all event triggered chirps + chirprate with gaussian kernel
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@ -6,7 +6,7 @@ import matplotlib.pyplot as plt
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from IPython import embed
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from pandas import read_csv
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from modules.logger import makeLogger
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from modules.datahandling import causal_kde1d
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from modules.datahandling import causal_kde1d, acausal_kde1d
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logger = makeLogger(__name__)
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@ -129,17 +129,18 @@ def event_triggered_chirps(
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event: np.ndarray,
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chirps:np.ndarray,
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time_before_event: int,
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time_after_event: int
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)-> tuple[np.ndarray, np.ndarray]:
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time_after_event: int,
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dt: float,
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width: float,
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)-> tuple[np.ndarray, np.ndarray, np.ndarray]:
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event_chirps = [] # chirps that are in specified window around event
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centered_chirps = [] # timestamps of chirps around event centered on the event timepoint
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for event_timestamp in event:
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start = event_timestamp - time_before_event # timepoint of window start
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stop = event_timestamp + time_after_event # timepoint of window ending
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chirps_around_event = [c for c in chirps if (c >= start) & (c <= stop)] # get chirps that are in a -5 to +5 sec window around event
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start = event_timestamp - time_before_event
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stop = event_timestamp + time_after_event
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chirps_around_event = [c for c in chirps if (c >= start) & (c <= stop)]
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event_chirps.append(chirps_around_event)
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if len(chirps_around_event) == 0:
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continue
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@ -147,7 +148,11 @@ def event_triggered_chirps(
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centered_chirps.append(chirps_around_event - event_timestamp)
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centered_chirps = np.concatenate(centered_chirps, axis=0) # convert list of arrays to one array for plotting
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return event_chirps, centered_chirps
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# Kernel density estimation
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time = np.arange(-time_before_event, time_after_event, dt)
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centered_chirps_convolved = (acausal_kde1d(centered_chirps, time, width)) / len(event)
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return event_chirps, centered_chirps, centered_chirps_convolved
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def main(datapath: str):
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@ -167,46 +172,79 @@ def main(datapath: str):
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category, timestamps = correct_chasing_events(category, timestamps)
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# split categories
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chasing_onset = timestamps[category == 0]
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chasing_offset = timestamps[category == 1]
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physical_contact = timestamps[category == 2]
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chasing_onsets = timestamps[category == 0]
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chasing_offsets = timestamps[category == 1]
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physical_contacts = timestamps[category == 2]
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chasing_durations = []
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# Calculate chasing duration to evaluate a nice time window for kernel density estimation
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for onset, offset in zip(chasing_onsets, chasing_offsets):
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duration = offset - onset
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chasing_durations.append(duration)
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fig, ax = plt.subplots()
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ax.boxplot(chasing_durations)
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plt.show()
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plt.close()
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# Get fish ids
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fish_ids = np.unique(chirps_fish_ids)
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##### Chasing triggered chirps CTC #####
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# Evaluate how many chirps were emitted in specific time window around the chasing onset events
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# # Associate chirps to individual fish
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# fish1 = chirps[chirps_fish_ids == fish_ids[0]]
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# fish2 = chirps[chirps_fish_ids == fish_ids[1]]
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# fish = [len(fish1), len(fish2)]
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# Define time window for chirp around event analysis
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time_before_event = 30
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time_after_event = 60
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dt = 0.01
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width = 1
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# Iterate over chasing onsets (later over fish)
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time_around_event = 5 # time window around the event in which chirps are counted, 5 = -5 to +5 sec around event
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#### Loop crashes at concatenate in function ####
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# for i in range(len(fish_ids)):
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# fish = fish_ids[i]
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# chirps = chirps[chirps_fish_ids == fish]
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# print(fish)
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for i in range(len(fish_ids)):
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fish = fish_ids[i]
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chirps_temp = chirps[chirps_fish_ids == fish]
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print(fish)
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chasing_chirps, centered_chasing_chirps = event_triggered_chirps(chasing_onset, chirps, time_around_event, time_around_event)
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physical_chirps, centered_physical_chirps = event_triggered_chirps(physical_contact, chirps, time_around_event, time_around_event)
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##### Chirps around events #####
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time = np.arange(-time_before_event, time_after_event, dt)
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# Kernel density estimation ???
