make chirp-chrip cross correlation. marcov model works but requires refinement. arrows from each node should sum up to 100pct ?!
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164
ethogram.py
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164
ethogram.py
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import os
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import sys
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import matplotlib.pyplot as plt
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import matplotlib.gridspec as gridspec
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import numpy as np
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import pandas as pd
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import scipy.stats as scp
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import networkx as nx
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from IPython import embed
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from event_time_correlations import load_and_converete_boris_events
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def plot_transition_matrix(matrix, labels):
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fig, ax = plt.subplots()
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im = ax.imshow(matrix)
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ax.set_xticks(list(range(len(matrix))))
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ax.set_yticks(list(range(len(matrix))))
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ax.set_xticklabels(labels)
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ax.set_yticklabels(labels)
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fig.colorbar(im)
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plt.show()
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def plot_transition_diagram(matrix, labels, node_size):
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matrix[matrix <= 5] = 0
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matrix = np.around(matrix, decimals=1)
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fig, ax = plt.subplots(figsize=(21 / 2.54, 19 / 2.54))
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fig.subplots_adjust(left=0.05, bottom=0.05, right=0.95, top=0.95)
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Graph = nx.from_numpy_array(matrix, create_using=nx.DiGraph)
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node_labels = dict(zip(Graph, labels))
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# Graph = nx.relabel_nodes(Graph, node_labels)
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edge_labels = nx.get_edge_attributes(Graph, 'weight')
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positions = nx.circular_layout(Graph)
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positions2 = nx.circular_layout(Graph)
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for p in positions:
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positions2[p][0] -= .1
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positions2[p][1] -= .1
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# ToDo: nodes
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nx.draw_networkx_nodes(Graph, pos=positions, node_size=node_size*2, ax=ax, alpha=0.5)
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nx.draw_networkx_labels(Graph, pos=positions, labels=node_labels)
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# google networkx drawing to get better graphs with networkx
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# nx.draw(Graph, pos=positions, node_size=node_size, label=labels, with_labels=True, ax=ax)
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# # ToDo: edges
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edge_width = [x / 10 for x in [*edge_labels.values()]]
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# nx.draw_networkx_edges(Graph, pos=positions, node_size=node_size, width=edge_width,
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# arrows=True, arrowsize=20,
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# min_target_margin=25, min_source_margin=10, connectionstyle="arc3, rad=0.0",
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# ax=ax)
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# nx.draw_networkx_edge_labels(Graph, positions, label_pos=0.5, edge_labels=edge_labels, ax=ax, rotate=False)
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nx.draw_networkx_edges(Graph, pos=positions, node_size=node_size, width=edge_width,
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arrows=True, arrowsize=20,
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min_target_margin=25, min_source_margin=10, connectionstyle="arc3, rad=0.0",
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ax=ax)
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nx.draw_networkx_edge_labels(Graph, positions, label_pos=0.2, edge_labels=edge_labels, ax=ax, rotate=False)
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ax.spines["top"].set_visible(False)
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ax.spines["bottom"].set_visible(False)
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ax.spines["right"].set_visible(False)
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ax.spines["left"].set_visible(False)
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# plt.title(title)
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plt.show()
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def main(base_path):
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trial_summary = pd.read_csv(os.path.join(base_path, 'trial_summary.csv'), index_col=0)
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chirp_notes = pd.read_csv(os.path.join(base_path, 'chirp_notes.csv'), index_col=0)
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# trial_summary = trial_summary[chirp_notes['good'] == 1]
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trial_mask = chirp_notes['good'] == 1
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all_marcov_matrix = []
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all_event_counts = []
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for index, trial in trial_summary.iterrows():
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trial_path = os.path.join(base_path, trial['recording'])
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if not trial_mask[index]:
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continue
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if trial['group'] < 5:
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continue
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if not os.path.exists(os.path.join(trial_path, 'led_idxs.csv')):
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continue
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if not os.path.exists(os.path.join(trial_path, 'LED_frames.npy')):
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continue
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if trial['draw'] == 1:
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continue
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ids = np.load(os.path.join(trial_path, 'analysis', 'ids.npy'))
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times = np.load(os.path.join(trial_path, 'times.npy'))
