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

remove unnecessary header files, combine obs by sum of counts

Browse Source
This commit is contained in:
Thibault Barnouin
2024-07-04 11:49:28 +02:00
parent 2b14ed84aa
commit 7a15b02b04
3 changed files with 93 additions and 90 deletions
Download Patch File Download Diff File Expand all files Collapse all files

28
package/FOC_reduction.py
View File

@@ -233,24 +233,24 @@ def main(target=None, proposal_id=None, infiles=None, output_dir="./data", crop=
# FWHM of FOC have been estimated at about 0.03" across 1500-5000 Angstrom band, which is about 2 detector pixels wide # FWHM of FOC have been estimated at about 0.03" across 1500-5000 Angstrom band, which is about 2 detector pixels wide
# see Jedrzejewski, R.; Nota, A.; Hack, W. J., A Comparison Between FOC and WFPC2 # see Jedrzejewski, R.; Nota, A.; Hack, W. J., A Comparison Between FOC and WFPC2
# Bibcode : 1995chst.conf...10J # Bibcode : 1995chst.conf...10J
I_stokes, Q_stokes, U_stokes, Stokes_cov = proj_red.compute_Stokes( I_stokes, Q_stokes, U_stokes, Stokes_cov, header_stokes = proj_red.compute_Stokes(
data_array, error_array, data_mask, headers, FWHM=smoothing_FWHM, scale=smoothing_scale, smoothing=smoothing_function, transmitcorr=transmitcorr data_array, error_array, data_mask, headers, FWHM=smoothing_FWHM, scale=smoothing_scale, smoothing=smoothing_function, transmitcorr=transmitcorr
) )
I_bkg, Q_bkg, U_bkg, S_cov_bkg = proj_red.compute_Stokes( I_bkg, Q_bkg, U_bkg, S_cov_bkg, header_bkg = proj_red.compute_Stokes(
background, background_error, np.array(True).reshape(1, 1), headers, FWHM=None, scale=smoothing_scale, smoothing=smoothing_function, transmitcorr=False background, background_error, np.array(True).reshape(1, 1), headers, FWHM=None, scale=smoothing_scale, smoothing=smoothing_function, transmitcorr=False
) )
# Step 3: # Step 3:
# Rotate images to have North up # Rotate images to have North up
if rotate_North: if rotate_North:
I_stokes, Q_stokes, U_stokes, Stokes_cov, data_mask, headers = proj_red.rotate_Stokes( I_stokes, Q_stokes, U_stokes, Stokes_cov, data_mask, header_stokes = proj_red.rotate_Stokes(
I_stokes, Q_stokes, U_stokes, Stokes_cov, data_mask, headers, SNRi_cut=None I_stokes, Q_stokes, U_stokes, Stokes_cov, data_mask, header_stokes, SNRi_cut=None
) )
I_bkg, Q_bkg, U_bkg, S_cov_bkg, _, _ = proj_red.rotate_Stokes(I_bkg, Q_bkg, U_bkg, S_cov_bkg, np.array(True).reshape(1, 1), headers, SNRi_cut=None) I_bkg, Q_bkg, U_bkg, S_cov_bkg, _, _ = proj_red.rotate_Stokes(I_bkg, Q_bkg, U_bkg, S_cov_bkg, np.array(True).reshape(1, 1), header_bkg, SNRi_cut=None)
# Compute polarimetric parameters (polarization degree and angle). # Compute polarimetric parameters (polarization degree and angle).
P, debiased_P, s_P, s_P_P, PA, s_PA, s_PA_P = proj_red.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 = proj_red.compute_pol(I_stokes, Q_stokes, U_stokes, Stokes_cov, header_stokes)
P_bkg, debiased_P_bkg, s_P_bkg, s_P_P_bkg, PA_bkg, s_PA_bkg, s_PA_P_bkg = proj_red.compute_pol(I_bkg, Q_bkg, U_bkg, S_cov_bkg, headers) P_bkg, debiased_P_bkg, s_P_bkg, s_P_P_bkg, PA_bkg, s_PA_bkg, s_PA_P_bkg = proj_red.compute_pol(I_bkg, Q_bkg, U_bkg, S_cov_bkg, header_bkg)
