raab/GP2023_chirp_detection
1
0
Fork 0
Code Issues Pull Requests Releases Wiki Activity

Merge branch 'master' of https://whale.am28.uni-tuebingen.de/git/raab/GP2023_chirp_detection

Browse Source
This commit is contained in:
wendtalexander
2023-01-25 20:49:28 +01:00
parent a57fa14ed6 6b4f3a42cc
commit f5c922c4ff
6 changed files with 171 additions and 125 deletions
Download Patch File Download Diff File Expand all files Collapse all files

271
code/plot_kdes.py
View File

@@ -18,49 +18,64 @@ logger = makeLogger(__name__)
ps = PlotStyle()
def jackknife(data, nresamples, subsetsize, kde_time, kernel_width):
if len(data) == 0:
return []
jackknifed_kdes = []
data = np.sort(data)
subsetsize = int(np.round(len(data)*subsetsize))
for n in range(nresamples):
subset = np.random.choice(data, subsetsize, replace=False)
subset_kde = acausal_kde1d(subset, time=kde_time, width=kernel_width)
jackknifed_kdes.append(subset_kde)
return jackknifed_kdes
def bootstrap(data, nresamples, kde_time, kernel_width, event_times, time_before, time_after):
bootstrapped_kdes = []
data = data[data <= 3*60*60] # only night time
if len(data) == 0:
logger.info('No data for bootstrap, added zeros')
return [np.zeros_like(kde_time) for i in range(nresamples)]
diff_data = np.diff(np.sort(data), prepend=np.sort(data)[0])
# diff_data = np.diff(np.sort(data), prepend=0)
# if len(data) != 0:
# mean_chirprate = (len(data) - 1) / (data[-1] - data[0])
for i in tqdm(range(nresamples)):
np.random.shuffle(diff_data)
bootstrapped_data = np.cumsum(diff_data)
# np.random.shuffle(diff_data)
# bootstrapped_data = np.cumsum(diff_data)
bootstrapped_data = data + np.random.randn(len(data)) * 10
bootstrap_data_centered = center_chirps(
bootstrapped_data, event_times, time_before, time_after)
bootstrapped_kde = acausal_kde1d(
bootstrap_data_centered, time=kde_time, width=kernel_width)
# bootstrapped_kdes = list(np.asarray(
# bootstrapped_kdes) / len(event_times))
bootstrapped_kdes.append(bootstrapped_kde)
return bootstrapped_kdes
def jackknife(data, nresamples, subsetsize, kde_time, kernel_width, event_times, time_before, time_after):
jackknife_kdes = []
data = data[data <= 3*60*60] # only night time
subsetsize = int(len(data) * subsetsize)
diff_data = np.diff(np.sort(data), prepend=0)
for i in tqdm(range(nresamples)):
bootstrapped_data = np.random.sample(data, subsetsize, replace=False)
bootstrapped_data = np.cumsum(diff_data)
bootstrap_data_centered = center_chirps(
bootstrapped_data, event_times, time_before, time_after)
bootstrapped_kde = acausal_kde1d(
bootstrap_data_centered, time=kde_time, width=kernel_width)
# bootstrapped_kdes = list(np.asarray(
# bootstrapped_kdes) / len(event_times))
jackknife_kdes.append(bootstrapped_kde)
return jackknife_kdes
def get_chirp_winner_loser(folder_name, Behavior, order_meta_df):
foldername = folder_name.split('/')[-2]
@@ -88,7 +103,7 @@ def get_chirp_winner_loser(folder_name, Behavior, order_meta_df):
def main(dataroot):
