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clean and complete docstrings

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This commit is contained in:
Thibault Barnouin
2022-07-05 12:49:01 +02:00
parent c75852e1ea
commit cc4625ffd5
6 changed files with 234 additions and 400 deletions
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4
src/lib/cross_correlation.py
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@@ -13,8 +13,8 @@ except ImportError:
import numpy as np
def _upsampled_dft(data, upsampled_region_size,
upsample_factor=1, axis_offsets=None):
def _upsampled_dft(data, upsampled_region_size, upsample_factor=1,
axis_offsets=None):
"""
Upsampled DFT by matrix multiplication.
This code is intended to provide the same result as if the following

239
src/lib/deconvolve.py
View File

@@ -1,5 +1,30 @@
"""
Library functions for the implementation of various deconvolution algorithms.
prototypes :
- gaussian_psf(FWHM, shape) -> kernel
Return the normalized gaussian point spread function over some kernel shape.
- from_file_psf(filename) -> kernel
Get the point spread function from an external FITS file.
- wiener(image, psf, alpha, clip) -> im_deconv
Implement the simplified Wiener filtering.
- van_cittert(image, psf, alpha, iterations, clip, filter_epsilon) -> im_deconv
Implement Van-Cittert iterative algorithm.
- richardson_lucy(image, psf, iterations, clip, filter_epsilon) -> im_deconv
Implement Richardson-Lucy iterative algorithm.
- one_step_gradient(image, psf, iterations, clip, filter_epsilon) -> im_deconv
Implement One-step gradient iterative algorithm.
- conjgrad(image, psf, alpha, error, iterations) -> im_deconv
Implement the Conjugate Gradient algorithm.
- deconvolve_im(image, psf, alpha, error, iterations, clip, filter_epsilon, algo) -> im_deconv
Prepare data for deconvolution using specified algorithm.
"""
import numpy as np
@@ -18,53 +43,108 @@ def abs2(x):
def zeropad(arr, shape):
"""Zero-pad array ARR to given shape.
"""
Zero-pad array ARR to given shape.
The contents of ARR is approximately centered in the result.
"""
rank = arr.ndim
if len(shape) != rank:
raise ValueError("bad number of dimensions")
diff = np.asarray(shape) - np.asarray(arr.shape)
if diff.min() < 0:
raise ValueError("output dimensions must be larger or equal input dimensions")
offset = diff//2
z = np.zeros(shape, dtype=arr.dtype)
if rank == 1:
i0 = offset[0]; n0 = i0 + arr.shape[0]
z[i0:n0] = arr
elif rank == 2:
i0 = offset[0]; n0 = i0 + arr.shape[0]
i1 = offset[1]; n1 = i1 + arr.shape[1]
z[i0:n0,i1:n1] = arr
elif rank == 3:
i0 = offset[0]; n0 = i0 + arr.shape[0]
i1 = offset[1]; n1 = i1 + arr.shape[1]
i2 = offset[2]; n2 = i2 + arr.shape[2]
z[i0:n0,i1:n1,i2:n2] = arr
elif rank == 4:
i0 = offset[0]; n0 = i0 + arr.shape[0]
i1 = offset[1]; n1 = i1 + arr.shape[1]
i2 = offset[2]; n2 = i2 + arr.shape[2]
i3 = offset[3]; n3 = i3 + arr.shape[3]
z[i0:n0,i1:n1,i2:n2,i3:n3] = arr
elif rank == 5:
i0 = offset[0]; n0 = i0 + arr.shape[0]
i1 = offset[1]; n1 = i1 + arr.shape[1]
i2 = offset[2]; n2 = i2 + arr.shape[2]
i3 = offset[3]; n3 = i3 + arr.shape[3]
i4 = offset[4]; n4 = i4 + arr.shape[4]
z[i0:n0,i1:n1,i2:n2,i3:n3,i4:n4] = arr
elif rank == 6:
i0 = offset[0]; n0 = i0 + arr.shape[0]
i1 = offset[1]; n1 = i1 + arr.shape[1]
i2 = offset[2]; n2 = i2 + arr.shape[2]
i3 = offset[3]; n3 = i3 + arr.shape[3]
i4 = offset[4]; n4 = i4 + arr.shape[4]
i5 = offset[5]; n5 = i5 + arr.shape[5]
z[i0:n0,i1:n1,i2:n2,i3:n3,i4:n4,i5:n5] = arr
else:
raise ValueError("too many dimensions")
return z
The contents of ARR is approximately centered in the result."""
