semantic changes to network examples (#124)

* semantic changes to network examples

* retuned parameters for extrinsic inputs, fixed py3 issue

* use standalone parameters module

* pip requirements file pointing to freestanding Neurotools parameters module

* pip requirements file pointing to freestanding Neurotools parameters module

* removed unused imports, fixed h5py and mpl DeprecationWarnings

examples/bioRxiv281717/example_parallel_network.py

@@ -136,6 +136,8 @@
 GNU General Public License for more details.
 '''
 from __future__ import division
+from mpi4py import MPI
+import neuron
 import matplotlib
 matplotlib.use('agg')
 import matplotlib.pyplot as plt
@@ -150,8 +152,6 @@
         h5py.version.hdf5_version))
 import os
 from time import time
-from mpi4py import MPI
-import neuron
 import LFPy
 from example_parallel_network_plotting import decimate
 

examples/bioRxiv281717/example_parallel_network_parameters.py

@@ -49,6 +49,27 @@
 neuron.h.load_file("import3d.hoc")
 
 
+#######################
+# Functions
+#######################
+def get_pre_m_type(post):
+    '''little helper function to return the most populuous excitatory m_type
+    within the layer of m_type post, assuming this is representative for
+    excitatory external connections onto postsynaptic cells '''
+    if post.startswith('L23'):
+        return 'L23_PC'
+    elif post.startswith('L4'):
+        return 'L4_PC'
+    elif post.startswith('L5'):
+        return 'L5_TTPC1'
+    elif post.startswith('L6'):
+        return 'L6_IPC'
+
+#######################
+# Parameters
+#######################
+
+
 # test mode (1 cell per pop, all-to-all connectivity)
 TESTING = False
 
@@ -202,21 +223,21 @@
         # Excitatory
         ('L4_PC',       'cAD', 'L4_PC_cADpyr230_1',        2674,
          dict(radius=210, loc=PSET.layer_data[3]['center'], scale=100., cap=[1078., 97.]),
-         dict(x=np.pi/2, y=0.), ['dend', 'apic'],            ['dend', 'apic'],   0.05,    5.),
+         dict(x=np.pi/2, y=0.), ['dend', 'apic'],            ['dend', 'apic'],   0.125,    5.),
         # Inhibitory
         ('L4_LBC',      'dNAC', 'L4_LBC_dNAC222_1',         122,
          dict(radius=210, loc=PSET.layer_data[3]['center'], scale=100., cap=[938., 670]),
-         dict(x=np.pi/2, y=0.), ['soma', 'dend', 'apic'],    ['dend', 'apic'],   0.05,    5.),
+         dict(x=np.pi/2, y=0.), ['soma', 'dend', 'apic'],    ['dend', 'apic'],   0.125,    5.),
 
         # Layer 5
         # Excitatory
         ('L5_TTPC1',    'cAD', 'L5_TTPC1_cADpyr232_1',     2403,
          dict(radius=210, loc=PSET.layer_data[4]['center'], scale=125., cap=[719, 73.]),
-         dict(x=np.pi/2, y=0.), ['dend', 'apic'],            ['dend', 'apic'],   0.045,   5.),
+         dict(x=np.pi/2, y=0.), ['dend', 'apic'],            ['dend', 'apic'],   0.1,   5.),
         # Inhibitory
         ('L5_MC',       'bAC', 'L5_MC_bAC217_1',            395,
          dict(radius=210, loc=PSET.layer_data[4]['center'], scale=125., cap=[378., 890]),
-         dict(x=np.pi/2, y=0.), ['soma', 'dend', 'apic'],    ['dend', 'apic'],   0.05,    5.),
+         dict(x=np.pi/2, y=0.), ['soma', 'dend', 'apic'],    ['dend', 'apic'],   0.125,    5.),
         ],
     dtype = [('m_type', '|{}32'.format(stringType)), ('e_type', '|{}32'.format(stringType)),
              ('me_type', '|{}32'.format(stringType)), ('POP_SIZE', 'i8'), ('pop_args', dict),
@@ -228,6 +249,7 @@
 # names as used to denote individual cell types
 # POP_SIZE :    number of neurons for each morphological type as given on https://bbp.epfl.ch/nmc-portal/microcircuit 
 
+
 # pop_args : dict, radius, mean position (loc) and standard deviation (scale) of the soma positions
 # rotation_args : dict, default rotations around x and y axis applied to each cell in the population using LFPy.NetworkCell.set_rotation() method. 
 
