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Sky matching for image mosaic¶

A module that provides functions for matching sky in overlapping images.

Authors: Mihai Cara, Warren Hack, Pey-Lian Lim
License: LICENSE

stsci.skypac.skymatch.TEAL_SkyMatch(input, skymethod='globalmin+match', match_down=True, skystat='mode', lower=None, upper=None, nclip=5, lsigma=4.0, usigma=4.0, binwidth=0.1, skyuser_kwd='SKYUSER', units_kwd='BUNIT', invsens_kwd=None, readonly=True, subtractsky=False, dq_bits=None, optimize='balanced', clobber=False, clean=True, verbose=True, logfile='skymatch.log')[source]¶

TEAL interface for skymatch(). Most parameters are identical to those of the skymatch(). Here we mention only the differences:

Parameters

logfile: str, optional: Store execution log in this file. Always openned in append mode. If not given (logfile is None), print to screen instead. NOTE: Unlike skymatch(), logfile can only be either a string file name or None.

stsci.skypac.skymatch.skymatch(input, skymethod='globalmin+match', \ skystat='mode', lower=None, upper=None, nclip=5, lsigma=4.0, usigma=4.0, \ binwidth=0.1, skyuser_kwd='SKYUSER', units_kwd='BUNIT', readonly=True, \ subtractsky=False, dq_bits=None, optimize='balanced', clobber=False, \ clean=True, verbose=True, flog='skymatch_log.txt')[source]¶

Standalone task to compute and/or “equalize” sky in input images.

Note

Sky matching (“equalization”) is possible only for overlapping exposures.

Warning

When readonly is False, image headers will be modified and image data will be background-subtracted if subtractsky is True. Remember to back up original copies as desired.

Warning

Unlike previous sky subtraction algorithm used by astrodrizzle, skymatch() accounts for differences in chip sensitivities by performing sky computations on data multiplied by inverse sensitivity (e.g., value of PHOTFLAM in image headers – see “Notes” section below).

Parameters

input: str, list of FileExtMaskInfo

A list of of FileExtMaskInfo objects or a string containing one of the following:

a comma-separated list of valid science image file names (see note below) and (optionally) extension specifications, e.g.: 'j1234567q_flt.fits[1], j1234568q_flt.fits[sci,2]';

an @-file name, e.g., '@files_to_match.txt'. See notes section for details on the format of the @-files.

Note

Valid science image file names are:

file names of existing FITS, GEIS, or WAIVER FITS files;

partial file names containing wildcard characters, e.g., '*_flt.fits';

Association (ASN) tables (must have _asn, or _asc suffix), e.g., 'j12345670_asn.fits'.

Warning

@-file names MAY NOT be followed by an extension specification.

Warning

If an association table or a partial file name with wildcard characters is followed by an extension specification, it will be considered that this extension specification applies to each file name in the association table or each file name obtained after wildcard expansion of the partial file name.

skymethod: {‘localmin’, ‘globalmin+match’, ‘globalmin’, ‘match’}, optional

Select the algorithm for sky computation:

‘localmin’: compute a common sky for all members of an exposure (see “Notes” section below). For a typical use, it will compute sky values for each chip/image extension (marked for sky subtraction in the input parameter) in an input image, and it will subtract the previously found minimum sky value from all chips (marked for sky subtraction) in that image. This process is repeated for each input image.

Note

This setting is recommended when regions of overlap between images are dominated by “pure” sky (as opposite to extended, diffuse sources).

Note

This is similar to the “skysub” algorithm used in previous versions of astrodrizzle.

‘globalmin’: compute a common sky value for all members of all exposures (see “Notes” section below). It will compute sky values for each chip/image extension (marked for sky subtraction in the input parameter) in all input images, find the minimum sky value, and then it will subtract the same minimum sky value from all chips (marked for sky subtraction) in all images. This method may useful when input images already have matched background values.

‘match’: compute differences in sky values between images in common (pair-wise) sky regions. In this case computed sky values will be relative (delta) to the sky computed in one of the input images whose sky value will be set to (reported to be) 0. This setting will “equalize” sky values between the images in large mosaics. However, this method is not recommended when used in conjunction with astrodrizzle because it computes relative sky values while astrodrizzle needs “measured” sky values for median image generation and CR rejection.

‘globalmin+match’: first find a minimum “global” sky value in all input images and then use 'match' method to equalize sky values between images.

Note

This is the recommended setting for images containing diffuse sources (e.g., galaxies, nebulae) covering significant parts of the image.

match_down: bool, optional

Specifies whether the sky differences should be subtracted from images with higher sky values (match_down is True) to match the image with the lowest sky or sky differences should be added to the images with lower sky values to match the sky of the image with the highest sky value (match_down is False).

Note

This setting applies only when skymethod parameter is either 'match' or 'globalmin+match'.

skystat: {‘mode’, ‘median’, ‘mode’, ‘midpt’}, optional

Statistical method for determining the sky value from the image pixel values. See computeSky for more detals.

lower: float, None, optional

Lower limit of usable pixel values for computing the sky. This value should be specified in the units of the input image(s).

upper: float, None, optional

Upper limit of usable pixel values for computing the sky. This value should be specified in the units of the input image(s).

nclip: int, optional

A non-negative number of clipping iterations to use when computing the sky value.

lsigma: float, optional

Lower clipping limit, in sigma, used when computing the sky value.

usigma: float, optional

Upper clipping limit, in sigma, used when computing the sky value.

binwidth: float, optional

Bin width, in sigma, used to sample the distribution of pixel brightness values in order to compute the sky background statistics.

skyuser_kwd: str, optional

Name of header keyword which records the sky value previously subtracted (if subtractsky is True) from the image data or the computed (if subtractsky is False) sky value. This keyword’s value will be updated by skymatch() (if readonly is False).

Warning

When subtractsky is True then skyuser_kwd is treated as a cummulative value. That is, subtracted sky value will be added to the skyuser_kwd value and thus skyuser_kwd represents total sky subtracted from the image by the user over the entire “history” of the image. If skyuser_kwd is missing in the input image, “previous” sky value will be considered to be 0.0.

When subtractsky is False then skyuser_kwd represents computed sky value and it is not treated as a cummulative value. Any previous value of the skyuser_kwd header keyword will be overwritten with the newly computed value.

Because of different meanings of the value represented by the skyuser_kwd header keyword depending on the value of the subtractsky parameter, it is important to be consistent and not to mix the two modes when using skymatch() multiple times on the same images.

units_kwd: str, optional

Name of header keyword which records the units of the data in the image.

invsens_kwd: str, None, optional

Name of header keyword which records the inverse sensitivity of the detector used to acquire data. For HST detectors, 'PHOTFLAM' is proportional to detector’s inverse sensitivity. It is used to convert electron counts-like to photon counts by multiplying count-like data (or count-rates) by the value indicated by this keyword.

By performing matching using photon counts (“flux units”), one can match images from heterogeneous instruments. Default value '' or None turns off use of inverse sensitivity.

readonly: bool, optional

Report the sky matching values but do not modify the input files.

subtractsky: bool, optional

Subtract computed sky value from image data and add this value to the existing value represented by skyuser_kwd (subtracted sky) or simply report the computed sky value in the header keyword specified by skyuser_kwd (computed sky).

Warning

Because subtractsky changes the meaning of the value of the header keyword skyuser_kwd it is important to be consistent in using subtractsky parameter: inconsistent use may lead to sky values reported in skyuser_kwd header keyword that do not reflect correct sky value computed for or subtracted from flat-fielded images. A possible workaround is to use different keywords for subtracted and computed sky, keeping in mind that the order of operation will affect reported computed sky values.

Also see warning for skyuser_kwd parameter.

Note

When readonly is True, reported sky values will be consistent with the setting specified by subtractsky (as if readonly is False), however sky values will NOT be subtracted from the image data when subtractsky is True.

Note

astrodrizzle does not subtract computed sky values from input flat-fielded images. Therefore, when using skymatch() on images that subsequently will be processed by astrodrizzle it is recommended to use the following suggestions:

If one plans to turn on sky subtraction step in astrodrizzle that will involve additional sky computation (as opposite to using astrodrizzle’s skyuser or skyfile parameters), then it is recommended to set subtractsky to False and set skyuser_kwd to the default value used by astrodrizzle: MDRIZSKY.
If one wants to effectively subtract the computed sky values from the flat-fielded image data, then it is recommended to set subtractsky to True, skyuser_kwd parameter to something different from MDRIZSKY, (e.g., SKYUSER), and set skyuser parameter in astrodrizzle to the same value as the values of skyuser_kwd used in the call to skymatch().

dq_bits: int, str, None, optional

Integer sum of all the DQ bit values from the input image’s DQ array that should be considered “good” when building masks for sky computations. For example, if pixels in the DQ array can be combinations of 1, 2, 4, and 8 flags and one wants to consider DQ “defects” having flags 2 and 4 as being acceptable for sky computations, then dq_bits should be set to 2+4=6. Then a DQ pixel having values 2,4, or 6 will be considered a good pixel, while a DQ pixel with a value, e.g., 1+2=3, 4+8=12, etc. will be flagged as a “bad” pixel.

Alternatively, one can enter a comma- or ‘+’-separated list of integer bit flags that should be added to obtain the final “good” bits. For example, both 4,8 and 4+8 are equivalent to setting dq_bits to 12.

Default value (0) will make all non-zero pixels in the DQ mask to be considered “bad” pixels, and the corresponding image pixels will not be used for sky computations.

Set dq_bits to None to turn off the use of image’s DQ array for sky computations.

