Tibeuleu/FOC_Reduction
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fix integrated flux display, analysis default snri

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This commit is contained in:
Thibault Barnouin
2024-03-28 17:31:58 +01:00
parent 16c0bdeb17
commit ce927c3409
3 changed files with 19 additions and 20 deletions
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4
src/FOC_reduction.py
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@@ -172,7 +172,6 @@ def main(target=None, proposal_id=None, infiles=None, output_dir="./data", crop=
figname = "_".join([figname, figtype]) if figtype != "" else figname
Stokes_test = proj_fits.save_Stokes(I_stokes, Q_stokes, U_stokes, Stokes_cov, P, debiased_P, s_P, s_P_P, PA, s_PA, s_PA_P,
headers, data_mask, figname, data_folder=data_folder, return_hdul=True)
data_mask = Stokes_test[-1].data.astype(bool)
# Step 5:
# crop to desired region of interest (roi)
@@ -181,8 +180,9 @@ def main(target=None, proposal_id=None, infiles=None, output_dir="./data", crop=
stokescrop = proj_plots.crop_Stokes(deepcopy(Stokes_test), norm=LogNorm())
stokescrop.crop()
stokescrop.write_to("/".join([data_folder, figname+".fits"]))
Stokes_test, data_mask, headers = stokescrop.hdul_crop, stokescrop.data_mask, [dataset.header for dataset in stokescrop.hdul_crop]
Stokes_test, headers = stokescrop.hdul_crop, [dataset.header for dataset in stokescrop.hdul_crop]
data_mask = Stokes_test['data_mask'].data.astype(bool)
print("F_int({0:.0f} Angs) = ({1} ± {2})e{3} ergs.cm^-2.s^-1.Angs^-1".format(headers[0]['photplam'], *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.))

2
src/analysis.py
View File

@@ -7,7 +7,7 @@ options = "hf:p:i:l:"
long_options = ["help", "fits=", "snrp=", "snri=", "lim="]
fits_path = None
SNRp_cut, SNRi_cut = 3, 30
SNRp_cut, SNRi_cut = 3, 3
flux_lim = None
out_txt = None

