.. 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/06_resampling_interval_data_2d.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_06_resampling_interval_data_2d.py>`
        to download the full example code.

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

.. _sphx_glr_auto_examples_examples_02_interval_sample_06_resampling_interval_data_2d.py:


Resampling 2D Interval Data
===========================

The Sink Float metallurgical test splits/fractionates samples by density.  The density fraction is often conducted by
size fraction, resulting in 2D fractionation (interval) data.

This example demonstrates how to resample 2D interval data using the IntervalSample object.

.. GENERATED FROM PYTHON SOURCE LINES 11-24

.. code-block:: Python


    import logging

    # noinspection PyUnresolvedReferences
    import numpy as np
    import pandas as pd
    import plotly.io

    from elphick.geomet import IntervalSample
    from elphick.geomet.datasets import datasets
    from elphick.geomet.utils.pandas import MeanIntervalIndex
    from elphick.geomet.utils.size import sizes_all








.. GENERATED FROM PYTHON SOURCE LINES 25-29

.. 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 30-34

Load Data
---------

We load some real data.

.. GENERATED FROM PYTHON SOURCE LINES 35-39

.. code-block:: Python


    df_data: pd.DataFrame = datasets.load_nordic_iron_ore_sink_float()
    df_data






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        }

        .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>size_retained</th>
          <th>size_passing</th>
          <th>density_lo</th>
          <th>density_hi</th>
          <th>mass_pct</th>
          <th>Fe</th>
          <th>SiO2</th>
          <th>P</th>
          <th>TiO2</th>
          <th>V</th>
        </tr>
      </thead>
      <tbody>
        <tr>
          <th>0</th>
          <td>1.000</td>
          <td>NaN</td>
          <td>NaN</td>
          <td>NaN</td>
          <td>0.4</td>
          <td>22.7</td>
          <td>50.9</td>
          <td>0.042</td>
          <td>0.170</td>
          <td>0.0049</td>
        </tr>
        <tr>
          <th>1</th>
          <td>0.100</td>
          <td>1.000</td>
          <td>NaN</td>
          <td>NaN</td>
          <td>67.1</td>
          <td>27.3</td>
          <td>47.7</td>
          <td>0.107</td>
          <td>0.178</td>
          <td>0.0062</td>
        </tr>
        <tr>
          <th>2</th>
          <td>0.063</td>
          <td>0.100</td>
          <td>NaN</td>
          <td>NaN</td>
          <td>12.7</td>
          <td>18.0</td>
          <td>57.8</td>
          <td>0.440</td>
          <td>0.200</td>
          <td>0.0056</td>
        </tr>
        <tr>
          <th>3</th>
          <td>0.040</td>
          <td>0.063</td>
          <td>NaN</td>
          <td>NaN</td>
          <td>8.2</td>
          <td>16.9</td>
          <td>57.1</td>
          <td>0.610</td>
          <td>0.235</td>
          <td>0.0057</td>
