Load Other Sensor Data

The DataWindow contains single-channel sensors, such as audio, and three-channel sensors, such as accelerometer. In this example we will take a look at the general procedure to obtain single-channel sensor data (barometer in this example), and three-channel sensor data, (magnetometer in this example).

For more Sensors available in the DataWindow, check out the Sensor documentation.

Running the example

The first step is to load RedVox data in a DataWindow.

from redvox.common.data_window import DataWindow

# Input Directory
input_dir = "path/to/redvox/data/dw_1648830257000498_2.pkl.lz4"

# Load data window from report
dw = DataWindow.deserialize(input_dir)

Single-channel sensors such as the barometer sensor load the same way as the audio sensor.

from redvox.common.data_window import DataWindow
import matplotlib.pyplot as plt

# Input Directory
input_dir = "path/to/redvox/data/dw_1648830257000498_2.pkl.lz4"

# Load data window from report
dw = DataWindow.deserialize(input_dir)

for station in dw.stations():
    # Single channel sensors such as barometer load the same way as audio
    # Check that there is barometer data in the first place
    if station.has_barometer_data():

        # Get barometer data and timestamps (format to seconds) 
        barometer_samples = station.barometer_sensor().get_pressure_data()
        barometer_time_micros = station.barometer_sensor().data_timestamps() - \
                                station.barometer_sensor().first_data_timestamp()
        barometer_time_s = barometer_time_micros*1E-6
    
        # Plot the barometer data
        plt.figure()
        plt.plot(barometer_time_s, barometer_samples)
        plt.title(f"RedVox Station ID {station.id()}")
        plt.xlabel(f"Seconds from {int(dw.start_date()*1E-6)} Unix epoch UTC")
        plt.ylabel("Barometer Pressure [kPa]")

plt.show()

Three-channel sensors such as the magnetometer sensor load the same as the accelerometer sensor.

from redvox.common.data_window import DataWindow
import matplotlib.pyplot as plt

# Input Directory
input_dir = "path/to/redvox/data/dw_1648830257000498_2.pkl.lz4"

# Load data window from report
dw = DataWindow.deserialize(input_dir)

for station in dw.stations():    
    # Multi-channel sensors such as magnetometer load the same as the accelerometer sensor
    # Check that there is magnetometer data in the first place
    if station.has_magnetometer_data():

        # Magnetometer has 3 channels - x, y and z
        magnetometer_x_samples = station.magnetometer_sensor().get_magnetometer_x_data()
        magnetometer_y_samples = station.magnetometer_sensor().get_magnetometer_y_data()
        magnetometer_z_samples = station.magnetometer_sensor().get_magnetometer_z_data()
        # The channels share the same timestamps
        magnetometer_time_micros = station.magnetometer_sensor().data_timestamps() - \
                                    station.magnetometer_sensor().first_data_timestamp()
        magnetometer_time_s = magnetometer_time_micros*1E-6
    
        # Plot the acceleration data - one subplot per channel
        fig, ax = plt.subplots(nrows=3, ncols=1, sharex='col')
        ax[0].plot(magnetometer_time_s, magnetometer_x_samples)
        ax[1].plot(magnetometer_time_s, magnetometer_y_samples)
        ax[2].plot(magnetometer_time_s, magnetometer_z_samples)
    
        # Set labels and subplot title
        ax[0].set_ylabel(r'Mag X [$\mu T$]')
        ax[1].set_ylabel(r'Mag Y [$\mu T$]')
        ax[2].set_ylabel(r'Mag Z [$\mu T$]')
        ax[2].set_xlabel(f"Seconds from {int(dw.start_date()*1E-6)} Unix epoch UTC")
    
        plt.suptitle(f"RedVox Station ID {station.id()}")

plt.show()

Example output

After running the above code snippets, you should see this graph for the barometer:

and this one for the magnetometer:

For a more complete example on how to load sensor data, visit Github.

For more information on Sensors, visit the Sensor Data Documentation and the Sensor Subclasses to see what X, Y, Z data and other possible functions are available per sensor.

The next example focuses on obtaining Metadata and state of health data.