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LucidtechAI/las-sdk-python | las/client.py | Client.put_document | def put_document(document_path: str, content_type: str, presigned_url: str) -> str:
"""Convenience method for putting a document to presigned url.
>>> from las import Client
>>> client = Client(endpoint='<api endpoint>')
>>> client.put_document(document_path='document.jpeg', content_typ... | python | def put_document(document_path: str, content_type: str, presigned_url: str) -> str:
"""Convenience method for putting a document to presigned url.
>>> from las import Client
>>> client = Client(endpoint='<api endpoint>')
>>> client.put_document(document_path='document.jpeg', content_typ... | [
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>>> from las import Client
>>> client = Client(endpoint='<api endpoint>')
>>> client.put_document(document_path='document.jpeg', content_type='image/jpeg',
>>> presigned_url='<presigned url>')
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owncloud/pyocclient | owncloud/owncloud.py | ShareInfo.get_expiration | def get_expiration(self):
"""Returns the expiration date.
:returns: expiration date
:rtype: datetime object
"""
exp = self._get_int('expiration')
if exp is not None:
return datetime.datetime.fromtimestamp(
exp
)
return None | python | def get_expiration(self):
"""Returns the expiration date.
:returns: expiration date
:rtype: datetime object
"""
exp = self._get_int('expiration')
if exp is not None:
return datetime.datetime.fromtimestamp(
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owncloud/pyocclient | owncloud/owncloud.py | Client.login | def login(self, user_id, password):
"""Authenticate to ownCloud.
This will create a session on the server.
:param user_id: user id
:param password: password
:raises: HTTPResponseError in case an HTTP error status was returned
"""
self._session = requests.session... | python | def login(self, user_id, password):
"""Authenticate to ownCloud.
This will create a session on the server.
:param user_id: user id
:param password: password
:raises: HTTPResponseError in case an HTTP error status was returned
"""
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This will create a session on the server.
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owncloud/pyocclient | owncloud/owncloud.py | Client.file_info | def file_info(self, path):
"""Returns the file info for the given remote file
:param path: path to the remote file
:returns: file info
:rtype: :class:`FileInfo` object or `None` if file
was not found
:raises: HTTPResponseError in case an HTTP error status was returne... | python | def file_info(self, path):
"""Returns the file info for the given remote file
:param path: path to the remote file
:returns: file info
:rtype: :class:`FileInfo` object or `None` if file
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owncloud/pyocclient | owncloud/owncloud.py | Client.get_file_contents | def get_file_contents(self, path):
"""Returns the contents of a remote file
:param path: path to the remote file
:returns: file contents
:rtype: binary data
:raises: HTTPResponseError in case an HTTP error status was returned
"""
path = self._normalize_path(path)... | python | def get_file_contents(self, path):
"""Returns the contents of a remote file
:param path: path to the remote file
:returns: file contents
:rtype: binary data
:raises: HTTPResponseError in case an HTTP error status was returned
"""
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owncloud/pyocclient | owncloud/owncloud.py | Client.get_directory_as_zip | def get_directory_as_zip(self, remote_path, local_file):
"""Downloads a remote directory as zip
:param remote_path: path to the remote directory to download
:param local_file: path and name of the target local file
:returns: True if the operation succeeded, False otherwise
:rais... | python | def get_directory_as_zip(self, remote_path, local_file):
"""Downloads a remote directory as zip
:param remote_path: path to the remote directory to download
:param local_file: path and name of the target local file
:returns: True if the operation succeeded, False otherwise
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owncloud/pyocclient | owncloud/owncloud.py | Client.put_directory | def put_directory(self, target_path, local_directory, **kwargs):
"""Upload a directory with all its contents
:param target_path: path of the directory to upload into
:param local_directory: path to the local directory to upload
:param \*\*kwargs: optional arguments that ``put_file`` acc... | python | def put_directory(self, target_path, local_directory, **kwargs):
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:param target_path: path of the directory to upload into
:param local_directory: path to the local directory to upload
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owncloud/pyocclient | owncloud/owncloud.py | Client._put_file_chunked | def _put_file_chunked(self, remote_path, local_source_file, **kwargs):
"""Uploads a file using chunks. If the file is smaller than
``chunk_size`` it will be uploaded directly.
