raab/GP2023_chirp_detection
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weygoldt 2023-04-11 15:33:07 +02:00
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26 changed files with 1177 additions and 810 deletions
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36
chirp_instantaneous_freq/filters.py
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@ -59,14 +59,14 @@ def instantaneous_frequency(
def inst_freq(signal, fs): def inst_freq(signal, fs):
""" """
Computes the instantaneous frequency of a periodic signal using zero-crossings. Computes the instantaneous frequency of a periodic signal using zero-crossings.
Parameters: Parameters:
----------- -----------
signal : array-like signal : array-like
The input signal. The input signal.
fs : float fs : float
The sampling frequency of the input signal. The sampling frequency of the input signal.
Returns: Returns:
-------- --------
freq : array-like freq : array-like
@ -74,29 +74,30 @@ def inst_freq(signal, fs):
""" """
# Compute the sign of the signal # Compute the sign of the signal
sign = np.sign(signal) sign = np.sign(signal)
# Compute the crossings of the sign signal with a zero line # Compute the crossings of the sign signal with a zero line
crossings = np.where(np.diff(sign))[0] crossings = np.where(np.diff(sign))[0]
# Compute the time differences between zero crossings # Compute the time differences between zero crossings
dt = np.diff(crossings) / fs dt = np.diff(crossings) / fs
# Compute the instantaneous frequency as the reciprocal of the time differences # Compute the instantaneous frequency as the reciprocal of the time differences
freq = 1 / dt freq = 1 / dt
# Gaussian filter the signal # Gaussian filter the signal
freq = gaussian_filter1d(freq, 10) freq = gaussian_filter1d(freq, 10)
# Pad the frequency vector with zeros to match the length of the input signal # Pad the frequency vector with zeros to match the length of the input signal
freq = np.pad(freq, (0, len(signal) - len(freq))) freq = np.pad(freq, (0, len(signal) - len(freq)))
return freq return freq
def bandpass_filter( def bandpass_filter(
signal: np.ndarray, signal: np.ndarray,
samplerate: float, samplerate: float,
lowf: float, lowf: float,
highf: float, highf: float,
) -> np.ndarray: ) -> np.ndarray:
"""Bandpass filter a signal. """Bandpass filter a signal.
@ -150,9 +151,7 @@ def highpass_filter(
def lowpass_filter( def lowpass_filter(
signal: np.ndarray, signal: np.ndarray, samplerate: float, cutoff: float
samplerate: float,
cutoff: float
) -> np.ndarray: ) -> np.ndarray:
"""Lowpass filter a signal. """Lowpass filter a signal.
@ -176,10 +175,9 @@ def lowpass_filter(
return filtered_signal return filtered_signal
def envelope(signal: np.ndarray, def envelope(
samplerate: float, signal: np.ndarray, samplerate: float, cutoff_frequency: float
cutoff_frequency: float ) -> np.ndarray:
) -> np.ndarray:
"""Calculate the envelope of a signal using a lowpass filter. """Calculate the envelope of a signal using a lowpass filter.
Parameters Parameters

34
chirp_instantaneous_freq/fish_signal.py
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@ -384,16 +384,14 @@ def chirps(
frequency = eodf * np.ones(n) frequency = eodf * np.ones(n)
am = np.ones(n) am = np.ones(n)
for time, width, size, kurtosis, contrast in zip(chirp_times, chirp_width, chirp_size, chirp_kurtosis, chirp_contrast): for time, width, size, kurtosis, contrast in zip(
chirp_times, chirp_width, chirp_size, chirp_kurtosis, chirp_contrast
):
# chirp frequency waveform: # chirp frequency waveform:
chirp_t = np.arange(-2.0 * width, 2.0 * width, 1.0 / samplerate) chirp_t = np.arange(-2.0 * width, 2.0 * width, 1.0 / samplerate)
chirp_sig = ( chirp_sig = 0.5 * width / (2.0 * np.log(10.0)) ** (0.5 / kurtosis)
0.5 * width / (2.0 * np.log(10.0)) ** (0.5 / kurtosis)
)
gauss = np.exp(-0.5 * ((chirp_t / chirp_sig) ** 2.0) ** kurtosis) gauss = np.exp(-0.5 * ((chirp_t / chirp_sig) ** 2.0) ** kurtosis)
# add chirps on baseline eodf: # add chirps on baseline eodf:
index = int(time * samplerate) index = int(time * samplerate)
i0 = index - len(gauss) // 2 i0 = index - len(gauss) // 2
@ -433,7 +431,7 @@ def rises(
Sampling rate in Hertz. Sampling rate in Hertz.
duration: float duration: float
Duration of the generated data in seconds. Duration of the generated data in seconds.
rise_times: list rise_times: list
Timestamp of each of the rises in seconds. Timestamp of each of the rises in seconds.
rise_size: list rise_size: list
Size of the respective rise (frequency increase above eodf) in Hertz. Size of the respective rise (frequency increase above eodf) in Hertz.
@ -452,15 +450,12 @@ def rises(
# baseline eod frequency: # baseline eod frequency:
frequency = eodf * np.ones(n) frequency = eodf * np.ones(n)
for time, size, riset, decayt in zip(rise_times, rise_size, rise_tau, decay_tau): for time, size, riset, decayt in zip(
rise_times, rise_size, rise_tau, decay_tau
):
# rise frequency waveform: # rise frequency waveform:
rise_t = np.arange(0.0, 5.0 * decayt, 1.0 / samplerate) rise_t = np.arange(0.0, 5.0 * decayt, 1.0 / samplerate)
rise = ( rise = size * (1.0 - np.exp(-rise_t / riset)) * np.exp(-rise_t / decayt)
size
* (1.0 - np.exp(-rise_t / riset))
* np.exp(-rise_t / decayt)
)
# add rises on baseline eodf: # add rises on baseline eodf:
index = int(time * samplerate) index = int(time * samplerate)
@ -472,13 +467,14 @@ def rises(
frequency[index : index + len(rise)] += rise frequency[index : index + len(rise)] += rise
return frequency return frequency
class FishSignal: class FishSignal:
def __init__(self, samplerate, duration, eodf, nchirps, nrises): def __init__(self, samplerate, duration, eodf, nchirps, nrises):
time = np.arange(0, duration, 1 / samplerate) time = np.arange(0, duration, 1 / samplerate)
chirp_times = np.random.uniform(0, duration, nchirps) chirp_times = np.random.uniform(0, duration, nchirps)
rise_times = np.random.uniform(0, duration, nrises) rise_times = np.random.uniform(0, duration, nrises)
# pick random parameters for chirps # pick random parameters for chirps
chirp_size = np.random.uniform(60, 200, nchirps) chirp_size = np.random.uniform(60, 200, nchirps)
chirp_width = np.random.uniform(0.01, 0.1, nchirps) chirp_width = np.random.uniform(0.01, 0.1, nchirps)
chirp_kurtosis = np.random.uniform(1, 1, nchirps) chirp_kurtosis = np.random.uniform(1, 1, nchirps)
@ -534,7 +530,6 @@ class FishSignal:
self.eodf = eodf self.eodf = eodf
def visualize(self): def visualize(self):
spec, freqs, spectime = ps.spectrogram( spec, freqs, spectime = ps.spectrogram(
data=self.signal, data=self.signal,
ratetime=self.samplerate, ratetime=self.samplerate,
@ -549,7 +544,12 @@ class FishSignal:
ax1.set_xlabel("Time (s)") ax1.set_xlabel("Time (s)")
ax1.set_title("EOD signal") ax1.set_title("EOD signal")
ax2.imshow(ps.decibel(spec), origin='lower', aspect="auto", extent=[spectime[0], spectime[-1], freqs[0], freqs[-1]]) ax2.imshow(
ps.decibel(spec),
origin="lower",
aspect="auto",
extent=[spectime[0], spectime[-1], freqs[0], freqs[-1]],
)
ax2.set_ylabel("Frequency (Hz)") ax2.set_ylabel("Frequency (Hz)")
ax2.set_xlabel("Time (s)") ax2.set_xlabel("Time (s)")
ax2.set_title("Spectrogram") ax2.set_title("Spectrogram")

124
chirp_instantaneous_freq/test_parameters.py
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@ -1,4 +1,4 @@
import numpy as np import numpy as np
import matplotlib.pyplot as plt import matplotlib.pyplot as plt
from fish_signal import chirps, wavefish_eods from fish_signal import chirps, wavefish_eods
from filters import bandpass_filter, instantaneous_frequency, inst_freq from filters import bandpass_filter, instantaneous_frequency, inst_freq
@ -6,18 +6,18 @@ from IPython import embed
def switch_test(test, defaultparams, testparams): def switch_test(test, defaultparams, testparams):
if test == 'width': if test == "width":
defaultparams['chirp_width'] = testparams['chirp_width'] defaultparams["chirp_width"] = testparams["chirp_width"]
key = 'chirp_width' key = "chirp_width"
elif test == 'size': elif test == "size":
defaultparams['chirp_size'] = testparams['chirp_size'] defaultparams["chirp_size"] = testparams["chirp_size"]
key = 'chirp_size' key = "chirp_size"
elif test == 'kurtosis': elif test == "kurtosis":
defaultparams['chirp_kurtosis'] = testparams['chirp_kurtosis'] defaultparams["chirp_kurtosis"] = testparams["chirp_kurtosis"]
key = 'chirp_kurtosis' key = "chirp_kurtosis"
elif test == 'contrast': elif test == "contrast":
defaultparams['chirp_contrast'] = testparams['chirp_contrast'] defaultparams["chirp_contrast"] = testparams["chirp_contrast"]
key = 'chirp_contrast' key = "chirp_contrast"
else: else:
raise ValueError("Test not recognized") raise ValueError("Test not recognized")
@ -29,31 +29,40 @@ def extract_dict(dict, index):
def main(test1, test2, resolution=10): def main(test1, test2, resolution=10):
assert test1 in [
assert test1 in ['width', 'size', 'kurtosis', 'contrast'], "Test1 not recognized" "width",
assert test2 in ['width', 'size', 'kurtosis', 'contrast'], "Test2 not recognized" "size",
"kurtosis",
# Define the parameters for the chirp simulations "contrast",
], "Test1 not recognized"
assert test2 in [
"width",
"size",
"kurtosis",
"contrast",
], "Test2 not recognized"
# Define the parameters for the chirp simulations
ntest = resolution ntest = resolution
defaultparams = dict( defaultparams = dict(
chirp_size = np.ones(ntest) * 100, chirp_size=np.ones(ntest) * 100,
chirp_width = np.ones(ntest) * 0.1, chirp_width=np.ones(ntest) * 0.1,
chirp_kurtosis = np.ones(ntest) * 1.0, chirp_kurtosis=np.ones(ntest) * 1.0,
chirp_contrast = np.ones(ntest) * 0.5, chirp_contrast=np.ones(ntest) * 0.5,
) )
testparams = dict( testparams = dict(
chirp_width = np.linspace(0.01, 0.2, ntest), chirp_width=np.linspace(0.01, 0.2, ntest),
chirp_size = np.linspace(50, 300, ntest), chirp_size=np.linspace(50, 300, ntest),
chirp_kurtosis = np.linspace(0.5, 1.5, ntest), chirp_kurtosis=np.linspace(0.5, 1.5, ntest),
chirp_contrast = np.linspace(0.01, 1.0, ntest), chirp_contrast=np.linspace(0.01, 1.0, ntest),
) )
key1, chirp_params = switch_test(test1, defaultparams, testparams) key1, chirp_params = switch_test(test1, defaultparams, testparams)
key2, chirp_params = switch_test(test2, chirp_params, testparams) key2, chirp_params = switch_test(test2, chirp_params, testparams)
# make the chirp trace # make the chirp trace
eodf = 500 eodf = 500
samplerate = 20000 samplerate = 20000
duration = 2 duration = 2
@ -63,40 +72,60 @@ def main(test1, test2, resolution=10):
tight_cutoffs = 10 tight_cutoffs = 10
distances = np.full((ntest, ntest), np.nan) distances = np.full((ntest, ntest), np.nan)
fig, axs = plt.subplots(ntest, ntest, figsize = (10, 10), sharex = True, sharey = True) fig, axs = plt.subplots(
ntest, ntest, figsize=(10, 10), sharex=True, sharey=True
)
axs = axs.flatten() axs = axs.flatten()
iter0 = 0 iter0 = 0
for iter1, test1_param in enumerate(chirp_params[key1]): for iter1, test1_param in enumerate(chirp_params[key1]):
for iter2, test2_param in enumerate(chirp_params[key2]): for iter2, test2_param in enumerate(chirp_params[key2]):
# get the chirp parameters for the current test # get the chirp parameters for the current test
inner_chirp_params = extract_dict(chirp_params, iter2) inner_chirp_params = extract_dict(chirp_params, iter2)
inner_chirp_params[key1] = test1_param inner_chirp_params[key1] = test1_param
inner_chirp_params[key2] = test2_param inner_chirp_params[key2] = test2_param
# make the chirp trace for the current chirp parameters # make the chirp trace for the current chirp parameters
sizes = np.ones(len(chirp_times)) * inner_chirp_params['chirp_size'] sizes = np.ones(len(chirp_times)) * inner_chirp_params["chirp_size"]
widths = np.ones(len(chirp_times)) * inner_chirp_params['chirp_width'] widths = (
kurtosis = np.ones(len(chirp_times)) * inner_chirp_params['chirp_kurtosis'] np.ones(len(chirp_times)) * inner_chirp_params["chirp_width"]
contrast = np.ones(len(chirp_times)) * inner_chirp_params['chirp_contrast'] )
kurtosis = (
np.ones(len(chirp_times)) * inner_chirp_params["chirp_kurtosis"]
)
contrast = (
np.ones(len(chirp_times)) * inner_chirp_params["chirp_contrast"]
)
# make the chirp trace # make the chirp trace
chirp_trace, ampmod = chirps(eodf, samplerate, duration, chirp_times, sizes, widths, kurtosis, contrast) chirp_trace, ampmod = chirps(
eodf,
samplerate,
duration,
chirp_times,
sizes,
widths,
kurtosis,
contrast,
)
signal = wavefish_eods( signal = wavefish_eods(
fish="Alepto", fish="Alepto",
frequency=chirp_trace, frequency=chirp_trace,
samplerate=samplerate, samplerate=samplerate,
duration=duration, duration=duration,
phase0=0.0, phase0=0.0,
noise_std=0.05 noise_std=0.05,
) )
signal = signal * ampmod signal = signal * ampmod
# apply broadband filter # apply broadband filter
wide_signal = bandpass_filter(signal, samplerate, eodf - wide_cutoffs, eodf + wide_cutoffs) wide_signal = bandpass_filter(
tight_signal = bandpass_filter(signal, samplerate, eodf - tight_cutoffs, eodf + tight_cutoffs) signal, samplerate, eodf - wide_cutoffs, eodf + wide_cutoffs
)
tight_signal = bandpass_filter(
signal, samplerate, eodf - tight_cutoffs, eodf + tight_cutoffs
)
# get the instantaneous frequency # get the instantaneous frequency
wide_frequency = inst_freq(wide_signal, samplerate) wide_frequency = inst_freq(wide_signal, samplerate)
@ -111,8 +140,9 @@ def main(test1, test2, resolution=10):
iter0 += 1 iter0 += 1
fig, ax = plt.subplots() fig, ax = plt.subplots()
ax.imshow(distances, cmap = 'jet') ax.imshow(distances, cmap="jet")
plt.show() plt.show()
if __name__ == "__main__": if __name__ == "__main__":
main('width', 'size') main("width", "size")

81
code/analysis.py
View File

@ -10,73 +10,84 @@ from modules.filters import bandpass_filter
def main(folder): def main(folder):
file = os.path.join(folder, 'traces-grid.raw') file = os.path.join(folder, "traces-grid.raw")
data = open_data(folder, 60.0, 0, channel=-1) data = open_data(folder, 60.0, 0, channel=-1)
time = np.load(folder + 'times.npy', allow_pickle=True) time = np.load(folder + "times.npy", allow_pickle=True)
freq = np.load(folder + 'fund_v.npy', allow_pickle=True) freq = np.load(folder + "fund_v.npy", allow_pickle=True)
ident = np.load(folder + 'ident_v.npy', allow_pickle=True) ident = np.load(folder + "ident_v.npy", allow_pickle=True)
idx = np.load(folder + 'idx_v.npy', allow_pickle=True) idx = np.load(folder + "idx_v.npy", allow_pickle=True)
t0 = 3*60*60 + 6*60 + 43.5 t0 = 3 * 60 * 60 + 6 * 60 + 43.5
dt = 60 dt = 60
data_oi = data[t0 * data.samplerate: (t0+dt)*data.samplerate, :] data_oi = data[t0 * data.samplerate : (t0 + dt) * data.samplerate, :]
for i in [10]: for i in [10]:
# getting the spectogramm # getting the spectogramm
spec_power, spec_freqs, spec_times = spectrogram( spec_power, spec_freqs, spec_times = spectrogram(
data_oi[:, i], ratetime=data.samplerate, freq_resolution=50, overlap_frac=0.0) data_oi[:, i],
fig, ax = plt.subplots(figsize=(20/2.54, 12/2.54)) ratetime=data.samplerate,
ax.pcolormesh(spec_times, spec_freqs, decibel( freq_resolution=50,
spec_power), vmin=-100, vmax=-50) overlap_frac=0.0,
)
fig, ax = plt.subplots(figsize=(20 / 2.54, 12 / 2.54))
ax.pcolormesh(
spec_times, spec_freqs, decibel(spec_power), vmin=-100, vmax=-50
)
for track_id in np.unique(ident): for track_id in np.unique(ident):
# window_index for time array in time window # window_index for time array in time window
window_index = np.arange(len(idx))[(ident == track_id) & window_index = np.arange(len(idx))[
(time[idx] >= t0) & (ident == track_id)
(time[idx] <= (t0+dt))] & (time[idx] >= t0)
& (time[idx] <= (t0 + dt))
]
freq_temp = freq[window_index] freq_temp = freq[window_index]
time_temp = time[idx[window_index]] time_temp = time[idx[window_index]]
#mean_freq = np.mean(freq_temp) # mean_freq = np.mean(freq_temp)
#fdata = bandpass_filter(data_oi[:, track_id], data.samplerate, mean_freq-5, mean_freq+200) # fdata = bandpass_filter(data_oi[:, track_id], data.samplerate, mean_freq-5, mean_freq+200)
ax.plot(time_temp - t0, freq_temp) ax.plot(time_temp - t0, freq_temp)
ax.set_ylim(500, 1000) ax.set_ylim(500, 1000)
plt.show() plt.show()
# filter plot # filter plot
id = 10. id = 10.0
i = 10 i = 10
window_index = np.arange(len(idx))[(ident == id) & window_index = np.arange(len(idx))[
(time[idx] >= t0) & (ident == id) & (time[idx] >= t0) & (time[idx] <= (t0 + dt))
(time[idx] <= (t0+dt))] ]
freq_temp = freq[window_index] freq_temp = freq[window_index]
time_temp = time[idx[window_index]] time_temp = time[idx[window_index]]
mean_freq = np.mean(freq_temp) mean_freq = np.mean(freq_temp)
fdata = bandpass_filter( fdata = bandpass_filter(
data_oi[:, i], rate=data.samplerate, lowf=mean_freq-5, highf=mean_freq+200) data_oi[:, i],
rate=data.samplerate,
lowf=mean_freq - 5,
highf=mean_freq + 200,
)
fig, ax = plt.subplots() fig, ax = plt.subplots()
ax.plot(np.arange(len(fdata))/data.samplerate, fdata, marker='*') ax.plot(np.arange(len(fdata)) / data.samplerate, fdata, marker="*")
# plt.show() # plt.show()
# freqency analyis of filtered data # freqency analyis of filtered data
time_fdata = np.arange(len(fdata))/data.samplerate time_fdata = np.arange(len(fdata)) / data.samplerate
roll_fdata = np.roll(fdata, shift=1) roll_fdata = np.roll(fdata, shift=1)
period_index = np.arange(len(fdata))[(roll_fdata < 0) & (fdata >= 0)] period_index = np.arange(len(fdata))[(roll_fdata < 0) & (fdata >= 0)]
plt.plot(time_fdata, fdata) plt.plot(time_fdata, fdata)
plt.scatter(time_fdata[period_index], fdata[period_index], c='r') plt.scatter(time_fdata[period_index], fdata[period_index], c="r")
plt.scatter(time_fdata[period_index-1], fdata[period_index-1], c='r') plt.scatter(time_fdata[period_index - 1], fdata[period_index - 1], c="r")
upper_bound = np.abs(fdata[period_index]) upper_bound = np.abs(fdata[period_index])
lower_bound = np.abs(fdata[period_index-1]) lower_bound = np.abs(fdata[period_index - 1])
upper_times = np.abs(time_fdata[period_index]) upper_times = np.abs(time_fdata[period_index])
lower_times = np.abs(time_fdata[period_index-1]) lower_times = np.abs(time_fdata[period_index - 1])
lower_ratio = lower_bound/(lower_bound+upper_bound) lower_ratio = lower_bound / (lower_bound + upper_bound)
upper_ratio = upper_bound/(lower_bound+upper_bound) upper_ratio = upper_bound / (lower_bound + upper_bound)
time_delta = upper_times-lower_times time_delta = upper_times - lower_times
true_zero = lower_times + time_delta*lower_ratio true_zero = lower_times + time_delta * lower_ratio
plt.scatter(true_zero, np.zeros(len(true_zero))) plt.scatter(true_zero, np.zeros(len(true_zero)))
@ -84,7 +95,7 @@ def main(folder):
inst_freq = 1 / np.diff(true_zero) inst_freq = 1 / np.diff(true_zero)
filtered_inst_freq = gaussian_filter1d(inst_freq, 0.005) filtered_inst_freq = gaussian_filter1d(inst_freq, 0.005)
fig, ax = plt.subplots() fig, ax = plt.subplots()
ax.plot(filtered_inst_freq, marker='.') ax.plot(filtered_inst_freq, marker=".")
# in 5 sekunden welcher fisch auf einer elektrode am # in 5 sekunden welcher fisch auf einer elektrode am
embed() embed()
@ -99,5 +110,7 @@ def main(folder):
pass pass
if __name__ == '__main__': if __name__ == "__main__":
main('/Users/acfw/Documents/uni_tuebingen/chirpdetection/gp_benda/data/2022-06-02-10_00/') main(
"/Users/acfw/Documents/uni_tuebingen/chirpdetection/gp_benda/data/2022-06-02-10_00/"
)

