The official colors of the FAU as matplotlib/seaborn colormaps

Overview

FAU - Colors

PyPI GitHub Code style: black PyPI - Downloads GitHub commit activity

The official colors of Friedrich-Alexander-Universität Erlangen-Nürnberg (FAU) as matplotlib / seaborn colormaps.

We support the old colors based on the 2019 CI-guidelines and the brand new 2021 Brand redesign.

Installation

pip install fau-colors

Quick Guide

2021 colormaps

2021 colors

import seaborn as sns

from fau_colors import register_cmaps
register_cmaps()

sns.set_palette("tech")

2019 colormaps

2019 colors

import seaborn as sns

from fau_colors.v2019 import register_cmaps
register_cmaps()

sns.set_palette("tech")

General Usage

The 2019 and the 2021 colors are available in the separate submodules fau_colors.v2019 and fau_colors.v2021 that contain equivalent functions.

Note: For convenience, the v2021 colors can also be accessed from the top-level. In the following examples we will use this shorter notation.

The methods below show the usage with the new color scheme. For the old colors simply replace the module name.

Registering color palettes

The easiest way to use the provided color palettes is to register them as global matplotlib colormaps. This can be done by calling the register_cmaps() function from the respective submodule. All available cmaps can be seen in the images above.

2021 colors

>>> from fau_colors import register_cmaps  # v2021 colors
>>> register_cmaps()

2019 colors

>>> from fau_colors.v2019 import register_cmaps
>>> register_cmaps()

WARNING: The 2019 and 2021 cmaps have overlapping names! This means you can not register both at the same time. You need to call unregister_cmaps from the correct module first, before you can register the other colormaps. If you need colormaps from both CI-guides, use them individually, as shown below.

Getting the raw colors

All primary faculty colors are stored in a namedtuple called colors.

2021 colors

>>> from fau_colors import colors  # v2021 colors
>>> colors
FacultyColors(fau='#002F6C', tech='#779FB5', phil='#FFB81C', med='#00A3E0', nat='#43B02A', wiso='#C8102E')
>>> colors.fau
'#002F6C'

2019 colors

>>> from fau_colors.v2019 import colors
>>> colors
FacultyColors(fau='#003865', tech='#98a4ae', phil='#c99313', med='#00b1eb', nat='#009b77', wiso='#8d1429')
>>> colors.fau
'##003865'

For the 2021 color scheme also the variable colors_dark and colors_all are available. They contain the dark variant of each color, as well as light and dark colors combined, respectively.

Manually getting the colormaps

The colormaps are stored in a namedtuple called cmaps. There are colormaps for the primary colors and colormaps with varying lightness using each color as the base color. The latter colormaps contain 5 colors each with 12.5, 25, 37.5, 62.5, and 100% value of the base color. If you need more than 5 colors see below.

2021 colors

>>> from fau_colors import cmaps  # v2021 colors
>>> # Only get the names here
>>> cmaps._fields
('faculties', 'faculties_dark', 'faculties_all', 'fau', 'fau_dark', 'tech', 'tech_dark', 'phil', 'phil_dark', 'med', 'med_dark', 'nat', 'nat_dark', 'wiso', 'wiso_dark')
>>> cmaps.fau_dark
[(0.01568627450980392, 0.11764705882352941, 0.25882352941176473), (0.3823913879277201, 0.4463667820069205, 0.5349480968858131), (0.629434832756632, 0.6678200692041523, 0.7209688581314879), (0.7529565551710881, 0.7785467128027682, 0.8139792387543252), (0.876478277585544, 0.889273356401384, 0.9069896193771626)]
>>> import seaborn as sns
>>> sns.set_palette(cmaps.fau_dark)

2019 colors

>>> from fau_colors.v2019 import cmaps
>>> # Only get the names here
>>> cmaps._fields
('faculties', 'fau', 'tech', 'phil', 'med', 'nat', 'wiso')
>>> cmaps.fau
[(0.0, 0.2196078431372549, 0.396078431372549), (0.37254901960784315, 0.5103421760861206, 0.6210688196847366), (0.6235294117647059, 0.7062053056516724, 0.772641291810842), (0.7490196078431373, 0.8041368704344483, 0.8484275278738946), (0.8745098039215686, 0.9020684352172241, 0.9242137639369473)]
>>> import seaborn as sns
>>> sns.set_palette(cmaps.fau)

Modifying the colormaps

Sometimes five colors are not enough for a colormap. The easiest way to generate more colors is to use one of the FAU colors as base and then create custom sequential palettes from it. This can be done using sns.light_palette or sns.dark_palette, as explained here.

