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experiment.py
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experiment.py
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"""
==========
Experiment
==========
Experiment runs the simulation.
"""
import sys
import os
import logging as log
import warnings
import pprint
import multiprocessing
from multiprocessing import pool as multipool
from typing import (
Any, Dict, Optional, Union, Tuple, Callable)
import math
import datetime
import time as clock
import uuid
from vivarium.composites.toys import Proton, Electron, Sine, PoQo, ToyDivider
from vivarium.core.store import hierarchy_depth, Store, generate_state
from vivarium.core.emitter import get_emitter
from vivarium.core.process import (
Process,
ParallelProcess,
serialize_value,
Composer,
)
from vivarium.library.topology import (
delete_in,
assoc_path,
inverse_topology
)
from vivarium.library.units import units
from vivarium.core.types import (
HierarchyPath, Topology, Schema, State, Update, Processes)
pretty = pprint.PrettyPrinter(indent=2)
def pp(x: Any) -> None:
pretty.pprint(x)
def pf(x: Any) -> str:
return pretty.pformat(x)
log.basicConfig(level=os.environ.get("LOGLEVEL", log.WARNING))
def starts_with(
a_list: HierarchyPath,
sub: HierarchyPath,
) -> bool:
return len(sub) <= len(a_list) and all(
a_list[i] == el
for i, el in enumerate(sub))
def invert_topology(
update: Update,
args: Tuple[HierarchyPath, Topology],
) -> State:
path, topology = args
return inverse_topology(path[:-1], update, topology)
def timestamp(dt: Optional[Any] = None) -> str:
if not dt:
dt = datetime.datetime.now()
return "%04d%02d%02d.%02d%02d%02d" % (
dt.year, dt.month, dt.day,
dt.hour, dt.minute, dt.second)
def invoke_process(
process: Process,
interval: float,
states: State,
) -> Update:
return process.next_update(interval, states)
class Defer:
def __init__(
self,
defer: Any,
f: Callable,
args: Tuple,
) -> None:
self.defer = defer
self.f = f
self.args = args
def get(self) -> Update:
return self.f(
self.defer.get(),
self.args)
class InvokeProcess:
def __init__(
self,
process: Process,
interval: float,
states: State,
) -> None:
self.process = process
self.interval = interval
self.states = states
self.update = invoke_process(
self.process,
self.interval,
self.states)
def get(self) -> Update:
return self.update
class MultiInvoke:
def __init__(
self,
pool: multipool.Pool
) -> None:
self.pool = pool
def invoke(
self,
process: Process,
interval: float,
states: State,
) -> Any:
args = (process, interval, states)
result = self.pool.apply_async(invoke_process, args)
return result
class Experiment:
def __init__(self, config: Dict[str, Any]) -> None:
"""Defines simulations
Arguments:
config (dict): A dictionary of configuration options. The
required options are:
* **processes** (:py:class:`dict`): A dictionary that
maps :term:`process` names to process objects. You
will usually get this from the ``processes``
attribute of the dictionary from
:py:meth:`vivarium.core.process.Composer.generate`.
* **topology** (:py:class:`dict`): A dictionary that
maps process names to sub-dictionaries. These
sub-dictionaries map the process's port names to
tuples that specify a path through the :term:`tree`
from the :term:`compartment` root to the
:term:`store` that will be passed to the process for
that port.
The following options are optional:
* **experiment_id** (:py:class:`uuid.UUID` or
:py:class:`str`): A unique identifier for the
experiment. A UUID will be generated if none is
provided.
* **description** (:py:class:`str`): A description of
the experiment. A blank string by default.
* **initial_state** (:py:class:`dict`): By default an
empty dictionary, this is the initial state of the
simulation.
* **emitter** (:py:class:`dict`): An emitter
configuration which must conform to the
specification in the documentation for
:py:func:`vivarium.core.emitter.get_emitter`. The
experiment ID will be added to the dictionary you
provide as the value for the key ``experiment_id``.
