Extracellular Features Extraction for MEA Data ============================================== This example shows how to use extracellular features implemented in eFEL for recordings from Microelectrode Arrays (MEA). Also see `Buccino et al., 2024 `__ and another `example `__ using these efeatures for more details. .. code:: ipython3 from efel.pyfeatures import extrafeats import numpy as np Currently, the following features are implemented in eFEL: .. code:: ipython3 extrafeats.all_1D_features .. parsed-literal:: ['peak_to_valley', 'halfwidth', 'peak_trough_ratio', 'repolarization_slope', 'recovery_slope', 'neg_peak_relative', 'pos_peak_relative', 'neg_peak_diff', 'pos_peak_diff', 'neg_image', 'pos_image'] These features are described as functions in extrafeats.py. You can find the docstrings of these functions by using the help function in Python. You can find more documentation of the features here: https://efel.readthedocs.io/en/latest/ .. code:: ipython3 help(extrafeats.peak_to_valley) .. parsed-literal:: Help on function peak_to_valley in module efel.pyfeatures.extrafeats: peak_to_valley(waveforms, sampling_frequency) Time between trough and peak. If the peak precedes the trough, peak_to_valley is negative. Parameters ---------- waveforms : numpy.ndarray (num_waveforms x num_samples) waveforms to compute feature for sampling_frequency : float rate at which the waveforms are sampled (Hz) Returns ------- np.ndarray (num_waveforms) peak_to_valley in seconds Let’s get the test MEA data and calculate the extracellular features. .. code:: ipython3 waveforms_fpath = "../../tests/testdata/extrafeats/mean_waveforms.dat" waveforms = np.loadtxt(waveforms_fpath) waveform = np.array([waveforms[0]]) sampling_freq = 10000 .. code:: ipython3 extrafeats.peak_to_valley(waveforms=waveform, sampling_frequency=sampling_freq) .. parsed-literal:: array([0.0013]) The ``waveform`` should have a structure similar to the one in eFEL/tests/testdata/extrafeats/mean_waveforms.datmean_waveforms.dat. We will use the ``calculate_features`` function from extrafeats to calculate all the features. .. code:: ipython3 feats = extrafeats.calculate_features( waveforms, sampling_freq ) for feature_name in extrafeats.all_1D_features: print(feature_name, feats[feature_name]) .. parsed-literal:: peak_to_valley [ 0.0013 -0.0001 -0.0016 -0.0013 -0.0011 -0.0013 -0.0012 0.0009 -0.0011 -0.0002 -0.0004 -0.0008 0.0031 -0.0018 0.0016 -0.0007 -0.0009 0.0009 -0.0007 -0.0018 -0.0013 -0.0005 -0.0015 -0.0011 0.0013 -0.0034 0.0018 0.001 -0.0019 -0.0015 -0.0014 0.0002 -0.0014 -0.0012 -0.0016 0.0008 0.0014 -0.0005 -0.0025 0.0009 0.0014 -0.0016 -0.0013 0.0009 -0.0008 -0.0016 -0.0016 -0.0019 -0.0013 -0.0014 -0.0011 0.0003 -0.0014 -0.0014 -0.001 -0.0013 -0.0006 -0.0024 0.0018 -0.0035 -0.0012 -0.0012 0.0002 -0.0013 -0.0023 -0.0002 -0.0001 0.0032 0.0024 -0.0013 0.0029 -0.0006 0.001 -0.001 0.0013 0.0022 0.0009 -0.0014 -0.0014 0.0018 0.0007 0.0008 -0.0017 0.0014 0.0007 -0.0016 -0.0008 0.0027 -0.0021 -0.0012 -0.0015 -0.0001 -0.0001 0.0009 0.0016 -0.0001 0.0006 0.0015 0.0011 -0.0009 0.0012 -0.0012 0.0013 -0.0012 0.0007 -0.0023 -0.0009 -0.0013 -0.0002 -0.001 -0.0011 -0.0012 -0.0015 -0.0011 -0.0012 -0.0014 0.0003 0.0009 0.0009 0.0006 0.0002 0.0008 -0.0006 0.0034 0.0014 -0.0013 -0.0011 -0.0008 0.0007 -0.0011 -0.0013 0.0009 -0.0013 0.0009 0.0009 -0.0018 -0.0012 0.0002 -0.0013 -0.0019 -0.0002 -0.0041 0.0009 -0.0009 -0.0006 -0.001 -0.0015 0.0009 -0.0012 -0.002 -0.0008 -0.0032 0.0015 0.0021 -0.0002 -0.0005 -0.0009 -0.0008 -0.001 -0.0013 0.0014 -0.001 0.0009 -0.0013 -0.0017 0.0021 -0.0016 -0.0011 -0.0014 -0.0019 0.0011 -0.0016 -0.0002 -0.001 -0.0018 -0.0015 -0.0001 0.002 0.0009 -0.0015 -0.0014 -0.0017 0.0009 -0.0007 0.0013 -0.0024 -0.0013 -0.0037 -0.0002 -0.0007 -0.0006 -0.0011 -0.0016 -0.0013 -0.0001 -0.001 -0.0014 -0.0015 -0.0005 -0.0004 -0.0015 -0.0001 -0.0015 -0.0011 0.0002 -0.0013 -0.0012 -0.0011 -0.0013] halfwidth [ 0.0015 nan -0.0028 -0.0093 -0.0025 -0.0017 -0.0018 0.0003 -0.0024 nan nan -0.0055 0.0017 -0.003 0.0006 -0.0017 -0.0014 nan -0.0011 nan -0.0025 -0.0068 -0.003 -0.0024 0.0007 -0.0054 0.0007 nan -0.0035 nan -0.0023 nan -0.0026 -0.0019 -0.0029 nan nan nan -0.0039 nan nan -0.0029 -0.0025 nan -0.0017 -0.0026 nan -0.0035 -0.0023 -0.0018 -0.0017 nan -0.0028 nan -0.0016 -0.0023 nan -0.0042 0.0008 -0.0045 -0.0022 -0.0019 nan -0.0027 -0.0026 -0.0008 nan 0.0009 0.0008 nan 0.0014 nan 0.0005 -0.0017 0.0007 0.0015 0.0012 -0.002 -0.0019 0.0012 0.0004 0.0009 -0.0033 0.0006 0.0005 -0.0031 -0.0018 0.0006 -0.0038 -0.0024 -0.0025 nan nan 0.002 0.0006 nan nan 0.0005 0.0006 -0.0022 nan -0.0026 nan -0.0025 nan -0.0039 -0.0019 -0.0018 nan -0.0016 -0.0015 -0.0024 -0.003 -0.0023 nan -0.002 nan nan nan nan nan nan nan 0.0019 nan -0.0018 -0.0017 -0.002 0.0005 -0.0017 -0.0023 0.002 -0.0067 0.0005 nan nan -0.0019 nan -0.0022 -0.0036 nan -0.0063 0.0005 -0.0015 -0.0018 -0.0023 -0.0019 nan -0.0026 -0.0037 -0.0019 -0.0051 0.0006 0.0008 nan nan -0.0019 -0.002 -0.0016 -0.0027 0.0007 -0.0024 nan -0.002 -0.003 0.0007 -0.0031 -0.0024 nan -0.0036 0.0014 nan nan -0.0016 -0.0035 -0.0081 nan 0.0005 0.0003 -0.0024 -0.0028 nan 0.0007 -0.0018 nan -0.0046 -0.0024 -0.0059 nan -0.0019 -0.0009 -0.0018 -0.003 -0.0024 nan -0.0016 -0.0028 -0.0029 -0.0073 -0.0044 -0.0025 nan -0.003 nan nan -0.0019 -0.0018 -0.0048 -0.0023] peak_trough_ratio [ 0.53804035 1.21802672 2.36845269 0.13818893 2.01288236 0.67247375 0.71054337 0.51431686 1.2270848 6.12281799 0.16201052 0.52271024 0.29541054 2.10863154 0.16400563 0.71021801 0.82259676 0.80606722 0.59820375 1.67405533 1.22329924 0.19102443 2.6186373 1.35055906 0.42408852 6.55820515 0.16284988 0.93212375 3.95194239 0.61651983 4.12676067 0.7973423 0.82446904 0.63280487 0.94402227 0.83385778 0.31889983 0.40030984 13.01325066 0.41722616 1.08734046 1.28142004 1.31052961 0.65915915 0.59007358 8.24318384 1.05782205 3.37112047 0.54651881 0.73548706 1.13247942 2.20866198 2.01767528 0.97482789 0.47452749 0.6706796 6.99816194 5.11126778 0.18733708 6.95707896 0.48524825 0.60336188 0.9870102 2.79221104 9.70193971 0.82660372 1.19167412 0.21163596 0.20311777 0.72466183 0.14251774 1.44321506 0.53334603 0.90387643 0.41733713 0.24190003 0.53594822 0.73317705 0.69903345 0.31630318 0.64635683 0.97632033 2.84277294 0.57842661 0.67426552 3.12048394 0.65460616 0.18484049 4.40800015 3.50836316 2.37435616 2.72846297 0.81115791 0.57493701 0.39853579 1.71386828 5.21433688 0.16672223 0.49929396 1.67457084 1.5233474 2.58614124 0.66188939 2.5763163 1.11925818 4.77781519 0.53758802 0.36358919 3.00864117 0.96411726 0.45938812 2.93000602 2.33622968 1.09317424 0.49026222 1.13618701 1.50261237 1.095218 0.38294177 0.8881845 3.26153958 0.8232666 0.5709424 0.22638494 2.34391786 0.59851957 0.40645699 1.3843255 0.75217496 0.58369778 0.85124889 0.5570616 0.33895087 0.48671009 0.6832888 2.75731074 0.58309425 7.37870261 0.62792104 4.58416164 6.30390847 4.72034757 0.59310884 0.67547227 0.22271289 1.72432913 0.28557396 0.69169406 1.80447415 4.37127311 0.96403529 9.13984054 0.36332231 0.18233296 0.81281747 0.39035498 1.56471095 1.34022733 0.79437089 4.12955594 0.39527652 2.11227852 0.73075634 1.26226321 1.79751798 0.1394111 2.89838121 1.93914669 0.69080368 3.72053666 0.32107693 1.08166173 1.62173755 0.83715129 3.76741793 