# Step 4: # Step 4:
# Save image to FITS. # Save image to FITS.
@@ -267,7 +267,7 @@ def main(target=None, proposal_id=None, infiles=None, output_dir="./data", crop=
PA, PA,
s_PA, s_PA,
s_PA_P, s_PA_P,
headers, header_stokes,
data_mask, data_mask,
figname, figname,
data_folder=data_folder, data_folder=data_folder,
@@ -286,21 +286,21 @@ def main(target=None, proposal_id=None, infiles=None, output_dir="./data", crop=
data_mask = Stokes_hdul["data_mask"].data.astype(bool) data_mask = Stokes_hdul["data_mask"].data.astype(bool)
print( print(
"F_int({0:.0f} Angs) = ({1} ± {2})e{3} ergs.cm^-2.s^-1.Angs^-1".format( "F_int({0:.0f} Angs) = ({1} ± {2})e{3} ergs.cm^-2.s^-1.Angs^-1".format(
headers[0]["photplam"], header_stokes["photplam"],
*sci_not( *sci_not(
Stokes_hdul[0].data[data_mask].sum() * headers[0]["photflam"], Stokes_hdul[0].data[data_mask].sum() * header_stokes["photflam"],
np.sqrt(Stokes_hdul[3].data[0, 0][data_mask].sum()) * headers[0]["photflam"], np.sqrt(Stokes_hdul[3].data[0, 0][data_mask].sum()) * header_stokes["photflam"],
2, 2,
out=int, out=int,
), ),
) )
) )
print("P_int = {0:.1f} ± {1:.1f} %".format(headers[0]["p_int"] * 100.0, np.ceil(headers[0]["sP_int"] * 1000.0) / 10.0)) print("P_int = {0:.1f} ± {1:.1f} %".format(header_stokes["p_int"] * 100.0, np.ceil(header_stokes["sP_int"] * 1000.0) / 10.0))
print("PA_int = {0:.1f} ± {1:.1f} °".format(princ_angle(headers[0]["pa_int"]), princ_angle(np.ceil(headers[0]["sPA_int"] * 10.0) / 10.0))) print("PA_int = {0:.1f} ± {1:.1f} °".format(princ_angle(header_stokes["pa_int"]), princ_angle(np.ceil(header_stokes["sPA_int"] * 10.0) / 10.0)))
# Background values # Background values
print( print(
"F_bkg({0:.0f} Angs) = ({1} ± {2})e{3} ergs.cm^-2.s^-1.Angs^-1".format( "F_bkg({0:.0f} Angs) = ({1} ± {2})e{3} ergs.cm^-2.s^-1.Angs^-1".format(
headers[0]["photplam"], *sci_not(I_bkg[0, 0] * headers[0]["photflam"], np.sqrt(S_cov_bkg[0, 0][0, 0]) * headers[0]["photflam"], 2, out=int) header_stokes["photplam"], *sci_not(I_bkg[0, 0] * header_stokes["photflam"], np.sqrt(S_cov_bkg[0, 0][0, 0]) * header_stokes["photflam"], 2, out=int)
) )
) )
print("P_bkg = {0:.1f} ± {1:.1f} %".format(debiased_P_bkg[0, 0] * 100.0, np.ceil(s_P_bkg[0, 0] * 1000.0) / 10.0)) print("P_bkg = {0:.1f} ± {1:.1f} %".format(debiased_P_bkg[0, 0] * 100.0, np.ceil(s_P_bkg[0, 0] * 1000.0) / 10.0))

35
package/lib/fits.py
View File

@@ -93,7 +93,7 @@ def get_obs_data(infiles, data_folder="", compute_flux=False):
def save_Stokes( def save_Stokes(
I_stokes, Q_stokes, U_stokes, Stokes_cov, P, debiased_P, s_P, s_P_P, PA, s_PA, s_PA_P, headers, data_mask, filename, data_folder="", return_hdul=False I_stokes, Q_stokes, U_stokes, Stokes_cov, P, debiased_P, s_P, s_P_P, PA, s_PA, s_PA_P, header_stokes, data_mask, filename, data_folder="", return_hdul=False
): ):
""" """
Save computed polarimetry parameters to a single fits file, Save computed polarimetry parameters to a single fits file,
@@ -130,9 +130,8 @@ def save_Stokes(
Only returned if return_hdul is True. Only returned if return_hdul is True.