rank = arr.ndim
if len(shape) != rank:
raise ValueError("bad number of dimensions")
diff = np.asarray(shape) - np.asarray(arr.shape)
if diff.min() < 0:
raise ValueError("output dimensions must be larger or equal input dimensions")
offset = diff//2
z = np.zeros(shape, dtype=arr.dtype)
if rank == 1:
i0 = offset[0]; n0 = i0 + arr.shape[0]
z[i0:n0] = arr
elif rank == 2:
i0 = offset[0]; n0 = i0 + arr.shape[0]
i1 = offset[1]; n1 = i1 + arr.shape[1]
z[i0:n0,i1:n1] = arr
elif rank == 3:
i0 = offset[0]; n0 = i0 + arr.shape[0]
i1 = offset[1]; n1 = i1 + arr.shape[1]
i2 = offset[2]; n2 = i2 + arr.shape[2]
z[i0:n0,i1:n1,i2:n2] = arr
elif rank == 4:
i0 = offset[0]; n0 = i0 + arr.shape[0]
i1 = offset[1]; n1 = i1 + arr.shape[1]
i2 = offset[2]; n2 = i2 + arr.shape[2]
i3 = offset[3]; n3 = i3 + arr.shape[3]
z[i0:n0,i1:n1,i2:n2,i3:n3] = arr
elif rank == 5:
i0 = offset[0]; n0 = i0 + arr.shape[0]
i1 = offset[1]; n1 = i1 + arr.shape[1]
i2 = offset[2]; n2 = i2 + arr.shape[2]
i3 = offset[3]; n3 = i3 + arr.shape[3]
i4 = offset[4]; n4 = i4 + arr.shape[4]
z[i0:n0,i1:n1,i2:n2,i3:n3,i4:n4] = arr
elif rank == 6:
i0 = offset[0]; n0 = i0 + arr.shape[0]
i1 = offset[1]; n1 = i1 + arr.shape[1]
i2 = offset[2]; n2 = i2 + arr.shape[2]
i3 = offset[3]; n3 = i3 + arr.shape[3]
i4 = offset[4]; n4 = i4 + arr.shape[4]
i5 = offset[5]; n5 = i5 + arr.shape[5]
z[i0:n0,i1:n1,i2:n2,i3:n3,i4:n4,i5:n5] = arr
else:
raise ValueError("too many dimensions")
return z
def gaussian2d(x, y, sigma):
return np.exp(-(x**2+y**2)/(2*sigma**2))/(2*np.pi*sigma**2)
def gaussian_psf(FWHM=1., shape=(5,5)):
"""
Define the gaussian Point-Spread-Function of chosen shape and FWHM.
----------
Inputs:
FWHM : float, optional
The Full Width at Half Maximum of the desired gaussian function for the
PSF in pixel increments.
Defaults to 1.
shape : tuple, optional
The shape of the PSF kernel. Must be of dimension 2.
Defaults to (5,5).
----------
Returns:
kernel : numpy.ndarray
Kernel containing the weights of the desired gaussian PSF.
"""
# Compute standard deviation from FWHM
stdev = FWHM/(2.*np.sqrt(2.*np.log(2.)))
# Create kernel of desired shape
x, y = np.meshgrid(np.arange(-shape[0]/2,shape[0]/2),np.arange(-shape[1]/2,shape[1]/2))
kernel = gaussian2d(x, y, stdev)
return kernel/kernel.sum()
def from_file_psf(filename):
"""
Get the Point-Spread-Function from an external FITS file.
Such PSF can be generated using the TinyTim standalone program by STSCI.
See:
[1] https://www.stsci.edu/hst/instrumentation/focus-and-pointing/focus/tiny-tim-hst-psf-modeling
[2] https://doi.org/10.1117/12.892762
----------
Inputs:
filename : str
----------
kernel : numpy.ndarray
Kernel containing the weights of the desired gaussian PSF.
"""
with fits.open(filename) as f:
psf = f[0].data
if (type(psf) != np.ndarray) or len(psf) != 2:
raise ValueError("Invalid PSF image in PrimaryHDU at {0:s}".format(filename))
#Return the normalized Point Spread Function
kernel = psf/psf.max()
return kernel
def wiener(image, psf, alpha=0.1, clip=True):
@@ -88,9 +168,6 @@ def wiener(image, psf, alpha=0.1, clip=True):
Returns:
im_deconv : ndarray
The deconvolved image.
