.. DO NOT EDIT.
.. THIS FILE WAS AUTOMATICALLY GENERATED BY SPHINX-GALLERY.
.. TO MAKE CHANGES, EDIT THE SOURCE PYTHON FILE:
.. "auto_examples/wba/_01_assembling_wbs.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_wba__01_assembling_wbs.py>`
        to download the full example code

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

.. _sphx_glr_auto_examples_wba__01_assembling_wbs.py:


Assembling WBs
==============

An important part of the Mobilise-D pipeline is that we output WBs based on a fixed set of definitions.
These definitions are build based on a consensus process within the Mobilise-D consortium [1]_ and additional normative
data from the literature to define realistic ranges for stride and WB level parameters.

To support the process of creating WBs based on these and further custom rules, we provide a framework of dynamic rule
objects that can be adjusted and combined and then used to create WBs from a list of strides.
This works in 3 steps:

1. First, we filter the list of strides based on the stride-level rules.
2. Second, we iterate through the remaining list of strides and add them to a WB, until we reach a "Termination
   Criterion".
3. The WBs found in this way are then finally filtered based on WB-level rules.

.. [1] Kluge F, Del Din S, Cereatti A, Gaßner H, Hansen C, Helbostad JL, et al. (2021) Consensus based framework for
      digital mobility monitoring. PLoS ONE 16(8): e0256541. https://doi.org/10.1371/journal.pone.0256541

.. GENERATED FROM PYTHON SOURCE LINES 22-24

.. code-block:: default

    # TODO: Update the example with real stride values and rules








.. GENERATED FROM PYTHON SOURCE LINES 25-33

Step 1: Stride Selection
------------------------
Stride selection is a relatively simple process.
Each stride is defined by its start and end time and a set of parameters.
Stride-level rules are basically just threshold rules for these parameters or the duration of the stride.

Let's start by creating a list of strides with dummy values.
We specifically create list of "left" and "right" strides and combine them at the end.

.. GENERATED FROM PYTHON SOURCE LINES 33-82

.. code-block:: default

    from itertools import count

    import matplotlib.pyplot as plt
    import numpy as np
    import pandas as pd

    id_counter = count(1)


    def window(start, end, **parameter):
        parameter = parameter or {}

        parameter = {**parameter, "duration": end - start}

        return dict(s_id=next(id_counter), start=start, end=end, **parameter)


    def naive_stride_list(start, stop, duration, foot=None, **paras):
        """A window list full of identical strides."""
        x = np.arange(start, stop + duration, duration, dtype="int64")
        start_end = zip(x[:-1], x[1:])

        return pd.DataFrame.from_records(
            [
                window(start=s, end=e, foot=foot, duration=duration, **paras)
                for s, e in start_end
            ]
        ).set_index("s_id")


    stride_list = [
        naive_stride_list(0, 5000, 100, foot="left"),
        naive_stride_list(50, 5050, 100, foot="right"),
        naive_stride_list(5000, 6020, 60, foot="left"),
        naive_stride_list(5050, 6070, 60, foot="right"),
        naive_stride_list(6020, 8000, 90, foot="left"),
        naive_stride_list(6070, 8050, 90, foot="right"),
    ]

    stride_list = pd.concat(stride_list).sort_values("start")
    # We reset the stride ids to have them in ascending order.
    stride_list = stride_list.reset_index(drop=True).rename_axis(index="s_id")
    stride_list.index += 1
    # We add some additional parameters, we can use to filter later on.
    large_sl_ids = [10, 11, 12, 13, 14, 18, 19, 20, 21, 56, 90, 91, 121, 122, 176]
    stride_list["stride_length"] = 1
    stride_list.loc[stride_list.index[large_sl_ids], "stride_length"] = 2
    stride_list






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        }
    </style>
    <table border="1" class="dataframe">
      <thead>
        <tr style="text-align: right;">
          <th></th>
          <th>start</th>
          <th>end</th>
          <th>foot</th>
          <th>duration</th>
          <th>stride_length</th>
        </tr>
        <tr>
          <th>s_id</th>
          <th></th>
          <th></th>
          <th></th>
          <th></th>
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        </tr>
      </thead>
      <tbody>
        <tr>
          <th>1</th>
          <td>0</td>
          <td>100</td>
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          <td>100</td>
          <td>1</td>
        </tr>
        <tr>
          <th>2</th>
          <td>50</td>
          <td>150</td>
          <td>right</td>
          <td>100</td>
          <td>1</td>
        </tr>
        <tr>
          <th>3</th>
          <td>100</td>
          <td>200</td>
          <td>left</td>
          <td>100</td>
          <td>1</td>
        </tr>
        <tr>
          <th>4</th>
          <td>150</td>
          <td>250</td>
          <td>right</td>
          <td>100</td>
          <td>1</td>
        </tr>
        <tr>
          <th>5</th>
          <td>200</td>
          <td>300</td>
          <td>left</td>
          <td>100</td>
          <td>1</td>
        </tr>
        <tr>
          <th>...</th>
          <td>...</td>
          <td>...</td>
          <td>...</td>
          <td>...</td>
          <td>...</td>
        </tr>
        <tr>
          <th>174</th>
          <td>7780</td>
          <td>7870</td>
          <td>right</td>
          <td>90</td>
          <td>1</td>
        </tr>
        <tr>
          <th>175</th>
          <td>7820</td>
          <td>7910</td>
          <td>left</td>
          <td>90</td>
          <td>1</td>
        </tr>
        <tr>
          <th>176</th>
          <td>7870</td>
          <td>7960</td>
          <td>right</td>
          <td>90</td>
          <td>1</td>
        </tr>
        <tr>
          <th>177</th>
          <td>7910</td>
          <td>8000</td>
          <td>left</td>
          <td>90</td>
          <td>2</td>
        </tr>
        <tr>
          <th>178</th>
          <td>7960</td>
          <td>8050</td>
          <td>right</td>
          <td>90</td>
          <td>1</td>
        </tr>
      </tbody>
    </table>
    <p>178 rows × 5 columns</p>
    </div>
    </div>
    <br />
    <br />

