images.imutil¶
The images.imutil package provides general fits image tools such as header editing and image arithimetic.
Notes¶
For questions or comments please see our github page. We encourage and appreciate user feedback.
Contents:
chpixtype¶
Please review the Notes section above before running any examples in this notebook
Chpixtype is a task that allows you to change the pixel type of a fits
image. There is built in functionality in astropy.io.fits to preform
this task with the scale method. Below you will find a table that
translates the chpixtype newpixtype options into their equivalent
numpy/astropy
type.
Type Conversions
| Chpixtype | Numpy/Astropy Type |
|---|---|
ushort |
uint16 |
short |
int16 |
int |
int32 |
long |
int64 |
real |
float32 |
double |
float64 |
# Astronomy Specific Imports
from astropy.io import fits
# Standard Imports
import numpy as np
# Astronomy Specific Imports
from astropy.io import fits
# Change this value to your desired data file, here were creating a filename
# for our new changed data
orig_data = '/eng/ssb/iraf_transition/test_data/iczgs3ygq_flt.fits'
new_data = '/eng/ssb/iraf_transition/test_data/iczgs3ygq_newdtype_flt.fits'
# Read in your fits file
hdu = fits.open(orig_data)
# Edit the datatype
hdu[1].scale(type='int32')
# Save changed hdu object to new file
# The clobber argument tells the writeto method to overwrite if file already exists
hdu.writeto(new_data, clobber=True)
hdu.close()
hedit¶
Please review the Notes section above before running any examples in this notebook
The hedit task allows users to edit an image header. This functioanlity
is covered in astropy.io.fits. Take note that to make changes to a
fits file, you must use the mode='update' keyword in the
fits.open call. Below you’ll find examples of editing a keyword if
it does/doesn’t exist, and how to delete keywords from the header.
# Astronomy Specific Imports
from astropy.io import fits
# Change this value to your desired data file
test_data = '/eng/ssb/iraf_transition/test_data/iczgs3ygq_flt.fits'
# Open fits file, include the mode='update' keyword
hdu = fits.open(test_data, mode='update')
# Simple header change, will add keyword if it doesn't not exist
hdu[0].header['MYKEY1'] = 'Editing this keyword'
# Only add keyword if it does not already exist:
if 'MYKEY2' not in hdu[0].header:
hdu[0].header['MYKEY2'] = 'Also editing this'
# To delete keywords, first check if they exist:
if 'MYKEY2' in hdu[0].header:
del hdu[0].header['MYKEY2']
# Close fits file, this will save your changes
hdu.close()
hselect¶
imarith-imdivide¶
Please review the Notes section above before running any examples in this notebook
Imarith and imdivide both provide functionality to apply basic operators
to whole image arrays. This task can be achieved with basic
astropy.io.fits functionality along with numpy array
functionality.
The basic operands (+,-,/,*) can all be used with
an assignment operator in python (+=,-=,/=,*=).
See http://www.tutorialspoint.com/python/python_basic_operators.htm for
more details
# Astronomy Specific Imports
from astropy.io import fits
# Basic operands (+,-,/,*)
# Change these values to your desired data files
test_data1 = '/eng/ssb/iraf_transition/test_data/iczgs3ygq_flt.fits'
test_data2 = '/eng/ssb/iraf_transition/test_data/iczgs3y5q_flt.fits'
output_data = '/eng/ssb/iraf_transition/test_data/imarith_out.fits'
# Open fits file
hdu1 = fits.open(test_data1)
hdu2 = fits.open(test_data2)
# Here we add hdu2-ext1 to hdu1-ext1 by using the shortcute += operator
hdu1[1].data += hdu2[1].data
# If you are dividing and need to avoid zeros in the image use indexing
indx_zeros = [hdu2[4].data == 0]
indx_nonzeros = [hdu2[4].data != 0]
# Set this value as you would the divzero parameter in imarith
set_zeros = 999.9
hdu1[4].data[indx_nonzeros] /= hdu2[4].data[indx_nonzeros]
hdu1[4].data[indx_zeros] = 999.9
# Save your new file
# The clobber argument tells the writeto method to overwrite if file already exists
hdu1.writeto(output_data, clobber=True)
# Close hdu files
hdu1.close()
hdu2.close()
imcopy¶
Please review the Notes section above before running any examples in this notebook
Imcopy allows users to copy a fits image to a new file. We can
accomplish this using astropy.io.fits by saving our fits file to a
new filename.
Imcopy will also make a cutout of an image and save the cutout to a new
file with an updated WCS. We show an exampe of this in Python using the
Cutout2D
tool in Astropy. For more information on how to use Cutout2D
please see this tutorial
page.