26
code/band_pass_problem.py
View File

@ -12,25 +12,27 @@ from modules.filehandling import LoadData
def main(folder): def main(folder):
data = LoadData(folder) data = LoadData(folder)
t0 = 3*60*60 + 6*60 + 43.5 t0 = 3 * 60 * 60 + 6 * 60 + 43.5
dt = 60 dt = 60
data_oi = data.raw[t0 * data.raw_rate: (t0+dt)*data.raw_rate, :] data_oi = data.raw[t0 * data.raw_rate : (t0 + dt) * data.raw_rate, :]
# good electrode # good electrode
electrode = 10 electrode = 10
data_oi = data_oi[:, electrode] data_oi = data_oi[:, electrode]
fig, axs = plt.subplots(2,1) fig, axs = plt.subplots(2, 1)
axs[0].plot( np.arange(data_oi.shape[0]) / data.raw_rate, data_oi) axs[0].plot(np.arange(data_oi.shape[0]) / data.raw_rate, data_oi)
for tr, track_id in enumerate(np.unique(data.ident[~np.isnan(data.ident)])): for tr, track_id in enumerate(np.unique(data.ident[~np.isnan(data.ident)])):
rack_window_index = np.arange(len(data.idx))[ rack_window_index = np.arange(len(data.idx))[
(data.ident == track_id) & (data.ident == track_id)
(data.time[data.idx] >= t0) & & (data.time[data.idx] >= t0)
(data.time[data.idx] <= (t0+dt))] & (data.time[data.idx] <= (t0 + dt))
]
freq_fish = data.freq[rack_window_index] freq_fish = data.freq[rack_window_index]
axs[1].plot(np.arange(freq_fish.shape[0]) / data.raw_rate, freq_fish) axs[1].plot(np.arange(freq_fish.shape[0]) / data.raw_rate, freq_fish)
plt.show() plt.show()
if __name__ == "__main__":
if __name__ == '__main__': main(
main('/Users/acfw/Documents/uni_tuebingen/chirpdetection/GP2023_chirp_detection/data/2022-06-02-10_00/') "/Users/acfw/Documents/uni_tuebingen/chirpdetection/GP2023_chirp_detection/data/2022-06-02-10_00/"
)

237
code/behavior.py
View File

@ -1,8 +1,8 @@
import os import os
import os import os
import numpy as np import numpy as np
import matplotlib.pyplot as plt import matplotlib.pyplot as plt
from IPython import embed from IPython import embed
from pandas import read_csv from pandas import read_csv
@ -11,51 +11,65 @@ from scipy.ndimage import gaussian_filter1d
logger = makeLogger(__name__) logger = makeLogger(__name__)
class Behavior: class Behavior:
"""Load behavior data from csv file as class attributes """Load behavior data from csv file as class attributes
Attributes Attributes
---------- ----------
behavior: 0: chasing onset, 1: chasing offset, 2: physical contact behavior: 0: chasing onset, 1: chasing offset, 2: physical contact
behavior_type: behavior_type:
behavioral_category: behavioral_category:
comment_start: comment_start:
comment_stop: comment_stop:
dataframe: pandas dataframe with all the data dataframe: pandas dataframe with all the data
duration_s: duration_s:
media_file: media_file:
observation_date: observation_date:
observation_id: observation_id:
start_s: start time of the event in seconds start_s: start time of the event in seconds
stop_s: stop time of the event in seconds stop_s: stop time of the event in seconds
total_length: total_length:
""" """
def __init__(self, folder_path: str) -> None: def __init__(self, folder_path: str) -> None:
LED_on_time_BORIS = np.load(
os.path.join(folder_path, "LED_on_time.npy"), allow_pickle=True
LED_on_time_BORIS = np.load(os.path.join(folder_path, 'LED_on_time.npy'), allow_pickle=True) )
self.time = np.load(os.path.join(folder_path, "times.npy"), allow_pickle=True) self.time = np.load(
csv_filename = [f for f in os.listdir(folder_path) if f.endswith('.csv')][0] # check if there are more than one csv file os.path.join(folder_path, "times.npy"), allow_pickle=True
)
csv_filename = [
f for f in os.listdir(folder_path) if f.endswith(".csv")
][
0
] # check if there are more than one csv file
self.dataframe = read_csv(os.path.join(folder_path, csv_filename)) self.dataframe = read_csv(os.path.join(folder_path, csv_filename))
self.chirps = np.load(os.path.join(folder_path, 'chirps.npy'), allow_pickle=True) self.chirps = np.load(
self.chirps_ids = np.load(os.path.join(folder_path, 'chirps_ids.npy'), allow_pickle=True) os.path.join(folder_path, "chirps.npy"), allow_pickle=True
)
self.chirps_ids = np.load(
os.path.join(folder_path, "chirps_ids.npy"), allow_pickle=True
)
for k, key in enumerate(self.dataframe.keys()): for k, key in enumerate(self.dataframe.keys()):
key = key.lower() key = key.lower()
if ' ' in key: if " " in key:
key = key.replace(' ', '_') key = key.replace(" ", "_")
if '(' in key: if "(" in key:
key = key.replace('(', '') key = key.replace("(", "")
key = key.replace(')', '') key = key.replace(")", "")
setattr(self, key, np.array(self.dataframe[self.dataframe.keys()[k]])) setattr(
self, key, np.array(self.dataframe[self.dataframe.keys()[k]])
)
last_LED_t_BORIS = LED_on_time_BORIS[-1] last_LED_t_BORIS = LED_on_time_BORIS[-1]
real_time_range = self.time[-1] - self.time[0] real_time_range = self.time[-1] - self.time[0]
factor = 1.034141 factor = 1.034141
shift = last_LED_t_BORIS - real_time_range * factor shift = last_LED_t_BORIS - real_time_range * factor
self.start_s = (self.start_s - shift) / factor self.start_s = (self.start_s - shift) / factor
self.stop_s = (self.stop_s - shift) / factor self.stop_s = (self.stop_s - shift) / factor
""" """
1 - chasing onset 1 - chasing onset
2 - chasing offset 2 - chasing offset
@ -83,77 +97,77 @@ temporal encpding needs to be corrected ... not exactly 25FPS.
behavior = data['Behavior'] behavior = data['Behavior']
""" """
def correct_chasing_events(
category: np.ndarray,
timestamps: np.ndarray
) -> tuple[np.ndarray, np.ndarray]:
onset_ids = np.arange( def correct_chasing_events(
len(category))[category == 0] category: np.ndarray, timestamps: np.ndarray
offset_ids = np.arange( ) -> tuple[np.ndarray, np.ndarray]:
len(category))[category == 1] onset_ids = np.arange(len(category))[category == 0]
offset_ids = np.arange(len(category))[category == 1]
# Check whether on- or offset is longer and calculate length difference # Check whether on- or offset is longer and calculate length difference
if len(onset_ids) > len(offset_ids): if len(onset_ids) > len(offset_ids):
len_diff = len(onset_ids) - len(offset_ids) len_diff = len(onset_ids) - len(offset_ids)
longer_array = onset_ids longer_array = onset_ids
shorter_array = offset_ids shorter_array = offset_ids
logger.info(f'Onsets are greater than offsets by {len_diff}') logger.info(f"Onsets are greater than offsets by {len_diff}")
elif len(onset_ids) < len(offset_ids): elif len(onset_ids) < len(offset_ids):
len_diff = len(offset_ids) - len(onset_ids) len_diff = len(offset_ids) - len(onset_ids)
longer_array = offset_ids longer_array = offset_ids
shorter_array = onset_ids shorter_array = onset_ids
logger.info(f'Offsets are greater than offsets by {len_diff}') logger.info(f"Offsets are greater than offsets by {len_diff}")
elif len(onset_ids) == len(offset_ids): elif len(onset_ids) == len(offset_ids):
logger.info('Chasing events are equal') logger.info("Chasing events are equal")
return category, timestamps return category, timestamps
# Correct the wrong chasing events; delete double events # Correct the wrong chasing events; delete double events
wrong_ids = [] wrong_ids = []
for i in range(len(longer_array)-(len_diff+1)): for i in range(len(longer_array) - (len_diff + 1)):
if (shorter_array[i] > longer_array[i]) & (shorter_array[i] < longer_array[i+1]): if (shorter_array[i] > longer_array[i]) & (
shorter_array[i] < longer_array[i + 1]
):
pass pass
else: else:
wrong_ids.append(longer_array[i]) wrong_ids.append(longer_array[i])
longer_array = np.delete(longer_array, i) longer_array = np.delete(longer_array, i)
category = np.delete( category = np.delete(category, wrong_ids)
category, wrong_ids) timestamps = np.delete(timestamps, wrong_ids)
timestamps = np.delete(
timestamps, wrong_ids)
return category, timestamps return category, timestamps
def event_triggered_chirps( 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]: ) -> tuple[np.ndarray, np.ndarray]:
event_chirps = [] # chirps that are in specified window around event
centered_chirps = (
event_chirps = [] # chirps that are in specified window around event []
centered_chirps = [] # timestamps of chirps around event centered on the event timepoint ) # 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 # timepoint of window start
stop = event_timestamp + time_after_event # timepoint of window ending stop = event_timestamp + time_after_event # timepoint of window ending
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)
] # get chirps that are in a -5 to +5 sec window around event
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
else: else:
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 return event_chirps, centered_chirps
def main(datapath: str): def main(datapath: str):
# behavior is pandas dataframe with all the data # behavior is pandas dataframe with all the data
bh = Behavior(datapath) bh = Behavior(datapath)
# chirps are not sorted in time (presumably due to prior groupings) # chirps are not sorted in time (presumably due to prior groupings)
# get and sort chirps and corresponding fish_ids of the chirps # get and sort chirps and corresponding fish_ids of the chirps
chirps = bh.chirps[np.argsort(bh.chirps)] chirps = bh.chirps[np.argsort(bh.chirps)]
@ -172,10 +186,34 @@ def main(datapath: str):
# First overview plot # First overview plot
fig1, ax1 = plt.subplots() fig1, ax1 = plt.subplots()
ax1.scatter(chirps, np.ones_like(chirps), marker='*', color='royalblue', label='Chirps') ax1.scatter(
ax1.scatter(chasing_onset, np.ones_like(chasing_onset)*2, marker='.', color='forestgreen', label='Chasing onset') chirps,
ax1.scatter(chasing_offset, np.ones_like(chasing_offset)*2.5, marker='.', color='firebrick', label='Chasing offset') np.ones_like(chirps),
ax1.scatter(physical_contact, np.ones_like(physical_contact)*3, marker='x', color='black', label='Physical contact') marker="*",
color="royalblue",
label="Chirps",
)
ax1.scatter(
chasing_onset,
np.ones_like(chasing_onset) * 2,
marker=".",
color="forestgreen",
label="Chasing onset",
)
ax1.scatter(
chasing_offset,
np.ones_like(chasing_offset) * 2.5,
marker=".",
color="firebrick",
label="Chasing offset",
)
ax1.scatter(
physical_contact,
np.ones_like(physical_contact) * 3,
marker="x",
color="black",
label="Physical contact",
)
plt.legend() plt.legend()
# plt.show() # plt.show()
plt.close() plt.close()
@ -187,29 +225,40 @@ def main(datapath: str):
# Evaluate how many chirps were emitted in specific time window around the chasing onset events # Evaluate how many chirps were emitted in specific time window around the chasing onset events
# Iterate over chasing onsets (later over fish) # Iterate over chasing onsets (later over fish)
time_around_event = 5 # time window around the event in which chirps are counted, 5 = -5 to +5 sec around event 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 #### #### Loop crashes at concatenate in function ####
# for i in range(len(fish_ids)): # for i in range(len(fish_ids)):
# fish = fish_ids[i] # fish = fish_ids[i]
# chirps = chirps[chirps_fish_ids == fish] # chirps = chirps[chirps_fish_ids == fish]
# print(fish) # print(fish)
chasing_chirps, centered_chasing_chirps = event_triggered_chirps(chasing_onset, chirps, time_around_event, time_around_event) chasing_chirps, centered_chasing_chirps = event_triggered_chirps(
physical_chirps, centered_physical_chirps = event_triggered_chirps(physical_contact, chirps, time_around_event, time_around_event) chasing_onset, chirps, time_around_event, time_around_event
)
physical_chirps, centered_physical_chirps = event_triggered_chirps(
physical_contact, chirps, time_around_event, time_around_event
)
# Kernel density estimation ??? # Kernel density estimation ???
# centered_chasing_chirps_convolved = gaussian_filter1d(centered_chasing_chirps, 5) # centered_chasing_chirps_convolved = gaussian_filter1d(centered_chasing_chirps, 5)
# centered_chasing = chasing_onset[0] - chasing_onset[0] ## get the 0 timepoint for plotting; set one chasing event to 0 # centered_chasing = chasing_onset[0] - chasing_onset[0] ## get the 0 timepoint for plotting; set one chasing event to 0
offsets = [0.5, 1] offsets = [0.5, 1]
fig4, ax4 = plt.subplots(figsize=(20 / 2.54, 12 / 2.54), constrained_layout=True) fig4, ax4 = plt.subplots(
ax4.eventplot(np.array([centered_chasing_chirps, centered_physical_chirps]), lineoffsets=offsets, linelengths=0.25, colors=['g', 'r']) figsize=(20 / 2.54, 12 / 2.54), constrained_layout=True
ax4.vlines(0, 0, 1.5, 'tab:grey', 'dashed', 'Timepoint of event') )
ax4.eventplot(
np.array([centered_chasing_chirps, centered_physical_chirps]),
lineoffsets=offsets,
linelengths=0.25,
colors=["g", "r"],
)
ax4.vlines(0, 0, 1.5, "tab:grey", "dashed", "Timepoint of event")
# ax4.plot(centered_chasing_chirps_convolved) # ax4.plot(centered_chasing_chirps_convolved)
ax4.set_yticks(offsets) ax4.set_yticks(offsets)
ax4.set_yticklabels(['Chasings', 'Physical \n contacts']) ax4.set_yticklabels(["Chasings", "Physical \n contacts"])
ax4.set_xlabel('Time[s]') ax4.set_xlabel("Time[s]")
ax4.set_ylabel('Type of event') ax4.set_ylabel("Type of event")
plt.show() plt.show()
# Associate chirps to inidividual fish # Associate chirps to inidividual fish
@ -219,22 +268,21 @@ def main(datapath: str):
### Plots: ### Plots:
# 1. All recordings, all fish, all chirps # 1. All recordings, all fish, all chirps
# One CTC, one PTC # One CTC, one PTC
# 2. All recordings, only winners # 2. All recordings, only winners
# One CTC, one PTC # One CTC, one PTC
# 3. All recordings, all losers # 3. All recordings, all losers
# One CTC, one PTC # One CTC, one PTC
#### Chirp counts per fish general ##### #### Chirp counts per fish general #####
fig2, ax2 = plt.subplots() fig2, ax2 = plt.subplots()
x = ['Fish1', 'Fish2'] x = ["Fish1", "Fish2"]
width = 0.35 width = 0.35
ax2.bar(x, fish, width=width) ax2.bar(x, fish, width=width)
ax2.set_ylabel('Chirp count') ax2.set_ylabel("Chirp count")
# plt.show() # plt.show()
plt.close() plt.close()
##### Count chirps emitted during chasing events and chirps emitted out of chasing events ##### ##### Count chirps emitted during chasing events and chirps emitted out of chasing events #####
chirps_in_chasings = [] chirps_in_chasings = []
for onset, offset in zip(chasing_onset, chasing_offset): for onset, offset in zip(chasing_onset, chasing_offset):
@ -251,23 +299,24 @@ def main(datapath: str):
counts_chirps_chasings += 1 counts_chirps_chasings += 1
# chirps in chasing events # chirps in chasing events
fig3 , ax3 = plt.subplots() fig3, ax3 = plt.subplots()
ax3.bar(['Chirps in chasing events', 'Chasing events without Chirps'], [counts_chirps_chasings, chasings_without_chirps], width=width) ax3.bar(
plt.ylabel('Count') ["Chirps in chasing events", "Chasing events without Chirps"],
[counts_chirps_chasings, chasings_without_chirps],
width=width,
)
plt.ylabel("Count")
# plt.show() # plt.show()
plt.close() plt.close()
# comparison between chasing events with and without chirps # comparison between chasing events with and without chirps
embed() embed()
exit() exit()
if __name__ == "__main__":
if __name__ == '__main__':
# Path to the data # Path to the data
datapath = '../data/mount_data/2020-05-13-10_00/' datapath = "../data/mount_data/2020-05-13-10_00/"
datapath = '../data/mount_data/2020-05-13-10_00/' datapath = "../data/mount_data/2020-05-13-10_00/"
main(datapath) main(datapath)

19
code/chirp_sim.py
View File

@ -8,30 +8,27 @@ from modules.datahandling import instantaneous_frequency
from modules.simulations import create_chirp from modules.simulations import create_chirp
# trying thunderfish fakefish chirp simulation --------------------------------- # trying thunderfish fakefish chirp simulation ---------------------------------
samplerate = 44100 samplerate = 44100
freq, ampl = fakefish.chirps(eodf=500, chirp_contrast=0.2) freq, ampl = fakefish.chirps(eodf=500, chirp_contrast=0.2)
data = fakefish.wavefish_eods(fish='Alepto', frequency=freq, phase0=3, samplerate=samplerate) data = fakefish.wavefish_eods(
fish="Alepto", frequency=freq, phase0=3, samplerate=samplerate
)
# filter signal with bandpass_filter # filter signal with bandpass_filter
data_filterd = bandpass_filter(data*ampl+1, samplerate, 0.01, 1.99) data_filterd = bandpass_filter(data * ampl + 1, samplerate, 0.01, 1.99)
embed() embed()
data_freq_time, data_freq = instantaneous_frequency(data, samplerate, 5) data_freq_time, data_freq = instantaneous_frequency(data, samplerate, 5)
fig, ax = plt.subplots(4, 1, figsize=(20 / 2.54, 12 / 2.54), sharex=True) fig, ax = plt.subplots(4, 1, figsize=(20 / 2.54, 12 / 2.54), sharex=True)
ax[0].plot(np.arange(len(data))/samplerate, data*ampl) ax[0].plot(np.arange(len(data)) / samplerate, data * ampl)
#ax[0].scatter(true_zero, np.zeros_like(true_zero), color='red') # ax[0].scatter(true_zero, np.zeros_like(true_zero), color='red')
ax[1].plot(np.arange(len(data_filterd))/samplerate, data_filterd) ax[1].plot(np.arange(len(data_filterd)) / samplerate, data_filterd)
ax[2].plot(np.arange(len(freq))/samplerate, freq) ax[2].plot(np.arange(len(freq)) / samplerate, freq)
ax[3].plot(data_freq_time, data_freq) ax[3].plot(data_freq_time, data_freq)
plt.show() plt.show()
embed() embed()

