.. 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 40-45 .. 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 46-52 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 52-54 .. code-block:: default imu_data = single_test.data["LowerBack"] imu_data .. rst-class:: sphx-glr-script-out .. code-block:: none /home/docs/checkouts/readthedocs.org/user_builds/mobgap/checkouts/v0.9.0/mobgap/data/_mobilised_matlab_loader.py:1189: UserWarning: Global caching is a little tricky to get right and our implementation is not yet battle-tested. Please double check that the results are correct and report any issues you find. return hybrid_cache(self.memory, 1)(_load_test_data_without_checks)( .. raw:: html

	acc_x	acc_y	acc_z	gyr_x	gyr_y	gyr_z
time
2020-08-21 10:30:50.479000092+00:00	9.257165	0.031602	-2.604847	-0.1608	0.2119	-0.3052
2020-08-21 10:30:50.489000082+00:00	9.268460	0.017997	-2.594873	-0.2712	-0.0757	-0.4693
2020-08-21 10:30:50.499000072+00:00	9.272030	0.040954	-2.617060	0.1157	-0.0892	-0.2648
2020-08-21 10:30:50.509000063+00:00	9.262215	0.046100	-2.615381	-0.0091	-0.2005	-0.3278
2020-08-21 10:30:50.519000052+00:00	9.267278	0.070575	-2.585830	0.0524	-0.2733	-0.0965
...	...	...	...	...	...	...
2020-08-21 10:30:58.239000082+00:00	9.162277	0.123379	-2.803288	1.1903	0.7700	0.2111
2020-08-21 10:30:58.249000072+00:00	9.146312	0.103306	-2.765232	1.3227	0.9281	0.4836
2020-08-21 10:30:58.259000063+00:00	9.153765	0.093615	-2.779604	0.9306	1.3176	0.1052
2020-08-21 10:30:58.269000053+00:00	9.125109	0.076351	-2.786753	0.7761	1.6184	0.1962
2020-08-21 10:30:58.279000044+00:00	9.138839	0.075038	-2.799398	0.8296	1.6837	0.4381

781 rows × 6 columns

.. GENERATED FROM PYTHON SOURCE LINES 55-60 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 60-63 .. 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	9.257165	0.031602	-2.604847	-0.1608	0.2119	-0.3052
2020-08-21 10:30:50.489000082+00:00	9.268460	0.017997	-2.594873	-0.2712	-0.0757	-0.4693
2020-08-21 10:30:50.499000072+00:00	9.272030	0.040954	-2.617060	0.1157	-0.0892	-0.2648
2020-08-21 10:30:50.509000063+00:00	9.262215	0.046100	-2.615381	-0.0091	-0.2005	-0.3278
2020-08-21 10:30:50.519000052+00:00	9.267278	0.070575	-2.585830	0.0524	-0.2733	-0.0965
...	...	...	...	...	...	...
2020-08-21 10:30:58.239000082+00:00	9.162277	0.123379	-2.803288	1.1903	0.7700	0.2111
2020-08-21 10:30:58.249000072+00:00	9.146312	0.103306	-2.765232	1.3227	0.9281	0.4836
2020-08-21 10:30:58.259000063+00:00	9.153765	0.093615	-2.779604	0.9306	1.3176	0.1052
2020-08-21 10:30:58.269000053+00:00	9.125109	0.076351	-2.786753	0.7761	1.6184	0.1962
2020-08-21 10:30:58.279000044+00:00	9.138839	0.075038	-2.799398	0.8296	1.6837	0.4381