:param remote_path: path to the target file. A target directory can
also be specified instead by appending a "/... | python | def _put_file_chunked(self, remote_path, local_source_file, **kwargs):
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:param remote_path: path to the target file. A target directory can
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owncloud/pyocclient | owncloud/owncloud.py | Client.list_open_remote_share | def list_open_remote_share(self):
"""List all pending remote shares
:returns: array of pending remote shares
:raises: HTTPResponseError in case an HTTP error status was returned
"""
res = self._make_ocs_request(
'GET',
self.OCS_SERVICE_SHARE,
... | python | def list_open_remote_share(self):
"""List all pending remote shares
:returns: array of pending remote shares
:raises: HTTPResponseError in case an HTTP error status was returned
"""
res = self._make_ocs_request(
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owncloud/pyocclient | owncloud/owncloud.py | Client.accept_remote_share | def accept_remote_share(self, share_id):
"""Accepts a remote share
:param share_id: Share ID (int)
:returns: True if the operation succeeded, False otherwise
:raises: HTTPResponseError in case an HTTP error status was returned
"""
if not isinstance(share_id, int):
... | python | def accept_remote_share(self, share_id):
"""Accepts a remote share
:param share_id: Share ID (int)
:returns: True if the operation succeeded, False otherwise
:raises: HTTPResponseError in case an HTTP error status was returned
"""
if not isinstance(share_id, int):
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owncloud/pyocclient | owncloud/owncloud.py | Client.update_share | def update_share(self, share_id, **kwargs):
"""Updates a given share
:param share_id: (int) Share ID
:param perms: (int) update permissions (see share_file_with_user() below)
:param password: (string) updated password for public link Share
:param public_upload: (boolean) enable/... | python | def update_share(self, share_id, **kwargs):
"""Updates a given share
:param share_id: (int) Share ID
:param perms: (int) update permissions (see share_file_with_user() below)
:param password: (string) updated password for public link Share
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owncloud/pyocclient | owncloud/owncloud.py | Client.share_file_with_link | def share_file_with_link(self, path, **kwargs):
"""Shares a remote file with link
:param path: path to the remote file to share
:param perms (optional): permission of the shared object
defaults to read only (1)
:param public_upload (optional): allows users to upload files or fol... | python | def share_file_with_link(self, path, **kwargs):
"""Shares a remote file with link
:param path: path to the remote file to share
:param perms (optional): permission of the shared object
defaults to read only (1)
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owncloud/pyocclient | owncloud/owncloud.py | Client.is_shared | def is_shared(self, path):
"""Checks whether a path is already shared
:param path: path to the share to be checked
:returns: True if the path is already shared, else False
:raises: HTTPResponseError in case an HTTP error status was returned
"""
# make sure that the path ... | python | def is_shared(self, path):
"""Checks whether a path is already shared
:param path: path to the share to be checked
:returns: True if the path is already shared, else False
:raises: HTTPResponseError in case an HTTP error status was returned
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# make sure that the path ... | [
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owncloud/pyocclient | owncloud/owncloud.py | Client.get_share | def get_share(self, share_id):
"""Returns share information about known share
:param share_id: id of the share to be checked
:returns: instance of ShareInfo class
:raises: ResponseError in case an HTTP error status was returned
"""
if (share_id is None) or not (isinstanc... | python | def get_share(self, share_id):
"""Returns share information about known share
:param share_id: id of the share to be checked
:returns: instance of ShareInfo class
:raises: ResponseError in case an HTTP error status was returned
"""
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owncloud/pyocclient | owncloud/owncloud.py | Client.get_shares | def get_shares(self, path='', **kwargs):
"""Returns array of shares
:param path: path to the share to be checked
:param reshares: (optional, boolean) returns not only the shares from
the current user but all shares from the given file (default: False)
:param subfiles: (optio... | python | def get_shares(self, path='', **kwargs):
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:param path: path to the share to be checked
:param reshares: (optional, boolean) returns not only the shares from
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It is not an error, if the user already existed before.
If you get back an error 999, then the provisioning API is not enabled.
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owncloud/pyocclient | owncloud/owncloud.py | Client.get_user_groups | def get_user_groups(self, user_name):
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:returns: attribute value if key was specified, or an array of tuples
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owncloud/pyocclient | owncloud/owncloud.py | Client.get_apps | def get_apps(self):
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owncloud/pyocclient | owncloud/owncloud.py | Client._make_dav_request | def _make_dav_request(self, method, path, **kwargs):
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dgovil/PySignal | PySignal.py | Signal.connect | def connect(self, slot):
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"""
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craffel/mir_eval | mir_eval/io.py | _open | def _open(file_or_str, **kwargs):
'''Either open a file handle, or use an existing file-like object.
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craffel/mir_eval | mir_eval/io.py | load_delimited | def load_delimited(filename, converters, delimiter=r'\s+'):
r"""Utility function for loading in data from an annotation file where columns
are delimited. The number of columns is inferred from the length of
the provided converters list.
Examples
--------
>>> # Load in a one-column list of even... | python | def load_delimited(filename, converters, delimiter=r'\s+'):
r"""Utility function for loading in data from an annotation file where columns
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craffel/mir_eval | mir_eval/io.py | load_events | def load_events(filename, delimiter=r'\s+'):
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... | python | def load_events(filename, delimiter=r'\s+'):
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craffel/mir_eval | mir_eval/io.py | load_labeled_events | def load_labeled_events(filename, delimiter=r'\s+'):
r"""Import labeled time-stamp events from an annotation file. The file should
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craffel/mir_eval | mir_eval/io.py | load_time_series | def load_time_series(filename, delimiter=r'\s+'):
r"""Import a time series from an annotation file. The file should consist of
two columns of numeric values corresponding to the time and value of each
sample of the time series.
Parameters
----------
filename : str
Path to the annotatio... | python | def load_time_series(filename, delimiter=r'\s+'):
r"""Import a time series from an annotation file. The file should consist of
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craffel/mir_eval | mir_eval/io.py | load_wav | def load_wav(path, mono=True):
"""Loads a .wav file as a numpy array using ``scipy.io.wavfile``.
Parameters
----------
path : str
Path to a .wav file
mono : bool
If the provided .wav has more than one channel, it will be
converted to mono if ``mono=True``. (Default value = T... | python | def load_wav(path, mono=True):
"""Loads a .wav file as a numpy array using ``scipy.io.wavfile``.
