Single-pass, parallel statistics algorithms for mean, variance, and standard deviation. See the rs.py file for example usage.
This code enables the ability to split up datasets and in parallel compute some descriptive statistics. Here is an example from the test_suit fuction in rs.py.
# Setup test data
t1 = np.random.randint(9, size=(1,2000)) * 8.9
t1 = list(t1[0])
t2 = np.random.randint(20, size=(1,200)) * 8.9
t2 = list(t2[0])
t3 = np.random.randint(100, size=(1,500)) * 8.9
t3 = list(t3[0])
t4 = t1 + t2 + t3
# crate instances of data structure
h1 = get_rolling_stats(t1)
h2 = get_rolling_stats(t2)
h3 = get_rolling_stats(t3)
h_1_2 = get_rolling_stats(t1 + t2)
h4 = get_rolling_stats(t4)
# Test map opperation and addition
l_rs = [h1,h2,h3]
h5 = aggrate_rolling_values(l_rs)
# Test addition
h6 = (h1 + h2) + h3