.. DO NOT EDIT. .. THIS FILE WAS AUTOMATICALLY GENERATED BY SPHINX-GALLERY. .. TO MAKE CHANGES, EDIT THE SOURCE PYTHON FILE: .. "auto_examples/data/_01_loading_example_data.py" .. LINE NUMBERS ARE GIVEN BELOW. .. only:: html .. note:: :class: sphx-glr-download-link-note :ref:`Go to the end ` to download the full example code .. rst-class:: sphx-glr-example-title .. _sphx_glr_auto_examples_data__01_loading_example_data.py: .. _data_loading_example: Loading example data ==================== This example shows how to use the loader functions to load example data. Laboratory data --------------- We provide a small subset of the TVS dataset of the Mobilise-D project as example data. The data only contains a small number of participants and only two (instead of the ten) laboratory tests of each participant. The data is stored in the `examples/data` folder of the repository in the Mobilise-D matlab format [1]_. .. [1] Palmerini L, Reggi L, Bonci T, Del Din S, Micó-Amigo ME, Salis F, Bertuletti S, Caruso M, Cereatti A, Gazit E, Paraschiv-Ionescu A, Soltani A, Kluge F, Küderle A, Ullrich M, Kirk C, Hiden H, D'Ascanio I, Hansen C, Rochester L, Mazzà C, Chiari L. Mobility recorded by wearable devices and gold standards: the Mobilise-D procedure for data standardization. Sci Data. 2023 Jan 19;10(1):38. doi: 10.1038/s41597-023-01930-9. PMID: 36658136; PMCID: PMC9852581. .. GENERATED FROM PYTHON SOURCE LINES 25-29 Dataset Class +++++++++++++ We provide a :class:`~mobgap.data.LabExampleDataset` class to load the example data. This is the easiest way to access the example data and allows you to select and iterate over the data in an easy way. .. GENERATED FROM PYTHON SOURCE LINES 29-32 .. code-block:: default from mobgap.data import LabExampleDataset example_data = LabExampleDataset() .. GENERATED FROM PYTHON SOURCE LINES 33-34 You can select the data you want using the ``get_subset`` method. .. GENERATED FROM PYTHON SOURCE LINES 34-37 .. code-block:: default ha_example_data = example_data.get_subset(cohort="HA") ha_example_data .. raw:: html

LabExampleDataset [6 groups/rows]

cohort participant_id time_measure test trial
0 HA 001 TimeMeasure1 Test5 Trial1
1 HA 001 TimeMeasure1 Test5 Trial2
2 HA 001 TimeMeasure1 Test11 Trial1
3 HA 002 TimeMeasure1 Test5 Trial1
4 HA 002 TimeMeasure1 Test5 Trial2
5 HA 002 TimeMeasure1 Test11 Trial1


.. GENERATED FROM PYTHON SOURCE LINES 38-40 Once you selected only a single row of the dataset (either by repeated ``get_subset`` or by iteration), you can load the actual data. .. GENERATED FROM PYTHON SOURCE LINES 40-43 .. code-block:: default single_test = ha_example_data.get_subset(participant_id="002", test="Test5", trial="Trial2") single_test .. raw:: html

LabExampleDataset [1 groups/rows]

cohort participant_id time_measure test trial
0 HA 002 TimeMeasure1 Test5 Trial2


.. GENERATED FROM PYTHON SOURCE LINES 44-50 The raw IMU data can be accessed in two ways: 1. ``.data`` which contains a dictionary with the data of all IMU sensors in the dataset. Per default, we only load the data of the "LowerBack" sensor for performance reasons. But, you can select the sensors to load using the ``raw_data_sensor`` and ``sensor_psotions`` argument of the Dataset class. .. GENERATED FROM PYTHON SOURCE LINES 50-52 .. code-block:: default imu_data = single_test.data["LowerBack"] imu_data .. raw:: html
acc_x acc_y acc_z gyr_x gyr_y gyr_z
time
2020-08-21 10:30:50.479000092+00:00 9.260327 0.031613 -2.605737 -0.1608 0.2119 -0.3052
2020-08-21 10:30:50.489000082+00:00 9.271626 0.018003 -2.595759 -0.2712 -0.0757 -0.4693
2020-08-21 10:30:50.499000072+00:00 9.275198 0.040968 -2.617954 0.1157 -0.0892 -0.2648
2020-08-21 10:30:50.509000063+00:00 9.265379 0.046116 -2.616274 -0.0091 -0.2005 -0.3278
2020-08-21 10:30:50.519000052+00:00 9.270444 0.070599 -2.586713 0.0524 -0.2733 -0.0965
... ... ... ... ... ... ...
2020-08-21 10:30:58.239000082+00:00 9.165407 0.123421 -2.804246 1.1903 0.7700 0.2111
2020-08-21 10:30:58.249000072+00:00 9.149437 0.103341 -2.766177 1.3227 0.9281 0.4836
2020-08-21 10:30:58.259000063+00:00 9.156892 0.093647 -2.780554 0.9306 1.3176 0.1052
2020-08-21 10:30:58.269000053+00:00 9.128226 0.076377 -2.787705 0.7761 1.6184 0.1962
2020-08-21 10:30:58.279000044+00:00 9.141961 0.075063 -2.800354 0.8296 1.6837 0.4381

