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craffel/mir_eval | mir_eval/util.py | _fast_hit_windows | def _fast_hit_windows(ref, est, window):
'''Fast calculation of windowed hits for time events.
Given two lists of event times ``ref`` and ``est``, and a
tolerance window, computes a list of pairings
``(i, j)`` where ``|ref[i] - est[j]| <= window``.
This is equivalent to, but more efficient than th... | python | def _fast_hit_windows(ref, est, window):
'''Fast calculation of windowed hits for time events.
Given two lists of event times ``ref`` and ``est``, and a
tolerance window, computes a list of pairings
``(i, j)`` where ``|ref[i] - est[j]| <= window``.
This is equivalent to, but more efficient than th... | [
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craffel/mir_eval | mir_eval/util.py | validate_events | def validate_events(events, max_time=30000.):
"""Checks that a 1-d event location ndarray is well-formed, and raises
errors if not.
Parameters
----------
events : np.ndarray, shape=(n,)
Array of event times
max_time : float
If an event is found above this time, a ValueError will... | python | def validate_events(events, max_time=30000.):
"""Checks that a 1-d event location ndarray is well-formed, and raises
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Parameters
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events : np.ndarray, shape=(n,)
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craffel/mir_eval | mir_eval/util.py | validate_frequencies | def validate_frequencies(frequencies, max_freq, min_freq,
allow_negatives=False):
"""Checks that a 1-d frequency ndarray is well-formed, and raises
errors if not.
Parameters
----------
frequencies : np.ndarray, shape=(n,)
Array of frequency values
max_freq : flo... | python | def validate_frequencies(frequencies, max_freq, min_freq,
allow_negatives=False):
"""Checks that a 1-d frequency ndarray is well-formed, and raises
errors if not.
Parameters
----------
frequencies : np.ndarray, shape=(n,)
Array of frequency values
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craffel/mir_eval | mir_eval/util.py | intervals_to_durations | def intervals_to_durations(intervals):
"""Converts an array of n intervals to their n durations.
Parameters
----------
intervals : np.ndarray, shape=(n, 2)
An array of time intervals, as returned by
:func:`mir_eval.io.load_intervals()`.
The ``i`` th interval spans time ``interva... | python | def intervals_to_durations(intervals):
"""Converts an array of n intervals to their n durations.
Parameters
----------
intervals : np.ndarray, shape=(n, 2)
An array of time intervals, as returned by
:func:`mir_eval.io.load_intervals()`.
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craffel/mir_eval | mir_eval/separation.py | validate | def validate(reference_sources, estimated_sources):
"""Checks that the input data to a metric are valid, and throws helpful
errors if not.
Parameters
----------
reference_sources : np.ndarray, shape=(nsrc, nsampl)
matrix containing true sources
estimated_sources : np.ndarray, shape=(nsr... | python | def validate(reference_sources, estimated_sources):
"""Checks that the input data to a metric are valid, and throws helpful
errors if not.
Parameters
----------
reference_sources : np.ndarray, shape=(nsrc, nsampl)
matrix containing true sources
estimated_sources : np.ndarray, shape=(nsr... | [
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craffel/mir_eval | mir_eval/separation.py | _any_source_silent | def _any_source_silent(sources):
"""Returns true if the parameter sources has any silent first dimensions"""
return np.any(np.all(np.sum(
sources, axis=tuple(range(2, sources.ndim))) == 0, axis=1)) | python | def _any_source_silent(sources):
"""Returns true if the parameter sources has any silent first dimensions"""
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craffel/mir_eval | mir_eval/separation.py | bss_eval_sources | def bss_eval_sources(reference_sources, estimated_sources,
compute_permutation=True):
"""
Ordering and measurement of the separation quality for estimated source
signals in terms of filtered true source, interference and artifacts.
The decomposition allows a time-invariant filter d... | python | def bss_eval_sources(reference_sources, estimated_sources,
compute_permutation=True):
"""
Ordering and measurement of the separation quality for estimated source
signals in terms of filtered true source, interference and artifacts.