In order to reverse the meaning of the dq_bits parameter from indicating values of the “good” DQ flags to indicating the “bad” DQ flags, prepend ‘~’ to the string value. For example, in order not to use pixels with DQ flags 4 and 8 for sky computations and to consider as “good” all other pixels (regardless of their DQ flag), set dq_bits to ~4+8, or ~4,8. To obtain the same effect with an int input value (except for 0), enter -(4+8+1)=-13. Following this convention, a dq_bits string value of '~0' would be equivalent to setting dq_bits=None.

Note

DQ masks (if used), will be combined with user masks specified in the input @-file.

optimize: {‘balanced’, ‘speed’, ‘inmemory’}, optional

Specifies whether to optimize execution for speed (maximum memory usage - loaded masks and images are not unloaded until the end of the execution) or use a balanced approach in which a minimal amount of image data is kept in memory and retrieved from disk as needed. The ‘inmemory’ option is similar to the ‘speed’ setting except that masks are never saved to the files on a physical disk but are created in memory. The default setting is recommended for most systems.

clobber: bool, optional

When a input image file is in GEIS or WAIVER FITS format it must be converted to simple/MEF FITS file format before it can be used by skymatch(). This setting specifies whether any existing simple/MEF files be overwritten during this conversion process. If clobber is False, existing simple/MEF FITS files will be opened. If clobber is True, input GEIS or WAIVER FITS will be first converted to simple FITS/MEF format overwritting (if necessary) existing files and then these newly created simple FITS/MEF files will be opened.

clean: bool, optional

Specifies whether to delete at the end of the execution any temporary files created by skymatch().

verbose: bool, optional

Specifies whether to print warning messages.

flog: str, file object, MultiFileLog, None, optional

Log file to which messages shoul be written. It can be a file name, file object, or a MultiFileLog object. The later two allow the log to be written to an existing open output stream passed from the calling function such as astrodrizzle. Log file is always openned in append mode. If not provided (None), print messages to screen only.

Raises

RuntimeError

Could not add an image to mosaic. Possibly this SkyLine does not intersect the mosaic.

TypeError: The input argument must be either a Python list of FileExtMaskInfo objects, or a string either containing either a comma-separated list file names, or an @-file name.

Notes

skymatch() provides new algorithms for sky value computations and enhances previously available algorithms used by, e.g., astrodrizzle.

First, the standard sky computation algorithm (see skymethod = 'localmin') was upgraded to be able to use DQ flags and user supplied masks to remove “bad” pixels from being used for sky statistics computations. Values different from zero in user-supplied masks indicate “good” data pixels.

Second, two new methods have been introduced: 'globalmin' and 'match', as well as a combination of the two – 'globalmin+match'.

The 'globalmin' method computes the minimum sky value across all chips in all input images. That sky value is then considered to be the background in all input images.
The 'match' algorithm is somewhat similar to the traditional sky subtraction method (skymethod = 'localmin') in the sense that it measures the sky indipendently in input images (or detector chips). The major differences are that, unlike the traditional method,
1. 'match' algorithm computes relative sky values with regard to the sky in a reference image chosen from the input list of images; and
2. Sky statistics is computed only in the part of the image that intersects other images.
This makes 'match' sky computation algorithm particularly useful for “equalizing” sky values in large mosaics in which one may have only (at least) pair-wise intersection of images without having a common intersection region (on the sky) in all images.

The 'match' method works in the following way: for each pair of intersecting images, an equation is written that requires that average surface brightness in the overlapping part of the sky be equal in both images. The final system of equations is then solved for unknown background levels.

Warning

Current algorithm is not capable of detecting cases when some groups of intersecting images (from the input list of images) do not intersect at all other groups of intersecting images (except for the simple case when single images do not intersect any other images). In these cases the algorithm will find equalizing sky values for each group. However since these groups of images do not intersect each other, sky will be matched only within each group and the “inter-group” sky mismatch could be significant.

Users are responsible for detecting such cases and adjusting processing accordingly.

Warning

Because this method computes relative sky values compared to a reference image (which will have its sky value set to 0), the sky values computed with this method usually are smaller than the “absolute” sky values computed, e.g., with the 'localmin' algorithm. Since astrodrizzle expects “true” (as opposite to relative) sky values in order to correctly compute the median image or to perform cosmic-ray detection, this algorithm in not recommended to be used alone for sky computations to be used with astrodrizzle.

For the same reason, IVM weighting in astrodrizzle should not be used with 'match' method: sky values reported in MDRIZSKY header keyword will be relative sky values (sky offsets) and derived weights will be incorrect.
The 'globalmin+match' algorithm combines 'match' and 'globalmin' methods in order to overcome the limitation of the 'match' method described in the note above: it uses 'globalmin' algorithm to find a baseline sky value common to all input images and the 'match' algorithm to “equalize” sky values in the mosaic. Thus, the sky value of the “reference” image will be equal to the baseline sky value (instead of 0 in 'match' algorithm alone) making this method acceptable for use in conjunction with astrodrizzle.

“Surface Brightness”:

skymatch() converts “raw” sky values (in image data units) obtained directly from image data to “surface brightness”-like units and all computations are performed in these units. Computed sky surface brightness values are converted back to image data units before being subtracted from the image data and/or reported in the skyuser_kwd in the image header.

This conversion from image data units to “surface brightness”-like units is necessary in order to perform correct sky computations for data from various intsruments/detectors. It accounts for differences in exposure times (if image data are in “counts” units) in each input image, differences in pixel scales of different detector chips (instruments), and detector sensitivities.

For images with data in “counts”-like units, the conversion from data units to surface brightness is given by:

\[sky_{\mathrm{surface\: brightness}} = sky_{\mathrm{data\: units}} \ \cdot \mathrm{PHOTFLAM} / (\mathrm{pixel\: Area}^2 \cdot \ \mathrm{EXPTIME})\]

and for image data in “count-rate”-like units, this conversion is given by:

\[sky_{\mathrm{surface\: brightness}} = sky_{\mathrm{data\: units}} \ \cdot \mathrm{PHOTFLAM} / \mathrm{pixel\: Area}^2 .\]

Important Header Keywords:

As discussed above, skymatch() uses values of various keywords in image headers to perform conversion of sky values to/from data units from/to surface brightness units. The most important keywords are:

BUNIT describes the units of the image data. The units of data are determined from the BUNIT header keyword by searching its value for the division sign '/'. If the division sign is not found, then the units are assumed to be “counts”. If the division sign is found in the BUNIT value and if the numerator is one of the following: 'ELECTRONS', 'COUNTS', or 'DN', and denumerator is either 'S', 'SEC', or 'SECOND', then the units are assumed to be count-rate.

If BUNIT is missing then for non-HST images the units will be assumed to be “count-rate”, while for HST images (header keyword TELESCOP = 'HST') the 'INSTRUME' and 'DETECTOR' keywords will be used to infer the units. For the NICMOS instrument, 'UNITCORR' will be used to infer the units. If relevant keywords are missing, the units of image data will be assumed to be “count-rate”. Check the log file for selected units.
EXPTIME – total exposure time, assumed to be in seconds. While the units of EXPTIME are not important for sky computation, it is important that all input images to skymatch() use the same units. This keyword is used only when inferred units for image data are “count-rates”. If EXPTIME is missing when image data units are counts, then variations in exposure time WILL NOT be accounted for. First, the primary header of the image file is searched for EXPTIME and if it is not found in the primary header, then image extension is searched for the presense of EXPTIME keyword.
PHOTFLAM – inverse sensitivity of the detector. At first skymatch() will try to detect PHOTFLAM in the image extension header and if not found, it will look for PHOTFLAM in the primary header. If PHOTFLAM is not present at all, the variations in detector sensitivity WILL NOT be accounted for.

Glossary:

Exposure – a subset of FITS image extensions in an input image that correspond to different chips in the detector used to acquire the image. The subset of image extensions that form an exposure is defined by specifying extensions to be used with input images (see parameter input).

See help for stsci.skypac.parseat.parse_at_line() for details on how to specify image extensions.

Footprint – the outline (edge) of the projection of a chip or of an exposure on the celestial sphere.

Note

Footprints are managed by the SphericalPolygon class.
Both footprints and associated exposures (image data, WCS information, and other header information) are managed by the SkyLine class.
Each SkyLine object contains one or more SkyLineMember objects that manage both footprints and associated chip data that form an exposure.

Remarks:

skymatch() works directly on geometrically distorted flat-fielded images thus avoiding the need to perform an additional drizzle step to perform distortion correction of input images.

Initially, the footprint of a chip in an image is aproximated by a 2D planar rectangle representing the borders of chip’s distorted image. After applying distortion model to this rectangle and progecting it onto the celestial sphere, it is approximated by spherical polygons. Footprints of exposures and mosaics are computed as unions of such spherical polygons while overlaps of image pairs are found by intersecting these spherical polygons.

@-File Format:

A catalog file containing a science image file and extension specifications and optionally followed by a comma-separated list of mask files and extension specifications (or None).

File names will be stripped of leading and trailing white spaces. If it is essential to keep these spaces, file names may be enclosed in single or double quotation marks. Quotation marks may also be required when file names contain special characters used to separate file names and extension specifications: ,[]{}

Extension specifications must follow the file name and must be delimited by either square or curly brackets. Curly brackets allow specifying multiple comma-separated extensions: integer extension numbers and/or tuples (‘ext name’, ext version).

