import os import sys import matplotlib.pyplot as plt import matplotlib.gridspec as gridspec from matplotlib.patches import Patch from matplotlib.lines import Line2D from mpl_toolkits.axes_grid1 import make_axes_locatable import numpy as np import pandas as pd import scipy.stats as scp import networkx as nx 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): 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, rotation=45) ax.set_yticklabels(labels) 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='', color_palet=glob_colors): matrix[matrix <= threshold] = 0 matrix = np.around(matrix, decimals=1) Graph = nx.from_numpy_array(matrix, create_using=nx.DiGraph) node_labels = dict(zip(Graph, labels)) # Graph = nx.relabel_nodes(Graph, node_labels) edge_labels = nx.get_edge_attributes(Graph, '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 # 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(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(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(color_palet)[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=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) 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(-1.3, 1.3) 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 # embed() # quit() matrix_origin[(matrix_origin == 0) & (matrix_target >= 5)] = 5 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)) # embed() # quit() 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_width = np.array([np.log(x) 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_width_prev = np.array([np.log(x) 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_labels) >= 1: if color_by_origin: edge_colors = np.array(color_palet)[np.array([*edge_labels.keys()], dtype=int)[:, 0]] elif color_by_target: edge_colors = np.array(color_palet)[np.array([*edge_labels.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_labels_prev) >= 1: if color_by_origin: edge_colors_prev = np.array(color_palet)[np.array([*edge_labels_prev.keys()], dtype=int)[:, 0]] elif color_by_target: edge_colors_prev = np.array(color_palet)[np.array([*edge_labels_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=edge_colors) if len(edge_labels_prev) > 0: 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=edge_colors_prev, 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_1_{scenario}' + '.png'), dpi=300) plt.close() # plt.show() def create_marcov_matrix(individual_event_times, individual_event_labels): event_times = [] event_labels = [] for ll, t in zip(individual_event_labels, individual_event_times): event_times.extend(t) event_labels.extend(np.full(len(t), ll)) time_sorter = np.argsort(event_times) event_times = np.array(event_times)[time_sorter] event_labels = np.array(event_labels)[time_sorter] marcov_matrix = np.zeros((len(individual_event_labels) + 1, len(individual_event_labels) + 1)) for enu_ori, label_ori in enumerate(individual_event_labels): for enu_tar, label_tar in enumerate(individual_event_labels): n = len(event_times[:-1][(event_labels[:-1] == label_ori) & (event_labels[1:] == label_tar) & ( np.diff(event_times) <= 5)]) marcov_matrix[enu_ori, enu_tar] = n for enu_tar, label_tar in enumerate(individual_event_labels): n = len(event_times[:-1][(event_labels[1:] == label_tar) & (np.diff(event_times) > 5)]) marcov_matrix[-1, enu_tar] = n marcov_matrix[-1, 5] = 0 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] chase_off_idx = np.where(event_labels == individual_event_labels[5])[0] helper_mask = np.ones_like(chase_on_idx) helper_mask[np.diff(event_times)[chase_on_idx] <= 5] = 0 helper_mask[np.diff(event_times)[chase_off_idx - 1] <= 5] = 0 marcov_matrix[4, 5] += np.sum(helper_mask) return marcov_matrix def fine_spec_plot(ax, example_1_path, trial_summary, example_ag_on_off): ex1_df_idx = trial_summary[trial_summary['recording'] == os.path.split(example_1_path)[-1]].index.to_numpy()[0] lose_id = trial_summary.iloc[ex1_df_idx]['lose_ID'] fine_spec_shape = np.load(os.path.join(example_1_path, 'fill_spec_shape.npy')) fine_spec = np.memmap(os.path.join(example_1_path, 