781 rows × 6 columns



.. GENERATED FROM PYTHON SOURCE LINES 53-58 2. ``.data_ss`` which contains only the data of the "single sensor". This is the data used as input for all algorithms in the provided pipelines. In most cases this is equivalent to the data of the "LowerBack" sensor, but a different position can be selected using the ``single_sensor_position`` (or ``single_sensor_name`` in some Dataset classes) argument of the Dataset class. .. GENERATED FROM PYTHON SOURCE LINES 58-61 .. code-block:: default single_sensor_data = single_test.data_ss single_sensor_data .. raw:: html
acc_x acc_y acc_z gyr_x gyr_y gyr_z
time
2020-08-21 10:30:50.479000092+00:00 9.260327 0.031613 -2.605737 -0.1608 0.2119 -0.3052
2020-08-21 10:30:50.489000082+00:00 9.271626 0.018003 -2.595759 -0.2712 -0.0757 -0.4693
2020-08-21 10:30:50.499000072+00:00 9.275198 0.040968 -2.617954 0.1157 -0.0892 -0.2648
2020-08-21 10:30:50.509000063+00:00 9.265379 0.046116 -2.616274 -0.0091 -0.2005 -0.3278
2020-08-21 10:30:50.519000052+00:00 9.270444 0.070599 -2.586713 0.0524 -0.2733 -0.0965
... ... ... ... ... ... ...
2020-08-21 10:30:58.239000082+00:00 9.165407 0.123421 -2.804246 1.1903 0.7700 0.2111
2020-08-21 10:30:58.249000072+00:00 9.149437 0.103341 -2.766177 1.3227 0.9281 0.4836
2020-08-21 10:30:58.259000063+00:00 9.156892 0.093647 -2.780554 0.9306 1.3176 0.1052
2020-08-21 10:30:58.269000053+00:00 9.128226 0.076377 -2.787705 0.7761 1.6184 0.1962
2020-08-21 10:30:58.279000044+00:00 9.141961 0.075063 -2.800354 0.8296 1.6837 0.4381

