# Python code to adjust Benjamini & Hochberg alpha values
# By Yilin Liu, Penn Center for Learning Analytics
# This code is released as public domain
import numpy as np

def benjamini_hochberg(p_values):
    n = len(p_values)

    # Pair each p-value with its index and sort by p-value
    sorted_pvals = sorted(enumerate(p_values), key=lambda x: x[1])

    # Initialize result arrays for both original and sorted views
    result_original = np.zeros((n, 3), dtype=object)
    result_sorted = np.zeros((n, 3), dtype=object)

    # Fill result arrays with p-values
    for i, (orig_index, pval) in enumerate(sorted_pvals):
        result_sorted[i, 0] = pval  # Sorted p-values
        result_original[orig_index, 0] = pval  # Original p-values

    # Apply the Benjamini-Hochberg procedure
    for rank, (orig_index, pval) in enumerate(sorted_pvals, start=1):
        alpha = (0.05 * rank) / n

        # Fill alpha values
        result_sorted[rank - 1, 1] = alpha
        result_original[orig_index, 1] = alpha

        # Mark significance
        significance = 'Significant' if pval <= alpha else 'Not Significant'
        result_sorted[rank - 1, 2] = significance
        result_original[orig_index, 2] = significance

    return result_original, result_sorted

result_original, result_sorted  = benjamini_hochberg([0.01, 0.09, 0.2, 0.03])
print("=== Original Order ===")
print("P-value | Alpha | Significance")
for row in result_original:
    print(f"{row[0]:.5f} | {row[1]:.5f} | {row[2]}")

print("\n=== Sorted by P-value ===")
print("P-value | Alpha | Significance")
for row in result_sorted:
    print(f"{row[0]:.3f} | {row[1]:.5f} | {row[2]}")