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index 8882ef9..6180610 100644
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index 65df305..5c38450 100644
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diff --git a/src/FOC_reduction.py b/src/FOC_reduction.py
index 4967b53..64a6ad2 100755
--- a/src/FOC_reduction.py
+++ b/src/FOC_reduction.py
@@ -8,6 +8,7 @@ Main script where are progressively added the steps for the FOC pipeline reducti
 import sys
 import numpy as np
 import copy
+import matplotlib.pyplot as plt
 import lib.fits as proj_fits        #Functions to handle fits files
 import lib.reduction as proj_red    #Functions used in reduction pipeline
 import lib.plots as proj_plots      #Functions for plotting data
@@ -85,6 +86,8 @@ def main():
         psf_scale = 'arcsec'
         psf_shape=(9,9)
         iterations = 10
+    # Cropping
+    display_crop = False
     # Error estimation
     error_sub_shape = (75,75)
     display_error = False
@@ -98,17 +101,17 @@ def main():
     align_center = 'image'        #If None will align image to image center
     display_data = False
     # Smoothing
-    smoothing_function = 'gaussian'  #gaussian_after, gaussian or combine
-    smoothing_FWHM = 0.10           #If None, no smoothing is done
+    smoothing_function = 'combine'  #gaussian_after, gaussian or combine
+    smoothing_FWHM = 0.20           #If None, no smoothing is done
     smoothing_scale = 'arcsec'       #pixel or arcsec
     # Rotation
     rotate_stokes = True           #rotation to North convention can give erroneous results
     rotate_data = False              #rotation to North convention can give erroneous results
     # Polarization map output
     figname = 'NGC1068_FOC'         #target/intrument name
-    figtype = '_gaussian_FWHM010_rot'    #additionnal informations
-    SNRp_cut = 3    #P measurments with SNR>3
-    SNRi_cut = 30   #I measurments with SNR>30, which implies an uncertainty in P of 4.7%.
+    figtype = '_combine_FWHM020_rot'    #additionnal informations
+    SNRp_cut = 20    #P measurments with SNR>3
+    SNRi_cut = 130   #I measurments with SNR>30, which implies an uncertainty in P of 4.7%.
     step_vec = 1    #plot all vectors in the array. if step_vec = 2, then every other vector will be plotted
 
     ##### Pipeline start
@@ -119,7 +122,7 @@ def main():
         if (data < 0.).any():
             print("ETAPE 1 : ", data)
     # Crop data to remove outside blank margins.
-    data_array, error_array = proj_red.crop_array(data_array, step=5, null_val=0., inside=True)
+    data_array, error_array = proj_red.crop_array(data_array, headers, step=5, null_val=0., inside=True, display=display_crop, savename=figname, plots_folder=plots_folder)
     for data in data_array:
         if (data < 0.).any():
             print("ETAPE 2 : ", data)
@@ -138,14 +141,14 @@ def main():
         if (data < 0.).any():
             print("ETAPE 4 : ", data)
     #Align and rescale images with oversampling.
-    data_array, error_array = proj_red.align_data(data_array, error_array, upsample_factor=int(Dxy.min()), ref_center=align_center, return_shifts=False)
+    data_array, error_array, data_mask = proj_red.align_data(data_array, headers, error_array, upsample_factor=int(Dxy.min()), ref_center=align_center, return_shifts=False)
     for data in data_array:
         if (data < 0.).any():
             print("ETAPE 5 : ", data)
     # Rotate data to have North up
     ref_header = copy.deepcopy(headers[0])
     if rotate_data:
-        data_array, error_array, headers = proj_red.rotate_data(data_array, error_array, headers, -ref_header['orientat'])
+        data_array, error_array, data_mask, headers = proj_red.rotate_data(data_array, error_array, data_mask, headers, -ref_header['orientat'])
         for data in data_array:
             if (data < 0.).any():
                 print("ETAPE 6 : ", data)
@@ -159,13 +162,13 @@ def main():
     # FWHM of FOC have been estimated at about 0.03" across 1500-5000 Angstrom band, which is about 2 detector pixels wide
     # see Jedrzejewski, R.; Nota, A.; Hack, W. J., A Comparison Between FOC and WFPC2
     # Bibcode : 1995chst.conf...10J
-    I_stokes, Q_stokes, U_stokes, Stokes_cov = proj_red.compute_Stokes(data_array, error_array, headers, FWHM=smoothing_FWHM, scale=smoothing_scale, smoothing=smoothing_function)
+    I_stokes, Q_stokes, U_stokes, Stokes_cov = proj_red.compute_Stokes(data_array, error_array, data_mask, headers, FWHM=smoothing_FWHM, scale=smoothing_scale, smoothing=smoothing_function)
 
     ## Step 3:
     # Rotate images to have North up
     if rotate_stokes:
         ref_header = copy.deepcopy(headers[0])
-        I_stokes, Q_stokes, U_stokes, Stokes_cov, headers = proj_red.rotate_Stokes(I_stokes, Q_stokes, U_stokes, Stokes_cov, headers, -ref_header['orientat'], SNRi_cut=None)
+        I_stokes, Q_stokes, U_stokes, Stokes_cov, data_mask, headers = proj_red.rotate_Stokes(I_stokes, Q_stokes, U_stokes, Stokes_cov, data_mask, headers, -ref_header['orientat'], SNRi_cut=None)
     # Compute polarimetric parameters (polarization degree and angle).
     P, debiased_P, s_P, s_P_P, PA, s_PA, s_PA_P = proj_red.compute_pol(I_stokes, Q_stokes, U_stokes, Stokes_cov, headers)
 
@@ -175,11 +178,11 @@ def main():
 
     ## Step 5:
     # Plot polarization map (Background is either total Flux, Polarization degree or Polarization degree error).
-    proj_plots.polarization_map(copy.deepcopy(Stokes_test), SNRp_cut=SNRp_cut, SNRi_cut=SNRi_cut, step_vec=step_vec, savename=figname+figtype, plots_folder=plots_folder, display=None)
-    proj_plots.polarization_map(copy.deepcopy(Stokes_test), SNRp_cut=SNRp_cut, SNRi_cut=SNRi_cut, step_vec=step_vec, savename=figname+figtype+"_P", plots_folder=plots_folder, display='Pol_deg')
-    proj_plots.polarization_map(copy.deepcopy(Stokes_test), SNRp_cut=SNRp_cut, SNRi_cut=SNRi_cut, step_vec=step_vec, savename=figname+figtype+"_P_err", plots_folder=plots_folder, display='Pol_deg_err')
-    proj_plots.polarization_map(copy.deepcopy(Stokes_test), SNRp_cut=SNRp_cut, SNRi_cut=SNRi_cut, step_vec=step_vec, savename=figname+figtype+"_SNRi", plots_folder=plots_folder, display='SNRi')
-    proj_plots.polarization_map(copy.deepcopy(Stokes_test), SNRp_cut=SNRp_cut, SNRi_cut=SNRi_cut, step_vec=step_vec, savename=figname+figtype+"_SNRp", plots_folder=plots_folder, display='SNRp')
+    proj_plots.polarization_map(copy.deepcopy(Stokes_test), data_mask, SNRp_cut=SNRp_cut, SNRi_cut=SNRi_cut, step_vec=step_vec, savename=figname+figtype, plots_folder=plots_folder, display=None)
+    proj_plots.polarization_map(copy.deepcopy(Stokes_test), data_mask, SNRp_cut=SNRp_cut, SNRi_cut=SNRi_cut, step_vec=step_vec, savename=figname+figtype+"_P", plots_folder=plots_folder, display='Pol_deg')
+    proj_plots.polarization_map(copy.deepcopy(Stokes_test), data_mask, SNRp_cut=SNRp_cut, SNRi_cut=SNRi_cut, step_vec=step_vec, savename=figname+figtype+"_P_err", plots_folder=plots_folder, display='Pol_deg_err')
+    proj_plots.polarization_map(copy.deepcopy(Stokes_test), data_mask, SNRp_cut=SNRp_cut, SNRi_cut=SNRi_cut, step_vec=step_vec, savename=figname+figtype+"_SNRi", plots_folder=plots_folder, display='SNRi')
+    proj_plots.polarization_map(copy.deepcopy(Stokes_test), data_mask, SNRp_cut=SNRp_cut, SNRi_cut=SNRi_cut, step_vec=step_vec, savename=figname+figtype+"_SNRp", plots_folder=plots_folder, display='SNRp')
 