@@ -651,13 +673,13 @@
 PSET.connParamsExtrinsic = dict(
     # synapse type
     syntype = 'ProbAMPANMDA_EMS',
-    # synapse parameters (chosen using pre==post)
+    # synapse parameters (assumes parameters of excitatory population in the layer)
     synparams = [dict(
-                        Use = syn_param_stats['{}:{}'.format(post, post)]['Use_mean'],
-                        Dep = syn_param_stats['{}:{}'.format(post, post)]['Dep_mean'],
-                        Fac = syn_param_stats['{}:{}'.format(post, post)]['Fac_mean'],
+                        Use = syn_param_stats['{}:{}'.format(get_pre_m_type(post), post)]['Use_mean'],
+                        Dep = syn_param_stats['{}:{}'.format(get_pre_m_type(post), post)]['Dep_mean'],
+                        Fac = syn_param_stats['{}:{}'.format(get_pre_m_type(post), post)]['Fac_mean'],
                         tau_r_AMPA = 0.2,
-                        tau_d_AMPA = syn_param_stats['{}:{}'.format(pre, post)]['tau_d_mean'],
+                        tau_d_AMPA = syn_param_stats['{}:{}'.format(get_pre_m_type(post), post)]['tau_d_mean'],
                         tau_r_NMDA = 0.29,
                         tau_d_NMDA = 43,
                         e=0,

examples/bioRxiv281717/figure_2.py

@@ -12,9 +12,8 @@
 import numpy as np
 import matplotlib.pyplot as plt
 from matplotlib.gridspec import GridSpec
-from mpl_toolkits.axes_grid.axislines import SubplotZero
-from matplotlib.collections import PolyCollection, LineCollection, PatchCollection
-from matplotlib import colors, patches
+from mpl_toolkits.axisartist.axislines import SubplotZero
+from matplotlib.collections import PolyCollection
 import neuron
 
 # set some plotting parameters
@@ -258,7 +257,7 @@ def draw_lineplot(
 
 
 def remove_axis_junk(ax, lines=['right', 'top']):
-    for loc, spine in ax.spines.iteritems():
+    for loc, spine in ax.spines.items():
         if loc in lines:
             spine.set_color('none')            
     ax.xaxis.set_ticks_position('bottom')
@@ -305,7 +304,7 @@ def remove_axis_junk(ax, lines=['right', 'top']):
 zips = []
 xz = cell.get_idx_polygons()
 for x, z in xz:
-    zips.append(zip(x, z))
+    zips.append(list(zip(x, z)))
 for ax in [ax0]:
     polycol = PolyCollection(zips,
                              linewidths=(0.5),
@@ -336,7 +335,7 @@ def remove_axis_junk(ax, lines=['right', 'top']):
 zips = []
 offset = 0.
 for x, z in cell.get_pt3d_polygons():
-    zips.append(zip(x-offset, z+offset))
+    zips.append(list(zip(x-offset, z+offset)))
 polycol = PolyCollection(zips,
                          edgecolors='none',
                          facecolors='gray',

examples/bioRxiv281717/figure_4.py

@@ -22,7 +22,6 @@
 from matplotlib.ticker import MaxNLocator
 import os
 import numpy as np
-import scipy.signal as ss
 import h5py
 from LFPy import NetworkCell
 import example_parallel_network_plotting as plotting

examples/bioRxiv281717/figure_5.py

@@ -18,15 +18,11 @@
 from __future__ import division
 import matplotlib.pyplot as plt
 from matplotlib.gridspec import GridSpec, GridSpecFromSubplotSpec
-from matplotlib.collections import PolyCollection
-from mpl_toolkits.axes_grid.axislines import SubplotZero
-import mpl_toolkits.axes_grid.axes_size as Size
-from mpl_toolkits.axes_grid import Divider
+from mpl_toolkits.axisartist.axislines import SubplotZero
 import os
 import numpy as np
-import scipy.signal as ss
 import h5py
-from LFPy import NetworkCell, FourSphereVolumeConductor, MEG
+from LFPy import FourSphereVolumeConductor, MEG
 import example_parallel_network_plotting as plotting
 from mpi4py import MPI
 
