Note
Click here to download the full example code
categories¶
107 sonic holes drill a tailings storage facility and log it as cover (CAP), sand (SAND), slimes (SLIME) and the clay
of the original ground (CLAY). a Categories scheme fixes the order, names and colors of the codes once, so shares,
plots and later models agree on them.
import sys
from pathlib import Path
HERE = Path(__file__).parent if "__file__" in globals() else Path.cwd()
sys.path.insert(0, str(HERE.parents[1]))
import boitata as bt
import matplotlib.pyplot as plt
from common import save
data = bt.datasets.tailings_reprocessing()
logged = bt.Drillholes.from_tables(data, intervals="lithology").samples()
length = logged["TO"] - logged["FROM"]
print(
f"{len(logged)} intervals, {length.sum():.0f} m logged, {length.min():.2f} to {length.max():.1f} m long"
)
Out:
3724 intervals, 1707 m logged, 0.05 to 5.8 m long
encode turns the logged names into integer codes in the order of the scheme, NaN for an unknown name. shares
gives the proportion of each code, by count or weighted. interval lengths differ a hundredfold, so the shares by
length describe the facility.
lithology = bt.Categories(
["CAP", "SAND", "SLIME", "CLAY"], colors=["#b8b8b8", "#d9b56c", "#5b7a99", "#7a4f35"]
)
codes = lithology.encode(logged["LITH"])
by_count, by_length = lithology.shares(codes), lithology.shares(codes, weights=length)
for name, a, b in zip(lithology.names, by_count, by_length, strict=True):
print(f"{name:<6}{a:6.1%} of intervals {b:6.1%} of length")
Out:
CAP 1.4% of intervals 2.2% of length
SAND 48.0% of intervals 48.2% of length
SLIME 47.7% of intervals 38.6% of length
CLAY 2.9% of intervals 11.0% of length
SLIME appears as often as SAND in thinner intervals, and the few CLAY intervals are long. by length, SLIME falls
from 47.7 % to 38.6 % and CLAY rises from 2.9 % to 11.0 %. plot.proportions draws the weighted shares as bars and
the unweighted ones as ticks; plot.category_swath stacks the shares per slice, as swath does for a grade.
fig, axes = plt.subplots(1, 3, figsize=(12, 3.4), layout="constrained", width_ratios=[1, 1.4, 1.4])
bt.plot.proportions(codes, weights=length, scheme=lithology, ax=axes[0])
axes[0].set_title("Share of length")
xyz = logged.coords
bt.plot.category_swath(xyz, codes, 50.0, axis="y", weights=length, scheme=lithology, ax=axes[1])
axes[1].set(title="Along northing, 50 m slices", xlabel="Northing (m)")
axes[1].get_legend().remove()
bt.plot.category_swath(xyz, codes, 2.0, axis="z", weights=length, scheme=lithology, ax=axes[2])
axes[2].set(title="In elevation, 2 m slices", xlabel="Elevation (m)")
save(fig, "categories")
sand dominates the north and slimes the south, where the fines settled; clay sits at the base and cover on top. a reprocessing plan that treats sand and slimes apart would model them as separate domains.
you can also read a scheme off the data. Categories.from_values keeps the names above min_share of the weight,
sorted, and lumps the rest into other, last: here CAP, under 5 % of the length.
found = bt.Categories.from_values(logged["LITH"], weights=length, min_share=0.05)
print(found.names)
Out:
['CLAY', 'SAND', 'SLIME', 'other']
Total running time of the script: ( 0 minutes 0.217 seconds)
Download Python source code: example_07_01.py