Sebastian Heimann
3 months ago
6 changed files with 130 additions and 445 deletions
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# Byte-compiled / optimized / DLL files |
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__pycache__/ |
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*.py[cod] |
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*$py.class |
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# C extensions |
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*.so |
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build/ |
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develop-eggs/ |
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downloads/ |
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eggs/ |
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lib/ |
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lib64/ |
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parts/ |
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sdist/ |
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var/ |
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wheels/ |
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pip-wheel-metadata/ |
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share/python-wheels/ |
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*.egg-info/ |
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.installed.cfg |
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*.egg |
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MANIFEST |
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# PyInstaller |
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# Usually these files are written by a python script from a template |
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# before PyInstaller builds the exe, so as to inject date/other infos into it. |
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.coverage.* |
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nosetests.xml |
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coverage.xml |
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*.cover |
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*.py,cover |
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# Translations |
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*.mo |
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*.pot |
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# Django stuff: |
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*.log |
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local_settings.py |
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# IPython |
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ipython_config.py |
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# pyenv |
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.python-version |
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# pipenv |
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# According to pypa/pipenv#598, it is recommended to include Pipfile.lock in version control. |
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celerybeat-schedule |
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*.sage.py |
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# Pyre type checker |
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.pyre/ |
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@ -1,21 +0,0 @@
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# NoiseCut |
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OBS noise reduction from horizontal and vertical components using harmonic-percussive |
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separation algorithms |
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## Documentation and installation |
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Find documentation and installation instructions in [NoiseCut's documentation |
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preview](https://NoiseCut.org/doc). |
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|
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## Contributing |
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Please see the file [`CONTRIBUTING.md`](CONTRIBUTING.md). |
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|
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## Licence |
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|
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NoiseCut is licenced under the [GNU Affero General Public License |
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(AGPLv3)](LICENCE). |
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## Contact |
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* Package maintainer: Zahra Zali, zali@uni-potsdam.de |
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@ -1,22 +0,0 @@
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from setuptools import setup |
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setup( |
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name='noisecut', |
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version='0.0.1', |
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author='Zahra Zali', |
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license='AGPLv3', |
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package_dir={ |
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'noisecut': 'src' |
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}, |
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packages=[ |
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'noisecut'], |
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install_requires=[ |
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'numpy', |
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'obspy', |
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'librosa', |
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], |
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extras_require={ |
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'obspy_compatibility': ['obspy'], |
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'pyrocko_compatibility': ['pyrocko'], |
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}, |
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) |
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@ -1,195 +0,0 @@
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# ----------------------------------------------------------------------------- |
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# NoiseCut |
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# |
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# This file is part of the NoiseCut library. For licensing information see the |
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# accompanying file `LICENSE`. |
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# ----------------------------------------------------------------------------- |
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|
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import math |
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import numpy as np |
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import librosa |
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from obspy import Trace |
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import librosa.display |
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import matplotlib.pyplot as plt |
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|
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def _next_pow2(n): |
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return int(round(2**np.ceil(np.log2(n)))) |
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|
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def _valid_win_length_samples(win_length_samples, win_length, sampling_rate): |
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if win_length_samples is None and win_length is None: |
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# fully automatic window length |
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win_length_samples = _next_pow2(120*sampling_rate) |
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|
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elif win_length_samples is None and win_length is not None: |
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win_length_samples = _next_pow2(win_length*sampling_rate) |
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|
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elif win_length_samples is not None and win_length_samples is not None: |
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raise ValueError( |
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'Parameters win_length and win_length_samples are mutually ' |
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'exclusive.') |
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elif win_length_samples is not None and win_length is None: |
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# check win_length_samples is a power of 2 |
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win_length_samples = int(win_length_samples) |
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if win_length_samples != _next_pow2(win_length_samples): |
