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https://github.com/erdogant/imagesc

Make quick and beautiful heatmaps
https://github.com/erdogant/imagesc

clustering clustermap d3js heatmap imagesc interactive plot seaborn seaborn-plots

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Make quick and beautiful heatmaps

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README 

        
# imagesc



[![Python](https://img.shields.io/pypi/pyversions/imagesc)](https://img.shields.io/pypi/pyversions/imagesc)

[![PyPI Version](https://img.shields.io/pypi/v/imagesc)](https://pypi.org/project/imagesc/)

[![License](https://img.shields.io/badge/license-MIT-green.svg)](https://github.com/erdogant/imagesc/blob/master/LICENSE)

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[![Downloads](https://pepy.tech/badge/imagesc/month)](https://pepy.tech/project/imagesc/month)

[![Downloads](https://pepy.tech/badge/imagesc)](https://pepy.tech/project/imagesc)



* imagesc is an Python package to create heatmaps. Various methods to create a heatmap are implemented, each with specific properties that can help to easily create your heatmap. The **fast** and **clean** method is optimized for speed, the **cluster** method provides clustering, the **seaborn** method contains many configuration settings, and finally, the **plot** as good as possible the imagesc from matlab.



# 

**Star this repo if you like it! ⭐️**

#



### Functions in imagesc

```python

# X is your numpy array

fig  = imagesc.seaborn(X)

fig  = imagesc.cluster(X)

fig  = imagesc.fast(X)

fig  = imagesc.clean(X)

fig  = imagesc.plot(X)

status = imagesc.savefig(fig)

path = imagesc.d3(df)



```



## Contents

- [Installation](#-installation)

- [Requirements](#-Requirements)

- [Quick Start](#-quick-start)

- [Contribute](#-contribute)

- [Citation](#-citation)

- [Maintainers](#-maintainers)

- [License](#-copyright)



## Installation

* Install imagesc from PyPI (recommended). imagesc is compatible with Python 3.6+ and runs on Linux, MacOS X and Windows. 

* It is distributed under the MIT license.



## Requirements

```python

# Note that: seaborn is only required when using **seaborn** or **cluster** functions.

pip install -r requirements.txt

```



## Installation from Pypi

```

pip install imagesc

```



## Import imagesc package

```python

import imagesc as imagesc

```



### d3

* Implemention is based on **d3**

* Interactive

* Stand-alone

* https://d3-graph-gallery.com



```python

df = pd.DataFrame(np.random.randint(0, 100, size=(50, 50)))

imagesc.d3(df, vmax=1)

```



  




### seaborn

* Underlying implemented is based on **seaborn**

* Large number of configurations

* Slow when using large datasets

* Grid is aligned to the cells

* See here for all parameters: https://seaborn.pydata.org/generated/seaborn.heatmap.html



```python

df = pd.DataFrame(np.random.randint(0,100,size=(10,20)))

A = imagesc.seaborn(df.values, df.index.values, df.columns.values)

B = imagesc.seaborn(df.values, df.index.values, df.columns.values, annot=True, annot_kws={"size": 12})

C = imagesc.seaborn(df.values, df.index.values, df.columns.values, annot=True, annot_kws={"size": 12}, cmap='rainbow')

D = imagesc.seaborn(df.values, df.index.values, df.columns.values, annot=True, annot_kws={"size": 12}, cmap='rainbow', linecolor='#ffffff')

```



  A

  B

  C

  D




### cluster

* Underlying implemented is based on **clustermap** 

* When you desire to cluster your heatmap

* Default distance setting: metric="euclidean", linkage="ward" (can be changed)

* Slow for large data sets

* Grid is aligned to the cells

* Possibilities to tweak

* Possible arguments: https://seaborn.pydata.org/generated/seaborn.clustermap.html



```python

df = pd.DataFrame(np.random.randint(0,100,size=(10,20)))

fig_C1 = imagesc.cluster(df.values, df.index.values, df.columns.values)

fig_C2 = imagesc.cluster(df.values, df.index.values, df.columns.values, cmap='rainbow')

fig_C3 = imagesc.cluster(df.values, df.index.values, df.columns.values, cmap='rainbow', linecolor='#ffffff')

fig_C4 = imagesc.cluster(df.values, df.index.values, df.columns.values, cmap='rainbow', linecolor='#ffffff', linewidth=0)

imagesc.savefig(fig_C1, './docs/figs/cluster4.png')