        </tr>
        <tr>
          <th>4</th>
          <td>0.000</td>
          <td>0.040</td>
          <td>NaN</td>
          <td>NaN</td>
          <td>11.6</td>
          <td>19.4</td>
          <td>51.6</td>
          <td>0.650</td>
          <td>0.310</td>
          <td>0.0072</td>
        </tr>
        <tr>
          <th>5</th>
          <td>0.100</td>
          <td>NaN</td>
          <td>NaN</td>
          <td>2.7</td>
          <td>54.2</td>
          <td>1.2</td>
          <td>1.6</td>
          <td>0.180</td>
          <td>0.084</td>
          <td>0.0030</td>
        </tr>
        <tr>
          <th>6</th>
          <td>0.100</td>
          <td>NaN</td>
          <td>2.7</td>
          <td>3.3</td>
          <td>9.7</td>
          <td>16.6</td>
          <td>42.6</td>
          <td>0.980</td>
          <td>0.380</td>
          <td>0.0100</td>
        </tr>
        <tr>
          <th>7</th>
          <td>0.100</td>
          <td>NaN</td>
          <td>3.3</td>
          <td>NaN</td>
          <td>36.1</td>
          <td>68.0</td>
          <td>78.8</td>
          <td>0.033</td>
          <td>0.285</td>
          <td>0.0120</td>
        </tr>
        <tr>
          <th>8</th>
          <td>0.063</td>
          <td>0.100</td>
          <td>NaN</td>
          <td>2.7</td>
          <td>24.0</td>
          <td>1.2</td>
          <td>79.5</td>
          <td>0.015</td>
          <td>0.060</td>
          <td>0.0070</td>
        </tr>
        <tr>
          <th>9</th>
          <td>0.063</td>
          <td>0.100</td>
          <td>2.7</td>
          <td>3.3</td>
          <td>11.9</td>
          <td>10.2</td>
          <td>54.0</td>
          <td>2.320</td>
          <td>0.280</td>
          <td>0.0080</td>
        </tr>
        <tr>
          <th>10</th>
          <td>0.063</td>
          <td>0.100</td>
          <td>3.3</td>
          <td>NaN</td>
          <td>64.1</td>
          <td>67.1</td>
          <td>1.4</td>
          <td>0.174</td>
          <td>0.530</td>
          <td>0.0020</td>
        </tr>
        <tr>
          <th>11</th>
          <td>0.040</td>
          <td>0.063</td>
          <td>NaN</td>
          <td>2.7</td>
          <td>76.6</td>
          <td>3.1</td>
          <td>71.3</td>
          <td>0.850</td>
          <td>0.145</td>
          <td>0.0130</td>
        </tr>
        <tr>
          <th>12</th>
          <td>0.040</td>
          <td>0.063</td>
          <td>2.7</td>
          <td>3.3</td>
          <td>4.3</td>
          <td>28.5</td>
          <td>24.5</td>
          <td>2.780</td>
          <td>0.460</td>
          <td>0.1100</td>
        </tr>
        <tr>
          <th>13</th>
          <td>0.040</td>
          <td>0.063</td>
          <td>3.3</td>
          <td>NaN</td>
          <td>19.1</td>
          <td>68.6</td>
          <td>0.6</td>
          <td>0.069</td>
          <td>0.480</td>
          <td>0.0130</td>
        </tr>
      </tbody>
    </table>
    </div>
    </div>
    <br />
    <br />

