436 lines
17 KiB
Python
436 lines
17 KiB
Python
import numpy as np
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import matplotlib.pyplot as plt
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import matplotlib.gridspec as gridspec
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from analysis import get_filtered_fit_info, get_behaviour_values, get_parameter_values, behaviour_correlations, parameter_correlations
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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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parameter_titles = {"input_scaling": r"$\alpha$", "delta_a": r"$\Delta_A$",
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"mem_tau": r"$\tau_m$", "noise_strength": r"$\sqrt{2D}$",
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"refractory_period": "$t_{ref}$", "tau_a": r"$\tau_A$",
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"v_offset": r"$I_{Bias}$", "dend_tau": r"$\tau_{dend}$"}
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behaviour_titles = {"baseline_frequency": "base rate", "Burstiness": "Burst", "coefficient_of_variation": "CV",
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"serial_correlation": "SC", "vector_strength": "VS",
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"f_inf_slope": r"$f_{\infty}$ Slope", "f_zero_slope": r"$f_0$ Slope",
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"f_zero_middle": r"$f_0$ middle"}
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def main():
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dir_path = "results/final_2/"
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fits_info = get_filtered_fit_info(dir_path)
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print("Cells left:", len(fits_info))
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cell_behaviour, model_behaviour = get_behaviour_values(fits_info)
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plot_cell_model_comp_baseline(cell_behaviour, model_behaviour)
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plot_cell_model_comp_adaption(cell_behaviour, model_behaviour)
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plot_cell_model_comp_burstiness(cell_behaviour, model_behaviour)
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#
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behaviour_correlations_plot(fits_info)
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#
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labels, corr_values, corrected_p_values = parameter_correlations(fits_info)
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par_labels = [parameter_titles[l] for l in labels]
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fig, ax = plt.subplots(1, 1)
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#ax, labels, correlations, p_values, title, y_label=True
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create_correlation_plot(ax, par_labels, corr_values, corrected_p_values, "")
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plt.savefig(consts.SAVE_FOLDER + "parameter_correlations.png")
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plt.close()
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# create_parameter_distributions(get_parameter_values(fits_info))
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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 create_parameter_distributions(par_values):
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fig, axes = plt.subplots(4, 2)
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if len(par_values.keys()) != 8:
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print("not eight parameters")
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labels = ["input_scaling", "v_offset", "mem_tau", "noise_strength",
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"tau_a", "delta_a", "dend_tau", "refractory_period"]
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axes_flat = axes.flatten()
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for i, l in enumerate(labels):
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min_v = min(par_values[l]) * 0.95
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max_v = max(par_values[l]) * 1.05
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step = (max_v - min_v) / 20
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bins = np.arange(min_v, max_v+step, step)
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axes_flat[i].hist(par_values[l], bins=bins, color=consts.COLOR_MODEL, alpha=0.75)
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axes_flat[i].set_title(parameter_titles[l])
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plt.tight_layout()
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plt.savefig(consts.SAVE_FOLDER + "parameter_distributions.png")
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plt.close()
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def behaviour_correlations_plot(fits_info):
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fig = plt.figure(tight_layout=True, figsize=consts.FIG_SIZE_MEDIUM_WIDE)
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gs = gridspec.GridSpec(2, 2, width_ratios=(1, 1), height_ratios=(5, 1), hspace=0.025, wspace=0.05)
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# fig, axes = plt.subplots(1, 2, figsize=consts.FIG_SIZE_MEDIUM_WIDE)
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keys, corr_values, corrected_p_values = behaviour_correlations(fits_info, model_values=False)
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labels = [behaviour_titles[k] for k in keys]
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img = create_correlation_plot(fig.add_subplot(gs[0, 0]), labels, corr_values, corrected_p_values, "Data")
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keys, corr_values, corrected_p_values = behaviour_correlations(fits_info, model_values=True)
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labels = [behaviour_titles[k] for k in keys]
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img = create_correlation_plot(fig.add_subplot(gs[0, 1]), labels, corr_values, corrected_p_values, "Model", y_label=False)
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ax_col = fig.add_subplot(gs[1, :])
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data = [np.arange(-1, 1.001, 0.01)] * 10
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ax_col.set_xticks([0, 25, 50, 75, 100, 125, 150, 175, 200])
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ax_col.set_xticklabels([-1, -0.75, -0.5, -0.25, 0, 0.25, 0.5, 0.75, 1])
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ax_col.set_yticks([])
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ax_col.imshow(data)
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ax_col.set_xlabel("Correlation Coefficients")
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plt.tight_layout()
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plt.savefig(consts.SAVE_FOLDER + "behaviour_correlations.png")
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plt.close()
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def create_correlation_plot(ax, labels, correlations, p_values, title, y_label=True):
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cleaned_cors = np.zeros(correlations.shape)
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for i in range(correlations.shape[0]):
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for j in range(correlations.shape[1]):
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if abs(p_values[i, j]) < 0.05:
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cleaned_cors[i, j] = correlations[i, j]
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else:
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cleaned_cors[i, j] = np.NAN
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im = ax.imshow(cleaned_cors, vmin=-1, vmax=1)
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# We want to show all ticks...
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ax.set_xticks(np.arange(len(labels)))
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ax.set_xticklabels(labels)
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# ... and label them with the respective list entries
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if y_label:
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ax.set_yticks(np.arange(len(labels)))
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ax.set_yticklabels(labels)
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else:
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ax.set_yticklabels([])
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ax.set_title(title)
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# Rotate the tick labels and set their alignment.
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plt.setp(ax.get_xticklabels(), rotation=45, ha="right",
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rotation_mode="anchor")
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# Loop over data dimensions and create text annotations.
