quantitative-literaturwissenschaft: import

quantitative-literaturwissenschaft/quantitative_literaturwissenschaft.py

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+import pandas as pd
+import numpy as np
+from typing import List, Tuple, Literal, Generator
+from numpy.typing import NDArray
+import itertools
+import math
+import random
+
+type Binary = Literal[0] | Literal[1]
+
+def get_transitions[A](series: List[A], step=1) -> List[Tuple[A, A]]:
+    cycled_series = series[step:] + series[:step]
+    return list(zip(series, cycled_series))
+
+def transition_matrix[A](transitions: List[Tuple[A, A]]) -> NDArray[np.integer]:
+    element, next_element = zip(*transitions)
+    crosstab = pd.crosstab(element, next_element)
+    return np.matrix(crosstab)
+
+
+def correlation(matrix: NDArray[np.integer]) -> float:
+    if matrix.shape != (2, 2):
+        raise ValueError("The input matrix must be 2x2")
+
+    main_diagonal_product = matrix[0, 0] * matrix[1, 1]
+    other_diagonal_product = matrix[0, 1] * matrix[1, 0]
+    difference = main_diagonal_product - other_diagonal_product
+
+    row_sums = matrix.sum(axis=1)
+    col_sums = matrix.sum(axis=0)
+    product_of_sums = np.prod(row_sums) * np.prod(col_sums)
+
+    sqrt_product_of_sums = np.sqrt(product_of_sums)
+
+    return difference / sqrt_product_of_sums
+
+def correlation_ranges[A](series: List[A]) -> Generator[float, None, None]:
+    step = 0
+    while True:
+        transitions = get_transitions(series, step=step)
+        matrix = transition_matrix(transitions)
+        current_correlation = correlation(matrix)
+        yield current_correlation
+        step += 1
+
+type MetricalSyllable = Literal["-"] | Literal["u"]
+
+sonett = list(14 * ((5 * "u-") + "u"))
+
+limerick = list(2 * "u-uu-uu-u" + 2 * "u-uu-" + "u-uu-uu-u")
+
+def get_hexameter(k: int = 1):
+    result = []
+    for _ in range(k):
+        hexameter = list("".join(random.choices(["-uu", "--"], k=5)) + random.choice(["--", "-u"]))
+        result.extend(hexameter)
+    return result
+
+mat = transition_matrix(get_transitions(sonett))
+print(mat)
+print(correlation(mat))
+
+mat = transition_matrix(get_transitions(limerick))
+print(mat)
+print(correlation(mat))
+
+
+mat = transition_matrix(get_transitions(get_hexameter(k=2000)))
+print(mat)
+print(correlation(mat))
+
+mat = transition_matrix(get_transitions(random.choices(["-", "u"], k=20000)))
+print(mat)
+print(correlation(mat))
+
+print(list(itertools.islice(correlation_ranges(get_hexameter(k=2000)), 40)))