import warnings
from collections import defaultdict
from copy import deepcopy
from typing import Any, Literal, Optional
import numpy as np
import psutil
import pynwb
from hdmf.data_utils import AbstractDataChunkIterator
from pydantic import FilePath
from spikeinterface import BaseRecording, BaseSorting, SortingAnalyzer
from spikeinterface.core.segmentutils import AppendSegmentRecording
from .spikeinterfacerecordingdatachunkiterator import (
SpikeInterfaceRecordingDataChunkIterator,
)
from ..nwb_helpers import (
BACKEND_NWB_IO,
HDF5BackendConfiguration,
ZarrBackendConfiguration,
configure_backend,
get_default_backend_configuration,
get_module,
make_nwbfile_from_metadata,
)
from ..nwb_helpers._metadata_and_file_helpers import (
_resolve_backend,
configure_and_write_nwbfile,
)
from ...utils import (
DeepDict,
calculate_regular_series_rate,
)
from ...utils.str_utils import human_readable_size
[docs]
def add_recording_to_nwbfile(
recording: BaseRecording,
nwbfile: pynwb.NWBFile,
metadata: dict | None = None,
write_as: Literal["raw", "processed", "lfp"] = "raw",
es_key: str | None = None,
iterator_type: str = "v2",
iterator_options: dict | None = None,
iterator_opts: dict | None = None,
always_write_timestamps: bool = False,
):
"""
Adds traces from recording object as ElectricalSeries to an NWBFile object.
Parameters
----------
recording : SpikeInterfaceRecording
A recording extractor from spikeinterface
nwbfile : NWBFile
nwb file to which the recording information is to be added
metadata : dict, optional
metadata info for constructing the nwb file.
Should be of the format::
metadata['Ecephys']['ElectricalSeries'] = dict(
name=my_name,
description=my_description
)
write_as : {'raw', 'processed', 'lfp'}
How to save the traces data in the nwb file. Options:
- 'raw': save it in acquisition
- 'processed': save it as FilteredEphys, in a processing module
- 'lfp': save it as LFP, in a processing module
es_key : str, optional
Key in metadata dictionary containing metadata info for the specific electrical series
iterator_type: {"v2", None}, default: 'v2'
The type of DataChunkIterator to use.
'v2' is the locally developed SpikeInterfaceRecordingDataChunkIterator, which offers full control over chunking.
None: write the TimeSeries with no memory chunking.
iterator_options: dict, optional
Dictionary of options for the iterator.
See https://hdmf.readthedocs.io/en/stable/hdmf.data_utils.html#hdmf.data_utils.GenericDataChunkIterator
for the full list of options.
iterator_opts: dict, optional
Deprecated. Use 'iterator_options' instead.
always_write_timestamps : bool, default: False
Set to True to always write timestamps.
By default (False), the function checks if the timestamps are uniformly sampled, and if so, stores the data
using a regular sampling rate instead of explicit timestamps. If set to True, timestamps will be written
explicitly, regardless of whether the sampling rate is uniform.
Notes
-----
Missing keys in an element of metadata['Ecephys']['ElectrodeGroup'] will be auto-populated with defaults
whenever possible.
"""
# Handle deprecated iterator_opts parameter
if iterator_opts is not None:
warnings.warn(
"The 'iterator_opts' parameter is deprecated and will be removed on or after March 2026. "
"Use 'iterator_options' instead.",
FutureWarning,
stacklevel=2,
)
if iterator_options is not None:
raise ValueError("Cannot specify both 'iterator_opts' and 'iterator_options'. Use 'iterator_options'.")
iterator_options = iterator_opts
if metadata is None:
metadata = _get_default_ecephys_metadata()
add_recording_metadata_to_nwbfile(recording=recording, nwbfile=nwbfile, metadata=metadata)
number_of_segments = recording.get_num_segments()
for segment_index in range(number_of_segments):
_add_recording_segment_to_nwbfile(
recording=recording,
nwbfile=nwbfile,
segment_index=segment_index,
metadata=metadata,
write_as=write_as,
es_key=es_key,
iterator_type=iterator_type,
iterator_opts=iterator_options,
always_write_timestamps=always_write_timestamps,
)
[docs]
def add_sorting_to_nwbfile(
sorting: BaseSorting,
nwbfile: pynwb.NWBFile | None = None,
unit_ids: list[str] | list[int] | None = None,
property_descriptions: dict | None = None,
skip_properties: list[str] | None = None,
write_as: Literal["units", "processing"] = "units",
units_name: str = "units",
units_description: str = "Autogenerated by neuroconv.",
waveform_means: np.ndarray | None = None,
waveform_sds: np.ndarray | None = None,
unit_electrode_indices: list[list[int]] | None = None,
null_values_for_properties: dict | None = None,
):
"""Add sorting data (units and their properties) to an NWBFile.
This function serves as a convenient wrapper around `add_units_table` to match
Spikeinterface's `SortingExtractor`
Parameters
----------
sorting : BaseSorting
The SortingExtractor object containing unit data.
nwbfile : pynwb.NWBFile, optional
The NWBFile object to write the unit data into.
unit_ids : list of int or str, optional
The specific unit IDs to write. If None, all units are written.
property_descriptions : dict, optional
Custom descriptions for unit properties. Keys should match property names in `sorting`,
and values will be used as descriptions in the Units table.
skip_properties : list of str, optional
Unit properties to exclude from writing.
write_as : {'units', 'processing'}, default: 'units'
Where to write the unit data:
- 'units': Write to the primary NWBFile.units table.
- 'processing': Write to the processing module (intermediate data).
units_name : str, default: 'units'
Name of the Units table. Must be 'units' if `write_as` is 'units'.
units_description : str, optional
Description for the Units table (e.g., sorting method, curation details).
waveform_means : np.ndarray, optional
Waveform mean (template) for each unit. Shape: (num_units, num_samples, num_channels).
waveform_sds : np.ndarray, optional
Waveform standard deviation for each unit. Shape: (num_units, num_samples, num_channels).
unit_electrode_indices : list of lists of int, optional
A list of lists of integers indicating the indices of the electrodes that each unit is associated with.
The length of the list must match the number of units in the sorting extractor.
null_values_for_properties : dict of str to Any
A dictionary mapping properties to their respective default values. If a property is not found in this
dictionary, a sensible default value based on the type of `sample_data` will be used.
"""
assert write_as in [
"units",
"processing",
], f"Argument write_as ({write_as}) should be one of 'units' or 'processing'!"
write_in_processing_module = False if write_as == "units" else True
_add_units_table_to_nwbfile(
sorting=sorting,
unit_ids=unit_ids,
nwbfile=nwbfile,
property_descriptions=property_descriptions,
skip_properties=skip_properties,
write_in_processing_module=write_in_processing_module,
units_table_name=units_name,
unit_table_description=units_description,
waveform_means=waveform_means,
waveform_sds=waveform_sds,
unit_electrode_indices=unit_electrode_indices,
null_values_for_properties=null_values_for_properties,
)
def _add_recording_segment_to_nwbfile(
recording: BaseRecording,
nwbfile: pynwb.NWBFile,
metadata: dict = None,
segment_index: int = 0,
write_as: Literal["raw", "processed", "lfp"] = "raw",
es_key: str = None,
iterator_type: str | None = "v2",
iterator_opts: dict | None = None,
always_write_timestamps: bool = False,
):
"""
See add_recording_to_nwbfile for details.
"""
assert write_as in [
"raw",
"processed",
"lfp",
], f"'write_as' should be 'raw', 'processed' or 'lfp', but instead received value {write_as}"
modality_signature = write_as.upper() if write_as == "lfp" else write_as.capitalize()
default_name = f"ElectricalSeries{modality_signature}"
default_description = dict(raw="Raw acquired data", lfp="Processed data - LFP", processed="Processed data")
eseries_kwargs = dict(name=default_name, description=default_description[write_as])
# Select and/or create module if lfp or processed data is to be stored.
if write_as in ["lfp", "processed"]:
ecephys_mod = get_module(
nwbfile=nwbfile,
name="ecephys",
description="Intermediate data from extracellular electrophysiology recordings, e.g., LFP.",
)
if write_as == "lfp" and "LFP" not in ecephys_mod.data_interfaces:
ecephys_mod.add(pynwb.ecephys.LFP(name="LFP"))
if write_as == "processed" and "Processed" not in ecephys_mod.data_interfaces:
ecephys_mod.add(pynwb.ecephys.FilteredEphys(name="Processed"))
if metadata is not None and "Ecephys" in metadata and es_key is not None:
assert es_key in metadata["Ecephys"], f"metadata['Ecephys'] dictionary does not contain key '{es_key}'"
eseries_kwargs.update(metadata["Ecephys"][es_key])
# If the recording extractor has more than 1 segment, append numbers to the names so that the names are unique.
# 0-pad these names based on the number of segments.
# If there are 10 segments use 2 digits, if there are 100 segments use 3 digits, etc.
if recording.get_num_segments() > 1:
width = int(np.ceil(np.log10((recording.get_num_segments()))))
eseries_kwargs["name"] += f"{segment_index:0{width}}"
# The add_electrodes adds a column with channel name to the electrode table.
add_electrodes_to_nwbfile(recording=recording, nwbfile=nwbfile, metadata=metadata)
# Create a region for the electrodes table by matching recording channels to table rows
channel_map = _build_channel_id_to_electrodes_table_map(recording=recording, nwbfile=nwbfile)
channel_ids = recording.get_channel_ids()
electrode_table_indices = [channel_map[channel_id] for channel_id in channel_ids]
electrode_table_region = nwbfile.create_electrode_table_region(
region=electrode_table_indices,
description="electrode_table_region",
)
eseries_kwargs.update(electrodes=electrode_table_region)
if recording.has_scaleable_traces():
# Spikeinterface gains and offsets are gains and offsets to micro volts.
# The units of the ElectricalSeries should be volts so we scale correspondingly.
micro_to_volts_conversion_factor = 1e-6
channel_gains_to_volts = recording.get_channel_gains() * micro_to_volts_conversion_factor
channel_offsets_to_volts = recording.get_channel_offsets() * micro_to_volts_conversion_factor
unique_gains = set(channel_gains_to_volts)
if len(unique_gains) == 1:
conversion_to_volts = channel_gains_to_volts[0]
eseries_kwargs.update(conversion=conversion_to_volts)
else:
eseries_kwargs.update(channel_conversion=channel_gains_to_volts)
unique_offset = set(channel_offsets_to_volts)
if len(unique_offset) > 1:
channel_ids = recording.get_channel_ids()
# This prints a user friendly error where the user is provided with a map from offset to channels
_report_variable_offset(recording=recording)
unique_offset = channel_offsets_to_volts[0]
eseries_kwargs.update(offset=unique_offset)
else:
warning_message = (
"The recording extractor does not have gains and offsets to convert to volts. "
"That means that correct units are not guaranteed. \n"
"Set the correct gains and offsets to the recording extractor before writing to NWB."
)
warnings.warn(warning_message, UserWarning, stacklevel=2)
# Iterator
ephys_data_iterator = _recording_traces_to_hdmf_iterator(
recording=recording,
segment_index=segment_index,
iterator_type=iterator_type,
iterator_opts=iterator_opts,
)
eseries_kwargs.update(data=ephys_data_iterator)
if always_write_timestamps:
timestamps = recording.get_times(segment_index=segment_index)
eseries_kwargs.update(timestamps=timestamps)
else:
# By default we write the rate if the timestamps are regular
recording_has_timestamps = recording.has_time_vector(segment_index=segment_index)
if recording_has_timestamps:
timestamps = recording.get_times(segment_index=segment_index)
rate = calculate_regular_series_rate(series=timestamps) # Returns None if it is not regular
recording_t_start = timestamps[0]
else:
rate = recording.get_sampling_frequency()
recording_t_start = recording._recording_segments[segment_index].t_start or 0
if rate:
starting_time = float(recording_t_start)
# Note that we call the sampling frequency again because the estimated rate might be different from the
# sampling frequency of the recording extractor by some epsilon.
eseries_kwargs.update(starting_time=starting_time, rate=recording.get_sampling_frequency())
else:
eseries_kwargs.update(timestamps=timestamps)
# Create ElectricalSeries object and add it to nwbfile
es = pynwb.ecephys.ElectricalSeries(**eseries_kwargs)
if write_as == "raw":
nwbfile.add_acquisition(es)
elif write_as == "processed":
ecephys_mod.data_interfaces["Processed"].add_electrical_series(es)
elif write_as == "lfp":
ecephys_mod.data_interfaces["LFP"].add_electrical_series(es)
def _get_default_ecephys_metadata():
"""
Returns fresh ecephys default metadata dictionary.
