src/2026-01-30/gen_correlated_data.py

@@ -1,38 +0,0 @@
-import numpy as np
-import matplotlib.pyplot as plt
-from numpy.typing import NDArray
-import argparse
-
-
-def twodim_array_to_pgfplots_table_string(a: NDArray):
-    return (
-        " \\\\\n".join([" ".join([str(vali) for vali in val]) for val in a]) + "\\\\\n"
-    )
-
-
-def main():
-    # Parse command line arguments
-
-    parser = argparse.ArgumentParser()
-    parser.add_argument("--correlation", "-c", type=np.float32, required=True)
-    parser.add_argument("-N", type=np.int32, required=True)
-    parser.add_argument("--plot", "-p", action="store_true")
-
-    args = parser.parse_args()
-
-    # Generate & plot data
-
-    means = np.array([0, 0])
-    cov = np.array([[1, args.correlation], [args.correlation, 1]])
-
-    x = np.random.multivariate_normal(means, cov, size=args.N)
-
-    print(twodim_array_to_pgfplots_table_string(x))
-
-    if args.plot:
-        plt.scatter(x[:, 0], x[:, 1])
-        plt.show()
-
-
-if __name__ == "__main__":
-    main()

src/2026-01-30/gen_histogram.py

@@ -1,40 +0,0 @@
-import argparse
-import numpy as np
-import matplotlib.pyplot as plt
-from scipy.special import binom
-
-
-def array_to_pgfplots_table_string(a):
-    return " ".join([f"({k}, {val})" for (k, val) in enumerate(a)]) + f" ({len(a)}, 0)"
-
-
-def P_binom(N, p, k):
-    return binom(N, k) * p**k * (1 - p) ** (N - k)
-
-
-def main():
-    # Parse command line arguments
-
-    parser = argparse.ArgumentParser()
-    parser.add_argument("-N", type=np.int32, required=True)
-    parser.add_argument("-p", type=np.float32, required=True)
-    parser.add_argument("--show", "-s", action="store_true")
-
-    args = parser.parse_args()
-
-    # Generate and show data
-
-    N = args.N
-    p = args.p
-
-    bars = np.array([P_binom(N, p, k) for k in range(N + 1)])
-
-    print(array_to_pgfplots_table_string(bars))
-
-    if args.show:
-        plt.stem(bars)
-        plt.show()
-
-
-if __name__ == "__main__":
-    main()