wx2d

stistools.wx2d.apply_trace(image, a2center, a2displ, subdiv, offset=0.0, shifta2=0.0, extname='SCI')

Add together ‘subdiv’ rows of ‘image’, following the trace.

Parameters:
image : ndarray

input 2-D image array, oversampled by ‘subdiv’ in axis 0

a2center : ndarray

1-D array of Y locations

a2displ : ndarray

array of traces, one for each a2center; the length of each trace must be the same as the number of columns in the input image

subdiv : int

number of rows to add together

offset : float

offset of the first row in ‘image’ from the beginning of the data block in the original file, needed for trace

shifta2 : float

offset of the row from nominal (from shifta2 keyword)

extname : string

which type of extension (SCI, ERR, DQ)?

Returns:
x2d : ndarray

resampled 2-D image array

Notes

The function value is a 2-D array containing the resampled image. This is binned by subdiv in Y (axis 0), after shifting by trace (multiplied by subdiv).

For extname = “ERR” the result differs in these ways:

  1. fractions of pixels at the endpoints of the extraction region are not included
  2. the values are combined as the average of the sum of the squares

For extname = “DQ” the result differs in these ways:

  1. the output is type int16
  2. the output values are nominally the same as the input, while for SCI the output are subdiv times larger than the input
  3. fractions of pixels at the endpoints of the extraction region are not included
  4. the values are combined via bitwise OR rather than an average or sum
stistools.wx2d.bin_traces(a2displ, binaxis1, ltv)

bin the traces by the factor binaxis1

Parameters:
a2displ : ndarray

an array of one or more arrays of Y displacements (traces)

binaxis1 : int

binning factor in the dispersion axis

ltv : float

offset in the dispersion axis (one indexing)

Returns:
a2displ : ndarray

an array of traces (a2displ), but with the trace arrays binned and shorter by the factor binaxis1

stistools.wx2d.extract(image, locn, subdiv)

Add together ‘subdiv’ rows of ‘image’, centered on ‘locn’.

Parameters:
image : ndarray

input array, oversampled by ‘subdiv’ in axis 0

locn : ndarray

a 1-D array giving the location at which to extract; an integer value corresponds to the center of the pixel. The length must be the same as the number of columns in the input image.

subdiv : int

number of rows to add together

Returns:
spec : ndarray

a 1-D array containing the extracted row

stistools.wx2d.extract_err(image, locn, subdiv)

Average ‘subdiv’ rows of ‘image’, centered on ‘locn’.

Parameters:
image : ndarray

input array, oversampled by ‘subdiv’ in axis 0

locn : ndarray

a 1-D array giving the location at which to extract; an integer value corresponds to the center of the pixel

subdiv : int

number of rows to add together

Returns:
spec : ndarray

a 1-D array containing the extracted row

Notes

This takes the square root of the average of the squares, intended to be used for interpolating the ERR array. Fractions of pixels at the upper and lower edges are excluded.

stistools.wx2d.extract_i16(image, locn, subdiv)

Bitwise OR ‘subdiv’ rows of ‘image’, centered on ‘locn’.

Parameters:
image : ndarray

input array, oversampled by ‘subdiv’ in axis 0

locn : ndarray

a 1-D array giving the location at which to extract; an integer value corresponds to the center of the pixel

subdiv : int

number of rows to add together

Returns:
spec : ndarray

a 1-D array containing the extracted row

stistools.wx2d.get_trace(tracefile, phdr, hdr)

Read 1-D traces from the 1dt table (sptrctab).

Parameters:
tracefile : string or array

either a trace array or the name of a FITS 1dt table

phdr : fits Header object

primary header of input file

hdr : fits Header object

extension header of input image (for binning info and time of exposure)

Returns:
trace_arrays : tuple of 2 arrays

a pair of arrays, one is the Y location at the middle column, and the other is an array of trace arrays

Notes

If ‘tracefile’ is already a trace array, it will just be returned, together with an arbitrary Y location of 0 (because that will always be within the image).

opt_elem and cenwave are criteria for selecting the relevant rows from the 1dt table. There will normally be several rows that match, and they should have different values of the Y location; the output list will be sorted on Y location.

stistools.wx2d.interpolate_trace(a2center, a2displ, y, length)

Interpolate within the array of traces, and return a trace.

Parameters:
a2center : ndarray

array of Y locations

a2displ : ndarray

array of traces, one trace for each element of a2center

y : float

Y location on the detector

length : int

length of a trace; needed only if traces is empty

stistools.wx2d.inv_avg_interp(order, image)
stistools.wx2d.inv_haar(image)
stistools.wx2d.kd_apply_trace(image, a2center, a2displ, offset=0.0, shifta2=0.0)

Kris Davidson’s resampling algorithm, following the trace.

Parameters:
image : ndarray

input 2-D image array

a2center : ndarray

array of Y locations

a2displ : ndarray

array of traces, one for each a2center; the length of each trace must be the same as the number of columns in ‘image’

offset : float

offset of the first row in ‘image’ from the beginning of the data block in the original file, needed for trace

shifta2 : float

offset of the row from nominal (from shifta2 keyword)

Returns:
x2d : ndarray

2-D array containing the resampled image

stistools.wx2d.kd_resampling(img, errimg, original_nrows, nrows, ncols, rows, a2center, a2displ, offset, shifta2)

Apply Kris Davidson’s resampling method.