"""
self.config = config
self.experiment_id = config.get(
'experiment_id', str(uuid.uuid1()))
self.processes = config['processes']
self.topology = config['topology']
self.initial_state = config.get('initial_state', {})
self.emit_step = config.get('emit_step', 1.0)
# display settings
self.experiment_name = config.get(
'experiment_name', self.experiment_id)
self.description = config.get('description', '')
self.display_info = config.get('display_info', True)
self.progress_bar = config.get('progress_bar', False)
self.time_created = timestamp()
if self.display_info:
self.print_display()
# parallel settings
self.invoke = config.get('invoke', InvokeProcess)
self.parallel: Dict[HierarchyPath, ParallelProcess] = {}
# get a mapping of all paths to processes
self.process_paths: Dict[HierarchyPath, Process] = {}
self.deriver_paths: Dict[HierarchyPath, Process] = {}
self.find_process_paths(self.processes)
# initialize the state
self.state = generate_state(
self.processes,
self.topology,
self.initial_state)
# emitter settings
emitter_config = config.get('emitter', 'timeseries')
if isinstance(emitter_config, str):
emitter_config = {'type': emitter_config}
else:
emitter_config = dict(emitter_config)
emitter_config['experiment_id'] = self.experiment_id
self.emitter = get_emitter(emitter_config)
self.experiment_time = 0.0
# run the derivers
self.send_updates([])
# run the emitter
self.emit_configuration()
self.emit_data()
# logging information
log.info('experiment %s', str(self.experiment_id))
log.info('\nPROCESSES:')
log.info(pf(self.processes))
log.info('\nTOPOLOGY:')
log.info(pf(self.topology))
# log.info('\nSTATE:')
# log.info(pf(self.state.get_value()))
#
# log.info('\nCONFIG:')
# log.info(pf(self.state.get_config(True)))
def add_process_path(
self,
process: Process,
path: HierarchyPath
) -> None:
if process.is_deriver():
self.deriver_paths[path] = process
else:
self.process_paths[path] = process
def find_process_paths(
self,
processes: Processes
) -> None:
tree = hierarchy_depth(processes)
for path, process in tree.items():
self.add_process_path(process, path)
def emit_configuration(self) -> None:
data: Dict[str, Any] = {
'time_created': self.time_created,
'experiment_id': self.experiment_id,
'name': self.experiment_name,
'description': self.description,
'topology': self.topology,
'processes': serialize_value(self.processes),
'state': serialize_value(self.state.get_config())
}
emit_config: Dict[str, Any] = {
'table': 'configuration',
'data': data}
# get size of data for emit
data_bytes = sys.getsizeof(str(emit_config))
if data_bytes < 26000000: # pymongo document size limit
self.emitter.emit(emit_config)
else:
warnings.warn('configuration size is too big for the emitter, '
'discarding process parameters')
for process_id in emit_config['data']['processes'].keys():
emit_config['data']['processes'][process_id] = None
self.emitter.emit(emit_config)
def invoke_process(
self,
process: Process,
path: HierarchyPath,
interval: float,
states: State,
) -> Any:
if process.parallel:
# add parallel process if it doesn't exist
if path not in self.parallel:
self.parallel[path] = ParallelProcess(process)
# trigger the computation of the parallel process
self.parallel[path].update(interval, states)
return self.parallel[path]
# if not parallel, perform a normal invocation
return self.invoke(process, interval, states)
def process_update(
self,
path: HierarchyPath,
process: Process,
store: Store,
states: State,
interval: float,
) -> Tuple[Defer, Store]:
update = self.invoke_process(
process,
path,
interval,
states)
absolute = Defer(
update,
invert_topology,
(path, store.topology))
return absolute, store
def process_state(
self,
path: HierarchyPath,
process: Process,
) -> Tuple[Store, State]:
store = self.state.get_path(path)
# translate the values from the tree structure into the form
# that this process expects, based on its declared topology
states = store.outer.schema_topology(process.schema, store.topology)
return store, states
def calculate_update(
self,
path: HierarchyPath,
process: Process,
interval: float
) -> Tuple[Defer, Store]:
store, states = self.process_state(path, process)
return self.process_update(
path, process, store, states, interval)
def apply_update(
self,
update: Update,
state: Store
) -> None:
topology_updates, process_updates, deletions = self.state.apply_update(
update, state)
if topology_updates:
for path, topology_update in topology_updates:
assoc_path(self.topology, path, topology_update)
if process_updates:
for path, process in process_updates:
assoc_path(self.processes, path, process)
self.add_process_path(process, path)
if deletions:
for deletion in deletions:
self.delete_path(deletion)
def delete_path(
self,
deletion: HierarchyPath
) -> None:
delete_in(self.processes, deletion)
delete_in(self.topology, deletion)
for path in list(self.process_paths.keys()):
if starts_with(path, deletion):
del self.process_paths[path]
for path in list(self.deriver_paths.keys()):
if starts_with(path, deletion):
del self.deriver_paths[path]
def run_derivers(self) -> None:
paths = list(self.deriver_paths.keys())
for path in paths:
# deriver could have been deleted by another deriver
deriver = self.deriver_paths.get(path)
if deriver:
