bug fix and small improvements
This commit is contained in:
Thibault Barnouin
@@ -485,17 +485,30 @@ def get_error(data_array, headers, error_array=None, data_mask=None,
|
||||
mask = zeropad(mask_c, data[0].shape).astype(bool)
|
||||
background = np.zeros((data.shape[0]))
|
||||
|
||||
#wavelength dependence of the polariser filters
|
||||
#estimated to less than 1%
|
||||
err_wav = data*0.01
|
||||
#difference in PSFs through each polarizers
|
||||
#estimated to less than 3%
|
||||
err_psf = data*0.03
|
||||
#flatfielding uncertainties
|
||||
#estimated to less than 3%
|
||||
err_flat = data*0.03
|
||||
|
||||
if (sub_type is None):
|
||||
n_data_array, n_error_array, headers, background = bkg_hist(data, error, mask, headers, subtract_error=subtract_error, display=display, savename=savename, plots_folder=plots_folder)
|
||||
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)
|
||||
elif type(sub_type)==str:
|
||||
if sub_type.lower() in ['auto']:
|
||||
n_data_array, n_error_array, headers, background = bkg_fit(data, error, mask, headers, subtract_error=subtract_error, display=display, savename=savename, plots_folder=plots_folder)
|
||||
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)
|
||||
else:
|
||||
n_data_array, n_error_array, headers, background = bkg_hist(data, error, mask, headers, sub_type=sub_type, subtract_error=subtract_error, display=display, savename=savename, plots_folder=plots_folder)
|
||||
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)
|
||||
elif type(sub_type)==tuple:
|
||||
n_data_array, n_error_array, headers, background = bkg_mini(data, error, mask, headers, sub_shape=sub_type, subtract_error=subtract_error, display=display, savename=savename, plots_folder=plots_folder)
|
||||
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)
|
||||
else:
|
||||
print("Warning: Invalid subtype.")
|
||||
|
||||
# Quadratically add uncertainties in the "correction factors" (see Kishimoto 1999)
|
||||
n_error_array = np.sqrt(err_wav**2+err_psf**2+err_flat**2+c_error_bkg**2)
|
||||
|
||||
if return_background:
|
||||
return n_data_array, n_error_array, headers, background
|
||||
@@ -553,29 +566,39 @@ def rebin_array(data_array, error_array, headers, pxsize, scale,
|
||||
(yet)".format(instr))
|
||||
|
||||
rebinned_data, rebinned_error, rebinned_headers = [], [], []
|
||||
Dxy = np.array([1, 1],dtype=int)
|
||||
Dxy = np.array([1., 1.])
|
||||
|
||||
# Routine for the FOC instrument
|
||||
if instr == 'FOC':
|
||||
HST_aper = 2400. # HST aperture in mm
|
||||
Dxy_arr = np.ones((data_array.shape[0],2))
|
||||
for i, enum in enumerate(list(zip(data_array, error_array, headers))):
|
||||
image, error, header = enum
|
||||
# Get current pixel size
|
||||
w = WCS(header).deepcopy()
|
||||
new_header = deepcopy(header)
|
||||
|
||||
# Compute binning ratio
|
||||
if scale.lower() in ['px', 'pixel']:
|
||||
Dxy = np.array([pxsize,]*2)
|
||||
Dxy_arr[i] = np.array([pxsize,]*2)
|
||||
elif scale.lower() in ['arcsec','arcseconds']:
|
||||
Dxy = np.floor(pxsize/np.abs(w.wcs.cdelt)/3600.).astype(int)
|
||||
Dxy_arr[i] = np.array(pxsize/np.abs(w.wcs.cdelt)/3600.)
|
||||
elif scale.lower() in ['full','integrate']:
|
||||
Dxy = np.floor(image.shape).astype(int)
|
||||
Dxy_arr[i] = image.shape
|
||||
else:
|
||||
raise ValueError("'{0:s}' invalid scale for binning.".format(scale))
|
||||
new_shape = np.ceil(min(image.shape/Dxy_arr,key=lambda x:x[0]+x[1])).astype(int)
|
||||
|
||||
for i, enum in enumerate(list(zip(data_array, error_array, headers))):
|
||||
image, error, header = enum
|
||||
# Get current pixel size
|
||||
w = WCS(header).deepcopy()
|
||||
new_header = deepcopy(header)
|
||||
|
||||
Dxy = image.shape/new_shape
|
||||
if (Dxy < 1.).any():
|
||||
raise ValueError("Requested pixel size is below resolution.")
