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

.. only:: html

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

        Click :ref:`here <sphx_glr_download_auto_examples_resampling_interval_data.py>`
        to download the full example code

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

.. _sphx_glr_auto_examples_resampling_interval_data.py:


Resampling Interval Data
========================

Interval (or fractional) data is common in metallurgy and mineral processing.  Samples are sized using sieves
in a laboratory and each resultant fraction is often assayed to determine chemical composition.
The typical nomenclature is of the interval edges is size_retained, size passing - any particle within an interval
or fraction was retained by the lower sieve size, but passed the sieve size above it.

.. GENERATED FROM PYTHON SOURCE LINES 11-20

.. code-block:: default

    import logging

    import numpy as np
    import pandas as pd
    import plotly

    from elphick.mass_composition import MassComposition
    from elphick.mass_composition.datasets.sample_data import size_by_assay








.. GENERATED FROM PYTHON SOURCE LINES 21-26

.. code-block:: default

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








.. GENERATED FROM PYTHON SOURCE LINES 27-32

Create a MassComposition object
-------------------------------

We get some demo data in the form of a pandas DataFrame
We create this object as 1D based on the pandas index

.. GENERATED FROM PYTHON SOURCE LINES 33-37

.. code-block:: default


    df_data: pd.DataFrame = size_by_assay()
    df_data






.. 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;
        }

        .dataframe thead th {
            text-align: right;
        }
    </style>
    <table border="1" class="dataframe">
      <thead>
        <tr style="text-align: right;">
          <th></th>
          <th></th>
          <th>mass_dry</th>
          <th>fe</th>
          <th>sio2</th>
          <th>al2o3</th>
        </tr>
        <tr>
          <th>size_retained</th>
          <th>size_passing</th>
          <th></th>
          <th></th>
          <th></th>
          <th></th>
        </tr>
      </thead>
      <tbody>
        <tr>
          <th>0.850</th>
          <th>2.000</th>
          <td>3.3</td>
          <td>64.15</td>
          <td>2.04</td>
          <td>2.68</td>
        </tr>
        <tr>
          <th>0.500</th>
          <th>0.850</th>
          <td>9.9</td>
          <td>64.33</td>
          <td>2.05</td>
          <td>2.23</td>
        </tr>
        <tr>
          <th>0.150</th>
          <th>0.500</th>
          <td>26.5</td>
          <td>64.52</td>
          <td>1.84</td>
          <td>2.19</td>
        </tr>
        <tr>
          <th>0.075</th>
          <th>0.150</th>
          <td>2.5</td>
          <td>62.65</td>
          <td>2.88</td>
          <td>3.32</td>
        </tr>
        <tr>
          <th>0.045</th>
          <th>0.075</th>
          <td>8.8</td>
          <td>62.81</td>
          <td>2.12</td>
          <td>2.25</td>
        </tr>
        <tr>
          <th>0.000</th>
          <th>0.045</th>
          <td>49.0</td>
          <td>55.95</td>
          <td>6.39</td>
          <td>6.34</td>
        </tr>
      </tbody>
    </table>
    </div>
    </div>
    <br />
    <br />

.. GENERATED FROM PYTHON SOURCE LINES 38-39

The size index is of the Interval type, maintaining the fractional information.

.. GENERATED FROM PYTHON SOURCE LINES 40-44

.. code-block:: default


    mc_size: MassComposition = MassComposition(df_data, name='Sample')
    mc_size.data.to_dataframe()






