reworked code to make nice plots for my presentation

code/ethogram.py

@@ -15,6 +15,7 @@ from thunderfish.powerspectrum import decibel
 from IPython import embed
 from event_time_correlations import load_and_converete_boris_events
 glob_colors = ['#BA2D22', '#53379B', '#F47F17', '#3673A4', '#AAB71B', '#DC143C', '#1E90FF', 'k']
+glob_colors_new = ['tab:green', 'tab:olive', 'tab:red', 'tab:orange', 'tab:blue', 'k']
 
 
 def plot_transition_matrix(matrix, labels):
@@ -42,7 +43,7 @@ def plot_transition_matrix(matrix, labels):
 
 
 def plot_transition_diagram(matrix, labels, node_size, ax, threshold=5,
-                            color_by_origin=False, color_by_target=False, title=''):
+                            color_by_origin=False, color_by_target=False, title='', color_palet=glob_colors):
 
 
 
@@ -61,16 +62,19 @@ def plot_transition_diagram(matrix, labels, node_size, ax, threshold=5,
         positions2[p][1] *= 1.2
 
     # 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_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_nodes(Graph, pos=positions, node_size=node_size, ax=ax, alpha=0.5, node_color=np.array(color_palet)[:len(node_size)])
     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]]
+        # edge_colors = np.array(glob_colors)[np.array([*edge_labels.keys()], dtype=int)[:, 0]]
+        edge_colors = np.array(color_palet)[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]]
+        # edge_colors = np.array(glob_colors)[np.array([*edge_labels.keys()], dtype=int)[:, 1]]
+        edge_colors = np.array(color_palet)[np.array([*edge_labels.keys()], dtype=int)[:, 1]]
     else:
         edge_colors = 'k'
 
@@ -79,7 +83,7 @@ def plot_transition_diagram(matrix, labels, node_size, ax, threshold=5,
 
     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=25, connectionstyle="arc3, rad=0.025",
+                           min_target_margin=25, min_source_margin=25, connectionstyle="arc3, rad=0.05", # 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)
 
@@ -92,6 +96,157 @@ def plot_transition_diagram(matrix, labels, node_size, ax, threshold=5,
     ax.set_ylim(-1.3, 1.3)
     ax.set_title(title, fontsize=12)
 
