slope_measure = StandardScaler().fit_transform(slopes_waves)
position_measure = StandardScaler().fit_transform(mean_position_waves)
location_measure = abs(slope_measure * position_measure)
location_measure *= np.sign(slopes_waves)
location_measure = slope_measure * channel_mean_scaled
location_measure = StandardScaler().fit_transform(location_measure)
#location_measure *= np.sign(mean_position_waves)
location_measure = pd.DataFrame(location_measure,
columns=df.columns,
index=df.index)
# Get smallest and largest measure and put together as ratio
largest = location_measure.idxmax(axis=1)
smallest = location_measure.idxmin(axis=1)
measure_coupling_1 = (largest + '_' + smallest).astype(str)
measure_coupling_2 = (smallest + '_' + largest).astype(str)
# make sure ratio is put together in correct order
isin = measure_coupling_2.isin(ratios.columns)
measure_coupling = measure_coupling_1
measure_coupling[isin] = measure_coupling_2[isin]
''' Try Smallest correlation '''
# Get smallest Correlation
smallest_correlation = correlation_waves.idxmin(axis=1)
''' Try No correlation '''
smallest_correlation = correlation_waves.idxmax(axis=1)
''' Try Smallest Correlation with smallest pairs '''
smallest_correlation = correlation_waves.idxmin(axis=1)
first = pd.DataFrame(location_measure.aud)
