.. DO NOT EDIT.
.. THIS FILE WAS AUTOMATICALLY GENERATED BY SPHINX-GALLERY.
.. TO MAKE CHANGES, EDIT THE SOURCE PYTHON FILE:
.. "auto_examples/pipeline/_01_gs_iterator.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_pipeline__01_gs_iterator.py>`
        to download the full example code

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

.. _sphx_glr_auto_examples_pipeline__01_gs_iterator.py:


.. _gs_iterator_example:

Gait Sequence Iterator
======================

As part of most pipelines, we need to iterate over the gait sequences to apply all further algorithms to them
individually.
This can be a bit cumbersome, as we need to iterate over the data and aggregate the results at the same time.
Hence, we provide some helpers for that.

We provide two ways of iterating.
The first one, only handles the iteration and does not aggregate the results.
The second approach attempts to also support you in aggregating the results.

Getting Some Example Data
-------------------------

.. GENERATED FROM PYTHON SOURCE LINES 20-32

.. code-block:: default


    import numpy as np
    import pandas as pd

    from mobgap.data import LabExampleDataset

    lab_example_data = LabExampleDataset(reference_system="INDIP")
    long_trial = lab_example_data.get_subset(cohort="MS", participant_id="001", test="Test11", trial="Trial1")
    long_trial_gs = long_trial.reference_parameters_.wb_list

    long_trial_gs






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    <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>start</th>
          <th>end</th>
          <th>n_strides</th>
          <th>duration_s</th>
          <th>length_m</th>
          <th>avg_speed_mps</th>
          <th>avg_cadence_spm</th>
          <th>avg_stride_length_m</th>
          <th>termination_reason</th>
        </tr>
        <tr>
          <th>wb_id</th>
          <th></th>
          <th></th>
          <th></th>
          <th></th>
          <th></th>
          <th></th>
          <th></th>
          <th></th>
          <th></th>
        </tr>
      </thead>
      <tbody>
        <tr>
          <th>1</th>
          <td>1019</td>
          <td>1768</td>
          <td>9</td>
          <td>7.48</td>
          <td>4.468932</td>
          <td>0.847668</td>
          <td>107.795850</td>
          <td>0.942678</td>
          <td>Pause</td>
        </tr>
        <tr>
          <th>2</th>
          <td>4534</td>
          <td>5549</td>
          <td>11</td>
          <td>10.14</td>
          <td>2.900453</td>
          <td>0.365176</td>
          <td>93.396106</td>
          <td>0.483923</td>
          <td>Pause</td>
        </tr>
        <tr>
          <th>3</th>
          <td>9665</td>
          <td>10569</td>
          <td>9</td>
          <td>9.03</td>
          <td>2.140232</td>
          <td>0.294058</td>
          <td>75.981133</td>
          <td>0.506458</td>
          <td>Pause</td>
        </tr>
        <tr>
          <th>4</th>
          <td>12337</td>
          <td>14633</td>
          <td>28</td>
          <td>22.95</td>
          <td>11.201110</td>
          <td>0.634425</td>
          <td>92.337768</td>
          <td>0.803933</td>
          <td>Pause</td>
        </tr>
        <tr>
          <th>5</th>
          <td>20151</td>
          <td>20982</td>
          <td>11</td>
          <td>8.30</td>
          <td>2.390709</td>
          <td>0.371746</td>
          <td>87.915774</td>
          <td>0.507484</td>
          <td>Pause</td>
        </tr>
        <tr>
          <th>6</th>
          <td>21378</td>
          <td>22129</td>
          <td>9</td>
          <td>7.50</td>
          <td>2.517558</td>
          <td>0.492965</td>
          <td>95.365740</td>
          <td>0.599360</td>
          <td>Pause</td>
        </tr>
      </tbody>
    </table>
    </div>
    </div>
    <br />
    <br />

.. GENERATED FROM PYTHON SOURCE LINES 33-43

Simple Functional Interface
---------------------------
We provide the :func:`~mobgap.pipeline.iter_gs` function to iterate over the gait sequences.
It simply takes the data and the gait sequence list and cuts the data accordingly to iterate over it.
The function yields the gait sequence information as tuple (i.e. the "row" of the gs dataframe as namedtuple) and the
data for each iteration.
Note that the index of the data is not changed.
Hence we recommend using `iloc` to access the data (`iloc[0]` will return the first sample of the gait sequence).

