Fix module level test groups (#4573)

Author: chrishalcrow

pyproject.toml

@@ -244,6 +244,11 @@ test-streaming-extractors = [
     {include-group = "test-common"},
     "pooch>=1.8.2",
     "datalad>=1.0.2",
+    "remfile",
+    "requests",
+    "aiohttp",
+    "fsspec",
+    "s3fs",
 ]
 
 test-preprocessing = [
@@ -254,9 +259,17 @@ test-preprocessing = [
     "torch",  # preprocessing tests exercise sortingcomponents.motion.dredge
 ]
 
-test-postprocessing = [{include-group = "test-common"}]
+test-postprocessing = [
+    {include-group = "test-common"},
+    "pandas<3",
+    ]
 test-metrics = [{include-group = "test-common"}]
-test-comparison = [{include-group = "test-common"}]
+
+test-comparison = [
+    {include-group = "test-common"},
+    "numba>=0.59"
+    ]
+
 test-sorters-internal = [
     {include-group = "test-common"},
     "torch",  # spyking_circus2 template matching
@@ -278,8 +291,15 @@ test-exporters = [{include-group = "test-common"}]
 test-sortingcomponents = [
     {include-group = "test-common"},
     "torch",  # neural network peak detection, motion, matching
+    "huggingface_hub", # for `test_single_channel_toy_denoiser_in_peak_pipeline`
 ]
-test-generation = [{include-group = "test-common"}]
+test-generation = [
+    {include-group = "test-common"},
+    "fsspec",
+    "s3fs",
+    "numba>=0.59", # numba and sklearn needed for motion correction of
+    "scikit-learn", # syntheic data used in `test_generate_hybrid_motion`
+    ]
 
 # Aggregate group for workflows that run the full test suite (nightly codecov).
 test-all = [

src/spikeinterface/generation/tests/test_hybrid_tools.py

@@ -39,7 +39,10 @@ def test_generate_hybrid_with_sorting():
 def test_generate_hybrid_motion():
     rec, _ = generate_ground_truth_recording(sampling_frequency=20000, durations=[10], num_channels=16, seed=0)
     _, motion_info = correct_motion(
-        rec, output_motion_info=True, estimate_motion_kwargs={"win_step_um": 20, "win_scale_um": 20}
+        rec,
+        output_motion_info=True,
+        preset="nonrigid_fast_and_accurate",
+        estimate_motion_kwargs={"win_step_um": 20, "win_scale_um": 20},
     )
     motion = motion_info["motion"]
     hybrid, sorting_hybrid = generate_hybrid_recording(rec, motion=motion, seed=0)