.. GENERATED FROM PYTHON SOURCE LINES 83-92

We can filter the stride list above based on a set of rules.
We start with some simple rules to demonstrate the process.
All rules must be subclasses of :class:`~mobgap.wba.BaseIntervalCriteria`.
At the moment we have implemented :class:`~mobgap.wba.IntervalParameterCriteria` and
:class:`~mobgap.wba.IntervalDurationCriteria`.
But you could implement custom subclasses, if you see a need for it.

Here we just implement a simple rule that filters strides based on their stride length for now.
With the thresholds, all strides, we marked as "large" above should be filtered out.

.. GENERATED FROM PYTHON SOURCE LINES 92-103

.. code-block:: default

    from mobgap.wba import IntervalParameterCriteria

    rules = [
        (
            "sl_thres",
            IntervalParameterCriteria(
                "stride_length", lower_threshold=0.5, upper_threshold=1.5
            ),
        )
    ]








.. GENERATED FROM PYTHON SOURCE LINES 104-105

We can now use these rules to filter the stride list.

.. GENERATED FROM PYTHON SOURCE LINES 105-113

.. code-block:: default

    from mobgap.wba import StrideSelection

    stride_selection = StrideSelection(rules)
    stride_selection.filter(stride_list, sampling_rate_hz=1)

    filtered_stride_list = stride_selection.filtered_stride_list_
    filtered_stride_list






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        <tr>
          <th>s_id</th>
          <th></th>
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          <td>50</td>
          <td>150</td>
          <td>right</td>
          <td>100</td>
          <td>1</td>
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        <tr>
          <th>3</th>
          <td>100</td>
          <td>200</td>
          <td>left</td>
          <td>100</td>
          <td>1</td>
        </tr>
        <tr>
          <th>4</th>
          <td>150</td>
          <td>250</td>
          <td>right</td>
          <td>100</td>
          <td>1</td>
        </tr>
        <tr>
          <th>5</th>
          <td>200</td>
          <td>300</td>
          <td>left</td>
          <td>100</td>
          <td>1</td>
        </tr>
        <tr>
          <th>...</th>
          <td>...</td>
          <td>...</td>
          <td>...</td>
          <td>...</td>
          <td>...</td>
        </tr>
        <tr>
          <th>173</th>
          <td>7730</td>
          <td>7820</td>
          <td>left</td>
          <td>90</td>
          <td>1</td>
        </tr>
        <tr>
          <th>174</th>
          <td>7780</td>
          <td>7870</td>
          <td>right</td>
          <td>90</td>
          <td>1</td>
        </tr>
        <tr>
          <th>175</th>
          <td>7820</td>
          <td>7910</td>
          <td>left</td>
          <td>90</td>
          <td>1</td>
        </tr>
        <tr>
          <th>176</th>
          <td>7870</td>
          <td>7960</td>
          <td>right</td>
          <td>90</td>
          <td>1</td>
        </tr>
        <tr>
          <th>178</th>
          <td>7960</td>
          <td>8050</td>
          <td>right</td>
          <td>90</td>
          <td>1</td>
        </tr>
      </tbody>
    </table>
    <p>163 rows × 5 columns</p>
    </div>
    </div>
    <br />
    <br />

.. GENERATED FROM PYTHON SOURCE LINES 114-116

.. code-block:: default

    filtered_stride_list["stride_length"].unique()





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

 .. code-block:: none


    array([1])



.. GENERATED FROM PYTHON SOURCE LINES 117-126

As we can see all the strides above the threshold were filtered out.

Let's make this a little more complicated and add a second rule that filters strides based on their duration.
This could either be done by a second rule targeting the parameter `duration` or by using the
:class:`~mobgap.wba.IntervalDurationCriteria`, which recalculates the duration of the stride based on the
`start` and `end` columns.
We use the latter here.

