alala package

Submodules

alala.alala module

SECTIONS:
  • RawData

    • Initialization; basic functions

    • Image diagnostics + pre-solving before stacking

    • Locating coordinates among raw data + writing extensions

    • Combining/dividing (WIRCam) cubes

    • Cropping images

    • Stacking (co-adding images)

  • Stack

    • Bad pixel masks / source masks / error arrays

    • Making images (plots)

    • Astrometry

    • PSF photometry

    • Aperture photometry

    • PSF/aperture photometry comparison

    • Source selection

exception alala.alala.NoDataError

Bases: Exception

Raise this error when a RawData object has no data.

exception alala.alala.TooFewMatchesError

Bases: Exception

Raise this error when, during PSF photometry, the no. of sources in the image which match those in some external catalogue is less than 3.

alala.alala.VALID_DATA_EXT = ('fits', 'fits.fz', 'flt')

Valid file types for the image data.

alala.alala.VALID_PLOT_EXT = ('pdf', 'jpg', 'bmp', 'png')

Valid file types for any plots which are produced.

class alala.alala.RawData(data_dir, stack_dir=None, qso_grade_limit=None, fmt='fits', plot_ext='png')

Bases: object

property data_dir

Directory containing the raw data

property stack_dir

Directory in which to store stacked images, if stacking

property fmt

Format (file type/extension) of the data files

property files

All of the data files

property plot_ext

Format (file type/extension) of any plots made/to be made

property instrument

Instrument (WIRCam, MegaCam, or MegaPrime) used to acquire data

property nextend

Number of extensions in the image data files

property qso_grade_limit

Queue Service Observer (QSO) grade limit; only relevant if using WIRCam data

property date

Date (YYYYMMDD) at the start of observations

property mjdate

Modified Julian Date (MJD) at the start of observations

property dates

All date(s) (YYYYMMDD) spanned by the data

property dates_dict

Dictionary with entries {date –> [file1,file2,…]}, e.g., ‘20190816’ –> [‘file1’,’file2’,’file3’]

property filters

Filters (bands) used in the observations which make up the data

property filters_dict

Dictionary with entries {filter –> [file1,file2,…]}, e.g., ‘i’ –> [‘file1’,’file2’,’file3’]

property J

Filenames for images acquired in the J-band

property H

Filenames for images acquired in the H-band

property Ks

Filenames for images acquired in the Ks-band

property Y

Filenames for images acquired in the Y-band

property u

Filenames for images acquired in the u-band

property g

Filenames for images acquired in the g-band

property r

Filenames for images acquired in the r-band

property i

Filenames for images acquired in the i-band

property z

Filenames for images acquired in the z-band

property uS

Filenames for images acquired in the uS-band

property gS

Filenames for images acquired in the gS-band

property rS

Filenames for images acquired in the rS-band

property iS

Filenames for images acquired in the iS-band

property zS

Filenames for images acquired in the zS-band

property stack_made

Whether a stack has already been made for the object

set_stack_dir(stack_dir)

Set the name of the directory in which to store any stacked images.

set_plot_ext(plot_ext)

Set the file extension of any plots which are produced.

exclude_date(date)

Exclude a specific date from the data, by parsing through the image files’ fits headers.

Parameters:

date (str) – A date of the format ‘YYYYMMDD’ or ‘YYYYMM’ or ‘YYYY’ to exclude from the raw data

exclude_object(obj)

Exclude a specific target object from the data, by parsing through the image files’ fits headers.

Parameters:

obj (str) – Name of a target object to exclude, e.g., ‘NGC457’

only_date(date)

Exclude all but one specific date in the data, by parsing through the image files’ fits headers.

Parameters:

date (str) – A date of the format ‘YYYYMMDD’ or ‘YYYYMM’ or ‘YYYY’ to select in the raw data

only_object(obj)

Excude all but one specific target object in the data to be used by parsing through the image files’ fits headers.

Parameters:

obj (str) – Name of the object, e.g., ‘NGC457’

copy()

Produces a (deep) copy of the object.

print_headers(ext, *headers)

Print particular fits header(s) for data files.

Parameters:

  • ext ({‘fits’, ‘fits.fz’, ‘flt’}) – Extension of interest for the image data to be parsed

  • headers (str) – Name(s) of any header(s) of interest

value_at(ra, dec)

Get the value at some RA and Dec, for all images.

Parameters:

ra, dec (float) – Right Ascension (RA) and Declination of interest

For all of the files contained in the RawData object, prints the ADU value at the given RA and Dec, if these coordinates are within the image’s bounds. Can be used to see if, e.g., the ADU was set to 0 over a source of interest during image de-trending.

background()

Estimate the background of the images.

Returns:

List containing the background level of each file

Return type:

list

Naively estimates the background as the median of the image’s ADU for every raw image in order to see how the data varies.

Notes

Does not mask sources, but this is not important for the purpose of this function: to see if any data is dubious

radial_PSFs(ra, dec, solved=True, adu_min=4000, adu_max=66000)

Produce radial PSFs for all images.

Parameters:

  • ra, dec (float) – Right Ascension (RA) and Declination of interest

  • solved (bool, optional) – Have the images already been solved with astrometry? (default True)

  • adu_min, adu_max (float, optional) – Minimum and maximum allowed ADU in the image (default 4000, 66000)

For all of the files in the raw data directory, produces and saves a figure of the radial profile around the input RA and Dec. If solved=False, first solves the image with astrometry.net. (A refined astrometric solution is required to get an accurate radial profile.) A crude technique for estimating the PSF of an image.

Notes

Best to pick a source which is bright, but not one which saturates the detectors.

solve_all(solved_dir=None, depth=None)

Find the astrometric solution for all images.

Parameters:

  • solved_dir (str, optional) – Directory in which to store the solved images (default None –> set automatically)

  • depth (int, optional) – Number of stars to use in solving with astrometry.net, e.g., depth=100 will use only the 100 brightest stars (default None –> no limit, use all stars)

Using astrometry.net, solve all files, and put them in a new directory. This is necessary when the astrometric solution obtained by CFHT is inaccurate and requires refining, or if you wish to plot the radial PSFs of some point in the raw data images.

Notes

For MegaCam images, detectors (0, 1, …, 17), 36, 37 (top half of camera) are oriented with North ANTI-parallel to the y-axis and East parallel to the x-axis. Detectors (18, 19, …, 35), 38, 39 (bottom half of the camera) are oriented with North parallel to the y-axis and East ANTI-parallel to the x-axis. When trying to make a stack from images in both the top and bottom halves, IRAF sometimes gets confused.

WCS_check(ra, dec, frac=1.0, verbose=True, checkall=True)

Check if the input coordinates are spanned by any of the data.

Parameters:

  • ra, dec (float) – Right Ascension (RA) and Declination of interest

  • frac (float, optional) – Fraction of the image to consider valid, e.g., 0.9 will exclude the outermost 10% of the image (default 1.0 –> whole image)

  • verbose (bool, optional) – Print lots of details? (default True)

  • checkall (bool, optional) – Check all files, or, just return the first matching file? (default True –> check all files)

Returns:

List of files which span the given coordinates

Return type:

list

locate_WCS(ra, dec, frac=1.0)

Given some coordinates and a multi-detector image, find the number ID of the detector where the coordinates are located.