# Astronomy Specific Imports
from astropy import wcs
from astropy.io import fits
from astropy.nddata import Cutout2D
Simple example of a file copy
# Change these values to your desired filenames
test_data = '/eng/ssb/iraf_transition/test_data/iczgs3ygq_flt.fits'
output_data = '/eng/ssb/iraf_transition/test_data/imcopy_out.fits'
hdulist = fits.open(test_data)
# The clobber argument tells the writeto method to overwrite if file already exists
hdulist.writeto(output_data, overwrite=True)
hdulist.close()
Example using a new cutout, here we will take a 100x100 pixel cutout from all image extensions centered at x:200, y:300
# Change these values to your desired filenames
test_data = '/eng/ssb/iraf_transition/test_data/jcw505010_drz.fits'
output_data = '/eng/ssb/iraf_transition/test_data/imcopy_cutout_out.fits'
hdulist = fits.open(test_data)
# Create iterable list of tuples to feed into Cutout2D,
# seperate list for extensions with wcs, as feeding the wcs
# back into the fits file takes more work.
ext_list = [1,2]
for ext in ext_list:
orig_wcs = wcs.WCS(hdulist[ext].header)
cutout = Cutout2D(hdulist[ext].data, (200,300), (50,50), wcs=orig_wcs)
hdulist[ext].data = cutout.data
hdulist[ext].header.update(cutout.wcs.to_header())
hdulist.writeto(output_data, overwrite=True)
hdulist.close()
imfunction-imexpr¶
Please review the Notes section above before running any examples in this notebook
Imfunction will apply a function to the image pixel values in an image array. Imexpr gives you similiar functionality with the added capability to combine different images using a user created expression. We can accomplish this using the built in funcitonality of the numpy library
If there is a particular function you would like to apply to your image
array that you cannot find in the numpy library you can use the
np.vectorize function, which can make any python function apply to
each element of your array. But keep in mind that
np.vectorize
is esentially looping over the array, and may not be the most efficient
method.
Example using exsisting numpy function:
# Standard Imports
import numpy as np
# Astronomy Specific Imports
from astropy.io import fits
# Change these values to your desired data files
test_data = '/eng/ssb/iraf_transition/test_data/iczgs3ygq_flt.fits'
output_data = '/eng/ssb/iraf_transition/test_data/imfunction_out.fits'
# Here we use the cosine function as an example
hdu = fits.open(test_data)
sci = hdu[1].data
# When you call your new function, make sure to reassign the array to
# the new values if the original function is not changing values in place
hdu[1].data = np.cos(hdu[1].data)
# Now save out to a new file, and close the original file, changes will
# not be applied to the oiginal fits file.
hdu.writeto(output_data, overwrite=True)
hdu.close()
Example using user defined function and np.vectorize:
# Change these values to your desired data files
test_data = '/eng/ssb/iraf_transition/test_data/iczgs3ygq_flt.fits'
output_data = '/eng/ssb/iraf_transition/test_data/imfunction2_out.fits'
# Here we use the following custom function as an example
def my_func(x):
return (x**2)+(x**3)
# Now we open our file, and vectorize our function
hdu = fits.open(test_data)
sci = hdu[1].data
vcos = np.vectorize(my_func)
# When you call your new function, make sure to reassign the array to
# the new values if the original function is not changing values in place
hdu[1].data = vcos(hdu[1].data)
# Now save out to a new file, and close the original file, changes will
# not be applied to the oiginal fits file.
hdu.writeto(output_data, overwrite=True)
hdu.close()
imheader¶
Please review the Notes section above before running any examples in this notebook
The imheader task allows the user to list header parameters for a list
of images. Here we can use the astropy convenience function,
fits.getheader()
# Standard Imports
import numpy as np
import glob
