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The dplPy Codebase

Welcome to dplPy.

Requirements, Installation and Accessibility

Requirements

  • Git
  • Python v3.10=< (although functional on Python v3.8=<, we suggest using Python v3.10=<)
  • Pip
  • Miniconda3 (suggestion: select the python 3.10 version that best fits your OS)

Suggested:

Installation

Future plans

dplPy is planned to be released as a pip and conda packages for easy installation (e.g., pip install dplpy or conda install -c conda-forge dplpy). However, the current installation process for dplPy requires manual steps to be performed after cloning the GitHub repository.

  1. Clone this repository to your personal machine: git clone https://github.com/OpenDendro/dplPy.git; move into the repository cd code/
  2. Build conda environment: conda env create -f environment.yml or mamba env create -f environment.yml if Mamba is installed; Activate environment: conda activate dplpy

Known Issues

The CSAPS package, required for smoothing splines, may fail to install in rare occasions. If that is the case, please install CSAPS manually by doing pip install -U csaps.

Accessing dplPy via Jupyter Notebook

Although dplPy is executable from the command line interface (CLI), e.g., BASH, ZSH, or a Cygwin terminal, the usage of Jupyter Notebooks is suggested for visualizing graphs.

Making the dplPy kernel findable

It is possible that your computer will not automatically find the dplPy [kernel](https://en.wikipedia.org/wiki/Kernel_(operating_system). If that is the case, execute the following command:

python -m ipykernel install --user --name dplpy --display-name "Python (dplpy)"

This will ensure that the dplPy environment created through conda is findable by Jupyter under the name Python (dplpy).

The dplPy Git repository contains:

- source code (`src/`)
- A jupyter notebook example (`runnable_example.ipynb`)
- Test files in `csv` and `rwl` formats (`tests/data/<format>/`)

Accessing Jupyter Notebook on Linux, MacOS

  1. In your VScode terminal, activate the conda environment with conda activate dplpy.
  2. From the terminal, execute jupyter notebook.
  3. If prompted to select a kernel, select dplpy. This will automatically load the correct environment.

Accessing Jupyter Notebook on Windows

In VScode:

  1. In your VSCode terminal window, activate the conda environment with conda activate dplpy.
  2. In the same terminal window, start a Jupyter Notebook with jupyter notebook. Jupyter will then return URLs that you can copy; Copy one of these URLs.
  3. When propted to select a kernel (top right), select Select Another Kernel > Existing Jupyter Server and paste the URL you have copied.
  4. Jupyter Notebook will now be able to access the environment created.

Import the dplpy library

dplPy currently exists as a python library; ensure you are in the correct folder prior to execution. In a Jupyter Notebook, execute the following lines:

import os
directory = os.getcwd().split("/")
if directory[-1] != 'src':
    os.chdir("./src")
import dplpy as dpl

Currently, the dplPy codebase is executable from within the src folder. There, you will find execution_sample.ipynb, which is an example file where you can execute dplPy.

User Manual

See the dplPy User Manual