partition stringclasses 3
values | func_name stringlengths 1 134 | docstring stringlengths 1 46.9k | path stringlengths 4 223 | original_string stringlengths 75 104k | code stringlengths 75 104k | docstring_tokens listlengths 1 1.97k | repo stringlengths 7 55 | language stringclasses 1
value | url stringlengths 87 315 | code_tokens listlengths 19 28.4k | sha stringlengths 40 40 |
|---|---|---|---|---|---|---|---|---|---|---|---|
test | spectral_bandwidth | Compute p'th-order spectral bandwidth:
(sum_k S[k] * (freq[k] - centroid)**p)**(1/p)
Parameters
----------
y : np.ndarray [shape=(n,)] or None
audio time series
sr : number > 0 [scalar]
audio sampling rate of `y`
S : np.ndarray [shape=(d, t)] or None
(optional) sp... | librosa/feature/spectral.py | def spectral_bandwidth(y=None, sr=22050, S=None, n_fft=2048, hop_length=512,
win_length=None, window='hann', center=True, pad_mode='reflect',
freq=None, centroid=None, norm=True, p=2):
'''Compute p'th-order spectral bandwidth:
(sum_k S[k] * (freq[k] - centroid)... | def spectral_bandwidth(y=None, sr=22050, S=None, n_fft=2048, hop_length=512,
win_length=None, window='hann', center=True, pad_mode='reflect',
freq=None, centroid=None, norm=True, p=2):
'''Compute p'th-order spectral bandwidth:
(sum_k S[k] * (freq[k] - centroid)... | [
"Compute",
"p",
"th",
"-",
"order",
"spectral",
"bandwidth",
":"
] | librosa/librosa | python | https://github.com/librosa/librosa/blob/180e8e6eb8f958fa6b20b8cba389f7945d508247/librosa/feature/spectral.py#L171-L311 | [
"def",
"spectral_bandwidth",
"(",
"y",
"=",
"None",
",",
"sr",
"=",
"22050",
",",
"S",
"=",
"None",
",",
"n_fft",
"=",
"2048",
",",
"hop_length",
"=",
"512",
",",
"win_length",
"=",
"None",
",",
"window",
"=",
"'hann'",
",",
"center",
"=",
"True",
... | 180e8e6eb8f958fa6b20b8cba389f7945d508247 |
test | spectral_contrast | Compute spectral contrast [1]_
.. [1] Jiang, Dan-Ning, Lie Lu, Hong-Jiang Zhang, Jian-Hua Tao,
and Lian-Hong Cai.
"Music type classification by spectral contrast feature."
In Multimedia and Expo, 2002. ICME'02. Proceedings.
2002 IEEE International Conference on, vol. 1, ... | librosa/feature/spectral.py | def spectral_contrast(y=None, sr=22050, S=None, n_fft=2048, hop_length=512,
win_length=None, window='hann', center=True, pad_mode='reflect',
freq=None, fmin=200.0, n_bands=6, quantile=0.02,
linear=False):
'''Compute spectral contrast [1]_
.. [1]... | def spectral_contrast(y=None, sr=22050, S=None, n_fft=2048, hop_length=512,
win_length=None, window='hann', center=True, pad_mode='reflect',
freq=None, fmin=200.0, n_bands=6, quantile=0.02,
linear=False):
'''Compute spectral contrast [1]_
.. [1]... | [
"Compute",
"spectral",
"contrast",
"[",
"1",
"]",
"_"
] | librosa/librosa | python | https://github.com/librosa/librosa/blob/180e8e6eb8f958fa6b20b8cba389f7945d508247/librosa/feature/spectral.py#L314-L481 | [
"def",
"spectral_contrast",
"(",
"y",
"=",
"None",
",",
"sr",
"=",
"22050",
",",
"S",
"=",
"None",
",",
"n_fft",
"=",
"2048",
",",
"hop_length",
"=",
"512",
",",
"win_length",
"=",
"None",
",",
"window",
"=",
"'hann'",
",",
"center",
"=",
"True",
"... | 180e8e6eb8f958fa6b20b8cba389f7945d508247 |
test | spectral_rolloff | Compute roll-off frequency.
The roll-off frequency is defined for each frame as the center frequency
for a spectrogram bin such that at least roll_percent (0.85 by default)
of the energy of the spectrum in this frame is contained in this bin and
the bins below. This can be used to, e.g., approximate th... | librosa/feature/spectral.py | def spectral_rolloff(y=None, sr=22050, S=None, n_fft=2048, hop_length=512,
win_length=None, window='hann', center=True, pad_mode='reflect',
freq=None, roll_percent=0.85):
'''Compute roll-off frequency.
The roll-off frequency is defined for each frame as the center freq... | def spectral_rolloff(y=None, sr=22050, S=None, n_fft=2048, hop_length=512,
win_length=None, window='hann', center=True, pad_mode='reflect',
freq=None, roll_percent=0.85):
'''Compute roll-off frequency.
The roll-off frequency is defined for each frame as the center freq... | [
"Compute",
"roll",
"-",
"off",
"frequency",
"."
] | librosa/librosa | python | https://github.com/librosa/librosa/blob/180e8e6eb8f958fa6b20b8cba389f7945d508247/librosa/feature/spectral.py#L484-L623 | [
"def",
"spectral_rolloff",
"(",
"y",
"=",
"None",
",",
"sr",
"=",
"22050",
",",
"S",
"=",
"None",
",",
"n_fft",
"=",
"2048",
",",
"hop_length",
"=",
"512",
",",
"win_length",
"=",
"None",
",",
"window",
"=",
"'hann'",
",",
"center",
"=",
"True",
",... | 180e8e6eb8f958fa6b20b8cba389f7945d508247 |
test | spectral_flatness | Compute spectral flatness
Spectral flatness (or tonality coefficient) is a measure to
quantify how much noise-like a sound is, as opposed to being
tone-like [1]_. A high spectral flatness (closer to 1.0)
indicates the spectrum is similar to white noise.
It is often converted to decibel.
.. [1]... | librosa/feature/spectral.py | def spectral_flatness(y=None, S=None, n_fft=2048, hop_length=512,
win_length=None, window='hann', center=True, pad_mode='reflect',
amin=1e-10, power=2.0):
'''Compute spectral flatness
Spectral flatness (or tonality coefficient) is a measure to
quantify how much n... | def spectral_flatness(y=None, S=None, n_fft=2048, hop_length=512,
win_length=None, window='hann', center=True, pad_mode='reflect',
amin=1e-10, power=2.0):
'''Compute spectral flatness
Spectral flatness (or tonality coefficient) is a measure to
quantify how much n... | [
"Compute",
"spectral",
"flatness"
] | librosa/librosa | python | https://github.com/librosa/librosa/blob/180e8e6eb8f958fa6b20b8cba389f7945d508247/librosa/feature/spectral.py#L626-L737 | [
"def",
"spectral_flatness",
"(",
"y",
"=",
"None",
",",
"S",
"=",
"None",
",",
"n_fft",
"=",
"2048",
",",
"hop_length",
"=",
"512",
",",
"win_length",
"=",
"None",
",",
"window",
"=",
"'hann'",
",",
"center",
"=",
"True",
",",
"pad_mode",
"=",
"'refl... | 180e8e6eb8f958fa6b20b8cba389f7945d508247 |
test | rms | Compute root-mean-square (RMS) value for each frame, either from the
audio samples `y` or from a spectrogram `S`.
Computing the RMS value from audio samples is faster as it doesn't require
a STFT calculation. However, using a spectrogram will give a more accurate
representation of energy over time beca... | librosa/feature/spectral.py | def rms(y=None, S=None, frame_length=2048, hop_length=512,
center=True, pad_mode='reflect'):
'''Compute root-mean-square (RMS) value for each frame, either from the
audio samples `y` or from a spectrogram `S`.
Computing the RMS value from audio samples is faster as it doesn't require
a STFT cal... | def rms(y=None, S=None, frame_length=2048, hop_length=512,
center=True, pad_mode='reflect'):
'''Compute root-mean-square (RMS) value for each frame, either from the
audio samples `y` or from a spectrogram `S`.
Computing the RMS value from audio samples is faster as it doesn't require
a STFT cal... | [
"Compute",
"root",
"-",
"mean",
"-",
"square",
"(",
"RMS",
")",
"value",
"for",
"each",
"frame",
"either",
"from",
"the",
"audio",
"samples",
"y",
"or",
"from",
"a",
"spectrogram",
"S",
"."
] | librosa/librosa | python | https://github.com/librosa/librosa/blob/180e8e6eb8f958fa6b20b8cba389f7945d508247/librosa/feature/spectral.py#L740-L828 | [
"def",
"rms",
"(",
"y",
"=",
"None",
",",
"S",
"=",
"None",
",",
"frame_length",
"=",
"2048",
",",
"hop_length",
"=",
"512",
",",
"center",
"=",
"True",
",",
"pad_mode",
"=",
"'reflect'",
")",
":",
"if",
"y",
"is",
"not",
"None",
"and",
"S",
"is"... | 180e8e6eb8f958fa6b20b8cba389f7945d508247 |
test | poly_features | Get coefficients of fitting an nth-order polynomial to the columns
of a spectrogram.
Parameters
----------
y : np.ndarray [shape=(n,)] or None
audio time series
sr : number > 0 [scalar]
audio sampling rate of `y`
S : np.ndarray [shape=(d, t)] or None
(optional) spectro... | librosa/feature/spectral.py | def poly_features(y=None, sr=22050, S=None, n_fft=2048, hop_length=512,
win_length=None, window='hann', center=True, pad_mode='reflect',
order=1, freq=None):
'''Get coefficients of fitting an nth-order polynomial to the columns
of a spectrogram.
Parameters
----------... | def poly_features(y=None, sr=22050, S=None, n_fft=2048, hop_length=512,
win_length=None, window='hann', center=True, pad_mode='reflect',
order=1, freq=None):
'''Get coefficients of fitting an nth-order polynomial to the columns
of a spectrogram.
Parameters
----------... | [
"Get",
"coefficients",
"of",
"fitting",
"an",
"nth",
"-",
"order",
"polynomial",
"to",
"the",
"columns",
"of",
"a",
"spectrogram",
"."
] | librosa/librosa | python | https://github.com/librosa/librosa/blob/180e8e6eb8f958fa6b20b8cba389f7945d508247/librosa/feature/spectral.py#L831-L958 | [
"def",
"poly_features",
"(",
"y",
"=",
"None",
",",
"sr",
"=",
"22050",
",",
"S",
"=",
"None",
",",
"n_fft",
"=",
"2048",
",",
"hop_length",
"=",
"512",
",",
"win_length",
"=",
"None",
",",
"window",
"=",
"'hann'",
",",
"center",
"=",
"True",
",",
... | 180e8e6eb8f958fa6b20b8cba389f7945d508247 |
test | zero_crossing_rate | Compute the zero-crossing rate of an audio time series.
Parameters
----------
y : np.ndarray [shape=(n,)]
Audio time series
frame_length : int > 0
Length of the frame over which to compute zero crossing rates
hop_length : int > 0
Number of samples to advance for each frame... | librosa/feature/spectral.py | def zero_crossing_rate(y, frame_length=2048, hop_length=512, center=True,
**kwargs):
'''Compute the zero-crossing rate of an audio time series.
Parameters
----------
y : np.ndarray [shape=(n,)]
Audio time series
frame_length : int > 0
Length of the frame over... | def zero_crossing_rate(y, frame_length=2048, hop_length=512, center=True,
**kwargs):
'''Compute the zero-crossing rate of an audio time series.
Parameters
----------
y : np.ndarray [shape=(n,)]
Audio time series
frame_length : int > 0
Length of the frame over... | [
"Compute",
"the",
"zero",
"-",
"crossing",
"rate",
"of",
"an",
"audio",
"time",
"series",
"."
] | librosa/librosa | python | https://github.com/librosa/librosa/blob/180e8e6eb8f958fa6b20b8cba389f7945d508247/librosa/feature/spectral.py#L961-L1019 | [
"def",
"zero_crossing_rate",
"(",
"y",
",",
"frame_length",
"=",
"2048",
",",
"hop_length",
"=",
"512",
",",
"center",
"=",
"True",
",",
"*",
"*",
"kwargs",
")",
":",
"util",
".",
"valid_audio",
"(",
"y",
")",
"if",
"center",
":",
"y",
"=",
"np",
"... | 180e8e6eb8f958fa6b20b8cba389f7945d508247 |
test | chroma_stft | Compute a chromagram from a waveform or power spectrogram.
This implementation is derived from `chromagram_E` [1]_
.. [1] Ellis, Daniel P.W. "Chroma feature analysis and synthesis"
2007/04/21
http://labrosa.ee.columbia.edu/matlab/chroma-ansyn/
Parameters
----------
y : np.n... | librosa/feature/spectral.py | def chroma_stft(y=None, sr=22050, S=None, norm=np.inf, n_fft=2048,
hop_length=512, win_length=None, window='hann', center=True,
pad_mode='reflect', tuning=None, **kwargs):
"""Compute a chromagram from a waveform or power spectrogram.
This implementation is derived from `chromagr... | def chroma_stft(y=None, sr=22050, S=None, norm=np.inf, n_fft=2048,
hop_length=512, win_length=None, window='hann', center=True,
pad_mode='reflect', tuning=None, **kwargs):
"""Compute a chromagram from a waveform or power spectrogram.
This implementation is derived from `chromagr... | [
"Compute",
"a",
"chromagram",
"from",
"a",
"waveform",
"or",
"power",
"spectrogram",
"."
] | librosa/librosa | python | https://github.com/librosa/librosa/blob/180e8e6eb8f958fa6b20b8cba389f7945d508247/librosa/feature/spectral.py#L1023-L1162 | [
"def",
"chroma_stft",
"(",
"y",
"=",
"None",
",",
"sr",
"=",
"22050",
",",
"S",
"=",
"None",
",",
"norm",
"=",
"np",
".",
"inf",
",",
"n_fft",
"=",
"2048",
",",
"hop_length",
"=",
"512",
",",
"win_length",
"=",
"None",
",",
"window",
"=",
"'hann'... | 180e8e6eb8f958fa6b20b8cba389f7945d508247 |
test | chroma_cqt | r'''Constant-Q chromagram
Parameters
----------
y : np.ndarray [shape=(n,)]
audio time series
sr : number > 0
sampling rate of `y`
C : np.ndarray [shape=(d, t)] [Optional]
a pre-computed constant-Q spectrogram
hop_length : int > 0
number of samples between suc... | librosa/feature/spectral.py | def chroma_cqt(y=None, sr=22050, C=None, hop_length=512, fmin=None,
norm=np.inf, threshold=0.0, tuning=None, n_chroma=12,
n_octaves=7, window=None, bins_per_octave=None, cqt_mode='full'):
r'''Constant-Q chromagram
Parameters
----------
y : np.ndarray [shape=(n,)]
a... | def chroma_cqt(y=None, sr=22050, C=None, hop_length=512, fmin=None,
norm=np.inf, threshold=0.0, tuning=None, n_chroma=12,
n_octaves=7, window=None, bins_per_octave=None, cqt_mode='full'):
r'''Constant-Q chromagram
Parameters
----------
y : np.ndarray [shape=(n,)]
a... | [
"r",
"Constant",
"-",
"Q",
"chromagram"
] | librosa/librosa | python | https://github.com/librosa/librosa/blob/180e8e6eb8f958fa6b20b8cba389f7945d508247/librosa/feature/spectral.py#L1165-L1280 | [
"def",
"chroma_cqt",
"(",
"y",
"=",
"None",
",",
"sr",
"=",
"22050",
",",
"C",
"=",
"None",
",",
"hop_length",
"=",
"512",
",",
"fmin",
"=",
"None",
",",
"norm",
"=",
"np",
".",
"inf",
",",
"threshold",
"=",
"0.0",
",",
"tuning",
"=",
"None",
"... | 180e8e6eb8f958fa6b20b8cba389f7945d508247 |
test | chroma_cens | r'''Computes the chroma variant "Chroma Energy Normalized" (CENS), following [1]_.
To compute CENS features, following steps are taken after obtaining chroma vectors using `chroma_cqt`:
1. L-1 normalization of each chroma vector
2. Quantization of amplitude based on "log-like" amplitude thresholds
3. (... | librosa/feature/spectral.py | def chroma_cens(y=None, sr=22050, C=None, hop_length=512, fmin=None,
tuning=None, n_chroma=12,
n_octaves=7, bins_per_octave=None, cqt_mode='full', window=None,
norm=2, win_len_smooth=41, smoothing_window='hann'):
r'''Computes the chroma variant "Chroma Energy Normaliz... | def chroma_cens(y=None, sr=22050, C=None, hop_length=512, fmin=None,
tuning=None, n_chroma=12,
n_octaves=7, bins_per_octave=None, cqt_mode='full', window=None,
norm=2, win_len_smooth=41, smoothing_window='hann'):
r'''Computes the chroma variant "Chroma Energy Normaliz... | [
"r",
"Computes",
"the",
"chroma",
"variant",
"Chroma",
"Energy",
"Normalized",
"(",
"CENS",
")",
"following",
"[",
"1",
"]",
"_",
"."
] | librosa/librosa | python | https://github.com/librosa/librosa/blob/180e8e6eb8f958fa6b20b8cba389f7945d508247/librosa/feature/spectral.py#L1283-L1426 | [
"def",
"chroma_cens",
"(",
"y",
"=",
"None",
",",
"sr",
"=",
"22050",
",",
"C",
"=",
"None",
",",
"hop_length",
"=",
"512",
",",
"fmin",
"=",
"None",
",",
"tuning",
"=",
"None",
",",
"n_chroma",
"=",
"12",
",",
"n_octaves",
"=",
"7",
",",
"bins_p... | 180e8e6eb8f958fa6b20b8cba389f7945d508247 |
test | tonnetz | Computes the tonal centroid features (tonnetz), following the method of
[1]_.
.. [1] Harte, C., Sandler, M., & Gasser, M. (2006). "Detecting Harmonic
Change in Musical Audio." In Proceedings of the 1st ACM Workshop
on Audio and Music Computing Multimedia (pp. 21-26).
Santa Barb... | librosa/feature/spectral.py | def tonnetz(y=None, sr=22050, chroma=None):
'''Computes the tonal centroid features (tonnetz), following the method of
[1]_.
.. [1] Harte, C., Sandler, M., & Gasser, M. (2006). "Detecting Harmonic
Change in Musical Audio." In Proceedings of the 1st ACM Workshop
on Audio and Music Comp... | def tonnetz(y=None, sr=22050, chroma=None):
'''Computes the tonal centroid features (tonnetz), following the method of
[1]_.
.. [1] Harte, C., Sandler, M., & Gasser, M. (2006). "Detecting Harmonic
Change in Musical Audio." In Proceedings of the 1st ACM Workshop
on Audio and Music Comp... | [
"Computes",
"the",
"tonal",
"centroid",
"features",
"(",
"tonnetz",
")",
"following",
"the",
"method",
"of",
"[",
"1",
"]",
"_",
"."