The thresholds we use here should filter out all the strides from above with a duration of 60

.. GENERATED FROM PYTHON SOURCE LINES 126-141

.. code-block:: default

    from mobgap.wba import IntervalDurationCriteria

    rules.append(
        (
            "dur_thres",
            IntervalDurationCriteria(min_duration_s=80, max_duration_s=120),
        )
    )

    stride_selection = StrideSelection(rules)
    stride_selection.filter(stride_list, sampling_rate_hz=1)

    filtered_stride_list = stride_selection.filtered_stride_list_
    filtered_stride_list






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          <th>stride_length</th>
        </tr>
        <tr>
          <th>s_id</th>
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          <th>3</th>
          <td>100</td>
          <td>200</td>
          <td>left</td>
          <td>100</td>
          <td>1</td>
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        <tr>
          <th>4</th>
          <td>150</td>
          <td>250</td>
          <td>right</td>
          <td>100</td>
          <td>1</td>
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        <tr>
          <th>5</th>
          <td>200</td>
          <td>300</td>
          <td>left</td>
          <td>100</td>
          <td>1</td>
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          <th>...</th>
          <td>...</td>
          <td>...</td>
          <td>...</td>
          <td>...</td>
          <td>...</td>
        </tr>
        <tr>
          <th>173</th>
          <td>7730</td>
          <td>7820</td>
          <td>left</td>
          <td>90</td>
          <td>1</td>
        </tr>
        <tr>
          <th>174</th>
          <td>7780</td>
          <td>7870</td>
          <td>right</td>
          <td>90</td>
          <td>1</td>
        </tr>
        <tr>
          <th>175</th>
          <td>7820</td>
          <td>7910</td>
          <td>left</td>
          <td>90</td>
          <td>1</td>
        </tr>
        <tr>
          <th>176</th>
          <td>7870</td>
          <td>7960</td>
          <td>right</td>
          <td>90</td>
          <td>1</td>
        </tr>
        <tr>
          <th>178</th>
          <td>7960</td>
          <td>8050</td>
          <td>right</td>
          <td>90</td>
          <td>1</td>
        </tr>
      </tbody>
    </table>
    <p>131 rows × 5 columns</p>
    </div>
    </div>
    <br />
    <br />

.. GENERATED FROM PYTHON SOURCE LINES 142-144

.. code-block:: default

    filtered_stride_list["duration"].unique()





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

 .. code-block:: none


    array([100,  90])



.. GENERATED FROM PYTHON SOURCE LINES 145-146

We can also explicitly inspect which strides are filtered out.

