update thesis figures

Figures_Baseline.py

@@ -9,12 +9,9 @@ import helperFunctions as hF
 
 from CellData import CellData
 from Baseline import BaselineCellData
-from FiCurve import FICurveCellData
+from FiCurve import FICurveCellData, FICurveModel
 import Figure_constants as consts
-MODEL_COLOR = "orange"
-DATA_COLOR = "blue"
-
-DATA_SAVE_PATH = "data/figure_data/"
+from ModelFit import get_best_fit
 
 EXAMPLE_CELL = "data/final/2012-12-20-ac-invivo-1"
 
@@ -25,13 +22,68 @@ def main():
     # data_fi_curve()
     p_unit_example()
     fi_point_detection()
+
+
+    # test_fi_curve_colors()
     pass
 
 
-def p_unit_example():
+def test_fi_curve_colors():
+    example_cell_fit = "results/final_2/2012-12-20-ac-invivo-1"
     cell = EXAMPLE_CELL
+
     cell_data = CellData(cell)
+    fit = get_best_fit(example_cell_fit)
+
+    fig, axes = plt.subplots(1, 3)
+
+    axes[0].set_title("Cell")
+    fi_curve = FICurveCellData(cell_data, cell_data.get_fi_contrasts(), save_dir=cell_data.get_data_path())
+    contrasts = cell_data.get_fi_contrasts()
+    f_zeros = fi_curve.get_f_zero_frequencies()
+    f_infs = fi_curve.get_f_inf_frequencies()
+    axes[0].plot(contrasts, f_zeros, ',', marker=consts.f0_marker, color=consts.COLOR_DATA_f0)
+    axes[0].plot(contrasts, f_infs, ',', marker=consts.finf_marker, color=consts.COLOR_DATA_finf)
+
+    x_values = np.arange(min(contrasts), max(contrasts), (max(contrasts) - min(contrasts)) / 1000)
+    f_inf_fit = fi_curve.f_inf_fit
+    f_zero_fit = fi_curve.f_zero_fit
+    f_zero_fit = [fu.full_boltzmann(x, f_zero_fit[0], f_zero_fit[1], f_zero_fit[2], f_zero_fit[3]) for x in x_values]
+    f_inf_fit = [fu.clipped_line(x, f_inf_fit[0], f_inf_fit[1]) for x in x_values]
+    axes[0].plot(x_values, f_zero_fit, color=consts.COLOR_DATA_f0)
+    axes[0].plot(x_values, f_inf_fit, color=consts.COLOR_DATA_finf)
+
+    axes[2].plot(x_values, f_zero_fit, color=consts.COLOR_DATA_f0)
+    axes[2].plot(x_values, f_inf_fit, color=consts.COLOR_DATA_finf)
+
+    axes[1].set_title("Model")
+    model = fit.get_model()
+    fi_curve = FICurveModel(model, contrasts, eod_frequency=cell_data.get_eod_frequency())
+
+    f_zeros = fi_curve.get_f_zero_frequencies()
+    f_infs = fi_curve.get_f_inf_frequencies()
+    axes[1].plot(contrasts, f_zeros, ',', marker=consts.f0_marker, color=consts.COLOR_MODEL_f0)
+    axes[1].plot(contrasts, f_infs, ',', marker=consts.finf_marker, color=consts.COLOR_MODEL_finf)
+
+    x_values = np.arange(min(contrasts), max(contrasts), (max(contrasts) - min(contrasts)) / 1000)
+    f_inf_fit = fi_curve.f_inf_fit
+    f_zero_fit = fi_curve.f_zero_fit
+    f_zero_fit = [fu.full_boltzmann(x, f_zero_fit[0], f_zero_fit[1], f_zero_fit[2], f_zero_fit[3]) for x in x_values]
+    f_inf_fit = [fu.clipped_line(x, f_inf_fit[0], f_inf_fit[1]) for x in x_values]
+    axes[1].plot(x_values, f_zero_fit, color=consts.COLOR_MODEL_f0)
+    axes[1].plot(x_values, f_inf_fit, color=consts.COLOR_MODEL_finf)
+
+    axes[2].plot(contrasts, f_zeros, ",", marker=consts.f0_marker, color=consts.COLOR_MODEL_f0)
+    axes[2].plot(contrasts, f_infs, ",", marker=consts.finf_marker, color=consts.COLOR_MODEL_finf)
+
+    plt.show()
+    plt.close()
+
 
+def p_unit_example():
+    cell = EXAMPLE_CELL
+    cell_data = CellData(cell)
+    print("p-unit example eodf:", cell_data.get_eod_frequency())
     base = BaselineCellData(cell_data)
     base.load_values(cell_data.get_data_path())
     print("burstiness of example cell:", base.get_burstiness())
@@ -51,12 +103,13 @@ def p_unit_example():
     start = 0
     duration = 0.10
 