] | librosa/librosa | python | https://github.com/librosa/librosa/blob/180e8e6eb8f958fa6b20b8cba389f7945d508247/librosa/feature/spectral.py#L1429-L1528 | [
"def",
"tonnetz",
"(",
"y",
"=",
"None",
",",
"sr",
"=",
"22050",
",",
"chroma",
"=",
"None",
")",
":",
"if",
"y",
"is",
"None",
"and",
"chroma",
"is",
"None",
":",
"raise",
"ParameterError",
"(",
"'Either the audio samples or the chromagram must be '",
"'pa... | 180e8e6eb8f958fa6b20b8cba389f7945d508247 |
test | mfcc | Mel-frequency cepstral coefficients (MFCCs)
Parameters
----------
y : np.ndarray [shape=(n,)] or None
audio time series
sr : number > 0 [scalar]
sampling rate of `y`
S : np.ndarray [shape=(d, t)] or None
log-power Mel spectrogram
n_mfcc: int > 0 [scalar]
numbe... | librosa/feature/spectral.py | def mfcc(y=None, sr=22050, S=None, n_mfcc=20, dct_type=2, norm='ortho', **kwargs):
"""Mel-frequency cepstral coefficients (MFCCs)
Parameters
----------
y : np.ndarray [shape=(n,)] or None
audio time series
sr : number > 0 [scalar]
sampling rate of `y`
S : np.ndarray [shape=(d,... | def mfcc(y=None, sr=22050, S=None, n_mfcc=20, dct_type=2, norm='ortho', **kwargs):
"""Mel-frequency cepstral coefficients (MFCCs)
Parameters
----------
y : np.ndarray [shape=(n,)] or None
audio time series
sr : number > 0 [scalar]
sampling rate of `y`
S : np.ndarray [shape=(d,... | [
"Mel",
"-",
"frequency",
"cepstral",
"coefficients",
"(",
"MFCCs",
")"
] | librosa/librosa | python | https://github.com/librosa/librosa/blob/180e8e6eb8f958fa6b20b8cba389f7945d508247/librosa/feature/spectral.py#L1532-L1628 | [
"def",
"mfcc",
"(",
"y",
"=",
"None",
",",
"sr",
"=",
"22050",
",",
"S",
"=",
"None",
",",
"n_mfcc",
"=",
"20",
",",
"dct_type",
"=",
"2",
",",
"norm",
"=",
"'ortho'",
",",
"*",
"*",
"kwargs",
")",
":",
"if",
"S",
"is",
"None",
":",
"S",
"=... | 180e8e6eb8f958fa6b20b8cba389f7945d508247 |
test | melspectrogram | Compute a mel-scaled spectrogram.
If a spectrogram input `S` is provided, then it is mapped directly onto
the mel basis `mel_f` by `mel_f.dot(S)`.
If a time-series input `y, sr` is provided, then its magnitude spectrogram
`S` is first computed, and then mapped onto the mel scale by
`mel_f.dot(S**p... | librosa/feature/spectral.py | def melspectrogram(y=None, sr=22050, S=None, n_fft=2048, hop_length=512,
win_length=None, window='hann', center=True, pad_mode='reflect',
power=2.0, **kwargs):
"""Compute a mel-scaled spectrogram.
If a spectrogram input `S` is provided, then it is mapped directly onto
... | def melspectrogram(y=None, sr=22050, S=None, n_fft=2048, hop_length=512,
win_length=None, window='hann', center=True, pad_mode='reflect',
power=2.0, **kwargs):
"""Compute a mel-scaled spectrogram.
If a spectrogram input `S` is provided, then it is mapped directly onto
... | [
"Compute",
"a",
"mel",
"-",
"scaled",
"spectrogram",
"."
] | librosa/librosa | python | https://github.com/librosa/librosa/blob/180e8e6eb8f958fa6b20b8cba389f7945d508247/librosa/feature/spectral.py#L1631-L1744 | [
"def",
"melspectrogram",
"(",
"y",
"=",
"None",
",",
"sr",
"=",
"22050",
",",
"S",
"=",
"None",
",",
"n_fft",
"=",
"2048",
",",
"hop_length",
"=",
"512",
",",
"win_length",
"=",
"None",
",",
"window",
"=",
"'hann'",
",",
"center",
"=",
"True",
",",... | 180e8e6eb8f958fa6b20b8cba389f7945d508247 |
test | estimate_tuning | Load an audio file and estimate tuning (in cents) | examples/estimate_tuning.py | def estimate_tuning(input_file):
'''Load an audio file and estimate tuning (in cents)'''
print('Loading ', input_file)
y, sr = librosa.load(input_file)
print('Separating harmonic component ... ')
y_harm = librosa.effects.harmonic(y)
print('Estimating tuning ... ')
# Just track the pitches... | def estimate_tuning(input_file):
'''Load an audio file and estimate tuning (in cents)'''
print('Loading ', input_file)
y, sr = librosa.load(input_file)
print('Separating harmonic component ... ')
y_harm = librosa.effects.harmonic(y)
print('Estimating tuning ... ')
# Just track the pitches... | [
"Load",
"an",
"audio",
"file",
"and",
"estimate",
"tuning",
"(",
"in",
"cents",
")"
] | librosa/librosa | python | https://github.com/librosa/librosa/blob/180e8e6eb8f958fa6b20b8cba389f7945d508247/examples/estimate_tuning.py#L15-L28 | [
"def",
"estimate_tuning",
"(",
"input_file",
")",
":",
"print",
"(",
"'Loading '",
",",
"input_file",
")",
"y",
",",
"sr",
"=",
"librosa",
".",
"load",
"(",
"input_file",
")",
"print",
"(",
"'Separating harmonic component ... '",
")",
"y_harm",
"=",
"librosa",... | 180e8e6eb8f958fa6b20b8cba389f7945d508247 |
test | __jaccard | Jaccard similarity between two intervals
Parameters
----------
int_a, int_b : np.ndarrays, shape=(2,)
Returns
-------
Jaccard similarity between intervals | librosa/util/matching.py | def __jaccard(int_a, int_b): # pragma: no cover
'''Jaccard similarity between two intervals
Parameters
----------
int_a, int_b : np.ndarrays, shape=(2,)
Returns
-------
Jaccard similarity between intervals
'''
ends = [int_a[1], int_b[1]]
if ends[1] < ends[0]:
ends.reve... | def __jaccard(int_a, int_b): # pragma: no cover
'''Jaccard similarity between two intervals
Parameters
----------
int_a, int_b : np.ndarrays, shape=(2,)
Returns
-------
Jaccard similarity between intervals
'''
ends = [int_a[1], int_b[1]]
if ends[1] < ends[0]:
ends.reve... | [
"Jaccard",
"similarity",
"between",
"two",
"intervals"
] | librosa/librosa | python | https://github.com/librosa/librosa/blob/180e8e6eb8f958fa6b20b8cba389f7945d508247/librosa/util/matching.py#L17-L45 | [
"def",
"__jaccard",
"(",
"int_a",
",",
"int_b",
")",
":",
"# pragma: no cover",
"ends",
"=",
"[",
"int_a",
"[",
"1",
"]",
",",
"int_b",
"[",
"1",
"]",
"]",
"if",
"ends",
"[",
"1",
"]",
"<",
"ends",
"[",
"0",
"]",
":",
"ends",
".",
"reverse",
"(... | 180e8e6eb8f958fa6b20b8cba389f7945d508247 |
test | __match_interval_overlaps | Find the best Jaccard match from query to candidates | librosa/util/matching.py | def __match_interval_overlaps(query, intervals_to, candidates): # pragma: no cover
'''Find the best Jaccard match from query to candidates'''
best_score = -1
best_idx = -1
for idx in candidates:
score = __jaccard(query, intervals_to[idx])
if score > best_score:
best_score,... | def __match_interval_overlaps(query, intervals_to, candidates): # pragma: no cover
'''Find the best Jaccard match from query to candidates'''
best_score = -1
best_idx = -1
for idx in candidates:
score = __jaccard(query, intervals_to[idx])
if score > best_score:
best_score,... | [
"Find",
"the",
"best",
"Jaccard",
"match",
"from",
"query",
"to",
"candidates"
] | librosa/librosa | python | https://github.com/librosa/librosa/blob/180e8e6eb8f958fa6b20b8cba389f7945d508247/librosa/util/matching.py#L49-L59 | [
"def",
"__match_interval_overlaps",
"(",
"query",
",",
"intervals_to",
",",
"candidates",
")",
":",
"# pragma: no cover",
"best_score",
"=",
"-",
"1",
"best_idx",
"=",
"-",
"1",
"for",
"idx",
"in",
"candidates",
":",
"score",
"=",
"__jaccard",
"(",
"query",
... | 180e8e6eb8f958fa6b20b8cba389f7945d508247 |
test | __match_intervals | Numba-accelerated interval matching algorithm. | librosa/util/matching.py | def __match_intervals(intervals_from, intervals_to, strict=True): # pragma: no cover
'''Numba-accelerated interval matching algorithm.
'''
# sort index of the interval starts
start_index = np.argsort(intervals_to[:, 0])
# sort index of the interval ends
end_index = np.argsort(intervals_to[:, ... | def __match_intervals(intervals_from, intervals_to, strict=True): # pragma: no cover
'''Numba-accelerated interval matching algorithm.
'''
# sort index of the interval starts
start_index = np.argsort(intervals_to[:, 0])
# sort index of the interval ends
end_index = np.argsort(intervals_to[:, ... | [
"Numba",
"-",
"accelerated",
"interval",
"matching",
"algorithm",
"."
] | librosa/librosa | python | https://github.com/librosa/librosa/blob/180e8e6eb8f958fa6b20b8cba389f7945d508247/librosa/util/matching.py#L63-L113 | [
"def",
"__match_intervals",
"(",
"intervals_from",
",",
"intervals_to",
",",
"strict",
"=",
"True",
")",
":",
"# pragma: no cover",
"# sort index of the interval starts",
"start_index",
"=",
"np",
".",
"argsort",
"(",
"intervals_to",
"[",
":",
",",
"0",
"]",
")",
... | 180e8e6eb8f958fa6b20b8cba389f7945d508247 |
test | match_intervals | Match one set of time intervals to another.
This can be useful for tasks such as mapping beat timings
to segments.
Each element `[a, b]` of `intervals_from` is matched to the
element `[c, d]` of `intervals_to` which maximizes the
Jaccard similarity between the intervals:
`max(0, |min(b, d... | librosa/util/matching.py | def match_intervals(intervals_from, intervals_to, strict=True):
'''Match one set of time intervals to another.
This can be useful for tasks such as mapping beat timings
to segments.
Each element `[a, b]` of `intervals_from` is matched to the
element `[c, d]` of `intervals_to` which maximizes the
... | def match_intervals(intervals_from, intervals_to, strict=True):
'''Match one set of time intervals to another.
This can be useful for tasks such as mapping beat timings
to segments.
Each element `[a, b]` of `intervals_from` is matched to the
element `[c, d]` of `intervals_to` which maximizes the
... | [
"Match",
"one",
"set",
"of",
"time",
"intervals",
"to",
"another",
"."
] | librosa/librosa | python | https://github.com/librosa/librosa/blob/180e8e6eb8f958fa6b20b8cba389f7945d508247/librosa/util/matching.py#L116-L210 | [
"def",
"match_intervals",
"(",
"intervals_from",
",",
"intervals_to",
",",
"strict",
"=",
"True",
")",
":",
"if",
"len",
"(",
"intervals_from",
")",
"==",
"0",
"or",
"len",
"(",
"intervals_to",
")",
"==",
"0",
":",
"raise",
"ParameterError",
"(",
"'Attempt... | 180e8e6eb8f958fa6b20b8cba389f7945d508247 |
test | match_events | Match one set of events to another.
This is useful for tasks such as matching beats to the nearest
detected onsets, or frame-aligned events to the nearest zero-crossing.
.. note:: A target event may be matched to multiple source events.
Examples
--------
>>> # Sources are multiples of 7
>... | librosa/util/matching.py | def match_events(events_from, events_to, left=True, right=True):
'''Match one set of events to another.
This is useful for tasks such as matching beats to the nearest
detected onsets, or frame-aligned events to the nearest zero-crossing.
.. note:: A target event may be matched to multiple source event... | def match_events(events_from, events_to, left=True, right=True):
'''Match one set of events to another.
This is useful for tasks such as matching beats to the nearest
detected onsets, or frame-aligned events to the nearest zero-crossing.
.. note:: A target event may be matched to multiple source event... | [
"Match",
"one",
"set",
"of",
"events",
"to",
"another",
"."
] | librosa/librosa | python | https://github.com/librosa/librosa/blob/180e8e6eb8f958fa6b20b8cba389f7945d508247/librosa/util/matching.py#L213-L298 | [
"def",
"match_events",
"(",
"events_from",
",",
"events_to",
",",
"left",
"=",
"True",
",",
"right",
"=",
"True",
")",
":",
"if",
"len",
"(",
"events_from",
")",
"==",
"0",
"or",
"len",
"(",
"events_to",
")",
"==",
"0",
":",
"raise",
"ParameterError",
... | 180e8e6eb8f958fa6b20b8cba389f7945d508247 |
test | salience | Harmonic salience function.
Parameters
----------
S : np.ndarray [shape=(d, n)]
input time frequency magnitude representation (stft, ifgram, etc).
Must be real-valued and non-negative.
freqs : np.ndarray, shape=(S.shape[axis])
The frequency values corresponding to S's elements a... | librosa/core/harmonic.py | def salience(S, freqs, h_range, weights=None, aggregate=None,
filter_peaks=True, fill_value=np.nan, kind='linear', axis=0):
"""Harmonic salience function.
Parameters
----------
S : np.ndarray [shape=(d, n)]
input time frequency magnitude representation (stft, ifgram, etc).
... | def salience(S, freqs, h_range, weights=None, aggregate=None,
filter_peaks=True, fill_value=np.nan, kind='linear', axis=0):
"""Harmonic salience function.
Parameters
----------
S : np.ndarray [shape=(d, n)]
input time frequency magnitude representation (stft, ifgram, etc).
... | [
"Harmonic",
"salience",
"function",
"."
] | librosa/librosa | python | https://github.com/librosa/librosa/blob/180e8e6eb8f958fa6b20b8cba389f7945d508247/librosa/core/harmonic.py#L13-L104 | [
"def",
"salience",
"(",
"S",
",",
"freqs",
",",
"h_range",
",",
"weights",
"=",
"None",
",",
"aggregate",
"=",
"None",
",",
"filter_peaks",
"=",
"True",
",",
"fill_value",
"=",
"np",
".",
"nan",
",",
"kind",
"=",
"'linear'",
",",
"axis",
"=",
"0",
... | 180e8e6eb8f958fa6b20b8cba389f7945d508247 |
test | interp_harmonics | Compute the energy at harmonics of time-frequency representation.
Given a frequency-based energy representation such as a spectrogram
or tempogram, this function computes the energy at the chosen harmonics
of the frequency axis. (See examples below.)
The resulting harmonic array can then be used as in... | librosa/core/harmonic.py | def interp_harmonics(x, freqs, h_range, kind='linear', fill_value=0, axis=0):
'''Compute the energy at harmonics of time-frequency representation.
Given a frequency-based energy representation such as a spectrogram
or tempogram, this function computes the energy at the chosen harmonics
of the frequency... | def interp_harmonics(x, freqs, h_range, kind='linear', fill_value=0, axis=0):
'''Compute the energy at harmonics of time-frequency representation.
Given a frequency-based energy representation such as a spectrogram
or tempogram, this function computes the energy at the chosen harmonics
of the frequency... | [
"Compute",
"the",
"energy",
"at",
"harmonics",
"of",
"time",
"-",
"frequency",
"representation",
"."
] | librosa/librosa | python | https://github.com/librosa/librosa/blob/180e8e6eb8f958fa6b20b8cba389f7945d508247/librosa/core/harmonic.py#L107-L218 | [
"def",
"interp_harmonics",
"(",
"x",
",",
"freqs",
",",
"h_range",
",",
"kind",
"=",
"'linear'",
",",
"fill_value",
"=",
"0",
",",
"axis",
"=",
"0",
")",
":",
"# X_out will be the same shape as X, plus a leading",
"# axis that has length = len(h_range)",
"out_shape",
... | 180e8e6eb8f958fa6b20b8cba389f7945d508247 |
test | harmonics_1d | Populate a harmonic tensor from a time-frequency representation.
Parameters
----------
harmonic_out : np.ndarray, shape=(len(h_range), X.shape)
The output array to store harmonics
X : np.ndarray
The input energy
freqs : np.ndarray, shape=(x.shape[axis])
The frequency value... | librosa/core/harmonic.py | def harmonics_1d(harmonic_out, x, freqs, h_range, kind='linear',
fill_value=0, axis=0):
'''Populate a harmonic tensor from a time-frequency representation.
Parameters
----------
harmonic_out : np.ndarray, shape=(len(h_range), X.shape)
The output array to store harmonics
X ... | def harmonics_1d(harmonic_out, x, freqs, h_range, kind='linear',
fill_value=0, axis=0):
'''Populate a harmonic tensor from a time-frequency representation.
Parameters
----------
harmonic_out : np.ndarray, shape=(len(h_range), X.shape)
The output array to store harmonics
X ... | [
"Populate",
"a",
"harmonic",
"tensor",
"from",
"a",
"time",
"-",
"frequency",
"representation",
"."
] | librosa/librosa | python | https://github.com/librosa/librosa/blob/180e8e6eb8f958fa6b20b8cba389f7945d508247/librosa/core/harmonic.py#L221-L328 | [
"def",
"harmonics_1d",
"(",
"harmonic_out",
",",
"x",
",",
"freqs",
",",
"h_range",
",",
"kind",
"=",
"'linear'",
",",
"fill_value",
"=",
"0",
",",
"axis",
"=",
"0",
")",
":",
"# Note: this only works for fixed-grid, 1d interpolation",
"f_interp",
"=",
"scipy",
... | 180e8e6eb8f958fa6b20b8cba389f7945d508247 |
test | harmonics_2d | Populate a harmonic tensor from a time-frequency representation with
time-varying frequencies.
Parameters
----------
harmonic_out : np.ndarray
The output array to store harmonics
x : np.ndarray
The input energy
freqs : np.ndarray, shape=x.shape
The frequency values cor... | librosa/core/harmonic.py | def harmonics_2d(harmonic_out, x, freqs, h_range, kind='linear', fill_value=0,
axis=0):
'''Populate a harmonic tensor from a time-frequency representation with
time-varying frequencies.
Parameters
----------
harmonic_out : np.ndarray
The output array to store harmonics
... | def harmonics_2d(harmonic_out, x, freqs, h_range, kind='linear', fill_value=0,
axis=0):
'''Populate a harmonic tensor from a time-frequency representation with
time-varying frequencies.
Parameters
----------
harmonic_out : np.ndarray
The output array to store harmonics
... | [
"Populate",
"a",
"harmonic",
"tensor",
"from",
"a",
"time",
"-",
"frequency",
"representation",
"with",
"time",
"-",
"varying",
"frequencies",
"."
] | librosa/librosa | python | https://github.com/librosa/librosa/blob/180e8e6eb8f958fa6b20b8cba389f7945d508247/librosa/core/harmonic.py#L331-L382 | [
"def",
"harmonics_2d",
"(",
"harmonic_out",
",",
"x",
",",
"freqs",
",",
"h_range",
",",
"kind",
"=",
"'linear'",
",",
"fill_value",
"=",
"0",
",",
"axis",
"=",
"0",
")",
":",
"idx_in",
"=",
"[",
"slice",
"(",
"None",
")",
"]",
"*",
"x",
".",
"nd... | 180e8e6eb8f958fa6b20b8cba389f7945d508247 |
test | load | Load an audio file as a floating point time series.
Audio will be automatically resampled to the given rate
(default `sr=22050`).
To preserve the native sampling rate of the file, use `sr=None`.
Parameters
----------
path : string, int, or file-like object
path to the input file.
... | librosa/core/audio.py | def load(path, sr=22050, mono=True, offset=0.0, duration=None,
dtype=np.float32, res_type='kaiser_best'):
"""Load an audio file as a floating point time series.
Audio will be automatically resampled to the given rate
(default `sr=22050`).
To preserve the native sampling rate of the file, use ... | def load(path, sr=22050, mono=True, offset=0.0, duration=None,
dtype=np.float32, res_type='kaiser_best'):
"""Load an audio file as a floating point time series.
Audio will be automatically resampled to the given rate
(default `sr=22050`).
To preserve the native sampling rate of the file, use ... | [
"Load",
"an",
"audio",
"file",
"as",
"a",
"floating",
"point",
"time",
"series",
"."
] | librosa/librosa | python | https://github.com/librosa/librosa/blob/180e8e6eb8f958fa6b20b8cba389f7945d508247/librosa/core/audio.py#L32-L152 | [
"def",
"load",
"(",
"path",
",",
"sr",
"=",
"22050",
",",
"mono",
"=",
"True",
",",
"offset",
"=",
"0.0",
",",
"duration",
"=",
"None",
",",
"dtype",
"=",
"np",
".",
"float32",
",",
"res_type",
"=",
"'kaiser_best'",
")",
":",
"try",
":",
"with",
... | 180e8e6eb8f958fa6b20b8cba389f7945d508247 |
test | __audioread_load | Load an audio buffer using audioread.
This loads one block at a time, and then concatenates the results. | librosa/core/audio.py | def __audioread_load(path, offset, duration, dtype):
'''Load an audio buffer using audioread.
This loads one block at a time, and then concatenates the results.
