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bug fix for crop_stokes.write_to and background subtraction

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Thibault Barnouin
2024-03-12 17:06:28 +01:00
parent 10c35b10fc
commit 762b857720
4 changed files with 7 additions and 14 deletions
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6
src/FOC_reduction.py
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@@ -33,7 +33,7 @@ def main(target=None, proposal_id=None, infiles=None, output_dir="./data", crop=
# Background estimation
error_sub_type = 'freedman-diaconis' # sqrt, sturges, rice, scott, freedman-diaconis (default) or shape (example (51, 51))
subtract_error = 1.00
subtract_error = 0.50
display_bkg = False
# Data binning
@@ -181,13 +181,13 @@ def main(target=None, proposal_id=None, infiles=None, output_dir="./data", crop=
figtype += "_crop"
stokescrop = proj_plots.crop_Stokes(deepcopy(Stokes_test), norm=LogNorm())
stokescrop.crop()
stokescrop.writeto("/".join([data_folder, "_".join([figname, figtype+".fits"])]))
stokescrop.write_to("/".join([data_folder, "_".join([figname, figtype+".fits"])]))
Stokes_test, data_mask, headers = stokescrop.hdul_crop, stokescrop.data_mask, [dataset.header for dataset in stokescrop.hdul_crop]
print("F_int({0:.0f} Angs) = ({1} ± {2})e{3} ergs.cm^-2.s^-1.Angs^-1".format(headers[0]['photplam'], *proj_plots.sci_not(
Stokes_test[0].data[data_mask].sum()*headers[0]['photflam'], np.sqrt(Stokes_test[3].data[0, 0][data_mask].sum())*headers[0]['photflam'], 2, out=int)))
print("P_int = {0:.1f} ± {1:.1f} %".format(headers[0]['p_int']*100., np.ceil(headers[0]['p_int_err']*1000.)/10.))
print("PA_int = {0:.1f} ±t {1:.1f} °".format(headers[0]['pa_int'], np.ceil(headers[0]['pa_int_err']*10.)/10.))
print("PA_int = {0:.1f} ± {1:.1f} °".format(headers[0]['pa_int'], np.ceil(headers[0]['pa_int_err']*10.)/10.))
# Background values
print("F_bkg({0:.0f} Angs) = ({1} ± {2})e{3} ergs.cm^-2.s^-1.Angs^-1".format(headers[0]['photplam'], *proj_plots.sci_not(
I_bkg[0, 0]*headers[0]['photflam'], np.sqrt(S_cov_bkg[0, 0][0, 0])*headers[0]['photflam'], 2, out=int)))

10
src/lib/background.py
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@@ -240,7 +240,7 @@ def bkg_fit(data, error, mask, headers, subtract_error=True, display=False, save
# Substract background
if 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] <= 0.01*bkg)] = 0.01*bkg
n_data_array[i][np.logical_and(mask, n_data_array[i] <= 1e-3*bkg)] = 1e-3*bkg
std_bkg[i] = image[np.abs(image-bkg)/bkg < 1.].std()
background[i] = bkg
@@ -327,10 +327,6 @@ def bkg_hist(data, error, mask, headers, sub_type=None, subtract_error=True, dis
histograms.append(hist)
binning.append(np.exp(bin_centers(bin_edges)))
# Take the background as the count-rate with the maximum number of pixels
# hist_max = binning[-1][np.argmax(hist)]
# bkg = np.sqrt(np.sum(image[np.abs(image-hist_max)/hist_max<0.5]**2)/image[np.abs(image-hist_max)/hist_max<0.5].size)
# Fit a gaussian to the log-intensity histogram
bins_stdev = binning[-1][hist > hist.max()/2.]
stdev = bins_stdev[-1]-bins_stdev[0]
@@ -346,7 +342,7 @@ def bkg_hist(data, error, mask, headers, sub_type=None, subtract_error=True, dis
# Substract background
if 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] < 0.)] = 0.
n_data_array[i][np.logical_and(mask, n_data_array[i] <= 1e-3*bkg)] = 1e-3*bkg
std_bkg[i] = image[np.abs(image-bkg)/bkg < 1.].std()
background[i] = bkg
@@ -443,7 +439,7 @@ def bkg_mini(data, error, mask, headers, sub_shape=(15, 15), subtract_error=True
# Substract background
if 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] <= 0.01*bkg)] = 0.01*bkg
n_data_array[i][np.logical_and(mask, n_data_array[i] <= 1e-3*bkg)] = 1e-3*bkg
std_bkg[i] = image[np.abs(image-bkg)/bkg < 1.].std()
background[i] = bkg

3
src/lib/plots.py
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@@ -1438,9 +1438,6 @@ class crop_Stokes(crop_map):
def data_mask(self):
return self.hdul_crop[-1].data.astype(int)
def write_to(self, filename):
save_Stokes(self.hdul_crop['I_stokes'], self.hdul_crop['Q_stokes'], self.hdul_crop['U_stokes'], self.hdul_crop['IQU_cov_matrix'], self.hdul_crop['Pol_deg'], self.hdul_crop['Pol_deg_debiased'], self.hdul_crop['Pol_deg_err'], self.hdul_crop['Pol_deg_err_Poisson_noise'], self.hdul_crop['Pol_ang'], self.hdul_crop['Pol_ang_err'], self.hdul_crop['Pol_ang_err_Poisson_noise'], [hdu.header for hdu in self.hdul_crop], self.hdul_crop['data_mask'], filename, data_folder="", return_hdul=False)
class image_lasso_selector(object):
def __init__(self, img, fig=None, ax=None):

2
src/lib/reduction.py
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@@ -164,7 +164,7 @@ def bin_ndarray(ndarray, new_shape, operation='sum'):
[342 350 358 366 374]]
"""
if not operation.lower() in ['sum', 'mean', 'average', 'avg']:
if operation.lower() not in ['sum', 'mean', 'average', 'avg']:
raise ValueError("Operation not supported.")
if ndarray.ndim != len(new_shape):
raise ValueError("Shape mismatch: {} -> {}".format(ndarray.shape,