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craffel/mir_eval | mir_eval/separation.py | bss_eval_sources_framewise | def bss_eval_sources_framewise(reference_sources, estimated_sources,
window=30*44100, hop=15*44100,
compute_permutation=False):
"""Framewise computation of bss_eval_sources
Please be aware that this function does not compute permutations (by
def... | python | def bss_eval_sources_framewise(reference_sources, estimated_sources,
window=30*44100, hop=15*44100,
compute_permutation=False):
"""Framewise computation of bss_eval_sources
Please be aware that this function does not compute permutations (by
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craffel/mir_eval | mir_eval/separation.py | bss_eval_images_framewise | def bss_eval_images_framewise(reference_sources, estimated_sources,
window=30*44100, hop=15*44100,
compute_permutation=False):
"""Framewise computation of bss_eval_images
Please be aware that this function does not compute permutations (by
default... | python | def bss_eval_images_framewise(reference_sources, estimated_sources,
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compute_permutation=False):
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craffel/mir_eval | mir_eval/separation.py | _project | def _project(reference_sources, estimated_source, flen):
"""Least-squares projection of estimated source on the subspace spanned by
delayed versions of reference sources, with delays between 0 and flen-1
"""
nsrc = reference_sources.shape[0]
nsampl = reference_sources.shape[1]
# computing coeff... | python | def _project(reference_sources, estimated_source, flen):
"""Least-squares projection of estimated source on the subspace spanned by
delayed versions of reference sources, with delays between 0 and flen-1
"""
nsrc = reference_sources.shape[0]
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craffel/mir_eval | mir_eval/separation.py | _bss_image_crit | def _bss_image_crit(s_true, e_spat, e_interf, e_artif):
"""Measurement of the separation quality for a given image in terms of
filtered true source, spatial error, interference and artifacts.
"""
# energy ratios
sdr = _safe_db(np.sum(s_true**2), np.sum((e_spat+e_interf+e_artif)**2))
isr = _safe_... | python | def _bss_image_crit(s_true, e_spat, e_interf, e_artif):
"""Measurement of the separation quality for a given image in terms of
filtered true source, spatial error, interference and artifacts.
"""
# energy ratios
sdr = _safe_db(np.sum(s_true**2), np.sum((e_spat+e_interf+e_artif)**2))
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craffel/mir_eval | mir_eval/separation.py | _safe_db | def _safe_db(num, den):
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"""
if den == 0:
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return 10 * np.log10(num / den) | python | def _safe_db(num, den):
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craffel/mir_eval | mir_eval/separation.py | evaluate | def evaluate(reference_sources, estimated_sources, **kwargs):
"""Compute all metrics for the given reference and estimated signals.
NOTE: This will always compute :func:`mir_eval.separation.bss_eval_images`
for any valid input and will additionally compute
:func:`mir_eval.separation.bss_eval_sources` f... | python | def evaluate(reference_sources, estimated_sources, **kwargs):
"""Compute all metrics for the given reference and estimated signals.
NOTE: This will always compute :func:`mir_eval.separation.bss_eval_images`
for any valid input and will additionally compute
:func:`mir_eval.separation.bss_eval_sources` f... | [
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craffel/mir_eval | mir_eval/sonify.py | clicks | def clicks(times, fs, click=None, length=None):
"""Returns a signal with the signal 'click' placed at each specified time
Parameters
----------
times : np.ndarray
times to place clicks, in seconds
fs : int
desired sampling rate of the output signal
click : np.ndarray
cli... | python | def clicks(times, fs, click=None, length=None):
"""Returns a signal with the signal 'click' placed at each specified time
Parameters
----------
times : np.ndarray
times to place clicks, in seconds
fs : int
desired sampling rate of the output signal
click : np.ndarray
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desired sampling rate of the output signal
click : np.ndarray
click signal, defaults to a 1 kHz blip
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craffel/mir_eval | mir_eval/sonify.py | time_frequency | def time_frequency(gram, frequencies, times, fs, function=np.sin, length=None,
n_dec=1):
"""Reverse synthesis of a time-frequency representation of a signal
Parameters
----------
gram : np.ndarray
``gram[n, m]`` is the magnitude of ``frequencies[n]``
from ``times[m]``... | python | def time_frequency(gram, frequencies, times, fs, function=np.sin, length=None,
n_dec=1):
"""Reverse synthesis of a time-frequency representation of a signal
Parameters
----------
gram : np.ndarray
``gram[n, m]`` is the magnitude of ``frequencies[n]``
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craffel/mir_eval | mir_eval/sonify.py | pitch_contour | def pitch_contour(times, frequencies, fs, amplitudes=None, function=np.sin,
length=None, kind='linear'):
'''Sonify a pitch contour.
Parameters
----------
times : np.ndarray
time indices for each frequency measurement, in seconds
frequencies : np.ndarray
frequency ... | python | def pitch_contour(times, frequencies, fs, amplitudes=None, function=np.sin,
length=None, kind='linear'):
'''Sonify a pitch contour.