Some possible ways of specifying extensions:

[1] – extension number

[‘sci’,2] – extension name and version

{1,4,(‘sci’,3)} – multiple extension specifications, including tuples

{(‘sci’,*)} – wildcard extension versions (i.e., all extensions with extension name ‘sci’)

[‘sci’] – equivalent to [‘sci’,1]

{‘sci’} – equivalent to {(‘sci’,*)}

For extensions in the science image for which no mask file is provided, the corresponding mask file names may be omitted (but a comma must still be used to show that no mask is provided in that position) or None can be used in place of the file name. NOTE: ‘None’ (in quotation marks) will be interpreted as a file named None.

Some examples of possible user input:

image1.fits{1,2,(‘sci’,3)},mask1.fits,,mask3.fits[0]

In this case:

image1.fits[1] is associated with mask1.fits[0];

image1.fits[2] does not have an associated mask;

image1.fits[‘sci’,3] is associated with mask3.fits[0].

– Assume image2.fits has 4 ‘SCI’ extensions:

image2.fits{‘sci’},None,,mask3.fits

In this case:

image2.fits[‘sci’,1] and image2.fits[‘sci’,2] and image2.fits[‘sci’,4] do not have an associated mask;

image2.fits[‘sci’,3] is associated with mask3.fits[0]

Note

User mask data that indicate what pixels in the input image should be used for sky computations (1) and which pixels should not be used for sky computations (0).

Limitations and Discussions:

Primary reason for introducing “sky match” algorithm was to try to equalize the sky in large mosaics in which computation of the “absolute” sky is difficult due to the presence of large diffuse sources in the image. As discussed above, skymatch() accomplishes this by comparing “sky values” in a pair of images in the overlap region (that is common to both images). Quite obviously the quality of sky “matching” will depend on how well these “sky values” can be estimated. We use quotation marks around sky values because for some image “true” background may not be present at all and the measured sky may be the surface brightness of large galaxy, nebula, etc.

Here is a brief list of possible limitations/factors that can affect the outcome of the matching (sky subtraction in general) algorithm:

Since sky subtraction is performed on flat-fielded but not distortion corrected images, it is important to keep in mind that flat-fielding is performed to obtain uniform surface brightness and not flux. This distinction is important for images that have not been distortion corrected. As a consequence, it is advisable that point-like sources be masked through the user-supplied mask files. Values different from zero in user-supplied masks indicate “good” data pixels. Alternatively, one can use upper parameter to limit the use of bright objects in sky computations.
Normally, distorted flat-fielded images contain cosmic rays. This algorithm does not perform CR cleaning. A possible way of minimizing the effect of the cosmic rays on sky computations is to use clipping (nclip > 0) and/or set upper parameter to a value larger than most of the sky background (or extended source) but lower than the values of most CR pixels.
In general, clipping is a good way of eliminating “bad” pixels: pixels affected by CR, hot/dead pixels, etc. However, for images with complicated backgrounds (extended galaxies, nebulae, etc.), affected by CR and noise, clipping process may mask different pixels in different images. If variations in the background are too strong, clipping may converge to different sky values in different images even when factoring in the “true” difference in the sky background between the two images.
In general images can have different “true” background values (we could measure it if images were not affected by large diffuse sources). However, arguments such as lower and upper will apply to all images regardless of the intrinsic differences in sky levels.

Examples

This task can be used to match skies of a set of ACS images simply with:

>>> from stsci.skypac import skymatch
>>> skymatch.skymatch('j*q_flt.fits')

The TEAL GUI can be used to run this task using:

>>> from stsci.skypac import skymatch
>>> epar skymatch

or from a general Python command line:

>>> from stsci.skypac import skymatch
>>> from stsci.tools import teal
>>> teal.teal('skymatch')

SkyLine (chip outline on the sky) management for image mosaic¶

This module provides support for working with footprints on the sky. Primary use case would use the following generalized steps:

Initialize SkyLine objects for each input image. This object would be the union of all the input image’s individual chips WCS footprints.
Determine overlap between all images. The determination would employ a recursive operation to return the extended list of all overlap values computed as [img1 vs [img2,img3,…,imgN],img2 vs [img3,…,imgN],…]
Select the pair with the largest overlap, or the pair which produces the largest overlap with the first input image. This defines the initial reference SkyLine object.
Perform some operation on the 2 images: for example, match sky in intersecting regions, or aligning second image with the first (reference) image.
Update the second image, either apply the sky value or correct the WCS, then generate a new SkyLine object for that image.
Create a new reference SkyLine object as the union of the initial reference object and the newly updated SkyLine object.
Repeat Steps 2-6 for all remaining input images.

This process will work reasonably fast as most operations are performed using the SkyLine objects and WCS information solely, not image data itself.

Authors: Mihai Cara, Warren Hack, Pey-Lian Lim
License: LICENSE

class stsci.skypac.skyline.SkyLine(mlist)[source]¶

Manage outlines on the sky.

Skylines are designed to capture and manipulate HST WCS image information as spherical polygons. They are represented by the SkyLine class, which is an extension of SphericalPolygon class.

Each skyline has a list of members, members, and a composite spherical polygon, polygon, members. The polygon has all the functionalities of SphericalPolygon.

Each SkyLine has a list of members and a composite polygon with all the functionalities of SphericalPolygon.

Each member in members belongs to the SkyLineMember class, which contains image name (with path if given), science extension(s), and composite WCS and polygon of the extension(s). All skylines start out with a single member from a single image. When operations are used to find composite or intersecting skylines, the resulting skyline can have multiple members.

For example, a skyline from an ACS/WFC full-frame image would give 1 member, which is a composite of extensions 1 and 4. A skyline from the union of 2 such images would have 2 members, and so forth.

Parameters

fname: str: FITS image. None to create empty SkyLine.
ext: a list of tuples (‘extname’,extver).

add_image(other)[source]¶

Return a new SkyLine that is the union of self and other.

Warning

SkyLine.union only returns polygon without members.

Parameters

other: `SkyLine` object

Examples

>>> s1 = SkyLine('image1.fits')
>>> s2 = SkyLine('image2.fits')
>>> s3 = s1.add_image(s2)

find_intersection(other)[source]¶

Return a new SkyLine that is the intersection of self and other.

Warning

SkyLine.intersection only returns polygon without members.

Parameters

other: `SkyLine` object

Examples

>>> s1 = SkyLine('image1.fits')
>>> s2 = SkyLine('image2.fits')
>>> s3 = s1.find_intersection(s2)

find_max_overlap(skylines)[source]¶

Find SkyLine from a list of skylines that overlaps the most with self.

Parameters

skylines: list: A list of SkyLine instances.

Returns

max_skyline: SkyLine instance or None: SkyLine that overlaps the most or None if no overlap found. This is not a copy.
max_overlap_area: float: Area of intersection.

property is_mf_mosaic¶: returns True if SkyLine members are from distinct image files (multi-file mosaic) and False otherwise.

static max_overlap_pair(skylines)[source]¶

Find a pair of skylines with maximum overlap.

Parameters

skylines: list: A list of SkyLine instances.

Returns

max_pair: tuple: Pair of SkyLine objects with max overlap among given skylines. If no overlap found, return None. These are not copies.

property members¶: List of SkyLineMember objects that belong to self. Duplicate members are discarded. Members are kept in the order of their additions to self.

property polygon¶: SphericalPolygon portion of SkyLine that contains the composite skyline from members belonging to self.

to_wcs()[source]¶: Combine HSTWCS objects from all members and return a new HSTWCS object. If no members, return None.

Warning

This cannot return WCS of intersection.

class stsci.skypac.skyline.SkyLineMember(image, ext, dq_bits=0, dqimage=None, dqext=None, usermask=None, usermask_ext=None)[source]¶

Container for SkyLine members that holds information about properties of a single extension (chip) in a FITS image such as:

WCS of the chip image;
bounding spherical polygon;
file name and extension from which the chip’s image has originated;
information required for unit conversions (EXPTIME, PHOTFLAM, BUNIT, etc.);
user mask and DQ array associated with chip’s image data.

Parameters

image: ImageRef

An ImageRef object that refers to an open FITS file

ext: tuple, int, str

Extension specification in the image the SkyLineMember object will be associated with.

An int ext specifies extension number. A tuple in the form (str, int) specifies extension name and number. A string ext specifies extension name and the extension version is assumed to be 1. See documentation for astropy.io.fits.getData for examples.

dq_bits: int, None (Default = 0)

Integer sum of all the DQ bit values from the input image’s DQ array that should be considered “good” when building masks for sky computations. For example, if pixels in the DQ array can be combinations of 1, 2, 4, and 8 flags and one wants to consider DQ “defects” having flags 2 and 4 as being acceptable for sky computations, then dq_bits should be set to 2+4=6. Then a DQ pixel having values 2,4, or 6 will be considered a good pixel, while a DQ pixel with a value, e.g., 1+2=3, 4+8=12, etc. will be flagged as a “bad” pixel.

Default value (0) will make all non-zero pixels in the DQ mask to be considered “bad” pixels, and the corresponding image pixels will not be used for sky computations.

Set dq_bits to None to turn off the use of image’s DQ array for sky computations.

Note

DQ masks (if used), will be combined with user masks specified by the usermask parameter.

dqimage: ImageRef

An ImageRef object that refers to an open FITS file that has DQ data of the input image.