+def plot_mixed_transition_diagram(og_matrix_origin, og_matrix_target, labels, node_size,
+                                  threshold=10, color_by_origin=False, color_by_target=False,
+                                  title='', color_palet=glob_colors):
+
+    old_mask = np.zeros_like(og_matrix_origin, dtype=bool)
+    for scenario in np.arange(1, 9):
+        fig, ax = plt.subplots(figsize=(21 / 2.54, 19 / 2.54))
+        fig.subplots_adjust(left=0.0, bottom=0.0, right=1, top=1)
+
+        matrix_origin = np.copy(og_matrix_origin)
+        matrix_target = np.copy(og_matrix_target)
+
+        matrix_origin[matrix_origin <= threshold] = 0
+        matrix_target[matrix_target <= threshold] = 0
+        helper_maks = matrix_origin >= matrix_target
+        matrix_origin[~helper_maks] = 0
+        matrix_target[helper_maks] = 0
+        matrix_origin_prev = np.copy(matrix_origin)
+        matrix_target_prev = np.copy(matrix_target)
+
+        mask = np.zeros_like(og_matrix_origin, dtype=bool)
+        if scenario == 1:
+            mask[-1, :] = 1
+        elif scenario == 2:
+            mask[1, :] = 1
+        elif scenario == 3:
+            mask[2, :] = 1
+        elif scenario == 4:
+            mask[0, :] = 1
+            mask[3, :] = 1
+            mask[0, 0] = 0
+        elif scenario == 5:
+            mask[4, :] = 1
+        elif scenario == 6:
+            mask = np.ones_like(matrix_origin, dtype=bool)
+            mask[0, 0] = 0
+        elif scenario == 7:
+            mask = np.zeros_like(matrix_origin, dtype=bool)
+            mask[5, 1] = 1
+            mask[1, 2] = 1
+            mask[2, 0] = 1
+            mask[0, 3] = 1
+            mask[3, 0] = 1
+        elif scenario == 8:
+            old_mask = np.ones_like(matrix_origin, dtype=bool)
+            old_mask[0, 0] = 0
+            mask = np.zeros_like(matrix_origin, dtype=bool)
+            mask[5, 1] = 1
+            mask[1, 2] = 1
+            mask[2, 0] = 1
+            mask[0, 3] = 1
+            mask[3, 0] = 1
+            mask[2, 3] = 1
+            mask[5, 2] = 1
+            old_mask[mask] = 0
+        matrix_origin[~mask] = 0
+        matrix_target[~mask] = 0
+        matrix_origin_prev[~old_mask] = 0
+        matrix_target_prev[~old_mask] = 0
+
+        matrix_origin = np.around(matrix_origin, decimals=1)
+        matrix_target = np.around(matrix_target, decimals=1)
+        matrix_origin_prev = np.around(matrix_origin_prev, decimals=1)
+        matrix_target_prev = np.around(matrix_target_prev, decimals=1)
+
+        Graph = nx.from_numpy_array(matrix_origin, create_using=nx.DiGraph)
+        Graph2 = nx.from_numpy_array(matrix_target, create_using=nx.DiGraph)
+        Graph_prev = nx.from_numpy_array(matrix_origin_prev, create_using=nx.DiGraph)
+        Graph2_prev = nx.from_numpy_array(matrix_target_prev, create_using=nx.DiGraph)
+
+        node_labels = dict(zip(Graph, labels))
+
+
+        edge_labels = nx.get_edge_attributes(Graph, 'weight')
+        edge_labels2 = nx.get_edge_attributes(Graph2, 'weight')
+        edge_labels_prev = nx.get_edge_attributes(Graph_prev, 'weight')
+        edge_labels2_prev = nx.get_edge_attributes(Graph2_prev, 'weight')
+
+        positions = nx.circular_layout(Graph)
+        positions2 = nx.circular_layout(Graph)
+        for p in positions:
+            positions2[p][0] *= 1.2
+            positions2[p][1] *= 1.2
+
+        nx.draw_networkx_nodes(Graph, pos=positions, node_size=node_size, ax=ax, alpha=0.5, node_color=np.array(color_palet)[:len(node_size)])
+        nx.draw_networkx_labels(Graph, pos=positions2, labels=node_labels, ax=ax)
+
+        edge_width = np.array([x / 5 for x in [*edge_labels.values()]])
+        edge_width2 = np.array([x / 5 for x in [*edge_labels2.values()]])
+        edge_width_prev = np.array([x / 5 for x in [*edge_labels_prev.values()]])
+        edge_width2_prev = np.array([x / 5 for x in [*edge_labels2_prev.values()]])
+
+        edge_width[edge_width >= 6] = 6
+        edge_width2[edge_width2 >= 6] = 6
+        edge_width_prev[edge_width_prev >= 6] = 6
+        edge_width2_prev[edge_width2_prev >= 6] = 6
+
+        if len(edge_labels2) >= 1:
+            if color_by_origin:
+                edge_colors = np.array(color_palet)[np.array([*edge_labels2.keys()], dtype=int)[:, 0]]
+            elif color_by_target:
+                edge_colors = np.array(color_palet)[np.array([*edge_labels2.keys()], dtype=int)[:, 1]]
+            else:
+                edge_colors = 'k'
+
+            nx.draw_networkx_edges(Graph2, pos=positions, node_size=node_size, width=edge_width2,
+                                   arrows=True, arrowsize=20, arrowstyle='->',
+                                   min_target_margin=25, min_source_margin=25, connectionstyle="arc3, rad=0.05",
+                                   # rad=0.025"
+                                   ax=ax, edge_color=edge_colors)
+
+        if len(edge_labels2_prev) >= 1:
+            if color_by_origin:
+                edge_colors_prev = np.array(color_palet)[np.array([*edge_labels2_prev.keys()], dtype=int)[:, 0]]
+            elif color_by_target:
+                edge_colors_prev = np.array(color_palet)[np.array([*edge_labels2_prev.keys()], dtype=int)[:, 1]]
+            else:
+                edge_colors_prev = 'k'
+
+
+            nx.draw_networkx_edges(Graph2_prev, pos=positions, node_size=node_size, width=edge_width2_prev,
+                                   arrows=True, arrowsize=20, arrowstyle='->',
+                                   min_target_margin=25, min_source_margin=25, connectionstyle="arc3, rad=0.05", # rad=0.025"
+                                   ax=ax, edge_color=edge_colors_prev, alpha=.25)
+
+        nx.draw_networkx_edges(Graph, pos=positions, node_size=node_size, width=edge_width,
+                               arrows=True, arrowsize=20, arrowstyle='->',
+                               min_target_margin=25, min_source_margin=25, connectionstyle="arc3, rad=0.05",
+                               ax=ax, edge_color='k')
+
+        nx.draw_networkx_edges(Graph_prev, pos=positions, node_size=node_size, width=edge_width_prev,
+                               arrows=True, arrowsize=20, arrowstyle='->',
+                               min_target_margin=25, min_source_margin=25, connectionstyle="arc3, rad=0.05",
+                               ax=ax, edge_color='k', alpha=.25)
+
+        ax.spines["top"].set_visible(False)
+        ax.spines["bottom"].set_visible(False)
+        ax.spines["right"].set_visible(False)
+        ax.spines["left"].set_visible(False)
+
+        ax.set_xlim(-1.4, 1.4)
+        ax.set_ylim(-1.3, 1.3)
+        ax.set_title(title, fontsize=12)
+
+        old_mask += mask
+
+        plt.savefig(os.path.join(os.path.split(__file__)[0], 'figures', 'markov', f'marcov_buildup_{scenario}' + '.png'), dpi=300)
+        plt.close()
+    # plt.show()
+
+
 def create_marcov_matrix(individual_event_times, individual_event_labels):
     event_times = []
     event_labels = []
@@ -114,7 +269,7 @@ def create_marcov_matrix(individual_event_times, individual_event_labels):
         marcov_matrix[-1, enu_tar] = n
     marcov_matrix[-1, 5] = 0
 