Parameters
----------
times : np.ndarray
time indices for each frequency measurement, in seconds
frequencies : np.ndarray
frequency ... | [
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craffel/mir_eval | mir_eval/sonify.py | chords | def chords(chord_labels, intervals, fs, **kwargs):
"""Synthesizes chord labels
Parameters
----------
chord_labels : list of str
List of chord label strings.
intervals : np.ndarray, shape=(len(chord_labels), 2)
Start and end times of each chord label
fs : int
Sampling rat... | python | def chords(chord_labels, intervals, fs, **kwargs):
"""Synthesizes chord labels
Parameters
----------
chord_labels : list of str
List of chord label strings.
intervals : np.ndarray, shape=(len(chord_labels), 2)
Start and end times of each chord label
fs : int
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craffel/mir_eval | mir_eval/onset.py | validate | def validate(reference_onsets, estimated_onsets):
"""Checks that the input annotations to a metric look like valid onset time
arrays, and throws helpful errors if not.
Parameters
----------
reference_onsets : np.ndarray
reference onset locations, in seconds
estimated_onsets : np.ndarray... | python | def validate(reference_onsets, estimated_onsets):
"""Checks that the input annotations to a metric look like valid onset time
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Parameters
----------
reference_onsets : np.ndarray
reference onset locations, in seconds
estimated_onsets : np.ndarray... | [
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craffel/mir_eval | mir_eval/onset.py | f_measure | def f_measure(reference_onsets, estimated_onsets, window=.05):
"""Compute the F-measure of correct vs incorrectly predicted onsets.
"Corectness" is determined over a small window.
Examples
--------
>>> reference_onsets = mir_eval.io.load_events('reference.txt')
>>> estimated_onsets = mir_eval.i... | python | def f_measure(reference_onsets, estimated_onsets, window=.05):
"""Compute the F-measure of correct vs incorrectly predicted onsets.
"Corectness" is determined over a small window.
Examples
--------
>>> reference_onsets = mir_eval.io.load_events('reference.txt')
>>> estimated_onsets = mir_eval.i... | [
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>>> reference_onsets = mir_eval.io.load_events('reference.txt')
>>> estimated_onsets = mir_eval.io.load_events('estimated.txt')
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craffel/mir_eval | mir_eval/transcription.py | validate | def validate(ref_intervals, ref_pitches, est_intervals, est_pitches):
"""Checks that the input annotations to a metric look like time intervals
and a pitch list, and throws helpful errors if not.
Parameters
----------
ref_intervals : np.ndarray, shape=(n,2)
Array of reference notes time int... | python | def validate(ref_intervals, ref_pitches, est_intervals, est_pitches):
"""Checks that the input annotations to a metric look like time intervals
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----------
ref_intervals : np.ndarray, shape=(n,2)
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craffel/mir_eval | mir_eval/transcription.py | validate_intervals | def validate_intervals(ref_intervals, est_intervals):
"""Checks that the input annotations to a metric look like time intervals,
and throws helpful errors if not.
Parameters
----------
ref_intervals : np.ndarray, shape=(n,2)
Array of reference notes time intervals (onset and offset times)
... | python | def validate_intervals(ref_intervals, est_intervals):
"""Checks that the input annotations to a metric look like time intervals,
and throws helpful errors if not.
Parameters
----------
ref_intervals : np.ndarray, shape=(n,2)
Array of reference notes time intervals (onset and offset times)
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craffel/mir_eval | mir_eval/transcription.py | match_note_offsets | def match_note_offsets(ref_intervals, est_intervals, offset_ratio=0.2,
offset_min_tolerance=0.05, strict=False):
"""Compute a maximum matching between reference and estimated notes,
only taking note offsets into account.
Given two note sequences represented by ``ref_intervals`` and
... | python | def match_note_offsets(ref_intervals, est_intervals, offset_ratio=0.2,
offset_min_tolerance=0.05, strict=False):
"""Compute a maximum matching between reference and estimated notes,
only taking note offsets into account.
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craffel/mir_eval | mir_eval/transcription.py | match_note_onsets | def match_note_onsets(ref_intervals, est_intervals, onset_tolerance=0.05,
strict=False):
"""Compute a maximum matching between reference and estimated notes,
only taking note onsets into account.
Given two note sequences represented by ``ref_intervals`` and
``est_intervals`` (see ... | python | def match_note_onsets(ref_intervals, est_intervals, onset_tolerance=0.05,
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"""Compute a maximum matching between reference and estimated notes,
only taking note onsets into account.