781 rows × 6 columns

.. GENERATED FROM PYTHON SOURCE LINES 64-69 .. 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 70-71 Test-level metadata: .. GENERATED FROM PYTHON SOURCE LINES 71-73 .. code-block:: default single_test.recording_metadata .. rst-class:: sphx-glr-script-out .. code-block:: none {'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, 'measurement_condition': 'laboratory', 'recording_identifier': ('TimeMeasure1', 'Test5', 'Trial2')} .. GENERATED FROM PYTHON SOURCE LINES 74-75 Participant-level metadata: .. GENERATED FROM PYTHON SOURCE LINES 75-77 .. code-block:: default single_test.participant_metadata .. rst-class:: sphx-glr-script-out .. code-block:: none {'cohort': 'HA', 'foot_length_cm': 26.4, 'handedness': 'right', 'height_m': 1.75, 'indip_data_used': 'All', 'sensor_attachment_su': 'Body-Worn', 'sensor_height_m': 1.08, 'sensor_type_su': 'MM+', 'walking_aid_used': False, 'weight_kg': 82.0} .. GENERATED FROM PYTHON SOURCE LINES 78-80 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 80-86 .. 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 87-93 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 93-95 .. code-block:: default ref_paras = single_trial_with_reference.reference_parameters_ .. GENERATED FROM PYTHON SOURCE LINES 96-97 This attribute contains the data for the outputs of the various steps of the processing pipeline. .. GENERATED FROM PYTHON SOURCE LINES 97-99 .. code-block:: default ref_paras.wb_list .. raw:: html

	start	end	n_strides	duration_s	length_m	avg_walking_speed_mps	avg_cadence_spm	avg_stride_length_m	termination_reason
wb_id
0	227	539	4	3.11	4.113707	1.372701	98.723298	1.669609	Pause

.. GENERATED FROM PYTHON SOURCE LINES 100-102 .. code-block:: default ref_paras.ic_list .. raw:: html

		ic	lr_label
wb_id	step_id
0	0	227	left
	1	295	right
	2	355	left
	3	413	right
	4	471	left
	5	538	right

.. GENERATED FROM PYTHON SOURCE LINES 103-105 .. code-block:: default ref_paras.turn_parameters .. raw:: html

		start	end	duration_s	angle_deg	direction
wb_id	turn_id

.. GENERATED FROM PYTHON SOURCE LINES 106-109 .. 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
0	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 110-114 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 114-122 .. 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 123-124 Then we can select the participant we want to load. .. GENERATED FROM PYTHON SOURCE LINES 124-127 .. 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 128-132 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 132-135 .. 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 136-137 We can access the data of a single test by using the test name as key. .. GENERATED FROM PYTHON SOURCE LINES 137-141 .. 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	9.503406	0.014716	-1.251254	0.7467	0.0411	-0.2598
2020-08-21 10:50:00.398000002+00:00	9.505303	-0.003993	-1.255329	0.7143	-0.2051	-0.2739
2020-08-21 10:50:00.407999992+00:00	9.519613	0.010139	-1.287047	0.5649	-0.0735	-0.4197
2020-08-21 10:50:00.417999983+00:00	9.506410	0.024640	-1.293516	0.3432	-0.0719	-0.2773
2020-08-21 10:50:00.427999973+00:00	9.495305	0.053672	-1.238819	0.2914	-0.0606	-0.3616
...	...	...	...	...	...	...
2020-08-21 10:52:40.177999973+00:00	9.558308	0.635739	0.264102	-0.1136	-0.9987	-0.4929
2020-08-21 10:52:40.187999964+00:00	9.585720	0.647438	0.268969	0.4132	-1.2662	-0.5346
2020-08-21 10:52:40.197999954+00:00	9.610060	0.659335	0.249631	0.5845	-1.0722	-0.2816
2020-08-21 10:52:40.207999945+00:00	9.614336	0.653949	0.214456	0.2529	-1.1498	-0.5514
2020-08-21 10:52:40.217999935+00:00	9.601851	0.654131	0.212804	0.2313	-1.6593	-0.4748