'fill_spec.npy'), dtype='float', mode='r', shape=(fine_spec_shape[0], fine_spec_shape[1]), order='F') fine_times = np.load(os.path.join(example_1_path, 'fill_times.npy')) spec_freqs = np.load(os.path.join(example_1_path, 'fill_freqs.npy')) times = np.load(os.path.join(example_1_path, 'times.npy')) fund_v = np.load(os.path.join(example_1_path, 'fund_v.npy')) ident_v = np.load(os.path.join(example_1_path, 'ident_v.npy')) idx_v = np.load(os.path.join(example_1_path, 'idx_v.npy')) # artificial_t_axis = np.linspace(times[0], times[-1], spec.shape[1]) # artificial_f_axis = np.linspace(0, 2000, spec.shape[0]) # plt.pcolormesh(artificial_t_axis, artificial_f_axis, decibel(spec), vmin=-100, vmax=-50) lose_freq_in_snippet = fund_v[(ident_v == lose_id) & (times[idx_v] > example_ag_on_off[0][0]-5) & (times[idx_v] < example_ag_on_off[0][1]+5)] max_f, min_f = np.max(lose_freq_in_snippet) + 25, np.min(lose_freq_in_snippet) - 25 min_f = 750 # print(example_1_path, example_ag_on_off[0][0]) f_idx0 = np.where(spec_freqs <= min_f)[0][-1] f_idx1 = np.where(spec_freqs >= max_f)[0][0] t_idx0 = np.where(fine_times <= example_ag_on_off[0][0] - 5)[0][-1] t_idx1 = np.where(fine_times >= example_ag_on_off[0][0] + 4)[0][0] ax.pcolormesh(fine_times[t_idx0:t_idx1+1] - example_ag_on_off[0][0], spec_freqs[f_idx0:f_idx1+1], decibel(fine_spec[f_idx0:f_idx1+1, t_idx0:t_idx1+1]), cmap='afmhot') t_idx0 = np.where(fine_times <= example_ag_on_off[0][1] - 5)[0][-1] t_idx1 = np.where(fine_times >= example_ag_on_off[0][1] + 5)[0][0] ax.pcolormesh(fine_times[t_idx0:t_idx1+1] - example_ag_on_off[0][1] + 10, spec_freqs[f_idx0:f_idx1+1], decibel(fine_spec[f_idx0:f_idx1+1, t_idx0:t_idx1+1]), cmap='afmhot') ax.set_yticks([750, 775, 800]) ax.fill_between([4, 5], [spec_freqs[f_idx0], spec_freqs[f_idx0]], [spec_freqs[f_idx1], spec_freqs[f_idx1]], color='white') 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] trial_mask = chirp_notes['good'] == 1 all_marcov_matrix = [] all_event_counts = [] all_agonistic_categorie = [] # agonistic categorie plot # fig = plt.figure(figsize=(20 / 2.54, 12 / 2.54)) # gs = gridspec.GridSpec(2, 1, left=0.1, bottom=0.1, right=0.9, top=0.95, height_ratios=[1, 4], hspace=0) # ax = fig.add_subplot(gs[1, 0]) # ax_spec = fig.add_subplot(gs[0, 0], sharex=ax) # plt.setp(ax_spec.get_xticklabels(), visible=False) # # for i in range(1, 5): # ax.fill_between([0, 4], np.array([-.2, -.2]) + i, np.array([.2, .2]) + i, color='tab:grey') # ax.fill_between([5, 10], np.array([-.2, -.2]) + i, np.array([.2, .2]) + i, color='tab:grey') # # fill_dots = np.arange(4, 5.1, 0.125) # ax.plot(fill_dots, np.ones_like(fill_dots)*i, '.', color='tab:grey', markersize=3) got_examples = [False, False, False] example_ag_on_off = [[], [], []] example_chirp_times = [[], [], []] example_rise_times = [[], [], []] example_1_path = '' example_skips = [15, 4, 3] #3, 5, 9, 15, 19 for index, trial in trial_summary.iterrows(): trial_path = os.path.join(base_path, trial['recording']) if not trial_mask[index]: continue if trial['group'] < 5: continue if not os.path.exists(os.path.join(trial_path, 'led_idxs.csv')): continue if not os.path.exists(os.path.join(trial_path, 'LED_frames.npy')): continue if trial['draw'] == 1: continue ids = np.load(os.path.join(trial_path, 'analysis', 'ids.npy')) times = np.load(os.path.join(trial_path, 'times.npy')) sorter = -1 if trial['win_ID'] != ids[0] else 1 ### event times --> BORIS behavior contact_t_GRID, ag_on_off_t_GRID, led_idx, led_frames = \ load_and_converete_boris_events(trial_path, trial['recording'], sr=20_000) ### communication if not os.path.exists(os.path.join(trial_path, 'chirp_times_cnn.npy')): continue chirp_t = np.load(os.path.join(trial_path, 'chirp_times_cnn.npy')) chirp_ids = np.load(os.path.join(trial_path, 'chirp_ids_cnn.npy')) chirp_times = [chirp_t[chirp_ids == trial['win_ID']], chirp_t[chirp_ids == trial['lose_ID']]] rise_idx = np.load(os.path.join(trial_path, 'analysis', 'rise_idx.npy'))[::sorter] rise_idx_int = [np.array(rise_idx[i][~np.isnan(rise_idx[i])], dtype=int) for i in