Note

When DQ data are located in the same FITS file as the science image data (e.g., HST/ACS, HST/WFC3, etc.), dqimage may point to the same ImageRef object. In this case the reference count of the ImageRef object must be increased adequately.

dqext: tuple, int, str

Extension specification of the dqimage that contains image’s DQ information. See help for ext for more details on acceptable formats for this parameter.

usermask: ImageRef

An ImageRef object that refers to an open FITS file that has user mask data that indicate what pixels in the input image should be used for sky computations (1) and which pixels should not be used for sky computations (0).

usermask_ext: tuple, int, str

Extension specification of the usermask mask file that contains user’s mask data that should be associated with the input image and ext. See help for ext for more details on acceptable formats for this parameter.

Utility functions for parsing user catalog files for skypac¶

Module for parsing @-files or user input strings for use by stsci.skypac module.

Authors: Mihai Cara
License: LICENSE

class stsci.skypac.parseat.FileExtMaskInfo(default_ext=('SCI', '*'), default_mask_ext=0, clobber=False, doNotOpenDQ=False, fnamesOnly=False, im_fmode='update', dq_fmode='readonly', msk_fmode='readonly')[source]¶

A class that holds image, dq, user masks, and extensions to be used with these files. It is designed to facilitate keeping track of user input in catalog files.

This class is intended to be used primarily for functions such as parse_at_line() and other related functions as a return value. It is also used to initialize skypac.skyline.SkyLine objects.

FileExtMaskInfo was designed to be used in a specific ordered workflow. Below is a typical use of this class:

Initialize the object with the desired settings for default extensions to be used with the files (when a specific extension for a file is not provided) and the open modes for the files;
Add image file using image();
Add image’s extension(s) using append_ext();
[Optional; can be performed at any later stage] Add DQ file and extensions using DQimage() and dqext() methods;
Append mask files and extensions using append_mask();
[Optional] Finalize the FileExtMaskInfo() object.

Parameters

default_ext: int, tuple, optional: Default extension to be used with image files that to not have an extension specified.
default_mask_ext: int, tuple, optional: Default extension to be used with image mask files that to not have an extension specified.
clobber: bool, optional: If a file being appended is in GEIS or WAIVER FITS format, should any existing MEF files be overwritten?
doNotOpenDQ: bool, optional: Should the DQ files be oppened when simultaneously with the image files?
fnamesOnly: bool, optional: Return file names only, or open the files and return ImageRef objects?
im_fmode: str, optional: File mode to be used to open image FITS file. See astropy.io.fits.open for more details.
dq_fmode: str, optional: File mode to be used to open DQ FITS file. This is valid only if the DQ model of the image file is ‘external’ (see documentation for ImageRef for more details). For ‘intrinsic’ DQ model the DQ files will use the same setting as for im_fmode.
msk_fmode: str, optional: File mode to be used to open mask files.

Attributes

clobber: bool: If a file being appended is in GEIS or WAIVER FITS format, should any existing MEF files be overwritten?
dq_bits: int: Bitmask specifying what pixels in the mask should be removed (or kept) with the precise interpretation being left to the user. This flag is not used by this class but was designed to be passed to other functions that will use FileExtMaskInfo.

property DQimage¶

DQ image (file or ImageRef object depending on the fnamesOnly value).

Getter: Get the ImageRef DQ image object.
Setter: Set the DQ file.
Type: str, ImageRef

append_ext(ext)[source]¶

Append extensions to the list of “selected” extensions for the image file.

Note

This function appends the extensions. If it is desired to set the extensions, use replace_ext() instead.

Parameters

ext: int, tuple, None, list: Extension specification: None, an integer extension number, a tuple (extension number, extension version) where extension version can be '*' which will be replaced with the extension versions of all extensions having given extension name. If ext is None, it will be replaced with the default extension specification set during the initialization of the FileExtMaskInfo object.

append_mask(mask, ext, mask_stat=None)[source]¶

Append a mask image and its extension(s).

Note

Mask files and extensions are kept in ordered lists and their order is significant: the first mask file-extension pair is associated with the first extension of the science image file set with append_ext() and so on.

Parameters

mask: str, ImageRef, None: Mask image file. Can be a string file name, an ImageRef object (only if fnamesOnly is False), or None (to act as a place holder in the ordered list of extensions).
ext: int, tuple, None, list: Extension specification: None, an integer extension number, a tuple (extension number, extension version) where extension version can be '*' which will be replaced with the extension versions of all extensions having given extension name. If ext is None, it will be replaced with the default extension specification for mask images set during the initialization of the FileExtMaskInfo object.
mask_stat: `os.stat_result`, optional: An os.stat_result structure for the input mask file. If None, then append_mask() will compute stat for the input mask file.

Raises

RuntimeError: Raised if attempting to add masks when the science image was not yet set.
AssertionError: Raised if finalized is True.
TypeError: Raised if mask is an ImageRef object but fnamesOnly is True or if mask argument is of incorrect type.
ValueError: If mask is an ImageRef, it must not be closed.

clear_masks()[source]¶: Remove all attached mask files and extensions.

convert2ImageRef()[source]¶

Replace any existing file names with opened ImageRef objects and change the fnamesOnly property to False.

Note

The finalized property will not be modified.

Warning

The FileExtMaskInfo must not have been finalized (finalized is False) and must contain file names only (fnamesOnly is True).

Raises

AssertionError: Raised if finalized is True or fnamesOnly is False.

See also

release_all_images

property count¶: Number of extensions associated with the image file.

property dqext¶: FITS extensions associated with the DQ file.

property fext¶: FITS extensions associated with the image file.

finalize(toImageRef=False)[source]¶

Finalize the object by trimming or extending mask image lists to match the number of science image extensions.

In principle, the number of mask files and their extensions need not be equal to the number of extensions specified for the science image. If the number of masks/extensions is smaller than the number of science extensions, the list of mask extensions will be appended with None (if fnamesOnly is True) or dummy ImageRef (if fnamesOnly is False) until the number of mask extensions is equal to the number of science image extensions. If the number of mask extensions is larger than the number of science image extensions, the list of mask extensions will be trimmed to match the number of science image extensions. The trimmed out mask files (if represented by ImageRef) will be “released”.

property finalized¶: Is the FileExtMaskInfo object finalized?

property fnamesOnly¶: Was the FileExtMaskInfo initialized to return file names or the ImageRef objects?

property image¶

Image file name or the associated ImageRef object (depending on the fnamesOnly value).

Getter: Get the ImageRef image object.
Setter: Set the image file.
Type: str, ImageRef, None

Note

Setting the image will re-initialize FileExtMaskInfo. All previous settings will be lost and previously attached files will be released/deleted.

info()[source]¶: Print information about the state of the object.

property mask_images¶: Mask image file names or ImageRef object depending on the fnamesOnly value.

property maskext¶: FITS extensions associated with the mask files.

release_all_images()[source]¶

Release all images if fnamesOnly is False and replace any existing ImageRef with their original file names.

Note

This will set the fnamesOnly property to True and the finalized property to False.

See also

convert2ImageRef

replace_fext(ext)[source]¶

Replace/set image file extension list.

See also

append_ext

stsci.skypac.parseat.parse_at_file(fname, default_ext=('SCI', '*'), default_mask_ext=0, clobber=False, fnamesOnly=False, doNotOpenDQ=False, match2Images=None, im_fmode='update', dq_fmode='readonly', msk_fmode='readonly', logfile=None, verbose=False)[source]¶

This function is similar to parse_at_line(), the main difference being that is can parse multiple (EOL terminated) lines of the format described in the documentation for parse_at_line().

Below we describe only differences bewtween this function and parse_at_line().

Parameters

fname: str: File name of the catalog file.
match2Images: list of str, list of ImageRef, None, optional: List of file names or ImageRef objects whose mask specifications are to be read from the catalog file. Mask specifications for other files in the catalog that do not match the files in the match2Images list will be ignored. If match2Images is None, then all files from the catalog will be read.
logfile: str, file, MultiFileLog, None, optional: Specifies the log file to which the messages should be printed. It can be a file name, a file object, a MultiFileLog object, or None.

Returns

list: Returns a list of filenames if fnamesOnly is True or a list of FileExtMaskInfo objects if fnamesOnly is False.

stsci.skypac.parseat.parse_at_line(fstring, default_ext=('SCI', '*'), default_mask_ext=0, \ clobber=False, fnamesOnly=False, doNotOpenDQ=False, \ im_fmode='update', dq_fmode='readonly', msk_fmode='readonly', verbose=False)[source]¶

Parse a line from a catalog file containing a science image file and extension specifications and optionally followed by a comma-separated list of mask files and extension specifications (or None).

File names will be stripped of leading and trailing white spaces. If it is essential to keep these spaces, file names may be enclosed in single or double quotation marks. Quotation marks may also be required when file names contain special characters used to separate file names and extension specifications: ,[]{}

Extension specifications must follow the file name and must be delimited by either square or curly brackets. Curly brackets allow specifying multiple comma-separated extensions: integer extension numbers and/or tuples (‘ext name’, ext version).

Some possible ways of specifying extensions:
[1] – extension number

[‘sci’,2] – extension name and version

{1,4,(‘sci’,3)} – multiple extension specifications, including tuples

{(‘sci’,*)} – wildcard extension versions (i.e., all extensions with extension name ‘sci’)

[‘sci’] – equivalent to [‘sci’, 1]

{‘sci’} – equivalent to {(‘sci’,*)}

For extensions in the science image for which no mask file is provided, the corresponding mask file names may be omitted (but a comma must still be used to show that no mask is provided in that position) or None can be used in place of the file name. NOTE: 'None' (in quotation marks) will be interpreted as a file named 'None'.

