diff --git a/.github/actions/build-test-environment/action.yml b/.github/actions/build-test-environment/action.yml
index 723e8a702f..c2524d2c16 100644
--- a/.github/actions/build-test-environment/action.yml
+++ b/.github/actions/build-test-environment/action.yml
@@ -1,41 +1,20 @@
 name: Install packages
 description: This action installs the package and its dependencies for testing
 
-inputs:
-  python-version:
-    description: 'Python version to set up'
-    required: false
-  os:
-    description: 'Operating system to set up'
-    required: false
-
 runs:
   using: "composite"
   steps:
     - name: Install dependencies
       run: |
-        sudo apt install git
         git config --global user.email "CI@example.com"
         git config --global user.name "CI Almighty"
-        python -m venv ${{ github.workspace }}/test_env # Environment used in the caching step
-        python -m pip install -U pip  # Official recommended way
-        source ${{ github.workspace }}/test_env/bin/activate
         pip install tabulate  # This produces summaries at the end
         pip install -e .[test,extractors,streaming_extractors,test_extractors,full]
       shell: bash
-    - name: Force installation of latest dev from key-packages when running dev (not release)
-      run: |
-        source ${{ github.workspace }}/test_env/bin/activate
-        spikeinterface_is_dev_version=$(python -c "import spikeinterface; print(spikeinterface.DEV_MODE)")
-        if [ $spikeinterface_is_dev_version = "True" ]; then
-          echo "Running spikeinterface dev version"
-          pip install --no-cache-dir git+https://github.com/NeuralEnsemble/python-neo
-          pip install --no-cache-dir git+https://github.com/SpikeInterface/probeinterface
-        fi
-          echo "Running tests for release, using pyproject.toml versions of neo and probeinterface"
+    - name: Install git-annex
       shell: bash
-    - name: git-annex install
       run: |
+        pip install datalad-installer
         wget https://downloads.kitenet.net/git-annex/linux/current/git-annex-standalone-amd64.tar.gz
         mkdir /home/runner/work/installation
         mv git-annex-standalone-amd64.tar.gz /home/runner/work/installation/
@@ -44,4 +23,14 @@ runs:
         tar xvzf git-annex-standalone-amd64.tar.gz
         echo "$(pwd)/git-annex.linux" >> $GITHUB_PATH
         cd $workdir
+        git config --global filter.annex.process "git-annex filter-process"  # recommended for efficiency
+    - name: Force installation of latest dev from key-packages when running dev (not release)
+      run: |
+        spikeinterface_is_dev_version=$(python -c "import spikeinterface; print(spikeinterface.DEV_MODE)")
+        if [ $spikeinterface_is_dev_version = "True" ]; then
+          echo "Running spikeinterface dev version"
+          pip install --no-cache-dir git+https://github.com/NeuralEnsemble/python-neo
+          pip install --no-cache-dir git+https://github.com/SpikeInterface/probeinterface
+        fi
+          echo "Running tests for release, using pyproject.toml versions of neo and probeinterface"
       shell: bash
diff --git a/.github/workflows/all-tests.yml b/.github/workflows/all-tests.yml
index dcaec8b272..a9c840d5d5 100644
--- a/.github/workflows/all-tests.yml
+++ b/.github/workflows/all-tests.yml
@@ -47,7 +47,7 @@ jobs:
             echo "$file was changed"
           done
 
-      - name: Set testing environment  # This decides which tests are run and whether to install especial dependencies
+      - name: Set testing environment  # This decides which tests are run and whether to install special dependencies
         shell: bash
         run: |
           changed_files="${{ steps.changed-files.outputs.all_changed_files }}"
diff --git a/.github/workflows/full-test-with-codecov.yml b/.github/workflows/full-test-with-codecov.yml
index 407c614ebf..f8ed2aa7a9 100644
--- a/.github/workflows/full-test-with-codecov.yml
+++ b/.github/workflows/full-test-with-codecov.yml
@@ -45,7 +45,6 @@ jobs:
         env:
           HDF5_PLUGIN_PATH: ${{ github.workspace }}/hdf5_plugin_path_maxwell
         run: |
-          source ${{ github.workspace }}/test_env/bin/activate
           pytest -m "not sorters_external" --cov=./ --cov-report xml:./coverage.xml -vv -ra --durations=0 | tee report_full.txt; test ${PIPESTATUS[0]} -eq 0 || exit 1
           echo "# Timing profile of full tests" >> $GITHUB_STEP_SUMMARY
           python ./.github/scripts/build_job_summary.py report_full.txt >> $GITHUB_STEP_SUMMARY
diff --git a/doc/get_started/quickstart.rst b/doc/get_started/quickstart.rst
index 3d45606a78..1349802ce5 100644
--- a/doc/get_started/quickstart.rst
+++ b/doc/get_started/quickstart.rst
@@ -673,7 +673,7 @@ compute quality metrics (some quality metrics require certain extensions
                               'min_spikes': 0,
                               'window_size_s': 1},
      'snr': {'peak_mode': 'extremum', 'peak_sign': 'neg'},
-     'synchrony': {'synchrony_sizes': (2, 4, 8)}}
+     'synchrony': {}
 
 
 Since the recording is very short, let’s change some parameters to
diff --git a/doc/modules/qualitymetrics/synchrony.rst b/doc/modules/qualitymetrics/synchrony.rst
index d244fd0c0f..696dacbd3c 100644
--- a/doc/modules/qualitymetrics/synchrony.rst
+++ b/doc/modules/qualitymetrics/synchrony.rst
@@ -12,7 +12,7 @@ trains. This way synchronous events can be found both in multi-unit and single-u
 Complexity is calculated by counting the number of spikes (i.e. non-empty bins) that occur at the same sample index,
 within and across spike trains.
 
-Synchrony metrics can be computed for different synchrony sizes (>1), defining the number of simultaneous spikes to count.
+Synchrony metrics are computed for 2, 4 and 8 synchronous spikes.
 
 
 
@@ -29,7 +29,7 @@ Example code
 
     import spikeinterface.qualitymetrics as sqm
     # Combine a sorting and recording into a sorting_analyzer
-    synchrony = sqm.compute_synchrony_metrics(sorting_analyzer=sorting_analyzer synchrony_sizes=(2, 4, 8))
+    synchrony = sqm.compute_synchrony_metrics(sorting_analyzer=sorting_analyzer)
     # synchrony is a tuple of dicts with the synchrony metrics for each unit
 
 
diff --git a/examples/tutorials/widgets/plot_2_sort_gallery.py b/examples/tutorials/widgets/plot_2_sort_gallery.py
index da5c611ce4..056b5e3a8d 100644
--- a/examples/tutorials/widgets/plot_2_sort_gallery.py
+++ b/examples/tutorials/widgets/plot_2_sort_gallery.py
@@ -31,14 +31,14 @@
 # plot_autocorrelograms()
 # ~~~~~~~~~~~~~~~~~~~~~~~~
 
-w_ach = sw.plot_autocorrelograms(sorting, window_ms=150.0, bin_ms=5.0, unit_ids=[1, 2, 5])
+w_ach = sw.plot_autocorrelograms(sorting, window_ms=150.0, bin_ms=5.0, unit_ids=['1', '2', '5'])
 
 ##############################################################################
 # plot_crosscorrelograms()
 # ~~~~~~~~~~~~~~~~~~~~~~~~
 
 
-w_cch = sw.plot_crosscorrelograms(sorting, window_ms=150.0, bin_ms=5.0, unit_ids=[1, 2, 5])
+w_cch = sw.plot_crosscorrelograms(sorting, window_ms=150.0, bin_ms=5.0, unit_ids=['1', '2', '5'])
 
 plt.show()
 
diff --git a/pyproject.toml b/pyproject.toml
index fc09ad9198..22fbdc7f22 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -73,7 +73,7 @@ extractors = [
 ]
 
 streaming_extractors = [
-    "ONE-api>=2.7.0", # alf sorter and streaming IBL
+    "ONE-api>=2.7.0,<2.10.0", # alf sorter and streaming IBL
     "ibllib>=2.36.0", # streaming IBL
     # Following dependencies are for streaming with nwb files
     "pynwb>=2.6.0",
diff --git a/src/spikeinterface/benchmark/benchmark_base.py b/src/spikeinterface/benchmark/benchmark_base.py
index b9cbf269c8..fc1b136d2d 100644
--- a/src/spikeinterface/benchmark/benchmark_base.py
+++ b/src/spikeinterface/benchmark/benchmark_base.py
@@ -208,10 +208,11 @@ def run(self, case_keys=None, keep=True, verbose=False, **job_kwargs):
         for key in case_keys:
 
             result_folder = self.folder / "results" / self.key_to_str(key)
+            sorter_folder = self.folder / "sorters" / self.key_to_str(key)
 
             if keep and result_folder.exists():
                 continue
-            elif not keep and result_folder.exists():
+            elif not keep and (result_folder.exists() or sorter_folder.exists()):
                 self.remove_benchmark(key)
             job_keys.append(key)
 
diff --git a/src/spikeinterface/benchmark/benchmark_motion_estimation.py b/src/spikeinterface/benchmark/benchmark_motion_estimation.py
index abb2a51bae..5a3c490d38 100644
--- a/src/spikeinterface/benchmark/benchmark_motion_estimation.py
+++ b/src/spikeinterface/benchmark/benchmark_motion_estimation.py
@@ -109,6 +109,8 @@ def run(self, **job_kwargs):
             estimate_motion=t4 - t3,
         )
 
+        self.result["peaks"] = peaks
+        self.result["peak_locations"] = peak_locations
         self.result["step_run_times"] = step_run_times
         self.result["raw_motion"] = motion
 
@@ -131,6 +133,8 @@ def compute_result(self, **result_params):
         self.result["motion"] = motion
 
