#import some libraries
import matplotlib.pyplot as plt, pandas as pd, numpy as np, matplotlib as mpl
from __future__ import print_function

mpl.rcParams['font.family'] = ['Bitstream Vera Sans']

import json #import json library
json_data = open('/home/dan-laptop/github/BasketballData/2016.NBA.Raw.SportVU.Game.Logs/0021500594.json') #import the data from wherever you saved it.
data = json.load(json_data) #load the data

data.keys()

data['events'][0].keys()

data['events'][0]['moments'][0]

def acquire_gameData(data):
    import requests
    header_data = { #I pulled this header from the py goldsberry library
        'Accept-Encoding': 'gzip, deflate, sdch',
        'Accept-Language': 'en-US,en;q=0.8',
        'Upgrade-Insecure-Requests': '1',
        'User-Agent': 'Mozilla/5.0 (Windows NT 10.0; WOW64)'\
        ' AppleWebKit/537.36 (KHTML, like Gecko) Chrome/48.0.2564.82 '\
        'Safari/537.36',
        'Accept': 'text/html,application/xhtml+xml,application/xml;q=0.9'\
        ',image/webp,*/*;q=0.8',
        'Cache-Control': 'max-age=0',
        'Connection': 'keep-alive'
    }
    game_url = 'http://stats.nba.com/stats/playbyplayv2?EndPeriod=0&EndRange=0&GameID='+data['gameid']+\
                '&RangeType=0&StartPeriod=0&StartRange=0' #address for querying the data
    response = requests.get(game_url,headers = header_data) #go get the data
    headers = response.json()['resultSets'][0]['headers'] #get headers of data
    gameData = response.json()['resultSets'][0]['rowSet'] #get actual data from json object
    df = pd.DataFrame(gameData, columns=headers) #turn the data into a pandas dataframe
    df = df[[df.columns[1], df.columns[2],df.columns[7],df.columns[9],df.columns[18]]] #there's a ton of data here, so I trim  it doown
    df['TEAM'] = df['PLAYER1_TEAM_ABBREVIATION']
    df = df.drop('PLAYER1_TEAM_ABBREVIATION', 1)
    return df

df = acquire_gameData(data)
df.head()
#EVENTMSGTYPE
#1 - Make 
#2 - Miss 
#3 - Free Throw 
#4 - Rebound 
#5 - out of bounds / Turnover / Steal 
#6 - Personal Foul 
#7 - Violation 
#8 - Substitution 
#9 - Timeout 
#10 - Jumpball 
#12 - Start Q1? 
#13 - Start Q2?

player_fields = data['events'][0]['home']['players'][0].keys()
home_players = pd.DataFrame(data=[i for i in data['events'][0]['home']['players']], columns=player_fields)
away_players = pd.DataFrame(data=[i for i in data['events'][0]['visitor']['players']], columns=player_fields)
players = pd.merge(home_players, away_players, how='outer')
jerseydict = dict(zip(players.playerid.values, players.jersey.values))

# Animation function / loop
def draw_court(axis):
    import matplotlib.image as mpimg
    img = mpimg.imread('./nba_court_T.png') #read image. I got this image from gmf05's github.
    plt.imshow(img,extent=axis, zorder=0) #show the image. 

def animate(n): #matplotlib's animation function loops through a function n times that draws a different frame on each iteration
    for i,ii in enumerate(player_xy[n]): #loop through all the players
        player_circ[i].center = (ii[1], ii[2]) #change each players xy position
        player_text[i].set_text(str(jerseydict[ii[0]])) #draw the text for each player. 
        player_text[i].set_x(ii[1]) #set the text x position
        player_text[i].set_y(ii[2]) #set text y position
    ball_circ.center = (ball_xy[n,0],ball_xy[n,1]) #change ball xy position
    ball_circ.radius = 1.1 #i could change the size of the ball according to its height, but chose to keep this constant
    return tuple(player_text) + tuple(player_circ) + (ball_circ,)

def init(): #this is what matplotlib's animation will create before drawing the first frame. 
    for i in range(10): #set up players
        player_text[i].set_text('')
        ax.add_patch(player_circ[i])
    ax.add_patch(ball_circ) #create ball
    ax.axis('off') #turn off axis
    dx = 5
    plt.xlim([0-dx,100+dx]) #set axis
    plt.ylim([0-dx,50+dx])
    return tuple(player_text) + tuple(player_circ) + (ball_circ,)

df[37:38]

#the order of events does not match up, so we have to use the eventIds. This loop finds the correct event for a given id#.
search_id = 41
def find_moment(search_id):
    for i,events in enumerate(data['events']):
        if events['eventId'] == str(search_id):
            finder = i
            break
    return finder

event_num = find_moment(search_id)
ball_xy = np.array([x[5][0][2:5] for x in data['events'][event_num]['moments']]) #create matrix of ball data
player_xy = np.array([np.array(x[5][1:])[:,1:4] for x in data['events'][event_num]['moments']]) #create matrix of player data

import matplotlib.animation as animation

fig = plt.figure(figsize=(15,7.5)) #create figure object
ax = plt.gca() #create axis object

draw_court([0,100,0,50]) #draw the court
player_text = range(10) #create player text vector
player_circ = range(10) #create player circle vector
ball_circ = plt.Circle((0,0), 1.1, color=[1, 0.4, 0]) #create circle object for bal
for i in range(10): #create circle object and text object for each player
    col=['w','k'] if i<5 else ['k','w'] #color scheme
    player_circ[i] = plt.Circle((0,0), 2.2, facecolor=col[0],edgecolor='k') #player circle
    player_text[i] = ax.text(0,0,'',color=col[1],ha='center',va='center') #player jersey # (text)

ani = animation.FuncAnimation(fig, animate, frames=np.arange(0,np.size(ball_xy,0)), init_func=init, blit=True, interval=5, repeat=False,\
                             save_count=0) #function for making video
ani.save('Event_%d.mp4' % (search_id),dpi=100,fps=25) #function for saving video
plt.close('all') #close the plot