     return 0
 
diff --git a/src/FOC_reduction2_ELR.py b/src/FOC_reduction2_ELR.py
deleted file mode 100755
index 2d80c97..0000000
--- a/src/FOC_reduction2_ELR.py
+++ /dev/null
@@ -1,156 +0,0 @@
-#!/usr/bin/python
-#-*- coding:utf-8 -*-
-"""
-Main script where are progressively added the steps for the FOC pipeline reduction.
-"""
-
-#Project libraries
-import sys
-import numpy as np
-import copy
-import lib.fits as proj_fits        #Functions to handle fits files
-import lib.reduction_ELR as proj_red    #Functions used in reduction pipeline
-import lib.plots_ELR as proj_plots      #Functions for plotting data
-
-
-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']
-#    globals()['plots_folder'] = "../plots/NGC1068_x274020/"
-
-#    globals()['data_folder'] = "../data/NGC1068_x14w010/"
-#    infiles = ['x14w0101t_c0f.fits','x14w0102t_c0f.fits','x14w0103t_c0f.fits',
-#            'x14w0104t_c0f.fits','x14w0105p_c0f.fits','x14w0106t_c0f.fits']
-#    infiles = ['x14w0101t_c1f.fits','x14w0102t_c1f.fits','x14w0103t_c1f.fits',
-#            'x14w0104t_c1f.fits','x14w0105p_c1f.fits','x14w0106t_c1f.fits']
-#    globals()['plots_folder'] = "../plots/NGC1068_x14w010/"
-
-    globals()['data_folder'] = "../data/3C405_x136060/"
-    infiles = ['x1360601t_c0f.fits','x1360602t_c0f.fits','x1360603t_c0f.fits']
-#    infiles = ['x1360601t_c1f.fits','x1360602t_c1f.fits','x1360603t_c1f.fits']
-    globals()['plots_folder'] = "../plots/3C405_x136060/"
-
-#    globals()['data_folder'] = "../data/CygnusA_x43w0/"
-#    infiles = ['x43w0101r_c0f.fits', 'x43w0104r_c0f.fits', 'x43w0107r_c0f.fits',
-#            'x43w0201r_c0f.fits', 'x43w0204r_c0f.fits', 'x43w0102r_c0f.fits',
-#            'x43w0105r_c0f.fits', 'x43w0108r_c0f.fits', 'x43w0202r_c0f.fits',
-#            'x43w0205r_c0f.fits', 'x43w0103r_c0f.fits', 'x43w0106r_c0f.fits',
-#            'x43w0109r_c0f.fits', 'x43w0203r_c0f.fits', 'x43w0206r_c0f.fits']
-#    globals()['plots_folder'] = "../plots/CygnusA_x43w0/"
-
-#    globals()['data_folder'] = "../data/3C109_x3mc010/"
-#    infiles = ['x3mc0101m_c0f.fits','x3mc0102m_c0f.fits','x3mc0103m_c0f.fits']
-#    globals()['plots_folder'] = "../plots/3C109_x3mc010/"
-
-#    globals()['data_folder'] = "../data/MKN463_x2rp030/"
-#    infiles = ['x2rp0201t_c0f.fits', 'x2rp0203t_c0f.fits', 'x2rp0205t_c0f.fits',
-#            'x2rp0207t_c0f.fits', 'x2rp0302t_c0f.fits', 'x2rp0304t_c0f.fits',
-#            'x2rp0306t_c0f.fits', 'x2rp0202t_c0f.fits', 'x2rp0204t_c0f.fits',
-#            'x2rp0206t_c0f.fits', 'x2rp0301t_c0f.fits', 'x2rp0303t_c0f.fits',
-#            'x2rp0305t_c0f.fits', 'x2rp0307t_c0f.fits']
-#    globals()['plots_folder'] = "../plots/MKN463_x2rp030/"
-
-#    globals()['data_folder'] = "../data/PG1630+377_x39510/"
-#    infiles = ['x3990201m_c0f.fits', 'x3990205m_c0f.fits', 'x3995101r_c0f.fits',
-#            'x3995105r_c0f.fits', 'x3995109r_c0f.fits', 'x3995201r_c0f.fits',
-#            'x3995205r_c0f.fits', 'x3990202m_c0f.fits', 'x3990206m_c0f.fits',
-#            'x3995102r_c0f.fits', 'x3995106r_c0f.fits', 'x399510ar_c0f.fits',
-#            'x3995202r_c0f.fits','x3995206r_c0f.fits']
-#    globals()['plots_folder'] = "../plots/PG1630+377_x39510/"
-
-    ## Reduction parameters
-    # Deconvolution
-    deconvolve = False
-    if deconvolve:
-        psf = 'gaussian'  #Can be user-defined as well
-        psf_FWHM = 0.10
-        psf_scale = 'arcsec'
-        psf_shape=(9,9)
-        iterations = 10
-    # Error estimation
-    error_sub_shape = (150,150)
-    display_error = True
-    # Data binning
-    rebin = True
-    if rebin:
-        pxsize = 15
-        px_scale = 'pixel'         #pixel or arcsec
-        rebin_operation = 'average'     #sum or average
-    # Alignement
-    align_center = 'image'        #If None will align image to image center
-    display_data = True
-    # Smoothing
-    smoothing_function = 'gaussian'  #gaussian or combine
-    smoothing_FWHM = 1           #If None, no smoothing is done
-    smoothing_scale = 'pixel'       #pixel or arcsec
-    # Rotation
-    rotate = False                  #rotation to North convention can give erroneous results
-    rotate_library = 'scipy'        #scipy or pillow
-    # Polarization map output
-    figname = '3C405_FOC'         #target/intrument name
-    figtype = ''    #additionnal informations
-    SNRp_cut = 3    #P measurments with SNR>3
-    SNRi_cut = 4   #I measurments with SNR>30, which implies an uncertainty in P of 4.7%.
-    step_vec = 1    #plot all vectors in the array. if step_vec = 2, then every other vector will be plotted
-
-    ##### Pipeline start
-    ## Step 1:
-    # Get data from fits files and translate to flux in erg/cm²/s/Angstrom.
-    data_array, headers = proj_fits.get_obs_data(infiles, data_folder=data_folder, compute_flux=False)
-    # Crop data to remove outside blank margins.
-    #data_array, error_array = proj_red.crop_array(data_array, step=5, null_val=0., inside=True)
-    # Deconvolve data using Richardson-Lucy iterative algorithm with a gaussian PSF of given FWHM.
-    if deconvolve:
-        data_array = proj_red.deconvolve_array(data_array, headers, psf=psf, FWHM=psf_FWHM, scale=psf_scale, shape=psf_shape, iterations=iterations)
-    # Estimate error from data background, estimated from sub-image of desired sub_shape.
-    data_array, error_array = proj_red.get_error(data_array, sub_shape=error_sub_shape, display=display_error, headers=headers, savename=figname+"_errors", plots_folder=plots_folder)
-    # Rebin data to desired pixel size.
-    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_array, error_array = proj_red.align_data(data_array, error_array, upsample_factor=int(Dxy.min()), ref_center=align_center, return_shifts=False)
-
-    #Plot array for checking output
-    if display_data:
-        proj_plots.plot_obs(data_array, headers, vmin=data_array.min(), vmax=data_array.max(), savename=figname+"_center_"+align_center, plots_folder=plots_folder)
-
-    ## Step 2:
-    # Compute Stokes I, Q, U with smoothed polarized images
-    # SMOOTHING DISCUSSION :
-    # FWHM of FOC have been estimated at about 0.03" across 1500-5000 Angstrom band, which is about 2 detector pixels wide
-    # see Jedrzejewski, R.; Nota, A.; Hack, W. J., A Comparison Between FOC and WFPC2
-    # Bibcode : 1995chst.conf...10J
-    I_stokes, Q_stokes, U_stokes, Stokes_cov = proj_red.compute_Stokes(data_array, error_array, headers, FWHM=smoothing_FWHM, scale=smoothing_scale, smoothing=smoothing_function)
-
-    ## Step 3:
-    # Rotate images to have North up
-    if rotate:
-        ref_header = copy.deepcopy(headers[0])
-        if rotate_library.lower() in ['scipy','scipy.ndimage']:
-            I_stokes, Q_stokes, U_stokes, Stokes_cov, headers = proj_red.rotate_sc_Stokes(I_stokes, Q_stokes, U_stokes, Stokes_cov, headers, -ref_header['orientat'])
-        if rotate_library.lower() in ['pillow','pil']:
-            I_stokes, Q_stokes, U_stokes, Stokes_cov, headers = proj_red.rotate_PIL_Stokes(I_stokes, Q_stokes, U_stokes, Stokes_cov, headers, -ref_header['orientat'])
-    # Compute polarimetric parameters (polarization degree and angle).
-    P, debiased_P, s_P, s_P_P, PA, s_PA, s_PA_P = proj_red.compute_pol(I_stokes, Q_stokes, U_stokes, Stokes_cov, headers)
-
-    ## Step 4:
-    # Save image to FITS.
-    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, figname+figtype, data_folder=data_folder, return_hdul=True)
-
-    ## Step 5:
-    # Plot polarization map (Background is either total Flux, Polarization degree or Polarization degree error).
-    proj_plots.polarization_map(copy.deepcopy(Stokes_test), SNRp_cut=SNRp_cut, SNRi_cut=SNRi_cut, step_vec=step_vec, savename=figname+figtype, plots_folder=plots_folder, display=None)
-    #proj_plots.polarization_map(copy.deepcopy(Stokes_test), SNRp_cut=SNRp_cut, SNRi_cut=SNRi_cut, step_vec=step_vec, savename=figname+figtype+"_P", plots_folder=plots_folder, display='Pol_deg')
-    #proj_plots.polarization_map(copy.deepcopy(Stokes_test), SNRp_cut=SNRp_cut, SNRi_cut=SNRi_cut, step_vec=step_vec, savename=figname+figtype+"_P_err", plots_folder=plots_folder, display='Pol_deg_err')
-    proj_plots.polarization_map(copy.deepcopy(Stokes_test), SNRp_cut=SNRp_cut, SNRi_cut=SNRi_cut, step_vec=step_vec, savename=figname+figtype+"_SNRi", plots_folder=plots_folder, display='SNRi')
-    proj_plots.polarization_map(copy.deepcopy(Stokes_test), SNRp_cut=SNRp_cut, SNRi_cut=SNRi_cut, step_vec=step_vec, savename=figname+figtype+"_SNRp", plots_folder=plots_folder, display='SNRp')
-
-    return 0
-
-
-if __name__ == "__main__":
-    sys.exit(main())
diff --git a/src/FOC_reduction_ELR.py b/src/FOC_reduction_ELR.py
deleted file mode 100644
index dbb29cc..0000000
--- a/src/FOC_reduction_ELR.py
+++ /dev/null
@@ -1,312 +0,0 @@
-#!/usr/bin/python
-#-*- coding:utf-8 -*-
-
-from pylab import *
-import numpy as np
-import matplotlib.pyplot as plt
-from astropy.io import fits
-from astropy.wcs import WCS
-from aplpy import FITSFigure
-from scipy import ndimage
-from scipy import signal
-from skimage.feature import register_translation
-from astropy.convolution import convolve
-from astropy.convolution import Gaussian2DKernel
-import os as os
-
-import lib.fits as proj_fits        #Functions to handle fits files
-import lib.reduction as proj_red    #Functions used in reduction pipeline
-import lib.plots as proj_plots      #Functions for plotting data
-
-def bin_ndarray(ndarray, new_shape, operation='sum'):
-    """
-    Bins an ndarray in all axes based on the target shape, by summing or
-        averaging.
-
-    Number of output dimensions must match number of input dimensions.
-
-    Example
-    -------
-    >>> m = np.arange(0,100,1).reshape((10,10))
-    >>> n = bin_ndarray(m, new_shape=(5,5), operation='sum')
-    >>> print(n)
-
-    [[ 22  30  38  46  54]
-     [102 110 118 126 134]
-     [182 190 198 206 214]
-     [262 270 278 286 294]
-     [342 350 358 366 374]]
-
-    """
-    if not operation.lower() in ['sum', 'median', 'std']:
-        raise ValueError("Operation not supported.")
-    if ndarray.ndim != len(new_shape):
-        raise ValueError("Shape mismatch: {} -> {}".format(ndarray.shape,
-                                                           new_shape))
-    compression_pairs = [(d, c//d) for d,c in zip(new_shape,
-                                                  ndarray.shape)]
-    flattened = [l for p in compression_pairs for l in p]
-    ndarray = ndarray.reshape(flattened)
-    for i in range(len(new_shape)):
-        if operation.lower() == "sum":
-            ndarray = ndarray.sum(-1*(i+1))
-        if operation.lower() == "median":
-            ndarray = ndarray.mean(-1*(i+1))
-        if operation.lower() == "std":
-            ndarray = ndarray.std(-1*(i+1))
-    return ndarray
-
-#load files
-def load_files(infiles):
-    data_array = []
-    for name in infiles['filenames']:
-        with fits.open(infiles['data_folder']+name) as f:
-            data_array.append(f[0].data)
-    data_array = np.array(data_array)
-    return data_array
-
-def rebin_array(data,config):
-    data_m = []
-    data_s = []
-    for ii in range(len(data)):
-        new_shape = (data[ii].shape[1]//config['downsample_factor'],\
-                data[ii].shape[0]//config['downsample_factor'])
-        data_m.append(bin_ndarray(data[ii], new_shape, operation='median'))
-        data_s.append(bin_ndarray(data[ii], new_shape, operation='std'))
-    return data_m, data_s
-
-def cross_corr(data):
-    shifts = []
-    data_array_c = []
-    for ii in range(len(data)):
-        shift, error, diffphase = register_translation(data[0], data_array[ii])
-        print('   -',infiles['filenames'][ii],shift)
-        shifts.append(shift)
-        ima_shifted = np.roll(data[ii],round(shifts[ii][0]),axis=0)
-        ima_shifted = np.roll(ima_shifted,round(shifts[ii][1]),axis=1)
-        data_array_c.append(ima_shifted)
-    return data_array_c
-
-plt.close('all')
-
-
-### User input ####
-#Create dictionaries
-infiles = {'data_folder':'../data/NGC1068_x274020/',\
-            'filenames':['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']}
-
-config = {'conserve_flux':True,'upsample_factor':1,\
-            'downsample_factor':8,\
-            'rotate':True,'reshape':True}
-
-fig = {'cmap':'magma','vmin':-20,'vmax':200,'font_size':40.0,'label_size':20,'lw':3,\
-        'SNRp_cut':1,'SNRi_cut':4,'scalevec':0.05,'step_vec':1,\
-        'vec_legend':10,'figname':'NGC1068_FOC_ELR'}
-#####################
-
-##### SCRIPT #####
-#load files
-print('--- Load files')
-print('  -',infiles['filenames'])
-data_array = load_files(infiles)
-#crosscorrelation
-print('--- Cross-correlate HWP PAs')
-data_array_c = cross_corr(data_array)
-#resample image
-print('--- Downsample image')
-data_array_cr_m, data_array_cr_s = rebin_array(data_array_c,config)
-#compute stokes
-print('--- Combine HWP PAs')
-#combine HWP PAs
-pol0   = (data_array_cr_m[0]+data_array_cr_m[1]+data_array_cr_m[2])
-pol60  = (data_array_cr_m[3]+data_array_cr_m[4]+data_array_cr_m[5]+data_array_cr_m[6])
-pol120 = (data_array_cr_m[7]+data_array_cr_m[8])
-#Stokes
-print('--- Compute Stokes')
-I_stokes = (2./3.)*(pol0 + pol60 + pol120)
-Q_stokes = (2./3.)*(2*pol0 - pol60 - pol120)
-U_stokes = (2./np.sqrt(3.))*(pol60 - pol120)
-#Rotate Stokes
-if config['rotate']:
-    print('--- Rotate Stokes')
-    ima = fits.open(infiles['data_folder']+infiles['filenames'][0])
-    PA = ima[0].header['ORIENTAT']
-    #roate images
-    I_stokes = ndimage.rotate(I_stokes,-PA,reshape=config['reshape'])
-    Q_stokes = ndimage.rotate(Q_stokes,-PA,reshape=config['reshape'])
-    U_stokes = ndimage.rotate(U_stokes,-PA,reshape=config['reshape'])
-    #rotate stokes
-    Q_stokes = Q_stokes*np.cos(np.deg2rad(PA)) - U_stokes*np.sin(np.deg2rad(PA))
-    U_stokes = Q_stokes*np.sin(np.deg2rad(PA)) + U_stokes*np.cos(np.deg2rad(PA))
-#smoothing
-print('--- Smoothing')
-kernel = Gaussian2DKernel(x_stddev=1)
-I_stokes = convolve(I_stokes,kernel)
-Q_stokes = convolve(Q_stokes,kernel)
-U_stokes = convolve(U_stokes,kernel)
-#remove annoying pixels due to rotation
-I_stokes[I_stokes == 0] = np.nan
-Q_stokes[Q_stokes == 0] = np.nan
-U_stokes[U_stokes == 0] = np.nan
-
-#Polarimetry
-PI  = np.sqrt(Q_stokes*Q_stokes + U_stokes*U_stokes)
-P   = PI/I_stokes*100
-PA  = 0.5*arctan2(U_stokes,Q_stokes)*180./np.pi+90
-s_P  = (np.sqrt(2.)*(I_stokes)**(-0.5)) ### Correct this line!!!
-s_PA = s_P/(P/100.)*180./np.pi
-
-### STEP 6. image to FITS with updated WCS
-img = fits.open(infiles['data_folder']+infiles['filenames'][0])
-new_wcs = WCS(naxis=2)
-new_wcs.wcs.crpix = [I_stokes.shape[0]/2, I_stokes.shape[1]/2]
-new_wcs.wcs.crval = [img[0].header['CRVAL1'], img[0].header['CRVAL2']]
-new_wcs.wcs.cunit = ["deg", "deg"]
-new_wcs.wcs.ctype = ["RA---TAN", "DEC--TAN"]
-new_wcs.wcs.cdelt = [img[0].header['CD1_1']*config['downsample_factor'],\
-                     img[0].header['CD1_2']*config['downsample_factor']]
-
-stkI=fits.PrimaryHDU(data=I_stokes,header=new_wcs.to_header())
-pol=fits.PrimaryHDU(data=P,header=new_wcs.to_header())
-pang=fits.PrimaryHDU(data=PA,header=new_wcs.to_header())
-pol_err=fits.PrimaryHDU(data=s_P,header=new_wcs.to_header())
-pang_err=fits.PrimaryHDU(data=s_PA,header=new_wcs.to_header())
-
-### STEP 7. polarization map
-#quality cuts
-pxscale = stkI.header['CDELT2']
-
-#apply quality cuts
-pol.data[stkI.data < 10] = np.nan
-
-
-#levelsI = 2**(np.arange(2,20,0.5))
-levelsI = 2**(np.arange(3,20,0.5))
-
-fig1 = plt.figure(figsize=(11,10))
-gc = FITSFigure(stkI,figure=fig1)
-gc.show_colorscale(cmap=fig['cmap'],vmin=fig['vmin'],vmax=fig['vmax'])
-gc.show_contour(levels=levelsI,colors='grey',linewidths=0.5)
-gc.show_vectors(pol,pang,scale=fig['scalevec'],\
-                step=fig['step_vec'],color='black',linewidth=2.0)
-gc.show_vectors(pol,pang,scale=fig['scalevec'],\
-                step=fig['step_vec'],color='white',linewidth=1.0)
-#legend vector
-vecscale = fig['scalevec'] * new_wcs.wcs.cdelt[0]
-gc.add_scalebar(fig['vec_legend']*vecscale,'P ='+np.str(fig['vec_legend'])+'%',\
-                corner='bottom right',frame=True,color='black',facecolor='blue')
-figname = figname = fig['figname']+'_test_polmap.pdf'
-fig1.savefig(figname,dpi=300)
-os.system('open '+figname)
-
-sys.exit()
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-fig2,((ax1,ax2,ax3),(ax4,ax5,ax6)) = plt.subplots(2,3,figsize=(40,20),dpi=200)
-CF = ax1.imshow(I_stokes,origin='lower')
-cbar = plt.colorbar(CF,ax=ax1)
-cbar.ax.tick_params(labelsize=20)
-ax1.tick_params(axis='both', which='major', labelsize=20)
-ax1.text(2,2,'I',color='white',fontsize=10)
-
-CF = ax2.imshow(Q_stokes,origin='lower')
-cbar = plt.colorbar(CF,ax=ax2)
-cbar.ax.tick_params(labelsize=20)
-ax2.tick_params(axis='both', which='major', labelsize=20)
-ax2.text(2,2,'Q',color='white',fontsize=10)
-
-CF = ax3.imshow(U_stokes,origin='lower')
-cbar = plt.colorbar(CF,ax=ax3)
-cbar.ax.tick_params(labelsize=20)
-ax3.tick_params(axis='both', which='major', labelsize=20)
-ax3.text(2,2,'U',color='white',fontsize=10)
-
-v = np.linspace(0,40,50)
-CF = ax4.imshow(P,origin='lower',vmin=0,vmax=40)
-cbar = plt.colorbar(CF,ax=ax4)
-cbar.ax.tick_params(labelsize=20)
-ax4.tick_params(axis='both', which='major', labelsize=20)
-ax4.text(2,2,'P',color='white',fontsize=10)
-
-CF = ax5.imshow(PA,origin='lower',vmin=0,vmax=180)
-cbar = plt.colorbar(CF,ax=ax5)
-cbar.ax.tick_params(labelsize=20)
-ax5.tick_params(axis='both', which='major', labelsize=20)
-ax5.text(2,2,'PA',color='white',fontsize=10)
-
-CF = ax6.imshow(PI,origin='lower')
-cbar = plt.colorbar(CF,ax=ax6)
-cbar.ax.tick_params(labelsize=20)
-ax6.tick_params(axis='both', which='major', labelsize=20)
-ax6.text(2,2,'PI',color='white',fontsize=10)
-
-figname = fig['figname']+'_test_Stokes.pdf'
-fig2.savefig(figname,dpi=300)
-#os.system('open '+figname)
-
-
-
-### Step 4. Binning and smoothing
-#Images can be binned and smoothed to improve SNR. This step can also be done
-#using the PolX images.
-
-
-### Step 5. Roate images to have North up
-#Images needs to be reprojected to have North up.
-#this procedure implies to rotate the Stokes QU using a rotation matrix
-
-
-### STEP 6. image to FITS with updated WCS
-img = fits.open(infiles['data_folder']+infiles['filenames'][0])
-new_wcs = WCS(naxis=2)
-new_wcs.wcs.crpix = [I_stokes.shape[0]/2, I_stokes.shape[1]/2]
-new_wcs.wcs.crval = [img[0].header['CRVAL1'], img[0].header['CRVAL2']]
-new_wcs.wcs.cunit = ["deg", "deg"]
-new_wcs.wcs.ctype = ["RA---TAN", "DEC--TAN"]
-new_wcs.wcs.cdelt = [img[0].header['CD1_1']*config['downsample_factor'],\
-                     img[0].header['CD1_2']*config['downsample_factor']]
-
-#hdu_ori = WCS(img[0])
-stkI=fits.PrimaryHDU(data=I_stokes,header=new_wcs.to_header())
-pol=fits.PrimaryHDU(data=P,header=new_wcs.to_header())
-pang=fits.PrimaryHDU(data=PA,header=new_wcs.to_header())
-pol_err=fits.PrimaryHDU(data=s_P,header=new_wcs.to_header())
-pang_err=fits.PrimaryHDU(data=s_PA,header=new_wcs.to_header())
-
-
-### STEP 7. polarization map
-#quality cuts
-pxscale = stkI.header['CDELT1']
-
-#apply quality cuts
-SNRp = pol.data/pol_err.data
-pol.data[SNRp < fig['SNRp_cut']] = np.nan
-
-SNRi = stkI.data/np.std(stkI.data[0:10,0:10])
-SNRi = fits.PrimaryHDU(SNRi,header=stkI.header)
-pol.data[SNRi.data < fig['SNRi_cut']] = np.nan
-print(np.max(SNRi))
-fig1 = plt.figure(figsize=(11,10))
-gc = FITSFigure(stkI,figure=fig1)
-gc.show_colorscale(fig['cmap'])
-#gc.show_contour(np.log10(SNRi.data),levels=np.linspace(np.log10(fig['SNRi_cut']),np.nanmax(np.log10(SNRi.data,20))),\
-#                filled=True,cmap='magma')
-gc.show_vectors(pol,pang,scale=scalevec,step=step_vec,color='white',linewidth=1.0)
-
-figname = figname = fig['figname']+'_test_polmap.pdf'
-fig1.savefig(figname,dpi=300)
-os.system('open '+figname)
diff --git a/src/FOC_reduction_Enrique.py b/src/FOC_reduction_Enrique.py
deleted file mode 100755
index 61304de..0000000
--- a/src/FOC_reduction_Enrique.py
+++ /dev/null
@@ -1,281 +0,0 @@
-#!/usr/bin/python
-#-*- coding:utf-8 -*-
-
-from pylab import *
-import numpy as np
-import matplotlib.pyplot as plt
-from astropy.io import fits
-from astropy.wcs import WCS
-from aplpy import FITSFigure
-import scipy.ndimage
-import os as os
-
-import lib.fits as proj_fits        #Functions to handle fits files
-import lib.reduction as proj_red    #Functions used in reduction pipeline
-import lib.plots as proj_plots      #Functions for plotting data
-
-plt.close('all')
-
-def bin_ndarray(ndarray, new_shape, operation='sum'):
-    """
-    Bins an ndarray in all axes based on the target shape, by summing or
-        averaging.
-
-    Number of output dimensions must match number of input dimensions.
-
-    Example
-    -------
-    >>> m = np.arange(0,100,1).reshape((10,10))
-    >>> n = bin_ndarray(m, new_shape=(5,5), operation='sum')
-    >>> print(n)
-
-    [[ 22  30  38  46  54]
-     [102 110 118 126 134]
-     [182 190 198 206 214]
-     [262 270 278 286 294]
-     [342 350 358 366 374]]
-
-    """
-    if not operation.lower() in ['sum', 'mean', 'average', 'avg']:
-        raise ValueError("Operation not supported.")
-    if ndarray.ndim != len(new_shape):
-        raise ValueError("Shape mismatch: {} -> {}".format(ndarray.shape,
-                                                           new_shape))
-    compression_pairs = [(d, c//d) for d,c in zip(new_shape,
-                                                  ndarray.shape)]
-    flattened = [l for p in compression_pairs for l in p]
-    ndarray = ndarray.reshape(flattened)
-    for i in range(len(new_shape)):
-        if operation.lower() == "sum":
-            ndarray = ndarray.sum(-1*(i+1))
-        elif operation.lower() in ["mean", "average", "avg"]:
-            ndarray = ndarray.mean(-1*(i+1))
-    return ndarray
-
-def plots(ax,data,vmax,vmin):
-    ax.imshow(data,vmax=vmax,vmin=vmin,origin='lower')
-
-
-
-### User input
-
-#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']
-
-globals()['data_folder'] = "../data/3C405_x136060/"
-infiles = ['x1360601t_c0f.fits','x1360602t_c0f.fits','x1360603t_c0f.fits']
-#infiles = ['x1360601t_c1f.fits','x1360602t_c1f.fits','x1360603t_c1f.fits']
-globals()['plots_folder'] = "../plots/3C405_x136060/"
-
-#Centroids
-#The centroids should be estimated by cross-correlating the images.
-#Here I used the position of the central source for each file as the reference pixel position.
-#ycen_0 = [304,306,303,296,295,295,294,305,304]
-#xcen_0 = [273,274,273,276,274,274,274,272,271]
-
-data_array = []
-for name in infiles:
-    with fits.open(data_folder+name) as f:
-        data_array.append(f[0].data)
-data_array = np.array(data_array)
-shape = data_array.shape
-data_array, error_array = proj_red.crop_array(data_array, step=5, null_val=0., inside=True)
-data_array, error_array, shifts, errors = proj_red.align_data(data_array, error_array)
-center = np.array([(np.array(shape[1:])/2).astype(int),]*len(infiles))-shifts
-xcen_0 = center[:,0].astype(int)
-ycen_0 = center[:,1].astype(int)
-
-#size, in pixels, of the FOV centered in x_cen_0,y_cen_0
-Dx = 500
-Dy = 500
-
-#set the new image size (Dxy x Dxy pixels binning)
-Dxy = 10
-new_shape   = (Dx//Dxy,Dy//Dxy)
-
-#figures
-#test alignment
-vmin = 0
-vmax = 6
-font_size=40.0
-label_size=20.
-lw = 3.
-
-#pol. map
-SNRp_cut = 3 #P measumentes with SNR>3
-SNRi_cut = 5 #I measuremntes with SNR>30, which implies an uncerrtainty in P of 4.7%.
-scalevec = 0.05 #length of vectors in pol. map
-step_vec = 1    #plot all vectors in the array. if step_vec = 2, then every other vector will be plotted
-vec_legend = 10 #% pol for legend
-#figname = 'NGC1068_FOC.png'
-figname = '3C405_FOC_Enrique'
-
-
-### SCRIPT ###
-### Step 1. Check input images before data reduction
-#this step is very simplistic.
-#Here I used the position of the central source for each file as the
-#reference pixel position.
-#The centroids should be estimated by cross-correlating the images,
-#and compare with the simplistic approach of using the peak pixel of the
-#object as the reference pixel.
-
-
-fig,axs = plt.subplots(3,3,figsize=(30,30),dpi=200,sharex=True,sharey=True)
-
-for jj, enum in enumerate(list(zip(axs.flatten(),data_array))):
-    a = enum[0]
-    img = fits.open(data_folder+infiles[jj])
-    ima = img[0].data
-    ima = ima[ycen_0[jj]-Dy:ycen_0[jj]+Dy,xcen_0[jj]-Dx:xcen_0[jj]+Dx]
-    ima = bin_ndarray(ima,new_shape=new_shape,operation='sum') #binning
-    exptime = img[0].header['EXPTIME']
-    fil = img[0].header['FILTNAM1']
-    ima = ima/exptime
-    globals()['ima_%s' % jj] = ima
-    #plots
-    plots(a,ima,vmax=vmax,vmin=vmin)
-    #position of centroid
-    a.plot([ima.shape[1]/2,ima.shape[1]/2],[0,ima.shape[0]-1],lw=1,color='black')
-    a.plot([0,ima.shape[1]-1],[ima.shape[1]/2,ima.shape[1]/2],lw=1,color='black')
-    a.text(2,2,infiles[jj][0:8],color='white',fontsize=10)
-    a.text(2,5,fil,color='white',fontsize=30)
-    a.text(ima.shape[1]-20,1,exptime,color='white',fontsize=20)
-fig.subplots_adjust(hspace=0,wspace=0)
-fig.savefig(plots_folder+figname+'_test_alignment.png',dpi=300)
-#os.system('open test_alignment.png')
-
-
-
-### Step 2. average of all images for a single polarizer to have them in the same units e/s.
-pol0   = ima_0#(ima_0 + ima_1 + ima_2)/3.
-pol60  = ima_1#(ima_3 + ima_4 + ima_5 + ima_6)/4.
-pol120 = ima_2#(ima_7 + ima_8)/2.
-
-fig1,(ax1,ax2,ax3) = plt.subplots(1,3,figsize=(26,8),dpi=200)
-CF = ax1.imshow(pol0,vmin=vmin,vmax=vmax,origin='lower')
-cbar = plt.colorbar(CF,ax=ax1)
-cbar.ax.tick_params(labelsize=20)
-ax1.tick_params(axis='both', which='major', labelsize=20)
-ax1.text(2,2,'POL0',color='white',fontsize=10)
-
-CF = ax2.imshow(pol60,vmin=vmin,vmax=vmax,origin='lower')
-cbar = plt.colorbar(CF,ax=ax2)
-cbar.ax.tick_params(labelsize=20)
-ax2.tick_params(axis='both', which='major', labelsize=20)
-ax2.text(2,2,'POL60',color='white',fontsize=10)
-
-CF = ax3.imshow(pol120,vmin=vmin,vmax=vmax,origin='lower')
-cbar = plt.colorbar(CF,ax=ax3)
-cbar.ax.tick_params(labelsize=20)
-ax3.tick_params(axis='both', which='major', labelsize=20)
-ax3.text(2,2,'POL120',color='white',fontsize=10)
-fig1.savefig(plots_folder+figname+'_test_combinePol.png',dpi=300)
-#os.system('open test_combinePol.png')
-
-
-### Step 3. Compute Stokes IQU, P, PA, PI
-#Stokes parameters
-I_stokes = (2./3.)*(pol0 + pol60 + pol120)
-Q_stokes = (2./3.)*(2*pol0 - pol60 - pol120)
-U_stokes = (2./np.sqrt(3.))*(pol60 - pol120)
-
-#Remove nan
-I_stokes[np.isnan(I_stokes)]=0.
-Q_stokes[np.isnan(Q_stokes)]=0.
-U_stokes[np.isnan(U_stokes)]=0.
-
-#Polarimetry
-PI  = np.sqrt(Q_stokes*Q_stokes + U_stokes*U_stokes)
-P   = PI/I_stokes*100
-PA  = 0.5*arctan2(U_stokes,Q_stokes)*180./np.pi+90
-s_P  = np.sqrt(2.)*(I_stokes)**(-0.5)
-s_PA = s_P/(P/100.)*180./np.pi
-
-fig2,((ax1,ax2,ax3),(ax4,ax5,ax6)) = plt.subplots(2,3,figsize=(40,20),dpi=200)
-CF = ax1.imshow(I_stokes,origin='lower')
-cbar = plt.colorbar(CF,ax=ax1)
-cbar.ax.tick_params(labelsize=20)
-ax1.tick_params(axis='both', which='major', labelsize=20)
-ax1.text(2,2,'I',color='white',fontsize=10)
-
-CF = ax2.imshow(Q_stokes,origin='lower')
-cbar = plt.colorbar(CF,ax=ax2)
-cbar.ax.tick_params(labelsize=20)
-ax2.tick_params(axis='both', which='major', labelsize=20)
-ax2.text(2,2,'Q',color='white',fontsize=10)
-
-CF = ax3.imshow(U_stokes,origin='lower')
-cbar = plt.colorbar(CF,ax=ax3)
-cbar.ax.tick_params(labelsize=20)
-ax3.tick_params(axis='both', which='major', labelsize=20)
-ax3.text(2,2,'U',color='white',fontsize=10)
-
-v = np.linspace(0,40,50)
-CF = ax4.imshow(P,origin='lower',vmin=0,vmax=40)
-cbar = plt.colorbar(CF,ax=ax4)
-cbar.ax.tick_params(labelsize=20)
-ax4.tick_params(axis='both', which='major', labelsize=20)
-ax4.text(2,2,'P',color='white',fontsize=10)
-
-CF = ax5.imshow(PA,origin='lower',vmin=0,vmax=180)
-cbar = plt.colorbar(CF,ax=ax5)
-cbar.ax.tick_params(labelsize=20)
-ax5.tick_params(axis='both', which='major', labelsize=20)
-ax5.text(2,2,'PA',color='white',fontsize=10)
-
-CF = ax6.imshow(PI,origin='lower')
-cbar = plt.colorbar(CF,ax=ax6)
-cbar.ax.tick_params(labelsize=20)
-ax6.tick_params(axis='both', which='major', labelsize=20)
-ax6.text(2,2,'PI',color='white',fontsize=10)
-
-fig2.savefig(plots_folder+figname+'_test_Stokes.png',dpi=300)
-#os.system('open test_Stokes.png')
-
-### Step 4. Binning and smoothing
-#Images can be binned and smoothed to improve SNR. This step can also be done
-#using the PolX images.
-
-
-### Step 5. Roate images to have North up
-#Images needs to be reprojected to have North up.
-#this procedure implies to rotate the Stokes QU using a rotation matrix
-
-
-### STEP 6. image to FITS with updated WCS
-new_wcs = WCS(naxis=2)
-new_wcs.wcs.crpix = [I_stokes.shape[0]/2, I_stokes.shape[1]/2]
-new_wcs.wcs.crval = [img[0].header['CRVAL1'], img[0].header['CRVAL2']]
-new_wcs.wcs.cunit = ["deg", "deg"]
-new_wcs.wcs.ctype = ["RA---TAN", "DEC--TAN"]
-new_wcs.wcs.cdelt = [img[0].header['CD1_1']*Dxy, img[0].header['CD1_2']*Dxy]
-
-#hdu_ori = WCS(img[0])
-stkI=fits.PrimaryHDU(data=I_stokes,header=new_wcs.to_header())
-pol=fits.PrimaryHDU(data=P,header=new_wcs.to_header())
-pang=fits.PrimaryHDU(data=PA,header=new_wcs.to_header())
-pol_err=fits.PrimaryHDU(data=s_P,header=new_wcs.to_header())
-pang_err=fits.PrimaryHDU(data=s_PA,header=new_wcs.to_header())
-
-
-### STEP 7. polarization map
-#quality cuts
-pxscale = stkI.header['CDELT1']
-
-#apply quality cuts
-SNRp = pol.data/pol_err.data
-pol.data[SNRp < SNRp_cut] = np.nan
-
-SNRi = stkI.data/np.std(stkI.data[0:10,0:10])
-pol.data[SNRi < SNRi_cut] = np.nan
-print(np.max(SNRi))
-fig = plt.figure(figsize=(11,10))
-gc = FITSFigure(stkI,figure=fig)
-gc.show_contour(np.log10(SNRi),levels=np.linspace(np.log10(SNRi_cut),np.max(np.log10(SNRi)),20),\
-                filled=True,cmap='magma')
-gc.show_vectors(pol,pang,scale=scalevec,step=step_vec,color='white',linewidth=1.0)
-
-fig.savefig(plots_folder+figname,dpi=300)
-#os.system('open '+figname)
diff --git a/src/lib/plots.py b/src/lib/plots.py
index 8e04b70..d2a9abe 100755
--- a/src/lib/plots.py
+++ b/src/lib/plots.py
@@ -12,10 +12,17 @@ prototypes :
 import numpy as np
 import matplotlib.pyplot as plt
 from matplotlib.patches import Rectangle
-from mpl_toolkits.axes_grid1.anchored_artists import AnchoredSizeBar
+import matplotlib.font_manager as fm
+from mpl_toolkits.axes_grid1.anchored_artists import AnchoredSizeBar, AnchoredDirectionArrows
 from astropy.wcs import WCS
 
