savety

ethogram.py

@@ -2,6 +2,7 @@ import os
 import sys
 import matplotlib.pyplot as plt
 import matplotlib.gridspec as gridspec
+from mpl_toolkits.axes_grid1 import make_axes_locatable
 import numpy as np
 import pandas as pd
 import scipy.stats as scp
@@ -13,21 +14,36 @@ glob_colors = ['#BA2D22', '#53379B', '#F47F17', '#3673A4', '#AAB71B', '#DC143C',
 
 
 def plot_transition_matrix(matrix, labels):
-    fig, ax = plt.subplots()
+    fig = plt.figure(figsize=(20/2.54, 20/2.54))
+    #gs = gridspec.GridSpec(1, 2, left=0.1, bottom=0.1, right=0.9, top=0.95, wspace=0.1, width_ratios=[8, 1])
+    gs = gridspec.GridSpec(1, 1, left=0.1, bottom=0.1, right=0.925, top=0.95)
+    ax = fig.add_subplot(gs[0, 0])
+
+    divider = make_axes_locatable(ax)
+    cax = divider.append_axes('right', size='5%', pad=0.05)
+
+    # cax = fig.add_subplot(gs[0, 1])
     im = ax.imshow(matrix)
     ax.set_xticks(list(range(len(matrix))))
     ax.set_yticks(list(range(len(matrix))))
-    ax.set_xticklabels(labels)
+    ax.set_xticklabels(labels, rotation=45)
     ax.set_yticklabels(labels)
-    fig.colorbar(im)
+
-    plt.show()
+    fig.colorbar(im, cax=cax, orientation='vertical')
+
+    ax.tick_params(labelsize=10)
+    cax.tick_params(labelsize=10)
+    plt.savefig(os.path.join(os.path.split(__file__)[0], 'figures', 'markov', 'event_counts' + '.png'), dpi=300)
+    plt.close()
+
+
+def plot_transition_diagram(matrix, labels, node_size, ax, threshold=5,
+                            color_by_origin=False, color_by_target=False, title=''):
+
 
 
-def plot_transition_diagram(matrix, labels, node_size, threshold=5, save_str = 'rdm'):
     matrix[matrix <= threshold] = 0
     matrix = np.around(matrix, decimals=1)
-    fig, ax = plt.subplots(figsize=(21 / 2.54, 19 / 2.54))
-    fig.subplots_adjust(left=0.05, bottom=0.05, right=0.95, top=0.95)
     Graph = nx.from_numpy_array(matrix, create_using=nx.DiGraph)
 
     node_labels = dict(zip(Graph, labels))
@@ -42,24 +58,24 @@ def plot_transition_diagram(matrix, labels, node_size, threshold=5, save_str = '
 
     # ToDo: nodes
     nx.draw_networkx_nodes(Graph, pos=positions, node_size=node_size, ax=ax, alpha=0.5, node_color=np.array(glob_colors)[:len(node_size)])
-    nx.draw_networkx_labels(Graph, pos=positions2, labels=node_labels)
+    nx.draw_networkx_labels(Graph, pos=positions2, labels=node_labels, ax=ax)
     # google networkx drawing to get better graphs with networkx
     # nx.draw(Graph, pos=positions, node_size=node_size, label=labels, with_labels=True, ax=ax)
     # # ToDo: edges
     edge_width = np.array([x / 5 for x in [*edge_labels.values()]])
+    if color_by_origin:
         edge_colors = np.array(glob_colors)[np.array([*edge_labels.keys()], dtype=int)[:, 0]]
+    elif color_by_target:
+        edge_colors = np.array(glob_colors)[np.array([*edge_labels.keys()], dtype=int)[:, 1]]
+    else:
+        edge_colors = 'k'
 
 
     edge_width[edge_width >= 6] = 6
-    # nx.draw_networkx_edges(Graph, pos=positions, node_size=node_size, width=edge_width,
-    #                        arrows=True, arrowsize=20,
-    #                        min_target_margin=25, min_source_margin=10, connectionstyle="arc3, rad=0.0",
-    #                        ax=ax)
-    # nx.draw_networkx_edge_labels(Graph, positions, label_pos=0.5, edge_labels=edge_labels, ax=ax, rotate=False)
 
     nx.draw_networkx_edges(Graph, pos=positions, node_size=node_size, width=edge_width,
                            arrows=True, arrowsize=20,
-                           min_target_margin=25, min_source_margin=10, connectionstyle="arc3, rad=0.025",
+                           min_target_margin=25, min_source_margin=25, connectionstyle="arc3, rad=0.025",
                            ax=ax, edge_color=edge_colors)
     nx.draw_networkx_edge_labels(Graph, positions, label_pos=0.2, edge_labels=edge_labels, ax=ax, rotate=True)
 
