コード例 #1
0
    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)
コード例 #2
0
ファイル: efeatures.py プロジェクト: Chris-crisur/BluePyOpt
    def calculate_score(self, responses):
        """Calculate the score"""

        efel_trace = self._construct_efel_trace(responses)

        if efel_trace is None:
            score = 250.0
        else:
            import efel
            efel.reset()

            if self.threshold:
                efel.setThreshold(self.threshold)

            score = efel.getDistance(
                efel_trace,
                self.efel_feature_name,
                self.exp_mean,
                self.exp_std)

            efel.reset()

        logger.debug('Calculated score for %s: %f', self.name, score)

        return score
コード例 #3
0
    def FFun_for_Features(self, mod_t, features_data, feature, k, args={}):

        exp_mean = 0
        exp_std = 0
        # generating time trace for efel
        t_gen = frange(
            0, self.option.run_controll_tstop + self.option.run_controll_dt,
            self.option.run_controll_dt)
        t = []
        for n in t_gen:
            t.append(n)
        t = t[0:len(mod_t)]

        # converting list to numpy array for efel
        mod_t_np = np.array(mod_t)
        t_np = np.array(t)

        temp_fit = 0
        trace = {}
        traces = []
        trace['T'] = t_np
        trace['V'] = mod_t_np
        trace['stim_start'] = [self.option.stim_del]
        trace['stim_end'] = [self.option.stim_del + self.option.stim_dur]
        traces.append(trace)

        efel.setThreshold(self.thres)

        traces_results = self.get_efel_values(traces, feature)

        exp_mean = features_data[feature]["mean"][k]
        exp_std = features_data[feature]["std"][k]
        mod_result = traces_results[0][feature]

        if mod_result is not None and mod_result.size > 1 and (
                feature == 'AP_rise_time' or feature == 'AP_amplitude'
                or feature == 'AP_duration_half_width'
                or feature == 'AP_begin_voltage' or feature == 'AP_rise_rate'):
            mod_result = scipy.mean(mod_result[1:])
        elif mod_result is not None and mod_result.size > 1:
            mod_result = scipy.mean(
                mod_result
            )  #for features (AHP_depth, AP_duration_half_width) that gives a list as a result, the mean of the results is used
        elif mod_result is not None and mod_result.size != 0:  # mod_result is a numpy array with one element, 0 element for AP1_amp or APlast_amp if no AP generated by the model
            mod_result = mod_result[0]

        if (mod_result == None or mod_result.size
                == 0) and (exp_mean != None and exp_std != None):
            temp_fit = 250
        elif exp_mean == None and exp_std == None:
            temp_fit = 0

        else:
            result = abs(exp_mean - mod_result) / exp_std
            temp_fit = result

        return temp_fit
コード例 #4
0
ファイル: efeatures.py プロジェクト: rxseadew/BluePyOpt
    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 not efel.FeatureNameExists(self.efel_feature_name):
            raise ValueError("eFEL doesn't have a feature called %s" %
                             self.efel_feature_name)
コード例 #5
0
ファイル: efeatures.py プロジェクト: jdcourcol/BluePyOpt
    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 not efel.FeatureNameExists(self.efel_feature_name):
            raise ValueError("eFEL doesn't have a feature called %s" %
                             self.efel_feature_name)
コード例 #6
0
ファイル: efeatures.py プロジェクト: wvangeit/BluePyOpt
    def calculate_score(self, responses):
        """Calculate the score"""

        efel_trace = self._construct_efel_trace(responses)

        import efel

        if self.threshold:
            efel.setThreshold(self.threshold)

        score = efel.getDistance(
            efel_trace,
            self.efel_feature_name,
            self.exp_mean,
            self.exp_std)

        logger.debug('Calculated score for %s: %f', self.name, score)

        return score
コード例 #7
0
ファイル: efeatures.py プロジェクト: apdavison/BluePyOpt
    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)
コード例 #8
0
    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 not efel.FeatureNameExists(self.efel_feature_name):
            raise ValueError("eFEL doesn't have a feature called %s" %
                             self.efel_feature_name)
コード例 #9
0
ファイル: test_allfeatures.py プロジェクト: BlueBrain/eFEL
def get_allfeature_values():
    """Get back all the feature names and value"""

    import efel
    efel.reset()
    import numpy

    all_featurenames = efel.getFeatureNames()