-    individual_event_labels.append('void')
+    individual_event_labels.append('start')
 
     ### get those cases where ag_on does not point to event and no event points to corresponding ag_off ... add thise cases in marcov matrix
     chase_on_idx = np.where(event_labels == individual_event_labels[4])[0]
@@ -400,24 +555,50 @@ def main(base_path):
     collective_marcov_matrix = np.sum(all_marcov_matrix, axis=0)
     collective_event_counts = np.sum(all_event_counts, axis=0)
 
+    for del_idx in [3, 2]:
+        collective_marcov_matrix = np.delete(collective_marcov_matrix, del_idx, 0)
+        collective_marcov_matrix = np.delete(collective_marcov_matrix, del_idx, 1)
+        collective_event_counts = np.delete(collective_event_counts, del_idx, 0)
+        individual_event_labels.pop(del_idx)
+
+
+    # collective_event_counts = np.ones_like(collective_event_counts) * collective_event_counts.mean()
+    ball_size = np.ones_like(collective_event_counts) * collective_event_counts.mean()
     plot_transition_matrix(collective_marcov_matrix, individual_event_labels)
 
+    # marcov by origin
     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,
-        individual_event_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()
+        individual_event_labels, ball_size, ax, threshold=5, color_by_origin=True, title='origin triggers target [%]', color_palet=glob_colors_new)
+    # plt.savefig(os.path.join(os.path.split(__file__)[0], 'figures', 'markov', 'markov_destination' + '.png'), dpi=300)
+    # plt.close()
 
+    # marcov by target
     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,
-                            individual_event_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()
+                            individual_event_labels, ball_size, ax, threshold=5, color_by_target=True,
+                            title='target triggered by origin [%]', color_palet=glob_colors_new)
+    # plt.savefig(os.path.join(os.path.split(__file__)[0], 'figures', 'markov', 'markov_origin' + '.png'), dpi=300)
+    # plt.close()
+
+    mm_origin = collective_marcov_matrix / collective_event_counts.reshape(len(collective_event_counts), 1) * 100
+    mm_target = collective_marcov_matrix / collective_event_counts * 100
+    max_collective_marcov = np.array([mm_origin, mm_target]).max(0)
+
+    fig, ax = plt.subplots(figsize=(21 / 2.54, 19 / 2.54))
+    fig.subplots_adjust(left=0.0, bottom=0.0, right=1, top=1)
+    plot_transition_diagram(max_collective_marcov,
+                            individual_event_labels, ball_size, ax, threshold=10, color_by_origin=True,
+                            title='best of both worlds', color_palet=glob_colors_new)
+
+    plt.show()
+
+    plot_mixed_transition_diagram(mm_origin, mm_target, individual_event_labels, ball_size,
+                                  color_by_target=True, color_palet=glob_colors_new)
 
     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))

code/event_time_analysis.py

@@ -420,12 +420,34 @@ def event_category_signal(all_event_t, all_contact_t, all_ag_on_t, all_ag_off_t,
     time_context_values.append(len(all_pre_chase_time) * 3 * 60 * 60 - np.sum(time_context_values))
     time_context_values /= np.sum(time_context_values)
 
-    # fig, ax = plt.subplots(figsize=(12/2.54,12/2.54))
+    event_context_values2 = []
+    time_context_values2 = []
+    for event_value, time_value in zip(event_context_values[:-1], time_context_values[:-1]):
+        event_context_values2.append(event_value)
+        time_context_values2.append(time_value)
+        if event_value >= time_value:
+            time_context_values2.append(event_value - time_value)
+            event_context_values2.append(0)
+        else:
+            event_context_values2.append(time_value - event_value)
+            time_context_values2.append(0)
+
+    event_context_values2.append(1-np.sum(event_context_values2))
+    time_context_values2.append(1-np.sum(time_context_values2))
+    # fig, ax_pie = plt.subplots(figsize=(12/2.54,12/2.54))
     size = 0.3
-    outer_colors = ['tab:red', 'tab:orange', 'yellow', 'tab:green', 'k', 'tab:brown', 'tab:grey']
-    ax_pie.pie(event_context_values, radius=1, colors=outer_colors,
+    # embed()
+    # quit()
+
+    outer_colors = ['tab:red', 'tab:grey',
+                    'tab:orange', 'tab:grey',
+                    'yellow', 'tab:grey',
+                    'tab:green', 'tab:grey',
+                    'k', 'tab:grey', 'tab:brown',
+                    'tab:grey', 'tab:grey']
+    ax_pie.pie(event_context_values2, radius=1, colors=outer_colors,
                wedgeprops=dict(width=size, edgecolor='w'), startangle=90, center=(0, 1))
-    ax_pie.pie(time_context_values, radius=1 - size, colors=outer_colors,
+    ax_pie.pie(time_context_values2, radius=1 - size, colors=outer_colors,
                wedgeprops=dict(width=size, edgecolor='w', alpha=.6), startangle=90, center=(0, 1))
 
     ax_pie.set_title(r'event context')
@@ -451,7 +473,7 @@ def event_category_signal(all_event_t, all_contact_t, all_ag_on_t, all_ag_off_t,
                        Patch(facecolor='tab:brown', alpha=0.6, edgecolor='w',
                              label='%.1f' % (time_context_values[5] * 100) + '%')]
 
