raab/GP2023_chirp_detection
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implemented bootstrapping, q5 of physical still weird

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sprause 2023-01-23 15:58:22 +01:00
parent 5a0853a023
commit b1b9d9d3f1
1 changed files with 85 additions and 48 deletions
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code/eventchirpsplots.py
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@ -195,64 +195,101 @@ def main(datapath: str):
# fish2 = chirps[chirps_fish_ids == fish_ids[1]]
# fish = [len(fish1), len(fish2)]
# Define time window for chirp around event analysis
# Define time window for chirps around event analysis
time_before_event = 30
time_after_event = 60
dt = 0.01
width = 1
time = np.arange(-time_before_event, time_after_event, dt)
##### Chirps around events, all fish, one recording #####
# Chirps around chasing onsets
_, centered_chasing_onset_chirps, cc_chasing_onset_chirps = event_triggered_chirps(chasing_onsets, chirps, time_before_event, time_after_event, dt, width)
# Chirps around chasing offsets
_, centered_chasing_offset_chirps, cc_chasing_offset_chirps = event_triggered_chirps(chasing_offsets, chirps, time_before_event, time_after_event, dt, width)
# Chirps around physical contacts
_, centered_physical_chirps, cc_physical_chirps = event_triggered_chirps(physical_contacts, chirps, time_before_event, time_after_event, dt, width)
## Shuffled chirps ##
nbootstrapping = 1000
nshuffled_chirps_onset = []
nshuffled_chirps_offset = []
nshuffled_chirps_physical = []
for i in range(nbootstrapping):
# Calculate interchirp intervals; add first chirp timestamp in beginning to get equal lengths
interchirp_intervals = np.append(np.array([chirps[0]]), np.diff(chirps))
np.random.shuffle(interchirp_intervals)
shuffled_chirps = np.cumsum(interchirp_intervals)
# Shuffled chasing onset chirps
_, _, cc_shuffled_onset_chirps = event_triggered_chirps(chasing_onsets, shuffled_chirps, time_before_event, time_after_event, dt, width)
nshuffled_chirps_onset.append(cc_shuffled_onset_chirps)
# Shuffled chasing offset chirps
_, _, cc_shuffled_offset_chirps = event_triggered_chirps(chasing_offsets, shuffled_chirps, time_before_event, time_after_event, dt, width)
nshuffled_chirps_offset.append(cc_shuffled_offset_chirps)
# Shuffled physical contact chirps
_, _, cc_shuffled_physical_chirps = event_triggered_chirps(physical_contacts, shuffled_chirps, time_before_event, time_after_event, dt, width)
nshuffled_chirps_physical.append(cc_shuffled_physical_chirps)
shuffled_q5_onset, shuffled_median_onset, shuffled_q95_onset = np.percentile(nshuffled_chirps_onset, (5, 50, 95), axis=0)
shuffled_q5_offset, shuffled_median_offset, shuffled_q95_offset = np.percentile(nshuffled_chirps_offset, (5, 50, 95), axis=0)
shuffled_q5_physical, shuffled_median_physical, shuffled_q95_physical = np.percentile(nshuffled_chirps_physical, (5, 50, 95), axis=0)
embed()
# Plot all events with all shuffled
fig, ax = plt.subplots(1, 3, figsize=(50 / 2.54, 15 / 2.54), constrained_layout=True, sharey='all')
offset = [1.35]
ax[0].set_xlabel('Time[s]')
# Plot chasing onsets
ax[0].set_ylabel('Chirp rate [Hz]')
ax[0].plot(time, cc_chasing_onset_chirps, color='tab:blue', zorder=100)
ax0 = ax[0].twinx()
ax0.eventplot(np.array([centered_chasing_onset_chirps]), lineoffsets=offset, linelengths=0.1, colors=['tab:green'], alpha=0.25, zorder=-100)
ax0.vlines(0, 0, 1.5, 'tab:grey', 'dashed')
ax0.set_yticklabels([])
ax0.set_yticks([])