'''
y = []
with audioread.audio_open(path) as input_file:
sr_native = input_file.samplerate
n_channels = input_file.chann... | def __audioread_load(path, offset, duration, dtype):
'''Load an audio buffer using audioread.
This loads one block at a time, and then concatenates the results.
'''
y = []
with audioread.audio_open(path) as input_file:
sr_native = input_file.samplerate
n_channels = input_file.chann... | [
"Load",
"an",
"audio",
"buffer",
"using",
"audioread",
"."
] | librosa/librosa | python | https://github.com/librosa/librosa/blob/180e8e6eb8f958fa6b20b8cba389f7945d508247/librosa/core/audio.py#L155-L208 | [
"def",
"__audioread_load",
"(",
"path",
",",
"offset",
",",
"duration",
",",
"dtype",
")",
":",
"y",
"=",
"[",
"]",
"with",
"audioread",
".",
"audio_open",
"(",
"path",
")",
"as",
"input_file",
":",
"sr_native",
"=",
"input_file",
".",
"samplerate",
"n_c... | 180e8e6eb8f958fa6b20b8cba389f7945d508247 |
test | to_mono | Force an audio signal down to mono.
Parameters
----------
y : np.ndarray [shape=(2,n) or shape=(n,)]
audio time series, either stereo or mono
Returns
-------
y_mono : np.ndarray [shape=(n,)]
`y` as a monophonic time-series
Notes
-----
This function caches at level ... | librosa/core/audio.py | def to_mono(y):
'''Force an audio signal down to mono.
Parameters
----------
y : np.ndarray [shape=(2,n) or shape=(n,)]
audio time series, either stereo or mono
Returns
-------
y_mono : np.ndarray [shape=(n,)]
`y` as a monophonic time-series
Notes
-----
This fu... | def to_mono(y):
'''Force an audio signal down to mono.
Parameters
----------
y : np.ndarray [shape=(2,n) or shape=(n,)]
audio time series, either stereo or mono
Returns
-------
y_mono : np.ndarray [shape=(n,)]
`y` as a monophonic time-series
Notes
-----
This fu... | [
"Force",
"an",
"audio",
"signal",
"down",
"to",
"mono",
"."
] | librosa/librosa | python | https://github.com/librosa/librosa/blob/180e8e6eb8f958fa6b20b8cba389f7945d508247/librosa/core/audio.py#L212-L246 | [
"def",
"to_mono",
"(",
"y",
")",
":",
"# Validate the buffer. Stereo is ok here.",
"util",
".",
"valid_audio",
"(",
"y",
",",
"mono",
"=",
"False",
")",
"if",
"y",
".",
"ndim",
">",
"1",
":",
"y",
"=",
"np",
".",
"mean",
"(",
"y",
",",
"axis",
"=",
... | 180e8e6eb8f958fa6b20b8cba389f7945d508247 |
test | resample | Resample a time series from orig_sr to target_sr
Parameters
----------
y : np.ndarray [shape=(n,) or shape=(2, n)]
audio time series. Can be mono or stereo.
orig_sr : number > 0 [scalar]
original sampling rate of `y`
target_sr : number > 0 [scalar]
target sampling rate
... | librosa/core/audio.py | def resample(y, orig_sr, target_sr, res_type='kaiser_best', fix=True, scale=False, **kwargs):
"""Resample a time series from orig_sr to target_sr
Parameters
----------
y : np.ndarray [shape=(n,) or shape=(2, n)]
audio time series. Can be mono or stereo.
orig_sr : number > 0 [scalar]
... | def resample(y, orig_sr, target_sr, res_type='kaiser_best', fix=True, scale=False, **kwargs):
"""Resample a time series from orig_sr to target_sr
Parameters
----------
y : np.ndarray [shape=(n,) or shape=(2, n)]
audio time series. Can be mono or stereo.
orig_sr : number > 0 [scalar]
... | [
"Resample",
"a",
"time",
"series",
"from",
"orig_sr",
"to",
"target_sr"
] | librosa/librosa | python | https://github.com/librosa/librosa/blob/180e8e6eb8f958fa6b20b8cba389f7945d508247/librosa/core/audio.py#L250-L355 | [
"def",
"resample",
"(",
"y",
",",
"orig_sr",
",",
"target_sr",
",",
"res_type",
"=",
"'kaiser_best'",
",",
"fix",
"=",
"True",
",",
"scale",
"=",
"False",
",",
"*",
"*",
"kwargs",
")",
":",
"# First, validate the audio buffer",
"util",
".",
"valid_audio",
... | 180e8e6eb8f958fa6b20b8cba389f7945d508247 |
test | get_duration | Compute the duration (in seconds) of an audio time series,
feature matrix, or filename.
Examples
--------
>>> # Load the example audio file
>>> y, sr = librosa.load(librosa.util.example_audio_file())
>>> librosa.get_duration(y=y, sr=sr)
61.45886621315193
>>> # Or directly from an audio... | librosa/core/audio.py | def get_duration(y=None, sr=22050, S=None, n_fft=2048, hop_length=512,
center=True, filename=None):
"""Compute the duration (in seconds) of an audio time series,
feature matrix, or filename.
Examples
--------
>>> # Load the example audio file
>>> y, sr = librosa.load(librosa.ut... | def get_duration(y=None, sr=22050, S=None, n_fft=2048, hop_length=512,
center=True, filename=None):
"""Compute the duration (in seconds) of an audio time series,
feature matrix, or filename.
Examples
--------
>>> # Load the example audio file
>>> y, sr = librosa.load(librosa.ut... | [
"Compute",
"the",
"duration",
"(",
"in",
"seconds",
")",
"of",
"an",
"audio",
"time",
"series",
"feature",
"matrix",
"or",
"filename",
"."
] | librosa/librosa | python | https://github.com/librosa/librosa/blob/180e8e6eb8f958fa6b20b8cba389f7945d508247/librosa/core/audio.py#L358-L462 | [
"def",
"get_duration",
"(",
"y",
"=",
"None",
",",
"sr",
"=",
"22050",
",",
"S",
"=",
"None",
",",
"n_fft",
"=",
"2048",
",",
"hop_length",
"=",
"512",
",",
"center",
"=",
"True",
",",
"filename",
"=",
"None",
")",
":",
"if",
"filename",
"is",
"n... | 180e8e6eb8f958fa6b20b8cba389f7945d508247 |
test | autocorrelate | Bounded auto-correlation
Parameters
----------
y : np.ndarray
array to autocorrelate
max_size : int > 0 or None
maximum correlation lag.
If unspecified, defaults to `y.shape[axis]` (unbounded)
axis : int
The axis along which to autocorrelate.
By default, t... | librosa/core/audio.py | def autocorrelate(y, max_size=None, axis=-1):
"""Bounded auto-correlation
Parameters
----------
y : np.ndarray
array to autocorrelate
max_size : int > 0 or None
maximum correlation lag.
If unspecified, defaults to `y.shape[axis]` (unbounded)
axis : int
The axi... | def autocorrelate(y, max_size=None, axis=-1):
"""Bounded auto-correlation
Parameters
----------
y : np.ndarray
array to autocorrelate
max_size : int > 0 or None
maximum correlation lag.
If unspecified, defaults to `y.shape[axis]` (unbounded)
axis : int
The axi... | [
"Bounded",
"auto",
"-",
"correlation"
] | librosa/librosa | python | https://github.com/librosa/librosa/blob/180e8e6eb8f958fa6b20b8cba389f7945d508247/librosa/core/audio.py#L465-L533 | [
"def",
"autocorrelate",
"(",
"y",
",",
"max_size",
"=",
"None",
",",
"axis",
"=",
"-",
"1",
")",
":",
"if",
"max_size",
"is",
"None",
":",
"max_size",
"=",
"y",
".",
"shape",
"[",
"axis",
"]",
"max_size",
"=",
"int",
"(",
"min",
"(",
"max_size",
... | 180e8e6eb8f958fa6b20b8cba389f7945d508247 |
test | lpc | Linear Prediction Coefficients via Burg's method
This function applies Burg's method to estimate coefficients of a linear
filter on `y` of order `order`. Burg's method is an extension to the
Yule-Walker approach, which are both sometimes referred to as LPC parameter
estimation by autocorrelation.
... | librosa/core/audio.py | def lpc(y, order):
"""Linear Prediction Coefficients via Burg's method
This function applies Burg's method to estimate coefficients of a linear
filter on `y` of order `order`. Burg's method is an extension to the
Yule-Walker approach, which are both sometimes referred to as LPC parameter
estimatio... | def lpc(y, order):
"""Linear Prediction Coefficients via Burg's method
This function applies Burg's method to estimate coefficients of a linear
filter on `y` of order `order`. Burg's method is an extension to the
Yule-Walker approach, which are both sometimes referred to as LPC parameter
estimatio... | [
"Linear",
"Prediction",
"Coefficients",
"via",
"Burg",
"s",
"method"
] | librosa/librosa | python | https://github.com/librosa/librosa/blob/180e8e6eb8f958fa6b20b8cba389f7945d508247/librosa/core/audio.py#L536-L607 | [
"def",
"lpc",
"(",
"y",
",",
"order",
")",
":",
"if",
"not",
"isinstance",
"(",
"order",
",",
"int",
")",
"or",
"order",
"<",
"1",
":",
"raise",
"ParameterError",
"(",
"\"order must be an integer > 0\"",
")",
"util",
".",
"valid_audio",
"(",
"y",
",",
... | 180e8e6eb8f958fa6b20b8cba389f7945d508247 |
test | zero_crossings | Find the zero-crossings of a signal `y`: indices `i` such that
`sign(y[i]) != sign(y[j])`.
If `y` is multi-dimensional, then zero-crossings are computed along
the specified `axis`.
Parameters
----------
y : np.ndarray
The input array
threshold : float > 0 or None
If speci... | librosa/core/audio.py | def zero_crossings(y, threshold=1e-10, ref_magnitude=None, pad=True,
zero_pos=True, axis=-1):
'''Find the zero-crossings of a signal `y`: indices `i` such that
`sign(y[i]) != sign(y[j])`.
If `y` is multi-dimensional, then zero-crossings are computed along
the specified `axis`.
... | def zero_crossings(y, threshold=1e-10, ref_magnitude=None, pad=True,
zero_pos=True, axis=-1):
'''Find the zero-crossings of a signal `y`: indices `i` such that
`sign(y[i]) != sign(y[j])`.
If `y` is multi-dimensional, then zero-crossings are computed along
the specified `axis`.
... | [
"Find",
"the",
"zero",
"-",
"crossings",
"of",
"a",
"signal",
"y",
":",
"indices",
"i",
"such",
"that",
"sign",
"(",
"y",
"[",
"i",
"]",
")",
"!",
"=",
"sign",
"(",
"y",
"[",
"j",
"]",
")",
"."
] | librosa/librosa | python | https://github.com/librosa/librosa/blob/180e8e6eb8f958fa6b20b8cba389f7945d508247/librosa/core/audio.py#L694-L815 | [
"def",
"zero_crossings",
"(",
"y",
",",
"threshold",
"=",
"1e-10",
",",
"ref_magnitude",
"=",
"None",
",",
"pad",
"=",
"True",
",",
"zero_pos",
"=",
"True",
",",
"axis",
"=",
"-",
"1",
")",
":",
"# Clip within the threshold",
"if",
"threshold",
"is",
"No... | 180e8e6eb8f958fa6b20b8cba389f7945d508247 |
test | clicks | Returns a signal with the signal `click` placed at each specified time
Parameters
----------
times : np.ndarray or None
times to place clicks, in seconds
frames : np.ndarray or None
frame indices to place clicks
sr : number > 0
desired sampling rate of the output signal
... | librosa/core/audio.py | def clicks(times=None, frames=None, sr=22050, hop_length=512,
click_freq=1000.0, click_duration=0.1, click=None, length=None):
"""Returns a signal with the signal `click` placed at each specified time
Parameters
----------
times : np.ndarray or None
times to place clicks, in seconds
... | def clicks(times=None, frames=None, sr=22050, hop_length=512,
click_freq=1000.0, click_duration=0.1, click=None, length=None):
"""Returns a signal with the signal `click` placed at each specified time
Parameters
----------
times : np.ndarray or None
times to place clicks, in seconds
... | [
"Returns",
"a",
"signal",
"with",
"the",
"signal",
"click",
"placed",
"at",
"each",
"specified",
"time"
] | librosa/librosa | python | https://github.com/librosa/librosa/blob/180e8e6eb8f958fa6b20b8cba389f7945d508247/librosa/core/audio.py#L818-L949 | [
"def",
"clicks",
"(",
"times",
"=",
"None",
",",
"frames",
"=",
"None",
",",
"sr",
"=",
"22050",
",",
"hop_length",
"=",
"512",
",",
"click_freq",
"=",
"1000.0",
",",
"click_duration",
"=",
"0.1",
",",
"click",
"=",
"None",
",",
"length",
"=",
"None"... | 180e8e6eb8f958fa6b20b8cba389f7945d508247 |
test | tone | Returns a pure tone signal. The signal generated is a cosine wave.
Parameters
----------
frequency : float > 0
frequency
sr : number > 0
desired sampling rate of the output signal
length : int > 0
desired number of samples in the output signal. When both `duration` and `le... | librosa/core/audio.py | def tone(frequency, sr=22050, length=None, duration=None, phi=None):
"""Returns a pure tone signal. The signal generated is a cosine wave.
Parameters
----------
frequency : float > 0
frequency
sr : number > 0
desired sampling rate of the output signal
length : int > 0
... | def tone(frequency, sr=22050, length=None, duration=None, phi=None):
"""Returns a pure tone signal. The signal generated is a cosine wave.
Parameters
----------
frequency : float > 0
frequency
sr : number > 0
desired sampling rate of the output signal
length : int > 0
... | [
"Returns",
"a",
"pure",
"tone",
"signal",
".",
"The",
"signal",
"generated",
"is",
"a",
"cosine",
"wave",
"."
] | librosa/librosa | python | https://github.com/librosa/librosa/blob/180e8e6eb8f958fa6b20b8cba389f7945d508247/librosa/core/audio.py#L952-L1017 | [
"def",
"tone",
"(",
"frequency",
",",
"sr",
"=",
"22050",
",",
"length",
"=",
"None",
",",
"duration",
"=",
"None",
",",
"phi",
"=",
"None",
")",
":",
"if",
"frequency",
"is",
"None",
":",
"raise",
"ParameterError",
"(",
"'\"frequency\" must be provided'",... | 180e8e6eb8f958fa6b20b8cba389f7945d508247 |
test | chirp | Returns a chirp signal that goes from frequency `fmin` to frequency `fmax`
Parameters
----------
fmin : float > 0
initial frequency
fmax : float > 0
final frequency
sr : number > 0
desired sampling rate of the output signal
length : int > 0
desired number of s... | librosa/core/audio.py | def chirp(fmin, fmax, sr=22050, length=None, duration=None, linear=False, phi=None):
"""Returns a chirp signal that goes from frequency `fmin` to frequency `fmax`
Parameters
----------
fmin : float > 0
initial frequency
fmax : float > 0
final frequency
sr : number > 0
... | def chirp(fmin, fmax, sr=22050, length=None, duration=None, linear=False, phi=None):
"""Returns a chirp signal that goes from frequency `fmin` to frequency `fmax`
Parameters
----------
fmin : float > 0
initial frequency
fmax : float > 0
final frequency
sr : number > 0
... | [
"Returns",
"a",
"chirp",
"signal",
"that",
"goes",
"from",
"frequency",
"fmin",
"to",
"frequency",
"fmax"
] | librosa/librosa | python | https://github.com/librosa/librosa/blob/180e8e6eb8f958fa6b20b8cba389f7945d508247/librosa/core/audio.py#L1020-L1118 | [
"def",
"chirp",
"(",
"fmin",
",",
"fmax",
",",
"sr",
"=",
"22050",
",",
"length",
"=",
"None",
",",
"duration",
"=",
"None",
",",
"linear",
"=",
"False",
",",
"phi",
"=",
"None",
")",
":",
"if",
"fmin",
"is",
"None",
"or",
"fmax",
"is",
"None",
... | 180e8e6eb8f958fa6b20b8cba389f7945d508247 |
test | tempogram | Compute the tempogram: local autocorrelation of the onset strength envelope. [1]_
.. [1] Grosche, Peter, Meinard Müller, and Frank Kurth.
"Cyclic tempogram - A mid-level tempo representation for music signals."
ICASSP, 2010.
Parameters
----------
y : np.ndarray [shape=(n,)] or None
... | librosa/feature/rhythm.py | def tempogram(y=None, sr=22050, onset_envelope=None, hop_length=512,
win_length=384, center=True, window='hann', norm=np.inf):
'''Compute the tempogram: local autocorrelation of the onset strength envelope. [1]_
.. [1] Grosche, Peter, Meinard Müller, and Frank Kurth.
"Cyclic tempogram - A... | def tempogram(y=None, sr=22050, onset_envelope=None, hop_length=512,
win_length=384, center=True, window='hann', norm=np.inf):
'''Compute the tempogram: local autocorrelation of the onset strength envelope. [1]_
.. [1] Grosche, Peter, Meinard Müller, and Frank Kurth.
"Cyclic tempogram - A... | [
"Compute",
"the",
"tempogram",
":",
"local",
"autocorrelation",
"of",
"the",
"onset",
"strength",
"envelope",
".",
"[",
"1",
"]",
"_"
] | librosa/librosa | python | https://github.com/librosa/librosa/blob/180e8e6eb8f958fa6b20b8cba389f7945d508247/librosa/feature/rhythm.py#L18-L178 | [
"def",
"tempogram",
"(",
"y",
"=",
"None",
",",
"sr",
"=",
"22050",
",",
"onset_envelope",
"=",
"None",
",",
"hop_length",
"=",
"512",
",",
"win_length",
"=",
"384",
",",
"center",
"=",
"True",
",",
"window",
"=",
"'hann'",
",",
"norm",
"=",
"np",
... | 180e8e6eb8f958fa6b20b8cba389f7945d508247 |
test | find_files | Get a sorted list of (audio) files in a directory or directory sub-tree.
Examples
--------
>>> # Get all audio files in a directory sub-tree
>>> files = librosa.util.find_files('~/Music')
>>> # Look only within a specific directory, not the sub-tree
>>> files = librosa.util.find_files('~/Music... | librosa/util/files.py | def find_files(directory, ext=None, recurse=True, case_sensitive=False,
limit=None, offset=0):
'''Get a sorted list of (audio) files in a directory or directory sub-tree.
Examples
--------
>>> # Get all audio files in a directory sub-tree
>>> files = librosa.util.find_files('~/Music'... | def find_files(directory, ext=None, recurse=True, case_sensitive=False,
limit=None, offset=0):
'''Get a sorted list of (audio) files in a directory or directory sub-tree.
Examples
--------
>>> # Get all audio files in a directory sub-tree
>>> files = librosa.util.find_files('~/Music'... | [
"Get",
"a",
"sorted",
"list",
"of",
"(",
"audio",
")",
"files",
"in",
"a",
"directory",
"or",
"directory",
"sub",
"-",
"tree",
"."
] | librosa/librosa | python | https://github.com/librosa/librosa/blob/180e8e6eb8f958fa6b20b8cba389f7945d508247/librosa/util/files.py#L49-L136 | [
"def",
"find_files",
"(",
"directory",
",",
"ext",
"=",
"None",
",",
"recurse",
"=",
"True",
",",
"case_sensitive",
"=",
"False",
",",
"limit",
"=",
"None",
",",
"offset",
"=",
"0",
")",
":",
"if",
"ext",
"is",
"None",
":",
"ext",
"=",
"[",
"'aac'"... | 180e8e6eb8f958fa6b20b8cba389f7945d508247 |
test | __get_files | Helper function to get files in a single directory | librosa/util/files.py | def __get_files(dir_name, extensions):
'''Helper function to get files in a single directory'''
# Expand out the directory
dir_name = os.path.abspath(os.path.expanduser(dir_name))
myfiles = set()
for sub_ext in extensions:
globstr = os.path.join(dir_name, '*' + os.path.extsep + sub_ext)
... | def __get_files(dir_name, extensions):
'''Helper function to get files in a single directory'''
# Expand out the directory
dir_name = os.path.abspath(os.path.expanduser(dir_name))
myfiles = set()
for sub_ext in extensions:
globstr = os.path.join(dir_name, '*' + os.path.extsep + sub_ext)
... | [
"Helper",
"function",
"to",
"get",
"files",
"in",
"a",
"single",
"directory"
] | librosa/librosa | python | https://github.com/librosa/librosa/blob/180e8e6eb8f958fa6b20b8cba389f7945d508247/librosa/util/files.py#L139-L151 | [
"def",
"__get_files",
"(",
"dir_name",
",",
"extensions",
")",
":",
"# Expand out the directory",
"dir_name",
"=",
"os",
".",
"path",
".",
"abspath",
"(",
"os",
".",
"path",
".",
"expanduser",
"(",
"dir_name",
")",
")",
"myfiles",
"=",
"set",
"(",
")",
"... | 180e8e6eb8f958fa6b20b8cba389f7945d508247 |
test | stretch_demo | Phase-vocoder time stretch demo function.
