raab/competition_experiments
1
0
Fork 0
Code Issues Pull Requests Releases Wiki Activity

stuff with figures and videos

Browse Source
This commit is contained in:
Till Raab 2023-12-05 15:15:09 +01:00
parent 35dc062164
commit 69190eb6db
4 changed files with 64 additions and 42 deletions
Show Stats Download Patch File Download Diff File Expand all files Collapse all files

BIN
code/__pycache__/event_time_correlations.cpython-310.pyc
View File

Binary file not shown.

92
code/ethogram.py
View File

@ -111,8 +111,14 @@ def plot_mixed_transition_diagram(og_matrix_origin, og_matrix_target, labels, no
matrix_origin[matrix_origin <= threshold] = 0
matrix_target[matrix_target <= threshold] = 0
helper_maks = matrix_origin >= matrix_target
matrix_origin[~helper_maks] = 0
# 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)
@ -157,22 +163,23 @@ def plot_mixed_transition_diagram(og_matrix_origin, og_matrix_target, labels, no
matrix_target_prev[~old_mask] = 0
matrix_origin = np.around(matrix_origin, decimals=1)
matrix_target = np.around(matrix_target, 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)
# 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)
# 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)
# 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_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')
# edge_labels2_prev = nx.get_edge_attributes(Graph2_prev, 'weight')
positions = nx.circular_layout(Graph)
positions2 = nx.circular_layout(Graph)
@ -183,53 +190,55 @@ def plot_mixed_transition_diagram(og_matrix_origin, og_matrix_target, labels, no
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 = 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_width2[edge_width2 >= 6] = 6
edge_width_prev[edge_width_prev >= 6] = 6
edge_width2_prev[edge_width2_prev >= 6] = 6
# edge_width2_prev[edge_width2_prev >= 6] = 6
if len(edge_labels2) >= 1:
if len(edge_labels) >= 1:
if color_by_origin:
edge_colors = np.array(color_palet)[np.array([*edge_labels2.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(color_palet)[np.array([*edge_labels2.keys()], dtype=int)[:, 1]]
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)
# 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 len(edge_labels_prev) >= 1:
if color_by_origin:
edge_colors_prev = np.array(color_palet)[np.array([*edge_labels2_prev.keys()], dtype=int)[:, 0]]
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_labels2_prev.keys()], dtype=int)[:, 1]]
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(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')
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='k', alpha=.25)
ax=ax, edge_color=edge_colors_prev, alpha=.25)
ax.spines["top"].set_visible(False)
ax.spines["bottom"].set_visible(False)
@ -242,7 +251,7 @@ def plot_mixed_transition_diagram(og_matrix_origin, og_matrix_target, labels, no
old_mask += mask
plt.savefig(os.path.join(os.path.split(__file__)[0], 'figures', 'markov', f'marcov_buildup_{scenario}' + '.png'), dpi=300)
plt.savefig(os.path.join(os.path.split(__file__)[0], 'figures', 'markov', f'marcov_buildup_1_{scenario}' + '.png'), dpi=300)
plt.close()
# plt.show()
@ -302,19 +311,23 @@ def fine_spec_plot(ax, example_1_path, trial_summary, example_ag_on_off):
# 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]))
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]))
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')
@ -466,7 +479,7 @@ def main(base_path):
### 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, 4], hspace=0)
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)
@ -495,6 +508,8 @@ def main(base_path):
# 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)
@ -508,6 +523,7 @@ def main(base_path):
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$_{lose}$'),
@ -518,7 +534,7 @@ def main(base_path):
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_categories' + '.png'), dpi=300)
plt.savefig(os.path.join(os.path.split(__file__)[0], 'figures', 'markov', 'agonistic_catego ries' + '.png'), dpi=300)
plt.show()
@ -598,7 +614,7 @@ def main(base_path):
plt.show()
plot_mixed_transition_diagram(mm_origin, mm_target, individual_event_labels, ball_size,
color_by_target=True, color_palet=glob_colors_new)
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))

8
code/event_videos.py
View File

@ -51,8 +51,14 @@ def main(folder, dt):
for rise_time in rise_times:
relevant_chirps = chirp_times[((chirp_times - rise_time) > 0 ) &
((chirp_times - rise_time) < dt * 3)]
if len(relevant_chirps) == 0:
name_appendix = '_rise_only'
pass
else:
name_appendix = ''
continue
# continue
rel_chirp_time = relevant_chirps - rise_time
@ -320,7 +326,7 @@ def main(folder, dt):
# video_name = ("./rise_video/%s_%2.f:%2.f:%2.f.mp4" % (win_lose_str, HH, MM, SS)).replace(' ', '0')
# command = "ffmpeg -r 25 -i './rise_video/frame%4d.jpg' -vf 'pad=ceil(iw/2)*2:ceil(ih/2)*2' -vcodec libx264 -y -an"
video_name = os.path.join(create_video_path, ("%s_%2.f:%2.f:%2.f.mp4" % (win_lose_str, HH, MM, SS)).replace(' ', '0'))
video_name = os.path.join(create_video_path, ("%s%s_%2.f:%2.f:%2.f.mp4" % (win_lose_str, name_appendix, HH, MM, SS)).replace(' ', '0'))
command1 = "ffmpeg -r 25 -i"
frames_path = '"%s"' % os.path.join(create_video_path, "frame%4d.jpg")
command2 = "-vf 'pad=ceil(iw/2)*2:ceil(ih/2)*2' -vcodec libx264 -y -an"

BIN
code/figures/markov/agonistic_categories.png
View File

Binary file not shown.
Side by Side Swipe Overlay

Before

Width:  |  Height:  |  Size: 116 KiB

After

Width:  |  Height:  |  Size: 109 KiB