Tibeuleu/FOC_Reduction
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plots clean-up, correct crop, prepare analysis

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Thibault Barnouin
2022-04-04 11:15:25 +02:00
parent 1ddff076b8
commit c4311bbb4b
399 changed files with 256 additions and 90 deletions
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src/FOC_reduction.py
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@@ -13,17 +13,18 @@ import lib.reduction as proj_red #Functions used in reduction pipeline
import lib.plots as proj_plots #Functions for plotting data
from lib.convex_hull import image_hull
from lib.deconvolve import from_file_psf
import matplotlib.pyplot as plt
def main():
##### User inputs
## Input and output locations
globals()['data_folder'] = "../data/NGC1068_x274020/"
infiles = ['x274020at.c0f.fits','x274020bt.c0f.fits','x274020ct.c0f.fits',
'x274020dt.c0f.fits','x274020et.c0f.fits','x274020ft.c0f.fits',
'x274020gt.c0f.fits','x274020ht.c0f.fits','x274020it.c0f.fits']
psf_file = 'NGC1068_f253m00.fits'
globals()['plots_folder'] = "../plots/NGC1068_x274020/"
# globals()['data_folder'] = "../data/NGC1068_x274020/"
# infiles = ['x274020at.c0f.fits','x274020bt.c0f.fits','x274020ct.c0f.fits',
# 'x274020dt.c0f.fits','x274020et.c0f.fits','x274020ft.c0f.fits',
# 'x274020gt.c0f.fits','x274020ht.c0f.fits','x274020it.c0f.fits']
# psf_file = 'NGC1068_f253m00.fits'
# globals()['plots_folder'] = "../plots/NGC1068_x274020/"
# globals()['data_folder'] = "../data/NGC1068_x14w010/"
# infiles = ['x14w0101t_c0f.fits','x14w0102t_c0f.fits','x14w0103t_c0f.fits',
@@ -62,10 +63,10 @@ def main():
# 'x3995202r_c0f.fits','x3995206r_c0f.fits']
# globals()['plots_folder'] = "../plots/PG1630+377_x39510/"
# globals()['data_folder'] = "../data/IC5063_x3nl030/"
# infiles = ['x3nl0301r_c0f.fits','x3nl0302r_c0f.fits','x3nl0303r_c0f.fits']
# psf_file = 'IC5063_f502m00.fits'
# globals()['plots_folder'] = "../plots/IC5063_x3nl030/"
globals()['data_folder'] = "../data/IC5063_x3nl030/"
infiles = ['x3nl0301r_c0f.fits','x3nl0302r_c0f.fits','x3nl0303r_c0f.fits']
psf_file = 'IC5063_f502m00.fits'
globals()['plots_folder'] = "../plots/IC5063_x3nl030/"
# globals()['data_folder'] = "../data/MKN3_x3nl010/"
# infiles = ['x3nl0101r_c0f.fits','x3nl0102r_c0f.fits','x3nl0103r_c0f.fits']
@@ -118,7 +119,7 @@ def main():
# Final crop
crop = False #Crop to desired ROI
# Polarization map output
figname = 'NGC1068_FOC' #target/intrument name
figname = 'IC5063_FOC' #target/intrument name
figtype = '_combine_FWHM020' #additionnal informations
SNRp_cut = 10. #P measurments with SNR>3
SNRi_cut = 100. #I measurments with SNR>30, which implies an uncertainty in P of 4.7%.
@@ -141,12 +142,12 @@ def main():
if rebin:
data_array, error_array, headers, Dxy = proj_red.rebin_array(data_array, error_array, headers, pxsize=pxsize, scale=px_scale, operation=rebin_operation)
# Align and rescale images with oversampling.
data_mask = np.zeros(data_array.shape[1:]).astype(bool)
data_mask = np.ones(data_array.shape[1:]).astype(bool)
if px_scale.lower() not in ['full','integrate']:
data_array, error_array, headers, data_mask = proj_red.align_data(data_array, headers, error_array, upsample_factor=int(Dxy.min()), ref_center=align_center, return_shifts=False)
if px_scale.lower() not in ['full','integrate']:
vertex = image_hull((1.-data_mask),step=5,null_val=0.,inside=True)
vertex = image_hull(data_mask,step=5,null_val=0.,inside=True)
else:
vertex = np.array([0.,0.,data_array.shape[2],data_array.shape[2]])
shape = np.array([vertex[1]-vertex[0],vertex[3]-vertex[2]])