Parameters
----------
path : str
Path to a .wav file
mono : bool
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craffel/mir_eval | mir_eval/io.py | load_ragged_time_series | def load_ragged_time_series(filename, dtype=float, delimiter=r'\s+',
header=False):
r"""Utility function for loading in data from a delimited time series
annotation file with a variable number of columns.
Assumes that column 0 contains time stamps and columns 1 through n contain
... | python | def load_ragged_time_series(filename, dtype=float, delimiter=r'\s+',
header=False):
r"""Utility function for loading in data from a delimited time series
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craffel/mir_eval | mir_eval/chord.py | pitch_class_to_semitone | def pitch_class_to_semitone(pitch_class):
r'''Convert a pitch class to semitone.
Parameters
----------
pitch_class : str
Spelling of a given pitch class, e.g. 'C#', 'Gbb'
Returns
-------
semitone : int
Semitone value of the pitch class.
'''
semitone = 0
for idx... | python | def pitch_class_to_semitone(pitch_class):
r'''Convert a pitch class to semitone.
Parameters
----------
pitch_class : str
Spelling of a given pitch class, e.g. 'C#', 'Gbb'
Returns
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semitone : int
Semitone value of the pitch class.
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craffel/mir_eval | mir_eval/chord.py | scale_degree_to_semitone | def scale_degree_to_semitone(scale_degree):
r"""Convert a scale degree to semitone.
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----------
scale degree : str
Spelling of a relative scale degree, e.g. 'b3', '7', '#5'
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semitone : int
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r"""Convert a scale degree to semitone.
Parameters
----------
scale degree : str
Spelling of a relative scale degree, e.g. 'b3', '7', '#5'
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craffel/mir_eval | mir_eval/chord.py | scale_degree_to_bitmap | def scale_degree_to_bitmap(scale_degree, modulo=False, length=BITMAP_LENGTH):
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Parameters
----------
scale_degree : str
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craffel/mir_eval | mir_eval/chord.py | quality_to_bitmap | def quality_to_bitmap(quality):
"""Return the bitmap for a given quality.
Parameters
----------
quality : str
Chord quality name.
Returns
-------
bitmap : np.ndarray
Bitmap representation of this quality (12-dim).
"""
if quality not in QUALITIES:
raise Inva... | python | def quality_to_bitmap(quality):
"""Return the bitmap for a given quality.
Parameters
----------
quality : str
Chord quality name.
Returns
-------
bitmap : np.ndarray
Bitmap representation of this quality (12-dim).
"""
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craffel/mir_eval | mir_eval/chord.py | validate_chord_label | def validate_chord_label(chord_label):
"""Test for well-formedness of a chord label.
Parameters
----------
chord : str
Chord label to validate.
"""
# This monster regexp is pulled from the JAMS chord namespace,
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# Hart... | python | def validate_chord_label(chord_label):
"""Test for well-formedness of a chord label.
Parameters
----------
chord : str
Chord label to validate.
"""
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craffel/mir_eval | mir_eval/chord.py | join | def join(chord_root, quality='', extensions=None, bass=''):
r"""Join the parts of a chord into a complete chord label.
Parameters
----------
chord_root : str
Root pitch class of the chord, e.g. 'C', 'Eb'
quality : str
Quality of the chord, e.g. 'maj', 'hdim7'
(Default value ... | python | def join(chord_root, quality='', extensions=None, bass=''):
r"""Join the parts of a chord into a complete chord label.
Parameters
----------
chord_root : str
Root pitch class of the chord, e.g. 'C', 'Eb'
quality : str
Quality of the chord, e.g. 'maj', 'hdim7'
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Root pitch class of the chord, e.g. 'C', 'Eb'
quality : str
Quality of the chord, e.g. 'maj', 'hdim7'
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craffel/mir_eval | mir_eval/chord.py | encode | def encode(chord_label, reduce_extended_chords=False,
strict_bass_intervals=False):
"""Translate a chord label to numerical representations for evaluation.
Parameters
----------
chord_label : str
Chord label to encode.
reduce_extended_chords : bool
Whether to map the uppe... | python | def encode(chord_label, reduce_extended_chords=False,
strict_bass_intervals=False):
"""Translate a chord label to numerical representations for evaluation.
Parameters
----------
chord_label : str
Chord label to encode.
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Chord label to encode.
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Whether to map the upper voicings of extended chords (9's, 11's, 13's)
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craffel/mir_eval | mir_eval/chord.py | encode_many | def encode_many(chord_labels, reduce_extended_chords=False):
"""Translate a set of chord labels to numerical representations for sane
evaluation.
Parameters
----------
chord_labels : list
Set of chord labels to encode.
reduce_extended_chords : bool
Whether to map the upper voici... | python | def encode_many(chord_labels, reduce_extended_chords=False):
"""Translate a set of chord labels to numerical representations for sane
evaluation.
Parameters
----------
chord_labels : list
Set of chord labels to encode.
reduce_extended_chords : bool
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craffel/mir_eval | mir_eval/chord.py | rotate_bitmap_to_root | def rotate_bitmap_to_root(bitmap, chord_root):
"""Circularly shift a relative bitmap to its asbolute pitch classes.
