[+] tqdm and scientific utils

hypy_utils/scientific_utils.py

@@ -0,0 +1,72 @@
+"""
+Importing this file requires numpy, matplotlib, and numba
+"""
+from __future__ import annotations
+
+import time
+from dataclasses import dataclass
+from typing import Callable
+
+import numpy as np
+from matplotlib import pyplot as plt
+from numba import njit
+
+
+@dataclass
+class Statistics:
+    mean: float
+    median: float
+    lower_quartile: float
+    upper_quartile: float
+    iqr: float
+    minimum: float
+    maximum: float
+    count: int
+    total: float
+
+    def get_metric_6(self) -> tuple[float, float, float, float, float, float]:
+        return self.mean, self.median, self.minimum, self.maximum, self.lower_quartile, self.upper_quartile
+
+
+@njit(cache=True)
+def _calc_col_stats_helper(col: np.ndarray) -> tuple[float, float, float, float, float, float, float, int, float]:
+    q1 = np.quantile(col, 0.25)
+    q3 = np.quantile(col, 0.75)
+    return (
+        float(np.mean(col)),
+        float(np.median(col)),
+        float(q1),
+        float(q3),
+        float(q3 - q1),
+        float(np.min(col)),
+        float(np.max(col)),
+        len(col),
+        float(np.sum(col))
+    )
+
+
+def calc_col_stats(col: np.ndarray | list) -> Statistics:
+    """
+    Compute statistics for a data column
+
+    :param col: Input column (tested on 1D array)
+    :return: Statistics
+    """
+    if isinstance(col, list):
+        col = np.array(col)
+    return Statistics(*_calc_col_stats_helper(col))
+
+
+def plot(**kwargs) -> plt:
+    """
+    Pyplot configurator shorthand
+
+    Example: plt_cfg(xlabel="X", ylabel="Y") is equivalent to plt.xlabel("X"); plt.ylabel("Y")
+    """
+    for k, args in kwargs.items():
+        if isinstance(args, dict):
+            getattr(plt, k)(**args)
+        else:
+            getattr(plt, k)(args)
+    return plt
+