.. GENERATED FROM PYTHON SOURCE LINES 40-42

The dataset contains size x assay, plus size x density x assay data.  We'll drop the size x assay data to leave the
sink / float data.

.. GENERATED FROM PYTHON SOURCE LINES 42-46

.. code-block:: Python


    df_sink_float: pd.DataFrame = df_data.dropna(subset=['density_lo', 'density_hi'], how='all').copy()
    df_sink_float






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    </style>
    <table border="1" class="dataframe">
      <thead>
        <tr style="text-align: right;">
          <th></th>
          <th>size_retained</th>
          <th>size_passing</th>
          <th>density_lo</th>
          <th>density_hi</th>
          <th>mass_pct</th>
          <th>Fe</th>
          <th>SiO2</th>
          <th>P</th>
          <th>TiO2</th>
          <th>V</th>
        </tr>
      </thead>
      <tbody>
        <tr>
          <th>5</th>
          <td>0.100</td>
          <td>NaN</td>
          <td>NaN</td>
          <td>2.7</td>
          <td>54.2</td>
          <td>1.2</td>
          <td>1.6</td>
          <td>0.180</td>
          <td>0.084</td>
          <td>0.003</td>
        </tr>
        <tr>
          <th>6</th>
          <td>0.100</td>
          <td>NaN</td>
          <td>2.7</td>
          <td>3.3</td>
          <td>9.7</td>
          <td>16.6</td>
          <td>42.6</td>
          <td>0.980</td>
          <td>0.380</td>
          <td>0.010</td>
        </tr>
        <tr>
          <th>7</th>
          <td>0.100</td>
          <td>NaN</td>
          <td>3.3</td>
          <td>NaN</td>
          <td>36.1</td>
          <td>68.0</td>
          <td>78.8</td>
          <td>0.033</td>
          <td>0.285</td>
          <td>0.012</td>
        </tr>
        <tr>
          <th>8</th>
          <td>0.063</td>
          <td>0.100</td>
          <td>NaN</td>
          <td>2.7</td>
          <td>24.0</td>
          <td>1.2</td>
          <td>79.5</td>
          <td>0.015</td>
          <td>0.060</td>
          <td>0.007</td>
        </tr>
        <tr>
          <th>9</th>
          <td>0.063</td>
          <td>0.100</td>
          <td>2.7</td>
          <td>3.3</td>
          <td>11.9</td>
          <td>10.2</td>
          <td>54.0</td>
          <td>2.320</td>
          <td>0.280</td>
          <td>0.008</td>
        </tr>
        <tr>
          <th>10</th>
          <td>0.063</td>
          <td>0.100</td>
          <td>3.3</td>
          <td>NaN</td>
          <td>64.1</td>
          <td>67.1</td>
          <td>1.4</td>
          <td>0.174</td>
          <td>0.530</td>
          <td>0.002</td>
        </tr>
        <tr>
          <th>11</th>
          <td>0.040</td>
          <td>0.063</td>
          <td>NaN</td>
          <td>2.7</td>
          <td>76.6</td>
          <td>3.1</td>
          <td>71.3</td>
          <td>0.850</td>
          <td>0.145</td>
          <td>0.013</td>
        </tr>
        <tr>
          <th>12</th>
          <td>0.040</td>
          <td>0.063</td>
          <td>2.7</td>
          <td>3.3</td>
          <td>4.3</td>
          <td>28.5</td>
          <td>24.5</td>
          <td>2.780</td>
          <td>0.460</td>
          <td>0.110</td>
        </tr>
        <tr>
          <th>13</th>
          <td>0.040</td>
          <td>0.063</td>
          <td>3.3</td>
          <td>NaN</td>
          <td>19.1</td>
          <td>68.6</td>
          <td>0.6</td>
          <td>0.069</td>
          <td>0.480</td>
          <td>0.013</td>
        </tr>
      </tbody>
    </table>
    </div>
    </div>
    <br />
    <br />

.. GENERATED FROM PYTHON SOURCE LINES 47-48

We will fill some nan values with assumptions

.. GENERATED FROM PYTHON SOURCE LINES 48-52

.. code-block:: Python

    df_sink_float['size_passing'].fillna(1.0, inplace=True)
    df_sink_float['density_lo'].fillna(1.5, inplace=True)
    df_sink_float['density_hi'].fillna(5.0, inplace=True)








.. GENERATED FROM PYTHON SOURCE LINES 53-54

Check the mass_pct by size

.. GENERATED FROM PYTHON SOURCE LINES 54-62

.. code-block:: Python


    mass_check: pd.DataFrame = df_sink_float[['size_passing', 'size_retained', 'mass_pct']].groupby(
        ['size_passing', 'size_retained']).sum()
    # check that all are 100
    assert np.all(mass_check['mass_pct'] == 100)

    mass_check






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    </style>
    <table border="1" class="dataframe">
      <thead>
        <tr style="text-align: right;">
          <th></th>
          <th></th>
          <th>mass_pct</th>
        </tr>
        <tr>
          <th>size_passing</th>
          <th>size_retained</th>
          <th></th>
        </tr>
      </thead>
      <tbody>
        <tr>
          <th>0.063</th>
          <th>0.040</th>
          <td>100.0</td>
        </tr>
        <tr>
          <th>0.100</th>
          <th>0.063</th>
          <td>100.0</td>
        </tr>
        <tr>
          <th>1.000</th>
          <th>0.100</th>
          <td>100.0</td>
        </tr>
      </tbody>
    </table>
    </div>
    </div>
    <br />
    <br />

.. GENERATED FROM PYTHON SOURCE LINES 63-65

This indicates that the mass_pct column is actually a density_mass_pct column.
We'll rename that but also need to get the size_mass_pct values for those sizes from the size dataset

