neural_deprojection

Using neural networks to deprojection astronomical observables

Install

Install miniconda.

DOWNLOAD_DIR=$HOME
GIT_DIR=$HOME/git
INSTALL_DIR=$HOME
wget https://repo.anaconda.com/miniconda/Miniconda3-latest-Linux-x86_64.sh -O $DOWNLOAD_DIR/miniconda.sh
bash $DOWNLOAD_DIR/miniconda.sh -b -p $INSTALL_DIR/miniconda3
. $INSTALL_DIR/miniconda3/etc/profile.d/conda.sh
echo ". $INSTALL_DIR/miniconda3/etc/profile.d/conda.sh" >> $HOME/.bashrc
hash -r 
conda config --set auto_activate_base false --set always_yes yes
conda update -q conda
conda info -a

Make a conda environment for this project. I'll call it tf_py because it will contain tensorflow.

conda create -n tf_py python=3.8

Activate tf_py

conda activate tf_py

Install all required packages

pip install numpy tensorflow tensorflow_probability matplotlib scipy pytest

and for yt we need the git master for now which has particle data volume rendering, pip install -e git+https://github.com/yt-project/yt.git#egg=yt

Note that AMUSE may require it's own special environment, with differences from this setup. You should still be able to follow the same method of modularising that environment.

Set up pycharm professional

Clone the package

git clone https://github.com/Joshuaalbert/neural_deprojection.git

Make a new project using .../neural_deprojection as the project path. Choose tf_py as your interpreter (verify it's in use after making the project). Go to Settings (Ctrl-Alt-s) then Tools>Python Integrate Tools and select pytest as default test runner and Google as docstring format. Go to Settings>Tools>Python Scientific and make sure show plots in tool window is checked. When you commit for first time you'll need to enter github login.