This collection of "observational" (really, comparison - as this includes some theory datasets as well) are used for comparisons within the VELOCIraptor library. The repository contains two main things:
- A selection of plain-text tables extracted from various comparison data sets.
- Accompanying scripts that convert these into h-corrected (and otherwise cosmology corrected) values for plotting, saving them in HDF5 files.
This functionality is supported by the ObservationalData class within the VELOCIraptor python library. The file format, and information on how to create a given file, is available in that documentation.
The build checks that:
- All python files are correctly formatted
- All conversions succeed with a Planck15 cosmology.
To contribute a dataset to this repository, you will need to save the data in a plain-text format (including a comment), and create a conversion script.
This conversion script should be as similar as possible to the example below:
from velociraptor.observations.objects import ObservationalData
import unyt
import numpy as np
import os
import sys
# Exec the master cosmology file passed as first argument
# These lines are _required_ and you are required to use
# the cosmology specified (this is an astropy.cosmology
# instance)
with open(sys.argv[1], "r") as handle:
exec(handle.read())
input_filename = "../raw/AuthorYear.txt"
delimiter = None
output_filename = "AuthorYear.hdf5"
output_directory = "../"
if not os.path.exists(output_directory):
os.mkdir(output_directory)
processed = ObservationalData()
raw = np.loadtxt(input_filename, delimiter=delimiter)
comment = f"Comment About Your Data. h-corrected for SWIFT using Cosmology: {cosmology.name}."
citation = "Author et al. (Year)"
bibcode = "Bibcode from ADS"
name = "Name of Plot"
plot_as = "points/line"
redshift = 0.000000
h = cosmology.h
x = convert_x_to_physical_units
y = convert_y_to_physical_units
# y_scatter should be a 1xN or 2xN array describing offsets from
# the median point 'y'
y_scatter = convert_y_scatter_to_physical_units
processed.associate_x(x, scatter=None, comoving=True, description="x Description")
processed.associate_y(y, scatter=y_scatter, comoving=True, description="y Description")
processed.associate_citation(citation, bibcode)
processed.associate_name(name)
processed.associate_comment(comment)
processed.associate_redshift(redshift)
processed.associate_plot_as(plot_as)
processed.associate_cosmology(cosmology)
output_path = f"{output_directory}/{output_filename}"
if os.path.exists(output_path):
os.remove(output_path)
processed.write(filename=output_path)There are several examples within the repository already.
You should then organise things as follows:
- Raw data:
data/PlotName/raw/AuthorYear.txt - Conversion script:
data/PlotName/conversion/convertAuthorYear.py
This ensures that it is picked up by the automatic conversion script.
Before creating a pull request, please ensure that you run the format_all.sh script.
You can preview your data file by using the included
plot_individual_dataset.py script, and passing the .hdf5 file
as the first argument.
The code is formatted with black, and follows all rules exactly apart from one;
strings that are longer than 88 characters should be broken into pieces so that
the maximal line length in a file is 88, such as:
long_string = (
"This would have been a really, really, really, really, really, really, really, "
"really, really, really, really, really, really, really really, really, really, "
"long string."
)
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