For clarity, the best explanation is an example. Given 'G:Maj', the root
and quality map are as follows::
root=5
quality=[1, 0, 0, 0, 1, 0, 0, 1, 0, 0, 0, 0] # Relative chor... | python | def rotate_bitmap_to_root(bitmap, chord_root):
"""Circularly shift a relative bitmap to its asbolute pitch classes.
For clarity, the best explanation is an example. Given 'G:Maj', the root
and quality map are as follows::
root=5
quality=[1, 0, 0, 0, 1, 0, 0, 1, 0, 0, 0, 0] # Relative chor... | [
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quality=[1, 0, 0, 0, 1, 0, 0, 1, 0, 0, 0, 0] # Relative chord shape
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craffel/mir_eval | mir_eval/chord.py | rotate_bitmaps_to_roots | def rotate_bitmaps_to_roots(bitmaps, roots):
"""Circularly shift a relative bitmaps to asbolute pitch classes.
See :func:`rotate_bitmap_to_root` for more information.
Parameters
----------
bitmap : np.ndarray, shape=(N, 12)
Bitmap of active notes, relative to the given root.
root : np.... | python | def rotate_bitmaps_to_roots(bitmaps, roots):
"""Circularly shift a relative bitmaps to asbolute pitch classes.
See :func:`rotate_bitmap_to_root` for more information.
Parameters
----------
bitmap : np.ndarray, shape=(N, 12)
Bitmap of active notes, relative to the given root.
root : np.... | [
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craffel/mir_eval | mir_eval/chord.py | validate | def validate(reference_labels, estimated_labels):
"""Checks that the input annotations to a comparison function look like
valid chord labels.
Parameters
----------
reference_labels : list, len=n
Reference chord labels to score against.
estimated_labels : list, len=n
Estimated ch... | python | def validate(reference_labels, estimated_labels):
"""Checks that the input annotations to a comparison function look like
valid chord labels.
Parameters
----------
reference_labels : list, len=n
Reference chord labels to score against.
estimated_labels : list, len=n
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Parameters
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reference_labels : list, len=n
Reference chord labels to score against.
estimated_labels : list, len=n
Estimated chord labels to score against. | [
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craffel/mir_eval | mir_eval/chord.py | weighted_accuracy | def weighted_accuracy(comparisons, weights):
"""Compute the weighted accuracy of a list of chord comparisons.
Examples
--------
>>> (ref_intervals,
... ref_labels) = mir_eval.io.load_labeled_intervals('ref.lab')
>>> (est_intervals,
... est_labels) = mir_eval.io.load_labeled_intervals('est... | python | def weighted_accuracy(comparisons, weights):
"""Compute the weighted accuracy of a list of chord comparisons.
Examples
--------
>>> (ref_intervals,
... ref_labels) = mir_eval.io.load_labeled_intervals('ref.lab')
>>> (est_intervals,
... est_labels) = mir_eval.io.load_labeled_intervals('est... | [
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Examples
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>>> (ref_intervals,
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>>> (est_intervals,
... est_labels) = mir_eval.io.load_labeled_intervals('est.lab')
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craffel/mir_eval | mir_eval/chord.py | thirds | def thirds(reference_labels, estimated_labels):
"""Compare chords along root & third relationships.
Examples
--------
>>> (ref_intervals,
... ref_labels) = mir_eval.io.load_labeled_intervals('ref.lab')
>>> (est_intervals,
... est_labels) = mir_eval.io.load_labeled_intervals('est.lab')
... | python | def thirds(reference_labels, estimated_labels):
"""Compare chords along root & third relationships.
Examples
--------
>>> (ref_intervals,
... ref_labels) = mir_eval.io.load_labeled_intervals('ref.lab')
>>> (est_intervals,
... est_labels) = mir_eval.io.load_labeled_intervals('est.lab')
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>>> (est_intervals,
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craffel/mir_eval | mir_eval/chord.py | thirds_inv | def thirds_inv(reference_labels, estimated_labels):
"""Score chords along root, third, & bass relationships.
Examples
--------
>>> (ref_intervals,
... ref_labels) = mir_eval.io.load_labeled_intervals('ref.lab')
>>> (est_intervals,
... est_labels) = mir_eval.io.load_labeled_intervals('est.... | python | def thirds_inv(reference_labels, estimated_labels):
"""Score chords along root, third, & bass relationships.
Examples
--------
>>> (ref_intervals,
... ref_labels) = mir_eval.io.load_labeled_intervals('ref.lab')
>>> (est_intervals,
... est_labels) = mir_eval.io.load_labeled_intervals('est.... | [
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craffel/mir_eval | mir_eval/chord.py | root | def root(reference_labels, estimated_labels):
"""Compare chords according to roots.
Examples
--------
>>> (ref_intervals,
... ref_labels) = mir_eval.io.load_labeled_intervals('ref.lab')
>>> (est_intervals,
... est_labels) = mir_eval.io.load_labeled_intervals('est.lab')
>>> est_interva... | python | def root(reference_labels, estimated_labels):
"""Compare chords according to roots.
Examples
--------
>>> (ref_intervals,
... ref_labels) = mir_eval.io.load_labeled_intervals('ref.lab')
>>> (est_intervals,
... est_labels) = mir_eval.io.load_labeled_intervals('est.lab')
>>> est_interva... | [
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>>> (est_intervals,
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craffel/mir_eval | mir_eval/chord.py | mirex | def mirex(reference_labels, estimated_labels):
"""Compare chords along MIREX rules.