0.19781369 1.34602015 0.22818912 0.85238754 5.67776085 7.99932939 2.45151786 0.3190137 1.05551016 1.1821116 3.85214816 1.04723524 4.71443887 0.30886562 0.39164463 0.41260801 0.51914942 3.12387873 1.49623728 1.28426178 0.87490264 2.41248035 2.18445792 0.24783548 0.2482083 3.15501273 1.11067998 2.65673549 0.37181242 6.12408649 0.64401332 1.50091732 0.48633446 0.72905253] repolarization_slope [7.31257213e+01 nan 6.50182478e-01 4.99764537e+01 1.01266031e+00 1.22505405e+01 3.66794226e+00 3.96887490e+01 2.08781290e+00 nan 2.66397081e+01 3.32739630e+01 3.99171396e+01 1.18709472e+00 1.89150163e+02 5.33856699e+00 4.44666871e+00 nan 6.59520145e+00 1.35164366e+00 2.82988737e+00 2.03543510e+01 4.85872501e-01 1.38560311e+00 8.93961074e+01 1.15821548e-01 9.74608965e+01 4.35300936e+01 2.48036396e-01 5.35377718e+00 4.19533385e-01 nan 3.87647107e+00 2.49081159e+01 2.53626726e+00 3.08366795e+02 1.12289446e+02 7.72393222e+00 1.02580692e-01 nan 5.83851918e+00 1.57677921e+00 1.92203478e+00 6.57320993e+01 1.54010808e+01 1.55766806e-01 2.41955804e+00 3.11922659e-01 1.52125356e+01 3.20115632e+00 4.41163627e+00 nan 7.79157923e-01 2.62026144e+00 6.73181141e+01 7.50472345e+00 nan 1.36751428e-01 5.34852086e+01 2.24802060e-01 5.15398199e+01 2.30980102e+01 nan 5.65438604e-01 3.84641022e-01 nan nan 1.90051711e+01 5.68442402e+01 6.28630703e+00 1.20718467e+01 nan 8.79612883e+01 3.09045447e+00 1.36219748e+02 1.07188996e+01 1.19465006e+02 3.74992327e+00 5.11446110e+00 3.21324376e+01 2.96762385e+02 2.87781921e+02 4.56945190e-01 1.36938154e+02 2.44544112e+02 3.82173278e-01 4.28763535e+00 1.25128169e+01 1.69948984e-01 4.05290769e-01 7.46124701e-01 nan nan 6.40825688e+01 7.90614702e+01 nan nan 5.41251231e+02 5.78885301e+01 1.34395745e+00 nan 7.11621115e-01 4.54818048e+01 6.68880108e-01 nan 1.63878756e-01 9.48684456e+00 1.27452569e+01 nan 7.83978570e+00 2.70935240e+01 5.88313421e-01 6.93437974e-01 2.65871059e+00 2.13100092e+01 2.53094985e+00 nan nan 1.06479171e+02 nan nan 1.97160273e+02 6.90330690e+00 4.15423798e+00 nan 7.32781106e+00 9.63477588e+01 1.89820897e+00 2.54461786e+02 6.19672821e+01 4.78810459e+00 1.05531879e+02 1.04548966e+01 1.24175790e+02 6.02423181e+01 9.37029728e-01 3.43643506e+01 nan 1.24964841e+01 1.56175850e-01 nan 1.77657282e-01 1.04539302e+02 2.10076218e+01 1.22608385e+01 1.41715843e+00 6.93772653e+00 1.63626246e+01 1.10691890e+00 1.69822463e-01 2.90615132e+00 9.16422355e-02 8.81610344e+01 3.49923843e+01 nan 1.45976613e+01 9.41618161e-01 1.76125600e+00 1.09905341e+01 3.63785992e-01 3.26088462e+01 9.29907719e-01 7.64039846e+01 2.86223544e+00 8.74881544e-01 4.62083858e+01 4.79718173e-01 1.10037652e+00 7.23525875e+00 2.33396674e-01 6.76706409e+01 2.92417470e+00 nan 1.39168207e+01 2.47310145e-01 2.75639683e+01 nan 1.20943344e+02 1.26141937e+01 3.12423625e-01 1.69656396e-01 1.80390321e+00 2.74981969e+01 2.43878552e+00 nan 2.14533667e-01 2.97934250e+00 1.55177673e-01 nan 6.74687462e+00 1.16212026e+01 6.70505162e+01 4.12664511e-01 2.44764617e+00 nan 9.28700444e+00 8.35177563e-01 7.86531878e-01 7.90299729e+00 2.12171085e+01 5.12327842e-01 nan 5.99487790e-01 2.87914657e+01 nan 5.07647764e+00 3.12094982e+00 5.37494106e+01 8.01730656e+00] recovery_slope [-3.63355521e+00 4.54342435e+00 -2.10494514e+00 -3.21612844e+01 -1.59355549e+01 -2.91941227e+01 -4.48096741e+00 -7.79253365e+00 -1.93142445e+01 -5.43590968e+00 -7.84073969e+01 -4.67504152e+01 -1.24537129e+00 9.04851649e-01 -9.23155491e+00 -5.20729298e+01 -2.88969544e+01 -1.92130265e+01 -4.31626734e+01 2.16413225e+00 -1.04642376e+01 -8.08408718e+01 -5.87632497e+00 -1.09565866e+01 -5.85332555e+00 -1.16608216e+00 -3.21579887e+00 -2.17739809e+01 -5.07126055e+00 -1.02362027e+01 -2.03299056e+00 -7.71686527e-01 -2.51163221e+00 -2.95649418e+01 -8.15932912e-01 -5.13599982e+01 -9.32113618e+00 -4.50290554e+01 -3.80578627e+00 -1.24285027e+01 -9.27802447e-01 -2.00340018e+00 -6.20759074e+00 -2.66042436e+01 -5.09533788e+01 -3.97681438e+00 -1.18503344e+00 -4.60929399e+00 -6.92211691e+00 -6.16165160e+00 -1.12842409e+01 -1.75193125e+00 -6.96007348e+00 -4.96107984e+00 -3.46614424e+01 -5.24159703e+00 -8.08372841e+00 -2.71314048e+00 -2.65011956e+00 5.62134860e-01 -1.78506949e+01 -1.81238510e+01 2.00340882e+00 -1.06992198e+01 -3.91580166e+00 -4.59364500e-01 2.75542585e+00 -3.77812716e-01 -1.39327173e+00 -1.32048883e+01 -1.86680577e-01 -3.93456185e+00 -7.64656083e+00 -1.68954306e+01 -4.34881443e+00 -8.69229998e-01 -1.19791230e+01 -6.67007742e+00 -1.23338715e+01 -3.92168564e+00 -3.26267482e+01 -4.89466829e+01 -5.50323478e+00 -4.06361556e+01 -2.04860872e+01 -6.46205930e+00 -2.42733005e+01 -3.29076079e-01 -2.18624572e+00 -8.64471967e+00 -2.57717988e+00 -4.83779739e-01 5.09670643e+00 -2.81096643e+01 -1.24310724e+01 -2.41915951e+00 -1.51126413e+00 -2.88979477e+01 -3.37945486e+00 -2.08947684e+01 -1.87378608e+01 -1.32479872e+01 -8.10451785e+00 -7.21654548e+00 -7.39351372e+00 -2.88267735e+00 -2.66157324e+01 -2.01121237e+01 -3.67737909e+00 -2.19363285e+01 -1.54238008e+01 -6.18020049e+00 -8.82350024e+00 -1.78646598e+01 1.80605389e+00 -4.14716330e+00 -5.43485978e+00 -1.06769351e+01 -5.30541741e+01 -2.97564361e+00 -3.98414646e-01 -2.97626033e+01 -4.44445283e+01 -7.16824627e-02 -7.93094190e+00 -1.32033245e+01 -2.81984189e+01 -3.04757744e+01 -3.08283335e+01 -4.18498964e+01 -6.57149163e+00 -4.39076727e+01 -1.37147948e+01 -1.12276678e+01 -2.03882091e+01 1.19113164e+00 -1.87193042e+01 -4.29831701e+00 -1.10030127e+01 -2.22290578e+00 -5.84355534e+00 1.97905425e+00 -1.04236166e+01 -3.58815562e+01 -4.53173319e+01 -2.11364258e+01 -1.20790837e+01 -1.46042168e+01 -1.16542322e+01 -2.16928796e+00 -3.84443397e+01 -1.10930702e+00 -1.35325880e+01 -1.02588094e+00 4.08781011e+00 -6.78991393e+01 -9.65564853e+00 -3.02388143e+01 -2.85310794e+01 -5.89109388e+00 -1.32740623e+00 -1.60552191e+01 -3.65615502e+01 -1.23223306e+01 -1.19126869e+00 -1.38971257e+00 -7.08911891e+00 -1.72027697e+01 -1.10750593e+01 -2.40240059e+00 -3.18275518e+00 -2.23994382e+00 -2.46730697e+00 -2.53630321e+01 -4.32395820e+00 -3.46648203e+01 8.31871590e+00 -2.99608434e+00 -1.09693318e+01 -1.76399365e+00 -5.12560876e+00 -3.74529408e+00 -2.74762612e+01 -3.48078286e+01 -1.38484473e+01 -3.84538670e+00 -1.56589216e+00 -1.16947634e+00 1.22042755e+01 -3.35964001e+01 -6.04604562e+01 -3.74568162e+01 -7.22145075e+00 -7.70012328e+00 6.40803600e+00 -1.85702048e+01 -1.07774241e+01 -1.07239397e+01 -3.73939408e+01 -7.05625361e+01 -2.21224102e+00 8.72531968e-01 -8.22518343e+00 -5.93215146e+00 3.04381793e+00 -9.72311749e+00 -1.89302412e+01 -3.75853073e+01 -8.14213856e+00] neg_peak_relative [9.51339213e-02 1.00388035e-02 3.28724224e-03 8.63910846e-02 1.23287678e-02 2.81335375e-02 1.35270547e-02 3.26901910e-02 1.75885984e-02 5.17095565e-03 1.10390041e-01 5.58803425e-02 8.93325636e-02 5.76913099e-03 4.77727744e-01 4.79924335e-02 2.80982170e-02 2.47240713e-02 4.01139776e-02 6.51939114e-03 2.71661034e-02 8.94666451e-02 4.32888964e-03 1.12655112e-02 1.44575946e-01 8.34988705e-04 2.75001421e-01 2.65226106e-02 2.80205172e-03 1.80731068e-02 1.04085591e-03 1.01575434e-02 2.66661308e-02 5.82650282e-02 1.86321664e-02 2.87389870e-01 5.76186146e-02 4.13010277e-02 6.84346712e-04 1.97046304e-02 3.54932940e-03 9.85326097e-03 1.91838933e-02 4.03909653e-02 5.65890648e-02 1.05026994e-03 1.00670272e-02 