2021 colors

>>> from fau_colors import colors  # v2021 colors
>>> import seaborn as sns
>>> sns.light_palette(colors.med, n_colors=8)
[(0.9370639121761148, 0.9445189791516921, 0.9520035391049294), (0.8047725363394869, 0.9014173378043252, 0.9416168802970363), (0.6688064000629526, 0.8571184286417537, 0.9309417031889239), (0.5365150242263246, 0.8140167872943868, 0.9205550443810308), (0.40054888794979027, 0.7697178781318151, 0.9098798672729183), (0.2682575121131623, 0.7266162367844482, 0.8994932084650251), (0.13229137583662798, 0.6823173276218767, 0.8888180313569127), (0.0, 0.6392156862745098, 0.8784313725490196)]

2019 colors

>>> from fau_colors.v2019 import colors
>>> import seaborn as sns
>>> sns.light_palette(colors.med, n_colors=8)
[(0.9363137612705862, 0.94473936725293, 0.9520047198366567), (0.8041282890912094, 0.9093574773431737, 0.9477078597351495), (0.6682709982401831, 0.8729927571581465, 0.9432916424086003), (0.5360855260608062, 0.8376108672483904, 0.9389947823070931), (0.40022823520978, 0.8012461470633632, 0.9345785649805439), (0.2680427630304031, 0.765864257153607, 0.9302817048790367), (0.13218547217937693, 0.7294995369685797, 0.9258654875524875), (0.0, 0.6941176470588235, 0.9215686274509803)]c
You might also like...
:small_red_triangle: Ternary plotting library for python with matplotlib
:small_red_triangle: Ternary plotting library for python with matplotlib

python-ternary This is a plotting library for use with matplotlib to make ternary plots plots in the two dimensional simplex projected onto a two dime

Joyplots in Python with matplotlib & pandas :chart_with_upwards_trend:
Joyplots in Python with matplotlib & pandas :chart_with_upwards_trend:

JoyPy JoyPy is a one-function Python package based on matplotlib + pandas with a single purpose: drawing joyplots (a.k.a. ridgeline plots). The code f

A python package for animating plots build on matplotlib.
A python package for animating plots build on matplotlib.

animatplot A python package for making interactive as well as animated plots with matplotlib. Requires Python = 3.5 Matplotlib = 2.2 (because slider

matplotlib: plotting with Python
matplotlib: plotting with Python

Matplotlib is a comprehensive library for creating static, animated, and interactive visualizations in Python. Check out our home page for more inform

Statistical data visualization using matplotlib

seaborn: statistical data visualization Seaborn is a Python visualization library based on matplotlib. It provides a high-level interface for drawing

:small_red_triangle: Ternary plotting library for python with matplotlib
:small_red_triangle: Ternary plotting library for python with matplotlib

python-ternary This is a plotting library for use with matplotlib to make ternary plots plots in the two dimensional simplex projected onto a two dime

Joyplots in Python with matplotlib & pandas :chart_with_upwards_trend:
Joyplots in Python with matplotlib & pandas :chart_with_upwards_trend:

JoyPy JoyPy is a one-function Python package based on matplotlib + pandas with a single purpose: drawing joyplots (a.k.a. ridgeline plots). The code f

A python package for animating plots build on matplotlib.
A python package for animating plots build on matplotlib.

animatplot A python package for making interactive as well as animated plots with matplotlib. Requires Python = 3.5 Matplotlib = 2.2 (because slider

Painlessly create beautiful matplotlib plots.
Painlessly create beautiful matplotlib plots.

Announcement Thank you to everyone who has used prettyplotlib and made it what it is today! Unfortunately, I no longer have the bandwidth to maintain

Comments
Releases(v1.4.3)
Owner
Machine Learning and Data Analytics Lab FAU
Public projects of the Machine Learning and Data Analytics Lab at the Friedrich-Alexander-University Erlangen-Nürnberg
Machine Learning and Data Analytics Lab FAU
This GitHub Repository contains Data Analysis projects that I have completed so far! While most of th project are focused on Data Analysis, some of them are also put here to show off other skills that I have learned.

Welcome to my Data Analysis projects page! This GitHub Repository contains Data Analysis projects that I have completed so far! While most of th proje

Kyle Dini 1 Jan 31, 2022
https://there.oughta.be/a/macro-keyboard

inkkeys Details and instructions can be found on https://there.oughta.be/a/macro-keyboard In contrast to most of my other projects, I decided to put t

Sebastian Staacks 209 Dec 21, 2022
LinkedIn connections analyzer

LinkedIn Connections Analyzer 🔗 https://linkedin-analzyer.herokuapp.com Hey hey 👋 , welcome to my LinkedIn connections analyzer. I recently found ou

Okkar Min 5 Sep 13, 2022
An open-source plotting library for statistical data.