Parameters:

  • ra, dec (float) – Right Ascension (RA) and Declination of interest

  • frac (float, optional) – Fraction of the image to consider valid, e.g., 0.9 will exclude the outermost 10% of the image (default 1.0 –> whole image)

Returns:

Number ID of the detector where the coordinates are located

Return type:

int

write_extensions(n_ext, exten_dir=None)

Write a specific extension of each multi-header fits file to individual files.

Parameters:

  • n_ext (int) – Extension number of interest (should be at least 1, n_ext=1 –> extract the 1st extension in the file which corresponds to the 0th detector/CCD)

  • exten_dir (str, optional) – Name for the directory which will hold the written extensions (default None –> set automatically)

Gets the header and image data for the given extension and writes them to a new .fits file. Does so for all raw data files. Stores them in a new subdirectory. Used to extract image data for one of many CCDs / detectors on either MegaPrime/WIRCam.

Notes

  • For a given dataset, once RawData.locate_WCS() has been used to find the extension containing the WCS of interest, run this function once to extract that specific extension. Can then use this newly made folder for stacking.

  • If the extensions are themselves cubes (sometimes the case for WIRCam), see RawData.combine_WIRCam() or RawData.divide_WIRCam().

  • Currently only works for cubes of the form fits.fz, and produces files of form fits.

write_extensions_all(all_exten_dir=None)

Write each extension of each multi-header fits file to individual files.

Parameters:

all_exten_dir (str, optional) – Name for the directory which will hold the written extensions (default None –> set automatically)

Gets the header and image data for all extensions of a multi-header fits file and writes each extension to a new .fits file. Useful for MegaPrime data where the scope moves a lot and the detectors are all calibrated to the same ADU level.

Notes

Currently only works for cubes of the form fits.fz, and produces files of form fits.

write_extensions_by_WCS(ra, dec, frac=1.0, wcs_exten_dir=None)

Same as RawData.write_extensions(), but select the extension using an RA and Dec.

Parameters:

  • ra, dec (float) – Right Ascension (RA) and Declination of interest

  • frac (float, optional) – Fraction of the image to consider valid, e.g., 0.9 will exclude the outermost 10% of the image (default 1.0 –> whole image)

  • wcs_exten_dir (str, optional) – Name for the directory which will hold the written extensions (default None –> set automatically)

For a directory full of multi-extension fits files, gets the extensions which contain the input RA, Dec and writes them to a new file.

Notes

Currently only works for cubes of the form fits.fz, and produces files of form fits.

write_source(name, ra, dec, frac=1.0)

Same as RawData.write_extensions_by_WCS(), but write the files to a new directory with name name.

Parameters:

  • name (str) – Name of the source of interest (e.g. some transient)

  • ra, dec (float) – Right Ascension (RA) and Declination of interest

  • frac (float, optional) – Fraction of the image to consider valid, e.g., 0.9 will exclude the outermost 10% of the image (default 1.0 –> whole image)

Parses all raw data and copies any images which contain the input RA, Dec to a new sub-directory with the name of the source.

combine_WIRCam()

For a directory full of WIRCam images, if the images are cubes, combine the multiple 2D arrays into single arrays.

Notes

Use this function once to take a folder full of cubes and turn them into single-frame fits files. Can not be called on non-cubes. Only used for WIRCam.

divide_WIRCam()

For a directory full of WIRCam images, if the images are cubes, divide the multiple 2D arrays into individual arrays.

Notes

Use this function once to take a folder full of cubes and turn them into single-frame fits files. Can not be called on non-cubes. Only used for WIRCam.

crop_images(frac_hori=[0, 1], frac_vert=[0, 1])

Crop several fits images.

Parameters:

frac_hori, frac_vert (length-two list, optional) – Horizontal and vertical fractions of the images to crop (default [0,1] for both –> no cropping)

For a directory full of WIRCam/MegaPrime images (should not be cubes), crops the images based on the input x-axis and y-axis boundaries and writes them all to a new directory.

crop_images_wcs(ra, dec, size)

Crop several fits images by picking out a source based on its RA and Dec.

Parameters:

  • ra, dec (float) – Right Ascension (RA) and Declination of interest, in degrees

  • size (float) – Size of a box (in pixels) to crop around the input coordinates

For a directory full of WIRCam/MegaPrime images (should not be cubes), crops the images based on the input WCS coordinates, creating a box centred on these coordinates and writing the new files to a new directory. If the given box extends beyond the bounds of the image, the box will be truncated at these bounds.

make_badpix_masks()

Builds a bad pixel mask for all image files, and places them in a new directory which is stored in Stack.bp_dir.

make_stacks(*filters)

Co-add images with PyRaf.

Parameters:

filters (str, optional) – Filters of interest (defaults to all of the filters for which there is raw data)

Uses PyRaf to coadd all of the raw data into stacks based on filter. Can specify a subset of these filters if you don’t want to process all of them at once.

extract_stack(filt)

Given a filter, create a Stack object which contains only stacks in that filter, from a base RawData object.

class alala.alala.Stack(location, stack_directory, qso_grade_limit, fmt, plot_ext, filt)

Bases: RawData

property stack_size

Number of files used in the stacking

property stack_time

Observation time in Modified Julian Date (MJD)

property image_data

Stacked image data

property image_header

Image fits header

property bp_dir

Directory containing the bad pixel mask

property calib_dir

Directory containing photometric/astrometric calibration files

property exptime

Total exposure time of the stacked image

property pixscale

Pixel scale of the instrument used

property x_size

x dimension of the image, in pixels

property y_size

y dimension of the image, in pixels

property astrometric_calib

Has astrometric calibration been performed?

property psf_fit

Has the effective Point-Spread Function (ePSF) been fit?

property photometric_calib

Has photometric calibration (i.e., obtaining a zero point) been performed?

property aperture_fit

Has aperture photometry been performed?

property bkg

Background-only image

property bp_mask

Bad pixel mask; array of bools

property source_mask

Mask of sources; array of ints, where 0=non-masked and 1=masked

property image_error

Estimate of the error (Gaussian + Poisson) across the image

property image_data_bkgsub

Background-subtracted image data

property xy_data

Table of (x, y) coordinates for sources found with astrometry.net

property xy_name

Name of the file containing Stack.xy_data

property epsf_data

Effective Point-Spread Function (ePSF) data

property epsf_radius

Radius of the effective Point Spread Function (ePSF), in pixels

property ref_cat

Vizier code/ID for the reference catalogue used in photometric calibration

property ref_cat_name

Name of the reference catalogue used in photometric calibration

property zp_mean

Mean zero point from photometric calibration

property zp_med

Median zero point from photometric calibration

property zp_std

Standard deviation (uncertainty) of the zero point from photometric calibration

property ra_offsets_mean

Mean offset in Right Ascension (RA) between the image and reference catalogue

property dec_offsets_mean

Mean offset in Declination between the image and reference catalogue

property nmatches

Number of matches between astrometry-detected sources in the image and the reference catalogue

property sep_mean

Mean separation between sources, in arcseconds

property mag_diff_mean

Mean difference between calibrated magnitudes and reference catalogue magnitudes

property psf_sources

Table of sources detected by point-spread function (PSF) photometry

property aperture_sources

Table of sources detected by aperture photometry

property limmag_sources

Table of “sources” for which we have a limiting magnitude

mask_bp()

Make a bad pixel mask of pixels = 0 or nan. The mask is an array of bools, contained in Stack.bp_mask

mask_source(sigma=3.0)

Make a mask of pixels containing sources.

