images.imgeom

The images.imgeom package contains various image spatial manipulation and interpolation tasks.

Notes

For questions or comments please see our github page. We encourage and appreciate user feedback.

Most of these notebooks rely on basic knowledge of the Astropy FITS I/O module. If you are unfamiliar with this module please see the Astropy FITS I/O user documentation before using this documentation.

General Note about this package:

The tasks in this IRAF package include support for WCS updates after the image manipulations are preformed. Efforst in the community are ongoing for these WCS capabilities. For the moment we have included the array manipulation part of these tasks in this notebook.

Boundary Condition and Interpolation Options:

In the IRAF package the interpolation options are as follows: nearest, linear, poly3, poly5, spline3, sinc/lsinc. In the scipy.ndimage functions, the interpolation option are spline degrees 0-5, where spline-0 is nearest and spline-1 is linear.

The boundary condition options for IRAF and scipy are the same: nearest, wrap, reflect, and constant.

Important Note to Users: There are some differences in algorithms between some of the IRAF and Python Interpolations. Proceed with care if you are comparing prior IRAF results to Python results. For more details on this issue see the filed Github issue.

Contents:

blkavg

Please review the Notes section above before running any examples in this notebook

The blkavg task takes in an arbitrary dimensioned image and performs a block average across the requested box size. We can preform the same task with the astropy.nddata.utils.block_reduce function. In fact, this function is more generalized as you can apply any function (not just averaging) that accepts an ndarray and axis keyword to the block filter.

Below we show an example that reads in a FITS file, runs block_reduce on the first image extension, and saves out the updated file.

# Standard Imports
import numpy as np

# Astronomy Specific Imports
from astropy.io import fits
from astropy.nddata.utils import block_reduce
from astroquery.mast import Observations

# Download test file using astroquery, this only needs to be run once
# and can be skipped if using your own data.
# Astroquery will only download file if not already present.
obsid = '2004663553'
Observations.download_products(obsid,productFilename="jczgx1ppq_flc.fits")

INFO: Found cached file ./mastDownload/HST/JCZGX1PPQ/jczgx1ppq_flc.fits with expected size 167964480. [astroquery.query]
Table length=1
Local PathStatusMessageURL
str47str8objectobject
./mastDownload/HST/JCZGX1PPQ/jczgx1ppq_flc.fitsCOMPLETENoneNone
# Change this value to your desired data file, here were creating a filename
# for our new changed data
orig_data = './mastDownload/HST/JCZGX1PPQ/jczgx1ppq_flc.fits'
new_fits = 'iczgs3ygq_newdtype_flt.fits'

# Read in your fits file
hdu = fits.open(orig_data)

# Print FITS summary
hdu.info()

# Run block reduce on first sci extension
red_data = block_reduce(hdu[1].data, [2,2], func=np.mean)

# Re-insert the data into the HDUList
hdu[1].data = red_data

# Save out fits to a new file
hdu.writeto(new_fits, overwrite=True)
hdu.close()

Filename: ./mastDownload/HST/JCZGX1PPQ/jczgx1ppq_flc.fits
No.    Name      Ver    Type      Cards   Dimensions   Format
  0  PRIMARY       1 PrimaryHDU     279   ()
  1  SCI           1 ImageHDU       200   (4096, 2048)   float32
  2  ERR           1 ImageHDU        56   (4096, 2048)   float32
  3  DQ            1 ImageHDU        48   (4096, 2048)   int16
  4  SCI           2 ImageHDU       198   (4096, 2048)   float32
  5  ERR           2 ImageHDU        56   (4096, 2048)   float32
  6  DQ            2 ImageHDU        48   (4096, 2048)   int16
  7  D2IMARR       1 ImageHDU        15   (64, 32)   float32
  8  D2IMARR       2 ImageHDU        15   (64, 32)   float32
  9  D2IMARR       3 ImageHDU        15   (64, 32)   float32
 10  D2IMARR       4 ImageHDU        15   (64, 32)   float32
 11  WCSDVARR      1 ImageHDU        15   (64, 32)   float32
 12  WCSDVARR      2 ImageHDU        15   (64, 32)   float32
 13  WCSDVARR      3 ImageHDU        15   (64, 32)   float32
 14  WCSDVARR      4 ImageHDU        15   (64, 32)   float32
 15  WCSCORR       1 BinTableHDU     59   14R x 24C   [40A, I, A, 24A, 24A, 24A, 24A, D, D, D, D, D, D, D, D, 24A, 24A, D, D, D, D, J, 40A, 128A]

