import numpy as np
from pandas.arrays import IntervalArray
[docs]def mean_size(size_intervals: IntervalArray) -> np.ndarray:
"""Geometric mean size
Size calculations are performed using the geometric mean, not the arithmetic mean
NOTE: If geometric mean is used for the pan fraction (0.0mm retained) it will return zero, which is an
edge size not mean size. So the mean ratio of the geometric mean to the arithmetic mean for all other
fractions is used for the bottom fraction.
Args:
size_intervals: A pandas IntervalArray
Returns:
"""
intervals = size_intervals.copy()
res = np.array((intervals.left * intervals.right) ** 0.5)
geomean_mean_ratio: float = float(np.mean((res[0:-1] / intervals.mid[0:-1])))
if np.isclose(size_intervals.min().left, 0.0):
res[np.isclose(size_intervals.left, 0.0)] = size_intervals.min().mid * geomean_mean_ratio
return res
# REF: https://www.globalgilson.com/blog/sieve-sizes
sizes_iso_565 = [63.0, 56.0, 53.0, 50.0, 45.0, 40.0, 37.5, 35.5, 31.5, 28.0, 26.5, 25.0, 22.4, 20.0,
19.0, 18.0, 16.0, 14.0, 13.2, 12.5, 11.2, 10.0, 9.5, 9.0, 8.0, 7.1, 6.7, 6.3, 5.6,
5.0, 4.75, 4.5, 4.0, 3.55, 3.35, 3.15, 2.8, 2.5, 2.36, 2.0, 1.8, 1.7, 1.6, 1.4, 1.25,
1.18, 1.12, 1.0, 0.900, 0.850, 0.800, 0.710, 0.630, 0.600, 0.560, 0.500, 0.450, 0.425,
0.400, 0.355, 0.315, 0.300, 0.280, 0.250, 0.224, 0.212, 0.200, 0.180, 0.160, 0.150, 0.140,
0.125, 0.112, 0.106, 0.100, 0.090, 0.080, 0.075, 0.071, 0.063, 0.056, 0.053, 0.050, 0.045,
0.040, 0.038, 0.036, 0.032, 0.025, 0.020]
sizes_astm_e11 = [100.0, 90.0, 75.0, 63.0, 53.0, 50.0, 45.0, 37.5, 31.5, 26.5, 25.0, 22.4, 19.0, 16.0,
13.2, 12.5, 11.2, 9.5, 8.0, 6.7, 6.3, 5.6, 4.75, 4.0, 3.35, 2.8, 2.36, 2.0, 1.7, 1.4,
1.18, 1.0, 0.850, 0.710, 0.600, 0.500, 0.425, 0.355, 0.300, 0.250, 0.212, 0.180, 0.150,
0.125, 0.106, 0.090, 0.075, 0.063, 0.053, 0.045, 0.038, 0.032, 0.025, 0.020]
sizes_all = sorted(list(set(sizes_astm_e11).union(set(sizes_iso_565))), reverse=True)