Tutorial: load datasets
Author: Robbin Romijnders
Last update: Tue 16 Jan 2024
Learning objectives
By the end of this tutorial:
- you can load data from a recording that belongs to one of the available datasets,
- you know which attributes are available for an instance of the
KielMATRecording - you can do some basic selecting and slicing of data
Imports
We start by importing some Python libraries. You should be familiar with most of them, and we will not discuss them here.
import numpy as np
import matplotlib.pyplot as plt
import os
from pathlib import Path
from kielmat.datasets import mobilised
Import data
Let us consider a single recording, namely of the randomly selected subject sub-3011 from the Mobilise-D dataset, and load the data. For that we use the load_recording() function that is available in the kielmat.datasets.mobilised module.
# load the data
recording = mobilised.load_recording()
We have loaded the data for two tracking systems, SU and SU_INDIP, and we have specified three tracked points. The data is assigned to the variable recording, so let us take a look at what we have got.
recording.__dict__
{'data': {'SU': LowerBack_ACCEL_x LowerBack_ACCEL_y LowerBack_ACCEL_z \
0 0.933334 0.084820 -0.302665
1 0.932675 0.084844 -0.300591
2 0.932350 0.082886 -0.310576
3 0.929716 0.081786 -0.303551
4 0.932825 0.077879 -0.308859
... ... ... ...
693471 -0.192553 -0.016052 -0.984290
693472 -0.189575 -0.016449 -0.988130
693473 -0.191176 -0.017954 -0.983820
693474 -0.189691 -0.014539 -0.986376
693475 -0.192993 -0.015306 -0.989452
LowerBack_GYRO_x LowerBack_GYRO_y LowerBack_GYRO_z \
0 5.600066 1.120697 0.489152
1 5.440734 1.401663 0.279477
2 5.196312 1.168802 0.435765
3 5.553083 1.116346 0.383447
4 5.437505 0.892803 -0.150115
... ... ... ...
693471 -0.225874 0.832856 0.704711
693472 -0.393438 0.598116 0.522755
693473 -0.430749 0.417541 0.282336
693474 -0.279277 0.559122 0.418693
693475 -0.563741 0.478618 0.411295
LowerBack_MAGN_x LowerBack_MAGN_y LowerBack_MAGN_z \
0 -93.972011 -25.023998 44.675028
1 -93.958012 -25.016007 44.610055
2 -93.946010 -25.000014 44.520078
3 -93.938007 -24.980018 44.411097
4 -93.935003 -24.957021 44.287113
... ... ... ...
693471 -50.718928 -36.997006 34.111960
693472 -50.649929 -37.003005 34.072972
693473 -50.579936 -37.008003 34.044986
693474 -50.515946 -37.011000 34.031004
693475 -50.460961 -37.010996 34.035025
LowerBack_BARO_n/a
0 990.394600
1 990.395100
2 990.395600
3 990.396199
4 990.396700
... ...
693471 990.204600
693472 990.204900
693473 990.205200
693474 990.205500
693475 990.205800
[693476 rows x 10 columns]},
'channels': {'SU': name component type tracked_point units \
0 LowerBack_ACCEL_x x ACCEL LowerBack g
1 LowerBack_ACCEL_y y ACCEL LowerBack g
2 LowerBack_ACCEL_z z ACCEL LowerBack g
3 LowerBack_GYRO_x x GYRO LowerBack deg/s
4 LowerBack_GYRO_y y GYRO LowerBack deg/s
5 LowerBack_GYRO_z z GYRO LowerBack deg/s
6 LowerBack_MAGN_x x MAGN LowerBack µT
7 LowerBack_MAGN_y y MAGN LowerBack µT
8 LowerBack_MAGN_z z MAGN LowerBack µT
9 LowerBack_BARO_n/a n/a BARO LowerBack hPa
sampling_frequency
0 100.0
1 100.0
2 100.0
3 100.0
4 100.0
5 100.0
6 100.0
7 100.0
8 100.0
9 100.0 },
'info': None,
'events': None,
'events_info': None}
That is a whole lot of output, so let us take a look at the attributes of instance one by one. First, print a list of all available attributes.
print(recording.__dict__.keys())
dict_keys(['data', 'channels', 'info', 'events', 'events_info'])
The contents of any individual attribute can be accessed in two ways, namely via the __dict__ or with dot indexing.
print(recording.data) # print(recording.__dict__["data"])
{'SU': LowerBack_ACCEL_x LowerBack_ACCEL_y LowerBack_ACCEL_z \
0 0.933334 0.084820 -0.302665
1 0.932675 0.084844 -0.300591
2 0.932350 0.082886 -0.310576
3 0.929716 0.081786 -0.303551
4 0.932825 0.077879 -0.308859
... ... ... ...
693471 -0.192553 -0.016052 -0.984290
693472 -0.189575 -0.016449 -0.988130
693473 -0.191176 -0.017954 -0.983820
693474 -0.189691 -0.014539 -0.986376
693475 -0.192993 -0.015306 -0.989452
LowerBack_GYRO_x LowerBack_GYRO_y LowerBack_GYRO_z \
0 5.600066 1.120697 0.489152
1 5.440734 1.401663 0.279477
2 5.196312 1.168802 0.435765
3 5.553083 1.116346 0.383447
4 5.437505 0.892803 -0.150115
... ... ... ...
693471 -0.225874 0.832856 0.704711
693472 -0.393438 0.598116 0.522755
693473 -0.430749 0.417541 0.282336
693474 -0.279277 0.559122 0.418693
693475 -0.563741 0.478618 0.411295
LowerBack_MAGN_x LowerBack_MAGN_y LowerBack_MAGN_z \
0 -93.972011 -25.023998 44.675028
1 -93.958012 -25.016007 44.610055
2 -93.946010 -25.000014 44.520078
3 -93.938007 -24.980018 44.411097
4 -93.935003 -24.957021 44.287113
... ... ... ...
693471 -50.718928 -36.997006 34.111960
693472 -50.649929 -37.003005 34.072972
693473 -50.579936 -37.008003 34.044986
693474 -50.515946 -37.011000 34.031004
693475 -50.460961 -37.010996 34.035025
LowerBack_BARO_n/a
0 990.394600
1 990.395100
2 990.395600
3 990.396199
4 990.396700
... ...
693471 990.204600
693472 990.204900
693473 990.205200
693474 990.205500
693475 990.205800
[693476 rows x 10 columns]}
We see that that data attribute is in the form of a Python dict, where the keys correspond to the tracking systems that we have requested when calling the load_recording() function. KielMAT is setup so that the keys of the channels attribute match with these keys, so that the channel descriptions are availbale per tracking system.
print(f"We have the following keys in recording.data: {recording.data.keys()}")
print(f"We have the same keys in recordings.channels: {recording.channels.keys()}")
We have the following keys in recording.data: dict_keys(['SU'])
We have the same keys in recordings.channels: dict_keys(['SU'])