foldernames, _ = get_valid_datasets(dataroot)
plot_all = False
plot_all = True
time_before = 60
time_after = 60
dt = 0.001
@@ -134,6 +149,9 @@ def main(dataroot):
bh = Behavior(folder)
category, timestamps = correct_chasing_events(bh.behavior, bh.start_s)
category = category[timestamps < 3*60*60] # only night time
timestamps = timestamps[timestamps < 3*60*60] # only night time
winner, loser = get_chirp_winner_loser(folder, bh, meta)
if winner is None:
@@ -162,6 +180,8 @@ def main(dataroot):
loser, physicals, time_before, time_after))
# bootstrap
# chirps = [winner, winner, winner, loser, loser, loser]
winner_onsets_boot.append(bootstrap(
winner,
nresamples=nbootstraps,
@@ -240,80 +260,80 @@ def main(dataroot):
ax[1, 2].plot(kde_time, loser_physicals_conv/len(physicals))
# # plot bootstrap lines
# for kde in winner_onsets_boot[-1]:
# ax[0, 0].plot(kde_time, kde/len(offsets),
# color='gray')
# for kde in winner_offsets_boot[-1]:
# ax[0, 1].plot(kde_time, kde/len(offsets),
# color='gray')
# for kde in winner_physicals_boot[-1]:
# ax[0, 2].plot(kde_time, kde/len(offsets),
# color='gray')
# for kde in loser_onsets_boot[-1]:
# ax[1, 0].plot(kde_time, kde/len(offsets),
# color='gray')
# for kde in loser_offsets_boot[-1]:
# ax[1, 1].plot(kde_time, kde/len(offsets),
# color='gray')
# for kde in loser_physicals_boot[-1]:
# ax[1, 2].plot(kde_time, kde/len(offsets),
# color='gray')
for kde in winner_onsets_boot[-1]:
ax[0, 0].plot(kde_time, kde/len(onsets),
color='gray')
for kde in winner_offsets_boot[-1]:
ax[0, 1].plot(kde_time, kde/len(offsets),
color='gray')
for kde in winner_physicals_boot[-1]:
ax[0, 2].plot(kde_time, kde/len(physicals),
color='gray')
for kde in loser_onsets_boot[-1]:
ax[1, 0].plot(kde_time, kde/len(onsets),
color='gray')
for kde in loser_offsets_boot[-1]:
ax[1, 1].plot(kde_time, kde/len(offsets),
color='gray')
for kde in loser_physicals_boot[-1]:
ax[1, 2].plot(kde_time, kde/len(physicals),
color='gray')
# plot bootstrap percentiles
ax[0, 0].fill_between(
kde_time,
np.percentile(winner_onsets_boot[-1], 5, axis=0)/len(onsets),
np.percentile(winner_onsets_boot[-1], 95, axis=0)/len(onsets),
color='gray',
alpha=0.5)
ax[0, 1].fill_between(
kde_time,
np.percentile(winner_offsets_boot[-1], 5, axis=0)/len(offsets),
np.percentile(
winner_offsets_boot[-1], 95, axis=0)/len(offsets),
color='gray',
alpha=0.5)
ax[0, 2].fill_between(
kde_time,
np.percentile(
winner_physicals_boot[-1], 5, axis=0)/len(physicals),
np.percentile(
winner_physicals_boot[-1], 95, axis=0)/len(physicals),
color='gray',
alpha=0.5)
ax[1, 0].fill_between(
kde_time,
np.percentile(loser_onsets_boot[-1], 5, axis=0)/len(onsets),
np.percentile(loser_onsets_boot[-1], 95, axis=0)/len(onsets),
color='gray',
alpha=0.5)
ax[1, 1].fill_between(
kde_time,
np.percentile(loser_offsets_boot[-1], 5, axis=0)/len(offsets),
np.percentile(loser_offsets_boot[-1], 95, axis=0)/len(offsets),
color='gray',
alpha=0.5)
ax[1, 2].fill_between(
kde_time,
