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fix solve_2D_DPSS bug when in bases are complex #972

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merged 2 commits into from
Aug 5, 2024
Merged

fix solve_2D_DPSS bug when in bases are complex #972

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f014721
fix solve_2D_DPSS bug where basis is complex
tyler-a-cox Aug 5, 2024
d21f35e
Merge branch 'main' into fix_solve_2D_DPSS
jsdillon Aug 5, 2024
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4 changes: 2 additions & 2 deletions hera_cal/smooth_cal.py
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Original file line number Diff line number Diff line change
Expand Up @@ -183,12 +183,12 @@ def solve_2D_DPSS(gains, weights, time_filters, freq_filters, method="pinv", cac
# einsum indices are (t -> time, f -> freq, i > time filter index, j -> freq filter index, m ->
# time filter index, n -> freq filter index)
XTX = jnp.einsum(
"ti,fj,tf,tm,fn->ijmn", time_filters, freq_filters, weights, time_filters, freq_filters, optimize=True
"ti,fj,tf,tm,fn->ijmn", time_filters.conj(), freq_filters.conj(), weights, time_filters, freq_filters, optimize=True
)
XTX = np.reshape(XTX, (ncomps, ncomps))

# Calculate X^T W y using the property (A \otimes B) vec(y) = (A Y B)
XTWy = jnp.ravel(jnp.dot(jnp.dot(jnp.transpose(time_filters), (gains * weights)), freq_filters))
XTWy = jnp.ravel(jnp.dot(jnp.dot(jnp.transpose(time_filters.conj()), (gains * weights)), freq_filters.conj()))

# Compute beta and reshape into a 2D array
beta, cached_output = _linear_fit(XTX, XTWy, solver=method, cached_input=cached_input)
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15 changes: 15 additions & 0 deletions hera_cal/tests/test_smooth_cal.py
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Expand Up @@ -115,6 +115,21 @@ def test_solve_2D_DPSS(self):
fit_lsq = X @ np.linalg.pinv((X.T * weights.ravel()) @ X) @ (X.T * weights.ravel()) @ gains.ravel()
np.testing.assert_array_almost_equal(fit_lsq, fit2.ravel())

# Check that this works when the basis functions are complex
freqs = np.linspace(100e6, 150e6, 100)
x = np.linspace(0, 2 * np.pi, 50)
X = dspec.dpss_operator(freqs, [0], [20e-9], eigenval_cutoff=[1e-12])[0].real
Y = dspec.dft_operator(x, [0], [0.1])

ncomps = X.shape[-1] * Y.shape[-1]
values = np.random.normal(0, 1, ncomps) + 1j * np.random.normal(0, 1, ncomps)
beta = np.reshape(values, (X.shape[1], Y.shape[1]))
gains = np.dot(np.dot(X, beta), np.transpose(Y))
weights = np.ones(gains.shape)

fit1, cached_output = smooth_cal.solve_2D_DPSS(gains, weights, X, Y)
np.testing.assert_array_almost_equal(gains, fit1)

def test_time_filter(self):
gains = np.ones((10, 10), dtype=complex)
gains[3, 5] = 10.0
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