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craffel/mir_eval | mir_eval/melody.py | validate_voicing | def validate_voicing(ref_voicing, est_voicing):
"""Checks that voicing inputs to a metric are in the correct format.
Parameters
----------
ref_voicing : np.ndarray
Reference boolean voicing array
est_voicing : np.ndarray
Estimated boolean voicing array
"""
if ref_voicing.si... | python | def validate_voicing(ref_voicing, est_voicing):
"""Checks that voicing inputs to a metric are in the correct format.
Parameters
----------
ref_voicing : np.ndarray
Reference boolean voicing array
est_voicing : np.ndarray
Estimated boolean voicing array
"""
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craffel/mir_eval | mir_eval/melody.py | hz2cents | def hz2cents(freq_hz, base_frequency=10.0):
"""Convert an array of frequency values in Hz to cents.
0 values are left in place.
Parameters
----------
freq_hz : np.ndarray
Array of frequencies in Hz.
base_frequency : float
Base frequency for conversion.
(Default value = 1... | python | def hz2cents(freq_hz, base_frequency=10.0):
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freq_hz : np.ndarray
Array of frequencies in Hz.
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Base frequency for conversion.
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craffel/mir_eval | mir_eval/melody.py | constant_hop_timebase | def constant_hop_timebase(hop, end_time):
"""Generates a time series from 0 to ``end_time`` with times spaced ``hop``
apart
Parameters
----------
hop : float
Spacing of samples in the time series
end_time : float
Time series will span ``[0, end_time]``
Returns
-------
... | python | def constant_hop_timebase(hop, end_time):
"""Generates a time series from 0 to ``end_time`` with times spaced ``hop``
apart
Parameters
----------
hop : float
Spacing of samples in the time series
end_time : float
Time series will span ``[0, end_time]``
Returns
-------
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craffel/mir_eval | mir_eval/segment.py | detection | def detection(reference_intervals, estimated_intervals,
window=0.5, beta=1.0, trim=False):
"""Boundary detection hit-rate.
A hit is counted whenever an reference boundary is within ``window`` of a
estimated boundary. Note that each boundary is matched at most once: this
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window=0.5, beta=1.0, trim=False):
"""Boundary detection hit-rate.
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craffel/mir_eval | mir_eval/segment.py | deviation | def deviation(reference_intervals, estimated_intervals, trim=False):
"""Compute the median deviations between reference
and estimated boundary times.
Examples
--------
>>> ref_intervals, _ = mir_eval.io.load_labeled_intervals('ref.lab')
>>> est_intervals, _ = mir_eval.io.load_labeled_intervals(... | python | def deviation(reference_intervals, estimated_intervals, trim=False):
"""Compute the median deviations between reference
and estimated boundary times.
Examples
--------
>>> ref_intervals, _ = mir_eval.io.load_labeled_intervals('ref.lab')
>>> est_intervals, _ = mir_eval.io.load_labeled_intervals(... | [
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craffel/mir_eval | mir_eval/segment.py | pairwise | def pairwise(reference_intervals, reference_labels,
estimated_intervals, estimated_labels,
frame_size=0.1, beta=1.0):
"""Frame-clustering segmentation evaluation by pair-wise agreement.
Examples
--------
>>> (ref_intervals,
... ref_labels) = mir_eval.io.load_labeled_inter... | python | def pairwise(reference_intervals, reference_labels,
estimated_intervals, estimated_labels,
frame_size=0.1, beta=1.0):
"""Frame-clustering segmentation evaluation by pair-wise agreement.
Examples
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>>> (ref_intervals,
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craffel/mir_eval | mir_eval/segment.py | _contingency_matrix | def _contingency_matrix(reference_indices, estimated_indices):
"""Computes the contingency matrix of a true labeling vs an estimated one.
Parameters
----------
reference_indices : np.ndarray
Array of reference indices
estimated_indices : np.ndarray
Array of estimated indices
Re... | python | def _contingency_matrix(reference_indices, estimated_indices):
"""Computes the contingency matrix of a true labeling vs an estimated one.
Parameters
----------
reference_indices : np.ndarray
Array of reference indices
estimated_indices : np.ndarray
Array of estimated indices
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craffel/mir_eval | mir_eval/segment.py | _adjusted_rand_index | def _adjusted_rand_index(reference_indices, estimated_indices):
"""Compute the Rand index, adjusted for change.
