecationWarning: This module was deprecated in version 0.18 in favor of the model_selection module into which all the refactored classes and functions are moved. This module will be removed in 0.20.\n", - " DeprecationWarning)\n" - ] - }, - { - "ename": "TypeError", - "evalue": "'DataTC' object is not iterable", - "output_type": "error", - "traceback": [ - "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", - "\u001b[0;31mTypeError\u001b[0m Traceback (most recent call last)", - "\u001b[0;32m\u001b[0m in \u001b[0;36m\u001b[0;34m()\u001b[0m\n\u001b[1;32m 60\u001b[0m \u001b[0mdlist_final_third\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mdlist\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m2\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdlist\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m/\u001b[0m\u001b[0mN\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m-\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 61\u001b[0m \u001b[0;32mfrom\u001b[0m \u001b[0mneuronunit\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0moptimization\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0moptimization_management\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 62\u001b[0;31m \u001b[0mdtc_first_third\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0moptimization_management\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mupdate_deap_pop\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdlist_first_third\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0melectro_tests\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtd\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 63\u001b[0m \u001b[0;31m#dlist_first_3rd = compute_chunk(dlist_first_third)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 64\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mpickle\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", - 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"\u001b[0;32m/opt/conda/lib/python3.5/site-packages/dask/bag/core.py\u001b[0m in \u001b[0;36mmap_chunk\u001b[0;34m()\u001b[0m\n\u001b[1;32m 1617\u001b[0m \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1618\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0ma\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mzip\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1619\u001b[0;31m \u001b[0;32myield\u001b[0m \u001b[0mf\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0ma\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 1620\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1621\u001b[0m \u001b[0;31m# Check that all iterators are fully exhausted\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", - "\u001b[0;32m~/neuronunit/neuronunit/optimization/optimization_management.py\u001b[0m in \u001b[0;36mtransform\u001b[0;34m()\u001b[0m\n\u001b[1;32m 195\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 196\u001b[0m \u001b[0mdtc\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mattrs\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m{\u001b[0m\u001b[0;34m}\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 197\u001b[0;31m \u001b[0;32mfor\u001b[0m \u001b[0mi\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mj\u001b[0m \u001b[0;32min\u001b[0m \u001b[0menumerate\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mind\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 198\u001b[0m \u001b[0mdtc\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mattrs\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mstr\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtd\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mi\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mj\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 199\u001b[0m \u001b[0mdtc\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mevaluated\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;32mFalse\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", - "\u001b[0;31mTypeError\u001b[0m: 'DataTC' object is not iterable" - ] - } - ], - "source": [ - "-" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "dtcpopg = [ dtc for dtc in dtcpopg if not None in (dtc.scores.values()) ]\n", - "dtcpopg = [ (dtc,sum(list(dtc.scores.values()))) for dtc in dtcpopg ]\n", - "\n", - "\n", - "sorted_grid = sorted(dtcpopg,key=lambda x:x[1])\n", - "sorted_grid = [dtc[0] for dtc in sorted_grid]\n", - "#print(sorted_grid)\n", - "mini = dtcpopg[0][1]\n", - "maxi = dtcpopg[-1][1]\n", - "minimagr = sorted_grid[0]\n", - "minimagr_dtc = sorted_grid[0]\n", - "minimagr_dtc_1 = sorted_grid[1]\n", - "minimagr_dtc_2 = sorted_grid[2]\n", - "from neuronunit.optimization.exhaustive_search import create_refined_grid\n", - "refined_grid = create_refined_grid(minimagr_dtc, minimagr_dtc_1,minimagr_dtc_2)\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": true - }, - "outputs": [], - "source": [ - "print(td)\n", - "def pop2dtc(pop1,DO,td):\n", - " '''\n", - " This function takes the DEAP population data type, and converts it to a more convenient\n", - " data transport object, which can more readily be used in plotting functions.\n", - " This a wasteful, recompute, which is in part necessitated because\n", - " deaps pareto front object, only returns gene individual objects (elements of population)\n", - " '''\n", - " from neuronunit.optimization import nsga_parallel\n", - " DO.td = td\n", - " assert DO.td == td\n", - " return_package = nsga_parallel.update_pop(pop1,td);\n", - " dtc_pop = []\n", - " for i,r in enumerate(return_package):\n", - " dtc_pop.append(r[0])\n", - " dtc_pop[i].error = None\n", - " dtc_pop[i].error = np.sqrt(np.mean(np.square(list(pop1[i].fitness.values))))\n", - " sorted_list = sorted([(dtc,dtc.error) for dtc in dtc_pop],key=lambda x:x[1])\n", - " dtc_pop = [dtc[0] for dtc in sorted_list]\n", - " print(dtc_pop,sorted_list)\n", - " return dtc_pop\n", - "\n", - "DO.td = td\n", - "print(hof[0])\n", - "print(len(hof))\n", - "dtc_pop = pop2dtc(hof[0:-1],DO,td)\n", - "\n", - "miniga = dtc_pop[0].error\n", - "maxiga = dtc_pop[-1].error\n", - "maximaga = dtc_pop[-1]\n", - "minimaga = dtc_pop[0]\n", - "\n", - "CACHE_PF = False" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": true - }, - "outputs": [], - "source": [ - "\n", - "if CACHE_PF == False:\n", - " h = list(history.genealogy_history.values())\n", - " evaluated_history = []\n", - " for i in h:\n", - " if hasattr(i,'rheobase'):\n", - " i.error = None\n", - " i.error = np.sqrt(np.mean(np.square(list(i.fitness.values))))\n", - " evaluated_history.append(i)\n", - " sorted_list = sorted([(i,i.error) for i in evaluated_history ],key=lambda x:x[1])\n", - "\n", - " with open('pf_dump.p','wb') as f:\n", - " pickle.dump([ sorted_list, evaluated_history ],f)\n", - "else: \n", - " \n", - " unpack = pickle.load(open('pf_dump.p','rb'))\n", - " print(unpack)\n", - " sorted_list_pf = unpack[0]\n", - " pareto_dtc = unpack[1] \n", - "\n", - "minimaga_ind = sorted_list[0][0]\n", - "maximaga_ind = sorted_list[-1][0]\n", - "miniga = sorted_list[0][1]\n", - "maxiga = sorted_list[-1][1]\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": true - }, - "outputs": [], - "source": [ - "import pandas as pd\n", - "\n", - "print(miniga)\n", - "print(maxiga)\n", - "print(minimaga_ind.fitness.values)\n", - "print(maximaga_ind.fitness.values)\n", - "print(len(minimaga_ind.fitness.values))\n", - "print(dtcpopg[0][0].rheobase)\n", - "print(dtcpopg[0][0].scores)\n", - "print(sorted_list[-1][0].rheobase)\n", - "print(sorted_list[-1][0].fitness.values)\n", - "\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": true - }, - "outputs": [], - "source": [ - "def use_dtc_to_plotting(dtcpop,minimagr):\n", - " from neuronunit.capabilities import spike_functions\n", - " import matplotlib.pyplot as plt\n", - " import numpy as np\n", - " plt.clf()\n", - " plt.style.use('ggplot')\n", - " fig, axes = plt.subplots(figsize=(10, 10), facecolor='white')\n", - " stored_min = []\n", - " stored_max = []\n", - " for dtc in dtcpop[1:-1]:\n", - " plt.plot(dtc.tvec, dtc.vm0,linewidth=3.5, color='grey')\n", - " stored_min.append(np.min(dtc.vm0))\n", - " stored_max.append(np.max(dtc.vm0))\n", - " \n", - " from neuronunit.models.reduced import ReducedModel\n", - " from