import json
import requests
import pandas as pd
import csv

auth = "Bearer " + "BQCHzulDBG1JQpx0MpuNWzZ9djJbhNbeNFmwHn9678G2hIbMrHOLg7mf8o_QMAP4hWkQstq2qfn49_s0VMJhe-RewkHa3GpyZJZc6Qui_XXI5p93FEoy7oD99Ol-wSDuFN4SzlyBjyTIzQ"

#Chill Tracks: 37i9dQZF1DX6VdMW310YC7
#Summer Heat: 37i9dQZF1DWX1UA045EoPG
response = requests.get('https://api.spotify.com/v1/users/spotify/playlists/37i9dQZF1DX6VdMW310YC7/tracks', headers={"Authorization": auth})

data = response.json()

ids = []

for item in data:
	ls = data.get("items")
	for n in range(len(ls)):
		id = ls[n].get("track").get("id")
		if id not in ids: ids.append(id)
		
	
df = pd.DataFrame(columns=['danceability',
  'energy',
  'key',
  'loudness',
  'mode',
  'speechiness',
  'acousticness',
  'instrumentalness',
  'liveness',
  'valence',
  'tempo',
  'type',
  'id',
  'uri',
  'track_href',
  'analysis_url',
  'duration_ms',
  'time_signature']) 
  



for id in ids:
	rq = "https://api.spotify.com/v1/audio-features/" + id
	response = requests.get(rq, headers={"Authorization": auth})
	newdf = pd.DataFrame(response.json(), index = [0])
	try:
		df = pd.concat([df,newdf])
	except:
		pass

df['on_playlist'] = 1
#df = df.drop('error',1).dropna()
df = df.dropna()

print df


with open('900_tracks.csv', 'rb') as f:
    reader = csv.reader(f)
    id_list = list(reader)



df2 = pd.DataFrame(columns=['danceability',
  'energy',
  'key',
  'loudness',
  'mode',
  'speechiness',
  'acousticness',
  'instrumentalness',
  'liveness',
  'valence',
  'tempo',
  'type',
  'id',
  'uri',
  'track_href',
  'analysis_url',
  'duration_ms',
  'time_signature']) 

for id in id_list:
	rq = "https://api.spotify.com/v1/audio-features/" + id[0]
	response = requests.get(rq, headers={"Authorization": auth})
	newdf = pd.DataFrame(response.json(), index = [0])
	try:
		df2 = pd.concat([df2,newdf])
	except:
		pass
	print df2

df2['on_playlist'] = 0
df2 = df2.drop('error',1).dropna()
print df2

final = pd.concat([df,df2])


final.to_csv('final_dataset_june12_chilltracks.csv')


import statsmodels.formula.api as sm

fml = "on_playlist ~  danceability + energy + key + loudness + mode  + speechiness + acousticness + instrumentalness + liveness + valence +  tempo"

result = sm.ols(fml, data=final).fit()

print(result.summary())