nilspodlib
Note
Click here to download the full example code
A simple example on how to work with a single Dataset.
Out:
Sensor ID: 7fad Start Date (UTC): 2019-04-30 07:33:12 Stop Date (UTC): 2019-04-30 07:33:59 Enabled Sensors: ('gyro', 'acc') Analog is disabled: True The acc recordings are 3D and have the length 9597 The new datastream has a length of 4798 Acc has now a length of 4798 Gyro has now a length of 4798 The old and the new are identical: True gyr_x gyr_y gyr_z acc_x acc_y acc_z n_samples 0 -0.092530 -0.026791 -0.166483 0.537133 0.294263 9.969488 1 -0.338874 -0.158994 -0.307865 0.522367 0.312731 10.003687 2 0.001125 -0.025516 -0.170005 0.527848 0.340135 9.983830 3 -0.314390 -0.047710 0.073775 0.600002 0.342938 10.001858 4 0.053510 -0.243648 -0.107525 0.642641 0.344317 9.979044
import matplotlib.pyplot as plt from pathlib import Path from nilspodlib import Dataset FILEPATH = Path("../tests/test_data/synced_sample_session/NilsPodX-7FAD_20190430_0933.bin") # Create a Dataset Object from the bin file dataset = Dataset.from_bin_file(FILEPATH) # You can access the metainformation about your dataset using the `info` attr. # For a full list of available attributes see nilspodlib.header._HeaderFields print("Sensor ID:", dataset.info.sensor_id) print("Start Date (UTC):", dataset.info.utc_datetime_start) print("Stop Date (UTC):", dataset.info.utc_datetime_stop) print("Enabled Sensors:", dataset.info.enabled_sensors) # You can access the individual sensor data directly from the dataset object using the names provided # in dataset.info.enabled_sensors datastream_acc = dataset.acc # If a sensor is disabled, this will return `None` print("Analog is disabled:", dataset.analog is None) # Access the data of a datastream object as a numpy.array using the `data` attribute print("The acc recordings are {}D and have the length {}".format(*datastream_acc.data.T.shape)) # Convenience methods are available for common operations. E.g. Norm or downsample plt.figure() plt.title("Acc Norm") plt.plot(datastream_acc.norm()) plt.show() downsampled_datastream = datastream_acc.downsample(factor=2) print("The new datastream has a length of", len(downsampled_datastream.data)) # However, for many operations it makes more sense to apply them to the Dataset instead of the Datastream. # This will apply the operations to all Datastream and return a new Dataset object downsampled_dataset = dataset.downsample(factor=2) print("Acc has now a length of", len(downsampled_dataset.acc.data)) print("Gyro has now a length of", len(downsampled_dataset.gyro.data)) # By default this returns a copy of the dataset and all datastreams. If this is a performance concern, the dataset can # be modified inplace: downsampled_dataset = dataset.downsample(factor=2, inplace=True) print("The old and the new are identical:", id(dataset) == id(downsampled_dataset)) # At this point you would usually apply a calibration to the IMU data (see other examples) # After calibration and initial operations on all datastreams, the easiest way to interface with further processing # pipelines is a conversion into a pandas DataFrame df = dataset.data_as_df() print(df.head()) df.plot() plt.show()
Total running time of the script: ( 0 minutes 4.355 seconds)
Estimated memory usage: 45 MB
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