.. DO NOT EDIT.
.. THIS FILE WAS AUTOMATICALLY GENERATED BY SPHINX-GALLERY.
.. TO MAKE CHANGES, EDIT THE SOURCE PYTHON FILE:
.. "auto_examples/examples/02_interval_sample/05_resampling_interval_data.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_examples_02_interval_sample_05_resampling_interval_data.py>`
        to download the full example code.

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

.. _sphx_glr_auto_examples_examples_02_interval_sample_05_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 11-21

.. code-block:: Python

    import logging

    import numpy as np
    import pandas as pd
    import plotly

    from elphick.geomet import IntervalSample
    from elphick.geomet.datasets.sample_data import size_by_assay
    from elphick.geomet.utils.size import sizes_all








.. GENERATED FROM PYTHON SOURCE LINES 22-27

.. code-block:: Python

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








.. GENERATED FROM PYTHON SOURCE LINES 28-33

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 34-38

.. code-block:: Python


    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 39-40

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

.. GENERATED FROM PYTHON SOURCE LINES 41-45

.. code-block:: Python


    size_fractions: IntervalSample = IntervalSample(df_data, name='Sample', moisture_in_scope=False)
    size_fractions.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>mass_dry</th>
          <th>Fe</th>
          <th>SiO2</th>
          <th>Al2O3</th>
        </tr>
        <tr>
          <th>size</th>
          <th></th>
          <th></th>
          <th></th>
          <th></th>
        </tr>
      </thead>
      <tbody>
        <tr>
          <th>[0.85, 2.0)</th>
          <td>3.3</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>64.33</td>
          <td>2.05</td>
          <td>2.23</td>
        </tr>
        <tr>
          <th>[0.15, 0.5)</th>
          <td>26.5</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>62.65</td>
          <td>2.88</td>
          <td>3.32</td>
        </tr>
        <tr>
          <th>[0.045, 0.075)</th>
          <td>8.8</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>55.95</td>
          <td>6.39</td>
          <td>6.34</td>
        </tr>
      </tbody>
    </table>
    </div>
    </div>
    <br />
    <br />

.. GENERATED FROM PYTHON SOURCE LINES 46-49

.. code-block:: Python


    size_fractions.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_dry</th>
          <th>Fe</th>
          <th>SiO2</th>
          <th>Al2O3</th>
        </tr>
      </thead>
      <tbody>
        <tr>
          <th>0</th>
          <td>100.0</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 50-53

Plot the original sample intervals
----------------------------------
First we'll plot the intervals of the original sample

.. GENERATED FROM PYTHON SOURCE LINES 53-58

.. code-block:: Python


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




.. raw:: html
    :file: images/sphx_glr_05_resampling_interval_data_001.html





.. GENERATED FROM PYTHON SOURCE LINES 59-61

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

.. GENERATED FROM PYTHON SOURCE LINES 62-67

.. code-block:: Python


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






.. raw:: html

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.. GENERATED FROM PYTHON SOURCE LINES 68-71

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

.. GENERATED FROM PYTHON SOURCE LINES 71-82

.. code-block:: Python


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

    upsampled: IntervalSample = size_fractions.resample_1d(interval_edges=new_edges, precision=3,
                                                           include_original_edges=True)

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




.. raw:: html
    :file: images/sphx_glr_05_resampling_interval_data_002.html





.. GENERATED FROM PYTHON SOURCE LINES 83-86

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 86-90

.. code-block:: Python


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






.. raw:: html

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.. GENERATED FROM PYTHON SOURCE LINES 91-96

Up-sample by a factor
---------------------
We can up-sample 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 96-101

.. code-block:: Python


    upsampled_2: IntervalSample = size_fractions.resample_1d(interval_edges=10, precision=3)
    fig = upsampled_2.plot_intervals(variables=['mass_dry', 'Fe', 'SiO2', 'Al2O3'], cumulative=False)
    fig






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    <br />

.. GENERATED FROM PYTHON SOURCE LINES 102-106

Up-sample to a sieve series
---------------------------
Standard sieve series account for the log nature of a particle size distribution.  This results in reasonably
equal width intervals on a log scale.  We will up-sample to a standard sieve series and plot the results.

.. GENERATED FROM PYTHON SOURCE LINES 106-111

.. code-block:: Python


    new_sizes = [s for s in sizes_all if
                 s >= size_fractions.data.index.left.min() and s <= size_fractions.data.index.right.max()]
    new_sizes = sorted(new_sizes)








.. GENERATED FROM PYTHON SOURCE LINES 112-116

.. code-block:: Python

    upsampled_3: IntervalSample = size_fractions.resample_1d(interval_edges=new_sizes, precision=3)
    fig = upsampled_3.plot_intervals(variables=['mass_dry', 'Fe', 'SiO2', 'Al2O3'], cumulative=False)
    fig






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

.. GENERATED FROM PYTHON SOURCE LINES 117-118

Validate the head grade against the original sample

.. GENERATED FROM PYTHON SOURCE LINES 118-128

.. code-block:: Python


    pd.testing.assert_frame_equal(size_fractions.aggregate.reset_index(drop=True),
                                  upsampled.aggregate.reset_index(drop=True))

    pd.testing.assert_frame_equal(size_fractions.aggregate.reset_index(drop=True),
                                  upsampled_2.aggregate.reset_index(drop=True))

    pd.testing.assert_frame_equal(size_fractions.aggregate.reset_index(drop=True),
                                  upsampled_3.aggregate.reset_index(drop=True))








.. GENERATED FROM PYTHON SOURCE LINES 129-130

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

.. GENERATED FROM PYTHON SOURCE LINES 130-141

.. code-block:: Python


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

    downsampled: IntervalSample = upsampled.resample_1d(interval_edges=original_edges, precision=3)
    downsampled_2: IntervalSample = upsampled_2.resample_1d(interval_edges=original_edges, precision=3)
    downsampled_3: IntervalSample = upsampled_3.resample_1d(interval_edges=original_edges, precision=3)

    pd.testing.assert_frame_equal(size_fractions.data, downsampled.data)
    pd.testing.assert_frame_equal(size_fractions.data, downsampled_2.data)
    pd.testing.assert_frame_equal(size_fractions.data, downsampled_3.data)








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

   **Total running time of the script:** (0 minutes 3.104 seconds)


.. _sphx_glr_download_auto_examples_examples_02_interval_sample_05_resampling_interval_data.py:

.. only:: html

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

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

      :download:`Download Jupyter notebook: 05_resampling_interval_data.ipynb <05_resampling_interval_data.ipynb>`

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

      :download:`Download Python source code: 05_resampling_interval_data.py <05_resampling_interval_data.py>`


.. only:: html

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

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