Pipeline

class phrosty.pipeline.Pipeline(diaobj, imgcol, band, science_images=None, template_images=None, science_csv=None, template_csv=None, nprocs=1, nwrite=5, verbose=False)

Bases: object

Phrosty’s top-level pipeline

Create the a pipeline object.

Parameters:

• diaobj (DiaObject) – The object we’re building a lightcurve for

• band (str) – One of R062, Z087, Y106, J129, H158, F184, K213

• science_images (list of snappl.image.Image) – The science images.

• template_images (list of snappl.image.Image) – The template images.

• science_csv (Path or str) –

  CSV file with the science images. The first line must be:

  path pointing sca mjd band
  

  subsequent lines must have that information for all the science images. path must be relative to ou24.images in config. Pipeline will extract the images from this file whose band matches the band of the pipeline (and ignore the rest)

• template_csv (Path or str) – CSV file with template images. Same format as science_csv.

• nprocs (int, default 1) – Number of cpus for the CPU multiprocessing segments of the pipeline. (GPU segments will run a single process.)

• nwrite (int, default 5) – Number of asynchronous FITS writer processes.

Methods Summary

__call__([through_step])	Run the pipeline.
add_to_results_dict(one_pair)
align_and_pre_convolve(templ_image, sci_image)	Align and pre convolve a single template/science pair.
clear_contents(directory)
do_stamps(sci_image, templ_image)
get_psfs()
make_lightcurve()
make_phot_info_dict(sci_image, templ_image)	"Do things.
phot_at_coords(img, psf[, pxcoords, ap_r])	Do photometry at forced set of pixel coordinates.
save_stamp_paths(sci_image, templ_image, paths)
sky_sub_all_images()
write_fits_file(data, header, savepath)

Methods Documentation

__call__(through_step=None)

Run the pipeline.

Parameters:

through_step (str, default None) –

Which step to run thorough? Runs them all if not given.

Steps in order are: * sky_subtract * get_psfs * align_and_preconvolve * subtract * find_decorrelation * apply_decorrelation * make_stamps * make_lightcurve

Returns:

ltcvpath – The path to the output lightcurve file if make_lightcurve was run, otherwise None.

Return type:

pathlib.Path or None

add_to_results_dict(one_pair)

align_and_pre_convolve(templ_image, sci_image)

Align and pre convolve a single template/science pair.

Parameters:

• sci_image (phrosty.PipelineImage) – The science (new) image.

• templ_image (phrosty.PipelineImage) – The template (ref) image that will be subtracted from sci_image.

Returns:

sfftifier – Use this object for futher SFFT work. Be sure to dereference it to free the prodigious amount of memory it allcoates.

Return type:

SpaceSFFT_CupyFlow

clear_contents(directory)

do_stamps(sci_image, templ_image)

get_psfs()

make_lightcurve()

make_phot_info_dict(sci_image, templ_image, ap_r=4)

“Do things.

Parmaeters

sci_image: PipelineImage

science image wrapper

temp_image: PipelineImage

template image wrapper

ap_r: float, default 4

Radius of aperture to use in something

returns:

something – It has things in it

rtype:

dict

phot_at_coords(img, psf, pxcoords=(50, 50), ap_r=4)

Do photometry at forced set of pixel coordinates.

Parameters:

• img (snappl.image.Image) – The image on which to do the photometry

• psf (snappl.psf.PSF) – The PSF.

• pxcoords (tuple of (int, int)) – The position on the image to do the photometry

• ap_r (float) – Radius of aperture.

Returns:

results – Keys and values are: * ‘aperture_sum’: flux in aperture of radius ap_r * ‘flux_fit’: flux from PSF photometry * ‘flux_fit_err’: uncertainty on flux_fit * ‘mag_fit’: instrumental magnitude (i.e. no zeropoint) from flux_fit * ‘mag_fit_err’: uncertainty on mag_fit

All values are floats.

Return type:

dict

save_stamp_paths(sci_image, templ_image, paths)

sky_sub_all_images()

write_fits_file(data, header, savepath)