:parameters:
- input_file : str
path to input audio
- output_file : str
path to save output (wav)
- speed : float > 0
speed up by this factor | examples/time_stretch.py | def stretch_demo(input_file, output_file, speed):
'''Phase-vocoder time stretch demo function.
:parameters:
- input_file : str
path to input audio
- output_file : str
path to save output (wav)
- speed : float > 0
speed up by this factor
'''
# 1. Load the... | def stretch_demo(input_file, output_file, speed):
'''Phase-vocoder time stretch demo function.
:parameters:
- input_file : str
path to input audio
- output_file : str
path to save output (wav)
- speed : float > 0
speed up by this factor
'''
# 1. Load the... | [
"Phase",
"-",
"vocoder",
"time",
"stretch",
"demo",
"function",
"."
] | librosa/librosa | python | https://github.com/librosa/librosa/blob/180e8e6eb8f958fa6b20b8cba389f7945d508247/examples/time_stretch.py#L13-L36 | [
"def",
"stretch_demo",
"(",
"input_file",
",",
"output_file",
",",
"speed",
")",
":",
"# 1. Load the wav file, resample",
"print",
"(",
"'Loading '",
",",
"input_file",
")",
"y",
",",
"sr",
"=",
"librosa",
".",
"load",
"(",
"input_file",
")",
"# 2. Time-stretch ... | 180e8e6eb8f958fa6b20b8cba389f7945d508247 |
test | process_arguments | Argparse function to get the program parameters | examples/time_stretch.py | def process_arguments(args):
'''Argparse function to get the program parameters'''
parser = argparse.ArgumentParser(description='Time stretching example')
parser.add_argument('input_file',
action='store',
help='path to the input file (wav, mp3, etc)')
p... | def process_arguments(args):
'''Argparse function to get the program parameters'''
parser = argparse.ArgumentParser(description='Time stretching example')
parser.add_argument('input_file',
action='store',
help='path to the input file (wav, mp3, etc)')
p... | [
"Argparse",
"function",
"to",
"get",
"the",
"program",
"parameters"
] | librosa/librosa | python | https://github.com/librosa/librosa/blob/180e8e6eb8f958fa6b20b8cba389f7945d508247/examples/time_stretch.py#L39-L59 | [
"def",
"process_arguments",
"(",
"args",
")",
":",
"parser",
"=",
"argparse",
".",
"ArgumentParser",
"(",
"description",
"=",
"'Time stretching example'",
")",
"parser",
".",
"add_argument",
"(",
"'input_file'",
",",
"action",
"=",
"'store'",
",",
"help",
"=",
... | 180e8e6eb8f958fa6b20b8cba389f7945d508247 |
test | hpss_demo | HPSS demo function.
:parameters:
- input_file : str
path to input audio
- output_harmonic : str
path to save output harmonic (wav)
- output_percussive : str
path to save output harmonic (wav) | examples/hpss.py | def hpss_demo(input_file, output_harmonic, output_percussive):
'''HPSS demo function.
:parameters:
- input_file : str
path to input audio
- output_harmonic : str
path to save output harmonic (wav)
- output_percussive : str
path to save output harmonic (wav)
'... | def hpss_demo(input_file, output_harmonic, output_percussive):
'''HPSS demo function.
:parameters:
- input_file : str
path to input audio
- output_harmonic : str
path to save output harmonic (wav)
- output_percussive : str
path to save output harmonic (wav)
'... | [
"HPSS",
"demo",
"function",
"."
] | librosa/librosa | python | https://github.com/librosa/librosa/blob/180e8e6eb8f958fa6b20b8cba389f7945d508247/examples/hpss.py#L13-L39 | [
"def",
"hpss_demo",
"(",
"input_file",
",",
"output_harmonic",
",",
"output_percussive",
")",
":",
"# 1. Load the wav file, resample",
"print",
"(",
"'Loading '",
",",
"input_file",
")",
"y",
",",
"sr",
"=",
"librosa",
".",
"load",
"(",
"input_file",
")",
"# Sep... | 180e8e6eb8f958fa6b20b8cba389f7945d508247 |
test | beat_track | r'''Dynamic programming beat tracker.
Beats are detected in three stages, following the method of [1]_:
1. Measure onset strength
2. Estimate tempo from onset correlation
3. Pick peaks in onset strength approximately consistent with estimated
tempo
.. [1] Ellis, Daniel PW. "Beat tra... | librosa/beat.py | def beat_track(y=None, sr=22050, onset_envelope=None, hop_length=512,
start_bpm=120.0, tightness=100, trim=True, bpm=None,
units='frames'):
r'''Dynamic programming beat tracker.
Beats are detected in three stages, following the method of [1]_:
1. Measure onset strength
... | def beat_track(y=None, sr=22050, onset_envelope=None, hop_length=512,
start_bpm=120.0, tightness=100, trim=True, bpm=None,
units='frames'):
r'''Dynamic programming beat tracker.
Beats are detected in three stages, following the method of [1]_:
1. Measure onset strength
... | [
"r",
"Dynamic",
"programming",
"beat",
"tracker",
"."
] | librosa/librosa | python | https://github.com/librosa/librosa/blob/180e8e6eb8f958fa6b20b8cba389f7945d508247/librosa/beat.py#L26-L199 | [
"def",
"beat_track",
"(",
"y",
"=",
"None",
",",
"sr",
"=",
"22050",
",",
"onset_envelope",
"=",
"None",
",",
"hop_length",
"=",
"512",
",",
"start_bpm",
"=",
"120.0",
",",
"tightness",
"=",
"100",
",",
"trim",
"=",
"True",
",",
"bpm",
"=",
"None",
... | 180e8e6eb8f958fa6b20b8cba389f7945d508247 |
test | tempo | Estimate the tempo (beats per minute)
Parameters
----------
y : np.ndarray [shape=(n,)] or None
audio time series
sr : number > 0 [scalar]
sampling rate of the time series
onset_envelope : np.ndarray [shape=(n,)]
pre-computed onset strength envelope
hop_length : in... | librosa/beat.py | def tempo(y=None, sr=22050, onset_envelope=None, hop_length=512, start_bpm=120,
std_bpm=1.0, ac_size=8.0, max_tempo=320.0, aggregate=np.mean):
"""Estimate the tempo (beats per minute)
Parameters
----------
y : np.ndarray [shape=(n,)] or None
audio time series
sr : number > 0 [sca... | def tempo(y=None, sr=22050, onset_envelope=None, hop_length=512, start_bpm=120,
std_bpm=1.0, ac_size=8.0, max_tempo=320.0, aggregate=np.mean):
"""Estimate the tempo (beats per minute)
Parameters
----------
y : np.ndarray [shape=(n,)] or None
audio time series
sr : number > 0 [sca... | [
"Estimate",
"the",
"tempo",
"(",
"beats",
"per",
"minute",
")"
] | librosa/librosa | python | https://github.com/librosa/librosa/blob/180e8e6eb8f958fa6b20b8cba389f7945d508247/librosa/beat.py#L203-L340 | [
"def",
"tempo",
"(",
"y",
"=",
"None",
",",
"sr",
"=",
"22050",
",",
"onset_envelope",
"=",
"None",
",",
"hop_length",
"=",
"512",
",",
"start_bpm",
"=",
"120",
",",
"std_bpm",
"=",
"1.0",
",",
"ac_size",
"=",
"8.0",
",",
"max_tempo",
"=",
"320.0",
... | 180e8e6eb8f958fa6b20b8cba389f7945d508247 |
test | __beat_tracker | Internal function that tracks beats in an onset strength envelope.
Parameters
----------
onset_envelope : np.ndarray [shape=(n,)]
onset strength envelope
bpm : float [scalar]
tempo estimate
fft_res : float [scalar]
resolution of the fft (sr / hop_length)
tightness: f... | librosa/beat.py | def __beat_tracker(onset_envelope, bpm, fft_res, tightness, trim):
"""Internal function that tracks beats in an onset strength envelope.
Parameters
----------
onset_envelope : np.ndarray [shape=(n,)]
onset strength envelope
bpm : float [scalar]
tempo estimate
fft_res : float ... | def __beat_tracker(onset_envelope, bpm, fft_res, tightness, trim):
"""Internal function that tracks beats in an onset strength envelope.
Parameters
----------
onset_envelope : np.ndarray [shape=(n,)]
onset strength envelope
bpm : float [scalar]
tempo estimate
fft_res : float ... | [
"Internal",
"function",
"that",
"tracks",
"beats",
"in",
"an",
"onset",
"strength",
"envelope",
"."
] | librosa/librosa | python | https://github.com/librosa/librosa/blob/180e8e6eb8f958fa6b20b8cba389f7945d508247/librosa/beat.py#L343-L395 | [
"def",
"__beat_tracker",
"(",
"onset_envelope",
",",
"bpm",
",",
"fft_res",
",",
"tightness",
",",
"trim",
")",
":",
"if",
"bpm",
"<=",
"0",
":",
"raise",
"ParameterError",
"(",
"'bpm must be strictly positive'",
")",
"# convert bpm to a sample period for searching",
... | 180e8e6eb8f958fa6b20b8cba389f7945d508247 |
test | __normalize_onsets | Maps onset strength function into the range [0, 1] | librosa/beat.py | def __normalize_onsets(onsets):
'''Maps onset strength function into the range [0, 1]'''
norm = onsets.std(ddof=1)
if norm > 0:
onsets = onsets / norm
return onsets | def __normalize_onsets(onsets):
'''Maps onset strength function into the range [0, 1]'''
norm = onsets.std(ddof=1)
if norm > 0:
onsets = onsets / norm
return onsets | [
"Maps",
"onset",
"strength",
"function",
"into",
"the",
"range",
"[",
"0",
"1",
"]"
] | librosa/librosa | python | https://github.com/librosa/librosa/blob/180e8e6eb8f958fa6b20b8cba389f7945d508247/librosa/beat.py#L399-L405 | [
"def",
"__normalize_onsets",
"(",
"onsets",
")",
":",
"norm",
"=",
"onsets",
".",
"std",
"(",
"ddof",
"=",
"1",
")",
"if",
"norm",
">",
"0",
":",
"onsets",
"=",
"onsets",
"/",
"norm",
"return",
"onsets"
] | 180e8e6eb8f958fa6b20b8cba389f7945d508247 |
test | __beat_local_score | Construct the local score for an onset envlope and given period | librosa/beat.py | def __beat_local_score(onset_envelope, period):
'''Construct the local score for an onset envlope and given period'''
window = np.exp(-0.5 * (np.arange(-period, period+1)*32.0/period)**2)
return scipy.signal.convolve(__normalize_onsets(onset_envelope),
window,
... | def __beat_local_score(onset_envelope, period):
'''Construct the local score for an onset envlope and given period'''
window = np.exp(-0.5 * (np.arange(-period, period+1)*32.0/period)**2)
return scipy.signal.convolve(__normalize_onsets(onset_envelope),
window,
... | [
"Construct",
"the",
"local",
"score",
"for",
"an",
"onset",
"envlope",
"and",
"given",
"period"
] | librosa/librosa | python | https://github.com/librosa/librosa/blob/180e8e6eb8f958fa6b20b8cba389f7945d508247/librosa/beat.py#L408-L414 | [
"def",
"__beat_local_score",
"(",
"onset_envelope",
",",
"period",
")",
":",
"window",
"=",
"np",
".",
"exp",
"(",
"-",
"0.5",
"*",
"(",
"np",
".",
"arange",
"(",
"-",
"period",
",",
"period",
"+",
"1",
")",
"*",
"32.0",
"/",
"period",
")",
"**",
... | 180e8e6eb8f958fa6b20b8cba389f7945d508247 |
test | __beat_track_dp | Core dynamic program for beat tracking | librosa/beat.py | def __beat_track_dp(localscore, period, tightness):
"""Core dynamic program for beat tracking"""
backlink = np.zeros_like(localscore, dtype=int)
cumscore = np.zeros_like(localscore)
# Search range for previous beat
window = np.arange(-2 * period, -np.round(period / 2) + 1, dtype=int)
# Make a... | def __beat_track_dp(localscore, period, tightness):
"""Core dynamic program for beat tracking"""
backlink = np.zeros_like(localscore, dtype=int)
cumscore = np.zeros_like(localscore)
# Search range for previous beat
window = np.arange(-2 * period, -np.round(period / 2) + 1, dtype=int)
# Make a... | [
"Core",
"dynamic",
"program",
"for",
"beat",
"tracking"
] | librosa/librosa | python | https://github.com/librosa/librosa/blob/180e8e6eb8f958fa6b20b8cba389f7945d508247/librosa/beat.py#L417-L459 | [
"def",
"__beat_track_dp",
"(",
"localscore",
",",
"period",
",",
"tightness",
")",
":",
"backlink",
"=",
"np",
".",
"zeros_like",
"(",
"localscore",
",",
"dtype",
"=",
"int",
")",
"cumscore",
"=",
"np",
".",
"zeros_like",
"(",
"localscore",
")",
"# Search ... | 180e8e6eb8f958fa6b20b8cba389f7945d508247 |
test | __last_beat | Get the last beat from the cumulative score array | librosa/beat.py | def __last_beat(cumscore):
"""Get the last beat from the cumulative score array"""
maxes = util.localmax(cumscore)
med_score = np.median(cumscore[np.argwhere(maxes)])
# The last of these is the last beat (since score generally increases)
return np.argwhere((cumscore * maxes * 2 > med_score)).max() | def __last_beat(cumscore):
"""Get the last beat from the cumulative score array"""
maxes = util.localmax(cumscore)
med_score = np.median(cumscore[np.argwhere(maxes)])
# The last of these is the last beat (since score generally increases)
return np.argwhere((cumscore * maxes * 2 > med_score)).max() | [
"Get",
"the",
"last",
"beat",
"from",
"the",
"cumulative",
"score",
"array"
] | librosa/librosa | python | https://github.com/librosa/librosa/blob/180e8e6eb8f958fa6b20b8cba389f7945d508247/librosa/beat.py#L462-L469 | [
"def",
"__last_beat",
"(",
"cumscore",
")",
":",
"maxes",
"=",
"util",
".",
"localmax",
"(",
"cumscore",
")",
"med_score",
"=",
"np",
".",
"median",
"(",
"cumscore",
"[",
"np",
".",
"argwhere",
"(",
"maxes",
")",
"]",
")",
"# The last of these is the last ... | 180e8e6eb8f958fa6b20b8cba389f7945d508247 |
test | __trim_beats | Final post-processing: throw out spurious leading/trailing beats | librosa/beat.py | def __trim_beats(localscore, beats, trim):
"""Final post-processing: throw out spurious leading/trailing beats"""
smooth_boe = scipy.signal.convolve(localscore[beats],
scipy.signal.hann(5),
'same')
if trim:
threshold = 0... | def __trim_beats(localscore, beats, trim):
"""Final post-processing: throw out spurious leading/trailing beats"""
smooth_boe = scipy.signal.convolve(localscore[beats],
scipy.signal.hann(5),
'same')
if trim:
threshold = 0... | [
"Final",
"post",
"-",
"processing",
":",
"throw",
"out",
"spurious",
"leading",
"/",
"trailing",
"beats"
] | librosa/librosa | python | https://github.com/librosa/librosa/blob/180e8e6eb8f958fa6b20b8cba389f7945d508247/librosa/beat.py#L472-L486 | [
"def",
"__trim_beats",
"(",
"localscore",
",",
"beats",
",",
"trim",
")",
":",
"smooth_boe",
"=",
"scipy",
".",
"signal",
".",
"convolve",
"(",
"localscore",
"[",
"beats",
"]",
",",
"scipy",
".",
"signal",
".",
"hann",
"(",
"5",
")",
",",
"'same'",
"... | 180e8e6eb8f958fa6b20b8cba389f7945d508247 |
test | recurrence_matrix | Compute a recurrence matrix from a data matrix.
`rec[i, j]` is non-zero if (`data[:, i]`, `data[:, j]`) are
k-nearest-neighbors and `|i - j| >= width`
The specific value of `rec[i, j]` can have several forms, governed
by the `mode` parameter below:
- Connectivity: `rec[i, j] = 1 or 0` indicat... | librosa/segment.py | def recurrence_matrix(data, k=None, width=1, metric='euclidean',
sym=False, sparse=False, mode='connectivity',
bandwidth=None, self=False, axis=-1):
'''Compute a recurrence matrix from a data matrix.
`rec[i, j]` is non-zero if (`data[:, i]`, `data[:, j]`) are
k-n... | def recurrence_matrix(data, k=None, width=1, metric='euclidean',
sym=False, sparse=False, mode='connectivity',
bandwidth=None, self=False, axis=-1):
'''Compute a recurrence matrix from a data matrix.
`rec[i, j]` is non-zero if (`data[:, i]`, `data[:, j]`) are
k-n... | [
"Compute",
"a",
"recurrence",
"matrix",
"from",
"a",
"data",
"matrix",
"."
] | librosa/librosa | python | https://github.com/librosa/librosa/blob/180e8e6eb8f958fa6b20b8cba389f7945d508247/librosa/segment.py#L54-L287 | [
"def",
"recurrence_matrix",
"(",
"data",
",",
"k",
"=",
"None",
",",
"width",
"=",
"1",
",",
"metric",
"=",
"'euclidean'",
",",
"sym",
"=",
"False",
",",
"sparse",
"=",
"False",
",",
"mode",
"=",
"'connectivity'",
",",
"bandwidth",
"=",
"None",
",",
... | 180e8e6eb8f958fa6b20b8cba389f7945d508247 |
test | recurrence_to_lag | Convert a recurrence matrix into a lag matrix.
`lag[i, j] == rec[i+j, j]`
Parameters
----------
rec : np.ndarray, or scipy.sparse.spmatrix [shape=(n, n)]
A (binary) recurrence matrix, as returned by `recurrence_matrix`
pad : bool
If False, `lag` matrix is square, which is equi... | librosa/segment.py | def recurrence_to_lag(rec, pad=True, axis=-1):
'''Convert a recurrence matrix into a lag matrix.
`lag[i, j] == rec[i+j, j]`
Parameters
----------
rec : np.ndarray, or scipy.sparse.spmatrix [shape=(n, n)]
A (binary) recurrence matrix, as returned by `recurrence_matrix`
pad : bool
... | def recurrence_to_lag(rec, pad=True, axis=-1):
'''Convert a recurrence matrix into a lag matrix.
`lag[i, j] == rec[i+j, j]`
Parameters
----------
rec : np.ndarray, or scipy.sparse.spmatrix [shape=(n, n)]
A (binary) recurrence matrix, as returned by `recurrence_matrix`
pad : bool
... | [
"Convert",
"a",
"recurrence",
"matrix",
"into",
"a",
"lag",
"matrix",
"."
] | librosa/librosa | python | https://github.com/librosa/librosa/blob/180e8e6eb8f958fa6b20b8cba389f7945d508247/librosa/segment.py#L290-L386 | [
"def",
"recurrence_to_lag",
"(",
"rec",
",",
"pad",
"=",
"True",
",",
"axis",
"=",
"-",
"1",
")",
":",
"axis",
"=",
"np",
".",
"abs",
"(",
"axis",
")",
"if",
"rec",
".",
"ndim",
"!=",
"2",
"or",
"rec",
".",
"shape",
"[",
"0",
"]",
"!=",
"rec"... | 180e8e6eb8f958fa6b20b8cba389f7945d508247 |
test | lag_to_recurrence | Convert a lag matrix into a recurrence matrix.