3.24793977e-03 7.57699886e-02 1.20001955e-02 1.74041886e-02 4.45264419e-03 6.35282086e-03 1.33618267e-02 1.03643170e-01 4.93752872e-02 3.23877510e-03 1.29991624e-03 1.79916050e-01 2.00675228e-03 9.85910621e-02 5.49954957e-02 1.24116089e-02 6.46973356e-03 1.52057421e-03 1.29378784e-02 1.64824978e-02 6.58365941e-02 1.47911176e-01 2.02344756e-02 5.88078649e-02 8.70456343e-03 1.11993825e-01 1.82114418e-02 1.15731751e-01 6.45806158e-02 1.49667367e-01 1.48037917e-02 1.83947157e-02 1.26785426e-01 2.93172551e-01 2.44271425e-01 4.66676594e-03 3.49910014e-01 2.49263853e-01 4.24394443e-03 2.39841963e-02 6.20606547e-02 1.40386954e-03 3.65911205e-03 2.80730871e-03 8.65798745e-03 1.67705222e-02 4.02185452e-02 2.58374175e-01 1.24129259e-02 1.19555561e-03 1.00000000e+00 6.84316052e-02 1.45336614e-02 1.38767710e-02 8.06665123e-03 2.41699581e-02 4.09648680e-03 1.80323783e-02 1.67985703e-03 3.35210216e-02 2.76382021e-02 7.01153240e-03 3.53932427e-02 7.93280312e-02 3.32079102e-03 6.57178672e-03 1.74822659e-02 7.32635742e-02 1.02818073e-02 7.33690637e-03 1.53876936e-02 9.23369396e-02 1.60976328e-02 4.62550161e-03 1.96154390e-01 3.60257600e-02 2.74494341e-02 5.33309832e-03 2.13874320e-02 1.33876755e-01 2.17033620e-02 2.26948459e-01 8.75767315e-02 3.61205975e-02 6.40393055e-02 2.34164085e-02 1.65463342e-01 3.68799608e-02 4.15603502e-03 6.46803276e-02 3.75104615e-03 6.13998454e-02 1.21713189e-03 2.64842149e-03 1.76493118e-03 1.21477080e-01 4.50434043e-02 5.25971651e-02 1.65012657e-02 2.51654796e-02 2.87957457e-02 9.98412841e-03 1.41134268e-03 3.34588127e-02 4.90472664e-04 2.65779037e-01 1.21801997e-01 1.59984875e-02 6.99526537e-02 6.97657454e-03 2.18151914e-02 3.91343011e-02 2.43429319e-03 4.56749521e-02 1.15116091e-02 4.38027355e-02 2.05676271e-02 6.21842810e-03 1.65450507e-01 5.48683405e-03 1.23075447e-02 1.82412304e-02 1.99493478e-03 1.02296150e-01 1.20691796e-02 1.45060461e-02 3.25582148e-02 2.48795939e-03 5.73896552e-02 1.75166794e-02 2.37964517e-01 1.95533483e-02 6.73091533e-04 1.09128246e-03 7.45454447e-03 7.32739006e-02 2.68284733e-02 1.49532548e-02 2.31259318e-03 2.79921566e-02 1.46586883e-03 3.83552881e-02 3.49975742e-02 6.03611556e-02 9.86314676e-02 4.82697766e-03 1.07961656e-02 2.16553606e-02 2.87630514e-02 8.80652699e-03 9.10429364e-03 4.56176900e-02 9.03832156e-02 1.79535329e-03 1.43656137e-02 6.85879100e-03 7.56633815e-02 2.27921586e-03 1.62732679e-02 2.73694031e-02 7.65484421e-02 4.10695154e-02] pos_peak_relative [0.2135929 0.0510241 0.03248875 0.04981713 0.10355585 0.07894711 0.04010794 0.07015922 0.09006216 0.13211701 0.07462935 0.12188679 0.11012156 0.05076304 0.32694581 0.14223321 0.09645002 0.08316255 0.10013397 0.04554214 0.1386745 0.07131594 0.04730297 0.06348937 0.25585218 0.02285084 0.18687832 0.10316358 0.04620865 0.04649609 0.01792406 0.03379637 0.09174257 0.15385596 0.07339765 1. 0.07667498 0.0689912 0.03716195 0.0343065 0.01610454 0.05268756 0.10491079 0.11109924 0.13933982 0.03612705 0.04443758 0.04568975 0.17279807 0.03682985 0.08224711 0.04103777 0.05348776 0.05435385 0.20522895 0.13818512 0.09458044 0.02772557 0.14064691 0.05825823 0.19963564 0.13846549 0.05111946 0.07538261 0.06156059 0.04462693 0.08196286 0.05814246 0.12536764 0.06118767 0.03497364 0.05242212 0.2492526 0.06868944 0.20154689 0.06518905 0.33472374 0.04529168 0.05365715 0.16734373 0.79073727 0.99517981 0.05535983 0.84458077 0.7013371 0.0552622 0.06551516 0.04786852 0.02582289 0.0535694 0.02781459 0.09857609 0.05676603 0.09649019 0.42968777 0.08877449 0.0260139 0.69571292 0.14257704 0.10155823 0.08821124 0.08705267 0.06675716 0.04404 0.08422089 0.03349178 0.0751975 0.04193313 0.08802781 