.. GENERATED FROM PYTHON SOURCE LINES 146-148

.. code-block:: default

    stride_selection.excluded_stride_list_






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          <th>12</th>
          <td>550</td>
          <td>650</td>
          <td>right</td>
          <td>100</td>
          <td>2</td>
        </tr>
        <tr>
          <th>13</th>
          <td>600</td>
          <td>700</td>
          <td>left</td>
          <td>100</td>
          <td>2</td>
        </tr>
        <tr>
          <th>14</th>
          <td>650</td>
          <td>750</td>
          <td>right</td>
          <td>100</td>
          <td>2</td>
        </tr>
        <tr>
          <th>15</th>
          <td>700</td>
          <td>800</td>
          <td>left</td>
          <td>100</td>
          <td>2</td>
        </tr>
        <tr>
          <th>19</th>
          <td>900</td>
          <td>1000</td>
          <td>left</td>
          <td>100</td>
          <td>2</td>
        </tr>
        <tr>
          <th>20</th>
          <td>950</td>
          <td>1050</td>
          <td>right</td>
          <td>100</td>
          <td>2</td>
        </tr>
        <tr>
          <th>21</th>
          <td>1000</td>
          <td>1100</td>
          <td>left</td>
          <td>100</td>
          <td>2</td>
        </tr>
        <tr>
          <th>22</th>
          <td>1050</td>
          <td>1150</td>
          <td>right</td>
          <td>100</td>
          <td>2</td>
        </tr>
        <tr>
          <th>57</th>
          <td>2800</td>
          <td>2900</td>
          <td>left</td>
          <td>100</td>
          <td>2</td>
        </tr>
        <tr>
          <th>91</th>
          <td>4500</td>
          <td>4600</td>
          <td>left</td>
          <td>100</td>
          <td>2</td>
        </tr>
        <tr>
          <th>92</th>
          <td>4550</td>
          <td>4650</td>
          <td>right</td>
          <td>100</td>
          <td>2</td>
        </tr>
        <tr>
          <th>101</th>
          <td>5000</td>
          <td>5060</td>
          <td>left</td>
          <td>60</td>
          <td>1</td>
        </tr>
        <tr>
          <th>102</th>
          <td>5050</td>
          <td>5110</td>
          <td>right</td>
          <td>60</td>
          <td>1</td>
        </tr>
        <tr>
          <th>103</th>
          <td>5060</td>
          <td>5120</td>
          <td>left</td>
          <td>60</td>
          <td>1</td>
        </tr>
        <tr>
          <th>104</th>
          <td>5110</td>
          <td>5170</td>
          <td>right</td>
          <td>60</td>
          <td>1</td>
        </tr>
        <tr>
          <th>105</th>
          <td>5120</td>
          <td>5180</td>
          <td>left</td>
          <td>60</td>
          <td>1</td>
        </tr>
        <tr>
          <th>106</th>
          <td>5170</td>
          <td>5230</td>
          <td>right</td>
          <td>60</td>
          <td>1</td>
        </tr>
        <tr>
          <th>107</th>
          <td>5180</td>
          <td>5240</td>
          <td>left</td>
          <td>60</td>
          <td>1</td>
        </tr>
        <tr>
          <th>108</th>
          <td>5230</td>
          <td>5290</td>
          <td>right</td>
          <td>60</td>
          <td>1</td>
        </tr>
        <tr>
          <th>109</th>
          <td>5240</td>
          <td>5300</td>
          <td>left</td>
          <td>60</td>
          <td>1</td>
        </tr>
        <tr>
          <th>110</th>
          <td>5290</td>
          <td>5350</td>
          <td>right</td>
          <td>60</td>
          <td>1</td>
        </tr>
        <tr>
          <th>111</th>
          <td>5300</td>
          <td>5360</td>
          <td>left</td>
          <td>60</td>
          <td>1</td>
        </tr>
        <tr>
          <th>112</th>
          <td>5350</td>
          <td>5410</td>
          <td>right</td>
          <td>60</td>
          <td>1</td>
        </tr>
        <tr>
          <th>113</th>
          <td>5360</td>
          <td>5420</td>
          <td>left</td>
          <td>60</td>
          <td>1</td>
        </tr>
        <tr>
          <th>114</th>
          <td>5410</td>
          <td>5470</td>
          <td>right</td>
          <td>60</td>
          <td>1</td>
        </tr>
        <tr>
          <th>115</th>
          <td>5420</td>
          <td>5480</td>
          <td>left</td>
          <td>60</td>
          <td>1</td>
        </tr>
        <tr>
          <th>116</th>
          <td>5470</td>
          <td>5530</td>
          <td>right</td>
          <td>60</td>
          <td>1</td>
        </tr>
        <tr>
          <th>117</th>
          <td>5480</td>
          <td>5540</td>
          <td>left</td>
          <td>60</td>
          <td>1</td>
        </tr>
        <tr>
          <th>118</th>
          <td>5530</td>
          <td>5590</td>
          <td>right</td>
          <td>60</td>
          <td>1</td>
        </tr>
        <tr>
          <th>119</th>
          <td>5540</td>
          <td>5600</td>
          <td>left</td>
          <td>60</td>
          <td>1</td>
        </tr>
        <tr>
          <th>120</th>
          <td>5590</td>
          <td>5650</td>
          <td>right</td>
          <td>60</td>
          <td>1</td>
        </tr>
        <tr>
          <th>121</th>
          <td>5600</td>
          <td>5660</td>
          <td>left</td>
          <td>60</td>
          <td>1</td>
        </tr>
        <tr>
          <th>122</th>
          <td>5650</td>
          <td>5710</td>
          <td>right</td>
          <td>60</td>
          <td>2</td>
        </tr>
        <tr>
          <th>123</th>
          <td>5660</td>
          <td>5720</td>
          <td>left</td>
          <td>60</td>
          <td>2</td>
        </tr>
        <tr>
          <th>124</th>
          <td>5710</td>
          <td>5770</td>
          <td>right</td>
          <td>60</td>
          <td>1</td>
        </tr>
        <tr>
          <th>125</th>
          <td>5720</td>
          <td>5780</td>
          <td>left</td>
          <td>60</td>
          <td>1</td>
        </tr>
        <tr>
          <th>126</th>
          <td>5770</td>
          <td>5830</td>
          <td>right</td>
          <td>60</td>
          <td>1</td>
        </tr>
        <tr>
          <th>127</th>
          <td>5780</td>
          <td>5840</td>
          <td>left</td>
          <td>60</td>
          <td>1</td>
        </tr>
        <tr>
          <th>128</th>
          <td>5830</td>
          <td>5890</td>
          <td>right</td>
          <td>60</td>
          <td>1</td>
        </tr>
        <tr>
          <th>129</th>
          <td>5840</td>
          <td>5900</td>
          <td>left</td>
          <td>60</td>
          <td>1</td>
        </tr>
        <tr>
          <th>130</th>
          <td>5890</td>
          <td>5950</td>
          <td>right</td>
          <td>60</td>
          <td>1</td>
        </tr>
        <tr>
          <th>131</th>
          <td>5900</td>
          <td>5960</td>
          <td>left</td>
          <td>60</td>
          <td>1</td>
        </tr>
        <tr>
          <th>132</th>
          <td>5950</td>
          <td>6010</td>
          <td>right</td>
          <td>60</td>
          <td>1</td>
        </tr>
        <tr>
          <th>133</th>
          <td>5960</td>
          <td>6020</td>
          <td>left</td>
          <td>60</td>
          <td>1</td>
        </tr>
        <tr>
          <th>134</th>
          <td>6010</td>
          <td>6070</td>
          <td>right</td>
          <td>60</td>
          <td>1</td>
        </tr>
        <tr>
          <th>177</th>
          <td>7910</td>
          <td>8000</td>
          <td>left</td>
          <td>90</td>
          <td>2</td>
        </tr>
      </tbody>
    </table>
    </div>
    </div>
    <br />
    <br />

.. GENERATED FROM PYTHON SOURCE LINES 149-151

And even see which rule filtered them out.
Note, that only the first rule that filtered the stride is shown.

