Pretty Plot

A utility for easily converting VNIR multispectral data from Mastcam-Z into a standardized, pretty plot for quick distribution of results. The tool accepts marslab-formatted files.

Installation / setup

Step 0: clone the repository to your computer:

If you have git installed on your computer, navigate in a terminal emulator to wherever you'd like to place the software and run git clone git@github.com:MillionConcepts/pretty-plot.git.

Alternatively, you can use GitHub Desktop to clone the repository. Install that program, run it, log in to your account, choose "Clone Repository...", click the "URL" tab, paste https://github.com/MillionConcepts/pretty-plot.git into the 'Repository URL' field, and click "Clone".

Step 1: install conda

Note: If you already have Anaconda or Miniconda installed on your computer, you can skip this step. If it's very old or not working well, you should uninstall it first. We strongly advise against installing multiple versions of conda unless you a very specific reason to do so.

We recommend using Miniforge. Follow the instructions on that website to download the installer script and set up your conda installation.

Step 2: create a pretty-plot conda environment:

Once you have conda installed, you can set up a Python environment to use pretty-plot. Open a terminal window: Anaconda Prompt on Windows, Terminal on macOS, or your terminal emulator of choice on Linux. (Windows might name the prompt "Miniconda Prompt" or something else instead; just search for "prompt" in the Start Menu and don't pick Windows Command Prompt.)

Now, navigate to the directory where you downloaded the repository and run the command:
conda env create -n pretty-plot --file environment.yml

Say yes at the prompts and let the installation finish. Then run conda env list. You should see pretty-plot in the list of environments.

Step 3: activate the conda environment and install pretty-plot:

Next, run conda activate pretty-plot to activate the Python environment that contains the packages pretty-plot needs.

To install the pretty-plot application into the environment, run pip install -e . You will never need to run this again unless you delete and recreate the pretty-plot environment.

Important: now that you've created this environment, you should always have it active whenever you work with pretty-plot. You can do this simply by running conda activate pretty-plot.

Tutorial

The Pretty Plot.ipynb file in this repo is a jupyter notebook tutorial for how to use pretty-plot. To access it make sure your pretty-plot conda environment has been activated (see above) and then type:

jupyter notebook

Now, follow the instructions output on the command line to open a jupyter notebook session in your browser (crtl+click on the url) and click on Pretty Plot.ipynb. For a streamlined description of other advanced options, click on brief_examples.ipynb.

From the command line

You can also run pretty-plot directly from the command line. To do that, make sure your pretty-plot conda environment has been activated, then run the pplot command. For example:

pplot /Users/username/Documents/spectra/marslab_file.csv

Updating Pretty Plot

Make sure the conda environment is active:
conda activate pretty-plot

Navigate to your pretty-plot directory. The exact path depends on where you initally installed pretty-plot, for example:
cd ~/Documents/GitHub/pretty-plot

Optional: run the command ls. If you see pplot.py in the output, then you are in the correct directory.

Download any updates from GitHub by running the git pull command. (Or by clicking the "Pull" button in GitHub Desktop.)

Troubleshooting

• Any time something is not working as expected, a good first step is to check if the pretty-plot conda environment is active.

• Normally you can run pretty-plot from any directory, but during initial setup the pip install -e . command must be run from within the pretty-plot directory.

  • If you are getting errors, try running the ls command. If pplot.py is listed in the output, then you are in the right directory. If you don't see that file, navigate to the correct directory with cd and try the pip install again.

• Sometimes a fresh conda environment will solve the problem. First navigate to your pretty-plot directory with cd. After that, the steps to remove and recreate the pretty-plot environment are:

  conda activate

  conda env remove --name pretty-plot

  conda env create --name pretty-plot --file environment.yml

  conda activate pretty-plot

  pip install -e .

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The contents of this repo are provided by the Western Washington University Reflectance Lab (PI: M. Rice) and Million Concepts (C. Million, M. St. Clair, S. Curtis, S.V. Brown) under a BSD 3-Clause License. You may do nearly anything that you want with this code. If you have any questions, leave us a Github Issue.

MERTools/MERspect is proprietary software (Arizona State University) for rover tactical operations made available on an as-needed basis.