-    ax_pie.legend(handles=legend_elements, loc='lower right', ncol=2, bbox_to_anchor=(1.15, -0.25), frameon=False,
+    ax_pie.legend(handles=legend_elements, loc='lower right', ncol=2, bbox_to_anchor=(1.15, -0.3), frameon=False,
                   fontsize=9)
 
     plt.savefig(os.path.join(os.path.split(__file__)[0], 'figures', 'event_time_corr', f'{event_name}_categories.png'),

code/event_time_correlations.py

@@ -1,6 +1,7 @@
 import os
 import sys
 import argparse
+import pathlib
 import time
 import itertools
 
@@ -91,7 +92,7 @@ def kde(event_dt, conv_t, kernal_w = 1, kernal_h = 0.2):
     return conv_array
 
 
-def permutation_kde(event_dt, conv_t, repetitions = 2000, max_mem_use_GB = 4, kernal_w = 1, kernal_h = 0.2):
+def permutation_kde(event_dt, conv_t, repetitions = 2000, max_mem_use_GB = 2, kernal_w = 1, kernal_h = 0.2):
     def chunk_permutation(select_event_dt, conv_tt, n_chuck, max_jitter, kernal_w, kernal_h):
         # array.shape = (120, 100, 15486) = (len(conv_t), repetitions, len(event_dt))
         # event_dt_perm = cp.tile(event_dt, (len(conv_t), repetitions, 1))
@@ -385,37 +386,7 @@ def main(base_path):
     conv_t = cp.arange(-max_dt, max_dt+conv_t_dt, conv_t_dt)
     conv_t_numpy = cp.asnumpy(conv_t)
 
-    # embed()
-    # quit()
-
-    # for centered_times, event_counts, title in \
-    #         [[lose_chrips_centered_on_ag_off_t, lose_chirp_count, r'chirp$_{lose}$ on chase$_{off}$'],
-    #          [lose_chrips_centered_on_ag_on_t, lose_chirp_count, r'chirp$_{lose}$ on chase$_{on}$'],
-    #          [lose_chrips_centered_on_contact_t, lose_chirp_count, r'chirp$_{lose}$ on contact'],
-    #          [lose_chrips_centered_on_win_rises, lose_chirp_count, r'chirp$_{lose}$ on rise$_{win}$'],
-    #          [lose_chrips_centered_on_win_chirp, lose_chirp_count, r'chirp$_{lose}$ on chirp$_{win}$'],
-    #          [lose_chirps_centered_on_lose_rises, lose_chirp_count, r'chirp$_{lose}$ on rises$_{lose}$'],
-    #
-    #          [win_chrips_centered_on_ag_off_t, win_chirp_count, r'chirp$_{win}$ on chase$_{off}$'],
-    #          [win_chrips_centered_on_ag_on_t, win_chirp_count, r'chirp$_{win}$ on chase$_{on}$'],
-    #          [win_chrips_centered_on_contact_t, win_chirp_count, r'chirp$_{win}$ on contact'],
-    #          [win_chrips_centered_on_lose_rises, win_chirp_count, r'chirp$_{win}$ on rise$_{lose}$'],
-    #          [win_chrips_centered_on_lose_chirp, win_chirp_count, r'chirp$_{win}$ on chirp$_{lose}$'],
-    #          [win_chirps_centered_on_win_rises, win_chirp_count, r'chirp$_{win}$ on rises$_{win}$'],
-    #
-    #          [lose_rises_centered_on_ag_off_t, lose_rises_count, r'rise$_{lose}$ on chase$_{off}$'],
-    #          [lose_rises_centered_on_ag_on_t, lose_rises_count, r'rise$_{lose}$ on chase$_{on}$'],
-    #          [lose_rises_centered_on_contact_t, lose_rises_count, r'rise$_{lose}$ on contact'],
-    #          [lose_rises_centered_on_win_chirps, lose_rises_count, r'rise$_{lose}$ on chirp$_{win}$'],
-    #
-    #          [win_rises_centered_on_ag_off_t, win_rises_count, r'rise$_{win}$ on chase$_{off}$'],
-    #          [win_rises_centered_on_ag_on_t, win_rises_count, r'rise$_{win}$ on chase$_{on}$'],
-    #          [win_rises_centered_on_contact_t, win_rises_count, r'rise$_{win}$ on contact'],
-    #          [win_rises_centered_on_lose_chirps, win_rises_count, r'rise$_{win}$ on chirp$_{lose}$'],
-    #
-    #          [ag_off_centered_on_ag_on, chase_count,  r'chase$_{off}$ on chase$_{on}$']]:
-
-    for centered_times, event_counts, title in \
+    for category_enu, (centered_times, event_counts, title) in enumerate(
             [[lose_chrips_centered_on_ag_off_t, chase_count, r'chirp$_{lose}$ on chase$_{off}$'],
              [lose_chrips_centered_on_ag_on_t, chase_count, r'chirp$_{lose}$ on chase$_{on}$'],
              [lose_chrips_centered_on_contact_t, contact_count, r'chirp$_{lose}$ on contact'],
@@ -440,77 +411,79 @@ def main(base_path):
              [win_rises_centered_on_contact_t, contact_count, r'rise$_{win}$ on contact'],
              [win_rises_centered_on_lose_chirps, lose_chirp_count, r'rise$_{win}$ on chirp$_{lose}$'],
 
