velinized docstrings

Author: bmcfee

mir_eval/alignment.py

@@ -324,7 +324,7 @@ def evaluate(reference_timestamps, estimated_timestamps, **kwargs):
         reference timestamp locations, in seconds
     estimated_timestamps : np.ndarray
         estimated timestamp locations, in seconds
-    kwargs
+    **kwargs
         Additional keyword arguments which will be passed to the
         appropriate metric or preprocessing functions.
 

mir_eval/beat.py

@@ -727,7 +727,7 @@ def evaluate(reference_beats, estimated_beats, **kwargs):
         Reference beat times, in seconds
     estimated_beats : np.ndarray
         Query beat times, in seconds
-    kwargs
+    **kwargs
         Additional keyword arguments which will be passed to the
         appropriate metric or preprocessing functions.
 

mir_eval/chord.py

@@ -177,7 +177,7 @@ def scale_degree_to_semitone(scale_degree):
 
     Parameters
     ----------
-    scale degree : str
+    scale_degree : str
         Spelling of a relative scale degree, e.g. 'b3', '7', '#5'
 
     Returns
@@ -345,7 +345,7 @@ def validate_chord_label(chord_label):
 
     Parameters
     ----------
-    chord : str
+    chord_label : str
         Chord label to validate.
 
     """
@@ -1551,20 +1551,16 @@ def evaluate(ref_intervals, ref_labels, est_intervals, est_labels, **kwargs):
     ref_intervals : np.ndarray, shape=(n, 2)
         Reference chord intervals, in the format returned by
         :func:`mir_eval.io.load_labeled_intervals`.
-
     ref_labels : list, shape=(n,)
         reference chord labels, in the format returned by
         :func:`mir_eval.io.load_labeled_intervals`.
-
     est_intervals : np.ndarray, shape=(m, 2)
         estimated chord intervals, in the format returned by
         :func:`mir_eval.io.load_labeled_intervals`.
-
     est_labels : list, shape=(m,)
         estimated chord labels, in the format returned by
         :func:`mir_eval.io.load_labeled_intervals`.
-
-    kwargs
+    **kwargs
         Additional keyword arguments which will be passed to the
         appropriate metric or preprocessing functions.
 

mir_eval/display.py

@@ -57,7 +57,6 @@ def __get_axes(ax=None, fig=None):
     ax : matplotlib.pyplot.axes
         An axis handle on which to draw the segmentation.
         If none is provided, a new set of axes is created.
-
     new_axes : bool
         If `True`, the axis object was newly constructed.
         If `False`, the axis object already existed.
@@ -98,33 +97,26 @@ def segments(
         segment intervals, in the format returned by
         :func:`mir_eval.io.load_intervals` or
         :func:`mir_eval.io.load_labeled_intervals`.
-
     labels : list, shape=(n,)
         reference segment labels, in the format returned by
         :func:`mir_eval.io.load_labeled_intervals`.
-
     base : number
         The vertical position of the base of the rectangles.
         By default, this will be the bottom of the plot.
-
     height : number
         The height of the rectangles.
         By default, this will be the top of the plot (minus ``base``).
-
     text : bool
         If true, each segment's label is displayed in its
         upper-left corner
-
     text_kw : dict
         If ``text == True``, the properties of the text
         object can be specified here.
         See ``matplotlib.pyplot.Text`` for valid parameters
-
     ax : matplotlib.pyplot.axes
         An axis handle on which to draw the segmentation.
         If none is provided, a new set of axes is created.
-
-    kwargs
+    **kwargs
         Additional keyword arguments to pass to
         ``matplotlib.patches.Rectangle``.
 
@@ -255,7 +247,7 @@ def labeled_intervals(
     tick : bool
         If ``True``, sets tick positions and labels on the y-axis.
 
-    kwargs
+    **kwargs
         Additional keyword arguments to pass to
         `matplotlib.collection.BrokenBarHCollection`.
 