Single source of truth for all ecephys default metadata.
Structure matches the output of recording interfaces and extractor functions.
Each call returns a new instance to prevent accidental mutation of global state.
"""
from neuroconv.tools.nwb_helpers import get_default_nwbfile_metadata
metadata = get_default_nwbfile_metadata()
metadata["Ecephys"] = {
"Device": [{"name": "Device", "description": "Ecephys probe. Automatically generated."}],
"ElectrodeGroup": [
{"name": "ElectrodeGroup", "description": "no description", "location": "unknown", "device": "Device"}
],
"ElectricalSeries": {"name": "ElectricalSeries", "description": "Acquisition traces for the ElectricalSeries."},
}
return metadata
[docs]
def add_devices_to_nwbfile(nwbfile: pynwb.NWBFile, metadata: DeepDict | None = None):
"""
Add device information to nwbfile object.
Will always ensure nwbfile has at least one device, but multiple
devices within the metadata list will also be created.
Parameters
----------
nwbfile: NWBFile
nwb file to which the recording information is to be added
metadata: DeepDict
metadata info for constructing the nwb file (optional).
Should be of the format::
metadata['Ecephys']['Device'] = [
{
'name': my_name,
'description': my_description
},
...
]
Missing keys in an element of metadata['Ecephys']['Device'] will be auto-populated with defaults.
"""
if nwbfile is not None:
assert isinstance(nwbfile, pynwb.NWBFile), "'nwbfile' should be of type pynwb.NWBFile"
# Get default device metadata from single source of truth
ecephys_defaults = _get_default_ecephys_metadata()
defaults = ecephys_defaults["Ecephys"]["Device"][0]
if metadata is None:
metadata = dict()
if "Ecephys" not in metadata:
metadata["Ecephys"] = dict()
if "Device" not in metadata["Ecephys"]:
metadata["Ecephys"]["Device"] = [defaults]
for device_metadata in metadata["Ecephys"]["Device"]:
if device_metadata.get("name", defaults["name"]) not in nwbfile.devices:
device_kwargs = dict(defaults, **device_metadata)
nwbfile.create_device(**device_kwargs)
[docs]
def add_electrode_groups_to_nwbfile(recording: BaseRecording, nwbfile: pynwb.NWBFile, metadata: dict | None = None):
"""
Deprecated. This will become a private method.
"""
warnings.warn("This function is deprecated and will be removed in future versions.", DeprecationWarning)
_add_electrode_groups_to_nwbfile(recording=recording, nwbfile=nwbfile, metadata=metadata)
def _add_electrode_groups_to_nwbfile(recording: BaseRecording, nwbfile: pynwb.NWBFile, metadata: dict | None = None):
"""
Add electrode group information to nwbfile object.
Will always ensure nwbfile has at least one electrode group.
Will auto-generate a linked device if the specified name does not exist in the nwbfile.
Parameters
----------
recording: spikeinterface.BaseRecording
nwbfile: pynwb.NWBFile
nwb file to which the recording information is to be added
metadata: dict
metadata info for constructing the nwb file (optional).
Should be of the format::
metadata['Ecephys']['ElectrodeGroup'] = [
{
'name': my_name,
'description': my_description,
'location': electrode_location,
'device': my_device_name
},
...
]
Missing keys in an element of ``metadata['Ecephys']['ElectrodeGroup']`` will be auto-populated with defaults.
Group names set by RecordingExtractor channel properties will also be included with passed metadata,
but will only use default description and location.
"""
assert isinstance(nwbfile, pynwb.NWBFile), "'nwbfile' should be of type pynwb.NWBFile"
if metadata is None:
metadata = dict()
if "Ecephys" not in metadata:
metadata["Ecephys"] = dict()
add_devices_to_nwbfile(nwbfile=nwbfile, metadata=metadata)
group_names = _get_group_name(recording=recording)
# Get default electrode group metadata from single source of truth
ecephys_defaults = _get_default_ecephys_metadata()
default_electrode_group = ecephys_defaults["Ecephys"]["ElectrodeGroup"][0]
defaults = [
dict(
name=group_name,
description=default_electrode_group["description"],
location=default_electrode_group["location"],
device=[i.name for i in nwbfile.devices.values()][0],
)
for group_name in group_names
]
if "ElectrodeGroup" not in metadata["Ecephys"]:
metadata["Ecephys"]["ElectrodeGroup"] = defaults
assert all(
[isinstance(x, dict) for x in metadata["Ecephys"]["ElectrodeGroup"]]
), "Expected metadata['Ecephys']['ElectrodeGroup'] to be a list of dictionaries!"
for group_metadata in metadata["Ecephys"]["ElectrodeGroup"]:
if group_metadata.get("name", defaults[0]["name"]) not in nwbfile.electrode_groups:
device_name = group_metadata.get("device", defaults[0]["device"])
if device_name not in nwbfile.devices:
new_device_metadata = dict(Ecephys=dict(Device=[dict(name=device_name)]))
add_devices_to_nwbfile(nwbfile=nwbfile, metadata=new_device_metadata)
warnings.warn(
f"Device '{device_name}' not detected in "
"attempted link to electrode group! Automatically generating."
)
electrode_group_kwargs = dict(defaults[0], **group_metadata)
electrode_group_kwargs.update(device=nwbfile.devices[device_name])
nwbfile.create_electrode_group(**electrode_group_kwargs)
# TODO: Check this, probably not necessary
if not nwbfile.electrode_groups:
device_name = list(nwbfile.devices.keys())[0]
device = nwbfile.devices[device_name]
if len(nwbfile.devices) > 1:
warnings.warn(
"More than one device found when adding electrode group "
f"via channel properties: using device '{device_name}'. To use a "
"different device, indicate it the metadata argument."
)
electrode_group_kwargs = dict(defaults[0])
electrode_group_kwargs.update(device=device)
for group_name in np.unique(recording.get_channel_groups()).tolist():
electrode_group_kwargs.update(name=str(group_name))
nwbfile.create_electrode_group(**electrode_group_kwargs)
def _get_electrode_name(recording: BaseRecording) -> np.ndarray | None:
"""
Extract electrode names from a recording when a probe is attached.
The purpose of this auxiliary method is to unify electrode name extraction from a recording
across the ecephys pipeline and neuroconv. This achieves consistency and avoids duplication of code.
For recordings with attached probes (via ProbeInterface), electrode names are derived from
probe contact identifiers. These represent physical electrodes, allowing multiple channels
(e.g., AP and LF bands in neuropixel) from the same electrode to be properly grouped.
When available, we use the electrode_name property. If not available but a probe is attached,
we extract contact_ids from the probe. Otherwise, we return None to indicate no probe-based
electrode information is available.
Parameters
----------
recording : BaseRecording
The recording object from which to extract electrode names.
Returns
-------
np.ndarray | None
An array containing electrode names if available from the recording property or probe.
Returns None if the recording has no electrode_name property and no probe attached.
"""
# Check if recording has electrode_name property already set
electrode_names = recording.get_property("electrode_name")
if electrode_names is not None:
return electrode_names
# Try to get electrode names from probe contact_ids
if recording.has_probe():
# get_probegroup() works for both single and multiple probes
probegroup = recording.get_probegroup()
# Collect contact_ids from all probes
all_contact_ids = []
for probe in probegroup.probes:
contact_ids = probe.contact_ids
# contact_ids is None for microelectrode arrays (Biocam, Maxwell, MEArec) - this is valid in probeinterface
# These formats have probes for geometry but rely on channel-based identification
if contact_ids is not None:
all_contact_ids.extend(contact_ids)
if len(all_contact_ids) > 0:
return np.array(all_contact_ids)
# Return None if no electrode names available
return None
def _get_channel_name(recording: BaseRecording) -> np.ndarray:
"""
Extract channel names from a recording.
The purpose of this auxiliary method is to unify channel name extraction from a recording
across the ecephys pipeline and neuroconv. This achieves consistency and avoids duplication of code.
Spike interface sometimes inherits channel names from neo, those are usually human
readable names in comparison to channel ids that only serve the purpose of unique identification.
When available, we use the channel names, otherwise we fallback to channel ids as strings.
Parameters
----------
recording : BaseRecording
The recording object from which to extract the channel names.
Returns
-------
np.ndarray
An array containing the channel names. If the `channel_name` property is not
available, the channel IDs as strings will be returned.
"""
# Uses either the `channel_name` property or the channel ids as string otherwise
channel_names = recording.get_property("channel_name")
if channel_names is None:
channel_names = recording.get_channel_ids().astype("str", copy=False)
return channel_names
def _get_group_name(recording: BaseRecording) -> np.ndarray:
"""
Extract the canonical `group_name` from the recording, which will be written
in the electrodes table.
Parameters
----------
recording : BaseRecording
The recording object from which to extract the group names.
Returns
-------
np.ndarray
An array containing the group names. If the `group_name` property is not
available, the channel groups will be returned. If the group names are
empty, a default value 'ElectrodeGroup' will be used.
Raises
------
ValueError
If the number of unique group names doesn't match the number of unique groups,
or if the mapping between group names and group numbers is inconsistent.
"""
# Get default electrode group name from single source of truth
ecephys_defaults = _get_default_ecephys_metadata()
default_group_name = ecephys_defaults["Ecephys"]["ElectrodeGroup"][0]["name"]
group_names = recording.get_property("group_name")
groups = recording.get_channel_groups()
if group_names is None:
group_names = groups
if group_names is None:
group_names = np.full(recording.get_num_channels(), fill_value=default_group_name)
# Always ensure group_names are strings
group_names = group_names.astype("str", copy=False)
# If for any reason the group names are empty, fill them with the default
group_names[group_names == ""] = default_group_name
# Validate group names against groups
if groups is not None:
unique_groups = set(groups)
unique_names = set(group_names)
if len(unique_names) != len(unique_groups):
raise ValueError("The number of group names must match the number of groups")
# Check consistency of group name to group number mapping
group_to_name_map = {}
for group, name in zip(groups, group_names):
if group in group_to_name_map:
if group_to_name_map[group] != name:
raise ValueError("Inconsistent mapping between group numbers and group names")
else:
group_to_name_map[group] = name
return group_names
def _build_channel_id_to_electrodes_table_map(
recording: BaseRecording, nwbfile: pynwb.NWBFile
) -> dict[str | int, int | None]:
"""
Build a mapping from recording channel IDs to electrode table row indices.
The purpose of this function is to work as a single source of truth for matching
recordings with electrode table rows in NWB.
The way that this works is that an element is fully identified by three fields:
- group_name
- electrode_name (if probe is attached)
- channel_name
We do the match in two passes, first for every row we build a virtual global id that has
this format:
{group_name}_{electrode_name}_{channel_name}
Where the electrode_name is "" if no probe is attached.
Then, to see to which row a channel belongs to, we build the same virtual id
and we do a lookup in the table.
**Matching Logic**:
- If recording has probe → match by (group_name, electrode_name, channel_name)
- If recording has no probe → match by (group_name, "", channel_name)
Parameters
----------
recording : BaseRecording
The recording object whose channels need to be mapped.
nwbfile : pynwb.NWBFile
The NWBFile containing the electrode table.
Returns
-------
dict[str | int, int | None]
Dictionary mapping channel ID to electrode table row index.
Value is None if the channel is not found in the table.
Keys are channel IDs from recording.get_channel_ids().