 
+def sci_not(v,err,rnd=1):
+    power = - int(('%E' % v)[-3:])+1
+    return r"({0} $\pm$ {1})e{2}".format(
+            round(v*10**power,rnd),round(err*10**power,rnd),-power)
+
+
 def plot_obs(data_array, headers, shape=None, vmin=0., vmax=6., rectangle=None,
         savename=None, plots_folder=""):
     """
@@ -66,8 +73,8 @@ def plot_obs(data_array, headers, shape=None, vmin=0., vmax=6., rectangle=None,
         #plots
         im = ax.imshow(data, vmin=vmin, vmax=vmax, origin='lower')
         if not(rectangle is None):
-            x, y, width, height = rectangle[i]
-            ax.add_patch(Rectangle((x, y), width, height, edgecolor='r', fill=False))
+            x, y, width, height, color = rectangle[i]
+            ax.add_patch(Rectangle((x, y), width, height, edgecolor=color, fill=False))
         #position of centroid
         ax.plot([data.shape[1]/2, data.shape[1]/2], [0,data.shape[0]-1], lw=1,
                 color='black')
@@ -137,7 +144,7 @@ def plot_Stokes(Stokes, savename=None, plots_folder=""):
     return 0
 
 
-def polarization_map(Stokes, SNRp_cut=3., SNRi_cut=30., step_vec=1,
+def polarization_map(Stokes, data_mask, SNRp_cut=3., SNRi_cut=30., step_vec=1,
         savename=None, plots_folder="", display=None):
     """
     Plots polarization map from Stokes HDUList.
@@ -177,7 +184,8 @@ def polarization_map(Stokes, SNRp_cut=3., SNRi_cut=30., step_vec=1,
     stkQ = Stokes[np.argmax([Stokes[i].header['datatype']=='Q_stokes' for i in range(len(Stokes))])]
     stkU = Stokes[np.argmax([Stokes[i].header['datatype']=='U_stokes' for i in range(len(Stokes))])]
     stk_cov = Stokes[np.argmax([Stokes[i].header['datatype']=='IQU_cov_matrix' for i in range(len(Stokes))])]
-    pol = Stokes[np.argmax([Stokes[i].header['datatype']=='Pol_deg' for i in range(len(Stokes))])]
+    #pol = Stokes[np.argmax([Stokes[i].header['datatype']=='Pol_deg' for i in range(len(Stokes))])]
+    pol = Stokes[np.argmax([Stokes[i].header['datatype']=='Pol_deg_debiased' for i in range(len(Stokes))])]
     pol_err = Stokes[np.argmax([Stokes[i].header['datatype']=='Pol_deg_err' for i in range(len(Stokes))])]
     #pol_err_Poisson = Stokes[np.argmax([Stokes[i].header['datatype']=='Pol_deg_err_Poisson_noise' for i in range(len(Stokes))])]
     pang = Stokes[np.argmax([Stokes[i].header['datatype']=='Pol_ang' for i in range(len(Stokes))])]
@@ -210,11 +218,12 @@ def polarization_map(Stokes, SNRp_cut=3., SNRi_cut=30., step_vec=1,
         print("No pixel with polarization information above requested SNR.")
 
     #Plot the map
+    plt.rcParams.update({'font.size': 16})
     fig = plt.figure(figsize=(15,15))
     ax = fig.add_subplot(111, projection=wcs)
     ax.set_facecolor('k')
-    fig.subplots_adjust(hspace=0, wspace=0, right=0.95)
-    cbar_ax = fig.add_axes([0.98, 0.12, 0.01, 0.75])
+    fig.subplots_adjust(hspace=0, wspace=0, right=0.9)
+    cbar_ax = fig.add_axes([0.95, 0.12, 0.01, 0.75])
 
     if display is None:
         # If no display selected, show intensity map
@@ -239,6 +248,7 @@ def polarization_map(Stokes, SNRp_cut=3., SNRi_cut=30., step_vec=1,
         im = ax.imshow(SNRi, extent=[-SNRi.shape[1]/2.,SNRi.shape[1]/2.,-SNRi.shape[0]/2.,SNRi.shape[0]/2.], vmin=vmin, vmax=vmax, aspect='auto', cmap='inferno', alpha=1.)
         cbar = plt.colorbar(im, cax=cbar_ax, label=r"$I_{Stokes}/\sigma_{I}$")
         levelsI = np.linspace(SNRi_cut, np.max(SNRi[SNRi > 0.]), 10)
+        #print(levelsI)
         cont = ax.contour(SNRi, extent=[-SNRi.shape[1]/2.,SNRi.shape[1]/2.,-SNRi.shape[0]/2.,SNRi.shape[0]/2.], levels=levelsI, colors='grey', linewidths=0.5)
     elif display.lower() in ['snrp']:
         # Display polarization degree signal-to-noise map
@@ -255,26 +265,31 @@ def polarization_map(Stokes, SNRp_cut=3., SNRi_cut=30., step_vec=1,
         levelsI = np.linspace(SNRi_cut, SNRi.max(), 10)
         cont = ax.contour(SNRi, extent=[-SNRi.shape[1]/2.,SNRi.shape[1]/2.,-SNRi.shape[0]/2.,SNRi.shape[0]/2.], levels=levelsI, colors='grey', linewidths=0.5)
 
+    fontprops = fm.FontProperties(size=16)
     px_size = wcs.wcs.get_cdelt()[0]
-    px_sc = AnchoredSizeBar(ax.transData, 1./px_size, '1 arcsec', 3, pad=0.5, sep=5, borderpad=0.5, frameon=False, size_vertical=0.005, color='w')
+    px_sc = AnchoredSizeBar(ax.transData, 1./px_size, '1 arcsec', 3, pad=0.5, sep=5, borderpad=0.5, frameon=False, size_vertical=0.005, color='w', fontproperties=fontprops)
     ax.add_artist(px_sc)
 
     X, Y = np.meshgrid(np.linspace(-stkI.data.shape[0]/2.,stkI.data.shape[0]/2.,stkI.data.shape[0]), np.linspace(-stkI.data.shape[1]/2.,stkI.data.shape[1]/2.,stkI.data.shape[1]))
     U, V = pol.data*np.cos(np.pi/2.+pang.data*np.pi/180.), pol.data*np.sin(np.pi/2.+pang.data*np.pi/180.)
     Q = ax.quiver(X[::step_vec,::step_vec],Y[::step_vec,::step_vec],U[::step_vec,::step_vec],V[::step_vec,::step_vec],units='xy',angles='uv',scale=50.,scale_units='xy',pivot='mid',headwidth=0.,headlength=0.,headaxislength=0.,width=0.1,color='w')
-    pol_sc = AnchoredSizeBar(ax.transData, 2., r"$P$= 100 %", 4, pad=0.5, sep=5, borderpad=0.5, frameon=False, size_vertical=0.005, color='w')
+    pol_sc = AnchoredSizeBar(ax.transData, 2., r"$P$= 100 %", 4, pad=0.5, sep=5, borderpad=0.5, frameon=False, size_vertical=0.005, color='w', fontproperties=fontprops)
     ax.add_artist(pol_sc)
 
+    north_dir = AnchoredDirectionArrows(ax.transAxes, "E", "N", length=-0.08, fontsize=0.03, loc=1, aspect_ratio=-1, sep_y=0.01, sep_x=0.01, angle=-Stokes[0].header['orientat'], color='w', arrow_props={'ec': 'w', 'fc': 'w', 'alpha': 1,'lw': 2})
+    ax.add_artist(north_dir)
+
     # Compute integrated parameters and associated errors for pixels in the cut
+    n_pix = mask.size
     I_int = stkI.data[mask].sum()
     Q_int = stkQ.data[mask].sum()
     U_int = stkU.data[mask].sum()
-    I_int_err = np.sqrt(np.sum(stk_cov.data[0,0][mask]))
-    Q_int_err = np.sqrt(np.sum(stk_cov.data[1,1][mask]))
-    U_int_err = np.sqrt(np.sum(stk_cov.data[2,2][mask]))
-    IQ_int_err = np.sqrt(np.sum(stk_cov.data[0,1][mask]**2))
-    IU_int_err = np.sqrt(np.sum(stk_cov.data[0,2][mask]**2))
-    QU_int_err = np.sqrt(np.sum(stk_cov.data[1,2][mask]**2))
+    I_int_err = np.sqrt(n_pix)*np.sqrt(np.sum(stk_cov.data[0,0][mask]))
+    Q_int_err = np.sqrt(n_pix)*np.sqrt(np.sum(stk_cov.data[1,1][mask]))
+    U_int_err = np.sqrt(n_pix)*np.sqrt(np.sum(stk_cov.data[2,2][mask]))
+    IQ_int_err = np.sqrt(n_pix)*np.sqrt(np.sum(stk_cov.data[0,1][mask]**2))
+    IU_int_err = np.sqrt(n_pix)*np.sqrt(np.sum(stk_cov.data[0,2][mask]**2))
+    QU_int_err = np.sqrt(n_pix)*np.sqrt(np.sum(stk_cov.data[1,2][mask]**2))
 
     P_int = np.sqrt(Q_int**2+U_int**2)/I_int*100.
     P_int_err = (100./I_int)*np.sqrt((Q_int**2*Q_int_err**2 + U_int**2*U_int_err**2 + 2.*Q_int*U_int*QU_int_err)/(Q_int**2 + U_int**2) + ((Q_int/I_int)**2 + (U_int/I_int)**2)*I_int_err**2 - 2.*(Q_int/I_int)*IQ_int_err - 2.*(U_int/I_int)*IU_int_err)
@@ -283,23 +298,26 @@ def polarization_map(Stokes, SNRp_cut=3., SNRi_cut=30., step_vec=1,
     PA_int_err = (90./(np.pi*(Q_int**2 + U_int**2)))*np.sqrt(U_int**2*Q_int_err**2 + Q_int**2*U_int_err**2 - 2.*Q_int*U_int*QU_int_err)
 
     # Compute integrated parameters and associated errors for all pixels
+    n_pix = stkI.data.size
     I_diluted = stkI.data.sum()
     Q_diluted = stkQ.data.sum()
     U_diluted = stkU.data.sum()
-    I_diluted_err = np.sqrt(np.sum(stk_cov.data[0,0]))
-    Q_diluted_err = np.sqrt(np.sum(stk_cov.data[1,1]))
-    U_diluted_err = np.sqrt(np.sum(stk_cov.data[2,2]))
-    IQ_diluted_err = np.sqrt(np.sum(stk_cov.data[0,1]**2))
-    IU_diluted_err = np.sqrt(np.sum(stk_cov.data[0,2]**2))
-    QU_diluted_err = np.sqrt(np.sum(stk_cov.data[1,2]**2))
+    I_diluted_err = np.sqrt(n_pix)*np.sqrt(np.sum(stk_cov.data[0,0]))
+    Q_diluted_err = np.sqrt(n_pix)*np.sqrt(np.sum(stk_cov.data[1,1]))
+    U_diluted_err = np.sqrt(n_pix)*np.sqrt(np.sum(stk_cov.data[2,2]))
+    IQ_diluted_err = np.sqrt(n_pix)*np.sqrt(np.sum(stk_cov.data[0,1]**2))
+    IU_diluted_err = np.sqrt(n_pix)*np.sqrt(np.sum(stk_cov.data[0,2]**2))
+    QU_diluted_err = np.sqrt(n_pix)*np.sqrt(np.sum(stk_cov.data[1,2]**2))
 
     P_diluted = np.sqrt(Q_diluted**2+U_diluted**2)/I_diluted*100.
     P_diluted_err = (100./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**2) + ((Q_diluted/I_diluted)**2 + (U_diluted/I_diluted)**2)*I_diluted_err**2 - 2.*(Q_diluted/I_diluted)*IQ_diluted_err - 2.*(U_diluted/I_diluted)*IU_diluted_err)
+    P_diluted_err = np.sqrt(2/n_pix)*100.
 
     PA_diluted = (90./np.pi)*np.arctan2(U_diluted,Q_diluted)+90.
     PA_diluted_err = (90./(np.pi*(Q_diluted**2 + U_diluted**2)))*np.sqrt(U_diluted**2*Q_diluted_err**2 + Q_diluted**2*U_diluted_err**2 - 2.*Q_diluted*U_diluted*QU_diluted_err)
+    PA_diluted_err = P_diluted_err/(2.*P_diluted)*180./np.pi
 
-    ax.annotate(r"$F_{{\lambda}}^{{int}}$({0:.0f} $\AA$) = {1:.1e} $\pm$ {2:.1e} $ergs \cdot cm^{{-2}} \cdot s^{{-1}} \cdot \AA^{{-1}}$".format(pivot_wav,I_int*convert_flux,I_int_err*convert_flux)+"\n"+r"$P^{{int}}$ = {0:.2f} $\pm$ {1:.2f} %".format(P_int,P_int_err)+"\n"+r"$\theta_{{P}}^{{int}}$ = {0:.2f} $\pm$ {1:.2f} °".format(PA_int,PA_int_err)+"\n"+r"$P^{{diluted}}$ = {0:.2f} $\pm$ {1:.2f} %".format(P_diluted,P_diluted_err)+"\n"+r"$\theta_{{P}}^{{diluted}}$ = {0:.2f} $\pm$ {1:.2f} °".format(PA_diluted,PA_diluted_err), color='white', fontsize=11, xy=(0.01, 0.90), xycoords='axes fraction')
+    ax.annotate(r"$F_{{\lambda}}^{{int}}$({0:.0f} $\AA$) = {1} $ergs \cdot cm^{{-2}} \cdot s^{{-1}} \cdot \AA^{{-1}}$".format(pivot_wav,sci_not(I_diluted*convert_flux,I_diluted_err*convert_flux,2))+"\n"+r"$P^{{int}}$ = {0:.1f} $\pm$ {1:.1f} %".format(P_diluted,P_diluted_err)+"\n"+r"$\theta_{{P}}^{{int}}$ = {0:.1f} $\pm$ {1:.1f} °".format(PA_diluted,PA_diluted_err), color='white', fontsize=16, xy=(0.01, 0.92), xycoords='axes fraction')
 
     ax.coords.grid(True, color='white', ls='dotted', alpha=0.5)
     ax.coords[0].set_axislabel('Right Ascension (J2000)')
@@ -311,7 +329,7 @@ def polarization_map(Stokes, SNRp_cut=3., SNRi_cut=30., step_vec=1,
     ax.axis('equal')
 
     if not savename is None:
-        fig.suptitle(savename)
+        #fig.suptitle(savename)
         fig.savefig(plots_folder+savename+".png",bbox_inches='tight',dpi=200)
 
