.. 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/03_incremental_separation.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_03_incremental_separation.py>`
        to download the full example code.

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

.. _sphx_glr_auto_examples_examples_02_interval_sample_03_incremental_separation.py:


Incremental Separation
======================

This method sorts by the provided direction prior to incrementally removing and discarding the first fraction
(of the remaining fractions) and recalculating the mass-composition and recovery of the portion remaining.
This is equivalent to incrementally applying a perfect separation (partition) at every interval edge.

The returned data can be used to assess the amenability of a fractionated sample (in the dimension of the sample).

This concept is only applicable in a single dimension where the mass-composition (sample) object is an interval index.

The example will use a dataset that represents a sample fractionated by size.

.. GENERATED FROM PYTHON SOURCE LINES 16-24

.. code-block:: Python

    import logging

    import pandas as pd
    import plotly

    from elphick.geomet import IntervalSample
    from elphick.geomet.datasets.sample_data import size_by_assay








.. GENERATED FROM PYTHON SOURCE LINES 25-28

.. code-block:: Python

    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 29-33

Create the sample
-----------------

The sample is a MassComposition object

.. GENERATED FROM PYTHON SOURCE LINES 33-37

.. 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>
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    </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 39-43

.. code-block:: Python


    mc_size: IntervalSample = IntervalSample(df_data, name='Sample', moisture_in_scope=False)
    mc_size.data






.. raw:: html

    <div class="output_subarea output_html rendered_html output_result">
    <div>
    <style scoped>
        .dataframe tbody tr th:only-of-type {
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        <tr style="text-align: right;">
          <th></th>
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          <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 44-49

Incrementally Separate
----------------------

Leverage the method to return the incremental perfect separation in the size dimension.
Here we will "de-slime" by discarding the smallest (lowest) sizes incrementally.

.. GENERATED FROM PYTHON SOURCE LINES 49-53

.. code-block:: Python


    results: pd.DataFrame = mc_size.ideal_incremental_separation(discard_from="lowest")
    results






.. raw:: html

    <div class="output_subarea output_html rendered_html output_result">
    <div>
    <style scoped>
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          <th>SiO2</th>
          <th>Al2O3</th>
        </tr>
        <tr>
          <th>size_cut-point</th>
          <th>attribute</th>
          <th></th>
          <th></th>
          <th></th>
          <th></th>
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      <tbody>
        <tr>
          <th>0.850</th>
          <th>composition</th>
          <td>3.300</td>
          <td>64.150000</td>
          <td>2.040000</td>
          <td>2.680000</td>
        </tr>
        <tr>
          <th>0.500</th>
          <th>composition</th>
          <td>13.200</td>
          <td>64.285000</td>
          <td>2.047500</td>
          <td>2.342500</td>
        </tr>
        <tr>
          <th>0.150</th>
          <th>composition</th>
          <td>39.700</td>
          <td>64.441864</td>
          <td>1.908992</td>
          <td>2.240705</td>
        </tr>
        <tr>
          <th>0.075</th>
          <th>composition</th>
          <td>42.200</td>
          <td>64.335711</td>
          <td>1.966517</td>
          <td>2.304645</td>
        </tr>
        <tr>
          <th>0.045</th>
          <th>composition</th>
          <td>51.000</td>
          <td>64.072451</td>
          <td>1.993000</td>
          <td>2.295216</td>
        </tr>
        <tr>
          <th>0.000</th>
          <th>composition</th>
          <td>100.000</td>
          <td>60.092450</td>
          <td>4.147530</td>
          <td>4.277160</td>
        </tr>
        <tr>
          <th>0.850</th>
          <th>recovery</th>
          <td>0.033</td>
          <td>0.035228</td>
          <td>0.016231</td>
          <td>0.020677</td>
        </tr>
        <tr>
          <th>0.500</th>
          <th>recovery</th>
          <td>0.132</td>
          <td>0.141209</td>
          <td>0.065164</td>
          <td>0.072293</td>
        </tr>
        <tr>
          <th>0.150</th>
          <th>recovery</th>
          <td>0.397</td>
          <td>0.425734</td>
          <td>0.182728</td>
          <td>0.207979</td>
        </tr>
        <tr>
          <th>0.075</th>
          <th>recovery</th>
          <td>0.422</td>
          <td>0.451798</td>
          <td>0.200088</td>
          <td>0.227385</td>
        </tr>
        <tr>
          <th>0.045</th>
          <th>recovery</th>
          <td>0.510</td>
          <td>0.543778</td>
          <td>0.245069</td>
          <td>0.273677</td>
        </tr>
        <tr>
          <th>0.000</th>
          <th>recovery</th>
          <td>1.000</td>
          <td>1.000000</td>
          <td>1.000000</td>
          <td>1.000000</td>
        </tr>
      </tbody>
    </table>
    </div>
    </div>
    <br />
    <br />

.. GENERATED FROM PYTHON SOURCE LINES 54-55

Repeat the process but by discarding the coarser sizes.