"""
num_channels = recording.get_num_channels()
channel_ids = recording.get_channel_ids()
# Initialize mapping with None (channel not in table)
channel_to_electrode_row = defaultdict(lambda: None)
# If no electrode table, all channels are unmapped
if nwbfile.electrodes is None or len(nwbfile.electrodes) == 0:
return {channel_id: None for channel_id in channel_ids}
# Determine which columns exist in the table
has_electrode_column = "electrode_name" in nwbfile.electrodes.colnames
has_channel_column = "channel_name" in nwbfile.electrodes.colnames
# Build lookup table from existing rows
# Note: We check if columns exist before trying to access them
table_lookup = {}
for row_index in range(len(nwbfile.electrodes)):
group = nwbfile.electrodes["group_name"][row_index]
electrode = nwbfile.electrodes["electrode_name"][row_index] if has_electrode_column else ""
channel = nwbfile.electrodes["channel_name"][row_index] if has_channel_column else ""
virtual_id = f"{group}_{electrode}_{channel}"
table_lookup[virtual_id] = row_index
# When there is no probe id information, the field is populated with empty strings
group_names = _get_group_name(recording=recording)
electrode_names = _get_electrode_name(recording=recording)
channel_names = _get_channel_name(recording=recording)
# Use empty strings for electrode names when no probe is attached
if electrode_names is None:
electrode_names = [""] * num_channels
for channel_id, group, electrode, channel in zip(channel_ids, group_names, electrode_names, channel_names):
virtual_id = f"{group}_{electrode}_{channel}"
channel_to_electrode_row[channel_id] = table_lookup.get(virtual_id, None)
return dict(channel_to_electrode_row)
def _get_null_value_for_property(property: str, sample_data: Any, null_values_for_properties: dict[str, Any]) -> Any:
"""
Retrieve the null value for a given property based on its data type or a provided mapping.
Also performs type checking to ensure the default value matches the type of the existing data.
Parameters
----------
sample_data : Any
The sample data for which the default value is being determined. This can be of any data type.
null_values_for_properties : dict of str to Any
A dictionary mapping properties to their respective default values. If a property is not found in this
dictionary, a sensible default value based on the type of `sample_data` will be used.
Returns
-------
Any
The default value for the specified property. The type of the default value will match the type of `sample_data`
or the type specified in `null_values_for_properties`.
Raises
------
ValueError
If a sensible default value cannot be determined for the given property and data type, or if the type of the
provided default value does not match the type of the existing data.
"""
type_to_default_value = {list: [], np.ndarray: np.array(np.nan), str: "", float: np.nan, complex: np.nan}
# Check for numpy scalar types
sample_data = sample_data.item() if isinstance(sample_data, np.generic) else sample_data
default_value = null_values_for_properties.get(property, None)
if default_value is None:
sample_data_type = type(sample_data)
default_value = type_to_default_value.get(sample_data_type, None)
if default_value is None:
error_msg = (
f"Could not find a sensible default value for property '{property}' of type {sample_data_type} \n"
"This can be fixed by by modifying the recording property or setting a sensible default value "
"using the `add_electrodes` function argument `null_values_for_properties` as in: \n"
"null_values_for_properties = {{property}': default_value}"
)
raise ValueError(error_msg)
if type(default_value) != sample_data_type:
error_msg = (
f"Default value for property '{property}' in null_values_for_properties dict has a "
f"different type {type(default_value)} than the currently existing data type {sample_data_type}. \n"
"Modify the recording property or the default value to match"
)
raise ValueError(error_msg)
return default_value
[docs]
def add_electrodes_to_nwbfile(
recording: BaseRecording,
nwbfile: pynwb.NWBFile,
metadata: dict | None = None,
exclude: tuple = (),
null_values_for_properties: dict | None = None,
):
"""
Build an electrode_table from the recording information and it to the nwbfile object.
Parameters
----------
recording: spikeinterface.BaseRecording
nwbfile: NWBFile
nwb file to which the recording information is to be added
metadata: dict
metadata info for constructing the nwb file (optional).
Should be of the format::
metadata['Ecephys']['Electrodes'] = [
{
'name': my_name,
'description': my_description
},
...
]
Note that data intended to be added to the electrodes table of the NWBFile should be set as channel
properties in the RecordingExtractor object.
Missing keys in an element of metadata['Ecephys']['ElectrodeGroup'] will be auto-populated with defaults
whenever possible.
If 'my_name' is set to one of the required fields for nwbfile
electrodes (id, x, y, z, imp, location, filtering, group_name),
then the metadata will override their default values.
Setting 'my_name' to metadata field 'group' is not supported as the linking to
nwbfile.electrode_groups is handled automatically; please specify the string 'group_name' in this case.
If no group information is passed via metadata, automatic linking to existing electrode groups,
possibly including the default, will occur.
exclude: tuple
An iterable containing the string names of channel properties in the RecordingExtractor
object to ignore when writing to the NWBFile.
null_values_for_properties : dict of str to Any
A dictionary mapping properties to their respective default values. If a property is not found in this
dictionary, a sensible default value based on the type of `sample_data` will be used.
"""
assert isinstance(
nwbfile, pynwb.NWBFile
), f"'nwbfile' should be of type pynwb.NWBFile but is of type {type(nwbfile)}"
null_values_for_properties = dict() if null_values_for_properties is None else null_values_for_properties
# Test that metadata has the expected structure
electrodes_metadata = list()
if metadata is not None:
electrodes_metadata = metadata.get("Ecephys", dict()).get("Electrodes", list())
required_keys = {"name", "description"}
assert all(
[isinstance(property, dict) and set(property.keys()) == required_keys for property in electrodes_metadata]
), (
"Expected metadata['Ecephys']['Electrodes'] to be a list of dictionaries, "
"containing the keys 'name' and 'description'"
)
assert all(
[property["name"] != "group" for property in electrodes_metadata]
), "The recording property 'group' is not allowed; please use 'group_name' instead!"
# Transform to a dict that maps property name to its description
property_descriptions = dict()
for property in electrodes_metadata:
property_descriptions[property["name"]] = property["description"]
# 1. Build columns details from extractor properties: dict(name: dict(description='',data=data, index=False))
data_to_add = dict()
recording_properties = recording.get_property_keys()
spikeinterface_special_cases = [
"offset_to_uV", # Written in the ElectricalSeries
"gain_to_uV", # Written in the ElectricalSeries
"gain_to_physical_unit", # Written in the ElectricalSeries
"offset_to_physical_unit", # Written in the ElectricalSeries
"physical_unit", # Written in the ElectricalSeries
"contact_vector", # Structured array representing the probe
"channel_name", # We handle this here with _get_channel_name
"channel_names", # Some formats from neo also have this property, skip it
"group_name", # We handle this here _get_group_name
"group", # We handle this here with _get_group_name
"contact_ids", # Handled via _get_electrode_name
]
excluded_properties = list(exclude) + spikeinterface_special_cases
properties_to_extract = [property for property in recording_properties if property not in excluded_properties]
for property in properties_to_extract:
data = np.asarray(recording.get_property(property)).copy() # Do not modify properties of the recording
index = isinstance(data[0], (list, np.ndarray, tuple))
if index and isinstance(data[0], np.ndarray):
index = data[0].ndim
# Fill with provided custom descriptions
description = property_descriptions.get(property, "no description")
data_to_add[property] = dict(description=description, data=data, index=index)
# Special cases properties
group_names = _get_group_name(recording=recording)
electrode_names = _get_electrode_name(recording=recording)
channel_names = _get_channel_name(recording=recording)
# Always write channel_name column
data_to_add["channel_name"] = dict(description="unique channel reference", data=channel_names, index=False)
# Write electrode_name column when probe is attached
if electrode_names is not None:
data_to_add["electrode_name"] = dict(
description="unique electrode reference from probe contact identifiers", data=electrode_names, index=False
)
data_to_add["group_name"] = dict(description="group_name", data=group_names, index=False)
# Location in spikeinterface is equivalent to rel_x, rel_y, rel_z in the nwb standard
if "location" in data_to_add:
data = data_to_add["location"]["data"]
column_number_to_property = {0: "rel_x", 1: "rel_y", 2: "rel_z"}
for column_number in range(data.shape[1]):
property = column_number_to_property[column_number]
data_to_add[property] = dict(description=property, data=data[:, column_number], index=False)
data_to_add.pop("location")
# In the electrode table location is the brain area of spikeinterface
if "brain_area" in data_to_add:
data_to_add["location"] = data_to_add["brain_area"]
data_to_add["location"].update(description="location")
data_to_add.pop("brain_area")
else:
default_location = _get_default_ecephys_metadata()["Ecephys"]["ElectrodeGroup"][0]["location"]
data = np.full(recording.get_num_channels(), fill_value=default_location)
data_to_add["location"] = dict(description="location", data=data, index=False)
# Add missing groups to the nwb file
groupless_names = [group_name for group_name in group_names if group_name not in nwbfile.electrode_groups]
if len(groupless_names) > 0:
electrode_group_list = [dict(name=group_name) for group_name in groupless_names]
missing_group_metadata = dict(Ecephys=dict(ElectrodeGroup=electrode_group_list))
_add_electrode_groups_to_nwbfile(recording=recording, nwbfile=nwbfile, metadata=missing_group_metadata)
group_list = [nwbfile.electrode_groups[group_name] for group_name in group_names]
data_to_add["group"] = dict(description="the ElectrodeGroup object", data=group_list, index=False)
schema_properties = {"group", "group_name", "location"}
properties_to_add = set(data_to_add)
electrode_table_previous_properties = set(nwbfile.electrodes.colnames) if nwbfile.electrodes else set()
# The schema properties are always added by rows because they are required
properties_to_add_by_rows = schema_properties.union(electrode_table_previous_properties)
properties_to_add_by_columns = properties_to_add.difference(properties_to_add_by_rows)
# Properties that were added before require null values to add by rows if data is missing
properties_requiring_null_values = electrode_table_previous_properties.difference(properties_to_add)
nul_values_for_rows = dict()
for property in properties_requiring_null_values:
sample_data = nwbfile.electrodes[property][:][0]
null_value = _get_null_value_for_property(
property=property,
sample_data=sample_data,
null_values_for_properties=null_values_for_properties,
)
nul_values_for_rows[property] = null_value
# We only add new electrodes to the table
# Use the mapping function to determine which channels are already in the table
channel_map = _build_channel_id_to_electrodes_table_map(recording=recording, nwbfile=nwbfile)
# Channels to add are those that don't have a row mapping (None)
channel_ids_to_add = [channel_id for channel_id, row in channel_map.items() if row is None]
# Create mapping from channel_id to channel_index for data array access
channel_ids = recording.get_channel_ids()
channel_id_to_index = {channel_id: index for index, channel_id in enumerate(channel_ids)}
properties_with_data = properties_to_add_by_rows.intersection(data_to_add)
for channel_id in channel_ids_to_add:
channel_index = channel_id_to_index[channel_id]
electrode_kwargs = nul_values_for_rows
data_dict = {property: data_to_add[property]["data"][channel_index] for property in properties_with_data}
electrode_kwargs.update(**data_dict)
nwbfile.add_electrode(**electrode_kwargs, enforce_unique_id=True)
# The channel_name/electrode_name column as we use it with group_name as a unique identifier
# We fill previously inexistent values with the electrode table ids
electrode_table_size = len(nwbfile.electrodes.id[:])
num_channels_added = len(channel_ids_to_add)
previous_table_size = electrode_table_size - num_channels_added
# Handle channel_name and electrode_name columns (both may be present for probe-based recordings)
if "channel_name" in properties_to_add_by_columns:
cols_args = data_to_add["channel_name"]
data = cols_args["data"]
previous_ids = nwbfile.electrodes.id[:previous_table_size]
default_value = np.array(previous_ids).astype("str")
extended_data = np.hstack([default_value, data])
cols_args["data"] = extended_data
nwbfile.add_electrode_column("channel_name", **cols_args)
if "electrode_name" in properties_to_add_by_columns:
cols_args = data_to_add["electrode_name"]
data = cols_args["data"]
default_value = np.array([""] * previous_table_size)
extended_data = np.hstack([default_value, data])
cols_args["data"] = extended_data
nwbfile.add_electrode_column("electrode_name", **cols_args)
# Now find indices for this recording in the updated table
# Rebuild the map after adding electrodes
channel_map = _build_channel_id_to_electrodes_table_map(recording=recording, nwbfile=nwbfile)
# Get indices where this recording's data goes (all should be found now)
all_indices = np.arange(electrode_table_size)
channel_ids = recording.get_channel_ids()
indices_for_new_data = [channel_map[channel_id] for channel_id in channel_ids]
indices_for_null_values = [index for index in all_indices if index not in indices_for_new_data]
extending_column = len(indices_for_null_values) > 0
# Add properties as columns (exclude channel_name and electrode_name as they were handled above)
for property in properties_to_add_by_columns - {"channel_name", "electrode_name"}:
cols_args = data_to_add[property]
data = cols_args["data"]
# This is the simple case, early return
if not extending_column:
nwbfile.add_electrode_column(property, **cols_args)
continue
adding_ragged_array = cols_args["index"]
if not adding_ragged_array:
sample_data = data[0]
dtype = data.dtype
extended_data = np.empty(shape=electrode_table_size, dtype=dtype)
extended_data[indices_for_new_data] = data
null_value = _get_null_value_for_property(
property=property,
sample_data=sample_data,
null_values_for_properties=null_values_for_properties,
)
extended_data[indices_for_null_values] = null_value
else:
dtype = np.ndarray
extended_data = np.empty(shape=electrode_table_size, dtype=dtype)
for index, value in enumerate(data):
index_in_extended_data = indices_for_new_data[index]
index_in_extended_data = indices_for_new_data[index]
extended_data[index_in_extended_data] = value.tolist()
for index in indices_for_null_values:
null_value = []
extended_data[index] = null_value
cols_args["data"] = extended_data
nwbfile.add_electrode_column(property, **cols_args)
def _check_if_recording_traces_fit_into_memory(recording: BaseRecording, segment_index: int = 0) -> None:
"""
Raises an error if the full traces of a recording extractor are larger than psutil.virtual_memory().available.