# Astronomy Specific Imports
from astropy.io import fits
# Change these values to your desired data files, glob will capture all wildcard matches
test_data = glob.glob('/eng/ssb/iraf_transition/test_data/iczgs3y*')
for filename in test_data:
# Pull the header from extension 1
head = fits.getheader(filename, ext=1)
print repr(head)
XTENSION= 'IMAGE ' / IMAGE extension
BITPIX = -32
NAXIS = 2
NAXIS1 = 1014
NAXIS2 = 1014
PCOUNT = 0 / required keyword; must = 0
GCOUNT = 1 / required keyword; must = 1
ORIGIN = 'HSTIO/CFITSIO March 2010'
DATE = '2016-06-02' / date this file was written (yyyy-mm-dd)
INHERIT = T / inherit the primary header
EXTNAME = 'SCI ' / extension name
EXTVER = 1 / extension version number
ROOTNAME= 'iczgs3ygq ' / rootname of the observation set
EXPNAME = 'iczgs3ygq ' / exposure identifier
BUNIT = 'ELECTRONS/S' / brightness units
/ World Coordinate System and Related Parameters
WCSAXES = 2 / number of World Coordinate System axes
CRPIX1 = 507.0 / x-coordinate of reference pixel
CRPIX2 = 507.0 / y-coordinate of reference pixel
CRVAL1 = 36.85374208875 / first axis value at reference pixel
CRVAL2 = 48.92264646942 / second axis value at reference pixel
CTYPE1 = 'RA---TAN-SIP' / the coordinate type for the first axis
CTYPE2 = 'DEC--TAN-SIP' / the coordinate type for the second axis
CD1_1 = -3.1758778512629E-05 / partial of first axis coordinate w.r.t. x
CD1_2 = -1.8099259044494E-05 / partial of first axis coordinate w.r.t. y
CD2_1 = -2.0157648752092E-05 / partial of second axis coordinate w.r.t. x
CD2_2 = 2.83052387051731E-05 / partial of second axis coordinate w.r.t. y
LTV1 = 0.0000000E+00 / offset in X to subsection start
LTV2 = 0.0000000E+00 / offset in Y to subsection start
LTM1_1 = 1.0 / reciprocal of sampling rate in X
LTM2_2 = 1.0 / reciprocal of sampling rate in Y
PA_APER = -32.556 / Position Angle of reference aperture center (de
VAFACTOR= 9.999085821139E-01 / velocity aberration plate scale factor
ORIENTAT= -32.556 / position angle of image y axis (deg. e of n)
RA_APER = 3.685374208875E+01 / RA of aperture reference position
DEC_APER= 4.892264646942E+01 / Declination of aperture reference position
/ REPEATED EXPOSURES INFORMATION
NCOMBINE= 1 / number of image sets combined during CR rejecti
/ READOUT DEFINITION PARAMETERS
CENTERA1= 513 / subarray axis1 center pt in unbinned dect. pix
CENTERA2= 513 / subarray axis2 center pt in unbinned dect. pix
SIZAXIS1= 1024 / subarray axis1 size in unbinned detector pixels
SIZAXIS2= 1024 / subarray axis2 size in unbinned detector pixels
BINAXIS1= 1 / axis1 data bin size in unbinned detector pixels
BINAXIS2= 1 / axis2 data bin size in unbinned detector pixels
/ READOUT PARAMETERS
SAMPNUM = 13 / MULTIACCUM sample number
SAMPTIME= 602.937317 / total integration time (sec)
DELTATIM= 50.000412 / integration time of this sample (sec)
ROUTTIME= 5.740229030181E+04 / UT time of array readout (MJD)
TDFTRANS= 0 / number of TDF transitions during current sample
/ DATA PACKET INFORMATION
FILLCNT = 0 / number of segments containing fill
ERRCNT = 0 / number of segments containing errors
PODPSFF = F / podps fill present (T/F)
STDCFFF = F / science telemetry fill data present (T=1/F=0)
STDCFFP = '0x5569' / science telemetry fill pattern (hex)
/ IMAGE STATISTICS AND DATA QUALITY FLAGS
NGOODPIX= 990475 / number of good pixels
SDQFLAGS= 31743 / serious data quality flags
GOODMIN = -2.8782272E+00 / minimum value of good pixels
GOODMAX = 1.1788658E+04 / maximum value of good pixels
GOODMEAN= 9.9831134E-01 / mean value of good pixels
SNRMIN = 1.8871337E-02 / minimum signal to noise of good pixels
SNRMAX = 6.3982178E+01 / maximum signal to noise of good pixels
SNRMEAN = 5.3425826E-02 / mean value of signal to noise of good pixels
SOFTERRS= 0 / number of soft error pixels (DQF=1)
MEANDARK= 1.2191877E+01 / average of the dark values subtracted
MEANBLEV= 1.4332316E+04 / average of all bias levels subtracted
RADESYS = 'ICRS '
