Features

Revision as of 22:17, 23 September 2016

Features in brief

• Basic information about the images or other FITS data, pixel statisitics.
• Conversion of FITS images to more easily presentable formats.
• Querying and modification of FITS header keywords.
• Calibration of raw images, including the masking of bad, saturated or otherwise unuseable pixels.
• Arithmetic operations on images, both on per-image and per-pixel basis.
• Combination of multiple images into a single one.
• Generic spatial geometric transformations of images (shifting, dilating, shrinking, clipping, higher order polynomial transformations, ...), including the registration of images to the same reference frame.
• Generation of artificial images.
• Detection and characterization of stellar profiles.
• Coordinate list manipulation (fit and evaluation of geometric transformations, point matching, pair matching, matching by identifiers, ...)
• Photometry (aperture, image subtraction, analytic profile modelling, PSF and various other sophisticated combinations of these).
• Regression analysis and general numeric data manipulation.
• Tasks are optimized for shell-level parallel processing.

Features by tasks

fiarith

• Basic arithmetic operations on images.
• Operations on individual pixels.
• Creation of images where the pixel intensities are are functions of the coordinates.

ficalib

• Performing simple arithmetic operations on a bunch of images.
• Performing pixel mask modification on a bunch of images.
• Trimming of a set of images.

ficombine

• Combination of a set of images into one image using various averaging and stacking methods.

ficonv

• Fitting optimal convolution transformation between a reference image and an input image.
• Application of existing convolution transformation on a reference image.
• Performing image subtraction using a reference image and an input image either by knowing the convolution transformation in advance or by fitting the optimal one during the process.
• Management of so-called kernel files describing these convolution transformations.

fiheader

• Extracting various FITS keywords and the associated values from individual or a bunch of files.
• Modification of FITS keywords in an individual or in a bunch of files.
• Adding or removing FITS keywords to/from an individual or to/from a bunch of files.

fiign

• Performing low-level manipulation of masks associated to FITS images.
• Adding or removing masks (and various mask layers) to/from a FITS image.
• Conversions between various mask types.
• Adding or removing mask bits as the function of the pixel values or based on the characteristics of pixels.

fiinfo

• Reporting a brief or a detailed summary about a FITS file, including primary image and various extensions (such as additional images, binary tables or textual tables).
• Extracting raw information from FITS files, including dumping of image pixel data, mask bits, binary table data and textual table data.
• Conversion of FITS images to PNM (PPM, PGM) format using
  • various scaling functions, such as linear, logarithmic, squared, square root, etc.,
  • scale limit determinations, such as minimum, maximum values, zscale, histrogram normalization, values derived from cumulative pixel intensity distributions, etc., and
  • various color palettes, including grey-scale, color, custom defined color as well as 8-bit and 16-bit dynamics.
• Extracting simple pixel statistics from FITS image files.

fiphot

• Performing aperture photometry on normal images.
• Performing aperture photometry on subtracted images by considering the information related to image convolution (if it has been applied in advance).
• Determination of background level using the classic ways (considering pixels in annuli around the targets) as well as treating the background to be known in advance.
• Support for arbitrary-shaped apertures using polygonal approximations by involving arbitrary number of boundary segments.
• Conversion of fluxes to magnitudes.
• Computation of flux and/or magnitude uncertainties by involving as many noise sources as possible (mainly background noise and photon noise).
• Computation shape centroid coordinates and shape parameters, refinement of centroid coordinates.
• Computation of the respective uncertainties of centroid coordinates and shape parameters by involving as many noise sources as possible.
• Fitting and adjusting of large-scale flux variations on subtracted images considering a dependence on the coordinates and intrinsic colors in a form of arbitrary-order polynomials.
• Treatment of nearby sources by removing them from the background determination.
• Flagging the sources using the constraints defined by the masks associated to the FITS images.

firandom

• Creation of images.
• Simulation of various noise sources, such as background noise and photon noise.
• Implantation of sources having various fluxes and shape parameters (including analytical shape models as well as external point-spread functions).
• Quantization of pixel flux values.
• Creation of random lists of objects.

fistar

• Extracting list of point sources and the corresponding geometric parameters (centroid coordinates, shape paremeters, etc.).
• Fitting of (optionally space-varying) point-spread function based on the detected sources, including sub-pixel level sampling.

fitrans

• Transformation of FITS images and the corresponding pixel masks.
• Extractions of a single or multiple layers from a FITS data cube.

grcollect

• Transposing large amount of data based on unique keys found in the input data series.

grmatch

• Matching two data files using various criteria:
  • identifier matching (unique identifiers or keys are matched),
  • coordinate matching (points are matched based on Cartesian distance and topological correspondence)
  • point matching and the derivation of the corresponding analytic transformation (only in planar case)
• Fine-tuning of point matching algorithms using various criteria

grtrans

• Derivation of best-fit spatial transformation between a matched list of coordinates.
• Application of transformation data to a list of coordinates.
• Application of astronomy-specific transformations on a list of coordinates.

lfit

• Regression analysis and best-fit parameters estimation of input data by involving various algorithms, such as
  • linear least squares fitting;
  • non-linear least squares fitting, based on
    • Levenberg-Marquard procedure,
    • downhill simplex minimization,
    • Markov Chain Monte Carlo, etc.
  • Mapping of χ² space
• Uncertainty estimations by involving various procedures, such as
  • Linear error propagation and covariance matrix analysis,
  • Markov Chain Monte Carlo,
  • Bootsrapping, etc.
• Evaluation of functions.
• Fully symbolic operations, incuding analytical computation of partial derivatives.
• Definition of additional functions during run-time.
• User-defined and time-consuming functions can be implemented using shared libraries.
• Various built-in functions useful in astronomical data analysis.