port over other PR

environments/environment-Linux.yml

@@ -15,10 +15,8 @@ dependencies:
       - flask == 2.3.2
       - flask-cors == 4.0.0
       - flask_restx == 1.1.0
-      # For stability, NeuroConv is pinned at a commit just prior to breaking SpikeInterface compatibility
-      - neuroconv @ git+https://github.com/catalystneuro/neuroconv.git@fa636458aa5c321f1c2c08f6e682b4a52d5a83f3#neuroconv[dandi,compressors,ecephys,ophys,behavior,text]
-      # For stability, pinning SpikeInterface to a version that works with NeuroConv and with tutorial generation
-      - spikeinterface == 0.100.5
+      # For stability, NeuroConv is pinned at a commit just prior to Pydantic schema inference refactor
+      - neuroconv @ git+https://github.com/catalystneuro/neuroconv.git@8fa1c8f46316a7192073713e3e4e78c25a2e0d36#neuroconv[dandi,compressors,ecephys,ophys,behavior,text]
       - scikit-learn == 1.4.0  # Tutorial data generation
       - tqdm_publisher >= 0.0.1  # Progress bars
       - tzlocal >= 5.2  # Frontend timezone handling

environments/environment-MAC-apple-silicon.yml

@@ -23,10 +23,8 @@ dependencies:
       - flask_restx == 1.1.0
       # NOTE: the NeuroConv wheel on PyPI includes sonpy which is not compatible with arm64, so build and install
       # NeuroConv from GitHub, which will remove the sonpy dependency when building from Mac arm64
-      # For stability, NeuroConv is pinned at a commit just prior to breaking SpikeInterface compatibility
-      - neuroconv @ git+https://github.com/catalystneuro/neuroconv.git@fa636458aa5c321f1c2c08f6e682b4a52d5a83f3#neuroconv[dandi,compressors,ecephys,ophys,behavior,text]
-      # For stability, pinning SpikeInterface to a version that works with NeuroConv and with tutorial generation
-      - spikeinterface == 0.100.5
+      # For stability, NeuroConv is pinned at a commit just prior to Pydantic schema inference refactor
+      - neuroconv @ git+https://github.com/catalystneuro/neuroconv.git@8fa1c8f46316a7192073713e3e4e78c25a2e0d36#neuroconv[dandi,compressors,ecephys,ophys,behavior,text]
       - scikit-learn == 1.4.0  # Tutorial data generation
       - tqdm_publisher >= 0.0.1  # Progress bars
       - tzlocal >= 5.2  # Frontend timezone handling

environments/environment-MAC-intel.yml

@@ -18,10 +18,8 @@ dependencies:
       - flask == 2.3.2
       - flask-cors == 4.0.0
       - flask_restx == 1.1.0
-      # For stability, NeuroConv is pinned at a commit just prior to breaking SpikeInterface compatibility
-      - neuroconv @ git+https://github.com/catalystneuro/neuroconv.git@fa636458aa5c321f1c2c08f6e682b4a52d5a83f3#neuroconv[dandi,compressors,ecephys,ophys,behavior,text]
-      # For stability, pinning SpikeInterface to a version that works with NeuroConv and with tutorial generation
-      - spikeinterface == 0.100.5
+      # For stability, NeuroConv is pinned at a commit just prior to Pydantic schema inference refactor
+      - neuroconv @ git+https://github.com/catalystneuro/neuroconv.git@8fa1c8f46316a7192073713e3e4e78c25a2e0d36#neuroconv[dandi,compressors,ecephys,ophys,behavior,text]
       - scikit-learn == 1.4.0  # Tutorial data generation
       - tqdm_publisher >= 0.0.1  # Progress bars
       - tzlocal >= 5.2  # Frontend timezone handling

