Skip to main content

imagesc is an Python package to create heatmaps in a easy way.

Project description

imagesc

Python PyPI Version License Coffee Github Forks GitHub Open Issues Project Status Downloads Downloads

  • 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.

Functions in imagesc

# 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

  • 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

# 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

import imagesc as imagesc

d3

df = pd.DataFrame(np.random.randint(0, 100, size=(50, 50)))
imagesc.d3(df, vmax=1)

seaborn

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
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

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

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

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:

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:

@misc{erdogant2019imagesc,
  title={imagesc},
  author={Erdogan Taskesen},
  year={2019},
  howpublished={\url{https://github.com/erdogant/imagesc}},
}

References

Maintainer

  • Erdogan Taskesen, github: erdogant
  • Contributions are welcome.
  • If you wish to buy me a Coffee for this work, it is very appreciated :)

Project details


Download files

Download the file for your platform. If you're not sure which to choose, learn more about installing packages.

Files for imagesc, version 0.2.1
Filename, size File type Python version Upload date Hashes
Filename, size imagesc-0.2.1-py3-none-any.whl (15.9 kB) File type Wheel Python version py3 Upload date Hashes View
Filename, size imagesc-0.2.1.tar.gz (14.1 kB) File type Source Python version None Upload date Hashes View

Supported by

Pingdom Pingdom Monitoring Google Google Object Storage and Download Analytics Sentry Sentry Error logging AWS AWS Cloud computing DataDog DataDog Monitoring Fastly Fastly CDN DigiCert DigiCert EV certificate StatusPage StatusPage Status page