predictive-maintenance-sim/install_notes.txt at master · joehahn/predictive-maintenance-sim · GitHub

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#install_notes.txt
#
#by Joe Hahn
#joe.hahn@oracle.com
#21 April 2019
#
#notes on installing predictive-maintenance-sim

1 clone this repo:

    git clone git@github.com:joehahn/predictive-maintenance-sim.git
    cd predictive-maintenance-sim

2 browse https://repo.anaconda.com/archive and pick a recent Anaconda release

    release=Anaconda2-2018.12-MacOSX-x86_64.sh

the above will install python 2.7.15 for my Mac laptop.

3 download and install Anaconda python:

    wget https://repo.anaconda.com/archive/$release
    chmod +x $release
    ./$release -b -p ./anaconda2
    ./anaconda2/bin/python --version

which installs Anaconda python in the predictive-maintenance-sim folder

4 set path to python, ipython, jupyter etc:

    PYTHON_PATH=./anaconda2/bin
    $PYTHON_PATH/python --version

5 also note python package version numbers

    $PYTHON_PATH/pip freeze | grep 'matplotlib\|numpy\|pandas\|scikit-learn\|seaborn\|jupyter'

which yields

    jupyter==1.0.0
    matplotlib==2.2.3
    numpy==1.15.1
    pandas==0.23.4
    scikit-learn==0.20.1
    seaborn==0.9.0

6 generate RTF data:

    $PYTHON_PATH/python ./pdm.py inputs_rtf.py

7 start Jupyter

    $PYTHON_PATH/jupyter notebook

8 execute the build_models.ipynb Jupyter notebook, to build & test the ML models

9 run demo in PdM mode:

    $PYTHON_PATH/python ./pdm.py inputs_pdm.py

10 execute the inspect_pdm.ipynb notebook, to assess well production while operating in PdM mode