----------
References:
[1]
"""
float_type = np.promote_types(image.dtype, np.float32)
image = image.astype(float_type, copy=False)
@@ -135,9 +212,6 @@ def van_cittert(image, psf, alpha=0.1, iterations=20, clip=True, filter_epsilon=
Returns:
im_deconv : ndarray
The deconvolved image.
----------
References
[1]
"""
float_type = np.promote_types(image.dtype, np.float32)
image = image.astype(float_type, copy=False)
@@ -231,9 +305,6 @@ def one_step_gradient(image, psf, iterations=20, clip=True, filter_epsilon=None)
Returns:
im_deconv : ndarray
The deconvolved image.
----------
References
[1]
"""
float_type = np.promote_types(image.dtype, np.float32)
image = image.astype(float_type, copy=False)
@@ -282,9 +353,6 @@ def conjgrad(image, psf, alpha=0.1, error=None, iterations=20):
Returns:
im_deconv : ndarray
The deconvolved image.
----------
References:
[1]
"""
float_type = np.promote_types(image.dtype, np.float32)
image = image.astype(float_type, copy=False)
@@ -402,8 +470,8 @@ def conjgrad(image, psf, alpha=0.1, error=None, iterations=20):
def deconvolve_im(image, psf, alpha=0.1, error=None, iterations=20, clip=True,
filter_epsilon=None, algo='richardson'):
"""
Prepare an image for deconvolution using Richardson-Lucy algorithm and
return results.
Prepare an image for deconvolution using a chosen algorithm and return
results.
----------
Inputs:
image : numpy.ndarray
@@ -466,54 +534,3 @@ def deconvolve_im(image, psf, alpha=0.1, error=None, iterations=20, clip=True,
im_deconv = pxmax*norm_deconv
return im_deconv
def gaussian2d(x,y,sigma):
return np.exp(-(x**2+y**2)/(2*sigma**2))/(2*np.pi*sigma**2)
def gaussian_psf(FWHM=1., shape=(5,5)):
"""
Define the gaussian Point-Spread-Function of chosen shape and FWHM.
----------
Inputs:
FWHM : float, optional
The Full Width at Half Maximum of the desired gaussian function for the
PSF in pixel increments.
Defaults to 1.
shape : tuple, optional
The shape of the PSF kernel. Must be of dimension 2.
Defaults to (5,5).
----------
Returns:
kernel : numpy.ndarray
Kernel containing the weights of the desired gaussian PSF.
"""
# Compute standard deviation from FWHM
stdev = FWHM/(2.*np.sqrt(2.*np.log(2.)))
# Create kernel of desired shape
x, y = np.meshgrid(np.arange(-shape[0]/2,shape[0]/2),np.arange(-shape[1]/2,shape[1]/2))
kernel = gaussian2d(x, y, stdev)
return kernel/kernel.sum()
def from_file_psf(filename):
"""
Get the Point-Spread-Function from an external FITS file.
Such PSF can be generated using the TinyTim standalone program by STSCI.
See:
[1] https://www.stsci.edu/hst/instrumentation/focus-and-pointing/focus/tiny-tim-hst-psf-modeling
[2] https://doi.org/10.1117/12.892762
----------
Inputs:
filename : str
----------
kernel : numpy.ndarray
Kernel containing the weights of the desired gaussian PSF.
"""
with fits.open(filename) as f:
psf = f[0].data
if (type(psf) != np.ndarray) or len(psf) != 2:
raise ValueError("Invalid PSF image in PrimaryHDU at {0:s}".format(filename))
#Return the normalized Point Spread Function
kernel = psf/psf.max()
return kernel

4
src/lib/fits.py
View File

@@ -5,7 +5,7 @@ prototypes :
- get_obs_data(infiles, data_folder) -> data_array, headers
Extract the observationnal data from fits files
- save_Stokes(I, Q, U, Stokes_cov, P, debiased_P, s_P, s_P_P, PA, s_PA, s_PA_P, ref_header, filename, data_folder, return_hdul) -> ( HDUL_data )
- save_Stokes(I, Q, U, Stokes_cov, P, debiased_P, s_P, s_P_P, PA, s_PA, s_PA_P, headers, data_mask, filename, data_folder, return_hdul) -> ( HDUL_data )
Save computed polarimetry parameters to a single fits file (and return HDUList)
"""
@@ -97,6 +97,8 @@ def save_Stokes(I_stokes, Q_stokes, U_stokes, Stokes_cov, P, debiased_P, s_P,
headers : header list
Header of reference some keywords will be copied from (CRVAL, CDELT,
INSTRUME, PROPOSID, TARGNAME, ORIENTAT, EXPTOT).
