PerfPlotter

Read data from CSV files using pandas and generate interactive plots using bokeh, which can then be embedded into HTML pages and served by server-client applications (or at least allow them to be viewed on a browser).

Contents

• Environment
• Setup
• Usage
• Distribution

Environment

• Python 3.8.x
• macOS 10.15.x
• Firefox 88.x

Setup

1. Clone and cd into the perfplotter directory
2. Install dependencies

   $ pipenv install --dev --python=/path/to/python3.8
   
   

3. Install pre-commit hooks

   $ pre-commit install
   $ pre-commit run --all-files
   
   

   • Optional: pre-commit autoupdate
4. Activate virtual environment
5. In the same directory as setup.py, install *perfplotter as an editable dependency:

   $ pip install -e .
   
   

   • Use bare pip here to exclude perfplotter from the Pipfile* (which is under version control)
   • Do pipenv clean to really make sure it's not included in the Pipfile*
6. Start and open any of the jupyter notebooks

   $ jupyter notebook --notebook-dir tutorials/notebooks
   
   

Usage

Show the main usage/help info

$ perfplotter --help

Show each command's usage/help info

$ perfplotter examples --help
$ perfplotter timeseries --help
$ perfplotter histogram --help

Supported plots

• Plot TimeSeries

  $ perfplotter timeseries --help
  $ perfplotter timeseries /path/to/my.csv -x 1 -y 2
  
  

• Plot Histogram

  $ perfplotter histogram --help
  $ perfplotter histogram /path/to/my.csv -x 1 -y 2
  
  

Use examples to test

$ perfplotter examples sample1
Generated 
   
    /out/sample1.csv

$ perfplotter timeseries out/sample1.csv

   

The last command should open up a browser with an interactive plot of sample1.csv

Distribution

1. In the same directory as setup.py:

   $ python setup.py sdist
   
   

   • This should create a dist/perfplotter-VERSION.tar.gz file
2. In another directory (i.e. a test env) where perfplotter will be used:

   $ pipenv install /path/to/perfplotter/dist/perfplotter-VERSION.tar.gz