import pyrelacs.DataLoader as dl
import numpy as np
import matplotlib.pyplot as plt
from IPython import embed
import os
import introduction.old_helper_functions as ohf
from thunderfish.eventdetection import detect_peaks
SAVEPATH = ""
def get_savepath():
global SAVEPATH
return SAVEPATH
def set_savepath(new_path):
global SAVEPATH
SAVEPATH = new_path
def main():
for folder in ohf.get_subfolder_paths("data/"):
filepath = folder + "/basespikes1.dat"
set_savepath("figures/" + folder.split('/')[1] + "/")
print("Folder:", folder)
if not os.path.exists(get_savepath()):
os.makedirs(get_savepath())
spiketimes = []
ran = False
for metadata, key, data in dl.iload(filepath):
ran = True
spikes = data[:, 0]
spiketimes.append(spikes) # save for calculation of vector strength
metadata = metadata[0]
#print(metadata)
# print('firing frequency1:', metadata['firing frequency1'])
# print(mean_firing_rate(spikes))
# print('Coefficient of Variation (CV):', metadata['CV1'])
# print(calculate_coefficient_of_variation(spikes))
if not ran:
print("------------ DIDN'T RUN")
isi_histogram(spiketimes)
times, eods = ohf.get_traces(folder, 2, 'BaselineActivity')
times, v1s = ohf.get_traces(folder, 1, 'BaselineActivity')
vs = calculate_vector_strength(times, eods, spiketimes, v1s)
# print("Calculated vector strength:", vs)
def mean_firing_rate(spiketimes):
# mean firing rate (number of spikes per time)
return len(spiketimes)/spiketimes[-1]*1000
def calculate_coefficient_of_variation(spiketimes):
# CV (stddev of ISI divided by mean ISI (np.diff(spiketimes))
isi = np.diff(spiketimes)
std = np.std(isi)
mean = np.mean(isi)
return std/mean
def isi_histogram(spiketimes):
# ISI histogram (play around with binsize! < 1ms)
isi = []
for spike_list in spiketimes:
isi.extend(np.diff(spike_list))
maximum = max(isi)
bins = np.arange(0, maximum*1.01, 0.1)
plt.title('Phase locking of ISI without stimulus')
plt.xlabel('ISI in ms')
plt.ylabel('Count')
plt.hist(isi, bins=bins)
plt.savefig(get_savepath() + 'phase_locking_without_stimulus.png')
plt.close()
def calculate_vector_strength(times, eods, spiketimes, v1s):
# Vectorstaerke (use EOD frequency from header (metadata)) VS > 0.8
# dl.iload_traces(repro='BaselineActivity')
relative_spike_times = []
eod_durations = []
if len(times) == 0:
print("-----LENGTH OF TIMES = 0")
for recording in range(len(times)):
rel_spikes, eod_durs = eods_around_spikes(times[recording], eods[recording], spiketimes[recording])
relative_spike_times.extend(rel_spikes)
eod_durations.extend(eod_durs)
vs = __vector_strength__(np.array(rel_spikes), eod_durs)
phases = calculate_phases(rel_spikes, eod_durs)
plot_polar(phases, "test_phase_locking_" + str(recording) + "_with_vs:" + str(round(vs, 3)) + ".png")
print("VS of recording", recording, ":", vs)
plot_phaselocking_testfigures(times[recording], eods[recording], spiketimes[recording], v1s[recording])
return __vector_strength__(np.array(relative_spike_times), eod_durations)
def eods_around_spikes(time, eod, spiketimes):
eod_durations = []
relative_spike_times = []
for spike in spiketimes:
index = spike * 20 # time in s given timestamp of spike in ms - recorded at 20kHz -> timestamp/1000*20000 = idx
if index != np.round(index):
print("INDEX NOT AN INTEGER in eods_around_spikes! index:", index)
continue
index = int(index)
start_time, end_time = search_eod_start_and_end_times(time, eod, index)
eod_durations.append(end_time-start_time)
relative_spike_times.append(spike/1000 - start_time)
return relative_spike_times, eod_durations
def search_eod_start_and_end_times(time, eod, index):
