jgrewe/chirp_probing
1
0
Fork 0
Code Issues Pull Requests Releases Wiki Activity

more dfs, more comparisons, fixed plot issue

Browse Source
This commit is contained in:
Jan Grewe 2021-02-18 19:42:28 +01:00
parent 3ce3117d93
commit d98d38c9be
3 changed files with 100 additions and 27 deletions
Show Stats Download Patch File Download Diff File Expand all files Collapse all files

50
plots.py
View File

@ -38,7 +38,7 @@ def plot_comparisons(args):
axes = []
for i, condition in enumerate(conditions):
# plot the signals
block = block_map[(all_contrasts[0], args.current_df, condition)]
block = block_map[(all_contrasts[0], args.deltaf, args.chirpsize, condition)]
_, self_freq, other_freq, time = get_signals(block)
self_eodf = block.metadata["stimulus parameter"]["eodfs"]["self"]
@ -54,47 +54,70 @@ def plot_comparisons(args):
color="#1f77b4", label="%iHz" % other_eodf)
ax.text(min_time-0.05, other_eodf, "%iHz" % other_eodf, color="#1f77b4", va="center", ha="right", fontsize=9)
# ax.set_title(condition_labels[i])
ax.set_ylim([735, 885])
ax.set_ylim([735, 895])
despine(ax, ["top", "bottom", "left", "right"], True)
axes.append(ax)
rects = []
rect = Rectangle((0.675, 740), 0.098, 140)
rect = Rectangle((0.675, 740), 0.098, 150)
rects.append(rect)
rect = Rectangle((0.57, 740), 0.098, 140)
rect = Rectangle((0.57, 740), 0.098, 150)
rects.append(rect)
pc = PatchCollection(rects, facecolor=None, alpha=0.15, edgecolor="k", ls="--")
axes[0].add_collection(pc)
axes[0].text(0.625, 860, "a)", ha="center", fontsize=7)
axes[0].text(0.724, 860, "b)", ha="center", fontsize=7)
rects = []
rect = Rectangle((0.675, 740), 0.098, 140)
rect = Rectangle((0.675, 740), 0.098, 150)
rects.append(rect)
rect = Rectangle((0.575, 740), 0.098, 140)
rect = Rectangle((0.57, 740), 0.098, 150)
rects.append(rect)
pc = PatchCollection(rects, facecolor=None, alpha=0.15, edgecolor="k", ls="--")
axes[1].add_collection(pc)
axes[1].text(0.625, 860, "c)", ha="center", fontsize=7)
axes[1].text(0.724, 860, "d)", ha="center", fontsize=7)
rects = []
rect = Rectangle((0.57, 740), 0.098, 140)
rect = Rectangle((0.57, 740), 0.098, 150)
rects.append(rect)
pc = PatchCollection(rects, facecolor=None, alpha=0.15, edgecolor="k", ls="--")
axes[2].add_collection(pc)
axes[2].text(0.625, 860, "e)", ha="center", fontsize=7)
con = ConnectionPatch(xyA=(0.625, 735), xyB=(0.625, 740), coordsA="data", coordsB="data",
axesA=axes[0], axesB=axes[1], arrowstyle="<->", shrinkB=5, connectionstyle="arc3,rad=.35")
axes[1].add_artist(con)
con = ConnectionPatch(xyA=(0.725, 885), xyB=(0.725, 880), coordsA="data", coordsB="data",
axesA=axes[0], axesB=axes[1], arrowstyle="<->", shrinkB=5, connectionstyle="arc3,rad=-.25")
con = ConnectionPatch(xyA=(0.725, 895), xyB=(0.725, 890), coordsA="data", coordsB="data",
axesA=axes[0], axesB=axes[1], arrowstyle="<->", shrinkB=5,
connectionstyle="arc3,rad=-.25")
axes[1].add_artist(con)
con = ConnectionPatch(xyA=(0.725, 735), xyB=(0.625, 740), coordsA="data", coordsB="data",
axesA=axes[1], axesB=axes[2], arrowstyle="<->", shrinkB=5, connectionstyle="arc3,rad=.35")
axes[1].add_artist(con)
con = ConnectionPatch(xyA=(0.625, 895), xyB=(0.725, 890), coordsA="data", coordsB="data",
axesA=axes[0], axesB=axes[1], arrowstyle="<->", shrinkB=5,
connectionstyle="arc3,rad=-.35")
axes[1].add_artist(con)
axes[0].text(1., 660, "2.")
axes[1].text(1.05, 660, "3.")
axes[0].text(1.1, 890, "1.")
con = ConnectionPatch(xyA=(0.615, 735), xyB=(0.735, 745), coordsA="data", coordsB="data",
axesA=axes[1], axesB=axes[1], arrowstyle="<->", shrinkB=5,
connectionstyle="arc3,rad=1.")
axes[1].add_artist(con)
con = ConnectionPatch(xyA=(0.625, 895), xyB=(0.625, 895), coordsA="data", coordsB="data",
axesA=axes[1], axesB=axes[2], arrowstyle="<->", shrinkB=5,
connectionstyle="arc3,rad=-.35")
axes[1].add_artist(con)
axes[0].text(1., 655, "2.")
axes[1].text(1.05, 655, "3.")
axes[0].text(1.1, 895, "1.")
axes[0].text(0.6, 925, "4.")
axes[1].text(0.675, 680, "5.")
axes[1].text(1.05, 895, "6.")
fig.subplots_adjust(bottom=0.1, top=0.8, left=0.1, right=0.9)
fig.savefig(args.outfile)
plt.close()
@ -333,7 +356,8 @@ def main():
help="Sub commands for plotting different figures",
description="", dest="explore_cmd")
comp_parser = subparsers.add_parser("comparisons", help="Create a didactic plot illustrating the comparisons")
comp_parser.add_argument("-df", "--deltaf", type=int, default=20, help="The difference frequency to used for plotting")
comp_parser.add_argument("-df", "--deltaf", type=int, default=20, help="The difference frequency to used for plotting. Defaults to 20 Hz")
comp_parser.add_argument("-cs", "--chirpsize", type=int, default=60, help="The chirpsize. Defaults to 60 Hz.")
comp_parser.add_argument("-o", "--outfile", default=os.path.join(figure_folder, "comparisons.pdf"), help="filename of the plot")
comp_parser.set_defaults(func=plot_comparisons)