    soma_data = numpy.loadtxt(os.path.join(testdata_dir, 'testdata.txt'))
    soma_time = soma_data[:, 0]
    soma_voltage = soma_data[:, 1]

    db_data = numpy.loadtxt(os.path.join(testdata_dir, 'testdbdata.txt'))
    db_time = db_data[:, 0]
    db_voltage = db_data[:, 1]

    bac_data = numpy.loadtxt(os.path.join(testdata_dir, 'testbacdata.txt'))
    bac_time = bac_data[:, 0]
    bac_voltage = bac_data[:, 1]

    bap1_data = numpy.loadtxt(os.path.join(testdata_dir, 'testbap1data.txt'))
    bap1_time = bap1_data[:, 0]
    bap1_voltage = bap1_data[:, 1]

    bap2_data = numpy.loadtxt(os.path.join(testdata_dir, 'testbap2data.txt'))
    bap2_time = bap2_data[:, 0]
    bap2_voltage = bap2_data[:, 1]

    trace = {}
    trace_db = {}

    trace['T'] = soma_time
    trace['V'] = soma_voltage
    trace['stim_start'] = [700]
    trace['stim_end'] = [2700]
    trace['T;location_AIS'] = soma_time
    trace['V;location_AIS'] = soma_voltage
    trace['stim_start;location_AIS'] = [700]
    trace['stim_end;location_AIS'] = [2700]
    trace['T;location_epsp'] = bac_time
    trace['V;location_epsp'] = bac_voltage
    trace['stim_start;location_epsp'] = [295]
    trace['stim_end;location_epsp'] = [600]
    trace['T;location_dend1'] = bap1_time
    trace['V;location_dend1'] = bap1_voltage
    trace['stim_start;location_dend1'] = [295]
    trace['stim_end;location_dend1'] = [500]
    trace['T;location_dend2'] = bap2_time
    trace['V;location_dend2'] = bap2_voltage
    trace['stim_start;location_dend2'] = [295]
    trace['stim_end;location_dend2'] = [500]

    trace_db['T'] = db_time
    trace_db['V'] = db_voltage
    trace_db['stim_start'] = [419.995]
    trace_db['stim_end'] = [1419.995]

    bpap_featurenames = [
        'BPAPHeightLoc1',
        'BPAPHeightLoc2',
        'BPAPAmplitudeLoc1',
        'BPAPAmplitudeLoc2']

    bac_featurenames = [
        'BAC_width']

    db_featurenames = [
        'depol_block']

    soma_featurenames = all_featurenames[:]

    for feature_name in bpap_featurenames:
        soma_featurenames.remove(feature_name)

    for feature_name in bac_featurenames:
        soma_featurenames.remove(feature_name)

    for feature_name in db_featurenames:
        soma_featurenames.remove(feature_name)

    import warnings
    with warnings.catch_warnings():
        warnings.simplefilter("ignore")
        feature_values = efel.getFeatureValues([trace], soma_featurenames)[0]

    with warnings.catch_warnings():
        warnings.simplefilter("ignore")
        feature_values = dict(
            list(feature_values.items()) +
            list(efel.getFeatureValues(
                [trace_db],
                db_featurenames)[0].items()))

    with warnings.catch_warnings():
        warnings.simplefilter("ignore")
        efel.setThreshold(-30)
        feature_values = dict(
            list(feature_values.items()) +
            list(efel.getFeatureValues(
                [trace],
                bpap_featurenames)[0].items()))

    with warnings.catch_warnings():
        warnings.simplefilter("ignore")
        efel.setThreshold(-55)
        feature_values = dict(
            list(feature_values.items()) +
            list(efel.getFeatureValues(
                [trace],
                bac_featurenames)[0].items()))

    for feature_name in feature_values:
        if feature_values[feature_name] is not None:
            feature_values[feature_name] = list(
                feature_values[feature_name])

    return feature_values
コード例 #10
0
ファイル: test_allfeatures.py プロジェクト: apdavison/eFEL
def get_allfeature_values():
    """Get back all the feature names and value"""

    import efel
    efel.reset()
    import numpy

    all_featurenames = efel.getFeatureNames()

    soma_data = numpy.loadtxt(os.path.join(testdata_dir, 'testdata.txt'))
    soma_time = soma_data[:, 0]
    soma_voltage = soma_data[:, 1]

