Tibeuleu/FOC_Reduction
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move alignement before rebinning, before background computation

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This commit is contained in:
Thibault Barnouin
2022-04-12 17:17:34 +02:00
parent 7bbd2bc2e8
commit 3770a78940
52 changed files with 269 additions and 187 deletions
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21
src/lib/plots.py
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@@ -1109,6 +1109,8 @@ class pol_map(object):
self.ax.reset_wcs(self.wcs)
self.ax_cosmetics()
self.display()
self.ax.set_xlim(0,self.I.shape[1])
self.ax.set_ylim(0,self.I.shape[0])
self.pol_vector()
else:
self.cropped = True
@@ -1229,26 +1231,31 @@ class pol_map(object):
def d_tf(event):
self.display_selection = 'total_flux'
self.display()
self.pol_int()
b_tf.on_clicked(d_tf)
def d_pf(event):
self.display_selection = 'pol_flux'
self.display()
self.pol_int()
b_pf.on_clicked(d_pf)
def d_p(event):
self.display_selection = 'pol_deg'
self.display()
self.pol_int()
b_p.on_clicked(d_p)
def d_snri(event):
self.display_selection = 'snri'
self.display()
self.pol_int()
b_snri.on_clicked(d_snri)
def d_snrp(event):
self.display_selection = 'snrp'
self.display()
self.pol_int()
b_snrp.on_clicked(d_snrp)
plt.show()
@@ -1359,8 +1366,6 @@ class pol_map(object):
if hasattr(self, 'im'):
self.im.remove()
self.im = ax.imshow(self.data, vmin=vmin, vmax=vmax, aspect='auto', cmap='inferno')
ax.set_xlim(0,self.data.shape[1])
ax.set_ylim(0,self.data.shape[0])
self.cbar = plt.colorbar(self.im, cax=self.cbar_ax, label=label)
fig.canvas.draw_idle()
return self.im
@@ -1412,12 +1417,12 @@ class pol_map(object):
I_reg = self.I[self.region].sum()
Q_reg = self.Q[self.region].sum()
U_reg = self.U[self.region].sum()
I_reg_err = np.sqrt(n_pix)*np.sqrt(np.sum(s_I[self.region]**2))
Q_reg_err = np.sqrt(n_pix)*np.sqrt(np.sum(s_Q[self.region]**2))
U_reg_err = np.sqrt(n_pix)*np.sqrt(np.sum(s_U[self.region]**2))
IQ_reg_err = np.sqrt(n_pix)*np.sqrt(np.sum(s_IQ[self.region]**2))
IU_reg_err = np.sqrt(n_pix)*np.sqrt(np.sum(s_IU[self.region]**2))
QU_reg_err = np.sqrt(n_pix)*np.sqrt(np.sum(s_QU[self.region]**2))
I_reg_err = np.sqrt(np.sum(s_I[self.region]**2))
Q_reg_err = np.sqrt(np.sum(s_Q[self.region]**2))
U_reg_err = np.sqrt(np.sum(s_U[self.region]**2))
IQ_reg_err = np.sqrt(np.sum(s_IQ[self.region]**2))
IU_reg_err = np.sqrt(np.sum(s_IU[self.region]**2))
QU_reg_err = np.sqrt(np.sum(s_QU[self.region]**2))
P_reg = np.sqrt(Q_reg**2+U_reg**2)/I_reg
P_reg_err = np.sqrt((Q_reg**2*Q_reg_err**2 + U_reg**2*U_reg_err**2 + 2.*Q_reg*U_reg*QU_reg_err)/(Q_reg**2 + U_reg**2) + ((Q_reg/I_reg)**2 + (U_reg/I_reg)**2)*I_reg_err**2 - 2.*(Q_reg/I_reg)*IQ_reg_err - 2.*(U_reg/I_reg)*IU_reg_err)/I_reg