138
code/chirpdetection.py
View File

@ -7,6 +7,7 @@ import matplotlib.pyplot as plt
import matplotlib.gridspec as gr import matplotlib.gridspec as gr
from scipy.signal import find_peaks from scipy.signal import find_peaks
from thunderfish.powerspectrum import spectrogram, decibel from thunderfish.powerspectrum import spectrogram, decibel
# from sklearn.preprocessing import normalize # from sklearn.preprocessing import normalize
from modules.filters import bandpass_filter, envelope, highpass_filter from modules.filters import bandpass_filter, envelope, highpass_filter
@ -18,7 +19,7 @@ from modules.datahandling import (
purge_duplicates, purge_duplicates,
group_timestamps, group_timestamps,
instantaneous_frequency, instantaneous_frequency,
instantaneous_frequency2, instantaneous_frequency2,
minmaxnorm, minmaxnorm,
) )
@ -59,7 +60,6 @@ class ChirpPlotBuffer:
frequency_peaks: np.ndarray frequency_peaks: np.ndarray
def plot_buffer(self, chirps: np.ndarray, plot: str) -> None: def plot_buffer(self, chirps: np.ndarray, plot: str) -> None:
logger.debug("Starting plotting") logger.debug("Starting plotting")
# make data for plotting # make data for plotting
@ -135,7 +135,6 @@ class ChirpPlotBuffer:
ax0.set_ylim(np.min(self.frequency) - 100, np.max(self.frequency) + 200) ax0.set_ylim(np.min(self.frequency) - 100, np.max(self.frequency) + 200)
for track_id in self.data.ids: for track_id in self.data.ids:
t0_track = self.t0_old - 5 t0_track = self.t0_old - 5
dt_track = self.dt + 10 dt_track = self.dt + 10
window_idx = np.arange(len(self.data.idx))[ window_idx = np.arange(len(self.data.idx))[
@ -176,10 +175,16 @@ class ChirpPlotBuffer:
# ) # )
ax0.axhline( ax0.axhline(
q50 - self.config.minimal_bandwidth / 2, color=ps.gblue1, lw=1, ls="dashed" q50 - self.config.minimal_bandwidth / 2,
color=ps.gblue1,
lw=1,
ls="dashed",
) )
ax0.axhline( ax0.axhline(
q50 + self.config.minimal_bandwidth / 2, color=ps.gblue1, lw=1, ls="dashed" q50 + self.config.minimal_bandwidth / 2,
color=ps.gblue1,
lw=1,
ls="dashed",
) )
ax0.axhline(search_lower, color=ps.gblue2, lw=1, ls="dashed") ax0.axhline(search_lower, color=ps.gblue2, lw=1, ls="dashed")
ax0.axhline(search_upper, color=ps.gblue2, lw=1, ls="dashed") ax0.axhline(search_upper, color=ps.gblue2, lw=1, ls="dashed")
@ -205,7 +210,11 @@ class ChirpPlotBuffer:
# plot waveform of filtered signal # plot waveform of filtered signal
ax1.plot( ax1.plot(
self.time, self.baseline * waveform_scaler, c=ps.gray, lw=lw, alpha=0.5 self.time,
self.baseline * waveform_scaler,
c=ps.gray,
lw=lw,
alpha=0.5,
) )
ax1.plot( ax1.plot(
self.time, self.time,
@ -216,7 +225,13 @@ class ChirpPlotBuffer:
) )
# plot waveform of filtered search signal # plot waveform of filtered search signal
ax2.plot(self.time, self.search * waveform_scaler, c=ps.gray, lw=lw, alpha=0.5) ax2.plot(
self.time,
self.search * waveform_scaler,
c=ps.gray,
lw=lw,
alpha=0.5,
)
ax2.plot( ax2.plot(
self.time, self.time,
self.search_envelope_unfiltered * waveform_scaler, self.search_envelope_unfiltered * waveform_scaler,
@ -238,9 +253,7 @@ class ChirpPlotBuffer:
# ax4.plot( # ax4.plot(
# self.time, self.baseline_envelope * waveform_scaler, c=ps.gblue1, lw=lw # self.time, self.baseline_envelope * waveform_scaler, c=ps.gblue1, lw=lw
# ) # )
ax4.plot( ax4.plot(self.time, self.baseline_envelope, c=ps.gblue1, lw=lw)
self.time, self.baseline_envelope, c=ps.gblue1, lw=lw
)
ax4.scatter( ax4.scatter(
(self.time)[self.baseline_peaks], (self.time)[self.baseline_peaks],
# (self.baseline_envelope * waveform_scaler)[self.baseline_peaks], # (self.baseline_envelope * waveform_scaler)[self.baseline_peaks],
@ -269,7 +282,9 @@ class ChirpPlotBuffer:
) )
# plot filtered instantaneous frequency # plot filtered instantaneous frequency
ax6.plot(self.frequency_time, self.frequency_filtered, c=ps.gblue3, lw=lw) ax6.plot(
self.frequency_time, self.frequency_filtered, c=ps.gblue3, lw=lw
)
ax6.scatter( ax6.scatter(
self.frequency_time[self.frequency_peaks], self.frequency_time[self.frequency_peaks],
self.frequency_filtered[self.frequency_peaks], self.frequency_filtered[self.frequency_peaks],
@ -303,7 +318,9 @@ class ChirpPlotBuffer:
# ax7.spines.bottom.set_bounds((0, 5)) # ax7.spines.bottom.set_bounds((0, 5))
ax0.set_xlim(0, self.config.window) ax0.set_xlim(0, self.config.window)
plt.subplots_adjust(left=0.165, right=0.975, top=0.98, bottom=0.074, hspace=0.2) plt.subplots_adjust(
left=0.165, right=0.975, top=0.98, bottom=0.074, hspace=0.2
)
fig.align_labels() fig.align_labels()
if plot == "show": if plot == "show":
@ -408,7 +425,9 @@ def extract_frequency_bands(
q25, q75 = q50 - minimal_bandwidth / 2, q50 + minimal_bandwidth / 2 q25, q75 = q50 - minimal_bandwidth / 2, q50 + minimal_bandwidth / 2
# filter baseline # filter baseline
filtered_baseline = bandpass_filter(raw_data, samplerate, lowf=q25, highf=q75) filtered_baseline = bandpass_filter(
raw_data, samplerate, lowf=q25, highf=q75
)
# filter search area # filter search area
filtered_search_freq = bandpass_filter( filtered_search_freq = bandpass_filter(
@ -453,12 +472,14 @@ def window_median_all_track_ids(
track_ids = [] track_ids = []
for _, track_id in enumerate(np.unique(data.ident[~np.isnan(data.ident)])): for _, track_id in enumerate(np.unique(data.ident[~np.isnan(data.ident)])):
# the window index combines the track id and the time window # the window index combines the track id and the time window
window_idx = np.arange(len(data.idx))[ window_idx = np.arange(len(data.idx))[
(data.ident == track_id) (data.ident == track_id)
& (data.time[data.idx] >= window_start_seconds) & (data.time[data.idx] >= window_start_seconds)
& (data.time[data.idx] <= (window_start_seconds + window_duration_seconds)) & (
data.time[data.idx]
<= (window_start_seconds + window_duration_seconds)
)
] ]
if len(data.freq[window_idx]) > 0: if len(data.freq[window_idx]) > 0:
@ -595,15 +616,15 @@ def find_searchband(
# iterate through theses tracks # iterate through theses tracks
if check_track_ids.size != 0: if check_track_ids.size != 0:
for j, check_track_id in enumerate(check_track_ids): for j, check_track_id in enumerate(check_track_ids):
q25_temp = q25[percentiles_ids == check_track_id] q25_temp = q25[percentiles_ids == check_track_id]
q75_temp = q75[percentiles_ids == check_track_id] q75_temp = q75[percentiles_ids == check_track_id]
bool_lower[search_window > q25_temp - config.search_res] = False bool_lower[search_window > q25_temp - config.search_res] = False
bool_upper[search_window < q75_temp + config.search_res] = False bool_upper[search_window < q75_temp + config.search_res] = False
search_window_bool[(bool_lower == False) & (bool_upper == False)] = False search_window_bool[
(bool_lower == False) & (bool_upper == False)
] = False
# find gaps in search window # find gaps in search window
search_window_indices = np.arange(len(search_window)) search_window_indices = np.arange(len(search_window))
@ -622,7 +643,9 @@ def find_searchband(
# if the first value is -1, the array starst with true, so a gap # if the first value is -1, the array starst with true, so a gap
if nonzeros[0] == -1: if nonzeros[0] == -1:
stops = search_window_indices[search_window_gaps == -1] stops = search_window_indices[search_window_gaps == -1]
starts = np.append(0, search_window_indices[search_window_gaps == 1]) starts = np.append(
0, search_window_indices[search_window_gaps == 1]
)
# if the last value is -1, the array ends with true, so a gap # if the last value is -1, the array ends with true, so a gap
if nonzeros[-1] == 1: if nonzeros[-1] == 1:
@ -659,7 +682,6 @@ def find_searchband(
def chirpdetection(datapath: str, plot: str, debug: str = "false") -> None: def chirpdetection(datapath: str, plot: str, debug: str = "false") -> None:
assert plot in [ assert plot in [
"save", "save",
"show", "show",
@ -729,7 +751,6 @@ def chirpdetection(datapath: str, plot: str, debug: str = "false") -> None:
multiwindow_ids = [] multiwindow_ids = []
for st, window_start_index in enumerate(window_start_indices): for st, window_start_index in enumerate(window_start_indices):
logger.info(f"Processing window {st+1} of {len(window_start_indices)}") logger.info(f"Processing window {st+1} of {len(window_start_indices)}")
window_start_seconds = window_start_index / data.raw_rate window_start_seconds = window_start_index / data.raw_rate
@ -744,8 +765,9 @@ def chirpdetection(datapath: str, plot: str, debug: str = "false") -> None:
) )
# iterate through all fish # iterate through all fish
for tr, track_id in enumerate(np.unique(data.ident[~np.isnan(data.ident)])): for tr, track_id in enumerate(
np.unique(data.ident[~np.isnan(data.ident)])
):
logger.debug(f"Processing track {tr} of {len(data.ids)}") logger.debug(f"Processing track {tr} of {len(data.ids)}")
# get index of track data in this time window # get index of track data in this time window
@ -773,16 +795,17 @@ def chirpdetection(datapath: str, plot: str, debug: str = "false") -> None:
nanchecker = np.unique(np.isnan(current_powers)) nanchecker = np.unique(np.isnan(current_powers))
if (len(nanchecker) == 1) and nanchecker[0] is True: if (len(nanchecker) == 1) and nanchecker[0] is True:
logger.warning( logger.warning(
f"No powers available for track {track_id} window {st}," "skipping." f"No powers available for track {track_id} window {st},"
"skipping."
) )
continue continue
# find the strongest electrodes for the current fish in the current # find the strongest electrodes for the current fish in the current
# window # window
best_electrode_index = np.argsort(np.nanmean(current_powers, axis=0))[ best_electrode_index = np.argsort(
-config.number_electrodes : np.nanmean(current_powers, axis=0)
] )[-config.number_electrodes :]
# find a frequency above the baseline of the current fish in which # find a frequency above the baseline of the current fish in which
# no other fish is active to search for chirps there # no other fish is active to search for chirps there
@ -802,9 +825,9 @@ def chirpdetection(datapath: str, plot: str, debug: str = "false") -> None:
# iterate through electrodes # iterate through electrodes
for el, electrode_index in enumerate(best_electrode_index): for el, electrode_index in enumerate(best_electrode_index):
logger.debug( logger.debug(
f"Processing electrode {el+1} of " f"{len(best_electrode_index)}" f"Processing electrode {el+1} of "
f"{len(best_electrode_index)}"
) )
# LOAD DATA FOR CURRENT ELECTRODE AND CURRENT FISH ------------ # LOAD DATA FOR CURRENT ELECTRODE AND CURRENT FISH ------------
@ -813,7 +836,9 @@ def chirpdetection(datapath: str, plot: str, debug: str = "false") -> None:
current_raw_data = data.raw[ current_raw_data = data.raw[
window_start_index:window_stop_index, electrode_index window_start_index:window_stop_index, electrode_index
] ]
current_raw_time = raw_time[window_start_index:window_stop_index] current_raw_time = raw_time[
window_start_index:window_stop_index
]
# EXTRACT FEATURES -------------------------------------------- # EXTRACT FEATURES --------------------------------------------
@ -839,8 +864,7 @@ def chirpdetection(datapath: str, plot: str, debug: str = "false") -> None:
# because the instantaneous frequency is not reliable there # because the instantaneous frequency is not reliable there
amplitude_mask = mask_low_amplitudes( amplitude_mask = mask_low_amplitudes(
baseline_envelope_unfiltered, baseline_envelope_unfiltered, config.baseline_min_amplitude
config.baseline_min_amplitude
) )
# highpass filter baseline envelope to remove slower # highpass filter baseline envelope to remove slower
@ -877,27 +901,30 @@ def chirpdetection(datapath: str, plot: str, debug: str = "false") -> None:
# filtered baseline such as the one we are working with. # filtered baseline such as the one we are working with.
baseline_frequency = instantaneous_frequency( baseline_frequency = instantaneous_frequency(
baselineband, baselineband,
data.raw_rate, data.raw_rate,
config.baseline_frequency_smoothing config.baseline_frequency_smoothing,
) )
# Take the absolute of the instantaneous frequency to invert # Take the absolute of the instantaneous frequency to invert
# troughs into peaks. This is nessecary since the narrow # troughs into peaks. This is nessecary since the narrow
# pass band introduces these anomalies. Also substract by the # pass band introduces these anomalies. Also substract by the
# median to set it to 0. # median to set it to 0.
baseline_frequency_filtered = np.abs( baseline_frequency_filtered = np.abs(
baseline_frequency - np.median(baseline_frequency) baseline_frequency - np.median(baseline_frequency)
) )
# check if there is at least one superthreshold peak on the # check if there is at least one superthreshold peak on the
# instantaneous and exit the loop if not. This is used to # instantaneous and exit the loop if not. This is used to
# prevent windows that do definetely not include a chirp # prevent windows that do definetely not include a chirp
# to enter normalization, where small changes due to noise # to enter normalization, where small changes due to noise
# would be amplified # would be amplified
if not has_chirp(baseline_frequency_filtered[amplitude_mask], config.baseline_frequency_peakheight): if not has_chirp(
baseline_frequency_filtered[amplitude_mask],
config.baseline_frequency_peakheight,
):
continue continue
# CUT OFF OVERLAP --------------------------------------------- # CUT OFF OVERLAP ---------------------------------------------
@ -912,14 +939,20 @@ def chirpdetection(datapath: str, plot: str, debug: str = "false") -> None:
current_raw_time = current_raw_time[no_edges] current_raw_time = current_raw_time[no_edges]
baselineband = baselineband[no_edges] baselineband = baselineband[no_edges]
baseline_envelope_unfiltered = baseline_envelope_unfiltered[no_edges] baseline_envelope_unfiltered = baseline_envelope_unfiltered[
no_edges
]
searchband = searchband[no_edges] searchband = searchband[no_edges]
baseline_envelope = baseline_envelope[no_edges] baseline_envelope = baseline_envelope[no_edges]
search_envelope_unfiltered = search_envelope_unfiltered[no_edges] search_envelope_unfiltered = search_envelope_unfiltered[
no_edges
]
search_envelope = search_envelope[no_edges] search_envelope = search_envelope[no_edges]
baseline_frequency = baseline_frequency[no_edges] baseline_frequency = baseline_frequency[no_edges]
baseline_frequency_filtered = baseline_frequency_filtered[no_edges] baseline_frequency_filtered = baseline_frequency_filtered[
no_edges
]
baseline_frequency_time = current_raw_time baseline_frequency_time = current_raw_time
# # get instantaneous frequency withoup edges # # get instantaneous frequency withoup edges
@ -960,13 +993,16 @@ def chirpdetection(datapath: str, plot: str, debug: str = "false") -> None:
) )
# detect peaks inst_freq_filtered # detect peaks inst_freq_filtered
frequency_peak_indices, _ = find_peaks( frequency_peak_indices, _ = find_peaks(
baseline_frequency_filtered, prominence=config.frequency_prominence baseline_frequency_filtered,
prominence=config.frequency_prominence,
) )
# DETECT CHIRPS IN SEARCH WINDOW ------------------------------ # DETECT CHIRPS IN SEARCH WINDOW ------------------------------
# get the peak timestamps from the peak indices # get the peak timestamps from the peak indices
baseline_peak_timestamps = current_raw_time[baseline_peak_indices] baseline_peak_timestamps = current_raw_time[
baseline_peak_indices
]
search_peak_timestamps = current_raw_time[search_peak_indices] search_peak_timestamps = current_raw_time[search_peak_indices]
frequency_peak_timestamps = baseline_frequency_time[ frequency_peak_timestamps = baseline_frequency_time[
@ -1015,7 +1051,6 @@ def chirpdetection(datapath: str, plot: str, debug: str = "false") -> None:
) )
if chirp_detected or (debug != "elecrode"): if chirp_detected or (debug != "elecrode"):
logger.debug("Detected chirp, ititialize buffer ...") logger.debug("Detected chirp, ititialize buffer ...")
# save data to Buffer # save data to Buffer
@ -1107,7 +1142,6 @@ def chirpdetection(datapath: str, plot: str, debug: str = "false") -> None:
multiwindow_chirps_flat = [] multiwindow_chirps_flat = []
multiwindow_ids_flat = [] multiwindow_ids_flat = []
for track_id in np.unique(multiwindow_ids): for track_id in np.unique(multiwindow_ids):
# get chirps for this fish and flatten the list # get chirps for this fish and flatten the list
current_track_bool = np.asarray(multiwindow_ids) == track_id current_track_bool = np.asarray(multiwindow_ids) == track_id
current_track_chirps = flatten( current_track_chirps = flatten(
@ -1116,7 +1150,9 @@ def chirpdetection(datapath: str, plot: str, debug: str = "false") -> None:
# add flattened chirps to the list # add flattened chirps to the list
multiwindow_chirps_flat.extend(current_track_chirps) multiwindow_chirps_flat.extend(current_track_chirps)
multiwindow_ids_flat.extend(list(np.ones_like(current_track_chirps) * track_id)) multiwindow_ids_flat.extend(
list(np.ones_like(current_track_chirps) * track_id)
)
# purge duplicates, i.e. chirps that are very close to each other # purge duplicates, i.e. chirps that are very close to each other
# duplites arise due to overlapping windows # duplites arise due to overlapping windows

