master: 
development: 
The version of the python packages listed below are the tested version. Any recent versions of the packages should technically work.
- Python (version >=3.5)
- NumPy (version 1.13.1)
- HealPy (version 1.11.0)
- configparser (version 3.5.0)
- AstroPy (version 1.2.1)
- SciPy (version 0.19.1)
- Matplotlib (version 2.0.0)
Generating the mock requires a completeness map and a redshift distribution. An example completeness map is provided at examples/data/example_coverage.npz. As for the redshift distribution, user can use a data challenge file which has the information for individual galaxies or use a fits file similar to the one provided at examples/data/example_zcat.fits.
In order to avoid any possible conficts with other packages, it is advised to create a clean conda environment. conda installation instructions can be found at this link. The new environment can be created as follows:
conda create -n paramockEnv python=PYTHON_VERSION numpy
The package has been tested with Python 3.5, 3.6 and 3.7. It will not work with Python 2 since Python 2 will retire in less than a year.
It should be noted that numpy is installed while creating the conda environment. This is due to an unexpected problem with easy_install not being able to install numpy properly.
Then, user needs to load the new conda environment as follows:
source activate paramockEnv
To install ParaMock:
git clone https://github.com/DESI-UR/catalog_generation.git
cd catalog_generation
python setup.py install
To fully simulate a signal due Baryon Acoustic Oscillations (BAO) in galaxy distribution one needs to run a full simulation of the Universe evolving from the era of decoupling to present day. It is very CPU consuming and thus limits the range of parameters that can be tested. At the same time future high statistics experiments will demand a control over the systematic uncertainties down to sub-percent level. Here, we suggest a model that would significantly reduce this computational time, while having main features present in the observation data.
The mock and random catalog generation requires two distributions: z distribution, P(r), and a completeness map of an experiment, Rang(θ, φ).
We start by randomly generating Ncenter new galaxies according to the product of Rang(θ, φ) and P(r). These galaxies form centers of BAO ”bubbles”. Then, for each BAO center we add Nrim galaxies located at a distance rBAO from the center and uniformly distributed over solid angle around the BAO center. Added galaxies can be discarded to correct for the acceptance in r and angles (θ, φ).
Then, Nflat random galaxies are generated according to the product of Rang(θ, φ) and P(r). After this, some ”clumping” is introduced around NRclump galaxies drawn randomly from the newly-created mock catalog to mimic galaxy clusters, with BAO centers having a different number of clumping partners because of the potential access of dark matter around the BAO centers. Clumping follows the following distribution: f(r) = Ax(r0/r)γ where r is the distance of the center of the cluster, A is a normalization parameter and r0 and γ are input parameters.
There are two executables provided within the package. The insight of the executables is explain in the following section. The arguments for the executable are shown below:
usage: generate_mock.py [-h] -c CONFIG [-o OUTPUT] [-d DIAGNOSTICS]
[-a ADD_TO_RIMS] [-e EXTENDED] [-p PICKLE] [-u STYLE]
optional arguments:
-h, --help show this help message and exit
-c CONFIG, --config CONFIG
Configuration filename
-o OUTPUT, --output OUTPUT
Output filename (this overwrites the output filename
in the configuration file)
-d DIAGNOSTICS, --diagnostics DIAGNOSTICS
Flag to get diagnostic messages
-a ADD_TO_RIMS, --add_to_rims ADD_TO_RIMS
Flag to add clumping galaxies around rim galaxies
-e EXTENDED, --extended EXTENDED
Flag to write the extended output for the fits file
-p PICKLE, --pickle PICKLE
Flag to write the extended pickle file
-u STYLE, --style STYLE
Flag to use the provided plotting style
--config argument is required for the configuration to be used.
Other arguments are optional and the explanation is provided with --help.
As for the random catalog generation, the list of arguments is similar to mock catalog generation but limited.
usage: generate_random.py [-h] -c CONFIG [-o OUTPUT]
optional arguments:
-h, --help show this help message and exit
-c CONFIG, --config CONFIG
Configuration filename
-o OUTPUT, --output OUTPUT
Output filename (this overwrites the output filename
in the configuration file)
The usage is shown in bin/generate_mock.py and bin/generate_random.py. However, the user can write own executables or use them as template for python notebook use.
In order to import the module, following lines are required in the python script:
from paramock.paramock import GeneralTools as generalTools
The examples shown here needs the object created with a configuration file. Example configuration file is at data/catgen.cfg
gen_tool = generationTools(CONFIGURATION_FILE, diagnostics=DIAGNOSTICS)
In the configuration file, user needs to provide either a sample catalog with redshifts or a redshift distribution as datafile. The mock and random catalog to be generated within a window given in angmask. For the users who do not have such a file or want to generate a catalog in a custom region in the sky, a python script, examples/generate_completeness_map.py, is included in the project. This script generates a completeness map for a region of interest. For more complicated completeness maps, users need to write their own scripts considering the data structure shown in examples/generate_completeness_map.py
One needs to generate the center galaxies to be used for introducing the BAO galaxies. The number of center galaxies are defined in the configuration file. The example line below shows how to generate center galaxies.
gen_tool.generate_center_galaxies()
The BAO signal is introduced around the center galaxies with a gaussian distribution (μ, σ)=(rBAO, sigmar_BAO). To generate them, one needs to use the generate_rim function. The position of the center galaxies are pulled from the catalog object within gen_tool.
gen_tool.generate_rim()
In addition to the center and rim galaxies, randomy distributed flat galaxies needed to be introduced. It is automatically done when the clumps are being generated. Then, a number of clump galaxies are injected centered around some selected galaxies. The clumps are categorized into two groups: center seeded clumps and flat seeded clumps. The defition of center seeded clumps is left open for the user. User can use rims for the center seed clumps. The second group, flat seeded clumps, are selected among the flat galaxies generated. In the example below, rim galaxies are added to the center seeded clumps by setting add_clumps_to_rims=True (which is False by default).
gen_tool.generate_clumps(add_clumps_to_rims=True)
There is no difference in generating both group of clumps, they both use the same power law distribution for their location with respect to the seed galaxy of a clump. The only difference is the number of clumps around a certain type of object. This is controlled by the two parameters in the configuration file: frac_f2c and frac_c2r. frac_f2c defines the fraction of flat seeded clumps to center seeded clumps and frac_c2r defines the fraction of center galaxies used with respect to the rim galaxies for the center seed galaxies. If the parameters are not defined in the configuration, gen_tool defaults to None and use random distributions for the selections
The output can be saved in two different formats, fits and pickle. .fits output is structured to run analysis easily and the fields stored can be extended to have the name and the parent name of the galaxies with the flag save_extended. The following line shows how to save the output in fits format.
gen_tool.write_to_fits(FILENAME, save_extended=True)
If the filename is not provided when calling the function, the module will use the filename defined in the configuration file. The second output type, .pkl, stores the whole catalog as an object so that the user can use the functions in the catalog and galaxy modules easily for postprocessing.
gen_tool.write_to_pickle(FILENAME)
Similarly, when the filename is not provided, the filename in the configuration file will be used after changing the extension to .pkl.