.. DO NOT EDIT.
.. THIS FILE WAS AUTOMATICALLY GENERATED BY SPHINX-GALLERY.
.. TO MAKE CHANGES, EDIT THE SOURCE PYTHON FILE:
.. "auto_examples/aggregation/_02_threshold_check.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_aggregation__02_threshold_check.py>`
        to download the full example code

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

.. _sphx_glr_auto_examples_aggregation__02_threshold_check.py:


.. _threshold_check_example:

Threshold Checker
====================

This example shows how to use the functions ``apply_thresholds`` to check if a DMO falls within the thresholds for a
specific cohort and height.

.. GENERATED FROM PYTHON SOURCE LINES 10-15

.. code-block:: default


    import pandas as pd
    from mobgap import PACKAGE_ROOT
    from mobgap.aggregation import apply_thresholds, get_mobilised_dmo_thresholds








.. GENERATED FROM PYTHON SOURCE LINES 16-22

Selecting Example Data
----------------------
We use some example DMO data.
Note, that the ``apply_thresholds`` function can only be used with the data of a single participant at the time,
as meta-data such as the participant's height is required.
Luckily, the all the example data is from the same participant, just recorded at different times.

.. GENERATED FROM PYTHON SOURCE LINES 22-32

.. code-block:: default


    DATA_PATH = (
        PACKAGE_ROOT.parent / "example_data/original_results/mobilised_aggregator"
    )

    data = pd.read_csv(
        DATA_PATH / "aggregation_test_input.csv", index_col=0
    ).set_index(["visit_type", "participant_id", "measurement_date", "wb_id"])
    data.head()






.. 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 {
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        .dataframe thead th {
            text-align: right;
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    </style>
    <table border="1" class="dataframe">
      <thead>
        <tr style="text-align: right;">
          <th></th>
          <th></th>
          <th></th>
          <th></th>
          <th>duration_s</th>
          <th>n_steps</th>
          <th>cadence_spm</th>
          <th>walking_speed_mps</th>
          <th>stride_length_m</th>
          <th>stride_duration_s</th>
          <th>n_turns</th>
        </tr>
        <tr>
          <th>visit_type</th>
          <th>participant_id</th>
          <th>measurement_date</th>
          <th>wb_id</th>
          <th></th>
          <th></th>
          <th></th>
          <th></th>
          <th></th>
          <th></th>
          <th></th>
        </tr>
      </thead>
      <tbody>
        <tr>
          <th rowspan="5" valign="top">T1</th>
          <th rowspan="5" valign="top">12345</th>
          <th rowspan="5" valign="top">2023-01-01</th>
          <th>0</th>
          <td>4.74702</td>
          <td>8</td>
          <td>99.82188</td>
          <td>1.16079</td>
          <td>2.51885</td>
          <td>1.58675</td>
          <td>0</td>
        </tr>
        <tr>
          <th>1</th>
          <td>5.13150</td>
          <td>7</td>
          <td>101.16429</td>
          <td>2.57881</td>
          <td>1.57243</td>
          <td>1.46537</td>
          <td>0</td>
        </tr>
        <tr>
          <th>2</th>
          <td>8.52727</td>
          <td>12</td>
          <td>86.53527</td>
          <td>1.60044</td>
          <td>1.66305</td>
          <td>2.56092</td>
          <td>2</td>
        </tr>
        <tr>
          <th>3</th>
          <td>16.24554</td>
          <td>27</td>
          <td>91.49977</td>
          <td>0.95558</td>
          <td>0.88961</td>
          <td>3.14549</td>
          <td>1</td>
        </tr>
        <tr>
          <th>4</th>
          <td>6.09907</td>
          <td>8</td>
          <td>93.69895</td>
          <td>2.33230</td>
          <td>1.95969</td>
          <td>2.35295</td>
          <td>0</td>
        </tr>
      </tbody>
    </table>
    </div>
    </div>
    <br />
    <br />

.. GENERATED FROM PYTHON SOURCE LINES 33-35

Load Thresholds
---------------

.. GENERATED FROM PYTHON SOURCE LINES 35-38

.. code-block:: default

    thresholds = get_mobilised_dmo_thresholds()
    thresholds






.. 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 tr th {
            text-align: left;
        }