neuronunit.optimization.get_neab import tests as T\n", - " from neuronunit.optimization import get_neab\n", - " from neuronunit.optimization import evaluate_as_module\n", - " from neuronunit.optimization.evaluate_as_module import pre_format\n", - " model = ReducedModel(get_neab.LEMS_MODEL_PATH,name=str('vanilla'),backend='NEURON')\n", - " import neuron\n", - " model._backend.reset_neuron(neuron)\n", - " model.set_attrs(minimagr.attrs)\n", - " model.rheobase = minimagr.rheobase['value']\n", - " minimagr = pre_format(minimagr)\n", - " parameter_list = list(minimagr.vtest.values())\n", - " model.inject_square_current(parameter_list[0])\n", - " model._backend.local_run()\n", - " assert model.get_spike_count() == 1\n", - " print(model.get_spike_count(),bool(model.get_spike_count() == 1))\n", - " brute_best = list(model.results['vm'])\n", - "\n", - " plt.plot(dtcpop[0].tvec, brute_best,linewidth=1, color='blue',label='best candidate via grid')#+str(mini.scores))\n", - " plt.plot(dtcpop[0].tvec,dtcpop[0].vm0,linewidth=1, color='red',label='best candidate via GA')#+str(miniga.scores))\n", - " plt.legend()\n", - " plt.ylabel('$V_{m}$ mV')\n", - " plt.xlabel('ms')\n", - " plt.show()\n", - "from neuronunit import plottools\n", - "from neuronunit.plottools import dtc_to_plotting\n", - "\n", - "CACHE_PLOTTING = False\n", - "if CACHE_PLOTTING == False:\n", - " dtc_pop = dview.map_sync(dtc_to_plotting,dtc_pop )\n", - " with open('plotting_dump.p','wb') as f:\n", - " pickle.dump(dtc_pop,f)\n", - "else: \n", - " dtc_pop = pickle.load(open('plotting_dump.p','rb'))\n", - "\n", - "use_dtc_to_plotting(dtc_pop,minimagr_dtc)\n", - " " - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Comment on plot\n", - "There is good agreement between traces produced by the best candidate found by Genetic Algorithm, and exhaustive grid search." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": true - }, - "outputs": [], - "source": [ - "import pandas as pd\n", - "\n", - "print(dtc_pop[0].scores)\n", - "print(minimagr_dtc.scores)\n", - "print(sum(list(dtc_pop[0].scores.values())))\n", - "print(sum(list(minimagr_dtc.scores.values())))\n", - "miniga = sum(list(dtc_pop[0].scores.values()))\n", - "print(miniga)\n" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Quantize distance between minimimum error and maximum error.\n", - "This step will allow the GA's performance to be located within or below the range of error found by grid search.\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": true - }, - "outputs": [], - "source": [ - "print(maxi)\n", - "print(mini)\n", - "print(miniga)\n", - "quantize_distance = list(np.linspace(mini,maxi,10))\n", - "\n", - "# check that the nsga error is in the bottom 1/5th of the entire error range.\n", - "print('Report: ')\n", - "print(\"Success\" if bool(miniga < quantize_distance[0]) else \"Failure\")\n", - "print(\"The nsga error %f is in the bottom 1/5th of the entire error range\" % miniga)\n", - "print(\"Minimum = %f; 20th percentile = %f; Maximum = %f\" % (mini,quantize_distance[0],maxi))" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "The code below reports on the differences between between attributes of best models found via grid versus attributes of best models found via GA search:\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": true - }, - "outputs": [], - "source": [ - "import pandas as pd\n", - "\n", - "from neuronunit.optimization import evaluate_as_module as eam\n", - "NSGAO = NSGA(0.85)\n", - "NSGAO.setnparams(nparams=nparams,provided_keys=provided_keys)\n", - "#td = eam.get_trans_dict(NSGAO.subset)\n", - "#print(td)\n", - "td = { v:k for k,v in enumerate(td) }\n", - "from neuronunit.optimization import model_parameters as modelp\n", - "mp = modelp.model_params\n", - "#minimaga = pareto_dtc[0]\n", - "for k,v in minimagr_dtc.attrs.items():\n", - " #hvgrid = np.linspace(np.min(mp[k]),np.max(mp[k]),10)\n", - " dimension_length = np.max(mp[k]) - np.min(mp[k])\n", - " solution_distance_in_1D = np.abs(float(hof[0][td[k]]))-np.abs(float(v))\n", - " \n", - " #solution_distance_in_1D = np.abs(float(minimaga.attrs[k]))-np.abs(float(v))\n", - " relative_distance = dimension_length/solution_distance_in_1D\n", - " print('the difference between brute force candidates model parameters and the GA\\'s model parameters:')\n", - " print(float(hof[0][td[k]])-float(v),hof[0][td[k]],v,k)\n", - " print('the relative distance scaled by the length of the parameter dimension of interest:')\n", - " print(relative_distance)\n", - "\n", - " \n", - " " - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": true - }, - "outputs": [], - "source": [ - "\n", - "print('the difference between the bf error and the GA\\'s error:')\n", - "print('grid search:')\n", - "from numpy import square, mean, sqrt\n", - "rmsg = sqrt(mean(square(list(minimagr_dtc.scores.values()))))\n", - "print(rmsg)\n", - "print('ga:')\n", - "rmsga = sqrt(mean(square(list(dtc_pop[0].scores.values()))))\n", - "print(rmsga)\n", - "print('Hall of Fame front')\n", - "print(sqrt(mean(square(list(hof[0].fitness.values)))))\n", - "print(miniga)\n" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "If any time is left over, may as well compute a more accurate grid, to better quantify GA performance in the future." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": true - }, - "outputs": [], - "source": [] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": true - }, - "outputs": [], - "source": [ - "from neuronunit.optimization import get_neab\n", - "#fi_basket = {'nlex_id':'NLXCELL:100201'}\n", - "neuron = {'nlex_id': 'nifext_50'} \n", - "\n", - "error_criterion, inh_observations = get_neab.get_neuron_criteria(fi_basket)\n", - "print(error_criterion)\n", - "\n", - "from bluepyopt.deapext.optimisations import DEAPOptimisation\n", - "\n", - "DO = DEAPOptimisation(error_criterion=error_criterion)\n", - "DO.setnparams(nparams = nparams, provided_keys = provided_keys)\n", - "pop, hof, log, history, td, gen_vs_hof = DO.run(offspring_size = MU, max_ngen = NGEN, cp_frequency=4,cp_filename='checkpointedGA.p')\n", - "with open('ga_dump.p','wb') as f:\n", - " pickle.dump([pop, log, history, hof, td],f)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": true - }, - "outputs": [], - "source": [ - "# Layer V pyramidal cell\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": true - }, - "outputs": [], - "source": [] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": true - }, - "outputs": [], - "source": [] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": true - }, - "outputs": [], - "source": [] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": true - }, - "outputs": [], - "source": [] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": true - }, - "outputs": [], - "source": [] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": true - }, - "outputs": [], - "source": [] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": true - }, - "outputs": [], - "source": [] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": true - }, - "outputs": [], - "source": [] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": true - }, - "outputs": [], - "source": [] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.5.2" - } - }, - "nbformat": 4, - "nbformat_minor": 2 -} diff --git a/neuronunit/unit_test/test_ga_versus_grid.py b/neuronunit/unit_test/test_ga_versus_grid.py deleted file mode 100644 index a66c91e88..000000000 --- a/neuronunit/unit_test/test_ga_versus_grid.py +++ /dev/null @@ -1,561 +0,0 @@ - -# coding: utf-8 - -# In[2]: - -import matplotlib as mpl -mpl.backend('Agg') -get_ipython().system('%matplotlib tk') -get_ipython().system('jupyter kernelspec install /tmp/share/jupyter/kernels/python3') - - - -# -# # Assumptions about this code: -# The NB was launched with a command that mounts two volumes inside a docker container. -# In the future invocation of this script will be simplified greatly. NU is from a specific fork and branch -b results https://github.com/russelljjarvis/neuronunit -# BluePyOpt is also from a specific fork and branch: -b elitism https://github.com/russelljjarvis/BluePyOpt -# -# Below BASH code for Ubuntu host: -# -# ``` bash -# cd ~/git/neuronunit; sudo docker run -it -v `pwd`:/home/jovyan/neuronunit -v ~/git/BluePyOpt:/home/jovyan/BluePyOpt neuronunit-optimization /bin/bash' -# ``` -# -# ## Parallel Environment. -# Parallelisation module: dask distributed. - -# In[2]: - -import os -os.system('jupyter trust test_ga_versus_grid.ipynb'); #suppress the untrusted notebook warning. - - - -# In[3]: - -import pickle -with open('dump_all_cells','rb') as f: - pipe_results = pickle.load(f) -import pprint -pprint.pprint(pipe_results) - - -# In[ ]: - - - - -# In[ ]: - -import pandas as pd -cell_names = list(pipe_results.keys()) -param_names = list(pipe_results[list(pipe_results.keys())[0]]['pop'][0].dtc.attrs.keys()) -df = pd.DataFrame(index=pipe_results.keys(),columns=param_names) - - -# In[ ]: - - - - -# In[ ]: - -import pandas as pd -for index, val in enumerate(pipe_results.values()): - if index == 0: - sci = pd.DataFrame(list(val['pop'][0].dtc.scores.values())).T - else: - sci = sci.append(pd.DataFrame(list(val['pop'][0].dtc.scores.values())).T) - -#sci.columns = val['pop'][0].dtc.scores.keys() -#print(sci) -print(sci) - - -# In[ ]: - -import pandas as pd -for index, val in enumerate(pipe_results.values()): - if index == 0: - attrs = pd.DataFrame(list(val['pop'][0].dtc.attrs.values())).T - else: - attrs = attrs.append(pd.DataFrame(list(val['pop'][0].dtc.attrs.values())).T) - -attrs.columns = val['pop'][0].dtc.attrs.keys() -#print(attrs) -attrs - - -# In[ ]: - -import pandas as pd - - -for index, val in enumerate(pipe_results.values()): - if index == 0: - #,columns=['Dice number','value'],index=[1,2,3,4]) - rheobase = pd.DataFrame([i.dtc.rheobase for i in val['pop']]).T - else: - rheobase = rheobase.append(pd.DataFrame([i.dtc.rheobase for i in val['pop']]).T) - -rheobase - -names = [ str('generation: ')+str(i) for i in range(0,len(rheobase)) ] - -rheobase - - -# In[ ]: - -import matplotlib -get_ipython().magic('matplotlib inline') - - -# ################ -# # GA parameters: -# about $10^{3}=30$ models will be made, excluding rheobase search. -# ################ -# -# -# # Choice of selection criteria is important. -# Here we use BluepyOpts IBEA, such that it can be compared to NSGA2. -# -# https://link.springer.com/article/10.1007/s00500-005-0027-5 -# -# -# - -# In[ ]: - -MU = 6; NGEN = 6; CXPB = 0.9 -USE_CACHED_GA = False - - -# ################ -# # Grid search parameters: -# $ 2^{10}=1024 $ models, will be made excluding rheobase search -# ################ - -# An oppurtunity to improve grid search, by increasing resolution of search intervals given a first pass: - -# In[ ]: - -from neuronunit.plottools import plot_surface - - -# # Below two error surface slices from the hypervolume are plotted. -# The data that is plotted consists of the error as experienced by the GA. -# Note: the GA performs an incomplete, and efficient sampling of the parameter space, and therefore sample points are irregularly spaced. Polygon interpolation is used to visualize error gradients. Existing plotting code from the package BluePyOpt has been extended for this purpose. -# Light blue dots indicate local minima's of error experienced by the NSGA algrorithm. - -# In[ ]: - -for index, val in enumerate(pipe_results.values()): - td = val['td_py'] - history = val['history'] - - plot_surface('a','b',td,history) - - -# In[ ]: - - -for index, val in enumerate(pipe_results.values()): - td = val['td_py'] - history = val['history'] - - plot_surface('v0','vt',td,history) - - - -# In[ ]: - -list(pipe_results.keys()) - - -# In[ ]: - -for index, val in enumerate(pipe_results.values()): - ''' - log = val['log'] - gen_numbers =[ i for i in range(0,len(log.select('gen'))) ] - hof = val['hof_py'] - mean = np.array([ np.sqrt(np.mean(np.square(i))) for i in log.select('avg')]) - std = np.array([ np.sqrt(np.mean(np.square(i))) for i in log.select('std')]) - minimum = np.array([ np.sqrt(np.mean(np.square(i))) for i in log.select('min')]) - best_line = np.array([ np.sqrt(np.mean(np.square(list(p.fitness.values)))) for p in hof]) - blg = [ best_line[h] for i, h in enumerate(gen_numbers) ] - - ''' - print(val['gen_vs_hof']) - - -# In[ ]: - - -get_ipython().magic('matplotlib inline') -import matplotlib.pyplot as plt -import numpy as np - -plt.style.use('ggplot') -fig, axes = plt.subplots(figsize=(10, 10), facecolor='white') - -for index, val in enumerate(pipe_results.values()): - - log = val['log'] - gen_numbers =[ i for i in range(0,len(log.select('gen'))) ] - hof = val['hof_py'] - mean = np.array([ np.sqrt(np.mean(np.square(i))) for i in log.select('avg')]) - std = np.array([ np.sqrt(np.mean(np.square(i))) for i in log.select('std')]) - minimum = np.array([ np.sqrt(np.mean(np.square(i))) for i in log.select('min')]) - best_line = np.array([ np.sqrt(np.mean(np.square(list(p.fitness.values)))) for p in hof]) - blg = [ best_line[h] for i, h in enumerate(gen_numbers) ] - - - mean_ = np.mean(val['gen_vs_hof']) - #std_ = np.std(val['gen_vs_hof']) - # print(gvh) - #import pdb - #pdb.set_trace() - - stdminus = mean - std - stdplus = mean + std - try: - assert len(gen_numbers) == len(stdminus) == len(stdplus) - except: - pass - - axes.plot( - gen_numbers, - mean, - color='black', - linewidth=2, - label='population average') - axes.fill_between(gen_numbers, stdminus, stdplus) - - #axes.plot([i for i in range(0,len(gvh))], gvh,'y--', linewidth=2, label='grid search error') - #axes.plot(gen_numbers, bl, 'go', linewidth=2, label='hall of fame error') - - #axes.plot(gen_numbers, stdminus, label='std variation lower limit') - #axes.plot(gen_numbers, stdplus, label='std variation upper limit') - - #axes.set_xlim(np.min(gen_numbers) - 1, np.max(gen_numbers) + 1) - #axes.set_xlabel('Generations') - #axes.set_ylabel('Sum of objectives') - #axes.legend() - #fig.tight_layout() - #fig.show() - - -# # Comment on plot -# The plot shows the mean error value of the population as the GA evolves it's population. The red interval at any instant is the standard deviation of the error. The fact that the mean GA error is able to have a net upwards trajectory, after experiencing a temporary downwards trajectory, demonstrates that the GA retains a drive to explore, and is resiliant against being stuck in a local minima. Also in the above plot population variance in error stays remarkably constant, in this way BluePyOpts selection criteria SELIBEA contrasts with DEAPs native selection strategy NSGA2 - - -# In[ ]: - -dtcpopg = [ dtc for dtc in dtcpopg if not None in (dtc.scores.values()) ] -dtcpopg = [ (dtc,sum(list(dtc.scores.values()))) for dtc in dtcpopg ] - - -sorted_grid = sorted(dtcpopg,key=lambda x:x[1]) -sorted_grid = [dtc[0] for dtc in sorted_grid] -#print(sorted_grid) -mini = dtcpopg[0][1] -maxi = dtcpopg[-1][1] -minimagr = sorted_grid[0] -minimagr_dtc = sorted_grid[0] -minimagr_dtc_1 = sorted_grid[1] -minimagr_dtc_2 = sorted_grid[2] -from neuronunit.optimization.exhaustive_search import create_refined_grid -refined_grid = create_refined_grid(minimagr_dtc, minimagr_dtc_1,minimagr_dtc_2) - - -# In[ ]: - -print(td) -def pop2dtc(pop1,DO,td): - ''' - This function takes the DEAP population data type, and converts it to a more convenient - data transport object, which can more readily be used in plotting functions. - This a wasteful, recompute, which is in part necessitated because - deaps pareto front object, only returns gene individual objects (elements of population) - ''' - from neuronunit.optimization import nsga_parallel - DO.td = td - assert DO.td == td - return_package = nsga_parallel.update_pop(pop1,td); - dtc_pop = [] - for i,r in enumerate(return_package): - dtc_pop.append(r[0]) - dtc_pop[i].error = None - dtc_pop[i].error = np.sqrt(np.mean(np.square(list(pop1[i].fitness.values)))) - sorted_list = sorted([(dtc,dtc.error) for dtc in dtc_pop],key=lambda x:x[1]) - dtc_pop = [dtc[0] for dtc in sorted_list] - print(dtc_pop,sorted_list) - return dtc_pop - -DO.td = td -print(hof[0]) -print(len(hof)) -dtc_pop = pop2dtc(hof[0:-1],DO,td) - -miniga = dtc_pop[0].error -maxiga = dtc_pop[-1].error -maximaga = dtc_pop[-1] -minimaga = dtc_pop[0] - -CACHE_PF = False - - -# In[ ]: - - -if CACHE_PF == False: - h = list(history.genealogy_history.values()) - evaluated_history = [] - for i in h: - if hasattr(i,'rheobase'): - i.error = None - i.error = np.sqrt(np.mean(np.square(list(i.fitness.values)))) - evaluated_history.append(i) - sorted_list = sorted([(i,i.error) for i in evaluated_history ],key=lambda x:x[1]) - - with open('pf_dump.p','wb') as f: - pickle.dump([ sorted_list, evaluated_history ],f) -else: - - unpack = pickle.load(open('pf_dump.p','rb')) - print(unpack) - sorted_list_pf = unpack[0] - pareto_dtc = unpack[1] - -minimaga_ind = sorted_list[0][0] -maximaga_ind = sorted_list[-1][0] -miniga = sorted_list[0][1] -maxiga = sorted_list[-1][1] - - -# In[ ]: - -import pandas as pd - -print(miniga) -print(maxiga) -print(minimaga_ind.fitness.values) -print(maximaga_ind.fitness.values) -print(len(minimaga_ind.fitness.values)) -print(dtcpopg[0][0].rheobase) -print(dtcpopg[0][0].scores) -print(sorted_list[-1][0].rheobase) -print(sorted_list[-1][0].fitness.values) - - - -# In[ ]: - -def use_dtc_to_plotting(dtcpop,minimagr): - from neuronunit.capabilities import spike_functions - import matplotlib.pyplot as plt - import numpy as np - plt.clf() - plt.style.use('ggplot') - fig, axes = plt.subplots(figsize=(10, 10), facecolor='white') - stored_min = [] - stored_max = [] - for dtc in dtcpop[1:-1]: - plt.plot(dtc.tvec, dtc.vm0,linewidth=3.5, color='grey') - stored_min.append(np.min(dtc.vm0)) - stored_max.append(np.max(dtc.vm0)) - - from neuronunit.models.reduced import ReducedModel - from neuronunit.optimization.get_neab import tests as T - from neuronunit.optimization import get_neab - from neuronunit.optimization import evaluate_as_module - from neuronunit.optimization.evaluate_as_module import pre_format - model = ReducedModel(get_neab.LEMS_MODEL_PATH,name=str('vanilla'),backend='NEURON') - import neuron - model._backend.reset_neuron(neuron) - model.set_attrs(minimagr.attrs) - model.rheobase = minimagr.rheobase['value'] - minimagr = pre_format(minimagr) - parameter_list = list(minimagr.vtest.values()) - model.inject_square_current(parameter_list[0]) - model._backend.local_run() - assert model.get_spike_count() == 1 - print(model.get_spike_count(),bool(model.get_spike_count() == 1)) - brute_best = list(model.results['vm']) - - plt.plot(dtcpop[0].tvec, brute_best,linewidth=1, color='blue',label='best candidate via grid')#+str(mini.scores)) - plt.plot(dtcpop[0].tvec,dtcpop[0].vm0,linewidth=1, color='red',label='best candidate via GA')#+str(miniga.scores)) - plt.legend() - plt.ylabel('$V_{m}$ mV') - plt.xlabel('ms') - plt.show() -from neuronunit import plottools -from neuronunit.plottools import dtc_to_plotting - -CACHE_PLOTTING = False -if CACHE_PLOTTING == False: - dtc_pop = dview.map_sync(dtc_to_plotting,dtc_pop ) - with open('plotting_dump.p','wb') as f: - pickle.dump(dtc_pop,f) -else: - dtc_pop = pickle.load(open('plotting_dump.p','rb')) - -use_dtc_to_plotting(dtc_pop,minimagr_dtc) - - - -# # Comment on plot -# There is good agreement between traces produced by the best candidate found by Genetic Algorithm, and exhaustive grid search. - -# In[ ]: - -import pandas as pd - -print(dtc_pop[0].scores) -print(minimagr_dtc.scores) -print(sum(list(dtc_pop[0].scores.values()))) -print(sum(list(minimagr_dtc.scores.values()))) -miniga = sum(list(dtc_pop[0].scores.values())) -print(miniga) - - -# # Quantize distance between minimimum error and maximum error. -# This step will allow the GA's performance to be located within or below the range of error found by grid search. -# - -# In[ ]: - -print(maxi) -print(mini) -print(miniga) -quantize_distance = list(np.linspace(mini,maxi,10)) - -# check that the nsga error is in the bottom 1/5th of the entire error range. -print('Report: ') -print("Success" if bool(miniga < quantize_distance[0]) else "Failure") -print("The nsga error %f is in the bottom 1/5th of the entire error range" % miniga) -print("Minimum = %f; 20th percentile = %f; Maximum = %f" % (mini,quantize_distance[0],maxi)) - - -# The code below reports on the differences between between attributes of best models found via grid versus attributes of best models found via GA search: -# - -# In[ ]: - -import pandas as pd - -from neuronunit.optimization import evaluate_as_module as eam -NSGAO = NSGA(0.85) -NSGAO.setnparams(nparams=nparams,provided_keys=provided_keys) -#td = eam.get_trans_dict(NSGAO.subset) -#print(td) -td = { v:k for k,v in enumerate(td) } -from neuronunit.optimization import model_parameters as modelp -mp = modelp.model_params -#minimaga = pareto_dtc[0] -for k,v in minimagr_dtc.attrs.items(): - #hvgrid = np.linspace(np.min(mp[k]),np.max(mp[k]),10) - dimension_length = np.max(mp[k]) - np.min(mp[k]) - solution_distance_in_1D = np.abs(float(hof[0][td[k]]))-np.abs(float(v)) - - #solution_distance_in_1D = np.abs(float(minimaga.attrs[k]))-np.abs(float(v)) - relative_distance = dimension_length/solution_distance_in_1D - print('the difference between brute force candidates model parameters and the GA\'s model parameters:') - print(float(hof[0][td[k]])-float(v),hof[0][td[k]],v,k) - print('the relative distance scaled by the length of the parameter dimension of interest:') - print(relative_distance) - - - - - -# In[ ]: - - -print('the difference between the bf error and the GA\'s error:') -print('grid search:') -from numpy import square, mean, sqrt -rmsg = sqrt(mean(square(list(minimagr_dtc.scores.values())))) -print(rmsg) -print('ga:') -rmsga = sqrt(mean(square(list(dtc_pop[0].scores.values())))) -print(rmsga) -print('Hall of Fame front') -print(sqrt(mean(square(list(hof[0].fitness.values))))) -print(miniga) - - -# If any time is left over, may as well compute a more accurate grid, to better quantify GA performance in the future. - -# In[ ]: - - - - -# In[ ]: - -from neuronunit.optimization import get_neab -#fi_basket = {'nlex_id':'NLXCELL:100201'} -neuron = {'nlex_id': 'nifext_50'} - -error_criterion, inh_observations = get_neab.get_neuron_criteria(fi_basket) -print(error_criterion) - -from bluepyopt.deapext.optimisations import DEAPOptimisation - -DO = DEAPOptimisation(error_criterion=error_criterion) -DO.setnparams(nparams = nparams, provided_keys = provided_keys) -pop, hof, log, history, td, gen_vs_hof = DO.run(offspring_size = MU, max_ngen = NGEN, cp_frequency=4,cp_filename='checkpointedGA.p') -with open('ga_dump.p','wb') as f: - pickle.dump([pop, log, history, hof, td],f) - - -# In[ ]: - -# Layer V pyramidal cell - - -# In[ ]: - - - - -# In[ ]: - - - - -# In[ ]: - - - - -# In[ ]: - - - - -# In[ ]: - - - - -# In[ ]: - - - - -# In[ ]: - - - - -# In[ ]: - - - - -# In[ ]: diff --git a/neuronunit/unit_test/test_glif.py b/neuronunit/unit_test/test_glif.py deleted file mode 100644 index c18315499..000000000 --- a/neuronunit/unit_test/test_glif.py +++ /dev/null @@ -1,38 +0,0 @@ -import os -import sys -import matplotlib -matplotlib.use('Agg') - -import matplotlib.pyplot as plt - -from neuronunit.models.interfaces import glif -gc = glif.GC() -from neuronunit.optimization import get_neab #import get_neuron_criteria, impute_criteria -from neuronunit.optimization.model_parameters import model_params - -import os -import pickle -electro_path = 'pipe_tests.p' - -assert os.path.isfile(electro_path) == True -with open(electro_path,'rb') as f: - electro_tests = pickle.load(f) - - -MU = 6; NGEN = 6; CXPB = 0.9 -USE_CACHED_GA = False - -#provided_keys = list(model_params.keys()) -USE_CACHED_GS = False -from bluepyopt.deapext.optimisations import DEAPOptimisation -npoints = 2 -nparams = 10 - -from dask import distributed -test = electro_tests[0][0] - -#for test, observation in electro_tests: -DO = DEAPOptimisation(error_criterion = test, selection = 'selIBEA', nparams = 10, provided_dict = model_params) -#DO = DEAPOptimisation(error_criterion = test, selection = 'selIBEA', backend = 'glif') -#DO.setnparams(nparams = nparams, provided_keys = provided_keys) -pop, hof_py, log, history, td_py, gen_vs_hof = DO.run(offspring_size = MU, max_ngen = NGEN, cp_frequency=0,cp_filename='ga_dumpnifext_50.p') diff --git a/neuronunit/unit_test/test_morphology.py b/neuronunit/unit_test/test_morphology.py deleted file mode 100755 index 1aba13043..000000000 --- a/neuronunit/unit_test/test_morphology.py +++ /dev/null @@ -1,301 +0,0 @@ -"""Tests of the 38 Druckmann 2013 test classes""" - -import unittest -from sciunit.suites import TestSuite -from neuronunit.models.morphology import SwcCellModel -from