Parameters
----------
lag : np.ndarray or scipy.sparse.spmatrix
A lag matrix, as produced by `recurrence_to_lag`
axis : int
The axis corresponding to the time dimension.
The alternate axis will be interpreted in lag coordinates.
... | librosa/segment.py | def lag_to_recurrence(lag, axis=-1):
'''Convert a lag matrix into a recurrence matrix.
Parameters
----------
lag : np.ndarray or scipy.sparse.spmatrix
A lag matrix, as produced by `recurrence_to_lag`
axis : int
The axis corresponding to the time dimension.
The alternate axi... | def lag_to_recurrence(lag, axis=-1):
'''Convert a lag matrix into a recurrence matrix.
Parameters
----------
lag : np.ndarray or scipy.sparse.spmatrix
A lag matrix, as produced by `recurrence_to_lag`
axis : int
The axis corresponding to the time dimension.
The alternate axi... | [
"Convert",
"a",
"lag",
"matrix",
"into",
"a",
"recurrence",
"matrix",
"."
] | librosa/librosa | python | https://github.com/librosa/librosa/blob/180e8e6eb8f958fa6b20b8cba389f7945d508247/librosa/segment.py#L389-L474 | [
"def",
"lag_to_recurrence",
"(",
"lag",
",",
"axis",
"=",
"-",
"1",
")",
":",
"if",
"axis",
"not",
"in",
"[",
"0",
",",
"1",
",",
"-",
"1",
"]",
":",
"raise",
"ParameterError",
"(",
"'Invalid target axis: {}'",
".",
"format",
"(",
"axis",
")",
")",
... | 180e8e6eb8f958fa6b20b8cba389f7945d508247 |
test | timelag_filter | Filtering in the time-lag domain.
This is primarily useful for adapting image filters to operate on
`recurrence_to_lag` output.
Using `timelag_filter` is equivalent to the following sequence of
operations:
>>> data_tl = librosa.segment.recurrence_to_lag(data)
>>> data_filtered_tl = function(d... | librosa/segment.py | def timelag_filter(function, pad=True, index=0):
'''Filtering in the time-lag domain.
This is primarily useful for adapting image filters to operate on
`recurrence_to_lag` output.
Using `timelag_filter` is equivalent to the following sequence of
operations:
>>> data_tl = librosa.segment.recur... | def timelag_filter(function, pad=True, index=0):
'''Filtering in the time-lag domain.
This is primarily useful for adapting image filters to operate on
`recurrence_to_lag` output.
Using `timelag_filter` is equivalent to the following sequence of
operations:
>>> data_tl = librosa.segment.recur... | [
"Filtering",
"in",
"the",
"time",
"-",
"lag",
"domain",
"."
] | librosa/librosa | python | https://github.com/librosa/librosa/blob/180e8e6eb8f958fa6b20b8cba389f7945d508247/librosa/segment.py#L477-L559 | [
"def",
"timelag_filter",
"(",
"function",
",",
"pad",
"=",
"True",
",",
"index",
"=",
"0",
")",
":",
"def",
"__my_filter",
"(",
"wrapped_f",
",",
"*",
"args",
",",
"*",
"*",
"kwargs",
")",
":",
"'''Decorator to wrap the filter'''",
"# Map the input data into t... | 180e8e6eb8f958fa6b20b8cba389f7945d508247 |
test | subsegment | Sub-divide a segmentation by feature clustering.
Given a set of frame boundaries (`frames`), and a data matrix (`data`),
each successive interval defined by `frames` is partitioned into
`n_segments` by constrained agglomerative clustering.
.. note::
If an interval spans fewer than `n_segments`... | librosa/segment.py | def subsegment(data, frames, n_segments=4, axis=-1):
'''Sub-divide a segmentation by feature clustering.
Given a set of frame boundaries (`frames`), and a data matrix (`data`),
each successive interval defined by `frames` is partitioned into
`n_segments` by constrained agglomerative clustering.
..... | def subsegment(data, frames, n_segments=4, axis=-1):
'''Sub-divide a segmentation by feature clustering.
Given a set of frame boundaries (`frames`), and a data matrix (`data`),
each successive interval defined by `frames` is partitioned into
`n_segments` by constrained agglomerative clustering.
..... | [
"Sub",
"-",
"divide",
"a",
"segmentation",
"by",
"feature",
"clustering",
"."
] | librosa/librosa | python | https://github.com/librosa/librosa/blob/180e8e6eb8f958fa6b20b8cba389f7945d508247/librosa/segment.py#L563-L655 | [
"def",
"subsegment",
"(",
"data",
",",
"frames",
",",
"n_segments",
"=",
"4",
",",
"axis",
"=",
"-",
"1",
")",
":",
"frames",
"=",
"util",
".",
"fix_frames",
"(",
"frames",
",",
"x_min",
"=",
"0",
",",
"x_max",
"=",
"data",
".",
"shape",
"[",
"ax... | 180e8e6eb8f958fa6b20b8cba389f7945d508247 |
test | agglomerative | Bottom-up temporal segmentation.
Use a temporally-constrained agglomerative clustering routine to partition
`data` into `k` contiguous segments.
Parameters
----------
data : np.ndarray
data to cluster
k : int > 0 [scalar]
number of segments to produce
clusterer... | librosa/segment.py | def agglomerative(data, k, clusterer=None, axis=-1):
"""Bottom-up temporal segmentation.
Use a temporally-constrained agglomerative clustering routine to partition
`data` into `k` contiguous segments.
Parameters
----------
data : np.ndarray
data to cluster
k : int > 0 [... | def agglomerative(data, k, clusterer=None, axis=-1):
"""Bottom-up temporal segmentation.
Use a temporally-constrained agglomerative clustering routine to partition
`data` into `k` contiguous segments.
Parameters
----------
data : np.ndarray
data to cluster
k : int > 0 [... | [
"Bottom",
"-",
"up",
"temporal",
"segmentation",
"."
] | librosa/librosa | python | https://github.com/librosa/librosa/blob/180e8e6eb8f958fa6b20b8cba389f7945d508247/librosa/segment.py#L658-L745 | [
"def",
"agglomerative",
"(",
"data",
",",
"k",
",",
"clusterer",
"=",
"None",
",",
"axis",
"=",
"-",
"1",
")",
":",
"# Make sure we have at least two dimensions",
"data",
"=",
"np",
".",
"atleast_2d",
"(",
"data",
")",
"# Swap data index to position 0",
"data",
... | 180e8e6eb8f958fa6b20b8cba389f7945d508247 |
test | path_enhance | Multi-angle path enhancement for self- and cross-similarity matrices.
This function convolves multiple diagonal smoothing filters with a self-similarity (or
recurrence) matrix R, and aggregates the result by an element-wise maximum.
Technically, the output is a matrix R_smooth such that
`R_smooth... | librosa/segment.py | def path_enhance(R, n, window='hann', max_ratio=2.0, min_ratio=None, n_filters=7,
zero_mean=False, clip=True, **kwargs):
'''Multi-angle path enhancement for self- and cross-similarity matrices.
This function convolves multiple diagonal smoothing filters with a self-similarity (or
recurrenc... | def path_enhance(R, n, window='hann', max_ratio=2.0, min_ratio=None, n_filters=7,
zero_mean=False, clip=True, **kwargs):
'''Multi-angle path enhancement for self- and cross-similarity matrices.
This function convolves multiple diagonal smoothing filters with a self-similarity (or
recurrenc... | [
"Multi",
"-",
"angle",
"path",
"enhancement",
"for",
"self",
"-",
"and",
"cross",
"-",
"similarity",
"matrices",
"."
] | librosa/librosa | python | https://github.com/librosa/librosa/blob/180e8e6eb8f958fa6b20b8cba389f7945d508247/librosa/segment.py#L748-L877 | [
"def",
"path_enhance",
"(",
"R",
",",
"n",
",",
"window",
"=",
"'hann'",
",",
"max_ratio",
"=",
"2.0",
",",
"min_ratio",
"=",
"None",
",",
"n_filters",
"=",
"7",
",",
"zero_mean",
"=",
"False",
",",
"clip",
"=",
"True",
",",
"*",
"*",
"kwargs",
")"... | 180e8e6eb8f958fa6b20b8cba389f7945d508247 |
test | onset_detect | Onset detection function
:parameters:
- input_file : str
Path to input audio file (wav, mp3, m4a, flac, etc.)
- output_file : str
Path to save onset timestamps as a CSV file | examples/onset_detector.py | def onset_detect(input_file, output_csv):
'''Onset detection function
:parameters:
- input_file : str
Path to input audio file (wav, mp3, m4a, flac, etc.)
- output_file : str
Path to save onset timestamps as a CSV file
'''
# 1. load the wav file and resample to 22.050 ... | def onset_detect(input_file, output_csv):
'''Onset detection function
:parameters:
- input_file : str
Path to input audio file (wav, mp3, m4a, flac, etc.)
- output_file : str
Path to save onset timestamps as a CSV file
'''
# 1. load the wav file and resample to 22.050 ... | [
"Onset",
"detection",
"function"
] | librosa/librosa | python | https://github.com/librosa/librosa/blob/180e8e6eb8f958fa6b20b8cba389f7945d508247/examples/onset_detector.py#L16-L51 | [
"def",
"onset_detect",
"(",
"input_file",
",",
"output_csv",
")",
":",
"# 1. load the wav file and resample to 22.050 KHz",
"print",
"(",
"'Loading '",
",",
"input_file",
")",
"y",
",",
"sr",
"=",
"librosa",
".",
"load",
"(",
"input_file",
",",
"sr",
"=",
"22050... | 180e8e6eb8f958fa6b20b8cba389f7945d508247 |
test | frame | Slice a time series into overlapping frames.
This implementation uses low-level stride manipulation to avoid
redundant copies of the time series data.
Parameters
----------
y : np.ndarray [shape=(n,)]
Time series to frame. Must be one-dimensional and contiguous
in memory.
fram... | librosa/util/utils.py | def frame(y, frame_length=2048, hop_length=512):
'''Slice a time series into overlapping frames.
This implementation uses low-level stride manipulation to avoid
redundant copies of the time series data.
Parameters
----------
y : np.ndarray [shape=(n,)]
Time series to frame. Must be one... | def frame(y, frame_length=2048, hop_length=512):
'''Slice a time series into overlapping frames.
This implementation uses low-level stride manipulation to avoid
redundant copies of the time series data.
Parameters
----------
y : np.ndarray [shape=(n,)]
Time series to frame. Must be one... | [
"Slice",
"a",
"time",
"series",
"into",
"overlapping",
"frames",
"."
] | librosa/librosa | python | https://github.com/librosa/librosa/blob/180e8e6eb8f958fa6b20b8cba389f7945d508247/librosa/util/utils.py#L34-L107 | [
"def",
"frame",
"(",
"y",
",",
"frame_length",
"=",
"2048",
",",
"hop_length",
"=",
"512",
")",
":",
"if",
"not",
"isinstance",
"(",
"y",
",",
"np",
".",
"ndarray",
")",
":",
"raise",
"ParameterError",
"(",
"'Input must be of type numpy.ndarray, '",
"'given ... | 180e8e6eb8f958fa6b20b8cba389f7945d508247 |
test | valid_audio | Validate whether a variable contains valid, mono audio data.
Parameters
----------
y : np.ndarray
The input data to validate
mono : bool
Whether or not to force monophonic audio
Returns
-------
valid : bool
True if all tests pass
Raises
------
ParameterEr... | librosa/util/utils.py | def valid_audio(y, mono=True):
'''Validate whether a variable contains valid, mono audio data.
Parameters
----------
y : np.ndarray
The input data to validate
mono : bool
Whether or not to force monophonic audio
Returns
-------
valid : bool
True if all tests pass
... | def valid_audio(y, mono=True):
'''Validate whether a variable contains valid, mono audio data.
Parameters
----------
y : np.ndarray
The input data to validate
mono : bool
Whether or not to force monophonic audio
Returns
-------
valid : bool
True if all tests pass
... | [
"Validate",
"whether",
"a",
"variable",
"contains",
"valid",
"mono",
"audio",
"data",
"."
] | librosa/librosa | python | https://github.com/librosa/librosa/blob/180e8e6eb8f958fa6b20b8cba389f7945d508247/librosa/util/utils.py#L111-L172 | [
"def",
"valid_audio",
"(",
"y",
",",
"mono",
"=",
"True",
")",
":",
"if",
"not",
"isinstance",
"(",
"y",
",",
"np",
".",
"ndarray",
")",
":",
"raise",
"ParameterError",
"(",
"'data must be of type numpy.ndarray'",
")",
"if",
"not",
"np",
".",
"issubdtype",... | 180e8e6eb8f958fa6b20b8cba389f7945d508247 |
test | valid_int | Ensure that an input value is integer-typed.
This is primarily useful for ensuring integrable-valued
array indices.
Parameters
----------
x : number
A scalar value to be cast to int
cast : function [optional]
A function to modify `x` before casting.
Default: `np.floor`
... | librosa/util/utils.py | def valid_int(x, cast=None):
'''Ensure that an input value is integer-typed.
This is primarily useful for ensuring integrable-valued
array indices.
Parameters
----------
x : number
A scalar value to be cast to int
cast : function [optional]
A function to modify `x` before c... | def valid_int(x, cast=None):
'''Ensure that an input value is integer-typed.
This is primarily useful for ensuring integrable-valued
array indices.
Parameters
----------
x : number
A scalar value to be cast to int
cast : function [optional]
A function to modify `x` before c... | [
"Ensure",
"that",
"an",
"input",
"value",
"is",
"integer",
"-",
"typed",
".",
"This",
"is",
"primarily",
"useful",
"for",
"ensuring",
"integrable",
"-",
"valued",
"array",
"indices",
"."
] | librosa/librosa | python | https://github.com/librosa/librosa/blob/180e8e6eb8f958fa6b20b8cba389f7945d508247/librosa/util/utils.py#L175-L206 | [
"def",
"valid_int",
"(",
"x",
",",
"cast",
"=",
"None",
")",
":",
"if",
"cast",
"is",
"None",
":",
"cast",
"=",
"np",
".",
"floor",
"if",
"not",
"six",
".",
"callable",
"(",
"cast",
")",
":",
"raise",
"ParameterError",
"(",
"'cast parameter must be cal... | 180e8e6eb8f958fa6b20b8cba389f7945d508247 |
test | valid_intervals | Ensure that an array is a valid representation of time intervals:
- intervals.ndim == 2
- intervals.shape[1] == 2
- intervals[i, 0] <= intervals[i, 1] for all i
Parameters
----------
intervals : np.ndarray [shape=(n, 2)]
set of time intervals
Returns
-------
va... | librosa/util/utils.py | def valid_intervals(intervals):
'''Ensure that an array is a valid representation of time intervals:
- intervals.ndim == 2
- intervals.shape[1] == 2
- intervals[i, 0] <= intervals[i, 1] for all i
Parameters
----------
intervals : np.ndarray [shape=(n, 2)]
set of time in... | def valid_intervals(intervals):
'''Ensure that an array is a valid representation of time intervals:
- intervals.ndim == 2
- intervals.shape[1] == 2
- intervals[i, 0] <= intervals[i, 1] for all i
Parameters
----------
intervals : np.ndarray [shape=(n, 2)]
set of time in... | [
"Ensure",
"that",
"an",
"array",
"is",
"a",
"valid",
"representation",
"of",
"time",
"intervals",
":"
] | librosa/librosa | python | https://github.com/librosa/librosa/blob/180e8e6eb8f958fa6b20b8cba389f7945d508247/librosa/util/utils.py#L209-L233 | [
"def",
"valid_intervals",
"(",
"intervals",
")",
":",
"if",
"intervals",
".",
"ndim",
"!=",
"2",
"or",
"intervals",
".",
"shape",
"[",
"-",
"1",
"]",
"!=",
"2",
":",
"raise",
"ParameterError",
"(",
"'intervals must have shape (n, 2)'",
")",
"if",
"np",
"."... | 180e8e6eb8f958fa6b20b8cba389f7945d508247 |
test | pad_center | Wrapper for np.pad to automatically center an array prior to padding.
This is analogous to `str.center()`
Examples
--------
>>> # Generate a vector
>>> data = np.ones(5)
>>> librosa.util.pad_center(data, 10, mode='constant')
array([ 0., 0., 1., 1., 1., 1., 1., 0., 0., 0.])
>>>... | librosa/util/utils.py | def pad_center(data, size, axis=-1, **kwargs):
'''Wrapper for np.pad to automatically center an array prior to padding.
This is analogous to `str.center()`
Examples
--------
>>> # Generate a vector
>>> data = np.ones(5)
>>> librosa.util.pad_center(data, 10, mode='constant')
array([ 0., ... | def pad_center(data, size, axis=-1, **kwargs):
'''Wrapper for np.pad to automatically center an array prior to padding.
This is analogous to `str.center()`
Examples
--------
>>> # Generate a vector
>>> data = np.ones(5)
>>> librosa.util.pad_center(data, 10, mode='constant')
array([ 0., ... | [
"Wrapper",
"for",
"np",
".",
"pad",
"to",
"automatically",
"center",
"an",
"array",
"prior",
"to",
"padding",
".",
"This",
"is",
"analogous",
"to",
"str",
".",
"center",
"()"
] | librosa/librosa | python | https://github.com/librosa/librosa/blob/180e8e6eb8f958fa6b20b8cba389f7945d508247/librosa/util/utils.py#L236-L306 | [
"def",
"pad_center",
"(",
"data",
",",
"size",
",",
"axis",
"=",
"-",
"1",
",",
"*",
"*",
"kwargs",
")",
":",
"kwargs",
".",
"setdefault",
"(",
"'mode'",
",",
"'constant'",
")",
"n",
"=",
"data",
".",
"shape",
"[",
"axis",
"]",
"lpad",
"=",
"int"... | 180e8e6eb8f958fa6b20b8cba389f7945d508247 |
test | fix_length | Fix the length an array `data` to exactly `size`.
If `data.shape[axis] < n`, pad according to the provided kwargs.
By default, `data` is padded with trailing zeros.
Examples
--------
>>> y = np.arange(7)
>>> # Default: pad with zeros
>>> librosa.util.fix_length(y, 10)
array([0, 1, 2, 3... | librosa/util/utils.py | def fix_length(data, size, axis=-1, **kwargs):
'''Fix the length an array `data` to exactly `size`.
If `data.shape[axis] < n`, pad according to the provided kwargs.
By default, `data` is padded with trailing zeros.
Examples
--------
>>> y = np.arange(7)
>>> # Default: pad with zeros
>>... | def fix_length(data, size, axis=-1, **kwargs):
'''Fix the length an array `data` to exactly `size`.
If `data.shape[axis] < n`, pad according to the provided kwargs.
By default, `data` is padded with trailing zeros.
Examples
--------
>>> y = np.arange(7)
>>> # Default: pad with zeros
>>... | [
"Fix",
"the",
"length",
"an",
"array",
"data",
"to",
"exactly",
"size",
"."
] | librosa/librosa | python | https://github.com/librosa/librosa/blob/180e8e6eb8f958fa6b20b8cba389f7945d508247/librosa/util/utils.py#L309-L367 | [
"def",
"fix_length",
"(",
"data",
",",
"size",
",",
"axis",
"=",
"-",
"1",
",",
"*",
"*",
"kwargs",
")",
":",
"kwargs",
".",
"setdefault",
"(",
"'mode'",
",",
"'constant'",
")",
"n",
"=",
"data",
".",
"shape",
"[",
"axis",
"]",
"if",
"n",
">",
... | 180e8e6eb8f958fa6b20b8cba389f7945d508247 |
test | fix_frames | Fix a list of frames to lie within [x_min, x_max]
Examples
--------
>>> # Generate a list of frame indices
>>> frames = np.arange(0, 1000.0, 50)
>>> frames
array([ 0., 50., 100., 150., 200., 250., 300., 350.,
400., 450., 500., 550., 600., 650., 700., 750.,
... | librosa/util/utils.py | def fix_frames(frames, x_min=0, x_max=None, pad=True):
'''Fix a list of frames to lie within [x_min, x_max]
Examples
--------
>>> # Generate a list of frame indices
>>> frames = np.arange(0, 1000.0, 50)
>>> frames
array([ 0., 50., 100., 150., 200., 250., 300., 350.,
40... | def fix_frames(frames, x_min=0, x_max=None, pad=True):
'''Fix a list of frames to lie within [x_min, x_max]
Examples
--------
>>> # Generate a list of frame indices
>>> frames = np.arange(0, 1000.0, 50)
>>> frames
array([ 0., 50., 100., 150., 200., 250., 300., 350.,
40... | [
"Fix",
"a",
"list",
"of",
"frames",
"to",
"lie",
"within",
"[",
"x_min",
"x_max",
"]"
] | librosa/librosa | python | https://github.com/librosa/librosa/blob/180e8e6eb8f958fa6b20b8cba389f7945d508247/librosa/util/utils.py#L370-L452 | [
"def",
"fix_frames",
"(",
"frames",
",",
"x_min",
"=",
"0",
",",
"x_max",
"=",
"None",
",",
"pad",
"=",
"True",
")",
":",
"frames",
"=",
"np",
".",
"asarray",
"(",
"frames",
")",
"if",
"np",
".",
"any",
"(",
"frames",
"<",
"0",
")",
":",
"raise... | 180e8e6eb8f958fa6b20b8cba389f7945d508247 |
test | axis_sort | Sort an array along its rows or columns.