781 rows × 6 columns



.. GENERATED FROM PYTHON SOURCE LINES 62-67 .. code-block:: default import matplotlib.pyplot as plt single_sensor_data.filter(like="gyr").plot() plt.show() .. image-sg:: /auto_examples/data/images/sphx_glr__01_loading_example_data_001.png :alt: 01 loading example data :srcset: /auto_examples/data/images/sphx_glr__01_loading_example_data_001.png :class: sphx-glr-single-img .. GENERATED FROM PYTHON SOURCE LINES 68-69 Test-level metadata: .. GENERATED FROM PYTHON SOURCE LINES 69-71 .. code-block:: default single_test.metadata .. rst-class:: sphx-glr-script-out .. code-block:: none MobilisedMetadata(start_date_time_iso='2020-08-21T12:30:50.479+02:00', time_zone='Europe/Berlin', sampling_rate_hz=100.0, reference_sampling_rate_hz=None) .. GENERATED FROM PYTHON SOURCE LINES 72-73 Participant-level metadata: .. GENERATED FROM PYTHON SOURCE LINES 73-75 .. code-block:: default single_test.participant_metadata .. rst-class:: sphx-glr-script-out .. code-block:: none {'Height': 175.0, 'Weight': 82.0, 'SensorHeight': 108.0, 'Handedness': 'R', 'FootSize': 26.4, 'WalkingAid_01': 0.0, 'INDIP_DataUsed': 'All', 'SensorType_SU': 'MM+', 'SensorAttachment_SU': 'Body-Worn'} .. GENERATED FROM PYTHON SOURCE LINES 76-78 You can also load the reference system data, by specifying the ``reference_system`` argument. All parameters related to the reference systems have a trailing underscore. .. GENERATED FROM PYTHON SOURCE LINES 78-84 .. code-block:: default example_data_with_reference = LabExampleDataset(reference_system="Stereophoto") single_trial_with_reference = example_data_with_reference.get_subset( cohort="HA", participant_id="002", test="Test5", trial="Trial2" ) single_trial_with_reference.raw_reference_parameters_ .. rst-class:: sphx-glr-script-out .. code-block:: none {'lwb': [{'Start': 2.28, 'End': 5.39, 'StrideFrequency': 49.36164888953828, 'Cadence': 98.72329777907656, 'Duration': 3.11, 'Length': 4.113706815831932, 'LengthFeet': 3.990970590722213, 'WalkingSpeed': 1.3727010349957065, 'AverageStrideLength': 1.6696087035787461, 'NumberStrides': 4.0, 'TerminationReason': 'Pause', 'Break_Start': array([], dtype=float64), 'Break_End': array([], dtype=float64), 'Break_Number': array([], dtype=float64), 'Break_Duration': array([], dtype=float64), 'Turning_Flag': 0.0, 'Turn_Start': array([], dtype=float64), 'Turn_End': array([], dtype=float64), 'Turn_Duration': array([], dtype=float64), 'Turn_Number': array([], dtype=float64), 'Turn_Angle': array([], dtype=float64), 'Turn_NumberStrides': array([], dtype=float64), 'Turn_AngularVelocity': array([], dtype=float64), 'Turn_PeakAngularVelocity': array([], dtype=float64), 'Turn_MeanAngularVelocity': array([], dtype=float64), 'Turn_Length': array([], dtype=float64), 'Stride_Duration': array([1.28, 1.18, 1.16, 1.25]), 'Stride_Length': array([1.6130588 , 1.59518995, 1.65295507, 1.81723099]), 'Stride_Height': array([ 0.00258984, 0.0019533 , -0.00603833, -0.01509003]), 'Stride_Speed': array([1.26020219, 