Pandas Block Model to OMF

This example demonstrates how to read a block model from an OMF file, modify it using pandas, and write it back to a new OMF file.

from pathlib import Path
import logging

import numpy as np
import pandas as pd
import omf
from omf import Project

from omfpandas import OMFPandasReader, OMFPandasWriter
from omfpandas.blockmodel import df_to_blockmodel

Instantiate

logging.basicConfig(level=logging.INFO,
                    format='%(asctime)s %(levelname)s %(module)s - %(funcName)s: %(message)s',
                    datefmt='%Y-%m-%dT%H:%M:%S%z')
test_omf_filepath: Path = Path('../assets/v2/test_file.omf')

Read the block model

Read the original block model from the OMF file.

blocks: pd.DataFrame = OMFPandasReader(filepath=test_omf_filepath).read_blockmodel(blockmodel_name='vol')
blocks.head()

						random attr
x	y	z	dx	dy	dz
10.5	10.5	-9.5	1.0	1.0	1.0	0.727986
11.5	10.5	-9.5	1.0	1.0	1.0	0.277389
12.5	10.5	-9.5	1.0	1.0	1.0	0.351741
13.5	10.5	-9.5	1.0	1.0	1.0	0.999272
14.5	10.5	-9.5	1.0	1.0	1.0	0.495092

Modify the BlockModel

We’ll modify the block model using Pandas and write it back to another OMF file. Add a new float column to the block model.

blocks['density'] = 2.7
# set the second density record to nan
blocks.iloc[1, blocks.columns.get_loc('density')] = np.nan

Create an integer variable

np.random.seed(42)
blocks['some_int'] = pd.Series(np.random.choice([0, 1, 2, np.nan], size=blocks.shape[0], ), dtype='Int32',
                               index=blocks.index)

Create a categorical variable

blocks['rock_class'] = np.random.choice(['A', 'B', 'C', np.nan], size=blocks.shape[0])
blocks['rock_class'] = blocks['rock_class'].astype('category')
blocks.head()

						random attr	density	some_int	rock_class
x	y	z	dx	dy	dz
10.5	10.5	-9.5	1.0	1.0	1.0	0.727986	2.7	2	nan
11.5	10.5	-9.5	1.0	1.0	1.0	0.277389	NaN	<NA>	B
12.5	10.5	-9.5	1.0	1.0	1.0	0.351741	2.7	0	B
13.5	10.5	-9.5	1.0	1.0	1.0	0.999272	2.7	2	nan
14.5	10.5	-9.5	1.0	1.0	1.0	0.495092	2.7	2	A

blocks.dtypes

random attr     float64
density         float64
some_int          Int32
rock_class     category
dtype: object

Write to OMF

new_omf_filepath: Path = Path('modified_test_file.omf')

bm = df_to_blockmodel(blocks, 'Modified Block Model')

OMFPandasWriter(filepath=new_omf_filepath).write_blockmodel(blocks=blocks, blockmodel_name='Modified Block Model',
                                                            allow_overwrite=True)
omfpr: OMFPandasReader = OMFPandasReader(filepath=new_omf_filepath)

2024-08-08T23:37:25+0000 INFO writer - __init__: Creating new OMF file: modified_test_file.omf
2024-08-08T23:37:25+0000 INFO writer - write_blockmodel: Writing dataframe to BlockModel: Modified Block Model
2024-08-08T23:37:25+0000 INFO timer - wrapper: Elapsed time for write_blockmodel: 0:00:00.016059

Confirm that the new variables are in the model we saved.

saved_blocks: pd.DataFrame = OMFPandasReader(filepath=new_omf_filepath).read_blockmodel(
    blockmodel_name='Modified Block Model')
saved_blocks.head()

						random attr	density	some_int	rock_class
x	y	z	dx	dy	dz
10.5	10.5	-9.5	1.0	1.0	1.0	0.727986	2.7	2	nan
11.5	10.5	-9.5	1.0	1.0	1.0	0.277389	NaN	<NA>	B
12.5	10.5	-9.5	1.0	1.0	1.0	0.351741	2.7	0	B
13.5	10.5	-9.5	1.0	1.0	1.0	0.999272	2.7	2	nan
14.5	10.5	-9.5	1.0	1.0	1.0	0.495092	2.7	2	A

saved_blocks.dtypes

random attr     float64
density         float64
some_int         object
rock_class     category
dtype: object

Validation

Load the two omf projects and compare the CU_pct data for each

project_0: Project = omf.load(str(test_omf_filepath))
project_1: Project = omf.load(str(new_omf_filepath))

bm_1: omf.TensorGridBlockModel = [element for element in project_0.elements if element.name == 'vol'][0]
bm_2: omf.TensorGridBlockModel = [element for element in project_1.elements if element.name == 'Modified Block Model'][
    0]

bm_cu_1 = bm_1.attributes[0]
bm_cu_2 = bm_2.attributes[0]

assert np.all(bm_cu_1.name == bm_cu_2.name)

assert np.all(bm_cu_1.array.array == bm_cu_2.array.array)