Parameters:

sigma (float, optional) – Detection sigma to use in image segmentation (default 3.0)

Use crude image segmentation to make a proto source mask, use the mask to get the background, and then perform proper image segmentation on the background-subtracted image data. The resulting segmentation image is a proper source mask, to be used in other steps in aperture photometry. The output source mask also flags bad pixels.

Notes

The mask is an array of ints, where 1=masked, 0=non-masked. Will be contained in Stack.source_mask, Not to be confused with Stack.bp_mask.

bkg_compute(box_size=50, filter_size=5, thresh_sigma=3.0)

Compute the background of the image.

Parameters:

  • box_size (int, optional) – Size of box for background estimation (default 50 –> 50x50 boxes)

  • filter_size (int, optional) – Size of the median filter applied during background estimation (default 5 –> 5x5 filter)

  • thresh_sigma (float, optional) – Detection to use in image segmentation when producing a source mask (default 3.0; only relevant if a source mask does not already exist and needs to be computed)

Computes the background of the image, the error on the background (as the RMS deviation), and the background-subtracted image. Assigns these to the attributes Stack.bkg, Stack.bkg_rms, Stack.image_data_bkgsub.

Notes

A new source mask will be obtained only if Stack.source_mask does not exist.

error_array(box_size=50, filter_size=5, thresh_sigma=3.0)

Compute the error (uncertainty) on the background-only image.

Parameters:

  • box_size (int, optional) – Size of box for background estimation (default 50 –> 50x50 boxes; only relevant if background not previously computed)

  • filter_size (int, optional) – Size of the median filter applied during background estimation (default 5 –> 5x5 filter, only relevant if background not previously computed)

  • thresh_sigma (float, optional) – Detection to use in image segmentation when producing a source mask (default 3.0; only relevant if source mask not previously computed)

Computes the error on the background-only image as the RMS deviation of the background, and then computes the total image error including the contribution of the Poisson noise from detected sources. Necessary for error propagation in aperture photometry.

make_image(bkgsub=False, border=False, sources=False, ra=None, dec=None, scale=None, title=None, output=None)

Make a plot of the stacked image.

Parameters:

  • bkgsub (bool, optional) – Plot the background-subtracted data? (default False)

  • border (bool, optional) – Demarcate the region in which sources are valid for photometry? (default False)

  • sources (bool, optional) – Drop circles on detected sources? (default False)

  • ra, dec (float, optional) – Right Ascension (RA) and Declination at which to place a crosshair (default None)

  • scale ({None, “log”, “asinh”}, optional) – Scale to apply to the images (default None –> linear scale)

  • title (str, optional) – Title to place above the plot (default None –> no title)

  • output (str, optional) – Name for the output image file (default None –> set automatically)

astrometry(verbose=0)

Perform source extraction with astrometry.net and solve the field.

Parameters:

verbose (int, optional) – Level of verbosity within astrometry.net (default 0)

Notes

  • After running this function, can obtain a list of detected sources’ (x, y) coordinates from attribute self.xy_data or by reading the fits file Stack.xy_name

  • Creates a new file appended with _updated.fits with the new astrometric solution

fit_PSF(nstars=40, thresh_sigma=5.0, pixelmin=20, elongation_lim=1.4, area_max=500, cutout=35, source_lim=None, write=False, output=None, plot_ePSF=True, ePSF_name=None, plot_residuals=False, resid_name=None)

Fit for the effective Point-Spread Function (ePSF) of the image.

Parameters:

  • nstars (int, optional) – Maximum number of stars to use in building the ePSF (default 40; set to None to impose no limit)

  • thresh_sigma (float, optional) – Sigma threshold for source detection with image segmentation (default 5.0)

  • pixelmin (int, optional) – Minimum number of contiguous pixels for an isophote to be considered a source (default 20)

  • elongation_lim (float, optional) – Maximum elongation (ratio of semimajor axis / semiminor axis) for an isophote to be considered a source (default 1.4)

  • area_max (float, optional) – Maximum pixel area (in pix**2) for an isophote to be considered a source (default 500)

  • cutout (int, optional) – Size of the cutout around each star, in pix (default 35; must be odd, will be rounded down if eve)

  • source_lim (int, optional) – Limit on the number of sources to fit with the ePSF (default None)

  • write (bool, optional) – Write the ePSF to a .fits file as an image? (default False)

  • output (str, optional) – Name for the output .fits file (default None –> set automatically; only relevant if write=True)

  • plot_ePSF (bool, optional) – Plot the ePSF? (default False)

  • ePSF_name (str, optional) – Name for the output plot of the ePSF? (default None –> set automatically; only relevant if plot_ePSF=True)

  • plot_residuals (bool, optional) – Plot the residuals of the iterative PSF fitting? (default False)

  • resid_name (str, optional) – Name for the output plot of the residuals (default None –> set automatically; only relevant if plot_residuals=True)

Uses image segmentation to obtain a list of sources in the smoothed, background-subtracted image (provided by astrometry.net) with their x, y coordinates. Uses EPSFBuilder to empirically obtain the ePSF of these stars. Uses astrometry.net to find all sources in the image, and fits them with the empirically obtained ePSF. Builds a table containing the instrumental magnitudes and corresponding uncertainties to be used in obtaining the zero point for PSF calibration, in Stack.psf_sources.

Notes

The ePSF obtained here should NOT be used in convolutions. Instead, it can serve as a tool for estimating the seeing of an image.

zero_point(sep_max=2.0, plot_corr=True, corr_name=None, plot_source_offsets=True, source_offs_name=None, plot_field_offsets=False, field_offs_name=None, gaussian_blur_sigma=30.0, cat_num=None)

Obtain the zero point of the image.

Parameters:

  • sep_max (float, optional) – Maximum allowed separation when cross-matching sources, in pix (default 2.0; corresponds to ~ 0.6” for WIRCam and ~ 0.37” for MegaPrime)

  • plot_corr (bool, optional) – Plot the correlation plot with a linear fit (i.e., instrumental source magnitude in the image versus calibrated catalogue magnitude)? (default True)

  • corr_name (str, optional) – Name for the output correlation plot (default None –> set automatically; only relevant if plot_corr=True)

  • plot_source_offsets (bool, optional) – Plot the offsets between the image and catalogue in Right Ascension (RA) and Declination (Dec) space, for all sources? (default True)

  • source_offs_name (str, optional) – Name for the output offsets plot (default None –> set automatically; only relevant if plot_source_offsets=True)

  • plot_field_offsets (bool, optional) – Plot the offsets over the image itself, with some Gaussian blur for legibility? (default False)

  • field_offs_name (str, optional) – Name for the output field offsets plot (default None –> set automatically; only relevant if plot_field_offsets=True)

  • gaussian_blur_sigma (float, optional) – Standard deviation (sigma) of the Gaussian blur to apply if plotting the field offsets (default 30.0; only relevant if plot_field_offsets=True)

  • cat_num (str, optional) – Vizier catalogue number for the catalogue to use in cross-matching (default None –> set automatically according to the filter in use)

Uses astroquery and Vizier to query an online catalogue for sources which match those detected by astrometry. Computes the offset between the apparent and instrumental magnitudes of the queried sources for photometric calibration. Computes the mean, median and standard deviation.