-             [ag_off_centered_on_ag_on, chase_count, r'chase$_{off}$ on chase$_{on}$']]:
+             [ag_off_centered_on_ag_on, chase_count, r'chase$_{off}$ on chase$_{on}$']]):
+
         save_str = title.replace('$', '').replace('{', '').replace('}', '').replace(' ', '_')
 
         ###########################################################################################################
         ### by pairing ###
-        centered_times_pairing = []
-        for sex_w, sex_l in itertools.product(['m', 'f'], repeat=2):
-            centered_times_pairing.append([])
-            for i in range(len(centered_times)):
-                if sex_w == sex_win[i] and sex_l == sex_lose[i]:
-                    centered_times_pairing[-1].append(centered_times[i])
-
-        event_counts_pairings = [np.nansum(np.array(event_counts)[(sex_win == 'm') & (sex_lose == 'm')]),
-                                 np.nansum(np.array(event_counts)[(sex_win == 'm') & (sex_lose == 'f')]),
-                                 np.nansum(np.array(event_counts)[(sex_win == 'f') & (sex_lose == 'm')]),
-                                 np.nansum(np.array(event_counts)[(sex_win == 'f') & (sex_lose == 'f')])]
-        color = [male_color, female_color, male_color, female_color]
-        linestyle = ['-', '--', '--', '-']
-
-        perm_p_pairings = []
-        jk_p_pairings = []
-        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)
-        ax = []
-        ax.append(fig.add_subplot(gs[0, 0]))
-        ax.append(fig.add_subplot(gs[0, 1], sharey=ax[0]))
-        ax.append(fig.add_subplot(gs[1, 0], sharex=ax[0]))
-        ax.append(fig.add_subplot(gs[1, 1], sharey=ax[2], sharex=ax[1]))
-
-        for enu, (centered_times_p, event_count_p) in enumerate(zip(centered_times_pairing, event_counts_pairings)):
-            boot_kde = permutation_kde(np.hstack(centered_times_p), conv_t, kernal_w=1, kernal_h=1)
-            jk_kde = jackknife_kde(np.hstack(centered_times_p), conv_t, jack_pct=jack_pct, kernal_w=1, kernal_h=1)
-
-            perm_p1, perm_p50, perm_p99 = np.percentile(boot_kde, (1, 50, 99), axis=0)
-            perm_p_pairings.append([perm_p1, perm_p50, perm_p99])
-
-            jk_p1, jk_p50, jk_p99 = np.percentile(jk_kde, (1, 50, 99), axis=0)
-            jk_p_pairings.append([jk_p1, jk_p50, jk_p99])
-
-            ax[enu].fill_between(conv_t_numpy, perm_p1 / event_count_p, perm_p99 / event_count_p, color='cornflowerblue', alpha=.8)
-            ax[enu].plot(conv_t_numpy, perm_p50 / event_count_p, color='dodgerblue', alpha=1, lw=3)
-
-            ax[enu].fill_between(conv_t_numpy, jk_p1 / event_count_p / jack_pct, jk_p99 / event_count_p / jack_pct, color=color[enu], alpha=.8)
-            ax[enu].plot(conv_t_numpy, jk_p50 / event_count_p / jack_pct, color=color[enu], alpha=1, lw=3, linestyle=linestyle[enu])
-
-            ax_m = ax[enu].twinx()
-            counter = 0
-            for enu2, centered_events in enumerate(centered_times_p):
-                Cevents = centered_events[np.abs(centered_events) <= max_dt]
-                if len(Cevents) != 0:
-                    ax_m.plot(Cevents, np.ones(len(Cevents)) * counter, '|', markersize=8, color='k', alpha=.1)
-                    counter += 1
-
-            ax_m.set_yticks([])
-            ax[enu].set_xlim(-max_dt, max_dt)
-            ax[enu].tick_params(labelsize=10)
-
-        plt.setp(ax[1].get_yticklabels(), visible=False)
-        plt.setp(ax[3].get_yticklabels(), visible=False)
-
-        plt.setp(ax[0].get_xticklabels(), visible=False)
-        plt.setp(ax[1].get_xticklabels(), visible=False)
-
-        ax[2].set_xlabel('time [s]', fontsize=12)
-        ax[3].set_xlabel('time [s]', fontsize=12)
-        ax[0].set_ylabel('event rate [Hz]', fontsize=12)
-        ax[2].set_ylabel('event rate [Hz]', fontsize=12)
-        fig.suptitle(title)
-
-        plt.savefig(os.path.join(os.path.split(__file__)[0], 'figures', 'event_time_corr', f'{save_str}_by_sexes.png'), dpi=300)
-        plt.close()
+        if True:
+            centered_times_pairing = []
