Metadata-Version: 2.1
Name: acmetric_plotting
Version: 1.3.1
Summary: A package to easily build ACMetric branded plots
Author-email: Ivan <ivan@acmetric.com>, Jakob <jakob@acmetric.com>
Project-URL: Homepage, https://github.com/ACMetric/acmetric_package
Project-URL: Bug Tracker, https://github.com/ACMetric/acmetric_package/issues
Classifier: Programming Language :: Python :: 3
Classifier: Operating System :: OS Independent
Requires-Python: >=3.7
Description-Content-Type: text/markdown
License-File: LICENCE
Requires-Dist: cycler>=0.11.0
Requires-Dist: matplotlib>=3.5
Requires-Dist: seaborn>=0.11.2
Requires-Dist: numpy>=1.20.0
Requires-Dist: scipy>=1.7.0

<a href='https://acmetric.com/' target='_blank'><img src='https://i.postimg.cc/ZnscgcFb/Acmetric-Social-3.jpg' height="100" border='0' alt='Acmetric-Social-3'/></a> 
# Introducing ACMetric package!
### Current version: `1.3.1`

This package is created to help you use ACMetric's brand colors and build plots without hours of tuning. Enjoy!

## Installing on Google Colab 
Setting up in Google Colab is described [here](https://github.com/ACMetric/acmetric_package/blob/master/colab_setup.md).

## Importing
We recommend importing it along with `matplotlib` and `seaborn`.

```python3
%matplotlib inline # display plots in the notebook right away
%config InlineBackend.figure_format='retina' # high resolution
import matplotlib.pyplot as plt
import seaborn as sns
import acmetric_plotting as ac
```

And it is ready to go!

### You can find code examples here: [Jupyter](https://github.com/ACMetric/acmetric_package/blob/master/notebooks/acmetric_package_intro.ipynb) | [Google Colab](https://colab.research.google.com/drive/14eYxEthMcPohkTFC9CLhe-nzHbQDoEsu?usp=sharing)
***
## Some things you need to know

`ac.display_colors()` will show you a table with all the colors available and their names.

`ac.colors` module contains ACMetric colors, you can access them by writing `ac.colors.coral`, `ac.colors.sky_60`, etc.  

`ac.palette` is a `matplotlib` color palette. You can call it and choose a color you like by index, e.g. `ac.palette[3]`.

`ac.cmap` is a gradient colormap that can be used in `seaborn` heatmap and other plots.

Run `ac.params.layout_color('black')` to make axes and text black. Run `ac.params.layout_color('default')` to make them grey again.

Now 4 kinds of plots are available in the package: bar chart, pie chart, scatter plot and box plot. You can make them using `ac.bar`, `ac.pie`, `ac.scatter` and `ac.box`. All the possible parameters can be found in the docstring.

**Note:** it doesn't mean you can't build other kinds of plots. Just import `matplotlib` or `seaborn`, and all the plots you create will also be ACMetric branded!