OCX10 = 0.000786257500294596
OCX11 = 0.1354287266731262
OCY10 = 0.1209582984447479
OCY11 = -0.00042557646520435
IDCSCALE= 0.1282500028610229
IDCTHETA= 45.0
IDCXREF = 507.0
IDCYREF = 507.0
IDCV2REF= 1.019000053405762
IDCV3REF= -0.5070000290870667
WCSNAMEO= 'OPUS '
WCSAXESO= 2
CRPIX1O = 507.0
CRPIX2O = 507.0
CDELT1O = 1.0
CDELT2O = 1.0
CUNIT1O = 'deg '
CUNIT2O = 'deg '
CTYPE1O = 'RA---TAN'
CTYPE2O = 'DEC--TAN'
CRVAL1O = 36.85374208875
CRVAL2O = 48.92264646942
LONPOLEO= 180.0
LATPOLEO= 48.92264646942
RADESYSO= 'ICRS '
CD1_1O = -3.17711E-05
CD1_2O = -1.80786E-05
CD2_1O = -2.01487E-05
CD2_2O = 2.83166E-05
IDCTAB = 'iref$w3m18525i_idc.fits'
B_1_3 = 1.69983940010457E-13
B_0_3 = -2.2777970488111E-10
A_2_2 = 1.11275247848408E-13
B_0_4 = 1.03978470894974E-12
A_0_4 = -2.0083179974495E-13
B_3_1 = 3.81044199963010E-13
A_3_0 = -1.9851733613323E-10
B_4_0 = -5.7352409055905E-13
B_0_2 = 2.98815054868485E-05
A_1_3 = 6.08832045645843E-13
A_4_0 = -3.2156784473326E-13
B_ORDER = 4
A_0_2 = 2.77482030873749E-08
A_2_1 = 1.22255499299390E-10
B_2_0 = 6.92276069494587E-06
A_2_0 = -2.0701735553551E-07
A_3_1 = 4.13947711822547E-13
A_1_2 = 3.11477338242516E-11
A_ORDER = 4
B_1_2 = 7.47270961118588E-11
B_2_2 = 1.38557115814168E-13
A_0_3 = 4.55691839657869E-11
B_2_1 = -2.3836656728517E-10
B_3_0 = 5.14014553890418E-11
B_1_1 = -2.8538202053351E-07
A_1_1 = 2.44176437155426E-05
WCSNAME = 'IDC_w3m18525i'
MDRIZSKY= 0.8125642368041847 / Sky value computed by AstroDrizzle
XTENSION= 'IMAGE ' / IMAGE extension
BITPIX = 32
NAXIS = 2
NAXIS1 = 1014
NAXIS2 = 1014
PCOUNT = 0 / required keyword; must = 0
GCOUNT = 1 / required keyword; must = 1
ORIGIN = 'HSTIO/CFITSIO March 2010'
DATE = '2016-06-02' / date this file was written (yyyy-mm-dd)
INHERIT = T / inherit the primary header
EXTNAME = 'SCI ' / extension name
EXTVER = 1 / extension version number
ROOTNAME= 'iczgs3ygq ' / rootname of the observation set
EXPNAME = 'iczgs3ygq ' / exposure identifier
BUNIT = 'ELECTRONS/S' / brightness units
/ World Coordinate System and Related Parameters
WCSAXES = 2 / number of World Coordinate System axes
CRPIX1 = 507.0 / x-coordinate of reference pixel
CRPIX2 = 507.0 / y-coordinate of reference pixel
CRVAL1 = 36.85374208875 / first axis value at reference pixel
CRVAL2 = 48.92264646942 / second axis value at reference pixel
CTYPE1 = 'RA---TAN-SIP' / the coordinate type for the first axis
CTYPE2 = 'DEC--TAN-SIP' / the coordinate type for the second axis
CD1_1 = -3.1758778512629E-05 / partial of first axis coordinate w.r.t. x
CD1_2 = -1.8099259044494E-05 / partial of first axis coordinate w.r.t. y
CD2_1 = -2.0157648752092E-05 / partial of second axis coordinate w.r.t. x
CD2_2 = 2.83052387051731E-05 / partial of second axis coordinate w.r.t. y
LTV1 = 0.0000000E+00 / offset in X to subsection start
LTV2 = 0.0000000E+00 / offset in Y to subsection start
LTM1_1 = 1.0 / reciprocal of sampling rate in X
LTM2_2 = 1.0 / reciprocal of sampling rate in Y
PA_APER = -32.556 / Position Angle of reference aperture center (de
VAFACTOR= 9.999085821139E-01 / velocity aberration plate scale factor
ORIENTAT= -32.556 / position angle of image y axis (deg. e of n)
RA_APER = 3.685374208875E+01 / RA of aperture reference position
DEC_APER= 4.892264646942E+01 / Declination of aperture reference position
/ REPEATED EXPOSURES INFORMATION
NCOMBINE= 1 / number of image sets combined during CR rejecti
/ READOUT DEFINITION PARAMETERS
CENTERA1= 513 / subarray axis1 center pt in unbinned dect. pix
CENTERA2= 513 / subarray axis2 center pt in unbinned dect. pix
SIZAXIS1= 1024 / subarray axis1 size in unbinned detector pixels
SIZAXIS2= 1024 / subarray axis2 size in unbinned detector pixels
BINAXIS1= 1 / axis1 data bin size in unbinned detector pixels
BINAXIS2= 1 / axis2 data bin size in unbinned detector pixels
/ READOUT PARAMETERS
SAMPNUM = 13 / MULTIACCUM sample number
SAMPTIME= 602.937317 / total integration time (sec)
