A Python library for plotting hockey rinks with Matplotlib.

Overview

Hockey Rink

A Python library for plotting hockey rinks with Matplotlib.

Installation

pip install hockey_rink

Current Rinks

The following shows the custom rinks currently available for plotting.

from hockey_rink import NHLRink, IIHFRink, NWHLRink
import matplotlib.pyplot as plt

fig, axs = plt.subplots(1, 3, sharey=True, figsize=(12, 6), gridspec_kw={"width_ratios": [1, 98.4/85, 1]})
nhl_rink = NHLRink(rotation=90)
iihf_rink = IIHFRink(rotation=90)
nwhl_rink = NWHLRink(rotation=90)
axs[0] = nhl_rink.draw(ax=axs[0])
axs[1] = iihf_rink.draw(ax=axs[1])
axs[2] = nwhl_rink.draw(ax=axs[2])

The NWHL logo comes from the NWHL site.

Customization

There is also room for customization. The image at the top was created as follows:

rink = Rink(rotation=45, boards={"length": 150, "width": 150, "radius": 75})

Rinks also allow for additional features to be added. Custom features should inherit from RinkFeature and override the _get_centered_xy method. The draw method can also be overridden if the desired feature can't be drawn with a matplotlib Polygon, though _get_centered_xy should still provide the feature's boundaries. CircularImage provides an example of this by inheriting from RinkCircle.

If a custom feature is to be constrained to only display within the rink, the returned object needs to have a set_clip_path method.

Plots

There are currently wrappers available for the following Matplotlib plots:
- plot
- scatter
- arrow
- hexbin
- pcolormesh (heatmap in Hockey Rink)
- contour
- contourf

If you'd like to bypass the wrappers, you can convert coordinates to the proper scale with convert_xy:

rink = Rink()
x, y = rink.convert_xy(x, y)

When plotting to a partially drawn surface, the plot will be applied to the entire rink, not what's visible. This can be avoided by setting plot_range (or plot_xlim and plot_ylim) in the plotting functions where they're available.

It's also important to realize that the plotting functions only allow arguments to be passed without keywords for the coordinates.
ie) hexbin(x, y, values) will throw an error.

The correct call is hexbin(x, y, values=values)

Examples

Let's look at some NWHL data via the Big Data Cup.

The first game is Minnesota vs Boston, so we'll go with that and do a scatter plot of each team's shots.

from hockey_rink import NWHLRink
import pandas as pd

df = pd.read_csv("https://raw.githubusercontent.com/bigdatacup/Big-Data-Cup-2021/main/hackathon_nwhl.csv")
game_df = df.loc[(df["Home Team"] == "Minnesota Whitecaps") & (df["Away Team"] == "Boston Pride")]
shots = game_df.loc[(game_df.Event.isin(["Shot", "Goal"]))]
boston_shots = shots[shots.Team == "Boston Pride"]
minnesota_shots = shots[shots.Team == "Minnesota Whitecaps"]
rink = NWHLRink(x_shift=100, y_shift=42.5)
ax = rink.draw()
rink.scatter(boston_shots["X Coordinate"], boston_shots["Y Coordinate"])
rink.scatter(200 - minnesota_shots["X Coordinate"], 85 - minnesota_shots["Y Coordinate"])

Extending the example, let's look at all of Boston's passes.

boston_passes = game_df.loc[(game_df.Team == "Boston Pride") & (game_df.Event == "Play")]
ax.clear()
rink.draw()
arrows = rink.arrow(boston_passes["X Coordinate"], boston_passes["Y Coordinate"], 
                    boston_passes["X Coordinate 2"], boston_passes["Y Coordinate 2"], color="yellow")

For some of the other plots, let's look at some NHL shooting percentages.

To mix things up a little, binsize will take different values in each plot and the heatmap won't include shots from below the goal line. We'll also throw in a colorbar for the contour plot.

from hockey_rink import NHLRink
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd

pbp = pd.read_csv("https://hockey-data.harryshomer.com/pbp/nhl_pbp20192020.csv.gz", compression="gzip")
pbp["goal"] = (pbp.Event == "GOAL").astype(int)
pbp["x"] = np.abs(pbp.xC)
pbp["y"] = pbp.yC * np.sign(pbp.xC)
shots = pbp.loc[(pbp.Ev_Zone == "Off") & ~pbp.x.isna() & ~pbp.y.isna() & (pbp.Event.isin(["GOAL", "SHOT", "MISS"]))]

fig, axs = plt.subplots(1, 3, figsize=(14, 8))
rink = NHLRink(rotation=270)
for i in range(3):
    rink.draw(ax=axs[i], display_range="ozone")
contour_img = rink.contourf(shots.x, shots.y, values=shots.goal, ax=axs[0], cmap="bwr", 
                            plot_range="ozone", binsize=10, levels=50, statistic="mean")
plt.colorbar(contour_img, ax=axs[0], orientation="horizontal")
rink.heatmap(shots.x, shots.y, values=shots.goal, ax=axs[1], cmap="magma",
             plot_xlim=(25, 89), statistic="mean", vmax=0.2, binsize=3)
rink.hexbin(shots.x, shots.y, values=shots.goal, ax=axs[2], binsize=(8, 12), plot_range="ozone", zorder=25, alpha=0.85)

Inspiration

This project was partly inspired by mplsoccer.

