127 lines
4.5 KiB
Python
127 lines
4.5 KiB
Python
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import numpy as np
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import matplotlib.pyplot as plt
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import pyrelacs.DataLoader as Dl
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test_cell = "data/final/2010-11-08-al-invivo-1/"
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def main():
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fi_spiketimes = get_unsorted_spiketimes(test_cell + "/fispikes1.dat")
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count = 0
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for info, key, time, x in Dl.iload_traces(test_cell, repro="FICurve", before=0.2, after=0.8):
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# time, v1, eod, local_eod, stimulus
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print(key)
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print(info)
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v1 = x[0]
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# eod = x[1]
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# local_eod = x[2]
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# stimulus = x[3]
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count = 0
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for i in range(len(fi_spiketimes)):
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if count >= 9:
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break
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count += 1
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height = max(v1)+i
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time_offset = 2 # expected time shift per stimulus: with 0.2 delay 1 sec step and 0.8s after stimulus, but doesn't work
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plt.eventplot(np.array(fi_spiketimes[i]), colors="black", lineoffsets=height)
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plt.plot(np.array(time), v1)
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plt.show()
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pass
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def get_unsorted_spiketimes(fi_file):
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spiketimes = []
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for metadata, key, data in Dl.iload(fi_file):
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spike_time_data = data[:, 0] / 1000
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spiketimes.append(spike_time_data)
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return spiketimes
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def get_fi_curve_spiketimes(fi_file):
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spiketimes = []
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pre_intensities = []
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pre_durations = []
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intensities = []
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trans_amplitudes = []
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pre_duration = -1
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index = -1
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skip = False
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trans_amplitude = float('nan')
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for metadata, key, data in Dl.iload(fi_file):
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if len(metadata) != 0:
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metadata_index = 0
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if '----- Control --------------------------------------------------------' in metadata[0].keys():
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metadata_index = 1
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pre_duration = float(metadata[0]["----- Pre-Intensities ------------------------------------------------"]["preduration"][:-2])
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trans_amplitude = float(metadata[0]["trans. amplitude"][:-2])
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if pre_duration == 0:
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skip = False
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else:
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skip = True
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continue
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else:
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if "preduration" in metadata[0].keys():
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pre_duration = float(metadata[0]["preduration"][:-2])
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trans_amplitude = float(metadata[0]["trans. amplitude"][:-2])
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if pre_duration == 0:
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skip = False
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else:
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skip = True
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continue
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if skip:
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continue
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if 'intensity' in metadata[metadata_index].keys():
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intensity = float(metadata[metadata_index]['intensity'][:-2])
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pre_intensity = float(metadata[metadata_index]['preintensity'][:-2])
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else:
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intensity = float(metadata[1-metadata_index]['intensity'][:-2])
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pre_intensity = float(metadata[1-metadata_index]['preintensity'][:-2])
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intensities.append(intensity)
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pre_durations.append(pre_duration)
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pre_intensities.append(pre_intensity)
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trans_amplitudes.append(trans_amplitude)
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spiketimes.append([])
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index += 1
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if skip:
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continue
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if data.shape[1] != 1:
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raise RuntimeError("DatParser:get_fi_curve_spiketimes():\n read data has more than one dimension!")
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spike_time_data = data[:, 0]/1000
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if len(spike_time_data) < 10:
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print("# ignoring spike-train that contains less than 10 spikes.")
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continue
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if spike_time_data[-1] < 1:
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print("# ignoring spike-train that ends before one second.")
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continue
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spiketimes[index].append(spike_time_data)
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# TODO Check if sorting works!
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# new_order = np.arange(0, len(intensities), 1)
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# intensities, new_order = zip(*sorted(zip(intensities, new_order)))
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# intensities = list(intensities)
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# spiketimes = [spiketimes[i] for i in new_order]
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# trans_amplitudes = [trans_amplitudes[i] for i in new_order]
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#
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# for i in range(len(intensities)-1, -1, -1):
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# if len(spiketimes[i]) < 3:
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# del intensities[i]
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# del spiketimes[i]
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# del trans_amplitudes[i]
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return trans_amplitudes, intensities, spiketimes
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if __name__ == '__main__':
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main()
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