115 lines
3.6 KiB
Python
115 lines
3.6 KiB
Python
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import matplotlib.pyplot as plt
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import numpy as np
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import os
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import helperFunctions as hF
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from CellData import CellData
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from Baseline import BaselineCellData
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from FiCurve import FICurveCellData
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MODEL_COLOR = "orange"
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DATA_COLOR = "blue"
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DATA_SAVE_PATH = "data/figure_data/"
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def main():
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# data_isi_histogram()
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data_mean_freq_step_stimulus_examples()
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# data_mean_freq_step_stimulus_with_detections()
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# data_fi_curve()
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pass
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def data_fi_curve():
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cell = "data/invivo/2013-04-17-ac-invivo-1/"
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cell_data = CellData(cell)
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fi = FICurveCellData(cell_data, cell_data.get_fi_contrasts())
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fi.plot_fi_curve()
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def data_mean_freq_step_stimulus_with_detections():
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cell = "data/invivo/2013-04-17-ac-invivo-1/"
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cell_data = CellData(cell)
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fi = FICurveCellData(cell_data, cell_data.get_fi_contrasts())
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time_traces, freq_traces = fi.get_time_and_freq_traces()
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mean_times, mean_freqs = fi.get_mean_time_and_freq_traces()
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idx = -1
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time = np.array(mean_times[idx])
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freq = np.array(mean_freqs[idx])
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f_inf = fi.f_inf_frequencies[idx]
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f_zero = fi.f_zero_frequencies[idx]
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plt.plot(time, freq, color=DATA_COLOR)
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plt.plot(time[freq == f_zero][0], f_zero, "o", color="black")
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f_inf_time = time[(0.2 < time) & (time < 0.4)]
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plt.plot(f_inf_time, [f_inf for _ in f_inf_time], color="black")
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plt.xlim((-0.1, 0.6))
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plt.show()
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def data_mean_freq_step_stimulus_examples():
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# todo smooth! add f_0, f_inf, f_base to it?
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cell = "data/invivo/2013-04-17-ac-invivo-1/"
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cell_data = CellData(cell)
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fi = FICurveCellData(cell_data, cell_data.get_fi_contrasts())
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time_traces, freq_traces = fi.get_time_and_freq_traces()
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mean_times, mean_freqs = fi.get_mean_time_and_freq_traces()
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used_idicies = (0, 7, -1)
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fig, axes = plt.subplots(len(used_idicies), figsize=(8, 12), sharex=True, sharey=True)
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for ax_idx, idx in enumerate(used_idicies):
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sv = fi.stimulus_values[idx]
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# for j in range(len(time_traces[i])):
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# axes[i].plot(time_traces[i][j], freq_traces[i][j], color="gray", alpha=0.5)
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axes[ax_idx].plot(mean_times[idx], mean_freqs[idx], color=DATA_COLOR)
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# plt.plot(mean_times[i], mean_freqs[i], color="black")
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axes[ax_idx].set_ylabel("Frequency [Hz]")
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axes[ax_idx].set_xlim((-0.2, 0.6))
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axes[ax_idx].set_title("Contrast {:.2f} ({:} trials)".format(sv, len(time_traces[idx])))
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axes[ax_idx].set_xlabel("Time [s]")
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plt.show()
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def data_isi_histogram(recalculate=True):
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# if isis loadable - load
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name = "isi_cell_data.npy"
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path = os.path.join(DATA_SAVE_PATH, name)
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if os.path.exists(path) and not recalculate:
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isis = np.load(path)
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print("loaded")
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else:
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# if not get them from the cell
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cell = "data/invivo/2013-04-17-ac-invivo-1/" # not bursty
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# cell = "data/invivo/2014-12-03-ad-invivo-1/" # half bursty
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# cell = "data/invivo/2015-01-20-ad-invivo-1/" # does triple peaks...
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# cell = "data/invivo/2018-05-08-ae-invivo-1/" # a bit bursty
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# cell = "data/invivo/2013-04-10-af-invivo-1/" # a bit bursty
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cell_data = CellData(cell)
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base = BaselineCellData(cell_data)
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isis = np.array(base.get_interspike_intervals())
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# base.plot_baseline(position=0,time_length=10)
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# save isis
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np.save(path, isis)
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isis = isis * 1000
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# plot histogram
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bins = np.arange(0, 30.1, 0.1)
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plt.hist(isis, bins=bins, color=DATA_COLOR)
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plt.xlabel("Inter spike intervals [ms]")
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plt.ylabel("Count")
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plt.tight_layout()
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plt.show()
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if __name__ == '__main__':
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main() |