Using our example data and gs, we can iterate over the data as follows:

.. GENERATED FROM PYTHON SOURCE LINES 43-53

.. code-block:: default

    from mobgap.pipeline import iter_gs

    for gs, data in iter_gs(long_trial.data["LowerBack"], long_trial_gs):
        # Note that the key to access the id is called "wb_id" here, as we loaded the WB from the reference system.
        # If this is an "actual" gait sequences, as calculated by one of the GSD algorithms, the key would be "gs_id".
        print("Gait Sequence: ", gs)
        print("Expected N-samples in gs: ", gs.end - gs.start)
        print("N-samples in gs: ", len(data))
        print("First sample of gs:\n", data.iloc[0], end="\n\n")





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

 .. code-block:: none

    Gait Sequence:  WalkingBout(wb_id=1, start=1019, end=1768)
    Expected N-samples in gs:  749
    N-samples in gs:  749
    First sample of gs:
     acc_x    10.750431
    acc_y     0.390207
    acc_z    -2.088885
    gyr_x    13.226900
    gyr_y    -4.914900
    gyr_z    19.874400
    Name: 2020-10-30 12:53:33.211999893+00:00, dtype: float64

    Gait Sequence:  WalkingBout(wb_id=2, start=4534, end=5549)
    Expected N-samples in gs:  1015
    N-samples in gs:  1015
    First sample of gs:
     acc_x     9.997300
    acc_y    -0.864568
    acc_z    -0.297291
    gyr_x     3.193700
    gyr_y    12.861200
    gyr_z    -5.371300
    Name: 2020-10-30 12:54:08.361999989+00:00, dtype: float64

    Gait Sequence:  WalkingBout(wb_id=3, start=9665, end=10569)
    Expected N-samples in gs:  904
    N-samples in gs:  904
    First sample of gs:
     acc_x    8.892269
    acc_y   -0.136856
    acc_z   -3.006764
    gyr_x   -7.543100
    gyr_y   -5.688700
    gyr_z    0.470900
    Name: 2020-10-30 12:54:59.671999931+00:00, dtype: float64

    Gait Sequence:  WalkingBout(wb_id=4, start=12337, end=14633)
    Expected N-samples in gs:  2296
    N-samples in gs:  2296
    First sample of gs:
     acc_x    10.236130
    acc_y    -1.686952
    acc_z    -0.285697
    gyr_x    84.994600
    gyr_y   -17.645800
    gyr_z   -24.538000
    Name: 2020-10-30 12:55:26.391999960+00:00, dtype: float64

    Gait Sequence:  WalkingBout(wb_id=5, start=20151, end=20982)
    Expected N-samples in gs:  831
    N-samples in gs:  831
    First sample of gs:
     acc_x      9.135405
    acc_y     -1.797439
    acc_z      0.479475
    gyr_x   -137.510300
    gyr_y     16.480800
    gyr_z    -21.008900
    Name: 2020-10-30 12:56:44.532000064+00:00, dtype: float64

    Gait Sequence:  WalkingBout(wb_id=6, start=21378, end=22129)
    Expected N-samples in gs:  751
    N-samples in gs:  751
    First sample of gs:
     acc_x     9.895224
    acc_y    -3.081051
    acc_z    -1.070252
    gyr_x    56.595700
    gyr_y    -5.242100
    gyr_z   -16.025800
    Name: 2020-10-30 12:56:56.802000046+00:00, dtype: float64





.. GENERATED FROM PYTHON SOURCE LINES 54-95

.. note:: The ``gs`` named-tuples returned by the iterator can either be of type ``GaitSequence`` or ``WalkingBout``.
          In both cases they contain the fields ``id``, ``start``, and ``end`` in this order.
          When using the named access the ``id`` field can also be accessed via the ``wb_id``/``gs_id`` field (
          depending on the type of the gait sequence).

You can see that this way it is pretty easy to iterate over the data.
However, if you are planning to run calculations on the data, you need to aggregate the results yourself.
If you are planning to collect multiple pieces of results, this can become cumbersome.
See the is `tpcp example <https://tpcp.readthedocs.io/en/latest/auto_examples/recipies/_04_typed_iterator.html>`__
for more information about this.
Therefore, we also provide an Iterator Class based on :class:`~tpcp.misc.TypedIterator`.

