Universal Software Lock-In Amplifier (ULIA)
===========================================
.. image:: https://gitlab.com/UhlDaniel/ulia/-/jobs/artifacts/master/raw/license.svg?job=badges
:target: https://gitlab.com/UhlDaniel/ulia/-/blob/master/LICENSE
.. image:: https://gitlab.com/UhlDaniel/ulia/-/jobs/artifacts/master/raw/pypi.svg?job=pypi
:target: https://pypi.org/project/ulia/
.. image:: https://img.shields.io/badge/DOI-10.1063%2F5.0059740-blue
:target: https://aip.scitation.org/doi/10.1063/5.0059740
An effective algorithm to emulate a Lock-In Amplifier.
Quickstart
==========
Installation
------------
To install `ulia` you can use `pip`.
`ulia` package can be installed directly from PyPI using `pip` (`pip3`).
.. code-block:: console
$ pip install git+https://gitlab.com/UhlDaniel/ulia.git
or
.. code-block:: console
$ pip install ulia
Dependencies
------------
This package depends on:
- Numpy
- Scipy
- Numba
Usage
-----
A simple example on how to utilize the ULIA.
.. code-block:: python
>>> import numpy as np
>>> import ulia
>>> modulation_frequency = 5000.0
>>> sampling_rate = 200000.0
>>> t = np.arange(0, 0.3*sampling_rate) / sampling_rate
>>> signal = np.cos(2*np.pi*t*modulation_frequency)
>>> reference = np.cos(2*np.pi*t*modulation_frequency)
>>> lia = ulia.ULIA(signal.size, sampling_rate, 0.03, 2, 0.2)
>>> lia.load_data(reference, signal)
>>> lia.execute()
Ignore the first 30% and last 10% of data due to filter artefacts.
>>> x = np.mean(lia.x[int(0.3*lia.x.size):int(0.9*lia.x.size)])
>>> y = np.mean(lia.y[int(0.3*lia.y.size):int(0.9*lia.y.size)])
>>> print(x + 1j * y)
Links
=====
* `ULIA on PyPi <https://pypi.org/project/ulia/>`_
* `Publication <https://doi.org/10.1063/5.0059740>`_
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"description": "Universal Software Lock-In Amplifier (ULIA)\n===========================================\n\n.. image:: https://gitlab.com/UhlDaniel/ulia/-/jobs/artifacts/master/raw/license.svg?job=badges\n :target: https://gitlab.com/UhlDaniel/ulia/-/blob/master/LICENSE\n\n.. image:: https://gitlab.com/UhlDaniel/ulia/-/jobs/artifacts/master/raw/pypi.svg?job=pypi\n :target: https://pypi.org/project/ulia/\n\n.. image:: https://img.shields.io/badge/DOI-10.1063%2F5.0059740-blue\n :target: https://aip.scitation.org/doi/10.1063/5.0059740\n\n\nAn effective algorithm to emulate a Lock-In Amplifier.\n\n\nQuickstart\n==========\n\nInstallation\n------------\n\nTo install `ulia` you can use `pip`.\n\n\n`ulia` package can be installed directly from PyPI using `pip` (`pip3`).\n\n.. code-block:: console\n\n $ pip install git+https://gitlab.com/UhlDaniel/ulia.git\n\nor\n\n.. code-block:: console\n\n $ pip install ulia\n\n\nDependencies\n------------\n\nThis package depends on:\n\n - Numpy\n - Scipy\n - Numba\n\n\n\nUsage\n-----\n\nA simple example on how to utilize the ULIA.\n\n.. code-block:: python\n\n >>> import numpy as np\n >>> import ulia\n\n\n >>> modulation_frequency = 5000.0\n >>> sampling_rate = 200000.0\n\n >>> t = np.arange(0, 0.3*sampling_rate) / sampling_rate\n >>> signal = np.cos(2*np.pi*t*modulation_frequency)\n >>> reference = np.cos(2*np.pi*t*modulation_frequency)\n\n >>> lia = ulia.ULIA(signal.size, sampling_rate, 0.03, 2, 0.2)\n >>> lia.load_data(reference, signal)\n >>> lia.execute()\n\n\n Ignore the first 30% and last 10% of data due to filter artefacts.\n >>> x = np.mean(lia.x[int(0.3*lia.x.size):int(0.9*lia.x.size)])\n >>> y = np.mean(lia.y[int(0.3*lia.y.size):int(0.9*lia.y.size)])\n\n >>> print(x + 1j * y)\n\n\n\nLinks\n=====\n\n * `ULIA on PyPi <https://pypi.org/project/ulia/>`_\n * `Publication <https://doi.org/10.1063/5.0059740>`_\n\n\n",
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