Change pipeline to work in counts/sec and convert on display, add 'gaussian_after' smoothing for convolution of Stokes I/Q/U
This commit is contained in:
Thibault Barnouin
@@ -33,19 +33,12 @@ prototypes :
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Compute polarization degree (in %) and angle (in degree) and their
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respective errors
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- rotate_sc_Stokes(I_stokes, Q_stokes, U_stokes, Stokes_cov, headers, ang) -> I_stokes, Q_stokes, U_stokes, Stokes_cov, headers
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- rotate_Stokes(I_stokes, Q_stokes, U_stokes, Stokes_cov, headers, ang) -> I_stokes, Q_stokes, U_stokes, Stokes_cov, headers
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Rotate I, Q, U given an angle in degrees using scipy functions.
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- rotate_PIL_Stokes(I_stokes, Q_stokes, U_stokes, Stokes_cov, headers, ang) -> I_stokes, Q_stokes, U_stokes, Stokes_cov, headers
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Rotate I, Q, U given an angle in degrees using Pillow function.
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- 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
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- rotate2_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
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Rotate I, Q, U, P, PA and associated errors given an angle in degrees
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using scipy functions.
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- 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
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Rotate I, Q, U, P, PA and associated errors given an angle in degrees
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using Pillow function.
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"""
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import copy
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@@ -55,7 +48,6 @@ import matplotlib.dates as mdates
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from datetime import datetime
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from scipy.ndimage import rotate as sc_rotate
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from scipy.ndimage import shift as sc_shift
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from PIL import Image
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from astropy.wcs import WCS
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from lib.deconvolve import deconvolve_im, gaussian_psf
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from lib.convex_hull import image_hull
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@@ -63,36 +55,6 @@ from lib.plots import plot_obs
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from lib.cross_correlation import phase_cross_correlation
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def PIL_rotate(ndarray, angle, reshape=False, cval=0.):
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"""
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Define Function with similar parameters as scipy.ndimage.rotate making use
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of the Pillow library.
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----------
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Inputs:
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ndarray : numpy.ndarray
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2D float array to be rotated using Pillow library functions.
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angle : float
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Counter-clockwise ngle in degrees that the input array should be
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rotated to.
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reshape : boolean, optional
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If True, output image will be of shape such that the full rotated
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input array is displayed. If False, output array will be of same
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shape than input array, cutting rotated edges.
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Defaults to False.
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cval : float, optional
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Values with which will be filled pixels entering the output array by
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rotation of the input array.
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Dafaults to 0.
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----------
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Returns:
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rotated_array : numpy.ndarray
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Rotated array using Pillow functions.
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"""
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image = Image.fromarray(ndarray)
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rotated = image.rotate(angle, expand=reshape, fillcolor=cval)
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return np.asarray(rotated)
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def get_row_compressor(old_dimension, new_dimension, operation='sum'):
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"""
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Return the matrix that allows to compress an array from an old dimension of
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@@ -660,8 +622,8 @@ def align_data(data_array, error_array=None, upsample_factor=1., ref_data=None,
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full_array = np.concatenate((data_array,[ref_data]),axis=0)
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err_array = np.concatenate((error_array,[np.zeros(ref_data.shape)]),axis=0)
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#full_array, err_array = crop_array(full_array, err_array, step=5,
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# inside=False)
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full_array, err_array = crop_array(full_array, err_array, step=5,
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inside=False)
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data_array, ref_data = full_array[:-1], full_array[-1]
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error_array = err_array[:-1]
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@@ -772,7 +734,7 @@ def smooth_data(data_array, error_array, headers, FWHM=1., scale='pixel',
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px_dim[0] = 50.
