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Update README.md

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Signed-off-by: David Rotermund <54365609+davrot@users.noreply.github.com>
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data_analysis/spectral_coherence/README.md
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@ -193,7 +193,6 @@ plt.show()
import numpy as np import numpy as np
import matplotlib.pyplot as plt import matplotlib.pyplot as plt
import pywt import pywt
from tqdm import trange
# Calculate the wavelet scales we requested # Calculate the wavelet scales we requested
@ -301,6 +300,66 @@ def calculate_wavelet_tf_complex_coeffs(
return (complex_spectrum, frequency_axis, t) return (complex_spectrum, frequency_axis, t)
def calculate_spectral_coherence(
n_trials: int,
y_a: np.ndarray,
y_b: np.ndarray,
number_of_frequences: int,
frequency_range_min: float,
frequency_range_max: float,
dt: float,
) -> tuple[np.ndarray, np.ndarray, np.ndarray]:
for trial_id in range(0, n_trials):
wave_data_a, frequency_axis, t = calculate_wavelet_tf_complex_coeffs(
data=y_a[..., trial_id],
number_of_frequences=number_of_frequences,
frequency_range_min=frequency_range_min,
frequency_range_max=frequency_range_max,
dt=dt,
)
wave_data_b, frequency_axis, t = calculate_wavelet_tf_complex_coeffs(
data=y_b[..., trial_id],
number_of_frequences=number_of_frequences,
frequency_range_min=frequency_range_min,
frequency_range_max=frequency_range_max,
dt=dt,
)
cone_of_influence = calculate_cone_of_influence(dt, frequency_axis)
wave_data_a = mask_cone_of_influence(
complex_spectrum=wave_data_a,
cone_of_influence=cone_of_influence,
fill_value=np.NaN,
)
wave_data_b = mask_cone_of_influence(
complex_spectrum=wave_data_b,
cone_of_influence=cone_of_influence,
fill_value=np.NaN,
)
if trial_id == 0:
calculation = wave_data_a * wave_data_b
norm_data_a = np.abs(wave_data_a) ** 2
norm_data_b = np.abs(wave_data_b) ** 2
else:
calculation += wave_data_a * wave_data_b
norm_data_a += np.abs(wave_data_a) ** 2
norm_data_b += np.abs(wave_data_b) ** 2
calculation /= float(n_trials)
norm_data_a /= float(n_trials)
norm_data_b /= float(n_trials)
coherence = np.abs(calculation) ** 2 / (norm_data_a * norm_data_b)
return np.nanmean(coherence, axis=-1), frequency_axis, t
# Parameters for the wavelet transform # Parameters for the wavelet transform
number_of_frequences: int = 25 # frequency bands number_of_frequences: int = 25 # frequency bands
frequency_range_min: float = 5 # Hz frequency_range_min: float = 5 # Hz
@ -338,72 +397,18 @@ if delay_enable:
else: else:
y_b = y_a.copy() y_b = y_a.copy()
for trial_id in trange(0, n_trials): coherence, frequency_axis, t = calculate_spectral_coherence(
wave_data_a, frequency_axis, t = calculate_wavelet_tf_complex_coeffs( n_trials=n_trials,
data=y_a[..., trial_id], y_a=y_a,
number_of_frequences=number_of_frequences, y_b=y_b,
frequency_range_min=frequency_range_min, number_of_frequences=number_of_frequences,
frequency_range_max=frequency_range_max, frequency_range_min=frequency_range_min,
dt=dt, frequency_range_max=frequency_range_max,
)
wave_data_b, frequency_axis, t = calculate_wavelet_tf_complex_coeffs(
data=y_b[..., trial_id],
number_of_frequences=number_of_frequences,
frequency_range_min=frequency_range_min,
frequency_range_max=frequency_range_max,
dt=dt,
)
cone_of_influence = calculate_cone_of_influence(dt, frequency_axis)
wave_data_a = mask_cone_of_influence(
complex_spectrum=wave_data_a,
cone_of_influence=cone_of_influence,
fill_value=np.NaN,
)
wave_data_b = mask_cone_of_influence(
complex_spectrum=wave_data_b,
cone_of_influence=cone_of_influence,
fill_value=np.NaN,
)
if trial_id == 0:
calculation = wave_data_a * wave_data_b
norm_data_a = np.abs(wave_data_a) ** 2
norm_data_b = np.abs(wave_data_b) ** 2
else:
calculation += wave_data_a * wave_data_b
norm_data_a += np.abs(wave_data_a) ** 2
norm_data_b += np.abs(wave_data_b) ** 2
calculation /= float(n_trials)
norm_data_a /= float(n_trials)
norm_data_b /= float(n_trials)
coherence = np.abs(calculation) ** 2 / (norm_data_a * norm_data_b)
y_reduction_to_ticks: int = 10
x_reduction_to_ticks: int = 10
y_round: int = 1
x_round: int = 1
freq_ticks, freq_values = get_y_ticks(
reduction_to_ticks=y_reduction_to_ticks,
frequency_axis=frequency_axis,
round=y_round,
)
time_ticks, time_values = get_x_ticks(
reduction_to_ticks=x_reduction_to_ticks,
dt=dt, dt=dt,
number_of_timesteps=t.shape[0],
round=x_round,
) )
plt.plot(frequency_axis, np.nanmean(coherence, axis=-1))
plt.plot(frequency_axis, coherence)
plt.ylabel("Spectral Coherence") plt.ylabel("Spectral Coherence")
plt.xlabel("Frequency [Hz]") plt.xlabel("Frequency [Hz]")
plt.show() plt.show()