""" """
# Create new WCS object given the modified images # Create new WCS object given the modified images
ref_header = headers[0] exp_tot = header_stokes['exptime']
exp_tot = np.array([header["exptime"] for header in headers]).sum() new_wcs = WCS(header_stokes).deepcopy()
new_wcs = WCS(ref_header).deepcopy()
if data_mask.shape != (1, 1): if data_mask.shape != (1, 1):
vertex = clean_ROI(data_mask) vertex = clean_ROI(data_mask)
@@ -141,23 +140,23 @@ def save_Stokes(
new_wcs.wcs.crpix = np.array(new_wcs.wcs.crpix) - vertex[0::-2] new_wcs.wcs.crpix = np.array(new_wcs.wcs.crpix) - vertex[0::-2]
header = new_wcs.to_header() header = new_wcs.to_header()
header["TELESCOP"] = (ref_header["telescop"] if "TELESCOP" in list(ref_header.keys()) else "HST", "telescope used to acquire data") header["TELESCOP"] = (header_stokes["telescop"] if "TELESCOP" in list(header_stokes.keys()) else "HST", "telescope used to acquire data")
header["INSTRUME"] = (ref_header["instrume"] if "INSTRUME" in list(ref_header.keys()) else "FOC", "identifier for instrument used to acuire data") header["INSTRUME"] = (header_stokes["instrume"] if "INSTRUME" in list(header_stokes.keys()) else "FOC", "identifier for instrument used to acuire data")
header["PHOTPLAM"] = (ref_header["photplam"], "Pivot Wavelength") header["PHOTPLAM"] = (header_stokes["photplam"], "Pivot Wavelength")
header["PHOTFLAM"] = (ref_header["photflam"], "Inverse Sensitivity in DN/sec/cm**2/Angst") header["PHOTFLAM"] = (header_stokes["photflam"], "Inverse Sensitivity in DN/sec/cm**2/Angst")
header["EXPTOT"] = (exp_tot, "Total exposure time in sec") header["EXPTOT"] = (exp_tot, "Total exposure time in sec")
header["PROPOSID"] = (ref_header["proposid"], "PEP proposal identifier for observation") header["PROPOSID"] = (header_stokes["proposid"], "PEP proposal identifier for observation")
header["TARGNAME"] = (ref_header["targname"], "Target name") header["TARGNAME"] = (header_stokes["targname"], "Target name")
header["ORIENTAT"] = (np.arccos(new_wcs.wcs.pc[0, 0]) * 180.0 / np.pi, "Angle between North and the y-axis of the image") header["ORIENTAT"] = (np.arccos(new_wcs.wcs.pc[0, 0]) * 180.0 / np.pi, "Angle between North and the y-axis of the image")
header["FILENAME"] = (filename, "Original filename") header["FILENAME"] = (filename, "Original filename")
header["BKG_TYPE"] = (ref_header["BKG_TYPE"], "Bkg estimation method used during reduction") header["BKG_TYPE"] = (header_stokes["BKG_TYPE"], "Bkg estimation method used during reduction")
header["BKG_SUB"] = (ref_header["BKG_SUB"], "Amount of bkg subtracted from images") header["BKG_SUB"] = (header_stokes["BKG_SUB"], "Amount of bkg subtracted from images")
header["SMOOTH"] = (ref_header["SMOOTH"], "Smoothing method used during reduction") header["SMOOTH"] = (header_stokes["SMOOTH"], "Smoothing method used during reduction")
header["SAMPLING"] = (ref_header["SAMPLING"], "Resampling performed during reduction") header["SAMPLING"] = (header_stokes["SAMPLING"], "Resampling performed during reduction")
header["P_INT"] = (ref_header["P_int"], "Integrated polarization degree") header["P_INT"] = (header_stokes["P_int"], "Integrated polarization degree")
header["sP_INT"] = (ref_header["sP_int"], "Integrated polarization degree error") header["sP_INT"] = (header_stokes["sP_int"], "Integrated polarization degree error")
header["PA_INT"] = (ref_header["PA_int"], "Integrated polarization angle") header["PA_INT"] = (header_stokes["PA_int"], "Integrated polarization angle")
header["sPA_INT"] = (ref_header["sPA_int"], "Integrated polarization angle error") header["sPA_INT"] = (header_stokes["sPA_int"], "Integrated polarization angle error")
# Crop Data to mask # Crop Data to mask
if data_mask.shape != (1, 1): if data_mask.shape != (1, 1):

110
package/lib/reduction.py
View File

@@ -521,23 +521,23 @@ def get_error(data_array, headers, error_array=None, data_mask=None, sub_type=No
n_data_array, c_error_bkg, headers, background = bkg_hist( n_data_array, c_error_bkg, headers, background = bkg_hist(
data, error, mask, headers, subtract_error=subtract_error, display=display, savename=savename, plots_folder=plots_folder data, error, mask, headers, subtract_error=subtract_error, display=display, savename=savename, plots_folder=plots_folder
) )
sub_type, subtract_error = "histogram", str(int(subtract_error>0.)) sub_type, subtract_error = "histogram ", str(int(subtract_error>0.))
