import numpy as np, sys, scipy.stats, pandas as pd, requests, json, matplotlib
import matplotlib.pyplot as plt
import pylab as pl
%matplotlib inline
pd.options.display.mpl_style = 'default'

df = None

players = []  
player_stats = {'name':None,'avg_dribbles':None,'avg_touch_time':None,'avg_shot_distance':None,'avg_defender_distance':None, 'shooting_%':None}

def find_stats(name,player_id,dateFrom,dateTo):  
    #NBA Stats API using selected player ID
    url = 'http://stats.nba.com/stats/playerdashptshotlog?'+ \
    'DateFrom='+dateFrom+'&DateTo='+dateTo+'&GameSegment=&LastNGames=0&LeagueID=00&' + \
    'Location=&Month=0&OpponentTeamID=0&Outcome=&Period=0&' + \
    'PlayerID='+player_id+'&Season=2014-15&SeasonSegment=&' + \
    'SeasonType=Regular+Season&TeamID=0&VsConference=&VsDivision='

    #Create Dict based on JSON response
    response = requests.get(url)
    shots = response.json()['resultSets'][0]['rowSet']
    data = json.loads(response.text)

    #Create df from data and find averages 
    headers = data['resultSets'][0]['headers']
    shot_data = data['resultSets'][0]['rowSet']
    df = pd.DataFrame(shot_data,columns=headers) 
    avg_def = df['CLOSE_DEF_DIST'].mean(axis=1)
    avg_dribbles = df['DRIBBLES'].mean(axis=1)
    avg_shot_distance = df['SHOT_DIST'].mean(axis=1)
    avg_touch_time = df['TOUCH_TIME'].mean(axis=1)

    #add Averages to dictionary then to list
    player_stats['name'] = name
    player_stats['avg_defender_distance']=avg_def
    player_stats['avg_shot_distance'] = avg_shot_distance
    player_stats['avg_touch_time'] = avg_touch_time
    player_stats['avg_dribbles'] = avg_dribbles
    player_stats['shooting_%'] = df['FGM'].mean(axis=1)
    players.append(player_stats.copy())
    
def find_stats2(name,player_id,dateFrom,dateTo):  
    #NBA Stats API using selected player ID
    url = 'http://stats.nba.com/stats/playerdashptshotlog?'+ \
    'DateFrom='+dateFrom+'&DateTo='+dateTo+'&GameSegment=&LastNGames=0&LeagueID=00&' + \
    'Location=&Month=0&OpponentTeamID=0&Outcome=&Period=0&' + \
    'PlayerID='+player_id+'&Season=2014-15&SeasonSegment=&' + \
    'SeasonType=Regular+Season&TeamID=0&VsConference=&VsDivision='

    #Create Dict based on JSON response
    response = requests.get(url)
    shots = response.json()['resultSets'][0]['rowSet']
    data = json.loads(response.text)

    #Create df from data and find averages 
    headers = data['resultSets'][0]['headers']
    shot_data = data['resultSets'][0]['rowSet']
    df = pd.DataFrame(shot_data,columns=headers) 
    return df

cols = ['name','avg_defender_distance','avg_dribbles','avg_shot_distance','avg_touch_time', 'shooting_%']  #name the columns that I will put data into

dateFrom='8/1/14' #some arbitrary before season date- dec22
dateTo='12/22/14' 
find_stats('andrew wiggins','203952', dateFrom, dateTo)
df = pd.DataFrame(players,columns = cols) 

dateFrom='12/22/14' #dec 22- through some arbitrary future date
dateTo='12/1/15'
find_stats('andrew wiggins','203952', dateFrom, dateTo)
df = pd.DataFrame(players,columns = cols) 

df.head()

df = None #lets create a new place for the data

dateFrom='8/1/14'
dateTo='12/22/14'
df = find_stats2('andrew wiggins','203952', dateFrom, dateTo);

df.head()

bins = [0,3,6,9,12,15,18,21,24,27]
binlabels = ['0','1-3','4-6','7-9','10-12','13-15','16-18','19-21','22-24','25-27']

fig = matplotlib.pyplot.gcf()
fig.set_size_inches(14,6)

plt.subplot(1,2,1)
n, bins, patches = plt.hist(df['SHOT_DIST'], bins=bins)
plt.xticks(bins+1.5,binlabels)
plt.xlabel('Shot Distance (ft)')
plt.title('Before Dec 22nd')
plt.ylabel('Bin Count');

dateFrom='12/22/14'
dateTo='12/1/15'
df2 = find_stats2('andrew wiggins','203952', dateFrom, dateTo);
plt.subplot(1,2,2)
n, bins, patches = plt.hist(df2['SHOT_DIST'], bins)
plt.xticks(bins+1.5,binlabels)
plt.title('After Dec 22nd')
plt.xlabel('Shot Distance (ft)');

fig = matplotlib.pyplot.gcf()
fig.set_size_inches(14,6)

made = df['SHOT_RESULT'] == 'made'
missed = df['SHOT_RESULT'] == 'missed'
plt.subplot(1,2,1)
n, bins, patches = plt.hist([df[made]['SHOT_DIST'],df[missed]['SHOT_DIST']], bins=bins, label=['shot made',' shot missed'])
plt.title('Before Dec 22nd')
plt.legend()
plt.xticks(bins+1.5,binlabels)
plt.xlabel('Shot Distance (ft)')
plt.ylabel('Bin Count');

made = df2['SHOT_RESULT'] == 'made'
missed = df2['SHOT_RESULT'] == 'missed'
plt.subplot(1,2,2)
plt.title('After Dec 22nd')
n, bins, patches = plt.hist([df2[made]['SHOT_DIST'],df2[missed]['SHOT_DIST']], bins,  label=['shot made',' shot missed'])
plt.xticks(bins+1.5,binlabels)
plt.legend()
plt.xlabel('Shot Distance (ft)');

fig = matplotlib.pyplot.gcf()
fig.set_size_inches(16,6)

plt.subplot(1,2,1)
plt.hist2d(df['SHOT_DIST'], df['DRIBBLES'])
plt.colorbar()
plt.title('Before Dec 22nd')
plt.xlabel('Shot Distance (ft)')
plt.ylabel('# Dribbles');

plt.subplot(1,2,2)
plt.hist2d(df2['SHOT_DIST'], df2['DRIBBLES'])
plt.colorbar()
plt.title('After Dec 22nd')
plt.xlabel('Shot Distance (ft)');

fig = matplotlib.pyplot.gcf()
fig.set_size_inches(16,6)

plt.subplot(1,2,1)
plt.hist2d(df['SHOT_DIST'], df['CLOSE_DEF_DIST'])
plt.colorbar()
plt.title('Before Dec 22nd')
plt.xlabel('Shot Distance (ft)')
plt.ylabel('Closest Defender (ft)');

plt.subplot(1,2,2)
plt.hist2d(df2['SHOT_DIST'], df2['CLOSE_DEF_DIST'])
plt.title('After Dec 22nd')
plt.colorbar()
plt.xlabel('Shot Distance (ft)');