6
punit_responses.py
View File

@ -220,7 +220,7 @@ def main():
parser = argparse.ArgumentParser(description="Simulate P-unit responses using the model parameters from the models.csv file. Calling it without any arguments works with the defaults, may need some time.")
parser.add_argument("-n", "--number", type=int, default=20, help="Number of simulated neurons. Randomly chosen from model list. Defaults to 20")
parser.add_argument("-t", "--trials", type=int, default=25, help="Number of stimulus repetitions, trials. Defaults to 25")
parser.add_argument("-dfs", "--deltafs", type=float, nargs="+", default=[-200, -100, -50, -20, -10, -5, 5, 10, 20, 50, 100, 200],
parser.add_argument("-dfs", "--deltafs", type=float, nargs="+", default=[-200, -100, -50, -20, -10, -5, 5, 10, 20, 50, 100, 150, 200, 250, 300, 350, 400, 500],
help="List of difference frequencies. Defaults to [-200, -100, -50, -20, -10, -5, 5, 10, 20, 50, 100, 200]")
parser.add_argument("-cs", "--chirpsizes", type=float, nargs="+", default=[40, 60, 100],
help="List of chirp sizes. Defaults to [40, 60, 100]")
@ -232,13 +232,13 @@ def main():
models = load_models("models.csv")
num_models = args.number
if args.number > len(models):
if num_models > len(models):
print("INFO: number of cells larger than number of available models. Reset to max number of models.")
num_models = len(models)
indices = list(range(len(models)))
np.random.shuffle(indices)
Parallel(n_jobs=args.jobs)(delayed(simulate_cell)(cell_id, models, args) for cell_id in indices)
Parallel(n_jobs=args.jobs)(delayed(simulate_cell)(cell_id, models, args) for cell_id in indices[:num_models])
if __name__ == "__main__":