Parameters
----------
recording : spikeinterface.BaseRecording
A recording extractor object from spikeinterface.
segment_index : int, optional
The segment index of the recording extractor object, by default 0
Raises
------
MemoryError
"""
element_size_in_bytes = recording.get_dtype().itemsize
num_channels = recording.get_num_channels()
num_frames = recording.get_num_samples(segment_index=segment_index)
traces_size_in_bytes = element_size_in_bytes * num_channels * num_frames
available_memory_in_bytes = psutil.virtual_memory().available
if traces_size_in_bytes > available_memory_in_bytes:
message = (
f"Memory error, full electrical series is {human_readable_size(traces_size_in_bytes, binary=True)} but only"
f" {human_readable_size(available_memory_in_bytes, binary=True)} are available. Use iterator_type='V2'"
)
raise MemoryError(message)
def _recording_traces_to_hdmf_iterator(
recording: BaseRecording,
segment_index: int = None,
return_scaled: bool = False,
iterator_type: str | None = "v2",
iterator_opts: dict = None,
) -> AbstractDataChunkIterator:
"""Function to wrap traces of spikeinterface recording into an AbstractDataChunkIterator.
Parameters
----------
recording : spikeinterface.BaseRecording
A recording extractor from spikeinterface
segment_index : int, optional
The recording segment to add to the NWBFile.
return_scaled : bool, defaults to False
When True recording extractor objects from spikeinterface return their traces in microvolts.
iterator_type: {"v2", None}, default: 'v2'
The type of DataChunkIterator to use.
'v2' is the locally developed SpikeInterfaceRecordingDataChunkIterator, which offers full control over chunking.
None: write the TimeSeries with no memory chunking.
iterator_opts: dict, optional
Dictionary of options for the iterator.
See https://hdmf.readthedocs.io/en/stable/hdmf.data_utils.html#hdmf.data_utils.GenericDataChunkIterator
for the full list of options.
Returns
-------
traces_as_iterator: AbstractDataChunkIterator
The traces of the recording extractor wrapped in an iterator object.
Raises
------
ValueError
If the iterator_type is not 'v2' or None.
"""
supported_iterator_types = ["v2", None]
if iterator_type not in supported_iterator_types:
message = f"iterator_type '{iterator_type}' is not supported. Must be either 'v2' (recommended) or None."
raise ValueError(message)
iterator_opts = dict() if iterator_opts is None else iterator_opts
if iterator_type is None:
_check_if_recording_traces_fit_into_memory(recording=recording, segment_index=segment_index)
traces_as_iterator = recording.get_traces(return_scaled=return_scaled, segment_index=segment_index)
elif iterator_type == "v2":
traces_as_iterator = SpikeInterfaceRecordingDataChunkIterator(
recording=recording,
segment_index=segment_index,
return_scaled=return_scaled,
**iterator_opts,
)
else:
raise ValueError("iterator_type must be None or 'v2'.")
return traces_as_iterator
def _report_variable_offset(recording: BaseRecording) -> None:
"""
Helper function to report variable offsets per channel IDs.
Groups the different available offsets per channel IDs and raises a ValueError.
"""
channel_offsets = recording.get_channel_offsets()
channel_ids = recording.get_channel_ids()
# Group the different offsets per channel IDs
offset_to_channel_ids = {}
for offset, channel_id in zip(channel_offsets, channel_ids):
offset = offset.item() if isinstance(offset, np.generic) else offset
channel_id = channel_id.item() if isinstance(channel_id, np.generic) else channel_id
if offset not in offset_to_channel_ids:
offset_to_channel_ids[offset] = []
offset_to_channel_ids[offset].append(channel_id)
# Create a user-friendly message
message_lines = ["Recording extractors with heterogeneous offsets are not supported."]
message_lines.append("Multiple offsets were found per channel IDs:")
for offset, ids in offset_to_channel_ids.items():
message_lines.append(f" Offset {offset}: Channel IDs {ids}")
message = "\n".join(message_lines)
raise ValueError(message)
[docs]
def add_recording_as_time_series_to_nwbfile(
recording: BaseRecording,
nwbfile: pynwb.NWBFile,
metadata: dict | None = None,
iterator_type: str | None = "v2",
iterator_opts: dict | None = None,
always_write_timestamps: bool = False,
time_series_name: Optional[str] = None,
metadata_key: str = "TimeSeries",
):
"""
Adds traces from recording object as TimeSeries to an NWBFile object.
Parameters
----------
recording : BaseRecording
A recording extractor from spikeinterface
nwbfile : NWBFile
nwb file to which the recording information is to be added
metadata : dict, optional
metadata info for constructing the nwb file.
Should be of the format::
metadata['TimeSeries'] = {
'metadata_key': {
"name": "my_name",
'description': 'my_description',
'unit': 'my_unit',
"offset": offset_to_unit_value,
"conversion": gain_to_unit_value,
'comments': 'comments',
...
}
}
Where the metadata_key is used to look up metadata in the metadata dictionary.
metadata_key: str
The entry in TimeSeries metadata to use.
iterator_type: {"v2", None}, default: 'v2'
The type of DataChunkIterator to use.
'v2' is the locally developed SpikeInterfaceRecordingDataChunkIterator, which offers full control over chunking.
None: write the TimeSeries with no memory chunking.
iterator_opts: dict, optional
Dictionary of options for the iterator.
See https://hdmf.readthedocs.io/en/stable/hdmf.data_utils.html#hdmf.data_utils.GenericDataChunkIterator
for the full list of options.
always_write_timestamps : bool, default: False
Set to True to always write timestamps.
By default (False), the function checks if the timestamps are uniformly sampled, and if so, stores the data
using a regular sampling rate instead of explicit timestamps. If set to True, timestamps will be written
explicitly, regardless of whether the sampling rate is uniform.
"""
if time_series_name is not None:
warnings.warn(
"The 'time_series_name' parameter is deprecated and will be removed in or after February 2026. "
"Use 'metadata_key' to specify the metadata entry instead.",
DeprecationWarning,
stacklevel=2,
)
num_segments = recording.get_num_segments()
for segment_index in range(num_segments):
_add_time_series_segment_to_nwbfile(
recording=recording,
nwbfile=nwbfile,
metadata=metadata,
segment_index=segment_index,
iterator_type=iterator_type,
iterator_opts=iterator_opts,
always_write_timestamps=always_write_timestamps,
time_series_name=time_series_name,
metadata_key=metadata_key,
)
def _add_time_series_segment_to_nwbfile(
recording: BaseRecording,
nwbfile: pynwb.NWBFile,
metadata: dict | None = None,
segment_index: int = 0,
iterator_type: str | None = "v2",
iterator_opts: dict | None = None,
always_write_timestamps: bool = False,
time_series_name: Optional[str] = None,
metadata_key: str = "time_series_metadata_key",
):
"""
See `add_recording_as_time_series_to_nwbfile` for details.
"""
# For backwards compatibility
metadata_key = time_series_name or metadata_key
metadata = DeepDict() if metadata is None else metadata
time_series_name = time_series_name or metadata["TimeSeries"][metadata_key].get("name", "TimeSeries")
tseries_kwargs = dict(name=time_series_name)
metadata = deepcopy(metadata)
# Apply metadata if available
if "TimeSeries" in metadata and metadata_key in metadata["TimeSeries"]:
time_series_metadata = metadata["TimeSeries"][metadata_key]
tseries_kwargs.update(time_series_metadata)
# If the recording extractor has more than 1 segment, append numbers to the names so that the names are unique.
# 0-pad these names based on the number of segments.
# If there are 10 segments use 2 digits, if there are 100 segments use 3 digits, etc.
if recording.get_num_segments() > 1:
width = int(np.ceil(np.log10((recording.get_num_segments()))))
tseries_kwargs["name"] += f"{segment_index:0{width}}"
# metadata "unit" has priority over recording properties
if "unit" not in tseries_kwargs:
# Get physical units from recording properties
units = recording.get_property("physical_unit")
# Get gain and offset from recording properties
gain_to_unit = recording.get_property("gain_to_physical_unit")
offset_to_unit = recording.get_property("offset_to_physical_unit")
channels_have_same_unit = len(set(units)) == 1 if units is not None else False
channels_have_same_gain = len(set(gain_to_unit)) == 1 if gain_to_unit is not None else False
channels_have_same_offest = len(set(offset_to_unit)) == 1 if offset_to_unit is not None else False
save_scaling_info = channels_have_same_unit and channels_have_same_gain and channels_have_same_offest
if save_scaling_info:
tseries_kwargs.update(unit=units[0], conversion=gain_to_unit[0], offset=offset_to_unit[0])
else:
warning_msg = (
"The recording extractor has heterogeneous units or is lacking scaling factors. "
"The time series will be saved with unit 'n.a.' and the conversion factors will not be set. "
"To fix this issue, either: "
"1) Set the unit in the metadata['TimeSeries'][time_series_name]['unit'] field, or "
"2) Set the `physical_unit`, `gain_to_physical_unit`, and `offset_to_physical_unit` properties "
"on the recording object with consistent units across all channels. "
f"Channel units: {units if units is not None else 'None'}, "
f"gain available: {gain_to_unit is not None}, "
f"offset available: {offset_to_unit is not None}"
)
warnings.warn(warning_msg, UserWarning, stacklevel=2)
tseries_kwargs.update(unit="n.a.")
# Iterator
data_iterator = _recording_traces_to_hdmf_iterator(
recording=recording,
segment_index=segment_index,
iterator_type=iterator_type,
iterator_opts=iterator_opts,
)
tseries_kwargs.update(data=data_iterator)
if always_write_timestamps:
timestamps = recording.get_times(segment_index=segment_index)
tseries_kwargs.update(timestamps=timestamps)
else:
# By default we write the rate if the timestamps are regular
recording_has_timestamps = recording.has_time_vector(segment_index=segment_index)
if recording_has_timestamps:
timestamps = recording.get_times(segment_index=segment_index)
rate = calculate_regular_series_rate(series=timestamps) # Returns None if it is not regular
recording_t_start = timestamps[0]
else:
rate = recording.get_sampling_frequency()
recording_t_start = recording._recording_segments[segment_index].t_start or 0
# Set starting time and rate or timestamps
if rate:
starting_time = float(recording_t_start)
# Note that we call the sampling frequency again because the estimated rate might be different from the
# sampling frequency of the recording extractor by some epsilon.
tseries_kwargs.update(starting_time=starting_time, rate=recording.get_sampling_frequency())
else:
tseries_kwargs.update(timestamps=timestamps)
# Create TimeSeries object and add it to nwbfile
time_series = pynwb.base.TimeSeries(**tseries_kwargs)
nwbfile.add_acquisition(time_series)
def _get_default_spatial_series_metadata():
"""
Returns fresh spatial series default metadata dictionary.
Single source of truth for all spatial series default metadata.
Structure matches the output of behavioral interfaces.
Each call returns a new instance to prevent accidental mutation of global state.