Lets-Plot Lets-Plot is an open-source plotting library for statistical data. It is implemented using the Kotlin programming language. The design of Le

JetBrains 820 Jan 06, 2023
A shimmer pre-load component for Plotly Dash

dash-loading-shimmer A shimmer pre-load component for Plotly Dash Installation Get it with pip: pip install dash-loading-extras Or maybe you prefer Pi

Lucas Durand 4 Oct 12, 2022
Investment and risk technologies maintained by Fortitudo Technologies.

Fortitudo Technologies Open Source This package allows you to freely explore open-source implementations of some of our fundamental technologies under

Fortitudo Technologies 11 Dec 14, 2022
Learn Data Science with focus on adding value with the most efficient tech stack.

DataScienceWithPython Get started with Data Science with Python An engaging journey to become a Data Scientist with Python TL;DR Download all Jupyter

Learn Python with Rune 110 Dec 22, 2022
又一个云探针

ServerStatus-Murasame 感谢ServerStatus-Hotaru,又一个云探针诞生了(大雾 本项目在ServerStatus-Hotaru的基础上使用fastapi重构了服务端,部分修改了客户端与前端 项目还在非常原始的阶段,可能存在严重的问题 演示站:https://stat

6 Oct 19, 2021
Quickly and accurately render even the largest data.

Turn even the largest data into images, accurately Build Status Coverage Latest dev release Latest release Docs Support What is it? Datashader is a da

HoloViz 2.9k Dec 28, 2022
100 Days of Code The Complete Python Pro Bootcamp for 2022

100-Day-With-Python 100 Days of Code - The Complete Python Pro Bootcamp for 2022. In this course, I spend with python language over 100 days, and I up

Rajdip Das 8 Jun 22, 2022
Python scripts for plotting audiograms and related data from Interacoustics Equinox audiometer and Otoaccess software.

audiometry Python scripts for plotting audiograms and related data from Interacoustics Equinox 2.0 audiometer and Otoaccess software. Maybe similar sc

Hamilton Lab at UT Austin 2 Jun 15, 2022
Matplotlib tutorial for beginner

matplotlib is probably the single most used Python package for 2D-graphics. It provides both a very quick way to visualize data from Python and publication-quality figures in many formats. We are goi

Nicolas P. Rougier 2.6k Dec 28, 2022
Visualization Data Drug in thailand during 2014 to 2020

Visualization Data Drug in thailand during 2014 to 2020 Data sorce from ข้อมูลเปิดภาครัฐ สำนักงาน ป.ป.ส Inttroducing program Using tkinter module for

Narongkorn 1 Jan 05, 2022
Python library that makes it easy for data scientists to create charts.

Chartify Chartify is a Python library that makes it easy for data scientists to create charts. Why use Chartify? Consistent input data format: Spend l

Spotify 3.2k Jan 04, 2023
Visualize the training curve from the *.csv file (tensorboard format).

Training-Curve-Vis Visualize the training curve from the *.csv file (tensorboard format). Feature Custom labels Curve smoothing Support for multiple c

Luckky 7 Feb 23, 2022
Manim is an animation engine for explanatory math videos.

A community-maintained Python framework for creating mathematical animations.

12.4k Dec 30, 2022
Schema validation for Xarray objects

xarray-schema Schema validation for Xarray installation This package is in the early stages of development. Install it from source: pip install git+gi

carbonplan 22 Oct 31, 2022
Streamlit dashboard examples - Twitter cashtags, StockTwits, WSB, Charts, SQL Pattern Scanner

streamlit-dashboards Streamlit dashboard examples - Twitter cashtags, StockTwits, WSB, Charts, SQL Pattern Scanner Tutorial Video https://ww

122 Dec 21, 2022
Small binja plugin to import header file to types

binja-import-header (v1.0.0) Author: matteyeux Import header file to Binary Ninja types view Description: Binary Ninja plugin to import types from C h

matteyeux 15 Dec 10, 2022
Analysis and plotting for motor/prop/ESC characterization, thrust vs RPM and torque vs thrust

esc_test This is a Python package used to plot and analyze data collected for the purpose of characterizing a particular propeller, motor, and ESC con

Alex Spitzer 1 Dec 28, 2021