Examples
--------
Visualize NMF output for a spectrogram S
>>> # Sort the columns of W by peak frequency bin
>>> y, sr = librosa.load(librosa.util.example_audio_file())
>>> S = np.abs(librosa.stft(y))
>>> W, H = librosa.decompose.decompose(S, n_comp... | librosa/util/utils.py | def axis_sort(S, axis=-1, index=False, value=None):
'''Sort an array along its rows or columns.
Examples
--------
Visualize NMF output for a spectrogram S
>>> # Sort the columns of W by peak frequency bin
>>> y, sr = librosa.load(librosa.util.example_audio_file())
>>> S = np.abs(librosa.st... | def axis_sort(S, axis=-1, index=False, value=None):
'''Sort an array along its rows or columns.
Examples
--------
Visualize NMF output for a spectrogram S
>>> # Sort the columns of W by peak frequency bin
>>> y, sr = librosa.load(librosa.util.example_audio_file())
>>> S = np.abs(librosa.st... | [
"Sort",
"an",
"array",
"along",
"its",
"rows",
"or",
"columns",
"."
] | librosa/librosa | python | https://github.com/librosa/librosa/blob/180e8e6eb8f958fa6b20b8cba389f7945d508247/librosa/util/utils.py#L455-L549 | [
"def",
"axis_sort",
"(",
"S",
",",
"axis",
"=",
"-",
"1",
",",
"index",
"=",
"False",
",",
"value",
"=",
"None",
")",
":",
"if",
"value",
"is",
"None",
":",
"value",
"=",
"np",
".",
"argmax",
"if",
"S",
".",
"ndim",
"!=",
"2",
":",
"raise",
"... | 180e8e6eb8f958fa6b20b8cba389f7945d508247 |
test | normalize | Normalize an array along a chosen axis.
Given a norm (described below) and a target axis, the input
array is scaled so that
`norm(S, axis=axis) == 1`
For example, `axis=0` normalizes each column of a 2-d array
by aggregating over the rows (0-axis).
Similarly, `axis=1` normalizes each row ... | librosa/util/utils.py | def normalize(S, norm=np.inf, axis=0, threshold=None, fill=None):
'''Normalize an array along a chosen axis.
Given a norm (described below) and a target axis, the input
array is scaled so that
`norm(S, axis=axis) == 1`
For example, `axis=0` normalizes each column of a 2-d array
by aggrega... | def normalize(S, norm=np.inf, axis=0, threshold=None, fill=None):
'''Normalize an array along a chosen axis.
Given a norm (described below) and a target axis, the input
array is scaled so that
`norm(S, axis=axis) == 1`
For example, `axis=0` normalizes each column of a 2-d array
by aggrega... | [
"Normalize",
"an",
"array",
"along",
"a",
"chosen",
"axis",
"."
] | librosa/librosa | python | https://github.com/librosa/librosa/blob/180e8e6eb8f958fa6b20b8cba389f7945d508247/librosa/util/utils.py#L553-L777 | [
"def",
"normalize",
"(",
"S",
",",
"norm",
"=",
"np",
".",
"inf",
",",
"axis",
"=",
"0",
",",
"threshold",
"=",
"None",
",",
"fill",
"=",
"None",
")",
":",
"# Avoid div-by-zero",
"if",
"threshold",
"is",
"None",
":",
"threshold",
"=",
"tiny",
"(",
... | 180e8e6eb8f958fa6b20b8cba389f7945d508247 |
test | localmax | Find local maxima in an array `x`.
An element `x[i]` is considered a local maximum if the following
conditions are met:
- `x[i] > x[i-1]`
- `x[i] >= x[i+1]`
Note that the first condition is strict, and that the first element
`x[0]` will never be considered as a local maximum.
Examples
... | librosa/util/utils.py | def localmax(x, axis=0):
"""Find local maxima in an array `x`.
An element `x[i]` is considered a local maximum if the following
conditions are met:
- `x[i] > x[i-1]`
- `x[i] >= x[i+1]`
Note that the first condition is strict, and that the first element
`x[0]` will never be considered as a... | def localmax(x, axis=0):
"""Find local maxima in an array `x`.
An element `x[i]` is considered a local maximum if the following
conditions are met:
- `x[i] > x[i-1]`
- `x[i] >= x[i+1]`
Note that the first condition is strict, and that the first element
`x[0]` will never be considered as a... | [
"Find",
"local",
"maxima",
"in",
"an",
"array",
"x",
"."
] | librosa/librosa | python | https://github.com/librosa/librosa/blob/180e8e6eb8f958fa6b20b8cba389f7945d508247/librosa/util/utils.py#L780-L835 | [
"def",
"localmax",
"(",
"x",
",",
"axis",
"=",
"0",
")",
":",
"paddings",
"=",
"[",
"(",
"0",
",",
"0",
")",
"]",
"*",
"x",
".",
"ndim",
"paddings",
"[",
"axis",
"]",
"=",
"(",
"1",
",",
"1",
")",
"x_pad",
"=",
"np",
".",
"pad",
"(",
"x",... | 180e8e6eb8f958fa6b20b8cba389f7945d508247 |
test | peak_pick | Uses a flexible heuristic to pick peaks in a signal.
A sample n is selected as an peak if the corresponding x[n]
fulfills the following three conditions:
1. `x[n] == max(x[n - pre_max:n + post_max])`
2. `x[n] >= mean(x[n - pre_avg:n + post_avg]) + delta`
3. `n - previous_n > wait`
where `prev... | librosa/util/utils.py | def peak_pick(x, pre_max, post_max, pre_avg, post_avg, delta, wait):
'''Uses a flexible heuristic to pick peaks in a signal.
A sample n is selected as an peak if the corresponding x[n]
fulfills the following three conditions:
1. `x[n] == max(x[n - pre_max:n + post_max])`
2. `x[n] >= mean(x[n - pre... | def peak_pick(x, pre_max, post_max, pre_avg, post_avg, delta, wait):
'''Uses a flexible heuristic to pick peaks in a signal.
A sample n is selected as an peak if the corresponding x[n]
fulfills the following three conditions:
1. `x[n] == max(x[n - pre_max:n + post_max])`
2. `x[n] >= mean(x[n - pre... | [
"Uses",
"a",
"flexible",
"heuristic",
"to",
"pick",
"peaks",
"in",
"a",
"signal",
"."
] | librosa/librosa | python | https://github.com/librosa/librosa/blob/180e8e6eb8f958fa6b20b8cba389f7945d508247/librosa/util/utils.py#L838-L1005 | [
"def",
"peak_pick",
"(",
"x",
",",
"pre_max",
",",
"post_max",
",",
"pre_avg",
",",
"post_avg",
",",
"delta",
",",
"wait",
")",
":",
"if",
"pre_max",
"<",
"0",
":",
"raise",
"ParameterError",
"(",
"'pre_max must be non-negative'",
")",
"if",
"pre_avg",
"<"... | 180e8e6eb8f958fa6b20b8cba389f7945d508247 |
test | sparsify_rows | Return a row-sparse matrix approximating the input `x`.
Parameters
----------
x : np.ndarray [ndim <= 2]
The input matrix to sparsify.
quantile : float in [0, 1.0)
Percentage of magnitude to discard in each row of `x`
Returns
-------
x_sparse : `scipy.sparse.csr_matrix` [s... | librosa/util/utils.py | def sparsify_rows(x, quantile=0.01):
'''
Return a row-sparse matrix approximating the input `x`.
Parameters
----------
x : np.ndarray [ndim <= 2]
The input matrix to sparsify.
quantile : float in [0, 1.0)
Percentage of magnitude to discard in each row of `x`
Returns
--... | def sparsify_rows(x, quantile=0.01):
'''
Return a row-sparse matrix approximating the input `x`.
Parameters
----------
x : np.ndarray [ndim <= 2]
The input matrix to sparsify.
quantile : float in [0, 1.0)
Percentage of magnitude to discard in each row of `x`
Returns
--... | [
"Return",
"a",
"row",
"-",
"sparse",
"matrix",
"approximating",
"the",
"input",
"x",
"."
] | librosa/librosa | python | https://github.com/librosa/librosa/blob/180e8e6eb8f958fa6b20b8cba389f7945d508247/librosa/util/utils.py#L1009-L1098 | [
"def",
"sparsify_rows",
"(",
"x",
",",
"quantile",
"=",
"0.01",
")",
":",
"if",
"x",
".",
"ndim",
"==",
"1",
":",
"x",
"=",
"x",
".",
"reshape",
"(",
"(",
"1",
",",
"-",
"1",
")",
")",
"elif",
"x",
".",
"ndim",
">",
"2",
":",
"raise",
"Para... | 180e8e6eb8f958fa6b20b8cba389f7945d508247 |
test | roll_sparse | Sparse matrix roll
This operation is equivalent to ``numpy.roll``, but operates on sparse matrices.
Parameters
----------
x : scipy.sparse.spmatrix or np.ndarray
The sparse matrix input
shift : int
The number of positions to roll the specified axis
axis : (0, 1, -1)
T... | librosa/util/utils.py | def roll_sparse(x, shift, axis=0):
'''Sparse matrix roll
This operation is equivalent to ``numpy.roll``, but operates on sparse matrices.
Parameters
----------
x : scipy.sparse.spmatrix or np.ndarray
The sparse matrix input
shift : int
The number of positions to roll the speci... | def roll_sparse(x, shift, axis=0):
'''Sparse matrix roll
This operation is equivalent to ``numpy.roll``, but operates on sparse matrices.
Parameters
----------
x : scipy.sparse.spmatrix or np.ndarray
The sparse matrix input
shift : int
The number of positions to roll the speci... | [
"Sparse",
"matrix",
"roll"
] | librosa/librosa | python | https://github.com/librosa/librosa/blob/180e8e6eb8f958fa6b20b8cba389f7945d508247/librosa/util/utils.py#L1101-L1167 | [
"def",
"roll_sparse",
"(",
"x",
",",
"shift",
",",
"axis",
"=",
"0",
")",
":",
"if",
"not",
"scipy",
".",
"sparse",
".",
"isspmatrix",
"(",
"x",
")",
":",
"return",
"np",
".",
"roll",
"(",
"x",
",",
"shift",
",",
"axis",
"=",
"axis",
")",
"# sh... | 180e8e6eb8f958fa6b20b8cba389f7945d508247 |
test | buf_to_float | Convert an integer buffer to floating point values.
This is primarily useful when loading integer-valued wav data
into numpy arrays.
See Also
--------
buf_to_float
Parameters
----------
x : np.ndarray [dtype=int]
The integer-valued data buffer
n_bytes : int [1, 2, 4]
... | librosa/util/utils.py | def buf_to_float(x, n_bytes=2, dtype=np.float32):
"""Convert an integer buffer to floating point values.
This is primarily useful when loading integer-valued wav data
into numpy arrays.
See Also
--------
buf_to_float
Parameters
----------
x : np.ndarray [dtype=int]
The inte... | def buf_to_float(x, n_bytes=2, dtype=np.float32):
"""Convert an integer buffer to floating point values.
This is primarily useful when loading integer-valued wav data
into numpy arrays.
See Also
--------
buf_to_float
Parameters
----------
x : np.ndarray [dtype=int]
The inte... | [
"Convert",
"an",
"integer",
"buffer",
"to",
"floating",
"point",
"values",
".",
"This",
"is",
"primarily",
"useful",
"when",
"loading",
"integer",
"-",
"valued",
"wav",
"data",
"into",
"numpy",
"arrays",
"."
] | librosa/librosa | python | https://github.com/librosa/librosa/blob/180e8e6eb8f958fa6b20b8cba389f7945d508247/librosa/util/utils.py#L1170-L1203 | [
"def",
"buf_to_float",
"(",
"x",
",",
"n_bytes",
"=",
"2",
",",
"dtype",
"=",
"np",
".",
"float32",
")",
":",
"# Invert the scale of the data",
"scale",
"=",
"1.",
"/",
"float",
"(",
"1",
"<<",
"(",
"(",
"8",
"*",
"n_bytes",
")",
"-",
"1",
")",
")"... | 180e8e6eb8f958fa6b20b8cba389f7945d508247 |
test | index_to_slice | Generate a slice array from an index array.
Parameters
----------
idx : list-like
Array of index boundaries
idx_min : None or int
idx_max : None or int
Minimum and maximum allowed indices
step : None or int
Step size for each slice. If `None`, then the default
... | librosa/util/utils.py | def index_to_slice(idx, idx_min=None, idx_max=None, step=None, pad=True):
'''Generate a slice array from an index array.
Parameters
----------
idx : list-like
Array of index boundaries
idx_min : None or int
idx_max : None or int
Minimum and maximum allowed indices
step : N... | def index_to_slice(idx, idx_min=None, idx_max=None, step=None, pad=True):
'''Generate a slice array from an index array.
Parameters
----------
idx : list-like
Array of index boundaries
idx_min : None or int
idx_max : None or int
Minimum and maximum allowed indices
step : N... | [
"Generate",
"a",
"slice",
"array",
"from",
"an",
"index",
"array",
"."
] | librosa/librosa | python | https://github.com/librosa/librosa/blob/180e8e6eb8f958fa6b20b8cba389f7945d508247/librosa/util/utils.py#L1206-L1259 | [
"def",
"index_to_slice",
"(",
"idx",
",",
"idx_min",
"=",
"None",
",",
"idx_max",
"=",
"None",
",",
"step",
"=",
"None",
",",
"pad",
"=",
"True",
")",
":",
"# First, normalize the index set",
"idx_fixed",
"=",
"fix_frames",
"(",
"idx",
",",
"idx_min",
",",... | 180e8e6eb8f958fa6b20b8cba389f7945d508247 |
test | sync | Synchronous aggregation of a multi-dimensional array between boundaries
.. note::
In order to ensure total coverage, boundary points may be added
to `idx`.
If synchronizing a feature matrix against beat tracker output, ensure
that frame index numbers are properly aligned and use th... | librosa/util/utils.py | def sync(data, idx, aggregate=None, pad=True, axis=-1):
"""Synchronous aggregation of a multi-dimensional array between boundaries
.. note::
In order to ensure total coverage, boundary points may be added
to `idx`.
If synchronizing a feature matrix against beat tracker output, ensure
... | def sync(data, idx, aggregate=None, pad=True, axis=-1):
"""Synchronous aggregation of a multi-dimensional array between boundaries
.. note::
In order to ensure total coverage, boundary points may be added
to `idx`.
If synchronizing a feature matrix against beat tracker output, ensure
... | [
"Synchronous",
"aggregation",
"of",
"a",
"multi",
"-",
"dimensional",
"array",
"between",
"boundaries"
] | librosa/librosa | python | https://github.com/librosa/librosa/blob/180e8e6eb8f958fa6b20b8cba389f7945d508247/librosa/util/utils.py#L1263-L1388 | [
"def",
"sync",
"(",
"data",
",",
"idx",
",",
"aggregate",
"=",
"None",
",",
"pad",
"=",
"True",
",",
"axis",
"=",
"-",
"1",
")",
":",
"if",
"aggregate",
"is",
"None",
":",
"aggregate",
"=",
"np",
".",
"mean",
"shape",
"=",
"list",
"(",
"data",
... | 180e8e6eb8f958fa6b20b8cba389f7945d508247 |
test | softmask | Robustly compute a softmask operation.
`M = X**power / (X**power + X_ref**power)`
Parameters
----------
X : np.ndarray
The (non-negative) input array corresponding to the positive mask elements
X_ref : np.ndarray
The (non-negative) array of reference or background elements.
... | librosa/util/utils.py | def softmask(X, X_ref, power=1, split_zeros=False):
'''Robustly compute a softmask operation.
`M = X**power / (X**power + X_ref**power)`
Parameters
----------
X : np.ndarray
The (non-negative) input array corresponding to the positive mask elements
X_ref : np.ndarray
The ... | def softmask(X, X_ref, power=1, split_zeros=False):
'''Robustly compute a softmask operation.
`M = X**power / (X**power + X_ref**power)`
Parameters
----------
X : np.ndarray
The (non-negative) input array corresponding to the positive mask elements
X_ref : np.ndarray
The ... | [
"Robustly",
"compute",
"a",
"softmask",
"operation",
"."
] | librosa/librosa | python | https://github.com/librosa/librosa/blob/180e8e6eb8f958fa6b20b8cba389f7945d508247/librosa/util/utils.py#L1391-L1507 | [
"def",
"softmask",
"(",
"X",
",",
"X_ref",
",",
"power",
"=",
"1",
",",
"split_zeros",
"=",
"False",
")",
":",
"if",
"X",
".",
"shape",
"!=",
"X_ref",
".",
"shape",
":",
"raise",
"ParameterError",
"(",
"'Shape mismatch: {}!={}'",
".",
"format",
"(",
"X... | 180e8e6eb8f958fa6b20b8cba389f7945d508247 |
test | tiny | Compute the tiny-value corresponding to an input's data type.
This is the smallest "usable" number representable in `x`'s
data type (e.g., float32).
This is primarily useful for determining a threshold for
numerical underflow in division or multiplication operations.
Parameters
----------
... | librosa/util/utils.py | def tiny(x):
'''Compute the tiny-value corresponding to an input's data type.
This is the smallest "usable" number representable in `x`'s
data type (e.g., float32).
This is primarily useful for determining a threshold for
numerical underflow in division or multiplication operations.
Parameter... | def tiny(x):
'''Compute the tiny-value corresponding to an input's data type.
This is the smallest "usable" number representable in `x`'s
data type (e.g., float32).
This is primarily useful for determining a threshold for
numerical underflow in division or multiplication operations.
Parameter... | [
"Compute",
"the",
"tiny",
"-",
"value",
"corresponding",
"to",
"an",
"input",
"s",
"data",
"type",
"."
] | librosa/librosa | python | https://github.com/librosa/librosa/blob/180e8e6eb8f958fa6b20b8cba389f7945d508247/librosa/util/utils.py#L1510-L1574 | [
"def",
"tiny",
"(",
"x",
")",
":",
"# Make sure we have an array view",
"x",
"=",
"np",
".",
"asarray",
"(",
"x",
")",
"# Only floating types generate a tiny",
"if",
"np",
".",
"issubdtype",
"(",
"x",
".",
"dtype",
",",
"np",
".",
"floating",
")",
"or",
"n... | 180e8e6eb8f958fa6b20b8cba389f7945d508247 |
test | fill_off_diagonal | Sets all cells of a matrix to a given ``value``
if they lie outside a constraint region.
In this case, the constraint region is the
Sakoe-Chiba band which runs with a fixed ``radius``
along the main diagonal.
When ``x.shape[0] != x.shape[1]``, the radius will be
expanded so that ``x[-1, -1] = 1`... | librosa/util/utils.py | def fill_off_diagonal(x, radius, value=0):
"""Sets all cells of a matrix to a given ``value``
if they lie outside a constraint region.
In this case, the constraint region is the
Sakoe-Chiba band which runs with a fixed ``radius``
along the main diagonal.
When ``x.shape[0] != x.shape[1]``, the ra... | def fill_off_diagonal(x, radius, value=0):
"""Sets all cells of a matrix to a given ``value``
if they lie outside a constraint region.