neuronunit.tests.morphology import * -import quantities as pq - -# SWC file and expected values are from: http://neuromorpho.org/neuron_info.jsp?neuron_name=VD110330-IDC -model = SwcCellModel('neuronunit/unit_test/morphology/pyramidalCell.swc') - -class MorphologyTestCase(unittest.TestCase): - # -------------------- Somatic ------------------------------ # - def test_SomaSurfaceAreaTest(self): - test = SomaSurfaceAreaTest({"mean":1739.11*pq.um**2, "std":1*pq.um**2,"n":1}) - z = test.judge(model) - self.assertLess(abs(z.score), 0.1) - - def test_NumberofStemsTest(self): - test = NumberofStemsTest({"mean":10, "std":1,"n":1}) - z = test.judge(model) - self.assertLess(abs(z.score), 0.1) - - - # -------------------- Whole Cell ------------------------------ # - def test_NumberofBifurcationsTest(self): - test = NumberofBifurcationsTest({"mean":138, "std":1,"n":1}) - z = test.judge(model) - self.assertLess(abs(z.score), 0.1) - - def test_NumberofBranchesTest(self): - test = NumberofBranchesTest({"mean":286, "std":1,"n":1}) - z = test.judge(model) - self.assertLess(abs(z.score), 0.1) - - def test_OverallWidthTest(self): - test = OverallWidthTest({"mean":771.2*pq.um, "std":1*pq.um,"n":1}) - z = test.judge(model) - self.assertLess(abs(z.score), 0.1) - - def test_OverallHeightTest(self): - test = OverallHeightTest({"mean":1672.09*pq.um, "std":1*pq.um,"n":1}) - z = test.judge(model) - self.assertLess(abs(z.score), 0.1) - - def test_OverallDepthTest(self): - test = OverallDepthTest({"mean":198.75*pq.um, "std":1*pq.um,"n":1}) - z = test.judge(model) - self.assertLess(abs(z.score), 0.1) - - def test_AverageDiameterTest(self): - test = AverageDiameterTest({"mean":0.38*pq.um, "std":1*pq.um,"n":1}) - z = test.judge(model) - self.assertLess(abs(z.score), 0.1) - - def test_TotalLengthTest(self): - test = TotalLengthTest({"mean":16949.9*pq.um, "std":1*pq.um,"n":1}) - z = test.judge(model) - self.assertLess(abs(z.score), 0.1) - - def test_TotalSurfaceTest(self): - test = TotalSurfaceTest({"mean":18620.5*pq.um**2, "std":1*pq.um**2,"n":1}) - z = test.judge(model) - self.assertLess(abs(z.score), 0.1) - - def test_TotalVolumeTest(self): - test = TotalVolumeTest({"mean":3558.79*pq.um**3, "std":1*pq.um**3,"n":1}) - z = test.judge(model) - self.assertLess(abs(z.score), 0.1) - - def test_MaxEuclideanDistanceTest(self): - test = MaxEuclideanDistanceTest({"mean":1099.26*pq.um, "std":1*pq.um,"n":1}) - z = test.judge(model) - self.assertLess(abs(z.score), 0.1) - - def test_MaxPathDistanceTest(self): - test = MaxPathDistanceTest({"mean":1368.54*pq.um, "std":1*pq.um,"n":1}) - z = test.judge(model) - self.assertLess(abs(z.score), 0.1) - - def test_MaxBranchOrderTest(self): - test = MaxBranchOrderTest({"mean":23, "std":1,"n":1}) - z = test.judge(model) - self.assertLess(abs(z.score), 0.1) - - def test_AverageContractionTest(self): - test = AverageContractionTest({"mean":0.86, "std":1,"n":1}) - z = test.judge(model) - self.assertLess(abs(z.score), 0.1) - - def test_PartitionAsymmetryTest(self): - test = PartitionAsymmetryTest({"mean":0.51, "std":1,"n":1}) - z = test.judge(model) - self.assertLess(abs(z.score), 0.1) - - def test_AverageRallsRatioTest(self): - test = AverageRallsRatioTest({"mean":1.6, "std":1,"n":1}) - z = test.judge(model) - self.assertLess(abs(z.score), 0.1) - - def test_AverageBifurcationAngleLocalTest(self): - test = AverageBifurcationAngleLocalTest({"mean":80.94, "std":1,"n":1}) - z = test.judge(model) - self.assertLess(abs(z.score), 0.1) - - def test_AverageBifurcationAngleRemoteTest(self): - test = AverageBifurcationAngleRemoteTest({"mean":71.75, "std":1,"n":1}) - z = test.judge(model) - self.assertLess(abs(z.score), 0.1) - - def test_FractalDimensionTest(self): - test = FractalDimensionTest({"mean":1.05, "std":1,"n":1}) - z = test.judge(model) - self.assertLess(abs(z.score), 0.1) - - # -------------------- APICAL ------------------------------ # - def test_ApicalDendriteNumberofBifurcationsTest(self): - test = ApicalDendriteNumberofBifurcationsTest({"mean": 138, "std": 1, "n": 1}) - z = test.judge(model) - self.assertLess(abs(z.score)-73, 0.1) - - def test_ApicalDendriteNumberofBranchesTest(self): - test = ApicalDendriteNumberofBranchesTest({"mean": 286, "std": 1, "n": 1}) - z = test.judge(model) - self.assertLess(abs(z.score)-155, 0.1) - - def test_ApicalDendriteOverallWidthTest(self): - test = ApicalDendriteOverallWidthTest({"mean": 771.2 * pq.um, "std": 1 * pq.um, "n": 1}) - z = test.judge(model) - self.assertLess(abs(z.score)-461.617, 0.1) - - def test_ApicalDendriteOverallHeightTest(self): - test = ApicalDendriteOverallHeightTest({"mean": 1672.09 * pq.um, "std": 1 * pq.um, "n": 1}) - z = test.judge(model) - self.assertLess(abs(z.score)-560.8, 0.1) - - def test_ApicalDendriteOverallDepthTest(self): - test = ApicalDendriteOverallDepthTest({"mean": 198.75 * pq.um, "std": 1 * pq.um, "n": 1}) - z = test.judge(model) - self.assertLess(abs(z.score)-8.16, 0.1) - - def test_ApicalDendriteAverageDiameterTest(self): - test = ApicalDendriteAverageDiameterTest({"mean": 0.38 * pq.um, "std": 1 * pq.um, "n": 1}) - z = test.judge(model) - self.assertLess(abs(z.score), 0.1) - - def test_ApicalDendriteTotalLengthTest(self): - test = ApicalDendriteTotalLengthTest({"mean": 7791.44 * pq.um, "std": 1 * pq.um, "n": 1}) - z = test.judge(model) - self.assertLess(abs(z.score), 0.1) - - def test_ApicalDendriteTotalSurfaceTest(self): - test = ApicalDendriteTotalSurfaceTest({"mean": 11094.4 * pq.um ** 2, "std": 1 * pq.um ** 2, "n": 1}) - z = test.judge(model) - self.assertLess(abs(z.score), 0.1) - - def test_ApicalDendriteTotalVolumeTest(self): - test = ApicalDendriteTotalVolumeTest({"mean": 2613.45 * pq.um ** 3, "std": 1 * pq.um ** 3, "n": 1}) - z = test.judge(model) - self.assertLess(abs(z.score), 0.1) - - def test_ApicalDendriteMaxEuclideanDistanceTest(self): - test = ApicalDendriteMaxEuclideanDistanceTest({"mean": 1099.26 * pq.um, "std": 1 * pq.um, "n": 1}) - z = test.judge(model) - self.assertLess(abs(z.score), 0.1) - - def test_ApicalDendriteMaxPathDistanceTest(self): - test = ApicalDendriteMaxPathDistanceTest({"mean": 1368.54 * pq.um, "std": 1 * pq.um, "n": 1}) - z = test.judge(model) - self.assertLess(abs(z.score), 0.1) - - def test_ApicalDendriteMaxBranchOrderTest(self): - test = ApicalDendriteMaxBranchOrderTest({"mean": 23, "std": 1, "n": 1}) - z = test.judge(model) - self.assertLess(abs(z.score), 0.1) - - def test_ApicalDendriteAverageContractionTest(self): - test = ApicalDendriteAverageContractionTest({"mean": 0.86, "std": 1, "n": 1}) - z = test.judge(model) - self.assertLess(abs(z.score), 0.1) - - def test_ApicalDendritePartitionAsymmetryTest(self): - test = ApicalDendritePartitionAsymmetryTest({"mean": 0.51, "std": 1, "n": 1}) - z = test.judge(model) - self.assertLess(abs(z.score), 0.1) - - def test_ApicalDendriteAverageRallsRatioTest(self): - test = ApicalDendriteAverageRallsRatioTest({"mean": 1.6, "std": 1, "n": 1}) - z = test.judge(model) - self.assertLess(abs(z.score), 0.1) - - def test_ApicalDendriteAverageBifurcationAngleLocalTest(self): - test = ApicalDendriteAverageBifurcationAngleLocalTest({"mean": 80.94, "std": 1, "n": 1}) - z = test.judge(model) - self.assertLess(abs(z.score)-0.52, 0.1) - - def test_ApicalDendriteAverageBifurcationAngleRemoteTest(self): - test = ApicalDendriteAverageBifurcationAngleRemoteTest({"mean": 71.75, "std": 1, "n": 1}) - z = test.judge(model) - self.assertLess(abs(z.score)-6.75, 0.1) - - def test_ApicalDendriteFractalDimensionTest(self): - test = ApicalDendriteFractalDimensionTest({"mean": 1.05, "std": 1, "n": 1}) - z = test.judge(model) - self.assertLess(abs(z.score), 0.1) - - - # -------------------- BASAL ------------------------------ # - def test_BasalDendriteNumberofBifurcationsTest(self): - test = BasalDendriteNumberofBifurcationsTest({"mean":138, "std":1,"n":1}) - z = test.judge(model) - self.assertLess(abs(z.score)-110, 0.1) - - def test_BasalDendriteNumberofBranchesTest(self): - test = BasalDendriteNumberofBranchesTest({"mean":286, "std":1,"n":1}) - z = test.judge(model) - self.assertLess(abs(z.score)-222, 0.1) - - def test_BasalDendriteOverallWidthTest(self): - test = BasalDendriteOverallWidthTest({"mean":771.2*pq.um, "std":1*pq.um,"n":1}) - z = test.judge(model) - self.assertLess(abs(z.score)-527.4, 