     _run_key_saved = [
+        ("peaks", "npy"),
+        ("peak_locations", "npy"),
         ("raw_motion", "Motion"),
         ("step_run_times", "pickle"),
     ]
@@ -161,7 +165,9 @@ def create_benchmark(self, key):
     def plot_true_drift(self, case_keys=None, scaling_probe=1.5, figsize=(8, 6)):
         self.plot_drift(case_keys=case_keys, tested_drift=False, scaling_probe=scaling_probe, figsize=figsize)
 
-    def plot_drift(self, case_keys=None, gt_drift=True, tested_drift=True, scaling_probe=1.0, figsize=(8, 6)):
+    def plot_drift(
+        self, case_keys=None, gt_drift=True, tested_drift=True, raster=False, scaling_probe=1.0, figsize=(8, 6)
+    ):
         import matplotlib.pyplot as plt
 
         if case_keys is None:
@@ -195,6 +201,13 @@ def plot_drift(self, case_keys=None, gt_drift=True, tested_drift=True, scaling_p
 
             # for i in range(self.gt_unit_positions.shape[1]):
             #     ax.plot(temporal_bins_s, self.gt_unit_positions[:, i], alpha=0.5, ls="--", c="0.5")
+            if raster:
+                peaks = bench.result["peaks"]
+                peak_locations = bench.result["peak_locations"]
+                rec = bench.recording
+                x = peaks["sample_index"] / rec.sampling_frequency
+                y = peak_locations[bench.direction]
+                ax.scatter(x, y, alpha=0.2, s=2, c=np.abs(peaks["amplitude"]), cmap="inferno")
 
             for i in range(gt_motion.displacement[0].shape[1]):
                 depth = motion.spatial_bins_um[i]
diff --git a/src/spikeinterface/benchmark/benchmark_sorter.py b/src/spikeinterface/benchmark/benchmark_sorter.py
index f9267c785a..3cf6dca04f 100644
--- a/src/spikeinterface/benchmark/benchmark_sorter.py
+++ b/src/spikeinterface/benchmark/benchmark_sorter.py
@@ -56,6 +56,15 @@ def create_benchmark(self, key):
         benchmark = SorterBenchmark(recording, gt_sorting, params, sorter_folder)
         return benchmark
 
+    def remove_benchmark(self, key):
+        BenchmarkStudy.remove_benchmark(self, key)
+
+        sorter_folder = self.folder / "sorters" / self.key_to_str(key)
+        import shutil
+
+        if sorter_folder.exists():
+            shutil.rmtree(sorter_folder)
+
     def get_performance_by_unit(self, case_keys=None):
         import pandas as pd
 
diff --git a/src/spikeinterface/core/basesorting.py b/src/spikeinterface/core/basesorting.py
index 2af48407a3..9a0e242d62 100644
--- a/src/spikeinterface/core/basesorting.py
+++ b/src/spikeinterface/core/basesorting.py
@@ -135,7 +135,7 @@ def get_total_duration(self) -> float:
 
     def get_unit_spike_train(
         self,
-        unit_id,
+        unit_id: str | int,
         segment_index: Union[int, None] = None,
         start_frame: Union[int, None] = None,
         end_frame: Union[int, None] = None,
diff --git a/src/spikeinterface/core/generate.py b/src/spikeinterface/core/generate.py
index 0316b3bab1..aa69fe585b 100644
--- a/src/spikeinterface/core/generate.py
+++ b/src/spikeinterface/core/generate.py
@@ -2,7 +2,7 @@
 import math
 import warnings
 import numpy as np
-from typing import Literal
+from typing import Literal, Optional
 from math import ceil
 
 from .basesorting import SpikeVectorSortingSegment
@@ -134,7 +134,7 @@ def generate_sorting(
     seed = _ensure_seed(seed)
     rng = np.random.default_rng(seed)
     num_segments = len(durations)
-    unit_ids = np.arange(num_units)
+    unit_ids = [str(idx) for idx in np.arange(num_units)]
 
     spikes = []
     for segment_index in range(num_segments):
@@ -1111,7 +1111,7 @@ def __init__(
 
         """
 
-        unit_ids = np.arange(num_units)
+        unit_ids = [str(idx) for idx in np.arange(num_units)]
         super().__init__(sampling_frequency, unit_ids)
 
         self.num_units = num_units
@@ -1138,6 +1138,7 @@ def __init__(
                 firing_rates=firing_rates,
                 refractory_period_seconds=self.refractory_period_seconds,
                 seed=segment_seed,
+                unit_ids=unit_ids,
                 t_start=None,
             )
             self.add_sorting_segment(segment)
@@ -1161,6 +1162,7 @@ def __init__(
         firing_rates: float | np.ndarray,
         refractory_period_seconds: float | np.ndarray,
         seed: int,
+        unit_ids: list[str],
         t_start: Optional[float] = None,
     ):
         self.num_units = num_units
@@ -1177,7 +1179,8 @@ def __init__(
             self.refractory_period_seconds = np.full(num_units, self.refractory_period_seconds, dtype="float64")
 
         self.segment_seed = seed
-        self.units_seed = {unit_id: self.segment_seed + hash(unit_id) for unit_id in range(num_units)}
+        self.units_seed = {unit_id: abs(self.segment_seed + hash(unit_id)) for unit_id in unit_ids}
+
         self.num_samples = math.ceil(sampling_frequency * duration)
         super().__init__(t_start)
 
@@ -1280,7 +1283,7 @@ def __init__(
         noise_block_size: int = 30000,
     ):
 
-        channel_ids = np.arange(num_channels)
+        channel_ids = [str(idx) for idx in np.arange(num_channels)]
         dtype = np.dtype(dtype).name  # Cast to string for serialization
         if dtype not in ("float32", "float64"):
             raise ValueError(f"'dtype' must be 'float32' or 'float64' but is {dtype}")
diff --git a/src/spikeinterface/core/tests/test_basesnippets.py b/src/spikeinterface/core/tests/test_basesnippets.py
index 64f7f76819..f243dd9d9f 100644
--- a/src/spikeinterface/core/tests/test_basesnippets.py
+++ b/src/spikeinterface/core/tests/test_basesnippets.py
@@ -41,8 +41,8 @@ def test_BaseSnippets(create_cache_folder):
     assert snippets.get_num_segments() == len(duration)
     assert snippets.get_num_channels() == num_channels
 
-    assert np.all(snippets.ids_to_indices([0, 1, 2]) == [0, 1, 2])
-    assert np.all(snippets.ids_to_indices([0, 1, 2], prefer_slice=True) == slice(0, 3, None))
+    assert np.all(snippets.ids_to_indices(["0", "1", "2"]) == [0, 1, 2])
+    assert np.all(snippets.ids_to_indices(["0", "1", "2"], prefer_slice=True) == slice(0, 3, None))
 
     # annotations / properties
     snippets.annotate(gre="ta")
@@ -60,7 +60,7 @@ def test_BaseSnippets(create_cache_folder):
     )
 
     # missing property
-    snippets.set_property("string_property", ["ciao", "bello"], ids=[0, 1])
+    snippets.set_property("string_property", ["ciao", "bello"], ids=["0", "1"])
     values = snippets.get_property("string_property")
     assert values[2] == ""
 
@@ -70,14 +70,14 @@ def test_BaseSnippets(create_cache_folder):
         snippets.set_property,
         key="string_property_nan",
         values=["hola", "chabon"],
-        ids=[0, 1],
+        ids=["0", "1"],
         missing_value=np.nan,
     )
 
     # int properties without missing values raise an error
     assert_raises(Exception, snippets.set_property, key="int_property", values=[5, 6], ids=[1, 2])
 
-    snippets.set_property("int_property", [5, 6], ids=[1, 2], missing_value=200)
+    snippets.set_property("int_property", [5, 6], ids=["1", "2"], missing_value=200)
     values = snippets.get_property("int_property")
     assert values.dtype.kind == "i"
 
diff --git a/src/spikeinterface/core/tests/test_channelsaggregationrecording.py b/src/spikeinterface/core/tests/test_channelsaggregationrecording.py
index 118b6092a9..99d6890dfd 100644
--- a/src/spikeinterface/core/tests/test_channelsaggregationrecording.py
+++ b/src/spikeinterface/core/tests/test_channelsaggregationrecording.py
@@ -38,10 +38,12 @@ def test_channelsaggregationrecording():
 
         assert np.allclose(traces1_1, recording_agg.get_traces(channel_ids=[str(channel_ids[1])], segment_index=seg))
         assert np.allclose(
-            traces2_0, recording_agg.get_traces(channel_ids=[str(num_channels + channel_ids[0])], segment_index=seg)
+            traces2_0,
+            recording_agg.get_traces(channel_ids=[str(num_channels + int(channel_ids[0]))], segment_index=seg),
         )
         assert np.allclose(
-            traces3_2, recording_agg.get_traces(channel_ids=[str(2 * num_channels + channel_ids[2])], segment_index=seg)
+            traces3_2,
+            recording_agg.get_traces(channel_ids=[str(2 * num_channels + int(channel_ids[2]))], segment_index=seg),
         )
         # all traces
         traces1 = recording1.get_traces(segment_index=seg)
diff --git a/src/spikeinterface/core/tests/test_sortinganalyzer.py b/src/spikeinterface/core/tests/test_sortinganalyzer.py
index 35ab18b5f2..15f089f784 100644
--- a/src/spikeinterface/core/tests/test_sortinganalyzer.py
+++ b/src/spikeinterface/core/tests/test_sortinganalyzer.py
@@ -31,6 +31,14 @@ def get_dataset():
         noise_kwargs=dict(noise_levels=5.0, strategy="tile_pregenerated"),
         seed=2205,
     )
+
+    # TODO: the tests or the sorting analyzer make assumptions about the ids being integers
+    # So keeping this the way it was
+    integer_channel_ids = [int(id) for id in recording.get_channel_ids()]
+    integer_unit_ids = [int(id) for id in sorting.get_unit_ids()]
+
+    recording = recording.rename_channels(new_channel_ids=integer_channel_ids)
+    sorting = sorting.rename_units(new_unit_ids=integer_unit_ids)
     return recording, sorting
 
 
diff --git a/src/spikeinterface/core/tests/test_unitsselectionsorting.py b/src/spikeinterface/core/tests/test_unitsselectionsorting.py
index 1e72b0ab28..3ecb702aa2 100644
--- a/src/spikeinterface/core/tests/test_unitsselectionsorting.py
+++ b/src/spikeinterface/core/tests/test_unitsselectionsorting.py
@@ -10,25 +10,29 @@
 def test_basic_functions():
     sorting = generate_sorting(num_units=3, durations=[0.100, 0.100], sampling_frequency=30000.0)
 
