Tibeuleu/FOC_Reduction
1
0
Fork 0
Code Issues Pull Requests Packages Projects Releases Wiki Activity

clean and complete docstrings

Browse Source
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
Download Patch File Download Diff File Expand all files Collapse all files

4
src/lib/cross_correlation.py
View File

@@ -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

256
src/test_Enrique_reduction.py
View File

@@ -1,256 +0,0 @@
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
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']
#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]
#size, in pixels, of the FOV centered in x_cen_0,y_cen_0
Dx = 200
Dy = 200
#set the new image size (Dxy x Dxy pixels binning)
Dxy = 5
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 = 30 #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'
### 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, a in enumerate(axs.flatten()):
img = fits.open(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('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_1 + ima_2)/3.
pol60 = (ima_3 + ima_4 + ima_5 + ima_6)/4.
pol120 = (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('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('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
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(figname,dpi=300)
os.system('open '+figname)