"""
from neuroconv.tools.nwb_helpers import get_default_nwbfile_metadata
metadata = get_default_nwbfile_metadata()
metadata["SpatialSeries"] = {
"name": "SpatialSeries",
"description": "Spatial or directional data",
"unit": "meters",
}
return metadata
[docs]
def add_recording_as_spatial_series_to_nwbfile(
recording: BaseRecording,
nwbfile: pynwb.NWBFile,
metadata: dict | None = None,
metadata_key: str = "SpatialSeries",
write_as: Literal["acquisition", "processing"] = "acquisition",
iterator_type: str = "v2",
iterator_options: dict | None = None,
always_write_timestamps: bool = False,
):
"""
Adds traces from recording object as SpatialSeries to an NWBFile object.
This function is designed for behavioral tracking data where the recording represents spatial
or directional information (e.g., position, head direction, gaze tracking).
Parameters
----------
recording : BaseRecording
A recording extractor from spikeinterface containing behavioral tracking data.
nwbfile : NWBFile
NWB file to which the spatial series information is to be added.
metadata : dict, optional
Metadata info for constructing the NWB file.
Should be of the format::
metadata['SpatialSeries'] = {
'metadata_key': {
'name': 'my_spatial_series',
'description': 'my_description',
'reference_frame': 'origin at top-left corner of arena...',
'unit': 'meters'
}
}
Where the metadata_key is used to look up metadata in the metadata dictionary.
metadata_key : str, default: 'SpatialSeries'
The entry in SpatialSeries metadata to use.
write_as : {'acquisition', 'processing'}, default: 'acquisition'
Where to save the spatial series data:
- 'acquisition': Save in nwbfile.acquisition
- 'processing': Save in a processing module under 'behavior'
iterator_type : {"v2", None}, default: 'v2'
The type of DataChunkIterator to use.
'v2' is the locally developed SpikeInterfaceRecordingDataChunkIterator.
None: write the SpatialSeries with no memory chunking.
iterator_options : dict, optional
Dictionary of options for the iterator.
always_write_timestamps : bool, default: False
Set to True to always write timestamps explicitly.
By default (False), the function checks if timestamps are uniformly sampled,
and if so, stores data using a regular sampling rate.
"""
# Validate write_as parameter
if write_as not in ["acquisition", "processing"]:
raise ValueError(f"write_as must be 'acquisition' or 'processing', got '{write_as}'")
# Handle multiple segments
number_of_segments = recording.get_num_segments()
for segment_index in range(number_of_segments):
_add_spatial_series_segment_to_nwbfile(
recording=recording,
nwbfile=nwbfile,
metadata=metadata,
metadata_key=metadata_key,
segment_index=segment_index,
write_as=write_as,
iterator_type=iterator_type,
iterator_options=iterator_options,
always_write_timestamps=always_write_timestamps,
)
def _add_spatial_series_segment_to_nwbfile(
recording: BaseRecording,
nwbfile: pynwb.NWBFile,
segment_index: int,
metadata: dict | None = None,
metadata_key: str = "SpatialSeries",
write_as: Literal["acquisition", "processing"] = "acquisition",
iterator_type: str | None = "v2",
iterator_options: dict | None = None,
always_write_timestamps: bool = False,
):
"""
See add_recording_as_spatial_series_to_nwbfile for details.
"""
# Validate recording dimensions for SpatialSeries
num_channels = recording.get_num_channels()
if num_channels > 3:
channel_names = _get_channel_name(recording)
raise ValueError(
f"SpatialSeries only supports 1D, 2D, or 3D data (up to 3 channels). "
f"This recording has {num_channels} channels: {channel_names}. "
f"Use recording.select_channels(channel_ids) to select only the spatial data channels, "
f"or verify you loaded the correct stream if working with multi-stream data."
)
# Get default metadata from single source of truth
default_metadata = _get_default_spatial_series_metadata()
default_spatial_series = default_metadata["SpatialSeries"]
metadata = metadata if metadata is not None else {}
# Get spatial series metadata or empty dict
spatial_series_metadata = metadata.get("SpatialSeries", {}).get(metadata_key, {})
# Copy user metadata to avoid mutation
series_kwargs = spatial_series_metadata.copy()
# Fill in missing required fields with defaults
required_fields = ["name", "description", "unit"]
for field in required_fields:
if field not in series_kwargs:
series_kwargs[field] = default_spatial_series[field]
# If multiple segments, append index to name
if recording.get_num_segments() > 1:
width = int(np.ceil(np.log10(recording.get_num_segments())))
series_kwargs["name"] += f"{segment_index:0{width}}"
# Add data iterator
data_iterator = _recording_traces_to_hdmf_iterator(
recording=recording,
segment_index=segment_index,
iterator_type=iterator_type,
iterator_opts=iterator_options,
)
series_kwargs["data"] = data_iterator
# Handle timestamps or rate
if always_write_timestamps:
timestamps = recording.get_times(segment_index=segment_index)
series_kwargs["timestamps"] = timestamps
else:
# Check if timestamps are regular
recording_has_timestamps = recording.has_time_vector(segment_index=segment_index)
if recording_has_timestamps:
timestamps = recording.get_times(segment_index=segment_index)
rate = calculate_regular_series_rate(series=timestamps)
recording_t_start = timestamps[0]
else:
rate = recording.get_sampling_frequency()
recording_t_start = recording.get_start_time(segment_index=segment_index)
if rate:
starting_time = float(recording_t_start)
series_kwargs["starting_time"] = starting_time
series_kwargs["rate"] = recording.get_sampling_frequency()
else:
series_kwargs["timestamps"] = timestamps
# Create the SpatialSeries instance
spatial_series = pynwb.behavior.SpatialSeries(**series_kwargs)
# Add to nwbfile
if write_as == "acquisition":
nwbfile.add_acquisition(spatial_series)
elif write_as == "processing":
# Create or get behavior processing module
behavior_module = get_module(
nwbfile=nwbfile,
name="behavior",
description="Processed behavioral data",
)
behavior_module.add(spatial_series)
[docs]
def write_recording_to_nwbfile(
recording: BaseRecording,
nwbfile_path: FilePath | None = None,
nwbfile: pynwb.NWBFile | None = None,
metadata: dict | None = None,
overwrite: bool = False,
verbose: bool = False,
write_as: Literal["raw", "processed", "lfp"] = "raw",
es_key: str | None = None,
*,
iterator_type: str | None = "v2",
iterator_options: dict | None = None,
iterator_opts: dict | None = None,
backend: Literal["hdf5", "zarr"] | None = None,
backend_configuration: HDF5BackendConfiguration | ZarrBackendConfiguration | None = None,
append_on_disk_nwbfile: bool = False,
) -> pynwb.NWBFile | None:
"""
Primary method for writing a RecordingExtractor object to an NWBFile.
Parameters
----------
recording : spikeinterface.BaseRecording
nwbfile_path : FilePath, optional
Path for where to write or load (if overwrite=False) the NWBFile.
If not provided, only adds data to the in-memory nwbfile without writing to disk.
**Deprecated**: Using this function without nwbfile_path is deprecated. Use `add_recording_to_nwbfile` instead.
nwbfile : NWBFile, optional
If passed, this function will fill the relevant fields within the NWBFile object.
E.g., calling::
write_recording(recording=my_recording_extractor, nwbfile=my_nwbfile)
will result in the appropriate changes to the my_nwbfile object.
metadata : dict
Metadata dictionary for constructing the NWB file. Required when nwbfile is not provided.
Must include at minimum the required NWBFile fields (session_description, identifier, session_start_time).
Should be of the format::
metadata['Ecephys'] = {
'Device': [
{
'name': my_name,
'description': my_description
},
...
]
'ElectrodeGroup': [
{
'name': my_name,
'description': my_description,
'location': electrode_location,
'device': my_device_name
},
...
]
'Electrodes': [
{
'name': my_name,
'description': my_description
},
...
]
'ElectricalSeries' = {
'name': my_name,
'description': my_description
}
Note that data intended to be added to the electrodes table of the NWBFile should be set as channel
properties in the RecordingExtractor object.
overwrite : bool, default: False
Whether to overwrite the NWBFile if one exists at the nwbfile_path.
verbose : bool, default: False
If 'nwbfile_path' is specified, informs user after a successful write operation.
write_as: {'raw', 'processed', 'lfp'}, optional
How to save the traces data in the nwb file.
- 'raw' will save it in acquisition
- 'processed' will save it as FilteredEphys, in a processing module
- 'lfp' will save it as LFP, in a processing module
es_key: str, optional
Key in metadata dictionary containing metadata info for the specific electrical series
iterator_type: {"v2", None}
The type of DataChunkIterator to use.
'v2' is the locally developed SpikeInterfaceRecordingDataChunkIterator, which offers full control over chunking.
None: write the TimeSeries with no memory chunking.
iterator_options: dict, optional
Dictionary of options for the RecordingExtractorDataChunkIterator (iterator_type='v2').
Valid options are:
* buffer_gb : float, default: 1.0
In units of GB. Recommended to be as much free RAM as available. Automatically calculates suitable
buffer shape.
* buffer_shape : tuple, optional
Manual specification of buffer shape to return on each iteration.
Must be a multiple of chunk_shape along each axis.
Cannot be set if `buffer_gb` is specified.
* chunk_mb : float. default: 1.0
Should be below 1 MB. Automatically calculates suitable chunk shape.
* chunk_shape : tuple, optional
Manual specification of the internal chunk shape for the HDF5 dataset.
Cannot be set if `chunk_mb` is also specified.
* display_progress : bool, default: False
Display a progress bar with iteration rate and estimated completion time.
* progress_bar_options : dict, optional
Dictionary of keyword arguments to be passed directly to tqdm.
See https://github.com/tqdm/tqdm#parameters for options.
backend : {"hdf5", "zarr"}, optional
The type of backend to use when writing the file.
If a `backend_configuration` is not specified, the default type will be "hdf5".
If a `backend_configuration` is specified, then the type will be auto-detected.
backend_configuration : HDF5BackendConfiguration or ZarrBackendConfiguration, optional
The configuration model to use when configuring the datasets for this backend.
To customize, call the `.get_default_backend_configuration(...)` method, modify the returned
BackendConfiguration object, and pass that instead.
Otherwise, all datasets will use default configuration settings.
append_on_disk_nwbfile : bool, default: False
Whether to append to an existing NWBFile on disk. If True, the `nwbfile` parameter must be None.
This is useful for appending data to an existing file without overwriting it.
Returns
-------
NWBFile or None
The NWBFile object when writing a new file or using an in-memory nwbfile.
Returns None when appending to an existing file on disk (append_on_disk_nwbfile=True).
**Deprecated**: Returning NWBFile in append mode is deprecated and will return None in or after March 2026.
"""
# Handle deprecated usage without nwbfile_path
if nwbfile_path is None:
warnings.warn(
"Using 'write_recording_to_nwbfile' without 'nwbfile_path' to only add data to an in-memory nwbfile is deprecated "
"and will be removed in or after March 2026. Use 'add_recording_to_nwbfile' instead.",
DeprecationWarning,
stacklevel=2,
)
if nwbfile is None:
raise ValueError(
"Either 'nwbfile_path' or 'nwbfile' must be provided. "
"To add data to an in-memory nwbfile, use 'add_recording_to_nwbfile' instead."
)
# Call add_recording_to_nwbfile for deprecated behavior
add_recording_to_nwbfile(
recording=recording,
nwbfile=nwbfile,
metadata=metadata,
write_as=write_as,
es_key=es_key,
iterator_type=iterator_type,
iterator_options=iterator_options if iterator_options is not None else iterator_opts,
)
return nwbfile
# Handle deprecated iterator_opts parameter
if iterator_opts is not None:
warnings.warn(
"The 'iterator_opts' parameter is deprecated and will be removed in or after March 2026. "
"Use 'iterator_options' instead.",
DeprecationWarning,
stacklevel=2,
)
if iterator_options is None:
iterator_options = iterator_opts
else:
raise ValueError(
"Both 'iterator_opts' and 'iterator_options' were specified. "
"Please use only 'iterator_options' as 'iterator_opts' is deprecated."