Examples
--------
>>> (ref_intervals,
... ref_labels) = mir_eval.io.load_labeled_intervals('ref.lab')
>>> (est_intervals,
... est_labels) = mir_eval.io.load_labeled_intervals('est.lab')
>>> est_interva... | python | def mirex(reference_labels, estimated_labels):
"""Compare chords along MIREX rules.
Examples
--------
>>> (ref_intervals,
... ref_labels) = mir_eval.io.load_labeled_intervals('ref.lab')
>>> (est_intervals,
... est_labels) = mir_eval.io.load_labeled_intervals('est.lab')
>>> est_interva... | [
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>>> (ref_intervals,
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>>> (est_intervals,
... est_labels) = mir_eval.io.load_labeled_intervals('est.lab')
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craffel/mir_eval | mir_eval/chord.py | seg | def seg(reference_intervals, estimated_intervals):
"""Compute the MIREX 'MeanSeg' score.
Examples
--------
>>> (ref_intervals,
... ref_labels) = mir_eval.io.load_labeled_intervals('ref.lab')
>>> (est_intervals,
... est_labels) = mir_eval.io.load_labeled_intervals('est.lab')
>>> score ... | python | def seg(reference_intervals, estimated_intervals):
"""Compute the MIREX 'MeanSeg' score.
Examples
--------
>>> (ref_intervals,
... ref_labels) = mir_eval.io.load_labeled_intervals('ref.lab')
>>> (est_intervals,
... est_labels) = mir_eval.io.load_labeled_intervals('est.lab')
>>> score ... | [
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>>> (ref_intervals,
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>>> (est_intervals,
... est_labels) = mir_eval.io.load_labeled_intervals('est.lab')
>>> score = mir_eval.chord.seg(ref_intervals, est_intervals)
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craffel/mir_eval | mir_eval/chord.py | merge_chord_intervals | def merge_chord_intervals(intervals, labels):
"""
Merge consecutive chord intervals if they represent the same chord.
Parameters
----------
intervals : np.ndarray, shape=(n, 2), dtype=float
Chord intervals to be merged, in the format returned by
:func:`mir_eval.io.load_labeled_inter... | python | def merge_chord_intervals(intervals, labels):
"""
Merge consecutive chord intervals if they represent the same chord.
Parameters
----------
intervals : np.ndarray, shape=(n, 2), dtype=float
Chord intervals to be merged, in the format returned by
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craffel/mir_eval | mir_eval/chord.py | evaluate | def evaluate(ref_intervals, ref_labels, est_intervals, est_labels, **kwargs):
"""Computes weighted accuracy for all comparison functions for the given
reference and estimated annotations.
Examples
--------
>>> (ref_intervals,
... ref_labels) = mir_eval.io.load_labeled_intervals('ref.lab')
... | python | def evaluate(ref_intervals, ref_labels, est_intervals, est_labels, **kwargs):
"""Computes weighted accuracy for all comparison functions for the given
reference and estimated annotations.
Examples
--------
>>> (ref_intervals,
... ref_labels) = mir_eval.io.load_labeled_intervals('ref.lab')
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craffel/mir_eval | mir_eval/pattern.py | _n_onset_midi | def _n_onset_midi(patterns):
"""Computes the number of onset_midi objects in a pattern
Parameters
----------
patterns :
A list of patterns using the format returned by
:func:`mir_eval.io.load_patterns()`
Returns
-------
n_onsets : int
Number of onsets within the pat... | python | def _n_onset_midi(patterns):
"""Computes the number of onset_midi objects in a pattern
Parameters
----------
patterns :
A list of patterns using the format returned by
:func:`mir_eval.io.load_patterns()`
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n_onsets : int
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craffel/mir_eval | mir_eval/pattern.py | validate | def validate(reference_patterns, estimated_patterns):
"""Checks that the input annotations to a metric look like valid pattern
lists, and throws helpful errors if not.
Parameters
----------
reference_patterns : list
The reference patterns using the format returned by
:func:`mir_eval... | python | def validate(reference_patterns, estimated_patterns):
"""Checks that the input annotations to a metric look like valid pattern
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Parameters
----------
reference_patterns : list
The reference patterns using the format returned by
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craffel/mir_eval | mir_eval/pattern.py | _occurrence_intersection | def _occurrence_intersection(occ_P, occ_Q):
"""Computes the intersection between two occurrences.
Parameters
----------
occ_P : list of tuples
(onset, midi) pairs representing the reference occurrence.
occ_Q : list
second list of (onset, midi) tuples
Returns
-------
S :... | python | def _occurrence_intersection(occ_P, occ_Q):
"""Computes the intersection between two occurrences.
Parameters
----------
occ_P : list of tuples
(onset, midi) pairs representing the reference occurrence.
occ_Q : list
second list of (onset, midi) tuples
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-------
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craffel/mir_eval | mir_eval/pattern.py | _compute_score_matrix | def _compute_score_matrix(P, Q, similarity_metric="cardinality_score"):
"""Computes the score matrix between the patterns P and Q.
Parameters
----------
P : list
Pattern containing a list of occurrences.
Q : list
Pattern containing a list of occurrences.
similarity_metric : str
... | python | def _compute_score_matrix(P, Q, similarity_metric="cardinality_score"):
"""Computes the score matrix between the patterns P and Q.