     plt.show()
diff --git a/src/lib/plots_ELR.py b/src/lib/plots_ELR.py
deleted file mode 100755
index 2f69476..0000000
--- a/src/lib/plots_ELR.py
+++ /dev/null
@@ -1,247 +0,0 @@
-"""
-Library functions for displaying  informations using matplotlib
-
-prototypes :
-    - plot_obs(data_array, headers, shape, vmin, vmax, savename, plots_folder)
-        Plots whole observation raw data in given display shape
-
-    - polarization_map(Stokes_hdul, SNRp_cut, SNRi_cut, step_vec, savename, plots_folder, display)
-        Plots polarization map of polarimetric parameters saved in an HDUList
-"""
-
-import numpy as np
-import matplotlib.pyplot as plt
-from matplotlib.patches import Rectangle
-from mpl_toolkits.axes_grid1.anchored_artists import AnchoredSizeBar
-from astropy.wcs import WCS
-
-
-def plot_obs(data_array, headers, shape=None, vmin=0., vmax=6., rectangle=None,
-        savename=None, plots_folder=""):
-    """
-    Plots raw observation imagery with some information on the instrument and
-    filters.
-    ----------
-    Inputs:
-    data_array : numpy.ndarray
-        Array of images (2D floats, aligned and of the same shape) of a
-        single observation with multiple polarizers of an instrument
-    headers : header list
-        List of headers corresponding to the images in data_array
-    shape : array-like of length 2, optional
-        Shape of the display, with shape = [#row, #columns]. If None, defaults
-        to the optimal square.
-        Defaults to None.
-    vmin : float, optional
-        Min pixel value that should be displayed.
-        Defaults to 0.
-    vmax : float, optional
-        Max pixel value that should be displayed.
-        Defaults to 6.
-    rectangle : numpy.ndarray, optional
-        Array of parameters for matplotlib.patches.Rectangle objects that will
-        be displayed on each output image. If None, no rectangle displayed.
-        Defaults to None.
-    savename : str, optional
-        Name of the figure the map should be saved to. If None, the map won't
-        be saved (only displayed).
-        Defaults to None.
-    plots_folder : str, optional
-        Relative (or absolute) filepath to the folder in wich the map will
-        be saved. Not used if savename is None.
-        Defaults to current folder.
-    """
-    if shape is None:
-        shape = np.array([np.ceil(np.sqrt(data_array.shape[0])).astype(int),]*2)
-    fig, ax = plt.subplots(shape[0], shape[1], figsize=(10,10), dpi=200,
-            sharex=True, sharey=True)
-
-    for i, enum in enumerate(list(zip(ax.flatten(),data_array))):
-        ax = enum[0]
-        data = enum[1]
-        instr = headers[i]['instrume']
-        rootname = headers[i]['rootname']
-        exptime = headers[i]['exptime']
-        filt = headers[i]['filtnam1']
-        #plots
-        im = ax.imshow(data, vmin=vmin, vmax=vmax, origin='lower')
-        if not(rectangle is None):
-            x, y, width, height = rectangle[i]
-            ax.add_patch(Rectangle((x, y), width, height, edgecolor='r', fill=False))
-        #position of centroid
-        ax.plot([data.shape[1]/2, data.shape[1]/2], [0,data.shape[0]-1], lw=1,
-                color='black')
-        ax.plot([0,data.shape[1]-1], [data.shape[1]/2, data.shape[1]/2], lw=1,
-                color='black')
-        ax.annotate(instr+":"+rootname, color='white', fontsize=5, xy=(0.02, 0.95), xycoords='axes fraction')
-        ax.annotate(filt, color='white', fontsize=10, xy=(0.02, 0.02), xycoords='axes fraction')
-        ax.annotate(exptime, color='white', fontsize=5, xy=(0.80, 0.02), xycoords='axes fraction')
-
-    fig.subplots_adjust(hspace=0, wspace=0, right=0.85)
-    cbar_ax = fig.add_axes([0.9, 0.12, 0.02, 0.75])
-    fig.colorbar(im, cax=cbar_ax)
-
-    if not (savename is None):
-        fig.suptitle(savename)
-        fig.savefig(plots_folder+savename+".png",bbox_inches='tight')
-    plt.show()
-    return 0
-
-def polarization_map(Stokes, SNRp_cut=3., SNRi_cut=30., step_vec=1,
-        savename=None, plots_folder="", display=None):
-    """
-    Plots polarization map from Stokes HDUList.
-    ----------
-    Inputs:
-    Stokes : astropy.io.fits.hdu.hdulist.HDUList
-        HDUList containing I, Q, U, P, s_P, PA, s_PA (in this particular order)
-        for one observation.
-    SNRp_cut : float, optional
-        Cut that should be applied to the signal-to-noise ratio on P.
-        Any SNR < SNRp_cut won't be displayed.
-        Defaults to 3.
-    SNRi_cut : float, optional
-        Cut that should be applied to the signal-to-noise ratio on I.
-        Any SNR < SNRi_cut won't be displayed.
-        Defaults to 30. This value implies an uncertainty in P of 4.7%
-    step_vec : int, optional
-        Number of steps between each displayed polarization vector.
-        If step_vec = 2, every other vector will be displayed.
-        Defaults to 1
-    savename : str, optional
-        Name of the figure the map should be saved to. If None, the map won't
-        be saved (only displayed).
-        Defaults to None.
-    plots_folder : str, optional
-        Relative (or absolute) filepath to the folder in wich the map will
-        be saved. Not used if savename is None.
-        Defaults to current folder.
-    display : str, optional
-        Choose the map to display between intensity (default), polarization
-        degree ('p','pol','pol_deg') or polarization degree error ('s_p',
-        'pol_err','pol_deg_err').
-        Defaults to None (intensity).
-    """
-    #Get data
-    stkI = Stokes[np.argmax([Stokes[i].header['datatype']=='I_stokes' for i in range(len(Stokes))])]
-    stkQ = Stokes[np.argmax([Stokes[i].header['datatype']=='Q_stokes' for i in range(len(Stokes))])]
-    stkU = Stokes[np.argmax([Stokes[i].header['datatype']=='U_stokes' for i in range(len(Stokes))])]
-    stk_cov = Stokes[np.argmax([Stokes[i].header['datatype']=='IQU_cov_matrix' for i in range(len(Stokes))])]
-    pol = Stokes[np.argmax([Stokes[i].header['datatype']=='Pol_deg' for i in range(len(Stokes))])]
-    pol_err = Stokes[np.argmax([Stokes[i].header['datatype']=='Pol_deg_err' for i in range(len(Stokes))])]
-    pol_err_Poisson = Stokes[np.argmax([Stokes[i].header['datatype']=='Pol_deg_err_Poisson_noise' for i in range(len(Stokes))])]
-    pang = Stokes[np.argmax([Stokes[i].header['datatype']=='Pol_ang' for i in range(len(Stokes))])]
-    pang_err = Stokes[np.argmax([Stokes[i].header['datatype']=='Pol_ang_err' for i in range(len(Stokes))])]
-
-    pivot_wav = Stokes[0].header['photplam']
-    convert_flux = Stokes[0].header['photflam']/Stokes[0].header['exptot']
-    wcs = WCS(Stokes[0]).deepcopy()
-
-    #Compute SNR and apply cuts
-    pol.data[pol.data == 0.] = np.nan
-    #SNRp = pol.data/pol_err.data
-    SNRp = pol.data/pol_err_Poisson.data
-    pol.data[SNRp < SNRp_cut] = np.nan
-    #SNRi = stkI.data/np.sqrt(stk_cov.data[0,0])
-    SNRi = stkI.data/np.std(stkI.data[0:10,0:10])
-    pol.data[SNRi < SNRi_cut] = np.nan
-
-    mask = (SNRp < SNRp_cut) * (SNRi < SNRi_cut)
-
-    # Look for pixel of max polarization
-    if np.isfinite(pol.data).any():
-        p_max = np.max(pol.data[np.isfinite(pol.data)])
-        x_max, y_max = np.unravel_index(np.argmax(pol.data==p_max),pol.data.shape)
-    else:
-        print("No pixel with polarization information above requested SNR.")
-
-    #Plot the map
-    fig = plt.figure(figsize=(15,15))
-    ax = fig.add_subplot(111, projection=wcs)
-    ax.set_facecolor('k')
-    fig.subplots_adjust(hspace=0, wspace=0, right=0.95)
-    cbar_ax = fig.add_axes([0.98, 0.12, 0.01, 0.75])
-
-    if display is None:
-        # If no display selected, show intensity map
-        vmin, vmax = 0., np.max(stkI.data[stkI.data > 0.]*convert_flux)
-        im = ax.imshow(stkI.data*convert_flux,extent=[-stkI.data.shape[1]/2.,stkI.data.shape[1]/2.,-stkI.data.shape[0]/2.,stkI.data.shape[0]/2.], vmin=vmin, vmax=vmax, aspect='auto', cmap='inferno', alpha=1.)
-        cbar = plt.colorbar(im, cax=cbar_ax, label=r"$F_{\lambda}$ [$ergs \cdot cm^{-2} \cdot s^{-1} \cdot \AA^{-1}$]")
-        levelsI = np.linspace(SNRi_cut, SNRi.max(), 10)
-        cont = ax.contour(SNRi, extent=[-SNRi.shape[1]/2.,SNRi.shape[1]/2.,-SNRi.shape[0]/2.,SNRi.shape[0]/2.], levels=levelsI, colors='grey', linewidths=0.5)
-    elif display.lower() in ['p','pol','pol_deg']:
-        # Display polarization degree map
-        vmin, vmax = 0., 100.
-        im = ax.imshow(pol.data,extent=[-pol.data.shape[1]/2.,pol.data.shape[1]/2.,-pol.data.shape[0]/2.,pol.data.shape[0]/2.], vmin=vmin, vmax=vmax, aspect='auto', cmap='inferno', alpha=1.)
-        cbar = plt.colorbar(im, cax=cbar_ax, label=r"$P$ [%]")
-    elif display.lower() in ['s_p','pol_err','pol_deg_err']:
-        # Display polarization degree error map
-        vmin, vmax = 0., 5.
-        im = ax.imshow(pol_err.data,extent=[-pol_err.data.shape[1]/2.,pol_err.data.shape[1]/2.,-pol_err.data.shape[0]/2.,pol_err.data.shape[0]/2.], vmin=vmin, vmax=vmax, aspect='auto', cmap='inferno', alpha=1.)
-        cbar = plt.colorbar(im, cax=cbar_ax, label=r"$\sigma_P$ [%]")
-    elif display.lower() in ['snr','snri']:
-        # Display I_stokes signal-to-noise map
-        vmin, vmax = 0., SNRi.max()
-        im = ax.imshow(SNRi, extent=[-SNRi.shape[1]/2.,SNRi.shape[1]/2.,-SNRi.shape[0]/2.,SNRi.shape[0]/2.], vmin=vmin, vmax=vmax, aspect='auto', cmap='inferno', alpha=1.)
-        cbar = plt.colorbar(im, cax=cbar_ax, label=r"$I_{Stokes}/\sigma_{I}$")
-        levelsI = np.linspace(SNRi_cut, SNRi.max(), 20)
-        cont = ax.contour(SNRi, extent=[-SNRi.shape[1]/2.,SNRi.shape[1]/2.,-SNRi.shape[0]/2.,SNRi.shape[0]/2.], levels=levelsI, colors='grey', linewidths=0.5)
-    elif display.lower() in ['snrp']:
-        # Display polarization degree signal-to-noise map
-        vmin, vmax = SNRp_cut, np.max(SNRp[SNRp > 0.])
-        im = ax.imshow(SNRp, extent=[-SNRp.shape[1]/2.,SNRp.shape[1]/2.,-SNRp.shape[0]/2.,SNRp.shape[0]/2.], vmin=vmin, vmax=vmax, aspect='auto', cmap='inferno', alpha=1.)
-        cbar = plt.colorbar(im, cax=cbar_ax, label=r"$P/\sigma_{P}$")
-        levelsP = np.linspace(SNRp_cut, np.max(SNRp[SNRp > 0.]), 10)
-        cont = ax.contour(SNRp, extent=[-SNRp.shape[1]/2.,SNRp.shape[1]/2.,-SNRp.shape[0]/2.,SNRp.shape[0]/2.], levels=levelsP, colors='grey', linewidths=0.5)
-    else:
-        # Defaults to intensity map
-        vmin, vmax = 0., np.max(stkI.data[stkI.data > 0.]*convert_flux)
-        im = ax.imshow(stkI.data*convert_flux,extent=[-stkI.data.shape[1]/2.,stkI.data.shape[1]/2.,-stkI.data.shape[0]/2.,stkI.data.shape[0]/2.], vmin=vmin, vmax=vmax, aspect='auto', cmap='inferno', alpha=1.)
-        cbar = plt.colorbar(im, cax=cbar_ax, label=r"$F_{\lambda}$ [$ergs \cdot cm^{-2} \cdot s^{-1} \cdot \AA$]")
-        levelsI = np.linspace(SNRi_cut, SNRi.max(), 10)
-        cont = ax.contour(SNRi, extent=[-SNRi.shape[1]/2.,SNRi.shape[1]/2.,-SNRi.shape[0]/2.,SNRi.shape[0]/2.], levels=levelsI, colors='grey', linewidths=0.5)
-
-    px_size = wcs.wcs.get_cdelt()[0]
-    px_sc = AnchoredSizeBar(ax.transData, 1./px_size, '1 arcsec', 3, pad=0.5, sep=5, borderpad=0.5, frameon=False, size_vertical=0.005, color='w')
-    ax.add_artist(px_sc)
-
-    X, Y = np.meshgrid(np.linspace(-stkI.data.shape[0]/2.,stkI.data.shape[0]/2.,stkI.data.shape[0]), np.linspace(-stkI.data.shape[1]/2.,stkI.data.shape[1]/2.,stkI.data.shape[1]))
-    U, V = pol.data*np.cos(np.pi/2.+pang.data*np.pi/180.), pol.data*np.sin(np.pi/2.+pang.data*np.pi/180.)
-    Q = ax.quiver(X[::step_vec,::step_vec],Y[::step_vec,::step_vec],U[::step_vec,::step_vec],V[::step_vec,::step_vec],units='xy',angles='uv',scale=50.,scale_units='xy',pivot='mid',headwidth=0.,headlength=0.,headaxislength=0.,width=0.1,color='w')
-    pol_sc = AnchoredSizeBar(ax.transData, 2., r"$P$= 100 %", 4, pad=0.5, sep=5, borderpad=0.5, frameon=False, size_vertical=0.005, color='w')
-    ax.add_artist(pol_sc)
-
-    # Compute integrated parameters and associated errors
-    I_int = stkI.data[mask].sum()
-    Q_int = stkQ.data[mask].sum()
-    U_int = stkU.data[mask].sum()
-    I_int_err = np.sqrt(np.sum(stk_cov.data[0,0][mask]))
-    Q_int_err = np.sqrt(np.sum(stk_cov.data[1,1][mask]))
-    U_int_err = np.sqrt(np.sum(stk_cov.data[2,2][mask]))
-    IQ_int_err = np.sqrt(np.sum(stk_cov.data[0,1][mask]**2))
-    IU_int_err = np.sqrt(np.sum(stk_cov.data[0,2][mask]**2))
-    QU_int_err = np.sqrt(np.sum(stk_cov.data[1,2][mask]**2))
-
-    P_int = np.sqrt(Q_int**2+U_int**2)/I_int*100.
-    P_int_err = np.sqrt(2.)*(I_int)**(-0.5)*100.#(100./I_int)*np.sqrt((Q_int**2*Q_int_err**2 + U_int**2*U_int_err**2 + 2.*Q_int*U_int*QU_int_err)/(Q_int**2 + U_int**2) + ((Q_int/I_int)**2 + (U_int/I_int)**2)*I_int_err**2 - 2.*(Q_int/I_int)*IQ_int_err - 2.*(U_int/I_int)*IU_int_err)
-
-    PA_int = (90./np.pi)*np.arctan2(U_int,Q_int)+90.
-    PA_int_err = P_int_err/(P_int)*180./np.pi#(90./(np.pi*(Q_int**2 + U_int**2)))*np.sqrt(U_int**2*Q_int_err**2 + Q_int**2*U_int_err**2 - 2.*Q_int*U_int*QU_int_err)
-
-    ax.annotate(r"$F_{{\lambda}}^{{int}}$({0:.0f} $\AA$) = {1:.1e} $\pm$ {2:.1e} $ergs \cdot cm^{{-2}} \cdot s^{{-1}} \cdot \AA^{{-1}}$".format(pivot_wav,I_int*convert_flux,I_int_err*convert_flux)+"\n"+r"$P^{{int}}$ = {0:.2f} $\pm$ {1:.2f} %".format(P_int,P_int_err)+"\n"+r"$\theta_{{P}}^{{int}}$ = {0:.2f} $\pm$ {1:.2f} °".format(PA_int,PA_int_err), color='white', fontsize=11, xy=(0.01, 0.94), xycoords='axes fraction')
-
-    ax.coords.grid(True, color='white', ls='dotted', alpha=0.5)
-    ax.coords[0].set_axislabel('Right Ascension (J2000)')
-    ax.coords[0].set_axislabel_position('t')
-    ax.coords[0].set_ticklabel_position('t')
-    ax.coords[1].set_axislabel('Declination (J2000)')
-    ax.coords[1].set_axislabel_position('l')
-    ax.coords[1].set_ticklabel_position('l')
-    ax.axis('equal')
-
-    if not savename is None:
-        fig.suptitle(savename)
-        fig.savefig(plots_folder+savename+".png",bbox_inches='tight',dpi=200)
-
-    plt.show()
-    return 0
diff --git a/src/lib/reduction.py b/src/lib/reduction.py
index 656b99e..027809b 100755
--- a/src/lib/reduction.py
+++ b/src/lib/reduction.py
@@ -45,6 +45,7 @@ import copy
 import numpy as np
 import matplotlib.pyplot as plt
 import matplotlib.dates as mdates
+from matplotlib.patches import Rectangle
 from datetime import datetime
 from scipy.ndimage import rotate as sc_rotate
 from scipy.ndimage import shift as sc_shift
@@ -166,7 +167,8 @@ def bin_ndarray(ndarray, new_shape, operation='sum'):
     return ndarray
 
 
-def crop_array(data_array, error_array=None, step=5, null_val=None, inside=False):
+def crop_array(data_array, headers, error_array=None, step=5, null_val=None,
+        inside=False, display=False, savename=None, plots_folder=""):
     """
     Homogeneously crop an array: all contained images will have the same shape.
     'inside' parameter will decide how much should be cropped.
@@ -175,6 +177,8 @@ def crop_array(data_array, error_array=None, step=5, null_val=None, inside=False
     data_array : numpy.ndarray
         Array containing the observation data (2D float arrays) to
         homogeneously crop.
+    headers : header list
+        Headers associated with the images in data_array.
     error_array : numpy.ndarray, optional
         Array of images (2D floats, aligned and of the same shape) containing
         the error in each pixel of the observation images in data_array.
@@ -197,6 +201,10 @@ def crop_array(data_array, error_array=None, step=5, null_val=None, inside=False
         inside the image. If False, the cropped image will be the minimum
         rectangle in which the whole image is included.
         Defaults to False.
+    display : boolean, optional
+        If True, data_array will be displayed with a rectangle around the
+        sub-image selected for region of interest.
+        Defaults to False.
     ----------
     Returns:
     cropped_array : numpy.ndarray
@@ -225,6 +233,46 @@ def crop_array(data_array, error_array=None, step=5, null_val=None, inside=False
         v_array[3] = np.max(vertex[:,3]).astype(int)
 
     new_shape = np.array([v_array[1]-v_array[0],v_array[3]-v_array[2]])
+    rectangle = [v_array[2], v_array[0], new_shape[1], new_shape[0], 'b']
+    if display:
+        fig, ax = plt.subplots()
+        data = data_array[0]
+        instr = headers[0]['instrume']
+        rootname = headers[0]['rootname']
+        exptime = headers[0]['exptime']
+        filt = headers[0]['filtnam1']
+        #plots
+        im = ax.imshow(data, vmin=data.min(), vmax=data.max(), origin='lower')
+        x, y, width, height, color = rectangle
+        ax.add_patch(Rectangle((x, y),width,height,edgecolor=color,fill=False))
+        #position of centroid
+        ax.plot([data.shape[1]/2, data.shape[1]/2], [0,data.shape[0]-1], lw=1,
+                color='black')
+        ax.plot([0,data.shape[1]-1], [data.shape[1]/2, data.shape[1]/2], lw=1,
+                color='black')
+        ax.annotate(instr+":"+rootname, color='white', fontsize=5,
+                xy=(0.02, 0.95), xycoords='axes fraction')
+        ax.annotate(filt, color='white', fontsize=10, xy=(0.02, 0.02),
+                xycoords='axes fraction')
+        ax.annotate(exptime, color='white', fontsize=5, xy=(0.80, 0.02),
+                xycoords='axes fraction')
+        ax.set(title="Location of cropped image.",
+                xlabel='pixel offset',
+                ylabel='pixel offset')
+
+        fig.subplots_adjust(hspace=0, wspace=0, right=0.85)
+        cbar_ax = fig.add_axes([0.9, 0.12, 0.02, 0.75])
+        fig.colorbar(im, cax=cbar_ax)
+
+        if not(savename is None):
+            fig.suptitle(savename+'_'+filt+'_crop_region')
+            fig.savefig(plots_folder+savename+'_'+filt+'_crop_region.png',
+                    bbox_inches='tight')
+            plot_obs(data_array, headers, vmin=data_array.min(),
+                    vmax=data_array.max(), rectangle=[rectangle,]*len(headers),
+                    savename=savename+'_crop_region',plots_folder=plots_folder)
+        plt.show()
+
     crop_array = np.zeros((data_array.shape[0],new_shape[0],new_shape[1]))
     crop_error_array = np.zeros((data_array.shape[0],new_shape[0],new_shape[1]))
     for i,image in enumerate(data_array):
@@ -360,7 +408,7 @@ def get_error(data_array, sub_shape=(15,15), display=False, headers=None,
         Only returned if return_background is True.
     """
     # Crop out any null edges
-    data, error = crop_array(data_array, step=5, null_val=0., inside=False)
+    data, error = crop_array(data_array, headers, step=5, null_val=0., inside=False)
 
     sub_shape = np.array(sub_shape)
     # Make sub_shape of odd values
@@ -371,7 +419,7 @@ def get_error(data_array, sub_shape=(15,15), display=False, headers=None,
     diff = (sub_shape-1).astype(int)
     temp = np.zeros((shape[0],shape[1]-diff[0],shape[2]-diff[1]))
     error_array = np.ones(data_array.shape)
-    rectangle = np.zeros((shape[0],4))
+    rectangle = []
     background = np.zeros((shape[0]))
 
     for i,image in enumerate(data):
@@ -384,7 +432,7 @@ def get_error(data_array, sub_shape=(15,15), display=False, headers=None,
     minima = np.unravel_index(np.argmin(temp.sum(axis=0)),temp.shape[1:])
 
     for i, image in enumerate(data):
-        rectangle[i] = minima[1], minima[0], sub_shape[1], sub_shape[0]
+        rectangle.append([minima[1], minima[0], sub_shape[1], sub_shape[0], 'r'])
         # Compute error : root mean square of the background
         sub_image = image[minima[0]:minima[0]+sub_shape[0],minima[1]:minima[1]+sub_shape[1]]
         #error =  np.std(sub_image)    # Previously computed using standard deviation over the background
@@ -398,6 +446,7 @@ def get_error(data_array, sub_shape=(15,15), display=False, headers=None,
 
     if display:
 
+        convert_flux = headers[0]['photflam']
         date_time = np.array([headers[i]['date-obs']+';'+headers[i]['time-obs']
             for i in range(len(headers))])
         date_time = np.array([datetime.strptime(d,'%Y-%m-%d;%H:%M:%S')
@@ -406,12 +455,13 @@ def get_error(data_array, sub_shape=(15,15), display=False, headers=None,
         dict_filt = {"POL0":'r', "POL60":'g', "POL120":'b'}
         c_filt = np.array([dict_filt[f] for f in filt])
 
-        fig,ax = plt.subplots(figsize=(10,6),constrained_layout=True)
+        fig,ax = plt.subplots(figsize=(10,6), constrained_layout=True)
         for f in np.unique(filt):
             mask = [fil==f for fil in filt]
-            ax.scatter(date_time[mask], background[mask], color=dict_filt[f],
-                    label="Filter : {0:s}".format(f))
-        ax.errorbar(date_time, background, yerr=error_array[:,0,0], fmt='+k',
+            ax.scatter(date_time[mask], background[mask]*convert_flux,
+                    color=dict_filt[f],label="Filter : {0:s}".format(f))
+        ax.errorbar(date_time, background*convert_flux,
+                yerr=error_array[:,0,0]*convert_flux, fmt='+k',
                 markersize=0, ecolor=c_filt)
         # Date handling
         locator = mdates.AutoDateLocator()
@@ -424,12 +474,12 @@ def get_error(data_array, sub_shape=(15,15), display=False, headers=None,
         plt.legend()
 
         if not(savename is None):
-            fig.suptitle(savename+"_background_flux.png")
+            fig.suptitle(savename+"_background_flux")
             fig.savefig(plots_folder+savename+"_background_flux.png",
                     bbox_inches='tight')
             vmin = np.min(np.log10(data[data > 0.]))
             vmax = np.max(np.log10(data[data > 0.]))
-            plot_obs(np.log10(data), headers, vmin=vmin, vmax=vmax,
+            plot_obs(data, headers, vmin=data.min(), vmax=data.max(),
                     rectangle=rectangle,
                     savename=savename+"_background_location",
                     plots_folder=plots_folder)
@@ -555,8 +605,8 @@ def rebin_array(data_array, error_array, headers, pxsize, scale,
     return rebinned_data, rebinned_error, rebinned_headers, Dxy
 
 
-def align_data(data_array, error_array=None, upsample_factor=1., ref_data=None,
-        ref_center=None, return_shifts=True):
+def align_data(data_array, headers, error_array=None, upsample_factor=1.,
+        ref_data=None, ref_center=None, return_shifts=True):
     """
     Align images in data_array using cross correlation, and rescale them to
     wider images able to contain any rotation of the reference image.
@@ -565,6 +615,8 @@ def align_data(data_array, error_array=None, upsample_factor=1., ref_data=None,
     Inputs:
     data_array : numpy.ndarray
         Array containing the data to align (2D float arrays).
+    headers : header list
+        List of headers corresponding to the images in data_array.
     error_array : numpy.ndarray, optional
         Array of images (2D floats, aligned and of the same shape) containing
         the error in each pixel of the observation images in data_array.
@@ -626,7 +678,7 @@ def align_data(data_array, error_array=None, upsample_factor=1., ref_data=None,
     full_array = np.concatenate((data_array,[ref_data]),axis=0)
     err_array = np.concatenate((error_array,[np.zeros(ref_data.shape)]),axis=0)
 
-    full_array, err_array = crop_array(full_array, err_array, step=5,
+    full_array, err_array = crop_array(full_array, headers, err_array, step=5,
             inside=False)
 
     data_array, ref_data = full_array[:-1], full_array[-1]
@@ -649,6 +701,7 @@ def align_data(data_array, error_array=None, upsample_factor=1., ref_data=None,
     res_shape = int(np.ceil(np.sqrt(2.5)*np.max(shape[1:])))
     rescaled_image = np.zeros((shape[0],res_shape,res_shape))
     rescaled_error = np.ones((shape[0],res_shape,res_shape))
+    rescaled_mask = np.ones((shape[0],res_shape,res_shape),dtype=bool)
     res_center = (np.array(rescaled_image.shape[1:])/2).astype(int)
 
     shifts, errors = [], []
@@ -665,9 +718,12 @@ def align_data(data_array, error_array=None, upsample_factor=1., ref_data=None,
                 res_shift[1]:res_shift[1]+shape[2]] = copy.deepcopy(image)
         rescaled_error[i,res_shift[0]:res_shift[0]+shape[1],
                 res_shift[1]:res_shift[1]+shape[2]] = copy.deepcopy(error_array[i])
+        rescaled_mask[i,res_shift[0]:res_shift[0]+shape[1],
+                res_shift[1]:res_shift[1]+shape[2]] = False
         # Shift images to align
         rescaled_image[i] = sc_shift(rescaled_image[i], shift, cval=0.)
         rescaled_error[i] = sc_shift(rescaled_error[i], shift, cval=background[i])
+        rescaled_mask[i] = sc_shift(rescaled_mask[i], shift, cval=True)
 
         rescaled_image[i][rescaled_image[i] < 0.] = 0.
 
