import os
import numpy as np
import pandas as pd
from functools import partial
from six import string_types
from bmtk.utils import sonata
from bmtk.utils.sonata.config import SonataConfig
from bmtk.utils.reports import SpikeTrains
from bmtk.utils.reports.spike_trains import plotting
from bmtk.simulator.utils import simulation_reports
def _find_spikes(spikes_file=None, config_file=None, population=None):
candidate_spikes = []
# Get spikes file(s)
if spikes_file:
# User has explicity set the location of the spike files
candidate_spikes.append(spikes_file)
elif config_file is not None:
# Otherwise search the config.json for all possible output spikes_files. We can use the simulation_reports
# module to find any spikes output file specified in config's "output" or "reports" section.
config = SonataConfig.from_json(config_file)
sim_reports = simulation_reports.from_config(config)
for report in sim_reports:
if report.module == 'spikes_report':
# BMTK can end up output the same spikes file in SONATA, CSV, and NWB format. Try fetching the SONATA
# version first, then CSV, and finally NWB if it exists.
spikes_sonata = report.params.get('spikes_file', None)
spikes_csv = report.params.get('spikes_file_csv', None)
spikes_nwb = report.params.get('spikes_file_nwb', None)
if spikes_sonata is not None:
candidate_spikes.append(spikes_sonata)
elif spikes_csv is not None:
candidate_spikes.append(spikes_csv)
elif spikes_csv is not None:
candidate_spikes.append(spikes_nwb)
# TODO: Should we also look in the "inputs" for displaying input spike statistics?
if not candidate_spikes:
raise ValueError('Could not find an output spikes-file. Use "spikes_file" parameter option.')
# Find file that contains spikes for the specified "population" of nodes. If "population" parameter is not
# specified try to guess that spikes that the user wants to visualize.
if population is not None:
spikes_obj = None
for spikes_f in candidate_spikes:
st = SpikeTrains.load(spikes_f)
if population in st.populations:
if spikes_obj is None:
spikes_obj = st
else:
spikes_obj.merge(st)
if spikes_obj is None:
raise ValueError('Could not fine spikes file with node population "{}".'.format(population))
else:
return population, spikes_obj
else:
if len(candidate_spikes) > 1:
raise ValueError('Found more than one spike-trains file')
spikes_f = candidate_spikes[0]
if not os.path.exists(spikes_f):
raise ValueError('Did not find spike-trains file {}. Make sure the simulation has completed.'.format(
spikes_f))
spikes_obj = SpikeTrains.load(spikes_f)
if len(spikes_obj.populations) > 1:
raise ValueError('Spikes file {} contains more than one node population.'.format(spikes_f))
else:
return spikes_obj.populations[0], spikes_obj
def _find_nodes(population, config=None, nodes_file=None, node_types_file=None):
if nodes_file is not None:
network = sonata.File(data_files=nodes_file, data_type_files=node_types_file)
if population not in network.nodes.population_names:
raise ValueError('node population "{}" not found in {}'.format(population, nodes_file))
return network.nodes[population]
elif config is not None:
for nodes_grp in config.nodes:
network = sonata.File(data_files=nodes_grp['nodes_file'], data_type_files=nodes_grp['node_types_file'])
if population in network.nodes.population_names:
return network.nodes[population]
raise ValueError('Could not find nodes file with node population "{}".'.format(population))
def _plot_helper(plot_fnc, config_file=None, population=None, times=None, title=None, show=True, save_as=None,
group_by=None, group_excludes=None,
spikes_file=None, nodes_file=None, node_types_file=None):
sonata_config = SonataConfig.from_json(config_file) if config_file else None
pop, spike_trains = _find_spikes(config_file=config_file, spikes_file=spikes_file, population=population)
# Create the title
title = title if title is not None else "Nodes in network '{}'".format(pop)
# Get start and stop times from config if needed
if sonata_config and times is None:
times = (sonata_config.tstart, sonata_config.tstop)
# Create node-groups
if group_by is not None:
node_groups = []
nodes = _find_nodes(population=pop, config=sonata_config, nodes_file=nodes_file,
node_types_file=node_types_file)
grouped_df = None
for grp in nodes.groups:
if group_by in grp.all_columns:
grp_df = grp.to_dataframe()
grp_df = grp_df[['node_id', group_by]]
grouped_df = grp_df if grouped_df is None else pd.concat([grouped_df, grp_df], ignore_index=True)
if grouped_df is None:
raise ValueError('Could not find any nodes with group_by attribute "{}"'.format(group_by))
# Convert from string to list so we can always use the isin() method for filtering
if isinstance(group_excludes, string_types):
group_excludes = [group_excludes]
elif group_excludes is None:
group_excludes = []
for grp_key, grp in grouped_df.groupby(group_by):
if grp_key in group_excludes:
continue
node_groups.append({'node_ids': np.array(grp['node_id']), 'label': grp_key})
else:
node_groups = None
return plot_fnc(
spike_trains=spike_trains, node_groups=node_groups, population=pop, times=times, title=title, show=show,
save_as=save_as
)
[docs]def plot_raster(config_file=None, population=None, with_histogram=True, times=None, title=None, show=True,
save_as=None, group_by=None, group_excludes=None,
spikes_file=None, nodes_file=None, node_types_file=None, plt_style=None):
"""Create a raster plot (plus optional histogram) from the results of the simulation.