Parameters
----------
P : list
Pattern containing a list of occurrences.
Q : list
Pattern containing a list of occurrences.
similarity_metric : str
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craffel/mir_eval | mir_eval/pattern.py | standard_FPR | def standard_FPR(reference_patterns, estimated_patterns, tol=1e-5):
"""Standard F1 Score, Precision and Recall.
This metric checks if the prototype patterns of the reference match
possible translated patterns in the prototype patterns of the estimations.
Since the sizes of these prototypes must be equa... | python | def standard_FPR(reference_patterns, estimated_patterns, tol=1e-5):
"""Standard F1 Score, Precision and Recall.
This metric checks if the prototype patterns of the reference match
possible translated patterns in the prototype patterns of the estimations.
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craffel/mir_eval | mir_eval/pattern.py | three_layer_FPR | def three_layer_FPR(reference_patterns, estimated_patterns):
"""Three Layer F1 Score, Precision and Recall. As described by Meridith.
Examples
--------
>>> ref_patterns = mir_eval.io.load_patterns("ref_pattern.txt")
>>> est_patterns = mir_eval.io.load_patterns("est_pattern.txt")
>>> F, P, R = m... | python | def three_layer_FPR(reference_patterns, estimated_patterns):
"""Three Layer F1 Score, Precision and Recall. As described by Meridith.
Examples
--------
>>> ref_patterns = mir_eval.io.load_patterns("ref_pattern.txt")
>>> est_patterns = mir_eval.io.load_patterns("est_pattern.txt")
>>> F, P, R = m... | [
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>>> ref_patterns = mir_eval.io.load_patterns("ref_pattern.txt")
>>> est_patterns = mir_eval.io.load_patterns("est_pattern.txt")
>>> F, P, R = mir_eval.pattern.three_layer_FPR(ref_patterns,
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craffel/mir_eval | mir_eval/pattern.py | first_n_three_layer_P | def first_n_three_layer_P(reference_patterns, estimated_patterns, n=5):
"""First n three-layer precision.
This metric is basically the same as the three-layer FPR but it is only
applied to the first n estimated patterns, and it only returns the
precision. In MIREX and typically, n = 5.
Examples
... | python | def first_n_three_layer_P(reference_patterns, estimated_patterns, n=5):
"""First n three-layer precision.
This metric is basically the same as the three-layer FPR but it is only
applied to the first n estimated patterns, and it only returns the
precision. In MIREX and typically, n = 5.
Examples
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craffel/mir_eval | mir_eval/pattern.py | first_n_target_proportion_R | def first_n_target_proportion_R(reference_patterns, estimated_patterns, n=5):
"""First n target proportion establishment recall metric.
This metric is similar is similar to the establishment FPR score, but it
only takes into account the first n estimated patterns and it only
outputs the Recall value of... | python | def first_n_target_proportion_R(reference_patterns, estimated_patterns, n=5):
"""First n target proportion establishment recall metric.
This metric is similar is similar to the establishment FPR score, but it
only takes into account the first n estimated patterns and it only
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craffel/mir_eval | mir_eval/pattern.py | evaluate | def evaluate(ref_patterns, est_patterns, **kwargs):
"""Load data and perform the evaluation.
Examples
--------
>>> ref_patterns = mir_eval.io.load_patterns("ref_pattern.txt")
>>> est_patterns = mir_eval.io.load_patterns("est_pattern.txt")
>>> scores = mir_eval.pattern.evaluate(ref_patterns, est... | python | def evaluate(ref_patterns, est_patterns, **kwargs):
"""Load data and perform the evaluation.
Examples
--------
>>> ref_patterns = mir_eval.io.load_patterns("ref_pattern.txt")
>>> est_patterns = mir_eval.io.load_patterns("est_pattern.txt")
>>> scores = mir_eval.pattern.evaluate(ref_patterns, est... | [
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craffel/mir_eval | mir_eval/transcription_velocity.py | validate | def validate(ref_intervals, ref_pitches, ref_velocities, est_intervals,
est_pitches, est_velocities):
"""Checks that the input annotations have valid time intervals, pitches,
and velocities, and throws helpful errors if not.
Parameters
----------
ref_intervals : np.ndarray, shape=(n,2)... | python | def validate(ref_intervals, ref_pitches, ref_velocities, est_intervals,
est_pitches, est_velocities):
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craffel/mir_eval | mir_eval/transcription_velocity.py | match_notes | def match_notes(
ref_intervals, ref_pitches, ref_velocities, est_intervals, est_pitches,
est_velocities, onset_tolerance=0.05, pitch_tolerance=50.0,
offset_ratio=0.2, offset_min_tolerance=0.05, strict=False,
velocity_tolerance=0.1):
"""Match notes, taking note velocity into considera... | python | def match_notes(
ref_intervals, ref_pitches, ref_velocities, est_intervals, est_pitches,
est_velocities, onset_tolerance=0.05, pitch_tolerance=50.0,
offset_ratio=0.2, offset_min_tolerance=0.05, strict=False,
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craffel/mir_eval | mir_eval/beat.py | validate | def validate(reference_beats, estimated_beats):
"""Checks that the input annotations to a metric look like valid beat time
arrays, and throws helpful errors if not.
