| Name | hpmoc JSON |
| Version |
1.0.0
JSON |
| download |
| home_page | None |
| Summary | HPMOC is an ultra high-performance, cross-platform toolset for working with |
| upload_time | 2023-08-08 05:08:46 |
| maintainer | None |
| docs_url | None |
| author | None |
| requires_python | >=3.8 |
| license | None |
| keywords |
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| VCS |
 |
| bugtrack_url |
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| requirements |
No requirements were recorded.
|
| Travis-CI |
No Travis.
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| coveralls test coverage |
No coveralls.
|
# hpmoc: HEALPix Multi-Order Coordinate Partial Skymaps
HPMOC is an ultra high-performance, cross-platform toolset for working with
multi-order coordinate (MOC) HEALPix_ images (i.e. images with multiple pixel
resolutions). MOC images are used by the [LIGO-Virgo-KAGRA collaboration](https://www.ligo.org/),
the [Interplanerary Network](https://ipn3.ssl.berkeley.edu/) and others to represent portions of
the sky with variable resolution. By only including pixels in regions of
interest, and only then at a resolution appropriate to how they were
observed/calculated, it is possible to reduce storage and computation costs by
several orders of magnitude.
HPMOC is the *only* library providing tools for loading partial/whole MOC
skymaps (as well as standard HEALPix skymaps), taking spatial intersections,
modifying resolution, plotting the skymaps, converting them to and from
[Astropy WCS](https://docs.astropy.org/en/stable/wcs/index.html) projections,
performing pointwise math, and generating PSF skymaps from point sources,
all using algorithms that minimize memory, computation, and storage costs.
It is based off of work on [LLAMA](https://multimessenger.science), the world's first Gravitational
Wave/High-Energy Neutrino low-latency search pipeline, which has been improved
and refactored into this separate module.
If you use `hpmoc` in published research, we ask that you cite [Stefan Countryman's thesis](https://academiccommons.columbia.edu/doi/10.7916/c8n9-p112).
`hpmoc` is introduced in section 4.5.13.
`hpmoc` is licensed under the terms of the [GNU General Public License, version 2 or later](https://www.gnu.org/licenses/old-licenses/gpl-2.0.en.html)
## Installation
`hpmoc` has only a few dependencies, but they are large numerical/scientific
libraries. You should therefore probably create a virtual environment of some
sort before installing. The easiest and best way to do this at the moment is to
use `conda`, which should come with an Anaconda distribution of Python:
```bash
conda create -n hpmoc
conda activate hpmoc
```
note that creating a new environment is optional and hpmoc can now be installed similar to any other python package.
### With pip
If you just want to use `hpmoc` and don't need to modify the source code, you
can install using pip:
```bash
pip install hpmoc
```
This should install all required dependencies for you.
### Developers
If you want to install from source (to try the latest, unreleased version, or
to make your own modifications, run tests, etc.), first clone the repository:
```bash
git clone https://github.com/markalab/hpmoc.git
cd hpmoc
```
Make sure the build tool, `flit`, is installed:
```bash
pip install flit
```
Then install an editable version of `hpmoc` with `flit`:
```bash
flit install --symlink
```
As with the `pip` installation method, this should install all requirements for
you. You should now be able to import `hpmoc`. Note that you'll need to quit
your `python` session (or restart the kernel in Jupyter) and reimport `hpmoc`
before your changes to the source code take effect (which is true for any
editable Python installation, FYI).
You can go ahead and run the tests with `pytest` (which should have been
installed automatically by `flit`):
```bash
py.test --doctest-modules --cov=hpmoc
```
Raw data
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"description": "# hpmoc: HEALPix Multi-Order Coordinate Partial Skymaps\n\nHPMOC is an ultra high-performance, cross-platform toolset for working with\nmulti-order coordinate (MOC) HEALPix_ images (i.e. images with multiple pixel\nresolutions). MOC images are used by the [LIGO-Virgo-KAGRA collaboration](https://www.ligo.org/),\nthe [Interplanerary Network](https://ipn3.ssl.berkeley.edu/) and others to represent portions of\nthe sky with variable resolution. By only including pixels in regions of\ninterest, and only then at a resolution appropriate to how they were\nobserved/calculated, it is possible to reduce storage and computation costs by\nseveral orders of magnitude.\n\nHPMOC is the *only* library providing tools for loading partial/whole MOC\nskymaps (as well as standard HEALPix skymaps), taking spatial intersections,\nmodifying resolution, plotting the skymaps, converting them to and from\n[Astropy WCS](https://docs.astropy.org/en/stable/wcs/index.html) projections,\nperforming pointwise math, and generating PSF skymaps from point sources,\nall using algorithms that minimize memory, computation, and storage costs.\nIt is based off of work on [LLAMA](https://multimessenger.science), the world's first Gravitational\nWave/High-Energy Neutrino low-latency search pipeline, which has been improved\nand refactored into this separate module.\n\nIf you use `hpmoc` in published research, we ask that you cite [Stefan Countryman's thesis](https://academiccommons.columbia.edu/doi/10.7916/c8n9-p112).\n`hpmoc` is introduced in section 4.5.13.\n\n`hpmoc` is licensed under the terms of the [GNU General Public License, version 2 or later](https://www.gnu.org/licenses/old-licenses/gpl-2.0.en.html)\n\n## Installation\n\n`hpmoc` has only a few dependencies, but they are large numerical/scientific\nlibraries. You should therefore probably create a virtual environment of some\nsort before installing. The easiest and best way to do this at the moment is to\nuse `conda`, which should come with an Anaconda distribution of Python:\n\n```bash\nconda create -n hpmoc\nconda activate hpmoc\n```\nnote that creating a new environment is optional and hpmoc can now be installed similar to any other python package. \n\n### With pip\n\nIf you just want to use `hpmoc` and don't need to modify the source code, you\ncan install using pip:\n\n```bash\npip install hpmoc\n```\n\nThis should install all required dependencies for you.\n\n### Developers\n\nIf you want to install from source (to try the latest, unreleased version, or\nto make your own modifications, run tests, etc.), first clone the repository:\n\n```bash\ngit clone https://github.com/markalab/hpmoc.git\ncd hpmoc\n```\n\nMake sure the build tool, `flit`, is installed:\n\n```bash\npip install flit\n```\n\nThen install an editable version of `hpmoc` with `flit`:\n\n```bash\nflit install --symlink\n```\n\nAs with the `pip` installation method, this should install all requirements for\nyou. You should now be able to import `hpmoc`. Note that you'll need to quit\nyour `python` session (or restart the kernel in Jupyter) and reimport `hpmoc`\nbefore your changes to the source code take effect (which is true for any\neditable Python installation, FYI).\n\nYou can go ahead and run the tests with `pytest` (which should have been\ninstalled automatically by `flit`):\n\n```bash\npy.test --doctest-modules --cov=hpmoc\n```\n",
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