173 lines
6.1 KiB
Python
173 lines
6.1 KiB
Python
import numpy as np
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import matplotlib.pyplot as plt
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from analysis import get_fit_info, get_behaviour_values, calculate_percent_errors
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from ModelFit import get_best_fit
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from Baseline import BaselineModel, BaselineCellData
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import Figure_constants as consts
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def main():
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dir_path = "results/final_2/"
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fits_info = get_fit_info(dir_path)
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# cell_behaviour, model_behaviour = get_behaviour_values(fits_info)
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# behaviour_overview_pairs(cell_behaviour, model_behaviour)
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# errors = calculate_percent_errors(fits_info)
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# create_boxplots(errors)
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example_good_hist_fits(dir_path)
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def example_good_hist_fits(dir_path):
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strong_bursty_cell = "2018-05-08-ac-invivo-1"
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bursty_cell = "2014-03-19-ad-invivo-1"
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non_bursty_cell = "2012-12-21-am-invivo-1"
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fig, axes = plt.subplots(1, 3, sharex="all", figsize=consts.FIG_SIZE_MEDIUM_WIDE)
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for i, cell in enumerate([non_bursty_cell, bursty_cell, strong_bursty_cell]):
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fit_dir = dir_path + cell + "/"
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fit = get_best_fit(fit_dir)
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cell_data = fit.get_cell_data()
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eodf = cell_data.get_eod_frequency()
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model = fit.get_model()
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baseline_model = BaselineModel(model, eodf, trials=5)
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model_isi = np.array(baseline_model.get_interspike_intervals()) * eodf
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cell_isi = BaselineCellData(cell_data).get_interspike_intervals() * eodf
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bins = np.arange(0, 0.025, 0.0001) * eodf
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axes[i].hist(cell_isi, bins=bins, density=True, alpha=0.5, color=consts.COLOR_DATA)
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axes[i].hist(model_isi, bins=bins, density=True, alpha=0.5, color=consts.COLOR_MODEL)
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axes[i].set_xlabel("ISI in EOD periods")
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axes[0].set_ylabel("Density")
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plt.tight_layout()
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consts.set_figure_labels(xoffset=-2.5)
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fig.label_axes()
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plt.savefig(consts.SAVE_FOLDER + "example_good_isi_hist_fits.png", transparent=True)
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plt.close()
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def create_boxplots(errors):
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labels = ["{}_n:{}".format(k, len(errors[k])) for k in sorted(errors.keys())]
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for k in sorted(errors.keys()):
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print("{}: median %-error: {:.2f}".format(k, np.median(errors[k])))
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y_values = [errors[k] for k in sorted(errors.keys())]
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plt.boxplot(y_values)
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plt.xticks(np.arange(1, len(y_values)+1, 1), labels, rotation=45)
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plt.tight_layout()
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plt.show()
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plt.close()
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def behaviour_overview_pairs(cell_behaviour, model_behaviour):
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# behaviour_keys = ["Burstiness", "coefficient_of_variation", "serial_correlation",
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# "vector_strength", "f_inf_slope", "f_zero_slope", "baseline_frequency"]
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pairs = [("baseline_frequency", "vector_strength", "serial_correlation"),
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("Burstiness", "coefficient_of_variation"),
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("f_inf_slope", "f_zero_slope")]
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for pair in pairs:
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cell = []
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model = []
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for behaviour in pair:
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cell.append(cell_behaviour[behaviour])
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model.append(model_behaviour[behaviour])
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overview_pair(cell, model, pair)
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def overview_pair(cell, model, titles):
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fig = plt.figure(figsize=(8, 6))
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columns = len(cell)
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# Add a gridspec with two rows and two columns and a ratio of 2 to 7 between
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# the size of the marginal axes and the main axes in both directions.
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# Also adjust the subplot parameters for a square plot.
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gs = fig.add_gridspec(2, columns, width_ratios=[5] * columns, height_ratios=[3, 7],
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left=0.1, right=0.9, bottom=0.1, top=0.9,
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wspace=0.2, hspace=0.05)
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for i in range(len(cell)):
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if titles[i] == "f_zero_slope":
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length_before = len(cell[i])
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idx = np.array(cell[i]) < 30000
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cell[i] = np.array(cell[i])[idx]
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model[i] = np.array(model[i])[idx]
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idx = np.array(model[i]) < 30000
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cell[i] = np.array(cell[i])[idx]
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model[i] = np.array(model[i])[idx]
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print("removed {} values from f_zero_slope plot.".format(length_before - len(cell[i])))
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ax = fig.add_subplot(gs[1, i])
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ax_histx = fig.add_subplot(gs[0, i], sharex=ax)
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scatter_hist(cell[i], model[i], ax, ax_histx, titles[i])
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# plt.tight_layout()
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plt.show()
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def grouped_error_overview_behaviour_dist(cell_behaviours, model_behaviours):
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# start with a square Figure
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fig = plt.figure(figsize=(12, 12))
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rows = 4
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columns = 2
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# Add a gridspec with two rows and two columns and a ratio of 2 to 7 between
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# the size of the marginal axes and the main axes in both directions.
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# Also adjust the subplot parameters for a square plot.
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gs = fig.add_gridspec(rows*2, columns, width_ratios=[5]*columns, height_ratios=[3, 7] * rows,
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left=0.1, right=0.9, bottom=0.1, top=0.9,
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wspace=0.2, hspace=0.5)
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for i, behaviour in enumerate(sorted(cell_behaviours.keys())):
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col = int(np.floor(i / rows))
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row = i - rows*col
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ax = fig.add_subplot(gs[row*2 + 1, col])
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ax_histx = fig.add_subplot(gs[row*2, col])
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# use the previously defined function
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scatter_hist(cell_behaviours[behaviour], model_behaviours[behaviour], ax, ax_histx, behaviour)
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plt.tight_layout()
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plt.show()
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def scatter_hist(cell_values, model_values, ax, ax_histx, behaviour, ax_histy=None):
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# copied from matplotlib
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# no labels
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ax_histx.tick_params(axis="cell", labelbottom=False)
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# ax_histy.tick_params(axis="model_values", labelleft=False)
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# the scatter plot:
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ax.scatter(cell_values, model_values)
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minimum = min(min(cell_values), min(model_values))
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maximum = max(max(cell_values), max(model_values))
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ax.plot((minimum, maximum), (minimum, maximum), color="grey")
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ax.set_xlabel("cell")
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ax.set_ylabel("model")
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ax_histx.hist(cell_values, color="blue", alpha=0.5)
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ax_histx.hist(model_values, color="orange", alpha=0.5)
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ax_labels = ax.get_xticklabels()
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ax_histx.set_xticklabels([])
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ax.set_xticklabels(ax_labels)
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ax_histx.set_xticks(ax.get_xticks())
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ax_histx.set_xlim(ax.get_xlim())
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ax_histx.set_title(behaviour)
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# ax_histy.hist(y, bins=bins, orientation='horizontal')
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if __name__ == '__main__':
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main()
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