1.35185589, 1.42496127, 1.45378479]), 'Stride_TrunkElevationChange': array([0.015336 , 0.00167007, 0.00736727, 0.03591326]), 'Stance_Duration': array([0.85, 0.74, 0.75, 0.73]), 'Swing_Duration': array([0.43, 0.44, 0.41, 0.52]), 'SingleSupport_Duration': array([ nan, 0.87, 0.85, 0.93]), 'DoubleSupport_Duration': array([ nan, 0.31, 0.31, 0.32]), 'Stance_Length': array([0.22870683, 0.20503215, 0.2409645 , 0.24516186]), 'Swing_Length': array([1.37562656, 1.38826616, 1.40289499, 1.56628489]), 'Stance_Speed': array([0.26906685, 0.27707047, 0.32128599, 0.33583817]), 'Swing_Speed': array([3.19913153, 3.15515037, 3.42169511, 3.01208632]), 'SingleSupport_Length': array([], dtype=float64), 'DoubleSupport_Length': array([], dtype=float64), 'SingleSupport_Speed': array([], dtype=float64), 'DoubleSupport_Speed': array([], dtype=float64), 'Stride_InitialContacts': array([[2.28, 3.56], [2.96, 4.14], [3.56, 4.72], [4.14, 5.39]]), 'InitialContact_Event': array([2.28, 2.96, 3.56, 4.14, 4.72, 5.39]), 'InitialContact_LeftRight': array(['Left', 'Right', 'Left', 'Right', 'Left', 'Right'], dtype=object), 'FinalContact_Event': array([3.13, 3.7 , 4.31, 4.87]), 'FinalContact_LeftRight': array(['Left', 'Right', 'Left', 'Right'], dtype=object), 'Step_Duration': array([0.68, 0.6 , 0.58, 0.58, 0.67]), 'Step_Length': array([0.78400307, 0.79455085, 0.78468028, 0.83038434, 0.92008828]), 'Step_Length_Feet': array([0.79563927, 0.81137347, 0.78349089, 0.87076342, 0.95103785]), 'Step_Speed': array([1.15294569, 1.32425142, 1.35289704, 1.43169713, 1.37326609]), 'Step_TrunkElevationChange': array([0.00490894, 0.01042706, 0.01209712, 0.01946439, 0.01644887])}], 'wb': [{'Start': 2.28, 'End': 5.39, 'StrideFrequency': 49.36164888953828, 'Cadence': 98.72329777907656, 'Duration': 3.11, 'Length': 4.113706815831932, 'LengthFeet': 3.990970590722213, 'WalkingSpeed': 1.3727010349957065, 'AverageStrideLength': 1.6696087035787461, 'NumberStrides': 4.0, 'TerminationReason': 'Pause', 'Break_Start': array([], dtype=float64), 'Break_End': array([], dtype=float64), 'Break_Number': array([], dtype=float64), 'Break_Duration': array([], dtype=float64), 'Turning_Flag': 0.0, 'Turn_Start': array([], dtype=float64), 'Turn_End': array([], dtype=float64), 'Turn_Duration': array([], dtype=float64), 'Turn_Number': array([], dtype=float64), 'Turn_Angle': array([], dtype=float64), 'Turn_NumberStrides': array([], dtype=float64), 'Turn_AngularVelocity': array([], dtype=float64), 'Turn_PeakAngularVelocity': array([], dtype=float64), 'Turn_MeanAngularVelocity': array([], dtype=float64), 'Turn_Length': array([], dtype=float64), 'Turning_SharpTurn_Flag': array([], dtype=float64), 'Incline_Start': array([], dtype=float64), 'Incline_End': array([], dtype=float64), 'Incline_Duration': array([], dtype=float64), 'Incline_Number': array([], dtype=float64), 'Incline_NumberStrides': array([], dtype=float64), 'Incline_PositiveElevation': array([], dtype=float64), 'Incline_NegativeElevation': array([], dtype=float64), 