Notes

  • For filters , “g”, “r”, “i”, “z”, “Y”, the default catalogue is Pan-STARRS 1

  • For filter “u”, the default catalogue is SDSS DR12

  • For filter “Ks” (“K”), the default catalogue is 2MASS

PSF_photometry(nstars=40, thresh_sigma=5.0, pixelmin=20, elongation_lim=1.4, area_max=500, cutout=35, source_lim=None, gaussian_blur_sigma=30.0, cat_num=None, sep_max=2.0, box_size=50, filter_size=5, write_ePSF=True, ePSF_data_name=None, plot_ePSF=True, ePSF_name=None, plot_resid=False, resid_name=None, plot_corr=True, corr_name=None, plot_source_offsets=True, source_offs_name=None, plot_field_offsets=False, field_offs_name=None)

Obtain the effective Point-Spread Function (ePSF) of the image, perform zero point calibration, and perform PSF photometry on all detected sources.

Parameters:

  • nstars (int, optional) – Maximum number of stars to use in building the ePSF (default 40; set to None to impose no limit)

  • thresh_sigma (float, optional) – Sigma threshold for source detection with image segmentation (default 5.0)

  • pixelmin (int, optional) – Minimum number of contiguous pixels for an isophote to be considered a source (default 20)

  • elongation_lim (float, optional) – Maximum elongation (ratio of semimajor axis / semiminor axis) for an isophote to be considered a source (default 1.4)

  • area_max (float, optional) – Maximum pixel area (in pix**2) for an isophote to be considered a source (default 500)

  • cutout (int, optional) – Size of the cutout around each star, in pix (default 35; must be odd, will be rounded down if eve)

  • source_lim (int, optional) – Limit on the number of sources to fit with the ePSF (default None)

  • gaussian_blur_sigma (float, optional) – Standard deviation (sigma) of the Gaussian blur to apply if plotting the field offsets (default 30.0; only relevant if plot_field_offsets=True)

  • cat_num (str, optional) – Vizier catalogue number for the catalogue to use in cross-matching (default None –> set automatically according to the filter in use)

  • sep_max (float, optional) – Maximum allowed separation when cross-matching sources, in pix (default 2.0; corresponds to ~ 0.6” for WIRCam and ~ 0.37” for MegaPrime)

  • box_size (int, optional) – Size of box for background estimation (default 50 –> 50x50 boxes; only relevant if background not previously computed)

  • filter_size (int, optional) – Size of the median filter applied during background estimation (default 5 –> 5x5 filter, only relevant if background not previously computed)

  • write_ePSF (bool, optional) – Write the ePSF to a .fits file as an image? (default False)

  • ePSF_data_name (str, optional) – Name for the output .fits file (default None –> set automatically; only relevant if write_ePSF=True)

  • plot_ePSF (bool, optional) – Plot the ePSF? (default False)

  • ePSF_name (str, optional) – Name for the output plot of the ePSF? (default None –> set automatically; only relevant if plot_ePSF=True)

  • plot_residuals (bool, optional) – Plot the residuals of the iterative PSF fitting? (default False)

  • resid_name (str, optional) – Name for the output plot of the residuals (default None –> set automatically; only relevant if plot_residuals=True)

  • plot_corr (bool, optional) – Plot the correlation plot with a linear fit (i.e., instrumental source magnitude in the image versus calibrated catalogue magnitude)? (default True)

  • corr_name (str, optional) – Name for the output correlation plot (default None –> set automatically; only relevant if plot_corr=True)

  • plot_source_offsets (bool, optional) – Plot the offsets between the image and catalogue in Right Ascension (RA) and Declination (Dec) space, for all sources? (default True)

  • source_offs_name (str, optional) – Name for the output offsets plot (default None –> set automatically; only relevant if plot_source_offsets=True)

  • plot_field_offsets (bool, optional) – Plot the offsets over the image itself, with some Gaussian blur for legibility? (default False)

  • field_offs_name (str, optional) – Name for the output field offsets plot (default None –> set automatically; only relevant if plot_field_offsets=True)

Using image segmentation, finds as many sources as possible in the image with an elongation below some elongation limit. Uses these sources to build an empirical effective PSF (ePSF). Using a list of sources found by astrometry.net, fits the ePSF to all of those sources. Computes the instrumental magnitude of all of these sources. Queries the correct online catalogue for the given filter to crossmatch sources in the image with those in the catalogue (e.g., Pan-STARRS 1). Finds the zero point which satisfies AB_mag = ZP + instrumental_mag and gets the calibrated AB mags for all PSF-fit sources.

write_PSF_photometry(nstars=40, thresh_sigma=5.0, pixelmin=20, elongation_lim=1.4, area_max=500, cutout=35, source_lim=None, gaussian_blur_sigma=30.0, cat_num=None, sep_max=2.0, verbose=True, box_size=50, filter_size=5, write_ePSF=False, ePSF_data_name=None, plot_ePSF=True, ePSF_name=None, plot_resid=False, resid_name=None, plot_corr=True, corr_name=None, plot_source_offsets=True, source_offs_name=None, plot_field_offsets=False, field_offs_name=None, output=None)

Write the results of PSF photometry.

Parameters:

  • … – all of the inputs to Stack.PSF_photometry()

  • output (str, optional) – Name of the output .fits file (default None –> set automatically)

Performs PSF photometry if it has not already been performed, and then writes a table of the PSF-fit sources to a .fits table.

aperture_photometry(ra_list, dec_list, sigma=None, bkgsub_verify=True, ap_radius=1.2, r1=2.0, r2=5.0, box_size=50, filter_size=5, thresh_sigma=3.0, plot_annulus=False, ann_name=None, plot_aperture=False, ap_name=None)

Do aperture photometry.

Parameters:

  • ra_list, dec_list (list **or* float*) – List or single float of the Right Ascension (RA), Declination around some source of interest

  • sigma (float, optional) – Limiting sigma below which a source is labeled as not detected, in which case the user should use Stack.limiting_magnitude()

  • ap_radius (float, optional) – Radius of the aperture, in arcseconds (default 1.2)

  • r1, r2 (float, optional) – Inner and outer radii of the annulus, in arcseconds (default 2.0, 5.0)

  • box_size (int, optional) – Size of box for background estimation (default 50 –> 50x50 boxes; only relevant if background not previously computed)

  • filter_size (int, optional) – Size of the median filter applied during background estimation (default 5 –> 5x5 filter, only relevant if background not previously computed)

  • thresh_sigma (float, optional) – Detection to use in image segmentation when producing a source mask (default 3.0; only relevant if a source mask does not already exist and needs to be computed)

  • plot_annulus (bool, optional) – Plot the image data contained in the annulus, centered on the ra, dec of interest? (default False)

  • ann_name (str, optional) – Name for the plot of the annulus image data (default None –> set automatically)

  • plot_aperture (bool, optional) – Plot the aperture and annulus as rings (default False)

  • ap_name (str, optional) – Name for the output aperture+annulus plot (default None –> set automatically)

Computes the the total flux in a defined aperture around the given RA, Dec, computes the background in an annulus around this aperture, and computes the background-subtracted flux of the “source” defined by the aperture. Can be called multiple times if a list of RA/Dec is given.

limiting_magnitude(ra, dec, sigma=5.0, thresh_sigma=3.0, box_size=50, filter_size=5, plot_annulus=True, ann_name=None, plot_aperture=None, ap_name=None, write=False, output=None)

Obtain the limiting magnitude at some coordinates for some sigma significance.