.. GENERATED FROM PYTHON SOURCE LINES 151-155

.. code-block:: default

    stride_selection.excluded_stride_list_.merge(
        stride_selection.exclusion_reasons_, left_index=True, right_index=True
    )






.. 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>
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      <thead>
        <tr style="text-align: right;">
          <th></th>
          <th>start</th>
          <th>end</th>
          <th>foot</th>
          <th>duration</th>
          <th>stride_length</th>
          <th>rule_name</th>
          <th>rule_obj</th>
        </tr>
        <tr>
          <th>s_id</th>
          <th></th>
          <th></th>
          <th></th>
          <th></th>
          <th></th>
          <th></th>
          <th></th>
        </tr>
      </thead>
      <tbody>
        <tr>
          <th>11</th>
          <td>500</td>
          <td>600</td>
          <td>left</td>
          <td>100</td>
          <td>2</td>
          <td>sl_thres</td>
          <td>IntervalParameterCriteria(inclusive=(False, Tr...</td>
        </tr>
        <tr>
          <th>12</th>
          <td>550</td>
          <td>650</td>
          <td>right</td>
          <td>100</td>
          <td>2</td>
          <td>sl_thres</td>
          <td>IntervalParameterCriteria(inclusive=(False, Tr...</td>
        </tr>
        <tr>
          <th>13</th>
          <td>600</td>
          <td>700</td>
          <td>left</td>
          <td>100</td>
          <td>2</td>
          <td>sl_thres</td>
          <td>IntervalParameterCriteria(inclusive=(False, Tr...</td>
        </tr>
        <tr>
          <th>14</th>
          <td>650</td>
          <td>750</td>
          <td>right</td>
          <td>100</td>
          <td>2</td>
          <td>sl_thres</td>
          <td>IntervalParameterCriteria(inclusive=(False, Tr...</td>
        </tr>
        <tr>
          <th>15</th>
          <td>700</td>
          <td>800</td>
          <td>left</td>
          <td>100</td>
          <td>2</td>
          <td>sl_thres</td>
          <td>IntervalParameterCriteria(inclusive=(False, Tr...</td>
        </tr>
        <tr>
          <th>19</th>
          <td>900</td>
          <td>1000</td>
          <td>left</td>
          <td>100</td>
          <td>2</td>
          <td>sl_thres</td>
          <td>IntervalParameterCriteria(inclusive=(False, Tr...</td>
        </tr>
        <tr>
          <th>20</th>
          <td>950</td>
          <td>1050</td>
          <td>right</td>
          <td>100</td>
          <td>2</td>
          <td>sl_thres</td>
          <td>IntervalParameterCriteria(inclusive=(False, Tr...</td>
        </tr>
        <tr>
          <th>21</th>
          <td>1000</td>
          <td>1100</td>
          <td>left</td>
          <td>100</td>
          <td>2</td>
          <td>sl_thres</td>
          <td>IntervalParameterCriteria(inclusive=(False, Tr...</td>
        </tr>
        <tr>
          <th>22</th>
          <td>1050</td>
          <td>1150</td>
          <td>right</td>
          <td>100</td>
          <td>2</td>
          <td>sl_thres</td>
          <td>IntervalParameterCriteria(inclusive=(False, Tr...</td>
        </tr>
        <tr>
          <th>57</th>
          <td>2800</td>
          <td>2900</td>
          <td>left</td>
          <td>100</td>
          <td>2</td>
          <td>sl_thres</td>
          <td>IntervalParameterCriteria(inclusive=(False, Tr...</td>
        </tr>
        <tr>
          <th>91</th>
          <td>4500</td>
          <td>4600</td>
          <td>left</td>
          <td>100</td>
          <td>2</td>
          <td>sl_thres</td>
          <td>IntervalParameterCriteria(inclusive=(False, Tr...</td>
        </tr>
        <tr>
          <th>92</th>
          <td>4550</td>
          <td>4650</td>
          <td>right</td>
          <td>100</td>
          <td>2</td>
          <td>sl_thres</td>
          <td>IntervalParameterCriteria(inclusive=(False, Tr...</td>
        </tr>
        <tr>
          <th>101</th>
          <td>5000</td>
          <td>5060</td>
          <td>left</td>
          <td>60</td>
          <td>1</td>
          <td>dur_thres</td>
          <td>IntervalDurationCriteria(inclusive=(False, Tru...</td>
        </tr>
        <tr>
          <th>102</th>
          <td>5050</td>
          <td>5110</td>
          <td>right</td>
          <td>60</td>
          <td>1</td>
          <td>dur_thres</td>