41
code/chirpdetector_conf.yml
View File

@ -1,37 +1,37 @@
# Path setup ------------------------------------------------------------------ # Path setup ------------------------------------------------------------------
dataroot: "../data/" # path to data dataroot: "../data/" # path to data
outputdir: "../output/" # path to save plots to outputdir: "../output/" # path to save plots to
# Rolling window parameters --------------------------------------------------- # Rolling window parameters ---------------------------------------------------
window: 5 # rolling window length in seconds window: 5 # rolling window length in seconds
overlap: 1 # window overlap in seconds overlap: 1 # window overlap in seconds
edge: 0.25 # window edge cufoffs to mitigate filter edge effects edge: 0.25 # window edge cufoffs to mitigate filter edge effects
# Electrode iteration parameters ---------------------------------------------- # Electrode iteration parameters ----------------------------------------------
number_electrodes: 2 # number of electrodes to go over number_electrodes: 2 # number of electrodes to go over
minimum_electrodes: 1 # mimumun number of electrodes a chirp must be on minimum_electrodes: 1 # mimumun number of electrodes a chirp must be on
# Feature extraction parameters ----------------------------------------------- # Feature extraction parameters -----------------------------------------------
search_df_lower: 20 # start searching this far above the baseline search_df_lower: 20 # start searching this far above the baseline
search_df_upper: 100 # stop searching this far above the baseline search_df_upper: 100 # stop searching this far above the baseline
search_res: 1 # search window resolution search_res: 1 # search window resolution
default_search_freq: 60 # search here if no need for a search frequency default_search_freq: 60 # search here if no need for a search frequency
minimal_bandwidth: 10 # minimal bandpass filter width for baseline minimal_bandwidth: 10 # minimal bandpass filter width for baseline
search_bandwidth: 10 # minimal bandpass filter width for search frequency search_bandwidth: 10 # minimal bandpass filter width for search frequency
baseline_frequency_smoothing: 3 # instantaneous frequency smoothing baseline_frequency_smoothing: 3 # instantaneous frequency smoothing
# Feature processing parameters ----------------------------------------------- # Feature processing parameters -----------------------------------------------
baseline_frequency_peakheight: 5 # the min peak height of the baseline instfreq baseline_frequency_peakheight: 5 # the min peak height of the baseline instfreq
baseline_min_amplitude: 0.0001 # the minimal value of the baseline envelope baseline_min_amplitude: 0.0001 # the minimal value of the baseline envelope
baseline_envelope_cutoff: 25 # envelope estimation cutoff baseline_envelope_cutoff: 25 # envelope estimation cutoff
baseline_envelope_bandpass_lowf: 2 # envelope badpass lower cutoff baseline_envelope_bandpass_lowf: 2 # envelope badpass lower cutoff
baseline_envelope_bandpass_highf: 100 # envelope bandbass higher cutoff baseline_envelope_bandpass_highf: 100 # envelope bandbass higher cutoff
search_envelope_cutoff: 10 # search envelope estimation cufoff search_envelope_cutoff: 10 # search envelope estimation cufoff
# Peak detecion parameters ---------------------------------------------------- # Peak detecion parameters ----------------------------------------------------
# baseline_prominence: 0.00005 # peak prominence threshold for baseline envelope # baseline_prominence: 0.00005 # peak prominence threshold for baseline envelope
@ -39,9 +39,8 @@ search_envelope_cutoff: 10 # search envelope estimation cufoff
# frequency_prominence: 2 # peak prominence threshold for baseline freq # frequency_prominence: 2 # peak prominence threshold for baseline freq
baseline_prominence: 0.3 # peak prominence threshold for baseline envelope baseline_prominence: 0.3 # peak prominence threshold for baseline envelope
search_prominence: 0.3 # peak prominence threshold for search envelope search_prominence: 0.3 # peak prominence threshold for search envelope
frequency_prominence: 0.3 # peak prominence threshold for baseline freq frequency_prominence: 0.3 # peak prominence threshold for baseline freq
# Classify events as chirps if they are less than this time apart # Classify events as chirps if they are less than this time apart
chirp_window_threshold: 0.02 chirp_window_threshold: 0.02

266
code/eventchirpsplots.py
View File

@ -35,28 +35,36 @@ class Behavior:
""" """
def __init__(self, folder_path: str) -> None: def __init__(self, folder_path: str) -> None:
print(f'{folder_path}') print(f"{folder_path}")
LED_on_time_BORIS = np.load(os.path.join( LED_on_time_BORIS = np.load(
folder_path, 'LED_on_time.npy'), allow_pickle=True) os.path.join(folder_path, "LED_on_time.npy"), allow_pickle=True
self.time = np.load(os.path.join( )
folder_path, "times.npy"), allow_pickle=True) self.time = np.load(
csv_filename = [f for f in os.listdir(folder_path) if f.endswith( os.path.join(folder_path, "times.npy"), allow_pickle=True
'.csv')][0] # check if there are more than one csv file )
csv_filename = [
f for f in os.listdir(folder_path) if f.endswith(".csv")
][
0
] # check if there are more than one csv file
self.dataframe = read_csv(os.path.join(folder_path, csv_filename)) self.dataframe = read_csv(os.path.join(folder_path, csv_filename))
self.chirps = np.load(os.path.join( self.chirps = np.load(
folder_path, 'chirps.npy'), allow_pickle=True) os.path.join(folder_path, "chirps.npy"), allow_pickle=True
self.chirps_ids = np.load(os.path.join( )
folder_path, 'chirp_ids.npy'), allow_pickle=True) self.chirps_ids = np.load(
os.path.join(folder_path, "chirp_ids.npy"), allow_pickle=True
)
for k, key in enumerate(self.dataframe.keys()): for k, key in enumerate(self.dataframe.keys()):
key = key.lower() key = key.lower()
if ' ' in key: if " " in key:
key = key.replace(' ', '_') key = key.replace(" ", "_")
if '(' in key: if "(" in key:
key = key.replace('(', '') key = key.replace("(", "")
key = key.replace(')', '') key = key.replace(")", "")
setattr(self, key, np.array( setattr(
self.dataframe[self.dataframe.keys()[k]])) self, key, np.array(self.dataframe[self.dataframe.keys()[k]])
)
last_LED_t_BORIS = LED_on_time_BORIS[-1] last_LED_t_BORIS = LED_on_time_BORIS[-1]
real_time_range = self.time[-1] - self.time[0] real_time_range = self.time[-1] - self.time[0]
@ -95,17 +103,14 @@ temporal encpding needs to be corrected ... not exactly 25FPS.
def correct_chasing_events( def correct_chasing_events(
category: np.ndarray, category: np.ndarray, timestamps: np.ndarray
timestamps: np.ndarray
) -> tuple[np.ndarray, np.ndarray]: ) -> tuple[np.ndarray, np.ndarray]:
onset_ids = np.arange(len(category))[category == 0]
offset_ids = np.arange(len(category))[category == 1]
onset_ids = np.arange( wrong_bh = np.arange(len(category))[category != 2][:-1][
len(category))[category == 0] np.diff(category[category != 2]) == 0
offset_ids = np.arange( ]
len(category))[category == 1]
wrong_bh = np.arange(len(category))[
category != 2][:-1][np.diff(category[category != 2]) == 0]
if onset_ids[0] > offset_ids[0]: if onset_ids[0] > offset_ids[0]:
offset_ids = np.delete(offset_ids, 0) offset_ids = np.delete(offset_ids, 0)
help_index = offset_ids[0] help_index = offset_ids[0]
@ -117,12 +122,12 @@ def correct_chasing_events(
# Check whether on- or offset is longer and calculate length difference # Check whether on- or offset is longer and calculate length difference
if len(onset_ids) > len(offset_ids): if len(onset_ids) > len(offset_ids):
len_diff = len(onset_ids) - len(offset_ids) len_diff = len(onset_ids) - len(offset_ids)
logger.info(f'Onsets are greater than offsets by {len_diff}') logger.info(f"Onsets are greater than offsets by {len_diff}")
elif len(onset_ids) < len(offset_ids): elif len(onset_ids) < len(offset_ids):
len_diff = len(offset_ids) - len(onset_ids) len_diff = len(offset_ids) - len(onset_ids)
logger.info(f'Offsets are greater than onsets by {len_diff}') logger.info(f"Offsets are greater than onsets by {len_diff}")
elif len(onset_ids) == len(offset_ids): elif len(onset_ids) == len(offset_ids):
logger.info('Chasing events are equal') logger.info("Chasing events are equal")
return category, timestamps return category, timestamps
@ -135,8 +140,7 @@ def event_triggered_chirps(
dt: float, dt: float,
width: float, width: float,
) -> tuple[np.ndarray, np.ndarray, np.ndarray]: ) -> 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
# timestamps of chirps around event centered on the event timepoint # timestamps of chirps around event centered on the event timepoint
centered_chirps = [] centered_chirps = []
@ -159,16 +163,19 @@ def event_triggered_chirps(
else: else:
# convert list of arrays to one array for plotting # convert list of arrays to one array for plotting
centered_chirps = np.concatenate(centered_chirps, axis=0) centered_chirps = np.concatenate(centered_chirps, axis=0)
centered_chirps_convolved = (acausal_kde1d( centered_chirps_convolved = (
centered_chirps, time, width)) / len(event) acausal_kde1d(centered_chirps, time, width)
) / len(event)
return event_chirps, centered_chirps, centered_chirps_convolved return event_chirps, centered_chirps, centered_chirps_convolved
def main(datapath: str): def main(datapath: str):
foldernames = [ foldernames = [
datapath + x + '/' for x in os.listdir(datapath) if os.path.isdir(datapath + x)] datapath + x + "/"
for x in os.listdir(datapath)
if os.path.isdir(datapath + x)
]
nrecording_chirps = [] nrecording_chirps = []
nrecording_chirps_fish_ids = [] nrecording_chirps_fish_ids = []
@ -179,7 +186,7 @@ def main(datapath: str):
# Iterate over all recordings and save chirp- and event-timestamps # Iterate over all recordings and save chirp- and event-timestamps
for folder in foldernames: for folder in foldernames:
# exclude folder with empty LED_on_time.npy # exclude folder with empty LED_on_time.npy
if folder == '../data/mount_data/2020-05-12-10_00/': if folder == "../data/mount_data/2020-05-12-10_00/":
continue continue
bh = Behavior(folder) bh = Behavior(folder)
@ -209,7 +216,7 @@ def main(datapath: str):
time_before_event = 30 time_before_event = 30
time_after_event = 60 time_after_event = 60
dt = 0.01 dt = 0.01
width = 1.5 # width of kernel for all recordings, currently gaussian kernel width = 1.5 # width of kernel for all recordings, currently gaussian kernel
recording_width = 2 # width of kernel for each recording recording_width = 2 # width of kernel for each recording
time = np.arange(-time_before_event, time_after_event, dt) time = np.arange(-time_before_event, time_after_event, dt)
@ -232,18 +239,47 @@ def main(datapath: str):
physical_contacts = nrecording_physicals[i] physical_contacts = nrecording_physicals[i]
# Chirps around chasing onsets # 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, recording_width) _,
centered_chasing_onset_chirps,
cc_chasing_onset_chirps,
) = event_triggered_chirps(
chasing_onsets,
chirps,
time_before_event,
time_after_event,
dt,
recording_width,
)
# Chirps around chasing offsets # 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, recording_width) _,
centered_chasing_offset_chirps,
cc_chasing_offset_chirps,
) = event_triggered_chirps(
chasing_offsets,
chirps,
time_before_event,
time_after_event,
dt,
recording_width,
)
# Chirps around physical contacts # Chirps around physical contacts
_, centered_physical_chirps, cc_physical_chirps = event_triggered_chirps( (
physical_contacts, chirps, time_before_event, time_after_event, dt, recording_width) _,
centered_physical_chirps,
cc_physical_chirps,
) = event_triggered_chirps(
physical_contacts,
chirps,
time_before_event,
time_after_event,
dt,
recording_width,
)
nrecording_centered_onset_chirps.append(centered_chasing_onset_chirps) nrecording_centered_onset_chirps.append(centered_chasing_onset_chirps)
nrecording_centered_offset_chirps.append( nrecording_centered_offset_chirps.append(centered_chasing_offset_chirps)
centered_chasing_offset_chirps)
nrecording_centered_physical_chirps.append(centered_physical_chirps) nrecording_centered_physical_chirps.append(centered_physical_chirps)
## Shuffled chirps ## ## Shuffled chirps ##
@ -331,12 +367,13 @@ def main(datapath: str):
# New bootstrapping approach # New bootstrapping approach
for n in range(nbootstrapping): for n in range(nbootstrapping):
diff_onset = np.diff( diff_onset = np.diff(np.sort(flatten(nrecording_centered_onset_chirps)))
np.sort(flatten(nrecording_centered_onset_chirps)))
diff_offset = np.diff( diff_offset = np.diff(
np.sort(flatten(nrecording_centered_offset_chirps))) np.sort(flatten(nrecording_centered_offset_chirps))
)
diff_physical = np.diff( diff_physical = np.diff(
np.sort(flatten(nrecording_centered_physical_chirps))) np.sort(flatten(nrecording_centered_physical_chirps))
)
np.random.shuffle(diff_onset) np.random.shuffle(diff_onset)
shuffled_onset = np.cumsum(diff_onset) shuffled_onset = np.cumsum(diff_onset)
@ -345,9 +382,11 @@ def main(datapath: str):
np.random.shuffle(diff_physical) np.random.shuffle(diff_physical)
shuffled_physical = np.cumsum(diff_physical) shuffled_physical = np.cumsum(diff_physical)
kde_onset (acausal_kde1d(shuffled_onset, time, width))/(27*100) kde_onset(acausal_kde1d(shuffled_onset, time, width)) / (27 * 100)
kde_offset = (acausal_kde1d(shuffled_offset, time, width))/(27*100) kde_offset = (acausal_kde1d(shuffled_offset, time, width)) / (27 * 100)
kde_physical = (acausal_kde1d(shuffled_physical, time, width))/(27*100) kde_physical = (acausal_kde1d(shuffled_physical, time, width)) / (
27 * 100
)
bootstrap_onset.append(kde_onset) bootstrap_onset.append(kde_onset)
bootstrap_offset.append(kde_offset) bootstrap_offset.append(kde_offset)
@ -355,11 +394,14 @@ def main(datapath: str):
# New shuffle approach q5, q50, q95 # New shuffle approach q5, q50, q95
onset_q5, onset_median, onset_q95 = np.percentile( onset_q5, onset_median, onset_q95 = np.percentile(
bootstrap_onset, [5, 50, 95], axis=0) bootstrap_onset, [5, 50, 95], axis=0
)
offset_q5, offset_median, offset_q95 = np.percentile( offset_q5, offset_median, offset_q95 = np.percentile(
bootstrap_offset, [5, 50, 95], axis=0) bootstrap_offset, [5, 50, 95], axis=0
)
physical_q5, physical_median, physical_q95 = np.percentile( physical_q5, physical_median, physical_q95 = np.percentile(
bootstrap_physical, [5, 50, 95], axis=0) bootstrap_physical, [5, 50, 95], axis=0
)
# vstack um 1. Dim zu cutten # vstack um 1. Dim zu cutten
# nrecording_shuffled_convolved_onset_chirps = np.vstack(nrecording_shuffled_convolved_onset_chirps) # nrecording_shuffled_convolved_onset_chirps = np.vstack(nrecording_shuffled_convolved_onset_chirps)
@ -378,45 +420,66 @@ def main(datapath: str):
# Flatten event timestamps # Flatten event timestamps
all_onsets = np.concatenate( all_onsets = np.concatenate(
nrecording_chasing_onsets).ravel() # not centered nrecording_chasing_onsets
).ravel() # not centered
all_offsets = np.concatenate( all_offsets = np.concatenate(
nrecording_chasing_offsets).ravel() # not centered nrecording_chasing_offsets
all_physicals = np.concatenate( ).ravel() # not centered
nrecording_physicals).ravel() # not centered all_physicals = np.concatenate(nrecording_physicals).ravel() # not centered
# Flatten all chirps around events # Flatten all chirps around events
all_onset_chirps = np.concatenate( all_onset_chirps = np.concatenate(
nrecording_centered_onset_chirps).ravel() # centered nrecording_centered_onset_chirps
).ravel() # centered
all_offset_chirps = np.concatenate( all_offset_chirps = np.concatenate(
nrecording_centered_offset_chirps).ravel() # centered nrecording_centered_offset_chirps
).ravel() # centered
all_physical_chirps = np.concatenate( all_physical_chirps = np.concatenate(
nrecording_centered_physical_chirps).ravel() # centered nrecording_centered_physical_chirps
).ravel() # centered
# Convolute all chirps # Convolute all chirps
# Divide by total number of each event over all recordings # Divide by total number of each event over all recordings
all_onset_chirps_convolved = (acausal_kde1d( all_onset_chirps_convolved = (
all_onset_chirps, time, width)) / len(all_onsets) acausal_kde1d(all_onset_chirps, time, width)
all_offset_chirps_convolved = (acausal_kde1d( ) / len(all_onsets)
all_offset_chirps, time, width)) / len(all_offsets) all_offset_chirps_convolved = (
all_physical_chirps_convolved = (acausal_kde1d( acausal_kde1d(all_offset_chirps, time, width)
all_physical_chirps, time, width)) / len(all_physicals) ) / len(all_offsets)
all_physical_chirps_convolved = (
acausal_kde1d(all_physical_chirps, time, width)
) / len(all_physicals)
# Plot all events with all shuffled # Plot all events with all shuffled
fig, ax = plt.subplots(1, 3, figsize=( fig, ax = plt.subplots(
28*ps.cm, 16*ps.cm, ), constrained_layout=True, sharey='all') 1,
3,
figsize=(
28 * ps.cm,
16 * ps.cm,
),
constrained_layout=True,
sharey="all",
)
# offsets = np.arange(1,28,1) # offsets = np.arange(1,28,1)
ax[0].set_xlabel('Time[s]') ax[0].set_xlabel("Time[s]")
# Plot chasing onsets # Plot chasing onsets
ax[0].set_ylabel('Chirp rate [Hz]') ax[0].set_ylabel("Chirp rate [Hz]")
ax[0].plot(time, all_onset_chirps_convolved, color=ps.yellow, zorder=2) ax[0].plot(time, all_onset_chirps_convolved, color=ps.yellow, zorder=2)
ax0 = ax[0].twinx() ax0 = ax[0].twinx()
nrecording_centered_onset_chirps = np.asarray( nrecording_centered_onset_chirps = np.asarray(
nrecording_centered_onset_chirps, dtype=object) nrecording_centered_onset_chirps, dtype=object
ax0.eventplot(np.array(nrecording_centered_onset_chirps), )
linelengths=0.5, colors=ps.gray, alpha=0.25, zorder=1) ax0.eventplot(
ax0.vlines(0, 0, 1.5, ps.white, 'dashed') np.array(nrecording_centered_onset_chirps),
ax[0].set_zorder(ax0.get_zorder()+1) linelengths=0.5,
colors=ps.gray,
alpha=0.25,
zorder=1,
)
ax0.vlines(0, 0, 1.5, ps.white, "dashed")
ax[0].set_zorder(ax0.get_zorder() + 1)
ax[0].patch.set_visible(False) ax[0].patch.set_visible(False)
ax0.set_yticklabels([]) ax0.set_yticklabels([])
ax0.set_yticks([]) ax0.set_yticks([])
@ -426,15 +489,21 @@ def main(datapath: str):
ax[0].plot(time, onset_median, color=ps.black) ax[0].plot(time, onset_median, color=ps.black)
# Plot chasing offets # Plot chasing offets
ax[1].set_xlabel('Time[s]') ax[1].set_xlabel("Time[s]")
ax[1].plot(time, all_offset_chirps_convolved, color=ps.orange, zorder=2) ax[1].plot(time, all_offset_chirps_convolved, color=ps.orange, zorder=2)
ax1 = ax[1].twinx() ax1 = ax[1].twinx()
nrecording_centered_offset_chirps = np.asarray( nrecording_centered_offset_chirps = np.asarray(
nrecording_centered_offset_chirps, dtype=object) nrecording_centered_offset_chirps, dtype=object
ax1.eventplot(np.array(nrecording_centered_offset_chirps), )
linelengths=0.5, colors=ps.gray, alpha=0.25, zorder=1) ax1.eventplot(
ax1.vlines(0, 0, 1.5, ps.white, 'dashed') np.array(nrecording_centered_offset_chirps),
ax[1].set_zorder(ax1.get_zorder()+1) linelengths=0.5,
colors=ps.gray,
alpha=0.25,
zorder=1,
)
ax1.vlines(0, 0, 1.5, ps.white, "dashed")
ax[1].set_zorder(ax1.get_zorder() + 1)
ax[1].patch.set_visible(False) ax[1].patch.set_visible(False)
ax1.set_yticklabels([]) ax1.set_yticklabels([])
ax1.set_yticks([]) ax1.set_yticks([])
@ -444,24 +513,31 @@ def main(datapath: str):
ax[1].plot(time, offset_median, color=ps.black) ax[1].plot(time, offset_median, color=ps.black)
# Plot physical contacts # Plot physical contacts
ax[2].set_xlabel('Time[s]') ax[2].set_xlabel("Time[s]")
ax[2].plot(time, all_physical_chirps_convolved, color=ps.maroon, zorder=2) ax[2].plot(time, all_physical_chirps_convolved, color=ps.maroon, zorder=2)
ax2 = ax[2].twinx() ax2 = ax[2].twinx()
nrecording_centered_physical_chirps = np.asarray( nrecording_centered_physical_chirps = np.asarray(
nrecording_centered_physical_chirps, dtype=object) nrecording_centered_physical_chirps, dtype=object
ax2.eventplot(np.array(nrecording_centered_physical_chirps), )
linelengths=0.5, colors=ps.gray, alpha=0.25, zorder=1) ax2.eventplot(
ax2.vlines(0, 0, 1.5, ps.white, 'dashed') np.array(nrecording_centered_physical_chirps),
ax[2].set_zorder(ax2.get_zorder()+1) linelengths=0.5,
colors=ps.gray,
alpha=0.25,
zorder=1,
)
ax2.vlines(0, 0, 1.5, ps.white, "dashed")
ax[2].set_zorder(ax2.get_zorder() + 1)
ax[2].patch.set_visible(False) ax[2].patch.set_visible(False)
ax2.set_yticklabels([]) ax2.set_yticklabels([])
ax2.set_yticks([]) ax2.set_yticks([])
# ax[2].fill_between(time, shuffled_q5_physical, shuffled_q95_physical, color=ps.gray, alpha=0.5) # ax[2].fill_between(time, shuffled_q5_physical, shuffled_q95_physical, color=ps.gray, alpha=0.5)
# ax[2].plot(time, shuffled_median_physical, ps.black) # ax[2].plot(time, shuffled_median_physical, ps.black)
ax[2].fill_between(time, physical_q5, physical_q95, ax[2].fill_between(
color=ps.gray, alpha=0.5) time, physical_q5, physical_q95, color=ps.gray, alpha=0.5
)
ax[2].plot(time, physical_median, ps.black) ax[2].plot(time, physical_median, ps.black)
fig.suptitle('All recordings') fig.suptitle("All recordings")
plt.show() plt.show()
plt.close() plt.close()
@ -587,7 +663,7 @@ def main(datapath: str):
#### Chirps around events, only losers, one recording #### #### Chirps around events, only losers, one recording ####
if __name__ == '__main__': if __name__ == "__main__":
# Path to the data # Path to the data
datapath = '../data/mount_data/' datapath = "../data/mount_data/"
main(datapath) main(datapath)