    bac_data = numpy.loadtxt(os.path.join(testdata_dir, 'testbacdata.txt'))
    bac_time = bac_data[:, 0]
    bac_voltage = bac_data[:, 1]

    bap1_data = numpy.loadtxt(os.path.join(testdata_dir, 'testbap1data.txt'))
    bap1_time = bap1_data[:, 0]
    bap1_voltage = bap1_data[:, 1]

    bap2_data = numpy.loadtxt(os.path.join(testdata_dir, 'testbap2data.txt'))
    bap2_time = bap2_data[:, 0]
    bap2_voltage = bap2_data[:, 1]

    trace = {}

    trace['T'] = soma_time
    trace['V'] = soma_voltage
    trace['stim_start'] = [700]
    trace['stim_end'] = [2700]
    trace['T;location_AIS'] = soma_time
    trace['V;location_AIS'] = soma_voltage
    trace['stim_start;location_AIS'] = [700]
    trace['stim_end;location_AIS'] = [2700]
    trace['T;location_epsp'] = bac_time
    trace['V;location_epsp'] = bac_voltage
    trace['stim_start;location_epsp'] = [295]
    trace['stim_end;location_epsp'] = [600]
    trace['T;location_dend1'] = bap1_time
    trace['V;location_dend1'] = bap1_voltage
    trace['stim_start;location_dend1'] = [295]
    trace['stim_end;location_dend1'] = [500]
    trace['T;location_dend2'] = bap2_time
    trace['V;location_dend2'] = bap2_voltage
    trace['stim_start;location_dend2'] = [295]
    trace['stim_end;location_dend2'] = [500]

    bpap_featurenames = [
        'BPAPHeightLoc1', 'BPAPHeightLoc2', 'BPAPAmplitudeLoc1',
        'BPAPAmplitudeLoc2'
    ]

    bac_featurenames = ['BAC_width']

    soma_featurenames = all_featurenames[:]

    for feature_name in bpap_featurenames:
        soma_featurenames.remove(feature_name)

    for feature_name in bac_featurenames:
        soma_featurenames.remove(feature_name)

    import warnings
    with warnings.catch_warnings():
        warnings.simplefilter("ignore")
        feature_values = efel.getFeatureValues([trace], soma_featurenames)[0]

    with warnings.catch_warnings():
        warnings.simplefilter("ignore")
        efel.setThreshold(-30)
        feature_values = dict(
            list(feature_values.items()) +
            list(efel.getFeatureValues([trace], bpap_featurenames)[0].items()))

    with warnings.catch_warnings():
        warnings.simplefilter("ignore")
        efel.setThreshold(-55)
        feature_values = dict(
            list(feature_values.items()) +
            list(efel.getFeatureValues([trace], bac_featurenames)[0].items()))

    for feature_name in feature_values:
        if feature_values[feature_name] is not None:
            feature_values[feature_name] = list(feature_values[feature_name])

    return feature_values
コード例 #11
0
#*It should be used in conjunction with the HEKAv2 MATLAB script

#Install and Import these scripts
import scipy.io
import efel
import numpy as np
from tkinter import filedialog
from matplotlib import pyplot as plt

#Enable EFEL Settings
efel.setDoubleSetting(
    'interp_step', 0.05)  #Must be set to the sampling rate (20,000 Hz = 0.05)
efel.setDoubleSetting('voltage_base_start_perc', 0.1)
efel.setDoubleSetting('voltage_base_end_perc', 0.8)
efel.setDerivativeThreshold(10)  #Value can be changed, but normally set to 10
efel.setThreshold(0)

#Create global variable
trace_results = dict()

#Opens a dialog box allowing the user to select the file and removes the extension from the file name & load in the file
file_path = filedialog.askopenfilename()
file = file_path[:-4]
mat = scipy.io.loadmat(file_path)

#Depending upon the number of cells present, split the data into a list of numpy arrays based on the number of experiments
print('There are ' + str(int(mat['CellNum'])) + ' cells in this file.')

if mat['CellNum'] == 1:
    Cell1 = list(np.hsplit(mat['C1data'], int(mat['C1Expts'])))
    print('Cell #1 has ' + str(int(mat['C1Expts'])) + ' experiments.')
コード例 #12
0
    def FFun_for_Features(self, mod_t, features_data, feature, k, args={}):

        exp_mean = 0
        exp_std = 0
        # generating time trace for efel
        t_gen = frange(
            0, self.option.run_controll_tstop + self.option.run_controll_dt,
            self.option.run_controll_dt)
        t = []
        for n in t_gen:
            t.append(n)
        t = t[0:len(mod_t)]