Parameters
----------
reference_beats : np.ndarray
reference beat times, in seconds
estimated_beats : np.ndarray
e... | python | def validate(reference_beats, estimated_beats):
"""Checks that the input annotations to a metric look like valid beat time
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Parameters
----------
reference_beats : np.ndarray
reference beat times, in seconds
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reference beat times, in seconds
estimated_beats : np.ndarray
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craffel/mir_eval | mir_eval/beat.py | _get_reference_beat_variations | def _get_reference_beat_variations(reference_beats):
"""Return metric variations of the reference beats
Parameters
----------
reference_beats : np.ndarray
beat locations in seconds
Returns
-------
reference_beats : np.ndarray
Original beat locations
off_beat : np.ndarra... | python | def _get_reference_beat_variations(reference_beats):
"""Return metric variations of the reference beats
Parameters
----------
reference_beats : np.ndarray
beat locations in seconds
Returns
-------
reference_beats : np.ndarray
Original beat locations
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craffel/mir_eval | mir_eval/beat.py | f_measure | def f_measure(reference_beats,
estimated_beats,
f_measure_threshold=0.07):
"""Compute the F-measure of correct vs incorrectly predicted beats.
"Correctness" is determined over a small window.
Examples
--------
>>> reference_beats = mir_eval.io.load_events('reference.txt'... | python | def f_measure(reference_beats,
estimated_beats,
f_measure_threshold=0.07):
"""Compute the F-measure of correct vs incorrectly predicted beats.
"Correctness" is determined over a small window.
Examples
--------
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craffel/mir_eval | mir_eval/beat.py | cemgil | def cemgil(reference_beats,
estimated_beats,
cemgil_sigma=0.04):
"""Cemgil's score, computes a gaussian error of each estimated beat.
Compares against the original beat times and all metrical variations.
Examples
--------
>>> reference_beats = mir_eval.io.load_events('referenc... | python | def cemgil(reference_beats,
estimated_beats,
cemgil_sigma=0.04):
"""Cemgil's score, computes a gaussian error of each estimated beat.
Compares against the original beat times and all metrical variations.
Examples
--------
>>> reference_beats = mir_eval.io.load_events('referenc... | [
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>>> reference_beats = mir_eval.io.load_events('reference.txt')
>>> reference_beats = mir_eval.beat.trim_beats(reference_beats)
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craffel/mir_eval | mir_eval/beat.py | goto | def goto(reference_beats,
estimated_beats,
goto_threshold=0.35,
goto_mu=0.2,
goto_sigma=0.2):
"""Calculate Goto's score, a binary 1 or 0 depending on some specific
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Examples
--------
>>> reference_beats = mir_eval.io.load_events('reference.txt')... | python | def goto(reference_beats,
estimated_beats,
goto_threshold=0.35,
goto_mu=0.2,
goto_sigma=0.2):
"""Calculate Goto's score, a binary 1 or 0 depending on some specific
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Examples
--------
>>> reference_beats = mir_eval.io.load_events('reference.txt')... | [
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>>> reference_beats = mir_eval.beat.trim_beats(reference_beats)
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craffel/mir_eval | mir_eval/beat.py | p_score | def p_score(reference_beats,
estimated_beats,
p_score_threshold=0.2):
"""Get McKinney's P-score.
Based on the autocorrelation of the reference and estimated beats
Examples
--------
>>> reference_beats = mir_eval.io.load_events('reference.txt')
>>> reference_beats = mir_e... | python | def p_score(reference_beats,
estimated_beats,
p_score_threshold=0.2):
"""Get McKinney's P-score.
Based on the autocorrelation of the reference and estimated beats
Examples
--------
>>> reference_beats = mir_eval.io.load_events('reference.txt')
>>> reference_beats = mir_e... | [
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>>> reference_beats = mir_eval.beat.trim_beats(reference_beats)
>>> estimated_beats = mir_eval.io.load_events('estimated.txt... | [
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craffel/mir_eval | mir_eval/beat.py | information_gain | def information_gain(reference_beats,
estimated_beats,
bins=41):
"""Get the information gain - K-L divergence of the beat error histogram
to a uniform histogram
Examples
--------
>>> reference_beats = mir_eval.io.load_events('reference.txt')
>>> referen... | python | def information_gain(reference_beats,
estimated_beats,
bins=41):
"""Get the information gain - K-L divergence of the beat error histogram
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Examples
--------
>>> reference_beats = mir_eval.io.load_events('reference.txt')
>>> referen... | [
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>>> reference_beats = mir_eval.beat.trim_beats(reference_beats)
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craffel/mir_eval | mir_eval/util.py | index_labels | def index_labels(labels, case_sensitive=False):
"""Convert a list of string identifiers into numerical indices.
Parameters
----------
labels : list of strings, shape=(n,)
A list of annotations, e.g., segment or chord labels from an
annotation file.
case_sensitive : bool
Set... | python | def index_labels(labels, case_sensitive=False):
"""Convert a list of string identifiers into numerical indices.
Parameters
----------
labels : list of strings, shape=(n,)
A list of annotations, e.g., segment or chord labels from an
annotation file.
case_sensitive : bool
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craffel/mir_eval | mir_eval/util.py | intervals_to_samples | def intervals_to_samples(intervals, labels, offset=0, sample_size=0.1,
fill_value=None):
"""Convert an array of labeled time intervals to annotated samples.