# timestep shouldn't influence derivers
# TODO(jerry): Do something cleaner than having
# generate_paths() add a schema attribute to the Deriver.
# PyCharm's type check reports:
# Type Process doesn't have expected attribute 'schema'
update, store = self.calculate_update(
path, deriver, 0)
self.apply_update(update.get(), store)
def emit_data(self) -> None:
data = self.state.emit_data()
data.update({
'time': self.experiment_time})
emit_config = {
'table': 'history',
'data': serialize_value(data)}
self.emitter.emit(emit_config)
def send_updates(
self,
update_tuples: list
) -> None:
for update_tuple in update_tuples:
update, state = update_tuple
self.apply_update(update.get(), state)
self.run_derivers()
def update(
self,
interval: float
) -> None:
""" Run each process for the given interval and update the states.
"""
time = 0.0
emit_time = self.emit_step
clock_start = clock.time()
def empty_front(t: float) -> Dict[str, Union[float, dict]]:
return {
'time': t,
'update': {}}
# keep track of which processes have simulated until when
front: Dict = {}
while time < interval:
full_step = math.inf
# find any parallel processes that were removed and terminate them
for terminated in self.parallel.keys() - self.process_paths.keys():
self.parallel[terminated].end()
del self.parallel[terminated]
# setup a way to track how far each process has simulated in time
front = {
path: progress
for path, progress in front.items()
if path in self.process_paths}
# go through each process and find those that are able to update
# based on their current time being less than the global time.
for path, process in self.process_paths.items():
if path not in front:
front[path] = empty_front(time)
process_time = front[path]['time']
if process_time <= time:
# get the time step
store, states = self.process_state(path, process)
requested_timestep = process.calculate_timestep(states)
# progress only to the end of interval
future = min(process_time + requested_timestep, interval)
process_timestep = future - process_time
# calculate the update for this process
# TODO(jerry): Do something cleaner than having
# generate_paths() add a schema attribute to the Process.
# PyCharm's type check reports:
# Type Process doesn't have expected attribute 'schema'
# TODO(chris): Is there any reason to generate a process's
# schema dynamically like this?
update = self.process_update(
path, process, store, states, process_timestep)
# store the update to apply at its projected time
front[path]['time'] = future
front[path]['update'] = update
# absolute timestep
timestep = future - time
if timestep < full_step:
full_step = timestep
else:
# don't shoot past processes that didn't run this time
process_delay = process_time - time
if process_delay < full_step:
full_step = process_delay
if full_step == math.inf:
# no processes ran, jump to next process
next_event = interval
for path in front.keys():
if front[path]['time'] < next_event:
next_event = front[path]['time']
time = next_event
else:
# at least one process ran
# increase the time, apply updates, and continue
time += full_step
self.experiment_time += full_step
updates = []
paths = []
for path, advance in front.items():
if advance['time'] <= time:
new_update = advance['update']
# new_update['_path'] = path
updates.append(new_update)
advance['update'] = {}
paths.append(path)
self.send_updates(updates)
# display and emit
if self.progress_bar:
print_progress_bar(time, interval)
if self.emit_step is None:
self.emit_data()
elif emit_time <= time:
while emit_time <= time:
self.emit_data()
emit_time += self.emit_step
# post-simulation
for advance in front.values():
assert advance['time'] == time == interval
assert len(advance['update']) == 0
clock_finish = clock.time() - clock_start
if self.display_info:
self.print_summary(clock_finish)
def end(self) -> None:
for parallel in self.parallel.values():
parallel.end()
def print_display(self) -> None:
date, time = self.time_created.split('.')