Class based Interface
---------------------
.. note:: Learn more about the general approach of using :class:`~tpcp.misc.TypedIterator` classes in this
          `tpcp example <https://tpcp.readthedocs.io/en/latest/auto_examples/recipies/_04_typed_iterator.html>`__.

Compared to the functional interface, the class interface attempts to also solve the problem of collecting the
and aggregating results that you produce per GS.
In a typical pipeline you might want to calculate the initial contacts, cadence, stride length, and gait speed for
each gait sequence.
With the class based interface, you can easily collect all of these results and then aggregate them into one
predefined data structure.

The class based interface can be used in two ways.
First in the "default" configuration, which is set up to work with the typical calculations and results that you would
expect from a typical processing pipeline.
And second, in a custom way, where you need to define expected "results" per iteration yourself.

The simple case
---------------
The simple case basically no more setup as the functional interface.
However, it assumes that your results are a subset of initial contacts, cadence, stride length, and gait speed, and
that all of them are stored in the expected mobgap datatypes (aka pandas dataframes).
The iterator will then automatically aggregate the results the dataframes per iteration into one combined dataframe,
handling the sample offsets of the gait sequences for you.

Below we will show how this works, by "simulating" the calculation of some initial contacts and cadence.

We start by setting up an iterator object.
We can leave everything at the default values, as we do not need any custom aggregation functions.

.. GENERATED FROM PYTHON SOURCE LINES 95-100

.. code-block:: default

    from mobgap.pipeline import GsIterator

    iterator = GsIterator()
    dt = iterator.data_type








.. GENERATED FROM PYTHON SOURCE LINES 101-102

The default result datatype per iteration is defined as follows:

.. GENERATED FROM PYTHON SOURCE LINES 102-108

.. code-block:: default

    import inspect

    from IPython.core.display_functions import display

    display(inspect.getsource(iterator.data_type))





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

 .. code-block:: none

    @dataclass
    class FullPipelinePerGsResult:
        """Default expected result type for the gait-sequence iterator.

        When using the :class:`~mobgap.pipeline.GsIterator` with the default configuration, an instance of this dataclass
        will be created for each gait-sequence.

        Each value is expected to be a dataframe.

        Attributes
        ----------
        ic_list
            The initial contacts for each gait-sequence.
            This is a dataframe with a column called ``ic``.
            The values of this ic-column are expected to be samples relative to the start of the gait-sequence.
        cad_per_sec
            The cadence values within each gait-sequence.
            This dataframe has no further requirements relevant for the iterator.
        stride_length
            The stride length values within each gait-sequence.
            This dataframe has no further requirements relevant for the iterator.
        gait_speed
            The gait speed values within each gait-sequence.
            This dataframe has no further requirements relevant for the iterator.

        """

        ic_list: pd.DataFrame
        cad_per_sec: pd.DataFrame
        stride_length: pd.DataFrame
        gait_speed: pd.DataFrame





.. GENERATED FROM PYTHON SOURCE LINES 109-115

This means you are only allowed to use the available attributes.
But, you don't need to specify all of them.
Below we will only "calculate" the initial contacts and cadence.

In each iteration the iterator will give us a tuple of the gait sequence information, the data for the iteration, and
a new empty result object.

.. GENERATED FROM PYTHON SOURCE LINES 115-128

.. code-block:: default

    from mobgap.utils.conversions import as_samples

    for (gs, data), result in iterator.iterate(long_trial.data["LowerBack"], long_trial_gs):
        # Now we can just "calculate" the initial contacts and set it on the result object.
        result.ic_list = pd.DataFrame(np.arange(0, len(data), 100), columns=["ic"]).rename_axis(index="step_id")
        # For cadence, we just set a dummy value to the wb_id for each 1 second bout of the data.
        n_seconds = int(len(data) // long_trial.sampling_rate_hz)
        result.cad_per_sec = pd.DataFrame(
            [gs.id] * n_seconds,
            columns=["cad_spm"],
            index=as_samples(np.arange(0, n_seconds) + 0.5, long_trial.sampling_rate_hz),
        ).rename_axis(index="sec_center_samples")








.. GENERATED FROM PYTHON SOURCE LINES 129-130

After the iteration, we can access the aggregated results either using the `results_` property of the iterator