elif isinstance(sub_type, str): elif isinstance(sub_type, str):
if sub_type.lower() in ["auto"]: if sub_type.lower() in ["auto"]:
n_data_array, c_error_bkg, headers, background = bkg_fit( n_data_array, c_error_bkg, headers, background = bkg_fit(
data, error, mask, headers, subtract_error=subtract_error, display=display, savename=savename, plots_folder=plots_folder data, error, mask, headers, subtract_error=subtract_error, display=display, savename=savename, plots_folder=plots_folder
) )
sub_type, subtract_error = "histogram fit", "bkg+%.1fsigma"%subtract_error sub_type, subtract_error = "histogram fit ", "mean+%.1fsigma"%subtract_error if subtract_error != 0. else 0.
else: else:
n_data_array, c_error_bkg, headers, background = bkg_hist( n_data_array, c_error_bkg, headers, background = bkg_hist(
data, error, mask, headers, sub_type=sub_type, subtract_error=subtract_error, display=display, savename=savename, plots_folder=plots_folder data, error, mask, headers, sub_type=sub_type, subtract_error=subtract_error, display=display, savename=savename, plots_folder=plots_folder
) )
sub_type, subtract_error = "histogram", str(int(subtract_error>0.)) sub_type, subtract_error = "histogram ", "mean+%.1fsigma"%subtract_error if subtract_error != 0. else 0.
elif isinstance(sub_type, tuple): elif isinstance(sub_type, tuple):
n_data_array, c_error_bkg, headers, background = bkg_mini( n_data_array, c_error_bkg, headers, background = bkg_mini(
data, error, mask, headers, sub_shape=sub_type, subtract_error=subtract_error, display=display, savename=savename, plots_folder=plots_folder data, error, mask, headers, sub_shape=sub_type, subtract_error=subtract_error, display=display, savename=savename, plots_folder=plots_folder
) )
sub_type, subtract_error = "minimal flux", str(int(subtract_error>0.)) sub_type, subtract_error = "minimal flux ", "mean+%.1fsigma"%subtract_error if subtract_error != 0. else 0.
else: else:
print("Warning: Invalid subtype.") print("Warning: Invalid subtype.")
@@ -964,7 +964,7 @@ def smooth_data(data_array, error_array, data_mask, headers, FWHM=1.5, scale="pi
raise ValueError("{} is not a valid smoothing option".format(smoothing)) raise ValueError("{} is not a valid smoothing option".format(smoothing))
for header in headers: for header in headers:
header["SMOOTH"] = (" ".join([smoothing,FWHM_size,scale]),"Smoothing method used during reduction") header["SMOOTH"] = (" ".join([smoothing,FWHM_size,FWHM_scale]),"Smoothing method used during reduction")
return smoothed, error return smoothed, error
@@ -1229,8 +1229,10 @@ def compute_Stokes(data_array, error_array, data_mask, headers, FWHM=None, scale
transmit *= transmit2 * transmit3 * transmit4 transmit *= transmit2 * transmit3 * transmit4
pol_eff = np.array([globals()["pol_efficiency"]["pol0"], globals()["pol_efficiency"]["pol60"], globals()["pol_efficiency"]["pol120"]]) pol_eff = np.array([globals()["pol_efficiency"]["pol0"], globals()["pol_efficiency"]["pol60"], globals()["pol_efficiency"]["pol120"]])
# Calculating correction factor # Calculating correction factor: allows all pol_filt to share same exptime and inverse sensitivity (taken to be the one from POL0)
corr = np.array([1.0 * h["photflam"] / h["exptime"] for h in pol_headers]) * pol_headers[0]["exptime"] / pol_headers[0]["photflam"] corr = np.array([1.0 * h["photflam"] / h["exptime"] for h in pol_headers]) * pol_headers[0]["exptime"] / pol_headers[0]["photflam"]