Some examples of possible user input:
image1.fits{1,2,('sci',3)},mask1.fits,,mask3.fits[0]

In this case:

image1.fits[1] is associated with mask1.fits[0];

image1.fits[2] does not have an associated mask;

image1.fits['sci',3] is associated with mask3.fits[0].

– Assume image2.fits has 4 ‘SCI’ extensions:

image2.fits{'sci'},None,,mask3.fits

In this case:

image2.fits['sci', 1] and image2.fits['sci', 2] and image2.fits['sci', 4] do not have an associated mask; image2.fits['sci', 3] is associated with mask3.fits[0]

Note

Wildcard extension version in extension specification can be expanded only when fnamesOnly is False.

Parameters

fstring: str

A comma-separated string describing the image file name and (optionally) followed by the extension specifier (e.g., [sci,1,2], or [sci]). The image file name may be followed (comma-separated) by optional mask file names (and their extension specifier).

File and extension names may NOT contain leading and/or trailing spaces, commas, and/or square or curly brakets.

default_ext: int, tuple, optional: Default extension to be used with image files that to not have an extension specified.
default_mask_ext: int, tuple, optional: Default extension to be used with image mask files that to not have an extension specified.
fnamesOnly: bool, optional: Return file names only, or open the files and return ImageRef objects?
doNotOpenDQ: bool, optional: Should the DQ files be oppened when simultaneously with the image files?
im_fmode: str, optional: File mode to be used to open image FITS file. See astropy.io.fits.open for more details.
dq_fmode: str, optional: File mode to be used to open DQ FITS file. This is valid only if the DQ model of the image file is ‘external’ (see documentation for ImageRef for more details). For ‘intrinsic’ DQ model the DQ files will use the same setting as for im_fmode.
msk_fmode: str, optional: File mode to be used to open mask files.
verbose: bool, optional: Specifies whether to print warning messages.

Returns

FileExtMaskInfo: A FileExtMaskInfo object.

Raises

ValueError

Input argument ‘fstring’ must be a Python string.
Input argument ‘fstring’ contains either unbalanced or nested square brackets.
Extension specification must be preceeded by a valid image file name.

stsci.skypac.parseat.parse_cs_line(csline, default_ext=('SCI', '*'), clobber=False, fnamesOnly=False, doNotOpenDQ=False, im_fmode='update', dq_fmode='readonly', msk_fmode='readonly', logfile=None, verbose=False)[source]¶

This function is similar to parse_at_line(), the main difference being the content of the input string: a list of comma-separated science image file names. No masks can be specified and file names must be valid (i.e., None is not allowed). Extension specifications are allowed and must folow the same sintax as described for parse_at_line().

Below we describe only differences bewtween this function and parse_at_line().

Parameters

csline: str

User input string that needs to be parsed containing one of the following:

a comma-separated list of valid science image file names (see note below) and (optionally) extension specifications, e.g.: 'j1234567q_flt.fits[1], j1234568q_flt.fits[sci,2]';

an @-file name, e.g., '@files_to_match.txt'.

Note

Valid science image file names are:

file names of existing FITS, GEIS, or WAIVER FITS files;
partial file names containing wildcard characters, e.g., '*_flt.fits';
Association (ASN) tables (must have _asn, or _asc suffix), e.g., 'j12345670_asn.fits'.

Warning

@-file names MAY NOT be followed by an extension specification.

Warning

If an association table or a partial file name with wildcard characters is followed by an extension specification, it will be considered that this extension specification applies to each file name in the association table or each file name obtained after wildcard expansion of the partial file name.

logfile: str, file, MultiFileLog, None, optional

Specifies the log file to which the messages should be printed. It can be a file name, a file object, a MultiFileLog object, or None.

Returns

list: Returns a list of filenames if fnamesOnly is True or a list of FileExtMaskInfo objects if fnamesOnly is False.

Polygon filling algorithm¶

Polygon filling algorithm.

Authors: Nadezhda Dencheva, Mihai Cara
License: LICENSE

class stsci.skypac.region.Edge(name=None, start=None, stop=None, next=None)[source]¶

Edge representation

An edge has a “start” and “stop” (x, y) vertices and an entry in the GET table of a polygon. The GET entry is a list of these values:

[ymax, x_at_ymin, delta_x/delta_y]

compute_AET_entry(edge)[source]¶

Compute the entry for an edge in the current Active Edge Table

[ymax, x_intersect, 1/m] note: currently 1/m is not used

compute_GET_entry()[source]¶

Compute the entry in the Global Edge Table

[ymax, x@ymin, 1/m]

class stsci.skypac.region.Polygon(rid, vertices, coord_system='Cartesian')[source]¶

Represents a 2D polygon region with multiple vertices

Parameters

rid: string: polygon id
vertices: list of (x,y) tuples or lists: The list is ordered in such a way that when traversed in a counterclockwise direction, the enclosed area is the polygon. The last vertex must coincide with the first vertex, minimum 4 vertices are needed to define a triangle.
coord_system: string: coordinate system

get_edges()[source]¶: Create a list of Edge objects from vertices

scan(data)[source]¶

This is the main function which scans the polygon and creates the mask

Parameters

data: array: the mask array it has all zeros initially, elements within a region are set to the region’s ID

Notes

Algorithm summary:

Set the Global Edge Table (GET)
Set y to be the smallest y coordinate that has an entry in GET
Initialize the Active Edge Table (AET) to be empty
For each scan line:
1. Add edges from GET to AET for which ymin==y
2. Remove edges from AET fro which ymax==y
3. Compute the intersection of the current scan line with all edges in the AET
4. Sort on X of intersection point
5. Set elements between pairs of X in the AET to the Edge’s ID

update_AET(y, AET)[source]¶

Update the Active Edge Table (AET)

Add edges from GET to AET for which ymin of the edge is equal to the y of the scan line. Remove edges from AET for which ymax of the edge is equal to y of the scan line.

class stsci.skypac.region.Region(rid, coordinate_system)[source]¶

Base class for regions.

Parameters

rid: int or string: region ID
coordinate_system: astropy.wcs.CoordinateSystem instance or a string: in the context of WCS this would be an instance of wcs.CoordinateSysem

scan(mask)[source]¶

Sets mask values to region id for all pixels within the region. Subclasses must define this method.

Parameters

mask: ndarray: a byte array with the shape of the observation to be used as a mask

Returns

mask: array where the value of the elements is the region ID or 0 (for: pixels which are not included in any region).

Sky statistics functions for skypac¶

Sky statistics computation class for skymatch and _weighted_sky.

Authors: Mihai Cara
License: LICENSE

class stsci.skypac.skystatistics.SkyStats(skystat='mean', **kwargs)[source]¶

This is a superclass build on top of stsci.imagestats.ImageStats. Compared to ImageStats, SkyStats has “persistent settings” in the sense that object’s parameters need to be set once and these settings will be applied to all subsequent computations on different data.

Initializes the SkyStats object.

Parameters

skystat: str: Sets the statistics that will be returned by the calc_sky. The following statistics are supported: 'mean', 'mode', 'midpt', and 'median'. First three statistics have the same meaning as in stsdas.toolbox.imgtools.gstatistics while skystat='median' will compute the median of the distribution.
kwargs: dict: A dictionary of optional arguments to be passed to ImageStats.

calc_sky(data)[source]¶

Computes statistics on data.

Parameters

data: numpy.ndarray: A numpy array of values for which the statistics needs to be computed.

Returns

statistics: tuple: A tuple of two values: (skyvalue, npix), where skyvalue is the statistics specified by the skystat parameter during the initialization of the SkyStats object and npix is the number of pixels used in comuting the statistics reported in skyvalue.

class stsci.skypac.skystatistics.SkyStats(skystat='mean', **kwargs)[source]¶

This is a superclass build on top of stsci.imagestats.ImageStats. Compared to ImageStats, SkyStats has “persistent settings” in the sense that object’s parameters need to be set once and these settings will be applied to all subsequent computations on different data.

Initializes the SkyStats object.

Parameters

skystat: str: Sets the statistics that will be returned by the calc_sky. The following statistics are supported: 'mean', 'mode', 'midpt', and 'median'. First three statistics have the same meaning as in stsdas.toolbox.imgtools.gstatistics while skystat='median' will compute the median of the distribution.
kwargs: dict: A dictionary of optional arguments to be passed to ImageStats.

calc_sky(data)[source]¶

Computes statistics on data.

Parameters

data: numpy.ndarray: A numpy array of values for which the statistics needs to be computed.

Returns

statistics: tuple: A tuple of two values: (skyvalue, npix), where skyvalue is the statistics specified by the skystat parameter during the initialization of the SkyStats object and npix is the number of pixels used in comuting the statistics reported in skyvalue.

Utility functions for skypac¶

This module provides utility functions for use by stsci.skypac module.

Authors: Mihai Cara
License: LICENSE

class stsci.skypac.utils.ImageRef(hdulist_refcnt=None)[source]¶

A lightweight class that supports reference counting for FITS images and holds a ResourceRefCount object. It provides several attributes that describe main characteristics of the image (file) and essential functions for manipulating reference count.

Parameters

hdulist_refcnt: ResourceRefCount: A ResourceRefCount object holding a astropy.io.fits.HDUList object.