        .dataframe thead tr:last-of-type th {
            text-align: right;
        }
    </style>
    <table border="1" class="dataframe">
      <thead>
        <tr>
          <th></th>
          <th>condition</th>
          <th colspan="2" halign="left">free_living</th>
          <th colspan="2" halign="left">laboratory</th>
          <th colspan="2" halign="left">literature</th>
          <th colspan="2" halign="left">global</th>
        </tr>
        <tr>
          <th></th>
          <th>threshold_type</th>
          <th>min</th>
          <th>max</th>
          <th>min</th>
          <th>max</th>
          <th>min</th>
          <th>max</th>
          <th>min</th>
          <th>max</th>
        </tr>
        <tr>
          <th>dmo</th>
          <th>cohort</th>
          <th></th>
          <th></th>
          <th></th>
          <th></th>
          <th></th>
          <th></th>
          <th></th>
          <th></th>
        </tr>
      </thead>
      <tbody>
        <tr>
          <th rowspan="6" valign="top">cadence_spm</th>
          <th>CHF</th>
          <td>40.594770</td>
          <td>167.942930</td>
          <td>53.617703</td>
          <td>122.657158</td>
          <td>36.20</td>
          <td>150.900</td>
          <td>40.000000</td>
          <td>172.900</td>
        </tr>
        <tr>
          <th>COPD</th>
          <td>38.880484</td>
          <td>151.558718</td>
          <td>59.451004</td>
          <td>164.969764</td>
          <td>71.20</td>
          <td>154.800</td>
          <td>40.000000</td>
          <td>172.900</td>
        </tr>
        <tr>
          <th>HA</th>
          <td>35.751898</td>
          <td>156.946955</td>
          <td>60.176644</td>
          <td>146.309393</td>
          <td>90.40</td>
          <td>169.200</td>
          <td>40.000000</td>
          <td>172.900</td>
        </tr>
        <tr>
          <th>MS</th>
          <td>38.443211</td>
          <td>155.394496</td>
          <td>44.016149</td>
          <td>140.939597</td>
          <td>45.60</td>
          <td>164.400</td>
          <td>40.000000</td>
          <td>172.900</td>
        </tr>
        <tr>
          <th>PD</th>
          <td>40.957969</td>
          <td>142.121947</td>
          <td>47.681576</td>
          <td>147.054849</td>
          <td>33.80</td>
          <td>77.600</td>
          <td>40.000000</td>
          <td>172.900</td>
        </tr>
        <tr>
          <th>PFF</th>
          <td>42.191719</td>
          <td>157.613665</td>
          <td>46.839404</td>
          <td>133.129276</td>
          <td>42.90</td>
          <td>172.900</td>
          <td>40.000000</td>
          <td>172.900</td>
        </tr>
        <tr>
          <th rowspan="6" valign="top">walking_speed_mps</th>
          <th>CHF</th>
          <td>0.112478</td>
          <td>1.757103</td>
          <td>0.273401</td>
          <td>1.528913</td>
          <td>0.63</td>
          <td>1.830</td>
          <td>0.081515</td>
          <td>2.220</td>
        </tr>
        <tr>
          <th>COPD</th>
          <td>0.090731</td>
          <td>1.641773</td>
          <td>0.159689</td>
          <td>1.918146</td>
          <td>0.60</td>
          <td>1.840</td>
          <td>0.081515</td>
          <td>2.220</td>
        </tr>
        <tr>
          <th>HA</th>
          <td>0.097413</td>
          <td>1.965728</td>
          <td>0.207887</td>
          <td>1.583779</td>
          <td>0.78</td>
          <td>2.220</td>
          <td>0.081515</td>
          <td>2.220</td>
        </tr>
        <tr>
          <th>MS</th>
          <td>0.085918</td>
          <td>1.920262</td>
          <td>0.203213</td>
          <td>1.877691</td>
          <td>0.52</td>
          <td>1.740</td>
          <td>0.081515</td>
          <td>2.220</td>
        </tr>
        <tr>
          <th>PD</th>
          <td>0.081515</td>
          <td>1.735348</td>
          <td>0.139654</td>
          <td>1.939697</td>
          <td>0.56</td>
          <td>2.100</td>
          <td>0.081515</td>
          <td>2.220</td>
        </tr>
        <tr>
          <th>PFF</th>
          <td>0.104547</td>
          <td>1.553186</td>
          <td>0.143724</td>
          <td>1.627383</td>
          <td>0.11</td>
          <td>1.590</td>
          <td>0.081515</td>
          <td>2.220</td>
        </tr>
        <tr>
          <th rowspan="6" valign="top">stride_length_m</th>
          <th>CHF</th>
          <td>0.185308</td>
          <td>2.166646</td>
          <td>0.444041</td>
          <td>1.568583</td>
          <td>0.53</td>
          <td>1.860</td>
          <td>0.150523</td>
          <td>2.190</td>
        </tr>
        <tr>
