Note

Click here to download the full example code

stepped sections

bt.plot.fence slices a block model on several parallel planes stepped along a corridor, all sharing one color scale. it draws a fence of cross sections through the stacked sulphide lenses in one call, where the maps in target distribution correction used a hand-rolled plt.subplots loop.

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
import numpy as np
from common import save

a grade model across the lenses

zinc kriged from the 5 m composites (as in domain change tables) onto a block model spanning the deposit, cells of 20 m, masked to the topography.

data = bt.datasets.stacked_sulphide_lenses()
intervals = bt.merge_intervals(data["assays"], data["lithology"])
drillholes = bt.Drillholes(data["collars"], data["surveys"], intervals)
composites = drillholes.composite(5.0, ["ZN_PCT"], categories=["LITH"])
known = composites.drop_null("ZN_PCT")

layers = (23.0, 55.0, 0.0)
variogram = bt.Variogram([("spherical", 0.8, 200.0)], nugget=0.2, rotation=layers, ratios=(0.8, 0.2))
search = bt.Search(300.0, max_samples=24, max_per_hole=6, rotation=layers, ratios=(1.0, 0.4))

center = np.array([12350.0, 29900.0, 0.0])
across = np.array([np.sin(np.radians(113.0)), np.cos(np.radians(113.0)), 0.0])
along = np.array([np.sin(np.radians(23.0)), np.cos(np.radians(23.0)), 0.0])
origin = center - 600.0 * across - 300.0 * along + (0.0, 0.0, -400.0)
model = bt.BlockModel(origin, (20.0, 20.0, 20.0), (60, 30, 40), rotation=(23.0, 0.0, 0.0))
topography = data["topography"]
model = model.filter(model.z < topography.sample(model.coords[:, :2], "Z"))
kriged = bt.OrdinaryKriging(variogram, search).fit(known, "ZN_PCT", holes="HOLE_ID").predict(model)
model = model.with_columns({"ZN_PCT": kriged}).drop_null("ZN_PCT")
print(f"{len(model):,} blocks of 20 m")

Out:

51,013 blocks of 20 m

the old way: a manual loop

target distribution correction drew its two maps by hand: plt.subplots(1, n, sharey=True), one bt.plot.section(..., colorbar=False, ax=ax) per panel, vmin/vmax matched by hand across them, and one fig.colorbar at the end reading the last panel's image. the same recipe, repeated for five sections stepped 125 m apart along strike:

positions = [center + t * along for t in np.linspace(-250.0, 250.0, 5)]
fig, axes = plt.subplots(1, len(positions), figsize=(13, 4), layout="constrained", sharey=True)
for ax, position in zip(axes, positions, strict=True):
    bt.plot.section(model, "ZN_PCT", plane=(position, 113.0, 90.0), vmin=0, vmax=8, colorbar=False, ax=ax)
for ax in axes[1:]:
    ax.set_ylabel("")
fig.colorbar(axes[-1].collections[0], ax=axes, shrink=0.8, label="ZN_PCT")
save(fig, "manual")

bt.plot.fence

the same five sections in one call. all panels share azimuth and dip, and fence finds vmin/vmax across them unless you give them, as section takes them.

fig, axes = bt.plot.fence(model, "ZN_PCT", positions, azimuth=113.0, dip=90.0)
save(fig, "fence")

Total running time of the script: ( 0 minutes 1.107 seconds)

Download Python source code: example_02_16.py

Download Jupyter notebook: example_02_16.ipynb

Gallery generated by mkdocs-gallery