+            for sex_w, sex_l in itertools.product(['m', 'f'], repeat=2):
+                centered_times_pairing.append([])
+                for i in range(len(centered_times)):
+                    if sex_w == sex_win[i] and sex_l == sex_lose[i]:
+                        centered_times_pairing[-1].append(centered_times[i])
+
+            event_counts_pairings = [np.nansum(np.array(event_counts)[(sex_win == 'm') & (sex_lose == 'm')]),
+                                     np.nansum(np.array(event_counts)[(sex_win == 'm') & (sex_lose == 'f')]),
+                                     np.nansum(np.array(event_counts)[(sex_win == 'f') & (sex_lose == 'm')]),
+                                     np.nansum(np.array(event_counts)[(sex_win == 'f') & (sex_lose == 'f')])]
+            color = [male_color, female_color, male_color, female_color]
+            linestyle = ['-', '--', '--', '-']
+
+            perm_p_pairings = []
+            jk_p_pairings = []
+            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)
+            ax = []
+            ax.append(fig.add_subplot(gs[0, 0]))
+            ax.append(fig.add_subplot(gs[0, 1], sharey=ax[0]))
+            ax.append(fig.add_subplot(gs[1, 0], sharex=ax[0]))
+            ax.append(fig.add_subplot(gs[1, 1], sharey=ax[2], sharex=ax[1]))
+
+            for enu, (centered_times_p, event_count_p) in enumerate(zip(centered_times_pairing, event_counts_pairings)):
+                boot_kde = permutation_kde(np.hstack(centered_times_p), conv_t, kernal_w=1, kernal_h=1)
+                jk_kde = jackknife_kde(np.hstack(centered_times_p), conv_t, jack_pct=jack_pct, kernal_w=1, kernal_h=1)
+
+                perm_p1, perm_p50, perm_p99 = np.percentile(boot_kde, (1, 50, 99), axis=0)
+                perm_p_pairings.append([perm_p1, perm_p50, perm_p99])
+
+                jk_p1, jk_p50, jk_p99 = np.percentile(jk_kde, (1, 50, 99), axis=0)
+                jk_p_pairings.append([jk_p1, jk_p50, jk_p99])
+
+                ax[enu].fill_between(conv_t_numpy, perm_p1 / event_count_p, perm_p99 / event_count_p, color='cornflowerblue', alpha=.8)
+                ax[enu].plot(conv_t_numpy, perm_p50 / event_count_p, color='dodgerblue', alpha=1, lw=3)
+
+                ax[enu].fill_between(conv_t_numpy, jk_p1 / event_count_p / jack_pct, jk_p99 / event_count_p / jack_pct, color=color[enu], alpha=.8)
+                ax[enu].plot(conv_t_numpy, jk_p50 / event_count_p / jack_pct, color=color[enu], alpha=1, lw=3, linestyle=linestyle[enu])
+
+                ax_m = ax[enu].twinx()
+                counter = 0
+                for enu2, centered_events in enumerate(centered_times_p):
+                    Cevents = centered_events[np.abs(centered_events) <= max_dt]
+                    if len(Cevents) != 0:
+                        ax_m.plot(Cevents, np.ones(len(Cevents)) * counter, '|', markersize=8, color='k', alpha=.1)
+                        counter += 1
+
+                ax_m.set_yticks([])
+                ax[enu].set_xlim(-max_dt, max_dt)
+                ax[enu].tick_params(labelsize=10)
+
+            plt.setp(ax[1].get_yticklabels(), visible=False)
+            plt.setp(ax[3].get_yticklabels(), visible=False)
+
+            plt.setp(ax[0].get_xticklabels(), visible=False)
+            plt.setp(ax[1].get_xticklabels(), visible=False)
+
+            ax[2].set_xlabel('time [s]', fontsize=12)
+            ax[3].set_xlabel('time [s]', fontsize=12)
+            ax[0].set_ylabel('event rate [Hz]', fontsize=12)
+            ax[2].set_ylabel('event rate [Hz]', fontsize=12)
+            fig.suptitle(title)
+
+            plt.savefig(os.path.join(os.path.split(__file__)[0], 'figures', 'event_time_corr', f'{save_str}_by_sexes.png'), dpi=300)
+            plt.close()
 
         ###########################################################################################################
         ### all pairings ###
@@ -520,24 +493,38 @@ def main(base_path):
         perm_p1, perm_p50, perm_p99 = np.percentile(boot_kde, (1, 50, 99), axis=0)
         jk_p1, jk_p50, jk_p99 = np.percentile(jk_kde, (1, 50, 99), axis=0)
 