Attributes

filename: str: Name of the opened file. Can be None for in-memory created astropy.io.fits.HDUList objects.
can_reload_data: bool: True for files attached to a physical file, False for in-memory astropy.io.fits.HDUList objects.
original_fname: str: Original name of the file as requested by the user. Note: may be different from filename if the orininal file was in GEIS or WAIVER FITS format and subsequently was converted to a MEF FITS format. In that case this attribute will show the name of the original GEIS or WAIVER FITS file.
original_ftype: str: Type of the original file. Can take one of the following values: ‘MEF’, ‘SIMPLE’, ‘GEIS’, ‘WAIVER’, or ‘UNKNOWN’.
original_exists: bool: Indicates if the physical file exists. It is False for in-memory images.
mef_fname: str, None: Name of the MEF FITS file if exists, None otherwise.
mef_exists: bool: Indicates whether the MEF FITS file exists.
DQ_model: str, None: Type of the DQ model: ‘external’ for WFPC, WFPC2, and FOC instruments (or non-HST data if set so) that have DQ data in a separate (from image) file and ‘intrinsic’ for ACS, etc. images that have DQ extensions in the image file. It is None if the image does not have DQ data.
telescope: str: Telescope that acquired the image.
instrument: str: Instrument used to acquire data.
fmode: str: File mode used to open FITS file. See astropy.io.fits.open for more details.
memmap: bool: Is the astropy.io.fits.HDUList memory mapped?

property closed¶: Is the ImageRef object closed?

property extname¶: Extension name of the first extension.

property hdu¶: astropy.io.fits.HDUList of the attached image.

hold()[source]¶: Increment the reference count of the attached resource.

info(self, fh=sys.stdout)[source]¶: Print a summary of the object attributes.

property refcount¶: Reference count of the attached astropy.io.fits.HDUList.

release()[source]¶: Decrement reference count of the attached resource. If the reference count reaches zero, the attached ResourceRefCount object will be closed and set to None.

set_HDUList_RefCount(hdulist_refcnt=None)[source]¶

Set (attach) a new ResourceRefCount object that holds a astropy.io.fits.HDUList object. This is allowed only if the already attached ResourceRefCount can be closed. The reference count of the ResourceRefCount being attached will be incremented.

Parameters

hdulist_refcnt: ResourceRefCount, None: A ResourceRefCount object containing a astropy.io.fits.HDUList object. If it is None,it will release and close the attached ResourceRefCount object.

Raises

ValueError: The (already) attached ResourceRefCount must have reference count <= 1 so that it can be closed before being replaced with a new resource.
ValueError: The resource being attached must be in an open state.

class stsci.skypac.utils.MultiFileLog(console=True, enableBold=True, flog=None, append=True, autoflush=True, appendEOL=True)[source]¶

This is a class that facilitates writting to multiple files. MultiFileLog stores multiple file objects and can write the same log entry to all of them. It also facilitates controlling when a function can close a log file. Finally, it provides some utility functions that automate such things as appending EOL at the end of the log entry, flushing the files (to avoid losing log entries in case of uncaught exceptions), displaying WARNING, ERROR, etc. in bold on standard streams (sys.stdout, etc.)

Parameters

console: bool, optional: Enables writting to the standard output.
enableBold: bool, optional: Enable or disable writing bold text to console, e.g., 'WARNING:' 'ERROR:', etc.
flog: str, file, None, list of str or file objects, optional: File name or file object to be added to MultiFileLog during the initialization. More files can be added lated with add_logfile().
append: bool, optional: Default open mode for the files that need to be opened (e.g., that are passed as file names). If append is True, new files added to a MultiFileLog object will be opened in the “append” mode: new log entries will be appended to existing files – same as mode ‘a’ of standard function open(). When append is False, any existing files will be overwritten.
autoflush: bool, optional: Indicates whether or not to flush files after each log entry.
appendEOL: bool, optional: Indicates whether or not to add EOL at the end of each log entry.

add_logfile(flog, initial_skip=2, can_close=None, mode=None)[source]¶

Add (and open, if necessary) a log file to the MultiFileLog object.

Parameters

flog: str, file, None, list of str or file objects, optional

File name or file object to be added.

initial_skip: int, optional

The number of blank line to be written to the file if not at the beginning of the file.

can_close: bool, None, optional

Indicates whether the file object can be closed by the close() function:

can_close is True – file can be closed by the close() function;
can_close is False – file will not be closed by the close() function;
can_close is None – automatic selection based on the type of the flog argument: True if flog is a string (e.g., file name), False otherwise.

mode: str, None, optional

File open mode: same meaning as the mode parameter of the Python’s built-in open() function. If None, the mode will be inherited from file open mode set during initialization.

Returns

flog: file object: File object of newly opened (or attached) file.

close()[source]¶

Close all files opened by MultiFileLog.

It will close all files opened by MultiFileLog - essentially, all files added as file name. It will not close files added as file handles.

property count¶: Return the number of files attached to the MultiFileLog object excluding the sys.stdout file.

enable_console(enable=True, enableBold=True)[source]¶

Enable output to the standard console – sys.stdout.

Parameters

enable: bool, optional: Enable or disable output to sys.stdout.
enableBold: bool, optional: Enable or disable writing bold text to console, e.g., 'WARNING:', 'ERROR:', etc.

error(msg, *fmt)[source]¶: Prints an error message. The word ‘ERROR:’ will be printed as bold on the console and enclosed in asterisks (*) in a disk file.

flush()[source]¶: Flush all files.

important(msg, *fmt)[source]¶: Prints an “important” message. The word ‘IMPORTANT:’ will be printed as bold on the console and enclosed in asterisks (*) in a disk file.

logentry(msg, *fmt, **skip)[source]¶

Write a log entry to the log files.

Parameters

msg: str: String to be printed. Can contain replacement fields delimited by braces {}.
fmt: Parameters to be passed to str.format() method for formatting the string msg.
skip: int: Number of empty lines that should follow the log message.

Examples

>>> logentry('Sky background for chip {} is {}', 'SCI1', 10.0, skip=2)
will print:
Sky background for chip SCI1 is 10.0
followed by two blank lines.

print_endlog_msg()[source]¶: Print a message (“Log written to…”) indicating the end of the log.

set_close_flag(fh, can_close=True)[source]¶

Modify the “can be closed” status of a given file.

Parameters

fh: file object
can_close: bool, optional: Indicates if the file can be closed by the close() function.

Note

File object fh must have been already added to the MultiFileLog object.

skip(nlines=1)[source]¶

Skip a specified number of blank lines.

Parameters

nlines: int, optional

unclose_copy()[source]¶

Return a copy of the MultiFileLog object with all the attached files marked as “keep open”, that is, the close() will not close these files.

This is useful before passing the MultiFileLog object to a function that may add its own log files, and then attempt to close the files with the close() method. Thus, by passing an “unclose copy”, one can be sure that the files opened at the top level will not be closed by other functions to which the MultiFileLog object may be passed.

warning(msg, *fmt)[source]¶: Prints a warning message. The word ‘WARNING:’ will be printed as bold on the console and enclosed in asterisks (*) in a disk file.

class stsci.skypac.utils.ResourceRefCount(resource, *close_args, resource_close_fnct=None, **close_kwargs)[source]¶

A class that implements reference counting for various resources: file objects, FITS HDU lists, etc. It is intended to be used as a mechanism of controlling the “lifespan” of resources that can be used in different parts of the code “indipendently”. The resource is kept open as long as the reference count is larger than zero. Once the reference count is decreased to 0, the resource is automatically closed.

Note

The reference count of the newly created ResourceRefCount object is set to 0. It is user’s responsibility to increment the reference count of this object through the call to hold() function.

Parameters

resource: An object who must be kept open or closed based on reference count.
close_args: tuple: Positional arguments to be passed to the resource_close_fnct() function.
resource_close_fnct: function, optional: The function (usually a method of the attached resource), that can “close” the resource.
close_kwargs: dict: Keyword arguments to be passed to the resource_close_fnct() function.

property closed¶: Indicates if the resource is “closed”.

hold()[source]¶: Increment the reference count of the attached resource.

is_subscribed_on_close(obj)[source]¶: Check if the object is subscribed to on close notifications.

property refcount¶: Reference count.

release()[source]¶: Decrement reference count of the attached resource. If the reference count reaches zero, call all registered “on close” notify callbacks, and then call the “close” method on the resource which was set at initialization of the ResourceRefCount object. Finally, the resource property of the ResourceRefCount object will be set to None.

property resource¶: Resource attached to a ResourceRefCount object.

subscribe_close_notify(obj, callback=None)[source]¶: Set the object and its method that need to be called when the resource is about to be closed.

unsubscribe_close_notify(obj)[source]¶: Remove the object (and its method) from the list of callbacks that need to be notified of impending closing of the resource.

stsci.skypac.utils.almost_equal(arr1, arr2, fp_accuracy=None, fp_precision=None)[source]¶

Compares two values, or values of numpy.ndarray and verifies that these values are close to each other. For exact type (integers and boolean) the comparison is exact. For inexact types (float, numpy.float32, etc.) it checks that the values (or all values in a numpy.ndarray) satisfy the following inequality:

\[|v1-v2| \leq a + 10^{-p} \cdot |v2|,\]

where a is the accuracy (“absolute error”) and p is precision (“relative error”).

Parameters

arr1: float, int, bool, str, numpy.ndarray, None, etc.: First value or array of values to be compared.
arr2: float, int, bool, str, numpy.ndarray, None, etc.: Second value or array of values to be compared.
fp_accuracy: int, float, None, optional: Accuracy to withing values should be close. Default value will use twice the value of the machine accuracy (machine epsilon) for the input type. This parameter has effect only when the values to be compared are of inexact type (e.g., float).
fp_precision: int, float, None, optional: Accuracy to withing values should be close. Default value will use twice the value of the machine precision (resolution) for the input type. This parameter has effect only when the values to be compared are of inexact type (e.g., float).