pol_headers[1]['photflam'], pol_headers[1]['exptime'] = pol_headers[0]['photflam'], pol_headers[1]['exptime']
pol_headers[2]['photflam'], pol_headers[2]['exptime'] = pol_headers[0]['photflam'], pol_headers[2]['exptime']
# Orientation and error for each polarizer # Orientation and error for each polarizer
# fmax = np.finfo(np.float64).max # fmax = np.finfo(np.float64).max
@@ -1441,25 +1443,34 @@ def compute_Stokes(data_array, error_array, data_mask, headers, FWHM=None, scale
Stokes_cov[1, 1] += s_Q2_axis + s_Q2_stat Stokes_cov[1, 1] += s_Q2_axis + s_Q2_stat
Stokes_cov[2, 2] += s_U2_axis + s_U2_stat Stokes_cov[2, 2] += s_U2_axis + s_U2_stat
# Save values to single header
header_stokes = pol_headers[0]
else: else:
all_I_stokes = np.zeros((np.unique(rotate).size, data_array.shape[1], data_array.shape[2])) all_I_stokes = np.zeros((np.unique(rotate).size, data_array.shape[1], data_array.shape[2]))
all_Q_stokes = np.zeros((np.unique(rotate).size, data_array.shape[1], data_array.shape[2])) all_Q_stokes = np.zeros((np.unique(rotate).size, data_array.shape[1], data_array.shape[2]))
all_U_stokes = np.zeros((np.unique(rotate).size, data_array.shape[1], data_array.shape[2])) all_U_stokes = np.zeros((np.unique(rotate).size, data_array.shape[1], data_array.shape[2]))
all_Stokes_cov = np.zeros((np.unique(rotate).size, 3, 3, data_array.shape[1], data_array.shape[2])) all_Stokes_cov = np.zeros((np.unique(rotate).size, 3, 3, data_array.shape[1], data_array.shape[2]))
all_header_stokes = [{},]*np.unique(rotate).size
for i,rot in enumerate(np.unique(rotate)): for i,rot in enumerate(np.unique(rotate)):
rot_mask = (rotate == rot) rot_mask = (rotate == rot)
all_I_stokes[i], all_Q_stokes[i], all_U_stokes[i], all_Stokes_cov[i] = compute_Stokes(data_array[rot_mask], error_array[rot_mask], data_mask, [headers[i] for i in np.arange(len(headers))[rot_mask]], FWHM=FWHM, scale=scale, smoothing=smoothing, transmitcorr=transmitcorr, integrate=False) all_I_stokes[i], all_Q_stokes[i], all_U_stokes[i], all_Stokes_cov[i], all_header_stokes[i] = compute_Stokes(data_array[rot_mask], error_array[rot_mask], data_mask, [headers[i] for i in np.arange(len(headers))[rot_mask]], FWHM=FWHM, scale=scale, smoothing=smoothing, transmitcorr=transmitcorr, integrate=False)
all_exp = np.array([float(h['exptime']) for h in all_header_stokes])
I_stokes = all_I_stokes.sum(axis=0)/np.unique(rotate).size I_stokes = np.sum([exp*I for exp, I in zip(all_exp, all_I_stokes)],axis=0) / all_exp.sum()
Q_stokes = all_Q_stokes.sum(axis=0)/np.unique(rotate).size Q_stokes = np.sum([exp*Q for exp, Q in zip(all_exp, all_Q_stokes)],axis=0) / all_exp.sum()
U_stokes = all_U_stokes.sum(axis=0)/np.unique(rotate).size U_stokes = np.sum([exp*U for exp, U in zip(all_exp, all_U_stokes)],axis=0) / all_exp.sum()
Stokes_cov = np.zeros((3, 3, I_stokes.shape[0], I_stokes.shape[1])) Stokes_cov = np.zeros((3, 3, I_stokes.shape[0], I_stokes.shape[1]))
for i in range(3): for i in range(3):
Stokes_cov[i,i] = np.sum(all_Stokes_cov[:,i,i],axis=0)/np.unique(rotate).size Stokes_cov[i,i] = np.sum([exp*cov for exp, cov in zip(all_exp, all_Stokes_cov[:,i,i])], axis=0) / all_exp.sum()