-    sorting2 = UnitsSelectionSorting(sorting, unit_ids=[0, 2])
-    assert np.array_equal(sorting2.unit_ids, [0, 2])
+    sorting2 = UnitsSelectionSorting(sorting, unit_ids=["0", "2"])
+    assert np.array_equal(sorting2.unit_ids, ["0", "2"])
     assert sorting2.get_parent() == sorting
 
-    sorting3 = UnitsSelectionSorting(sorting, unit_ids=[0, 2], renamed_unit_ids=["a", "b"])
+    sorting3 = UnitsSelectionSorting(sorting, unit_ids=["0", "2"], renamed_unit_ids=["a", "b"])
     assert np.array_equal(sorting3.unit_ids, ["a", "b"])
 
     assert np.array_equal(
-        sorting.get_unit_spike_train(0, segment_index=0), sorting2.get_unit_spike_train(0, segment_index=0)
+        sorting.get_unit_spike_train(unit_id="0", segment_index=0),
+        sorting2.get_unit_spike_train(unit_id="0", segment_index=0),
     )
     assert np.array_equal(
-        sorting.get_unit_spike_train(0, segment_index=0), sorting3.get_unit_spike_train("a", segment_index=0)
+        sorting.get_unit_spike_train(unit_id="0", segment_index=0),
+        sorting3.get_unit_spike_train(unit_id="a", segment_index=0),
     )
 
     assert np.array_equal(
-        sorting.get_unit_spike_train(2, segment_index=0), sorting2.get_unit_spike_train(2, segment_index=0)
+        sorting.get_unit_spike_train(unit_id="2", segment_index=0),
+        sorting2.get_unit_spike_train(unit_id="2", segment_index=0),
     )
     assert np.array_equal(
-        sorting.get_unit_spike_train(2, segment_index=0), sorting3.get_unit_spike_train("b", segment_index=0)
+        sorting.get_unit_spike_train(unit_id="2", segment_index=0),
+        sorting3.get_unit_spike_train(unit_id="b", segment_index=0),
     )
 
 
@@ -36,13 +40,13 @@ def test_failure_with_non_unique_unit_ids():
     seed = 10
     sorting = generate_sorting(num_units=3, durations=[0.100], sampling_frequency=30000.0, seed=seed)
     with pytest.raises(AssertionError):
-        sorting2 = UnitsSelectionSorting(sorting, unit_ids=[0, 2], renamed_unit_ids=["a", "a"])
+        sorting2 = UnitsSelectionSorting(sorting, unit_ids=["0", "2"], renamed_unit_ids=["a", "a"])
 
 
 def test_custom_cache_spike_vector():
     sorting = generate_sorting(num_units=3, durations=[0.100, 0.100], sampling_frequency=30000.0)
 
-    sub_sorting = UnitsSelectionSorting(sorting, unit_ids=[2, 0], renamed_unit_ids=["b", "a"])
+    sub_sorting = UnitsSelectionSorting(sorting, unit_ids=["2", "0"], renamed_unit_ids=["b", "a"])
     cached_spike_vector = sub_sorting.to_spike_vector(use_cache=True)
     computed_spike_vector = sub_sorting.to_spike_vector(use_cache=False)
     assert np.all(cached_spike_vector == computed_spike_vector)
diff --git a/src/spikeinterface/curation/tests/common.py b/src/spikeinterface/curation/tests/common.py
index 9cd20f4bfc..e9c4c4a463 100644
--- a/src/spikeinterface/curation/tests/common.py
+++ b/src/spikeinterface/curation/tests/common.py
@@ -19,6 +19,11 @@ def make_sorting_analyzer(sparse=True):
         seed=2205,
     )
 
+    channel_ids_as_integers = [id for id in range(recording.get_num_channels())]
+    unit_ids_as_integers = [id for id in range(sorting.get_num_units())]
+    recording = recording.rename_channels(new_channel_ids=channel_ids_as_integers)
+    sorting = sorting.rename_units(new_unit_ids=unit_ids_as_integers)
+
     sorting_analyzer = create_sorting_analyzer(sorting=sorting, recording=recording, format="memory", sparse=sparse)
     sorting_analyzer.compute("random_spikes")
     sorting_analyzer.compute("waveforms", **job_kwargs)
diff --git a/src/spikeinterface/curation/tests/test_sortingview_curation.py b/src/spikeinterface/curation/tests/test_sortingview_curation.py
index 945aca7937..ff80be365d 100644
--- a/src/spikeinterface/curation/tests/test_sortingview_curation.py
+++ b/src/spikeinterface/curation/tests/test_sortingview_curation.py
@@ -49,6 +49,9 @@ def test_gh_curation():
     Test curation using GitHub URI.
     """
     sorting = generate_sorting(num_units=10)
+    unit_ids_as_int = [id for id in range(sorting.get_num_units())]
+    sorting = sorting.rename_units(new_unit_ids=unit_ids_as_int)
+
     # curated link:
     # https://figurl.org/f?v=npm://@fi-sci/figurl-sortingview@12/dist&d=sha1://058ab901610aa9d29df565595a3cc2a81a1b08e5
     gh_uri = "gh://SpikeInterface/spikeinterface/main/src/spikeinterface/curation/tests/sv-sorting-curation.json"
@@ -76,6 +79,8 @@ def test_sha1_curation():
     Test curation using SHA1 URI.
     """
     sorting = generate_sorting(num_units=10)
+    unit_ids_as_int = [id for id in range(sorting.get_num_units())]
+    sorting = sorting.rename_units(new_unit_ids=unit_ids_as_int)
 
     # from SHA1
     # curated link:
@@ -105,6 +110,8 @@ def test_json_curation():
     Test curation using a JSON file.
     """
     sorting = generate_sorting(num_units=10)
+    unit_ids_as_int = [id for id in range(sorting.get_num_units())]
+    sorting = sorting.rename_units(new_unit_ids=unit_ids_as_int)
 
     # from curation.json
     json_file = parent_folder / "sv-sorting-curation.json"
@@ -248,6 +255,8 @@ def test_json_no_merge_curation():
     Test curation with no merges using a JSON file.
     """
     sorting = generate_sorting(num_units=10)
+    unit_ids_as_int = [id for id in range(sorting.get_num_units())]
+    sorting = sorting.rename_units(new_unit_ids=unit_ids_as_int)
 
     json_file = parent_folder / "sv-sorting-curation-no-merge.json"
     sorting_curated = apply_sortingview_curation(sorting, uri_or_json=json_file)
diff --git a/src/spikeinterface/extractors/nwbextractors.py b/src/spikeinterface/extractors/nwbextractors.py
index d797e64910..171992f6b1 100644
--- a/src/spikeinterface/extractors/nwbextractors.py
+++ b/src/spikeinterface/extractors/nwbextractors.py
@@ -599,6 +599,8 @@ def __init__(
         else:
             gains, offsets, locations, groups = self._fetch_main_properties_backend()
             self.extra_requirements.append("h5py")
+        if stream_mode is not None:
+            self.extra_requirements.append(stream_mode)
         self.set_channel_gains(gains)
         self.set_channel_offsets(offsets)
         if locations is not None:
@@ -1100,6 +1102,8 @@ def __init__(
             for property_name, property_values in properties.items():
                 values = [x.decode("utf-8") if isinstance(x, bytes) else x for x in property_values]
                 self.set_property(property_name, values)
+        if stream_mode is not None:
+            self.extra_requirements.append(stream_mode)
 
         if stream_mode is None and file_path is not None:
             file_path = str(Path(file_path).resolve())
diff --git a/src/spikeinterface/extractors/tests/test_mdaextractors.py b/src/spikeinterface/extractors/tests/test_mdaextractors.py
index 0ef6697c6c..78e6afb65e 100644
--- a/src/spikeinterface/extractors/tests/test_mdaextractors.py
+++ b/src/spikeinterface/extractors/tests/test_mdaextractors.py
@@ -9,6 +9,12 @@ def test_mda_extractors(create_cache_folder):
     cache_folder = create_cache_folder
     rec, sort = generate_ground_truth_recording(durations=[10.0], num_units=10)
 