Parameters:

  • ra_list, dec_list (list **or* float*) – List or single float of the Right Ascension (RA), Declination around some source of interest

  • sigma (float, optional) – Detection significance (default 5.0)

  • thresh_sigma (float, optional) – Detection to use in image segmentation when producing a source mask (default 3.0; only relevant if a source mask does not already exist and needs to be computed)

  • box_size (int, optional) – Size of box for background estimation (default 50 –> 50x50 boxes; only relevant if background not previously computed)

  • filter_size (int, optional) – Size of the median filter applied during background estimation (default 5 –> 5x5 filter, only relevant if background not previously computed)

  • plot_annulus (bool, optional) – Plot the image data contained in the annulus, centered on the ra, dec of interest? (default True)

  • ann_name (str, optional) – Name for the plot of the annulus image data (default None –> set automatically)

  • plot_aperture (bool, optional) – Plot the aperture and annulus as rings (default False)

  • ap_name (str, optional) – Name for the output aperture+annulus plot (default None –> set automatically)

  • write (bool, optional) – Write the results to a table? (default False)

  • output (str, optional) – Name for the output table (default None –> set automatically; only relevant if write=True)

Returns:

Limiting magnitude

Return type:

float

Notes

For a given RA, Dec, finds the limiting magnitude at its location. If a source was previously detected <= 3” away from the given coords by astrometry.net, the limiting magnitude at the RA, Dec given will be invalid, and so the aperture will randomly jitter until a valid RA, Dec is found.

write_aperture_photometry(output=None)

Write the sources detected by aperture photometry to a table.

write_limiting_magnitude(output=None)

Write the “sources” with limiting magnitudes computed by aperture photometry to a table.

compare_photometry(ap_radius=1.2, r1=2.0, r2=5.0, nsamples=100, output=None)

Compare the calibrated magnitude obtained with aperture photometry (Stack.aperture_photometry()) to that obtained with PSF photometry (Stack.PSF_photometry()).

Parameters:

  • ap_radius (float, optional) – Radius of the aperture, in arcseconds (default 1.2)

  • r1, r2 (float, optional) – Inner and outer annuli radii, where the annulus is used for estimating the background (default 2.0, 5.0)

  • nsamples (int, optional) – Number of samples to take from PSF photometry

  • output (str, optional) – Name of the output figure comparing the two photometries (default None –> set automatically)

source_selection(ra, dec, radius=1.0)

Parse the table of sources provided by PSF photometry (Stack.PSF_photometry()) for sources within some distance of the given coordinates.

Parameters:

  • ra, dec (float) – Right Ascension (RA) and Declination for the source of interest

  • radius (float) – Radius to search in, in arcseconds (default 1.0)

Returns:

Table of the source(s) satisfying the conditions

Return type:

astropy.table.Table

write_selection(ra, dec, radius=1.0, output=None)

Write some selection of sources based on coordinates.

Parameters:

  • … – all of the inputs to Stack.source_selection()

  • output (str, optional) – Name of the output .fits table (default None –> set automatically)

alala.apphotom module

CONTENTS:
NON-STANDARD PYTHON DEPENDENCIES:

  • astropy

  • photutils

NON-PYTHON DEPENDENCIES:

  • astrometry.net

alala.apphotom.image2xy(image_file, astrom_sigma=5.0, psf_sigma=5.0, alim=10000, write=False, output=None)

Use astrometry.net’s image2xy to detect sources in an image.

Parameters:

  • image_file (str) – Filename for NOT background-subtracted image

  • astrom_sigma (float, optional) – Sigma threshold for astrometry.net source detection image (default 5.0)

  • psf_sigma (float, optional) – Sigma of the Gaussian PSF of the image (default 5.0)

  • alim (int, optional) – Maximum allowed source area, in pixels**2, beyond which sources will be deblended (default 10000)

  • write (bool, optional) – Write the output list of sources? (default False)

  • output (str, optional) – Name for output source list file (default set by function)

Returns:

List of sources

Return type:

astropy.io.fits.fitsrec.FITS_rec

Notes

Can write sources to a list of sources and/or a file to be used as a source mask in aperture photometry.

alala.apphotom.imsegm_make_source_mask(image_file, mask_file=None, sigma=3.0, write=False, output=None)

Make a mask which contains sources in the image using image segmentation.

Parameters:

  • image_file (str) – Filename for NOT background-subtracted image

  • mask_file (str, optional) – Filename for a bad pixel mask (default None)

  • sigma (float, optional) – Detection sigma to use in image segmentation (default 3.0)

  • write (bool, optional) – Write the source mask to a file? (default False)

  • output (str, optional) – Name for output file (default set by function)

Returns:

Source mask where 0 = background, 1 = source

Return type:

np.ndarray

Notes

Use crude image segmentation to make a proto source mask, use the mask to get the background, and then perform proper image segmentation on the background-subtracted image data. The resulting segmentation image is a source mask, to be used in other steps in aperture photometry. The output source mask also flags bad pixels.

alala.apphotom.error_array(image_file, source_mask=None, sigma=3.0, write=True, output=None)

Make an array of the error in an image, including root-mean-squared deviation on the background and Poisson noise for detected sources.

Parameters:

  • image_file (str) – Filename for NOT background-subtracted image

  • source_mask (np.ndarray, str, optional) – Filename for a source mask or the mask data itself (default None; in which case a source mask is made)

  • sigma (float, optional) – Detection sigma to use in image segmentation, if a source mask is to be made (default 3.0; only relevant if source_mask = None)

  • write (bool, optional) – Write the error array to a file? (default False)

  • output (str, optional) – Name for output file (default set by function)

Returns:

Image error array

Return type:

np.ndarray

Notes

Computes the error on the background-only image as the RMS deviation of the background, and then computes the total image error including the contribution of the Poisson noise for detected sources. Necessary for error propagation in aperture photometry.

alala.apphotom.aperture_photom(image_file, ra, dec, mask=None, error=None, sigma=None, bkgsub_verify=True, ap_radius=1.2, r1=2.0, r2=5.0, thresh_sigma=3.0, plot_annulus=True, plot_aperture=True, ann_output=None, ap_output=None, write=False, output=None, cmap='bone')

Perform aperture photometry on some image.

