docstyle pass

mir_eval/display.py

@@ -17,8 +17,7 @@
 
 
 def __expand_limits(ax, limits, which="x"):
-    """Helper function to expand axis limits"""
-
+    """Expand axis limits"""
     if which == "x":
         getter, setter = ax.get_xlim, ax.set_xlim
     elif which == "y":
@@ -60,9 +59,7 @@ def __get_axes(ax=None, fig=None):
     new_axes : bool
         If `True`, the axis object was newly constructed.
         If `False`, the axis object already existed.
-
     """
-
     new_axes = False
 
     if ax is not None:
@@ -256,7 +253,6 @@ def labeled_intervals(
     ax : matplotlib.pyplot.axes._subplots.AxesSubplot
         A handle to the (possibly constructed) plot axes
     """
-
     # Get the axes handle
     ax, _ = __get_axes(ax=ax)
 
@@ -347,6 +343,7 @@ def __init__(self, base, ticks):
         self._map = {int(k): v for k, v in zip(base, ticks)}
 
     def __call__(self, x, pos=None):
+        """Map the input position to its corresponding interval label"""
         return self._map.get(int(x), "")
 
 
@@ -379,7 +376,6 @@ def hierarchy(intervals_hier, labels_hier, levels=None, ax=None, **kwargs):
     ax : matplotlib.pyplot.axes._subplots.AxesSubplot
         A handle to the (possibly constructed) plot axes
     """
-
     # This will break if a segment label exists in multiple levels
     if levels is None:
         levels = list(range(len(intervals_hier)))
@@ -529,7 +525,6 @@ def pitch(times, frequencies, midi=False, unvoiced=False, ax=None, **kwargs):
     ax : matplotlib.pyplot.axes._subplots.AxesSubplot
         A handle to the (possibly constructed) plot axes
     """
-
     ax, _ = __get_axes(ax=ax)
 
     times = np.asarray(times)
@@ -616,7 +611,6 @@ def multipitch(times, frequencies, midi=False, unvoiced=False, ax=None, **kwargs
     ax : matplotlib.pyplot.axes._subplots.AxesSubplot
         A handle to the (possibly constructed) plot axes
     """
-
     # Get the axes handle
     ax, _ = __get_axes(ax=ax)
 
@@ -697,7 +691,6 @@ def piano_roll(intervals, pitches=None, midi=None, ax=None, **kwargs):
     ax : matplotlib.pyplot.axes._subplots.AxesSubplot
         A handle to the (possibly constructed) plot axes
     """
-
     if midi is None:
         if pitches is None:
             raise ValueError("At least one of `midi` or `pitches` " "must be provided.")
@@ -745,7 +738,6 @@ def separation(sources, fs=22050, labels=None, alpha=0.75, ax=None, **kwargs):
     ax
         The axis handle for this plot
     """
-
     # Get the axes handle
     ax, new_axes = __get_axes(ax=ax)
 
@@ -807,12 +799,11 @@ def separation(sources, fs=22050, labels=None, alpha=0.75, ax=None, **kwargs):
 
 
 def __ticker_midi_note(x, pos):
-    """A ticker function for midi notes.
+    """Format midi notes for ticker decoration.
 
     Inputs x are interpreted as midi numbers, and converted
     to [NOTE][OCTAVE]+[cents].
     """
-
     NOTES = ["C", "C#", "D", "D#", "E", "F", "F#", "G", "G#", "A", "A#", "B"]
 
     cents = float(np.mod(x, 1.0))
@@ -830,12 +821,11 @@ def __ticker_midi_note(x, pos):
 
 
 def __ticker_midi_hz(x, pos):
-    """A ticker function for midi pitches.
+    """Format midi pitches for ticker decoration.
 
     Inputs x are interpreted as midi numbers, and converted
     to Hz.
     """
-
     return "{:g}".format(midi_to_hz(x))
 
 

mir_eval/segment.py

@@ -85,7 +85,7 @@
 
 
 def validate_boundary(reference_intervals, estimated_intervals, trim):
-    """Checks that the input annotations to a segment boundary estimation
+    """Check that the input annotations to a segment boundary estimation
     metric (i.e. one that only takes in segment intervals) look like valid
     segment times, and throws helpful errors if not.
 
@@ -101,9 +101,7 @@ def validate_boundary(reference_intervals, estimated_intervals, trim):
         :func:`mir_eval.io.load_labeled_intervals`.
     trim : bool
         will the start and end events be trimmed?
-
     """
-
     if trim:
         # If we're trimming, then we need at least 2 intervals
         min_size = 2
@@ -124,7 +122,7 @@ def validate_boundary(reference_intervals, estimated_intervals, trim):
 def validate_structure(
     reference_intervals, reference_labels, estimated_intervals, estimated_labels
 ):
-    """Checks that the input annotations to a structure estimation metric (i.e.
+    """Check that the input annotations to a structure estimation metric (i.e.
     one that takes in both segment boundaries and their labels) look like valid
     segment times and labels, and throws helpful errors if not.
 