DELTATIM= 50.000412 / integration time of this sample (sec)
ROUTTIME= 5.740229030181E+04 / UT time of array readout (MJD)
TDFTRANS= 0 / number of TDF transitions during current sample
/ DATA PACKET INFORMATION
FILLCNT = 0 / number of segments containing fill
ERRCNT = 0 / number of segments containing errors
PODPSFF = F / podps fill present (T/F)
STDCFFF = F / science telemetry fill data present (T=1/F=0)
STDCFFP = '0x5569' / science telemetry fill pattern (hex)
/ IMAGE STATISTICS AND DATA QUALITY FLAGS
NGOODPIX= 990475 / number of good pixels
SDQFLAGS= 31743 / serious data quality flags
GOODMIN = -2.8782272E+00 / minimum value of good pixels
GOODMAX = 1.1788658E+04 / maximum value of good pixels
GOODMEAN= 9.9831134E-01 / mean value of good pixels
SNRMIN = 1.8871337E-02 / minimum signal to noise of good pixels
SNRMAX = 6.3982178E+01 / maximum signal to noise of good pixels
SNRMEAN = 5.3425826E-02 / mean value of signal to noise of good pixels
SOFTERRS= 0 / number of soft error pixels (DQF=1)
MEANDARK= 1.2191877E+01 / average of the dark values subtracted
MEANBLEV= 1.4332316E+04 / average of all bias levels subtracted
RADESYS = 'ICRS '
OCX10 = 0.000786257500294596
OCX11 = 0.1354287266731262
OCY10 = 0.1209582984447479
OCY11 = -0.00042557646520435
IDCSCALE= 0.1282500028610229
IDCTHETA= 45.0
IDCXREF = 507.0
IDCYREF = 507.0
IDCV2REF= 1.019000053405762
IDCV3REF= -0.5070000290870667
WCSNAMEO= 'OPUS '
WCSAXESO= 2
CRPIX1O = 507.0
CRPIX2O = 507.0
CDELT1O = 1.0
CDELT2O = 1.0
CUNIT1O = 'deg '
CUNIT2O = 'deg '
CTYPE1O = 'RA---TAN'
CTYPE2O = 'DEC--TAN'
CRVAL1O = 36.85374208875
CRVAL2O = 48.92264646942
LONPOLEO= 180.0
LATPOLEO= 48.92264646942
RADESYSO= 'ICRS '
CD1_1O = -3.17711E-05
CD1_2O = -1.80786E-05
CD2_1O = -2.01487E-05
CD2_2O = 2.83166E-05
IDCTAB = 'iref$w3m18525i_idc.fits'
B_1_3 = 1.69983940010457E-13
B_0_3 = -2.2777970488111E-10
A_2_2 = 1.11275247848408E-13
B_0_4 = 1.03978470894974E-12
A_0_4 = -2.0083179974495E-13
B_3_1 = 3.81044199963010E-13
A_3_0 = -1.9851733613323E-10
B_4_0 = -5.7352409055905E-13
B_0_2 = 2.98815054868485E-05
A_1_3 = 6.08832045645843E-13
A_4_0 = -3.2156784473326E-13
B_ORDER = 4
A_0_2 = 2.77482030873749E-08
A_2_1 = 1.22255499299390E-10
B_2_0 = 6.92276069494587E-06
A_2_0 = -2.0701735553551E-07
A_3_1 = 4.13947711822547E-13
A_1_2 = 3.11477338242516E-11
A_ORDER = 4
B_1_2 = 7.47270961118588E-11
B_2_2 = 1.38557115814168E-13
A_0_3 = 4.55691839657869E-11
B_2_1 = -2.3836656728517E-10
B_3_0 = 5.14014553890418E-11
B_1_1 = -2.8538202053351E-07
A_1_1 = 2.44176437155426E-05
WCSNAME = 'IDC_w3m18525i'
MDRIZSKY= 0.8125642368041847 / Sky value computed by AstroDrizzle
XTENSION= 'IMAGE ' / IMAGE extension
BITPIX = -32
NAXIS = 2
NAXIS1 = 1014
NAXIS2 = 1014
PCOUNT = 0 / required keyword; must = 0
GCOUNT = 1 / required keyword; must = 1
ORIGIN = 'HSTIO/CFITSIO March 2010'
DATE = '2016-06-02' / date this file was written (yyyy-mm-dd)
INHERIT = T / inherit the primary header
EXTNAME = 'SCI ' / extension name
EXTVER = 1 / extension version number
ROOTNAME= 'iczgs3y5q ' / rootname of the observation set
EXPNAME = 'iczgs3y5q ' / exposure identifier
BUNIT = 'ELECTRONS/S' / brightness units
/ World Coordinate System and Related Parameters
WCSAXES = 2 / number of World Coordinate System axes
CRPIX1 = 507.0 / x-coordinate of reference pixel
CRPIX2 = 507.0 / y-coordinate of reference pixel
CRVAL1 = 36.85747964213 / first axis value at reference pixel
CRVAL2 = 48.92227663477 / second axis value at reference pixel
CTYPE1 = 'RA---TAN-SIP' / the coordinate type for the first axis
CTYPE2 = 'DEC--TAN-SIP' / the coordinate type for the second axis
CD1_1 = -3.1760811272930E-05 / partial of first axis coordinate w.r.t. x
CD1_2 = -1.8097365221752E-05 / partial of first axis coordinate w.r.t. y
CD2_1 = -2.0155198493371E-05 / partial of second axis coordinate w.r.t. x
CD2_2 = 2.83091348126201E-05 / partial of second axis coordinate w.r.t. y
LTV1 = 0.0000000E+00 / offset in X to subsection start
