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This program utilizes machine learning to improve on mirror temperature settings for the Dark Energy Spectroscopic Instrument at Mayall 4-m telescope.

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desiforecast

Links to Automation (Work in Progress)

Full Documentation

Please visit desiforecast on Read the Docs

Documentation Status

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Travis Build Status

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Test Coverage Status

Introduction

This package contains notebooks and script that apply machine learning to the DESI telemetry data to prediction in facilitate temperature predictions.

Installation (Work in Progress)

  1. This package can be installed (and uninstalled) using the following command(s):

    python setup.py develop --prefix=$INSTALL_DIR.
    python setup.py develop --prefix=$INSTALL_DIR --uninstall.
    
  2. Fixed versions of the code can be installed as follows:

    python setup.py install --prefix=$INSTALL_DIR.
    
  3. Installing using pip:

    pip install git+https://github.com/desihub/desiforecast.git@1.1.0
    

Running the Scripts

Data Visulization Notebooks (Work in Progress)

Data visualization can be found in the folder doc/nb. Below is a list the the currently available notebooks:

time_plots.ipynb
correlation_plots.ipynb

Executable Scripts (Work in Progress)

Python scripts can be found in the folder py/desiforecast. Below is a list of the currently available scripts:

query_database.py
neural_network.py

License

desiforecast is free software licensed under a 3-clause BSD-style license. For details see the LICENSE.rst file.

About

This program utilizes machine learning to improve on mirror temperature settings for the Dark Energy Spectroscopic Instrument at Mayall 4-m telescope.

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