for j in [x for x in range(3) if x!=i]: for j in [x for x in range(3) if x!=i]:
Stokes_cov[i,j] = np.sqrt(np.sum(all_Stokes_cov[:,i,j]**2,axis=0)/np.unique(rotate).size) Stokes_cov[i,j] = np.sqrt(np.sum([exp*cov**2 for exp, cov in zip(all_exp, all_Stokes_cov[:,i,j])], axis=0) / all_exp.sum())
Stokes_cov[j,i] = np.sqrt(np.sum(all_Stokes_cov[:,j,i]**2,axis=0)/np.unique(rotate).size) Stokes_cov[j,i] = np.sqrt(np.sum([exp*cov**2 for exp, cov in zip(all_exp, all_Stokes_cov[:,j,i])], axis=0) / all_exp.sum())
# Save values to single header
header_stokes = all_header_stokes[0]
header_stokes['exptime'] = all_exp.sum()
# Nan handling : # Nan handling :
fmax = np.finfo(np.float64).max fmax = np.finfo(np.float64).max
@@ -1497,16 +1508,15 @@ def compute_Stokes(data_array, error_array, data_mask, headers, FWHM=None, scale
U_diluted**2 * Q_diluted_err**2 + Q_diluted**2 * U_diluted_err**2 - 2.0 * Q_diluted * U_diluted * QU_diluted_err U_diluted**2 * Q_diluted_err**2 + Q_diluted**2 * U_diluted_err**2 - 2.0 * Q_diluted * U_diluted * QU_diluted_err
) )
for header in headers: header_stokes["P_int"] = (P_diluted, "Integrated polarization degree")
header["P_int"] = (P_diluted, "Integrated polarization degree") header_stokes["sP_int"] = (np.ceil(P_diluted_err * 1000.0) / 1000.0, "Integrated polarization degree error")
header["sP_int"] = (np.ceil(P_diluted_err * 1000.0) / 1000.0, "Integrated polarization degree error") header_stokes["PA_int"] = (PA_diluted, "Integrated polarization angle")
header["PA_int"] = (PA_diluted, "Integrated polarization angle") header_stokes["sPA_int"] = (np.ceil(PA_diluted_err * 10.0) / 10.0, "Integrated polarization angle error")
header["sPA_int"] = (np.ceil(PA_diluted_err * 10.0) / 10.0, "Integrated polarization angle error")
return I_stokes, Q_stokes, U_stokes, Stokes_cov return I_stokes, Q_stokes, U_stokes, Stokes_cov, header_stokes
def compute_pol(I_stokes, Q_stokes, U_stokes, Stokes_cov, headers): def compute_pol(I_stokes, Q_stokes, U_stokes, Stokes_cov, header_stokes):
""" """
Compute the polarization 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. respective errors from given Stokes parameters.
@@ -1523,8 +1533,8 @@ def compute_pol(I_stokes, Q_stokes, U_stokes, Stokes_cov, headers):
+45/-45deg linear polarization intensity +45/-45deg linear polarization intensity
Stokes_cov : numpy.ndarray Stokes_cov : numpy.ndarray
Covariance matrix of the Stokes parameters I, Q, U. Covariance matrix of the Stokes parameters I, Q, U.
headers : header list header_stokes : astropy.fits.header.Header
List of headers corresponding to the images in data_array. Header file associated with the Stokes fluxes.
---------- ----------
Returns: Returns:
P : numpy.ndarray P : numpy.ndarray
@@ -1543,9 +1553,6 @@ def compute_pol(I_stokes, Q_stokes, U_stokes, Stokes_cov, headers):
s_PA_P : numpy.ndarray s_PA_P : numpy.ndarray
Image (2D floats) containing the Poisson noise error on the Image (2D floats) containing the Poisson noise error on the
polarization angle. polarization angle.
new_headers : header list
Updated list of headers corresponding to the reduced images accounting
for the new orientation angle.