.. GENERATED FROM PYTHON SOURCE LINES 55-59

.. code-block:: Python


    results_2: pd.DataFrame = mc_size.ideal_incremental_separation(discard_from="highest")
    results_2






.. raw:: html

    <div class="output_subarea output_html rendered_html output_result">
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          <th>SiO2</th>
          <th>Al2O3</th>
        </tr>
        <tr>
          <th>size_cut-point</th>
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          <td>60.092450</td>
          <td>4.147530</td>
          <td>4.277160</td>
        </tr>
        <tr>
          <th>0.850</th>
          <th>composition</th>
          <td>96.700</td>
          <td>59.953981</td>
          <td>4.219452</td>
          <td>4.331665</td>
        </tr>
        <tr>
          <th>0.500</th>
          <th>composition</th>
          <td>86.800</td>
          <td>59.454873</td>
          <td>4.466889</td>
          <td>4.571371</td>
        </tr>
        <tr>
          <th>0.150</th>
          <th>composition</th>
          <td>60.300</td>
          <td>57.228905</td>
          <td>5.621327</td>
          <td>5.617910</td>
        </tr>
        <tr>
          <th>0.075</th>
          <th>composition</th>
          <td>57.800</td>
          <td>56.994429</td>
          <td>5.739896</td>
          <td>5.717301</td>
        </tr>
        <tr>
          <th>0.045</th>
          <th>composition</th>
          <td>49.000</td>
          <td>55.950000</td>
          <td>6.390000</td>
          <td>6.340000</td>
        </tr>
        <tr>
          <th>2.000</th>
          <th>recovery</th>
          <td>1.000</td>
          <td>1.000000</td>
          <td>1.000000</td>
          <td>1.000000</td>
        </tr>
        <tr>
          <th>0.850</th>
          <th>recovery</th>
          <td>0.967</td>
          <td>0.964772</td>
          <td>0.983769</td>
          <td>0.979323</td>
        </tr>
        <tr>
          <th>0.500</th>
          <th>recovery</th>
          <td>0.868</td>
          <td>0.858791</td>
          <td>0.934836</td>
          <td>0.927707</td>
        </tr>
        <tr>
          <th>0.150</th>
          <th>recovery</th>
          <td>0.603</td>
          <td>0.574266</td>
          <td>0.817272</td>
          <td>0.792021</td>
        </tr>
        <tr>
          <th>0.075</th>
          <th>recovery</th>
          <td>0.578</td>
          <td>0.548202</td>
          <td>0.799912</td>
          <td>0.772615</td>
        </tr>
        <tr>
          <th>0.045</th>
          <th>recovery</th>
          <td>0.490</td>
          <td>0.456222</td>
          <td>0.754931</td>
          <td>0.726323</td>
        </tr>
      </tbody>
    </table>
    </div>
    </div>
    <br />
    <br />

.. GENERATED FROM PYTHON SOURCE LINES 60-62

Plot Grade-Recovery
-------------------

.. GENERATED FROM PYTHON SOURCE LINES 62-67

.. code-block:: Python


    fig = mc_size.plot_grade_recovery(target_analyte='Fe')
    fig.update_layout(height=800)
    fig






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

.. GENERATED FROM PYTHON SOURCE LINES 68-69

Discard the highest (coarsest) sizes.  As expected the response differs.

.. GENERATED FROM PYTHON SOURCE LINES 69-73

.. code-block:: Python


    fig = mc_size.plot_grade_recovery(target_analyte='Fe', discard_from="highest")
    fig.update_layout(height=800)






.. raw:: html

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

.. GENERATED FROM PYTHON SOURCE LINES 74-83

Plot Amenability
----------------

The Amenability Index (AI) will generally range between zero and one, but can in fact be legitimately negative.
The closer the AI is to 1.0, the more amenable the ore is to separation of that particular gangue component relative
to the target analyte.  The AI is shown in the legend (in brackets).

The plot below suggests that SiO2 is marginally more amenable than Al2O3 across the
spectrum of yield for this sample.

.. GENERATED FROM PYTHON SOURCE LINES 83-89

.. code-block:: Python


    fig = mc_size.plot_amenability(target_analyte='Fe')
    fig.update_layout(height=800)
    # noinspection PyTypeChecker
    plotly.io.show(fig)




.. raw:: html
    :file: images/sphx_glr_03_incremental_separation_001.html





.. GENERATED FROM PYTHON SOURCE LINES 90-93

Discard the highest (coarsest) sizes.  As expected the response differs.  The Amenability indices are negative
indicating a downgrade, rather than an upgrade.  This demonstrates that desliming is a plausible pathway to
beneficiating this sample, while "coarse scalping" is not.

.. GENERATED FROM PYTHON SOURCE LINES 93-97

.. code-block:: Python


    fig = mc_size.plot_amenability(target_analyte='Fe', discard_from="highest")
    fig.update_layout(height=800)
    fig





.. raw:: html

    <div class="output_subarea output_html rendered_html output_result">
    <div>                        <script type="text/javascript">window.PlotlyConfig = {MathJaxConfig: 'local'};</script>
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.. _sphx_glr_download_auto_examples_examples_02_interval_sample_03_incremental_separation.py:

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