Azalea (on HyDEV-Daisy)
70 lines
1.9 KiB
Python
70 lines
1.9 KiB
Python
from __future__ import annotations
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from dataclasses import dataclass
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from pathlib import Path
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import numpy as np
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import matplotlib.pyplot as plt
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@dataclass
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class Statistics:
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mean: float
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median: float
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lower_quartile: float
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upper_quartile: float
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iqr: float
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minimum: float
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maximum: float
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count: int
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total: float
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stddev: float
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def get_metric_6(self) -> tuple[float, float, float, float, float, float]:
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return self.mean, self.median, self.minimum, self.maximum, self.lower_quartile, self.upper_quartile
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def print(self, dec: int = 2):
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print(f'> Mean: {round(self.mean, dec)}, Median: {round(self.median, dec)}')
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print(f'> Min: {round(self.minimum, dec)}, Max: {round(self.maximum, dec)}')
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print(f'> Q1: {round(self.lower_quartile, dec)}, Q3: {round(self.upper_quartile, dec)}')
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print(f'> StdDev: {round(self.stddev, dec)}, IQR: {round(self.iqr, dec)}')
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print(f'> N: {self.count}')
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def _calc_col_stats_helper(col: np.ndarray) -> tuple[float, float, float, float, float, float, float, int, float, float]:
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q1 = np.quantile(col, 0.25)
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q3 = np.quantile(col, 0.75)
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return (
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float(np.mean(col)),
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float(np.median(col)),
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float(q1),
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float(q3),
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float(q3 - q1),
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float(np.min(col)),
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float(np.max(col)),
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len(col),
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float(np.sum(col)),
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float(np.std(col))
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)
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def calc_col_stats(col: np.ndarray | list) -> Statistics:
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"""
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Compute statistics for a data column
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:param col: Input column (tested on 1D array)
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:return: Statistics
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"""
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if isinstance(col, list):
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col = np.array(col)
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return Statistics(*_calc_col_stats_helper(col))
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if __name__ == '__main__':
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txt = Path('action-sizes.log').read_text('utf-8').split('\n')
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nums = [int(line) for line in txt if line.isnumeric()]
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# print(nums)
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calc_col_stats(nums).print()
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plt.hist(nums, bins=50)
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plt.show()
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