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Merge pull request #7 from nvlinhvn/linh-dev
Package and modulize hstransform
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| """ | ||
| This is the setup module for the HSTransform package. (Beta test) | ||
| S-transform with Hyperbolic Window | ||
| Combines the best properties of the Short-time Fourier Transform | ||
| and the Wavelet Transform. | ||
| """ | ||
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| from setuptools import setup, find_packages | ||
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| with open("README.md", "r", encoding="utf-8") as fh: | ||
| long_description = fh.read() | ||
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| setup( | ||
| name='HSTransform', | ||
| version='0.1.5', | ||
| url='https://github.com/nvlinhvn/HSTransform', | ||
| packages=find_packages(), | ||
| license='MIT', | ||
| author='Linh V Nguyen', | ||
| author_email='linhvietnguyen.ee@gmail.com', | ||
| description='A Package to Compute S-transform with Hyperbolic Window', | ||
| python_requires='>=3.10', | ||
| long_description=long_description, | ||
| long_description_content_type="text/markdown", | ||
| install_requires=[ | ||
| 'numpy>=1.21.2', | ||
| 'scipy>=1.7.1', | ||
| 'pandas>=1.3.3', | ||
| 'pytest>=6.2.5' | ||
| ], | ||
| classifiers=[ | ||
| 'Development Status :: 4 - Beta', | ||
| 'License :: OSI Approved :: MIT License', | ||
| 'Programming Language :: Python :: 3.10', | ||
| ], | ||
| test_suite='tests' | ||
| ) |
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| numpy>=1.21.2 | ||
| scipy>=1.7.1 | ||
| pandas>=1.3.3 | ||
| matplotlib>=3.4.3 | ||
| pytest>=6.2.5 |
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| hstransform | ||
| tests |
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| import dataclasses | ||
| from typing import Union | ||
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| import numpy as np | ||
| from scipy.fft import fft, ifft | ||
| import pandas as pd | ||
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| @dataclasses.dataclass | ||
| class HSTransform: | ||
| """ | ||
| A class used to represent the S-transform with a hyperbolic Gaussian window. | ||
| ... | ||
| Attributes | ||
| ---------- | ||
| forwardtaper : float | ||
| forward taper value | ||
| backwardtaper : float | ||
| backward taper value | ||
| curvature : float | ||
| curvature value | ||
| """ | ||
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| forwardtaper: float = 0.2 | ||
| backwardtaper: float = 0.1 | ||
| curvature: float = 312.5 | ||
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| def _input_validation(self, input_signal): | ||
| """ | ||
| Validates the input signal. | ||
| Parameters | ||
| ---------- | ||
| input_signal : np.ndarray, pd.Series, or list | ||
| Raises | ||
| ------ | ||
| TypeError: If input_signal is not a numpy array, pandas Series, or list. | ||
| ValueError: If input_signal contains non-numerical values. | ||
| """ | ||
| valid_types = (np.ndarray, pd.Series, list) | ||
| if not isinstance(input_signal, valid_types): | ||
| raise TypeError(f"input_signal must be one of the following types: {valid_types}, not {type(input_signal)}.") | ||
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| input_array = np.array(input_signal) | ||
| if np.isnan(input_array).any(): | ||
| raise ValueError("input_signal contains null values.") | ||
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| if not np.issubdtype(input_array.dtype, np.number): | ||
| raise ValueError("input_signal should only contain numerical values.") | ||
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| def _compute_hyperbolic_gaussian(self, l: int, n: int, time: np.ndarray) -> np.ndarray: | ||
| """ | ||
| Computes the hyperbolic Gaussian window. | ||
| Parameters | ||
| ---------- | ||
| l : int | ||
| length of the signal | ||
| n : int | ||
| frequency point | ||
| time : np.ndarray | ||
| time values of the signal | ||
| Returns | ||
| ------- | ||
| g : np.ndarray | ||
| hyperbolic Gaussian window | ||
| """ | ||
| vectorf = np.arange(0, l) | ||
| vectorf1 = vectorf**2 | ||
| lambdaf = self.forwardtaper | ||
| lambdab = self.backwardtaper | ||
| lambda_val = self.curvature | ||
| x = (lambdaf + lambdab) * time / (2 * lambdaf * lambdab) + (lambdaf - lambdab) * np.sqrt(time**2 + lambda_val) / (2 * lambdaf * lambdab) | ||
| x = np.tile(x, (1, 2)).T | ||
| vectorf2 = -vectorf1 * x**2 / (2 * n**2) | ||
| g = 2 * np.abs(vectorf) * np.exp(vectorf2) / ((lambdaf + lambdab) * np.sqrt(2 * np.pi)) | ||
| return np.sum(g) | ||
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| def fit_transform(self, | ||
| time_values: Union[pd.Series, np.ndarray, list], | ||
| input_signal: Union[pd.Series, np.ndarray, list], | ||
| minf: int = 0, | ||
| fsamplingrate: int = 1) -> np.ndarray: | ||
| """ | ||
| Computes the S-transform of the input signal. | ||
| Parameters | ||
| ---------- | ||
| time_values : np.ndarray | ||
| time values of the signal | ||
| input_signal : np.ndarray | ||
| input signal | ||
| minf : int | ||
| minimum frequency point | ||
| fsamplingrate : int | ||
| frequency sampling rate | ||
| Returns | ||
| ------- | ||
| S : np.ndarray | ||
| S-transform of the input signal | ||
| """ | ||
| # Validate the input | ||
| self._input_validation(input_signal) | ||
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| # Convert to numpy arrays if they are not array types | ||
| if not isinstance(time_values, np.ndarray): | ||
| time_values = np.array(time_values) | ||
| if not isinstance(input_signal, np.ndarray): | ||
| input_signal = np.array(input_signal) | ||
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| n = len(input_signal) | ||
| # Make sure the max frequency to be optimized (Cover the 6th, 12th, or 18th harmonic respectively) | ||
| maxf = min(900, n // 2) | ||
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| # Compute the fft of input | ||
| h = fft(input_signal) | ||
| h = np.concatenate((h, h)) | ||
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| # S output | ||
| s = np.zeros(((maxf - minf + 1) // fsamplingrate, n), dtype='complex') | ||
| s[0, :] = np.mean(input_signal) * (1 & np.arange(1, n + 1)) | ||
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| # Increment the frequency point | ||
| for k in range(fsamplingrate, maxf - minf + 1, fsamplingrate): | ||
| w_hy = self._compute_hyperbolic_gaussian(n, minf + k, time_values) | ||
| s[k // fsamplingrate, :] = ifft(h[minf + k + 1:minf + k + n+1] * w_hy) | ||
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| return s | ||
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| Original file line number | Diff line number | Diff line change |
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| @@ -0,0 +1 @@ | ||
| from .hstransform import HSTransform |
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| Original file line number | Diff line number | Diff line change |
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| @@ -0,0 +1 @@ | ||
| from .hstransform import HSTransform |
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