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cleanup and add .gitignore

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Sebastian Heimann 8 months ago
parent
commit
0b8c1a3422
  1. 130
      .gitignore
  2. 21
      .ipynb_checkpoints/README-checkpoint.md
  3. 22
      .ipynb_checkpoints/setup-checkpoint.py
  4. 195
      src/.ipynb_checkpoints/noisecut-checkpoint.py
  5. 195
      test/.ipynb_checkpoints/noisecut-checkpoint.py
  6. 12
      test/.ipynb_checkpoints/test_noisecut-checkpoint.py

130
.gitignore vendored

@ -0,0 +1,130 @@
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*.so
# Distribution / packaging
.Python
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*.egg
MANIFEST
# PyInstaller
# Usually these files are written by a python script from a template
# before PyInstaller builds the exe, so as to inject date/other infos into it.
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pip-delete-this-directory.txt
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htmlcov/
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# Translations
*.mo
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# Django stuff:
*.log
local_settings.py
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db.sqlite3-journal
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instance/
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.scrapy
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docs/_build/
# PyBuilder
target/
# Jupyter Notebook
.ipynb_checkpoints
# IPython
profile_default/
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# pyenv
.python-version
# pipenv
# According to pypa/pipenv#598, it is recommended to include Pipfile.lock in version control.
# However, in case of collaboration, if having platform-specific dependencies or dependencies
# having no cross-platform support, pipenv may install dependencies that don't work, or not
# install all needed dependencies.
#Pipfile.lock
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__pypackages__/
# Celery stuff
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dmypy.json
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.pyre/

21
.ipynb_checkpoints/README-checkpoint.md

@ -1,21 +0,0 @@
# NoiseCut
OBS noise reduction from horizontal and vertical components using harmonic-percussive
separation algorithms
## Documentation and installation
Find documentation and installation instructions in [NoiseCut's documentation
preview](https://NoiseCut.org/doc).
## Contributing
Please see the file [`CONTRIBUTING.md`](CONTRIBUTING.md).
## Licence
NoiseCut is licenced under the [GNU Affero General Public License
(AGPLv3)](LICENCE).
## Contact
* Package maintainer: Zahra Zali, zali@uni-potsdam.de

22
.ipynb_checkpoints/setup-checkpoint.py

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from setuptools import setup
setup(
name='noisecut',
version='0.0.1',
author='Zahra Zali',
license='AGPLv3',
package_dir={
'noisecut': 'src'
},
packages=[
'noisecut'],
install_requires=[
'numpy',
'obspy',
'librosa',
],
extras_require={
'obspy_compatibility': ['obspy'],
'pyrocko_compatibility': ['pyrocko'],
},
)

195
src/.ipynb_checkpoints/noisecut-checkpoint.py

@ -1,195 +0,0 @@
# -----------------------------------------------------------------------------
# NoiseCut
#
# This file is part of the NoiseCut library. For licensing information see the
# accompanying file `LICENSE`.
# -----------------------------------------------------------------------------
import math
import numpy as np
import librosa
from obspy import Trace
import librosa.display
import matplotlib.pyplot as plt
def _next_pow2(n):
return int(round(2**np.ceil(np.log2(n))))
def _valid_win_length_samples(win_length_samples, win_length, sampling_rate):
if win_length_samples is None and win_length is None:
# fully automatic window length
win_length_samples = _next_pow2(120*sampling_rate)
elif win_length_samples is None and win_length is not None:
win_length_samples = _next_pow2(win_length*sampling_rate)
elif win_length_samples is not None and win_length_samples is not None:
raise ValueError(
'Parameters win_length and win_length_samples are mutually '
'exclusive.')
elif win_length_samples is not None and win_length is None:
# check win_length_samples is a power of 2
win_length_samples = int(win_length_samples)
if win_length_samples != _next_pow2(win_length_samples):
raise ValueError(
'Parameter win_length_samples must be a power of 2.')
return win_length_samples
def noisecut(
trace,
ret_spectrograms=False,
win_length_samples=None,
win_length=None):
'''
Reduce noise from all the components of the OBS data using HPS noise
reduction algorithms.
:param win_length_samples:
Window length in samples. Must be a power of 2. Alternatively it can be
set with `win_length`.
:type win_length_samples:
int
:param win_length:
Window length [s]. Alternatively it can be set with
`win_length_samples`.
:type win_length:
float
:returns:
The HPS trace and the spectrograms of the original, noise, and hps
trace as well as an array with the frequencies.
:return_type:
tuple ``(hps_trace, (s_original, s_noise, s_hps, frequencies))``
'''
x = trace.data.astype(float)
win_length_samples = _valid_win_length_samples(
win_length_samples, win_length, trace.stats.sampling_rate)
hop_length = win_length_samples // 4
n_fft = win_length_samples
# Compute the spectrogram amplitude and phase
S_full, phase = librosa.magphase(librosa.stft(
x,
n_fft=n_fft,
hop_length=hop_length,
win_length=win_length_samples))
l1 = math.floor((0.1 * win_length_samples) / trace.stats.sampling_rate)
l2 = math.ceil((1 * win_length_samples) / trace.stats.sampling_rate)
# We consider the frequency range out of the [0.1-1] Hz for the first step
S_full2 = np.zeros((S_full.shape[0], S_full.shape[1]))
S_full2[l1:l2, :] = S_full[l1:l2, :]
# We consider the frequency range of [0.1-1] Hz for the second step
S_full1 = np.zeros((S_full.shape[0], S_full.shape[1]))
S_full1[:l1, :] = S_full[:l1, :]
S_full1[l2:, :] = S_full[l2:, :]
# We'll compare frames using cosine similarity, and aggregate similar
# frames by taking their (per-frequency) median value.
S_filter = librosa.decompose.nn_filter(
S_full1,
aggregate=np.median,
metric='cosine', width=200)
# The output of the filter shouldn't be greater than the input
S_filter = np.minimum(np.abs(S_full1), np.abs(S_filter))
margin_i = 1
power = 2
# Once we have the masks, simply multiply them with the input spectrogram
# to separate the components.
mask_i = librosa.util.softmask(
S_filter,
margin_i * (S_full1 - S_filter),
power=power)
S_background = mask_i * S_full1
# In the second step we apply a median filter
D_harmonic, D_percussive = librosa.decompose.hpss(
S_full2,
kernel_size=80,
margin=5)
S_background = S_background + D_harmonic
f = S_background * phase
L = x.shape[0]
new = librosa.istft(
f,
hop_length=hop_length,
win_length=win_length_samples,
window='hann',
length=L)
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)

