Tibeuleu/FOC_Reduction
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looking for displacement of WCS in pipeline

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Thibault Barnouin
2025-04-01 16:20:40 +02:00
parent 02f8d5213c
commit 71e9d89091
3 changed files with 4 additions and 3 deletions
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1
package/FOC_reduction.py
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@@ -20,7 +20,6 @@ import lib.reduction as proj_red # Functions used in reduction pipeline
import numpy as np import numpy as np
from lib.utils import princ_angle, sci_not from lib.utils import princ_angle, sci_not
from matplotlib.colors import LogNorm from matplotlib.colors import LogNorm
from astropy.wcs import WCS
def main(target=None, proposal_id=None, infiles=None, output_dir="./data", crop=False, interactive=False): def main(target=None, proposal_id=None, infiles=None, output_dir="./data", crop=False, interactive=False):

2
package/lib/plots.py
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@@ -549,12 +549,12 @@ def polarization_map(
else: else:
vmin, vmax = 1.0 / 2.0 * np.median(np.sqrt(stk_cov[0, 0][stkI > 0.0]) * convert_flux), np.max(stkI[stkI > 0.0] * convert_flux) vmin, vmax = 1.0 / 2.0 * np.median(np.sqrt(stk_cov[0, 0][stkI > 0.0]) * convert_flux), np.max(stkI[stkI > 0.0] * convert_flux)
im = ax.imshow(stkI * convert_flux, norm=LogNorm(vmin, vmax), aspect="equal", cmap=kwargs["cmap"], alpha=1.0) im = ax.imshow(stkI * convert_flux, norm=LogNorm(vmin, vmax), aspect="equal", cmap=kwargs["cmap"], alpha=1.0)
ax.plot(*WCS(Stokes[1]).wcs.crpix, "g+")
fig.colorbar(im, ax=ax, aspect=50, shrink=0.60, pad=0.025, label=r"$F_{\lambda}$ [$ergs \cdot cm^{-2} \cdot s^{-1} \cdot \AA^{-1}$]") fig.colorbar(im, ax=ax, aspect=50, shrink=0.60, pad=0.025, label=r"$F_{\lambda}$ [$ergs \cdot cm^{-2} \cdot s^{-1} \cdot \AA^{-1}$]")
# levelsI = np.array([0.8, 2.0, 5.0, 10.0, 20.0, 50.0]) / 100.0 * vmax # levelsI = np.array([0.8, 2.0, 5.0, 10.0, 20.0, 50.0]) / 100.0 * vmax
# print("Stokes I contour levels : ", levelsI) # print("Stokes I contour levels : ", levelsI)
# ax.contour(stkI * convert_flux, levels=levelsI, colors="grey", linewidths=0.5) # ax.contour(stkI * convert_flux, levels=levelsI, colors="grey", linewidths=0.5)
levelsF = np.array([0.8, 2.0, 5.0, 10.0, 20.0, 50.0]) / 100.0 * np.max(flux[flux > 0.0]) levelsF = np.array([0.8, 2.0, 5.0, 10.0, 20.0, 50.0]) / 100.0 * np.max(flux[flux > 0.0])
print("Flux density contour levels : ", levelsF)
ax.contour(flux, levels=levelsF, transform=ax.get_transform(WCS(Stokes[0].header).celestial), colors="grey", linewidths=0.5) ax.contour(flux, levels=levelsF, transform=ax.get_transform(WCS(Stokes[0].header).celestial), colors="grey", linewidths=0.5)
elif display.lower() in ["pf", "pol_flux"]: elif display.lower() in ["pf", "pol_flux"]:
# Display polarization flux # Display polarization flux

4
package/lib/reduction.py
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@@ -647,7 +647,8 @@ def rebin_array(data_array, error_array, headers, pxsize=2, scale="px", operatio
pxsize, scale = "", "full" pxsize, scale = "", "full"
else: else:
raise ValueError("'{0:s}' invalid scale for binning.".format(scale)) 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) new_shape_float = min(image.shape / Dxy_arr, key=lambda x: x[0] + x[1])
new_shape = np.ceil(new_shape_float).astype(int)
for i, (image, error, header) in enumerate(list(zip(data_array, error_array, headers))): for i, (image, error, header) in enumerate(list(zip(data_array, error_array, headers))):
# Get current pixel size # Get current pixel size
@@ -676,6 +677,7 @@ def rebin_array(data_array, error_array, headers, pxsize=2, scale="px", operatio
# Update header # Update header
nw = w.deepcopy() nw = w.deepcopy()
nw.wcs.cdelt *= Dxy nw.wcs.cdelt *= Dxy
# nw.wcs.crpix += np.abs(new_shape_float - new_shape) * np.array(new_shape) / Dxy
nw.wcs.crpix /= Dxy nw.wcs.crpix /= Dxy
nw.array_shape = new_shape nw.array_shape = new_shape
nw.wcs.set() nw.wcs.set()