data_mask : numpy.ndarray
2D boolean array delimiting the data to work on.
filename : str
Name that will be given to the file on writing (will appear in header).
data_folder : str, optional

53
src/lib/plots.py
View File

@@ -2,11 +2,38 @@
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
- plot_obs(data_array, headers, shape, vmin, vmax, rectangle, savename, plots_folder)
Plots whole observation raw data in given display shape.
- plot_Stokes(Stokes, savename, plots_folder)
Plot the I/Q/U maps from the Stokes HDUList.
- polarization_map(Stokes_hdul, SNRp_cut, SNRi_cut, step_vec, savename, plots_folder, display)
Plots polarization map of polarimetric parameters saved in an HDUList
- polarization_map(Stokes, data_mask, rectangle, SNRp_cut, SNRi_cut, step_vec, savename, plots_folder, display) -> fig, ax
Plots polarization map of polarimetric parameters saved in an HDUList.
class align_maps(map, other_map, **kwargs)
Class to interactively align maps with different WCS.
class overplot_radio(align_maps)
Class inherited from align_maps to overplot radio data as contours.
class overplot_pol(align_maps)
Class inherited from align_maps to overplot UV polarization vectors on other maps.
class crop_map(hdul, fig, ax)
Class to interactively crop a region of interest of a HDUList.
class crop_Stokes(crop_map)
Class inherited from crop_map to work on polarization maps.
class image_lasso_selector(img, fig, ax)
Class to interactively select part of a map to work on.
class aperture(img, cdelt, radius, fig, ax)
Class to interactively simulate aperture integration.
class pol_map(Stokes, SNRp_cut, SNRi_cut, selection)
Class to interactively study polarization maps making use of the cropping and selecting tools.
"""
from copy import deepcopy
@@ -946,7 +973,8 @@ class crop_Stokes(crop_map):
def data_mask(self):
return self.hdul_crop[-1].data
class image_lasso_selector:
class image_lasso_selector(object):
def __init__(self, img, fig=None, ax=None):
"""
img must have shape (X, Y)
@@ -1012,7 +1040,7 @@ class image_lasso_selector:
self.on_close()
class aperture:
class aperture(object):
def __init__(self, img, cdelt=np.array([1.,1.]), radius=1., fig=None, ax=None):
"""
img must have shape (X, Y)
@@ -1119,7 +1147,7 @@ class pol_map(object):
"""
Class to interactively study polarization maps.
"""
def __init__(self,Stokes, SNRp_cut=3., SNRi_cut=30., selection=None):
def __init__(self, Stokes, SNRp_cut=3., SNRi_cut=30., selection=None):
if type(Stokes) == str:
Stokes = fits.open(Stokes)
@@ -1194,6 +1222,7 @@ class pol_map(object):
self.data = self.Stokes[0].data
if self.selected:
self.selected = False
self.select_instance.update_mask()
self.region = deepcopy(self.select_instance.mask.astype(bool))
self.select_instance.displayed.remove()
for coll in self.select_instance.cont.collections[:]:
@@ -1206,11 +1235,6 @@ class pol_map(object):
self.region = None
self.select_instance = aperture(self.data, fig=self.fig, ax=self.ax, cdelt=self.wcs.wcs.cdelt, radius=s_aper_radius.val)
self.select_instance.circ.set_visible(True)
#k = 0
#while not self.select_instance.selected and k<60:
# self.fig.canvas.start_event_loop(timeout=1)
# k+=1
#select_aperture(event)
self.fig.canvas.draw_idle()
@@ -1617,7 +1641,10 @@ class pol_map(object):
if hasattr(self, 'cont'):
for coll in self.cont.collections:
coll.remove()
try:
coll.remove()
except:
return
del self.cont
if fig is None:
fig = self.fig

78
src/lib/reduction.py
View File

@@ -5,36 +5,38 @@ 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
- crop_array(data_array, error_array, data_mask, step, null_val, inside, display, savename, plots_folder) -> crop_data_array, crop_error_array (, crop_mask), crop_headers
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
- deconvolve_array(data_array, headers, psf, FWHM, scale, shape, iterations, algo) -> deconvolved_data_array
Homogeneously deconvolve a data array using a chosen deconvolution algorithm.