'Stride_Duration': array([1.28, 1.18, 1.16, 1.25]), 'Stride_Length': array([1.6130588 , 1.59518995, 1.65295507, 1.81723099]), 'Stride_Height': array([ 0.00258984, 0.0019533 , -0.00603833, -0.01509003]), 'Stride_Speed': array([1.26020219, 1.35185589, 1.42496127, 1.45378479]), 'Stride_TrunkElevationChange': array([0.015336 , 0.00167007, 0.00736727, 0.03591326]), 'Stance_Duration': array([0.85, 0.74, 0.75, 0.73]), 'Swing_Duration': array([0.43, 0.44, 0.41, 0.52]), 'SingleSupport_Duration': array([ nan, 0.87, 0.85, 0.93]), 'DoubleSupport_Duration': array([ nan, 0.31, 0.31, 0.32]), 'Stance_Length': array([0.22870683, 0.20503215, 0.2409645 , 0.24516186]), 'Swing_Length': array([1.37562656, 1.38826616, 1.40289499, 1.56628489]), 'Stance_Speed': array([0.26906685, 0.27707047, 0.32128599, 0.33583817]), 'Swing_Speed': array([3.19913153, 3.15515037, 3.42169511, 3.01208632]), 'SingleSupport_Length': array([], dtype=float64), 'DoubleSupport_Length': array([], dtype=float64), 'SingleSupport_Speed': array([], dtype=float64), 'DoubleSupport_Speed': array([], dtype=float64), 'Stride_InitialContacts': array([[2.28, 3.56], [2.96, 4.14], [3.56, 4.72], [4.14, 5.39]]), 'InitialContact_Event': array([2.28, 2.96, 3.56, 4.14, 4.72, 5.39]), 'InitialContact_LeftRight': array(['Left', 'Right', 'Left', 'Right', 'Left', 'Right'], dtype=object), 'FinalContact_Event': array([3.13, 3.7 , 4.31, 4.87]), 'FinalContact_LeftRight': array(['Left', 'Right', 'Left', 'Right'], dtype=object), 'Step_Duration': array([0.68, 0.6 , 0.58, 0.58, 0.67]), 'Step_Length': array([0.78400307, 0.79455085, 0.78468028, 0.83038434, 0.92008828]), 'Step_Length_Feet': array([0.79563927, 0.81137347, 0.78349089, 0.87076342, 0.95103785]), 'Step_Speed': array([1.15294569, 1.32425142, 1.35289704, 1.43169713, 1.37326609]), 'Step_TrunkElevationChange': array([0.00490894, 0.01042706, 0.01209712, 0.01946439, 0.01644887])}]} .. GENERATED FROM PYTHON SOURCE LINES 85-91 The data you can see above is the "raw" reference data. Including both the information for walking bouts and level-walking bouts. To access the data in format that can be directly compared to the output of the mobgap algorithms or used as input to algorithms further down the processing pipeline, you can use the ``reference_parameters_`` attribute. If the data is extracted from the normal walking bouts or the level walking bouts is controlled by the ``reference_para_level`` parameter of the Dataset class (default is ``wb``). .. GENERATED FROM PYTHON SOURCE LINES 91-93 .. code-block:: default ref_paras = single_trial_with_reference.reference_parameters_ .. GENERATED FROM PYTHON SOURCE LINES 94-95 This attribute contains the data for the outputs of the various steps of the processing pipeline. .. GENERATED FROM PYTHON SOURCE LINES 95-97 .. code-block:: default ref_paras.wb_list .. raw:: html
start end n_strides duration_s length_m avg_speed_mps avg_cadence_spm avg_stride_length_m termination_reason
wb_id
1 227 539 4 3.11 4.113707 1.372701 98.723298 1.669609 Pause


