make_ds_mask.py
dd1c0248d00daw_solar

make_ds_mask.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
			from features import Training, Testing

			data_path = sys.argv[1]
			dataset = sys.argv[2]
			features = sys.argv[3].split(",")
			targets = sys.argv[4].split(",")
			mask = sys.argv[5]
			val_ratio = float(sys.argv[6])
			year = 2001

			# set location masks for the training and test
			train_locs = "locations/"+mask+"_train.txt"
			test_locs = "locations/"+mask+"_test.txt"

			new_set = Training(data_path, dataset, features, targets)
			new_set.make_dataset(year = year, sample_name = train_locs, test_name = test_locs)

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