# TODO might break if a spike is in the cut off first or last eod!
# search start_time:
previous = index
working_idx = index-1
while True:
if eod[working_idx] < 0 < eod[previous]:
first_value = eod[working_idx]
second_value = eod[previous]
dif = second_value - first_value
part = np.abs(first_value/dif)
time_dif = np.abs(time[previous] - time[working_idx])
start_time = time[working_idx] + time_dif*part
break
previous = working_idx
working_idx -= 1
# search end_time
previous = index
working_idx = index + 1
while True:
if eod[previous] < 0 < eod[working_idx]:
first_value = eod[previous]
second_value = eod[working_idx]
dif = second_value - first_value
part = np.abs(first_value / dif)
time_dif = np.abs(time[previous] - time[working_idx])
end_time = time[working_idx] + time_dif * part
break
previous = working_idx
working_idx += 1
return start_time, end_time
def search_closest_index(array, value, start=0, end=-1):
# searches the array to find the closest value in the array to the given value and returns its index.
# expects sorted array!
# start hast to be smaller than end
if end == -1:
end = len(array)-1
while True:
if end-start <= 1:
return end if np.abs(array[end]-value) < np.abs(array[start]-value) else start
middle = int(np.floor((end-start)/2)+start)
if array[middle] == value:
return middle
elif array[middle] > value:
end = middle
continue
else:
start = middle
continue
def __vector_strength__(relative_spike_times, eod_durations):
# adapted from Ramona
n = len(relative_spike_times)
if n == 0:
return 0
phase_times = (relative_spike_times / eod_durations) * 2 * np.pi
vs = np.sqrt((1 / n * sum(np.cos(phase_times))) ** 2 + (1 / n * sum(np.sin(phase_times))) ** 2)
return vs
def calculate_phases(relative_spike_times, eod_durations):
phase_times = np.zeros(len(relative_spike_times))
for i in range(len(relative_spike_times)):
phase_times[i] = (relative_spike_times[i] / eod_durations[i]) * 2 * np.pi
return phase_times
def plot_polar(phases, name=""):
fig = plt.figure()
ax = fig.add_subplot(111, polar=True)
# r = np.arange(0, 1, 0.001)
# theta = 2 * 2 * np.pi * r
# line, = ax.plot(theta, r, color='#ee8d18', lw=3)
bins = np.arange(0, np.pi*2, 0.05)
ax.hist(phases, bins=bins)
if name == "":
plt.show()
else:
plt.savefig(get_savepath() + name)
plt.close()
def plot_phaselocking_testfigures(time, eod, spiketimes, v1):
eod_start_times = []
eod_end_times = []
for spike in spiketimes:
index = spike * 20 # time in s given timestamp of spike in ms - recorded at 20kHz -> timestamp/1000*20000 = idx
if index != np.round(index):
print("INDEX NOT AN INTEGER in eods_around_spikes! index:", index)
continue
index = int(index)
start_time, end_time = search_eod_start_and_end_times(time, eod, index)
eod_start_times.append(start_time)
eod_end_times.append(end_time)
cutoff_in_sec = 2
sampling = 20000
max_idx = cutoff_in_sec*sampling
spikes_part = [x/1000 for x in spiketimes if x/1000 < cutoff_in_sec]
count_spikes = len(spikes_part)
print(spiketimes)
print(len(spikes_part))
x_axis = time[0:max_idx]
plt.plot(spikes_part, np.ones(len(spikes_part))*-20, 'o')
plt.plot(x_axis, v1[0:max_idx])
plt.plot(eod_start_times[: count_spikes], np.zeros(count_spikes), 'o')
plt.plot(eod_end_times[: count_spikes], np.zeros(count_spikes), 'o')
plt.show()
plt.close()
if __name__ == '__main__':
main()