        # converting list to numpy array for efel
        mod_t_np = np.array(mod_t)
        t_np = np.array(t)

        temp_fit = 0
        trace = {}
        traces = []
        trace['T'] = t_np
        trace['V'] = mod_t_np
        trace['stim_start'] = [self.option.stim_del]
        trace['stim_end'] = [self.option.stim_del + self.option.stim_dur]
        traces.append(trace)

        efel.setThreshold(self.thres)
        '''

        used_features=list(features)

        AP_num = efel.getFeatureValues(traces,['Spikecount'])
        for i in range (len(features)):
            if AP_num[0]['Spikecount'] == 0 and features_data[features[i]]["requires_spike"]=="True":
                used_features.remove(features[i])
                temp_fit += 10  #MEnnyi legyen a bunti?

            if AP_num[0]['Spikecount'] <2 and "ISI" in features_data[features[i]]:
                used_features.remove(features[i])
                temp_fit += 1

        '''

        #traces_results = efel.getFeatureValues(traces,[feature])      #list
        traces_results = self.get_efel_values(traces, feature)
        #print traces_results

        exp_mean = features_data[feature]["mean"][k]
        exp_std = features_data[feature]["std"][k]
        mod_result = traces_results[0][feature]
        #print feature

        #print "exp mean", exp_mean
        #print "exp std", exp_std
        #print "model", mod_result

        if mod_result is not None and mod_result.size > 1:
            mod_result = scipy.mean(
                mod_result
            )  #for features (AHP_depth, AP_duration_half_width) that gives a list as a result, the mean of the results is used
        elif mod_result is not None and mod_result.size != 0:  # mod_result is a numpy array with one element, 0 element for AP1_amp or APlast_amp if no AP generated by the model
            mod_result = mod_result[0]

        if (mod_result == None or mod_result == 0 or mod_result.size
                == 0) and (exp_mean != None and exp_std != None):
            temp_fit = 250
        elif exp_mean == None and exp_std == None:
            temp_fit = 0
        #elif exp_mean != None and exp_std != None :
        else:
            result = abs(exp_mean - mod_result) / exp_std
            temp_fit = result

        #print temp_fit
        return temp_fit
コード例 #13
0
from scipy.io import loadmat, matlab
import efel
import pandas as pd
import numpy as np
from tkinter import filedialog
from matplotlib import pyplot as plt
from csv import DictWriter
import csv
from os import chdir, getcwd

#Enable EFEL Settings
efel.setDoubleSetting('interp_step',0.05) #Must be set to the sampling rate (20,000 Hz = 0.05)
efel.setDoubleSetting('voltage_base_start_perc',0.1)
efel.setDoubleSetting('voltage_base_end_perc',0.8)
efel.setDerivativeThreshold(10) #Value can be changed, but normally set to 10
efel.setThreshold(10)

#Create global variable to store parameter information
Results = list()
trace_calc = dict()
User_choices = dict()
Cell = dict()

#Function that loads nested mat structures into a dictionary instead of using dot notation to
#extract individual pieces of data
def load_mat(filename):
    """
    This function should be called instead of direct scipy.io.loadmat
    as it cures the problem of not properly recovering python dictionaries
    from mat files. It calls the function check keys to cure all entries
    which are still mat-objects
コード例 #14
0
    def FFun_for_Features(self, mod_t, features_data, feature, k, args={}):

        exp_mean = 0
        exp_std = 0
        # generating time trace for efel
        t_gen = frange(0, self.option.run_controll_tstop + self.option.run_controll_dt, self.option.run_controll_dt)
        t = []
        for n in t_gen:
            t.append(n)
        t = t[0:len(mod_t)]

        # converting list to numpy array for efel
        mod_t_np=np.array(mod_t)
        t_np=np.array(t)

        temp_fit=0
        trace = {}
        traces=[]
        trace['T'] = t_np
        trace['V'] = mod_t_np
        trace['stim_start'] = [self.option.stim_del]
        trace['stim_end'] = [self.option.stim_del + self.option.stim_dur]
        traces.append(trace)

        efel.setThreshold(self.thres)