np.percentile(
loser_physicals_boot[-1], 5, axis=0)/len(physicals),
np.percentile(
loser_physicals_boot[-1], 95, axis=0)/len(physicals),
color='gray',
alpha=0.5)
# ax[0, 0].fill_between(
# kde_time,
# np.percentile(winner_onsets_boot[-1], 5, axis=0),
# np.percentile(winner_onsets_boot[-1], 95, axis=0),
# color='gray',
# alpha=0.5)
# ax[0, 1].fill_between(
# kde_time,
# np.percentile(winner_offsets_boot[-1], 5, axis=0),
# np.percentile(
# winner_offsets_boot[-1], 95, axis=0),
# color='gray',
# alpha=0.5)
# ax[0, 2].fill_between(
# kde_time,
# np.percentile(
# winner_physicals_boot[-1], 5, axis=0),
# np.percentile(
# winner_physicals_boot[-1], 95, axis=0),
# color='gray',
# alpha=0.5)
# ax[1, 0].fill_between(
# kde_time,
# np.percentile(loser_onsets_boot[-1], 5, axis=0),
# np.percentile(loser_onsets_boot[-1], 95, axis=0),
# color='gray',
# alpha=0.5)
# ax[1, 1].fill_between(
# kde_time,
# np.percentile(loser_offsets_boot[-1], 5, axis=0),
# np.percentile(loser_offsets_boot[-1], 95, axis=0),
# color='gray',
# alpha=0.5)
# ax[1, 2].fill_between(
# kde_time,
# np.percentile(
# loser_physicals_boot[-1], 5, axis=0),
# np.percentile(
# loser_physicals_boot[-1], 95, axis=0),
# color='gray',
# alpha=0.5)
ax[0, 0].plot(kde_time, np.median(winner_onsets_boot[-1], axis=0)/len(onsets),
color='black', linewidth=2)
ax[0, 1].plot(kde_time, np.median(winner_offsets_boot[-1], axis=0)/len(offsets),
color='black', linewidth=2)
ax[0, 2].plot(kde_time, np.median(winner_physicals_boot[-1], axis=0)/len(physicals),
color='black', linewidth=2)
ax[1, 0].plot(kde_time, np.median(loser_onsets_boot[-1], axis=0)/len(onsets),
color='black', linewidth=2)
ax[1, 1].plot(kde_time, np.median(loser_offsets_boot[-1], axis=0)/len(offsets),
color='black', linewidth=2)
ax[1, 2].plot(kde_time, np.median(loser_physicals_boot[-1], axis=0)/len(physicals),
color='black', linewidth=2)
# ax[0, 0].plot(kde_time, np.median(winner_onsets_boot[-1], axis=0),
# color='black', linewidth=2)
# ax[0, 1].plot(kde_time, np.median(winner_offsets_boot[-1], axis=0),
# color='black', linewidth=2)
# ax[0, 2].plot(kde_time, np.median(winner_physicals_boot[-1], axis=0),
# color='black', linewidth=2)
# ax[1, 0].plot(kde_time, np.median(loser_onsets_boot[-1], axis=0),
# color='black', linewidth=2)
# ax[1, 1].plot(kde_time, np.median(loser_offsets_boot[-1], axis=0),
# color='black', linewidth=2)
# ax[1, 2].plot(kde_time, np.median(loser_physicals_boot[-1], axis=0),
# color='black', linewidth=2)
ax[0, 0].set_xlim(-30, 30)
plt.show()
@@ -345,20 +365,18 @@ def main(dataroot):
loser_offsets_conv = loser_offsets_conv / offset_count
loser_physicals_conv = loser_physicals_conv / physical_count
embed()
winner_onsets_boot = np.concatenate(
winner_onsets_boot) / onset_count
winner_onsets_boot)
winner_offsets_boot = np.concatenate(
winner_offsets_boot) / offset_count
winner_offsets_boot)
winner_physicals_boot = np.concatenate(
winner_physicals_boot) / physical_count
winner_physicals_boot)
