Row 79457

Row ID: 79457 | Dataset Entry | Axioma AXP Content Repository

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Hey everyone,

I made a python package for [S-transform](https://en.wikipedia.org/wiki/S_transform) with Hyperbolic window (Hyperbolic S-transform or HSTransform package). This is my first time publishing a python package, so the project is still far from stable and still under beta release.

* This transformation is applied to signal processing, analyzing transient changes of a signal during very short-time. Some special use case can be in power system signal, or Geophysical signal analysis, or MRI ... This is mainly for time-series data * The comparison with Wavelet Transform has been shown. (which probably shows more potential in detecting transient changes)

I would highly appreciate some feedback, before progressing further. So far the next steps in my plan is to:

* including Pydantic * Move from [setup.py](http://setup.py/) to pyproject.toml * Add a license, pre-commit hooks, Pypi build, and upload ci/cd * Add a src/project structure * Add style checking * Nox for running/testing many builds * Sphinx for documentation * Include inverse S-transform (to original data)

HSTransform is available on [pypi](https://pypi.org/project/HSTransform/).

[Link to source code in github](https://github.com/nvlinhvn/HSTransform)

Thanks everyone.

# Quick Usage

import numpy as np from hstransform import HSTransform # Create input signal (for example: Voltage signal) t = np.linspace(0, 10, 100) # timeseries V_m = 220*np.sqrt(2) # peak voltage f_V = 50 # frequency phi_V = 0 # phase V_clean = V_m * np.sin(2 * np.pi * f_V * t + phi_V) # Create voltage sag/dip (80% of the nominal voltage for 0.15 second) V_sag = np.where((t >= 2) & (t <= 3.5), 0.5 * V_clean, V_clean) # Create an instance of HSTransform hs = HSTransform() # Perform the transform signal = V_sag S_transformed = hs.fit_transform(t, signal)

FieldValue
text Hey everyone, I made a python package for [S-transform](https://en.wikipedia.org/wiki/S_transform) with Hyperbolic window (Hyperbolic S-transform or HSTransform package). This is my first time publishing a python package, so the project is still far from stable and still under beta release. * This transformation is applied to signal processing, analyzing transient changes of a signal during very short-time. Some special use case can be in power system signal, or Geophysical signal analysis, or…
label r/datascience
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communityName r/datascience
datetime 2024-05-24
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Raw Record

{
  "text": "Hey everyone,\n\nI made a python package for [S-transform](https://en.wikipedia.org/wiki/S_transform) with Hyperbolic window (Hyperbolic S-transform or HSTransform package). This is my first time publishing a python package, so the project is still far from stable and still under beta release.\n\n* This transformation is applied to signal processing, analyzing transient changes of a signal during very short-time. Some special use case can be in power system signal, or Geophysical signal analysis, or MRI ... This is mainly for time-series data \n* The comparison with Wavelet Transform has been shown. (which probably shows more potential in detecting transient changes)\n\nI would highly appreciate some feedback, before progressing further. So far the next steps in my plan is to:\n\n* including Pydantic\n* Move from [setup.py](http://setup.py/) to pyproject.toml\n* Add a license, pre-commit hooks, Pypi build, and upload ci/cd\n* Add a src/project structure\n* Add style checking\n* Nox for running/testing many builds\n* Sphinx for documentation\n* Include inverse S-transform (to original data)\n\nHSTransform is available on [pypi](https://pypi.org/project/HSTransform/).\n\n[Link to source code in github](https://github.com/nvlinhvn/HSTransform)\n\nThanks everyone.\n\n# Quick Usage\n\n    import numpy as np\n    from hstransform import HSTransform\n    \n    # Create input signal (for example: Voltage signal)\n    t = np.linspace(0, 10, 100) # timeseries\n    V_m = 220*np.sqrt(2)  # peak voltage\n    f_V = 50  # frequency\n    phi_V = 0  # phase\n    \n    V_clean = V_m * np.sin(2 * np.pi * f_V * t + phi_V)\n    # Create voltage sag/dip (80% of the nominal voltage for 0.15 second)\n    V_sag = np.where((t >= 2) & (t <= 3.5), 0.5 * V_clean, V_clean)\n    \n    # Create an instance of HSTransform\n    \n    hs = HSTransform()\n    \n    # Perform the transform\n    signal = V_sag\n    S_transformed = hs.fit_transform(t, signal)",
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