+    ids_as_integers = [id for id in range(rec.get_num_channels())]
+    rec = rec.rename_channels(new_channel_ids=ids_as_integers)
+
+    ids_as_integers = [id for id in range(sort.get_num_units())]
+    sort = sort.rename_units(new_unit_ids=ids_as_integers)
+
     MdaRecordingExtractor.write_recording(rec, cache_folder / "mdatest")
     rec_mda = MdaRecordingExtractor(cache_folder / "mdatest")
     probe = rec_mda.get_probe()
diff --git a/src/spikeinterface/postprocessing/template_metrics.py b/src/spikeinterface/postprocessing/template_metrics.py
index 6e7bcf21b8..1969480503 100644
--- a/src/spikeinterface/postprocessing/template_metrics.py
+++ b/src/spikeinterface/postprocessing/template_metrics.py
@@ -63,23 +63,10 @@ class ComputeTemplateMetrics(AnalyzerExtension):
     include_multi_channel_metrics : bool, default: False
         Whether to compute multi-channel metrics
     delete_existing_metrics : bool, default: False
-        If True, any template metrics attached to the `sorting_analyzer` are deleted. If False, any metrics which were previously calculated but are not included in `metric_names` are kept, provided the `metrics_kwargs` are unchanged.
-    metrics_kwargs : dict
-        Additional arguments to pass to the metric functions. Including:
-            * recovery_window_ms: the window in ms after the peak to compute the recovery_slope, default: 0.7
-            * peak_relative_threshold: the relative threshold to detect positive and negative peaks, default: 0.2
-            * peak_width_ms: the width in samples to detect peaks, default: 0.2
-            * depth_direction: the direction to compute velocity above and below, default: "y" (see notes)
-            * min_channels_for_velocity: the minimum number of channels above or below to compute velocity, default: 5
-            * min_r2_velocity: the minimum r2 to accept the velocity fit, default: 0.7
-            * exp_peak_function: the function to use to compute the peak amplitude for the exp decay, default: "ptp"
-            * min_r2_exp_decay: the minimum r2 to accept the exp decay fit, default: 0.5
-            * spread_threshold: the threshold to compute the spread, default: 0.2
-            * spread_smooth_um: the smoothing in um to compute the spread, default: 20
-            * column_range: the range in um in the horizontal direction to consider channels for velocity, default: None
-                - If None, all channels all channels are considered
-                - If 0 or 1, only the "column" that includes the max channel is considered
-                - If > 1, only channels within range (+/-) um from the max channel horizontal position are used
+        If True, any template metrics attached to the `sorting_analyzer` are deleted. If False, any metrics which were previously calculated but are not included in `metric_names` are kept, provided the `metric_params` are unchanged.
+    metric_params : dict of dicts or None, default: None
+        Dictionary with parameters for template metrics calculation.
+        Default parameters can be obtained with: `si.postprocessing.template_metrics.get_default_tm_params()`
 
     Returns
     -------
@@ -100,15 +87,29 @@ class ComputeTemplateMetrics(AnalyzerExtension):
     need_recording = False
     use_nodepipeline = False
     need_job_kwargs = False
+    need_backward_compatibility_on_load = True
 
     min_channels_for_multi_channel_warning = 10
 
+    def _handle_backward_compatibility_on_load(self):
+
+        # For backwards compatibility - this reformats metrics_kwargs as metric_params
+        if (metrics_kwargs := self.params.get("metrics_kwargs")) is not None:
+
+            metric_params = {}
+            for metric_name in self.params["metric_names"]:
+                metric_params[metric_name] = deepcopy(metrics_kwargs)
+            self.params["metric_params"] = metric_params
+
+            del self.params["metrics_kwargs"]
+
     def _set_params(
         self,
         metric_names=None,
         peak_sign="neg",
         upsampling_factor=10,
         sparsity=None,
+        metric_params=None,
         metrics_kwargs=None,
         include_multi_channel_metrics=False,
         delete_existing_metrics=False,
@@ -134,33 +135,24 @@ def _set_params(
         if include_multi_channel_metrics:
             metric_names += get_multi_channel_template_metric_names()
 
-        if metrics_kwargs is None:
-            metrics_kwargs_ = _default_function_kwargs.copy()
-            if len(other_kwargs) > 0:
-                for m in other_kwargs:
-                    if m in metrics_kwargs_:
-                        metrics_kwargs_[m] = other_kwargs[m]
-        else:
-            metrics_kwargs_ = _default_function_kwargs.copy()
-            metrics_kwargs_.update(metrics_kwargs)
+        if metrics_kwargs is not None and metric_params is None:
+            deprecation_msg = "`metrics_kwargs` is deprecated and will be removed in version 0.104.0. Please use `metric_params` instead"
+            deprecation_msg = "`metrics_kwargs` is deprecated and will be removed in version 0.104.0. Please use `metric_params` instead"
+
+            metric_params = {}
+            for metric_name in metric_names:
+                metric_params[metric_name] = deepcopy(metrics_kwargs)
+
+        metric_params_ = get_default_tm_params(metric_names)
+        for k in metric_params_:
+            if metric_params is not None and k in metric_params:
+                metric_params_[k].update(metric_params[k])
 
         metrics_to_compute = metric_names
         tm_extension = self.sorting_analyzer.get_extension("template_metrics")
         if delete_existing_metrics is False and tm_extension is not None:
 
-            existing_params = tm_extension.params["metrics_kwargs"]
-            # checks that existing metrics were calculated using the same params
-            if existing_params != metrics_kwargs_:
-                warnings.warn(
-                    f"The parameters used to calculate the previous template metrics are different"
-                    f"than those used now.\nPrevious parameters: {existing_params}\nCurrent "
-                    f"parameters:  {metrics_kwargs_}\nDeleting previous template metrics..."
-                )
-                tm_extension.params["metric_names"] = []
-                existing_metric_names = []
-            else:
-                existing_metric_names = tm_extension.params["metric_names"]
-
+            existing_metric_names = tm_extension.params["metric_names"]
             existing_metric_names_propogated = [
                 metric_name for metric_name in existing_metric_names if metric_name not in metrics_to_compute
             ]
@@ -171,7 +163,7 @@ def _set_params(
             sparsity=sparsity,
             peak_sign=peak_sign,
             upsampling_factor=int(upsampling_factor),
-            metrics_kwargs=metrics_kwargs_,
+            metric_params=metric_params_,
             delete_existing_metrics=delete_existing_metrics,
             metrics_to_compute=metrics_to_compute,
         )
@@ -273,7 +265,7 @@ def _compute_metrics(self, sorting_analyzer, unit_ids=None, verbose=False, metri
                             sampling_frequency=sampling_frequency_up,
                             trough_idx=trough_idx,
                             peak_idx=peak_idx,
-                            **self.params["metrics_kwargs"],
+                            **self.params["metric_params"][metric_name],
                         )
                     except Exception as e:
                         warnings.warn(f"Error computing metric {metric_name} for unit {unit_id}: {e}")
@@ -312,7 +304,7 @@ def _compute_metrics(self, sorting_analyzer, unit_ids=None, verbose=False, metri
                         template_upsampled,
                         channel_locations=channel_locations_sparse,
                         sampling_frequency=sampling_frequency_up,
-                        **self.params["metrics_kwargs"],
+                        **self.params["metric_params"][metric_name],
                     )
                 except Exception as e:
                     warnings.warn(f"Error computing metric {metric_name} for unit {unit_id}: {e}")
@@ -326,8 +318,8 @@ def _compute_metrics(self, sorting_analyzer, unit_ids=None, verbose=False, metri
 
     def _run(self, verbose=False):
 
-        delete_existing_metrics = self.params["delete_existing_metrics"]
         metrics_to_compute = self.params["metrics_to_compute"]
+        delete_existing_metrics = self.params["delete_existing_metrics"]
 
         # compute the metrics which have been specified by the user
         computed_metrics = self._compute_metrics(
@@ -343,9 +335,21 @@ def _run(self, verbose=False):
         ):
             existing_metrics = tm_extension.params["metric_names"]
 
+        existing_metrics = []
+        # here we get in the loaded via the dict only (to avoid full loading from disk after params reset)
+        tm_extension = self.sorting_analyzer.extensions.get("template_metrics", None)
+        if (
+            delete_existing_metrics is False
+            and tm_extension is not None
+            and tm_extension.data.get("metrics") is not None
+        ):
+            existing_metrics = tm_extension.params["metric_names"]
+
         # append the metrics which were previously computed
         for metric_name in set(existing_metrics).difference(metrics_to_compute):
-            computed_metrics[metric_name] = tm_extension.data["metrics"][metric_name]
+            # some metrics names produce data columns with other names. This deals with that.
+            for column_name in tm_compute_name_to_column_names[metric_name]:
+                computed_metrics[column_name] = tm_extension.data["metrics"][column_name]
 
         self.data["metrics"] = computed_metrics
 
@@ -372,6 +376,35 @@ def _get_data(self):
 )
 
 
+def get_default_tm_params(metric_names):
+    if metric_names is None:
+        metric_names = get_template_metric_names()
+
+    base_tm_params = _default_function_kwargs
+
+    metric_params = {}
+    for metric_name in metric_names:
+        metric_params[metric_name] = deepcopy(base_tm_params)
+
+    return metric_params
+
+
+# a dict converting the name of the metric for computation to the output of that computation
+tm_compute_name_to_column_names = {
+    "peak_to_valley": ["peak_to_valley"],
+    "peak_trough_ratio": ["peak_trough_ratio"],
+    "half_width": ["half_width"],
+    "repolarization_slope": ["repolarization_slope"],
+    "recovery_slope": ["recovery_slope"],
+    "num_positive_peaks": ["num_positive_peaks"],
+    "num_negative_peaks": ["num_negative_peaks"],
+    "velocity_above": ["velocity_above"],
+    "velocity_below": ["velocity_below"],
+    "exp_decay": ["exp_decay"],
+    "spread": ["spread"],
+}
+
+
 def get_trough_and_peak_idx(template):
     """
     Return the indices into the input template of the detected trough
diff --git a/src/spikeinterface/postprocessing/tests/test_multi_extensions.py b/src/spikeinterface/postprocessing/tests/test_multi_extensions.py
index bf0000135c..be0070d94a 100644
--- a/src/spikeinterface/postprocessing/tests/test_multi_extensions.py
+++ b/src/spikeinterface/postprocessing/tests/test_multi_extensions.py
@@ -23,6 +23,11 @@ def get_dataset():
         seed=2205,
     )
 