Hopefully, it can lower a barrier for someone looking to get involved in hockey analytics.

Contact

You can find me on twitter @the_bucketless or email me at [email protected] if you'd like to get in touch.

Import, visualize, and analyze SpiderFoot OSINT data in Neo4j, a graph database

SpiderFoot Neo4j Tools Import, visualize, and analyze SpiderFoot OSINT data in Neo4j, a graph database Step 1: Installation NOTE: This installs the sf

Black Lantern Security 42 Dec 26, 2022
a simple REPL display lib for circuitpython

Circuitpython-termio-lib a simple REPL display lib for circuitpython Fonctions cls clear terminal screen and set cursor on top left : coords 0,0 usage

BeBoXoS 1 Nov 17, 2021
Make scripted visualizations in blender

Scripted visualizations in blender The goal of this project is to script 3D scientific visualizations using blender. To achieve this, we aim to bring

Praneeth Namburi 10 Jun 01, 2022
The Metabolomics Integrator (MINT) is a post-processing tool for liquid chromatography-mass spectrometry (LCMS) based metabolomics.

MINT (Metabolomics Integrator) The Metabolomics Integrator (MINT) is a post-processing tool for liquid chromatography-mass spectrometry (LCMS) based m

Sören Wacker 0 May 04, 2022
Decision Border Visualizer for Classification Algorithms

dbv Decision Border Visualizer for Classification Algorithms Project description A python package for Machine Learning Engineers who want to visualize

Sven Eschlbeck 1 Nov 01, 2021
Generate the report for OCULTest.

Sample report generated in this function Usage example from utils.gen_report import generate_report if __name__ == '__main__': # def generate_rep

Philip Guo 1 Mar 10, 2022
Lightweight, extensible data validation library for Python

Cerberus Cerberus is a lightweight and extensible data validation library for Python. v = Validator({'name': {'type': 'string'}}) v.validate({

eve 2.9k Dec 27, 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
Voilà, install macOS on ANY Computer! This is really and magic easiest way!

OSX-PROXMOX - Run macOS on ANY Computer - AMD & Intel Install Proxmox VE v7.02 - Next, Next & Finish (NNF). Open Proxmox Web Console - Datacenter N

Gabriel Luchina 654 Jan 09, 2023
A workshop on data visualization in Python with notebooks and exercises for following along.

Beyond the Basics: Data Visualization in Python The human brain excels at finding patterns in visual representations, which is why data visualizations

Stefanie Molin 162 Dec 05, 2022
I'm doing Genuary, an aritifiacilly generated month to build code that make beautiful things

Genuary 2022 I'm doing Genuary, an aritifiacilly generated month to build code that make beautiful things. Every day there is a new prompt for making

Joaquín Feltes 1 Jan 10, 2022
Jupyter Notebook extension leveraging pandas DataFrames by integrating DataTables and ChartJS.

Jupyter DataTables Jupyter Notebook extension to leverage pandas DataFrames by integrating DataTables JS. About Data scientists and in fact many devel

Marek Čermák 142 Dec 28, 2022
A data visualization curriculum of interactive notebooks.

A data visualization curriculum of interactive notebooks, using Vega-Lite and Altair. This repository contains a series of Python-based Jupyter notebooks.

UW Interactive Data Lab 1.2k Dec 30, 2022
Regress.me is an easy to use data visualization tool powered by Dash/Plotly.

Regress.me Regress.me is an easy to use data visualization tool powered by Dash/Plotly. Regress.me.-.Google.Chrome.2022-05-10.15-58-59.mp4 Get Started

Amar 14 Aug 14, 2022
A Python wrapper of Neighbor Retrieval Visualizer (NeRV)

PyNeRV A Python wrapper of the dimensionality reduction algorithm Neighbor Retrieval Visualizer (NeRV) Compile Set up the paths in Makefile then make.

2 Aug 29, 2021
A high performance implementation of HDBSCAN clustering. http://hdbscan.readthedocs.io/en/latest/

HDBSCAN Now a part of scikit-learn-contrib HDBSCAN - Hierarchical Density-Based Spatial Clustering of Applications with Noise. Performs DBSCAN over va

Leland McInnes 91 Dec 29, 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
Plot and save the ground truth and predicted results of human 3.6 M and CMU mocap dataset.

Visualization-of-Human3.6M-Dataset Plot and save the ground truth and predicted results of human 3.6 M and CMU mocap dataset. human-motion-prediction

Gaurav Kumar Yadav 5 Nov 18, 2022
Script to create an animated data visualisation for categorical timeseries data - GIF choropleth map with annotations.

choropleth_ldn Simple script to create a chloropleth map of London with categorical timeseries data. The script in main.py creates a gif of the most f

1 Oct 07, 2021
A simple python tool for explore your object detection dataset

A simple tool for explore your object detection dataset. The goal of this library is to provide simple and intuitive visualizations from your dataset and automatically find the best parameters for ge

GRADIANT - Centro Tecnolóxico de Telecomunicacións de Galicia 142 Dec 25, 2022