)
appending_to_in_memory_nwbfile = nwbfile is not None
file_initially_exists = nwbfile_path.exists()
allowed_to_modify_existing = overwrite or append_on_disk_nwbfile
if file_initially_exists and not allowed_to_modify_existing:
raise ValueError(
f"The file at '{nwbfile_path}' already exists. Set overwrite=True to overwrite the existing file "
"or append_on_disk_nwbfile=True to append to the existing file."
)
if append_on_disk_nwbfile and appending_to_in_memory_nwbfile:
raise ValueError(
"Cannot append to an existing file while also providing an in-memory NWBFile. "
"Either set overwrite=True to replace the existing file, or remove the nwbfile parameter to append to the existing file on disk."
)
# Resolve backend
backend = _resolve_backend(backend=backend, backend_configuration=backend_configuration)
# Determine if we're writing a new file or appending
writing_new_file = not append_on_disk_nwbfile
if writing_new_file:
# Writing mode: create or use provided nwbfile and write
if nwbfile is None:
# Create new nwbfile from metadata
if metadata is None:
raise ValueError(
"metadata is required when nwbfile is not provided. "
"Please provide metadata dictionary with at least the required NWBFile fields."
)
nwbfile = make_nwbfile_from_metadata(metadata=metadata)
add_recording_to_nwbfile(
recording=recording,
nwbfile=nwbfile,
metadata=metadata,
write_as=write_as,
es_key=es_key,
iterator_type=iterator_type,
iterator_options=iterator_options,
)
if backend_configuration is None:
backend_configuration = get_default_backend_configuration(nwbfile=nwbfile, backend=backend)
configure_and_write_nwbfile(
nwbfile=nwbfile,
nwbfile_path=nwbfile_path,
backend=backend,
backend_configuration=backend_configuration,
)
if verbose:
print(f"NWB file saved at {nwbfile_path}!")
return nwbfile
else:
# Append mode: read existing file, add data, write back
warnings.warn(
"Returning an NWBFile object when using append_on_disk_nwbfile=True is deprecated "
"and will return None in or after March 2026.",
DeprecationWarning,
stacklevel=2,
)
IO = BACKEND_NWB_IO[backend]
with IO(path=str(nwbfile_path), mode="r+", load_namespaces=True) as io:
nwbfile = io.read()
add_recording_to_nwbfile(
recording=recording,
nwbfile=nwbfile,
metadata=metadata,
write_as=write_as,
es_key=es_key,
iterator_type=iterator_type,
iterator_options=iterator_options,
)
if backend_configuration is None:
backend_configuration = get_default_backend_configuration(nwbfile=nwbfile, backend=backend)
configure_backend(nwbfile=nwbfile, backend_configuration=backend_configuration)
io.write(nwbfile)
if verbose:
print(f"NWB file saved at {nwbfile_path}!")
return nwbfile # Will return None in March 2026
def _add_units_table_to_nwbfile(
sorting: BaseSorting,
nwbfile: pynwb.NWBFile,
unit_ids: list[str | int] | None = None,
property_descriptions: dict | None = None,
skip_properties: list[str] | None = None,
units_table_name: str = "units",
unit_table_description: str | None = None,
write_in_processing_module: bool = False,
waveform_means: np.ndarray | None = None,
waveform_sds: np.ndarray | None = None,
unit_electrode_indices: list[list[int]] | None = None,
null_values_for_properties: dict | None = None,
):
"""
Add sorting data to a NWBFile object as a Units table.
This function extracts unit properties from a SortingExtractor object and writes them
to an NWBFile Units table, either in the primary units interface or the processing
module (for intermediate/historical data). It handles unit selection, property customization,
waveform data, and electrode mapping.
Parameters
----------
sorting : spikeinterface.BaseSorting
The SortingExtractor object containing unit data.
nwbfile : pynwb.NWBFile
The NWBFile object to write the unit data into.
unit_ids : list of int or str, optional
The specific unit IDs to write. If None, all units are written.
property_descriptions : dict, optional
Custom descriptions for unit properties. Keys should match property names in `sorting`,
and values will be used as descriptions in the Units table.
skip_properties : list of str, optional
Unit properties to exclude from writing.
units_table_name : str, default: 'units'
Name of the Units table. Must be 'units' if `write_in_processing_module` is False.
unit_table_description : str, optional
Description for the Units table (e.g., sorting method, curation details).
write_in_processing_module : bool, default: False
If True, write to the processing module (intermediate data). If False, write to
the primary NWBFile.units table.
waveform_means : np.ndarray, optional
Waveform mean (template) for each unit. Shape: (num_units, num_samples, num_channels).
waveform_sds : np.ndarray, optional
Waveform standard deviation for each unit. Shape: (num_units, num_samples, num_channels).
unit_electrode_indices : list of lists of int, optional
A list of lists of integers indicating the indices of the electrodes that each unit is associated with.
The length of the list must match the number of units in the sorting extractor.
"""
unit_table_description = unit_table_description or "Autogenerated by neuroconv."
assert isinstance(
nwbfile, pynwb.NWBFile
), f"'nwbfile' should be of type pynwb.NWBFile but is of type {type(nwbfile)}"
if unit_electrode_indices is not None:
electrodes_table = nwbfile.electrodes
if electrodes_table is None:
raise ValueError(
"Electrodes table is required to map units to electrodes. Add an electrode table to the NWBFile first."
)
null_values_for_properties = dict() if null_values_for_properties is None else null_values_for_properties
if not write_in_processing_module and units_table_name != "units":
raise ValueError("When writing to the nwbfile.units table, the name of the table must be 'units'!")
if write_in_processing_module:
ecephys_mod = get_module(
nwbfile=nwbfile,
name="ecephys",
description="Intermediate data from extracellular electrophysiology recordings, e.g., LFP.",
)
write_table_first_time = units_table_name not in ecephys_mod.data_interfaces
if write_table_first_time:
units_table = pynwb.misc.Units(name=units_table_name, description=unit_table_description)
ecephys_mod.add(units_table)
units_table = ecephys_mod[units_table_name]
else:
write_table_first_time = nwbfile.units is None
if write_table_first_time:
nwbfile.units = pynwb.misc.Units(name="units", description=unit_table_description)
units_table = nwbfile.units
default_descriptions = dict(
isi_violation="Quality metric that measures the ISI violation ratio as a proxy for the purity of the unit.",
firing_rate="Number of spikes per unit of time.",
template="The extracellular average waveform.",
max_channel="The recording channel id with the largest amplitude.",
halfwidth="The full-width half maximum of the negative peak computed on the maximum channel.",
peak_to_valley="The duration between the negative and the positive peaks computed on the maximum channel.",
snr="The signal-to-noise ratio of the unit.",
quality="Quality of the unit as defined by phy (good, mua, noise).",
spike_amplitude="Average amplitude of peaks detected on the channel.",
spike_rate="Average rate of peaks detected on the channel.",
unit_name="Unique reference for each unit.",
)
if property_descriptions is None:
property_descriptions = dict()
if skip_properties is None:
skip_properties = list()
property_descriptions = dict(default_descriptions, **property_descriptions)
data_to_add = defaultdict(dict)
sorting_properties = sorting.get_property_keys()
excluded_properties = list(skip_properties) + ["contact_vector"]
properties_to_extract = [property for property in sorting_properties if property not in excluded_properties]
if unit_ids is not None:
sorting = sorting.select_units(unit_ids=unit_ids)
if unit_electrode_indices is not None:
unit_electrode_indices = np.array(unit_electrode_indices)[sorting.ids_to_indices(unit_ids)]
unit_ids = sorting.unit_ids
# Extract properties
for property in properties_to_extract:
data = sorting.get_property(property)
index = isinstance(data[0], (list, np.ndarray, tuple))
if index and isinstance(data[0], np.ndarray):
index = data[0].ndim
description = property_descriptions.get(property, "No description.")
data_to_add[property].update(description=description, data=data, index=index)
if property in ["max_channel", "max_electrode"] and nwbfile.electrodes is not None:
data_to_add[property].update(table=nwbfile.electrodes)
# Unit name logic
if "unit_name" in data_to_add:
# if 'unit_name' is set as a property, it is used to override default unit_ids (and "id")
unit_name_array = data_to_add["unit_name"]["data"]
else:
unit_name_array = unit_ids.astype("str", copy=False)
data_to_add["unit_name"].update(description="Unique reference for each unit.", data=unit_name_array)
units_table_previous_properties = set(units_table.colnames).difference({"spike_times"})
properties_to_add = set(data_to_add)
properties_to_add_by_rows = units_table_previous_properties.union({"id"})
properties_to_add_by_columns = properties_to_add - properties_to_add_by_rows
# Properties that were added before require null values to add by rows if data is missing
properties_requiring_null_values = units_table_previous_properties.difference(properties_to_add)
null_values_for_row = {}
for property in properties_requiring_null_values - {"electrodes"}: # TODO, fix electrodes
sample_data = units_table[property][:][0]
null_value = _get_null_value_for_property(
property=property,
sample_data=sample_data,
null_values_for_properties=null_values_for_properties,
)
null_values_for_row[property] = null_value
# Special case
null_values_for_row["id"] = None
# Add data by rows excluding the rows with previously added unit names
unit_names_used_previously = []
if "unit_name" in units_table_previous_properties:
unit_names_used_previously = units_table["unit_name"].data
has_electrodes_column = "electrodes" in units_table.colnames
properties_with_data = {property for property in properties_to_add_by_rows if "data" in data_to_add[property]}
rows_in_data = [index for index in range(sorting.get_num_units())]
if not has_electrodes_column:
rows_to_add = [index for index in rows_in_data if unit_name_array[index] not in unit_names_used_previously]
else:
rows_to_add = []
for index in rows_in_data:
if unit_name_array[index] not in unit_names_used_previously:
rows_to_add.append(index)
else:
unit_name = unit_name_array[index]
previous_electrodes = units_table[np.where(units_table["unit_name"][:] == unit_name)[0]].electrodes
if list(previous_electrodes.values[0]) != list(unit_electrode_indices[index]):
rows_to_add.append(index)
for row in rows_to_add:
unit_kwargs = null_values_for_row
for property in properties_with_data:
unit_kwargs[property] = data_to_add[property]["data"][row]
spike_times = []
# Extract and concatenate the spike times from multiple segments
for segment_index in range(sorting.get_num_segments()):
segment_spike_times = sorting.get_unit_spike_train(
unit_id=unit_ids[row], segment_index=segment_index, return_times=True
)
spike_times.append(segment_spike_times)
spike_times = np.concatenate(spike_times)
if waveform_means is not None:
unit_kwargs["waveform_mean"] = waveform_means[row]
if waveform_sds is not None:
unit_kwargs["waveform_sd"] = waveform_sds[row]
if unit_electrode_indices is not None:
unit_kwargs["electrodes"] = unit_electrode_indices[row]
units_table.add_unit(spike_times=spike_times, **unit_kwargs, enforce_unique_id=True)
# Add unit_name as a column and fill previously existing rows with unit_name equal to str(ids)
unit_table_size = len(units_table.id[:])
previous_table_size = len(units_table.id[:]) - len(unit_name_array)
if "unit_name" in properties_to_add_by_columns:
cols_args = data_to_add["unit_name"]
data = cols_args["data"]
previous_ids = units_table.id[:previous_table_size]
default_value = np.array(previous_ids).astype("str")
extended_data = np.hstack([default_value, data])
cols_args["data"] = extended_data
units_table.add_column("unit_name", **cols_args)
# Build a channel name to electrode table index map
table_df = units_table.to_dataframe().reset_index()
unit_name_to_electrode_index = {
unit_name: table_df.query(f"unit_name=='{unit_name}'").index[0] for unit_name in unit_name_array
}
indices_for_new_data = [unit_name_to_electrode_index[unit_name] for unit_name in unit_name_array]
indices_for_null_values = table_df.index.difference(indices_for_new_data).values
extending_column = len(indices_for_null_values) > 0
# Add properties as columns
for property in properties_to_add_by_columns - {"unit_name"}:
cols_args = data_to_add[property]
data = cols_args["data"]
# This is the simple case, early return
if not extending_column:
units_table.add_column(property, **cols_args)
continue
# Extending the columns is done differently for ragged arrays
adding_ragged_array = cols_args["index"]
if not adding_ragged_array:
sample_data = data[0]
dtype = data.dtype
extended_data = np.empty(shape=unit_table_size, dtype=dtype)
extended_data[indices_for_new_data] = data
null_value = _get_null_value_for_property(
property=property,
sample_data=sample_data,
null_values_for_properties=null_values_for_properties,
)
extended_data[indices_for_null_values] = null_value
else:
dtype = np.ndarray
extended_data = np.empty(shape=unit_table_size, dtype=dtype)
for index, value in enumerate(data):
index_in_extended_data = indices_for_new_data[index]
extended_data[index_in_extended_data] = value.tolist()
for index in indices_for_null_values:
null_value = []
extended_data[index] = null_value
# Add the data
cols_args["data"] = extended_data
units_table.add_column(property, **cols_args)
[docs]
def write_sorting_to_nwbfile(
sorting: BaseSorting,
nwbfile_path: FilePath | None = None,
nwbfile: pynwb.NWBFile | None = None,
metadata: dict | None = None,
overwrite: bool = False,
verbose: bool = False,
unit_ids: list[str | int] | None = None,
property_descriptions: dict | None = None,
skip_properties: list[str] | None = None,
write_as: Literal["units", "processing"] = "units",
units_name: str = "units",
units_description: str = "Autogenerated by neuroconv.",
waveform_means: np.ndarray | None = None,
waveform_sds: np.ndarray | None = None,
unit_electrode_indices=None,
*,
backend: Literal["hdf5", "zarr"] | None = None,
backend_configuration: HDF5BackendConfiguration | ZarrBackendConfiguration | None = None,
append_on_disk_nwbfile: bool = False,
) -> pynwb.NWBFile | None:
"""
Primary method for writing a SortingExtractor object to an NWBFile.