          <th>COPD</th>
          <td>0.176403</td>
          <td>1.711645</td>
          <td>0.223537</td>
          <td>1.623733</td>
          <td>1.03</td>
          <td>2.190</td>
          <td>0.150523</td>
          <td>2.190</td>
        </tr>
        <tr>
          <th>HA</th>
          <td>0.155126</td>
          <td>2.024694</td>
          <td>0.290283</td>
          <td>1.669609</td>
          <td>0.87</td>
          <td>1.980</td>
          <td>0.150523</td>
          <td>2.190</td>
        </tr>
        <tr>
          <th>MS</th>
          <td>0.191099</td>
          <td>1.940537</td>
          <td>0.363004</td>
          <td>1.721322</td>
          <td>0.72</td>
          <td>2.100</td>
          <td>0.150523</td>
          <td>2.190</td>
        </tr>
        <tr>
          <th>PD</th>
          <td>0.150523</td>
          <td>1.982926</td>
          <td>0.253069</td>
          <td>1.772536</td>
          <td>0.56</td>
          <td>1.620</td>
          <td>0.150523</td>
          <td>2.190</td>
        </tr>
        <tr>
          <th>PFF</th>
          <td>0.206787</td>
          <td>1.697251</td>
          <td>0.233460</td>
          <td>1.587144</td>
          <td>0.71</td>
          <td>1.930</td>
          <td>0.150523</td>
          <td>2.190</td>
        </tr>
        <tr>
          <th rowspan="6" valign="top">stride_duration_s</th>
          <th>CHF</th>
          <td>0.702000</td>
          <td>3.030857</td>
          <td>0.980000</td>
          <td>2.274545</td>
          <td>0.46</td>
          <td>1.320</td>
          <td>0.460000</td>
          <td>3.000</td>
        </tr>
        <tr>
          <th>COPD</th>
          <td>0.770000</td>
          <td>3.000000</td>
          <td>0.727500</td>
          <td>2.160000</td>
          <td>0.68</td>
          <td>1.410</td>
          <td>0.460000</td>
          <td>3.000</td>
        </tr>
        <tr>
          <th>HA</th>
          <td>0.735435</td>
          <td>3.254400</td>
          <td>0.829091</td>
          <td>2.142000</td>
          <td>0.69</td>
          <td>2.304</td>
          <td>0.460000</td>
          <td>3.000</td>
        </tr>
        <tr>
          <th>MS</th>
          <td>0.775597</td>
          <td>3.057750</td>
          <td>0.855000</td>
          <td>2.731667</td>
          <td>0.69</td>
          <td>2.304</td>
          <td>0.460000</td>
          <td>3.000</td>
        </tr>
        <tr>
          <th>PD</th>
          <td>0.836253</td>
          <td>2.928000</td>
          <td>0.818571</td>
          <td>2.522000</td>
          <td>0.76</td>
          <td>1.690</td>
          <td>0.460000</td>
          <td>3.000</td>
        </tr>
        <tr>
          <th>PFF</th>
          <td>0.744000</td>
          <td>2.769000</td>
          <td>0.902727</td>
          <td>2.584545</td>
          <td>0.14</td>
          <td>0.980</td>
          <td>0.460000</td>
          <td>3.000</td>
        </tr>
        <tr>
          <th rowspan="6" valign="top">step_duration_s</th>
          <th>CHF</th>
          <td>0.376000</td>
          <td>1.504500</td>
          <td>0.498000</td>
          <td>1.144167</td>
          <td>0.14</td>
          <td>0.980</td>
          <td>0.140000</td>
          <td>2.124</td>
        </tr>
        <tr>
          <th>COPD</th>
          <td>0.390400</td>
          <td>1.788000</td>
          <td>0.366000</td>
          <td>1.181429</td>
          <td>0.16</td>
          <td>0.920</td>
          <td>0.140000</td>
          <td>2.124</td>
        </tr>
        <tr>
          <th>HA</th>
          <td>0.367972</td>
          <td>1.730400</td>
          <td>0.410000</td>
          <td>1.137500</td>
          <td>0.25</td>
          <td>1.040</td>
          <td>0.140000</td>
          <td>2.124</td>
        </tr>
        <tr>
          <th>MS</th>
          <td>0.388084</td>
          <td>1.905000</td>
          <td>0.432857</td>
          <td>1.360000</td>
          <td>0.25</td>
          <td>1.090</td>
          <td>0.140000</td>
          <td>2.124</td>
        </tr>
        <tr>
          <th>PD</th>
          <td>0.417216</td>
          <td>1.605600</td>
          <td>0.415000</td>
          <td>1.450000</td>
          <td>0.19</td>
          <td>1.100</td>
          <td>0.140000</td>
          <td>2.124</td>
        </tr>
        <tr>
          <th>PFF</th>
          <td>0.371429</td>
          <td>2.124000</td>
          <td>0.430000</td>
          <td>1.320000</td>
          <td>0.35</td>
          <td>1.100</td>
          <td>0.140000</td>
          <td>2.124</td>
        </tr>
      </tbody>
    </table>
    </div>
    </div>
    <br />
    <br />