@@ -70,11 +86,11 @@ def plot_transition_diagram(matrix, labels, node_size, threshold=5, save_str = '
 
     ax.set_xlim(-1.3, 1.3)
     ax.set_ylim(-1.3, 1.3)
-    # plt.title(title)
+    ax.set_title(title, fontsize=12)
-    plt.savefig(os.path.join(os.path.split(__file__)[0], 'figures', save_str + '.png'), dpi=300)
-    plt.close()
-
 def main(base_path):
+    if not os.path.exists(os.path.join(os.path.split(__file__)[0], 'figures', 'markov')):
+        os.makedirs(os.path.join(os.path.split(__file__)[0], 'figures', 'markov'))
+
     trial_summary = pd.read_csv(os.path.join(base_path, 'trial_summary.csv'), index_col=0)
     chirp_notes = pd.read_csv(os.path.join(base_path, 'chirp_notes.csv'), index_col=0)
     # trial_summary = trial_summary[chirp_notes['good'] == 1]
@@ -130,7 +146,9 @@ def main(base_path):
         event_times = np.array(event_times)[time_sorter]
         event_labels = np.array(event_labels)[time_sorter]
 
+        ###  create marcov_matrix 1: which beh 2 is triggered by beh. 1 ?
         marcov_matrix = np.zeros((len(loop_labels)+1, len(loop_labels)+1))
+        ###  create marcov_matrix 2: beh 2 is triggered by which beh. 1 ?
 
         for enu_ori, label_ori in enumerate(loop_labels):
             for enu_tar, label_tar in enumerate(loop_labels):
@@ -164,14 +182,43 @@ def main(base_path):
     collective_marcov_matrix = np.sum(all_marcov_matrix, axis=0)
     collective_event_counts = np.sum(all_event_counts, axis=0)
 
+
     plot_transition_matrix(collective_marcov_matrix, loop_labels)
+
+    fig, ax = plt.subplots(figsize=(21 / 2.54, 19 / 2.54))
+    fig.subplots_adjust(left=0.05, bottom=0.05, right=0.95, top=0.95)
+
     plot_transition_diagram(collective_marcov_matrix / collective_event_counts.reshape(len(collective_event_counts), 1) * 100,
-                            loop_labels, collective_event_counts, threshold=5, save_str='markov_all')
+                            loop_labels, collective_event_counts, ax, threshold=5, color_by_origin=True, title='origin triggers target [%]')
+    plt.savefig(os.path.join(os.path.split(__file__)[0], 'figures', 'markov', 'markov_destination' + '.png'), dpi=300)
+    plt.close()
+
+    fig, ax = plt.subplots(figsize=(21 / 2.54, 19 / 2.54))
+    fig.subplots_adjust(left=0.05, bottom=0.05, right=0.95, top=0.95)
+    plot_transition_diagram(collective_marcov_matrix / collective_event_counts * 100,
+                            loop_labels, collective_event_counts, ax, threshold=5, color_by_target=True, title='target triggered by origin [%]')
+    plt.savefig(os.path.join(os.path.split(__file__)[0], 'figures', 'markov', 'markov_origin' + '.png'), dpi=300)
+    plt.close()
+
+    for i, (marcov_matrix, event_counts) in enumerate(zip(all_marcov_matrix, all_event_counts)):
+        fig, ax = plt.subplots(figsize=(21 / 2.54, 19 / 2.54))
+        fig.subplots_adjust(left=0.05, bottom=0.05, right=0.95, top=0.95)
+
+        plot_transition_diagram(
+            marcov_matrix / event_counts.reshape(len(event_counts), 1) * 100,
+            loop_labels, event_counts, ax, threshold=5, color_by_origin=True,
+            title='origin triggers target [%]')
+        plt.savefig(os.path.join(os.path.split(__file__)[0], 'figures', 'markov', f'markov_{i}_destination' + '.png'), dpi=300)
+        plt.close()
+
+        fig, ax = plt.subplots(figsize=(21 / 2.54, 19 / 2.54))
+        fig.subplots_adjust(left=0.05, bottom=0.05, right=0.95, top=0.95)
+        plot_transition_diagram(marcov_matrix / event_counts * 100,
+                                loop_labels, event_counts, ax, threshold=5, color_by_target=True,
+                                title='target triggered by origin [%]')
+        plt.savefig(os.path.join(os.path.split(__file__)[0], 'figures', 'markov', f'markov_{i}_origin' + '.png'), dpi=300)
+        plt.close()
 