37
code/extract_chirps.py
View File

@ -8,50 +8,51 @@ from IPython import embed
def get_valid_datasets(dataroot): def get_valid_datasets(dataroot):
datasets = sorted(
datasets = sorted([name for name in os.listdir(dataroot) if os.path.isdir( [
os.path.join(dataroot, name))]) name
for name in os.listdir(dataroot)
if os.path.isdir(os.path.join(dataroot, name))
]
)
valid_datasets = [] valid_datasets = []
for dataset in datasets: for dataset in datasets:
path = os.path.join(dataroot, dataset) path = os.path.join(dataroot, dataset)
csv_name = '-'.join(dataset.split('-')[:3]) + '.csv' csv_name = "-".join(dataset.split("-")[:3]) + ".csv"
if os.path.exists(os.path.join(path, csv_name)) is False: if os.path.exists(os.path.join(path, csv_name)) is False:
continue continue
if os.path.exists(os.path.join(path, 'ident_v.npy')) is False: if os.path.exists(os.path.join(path, "ident_v.npy")) is False:
continue continue
ident = np.load(os.path.join(path, 'ident_v.npy')) ident = np.load(os.path.join(path, "ident_v.npy"))
number_of_fish = len(np.unique(ident[~np.isnan(ident)])) number_of_fish = len(np.unique(ident[~np.isnan(ident)]))
if number_of_fish != 2: if number_of_fish != 2:
continue continue
valid_datasets.append(dataset) valid_datasets.append(dataset)
datapaths = [os.path.join(dataroot, dataset) + datapaths = [
'/' for dataset in valid_datasets] os.path.join(dataroot, dataset) + "/" for dataset in valid_datasets
]
return datapaths, valid_datasets return datapaths, valid_datasets
def main(datapaths): def main(datapaths):
for path in datapaths: for path in datapaths:
chirpdetection(path, plot='show') chirpdetection(path, plot="show")
if __name__ == '__main__':
dataroot = '../data/mount_data/'
if __name__ == "__main__":
dataroot = "../data/mount_data/"
datapaths, valid_datasets= get_valid_datasets(dataroot) datapaths, valid_datasets = get_valid_datasets(dataroot)
recs = pd.DataFrame(columns=['recording'], data=valid_datasets) recs = pd.DataFrame(columns=["recording"], data=valid_datasets)
recs.to_csv('../recs.csv', index=False) recs.to_csv("../recs.csv", index=False)
# datapaths = ['../data/mount_data/2020-03-25-10_00/'] # datapaths = ['../data/mount_data/2020-03-25-10_00/']
main(datapaths) main(datapaths)

44
code/get_behaviour.py
View File

@ -1,4 +1,4 @@
import os import os
from paramiko import SSHClient from paramiko import SSHClient
from scp import SCPClient from scp import SCPClient
from IPython import embed from IPython import embed
@ -7,29 +7,41 @@ from pandas import read_csv
ssh = SSHClient() ssh = SSHClient()
ssh.load_system_host_keys() ssh.load_system_host_keys()
ssh.connect(hostname='kraken', ssh.connect(
username='efish', hostname="kraken",
password='fwNix4U', username="efish",
) password="fwNix4U",
)
# SCPCLient takes a paramiko transport as its only argument # SCPCLient takes a paramiko transport as its only argument
scp = SCPClient(ssh.get_transport()) scp = SCPClient(ssh.get_transport())
data = read_csv('../recs.csv') data = read_csv("../recs.csv")
foldernames = data['recording'].values foldernames = data["recording"].values
directory = f'/Users/acfw/Documents/uni_tuebingen/chirpdetection/GP2023_chirp_detection/data/mount_data/' directory = f"/Users/acfw/Documents/uni_tuebingen/chirpdetection/GP2023_chirp_detection/data/mount_data/"
for foldername in foldernames: for foldername in foldernames:
if not os.path.exists(directory + foldername):
if not os.path.exists(directory+foldername): os.makedirs(directory + foldername)
os.makedirs(directory+foldername)
files = [
files = [('-').join(foldername.split('-')[:3])+'.csv','chirp_ids.npy', 'chirps.npy', 'fund_v.npy', 'ident_v.npy', 'idx_v.npy', 'times.npy', 'spec.npy', 'LED_on_time.npy', 'sign_v.npy'] ("-").join(foldername.split("-")[:3]) + ".csv",
"chirp_ids.npy",
"chirps.npy",
"fund_v.npy",
"ident_v.npy",
"idx_v.npy",
"times.npy",
"spec.npy",
"LED_on_time.npy",
"sign_v.npy",
]
for f in files: for f in files:
scp.get(f'/home/efish/behavior/2019_tube_competition/{foldername}/{f}', scp.get(
directory+foldername) f"/home/efish/behavior/2019_tube_competition/{foldername}/{f}",
directory + foldername,
)
scp.close() scp.close()

96
code/modules/behaviour_handling.py
View File

@ -30,12 +30,12 @@ class Behavior:
""" """
def __init__(self, folder_path: str) -> None: def __init__(self, folder_path: str) -> None:
LED_on_time_BORIS = np.load(
LED_on_time_BORIS = np.load(os.path.join( os.path.join(folder_path, "LED_on_time.npy"), allow_pickle=True
folder_path, 'LED_on_time.npy'), allow_pickle=True) )
csv_filename = os.path.split(folder_path[:-1])[-1] csv_filename = os.path.split(folder_path[:-1])[-1]
csv_filename = '-'.join(csv_filename.split('-')[:-1]) + '.csv' csv_filename = "-".join(csv_filename.split("-")[:-1]) + ".csv"
# embed() # embed()
# csv_filename = [f for f in os.listdir( # csv_filename = [f for f in os.listdir(
@ -43,31 +43,39 @@ class Behavior:
# logger.info(f'CSV file: {csv_filename}') # logger.info(f'CSV file: {csv_filename}')
self.dataframe = read_csv(os.path.join(folder_path, csv_filename)) self.dataframe = read_csv(os.path.join(folder_path, csv_filename))
self.chirps = np.load(os.path.join( self.chirps = np.load(
folder_path, 'chirps.npy'), allow_pickle=True) os.path.join(folder_path, "chirps.npy"), allow_pickle=True
self.chirps_ids = np.load(os.path.join( )
folder_path, 'chirp_ids.npy'), allow_pickle=True) self.chirps_ids = np.load(
os.path.join(folder_path, "chirp_ids.npy"), allow_pickle=True
self.ident = np.load(os.path.join( )
folder_path, 'ident_v.npy'), allow_pickle=True)
self.idx = np.load(os.path.join( self.ident = np.load(
folder_path, 'idx_v.npy'), allow_pickle=True) os.path.join(folder_path, "ident_v.npy"), allow_pickle=True
self.freq = np.load(os.path.join( )
folder_path, 'fund_v.npy'), allow_pickle=True) self.idx = np.load(
self.time = np.load(os.path.join( os.path.join(folder_path, "idx_v.npy"), allow_pickle=True
folder_path, "times.npy"), allow_pickle=True) )
self.spec = np.load(os.path.join( self.freq = np.load(
folder_path, "spec.npy"), allow_pickle=True) os.path.join(folder_path, "fund_v.npy"), allow_pickle=True
)
self.time = np.load(
os.path.join(folder_path, "times.npy"), allow_pickle=True
)
self.spec = np.load(
os.path.join(folder_path, "spec.npy"), allow_pickle=True
)
for k, key in enumerate(self.dataframe.keys()): for k, key in enumerate(self.dataframe.keys()):
key = key.lower() key = key.lower()
if ' ' in key: if " " in key:
key = key.replace(' ', '_') key = key.replace(" ", "_")
if '(' in key: if "(" in key:
key = key.replace('(', '') key = key.replace("(", "")
key = key.replace(')', '') key = key.replace(")", "")
setattr(self, key, np.array( setattr(
self.dataframe[self.dataframe.keys()[k]])) self, key, np.array(self.dataframe[self.dataframe.keys()[k]])
)
last_LED_t_BORIS = LED_on_time_BORIS[-1] last_LED_t_BORIS = LED_on_time_BORIS[-1]
real_time_range = self.time[-1] - self.time[0] real_time_range = self.time[-1] - self.time[0]
@ -78,22 +86,19 @@ class Behavior:
def correct_chasing_events( def correct_chasing_events(
category: np.ndarray, category: np.ndarray, timestamps: np.ndarray
timestamps: np.ndarray
) -> tuple[np.ndarray, np.ndarray]: ) -> tuple[np.ndarray, np.ndarray]:
onset_ids = np.arange(len(category))[category == 0]
offset_ids = np.arange(len(category))[category == 1]
onset_ids = np.arange( wrong_bh = np.arange(len(category))[category != 2][:-1][
len(category))[category == 0] np.diff(category[category != 2]) == 0
offset_ids = np.arange( ]
len(category))[category == 1]
wrong_bh = np.arange(len(category))[
category != 2][:-1][np.diff(category[category != 2]) == 0]
if category[category != 2][-1] == 0: if category[category != 2][-1] == 0:
wrong_bh = np.append( wrong_bh = np.append(
wrong_bh, wrong_bh, np.arange(len(category))[category != 2][-1]
np.arange(len(category))[category != 2][-1]) )
if onset_ids[0] > offset_ids[0]: if onset_ids[0] > offset_ids[0]:
offset_ids = np.delete(offset_ids, 0) offset_ids = np.delete(offset_ids, 0)
@ -103,18 +108,16 @@ def correct_chasing_events(
category = np.delete(category, wrong_bh) category = np.delete(category, wrong_bh)
timestamps = np.delete(timestamps, wrong_bh) timestamps = np.delete(timestamps, wrong_bh)
new_onset_ids = np.arange( new_onset_ids = np.arange(len(category))[category == 0]
len(category))[category == 0] new_offset_ids = np.arange(len(category))[category == 1]
new_offset_ids = np.arange(
len(category))[category == 1]
# Check whether on- or offset is longer and calculate length difference # Check whether on- or offset is longer and calculate length difference
if len(new_onset_ids) > len(new_offset_ids): if len(new_onset_ids) > len(new_offset_ids):
embed() embed()
logger.warning('Onsets are greater than offsets') logger.warning("Onsets are greater than offsets")
elif len(new_onset_ids) < len(new_offset_ids): elif len(new_onset_ids) < len(new_offset_ids):
logger.warning('Offsets are greater than onsets') logger.warning("Offsets are greater than onsets")
elif len(new_onset_ids) == len(new_offset_ids): elif len(new_onset_ids) == len(new_offset_ids):
# logger.info('Chasing events are equal') # logger.info('Chasing events are equal')
pass pass
@ -130,13 +133,11 @@ def center_chirps(
# dt: float, # dt: float,
# width: float, # width: float,
) -> tuple[np.ndarray, np.ndarray, np.ndarray]: ) -> 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
# timestamps of chirps around event centered on the event timepoint # timestamps of chirps around event centered on the event timepoint
centered_chirps = [] centered_chirps = []
for event_timestamp in events: for event_timestamp in events:
start = event_timestamp - time_before_event start = event_timestamp - time_before_event
stop = event_timestamp + time_after_event stop = event_timestamp + time_after_event
chirps_around_event = [c for c in chirps if (c >= start) & (c <= stop)] chirps_around_event = [c for c in chirps if (c >= start) & (c <= stop)]
@ -152,7 +153,8 @@ def center_chirps(
if len(centered_chirps) != len(event_chirps): if len(centered_chirps) != len(event_chirps):
raise ValueError( raise ValueError(
'Non centered chirps and centered chirps are not equal') "Non centered chirps and centered chirps are not equal"
)
# time = np.arange(-time_before_event, time_after_event, dt) # time = np.arange(-time_before_event, time_after_event, dt)

21
code/modules/datahandling.py
View File

@ -23,7 +23,9 @@ def minmaxnorm(data):
return (data - np.min(data)) / (np.max(data) - np.min(data)) return (data - np.min(data)) / (np.max(data) - np.min(data))
def instantaneous_frequency2(signal: np.ndarray, fs: float, interpolation: str = 'linear') -> np.ndarray: def instantaneous_frequency2(
signal: np.ndarray, fs: float, interpolation: str = "linear"
) -> np.ndarray:
""" """
Compute the instantaneous frequency of a periodic signal using zero crossings and resample the frequency using linear Compute the instantaneous frequency of a periodic signal using zero crossings and resample the frequency using linear
or cubic interpolation to match the dimensions of the input array. or cubic interpolation to match the dimensions of the input array.
@ -55,10 +57,10 @@ def instantaneous_frequency2(signal: np.ndarray, fs: float, interpolation: str =
orig_len = len(signal) orig_len = len(signal)
freq = resample(freq, orig_len) freq = resample(freq, orig_len)
if interpolation == 'linear': if interpolation == "linear":
freq = np.interp(np.arange(0, orig_len), np.arange(0, orig_len), freq) freq = np.interp(np.arange(0, orig_len), np.arange(0, orig_len), freq)
elif interpolation == 'cubic': elif interpolation == "cubic":
freq = resample(freq, orig_len, window='cubic') freq = resample(freq, orig_len, window="cubic")
return freq return freq
@ -67,7 +69,7 @@ def instantaneous_frequency(
signal: np.ndarray, signal: np.ndarray,
samplerate: int, samplerate: int,
smoothing_window: int, smoothing_window: int,
interpolation: str = 'linear', interpolation: str = "linear",
) -> np.ndarray: ) -> np.ndarray:
""" """
Compute the instantaneous frequency of a signal that is approximately Compute the instantaneous frequency of a signal that is approximately
@ -120,11 +122,10 @@ def instantaneous_frequency(
orig_len = len(signal) orig_len = len(signal)
freq = resample(instantaneous_frequency, orig_len) freq = resample(instantaneous_frequency, orig_len)
if interpolation == 'linear': if interpolation == "linear":
freq = np.interp(np.arange(0, orig_len), np.arange(0, orig_len), freq) freq = np.interp(np.arange(0, orig_len), np.arange(0, orig_len), freq)
elif interpolation == 'cubic': elif interpolation == "cubic":
freq = resample(freq, orig_len, window='cubic') freq = resample(freq, orig_len, window="cubic")
return freq return freq
@ -160,7 +161,6 @@ def purge_duplicates(
group = [timestamps[0]] group = [timestamps[0]]
for i in range(1, len(timestamps)): for i in range(1, len(timestamps)):
# check the difference between current timestamp and previous # check the difference between current timestamp and previous
# timestamp is less than the threshold # timestamp is less than the threshold
if timestamps[i] - timestamps[i - 1] < threshold: if timestamps[i] - timestamps[i - 1] < threshold:
@ -379,7 +379,6 @@ def acausal_kde1d(spikes, time, width):
if __name__ == "__main__": if __name__ == "__main__":
timestamps = [ timestamps = [
[1.2, 1.5, 1.3], [1.2, 1.5, 1.3],
[], [],

1
code/modules/filehandling.py
View File

@ -35,7 +35,6 @@ class LoadData:
""" """
def __init__(self, datapath: str) -> None: def __init__(self, datapath: str) -> None:
# load raw data # load raw data
self.datapath = datapath self.datapath = datapath
self.file = os.path.join(datapath, "traces-grid1.raw") self.file = os.path.join(datapath, "traces-grid1.raw")

19
code/modules/filters.py
View File

@ -3,10 +3,10 @@ import numpy as np
def bandpass_filter( def bandpass_filter(
signal: np.ndarray, signal: np.ndarray,
samplerate: float, samplerate: float,
lowf: float, lowf: float,
highf: float, highf: float,
) -> np.ndarray: ) -> np.ndarray:
"""Bandpass filter a signal. """Bandpass filter a signal.
@ -60,9 +60,7 @@ def highpass_filter(
def lowpass_filter( def lowpass_filter(
signal: np.ndarray, signal: np.ndarray, samplerate: float, cutoff: float
samplerate: float,
cutoff: float
) -> np.ndarray: ) -> np.ndarray:
"""Lowpass filter a signal. """Lowpass filter a signal.
@ -86,10 +84,9 @@ def lowpass_filter(
return filtered_signal return filtered_signal
def envelope(signal: np.ndarray, def envelope(
samplerate: float, signal: np.ndarray, samplerate: float, cutoff_frequency: float
cutoff_frequency: float ) -> np.ndarray:
) -> np.ndarray:
"""Calculate the envelope of a signal using a lowpass filter. """Calculate the envelope of a signal using a lowpass filter.
Parameters Parameters

8
code/modules/logger.py
View File

@ -2,12 +2,13 @@ import logging
def makeLogger(name: str): def makeLogger(name: str):
# create logger formats for file and terminal # create logger formats for file and terminal
file_formatter = logging.Formatter( file_formatter = logging.Formatter(
"[ %(levelname)s ] ~ %(asctime)s ~ %(module)s.%(funcName)s: %(message)s") "[ %(levelname)s ] ~ %(asctime)s ~ %(module)s.%(funcName)s: %(message)s"
)
console_formatter = logging.Formatter( console_formatter = logging.Formatter(
"[ %(levelname)s ] in %(module)s.%(funcName)s: %(message)s") "[ %(levelname)s ] in %(module)s.%(funcName)s: %(message)s"
)
# create logging file if loglevel is debug # create logging file if loglevel is debug
file_handler = logging.FileHandler(f"gridtools_log.log", mode="w") file_handler = logging.FileHandler(f"gridtools_log.log", mode="w")
@ -29,7 +30,6 @@ def makeLogger(name: str):
if __name__ == "__main__": if __name__ == "__main__":
# initiate logger # initiate logger
mylogger = makeLogger(__name__) mylogger = makeLogger(__name__)