Parameters
----------
sorting : spikeinterface.BaseSorting
nwbfile_path : FilePath, optional
Path for where to write or load (if overwrite=False) the NWBFile.
If not provided, only adds data to the in-memory nwbfile without writing to disk.
**Deprecated**: Using this function without nwbfile_path is deprecated. Use `add_sorting_to_nwbfile` instead.
nwbfile : NWBFile, optional
If passed, this function will fill the relevant fields within the NWBFile object.
E.g., calling::
write_recording(recording=my_recording_extractor, nwbfile=my_nwbfile)
will result in the appropriate changes to the my_nwbfile object.
metadata : dict
Metadata dictionary for constructing the NWB file. Required when nwbfile is not provided.
Must include at minimum the required NWBFile fields (session_description, identifier, session_start_time).
overwrite : bool, default: False
Whether to overwrite the NWBFile if one exists at the nwbfile_path.
The default is False (append mode).
verbose : bool, default: False
If 'nwbfile_path' is specified, informs user after a successful write operation.
unit_ids : list, optional
Controls the unit_ids that will be written to the nwb file. If None (default), all
units are written.
property_descriptions : dict, optional
For each key in this dictionary which matches the name of a unit
property in sorting, adds the value as a description to that
custom unit column.
skip_properties : list of str, optional
Each string in this list that matches a unit property will not be written to the NWBFile.
write_as : {'units', 'processing'}
How to save the units table in the nwb file. Options:
- 'units' will save it to the official NWBFile.Units position; recommended only for the final form of the data.
- 'processing' will save it to the processing module to serve as a historical provenance for the official table.
units_name : str, default: 'units'
The name of the units table. If write_as=='units', then units_name must also be 'units'.
units_description : str, default: 'Autogenerated by neuroconv.'
waveform_means : np.ndarray, optional
Waveform mean (template) for each unit. Shape: (num_units, num_samples, num_channels).
waveform_sds : np.ndarray, optional
Waveform standard deviation for each unit. Shape: (num_units, num_samples, num_channels).
unit_electrode_indices : list of lists of int, optional
For each unit, a list of electrode indices corresponding to waveform data.
backend : {"hdf5", "zarr"}, optional
The type of backend to use when writing the file.
If a `backend_configuration` is not specified, the default type will be "hdf5".
If a `backend_configuration` is specified, then the type will be auto-detected.
backend_configuration : HDF5BackendConfiguration or ZarrBackendConfiguration, optional
The configuration model to use when configuring the datasets for this backend.
To customize, call the `.get_default_backend_configuration(...)` method, modify the returned
BackendConfiguration object, and pass that instead.
Otherwise, all datasets will use default configuration settings.
append_on_disk_nwbfile : bool, default: False
Whether to append to an existing NWBFile on disk. If True, the `nwbfile` parameter must be None.
This is useful for appending data to an existing file without overwriting it.
Returns
-------
NWBFile or None
The NWBFile object when writing a new file or using an in-memory nwbfile.
Returns None when appending to an existing file on disk (append_on_disk_nwbfile=True).
**Deprecated**: Returning NWBFile in append mode is deprecated and will return None in or after March 2026.
"""
# Handle deprecated usage without nwbfile_path
if nwbfile_path is None:
warnings.warn(
"Using 'write_sorting_to_nwbfile' without 'nwbfile_path' to only add data to an in-memory nwbfile is deprecated "
"and will be removed in or after March 2026. Use 'add_sorting_to_nwbfile' instead.",
DeprecationWarning,
stacklevel=2,
)
if nwbfile is None:
raise ValueError(
"Either 'nwbfile_path' or 'nwbfile' must be provided. "
"To add data to an in-memory nwbfile, use 'add_sorting_to_nwbfile' instead."
)
# Call add_sorting_to_nwbfile for deprecated behavior
add_sorting_to_nwbfile(
sorting=sorting,
nwbfile=nwbfile,
unit_ids=unit_ids,
property_descriptions=property_descriptions,
skip_properties=skip_properties,
write_as=write_as,
units_name=units_name,
units_description=units_description,
waveform_means=waveform_means,
waveform_sds=waveform_sds,
unit_electrode_indices=unit_electrode_indices,
)
return nwbfile
appending_to_in_memory_nwbfile = nwbfile is not None
file_initially_exists = nwbfile_path.exists()
allowed_to_modify_existing = overwrite or append_on_disk_nwbfile
if file_initially_exists and not allowed_to_modify_existing:
raise ValueError(
f"The file at '{nwbfile_path}' already exists. Set overwrite=True to overwrite the existing file "
"or append_on_disk_nwbfile=True to append to the existing file."
)
if append_on_disk_nwbfile and appending_to_in_memory_nwbfile:
raise ValueError(
"Cannot append to an existing file while also providing an in-memory NWBFile. "
"Either set overwrite=True to replace the existing file, or remove the nwbfile parameter to append to the existing file on disk."
)
# Resolve backend
backend = _resolve_backend(backend=backend, backend_configuration=backend_configuration)
# Determine if we're writing a new file or appending
writing_new_file = not append_on_disk_nwbfile
if writing_new_file:
# Writing mode: create or use provided nwbfile and write
if nwbfile is None:
# Create new nwbfile from metadata
if metadata is None:
raise ValueError(
"metadata is required when nwbfile is not provided. "
"Please provide metadata dictionary with at least the required NWBFile fields."
)
nwbfile = make_nwbfile_from_metadata(metadata=metadata)
add_sorting_to_nwbfile(
sorting=sorting,
nwbfile=nwbfile,
unit_ids=unit_ids,
property_descriptions=property_descriptions,
skip_properties=skip_properties,
write_as=write_as,
units_name=units_name,
units_description=units_description,
waveform_means=waveform_means,
waveform_sds=waveform_sds,
unit_electrode_indices=unit_electrode_indices,
)
if backend_configuration is None:
backend_configuration = get_default_backend_configuration(nwbfile=nwbfile, backend=backend)
configure_and_write_nwbfile(
nwbfile=nwbfile,
nwbfile_path=nwbfile_path,
backend=backend,
backend_configuration=backend_configuration,
)
if verbose:
print(f"NWB file saved at {nwbfile_path}!")
return nwbfile
else:
# Append mode: read existing file, add data, write back
warnings.warn(
"Returning an NWBFile object when using append_on_disk_nwbfile=True is deprecated "
"and will return None in or after March 2026.",
DeprecationWarning,
stacklevel=2,
)
IO = BACKEND_NWB_IO[backend]
with IO(path=str(nwbfile_path), mode="r+", load_namespaces=True) as io:
nwbfile = io.read()
add_sorting_to_nwbfile(
sorting=sorting,
nwbfile=nwbfile,
unit_ids=unit_ids,
property_descriptions=property_descriptions,
skip_properties=skip_properties,
write_as=write_as,
units_name=units_name,
units_description=units_description,
waveform_means=waveform_means,
waveform_sds=waveform_sds,
unit_electrode_indices=unit_electrode_indices,
)
if backend_configuration is None:
backend_configuration = get_default_backend_configuration(nwbfile=nwbfile, backend=backend)
configure_backend(nwbfile=nwbfile, backend_configuration=backend_configuration)
io.write(nwbfile)
if verbose:
print(f"NWB file saved at {nwbfile_path}!")
return nwbfile # Will return None in March 2026
[docs]
def add_sorting_analyzer_to_nwbfile(
sorting_analyzer: SortingAnalyzer,
nwbfile: pynwb.NWBFile | None = None,
metadata: dict | None = None,
recording: BaseRecording | None = None,
unit_ids: list[str] | list[int] | None = None,
skip_properties: list[str] | None = None,
property_descriptions: dict | None = None,
write_as: Literal["units", "processing"] = "units",
units_name: str = "units",
units_description: str = "Autogenerated by neuroconv.",
):
"""
Convenience function to write directly a sorting analyzer object to an nwbfile.
The function adds the data of the recording and the sorting plus the following information from the sorting analyzer:
- quality metrics
- template mean and std
- template metrics
Parameters
----------
sorting_analyzer : spikeinterface.SortingAnalyzer
The sorting analyzer object to be written to the NWBFile.
nwbfile : NWBFile, optional
If passed, this function will fill the relevant fields within the NWBFile object.
E.g., calling::
write_recording(recording=my_recording_extractor, nwbfile=my_nwbfile)
will result in the appropriate changes to the my_nwbfile object.
If neither 'nwbfile_path' nor 'nwbfile' are specified, an NWBFile object will be automatically generated
and returned by the function.
metadata : dict, optional
Metadata dictionary with information used to create the NWBFile when one does not exist or overwrite=True.
The "Ecephys" section of metadata is also used to create electrodes and electrical series fields.
recording : BaseRecording, optional
If the sorting_analyzer is 'recordingless', this argument is required to save electrode info.
unit_ids : list, optional
Controls the unit_ids that will be written to the nwb file. If None (default), all
units are written.
property_descriptions : dict, optional
For each key in this dictionary which matches the name of a unit
property in sorting, adds the value as a description to that
custom unit column.
skip_properties : list of str, optional
Each string in this list that matches a unit property will not be written to the NWBFile.
write_as : {'units', 'processing'}
How to save the units table in the nwb file. Options:
- 'units' will save it to the official NWBFile.Units position; recommended only for the final form of the data.
- 'processing' will save it to the processing module to serve as a historical provenance for the official table.
units_name : str, optional, default: 'units'
The name of the units table. If write_as=='units', then units_name must also be 'units'.
units_description : str, default: 'Autogenerated by neuroconv.'
"""
# TODO: move into add_units
assert write_as in [
"units",
"processing",
], f"Argument write_as ({write_as}) should be one of 'units' or 'processing'!"
if write_as == "units":
assert units_name == "units", "When writing to the nwbfile.units table, the name of the table must be 'units'!"
write_in_processing_module = False if write_as == "units" else True
# retrieve templates and stds
template_extension = sorting_analyzer.get_extension("templates")
if template_extension is None:
raise ValueError("No templates found in the sorting analyzer.")
template_means = template_extension.get_templates()
template_stds = template_extension.get_templates(operator="std")
sorting = sorting_analyzer.sorting
if unit_ids is not None:
unit_indices = sorting.ids_to_indices(unit_ids)
template_means = template_means[unit_indices]
template_stds = template_stds[unit_indices]
# metrics properties (quality, template) are added as properties to the sorting copy
sorting_copy = sorting.select_units(unit_ids=sorting.unit_ids)
if sorting_analyzer.has_extension("quality_metrics"):
qm = sorting_analyzer.get_extension("quality_metrics").get_data()
for prop in qm.columns:
if prop not in sorting_copy.get_property_keys():
sorting_copy.set_property(prop, qm[prop])
if sorting_analyzer.has_extension("template_metrics"):
tm = sorting_analyzer.get_extension("template_metrics").get_data()
for prop in tm.columns:
if prop not in sorting_copy.get_property_keys():
sorting_copy.set_property(prop, tm[prop])
# if recording is given, it takes precedence over the recording in the sorting analyzer
if recording is None:
assert sorting_analyzer.has_recording(), (
"recording not found. To add the electrode table, the sorting_analyzer "
"needs to have a recording attached or the 'recording' argument needs to be used."