@@ -678,13 +734,13 @@ def align_data(data_array, error_array=None, upsample_factor=1., ref_data=None,
     errors = np.array(errors)
 
     if return_shifts:
-        return rescaled_image, rescaled_error, shifts, errors
+        return rescaled_image, rescaled_error, rescaled_mask.any(axis=0), shifts, errors
     else:
-        return rescaled_image, rescaled_error
+        return rescaled_image, rescaled_error, rescaled_mask.any(axis=0)
 
 
-def smooth_data(data_array, error_array, headers, FWHM=1., scale='pixel',
-        smoothing='gaussian'):
+def smooth_data(data_array, error_array, data_mask, headers, FWHM=1.,
+        scale='pixel', smoothing='gaussian'):
     """
     Smooth a data_array using selected function.
     ----------
@@ -746,6 +802,7 @@ def smooth_data(data_array, error_array, headers, FWHM=1., scale='pixel',
 
     #Define gaussian stdev
     stdev = FWHM/(2.*np.sqrt(2.*np.log(2)))
+    fmax = np.finfo(np.float64).max
 
     if smoothing.lower() in ['combine','combining']:
         # Smooth using N images combination algorithm
@@ -761,11 +818,15 @@ def smooth_data(data_array, error_array, headers, FWHM=1., scale='pixel',
         for r in range(smoothed.shape[0]):
             for c in range(smoothed.shape[1]):
                 # Compute distance from current pixel
-                dist_rc = np.sqrt((r-xx)**2+(c-yy)**2)
+                dist_rc = np.where(data_mask, fmax, np.sqrt((r-xx)**2+(c-yy)**2))
                 g_rc = np.array([np.exp(-0.5*(dist_rc/stdev)**2),]*len(data_array))
                 # Apply weighted combination
-                smoothed[r,c] = np.sum(data_array*weight*g_rc)/np.sum(weight*g_rc)
-                error[r,c] = np.sqrt(np.sum(weight*g_rc**2))/np.sum(weight*g_rc)
+                if data_mask[r,c]:
+                    smoothed[r,c] = 0.
+                    error[r,c] = 1.
+                else:
+                    smoothed[r,c] = np.sum(data_array*weight*g_rc)/np.sum(weight*g_rc)
+                    error[r,c] = np.sqrt(np.sum(weight*g_rc**2))/np.sum(weight*g_rc)
 
     elif smoothing.lower() in ['gauss','gaussian']:
         #Convolution with gaussian function
@@ -775,10 +836,14 @@ def smooth_data(data_array, error_array, headers, FWHM=1., scale='pixel',
             xx, yy = np.indices(image.shape)
             for r in range(image.shape[0]):
                 for c in range(image.shape[1]):
-                    dist_rc = np.sqrt((r-xx)**2+(c-yy)**2)
+                    dist_rc = np.where(data_mask, fmax, np.sqrt((r-xx)**2+(c-yy)**2))
                     g_rc = np.exp(-0.5*(dist_rc/stdev)**2)/(2.*np.pi*stdev**2)
-                    smoothed[i][r,c] = np.sum(image*g_rc)
-                    error[i][r,c] = np.sum(error_array*g_rc**2)
+                if data_mask[r,c]:
+                    smoothed[r,c] = 0.
+                    error[r,c] = 1.
+                else:
+                    smoothed[r,c] = np.sum(data_array*weight*g_rc)/np.sum(weight*g_rc)
+                    error[r,c] = np.sqrt(np.sum(weight*g_rc**2))/np.sum(weight*g_rc)
 
     else:
         raise ValueError("{} is not a valid smoothing option".format(smoothing))
@@ -786,8 +851,8 @@ def smooth_data(data_array, error_array, headers, FWHM=1., scale='pixel',
     return smoothed, error
 
 
-def polarizer_avg(data_array, error_array, headers, FWHM=None, scale='pixel',
-        smoothing='gaussian'):
+def polarizer_avg(data_array, error_array, data_mask, headers, FWHM=None,
+        scale='pixel', smoothing='gaussian'):
     """
     Make the average image from a single polarizer for a given instrument.
     -----------
@@ -862,11 +927,11 @@ def polarizer_avg(data_array, error_array, headers, FWHM=None, scale='pixel',
 
         if not(FWHM is None) and (smoothing.lower() in ['combine','combining']):
             # Smooth by combining each polarizer images
-            pol0, err0 = smooth_data(pol0_array, err0_array, headers0,
+            pol0, err0 = smooth_data(pol0_array, err0_array, data_mask, headers0,
                     FWHM=FWHM, scale=scale, smoothing=smoothing)
-            pol60, err60 = smooth_data(pol60_array, err60_array, headers60,
+            pol60, err60 = smooth_data(pol60_array, err60_array, data_mask, headers60,
                     FWHM=FWHM, scale=scale, smoothing=smoothing)
-            pol120, err120 = smooth_data(pol120_array, err120_array, headers120,
+            pol120, err120 = smooth_data(pol120_array, err120_array, data_mask, headers120,
                     FWHM=FWHM, scale=scale, smoothing=smoothing)
 
         else:
@@ -908,7 +973,7 @@ def polarizer_avg(data_array, error_array, headers, FWHM=None, scale='pixel',
             if not(FWHM is None) and (smoothing.lower() in ['gaussian','gauss']):
                 # Smooth by convoluting with a gaussian each polX image.
                 pol_array, polerr_array = smooth_data(pol_array, polerr_array,
-                        headers_array, FWHM=FWHM, scale=scale)
+                        data_mask, headers_array, FWHM=FWHM, scale=scale)
                 pol0, pol60, pol120 = pol_array
                 err0, err60, err120 = polerr_array
 
@@ -931,8 +996,8 @@ def polarizer_avg(data_array, error_array, headers, FWHM=None, scale='pixel',
     return polarizer_array, polarizer_cov
 
 
-def compute_Stokes(data_array, error_array, headers, FWHM=None,
-        scale='pixel', smoothing='gaussian_after'):
+def compute_Stokes(data_array, error_array, data_mask, headers,
+        FWHM=None, scale='pixel', smoothing='gaussian_after'):
     """
     Compute the Stokes parameters I, Q and U for a given data_set
     ----------
@@ -989,8 +1054,8 @@ def compute_Stokes(data_array, error_array, headers, FWHM=None,
     # Routine for the FOC instrument
     if instr == 'FOC':
         # Get image from each polarizer and covariance matrix
-        pol_array, pol_cov = polarizer_avg(data_array, error_array, headers,
-                FWHM=FWHM, scale=scale, smoothing=smoothing)
+        pol_array, pol_cov = polarizer_avg(data_array, error_array, data_mask,
+                headers, FWHM=FWHM, scale=scale, smoothing=smoothing)
         pol0, pol60, pol120 = pol_array
 
         if (pol0 < 0.).any() or (pol60 < 0.).any() or (pol120 < 0.).any():
@@ -1012,10 +1077,10 @@ def compute_Stokes(data_array, error_array, headers, FWHM=None,
         if mask.any():
             print("WARNING : I_pol > I_stokes : ", len(I_stokes[mask]))
 
-            plt.imshow(np.sqrt(Q_stokes**2+U_stokes**2)/I_stokes*mask, origin='lower')
-            plt.colorbar()
-            plt.title(r"$I_{pol}/I_{tot}$")
-            plt.show()
+            #plt.imshow(np.sqrt(Q_stokes**2+U_stokes**2)/I_stokes*mask, origin='lower')
+            #plt.colorbar()
+            #plt.title(r"$I_{pol}/I_{tot}$")
+            #plt.show()
 
             #I_stokes[mask]=0.
             #Q_stokes[mask]=0.
@@ -1107,11 +1172,12 @@ def compute_pol(I_stokes, Q_stokes, U_stokes, Stokes_cov, headers):
     #Compute the total exposure time so that
     #I_stokes*exp_tot = N_tot the total number of events
     exp_tot = np.array([header['exptime'] for header in headers]).sum()
+    print("Total exposure time : {} sec".format(exp_tot))
     N_obs = I_stokes*exp_tot
 
     #Errors on P, PA supposing Poisson noise
     s_P_P = np.sqrt(2.)/np.sqrt(N_obs)*100.
-    s_PA_P = s_P_P/(2.*P/100.)*180./np.pi
+    s_PA_P = s_P_P/(2.*P)*180./np.pi
 
     # Nan handling :
     fmax = np.finfo(np.float64).max
@@ -1126,7 +1192,7 @@ def compute_pol(I_stokes, Q_stokes, U_stokes, Stokes_cov, headers):
     return P, debiased_P, s_P, s_P_P, PA, s_PA, s_PA_P
 
 
-def rotate_data(data_array, error_array, headers, ang):
+def rotate_data(data_array, error_array, data_mask, headers, ang):
     """
     Use scipy.ndimage.rotate to rotate I_stokes to an angle, and a rotation
     matrix to rotate Q, U of a given angle in degrees and update header
@@ -1164,6 +1230,7 @@ def rotate_data(data_array, error_array, headers, ang):
         new_error_array.append(sc_rotate(error_array[i], ang, reshape=False,
             cval=error_array.mean()))
     new_data_array = np.array(new_data_array)
+    new_data_mask = sc_rotate(data_mask, ang, reshape=False, cval=True)
     new_error_array = np.array(new_error_array)
 
     for i in range(len(new_data_array)):
@@ -1192,10 +1259,10 @@ def rotate_data(data_array, error_array, headers, ang):
 
         new_headers.append(new_header)
 
-    return new_data_array, new_error_array, new_headers
+    return new_data_array, new_error_array, new_data_mask, new_headers
 
 
-def rotate_Stokes(I_stokes, Q_stokes, U_stokes, Stokes_cov, headers, ang, SNRi_cut=None):
+def rotate_Stokes(I_stokes, Q_stokes, U_stokes, Stokes_cov, data_mask, headers, ang, SNRi_cut=None):
     """
     Use scipy.ndimage.rotate to rotate I_stokes to an angle, and a rotation
     matrix to rotate Q, U of a given angle in degrees and update header
@@ -1266,16 +1333,14 @@ def rotate_Stokes(I_stokes, Q_stokes, U_stokes, Stokes_cov, headers, ang, SNRi_c
     new_Stokes_cov[1,2] = new_Stokes_cov[2,1] = np.cos(2.*alpha)*np.sin(2.*alpha)*(Stokes_cov[2,2] - Stokes_cov[1,1]) + (np.cos(2.*alpha)**2 - np.sin(2.*alpha)**2)*Stokes_cov[1,2]
 
     #Rotate original images using scipy.ndimage.rotate
-    new_I_stokes = sc_rotate(new_I_stokes, ang, reshape=False,
-            cval=0.0*np.sqrt(new_Stokes_cov[0,0][0,0]))
-    new_Q_stokes = sc_rotate(new_Q_stokes, ang, reshape=False,
-            cval=0.0*np.sqrt(new_Stokes_cov[1,1][0,0]))
-    new_U_stokes = sc_rotate(new_U_stokes, ang, reshape=False,
-            cval=0.0*np.sqrt(new_Stokes_cov[2,2][0,0]))
+    new_I_stokes = sc_rotate(new_I_stokes, ang, reshape=False, cval=0.)
+    new_Q_stokes = sc_rotate(new_Q_stokes, ang, reshape=False, cval=0.)
+    new_U_stokes = sc_rotate(new_U_stokes, ang, reshape=False, cval=0.)
+    new_data_mask = sc_rotate(data_mask, ang, reshape=False, cval=True)
     for i in range(3):
         for j in range(3):
-            new_Stokes_cov[i,j] = sc_rotate(new_Stokes_cov[i,j], ang, reshape=False,
-                    cval=0.0*new_Stokes_cov[i,j].mean())
+            new_Stokes_cov[i,j] = sc_rotate(new_Stokes_cov[i,j], ang,
+                    reshape=False, cval=0.)
 
     #Update headers to new angle
     new_headers = []
@@ -1310,10 +1375,10 @@ def rotate_Stokes(I_stokes, Q_stokes, U_stokes, Stokes_cov, headers, ang, SNRi_c
     new_U_stokes[np.isnan(new_U_stokes)] = 0.
     new_Stokes_cov[np.isnan(new_Stokes_cov)] = fmax
 
-    return new_I_stokes, new_Q_stokes, new_U_stokes, new_Stokes_cov, new_headers
+    return new_I_stokes, new_Q_stokes, new_U_stokes, new_Stokes_cov, new_data_mask, new_headers
 
 
-def rotate2_Stokes(I_stokes, Q_stokes, U_stokes, Stokes_cov, headers, ang):
+def rotate2_Stokes(I_stokes, Q_stokes, U_stokes, Stokes_cov, data_mask, headers, ang):
     """
     Use scipy.ndimage.rotate to rotate I_stokes to an angle, and a rotation
     matrix to rotate Q, U of a given angle in degrees and update header
@@ -1443,4 +1508,4 @@ def rotate2_Stokes(I_stokes, Q_stokes, U_stokes, Stokes_cov, headers, ang):
     s_P_P[np.isnan(s_P_P)] = fmax
     s_PA_P[np.isnan(s_PA_P)] = fmax
 