33
src/lib/plots.py
View File

@@ -1408,16 +1408,16 @@ class crop_Stokes(crop_map):
self.rect_selector = RectangleSelector(self.ax, self.onselect_crop,
button=[1])
# Update integrated values
mask = np.logical_and(self.hdul_crop[-1].data.astype(bool), self.hdul_crop[0].data > 0)
I_diluted = self.hdul_crop[0].data[mask].sum()
Q_diluted = self.hdul_crop[1].data[mask].sum()
U_diluted = self.hdul_crop[2].data[mask].sum()
I_diluted_err = np.sqrt(np.sum(self.hdul_crop[3].data[0, 0][mask]))
Q_diluted_err = np.sqrt(np.sum(self.hdul_crop[3].data[1, 1][mask]))
U_diluted_err = np.sqrt(np.sum(self.hdul_crop[3].data[2, 2][mask]))
IQ_diluted_err = np.sqrt(np.sum(self.hdul_crop[3].data[0, 1][mask]**2))
IU_diluted_err = np.sqrt(np.sum(self.hdul_crop[3].data[0, 2][mask]**2))
QU_diluted_err = np.sqrt(np.sum(self.hdul_crop[3].data[1, 2][mask]**2))
mask = np.logical_and(self.hdul_crop['data_mask'].data.astype(bool), self.hdul_crop[0].data > 0)
I_diluted = self.hdul_crop['i_stokes'].data[mask].sum()
Q_diluted = self.hdul_crop['q_stokes'].data[mask].sum()
U_diluted = self.hdul_crop['u_stokes'].data[mask].sum()
I_diluted_err = np.sqrt(np.sum(self.hdul_crop['iqu_cov_matrix'].data[0, 0][mask]))
Q_diluted_err = np.sqrt(np.sum(self.hdul_crop['iqu_cov_matrix'].data[1, 1][mask]))
U_diluted_err = np.sqrt(np.sum(self.hdul_crop['iqu_cov_matrix'].data[2, 2][mask]))
IQ_diluted_err = np.sqrt(np.sum(self.hdul_crop['iqu_cov_matrix'].data[0, 1][mask]**2))
IU_diluted_err = np.sqrt(np.sum(self.hdul_crop['iqu_cov_matrix'].data[0, 2][mask]**2))
QU_diluted_err = np.sqrt(np.sum(self.hdul_crop['iqu_cov_matrix'].data[1, 2][mask]**2))
P_diluted = np.sqrt(Q_diluted**2+U_diluted**2)/I_diluted
P_diluted_err = (1./I_diluted)*np.sqrt((Q_diluted**2*Q_diluted_err**2 + U_diluted**2*U_diluted_err**2 + 2.*Q_diluted*U_diluted*QU_diluted_err)/(Q_diluted**2 + U_diluted **
@@ -1436,7 +1436,7 @@ class crop_Stokes(crop_map):
@property
def data_mask(self):
return self.hdul_crop[-1].data.astype(int)
return self.hdul_crop['data_mask'].data.astype(int)
class image_lasso_selector(object):
@@ -2341,12 +2341,12 @@ class pol_map(object):
if hasattr(self, 'quiver'):
self.quiver.remove()
self.quiver = ax.quiver(X, Y, XY_U, XY_V, units='xy', scale=1./self.vec_scale, scale_units='xy', pivot='mid', headwidth=0.,
headlength=0., headaxislength=0., width=0.15, linewidth=0.5, color='white', edgecolor='black')
headlength=0., headaxislength=0., width=0.2, linewidth=0.3, color='white', edgecolor='black')
fig.canvas.draw_idle()
return self.quiver
else:
ax.quiver(X, Y, XY_U, XY_V, units='xy', scale=1./self.vec_scale, scale_units='xy', pivot='mid', headwidth=0.,
headlength=0., headaxislength=0., width=0.15, linewidth=0.5, color='white', edgecolor='black')
headlength=0., headaxislength=0., width=0.2, linewidth=0.3, color='white', edgecolor='black')
fig.canvas.draw_idle()
def pol_int(self, fig=None, ax=None):
@@ -2380,9 +2380,8 @@ class pol_map(object):
P_cut_err = np.sqrt((Q_cut**2*Q_cut_err**2 + U_cut**2*U_cut_err**2 + 2.*Q_cut*U_cut*QU_cut_err)/(Q_cut**2 + U_cut**2) +
((Q_cut/I_cut)**2 + (U_cut/I_cut)**2)*I_cut_err**2 - 2.*(Q_cut/I_cut)*IQ_cut_err - 2.*(U_cut/I_cut)*IU_cut_err)/I_cut
PA_cut = princ_angle(np.degrees((1./2.)*np.arctan2(U_cut, Q_cut)))
PA_cut_err = princ_angle(np.degrees((1./(2.*(Q_cut**2+U_cut**2)))*np.sqrt(U_cut**2 *
Q_cut_err**2 + Q_cut**2*U_cut_err**2 - 2.*Q_cut*U_cut*QU_cut_err)))
PA_cut = princ_angle((90./np.pi)*np.arctan2(U_cut, Q_cut))
PA_cut_err = (90./(np.pi*(Q_cut**2+U_cut**2)))*np.sqrt(U_cut**2*Q_cut_err**2 + Q_cut**2*U_cut_err**2 - 2.*Q_cut*U_cut*QU_cut_err)
else:
s_I = np.sqrt(self.IQU_cov[0, 0])
@@ -2424,7 +2423,7 @@ class pol_map(object):
P_cut_err = np.sqrt((Q_cut**2*Q_cut_err**2 + U_cut**2*U_cut_err**2 + 2.*Q_cut*U_cut*QU_cut_err)/(Q_cut**2 + U_cut**2) +
((Q_cut/I_cut)**2 + (U_cut/I_cut)**2)*I_cut_err**2 - 2.*(Q_cut/I_cut)*IQ_cut_err - 2.*(U_cut/I_cut)*IU_cut_err)/I_cut
PA_cut = 360.-princ_angle((90./np.pi)*np.arctan2(U_cut, Q_cut))
PA_cut = princ_angle((90./np.pi)*np.arctan2(U_cut, Q_cut))
PA_cut_err = (90./(np.pi*(Q_cut**2+U_cut**2)))*np.sqrt(U_cut**2*Q_cut_err**2 + Q_cut**2*U_cut_err**2 - 2.*Q_cut*U_cut*QU_cut_err)
if hasattr(self, 'cont'):