Parameters
----------
reference_indices : np.ndarray
Array of reference indices
estimated_indices : np.ndarray
Array of estimated indices
Returns
-------
ari ... | python | def _adjusted_rand_index(reference_indices, estimated_indices):
"""Compute the Rand index, adjusted for change.
Parameters
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reference_indices : np.ndarray
Array of reference indices
estimated_indices : np.ndarray
Array of estimated indices
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craffel/mir_eval | mir_eval/segment.py | _mutual_info_score | def _mutual_info_score(reference_indices, estimated_indices, contingency=None):
"""Compute the mutual information between two sequence labelings.
Parameters
----------
reference_indices : np.ndarray
Array of reference indices
estimated_indices : np.ndarray
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reference_indices : np.ndarray
Array of reference indices
estimated_indices : np.ndarray
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craffel/mir_eval | mir_eval/segment.py | _entropy | def _entropy(labels):
"""Calculates the entropy for a labeling.
Parameters
----------
labels : list-like
List of labels.
Returns
-------
entropy : float
Entropy of the labeling.
.. note:: Based on sklearn.metrics.cluster.entropy
"""
if len(labels) == 0:
... | python | def _entropy(labels):
"""Calculates the entropy for a labeling.
Parameters
----------
labels : list-like
List of labels.
Returns
-------
entropy : float
Entropy of the labeling.
.. note:: Based on sklearn.metrics.cluster.entropy
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craffel/mir_eval | mir_eval/tempo.py | validate_tempi | def validate_tempi(tempi, reference=True):
"""Checks that there are two non-negative tempi.
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Parameters
----------
tempi : np.ndarray
length-2 array of tempo, in bpm
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"""Checks that there are two non-negative tempi.
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length-2 array of tempo, in bpm
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craffel/mir_eval | mir_eval/tempo.py | validate | def validate(reference_tempi, reference_weight, estimated_tempi):
"""Checks that the input annotations to a metric look like valid tempo
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Parameters
----------
reference_tempi : np.ndarray
reference tempo values, in bpm
reference_weight : float
perceptual weight of ... | python | def validate(reference_tempi, reference_weight, estimated_tempi):
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reference_tempi : np.ndarray
reference tempo values, in bpm
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perceptual weight of ... | [
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craffel/mir_eval | mir_eval/tempo.py | detection | def detection(reference_tempi, reference_weight, estimated_tempi, tol=0.08):
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Parameters
----------
reference_tempi : np.ndarray, shape=(2,)
Two non-negative reference tempi
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craffel/mir_eval | mir_eval/multipitch.py | validate | def validate(ref_time, ref_freqs, est_time, est_freqs):
"""Checks that the time and frequency inputs are well-formed.
Parameters
----------
ref_time : np.ndarray
reference time stamps in seconds
ref_freqs : list of np.ndarray
reference frequencies in Hz
est_time : np.ndarray
... | python | def validate(ref_time, ref_freqs, est_time, est_freqs):
"""Checks that the time and frequency inputs are well-formed.
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ref_time : np.ndarray
reference time stamps in seconds
ref_freqs : list of np.ndarray
reference frequencies in Hz
est_time : np.ndarray
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craffel/mir_eval | mir_eval/multipitch.py | resample_multipitch | def resample_multipitch(times, frequencies, target_times):
"""Resamples multipitch time series to a new timescale. Values in
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----------
times : np.ndarray
Array of time stamps
frequencies : list of np.n... | python | def resample_multipitch(times, frequencies, target_times):
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craffel/mir_eval | mir_eval/multipitch.py | compute_num_true_positives | def compute_num_true_positives(ref_freqs, est_freqs, window=0.5, chroma=False):
"""Compute the number of true positives in an estimate given a reference.
A frequency is correct if it is within a quartertone of the
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Parameters
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ref_freqs : list of np.ndarray
r... | python | def compute_num_true_positives(ref_freqs, est_freqs, window=0.5, chroma=False):
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craffel/mir_eval | mir_eval/multipitch.py | compute_accuracy | def compute_accuracy(true_positives, n_ref, n_est):
"""Compute accuracy metrics.
Parameters
----------
true_positives : np.ndarray
Array containing the number of true positives at each time point.
n_ref : np.ndarray
Array containing the number of reference frequencies at each time
... | python | def compute_accuracy(true_positives, n_ref, n_est):
"""Compute accuracy metrics.