-    # for i in range(len(all_marcov_matrix)):
-    #     plot_transition_diagram(
-    #         all_marcov_matrix[i] / all_event_counts[i].reshape(len(all_event_counts[i]), 1) * 100,
-    #         loop_labels, all_event_counts[i], threshold=5)
     embed()
     quit()
     pass

event_time_analysis.py

@@ -13,9 +13,23 @@ from event_time_correlations import load_and_converete_boris_events, kde, gauss
 female_color, male_color = '#e74c3c', '#3498db'
 
 def iei_analysis(event_times, win_sex, lose_sex, kernal_w, title=''):
+    # ToDo: finish this !!!
     iei = []
+    weighted_mean_iei = []
+    median_iei = []
     for i in range(len(event_times)):
-        iei.append(np.diff(event_times[i]))
+        trial_iei = np.diff(event_times[i][event_times[i] <= 3600*3])
+        iei.append(trial_iei)
+
+        if len(trial_iei) == 0:
+            weighted_mean_iei.append(np.nan)
+            median_iei.append(np.nan)
+        else:
+            weighted_mean_iei.append(np.sum((trial_iei) * trial_iei) / np.sum(trial_iei))
+            median_iei.append(np.median(trial_iei))
+
+    weighted_mean_iei = np.array(weighted_mean_iei)
+    median_iei = np.array(median_iei)
 
     fig = plt.figure(figsize=(20 / 2.54, 12 / 2.54))
     gs = gridspec.GridSpec(2, 2, left=0.1, bottom=0.1, right=0.95, top=0.9)
@@ -41,13 +55,16 @@ def iei_analysis(event_times, win_sex, lose_sex, kernal_w, title=''):
                 color, linestyle = female_color, '-'
                 sp = 3
 
-
         conv_y = np.arange(0, np.percentile(np.hstack(iei), 80), .5)
         kde_array = kde(iei[i], conv_y, kernal_w=kernal_w, kernal_h=1)
 
         # kde_array /= np.sum(kde_array)
         ax[sp].plot(conv_y, kde_array, zorder=2, color=color, linestyle=linestyle, lw=2)
 
+    # ax_m = ax[0].twinx()
+    # ax_m.boxplot([weighted_mean_iei[(win_sex == 'm') & (win_sex == 'm') & ~np.isnan(weighted_mean_iei)],
+    #               median_iei[(win_sex == 'm') & (win_sex == 'm') & ~np.isnan(median_iei)]], sym='', vert=False)
+
     ax[0].set_xlim(conv_y[0], conv_y[-1])
     ax[0].set_ylabel('KDE', fontsize=12)
     ax[2].set_ylabel('KDE', fontsize=12)
@@ -170,7 +187,6 @@ def relative_rate_progression(all_event_t, title=''):
 
 
 def main(base_path):
-    # ToDo: for chirp and rise analysis different datasets!!!
     if not os.path.exists(os.path.join(os.path.split(__file__)[0], 'figures', 'event_meta')):
         os.makedirs(os.path.join(os.path.split(__file__)[0], 'figures', 'event_meta'))
 
@@ -178,7 +194,6 @@ def main(base_path):
         os.makedirs(os.path.join(os.path.split(__file__)[0], 'figures', 'event_time_corr'))
 
 
-
     trial_summary = pd.read_csv(os.path.join(base_path, 'trial_summary.csv'), index_col=0)
     chirp_notes = pd.read_csv(os.path.join(base_path, 'chirp_notes.csv'), index_col=0)
     trial_mask = chirp_notes['good'] == 1