import numpy as np
import matplotlib.pyplot as plt
import pyrelacs.DataLoader as dl
import os
import helperFunctions as hf
from IPython import embed
from scipy.optimize import curve_fit
import warnings
SAMPLING_INTERVAL = 1/20000
STIMULUS_START = 0
STIMULUS_DURATION = 0.400
PRE_DURATION = 0.250
TOTAL_DURATION = 1.25
def main():
for folder in hf.get_subfolder_paths("data/"):
filepath = folder + "/fispikes1.dat"
set_savepath("figures/" + folder.split('/')[1] + "/")
print("Folder:", folder)
if not os.path.exists(get_savepath()):
os.makedirs(get_savepath())
spiketimes = []
intensities = []
index = -1
for metadata, key, data in dl.iload(filepath):
# embed()
if len(metadata) != 0:
metadata_index = 0
if '----- Control --------------------------------------------------------' in metadata[0].keys():
metadata_index = 1
print(metadata)
i = float(metadata[metadata_index]['intensity'][:-2])
intensities.append(i)
spiketimes.append([])
index += 1
spiketimes[index].append(data[:, 0]/1000)
intensities, spiketimes = hf.merge_similar_intensities(intensities, spiketimes)
# Sort the lists so that intensities are increasing
x = [list(x) for x in zip(*sorted(zip(intensities, spiketimes), key=lambda pair: pair[0]))]
intensities = x[0]
spiketimes = x[1]
mean_frequencies = calculate_mean_frequencies(intensities, spiketimes)
popt, pcov = fit_exponential(intensities, mean_frequencies)
plot_frequency_curve(intensities, mean_frequencies)
f_baseline = calculate_f_baseline(mean_frequencies)
f_infinity = calculate_f_infinity(mean_frequencies)
f_zero = calculate_f_zero(mean_frequencies)
# plot_fi_curve(intensities, f_baseline, f_zero, f_infinity)
# TODO !!
def fit_exponential(intensities, mean_frequencies):
start_idx = int((PRE_DURATION + STIMULUS_START+0.005) / SAMPLING_INTERVAL)
end_idx = int((PRE_DURATION + STIMULUS_START + 0.1) / SAMPLING_INTERVAL)
time_constants = []
#print(start_idx, end_idx)
popts = []
pcovs = []
for i in range(len(mean_frequencies)):
freq = mean_frequencies[i]
y_values = freq[start_idx:end_idx+1]
x_values = np.arange(start_idx*SAMPLING_INTERVAL, end_idx*SAMPLING_INTERVAL, SAMPLING_INTERVAL)
try:
popt, pcov = curve_fit(exponential_function, x_values, y_values, p0=(1/(np.power(1, 10)), .5, 50, 180), maxfev=10000)
except RuntimeError:
print("RuntimeError happened in fit_exponential.")
continue
#print(popt)
#print(pcov)
#print()
popts.append(popt)
pcovs.append(pcov)
plt.plot(np.arange(-PRE_DURATION, TOTAL_DURATION, SAMPLING_INTERVAL), freq)
plt.plot(x_values-PRE_DURATION, [exponential_function(x, popt[0], popt[1], popt[2], popt[3]) for x in x_values])
# plt.show()
save_path = get_savepath() + "exponential_fits/"
if not os.path.exists(save_path):
os.makedirs(save_path)
plt.savefig(save_path + "fit_intensity:" + str(round(intensities[i], 4)) + ".png")
plt.close()
return popts, pcovs
def calculate_mean_frequency(freqs):
mean_freq = [sum(e) / len(e) for e in zip(*freqs)]
return mean_freq
def gaussian_kernel(sigma, dt):
x = np.arange(-4. * sigma, 4. * sigma, dt)
y = np.exp(-0.5 * (x / sigma) ** 2) / np.sqrt(2. * np.pi) / sigma
return y
def calculate_kernel_frequency(spiketimes, time, sampling_interval):
sp = spiketimes
t = time # Probably goes from -200ms to some amount of ms in the positive ~1200?
