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
Gaphas is the diagramming widget library for Python.

Gaphas Gaphas is the diagramming widget library for Python. Gaphas is a library that provides the user interface component (widget) for drawing diagra

Gaphor 144 Dec 14, 2022
Example Code Notebooks for Data Visualization in Python

This repository contains sample code scripts for creating awesome data visualizations from scratch using different python libraries (such as matplotli

Javed Ali 27 Jan 04, 2023
An adaptable Snakemake workflow which uses GATKs best practice recommendations to perform germline mutation calling starting with BAM files

Germline Mutation Calling This Snakemake workflow follows the GATK best-practice recommandations to call small germline variants. The pipeline require

12 Dec 24, 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
A command line tool for visualizing CSV/spreadsheet-like data

PerfPlotter Read data from CSV files using pandas and generate interactive plots using bokeh, which can then be embedded into HTML pages and served by

Gino Mempin 0 Jun 25, 2022
basemap - Plot on map projections (with coastlines and political boundaries) using matplotlib.

Basemap Plot on map projections (with coastlines and political boundaries) using matplotlib. ⚠️ Warning: this package is being deprecated in favour of

Matplotlib Developers 706 Dec 28, 2022
Visualization ideas for data science

Nuance I use Nuance to curate varied visualization thoughts during my data scientist career. It is not yet a package but a list of small ideas. Welcom

Li Jiangchun 16 Nov 03, 2022
coordinate to draw the nimbus logo on the graffitiwall

This is a community effort to draw the nimbus logo on beaconcha.in's graffitiwall. get started clone repo with git clone https://github.com/tennisbowl

4 Apr 04, 2022
Small project demonstrating the use of Grafana and InfluxDB for monitoring the speed of an internet connection

Speedtest monitor for Grafana A small project that allows internet speed monitoring using Grafana, InfluxDB 2 and Speedtest. Demo Requirements Docker

Joshua Ghali 3 Aug 06, 2021
Geospatial Data Visualization using PyGMT

Example script to visualize topographic data, earthquake data, and tomographic data on a map

Utpal Kumar 2 Jul 30, 2022
Smarthome Dashboard with Grafana & InfluxDB

Smarthome Dashboard with Grafana & InfluxDB This is a complete overhaul of my Raspberry Dashboard done with Flask. I switched from sqlite to InfluxDB

6 Oct 20, 2022
nvitop, an interactive NVIDIA-GPU process viewer, the one-stop solution for GPU process management

An interactive NVIDIA-GPU process viewer, the one-stop solution for GPU process management.

Xuehai Pan 1.3k Jan 02, 2023
Learning Convolutional Neural Networks with Interactive Visualization.

CNN Explainer An interactive visualization system designed to help non-experts learn about Convolutional Neural Networks (CNNs) For more information,

Polo Club of Data Science 6.3k Jan 01, 2023
This is simply repo for line drawing rendering using freestyle in Blender.

blender_freestyle_line_drawing This is simply repo for line drawing rendering using freestyle in Blender. how to use blender2935 --background --python

MaxLin 3 Jul 02, 2022
WhatsApp Chat Analyzer is a WebApp and it can be used by anyone to analyze their chat. 😄

WhatsApp-Chat-Analyzer You can view the working project here. WhatsApp chat Analyzer is a WebApp where anyone either tech or non-tech person can analy

Prem Chandra Singh 26 Nov 02, 2022
Realtime Web Apps and Dashboards for Python and R

H2O Wave Realtime Web Apps and Dashboards for Python and R New! R Language API Build and control Wave dashboards using R! New! Easily integrate AI/ML

H2O.ai 3.4k Jan 06, 2023
Using SQLite within Python to create database and analyze Starcraft 2 units data (Pandas also used)

SQLite python Starcraft 2 English This project shows the usage of SQLite with python. To create, modify and communicate with the SQLite database from

1 Dec 30, 2021
The Spectral Diagram (SD) is a new tool for the comparison of time series in the frequency domain

The Spectral Diagram (SD) is a new tool for the comparison of time series in the frequency domain. The SD provides a novel way to display the coherence function, power, amplitude, phase, and skill sc

Mabel 3 Oct 10, 2022
VDLdraw - Batch plot the log files exported from VisualDL using Matplotlib

VDLdraw Batch plot the log files exported from VisualDL using Matplotlib. At pre

Yizhou Chen 5 Sep 26, 2022
Python scripts to manage Chia plots and drive space, providing full reports. Also monitors the number of chia coins you have.

Chia Plot, Drive Manager & Coin Monitor (V0.5 - April 20th, 2021) Multi Server Chia Plot and Drive Management Solution Be sure to ⭐ my repo so you can

338 Nov 25, 2022