100 lines
2.4 KiB
Python
100 lines
2.4 KiB
Python
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import pyrelacs.DataLoader as dl
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import os
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import numpy as np
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def get_subfolder_paths(basepath):
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subfolders = []
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for content in os.listdir(basepath):
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content_path = basepath + content
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if os.path.isdir(content_path):
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subfolders.append(content_path)
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return sorted(subfolders)
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def get_traces(directory, trace_type, repro):
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# trace_type = 1: Voltage p-unit
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# trace_type = 2: EOD
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# trace_type = 3: local EOD ~(EOD + stimulus)
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# trace_type = 4: Stimulus
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load_iter = dl.iload_traces(directory, repro=repro)
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time_traces = []
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value_traces = []
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nothing = True
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for info, key, time, x in load_iter:
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nothing = False
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time_traces.append(time)
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value_traces.append(x[trace_type-1])
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if nothing:
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print("iload_traces found nothing for the BaselineActivity repro!")
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return time_traces, value_traces
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def get_all_traces(directory, repro):
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load_iter = dl.iload_traces(directory, repro=repro)
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time_traces = []
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v1_traces = []
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eod_traces = []
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local_eod_traces = []
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stimulus_traces = []
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nothing = True
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for info, key, time, x in load_iter:
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nothing = False
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time_traces.append(time)
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v1_traces.append(x[0])
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eod_traces.append(x[1])
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local_eod_traces.append(x[2])
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stimulus_traces.append(x[3])
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print(info)
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traces = [v1_traces, eod_traces, local_eod_traces, stimulus_traces]
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if nothing:
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print("iload_traces found nothing for the BaselineActivity repro!")
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return time_traces, traces
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def crappy_smoothing(signal:list, window_size:int = 5) -> list:
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smoothed = []
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for i in range(len(signal)):
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k = window_size
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if i < window_size:
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k = i
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j = window_size
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if i + j > len(signal):
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j = len(signal) - i
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smoothed.append(np.mean(signal[i-k:i+j]))
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return smoothed
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def plot_frequency_curve(cell_data, save_path: str = None, indices: list = None):
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contrast = cell_data.get_fi_contrasts()
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time_axes = cell_data.get_time_axes_fi_curve_mean_frequencies()
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mean_freqs = cell_data.get_mean_fi_curve_isi_frequencies()
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if indices is None:
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indices = np.arange(len(contrast))
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for i in indices:
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plt.plot(time_axes[i], mean_freqs[i], label=str(round(contrast[i], 2)))
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if save_path is None:
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plt.show()
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else:
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plt.savefig(save_path + "mean_frequency_curves.png")
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plt.close() |