Will using the SONATA simulation configs "output" section to locate where the spike-trains file was created and
display them::
plot_raster(config_file='config.json')
If the path the the report is different (or missing) than what's in the SONATA config then use the "spikes_file"
option instead::
plot_raster(spikes_file='/my/path/to/membrane_potential.h5')
You may also group together different subsets of nodes using specific attributes of the network using the "group_by"
option, and the "group_excludes" option to exclude specific subsets. For example to color and label different
subsets of nodes based on their cortical "layer", but exlcude plotting the L1 nodes::
plot_raster(config_file='config.json', groupy_by='layer', group_excludes='L1')
:param config_file: path to SONATA simulation configuration.
:param population: name of the membrane_report "report" which will be plotted. If only one compartment report
in the simulation config then function will find it automatically.
:param with_histogram: If True the a histogram will be shown as a small subplot below the scatter plot. Default
True.
:param times: (float, float), start and stop times of simulation. By default will get values from simulation
configs "run" section.
:param title: str, adds a title to the plot. If None (default) then name will be automatically generated using the
report_name.
:param show: bool to display or not display plot. default True.
:param save_as: None or str: file-name/path to save the plot as a png/jpeg/etc. If None or empty string will not
save plot.
:param group_by: Attribute of the "nodes" file used to group and average subsets of nodes.
:param group_excludes: list of strings or None. When using the "group_by", allows users to exclude certain groupings
based on the attribute value.
:param spikes_file: Path to SONATA spikes file. Do not use with "config_file" options.
:param nodes_file: path to nodes hdf5 file containing "population". By default this will be resolved using the
config.
:param node_types_file: path to node-types csv file containing "population". By default this will be resolved using
the config.
:return: matplotlib figure.Figure object
"""
plot_fnc = partial(plotting.plot_raster, with_histogram=with_histogram, plt_style=plt_style)
return _plot_helper(
plot_fnc,
config_file=config_file, population=population, times=times, title=title, show=show, save_as=save_as,
group_by=group_by, group_excludes=group_excludes,
spikes_file=spikes_file, nodes_file=nodes_file, node_types_file=node_types_file
)
[docs]def plot_rates(config_file=None, population=None, smoothing=False, smoothing_params=None, times=None, title=None,
show=True, save_as=None, group_by=None, group_excludes=None, spikes_file=None, nodes_file=None,
node_types_file=None, plt_style=None):
"""Calculate and plot the rates of each node recorded during the simulation - averaged across the entirety of the
simulation.
Will using the SONATA simulation configs "output" section to locate where the spike-trains file was created and
display them::
plot_rates(config_file='config.json')
If the path the the report is different (or missing) than what's in the SONATA config then use the "spikes_file"
option instead::
plot_rates(spikes_file='/my/path/to/membrane_potential.h5')
You may also group together different subsets of nodes using specific attributes of the network using the "group_by"
option, and the "group_excludes" option to exclude specific subsets. For example to color and label different
subsets of nodes based on their cortical "layer", but exlcude plotting the L1 nodes::
plot_rates(config_file='config.json', groupy_by='layer', group_excludes='L1')
:param config_file: path to SONATA simulation configuration.
:param population: name of the membrane_report "report" which will be plotted. If only one compartment report
in the simulation config then function will find it automatically.
:param smoothing: Bool or function. Used to smooth the data. By default (False) no smoothing will be done. If True
will using a moving average smoothing function. Or use a function pointer.
:param smoothing_params: dict, parameters when using a function pointer smoothing value.
:param times: (float, float), start and stop times of simulation. By default will get values from simulation
configs "run" section.
:param title: str, adds a title to the plot. If None (default) then name will be automatically generated using the
report_name.
:param show: bool to display or not display plot. default True.
:param save_as: None or str: file-name/path to save the plot as a png/jpeg/etc. If None or empty string will not
save plot.
:param group_by: Attribute of the "nodes" file used to group and average subsets of nodes.
:param group_excludes: list of strings or None. When using the "group_by", allows users to exclude certain groupings
based on the attribute value.
:param spikes_file: Path to SONATA spikes file. Do not use with "config_file" options.
:param nodes_file: Path to nodes hdf5 file containing "population". By default this will be resolved using the
config.
:param node_types_file: Path to node-types csv file containing "population". By default this will be resolved using
the config.