Returns

almost_equal: bool: Returns True if input values are close enough to each other or False otherwise.

Raises

ValueError: If fp_accuracy is negative.
TypeError: If fp_precision is not int, float, or None.

stsci.skypac.utils.count_extensions(img, extname='SCI')[source]¶

Return the number of extname extensions in the input image img. If extname is None, return the number of all image-like extensions.

Input parameters are identical to those of get_extver_list().

See also

get_extver_list, get_ext_list

Examples

>>> count_extensions('j9irw1rqq_flt.fits',extname='SCI')
2
>>> count_extensions('j9irw1rqq_flt.fits',extname=None)
10

stsci.skypac.utils.ext2str(ext, compact=False, default_extver=1)[source]¶

Return a string representation of an extension specification.

Parameters

ext: tuple, int, str: Extension specification can be a tuple of the form (str,int), e.g., (‘sci’,1), an integer (extension number), or a string (extension name).
compact: bool, optional: If compact is True the returned string will have extension name quoted and separated by a comma from the extension number, e.g., "'sci',1". If compact is False the returned string will have extension version immediately follow the extension name, e.g., 'sci1'.
default_extver: int, optional: Specifies the extension version to be used when the ext parameter is a string (extension name).

Returns

strext: str: String representation of extension specification ext.

Raises

TypeError: Unexpected extension type.

Examples

>>> ext2str('sci',compact=False,default_extver=6)
"'sci',6"
>>> ext2str(('sci',2))
"'sci',2"
>>> ext2str(4)
'4'
>>> ext2str('dq')
"'dq',1"
>>> ext2str('dq',default_extver=2)
"'dq',2"
>>> ext2str('sci',compact=True,default_extver=2)
'sci2'

stsci.skypac.utils.file_name_components(fname, detect_HST_FITS_suffix=True)[source]¶

Splits base file name into a root, suffix, and extension. Given a full path, this function extracts the base name, and splits it into three components: root name, suffix, and file extension.

Parameters

fname: str: file name
detect_HST_FITS_suffix: bool, optional: If True, detects the suffix of most HST files by looking for the rightmost occurence of the underscore (‘_’) in the file name.

Returns

root: str: Root name of the file. When detect_HST_FITS_suffix is True, this is the part of the file name preceding the rightmost suffix separator (‘_’). Otherwise, it is the base file name without file extension.
suffix: str: If detect_HST_FITS_suffix is True, this field will contain the suffix of most HST files, i.e., the part of the file name contained between the rightmost suffix separator (‘_’) and file extension separator. This return value will be an empty string if If detect_HST_FITS_suffix is False or if the file name has no extension separator.
fext: str: File extension

Examples

>>> file_name_components('/data/m87/ua0x5001m_c0f.fits')
('ua0x5001m', 'c0f', '.fits')
>>> file_name_components('/data/m87/ua0x5001m_c0f.fits',False)
('ua0x5001m_c0f', '', '.fits')

stsci.skypac.utils.get_ext_list(img, extname='SCI')[source]¶

Return a list of all extension versions of extname extensions. img can be either a file name or a astropy.io.fits.HDUList object.

This function is similar to get_extver_list(), the main difference being that it returns a list of fully qualified extensions: either tuples of the form (extname, extver) or integer extension numbers (when extname is None).

See also

get_extver_list, count_extensions

Examples

>>> get_ext_list('j9irw1rqq_flt.fits',extname='SCI')
[('SCI', 1), ('SCI', 2)]
>>> get_ext_list('j9irw1rqq_flt.fits',extname=None)
[1, 2, 3, 4, 5, 6, 8, 9, 10, 11]

stsci.skypac.utils.get_extver_list(img, extname='SCI')[source]¶

Return a list of all extension versions with extname extension names. If extname is None, return extension numbers of all image-like extensions.

Note

If input image is a ImageRef, this function will not modify its reference count.

Parameters

img: str, `astropy.io.fits.HDUList`, or `~skypac.utils.ImageRef`: Input image object. If img is a string object (file name) then that file will be opened. If the file pointed to by the file name is a GEIS or WAIVER FITS file, it will be converted to a simple/MEF FITS format if clobber is True.
extname: str, optional: Indicates extension name for which all existing extension versions should be found. If extname is None, then get_extver_list will return a list of extension numbers of all image-like extensions.

Returns

extver: list: List of extension versions corresponding to the input extname. If extname is None, it will return a list of extension numbers of all image-like extensions.

Raises

IOError: Unable to open input image file.
TypeError: Argument img must be either a file name (str), an ImageRef, or a astropy.io.fits.HDUList object.
TypeError: Argument extname must be either a string or None.

See also

get_ext_list, count_extensions

Examples

>>> get_extver_list('j9irw1rqq_flt.fits',extname='sci')
[1, 2]
>>> get_extver_list('j9irw1rqq_flt.fits',extname=None)
[1, 2, 3, 4, 5, 6, 8, 9, 10, 11

stsci.skypac.utils.is_countrate(hdulist, ext, units_kwd='BUNIT', guess_if_missing=True, telescope=None, instrument=None, verbose=True, flog=None)[source]¶

Infer the units of the data of the input image from the input image. Specifically, it tries to infer whether the units are counts (or count-like) or if the units are count-rate.

The units of data are determined from the BUNIT header keyword by searching its value for the division sign '/'. If the division sign is not found, then the units are assumed to be “counts”. If the division sign is found in the BUNIT value and if the numerator is one of the following: ‘ELECTRONS’,’COUNTS’, or ‘DN’, and denumerator is either ‘S’,’SEC’, or ‘SECOND’, then the units are assumed to be count-rate.

Parameters

hdulist: `astropy.io.fits.HDUList`: astropy.io.fits.HDUList of the image.
ext: tuple, int, str: Extension specification for whose data the units need to be inferred. An int ext specifies extension number. A tuple in the form (str, int) specifies extension name and number. A string ext specifies extension name and the extension version is assumed to be 1. See documentation for astropy.io.fits.getData for examples.
units_kwd: str, optional: FITS header keyword describing data units of the image. This keyword is assumed to be in the header of the extension specified by the ext parameter.
guess_if_missing: bool, optional: Instructs to try make best guess on image units when the keyword specified by units_kwd is not found in the image header. The first action will be to look for this keyword in the primary header, and if not found, infer the units based on the telescope, instrument, and detector information.
telescope: str, None, optional: Specifies the telescope from which the data came. If not specified, the value specified in the 'TELESCOP' keyword in the primary header will be used.
instrument: str, None, optional: Specifies the instrument used for acquiring data. If not specified, the value specified in the 'INSTRUME' keyword in the primary header will be used.
verbose: bool, optional: Specifies whether to print warning messages.
flog: str, file, MultiFileLog, None, optional: Specifies the log file to which the messages should be printed. It can be a file name, a file object, a MultiFileLog object, or None.

Returns

bool, None: Returns True if the units of the input image for a given extension are count-rate-like and False if the units are count-like. Returns None if the units cannot be inferred from the header.

stsci.skypac.utils.openImageEx(filename, mode='readonly', dqmode='readonly', memmap=True, saveAsMEF=True, output_base_fitsname=None, clobber=True, imageOnly=False, openImageHDU=True, openDQHDU=False, preferMEF=True, verbose=False)[source]¶

Open an image file and (if requested) the associated DQ file and return corresponding ImageRef objects.

This function is an enhanced version of stsci.tools.fileutil.openImage() function in that it can open both the image file and the associated DQ image. It also provides additional inormation about the opened files: file type, original file name, DQ model (“intrinsic”, where DQ data are placed in the same file as the science data, or “extrinsic” when DQ data are in a separate file from the science data), etc. Because of the way it was implemented, it requires half of the number of calls to astropy.io.fits.open thus making it almost twice as fast as the openImage() function.

Parameters

filename: str: File name of the file to be opened. The image file formats are recognized: simple/MEF FITS, HST GEIS format, or WAIVER FITS format.
mode: str, optional: File mode used to open main image FITS file. See astropy.io.fits.open for more details.
dqmode: str, optional: File mode used to open DQ image FITS file. See parameter mode above for more details.
memmap: bool, optional: Should memory mapping to be used whe opening simple/MEF FITS?
saveAsMEF: bool, optional: Should an input GEIS or WAIVER FITS be converted to simple/MEF FITS?
output_base_fitsname: str, None, optional: The base name of the output simple/MEF FITS when saveAsMEF is True. If it is None, the file name of the converted file will be determined according to HST file naming conventions.
clobber: bool, optional: If saveAsMEF is True, should any existing output files be overwritten?
imageOnly: bool, optional: Should this function open the image file only? If True, then the DQ-related attributes will not be valid.
openImageHDU: bool, optional: Indicates whether the returned ImageRef object corresponding to the science image file should be in an open or closed state.
openDQHDU: bool, optional: Indicates whether the returned ImageRef object corresponding to the DQ image file should be in an open or closed state.
preferMEF: bool, optional: Should this function open an existing MEF file that complies with HST naming convention when the input file is in GEIS or WAIVER FITS format, even when saveAsMEF is False or clobber is False?
verbose: bool, optional: If True, some addition information will be printed out to standard output.

Returns

(img, dqimg): (ImageRef, ImageRef): A tuple of ImageRef objects corresponding to the science image and to the DQ image. Each object in the returned tuple open or closed depending on the input arguments.

Note

If the returned object is open, then its reference count will be at least 1. The caller is responsible for “releasing” the object when it is no longer needed.