Parameters
----------
true_positives : np.ndarray
Array containing the number of true positives at each time point.
n_ref : np.ndarray
Array containing the number of reference frequencies at each time
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craffel/mir_eval | mir_eval/multipitch.py | compute_err_score | def compute_err_score(true_positives, n_ref, n_est):
"""Compute error score metrics.
Parameters
----------
true_positives : np.ndarray
Array containing the number of true positives at each time point.
n_ref : np.ndarray
Array containing the number of reference frequencies at each ti... | python | def compute_err_score(true_positives, n_ref, n_est):
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true_positives : np.ndarray
Array containing the number of true positives at each time point.
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craffel/mir_eval | mir_eval/hierarchy.py | _hierarchy_bounds | def _hierarchy_bounds(intervals_hier):
'''Compute the covered time range of a hierarchical segmentation.
Parameters
----------
intervals_hier : list of ndarray
A hierarchical segmentation, encoded as a list of arrays of segment
intervals.
Returns
-------
t_min : float
t... | python | def _hierarchy_bounds(intervals_hier):
'''Compute the covered time range of a hierarchical segmentation.
Parameters
----------
intervals_hier : list of ndarray
A hierarchical segmentation, encoded as a list of arrays of segment
intervals.
Returns
-------
t_min : float
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craffel/mir_eval | mir_eval/hierarchy.py | _align_intervals | def _align_intervals(int_hier, lab_hier, t_min=0.0, t_max=None):
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Parameters
----------
int_hier : list of list of intervals
lab_hier : list of list of str
Hierarchical segment annotations, encoded as a
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craffel/mir_eval | mir_eval/hierarchy.py | _compare_frame_rankings | def _compare_frame_rankings(ref, est, transitive=False):
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Parameters
----------
ref : np.ndarray, shape=(n,)
est : np.ndarray, shape=(n,)
Reference and estimate ranked lists.
`ref[i]` is the relevance score for point `i`.
... | python | def _compare_frame_rankings(ref, est, transitive=False):
'''Compute the number of ranking disagreements in two lists.
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craffel/mir_eval | mir_eval/hierarchy.py | validate_hier_intervals | def validate_hier_intervals(intervals_hier):
'''Validate a hierarchical segment annotation.
Parameters
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intervals_hier : ordered list of segmentations
Raises
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ValueError
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... | python | def validate_hier_intervals(intervals_hier):
'''Validate a hierarchical segment annotation.
Parameters
----------
intervals_hier : ordered list of segmentations
Raises
------
ValueError
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craffel/mir_eval | mir_eval/hierarchy.py | evaluate | def evaluate(ref_intervals_hier, ref_labels_hier,
est_intervals_hier, est_labels_hier, **kwargs):
'''Compute all hierarchical structure metrics for the given reference and
estimated annotations.
Examples
--------
A toy example with two two-layer annotations
>>> ref_i = [[[0, 30], ... | python | def evaluate(ref_intervals_hier, ref_labels_hier,
est_intervals_hier, est_labels_hier, **kwargs):
'''Compute all hierarchical structure metrics for the given reference and
estimated annotations.
Examples
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craffel/mir_eval | mir_eval/display.py | __expand_limits | def __expand_limits(ax, limits, which='x'):
'''Helper function to expand axis limits'''
if which == 'x':
getter, setter = ax.get_xlim, ax.set_xlim
elif which == 'y':
getter, setter = ax.get_ylim, ax.set_ylim
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old_lims ... | python | def __expand_limits(ax, limits, which='x'):
'''Helper function to expand axis limits'''
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getter, setter = ax.get_ylim, ax.set_ylim
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craffel/mir_eval | mir_eval/display.py | __get_axes | def __get_axes(ax=None, fig=None):
'''Get or construct the target axes object for a new plot.
Parameters
----------
ax : matplotlib.pyplot.axes, optional
If provided, return this axes object directly.
fig : matplotlib.figure.Figure, optional
The figure to query for axes.
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craffel/mir_eval | mir_eval/display.py | segments | def segments(intervals, labels, base=None, height=None, text=False,
text_kw=None, ax=None, **kwargs):
'''Plot a segmentation as a set of disjoint rectangles.
Parameters
----------
intervals : np.ndarray, shape=(n, 2)
segment intervals, in the format returned by
:func:`mir_e... | python | def segments(intervals, labels, base=None, height=None, text=False,
text_kw=None, ax=None, **kwargs):
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Parameters
----------
intervals : np.ndarray, shape=(n, 2)
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craffel/mir_eval | mir_eval/display.py | labeled_intervals | def labeled_intervals(intervals, labels, label_set=None,
base=None, height=None, extend_labels=True,
ax=None, tick=True, **kwargs):
'''Plot labeled intervals with each label on its own row.
