cleanup and add .gitignore
Sebastian Heimann
1 year ago
parent
e974bab001
commit
0b8c1a3422
@ -0,0 +1,130 @@
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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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# Distribution / packaging
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.Python
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build/
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develop-eggs/
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dist/
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downloads/
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eggs/
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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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*.manifest
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*.spec
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||||
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# Installer logs
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||||
pip-log.txt
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pip-delete-this-directory.txt
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# Unit test / coverage reports
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||||
htmlcov/
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.tox/
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.nox/
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.coverage
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.coverage.*
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.cache
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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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.hypothesis/
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.pytest_cache/
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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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db.sqlite3
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db.sqlite3-journal
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# Flask stuff:
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instance/
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.webassets-cache
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||||
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# Scrapy stuff:
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.scrapy
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||||
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# Sphinx documentation
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||||
docs/_build/
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||||
# PyBuilder
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target/
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# Jupyter Notebook
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.ipynb_checkpoints
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# IPython
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profile_default/
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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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# However, in case of collaboration, if having platform-specific dependencies or dependencies
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# having no cross-platform support, pipenv may install dependencies that don't work, or not
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# install all needed dependencies.
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#Pipfile.lock
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# PEP 582; used by e.g. github.com/David-OConnor/pyflow
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__pypackages__/
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# Celery stuff
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celerybeat-schedule
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celerybeat.pid
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||||
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# SageMath parsed files
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*.sage.py
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||||
# Environments
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.env
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.venv
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env/
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venv/
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ENV/
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env.bak/
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venv.bak/
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||||
|
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# Spyder project settings
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||||
.spyderproject
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.spyproject
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# Rope project settings
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.ropeproject
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# mkdocs documentation
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/site
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# mypy
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.mypy_cache/
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.dmypy.json
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dmypy.json
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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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## Contributing
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Please see the file [`CONTRIBUTING.md`](CONTRIBUTING.md).
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## Licence
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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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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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def _next_pow2(n):
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return int(round(2**np.ceil(np.log2(n))))
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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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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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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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: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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# 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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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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# 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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# 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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# 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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# 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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# 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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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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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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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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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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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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def _next_pow2(n):
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return int(round(2**np.ceil(np.log2(n))))
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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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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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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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|
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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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# 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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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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# 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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# 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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# 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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# 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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# 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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|
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z = x - new
|
||||
stats = trace.stats
|
||||
stats.location = 'NC'
|
||||
|
||||
hps_trace = Trace(data=z, header=stats)
|
||||
hps_trace.write( 'NoiseCut.mseed', format='MSEED', encoding=5, reclen=4096)
|
||||
|
||||
if ret_spectrograms:
|
||||
|
||||
S_hps = S_full - S_background
|
||||
|
||||
df = trace.stats.sampling_rate/win_length_samples
|
||||
frequencies = np.arange(S_hps.shape[0]) * df
|
||||
times = np.arange(S_hps.shape[1]) * hop_length
|
||||
|
||||
return hps_trace, (S_full, S_background, S_hps, frequencies, times)
|
||||
else:
|
||||
return hps_trace
|
||||
|
||||
|
||||
|
||||
def plot_noisecut_spectrograms(
|
||||
S_full, S_background, S_hps, frequencies, times):
|
||||
|
||||
fig= plt.figure(figsize=(15, 9))
|
||||
|
||||
axs=fig.add_subplot(311)
|
||||
pcm=axs.pcolormesh(times, frequencies, librosa.power_to_db(np.abs(S_full)), cmap = 'magma', shading= 'auto')
|
||||
plt.ylim(0,1)
|
||||
plt.title('Full spectrogram', fontsize=14)
|
||||
plt.ylabel('Frequency (Hz)', fontsize=14)
|
||||
plt.yticks (fontsize= 14)
|
||||
axs.set_xticks([])
|
||||
cbar=fig.colorbar(pcm, ax=axs, pad= 0.01)
|
||||
cbar.ax.tick_params(labelsize=14)
|
||||
|
||||
axs=fig.add_subplot(312)
|
||||
pcm=axs.pcolormesh(times, frequencies, librosa.power_to_db(np.abs(S_background)), cmap = 'magma', shading= 'auto')
|
||||
plt.ylim(0,1)
|
||||
plt.title('Noise spectrogram', fontsize=14)
|
||||
plt.ylabel('Frequency (Hz)', fontsize=14)
|
||||
plt.yticks (fontsize= 14)
|
||||
axs.set_xticks([])
|
||||
cbar=fig.colorbar(pcm, ax=axs, pad= 0.01)
|
||||
cbar.ax.tick_params(labelsize=14)
|
||||
|
||||
axs=fig.add_subplot(313)
|
||||
pcm=axs.pcolormesh(times, frequencies, librosa.power_to_db(np.abs(S_hps)), cmap = 'magma', shading= 'auto')
|
||||
plt.ylim(0,1)
|
||||
plt.title('Denoised spectrogram', fontsize=14)
|
||||
plt.ylabel('Frequency (Hz)', fontsize=14)
|
||||
plt.yticks (fontsize= 14)
|
||||
cbar=fig.colorbar(pcm, ax=axs, pad= 0.01)
|
||||
cbar.ax.tick_params(labelsize=14)
|
||||
labelsx = [0,4,8,12,16,20,24]
|
||||
plt.xticks(np.arange(0,times[-1],(times[-1]/6)-1), labelsx, fontsize= 14)
|
||||
|
||||
fig.savefig ('NoiseCut spectrograms.png', dpi=100)
|
||||
plt.show()
|
||||
plt.close(fig)
|
||||
@ -1,12 +0,0 @@
|
||||
import noisecut
|
||||
import obspy
|
||||
|
||||
|
||||
st= obspy.read('D10.DO.HH4..D.2012.080.000000')
|
||||
hps_trace, spectrograms = noisecut.noisecut (st[0], ret_spectrograms= True)
|
||||
noisecut.plot_noisecut_spectrograms (*spectrograms)
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
Loading…
Reference in New Issue