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correct wrong gaussian smoothing function

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This commit is contained in:
Thibault Barnouin
2021-10-10 01:07:42 +02:00
parent 978ff642a4
commit d03ae5ffc5
3 changed files with 20 additions and 12 deletions
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9
src/lib/plots.py
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@@ -372,8 +372,15 @@ def polarization_map(Stokes, data_mask, rectangle=None, SNRp_cut=3., SNRi_cut=30
elif display.lower() in ['s_p','pol_err','pol_deg_err']:
# Display polarization degree error map
vmin, vmax = 0., 10.
im = ax.imshow(pol_err.data*100.,extent=[-pol_err.data.shape[1]/2.,pol_err.data.shape[1]/2.,-pol_err.data.shape[0]/2.,pol_err.data.shape[0]/2.], vmin=vmin, vmax=vmax, aspect='auto', cmap='inferno', alpha=1.)
p_err = pol_err.data.copy()
p_err[p_err*100. > vmax] = np.nan
im = ax.imshow(p_err*100.,extent=[-pol_err.data.shape[1]/2.,pol_err.data.shape[1]/2.,-pol_err.data.shape[0]/2.,pol_err.data.shape[0]/2.], vmin=vmin, vmax=vmax, aspect='auto', cmap='inferno', alpha=1.)
cbar = plt.colorbar(im, cax=cbar_ax, label=r"$\sigma_P$ [%]")
elif display.lower() in ['s_i','i_err']:
# Display intensity error map
vmin, vmax = 0., np.max(np.sqrt(stk_cov.data[0,0][stk_cov.data[0,0] > 0.])*convert_flux)
im = ax.imshow(np.sqrt(stk_cov.data[0,0])*convert_flux,extent=[-stkI.data.shape[1]/2.,stkI.data.shape[1]/2.,-stkI.data.shape[0]/2.,stkI.data.shape[0]/2.], vmin=vmin, vmax=vmax, aspect='auto', cmap='inferno', alpha=1.)
cbar = plt.colorbar(im, cax=cbar_ax, label=r"$\sigma_I$ [$ergs \cdot cm^{-2} \cdot s^{-1} \cdot \AA^{-1}$]")
elif display.lower() in ['snr','snri']:
# Display I_stokes signal-to-noise map
vmin, vmax = 0., np.max(SNRi[SNRi > 0.])

10
src/lib/reduction.py
View File

@@ -863,13 +863,13 @@ def smooth_data(data_array, error_array, data_mask, headers, FWHM=1.,
for c in range(image.shape[1]):
dist_rc = np.where(data_mask, fmax, np.sqrt((r-xx)**2+(c-yy)**2))
g_rc = np.exp(-0.5*(dist_rc/stdev)**2)/(2.*np.pi*stdev**2)
smoothed[i][r,c] = (1.-data_mask[r,c])*np.sum(data_array*weight*g_rc)/np.sum(weight*g_rc)
error[i][r,c] = np.sqrt(np.sum(weight*g_rc**2))/np.sum(weight*g_rc)
smoothed[i][r,c] = (1.-data_mask[r,c])*np.sum(image*g_rc)
error[i][r,c] = np.sqrt(np.sum(error_array[i]*g_rc**2))
# Nan handling
error[i][np.isnan(smoothed)] = 0.
smoothed[i][np.isnan(smoothed)] = 0.
error[i][np.isnan(error)] = 0.
error[i][np.isnan(smoothed[i])] = 0.
smoothed[i][np.isnan(smoothed[i])] = 0.
error[i][np.isnan(error[i])] = 0.
else:
raise ValueError("{} is not a valid smoothing option".format(smoothing))