pyLDT-cosmo 0.3.12

Package for PDF calculations in Large Deviation Theory

pyLDT

Python code to generate matter PDF predictions in Large Deviation Theory for LCDM and alternative cosmologies

Installation and testing

(1) If not yet available on your machine, install julia (all platforms: download it from julialang.org; for macOS only you can alternatively

brew install --cask julia 

with Homebrew)

(2) make sure your system has a recent pip installation by running

python -m pip install --upgrade pip

(3) for a clean install of pyLDT create a virtual environment first. I will use virtualenvwrapper, but conda or any other environment manager will do. For more details on how to install and configure virtualenvwrapper visit https://virtualenvwrapper.readthedocs.io/en/latest/index.html

(4) Once virtualenvwrapper is setup, create simultaneously a project and an environment (e.g., pyLDTenv) typing in terminal

mkproject pyLDTenv 

If the envornment is not yet activated, type

workon pyLDTenv 

This should take you directly into the pyLDTenv directory associated with the pyLDTenv project.

(5) Install PyJulia by running

python3 -m pip install julia

(6) To install the Julia packages required by PyJulia launch a Python REPL and run the following code

>>> import julia 
>>> julia.install() 

(7) Install diffeqpy by running

pip install diffeqpy

(8) To install Julia packages required for diffeqpy, open up the Python interpreter and run

>>> import diffeqpy
>>> diffeqpy.install()

(9) Now run

pip install --index-url https://test.pypi.org/simple/ --extra-index-url https://pypi.org/simple pyLDT-cosmo 

hopefully at this stage all remaining Python dependencies will be automatically installed too

(10) To check everything is working as expected install pytest by issuing the command

pip install pytest 

and run

pytest --pyargs pyLDT_cosmo 

A test routine starts cruching the numbers (it should take about 90 sec.) and if pyLDT is correctly installed it should give 1 passed tests

Jupyter notebook

Go to https://github.com/mcataneo/pyLDT-cosmo/tree/main and download the example jupyter notebook showing how to use pyLDT. Move the notebook into the pyLDTenv directory. To fully exploit the notebook functionalities you'll need to 'pip install matplotlib' first.

That's all! Have fun!