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# centered_chasing_chirps_convolved = gaussian_filter1d(centered_chasing_chirps, 5)
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# Chirps around chasing onsets
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_, centered_chasing_onset_chirps, cc_chasing_onset_chirps = event_triggered_chirps(chasing_onsets, chirps_temp, time_before_event, time_after_event, dt, width)
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# Chirps around chasing offsets
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_, centered_chasing_offset_chirps, cc_chasing_offset_chirps = event_triggered_chirps(chasing_offsets, chirps_temp, time_before_event, time_after_event, dt, width)
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# Chirps around physical contacts
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_, centered_physical_chirps, cc_physical_chirps = event_triggered_chirps(physical_contacts, chirps_temp, time_before_event, time_after_event, dt, width)
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# centered_chasing = chasing_onset[0] - chasing_onset[0] ## get the 0 timepoint for plotting; set one chasing event to 0
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offsets = [0.5, 1]
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fig4, ax4 = plt.subplots(figsize=(20 / 2.54, 12 / 2.54), constrained_layout=True)
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ax4.eventplot(np.array([centered_chasing_chirps, centered_physical_chirps]), lineoffsets=offsets, linelengths=0.25, colors=['g', 'r'])
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ax4.vlines(0, 0, 1.5, 'tab:grey', 'dashed', 'Timepoint of event')
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# ax4.plot(centered_chasing_chirps_convolved)
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ax4.set_yticks(offsets)
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ax4.set_yticklabels(['Chasings', 'Physical \n contacts'])
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ax4.set_xlabel('Time[s]')
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ax4.set_ylabel('Type of event')
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plt.show()
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# Associate chirps to inidividual fish
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fish1 = chirps[chirps_fish_ids == fish_ids[0]]
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fish2 = chirps[chirps_fish_ids == fish_ids[1]]
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fish = [len(fish1), len(fish2)]
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fig, ax = plt.subplots(1, 3, figsize=(50 / 2.54, 15 / 2.54), constrained_layout=True, sharey='all')
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offset = [0.25]
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ax[0].set_xlabel('Time[s]')
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# Plot chasing onsets
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ax[0].set_ylabel('Chirp rate [Hz]')
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ax[0].plot(time, cc_chasing_onset_chirps, color='tab:blue')
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ax0 = ax[0].twinx()
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ax0.eventplot(np.array([centered_chasing_onset_chirps]), lineoffsets=offset, linelengths=0.1, colors=['tab:green'])
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ax0.vlines(0, 0, 1.5, 'tab:grey', 'dashed')
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ax0.set_yticklabels([])
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ax0.set_yticks([])
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# Plot chasing offets
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ax[1].set_xlabel('Time[s]')
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ax[1].plot(time, cc_chasing_offset_chirps, color='tab:blue')
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ax1 = ax[1].twinx()
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ax1.eventplot(np.array([centered_chasing_offset_chirps]), lineoffsets=offset, linelengths=0.1, colors=['tab:purple'])
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ax1.vlines(0, 0, 1.5, 'tab:grey', 'dashed')
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ax1.set_yticklabels([])
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ax1.set_yticks([])
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# Plot physical contacts
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ax[2].set_xlabel('Time[s]')
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ax[2].plot(time, cc_physical_chirps, color='tab:blue')
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ax2 = ax[2].twinx()
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ax2.eventplot(np.array([centered_physical_chirps]), lineoffsets=offset, linelengths=0.1, colors=['tab:red'])
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ax2.vlines(0, 0, 1.5, 'tab:grey', 'dashed')
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ax2.set_yticklabels([])
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ax2.set_yticks([])
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plt.show()
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### Plots:
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# 1. All recordings, all fish, all chirps
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