.. GENERATED FROM PYTHON SOURCE LINES 130-132

.. code-block:: default

    iterator.results_.ic_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>ic</th>
        </tr>
        <tr>
          <th>wb_id</th>
          <th>step_id</th>
          <th></th>
        </tr>
      </thead>
      <tbody>
        <tr>
          <th rowspan="5" valign="top">1</th>
          <th>0</th>
          <td>1019</td>
        </tr>
        <tr>
          <th>1</th>
          <td>1119</td>
        </tr>
        <tr>
          <th>2</th>
          <td>1219</td>
        </tr>
        <tr>
          <th>3</th>
          <td>1319</td>
        </tr>
        <tr>
          <th>4</th>
          <td>1419</td>
        </tr>
        <tr>
          <th>...</th>
          <th>...</th>
          <td>...</td>
        </tr>
        <tr>
          <th rowspan="5" valign="top">6</th>
          <th>3</th>
          <td>21678</td>
        </tr>
        <tr>
          <th>4</th>
          <td>21778</td>
        </tr>
        <tr>
          <th>5</th>
          <td>21878</td>
        </tr>
        <tr>
          <th>6</th>
          <td>21978</td>
        </tr>
        <tr>
          <th>7</th>
          <td>22078</td>
        </tr>
      </tbody>
    </table>
    <p>69 rows × 1 columns</p>
    </div>
    </div>
    <br />
    <br />

.. GENERATED FROM PYTHON SOURCE LINES 133-135

Or via direct dynamic property access, where we add a trailing underscore to the name of the result
(`result.ic_list` -> `iterator.ic_list_`).

.. GENERATED FROM PYTHON SOURCE LINES 135-137

.. code-block:: default

    iterator.ic_list_






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    <div>
    <style scoped>
        .dataframe tbody tr th:only-of-type {
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        .dataframe tbody tr th {
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            text-align: right;
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    </style>
    <table border="1" class="dataframe">
      <thead>
        <tr style="text-align: right;">
          <th></th>
          <th></th>
          <th>ic</th>
        </tr>
        <tr>
          <th>wb_id</th>
          <th>step_id</th>
          <th></th>
        </tr>
      </thead>
      <tbody>
        <tr>
          <th rowspan="5" valign="top">1</th>
          <th>0</th>
          <td>1019</td>
        </tr>
        <tr>
          <th>1</th>
          <td>1119</td>
        </tr>
        <tr>
          <th>2</th>
          <td>1219</td>
        </tr>
        <tr>
          <th>3</th>
          <td>1319</td>
        </tr>
        <tr>
          <th>4</th>
          <td>1419</td>
        </tr>
        <tr>
          <th>...</th>
          <th>...</th>
          <td>...</td>
        </tr>
        <tr>
          <th rowspan="5" valign="top">6</th>
          <th>3</th>
          <td>21678</td>
        </tr>
        <tr>
          <th>4</th>
          <td>21778</td>
        </tr>
        <tr>
          <th>5</th>
          <td>21878</td>
        </tr>
        <tr>
          <th>6</th>
          <td>21978</td>
        </tr>
        <tr>
          <th>7</th>
          <td>22078</td>
        </tr>
      </tbody>
    </table>
    <p>69 rows × 1 columns</p>
    </div>
    </div>
    <br />
    <br />

.. GENERATED FROM PYTHON SOURCE LINES 138-145

We can see that we only get a single dataframe with all the results.
And all ICs are offset, so that they are relative to the start of the recording and not the start of the gait
sequence anymore.

For the cadence value, the index represents the sample of the center of the second the cadence value belongs to.
This value was originally relative to the start of the GS.
We can see that in the aggregated results this is transformed back to be relative to the start of the recording.