+    channel_ids_as_integers = [id for id in range(recording.get_num_channels())]
+    unit_ids_as_integers = [id for id in range(sorting.get_num_units())]
+    recording = recording.rename_channels(new_channel_ids=channel_ids_as_integers)
+    sorting = sorting.rename_units(new_unit_ids=unit_ids_as_integers)
+
     # since templates are going to be averaged and this might be a problem for amplitude scaling
     # we select the 3 units with the largest templates to split
     analyzer_raw = create_sorting_analyzer(sorting, recording, format="memory", sparse=False)
diff --git a/src/spikeinterface/postprocessing/tests/test_template_metrics.py b/src/spikeinterface/postprocessing/tests/test_template_metrics.py
index 5056d4ff2a..1bf49f64c1 100644
--- a/src/spikeinterface/postprocessing/tests/test_template_metrics.py
+++ b/src/spikeinterface/postprocessing/tests/test_template_metrics.py
@@ -1,5 +1,5 @@
 from spikeinterface.postprocessing.tests.common_extension_tests import AnalyzerExtensionCommonTestSuite
-from spikeinterface.postprocessing import ComputeTemplateMetrics
+from spikeinterface.postprocessing import ComputeTemplateMetrics, compute_template_metrics
 import pytest
 import csv
 
@@ -8,6 +8,49 @@
 template_metrics = list(_single_channel_metric_name_to_func.keys())
 
 
+def test_different_params_template_metrics(small_sorting_analyzer):
+    """
+    Computes template metrics using different params, and check that they are
+    actually calculated using the different params.
+    """
+    compute_template_metrics(
+        sorting_analyzer=small_sorting_analyzer,
+        metric_names=["exp_decay", "spread", "half_width"],
+        metric_params={"exp_decay": {"recovery_window_ms": 0.8}, "spread": {"spread_smooth_um": 15}},
+    )
+
+    tm_extension = small_sorting_analyzer.get_extension("template_metrics")
+    tm_params = tm_extension.params["metric_params"]
+
+    assert tm_params["exp_decay"]["recovery_window_ms"] == 0.8
+    assert tm_params["spread"]["recovery_window_ms"] == 0.7
+    assert tm_params["half_width"]["recovery_window_ms"] == 0.7
+
+    assert tm_params["spread"]["spread_smooth_um"] == 15
+    assert tm_params["exp_decay"]["spread_smooth_um"] == 20
+    assert tm_params["half_width"]["spread_smooth_um"] == 20
+
+
+def test_backwards_compat_params_template_metrics(small_sorting_analyzer):
+    """
+    Computes template metrics using the metrics_kwargs keyword
+    """
+    compute_template_metrics(
+        sorting_analyzer=small_sorting_analyzer,
+        metric_names=["exp_decay", "spread"],
+        metrics_kwargs={"recovery_window_ms": 0.8},
+    )
+
+    tm_extension = small_sorting_analyzer.get_extension("template_metrics")
+    tm_params = tm_extension.params["metric_params"]
+
+    assert tm_params["exp_decay"]["recovery_window_ms"] == 0.8
+    assert tm_params["spread"]["recovery_window_ms"] == 0.8
+
+    assert tm_params["spread"]["spread_smooth_um"] == 20
+    assert tm_params["exp_decay"]["spread_smooth_um"] == 20
+
+
 def test_compute_new_template_metrics(small_sorting_analyzer):
     """
     Computes template metrics then computes a subset of template metrics, and checks
@@ -17,6 +60,8 @@ def test_compute_new_template_metrics(small_sorting_analyzer):
     are deleted.
     """
 
+    small_sorting_analyzer.delete_extension("template_metrics")
+
     # calculate just exp_decay
     small_sorting_analyzer.compute({"template_metrics": {"metric_names": ["exp_decay"]}})
     template_metric_extension = small_sorting_analyzer.get_extension("template_metrics")
@@ -47,7 +92,7 @@ def test_compute_new_template_metrics(small_sorting_analyzer):
 
     # check that, when parameters are changed, the old metrics are deleted
     small_sorting_analyzer.compute(
-        {"template_metrics": {"metric_names": ["exp_decay"], "metrics_kwargs": {"recovery_window_ms": 0.6}}}
+        {"template_metrics": {"metric_names": ["exp_decay"], "metric_params": {"recovery_window_ms": 0.6}}}
     )
 
 
diff --git a/src/spikeinterface/postprocessing/unit_locations.py b/src/spikeinterface/postprocessing/unit_locations.py
index 3f6dd47eec..df19458316 100644
--- a/src/spikeinterface/postprocessing/unit_locations.py
+++ b/src/spikeinterface/postprocessing/unit_locations.py
@@ -26,7 +26,7 @@ class ComputeUnitLocations(AnalyzerExtension):
     ----------
     sorting_analyzer : SortingAnalyzer
         A SortingAnalyzer object
-    method : "center_of_mass" | "monopolar_triangulation" | "grid_convolution", default: "center_of_mass"
+    method : "monopolar_triangulation" |  "center_of_mass" | "grid_convolution", default: "monopolar_triangulation"
         The method to use for localization
     **method_kwargs : dict, default: {}
         Kwargs which are passed to the method function. These can be found in the docstrings of `compute_center_of_mass`, `compute_grid_convolution` and `compute_monopolar_triangulation`.
diff --git a/src/spikeinterface/preprocessing/silence_periods.py b/src/spikeinterface/preprocessing/silence_periods.py
index 85169011d8..00d9a1a407 100644
--- a/src/spikeinterface/preprocessing/silence_periods.py
+++ b/src/spikeinterface/preprocessing/silence_periods.py
@@ -97,7 +97,8 @@ def __init__(self, recording, list_periods, mode="zeros", noise_levels=None, see
             rec_segment = SilencedPeriodsRecordingSegment(parent_segment, periods, mode, noise_generator, seg_index)
             self.add_recording_segment(rec_segment)
 
-        self._kwargs = dict(recording=recording, list_periods=list_periods, mode=mode, noise_generator=noise_generator)
+        self._kwargs = dict(recording=recording, list_periods=list_periods, mode=mode, seed=seed)
+        self._kwargs.update(random_chunk_kwargs)
 
 
 class SilencedPeriodsRecordingSegment(BasePreprocessorSegment):
diff --git a/src/spikeinterface/preprocessing/tests/test_clip.py b/src/spikeinterface/preprocessing/tests/test_clip.py
index 724ba2c963..c18c7d37af 100644
--- a/src/spikeinterface/preprocessing/tests/test_clip.py
+++ b/src/spikeinterface/preprocessing/tests/test_clip.py
@@ -14,12 +14,12 @@ def test_clip():
     rec1 = clip(rec, a_min=-1.5)
     rec1.save(verbose=False)
 
-    traces0 = rec0.get_traces(segment_index=0, channel_ids=[1])
+    traces0 = rec0.get_traces(segment_index=0, channel_ids=["1"])
     assert traces0.shape[1] == 1
 
     assert np.all(-2 <= traces0[0] <= 3)
 
-    traces1 = rec1.get_traces(segment_index=0, channel_ids=[0, 1])
+    traces1 = rec1.get_traces(segment_index=0, channel_ids=["0", "1"])
     assert traces1.shape[1] == 2
 
     assert np.all(-1.5 <= traces1[1])
@@ -34,11 +34,11 @@ def test_blank_staturation():
     rec1 = blank_staturation(rec, quantile_threshold=0.01, direction="both", chunk_size=10000)
     rec1.save(verbose=False)
 
-    traces0 = rec0.get_traces(segment_index=0, channel_ids=[1])
+    traces0 = rec0.get_traces(segment_index=0, channel_ids=["1"])
     assert traces0.shape[1] == 1
     assert np.all(traces0 < 3.0)
 
-    traces1 = rec1.get_traces(segment_index=0, channel_ids=[0])
+    traces1 = rec1.get_traces(segment_index=0, channel_ids=["0"])
     assert traces1.shape[1] == 1
     # use a smaller value to be sure
     a_min = rec1._recording_segments[0].a_min
diff --git a/src/spikeinterface/preprocessing/tests/test_interpolate_bad_channels.py b/src/spikeinterface/preprocessing/tests/test_interpolate_bad_channels.py
index 1189f04f7d..06bde4e3d1 100644
--- a/src/spikeinterface/preprocessing/tests/test_interpolate_bad_channels.py
+++ b/src/spikeinterface/preprocessing/tests/test_interpolate_bad_channels.py
@@ -163,7 +163,9 @@ def test_output_values():
     expected_weights = np.r_[np.tile(np.exp(-2), 3), np.exp(-4)]
     expected_weights /= np.sum(expected_weights)
 
-    si_interpolated_recording = spre.interpolate_bad_channels(recording, bad_channel_indexes, sigma_um=1, p=1)
+    si_interpolated_recording = spre.interpolate_bad_channels(
+        recording, bad_channel_ids=bad_channel_ids, sigma_um=1, p=1
+    )
     si_interpolated = si_interpolated_recording.get_traces()
 
     expected_ts = si_interpolated[:, 1:] @ expected_weights
diff --git a/src/spikeinterface/preprocessing/tests/test_normalize_scale.py b/src/spikeinterface/preprocessing/tests/test_normalize_scale.py
index 576b570832..151752e0e6 100644
--- a/src/spikeinterface/preprocessing/tests/test_normalize_scale.py
+++ b/src/spikeinterface/preprocessing/tests/test_normalize_scale.py
@@ -15,7 +15,7 @@ def test_normalize_by_quantile():
     rec2 = normalize_by_quantile(rec, mode="by_channel")
     rec2.save(verbose=False)
 
-    traces = rec2.get_traces(segment_index=0, channel_ids=[1])
+    traces = rec2.get_traces(segment_index=0, channel_ids=["1"])
     assert traces.shape[1] == 1
 
     rec2 = normalize_by_quantile(rec, mode="pool_channel")
diff --git a/src/spikeinterface/preprocessing/tests/test_rectify.py b/src/spikeinterface/preprocessing/tests/test_rectify.py
index b8bb31015e..a2a06e7a1f 100644
--- a/src/spikeinterface/preprocessing/tests/test_rectify.py
+++ b/src/spikeinterface/preprocessing/tests/test_rectify.py
@@ -15,7 +15,7 @@ def test_rectify():
     rec2 = rectify(rec)
     rec2.save(verbose=False)
 