)
recording = sorting_analyzer.recording
add_recording_metadata_to_nwbfile(recording, nwbfile=nwbfile, metadata=metadata)
electrode_group_indices = _get_electrode_group_indices(recording, nwbfile=nwbfile)
unit_electrode_indices = [electrode_group_indices] * len(sorting.unit_ids)
_add_units_table_to_nwbfile(
sorting=sorting_copy,
nwbfile=nwbfile,
unit_ids=unit_ids,
property_descriptions=property_descriptions,
skip_properties=skip_properties,
write_in_processing_module=write_in_processing_module,
units_table_name=units_name,
unit_table_description=units_description,
waveform_means=template_means,
waveform_sds=template_stds,
unit_electrode_indices=unit_electrode_indices,
)
[docs]
def write_sorting_analyzer_to_nwbfile(
sorting_analyzer: SortingAnalyzer,
nwbfile_path: FilePath | None = None,
nwbfile: pynwb.NWBFile | None = None,
metadata: dict | None = None,
overwrite: bool = False,
recording: BaseRecording | None = None,
verbose: bool = False,
unit_ids: list[str] | list[int] | None = None,
write_electrical_series: bool = False,
add_electrical_series_kwargs: dict | None = None,
skip_properties: list[str] | None = None,
property_descriptions: dict | None = None,
write_as: Literal["units", "processing"] = "units",
units_name: str = "units",
units_description: str = "Autogenerated by neuroconv.",
*,
backend: Literal["hdf5", "zarr"] | None = None,
backend_configuration: HDF5BackendConfiguration | ZarrBackendConfiguration | None = None,
append_on_disk_nwbfile: bool = False,
) -> pynwb.NWBFile | None:
"""
Convenience function to write directly a sorting analyzer object to an nwbfile.
The function adds the data of the recording and the sorting plus the following information from the sorting analyzer:
- quality metrics
- template mean and std
- template metrics
Parameters
----------
sorting_analyzer : spikeinterface.SortingAnalyzer
The sorting analyzer object to be written to the NWBFile.
nwbfile_path : FilePath, optional
Path for where to write or load (if overwrite=False) the NWBFile.
If not provided, only adds data to the in-memory nwbfile without writing to disk.
**Deprecated**: Using this function without nwbfile_path is deprecated. Use `add_sorting_analyzer_to_nwbfile` instead.
nwbfile : NWBFile, optional
If passed, this function will fill the relevant fields within the NWBFile object.
E.g., calling::
write_recording(recording=my_recording_extractor, nwbfile=my_nwbfile)
will result in the appropriate changes to the my_nwbfile object.
metadata : dict
Metadata dictionary for constructing the NWB file. Required when nwbfile is not provided.
Must include at minimum the required NWBFile fields (session_description, identifier, session_start_time).
The "Ecephys" section of metadata is also used to create electrodes and electrical series fields.
overwrite : bool, default: False
Whether to overwrite the NWBFile if one exists at the nwbfile_path.
recording : BaseRecording, optional
If the sorting_analyzer is 'recordingless', this argument is required to be passed to save electrode info.
verbose : bool, default: False
If 'nwbfile_path' is specified, informs user after a successful write operation.
unit_ids : list, optional
Controls the unit_ids that will be written to the nwb file. If None (default), all
units are written.
write_electrical_series : bool, default: False
If True, the recording object associated to the analyzer is written as an electrical series.
add_electrical_series_kwargs: dict, optional
Keyword arguments to control the `add_electrical_series()` function in case write_electrical_series=True
property_descriptions: dict, optional
For each key in this dictionary which matches the name of a unit
property in sorting, adds the value as a description to that
custom unit column.
skip_properties: list of str, optional
Each string in this list that matches a unit property will not be written to the NWBFile.
write_as: {'units', 'processing'}
How to save the units table in the nwb file. Options:
- 'units' will save it to the official NWBFile.Units position; recommended only for the final form of the data.
- 'processing' will save it to the processing module to serve as a historical provenance for the official table.
units_name : str, default: 'units'
The name of the units table. If write_as=='units', then units_name must also be 'units'.
units_description : str, default: 'Autogenerated by neuroconv.'
backend : {"hdf5", "zarr"}, optional
The type of backend to use when writing the file.
If a `backend_configuration` is not specified, the default type will be "hdf5".
If a `backend_configuration` is specified, then the type will be auto-detected.
backend_configuration : HDF5BackendConfiguration or ZarrBackendConfiguration, optional
The configuration model to use when configuring the datasets for this backend.
To customize, call the `.get_default_backend_configuration(...)` method, modify the returned
BackendConfiguration object, and pass that instead.
Otherwise, all datasets will use default configuration settings.
append_on_disk_nwbfile : bool, default: False
Whether to append to an existing NWBFile on disk. If True, the `nwbfile` parameter must be None.
This is useful for appending data to an existing file without overwriting it.
Returns
-------
nwbfile : pynwb.NWBFile or None
The in-memory NWBFile object. Returns None when append_on_disk_nwbfile=True (to be implemented in or after March 2026).
"""
# Handle deprecated usage without nwbfile_path
if nwbfile_path is None:
warnings.warn(
"Using 'write_sorting_analyzer_to_nwbfile' without 'nwbfile_path' to only add data to an in-memory nwbfile is deprecated "
"and will be removed in or after March 2026. Use 'add_sorting_analyzer_to_nwbfile' instead.",
DeprecationWarning,
stacklevel=2,
)
if nwbfile is None:
raise ValueError(
"Either 'nwbfile_path' or 'nwbfile' must be provided. "
"To add data to an in-memory nwbfile, use 'add_sorting_analyzer_to_nwbfile' instead."
)
# Ensure metadata exists
metadata = metadata if metadata is not None else dict()
# Get recording
if recording is None and sorting_analyzer.has_recording():
recording = sorting_analyzer.recording
assert recording is not None, (
"recording not found. To add the electrode table, the sorting_analyzer "
"needs to have a recording attached or the 'recording' argument needs to be used."
)
# Call add_sorting_analyzer_to_nwbfile for deprecated behavior
if write_electrical_series:
add_electrical_series_kwargs = add_electrical_series_kwargs or dict()
add_recording_to_nwbfile(
recording=recording, nwbfile=nwbfile, metadata=metadata, **add_electrical_series_kwargs
)
add_sorting_analyzer_to_nwbfile(
sorting_analyzer=sorting_analyzer,
nwbfile=nwbfile,
metadata=metadata,
recording=recording,
unit_ids=unit_ids,
skip_properties=skip_properties,
property_descriptions=property_descriptions,
write_as=write_as,
units_name=units_name,
units_description=units_description,
)
return nwbfile
# Ensure metadata exists
if metadata is None:
metadata = dict()
# if recording is given, it takes precedence over the recording in the sorting analyzer
if recording is None and sorting_analyzer.has_recording():
recording = sorting_analyzer.recording
assert recording is not None, (
"recording not found. To add the electrode table, the sorting_analyzer "
"needs to have a recording attached or the 'recording' argument needs to be used."
)
# Resolve backend
backend = _resolve_backend(backend=backend, backend_configuration=backend_configuration)
appending_to_in_memory_nwbfile = nwbfile is not None
file_initially_exists = nwbfile_path.exists()
allowed_to_modify_existing = overwrite or append_on_disk_nwbfile
if file_initially_exists and not allowed_to_modify_existing:
raise ValueError(
f"The file at '{nwbfile_path}' already exists. Set overwrite=True to overwrite the existing file "
"or append_on_disk_nwbfile=True to append to the existing file."
)
if append_on_disk_nwbfile and appending_to_in_memory_nwbfile:
raise ValueError(
"Cannot append to an existing file while also providing an in-memory NWBFile. "
"Either set overwrite=True to replace the existing file, or remove the nwbfile parameter to append to the existing file on disk."
)
# Determine if we're writing a new file or appending
writing_new_file = not append_on_disk_nwbfile
if writing_new_file:
# Writing mode: create or use provided nwbfile and write
if nwbfile is None:
# Create new nwbfile from metadata
if not metadata:
raise ValueError(
"metadata is required when nwbfile is not provided. "
"Please provide metadata dictionary with at least the required NWBFile fields."
)
nwbfile = make_nwbfile_from_metadata(metadata=metadata)
if write_electrical_series:
add_electrical_series_kwargs = add_electrical_series_kwargs or dict()
add_recording_to_nwbfile(
recording=recording, nwbfile=nwbfile, metadata=metadata, **add_electrical_series_kwargs
)
add_sorting_analyzer_to_nwbfile(
sorting_analyzer=sorting_analyzer,
nwbfile=nwbfile,
metadata=metadata,
recording=recording,
unit_ids=unit_ids,
skip_properties=skip_properties,
property_descriptions=property_descriptions,
write_as=write_as,
units_name=units_name,
units_description=units_description,
)
if backend_configuration is None:
backend_configuration = get_default_backend_configuration(nwbfile=nwbfile, backend=backend)
configure_and_write_nwbfile(
nwbfile=nwbfile,
nwbfile_path=nwbfile_path,
backend=backend,
backend_configuration=backend_configuration,
)
if verbose:
print(f"NWB file saved at {nwbfile_path}!")
return nwbfile
else:
# Append mode: read existing file, add data, write back
warnings.warn(
"Returning an NWBFile object in append mode is deprecated and will return None in or after March 2026.",
DeprecationWarning,
stacklevel=2,
)
IO = BACKEND_NWB_IO[backend]
with IO(path=str(nwbfile_path), mode="r+", load_namespaces=True) as io:
nwbfile = io.read()
if write_electrical_series:
add_electrical_series_kwargs = add_electrical_series_kwargs or dict()
add_recording_to_nwbfile(
recording=recording, nwbfile=nwbfile, metadata=metadata, **add_electrical_series_kwargs
)
add_sorting_analyzer_to_nwbfile(
sorting_analyzer=sorting_analyzer,
nwbfile=nwbfile,
metadata=metadata,
recording=recording,
unit_ids=unit_ids,
skip_properties=skip_properties,
property_descriptions=property_descriptions,
write_as=write_as,
units_name=units_name,
units_description=units_description,
)
if backend_configuration is None:
backend_configuration = get_default_backend_configuration(nwbfile=nwbfile, backend=backend)
configure_backend(nwbfile=nwbfile, backend_configuration=backend_configuration)
io.write(nwbfile)
if verbose:
print(f"NWB file saved at {nwbfile_path}!")
return nwbfile # Will return None in March 2026
def _get_electrode_group_indices(recording, nwbfile):
""" """
if "group_name" in recording.get_property_keys():
group_names = np.unique(recording.get_property("group_name"))
elif "group" in recording.get_property_keys():
group_names = np.unique(recording.get_property("group"))
else:
group_names = None
if group_names is None:
electrode_group_indices = None
else:
group_names = [str(group_name) for group_name in group_names]
electrode_group_indices = nwbfile.electrodes.to_dataframe().query(f"group_name in {group_names}").index.values
return electrode_group_indices
def _stub_recording(recording: BaseRecording, *, stub_samples: int = 100) -> BaseRecording:
"""
Create a stub recording object for the NWBFile.
Parameters
----------
recording : BaseRecording
The recording extractor object.
stub_samples : int, default: 100
The length of the stub recording in frames.
"""
number_of_segments = recording.get_num_segments()
recording_segments = [recording.select_segments([index]) for index in range(number_of_segments)]
# We clip the stub_samples if they are more than the number of samples in the segment
end_frame_list = [min(stub_samples, segment.get_num_samples()) for segment in recording_segments]
recording_segments_stubbed = [
segment.frame_slice(start_frame=0, end_frame=end_frame)
for segment, end_frame in zip(recording_segments, end_frame_list)
]
recording_stubbed = AppendSegmentRecording(recording_list=recording_segments_stubbed)
return recording_stubbed