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README.md

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+# Data Morph
+
+[![PyPI Latest Release](https://img.shields.io/pypi/v/data-morph-ai.svg)](https://pypi.org/project/data-morph-ai/)
+[![License](https://img.shields.io/pypi/l/data-morph-ai.svg)](https://github.com/stefmolin/data-morph/blob/main/LICENSE)
+[![codecov](https://codecov.io/gh/stefmolin/data-morph/branch/main/graph/badge.svg?token=3SEEG9SZQO)](https://codecov.io/gh/stefmolin/data-morph)
+[![CI](https://github.com/stefmolin/data-morph/actions/workflows/ci.yml/badge.svg)](https://github.com/stefmolin/data-morph/actions/workflows/ci.yml)
+[![Deploy Docs](https://github.com/stefmolin/data-morph/actions/workflows/docs.yml/badge.svg)](https://github.com/stefmolin/data-morph/actions/workflows/docs.yml)
+
+
+Morph an input dataset of 2D points into select shapes, while preserving the summary
+statistics to a given number of decimal points through simulated annealing.
+
+![Morphing the panda dataset into the star shape.](https://raw.githubusercontent.com/stefmolin/data-morph/main/docs/_static/panda-to-star-eased.gif)
+
+## Installation
+
+Data Morph can be installed with `pip`:
+
+```console
+$ pip install data-morph-ai
+```
+
+## Usage
+
+Once installed, Data Morph can be used on the command line or as an importable Python package.
+Below are some examples; be sure to check out the
+[documentation](https://stefmolin.github.io/data-morph) for more information.
+
+
+### Command Line Usage
+
+Run `data-morph` on the command line:
+
+```console
+$ data-morph --start-shape panda --target-shape star
+```
+
+This produces the animation in the newly-created `morphed_data` directory
+within your current working directory (shown above).
+
+----
+
+See all available CLI options by passing in `--help`:
+
+```console
+$ data-morph --help
+```
+
+### Python Usage
+
+The `DataMorpher` class performs the morphing from a `Dataset` to a `Shape`.
+Any `pandas.DataFrame` with numeric columns `x` and `y` can be a `Dataset`.
+Use the `DataLoader` to create the `Dataset` from a file or use a built-in dataset:
+
+```python
+from data_morph.data.loader import DataLoader
+
+dataset = DataLoader.load_dataset('panda')
+```
+
+For morphing purposes, all target shapes are placed/sized based on aspects of the `Dataset` class.
+All shapes are accessible via the `ShapeFactory` class:
+
+```python
+from data_morph.shapes.factory import ShapeFactory
+
+shape_factory = ShapeFactory(dataset)
+target_shape = shape_factory.generate_shape('star')
+```
+
+With the `Dataset` and `Shape` created, here is a minimal example of morphing:
+
+```python
+from data_morph.morpher import DataMorpher
+
+morpher = DataMorpher(
+    decimals=2,
+    in_notebook=False,  # whether you are running in a Jupyter Notebook
+    output_dir='data_morph/output',
+)
+
+result = morpher.morph(start_shape=dataset, target_shape=target_shape)
+```
+
+Note that the `result` variable in the above code block is a `pandas.DataFrame` of the data after completing the specified iterations of the simulated annealing process. The `DataMorpher.morph()` method is also saving plots to visualize the output periodically and make an animation; these end up in `data_morph/output`, which we set as `DataMorpher.output_dir`.
+
+
+----
+
+In this example, we morphed the built-in panda `Dataset` into the star `Shape`. Be sure to try out the other built-in options:
+
+* The `DataLoader.AVAILABLE_DATASETS` attribute contains a list of available datasets, which are also visualized in the `DataLoader` documentation.
+
+* The `ShapeFactory.AVAILABLE_SHAPES` attribute contains a list of available shapes, which are also visualized in the `ShapeFactory` documentation.
+
+## Acknowledgements
+
+This code has been altered by Stefanie Molin ([@stefmolin](https://github.com/stefmolin)) to work for other input datasets by parameterizing the target shapes with information from the input shape. The original code works for a specific dataset called the "Datasaurus" and was created for the paper *Same Stats, Different Graphs: Generating Datasets with Varied Appearance and Identical Statistics through Simulated Annealing* by Justin Matejka and George Fitzmaurice (ACM CHI 2017).
+
+The paper, video, and associated code and datasets can be found on the
+Autodesk Research website [here](https://www.autodeskresearch.com/publications/samestats>). The version of this code placed on GitHub at [jmatejka/same-stats-different-graphs](https://github.com/jmatejka/same-stats-different-graphs), served as the starting point for the Data Morph code base, which is on GitHub at [stefmolin/data-morph](https://github.com/stefmolin/data-morph).

pyproject.toml

@@ -13,7 +13,7 @@ maintainers = [
     {name = "Stefanie Molin"},
 ]
 description = "Teaching tool on the importance of data visualization."
-readme = "README.rst"
+readme = "README.md"
 keywords = ["data visualization", "summary statistics", "data animation"]
 license = {file = "LICENSE"}
 classifiers = [