Note

If the DQ model of the opened file is “intrinsic”, then the dqimg component of the returned tuple will hold a reference counter to the same image. Thus, for “intrinsic” DQ data models, the reference count of the returned objects may be 2 (if both science openImageHDU and openDQHDU are True).

Raises

ValueError: If input file is neither a GEIS file nor a FITS file.
ValueError: Errors occured while accessing/reading the file possibly due to corrupted file, non-compliant file format, etc.s

stsci.skypac.utils.skyval2txt(files='*_flt.fits', skyfile='skyfile.txt', skykwd='SKYUSER', default_ext=('SCI', '*'))[source]¶

A convenience function that allows retrieving computed sky background values from image headers and storing them in a text file that can be re-used by drizzlepac.astrodrizzle.AstroDrizzle(). This is particularly useful when performing sky matching on a large number of images which takes considerable time. Saving computed sky values to a text file allows re-running AstroDrizzle() without re-computing sky values.

Warning

The file specified by skyfile is overwritten without warning.

Note

Images that do not have specified extensions will be ignored.

Parameters

files: str

File name(s), including extension specification if necessary, from which sky values should be retrieved:

a comma-separated list of valid science image file names (see note below) and (optionally) extension specifications, e.g.: 'j1234567q_flt.fits[1], j1234568q_flt.fits[sci,2]';

an @-file name, e.g., '@fits_files_with_skyvals.txt'.

Note

Valid science image file names are:

file names of existing FITS, GEIS, or WAIVER FITS files;
partial file names containing wildcard characters, e.g., '*_flt.fits';
Association (ASN) tables (must have _asn, or _asc suffix), e.g., 'j12345670_asn.fits'.

Warning

@-file names MAY NOT be followed by an extension specification.

Warning

If an association table or a partial file name with wildcard characters is followed by an extension specification, it will be considered that this extension specification applies to each file name in the association table or each file name obtained after wildcard expansion of the partial file name.

skyfile: str, optional

Output “skyfile” to which sky values from the image headers should be written out.

skykwd: str, optional

Header keyword holding the value of the computed sky background.

default_ext: int, tuple, optional

Default extension to be used with image files that to not have an extension specified.

stsci.skypac.utils.temp_mask_file(rootname, suffix, ext, data, dir=os.path.curdir, fnameOnly=False)[source]¶

Saves 2D data array to temporary simple FITS file. The name of the emporary file is generated based on the input parameters.

Parameters

data: numpy array: Data to be written to the temporary FITS file. Data will be written in the primary HDU.
rootname: str: Root name of the file.
prefix: str, optional: Prefix to be added in front of the root name. If randomize_prefix is True, then a random string will be added to the right of the string specified by prefix (with no separator between them). Prefix (or the randomized prefix) will be separated from the root name by the string specified in sep. If prefix is an empty string ('') then no prefix will be prepended to the root file name.
suffix: str, optional: Suffix to be added to the root name. Suffix will be separated from the root name by the string specified in sep.
ext: int, str, or tuple of the form (str, int), optional: Extention to be added to the temporary file after the suffix. Extension name string will be separated from the suffix by the string specified in sep.
sep: str, optional: Separator string to be inserted between (randomized) prefix and root name, root name and suffix, and suffix and extension.
randomize_prefix: bool, optional: Specifies whether to add (postpend) a random string to string specified by prefix.
dir: str, optional: Directory to which the temporary file should be written. If directory dir is None then the file will be written to the default (for more details, see the explanation for argument dir to the tempfile.mstemp function).
fnameOnly: bool, optional: Specifies what should temp_mask_file return: file name of the created file (if fnameOnly is True), or a tuple with the file name of the created file and an open ImageRef object of that file.

Returns

fname: str: File name of the temporary file.
mask: ImageRef: An open ImageRef object of the temporary FITS file. This is returned as a tuple together with the file name only when fnameOnly is False.

Note

Mask data will be in the Primary HDU.

Raises

TypeError: Extension specifier must be either an integer, a string, or a tuple of the form (str, int).

Examples

>>> import numpy as np
>>> from stsci import skypac
>>> mask=np.ones((800,800),dtype=np.uint8)
>>> skypac.utils.temp_mask_file(mask, 'ua0x5001m',
...     suffix='skymatch_mask', ext=('sci',4), dir='/data/m87',
...     fnameOnly=True)
'/data/m87/tmp39gCpw_ua0x5001m_skymatch_mask_sci4.fits'
>>> skypac.utils.temp_mask_file(mask, 'ua0x5001m',
...     suffix='skymatch_mask', ext=('sci',4), dir='.', fnameOnly=True)
'tmpxl7LTO_ua0x5001m_skymatch_mask_sci4.fits'
>>> skypac.utils.temp_mask_file(mask, 'ua0x5001m',
...     suffix='skymatch_mask', ext=('sci',4), dir='.',fnameOnly=False)
('tmpxMcL5g_ua0x5001m_skymatch_mask_sci4.fits', <skypac.utils.ImageRef object at 0x101f5a3d0>)

Pixel Area Map (PAM) utilities¶

A module that provides functions for computing Pixel Area Maps (PAM) based on polynomial distortion model contained in a FITS WCS. Tabular distortions NPOL and DET2IM used to describe HST/ACS distortions are ignored.

Authors: Mihai Cara
License: LICENSE

stsci.skypac.pamutils.pam_from_file(image, ext, output_pam, ignore_vacorr=False, normalize_at=None)[source]¶

Generate a Pixel Area Map (PAM) file from the FITS WCS contained in an image extension of a calibrated HST image file specified by image.

Note

PAM computation is performed using the distortion model defined in the WCS and described through Simple Image Polynomials (SIP). Non-polynomial distortions are ignored!

Parameters

image: str: File name of a FITS image that will provide a FITS WCS (stwcs.wcsutils.HSTWCS or astropy.wcs.WCS).
ext: int, str, tuple of (str, int): Extension specification. May be an integer extension number, a string extension name, or a tuple of extension name and extension version.
output_pam: str: Output file name to which PAM will be written.

Warning

If the output file already exists, it will be overwritten without warnings.
ignore_vacorr: bool, optional: When set to True, PAM will be generated as if vellocity aberration has not applied to the WCS.

Warning

This function does not know whether velocity aberration (VA) correction has been applied to the WCS or not. It is user’s responsibility to check the appropriateness of settung this parameter to True. Setting ignore_vacorr to True when WCS was not VA-corrected will result in larger errors in computed PAM. Default value is highly recommended!
normalize_at: tuple of int, optional: Indicates whether to normalize computed PAM to 1 at the provided zero-based pixel position. By default, PAM is computed relative to (or, in units of) the idcscale (for HST instruments) value when present or to the pixel scale at CRPIX when the wcs object does not have an idcscale property. Default setting should produce results analogous to the drizzle/blot method.

Note

HST/WFC3 PAM historically are normalized to 1 at specific pixel positions. See http://www.stsci.edu/hst/wfc3/pam/pixel_area_maps for further details.

stsci.skypac.pamutils.pam_from_wcs(wcs, shape=None, ignore_vacorr=False, normalize_at=None)[source]¶

Generate a Pixel Area Map (PAM) file from a FITS WCS.

Note

PAM computation is performed using the distortion model defined in the WCS and described through Simple Image Polynomials (SIP). Non-polynomial distortions are ignored!

Parameters

wcs: stwcs.wcsutils.HSTWCS, astropy.wcs.WCS: An WCS object to be used for generating PAM file.
shape: tuple of two int, None, optional: Shape of the output image (ny, nx). If se to default None, this function will try to deduce the shape of the output image from the value of array_shape attribute of the input wcs object.
ignore_vacorr: bool, optional: When set to True, PAM will be generated as if vellocity aberration has not applied to the WCS.

Warning

This function does not know whether velocity aberration (VA) correction has been applied to the WCS or not. It is user’s responsibility to check the appropriateness of settung this parameter to True. Setting ignore_vacorr to True when WCS was not VA-corrected will result in larger errors in computed PAM. Default value is highly recommended!
normalize_at: tuple of int, optional: Indicates whether to normalize computed PAM to 1 at the provided zero-based pixel position. By default, PAM is computed relative to (or, in units of) the idcscale (for HST instruments) value when present or to the pixel scale at CRPIX when the wcs object does not have an idcscale property. Default setting should produce results analogous to the drizzle/blot method.

Note

HST/WFC3 PAM historically are normalized to 1 at specific pixel positions. See http://www.stsci.edu/hst/wfc3/pam/pixel_area_maps for further details.

Returns

pam: numpy.ndarray: A 2D numpy.ndarray containing PAM.

Raises

ValueError: When both shape and wcs.array_shape attribute are None.

LICENSE¶

Redistribution and use in source and binary forms, with or without modification, are permitted provided that the following conditions are met:

Redistributions of source code must retain the above copyright notice, this list of conditions and the following disclaimer.

Redistributions in binary form must reproduce the above copyright notice, this list of conditions and the following disclaimer in the documentation and/or other materials provided with the distribution.

Neither the name of the copyright holder nor the names of its contributors may be used to endorse or promote products derived from this software without specific prior written permission.

THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS “AS IS” AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.

Welcome to skypac’s documentation!¶

Content¶

Sky matching for image mosaic¶

SkyLine (chip outline on the sky) management for image mosaic¶

Utility functions for parsing user catalog files for skypac¶

Polygon filling algorithm¶

Sky statistics functions for skypac¶

Utility functions for skypac¶

Pixel Area Map (PAM) utilities¶

LICENSE¶

Indices and tables¶