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raise ValueError( |
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'Parameter win_length_samples must be a power of 2.') |
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return win_length_samples |
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def noisecut( |
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trace, |
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ret_spectrograms=False, |
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win_length_samples=None, |
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win_length=None): |
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''' |
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Reduce noise from all the components of the OBS data using HPS noise |
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reduction algorithms. |
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|
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:param win_length_samples: |
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Window length in samples. Must be a power of 2. Alternatively it can be |
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set with `win_length`. |
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:type win_length_samples: |
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int |
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:param win_length: |
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Window length [s]. Alternatively it can be set with |
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`win_length_samples`. |
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:type win_length: |
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float |
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:returns: |
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The HPS trace and the spectrograms of the original, noise, and hps |
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trace as well as an array with the frequencies. |
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:return_type: |
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tuple ``(hps_trace, (s_original, s_noise, s_hps, frequencies))`` |
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''' |
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x = trace.data.astype(float) |
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win_length_samples = _valid_win_length_samples( |
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win_length_samples, win_length, trace.stats.sampling_rate) |
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hop_length = win_length_samples // 4 |
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n_fft = win_length_samples |
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|
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# Compute the spectrogram amplitude and phase |
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S_full, phase = librosa.magphase(librosa.stft( |
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x, |
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n_fft=n_fft, |
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hop_length=hop_length, |
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win_length=win_length_samples)) |
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|
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l1 = math.floor((0.1 * win_length_samples) / trace.stats.sampling_rate) |
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l2 = math.ceil((1 * win_length_samples) / trace.stats.sampling_rate) |
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|
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# We consider the frequency range out of the [0.1-1] Hz for the first step |
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S_full2 = np.zeros((S_full.shape[0], S_full.shape[1])) |
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S_full2[l1:l2, :] = S_full[l1:l2, :] |
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|
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# We consider the frequency range of [0.1-1] Hz for the second step |
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S_full1 = np.zeros((S_full.shape[0], S_full.shape[1])) |
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S_full1[:l1, :] = S_full[:l1, :] |
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S_full1[l2:, :] = S_full[l2:, :] |
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|
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# We'll compare frames using cosine similarity, and aggregate similar |
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# frames by taking their (per-frequency) median value. |
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S_filter = librosa.decompose.nn_filter( |
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S_full1, |
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aggregate=np.median, |
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metric='cosine', width=200) |
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|
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# The output of the filter shouldn't be greater than the input |
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S_filter = np.minimum(np.abs(S_full1), np.abs(S_filter)) |
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margin_i = 1 |
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power = 2 |
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|
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# Once we have the masks, simply multiply them with the input spectrogram |
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# to separate the components. |
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mask_i = librosa.util.softmask( |
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S_filter, |
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margin_i * (S_full1 - S_filter), |
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power=power) |
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S_background = mask_i * S_full1 |
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# In the second step we apply a median filter |
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D_harmonic, D_percussive = librosa.decompose.hpss( |
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S_full2, |
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kernel_size=80, |
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margin=5) |
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S_background = S_background + D_harmonic |
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f = S_background * phase |
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L = x.shape[0] |
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new = librosa.istft( |
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f, |
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hop_length=hop_length, |
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win_length=win_length_samples, |
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window='hann', |
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length=L) |
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z = x - new |
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stats = trace.stats |
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stats.location = 'NC' |
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hps_trace = Trace(data=z, header=stats) |
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hps_trace.write( 'NoiseCut.mseed', format='MSEED', encoding=5, reclen=4096) |
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if ret_spectrograms: |
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S_hps = S_full - S_background |
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df = trace.stats.sampling_rate/win_length_samples |
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frequencies = np.arange(S_hps.shape[0]) * df |
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times = np.arange(S_hps.shape[1]) * hop_length |
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return hps_trace, (S_full, S_background, S_hps, frequencies, times) |
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else: |
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return hps_trace |
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|
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def plot_noisecut_spectrograms( |
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S_full, S_background, S_hps, frequencies, times): |
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fig= plt.figure(figsize=(15, 9)) |
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axs=fig.add_subplot(311) |
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pcm=axs.pcolormesh(times, frequencies, librosa.power_to_db(np.abs(S_full)), cmap = 'magma', shading= 'auto') |
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plt.ylim(0,1) |
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plt.title('Full spectrogram', fontsize=14) |
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plt.ylabel('Frequency (Hz)', fontsize=14) |
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plt.yticks (fontsize= 14) |