16
code/modules/plotstyle.py
View File

@ -7,7 +7,6 @@ from matplotlib.colors import ListedColormap
def PlotStyle() -> None: def PlotStyle() -> None:
class style: class style:
# lightcmap = cmocean.tools.lighten(cmocean.cm.haline, 0.8) # lightcmap = cmocean.tools.lighten(cmocean.cm.haline, 0.8)
# units # units
@ -76,13 +75,15 @@ def PlotStyle() -> None:
va="center", va="center",
zorder=1000, zorder=1000,
bbox=dict( bbox=dict(
boxstyle=f"circle, pad={padding}", fc="white", ec="black", lw=1 boxstyle=f"circle, pad={padding}",
fc="white",
ec="black",
lw=1,
), ),
) )
@classmethod @classmethod
def fade_cmap(cls, cmap): def fade_cmap(cls, cmap):
my_cmap = cmap(np.arange(cmap.N)) my_cmap = cmap(np.arange(cmap.N))
my_cmap[:, -1] = np.linspace(0, 1, cmap.N) my_cmap[:, -1] = np.linspace(0, 1, cmap.N)
my_cmap = ListedColormap(my_cmap) my_cmap = ListedColormap(my_cmap)
@ -295,7 +296,6 @@ def PlotStyle() -> None:
if __name__ == "__main__": if __name__ == "__main__":
s = PlotStyle() s = PlotStyle()
import matplotlib.cbook as cbook import matplotlib.cbook as cbook
@ -347,7 +347,8 @@ if __name__ == "__main__":
for ax in axs: for ax in axs:
ax.yaxis.grid(True) ax.yaxis.grid(True)
ax.set_xticks( ax.set_xticks(
[y + 1 for y in range(len(all_data))], labels=["x1", "x2", "x3", "x4"] [y + 1 for y in range(len(all_data))],
labels=["x1", "x2", "x3", "x4"],
) )
ax.set_xlabel("Four separate samples") ax.set_xlabel("Four separate samples")
ax.set_ylabel("Observed values") ax.set_ylabel("Observed values")
@ -396,7 +397,10 @@ if __name__ == "__main__":
grid = np.random.rand(4, 4) grid = np.random.rand(4, 4)
fig, axs = plt.subplots( fig, axs = plt.subplots(
nrows=3, ncols=6, figsize=(9, 6), subplot_kw={"xticks": [], "yticks": []} nrows=3,
ncols=6,
figsize=(9, 6),
subplot_kw={"xticks": [], "yticks": []},
) )
for ax, interp_method in zip(axs.flat, methods): for ax, interp_method in zip(axs.flat, methods):

16
code/modules/plotstyle1.py
View File

@ -7,7 +7,6 @@ from matplotlib.colors import ListedColormap
def PlotStyle() -> None: def PlotStyle() -> None:
class style: class style:
# lightcmap = cmocean.tools.lighten(cmocean.cm.haline, 0.8) # lightcmap = cmocean.tools.lighten(cmocean.cm.haline, 0.8)
# units # units
@ -76,13 +75,15 @@ def PlotStyle() -> None:
va="center", va="center",
zorder=1000, zorder=1000,
bbox=dict( bbox=dict(
boxstyle=f"circle, pad={padding}", fc="white", ec="black", lw=1 boxstyle=f"circle, pad={padding}",
fc="white",
ec="black",
lw=1,
), ),
) )
@classmethod @classmethod
def fade_cmap(cls, cmap): def fade_cmap(cls, cmap):
my_cmap = cmap(np.arange(cmap.N)) my_cmap = cmap(np.arange(cmap.N))
my_cmap[:, -1] = np.linspace(0, 1, cmap.N) my_cmap[:, -1] = np.linspace(0, 1, cmap.N)
my_cmap = ListedColormap(my_cmap) my_cmap = ListedColormap(my_cmap)
@ -295,7 +296,6 @@ def PlotStyle() -> None:
if __name__ == "__main__": if __name__ == "__main__":
s = PlotStyle() s = PlotStyle()
import matplotlib.cbook as cbook import matplotlib.cbook as cbook
@ -347,7 +347,8 @@ if __name__ == "__main__":
for ax in axs: for ax in axs:
ax.yaxis.grid(True) ax.yaxis.grid(True)
ax.set_xticks( ax.set_xticks(
[y + 1 for y in range(len(all_data))], labels=["x1", "x2", "x3", "x4"] [y + 1 for y in range(len(all_data))],
labels=["x1", "x2", "x3", "x4"],
) )
ax.set_xlabel("Four separate samples") ax.set_xlabel("Four separate samples")
ax.set_ylabel("Observed values") ax.set_ylabel("Observed values")
@ -396,7 +397,10 @@ if __name__ == "__main__":
grid = np.random.rand(4, 4) grid = np.random.rand(4, 4)
fig, axs = plt.subplots( fig, axs = plt.subplots(
nrows=3, ncols=6, figsize=(9, 6), subplot_kw={"xticks": [], "yticks": []} nrows=3,
ncols=6,
figsize=(9, 6),
subplot_kw={"xticks": [], "yticks": []},
) )
for ax, interp_method in zip(axs.flat, methods): for ax, interp_method in zip(axs.flat, methods):

16
code/modules/plotstyle_dark.py
View File

@ -7,7 +7,6 @@ from matplotlib.colors import ListedColormap
def PlotStyle() -> None: def PlotStyle() -> None:
class style: class style:
# lightcmap = cmocean.tools.lighten(cmocean.cm.haline, 0.8) # lightcmap = cmocean.tools.lighten(cmocean.cm.haline, 0.8)
# units # units
@ -76,13 +75,15 @@ def PlotStyle() -> None:
va="center", va="center",
zorder=1000, zorder=1000,
bbox=dict( bbox=dict(
boxstyle=f"circle, pad={padding}", fc="white", ec="black", lw=1 boxstyle=f"circle, pad={padding}",
fc="white",
ec="black",
lw=1,
), ),
) )
@classmethod @classmethod
def fade_cmap(cls, cmap): def fade_cmap(cls, cmap):
my_cmap = cmap(np.arange(cmap.N)) my_cmap = cmap(np.arange(cmap.N))
my_cmap[:, -1] = np.linspace(0, 1, cmap.N) my_cmap[:, -1] = np.linspace(0, 1, cmap.N)
my_cmap = ListedColormap(my_cmap) my_cmap = ListedColormap(my_cmap)
@ -295,7 +296,6 @@ def PlotStyle() -> None:
if __name__ == "__main__": if __name__ == "__main__":
s = PlotStyle() s = PlotStyle()
import matplotlib.cbook as cbook import matplotlib.cbook as cbook
@ -347,7 +347,8 @@ if __name__ == "__main__":
for ax in axs: for ax in axs:
ax.yaxis.grid(True) ax.yaxis.grid(True)
ax.set_xticks( ax.set_xticks(
[y + 1 for y in range(len(all_data))], labels=["x1", "x2", "x3", "x4"] [y + 1 for y in range(len(all_data))],
labels=["x1", "x2", "x3", "x4"],
) )
ax.set_xlabel("Four separate samples") ax.set_xlabel("Four separate samples")
ax.set_ylabel("Observed values") ax.set_ylabel("Observed values")
@ -396,7 +397,10 @@ if __name__ == "__main__":
grid = np.random.rand(4, 4) grid = np.random.rand(4, 4)
fig, axs = plt.subplots( fig, axs = plt.subplots(
nrows=3, ncols=6, figsize=(9, 6), subplot_kw={"xticks": [], "yticks": []} nrows=3,
ncols=6,
figsize=(9, 6),
subplot_kw={"xticks": [], "yticks": []},
) )
for ax, interp_method in zip(axs.flat, methods): for ax, interp_method in zip(axs.flat, methods):

2
code/modules/simulations.py
View File

@ -37,7 +37,7 @@ def create_chirp(
ck = 0 ck = 0
csig = 0.5 * chirpduration / np.power(2.0 * np.log(10.0), 0.5 / kurtosis) csig = 0.5 * chirpduration / np.power(2.0 * np.log(10.0), 0.5 / kurtosis)
#csig = csig*-1 # csig = csig*-1
for k, t in enumerate(time): for k, t in enumerate(time):
a = 1.0 a = 1.0
f = eodf f = eodf

291
code/plot_chirp_size.py
View File

@ -16,26 +16,25 @@ logger = makeLogger(__name__)
def get_chirp_winner_loser(folder_name, Behavior, order_meta_df): def get_chirp_winner_loser(folder_name, Behavior, order_meta_df):
foldername = folder_name.split("/")[-2]
foldername = folder_name.split('/')[-2] winner_row = order_meta_df[order_meta_df["recording"] == foldername]
winner_row = order_meta_df[order_meta_df['recording'] == foldername] winner = winner_row["winner"].values[0].astype(int)
winner = winner_row['winner'].values[0].astype(int) winner_fish1 = winner_row["fish1"].values[0].astype(int)
winner_fish1 = winner_row['fish1'].values[0].astype(int) winner_fish2 = winner_row["fish2"].values[0].astype(int)
winner_fish2 = winner_row['fish2'].values[0].astype(int)
if winner > 0: if winner > 0:
if winner == winner_fish1: if winner == winner_fish1:
winner_fish_id = winner_row['rec_id1'].values[0] winner_fish_id = winner_row["rec_id1"].values[0]
loser_fish_id = winner_row['rec_id2'].values[0] loser_fish_id = winner_row["rec_id2"].values[0]
elif winner == winner_fish2: elif winner == winner_fish2:
winner_fish_id = winner_row['rec_id2'].values[0] winner_fish_id = winner_row["rec_id2"].values[0]
loser_fish_id = winner_row['rec_id1'].values[0] loser_fish_id = winner_row["rec_id1"].values[0]
chirp_winner = len( chirp_winner = len(
Behavior.chirps[Behavior.chirps_ids == winner_fish_id]) Behavior.chirps[Behavior.chirps_ids == winner_fish_id]
chirp_loser = len( )
Behavior.chirps[Behavior.chirps_ids == loser_fish_id]) chirp_loser = len(Behavior.chirps[Behavior.chirps_ids == loser_fish_id])
return chirp_winner, chirp_loser return chirp_winner, chirp_loser
else: else:
@ -43,24 +42,24 @@ def get_chirp_winner_loser(folder_name, Behavior, order_meta_df):
def get_chirp_size(folder_name, Behavior, order_meta_df, id_meta_df): def get_chirp_size(folder_name, Behavior, order_meta_df, id_meta_df):
foldername = folder_name.split("/")[-2]
foldername = folder_name.split('/')[-2] folder_row = order_meta_df[order_meta_df["recording"] == foldername]
folder_row = order_meta_df[order_meta_df['recording'] == foldername] fish1 = folder_row["fish1"].values[0].astype(int)
fish1 = folder_row['fish1'].values[0].astype(int) fish2 = folder_row["fish2"].values[0].astype(int)
fish2 = folder_row['fish2'].values[0].astype(int) winner = folder_row["winner"].values[0].astype(int)
winner = folder_row['winner'].values[0].astype(int)
groub = folder_row["group"].values[0].astype(int)
groub = folder_row['group'].values[0].astype(int) size_fish1_row = id_meta_df[
size_fish1_row = id_meta_df[(id_meta_df['group'] == groub) & ( (id_meta_df["group"] == groub) & (id_meta_df["fish"] == fish1)
id_meta_df['fish'] == fish1)] ]
size_fish2_row = id_meta_df[(id_meta_df['group'] == groub) & ( size_fish2_row = id_meta_df[
id_meta_df['fish'] == fish2)] (id_meta_df["group"] == groub) & (id_meta_df["fish"] == fish2)
]
size_winners = [size_fish1_row[col].values[0]
for col in ['l1', 'l2', 'l3']] size_winners = [size_fish1_row[col].values[0] for col in ["l1", "l2", "l3"]]
size_fish1 = np.nanmean(size_winners) size_fish1 = np.nanmean(size_winners)
size_losers = [size_fish2_row[col].values[0] for col in ['l1', 'l2', 'l3']] size_losers = [size_fish2_row[col].values[0] for col in ["l1", "l2", "l3"]]
size_fish2 = np.nanmean(size_losers) size_fish2 = np.nanmean(size_losers)
if winner == fish1: if winner == fish1:
@ -75,8 +74,8 @@ def get_chirp_size(folder_name, Behavior, order_meta_df, id_meta_df):
size_diff_bigger = 0 size_diff_bigger = 0
size_diff_smaller = 0 size_diff_smaller = 0
winner_fish_id = folder_row['rec_id1'].values[0] winner_fish_id = folder_row["rec_id1"].values[0]
loser_fish_id = folder_row['rec_id2'].values[0] loser_fish_id = folder_row["rec_id2"].values[0]
elif winner == fish2: elif winner == fish2:
if size_fish2 > size_fish1: if size_fish2 > size_fish1:
@ -90,39 +89,39 @@ def get_chirp_size(folder_name, Behavior, order_meta_df, id_meta_df):
size_diff_bigger = 0 size_diff_bigger = 0
size_diff_smaller = 0 size_diff_smaller = 0
winner_fish_id = folder_row['rec_id2'].values[0] winner_fish_id = folder_row["rec_id2"].values[0]
loser_fish_id = folder_row['rec_id1'].values[0] loser_fish_id = folder_row["rec_id1"].values[0]
else: else:
size_diff_bigger = np.nan size_diff_bigger = np.nan
size_diff_smaller = np.nan size_diff_smaller = np.nan
winner_fish_id = np.nan winner_fish_id = np.nan
loser_fish_id = np.nan loser_fish_id = np.nan
return size_diff_bigger, size_diff_smaller, winner_fish_id, loser_fish_id return (
size_diff_bigger,
size_diff_smaller,
winner_fish_id,
loser_fish_id,
)
chirp_winner = len( chirp_winner = len(Behavior.chirps[Behavior.chirps_ids == winner_fish_id])
Behavior.chirps[Behavior.chirps_ids == winner_fish_id]) chirp_loser = len(Behavior.chirps[Behavior.chirps_ids == loser_fish_id])
chirp_loser = len(
Behavior.chirps[Behavior.chirps_ids == loser_fish_id])
return size_diff_bigger, chirp_winner, size_diff_smaller, chirp_loser return size_diff_bigger, chirp_winner, size_diff_smaller, chirp_loser
def get_chirp_freq(folder_name, Behavior, order_meta_df): def get_chirp_freq(folder_name, Behavior, order_meta_df):
foldername = folder_name.split("/")[-2]
folder_row = order_meta_df[order_meta_df["recording"] == foldername]
fish1 = folder_row["fish1"].values[0].astype(int)
fish2 = folder_row["fish2"].values[0].astype(int)
foldername = folder_name.split('/')[-2] fish1_freq = folder_row["rec_id1"].values[0].astype(int)
folder_row = order_meta_df[order_meta_df['recording'] == foldername] fish2_freq = folder_row["rec_id2"].values[0].astype(int)
fish1 = folder_row['fish1'].values[0].astype(int)
fish2 = folder_row['fish2'].values[0].astype(int)
fish1_freq = folder_row['rec_id1'].values[0].astype(int) chirp_freq_fish1 = np.nanmedian(Behavior.freq[Behavior.ident == fish1_freq])
fish2_freq = folder_row['rec_id2'].values[0].astype(int) chirp_freq_fish2 = np.nanmedian(Behavior.freq[Behavior.ident == fish2_freq])
winner = folder_row["winner"].values[0].astype(int)
chirp_freq_fish1 = np.nanmedian(
Behavior.freq[Behavior.ident == fish1_freq])
chirp_freq_fish2 = np.nanmedian(
Behavior.freq[Behavior.ident == fish2_freq])
winner = folder_row['winner'].values[0].astype(int)
if winner == fish1: if winner == fish1:
# if chirp_freq_fish1 > chirp_freq_fish2: # if chirp_freq_fish1 > chirp_freq_fish2:
@ -138,9 +137,9 @@ def get_chirp_freq(folder_name, Behavior, order_meta_df):
# winner_fish_id = np.nan # winner_fish_id = np.nan
# loser_fish_id = np.nan # loser_fish_id = np.nan
winner_fish_id = folder_row['rec_id1'].values[0] winner_fish_id = folder_row["rec_id1"].values[0]
winner_fish_freq = chirp_freq_fish1 winner_fish_freq = chirp_freq_fish1
loser_fish_id = folder_row['rec_id2'].values[0] loser_fish_id = folder_row["rec_id2"].values[0]
loser_fish_freq = chirp_freq_fish2 loser_fish_freq = chirp_freq_fish2
elif winner == fish2: elif winner == fish2:
@ -157,9 +156,9 @@ def get_chirp_freq(folder_name, Behavior, order_meta_df):
# winner_fish_id = np.nan # winner_fish_id = np.nan
# loser_fish_id = np.nan # loser_fish_id = np.nan
winner_fish_id = folder_row['rec_id2'].values[0] winner_fish_id = folder_row["rec_id2"].values[0]
winner_fish_freq = chirp_freq_fish2 winner_fish_freq = chirp_freq_fish2
loser_fish_id = folder_row['rec_id1'].values[0] loser_fish_id = folder_row["rec_id1"].values[0]
loser_fish_freq = chirp_freq_fish1 loser_fish_freq = chirp_freq_fish1
else: else:
winner_fish_freq = np.nan winner_fish_freq = np.nan
@ -168,25 +167,25 @@ def get_chirp_freq(folder_name, Behavior, order_meta_df):
loser_fish_id = np.nan loser_fish_id = np.nan
return winner_fish_freq, winner_fish_id, loser_fish_freq, loser_fish_id return winner_fish_freq, winner_fish_id, loser_fish_freq, loser_fish_id
chirp_winner = len( chirp_winner = len(Behavior.chirps[Behavior.chirps_ids == winner_fish_id])
Behavior.chirps[Behavior.chirps_ids == winner_fish_id]) chirp_loser = len(Behavior.chirps[Behavior.chirps_ids == loser_fish_id])
chirp_loser = len(
Behavior.chirps[Behavior.chirps_ids == loser_fish_id])
return winner_fish_freq, chirp_winner, loser_fish_freq, chirp_loser return winner_fish_freq, chirp_winner, loser_fish_freq, chirp_loser
def main(datapath: str): def main(datapath: str):
foldernames = [ foldernames = [
datapath + x + '/' for x in os.listdir(datapath) if os.path.isdir(datapath+x)] datapath + x + "/"
for x in os.listdir(datapath)
if os.path.isdir(datapath + x)
]
foldernames, _ = get_valid_datasets(datapath) foldernames, _ = get_valid_datasets(datapath)
path_order_meta = ( path_order_meta = ("/").join(
'/').join(foldernames[0].split('/')[:-2]) + '/order_meta.csv' foldernames[0].split("/")[:-2]
) + "/order_meta.csv"
order_meta_df = read_csv(path_order_meta) order_meta_df = read_csv(path_order_meta)
order_meta_df['recording'] = order_meta_df['recording'].str[1:-1] order_meta_df["recording"] = order_meta_df["recording"].str[1:-1]
path_id_meta = ( path_id_meta = ("/").join(foldernames[0].split("/")[:-2]) + "/id_meta.csv"
'/').join(foldernames[0].split('/')[:-2]) + '/id_meta.csv'
id_meta_df = read_csv(path_id_meta) id_meta_df = read_csv(path_id_meta)
chirps_winner = [] chirps_winner = []
@ -202,10 +201,9 @@ def main(datapath: str):
freq_chirps_winner = [] freq_chirps_winner = []
freq_chirps_loser = [] freq_chirps_loser = []
for foldername in foldernames: for foldername in foldernames:
# behabvior is pandas dataframe with all the data # behabvior is pandas dataframe with all the data
if foldername == '../data/mount_data/2020-05-12-10_00/': if foldername == "../data/mount_data/2020-05-12-10_00/":
continue continue
bh = Behavior(foldername) bh = Behavior(foldername)
# chirps are not sorted in time (presumably due to prior groupings) # chirps are not sorted in time (presumably due to prior groupings)
@ -217,15 +215,24 @@ def main(datapath: str):
category, timestamps = correct_chasing_events(category, timestamps) category, timestamps = correct_chasing_events(category, timestamps)
winner_chirp, loser_chirp = get_chirp_winner_loser( winner_chirp, loser_chirp = get_chirp_winner_loser(
foldername, bh, order_meta_df) foldername, bh, order_meta_df
)
chirps_winner.append(winner_chirp) chirps_winner.append(winner_chirp)
chirps_loser.append(loser_chirp) chirps_loser.append(loser_chirp)
size_diff_bigger, chirp_winner, size_diff_smaller, chirp_loser = get_chirp_size( (
foldername, bh, order_meta_df, id_meta_df) size_diff_bigger,
chirp_winner,
size_diff_smaller,
chirp_loser,
) = get_chirp_size(foldername, bh, order_meta_df, id_meta_df)
freq_winner, chirp_freq_winner, freq_loser, chirp_freq_loser = get_chirp_freq( (
foldername, bh, order_meta_df) freq_winner,
chirp_freq_winner,
freq_loser,
chirp_freq_loser,
) = get_chirp_freq(foldername, bh, order_meta_df)
freq_diffs_higher.append(freq_winner) freq_diffs_higher.append(freq_winner)
freq_diffs_lower.append(freq_loser) freq_diffs_lower.append(freq_loser)
@ -242,82 +249,124 @@ def main(datapath: str):
pearsonr(size_diffs_winner, size_chirps_winner) pearsonr(size_diffs_winner, size_chirps_winner)
pearsonr(size_diffs_loser, size_chirps_loser) pearsonr(size_diffs_loser, size_chirps_loser)
fig, (ax1, ax2, ax3) = plt.subplots(1, 3, figsize=( fig, (ax1, ax2, ax3) = plt.subplots(
21*ps.cm, 7*ps.cm), width_ratios=[1, 0.8, 0.8], sharey=True) 1,
plt.subplots_adjust(left=0.11, right=0.948, top=0.86, 3,
wspace=0.343, bottom=0.198) figsize=(21 * ps.cm, 7 * ps.cm),
width_ratios=[1, 0.8, 0.8],
sharey=True,
)
plt.subplots_adjust(
left=0.11, right=0.948, top=0.86, wspace=0.343, bottom=0.198
)
scatterwinner = 1.15 scatterwinner = 1.15
scatterloser = 1.85 scatterloser = 1.85
chirps_winner = np.asarray(chirps_winner)[~np.isnan(chirps_winner)] chirps_winner = np.asarray(chirps_winner)[~np.isnan(chirps_winner)]
chirps_loser = np.asarray(chirps_loser)[~np.isnan(chirps_loser)] chirps_loser = np.asarray(chirps_loser)[~np.isnan(chirps_loser)]
embed() embed()
exit() exit()
freq_diffs_higher = np.asarray( freq_diffs_higher = np.asarray(freq_diffs_higher)[
freq_diffs_higher)[~np.isnan(freq_diffs_higher)] ~np.isnan(freq_diffs_higher)
freq_diffs_lower = np.asarray(freq_diffs_lower)[ ]
~np.isnan(freq_diffs_lower)] freq_diffs_lower = np.asarray(freq_diffs_lower)[~np.isnan(freq_diffs_lower)]
freq_chirps_winner = np.asarray( freq_chirps_winner = np.asarray(freq_chirps_winner)[
freq_chirps_winner)[~np.isnan(freq_chirps_winner)] ~np.isnan(freq_chirps_winner)
freq_chirps_loser = np.asarray( ]
freq_chirps_loser)[~np.isnan(freq_chirps_loser)] freq_chirps_loser = np.asarray(freq_chirps_loser)[
~np.isnan(freq_chirps_loser)
]
stat = wilcoxon(chirps_winner, chirps_loser) stat = wilcoxon(chirps_winner, chirps_loser)
print(stat) print(stat)
winner_color = ps.gblue2 winner_color = ps.gblue2
loser_color = ps.gblue1 loser_color = ps.gblue1
bplot1 = ax1.boxplot(chirps_winner, positions=[ bplot1 = ax1.boxplot(
0.9], showfliers=False, patch_artist=True) chirps_winner, positions=[0.9], showfliers=False, patch_artist=True
)
bplot2 = ax1.boxplot(chirps_loser, positions=[
2.1], showfliers=False, patch_artist=True) bplot2 = ax1.boxplot(
chirps_loser, positions=[2.1], showfliers=False, patch_artist=True
ax1.scatter(np.ones(len(chirps_winner)) * )
scatterwinner, chirps_winner, color=winner_color)
ax1.scatter(np.ones(len(chirps_loser)) * ax1.scatter(
scatterloser, chirps_loser, color=loser_color) np.ones(len(chirps_winner)) * scatterwinner,
ax1.set_xticklabels(['Winner', 'Loser']) chirps_winner,
color=winner_color,
ax1.text(0.1, 0.85, f'n={len(chirps_loser)}', )
transform=ax1.transAxes, color=ps.white) ax1.scatter(
np.ones(len(chirps_loser)) * scatterloser,
chirps_loser,
color=loser_color,
)
ax1.set_xticklabels(["Winner", "Loser"])
ax1.text(
0.1,
0.85,
f"n={len(chirps_loser)}",
transform=ax1.transAxes,
color=ps.white,
)
for w, l in zip(chirps_winner, chirps_loser): for w, l in zip(chirps_winner, chirps_loser):
ax1.plot([scatterwinner, scatterloser], [w, l], ax1.plot(
color=ps.white, alpha=0.6, linewidth=1, zorder=-1) [scatterwinner, scatterloser],
ax1.set_ylabel('Chirp counts', color=ps.white) [w, l],
ax1.set_xlabel('Competition outcome', color=ps.white) color=ps.white,
alpha=0.6,
linewidth=1,
zorder=-1,
)
ax1.set_ylabel("Chirp counts", color=ps.white)
ax1.set_xlabel("Competition outcome", color=ps.white)
ps.set_boxplot_color(bplot1, winner_color) ps.set_boxplot_color(bplot1, winner_color)
ps.set_boxplot_color(bplot2, loser_color) ps.set_boxplot_color(bplot2, loser_color)
ax2.scatter(size_diffs_winner, size_chirps_winner, ax2.scatter(
color=winner_color, label='Winner') size_diffs_winner,
ax2.scatter(size_diffs_loser, size_chirps_loser, size_chirps_winner,
color=loser_color, label='Loser') color=winner_color,
label="Winner",
ax2.text(0.05, 0.85, f'n={len(size_chirps_loser)}', )
transform=ax2.transAxes, color=ps.white) ax2.scatter(
size_diffs_loser, size_chirps_loser, color=loser_color, label="Loser"
ax2.set_xlabel('Size difference [cm]') )
ax2.text(
0.05,
0.85,
f"n={len(size_chirps_loser)}",
transform=ax2.transAxes,
color=ps.white,
)
ax2.set_xlabel("Size difference [cm]")
# ax2.set_xticks(np.arange(-10, 10.1, 2)) # ax2.set_xticks(np.arange(-10, 10.1, 2))
ax3.scatter(freq_diffs_higher, freq_chirps_winner, color=winner_color) ax3.scatter(freq_diffs_higher, freq_chirps_winner, color=winner_color)
ax3.scatter(freq_diffs_lower, freq_chirps_loser, color=loser_color) ax3.scatter(freq_diffs_lower, freq_chirps_loser, color=loser_color)
ax3.text(0.1, 0.85, f'n={len(np.asarray(freq_chirps_winner)[~np.isnan(freq_chirps_loser)])}', ax3.text(
transform=ax3.transAxes, color=ps.white) 0.1,
0.85,
f"n={len(np.asarray(freq_chirps_winner)[~np.isnan(freq_chirps_loser)])}",
transform=ax3.transAxes,
color=ps.white,
)
ax3.set_xlabel('EODf [Hz]') ax3.set_xlabel("EODf [Hz]")
handles, labels = ax2.get_legend_handles_labels() handles, labels = ax2.get_legend_handles_labels()
fig.legend(handles, labels, loc='upper center', fig.legend(
ncol=2, bbox_to_anchor=(0.5, 1.04)) handles, labels, loc="upper center", ncol=2, bbox_to_anchor=(0.5, 1.04)
)
# pearson r # pearson r
plt.savefig('../poster/figs/chirps_winner_loser.pdf') plt.savefig("../poster/figs/chirps_winner_loser.pdf")
plt.show() plt.show()
if __name__ == '__main__': if __name__ == "__main__":
# Path to the data # Path to the data
datapath = '../data/mount_data/' datapath = "../data/mount_data/"
main(datapath) main(datapath)

