Tibeuleu/FOC_Reduction
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some more formatting

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This commit is contained in:
Thibault Barnouin
2025-04-08 19:48:46 +02:00
parent 05f800b282
commit 29786c2fa4
3 changed files with 141 additions and 893 deletions
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7
package/FOC_reduction.py
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@@ -290,12 +290,7 @@ def main(target=None, proposal_id=None, infiles=None, output_dir="./data", crop=
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(
flux_head["PHOTPLAM"], flux_head["PHOTPLAM"],
*sci_not( *sci_not(flux_data[flux_mask].sum() * flux_head["PHOTFLAM"], np.sqrt(np.sum(flux_error[flux_mask] ** 2)) * flux_head["PHOTFLAM"], 2, out=int),
flux_data[flux_mask].sum() * flux_head["PHOTFLAM"],
np.sqrt(np.sum(flux_error[flux_mask] ** 2)) * flux_head["PHOTFLAM"],
2,
out=int,
),
) )
) )
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("P_int = {0:.1f} ± {1:.1f} %".format(header_stokes["p_int"] * 100.0, np.ceil(header_stokes["sP_int"] * 1000.0) / 10.0))

996
package/lib/plots.py
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File diff suppressed because it is too large Load Diff

29
package/lib/reduction.py
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@@ -191,7 +191,7 @@ def bin_ndarray(ndarray, new_shape, operation="sum"):
Example Example
------- -------
>>> m = np.arange(0, 100, 1).reshape((10, 10)) >>> m = np.arange(0, 100, 1).reshape((10, 10))
>>> n = bin_ndarray(m, new_shape=(5,5), operation='sum') >>> n = bin_ndarray(m, new_shape=(5, 5), operation="sum")
>>> print(n) >>> print(n)
[[ 22 30 38 46 54] [[ 22 30 38 46 54]
@@ -278,9 +278,7 @@ def crop_array(data_array, headers, error_array=None, data_mask=None, step=5, nu
if null_val is None: if null_val is None:
null_val = [1.00 * error.mean() for error in error_array] null_val = [1.00 * error.mean() for error in error_array]
elif type(null_val) is float: elif type(null_val) is float:
null_val = [ null_val = [null_val] * error_array.shape[0]
null_val,
] * error_array.shape[0]
vertex = np.zeros((data_array.shape[0], 4), dtype=int) vertex = np.zeros((data_array.shape[0], 4), dtype=int)
for i, image in enumerate(data_array): # Get vertex of the rectangular convex hull of each image for i, image in enumerate(data_array): # Get vertex of the rectangular convex hull of each image
@@ -349,10 +347,7 @@ def crop_array(data_array, headers, error_array=None, data_mask=None, step=5, nu
headers, headers,
vmin=convert_flux * data_array[data_array > 0.0].mean() / 5.0, vmin=convert_flux * data_array[data_array > 0.0].mean() / 5.0,
vmax=convert_flux * data_array[data_array > 0.0].max(), vmax=convert_flux * data_array[data_array > 0.0].max(),
rectangle=[ rectangle=[rectangle] * len(headers),
rectangle,
]
* len(headers),
savename=savename + "_crop_region", savename=savename + "_crop_region",
plots_folder=plots_folder, plots_folder=plots_folder,
) )
@@ -632,12 +627,7 @@ def rebin_array(data_array, error_array, headers, pxsize=2, scale="px", operatio
# Compute binning ratio # Compute binning ratio
if scale.lower() in ["px", "pixel"]: if scale.lower() in ["px", "pixel"]:
Dxy_arr[i] = np.array( Dxy_arr[i] = np.array([pxsize] * 2)
[
pxsize,
]
* 2
)
scale = "px" scale = "px"
elif scale.lower() in ["arcsec", "arcseconds"]: elif scale.lower() in ["arcsec", "arcseconds"]:
Dxy_arr[i] = np.array(pxsize / np.abs(w.wcs.cdelt) / 3600.0) Dxy_arr[i] = np.array(pxsize / np.abs(w.wcs.cdelt) / 3600.0)
@@ -947,12 +937,7 @@ def smooth_data(data_array, error_array, data_mask, headers, FWHM=1.5, scale="pi
dist_rc = np.where(data_mask, np.sqrt((r - xx) ** 2 + (c - yy) ** 2), fmax) dist_rc = np.where(data_mask, np.sqrt((r - xx) ** 2 + (c - yy) ** 2), fmax)
# Catch expected "OverflowWarning" as we overflow values that are not in the image # Catch expected "OverflowWarning" as we overflow values that are not in the image
with warnings.catch_warnings(record=True) as w: with warnings.catch_warnings(record=True) as w:
g_rc = np.array( g_rc = np.array([np.exp(-0.5 * (dist_rc / stdev) ** 2) / (2.0 * np.pi * stdev**2)] * data_array.shape[0])
[
np.exp(-0.5 * (dist_rc / stdev) ** 2) / (2.0 * np.pi * stdev**2),
]
* data_array.shape[0]
)
# Apply weighted combination # Apply weighted combination
smoothed[r, c] = np.where(data_mask[r, c], np.sum(data_array * weight * g_rc) / np.sum(weight * g_rc), data_array.mean(axis=0)[r, c]) smoothed[r, c] = np.where(data_mask[r, c], np.sum(data_array * weight * g_rc) / np.sum(weight * g_rc), data_array.mean(axis=0)[r, c])
error[r, c] = np.where( error[r, c] = np.where(
@@ -1447,9 +1432,7 @@ def compute_Stokes(data_array, error_array, data_mask, headers, FWHM=None, scale
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 = [ all_header_stokes = [{}] * np.unique(rotate).size
{},
] * 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