-    traces = rec2.get_traces(segment_index=0, channel_ids=[1])
+    traces = rec2.get_traces(segment_index=0, channel_ids=["1"])
     assert traces.shape[1] == 1
 
     # import matplotlib.pyplot as plt
diff --git a/src/spikeinterface/qualitymetrics/__init__.py b/src/spikeinterface/qualitymetrics/__init__.py
index 9d604f6ae2..754c82d8e3 100644
--- a/src/spikeinterface/qualitymetrics/__init__.py
+++ b/src/spikeinterface/qualitymetrics/__init__.py
@@ -6,4 +6,4 @@
     get_default_qm_params,
 )
 from .pca_metrics import get_quality_pca_metric_list
-from .misc_metrics import get_synchrony_counts
+from .misc_metrics import _get_synchrony_counts
diff --git a/src/spikeinterface/qualitymetrics/misc_metrics.py b/src/spikeinterface/qualitymetrics/misc_metrics.py
index 8dfd41cf88..6007de379c 100644
--- a/src/spikeinterface/qualitymetrics/misc_metrics.py
+++ b/src/spikeinterface/qualitymetrics/misc_metrics.py
@@ -520,7 +520,7 @@ def compute_sliding_rp_violations(
 )
 
 
-def get_synchrony_counts(spikes, synchrony_sizes, all_unit_ids):
+def _get_synchrony_counts(spikes, synchrony_sizes, all_unit_ids):
     """
     Compute synchrony counts, the number of simultaneous spikes with sizes `synchrony_sizes`.
 
@@ -528,10 +528,10 @@ def get_synchrony_counts(spikes, synchrony_sizes, all_unit_ids):
     ----------
     spikes : np.array
         Structured numpy array with fields ("sample_index", "unit_index", "segment_index").
-    synchrony_sizes : numpy array
-        The synchrony sizes to compute. Should be pre-sorted.
     all_unit_ids : list or None, default: None
         List of unit ids to compute the synchrony metrics. Expecting all units.
+    synchrony_sizes : None or np.array, default: None
+        The synchrony sizes to compute. Should be pre-sorted.
 
     Returns
     -------
@@ -565,37 +565,38 @@ def get_synchrony_counts(spikes, synchrony_sizes, all_unit_ids):
     return synchrony_counts
 
 
-def compute_synchrony_metrics(sorting_analyzer, synchrony_sizes=(2, 4, 8), unit_ids=None):
+def compute_synchrony_metrics(sorting_analyzer, unit_ids=None, synchrony_sizes=None):
     """
     Compute synchrony metrics. Synchrony metrics represent the rate of occurrences of
-    "synchrony_size" spikes at the exact same sample index.
+    spikes at the exact same sample index, with synchrony sizes 2, 4 and 8.
 
     Parameters
     ----------
     sorting_analyzer : SortingAnalyzer
         A SortingAnalyzer object.
-    synchrony_sizes : list or tuple, default: (2, 4, 8)
-        The synchrony sizes to compute.
     unit_ids : list or None, default: None
         List of unit ids to compute the synchrony metrics. If None, all units are used.
+    synchrony_sizes: None, default: None
+        Deprecated argument. Please use private `_get_synchrony_counts` if you need finer control over number of synchronous spikes.
 
     Returns
     -------
     sync_spike_{X} : dict
         The synchrony metric for synchrony size X.
-        Returns are as many as synchrony_sizes.
 
     References
     ----------
     Based on concepts described in [Grün]_
     This code was adapted from `Elephant - Electrophysiology Analysis Toolkit <https://github.com/NeuralEnsemble/elephant/blob/master/elephant/spike_train_synchrony.py#L245>`_
     """
-    assert min(synchrony_sizes) > 1, "Synchrony sizes must be greater than 1"
-    # Sort the synchrony times so we can slice numpy arrays, instead of using dicts
-    synchrony_sizes_np = np.array(synchrony_sizes, dtype=np.int16)
-    synchrony_sizes_np.sort()
 
-    res = namedtuple("synchrony_metrics", [f"sync_spike_{size}" for size in synchrony_sizes_np])
+    if synchrony_sizes is not None:
+        warning_message = "Custom `synchrony_sizes` is deprecated; the `synchrony_metrics` will be computed using `synchrony_sizes = [2,4,8]`"
+        warnings.warn(warning_message, DeprecationWarning, stacklevel=2)
+
+    synchrony_sizes = np.array([2, 4, 8])
+
+    res = namedtuple("synchrony_metrics", [f"sync_spike_{size}" for size in synchrony_sizes])
 
     sorting = sorting_analyzer.sorting
 
@@ -606,10 +607,10 @@ def compute_synchrony_metrics(sorting_analyzer, synchrony_sizes=(2, 4, 8), unit_
 
     spikes = sorting.to_spike_vector()
     all_unit_ids = sorting.unit_ids
-    synchrony_counts = get_synchrony_counts(spikes, synchrony_sizes_np, all_unit_ids)
+    synchrony_counts = _get_synchrony_counts(spikes, synchrony_sizes, all_unit_ids)
 
     synchrony_metrics_dict = {}
-    for sync_idx, synchrony_size in enumerate(synchrony_sizes_np):
+    for sync_idx, synchrony_size in enumerate(synchrony_sizes):
         sync_id_metrics_dict = {}
         for i, unit_id in enumerate(all_unit_ids):
             if unit_id not in unit_ids:
@@ -623,7 +624,7 @@ def compute_synchrony_metrics(sorting_analyzer, synchrony_sizes=(2, 4, 8), unit_
     return res(**synchrony_metrics_dict)
 
 
-_default_params["synchrony"] = dict(synchrony_sizes=(2, 4, 8))
+_default_params["synchrony"] = dict()
 
 
 def compute_firing_ranges(sorting_analyzer, bin_size_s=5, percentiles=(5, 95), unit_ids=None):
diff --git a/src/spikeinterface/qualitymetrics/pca_metrics.py b/src/spikeinterface/qualitymetrics/pca_metrics.py
index 4c68dfea59..ca21f1e45f 100644
--- a/src/spikeinterface/qualitymetrics/pca_metrics.py
+++ b/src/spikeinterface/qualitymetrics/pca_metrics.py
@@ -6,6 +6,7 @@
 from copy import deepcopy
 import platform
 from tqdm.auto import tqdm
+from warnings import warn
 
 import numpy as np
 
@@ -52,6 +53,7 @@ def get_quality_pca_metric_list():
 def compute_pc_metrics(
     sorting_analyzer,
     metric_names=None,
+    metric_params=None,
     qm_params=None,
     unit_ids=None,
     seed=None,
@@ -70,7 +72,7 @@ def compute_pc_metrics(
     metric_names : list of str, default: None
         The list of PC metrics to compute.
         If not provided, defaults to all PC metrics.
-    qm_params : dict or None
+    metric_params : dict or None
         Dictionary with parameters for each PC metric function.
     unit_ids : list of int or None
         List of unit ids to compute metrics for.
@@ -86,6 +88,14 @@ def compute_pc_metrics(
     pc_metrics : dict
         The computed PC metrics.
     """
+
+    if qm_params is not None and metric_params is None:
+        deprecation_msg = (
+            "`qm_params` is deprecated and will be removed in version 0.104.0. Please use metric_params instead"
+        )
+        warn(deprecation_msg, category=DeprecationWarning, stacklevel=2)
+        metric_params = qm_params
+
     pca_ext = sorting_analyzer.get_extension("principal_components")
     assert pca_ext is not None, "calculate_pc_metrics() need extension 'principal_components'"
 
@@ -93,8 +103,8 @@ def compute_pc_metrics(
 
     if metric_names is None:
         metric_names = _possible_pc_metric_names.copy()
-    if qm_params is None:
-        qm_params = _default_params
+    if metric_params is None:
+        metric_params = _default_params
 
     extremum_channels = get_template_extremum_channel(sorting_analyzer)
 
@@ -147,7 +157,7 @@ def compute_pc_metrics(
         pcs = dense_projections[np.isin(all_labels, neighbor_unit_ids)][:, :, neighbor_channel_indices]
         pcs_flat = pcs.reshape(pcs.shape[0], -1)
 
-        func_args = (pcs_flat, labels, non_nn_metrics, unit_id, unit_ids, qm_params, max_threads_per_process)
+        func_args = (pcs_flat, labels, non_nn_metrics, unit_id, unit_ids, metric_params, max_threads_per_process)
         items.append(func_args)
 
     if not run_in_parallel and non_nn_metrics:
@@ -184,7 +194,7 @@ def compute_pc_metrics(
             units_loop = tqdm(units_loop, desc=f"calculate {metric_name} metric", total=len(unit_ids))
 
         func = _nn_metric_name_to_func[metric_name]
-        metric_params = qm_params[metric_name] if metric_name in qm_params else {}
+        metric_params = metric_params[metric_name] if metric_name in metric_params else {}
 
         for _, unit_id in units_loop:
             try:
@@ -213,7 +223,7 @@ def compute_pc_metrics(
 
 
 def calculate_pc_metrics(
-    sorting_analyzer, metric_names=None, qm_params=None, unit_ids=None, seed=None, n_jobs=1, progress_bar=False
+    sorting_analyzer, metric_names=None, metric_params=None, unit_ids=None, seed=None, n_jobs=1, progress_bar=False
 ):
     warnings.warn(
         "The `calculate_pc_metrics` function is deprecated and will be removed in 0.103.0. Please use compute_pc_metrics instead",
@@ -224,7 +234,7 @@ def calculate_pc_metrics(
     pc_metrics = compute_pc_metrics(
         sorting_analyzer,
         metric_names=metric_names,
-        qm_params=qm_params,
+        metric_params=metric_params,
         unit_ids=unit_ids,
         seed=seed,
         n_jobs=n_jobs,
@@ -977,16 +987,16 @@ def _compute_isolation(pcs_target_unit, pcs_other_unit, n_neighbors: int):
 
 
 def pca_metrics_one_unit(args):
-    (pcs_flat, labels, metric_names, unit_id, unit_ids, qm_params, max_threads_per_process) = args
+    (pcs_flat, labels, metric_names, unit_id, unit_ids, metric_params, max_threads_per_process) = args
 
     if max_threads_per_process is None:
-        return _pca_metrics_one_unit(pcs_flat, labels, metric_names, unit_id, unit_ids, qm_params)
+        return _pca_metrics_one_unit(pcs_flat, labels, metric_names, unit_id, unit_ids, metric_params)
     else:
         with threadpool_limits(limits=int(max_threads_per_process)):
-            return _pca_metrics_one_unit(pcs_flat, labels, metric_names, unit_id, unit_ids, qm_params)
+            return _pca_metrics_one_unit(pcs_flat, labels, metric_names, unit_id, unit_ids, metric_params)
 