69
response_discriminability.py
View File

@ -103,8 +103,8 @@ def foreign_fish_detection_beat(block_map, df, cs, all_contrasts, all_conditions
self_rates, _ = get_rates(self_spikes, duration, dt, kernel_width)
# get the response snippets between chrips
no_other_snippets = np.zeros((len(interchirp_starts) * no_other_rates.shape[0], int(ici / dt)))
self_snippets = np.zeros_like(no_other_snippets)
no_other_snippets = np.zeros((len(interchirp_starts) * no_other_rates.shape[0], int(ici / dt))) # section b, alone, no chirps
self_snippets = np.zeros_like(no_other_snippets) # section d, in company, no chirps, just beat
for i in range(no_other_rates.shape[0]):
for j, start in enumerate(interchirp_starts):
start_index = int(start/dt)
@ -188,10 +188,10 @@ def foreign_fish_detection_chirp(block_map, df, cs, all_contrasts, all_condition
other_rates, _ = get_rates(other_spikes, duration, dt, kernel_width)
# get the chirp response snippets
alone_chirping_snippets = np.zeros((len(chirp_times) * no_other_rates.shape[0], int(chirp_duration / dt)))
self_snippets = np.zeros_like(alone_chirping_snippets)
other_snippets = np.zeros_like(alone_chirping_snippets)
silence_snippets = np.zeros_like(alone_chirping_snippets)
alone_chirping_snippets = np.zeros((len(chirp_times) * no_other_rates.shape[0], int(chirp_duration / dt))) # section a, alone self-chirping
self_snippets = np.zeros_like(alone_chirping_snippets) # section c, self chirping in company
other_snippets = np.zeros_like(alone_chirping_snippets) # section e, other chirping in company
silence_snippets = np.zeros_like(alone_chirping_snippets) # section d, in company no one chirping
for i in range(no_other_rates.shape[0]):
for j, chirp_time in enumerate(chirp_times):
@ -210,10 +210,15 @@ def foreign_fish_detection_chirp(block_map, df, cs, all_contrasts, all_condition
# 2. Nobody chirps, all alone aka baseline response
# 3. I chirp while the other is present compared to self chirping without the other one present
# 4. the otherone chrips to me compared to baseline with anyone chirping
alone_chirping_dist = within_group_distance(alone_chirping_snippets)
silence_dist = within_group_distance(silence_snippets)
self_vs_alone_dist = across_group_distance(alone_chirping_snippets, self_snippets)
other_vs_silence_dist = across_group_distance(silence_snippets, other_snippets)
alone_chirping_dist = within_group_distance(alone_chirping_snippets) # within section a
silence_dist = within_group_distance(silence_snippets) # within section d
other_chirp_dist = within_group_distance(other_snippets) # within section e
self_vs_alone_dist = across_group_distance(alone_chirping_snippets, self_snippets) # section a vs. section c
other_vs_silence_dist = across_group_distance(silence_snippets, other_snippets) # section d vs. section e
self_other_chirp_dist = across_group_distance(self_snippets, other_snippets) # section c vs. section e
self_chirp_beat_dist = across_group_distance(self_snippets, silence_snippets) # section c vs. section d
alone_chirp_beat_dist = across_group_distance(alone_chirping_snippets, silence_snippets) # section a vs. section d
# sort and perfom ROC analysis for two comparisons
# 1. soliloquy vs. self chirping in company
@ -225,12 +230,25 @@ def foreign_fish_detection_chirp(block_map, df, cs, all_contrasts, all_condition
valid_silence_distances = silence_dist[triangle_indices]
silence_temp = np.zeros_like(valid_silence_distances)
valid_other_chirp_distances = other_chirp_dist[triangle_indices]
other_chirp_temp = np.zeros_like(valid_other_chirp_distances)
valid_self_vs_alone_distances = self_vs_alone_dist.ravel()
self_vs_alone_temp = np.ones_like(valid_self_vs_alone_distances)
valid_other_vs_silence_distances = other_vs_silence_dist.ravel()
other_vs_silence_temp = np.ones_like(valid_other_vs_silence_distances)