0.14239239 0.15206981 0.04060192 0.06406717 0.07974871 0.14988324 0.04874789 0.0460041 0.07032515 0.14755189 0.05966254 0.06295323 0.67386839 0.08583057 0.02593089 0.05216252 0.05341627 0.22706821 0.12537236 0.71233235 0.21331104 0.12830632 0.14886287 0.03312025 0.33605371 0.10515533 0.04781911 0.15737927 0.11549654 0.16088252 0.02328274 0.06966803 0.03476469 0.30065283 0.12696245 0.04888147 0.11873369 0.02998889 0.08311491 0.07517914 0.02574406 0.13459844 0.01870639 0.40294832 0.09267362 0.05426359 0.11394637 0.04555257 0.122004 0.12972314 0.04194815 0.07533828 0.10146676 0.13357045 0.10833547 0.04664342 0.09625028 0.06636115 0.09959067 0.052583 0.03097211 0.13705818 0.05447607 0.09816715 0.11373682 0.03911323 0.04737252 0.09838749 0.22659155 0.06954962 0.01594732 0.03642733 0.07625929 0.0975428 0.11816665 0.07376168 0.03717396 0.12232555 0.02883777 0.04943464 0.05719613 0.10392781 0.21367043 0.06292251 0.06740724 0.11605278 0.10501014 0.08865536 0.08299014 0.04717733 0.09361396 0.02363674 0.0665809 0.07603831 0.11739408 0.05824563 0.04373269 0.17141888 0.15534882 0.12494387] neg_peak_diff [ 0.0009 0. 0.0018 0.0011 0.0019 0.0013 0.0014 0. 0.0019 0. 0.0003 0.0011 -0.0001 0.0018 0.0001 0.0014 0.0017 -0.0001 0.0015 0.0017 0.0018 0.0003 0.0024 0.0019 0.0008 0.0036 0.0001 -0.0001 0.0029 0.0014 0.0016 0. 0.0016 0.0013 0.0018 0.0007 0. 0.0004 0.0033 -0.0001 0.0002 0.0018 0.0018 -0.0001 0.0016 0.0023 0.0015 0.0029 0.0014 0.0014 0.0015 -0.0001 0.0022 0.0015 0.001 0.0014 0.0007 0.0035 0.0002 0.0033 0.0013 0.0013 -0.0001 0.0022 0.0025 0.0007 0. -0.0001 -0.0001 0.0014 0.0004 0.0006 0.0008 0.0018 0.0011 0.0003 0.0008 0.0014 0.0014 0.0001 0.0008 0.0007 0.0026 0.0001 0.0008 0.0026 0.0016 0.0004 0.0031 0.002 0.0017 -0.0001 0. -0.0001 0. 0.0002 0.0002 0. 0.0009 0.0017 -0.0002 0.0021 0. 0.0019 -0.0003 0.0033 0.0017 0.0014 0. 0.0014 0.0014 0.0019 0.0024 0.002 0.0015 0.0014 -0.0001 -0.0001 -0.0002 -0.0002 -0.0001 0.0007 0.0005 0.0005 -0.0002 0.0014 0.0012 0.0016 0.0008 0.0011 0.0015 -0.0001 0.0014 0.0008 -0.0001 0.0018 0.0013 -0.0001 0.0014 0.0029 0.0002 0.0039 0.0008 0.0013 0.0014 0.0018 0.0014 -0.0001 0.002 0.003 0.0015 0.0034 0.0001 0.0003 -0.0001 0.0004 0.0016 0.0016 0.0018 0.002 0.0009 0.0018 -0.0001 0.0017 0.0019 0.0002 0.0025 0.002 0.0015 0.0028 0.0009 0.0015 0.0004 0.0013 0.0028 0.0013 -0.0002 -0.0001 0. 0.0017 0.0022 0.0017 -0.0001 0.0015 -0.0002 0.0036 0.0015 0.0039 0.0004 0.0015 0.0014 0.0012 0.0025 0.0017 -0.0002 0.0014 0.0022 0.0024 0.0004 0.0003 0.0017 0.0002 0.0024 0.0014 -0.0001 0.0014 0.0017 0.0011 0.0014] pos_peak_diff [ 0.0007 -0.0016 -0.0013 -0.0017 -0.0007 -0.0015 -0.0013 -0.0006 -0.0007 -0.0017 -0.0016 -0.0012 0.0015 -0.0015 0.0002 -0.0008 -0.0007 -0.0007 -0.0007 -0.0016 -0.001 -0.0017 -0.0006 -0.0007 0.0006 -0.0013 0.0004 -0.0006 -0.0005 -0.0016 -0.0013 -0.0013 -0.0013 -0.0014 -0.0013 0. -0.0001 -0.0016 -0.0007 -0.0007 0.0001 -0.0013 -0.001 -0.0007 -0.0007 -0.0008 -0.0016 -0.0005 -0.0014 -0.0015 -0.0011 -0.0013 -0.0007 -0.0014 -0.0015 -0.0014 -0.0014 -0.0004 0.0005 -0.0017 -0.0014 -0.0014 -0.0014 -0.0006 -0.0013 -0.001 -0.0016 0.0016 0.0008 -0.0014 0.0018 -0.0015 0.0003 -0.0007 0.0009 0.001 0.0002 -0.0015 -0.0015 0.0004 0. 0. -0.0006 0. 0. -0.0005 -0.0007 0.0016 -0.0005 -0.0007 -0.0013 -0.0017 -0.0016 -0.0007 0.0001 -0.0014 -0.0007 0. 