print('\nExperiment ID: {}'.format(self.experiment_id))
print('Created: {} at {}'.format(
date[4:6] + '/' + date[6:8] + '/' + date[0:4],
time[0:2] + ':' + time[2:4] + ':' + time[4:6]))
if self.experiment_name is not self.experiment_id:
print('Name: {}'.format(self.experiment_name))
if self.description:
print('Description: {}'.format(self.description))
def print_summary(
self,
clock_finish: float
) -> None:
if clock_finish < 1:
print('Completed in {:.6f} seconds'.format(clock_finish))
else:
print('Completed in {:.2f} seconds'.format(clock_finish))
def print_progress_bar(
iteration: float,
total: float,
decimals: float = 1,
length: int = 50,
) -> None:
""" Call in a loop to create terminal progress bar
Arguments:
iteration: (Required) current iteration
total: (Required) total iterations
decimals: (Optional) positive number of decimals in percent complete
length: (Optional) character length of bar
"""
progress: str = ("{0:." + str(decimals) + "f}").format(total - iteration)
filled_length: int = int(length * iteration // total)
filled_bar = '█' * filled_length + '-' * (length - filled_length)
print(
f'\rProgress:|{filled_bar}| {progress}/{float(total)} '
f'simulated seconds remaining ', end='\r')
# Print New Line on Complete
if iteration == total:
print()
def make_proton(
parallel: bool = False
) -> Dict[str, Any]:
processes = {
'proton': Proton({'_parallel': parallel}),
'electrons': {
'a': {
'electron': Electron({'_parallel': parallel})},
'b': {
'electron': Electron()}}}
spin_path = ('internal', 'spin')
radius_path = ('structure', 'radius')
topology = {
'proton': {
'radius': radius_path,
'quarks': ('internal', 'quarks'),
'electrons': {
'_path': ('electrons',),
'*': {
'orbital': ('shell', 'orbital'),
'spin': spin_path}}},
'electrons': {
'a': {
'electron': {
'spin': spin_path,
'proton': {
'_path': ('..', '..'),
'radius': radius_path}}},
'b': {
'electron': {
'spin': spin_path,
'proton': {
'_path': ('..', '..'),
'radius': radius_path}}}}}
initial_state = {
'structure': {
'radius': 0.7},
'internal': {
'quarks': {
'x': {
'color': 'green',
'spin': 'up'},
'y': {
'color': 'red',
'spin': 'up'},
'z': {
'color': 'blue',
'spin': 'down'}}}}
return {
'processes': processes,
'topology': topology,
'initial_state': initial_state}
def test_recursive_store() -> None:
environment_config = {
'environment': {
'temperature': {
'_default': 0.0,
'_updater': 'accumulate'},
'fields': {
(0, 1): {
'enzymeX': {
'_default': 0.0,
'_updater': 'set'},
'enzymeY': {
'_default': 0.0,
'_updater': 'set'}},
(0, 2): {
'enzymeX': {
'_default': 0.0,
'_updater': 'set'},
'enzymeY': {
'_default': 0.0,
'_updater': 'set'}}},
'agents': {
'1': {
'location': {
'_default': (0, 0),
'_updater': 'set'},
'boundary': {
'external': {
'_default': 0.0,
'_updater': 'set'},
'internal': {
'_default': 0.0,
'_updater': 'set'}},
'transcripts': {
'flhDC': {
'_default': 0,
'_updater': 'accumulate'},
'fliA': {
'_default': 0,
'_updater': 'accumulate'}},
'proteins': {
'ribosome': {
'_default': 0,
'_updater': 'set'},
'flagella': {
'_default': 0,
'_updater': 'accumulate'}}},
'2': {
'location': {
'_default': (0, 0),
'_updater': 'set'},
'boundary': {
'external': {
'_default': 0.0,
'_updater': 'set'},
'internal': {
'_default': 0.0,
'_updater': 'set'}},
'transcripts': {
'flhDC': {
'_default': 0,
'_updater': 'accumulate'},
'fliA': {
'_default': 0,
'_updater': 'accumulate'}},
'proteins': {
'ribosome': {
'_default': 0,
'_updater': 'set'},
'flagella': {
'_default': 0,
'_updater': 'accumulate'}}}}}}
state = Store(environment_config)
state.apply_update({})
state.state_for(['environment'], ['temperature'])
def test_topology_ports() -> None:
proton = make_proton()
experiment = Experiment(proton)
log.debug(pf(experiment.state.get_config(True)))
experiment.update(10.0)
log.debug(pf(experiment.state.get_config(True)))
log.debug(pf(experiment.state.divide_value()))
def test_timescales() -> None:
class Slow(Process):
name = 'slow'
defaults = {'timestep': 3.0}
def __init__(self, config: Optional[dict] = None) -> None:
super().__init__(config)
def ports_schema(self) -> Schema:
return {
'state': {
'base': {
'_default': 1.0}}}
def next_update(
self,
timestep: Union[float, int],
states: State) -> Update:
base = states['state']['base']
next_base = timestep * base * 0.1
return {
'state': {'base': next_base}}
class Fast(Process):
name = 'fast'
defaults = {'timestep': 0.3}