""" """
# Polarization degree and angle computation # Polarization degree and angle computation
mask = I_stokes > 0.0 mask = I_stokes > 0.0
@@ -1595,7 +1602,7 @@ def compute_pol(I_stokes, Q_stokes, U_stokes, Stokes_cov, headers):
# Compute the total exposure time so that # Compute the total exposure time so that
# I_stokes*exp_tot = N_tot the total number of events # I_stokes*exp_tot = N_tot the total number of events
exp_tot = np.array([header["exptime"] for header in headers]).sum() exp_tot = header_stokes["exptime"]
# print("Total exposure time : {} sec".format(exp_tot)) # print("Total exposure time : {} sec".format(exp_tot))
N_obs = I_stokes * exp_tot N_obs = I_stokes * exp_tot
@@ -1616,7 +1623,7 @@ def compute_pol(I_stokes, Q_stokes, U_stokes, Stokes_cov, headers):
return P, debiased_P, s_P, s_P_P, PA, s_PA, s_PA_P return P, debiased_P, s_P, s_P_P, PA, s_PA, s_PA_P
def rotate_Stokes(I_stokes, Q_stokes, U_stokes, Stokes_cov, data_mask, headers, SNRi_cut=None): def rotate_Stokes(I_stokes, Q_stokes, U_stokes, Stokes_cov, data_mask, header_stokes, SNRi_cut=None):
""" """
Use scipy.ndimage.rotate to rotate I_stokes to an angle, and a rotation Use scipy.ndimage.rotate to rotate I_stokes to an angle, and a rotation
matrix to rotate Q, U of a given angle in degrees and update header matrix to rotate Q, U of a given angle in degrees and update header
@@ -1636,8 +1643,8 @@ def rotate_Stokes(I_stokes, Q_stokes, U_stokes, Stokes_cov, data_mask, headers,
Covariance matrix of the Stokes parameters I, Q, U. Covariance matrix of the Stokes parameters I, Q, U.
data_mask : numpy.ndarray data_mask : numpy.ndarray
2D boolean array delimiting the data to work on. 2D boolean array delimiting the data to work on.
headers : header list header_stokes : astropy.fits.header.Header
List of headers corresponding to the reduced images. Header file associated with the Stokes fluxes.
SNRi_cut : float, optional SNRi_cut : float, optional
Cut that should be applied to the signal-to-noise ratio on I. Cut that should be applied to the signal-to-noise ratio on I.
Any SNR < SNRi_cut won't be displayed. If None, cut won't be applied. Any SNR < SNRi_cut won't be displayed. If None, cut won't be applied.
@@ -1655,8 +1662,8 @@ def rotate_Stokes(I_stokes, Q_stokes, U_stokes, Stokes_cov, data_mask, headers,
accounting for +45/-45deg linear polarization intensity. accounting for +45/-45deg linear polarization intensity.
new_Stokes_cov : numpy.ndarray new_Stokes_cov : numpy.ndarray
Updated covariance matrix of the Stokes parameters I, Q, U. Updated covariance matrix of the Stokes parameters I, Q, U.
new_headers : header list new_header_stokes : astropy.fits.header.Header
Updated list of headers corresponding to the reduced images accounting Updated Header file associated with the Stokes fluxes accounting
for the new orientation angle. for the new orientation angle.
new_data_mask : numpy.ndarray new_data_mask : numpy.ndarray
Updated 2D boolean array delimiting the data to work on. Updated 2D boolean array delimiting the data to work on.
@@ -1674,11 +1681,12 @@ def rotate_Stokes(I_stokes, Q_stokes, U_stokes, Stokes_cov, data_mask, headers,
U_stokes[i, j] = eps * np.sqrt(Stokes_cov[2, 2][i, j]) U_stokes[i, j] = eps * np.sqrt(Stokes_cov[2, 2][i, j])
# Rotate I_stokes, Q_stokes, U_stokes using rotation matrix # Rotate I_stokes, Q_stokes, U_stokes using rotation matrix
ang = np.zeros((len(headers),)) # ang = np.zeros((len(headers),))
for i, head in enumerate(headers): # for i, head in enumerate(headers):
pc = WCS(head).celestial.wcs.pc[0,0] # pc = WCS(head).celestial.wcs.pc[0,0]
ang[i] = -np.arccos(WCS(head).celestial.wcs.pc[0,0]) * 180.0 / np.pi if np.abs(pc) < 1. else 0. # ang[i] = -np.arccos(WCS(head).celestial.wcs.pc[0,0]) * 180.0 / np.pi if np.abs(pc) < 1. else 0.
ang = ang.mean() pc = WCS(header_stokes).celestial.wcs.pc[0,0]
ang = -np.arccos(WCS(header_stokes).celestial.wcs.pc[0,0]) * 180.0 / np.pi if np.abs(pc) < 1. else 0.