```



  C1

  C2

  C3

  C4




### fast

* Underlying implemented is based on **pcolorfast** 

* Fast

* Not so much tweakable

* Grid is **not** aligned to the cells

* Possible arguments: https://matplotlib.org/3.1.1/api/_as_gen/matplotlib.axes.Axes.pcolorfast.html



```python

df = pd.DataFrame(np.random.randint(0,100,size=(10,20)))

fig_F1 = imagesc.fast(df.values, df.index.values, df.columns.values)

fig_F2 = imagesc.fast(df.values, df.index.values, df.columns.values, grid=False)

fig_F3 = imagesc.fast(df.values, df.index.values, df.columns.values, grid=False, cbar=False)

fig_F4 = imagesc.fast(df.values, df.index.values, df.columns.values, grid=True, cbar=False)

fig_F5 = imagesc.fast(df.values, df.index.values, df.columns.values, cmap='rainbow')

fig_F6 = imagesc.fast(df.values, df.index.values, df.columns.values, cmap='rainbow', linewidth=0.5, grid=True)

imagesc.savefig(fig_C1, './docs/figs/fast1.png')

```



  F1

  F2

  F3

  F4

  F5

  F6




### clean

* Underlying implemented is based on **pcolorfast** 

* Fast

* No Grid

* Limited configurations

* Ideal for photos

* Possible arguments: https://matplotlib.org/3.1.1/api/_as_gen/matplotlib.axes.Axes.pcolorfast.html



```python

df = pd.DataFrame(np.random.randint(0,100,size=(10,20)))

fig_FC1 = imagesc.clean(df.values)

fig_FC2 = imagesc.clean(df.values, cmap='rainbow')

imagesc.savefig(fig_C1, './docs/figs/clean1.png')

```



  F1

  F2




### plot

* Underlying implemented is based on **imshow** 

* implementation will behave more-or-less as the one of matlab

* Medium speed

* Various configurations are possible but less then **seaborn**

* Grid is aligned to the cells

* Possible arguments: https://matplotlib.org/3.1.1/api/_as_gen/matplotlib.pyplot.imshow.html



```python

df = pd.DataFrame(np.random.randint(0,100,size=(10,20)))

fig_M1 = imagesc.plot(df.values)

fig_M2 = imagesc.plot(df.values, cbar=False)

fig_M3 = imagesc.plot(df.values, cbar=False, axis=False)

fig_M4 = imagesc.plot(df.values, cbar=False, axis=True, linewidth=0.2)

fig_M5 = imagesc.plot(df.values, df.index.values, df.columns.values)

fig_M6 = imagesc.plot(df.values, df.index.values, df.columns.values, cbar=False, linewidth=0.2)

fig_M7 = imagesc.plot(df.values, df.index.values, df.columns.values, grid=True, cbar=False, linewidth=0.2)

fig_M8 = imagesc.plot(df.values, df.index.values, df.columns.values, grid=False, cbar=False, linewidth=0.2)

fig_M9 = imagesc.plot(df.values, df.index.values, df.columns.values, grid=True, cbar=False, linewidth=0.8, linecolor='#ffffff')

fig_M10 = imagesc.plot(df.values, df.index.values, df.columns.values, grid=True, cbar=False, linewidth=0.8, linecolor='#ffffff', cmap='rainbow')

imagesc.savefig(fig, './docs/figs/plot10.png')imagesc.savefig(fig_C1, './docs/figs/fast1.png')

```



  M1

  M2

  M3

  M4

  M5

  M6

  M7

  M8

  M9

  M10




### Speed:

```python

import matplotlib.image as mpimg

img=mpimg.imread('./docs/figs/lenna.png')



fig = imagesc.clean(img)

# runtime 1.49



fig = imagesc.fast(img, cbar=False, axis=False)

# runtime: 2.931 seconds



fig = imagesc.plot(img, linewidth=0, cbar=False)

# runtime: 11.042

```



  **fast**

  **clean**

  **plot**





  




### Citation

Please cite imagesc in your publications if this is useful for your research. Here is an example BibTeX entry:

```BibTeX

@misc{erdogant2019imagesc,

  title={imagesc},

  author={Erdogan Taskesen},

  year={2019},

  howpublished={\url{https://github.com/erdogant/imagesc}},

}

```



### References

* seaborn

https://seaborn.pydata.org/generated/seaborn.heatmap.html

* clustermap

https://seaborn.pydata.org/generated/seaborn.clustermap.html

* fast and clean

https://matplotlib.org/3.1.1/api/_as_gen/matplotlib.axes.Axes.pcolor.html

* plot

 https://matplotlib.org/3.1.1/api/_as_gen/matplotlib.pyplot.imshow.html

* Other

https://matplotlib.org/3.1.1/gallery/images_contours_and_fields/image_annotated_heatmap.html

* Colormap

https://matplotlib.org/examples/color/colormaps_reference.html



### Maintainer

* Erdogan Taskesen, github: [erdogant](https://github.com/erdogant)

* Contributions are welcome.

* If you wish to buy me a Coffee for this work, it is very appreciated :)