@@ -369,17 +361,14 @@ def hierarchy(intervals_hier, labels_hier, levels=None, ax=None, **kwargs):
         :func:`mir_eval.io.load_intervals` or
         :func:`mir_eval.io.load_labeled_intervals`.
         Segmentations should be ordered by increasing specificity.
-
     labels_hier : list of list-like
         A list of segmentation labels.  Each element should
         be a list of labels for the corresponding element in
         `intervals_hier`.
-
     levels : list of string
         Each element ``levels[i]`` is a label for the ```i`` th segmentation.
         This is used in the legend to denote the levels in a segment hierarchy.
-
-    kwargs
+    **kwargs
         Additional keyword arguments to `labeled_intervals`.
 
     Returns
@@ -416,29 +405,23 @@ def events(times, labels=None, base=None, height=None, ax=None, text_kw=None, **
         event times, in the format returned by
         :func:`mir_eval.io.load_events` or
         :func:`mir_eval.io.load_labeled_events`.
-
     labels : list, shape=(n,), optional
         event labels, in the format returned by
         :func:`mir_eval.io.load_labeled_events`.
-
     base : number
         The vertical position of the base of the line.
         By default, this will be the bottom of the plot.
-
     height : number
         The height of the lines.
         By default, this will be the top of the plot (minus `base`).
-
     ax : matplotlib.pyplot.axes
         An axis handle on which to draw the segmentation.
         If none is provided, a new set of axes is created.
-
     text_kw : dict
         If `labels` is provided, the properties of the text
         objects can be specified here.
         See `matplotlib.pyplot.Text` for valid parameters
-
-    kwargs
+    **kwargs
         Additional keyword arguments to pass to
         `matplotlib.pyplot.vlines`.
 
@@ -535,7 +518,7 @@ def pitch(times, frequencies, midi=False, unvoiced=False, ax=None, **kwargs):
         An axis handle on which to draw the pitch contours.
         If none is provided, a new set of axes is created.
 
-    kwargs
+    **kwargs
         Additional keyword arguments to `matplotlib.pyplot.plot`.
 
     Returns
@@ -622,7 +605,7 @@ def multipitch(times, frequencies, midi=False, unvoiced=False, ax=None, **kwargs
         An axis handle on which to draw the pitch contours.
         If none is provided, a new set of axes is created.
 
-    kwargs
+    **kwargs
         Additional keyword arguments to `plt.scatter`.
 
     Returns
@@ -703,7 +686,7 @@ def piano_roll(intervals, pitches=None, midi=None, ax=None, **kwargs):
         An axis handle on which to draw the intervals.
         If none is provided, a new set of axes is created.
 
-    kwargs
+    **kwargs
         Additional keyword arguments to :func:`labeled_intervals`.
 
     Returns
@@ -742,21 +725,16 @@ def separation(sources, fs=22050, labels=None, alpha=0.75, ax=None, **kwargs):
     ----------
     sources : np.ndarray, shape=(nsrc, nsampl)
         A list of waveform buffers corresponding to each source
-
     fs : number > 0
         The sampling rate
-
     labels : list of strings
         An optional list of descriptors corresponding to each source
-
     alpha : float in [0, 1]
         Maximum alpha (opacity) of spectrogram values.
-
     ax : matplotlib.pyplot.axes
         An axis handle on which to draw the spectrograms.
         If none is provided, a new set of axes is created.
-
-    kwargs
+    **kwargs
         Additional keyword arguments to ``scipy.signal.spectrogram``
 
     Returns

mir_eval/hierarchy.py

@@ -68,7 +68,6 @@ def _round(t, frame_size):
     ----------
     t : number or ndarray
         The time-stamp to round
-
     frame_size : number > 0
         The resolution to round to
 
@@ -110,10 +109,8 @@ def _align_intervals(int_hier, lab_hier, t_min=0.0, t_max=None):
         Hierarchical segment annotations, encoded as a
         list of list of intervals (int_hier) and list of
         list of strings (lab_hier)
-
     t_min : None or number >= 0
         The minimum time value for the segmentation
-
     t_max : None or number >= t_min
         The maximum time value for the segmentation
 
@@ -149,7 +146,6 @@ def _lca(intervals_hier, frame_size):
     intervals_hier : list of ndarray
         An ordered list of segment interval arrays.
         The list is assumed to be ordered by increasing specificity (depth).
-
     frame_size : number
         The length of the sample frames (in seconds)
 