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sorter = -1 if trial['win_ID'] != ids[0] else 1
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### event times --> BORIS behavior
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contact_t_GRID, ag_on_off_t_GRID, led_idx, led_frames = \
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load_and_converete_boris_events(trial_path, trial['recording'], sr=20_000)
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### communication
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if not os.path.exists(os.path.join(trial_path, 'chirp_times_cnn.npy')):
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continue
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chirp_t = np.load(os.path.join(trial_path, 'chirp_times_cnn.npy'))
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chirp_ids = np.load(os.path.join(trial_path, 'chirp_ids_cnn.npy'))
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chirp_times = [chirp_t[chirp_ids == trial['win_ID']], chirp_t[chirp_ids == trial['lose_ID']]]
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rise_idx = np.load(os.path.join(trial_path, 'analysis', 'rise_idx.npy'))[::sorter]
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rise_idx_int = [np.array(rise_idx[i][~np.isnan(rise_idx[i])], dtype=int) for i in range(len(rise_idx))]
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rise_times = [times[rise_idx_int[0]], times[rise_idx_int[1]]]
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event_times = []
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event_labels = []
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loop_times = [chirp_times[1], rise_times[1], chirp_times[0], rise_times[0], ag_on_off_t_GRID[:, 0],
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ag_on_off_t_GRID[:, 1], contact_t_GRID]
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loop_labels = [r'chirp$_{lose}$', r'rise$_{lose}$', r'chirp$_{win}$', r'rise$_{win}$', r'chace$_{on}$', r'chace$_{off}$', 'contact']
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event_counts = np.array([len(chirp_times[1]), len(rise_times[1]), len(chirp_times[0]), len(rise_times[0]), len(ag_on_off_t_GRID), len(ag_on_off_t_GRID), len(contact_t_GRID)])
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for ll, t in zip(loop_labels, loop_times):
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event_times.extend(t)
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event_labels.extend(np.full(len(t), ll))
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time_sorter = np.argsort(event_times)
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event_times = np.array(event_times)[time_sorter]
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event_labels = np.array(event_labels)[time_sorter]
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marcov_matrix = np.zeros((len(loop_labels), len(loop_labels)))
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for enu_ori, label_ori in enumerate(loop_labels):
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for enu_tar, label_tar in enumerate(loop_labels):
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n = len(event_times[:-1][(event_labels[:-1] == label_ori) & (event_labels[1:] == label_tar) & (np.diff(event_times) <= 5)])
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marcov_matrix[enu_ori, enu_tar] = n
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### 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
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chase_on_idx = np.where(event_labels == loop_labels[4])[0]
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chase_off_idx = np.where(event_labels == loop_labels[5])[0]
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helper_mask = np.ones_like(chase_on_idx)
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helper_mask[np.diff(event_times)[chase_on_idx] <= 5] = 0
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helper_mask[np.diff(event_times)[chase_off_idx-1] <= 5] = 0
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marcov_matrix[4, 5] += np.sum(helper_mask)
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all_marcov_matrix.append(marcov_matrix)
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all_event_counts.append(event_counts)
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# plot_transition_matrix(marcov_matrix, loop_labels)
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# plot_transition_diagram(marcov_matrix, loop_labels, node_size=event_counts)
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# plot_transition_diagram(marcov_matrix / event_counts.reshape(len(event_counts), 1) * 100, loop_labels, node_size=event_counts)
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all_marcov_matrix = np.array(all_marcov_matrix)
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all_event_counts = np.array(all_event_counts)
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collective_marcov_matrix = np.sum(all_marcov_matrix, axis=0)
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collective_event_counts = np.sum(all_event_counts, axis=0)
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plot_transition_matrix(collective_marcov_matrix, loop_labels)
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plot_transition_diagram(collective_marcov_matrix / collective_event_counts.reshape(len(collective_event_counts), 1) * 100, loop_labels, collective_event_counts)
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embed()
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quit()
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pass
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if __name__ == '__main__':
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main(sys.argv[1])
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@ -240,15 +240,17 @@ def single_kde(event_dt, conv_t, kernal_w = 1, kernal_h = 0.2):
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return cp.asnumpy(single_kdes)
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def main(base_path):
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if not os.path.exists(os.path.join(os.path.split(__file__)[0], 'figures', 'event_time_corr')):
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os.makedirs(os.path.join(os.path.split(__file__)[0], 'figures', 'event_time_corr'))
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trial_summary = pd.read_csv('trial_summary.csv', index_col=0)
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trial_summary = pd.read_csv(os.path.join(base_path, 'trial_summary.csv'), index_col=0)
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chirp_notes = pd.read_csv(os.path.join(base_path, 'chirp_notes.csv'), index_col=0)
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# trial_summary = trial_summary[chirp_notes['good'] == 1]
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trial_mask = chirp_notes['good'] == 1
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# ToDo: do chirp on chirp and rise on rise
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lose_chrips_centered_on_ag_off_t = []
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lose_chrips_centered_on_ag_on_t = []
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lose_chrips_centered_on_contact_t = []
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