.. DO NOT EDIT.
.. THIS FILE WAS AUTOMATICALLY GENERATED BY SPHINX-GALLERY.
.. TO MAKE CHANGES, EDIT THE SOURCE PYTHON FILE:
.. "auto_examples/202_interval_data_advanced.py"
.. LINE NUMBERS ARE GIVEN BELOW.

.. only:: html

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

        Click :ref:`here <sphx_glr_download_auto_examples_202_interval_data_advanced.py>`
        to download the full example code

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

.. _sphx_glr_auto_examples_202_interval_data_advanced.py:


Interval Data - Advanced
========================

Intervals are encountered in Metallurgy, aka fractions,
e.g. size fractions.  In that case the typical nomenclature is size_retained, size passing, since the data
originates from a sieve stack.

This example walks through unifying the intervals across samples to be represented in the same network.
Consider the case where the feed, oversize and undersize from a screening operation are sampled and sieved.
It is likely that the undersize size distribution will be generated with fewer sieves in the sieve stack.
If this data is not unified (made consistent) the network cannot be constructed - by unifying it we can
construct a network and check the magnitude of any imbalance.

.. GENERATED FROM PYTHON SOURCE LINES 15-28

.. code-block:: default


    import logging
    from functools import partial
    from pathlib import Path

    import pandas as pd
    import plotly

    from elphick.mass_composition import MassComposition
    from elphick.mass_composition.datasets.sample_data import size_by_assay
    from elphick.mass_composition.flowsheet import Flowsheet
    from elphick.mass_composition.utils.partition import napier_munn








.. GENERATED FROM PYTHON SOURCE LINES 29-33

.. 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 34-38

Create some data
----------------

We get some demo sizing data, split it with a partition, and manually drop sieves for the undersize stream.

.. GENERATED FROM PYTHON SOURCE LINES 39-46

.. code-block:: default


    # We create this object as 1D based on the pandas index.

    df_data: pd.DataFrame = size_by_assay()
    mc_feed: MassComposition = MassComposition(df_data, name='FEED')
    mc_feed.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 47-48

We partially initialise a partition function, and split the feed stream accordingly.

.. GENERATED FROM PYTHON SOURCE LINES 48-52

.. code-block:: default


    partition = partial(napier_munn, d50=0.150, ep=0.05, dim='size')
    mc_oversize, mc_undersize = mc_feed.split_by_partition(partition_definition=partition, name_1='OS', name_2='US')








.. GENERATED FROM PYTHON SOURCE LINES 53-56

Drop the two size fractions from mc_fine that have near zero mass.

This simulates a common situation where fines samples will likely have fewer fractions reported in the results.

.. GENERATED FROM PYTHON SOURCE LINES 56-63

.. code-block:: default


    df_fine: pd.DataFrame = mc_undersize.data.to_dataframe()
    df_fine = df_fine.loc[df_fine.index.left < 0.5, :]

    mc_undersize.set_data(df_fine)
    mc_undersize.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>[0.15, 0.5)</th>
          <td>1.633999</td>
          <td>1.633999</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>1.810638</td>
          <td>1.810638</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>7.769423</td>
          <td>7.769423</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>46.291601</td>
          <td>46.291601</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 64-65

Notice that the top two fractions are now missing.

.. GENERATED FROM PYTHON SOURCE LINES 67-74

Unifying Indexes
----------------

If the dataset contains a single IntervalIndex called 'size' missing coarse size fractions will be added
automatically.

That said, the remaining sizes must be consistent.  Alignment of sizes across streams/mc objects is coming soon.

.. GENERATED FROM PYTHON SOURCE LINES 75-80

.. code-block:: default


    fs: Flowsheet = Flowsheet().from_streams([mc_feed, mc_oversize, mc_undersize])
    fig = fs.table_plot()
    fig





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

 .. code-block:: none

    /home/runner/work/mass-composition/mass-composition/elphick/mass_composition/flowsheet.py:1014: FutureWarning:

    The previous implementation of stack is deprecated and will be removed in a future version of pandas. See the What's New notes for pandas 2.1.0 for details. Specify future_stack=True to adopt the new implementation and silence this warning.



.. raw:: html

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

.. GENERATED FROM PYTHON SOURCE LINES 81-86

Troubleshooting the imbalance
-----------------------------

So we now have our network, but it does not balance.  Perhaps the fractions we removed to generate our test data
contained enough mass to breach our balance threshold?  Let's dig deeper with our balance plot.

.. GENERATED FROM PYTHON SOURCE LINES 87-92

.. code-block:: default


    fig = fs.plot_balance(color='size')
    # noinspection PyTypeChecker
    plotly.io.show(fig)  # this call to show will set the thumbnail for the gallery




.. raw:: html
    :file: images/sphx_glr_202_interval_data_advanced_001.html





.. GENERATED FROM PYTHON SOURCE LINES 93-94

What is the balance threshold set at?

.. GENERATED FROM PYTHON SOURCE LINES 94-97

.. code-block:: default


    print('Node error tolerance:', fs.graph.nodes[1]['mc']._tolerance)





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

 .. code-block:: none

    Node error tolerance: 1.1920929e-07




.. GENERATED FROM PYTHON SOURCE LINES 98-99

That plot does not reveal the problem, so we'll resort to another report.

.. GENERATED FROM PYTHON SOURCE LINES 99-102

.. code-block:: default


    fs.graph.nodes[1]['mc']._balance_errors






.. 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>Fe</th>
          <th>SiO2</th>
          <th>Al2O3</th>
        </tr>
        <tr>
          <th>size</th>
          <th></th>
          <th></th>
          <th></th>
          <th></th>
          <th></th>
        </tr>
      </thead>
      <tbody>
        <tr>
          <th>[0.5, 0.85)</th>
          <td>0.00016</td>
          <td>0.00016</td>
          <td>0.000103</td>
          <td>0.000003</td>
          <td>0.000004</td>
        </tr>
      </tbody>
    </table>
    </div>
    </div>
    <br />
    <br />

.. GENERATED FROM PYTHON SOURCE LINES 103-104

Let's change the node error tolerance.

.. GENERATED FROM PYTHON SOURCE LINES 104-109

.. code-block:: default


    fs.graph.nodes[1]['mc']._tolerance = 0.001
    fig = fs.table_plot()
    fig






.. raw:: html

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