@@ -194,11 +190,9 @@ def _meet(intervals_hier, labels_hier, frame_size):
     intervals_hier : list of ndarray
         An ordered list of segment interval arrays.
         The list is assumed to be ordered by increasing specificity (depth).
-
     labels_hier : list of list of str
         ``labels_hier[i]`` contains the segment labels for the
         ``i``th layer of the annotations
-
     frame_size : number
         The length of the sample frames (in seconds)
 
@@ -251,6 +245,7 @@ def _gauc(ref_lca, est_lca, transitive, window):
     Parameters
     ----------
     ref_lca : scipy.sparse
+
     est_lca : scipy.sparse
         The least common ancestor matrices for the reference and
         estimated annotations
@@ -380,7 +375,6 @@ def _compare_frame_rankings(ref, est, transitive=False):
     est : np.ndarray, shape=(n,)
         Reference and estimate ranked lists.
         `ref[i]` is the relevance score for point `i`.
-
     transitive : bool
         If true, all pairs of reference levels are compared.
         If false, only adjacent pairs of reference levels are compared.
@@ -390,7 +384,6 @@ def _compare_frame_rankings(ref, est, transitive=False):
     inversions : int
         The number of pairs of indices `i, j` where
         `ref[i] < ref[j]` but `est[i] >= est[j]`.
-
     normalizer : float
         The total number of pairs (i, j) under consideration.
         If transitive=True, then this is |{(i,j) : ref[i] < ref[j]}|
@@ -493,32 +486,25 @@ def tmeasure(
         (in seconds) for the ``i`` th layer of the annotations.  Layers are
         ordered from top to bottom, so that the last list of intervals should
         be the most specific.
-
     estimated_intervals_hier : list of ndarray
         Like ``reference_intervals_hier`` but for the estimated annotation
-
     transitive : bool
         whether to compute the t-measures using transitivity or not.
-
     window : float > 0
         size of the window (in seconds).  For each query frame q,
         result frames are only counted within q +- window.
-
     frame_size : float > 0
         length (in seconds) of frames.  The frame size cannot be longer than
         the window.
-
     beta : float > 0
         beta parameter for the F-measure.
 
     Returns
     -------
     t_precision : number [0, 1]
         T-measure Precision
-
     t_recall : number [0, 1]
         T-measure Recall
-
     t_measure : number [0, 1]
         F-beta measure for ``(t_precision, t_recall)``
 
@@ -583,31 +569,25 @@ def lmeasure(
         (in seconds) for the ``i`` th layer of the annotations.  Layers are
         ordered from top to bottom, so that the last list of intervals should
         be the most specific.
-
     reference_labels_hier : list of list of str
         ``reference_labels_hier[i]`` contains the segment labels for the
         ``i``th layer of the annotations
-
     estimated_intervals_hier : list of ndarray
     estimated_labels_hier : list of ndarray
         Like ``reference_intervals_hier`` and ``reference_labels_hier``
         but for the estimated annotation
-
     frame_size : float > 0
         length (in seconds) of frames.  The frame size cannot be longer than
         the window.
-
     beta : float > 0
         beta parameter for the F-measure.
 
     Returns
     -------
     l_precision : number [0, 1]
         L-measure Precision
-
     l_recall : number [0, 1]
         L-measure Recall
-
     l_measure : number [0, 1]
         F-beta measure for ``(l_precision, l_recall)``
 
@@ -694,7 +674,6 @@ def evaluate(
      'T-Recall full': 0.6523334654992341,
      'T-Recall reduced': 0.60799919710921635}
 
-
     Parameters
     ----------
     ref_intervals_hier : list of list-like
@@ -705,13 +684,12 @@ def evaluate(
         of segmentations.  Each segmentation itself is a list (or list-like)
         of intervals (\*_intervals_hier) and a list of lists of labels
         (\*_labels_hier).
-
-    kwargs
+    **kwargs
         additional keyword arguments to the evaluation metrics.
 
     Returns
     -------
-    scores :  OrderedDict
+    scores : OrderedDict
         Dictionary of scores, where the key is the metric name (str) and
         the value is the (float) score achieved.