.. GENERATED FROM PYTHON SOURCE LINES 65-84

.. code-block:: Python


    df_sink_float.rename(columns={'mass_pct': 'density_mass_pct'}, inplace=True)

    df_size: pd.DataFrame = df_data.loc[np.all(df_data[['density_lo', 'density_hi']].isna(), axis=1), :].copy()
    df_size.dropna(how='all', axis=1, inplace=True)
    assert df_size['mass_pct'].sum() == 100

    size_pairs = set(list((round(r, 5), round(p, 5)) for r, p in
                          zip(df_sink_float['size_retained'].values, df_sink_float['size_passing'].values)))
    for r, p in size_pairs:
        df_sink_float.loc[(df_sink_float['size_retained'] == r) & (df_sink_float['size_passing'] == p), 'size_mass_pct'] = \
            df_size.loc[(df_size['size_retained'] == r) & (df_size['size_passing'] == p), 'mass_pct'].values[0]
    # relocate the size_mass_pct column to the correct position, after size_passing
    df_sink_float.insert(2, df_sink_float.columns[-1], df_sink_float.pop(df_sink_float.columns[-1]))
    # add the mass_pct column
    df_sink_float.insert(loc=6, column='mass_pct',
                         value=df_sink_float['density_mass_pct'] * df_sink_float['size_mass_pct'] / 100)
    df_sink_float






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    <table border="1" class="dataframe">
      <thead>
        <tr style="text-align: right;">
          <th></th>
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          <th>size_mass_pct</th>
          <th>density_lo</th>
          <th>density_hi</th>
          <th>density_mass_pct</th>
          <th>mass_pct</th>
          <th>Fe</th>
          <th>SiO2</th>
          <th>P</th>
          <th>TiO2</th>
          <th>V</th>
        </tr>
      </thead>
      <tbody>
        <tr>
          <th>5</th>
          <td>0.100</td>
          <td>1.000</td>
          <td>67.1</td>
          <td>1.5</td>
          <td>2.7</td>
          <td>54.2</td>
          <td>36.3682</td>
          <td>1.2</td>
          <td>1.6</td>
          <td>0.180</td>
          <td>0.084</td>
          <td>0.003</td>
        </tr>
        <tr>
          <th>6</th>
          <td>0.100</td>
          <td>1.000</td>
          <td>67.1</td>
          <td>2.7</td>
          <td>3.3</td>
          <td>9.7</td>
          <td>6.5087</td>
          <td>16.6</td>
          <td>42.6</td>
          <td>0.980</td>
          <td>0.380</td>
          <td>0.010</td>
        </tr>
        <tr>
          <th>7</th>
          <td>0.100</td>
          <td>1.000</td>
          <td>67.1</td>
          <td>3.3</td>
          <td>5.0</td>
          <td>36.1</td>
          <td>24.2231</td>
          <td>68.0</td>
          <td>78.8</td>
          <td>0.033</td>
          <td>0.285</td>
          <td>0.012</td>
        </tr>
        <tr>
          <th>8</th>
          <td>0.063</td>
          <td>0.100</td>
          <td>12.7</td>
          <td>1.5</td>
          <td>2.7</td>
          <td>24.0</td>
          <td>3.0480</td>
          <td>1.2</td>
          <td>79.5</td>
          <td>0.015</td>
          <td>0.060</td>
          <td>0.007</td>
        </tr>
        <tr>
          <th>9</th>
          <td>0.063</td>
          <td>0.100</td>
          <td>12.7</td>
          <td>2.7</td>
          <td>3.3</td>
          <td>11.9</td>
          <td>1.5113</td>
          <td>10.2</td>
          <td>54.0</td>
          <td>2.320</td>
          <td>0.280</td>
          <td>0.008</td>
        </tr>
        <tr>
          <th>10</th>
          <td>0.063</td>
          <td>0.100</td>
          <td>12.7</td>
          <td>3.3</td>
          <td>5.0</td>
          <td>64.1</td>
          <td>8.1407</td>
          <td>67.1</td>
          <td>1.4</td>
          <td>0.174</td>
          <td>0.530</td>
          <td>0.002</td>
        </tr>
        <tr>
          <th>11</th>
          <td>0.040</td>
          <td>0.063</td>
          <td>8.2</td>
          <td>1.5</td>
          <td>2.7</td>
          <td>76.6</td>
          <td>6.2812</td>
          <td>3.1</td>
          <td>71.3</td>
          <td>0.850</td>
          <td>0.145</td>
          <td>0.013</td>
        </tr>
        <tr>
          <th>12</th>
          <td>0.040</td>
          <td>0.063</td>
          <td>8.2</td>
          <td>2.7</td>
          <td>3.3</td>
          <td>4.3</td>
          <td>0.3526</td>
          <td>28.5</td>
          <td>24.5</td>
          <td>2.780</td>
          <td>0.460</td>
          <td>0.110</td>
        </tr>
        <tr>
          <th>13</th>
          <td>0.040</td>
          <td>0.063</td>
          <td>8.2</td>
          <td>3.3</td>
          <td>5.0</td>
          <td>19.1</td>
          <td>1.5662</td>
          <td>68.6</td>
          <td>0.6</td>
          <td>0.069</td>
          <td>0.480</td>
          <td>0.013</td>
        </tr>
      </tbody>
    </table>
    </div>
    </div>
    <br />
    <br />