def __init__(self, config: Optional[dict] = None) -> None:
super().__init__(config)
def ports_schema(self) -> Schema:
return {
'state': {
'base': {
'_default': 1.0},
'motion': {
'_default': 0.0}}}
def next_update(
self,
timestep: Union[float, int],
states: State) -> Update:
base = states['state']['base']
motion = timestep * base * 0.001
return {
'state': {'motion': motion}}
processes = {
'slow': Slow(),
'fast': Fast()}
states = {
'state': {
'base': 1.0,
'motion': 0.0}}
topology = {
'slow': {'state': ('state',)},
'fast': {'state': ('state',)}}
emitter = {'type': 'null'}
experiment = Experiment({
'processes': processes,
'topology': topology,
'emitter': emitter,
'initial_state': states})
experiment.update(10.0)
def test_2_store_1_port() -> None:
"""
Split one port of a processes into two stores
"""
class OnePort(Process):
name = 'one_port'
def ports_schema(self) -> Schema:
return {
'A': {
'a': {
'_default': 0,
'_emit': True},
'b': {
'_default': 0,
'_emit': True}
}
}
def next_update(
self,
timestep: Union[float, int],
states: State) -> Update:
return {
'A': {
'a': 1,
'b': 2}}
class SplitPort(Composer):
"""splits OnePort's ports into two stores"""
name = 'split_port_composer'
def generate_processes(
self, config: Optional[dict]) -> Dict[str, Any]:
return {
'one_port': OnePort({})}
def generate_topology(self, config: Optional[dict]) -> Topology:
return {
'one_port': {
'A': {
'a': ('internal', 'a',),
'b': ('external', 'a',)
}
}}
# run experiment
split_port = SplitPort({})
network = split_port.generate()
exp = Experiment({
'processes': network['processes'],
'topology': network['topology']})
exp.update(2)
output = exp.emitter.get_timeseries()
expected_output = {
'external': {'a': [0, 2, 4]},
'internal': {'a': [0, 1, 2]},
'time': [0.0, 1.0, 2.0]}
assert output == expected_output
def test_multi_port_merge() -> None:
class MultiPort(Process):
name = 'multi_port'
def ports_schema(self) -> Schema:
return {
'A': {
'a': {
'_default': 0,
'_emit': True}},
'B': {
'a': {
'_default': 0,
'_emit': True}},
'C': {
'a': {
'_default': 0,
'_emit': True}}}
def next_update(
self,
timestep: Union[float, int],
states: State) -> Update:
return {
'A': {'a': 1},
'B': {'a': 1},
'C': {'a': 1}}
class MergePort(Composer):
"""combines both of MultiPort's ports into one store"""
name = 'multi_port_composer'
def generate_processes(
self, config: Optional[dict]) -> Dict[str, Any]:
return {
'multi_port': MultiPort({})}
def generate_topology(self, config: Optional[dict]) -> Topology:
return {
'multi_port': {
'A': ('aaa',),
'B': ('aaa',),
'C': ('aaa',)}}
# run experiment
merge_port = MergePort({})
network = merge_port.generate()
exp = Experiment({
'processes': network['processes'],
'topology': network['topology']})
exp.update(2)
output = exp.emitter.get_timeseries()
expected_output = {
'aaa': {'a': [0, 3, 6]},
'time': [0.0, 1.0, 2.0]}
assert output == expected_output
def test_complex_topology() -> None:
# make the experiment
outer_path = ('universe', 'agent')
pq = PoQo({})
pq_composite = pq.generate(path=outer_path)
experiment = Experiment(pq_composite)
# get the initial state
initial_state = experiment.state.get_value()
print('time 0:')
pp(initial_state)
# simulate for 1 second
experiment.update(1)
next_state = experiment.state.get_value()
print('time 1:')
pp(next_state)
# pull out the agent state
initial_agent_state = initial_state['universe']['agent']
agent_state = next_state['universe']['agent']
assert agent_state['aaa']['a1'] == initial_agent_state['aaa']['a1'] + 1
assert agent_state['aaa']['x'] == initial_agent_state['aaa']['x'] - 9
assert agent_state['ccc']['a3'] == initial_agent_state['ccc']['a3'] + 1
def test_multi() -> None:
with multiprocessing.Pool(processes=4) as pool:
multi = MultiInvoke(pool)
proton = make_proton()
experiment = Experiment({**proton, 'invoke': multi.invoke})
log.debug(pf(experiment.state.get_config(True)))
experiment.update(10.0)
log.debug(pf(experiment.state.get_config(True)))
log.debug(pf(experiment.state.divide_value()))
def test_parallel() -> None:
proton = make_proton(parallel=True)
experiment = Experiment(proton)
log.debug(pf(experiment.state.get_config(True)))
experiment.update(10.0)
log.debug(pf(experiment.state.get_config(True)))
log.debug(pf(experiment.state.divide_value()))
experiment.end()
def test_depth() -> None:
nested = {
'A': {
'AA': 5,
'AB': {
'ABC': 11}},
'B': {
'BA': 6}}