https://github.com/simonsobs/pspipe
Tools for power spectra estimation
https://github.com/simonsobs/pspipe
cmb pipeline
Last synced: about 1 year ago
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Tools for power spectra estimation
- Host: GitHub
- URL: https://github.com/simonsobs/pspipe
- Owner: simonsobs
- License: other
- Created: 2018-11-15T18:54:50.000Z (over 7 years ago)
- Default Branch: master
- Last Pushed: 2025-03-24T21:16:13.000Z (over 1 year ago)
- Last Synced: 2025-04-04T01:03:21.418Z (about 1 year ago)
- Topics: cmb, pipeline
- Language: Python
- Homepage:
- Size: 126 MB
- Stars: 13
- Watchers: 10
- Forks: 10
- Open Issues: 5
-
Metadata Files:
- Readme: README.rst
- License: LICENSE
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README
.. raw:: html
.. contents:: **Table of Contents**
The package
===========
.. image:: https://travis-ci.com/simonsobs/PSpipe.svg?branch=master
:target: https://travis-ci.com/simonsobs/PSpipe
``PSpipe`` is a pipeline creator for the analysis of the high resolution maps of the large aperture
telescope of the Simons Observatory. It contains tools for estimating power spectra and a
multi-frequency likelihood interfaced with the ``cobaya`` MCMC sampler.
The pipelines are mainly written in python and make use of three different codes,
* ``pspy`` : python library for power spectrum estimation (https://github.com/simonsobs/pspy)
* ``namaster`` : C library + python wrapper for power spectrum estimation (https://github.com/LSSTDESC/NaMaster)
* ``mflike`` : mutlifrequency likelihood interfaced with ``cobaya`` (https://github.com/simonsobs/LAT_MFLike)
The package is licensed under the BSD license.
Requirements
============
* Python >= 3.8
* `FFTW `_: version 3 required
Installing
==========
Using `python/pip`
------------------
If the previous requirements are fulfilled, you can install the ``PSpipe`` package with its
dependencies by doing
.. code:: shell
$ pip install --user git+https://github.com/simonsobs/PSpipe.git
If you plan to develop or want to use the different projects, it is better to checkout the latest
version by doing
.. code:: shell
$ git clone https://github.com/simonsobs/PSpipe.git /where/to/clone
Then you can install the ``PSpipe`` library and its dependencies *via*
.. code:: shell
$ pip install --user /where/to/clone
Using ``docker``
----------------
Given the number of requirements, you can use a ``docker`` image already made with the needed
libraries and everything compiled. You should first install `docker
`_ for your operating system.
Bash installation
~~~~~~~~~~~~~~~~~~
We have written a simple bash script to install the ``PSpipe`` docker and to clone the main ``PSpipe`` libraries.
Just copy the script in a directory where you want to work with pspipe and run
.. code:: shell
$ ./run_docker.sh
This will open a new ``bash`` terminal with a full installation of ``PSpipe``, ``pixell``,
``NaMaster``, ``pspy``... For instance, you can start the ``ipython`` interpreter and run the following
``import`` command
.. code:: shell
$ ipython
Python 3.6.9 (default, Nov 7 2019, 10:44:02)
Type 'copyright', 'credits' or 'license' for more information
IPython 7.11.1 -- An enhanced Interactive Python. Type '?' for help.
In [1]: import pixell, pymaster, pspy
You can run the python scripts from the tutorials directory of ``PSpipe``.
When you are done with the image, just type ``exit`` and you will go back to your local machine prompt.
Running ``jupyter`` notebook from docker
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
It is also possible to start a ``jupyter`` server from the ``PSpipe`` image and run it into your web
browser. Inside the image terminal, you have to start the ``jupyter`` server by typing
.. code:: shell
$ jupyter notebook --ip 0.0.0.0
Finally open the ``http`` link (something like ``http://127.0.0.1:8888/?token...``) within your web
browser and you should be able to run one of the ``python`` notebook.
Sharing data between the ``docker`` container and the host
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
Everything perfomed within the ``/home/pspipe/workspace`` directory will be reflected into
the ``/where/to/work_with_pspipe`` on your host machine. You can then share configuration files, source codes, data
files... between the running ``docker`` container and your local machine. Nothing will be lost after
you exit from the ``docker`` container.
Cryptic ``Killed`` message
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
Docker for Mac limits the resource available to 2Gb of RAM by default, This might cause the code to crash unexpectedly with a cryptic ``Killed`` message. It can easily be modified, click on the docker logo (top right of your screen), go in Preferences/Resources and increase the RAM allocated to Docker.
Youtube video
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
You are not ready for it: `youtube `_