@@ -53,7 +49,7 @@ def plot_spike_raster(ax, PSET, T):
     for i, (m_name, name) in enumerate(zip(PSET.populationParameters['m_type'], PSET.populationParameters['me_type'])):
         x = []
         y = []
-        ax.hlines(f['SPIKES'][name]['gids'].value.min(), T[0], T[1], 'k', lw=0.25)
+        ax.hlines(f['SPIKES'][name]['gids'][()].min(), T[0], T[1], 'k', lw=0.25)
         for gid, spt in zip(f['SPIKES'][name]['gids'], f['SPIKES'][name]['times']):
             if len(spt) > 0:
                 y += [gid]*spt.size
@@ -121,7 +117,7 @@ def plot_spike_raster(ax, PSET, T):
     for i, (m_name, name) in enumerate(zip(PSET.populationParameters['m_type'], PSET.populationParameters['me_type'])):
         ax = axes[i]
         plotting.remove_axis_junk(ax)
-        data = np.hstack(f['SPIKES'][name]['times'].value.flat)
+        data = np.hstack(f['SPIKES'][name]['times'][()].flat)
         ax.hist(data, bins=bins, color=colors[i][:-1], label=m_name)
         ax.axis(ax.axis('tight'))
         ax.set_xlim(PSET.TRANSIENT, PSET.TRANSIENT+1000.)
@@ -146,7 +142,7 @@ def plot_spike_raster(ax, PSET, T):
     ax = fig.add_subplot(gs0[:-2])
     f = h5py.File(os.path.join(PSET.OUTPUTPATH, 'example_parallel_network_output.h5'), 'r')
     for data, title, color in zip(
-                           [f['SUMMED_OUTPUT'].value['imem']],
+                           [f['SUMMED_OUTPUT'][()]['imem']],
                            ['extracellular potentials, summed'],
                            ['k']):
         ax.set_title(title)
@@ -169,12 +165,12 @@ def plot_spike_raster(ax, PSET, T):
     # PANEL D ECoG potential
     ax = fig.add_subplot(gs0[-1])
     f = h5py.File(os.path.join(PSET.OUTPUTPATH, 'example_parallel_network_output.h5'), 'r')
-    data = f['SUMMED_ECOG'].value['imem']
+    data = f['SUMMED_ECOG'][()]['imem']
     title = 'ECoG potential, summed'
     color = 'k'
     ax.set_title(title)
     vlimround = plotting.draw_lineplot(ax=ax,
-                               data=plotting.decimate(f['SUMMED_OUTPUT'].value['imem'][0, ].reshape((1, -1)),
+                               data=plotting.decimate(f['SUMMED_OUTPUT'][()]['imem'][0, ].reshape((1, -1)),
                                                       q=PSET.decimate_q),
                               dt=PSET.dt*PSET.decimate_q,
                               scalebar=False,

examples/bioRxiv281717/figure_6.py

@@ -18,15 +18,9 @@
 from __future__ import division
 import matplotlib.pyplot as plt
 from matplotlib.gridspec import GridSpec
-from matplotlib.collections import PolyCollection
-from mpl_toolkits.axes_grid.axislines import SubplotZero
-import mpl_toolkits.axes_grid.axes_size as Size
-from mpl_toolkits.axes_grid import Divider
 import os
 import numpy as np
-import scipy.signal as ss
 import h5py
-from LFPy import NetworkCell, FourSphereVolumeConductor, MEG
 import example_parallel_network_plotting as plotting
 from mpi4py import MPI
 