Parameters:

  • image_file (str) – Filename for NOT background-subtracted image

  • ra, dec (float, list) – Floats or lists of RA, Dec of interest

  • mask (np.ndarray, str, optional) – Filename for a source mask or the mask data itself (default None; in which case a source mask is made)

  • error (np.ndarray, str, optional) – Filename for image error or the image error itself (default None; in which case an error array is made)

  • sigma (float, optional) – Sigma below which to reject a source (default None –> do not reject any sources)

  • bkgsub_verify (bool, optional) – Verify that the background-subtracted flux is non-negative? (default True)

  • ap_radius (float, optional) – Aperture radius, in arcseconds (default 1.2”)

  • r1, r2 (float, optional) – Inner and outer radius of the annulus, in arcseconds (default 2.0”, 5.0”)

  • thresh_sigma (float, optional) – Detection sigma to use in image segmentation (default 3.0; only relevant if mask and/or error are not given)

  • plot_annulus (bool, optional) – Plot the annulus? (default True)

  • plot_aperture (bool, optional) – Plot the aperture? (default True)

  • ann_output (str, optional) – Name for output annulus plot (default None; set by function if plot_annulus = True)

  • ap_output (str, optional) – Name for output aperture plot (default None; set by function if plot_aperture = True)

  • write (bool, optional) – Write the resultant table? (default False)

  • output (str, optional) – Name for the output table file (default None; set by function if write = True)

  • cmap (str, matplotlib.colors.ListedColormap, optional) – Colormap for plots (default “bone”)

Returns:

Table containing the results of aperture photometry

Return type:

TYPE

Notes

Finds the total flux in a defined aperture, computes the background in an annulus around this aperture, and computes the background-subtracted flux of the “source” defined by the aperture. Will compute aperture photometry for multiple sources if a list of RA/Decs is given.

If the background-subtracted flux at some location is negative, make sure that no sources remain in the annulus of the data, or consider getting a limiting magnitude at the RA, Dec of interest instead with limiting_magnitude()

alala.apphotom.limiting_magnitude(image_file, ra, dec, source_list=None, mask=None, error=None, sigma=5.0, astrom_sigma=5.0, psf_sigma=5.0, alim=10000, ap_radius=1.2, r1=2.0, r2=10.0, thresh_sigma=3.0, plot_annulus=True, plot_aperture=True, ann_output=None, ap_output=None, write=False, output=None, cmap='bone')

Obtain the limiting magnitude of some image.

Parameters:

  • image_file (str) – Filename for NOT background-subtracted image

  • ra, dec (float, list) – Floats or lists of RA, Dec of interest

  • source_list (str, astropy.io.fits.fitsrec.FITS_rec, optional) – Filename for source list or the source list fits bintable itself (default None –> make a list)

  • mask (np.ndarray, str, optional) – Filename for a source mask or the mask data itself (default None; in which case a source mask is made)

  • error (np.ndarray, str, optional) – Filename for image error or the image error itself (default None; in which case an error array is made)

  • sigma (float, optional) – Sigma to use when computing limiting magnitude (default 5.0)

  • astrom_sigma (float, optional) – Sigma threshold for astrometry.net source detection image (default 5.0)

  • psf_sigma (float, optional) – Sigma of the Gaussian PSF of the image to be used in astrometry.net(default 5.0)

  • alim (int, optional) – Maximum allowed source area, in pixels**2, beyond which sources will be deblended in astrometry.net (default 10000)

  • ap_radius (float, optional) – Aperture radius, in arcseconds (default 1.2”)

  • r1, r2 (float, optional) – Inner and outer radius of the annulus, in arcseconds (default 2.0”, 10.0”)

  • thresh_sigma (float, optional) – Detection sigma to use in image segmentation (default 3.0; only relevant if mask and/or error are not given)

  • plot_annulus (bool, optional) – Plot the annulus? (default True)

  • plot_aperture (bool, optional) – Plot the aperture? (default True)

  • ann_output (str, optional) – Name for output annulus plot (default None; set by function if plot_annulus = True)

  • ap_output (str, optional) – Name for output aperture plot (default None; set by function if plot_aperture = True)

  • write (bool, optional) – Write the resultant table? (default False)

  • output (str, optional) – Name for the output table file (default None; set by function if write = True)

  • cmap (str, matplotlib.colors.ListedColormap, optional) – Colormap for plots (default “bone”)

Returns:

Table containing RA, Dec, calibrated magnitude, filter used, and MJD for the limiting magnitude

Return type:

TYPE

Notes

For a given RA, Dec, finds the limiting magnitude at its location. If a source was previously detected <= 3” away from the given coords by astrometry.net, the aperture will randomly move about until a valid RA, Dec is found.

alala.apphotom.ellipse_photom(image_file, ra, dec, mask=None, guess_sma=0.9, guess_eps=0.3, guess_pa=60.0, sma_min=0.2, sma_max=2.5, r1=4.0, r2=12.0, thresh_sigma=3.0, flux_sub=0, plot_annulus=True, plot_ellipses=True, ann_output=None, ell_output=None, write=False, output=None, cmap='bone')

Like aperture_photom(), but use an elliptical aperture.

Parameters:

  • image_file (str) – Filename for NOT background-subtracted image

  • ra, dec (float, list) – Floats or lists of RA, Dec of interest

  • mask (np.ndarray, str, optional) – Filename for a source mask or the mask data itself (default None; in which case a source mask is made)

  • guess_sma (float, optional) – Initial guess for semi-major of ellipse, in arcseconds (default 1.0)

  • guess_eps (float, optional) – Initial guess for ellipticity (default 0.3)

  • guess_pa (float, optional) – Initial guess for position angle of ellipse, in degrees (default 60.0)

  • sma_min, sma_max (float, optional) – Minimum/maximum allowed semi-major axis of ellipse, in arcseconds (default 0.2)

  • r1, r2 (float, optional) – Inner and outer radius of the annulus, in arcseconds (default 4.0”, 12.0”)

  • thresh_sigma (float, optional) – Detection sigma to use in image segmentation (default 3.0)

  • flux_sub (float, optional) – Additional flux to subtract, if, e.g., want to subtract the flux of some galaxy (default 0)

  • plot_annulus (bool, optional) – Plot the annulus? (default True)

  • plot_ellipses (bool, optional) – Plot the elliptical isotophotes? (default True)

  • ann_output (str, optional) – Name for output annulus plot (default None; set by function if plot_annulus = True)

  • ell_output (str, optional) – Name for output ellipses plot (default None; set by function if plot_ellipses = True)

  • write (bool, optional) – Write the resultant table? (default False)

  • output (str, optional) – Name for the output table file (default None; set by function if write = True)

  • cmap (str, matplotlib.colors.ListedColormap, optional) – Colormap for plots (default “bone”)

Returns:

Table containing the results of “ellipse” photometry

Return type:

TYPE

alala.lightcurve module

Combine aperture and/or PSF photometry generated with apphotom and/or psfphotom modules, to generate light curves.