.. GENERATED FROM PYTHON SOURCE LINES 145-148

.. code-block:: default

    iterator.results_.cad_per_sec







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    <div>
    <style scoped>
        .dataframe tbody tr th:only-of-type {
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        }
    </style>
    <table border="1" class="dataframe">
      <thead>
        <tr style="text-align: right;">
          <th></th>
          <th></th>
          <th>cad_spm</th>
        </tr>
        <tr>
          <th>wb_id</th>
          <th>sec_center_samples</th>
          <th></th>
        </tr>
      </thead>
      <tbody>
        <tr>
          <th rowspan="5" valign="top">1</th>
          <th>1069</th>
          <td>1</td>
        </tr>
        <tr>
          <th>1169</th>
          <td>1</td>
        </tr>
        <tr>
          <th>1269</th>
          <td>1</td>
        </tr>
        <tr>
          <th>1369</th>
          <td>1</td>
        </tr>
        <tr>
          <th>1469</th>
          <td>1</td>
        </tr>
        <tr>
          <th>...</th>
          <th>...</th>
          <td>...</td>
        </tr>
        <tr>
          <th rowspan="5" valign="top">6</th>
          <th>21628</th>
          <td>6</td>
        </tr>
        <tr>
          <th>21728</th>
          <td>6</td>
        </tr>
        <tr>
          <th>21828</th>
          <td>6</td>
        </tr>
        <tr>
          <th>21928</th>
          <td>6</td>
        </tr>
        <tr>
          <th>22028</th>
          <td>6</td>
        </tr>
      </tbody>
    </table>
    <p>63 rows × 1 columns</p>
    </div>
    </div>
    <br />
    <br />

.. GENERATED FROM PYTHON SOURCE LINES 149-164

But what to do, if you don't want to use the default result datatype?

Custom Results
--------------

This requires a little bit more setup.
First we need to decide what results we expect.
This is done by defining a dataclass that represents the results.

Here we create a new dataclass that only expect two dummy results, but you can add as many as you want.
You could also subclass the default dataclass and just add the additional results.

The first result here is ``n_samples`` which is just a dummy results indicating the number of samples the data has.
The second result is ``filtered_data`` (we will just add some dummy data here).
This is expected to be a pd.DataFrame to demonstrate that you can also return more complex results.

.. GENERATED FROM PYTHON SOURCE LINES 164-173

.. code-block:: default

    from dataclasses import dataclass


    @dataclass
    class ResultType:
        n_samples: int
        filtered_data: pd.DataFrame









.. GENERATED FROM PYTHON SOURCE LINES 174-183

For each iteration (i.e. for each gait sequence), we will create one instance of this dataclass.
The list of these instances will be available as the `raw_results_` attribute of the iterator.

We can also decide to aggregate the results.
We provide some default aggregations functions (see ``GsIterator.DEFAULT_AGGREGATORS``), that you could use.
However, here we will create our own aggregation function.

It might be nice to turn the ``n_samples`` into a pandas series with the gs identifier as index.
For this we define an aggregation function that expects the list of inputs and the list of results as inputs.

.. GENERATED FROM PYTHON SOURCE LINES 183-192

.. code-block:: default



    def aggregate_n_samples(inputs, results):
        gait_sequences, _ = zip(*inputs)
        return pd.Series(results, index=[gs.id for gs in gait_sequences], name="N-Samples")


    aggregations = [("n_samples", aggregate_n_samples)]








.. GENERATED FROM PYTHON SOURCE LINES 193-197

Now we can create an instance of the iterator.
Note, that if we want to correctly infer the result type, we need to use the somewhat weird square bracket-typing
syntax, when creating the iterator.
This will allow to autocomplete the attributes of the result type.

.. GENERATED FROM PYTHON SOURCE LINES 197-201

.. code-block:: default

    from mobgap.pipeline import GsIterator

    custom_iterator = GsIterator[ResultType](ResultType, aggregations=aggregations)








.. GENERATED FROM PYTHON SOURCE LINES 202-204

Iterating over the iterator now provides us the row from the gait sequence list (which we ignore here), the data for
each iteration, and the empty result object, we can fill up each iteration.

.. GENERATED FROM PYTHON SOURCE LINES 204-212

.. code-block:: default


    for (_, data), custom_result in custom_iterator.iterate(long_trial.data["LowerBack"], long_trial_gs):
        # We just calculate the length, but you can image any other calculation here.
        # Then we just set the result.
        custom_result.n_samples = len(data)
        # For the "filtered" data we just subtract 1 form the input
        custom_result.filtered_data = data - 1








.. GENERATED FROM PYTHON SOURCE LINES 213-214

Then we can easily inspect the aggregated results.

.. GENERATED FROM PYTHON SOURCE LINES 214-216

.. code-block:: default

    custom_iterator.results_.n_samples





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

 .. code-block:: none


    1     749
    2    1015
    3     904
    4    2296
    5     831
    6     751
    Name: N-Samples, dtype: int64



.. GENERATED FROM PYTHON SOURCE LINES 217-218

For the filtered data, we did not apply any aggregation and hence just get a list of all results.

