.py and .mat files from ./doc/notebooks necessary for tutorials/how-t…

…os copied to ./notebooks

notebooks/batlab2seq.py

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+import numpy as np
+from scipy.io import loadmat
+
+from crowsetta.classes import Sequence
+
+
+def batlab2seq(mat_file):
+    """unpack BatLAB annotation into list of Sequence objects
+
+    example of a function that unpacks annotation from
+    a complicated data structure and returns the necessary
+    data as a Sequence object
+
+    Parameters
+    ----------
+    mat_file : str
+        filename of .mat file created by BatLAB
+
+    Returns
+    -------
+    seq_list : list
+        of Sequence objects
+    """
+    mat = loadmat(mat_file, squeeze_me=True)
+    seq_list = []
+    # annotation structure loads as a Python dictionary with two keys
+    # one maps to a list of filenames,
+    # and the other to a Numpy array where each element is the annotation
+    # coresponding to the filename at the same index in the list.
+    # We can iterate over both by using the zip() function.
+    for filename, annotation in zip(mat['filenames'], mat['annotations']):
+        # below, .tolist() does not actually create a list,
+        # instead gets ndarray out of a zero-length ndarray of dtype=object.
+        # This is just weirdness that results from loading complicated data
+        # structure in .mat file.
+        seg_start_times = annotation['segFileStartTimes'].tolist()
+        seg_end_times = annotation['segFileEndTimes'].tolist()
+        seg_types = annotation['segType'].tolist()
+        if type(seg_types) == int:
+            # this happens when there's only one syllable in the file
+            # with only one corresponding label
+            seg_types = np.asarray([seg_types])  # so make it a one-element list
+        elif type(seg_types) == np.ndarray:
+            # this should happen whenever there's more than one label
+            pass
+        else:
+            # something unexpected happened
+            raise ValueError("Unable to load labels from {}, because "
+                             "the segType parsed as type {} which is "
+                             "not recognized.".format(wav_filename,
+                                                      type(seg_types)))
+        samp_freq = annotation['fs'].tolist()
+        seg_start_times_Hz = np.round(seg_start_times * samp_freq).astype(int)
+        seg_end_times_Hz = np.round(seg_end_times * samp_freq).astype(int)
+
+        seq = Sequence.from_keyword(file=filename,
+                                    labels=seg_types,
+                                    onsets_s=seg_start_times,
+                                    offsets_s=seg_end_times,
+                                    onsets_Hz=seg_start_times_Hz,
+                                    offsets_Hz=seg_end_times_Hz)
+        seq_list.append(seq)
+    return seq_list

notebooks/parsebat.py

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+import numpy as np
+from scipy.io import loadmat
+
+
+def parse_batlab_mat(mat_file):
+    """parse batlab annotation.mat file"""
+    mat = loadmat(mat_file, squeeze_me=True)
+    annot_list = []
+    # annotation structure loads as a Python dictionary with two keys
+    # one maps to a list of filenames,
+    # and the other to a Numpy array where each element is the annotation
+    # coresponding to the filename at the same index in the list.
+    # We can iterate over both by using the zip() function.
+    for filename, annotation in zip(mat['filenames'], mat['annotations']):
+        # below, .tolist() does not actually create a list,
+        # instead gets ndarray out of a zero-length ndarray of dtype=object.
+        # This is just weirdness that results from loading complicated data
+        # structure in .mat file.
+        seg_start_times = annotation['segFileStartTimes'].tolist()
+        seg_end_times = annotation['segFileEndTimes'].tolist()
+        seg_types = annotation['segType'].tolist()
+        if type(seg_types) == int:
+            # this happens when there's only one syllable in the file
+            # with only one corresponding label
+            seg_types = np.asarray([seg_types])  # so make it a one-element list
+        elif type(seg_types) == np.ndarray:
+            # this should happen whenever there's more than one label
+            pass
+        else:
+            # something unexpected happened
+            raise ValueError("Unable to load labels from {}, because "
+                             "the segType parsed as type {} which is "
+                             "not recognized.".format(filename,
+                                                      type(seg_types)))
+        samp_freq = annotation['fs'].tolist()
+        seg_start_times_Hz = np.round(seg_start_times * samp_freq).astype(int)
+        seg_end_times_Hz = np.round(seg_end_times * samp_freq).astype(int)
+        annot_dict = {
+            'audio_file': filename,
+            'seg_types': seg_types,
+            'seg_start_times': seg_start_times,
+            'seg_end_times': seg_end_times,
+            'seg_start_times_Hz': seg_start_times_Hz,
+            'seg_end_times_Hz': seg_end_times_Hz,
+        }
+        annot_list.append(annot_dict)
+
+    return annot_list