-    ax.plot(np.array(time[:int(duration/step)]) - start, v1[:int(duration/step)])
-    ax.eventplot([s for s in spiketimes if start < s < start + duration], lineoffsets=max(v1[:int(duration/step)])+0.75, color="black")
+    ax.plot((np.array(time[:int(duration/step)]) - start) * 1000, v1[:int(duration/step)], consts.COLOR_DATA)
+    ax.eventplot([s * 1000 for s in spiketimes if start < s < start + duration], lineoffsets=max(v1[:int(duration/step)])+1.25,
+                 color="black", linelengths=2)
     ax.set_ylabel('Voltage in mV')
-    ax.set_xlabel('Time in s')
+    ax.set_xlabel('Time in ms')
     ax.set_title("Baseline Firing")
-    ax.set_xlim((0, duration))
+    ax.set_xlim((0, duration*1000))
 
 
     # ISI-hist
@@ -66,19 +119,19 @@ def p_unit_example():
     isi = np.array(base.get_interspike_intervals()) / eod_period  # ISI in ms
     maximum = max(isi)
     bins = np.arange(0, maximum * 1.01, 0.1)
-    ax.hist(isi, bins=bins)
-    ax.set_ylabel("Count")
+    ax.hist(isi, bins=bins, color=consts.COLOR_DATA, density=True)
+    ax.set_ylabel("Density")
     ax.set_xlabel("ISI in EOD periods")
     ax.set_title("ISI Histogram")
 
 
     # Serial correlation
-    ax = fig.add_subplot(gs[2, 0])
+    ax = fig.add_subplot(gs[1, 1])
 
     sc = base.get_serial_correlation(10)
     ax.plot(range(11), [0 for _ in range(11)], color="darkgrey", alpha=0.8)
-    ax.plot(range(11), [1] + list(sc))
-    ax.plot(range(11), [1] + list(sc), '+')
+    ax.plot(range(11), [1] + list(sc), color=consts.COLOR_DATA)
+    ax.plot(range(11), [1] + list(sc), '+', color="black")
 
     ax.set_xlabel("Lag")
     ax.set_ylabel("SC")
@@ -90,14 +143,14 @@ def p_unit_example():
 
 
     # FI-Curve trace
-    ax = fig.add_subplot(gs[1, 1])
+    ax = fig.add_subplot(gs[2, 0])
 
     f_trace_times, f_traces = fi.get_mean_time_and_freq_traces()
 
     part = 0.4 + 0.2 + 0.2  # stim duration + delay up front and a part of the "delay" at the back
     idx = int(part/step)
 
-    ax.plot(f_trace_times[-1][:idx], f_traces[-1][:idx])
+    ax.plot(f_trace_times[-1][:idx], f_traces[-1][:idx], color=consts.COLOR_DATA)
     # strength = 200
     # smoothed = np.convolve(f_traces[-1][:idx], np.ones(strength)/strength)
     # ax.plot(f_trace_times[-1][:idx], smoothed[int(strength/2):idx + int(strength/2)])
@@ -116,22 +169,28 @@ def p_unit_example():
     f_zeros = fi.get_f_zero_frequencies()
     f_infties = fi.get_f_inf_frequencies()
 
-    ax.plot(contrasts, f_zeros, 'x')
-    ax.plot(contrasts, f_infties, 'o')
+    ax.plot(contrasts, f_zeros, ',', marker=consts.f0_marker, color=consts.COLOR_DATA_f0)
+    ax.plot(contrasts, f_infties, ',', marker=consts.finf_marker, color=consts.COLOR_DATA_finf)
 
     x_values = np.arange(min(contrasts), max(contrasts) + 0.0001, (max(contrasts)-min(contrasts)) / 1000)
     f_zero_fit = [fu.full_boltzmann(x, fi.f_zero_fit[0], fi.f_zero_fit[1], fi.f_zero_fit[2], fi.f_zero_fit[3]) for x in x_values]
     f_inf_fit = [fu.clipped_line(x, fi.f_inf_fit[0], fi.f_inf_fit[1]) for x in x_values]
-    ax.plot(x_values, f_zero_fit)
-    ax.plot(x_values, f_inf_fit)
+    ax.plot(x_values, f_zero_fit, color=consts.COLOR_DATA_f0)
+    ax.plot(x_values, f_inf_fit, color=consts.COLOR_DATA_finf)
 