 
-def _pca_metrics_one_unit(pcs_flat, labels, metric_names, unit_id, unit_ids, qm_params):
+def _pca_metrics_one_unit(pcs_flat, labels, metric_names, unit_id, unit_ids, metric_params):
     pc_metrics = {}
     # metrics
     if "isolation_distance" in metric_names or "l_ratio" in metric_names:
@@ -1015,7 +1025,7 @@ def _pca_metrics_one_unit(pcs_flat, labels, metric_names, unit_id, unit_ids, qm_
     if "nearest_neighbor" in metric_names:
         try:
             nn_hit_rate, nn_miss_rate = nearest_neighbors_metrics(
-                pcs_flat, labels, unit_id, **qm_params["nearest_neighbor"]
+                pcs_flat, labels, unit_id, **metric_params["nearest_neighbor"]
             )
         except:
             nn_hit_rate = np.nan
@@ -1024,7 +1034,7 @@ def _pca_metrics_one_unit(pcs_flat, labels, metric_names, unit_id, unit_ids, qm_
         pc_metrics["nn_miss_rate"] = nn_miss_rate
 
     if "silhouette" in metric_names:
-        silhouette_method = qm_params["silhouette"]["method"]
+        silhouette_method = metric_params["silhouette"]["method"]
         if "simplified" in silhouette_method:
             try:
                 unit_silhouette_score = simplified_silhouette_score(pcs_flat, labels, unit_id)
diff --git a/src/spikeinterface/qualitymetrics/quality_metric_calculator.py b/src/spikeinterface/qualitymetrics/quality_metric_calculator.py
index 6fdc21bac2..1f79acaa8b 100644
--- a/src/spikeinterface/qualitymetrics/quality_metric_calculator.py
+++ b/src/spikeinterface/qualitymetrics/quality_metric_calculator.py
@@ -6,6 +6,7 @@
 from copy import deepcopy
 
 import numpy as np
+from warnings import warn
 
 from spikeinterface.core.job_tools import fix_job_kwargs
 from spikeinterface.core.sortinganalyzer import register_result_extension, AnalyzerExtension
@@ -17,6 +18,7 @@
     _possible_pc_metric_names,
     compute_name_to_column_names,
     column_name_to_column_dtype,
+
 )
 from .misc_metrics import _default_params as misc_metrics_params
 from .pca_metrics import _default_params as pca_metrics_params
@@ -32,7 +34,7 @@ class ComputeQualityMetrics(AnalyzerExtension):
         A SortingAnalyzer object.
     metric_names : list or None
         List of quality metrics to compute.
-    qm_params : dict or None
+    metric_params : dict of dicts or None
         Dictionary with parameters for quality metrics calculation.
         Default parameters can be obtained with: `si.qualitymetrics.get_default_qm_params()`
     skip_pc_metrics : bool, default: False
@@ -55,10 +57,18 @@ class ComputeQualityMetrics(AnalyzerExtension):
     need_recording = False
     use_nodepipeline = False
     need_job_kwargs = True
+    need_backward_compatibility_on_load = True
+
+    def _handle_backward_compatibility_on_load(self):
+        # For backwards compatibility - this renames qm_params as metric_params
+        if (qm_params := self.params.get("qm_params")) is not None:
+            self.params["metric_params"] = qm_params
+            del self.params["qm_params"]
 
     def _set_params(
         self,
         metric_names=None,
+        metric_params=None,
         qm_params=None,
         peak_sign=None,
         seed=None,
@@ -66,6 +76,12 @@ def _set_params(
         delete_existing_metrics=False,
         metrics_to_compute=None,
     ):
+        if qm_params is not None and metric_params is None:
+            deprecation_msg = (
+                "`qm_params` is deprecated and will be removed in version 0.104.0 Please use metric_params instead"
+            )
+            metric_params = qm_params
+            warn(deprecation_msg, category=DeprecationWarning, stacklevel=2)
 
         if metric_names is None:
             metric_names = list(_misc_metric_name_to_func.keys())
@@ -81,12 +97,12 @@ def _set_params(
                 if "drift" in metric_names:
                     metric_names.remove("drift")
 
-        qm_params_ = get_default_qm_params()
-        for k in qm_params_:
-            if qm_params is not None and k in qm_params:
-                qm_params_[k].update(qm_params[k])
-            if "peak_sign" in qm_params_[k] and peak_sign is not None:
-                qm_params_[k]["peak_sign"] = peak_sign
+        metric_params_ = get_default_qm_params()
+        for k in metric_params_:
+            if metric_params is not None and k in metric_params:
+                metric_params_[k].update(metric_params[k])
+            if "peak_sign" in metric_params_[k] and peak_sign is not None:
+                metric_params_[k]["peak_sign"] = peak_sign
 
         metrics_to_compute = metric_names
         qm_extension = self.sorting_analyzer.get_extension("quality_metrics")
@@ -102,7 +118,7 @@ def _set_params(
             metric_names=metric_names,
             peak_sign=peak_sign,
             seed=seed,
-            qm_params=qm_params_,
+            metric_params=metric_params_,
             skip_pc_metrics=skip_pc_metrics,
             delete_existing_metrics=delete_existing_metrics,
             metrics_to_compute=metrics_to_compute,
@@ -149,7 +165,7 @@ def _compute_metrics(self, sorting_analyzer, unit_ids=None, verbose=False, metri
         """
         import pandas as pd
 
-        qm_params = self.params["qm_params"]
+        metric_params = self.params["metric_params"]
         # sparsity = self.params["sparsity"]
         seed = self.params["seed"]
 
@@ -185,7 +201,7 @@ def _compute_metrics(self, sorting_analyzer, unit_ids=None, verbose=False, metri
 
             func = _misc_metric_name_to_func[metric_name]
 
-            params = qm_params[metric_name] if metric_name in qm_params else {}
+            params = metric_params[metric_name] if metric_name in metric_params else {}
             res = func(sorting_analyzer, unit_ids=non_empty_unit_ids, **params)
             # QM with uninstall dependencies might return None
             if res is not None:
@@ -213,7 +229,7 @@ def _compute_metrics(self, sorting_analyzer, unit_ids=None, verbose=False, metri
                 # sparsity=sparsity,
                 progress_bar=progress_bar,
                 n_jobs=n_jobs,
-                qm_params=qm_params,
+                metric_params=metric_params,
                 seed=seed,
             )
             for col, values in pc_metrics.items():
@@ -269,7 +285,7 @@ def _run(self, verbose=False, **job_kwargs):
         # append the metrics which were previously computed
         for metric_name in set(existing_metrics).difference(metrics_to_compute):
             # some metrics names produce data columns with other names. This deals with that.
-            for column_name in compute_name_to_column_names[metric_name]:
+            for column_name in qm_compute_name_to_column_names[metric_name]:
                 computed_metrics[column_name] = qm_extension.data["metrics"][column_name]
 
         self.data["metrics"] = computed_metrics
diff --git a/src/spikeinterface/qualitymetrics/quality_metric_list.py b/src/spikeinterface/qualitymetrics/quality_metric_list.py
index 685aaddc83..fc7ae906e7 100644
--- a/src/spikeinterface/qualitymetrics/quality_metric_list.py
+++ b/src/spikeinterface/qualitymetrics/quality_metric_list.py
@@ -55,7 +55,7 @@
 }
 
 # a dict converting the name of the metric for computation to the output of that computation
-compute_name_to_column_names = {
+qm_compute_name_to_column_names = {
     "num_spikes": ["num_spikes"],
     "firing_rate": ["firing_rate"],
     "presence_ratio": ["presence_ratio"],
diff --git a/src/spikeinterface/qualitymetrics/tests/conftest.py b/src/spikeinterface/qualitymetrics/tests/conftest.py
index 01fa16c8d7..ac1789a375 100644
--- a/src/spikeinterface/qualitymetrics/tests/conftest.py
+++ b/src/spikeinterface/qualitymetrics/tests/conftest.py
@@ -16,6 +16,11 @@ def small_sorting_analyzer():
         seed=1205,
     )
 
+    channel_ids_as_integers = [id for id in range(recording.get_num_channels())]
+    unit_ids_as_integers = [id for id in range(sorting.get_num_units())]
+    recording = recording.rename_channels(new_channel_ids=channel_ids_as_integers)
+    sorting = sorting.rename_units(new_unit_ids=unit_ids_as_integers)
+
     sorting = sorting.select_units([2, 7, 0], ["#3", "#9", "#4"])
 
     sorting_analyzer = create_sorting_analyzer(recording=recording, sorting=sorting, format="memory")
@@ -60,6 +65,11 @@ def sorting_analyzer_simple():
         seed=1205,
     )
 