74
code/plot_chirps_in_chasing.py
View File

@ -21,14 +21,16 @@ logger = makeLogger(__name__)
def main(datapath: str): def main(datapath: str):
foldernames = [ foldernames = [
datapath + x + '/' for x in os.listdir(datapath) if os.path.isdir(datapath+x)] datapath + x + "/"
for x in os.listdir(datapath)
if os.path.isdir(datapath + x)
]
time_precents = [] time_precents = []
chirps_percents = [] chirps_percents = []
for foldername in foldernames: for foldername in foldernames:
# behabvior is pandas dataframe with all the data # behabvior is pandas dataframe with all the data
if foldername == '../data/mount_data/2020-05-12-10_00/': if foldername == "../data/mount_data/2020-05-12-10_00/":
continue continue
bh = Behavior(foldername) bh = Behavior(foldername)
@ -46,50 +48,70 @@ def main(datapath: str):
chirps_in_chasings = [] chirps_in_chasings = []
for onset, offset in zip(chasing_onset, chasing_offset): for onset, offset in zip(chasing_onset, chasing_offset):
chirps_in_chasing = [ chirps_in_chasing = [
c for c in bh.chirps if (c > onset) & (c < offset)] c for c in bh.chirps if (c > onset) & (c < offset)
]
chirps_in_chasings.append(chirps_in_chasing) chirps_in_chasings.append(chirps_in_chasing)
try: try:
time_chasing = np.sum( time_chasing = np.sum(
chasing_offset[chasing_offset < 3*60*60] - chasing_onset[chasing_onset < 3*60*60]) chasing_offset[chasing_offset < 3 * 60 * 60]
- chasing_onset[chasing_onset < 3 * 60 * 60]
)
except: except:
time_chasing = np.sum( time_chasing = np.sum(
chasing_offset[chasing_offset < 3*60*60] - chasing_onset[chasing_onset < 3*60*60][:-1]) chasing_offset[chasing_offset < 3 * 60 * 60]
- chasing_onset[chasing_onset < 3 * 60 * 60][:-1]
)
time_chasing_percent = (time_chasing/(3*60*60))*100 time_chasing_percent = (time_chasing / (3 * 60 * 60)) * 100
chirps_chasing = np.asarray(flatten(chirps_in_chasings)) chirps_chasing = np.asarray(flatten(chirps_in_chasings))
chirps_chasing_new = chirps_chasing[chirps_chasing < 3*60*60] chirps_chasing_new = chirps_chasing[chirps_chasing < 3 * 60 * 60]
chirps_percent = (len(chirps_chasing_new) / chirps_percent = (
len(bh.chirps[bh.chirps < 3*60*60]))*100 len(chirps_chasing_new) / len(bh.chirps[bh.chirps < 3 * 60 * 60])
) * 100
time_precents.append(time_chasing_percent) time_precents.append(time_chasing_percent)
chirps_percents.append(chirps_percent) chirps_percents.append(chirps_percent)
fig, ax = plt.subplots(1, 1, figsize=(7*ps.cm, 7*ps.cm)) fig, ax = plt.subplots(1, 1, figsize=(7 * ps.cm, 7 * ps.cm))
scatter_time = 1.20 scatter_time = 1.20
scatter_chirps = 1.80 scatter_chirps = 1.80
size = 10 size = 10
bplot1 = ax.boxplot([time_precents, chirps_percents], bplot1 = ax.boxplot(
showfliers=False, patch_artist=True) [time_precents, chirps_percents], showfliers=False, patch_artist=True
)
ps.set_boxplot_color(bplot1, ps.gray) ps.set_boxplot_color(bplot1, ps.gray)
ax.set_xticklabels(['Time \nchasing', 'Chirps \nin chasing']) ax.set_xticklabels(["Time \nchasing", "Chirps \nin chasing"])
ax.set_ylabel('Percent') ax.set_ylabel("Percent")
ax.scatter(np.ones(len(time_precents))*scatter_time, time_precents, ax.scatter(
facecolor=ps.white, s=size) np.ones(len(time_precents)) * scatter_time,
ax.scatter(np.ones(len(chirps_percents))*scatter_chirps, chirps_percents, time_precents,
facecolor=ps.white, s=size) facecolor=ps.white,
s=size,
)
ax.scatter(
np.ones(len(chirps_percents)) * scatter_chirps,
chirps_percents,
facecolor=ps.white,
s=size,
)
for i in range(len(time_precents)): for i in range(len(time_precents)):
ax.plot([scatter_time, scatter_chirps], [time_precents[i], ax.plot(
chirps_percents[i]], alpha=0.6, linewidth=1, color=ps.white) [scatter_time, scatter_chirps],
[time_precents[i], chirps_percents[i]],
ax.text(0.1, 0.9, f'n={len(time_precents)}', transform=ax.transAxes) alpha=0.6,
linewidth=1,
color=ps.white,
)
ax.text(0.1, 0.9, f"n={len(time_precents)}", transform=ax.transAxes)
plt.subplots_adjust(left=0.221, bottom=0.186, right=0.97, top=0.967) plt.subplots_adjust(left=0.221, bottom=0.186, right=0.97, top=0.967)
plt.savefig('../poster/figs/chirps_in_chasing.pdf') plt.savefig("../poster/figs/chirps_in_chasing.pdf")
plt.show() plt.show()
if __name__ == '__main__': if __name__ == "__main__":
# Path to the data # Path to the data
datapath = '../data/mount_data/' datapath = "../data/mount_data/"
main(datapath) main(datapath)

80
code/plot_event_timeline.py
View File

@ -13,6 +13,7 @@ from modules.plotstyle import PlotStyle
from modules.behaviour_handling import Behavior, correct_chasing_events from modules.behaviour_handling import Behavior, correct_chasing_events
from extract_chirps import get_valid_datasets from extract_chirps import get_valid_datasets
ps = PlotStyle() ps = PlotStyle()
logger = makeLogger(__name__) logger = makeLogger(__name__)
@ -20,13 +21,16 @@ logger = makeLogger(__name__)
def main(datapath: str): def main(datapath: str):
foldernames = [ foldernames = [
datapath + x + '/' for x in os.listdir(datapath) if os.path.isdir(datapath+x)] datapath + x + "/"
for x in os.listdir(datapath)
if os.path.isdir(datapath + x)
]
foldernames, _ = get_valid_datasets(datapath) foldernames, _ = get_valid_datasets(datapath)
for foldername in foldernames[3:4]: for foldername in foldernames[3:4]:
print(foldername) print(foldername)
# foldername = foldernames[0] # foldername = foldernames[0]
if foldername == '../data/mount_data/2020-05-12-10_00/': if foldername == "../data/mount_data/2020-05-12-10_00/":
continue continue
# behabvior is pandas dataframe with all the data # behabvior is pandas dataframe with all the data
bh = Behavior(foldername) bh = Behavior(foldername)
@ -52,18 +56,43 @@ def main(datapath: str):
exit() exit()
fish1_color = ps.gblue2 fish1_color = ps.gblue2
fish2_color = ps.gblue1 fish2_color = ps.gblue1
fig, ax = plt.subplots(5, 1, figsize=( fig, ax = plt.subplots(
21*ps.cm, 10*ps.cm), height_ratios=[0.5, 0.5, 0.5, 0.2, 6], sharex=True) 5,
1,
figsize=(21 * ps.cm, 10 * ps.cm),
height_ratios=[0.5, 0.5, 0.5, 0.2, 6],
sharex=True,
)
# marker size # marker size
s = 80 s = 80
ax[0].scatter(physical_contact, np.ones( ax[0].scatter(
len(physical_contact)), color=ps.gray, marker='|', s=s) physical_contact,
ax[1].scatter(chasing_onset, np.ones(len(chasing_onset)), np.ones(len(physical_contact)),
color=ps.gray, marker='|', s=s) color=ps.gray,
ax[2].scatter(fish1, np.ones(len(fish1))-0.25, marker="|",
color=fish1_color, marker='|', s=s) s=s,
ax[2].scatter(fish2, np.zeros(len(fish2))+0.25, )
color=fish2_color, marker='|', s=s) ax[1].scatter(
chasing_onset,
np.ones(len(chasing_onset)),
color=ps.gray,
marker="|",
s=s,
)
ax[2].scatter(
fish1,
np.ones(len(fish1)) - 0.25,
color=fish1_color,
marker="|",
s=s,
)
ax[2].scatter(
fish2,
np.zeros(len(fish2)) + 0.25,
color=fish2_color,
marker="|",
s=s,
)
freq_temp = bh.freq[bh.ident == fish1_id] freq_temp = bh.freq[bh.ident == fish1_id]
time_temp = bh.time[bh.idx[bh.ident == fish1_id]] time_temp = bh.time[bh.idx[bh.ident == fish1_id]]
@ -94,35 +123,38 @@ def main(datapath: str):
ax[2].set_xticks([]) ax[2].set_xticks([])
ps.hide_ax(ax[2]) ps.hide_ax(ax[2])
ax[4].axvspan(0, 3, 0, 5, facecolor='grey', alpha=0.5) ax[4].axvspan(0, 3, 0, 5, facecolor="grey", alpha=0.5)
ax[4].set_xticks(np.arange(0, 6.1, 0.5)) ax[4].set_xticks(np.arange(0, 6.1, 0.5))
ps.hide_ax(ax[3]) ps.hide_ax(ax[3])
labelpad = 30 labelpad = 30
fsize = 12 fsize = 12
ax[0].set_ylabel('Contact', rotation=0, ax[0].set_ylabel(
labelpad=labelpad, fontsize=fsize) "Contact", rotation=0, labelpad=labelpad, fontsize=fsize
)
ax[0].yaxis.set_label_coords(-0.062, -0.08) ax[0].yaxis.set_label_coords(-0.062, -0.08)
ax[1].set_ylabel('Chasing', rotation=0, ax[1].set_ylabel(
labelpad=labelpad, fontsize=fsize) "Chasing", rotation=0, labelpad=labelpad, fontsize=fsize
)
ax[1].yaxis.set_label_coords(-0.06, -0.08) ax[1].yaxis.set_label_coords(-0.06, -0.08)
ax[2].set_ylabel('Chirps', rotation=0, ax[2].set_ylabel(
labelpad=labelpad, fontsize=fsize) "Chirps", rotation=0, labelpad=labelpad, fontsize=fsize
)
ax[2].yaxis.set_label_coords(-0.07, -0.08) ax[2].yaxis.set_label_coords(-0.07, -0.08)
ax[4].set_ylabel('EODf') ax[4].set_ylabel("EODf")
ax[4].set_xlabel('Time [h]') ax[4].set_xlabel("Time [h]")
# ax[0].set_title(foldername.split('/')[-2]) # ax[0].set_title(foldername.split('/')[-2])
# 2020-03-31-9_59 # 2020-03-31-9_59
plt.subplots_adjust(left=0.158, right=0.987, top=0.918, bottom=0.136) plt.subplots_adjust(left=0.158, right=0.987, top=0.918, bottom=0.136)
plt.savefig('../poster/figs/timeline.svg') plt.savefig("../poster/figs/timeline.svg")
plt.show() plt.show()
# plot chirps # plot chirps
if __name__ == '__main__': if __name__ == "__main__":
# Path to the data # Path to the data
datapath = '../data/mount_data/' datapath = "../data/mount_data/"
main(datapath) main(datapath)