- get_error(data_array, sub_shape, display, headers, savename, plots_folder) -> data_array, error_array
- get_error(data_array, headers, error_array, data_mask, sub_shape, display, savename, plots_folder, return_background) -> data_array, error_array, headers (, background)
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
- rebin_array(data_array, error_array, headers, pxsize, scale, operation, data_mask) -> rebinned_data, rebinned_error, rebinned_headers, Dxy (, data_mask)
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(data_array, error_array, data_mask, headers, FWHM, scale, smoothing) -> smoothed_array, smoothed_error
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.
- polarizer_avg(data_array, error_array, data_mask, headers, FWHM, scale, smoothing) -> polarizer_array, polarizer_cov
Average images in data_array on each used polarizer filter and compute correlated errors.
- compute_Stokes(data_array, error_array, headers, FWHM, scale, smoothing) -> I_stokes, Q_stokes, U_stokes, Stokes_cov, pol_flux
Compute Stokes parameters I, Q and U and their respective errors from data_array.
- compute_Stokes(data_array, error_array, data_mask, headers, FWHM, scale, smoothing) -> I_stokes, Q_stokes, U_stokes, Stokes_cov
Compute Stokes parameters I, Q and U and their respective correlated errors from data_array.
- compute_pol(I_stokes, Q_stokes, U_stokes, Stokes_cov, pol_flux, 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
- 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_Stokes(I_stokes, Q_stokes, U_stokes, Stokes_cov, pol_flux, headers, ang) -> I_stokes, Q_stokes, U_stokes, Stokes_cov, pol_flux, headers
- rotate_Stokes(I_stokes, Q_stokes, U_stokes, Stokes_cov, data_mask, headers, ang, SNRi_cut) -> I_stokes, Q_stokes, U_stokes, Stokes_cov, data_mask, headers
Rotate I, Q, U given an angle in degrees using scipy functions.
- rotate_data(data_array, error_array, data_mask, headers, ang) -> data_array, error_array, data_mask, headers
Rotate data before reduction given an angle in degrees using scipy functions.
"""
from copy import deepcopy
@@ -206,6 +208,10 @@ def crop_array(data_array, headers, error_array=None, data_mask=None, step=5, nu
the error in each pixel of the observation images in data_array.
If None, will be initialized to zeros.
Defaults to None.
data_mask : numpy.ndarray, optional
2D boolean array delimiting the data to work on.
If None, will be initialized with a full true mask.
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
@@ -345,9 +351,14 @@ def deconvolve_array(data_array, headers, psf='gaussian', FWHM=1., scale='px',
relevant values of 'psf' variable.
Defaults to (9,9).
iterations : int, optional
Number of iterations of Richardson-Lucy deconvolution algorithm. Act as
Number of iterations for iterative deconvolution algorithms. Act as
as a regulation of the process.
Defaults to 20.
algo : str, optional
Name of the deconvolution algorithm that will be used. Implemented
algorithms are the following : 'Wiener', 'Van-Cittert',
'One Step Gradient', 'Conjugate Gradient' and 'Richardson-Lucy'.
Defaults to 'Richardson-Lucy'.
----------
Returns:
deconv_array : numpy.ndarray
@@ -394,6 +405,15 @@ def get_error(data_array, headers, error_array=None, data_mask=None, sub_shape=N
Array containing the data to study (2D float arrays).
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.
If None, will be initialized to zeros.
Defaults to None.
data_mask : numpy.ndarray, optional
2D boolean array delimiting the data to work on.
If None, will be initialized with a full true mask.
Defaults to None.
sub_shape : tuple, optional
Shape of the sub-image to look for. Must be odd.
Defaults to 10% of input array.
@@ -587,6 +607,10 @@ def rebin_array(data_array, error_array, headers, pxsize, scale,
Set the way original components of the matrix are put together
between summing ('sum') and averaging ('average', 'avg', 'mean') them.
Defaults to 'sum'.
data_mask : numpy.ndarray, optional
2D boolean array delimiting the data to work on.
If None, will be initialized with a full true mask.