In this case, the constraint region is the
Sakoe-Chiba band which runs with a fixed ``radius``
along the main diagonal.
When ``x.shape[0] != x.shape[1]``, the ra... | [
"Sets",
"all",
"cells",
"of",
"a",
"matrix",
"to",
"a",
"given",
"value",
"if",
"they",
"lie",
"outside",
"a",
"constraint",
"region",
".",
"In",
"this",
"case",
"the",
"constraint",
"region",
"is",
"the",
"Sakoe",
"-",
"Chiba",
"band",
"which",
"runs",
... | librosa/librosa | python | https://github.com/librosa/librosa/blob/180e8e6eb8f958fa6b20b8cba389f7945d508247/librosa/util/utils.py#L1577-L1640 | [
"def",
"fill_off_diagonal",
"(",
"x",
",",
"radius",
",",
"value",
"=",
"0",
")",
":",
"nx",
",",
"ny",
"=",
"x",
".",
"shape",
"# Calculate the radius in indices, rather than proportion",
"radius",
"=",
"np",
".",
"round",
"(",
"radius",
"*",
"np",
".",
"... | 180e8e6eb8f958fa6b20b8cba389f7945d508247 |
test | frames2video | Read the frame images from a directory and join them as a video
Args:
frame_dir (str): The directory containing video frames.
video_file (str): Output filename.
fps (float): FPS of the output video.
fourcc (str): Fourcc of the output video, this should be compatible
with... | mmcv/video/io.py | def frames2video(frame_dir,
video_file,
fps=30,
fourcc='XVID',
filename_tmpl='{:06d}.jpg',
start=0,
end=0,
show_progress=True):
"""Read the frame images from a directory and join them as a video
... | def frames2video(frame_dir,
video_file,
fps=30,
fourcc='XVID',
filename_tmpl='{:06d}.jpg',
start=0,
end=0,
show_progress=True):
"""Read the frame images from a directory and join them as a video
... | [
"Read",
"the",
"frame",
"images",
"from",
"a",
"directory",
"and",
"join",
"them",
"as",
"a",
"video"
] | open-mmlab/mmcv | python | https://github.com/open-mmlab/mmcv/blob/0d77f61450aab4dde8b8585a577cc496acb95d7f/mmcv/video/io.py#L288-L332 | [
"def",
"frames2video",
"(",
"frame_dir",
",",
"video_file",
",",
"fps",
"=",
"30",
",",
"fourcc",
"=",
"'XVID'",
",",
"filename_tmpl",
"=",
"'{:06d}.jpg'",
",",
"start",
"=",
"0",
",",
"end",
"=",
"0",
",",
"show_progress",
"=",
"True",
")",
":",
"if",... | 0d77f61450aab4dde8b8585a577cc496acb95d7f |
test | VideoReader.read | Read the next frame.
If the next frame have been decoded before and in the cache, then
return it directly, otherwise decode, cache and return it.
Returns:
ndarray or None: Return the frame if successful, otherwise None. | mmcv/video/io.py | def read(self):
"""Read the next frame.
If the next frame have been decoded before and in the cache, then
return it directly, otherwise decode, cache and return it.
Returns:
ndarray or None: Return the frame if successful, otherwise None.
"""
# pos = self._p... | def read(self):
"""Read the next frame.
If the next frame have been decoded before and in the cache, then
return it directly, otherwise decode, cache and return it.
Returns:
ndarray or None: Return the frame if successful, otherwise None.
"""
# pos = self._p... | [
"Read",
"the",
"next",
"frame",
"."
] | open-mmlab/mmcv | python | https://github.com/open-mmlab/mmcv/blob/0d77f61450aab4dde8b8585a577cc496acb95d7f/mmcv/video/io.py#L142-L166 | [
"def",
"read",
"(",
"self",
")",
":",
"# pos = self._position",
"if",
"self",
".",
"_cache",
":",
"img",
"=",
"self",
".",
"_cache",
".",
"get",
"(",
"self",
".",
"_position",
")",
"if",
"img",
"is",
"not",
"None",
":",
"ret",
"=",
"True",
"else",
... | 0d77f61450aab4dde8b8585a577cc496acb95d7f |
test | VideoReader.get_frame | Get frame by index.
Args:
frame_id (int): Index of the expected frame, 0-based.
Returns:
ndarray or None: Return the frame if successful, otherwise None. | mmcv/video/io.py | def get_frame(self, frame_id):
"""Get frame by index.
Args:
frame_id (int): Index of the expected frame, 0-based.
Returns:
ndarray or None: Return the frame if successful, otherwise None.
"""
if frame_id < 0 or frame_id >= self._frame_cnt:
ra... | def get_frame(self, frame_id):
"""Get frame by index.
Args:
frame_id (int): Index of the expected frame, 0-based.
Returns:
ndarray or None: Return the frame if successful, otherwise None.
"""
if frame_id < 0 or frame_id >= self._frame_cnt:
ra... | [
"Get",
"frame",
"by",
"index",
"."
] | open-mmlab/mmcv | python | https://github.com/open-mmlab/mmcv/blob/0d77f61450aab4dde8b8585a577cc496acb95d7f/mmcv/video/io.py#L168-L194 | [
"def",
"get_frame",
"(",
"self",
",",
"frame_id",
")",
":",
"if",
"frame_id",
"<",
"0",
"or",
"frame_id",
">=",
"self",
".",
"_frame_cnt",
":",
"raise",
"IndexError",
"(",
"'\"frame_id\" must be between 0 and {}'",
".",
"format",
"(",
"self",
".",
"_frame_cnt"... | 0d77f61450aab4dde8b8585a577cc496acb95d7f |
test | VideoReader.cvt2frames | Convert a video to frame images
Args:
frame_dir (str): Output directory to store all the frame images.
file_start (int): Filenames will start from the specified number.
filename_tmpl (str): Filename template with the index as the
placeholder.
star... | mmcv/video/io.py | def cvt2frames(self,
frame_dir,
file_start=0,
filename_tmpl='{:06d}.jpg',
start=0,
max_num=0,
show_progress=True):
"""Convert a video to frame images
Args:
frame_dir (str): Outp... | def cvt2frames(self,
frame_dir,
file_start=0,
filename_tmpl='{:06d}.jpg',
start=0,
max_num=0,
show_progress=True):
"""Convert a video to frame images
Args:
frame_dir (str): Outp... | [
"Convert",
"a",
"video",
"to",
"frame",
"images"
] | open-mmlab/mmcv | python | https://github.com/open-mmlab/mmcv/blob/0d77f61450aab4dde8b8585a577cc496acb95d7f/mmcv/video/io.py#L207-L250 | [
"def",
"cvt2frames",
"(",
"self",
",",
"frame_dir",
",",
"file_start",
"=",
"0",
",",
"filename_tmpl",
"=",
"'{:06d}.jpg'",
",",
"start",
"=",
"0",
",",
"max_num",
"=",
"0",
",",
"show_progress",
"=",
"True",
")",
":",
"mkdir_or_exist",
"(",
"frame_dir",
... | 0d77f61450aab4dde8b8585a577cc496acb95d7f |
test | track_progress | Track the progress of tasks execution with a progress bar.
Tasks are done with a simple for-loop.
Args:
func (callable): The function to be applied to each task.
tasks (list or tuple[Iterable, int]): A list of tasks or
(tasks, total num).
bar_width (int): Width of progress ... | mmcv/utils/progressbar.py | def track_progress(func, tasks, bar_width=50, **kwargs):
"""Track the progress of tasks execution with a progress bar.
Tasks are done with a simple for-loop.
Args:
func (callable): The function to be applied to each task.
tasks (list or tuple[Iterable, int]): A list of tasks or
... | def track_progress(func, tasks, bar_width=50, **kwargs):
"""Track the progress of tasks execution with a progress bar.
Tasks are done with a simple for-loop.
Args:
func (callable): The function to be applied to each task.
tasks (list or tuple[Iterable, int]): A list of tasks or
... | [
"Track",
"the",
"progress",
"of",
"tasks",
"execution",
"with",
"a",
"progress",
"bar",
"."
] | open-mmlab/mmcv | python | https://github.com/open-mmlab/mmcv/blob/0d77f61450aab4dde8b8585a577cc496acb95d7f/mmcv/utils/progressbar.py#L63-L94 | [
"def",
"track_progress",
"(",
"func",
",",
"tasks",
",",
"bar_width",
"=",
"50",
",",
"*",
"*",
"kwargs",
")",
":",
"if",
"isinstance",
"(",
"tasks",
",",
"tuple",
")",
":",
"assert",
"len",
"(",
"tasks",
")",
"==",
"2",
"assert",
"isinstance",
"(",
... | 0d77f61450aab4dde8b8585a577cc496acb95d7f |
test | track_parallel_progress | Track the progress of parallel task execution with a progress bar.
The built-in :mod:`multiprocessing` module is used for process pools and
tasks are done with :func:`Pool.map` or :func:`Pool.imap_unordered`.
Args:
func (callable): The function to be applied to each task.
tasks (list or tu... | mmcv/utils/progressbar.py | def track_parallel_progress(func,
tasks,
nproc,
initializer=None,
initargs=None,
bar_width=50,
chunksize=1,
skip_first=False... | def track_parallel_progress(func,
tasks,
nproc,
initializer=None,
initargs=None,
bar_width=50,
chunksize=1,
skip_first=False... | [
"Track",
"the",
"progress",
"of",
"parallel",
"task",
"execution",
"with",
"a",
"progress",
"bar",
"."
] | open-mmlab/mmcv | python | https://github.com/open-mmlab/mmcv/blob/0d77f61450aab4dde8b8585a577cc496acb95d7f/mmcv/utils/progressbar.py#L108-L174 | [
"def",
"track_parallel_progress",
"(",
"func",
",",
"tasks",
",",
"nproc",
",",
"initializer",
"=",
"None",
",",
"initargs",
"=",
"None",
",",
"bar_width",
"=",
"50",
",",
"chunksize",
"=",
"1",
",",
"skip_first",
"=",
"False",
",",
"keep_order",
"=",
"T... | 0d77f61450aab4dde8b8585a577cc496acb95d7f |
test | imflip | Flip an image horizontally or vertically.
Args:
img (ndarray): Image to be flipped.
direction (str): The flip direction, either "horizontal" or "vertical".
Returns:
ndarray: The flipped image. | mmcv/image/transforms/geometry.py | def imflip(img, direction='horizontal'):
"""Flip an image horizontally or vertically.
Args:
img (ndarray): Image to be flipped.
direction (str): The flip direction, either "horizontal" or "vertical".
Returns:
ndarray: The flipped image.
"""
assert direction in ['horizontal'... | def imflip(img, direction='horizontal'):
"""Flip an image horizontally or vertically.
Args:
img (ndarray): Image to be flipped.
direction (str): The flip direction, either "horizontal" or "vertical".
Returns:
ndarray: The flipped image.
"""
assert direction in ['horizontal'... | [
"Flip",
"an",
"image",
"horizontally",
"or",
"vertically",
"."
] | open-mmlab/mmcv | python | https://github.com/open-mmlab/mmcv/blob/0d77f61450aab4dde8b8585a577cc496acb95d7f/mmcv/image/transforms/geometry.py#L7-L21 | [
"def",
"imflip",
"(",
"img",
",",
"direction",
"=",
"'horizontal'",
")",
":",
"assert",
"direction",
"in",
"[",
"'horizontal'",
",",
"'vertical'",
"]",
"if",
"direction",
"==",
"'horizontal'",
":",
"return",
"np",
".",
"flip",
"(",
"img",
",",
"axis",
"=... | 0d77f61450aab4dde8b8585a577cc496acb95d7f |
test | imrotate | Rotate an image.
Args:
img (ndarray): Image to be rotated.
angle (float): Rotation angle in degrees, positive values mean
clockwise rotation.
center (tuple): Center of the rotation in the source image, by default
it is the center of the image.
scale (float): ... | mmcv/image/transforms/geometry.py | def imrotate(img,
angle,
center=None,
scale=1.0,
border_value=0,
auto_bound=False):
"""Rotate an image.
Args:
img (ndarray): Image to be rotated.
angle (float): Rotation angle in degrees, positive values mean
clockwise... | def imrotate(img,
angle,
center=None,
scale=1.0,
border_value=0,
auto_bound=False):
"""Rotate an image.
Args:
img (ndarray): Image to be rotated.
angle (float): Rotation angle in degrees, positive values mean
clockwise... | [
"Rotate",
"an",
"image",
"."
] | open-mmlab/mmcv | python | https://github.com/open-mmlab/mmcv/blob/0d77f61450aab4dde8b8585a577cc496acb95d7f/mmcv/image/transforms/geometry.py#L24-L64 | [
"def",
"imrotate",
"(",
"img",
",",
"angle",
",",
"center",
"=",
"None",
",",
"scale",
"=",
"1.0",
",",
"border_value",
"=",
"0",
",",
"auto_bound",
"=",
"False",
")",
":",
"if",
"center",
"is",
"not",
"None",
"and",
"auto_bound",
":",
"raise",
"Valu... | 0d77f61450aab4dde8b8585a577cc496acb95d7f |
test | bbox_clip | Clip bboxes to fit the image shape.
Args:
bboxes (ndarray): Shape (..., 4*k)
img_shape (tuple): (height, width) of the image.
Returns:
ndarray: Clipped bboxes. | mmcv/image/transforms/geometry.py | def bbox_clip(bboxes, img_shape):
"""Clip bboxes to fit the image shape.
Args:
bboxes (ndarray): Shape (..., 4*k)
img_shape (tuple): (height, width) of the image.
Returns:
ndarray: Clipped bboxes.
"""
assert bboxes.shape[-1] % 4 == 0
clipped_bboxes = np.empty_like(bboxe... | def bbox_clip(bboxes, img_shape):
"""Clip bboxes to fit the image shape.
Args:
bboxes (ndarray): Shape (..., 4*k)
img_shape (tuple): (height, width) of the image.
Returns:
ndarray: Clipped bboxes.
"""
assert bboxes.shape[-1] % 4 == 0
clipped_bboxes = np.empty_like(bboxe... | [
"Clip",
"bboxes",
"to",
"fit",
"the",
"image",
"shape",
"."
] | open-mmlab/mmcv | python | https://github.com/open-mmlab/mmcv/blob/0d77f61450aab4dde8b8585a577cc496acb95d7f/mmcv/image/transforms/geometry.py#L67-L83 | [
"def",
"bbox_clip",
"(",
"bboxes",
",",
"img_shape",
")",
":",
"assert",
"bboxes",
".",
"shape",
"[",
"-",
"1",
"]",
"%",
"4",
"==",
"0",
"clipped_bboxes",
"=",
"np",
".",
"empty_like",
"(",
"bboxes",
",",
"dtype",
"=",
"bboxes",
".",
"dtype",
")",
... | 0d77f61450aab4dde8b8585a577cc496acb95d7f |
test | bbox_scaling | Scaling bboxes w.r.t the box center.
Args:
bboxes (ndarray): Shape(..., 4).
scale (float): Scaling factor.
clip_shape (tuple, optional): If specified, bboxes that exceed the
boundary will be clipped according to the given shape (h, w).
Returns:
ndarray: Scaled bboxe... | mmcv/image/transforms/geometry.py | def bbox_scaling(bboxes, scale, clip_shape=None):
"""Scaling bboxes w.r.t the box center.
Args:
bboxes (ndarray): Shape(..., 4).
scale (float): Scaling factor.
clip_shape (tuple, optional): If specified, bboxes that exceed the
boundary will be clipped according to the given ... | def bbox_scaling(bboxes, scale, clip_shape=None):
"""Scaling bboxes w.r.t the box center.
Args:
bboxes (ndarray): Shape(..., 4).
scale (float): Scaling factor.
clip_shape (tuple, optional): If specified, bboxes that exceed the
boundary will be clipped according to the given ... | [
"Scaling",
"bboxes",
"w",
".",
"r",
".",
"t",
"the",
"box",
"center",
"."
] | open-mmlab/mmcv | python | https://github.com/open-mmlab/mmcv/blob/0d77f61450aab4dde8b8585a577cc496acb95d7f/mmcv/image/transforms/geometry.py#L86-L109 | [
"def",
"bbox_scaling",
"(",
"bboxes",
",",
"scale",
",",
"clip_shape",
"=",
"None",
")",
":",
"if",
"float",
"(",
"scale",
")",
"==",
"1.0",
":",
"scaled_bboxes",
"=",
"bboxes",
".",
"copy",
"(",
")",
"else",
":",
"w",
"=",
"bboxes",
"[",
"...",
",... | 0d77f61450aab4dde8b8585a577cc496acb95d7f |
test | imcrop | Crop image patches.
3 steps: scale the bboxes -> clip bboxes -> crop and pad.
Args:
img (ndarray): Image to be cropped.
bboxes (ndarray): Shape (k, 4) or (4, ), location of cropped bboxes.
scale (float, optional): Scale ratio of bboxes, the default value
1.0 means no paddin... | mmcv/image/transforms/geometry.py | def imcrop(img, bboxes, scale=1.0, pad_fill=None):
"""Crop image patches.
3 steps: scale the bboxes -> clip bboxes -> crop and pad.
Args:
img (ndarray): Image to be cropped.
bboxes (ndarray): Shape (k, 4) or (4, ), location of cropped bboxes.
scale (float, optional): Scale ratio of... | def imcrop(img, bboxes, scale=1.0, pad_fill=None):
"""Crop image patches.
3 steps: scale the bboxes -> clip bboxes -> crop and pad.
Args:
img (ndarray): Image to be cropped.
bboxes (ndarray): Shape (k, 4) or (4, ), location of cropped bboxes.
scale (float, optional): Scale ratio of... | [
"Crop",
"image",
"patches",
"."
] | open-mmlab/mmcv | python | https://github.com/open-mmlab/mmcv/blob/0d77f61450aab4dde8b8585a577cc496acb95d7f/mmcv/image/transforms/geometry.py#L112-L163 | [
"def",
"imcrop",
"(",
"img",
",",
"bboxes",
",",
"scale",
"=",
"1.0",
",",
"pad_fill",
"=",
"None",
")",
":",
"chn",
"=",
"1",
"if",
"img",
".",
"ndim",
"==",
"2",
"else",
"img",
".",
"shape",
"[",
"2",
"]",
"if",
"pad_fill",
"is",
"not",
"None... | 0d77f61450aab4dde8b8585a577cc496acb95d7f |
test | impad | Pad an image to a certain shape.
Args:
img (ndarray): Image to be padded.
shape (tuple): Expected padding shape.
pad_val (number or sequence): Values to be filled in padding areas.
Returns:
ndarray: The padded image. | mmcv/image/transforms/geometry.py | def impad(img, shape, pad_val=0):
"""Pad an image to a certain shape.
Args:
img (ndarray): Image to be padded.
shape (tuple): Expected padding shape.
pad_val (number or sequence): Values to be filled in padding areas.
Returns:
ndarray: The padded image.
"""
if not i... | def impad(img, shape, pad_val=0):
"""Pad an image to a certain shape.
Args:
img (ndarray): Image to be padded.
shape (tuple): Expected padding shape.
pad_val (number or sequence): Values to be filled in padding areas.
Returns:
ndarray: The padded image.
"""
if not i... | [
"Pad",
"an",
"image",
"to",
"a",
"certain",
"shape",
"."
] | open-mmlab/mmcv | python | https://github.com/open-mmlab/mmcv/blob/0d77f61450aab4dde8b8585a577cc496acb95d7f/mmcv/image/transforms/geometry.py#L166-L187 | [
"def",
"impad",
"(",
"img",
",",
"shape",
",",
"pad_val",
"=",
"0",
")",
":",
"if",
"not",
"isinstance",
"(",
"pad_val",
",",
"(",
"int",
",",
"float",
")",
")",
":",
"assert",
"len",
"(",
"pad_val",
")",
"==",
"img",
".",
"shape",
"[",
"-",
"1... | 0d77f61450aab4dde8b8585a577cc496acb95d7f |
test | impad_to_multiple | Pad an image to ensure each edge to be multiple to some number.
Args:
img (ndarray): Image to be padded.
divisor (int): Padded image edges will be multiple to divisor.
pad_val (number or sequence): Same as :func:`impad`.
