Note
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Validate BlockModel
This example demonstrates how to validate a block model prior to writing it to an OMF file.
import shutil
import tempfile
from pathlib import Path
import pandas as pd
from omfpandas import OMFPandasReader, OMFPandasWriter
Instantiate
Create the object OMFPandas with the path to the OMF file.
test_omf_path: Path = Path('./../assets/v2/test_file.omf')
# create a temporary copy to preserve the original file
temp_omf_path: Path = Path(tempfile.gettempdir()) / 'test_file_copy.omf'
shutil.copy(test_omf_path, temp_omf_path)
# Display the head of the original block model
blocks: pd.DataFrame = OMFPandasReader(filepath=temp_omf_path).read_blockmodel(blockmodel_name='vol')
blocks.head()
Write with Schema
The schema used to validate the dataframe is a Pandera schema. The schema can be a YAML file (or Pandera compatible dictionary) that is loaded by the Pandera library. The schema is used to validate the dataframe before writing it to the OMF file.
omfpw: OMFPandasWriter = OMFPandasWriter(filepath=temp_omf_path)
omfpw.write_blockmodel(blocks=blocks, blockmodel_name='vol',
pd_schema=test_omf_path.with_suffix('.schema.yaml'),
allow_overwrite=True)
The schema is persisted inside the omf file. This enables the schema to be used to validate modifications.
bm = omfpw.get_element_by_name('vol')
print(bm.metadata.get('pd_schema'))
{"schema_type": "dataframe", "version": "0.20.3", "columns": {"random attr": {"title": "Random Attribute", "description": "A random attribute for testing", "dtype": "float64", "nullable": false, "checks": {"greater_than_or_equal_to": 0.0, "less_than_or_equal_to": 1.0}, "unique": false, "coerce": false, "required": true, "regex": false}}, "checks": null, "index": null, "dtype": null, "coerce": true, "strict": false, "name": null, "ordered": false, "unique": null, "report_duplicates": "all", "unique_column_names": false, "add_missing_columns": false, "title": "Test Schema", "description": "A test dataset schema."}
Demonstrate Failure
We’ll demonstrate a failure by modifying the block model in a way that violates the schema.
blocks['random attr'] = blocks['random attr'] * 2
try:
omfpw.write_blockmodel(blocks=blocks, blockmodel_name='vol',
pd_schema=test_omf_path.with_suffix('.schema.yaml'),
allow_overwrite=True)
except Exception as e:
print(e)
Column 'random attr' failed element-wise validator number 1: less_than_or_equal_to(1.0) failure cases: 1.4559720737457786, 1.9985444352433313, 1.5907896431626796, 1.8440280921144294, 1.029931558509397, 1.3151572983059887, 1.0590145853319994, 1.2071569408544958, 1.4762044042932776, 1.659734334331123, 1.5458775431601834, 1.5284277086895106, 1.429773073123145, 1.36691049180721, 1.8428843020771741, 1.7181286907304707, 1.7232965755778566, 1.2805422743154005, 1.8140380167622396, 1.0495087164113834, 1.697546460456173, 1.830127740647213, 1.426383448407208, 1.8715684118119214, 1.811018412078782, 1.5636218408160385, 1.670402823040675, 1.639066773513726, 1.2413873316005761, 1.088998056678794, 1.4417580551698117, 1.1992796211513195, 1.122879179466444, 1.0527540583183441, 1.6364404326145545, 1.3339029696035074, 1.9027847554811121, 1.5358564377066184, 1.2234817990647127, 1.6785281981864928, 1.5953763016815006, 1.7585570130681376, 1.5778669273408064, 1.945116455601474, 1.5948025441299234, 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1.2134333611771773, 1.161837407036548
Update with valid data
omfpw.write_blockmodel_attribute(blockmodel_name='vol', series=blocks['random attr'] / 4, allow_overwrite=True)
blocks: pd.DataFrame = OMFPandasReader(filepath=temp_omf_path).read_blockmodel(blockmodel_name='vol')
blocks.head()
View the changelog
omfpw.changelog
Clean up
temp_omf_path.unlink()
Total running time of the script: (0 minutes 0.145 seconds)