Defaults to None.
----------
Returns:
rebinned_data, rebinned_error : numpy.ndarray
@@ -595,6 +619,9 @@ def rebin_array(data_array, error_array, headers, pxsize, scale,
Updated headers corresponding to the images in rebinned_data.
Dxy : numpy.ndarray
Array containing the rebinning factor in each direction of the image.
data_mask : numpy.ndarray, optional
Updated 2D boolean array delimiting the data to work on.
Only returned if inputed data_mask is not None.
"""
# Check that all images are from the same instrument
ref_header = headers[0]
@@ -716,6 +743,8 @@ def align_data(data_array, headers, error_array=None, upsample_factor=1.,
image with margins of value 0.
rescaled_error : numpy.ndarray
Array containing the errors on the aligned images in the rescaled array.
data_mask : numpy.ndarray
2D boolean array delimiting the data to work on.
shifts : numpy.ndarray
Array containing the pixel shifts on the x and y directions from
the reference image.
@@ -839,6 +868,8 @@ def smooth_data(data_array, error_array, data_mask, headers, FWHM=1.,
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.
data_mask : numpy.ndarray
2D boolean array delimiting the data to work on.
headers : header list
List of headers corresponding to the images in data_array.
FWHM : float, optional
@@ -942,6 +973,8 @@ def polarizer_avg(data_array, error_array, data_mask, headers, FWHM=None,
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.
data_mask : numpy.ndarray
2D boolean array delimiting the data to work on.
headers : header list
List of headers corresponding to the images in data_array.
FWHM : float, optional
@@ -1089,6 +1122,8 @@ def compute_Stokes(data_array, error_array, data_mask, headers,
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.
data_mask : numpy.ndarray
2D boolean array delimiting the data to work on.
headers : header list
List of headers corresponding to the images in data_array.
FWHM : float, optional
@@ -1376,6 +1411,8 @@ def rotate_Stokes(I_stokes, Q_stokes, U_stokes, Stokes_cov, data_mask, headers,
+45/-45deg linear polarization intensity
Stokes_cov : numpy.ndarray
Covariance matrix of the Stokes parameters I, Q, U.
data_mask : numpy.ndarray
2D boolean array delimiting the data to work on.
headers : header list
List of headers corresponding to the reduced images.
ang : float
@@ -1401,6 +1438,8 @@ def rotate_Stokes(I_stokes, Q_stokes, U_stokes, Stokes_cov, data_mask, headers,
new_headers : header list
Updated list of headers corresponding to the reduced images accounting
for the new orientation angle.
new_data_mask : numpy.ndarray
Updated 2D boolean array delimiting the data to work on.
"""
#Apply cuts
if not(SNRi_cut is None):
@@ -1520,7 +1559,8 @@ def rotate_Stokes(I_stokes, Q_stokes, U_stokes, Stokes_cov, data_mask, headers,
header['PA_int_err'] = (PA_diluted_err, 'Integrated polarization angle error')
return new_I_stokes, new_Q_stokes, new_U_stokes, new_Stokes_cov, new_headers, new_data_mask
return new_I_stokes, new_Q_stokes, new_U_stokes, new_Stokes_cov, new_data_mask, new_headers
def rotate_data(data_array, error_array, data_mask, headers, ang):
"""
@@ -1533,6 +1573,8 @@ def rotate_data(data_array, error_array, data_mask, headers, ang):
Array of images (2D floats) to be rotated by angle ang.
error_array : numpy.ndarray
Array of error associated to images in data_array.
data_mask : numpy.ndarray
2D boolean array delimiting the data to work on.
headers : header list
List of headers corresponding to the reduced images.
ang : float
@@ -1547,6 +1589,8 @@ def rotate_data(data_array, error_array, data_mask, headers, ang):
new_headers : header list
Updated list of headers corresponding to the reduced images accounting
for the new orientation angle.
new_data_mask : numpy.ndarray
Updated 2D boolean array delimiting the data to work on.
"""
#Rotate I_stokes, Q_stokes, U_stokes using rotation matrix
alpha = ang*np.pi/180.
@@ -1609,4 +1653,4 @@ def rotate_data(data_array, error_array, data_mask, headers, ang):
new_headers.append(new_header)
globals()['theta'] = theta - alpha
return new_data_array, new_error_array, new_headers, new_data_mask
return new_data_array, new_error_array, new_data_mask, new_headers