@@ -69,7 +63,7 @@
         ax = fig.add_subplot(gs[:8, j])
         f = h5py.File(os.path.join(PSET.OUTPUTPATH, 'example_parallel_network_output.h5'), 'r')
         for data, title, color in zip(
-                               [f['SUMMED_OUTPUT'].value[me_type]],
+                               [f['SUMMED_OUTPUT'][()][me_type]],
                                [m_type],
                                ['k']):
             ax.set_title(title)
@@ -127,7 +121,7 @@
     f = h5py.File(os.path.join(PSET.OUTPUTPATH, 'example_parallel_network_output.h5'), 'r')
 
     for m_type, me_type, color in zip(list(PSET.populationParameters['m_type'])+['summed'], list(PSET.populationParameters['me_type'])+['imem'], colors+['k']):
-        data = f['SUMMED_OUTPUT'].value[me_type]
+        data = f['SUMMED_OUTPUT'][()][me_type]
         ax.semilogx(data[:, tind:].var(axis=1), y, lw=2, label=m_type, color=color)
     f.close()
     ax.set_yticks(y)

examples/bioRxiv281717/figure_7_8/example_parallel_network.py

@@ -125,6 +125,8 @@
 GNU General Public License for more details.
 '''
 from __future__ import division
+from mpi4py import MPI
+import neuron
 import matplotlib
 matplotlib.use('agg')
 import matplotlib.pyplot as plt
@@ -139,12 +141,10 @@
         h5py.version.hdf5_version))
 import os
 from time import time
-from mpi4py import MPI
-import neuron
 import LFPy
 from example_parallel_network_plotting import decimate
 import sys
-import NeuroTools.parameters as ps
+import parameters as ps
 
 
 # set up MPI environment
@@ -263,7 +263,7 @@
     if RANK == 0:
         parameters_time = time() - tic
         # open file object for writing
-        logfile = file(os.path.join(PSET.OUTPUTPATH, 'log.txt'), 'w', 0)
+        logfile = open(os.path.join(PSET.OUTPUTPATH, 'log.txt'), 'w')
         logfile.write('initialization {}\n'.format(initialization_time))
         print('Parameters in {} seconds'.format(parameters_time))
         logfile.write('parameters {}\n'.format(parameters_time))
@@ -574,7 +574,6 @@
                                      label=name,
                                      )
             ax.add_collection(polycol)
-        ax.axis(ax.axis('equal'))
         ax.set_xlim(-400, 400)
         axis = ax.axis()
         ax.hlines(np.r_[0., -PSET.layer_data['thickness'].cumsum()],

examples/bioRxiv281717/figure_7_8/example_parallel_network_parameters.py

@@ -44,6 +44,27 @@
 RANK = COMM.Get_rank()
 
 
+#######################
+# Functions
+#######################
+def get_pre_m_type(post):
+    '''little helper function to return the most populuous excitatory m_type
+    within the layer of m_type post, assuming this is representative for
+    excitatory external connections onto postsynaptic cells '''
+    if post.startswith('L23'):
+        return 'L23_PC'
+    elif post.startswith('L4'):
+        return 'L4_PC'
+    elif post.startswith('L5'):
+        return 'L5_TTPC1'
+    elif post.startswith('L6'):
+        return 'L6_IPC'
+
+#######################
+# Parameters
+#######################
+
+
 # load some neuron-interface files needed for the EPFL cell types
 neuron.h.load_file("stdrun.hoc")
 neuron.h.load_file("import3d.hoc")
@@ -205,21 +226,21 @@
         # # Excitatory
         # ('L4_PC',       'cAD', 'L4_PC_cADpyr230_1',        2674,
         #  dict(radius=210, loc=PSET.layer_data[3]['center'], scale=100., cap=[1078., 97.]),
-        #  dict(x=np.pi/2, y=0.), ['dend', 'apic'],            ['dend', 'apic'],   0.05,    5.),
+        #  dict(x=np.pi/2, y=0.), ['dend', 'apic'],            ['dend', 'apic'],   0.125,    5.),
         # # Inhibitory
         # ('L4_LBC',      'dNAC', 'L4_LBC_dNAC222_1',         122,
         #  dict(radius=210, loc=PSET.layer_data[3]['center'], scale=100., cap=[938., 670]),
-        #  dict(x=np.pi/2, y=0.), ['soma', 'dend', 'apic'],    ['dend', 'apic'],   0.05,    5.),
+        #  dict(x=np.pi/2, y=0.), ['soma', 'dend', 'apic'],    ['dend', 'apic'],   0.125,    5.),
 
