# coding: utf-8
from csv import reader, writer
import numpy as np

fname = "../public/data/all_data.csv"
outname = "../public/data/normalized_data.csv"

vars_by_year = {}
updated_by_year = {}
headers = []
regions = []

with open(fname, encoding="utf-8") as fh:
	for row in reader(fh, delimiter=','):
		if len(headers) == 0:
			# Save the headers for later use
			headers = row
			# and also skip the headers line from processing
			continue
		year = row[0]
		#print(year)
		if year not in vars_by_year:
			vars_by_year[year] = []
		vars_by_year[year].append(row[1:])
		#print(', '.join(row))

print(vars_by_year)

for (year, data) in vars_by_year.copy().items():
	y_mat = np.matrix(data)
	# Remove years for which we do not have data for each pilot region
	if (y_mat.shape[0] < 9):
		del vars_by_year[year]
		continue
	#print(year)
	#print(y_mat.shape)

for (year, data) in vars_by_year.items():
	y_mat = np.matrix(data)
	# Save all pilot region names (can't be read elsewhere)
	if len(regions) == 0:
		regions = np.squeeze(y_mat[:, -1]).tolist()[0]
		#print(regions)
	y_mat = y_mat[:, :-1].astype(float)
	#print(y_mat.dtype)
	maxs = y_mat.max(axis=0)
	#print(maxs)
	mins = y_mat.min(axis=0)
	#print(mins)
	spans = maxs-mins
	#print(spans)
	y_mat = y_mat - mins
	y_mat = y_mat / spans
	#print(y_mat)
	updated_by_year[year] = y_mat

out = open(outname, 'w', encoding="utf-8", newline="")
cw = writer(out, delimiter=',', quotechar='"')
cw.writerow(headers)
for i in range(0, 8):
	for (year, data) in updated_by_year.items():
		row = data[i, :].astype(str).tolist()[0]
		row.insert(0, year)
		row.append(regions[i])
		#print(row)
		cw.writerow(row)
out.close()