https://github.com/cropbox/rda

RDA Phenology Modeling Project
https://github.com/cropbox/rda

Last synced: about 1 month ago
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RDA Phenology Modeling Project

• Host: GitHub
• URL: https://github.com/cropbox/rda
• Owner: cropbox
• Created: 2015-07-16T20:22:36.000Z (over 9 years ago)
• Default Branch: master
• Last Pushed: 2021-11-09T04:58:18.000Z (about 3 years ago)
• Last Synced: 2024-03-19T15:32:34.007Z (10 months ago)
• Language: Jupyter Notebook
• Homepage:
• Size: 40.5 MB
• Stars: 0
• Watchers: 4
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.md

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README

        
## Dependencies

The project requires Python 3.4. It used to be developed on Python 2.7, so most parts are still expected to work, but no longer guaranteed.

Here is a list of required modules:

- [numpy](https://github.com/numpy/numpy) : for any math

- [scipy](https://github.com/scipy/scipy): optimization

- [matplotlib](https://github.com/matplotlib/matplotlib): graphs

  - [seaborn](https://github.com/mwaskom/seaborn): more pretty graphs

- [pandas](https://github.com/pydata/pandas): structured data

  - [tables](https://github.com/PyTables/PyTables): read/writes HDF5 files

  - [xlrd](https://github.com/python-excel/xlrd): reads Excel files

- [ipython](https://github.com/ipython/ipython): better console

  - [tornado](https://github.com/tornadoweb/tornado): run [IPython Notebook](http://ipython.org/notebook.html)

  - [jsonschema](https://github.com/Julian/jsonschema)

Use `pip` to install them. Creating its own virtual environment with [virtualenvwrapper](https://bitbucket.org/dhellmann/virtualenvwrapper/) would be a good idea. If you use [Anaconda](https://store.continuum.io/cshop/anaconda/), they are probably already available on your system.

## Directory

- `code`: `main` module to run `pheno` package

  - `pheno`

    - `estimation`

    - `model`

    - `util`

- `input`: meteorological/observation datasets (.h5)

  - `raw`: original datasets from each source

    - `met`

    - `obs`

  - `df`: converted datasets in pandas `DataFrame`

    - `met`

    - `obs`

- `output`: generated results (.csv, .png)

  - `...`

  - `current`

    - `coeffs`: calibrated parameter sets

    - `suite`: from `ModelSuite` (a suite of models run on a single location/cultivar)

    - `group`: from `ModelGroup` (a group of suites run on a dataset, likely consists of multiple locations/cultivars)

    - `collection`: from `ModelCollection` (a collection of groups run on multiple datasets, mostly for aggregating cross-validation results)

Note that the datasets for `input` directory are **not** included in this repository due to file size limit. They can be found at the SPACE drive:

- Windows: `\\main.sefs.uw.edu\main\Space\Kim\Projects\RDA\work\input`

- Mac/Linux: `smb://main.sefs.uw.edu/main/Space/Kim/Projects/RDA/work/input`

Copy the contents of `input` into your local repository. Make sure you have the exactly same directory structure as above. In the future, they may be moved into [Git Large File Storage](https://git-lfs.github.com/) when it becomes available.

## Get Started

Run `IPython` on `code` directory to access `pheno` package. You may also want to read [the tutorial](code/Tutorial.ipynb) written in IPython notebook.