Parameters:
img : ndarray

SCI image array (could be a subset of full image)

errimg : ndarray

ERR image array (could be a subset of full image)

original_nrows : int

number of image lines (NAXIS2) in input image

nrows : int

number of image lines in subset

ncols : int

number of image columns (NAXIS1)

rows : tuple

tuple giving the slice of rows to process

a2center : ndarray

1-D array of Y locations

a2displ : ndarray

array of traces, one for each a2center; the length of each trace must be the same as the number of columns in the input image

offset : float

offset of the first row in ‘image’ from the beginning of the data block in the original file, needed for trace

shifta2 : float

offset of the row from nominal (from shifta2 keyword)

Returns:
img_arr : tuple

the image and error arrays (to replace the input img and errimg)

stistools.wx2d.polynomial(x, y, z, n)

used for interpolation

Parameters:
x : ndarray

the integer values from 0 through n-1 inclusive (but float64)

y : ndarray

a 2-D array, axis 0 of length n

z : float

n / 2.

n : int

1 + order of polynomial fit

stistools.wx2d.stis_psf(x, a)

Evaluate the cross-dispersion PSF at x.

Parameters:
x : float

offset in pixels from the center of the profile

a : float

a measure of the width of the PSF

Returns:
val : float

the PSF evaluated at x

stistools.wx2d.trace_name(trace, phdr)

Return the 1dt table name or array.

Parameters:
trace : string or array or None

if trace is None the header keyword SPTRCTAB will be gotten from phdr; else if this is a string it should be the name of a trace file (possibly using an environment variable); otherwise, it should be a trace, in which case it will be returned unchanged

phdr : fits Header object

primary header, used only if trace is None

Returns:
tracefile : string or array

name of a trace file (with environment variable expanded), or an actual trace array

stistools.wx2d.wavelet_resampling(hdu, img, errimg, original_nrows, nrows, ncols, rows, a2center, a2displ, offset, shifta2, imset, order, subdiv, psf_width, subsampled, convolved)

Resample img and errimg using wavelets.

Parameters:
hdu : fits header/data unit object

header/data unit for a SCI extension

img : ndarray

SCI image array (could be a subset of full image)

errimg : ndarray

ERR image array (could be a subset of full image)

original_nrows : int

number of image lines (NAXIS2) in input image

nrows : int

number of image lines in subset

ncols : int

number of image columns (NAXIS1)

rows : tuple

tuple giving the slice of rows to process

a2center : ndarray

1-D array of Y locations

a2displ : ndarray

array of traces, one for each a2center; the length of each trace must be the same as the number of columns in the input image

offset : float

offset of the first row in ‘image’ from the beginning of the data block in the original file, needed for trace

shifta2 : float

offset of the row from nominal (from shifta2 keyword)

imset : int

number of the current image set (keyword EXTVER)

order : int

polynomial order

subdiv : int

number of subpixels per input pixel

psf_width : float

width of PSF for convolution (e.g. 1.3);

subsampled : string or None

name of the output file with the subsampled image

convolved : string or None

name of the output file with the convolved image

Returns:
img_arr: tuple of ndarrays

the image and error arrays (to replace the input img and errimg)

stistools.wx2d.wx2d(input, output, wavelengths=None, helcorr='', algorithm='wavelet', trace=None, order=7, subdiv=8, psf_width=0.0, rows=None, subsampled=None, convolved=None)

Resample the input, correcting for geometric distortion.

Parameters:
input : string

name of input file containing an image set

output : string

name of the output file

wavelengths : string, optional [Default: None]

name of the output file for wavelengths

helcorr : string

specify “perform” or “omit” to override header keyword

algorithm : {‘wavelet’, ‘kd’}

algorithm to use in resampling the input

trace : string or array, or None

trace array, or name of FITS table containing trace(s)

order : int [Default: 7]

polynomial order (an odd number, e.g. 5 or 7)

subdiv : int [Default: 8]

number of subpixels (a power of 2, e.g. 8 or 16)

psf_width : float [Default: 0.]

width of PSF for convolution (e.g. 1.3); 0 means no convolution

rows : tuple, optional [Default: None]

a tuple giving the slice of rows to process; output values in all other rows will be set to zero. The default of None means all rows, same as (0, 1024)

subsampled : string, optional [Default: None]

name of the output file with the subsampled image

convolved : string, optional [Default: None]

name of the output file with the convolved image

stistools.wx2d.wx2d_imset(ft, imset, output, wavelengths, helcorr, algorithm, tracefile, order, subdiv, psf_width, rows, subsampled, convolved)

Resample one image set, and append to output file(s).

Parameters:
ft : HDUList

Fits HDUList object for the input file.

imset : int

one-indexed image set number

output : string

name of the output file

wavelengths : string or None

name of the output file for wavelengths

helcorr : {‘perform’, ‘omit’}

specify “perform” or “omit” to override header keyword

algorithm : {“wavelet”,”kd”}

algorithm to use to process input

tracefile : string or array

trace array, or name of FITS table containing trace(s)

order : int

polynomial order

subdiv : int

number of subpixels

psf_width : float

width of PSF for convolution

rows : tuple

a tuple giving the slice of rows to process

subsampled : string, or None

name of the output file with the subsampled image

convolved : string, or None

name of the output file with the convolved image