     # ax.set_xlim((0, 10))
     # ax.set_ylim((-1, 1))
     ax.set_xlabel("Contrast")
     ax.set_ylabel("Frequency in Hz")
+    ax.set_xticks([-0.2, -0.1, 0, 0.1, 0.2])
+    ax.set_xlim((-0.21, 0.2))
+    ylim = ax.get_ylim()
+    ax.set_ylim((0, ylim[1]))
     ax.set_title("f-I Curve")
 
     plt.tight_layout()
+    consts.set_figure_labels(xoffset=-2.5, yoffset=2.2)
+    fig.label_axes()
     plt.savefig("thesis/figures/p_unit_example.png", transparent=True)
     plt.close()
 
@@ -154,10 +213,13 @@ def fi_point_detection():
 
     f_inf = fi.get_f_inf_frequencies()[-1]
     f_inf_idx = fi.indices_f_inf[-1]
+    f_baseline = fi.get_f_baseline_frequencies()[-1]
+    f_base_idx = fi.indices_f_baseline[-1]
 
     axes[0].plot(f_trace_times[-1][:idx], f_traces[-1][:idx])
-    axes[0].plot([f_trace_times[-1][idx] for idx in f_zero_idx], (f_zero, ), "o")
-    axes[0].plot([f_trace_times[-1][idx] for idx in f_inf_idx], (f_inf, f_inf), color="orange", linewidth=4)
+    axes[0].plot([f_trace_times[-1][idx] for idx in f_zero_idx], (f_zero, ), ",", marker=consts.f0_marker, color=consts.COLOR_DATA_f0)
+    axes[0].plot([f_trace_times[-1][idx] for idx in f_inf_idx], (f_inf, f_inf), color=consts.COLOR_DATA_finf, linewidth=4)
+    axes[0].plot([f_trace_times[-1][idx] for idx in f_base_idx], (f_baseline, f_baseline), color="grey", linewidth=4)
 
     # mark stim start and end:
     stim_start = cell_data.get_stimulus_start()
@@ -176,15 +238,15 @@ def fi_point_detection():
     f_zeros = fi.get_f_zero_frequencies()
     f_infties = fi.get_f_inf_frequencies()
 
-    axes[1].plot(contrasts, f_zeros, 'x')
-    axes[1].plot(contrasts, f_infties, 'o')
+    axes[1].plot(contrasts, f_zeros, ",", marker=consts.f0_marker, color=consts.COLOR_DATA_f0)
+    axes[1].plot(contrasts, f_infties, ",", marker=consts.finf_marker, color=consts.COLOR_DATA_finf)
 
     x_values = np.arange(min(contrasts), max(contrasts) + 0.0001, (max(contrasts) - min(contrasts)) / 1000)
     f_zero_fit = [fu.full_boltzmann(x, fi.f_zero_fit[0], fi.f_zero_fit[1], fi.f_zero_fit[2], fi.f_zero_fit[3]) for x in
                   x_values]
     f_inf_fit = [fu.clipped_line(x, fi.f_inf_fit[0], fi.f_inf_fit[1]) for x in x_values]
-    axes[1].plot(x_values, f_zero_fit)
-    axes[1].plot(x_values, f_inf_fit)
+    axes[1].plot(x_values, f_zero_fit, color=consts.COLOR_DATA_f0)
+    axes[1].plot(x_values, f_inf_fit, color=consts.COLOR_DATA_finf)
 
     axes[1].set_xlabel("Contrast in %")
     # axes[1].set_ylabel("Frequency in Hz")
@@ -193,6 +255,8 @@ def fi_point_detection():
 
 
     plt.tight_layout()
+    consts.set_figure_labels(xoffset=-2.5)
+    fig.label_axes()
     plt.savefig("thesis/figures/f_point_detection.png", transparent=True)
     plt.close()
 

Figures_results.py

@@ -1,18 +1,56 @@
 import numpy as np
 import matplotlib.pyplot as plt
 from analysis import get_fit_info, get_behaviour_values, calculate_percent_errors
+from ModelFit import get_best_fit
+from Baseline import BaselineModel, BaselineCellData
+import Figure_constants as consts
 
 
 def main():
-    dir_path = "results/final_1/"
+    dir_path = "results/final_2/"
     fits_info = get_fit_info(dir_path)
 
-    cell_behaviour, model_behaviour = get_behaviour_values(fits_info)
+    # cell_behaviour, model_behaviour = get_behaviour_values(fits_info)
     # behaviour_overview_pairs(cell_behaviour, model_behaviour)
 