.. 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;
        }

        .dataframe thead th {
            text-align: right;
        }
    </style>
    <table border="1" class="dataframe">
      <thead>
        <tr style="text-align: right;">
          <th></th>
          <th>mass_wet</th>
          <th>mass_dry</th>
          <th>H2O</th>
          <th>Fe</th>
          <th>SiO2</th>
          <th>Al2O3</th>
        </tr>
        <tr>
          <th>size</th>
          <th></th>
          <th></th>
          <th></th>
          <th></th>
          <th></th>
          <th></th>
        </tr>
      </thead>
      <tbody>
        <tr>
          <th>[0.85, 2.0)</th>
          <td>3.3</td>
          <td>3.3</td>
          <td>0.0</td>
          <td>64.15</td>
          <td>2.04</td>
          <td>2.68</td>
        </tr>
        <tr>
          <th>[0.5, 0.85)</th>
          <td>9.9</td>
          <td>9.9</td>
          <td>0.0</td>
          <td>64.33</td>
          <td>2.05</td>
          <td>2.23</td>
        </tr>
        <tr>
          <th>[0.15, 0.5)</th>
          <td>26.5</td>
          <td>26.5</td>
          <td>0.0</td>
          <td>64.52</td>
          <td>1.84</td>
          <td>2.19</td>
        </tr>
        <tr>
          <th>[0.075, 0.15)</th>
          <td>2.5</td>
          <td>2.5</td>
          <td>0.0</td>
          <td>62.65</td>
          <td>2.88</td>
          <td>3.32</td>
        </tr>
        <tr>
          <th>[0.045, 0.075)</th>
          <td>8.8</td>
          <td>8.8</td>
          <td>0.0</td>
          <td>62.81</td>
          <td>2.12</td>
          <td>2.25</td>
        </tr>
        <tr>
          <th>[0.0, 0.045)</th>
          <td>49.0</td>
          <td>49.0</td>
          <td>0.0</td>
          <td>55.95</td>
          <td>6.39</td>
          <td>6.34</td>
        </tr>
      </tbody>
    </table>
    </div>
    </div>
    <br />
    <br />

.. GENERATED FROM PYTHON SOURCE LINES 45-48

.. code-block:: default


    mc_size.aggregate()






.. 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;
        }

        .dataframe thead th {
            text-align: right;
        }
    </style>
    <table border="1" class="dataframe">
      <thead>
        <tr style="text-align: right;">
          <th></th>
          <th>mass_wet</th>
          <th>mass_dry</th>
          <th>H2O</th>
          <th>Fe</th>
          <th>SiO2</th>
          <th>Al2O3</th>
        </tr>
        <tr>
          <th>name</th>
          <th></th>
          <th></th>
          <th></th>
          <th></th>
          <th></th>
          <th></th>
        </tr>
      </thead>
      <tbody>
        <tr>
          <th>Sample</th>
          <td>100.0</td>
          <td>100.0</td>
          <td>0.0</td>
          <td>60.09245</td>
          <td>4.14753</td>
          <td>4.27716</td>
        </tr>
      </tbody>
    </table>
    </div>
    </div>
    <br />
    <br />

.. GENERATED FROM PYTHON SOURCE LINES 49-50

First we'll plot the intervals

.. GENERATED FROM PYTHON SOURCE LINES 51-56

.. code-block:: default


    fig = mc_size.plot_intervals(variables=['mass_dry', 'Fe', 'SiO2', 'Al2O3'],
                                 cumulative=False)
    fig






.. raw:: html

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    <br />

.. GENERATED FROM PYTHON SOURCE LINES 57-59

Size distributions are often plotted in the cumulative form. Cumulative passing is achieved by setting the
direction = ascending.

.. GENERATED FROM PYTHON SOURCE LINES 60-65

.. code-block:: default


    fig = mc_size.plot_intervals(variables=['mass_dry', 'Fe', 'SiO2', 'Al2O3'],
                                 cumulative=True, direction='ascending')
    fig





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

 .. code-block:: none

    /home/runner/work/mass-composition/mass-composition/elphick/mass_composition/mc_xarray.py:157: FutureWarning:

    The return type of `Dataset.dims` will be changed to return a set of dimension names in future, in order to be more consistent with `DataArray.dims`. To access a mapping from dimension names to lengths, please use `Dataset.sizes`.



