Upload stats.py

stats.py

@@ -0,0 +1,35 @@
+import numpy as np
+from numpy import random
+from matplotlib import pyplot as plt
+
+
+def t_test(sample1, sample2):
+    # this is a t-test assuming equal sample sizes
+    assert len(sample1) == len(sample2)
+    difference = sample2.mean() - sample1.mean()
+    n_1 = len(sample1)
+    n_2 = len(sample2)
+    mu_measure_var1 = sample1.var(ddof=1) / len(sample1)
+    mu_measure_var2 = sample2.var(ddof=1) / len(sample2)
+    mu_std_err = np.sqrt(mu_measure_var1 + mu_measure_var2)
+    dof = mu_std_err**4 / (
+        mu_measure_var1**2 / (n_1 - 1) + mu_measure_var2**2 / (n_2 - 1)
+    )
+    t_val = difference / mu_std_err
+    t_null_dist = np.random.standard_t(dof, 100_000)
+    p_val = np.mean(np.abs(t_val) > t_null_dist) / 2
+    return f"""\
+t-value: {t_val}
+degrees of freedom: {dof}
+p-value: {p_val}"""
+
+
+def freqpoly(x1, x2, binwidth, xlim):
+    all_data = np.concatenate([x1, x2])
+    x_low = min([all_data.min(), xlim[0]])
+    x_high = max([all_data.max(), xlim[1]])
+    bins = np.arange(x_low, x_high + binwidth, binwidth)
+    fig, ax = plt.subplots()
+    ax.hist(x1, bins, density=True, range=xlim, alpha=0.5)
+    ax.hist(x2, bins, density=True, range=xlim, alpha=0.5)
+    return fig