LTV2 = 0.0000000E+00 / offset in Y to subsection start
LTM1_1 = 1.0 / reciprocal of sampling rate in X
LTM2_2 = 1.0 / reciprocal of sampling rate in Y
PA_APER = -32.5531 / Position Angle of reference aperture center (de
VAFACTOR= 9.999381116940E-01 / velocity aberration plate scale factor
ORIENTAT= -32.5531 / position angle of image y axis (deg. e of n)
RA_APER = 3.685747964213E+01 / RA of aperture reference position
DEC_APER= 4.892227663477E+01 / Declination of aperture reference position
/ REPEATED EXPOSURES INFORMATION
NCOMBINE= 1 / number of image sets combined during CR rejecti
/ READOUT DEFINITION PARAMETERS
CENTERA1= 513 / subarray axis1 center pt in unbinned dect. pix
CENTERA2= 513 / subarray axis2 center pt in unbinned dect. pix
SIZAXIS1= 1024 / subarray axis1 size in unbinned detector pixels
SIZAXIS2= 1024 / subarray axis2 size in unbinned detector pixels
BINAXIS1= 1 / axis1 data bin size in unbinned detector pixels
BINAXIS2= 1 / axis2 data bin size in unbinned detector pixels
/ READOUT PARAMETERS
SAMPNUM = 14 / MULTIACCUM sample number
SAMPTIME= 652.937744 / total integration time (sec)
DELTATIM= 50.000412 / integration time of this sample (sec)
ROUTTIME= 5.740226431774E+04 / UT time of array readout (MJD)
TDFTRANS= 0 / number of TDF transitions during current sample
/ DATA PACKET INFORMATION
FILLCNT = 0 / number of segments containing fill
ERRCNT = 0 / number of segments containing errors
PODPSFF = F / podps fill present (T/F)
STDCFFF = F / science telemetry fill data present (T=1/F=0)
STDCFFP = '0x5569' / science telemetry fill pattern (hex)
/ IMAGE STATISTICS AND DATA QUALITY FLAGS
NGOODPIX= 990476 / number of good pixels
SDQFLAGS= 31743 / serious data quality flags
GOODMIN = -2.9155195E+00 / minimum value of good pixels
GOODMAX = 2.6231844E+04 / maximum value of good pixels
GOODMEAN= 9.3451303E-01 / mean value of good pixels
SNRMIN = 1.1295157E-02 / minimum signal to noise of good pixels
SNRMAX = 9.8745354E+01 / maximum signal to noise of good pixels
SNRMEAN = 4.9034115E-02 / mean value of signal to noise of good pixels
SOFTERRS= 0 / number of soft error pixels (DQF=1)
MEANDARK= 1.3298962E+01 / average of the dark values subtracted
MEANBLEV= 1.4334856E+04 / average of all bias levels subtracted
RADESYS = 'ICRS '
OCX10 = 0.000779107213020324
OCX11 = 0.1354261934757233
OCY10 = 0.120962917804718
OCY11 = -0.00042105099419131
IDCSCALE= 0.1282500028610229
IDCTHETA= 45.0
IDCXREF = 507.0
IDCYREF = 507.0
IDCV2REF= 1.019000053405762
IDCV3REF= -0.5070000290870667
WCSNAMEO= 'OPUS '
WCSAXESO= 2
CRPIX1O = 507.0
CRPIX2O = 507.0
CDELT1O = 1.0
CDELT2O = 1.0
CUNIT1O = 'deg '
CUNIT2O = 'deg '
CTYPE1O = 'RA---TAN'
CTYPE2O = 'DEC--TAN'
CRVAL1O = 36.85747964213
CRVAL2O = 48.92227663477
LONPOLEO= 180.0
LATPOLEO= 48.92227663477
RADESYSO= 'ICRS '
CD1_1O = -3.17721E-05
CD1_2O = -1.80771E-05
CD2_1O = -2.01471E-05
CD2_2O = 2.83175E-05
IDCTAB = 'iref$w3m18525i_idc.fits'
B_1_2 = 2.35150691092754E-11
A_3_0 = -1.8769691205859E-10
B_ORDER = 4
A_2_1 = 9.33802326056672E-11
A_1_1 = 2.44489619913889E-05
A_2_2 = 5.99856272799014E-15
B_0_3 = -2.0092851573342E-10
B_3_1 = 1.00607112230593E-13
B_3_0 = 3.66824943640799E-11
A_2_0 = -1.8678411786277E-07
B_1_3 = -6.9677270201133E-15
A_0_2 = 4.73630640333079E-08
A_1_3 = 5.55221560333543E-13
B_0_4 = 7.52827599670567E-13
B_2_2 = -1.1683621160870E-13
A_0_4 = -2.0852050771470E-13
B_0_2 = 2.99875048026693E-05
A_4_0 = -3.1314754837293E-13
B_4_0 = -6.4384058620497E-13
A_ORDER = 4
A_0_3 = 2.65011000430244E-11
B_2_1 = -2.8558390691514E-10
A_1_2 = 5.07616164062598E-11
B_1_1 = -2.0379403931148E-07
A_3_1 = 5.25748787891111E-13
B_2_0 = 6.97816138011029E-06
WCSNAME = 'IDC_w3m18525i'
MDRIZSKY= 0.7757664823972165 / Sky value computed by AstroDrizzle
imhistogram¶
Please review the Notes section above before running any examples in this notebook
Imhistogram will plot a customized histogram of the provided image data.