-    return new_I_stokes, new_Q_stokes, new_U_stokes, new_Stokes_cov, P, debiased_P, s_P, s_P_P, PA, s_PA, s_PA_P, new_headers
+    return new_I_stokes, new_Q_stokes, new_U_stokes, new_Stokes_cov, data_mask, P, debiased_P, s_P, s_P_P, PA, s_PA, s_PA_P, new_headers
diff --git a/src/lib/reduction_ELR.py b/src/lib/reduction_ELR.py
deleted file mode 100755
index 9eaae6f..0000000
--- a/src/lib/reduction_ELR.py
+++ /dev/null
@@ -1,1515 +0,0 @@
-"""
-Library function computing various steps of the reduction pipeline.
-
-prototypes :
-    - bin_ndarray(ndarray, new_shape, operation) -> ndarray
-        Bins an ndarray to new_shape.
-
-    - crop_array(data_array, error_array, step, null_val, inside) -> crop_data_array, crop_error_array
-        Homogeneously crop out null edges off a data array.
-
-    - deconvolve_array(data_array, psf, FWHM, iterations) -> deconvolved_data_array
-        Homogeneously deconvolve a data array using Richardson-Lucy iterative algorithm
-
-    - get_error(data_array, sub_shape, display, headers, savename, plots_folder) -> data_array, error_array
-        Compute the error (noise) on each image of the input array.
-
-    - rebin_array(data_array, error_array, headers, pxsize, scale, operation) -> rebinned_data, rebinned_error, rebinned_headers, Dxy
-        Homegeneously rebin a data array given a target pixel size in scale units.
-
-    - align_data(data_array, error_array, upsample_factor, ref_data, ref_center, return_shifts) -> data_array, error_array (, shifts, errors)
-        Align data_array on ref_data by cross-correlation.
-
-    - smooth_data(data_array, error_array, FWHM, scale, smoothing) -> smoothed_array
-        Smooth data by convoluting with a gaussian or by combining weighted images
-
-    - polarizer_avg(data_array, error_array, headers, FWHM, scale, smoothing) -> polarizer_array, pol_error_array
-        Average images in data_array on each used polarizer filter.
-
-    - compute_Stokes(data_array, error_array, headers, FWHM, scale, smoothing) -> I_stokes, Q_stokes, U_stokes, Stokes_cov
-        Compute Stokes parameters I, Q and U and their respective errors from data_array.
-
-    - compute_pol(I_stokes, Q_stokes, U_stokes, Stokes_cov, headers) -> P, debiased_P, s_P, s_P_P, PA, s_PA, s_PA_P
-        Compute polarization degree (in %) and angle (in degree) and their
-        respective errors
-
-    - rotate_sc_Stokes(I_stokes, Q_stokes, U_stokes, Stokes_cov, headers, ang) -> I_stokes, Q_stokes, U_stokes, Stokes_cov, headers
-        Rotate I, Q, U given an angle in degrees using scipy functions.
-
-    - rotate_PIL_Stokes(I_stokes, Q_stokes, U_stokes, Stokes_cov, headers, ang) -> I_stokes, Q_stokes, U_stokes, Stokes_cov, headers
-        Rotate I, Q, U given an angle in degrees using Pillow function.
-
-    - rotate2_sc_Stokes(I_stokes, Q_stokes, U_stokes, Stokes_cov, headers, ang) -> I_stokes, Q_stokes, U_stokes, Stokes_cov, P, debiased_P, s_P, s_P_P, PA, s_PA, s_PA_P, headers
-        Rotate I, Q, U, P, PA and associated errors given an angle in degrees
-        using scipy functions.
-
-    - rotate2_PIL_Stokes(I_stokes, Q_stokes, U_stokes, Stokes_cov, headers, ang) -> I_stokes, Q_stokes, U_stokes, Stokes_cov, P, debiased_P, s_P, s_P_P, PA, s_PA, s_PA_P, headers
-        Rotate I, Q, U, P, PA and associated errors given an angle in degrees
-        using Pillow function.
-"""
-
-import copy
-import numpy as np
-import matplotlib.pyplot as plt
-import matplotlib.dates as mdates
-from datetime import datetime
-from scipy.ndimage import rotate as sc_rotate
-from scipy.ndimage import shift as sc_shift
-from PIL import Image
-from astropy.wcs import WCS
-from lib.deconvolve import deconvolve_im, gaussian_psf
-from lib.convex_hull import image_hull
-from lib.plots import plot_obs
-from lib.cross_correlation import phase_cross_correlation
-
-
-def PIL_rotate(ndarray, angle, reshape=False, cval=0.):
-    """
-    Define Function with similar parameters as scipy.ndimage.rotate making use
-    of the Pillow library.
-    ----------
-    Inputs:
-    ndarray : numpy.ndarray
-        2D float array to be rotated using Pillow library functions.
-    angle : float
-        Counter-clockwise ngle in degrees that the input array should be
-        rotated to.
-    reshape : boolean, optional
-        If True, output image will be of shape such that the full rotated
-        input array is displayed. If False, output array will be of same
-        shape than input array, cutting rotated edges.
-        Defaults to False.
-    cval : float, optional
-        Values with which will be filled pixels entering the output array by
-        rotation of the input array.
-        Dafaults to 0.
-    ----------
-    Returns:
-    rotated_array : numpy.ndarray
-        Rotated array using Pillow functions.
-    """
-    image = Image.fromarray(ndarray)
-    rotated = image.rotate(angle, expand=reshape, fillcolor=cval)
-    return np.asarray(rotated)
-
-
-def get_row_compressor(old_dimension, new_dimension, operation='sum'):
-    """
-    Return the matrix that allows to compress an array from an old dimension of
-    rows to a new dimension of rows, can be done by summing the original
-    components or averaging them.
-    ----------
-    Inputs:
-    old_dimension, new_dimension : int
-        Number of rows in the original and target matrices.
-    operation : str, optional
-        Set the way original components of the matrix are put together
-        between summing ('sum') and averaging ('average', 'avg', 'mean') them.
-        Defaults to 'sum'.
-    ----------
-    Returns:
-    dim_compressor : numpy.ndarray
-        2D matrix allowing row compression by matrix multiplication to the left
-        of the original matrix.
-    """
-    dim_compressor = np.zeros((new_dimension, old_dimension))
-    bin_size = float(old_dimension) / new_dimension
-    next_bin_break = bin_size
-    which_row, which_column = 0, 0
-
-    while which_row < dim_compressor.shape[0] and which_column < dim_compressor.shape[1]:
-        if round(next_bin_break - which_column, 10) >= 1:
-            dim_compressor[which_row, which_column] = 1
-            which_column += 1
-        elif next_bin_break == which_column:
-
-            which_row += 1
-            next_bin_break += bin_size
-        else:
-            partial_credit = next_bin_break - which_column
-            dim_compressor[which_row, which_column] = partial_credit
-            which_row += 1
-            dim_compressor[which_row, which_column] = 1 - partial_credit
-            which_column += 1
-            next_bin_break += bin_size
-
-    if operation.lower() in ["mean", "average", "avg"]:
-        dim_compressor /= bin_size
-
-    return dim_compressor
-
-
-def get_column_compressor(old_dimension, new_dimension, operation='sum'):
-    """
-    Return the matrix that allows to compress an array from an old dimension of
-    columns to a new dimension of columns, can be done by summing the original
-    components or averaging them.
-    ----------
-    Inputs:
-    old_dimension, new_dimension : int
-        Number of columns in the original and target matrices.
-    operation : str, optional
-        Set the way original components of the matrix are put together
-        between summing ('sum') and averaging ('average', 'avg', 'mean') them.
-        Defaults to 'sum'.
-    ----------
-    Returns:
-    dim_compressor : numpy.ndarray
-        2D matrix allowing columns compression by matrix multiplication to the
-        right of the original matrix.
-    """
-    return get_row_compressor(old_dimension, new_dimension, operation).transpose()
-
-
-def bin_ndarray(ndarray, new_shape, operation='sum'):
-    """
-    Bins an ndarray in all axes based on the target shape, by summing or
-        averaging.
-
-    Number of output dimensions must match number of input dimensions.
-
-    Example
-    -------
-    >>> m = np.arange(0,100,1).reshape((10,10))
-    >>> n = bin_ndarray(m, new_shape=(5,5), operation='sum')
-    >>> print(n)
-
-    [[ 22  30  38  46  54]
-     [102 110 118 126 134]
-     [182 190 198 206 214]
-     [262 270 278 286 294]
-     [342 350 358 366 374]]
-
-    """
-    if not operation.lower() in ['sum', 'mean', 'average', 'avg']:
-        raise ValueError("Operation not supported.")
-    if ndarray.ndim != len(new_shape):
-        raise ValueError("Shape mismatch: {} -> {}".format(ndarray.shape,
-                                                           new_shape))
-    if (np.array(ndarray.shape)%np.array(new_shape) == np.array([0.,0.])).all():
-        compression_pairs = [(d, c//d) for d,c in zip(new_shape, ndarray.shape)]
-        flattened = [l for p in compression_pairs for l in p]
-        ndarray = ndarray.reshape(flattened)
-
-        for i in range(len(new_shape)):
-            if operation.lower() == "sum":
-                ndarray = ndarray.sum(-1*(i+1))
-            elif operation.lower() in ["mean", "average", "avg"]:
-                ndarray = ndarray.mean(-1*(i+1))
-    else:
-        row_comp = np.mat(get_row_compressor(ndarray.shape[0], new_shape[0], operation))
-        col_comp = np.mat(get_column_compressor(ndarray.shape[1], new_shape[1], operation))
-        ndarray = np.array(row_comp * np.mat(ndarray) * col_comp)
-
-    return ndarray
-
-
-def crop_array(data_array, error_array=None, step=5, null_val=None, inside=False):
-    """
-    Homogeneously crop an array: all contained images will have the same shape.
-    'inside' parameter will decide how much should be cropped.
-    ----------
-    Inputs:
-    data_array : numpy.ndarray
-        Array containing the observation data (2D float arrays) to
-        homogeneously crop.
-    error_array : numpy.ndarray, optional
-        Array of images (2D floats, aligned and of the same shape) containing
-        the error in each pixel of the observation images in data_array.
-        If None, will be initialized to zeros.
-        Defaults to None.
-    step : int, optional
-        For images with straight edges, not all lines and columns need to be
-        browsed in order to have a good convex hull. Step value determine
-        how many row/columns can be jumped. With step=2 every other line will
-        be browsed.
-        Defaults to 5.
-    null_val : float or array-like, optional
-        Pixel value determining the threshold for what is considered 'outside'
-        the image. All border pixels below this value will be taken out.
-        If None, will be put to 75% of the mean value of the associated error
-        array.
-        Defaults to None.
-    inside : boolean, optional
-        If True, the cropped image will be the maximum rectangle included
-        inside the image. If False, the cropped image will be the minimum
-        rectangle in which the whole image is included.
-        Defaults to False.
-    ----------
-    Returns:
-    cropped_array : numpy.ndarray
-        Array containing the observationnal data homogeneously cropped.
-    """
-    if error_array is None:
-        error_array = np.zeros(data_array.shape)
-    if null_val is None:
-        null_val = [1.00*error.mean() for error in error_array]
-    elif type(null_val) is float:
-        null_val = [null_val,]*len(error_array)
-
-    vertex = np.zeros((data_array.shape[0],4),dtype=int)
-    for i,image in enumerate(data_array):
-        vertex[i] = image_hull(image,step=step,null_val=null_val[i],inside=inside)
-    v_array = np.zeros(4,dtype=int)
-    if inside:
-        v_array[0] = np.max(vertex[:,0]).astype(int)
-        v_array[1] = np.min(vertex[:,1]).astype(int)
-        v_array[2] = np.max(vertex[:,2]).astype(int)
-        v_array[3] = np.min(vertex[:,3]).astype(int)
-    else:
-        v_array[0] = np.min(vertex[:,0]).astype(int)
-        v_array[1] = np.max(vertex[:,1]).astype(int)
-        v_array[2] = np.min(vertex[:,2]).astype(int)
-        v_array[3] = np.max(vertex[:,3]).astype(int)
-
-    new_shape = np.array([v_array[1]-v_array[0],v_array[3]-v_array[2]])
-    crop_array = np.zeros((data_array.shape[0],new_shape[0],new_shape[1]))
-    crop_error_array = np.zeros((data_array.shape[0],new_shape[0],new_shape[1]))
-    for i,image in enumerate(data_array):
-        crop_array[i] = image[v_array[0]:v_array[1],v_array[2]:v_array[3]]
-        crop_error_array[i] = error_array[i][v_array[0]:v_array[1],v_array[2]:v_array[3]]
-
-    return crop_array, crop_error_array
-
-
-def deconvolve_array(data_array, headers, psf='gaussian', FWHM=1., scale='px',
-        shape=(9,9), iterations=20):
-    """
-    Homogeneously deconvolve a data array using Richardson-Lucy iterative algorithm.
-    ----------
-    Inputs:
-    data_array : numpy.ndarray
-        Array containing the observation data (2D float arrays) to
-        homogeneously deconvolve.
-    headers : header list
-        Headers associated with the images in data_array.
-    psf : str or numpy.ndarray, optionnal
-        String designing the type of desired Point Spread Function or array
-        of dimension 2 corresponding to the weights of a PSF.
-        Defaults to 'gaussian' type PSF.
-    FWHM : float, optional
-        Full Width at Half Maximum for desired PSF in 'scale units. Only used
-        for relevant values of 'psf' variable.
-        Defaults to 1.
-    scale : str, optional
-        Scale units for the FWHM of the PSF between 'pixel' and 'arcsec'.
-        Defaults to 'pixel'.
-    shape : tuple, optional
-        Shape of the kernel of the PSF. Must be of dimension 2. Only used for
-        relevant values of 'psf' variable.
-        Defaults to (9,9).
-    iterations : int, optional
-        Number of iterations of Richardson-Lucy deconvolution algorithm. Act as
-        as a regulation of the process.
-        Defaults to 20.
-    ----------
-    Returns:
-    deconv_array : numpy.ndarray
-        Array containing the deconvolved data (2D float arrays) using given
-        point spread function.
-    """
-    # If chosen FWHM scale is 'arcsec', compute FWHM in pixel scale
-    if scale.lower() in ['arcsec','arcseconds']:
-        pxsize = np.zeros((data_array.shape[0],2))
-        for i,header in enumerate(headers):
-            # Get current pixel size
-            w = WCS(header).deepcopy()
-            if w.wcs.has_cd():
-                del w.wcs.cd
-                keys = list(w.to_header().keys())+['CD1_1','CD1_2','CD2_1','CD2_2']
-                for key in keys:
-                    header.remove(key, ignore_missing=True)
-                w.wcs.cdelt = 3600.*np.sqrt(np.sum(w.wcs.get_pc()**2,axis=1))
-            if (w.wcs.cdelt == np.array([1., 1.])).all() and \
-                    (w.array_shape in [(512, 512),(1024,512),(512,1024),(1024,1024)]):
-                # Update WCS with relevant information
-                HST_aper = 2400.    # HST aperture in mm
-                f_ratio = header['f_ratio']
-                px_dim = np.array([25., 25.])   # Pixel dimension in µm
-                if header['pxformt'].lower() == 'zoom':
-                    px_dim[0] = 50.
-                w.wcs.cdelt = 206.3*px_dim/(f_ratio*HST_aper)
-            header.update(w.to_header())
-            pxsize[i] = np.round(w.wcs.cdelt,5)
-        if (pxsize != pxsize[0]).any():
-            raise ValueError("Not all images in array have same pixel size")
-        FWHM /= pxsize[0].min()
-
-    # Define Point-Spread-Function kernel
-    if psf.lower() in ['gauss','gaussian']:
-        kernel = gaussian_psf(FWHM=FWHM, shape=shape)
-    elif (type(psf) == np.ndarray) and (len(psf.shape) == 2):
-        kernel = psf
-    else:
-        raise ValueError("{} is not a valid value for 'psf'".format(psf))
-
-    # Deconvolve images in the array using given PSF
-    deconv_array = np.zeros(data_array.shape)
-    for i,image in enumerate(data_array):
-        deconv_array[i] = deconvolve_im(image, kernel, iterations=iterations,
-                clip=True, filter_epsilon=None)
-
-    return deconv_array
-
-
-def get_error(data_array, sub_shape=(15,15), display=False, headers=None,
-        savename=None, plots_folder="", return_background=False):
-    """
-    Look for sub-image of shape sub_shape that have the smallest integrated
-    flux (no source assumption) and define the background on the image by the
-    standard deviation on this sub-image.
-    ----------
-    Inputs:
-    data_array : numpy.ndarray
-        Array containing the data to study (2D float arrays).
-    sub_shape : tuple, optional
-        Shape of the sub-image to look for. Must be odd.
-        Defaults to (15,15).
-    display : boolean, optional
-        If True, data_array will be displayed with a rectangle around the
-        sub-image selected for background computation.
-        Defaults to False.
-    headers : header list, optional
-        Headers associated with the images in data_array. Will only be used if
-        display is True.
-        Defaults to None.
-    savename : str, optional
-        Name of the figure the map should be saved to. If None, the map won't
-        be saved (only displayed). Only used if display is True.
-        Defaults to None.
-    plots_folder : str, optional
-        Relative (or absolute) filepath to the folder in wich the map will
-        be saved. Not used if savename is None.
-        Defaults to current folder.
-    return_background : boolean, optional
-        If True, the pixel background value for each image in data_array is
-        returned.
-        Defaults to False.
-    ----------
-    Returns:
-    data_array : numpy.ndarray
-        Array containing the data to study minus the background.
-    error_array : numpy.ndarray
-        Array containing the background values associated to the images in
-        data_array.
-    background : numpy.ndarray
-        Array containing the pixel background value for each image in
-        data_array.
-        Only returned if return_background is True.
-    """
-    # Crop out any null edges
-    data, error = crop_array(data_array, step=5, null_val=0., inside=False)
-
-    sub_shape = np.array(sub_shape)
-    # Make sub_shape of odd values
-    if not(np.all(sub_shape%2)):
-        sub_shape += 1-sub_shape%2
-
-    shape = np.array(data.shape)
-    diff = (sub_shape-1).astype(int)
-    temp = np.zeros((shape[0],shape[1]-diff[0],shape[2]-diff[1]))
-    error_array = np.ones(data_array.shape)
-    rectangle = np.zeros((shape[0],4))
-    background = np.zeros((shape[0]))
-
-    for i,image in enumerate(data):
-        # Find the sub-image of smallest integrated flux (suppose no source)
-        #sub-image dominated by background
-        for r in range(temp.shape[1]):
-            for c in range(temp.shape[0]):
-                temp[i][r,c] = image[r:r+diff[0],c:c+diff[1]].sum()
-
-    minima = np.unravel_index(np.argmin(temp.sum(axis=0)),temp.shape[1:])
-
-    for i, image in enumerate(data):
-        rectangle[i] = minima[0], minima[1], sub_shape[0], sub_shape[1]
-        # Compute error : root mean square of the background
-        sub_image = image[minima[0]:minima[0]+sub_shape[0],minima[1]:minima[1]+sub_shape[1]]
-        #error_array[i] *= np.std(sub_image)    # Previously computed using standard deviation over the background
-        error_array[i] *= np.sqrt(np.sum(sub_image**2)/sub_image.size)
-        background[i] = sub_image.sum()
-        #data_array[i] = np.abs(data_array[i] - sub_image.mean())
-
-    if display:
-
-        date_time = np.array([headers[i]['date-obs']+';'+headers[i]['time-obs']
-            for i in range(len(headers))])
-        date_time = np.array([datetime.strptime(d,'%Y-%m-%d;%H:%M:%S')
-            for d in date_time])
-        filt = np.array([headers[i]['filtnam1'] for i in range(len(headers))])
-        dict_filt = {"POL0":'r', "POL60":'g', "POL120":'b'}
-        c_filt = np.array([dict_filt[f] for f in filt])
-
-        fig,ax = plt.subplots(figsize=(10,6),constrained_layout=True)
-        for f in np.unique(filt):
-            mask = [fil==f for fil in filt]
-            ax.scatter(date_time[mask], background[mask], color=dict_filt[f],
-                    label="Filter : {0:s}".format(f))
-        ax.errorbar(date_time, background, yerr=error_array[:,0,0], fmt='+k',
-                markersize=0, ecolor=c_filt)
-        # Date handling
-        locator = mdates.AutoDateLocator()
-        formatter = mdates.ConciseDateFormatter(locator)
-        ax.xaxis.set_major_locator(locator)
-        ax.xaxis.set_major_formatter(formatter)
-        ax.set_xlabel("Observation date and time")
-        ax.set_ylabel(r"Flux [$ergs \cdot cm^{-2} \cdot s^{-1} \cdot \AA^{-1}$]")
-        ax.set_title("Background flux and error computed for each image")
-        plt.legend()
-
-        if not(savename is None):
-            fig.suptitle(savename+"_background_flux.png")
-            fig.savefig(plots_folder+savename+"_background_flux.png",
-                    bbox_inches='tight')
-            vmin = np.min(np.log10(data[data > 0.]))
-            vmax = np.max(np.log10(data[data > 0.]))
-            plot_obs(np.log10(data), headers, vmin=vmin, vmax=vmax,
-                    rectangle=rectangle,
-                    savename=savename+"_background_location",
-                    plots_folder=plots_folder)
-
-        else:
-            vmin = np.min(np.log10(data[data > 0.]))
-            vmax = np.max(np.log10(data[data > 0.]))
-            plot_obs(np.log10(data), headers, vmin=vmin, vmax=vmax,
-                    rectangle=rectangle)
-
-        plt.show()
-
-    if return_background:
-        return data_array, error_array, np.array([error_array[i][0,0] for i in range(error_array.shape[0])])
-    else:
-        return data_array, error_array
-
-
-def rebin_array(data_array, error_array, headers, pxsize, scale,
-        operation='sum'):
-    """
-    Homogeneously rebin a data array to get a new pixel size equal to pxsize
-    where pxsize is given in arcsec.
-    ----------
-    Inputs:
-    data_array, error_array : numpy.ndarray
-        Arrays containing the images (2D float arrays) and their associated
-        errors that will be rebinned.
-    headers : header list
-        List of headers corresponding to the images in data_array.
-    pxsize : float
-        Physical size of the pixel in arcseconds that should be obtain with
-        the rebinning.
-    scale : str, optional
-        Scale units for the FWHM between 'pixel' and 'arcsec'.
-        Defaults to 'pixel'.
-    operation : str, optional
-        Set the way original components of the matrix are put together
-        between summing ('sum') and averaging ('average', 'avg', 'mean') them.
-        Defaults to 'sum'.
-    ----------
-    Returns:
-    rebinned_data, rebinned_error : numpy.ndarray
-        Rebinned arrays containing the images and associated errors.
-    rebinned_headers : header list
-        Updated headers corresponding to the images in rebinned_data.
-    Dxy : numpy.ndarray
-        Array containing the rebinning factor in each direction of the image.
-    """
-    # Check that all images are from the same instrument
-    ref_header = headers[0]
-    instr = ref_header['instrume']
-    same_instr = np.array([instr == header['instrume'] for header in headers]).all()
-    if not same_instr:
-        raise ValueError("All images in data_array are not from the same\
-                instrument, cannot proceed.")
-    if not instr in ['FOC']:
-        raise ValueError("Cannot reduce images from {0:s} instrument\
-                (yet)".format(instr))
-
-    rebinned_data, rebinned_error, rebinned_headers = [], [], []