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axs.set_xticks([]) |
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cbar=fig.colorbar(pcm, ax=axs, pad= 0.01) |
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cbar.ax.tick_params(labelsize=14) |
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|
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axs=fig.add_subplot(312) |
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pcm=axs.pcolormesh(times, frequencies, librosa.power_to_db(np.abs(S_background)), cmap = 'magma', shading= 'auto') |
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plt.ylim(0,1) |
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plt.title('Noise spectrogram', fontsize=14) |
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plt.ylabel('Frequency (Hz)', fontsize=14) |
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plt.yticks (fontsize= 14) |
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axs.set_xticks([]) |
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cbar=fig.colorbar(pcm, ax=axs, pad= 0.01) |
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cbar.ax.tick_params(labelsize=14) |
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|
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axs=fig.add_subplot(313) |
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pcm=axs.pcolormesh(times, frequencies, librosa.power_to_db(np.abs(S_hps)), cmap = 'magma', shading= 'auto') |
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plt.ylim(0,1) |
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plt.title('Denoised spectrogram', fontsize=14) |
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plt.ylabel('Frequency (Hz)', fontsize=14) |
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plt.yticks (fontsize= 14) |
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cbar=fig.colorbar(pcm, ax=axs, pad= 0.01) |
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cbar.ax.tick_params(labelsize=14) |
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labelsx = [0,4,8,12,16,20,24] |
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plt.xticks(np.arange(0,times[-1],(times[-1]/6)-1), labelsx, fontsize= 14) |
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|
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fig.savefig ('NoiseCut spectrograms.png', dpi=100) |
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plt.show() |
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plt.close(fig) |
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@ -1,195 +0,0 @@
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# ----------------------------------------------------------------------------- |
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# NoiseCut |
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# |
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# This file is part of the NoiseCut library. For licensing information see the |
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# accompanying file `LICENSE`. |
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# ----------------------------------------------------------------------------- |
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|
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import math |
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import numpy as np |
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import librosa |
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from obspy import Trace |
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import librosa.display |
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import matplotlib.pyplot as plt |
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|
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|
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|
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def _next_pow2(n): |
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return int(round(2**np.ceil(np.log2(n)))) |
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|
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|
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def _valid_win_length_samples(win_length_samples, win_length, sampling_rate): |
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if win_length_samples is None and win_length is None: |
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# fully automatic window length |
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win_length_samples = _next_pow2(120*sampling_rate) |
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|
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elif win_length_samples is None and win_length is not None: |
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win_length_samples = _next_pow2(win_length*sampling_rate) |
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|
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elif win_length_samples is not None and win_length_samples is not None: |
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raise ValueError( |
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'Parameters win_length and win_length_samples are mutually ' |
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'exclusive.') |
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elif win_length_samples is not None and win_length is None: |
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# check win_length_samples is a power of 2 |
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win_length_samples = int(win_length_samples) |
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if win_length_samples != _next_pow2(win_length_samples): |
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raise ValueError( |
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'Parameter win_length_samples must be a power of 2.') |
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return win_length_samples |
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|
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|
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def noisecut( |
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trace, |
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ret_spectrograms=False, |
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win_length_samples=None, |
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win_length=None): |
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''' |
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Reduce noise from all the components of the OBS data using HPS noise |
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reduction algorithms. |
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|
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:param win_length_samples: |
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Window length in samples. Must be a power of 2. Alternatively it can be |
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set with `win_length`. |
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:type win_length_samples: |
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int |
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|
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:param win_length: |
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Window length [s]. Alternatively it can be set with |
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`win_length_samples`. |
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:type win_length: |
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float |
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|
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:returns: |
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The HPS trace and the spectrograms of the original, noise, and hps |
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trace as well as an array with the frequencies. |
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:return_type: |
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tuple ``(hps_trace, (s_original, s_noise, s_hps, frequencies))`` |
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''' |
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|
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x = trace.data.astype(float) |
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|
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win_length_samples = _valid_win_length_samples( |
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win_length_samples, win_length, trace.stats.sampling_rate) |
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|
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hop_length = win_length_samples // 4 |
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n_fft = win_length_samples |
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|
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# Compute the spectrogram amplitude and phase |
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S_full, phase = librosa.magphase(librosa.stft( |
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x, |
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n_fft=n_fft, |
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hop_length=hop_length, |
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win_length=win_length_samples)) |
