Add support for PSI click ABRs

Author: bburan

abr/main_window.enaml

@@ -332,7 +332,10 @@ def add_dock_item(dock_area, model, presenter):
     items = dock_area.dock_items()
     n_items = len(items) - 1
     target = items[-1].name if n_items else ''
-    title = f'{model.frequency * 1e-3:.2f} kHz - {model.parent.name}'
+    if model.frequency == -1:
+        title = f'Click - {model.parent.name}'
+    else:
+        title = f'{model.frequency * 1e-3:.2f} kHz - {model.parent.name}'
     item = MPLDockItem(dock_area, name='dock_{}'.format(n_items), title=title,
                        presenter=presenter)
     op = InsertTab(item=item.name, target=target)

abr/parsers/PSI.py

@@ -38,7 +38,8 @@ def read_file(filename):
             line = fh.readline()
             if line.startswith('time'):
                 break
-            name, *keys = line.split(',')
+            name, *keys = line.strip().split(',')
+            keys = [-1 if k == 'click' else float(k) for k in keys]
             header[name] = np.array(keys).astype('f')
         data = pd.read_csv(fh, index_col=0, header=None)
 
@@ -116,13 +117,14 @@ def get_series(self, filter_settings=None):
         series = ABRSeries(waveforms, self.frequency)
         series.filename = self.parent.filename
         series.id = self.parent.filename.parent.name
+        series.dataset = self
         return series
 
 
 def iter_all(path):
     results = []
     path = Path(path)
-    if path.stem.endswith('abr_io'):
+    if 'abr_io' in path.stem:
         yield from PSIDataCollection(path).iter_frequencies()
     else:
         for subpath in path.glob('**/*abr_io'):

abr/parsers/__init__.py

@@ -53,13 +53,22 @@ def filter_string(waveform):
 
 def load_analysis(fname):
     th_match = re.compile('Threshold \(dB SPL\): ([\w.-]+)')
-    freq_match = re.compile('Frequency \(kHz\): ([\d.]+)')
+    freq_match = re.compile('Frequency \(kHz\): ([\d.]+)|Stimulus: ([.\w]+)?')
     with open(fname) as fh:
         text = fh.readline()
         th = th_match.search(text).group(1)
         th = None if th == 'None' else float(th)
         text = fh.readline()
-        freq = float(freq_match.search(text).group(1))
+
+        match = freq_match.search(text)
+        if match.group(1) is not None:
+            freq = float(match.group(1))
+        else:
+            freq = match.group(2)
+            if freq == 'click':
+                freq = -1
+            else:
+                freq = float(freq)
 
         for line in fh:
             if line.startswith('NOTE'):
@@ -108,8 +117,6 @@ def parse_peaks(peaks, threshold):
 
 class Parser(object):
 
-    filename_template = '{filename}-{frequency}kHz-{user}analyzed.txt'
-
     def __init__(self, file_format, filter_settings, user=None):
         '''
         Parameters
@@ -124,24 +131,13 @@ def __init__(self, file_format, filter_settings, user=None):
         '''
         self._file_format = file_format
         self._filter_settings = filter_settings
-        self._user = user
+        self._rater = user
         self._module_name = f'abr.parsers.{file_format}'
         self._module = importlib.import_module(self._module_name)
 
     def load(self, fs):
         return fs.get_series(self._filter_settings)
 
