********* Neo RawIO ********* .. currentmodule:: neo.rawio .. _neo_rawio_API: For performance and memory consumption reasons a new layer has been added to Neo. In brief: * **neo.io** is the user-oriented read/write layer. Reading consists of getting a tree of Neo objects from a data source (file, url, or directory). When reading, all Neo objects are correctly scaled to the correct units. Writing consists of making a set of Neo objects persistent in a file format. * **neo.rawio** is a low-level layer for reading data only. Reading consists of getting NumPy buffers (often int16/int64) of signals/spikes/events. Scaling to real values (microV, times, ...) is done in a second step. Here the underlying objects must be consistent across Blocks and Segments for a given data source. The neo.rawio API has been added for developers. The neo.rawio is close to what could be a C API for reading data but in Python/NumPy. Not all IOs are implemented in :mod:`neo.rawio` but all classes implemented in :mod:`neo.rawio` are also available in :mod:`neo.io`. Possible uses of the :mod:`neo.rawio` API are: * fast reading chunks of signals in int16 and do the scaling of units (uV) on a GPU while scaling the zoom. This should improve bandwith HD to RAM and RAM to GPU memory. * load only some small chunk of data for heavy computations. For instance the spike sorting module tridesclous_ does this. The :mod:`neo.rawio` API is less flexible than :mod:`neo.io` and has some limitations: * read-only * AnalogSignals must have the same characteristcs across all Blocks and Segments: ``sampling_rate``, ``shape[1]``, ``dtype`` * AnalogSignals should all have the same value of ``sampling_rate``, otherwise they won't be read at the same time. * Units must have SpikeTrain event if empty across all Block and Segment * Epoch and Event are processed the same way (with ``durations=None`` for Event). For an intuitive comparison of :mod:`neo.io` and :mod:`neo.rawio` see: * :file:`example/read_file_neo_io.py` * :file:`example/read_file_neo_rawio.py` One speculative benefit of the :mod:`neo.rawio` API should be that a developer should be able to code a new RawIO class with little knowledge of the Neo tree of objects or of the :mod:`quantities` package. Basic usage =========== First create a reader from a class:: >>> from neo.rawio import PlexonRawIO >>> reader = PlexonRawIO(filename='File_plexon_3.plx') Then browse the internal header and display information:: >>> reader.parse_header() >>> print(reader) PlexonRawIO: File_plexon_3.plx nb_block: 1 nb_segment: [1] signal_channels: [V1] unit_channels: [Wspk1u, Wspk2u, Wspk4u, Wspk5u ... Wspk29u Wspk30u Wspk31u Wspk32u] event_channels: [] You get the number of blocks and segments per block. You have information about channels: **signal_channels**, **unit_channels**, **event_channels**. All this information is internally available in the *header* dict:: >>> for k, v in reader.header.items(): ... print(k, v) signal_channels [('V1', 0, 1000., 'int16', '', 2.44140625, 0., 0)] event_channels [] nb_segment [1] nb_block 1 unit_channels [('Wspk1u', 'ch1#0', '', 0.00146484, 0., 0, 30000.) ('Wspk2u', 'ch2#0', '', 0.00146484, 0., 0, 30000.) ... Read signal chunks of data and scale them:: >>> channel_indexes = None  #could be channel_indexes = [0] >>> raw_sigs = reader.get_analogsignal_chunk(block_index=0, seg_index=0, i_start=1024, i_stop=2048, channel_indexes=channel_indexes) >>> float_sigs = reader.rescale_signal_raw_to_float(raw_sigs, dtype='float64') >>> sampling_rate = reader.get_signal_sampling_rate() >>> t_start = reader.get_signal_t_start(block_index=0, seg_index=0) >>> units =reader.header['signal_channels'][0]['units'] >>> print(raw_sigs.shape, raw_sigs.dtype) >>> print(float_sigs.shape, float_sigs.dtype) >>> print(sampling_rate, t_start, units) (1024, 1) int16 (1024, 1) float64 1000.0 0.0 V There are 3 ways to select a subset of channels: by index (0 based), by id or by name. By index is not ambiguous 0 to n-1 (included), for some IOs channel_names (and sometimes channel_ids) have no guarantees to be unique, in such cases it would raise an error. Example with BlackrockRawIO for the file FileSpec2.3001:: >>> raw_sigs = reader.get_analogsignal_chunk(channel_indexes=None) #Take all channels >>> raw_sigs1 = reader.get_analogsignal_chunk(channel_indexes=[0, 2, 4])) #Take 0 2 and 4 >>> raw_sigs2 = reader.get_analogsignal_chunk(channel_ids=[1, 3, 5]) # Same but with there id (1 based) >>> raw_sigs3 = reader.get_analogsignal_chunk(channel_names=['chan1', 'chan3', 'chan5'])) # Same but with there name print(raw_sigs1.shape[1], raw_sigs2.shape[1], raw_sigs3.shape[1]) 3, 3, 3 Inspect units channel. Each channel gives a SpikeTrain for each Segment. Note that for many formats a physical channel can have several units after spike sorting. So the nb_unit could be more than physical channel or signal channels. >>> nb_unit = reader.unit_channels_count() >>> print('nb_unit', nb_unit) nb_unit 30 >>> for unit_index in range(nb_unit): ... nb_spike = reader.spike_count(block_index=0, seg_index=0, unit_index=unit_index) ... print('unit_index', unit_index, 'nb_spike', nb_spike) unit_index 0 nb_spike 701 unit_index 1 nb_spike 716 unit_index 2 nb_spike 69 unit_index 3 nb_spike 12 unit_index 4 nb_spike 95 unit_index 5 nb_spike 37 unit_index 6 nb_spike 25 unit_index 7 nb_spike 15 unit_index 8 nb_spike 33 ... Get spike timestamps only between 0 and 10 seconds and convert them to spike times:: >>> spike_timestamps = reader.spike_timestamps(block_index=0, seg_index=0, unit_index=0, t_start=0., t_stop=10.) >>> print(spike_timestamps.shape, spike_timestamps.dtype, spike_timestamps[:5]) (424,) int64 [ 90 420 708 1020 1310] >>> spike_times = reader.rescale_spike_timestamp( spike_timestamps, dtype='float64') >>> print(spike_times.shape, spike_times.dtype, spike_times[:5]) (424,) float64 [ 0.003 0.014 0.0236 0.034 0.04366667] Get spike waveforms between 0 and 10 s:: >>> raw_waveforms = reader.spike_raw_waveforms( block_index=0, seg_index=0, unit_index=0, t_start=0., t_stop=10.) >>> print(raw_waveforms.shape, raw_waveforms.dtype, raw_waveforms[0,0,:4]) (424, 1, 64) int16 [-449 -206 34 40] >>> float_waveforms = reader.rescale_waveforms_to_float(raw_waveforms, dtype='float32', unit_index=0) >>> print(float_waveforms.shape, float_waveforms.dtype, float_waveforms[0,0,:4]) (424, 1, 64) float32 [-0.65771484 -0.30175781 0.04980469 0.05859375] Count events per channel:: >>> reader = PlexonRawIO(filename='File_plexon_2.plx') >>> reader.parse_header() >>> nb_event_channel = reader.event_channels_count() nb_event_channel 28 >>> print('nb_event_channel', nb_event_channel) >>> for chan_index in range(nb_event_channel): ... nb_event = reader.event_count(block_index=0, seg_index=0, event_channel_index=chan_index) ... print('chan_index',chan_index, 'nb_event', nb_event) chan_index 0 nb_event 1 chan_index 1 nb_event 0 chan_index 2 nb_event 0 chan_index 3 nb_event 0 ... Read event timestamps and times for chanindex=0 and with time limits (t_start=None, t_stop=None):: >>> ev_timestamps, ev_durations, ev_labels = reader.event_timestamps(block_index=0, seg_index=0, event_channel_index=0, t_start=None, t_stop=None) >>> print(ev_timestamps, ev_durations, ev_labels) [1268] None ['0'] >>> ev_times = reader.rescale_event_timestamp(ev_timestamps, dtype='float64') >>> print(ev_times) [ 0.0317] .. _tridesclous: https://github.com/tridesclous/tridesclous