.. GENERATED FROM PYTHON SOURCE LINES 98-100 .. code-block:: default ref_paras.ic_list .. raw:: html
ic lr_label
wb_id step_id
1 0 227 left
1 295 right
2 355 left
3 413 right
4 471 left
5 538 right


.. GENERATED FROM PYTHON SOURCE LINES 101-103 .. code-block:: default ref_paras.turn_parameters .. raw:: html
start end duration_s angle_deg
wb_id turn_id


.. GENERATED FROM PYTHON SOURCE LINES 104-107 .. code-block:: default ref_paras.stride_parameters .. raw:: html
start end duration_s length_m speed_mps stance_time_s swing_time_s lr_label
wb_id s_id
1 0 227 356 1.28 1.613059 1.260202 0.85 0.43 left
1 295 414 1.18 1.595190 1.351856 0.74 0.44 right
2 355 472 1.16 1.652955 1.424961 0.75 0.41 left
3 413 539 1.25 1.817231 1.453785 0.73 0.52 right


.. GENERATED FROM PYTHON SOURCE LINES 108-112 Functional interface ++++++++++++++++++++ We can get the local path to the example data using :func:`~mobgap.data.get_all_lab_example_data_paths` and then use :func:`~mobgap.data.load_mobilised_matlab_format` to load the data. .. GENERATED FROM PYTHON SOURCE LINES 112-117 .. code-block:: default from mobgap.data import get_all_lab_example_data_paths, load_mobilised_matlab_format all_example_data_paths = get_all_lab_example_data_paths() list(all_example_data_paths.keys()) .. rst-class:: sphx-glr-script-out .. code-block:: none [('HA', '001'), ('HA', '002'), ('MS', '001')] .. GENERATED FROM PYTHON SOURCE LINES 118-119 Then we can select the participant we want to load. .. GENERATED FROM PYTHON SOURCE LINES 119-122 .. code-block:: default example_participant_path = all_example_data_paths[("HA", "002")] data = load_mobilised_matlab_format(example_participant_path / "data.mat") .. GENERATED FROM PYTHON SOURCE LINES 123-127 Calling the loader function without any further arguments, will load the "SU" (normal lower-back sensor) only. The returned dictionary contains the test names as keys and the loaded data as :class:`~mobgap.data.MobilisedTestData` objects. This allows for easy access to the data and metadata without traversing a nested data structure. .. GENERATED FROM PYTHON SOURCE LINES 127-130 .. code-block:: default test_list = list(data.keys()) test_list .. rst-class:: sphx-glr-script-out .. code-block:: none [('TimeMeasure1', 'Test5', 'Trial1'), ('TimeMeasure1', 'Test5', 'Trial2'), ('TimeMeasure1', 'Test11', 'Trial1')] .. GENERATED FROM PYTHON SOURCE LINES 131-132 We can access the data of a single test by using the test name as key. .. GENERATED FROM PYTHON SOURCE LINES 132-136 .. code-block:: default test_11_data = data[test_list[2]] imu_data = test_11_data.imu_data["LowerBack"] imu_data .. raw:: html
acc_x acc_y acc_z gyr_x gyr_y gyr_z
time
2020-08-21 10:50:00.388000011+00:00 9.506652 0.014721 -1.251681 0.7467 0.0411 -0.2598
2020-08-21 10:50:00.398000002+00:00 9.508550 -0.003995 -1.255758 0.7143 -0.2051 -0.2739
2020-08-21 10:50:00.407999992+00:00 9.522865 0.010142 -1.287486 0.5649 -0.0735 -0.4197
2020-08-21 10:50:00.417999983+00:00 9.509657 0.024649 -1.293958 0.3432 -0.0719 -0.2773
2020-08-21 10:50:00.427999973+00:00 9.498548 0.053690 -1.239242 0.2914 -0.0606 -0.3616
... ... ... ... ... ... ...
2020-08-21 10:52:40.177999973+00:00 9.561574 0.635956 0.264192 -0.1136 -0.9987 -0.4929
2020-08-21 10:52:40.187999964+00:00 9.588995 0.647659 0.269060 0.4132 -1.2662 -0.5346
2020-08-21 10:52:40.197999954+00:00 9.613343 0.659560 0.249716 0.5845 -1.0722 -0.2816
2020-08-21 10:52:40.207999945+00:00 9.617620 0.654173 0.214529 0.2529 -1.1498 -0.5514
2020-08-21 10:52:40.217999935+00:00 9.605131 0.654354 0.212877 0.2313 -1.6593 -0.4748