.. GENERATED FROM PYTHON SOURCE LINES 218-219

.. code-block:: default

    custom_iterator.results_.filtered_data




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

 .. code-block:: none


    [                                        acc_x     acc_y  ...    gyr_y    gyr_z
    time                                                     ...                  
    2020-10-30 12:53:33.211999893+00:00  9.750431 -0.609793  ...  -5.9149  18.8744
    2020-10-30 12:53:33.221999884+00:00  9.216030 -1.843504  ... -16.0051  24.6507
    2020-10-30 12:53:33.232000113+00:00  8.318355 -2.916044  ... -21.3116  26.9805
    2020-10-30 12:53:33.242000103+00:00  7.360369 -3.854483  ... -22.8495  25.4291
    2020-10-30 12:53:33.252000092+00:00  6.388862 -4.409548  ... -21.4698  21.1185
    ...                                       ...       ...  ...      ...      ...
    2020-10-30 12:53:40.651999950+00:00  7.844738 -3.163485  ...   3.7649 -10.4524
    2020-10-30 12:53:40.661999941+00:00  7.819128 -3.057138  ...   5.7176 -11.1201
    2020-10-30 12:53:40.671999931+00:00  7.900424 -2.964919  ...   6.3879 -11.5961
    2020-10-30 12:53:40.681999922+00:00  7.916549 -2.904721  ...   5.8304 -12.1774
    2020-10-30 12:53:40.691999912+00:00  8.065151 -2.800718  ...   6.3839 -12.1929

    [749 rows x 6 columns],                                         acc_x     acc_y  ...    gyr_y    gyr_z
    time                                                     ...                  
    2020-10-30 12:54:08.361999989+00:00  8.997300 -1.864568  ...  11.8612  -6.3713
    2020-10-30 12:54:08.371999979+00:00  9.010271 -1.821648  ...  11.6298  -4.6801
    2020-10-30 12:54:08.381999969+00:00  9.069629 -1.717282  ...  11.0973  -3.1574
    2020-10-30 12:54:08.391999960+00:00  9.392935 -1.495418  ...   9.9199  -0.4397
    2020-10-30 12:54:08.401999950+00:00  9.784886 -1.396940  ...   7.7379   3.6206
    ...                                       ...       ...  ...      ...      ...
    2020-10-30 12:54:18.461999893+00:00  7.047536  0.514839  ... -27.7005  35.8898
    2020-10-30 12:54:18.471999884+00:00  6.969416  0.439982  ... -26.9190  37.7245
    2020-10-30 12:54:18.482000113+00:00  6.719520  0.505765  ... -26.5421  38.8936
    2020-10-30 12:54:18.492000103+00:00  6.450755  0.743554  ... -26.5874  39.8177
    2020-10-30 12:54:18.502000093+00:00  6.194022  0.940628  ... -26.6665  40.8314

    [1015 rows x 6 columns],                                         acc_x     acc_y  ...    gyr_y   gyr_z
    time                                                     ...                 
    2020-10-30 12:54:59.671999931+00:00  7.892269 -1.136856  ...  -6.6887 -0.5291
    2020-10-30 12:54:59.681999922+00:00  7.990240 -1.144530  ...  -7.2629 -0.7600
    2020-10-30 12:54:59.691999912+00:00  8.077780 -1.182633  ...  -7.6096 -1.1997
    2020-10-30 12:54:59.701999903+00:00  8.153184 -1.142374  ...  -7.9950 -1.5970
    2020-10-30 12:54:59.711999893+00:00  8.131641 -1.144750  ...  -8.3241 -1.6867
    ...                                       ...       ...  ...      ...     ...
    2020-10-30 12:55:08.661999941+00:00  8.136143 -2.346360  ...  16.1862 -7.2686
    2020-10-30 12:55:08.671999931+00:00  8.152235 -2.268232  ...  17.1322 -8.0022
    2020-10-30 12:55:08.681999922+00:00  8.182559 -2.251994  ...  17.5279 -8.3387
    2020-10-30 12:55:08.691999912+00:00  8.305513 -2.482470  ...  18.2932 -8.8593
    2020-10-30 12:55:08.701999903+00:00  8.434217 -2.731595  ...  18.5796 -7.7286

