Allow amplitude discriminators to be specified with arbitrary units

Closes #76

README.rst

@@ -45,6 +45,7 @@ To use the app, first organize your datasets in a *metadata file* like this
         amplitude_discriminators:  # used only if fast loading is off (lazy=False)
             - name: B3 neuron
               channel: BN2
+              units: uV
               amplitude: [50, 100]
             # etc
 

docs/metadata.rst

@@ -409,11 +409,13 @@ for each amplitude discriminator.
 
             - name: Unit 1
               channel: Extracellular
-              amplitude: [50, 150] # uV
+              units: uV
+              amplitude: [50, 150]
 
             - name: Unit 2
               channel: Extracellular
-              amplitude: [20, 50] # uV
+              units: uV
+              amplitude: [20, 50]
               epoch: Unit 2 activity
 
 Here two units are detected on the same channel with different amplitude

docs/overview.rst

@@ -35,6 +35,7 @@ this (see :ref:`config-metadata`):
         amplitude_discriminators:  # used only if fast loading is off (lazy=False)
             - name: B3 neuron
               channel: BN2
+              units: uV
               amplitude: [50, 100]
             # etc
 

neurotic/datasets/data.py

@@ -463,8 +463,8 @@ def DetectSpikes(sig, discriminator, epochs):
     else:
         raise ValueError('amplitude discriminator must have two nonnegative thresholds or two nonpositive thresholds: {}'.format(discriminator))
 
-    spikes_crossing_min = elephant.spike_train_generation.peak_detection(sig, min_threshold*pq.uV, sign, 'raw')
-    spikes_crossing_max = elephant.spike_train_generation.peak_detection(sig, max_threshold*pq.uV, sign, 'raw')
+    spikes_crossing_min = elephant.spike_train_generation.peak_detection(sig, pq.Quantity(min_threshold, discriminator['units']), sign, 'raw')
+    spikes_crossing_max = elephant.spike_train_generation.peak_detection(sig, pq.Quantity(max_threshold, discriminator['units']), sign, 'raw')
     if sign == 'above':
         spikes_between_min_and_max = np.setdiff1d(spikes_crossing_min, spikes_crossing_max)
     elif sign == 'below':

neurotic/datasets/metadata.py

@@ -124,8 +124,8 @@ def _defaults_for_key(key):
         # list of labels for epoch encoder
         'epoch_encoder_possible_labels': ['Type 1', 'Type 2', 'Type 3'],
 
-        # list of dicts giving name, channel, amplitude window, epoch window for each unit
-        # - e.g. [{'name': 'Unit X', 'channel': 'Channel A', 'amplitude': [75, 150], 'epoch': 'Type 1'}, ...]
+        # list of dicts giving name, channel, units, amplitude window, epoch window for each unit
+        # - e.g. [{'name': 'Unit X', 'channel': 'Channel A', 'units': 'uV', 'amplitude': [75, 150], 'epoch': 'Type 1'}, ...]
         'amplitude_discriminators': None,
 
         # the output file of a tridesclous spike sorting analysis

neurotic/example/metadata.yml

@@ -48,13 +48,16 @@ example dataset:
 
         - name: B3
           channel: BN2
-          amplitude: [50, 150] # uV
+          units: uV
+          amplitude: [50, 150]
 
         - name: B38
           channel: BN2
-          amplitude: [17, 26] # uV
+          units: uV
+          amplitude: [17, 26]
           epoch: B38 activity
 
         - name: B4/B5
           channel: BN3
-          amplitude: [85, 200] # uV
+          units: uV
+          amplitude: [85, 200]