raab/GP2023_chirp_detection
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all event triggered chirps + chirprate with gaussian kernel

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sprause 2023-01-23 13:48:40 +01:00
parent 8b327fdcbf
commit 5a0853a023
1 changed files with 80 additions and 42 deletions
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122
code/eventchirpsplots.py
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@ -6,7 +6,7 @@ import matplotlib.pyplot as plt
from IPython import embed from IPython import embed
from pandas import read_csv from pandas import read_csv
from modules.logger import makeLogger from modules.logger import makeLogger
from modules.datahandling import causal_kde1d from modules.datahandling import causal_kde1d, acausal_kde1d
logger = makeLogger(__name__) logger = makeLogger(__name__)
@ -129,17 +129,18 @@ def event_triggered_chirps(
event: np.ndarray, event: np.ndarray,
chirps:np.ndarray, chirps:np.ndarray,
time_before_event: int, time_before_event: int,
time_after_event: int time_after_event: int,
)-> tuple[np.ndarray, np.ndarray]: dt: float,
width: float,
)-> tuple[np.ndarray, np.ndarray, np.ndarray]:
event_chirps = [] # chirps that are in specified window around event event_chirps = [] # chirps that are in specified window around event
centered_chirps = [] # timestamps of chirps around event centered on the event timepoint centered_chirps = [] # timestamps of chirps around event centered on the event timepoint
for event_timestamp in event: for event_timestamp in event:
start = event_timestamp - time_before_event # timepoint of window start start = event_timestamp - time_before_event
stop = event_timestamp + time_after_event # timepoint of window ending stop = event_timestamp + time_after_event
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 chirps_around_event = [c for c in chirps if (c >= start) & (c <= stop)]
event_chirps.append(chirps_around_event) event_chirps.append(chirps_around_event)
if len(chirps_around_event) == 0: if len(chirps_around_event) == 0:
continue continue
@ -147,7 +148,11 @@ def event_triggered_chirps(
centered_chirps.append(chirps_around_event - event_timestamp) centered_chirps.append(chirps_around_event - event_timestamp)
centered_chirps = np.concatenate(centered_chirps, axis=0) # convert list of arrays to one array for plotting centered_chirps = np.concatenate(centered_chirps, axis=0) # convert list of arrays to one array for plotting
return event_chirps, centered_chirps # Kernel density estimation
time = np.arange(-time_before_event, time_after_event, dt)
centered_chirps_convolved = (acausal_kde1d(centered_chirps, time, width)) / len(event)
return event_chirps, centered_chirps, centered_chirps_convolved
def main(datapath: str): def main(datapath: str):
@ -167,46 +172,79 @@ def main(datapath: str):
category, timestamps = correct_chasing_events(category, timestamps) category, timestamps = correct_chasing_events(category, timestamps)
# split categories # split categories
chasing_onset = timestamps[category == 0] chasing_onsets = timestamps[category == 0]
chasing_offset = timestamps[category == 1] chasing_offsets = timestamps[category == 1]
physical_contact = timestamps[category == 2] physical_contacts = timestamps[category == 2]
chasing_durations = []
# Calculate chasing duration to evaluate a nice time window for kernel density estimation
for onset, offset in zip(chasing_onsets, chasing_offsets):
duration = offset - onset
chasing_durations.append(duration)
fig, ax = plt.subplots()
ax.boxplot(chasing_durations)
plt.show()
plt.close()
# Get fish ids # Get fish ids
fish_ids = np.unique(chirps_fish_ids) fish_ids = np.unique(chirps_fish_ids)
##### Chasing triggered chirps CTC ##### # # Associate chirps to individual fish
# Evaluate how many chirps were emitted in specific time window around the chasing onset events # fish1 = chirps[chirps_fish_ids == fish_ids[0]]
# fish2 = chirps[chirps_fish_ids == fish_ids[1]]
# Iterate over chasing onsets (later over fish) # fish = [len(fish1), len(fish2)]
time_around_event = 5 # time window around the event in which chirps are counted, 5 = -5 to +5 sec around event
#### Loop crashes at concatenate in function ####
# for i in range(len(fish_ids)):
# fish = fish_ids[i]
# chirps = chirps[chirps_fish_ids == fish]
# print(fish)
chasing_chirps, centered_chasing_chirps = event_triggered_chirps(chasing_onset, chirps, time_around_event, time_around_event) # Define time window for chirp around event analysis
physical_chirps, centered_physical_chirps = event_triggered_chirps(physical_contact, chirps, time_around_event, time_around_event) time_before_event = 30
time_after_event = 60
dt = 0.01
width = 1
# Kernel density estimation ??? #### Loop crashes at concatenate in function ####
# centered_chasing_chirps_convolved = gaussian_filter1d(centered_chasing_chirps, 5) for i in range(len(fish_ids)):
fish = fish_ids[i]
# centered_chasing = chasing_onset[0] - chasing_onset[0] ## get the 0 timepoint for plotting; set one chasing event to 0 chirps_temp = chirps[chirps_fish_ids == fish]
offsets = [0.5, 1] print(fish)
fig4, ax4 = plt.subplots(figsize=(20 / 2.54, 12 / 2.54), constrained_layout=True)
ax4.eventplot(np.array([centered_chasing_chirps, centered_physical_chirps]), lineoffsets=offsets, linelengths=0.25, colors=['g', 'r']) ##### Chirps around events #####
ax4.vlines(0, 0, 1.5, 'tab:grey', 'dashed', 'Timepoint of event') time = np.arange(-time_before_event, time_after_event, dt)
# ax4.plot(centered_chasing_chirps_convolved)
ax4.set_yticks(offsets) # Chirps around chasing onsets
ax4.set_yticklabels(['Chasings', 'Physical \n contacts']) _, centered_chasing_onset_chirps, cc_chasing_onset_chirps = event_triggered_chirps(chasing_onsets, chirps_temp, time_before_event, time_after_event, dt, width)
ax4.set_xlabel('Time[s]') # Chirps around chasing offsets
ax4.set_ylabel('Type of event') _, centered_chasing_offset_chirps, cc_chasing_offset_chirps = event_triggered_chirps(chasing_offsets, chirps_temp, time_before_event, time_after_event, dt, width)
plt.show() # 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)
# Associate chirps to inidividual fish
fish1 = chirps[chirps_fish_ids == fish_ids[0]] fig, ax = plt.subplots(1, 3, figsize=(50 / 2.54, 15 / 2.54), constrained_layout=True, sharey='all')
fish2 = chirps[chirps_fish_ids == fish_ids[1]] offset = [0.25]
fish = [len(fish1), len(fish2)] 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: ### Plots:
# 1. All recordings, all fish, all chirps # 1. All recordings, all fish, all chirps