Remove iterator_type v1 and rate in trace metadata deprecations

CHANGELOG.md

@@ -1,6 +1,8 @@
 # v0.9.4 (Upcoming)
 
 ## Removals, Deprecations and Changes
+* Removed the deprecated `iterator_type='v1'` option from `_imaging_frames_to_hdmf_iterator`. Use `iterator_type='v2'` (default) instead. [PR #1679](https://github.com/catalystneuro/neuroconv/pull/1679)
+* Removed support for passing `rate` in trace metadata for fluorescence traces. The rate is now always calculated automatically from the segmentation extractor's timestamps or sampling frequency. [PR #1679](https://github.com/catalystneuro/neuroconv/pull/1679)
 * Removed the deprecated `stub_frames` parameter from ophys interfaces (`BaseImagingExtractorInterface`, `BaseSegmentationExtractorInterface`, `BrukerTiffMultiPlaneConverter`, `BrukerTiffSinglePlaneConverter`, `MiniscopeConverter`, `Suite2pSegmentationInterface`, `MinianSegmentationInterface`, `MiniscopeImagingDataInterface`). Use `stub_samples` instead. [PR #1676](https://github.com/catalystneuro/neuroconv/pull/1676)
 * Removed deprecated wrapper functions from `roiextractors_pending_deprecation.py` (March 2026 deadline): `add_imaging_plane_to_nwbfile`, `add_image_segmentation_to_nwbfile`, `add_photon_series_to_nwbfile`, `add_plane_segmentation_to_nwbfile`, `add_background_plane_segmentation_to_nwbfile`, `add_background_fluorescence_traces_to_nwbfile`, `add_summary_images_to_nwbfile`. [PR #1680](https://github.com/catalystneuro/neuroconv/pull/1680)
 * Added `*args` positional argument deprecation to `add_imaging_to_nwbfile` to enforce keyword-only arguments. Will be enforced on or after September 2026. [PR #1680](https://github.com/catalystneuro/neuroconv/pull/1680)

src/neuroconv/tools/roiextractors/roiextractors.py

@@ -7,7 +7,6 @@
 import psutil
 
 # from hdmf.common import VectorData
-from hdmf.data_utils import DataChunkIterator
 from pydantic import FilePath
 from pynwb import NWBFile
 from pynwb.base import Images
@@ -895,7 +894,6 @@ def _imaging_frames_to_hdmf_iterator(
         The type of iterator for chunked data writing.
         'v2': Uses iterative write with control over chunking and progress bars.
         None: Loads all data into memory before writing (not recommended for large datasets).
-        Note: 'v1' is deprecated and will be removed on or after March 2026.
     iterator_options : dict, optional
         Options for controlling the iterative write process. See the
         `pynwb tutorial on iterative write <https://pynwb.readthedocs.io/en/stable/tutorials/advanced_io/plot_iterative_write.html#sphx-glr-tutorials-advanced-io-plot-iterative-write-py>`_
@@ -907,29 +905,13 @@ def _imaging_frames_to_hdmf_iterator(
         The frames of the imaging extractor wrapped in an iterator for chunked writing.
     """
 
-    def data_generator(imaging):
-        num_samples = imaging.get_num_samples()
-        for i in range(num_samples):
-            yield imaging.get_series(start_sample=i, end_sample=i + 1).squeeze().T
-
-    assert iterator_type in ["v1", "v2", None], "'iterator_type' must be either 'v2' (recommended) or None."
+    assert iterator_type in ["v2", None], "'iterator_type' must be either 'v2' (recommended) or None."
     iterator_options = dict() if iterator_options is None else iterator_options
 
     if iterator_type is None:
         _check_if_imaging_fits_into_memory(imaging=imaging)
         return imaging.get_series().transpose((0, 2, 1))
 
-    if iterator_type == "v1":
-        warnings.warn(
-            "iterator_type='v1' is deprecated and will be removed on or after March 2026. "
-            "Use iterator_type='v2' for better chunking control and progress bar support.",
-            FutureWarning,
-            stacklevel=2,
-        )
-        if "buffer_size" not in iterator_options:
-            iterator_options.update(buffer_size=10)
-        return DataChunkIterator(data=data_generator(imaging), **iterator_options)
-
     return ImagingExtractorDataChunkIterator(imaging_extractor=imaging, **iterator_options)
 