dt = sampling_interval
kernel_width = 0.01 # kernel width is a time in seconds how sharp the frequency should be counted
binary = np.zeros(t.shape)
spike_indices = ((sp - t[0]) / dt).astype(int)
binary[spike_indices[(spike_indices >= 0) & (spike_indices < len(binary))]] = 1
g = gaussian_kernel(kernel_width, dt)
rate = np.convolve(binary, g, mode='same')
return rate
def calculate_isi_frequency(spiketimes, time):
first_isi = spiketimes[0] - (-PRE_DURATION) # diff to the start at 0
last_isi = TOTAL_DURATION - spiketimes[-1] # diff from the last spike to the end of time :D
isis = [first_isi]
isis.extend(np.diff(spiketimes))
isis.append(last_isi)
if np.isnan(first_isi):
print(spiketimes[:10])
print(isis[0:10])
quit()
rate = []
for isi in isis:
if isi == 0:
print("probably a problem")
isi = 0.0000000001
freq = 1/isi
frequency_step = int(round(isi*(1/SAMPLING_INTERVAL)))*[freq]
rate.extend(frequency_step)
#plt.plot((np.arange(len(rate))-PRE_DURATION)/(1/SAMPLING_INTERVAL), rate)
#plt.plot([sum(isis[:i+1]) for i in range(len(isis))], [200 for i in isis], 'o')
#plt.plot(time, [100 for t in time])
#plt.show()
if len(rate) != len(time):
if "12-13-af" in get_savepath():
warnings.warn("preStimulus duration > 0 still not supported")
return [1]*len(time)
else:
print(len(rate), len(time), len(rate) - len(time))
print(rate)
print(isis)
print("Quitting because time and rate aren't the same length")
quit()
return rate
def calculate_mean_frequencies(intensities, spiketimes):
time = np.arange(-PRE_DURATION, TOTAL_DURATION, SAMPLING_INTERVAL)
mean_frequencies = []
for i in range(len(intensities)):
freqs = []
for spikes in spiketimes[i]:
if len(spikes) < 2:
continue
freq = calculate_isi_frequency(spikes, time)
freqs.append(freq)
mf = calculate_mean_frequency(freqs)
mean_frequencies.append(mf)
return mean_frequencies
def calculate_f_baseline(mean_frequencies):
buffer_time = 0.05
start_idx = int(0.05/SAMPLING_INTERVAL)
end_idx = int((PRE_DURATION - STIMULUS_START - buffer_time)/SAMPLING_INTERVAL)
f_zeros = []
for freq in mean_frequencies:
f_0 = np.mean(freq[start_idx:end_idx])
f_zeros.append(f_0)
return f_zeros
def calculate_f_infinity(mean_frequencies):
buffer_time = 0.05
start_idx = int((PRE_DURATION + STIMULUS_START + STIMULUS_DURATION - 0.15 - buffer_time) / SAMPLING_INTERVAL)
end_idx = int((PRE_DURATION + STIMULUS_START + STIMULUS_DURATION - buffer_time) / SAMPLING_INTERVAL)
f_infinity = []
for freq in mean_frequencies:
f_inf = np.mean(freq[start_idx:end_idx])
f_infinity.append(f_inf)
return f_infinity
def calculate_f_zero(mean_frequencies):
buffer_time = 0.1
start_idx = int((PRE_DURATION + STIMULUS_START - buffer_time) / SAMPLING_INTERVAL)
end_idx = int((PRE_DURATION + STIMULUS_START + buffer_time) / SAMPLING_INTERVAL)
f_peaks = []
for freq in mean_frequencies:
fp = np.mean(freq[start_idx-500:start_idx])
for i in range(start_idx+1, end_idx):
if abs(freq[i] - freq[start_idx]) > abs(fp - freq[start_idx]):
fp = freq[i]
f_peaks.append(fp)
return f_peaks
def plot_fi_curve(intensities, f_baseline, f_zero, f_infinity):
plt.plot(intensities, f_baseline, label="f_baseline")
plt.plot(intensities, f_zero, 'o', label="f_zero")
plt.plot(intensities, f_infinity, label="f_infinity")
max_f0 = float(max(f_zero))
mean_int = float(np.mean(intensities))
start_k = float(((f_zero[-1] - f_zero[0]) / (intensities[-1] - intensities[0])*4)/f_zero[-1])
popt, pcov = curve_fit(fill_boltzmann, intensities, f_zero, p0=(max_f0, start_k, mean_int), maxfev=10000)
print(popt)
min_x = min(intensities)
max_x = max(intensities)
step = (max_x - min_x) / 5000
x_values_boltzmann_fit = np.arange(min_x, max_x, step)
plt.plot(x_values_boltzmann_fit, [fill_boltzmann(i, popt[0], popt[1], popt[2]) for i in x_values_boltzmann_fit], label='fit')
plt.title("FI-Curve")
plt.ylabel("Frequency in Hz")
plt.xlabel("Intensity in mV")
plt.legend()
# plt.show()
plt.savefig(get_savepath() + "fi_curve.png")
plt.close()
def plot_frequency_curve(intensities, mean_frequencies):
colors = ["red", "green", "blue", "violet", "orange", "grey"]
time = np.arange(-PRE_DURATION, TOTAL_DURATION, SAMPLING_INTERVAL)
for i in range(len(intensities)):
plt.plot(time, mean_frequencies[i], color=colors[i % 6], label=str(intensities[i]))
plt.plot((0, 0), (0, 500), color="black")
plt.plot((0.4, 0.4), (0, 500), color="black")
plt.legend()
plt.xlabel("Time in seconds")
plt.ylabel("Frequency in Hz")
plt.title("Frequency curve")
plt.savefig(get_savepath() + "mean_frequency_curves.png")
plt.close()
def exponential_function(x, a, b, c, d):
return a*np.exp(-c*(x-b))+d
def upper_boltzmann(x, f_max, k, x_zero):
return f_max * np.clip((2 / (1+np.power(np.e, -k*(x - x_zero)))) - 1, 0, None)
def fill_boltzmann(x, f_max, k, x_zero):
return f_max * (1 / (1 + np.power(np.e, -k * (x - x_zero))))
SAVEPATH = ""
def get_savepath():
global SAVEPATH
return SAVEPATH
def set_savepath(new_path):
global SAVEPATH
SAVEPATH = new_path
if __name__ == '__main__':
main()