.. GENERATED FROM PYTHON SOURCE LINES 39-41

Apply Thresholds
----------------

.. GENERATED FROM PYTHON SOURCE LINES 41-50

.. code-block:: default

    data_mask = apply_thresholds(
        data,
        thresholds,
        cohort="CHF",
        height_m=1.75,
        measurement_condition="free_living",
    )
    data_mask.head()






.. 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;
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          <th></th>
          <th></th>
          <th></th>
          <th>duration_s</th>
          <th>n_steps</th>
          <th>cadence_spm</th>
          <th>walking_speed_mps</th>
          <th>stride_length_m</th>
          <th>stride_duration_s</th>
          <th>n_turns</th>
        </tr>
        <tr>
          <th>visit_type</th>
          <th>participant_id</th>
          <th>measurement_date</th>
          <th>wb_id</th>
          <th></th>
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          <th></th>
          <th></th>
          <th></th>
          <th></th>
          <th></th>
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      <tbody>
        <tr>
          <th rowspan="5" valign="top">T1</th>
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          <th>0</th>
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        <tr>
          <th>1</th>
          <td>NaN</td>
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          <td>False</td>
          <td>True</td>
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          <th>2</th>
          <td>NaN</td>
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          <td>True</td>
          <td>True</td>
          <td>True</td>
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        </tr>
        <tr>
          <th>3</th>
          <td>NaN</td>
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          <td>True</td>
          <td>True</td>
          <td>True</td>
          <td>False</td>
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        </tr>
        <tr>
          <th>4</th>
          <td>NaN</td>
          <td>NaN</td>
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          <td>False</td>
          <td>True</td>
          <td>True</td>
          <td>NaN</td>
        </tr>
      </tbody>
    </table>
    </div>
    </div>
    <br />
    <br />

.. GENERATED FROM PYTHON SOURCE LINES 51-55

We can see that the output has exactly the same structure as the input data, but with boolean values indicating if
the DMO falls within the thresholds or not.
Columns for which no thresholds were provided are simply NaN, indicating that no filtering was applied.
It is up to you how you want to use this information.

.. GENERATED FROM PYTHON SOURCE LINES 57-60

This output data can be used to filter the input data for DMOs that fall within the thresholds.
This can be done in combination with the aggregation algorithm to only include DMOs that fall within the thresholds.
See the :ref:`mobilised_aggregator example <mobilised_aggregator_example>` for more information.


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

   **Total running time of the script:** (0 minutes 0.734 seconds)

**Estimated memory usage:**  9 MB


.. _sphx_glr_download_auto_examples_aggregation__02_threshold_check.py:

.. only:: html

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




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

      :download:`Download Python source code: _02_threshold_check.py <_02_threshold_check.py>`

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

      :download:`Download Jupyter notebook: _02_threshold_check.ipynb <_02_threshold_check.ipynb>`


.. only:: html

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

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