Parameters
----------
intervals : np.ndarray, shape=(n, d)
An array of time intervals, as returned by
:fu... | python | def intervals_to_samples(intervals, labels, offset=0, sample_size=0.1,
fill_value=None):
"""Convert an array of labeled time intervals to annotated samples.
Parameters
----------
intervals : np.ndarray, shape=(n, d)
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craffel/mir_eval | mir_eval/util.py | interpolate_intervals | def interpolate_intervals(intervals, labels, time_points, fill_value=None):
"""Assign labels to a set of points in time given a set of intervals.
Time points that do not lie within an interval are mapped to `fill_value`.
Parameters
----------
intervals : np.ndarray, shape=(n, 2)
An array o... | python | def interpolate_intervals(intervals, labels, time_points, fill_value=None):
"""Assign labels to a set of points in time given a set of intervals.
Time points that do not lie within an interval are mapped to `fill_value`.
Parameters
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intervals : np.ndarray, shape=(n, 2)
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craffel/mir_eval | mir_eval/util.py | sort_labeled_intervals | def sort_labeled_intervals(intervals, labels=None):
'''Sort intervals, and optionally, their corresponding labels
according to start time.
Parameters
----------
intervals : np.ndarray, shape=(n, 2)
The input intervals
labels : list, optional
Labels for each interval
Return... | python | def sort_labeled_intervals(intervals, labels=None):
'''Sort intervals, and optionally, their corresponding labels
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intervals : np.ndarray, shape=(n, 2)
The input intervals
labels : list, optional
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craffel/mir_eval | mir_eval/util.py | f_measure | def f_measure(precision, recall, beta=1.0):
"""Compute the f-measure from precision and recall scores.
Parameters
----------
precision : float in (0, 1]
Precision
recall : float in (0, 1]
Recall
beta : float > 0
Weighting factor for f-measure
(Default value = 1.0... | python | def f_measure(precision, recall, beta=1.0):
"""Compute the f-measure from precision and recall scores.
Parameters
----------
precision : float in (0, 1]
Precision
recall : float in (0, 1]
Recall
beta : float > 0
Weighting factor for f-measure
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craffel/mir_eval | mir_eval/util.py | intervals_to_boundaries | def intervals_to_boundaries(intervals, q=5):
"""Convert interval times into boundaries.
Parameters
----------
intervals : np.ndarray, shape=(n_events, 2)
Array of interval start and end-times
q : int
Number of decimals to round to. (Default value = 5)
Returns
-------
bo... | python | def intervals_to_boundaries(intervals, q=5):
"""Convert interval times into boundaries.
Parameters
----------
intervals : np.ndarray, shape=(n_events, 2)
Array of interval start and end-times
q : int
Number of decimals to round to. (Default value = 5)
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craffel/mir_eval | mir_eval/util.py | boundaries_to_intervals | def boundaries_to_intervals(boundaries):
"""Convert an array of event times into intervals
Parameters
----------
boundaries : list-like
List-like of event times. These are assumed to be unique
timestamps in ascending order.
Returns
-------
intervals : np.ndarray, shape=(n_... | python | def boundaries_to_intervals(boundaries):
"""Convert an array of event times into intervals
Parameters
----------
boundaries : list-like
List-like of event times. These are assumed to be unique
timestamps in ascending order.
Returns
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intervals : np.ndarray, shape=(n_... | [
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] | f41c8dafaea04b411252a516d1965af43c7d531b | https://github.com/craffel/mir_eval/blob/f41c8dafaea04b411252a516d1965af43c7d531b/mir_eval/util.py#L257-L277 | train |
craffel/mir_eval | mir_eval/util.py | merge_labeled_intervals | def merge_labeled_intervals(x_intervals, x_labels, y_intervals, y_labels):
r"""Merge the time intervals of two sequences.
Parameters
----------
x_intervals : np.ndarray
Array of interval times (seconds)
x_labels : list or None
List of labels
y_intervals : np.ndarray
Arra... | python | def merge_labeled_intervals(x_intervals, x_labels, y_intervals, y_labels):
r"""Merge the time intervals of two sequences.
Parameters
----------
x_intervals : np.ndarray
Array of interval times (seconds)
x_labels : list or None
List of labels
y_intervals : np.ndarray
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Parameters
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x_intervals : np.ndarray
Array of interval times (seconds)
x_labels : list or None
List of labels
y_intervals : np.ndarray
Array of interval times (seconds)
y_labels : list or None
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] | f41c8dafaea04b411252a516d1965af43c7d531b | https://github.com/craffel/mir_eval/blob/f41c8dafaea04b411252a516d1965af43c7d531b/mir_eval/util.py#L501-L544 | train |
craffel/mir_eval | mir_eval/util.py | match_events | def match_events(ref, est, window, distance=None):
"""Compute a maximum matching between reference and estimated event times,
subject to a window constraint.
Given two lists of event times ``ref`` and ``est``, we seek the largest set
of correspondences ``(ref[i], est[j])`` such that
``distance(ref[... | python | def match_events(ref, est, window, distance=None):
"""Compute a maximum matching between reference and estimated event times,
subject to a window constraint.
Given two lists of event times ``ref`` and ``est``, we seek the largest set
of correspondences ``(ref[i], est[j])`` such that
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Given two lists of event times ``ref`` and ``est``, we seek the largest set
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``distance(ref[i], est[j]) <= window``, and each
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