15984 rows × 6 columns



.. GENERATED FROM PYTHON SOURCE LINES 137-138 We can also access the metadata of the test. .. GENERATED FROM PYTHON SOURCE LINES 138-140 .. code-block:: default test_11_data.metadata .. rst-class:: sphx-glr-script-out .. code-block:: none MobilisedMetadata(start_date_time_iso='2020-08-21T12:50:00.388+02:00', time_zone='Europe/Berlin', sampling_rate_hz=100.0, reference_sampling_rate_hz=None) .. GENERATED FROM PYTHON SOURCE LINES 141-146 To load reference data as well, we can use the ``reference_system`` argument. Note, that we don't have a way to load the raw data of the reference system. We only load the calculated parameters. The available reference systems will depend on the data. .. GENERATED FROM PYTHON SOURCE LINES 146-150 .. code-block:: default data_with_reference = load_mobilised_matlab_format(example_participant_path / "data.mat", reference_system="INDIP") selected_test = data_with_reference[test_list[2]] .. GENERATED FROM PYTHON SOURCE LINES 151-152 The returned :class:`~mobgap.data.MobilisedTestData` objects now contain the reference parameters. .. GENERATED FROM PYTHON SOURCE LINES 152-154 .. code-block:: default raw_reference_data = selected_test.raw_reference_parameters .. GENERATED FROM PYTHON SOURCE LINES 155-156 And metadata about the reference system is available as well. .. GENERATED FROM PYTHON SOURCE LINES 156-159 .. code-block:: default ref_sampling_rate_hz = selected_test.metadata.reference_sampling_rate_hz ref_sampling_rate_hz .. rst-class:: sphx-glr-script-out .. code-block:: none 100.0 .. GENERATED FROM PYTHON SOURCE LINES 160-162 To parse the reference data into better data structures, we can use the :func:`~mobgap.data.parse_reference_parameters` function. .. GENERATED FROM PYTHON SOURCE LINES 162-170 .. code-block:: default from mobgap.data import parse_reference_parameters data_sampling_rate_hz = selected_test.metadata.sampling_rate_hz ref_paras_functional = parse_reference_parameters( raw_reference_data["wb"], data_sampling_rate_hz=data_sampling_rate_hz, ref_sampling_rate_hz=ref_sampling_rate_hz ) .. GENERATED FROM PYTHON SOURCE LINES 171-172 They have the same structure the reference parameters of the Dataset class. .. GENERATED FROM PYTHON SOURCE LINES 172-173 .. code-block:: default ref_paras_functional .. rst-class:: sphx-glr-script-out .. code-block:: none ReferenceData(wb_list= start end ... avg_stride_length_m termination_reason wb_id ... 1 485 1131 ... 0.747408 Pause 2 1746 3554 ... 0.463619 Pause 3 6083 7708 ... 0.809791 Pause [3 rows x 9 columns], ic_list= ic lr_label wb_id step_id 1 0 485 right 1 553 left 2 619 right 3 692 left 4 742 right 5 779 left 6 862 right 8 1016 right 10 1130 right 2 11 1746 right 12 1768 left 13 1874 left 14 2005 right 15 2120 left 16 2196 right 18 2399 right 19 2489 left 20 2655 right 21 2675 left 22 2752 left 23 2880 right 25 3068 right 26 3139 left 27 3199 right 28 3289 left 30 3426 left 32 3553 left 3 33 6083 left 34 6151 right 35 6199 left 36 6277 right 37 6358 left 38 6425 right 39 6480 left 40 6534 right 41 6630 left 42 6758 right 43 6785 left 44 6847 right 45 6919 left 46 6985 right 47 7065 left 48 7112 right 49 7228 right 51 7476 right 53 7707 right, turn_parameters= start end duration_s angle_deg wb_id turn_id 1 0 5 10 4.27 -135.426119 3 1 61 64 2.58 -147.071110 2 68 72 2.40 149.190550, stride_parameters= start end duration_s ... stance_time_s swing_time_s lr_label wb_id s_id ... 1 0 485 620 1.34 ... 0.88 0.46 right 1 553 693 1.39 ... 0.84 0.55 left 2 619 743 1.23 ... 0.86 0.37 right 3 692 780 NaN ... NaN NaN left 4 742 863 1.20 ... NaN NaN right 6 862 1017 1.54 ... 1.15 0.39 right 8 1016 1131 1.14 ... 0.56 0.58 right 2 9 1746 2006 2.59 ... 1.43 1.16 right 10 1768 1875 NaN ... NaN NaN left 11 1874 2121 2.46 ... 1.07 1.39 left 12 2005 2197 1.91 ... 1.30 0.61 right 14 2196 2400 2.03 ... 1.40 0.63 right 16 2399 2656 2.56 ... 1.18 1.38 right 17 2489 2676 NaN ... NaN NaN left 18 2655 2881 2.25 ... 0.91 1.34 right 19 2675 2753 0.77 ... 0.16 0.61 left 21 2880 3069 1.88 ... 1.13 0.75 right 23 3068 3200 1.31 ... 1.00 0.31 right 24 3139 3290 1.50 ... 0.65 0.85 left 26 3289 3427 1.37 ... NaN NaN left 28 3426 3554 1.27 ... 0.91 0.36 left 3 29 6083 6200 1.16 ... 0.84 0.32 left 30 6151 6278 NaN ... NaN NaN right 31 6199 6359 1.59 ... 0.83 0.76 left 32 6277 6426 1.48 ... 0.89 0.59 right 33 6358 6481 1.22 ... 0.45 0.77 left 34 6425 6535 1.09 ... 0.71 0.38 right 35 6480 6631 1.50 ... 1.28 0.22 left 36 6534 6759 2.24 ... 1.14 1.10 right 37 6630 6786 1.55 ... 0.99 0.56 left 38 6758 6848 0.89 ... 0.44 0.45 right 39 6785 6920 1.34 ... 0.78 0.56 left 40 6847 6986 1.38 ... 0.89 0.49 right 41 6919 7066 1.46 ... 0.83 0.63 left 42 6985 7113 1.27 ... 0.87 0.40 right 44 7112 7229 NaN ... NaN NaN right 45 7228 7477 2.48 ... 1.97 0.51 right 47 7476 7708 2.31 ... 1.03 1.28 right [38 rows x 8 columns]) .. rst-class:: sphx-glr-timing **Total running time of the script:** (0 minutes 4.644 seconds) **Estimated memory usage:** 24 MB .. _sphx_glr_download_auto_examples_data__01_loading_example_data.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_loading_example_data.py <_01_loading_example_data.py>` .. container:: sphx-glr-download sphx-glr-download-jupyter :download:`Download Jupyter notebook: _01_loading_example_data.ipynb <_01_loading_example_data.ipynb>` .. only:: html .. rst-class:: sphx-glr-signature `Gallery generated by Sphinx-Gallery `_