range(len(rise_idx))] rise_times = [times[rise_idx_int[0]], times[rise_idx_int[1]]] # trial marcov matrix individual_event_times = [chirp_times[1], rise_times[1], chirp_times[0], rise_times[0], ag_on_off_t_GRID[:, 0], ag_on_off_t_GRID[:, 1], contact_t_GRID] individual_event_labels = [r'chirp$_{lose}$', r'rise$_{lose}$', r'chirp$_{win}$', r'rise$_{win}$', r'chace$_{on}$', r'chace$_{off}$', 'contact'] marcov_matrix = create_marcov_matrix(individual_event_times, individual_event_labels) all_marcov_matrix.append(marcov_matrix) # compute and store trial event counts event_counts = np.array(list(map(lambda x: len(x), individual_event_times))) event_counts = np.append(event_counts, marcov_matrix[-1].sum()) all_event_counts.append(event_counts) # agonistic categories agonitic_categorie = np.zeros(len(ag_on_off_t_GRID)) for enu, (chase_on_time, chase_off_time) in enumerate(ag_on_off_t_GRID): chase_dur = chase_off_time - chase_on_time chirp_dt = chase_dur if chase_dur < 5 else 5 max_dt = 5 # check if rise before chase / chirp at end rise_before, chirp_arround_end = False, False if np.any(((chase_on_time - rise_times[1]) > 0) & ((chase_on_time - rise_times[1]) < max_dt)): rise_times_oi = rise_times[1][((chase_on_time - rise_times[1]) > 0) & ((chase_on_time - rise_times[1]) < max_dt)] rise_before = True if np.any( ((chase_off_time - chirp_times[1]) < chirp_dt) & ((chirp_times[1] - chase_off_time) < max_dt)): chirp_time_oi = chirp_times[1][((chase_off_time - chirp_times[1]) < chase_dur) & ((chirp_times[1] - chase_off_time) < max_dt)] chirp_arround_end = True # define agonistic categorie based on rise/chirp occurance if rise_before: if chirp_arround_end: agonitic_categorie[enu] = 1 else: agonitic_categorie[enu] = 2 else: if chirp_arround_end: agonitic_categorie[enu] = 3 else: agonitic_categorie[enu] = 4 if agonitic_categorie[enu] == 1 and not got_examples[0]: if chase_dur > 10: if np.any((chirp_time_oi - chase_off_time) < 0) and np.any((chirp_time_oi - chase_off_time) > 0): if example_skips[int(agonitic_categorie[enu] - 1)] == 0: example_ag_on_off[int(agonitic_categorie[enu] - 1)].extend([chase_on_time, chase_off_time]) example_chirp_times[int(agonitic_categorie[enu] - 1)].extend(chirp_time_oi) example_rise_times[int(agonitic_categorie[enu] - 1)].extend(rise_times_oi) example_1_path = trial_path got_examples[0] = True else: example_skips[int(agonitic_categorie[enu] - 1)] -= 1 elif agonitic_categorie[enu] == 2 and not got_examples[1]: if chase_dur > 10: if example_skips[int(agonitic_categorie[enu] - 1)] == 0: example_ag_on_off[int(agonitic_categorie[enu] - 1)].extend([chase_on_time, chase_off_time]) example_rise_times[int(agonitic_categorie[enu] - 1)].extend(rise_times_oi) got_examples[1] = True else: example_skips[int(agonitic_categorie[enu] - 1)] -= 1 elif agonitic_categorie[enu] == 3 and not got_examples[2]: if chase_dur > 10: if np.any((chirp_time_oi - chase_off_time) < 0) and np.any((chirp_time_oi - chase_off_time) > 0): if example_skips[int(agonitic_categorie[enu] - 1)] == 0: example_ag_on_off[int(agonitic_categorie[enu] - 1)].extend([chase_on_time, chase_off_time]) example_chirp_times[int(agonitic_categorie[enu] - 1)].extend(chirp_time_oi) got_examples[2] = True else: example_skips[int(agonitic_categorie[enu] - 1)] -= 1 else: pass all_agonistic_categorie.append(agonitic_categorie) ### agonistic categorie example figure fig = plt.figure(figsize=(20 / 2.54, 12 / 2.54)) gs = gridspec.GridSpec(2, 1, left=0.1, bottom=0.1, right=0.9, top=0.9, height_ratios=[1, 3], hspace=0) ax = fig.add_subplot(gs[1, 0]) ax_spec = fig.add_subplot(gs[0, 0], sharex=ax) plt.setp(ax_spec.get_xticklabels(), visible=False) for i in range(1, 5): ax.fill_between([0, 4], np.array([-.2, -.2]) + i, np.array([.2, .2]) + i, color='tab:grey') ax.fill_between([5, 10], np.array([-.2, -.2]) + i, np.array([.2, .2]) + i, color='tab:grey') fill_dots = np.arange(4, 5.1, 0.125) ax.plot(fill_dots, np.ones_like(fill_dots)*i, '.', color='tab:grey', markersize=3) for enu, (chirp_time_oi, rise_times_oi, ag_on_off) in enumerate(zip(example_chirp_times, example_rise_times, example_ag_on_off)): chase_on_time, chase_off_time = ag_on_off for ct in chirp_time_oi: ax.plot([ct - chase_off_time + 10, ct - chase_off_time + 10], [enu + .8, enu + 1.2], color='k', lw=2) for rt in rise_times_oi: ax.plot([rt - chase_on_time, rt - chase_on_time], [enu + .8, enu + 1.2], color='firebrick', lw=2) stacked_agonistic_categories = np.hstack(all_agonistic_categorie) pct_each_categorie = np.zeros(4) for enu, cat in enumerate(range(1, 5)): pct_each_categorie[enu] = len(stacked_agonistic_categories[stacked_agonistic_categories == cat]) / len(stacked_agonistic_categories) ax.text(15.2, enu + 1, f'{pct_each_categorie[enu] * 100:.1f}' + ' $\%$', clip_on=False, fontsize=14, ha='left', va='center') # plot correct spectrogram fine_spec_plot(ax_spec, example_1_path, trial_summary, example_ag_on_off) # for a in [ax, ax_spec]: # a.tick_params(labelsize=10) ########################################## ax.plot([0, 0], [0.5, 5], '--', color='k', lw=1) ax.plot([10, 10], [0.5, 5], '--', color='k', lw=1) ax.set_ylim(0.5, 4.5) ax.set_xlim(-5, 15) ax.set_yticks([1, 2, 3, 4]) # ax.set_yticklabels([r'rise$_{pre}$ $&$ chirp$_{end}$', r'only rise$_{pre}$', r'only chirp$_{end}$', 'no communication']) ax.set_yticklabels(['A ', 'B ', 'C ', 'D ']) ax.invert_yaxis() ax.set_xlabel('time [s]', fontsize=12) ax.tick_params(axis='y', labelsize=20) ax.tick_params(axis = 'x', labelsize=10) ax_spec.tick_params(labelsize=10) legend_elements = [Line2D([0], [0], color='firebrick', lw=2, label=r'rise$_{lose}$'), Line2D([0], [0], color='k', lw=2, label=r'chirp$_{lose}$'), Patch(facecolor='tab:grey', edgecolor='w', label= 'chase event')] ax_spec.legend(handles=legend_elements, loc='lower right', ncol=3, bbox_to_anchor=(1, 1), frameon=False, fontsize=10, facecolor='white') ax_spec.set_ylabel('EODf [Hz]', fontsize=12) ax.spines[['right', 'top']].set_visible(False) plt.savefig(os.path.join(os.path.split(__file__)[0], 'figures', 'markov', 'agonistic_catego ries' + '.png'), dpi=300) plt.show() ### bar plot - agonistic categories counts/pct ##################################################################### fig, ax = plt.subplots(figsize=(20/2.54, 12/2.54)) ax.bar(np.arange(4), [len(stacked_agonistic_categories[stacked_agonistic_categories == 1]), len(stacked_agonistic_categories[stacked_agonistic_categories == 2]), len(stacked_agonistic_categories[stacked_agonistic_categories == 3]), len(stacked_agonistic_categories[stacked_agonistic_categories == 4])]) ax.set_xticks(np.arange(4)) ax.set_xticklabels([r'rise$_{pre}$ + chirp$_{end}$', r'rise$_{pre}$ + _', r'_ + chirp$_{end}$', '_ + _']) plt.show() # pct pct_agon_categorie = np.zeros(shape=(len(all_agonistic_categorie), 4)) for enu, agonitic_categorie in enumerate(all_agonistic_categorie): for cat in np.arange(4): pct_agon_categorie[enu, cat] = len(agonitic_categorie[agonitic_categorie == cat+1]) / len(agonitic_categorie) fig, ax = plt.subplots(figsize=(20 / 2.54, 12 / 2.54)) ax.bar(np.arange(4), pct_agon_categorie.mean(0)) ax.errorbar(np.arange(4), pct_agon_categorie.mean(0), yerr=pct_agon_categorie.std(0), fmt='', color='k', linestyle='None') ax.set_xticks(np.arange(4)) ax.set_xticklabels([r'rise$_{pre}$ + chirp$_{end}$', r'rise$_{pre}$ + _', r'_ + chirp$_{end}$', '_ + _']) plt.show() ### marcov models plots ############################################################################################ all_marcov_matrix = np.array(all_marcov_matrix) all_event_counts = np.array(all_event_counts) 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, 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, 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_origin=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)) 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, individual_event_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, individual_event_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() #################################################################################################################### embed() quit() pass if __name__ == '__main__': main(sys.argv[1])