Parameters
----------
intervals : np.ndarray, shape=(n, 2)
se... | python | def labeled_intervals(intervals, labels, label_set=None,
base=None, height=None, extend_labels=True,
ax=None, tick=True, **kwargs):
'''Plot labeled intervals with each label on its own row.
Parameters
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intervals : np.ndarray, shape=(n, 2)
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craffel/mir_eval | mir_eval/display.py | hierarchy | def hierarchy(intervals_hier, labels_hier, levels=None, ax=None, **kwargs):
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Parameters
----------
intervals_hier : list of np.ndarray
A list of segmentation intervals. Each element should be
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craffel/mir_eval | mir_eval/display.py | events | def events(times, labels=None, base=None, height=None, ax=None, text_kw=None,
**kwargs):
'''Plot event times as a set of vertical lines
Parameters
----------
times : np.ndarray, shape=(n,)
event times, in the format returned by
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:func... | python | def events(times, labels=None, base=None, height=None, ax=None, text_kw=None,
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Parameters
----------
times : np.ndarray, shape=(n,)
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craffel/mir_eval | mir_eval/display.py | pitch | def pitch(times, frequencies, midi=False, unvoiced=False, ax=None, **kwargs):
'''Visualize pitch contours
Parameters
----------
times : np.ndarray, shape=(n,)
Sample times of frequencies
frequencies : np.ndarray, shape=(n,)
frequencies (in Hz) of the pitch contours.
Voicing... | python | def pitch(times, frequencies, midi=False, unvoiced=False, ax=None, **kwargs):
'''Visualize pitch contours
Parameters
----------
times : np.ndarray, shape=(n,)
Sample times of frequencies
frequencies : np.ndarray, shape=(n,)
frequencies (in Hz) of the pitch contours.
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craffel/mir_eval | mir_eval/display.py | multipitch | def multipitch(times, frequencies, midi=False, unvoiced=False, ax=None,
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times : np.ndarray, shape=(n,)
Sample times of frequencies
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craffel/mir_eval | mir_eval/display.py | piano_roll | def piano_roll(intervals, pitches=None, midi=None, ax=None, **kwargs):
'''Plot a quantized piano roll as intervals
Parameters
----------
intervals : np.ndarray, shape=(n, 2)
timing intervals for notes
pitches : np.ndarray, shape=(n,), optional
pitches of notes (in Hz).
midi : ... | python | def piano_roll(intervals, pitches=None, midi=None, ax=None, **kwargs):
'''Plot a quantized piano roll as intervals
Parameters
----------
intervals : np.ndarray, shape=(n, 2)
timing intervals for notes
pitches : np.ndarray, shape=(n,), optional
pitches of notes (in Hz).
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timing intervals for notes
pitches : np.ndarray, shape=(n,), optional
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midi : np.ndarray, shape=(n,), optional
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craffel/mir_eval | mir_eval/display.py | separation | def separation(sources, fs=22050, labels=None, alpha=0.75, ax=None, **kwargs):
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A list of waveform buffers corresponding to each source
fs : number > 0
The sampling rate
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craffel/mir_eval | mir_eval/display.py | __ticker_midi_note | def __ticker_midi_note(x, pos):
'''A ticker function for midi notes.
Inputs x are interpreted as midi numbers, and converted
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'''
NOTES = ['C', 'C#', 'D', 'D#', 'E', 'F', 'F#', 'G', 'G#', 'A', 'A#', 'B']
cents = float(np.mod(x, 1.0))
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craffel/mir_eval | mir_eval/display.py | ticker_notes | def ticker_notes(ax=None):
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The axes handle to apply the ticker.
