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import numpy as np
import pandas as pd
import scipy.stats as ss
def create_combined_vector(assessment_file):
comb_df = pd.read_csv(assessment_file)
# Seperate out vectors
days = [0 for i in range(len(comb_df.values))]
trend = comb_df['trend'].values
close = comb_df['adjusted_close'].values
# Resize and normalize
days = days[1:]
trend = ss.zscore(trend[1:])
close = ss.zscore(np.diff(close))
return (trend, close, days)
# Generate combined vecotr ready for ingestion by the
# Granger Causality function (days used for later graph)
(trend, close, days) = create_combined_vector(filename)
combined_vector = []
for i in range(len(trend)):
combined_vector.append((trend[i], close[i]))
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