          <td>IntervalDurationCriteria(inclusive=(False, Tru...</td>
        </tr>
        <tr>
          <th>103</th>
          <td>5060</td>
          <td>5120</td>
          <td>left</td>
          <td>60</td>
          <td>1</td>
          <td>dur_thres</td>
          <td>IntervalDurationCriteria(inclusive=(False, Tru...</td>
        </tr>
        <tr>
          <th>104</th>
          <td>5110</td>
          <td>5170</td>
          <td>right</td>
          <td>60</td>
          <td>1</td>
          <td>dur_thres</td>
          <td>IntervalDurationCriteria(inclusive=(False, Tru...</td>
        </tr>
        <tr>
          <th>105</th>
          <td>5120</td>
          <td>5180</td>
          <td>left</td>
          <td>60</td>
          <td>1</td>
          <td>dur_thres</td>
          <td>IntervalDurationCriteria(inclusive=(False, Tru...</td>
        </tr>
        <tr>
          <th>106</th>
          <td>5170</td>
          <td>5230</td>
          <td>right</td>
          <td>60</td>
          <td>1</td>
          <td>dur_thres</td>
          <td>IntervalDurationCriteria(inclusive=(False, Tru...</td>
        </tr>
        <tr>
          <th>107</th>
          <td>5180</td>
          <td>5240</td>
          <td>left</td>
          <td>60</td>
          <td>1</td>
          <td>dur_thres</td>
          <td>IntervalDurationCriteria(inclusive=(False, Tru...</td>
        </tr>
        <tr>
          <th>108</th>
          <td>5230</td>
          <td>5290</td>
          <td>right</td>
          <td>60</td>
          <td>1</td>
          <td>dur_thres</td>
          <td>IntervalDurationCriteria(inclusive=(False, Tru...</td>
        </tr>
        <tr>
          <th>109</th>
          <td>5240</td>
          <td>5300</td>
          <td>left</td>
          <td>60</td>
          <td>1</td>
          <td>dur_thres</td>
          <td>IntervalDurationCriteria(inclusive=(False, Tru...</td>
        </tr>
        <tr>
          <th>110</th>
          <td>5290</td>
          <td>5350</td>
          <td>right</td>
          <td>60</td>
          <td>1</td>
          <td>dur_thres</td>
          <td>IntervalDurationCriteria(inclusive=(False, Tru...</td>
        </tr>
        <tr>
          <th>111</th>
          <td>5300</td>
          <td>5360</td>
          <td>left</td>
          <td>60</td>
          <td>1</td>
          <td>dur_thres</td>
          <td>IntervalDurationCriteria(inclusive=(False, Tru...</td>
        </tr>
        <tr>
          <th>112</th>
          <td>5350</td>
          <td>5410</td>
          <td>right</td>
          <td>60</td>
          <td>1</td>
          <td>dur_thres</td>
          <td>IntervalDurationCriteria(inclusive=(False, Tru...</td>
        </tr>
        <tr>
          <th>113</th>
          <td>5360</td>
          <td>5420</td>
          <td>left</td>
          <td>60</td>
          <td>1</td>
          <td>dur_thres</td>
          <td>IntervalDurationCriteria(inclusive=(False, Tru...</td>
        </tr>
        <tr>
          <th>114</th>
          <td>5410</td>
          <td>5470</td>
          <td>right</td>
          <td>60</td>
          <td>1</td>
          <td>dur_thres</td>
          <td>IntervalDurationCriteria(inclusive=(False, Tru...</td>
        </tr>
        <tr>
          <th>115</th>
          <td>5420</td>
          <td>5480</td>
          <td>left</td>
          <td>60</td>
          <td>1</td>
          <td>dur_thres</td>
          <td>IntervalDurationCriteria(inclusive=(False, Tru...</td>
        </tr>
        <tr>
          <th>116</th>
          <td>5470</td>
          <td>5530</td>
          <td>right</td>
          <td>60</td>
          <td>1</td>
          <td>dur_thres</td>
          <td>IntervalDurationCriteria(inclusive=(False, Tru...</td>
        </tr>
        <tr>
          <th>117</th>
          <td>5480</td>
          <td>5540</td>
          <td>left</td>
          <td>60</td>
          <td>1</td>
          <td>dur_thres</td>
          <td>IntervalDurationCriteria(inclusive=(False, Tru...</td>
        </tr>
        <tr>
          <th>118</th>
          <td>5530</td>
          <td>5590</td>
          <td>right</td>
          <td>60</td>
          <td>1</td>
          <td>dur_thres</td>
          <td>IntervalDurationCriteria(inclusive=(False, Tru...</td>
        </tr>
        <tr>
          <th>119</th>
          <td>5540</td>
          <td>5600</td>
          <td>left</td>
          <td>60</td>