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for i in range(len(labels)):
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for j in range(len(labels)):
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if p_values[i][j] < 0.0001:
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text = ax.text(j, i, "***", ha="center", va="center", color="b")
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elif p_values[i][j] < 0.001:
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text = ax.text(j, i, "**", ha="center", va="center", color="b")
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elif p_values[i][j] < 0.05:
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text = ax.text(j, i, "*", ha="center", va="center", color="b")
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return im
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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 plot_cell_model_comp_baseline(cell_behavior, model_behaviour):
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fig = plt.figure(figsize=(12, 6))
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# ("baseline_frequency", "vector_strength", "serial_correlation")
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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, 3, width_ratios=[5, 5, 5], 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=0.25, hspace=0.05)
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num_of_bins = 20
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# baseline freq plot:
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i = 0
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cell = cell_behavior["baseline_frequency"]
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model = model_behaviour["baseline_frequency"]
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minimum = min(min(cell), min(model))
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maximum = max(max(cell), max(model))
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step = (maximum - minimum) / num_of_bins
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bins = np.arange(minimum, maximum + step, step)
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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, model, ax, ax_histx, behaviour_titles["baseline_frequency"], bins)
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i += 1
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cell = cell_behavior["vector_strength"]
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model = model_behaviour["vector_strength"]
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minimum = min(min(cell), min(model))
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maximum = max(max(cell), max(model))
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step = (maximum - minimum) / num_of_bins
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bins = np.arange(minimum, maximum + step, step)
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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, model, ax, ax_histx, behaviour_titles["vector_strength"], bins)
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i += 1
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cell = cell_behavior["serial_correlation"]
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model = model_behaviour["serial_correlation"]
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minimum = min(min(cell), min(model))
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maximum = max(max(cell), max(model))
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step = (maximum - minimum) / num_of_bins
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bins = np.arange(minimum, maximum + step, step)
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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, model, ax, ax_histx, behaviour_titles["serial_correlation"], bins)
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i += 1
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plt.tight_layout()
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plt.savefig(consts.SAVE_FOLDER + "fit_baseline_comparison.png", transparent=True)
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plt.close()
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def plot_cell_model_comp_adaption(cell_behavior, model_behaviour):
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fig = plt.figure(figsize=(8, 6))
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# ("f_inf_slope", "f_zero_slope")
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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, 2, width_ratios=[5, 5], 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=0.25, hspace=0.05)
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num_of_bins = 20
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# baseline freq plot:
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i = 0
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cell = cell_behavior["f_inf_slope"]
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model = model_behaviour["f_inf_slope"]
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minimum = min(min(cell), min(model))
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maximum = max(max(cell), max(model))
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step = (maximum - minimum) / num_of_bins
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bins = np.arange(minimum, maximum + step, step)
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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, model, ax, ax_histx, behaviour_titles["f_inf_slope"], bins)
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i += 1
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cell = cell_behavior["f_zero_slope"]
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model = model_behaviour["f_zero_slope"]
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length_before = len(cell)
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idx = np.array(cell) < 25000
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cell = np.array(cell)[idx]
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model = np.array(model)[idx]
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idx = np.array(model) < 25000
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cell = np.array(cell)[idx]
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model = np.array(model)[idx]
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print("removed {} values from f_zero_slope plot.".format(length_before - len(cell)))
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minimum = min(min(cell), min(model))
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maximum = max(max(cell), max(model))
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step = (maximum - minimum) / num_of_bins
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bins = np.arange(minimum, maximum + step, step)
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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, model, ax, ax_histx, behaviour_titles["f_zero_slope"], bins)
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plt.tight_layout()
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plt.savefig(consts.SAVE_FOLDER + "fit_adaption_comparison.png", transparent=True)
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plt.close()
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def plot_cell_model_comp_burstiness(cell_behavior, model_behaviour):
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fig = plt.figure(figsize=(8, 6))
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# ("Burstiness", "coefficient_of_variation")
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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, 2, width_ratios=[5, 5], 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=0.25, hspace=0.05)
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num_of_bins = 20
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# baseline freq plot:
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i = 0
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cell = cell_behavior["Burstiness"]
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model = model_behaviour["Burstiness"]
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minimum = min(min(cell), min(model))
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maximum = max(max(cell), max(model))
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step = (maximum - minimum) / num_of_bins
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bins = np.arange(minimum, maximum + step, step)
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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, model, ax, ax_histx, behaviour_titles["Burstiness"], bins)
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i += 1
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cell = cell_behavior["coefficient_of_variation"]
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model = model_behaviour["coefficient_of_variation"]
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minimum = min(min(cell), min(model))
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maximum = max(max(cell), max(model))
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step = (maximum - minimum) / num_of_bins
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bins = np.arange(minimum, maximum + step, step)
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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, model, ax, ax_histx, behaviour_titles["coefficient_of_variation"], bins)
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plt.tight_layout()
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plt.savefig(consts.SAVE_FOLDER + "fit_burstiness_comparison.png", transparent=True)
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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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#
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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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#
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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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#
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#
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# def overview_pair(cell, model, titles):
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# fig = plt.figure(figsize=(8, 6))
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#
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# columns = len(cell)
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#
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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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#
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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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#
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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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#
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# # plt.tight_layout()
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# plt.show()
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#
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#
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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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#
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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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#
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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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#
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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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#
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# plt.tight_layout()
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# plt.show()
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#
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def scatter_hist(cell_values, model_values, ax, ax_histx, behaviour, bins, 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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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.scatter(cell_values, model_values, color="black")
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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, bins=bins, color=consts.COLOR_DATA, alpha=0.75)
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ax_histx.hist(model_values, bins=bins, color=consts.COLOR_MODEL, alpha=0.75)
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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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