Source code for neuroconv.datainterfaces.ophys.sbx.sbxdatainterface
import warnings
from typing import Literal
from pydantic import FilePath, validate_call
from ..baseimagingextractorinterface import BaseImagingExtractorInterface
from ....utils import DeepDict
[docs]
class SbxImagingInterface(BaseImagingExtractorInterface):
"""Data Interface for SbxImagingExtractor."""
display_name = "Scanbox Imaging"
associated_suffixes = (".sbx",)
info = "Interface for Scanbox imaging data."
@validate_call
def __init__(
self,
file_path: FilePath,
*args, # TODO: change to * (keyword only) on or after August 2026
sampling_frequency: float | None = None,
verbose: bool = False,
photon_series_type: Literal["OnePhotonSeries", "TwoPhotonSeries"] = "TwoPhotonSeries",
):
"""
Parameters
----------
file_path : FilePath
Path to .sbx file.
sampling_frequency : float, optional
verbose : bool, default: False
"""
# Handle deprecated positional arguments
if args:
parameter_names = [
"sampling_frequency",
"verbose",
"photon_series_type",
]
num_positional_args_before_args = 1 # file_path
if len(args) > len(parameter_names):
raise TypeError(
f"__init__() takes at most {len(parameter_names) + num_positional_args_before_args + 1} positional arguments but "
f"{len(args) + num_positional_args_before_args + 1} were given. "
"Note: Positional arguments are deprecated and will be removed on or after August 2026. "
"Please use keyword arguments."
)
positional_values = dict(zip(parameter_names, args))
passed_as_positional = list(positional_values.keys())
warnings.warn(
f"Passing arguments positionally to SbxImagingInterface.__init__() is deprecated "
f"and will be removed on or after August 2026. "
f"The following arguments were passed positionally: {passed_as_positional}. "
"Please use keyword arguments instead.",
FutureWarning,
stacklevel=2,
)
sampling_frequency = positional_values.get("sampling_frequency", sampling_frequency)
verbose = positional_values.get("verbose", verbose)
photon_series_type = positional_values.get("photon_series_type", photon_series_type)
super().__init__(
file_path=file_path,
sampling_frequency=sampling_frequency,
verbose=verbose,
photon_series_type=photon_series_type,
)