          <td>1</td>
          <td>dur_thres</td>
          <td>IntervalDurationCriteria(inclusive=(False, Tru...</td>
        </tr>
        <tr>
          <th>120</th>
          <td>5590</td>
          <td>5650</td>
          <td>right</td>
          <td>60</td>
          <td>1</td>
          <td>dur_thres</td>
          <td>IntervalDurationCriteria(inclusive=(False, Tru...</td>
        </tr>
        <tr>
          <th>121</th>
          <td>5600</td>
          <td>5660</td>
          <td>left</td>
          <td>60</td>
          <td>1</td>
          <td>dur_thres</td>
          <td>IntervalDurationCriteria(inclusive=(False, Tru...</td>
        </tr>
        <tr>
          <th>122</th>
          <td>5650</td>
          <td>5710</td>
          <td>right</td>
          <td>60</td>
          <td>2</td>
          <td>sl_thres</td>
          <td>IntervalParameterCriteria(inclusive=(False, Tr...</td>
        </tr>
        <tr>
          <th>123</th>
          <td>5660</td>
          <td>5720</td>
          <td>left</td>
          <td>60</td>
          <td>2</td>
          <td>sl_thres</td>
          <td>IntervalParameterCriteria(inclusive=(False, Tr...</td>
        </tr>
        <tr>
          <th>124</th>
          <td>5710</td>
          <td>5770</td>
          <td>right</td>
          <td>60</td>
          <td>1</td>
          <td>dur_thres</td>
          <td>IntervalDurationCriteria(inclusive=(False, Tru...</td>
        </tr>
        <tr>
          <th>125</th>
          <td>5720</td>
          <td>5780</td>
          <td>left</td>
          <td>60</td>
          <td>1</td>
          <td>dur_thres</td>
          <td>IntervalDurationCriteria(inclusive=(False, Tru...</td>
        </tr>
        <tr>
          <th>126</th>
          <td>5770</td>
          <td>5830</td>
          <td>right</td>
          <td>60</td>
          <td>1</td>
          <td>dur_thres</td>
          <td>IntervalDurationCriteria(inclusive=(False, Tru...</td>
        </tr>
        <tr>
          <th>127</th>
          <td>5780</td>
          <td>5840</td>
          <td>left</td>
          <td>60</td>
          <td>1</td>
          <td>dur_thres</td>
          <td>IntervalDurationCriteria(inclusive=(False, Tru...</td>
        </tr>
        <tr>
          <th>128</th>
          <td>5830</td>
          <td>5890</td>
          <td>right</td>
          <td>60</td>
          <td>1</td>
          <td>dur_thres</td>
          <td>IntervalDurationCriteria(inclusive=(False, Tru...</td>
        </tr>
        <tr>
          <th>129</th>
          <td>5840</td>
          <td>5900</td>
          <td>left</td>
          <td>60</td>
          <td>1</td>
          <td>dur_thres</td>
          <td>IntervalDurationCriteria(inclusive=(False, Tru...</td>
        </tr>
        <tr>
          <th>130</th>
          <td>5890</td>
          <td>5950</td>
          <td>right</td>
          <td>60</td>
          <td>1</td>
          <td>dur_thres</td>
          <td>IntervalDurationCriteria(inclusive=(False, Tru...</td>
        </tr>
        <tr>
          <th>131</th>
          <td>5900</td>
          <td>5960</td>
          <td>left</td>
          <td>60</td>
          <td>1</td>
          <td>dur_thres</td>
          <td>IntervalDurationCriteria(inclusive=(False, Tru...</td>
        </tr>
        <tr>
          <th>132</th>
          <td>5950</td>
          <td>6010</td>
          <td>right</td>
          <td>60</td>
          <td>1</td>
          <td>dur_thres</td>
          <td>IntervalDurationCriteria(inclusive=(False, Tru...</td>
        </tr>
        <tr>
          <th>133</th>
          <td>5960</td>
          <td>6020</td>
          <td>left</td>
          <td>60</td>
          <td>1</td>
          <td>dur_thres</td>
          <td>IntervalDurationCriteria(inclusive=(False, Tru...</td>
        </tr>
        <tr>
          <th>134</th>
          <td>6010</td>
          <td>6070</td>
          <td>right</td>
          <td>60</td>
          <td>1</td>
          <td>dur_thres</td>
          <td>IntervalDurationCriteria(inclusive=(False, Tru...</td>
        </tr>
        <tr>
          <th>177</th>
          <td>7910</td>
          <td>8000</td>
          <td>left</td>
          <td>90</td>
          <td>2</td>
          <td>sl_thres</td>
          <td>IntervalParameterCriteria(inclusive=(False, Tr...</td>
        </tr>
      </tbody>
    </table>
    </div>
    </div>
    <br />
    <br />

