Metadata-Version: 2.4
Name: wedap
Version: 1.1.0
Summary: Weighted Ensemble Data Analysis and Plotting
Author-email: "Darian T. Yang" <dty7@pitt.edu>
License: BSD 3-Clause License
        
        Copyright (c) 2021, Darian Yang
        All rights reserved.
        
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Project-URL: Source, https://github.com/darianyang/wedap
Project-URL: Documentation, https://darianyang.github.io/wedap
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Classifier: Programming Language :: Python :: 3.9
Classifier: Programming Language :: Python :: 3.10
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<p align="left">
    <img src="https://github.com/darianyang/wedap/blob/main/docs/_static/wedap_logo.png?raw=true" alt="wedap logo" width="400">
</p>

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**WEDAP** : **w**eighted **e**nsemble **d**ata **a**nalysis and **p**lotting (pronounced we-dap)

`wedap` is primarily used to plot H5 files produced from running [WESTPA](https://github.com/westpa/westpa).

`mdap` can be used to plot data files from analysis of standard MD simulations.

`wekap` can be used to plot flux values as rates from a WESTPA created direct.h5 file. 

For a demo and summary of features, see this [jupyter notebook](docs/notebook/wedap_demo.ipynb).

Or view the same demo notebook on the [documentation web page](https://darianyang.github.io/wedap/docs/html/notebook/wedap_demo.html).

## What's New in v1.1.0

This release modernizes the tooling and adds several requested features. Most changes are **backwards-compatible**; the breaking items below are limited to environment/dependency requirements and the removal of the deprecated GUI. See CHANGELOG.md for more info.

### Breaking changes

- **GUI removed.** The `Gooey`-based graphical interface (unmaintained upstream since 2021) and all of its mentions have been removed. Use the CLI or Python API instead. If you relied on the GUI, pin to `wedap<1.1.0`.
- **Python & dependency floors raised.** Minimum Python is now **3.9** (numpy 2 floor). The old `numpy<2` / `matplotlib<=3.7.0` upper caps have been **dropped** — `wedap` now supports **numpy 2.x** and recent matplotlib (tested through numpy 2.5 / matplotlib 3.11 on Python 3.12). If you must stay on an older interpreter, pin to `wedap<1.1.0`.


### Requirements

- numpy
- matplotlib
- h5py
- tqdm
- gif

## Installation

``` bash
pip install wedap
```

Or install the latest development version from source:
``` bash
git clone https://github.com/darianyang/wedap.git
cd wedap
pip install -e .
```

## Examples

After installation, to run the CLI version and view available options:
``` bash
wedap --help
```
Or:
``` bash
wedap -h
```
To visualize the evolution of the pcoord for the example p53.h5 file via CLI:
``` bash
wedap -h5 wedap/data/p53.h5
```
To do the same with the API:
``` Python
import wedap
import matplotlib.pyplot as plt

wedap.H5_Plot(h5="wedap/data/p53.h5", data_type="evolution").plot()
plt.show()
```
The resulting `p53.h5` file evolution plot will look like this:
<p align="left">
    <img src="https://github.com/darianyang/wedap/blob/main/docs/_static/p53_evo.png?raw=true" alt="p53 evo plot" width="400">
</p>

See the examples directory for more realistic applications using the Python API.

Evolution plots are created by default using the CLI but average and instant probability distribution options are also available. To use one of your auxiliary datasets instead of the progress coordinate, just include the name of the aux dataset from your h5 file in the `--Xname` or `--Yname` fields:
``` bash
wedap -h5 wedap/data/p53.h5 --data_type average --Xname dihedral_10 --Yname dihedral_11
```
Or:
``` bash
wedap -h5 wedap/data/p53.h5 -dt average -X dihedral_10 -Y dihedral_11
```

The resulting `p53.h5` file average plot of the dihedral aux datasets will look like this:
<p align="left">
    <img src="https://github.com/darianyang/wedap/blob/main/docs/_static/p53_avg_aux.png?raw=true" alt="p53 avg aux plot" width="400">
</p>

If you used a multi-dimensional progress coordinate and you want to use your pcoord for both the X and Y dimensions in a 2D average or instant plot, just use `pcoord` with the corresponding index set to the appropriate dimension (this also works with aux datasets which may have an additional dimension):
``` bash
wedap -h5 wedap/data/p53.h5 --data_type average --Xname pcoord --Xindex 0 --Yname pcoord --Yindex 1
```
Or:
``` bash
wedap -h5 wedap/data/p53.h5 -dt average -X pcoord -Xi 0 -Y pcoord -Yi 1
```
Or (since the default X options are the first pcoord, only the second pcoord needs to be specified):
``` bash
wedap -h5 wedap/data/p53.h5 -dt average -Y pcoord -Yi 1
```

The resulting `p53.h5` file average plot of the pcoord datasets will look like this:
<p align="left">
    <img src="https://github.com/darianyang/wedap/blob/main/docs/_static/p53_avg_pcoord.png?raw=true" alt="p53 avg pcoord plot" width="400">
</p>

## Motivation
`WESTPA` already comes with some excellent analysis tools for generating probability distributions, so why is `wedap` needed?

`wedap` was originally built as a way to simplify the original `WESTPA` plotting pipeline:

Native `WESTPA` CLI-based Analysis Tools:

    ┌───────┐       w_pdist        ┌────────┐        plothist         ┌────────┐
    │west.h5├─────────────────────►│pdist.h5├────────────────────────►│plot.pdf│
    └───────┘ --construct-dataset  └────────┘ --postprocess-function  └────────┘
                   module.py                      plot_settings.py


Analysis using `wedap`:

    ┌───────┐     wedap      ┌────────┐
    │west.h5├───────────────►│plot.pdf│
    └───────┘  CLI/Python    └────────┘

So `wedap` can generate plots with more flexibilty and less intermediate files, providing an especially useful way to plot aux datasets and explore your h5 file. 
* The Python interface allows for advanced users to quickly generate a plot as a matplotlib axes object which can be further customized all in one Python script.
    * For example, the `moviepy` or `gif` package can be used with wedap to easily create a gif of your h5 file (see an example of this in `wedap/h5_movie.py`).
    * The actual data can also be easily extracted and then analyzed (see `wedap/h5_cluster.py` for an example of k-means clustering using the data from a WESTPA west.h5 file). 
* A CLI is also available if using wedap on a system without access to a display.

Since the original implementation of `wedap`, many more features have been added that are not available using the `WESTPA` `w_pdist` and `plothist` tools, these include the following:
* Easy WE tracing and plotting by inputing an iteration and segment, or by inputing the X and Y value to then query and trace.
* 3D plots that replace the probability with another pcoord or aux dataset (`plot_mode="scatter3d"`).
* Selective basis states (if you have multiple basis states, only plot the probability contributions from specific states).
    * See the `skip_basis` argument (available through the Python API only currently).
* More to come!

Note that the `WESTPA` analysis tools have features not available in `wedap` and may still be of interest to you.

## Contributing

Have an idea for a feature to add to wedap? Let me know and I may be able to incorporate it (dty7@pitt.edu).

Or feel free to try developing it yourself! Features should be developed on branches. To create and switch to a branch, use the command:

`git checkout -b new_branch_name`

To switch to an existing branch, use:

`git checkout branch_name`

To submit your feature to be incorporated into the main branch, you should submit a `Pull Request`. The repository maintainers will review your pull request before accepting your changes.

## Copyright

Copyright (c) 2021-2026, Darian Yang
