import pandas as pd
import re, string
import numpy as np
from scipy.spatial.distance import pdist, squareform, euclidean

def firmmeans(data,year,key):
	d = data[data['bvdid'].isin(key['bvdid'][key['year']==year].tolist())]
	d[['year']] = d[['year']].astype(int)
	d = d[d['year']<=year]
	cols = [col for col in d.columns.values if col not in ['year','appln_id']]
	d = d[cols].groupby('bvdid',as_index=False)
	d = d.aggregate(np.mean)
	cols = [col for col in d.columns.values if col not in ['bvdid']]
	firms = d['bvdid'].tolist()
	d = d[cols].as_matrix()
	d = pdist(d,metric='euclidean')
	d = squareform(d)
	d = pd.DataFrame(d,columns=firms)
	d['bvdid'] = firms
	d = d.set_index('bvdid')
	return d

if __name__ == '__main__':
	pattern = re.compile('[\W_]+')
	df = pd.read_csv('bvd_small.csv')
	df = df[['bvd_id','year']]
	patents = pd.read_csv('labelledpredictions.txt',header=None)
	link = pd.read_csv('bvdid_patents_link.csv')
	date = pd.read_csv('appln_date.csv')
	new_columns = df.columns.values
	new_columns[0] = 'bvdid'
	df.columns = new_columns
	new_columns = patents.columns.values.tolist()
	new_columns[0] = 'appln_id'
	patents.columns = new_columns
	new_columns = link.columns.values
	new_columns[0] = 'bvdid'
	link.columns = new_columns
	link['bvdid'] = [pattern.sub('', x) for x in link['bvdid']]
	df['bvdid'] = [pattern.sub('', x) for x in df['bvdid']]
	date['appln_date'] = [x[-4:] for x in date['appln_date']]
	new_columns = date.columns.values
	new_columns[1] = 'year'
	date.columns = new_columns
	link = link[link['bvdid'].isin(df['bvdid'].unique().tolist())]
	patents = patents.dropna()
	patents['appln_id'] = patents[['appln_id']].astype(int)
	patents = patents[patents['appln_id'].isin(link['appln_id'].tolist())]
	data = patents.merge(date,on='appln_id',how='left')
	data = data.merge(link,on='appln_id',how='left')
	for i in df['year'].unique().tolist():
		a = firmmeans(data,i,df)
		year = str(i)
		name = 'firmtechdist'+year+'.csv'
		a.to_csv(path=str(name))