Light curves can include 3 kinds of data points:

  1. Magnitudes: Measured magnitudes, including error bars

  2. Limiting magnitudes: Limiting magnitudes, which do not have error bars

  3. Reference magnitudes: Reference magnitudes obtained from some other work / analysis, with (optional) error barts

alala.lightcurve.plotting_to_agg()

Switch matplotlib backend to ‘agg’

alala.lightcurve.plotting_to_Qt4Agg()

Switch matplotlib backend to ‘Qt4Agg’

alala.lightcurve.VALID_PHOT_FILTS = ['u', 'g', 'r', 'i', 'z', 'Y', 'J', 'H', 'K']

Valid photometric filters.

alala.lightcurve.DEFAULT_PLOT_INSTRUCTIONS = {'H': ['#fac205', 's'], 'J': ['#029386', 's'], 'K': ['#c04e01', 's'], 'Y': ['#8e82fe', 's'], 'g': ['#0165fc', 'o'], 'i': ['#ff9408', 'o'], 'r': ['#00ffff', 'o'], 'u': ['#be03fd', 'o'], 'z': ['#ff474c', 'o']}

Default instructions for plotting magnitudes.

alala.lightcurve.DEFAULT_PLOT_INSTRUCTIONS_LIM_MAGS = {'H': ['#fac205', 'v'], 'J': ['#ff028d', 'v'], 'K': ['#c04e01', 'v'], 'Y': ['#c65102', 'v'], 'g': ['#0165fc', 'v'], 'i': ['#ff9408', 'v'], 'r': ['#00ffff', 'v'], 'u': ['#be03fd', 'v'], 'z': ['#ff474c', 'v']}

Default instructions for plotting limiting magnitudes.

alala.lightcurve.DEFAULT_PLOT_INSTRUCTIONS_REF_MAGS = {'H': '#fac205', 'J': '#ff028d', 'K': '#c04e01', 'Y': '#c65102', 'g': '#0165fc', 'i': '#ff9408', 'r': '#00ffff', 'u': '#be03fd', 'z': '#ff474c'}

Default instructions for plotting reference magnitudes.

alala.lightcurve.PLOT_INSTRUCTIONS_VIEIRA20 = {'g': ['#76cd26', 's'], 'i': ['#0165fc', 'o'], 'z': ['#ff474c', 'D']}

Plotting instructions used in Vieira et al. (2020).

alala.lightcurve.PLOT_INSTRUCTIONS_LIM_MAGS_VIEIRA20 = {'g': ['#76cd26', 'v'], 'i': ['#0165fc', 'v'], 'z': ['#ff474c', 'v']}

Plotting instructions for limiting magnitudes used in Vieira et al. (2020).

alala.lightcurve.PLOT_INSTRUCTIONS_REF_MAGS_VIEIRA20 = {'g': '#76cd26', 'i': '#0165fc', 'z': '#ff474c'}

Plotting instructions for reference magnitudes used in Vieira et al. (2020).

alala.lightcurve.fromfile(readfile)

Read a single .fits table file containing either magnitudes or limiting magnitudes

Returns:

New LightCurve object

Return type:

LightCurve

Notes

Does not work for reference magnitudes. Will assume magnitude is just a regular magnitude. Reference magnitudes must be added one file at a time (see add_ref_files() and add_ref_tables()).

alala.lightcurve.fromdirectory(directory)

Search a directory for .fits files and read in magnitudes or limiting magnitudes

Returns:

New LightCurve object

Return type:

LightCurve

Notes

Assumes that all files in the directory contain magnitudes or limiting magnitudes. Reference magnitudes must be added one file at a time (see add_ref_files() and add_ref_tables()).

alala.lightcurve.frompoint(ra, dec, mag, mag_err, filt, mjd)

Generate a new light curve from a single specified data point

Parameters:

  • ra, dec (float) – RA and Dec of the source of interest

  • mag (float) – Magnitude

  • mag_err (float) – Error on the magnitude

  • filt (str) – Photometric filter used during observation

  • mjd (float) – MJD at time of observation

Returns:

New LightCurve object

Return type:

LightCurve

Notes

Should not be used for a limiting or reference magnitude.

class alala.lightcurve.LightCurve(mag_tab=None, lim_mag_tab=None, ref_mag_tab=None)

Bases: object

copy()

Return a (deep) copy of the object

property mags

Table of magnitudes

property limmags

Table of limiting magnitudes

property refmags

Table of reference magnitudes

property plot_instructions

Instructions for plotting magnitudes

property plot_instructions_lim_mags

Instructions for plotting limiting magnitudes

property plot_instructions_ref_mags

Instructions for plotting reference magnitudes

property mag_colnames

Columns to use as keys when searching for magnitudes in input files/tables

property limmag_colnames

Columns to use as keys when searching for limiting magnitudes in input files/tables

add_tables(*tables)

Add one or more new tables to the existing LightCurve’s magnitude table and/or limiting magnitude table

Notes

Changes the LightCurve in place. Assumes that the tables contain either magnitudes or limiting magnitudes. For adding in reference magnitudes, see add_ref_tables().

add_files(*files)

Read in from files and then append any new tables to the existing LightCurve’s magnitude table and/or limiting magnitude table

Notes

Changes the LightCurve in place. Assumes that the files contain tables with either magnitudes or limiting magnitudes. For adding in reference magnitudes, see add_ref_files().

add_ref_tables(*tables)

Same as add_tables(), but for reference magnitudes

add_ref_files(*files)

Same as add_files(), but for reference magnitudes

add_mag(ra, dec, mag, mag_err, filt, mjd)

Manually add a single point to the light curve’s magnitude table

Parameters:

  • ra, dec (float) – RA and Dec of the source of interest

  • mag (float) – Magnitude

  • mag_err (float) – Error on the magnitude

  • filt (str) – Photometric filter used during observation

  • mjd (float) – MJD at time of observation

add_limmag(ra, dec, mag, filt, mjd)

Manually add a single point to the light curve’s limiting magnitude table

Parameters:

  • ra, dec (float) – RA and Dec of the source of interest

  • mag (float) – Limiting magnitude

  • filt (str) – Photometric filter used during observation

  • mjd (float) – MJD at time of observation

add_refmag(ra, dec, mag, filt, mjd, mag_err=None)

Manually add a single point to the light curve’s reference magnitude table

Parameters:

  • ra, dec (float) – RA and Dec of the source of interest

  • mag (float) – Reference magnitude

  • filt (str) – Photometric filter used during observation

  • mjd (float) – MJD at time of observation

  • mag_err (float, optional) – Error on the magnitude, if any (default None)

coords_select(ra, dec, sep=1.0)

Given some light curve, select only data points for measurements taken within sep arcseconds of RA ra and Dec dec

Parameters:

  • ra, dec (float) – RA, Dec of the source of interest

  • sep (float, optional) – Maximum allowed separation from the RA, Dec, in arcseconds (default 1.0)

Returns:

New LightCurve object containing only sources satisfying the condition

Return type:

LightCurve

Notes

Does NOT change the object in-place. Returns a new object.

set_plot_instructions(filename)

Set the plotting instructions

Parameters:

filename (str) – Name of .csv file containing plotting instructions

Notes

The .csv should have no header, and should contain separate rows of the form

“u”,”red”,”o”

“g”,”#00ffff”,”s”

…

Indicating the photometric filter, line/marker colour, and marker style to use.

Will not change the marker style for limiting magnitudes, which are fixed at “v” (downwards caret).

plot(*filters, output='lightcurve.png', title=None, tmerger=None, show_legend=True, connect=True, text=None, mag_min=None, mag_max=None, limmag_min=None, limmag_max=None, refmag_min=None, refmag_max=None)

Plot your light curve!