0.1) - - def test_BasalDendriteOverallHeightTest(self): - test = BasalDendriteOverallHeightTest({"mean":1672.09*pq.um, "std":1*pq.um,"n":1}) - z = test.judge(model) - self.assertLess(abs(z.score)-1454.7, 0.1) - - def test_BasalDendriteOverallDepthTest(self): - test = BasalDendriteOverallDepthTest({"mean":198.75*pq.um, "std":1*pq.um,"n":1}) - z = test.judge(model) - self.assertLess(abs(z.score)-119.26, 0.1) - - def test_BasalDendriteAverageDiameterTest(self): - test = BasalDendriteAverageDiameterTest({"mean":0.38*pq.um, "std":1*pq.um,"n":1}) - z = test.judge(model) - self.assertLess(abs(z.score), 0.1) - - def test_BasalDendriteTotalLengthTest(self): - test = BasalDendriteTotalLengthTest({"mean":16949.9*pq.um, "std":1*pq.um,"n":1}) - z = test.judge(model) - self.assertLess(abs(z.score)-13958, 0.1) - - def test_BasalDendriteTotalSurfaceTest(self): - test = BasalDendriteTotalSurfaceTest({"mean":18620.5*pq.um**2, "std":1*pq.um**2,"n":1}) - z = test.judge(model) - self.assertLess(abs(z.score)-15099.17, 0.1) - - def test_BasalDendriteTotalVolumeTest(self): - test = BasalDendriteTotalVolumeTest({"mean":3558.79*pq.um**3, "std":1*pq.um**3,"n":1}) - z = test.judge(model) - self.assertLess(abs(z.score)-2982.2, 0.1) - - def test_BasalDendriteMaxEuclideanDistanceTest(self): - test = BasalDendriteMaxEuclideanDistanceTest({"mean":1099.26*pq.um, "std":1*pq.um,"n":1}) - z = test.judge(model) - self.assertLess(abs(z.score)-900.56, 0.1) - - def test_BasalDendriteMaxPathDistanceTest(self): - test = BasalDendriteMaxPathDistanceTest({"mean":1368.54*pq.um, "std":1*pq.um,"n":1}) - z = test.judge(model) - self.assertLess(abs(z.score)-1130.2, 0.1) - - def test_BasalDendriteMaxBranchOrderTest(self): - test = BasalDendriteMaxBranchOrderTest({"mean":23, "std":1,"n":1}) - z = test.judge(model) - self.assertLess(abs(z.score)-19, 0.1) - - def test_BasalDendriteAverageContractionTest(self): - test = BasalDendriteAverageContractionTest({"mean":0.86, "std":1,"n":1}) - z = test.judge(model) - self.assertLess(abs(z.score), 0.1) - - def test_BasalDendritePartitionAsymmetryTest(self): - test = BasalDendritePartitionAsymmetryTest({"mean":0.51, "std":1,"n":1}) - z = test.judge(model) - self.assertLess(abs(z.score)-0.186, 0.1) - - def test_BasalDendriteAverageRallsRatioTest(self): - test = BasalDendriteAverageRallsRatioTest({"mean":1.6, "std":1,"n":1}) - z = test.judge(model) - self.assertLess(abs(z.score)-0.37, 0.1) - - def test_BasalDendriteAverageBifurcationAngleLocalTest(self): - test = BasalDendriteAverageBifurcationAngleLocalTest({"mean":80.94, "std":1,"n":1}) - z = test.judge(model) - self.assertLess(abs(z.score)-16.33, 0.1) - - def test_BasalDendriteAverageBifurcationAngleRemoteTest(self): - test = BasalDendriteAverageBifurcationAngleRemoteTest({"mean":71.75, "std":1,"n":1}) - z = test.judge(model) - self.assertLess(abs(z.score)-11.2, 0.1) - - def test_BasalDendriteFractalDimensionTest(self): - test = BasalDendriteFractalDimensionTest({"mean":1.05, "std":1,"n":1}) - z = test.judge(model) - self.assertLess(abs(z.score), 0.1) - - -if __name__ == '__main__': - unittest.main() \ No newline at end of file diff --git a/neuronunit/unit_test/test_optimization.py b/neuronunit/unit_test/test_optimization.py deleted file mode 100644 index f1c3cb76a..000000000 --- a/neuronunit/unit_test/test_optimization.py +++ /dev/null @@ -1,455 +0,0 @@ -"""Tests of NeuronUnit test classes""" -import unittest -import os -import sys -from sciunit.utils import NotebookTools -import dask -from dask import bag -import dask.bag as db -import matplotlib as mpl -mpl.use('Agg') # Avoid any problems with Macs or headless displays. - -from sciunit.utils import NotebookTools,import_all_modules -from neuronunit import neuroelectro,bbp,aibs - -from .base import * - -class DataTCTestCase(unittest.TestCase): - def test_get_ss(self): - from neuronunit.optimization.data_transport_container import DataTC - dtc = DataTC() - self.assertIsNone(dtc.get_ss()) - dtc.scores = {'score1' : 1} - self.assertEqual(dtc.get_ss(), 1) - dtc.scores = {'score1' : 1, 'score2' : 2} - self.assertEqual(dtc.get_ss(), 3) - -def grid_points(): - from neuronunit.optimization.optimization_management import map_wrapper - - npoints = 2 - nparams = 10 - from neuronunit.optimization.model_parameters import model_params - provided_keys = list(model_params.keys()) - USE_CACHED_GS = False - from neuronunit.optimization import exhaustive_search - from neuronunit.optimization.optimization_management import map_wrapper - ## exhaustive_search - - grid_points = exhaustive_search.create_grid(npoints = npoints,nparams = nparams) - b0 = db.from_sequence(grid_points[0:2], npartitions=8) - dtcpop = list(db.map(exhaustive_search.update_dtc_grid,b0).compute()) - assert dtcpop is not None - return dtcpop - -def test_compute_score(dtcpop): - from neuronunit.optimization.optimization_management import map_wrapper - from neuronunit.optimization import get_neab - from neuronunit.optimization.optimization_management import dtc_to_rheo - from neuronunit.optimization.optimization_management import nunit_evaluation - from neuronunit.optimization.optimization_management import format_test - dtclist = list(map(dtc_to_rheo,dtcpop)) - for d in dtclist: - assert len(list(d.attrs.values())) > 0 - dtclist = map_wrapper(format_test, dtclist) - dtclist = map_wrapper(nunit_evaluation, dtclist) - return dtclist - -class testOptimizationBackend(NotebookTools,unittest.TestCase): - - def setUp(self): - self.predictions = None - self.predictionp = None - self.score_p = None - self.score_s = None - self.grid_points = grid_points() - dtcpop = self.grid_points - self.test_compute_score = test_compute_score - self.dtcpop = test_compute_score(dtcpop) - self.dtc = self.dtcpop[0] - self.rheobase = self.dtc.rheobase - from neuronunit.models.reduced import ReducedModel - from neuronunit.optimization import get_neab - self.standard_model = ReducedModel(get_neab.LEMS_MODEL_PATH, backend='NEURON') - self.model = ReducedModel(get_neab.LEMS_MODEL_PATH, backend='NEURON') - - def get_observation(self, cls): - print(cls.__name__) - neuron = {'nlex_id': 'nifext_50'} # Layer V pyramidal cell - return cls.neuroelectro_summary_observation(neuron) - - def run_test(self, cls, pred =None): - observation = self.get_observation(cls) - test = cls(observation=observation) - score = test.judge(self.standard_model, stop_on_error = True, deep_error = True) - return score - - - def test_map_wrapper(self): - npoints = 2 - nparams = 3 - from neuronunit.optimization.model_parameters import model_params - provided_keys = list(model_params.keys()) - USE_CACHED_GS = False - from neuronunit.optimization import exhaustive_search - from neuronunit.optimization.optimization_management import map_wrapper - grid_points = exhaustive_search.create_grid(npoints = npoints,nparams = nparams) - b0 = db.from_sequence(grid_points[0:2], npartitions=8) - dtcpop = list(db.map(exhaustive_search.update_dtc_grid,b0).compute()) - assert dtcpop is not None - dtcpop_compare = map_wrapper(exhaustive_search.update_dtc_grid,grid_points[0:2]) - for i,j in enumerate(dtcpop): - for k,v in dtcpop_compare[i].attrs.items(): - print(k,v,i,j) - self.assertEqual(j.attrs[k],v) - return True - - def test_grid_dimensions(self): - from neuronunit.optimization.model_parameters import model_params - provided_keys = list(model_params.keys()) - USE_CACHED_GS = False - from neuronunit.optimization import exhaustive_search - from neuronunit.optimization.optimization_management import map_wrapper - import dask.bag as db - npoints = 2 - nparams = 3 - for i in range(1,10): - for j in range(1,10): - grid_points = exhaustive_search.create_grid(npoints = i, nparams = j) - b0 = db.from_sequence(grid_points[0:2], npartitions=8) - dtcpop = list(db.map(exhaustive_search.update_dtc_grid,b0).compute()) - self.assertEqual(i*j,len(dtcpop)) - self.assertNotEqual(dtcpop,None) - dtcpop_compare = map_wrapper(exhaustive_search.update_dtc_grid,grid_points[0:2]) - self.assertNotEqual(dtcpop_compare,None) - self.assertEqual(len(dtcpop_compare),len(dtcpop)) - for i,j in enumerate(dtcpop): - for k,v in dtcpop_compare[i].attrs.items(): - print(k,v,i,j) - self.assertEqual(j.attrs[k],v) - - return True - - - def test_get_rate_CV(self): - from