195
test/.ipynb_checkpoints/noisecut-checkpoint.py

@ -1,195 +0,0 @@
# -----------------------------------------------------------------------------
# NoiseCut
#
# This file is part of the NoiseCut library. For licensing information see the
# accompanying file `LICENSE`.
# -----------------------------------------------------------------------------
import math
import numpy as np
import librosa
from obspy import Trace
import librosa.display
import matplotlib.pyplot as plt
def _next_pow2(n):
return int(round(2**np.ceil(np.log2(n))))
def _valid_win_length_samples(win_length_samples, win_length, sampling_rate):
if win_length_samples is None and win_length is None:
# fully automatic window length
win_length_samples = _next_pow2(120*sampling_rate)
elif win_length_samples is None and win_length is not None:
win_length_samples = _next_pow2(win_length*sampling_rate)
elif win_length_samples is not None and win_length_samples is not None:
raise ValueError(
'Parameters win_length and win_length_samples are mutually '
'exclusive.')
elif win_length_samples is not None and win_length is None:
# check win_length_samples is a power of 2
win_length_samples = int(win_length_samples)
if win_length_samples != _next_pow2(win_length_samples):
raise ValueError(
'Parameter win_length_samples must be a power of 2.')
return win_length_samples
def noisecut(
trace,
ret_spectrograms=False,
win_length_samples=None,
win_length=None):
'''
Reduce noise from all the components of the OBS data using HPS noise
reduction algorithms.
:param win_length_samples:
Window length in samples. Must be a power of 2. Alternatively it can be
set with `win_length`.
:type win_length_samples:
int
:param win_length:
Window length [s]. Alternatively it can be set with
`win_length_samples`.
:type win_length:
float
:returns:
The HPS trace and the spectrograms of the original, noise, and hps
trace as well as an array with the frequencies.
:return_type:
tuple ``(hps_trace, (s_original, s_noise, s_hps, frequencies))``
'''
x = trace.data.astype(float)
win_length_samples = _valid_win_length_samples(
win_length_samples, win_length, trace.stats.sampling_rate)
hop_length = win_length_samples // 4
n_fft = win_length_samples
# Compute the spectrogram amplitude and phase
S_full, phase = librosa.magphase(librosa.stft(
x,
n_fft=n_fft,
hop_length=hop_length,
win_length=win_length_samples))
l1 = math.floor((0.1 * win_length_samples) / trace.stats.sampling_rate)
l2 = math.ceil((1 * win_length_samples) / trace.stats.sampling_rate)
# We consider the frequency range out of the [0.1-1] Hz for the first step
S_full2 = np.zeros((S_full.shape[0], S_full.shape[1]))
S_full2[l1:l2, :] = S_full[l1:l2, :]
# We consider the frequency range of [0.1-1] Hz for the second step
S_full1 = np.zeros((S_full.shape[0], S_full.shape[1]))
S_full1[:l1, :] = S_full[:l1, :]
S_full1[l2:, :] = S_full[l2:, :]
# We'll compare frames using cosine similarity, and aggregate similar
# frames by taking their (per-frequency) median value.
S_filter = librosa.decompose.nn_filter(
S_full1,
aggregate=np.median,
metric='cosine', width=200)
# The output of the filter shouldn't be greater than the input
S_filter = np.minimum(np.abs(S_full1), np.abs(S_filter))
margin_i = 1
power = 2
# Once we have the masks, simply multiply them with the input spectrogram
# to separate the components.
mask_i = librosa.util.softmask(
S_filter,
margin_i * (S_full1 - S_filter),
power=power)
S_background = mask_i * S_full1
# In the second step we apply a median filter
D_harmonic, D_percussive = librosa.decompose.hpss(
S_full2,
kernel_size=80,
margin=5)
S_background = S_background + D_harmonic
f = S_background * phase
L = x.shape[0]
new = librosa.istft(
f,
hop_length=hop_length,
win_length=win_length_samples,
window='hann',
length=L)
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)

12
test/.ipynb_checkpoints/test_noisecut-checkpoint.py

@ -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)
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