more reformatting
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@@ -1079,7 +1079,7 @@ def polarizer_avg(data_array, error_array, data_mask, headers, FWHM=None, scale=
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err120 = np.sqrt(np.sum(err120_array**2, axis=0)) / pol120_t
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polerr_array = np.array([err0, err60, err120])
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if not (FWHM is None) and (smoothing.lower() in ["gaussian", "gauss", "weighted_gaussian", "weight_gauss"]):
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if (FWHM is not None) and (smoothing.lower() in ["gaussian", "gauss", "weighted_gaussian", "weight_gauss"]):
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# Smooth by convoluting with a gaussian each polX image.
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pol_array, polerr_array = smooth_data(pol_array, polerr_array, data_mask, pol_headers, FWHM=FWHM, scale=scale, smoothing=smoothing)
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pol0, pol60, pol120 = pol_array
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@@ -1251,7 +1251,7 @@ def compute_Stokes(data_array, error_array, data_mask, headers, FWHM=None, scale
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I_stokes[i, j], Q_stokes[i, j], U_stokes[i, j] = np.dot(coeff_stokes, pol_flux[:, i, j]).T
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Stokes_cov[:, :, i, j] = np.dot(coeff_stokes, np.dot(pol_cov[:, :, i, j], coeff_stokes.T))
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if not (FWHM is None) and (smoothing.lower() in ["weighted_gaussian_after", "weight_gauss_after", "gaussian_after", "gauss_after"]):
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if (FWHM is not None) and (smoothing.lower() in ["weighted_gaussian_after", "weight_gauss_after", "gaussian_after", "gauss_after"]):
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smoothing = smoothing.lower()[:-6]
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Stokes_array = np.array([I_stokes, Q_stokes, U_stokes])
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Stokes_error = np.array([np.sqrt(Stokes_cov[i, i]) for i in range(3)])
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