56
code/plot_introduction_specs.py
View File

@ -11,7 +11,6 @@ ps = PlotStyle()
def main(): def main():
# Load data # Load data
datapath = "../data/2022-06-02-10_00/" datapath = "../data/2022-06-02-10_00/"
data = LoadData(datapath) data = LoadData(datapath)
@ -24,26 +23,31 @@ def main():
timescaler = 1000 timescaler = 1000
raw = data.raw[window_start_index:window_start_index + raw = data.raw[
window_duration_index, 10] window_start_index : window_start_index + window_duration_index, 10
]
fig, (ax1, ax2) = plt.subplots( fig, (ax1, ax2) = plt.subplots(
1, 2, figsize=(21 * ps.cm, 8*ps.cm), sharex=True, sharey=True) 1, 2, figsize=(21 * ps.cm, 8 * ps.cm), sharex=True, sharey=True
)
# plot instantaneous frequency # plot instantaneous frequency
filtered1 = bandpass_filter( filtered1 = bandpass_filter(
signal=raw, lowf=750, highf=1200, samplerate=data.raw_rate) signal=raw, lowf=750, highf=1200, samplerate=data.raw_rate
)
filtered2 = bandpass_filter( filtered2 = bandpass_filter(
signal=raw, lowf=550, highf=700, samplerate=data.raw_rate) signal=raw, lowf=550, highf=700, samplerate=data.raw_rate
)
freqtime1, freq1 = instantaneous_frequency( freqtime1, freq1 = instantaneous_frequency(
filtered1, data.raw_rate, smoothing_window=3) filtered1, data.raw_rate, smoothing_window=3
)
freqtime2, freq2 = instantaneous_frequency( freqtime2, freq2 = instantaneous_frequency(
filtered2, data.raw_rate, smoothing_window=3) filtered2, data.raw_rate, smoothing_window=3
)
ax1.plot(freqtime1*timescaler, freq1, color=ps.g, lw=2, label="Fish 1") ax1.plot(freqtime1 * timescaler, freq1, color=ps.g, lw=2, label="Fish 1")
ax1.plot(freqtime2*timescaler, freq2, color=ps.gray, ax1.plot(freqtime2 * timescaler, freq2, color=ps.gray, lw=2, label="Fish 2")
lw=2, label="Fish 2")
# ax.legend(bbox_to_anchor=(1.04, 1), borderaxespad=0) # ax.legend(bbox_to_anchor=(1.04, 1), borderaxespad=0)
# # ps.hide_xax(ax1) # # ps.hide_xax(ax1)
@ -62,8 +66,8 @@ def main():
ax1.imshow( ax1.imshow(
decibel(spec_power[fmask, :]), decibel(spec_power[fmask, :]),
extent=[ extent=[
spec_times[0]*timescaler, spec_times[0] * timescaler,
spec_times[-1]*timescaler, spec_times[-1] * timescaler,
spec_freqs[fmask][0], spec_freqs[fmask][0],
spec_freqs[fmask][-1], spec_freqs[fmask][-1],
], ],
@ -87,8 +91,8 @@ def main():
ax2.imshow( ax2.imshow(
decibel(spec_power[fmask, :]), decibel(spec_power[fmask, :]),
extent=[ extent=[
spec_times[0]*timescaler, spec_times[0] * timescaler,
spec_times[-1]*timescaler, spec_times[-1] * timescaler,
spec_freqs[fmask][0], spec_freqs[fmask][0],
spec_freqs[fmask][-1], spec_freqs[fmask][-1],
], ],
@ -98,9 +102,8 @@ def main():
alpha=1, alpha=1,
) )
# ps.hide_xax(ax3) # ps.hide_xax(ax3)
ax2.plot(freqtime1*timescaler, freq1, color=ps.g, lw=2, label="_") ax2.plot(freqtime1 * timescaler, freq1, color=ps.g, lw=2, label="_")
ax2.plot(freqtime2*timescaler, freq2, color=ps.gray, ax2.plot(freqtime2 * timescaler, freq2, color=ps.gray, lw=2, label="_")
lw=2, label="_")
ax2.set_xlim(75, 200) ax2.set_xlim(75, 200)
ax1.set_ylim(400, 1200) ax1.set_ylim(400, 1200)
@ -109,15 +112,22 @@ def main():
fig.supylabel("Frequency [Hz]", fontsize=14) fig.supylabel("Frequency [Hz]", fontsize=14)
handles, labels = ax1.get_legend_handles_labels() handles, labels = ax1.get_legend_handles_labels()
ax2.legend(handles, labels, bbox_to_anchor=(1.04, 1), loc="upper left", ncol=1,) ax2.legend(
handles,
labels,
bbox_to_anchor=(1.04, 1),
loc="upper left",
ncol=1,
)
ps.letter_subplots(xoffset=[-0.27, -0.1], yoffset=1.05) ps.letter_subplots(xoffset=[-0.27, -0.1], yoffset=1.05)
plt.subplots_adjust(left=0.12, right=0.85, top=0.89, plt.subplots_adjust(
bottom=0.18, hspace=0.35) left=0.12, right=0.85, top=0.89, bottom=0.18, hspace=0.35
)
plt.savefig('../poster/figs/introplot.pdf') plt.savefig("../poster/figs/introplot.pdf")
if __name__ == '__main__': if __name__ == "__main__":
main() main()

208
code/plot_kdes.py
View File

@ -1,7 +1,9 @@
from modules.plotstyle import PlotStyle from modules.plotstyle import PlotStyle
from modules.behaviour_handling import ( from modules.behaviour_handling import (
Behavior, correct_chasing_events, center_chirps) Behavior,
correct_chasing_events,
center_chirps,
)
from modules.datahandling import flatten, causal_kde1d, acausal_kde1d from modules.datahandling import flatten, causal_kde1d, acausal_kde1d
from modules.logger import makeLogger from modules.logger import makeLogger
from pandas import read_csv from pandas import read_csv
@ -18,80 +20,93 @@ logger = makeLogger(__name__)
ps = PlotStyle() ps = PlotStyle()
def bootstrap(data, nresamples, kde_time, kernel_width, event_times, time_before, time_after): def bootstrap(
data,
nresamples,
kde_time,
kernel_width,
event_times,
time_before,
time_after,
):
bootstrapped_kdes = [] bootstrapped_kdes = []
data = data[data <= 3*60*60] # only night time data = data[data <= 3 * 60 * 60] # only night time
diff_data = np.diff(np.sort(data), prepend=0) diff_data = np.diff(np.sort(data), prepend=0)
# if len(data) != 0: # if len(data) != 0:
# mean_chirprate = (len(data) - 1) / (data[-1] - data[0]) # mean_chirprate = (len(data) - 1) / (data[-1] - data[0])
for i in tqdm(range(nresamples)): for i in tqdm(range(nresamples)):
np.random.shuffle(diff_data) np.random.shuffle(diff_data)
bootstrapped_data = np.cumsum(diff_data) bootstrapped_data = np.cumsum(diff_data)
# bootstrapped_data = data + np.random.randn(len(data)) * 10 # bootstrapped_data = data + np.random.randn(len(data)) * 10
bootstrap_data_centered = center_chirps( bootstrap_data_centered = center_chirps(
bootstrapped_data, event_times, time_before, time_after) bootstrapped_data, event_times, time_before, time_after
)
bootstrapped_kde = acausal_kde1d( bootstrapped_kde = acausal_kde1d(
bootstrap_data_centered, time=kde_time, width=kernel_width) bootstrap_data_centered, time=kde_time, width=kernel_width
)
bootstrapped_kde = list(np.asarray( bootstrapped_kde = list(np.asarray(bootstrapped_kde) / len(event_times))
bootstrapped_kde) / len(event_times))
bootstrapped_kdes.append(bootstrapped_kde) bootstrapped_kdes.append(bootstrapped_kde)
return bootstrapped_kdes return bootstrapped_kdes
def jackknife(data, nresamples, subsetsize, kde_time, kernel_width, event_times, time_before, time_after): def jackknife(
data,
nresamples,
subsetsize,
kde_time,
kernel_width,
event_times,
time_before,
time_after,
):
jackknife_kdes = [] jackknife_kdes = []
data = data[data <= 3*60*60] # only night time data = data[data <= 3 * 60 * 60] # only night time
subsetsize = int(len(data) * subsetsize) subsetsize = int(len(data) * subsetsize)
diff_data = np.diff(np.sort(data), prepend=0) diff_data = np.diff(np.sort(data), prepend=0)
for i in tqdm(range(nresamples)): for i in tqdm(range(nresamples)):
jackknifed_data = np.random.choice(diff_data, subsetsize, replace=False)
jackknifed_data = np.random.choice(
diff_data, subsetsize, replace=False)
jackknifed_data = np.cumsum(jackknifed_data) jackknifed_data = np.cumsum(jackknifed_data)
jackknifed_data_centered = center_chirps( jackknifed_data_centered = center_chirps(
jackknifed_data, event_times, time_before, time_after) jackknifed_data, event_times, time_before, time_after
)
jackknifed_kde = acausal_kde1d( jackknifed_kde = acausal_kde1d(
jackknifed_data_centered, time=kde_time, width=kernel_width) jackknifed_data_centered, time=kde_time, width=kernel_width
)
jackknifed_kde = list(np.asarray( jackknifed_kde = list(np.asarray(jackknifed_kde) / len(event_times))
jackknifed_kde) / len(event_times))
jackknife_kdes.append(jackknifed_kde) jackknife_kdes.append(jackknifed_kde)
return jackknife_kdes return jackknife_kdes
def get_chirp_winner_loser(folder_name, Behavior, order_meta_df): def get_chirp_winner_loser(folder_name, Behavior, order_meta_df):
foldername = folder_name.split("/")[-2]
foldername = folder_name.split('/')[-2] winner_row = order_meta_df[order_meta_df["recording"] == foldername]
winner_row = order_meta_df[order_meta_df['recording'] == foldername] winner = winner_row["winner"].values[0].astype(int)
winner = winner_row['winner'].values[0].astype(int) winner_fish1 = winner_row["fish1"].values[0].astype(int)
winner_fish1 = winner_row['fish1'].values[0].astype(int) winner_fish2 = winner_row["fish2"].values[0].astype(int)
winner_fish2 = winner_row['fish2'].values[0].astype(int)
if winner > 0: if winner > 0:
if winner == winner_fish1: if winner == winner_fish1:
winner_fish_id = winner_row['rec_id1'].values[0] winner_fish_id = winner_row["rec_id1"].values[0]
loser_fish_id = winner_row['rec_id2'].values[0] loser_fish_id = winner_row["rec_id2"].values[0]
elif winner == winner_fish2: elif winner == winner_fish2:
winner_fish_id = winner_row['rec_id2'].values[0] winner_fish_id = winner_row["rec_id2"].values[0]
loser_fish_id = winner_row['rec_id1'].values[0] loser_fish_id = winner_row["rec_id1"].values[0]
chirp_winner = Behavior.chirps[Behavior.chirps_ids == winner_fish_id] chirp_winner = Behavior.chirps[Behavior.chirps_ids == winner_fish_id]
chirp_loser = Behavior.chirps[Behavior.chirps_ids == loser_fish_id] chirp_loser = Behavior.chirps[Behavior.chirps_ids == loser_fish_id]
@ -101,7 +116,6 @@ def get_chirp_winner_loser(folder_name, Behavior, order_meta_df):
def main(dataroot): def main(dataroot):
foldernames, _ = np.asarray(get_valid_datasets(dataroot)) foldernames, _ = np.asarray(get_valid_datasets(dataroot))
plot_all = True plot_all = True
time_before = 90 time_before = 90
@ -111,10 +125,9 @@ def main(dataroot):
kde_time = np.arange(-time_before, time_after, dt) kde_time = np.arange(-time_before, time_after, dt)
nbootstraps = 50 nbootstraps = 50
meta_path = ( meta_path = ("/").join(foldernames[0].split("/")[:-2]) + "/order_meta.csv"
'/').join(foldernames[0].split('/')[:-2]) + '/order_meta.csv'
meta = pd.read_csv(meta_path) meta = pd.read_csv(meta_path)
meta['recording'] = meta['recording'].str[1:-1] meta["recording"] = meta["recording"].str[1:-1]
winner_onsets = [] winner_onsets = []
winner_offsets = [] winner_offsets = []
@ -143,24 +156,24 @@ def main(dataroot):
# loser_onset_chirpcount = 0 # loser_onset_chirpcount = 0
# loser_offset_chirpcount = 0 # loser_offset_chirpcount = 0
# loser_physical_chirpcount = 0 # loser_physical_chirpcount = 0
fig, ax = plt.subplots(1, 2, figsize=( fig, ax = plt.subplots(
14 * ps.cm, 7*ps.cm), sharey=True, sharex=True) 1, 2, figsize=(14 * ps.cm, 7 * ps.cm), sharey=True, sharex=True
)
# Iterate over all recordings and save chirp- and event-timestamps # Iterate over all recordings and save chirp- and event-timestamps
good_recs = np.asarray([0, 15]) good_recs = np.asarray([0, 15])
for i, folder in tqdm(enumerate(foldernames[good_recs])): for i, folder in tqdm(enumerate(foldernames[good_recs])):
foldername = folder.split("/")[-2]
foldername = folder.split('/')[-2]
# logger.info('Loading data from folder: {}'.format(foldername)) # logger.info('Loading data from folder: {}'.format(foldername))
broken_folders = ['../data/mount_data/2020-05-12-10_00/'] broken_folders = ["../data/mount_data/2020-05-12-10_00/"]
if folder in broken_folders: if folder in broken_folders:
continue continue
bh = Behavior(folder) bh = Behavior(folder)
category, timestamps = correct_chasing_events(bh.behavior, bh.start_s) category, timestamps = correct_chasing_events(bh.behavior, bh.start_s)
category = category[timestamps < 3*60*60] # only night time category = category[timestamps < 3 * 60 * 60] # only night time
timestamps = timestamps[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) winner, loser = get_chirp_winner_loser(folder, bh, meta)
if winner is None: if winner is None:
@ -168,27 +181,33 @@ def main(dataroot):
# winner_count += len(winner) # winner_count += len(winner)
# loser_count += len(loser) # loser_count += len(loser)
onsets = (timestamps[category == 0]) onsets = timestamps[category == 0]
offsets = (timestamps[category == 1]) offsets = timestamps[category == 1]
physicals = (timestamps[category == 2]) physicals = timestamps[category == 2]
onset_count += len(onsets) onset_count += len(onsets)
offset_count += len(offsets) offset_count += len(offsets)
physical_count += len(physicals) physical_count += len(physicals)
winner_onsets.append(center_chirps( winner_onsets.append(
winner, onsets, time_before, time_after)) center_chirps(winner, onsets, time_before, time_after)
winner_offsets.append(center_chirps( )
winner, offsets, time_before, time_after)) winner_offsets.append(
winner_physicals.append(center_chirps( center_chirps(winner, offsets, time_before, time_after)
winner, physicals, time_before, time_after)) )
winner_physicals.append(
loser_onsets.append(center_chirps( center_chirps(winner, physicals, time_before, time_after)
loser, onsets, time_before, time_after)) )
loser_offsets.append(center_chirps(
loser, offsets, time_before, time_after)) loser_onsets.append(
loser_physicals.append(center_chirps( center_chirps(loser, onsets, time_before, time_after)
loser, physicals, time_before, time_after)) )
loser_offsets.append(
center_chirps(loser, offsets, time_before, time_after)
)
loser_physicals.append(
center_chirps(loser, physicals, time_before, time_after)
)
# winner_onset_chirpcount += len(winner_onsets[-1]) # winner_onset_chirpcount += len(winner_onsets[-1])
# winner_offset_chirpcount += len(winner_offsets[-1]) # winner_offset_chirpcount += len(winner_offsets[-1])
@ -232,14 +251,17 @@ def main(dataroot):
# event_times=onsets, # event_times=onsets,
# time_before=time_before, # time_before=time_before,
# time_after=time_after)) # time_after=time_after))
loser_offsets_boot.append(bootstrap( loser_offsets_boot.append(
loser, bootstrap(
nresamples=nbootstraps, loser,
kde_time=kde_time, nresamples=nbootstraps,
kernel_width=kernel_width, kde_time=kde_time,
event_times=offsets, kernel_width=kernel_width,
time_before=time_before, event_times=offsets,
time_after=time_after)) time_before=time_before,
time_after=time_after,
)
)
# loser_physicals_boot.append(bootstrap( # loser_physicals_boot.append(bootstrap(
# loser, # loser,
# nresamples=nbootstraps, # nresamples=nbootstraps,
@ -249,18 +271,17 @@ def main(dataroot):
# time_before=time_before, # time_before=time_before,
# time_after=time_after)) # time_after=time_after))
# loser_offsets_jackknife = jackknife( # loser_offsets_jackknife = jackknife(
# loser, # loser,
# nresamples=nbootstraps, # nresamples=nbootstraps,
# subsetsize=0.9, # subsetsize=0.9,
# kde_time=kde_time, # kde_time=kde_time,
# kernel_width=kernel_width, # kernel_width=kernel_width,
# event_times=offsets, # event_times=offsets,
# time_before=time_before, # time_before=time_before,
# time_after=time_after) # time_after=time_after)
if plot_all: if plot_all:
# winner_onsets_conv = acausal_kde1d( # winner_onsets_conv = acausal_kde1d(
# winner_onsets[-1], kde_time, kernel_width) # winner_onsets[-1], kde_time, kernel_width)
# winner_offsets_conv = acausal_kde1d( # winner_offsets_conv = acausal_kde1d(
@ -271,24 +292,35 @@ def main(dataroot):
# loser_onsets_conv = acausal_kde1d( # loser_onsets_conv = acausal_kde1d(
# loser_onsets[-1], kde_time, kernel_width) # loser_onsets[-1], kde_time, kernel_width)
loser_offsets_conv = acausal_kde1d( loser_offsets_conv = acausal_kde1d(
loser_offsets[-1], kde_time, kernel_width) loser_offsets[-1], kde_time, kernel_width
)
# loser_physicals_conv = acausal_kde1d( # loser_physicals_conv = acausal_kde1d(
# loser_physicals[-1], kde_time, kernel_width) # loser_physicals[-1], kde_time, kernel_width)
ax[i].plot(kde_time, loser_offsets_conv / ax[i].plot(
len(offsets), lw=2, zorder=100, c=ps.gblue1) kde_time,
loser_offsets_conv / len(offsets),
lw=2,
zorder=100,
c=ps.gblue1,
)
ax[i].fill_between( ax[i].fill_between(
kde_time, kde_time,
np.percentile(loser_offsets_boot[-1], 1, axis=0), np.percentile(loser_offsets_boot[-1], 1, axis=0),
np.percentile(loser_offsets_boot[-1], 99, axis=0), np.percentile(loser_offsets_boot[-1], 99, axis=0),
color='gray', color="gray",
alpha=0.8) alpha=0.8,
)
ax[i].plot(kde_time, np.median(loser_offsets_boot[-1], axis=0), ax[i].plot(
color=ps.black, linewidth=2) kde_time,
np.median(loser_offsets_boot[-1], axis=0),
color=ps.black,
linewidth=2,
)
ax[i].axvline(0, color=ps.gray, linestyle='--') ax[i].axvline(0, color=ps.gray, linestyle="--")
# ax[i].fill_between( # ax[i].fill_between(
# kde_time, # kde_time,
@ -300,8 +332,8 @@ def main(dataroot):
# color=ps.white, linewidth=2) # color=ps.white, linewidth=2)
ax[i].set_xlim(-60, 60) ax[i].set_xlim(-60, 60)
fig.supylabel('Chirp rate (a.u.)', fontsize=14) fig.supylabel("Chirp rate (a.u.)", fontsize=14)
fig.supxlabel('Time (s)', fontsize=14) fig.supxlabel("Time (s)", fontsize=14)
# fig, ax = plt.subplots(2, 3, figsize=( # fig, ax = plt.subplots(2, 3, figsize=(
# 21*ps.cm, 10*ps.cm), sharey=True, sharex=True) # 21*ps.cm, 10*ps.cm), sharey=True, sharex=True)
@ -521,9 +553,9 @@ def main(dataroot):
# color=ps.gray, # color=ps.gray,
# alpha=0.5) # alpha=0.5)
plt.subplots_adjust(bottom=0.21, top=0.93) plt.subplots_adjust(bottom=0.21, top=0.93)
plt.savefig('../poster/figs/kde.pdf') plt.savefig("../poster/figs/kde.pdf")
plt.show() plt.show()
if __name__ == '__main__': if __name__ == "__main__":
main('../data/mount_data/') main("../data/mount_data/")