0.0005 -0.0007 -0.0005 -0.0006 -0.0002 -0.0008 -0.0011 -0.0005 -0.0007 -0.0014 -0.0017 -0.0011 -0.0012 -0.0008 -0.0006 -0.0006 -0.0012 -0.0015 -0.0013 -0.0007 -0.0008 -0.0011 -0.0014 0. -0.0016 0.0024 -0.0003 -0.0014 -0.0014 -0.0007 0. -0.0015 -0.0013 -0.0007 -0.0014 0.0002 -0.0007 -0.0015 -0.0014 -0.0014 -0.0014 -0.0005 -0.0015 -0.0017 0.0002 -0.0011 -0.0007 -0.0007 -0.0016 -0.0007 -0.0007 -0.0005 -0.0008 -0.0013 0.0001 0.0009 -0.0018 -0.0016 -0.0008 -0.0007 -0.0007 -0.0008 0.0008 -0.0007 -0.0007 -0.0011 -0.0013 0.0008 -0.0006 -0.0006 -0.0014 -0.0006 0.0005 -0.0016 -0.0013 -0.0012 -0.0005 -0.0017 -0.0018 0.0004 -0.0006 -0.0013 -0.0007 -0.0015 -0.0007 -0.0007 -0.0004 -0.0003 -0.0013 -0.0013 -0.0013 -0.0007 -0.0007 -0.0014 -0.0006 -0.0011 -0.0018 -0.0011 -0.0007 -0.0006 -0.0016 -0.0016 -0.0013 -0.0014 -0.0006 -0.0012 -0.0014 -0.0014 -0.001 -0.0015 -0.0014] neg_image [ 6.08446809e-02 1.00388035e-02 -6.05265898e-03 1.78245655e-02 -1.24821376e-02 -1.89190656e-02 3.08945388e-03 3.26901910e-02 -1.43230784e-02 5.17095565e-03 1.29688093e-02 -2.57703947e-02 6.39995784e-02 -1.21649715e-02 4.62402535e-01 -2.66267054e-02 -5.11740429e-03 1.74402736e-02 -5.07638723e-03 -9.52536749e-04 -1.86204841e-02 2.88399853e-02 -6.97229695e-03 -1.06917556e-02 -5.48151898e-02 -2.11465317e-03 2.35417100e-01 2.28362432e-02 -5.24303852e-03 -9.99384449e-03 -3.55590704e-03 1.01575434e-02 -1.70972605e-02 -3.36730026e-02 -1.28623455e-02 -7.87008393e-02 5.76186146e-02 -1.48221564e-02 -6.83807751e-03 1.14414297e-02 -1.18791361e-03 -9.58097136e-03 -1.66246015e-02 2.66268916e-02 1.25091639e-02 -4.03529947e-03 -7.10377009e-03 -5.44089702e-03 -3.53217079e-02 -8.82598847e-03 -1.31622694e-02 -2.53898707e-03 -8.64855042e-03 2.80748180e-03 -4.91815329e-02 -2.77268095e-02 -6.11335231e-03 -3.34263389e-03 1.15050169e-01 2.83327962e-04 -4.34206887e-02 -3.09019508e-02 7.93579896e-03 -8.61473952e-03 -2.30328131e-03 -4.93573286e-03 1.64824978e-02 4.16151399e-02 1.13755709e-01 3.73595986e-03 -3.47312106e-03 -1.25625570e-02 -3.13228925e-02 -5.06339728e-03 -3.39662070e-02 3.01342044e-02 7.55061262e-02 -1.08538003e-02 -1.28585217e-02 1.00740279e-01 -1.01669584e-01 1.04805660e-01 -6.86061335e-03 3.19225849e-01 -6.08905680e-02 -6.03417942e-03 -6.53320778e-03 1.88661022e-02 -3.92897568e-03 -1.15966324e-03 -5.42422620e-03 6.58619445e-03 1.67705222e-02 2.54909612e-02 2.58374175e-01 -4.02927754e-03 -5.09458215e-03 1.00000000e+00 -1.13930148e-02 -1.47001049e-02 -1.11415553e-02 -9.83958346e-03 2.41699581e-02 -5.37060702e-03 -1.33439461e-02 -3.99577596e-03 9.69961083e-03 -4.04012984e-03 7.01153240e-03 -2.28320677e-02 -1.07702337e-02 -6.36215860e-03 -8.74680377e-03 -4.18488252e-03 3.77310393e-03 -1.16820558e-02 -1.93667216e-03 1.28458224e-02 4.37043940e-02 -8.55250833e-03 1.33614096e-03 -2.74869251e-02 -1.85565685e-02 5.79815554e-03 -4.91421357e-03 -1.06350172e-02 -5.28184043e-02 -1.84791937e-02 5.41836128e-02 -5.11183439e-02 -2.41262529e-02 3.29196935e-02 -4.87750652e-03 -5.57221094e-02 2.77128268e-02 -1.14594800e-02 -3.51176076e-02 -1.63332712e-03 -3.27090474e-02 -3.41422013e-03 -1.66954067e-02 -1.17571147e-03 7.65362183e-03 -2.54347316e-02 -7.01149193e-03 -1.48849940e-02 -4.70009869e-03 2.29834419e-02 -1.12293652e-02 -3.77008421e-03 -2.28329429e-02 -2.20408006e-03 2.48999662e-01 7.39586173e-02 4.43774582e-04 -1.12027798e-02 1.50636568e-03 -1.77538970e-02 2.02450197e-02 -1.96999141e-03 -1.37745967e-02 -1.22174924e-02 2.60673799e-02 -1.66182621e-02 -8.21762118e-03 6.08330391e-02 -7.19549430e-03 -1.17398307e-02 2.55255787e-03 -4.71851271e-03 4.82709768e-02 1.93112806e-04 -1.16513332e-03 -1.94897140e-02 -4.81753971e-03 6.05581856e-04 -8.41006117e-03 1.80700429e-01 1.95533483e-02 -3.12721720e-03 -8.65113939e-03 -1.82749489e-02 4.32173811e-02 -1.97172171e-02 -7.71759605e-03 -3.01673378e-03 -2.17012063e-02 -2.14954356e-03 -1.35687955e-03 -7.51241270e-03 -4.17973323e-03 -4.94607047e-02 -7.16576989e-03 -1.02791855e-02 -5.18789896e-03 -1.76493478e-02 -1.07474676e-02 -9.33063939e-03 4.88762801e-03 9.14357372e-03 -4.57420933e-03 -3.35417773e-03 -7.99455792e-03 1.85335304e-02 -1.92801512e-03 1.02063490e-02 -1.86219774e-02 -3.72281451e-02 -2.57639530e-02] pos_image [ 1.08501167e-01 -1.38519656e-02 -8.62022439e-03 -2.06040891e-01 -1.79766671e-02 -9.78701610e-02 -5.58041488e-02 1.84963418e-02 -4.93451784e-02 1.37370617e-02 -7.37622137e-02 -9.94313678e-02 -1.18824789e-01 -1.95720633e-02 2.81616741e-01 -1.94448349e-01 -1.06678543e-01 -5.87641944e-02 -1.67391070e-01 -2.47799004e-02 -9.71399793e-02 -6.68305654e-02 1.10565445e-02 -2.21346697e-02 1.48155547e-01 1.30670198e-02 1.03530938e-01 -3.37769755e-02 2.33584482e-02 -7.10758824e-02 -3.87808177e-03 -3.89553391e-02 -1.08534293e-01 -1.93999750e-01 -6.14097940e-02 1.00000000e+00 6.82101090e-02 -9.59625210e-02 1.17900262e-02 -5.24792533e-02 1.48255488e-02 -3.00570823e-02 -5.99721529e-02 -8.23330821e-02 -2.21169173e-01 4.99799369e-03 -4.20085603e-02 2.53395523e-02 -2.70302090e-01 -4.83977594e-02 -7.26257011e-02 2.77995504e-02 -1.21964756e-03 -5.57573843e-02 -1.24945457e-01 -1.94711921e-01 1.01392275e-03 1.92395461e-02 3.55537231e-02 2.15808434e-02 -2.77599157e-01 -1.79099660e-01 -2.65259729e-02 9.48269964e-03 -1.13702423e-03 2.73471808e-03 -2.74834528e-02 -3.98016538e-02 5.93752186e-02 -8.29345619e-02 -9.66860512e-02 3.57025466e-03 2.08817380e-01 -5.76370788e-02 7.54080715e-05 -1.02562228e-01 2.78335925e-01 -6.04362390e-02 -7.27901478e-02 6.75434944e-02 7.90737268e-01 9.95179813e-01 2.12303735e-02 8.44580774e-01 7.01337105e-01 2.62646044e-02 -9.73851748e-02 -7.26134305e-02 1.62556610e-02 -6.24921544e-03 -9.93458290e-03 5.11836886e-03 -1.49627525e-02 -8.97409188e-02 4.15328294e-01 -2.76223181e-03 1.60881718e-02 6.95712920e-01 7.71219698e-02 -4.72679225e-02 6.96080174e-04 5.44668299e-03 5.80394053e-02 -9.98433280e-03 -2.95011461e-02 2.09944738e-02 -1.21975646e-01 -1.02535812e-01 7.61346209e-03 -1.46825996e-01 -3.05124429e-01 -6.01563021e-03 9.78195653e-03 -2.91949742e-02 -3.05720570e-01 -4.27620740e-02 4.07094422e-03 -1.90400710e-02 -1.67519563e-01 7.73910420e-04 -1.91953275e-02 6.73868393e-01 -1.08789703e-01 -8.49929088e-02 2.97077167e-02 -8.15660190e-02 -2.73192579e-01 -8.31348899e-02 7.12332350e-01 -1.29242840e-01 -1.50727149e-01 -1.46132154e-01 -8.65385479e-02 2.91433351e-01 -6.28626016e-02 -1.31243287e-02 -1.92992638e-01 1.57161322e-02 -2.28444937e-01 1.34367481e-02 1.16189420e-02 2.34086650e-02 2.64145675e-01 -1.38216002e-01 -2.04069815e-01 -3.76140374e-02 -1.00001743e-01 -3.24994651e-02 -1.33672327e-02 1.63751749e-02 -1.39619823e-01 8.87501353e-03 3.79814657e-01 -1.30388820e-02 1.38912513e-02 -9.41480201e-02 -2.86422267e-02 -8.54048672e-02 -1.08057469e-01 -2.75544036e-03 -2.71497552e-03 -2.06840393e-02 -1.36119696e-01 -7.42773676e-02 -1.03460912e-02 -3.48134643e-02 2.23164246e-02 -5.47293966e-03 -7.61185814e-02 1.59368359e-02 -8.36007928e-03 -5.03633149e-02 1.37868462e-02 -1.25896227e-01 1.96586742e-02 -1.84120761e-01 1.03126190e-02 1.72570998e-01 1.82149623e-03 -2.23764069e-03 3.39241504e-03 -3.04666832e-02 -7.51461458e-02 -1.11952170e-01 2.42018668e-02 2.77979044e-02 -1.16808089e-01 2.04911006e-02 1.76164628e-02 -1.46040900e-01 -2.37644831e-01 -2.07194603e-01 1.82503388e-02 -4.13795071e-02 1.30805556e-02 -1.14840546e-01 2.49402162e-03 1.65120438e-02 -1.12337528e-01 -8.06768107e-02 -5.84977379e-03 -4.73614935e-03 2.48863050e-02 -2.92409448e-01 3.56957519e-03 -6.70057126e-02 -1.06023022e-01 -9.88549097e-02 -1.63652624e-01] Another example of extraction of these features from the experimental data can be found in the `multimodalfitting `__ repository.