@@ -226,9 +224,7 @@ def detection(
         recall of reference reference boundaries
     f_measure : float
         F-measure (weighted harmonic mean of ``precision`` and ``recall``)
-
     """
-
     validate_boundary(reference_intervals, estimated_intervals, trim)
 
     # Convert intervals to boundaries
@@ -288,9 +284,7 @@ def deviation(reference_intervals, estimated_intervals, trim=False):
     estimated_to_reference : float
         median time from each estimated boundary to the
         closest reference boundary
-
     """
-
     validate_boundary(reference_intervals, estimated_intervals, trim)
 
     # Convert intervals to boundaries
@@ -471,9 +465,7 @@ def rand_index(
     -------
     rand_index : float > 0
         Rand index
-
     """
-
     validate_structure(
         reference_intervals, reference_labels, estimated_intervals, estimated_labels
     )
@@ -519,7 +511,7 @@ def rand_index(
 
 
 def _contingency_matrix(reference_indices, estimated_indices):
-    """Computes the contingency matrix of a true labeling vs an estimated one.
+    """Compute the contingency matrix of a true labeling vs an estimated one.
 
     Parameters
     ----------
@@ -602,7 +594,7 @@ def ari(
     estimated_labels,
     frame_size=0.1,
 ):
-    """Adjusted Rand Index (ARI) for frame clustering segmentation evaluation.
+    """Compute the Adjusted Rand Index (ARI) for frame clustering segmentation evaluation.
 
     Examples
     --------
@@ -715,7 +707,7 @@ def _mutual_info_score(reference_indices, estimated_indices, contingency=None):
 
 
 def _entropy(labels):
-    """Calculates the entropy for a labeling.
+    """Calculate the entropy for a labeling.
 
     Parameters
     ----------
@@ -1044,9 +1036,7 @@ def nce(
         If `|y_ref|==1`, then `S_under` will be 0.
     S_F
         F-measure for (S_over, S_under)
-
     """
-
     validate_structure(
         reference_intervals, reference_labels, estimated_intervals, estimated_labels
     )
@@ -1177,9 +1167,7 @@ def vmeasure(
         If `|y_ref|==1`, then `V_recall` will be 0.
     V_F
         F-measure for (V_precision, V_recall)
-
     """
-
     return nce(
         reference_intervals,
         reference_labels,
@@ -1226,9 +1214,7 @@ def evaluate(ref_intervals, ref_labels, est_intervals, est_labels, **kwargs):
     scores : dict
         Dictionary of scores, where the key is the metric name (str) and
         the value is the (float) score achieved.
-
     """
-
     # Adjust timespan of estimations relative to ground truth
     ref_intervals, ref_labels = util.adjust_intervals(
         ref_intervals, labels=ref_labels, t_min=0.0

mir_eval/separation.py

@@ -60,7 +60,7 @@
 
 
 def validate(reference_sources, estimated_sources):
-    """Checks that the input data to a metric are valid, and throws helpful
+    """Check that the input data to a metric are valid, and throws helpful
     errors if not.
 
     Parameters
@@ -71,7 +71,6 @@ def validate(reference_sources, estimated_sources):
         matrix containing estimated sources
 
     """
-
     if reference_sources.shape != estimated_sources.shape:
         raise ValueError(
             "The shape of estimated sources and the true "
@@ -136,7 +135,7 @@ def validate(reference_sources, estimated_sources):
 
 
 def _any_source_silent(sources):
-    """Returns true if the parameter sources has any silent first dimensions"""
+    """Return 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)
     )
@@ -198,7 +197,6 @@ def bss_eval_sources(reference_sources, estimated_sources, compute_permutation=T
         92, pp. 1928-1936, 2012.
 
     """
-
     # make sure the input is of shape (nsrc, nsampl)
     if estimated_sources.ndim == 1:
         estimated_sources = estimated_sources[np.newaxis, :]
@@ -319,9 +317,7 @@ def bss_eval_sources_framewise(
         the mean SIR sense (estimated source number ``perm[j]`` corresponds to
         true source number ``j``).  Note: ``perm`` will be ``range(nsrc)`` for
         all windows if ``compute_permutation`` is ``False``
-
     """
-
     # make sure the input is of shape (nsrc, nsampl)
     if estimated_sources.ndim == 1:
         estimated_sources = estimated_sources[np.newaxis, :]
@@ -367,7 +363,7 @@ def bss_eval_sources_framewise(
 
 
 def bss_eval_images(reference_sources, estimated_sources, compute_permutation=True):
-    """Implementation of the bss_eval_images function from the
+    """Compute the bss_eval_images function from the
     BSS_EVAL Matlab toolbox.
 
