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:

imagendarray: input 2-D image array, oversampled by 'subdiv' in axis 0
a2centerndarray: 1-D array of Y locations
a2displndarray: 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
subdivint: number of rows to add together
offsetfloat: offset of the first row in 'image' from the beginning of the data block in the original file, needed for trace
shifta2float: offset of the row from nominal (from shifta2 keyword)
extnamestring: which type of extension (SCI, ERR, DQ)?

Returns:

x2dndarray: 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:

fractions of pixels at the endpoints of the extraction region are not included

the values are combined as the average of the sum of the squares

For extname = "DQ" the result differs in these ways:

the output is type int16

the output values are nominally the same as the input, while for SCI the output are subdiv times larger than the input

fractions of pixels at the endpoints of the extraction region are not included

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:

a2displndarray: an array of one or more arrays of Y displacements (traces)
binaxis1int: binning factor in the dispersion axis
ltvfloat: offset in the dispersion axis (one indexing)

Returns:

a2displndarray: 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:

imagendarray: input array, oversampled by 'subdiv' in axis 0
locnndarray: 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.
subdivint: number of rows to add together

Returns:

specndarray: a 1-D array containing the extracted row

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

Average 'subdiv' rows of 'image', centered on 'locn'.

Parameters:

imagendarray: input array, oversampled by 'subdiv' in axis 0
locnndarray: a 1-D array giving the location at which to extract; an integer value corresponds to the center of the pixel
subdivint: number of rows to add together

Returns:

specndarray: 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:

imagendarray: input array, oversampled by 'subdiv' in axis 0
locnndarray: a 1-D array giving the location at which to extract; an integer value corresponds to the center of the pixel
subdivint: number of rows to add together

Returns:

specndarray: 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:

tracefilestring or array: either a trace array or the name of a FITS 1dt table
phdrfits Header object: primary header of input file
hdrfits Header object: extension header of input image (for binning info and time of exposure)

Returns:

trace_arraystuple 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:

a2centerndarray: array of Y locations
a2displndarray: array of traces, one trace for each element of a2center
yfloat: Y location on the detector
lengthint: 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:

imagendarray: input 2-D image array
a2centerndarray: array of Y locations
a2displndarray: array of traces, one for each a2center; the length of each trace must be the same as the number of columns in 'image'
offsetfloat: offset of the first row in 'image' from the beginning of the data block in the original file, needed for trace
shifta2float: offset of the row from nominal (from shifta2 keyword)

Returns:

x2dndarray: 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:

imgndarray: SCI image array (could be a subset of full image)
errimgndarray: ERR image array (could be a subset of full image)
original_nrowsint: number of image lines (NAXIS2) in input image
nrowsint: number of image lines in subset
ncolsint: number of image columns (NAXIS1)
rowstuple: tuple giving the slice of rows to process
a2centerndarray: 1-D array of Y locations
a2displndarray: 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
offsetfloat: offset of the first row in 'image' from the beginning of the data block in the original file, needed for trace
shifta2float: offset of the row from nominal (from shifta2 keyword)

Returns:

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

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

used for interpolation

Parameters:

xndarray: the integer values from 0 through n-1 inclusive (but float64)
yndarray: a 2-D array, axis 0 of length n
zfloat: n / 2.
nint: 1 + order of polynomial fit

stistools.wx2d.stis_psf(x, a)¶

Evaluate the cross-dispersion PSF at x.

Parameters:

xfloat: offset in pixels from the center of the profile
afloat: a measure of the width of the PSF

Returns:

valfloat: the PSF evaluated at x

stistools.wx2d.trace_name(trace, phdr)¶

Return the 1dt table name or array.

Parameters:

tracestring 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
phdrfits Header object: primary header, used only if trace is None

Returns:

tracefilestring 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:

hdufits header/data unit object: header/data unit for a SCI extension
imgndarray: SCI image array (could be a subset of full image)
errimgndarray: ERR image array (could be a subset of full image)
original_nrowsint: number of image lines (NAXIS2) in input image
nrowsint: number of image lines in subset
ncolsint: number of image columns (NAXIS1)
rowstuple: tuple giving the slice of rows to process
a2centerndarray: 1-D array of Y locations
a2displndarray: 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
offsetfloat: offset of the first row in 'image' from the beginning of the data block in the original file, needed for trace
shifta2float: offset of the row from nominal (from shifta2 keyword)
imsetint: number of the current image set (keyword EXTVER)
orderint: polynomial order
subdivint: number of subpixels per input pixel
psf_widthfloat: width of PSF for convolution (e.g. 1.3);
subsampledstring or None: name of the output file with the subsampled image
convolvedstring 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:

inputstring: name of input file containing an image set
outputstring: name of the output file
wavelengthsstring, optional [Default: None]: name of the output file for wavelengths
helcorrstring: specify "perform" or "omit" to override header keyword
algorithm{'wavelet', 'kd'}: algorithm to use in resampling the input
tracestring or array, or None: trace array, or name of FITS table containing trace(s)
orderint [Default: 7]: polynomial order (an odd number, e.g. 5 or 7)
subdivint [Default: 8]: number of subpixels (a power of 2, e.g. 8 or 16)
psf_widthfloat [Default: 0.]: width of PSF for convolution (e.g. 1.3); 0 means no convolution
rowstuple, 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)
subsampledstring, optional [Default: None]: name of the output file with the subsampled image
convolvedstring, 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:

ftHDUList: Fits HDUList object for the input file.
imsetint: one-indexed image set number
outputstring: name of the output file
wavelengthsstring 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
tracefilestring or array: trace array, or name of FITS table containing trace(s)
orderint: polynomial order
subdivint: number of subpixels
psf_widthfloat: width of PSF for convolution
rowstuple: a tuple giving the slice of rows to process
subsampledstring, or None: name of the output file with the subsampled image
convolvedstring, or None: name of the output file with the convolved image