ax[0].fill_between(time, shuffled_q5_onset, shuffled_q95_onset, color='tab:gray', alpha=0.5)
ax[0].plot(time, shuffled_median_onset, color='k')
# Plot chasing offets
ax[1].set_xlabel('Time[s]')
ax[1].plot(time, cc_chasing_offset_chirps, color='tab:blue', zorder=100)
ax1 = ax[1].twinx()
ax1.eventplot(np.array([centered_chasing_offset_chirps]), lineoffsets=offset, linelengths=0.1, colors=['tab:purple'], alpha=0.25, zorder=-100)
ax1.vlines(0, 0, 1.5, 'tab:grey', 'dashed')
ax1.set_yticklabels([])
ax1.set_yticks([])
ax[1].fill_between(time, shuffled_q5_offset, shuffled_q95_offset, color='tab:gray', alpha=0.5)
ax[1].plot(time, shuffled_median_offset, color='k')
# Plot physical contacts
ax[2].set_xlabel('Time[s]')
ax[2].plot(time, cc_physical_chirps, color='tab:blue', zorder=100)
ax2 = ax[2].twinx()
ax2.eventplot(np.array([centered_physical_chirps]), lineoffsets=offset, linelengths=0.1, colors=['tab:red'], alpha=0.25, zorder=-100)
ax2.vlines(0, 0, 1.5, 'tab:grey', 'dashed')
ax2.set_yticklabels([])
ax2.set_yticks([])
ax[2].fill_between(time, shuffled_q5_physical, shuffled_q95_physical, color='tab:gray', alpha=0.5)
ax[2].plot(time, shuffled_median_physical, color='k')
plt.show()
embed()
exit()
#### Loop crashes at concatenate in function ####
#### Chirps around events, only winners, one recording ####
for i in range(len(fish_ids)):
fish = fish_ids[i]
chirps_temp = chirps[chirps_fish_ids == fish]
print(fish)
##### Chirps around events #####
time = np.arange(-time_before_event, time_after_event, dt)
# Chirps around chasing onsets
_, centered_chasing_onset_chirps, cc_chasing_onset_chirps = event_triggered_chirps(chasing_onsets, chirps_temp, time_before_event, time_after_event, dt, width)
# Chirps around chasing offsets
_, centered_chasing_offset_chirps, cc_chasing_offset_chirps = event_triggered_chirps(chasing_offsets, chirps_temp, time_before_event, time_after_event, dt, width)
# Chirps around physical contacts
_, centered_physical_chirps, cc_physical_chirps = event_triggered_chirps(physical_contacts, chirps_temp, time_before_event, time_after_event, dt, width)
fig, ax = plt.subplots(1, 3, figsize=(50 / 2.54, 15 / 2.54), constrained_layout=True, sharey='all')
offset = [0.25]
ax[0].set_xlabel('Time[s]')
# Plot chasing onsets
ax[0].set_ylabel('Chirp rate [Hz]')
ax[0].plot(time, cc_chasing_onset_chirps, color='tab:blue')
ax0 = ax[0].twinx()
ax0.eventplot(np.array([centered_chasing_onset_chirps]), lineoffsets=offset, linelengths=0.1, colors=['tab:green'])
ax0.vlines(0, 0, 1.5, 'tab:grey', 'dashed')
ax0.set_yticklabels([])
ax0.set_yticks([])
# Plot chasing offets
ax[1].set_xlabel('Time[s]')
ax[1].plot(time, cc_chasing_offset_chirps, color='tab:blue')
ax1 = ax[1].twinx()
ax1.eventplot(np.array([centered_chasing_offset_chirps]), lineoffsets=offset, linelengths=0.1, colors=['tab:purple'])
ax1.vlines(0, 0, 1.5, 'tab:grey', 'dashed')
ax1.set_yticklabels([])
ax1.set_yticks([])
# Plot physical contacts
ax[2].set_xlabel('Time[s]')
ax[2].plot(time, cc_physical_chirps, color='tab:blue')
ax2 = ax[2].twinx()
ax2.eventplot(np.array([centered_physical_chirps]), lineoffsets=offset, linelengths=0.1, colors=['tab:red'])
ax2.vlines(0, 0, 1.5, 'tab:grey', 'dashed')
ax2.set_yticklabels([])
ax2.set_yticks([])
plt.show()
### Plots:
# 1. All recordings, all fish, all chirps
# One CTC, one PTC
# 2. All recordings, only winners
# One CTC, one PTC
# 3. All recordings, all losers
# One CTC, one PTC
#### Chirps around events, only losers, one recording ####
embed()