Returns:
ndarray: The padded image. | mmcv/image/transforms/geometry.py | def impad_to_multiple(img, divisor, pad_val=0):
"""Pad an image to ensure each edge to be multiple to some number.
Args:
img (ndarray): Image to be padded.
divisor (int): Padded image edges will be multiple to divisor.
pad_val (number or sequence): Same as :func:`impad`.
Returns:
... | def impad_to_multiple(img, divisor, pad_val=0):
"""Pad an image to ensure each edge to be multiple to some number.
Args:
img (ndarray): Image to be padded.
divisor (int): Padded image edges will be multiple to divisor.
pad_val (number or sequence): Same as :func:`impad`.
Returns:
... | [
"Pad",
"an",
"image",
"to",
"ensure",
"each",
"edge",
"to",
"be",
"multiple",
"to",
"some",
"number",
"."
] | open-mmlab/mmcv | python | https://github.com/open-mmlab/mmcv/blob/0d77f61450aab4dde8b8585a577cc496acb95d7f/mmcv/image/transforms/geometry.py#L190-L203 | [
"def",
"impad_to_multiple",
"(",
"img",
",",
"divisor",
",",
"pad_val",
"=",
"0",
")",
":",
"pad_h",
"=",
"int",
"(",
"np",
".",
"ceil",
"(",
"img",
".",
"shape",
"[",
"0",
"]",
"/",
"divisor",
")",
")",
"*",
"divisor",
"pad_w",
"=",
"int",
"(",
... | 0d77f61450aab4dde8b8585a577cc496acb95d7f |
test | _scale_size | Rescale a size by a ratio.
Args:
size (tuple): w, h.
scale (float): Scaling factor.
Returns:
tuple[int]: scaled size. | mmcv/image/transforms/resize.py | def _scale_size(size, scale):
"""Rescale a size by a ratio.
Args:
size (tuple): w, h.
scale (float): Scaling factor.
Returns:
tuple[int]: scaled size.
"""
w, h = size
return int(w * float(scale) + 0.5), int(h * float(scale) + 0.5) | def _scale_size(size, scale):
"""Rescale a size by a ratio.
Args:
size (tuple): w, h.
scale (float): Scaling factor.
Returns:
tuple[int]: scaled size.
"""
w, h = size
return int(w * float(scale) + 0.5), int(h * float(scale) + 0.5) | [
"Rescale",
"a",
"size",
"by",
"a",
"ratio",
"."
] | open-mmlab/mmcv | python | https://github.com/open-mmlab/mmcv/blob/0d77f61450aab4dde8b8585a577cc496acb95d7f/mmcv/image/transforms/resize.py#L6-L17 | [
"def",
"_scale_size",
"(",
"size",
",",
"scale",
")",
":",
"w",
",",
"h",
"=",
"size",
"return",
"int",
"(",
"w",
"*",
"float",
"(",
"scale",
")",
"+",
"0.5",
")",
",",
"int",
"(",
"h",
"*",
"float",
"(",
"scale",
")",
"+",
"0.5",
")"
] | 0d77f61450aab4dde8b8585a577cc496acb95d7f |
test | imresize | Resize image to a given size.
Args:
img (ndarray): The input image.
size (tuple): Target (w, h).
return_scale (bool): Whether to return `w_scale` and `h_scale`.
interpolation (str): Interpolation method, accepted values are
"nearest", "bilinear", "bicubic", "area", "lanc... | mmcv/image/transforms/resize.py | def imresize(img, size, return_scale=False, interpolation='bilinear'):
"""Resize image to a given size.
Args:
img (ndarray): The input image.
size (tuple): Target (w, h).
return_scale (bool): Whether to return `w_scale` and `h_scale`.
interpolation (str): Interpolation method, a... | def imresize(img, size, return_scale=False, interpolation='bilinear'):
"""Resize image to a given size.
Args:
img (ndarray): The input image.
size (tuple): Target (w, h).
return_scale (bool): Whether to return `w_scale` and `h_scale`.
interpolation (str): Interpolation method, a... | [
"Resize",
"image",
"to",
"a",
"given",
"size",
"."
] | open-mmlab/mmcv | python | https://github.com/open-mmlab/mmcv/blob/0d77f61450aab4dde8b8585a577cc496acb95d7f/mmcv/image/transforms/resize.py#L29-L51 | [
"def",
"imresize",
"(",
"img",
",",
"size",
",",
"return_scale",
"=",
"False",
",",
"interpolation",
"=",
"'bilinear'",
")",
":",
"h",
",",
"w",
"=",
"img",
".",
"shape",
"[",
":",
"2",
"]",
"resized_img",
"=",
"cv2",
".",
"resize",
"(",
"img",
","... | 0d77f61450aab4dde8b8585a577cc496acb95d7f |
test | imresize_like | Resize image to the same size of a given image.
Args:
img (ndarray): The input image.
dst_img (ndarray): The target image.
return_scale (bool): Whether to return `w_scale` and `h_scale`.
interpolation (str): Same as :func:`resize`.
Returns:
tuple or ndarray: (`resized_i... | mmcv/image/transforms/resize.py | def imresize_like(img, dst_img, return_scale=False, interpolation='bilinear'):
"""Resize image to the same size of a given image.
Args:
img (ndarray): The input image.
dst_img (ndarray): The target image.
return_scale (bool): Whether to return `w_scale` and `h_scale`.
interpolat... | def imresize_like(img, dst_img, return_scale=False, interpolation='bilinear'):
"""Resize image to the same size of a given image.
Args:
img (ndarray): The input image.
dst_img (ndarray): The target image.
return_scale (bool): Whether to return `w_scale` and `h_scale`.
interpolat... | [
"Resize",
"image",
"to",
"the",
"same",
"size",
"of",
"a",
"given",
"image",
"."
] | open-mmlab/mmcv | python | https://github.com/open-mmlab/mmcv/blob/0d77f61450aab4dde8b8585a577cc496acb95d7f/mmcv/image/transforms/resize.py#L54-L68 | [
"def",
"imresize_like",
"(",
"img",
",",
"dst_img",
",",
"return_scale",
"=",
"False",
",",
"interpolation",
"=",
"'bilinear'",
")",
":",
"h",
",",
"w",
"=",
"dst_img",
".",
"shape",
"[",
":",
"2",
"]",
"return",
"imresize",
"(",
"img",
",",
"(",
"w"... | 0d77f61450aab4dde8b8585a577cc496acb95d7f |
test | imrescale | Resize image while keeping the aspect ratio.
Args:
img (ndarray): The input image.
scale (float or tuple[int]): The scaling factor or maximum size.
If it is a float number, then the image will be rescaled by this
factor, else if it is a tuple of 2 integers, then the image wi... | mmcv/image/transforms/resize.py | def imrescale(img, scale, return_scale=False, interpolation='bilinear'):
"""Resize image while keeping the aspect ratio.
Args:
img (ndarray): The input image.
scale (float or tuple[int]): The scaling factor or maximum size.
If it is a float number, then the image will be rescaled by... | def imrescale(img, scale, return_scale=False, interpolation='bilinear'):
"""Resize image while keeping the aspect ratio.
Args:
img (ndarray): The input image.
scale (float or tuple[int]): The scaling factor or maximum size.
If it is a float number, then the image will be rescaled by... | [
"Resize",
"image",
"while",
"keeping",
"the",
"aspect",
"ratio",
"."
] | open-mmlab/mmcv | python | https://github.com/open-mmlab/mmcv/blob/0d77f61450aab4dde8b8585a577cc496acb95d7f/mmcv/image/transforms/resize.py#L71-L107 | [
"def",
"imrescale",
"(",
"img",
",",
"scale",
",",
"return_scale",
"=",
"False",
",",
"interpolation",
"=",
"'bilinear'",
")",
":",
"h",
",",
"w",
"=",
"img",
".",
"shape",
"[",
":",
"2",
"]",
"if",
"isinstance",
"(",
"scale",
",",
"(",
"float",
",... | 0d77f61450aab4dde8b8585a577cc496acb95d7f |
test | load | Load data from json/yaml/pickle files.
This method provides a unified api for loading data from serialized files.
Args:
file (str or file-like object): Filename or a file-like object.
file_format (str, optional): If not specified, the file format will be
inferred from the file exte... | mmcv/fileio/io.py | def load(file, file_format=None, **kwargs):
"""Load data from json/yaml/pickle files.
This method provides a unified api for loading data from serialized files.
Args:
file (str or file-like object): Filename or a file-like object.
file_format (str, optional): If not specified, the file for... | def load(file, file_format=None, **kwargs):
"""Load data from json/yaml/pickle files.
This method provides a unified api for loading data from serialized files.
Args:
file (str or file-like object): Filename or a file-like object.
file_format (str, optional): If not specified, the file for... | [
"Load",
"data",
"from",
"json",
"/",
"yaml",
"/",
"pickle",
"files",
"."
] | open-mmlab/mmcv | python | https://github.com/open-mmlab/mmcv/blob/0d77f61450aab4dde8b8585a577cc496acb95d7f/mmcv/fileio/io.py#L13-L40 | [
"def",
"load",
"(",
"file",
",",
"file_format",
"=",
"None",
",",
"*",
"*",
"kwargs",
")",
":",
"if",
"file_format",
"is",
"None",
"and",
"is_str",
"(",
"file",
")",
":",
"file_format",
"=",
"file",
".",
"split",
"(",
"'.'",
")",
"[",
"-",
"1",
"... | 0d77f61450aab4dde8b8585a577cc496acb95d7f |
test | dump | Dump data to json/yaml/pickle strings or files.
This method provides a unified api for dumping data as strings or to files,
and also supports custom arguments for each file format.
Args:
obj (any): The python object to be dumped.
file (str or file-like object, optional): If not specified, ... | mmcv/fileio/io.py | def dump(obj, file=None, file_format=None, **kwargs):
"""Dump data to json/yaml/pickle strings or files.
This method provides a unified api for dumping data as strings or to files,
and also supports custom arguments for each file format.
Args:
obj (any): The python object to be dumped.
... | def dump(obj, file=None, file_format=None, **kwargs):
"""Dump data to json/yaml/pickle strings or files.
This method provides a unified api for dumping data as strings or to files,
and also supports custom arguments for each file format.
Args:
obj (any): The python object to be dumped.
... | [
"Dump",
"data",
"to",
"json",
"/",
"yaml",
"/",
"pickle",
"strings",
"or",
"files",
"."
] | open-mmlab/mmcv | python | https://github.com/open-mmlab/mmcv/blob/0d77f61450aab4dde8b8585a577cc496acb95d7f/mmcv/fileio/io.py#L43-L76 | [
"def",
"dump",
"(",
"obj",
",",
"file",
"=",
"None",
",",
"file_format",
"=",
"None",
",",
"*",
"*",
"kwargs",
")",
":",
"if",
"file_format",
"is",
"None",
":",
"if",
"is_str",
"(",
"file",
")",
":",
"file_format",
"=",
"file",
".",
"split",
"(",
... | 0d77f61450aab4dde8b8585a577cc496acb95d7f |
test | _register_handler | Register a handler for some file extensions.
Args:
handler (:obj:`BaseFileHandler`): Handler to be registered.
file_formats (str or list[str]): File formats to be handled by this
handler. | mmcv/fileio/io.py | def _register_handler(handler, file_formats):
"""Register a handler for some file extensions.
Args:
handler (:obj:`BaseFileHandler`): Handler to be registered.
file_formats (str or list[str]): File formats to be handled by this
handler.
"""
if not isinstance(handler, BaseFil... | def _register_handler(handler, file_formats):
"""Register a handler for some file extensions.
Args:
handler (:obj:`BaseFileHandler`): Handler to be registered.
file_formats (str or list[str]): File formats to be handled by this
handler.
"""
if not isinstance(handler, BaseFil... | [
"Register",
"a",
"handler",
"for",
"some",
"file",
"extensions",
"."
] | open-mmlab/mmcv | python | https://github.com/open-mmlab/mmcv/blob/0d77f61450aab4dde8b8585a577cc496acb95d7f/mmcv/fileio/io.py#L79-L96 | [
"def",
"_register_handler",
"(",
"handler",
",",
"file_formats",
")",
":",
"if",
"not",
"isinstance",
"(",
"handler",
",",
"BaseFileHandler",
")",
":",
"raise",
"TypeError",
"(",
"'handler must be a child of BaseFileHandler, not {}'",
".",
"format",
"(",
"type",
"("... | 0d77f61450aab4dde8b8585a577cc496acb95d7f |
test | get_priority | Get priority value.
Args:
priority (int or str or :obj:`Priority`): Priority.
Returns:
int: The priority value. | mmcv/runner/priority.py | def get_priority(priority):
"""Get priority value.
Args:
priority (int or str or :obj:`Priority`): Priority.
Returns:
int: The priority value.
"""
if isinstance(priority, int):
if priority < 0 or priority > 100:
raise ValueError('priority must be between 0 and 1... | def get_priority(priority):
"""Get priority value.
Args:
priority (int or str or :obj:`Priority`): Priority.
Returns:
int: The priority value.
"""
if isinstance(priority, int):
if priority < 0 or priority > 100:
raise ValueError('priority must be between 0 and 1... | [
"Get",
"priority",
"value",
"."
] | open-mmlab/mmcv | python | https://github.com/open-mmlab/mmcv/blob/0d77f61450aab4dde8b8585a577cc496acb95d7f/mmcv/runner/priority.py#L35-L53 | [
"def",
"get_priority",
"(",
"priority",
")",
":",
"if",
"isinstance",
"(",
"priority",
",",
"int",
")",
":",
"if",
"priority",
"<",
"0",
"or",
"priority",
">",
"100",
":",
"raise",
"ValueError",
"(",
"'priority must be between 0 and 100'",
")",
"return",
"pr... | 0d77f61450aab4dde8b8585a577cc496acb95d7f |
test | quantize | Quantize an array of (-inf, inf) to [0, levels-1].
Args:
arr (ndarray): Input array.
min_val (scalar): Minimum value to be clipped.
max_val (scalar): Maximum value to be clipped.
levels (int): Quantization levels.
dtype (np.type): The type of the quantized array.
Return... | mmcv/arraymisc/quantization.py | def quantize(arr, min_val, max_val, levels, dtype=np.int64):
"""Quantize an array of (-inf, inf) to [0, levels-1].
Args:
arr (ndarray): Input array.
min_val (scalar): Minimum value to be clipped.
max_val (scalar): Maximum value to be clipped.
levels (int): Quantization levels.
... | def quantize(arr, min_val, max_val, levels, dtype=np.int64):
"""Quantize an array of (-inf, inf) to [0, levels-1].
Args:
arr (ndarray): Input array.
min_val (scalar): Minimum value to be clipped.
max_val (scalar): Maximum value to be clipped.
levels (int): Quantization levels.
... | [
"Quantize",
"an",
"array",
"of",
"(",
"-",
"inf",
"inf",
")",
"to",
"[",
"0",
"levels",
"-",
"1",
"]",
"."
] | open-mmlab/mmcv | python | https://github.com/open-mmlab/mmcv/blob/0d77f61450aab4dde8b8585a577cc496acb95d7f/mmcv/arraymisc/quantization.py#L4-L29 | [
"def",
"quantize",
"(",
"arr",
",",
"min_val",
",",
"max_val",
",",
"levels",
",",
"dtype",
"=",
"np",
".",
"int64",
")",
":",
"if",
"not",
"(",
"isinstance",
"(",
"levels",
",",
"int",
")",
"and",
"levels",
">",
"1",
")",
":",
"raise",
"ValueError... | 0d77f61450aab4dde8b8585a577cc496acb95d7f |
test | dequantize | Dequantize an array.
Args:
arr (ndarray): Input array.
min_val (scalar): Minimum value to be clipped.
max_val (scalar): Maximum value to be clipped.
levels (int): Quantization levels.
dtype (np.type): The type of the dequantized array.
Returns:
tuple: Dequantize... | mmcv/arraymisc/quantization.py | def dequantize(arr, min_val, max_val, levels, dtype=np.float64):
"""Dequantize an array.
Args:
arr (ndarray): Input array.
min_val (scalar): Minimum value to be clipped.
max_val (scalar): Maximum value to be clipped.
levels (int): Quantization levels.
dtype (np.type): Th... | def dequantize(arr, min_val, max_val, levels, dtype=np.float64):
"""Dequantize an array.
Args:
arr (ndarray): Input array.
min_val (scalar): Minimum value to be clipped.
max_val (scalar): Maximum value to be clipped.
levels (int): Quantization levels.
dtype (np.type): Th... | [
"Dequantize",
"an",
"array",
"."
] | open-mmlab/mmcv | python | https://github.com/open-mmlab/mmcv/blob/0d77f61450aab4dde8b8585a577cc496acb95d7f/mmcv/arraymisc/quantization.py#L32-L56 | [
"def",
"dequantize",
"(",
"arr",
",",
"min_val",
",",
"max_val",
",",
"levels",
",",
"dtype",
"=",
"np",
".",
"float64",
")",
":",
"if",
"not",
"(",
"isinstance",
"(",
"levels",
",",
"int",
")",
"and",
"levels",
">",
"1",
")",
":",
"raise",
"ValueE... | 0d77f61450aab4dde8b8585a577cc496acb95d7f |
test | Config.auto_argparser | Generate argparser from config file automatically (experimental) | mmcv/utils/config.py | def auto_argparser(description=None):
"""Generate argparser from config file automatically (experimental)
"""
partial_parser = ArgumentParser(description=description)
partial_parser.add_argument('config', help='config file path')
cfg_file = partial_parser.parse_known_args()[0].co... | def auto_argparser(description=None):
"""Generate argparser from config file automatically (experimental)
"""
partial_parser = ArgumentParser(description=description)
partial_parser.add_argument('config', help='config file path')
cfg_file = partial_parser.parse_known_args()[0].co... | [
"Generate",
"argparser",
"from",
"config",
"file",
"automatically",
"(",
"experimental",
")"
] | open-mmlab/mmcv | python | https://github.com/open-mmlab/mmcv/blob/0d77f61450aab4dde8b8585a577cc496acb95d7f/mmcv/utils/config.py#L100-L110 | [
"def",
"auto_argparser",
"(",
"description",
"=",
"None",
")",
":",
"partial_parser",
"=",
"ArgumentParser",
"(",
"description",
"=",
"description",
")",
"partial_parser",
".",
"add_argument",
"(",
"'config'",
",",
"help",
"=",
"'config file path'",
")",
"cfg_file... | 0d77f61450aab4dde8b8585a577cc496acb95d7f |
test | collate | Puts each data field into a tensor/DataContainer with outer dimension
batch size.
Extend default_collate to add support for
:type:`~mmcv.parallel.DataContainer`. There are 3 cases.
1. cpu_only = True, e.g., meta data
2. cpu_only = False, stack = True, e.g., images tensors
3. cpu_only = False, ... | mmcv/parallel/collate.py | def collate(batch, samples_per_gpu=1):
"""Puts each data field into a tensor/DataContainer with outer dimension
batch size.
Extend default_collate to add support for
:type:`~mmcv.parallel.DataContainer`. There are 3 cases.
1. cpu_only = True, e.g., meta data
2. cpu_only = False, stack = True, ... | def collate(batch, samples_per_gpu=1):
"""Puts each data field into a tensor/DataContainer with outer dimension
batch size.
Extend default_collate to add support for
:type:`~mmcv.parallel.DataContainer`. There are 3 cases.
1. cpu_only = True, e.g., meta data
2. cpu_only = False, stack = True, ... | [
"Puts",
"each",
"data",
"field",
"into",
"a",
"tensor",
"/",
"DataContainer",
"with",
"outer",
"dimension",
"batch",
"size",
"."
] | open-mmlab/mmcv | python | https://github.com/open-mmlab/mmcv/blob/0d77f61450aab4dde8b8585a577cc496acb95d7f/mmcv/parallel/collate.py#L10-L66 | [
"def",
"collate",
"(",
"batch",
",",
"samples_per_gpu",
"=",
"1",
")",
":",
"if",
"not",
"isinstance",
"(",
"batch",
",",
"collections",
".",
"Sequence",
")",
":",
"raise",
"TypeError",
"(",
"\"{} is not supported.\"",
".",
"format",
"(",
"batch",
".",
"dt... | 0d77f61450aab4dde8b8585a577cc496acb95d7f |
Subsets and Splits
No community queries yet
The top public SQL queries from the community will appear here once available.