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C.A.P. Linssen
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Jul 15, 2024
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import os | ||
import numpy as np | ||
import matplotlib.pyplot as plt | ||
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def read_isis_from_files(neuron_models): | ||
data = {} | ||
for neuron_model in neuron_models: | ||
data[neuron_model] = {"isis": []} | ||
iteration = 0 | ||
rank = 0 | ||
while True: | ||
filename = "isi_distribution_[simulated_neuron=" + neuron_model + "]_[network_scale=20000]_[iteration=" + str(iteration) + "]_[nodes=2]_[rank=" + str(rank) + "]_isi_list.txt" | ||
if not os.path.exists(filename): | ||
break | ||
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with open(filename, 'r') as file: | ||
isis = [float(line.strip()) for line in file] | ||
data[neuron_model]["isis"].append(isis) | ||
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#iteration += 1 | ||
rank += 1 | ||
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return data | ||
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def analyze_data(data, bin_size): | ||
# Determine the range for the bins | ||
min_val = np.inf | ||
max_val = -np.inf | ||
for neuron_model in data.keys(): | ||
isi_list = data[neuron_model]["isis"] | ||
for isi in isi_list: | ||
min_val = min(min_val, min(isi)) | ||
max_val = max(max_val, max(isi)) | ||
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bins = np.arange(min_val, max_val + bin_size, bin_size) | ||
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for neuron_model in data.keys(): | ||
isi_list = data[neuron_model]["isis"] | ||
data[neuron_model]["counts"] = len(isi_list) * [None] | ||
for i, isi in enumerate(isi_list): | ||
counts, bin_edges = np.histogram(isi, bins=bins) | ||
data[neuron_model]["counts"][i] = counts | ||
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data[neuron_model]["counts_mean"] = np.mean(np.array(data[neuron_model]["counts"]), axis=0) | ||
data[neuron_model]["counts_std"] = np.std(np.array(data[neuron_model]["counts"]), axis=0) | ||
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data["bin_centers"] = (bin_edges[:-1] + bin_edges[1:]) / 2 | ||
data["min_val"] = min_val | ||
data["max_val"] = max_val | ||
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def plot_isi_distributions(neuron_models, data): | ||
plt.figure(figsize=(10, 6)) | ||
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for neuron_model in neuron_models: | ||
plt.step(data["bin_centers"], data[neuron_model]["counts_mean"], label=neuron_model, linewidth=2, alpha=.5) | ||
plt.errorbar(data["bin_centers"], data[neuron_model]["counts_mean"], yerr=data[neuron_model]["counts_std"], fmt='o', color='black', capsize=5, label='Variance') | ||
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plt.xlabel('ISI (ms)') | ||
plt.ylabel('Frequency') | ||
plt.title('ISI Distributions') | ||
plt.legend() | ||
plt.grid(True) | ||
plt.show() | ||
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# Example usage | ||
neuron_models = ["aeif_psc_alpha_neuron_Nestml_Plastic_noco__with_stdp_synapse_Nestml_Plastic_noco", "aeif_psc_alpha_neuron_Nestml_Plastic__with_stdp_synapse_Nestml_Plastic", "aeif_psc_alpha_neuron_Nestml", "aeif_psc_alpha"] | ||
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bin_size = 5 # Adjust the bin size as needed | ||
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data = read_isis_from_files(neuron_models) | ||
analyze_data(data, bin_size) | ||
plot_isi_distributions(neuron_models, data) | ||
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