+        if category_enu == 0:
+            # chirp on chase off
+            chirp_on_chase_off = [perm_p1, perm_p50, perm_p99, jk_p1, jk_p50, jk_p99, event_counts]
+        elif category_enu == 1:
+            # chirp on chase on
+            chirp_on_chase_on = [perm_p1, perm_p50, perm_p99, jk_p1, jk_p50, jk_p99, event_counts]
+        elif category_enu == 20:
+            # chase off on chase on
+            chase_off_on_chase_on = [perm_p1, perm_p50, perm_p99, jk_p1, jk_p50, jk_p99, event_counts]
+
+        elif category_enu == 12:
+            rise_on_chase_off = [perm_p1, perm_p50, perm_p99, jk_p1, jk_p50, jk_p99, event_counts]
+        elif category_enu == 13:
+            rise_on_chase_on = [perm_p1, perm_p50, perm_p99, jk_p1, jk_p50, jk_p99, event_counts]
+
+        elif category_enu == 2:
+            chirp_on_contact = [perm_p1, perm_p50, perm_p99, jk_p1, jk_p50, jk_p99, event_counts]
+        elif category_enu == 14:
+            rise_on_contact = [perm_p1, perm_p50, perm_p99, jk_p1, jk_p50, jk_p99, event_counts]
+
         fig = plt.figure(figsize=(20/2.54, 12/2.54))
         gs = gridspec.GridSpec(1, 1, left=0.1, bottom=0.1, right=0.95, top=0.95)
         ax = fig.add_subplot(gs[0, 0])
 
-        # ax.fill_between(conv_t_numpy, perm_p1/len(np.hstack(centered_times)), perm_p99/len(np.hstack(centered_times)), color='cornflowerblue', alpha=.8)
-        # ax.plot(conv_t_numpy, perm_p50/len(np.hstack(centered_times)), color='dodgerblue', alpha=1, lw=3)
-        #
-        # ax.fill_between(conv_t_numpy, jk_p1/len(np.hstack(centered_times))/jack_pct, jk_p99/len(np.hstack(centered_times))/jack_pct, color='tab:red', alpha=.8)
-        # ax.plot(conv_t_numpy, jk_p50/len(np.hstack(centered_times))/jack_pct, color='firebrick', alpha=1, lw=3)
-
         ax.fill_between(conv_t_numpy, perm_p1/np.nansum(event_counts), perm_p99/np.nansum(event_counts), color='cornflowerblue', alpha=.8)
         ax.plot(conv_t_numpy, perm_p50/np.nansum(event_counts), color='dodgerblue', alpha=1, lw=3)
 
         ax.fill_between(conv_t_numpy, jk_p1/np.nansum(event_counts)/jack_pct, jk_p99/np.nansum(event_counts)/jack_pct, color='tab:red', alpha=.8)
         ax.plot(conv_t_numpy, jk_p50/np.nansum(event_counts)/jack_pct, color='firebrick', alpha=1, lw=3)
 
-
         ax_m = ax.twinx()
+        counter = 0
         for enu, centered_events in enumerate(centered_times):
             Cevents = centered_events[np.abs(centered_events) <= max_dt]
             if len(Cevents) != 0:
@@ -555,6 +542,104 @@ def main(base_path):
         plt.savefig(os.path.join(os.path.split(__file__)[0], 'figures', 'event_time_corr', f'{save_str}.png'), dpi=300)
         plt.close()
 
