Python API

eFEL Python API functions.

This module provides the user-facing Python API of the eFEL. The convenience functions defined here call the underlying ‘cppcore’ library to hide the lower level API from the user. All functions in this module can be called as efel.functionname, it is not necessary to include ‘api’ as in efel.api.functionname.

Copyright (c) 2015, EPFL/Blue Brain Project

This file is part of eFEL <https://github.com/BlueBrain/eFEL>

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 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.

efel.api.FeatureNameExists(feature_name)[source]

Does a certain feature name exist ?

Parameters:feature_name (string) – Name of the feature to check
Returns:FeatureNameExists – True if feature_name exists, otherwise False
Return type:bool
efel.api.getDependencyFileLocation()[source]

Get the location of the Dependency file

The eFEL uses ‘Dependency’ files to let the user define which versions of certain features are used to calculate. The installation directory of the eFEL contains a default ‘DependencyV5.txt’ file.

Returns:location – path to the location of a Dependency file
Return type:string
efel.api.getDistance(trace, featureName, mean, std, trace_check=True, error_dist=250)[source]

Calculate distance value for a list of traces.

Parameters:
  • trace (trace dicts) – Trace dict that represents one trace. The dict should have the following keys: ‘T’, ‘V’, ‘stim_start’, ‘stim_end’
  • featureName (string) – Name of the the features for which to calculate the distance
  • mean (float) – Mean to calculate the distance from
  • std (float) – Std to scale the distance with
  • trace_check (float) – Let the library check if there are spikes outside of stimulus interval, default is True
  • error_dist (float) – Distance returned when error, default is 250
Returns:

distance – The absolute number of standard deviation the feature is away from the mean. In case of anomalous results a value of ‘error_dist’ standard deviations is returned. This can happen if: a feature generates an error, there are spikes outside of the stimulus interval, the feature returns a NaN, etc.

Return type:

float

efel.api.getFeatureNames()[source]

Return a list with the name of all the available features

Returns:feature_names – A list that contains all the feature names available in the eFEL. These names can be used in the featureNames argument of e.g. getFeatureValues()
Return type:list of strings
efel.api.getFeatureValues(traces, featureNames, parallel_map=None, return_list=True, raise_warnings=True)[source]

Calculate feature values for a list of traces.

This function is the core of the eFEL API. A list of traces (in the form of dictionaries) is passed as argument, together with a list of feature names.

The return value consists of a list of dictionaries, one for each input trace. The keys in the dictionaries are the names of the calculated features, the corresponding values are lists with the feature values. Beware that every feature returns an array of values. E.g. AP_amplitude will return a list with the amplitude of every action potential.

Parameters:
  • traces (list of trace dicts) – Every trace dict represent one trace. The dict should have the following keys: ‘T’, ‘V’, ‘stim_start’, ‘stim_end’
  • feature_names (list of string) – List with the names of the features to be calculated on all the traces.
  • parallel_map (map function) – Map function to parallelise over the traces. Default is the serial map() function
  • return_list (boolean) – By default the function returns a list of dicts. This optional argument can disable this, so that the result of the parallel_map() is returned. Can be useful for performance reasons when an iterator is preferred.
  • raise_warnings (boolean) – Raise warning when efel c++ returns an error
Returns:

feature_values – For every input trace a feature value dict is return (in the same order). The dict contains the keys of ‘feature_names’, every key contains a numpy array with the feature values returned by the C++ efel code. The value is None if an error occured during the calculation of the feature.

Return type:

list of dicts

efel.api.getMeanFeatureValues(traces, featureNames, raise_warnings=True)[source]

Convenience function that returns mean values from getFeatureValues()

Instead of return a list of values for every feature as getFeatureValues() does, this function returns per trace one value for every feature, namely the mean value.

Parameters:
  • traces (list of trace dicts) – Every trace dict represent one trace. The dict should have the following keys: ‘T’, ‘V’, ‘stim_start’, ‘stim_end’
  • feature_names (list of string) – List with the names of the features to be calculated on all the traces.
  • raise_warnings (boolean) – Raise warning when efel c++ returns an error
Returns:

feature_values – For every input trace a feature value dict is return (in the same order). The dict contains the keys of ‘feature_names’, every key contains the mean of the array that is returned by getFeatureValues() The value is None if an error occured during the calculation of the feature, or if the feature value array was empty.

Return type:

list of dicts

efel.api.get_cpp_feature(featureName, raise_warnings=None)[source]

Return value of feature implemented in cpp

efel.api.get_py_feature(featureName)[source]

Return python feature

efel.api.reset()[source]

Resets the efel to its initial state

The user can set certain values in the efel, like the spike threshold. These values are persisten. This function will reset these value to their original state.

efel.api.setDependencyFileLocation(location)[source]

Set the location of the Dependency file

The eFEL uses ‘Dependency’ files to let the user define which versions of certain features are used to calculate. The installation directory of the eFEL contains a default ‘DependencyV5.txt’ file. Unless the user wants to change this file, it is not necessary to call this function.

Parameters:location (string) – path to the location of a Dependency file
efel.api.setDerivativeThreshold(newDerivativeThreshold)[source]

Set the threshold for the derivate for detecting the spike onset

Some featurea use a threshold on dV/dt to calculate the beginning of an action potential. This function allows you to set this threshold.

Parameters:derivative_threshold (float) – The new derivative threshold value (in the same units as the traces, e.g. mV/ms).
efel.api.setDoubleSetting(setting_name, new_value)[source]

Set a certain double setting to a new value

efel.api.setIntSetting(setting_name, new_value)[source]

Set a certain integer setting to a new value

efel.api.setThreshold(newThreshold)[source]

Set the spike detection threshold in the eFEL

Parameters:threshold (float) – The new spike detection threshold value (in the same units as the traces, e.g. mV).