Port SpikeInterface update for tutorial generation

environments/environment-Linux.yml

@@ -15,13 +15,11 @@ dependencies:
       - flask-cors == 4.0.0
       - flask_restx == 1.1.0
       - werkzeug < 3.0 # werkzeug 3.0 deprecates features used by flask 2.3.2. Remove this when updating flask.
-      # 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
+      - neuroconv[dandi,compressors,ecephys,ophys,behavior,text] == 0.6.0
       - scikit-learn == 1.4.0 # Tutorial data generation
       - tqdm_publisher >= 0.0.1 # Progress bars
       - tzlocal >= 5.2 # Frontend timezone handling
       - ndx-pose == 0.1.1
-      - nwbinspector==0.6.2
+      - nwbinspector == 0.6.2
       - tables
+      - numcodecs < 0.16.0  # numcodecs 0.16.0 is not compatible with zarr 2.18.5

environments/environment-MAC-apple-silicon.yml

@@ -23,12 +23,10 @@ dependencies:
       - werkzeug < 3.0 # werkzeug 3.0 deprecates features used by flask 2.3.2. Remove this when updating flask.
       # 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
+      - neuroconv[dandi,compressors,ecephys,ophys,behavior,text] == 0.6.0
       - scikit-learn == 1.4.0 # Tutorial data generation
       - tqdm_publisher >= 0.0.1 # Progress bars
       - tzlocal >= 5.2 # Frontend timezone handling
       - ndx-pose == 0.1.1
-      - nwbinspector==0.6.2
+      - nwbinspector == 0.6.2
+      - numcodecs < 0.16.0  # numcodecs 0.16.0 is not compatible with zarr 2.18.5

environments/environment-MAC-intel.yml

@@ -7,7 +7,6 @@ dependencies:
   - nodejs = 18.16.1
   # install these from conda-forge so that dependent packages get included in the distributable
   - jsonschema = 4.18.0 # installs jsonschema-specifications
-  - pytables = 3.10.2 # Install from conda-forge because PyPI version results in hdf5 conflicts and missing libs
   - pip
   - pip:
       - setuptools==70.0.0
@@ -19,12 +18,13 @@ dependencies:
       - flask-cors == 4.0.0
       - flask_restx == 1.1.0
       - werkzeug < 3.0 # werkzeug 3.0 deprecates features used by flask 2.3.2. Remove this when updating flask.
-      # 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
+      - neuroconv[dandi,compressors,ecephys,ophys,behavior,text] == 0.6.0
       - scikit-learn == 1.4.0 # Tutorial data generation
       - tqdm_publisher >= 0.0.1 # Progress bars
       - tzlocal >= 5.2 # Frontend timezone handling
       - ndx-pose == 0.1.1
-      - nwbinspector==0.6.2
+      - nwbinspector == 0.6.2
+      - numcodecs < 0.16.0  # numcodecs 0.16.0 is not compatible with zarr 2.18.5
+      - h5py == 3.12.1 # 3.13.0 uses features in hdf5 1.14.4 that is not available in earlier hdf5 libs packaged
+                       # with tables==3.9.1 (latest that can be used by neuroconv 0.6.0).
+                       # h5py and tables need to be consistent for electron build for unknown reason

environments/environment-Windows.yml

@@ -18,13 +18,11 @@ dependencies:
       - flask-cors === 3.0.10
       - flask_restx == 1.1.0
       - werkzeug < 3.0 # werkzeug 3.0 deprecates features used by flask 2.3.2. Remove this when updating flask.
-      # 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
+      - neuroconv[dandi,compressors,ecephys,ophys,behavior,text] == 0.6.0
       - scikit-learn == 1.4.0 # Tutorial data generation
       - tqdm_publisher >= 0.0.1 # Progress bars
       - tzlocal >= 5.2 # Frontend timezone handling
       - ndx-pose == 0.1.1
-      - nwbinspector==0.6.2
+      - nwbinspector == 0.6.2
       - tables
+      - numcodecs < 0.16.0  # numcodecs 0.16.0 is not compatible with zarr 2.18.5

src/pyflask/manageNeuroconv/manage_neuroconv.py

@@ -1668,11 +1668,13 @@ 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
+    import spikeinterface.exporters
+    import spikeinterface.preprocessing
+
+    spikeinterface.set_global_job_kwargs(n_jobs=-1)
 
     base_path = Path(output_path)
     spikeglx_output_folder = base_path / "spikeglx"
@@ -1687,8 +1689,8 @@ 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,
@@ -1697,12 +1699,18 @@ def generate_test_data(output_path: str):
         seed=0,  # Fixed seed for reproducibility
     )
 
-    unscaled_artificial_ap_band = scale(recording=artificial_ap_band_in_uV, gain=1 / conversion_factor_to_uV)
+    unscaled_artificial_ap_band = spikeinterface.preprocessing.scale(
+        recording=artificial_ap_band_in_uV, gain=1 / conversion_factor_to_uV
+    )
     int16_artificial_ap_band = unscaled_artificial_ap_band.astype(dtype="int16")
     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_filter = spikeinterface.preprocessing.bandpass_filter(
+        recording=unscaled_artificial_ap_band, freq_min=0.5, freq_max=1_000
+    )
+    unscaled_artificial_lf_band = spikeinterface.preprocessing.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)
 
@@ -1725,13 +1733,16 @@ def generate_test_data(output_path: str):
     with open(file=lf_meta_file_path, mode="w") as io:
         io.write(lf_meta_content)
 
-    # Make Phy folder
-    waveform_extractor = spikeinterface.extract_waveforms(
-        recording=artificial_ap_band_in_uV, sorting=spiking, mode="memory"
+    # Make Phy folder - see https://spikeinterface.readthedocs.io/en/latest/modules/exporters.html
+    sorting_analyzer = spikeinterface.create_sorting_analyzer(
+        sorting=sorting, recording=artificial_ap_band_in_uV, mode="memory", sparse=False
     )
+    sorting_analyzer.compute(["random_spikes", "waveforms", "templates", "noise_levels"])
+    sorting_analyzer.compute("spike_amplitudes")
+    sorting_analyzer.compute("principal_components", n_components=5, mode="by_channel_local")
 
-    export_to_phy(
-        waveform_extractor=waveform_extractor, output_folder=phy_output_folder, remove_if_exists=True, copy_binary=False
+    spikeinterface.exporters.export_to_phy(
+        sorting_analyzer=sorting_analyzer, output_folder=phy_output_folder, remove_if_exists=True, copy_binary=False
     )