To make a histogram in Python we are going to use matplotlibs hist
function. See the hist
documentation for
options to change the histogram type, scaling, bin sizes, and more.
# Standard Imports
import numpy as np
# Astronomy Specific Imports
from astropy.io import fits
# Plotting Imports/Setup
import matplotlib.pyplot as plt
%matplotlib inline
# Change these values to your desired data files
test_data = '/eng/ssb/iraf_transition/test_data/iczgs3ygq_flt.fits'
# Pull out the first science array, we also need to flatten the data before sending it to hist
sci1 = fits.getdata(test_data,ext=1)
sci1f = sci1.flatten()
# Now we can plot our histogram, using some of the optional keywords in hist
# The hist function returns the values of the histogram bins (n), the edges
# of the bins (obins), and the patches used to create the histogram
n, obins, patches = plt.hist(sci1f,bins=100,range=(0,2))
imreplace¶
Please review the Notes section above before running any examples in this notebook
We can use simple numpy array manipulation to replicate imreplace.
For details on how to grow the boolean array for replacement see crgrow,
or the skimage.dilation
documentation.
# Standard Imports
import numpy as np
# Astronomy Specific Imports
from astropy.io import fits
# Change these values to your desired data files
test_data = '/eng/ssb/iraf_transition/test_data/iczgs3ygq_flt.fits'
# Pull out the first science array, make boolean mask with your requirements
hdu = fits.open(test_data)
sci1 = hdu[1].data
hdu.close()
mask1 = np.logical_and(sci1>0.5, sci1<0.6)
# Use mask to replace values
sci1[mask1] = 999
# We can also use numpy where to pull out index numbers
mask2 = np.where(sci1 > 1000)
print mask2
(array([ 474, 474, 606, 607, 607, 607, 608, 608, 608, 608, 609,
609, 609, 609, 610, 610, 610, 804, 804, 809, 809, 810,
883, 883, 1002, 1013]), array([455, 456, 285, 284, 285, 286, 284, 285, 286, 287, 284, 285, 286,
287, 284, 285, 286, 349, 350, 53, 575, 53, 161, 162, 104, 460]))
imslice¶
Please review the Notes section above before running any examples in this notebook
Imslice can take a 3-D datacube fits image and return multiple 2D images sliced through the chosen dimension. Keep in mind for the python equivalent workflow that the header file from the original input image will be used for all output images, including WCS information. We will be using numpy.split.
# Astronomy Specific Imports
from astropy.io import fits
# Pull image data array and image header
orig_hdu = fits.open('/eng/ssb/iraf_transition/test_data/imstack_out.fits')
header1 = orig_hdu[0].header
image1 = orig_hdu[0].data
orig_hdu.close()
# Slice images easily by using numpy.split, which returns a list of the output arrays
arr_list = np.split(image1, 2)
print("final shape of a slice is:")
print(arr_list[0].shape)
# Now we can write this new array into a new fits files by packing it back into an HDU object
hdu1 = fits.PrimaryHDU(arr_list[0],header1)
hdu1.writeto('/eng/ssb/iraf_transition/test_data/imslice_out1.fits', clobber=True)
hdu2 = fits.PrimaryHDU(arr_list[1],header1)
hdu2.writeto('/eng/ssb/iraf_transition/test_data/imslice_out2.fits', clobber=True)
final shape of a slice is:
(1, 2048, 4096)
imstack¶
Please review the Notes section above before running any examples in this notebook
imstack can take multiple fits images and stack the data, writing out a
new file where the fits data is 1-dimension higher then the input
images. Here we show that manipulation using the astropy library and
numpy.stack.