 
@@ -1101,7 +1083,6 @@ def write_imaging_to_nwbfile(
         The type of iterator for chunked data writing.
         'v2': Uses iterative write with control over chunking and progress bars.
         None: Loads all data into memory before writing (not recommended for large datasets).
-        Note: 'v1' is deprecated and will be removed on or after March 2026.
     iterator_options : dict, optional
         Options for controlling the iterative write process. See the
         `pynwb tutorial on iterative write <https://pynwb.readthedocs.io/en/stable/tutorials/advanced_io/plot_iterative_write.html#sphx-glr-tutorials-advanced-io-plot-iterative-write-py>`_
@@ -1706,14 +1687,8 @@ def _add_fluorescence_traces_to_nwbfile(
         roi_response_series_kwargs["data"] = SliceableDataChunkIterator(trace, **iterator_options)
         roi_response_series_kwargs["rois"] = roi_table_region
 
-        # Deprecation warning for user-provided rate in metadata
-        if user_trace_metadata is not None and "rate" in user_trace_metadata:
-            warnings.warn(
-                f"Passing 'rate' in metadata for trace '{trace_name}' is deprecated and will be removed on or after March 2026. "
-                f"The rate will be automatically calculated from the segmentation extractor's timestamps or sampling frequency.",
-                FutureWarning,
-                stacklevel=2,
-            )
+        # Remove user-provided rate from metadata (rate is always calculated automatically)
+        roi_response_series_kwargs.pop("rate", None)
 
         # Resolve timestamps: user-set > native hardware > none
         timestamps_were_set = segmentation_extractor.has_time_vector()
@@ -1735,12 +1710,8 @@ def _add_fluorescence_traces_to_nwbfile(
 
         if timestamps_are_regular:
             roi_response_series_kwargs["starting_time"] = starting_time
-            # Use metadata rate if provided, otherwise use estimated/sampled rate
-            if "rate" not in roi_response_series_kwargs:
-                roi_response_series_kwargs["rate"] = rate
+            roi_response_series_kwargs["rate"] = rate
         else:
-            # Irregular timestamps - remove rate from metadata if present (can't specify both)
-            roi_response_series_kwargs.pop("rate", None)
             roi_response_series_kwargs["timestamps"] = timestamps
 
         # Build the roi response series

tests/test_modalities/test_ophys/test_tools_roiextractors.py

@@ -12,7 +12,6 @@
 import numpy as np
 import psutil
 import pytest
-from hdmf.data_utils import DataChunkIterator
 from hdmf.testing import TestCase
 from numpy.testing import assert_array_equal, assert_raises
 from numpy.typing import ArrayLike
@@ -1537,30 +1536,6 @@ def test_non_iterative_two_photon(self):
         expected_two_photon_series_data = self.imaging_extractor.get_series().transpose((0, 2, 1))
         assert_array_equal(two_photon_series_extracted, expected_two_photon_series_data)
 
-    def test_deprecated_v1_iterator_two_photon(self):
-        """Test adding two photon series with deprecated v1 iterator type."""
-        with self.assertWarns(FutureWarning):
-            _add_photon_series_to_nwbfile_old_list_format(
-                imaging=self.imaging_extractor,
-                nwbfile=self.nwbfile,
-                metadata=self.two_photon_series_metadata,
-                iterator_type="v1",
-            )
-
-        # Check data
-        acquisition_modules = self.nwbfile.acquisition
-        assert self.two_photon_series_name in acquisition_modules
-        data_chunk_iterator = acquisition_modules[self.two_photon_series_name].data
-        assert isinstance(data_chunk_iterator, DataChunkIterator)
-        self.assertEqual(data_chunk_iterator.buffer_size, 10)
-
-        two_photon_series_extracted = np.concatenate([data_chunk.data for data_chunk in data_chunk_iterator])
-        # NWB stores images as num_columns x num_rows
-        expected_two_photon_series_shape = (self.num_samples, self.num_columns, self.num_rows)
-        assert two_photon_series_extracted.shape == expected_two_photon_series_shape
-        expected_two_photon_series_data = self.imaging_extractor.get_series().transpose((0, 2, 1))
-        assert_array_equal(two_photon_series_extracted, expected_two_photon_series_data)
-
     def test_iterator_options_propagation(self):
         """Test that iterator options are propagated to the data chunk iterator."""
         buffer_shape = (20, 5, 5)