-    Dxy = np.array([1, 1],dtype=int)
-
-    # Routine for the FOC instrument
-    if instr == 'FOC':
-        HST_aper = 2400.    # HST aperture in mm
-        for i, enum in enumerate(list(zip(data_array, error_array, headers))):
-            image, error, header = enum
-            # Get current pixel size
-            w = WCS(header).deepcopy()
-            if w.wcs.has_cd():
-                del w.wcs.cd
-                keys = list(w.to_header().keys())+['CD1_1','CD1_2','CD2_1','CD2_2']
-                for key in keys:
-                    header.remove(key, ignore_missing=True)
-                w.wcs.cdelt = 3600.*np.sqrt(np.sum(w.wcs.get_pc()**2,axis=1))
-            if (w.wcs.cdelt == np.array([1., 1.])).all() and \
-                    (w.array_shape in [(512, 512),(1024,512),(512,1024),(1024,1024)]):
-                # Update WCS with relevant information
-                f_ratio = header['f_ratio']
-                px_dim = np.array([25., 25.])   # Pixel dimension in µm
-                if header['pxformt'].lower() == 'zoom':
-                    px_dim[0] = 50.
-                w.wcs.cdelt = 206.3*px_dim/(f_ratio*HST_aper)
-            header.update(w.to_header())
-
-            # Compute binning ratio
-            if scale.lower() in ['px', 'pixel']:
-                Dxy = np.array([pxsize,]*2)
-            elif scale.lower() in ['arcsec','arcseconds']:
-                Dxy = np.floor(pxsize/w.wcs.cdelt).astype(int)
-            else:
-                raise ValueError("'{0:s}' invalid scale for binning.".format(scale))
-
-            if (Dxy <= 1.).any():
-                raise ValueError("Requested pixel size is below resolution.")
-            new_shape = (image.shape//Dxy).astype(int)
-
-            # Rebin data
-            rebinned_data.append(bin_ndarray(image, new_shape=new_shape,
-                operation=operation))
-
-            # Propagate error
-            rms_image = np.sqrt(bin_ndarray(image**2, new_shape=new_shape,
-                operation='average'))
-            #std_image = np.sqrt(bin_ndarray(image**2, new_shape=new_shape,
-            #    operation='average') - bin_ndarray(image, new_shape=new_shape,
-            #        operation='average')**2)
-            new_error = np.sqrt(Dxy[0]*Dxy[1])*bin_ndarray(error,
-                    new_shape=new_shape, operation='average')
-            rebinned_error.append(np.sqrt(rms_image**2 + new_error**2))
-
-            # Update header
-            w = w.slice((np.s_[::Dxy[0]], np.s_[::Dxy[1]]))
-            header['NAXIS1'],header['NAXIS2'] = w.array_shape
-            header.update(w.to_header())
-            rebinned_headers.append(header)
-
-
-    rebinned_data = np.array(rebinned_data)
-    rebinned_error = np.array(rebinned_error)
-
-    return rebinned_data, rebinned_error, rebinned_headers, Dxy
-
-
-def align_data(data_array, error_array=None, upsample_factor=1., ref_data=None,
-        ref_center=None, return_shifts=True):
-    """
-    Align images in data_array using cross correlation, and rescale them to
-    wider images able to contain any rotation of the reference image.
-    All images in data_array must have the same shape.
-    ----------
-    Inputs:
-    data_array : numpy.ndarray
-        Array containing the data to align (2D float arrays).
-    error_array : numpy.ndarray, optional
-        Array of images (2D floats, aligned and of the same shape) containing
-        the error in each pixel of the observation images in data_array.
-        If None, will be initialized to zeros.
-        Defaults to None.
-    upsample_factor : float, optional
-        Oversampling factor for the cross-correlation, will allow sub-
-        pixel alignement as small as one over the factor of a pixel.
-        Defaults to one (no over-sampling).
-    ref_data : numpy.ndarray, optional
-        Reference image (2D float array) the data_array should be
-        aligned to. If "None", the ref_data will be the first image
-        of the data_array.
-        Defaults to None.
-    ref_center : numpy.ndarray, optional
-        Array containing the coordinates of the center of the reference
-        image or a string in 'max', 'flux', 'maxflux', 'max_flux'. If None,
-        will fallback to the center of the image.
-        Defaults to None.
-    return_shifts : boolean, optional
-        If False, calling the function will only return the array of
-        rescaled images. If True, will also return the shifts and
-        errors.
-        Defaults to True.
-    ----------
-    Returns:
-    rescaled : numpy.ndarray
-        Array containing the aligned data from data_array, rescaled to wider
-        image with margins of value 0.
-    rescaled_error : numpy.ndarray
-        Array containing the errors on the aligned images in the rescaled array.
-    shifts : numpy.ndarray
-        Array containing the pixel shifts on the x and y directions from
-        the reference image.
-        Only returned if return_shifts is True.
-    errors : numpy.ndarray
-        Array containing the relative error computed on every shift value.
-        Only returned if return_shifts is True.
-    """
-    if ref_data is None:
-        # Define the reference to be the first image of the inputed array
-        #if None have been specified
-        ref_data = data_array[0]
-    same = 1
-    for array in data_array:
-        # Check if all images have the same shape. If not, cross-correlation
-        #cannot be computed.
-        same *= (array.shape == ref_data.shape)
-    if not same:
-        raise ValueError("All images in data_array must have same shape as\
-            ref_data")
-    if error_array is None:
-        _, error_array, background = get_error(data_array, return_background=True)
-    else:
-        _, _, background = get_error(data_array, return_background=True)
-
-    # Crop out any null edges
-    #(ref_data must be cropped as well)
-    full_array = np.concatenate((data_array,[ref_data]),axis=0)
-    err_array = np.concatenate((error_array,[np.zeros(ref_data.shape)]),axis=0)
-
-    #full_array, err_array = crop_array(full_array, err_array, step=5,
-    #        inside=False)
-
-    data_array, ref_data = full_array[:-1], full_array[-1]
-    error_array = err_array[:-1]
-
-    if ref_center is None:
-        # Define the center of the reference image to be the center pixel
-        #if None have been specified
-        ref_center = (np.array(ref_data.shape)/2).astype(int)
-    elif ref_center.lower() in ['max', 'flux', 'maxflux', 'max_flux']:
-        # Define the center of the reference image to be the pixel of max flux.
-        ref_center = np.unravel_index(np.argmax(ref_data),ref_data.shape)
-    else:
-        # Default to image center.
-        ref_center = (np.array(ref_data.shape)/2).astype(int)
-
-    # Create a rescaled null array that can contain any rotation of the
-    #original image (and shifted images)
-    shape = data_array.shape
-    res_shape = int(np.ceil(np.sqrt(2)*np.max(shape[1:])))
-    rescaled_image = np.ones((shape[0],res_shape,res_shape))
-    cropped_image = np.ones(shape)
-    rescaled_error = np.ones((shape[0],res_shape,res_shape))
-    cropped_error = np.ones(shape)
-    res_center = (np.array(rescaled_image.shape[1:])/2).astype(int)
-
-    shifts, errors = [], []
-    for i,image in enumerate(data_array):
-        # Initialize rescaled images to background values
-        rescaled_image[i] *= 0.1*background[i]
-        rescaled_error[i] *= background[i]
-        # Get shifts and error by cross-correlation to ref_data
-        shift, error, phase_diff = phase_cross_correlation(ref_data, image,
-                upsample_factor=upsample_factor)
-        # Rescale image to requested output
-        center = np.fix(ref_center-shift).astype(int)
-        res_shift = res_center-ref_center
-        rescaled_image[i,res_shift[0]:res_shift[0]+shape[1],
-                res_shift[1]:res_shift[1]+shape[2]] = copy.deepcopy(image)
-        rescaled_error[i,res_shift[0]:res_shift[0]+shape[1],
-                res_shift[1]:res_shift[1]+shape[2]] = copy.deepcopy(error_array[i])
-        # Shift images to align
-        rescaled_image[i] = sc_shift(rescaled_image[i], shift, cval=0.1*background[i])
-        rescaled_error[i] = sc_shift(rescaled_error[i], shift, cval=background[i])
-
-        cropped_image[i] = rescaled_image[i,res_center[0]-int(shape[1]/2):res_center[0]+int(shape[1]/2),res_center[1]-int(shape[2]/2):res_center[1]+int(shape[2]/2)]
-        cropped_error[i] = rescaled_error[i,res_center[0]-int(shape[1]/2):res_center[0]+int(shape[1]/2),res_center[1]-int(shape[2]/2):res_center[1]+int(shape[2]/2)]
-
-        shifts.append(shift)
-        errors.append(error)
-
-    shifts = np.array(shifts)
-    errors = np.array(errors)
-
-    if return_shifts:
-        return cropped_image, cropped_error, shifts, errors
-    else:
-        return cropped_image, cropped_error
-
-
-def smooth_data(data_array, error_array, headers, FWHM=1., scale='pixel',
-        smoothing='gaussian'):
-    """
-    Smooth a data_array using selected function.
-    ----------
-    Inputs:
-    data_array : numpy.ndarray
-        Array containing the data to smooth (2D float arrays).
-    error_array : numpy.ndarray
-        Array of images (2D floats, aligned and of the same shape) containing
-        the error in each pixel of the observation images in data_array.
-    headers : header list
-        List of headers corresponding to the images in data_array.
-    FWHM : float, optional
-        Full Width at Half Maximum for desired smoothing in 'scale' units.
-        Defaults to 1.
-    scale : str, optional
-        Scale units for the FWHM between 'pixel' and 'arcsec'.
-        Defaults to 'pixel'.
-    smoothing : str, optional
-        Smoothing algorithm to be used on the input data array.
-        -'combine','combining' use the N images combining algorithm with
-          weighted pixels (inverse square of errors).
-        -'gauss','gaussian' convolve any input image with a gaussian of
-          standard deviation stdev = FWHM/(2*sqrt(2*log(2))).
-        Defaults to 'gaussian'. Won't be used if FWHM is None.
-    ----------
-    Returns:
-    smoothed_array : numpy.ndarray
-        Array containing the smoothed images.
-    error_array : numpy.ndarray
-        Array containing the error images corresponding to the images in
-        smoothed_array.
-    """
-    # If chosen FWHM scale is 'arcsec', compute FWHM in pixel scale
-    if scale.lower() in ['arcsec','arcseconds']:
-        pxsize = np.zeros((data_array.shape[0],2))
-        for i,header in enumerate(headers):
-            # Get current pixel size
-            w = WCS(header).deepcopy()
-            if w.wcs.has_cd():
-                del w.wcs.cd
-                keys = list(w.to_header().keys())+['CD1_1','CD1_2','CD2_1','CD2_2']
-                for key in keys:
-                    header.remove(key, ignore_missing=True)
-                w.wcs.cdelt = 3600.*np.sqrt(np.sum(w.wcs.get_pc()**2,axis=1))
-            if (w.wcs.cdelt == np.array([1., 1.])).all() and \
-                    (w.array_shape in [(512, 512),(1024,512),(512,1024),(1024,1024)]):
-                # Update WCS with relevant information
-                HST_aper = 2400.    # HST aperture in mm
-                f_ratio = header['f_ratio']
-                px_dim = np.array([25., 25.])   # Pixel dimension in µm
-                if header['pxformt'].lower() == 'zoom':
-                    px_dim[0] = 50.
-                w.wcs.cdelt = 206.3*px_dim/(f_ratio*HST_aper)
-            header.update(w.to_header())
-            pxsize[i] = np.round(w.wcs.cdelt,5)
-        if (pxsize != pxsize[0]).any():
-            raise ValueError("Not all images in array have same pixel size")
-        FWHM /= pxsize[0].min()
-
-    #Define gaussian stdev
-    stdev = FWHM/(2.*np.sqrt(2.*np.log(2)))
-
-    if smoothing.lower() in ['combine','combining']:
-        # Smooth using N images combination algorithm
-        # Weight array
-        weight = 1./error_array**2
-        # Prepare pixel distance matrix
-        xx, yy = np.indices(data_array[0].shape)
-        # Initialize smoothed image and error arrays
-        smoothed = np.zeros(data_array[0].shape)
-        error = np.zeros(data_array[0].shape)
-
-        # Combination smoothing algorithm
-        for r in range(smoothed.shape[0]):
-            for c in range(smoothed.shape[1]):
-                # Compute distance from current pixel
-                dist_rc = np.sqrt((r-xx)**2+(c-yy)**2)
-                g_rc = np.array([np.exp(-0.5*(dist_rc/stdev)**2),]*len(data_array))
-                # Apply weighted combination
-                smoothed[r,c] = np.sum(data_array*weight*g_rc)/np.sum(weight*g_rc)
-                error[r,c] = np.sqrt(np.sum(weight*g_rc**2))/np.sum(weight*g_rc)
-
-    elif smoothing.lower() in ['gauss','gaussian']:
-        #Convolution with gaussian function
-        smoothed = np.zeros(data_array.shape)
-        error = np.zeros(error_array.shape)
-        for i,image in enumerate(data_array):
-            xx, yy = np.indices(image.shape)
-            for r in range(image.shape[0]):
-                for c in range(image.shape[1]):
-                    dist_rc = np.sqrt((r-xx)**2+(c-yy)**2)
-                    g_rc = np.exp(-0.5*(dist_rc/stdev)**2)/(2.*np.pi*stdev**2)
-                    smoothed[i][r,c] = np.sum(image*g_rc)
-                    error[i][r,c] = np.sum(error_array*g_rc**2)
-
-    else:
-        raise ValueError("{} is not a valid smoothing option".format(smoothing))
-
-    return smoothed, error
-
-
-def polarizer_avg(data_array, error_array, headers, FWHM=None, scale='pixel',
-        smoothing='gaussian'):
-    """
-    Make the average image from a single polarizer for a given instrument.
-    -----------
-    Inputs:
-    data_array : numpy.ndarray
-        Array of images (2D floats, aligned and of the same shape) of a
-        single observation with multiple polarizers of an instrument.
-    error_array : numpy.ndarray
-        Array of images (2D floats, aligned and of the same shape) containing
-        the error in each pixel of the observation images in data_array.
-    headers : header list
-        List of headers corresponding to the images in data_array.
-    FWHM : float, optional
-        Full Width at Half Maximum of the detector for smoothing of the
-        data on each polarizer filter in 'scale' units. If None, no smoothing
-        is done.
-        Defaults to None.
-    scale : str, optional
-        Scale units for the FWHM between 'pixel' and 'arcsec'.
-        Defaults to 'pixel'.
-    smoothing : str, optional
-        Smoothing algorithm to be used on the input data array.
-        -'combine','combining' use the N images combining algorithm with
-          weighted pixels (inverse square of errors).
-        -'gaussian' convolve any input image with a gaussian of standard
-          deviation stdev = FWHM/(2*sqrt(2*log(2))).
-        Defaults to 'gaussian'. Won't be used if FWHM is None.
-    ----------
-    Returns:
-    polarizer_array : numpy.ndarray
-        Array of images averaged on each polarizer filter of the instrument
-    polarizer_cov : numpy.ndarray
-        Covariance matrix between the polarizer images in polarizer_array
-    """
-    # Check that all images are from the same instrument
-    instr = headers[0]['instrume']
-    same_instr = np.array([instr == header['instrume'] for header in headers]).all()
-    if not same_instr:
-        raise ValueError("All images in data_array are not from the same\
-                instrument, cannot proceed.")
-    if not instr in ['FOC']:
-        raise ValueError("Cannot reduce images from {0:s} instrument\
-                (yet)".format(instr))
-
-    # Routine for the FOC instrument
-    if instr == 'FOC':
-        # Sort images by polarizer filter : can be 0deg, 60deg, 120deg for the FOC
-        is_pol0 = np.array([header['filtnam1']=='POL0' for header in headers])
-        if (1-is_pol0).all():
-            print("Warning : no image for POL0 of FOC found, averaged data\
-                    will be NAN")
-        is_pol60 = np.array([header['filtnam1']=='POL60' for header in headers])
-        if (1-is_pol60).all():
-            print("Warning : no image for POL60 of FOC found, averaged data\
-                    will be NAN")
-        is_pol120 = np.array([header['filtnam1']=='POL120' for header in headers])
-        if (1-is_pol120).all():
-            print("Warning : no image for POL120 of FOC found, averaged data\
-                    will be NAN")
-        # Put each polarizer images in separate arrays
-        pol0_array = data_array[is_pol0]
-        pol60_array = data_array[is_pol60]
-        pol120_array = data_array[is_pol120]
-
-        err0_array = error_array[is_pol0]
-        err60_array = error_array[is_pol60]
-        err120_array = error_array[is_pol120]
-
-        headers0 = [header for header in headers if header['filtnam1']=='POL0']
-        headers60 = [header for header in headers if header['filtnam1']=='POL60']
-        headers120 = [header for header in headers if header['filtnam1']=='POL120']
-
-        if not(FWHM is None) and (smoothing.lower() in ['combine','combining']):
-            # Smooth by combining each polarizer images
-            pol0, err0 = smooth_data(pol0_array, err0_array, headers0,
-                    FWHM=FWHM, scale=scale, smoothing=smoothing)
-            pol60, err60 = smooth_data(pol60_array, err60_array, headers60,
-                    FWHM=FWHM, scale=scale, smoothing=smoothing)
-            pol120, err120 = smooth_data(pol120_array, err120_array, headers120,
-                    FWHM=FWHM, scale=scale, smoothing=smoothing)
-
-        else:
-            if not(FWHM is None):
-                # Smooth by convoluting with a gaussian each polX image.
-                pol0, err0 = smooth_data(pol0_array, err0_array, headers0,
-                        FWHM=FWHM, scale=scale)
-                pol60, err60 = smooth_data(pol60_array, err60_array, headers60,
-                        FWHM=FWHM, scale=scale)
-                pol120, err120 = smooth_data(pol120_array, err120_array, headers120,
-                        FWHM=FWHM, scale=scale)
-
-            # Sum on each polarization filter.
-            pol0 = pol0.sum(axis=0)
-            pol60 = pol60.sum(axis=0)
-            pol120 = pol120.sum(axis=0)
-
-            # Propagate uncertainties quadratically
-            err0 = np.mean(err0_array,axis=0)/np.sqrt(err0_array.shape[0])
-            err60 = np.mean(err60_array,axis=0)/np.sqrt(err60_array.shape[0])
-            err120 = np.mean(err120_array,axis=0)/np.sqrt(err120_array.shape[0])
-
-            headers_array = headers0 + headers60 + headers120
-        # Get image shape
-        shape = pol0.shape
-
-        # Construct the polarizer array
-        polarizer_array = np.zeros((3,shape[0],shape[1]))
-        polarizer_array[0] = pol0
-        polarizer_array[1] = pol60
-        polarizer_array[2] = pol120
-
-        # Define the covariance matrix for the polarizer images
-        #We assume cross terms are null
-        polarizer_cov = np.zeros((3,3,shape[0],shape[1]))
-        polarizer_cov[0,0] = err0**2
-        polarizer_cov[1,1] = err60**2
-        polarizer_cov[2,2] = err120**2
-
-    return polarizer_array, polarizer_cov
-
-
-def compute_Stokes(data_array, error_array, headers, FWHM=None,
-        scale='pixel', smoothing='gaussian'):
-    """
-    Compute the Stokes parameters I, Q and U for a given data_set
-    ----------
-    Inputs:
-    data_array : numpy.ndarray
-        Array of images (2D floats, aligned and of the same shape) of a
-        single observation with multiple polarizers of an instrument.
-    error_array : numpy.ndarray
-        Array of images (2D floats, aligned and of the same shape) containing
-        the error in each pixel of the observation images in data_array.
-    headers : header list
-        List of headers corresponding to the images in data_array.
-    FWHM : float, optional
-        Full Width at Half Maximum of the detector for smoothing of the
-        data on each polarizer filter in scale units. If None, no smoothing
-        is done.
-        Defaults to None.
-    scale : str, optional
-        Scale units for the FWHM between 'pixel' and 'arcsec'.
-        Defaults to 'pixel'.
-    smoothing : str, optional
-        Smoothing algorithm to be used on the input data array.
-        -'combine','combining' use the N images combining algorithm with
-          weighted pixels (inverse square of errors).
-        -'gaussian' convolve any input image with a gaussian of standard
-          deviation stdev = FWHM/(2*sqrt(2*log(2))).
-        Defaults to 'gaussian'. Won't be used if FWHM is None.
-    ----------
-    Returns:
-    I_stokes : numpy.ndarray
-        Image (2D floats) containing the Stokes parameters accounting for
-        total intensity
-    Q_stokes : numpy.ndarray
-        Image (2D floats) containing the Stokes parameters accounting for
-        vertical/horizontal linear polarization intensity
-    U_stokes : numpy.ndarray
-        Image (2D floats) containing the Stokes parameters accounting for
-        +45/-45deg linear polarization intensity
-    Stokes_cov : numpy.ndarray
-        Covariance matrix of the Stokes parameters I, Q, U.
-    """
-    # Check that all images are from the same instrument
-    instr = headers[0]['instrume']
-    same_instr = np.array([instr == header['instrume'] for header in headers]).all()
-    if not same_instr:
-        raise ValueError("All images in data_array are not from the same\
-                instrument, cannot proceed.")
-    if not instr in ['FOC']:
-        raise ValueError("Cannot reduce images from {0:s} instrument\
-                (yet)".format(instr))
-
-    # Routine for the FOC instrument
-    if instr == 'FOC':
-        # Get image from each polarizer and covariance matrix
-        pol_array, pol_cov = polarizer_avg(data_array, error_array, headers,
-                FWHM=FWHM, scale=scale, smoothing=smoothing)
-        pol0, pol60, pol120 = pol_array
-
-        #Stokes parameters
-        I_stokes = (2./3.)*(pol0 + pol60 + pol120)
-        Q_stokes = (2./3.)*(2*pol0 - pol60 - pol120)
-        U_stokes = (2./np.sqrt(3.))*(pol60 - pol120)
-
-        #Stokes covariance matrix
-        Stokes_cov = np.zeros((3,3,I_stokes.shape[0],I_stokes.shape[1]))
-        Stokes_cov[0,0] = (4./9.)*(pol_cov[0,0]+pol_cov[1,1]+pol_cov[2,2]) + (8./9.)*(pol_cov[0,1]+pol_cov[0,2]+pol_cov[1,2])
-        Stokes_cov[1,1] = (4./3.)*(pol_cov[1,1]+pol_cov[2,2]) - (8./3.)*pol_cov[1,2]
-        Stokes_cov[2,2] = (4./9.)*(4.*pol_cov[0,0]+pol_cov[1,1]+pol_cov[2,2]) - (8./3.)*(2.*pol_cov[0,1]+2.*pol_cov[0,2]-pol_cov[1,2])
-        Stokes_cov[0,1] = Stokes_cov[1,0] = (4./(3.*np.sqrt(3.)))*(pol_cov[1,1]-pol_cov[2,2]+pol_cov[0,1]-pol_cov[0,2])
-        Stokes_cov[0,2] = Stokes_cov[2,0] = (4./9.)*(2.*pol_cov[0,0]-pol_cov[1,1]-pol_cov[2,2]+pol_cov[0,1]+pol_cov[0,2]-2.*pol_cov[1,2])
-        Stokes_cov[1,2] = Stokes_cov[2,1] = (4./(3.*np.sqrt(3.)))*(-pol_cov[1,1]+pol_cov[2,2]+2.*pol_cov[0,1]-2.*pol_cov[0,2])
-
-        #Remove nan
-        I_stokes[np.isnan(I_stokes)]=0.
-        Q_stokes[np.isnan(Q_stokes)]=0.
-        U_stokes[np.isnan(U_stokes)]=0.
-
-    return I_stokes, Q_stokes, U_stokes, Stokes_cov
-
-
-def compute_pol(I_stokes, Q_stokes, U_stokes, Stokes_cov, headers):
-    """
-    Compute the polarization degree (in %) and angle (in deg) and their
-    respective errors from given Stokes parameters.
