Metadata-Version: 2.1
Name: mlmicrophysics
Version: 0.1
Summary: Machine learning emulator testbed for microphysics.
Home-page: https://github.com/NCAR/mlmicrophysics
Author: David John Gagne and Gabrielle Gantos
Author-email: dgagne@ucar.edu
License: MIT
Description: # mlmicrophysics
        Machine learning emulators for microphysical processes in CESM.
        
        ## Requirements
        
        The library has been tested with Python 3.6.
        The mlmicrophysics library requires the following Python libraries:
        * numpy
        * scipy
        * matplotlib
        * scikit-learn
        * tensorflow
        * keras
        * pandas
        * xarray
        * pyyaml
        * netcdf4
        
        You can install the dependencies using conda or pip depending on your local
        Python installation. In order to compile the fortran code within the library,
        you will need gfortran on your system.
        
        ## Installation
        To install and compile the library, run the following command:
        ```bash
        git clone https://github.com/NCAR/mlmicrophysics.git
        cd mlmicrophysics
        pip install .
        ```
        
        ## Running
        To train a new microphysics neural network emulator, you will first need to process
        the CESM CAM output files using the `scripts/process_cesm_output.py` script. The
        process script converts the CAM netCDF files to a set of csv files and filters
        out non-cloud grid cells. The script requires a yaml config file. See `config/cesm_tau_run5_full_process.yml` for
        an example. To run the processing script:
        ```bash
        cd ~/mlmicrophysics/scripts
        python -u process_cesm_output.py ../config/cesm_tau_run5_full_process.yml -p 5 >& tau_run5_process.log
        ```
        
        Once the data are processed, you can train a set of neural network emulators with `scripts/train_mp_neural_nets.py`.
        This script pre-processes the training and validation data, trains a set of neural networks
        and saves them and their verification statistics to an output directory.
        
        ## Contact
        If you have issues with the library, please create an issue on the github page.
        General questions can be sent to David John Gagne at dgagne@ucar.edu.
        
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