Tibeuleu/FOC_Reduction
1
0
Fork 0
Code Issues Pull Requests Packages Projects Releases Wiki Activity

polarisation -> polarization everywhere

Browse Source
This commit is contained in:
Thibault Barnouin
2024-06-04 11:59:33 +02:00
parent 5e27f36869
commit 97479f90fb
5 changed files with 90 additions and 96 deletions
Download Patch File Download Diff File Expand all files Collapse all files

54
package/lib/reduction.py
View File

@@ -30,7 +30,7 @@ prototypes :
Compute Stokes parameters I, Q and U and their respective correlated errors from data_array.
- compute_pol(I_stokes, Q_stokes, U_stokes, Stokes_cov, headers) -> P, debiased_P, s_P, s_P_P, PA, s_PA, s_PA_P
Compute polarisation degree (in %) and angle (in degree) and their respective errors.
Compute polarization degree (in %) and angle (in degree) and their respective errors.
- rotate_Stokes(I_stokes, Q_stokes, U_stokes, Stokes_cov, data_mask, headers, ang, SNRi_cut) -> I_stokes, Q_stokes, U_stokes, Stokes_cov, data_mask, headers
Rotate I, Q, U given an angle in degrees using scipy functions.
@@ -466,7 +466,7 @@ def get_error(data_array, headers, error_array=None, data_mask=None, sub_type=No
mask = zeropad(mask_c, data[0].shape).astype(bool)
background = np.zeros((data.shape[0]))
# wavelength dependence of the polariser filters
# wavelength dependence of the polarizer filters
# estimated to less than 1%
err_wav = data*0.01
# difference in PSFs through each polarizers
@@ -963,7 +963,7 @@ def polarizer_avg(data_array, error_array, data_mask, headers, FWHM=None, scale=
pol120, err120 = smooth_data(pol120_array, err120_array, data_mask, headers120, FWHM=FWHM, scale=scale, smoothing=smoothing)
else:
# Sum on each polarisation filter.
# Sum on each polarization filter.
pol0_t = np.sum([header['exptime'] for header in headers0])
pol60_t = np.sum([header['exptime'] for header in headers60])
pol120_t = np.sum([header['exptime'] for header in headers120])
@@ -1070,10 +1070,10 @@ def compute_Stokes(data_array, error_array, data_mask, headers, FWHM=None, scale
total intensity
Q_stokes : numpy.ndarray
Image (2D floats) containing the Stokes parameters accounting for
vertical/horizontal linear polarisation intensity
vertical/horizontal linear polarization intensity
U_stokes : numpy.ndarray
Image (2D floats) containing the Stokes parameters accounting for
+45/-45deg linear polarisation intensity
+45/-45deg linear polarization intensity
Stokes_cov : numpy.ndarray
Covariance matrix of the Stokes parameters I, Q, U.
"""
@@ -1236,17 +1236,17 @@ def compute_Stokes(data_array, error_array, data_mask, headers, FWHM=None, scale
2 + Q_diluted**2*U_diluted_err**2 - 2.*Q_diluted*U_diluted*QU_diluted_err)
for header in headers:
header['P_int'] = (P_diluted, 'Integrated polarisation degree')
header['P_int_err'] = (np.ceil(P_diluted_err*1000.)/1000., 'Integrated polarisation degree error')
header['PA_int'] = (PA_diluted, 'Integrated polarisation angle')
header['PA_int_err'] = (np.ceil(PA_diluted_err*10.)/10., 'Integrated polarisation angle error')
header['P_int'] = (P_diluted, 'Integrated polarization degree')
header['P_int_err'] = (np.ceil(P_diluted_err*1000.)/1000., 'Integrated polarization degree error')
header['PA_int'] = (PA_diluted, 'Integrated polarization angle')
header['PA_int_err'] = (np.ceil(PA_diluted_err*10.)/10., 'Integrated polarization angle error')
return I_stokes, Q_stokes, U_stokes, Stokes_cov
def compute_pol(I_stokes, Q_stokes, U_stokes, Stokes_cov, headers):
"""
Compute the polarisation degree (in %) and angle (in deg) and their
Compute the polarization degree (in %) and angle (in deg) and their
respective errors from given Stokes parameters.
----------
Inputs:
@@ -1255,10 +1255,10 @@ def compute_pol(I_stokes, Q_stokes, U_stokes, Stokes_cov, headers):
total intensity
Q_stokes : numpy.ndarray
Image (2D floats) containing the Stokes parameters accounting for
