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+#!/usr/bin/env nix-shell
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+#!nix-shell -i python3 -p python3 python3Packages.pandas python3Packages.numpy python3Packages.scipy python3Packages.pyarrow
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+# coding: utf-8
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+
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+from scipy.integrate import quad
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+import pandas as pd
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+import numpy as np
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+
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+from pathlib import Path
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+
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+
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+def normalProbabilityDensity(x):
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+ constant = 1.0 / np.sqrt(2*np.pi)
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+ return (constant * np.exp((-x**2) / 2.0))
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+
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+home = Path.home()
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+snt_path = Path(home, ".standard-normal.csv")
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+
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+if snt_path.exists():
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+ snt = pd.read_csv(snt_path, index_col=0)
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+else:
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+ snt = pd.DataFrame(data = [],
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+ index = np.round(np.arange(0, 3.5, 0.1), 2),
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+ columns = np.round(np.arange(0.00, 0.1, 0.01), 2))
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+
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+ for index in snt.index:
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+ for column in snt.columns:
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+ z = np.round(index + column, 2)
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+ value, _ = quad(normalProbabilityDensity, np.NINF, z)
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+ value = np.round(value - 0.5, 4)
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+ snt.loc[index,column] = value
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+ snt.to_csv(snt_path)
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+
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+def find_z(p):
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+ posns = []
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+ res = snt.isin([p])
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+ srs = res.any()
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+ cols = list(srs[srs == True].index)
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+ for col in cols:
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+ rows = list(res[col][res[col] == True].index)
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+ for row in rows:
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+ posns.append(float(col) + row)
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+ return np.max(posns)
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+
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+mean = float(input("Sample Mean: "))
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+std = float(input("Standard Deviation: "))
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+N = int(input("Sample Size: "))
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+alpha = float(input("Alpha [0, 1): "))
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+alpha_2 = alpha / 2
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+
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+Z_alpha_2 = find_z(alpha_2)
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+
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+plus_minus = np.round(Z_alpha_2 * (std / np.sqrt(N)), 4)
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+
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+print(f"{mean} +/- {plus_minus}")
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+print(f"[{mean - plus_minus}, {mean + plus_minus}]")
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+
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+### Local Variables:
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+### mode: python
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+### End:
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