valid_self_vs_other_chirp_distances = self_other_chirp_dist.ravel()
self_vs_other_chirps_temp = np.ones_like(valid_self_vs_other_chirp_distances)
valid_self_beat_distances = self_chirp_beat_dist.ravel()
self_vs_beat_temp = np.ones_like(valid_self_beat_distances)
valid_alone_chirp_beat_distance = alone_chirp_beat_dist.ravel()
alone_chirp_beat_temp = np.ones_like(valid_alone_chirp_beat_distance)
# Comparison 2: alone chirping (soliloquy) vs. self-chirping in company
group = np.hstack((no_other_temp, self_vs_alone_temp))
score = np.hstack((valid_no_other_distances, valid_self_vs_alone_distances))
auc = roc_auc_score(group, score)
@ -239,6 +257,8 @@ def foreign_fish_detection_chirp(block_map, df, cs, all_contrasts, all_condition
detection_performances.append({"cell": cell_name, "detection_task": "self vs soliloquy", "contrast": contrast, "df": df, "kernel_width": kernel_width, "chirpsize": cs, "auc": auc, "true_positives": tpr, "false_positives": fpr})
else:
detection_performances.append({"cell": cell_name, "detection_task": "self vs soliloquy", "contrast": contrast, "df": df, "kernel_width": kernel_width, "chirpsize": cs, "auc": auc})
# Comparison 3: other fish chirping vs. beat
group = np.hstack((silence_temp, other_vs_silence_temp))
score = np.hstack((valid_silence_distances, valid_other_vs_silence_distances))
auc = roc_auc_score(group, score)
@ -248,6 +268,35 @@ def foreign_fish_detection_chirp(block_map, df, cs, all_contrasts, all_condition
else:
detection_performances.append({"cell": cell_name, "detection_task": "other vs quietness", "contrast": contrast, "df": df, "kernel_width": kernel_width, "chirpsize": cs, "auc": auc})
# Comparison 4: soliloquy vs. beat
group = np.hstack((no_other_temp, alone_chirp_beat_temp))
score = np.hstack((valid_no_other_distances, valid_alone_chirp_beat_distance))
auc = roc_auc_score(group, score)
if store_roc:
fpr, tpr, _ = roc_curve(group, score, pos_label=1)
detection_performances.append({"cell": cell_name, "detection_task": "soliliquy vs beat", "contrast": contrast, "df": df, "kernel_width": kernel_width, "chirpsize": cs, "auc": auc, "true_positives": tpr, "false_positives": fpr})
else:
detection_performances.append({"cell": cell_name, "detection_task": "soliliquy vs beat", "contrast": contrast, "df": df, "kernel_width": kernel_width, "chirpsize": cs, "auc": auc})
# Comparison 5: beat vs self-chirping in company
group = np.hstack((silence_temp, self_vs_beat_temp))
score = np.hstack((valid_silence_distances, valid_alone_chirp_beat_distance))
auc = roc_auc_score(group, score)
if store_roc:
fpr, tpr, _ = roc_curve(group, score, pos_label=1)
detection_performances.append({"cell": cell_name, "detection_task": "beat vs self", "contrast": contrast, "df": df, "kernel_width": kernel_width, "chirpsize": cs, "auc": auc, "true_positives": tpr, "false_positives": fpr})
else:
detection_performances.append({"cell": cell_name, "detection_task": "beat vs self", "contrast": contrast, "df": df, "kernel_width": kernel_width, "chirpsize": cs, "auc": auc})
# Comparison 6: self vs other-chirping in company
group = np.hstack((other_chirp_temp, self_vs_other_chirps_temp))
score = np.hstack((valid_other_chirp_distances, valid_self_vs_other_chirp_distances))
auc = roc_auc_score(group, score)
if store_roc:
fpr, tpr, _ = roc_curve(group, score, pos_label=1)
detection_performances.append({"cell": cell_name, "detection_task": "self vs other", "contrast": contrast, "df": df, "kernel_width": kernel_width, "chirpsize": cs, "auc": auc, "true_positives": tpr, "false_positives": fpr})
else:
detection_performances.append({"cell": cell_name, "detection_task": "self vs other", "contrast": contrast, "df": df, "kernel_width": kernel_width, "chirpsize": cs, "auc": auc})
print("\n")
return detection_performances