Parameters:

  • *filters (str, optional) – Filter(s) of choice, if we wish to plot only the points for these filters (default None; valid options in VALID_PHOT_FILTERS)

  • output (str, optional) – Filename for output figure (default “lightcurve.png”; set to None to save no figure)

  • title (str, optional) – Title for the plot (default None)

  • tmerger (float, optional) – Time of the merger, in MJD, to plot in time elapsed since merger (default None –> just plot MJD)

  • show_legend (bool, optional) – Include a legend? (default True)

  • connect (bool, optional) – Connect points of the same photometric filter with a line, for legibility? (default True)

  • text (tuple, optional) – Text to place in a text box, in the form (x, y, ‘text you want printed’) (default None)

  • mag_min, mag_max (float, optional) – Lower, upper limits on the magnitudes in the light curve; omit datapoints outside bounds (default None)

  • limmag_min, limmag_max (float, optional) – Lower, upper limits on the limiting magnitudes in the light curve; omit datapoints outside bounds (default None)

  • refmag_min, refmag_max (float, optional) – Lower, upper limits on the reference magnitudes in the light curve; omit datapoints outside bounds (default None)

Returns:

Figure object, after all plotting

Return type:

matplotlib.figure.Figure

Notes

“lower” and “upper” limits on the data points are understood in terms of the magnitude system. I.e., m=26 is a lower limit, m=21 is an upper limit.

alala.psfphotom module

CONTENTS:

  • PSF_photometry(): Get the effective Point Spread Function (ePSF) of an image, fit all (or as many as desired) stars in the image with the ePSF, get instrumental magnitudes, and then crossmatch with external catalogue to get calibrated magnitudes

  • ePSF_FWHM(): Get the full width at half-max (FWHM) of an ePSF

  • copy_zero_point(): Copy zero point headers from one image to another

NON-STANDARD PYTHON DEPENDENCIES:

  • astropy

  • photutils

  • astroquery

NON-PYTHON DEPENDENCIES:

  • astrometry.net

alala.psfphotom.PSF_photometry(image_file, mask_file=None, nstars=40, thresh_sigma=5.0, pixelmin=20, elongation_lim=1.4, area_max=500, cutout=35, astrom_sigma=5.0, psf_sigma=5.0, alim=10000, write_xy=False, nsources=None, cat_num=None, sep_max=2.0, write_ePSF=False, ePSF_output=None, plot_ePSF=True, ePSF_plot_output=None, plot_resids=False, resids_output=None, plot_corr=False, corr_output=None, plot_source_offs=False, source_offs_output=None, plot_field_offs=False, field_offs_output=None, gaussian_blur_sigma=30.0, write=False, output=None)
Parameters:

  • image_file (str) – Filename for background-subtracted image

  • mask_file (str, optional) – Filename for a bad pixel mask (default None)

  • nstars (int, optional) – Maximum number of stars to use in constructing the ePSF (default 40; set to None to impose no limit)

  • thresh_sigma (float, optional) – Detection sigma to use in image segmentation (default 5.0)

  • pixelmin (int, optional) – Minimum allowed number of pixels in isophotes to be used in building the ePSF (default 20)

  • elongation_lim (float, optional) – Maximum allowed elongation of isophotes to used be in building the ePSF (default 1.4)

  • area_max (float, optional) – Maximum alllowed area for sources to be used in building the ePSF, in square pixels (default 500)

  • cutout (int, optional) – Size of cutout around each star, in pixels (default 35; must be odd, will be rounded down if even)

  • astrom_sigma (float, optional) – Sigma threshold for astrometry.net source detection image (default 5.0)

  • psf_sigma (float, optional) – Sigma of the Gaussian PSF of the image to be used by astrometry.net (default 5.0)

  • alim (int, optional) – Maximum allowed source area, in pixels**2, beyond which sources will be deblended in astrometry.net (default 10000)

  • write_xy (bool, optional) – Keep the .xy.fits source list produced by astrometry.net? (default False)

  • nsources (int, optional) – Limit on number of sources to fit with ePSF for obtaining the zero point (default None –> impose no limit)

  • cat_num (str, optional) – Vizier catalog number to determine which catalog to cross-match ( default None –> set to PanStarrs 1, SDSS DR12, or 2MASS depending on filter used during observations)

  • sep_max (float, optional) – Maximum allowed separation between sources when cross-matching to catalog, in pixels (default 2.0 ~ 0.6 arcseconds for WIRCam, 0.37 arcseconds for MegaCam)

  • write_ePSF (bool, optional) – Write the derived ePSF to a .fits file? (default False)

  • output_ePSF (str, optional) – Filename for output ePSF fits file (default None –> set by function)

  • plot_ePSF (bool, optional) – Plot the ePSF? (default True)

  • ePSF_plot_output (str, optional) – Filename for output ePSF plot (default None –> set by function)

  • plot_resids (bool, optional) – Plot the residuals of the iterative ePSF fitting? (default False)

  • resids_output (str, optional) – Filename for output residuals plot (default None –> set by function)

  • plot_corr (bool, optional) – Plot the instrumental magnitude versus castalogue magnitude correlation used to obtain the zero point (default False)

  • corr_output (str, optional) – Filename for output correlation plot (default None –> set by function)

  • plot_source_offs (bool, optional) – Plot offsets between image WCS and catalog WCS (default False)

  • source_offs_output (str, optional) – Filename for output source offsets plot (default None –> set by function)

  • plot_field_offs (bool, optional) – Plot offsets across the field, with a Gaussian blur to visualize large-scale structure in the offsets, if any

  • field_offs_output (str, optional) – Filename for output field offsets plot (default None –> set by function)

  • gaussian_blur_sigma (float, optional) – Sigma of the Gaussian blur to use in the field offsets plot, if plotting (default 30.0)

  • write (bool, optional) – Write the table of calibrated sources with PSF photometry to a file? (default False)

  • output (str, optional) – Name for output fits table of sources (default None –> set by function)

Returns:

Table of all PSF-fit sources, with their calibrated magnitudes

Return type:

TYPE

Notes

Using image segmentation, find as many sources as possible in the image with some constraints on number of pixels, elongation, and area. Use these sources to build an empirical effective PSF (ePSF). Use a list of sources found by astrometry.net to fit the ePSF to all of those sources. Compute the instrumental magnitude of all of these sources. Query the correct online catalogue for the given filter to crossmatch sources in the image with those in the catalogue (e.g., Pan-STARRS 1). Find the zero point which satisfies AB_mag = ZP + instrumental_mag and gets the calibrated AB mags for all PSF-fit sources.

alala.psfphotom.ePSF_FWHM(epsf_data)

Obtain the full width at half-max (FWHM) of an ePSF.

Parameters:

epsf_data (str, np.ndarray) – Fits file containing ePSF data or the data array itself

Returns:

FWHM of the ePSF

Return type:

float

alala.psfphotom.copy_zero_point(source_file, target_file)

Copy the zero point headers from source_file to target_file

Returns:

  • zp_mean (float) – Zero points mean

  • zp_med (float) – Zero points median

  • zp_std (float) – Zero points standard deviation

Notes

Copy the zero point obtained via PSF photometry from one fits header to another. Useful when one uses a background-subtracted image to do PSF photometry, obtains the zero point, and then wants to perform aperture photometry on the original, unsubtracted image.

alala.stack module

Module contents

Top-level package for alala.