mild spellchecking

.codespell_ignore_list

@@ -0,0 +1,5 @@
+nce
+FPR
+Shepard
+dum
+Theis

.github/workflows/lint_python.yml

@@ -44,7 +44,7 @@ jobs:
 
     - name: Spell check package
       shell: bash -l {0}
-      run: codespell --ignore-words-list="nce,FPR,Shepard,dum" mir_eval
+      run: codespell --ignore-words .codespell_ignore_list mir_eval
 
     - name: Security check
       shell: bash -l {0}

mir_eval/chord.py

@@ -67,7 +67,7 @@
   entire quality in closed voicing, i.e. spanning only a single octave;
   extended chords (9's, 11's and 13's) are rolled into a single octave with any
   upper voices included as extensions. For example, ('A:7', 'A:9') are
-  equivlent but ('A:7', 'A:maj7') are not.
+  equivalent but ('A:7', 'A:maj7') are not.
 
 * :func:`mir_eval.chord.tetrads_inv`: Same as above, with inversions (bass
   relationships).
@@ -557,7 +557,7 @@ def encode_many(chord_labels, reduce_extended_chords=False):
 
 
 def rotate_bitmap_to_root(bitmap, chord_root):
-    """Circularly shift a relative bitmap to its asbolute pitch classes.
+    """Circularly shift a relative bitmap to its absolute pitch classes.
 
     For clarity, the best explanation is an example. Given 'G:Maj', the root
     and quality map are as follows::
@@ -592,7 +592,7 @@ def rotate_bitmap_to_root(bitmap, chord_root):
 
 
 def rotate_bitmaps_to_roots(bitmaps, roots):
-    """Circularly shift a relative bitmaps to asbolute pitch classes.
+    """Circularly shift a relative bitmaps to absolute pitch classes.
 
     See :func:`rotate_bitmap_to_root` for more information.
 

mir_eval/io.py

@@ -319,7 +319,7 @@ def load_time_series(filename, delimiter=r'\s+', comment='#'):
 
 
 def load_patterns(filename):
-    """Loads the patters contained in the filename and puts them into a list
+    """Loads the patterns contained in the filename and puts them into a list
     of patterns, each pattern being a list of occurrence, and each
     occurrence being a list of (onset, midi) pairs.
 

mir_eval/multipitch.py

@@ -420,7 +420,7 @@ def metrics(ref_time, ref_freqs, est_time, est_freqs, **kwargs):
     ref_freqs_chroma = midi_to_chroma(ref_freqs_midi)
     est_freqs_chroma = midi_to_chroma(est_freqs_midi)
 
-    # count number of occurences
+    # count number of occurrences
     n_ref = compute_num_freqs(ref_freqs_midi)
     n_est = compute_num_freqs(est_freqs_midi)
 

mir_eval/onset.py

@@ -19,7 +19,7 @@
 -------
 
 * :func:`mir_eval.onset.f_measure`: Precision, Recall, and F-measure scores
-  based on the number of esimated onsets which are sufficiently close to
+  based on the number of estimated onsets which are sufficiently close to
   reference onsets.
 '''
 
@@ -55,7 +55,7 @@ def validate(reference_onsets, estimated_onsets):
 
 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.
+    "Correctness" is determined over a small window.
 
     Examples
     --------

mir_eval/pattern.py

@@ -16,7 +16,7 @@
 The input format can be automatically generated by calling
 :func:`mir_eval.io.load_patterns`.  This format is a list of a list of
 tuples.  The first list collections patterns, each of which is a list of
-occurences, and each occurrence is a list of MIDI onset tuples of
+occurrences, and each occurrence is a list of MIDI onset tuples of
 ``(onset_time, mid_note)``
 
 A pattern is a list of occurrences. The first occurrence must be the prototype
@@ -176,7 +176,7 @@ def standard_FPR(reference_patterns, estimated_patterns, tol=1e-5):
     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 equal, this metric is quite
-    restictive and it tends to be 0 in most of 2013 MIREX results.
+    restrictive and it tends to be 0 in most of 2013 MIREX results.
 
     Examples
     --------
@@ -467,7 +467,7 @@ def compute_second_layer_PR(ref_pattern, est_pattern):
 
     def compute_layer(ref_elements, est_elements, layer=1):
         """Computes the F-measure matrix for a given layer. The reference and
-        estimated elements can be either patters or occurrences, depending
+        estimated elements can be either patterns or occurrences, depending
         on the layer.
 
         For layer 1, the elements must be occurrences.

mir_eval/separation.py

@@ -471,7 +471,7 @@ def bss_eval_images(reference_sources, estimated_sources,
         isr = np.empty(nsrc)
         sir = np.empty(nsrc)
         sar = np.empty(nsrc)
-        Gj = [0] * nsrc  # prepare G matrics with zeroes
+        Gj = [0] * nsrc  # prepare G matrices with zeroes
         G = np.zeros(1)
         for j in range(nsrc):
             # save G matrix to avoid recomputing it every call
@@ -638,7 +638,7 @@ def _bss_decomp_mtifilt_images(reference_sources, estimated_source, j, flen,
     Improved performance can be gained by passing Gj and G parameters initially
     as all zeros. These parameters store the results from the computation of
     the G matrix in _project_images and then return them for subsequent calls
-    to this function. This only works when not computing permuations.
+    to this function. This only works when not computing permutations.
     """
     nsampl = np.shape(estimated_source)[0]
     nchan = np.shape(estimated_source)[1]

mir_eval/sonify.py

@@ -215,7 +215,7 @@ def pitch_contour(times, frequencies, fs, amplitudes=None, function=np.sin,
         desired sampling rate of the output signal
 
     amplitudes : np.ndarray
-        amplitude measurments, nonnegative
+        amplitude measurements, nonnegative
         defaults to ``np.ones((length,))``
 
     function : function