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Foreground modeling/removal and Power Spectra generation

Project description

Python package for foreground removal and power spectra calculation:


From git (development version):

git clone
pip install -f ./ps_eor

from pypi (latest stable version):

pip install ps_eor

The following packages will also be installed:

  • click
  • numpy
  • scipy
  • astropy
  • matplotlib
  • pytable (for saving loading in h5 format)
  • pyfftw (optional, can save time)
  • sklearn (for PCA)
  • GPy (for GPR)
  • healpy,reproject (optional)

Command-line usage:

The ps_eor package comes with a command line tool: pstool, which can:

  • Generate gridded visibility cube from fits images cubes (gen_vus_cube)
  • Produce power-spectra from gridded visibility cube (make_ps)
  • Combine different visibility cube (combine)
  • Remove foregrounds using GPR (run_gpr)
  • ... and several more

Usage of pstool is as follow:

Usage: pstool [OPTIONS] COMMAND [ARGS]...

  --version  Show the version and exit.
  --help     Show this message and exit.

  combine               Combine all datacubes listed in FILE_LIST...
  combine_sph           Combine all sph datacubes listed in FILE_LIST...
  diff_cube             Compute the difference between CUBE1 and CUBE2
  even_odd_to_sum_diff  Create SUM / DIFF datacubes from EVEN / ODD...
  gen_vis_cube          Create a datacube from image and psf fits files.
  make_ps               Produce power-spectra of datacubes FILE_I: Input...
  run_flagger           Run flagger on datacubes and save flag.
  run_gpr               Run GPR & generate power spectra FILE_I: Input...
  simu_noise_img        Compute noise fits cube from a simulated UV

  simu_noise_ps         Compute noise PS from a simulated UV coverage...
  simu_uv               Compute gridded UV coverage.
  vis_to_sph            Load visibilities datacubes VIS_CUBE, transform to...

To get help for any of the pstool command, execute:

pstool CMD --help

API usage:

This package provide a simple interface for Foreground removal and PS estimation tasks which can complete the command line tool.

Below we briefly document the main tasks that can be perform with this package:

  1. Loading your data:

data_cube_i = datacube.CartDataCube.load_from_fits_image(files, umin, umax, theta_fov)

This will do the following steps:

  • Read Fits image file
  • Trim image to theta_fov
  • Convert image from Jy/PSF to K, using imager_scale_factor or WSCNORMF attribute to get PSF "solid angle" (otherwise use Gaussian approx of the PSF)
  • FFT image per frequencies to get visibilities
  • Keep only non-zero visibilities between umin and umax. and return a CartDataCube object storing the visibilities in an ungridded way.

It is possible (recommended) to save/load to an h5 format with the save()/load() method.

One can also regrid the data and make an image with the regrid() and image() method.

  1. Run FG removal algorithm:

The main FG removal code is GPR, but PCA, GMCA (the python version) and Polynamial fitting are also implemented.

To run GPR, one do the following:

data_cube_i = datacube.CartDataCube.load_from_fits_image(files_i, umin, umax, theta_fov)
data_cube_v = datacube.CartDataCube.load_from_fits_image(files_v, umin, umax, theta_fov)

eor_bin_list = pspec.EorBinList(data_cube_i.freqs)
# Create an EoR bin 122-134 MHz with a 120-136MHz range for the FG fitting
eor_bin_list.add_freq(1, 122, 134, 120, 136)
eor = eor_bin_list.get(1)

gpr_config = fitutil.GprConfig.load(gpr_config_filename)
gpr_fit = fgfit.GprForegroundFit(gpr_config)
gpr_res =, eor.get_slice_fg(data_cube_v))

This return a GprForegroundResult object which have the following attributes:

  • fit: The FG model in a form of a CartDataCube object
  • sub: The residual
  • pre_fit: The pre-fit FG model
  • post_fit: The post-fit FG model

And the following method:

  • get_fg_model(): return the GPR fg model
  • get_eor_model(): return the GPR eor model

On can then save/load those CartDataCube as needed for later processing.

The CartDataCube cubes of the GPR model and residual contains the error covariance from the GPR model that need to be taken into account when generating the power spectra.

Look at gpr_config.parset and gpr_config_v.parset for examples of GPR configuration.

  1. Generate Power Spectra:

The PS code into account automatically the error covariance of the GPR model.

It is possible to generate spatial only PS, Cylindrically averaged PS (2D) or spherically averaged PS (3D).

# Create a PS configuration
el = 2 * np.pi * (np.arange(,, du))
ps_conf = pspec.PowerSpectraConfig(el)

pb = datacube.LofarHBAPrimaryBeam()

# Create a PS generation object
ps_gen = pspec.PowerSpectraCart(eor, ps_conf, pb)

# Create a Spatial PS, plot it and save it to a file
ps = ps_gen.get_ps(data_cube)
ps.plot(title='Spatial power spectra')

# Create a Cylindrically averaged PS
ps2d = ps_gen.get_ps2d(data_cube)
ps2d.plot(title='Cylindrically averaged power spectra')

# Create a Spherically averaged PS
kbins = np.logspace(np.log10(ps_gen.kmin), np.log10(0.5), 10)
ps3d = ps_gen.get_ps3d(kbins, data_cube)
ps3d.plot(title='Spherically averaged power spectra')

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