:return: matplotlib figure.Figure object
"""
plot_fnc = partial(plotting.plot_rates, smoothing=smoothing, smoothing_params=smoothing_params, plt_style=plt_style)
return _plot_helper(
plot_fnc,
config_file=config_file, population=population, times=times, title=title, show=show, save_as=save_as,
group_by=group_by, group_excludes=group_excludes,
spikes_file=spikes_file, nodes_file=nodes_file, node_types_file=node_types_file
)
[docs]def plot_rates_boxplot(config_file=None, population=None, times=None, title=None, show=True, save_as=None,
group_by=None, group_excludes=None,
spikes_file=None, nodes_file=None, node_types_file=None, plt_style=None):
"""Creates a box plot of the firing rates taken from nodes recorded during the simulation.
Will using the SONATA simulation configs "output" section to locate where the spike-trains file was created and
display them::
plot_rates_boxplot(config_file='config.json')
If the path the the report is different (or missing) than what's in the SONATA config then use the "spikes_file"
option instead::
plot_rates_boxplot(spikes_file='/my/path/to/membrane_potential.h5')
You may also group together different subsets of nodes using specific attributes of the network using the "group_by"
option, and the "group_excludes" option to exclude specific subsets. For example to color and label different
subsets of nodes based on their cortical "layer", but exlcude plotting the L1 nodes::
plot_rates_boxplot(config_file='config.json', groupy_by='layer', group_excludes='L1')
:param config_file: path to SONATA simulation configuration.
:param population: name of the membrane_report "report" which will be plotted. If only one compartment report
in the simulation config then function will find it automatically.
:param times: (float, float), start and stop times of simulation. By default will get values from simulation
configs "run" section.
:param title: str, adds a title to the plot. If None (default) then name will be automatically generated using the
report_name.
:param show: bool to display or not display plot. default True.
:param save_as: None or str: file-name/path to save the plot as a png/jpeg/etc. If None or empty string will not
save plot.
:param group_by: Attribute of the "nodes" file used to group and average subsets of nodes.
:param group_excludes: list of strings or None. When using the "group_by", allows users to exclude certain groupings
based on the attribute value.
:param spikes_file: Path to SONATA spikes file. Do not use with "config_file" options.
:param nodes_file: Path to nodes hdf5 file containing "population". By default this will be resolved using the
config.
:param node_types_file: Path to node-types csv file containing "population". By default this will be resolved using
the config.
:return: matplotlib figure.Figure object
"""
plot_fnc = partial(plotting.plot_rates_boxplot, plt_style=plt_style)
return _plot_helper(
plot_fnc,
config_file=config_file, population=population, times=times, title=title, show=show, save_as=save_as,
group_by=group_by, group_excludes=group_excludes,
spikes_file=spikes_file, nodes_file=nodes_file, node_types_file=node_types_file
)
[docs]def spike_statistics(spikes_file, simulation=None, population=None, simulation_time=None, group_by=None, network=None,
config_file=None, **filterparams):
"""Get spike statistics (firing_rate, counts, inter-spike interval) of the nodes.
:param spikes_file: Path to SONATA spikes file. Do not use with "config_file" options.
:param simulation:
:param population:
:param simulation_time:
:param groupby:
:param network:
:param config_file:
:param filterparams:
:return: pandas dataframe
"""
# TODO: Should be implemented in bmtk.utils.spike_trains.stats.py
pop, spike_trains = _find_spikes(config_file=config_file, spikes_file=spikes_file, population=population)
# spike_trains = SpikeTrains.load(spikes_file)
def calc_stats(r):
d = {}
vals = np.sort(r['timestamps'])
diffs = np.diff(vals)
if diffs.size > 0:
d['isi'] = np.mean(np.diff(vals))
else:
d['isi'] = 0.0
d['count'] = len(vals)
return pd.Series(d, index=['count', 'isi'])
spike_counts_df = spike_trains.to_dataframe().groupby(['population', 'node_ids']).apply(calc_stats)
spike_counts_df = spike_counts_df.rename({'timestamps': 'counts'}, axis=1)
spike_counts_df.index.names = ['population', 'node_id']
if simulation is not None:
nodes_df = simulation.net.node_properties(**filterparams)
sim_time_s = simulation.simulation_time(units='s')
spike_counts_df['firing_rate'] = spike_counts_df['count'] / sim_time_s
vals_df = pd.merge(nodes_df, spike_counts_df, left_index=True, right_index=True, how='left')
vals_df = vals_df.fillna({'count': 0.0, 'firing_rate': 0.0, 'isi': 0.0})
vals_df = vals_df.groupby(group_by)[['firing_rate', 'count', 'isi']].agg([np.mean, np.std])
return vals_df
else:
return spike_counts_df
[docs]def to_dataframe(config_file, spikes_file=None, population=None):
"""
:param config_file:
:param spikes_file:
:param population:
:return:
"""
# _, spike_trains = _find_spikes(config_file=config_file, spikes_file=spikes_file, population=population)
pop, spike_trains = _find_spikes(config_file=config_file, spikes_file=spikes_file, population=population)
return spike_trains.to_dataframe()