# Standard Imports
import numpy as np
# Astronomy Specific Imports
from astropy.io import fits
# Pull two image data arrays and image header
header1 = fits.getheader('/eng/ssb/iraf_transition/test_data/jczgx1ppq_flc.fits',ext=1)
image1 = fits.getdata('/eng/ssb/iraf_transition/test_data/jczgx1ppq_flc.fits')
image2 = fits.getdata('/eng/ssb/iraf_transition/test_data/jczgx1q1q_flc.fits')
# Stack arrays, the new dimension will be put first, unless otherwise specified with the axis keyword
outstack = np.stack((image1,image2))
print("final shape is:")
print(outstack.shape)
# Now we can write this new array into a new fits file by packing it back into an HDU object
hdu = fits.PrimaryHDU(outstack,header1)
hdu.writeto('/eng/ssb/iraf_transition/test_data/imstack_out.fits', clobber=True)
final shape is:
(2, 2048, 4096)
imstatistics¶
Please review the Notes section above before running any examples in this notebook
We will use the astropy.stats.sigma_clipped_stats function here,
which has some wider capabilites then the imstatistics function. Please
see the stats package
documentation
for details on the advanced usage .
# Astronomy Specific Imports
from astropy.io import fits
from astropy import stats
# Change these values to your desired data files
test_data = '/eng/ssb/iraf_transition/test_data/iczgs3ygq_flt.fits'
sci1 = fits.getdata(test_data, ext=1)
# The sigma_clipped_stats function returns the mean, median, and stddev respectively
output = stats.sigma_clipped_stats(sci1, sigma=2.0, iters=3)
print output
(0.82121155347072006, 0.81694626808166504, 0.058198063937460652)
imsum¶
Please review the Notes section above before running any examples in this notebook
We will be using the ccdproc Combiner class here. Keep in mind
that the original fits header is not retained in the CCDData object.
Please see the ccdproc
documentation
for more details.
# Astronomy Specific Imports
from astropy.io import fits
from astropy import units
from ccdproc import CCDData, Combiner
# Change these values to your desired data files
test_data1 = '/eng/ssb/iraf_transition/test_data/iczgs3y5q_flt.fits'
test_data2 = '/eng/ssb/iraf_transition/test_data/iczgs3ygq_flt.fits'
# First we need to pull out the science arrays to create CCDData objects
# Our acutal unit is electrons/sec, this is not accepted by the current
# set of units
cdata1 = CCDData.read(test_data1, hdu=1, unit=units.electron/units.s)
cdata2 = cdata1.copy()
cdata3 = CCDData.read(test_data2, hdu=1, unit=units.electron/units.s)
cdata4 = cdata3.copy()
combiner = Combiner([cdata1, cdata2, cdata3, cdata4])
# Now we can make our mask for extrema clipping
# The equivalent of low_reject, high_reject parameter
combiner.clip_extrema(nlow=1, nhigh=1)
# And finally to combine...
final_combine = combiner.average_combine()
print final_combine.data
INFO: using the unit electron / s passed to the FITS reader instead of the unit ELECTRONS/S in the FITS file. [ccdproc.ccddata]
INFO: using the unit electron / s passed to the FITS reader instead of the unit ELECTRONS/S in the FITS file. [ccdproc.ccddata]
[[ 0.87720111 0.82106587 0.79521415 ..., 3.87308204 7.41545987
9.01969481]
[ 0.89028609 0.7884455 0.8240625 ..., 0.86163342 4.53510189
0.99109203]
[ 0.81683022 0.83273572 0.82175627 ..., 3.60699821 -7.82266164
2.95994186]
...,
[ 40.72796059 15.36561799 -8.79329443 ..., 22.68277168 25.31048012
28.829813 ]
[ 46.28870392 -4.50218874 1.74757147 ..., 13.24364138 25.70440292
11.0971849 ]
[ 42.8106432 29.66250706 63.18441772 ..., 0. 9.80057049
22.66858006]]
listpixels¶
Please review the Notes section above before running any examples in this notebook
listpixels was used to list an indexed section of a FITs data array.
This is easy to do using astropy, but keep in mind that Python
indexs from zero, and with the y-axis leading, i.e. [y,x]. You also want
to end the cut with the pixel after the end pixel. So to get 1-10 in x
and 5-15 in y, you will index like so: array[4:15,0:10]
# Astronomy Specific Imports
from astropy.io import fits
# Change these values to your desired data files
test_data1 = '/eng/ssb/iraf_transition/test_data/iczgs3y5q_flt.fits'
# To quickly pull out the data array you can use the astropy convience fucntion
data_arr = fits.getdata(test_data1,ext=1)
# Now we can index the array as desired, we're cutting out 5 in y, and 2 in x
print data_arr[0:5,0:2]
[[ 0.86692303 0.80678135]
[ 0.83312052 0.76854318]
[ 0.77341086 0.80276382]
[ 0.80539584 0.78261763]
[ 0.78274417 0.82206035]]
Not Replacing¶
- imrename - can use command line utilities or the Python
ospackage for this functionality. - imdelete - can use command line utilities or the Python
ospackage for this functionality. - imtile - may replace infuture
- sections - IRAF utility function
- imgets - see images.imutil.hselect
- minmax - see images.imutil.imstatistics