neuronunit.tests import dynamics - import quantities as pq - doi = 'doi:10.1113/jphysiol.2010.200683' - observation = {} - observation[doi] = {} - observation[doi]['isi'] = 598.0*pq.ms - observation[doi]['mean'] = 598.0*pq.ms - observation[doi]['std'] = 37.0*pq.ms - isi_test = dynamics.ISITest(observation = observation[doi]) - # Put them together in a test suite - - observation = {} - observation[doi] = {} - observation[doi]['cv'] = 0.210526*pq.dimensionless - observation[doi]['mean'] = 0.210526*pq.dimensionless - observation[doi]['std'] = 0.105263*pq.dimensionless - #si_test = dynamics.ISITest(observation = observation[doi]) - - cv_test = dynamics.CVTest(observation = observation[doi]) - ap_tests = sciunit.TestSuite([isi_test,cv_test], name="AP CV and ISI Tests") - - from neuronunit.models import static_model - true_model = self.model - params = {} - params['injected_square_current'] = {} - params['injected_square_current']['amplitude'] = self.rheobase*2.01 - model.inject_square_current(params) - vm = model.get_membrane_potential() - proxy_model = static_model.StaticModel(vm = vm, st = None) - score_matrix = ap_tests.judge(proxy_model) - for score in score_matrix: - self.assertNotEqual(score,None) - - - # Put them together in a test suite - #ap_tests = sciunit.TestSuite([ap_width_test,ap_amplitude_test], name="AP Tests") - - - #@unittest.skip("Not fully developed yet") - def test_get_inhibitory_neuron(self): - from neuronunit import tests as nu_tests, neuroelectro - from neuronunit.tests import passive, waveform, fi - fi_basket = {'nlex_id':'NLXCELL:100201'} - observation = fi.neuroelectro_summary_observation(fi_basket) - test_class_params = [(fi.RheobaseTest,None), - (passive.InputResistanceTest,None), - (passive.TimeConstantTest,None), - (passive.CapacitanceTest,None), - (passive.RestingPotentialTest,None), - (waveform.InjectedCurrentAPWidthTest,None), - (waveform.InjectedCurrentAPAmplitudeTest,None), - (waveform.InjectedCurrentAPThresholdTest,None)]#, - inh_observations = [] - for cls,params in test_class_params: - inh_observations.append(cls.neuroelectro_summary_observation(fi_basket)) - self.inh_observations = inh_observations - return inh_observations - - def test_rheobase(self): - from neuronunit.models.reduced import ReducedModel - from neuronunit.optimization import get_neab - import copy - import unittest - dtc = copy.copy(self.dtc) - self.assertNotEqual(self.dtc,None) - dtc.scores = {} - size = len([ v for v in dtc.attrs.values()]) - assert size > 0 - self.assertGreater(size,0) - model = ReducedModel(get_neab.LEMS_MODEL_PATH, name= str('vanilla'), backend=('NEURON', {'DTC':dtc})) - temp = [ v for v in model.attrs.values() ] - assert len(temp) > 0 - self.assertGreater(len(temp),0) - rbt = get_neab.tests[0] - scoreN = rbt.judge(model,stop_on_error = False, deep_error = True) - import copy - dtc.scores[str(rbt)] = copy.copy(scoreN.norm_score) - assert scoreN.norm_score is not None - self.assertTrue(scoreN.norm_score is not None) - dtc.rheobase = copy.copy(scoreN.prediction) - return dtc - - def test_rheobase_on_list(self): - from neuronunit.optimization import exhaustive_search - grid_points = self.grid_points - second_point = grid_points[int(len(grid_points)/2)] - three_points = [grid_points[0],second_point,grid_points[-1]] - self.assertEqual(len(three_points),3) - dtcpop = list(map(exhaustive_search.update_dtc_grid,three_points)) - for d in self.dtcpop: - assert len(list(d.attrs.values())) > 0 - dtcpop = self.test_compute_score(self.dtcpop) - self.dtcpop = dtcpop - return dtcpop - - def test_neuron_set_attrs(self): - from neuronunit.models.reduced import ReducedModel - from neuronunit.optimization import get_neab - self.assertNotEqual(self.dtcpop,None) - dtc = self.dtcpop[0] - self.model = ReducedModel(get_neab.LEMS_MODEL_PATH, backend=('NEURON',{'DTC':dtc})) - temp = [ v for v in self.model.attrs.values() ] - assert len(temp) > 0 - self.assertGreater(len(temp),0) - - def test_rheobase_serial(self): - from neuronunit.optimization import data_transport_container - from neuronunit.tests.fi import RheobaseTest as T - score = self.run_test(T) - self.rheobase = score.prediction - self.assertNotEqual(self.rheobase,None) - - - def test_inputresistance(self): - from neuronunit.tests.passive import InputResistanceTest as T - score = self.run_test(T) - print(score) - print(score.norm_score) - assert score.norm_score is not None - self.assertTrue(score.norm_score is not None) - - self.assertTrue(-0.6 < score < -0.5) - - def test_restingpotential(self): - from neuronunit.tests.passive import RestingPotentialTest as T - score = self.run_test(T) - print(score) - print(score.norm_score) - assert score.norm_score is not None - self.assertTrue(score.norm_score is not None) - self.assertTrue(1.2 < score < 1.3) - - - def test_capacitance(self): - from neuronunit.tests.passive import CapacitanceTest as T - score = self.run_test(T) - print(score) - print(score.norm_score) - assert score.norm_score is not None - self.assertTrue(score.norm_score is not None) - - self.assertTrue(-0.15 < score < -0.05) - - def test_timeconstant(self): - from neuronunit.tests.passive import TimeConstantTest as T - score = self.run_test(T) - print(score) - print(score.norm_score) - print(score) - assert score.norm_score is not None - self.assertTrue(score.norm_score is not None) - - self.assertTrue(-1.45 < score < -1.35) - - - - def test_ap_width(self): - from neuronunit.models.reduced import ReducedModel - from neuronunit.optimization import get_neab - from neuronunit.tests.waveform import InjectedCurrentAPWidthTest as T - - #self.update_amplitude(T) - score = self.run_test(T,pred=self.rheobase) - assert score.norm_score is not None - self.assertTrue(score.norm_score is not None) - self.assertTrue(-0.6 < score < -0.5) - - def test_ap_amplitude(self): - from neuronunit.models.reduced import ReducedModel - from neuronunit.optimization import get_neab - from neuronunit.tests.waveform import InjectedCurrentAPAmplitudeTest as T - score = self.run_test(T,pred=self.rheobase) - assert score.norm_score is not None - self.assertTrue(score.norm_score is not None) - self.assertTrue(-1.7 < score < -1.6) - - def test_ap_threshold(self): - from neuronunit.models.reduced import ReducedModel - from neuronunit.optimization import get_neab - from neuronunit.tests.waveform import InjectedCurrentAPThresholdTest as T - from neuronunit.optimization.optimization_management import format_test - from neuronunit.optimization.data_transport_container import DataTC - dtc = DataTC() - dtc.rheobase = self.rheobase - dtc = format_test(dtc) - self.model = ReducedModel(get_neab.LEMS_MODEL_PATH, backend=('NEURON',{'DTC':dtc})) - #score = self.run_test(T) - score = self.run_test(T,pred=self.rheobase) - assert score.norm_score is not None - self.assertTrue(score.norm_score is not None) - - def test_rheobase_single_value_parallel_and_serial_comparison(self): - from neuronunit.tests.fi import RheobaseTest, RheobaseTestP - from neuronunit.optimization import get_neab - from neuronunit.models.reduced import ReducedModel - from neuronunit import aibs - import os - dataset_id = 354190013 # Internal ID that AIBS uses for a particular Scnn1a-Tg2-Cre - # Primary visual area, layer 5 neuron. - observation = aibs.get_observation(dataset_id,'rheobase') - rt = RheobaseTest(observation = observation) - rtp = RheobaseTestP(observation = observation) - model = ReducedModel(get_neab.LEMS_MODEL_PATH, backend=('NEURON')) - - #model = ReducedModel(get_neab.LEMS_MODEL_PATH,name=str('vanilla'),backend='NEURON') - self.score_p = rtp.judge(model,stop_on_error = False, deep_error = True) - self.predictionp = self.score_p.prediction - self.score_p = self.score_p.norm_score - #model = ReducedModel(get_neab.LEMS_MODEL_PATH,name=str('vanilla'),backend='NEURON') - - serial_model = ReducedModel(get_neab.LEMS_MODEL_PATH,name=str('vanilla'),backend='NEURON') - self.score_s = rt.judge(serial_model,stop_on_error = False, deep_error = True) - self.predictions = self.score_s.prediction - self.score_s = self.score_s.norm_score - import numpy as np - check_less_thresh = float(np.abs(self.predictionp['value'] - self.predictions['value'])) - self.assertLessEqual(check_less_thresh, 2.0) - - def