-    ----------
-    Inputs:
-    I_stokes : numpy.ndarray
-        Image (2D floats) containing the Stokes parameters accounting for
-        total intensity
-    Q_stokes : numpy.ndarray
-        Image (2D floats) containing the Stokes parameters accounting for
-        vertical/horizontal linear polarization intensity
-    U_stokes : numpy.ndarray
-        Image (2D floats) containing the Stokes parameters accounting for
-        +45/-45deg linear polarization intensity
-    Stokes_cov : numpy.ndarray
-        Covariance matrix of the Stokes parameters I, Q, U.
-    headers : header list
-        List of headers corresponding to the images in data_array.
-    ----------
-    Returns:
-    P : numpy.ndarray
-        Image (2D floats) containing the polarization degree (in %).
-    debiased_P : numpy.ndarray
-        Image (2D floats) containing the debiased polarization degree (in %).
-    s_P : numpy.ndarray
-        Image (2D floats) containing the error on the polarization degree.
-    s_P_P : numpy.ndarray
-        Image (2D floats) containing the Poisson noise error on the
-        polarization degree.
-    PA : numpy.ndarray
-        Image (2D floats) containing the polarization angle.
-    s_PA : numpy.ndarray
-        Image (2D floats) containing the error on the polarization angle.
-    s_PA_P : numpy.ndarray
-        Image (2D floats) containing the Poisson noise error on the
-        polarization angle.
-    new_headers : header list
-        Updated list of headers corresponding to the reduced images accounting
-        for the new orientation angle.
-    """
-    #Polarization degree and angle computation
-    I_pol = np.sqrt(Q_stokes**2 + U_stokes**2)
-    P = I_pol/I_stokes*100.
-    PA = (90./np.pi)*np.arctan2(U_stokes,Q_stokes)+90
-
-    if (np.isfinite(P)>100.).any():
-        print("WARNING : found pixels for which P > 100%")
-
-    #Associated errors
-    s_P = (100./I_stokes)*np.sqrt((Q_stokes**2*Stokes_cov[1,1] + U_stokes**2*Stokes_cov[2,2] + 2.*Q_stokes*U_stokes*Stokes_cov[1,2])/(Q_stokes**2 + U_stokes**2) + ((Q_stokes/I_stokes)**2 + (U_stokes/I_stokes)**2)*Stokes_cov[0,0] - 2.*(Q_stokes/I_stokes)*Stokes_cov[0,1] - 2.*(U_stokes/I_stokes)*Stokes_cov[0,2])
-
-    s_PA = (90./(np.pi*(Q_stokes**2 + U_stokes**2)))*np.sqrt(U_stokes**2*Stokes_cov[1,1] + Q_stokes**2*Stokes_cov[2,2] - 2.*Q_stokes*U_stokes*Stokes_cov[1,2])
-
-    debiased_P = np.sqrt(P**2 - s_P**2)
-
-    #Compute the total exposure time so that
-    #I_stokes*exp_tot = N_tot the total number of events
-    exp_tot = np.array([header['exptime'] for header in headers]).sum()
-    N_obs = I_stokes/np.array([header['photflam'] for header in headers]).mean()*exp_tot
-
-    #Errors on P, PA supposing Poisson noise
-    s_P_P = np.sqrt(2.)*(I_stokes)**(-0.5)*100.
-    s_PA_P = s_P_P/(2.*P/100.)*180./np.pi
-
-    return P, debiased_P, s_P, s_P_P, PA, s_PA, s_PA_P
-
-
-def rotate_sc_Stokes(I_stokes, Q_stokes, U_stokes, Stokes_cov, headers, ang):
-    """
-    Use scipy.ndimage.rotate to rotate I_stokes to an angle, and a rotation
-    matrix to rotate Q, U of a given angle in degrees and update header
-    orientation keyword.
-    ----------
-    Inputs:
-    I_stokes : numpy.ndarray
-        Image (2D floats) containing the Stokes parameters accounting for
-        total intensity
-    Q_stokes : numpy.ndarray
-        Image (2D floats) containing the Stokes parameters accounting for
-        vertical/horizontal linear polarization intensity
-    U_stokes : numpy.ndarray
-        Image (2D floats) containing the Stokes parameters accounting for
-        +45/-45deg linear polarization intensity
-    Stokes_cov : numpy.ndarray
-        Covariance matrix of the Stokes parameters I, Q, U.
-    headers : header list
-        List of headers corresponding to the reduced images.
-    ang : float
-        Rotation angle (in degrees) that should be applied to the Stokes
-        parameters
-    ----------
-    Returns:
-    new_I_stokes : numpy.ndarray
-        Rotated mage (2D floats) containing the rotated Stokes parameters
-        accounting for total intensity
-    new_Q_stokes : numpy.ndarray
-        Rotated mage (2D floats) containing the rotated Stokes parameters
-        accounting for vertical/horizontal linear polarization intensity
-    new_U_stokes : numpy.ndarray
-        Rotated image (2D floats) containing the rotated Stokes parameters
-        accounting for +45/-45deg linear polarization intensity.
-    new_Stokes_cov : numpy.ndarray
-        Updated covariance matrix of the Stokes parameters I, Q, U.
-    new_headers : header list
-        Updated list of headers corresponding to the reduced images accounting
-        for the new orientation angle.
-    """
-    #Rotate I_stokes, Q_stokes, U_stokes using rotation matrix
-    alpha = ang*np.pi/180.
-    new_I_stokes = 1.*I_stokes
-    new_Q_stokes = np.cos(2*alpha)*Q_stokes + np.sin(2*alpha)*U_stokes
-    new_U_stokes = -np.sin(2*alpha)*Q_stokes + np.cos(2*alpha)*U_stokes
-
-    #Compute new covariance matrix on rotated parameters
-    new_Stokes_cov = copy.deepcopy(Stokes_cov)
-    new_Stokes_cov[1,1] = np.cos(2.*alpha)**2*Stokes_cov[1,1] + np.sin(2.*alpha)**2*Stokes_cov[2,2] + 2.*np.cos(2.*alpha)*np.sin(2.*alpha)*Stokes_cov[1,2]
-    new_Stokes_cov[2,2] = np.sin(2.*alpha)**2*Stokes_cov[1,1] + np.cos(2.*alpha)**2*Stokes_cov[2,2] - 2.*np.cos(2.*alpha)*np.sin(2.*alpha)*Stokes_cov[1,2]
-    new_Stokes_cov[0,1] = new_Stokes_cov[1,0] = np.cos(2.*alpha)*Stokes_cov[0,1] + np.sin(2.*alpha)*Stokes_cov[0,2]
-    new_Stokes_cov[0,2] = new_Stokes_cov[2,0] = -np.sin(2.*alpha)*Stokes_cov[0,1] + np.cos(2.*alpha)*Stokes_cov[0,2]
-    new_Stokes_cov[1,2] = new_Stokes_cov[2,1] = np.cos(2.*alpha)*np.sin(2.*alpha)*(Stokes_cov[2,2] - Stokes_cov[1,1]) + (np.cos(2.*alpha)**2 - np.sin(2.*alpha)**2)*Stokes_cov[1,2]
-
-    #Rotate original images using scipy.ndimage.rotate
-    new_I_stokes = sc_rotate(new_I_stokes, ang, reshape=False,
-            cval=np.sqrt(new_Stokes_cov[0,0][0,0]))
-    new_Q_stokes = sc_rotate(new_Q_stokes, ang, reshape=False,
-            cval=np.sqrt(new_Stokes_cov[1,1][0,0]))
-    new_U_stokes = sc_rotate(new_U_stokes, ang, reshape=False,
-            cval=np.sqrt(new_Stokes_cov[2,2][0,0]))
-    for i in range(3):
-        for j in range(3):
-            new_Stokes_cov[i,j] = sc_rotate(new_Stokes_cov[i,j], ang, reshape=False,
-                    cval=new_Stokes_cov[i,j].mean())
-
-    #Update headers to new angle
-    new_headers = []
-    mrot = np.array([[np.cos(-alpha), -np.sin(-alpha)],
-        [np.sin(-alpha), np.cos(-alpha)]])
-    for header in headers:
-        new_header = copy.deepcopy(header)
-        new_header['orientat'] = header['orientat'] + ang
-
-        new_wcs = WCS(header).deepcopy()
-        if new_wcs.wcs.has_cd():    # CD matrix
-            del w.wcs.cd
-            keys = ['CD1_1','CD1_2','CD2_1','CD2_2']
-            for key in keys:
-                new_header.remove(key, ignore_missing=True)
-            w.wcs.cdelt = 3600.*np.sqrt(np.sum(w.wcs.get_pc()**2,axis=1))
-        elif new_wcs.wcs.has_pc():      # PC matrix + CDELT
-            newpc = np.dot(mrot, new_wcs.wcs.get_pc())
-            new_wcs.wcs.pc = newpc
-        new_wcs.wcs.set()
-        new_header.update(new_wcs.to_header())
-
-        new_headers.append(new_header)
-
-    return new_I_stokes, new_Q_stokes, new_U_stokes, new_Stokes_cov, new_headers
-
-
-def rotate_PIL_Stokes(I_stokes, Q_stokes, U_stokes, Stokes_cov, headers, ang):
-    """
-    Use scipy.ndimage.rotate to rotate I_stokes to an angle, and a rotation
-    matrix to rotate Q, U of a given angle in degrees and update header
-    orientation keyword.
-    ----------
-    Inputs:
-    I_stokes : numpy.ndarray
-        Image (2D floats) containing the Stokes parameters accounting for
-        total intensity
-    Q_stokes : numpy.ndarray
-        Image (2D floats) containing the Stokes parameters accounting for
-        vertical/horizontal linear polarization intensity
-    U_stokes : numpy.ndarray
-        Image (2D floats) containing the Stokes parameters accounting for
-        +45/-45deg linear polarization intensity
-    Stokes_cov : numpy.ndarray
-        Covariance matrix of the Stokes parameters I, Q, U.
-    headers : header list
-        List of headers corresponding to the reduced images.
-    ang : float
-        Rotation angle (in degrees) that should be applied to the Stokes
-        parameters
-    ----------
-    Returns:
-    new_I_stokes : numpy.ndarray
-        Rotated mage (2D floats) containing the rotated Stokes parameters
-        accounting for total intensity
-    new_Q_stokes : numpy.ndarray
-        Rotated mage (2D floats) containing the rotated Stokes parameters
-        accounting for vertical/horizontal linear polarization intensity
-    new_U_stokes : numpy.ndarray
-        Rotated image (2D floats) containing the rotated Stokes parameters
-        accounting for +45/-45deg linear polarization intensity.
-    new_Stokes_cov : numpy.ndarray
-        Updated covariance matrix of the Stokes parameters I, Q, U.
-    new_headers : header list
-        Updated list of headers corresponding to the reduced images accounting
-        for the new orientation angle.
-    """
-    #Rotate I_stokes, Q_stokes, U_stokes using rotation matrix
-    alpha = ang*np.pi/180.
-    new_I_stokes = 1.*I_stokes
-    new_Q_stokes = np.cos(2*alpha)*Q_stokes + np.sin(2*alpha)*U_stokes
-    new_U_stokes = -np.sin(2*alpha)*Q_stokes + np.cos(2*alpha)*U_stokes
-
-    #Compute new covariance matrix on rotated parameters
-    new_Stokes_cov = copy.deepcopy(Stokes_cov)
-    new_Stokes_cov[1,1] = np.cos(2.*alpha)**2*Stokes_cov[1,1] + np.sin(2.*alpha)**2*Stokes_cov[2,2] + 2.*np.cos(2.*alpha)*np.sin(2.*alpha)*Stokes_cov[1,2]
-    new_Stokes_cov[2,2] = np.sin(2.*alpha)**2*Stokes_cov[1,1] + np.cos(2.*alpha)**2*Stokes_cov[2,2] - 2.*np.cos(2.*alpha)*np.sin(2.*alpha)*Stokes_cov[1,2]
-    new_Stokes_cov[0,1] = new_Stokes_cov[1,0] = np.cos(2.*alpha)*Stokes_cov[0,1] + np.sin(2.*alpha)*Stokes_cov[0,2]
-    new_Stokes_cov[0,2] = new_Stokes_cov[2,0] = -np.sin(2.*alpha)*Stokes_cov[0,1] + np.cos(2.*alpha)*Stokes_cov[0,2]
-    new_Stokes_cov[1,2] = new_Stokes_cov[2,1] = np.cos(2.*alpha)*np.sin(2.*alpha)*(Stokes_cov[2,2] - Stokes_cov[1,1]) + (np.cos(2.*alpha)**2 - np.sin(2.*alpha)**2)*Stokes_cov[1,2]
-
-    #Rotate original images using scipy.ndimage.rotate
-    new_I_stokes = PIL_rotate(new_I_stokes, ang, reshape=False,
-            cval=np.sqrt(new_Stokes_cov[0,0][0,0]))
-    new_Q_stokes = PIL_rotate(new_Q_stokes, ang, reshape=False,
-            cval=np.sqrt(new_Stokes_cov[1,1][0,0]))
-    new_U_stokes = PIL_rotate(new_U_stokes, ang, reshape=False,
-            cval=np.sqrt(new_Stokes_cov[2,2][0,0]))
-    for i in range(3):
-        for j in range(3):
-            new_Stokes_cov[i,j] = PIL_rotate(new_Stokes_cov[i,j], ang,
-                    reshape=False, cval=new_Stokes_cov[i,j].mean())
-
-    #Update headers to new angle
-    new_headers = []
-    mrot = np.array([[np.cos(-alpha), -np.sin(-alpha)],
-        [np.sin(-alpha), np.cos(-alpha)]])
-    for header in headers:
-        new_header = copy.deepcopy(header)
-        new_header['orientat'] = header['orientat'] + ang
-
-        new_wcs = WCS(header).deepcopy()
-        if new_wcs.wcs.has_cd():    # CD matrix
-            del w.wcs.cd
-            keys = ['CD1_1','CD1_2','CD2_1','CD2_2']
-            for key in keys:
-                new_header.remove(key, ignore_missing=True)
-            w.wcs.cdelt = 3600.*np.sqrt(np.sum(w.wcs.get_pc()**2,axis=1))
-        elif new_wcs.wcs.has_pc():      # PC matrix + CDELT
-            newpc = np.dot(mrot, new_wcs.wcs.get_pc())
-            new_wcs.wcs.pc = newpc
-        new_wcs.wcs.set()
-        new_header.update(new_wcs.to_header())
-
-        new_headers.append(new_header)
-
-    return new_I_stokes, new_Q_stokes, new_U_stokes, new_Stokes_cov, new_headers
-
-
-def rotate2_sc_Stokes(I_stokes, Q_stokes, U_stokes, Stokes_cov, headers, ang):
-    """
-    Use scipy.ndimage.rotate to rotate I_stokes to an angle, and a rotation
-    matrix to rotate Q, U of a given angle in degrees and update header
-    orientation keyword.
-    ----------
-    Inputs:
-    I_stokes : numpy.ndarray
-        Image (2D floats) containing the Stokes parameters accounting for
-        total intensity
-    Q_stokes : numpy.ndarray
-        Image (2D floats) containing the Stokes parameters accounting for
-        vertical/horizontal linear polarization intensity
-    U_stokes : numpy.ndarray
-        Image (2D floats) containing the Stokes parameters accounting for
-        +45/-45deg linear polarization intensity
-    Stokes_cov : numpy.ndarray
-        Covariance matrix of the Stokes parameters I, Q, U.
-    headers : header list
-        List of headers corresponding to the reduced images.
-    ang : float
-        Rotation angle (in degrees) that should be applied to the Stokes
-        parameters
-    ----------
-    Returns:
-    new_I_stokes : numpy.ndarray
-        Rotated mage (2D floats) containing the rotated Stokes parameters
-        accounting for total intensity
-    new_Q_stokes : numpy.ndarray
-        Rotated mage (2D floats) containing the rotated Stokes parameters
-        accounting for vertical/horizontal linear polarization intensity
-    new_U_stokes : numpy.ndarray
-        Rotated image (2D floats) containing the rotated Stokes parameters
-        accounting for +45/-45deg linear polarization intensity.
-    new_Stokes_cov : numpy.ndarray
-        Updated covariance matrix of the Stokes parameters I, Q, U.
-    P : numpy.ndarray
-        Image (2D floats) containing the polarization degree (in %).
-    s_P : numpy.ndarray
-        Image (2D floats) containing the error on the polarization degree.
-    PA : numpy.ndarray
-        Image (2D floats) containing the polarization angle.
-    s_PA : numpy.ndarray
-        Image (2D floats) containing the error on the polarization angle.
-    debiased_P : numpy.ndarray
-        Image (2D floats) containing the debiased polarization degree (in %).
-    s_P_P : numpy.ndarray
-        Image (2D floats) containing the Poisson noise error on the
-        polarization degree.
-    s_PA_P : numpy.ndarray
-        Image (2D floats) containing the Poisson noise error on the
-        polarization angle.
-    """
-    # Rotate I_stokes, Q_stokes, U_stokes using rotation matrix
-    alpha = ang*np.pi/180.
-    new_I_stokes = 1.*I_stokes
-    new_Q_stokes = np.cos(2*alpha)*Q_stokes + np.sin(2*alpha)*U_stokes
-    new_U_stokes = -np.sin(2*alpha)*Q_stokes + np.cos(2*alpha)*U_stokes
-
-    # Compute new covariance matrix on rotated parameters
-    new_Stokes_cov = copy.deepcopy(Stokes_cov)
-    new_Stokes_cov[1,1] = np.cos(2.*alpha)**2*Stokes_cov[1,1] + np.sin(2.*alpha)**2*Stokes_cov[2,2] + 2.*np.cos(2.*alpha)*np.sin(2.*alpha)*Stokes_cov[1,2]
-    new_Stokes_cov[2,2] = np.sin(2.*alpha)**2*Stokes_cov[1,1] + np.cos(2.*alpha)**2*Stokes_cov[2,2] - 2.*np.cos(2.*alpha)*np.sin(2.*alpha)*Stokes_cov[1,2]
-    new_Stokes_cov[0,1] = new_Stokes_cov[1,0] = np.cos(2.*alpha)*Stokes_cov[0,1] + np.sin(2.*alpha)*Stokes_cov[0,2]
-    new_Stokes_cov[0,2] = new_Stokes_cov[2,0] = -np.sin(2.*alpha)*Stokes_cov[0,1] + np.cos(2.*alpha)*Stokes_cov[0,2]
-    new_Stokes_cov[1,2] = new_Stokes_cov[2,1] = np.cos(2.*alpha)*np.sin(2.*alpha)*(Stokes_cov[2,2] - Stokes_cov[1,1]) + (np.cos(2.*alpha)**2 - np.sin(2.*alpha)**2)*Stokes_cov[1,2]
-
-    # Compute new polarization parameters
-    P, debiased_P, s_P, s_P_P, PA, s_PA, s_PA_P = compute_pol(new_I_stokes,
-            new_Q_stokes, new_U_stokes, new_Stokes_cov, headers)
-
-    # Rotate original images using scipy.ndimage.rotate
-    new_I_stokes = sc_rotate(new_I_stokes, ang, reshape=False,
-            cval=np.sqrt(new_Stokes_cov[0,0][0,0]))
-    new_Q_stokes = sc_rotate(new_Q_stokes, ang, reshape=False,
-            cval=np.sqrt(new_Stokes_cov[1,1][0,0]))
-    new_U_stokes = sc_rotate(new_U_stokes, ang, reshape=False,
-            cval=np.sqrt(new_Stokes_cov[2,2][0,0]))
-    P = sc_rotate(P, ang, reshape=False, cval=P.mean())
-    debiased_P = sc_rotate(debiased_P, ang, reshape=False,
-            cval=debiased_P.mean())
-    s_P = sc_rotate(s_P, ang, reshape=False, cval=s_P.mean())
-    s_P_P = sc_rotate(s_P_P, ang, reshape=False, cval=s_P_P.mean())
-    PA = sc_rotate(PA, ang, reshape=False, cval=PA.mean())
-    s_PA = sc_rotate(s_PA, ang, reshape=False, cval=s_PA.mean())
-    s_PA_P = sc_rotate(s_PA_P, ang, reshape=False, cval=s_PA_P.mean())
-    for i in range(3):
-        for j in range(3):
-            new_Stokes_cov[i,j] = sc_rotate(new_Stokes_cov[i,j], ang,
-                    reshape=False, cval=new_Stokes_cov[i,j].mean())
-
-    #Update headers to new angle
-    new_headers = []
-    mrot = np.array([[np.cos(-alpha), -np.sin(-alpha)],
-        [np.sin(-alpha), np.cos(-alpha)]])
-    for header in headers:
-        new_header = copy.deepcopy(header)
-        new_header['orientat'] = header['orientat'] + ang
-
-        new_wcs = WCS(header).deepcopy()
-        if new_wcs.wcs.has_cd():    # CD matrix
-            del w.wcs.cd
-            keys = ['CD1_1','CD1_2','CD2_1','CD2_2']
-            for key in keys:
-                new_header.remove(key, ignore_missing=True)
-            w.wcs.cdelt = 3600.*np.sqrt(np.sum(w.wcs.get_pc()**2,axis=1))
-        elif new_wcs.wcs.has_pc():      # PC matrix + CDELT
-            newpc = np.dot(mrot, new_wcs.wcs.get_pc())
-            new_wcs.wcs.pc = newpc
-        new_wcs.wcs.set()
-        new_header.update(new_wcs.to_header())
-
-        new_headers.append(new_header)
-
-    return new_I_stokes, new_Q_stokes, new_U_stokes, new_Stokes_cov, P, debiased_P, s_P, s_P_P, PA, s_PA, s_PA_P, new_headers
-
-
-def rotate2_PIL_Stokes(I_stokes, Q_stokes, U_stokes, Stokes_cov, headers, ang):
-    """
-    Use scipy.ndimage.rotate to rotate I_stokes to an angle, and a rotation
-    matrix to rotate Q, U of a given angle in degrees and update header
-    orientation keyword.
-    ----------
-    Inputs:
-    I_stokes : numpy.ndarray
-        Image (2D floats) containing the Stokes parameters accounting for
-        total intensity
-    Q_stokes : numpy.ndarray
-        Image (2D floats) containing the Stokes parameters accounting for
-        vertical/horizontal linear polarization intensity
-    U_stokes : numpy.ndarray
-        Image (2D floats) containing the Stokes parameters accounting for
-        +45/-45deg linear polarization intensity
-    Stokes_cov : numpy.ndarray
-        Covariance matrix of the Stokes parameters I, Q, U.
-    headers : header list
-        List of headers corresponding to the reduced images.
-    ang : float
-        Rotation angle (in degrees) that should be applied to the Stokes
-        parameters
-    ----------
-    Returns:
-    new_I_stokes : numpy.ndarray
-        Rotated mage (2D floats) containing the rotated Stokes parameters
-        accounting for total intensity
-    new_Q_stokes : numpy.ndarray
-        Rotated mage (2D floats) containing the rotated Stokes parameters
-        accounting for vertical/horizontal linear polarization intensity
-    new_U_stokes : numpy.ndarray
-        Rotated image (2D floats) containing the rotated Stokes parameters
-        accounting for +45/-45deg linear polarization intensity.
-    new_Stokes_cov : numpy.ndarray
-        Updated covariance matrix of the Stokes parameters I, Q, U.
-    P : numpy.ndarray
-        Image (2D floats) containing the polarization degree (in %).
-    s_P : numpy.ndarray
-        Image (2D floats) containing the error on the polarization degree.
-    PA : numpy.ndarray
-        Image (2D floats) containing the polarization angle.
-    s_PA : numpy.ndarray
-        Image (2D floats) containing the error on the polarization angle.
-    debiased_P : numpy.ndarray
-        Image (2D floats) containing the debiased polarization degree (in %).
-    s_P_P : numpy.ndarray
-        Image (2D floats) containing the Poisson noise error on the
-        polarization degree.
-    s_PA_P : numpy.ndarray
-        Image (2D floats) containing the Poisson noise error on the
-        polarization angle.
-    """
-    # Rotate I_stokes, Q_stokes, U_stokes using rotation matrix
-    alpha = ang*np.pi/180.
-    new_I_stokes = 1.*I_stokes
-    new_Q_stokes = np.cos(2*alpha)*Q_stokes + np.sin(2*alpha)*U_stokes
-    new_U_stokes = -np.sin(2*alpha)*Q_stokes + np.cos(2*alpha)*U_stokes
-
-    # Compute new covariance matrix on rotated parameters
-    new_Stokes_cov = copy.deepcopy(Stokes_cov)
-    new_Stokes_cov[1,1] = np.cos(2.*alpha)**2*Stokes_cov[1,1] + np.sin(2.*alpha)**2*Stokes_cov[2,2] + 2.*np.cos(2.*alpha)*np.sin(2.*alpha)*Stokes_cov[1,2]
-    new_Stokes_cov[2,2] = np.sin(2.*alpha)**2*Stokes_cov[1,1] + np.cos(2.*alpha)**2*Stokes_cov[2,2] - 2.*np.cos(2.*alpha)*np.sin(2.*alpha)*Stokes_cov[1,2]
-    new_Stokes_cov[0,1] = new_Stokes_cov[1,0] = np.cos(2.*alpha)*Stokes_cov[0,1] + np.sin(2.*alpha)*Stokes_cov[0,2]
-    new_Stokes_cov[0,2] = new_Stokes_cov[2,0] = -np.sin(2.*alpha)*Stokes_cov[0,1] + np.cos(2.*alpha)*Stokes_cov[0,2]
-    new_Stokes_cov[1,2] = new_Stokes_cov[2,1] = np.cos(2.*alpha)*np.sin(2.*alpha)*(Stokes_cov[2,2] - Stokes_cov[1,1]) + (np.cos(2.*alpha)**2 - np.sin(2.*alpha)**2)*Stokes_cov[1,2]
-
-    # Compute new polarization parameters
-    P, debiased_P, s_P, s_P_P, PA, s_PA, s_PA_P = compute_pol(new_I_stokes,
-            new_Q_stokes, new_U_stokes, new_Stokes_cov, headers)
-
-    # Rotate original images using scipy.ndimage.rotate
-    new_I_stokes = PIL_rotate(new_I_stokes, ang, reshape=False,
-            cval=np.sqrt(new_Stokes_cov[0,0][0,0]))
-    new_Q_stokes = PIL_rotate(new_Q_stokes, ang, reshape=False,
-            cval=np.sqrt(new_Stokes_cov[1,1][0,0]))
-    new_U_stokes = PIL_rotate(new_U_stokes, ang, reshape=False,
-            cval=np.sqrt(new_Stokes_cov[2,2][0,0]))
-    P = PIL_rotate(P, ang, reshape=False, cval=P.mean())+0.
-    debiased_P = PIL_rotate(debiased_P, ang, reshape=False,
-            cval=debiased_P.mean())
-    s_P = PIL_rotate(s_P, ang, reshape=False, cval=s_P.mean())
-    s_P_P = PIL_rotate(s_P_P, ang, reshape=False, cval=s_P_P.mean())
-    PA = PIL_rotate(PA, ang, reshape=False, cval=PA.mean())
-    s_PA = PIL_rotate(s_PA, ang, reshape=False, cval=s_PA.mean())
-    s_PA_P = PIL_rotate(s_PA_P, ang, reshape=False, cval=s_PA_P.mean())
-    for i in range(3):
-        for j in range(3):
-            new_Stokes_cov[i,j] = PIL_rotate(new_Stokes_cov[i,j], ang,
-                    reshape=False, cval=new_Stokes_cov[i,j].mean())
-
-    #Update headers to new angle
-    new_headers = []
-    mrot = np.array([[np.cos(-alpha), -np.sin(-alpha)],
-        [np.sin(-alpha), np.cos(-alpha)]])
-    for header in headers:
-        new_header = copy.deepcopy(header)
-        new_header['orientat'] = header['orientat'] + ang
-
-        new_wcs = WCS(header).deepcopy()
-        if new_wcs.wcs.has_cd():    # CD matrix
-            del w.wcs.cd
-            keys = ['CD1_1','CD1_2','CD2_1','CD2_2']
-            for key in keys:
-                new_header.remove(key, ignore_missing=True)
-            w.wcs.cdelt = 3600.*np.sqrt(np.sum(w.wcs.get_pc()**2,axis=1))
-        elif new_wcs.wcs.has_pc():      # PC matrix + CDELT
-            newpc = np.dot(mrot, new_wcs.wcs.get_pc())
-            new_wcs.wcs.pc = newpc
-        new_wcs.wcs.set()
-        new_header.update(new_wcs.to_header())
-
-        new_headers.append(new_header)
-
-    return new_I_stokes, new_Q_stokes, new_U_stokes, new_Stokes_cov, P, debiased_P, s_P, s_P_P, PA, s_PA, s_PA_P, new_headers