Reblocking

import tempfile

import pandas as pd
from pathlib import Path
import pyvista as pv

from parq_blockmodel import ParquetBlockModel

Create a Parquet Block Model

We leverage the create_demo_block_model class method to create a Parquet Block Model.

temp_dir = Path(tempfile.gettempdir()) / "block_model_example"
temp_dir.mkdir(parents=True, exist_ok=True)

pbm: ParquetBlockModel = ParquetBlockModel.create_demo_block_model(
    filename=temp_dir / "demo_block_model.parquet")
pbm

ParquetBlockModel(name=demo_block_model, path=/tmp/block_model_example/demo_block_model.pbm.parquet)

Visualise the Model

A continuous attribute.

pbm.plot(scalar='depth', threshold=False, enable_picking=True).show()

A categorical attribute.

pbm.plot(scalar='depth_category', threshold=False, enable_picking=True).show()

Upsampling

Create a new grid with smaller blocks, and visualise.

upsampled_pbm: ParquetBlockModel = pbm.upsample(new_block_size=(0.5, 0.5, 0.5),
                                                interpolation_config={
                                                    'depth': 'linear',
                                                    'depth_category': 'nearest'
                                                })

upsampled_pbm.plot(scalar='depth', threshold=False, enable_picking=True).show()

Visualise a reblocked categorical attribute.

upsampled_pbm.plot(scalar='depth_category', threshold=False, enable_picking=True).show()

Downsample

Downsample the upsampled model back to the original block size.

downsampled_pbm: ParquetBlockModel = upsampled_pbm.downsample(new_block_size=(1.0, 1.0, 1.0),
                                                              aggregation_config={
                                                                  'depth': {'method': 'mean'},
                                                                  'depth_category': {'method': 'mode'}
                                                              })

downsampled_pbm.plot(scalar='depth', threshold=False, enable_picking=True).show()
downsampled_pbm.plot(scalar='depth_category', threshold=False, enable_picking=True).show()

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Validate

Confirm the round trip returned the original data

blocks_original: pd.DataFrame = pbm.data.to_pandas()
blocks_up_down: pd.DataFrame = pbm.data.to_pandas()

pd.testing.assert_frame_equal(blocks_original, blocks_up_down)