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w.wcs.cdelt = 206.3*px_dim/(f_ratio*HST_aper)
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header.update(w.to_header())
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pxsize[i] = np.round(w.wcs.cdelt,5)
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pxsize[i] = np.round(w.wcs.cdelt,4)
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if (pxsize != pxsize[0]).any():
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raise ValueError("Not all images in array have same pixel size")
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FWHM /= pxsize[0].min()
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@@ -915,8 +877,17 @@ def polarizer_avg(data_array, error_array, headers, FWHM=None, scale='pixel',
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err120 = np.mean(err120_array,axis=0)/np.sqrt(err120_array.shape[0])
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polerr_array = np.array([err0, err60, err120])
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headers_array = headers0 + headers60 + headers120
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if not(FWHM is None):
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# Update headers
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for header in headers:
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if header['filtnam1']=='POL0':
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list_head = headers0
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elif header['filtnam1']=='POL60':
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list_head = headers60
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else:
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list_head = headers120
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header['exptime'] = np.mean([head['exptime'] for head in list_head])/len(list_head)
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headers_array = [headers0[0], headers60[0], headers120[0]]
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if not(FWHM is None) and (smoothing.lower() in ['gaussian','gauss']):
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# Smooth by convoluting with a gaussian each polX image.
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pol_array, polerr_array = smooth_data(pol_array, polerr_array,
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headers_array, FWHM=FWHM, scale=scale)
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@@ -943,7 +914,7 @@ def polarizer_avg(data_array, error_array, headers, FWHM=None, scale='pixel',
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def compute_Stokes(data_array, error_array, headers, FWHM=None,
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scale='pixel', smoothing='gaussian'):
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scale='pixel', smoothing='gaussian_after'):
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"""
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Compute the Stokes parameters I, Q and U for a given data_set
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----------
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@@ -970,7 +941,9 @@ def compute_Stokes(data_array, error_array, headers, FWHM=None,
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weighted pixels (inverse square of errors).
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-'gaussian' convolve any input image with a gaussian of standard
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deviation stdev = FWHM/(2*sqrt(2*log(2))).
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Defaults to 'gaussian'. Won't be used if FWHM is None.
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-'gaussian_after' convolve output Stokes I/Q/U with a gaussian of
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standard deviation stdev = FWHM/(2*sqrt(2*log(2))).
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Defaults to 'gaussian_after'. Won't be used if FWHM is None.
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----------
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Returns:
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I_stokes : numpy.ndarray
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@@ -1021,6 +994,17 @@ def compute_Stokes(data_array, error_array, headers, FWHM=None,
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Q_stokes[np.isnan(Q_stokes)]=0.
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U_stokes[np.isnan(U_stokes)]=0.
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if not(FWHM is None) and (smoothing.lower() in ['gaussian_after','gauss_after']):
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Stokes_array = np.array([I_stokes, Q_stokes, U_stokes])
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Stokes_error = np.array([np.sqrt(Stokes_cov[i,i]) for i in range(3)])
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Stokes_headers = headers[0:3]
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Stokes_array, Stokes_error = smooth_data(Stokes_array, Stokes_error,
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headers=Stokes_headers, FWHM=FWHM, scale=scale)
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I_stokes, Q_stokes, U_stokes = Stokes_array
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Stokes_cov[0,0], Stokes_cov[1,1], Stokes_cov[2,2] = Stokes_error**2
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return I_stokes, Q_stokes, U_stokes, Stokes_cov
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@@ -1092,7 +1076,7 @@ def compute_pol(I_stokes, Q_stokes, U_stokes, Stokes_cov, headers):
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return P, debiased_P, s_P, s_P_P, PA, s_PA, s_PA_P
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def rotate_sc_Stokes(I_stokes, Q_stokes, U_stokes, Stokes_cov, headers, ang):
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def rotate_Stokes(I_stokes, Q_stokes, U_stokes, Stokes_cov, headers, ang):
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"""
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Use scipy.ndimage.rotate to rotate I_stokes to an angle, and a rotation
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matrix to rotate Q, U of a given angle in degrees and update header
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@@ -1184,99 +1168,7 @@ def rotate_sc_Stokes(I_stokes, Q_stokes, U_stokes, Stokes_cov, headers, ang):
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return new_I_stokes, new_Q_stokes, new_U_stokes, new_Stokes_cov, new_headers
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def rotate_PIL_Stokes(I_stokes, Q_stokes, U_stokes, Stokes_cov, headers, ang):
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"""
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Use scipy.ndimage.rotate to rotate I_stokes to an angle, and a rotation
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matrix to rotate Q, U of a given angle in degrees and update header
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orientation keyword.
