g12 update
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@@ -100,6 +100,7 @@ def _power_fact_array(x, n):
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return result
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return result
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@numba.njit()
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def abs2(z: np.ndarray) -> np.ndarray:
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def abs2(z: np.ndarray) -> np.ndarray:
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return z.real**2 + z.imag**2
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return z.real**2 + z.imag**2
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@@ -672,23 +672,23 @@ def g12(values):
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Parameters
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Parameters
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----------
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----------
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values : np.ndarray, shape (..., m, n)
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values : np.ndarray, shape (..., m, n, nt)
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complex values following sc-ordering
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complex values following sc-ordering
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Returns
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Returns
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-------
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-------
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np.ndarray, shape (..., n)
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np.ndarray, shape (..., n, nt)
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coherence function
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coherence function
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"""
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"""
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# Create all the possible pairs of values
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# Create all the possible pairs of values
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n = len(values)
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n = len(values)
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field_pairs = itertools.combinations(values, 2)
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field_pairs = itertools.combinations(values, 2)
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mean_spec = np.mean(math.abs2(values), axis=-2)
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mean_spec = np.mean(math.abs2(values), axis=-3)
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mask = mean_spec > 1e-15 * mean_spec.max()
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mask = mean_spec > 1e-15 * mean_spec.max()
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corr = np.zeros_like(values[0])
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corr = np.zeros_like(values[0])
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for pair in field_pairs:
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for left, right in field_pairs:
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corr[mask] += pair[0][mask].conj() * pair[1][mask]
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corr[mask] += left[mask].conj() * right[mask]
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corr[mask] = corr[mask] / (n * (n - 1) / 2 * mean_spec[mask])
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corr[mask] = corr[mask] / (n * (n - 1) / 2 * mean_spec[mask])
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return np.abs(corr)
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return np.abs(corr)
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