         # Layer 5
         # Excitatory
         ('L5_TTPC1',    'cAD', 'L5_TTPC1_cADpyr232_1',     2403,
          dict(radius=210, loc=PSET.layer_data[4]['center'], scale=125., cap=[719, 73.]),
-         dict(x=np.pi/2, y=0.), ['dend', 'apic'],            ['dend', 'apic'],   0.045,   5.),
+         dict(x=np.pi/2, y=0.), ['dend', 'apic'],            ['dend', 'apic'],   0.1,   5.),
         # Inhibitory
         ('L5_MC',       'bAC', 'L5_MC_bAC217_1',            395,
          dict(radius=210, loc=PSET.layer_data[4]['center'], scale=125., cap=[378., 890]),
-         dict(x=np.pi/2, y=0.), ['soma', 'dend', 'apic'],    ['dend', 'apic'],   0.05,    5.),
+         dict(x=np.pi/2, y=0.), ['soma', 'dend', 'apic'],    ['dend', 'apic'],   0.125,    5.),
         ],
     dtype = [('m_type', '|{}32'.format(stringType)), ('e_type', '|{}32'.format(stringType)),
              ('me_type', '|{}32'.format(stringType)), ('POP_SIZE', 'i8'), ('pop_args', dict),
@@ -654,13 +675,13 @@
 PSET.connParamsExtrinsic = dict(
     # synapse type
     syntype = 'ProbAMPANMDA_EMS',
-    # synapse parameters (chosen using pre==post)
+    # synapse parameters (assumes parameters of excitatory population in the layer)
     synparams = [dict(
-                        Use = syn_param_stats['{}:{}'.format(post, post)]['Use_mean'],
-                        Dep = syn_param_stats['{}:{}'.format(post, post)]['Dep_mean'],
-                        Fac = syn_param_stats['{}:{}'.format(post, post)]['Fac_mean'],
+                        Use = syn_param_stats['{}:{}'.format(get_pre_m_type(post), post)]['Use_mean'],
+                        Dep = syn_param_stats['{}:{}'.format(get_pre_m_type(post), post)]['Dep_mean'],
+                        Fac = syn_param_stats['{}:{}'.format(get_pre_m_type(post), post)]['Fac_mean'],
                         tau_r_AMPA = 0.2,
-                        tau_d_AMPA = syn_param_stats['{}:{}'.format(pre, post)]['tau_d_mean'],
+                        tau_d_AMPA = syn_param_stats['{}:{}'.format(get_pre_m_type(post), post)]['tau_d_mean'],
                         tau_r_NMDA = 0.29,
                         tau_d_NMDA = 43,
                         e=0,

examples/bioRxiv281717/figure_7_8/example_parallel_network_parameterspace.py

@@ -6,7 +6,7 @@
 import operator
 import pickle
 import hashlib
-import NeuroTools.parameters as ps
+import parameters as ps
 
 
 def sort_deep_dict(d):

examples/bioRxiv281717/figure_7_8/figure_7_8.py

@@ -17,7 +17,6 @@
 import os
 import numpy as np
 import example_parallel_network_parameterspace as ps
-#from example_parallel_network_parameterspace import *
 from example_parallel_network_parameters import PSET
 import matplotlib.pyplot as plt
 from matplotlib.ticker import ScalarFormatter

examples/bioRxiv281717/figure_7_8/requirements.txt

@@ -0,0 +1 @@
+-e git+git@github.com:espenhgn/parameters.git@fix_py3#egg=parameters

examples/bioRxiv281717/requirements.txt

@@ -1,2 +1 @@
-#essential
-parameters>=0.2.1
+-e git+git@github.com:espenhgn/parameters.git@fix_py3#egg=parameters