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Welcome to Pyglaze

Source Code: https://github.com/GlazeTech/pyglaze

Documentation Version: 0.4.2

This is the Pyglaze API documentation. Pyglaze is a python library used to operate the devices of Glaze Technologies. If you have a feature request or discover a bug, please create an issue here and we will look at it ASAP!

Usage

Pyglaze provides two main interfaces for operating Glaze devices: The Scanner and the The GlazeClient, where Scanneris a synchronous scanner, only scanning when requested, and GlazeClient is an asynchronous scanner, continuously scanning in the background.

GlazeClient

Using the GlazeClientis the preferred way to acquire scans. Before starting the scanner, a device configuration must be created. Depending on the type of device, different configurations are required, see e.g. a definition here. Be sure to replace mock_device and mock_delay with suitable values. Here, we will use a LeDeviceConfiguration.

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import json
from pathlib import Path

from pyglaze.device import Interval, LeDeviceConfiguration
from pyglaze.scanning import GlazeClient

device_config = LeDeviceConfiguration(
    amp_port="mock_device",
    integration_periods=10,
    scan_intervals=[
        Interval(0.5, 1.0),
        Interval(1.0, 0.0),
        Interval(0.0, 0.5),
    ],
)
When defining the configuration, a list of scan_intervals is set, determining which parts of the available timewindow should be scanned. Here, we scan a triangular waveform. Next, let's perform a scan.

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with GlazeClient(config=device_config) as client:
    scans = client.read(n_pulses=1)
    pulses = [
        pulse.from_triangular_waveform(ramp="down")
        .reconstruct(method="cubic_spline")
        .as_pulse()
        for pulse in scans
    ]

The client returns a list of UnprocessedWaveform, which can have many shapes and forms depending on the scan_intervals. Here, we extract the part of the waveforms corresponding to the down-ramp of the triangular waveform, then we perform a reconstruction to ensure we have pulses with equidistant times. Finally, having preprocessed the waveforms, we convert it to a list ofPulse. The pulse has attributes such as pulse.time, pulse.signal, pulse.frequency and pulse.fft in addition to many different convenience methods such as pulse.filter() for applying low- and highpass fitlers, pulse.spectrum_dB() for calculating the spectrum on a dB-scale and pulse.to_native_dict() for e.g. saving the pulse. Finally, we'll use the latter to save the results to disk

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with Path("scan_result.json").open("w") as f:
    json.dump([pulse.to_native_dict() for pulse in pulses], f, indent=4)

Scanner

Much like the GlazeClient, a Scanneris instantiated by first defining a configuration. Once instantiated, scans can be acquired by calling the scanner.scan() method.

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import json
from pathlib import Path

from pyglaze.device import Interval, LeDeviceConfiguration
from pyglaze.scanning import Scanner

device_config = LeDeviceConfiguration(
    amp_port="mock_device",
    integration_periods=10,
    n_points=100,
    scan_intervals=[
        Interval(0.5, 1.0),
        Interval(1.0, 0.0),
        Interval(0.0, 0.5),
    ],
)

scanner = Scanner(config=device_config)
device_config = LeDeviceConfiguration(
    amp_port="mock_device",
    integration_periods=10,
    scan_intervals=[
        Interval(0.5, 1.0),
        Interval(1.0, 0.0),
        Interval(0.0, 0.5),
    ],
)
waveform = scanner.scan()
pulse = (
    waveform.from_triangular_waveform(ramp="down")
    .reconstruct(method="cubic_spline")
    .as_pulse()
)
Like before, the pulse is now ready for further analysis or for saving to disk.

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with Path("scan_result_scanner.json").open("w") as f:
    json.dump(pulse.to_native_dict(), f, indent=4)