Tibeuleu/FOC_Reduction
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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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78
src/lib/reduction.py
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@@ -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