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----------
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Inputs:
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I_stokes : numpy.ndarray
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Image (2D floats) containing the Stokes parameters accounting for
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total intensity
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Q_stokes : numpy.ndarray
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Image (2D floats) containing the Stokes parameters accounting for
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vertical/horizontal linear polarization intensity
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U_stokes : numpy.ndarray
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Image (2D floats) containing the Stokes parameters accounting for
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+45/-45deg linear polarization intensity
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Stokes_cov : numpy.ndarray
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Covariance matrix of the Stokes parameters I, Q, U.
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headers : header list
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List of headers corresponding to the reduced images.
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ang : float
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Rotation angle (in degrees) that should be applied to the Stokes
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parameters
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----------
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Returns:
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new_I_stokes : numpy.ndarray
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Rotated mage (2D floats) containing the rotated Stokes parameters
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accounting for total intensity
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new_Q_stokes : numpy.ndarray
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Rotated mage (2D floats) containing the rotated Stokes parameters
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accounting for vertical/horizontal linear polarization intensity
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new_U_stokes : numpy.ndarray
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Rotated image (2D floats) containing the rotated Stokes parameters
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accounting for +45/-45deg linear polarization intensity.
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new_Stokes_cov : numpy.ndarray
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Updated covariance matrix of the Stokes parameters I, Q, U.
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new_headers : header list
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Updated list of headers corresponding to the reduced images accounting
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for the new orientation angle.
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"""
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#Rotate I_stokes, Q_stokes, U_stokes using rotation matrix
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alpha = ang*np.pi/180.
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new_I_stokes = 1.*I_stokes
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new_Q_stokes = np.cos(2*alpha)*Q_stokes + np.sin(2*alpha)*U_stokes
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new_U_stokes = -np.sin(2*alpha)*Q_stokes + np.cos(2*alpha)*U_stokes
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#Compute new covariance matrix on rotated parameters
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new_Stokes_cov = copy.deepcopy(Stokes_cov)
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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]
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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]
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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]
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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]
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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]
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#Rotate original images using scipy.ndimage.rotate
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new_I_stokes = PIL_rotate(new_I_stokes, ang, reshape=False,
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cval=np.sqrt(new_Stokes_cov[0,0][0,0]))
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new_Q_stokes = PIL_rotate(new_Q_stokes, ang, reshape=False,
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cval=np.sqrt(new_Stokes_cov[1,1][0,0]))
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new_U_stokes = PIL_rotate(new_U_stokes, ang, reshape=False,
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cval=np.sqrt(new_Stokes_cov[2,2][0,0]))
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for i in range(3):
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for j in range(3):
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new_Stokes_cov[i,j] = PIL_rotate(new_Stokes_cov[i,j], ang,
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reshape=False, cval=new_Stokes_cov[i,j].mean())
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#Update headers to new angle
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new_headers = []
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mrot = np.array([[np.cos(-alpha), -np.sin(-alpha)],
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[np.sin(-alpha), np.cos(-alpha)]])
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for header in headers:
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new_header = copy.deepcopy(header)
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new_header['orientat'] = header['orientat'] + ang
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new_wcs = WCS(header).deepcopy()
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if new_wcs.wcs.has_cd(): # CD matrix
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del w.wcs.cd
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keys = ['CD1_1','CD1_2','CD2_1','CD2_2']
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for key in keys:
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new_header.remove(key, ignore_missing=True)
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w.wcs.cdelt = 3600.*np.sqrt(np.sum(w.wcs.get_pc()**2,axis=1))
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elif new_wcs.wcs.has_pc(): # PC matrix + CDELT
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newpc = np.dot(mrot, new_wcs.wcs.get_pc())
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new_wcs.wcs.pc = newpc
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new_wcs.wcs.set()
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new_header.update(new_wcs.to_header())
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new_headers.append(new_header)
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return new_I_stokes, new_Q_stokes, new_U_stokes, new_Stokes_cov, new_headers
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def rotate2_sc_Stokes(I_stokes, Q_stokes, U_stokes, Stokes_cov, headers, ang):
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def rotate2_Stokes(I_stokes, Q_stokes, U_stokes, Stokes_cov, headers, ang):
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"""
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Use scipy.ndimage.rotate to rotate I_stokes to an angle, and a rotation
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matrix to rotate Q, U of a given angle in degrees and update header
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@@ -1391,120 +1283,3 @@ def rotate2_sc_Stokes(I_stokes, Q_stokes, U_stokes, Stokes_cov, headers, ang):
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new_headers.append(new_header)
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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
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def rotate2_PIL_Stokes(I_stokes, Q_stokes, U_stokes, Stokes_cov, headers, ang):
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"""
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Use scipy.ndimage.rotate to rotate I_stokes to an angle, and a rotation
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matrix to rotate Q, U of a given angle in degrees and update header
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orientation keyword.
