https://github.com/scienxlab/unmap

Unmap data from a pseudocolor image, with or without knowing the colormap.
https://github.com/scienxlab/unmap

plotting python recovering-data reproducible-research reproducible-science

Last synced: 5 months ago
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Unmap data from a pseudocolor image, with or without knowing the colormap.

• Host: GitHub
• URL: https://github.com/scienxlab/unmap
• Owner: scienxlab
• License: apache-2.0
• Created: 2022-09-16T16:35:06.000Z (over 3 years ago)
• Default Branch: main
• Last Pushed: 2023-04-04T21:20:19.000Z (almost 3 years ago)
• Last Synced: 2025-09-13T06:16:16.409Z (6 months ago)
• Topics: plotting, python, recovering-data, reproducible-research, reproducible-science
• Language: Python
• Homepage: https://scienxlab.org/unmap
• Size: 15.2 MB
• Stars: 18
• Watchers: 1
• Forks: 2
• Open Issues: 8
• Metadata Files:
  • Readme: README.md
  • Contributing: CONTRIBUTING.md
  • License: LICENSE
  • Citation: CITATION.cff
  • Authors: AUTHORS.md

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README

          
# unmap

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[![PyPI versions](https://img.shields.io/pypi/pyversions/unmap.svg)](https://pypi.org/project/unmap//)

[![PyPI license](https://img.shields.io/pypi/l/unmap.svg)](https://pypi.org/project/unmap/)

Unmap data from pseudocolor images, with or without knowledge of the colourmap. This tool has 2 main components:

1. Guess the colourmap that was used for a pseudocolour visualization, in cases where it's unknown and a colourbar is not included in the image.

2. 'Unmap' a pseudocolour visualization, separating the data from the image; essentially this is the opposite of what `plt.imshow()` does. 

## Similar projects

There are some other approaches to both Task 1 (above) and Task 2:

- [`unmap`](https://github.com/jperryhouts/unmap) (I swear I didn't know about this tool when I named mine!) — does the data ripping part. The colourmap must be provided, but the tool also provides a way to interactively identify a colourbar in the image.

- [Poco et al.](https://ieeexplore.ieee.org/document/8017646) ([GitHub](https://github.com/uwdata/rev)) attempts to both find the colourbar in a visualization, then use it to perform Task 2. The visualization must contain a colourbar.

- [Yuan et al.](https://github.com/yuanlinping/deep_colormap_extraction) attempts Task 1 using deep learning. The prediction from a CNN is refined with either Laplacian eigenmapping (manifold+based dimensionality reduction, for continuous colourmaps) or DBSCAN (for categorical colourmaps).

Of these projects, only Yuan et al. ('deep colormap extraction') requires no _a priori_ knowledge of the colourmap. 

## Stack Exchange questions about this topic

- [Can I get numeric data from a color map?](https://datascience.stackexchange.com/questions/27247/can-i-get-numeric-data-from-a-color-map)

- [How to extract a colormap from a colorbar image and use it in a heatmap?](https://stackoverflow.com/questions/71090534/how-to-extract-a-colormap-from-a-colorbar-image-and-use-it-in-a-heatmap)

- [Given a JPG of 2D colorplot colorbar how can I sample the image to extract data?](https://stackoverflow.com/questions/63233529/given-a-jpg-of-2d-colorplot-colorbar-how-can-i-sample-the-image-to-extract-n)

- [How to reverse a colormap image to scalar values?](https://stackoverflow.com/questions/3720840/how-to-reverse-a-color-map-image-to-scalar-values)

- [Extract color table values?](https://stackoverflow.com/questions/62267694/extract-color-table-values)

- [Invert not reverse a colormap in maptplotlib](https://stackoverflow.com/questions/14445102/invert-not-reverse-a-colormap-in-matplotlib)

## Installation

You can install this package with `pip`:

    pip install unmap

There are `dev`, `test` and `docs` options for installing dependencies for those purposes, eg `pip install unmap[dev]`.

## Documentation

Read [the documentation](https://scienxlab.org/unmap), especially [the examples](https://scienxlab.org/unmap/userguide/Unmap_data_from_an_image.html).

## Contributing

Take a look at [`CONTRIBUTING.md`](https://github.com/scienxlab/unmap/blob/main/CONTRIBUTING.md).

## Testing

After cloning this repository and installing the dependencies required for testing, you can run the tests (requires `pytest` and `pytest-cov`) with

    pytest

## Building

This repo uses PEP 517-style packaging, with the entire build system and requirements defined in the `pyproject.toml` file. [Read more about this](https://setuptools.pypa.io/en/latest/build_meta.html) and [about Python packaging in general](https://packaging.python.org/en/latest/tutorials/packaging-projects/).

Building the project requires `build`, so first:

    pip install build

Then to build `unmap` locally:

    python -m build

The builds both `.tar.gz` and `.whl` files, either of which you can install with `pip`.