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Moved file-open operation in BinaryRecordingSegment out of constructor #3296

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Moved file-open operation in BinaryRecordingSegment out of constructor #3296

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1414e62
Move file open from BinaryRecordingSegment constructor to get_traces(…
Aug 8, 2024
18f6dc0
Move return within open statement block
Aug 9, 2024
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44 changes: 22 additions & 22 deletions src/spikeinterface/core/binaryrecordingextractor.py
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Original file line number Diff line number Diff line change
Expand Up @@ -170,7 +170,6 @@ def __init__(self, file_path, sampling_frequency, t_start, num_channels, dtype,
self.file_offset = file_offset
self.time_axis = time_axis
self.file_path = file_path
self.file = open(self.file_path, "rb")
self.bytes_per_sample = self.num_channels * self.dtype.itemsize
self.data_size_in_bytes = Path(file_path).stat().st_size - file_offset
self.num_samples = self.data_size_in_bytes // self.bytes_per_sample
Expand Down Expand Up @@ -205,31 +204,32 @@ def get_traces(
# the memmap offset to a multiple of ALLOCATIONGRANULARITY
length += start_offset

# Create the mmap object
memmap_obj = mmap.mmap(self.file.fileno(), length=length, access=mmap.ACCESS_READ, offset=memmap_offset)
with open(self.file_path, "rb") as file:
# Create the mmap object
memmap_obj = mmap.mmap(file.fileno(), length=length, access=mmap.ACCESS_READ, offset=memmap_offset)

# Create a numpy array using the mmap object as the buffer
# Note that the shape must be recalculated based on the new data chunk
if self.time_axis == 0:
shape = ((end_frame - start_frame), self.num_channels)
else:
shape = (self.num_channels, (end_frame - start_frame))

# Now the entire array should correspond to the data between start_frame and end_frame, so we can use it directly
traces = np.ndarray(
shape=shape,
dtype=self.dtype,
buffer=memmap_obj,
offset=start_offset,
)
# Create a numpy array using the mmap object as the buffer
# Note that the shape must be recalculated based on the new data chunk
if self.time_axis == 0:
shape = ((end_frame - start_frame), self.num_channels)
else:
shape = (self.num_channels, (end_frame - start_frame))

# Now the entire array should correspond to the data between start_frame and end_frame, so we can use it directly
traces = np.ndarray(
shape=shape,
dtype=self.dtype,
buffer=memmap_obj,
offset=start_offset,
)

if self.time_axis == 1:
traces = traces.T
if self.time_axis == 1:
traces = traces.T

if channel_indices is not None:
traces = traces[:, channel_indices]
if channel_indices is not None:
traces = traces[:, channel_indices]

return traces
return traces


# For backward compatibility (old good time)
Expand Down