Visualizations of GCMS data from our plant samples.

This Jupyter notebook to create plots of our GCMS data was written by Manvitha Ponnapati to visualize data collected by John De La Parra.

Example GCMS data plot

• Jupyter is an interactive development system for code + data + comments. Useful for small scientific and math projects you want to analyze / visualize.
• Jupyter starts a local web server + kernel that will let you edit & run your 'document'.
• Jupyter documents are portable and easily shareable. They are in a human readable text format (JSON).
• We use Jupyter because we frequently have to edit this bit of code to parse the data and make these plots for publication.

• Install docs
• Install steps used on OSX 10.14.2 (with Python 3.6.5 already installed globally)

python3 -m pip install --upgrade pip
python3 -m pip install jupyter
python3 -m pip install bokeh
python3 -m pip install pandas
python3 -m pip install numpy

This is how we run our notebook. Make sure you have pre-processed the GCMS data into a data.csv file.

jupyter notebook gcms.ipynb

• Using Jupyter
• Examples
• Bokeh charting package

(Still TBD by Rob when he gets Manu's notes)

1. I have a script somewhere that automatically splits a CSV into multiple small csv files based on where the CSV file has a line With Path etc.
2. There are some cases when the samples all dont have the equal timestamps - like the last sample might be missing for some. In those cases - I do an assert and append an empty row to the dataframe if the dataframe files.
3. I recommend usually adding the indexes yourself for different samples so you know exactly what you are plotting.