+    embed()
+    quit()
+    event_time_plot_with_agonistic_dur(conv_t_numpy, chirp_on_chase_off, chase_off_on_chase_on,
+                                       lose_chrips_centered_on_ag_off_t, jack_pct, max_dt,
+                                       title=r'chirp$_{lose}$ on chase$_{off}$', chase_on_centered=False)
+
+    event_time_plot_with_agonistic_dur(conv_t_numpy, chirp_on_chase_on, chase_off_on_chase_on,
+                                       lose_chrips_centered_on_ag_on_t, jack_pct, max_dt,
+                                       title=r'chirp$_{lose}$ on chase$_{on}$', chase_on_centered=True)
+
+
+    event_time_plot_with_agonistic_dur(conv_t_numpy, rise_on_chase_off, chase_off_on_chase_on,
+                                       lose_rises_centered_on_ag_off_t, jack_pct, max_dt,
+                                       title=r'rise$_{lose}$ on chase$_{off}$', chase_on_centered=False)
+
+    event_time_plot_with_agonistic_dur(conv_t_numpy, rise_on_chase_on, chase_off_on_chase_on,
+                                       lose_rises_centered_on_ag_on_t, jack_pct, max_dt,
+                                       title=r'rise$_{lose}$ on chase$_{on}$', chase_on_centered=True)
+
+
+    event_time_plot_with_agonistic_dur(conv_t_numpy, rise_on_contact, None,
+                                       lose_rises_centered_on_contact_t, jack_pct, max_dt,
+                                       title=r'rise$_{lose}$ on contact')
+
+    event_time_plot_with_agonistic_dur(conv_t_numpy, chirp_on_contact, None,
+                                       lose_chrips_centered_on_contact_t, jack_pct, max_dt,
+                                       title=r'chirp$_{lose}$ on contact')
+
+
+
+
+
+def event_time_plot_with_agonistic_dur(conv_t_numpy, centered_communication, chase_dur_dist,
+                                       centered_raster_times, jack_pct, max_dt, title='', chase_on_centered=True):
+    fig = plt.figure(figsize=(20 / 2.54, 12 / 2.54))
+    gs = gridspec.GridSpec(1, 1, left=0.1, bottom=0.125 , right=0.9, top=0.95)
+    ax = fig.add_subplot(gs[0, 0])
+    perm_p1, perm_p50, perm_p99, jk_p1, jk_p50, jk_p99, event_counts = centered_communication
+
+    ax.fill_between(conv_t_numpy, perm_p1 / np.nansum(event_counts), perm_p99 / np.nansum(event_counts),
+                    color='tab:gray', alpha=.8)
+    ax.plot(conv_t_numpy, perm_p50 / np.nansum(event_counts), color='tab:gray', alpha=1, lw=3)
+
+    ax.fill_between(conv_t_numpy, jk_p1 / np.nansum(event_counts) / jack_pct,
+                    jk_p99 / np.nansum(event_counts) / jack_pct, color='tab:red', alpha=.8)
+    ax.plot(conv_t_numpy, jk_p50 / np.nansum(event_counts) / jack_pct, color='firebrick', alpha=1, lw=3)
+
+    ax.set_xlabel('time [s]', fontsize=14)
+    ax.set_ylabel('event rate [Hz]', fontsize=14)
+    ax.set_title(title, fontsize=14)
+    ax.set_xlim(-max_dt, max_dt)
+    ax.tick_params(labelsize=12)
+
+    ### chasing dist
+    if hasattr(chase_dur_dist, '__len__'):
+        ax_m = ax.twinx()
+        perm_p1, perm_p50, perm_p99, jk_p1, jk_p50, jk_p99, event_counts = chase_dur_dist
+        if chase_on_centered == False:
+            y1label = r'p(chase$_{start}$)'
+            jk_p1 = jk_p1[::-1]
+            jk_p50 = jk_p50[::-1]
+            jk_p99 = jk_p99[::-1]
+            mask = conv_t_numpy <= 0
+        else:
+            y1label = r'p(chase$_{end}$)'
+            mask = conv_t_numpy >= 0
+
+        ax_m.fill_between(conv_t_numpy[mask], jk_p1[mask] / np.nansum(event_counts) / jack_pct,
+                        jk_p99[mask] / np.nansum(event_counts) / jack_pct, color='tab:blue', alpha=.6)
+        ax_m.plot(conv_t_numpy[mask], jk_p50[mask] / np.nansum(event_counts) / jack_pct, color='tab:blue', alpha=.75, lw=3)
+
+
+        ax_m.set_ylabel(y1label, fontsize=14)
+        ax_m.yaxis.label.set_color('tab:blue')
+        ax_m.spines["right"].set_edgecolor('tab:blue')
+        ax_m.tick_params(axis='y', colors='tab:blue', labelsize=12)
+
+    counter = 0
+    ax_m2 = ax.twinx()
+    for enu, centered_events in enumerate(centered_raster_times):
+        Cevents = centered_events[np.abs(centered_events) <= max_dt]
+        if len(Cevents) != 0:
+            ax_m2.plot(Cevents, np.ones(len(Cevents)) * counter, '|', markersize=8, color='k', alpha=.1)
+            counter += 1
+
+    ax_m2.plot([0, 0], [-1, counter], '--', color='k', lw=2)
+    ax_m2.set_ylim(-1, counter)
+
+    ax_m2.yaxis.set_visible(False)
+    # embed()
+    # quit()
+    save_path = pathlib.Path(__file__).parent / 'figures' / 'event_time_corr_new'
+    save_str = title.replace(' ', '_').replace('{', '').replace('}', '').replace('$', '')
+    if not save_path.exists():
+        save_path.mkdir(parents=True, exist_ok=True)
+    plt.savefig(save_path / f'{save_str}.png', dpi=300)
+    plt.close()
+
 
 if __name__ == '__main__':
     main(sys.argv[1])

code/event_videos.py

@@ -15,6 +15,9 @@ def main(folder, dt):
     create_video_path = os.path.join(folder, 'rise_video')
     if not os.path.exists(create_video_path):
         os.mkdir(create_video_path)
+
+    # embed()
+    # quit()
     video = cv2.VideoCapture(video_path) #  was 'cap'
 
     # fish_freqs = np.load(os.path.join(folder, 'analysis', 'fish_freq_interp.npy'))
@@ -29,6 +32,8 @@ def main(folder, dt):
     fill_spec = np.memmap(os.path.join(folder, 'fill_spec.npy'), dtype='float', mode='r',
                                shape=(fill_spec_shape[0], fill_spec_shape[1]), order='F')
     #######################################
+    # embed()
+    # quit()
     for fish_nr in np.arange(2)[::-1]:
 
         for idx_oi in tqdm(np.array(rise_idx[fish_nr][~np.isnan(rise_idx[fish_nr])], dtype=int)):
@@ -98,7 +103,7 @@ def main(folder, dt):
 
             # plt.ion()
             for i in tqdm(np.arange(len(frames_oi))):
-                break
+                # break
                 video.set(cv2.CAP_PROP_POS_FRAMES, int(frames_oi[i]))
                 ret, frame = video.read()