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|
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l1 = math.floor((0.1 * win_length_samples) / trace.stats.sampling_rate) |
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l2 = math.ceil((1 * win_length_samples) / trace.stats.sampling_rate) |
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|
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# We consider the frequency range out of the [0.1-1] Hz for the first step |
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S_full2 = np.zeros((S_full.shape[0], S_full.shape[1])) |
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S_full2[l1:l2, :] = S_full[l1:l2, :] |
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|
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# We consider the frequency range of [0.1-1] Hz for the second step |
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S_full1 = np.zeros((S_full.shape[0], S_full.shape[1])) |
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S_full1[:l1, :] = S_full[:l1, :] |
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S_full1[l2:, :] = S_full[l2:, :] |
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|
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# We'll compare frames using cosine similarity, and aggregate similar |
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# frames by taking their (per-frequency) median value. |
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S_filter = librosa.decompose.nn_filter( |
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S_full1, |
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aggregate=np.median, |
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metric='cosine', width=200) |
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|
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# The output of the filter shouldn't be greater than the input |
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S_filter = np.minimum(np.abs(S_full1), np.abs(S_filter)) |
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margin_i = 1 |
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power = 2 |
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|
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# Once we have the masks, simply multiply them with the input spectrogram |
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# to separate the components. |
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mask_i = librosa.util.softmask( |
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S_filter, |
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margin_i * (S_full1 - S_filter), |
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power=power) |
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|
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S_background = mask_i * S_full1 |
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|
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# In the second step we apply a median filter |
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D_harmonic, D_percussive = librosa.decompose.hpss( |
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S_full2, |
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kernel_size=80, |
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margin=5) |
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|
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S_background = S_background + D_harmonic |
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|
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f = S_background * phase |
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L = x.shape[0] |
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new = librosa.istft( |
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f, |
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hop_length=hop_length, |
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win_length=win_length_samples, |
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window='hann', |
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length=L) |
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|
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z = x - new |
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stats = trace.stats |
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stats.location = 'NC' |
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|
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hps_trace = Trace(data=z, header=stats) |
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hps_trace.write( 'NoiseCut.mseed', format='MSEED', encoding=5, reclen=4096) |
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|
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if ret_spectrograms: |
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|
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S_hps = S_full - S_background |
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|
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df = trace.stats.sampling_rate/win_length_samples |
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frequencies = np.arange(S_hps.shape[0]) * df |
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times = np.arange(S_hps.shape[1]) * hop_length |
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|
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return hps_trace, (S_full, S_background, S_hps, frequencies, times) |
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else: |
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return hps_trace |
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|
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|
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|
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def plot_noisecut_spectrograms( |
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S_full, S_background, S_hps, frequencies, times): |
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|
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fig= plt.figure(figsize=(15, 9)) |
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|
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axs=fig.add_subplot(311) |
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pcm=axs.pcolormesh(times, frequencies, librosa.power_to_db(np.abs(S_full)), cmap = 'magma', shading= 'auto') |
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plt.ylim(0,1) |
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plt.title('Full spectrogram', fontsize=14) |
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plt.ylabel('Frequency (Hz)', fontsize=14) |
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plt.yticks (fontsize= 14) |
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axs.set_xticks([]) |
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cbar=fig.colorbar(pcm, ax=axs, pad= 0.01) |
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cbar.ax.tick_params(labelsize=14) |
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|
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axs=fig.add_subplot(312) |
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pcm=axs.pcolormesh(times, frequencies, librosa.power_to_db(np.abs(S_background)), cmap = 'magma', shading= 'auto') |
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plt.ylim(0,1) |
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plt.title('Noise spectrogram', fontsize=14) |
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plt.ylabel('Frequency (Hz)', fontsize=14) |
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plt.yticks (fontsize= 14) |
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axs.set_xticks([]) |
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cbar=fig.colorbar(pcm, ax=axs, pad= 0.01) |
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cbar.ax.tick_params(labelsize=14) |
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|
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axs=fig.add_subplot(313) |
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pcm=axs.pcolormesh(times, frequencies, librosa.power_to_db(np.abs(S_hps)), cmap = 'magma', shading= 'auto') |
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plt.ylim(0,1) |
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plt.title('Denoised spectrogram', fontsize=14) |
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plt.ylabel('Frequency (Hz)', fontsize=14) |
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plt.yticks (fontsize= 14) |
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cbar=fig.colorbar(pcm, ax=axs, pad= 0.01) |
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cbar.ax.tick_params(labelsize=14) |
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labelsx = [0,4,8,12,16,20,24] |
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plt.xticks(np.arange(0,times[-1],(times[-1]/6)-1), labelsx, fontsize= 14) |
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|
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fig.savefig ('NoiseCut spectrograms.png', dpi=100) |
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plt.show() |
||||
plt.close(fig) |
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Loading…
Reference in new issue