vertical/horizontal linear polarisation intensity
vertical/horizontal linear polarization intensity
U_stokes : numpy.ndarray
Image (2D floats) containing the Stokes parameters accounting for
+45/-45deg linear polarisation intensity
+45/-45deg linear polarization intensity
Stokes_cov : numpy.ndarray
Covariance matrix of the Stokes parameters I, Q, U.
headers : header list
@@ -1266,21 +1266,21 @@ def compute_pol(I_stokes, Q_stokes, U_stokes, Stokes_cov, headers):
----------
Returns:
P : numpy.ndarray
Image (2D floats) containing the polarisation degree (in %).
Image (2D floats) containing the polarization degree (in %).
debiased_P : numpy.ndarray
Image (2D floats) containing the debiased polarisation degree (in %).
Image (2D floats) containing the debiased polarization degree (in %).
s_P : numpy.ndarray
Image (2D floats) containing the error on the polarisation degree.
Image (2D floats) containing the error on the polarization degree.
s_P_P : numpy.ndarray
Image (2D floats) containing the Poisson noise error on the
polarisation degree.
polarization degree.
PA : numpy.ndarray
Image (2D floats) containing the polarisation angle.
Image (2D floats) containing the polarization angle.
s_PA : numpy.ndarray
Image (2D floats) containing the error on the polarisation angle.
Image (2D floats) containing the error on the polarization angle.
s_PA_P : numpy.ndarray
Image (2D floats) containing the Poisson noise error on the
polarisation angle.
polarization angle.
new_headers : header list
Updated list of headers corresponding to the reduced images accounting
for the new orientation angle.
@@ -1354,10 +1354,10 @@ def rotate_Stokes(I_stokes, Q_stokes, U_stokes, Stokes_cov, data_mask, headers,
total intensity
Q_stokes : numpy.ndarray
Image (2D floats) containing the Stokes parameters accounting for
vertical/horizontal linear polarisation intensity
vertical/horizontal linear polarization intensity
U_stokes : numpy.ndarray
Image (2D floats) containing the Stokes parameters accounting for
+45/-45deg linear polarisation intensity
+45/-45deg linear polarization intensity
Stokes_cov : numpy.ndarray
Covariance matrix of the Stokes parameters I, Q, U.
data_mask : numpy.ndarray
@@ -1379,10 +1379,10 @@ def rotate_Stokes(I_stokes, Q_stokes, U_stokes, Stokes_cov, data_mask, headers,
accounting for total intensity
new_Q_stokes : numpy.ndarray
Rotated mage (2D floats) containing the rotated Stokes parameters
accounting for vertical/horizontal linear polarisation intensity
accounting for vertical/horizontal linear polarization intensity
new_U_stokes : numpy.ndarray
Rotated image (2D floats) containing the rotated Stokes parameters
accounting for +45/-45deg linear polarisation intensity.
accounting for +45/-45deg linear polarization intensity.
new_Stokes_cov : numpy.ndarray
Updated covariance matrix of the Stokes parameters I, Q, U.
new_headers : header list
@@ -1497,10 +1497,10 @@ def rotate_Stokes(I_stokes, Q_stokes, U_stokes, Stokes_cov, data_mask, headers,
2 + Q_diluted**2*U_diluted_err**2 - 2.*Q_diluted*U_diluted*QU_diluted_err)
for header in new_headers:
header['P_int'] = (P_diluted, 'Integrated polarisation degree')
header['P_int_err'] = (np.ceil(P_diluted_err*1000.)/1000., 'Integrated polarisation degree error')
header['PA_int'] = (PA_diluted, 'Integrated polarisation angle')
header['PA_int_err'] = (np.ceil(PA_diluted_err*10.)/10., 'Integrated polarisation angle error')
header['P_int'] = (P_diluted, 'Integrated polarization degree')
header['P_int_err'] = (np.ceil(P_diluted_err*1000.)/1000., 'Integrated polarization degree error')
header['PA_int'] = (PA_diluted, 'Integrated polarization angle')
header['PA_int_err'] = (np.ceil(PA_diluted_err*10.)/10., 'Integrated polarization angle error')
return new_I_stokes, new_Q_stokes, new_U_stokes, new_Stokes_cov, new_data_mask, new_headers