        '''

        used_features=list(features)

        AP_num = efel.getFeatureValues(traces,['Spikecount'])
        for i in range (len(features)):
            if AP_num[0]['Spikecount'] == 0 and features_data[features[i]]["requires_spike"]=="True":
                used_features.remove(features[i])
                temp_fit += 10  #MEnnyi legyen a bunti?

            if AP_num[0]['Spikecount'] <2 and "ISI" in features_data[features[i]]:
                used_features.remove(features[i])
                temp_fit += 1

        '''

        #traces_results = efel.getFeatureValues(traces,[feature])      #list
        traces_results = self.get_efel_values(traces, feature)
        #print traces_results

        exp_mean = features_data[feature]["mean"][k]
        exp_std = features_data[feature]["std"][k]
        mod_result=traces_results[0][feature]
        #print feature

        #print "exp mean", exp_mean
        #print "exp std", exp_std
        #print "model", mod_result

        if mod_result is not None and mod_result.size > 1:
            mod_result = scipy.mean(mod_result)      #for features (AHP_depth, AP_duration_half_width) that gives a list as a result, the mean of the results is used
        elif mod_result is not None and mod_result.size!=0:                    # mod_result is a numpy array with one element, 0 element for AP1_amp or APlast_amp if no AP generated by the model
            mod_result=mod_result[0]

        if (mod_result == None or mod_result == 0 or mod_result.size==0) and (exp_mean != None and exp_std != None):
            temp_fit=250
        elif exp_mean == None and exp_std == None :
            temp_fit=0
        #elif exp_mean != None and exp_std != None :
        else:
            result = abs(exp_mean - mod_result) / exp_std
            temp_fit = result

        #print temp_fit
        return temp_fit
def three_feature_sets_on_static_models(model,
                                        unit_test=False,
                                        challenging=False):
    '''
    Conventions:
        variables ending with 15 refer to 1.5 current injection protocols.
        variables ending with 30 refer to 3.0 current injection protocols.
    Inputs:
        NML-DB models, a method designed to be called inside an iteration loop, where a list of
        models is iterated over, and on each iteration a new model is supplied to this method.

    Outputs:
        A dictionary of dataframes, for features sought according to: Druckman, EFEL, AllenSDK

    '''

    ##
    # wrangle data in preperation for computing
    # Allen Features
    ##

    #import pdb; pdb.set_trace()
    if type(model) is type(DataTC()):
        model = model.dtc_to_model()
    if not hasattr(model, 'vm30'):
        model.inject_square_current(model.rheobase * 3.0)
        model.vm30 = model.get_membrane_potential()
        ['amplitude']
        model.inject_square_current()
        model.vm15 = model.get_membrane_potential()

    times = np.array([float(t) for t in model.vm30.times])
    volts = np.array([float(v) for v in model.vm30])
    try:
        import asciiplotlib as apl
        fig = apl.figure()
        fig.plot(times,
                 volts,
                 label="V_{m} (mV), versus time (ms)",
                 width=100,
                 height=80)
        fig.show()
    except:
        pass
    ##
    # Allen Features
    ##
    #frame_shape,frame_dynamics,per_spike_info, meaned_features_overspikes
    all_allen_features30, allen_features30 = allen_format(
        volts, times, optional_vm=model.vm30)
    #if frame30 is not None:
    #    frame30['protocol'] = 3.0
    ##

    # wrangle data in preperation for computing
    # Allen Features
    ##
    times = np.array([float(t) for t in model.vm15.times])
    volts = np.array([float(v) for v in model.vm15])

    ##
    # Allen Features
    ##

    all_allen_features15, allen_features15 = allen_format(
        volts, times, optional_vm=model.vm15)
    ##
    # Get Druckman features, this is mainly handled in external files.
    ##
    #if model.ir_currents
    DMTNMLO = dm_test_interoperable.DMTNMLO()

    if hasattr(model,
               'druckmann2013_input_resistance_currents') and not hasattr(
                   model, 'allen'):
        DMTNMLO.test_setup(None, None, model=model)