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CartoDB/carto-python | carto/file_import.py | FileImportJob.run | def run(self, **import_params):
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CartoDB/carto-python | carto/auth.py | AuthAPIClient.is_valid_api_key | def is_valid_api_key(self):
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CartoDB/carto-python | carto/datasets.py | DatasetManager.is_sync_table | def is_sync_table(self, archive, interval, **import_args):
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CartoDB/carto-python | carto/datasets.py | DatasetManager.create | def create(self, archive, interval=None, **import_args):
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CartoDB/carto-python | carto/visualizations.py | VisualizationManager.send | def send(self, url, http_method, **client_args):
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CartoDB/carto-python | carto/exceptions.py | CartoRateLimitException.is_rate_limited | def is_rate_limited(response):
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CartoDB/carto-python | carto/maps.py | NamedMap.update_from_dict | def update_from_dict(self, attribute_dict):
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setattr(self,
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Method overriden from the base class
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CartoDB/carto-python | carto/sql.py | BatchSQLClient.cancel | def cancel(self, job_id):
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CartoDB/carto-python | carto/sync_tables.py | SyncTableJob.run | def run(self, **import_params):
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ikki407/stacking | stacking/base.py | BaseModel.set_prob_type | def set_prob_type(cls, problem_type, classification_type, eval_type):
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assert problem_type in problem_type_list, 'Need to set Problem Type'
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assert classification_type in classification_type_list,\
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cols = map(lambda x: x + name, cols)
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cols = map(lambda x: x + name, cols)
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A method to parse an ISO9660 Path Table Record out of a string.
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data - The string to parse.
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A method to parse an ISO9660 Path Table Record out of a string.
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An internal method to generate a string representing this Path Table Record.
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clalancette/pycdlib | pycdlib/path_table_record.py | PathTableRecord._new | def _new(self, name, parent_dir_num):
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An internal method to create a new Path Table Record.
Parameters:
name - The name for this Path Table Record.
parent_dir_num - The directory number of the parent of this Path Table
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clalancette/pycdlib | pycdlib/path_table_record.py | PathTableRecord.update_extent_location | def update_extent_location(self, extent_loc):
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A method to update the extent location for this Path Table Record.
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extent_loc - The new extent location.
Returns:
Nothing.
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extent_loc - The new extent location.
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clalancette/pycdlib | pycdlib/path_table_record.py | PathTableRecord.update_parent_directory_number | def update_parent_directory_number(self, parent_dir_num):
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clalancette/pycdlib | pycdlib/path_table_record.py | PathTableRecord.equal_to_be | def equal_to_be(self, be_record):
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clalancette/pycdlib | pycdlib/utils.py | copy_data | def copy_data(data_length, blocksize, infp, outfp):
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'''
A utility function to copy data from the input file object to the output
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clalancette/pycdlib | pycdlib/utils.py | encode_space_pad | def encode_space_pad(instr, length, encoding):
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# type: (bytes, int, str) -> bytes
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The space is first encoded into the specified encoding, then appended to
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Parameters:
instr - The input string to encode and pad.
length - The length to pad the input string to.... | [
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clalancette/pycdlib | pycdlib/utils.py | gmtoffset_from_tm | def gmtoffset_from_tm(tm, local):
# type: (float, time.struct_time) -> int
'''
A function to compute the GMT offset from the time in seconds since the epoch
and the local time object.
Parameters:
tm - The time in seconds since the epoch.
local - The struct_time object representing the loc... | python | def gmtoffset_from_tm(tm, local):
# type: (float, time.struct_time) -> int
'''
A function to compute the GMT offset from the time in seconds since the epoch
and the local time object.
Parameters:
tm - The time in seconds since the epoch.
local - The struct_time object representing the loc... | [
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Parameters:
tm - The time in seconds since the epoch.
local - The struct_time object representing the local time.
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] | 1e7b77a809e905d67dc71e12d70e850be26b6233 | https://github.com/clalancette/pycdlib/blob/1e7b77a809e905d67dc71e12d70e850be26b6233/pycdlib/utils.py#L223-L246 | train |
clalancette/pycdlib | pycdlib/utils.py | zero_pad | def zero_pad(fp, data_size, pad_size):
# type: (BinaryIO, int, int) -> None
'''
A function to write padding out from data_size up to pad_size
efficiently.
Parameters:
fp - The file object to use to write padding out to.
data_size - The current size of the data.
pad_size - The boundar... | python | def zero_pad(fp, data_size, pad_size):
# type: (BinaryIO, int, int) -> None
'''
A function to write padding out from data_size up to pad_size
efficiently.
Parameters:
fp - The file object to use to write padding out to.
data_size - The current size of the data.
pad_size - The boundar... | [
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Parameters:
fp - The file object to use to write padding out to.
data_size - The current size of the data.
pad_size - The boundary size of data to pad out to.
Returns:
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] | 1e7b77a809e905d67dc71e12d70e850be26b6233 | https://github.com/clalancette/pycdlib/blob/1e7b77a809e905d67dc71e12d70e850be26b6233/pycdlib/utils.py#L249-L268 | train |
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