.. DO NOT EDIT.
.. THIS FILE WAS AUTOMATICALLY GENERATED BY SPHINX-GALLERY.
.. TO MAKE CHANGES, EDIT THE SOURCE PYTHON FILE:
.. "auto_examples/06_reading_multiple_blockmodels.py"
.. LINE NUMBERS ARE GIVEN BELOW.

.. only:: html

    .. note::
        :class: sphx-glr-download-link-note

        :ref:`Go to the end <sphx_glr_download_auto_examples_06_reading_multiple_blockmodels.py>`
        to download the full example code.

.. rst-class:: sphx-glr-example-title

.. _sphx_glr_auto_examples_06_reading_multiple_blockmodels.py:


Reading Multiple Block Models
=============================

OMF allows storage of multiple block models in a single file.  This example demonstrates how to read multiple
block models to return a single pandas dataframe

.. GENERATED FROM PYTHON SOURCE LINES 8-18

.. code-block:: Python


    import tempfile
    from pathlib import Path

    import numpy as np
    import omf

    from omfpandas import OMFPandasReader









.. GENERATED FROM PYTHON SOURCE LINES 19-22

Instantiate
-----------
Create a function to generate a temporary omf file containing two block models.

.. GENERATED FROM PYTHON SOURCE LINES 22-60

.. code-block:: Python


    def create_demo_tensor_model(shape: tuple[int, int, int],
                                 cell_size: tuple[float, float, float],
                                 name: str,
                                 attr_names: list[str]) -> omf.TensorGridBlockModel:
        """Create a tensor grid block model with random attributes"""
        return omf.TensorGridBlockModel(
            name=name,
            tensor_u=np.full(shape[0], cell_size[0], dtype='float32'),
            tensor_v=np.full(shape[1], cell_size[1], dtype='float32'),
            tensor_w=np.full(shape[2], cell_size[2], dtype='float32'),
            attributes=[omf.NumericAttribute(name=attr_name, location="cells",
                                             array=np.random.rand(shape[0] * shape[1] * shape[2]).ravel()) for
                        attr_name in attr_names])


    def create_temp_omf_file() -> Path:
        """Creates an omf file directly using omf and numpy"""

        project = omf.Project(name='Test Project')

        # Create block models
        block_model_1 = create_demo_tensor_model((10, 10, 10), (10, 10, 10), 'BlockModel1', ['attr1', 'attr2'])

        block_model_2 = create_demo_tensor_model((10, 10, 10), (10, 10, 10), 'BlockModel2', ['attr3', 'attr4'])

        project.elements = [block_model_1, block_model_2]

        # Save to a temporary file
        temp_file = tempfile.NamedTemporaryFile(delete=False, suffix='.omf')
        omf.save(project, temp_file.name, mode='w')
        return Path(temp_file.name)


    temp_omf_path = create_temp_omf_file()
    reader = OMFPandasReader(temp_omf_path)
    reader





.. rst-class:: sphx-glr-script-out

 .. code-block:: none


    OMF file(/tmp/tmp2q8nao1s.omf)
    Elements: {'BlockModel1': 'TensorGridBlockModel', 'BlockModel2': 'TensorGridBlockModel'}



.. GENERATED FROM PYTHON SOURCE LINES 61-67

Read from block models
----------------------
We'll demonstrate how to selectively merge attributes from each of the two block models to create a single dataframe.
The `read_block_models` method accepts a dictionary where the keys are the block model names and the values are lists
of attribute names to include in the dataframe.  If None is provided for the attribute list, all attributes


.. GENERATED FROM PYTHON SOURCE LINES 67-73

.. code-block:: Python


    blockmodel_attributes = {'BlockModel1': None, 'BlockModel2': ['attr4']}

    df = reader.read_block_models(blockmodel_attributes)
    df.head(20)






.. raw:: html

    <div class="output_subarea output_html rendered_html output_result">
    <div>
    <style scoped>
        .dataframe tbody tr th:only-of-type {
            vertical-align: middle;
        }

        .dataframe tbody tr th {
            vertical-align: top;
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        .dataframe thead th {
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        </tr>
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          <td>0.518365</td>
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    <br />
    <br />

.. GENERATED FROM PYTHON SOURCE LINES 74-75

Check if the DataFrame contains the expected columns

.. GENERATED FROM PYTHON SOURCE LINES 75-79

.. code-block:: Python


    expected_columns = ['attr1', 'attr2', 'attr4']
    assert all(col in df.columns for col in expected_columns)








.. GENERATED FROM PYTHON SOURCE LINES 80-81

Clean up temporary file

.. GENERATED FROM PYTHON SOURCE LINES 81-83

.. code-block:: Python


    temp_omf_path.unlink()








.. rst-class:: sphx-glr-timing

   **Total running time of the script:** (0 minutes 0.050 seconds)


.. _sphx_glr_download_auto_examples_06_reading_multiple_blockmodels.py:

.. only:: html

  .. container:: sphx-glr-footer sphx-glr-footer-example

    .. container:: sphx-glr-download sphx-glr-download-jupyter

      :download:`Download Jupyter notebook: 06_reading_multiple_blockmodels.ipynb <06_reading_multiple_blockmodels.ipynb>`

    .. container:: sphx-glr-download sphx-glr-download-python

      :download:`Download Python source code: 06_reading_multiple_blockmodels.py <06_reading_multiple_blockmodels.py>`


.. only:: html

 .. rst-class:: sphx-glr-signature

    `Gallery generated by Sphinx-Gallery <https://sphinx-gallery.github.io>`_