    [904 rows x 6 columns],                                         acc_x     acc_y  ...    gyr_y    gyr_z
    time                                                     ...                  
    2020-10-30 12:55:26.391999960+00:00  9.236130 -2.686952  ... -18.6458 -25.5380
    2020-10-30 12:55:26.401999950+00:00  9.366706 -2.470049  ... -20.7625 -24.8423
    2020-10-30 12:55:26.411999941+00:00  9.266174 -2.263981  ... -18.0376 -24.7323
    2020-10-30 12:55:26.421999931+00:00  8.996807 -2.105405  ... -10.3876 -24.2681
    2020-10-30 12:55:26.431999922+00:00  8.920447 -1.906488  ...  -1.9655 -23.3928
    ...                                       ...       ...  ...      ...      ...
    2020-10-30 12:55:49.302000046+00:00  7.716993  0.326495  ...  15.9933   7.4403
    2020-10-30 12:55:49.312000036+00:00  7.728400  0.574557  ...  15.3581   5.5792
    2020-10-30 12:55:49.322000027+00:00  7.816767  0.665758  ...  15.8199   4.5902
    2020-10-30 12:55:49.332000017+00:00  8.074722  0.309418  ...  17.9715   3.6861
    2020-10-30 12:55:49.342000008+00:00  8.270306 -0.338238  ...  20.9984   3.2169

    [2296 rows x 6 columns],                                         acc_x     acc_y  ...    gyr_y    gyr_z
    time                                                     ...                  
    2020-10-30 12:56:44.532000064+00:00  8.135405 -2.797439  ...  15.4808 -22.0089
    2020-10-30 12:56:44.542000055+00:00  8.475562 -2.708892  ...  16.3716 -23.4909
    2020-10-30 12:56:44.552000046+00:00  8.853603 -2.523719  ...  15.7210 -24.0634
    2020-10-30 12:56:44.562000036+00:00  9.352082 -2.173552  ...  14.0635 -24.1058
    2020-10-30 12:56:44.572000027+00:00  9.733701 -1.799136  ...  12.3673 -23.7012
    ...                                       ...       ...  ...      ...      ...
    2020-10-30 12:56:52.792000055+00:00  8.036729 -2.491177  ...  13.5385   1.7647
    2020-10-30 12:56:52.802000046+00:00  8.545972 -2.340976  ...  11.4386   6.4245
    2020-10-30 12:56:52.812000036+00:00  8.822208 -2.348849  ...  12.2948  10.9618
    2020-10-30 12:56:52.822000027+00:00  9.126596 -2.336454  ...  15.8081  14.4861
    2020-10-30 12:56:52.832000017+00:00  9.465227 -2.308985  ...  17.4874  17.1938

    [831 rows x 6 columns],                                         acc_x     acc_y  ...    gyr_y    gyr_z
    time                                                     ...                  
    2020-10-30 12:56:56.802000046+00:00  8.895224 -4.081051  ...  -6.2421 -17.0258
    2020-10-30 12:56:56.812000036+00:00  8.844582 -4.294596  ... -12.8417 -14.2214
    2020-10-30 12:56:56.822000027+00:00  8.441273 -4.354333  ... -10.4114 -12.7980
    2020-10-30 12:56:56.832000017+00:00  7.944172 -4.372805  ...  -0.0473 -10.9684
    2020-10-30 12:56:56.842000008+00:00  7.882891 -4.435929  ...  11.1010  -7.4715
    ...                                       ...       ...  ...      ...      ...
    2020-10-30 12:57:04.262000084+00:00  7.601535  1.485462  ...  -2.9975  -8.6763
    2020-10-30 12:57:04.272000074+00:00  8.019890  1.890526  ...  -1.3660  -9.6691
    2020-10-30 12:57:04.282000065+00:00  8.437574  2.206942  ...  -0.3714  -8.8523
    2020-10-30 12:57:04.292000055+00:00  8.749383  2.304567  ...  -2.6950  -7.8553
    2020-10-30 12:57:04.302000046+00:00  9.103801  2.274051  ... -10.0859  -6.3404

    [751 rows x 6 columns]]




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

   **Total running time of the script:** (0 minutes 2.930 seconds)

**Estimated memory usage:**  9 MB


.. _sphx_glr_download_auto_examples_pipeline__01_gs_iterator.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_gs_iterator.py <_01_gs_iterator.py>`

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

      :download:`Download Jupyter notebook: _01_gs_iterator.ipynb <_01_gs_iterator.ipynb>`


.. only:: html

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

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