Source code for bluepyopt.ephys.efeatures

"""eFeature classes"""

Copyright (c) 2016-2020, 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 GNU 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

 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=R0914

import logging

from bluepyopt.ephys.base import BaseEPhys
from bluepyopt.ephys.serializer import DictMixin

logger = logging.getLogger(__name__)

[docs]class EFeature(BaseEPhys): """EPhys feature""" pass
[docs]class eFELFeature(EFeature, DictMixin): """eFEL feature""" SERIALIZED_FIELDS = ('name', 'efel_feature_name', 'recording_names', 'stim_start', 'stim_end', 'exp_mean', 'exp_std', 'threshold', 'comment') def __init__( self, name, efel_feature_name=None, recording_names=None, stim_start=None, stim_end=None, exp_mean=None, exp_std=None, threshold=None, stimulus_current=None, comment='', interp_step=None, double_settings=None, int_settings=None, string_settings=None, force_max_score=False, max_score=250 ): """Constructor Args: name (str): name of the eFELFeature object efel_feature_name (str): name of the eFeature in the eFEL library (ex: 'AP1_peak') recording_names (dict): eFEL features can accept several recordings as input stim_start (float): stimulation start time (ms) stim_end (float): stimulation end time (ms) exp_mean (float): experimental mean of this eFeature exp_std(float): experimental standard deviation of this eFeature threshold(float): spike detection threshold (mV) comment (str): comment interp_step(float): interpolation step (ms) double_settings(dict): dictionary with efel double settings that should be set before extracting the features int_settings(dict): dictionary with efel int settings that should be set before extracting the features string_settings(dict): dictionary with efel string settings that should be set before extracting the features """ super(eFELFeature, self).__init__(name, comment) self.recording_names = recording_names self.efel_feature_name = efel_feature_name self.exp_mean = exp_mean self.exp_std = exp_std self.stim_start = stim_start self.stim_end = stim_end self.threshold = threshold self.interp_step = interp_step self.stimulus_current = stimulus_current self.double_settings = double_settings self.int_settings = int_settings self.string_settings = string_settings self.force_max_score = force_max_score self.max_score = max_score def _construct_efel_trace(self, responses): """Construct trace that can be passed to eFEL""" trace = {} if '' not in self.recording_names: raise Exception( 'eFELFeature: \'\' needs to be in recording_names') for location_name, recording_name in self.recording_names.items(): if location_name == '': postfix = '' else: postfix = ';%s' % location_name if recording_name not in responses: logger.debug( "Recording named %s not found in responses %s", recording_name, str(responses)) return None if responses[self.recording_names['']] is None or \ responses[recording_name] is None: return None trace['T%s' % postfix] = \ responses[self.recording_names['']]['time'] trace['V%s' % postfix] = responses[recording_name]['voltage'] trace['stim_start%s' % postfix] = [self.stim_start] trace['stim_end%s' % postfix] = [self.stim_end] return trace def _setup_efel(self): """Set up efel before extracting the feature""" import efel efel.reset() if self.threshold is not None: efel.setThreshold(self.threshold) if self.stimulus_current is not None: efel.setDoubleSetting('stimulus_current', self.stimulus_current) if self.interp_step is not None: efel.setDoubleSetting('interp_step', self.interp_step) if self.double_settings is not None: for setting_name, setting_value in self.double_settings.items(): efel.setDoubleSetting(setting_name, setting_value) if self.int_settings is not None: for setting_name, setting_value in self.int_settings.items(): efel.setIntSetting(setting_name, setting_value) if self.string_settings is not None: for setting_name, setting_value in self.string_settings.items(): efel.setStrSetting(setting_name, setting_value)
[docs] def calculate_feature(self, responses, raise_warnings=False): """Calculate feature value""" efel_trace = self._construct_efel_trace(responses) if efel_trace is None: feature_value = None else: self._setup_efel() import efel values = efel.getMeanFeatureValues( [efel_trace], [self.efel_feature_name], raise_warnings=raise_warnings) feature_value = values[0][self.efel_feature_name] efel.reset() logger.debug( 'Calculated value for %s: %s',, str(feature_value)) return feature_value
[docs] def calculate_score(self, responses, trace_check=False): """Calculate the score""" efel_trace = self._construct_efel_trace(responses) if efel_trace is None: score = self.max_score else: self._setup_efel() import efel score = efel.getDistance( efel_trace, self.efel_feature_name, self.exp_mean, self.exp_std, trace_check=trace_check, error_dist=self.max_score ) if self.force_max_score: score = min(score, self.max_score) efel.reset() logger.debug('Calculated score for %s: %f',, score) return score
def __str__(self): """String representation""" return "%s for %s with stim start %s and end %s, " \ "exp mean %s and std %s and AP threshold override %s" % \ (self.efel_feature_name, self.recording_names, self.stim_start, self.stim_end, self.exp_mean, self.exp_std, self.threshold)