raab/GP2023_chirp_detection
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This commit is contained in:
weygoldt
2023-04-11 15:33:07 +02:00
parent 12b1fdccae
commit 282c846b05
26 changed files with 1165 additions and 798 deletions
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36
chirp_instantaneous_freq/filters.py
View File

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

34
chirp_instantaneous_freq/fish_signal.py
View File

@@ -384,16 +384,14 @@ def chirps(
frequency = eodf * 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_t = np.arange(-2.0 * width, 2.0 * width, 1.0 / samplerate)
chirp_sig = (
0.5 * width / (2.0 * np.log(10.0)) ** (0.5 / kurtosis)
)
chirp_sig = 0.5 * width / (2.0 * np.log(10.0)) ** (0.5 / kurtosis)
gauss = np.exp(-0.5 * ((chirp_t / chirp_sig) ** 2.0) ** kurtosis)
# add chirps on baseline eodf:
index = int(time * samplerate)
i0 = index - len(gauss) // 2
@@ -433,7 +431,7 @@ def rises(
Sampling rate in Hertz.
duration: float
Duration of the generated data in seconds.
rise_times: list
rise_times: list
Timestamp of each of the rises in seconds.
rise_size: list
Size of the respective rise (frequency increase above eodf) in Hertz.
@@ -452,15 +450,12 @@ def rises(
# baseline eod frequency:
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_t = np.arange(0.0, 5.0 * decayt, 1.0 / samplerate)
rise = (
size
* (1.0 - np.exp(-rise_t / riset))
* np.exp(-rise_t / decayt)
)
rise = size * (1.0 - np.exp(-rise_t / riset)) * np.exp(-rise_t / decayt)
# add rises on baseline eodf:
index = int(time * samplerate)
@@ -472,13 +467,14 @@ def rises(
frequency[index : index + len(rise)] += rise
return frequency
class FishSignal:
def __init__(self, samplerate, duration, eodf, nchirps, nrises):
time = np.arange(0, duration, 1 / samplerate)
chirp_times = np.random.uniform(0, duration, nchirps)
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_width = np.random.uniform(0.01, 0.1, nchirps)
chirp_kurtosis = np.random.uniform(1, 1, nchirps)
@@ -534,7 +530,6 @@ class FishSignal:
self.eodf = eodf
def visualize(self):
spec, freqs, spectime = ps.spectrogram(
data=self.signal,
ratetime=self.samplerate,
@@ -549,7 +544,12 @@ class FishSignal:
ax1.set_xlabel("Time (s)")
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_xlabel("Time (s)")
ax2.set_title("Spectrogram")

122
chirp_instantaneous_freq/test_parameters.py
View File

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