     Ordering and measurement of the separation quality for estimated source
@@ -423,9 +419,7 @@ def bss_eval_images(reference_sources, estimated_sources, compute_permutation=Tr
         Lutter and Ngoc Q.K. Duong, "The Signal Separation Evaluation Campaign
         (2007-2010): Achievements and remaining challenges", Signal Processing,
         92, pp. 1928-1936, 2012.
-
     """
-
     # make sure the input has 3 dimensions
     # assuming input is in shape (nsampl) or (nsrc, nsampl)
     estimated_sources = np.atleast_3d(estimated_sources)
@@ -570,9 +564,7 @@ def bss_eval_images_framewise(
         true source number j)
         Note: perm will be range(nsrc) for all windows if compute_permutation
         is False
-
     """
-
     # make sure the input has 3 dimensions
     # assuming input is in shape (nsampl) or (nsrc, nsampl)
     estimated_sources = np.atleast_3d(estimated_sources)

mir_eval/sonify.py

@@ -12,7 +12,7 @@
 
 
 def clicks(times, fs, click=None, length=None):
-    """Returns a signal with the signal 'click' placed at each specified time
+    """Return a signal with the signal 'click' placed at each specified time
 
     Parameters
     ----------
@@ -63,7 +63,7 @@ def clicks(times, fs, click=None, length=None):
 def time_frequency(
     gram, frequencies, times, fs, function=np.sin, length=None, n_dec=1, threshold=0.01
 ):
-    """Reverse synthesis of a time-frequency representation of a signal
+    r"""Reverse synthesis of a time-frequency representation of a signal
 
     Parameters
     ----------
@@ -126,7 +126,7 @@ def time_frequency(
     sample_intervals = np.round(times * fs).astype(int)
 
     def _fast_synthesize(frequency):
-        """A faster way to synthesize a signal.
+        """Efficiently synthesize a signal.
         Generate one cycle, and simulate arbitrary repetitions
         using array indexing tricks.
         """
@@ -217,7 +217,7 @@ def __interpolator(x):
 def pitch_contour(
     times, frequencies, fs, amplitudes=None, function=np.sin, length=None, kind="linear"
 ):
-    """Sonify a pitch contour.
+    r"""Sonify a pitch contour.
 
     Parameters
     ----------
@@ -245,7 +245,6 @@ def pitch_contour(
     output : np.ndarray
         synthesized version of the pitch contour
     """
-
     fs = float(fs)
 
     if length is None:

mir_eval/tempo.py

@@ -27,7 +27,7 @@
 
 
 def validate_tempi(tempi, reference=True):
-    """Checks that there are two non-negative tempi.
+    """Check that there are two non-negative tempi.
     For a reference value, at least one tempo has to be greater than zero.
 
     Parameters
@@ -36,9 +36,7 @@ def validate_tempi(tempi, reference=True):
         length-2 array of tempo, in bpm
     reference : bool
         indicates a reference value
-
     """
-
     if tempi.size != 2:
         raise ValueError("tempi must have exactly two values")
 
@@ -52,7 +50,7 @@ def validate_tempi(tempi, reference=True):
 
 
 def validate(reference_tempi, reference_weight, estimated_tempi):
-    """Checks that the input annotations to a metric look like valid tempo
+    """Check that the input annotations to a metric look like valid tempo
     annotations.
 
     Parameters
@@ -109,7 +107,6 @@ def detection(reference_tempi, reference_weight, estimated_tempi, tol=0.08):
 
         If ``tol < 0`` or ``tol > 1``.
     """
-
     validate(reference_tempi, reference_weight, estimated_tempi)
 
     if tol < 0 or tol > 1:

mir_eval/transcription.py

@@ -115,7 +115,7 @@
 
 
 def validate(ref_intervals, ref_pitches, est_intervals, est_pitches):
-    """Checks that the input annotations to a metric look like time intervals
+    """Check that the input annotations to a metric look like time intervals
     and a pitch list, and throws helpful errors if not.
 
     Parameters
@@ -146,7 +146,7 @@ def validate(ref_intervals, ref_pitches, est_intervals, est_pitches):
 
 
 def validate_intervals(ref_intervals, est_intervals):
-    """Checks that the input annotations to a metric look like time intervals,
+    """Check that the input annotations to a metric look like time intervals,
     and throws helpful errors if not.
 
     Parameters

mir_eval/transcription_velocity.py

@@ -67,7 +67,7 @@ def validate(
     est_pitches,
     est_velocities,
 ):
-    """Checks that the input annotations have valid time intervals, pitches,
+    """Check that the input annotations have valid time intervals, pitches,
     and velocities, and throws helpful errors if not.
 
     Parameters