Tibeuleu/FOC_Reduction
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clean Combine.py add orientat computation from wcs

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Thibault Barnouin
2024-07-08 17:06:11 +02:00
parent 155717a585
commit 91a59f9664
3 changed files with 37 additions and 32 deletions
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48
package/Combine.py
View File

@@ -157,8 +157,8 @@ def combine_Stokes(infiles):
def main(infiles, target=None, output_dir="./data/"):
""" """
from lib.fits import save_Stokes
from lib.reduction import compute_pol
from lib.plots import pol_map
from lib.reduction import compute_pol, rotate_Stokes
if target is None:
target = input("Target name:\n>")
@@ -167,48 +167,38 @@ def main(infiles, target=None, output_dir="./data/"):
data_folder = prod[0][0]
files = [p[1] for p in prod]
# Reduction parameters
kwargs = {}
# Polarization map output
kwargs["SNRp_cut"] = 3.0
kwargs["SNRi_cut"] = 1.0
kwargs["flux_lim"] = 1e-19, 3e-17
kwargs["scale_vec"] = 5
kwargs["step_vec"] = 1
if not same_reduction(infiles):
from FOC_reduction import main as FOC_reduction
# Reduction parameters
kwargs = {}
# Background estimation
kwargs["error_sub_type"] = "freedman-diaconis" # sqrt, sturges, rice, scott, freedman-diaconis (default) or shape (example (51, 51))
kwargs["subtract_error"] = 0.7
# Data binning
kwargs["pxsize"] = 0.1
kwargs["pxscale"] = "arcsec" # pixel, arcsec or full
# Smoothing
kwargs["smoothing_function"] = "combine" # gaussian_after, weighted_gaussian_after, gaussian, weighted_gaussian or combine
kwargs["smoothing_FWHM"] = 0.2 # If None, no smoothing is done
kwargs["smoothing_scale"] = "arcsec" # pixel or arcsec
# Polarization map output
kwargs["SNRp_cut"] = 3.0 # P measurments with SNR>3
kwargs["SNRi_cut"] = 1.0 # I measurments with SNR>30, which implies an uncertainty in P of 4.7%.
kwargs["flux_lim"] = 1e-19, 3e-17 # lowest and highest flux displayed on plot, defaults to bkg and maximum in cut if None
kwargs["scale_vec"] = 5
kwargs["step_vec"] = (
1 # plot all vectors in the array. if step_vec = 2, then every other vector will be plotted if step_vec = 0 then all vectors are displayed at full length
)
grouped_infiles = same_obs(files, data_folder)
new_infiles = []
for i, group in enumerate(grouped_infiles):
new_infiles.append(
FOC_reduction(target=target + "-" + str(i + 1), infiles=["/".join([data_folder, file]) for file in group], interactive=True, **kwargs)
FOC_reduction(target=target + "-" + str(i + 1), infiles=["/".join([data_folder, file]) for file in group], interactive=True)[0]
)
infiles = new_infiles
I_combined, Q_combined, U_combined, IQU_cov_combined, data_mask_combined, header_combined = combine_Stokes(infiles)
I_combined, Q_combined, U_combined, IQU_cov_combined, data_mask_combined, header_combined = combine_Stokes(infiles=infiles)
I_combined, Q_combined, U_combined, IQU_cov_combined, data_mask_combined, header_combined = rotate_Stokes(
I_stokes=I_combined, Q_stokes=Q_combined, U_stokes=U_combined, Stokes_cov=IQU_cov_combined, data_mask=data_mask_combined, header_stokes=header_combined
)
P, debiased_P, s_P, s_P_P, PA, s_PA, s_PA_P = compute_pol(
I_stokes=I_combined, Q_stokes=Q_combined, U_stokes=U_combined, Stokes_cov=IQU_cov_combined, header_stokes=header_combined
)
filename = header_combined["FILENAME"]
figname = "_".join([target, filename[filename.find("FOC_"):], "combined"])
figname = "_".join([target, filename[filename.find("FOC_") :], "combined"])
Stokes_combined = save_Stokes(
I_stokes=I_combined,
Q_stokes=Q_combined,
@@ -228,9 +218,9 @@ def main(infiles, target=None, output_dir="./data/"):
return_hdul=True,
)
pol_map(Stokes_combined)
pol_map(Stokes_combined, **kwargs)
return "/".join([data_folder, figname+".fits"])
return "/".join([data_folder, figname + ".fits"])
if __name__ == "__main__":

6
package/lib/background.py
View File

@@ -258,7 +258,7 @@ def bkg_fit(data, error, mask, headers, subtract_error=True, display=False, save
n_error_array[i] = np.sqrt(n_error_array[i] ** 2 + error_bkg[i] ** 2)
# Substract background
if subtract_error > 0:
if np.abs(subtract_error) > 0:
n_data_array[i][mask] = n_data_array[i][mask] - bkg
n_data_array[i][np.logical_and(mask, n_data_array[i] <= 1e-3 * bkg)] = 1e-3 * bkg
@@ -367,7 +367,7 @@ def bkg_hist(data, error, mask, headers, sub_type=None, subtract_error=True, dis
n_error_array[i] = np.sqrt(n_error_array[i] ** 2 + error_bkg[i] ** 2)
# Substract background
if subtract_error > 0:
if np.abs(subtract_error) > 0:
n_data_array[i][mask] = n_data_array[i][mask] - bkg
n_data_array[i][np.logical_and(mask, n_data_array[i] <= 1e-3 * bkg)] = 1e-3 * bkg
@@ -464,7 +464,7 @@ def bkg_mini(data, error, mask, headers, sub_shape=(15, 15), subtract_error=True
n_error_array[i] = np.sqrt(n_error_array[i] ** 2 + error_bkg[i] ** 2)
# Substract background
if subtract_error > 0.0:
if np.abs(subtract_error) > 0:
n_data_array[i][mask] = n_data_array[i][mask] - bkg
n_data_array[i][np.logical_and(mask, n_data_array[i] <= 1e-3 * bkg)] = 1e-3 * bkg

15
package/lib/utils.py
View File

@@ -45,3 +45,18 @@ def sci_not(v, err, rnd=1, out=str):
return output[0] + r")e{0}".format(-power)
else:
return *output[1:], -power
def wcs_PA(PC21, PC22):
"""
Return the position angle in degrees to the North direction of a wcs
from the values of coefficient of its transformation matrix.
"""
if (abs(PC21) > abs(PC22)) and (PC21 >= 0):
orient = -np.arccos(PC22) * 180.0 / np.pi
elif (abs(PC21) > abs(PC22)) and (PC21 < 0):
orient = np.arccos(PC22) * 180.0 / np.pi
elif (abs(PC21) < abs(PC22)) and (PC22 >= 0):
orient = np.arccos(PC22) * 180.0 / np.pi
elif (abs(PC21) < abs(PC22)) and (PC22 < 0):
orient = -np.arccos(PC22) * 180.0 / np.pi
return orient