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    <br />
    <br />

.. GENERATED FROM PYTHON SOURCE LINES 66-67

Now we will resample on a defined grid (interval edges) and view the resampled fractions

.. GENERATED FROM PYTHON SOURCE LINES 68-77

.. code-block:: default

    new_edges = np.unique(np.geomspace(1.0e-03, mc_size.data.to_dataframe().index.right.max() * 3, 50))
    new_coords = np.insert(new_edges, 0, 0)

    mc_upsampled: MassComposition = mc_size.resample_1d(interval_edges=new_edges, precision=3, include_original_edges=True)

    fig = mc_upsampled.plot_intervals(variables=['mass_dry', 'Fe', 'SiO2', 'Al2O3'], cumulative=False)
    # noinspection PyTypeChecker
    plotly.io.show(fig)




.. raw:: html
    :file: images/sphx_glr_resampling_interval_data_001.html





.. GENERATED FROM PYTHON SOURCE LINES 78-81

Close inspection of the plot above reals some sharp dips for some mass intervals.  This is caused by those intervals
being narrower than the adjacent neighbours, hence they have less absolute mass.
This is a visual artefact only, numerically it is correct, as shown by the cumulative plot.

.. GENERATED FROM PYTHON SOURCE LINES 81-85

.. code-block:: default


    fig = mc_upsampled.plot_intervals(variables=['mass_dry', 'Fe', 'SiO2', 'Al2O3'], cumulative=True, direction='ascending')
    fig





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

 .. code-block:: none

    /home/runner/work/mass-composition/mass-composition/elphick/mass_composition/mc_xarray.py:157: FutureWarning:

    The return type of `Dataset.dims` will be changed to return a set of dimension names in future, in order to be more consistent with `DataArray.dims`. To access a mapping from dimension names to lengths, please use `Dataset.sizes`.



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    <br />

.. GENERATED FROM PYTHON SOURCE LINES 86-89

We can upsample each of the original fraction by a factor.  Since adjacent fractions are similar, the fractional
plot is reasonably smooth.  Note however, that fraction widths are still different, caused by the original sieve
selection.

.. GENERATED FROM PYTHON SOURCE LINES 89-94

.. code-block:: default


    mc_upsampled_2: MassComposition = mc_size.resample_1d(interval_edges=10, precision=3)
    fig = mc_upsampled_2.plot_intervals(variables=['mass_dry', 'Fe', 'SiO2', 'Al2O3'], cumulative=False)
    fig






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    </div>
    <br />
    <br />

.. GENERATED FROM PYTHON SOURCE LINES 95-96

Validate the head grade against the original sample

.. GENERATED FROM PYTHON SOURCE LINES 96-103

.. code-block:: default


    pd.testing.assert_frame_equal(mc_size.aggregate().reset_index(drop=True),
                                  mc_upsampled.aggregate().reset_index(drop=True))

    pd.testing.assert_frame_equal(mc_size.aggregate().reset_index(drop=True),
                                  mc_upsampled_2.aggregate().reset_index(drop=True))








.. GENERATED FROM PYTHON SOURCE LINES 104-105

Complete a round trip by converting the up-sampled objects back to the original intervals and validate.

.. GENERATED FROM PYTHON SOURCE LINES 105-115

.. code-block:: default


    orig_index = mc_size.data.to_dataframe().index
    original_edges: np.ndarray = np.sort(np.unique(list(orig_index.left) + list(orig_index.right)))

    mc_downsampled: MassComposition = mc_upsampled.resample_1d(interval_edges=original_edges, precision=3)
    mc_downsampled_2: MassComposition = mc_upsampled_2.resample_1d(interval_edges=original_edges, precision=3)

    pd.testing.assert_frame_equal(mc_size.data.to_dataframe(), mc_downsampled.data.to_dataframe())
    pd.testing.assert_frame_equal(mc_size.data.to_dataframe(), mc_downsampled_2.data.to_dataframe())









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

   **Total running time of the script:** ( 0 minutes  0.906 seconds)


.. _sphx_glr_download_auto_examples_resampling_interval_data.py:

.. only:: html

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


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

      :download:`Download Python source code: resampling_interval_data.py <resampling_interval_data.py>`

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

      :download:`Download Jupyter notebook: resampling_interval_data.ipynb <resampling_interval_data.ipynb>`


.. only:: html

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

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