     # errors = calculate_percent_errors(fits_info)
     # create_boxplots(errors)
 
+    example_good_hist_fits(dir_path)
+
+
+def example_good_hist_fits(dir_path):
+    strong_bursty_cell = "2018-05-08-ac-invivo-1"
+    bursty_cell = "2014-03-19-ad-invivo-1"
+    non_bursty_cell = "2012-12-21-am-invivo-1"
+
+    fig, axes = plt.subplots(1, 3, sharex="all", figsize=consts.FIG_SIZE_MEDIUM_WIDE)
+
+    for i, cell in enumerate([non_bursty_cell, bursty_cell, strong_bursty_cell]):
+        fit_dir = dir_path + cell + "/"
+        fit = get_best_fit(fit_dir)
+
+        cell_data = fit.get_cell_data()
+        eodf = cell_data.get_eod_frequency()
+
+        model = fit.get_model()
+        baseline_model = BaselineModel(model, eodf, trials=5)
+
+        model_isi = np.array(baseline_model.get_interspike_intervals()) * eodf
+        cell_isi = BaselineCellData(cell_data).get_interspike_intervals() * eodf
+
+        bins = np.arange(0, 0.025, 0.0001) * eodf
+        axes[i].hist(cell_isi, bins=bins, density=True, alpha=0.5, color=consts.COLOR_DATA)
+        axes[i].hist(model_isi, bins=bins, density=True, alpha=0.5, color=consts.COLOR_MODEL)
+        axes[i].set_xlabel("ISI in EOD periods")
+    axes[0].set_ylabel("Density")
+    plt.tight_layout()
+    consts.set_figure_labels(xoffset=-2.5)
+    fig.label_axes()
+
+    plt.savefig(consts.SAVE_FOLDER + "example_good_isi_hist_fits.png", transparent=True)
+    plt.close()
+
 
 def create_boxplots(errors):
 
@@ -58,11 +96,21 @@ def overview_pair(cell, model, titles):
                           wspace=0.2, hspace=0.05)
 
     for i in range(len(cell)):
+        if titles[i] == "f_zero_slope":
+            length_before = len(cell[i])
+            idx = np.array(cell[i]) < 30000
+            cell[i] = np.array(cell[i])[idx]
+            model[i] = np.array(model[i])[idx]
+
+            idx = np.array(model[i]) < 30000
+            cell[i] = np.array(cell[i])[idx]
+            model[i] = np.array(model[i])[idx]
+            print("removed {} values from f_zero_slope plot.".format(length_before - len(cell[i])))
         ax = fig.add_subplot(gs[1, i])
         ax_histx = fig.add_subplot(gs[0, i], sharex=ax)
         scatter_hist(cell[i], model[i], ax, ax_histx, titles[i])
 
-    plt.tight_layout()
+    # plt.tight_layout()
     plt.show()
 
 
@@ -88,7 +136,7 @@ def grouped_error_overview_behaviour_dist(cell_behaviours, model_behaviours):
         # use the previously defined function
         scatter_hist(cell_behaviours[behaviour], model_behaviours[behaviour], ax, ax_histx, behaviour)
 
-    # plt.tight_layout()
+    plt.tight_layout()
     plt.show()
 
 
@@ -96,9 +144,8 @@ def scatter_hist(cell_values, model_values, ax, ax_histx, behaviour, ax_histy=No
     # copied from matplotlib
 
     # no labels
-    ax_histx.tick_params(axis="cell_values", labelbottom=False)
+    ax_histx.tick_params(axis="cell", labelbottom=False)
     # ax_histy.tick_params(axis="model_values", labelleft=False)
-
     # the scatter plot:
     ax.scatter(cell_values, model_values)
 
@@ -109,17 +156,13 @@ def scatter_hist(cell_values, model_values, ax, ax_histx, behaviour, ax_histy=No
     ax.set_xlabel("cell")
     ax.set_ylabel("model")
 
-    # now determine nice limits by hand:
-    binwidth = 0.25
-    xymax = max(np.max(np.abs(cell_values)), np.max(np.abs(model_values)))
-    lim = (int(xymax/binwidth) + 1) * binwidth
-
-    bins = np.arange(-lim, lim + binwidth, binwidth)
     ax_histx.hist(cell_values, color="blue", alpha=0.5)
     ax_histx.hist(model_values, color="orange", alpha=0.5)
-    # ax_histx.set_xticklabels([])
-    # ax_histx.set_xticks(ax.get_xticks())
-    # ax_histx.set_xlim(ax.get_xlim())
+    ax_labels = ax.get_xticklabels()
+    ax_histx.set_xticklabels([])
+    ax.set_xticklabels(ax_labels)
+    ax_histx.set_xticks(ax.get_xticks())
+    ax_histx.set_xlim(ax.get_xlim())
     ax_histx.set_title(behaviour)
 
     # ax_histy.hist(y, bins=bins, orientation='horizontal')