Tibeuleu/FOC_Reduction
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add correction factor for all polarizer filters to have same transmission efficiency

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Tibeuleu
2022-10-25 11:17:50 +02:00
parent 23d802cabb
commit 700cc7e4df
15 changed files with 24 additions and 15 deletions
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26
src/lib/reduction.py
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@@ -509,11 +509,11 @@ def get_error(data_array, headers, error_array=None, data_mask=None,
background[i] = sub_image.sum()
if (data_array[i] < 0.).any():
print(data_array[i])
if i==0:
np.savetxt("output/s_bg.txt",error_bkg[i])
np.savetxt("output/s_wav.txt",err_wav)
np.savetxt("output/s_psf.txt",err_psf)
np.savetxt("output/s_flat.txt",err_flat)
#if i==0:
#np.savetext("output/s_bg.txt",error_bkg[i])
#np.savetext("output/s_wav.txt",err_wav)
#np.savetext("output/s_psf.txt",err_psf)
#np.savetext("output/s_flat.txt",err_flat)
if display:
plt.rcParams.update({'font.size': 10})
@@ -843,8 +843,8 @@ def align_data(data_array, headers, error_array=None, upsample_factor=1.,
#sum quadratically the errors
rescaled_error[i] = np.sqrt(rescaled_error[i]**2 + error_shift**2)
if i==1:
np.savetxt("output/s_shift.txt",error_shift)
#if i==1:
#np.savetext("output/s_shift.txt",error_shift)
shifts.append(shift)
errors.append(error)
@@ -1204,12 +1204,14 @@ def compute_Stokes(data_array, error_array, data_mask, headers,
transmit3 = np.min([trans3[header['filtnam3'].lower()] for header in headers])
transmit4 = np.min([trans4[header['filtnam4'].lower()] for header in headers])
transmit *= transmit2*transmit3*transmit4
pol_eff = np.array([pol_efficiency['pol0'], pol_efficiency['pol60'], pol_efficiency['pol120']])
#Calculating correction factor
corr = np.array([1.0*h['photflam']/h['exptime'] for h in pol_headers])*pol_headers[0]['exptime']/pol_headers[0]['photflam']
# Orientation and error for each polarizer
fmax = np.finfo(np.float64).max
pol_flux = np.array([pol0, pol60, pol120])
pol_flux = np.array([corr[0]*pol0, corr[1]*pol60, corr[2]*pol120])
coeff_stokes = np.zeros((3,3))
# Coefficients linking each polarizer flux to each Stokes parameter
@@ -1255,9 +1257,9 @@ def compute_Stokes(data_array, error_array, data_mask, headers,
s_I2_axis = np.sum([dI_dtheta[i]**2 * sigma_theta[i]**2 for i in range(len(sigma_theta))],axis=0)
s_Q2_axis = np.sum([dQ_dtheta[i]**2 * sigma_theta[i]**2 for i in range(len(sigma_theta))],axis=0)
s_U2_axis = np.sum([dU_dtheta[i]**2 * sigma_theta[i]**2 for i in range(len(sigma_theta))],axis=0)
np.savetxt("output/sI_dir.txt", np.sqrt(s_I2_axis))
np.savetxt("output/sQ_dir.txt", np.sqrt(s_Q2_axis))
np.savetxt("output/sU_dir.txt", np.sqrt(s_U2_axis))
#np.savetext("output/sI_dir.txt", np.sqrt(s_I2_axis))
#np.savetext("output/sQ_dir.txt", np.sqrt(s_Q2_axis))
#np.savetext("output/sU_dir.txt", np.sqrt(s_U2_axis))
# Add quadratically the uncertainty to the Stokes covariance matrix
Stokes_cov[0,0] += s_I2_axis