make_ds_rand_month.py
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	#!/usr/bin/env python
	import numpy as np
	import pandas as pd
	import xarray as xr
	import os
	import sys
	import time
	import util
	from features import Training, Testing
	from meteo_data import Meteo_Reader

	data_path = sys.argv[1]
	dataset = sys.argv[2]
	features = sys.argv[3].split(",")
	targets = sys.argv[4].split(",")
	sample_size = float(sys.argv[5])*1000000
	val_ratio = float(sys.argv[6])
	month = int(sys.argv[7])
	year = 2001

	start_date, end_date = util.month_to_dates(year, month)
	train_locs = "locations/all_train.txt"
	test_locs = "locations/all_test.txt"

	tt = util.Timer()
	ds = Meteo_Reader(os.path.join(data_path, 'raw_data'), variables = targets)
	ds.read_data(start_date,end_date)
	train_data = ds.get_subset(filename = train_locs, sample_name = 'train')
	test_data = ds.get_subset(filename = test_locs, sample_name = 'test')
	tbl_train = ds.make_table(indata = train_data, ftrs = features)
	tbl_test = ds.make_table(indata = test_data, ftrs = features)
	tt.stop()

	# sample_size = min(sample_size, len(tbl))
	N_train = min(int(0.8*sample_size), len(tbl_train))
	N_test = min(int(0.2*sample_size), len(tbl_test))

	new_set = Training(data_path, dataset, features, targets, data_type = 'table');
	new_set.make_dataset(table = tbl_train.sample(N_train), test_table = tbl_test.sample(N_test))

	new_set.normalize_all(feature_norm = 'mean', target_norm = 'mean', val_ratio = val_ratio, force_normalization = True)