blkrep

Please review the Notes section above before running any examples in this notebook

The task blkrep is used to block replicate an n-dimensional image. Astropy has the equivalent function block_replicate.

For an example of how to read in a FITS extension, edit the image array, and save out the updated file, see blkavg above.

# Standard Imports
import numpy as np

# Astronomy Specific Imports
from astropy.nddata.utils import block_replicate

# test data
my_arr = np.array(([[0., 1.], [2., 3.]]))
print("input array")
print(my_arr)

# conservation of the array sum is the default
out = block_replicate(my_arr, 3)
print("sum conservation")
print(out)

# you can changes this using conserve_sum=False
out = block_replicate(my_arr, 3, conserve_sum=False)
print("no sum conservation")
print(out)

input array
[[ 0.  1.]
 [ 2.  3.]]
sum conservation
[[ 0.          0.          0.          0.11111111  0.11111111  0.11111111]
 [ 0.          0.          0.          0.11111111  0.11111111  0.11111111]
 [ 0.          0.          0.          0.11111111  0.11111111  0.11111111]
 [ 0.22222222  0.22222222  0.22222222  0.33333333  0.33333333  0.33333333]
 [ 0.22222222  0.22222222  0.22222222  0.33333333  0.33333333  0.33333333]
 [ 0.22222222  0.22222222  0.22222222  0.33333333  0.33333333  0.33333333]]
no sum conservation
[[ 0.  0.  0.  1.  1.  1.]
 [ 0.  0.  0.  1.  1.  1.]
 [ 0.  0.  0.  1.  1.  1.]
 [ 2.  2.  2.  3.  3.  3.]
 [ 2.  2.  2.  3.  3.  3.]
 [ 2.  2.  2.  3.  3.  3.]]

im3dtran-imtranspose

Please review the Notes section above before running any examples in this notebook

Tasks used to transpose images. numpy.transpose can handle any number of dimensions.

For an example of how to read in a FITS extension, edit the image array, and save out the updated file, see blkavg above.

# Standard Imports
import numpy as np

# in_array constructs a 3 x 5 array of integer values from 0 to 14
in_array = np.arange(15).reshape(5,3)
# we then transpose it it to a 5 x 3 array
out_array = np.transpose(in_array)

print('Original array:')
print(in_array)
print('Transpose of original array')
print(out_array)

Original array:
[[ 0  1  2]
 [ 3  4  5]
 [ 6  7  8]
 [ 9 10 11]
 [12 13 14]]
Transpose of original array
[[ 0  3  6  9 12]
 [ 1  4  7 10 13]
 [ 2  5  8 11 14]]

imshift-shiftlines

Please review the Notes section above before running any examples in this notebook

The task imshift can shift an image in x and y by float values and will use interpolation to create the output image. Shiftlines preformed similar functionality but We will be using scipy.ndimage.shift, where you can shift in any axis of your image. See the Notes at the top of the notebook for fitting and boundary options.

For an example of how to read in a FITS extension, edit the image array, and save out the updated file, see blkavg above.