alpha = np.pi / 180.0 * ang alpha = np.pi / 180.0 * ang
mrot = np.array([[1.0, 0.0, 0.0], [0.0, np.cos(2.0 * alpha), np.sin(2.0 * alpha)], [0, -np.sin(2.0 * alpha), np.cos(2.0 * alpha)]]) mrot = np.array([[1.0, 0.0, 0.0], [0.0, np.cos(2.0 * alpha), np.sin(2.0 * alpha)], [0, -np.sin(2.0 * alpha), np.cos(2.0 * alpha)]])
@@ -1714,25 +1722,22 @@ def rotate_Stokes(I_stokes, Q_stokes, U_stokes, Stokes_cov, data_mask, headers,
new_Stokes_cov[:, :, i, j] = np.dot(mrot, np.dot(new_Stokes_cov[:, :, i, j], mrot.T)) new_Stokes_cov[:, :, i, j] = np.dot(mrot, np.dot(new_Stokes_cov[:, :, i, j], mrot.T))
# Update headers to new angle # Update headers to new angle
new_headers = []
mrot = np.array([[np.cos(-alpha), -np.sin(-alpha)], [np.sin(-alpha), np.cos(-alpha)]]) mrot = np.array([[np.cos(-alpha), -np.sin(-alpha)], [np.sin(-alpha), np.cos(-alpha)]])
for header in headers:
new_header = deepcopy(header) new_header_stokes = deepcopy(header_stokes)
new_header["orientat"] = header["orientat"] + ang new_header_stokes["orientat"] = header_stokes["orientat"] + ang
new_wcs = WCS(header).celestial.deepcopy() new_wcs = WCS(header_stokes).celestial.deepcopy()
new_wcs.wcs.pc = np.dot(mrot, new_wcs.wcs.pc) new_wcs.wcs.pc = np.dot(mrot, new_wcs.wcs.pc)
new_wcs.wcs.crpix = np.dot(mrot, new_wcs.wcs.crpix - old_center[::-1]) + new_center[::-1] new_wcs.wcs.crpix = np.dot(mrot, new_wcs.wcs.crpix - old_center[::-1]) + new_center[::-1]
new_wcs.wcs.set() new_wcs.wcs.set()
for key, val in new_wcs.to_header().items(): for key, val in new_wcs.to_header().items():
new_header.set(key, val) new_header_stokes.set(key, val)
if new_wcs.wcs.pc[0, 0] == 1.0: if new_wcs.wcs.pc[0, 0] == 1.0:
new_header.set("PC1_1", 1.0) new_header_stokes.set("PC1_1", 1.0)
if new_wcs.wcs.pc[1, 1] == 1.0: if new_wcs.wcs.pc[1, 1] == 1.0:
new_header.set("PC2_2", 1.0) new_header_stokes.set("PC2_2", 1.0)
new_header["orientat"] = header["orientat"] + ang new_header_stokes["orientat"] = header_stokes["orientat"] + ang
new_headers.append(new_header)
# Nan handling : # Nan handling :
fmax = np.finfo(np.float64).max fmax = np.finfo(np.float64).max
@@ -1769,13 +1774,12 @@ def rotate_Stokes(I_stokes, Q_stokes, U_stokes, Stokes_cov, data_mask, headers,
U_diluted**2 * Q_diluted_err**2 + Q_diluted**2 * U_diluted_err**2 - 2.0 * Q_diluted * U_diluted * QU_diluted_err U_diluted**2 * Q_diluted_err**2 + Q_diluted**2 * U_diluted_err**2 - 2.0 * Q_diluted * U_diluted * QU_diluted_err
) )
for header in new_headers: new_header_stokes["P_int"] = (P_diluted, "Integrated polarization degree")
header["P_int"] = (P_diluted, "Integrated polarization degree") new_header_stokes["sP_int"] = (np.ceil(P_diluted_err * 1000.0) / 1000.0, "Integrated polarization degree error")
header["sP_int"] = (np.ceil(P_diluted_err * 1000.0) / 1000.0, "Integrated polarization degree error") new_header_stokes["PA_int"] = (PA_diluted, "Integrated polarization angle")
header["PA_int"] = (PA_diluted, "Integrated polarization angle") new_header_stokes["sPA_int"] = (np.ceil(PA_diluted_err * 10.0) / 10.0, "Integrated polarization angle error")
header["sPA_int"] = (np.ceil(PA_diluted_err * 10.0) / 10.0, "Integrated polarization angle error")
return new_I_stokes, new_Q_stokes, new_U_stokes, new_Stokes_cov, new_data_mask, new_headers return new_I_stokes, new_Q_stokes, new_U_stokes, new_Stokes_cov, new_data_mask, new_header_stokes
def rotate_data(data_array, error_array, data_mask, headers): def rotate_data(data_array, error_array, data_mask, headers):