|
||||
new_shape = (image.shape//Dxy).astype(int)
|
||||
new_shape = np.ceil(image.shape/Dxy).astype(int)
|
||||
|
||||
# Rebin data
|
||||
rebin_data = bin_ndarray(image, new_shape=new_shape,
|
||||
@@ -606,10 +629,15 @@ def rebin_array(data_array, error_array, headers, pxsize, scale,
|
||||
rebinned_error.append(np.sqrt(rms_image**2 + new_error**2))
|
||||
|
||||
# Update header
|
||||
w = w.slice((np.s_[::Dxy[0]], np.s_[::Dxy[1]]))
|
||||
header['NAXIS1'],header['NAXIS2'] = w.array_shape
|
||||
header.update(w.to_header())
|
||||
rebinned_headers.append(header)
|
||||
#nw = w.slice((np.s_[::Dxy[0]], np.s_[::Dxy[1]]))
|
||||
nw = w.deepcopy()
|
||||
nw.wcs.cdelt *= Dxy
|
||||
nw.wcs.crpix /= Dxy
|
||||
nw.array_shape = new_shape
|
||||
new_header['NAXIS1'],new_header['NAXIS2'] = nw.array_shape
|
||||
for key, val in nw.to_header().items():
|
||||
new_header.set(key,val)
|
||||
rebinned_headers.append(new_header)
|
||||
if not data_mask is None:
|
||||
data_mask = bin_ndarray(data_mask,new_shape=new_shape,operation='average') > 0.80
|
||||
|
||||
@@ -1180,6 +1208,12 @@ def compute_Stokes(data_array, error_array, data_mask, headers,
|
||||
if mask.any():
|
||||
print("WARNING : found {0:d} pixels for which I_pol > I_stokes".format(I_stokes[mask].size))
|
||||
|
||||
# Statistical error: Poisson noise is assumed
|
||||
sigma_flux = np.array([np.sqrt(flux/head['exptime']) for flux,head in zip(pol_flux,pol_headers)])
|
||||
s_I2_stat = np.sum([coeff_stokes[0,i]**2*sigma_flux[i]**2 for i in range(len(sigma_flux))],axis=0)
|
||||
s_Q2_stat = np.sum([coeff_stokes[1,i]**2*sigma_flux[i]**2 for i in range(len(sigma_flux))],axis=0)
|
||||
s_U2_stat = np.sum([coeff_stokes[2,i]**2*sigma_flux[i]**2 for i in range(len(sigma_flux))],axis=0)
|
||||
|
||||
# Compute the derivative of each Stokes parameter with respect to the polarizer orientation
|
||||
dI_dtheta1 = 2.*pol_eff[0]/A*(pol_eff[2]*np.cos(-2.*theta[2]+2.*theta[0])*(pol_flux[1]-I_stokes) - pol_eff[1]*np.cos(-2.*theta[0]+2.*theta[1])*(pol_flux[2]-I_stokes))
|
||||
dI_dtheta2 = 2.*pol_eff[1]/A*(pol_eff[0]*np.cos(-2.*theta[0]+2.*theta[1])*(pol_flux[2]-I_stokes) - pol_eff[2]*np.cos(-2.*theta[1]+2.*theta[2])*(pol_flux[0]-I_stokes))
|
||||
@@ -1205,9 +1239,9 @@ def compute_Stokes(data_array, error_array, data_mask, headers,
|
||||
#np.savetxt("output/sU_dir.txt", np.sqrt(s_U2_axis))
|
||||
|
||||
# Add quadratically the uncertainty to the Stokes covariance matrix
|
||||
Stokes_cov[0,0] += s_I2_axis
|
||||
Stokes_cov[1,1] += s_Q2_axis
|
||||
Stokes_cov[2,2] += s_U2_axis
|
||||
Stokes_cov[0,0] += s_I2_axis + s_I2_stat
|
||||
Stokes_cov[1,1] += s_Q2_axis + s_Q2_stat
|
||||
Stokes_cov[2,2] += s_U2_axis + s_U2_stat
|
||||
|
||||
#Compute integrated values for P, PA before any rotation
|
||||
mask = np.logical_and(data_mask.astype(bool), (I_stokes > 0.))
|
||||
|
||||
Reference in New Issue
Block a user