# Standard Imports
import numpy as np
from scipy.ndimage import shift

# Don't forget that Python uses (y,x) format when specifiying shifts
in_array = np.arange(25).reshape(5,5)
out_array = shift(x, (0.8,0.8), order=3, mode='constant', cval=2)

print('Original array:')
print(in_array)
print('A zoom of 0.5 in y and 2 in x with nearest')
print(out_array)

Original array:
[[ 0  1  2  3  4]
 [ 5  6  7  8  9]
 [10 11 12 13 14]
 [15 16 17 18 19]
 [20 21 22 23 24]]
A zoom of 0.5 in y and 2 in x with nearest
[[ 2  2  2  2  2]
 [ 2  0  2  2  4]
 [ 2  6  7  8  9]
 [ 2 11 12 13 14]
 [ 2 16 18 19 20]]

magnify

Please review the Notes section above before running any examples in this notebook

The task magnify takes an image and magnifies the image by the desired amount, using a chosen iterpolation. The interpolation options avaialable for the magnify task are nearest, linear, poly3, poly5, spine3, sinc, lsinc, and drizzle. We will be using scipy.ndimage.zoom as a python equivalent. For this task, the available interpolation options are nearest, and spline0-5 fits.

For an example of how to read in a FITS extension, edit the image array, and save out the updated file, see blkavg above.

# Standard Imports
import numpy as np
from scipy.ndimage import zoom

# Don't forget that Python uses (y,x) format when specifiying magnification
in_array = np.arange(25).reshape(5,5)
out_array = zoom(in_array, (0.5,2.5), order=0)

print('Original array:')
print(in_array)
print('A zoom of 0.5 in y and 2.5 in x with nearest')
print(out_array)

Original array:
[[ 0  1  2  3  4]
 [ 5  6  7  8  9]
 [10 11 12 13 14]
 [15 16 17 18 19]
 [20 21 22 23 24]]
A zoom of 0.5 in y and 2.5 in x with nearest
[[ 0  0  1  1  1  2  2  3  3  3  4  4]
 [20 20 21 21 21 22 22 23 23 23 24 24]]

rotate

Please review the Notes section above before running any examples in this notebook

The task rotate is used to rotate and shift images. We will only cover rotation here, for shifting please see shiftlines. We will be using scipy.ndimage.rotate for rotation using interpolation. For a simple 90 degree unit rotation we will use numpy.rot90 (you can do a 90 degree rotation using scipy.ndimage.rotate).

For an example of how to read in a FITS extension, edit the image array, and save out the updated file, see blkavg above.

Rotation using interpolation:

# Standard Imports
import numpy as np
from scipy.ndimage import rotate

in_array = np.arange(25).reshape(5,5)
# Rotate by 60 degrees
out_array = rotate(in_array, 60, axes=(1,0))

print('Original array:')
print(in_array)
print('A rotation of 60 degrees')
print(out_array)

Original array:
[[ 0  1  2  3  4]
 [ 5  6  7  8  9]
 [10 11 12 13 14]
 [15 16 17 18 19]
 [20 21 22 23 24]]
A rotation of 60 degrees
[[ 0  0  0  0  0  0  0]
 [ 0  0  3  9  0  0  0]
 [ 0  0  5 11 15 21  0]
 [ 0  2  7 12 17 22  0]
 [ 0  3  9 13 19  0  0]
 [ 0  0  0 15 21  0  0]
 [ 0  0  0  0  0  0  0]]

Rotation in increments of 90 degrees:

# Standard Imports
import numpy as np

in_array = np.arange(25).reshape(5,5)
# Rotate by 270 degrees
out_array = np.rot90(in_array, 3)

print('Original array:')
print(in_array)
print('A rotation of 270 degrees')
print(out_array)

Original array:
[[ 0  1  2  3  4]
 [ 5  6  7  8  9]
 [10 11 12 13 14]
 [15 16 17 18 19]
 [20 21 22 23 24]]
A rotation of 270 degrees
[[20 15 10  5  0]
 [21 16 11  6  1]
 [22 17 12  7  2]
 [23 18 13  8  3]
 [24 19 14  9  4]]