test_set_model_attrs(self): - from neuronunit.optimization.model_parameters import model_params - provided_keys = list(model_params.keys()) - from bluepyopt.deapext.optimisations import DEAPOptimisation - DO = DEAPOptimisation() - for i in range(1,10): - for j in range(1,10): - provided_keys = list(model_params.keys())[j] - DO.setnparams(nparams = i, provided_keys = provided_keys) - - def test_opt_set_GA_params(self): - ''' - Test to check if making the population size bigger, or increasing the run_number - of generations the GA consumes actually increases the fitness. - - Note only test for better fitness every 3rd count, because GAs are stochastic, - and by chance occassionally fitness may not increase, just because the GA has - more genes to sample with. - - A fairer test of the GA would probably test if the mean of mean fitness is improving - or use a sliding window. - ''' - from neuronunit.optimization.model_parameters import model_params - provided_keys = list(model_params.keys()) - from bluepyopt.deapext.optimisations import DEAPOptimisation - DO = DEAPOptimisation() - nparams = 5 - cnt = 0 - provided_keys = list(model_params.keys())[nparams] - DO.setnparams(nparams = nparams, provided_keys = provided_keys) - list_check_increase = [] - for NGEN in range(1,30): - for MU in range(5,40): - pop, hof, log, history, td, gen_vs_hof = DO.run(offspring_size = MU, max_ngen = NGEN, cp_frequency=4,cp_filename='checkpointedGA.p') - avgf = np.mean([ h.fitness for h in hof ]) - list_check_increase.append(avgf) - - if cnt % 3 == 0: - avgf = np.mean([ h.fitness for h in hof ]) - old_avg = np.mean(list_check_increase) - self.assertGreater(np.mean(list_check_increase),old_avg) - self.assertGreater(avgf,old) - - old = avgf - elif cnt == 0: - avgf = np.mean([ h.fitness for h in hof ]) - old = avgf - old_avg = np.mean(list_check_increase) - - - - cnt += 1 - print(old,cnt) - - - def test_frp(self): - from neuronunit.models.reduced import ReducedModel - from neuronunit.optimization import get_neab - model = ReducedModel(get_neab.LEMS_MODEL_PATH,name=str('vanilla'),backend=('NEURON')) - attrs = {'a':0.02, 'b':0.2, 'c':-65+15*0.5, 'd':8-0.5**2 } - from neuronunit.optimization.data_transport_container import DataTC - dtc = DataTC() - from neuronunit.tests import fi - model.set_attrs(attrs) - from neuronunit.optimization import get_neab - rtp = get_neab.tests[0] - rheobase = rtp.generate_prediction(model) - self.assertTrue(float(rheobase['value'])) - - - def test_parallelnb(self): - ''' - Lastly test the notebook - ''' - self.do_notebook('test_ga_versus_grid') - - - """Testing notebooks""" - @unittest.skip("takes too long") - - @unittest.skip("Not implemented") - def test_subset(self): - from neuronunit.optimization import create_subset - create_subset(5) - - @unittest.skip("Not implemented") - def test_update_deap_pop(self): - from neuronunit.optimization import update_deap_pop - - @unittest.skip("Not implemented") - def test_dtc_to_rheo(self): - from neuronunit.optimization import dtc_to_rheo - dtc_to_rheo(dtc) - - @unittest.skip("Not implemented") - def test_evaluate(self,dtc): - from neuronunit.optimization_management import evaluate - assert dtc.scores is not None - evauate(dtc) - - -if __name__ == '__main__': - unittest.main() diff --git a/neuronunit/unit_test/validate_observation_vm.ipynb b/neuronunit/unit_test/validate_observation_vm.ipynb index 29729a3dd..929ab4cb4 100644 --- a/neuronunit/unit_test/validate_observation_vm.ipynb +++ b/neuronunit/unit_test/validate_observation_vm.ipynb @@ -26,12 +26,14 @@ "metadata": {}, "outputs": [ { - "output_type": "execute_result", "data": { - "text/plain": "True" + "text/plain": [ + "True" + ] }, + "execution_count": 3, "metadata": {}, - "execution_count": 3 + "output_type": "execute_result" } ], "source": [ @@ -46,9 +48,11 @@ }, "outputs": [ { - "output_type": "stream", "name": "stdout", - "text": "{'observation': ['none or more than one rule validate', {'oneof definition 0': [{'std': ['required field']}], 'oneof definition 1': [{'n': ['required field'], 'sem': ['required field']}]}]}\n" + "output_type": "stream", + "text": [ + "{'observation': ['none or more than one rule validate', {'oneof definition 0': [{'std': ['required field']}], 'oneof definition 1': [{'n': ['required field'], 'sem': ['required field']}]}]}\n" + ] } ], "source": [ @@ -66,9 +70,11 @@ }, "outputs": [ { - "output_type": "stream", "name": "stdout", - "text": "{'observation': ['none or more than one rule validate', {'oneof definition 0': [{'std': [\"Must have units of 'millisecond'\"]}], 'oneof definition 1': [{'n': ['required field'], 'sem': ['required field'], 'std': ['unknown field']}]}]}\n" + "output_type": "stream", + "text": [ + "{'observation': ['none or more than one rule validate', {'oneof definition 0': [{'std': [\"Must have units of 'millisecond'\"]}], 'oneof definition 1': [{'n': ['required field'], 'sem': ['required field'], 'std': ['unknown field']}]}]}\n" + ] } ], "source": [ @@ -86,9 +92,11 @@ }, "outputs": [ { - "output_type": "stream", "name": "stdout", - "text": "{'observation': ['none or more than one rule validate', {'oneof definition 0': [{'sem': ['unknown field']}], 'oneof definition 1': [{'n': ['required field'], 'std': ['unknown field']}]}]}\n" + "output_type": "stream", + "text": [ + "{'observation': ['none or more than one rule validate', {'oneof definition 0': [{'sem': ['unknown field']}], 'oneof definition 1': [{'n': ['required field'], 'std': ['unknown field']}]}]}\n" + ] } ], "source": [ @@ -104,12 +112,14 @@ "metadata": {}, "outputs": [ { - "output_type": "execute_result", "data": { - "text/plain": "" + "text/plain": [ + "" + ] }, + "execution_count": 7, "metadata": {}, - "execution_count": 7 + "output_type": "execute_result" } ], "source": [ @@ -133,9 +143,9 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.8.5-final" + "version": "3.7.4" } }, "nbformat": 4, "nbformat_minor": 2 -} \ No newline at end of file +} diff --git a/neuronunit/unit_test/xppaut.ipynb b/neuronunit/unit_test/xppaut.ipynb deleted file mode 100644 index 7a31fcdc4..000000000 --- a/neuronunit/unit_test/xppaut.ipynb +++ /dev/null @@ -1,65 +0,0 @@ -{ - "cells": [ - { - "cell_type": "code", - "execution_count": 1, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Collecting git+https://github.com/social-hacks-for-mental-health/Py_XPPCALL\n", - " Cloning https://github.com/social-hacks-for-mental-health/Py_XPPCALL to /tmp/pip-req-build-zax4bykl\n", - " Complete output from command python setup.py egg_info:\n", - " Traceback (most recent call last):\n", - " File \"\", line 1, in \n", - " File \"/opt/conda/lib/python3.5/tokenize.py\", line 454, in open\n", - " buffer = _builtin_open(filename, 'rb')\n", - " FileNotFoundError: [Errno 2] No such file or directory: '/tmp/pip-req-build-zax4bykl/setup.py'\n", - " \n", - " ----------------------------------------\n", - "\u001b[31mCommand \"python setup.py egg_info\" failed with error code 1 in /tmp/pip-req-build-zax4bykl/\u001b[0m\n", - "\u001b[33mYou are using pip version 10.0.1, however version 18.1 is available.\n", - "You should consider upgrading via the 'pip install --upgrade pip' command.\u001b[0m\n" - ] - } - ], - "source": [ - "!pip install git+https://github.com/social-hacks-for-mental-health/Py_XPPCALL" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": true - }, - "outputs": [], - "source": [] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.5.2" - } - }, - "nbformat": 4, - "nbformat_minor": 2 -} diff --git a/requirements.txt b/requirements.txt new file mode 100644 index 000000000..90a0aa35c --- /dev/null +++ b/requirements.txt @@ -0,0 +1,35 @@ +typing +typing-extensions +dask +lxml==4.4.1 +elephant==0.4.1 +mock==3.0.5 +tqdm==4.48.2 +neo==0.5.2 +plotly==4.5.2 +simplejson==3.17.0 +future==0.17.1 +streamlit +pandas==1.1.0 +matplotlib +seaborn +quantities==0.12.4 +Jinja2==2.10.3 +Pebble==4.5.3 +backports.statistics==0.1.0 +python_dateutil +scikit_learn==0.23.2 +frozendict==1.2 +neo==0.5.2 +numba +tqdm==4.48.2 +allensdk==0.16.3 +natsort==7.0.1 +nbformat==4.4.0 +quantities==0.12.4 +dask[bag] +fsspec>=0.3.3 +cython +git+https://github.com/russelljjarvis/sciunit@dev +git+https://github.com/russelljjarvis/BluePyOpt@neuronunit_reduced_cells +efel