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----------
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Inputs:
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I_stokes : numpy.ndarray
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Image (2D floats) containing the Stokes parameters accounting for
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total intensity
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Q_stokes : numpy.ndarray
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Image (2D floats) containing the Stokes parameters accounting for
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vertical/horizontal linear polarization intensity
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U_stokes : numpy.ndarray
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Image (2D floats) containing the Stokes parameters accounting for
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+45/-45deg linear polarization intensity
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Stokes_cov : numpy.ndarray
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Covariance matrix of the Stokes parameters I, Q, U.
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headers : header list
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List of headers corresponding to the reduced images.
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ang : float
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Rotation angle (in degrees) that should be applied to the Stokes
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parameters
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----------
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Returns:
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new_I_stokes : numpy.ndarray
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Rotated mage (2D floats) containing the rotated Stokes parameters
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accounting for total intensity
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new_Q_stokes : numpy.ndarray
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Rotated mage (2D floats) containing the rotated Stokes parameters
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accounting for vertical/horizontal linear polarization intensity
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new_U_stokes : numpy.ndarray
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Rotated image (2D floats) containing the rotated Stokes parameters
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accounting for +45/-45deg linear polarization intensity.
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new_Stokes_cov : numpy.ndarray
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Updated covariance matrix of the Stokes parameters I, Q, U.
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P : numpy.ndarray
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Image (2D floats) containing the polarization degree (in %).
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s_P : numpy.ndarray
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Image (2D floats) containing the error on the polarization degree.
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PA : numpy.ndarray
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Image (2D floats) containing the polarization angle.
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s_PA : numpy.ndarray
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Image (2D floats) containing the error on the polarization angle.
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debiased_P : numpy.ndarray
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Image (2D floats) containing the debiased polarization degree (in %).
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s_P_P : numpy.ndarray
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Image (2D floats) containing the Poisson noise error on the
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polarization degree.
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s_PA_P : numpy.ndarray
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Image (2D floats) containing the Poisson noise error on the
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polarization angle.
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"""
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# Rotate I_stokes, Q_stokes, U_stokes using rotation matrix
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alpha = ang*np.pi/180.
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new_I_stokes = 1.*I_stokes
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new_Q_stokes = np.cos(2*alpha)*Q_stokes + np.sin(2*alpha)*U_stokes
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new_U_stokes = -np.sin(2*alpha)*Q_stokes + np.cos(2*alpha)*U_stokes
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# Compute new covariance matrix on rotated parameters
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new_Stokes_cov = copy.deepcopy(Stokes_cov)
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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]
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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]
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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]
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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]
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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]
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# Compute new polarization parameters
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P, debiased_P, s_P, s_P_P, PA, s_PA, s_PA_P = compute_pol(new_I_stokes,
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new_Q_stokes, new_U_stokes, new_Stokes_cov, headers)
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# Rotate original images using scipy.ndimage.rotate
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new_I_stokes = PIL_rotate(new_I_stokes, ang, reshape=False,
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cval=np.sqrt(new_Stokes_cov[0,0][0,0]))
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new_Q_stokes = PIL_rotate(new_Q_stokes, ang, reshape=False,
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cval=np.sqrt(new_Stokes_cov[1,1][0,0]))
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new_U_stokes = PIL_rotate(new_U_stokes, ang, reshape=False,
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cval=np.sqrt(new_Stokes_cov[2,2][0,0]))
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P = PIL_rotate(P, ang, reshape=False, cval=P.mean())+0.
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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
|
||||
|
||||
Reference in New Issue
Block a user