.. GENERATED FROM PYTHON SOURCE LINES 85-87

Create MeanIntervalIndexes
--------------------------

.. GENERATED FROM PYTHON SOURCE LINES 87-102

.. code-block:: Python


    size_intervals = pd.arrays.IntervalArray.from_arrays(df_sink_float['size_retained'], df_sink_float['size_passing'],
                                                         closed='left')
    size_index = MeanIntervalIndex(size_intervals)
    size_index.name = 'size'

    density_intervals = pd.arrays.IntervalArray.from_arrays(df_sink_float['density_lo'], df_sink_float['density_hi'],
                                                            closed='left')
    density_index = MeanIntervalIndex(density_intervals)
    density_index.name = 'density'

    df_sink_float.index = pd.MultiIndex.from_arrays([size_index, density_index])
    df_sink_float.drop(columns=['size_retained', 'size_passing', 'density_lo', 'density_hi'], inplace=True)
    df_sink_float






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      <thead>
        <tr style="text-align: right;">
          <th></th>
          <th></th>
          <th>size_mass_pct</th>
          <th>density_mass_pct</th>
          <th>mass_pct</th>
          <th>Fe</th>
          <th>SiO2</th>
          <th>P</th>
          <th>TiO2</th>
          <th>V</th>
        </tr>
        <tr>
          <th>size</th>
          <th>density</th>
          <th></th>
          <th></th>
          <th></th>
          <th></th>
          <th></th>
          <th></th>
          <th></th>
          <th></th>
        </tr>
      </thead>
      <tbody>
        <tr>
          <th rowspan="3" valign="top">[0.1, 1.0)</th>
          <th>[1.5, 2.7)</th>
          <td>67.1</td>
          <td>54.2</td>
          <td>36.3682</td>
          <td>1.2</td>
          <td>1.6</td>
          <td>0.180</td>
          <td>0.084</td>
          <td>0.003</td>
        </tr>
        <tr>
          <th>[2.7, 3.3)</th>
          <td>67.1</td>
          <td>9.7</td>
          <td>6.5087</td>
          <td>16.6</td>
          <td>42.6</td>
          <td>0.980</td>
          <td>0.380</td>
          <td>0.010</td>
        </tr>
        <tr>
          <th>[3.3, 5.0)</th>
          <td>67.1</td>
          <td>36.1</td>
          <td>24.2231</td>
          <td>68.0</td>
          <td>78.8</td>
          <td>0.033</td>
          <td>0.285</td>
          <td>0.012</td>
        </tr>
        <tr>
          <th rowspan="3" valign="top">[0.063, 0.1)</th>
          <th>[1.5, 2.7)</th>
          <td>12.7</td>
          <td>24.0</td>
          <td>3.0480</td>
          <td>1.2</td>
          <td>79.5</td>
          <td>0.015</td>
          <td>0.060</td>
          <td>0.007</td>
        </tr>
        <tr>
          <th>[2.7, 3.3)</th>
          <td>12.7</td>
          <td>11.9</td>
          <td>1.5113</td>
          <td>10.2</td>
          <td>54.0</td>
          <td>2.320</td>
          <td>0.280</td>
          <td>0.008</td>
        </tr>
        <tr>
          <th>[3.3, 5.0)</th>
          <td>12.7</td>
          <td>64.1</td>
          <td>8.1407</td>
          <td>67.1</td>
          <td>1.4</td>
          <td>0.174</td>
          <td>0.530</td>
