https://github.com/saforem2/ambivalent

Minimal, beautiful (+ highly-customizable) styles for Matplotlib.
https://github.com/saforem2/ambivalent

matplotlib minimal python

Last synced: 4 days ago
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Minimal, beautiful (+ highly-customizable) styles for Matplotlib.

• Host: GitHub
• URL: https://github.com/saforem2/ambivalent
• Owner: saforem2
• Created: 2023-12-28T18:01:23.000Z (10 months ago)
• Default Branch: main
• Last Pushed: 2024-10-10T14:31:30.000Z (24 days ago)
• Last Synced: 2024-10-13T03:50:54.938Z (22 days ago)
• Topics: matplotlib, minimal, python
• Language: Python
• Homepage: https://saforem2.github.io/ambivalent/
• Size: 39.5 MB
• Stars: 15
• Watchers: 1
• Forks: 0
• Open Issues: 1
• Metadata Files:
  • Readme: docs/README.md

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README

        
# `ambivalent` 🤷🏻‍

Sam Foreman

2024-05-13

[~~`opinionated`~~](https://github.com/saforem2/opinionated)

$\longrightarrow$

[**`ambivalent`**](https://github.com/saforem2/ambivalent) 🤷🏻‍

Clean, simple style for Matplotlib figures.

Transparent backgrounds with grey text

$\textcolor{#838383}{\blacksquare}$ that are accessible / legible and

`{light, dark}`-mode independent.

## Install

``` bash

python3 -m pip install ambivalent

```

## Getting Started

``` python

import ambivalent

import matplotlib.pyplot as plt

plt.style.use(ambivalent.STYLES['ambivalent'])

```

## Examples

### `seaborn` Tips Dataset

- [Seaborn Gallery](https://seaborn.pydata.org/examples/index.html)

  - [Tips Dataset

    Example](https://seaborn.pydata.org/generated/seaborn.kdeplot.html)

Code

``` python

import seaborn as sns

tips = sns.load_dataset("tips")

tips.head()

fig, ax = plt.subplots(figsize=(6, 6))  # , ncols=2)

_ = sns.kdeplot(

   data=tips, x="total_bill", hue="size",

   fill=True, common_norm=False, palette="flare_r",

   alpha=.3, linewidth=0,

   ax=ax,  # [0],

)

_ = ax.set_ylabel('')

plt.show()

```

![](index_files/figure-commonmark/fig-py-tips-density-output-1.svg)

### `seaborn` Scatter Plot

Code

``` python

import seaborn as sns

import matplotlib.pyplot as plt

# Load the example diamonds dataset

diamonds = sns.load_dataset("diamonds")

# Draw a scatter plot while assigning point colors and sizes to different

# variables in the dataset

f, ax = plt.subplots(figsize=(6, 6))

_ = sns.despine(f, left=True, bottom=True)

_ = clarity_ranking = ["I1", "SI2", "SI1", "VS2", "VS1", "VVS2", "VVS1", "IF"]

_ = sns.scatterplot(x="carat", y="price",

                hue="clarity", size="depth",

                palette="flare",

                hue_order=clarity_ranking,

                sizes=(1, 8), linewidth=0,

                data=diamonds, ax=ax)

```

![](index_files/figure-commonmark/fig-py-diamonds-scatter-output-1.svg)

### Histogram + Scatter Plot

Code

``` python

import numpy as np

import seaborn as sns

import matplotlib.pyplot as plt

# Simulate data from a bivariate Gaussian

n = 10000

mean = [0, 0]

cov = [(2, .4), (.4, .2)]

rng = np.random.RandomState(0)

x, y = rng.multivariate_normal(mean, cov, n).T

# Draw a combo histogram and scatterplot with density contours

f, ax = plt.subplots(figsize=(6, 6))

_ = sns.scatterplot(x=x, y=y, s=5, color="#666666", alpha=0.3)

_ = sns.histplot(x=x, y=y, bins=50, pthresh=.1, cmap="flare_r")

_ = sns.kdeplot(x=x, y=y, levels=5, color="w", linewidths=1)

_ = ax.set_xlabel('x')

_ = ax.set_ylabel('y')

_ = plt.show()

```

![](index_files/figure-commonmark/fig-py-hist-scatter-output-1.svg)

### Jointplot

Code

``` python

import seaborn as sns

# Load the penguins dataset

penguins = sns.load_dataset("penguins")

# Show the joint distribution using kernel density estimation

import matplotlib as mpl

with mpl.rc_context(plt.rcParams.update({'axes.grid': False})):

  g = sns.jointplot(

      data=penguins,

      x="bill_length_mm",

      y="bill_depth_mm",

      hue="species",

      edgecolors='none',

      alpha=0.4,

  )

  _ = plt.grid(False)

  plt.show()

```

![](index_files/figure-commonmark/fig-py-kde-2d-output-1.svg)

### Matplotlib Histograms

Code

``` python

import matplotlib.pyplot as plt

import numpy as np

n_bins = 10

x = np.random.randn(1000, 3)

plt.rcParams['axes.grid'] = True

fig, ((ax0, ax1), (ax2, ax3)) = plt.subplots(nrows=2, ncols=2)

colors = ['#333333', '#666666', '#999999']

ax0.hist(x, n_bins, density=True, histtype='bar', color=colors, label=colors)

_ = ax0.legend()

_ = ax0.set_title('bars with legend')

_ = ax1.hist(x, n_bins, density=True, histtype='bar', stacked=True, alpha=0.4)

_ = ax1.set_title('stacked bar')

_ = ax2.hist(x, n_bins, histtype='step', stacked=True, fill=False)

_ = ax2.set_title('stack step (unfilled)')

# Make a multiple-histogram of data-sets with different length.

x_multi = [np.random.randn(n) for n in [10000, 5000, 2000]]

_ = ax3.hist(x_multi, n_bins, histtype='bar')

_ = ax3.set_title('different sample sizes')

_ = fig.tight_layout()

plt.show()

```

![](index_files/figure-commonmark/fig-py-mpl-hists-output-1.svg)

## Gallery[^1]

More Examples…































> [!TIP]

>

> ### 💝 Status

>

> ![](https://hits.seeyoufarm.com/api/count/incr/badge.svg?url=https%3A%2F%2Fsaforem2.github.io%2Fambivalent&count_bg=%23222222&title_bg=%23303030&icon=&icon_color=%23E7E7E7)

>

> 
Last Updated: 05/13/2024 @ 21:56:28

> 


[^1]: Examples from [Matplotlib

    Examples](https://matplotlib.org/stable/gallery/index.html)