[bids_import] Added validSamples to index.json + Self-descriptive renames

python/react-series-data-viewer/chunking.py

@@ -10,33 +10,33 @@
 from protocol_buffers import chunk_pb2 as chunk_pb
 
 
-def pad_channels(channels, chunk_size):
-    num_chunks = math.ceil(channels.shape[-1] / chunk_size)
+def pad_values(values, chunk_size):
+    num_chunks = math.ceil(values.shape[-1] / chunk_size)
     total_chunked_points = num_chunks * chunk_size
-    padding = total_chunked_points - channels.shape[-1]
-    padding_seq = [(0, 0) for _ in channels.shape]
+    padding = total_chunked_points - values.shape[-1]
+    padding_seq = [(0, 0) for _ in values.shape]
     padding_seq[-1] = (0, padding)
-    padded_channels = np.pad(channels, padding_seq, 'edge')
-    return padded_channels
+    padded_values = np.pad(values, padding_seq, 'edge')
+    return padded_values
 
 
-def channels_to_chunks(channels, chunk_size):
-    padded_channels = pad_channels(channels, chunk_size)
-    padded_channels = np.expand_dims(padded_channels, axis=-2)
-    shape = list(padded_channels.shape)
-    shape[-2] = int(shape[-1] / chunk_size)
-    shape[-1] = chunk_size
+def values_to_chunks(values, chunk_size):
+    padded_values = pad_values(values, chunk_size)
+    padded_values = np.expand_dims(padded_values, axis=-2)
+    shape = list(padded_values.shape)
+    shape[-2] = int(shape[-1] / chunk_size)         # # of chunks
+    shape[-1] = chunk_size                          # # of samples per chunk
     shape = tuple(shape)
-    padded_channels = padded_channels.reshape(shape)
-    return padded_channels
+    padded_values = padded_values.reshape(shape)
+    return padded_values
 
 
-def create_chunks_from_channels_list(channels_list, chunk_size):
-    channel_chunks_list = [
-        channels_to_chunks(channels, chunk_size)
-        for channels in channels_list
+def create_chunks_from_values_lists(values_lists, chunk_size):
+    chunks_lists = [
+        values_to_chunks(values, chunk_size)
+        for values in values_lists
     ]
-    return channel_chunks_list
+    return chunks_lists
 
 
 def downsample_channel(channel, chunk_size, downsampling):
@@ -46,11 +46,10 @@ def downsample_channel(channel, chunk_size, downsampling):
     downsampled_size = channel.shape[-1] / down
     if downsampled_size <= chunk_size * 2:
         downsampled_size = chunk_size * 2
-        down = math.floor(channel.shape[-1] / (chunk_size * 2))
     return signal.resample(channel, downsampled_size, axis=-1)
 
 
-def create_downsampled_channels_list(channel, chunk_size):
+def create_downsampled_values_lists(channel, chunk_size):
     downsamplings = math.ceil(math.log(channel.shape[-1]) / math.log(chunk_size))
     downsamplings = range(downsamplings - 1, -1, -1)
     downsampled_channels = [
@@ -95,6 +94,7 @@ def write_index_json(
     chunk_size,
     downsamplings,
     channel_chunks_list,
+    valid_samples_in_last_chunk,
     trace_types={}
 ):
     json_dict = OrderedDict([
@@ -106,6 +106,7 @@ def write_index_json(
             list(downsampled.shape)
             for downsampled in channel_chunks_list
         ]),
+        ('validSamples', valid_samples_in_last_chunk),
         ('traceTypes', trace_types),
         ('channelMetadata', [
             {
@@ -181,6 +182,8 @@ def mne_file_to_chunks(path, chunk_size, loader, from_channel_name, channel_coun
     channel_ranges = []
     signal_range = [np.PINF, np.NINF]
     channel_chunks_list = []
+    selected_channels = []
+    valid_samples_in_last_chunk = []
 
     if from_channel_name:
         from_channel_index = channel_names.index(from_channel_name)
@@ -198,23 +201,31 @@ def mne_file_to_chunks(path, chunk_size, loader, from_channel_name, channel_coun
         signal_range = [min(channel_min, signal_range[0]), max(channel_max, signal_range[1])]
 
         channel = np.expand_dims(channel, axis=-2)
-        downsampled_channels = create_downsampled_channels_list(channel, chunk_size)
-        chunks = create_chunks_from_channels_list(downsampled_channels, chunk_size)
+        downsampled_values_lists = create_downsampled_values_lists(channel, chunk_size)
+        chunks = create_chunks_from_values_lists(downsampled_values_lists, chunk_size)
+
         if not channel_chunks_list:
             channel_chunks_list = chunks
+            # Assuming all channels have the same recording length as first channel
+            valid_samples_in_last_chunk = [
+                num_values % chunk_size or chunk_size   # chunk size if 0
+                for num_values in map(lambda values: len(values[0][0]), downsampled_values_lists)
+            ]
         else:
             for j, chunk in enumerate(chunks):
                 channel_chunks_list[j] = np.append(channel_chunks_list[j], chunk, axis=0)
 
-    return channel_chunks_list, time_interval, signal_range, channel_names, channel_ranges
+    return channel_chunks_list, time_interval, signal_range, channel_names, channel_ranges, valid_samples_in_last_chunk
 
 
 def write_chunk_directory(path, chunk_size, loader, from_channel_index=0, from_channel_name=None, 
                           channel_count=None, downsamplings=None, prefix=None, destination=None):
 
     chunk_dir = chunk_dir_path(path, prefix=prefix, destination=destination)
-    channel_chunks_list, time_interval, signal_range, channel_names, channel_ranges = mne_file_to_chunks(path, chunk_size, loader, 
-	                                                                                                     from_channel_name, channel_count)
+    channel_chunks_list, time_interval, signal_range, \
+        channel_names, channel_ranges, valid_samples_in_last_chunk = \
+        mne_file_to_chunks(path, chunk_size, loader, from_channel_name, channel_count)
+
     if downsamplings is not None:
         channel_chunks_list = channel_chunks_list[:downsamplings]
     write_index_json(
@@ -227,6 +238,7 @@ def write_chunk_directory(path, chunk_size, loader, from_channel_index=0, from_c
         channel_ranges,
         chunk_size,
         range(len(channel_chunks_list)),
-        channel_chunks_list
+        channel_chunks_list,
+        valid_samples_in_last_chunk
     )
     write_chunks(chunk_dir, channel_chunks_list, from_channel_index)