Move code from lessons to notebooks

.gitignore

@@ -0,0 +1,17 @@
+# Byte-compiled / optimized / DLL files
+__pycache__/
+*.py[cod]
+
+# VS Code
+.vscode/
+
+# Jupyter NB Checkpoints
+.ipynb_checkpoints/
+
+# Mac OS-specific storage files
+.DS_Store
+
+*.profraw
+
+# Own
+data/

README.md

@@ -6,3 +6,30 @@ The sample data can be found in Metrica's github repository here: https://github
 We'll be updating this repo as the friends of tracking series develops, adding code for measuring player velocity and acceleration, measuring team formations, and evaluating pitch control using a model published by Will Spearman. 
 
 To create movies from the tracking data you'll need to have ffmpeg installed. You can do this by following the instructions here: https://anaconda.org/conda-forge/ffmpeg (you may need to restart python afterwards).
+
+## Notebooks
+If you want to run the notebooks for each session, you can either install the repository locally or run it directly on Binder or Google Colab. In case you do not know, use Google Colab!  
+- Run each cell in the notebook and ignore the warning about the unknown environment.
+- The data is downloaded directly from Metricas repository.  
+- Give it some time when it says "Readining team home" in a cell.
+
+| Lesson | Binder | Colab |
+| --- | --- | --- |
+| [Lesson 4 - Basic Plotting of Event and Tracking Data](notebooks/Lesson4.ipynb) | [![Binder](https://mybinder.org/badge_logo.svg)](https://mybinder.org/v2/gh/seidlr/LaurieOnTracking/master?filepath=notebooks/Lesson4.ipynb) |  [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/seidlr/LaurieOnTracking/blob/master/notebooks/Lesson4.ipynb) |
+| [Lesson 5 - Advanced Plotting and Summary Statistics](notebooks/Lesson5.ipynb) | [![Binder](https://mybinder.org/badge_logo.svg)](https://mybinder.org/v2/gh/seidlr/LaurieOnTracking/master?filepath=notebooks/Lesson5.ipynb) |  [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/seidlr/LaurieOnTracking/blob/master/notebooks/Lesson5.ipynb) |
+| [Lesson 6 - Pitch Control](notebooks/Lesson6.ipynb) | [![Binder](https://mybinder.org/badge_logo.svg)](https://mybinder.org/v2/gh/seidlr/LaurieOnTracking/master?filepath=notebooks/Lesson6.ipynb) |  [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/seidlr/LaurieOnTracking/blob/master/notebooks/Lesson6.ipynb) |
+
+
+
+### Local installation
+1. Download Anaconda
+2. Clone repository
+3. Create environment `fotd-laurie-on-tracking`:
+```
+conda env update
+```
+4. Start jupyter notebook server
+```
+jupyter notebook
+```
+5. Select the kernel `fotd-laurie-on-tracking` for your notebook

environment.yml

@@ -0,0 +1,10 @@
+name: fotd-laurie-on-tracking
+channels:
+  - conda-forge
+dependencies:
+  - ipykernel
+  - numpy=1.18.1
+  - matplotlib=3.2.1
+  - pandas=1.0.3
+  - requests=2.23.0
+  - scipy=1.4.1

Metrica_IO.py → friendsoftracking/metrica/IO.py

@@ -9,39 +9,46 @@
 
 @author: Laurie Shaw (@EightyFivePoint)
 """
-
+import requests
 import pandas as pd
 import csv as csv
 
-def read_match_data(DATADIR,gameid):
+BASE_URL = 'https://raw.githubusercontent.com/metrica-sports/sample-data/master/data/'
+EVENT_URL = 'Sample_Game_{game_id}/Sample_Game_{game_id}_RawEventsData.csv'
+TRACKING_URL = 'Sample_Game_{game_id}/Sample_Game_{game_id}_RawTrackingData_{teamname}_Team.csv'
+
+def read_match_data(game_id):
     '''
-    read_match_data(DATADIR,gameid):
+    read_match_data(gameid):
     read all Metrica match data (tracking data for home & away teams, and ecvent data)
     '''
-    tracking_home = tracking_data(DATADIR,gameid,'Home')
-    tracking_away = tracking_data(DATADIR,gameid,'Away')
-    events = read_event_data(DATADIR,gameid)
+    tracking_home = read_tracking_data(game_id, 'Home')
+    tracking_away = read_tracking_data(game_id, 'Away')
+    events = read_event_data(game_id)
     return tracking_home,tracking_away,events
 
-def read_event_data(DATADIR,game_id):
+def read_event_data(game_id):
     '''
-    read_event_data(DATADIR,game_id):
+    read_event_data(game_id):
     read Metrica event data  for game_id and return as a DataFrame
     '''
-    eventfile = '/Sample_Game_%d/Sample_Game_%d_RawEventsData.csv' % (game_id,game_id) # filename
-    events = pd.read_csv('{}/{}'.format(DATADIR, eventfile)) # read data
+    url = BASE_URL + EVENT_URL
+    events = pd.read_csv(url.format(game_id=game_id)) # read data
     return events
 
-def tracking_data(DATADIR,game_id,teamname):
+def read_tracking_data(game_id,teamname):
     '''
-    tracking_data(DATADIR,game_id,teamname):
+    read_tracking_data(game_id,teamname):
     read Metrica tracking data for game_id and return as a DataFrame. 
     teamname is the name of the team in the filename. For the sample data this is either 'Home' or 'Away'.
     '''
-    teamfile = '/Sample_Game_%d/Sample_Game_%d_RawTrackingData_%s_Team.csv' % (game_id,game_id,teamname)
-    # First:  deal with file headers so that we can get the player names correct
-    csvfile =  open('{}/{}'.format(DATADIR, teamfile), 'r') # create a csv file reader
-    reader = csv.reader(csvfile) 
+
+    url  = BASE_URL + TRACKING_URL
+    url = url.format(game_id=game_id, teamname=teamname)
+    response = requests.get(url)        
+    decoded = response.content.decode('utf-8')
+
+    reader = csv.reader(decoded.splitlines()) 
     teamnamefull = next(reader)[3].lower()
     print("Reading team: %s" % teamnamefull)
     # construct column names
@@ -53,7 +60,7 @@ def tracking_data(DATADIR,game_id,teamname):
     columns[-2] = "ball_x" # column headers for the x & y positions of the ball
     columns[-1] = "ball_y"
     # Second: read in tracking data and place into pandas Dataframe
-    tracking = pd.read_csv('{}/{}'.format(DATADIR, teamfile), names=columns, index_col='Frame', skiprows=3)
+    tracking = pd.read_csv(url, names=columns, index_col='Frame', skiprows=3)
     return tracking
 
 def merge_tracking_data(home,away):