loser_onsets_boot = np.concatenate(
loser_onsets_boot) / onset_count
loser_onsets_boot)
loser_offsets_boot = np.concatenate(
loser_offsets_boot) / offset_count
loser_offsets_boot)
loser_physicals_boot = np.concatenate(
loser_physicals_boot) / physical_count
loser_physicals_boot)
percs = [5, 50, 95]
winner_onsets_boot_quarts = np.percentile(
@@ -378,52 +396,73 @@ def main(dataroot):
21*ps.cm, 10*ps.cm), sharey=True, sharex=True)
ax[0, 0].plot(kde_time, winner_onsets_conv)
ax[0, 1].plot(kde_time, winner_offsets_conv)
ax[0, 2].plot(kde_time, winner_physicals_conv)
ax[1, 0].plot(kde_time, loser_onsets_conv)
ax[1, 1].plot(kde_time, loser_offsets_conv)
ax[1, 2].plot(kde_time, loser_physicals_conv)
ax[0, 0].plot(kde_time, winner_onsets_boot_quarts[1], c=ps.black)
ax[0, 1].plot(kde_time, winner_offsets_boot_quarts[1], c=ps.black)
ax[0, 2].plot(kde_time, winner_physicals_boot_quarts[1], c=ps.black)
ax[1, 0].plot(kde_time, loser_onsets_boot_quarts[1], c=ps.black)
ax[1, 1].plot(kde_time, loser_offsets_boot_quarts[1], c=ps.black)
ax[1, 2].plot(kde_time, loser_physicals_boot_quarts[1], c=ps.black)
# for kde in winner_onsets_boot:
# ax[0, 0].plot(kde_time, kde,
# color='gray')
# for kde in winner_offsets_boot:
# ax[0, 1].plot(kde_time, kde,
# color='gray')
# for kde in winner_physicals_boot:
# ax[0, 2].plot(kde_time, kde,
# color='gray')
# for kde in loser_onsets_boot:
# ax[1, 0].plot(kde_time, kde,
# color='gray')
# for kde in loser_offsets_boot:
# ax[1, 1].plot(kde_time, kde,
# color='gray')
# for kde in loser_physicals_boot:
# ax[1, 2].plot(kde_time, kde,
# color='gray')
ax[0, 0].fill_between(kde_time,
winner_onsets_boot_quarts[0],
winner_onsets_boot_quarts[2],
color=ps.gray,
alpha=0.5)
ax[0, 0].plot(kde_time, winner_onsets_boot_quarts[1], c=ps.black)
ax[0, 1].plot(kde_time, winner_offsets_conv)
ax[0, 1].fill_between(kde_time,
winner_offsets_boot_quarts[0],
winner_offsets_boot_quarts[2],
color=ps.gray,
alpha=0.5)
ax[0, 1].plot(kde_time, winner_offsets_boot_quarts[1], c=ps.black)
ax[0, 2].plot(kde_time, winner_physicals_conv)
ax[0, 2].fill_between(kde_time,
loser_physicals_boot_quarts[0],
loser_physicals_boot_quarts[2],
color=ps.gray,
alpha=0.5)
ax[0, 2].plot(kde_time, winner_physicals_boot_quarts[1], c=ps.black)
ax[1, 0].plot(kde_time, loser_onsets_conv)
ax[1, 0].fill_between(kde_time,
loser_onsets_boot_quarts[0],
loser_onsets_boot_quarts[2],
color=ps.gray,
alpha=0.5)
ax[1, 0].plot(kde_time, loser_onsets_boot_quarts[1], c=ps.black)
ax[1, 1].plot(kde_time, loser_offsets_conv)
ax[1, 1].fill_between(kde_time,
loser_offsets_boot_quarts[0],
loser_offsets_boot_quarts[2],
color=ps.gray,
alpha=0.5)
ax[1, 1].plot(kde_time, loser_offsets_boot_quarts[1], c=ps.black)
ax[1, 2].plot(kde_time, loser_physicals_conv)
ax[1, 2].fill_between(kde_time,
loser_physicals_boot_quarts[0],
loser_physicals_boot_quarts[2],
color=ps.gray,
alpha=0.5)
ax[1, 2].plot(kde_time, loser_physicals_boot_quarts[1], c=ps.black)
plt.show()