          <td>0.002</td>
        </tr>
        <tr>
          <th rowspan="3" valign="top">[0.04, 0.063)</th>
          <th>[1.5, 2.7)</th>
          <td>8.2</td>
          <td>76.6</td>
          <td>6.2812</td>
          <td>3.1</td>
          <td>71.3</td>
          <td>0.850</td>
          <td>0.145</td>
          <td>0.013</td>
        </tr>
        <tr>
          <th>[2.7, 3.3)</th>
          <td>8.2</td>
          <td>4.3</td>
          <td>0.3526</td>
          <td>28.5</td>
          <td>24.5</td>
          <td>2.780</td>
          <td>0.460</td>
          <td>0.110</td>
        </tr>
        <tr>
          <th>[3.3, 5.0)</th>
          <td>8.2</td>
          <td>19.1</td>
          <td>1.5662</td>
          <td>68.6</td>
          <td>0.6</td>
          <td>0.069</td>
          <td>0.480</td>
          <td>0.013</td>
        </tr>
      </tbody>
    </table>
    </div>
    </div>
    <br />
    <br />

.. GENERATED FROM PYTHON SOURCE LINES 103-105

Create a 2D IntervalSample
--------------------------

.. GENERATED FROM PYTHON SOURCE LINES 105-113

.. code-block:: Python


    interval_sample = IntervalSample(df_sink_float, name='SINK_FLOAT', moisture_in_scope=False, mass_dry_var='mass_pct')
    print(interval_sample.is_2d_grid())
    print(interval_sample.is_rectilinear_grid)

    fig = interval_sample.plot_heatmap(components=['mass_pct'])
    plotly.io.show(fig)




.. raw:: html
    :file: images/sphx_glr_06_resampling_interval_data_2d_001.html


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

 .. code-block:: none

    False
    False




.. GENERATED FROM PYTHON SOURCE LINES 114-117

Upsample
--------
We will upsample the data to a new grid

.. GENERATED FROM PYTHON SOURCE LINES 117-127

.. code-block:: Python


    size_grid = sorted([s for s in sizes_all if s >= size_index.left.min() and s <= size_index.right.max()])
    density_grid = np.arange(1.5, 5.1, 0.1)
    new_grids: dict = {'size': size_grid, 'density': density_grid}

    upsampled: IntervalSample = interval_sample.resample_2d(interval_edges=new_grids, precision=3)

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

    fig = upsampled.plot_heatmap(components=['mass_pct'])
    plotly.io.show(fig)


.. raw:: html
    :file: images/sphx_glr_06_resampling_interval_data_2d_002.html


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

 .. code-block:: none

    (38, 36, 6)





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

   **Total running time of the script:** (0 minutes 0.781 seconds)


.. _sphx_glr_download_auto_examples_examples_02_interval_sample_06_resampling_interval_data_2d.py:

.. only:: html

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

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

      :download:`Download Jupyter notebook: 06_resampling_interval_data_2d.ipynb <06_resampling_interval_data_2d.ipynb>`

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

      :download:`Download Python source code: 06_resampling_interval_data_2d.py <06_resampling_interval_data_2d.py>`


.. only:: html

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

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