.. GENERATED FROM PYTHON SOURCE LINES 156-175

Step 2,3: WB Assembly
---------------------
Now that we have a list of strides that we consider valid, we want to group them into WBs.
For this we define a set of rules that define when a WB should be terminated and if a preliminary WB fulfills
the criteria to be a valid WB.
These rules are subclasses of :class:`~mobgap.wba.BaseWbCriteria` and each rule can act as both a termination
criterion and an inclusion criterion.
Have a look at the documentation of the specific rules for more details.
For more details on how the rules are applied, have a look at the documentation of the
:class:`~mobgap.wba.WbAssembly`.

For this example, we use two rules:

1. :class:`~mobgap.wba.MaxBreakCriteria`: This rule terminates a WB if the time between two strides is larger
   than a given threshold.
   It acts as a termination criterion.
2. :class:`~mobgap.wba.NStridesCriteria`: This rule excludes preliminary WBs that have less than a given number of
   strides.
   It acts as an inclusion criterion.

.. GENERATED FROM PYTHON SOURCE LINES 175-193

.. code-block:: default

    from mobgap.wba import MaxBreakCriteria, NStridesCriteria, WbAssembly

    rules = [
        (
            "max_break",
            MaxBreakCriteria(
                max_break_s=10,
                remove_last_ic="per_foot",
                consider_end_as_break=True,
            ),
        ),
        ("min_strides", NStridesCriteria(min_strides=5)),
    ]

    wb_assembly = WbAssembly(rules)
    # Note, that we use the filtered stride list from above.
    wb_assembly.assemble(filtered_stride_list, sampling_rate_hz=1)





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

 .. code-block:: none


    WbAssembly(rules=[('max_break', MaxBreakCriteria(consider_end_as_break=True, max_break_s=10, remove_last_ic='per_foot')), ('min_strides', NStridesCriteria(min_strides=5, min_strides_left=None, min_strides_right=None))])



.. GENERATED FROM PYTHON SOURCE LINES 194-202

The wb_assembly object now contains a grouping of each stride to a WB.
Depending on how we want to further process the data, we can either use the `wbs_` attribute, which is a dictionary
mapping a WB id to a list of strides, or the `annotated_stride_list_` attribute, which is a copy of the stride list
containing an additional column `wb_id` that contains the id of the WB the stride belongs to.
Note, that both outputs only contain the strides that belong to a final WB.
Strides belonging to a WB that was ultimately filtered out, or never belonged to a WB in the first place are not
included.
They can be accessed via the `excluded_stride_list_` and `excluded_wbs` attributes.

.. GENERATED FROM PYTHON SOURCE LINES 202-204

.. code-block:: default

    wb_assembly.annotated_stride_list_






.. 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>start</th>
          <th>end</th>
          <th>foot</th>
          <th>duration</th>
          <th>stride_length</th>
        </tr>
        <tr>
          <th>wb_id</th>
          <th>s_id</th>
          <th></th>
          <th></th>
          <th></th>
          <th></th>
          <th></th>
        </tr>
      </thead>
      <tbody>
        <tr>
          <th rowspan="5" valign="top">0</th>
          <th>1</th>
          <td>0</td>
          <td>100</td>
          <td>left</td>
          <td>100</td>
          <td>1</td>
        </tr>
        <tr>
          <th>2</th>
          <td>50</td>
          <td>150</td>
          <td>right</td>
          <td>100</td>
          <td>1</td>
        </tr>
        <tr>
          <th>3</th>
          <td>100</td>
          <td>200</td>
          <td>left</td>
          <td>100</td>
          <td>1</td>
        </tr>
        <tr>
          <th>4</th>
          <td>150</td>
          <td>250</td>
          <td>right</td>
          <td>100</td>
          <td>1</td>
        </tr>
        <tr>
          <th>5</th>
          <td>200</td>
          <td>300</td>
          <td>left</td>
          <td>100</td>
          <td>1</td>
        </tr>
        <tr>
          <th>...</th>
          <th>...</th>
          <td>...</td>
          <td>...</td>
          <td>...</td>
          <td>...</td>
          <td>...</td>
        </tr>
        <tr>
          <th rowspan="5" valign="top">3</th>
          <th>172</th>
          <td>7690</td>
          <td>7780</td>
          <td>right</td>
          <td>90</td>
          <td>1</td>
        </tr>
        <tr>
          <th>173</th>
          <td>7730</td>
          <td>7820</td>
          <td>left</td>
          <td>90</td>
          <td>1</td>
        </tr>
        <tr>
          <th>174</th>
          <td>7780</td>
          <td>7870</td>
          <td>right</td>
          <td>90</td>
          <td>1</td>
        </tr>
        <tr>
          <th>175</th>
          <td>7820</td>
          <td>7910</td>
          <td>left</td>
          <td>90</td>
          <td>1</td>
        </tr>
        <tr>
          <th>176</th>
          <td>7870</td>
          <td>7960</td>
          <td>right</td>
          <td>90</td>
          <td>1</td>
        </tr>
      </tbody>
    </table>
    <p>121 rows × 5 columns</p>
    </div>
    </div>
    <br />
    <br />

.. GENERATED FROM PYTHON SOURCE LINES 205-207

.. code-block:: default

    print(f"The method identified {len(wb_assembly.wbs_)} WBs.")





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

 .. code-block:: none

    The method identified 4 WBs.




.. GENERATED FROM PYTHON SOURCE LINES 208-209

We can also get an idea of how the final results looks, by plotting the wba outputs.

.. GENERATED FROM PYTHON SOURCE LINES 209-213

.. code-block:: default

    from mobgap.wba.plot import plot_wba_results

    plot_wba_results(wb_assembly, stride_selection=stride_selection)
    plt.show()



.. image-sg:: /auto_examples/wba/images/sphx_glr__01_assembling_wbs_001.png
   :alt:  01 assembling wbs
   :srcset: /auto_examples/wba/images/sphx_glr__01_assembling_wbs_001.png
   :class: sphx-glr-single-img






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

   **Total running time of the script:** (0 minutes 2.612 seconds)

**Estimated memory usage:**  9 MB


.. _sphx_glr_download_auto_examples_wba__01_assembling_wbs.py:

.. only:: html

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




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

      :download:`Download Python source code: _01_assembling_wbs.py <_01_assembling_wbs.py>`

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

      :download:`Download Jupyter notebook: _01_assembling_wbs.ipynb <_01_assembling_wbs.ipynb>`


.. only:: html

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

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