Consuming OMF

This example demonstrates how to consume an Open Mining Format file

import tempfile
from pathlib import Path

import pandas as pd
import pooch
from omfpandas import OMFPandasReader

from elphick.geomet.block_model import BlockModel

/home/runner/work/geometallurgy/geometallurgy/.venv/lib/python3.12/site-packages/stringcase.py:247: SyntaxWarning:

invalid escape sequence '\W'

Load

# Cache an omf2 file locally
filepath: Path = Path(pooch.retrieve(
    url="https://raw.githubusercontent.com/elphick/omfpandas/main/assets/copper_deposit.omf",
    known_hash=None,
    path=Path(tempfile.gettempdir()) / "geometallurgy"))

omfpr: OMFPandasReader = OMFPandasReader(filepath=filepath)
blocks: pd.DataFrame = omfpr.read_blockmodel(blockmodel_name='Block Model')

Create mass

The mass of each block is calculated from the volume and a density of 2.265 g/cm3.

blocks.reset_index(level=['dx', 'dy', 'dz'], inplace=True)
blocks['volume'] = blocks.dx * blocks.dy * blocks.dz
blocks.reset_index().set_index(keys=['x', 'y', 'z', 'dx', 'dy', 'dz'], inplace=True)
blocks.rename(columns={'CU_pct': 'Cu'}, inplace=True)
blocks['mass'] = blocks['volume'] * 2.265

Create a BlockModel

The BlockModel is created from the DataFrame and visualised. This model can be used in a Flowsheet model to make metallurgical predictions and preserve the spatial context.

bm: BlockModel = BlockModel(name=filepath.stem, data=blocks, mass_dry_var='mass', moisture_in_scope=False)

p = bm.plot(scalar='Cu')
p.title = filepath.stem
p.show()

Create from the omf directly

The BlockModel can be created directly from the OMF file. This is useful when the data is not yet in a DataFrame.

Note

This requires components to be chemical symbols. This is not the case for the sample omf file (at this stage).

# bm: BlockModel = BlockModel.from_omf(omf_filepath=filepath, element_name='Block Model', mass_dry_var='mass',
#                                      moisture_in_scope=False)