15984 rows × 6 columns

.. GENERATED FROM PYTHON SOURCE LINES 142-143 We can also access the metadata of the test. .. GENERATED FROM PYTHON SOURCE LINES 143-145 .. code-block:: default test_11_data.metadata .. rst-class:: sphx-glr-script-out .. code-block:: none {'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 146-151 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 151-157 .. 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 158-159 The returned :class:`~mobgap.data.MobilisedTestData` objects now contain the reference parameters. .. GENERATED FROM PYTHON SOURCE LINES 159-161 .. code-block:: default raw_reference_data = selected_test.raw_reference_parameters .. GENERATED FROM PYTHON SOURCE LINES 162-163 And metadata about the reference system is available as well. .. GENERATED FROM PYTHON SOURCE LINES 163-166 .. 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 167-169 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 169-179 .. 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 180-181 They have the same structure the reference parameters of the Dataset class. .. GENERATED FROM PYTHON SOURCE LINES 181-182 .. 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 ... 0 485 1131 ... 0.747408 Pause 1 1746 3554 ... 0.463619 Pause 2 6083 7708 ... 0.809791 Pause [3 rows x 9 columns], ic_list= ic lr_label wb_id step_id 0 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 1 0 1746 right 1 1768 left 2 1874 left 3 2005 right 4 2120 left 5 2196 right 7 2399 right 8 2489 left 9 2655 right 10 2675 left 11 2752 left 12 2880 right 14 3068 right 15 3139 left 16 3199 right 17 3289 left 19 3426 left 21 3553 left 2 0 6083 left 1 6151 right 2 6199 left 3 6277 right 4 6358 left 5 6425 right 6 6480 left 7 6534 right 8 6630 left 9 6758 right 10 6785 left 11 6847 right 12 6919 left 13 6985 right 14 7065 left 15 7112 right 16 7228 right 18 7476 right 20 7707 right, turn_parameters= start end duration_s angle_deg direction wb_id turn_id 0 0 621 1049 4.27 -135.426119 right 2 0 6231 6490 2.58 -147.071110 right 1 6981 7222 2.40 149.190550 left, stride_parameters= start end duration_s ... stance_time_s swing_time_s lr_label wb_id s_id ... 0 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 1 0 1746 2006 2.59 ... 1.43 1.16 right 1 1768 1875 NaN ... NaN NaN left 2 1874 2121 2.46 ... 1.07 1.39 left 3 2005 2197 1.91 ... 1.30 0.61 right 5 2196 2400 2.03 ... 1.40 0.63 right 7 2399 2656 2.56 ... 1.18 1.38 right 8 2489 2676 NaN ... NaN NaN left 9 2655 2881 2.25 ... 0.91 1.34 right 10 2675 2753 0.77 ... 0.16 0.61 left 12 2880 3069 1.88 ... 1.13 0.75 right 14 3068 3200 1.31 ... 1.00 0.31 right 15 3139 3290 1.50 ... 0.65 0.85 left 17 3289 3427 1.37 ... NaN NaN left 19 3426 3554 1.27 ... 0.91 0.36 left 2 0 6083 6200 1.16 ... 0.84 0.32 left 1 6151 6278 NaN ... NaN NaN right 2 6199 6359 1.59 ... 0.83 0.76 left 3 6277 6426 1.48 ... 0.89 0.59 right 4 6358 6481 1.22 ... 0.45 0.77 left 5 6425 6535 1.09 ... 0.71 0.38 right 6 6480 6631 1.50 ... 1.28 0.22 left 7 6534 6759 2.24 ... 1.14 1.10 right 8 6630 6786 1.55 ... 0.99 0.56 left 9 6758 6848 0.89 ... 0.44 0.45 right 10 6785 6920 1.34 ... 0.78 0.56 left 11 6847 6986 1.38 ... 0.89 0.49 right 12 6919 7066 1.46 ... 0.83 0.63 left 13 6985 7113 1.27 ... 0.87 0.40 right 15 7112 7229 NaN ... NaN NaN right 16 7228 7477 2.48 ... 1.97 0.51 right 18 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 4.061 seconds) **Estimated memory usage:** 17 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 `_