environments/environment-Windows.yml

@@ -18,10 +18,8 @@ dependencies:
       - flask == 2.3.2
       - flask-cors === 3.0.10
       - flask_restx == 1.1.0
-      # For stability, NeuroConv is pinned at a commit just prior to breaking SpikeInterface compatibility
-      - neuroconv @ git+https://github.com/catalystneuro/neuroconv.git@fa636458aa5c321f1c2c08f6e682b4a52d5a83f3#neuroconv[dandi,compressors,ecephys,ophys,behavior,text]
-      # For stability, pinning SpikeInterface to a version that works with NeuroConv and with tutorial generation
-      - spikeinterface == 0.100.5
+      # For stability, NeuroConv is pinned at a commit just prior to Pydantic schema inference refactor
+      - neuroconv @ git+https://github.com/catalystneuro/neuroconv.git@8fa1c8f46316a7192073713e3e4e78c25a2e0d36#neuroconv[dandi,compressors,ecephys,ophys,behavior,text]
       - scikit-learn == 1.4.0  # Tutorial data generation
       - tqdm_publisher >= 0.0.1  # Progress bars
       - tzlocal >= 5.2  # Frontend timezone handling

src/pyflask/manageNeuroconv/manage_neuroconv.py

@@ -1665,11 +1665,11 @@ def generate_test_data(output_path: str):
     """
     Autogenerate the data formats needed for the tutorial pipeline.
 
-    Consists of a single-probe single-segment SpikeGLX recording (both AP and LF bands) as well as Phy spiking data.
+    Consists of a single-probe single-segment SpikeGLX recording (both AP and LF bands) as well as Phy sorting data.
     """
     import spikeinterface
     from spikeinterface.exporters import export_to_phy
-    from spikeinterface.preprocessing import bandpass_filter, resample, scale
+    from spikeinterface.preprocessing import bandpass_filter, decimate, scale
 
     base_path = Path(output_path)
     spikeglx_output_folder = base_path / "spikeglx"
@@ -1684,8 +1684,8 @@ def generate_test_data(output_path: str):
     lf_sampling_frequency = 2_500.0
     downsample_factor = int(ap_sampling_frequency / lf_sampling_frequency)
 
-    # Generate synthetic spiking and voltage traces with waveforms around them
-    artificial_ap_band_in_uV, spiking = spikeinterface.generate_ground_truth_recording(
+    # Generate synthetic sorting and voltage traces with waveforms around them
+    artificial_ap_band_in_uV, sorting = spikeinterface.generate_ground_truth_recording(
         durations=[duration_in_s],
         sampling_frequency=ap_sampling_frequency,
         num_channels=number_of_channels,
@@ -1699,7 +1699,7 @@ def generate_test_data(output_path: str):
     int16_artificial_ap_band.set_channel_gains(conversion_factor_to_uV)
 
     unscaled_artificial_lf_filter = bandpass_filter(recording=unscaled_artificial_ap_band, freq_min=0.5, freq_max=1_000)
-    unscaled_artificial_lf_band = resample(recording=unscaled_artificial_lf_filter, resample_rate=2_500)
+    unscaled_artificial_lf_band = decimate(recording=unscaled_artificial_lf_filter, decimation_factor=downsample_factor)
     int16_artificial_lf_band = unscaled_artificial_lf_band.astype(dtype="int16")
     int16_artificial_lf_band.set_channel_gains(conversion_factor_to_uV)
 
@@ -1723,12 +1723,12 @@ def generate_test_data(output_path: str):
         io.write(lf_meta_content)
 
     # Make Phy folder
-    waveform_extractor = spikeinterface.extract_waveforms(
-        recording=artificial_ap_band_in_uV, sorting=spiking, mode="memory"
+    sorting_analyzer = si.create_sorting_analyzer(
+        sorting=sorting, recording=artificial_ap_band_in_uV, mode="memory", sparse=False
     )
 
     export_to_phy(
-        waveform_extractor=waveform_extractor, output_folder=phy_output_folder, remove_if_exists=True, copy_binary=False
+        sorting_analyzer=sorting_analyzer, output_folder=phy_output_folder, remove_if_exists=True, copy_binary=False
     )