    else:
        DMTNMLO.test_setup(None, None, model=model, ir_current_limited=True)
    dm_test_features = DMTNMLO.runTest()
    ##
    # Wrangle data to prepare for EFEL feature calculation.
    ##
    trace3 = {}
    trace3['T'] = [float(t) for t in model.vm30.times.rescale('ms')]
    trace3['V'] = [float(v) for v in model.vm30.magnitude]  #temp_vm
    trace3['stimulus_current'] = [model.druckmann2013_strong_current]
    if not hasattr(model, 'allen'):
        trace3['stim_end'] = [trace3['T'][-1]]
        trace3['stim_start'] = [float(model.protocol['Time_Start'])]

    else:
        trace3['stim_end'] = [float(model.protocol['Time_End']) * 1000.0]
        trace3['stim_start'] = [float(model.protocol['Time_Start']) * 1000.0]

    traces3 = [
        trace3
    ]  # Now we pass 'traces' to the efel and ask it to calculate the feature# values

    trace15 = {}
    trace15['T'] = [float(t) for t in model.vm15.times.rescale('ms')]
    trace15['V'] = [float(v) for v in model.vm15.magnitude]  #temp_vm

    if not hasattr(model, 'allen'):
        trace15['stim_end'] = [trace15['T'][-1]]
        trace15['stim_start'] = [float(model.protocol['Time_Start'])]
    else:
        trace15['stim_end'] = [float(model.protocol['Time_End']) * 1000.0]
        trace15['stim_start'] = [float(model.protocol['Time_Start']) * 1000.0]
    trace15['stimulus_current'] = [model.druckmann2013_standard_current]
    trace15['stim_end'] = [trace15['T'][-1]]
    traces15 = [
        trace15
    ]  # Now we pass 'traces' to the efel and ask it to calculate the feature# values

    ##
    # Compute
    # EFEL features (HBP)
    ##
    efel.reset()

    if len(threshold_detection(model.vm15, threshold=0)):
        #pass
        threshold = float(
            np.max(model.vm15.magnitude) -
            0.5 * np.abs(np.std(model.vm15.magnitude)))

        print(len(threshold_detection(model.vm15, threshold=threshold)))
        print(threshold, 'threshold', np.max(model.vm15.magnitude),
              np.min(model.vm15.magnitude))

        #efel_15 = efel.getMeanFeatureValues(traces15,list(efel.getFeatureNames()))#
    else:
        threshold = float(
            np.max(model.vm15.magnitude) -
            0.2 * np.abs(np.std(model.vm15.magnitude)))

        efel.setThreshold(threshold)
        print(len(threshold_detection(model.vm15, threshold=threshold)))
        print(threshold, 'threshold', np.max(model.vm15.magnitude))

    if np.min(model.vm15.magnitude) < 0:
        efel_15 = efel.getMeanFeatureValues(traces15,
                                            list(efel.getFeatureNames()))
    else:
        efel_15 = None
    efel.reset()

    if len(threshold_detection(model.vm30, threshold=0)):
        threshold = float(
            np.max(model.vm30.magnitude) -
            0.5 * np.abs(np.std(model.vm30.magnitude)))

        print(len(threshold_detection(model.vm30, threshold=threshold)))
        print(threshold, 'threshold', np.max(model.vm30.magnitude),
              np.min(model.vm30.magnitude))

    #efel_30 = efel.getMeanFeatureValues(traces3,list(efel.getFeatureNames()))

    else:
        threshold = float(
            np.max(model.vm30.magnitude) -
            0.2 * np.abs(np.std(model.vm30.magnitude)))
        efel.setThreshold(threshold)
        print(len(threshold_detection(model.vm15, threshold=threshold)))
        print(threshold, 'threshold', np.max(model.vm15.magnitude))

    if np.min(model.vm30.magnitude) < 0:
        efel_30 = efel.getMeanFeatureValues(traces3,
                                            list(efel.getFeatureNames()))
    else:
        efel_30 = None

    efel.reset()

    if hasattr(model, 'information'):
        return {
            'model_id': model.name,
            'model_information': model.information,
            'efel_15': efel_15,
            'efel_30': efel_30,
            'dm': dm_test_features,
            'allen_15': all_allen_features15,
            'allen_30': all_allen_features30
        }
    else:
        return {
            'model_id': model.name,
            'model_information': 'allen_data',
            'efel_15': efel_15,
            'efel_30': efel_30,
            'dm': dm_test_features,
            'allen_15': all_allen_features15,
            'allen_30': all_allen_features30
        }