206 lines
6.1 KiB
Python
206 lines
6.1 KiB
Python
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from Baseline import get_baseline_class
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from CellData import CellData, icelldata_of_dir
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from models.LIFACnoise import LifacNoiseModel
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from Baseline import BaselineCellData, BaselineModel
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from os import listdir
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import numpy as np
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from IPython import embed
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import pyrelacs.DataLoader as Dl
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from ModelFit import ModelFit
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from FiCurve import FICurveModel, FICurveCellData
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import os
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import matplotlib.pyplot as plt
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import functions as fu
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from scipy.optimize import curve_fit
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from scipy.signal import find_peaks
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from thunderfish.eventdetection import threshold_crossing_times, threshold_crossings, detect_peaks
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isis = np.random.normal(0, 1, 200000)
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bins = np.arange(-5, 5, 0.1)
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counts = np.array([np.sum((isis >= b) & (isis < b+0.1)) for b in bins]) / len(isis)
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plt.plot(counts)
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plt.show()
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quit()
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folder = "./results/test_data/start_parameter_1_err_28.43"
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fit = ModelFit(folder)
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print(fit.get_fit_routine_error())
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fit.generate_master_plot()
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quit()
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for item in os.listdir(folder):
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fit_path = os.path.join(folder, item)
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fit = ModelFit(fit_path)
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print(fit.get_fit_routine_error())
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fit.generate_master_plot()
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quit()
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def indices_of_peaks_of_distribution(y_values, stepsize_ms, eod_freq):
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eod_freq_ms = eod_freq / 1000
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distance = int(0.75*eod_freq_ms / stepsize_ms)
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print(distance*stepsize_ms)
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peaks, _ = find_peaks(np.array(y_values), distance=distance)
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return peaks
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def remove_close_peaks(maxima_idx, peaks, closeness=2):
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to_del = []
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maxima_idx = list(maxima_idx)
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for idx in maxima_idx:
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for i in range(-1*closeness,closeness+1, 1):
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if 0 <= idx + i < len(peaks):
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if peaks[idx+i] > peaks[idx]:
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to_del.append(idx)
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break
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for val in to_del:
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maxima_idx.remove(val)
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return maxima_idx
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def find_local_maxima(values):
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local_max_idx = []
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for i in range(len(values)):
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maxima = True
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for j in (-1, 1):
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if 0 <= i+j < len(values):
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if values[i+j] > values[i]:
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maxima = False
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break
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else:
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continue
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if maxima:
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local_max_idx.append(i)
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return local_max_idx
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def rms(array):
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square = np.array(array)**2
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return np.sqrt(np.mean(square))
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def perc_smaller_value(isis, value):
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isis = np.array(isis)
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fullfilled = isis < value
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return np.sum(fullfilled) / len(fullfilled)
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cell_datas = [] # [CellData("data/invivo/2014-12-03-ad-invivo-1/")]
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for cell_data in icelldata_of_dir("data/invivo/", test_for_v1_trace=False):
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cell_datas.append(cell_data)
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for cell_data in icelldata_of_dir("data/invivo_bursty/"):
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cell_datas.append(cell_data)
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burstiness = []
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for cell_data in cell_datas:
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base = BaselineCellData(cell_data)
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burstiness.append(base.get_burstiness())
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cell_data_idx = np.arange(0, len(cell_datas), 1)
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burstiness, cell_data_idx = (list(t) for t in zip(*sorted(zip(burstiness, cell_data_idx))))
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for i in range(len(burstiness)):
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base = BaselineCellData(cell_datas[cell_data_idx[i]])
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isis = np.array(base.get_interspike_intervals()) * 1000
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bins = np.arange(0, 30.1, 0.2)
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plt.hist(isis, bins=bins)
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plt.title(str(burstiness[i]))
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plt.show()
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quit()
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for cell_data in cell_datas:
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base = BaselineCellData(cell_data)
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isis = np.array(base.get_interspike_intervals()) * 1000
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eod_freq = cell_data.get_eod_frequency()
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bins = np.arange(0, 30.1, 0.2)
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# y_values = plt.hist(isis, bins=bins, cumulative=True, density=True, alpha=0.5)
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# y_values2 = plt.hist(isis, bins=bins, density=True)
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value = perc_smaller_value(isis, 2.5/(eod_freq/1000)) * np.mean(isis)
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dif_mean_median.append(value)
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# plt.title("Diff % < 2.5eod / mean= {:.2f}".format(value))
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# peaks, _ = detect_peaks(y_values[0], 0.5*np.std(y_values[0]))
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# hist_x = bins[peaks]
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# hist_peaks = y_values[0][peaks]
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# plt.plot(hist_x, hist_peaks, '+')
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# plt.plot([2.5/(eod_freq/1000)]*2, (0, 1), ":", color="black")
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# plt.plot([np.median(isis)]*2, (0, 1), "--", color="darkblue")
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# plt.plot([np.mean(isis)]*2, (0, 1), "--", color="darkgreen")
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# plt.plot([rms(isis)]*2, (0, 1), "--", color="red")
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if value < 1:
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cells_sorted["below_one"].append(cell_data)
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elif value < 3:
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cells_sorted["below_three"].append(cell_data)
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else:
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cells_sorted["other"].append(cell_data)
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count = 0
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for cell_data in cells_sorted["below_one"]:
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count += 1
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if count <= 10:
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base = BaselineCellData(cell_data)
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isis = np.array(base.get_interspike_intervals()) * 1000
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eod_freq = cell_data.get_eod_frequency()
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value = perc_smaller_value(isis, 2.5 / (eod_freq / 1000)) * np.mean(isis)
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bins = np.arange(0, 30.1, 0.2)
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plt.title("Value < 1: {:.2f}".format(value))
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plt.hist(isis, bins=bins, density=True)
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plt.show()
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plt.close()
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count = 0
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for cell_data in cells_sorted["below_three"]:
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count += 1
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if count <= 10:
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base = BaselineCellData(cell_data)
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isis = np.array(base.get_interspike_intervals()) * 1000
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eod_freq = cell_data.get_eod_frequency()
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value = perc_smaller_value(isis, 2.5 / (eod_freq / 1000)) * np.mean(isis)
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bins = np.arange(0, 30.1, 0.2)
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plt.title("1 < Value < 3: {:.2f}".format(value))
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plt.hist(isis, bins=bins, density=True)
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plt.show()
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plt.close()
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count = 0
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for cell_data in cells_sorted["other"]:
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count += 1
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if count <= 10:
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base = BaselineCellData(cell_data)
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isis = np.array(base.get_interspike_intervals()) * 1000
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eod_freq = cell_data.get_eod_frequency()
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value = perc_smaller_value(isis, 2.5 / (eod_freq / 1000)) * np.mean(isis)
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bins = np.arange(0, 30.1, 0.2)
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plt.title("Value >=3: {:.2f}".format(value))
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plt.hist(isis, bins=bins, density=True)
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plt.show()
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plt.close()
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print("< one:", len(cells_sorted["below_one"]))
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print("< three:", len(cells_sorted["below_three"]))
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print("< more:", len(cells_sorted["other"]))
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quit()
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for cell_data in icelldata_of_dir("data/"):
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baseline = get_baseline_class(cell_data)
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baseline.get_burstiness() |