Tibeuleu/FOC_Reduction
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save raw flux in fits file and display

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Thibault Barnouin
2025-03-19 16:38:36 +01:00
parent dea36b2535
commit 02f8d5213c
4 changed files with 230 additions and 138 deletions
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22
package/lib/reduction.py
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@@ -52,7 +52,6 @@ from scipy.ndimage import rotate as sc_rotate
from scipy.ndimage import shift as sc_shift
from scipy.signal import fftconvolve
from .background import bkg_fit, bkg_hist, bkg_mini
from .convex_hull import image_hull
from .cross_correlation import phase_cross_correlation
from .deconvolve import deconvolve_im, gaussian2d, gaussian_psf, zeropad
@@ -310,6 +309,8 @@ def crop_array(data_array, headers, error_array=None, data_mask=None, step=5, nu
# Update CRPIX value in the associated header
curr_wcs = WCS(crop_headers[i]).celestial.deepcopy()
curr_wcs.wcs.crpix[:2] = curr_wcs.wcs.crpix[:2] - np.array([v_array[2], v_array[0]])
curr_wcs.array_shape = crop_array[i].shape
curr_wcs.wcs.set()
crop_headers[i].update(curr_wcs.to_header())
crop_headers[i]["naxis1"], crop_headers[i]["naxis2"] = crop_array[i].shape
@@ -528,20 +529,22 @@ def get_error(
# estimated to less than 3%
err_flat = data * 0.03
from .background import bkg_fit, bkg_hist, bkg_mini
if sub_type is None:
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
)
sub_type, subtract_error = "histogram ", str(int(subtract_error > 0.0))
elif isinstance(sub_type, str):
if sub_type.lower() in ["auto"]:
if sub_type.lower() in ["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
)
sub_type, subtract_error = "histogram fit ", "mean+%.1fsigma" % subtract_error if subtract_error != 0.0 else 0.0
else:
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, n_bins=sub_type, subtract_error=subtract_error, display=display, savename=savename, plots_folder=plots_folder
)
sub_type, subtract_error = "histogram ", "mean+%.1fsigma" % subtract_error if subtract_error != 0.0 else 0.0
elif isinstance(sub_type, tuple):
@@ -675,7 +678,9 @@ def rebin_array(data_array, error_array, headers, pxsize=2, scale="px", operatio
nw.wcs.cdelt *= Dxy
nw.wcs.crpix /= Dxy
nw.array_shape = new_shape
nw.wcs.set()
new_header["NAXIS1"], new_header["NAXIS2"] = nw.array_shape
new_header["PXAREA"] *= Dxy[0] * Dxy[1]
for key, val in nw.to_header().items():
new_header.set(key, val)
new_header["SAMPLING"] = (str(pxsize) + scale, "Resampling performed during reduction")
@@ -853,7 +858,10 @@ def align_data(
new_crpix = np.array([wcs.wcs.crpix for wcs in headers_wcs]) + shifts[:, ::-1] + res_shift[::-1]
for i in range(len(headers_wcs)):
headers_wcs[i].wcs.crpix = new_crpix[0]
headers_wcs[i].array_shape = (res_shape, res_shape)
headers_wcs[i].wcs.set()
headers[i].update(headers_wcs[i].to_header())
headers[i]["NAXIS1"], headers[i]["NAXIS2"] = res_shape, res_shape
data_mask = rescaled_mask.all(axis=0)
data_array, error_array, data_mask, headers = crop_array(rescaled_image, headers, rescaled_error, data_mask, null_val=0.01 * background)
@@ -1722,13 +1730,11 @@ def rotate_Stokes(I_stokes, Q_stokes, U_stokes, Stokes_cov, data_mask, header_st
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.array_shape = shape
new_wcs.wcs.set()
for key, val in new_wcs.to_header().items():
new_header_stokes.set(key, val)
if new_wcs.wcs.pc[0, 0] == 1.0:
new_header_stokes.set("PC1_1", 1.0)
if new_wcs.wcs.pc[1, 1] == 1.0:
new_header_stokes.set("PC2_2", 1.0)
new_header_stokes["NAXIS1"], new_header_stokes["NAXIS2"] = new_wcs.array_shape
new_header_stokes["ORIENTAT"] += ang
# Nan handling :
@@ -1829,9 +1835,11 @@ def rotate_data(data_array, error_array, data_mask, headers):
new_wcs = WCS(header).celestial.deepcopy()
new_wcs.wcs.pc[:2, :2] = np.dot(mrot, new_wcs.wcs.pc[:2, :2])
new_wcs.wcs.crpix[:2] = np.dot(mrot, new_wcs.wcs.crpix[:2] - old_center[::-1]) + new_center[::-1]
new_wcs.array_shape = shape
new_wcs.wcs.set()
for key, val in new_wcs.to_header().items():
new_header[key] = val
new_header["NAXIS1"], new_header["NAXIS2"] = new_wcs.array_shape
new_header["ORIENTAT"] = np.arccos(new_wcs.celestial.wcs.pc[0, 0]) * 180.0 / np.pi
new_header["ROTATE"] = ang
new_headers.append(new_header)