-    def get_save_filename(self, filename, frequency):
-        # Round frequency to nearest 8 places to minimize floating-point
-        # errors.
-        user_name = self._user + '-' if self._user else ''
-        frequency = round(frequency * 1e-3, 8)
-        save_filename = self.filename_template.format(
-            filename=filename.with_suffix(''),
-            frequency=frequency,
-            user=user_name)
-        return Path(save_filename)
-
     def save(self, model):
         # Assume that all waveforms were filtered identically
         filter_history = filter_string(model.waveforms[-1])
@@ -157,14 +153,21 @@ def save(self, model):
         columns = '\t'.join(columns)
         spreadsheet = '\n'.join(waveform_string(w) \
                                 for w in reversed(model.waveforms))
+
+        if model.freq == -1:
+            stimulus = 'Stimulus: click'
+        else:
+            stimulus = f'Stimulus: {model.freq*1e-3:.2f} kHz'
+
         content = CONTENT.format(threshold=model.threshold,
-                                 frequency=model.freq*1e-3,
+                                 stimulus=stimulus,
                                  filter_history=filter_history,
                                  columns=columns,
                                  spreadsheet=spreadsheet,
                                  version=abr.__version__)
 
-        filename = self.get_save_filename(model.filename, model.freq)
+
+        filename = model.dataset.get_analyzed_filename(self._rater)
         with open(filename, 'w') as fh:
             fh.writelines(content)
 
@@ -173,12 +176,12 @@ def iter_all(self, path):
 
     def find_processed(self, path):
         for ds in self.iter_all(path):
-            if self.get_save_filename(ds.filename, ds.frequency).exists():
+            if ds.get_analyzed_filename(self._rater).exists():
                 yield ds
 
     def find_unprocessed(self, path):
         for ds in self.iter_all(path):
-            if not self.get_save_filename(ds.filename, ds.frequency).exists():
+            if not ds.get_analyzed_filename(self._rater).exists():
                 yield ds
 
     def find_analyses(self, study_directory):
@@ -215,9 +218,9 @@ def load_analyses(self, study_directory):
 
 CONTENT = '''
 Threshold (dB SPL): {threshold:.2f}
-Frequency (kHz): {frequency:.2f}
+{stimulus}
 Filter history (zpk format): {filter_history}
-file_format_version: 0.0.2
+file_format_version: 0.0.3
 code_version: {version}
 NOTE: Negative latencies indicate no peak. NaN for amplitudes indicate peak was unscorable.
 {columns}

abr/parsers/dataset.py

@@ -3,7 +3,7 @@
 import re
 
 
-P_RATER = re.compile('.*kHz(?:-(\w+))?-analyzed.txt')
+P_RATER = re.compile('.*(?:kHz|click)(?:-(\w+))?-analyzed.txt')
 
 
 def get_rater(filename):
@@ -41,7 +41,7 @@ def iter_frequencies(self):
 @functools.total_ordering
 class Dataset:
 
-    filename_template = '{filename}-{frequency}kHz-{rater}analyzed.txt'
+    filename_template = '{filename}-{frequency}-{rater}analyzed.txt'
 
     def __init__(self, parent, frequency):
         self.parent = parent
@@ -59,16 +59,19 @@ def get_series(self, filter_settings=None):
         raise NotImplementedError
 
     def get_analyzed_filename(self, rater):
-        frequency = round(self.frequency * 1e-3, 8)
+        if self.frequency == -1:
+            frequency = 'click'
+        else:
+            frequency = round(self.frequency * 1e-3, 8)
+            frequency = f'{frequency}kHz'
+
         filename = self.filename.with_suffix('')
         if rater != '*':
             rater = rater + '-'
-        return self.filename_template\
+        return self.filename_template \
             .format(filename=filename, frequency=frequency, rater=rater)
 
     def find_analyzed_files(self):
-        frequency = round(self.frequency * 1e-3, 8)
-        filename = self.filename.with_suffix('')
         glob_pattern = self.get_analyzed_filename('*')
         path = Path(glob_pattern)
         return list(path.parent.glob(path.name))
@@ -78,7 +81,9 @@ def list_raters(self):
 
     def load_analysis(self, rater):
         from . import load_analysis
-        return load_analysis(self.get_analyzed_filename(rater))
+        r = load_analysis(self.get_analyzed_filename(rater))
+        print(r)
+        return r
 
     def __lt__(self, other):
         if not isinstance(other, Dataset):