+    channel_ids_as_integers = [id for id in range(recording.get_num_channels())]
+    unit_ids_as_integers = [id for id in range(sorting.get_num_units())]
+    recording = recording.rename_channels(new_channel_ids=channel_ids_as_integers)
+    sorting = sorting.rename_units(new_unit_ids=unit_ids_as_integers)
+
     sorting_analyzer = create_sorting_analyzer(sorting, recording, format="memory", sparse=True)
 
     sorting_analyzer.compute("random_spikes", max_spikes_per_unit=300, seed=1205)
diff --git a/src/spikeinterface/qualitymetrics/tests/test_metrics_functions.py b/src/spikeinterface/qualitymetrics/tests/test_metrics_functions.py
index 4c0890b62b..18b49cd862 100644
--- a/src/spikeinterface/qualitymetrics/tests/test_metrics_functions.py
+++ b/src/spikeinterface/qualitymetrics/tests/test_metrics_functions.py
@@ -39,7 +39,7 @@
     compute_firing_ranges,
     compute_amplitude_cv_metrics,
     compute_sd_ratio,
-    get_synchrony_counts,
+    _get_synchrony_counts,
     compute_quality_metrics,
 )
 
@@ -69,7 +69,7 @@ def test_compute_new_quality_metrics(small_sorting_analyzer):
     assert calculated_metrics == ["snr"]
 
     small_sorting_analyzer.compute(
-        {"quality_metrics": {"metric_names": list(qm_params.keys()), "qm_params": qm_params}}
+        {"quality_metrics": {"metric_names": list(qm_params.keys()), "metric_params": qm_params}}
     )
     small_sorting_analyzer.compute({"quality_metrics": {"metric_names": ["snr"]}})
 
@@ -96,13 +96,13 @@ def test_compute_new_quality_metrics(small_sorting_analyzer):
     # check that, when parameters are changed, the data and metadata are updated
     old_snr_data = deepcopy(quality_metric_extension.get_data()["snr"].values)
     small_sorting_analyzer.compute(
-        {"quality_metrics": {"metric_names": ["snr"], "qm_params": {"snr": {"peak_mode": "peak_to_peak"}}}}
+        {"quality_metrics": {"metric_names": ["snr"], "metric_params": {"snr": {"peak_mode": "peak_to_peak"}}}}
     )
     new_quality_metric_extension = small_sorting_analyzer.get_extension("quality_metrics")
     new_snr_data = new_quality_metric_extension.get_data()["snr"].values
 
     assert np.all(old_snr_data != new_snr_data)
-    assert new_quality_metric_extension.params["qm_params"]["snr"]["peak_mode"] == "peak_to_peak"
+    assert new_quality_metric_extension.params["metric_params"]["snr"]["peak_mode"] == "peak_to_peak"
 
     # check that all quality metrics are deleted when parents are recomputed, even after
     # recomputation
@@ -280,10 +280,10 @@ def test_unit_id_order_independence(small_sorting_analyzer):
     }
 
     quality_metrics_1 = compute_quality_metrics(
-        small_sorting_analyzer, metric_names=get_quality_metric_list(), qm_params=qm_params
+        small_sorting_analyzer, metric_names=get_quality_metric_list(), metric_params=qm_params
     )
     quality_metrics_2 = compute_quality_metrics(
-        small_sorting_analyzer_2, metric_names=get_quality_metric_list(), qm_params=qm_params
+        small_sorting_analyzer_2, metric_names=get_quality_metric_list(), metric_params=qm_params
     )
 
     for metric, metric_2_data in quality_metrics_2.items():
@@ -352,7 +352,7 @@ def test_synchrony_counts_no_sync():
     one_spike["sample_index"] = spike_times
     one_spike["unit_index"] = spike_units
 
-    sync_count = get_synchrony_counts(one_spike, np.array((2)), [0])
+    sync_count = _get_synchrony_counts(one_spike, np.array([2, 4, 8]), [0])
 
     assert np.all(sync_count[0] == np.array([0]))
 
@@ -372,7 +372,7 @@ def test_synchrony_counts_one_sync():
     two_spikes["sample_index"] = np.concatenate((spike_indices, added_spikes_indices))
     two_spikes["unit_index"] = np.concatenate((spike_labels, added_spikes_labels))
 
-    sync_count = get_synchrony_counts(two_spikes, np.array((2)), [0, 1])
+    sync_count = _get_synchrony_counts(two_spikes, np.array([2, 4, 8]), [0, 1])
 
     assert np.all(sync_count[0] == np.array([1, 1]))
 
@@ -392,7 +392,7 @@ def test_synchrony_counts_one_quad_sync():
     four_spikes["sample_index"] = np.concatenate((spike_indices, added_spikes_indices))
     four_spikes["unit_index"] = np.concatenate((spike_labels, added_spikes_labels))
 
-    sync_count = get_synchrony_counts(four_spikes, np.array((2, 4)), [0, 1, 2, 3])
+    sync_count = _get_synchrony_counts(four_spikes, np.array([2, 4, 8]), [0, 1, 2, 3])
 
     assert np.all(sync_count[0] == np.array([1, 1, 1, 1]))
     assert np.all(sync_count[1] == np.array([1, 1, 1, 1]))
@@ -409,7 +409,7 @@ def test_synchrony_counts_not_all_units():
     three_spikes["sample_index"] = np.concatenate((spike_indices, added_spikes_indices))
     three_spikes["unit_index"] = np.concatenate((spike_labels, added_spikes_labels))
 
-    sync_count = get_synchrony_counts(three_spikes, np.array((2)), [0, 1, 2])
+    sync_count = _get_synchrony_counts(three_spikes, np.array([2, 4, 8]), [0, 1, 2])
 
     assert np.all(sync_count[0] == np.array([0, 1, 1]))
 
@@ -610,9 +610,9 @@ def test_calculate_rp_violations(sorting_analyzer_violations):
 def test_synchrony_metrics(sorting_analyzer_simple):
     sorting_analyzer = sorting_analyzer_simple
     sorting = sorting_analyzer.sorting
-    synchrony_sizes = (2, 3, 4)
-    synchrony_metrics = compute_synchrony_metrics(sorting_analyzer, synchrony_sizes=synchrony_sizes)
-    print(synchrony_metrics)
+    synchrony_metrics = compute_synchrony_metrics(sorting_analyzer)
+
+    synchrony_sizes = np.array([2, 4, 8])
 
     # check returns
     for size in synchrony_sizes:
@@ -625,10 +625,8 @@ def test_synchrony_metrics(sorting_analyzer_simple):
         sorting_sync = add_synchrony_to_sorting(sorting, sync_event_ratio=sync_level)
         sorting_analyzer_sync = create_sorting_analyzer(sorting_sync, sorting_analyzer.recording, format="memory")
 
-        previous_synchrony_metrics = compute_synchrony_metrics(
-            previous_sorting_analyzer, synchrony_sizes=synchrony_sizes
-        )
-        current_synchrony_metrics = compute_synchrony_metrics(sorting_analyzer_sync, synchrony_sizes=synchrony_sizes)
+        previous_synchrony_metrics = compute_synchrony_metrics(previous_sorting_analyzer)
+        current_synchrony_metrics = compute_synchrony_metrics(sorting_analyzer_sync)
         print(current_synchrony_metrics)
         # check that all values increased
         for i, col in enumerate(previous_synchrony_metrics._fields):
@@ -647,22 +645,17 @@ def test_synchrony_metrics_unit_id_subset(sorting_analyzer_simple):
 
     unit_ids_subset = [3, 7]
 
-    synchrony_sizes = (2,)
-    (synchrony_metrics,) = compute_synchrony_metrics(
-        sorting_analyzer_simple, synchrony_sizes=synchrony_sizes, unit_ids=unit_ids_subset
-    )
+    synchrony_metrics = compute_synchrony_metrics(sorting_analyzer_simple, unit_ids=unit_ids_subset)
 
-    assert list(synchrony_metrics.keys()) == [3, 7]
+    assert list(synchrony_metrics.sync_spike_2.keys()) == [3, 7]
+    assert list(synchrony_metrics.sync_spike_4.keys()) == [3, 7]
+    assert list(synchrony_metrics.sync_spike_8.keys()) == [3, 7]
 
 
 def test_synchrony_metrics_no_unit_ids(sorting_analyzer_simple):
 
-    # all_unit_ids = sorting_analyzer_simple.sorting.unit_ids
-
-    synchrony_sizes = (2,)
-    (synchrony_metrics,) = compute_synchrony_metrics(sorting_analyzer_simple, synchrony_sizes=synchrony_sizes)
-
-    assert np.all(list(synchrony_metrics.keys()) == sorting_analyzer_simple.unit_ids)
+    synchrony_metrics = compute_synchrony_metrics(sorting_analyzer_simple)
+    assert np.all(list(synchrony_metrics.sync_spike_2.keys()) == sorting_analyzer_simple.unit_ids)
 
 
 @pytest.mark.sortingcomponents
diff --git a/src/spikeinterface/qualitymetrics/tests/test_quality_metric_calculator.py b/src/spikeinterface/qualitymetrics/tests/test_quality_metric_calculator.py
index 71569e7b2b..ea8939ebb4 100644
--- a/src/spikeinterface/qualitymetrics/tests/test_quality_metric_calculator.py
+++ b/src/spikeinterface/qualitymetrics/tests/test_quality_metric_calculator.py
@@ -24,14 +24,14 @@ def test_compute_quality_metrics(sorting_analyzer_simple):
     metrics = compute_quality_metrics(
         sorting_analyzer,
         metric_names=["snr"],
-        qm_params=dict(isi_violation=dict(isi_threshold_ms=2)),
+        metric_params=dict(isi_violation=dict(isi_threshold_ms=2)),
         skip_pc_metrics=True,
         seed=2205,
     )
     # print(metrics)
 
     qm = sorting_analyzer.get_extension("quality_metrics")
-    assert qm.params["qm_params"]["isi_violation"]["isi_threshold_ms"] == 2
+    assert qm.params["metric_params"]["isi_violation"]["isi_threshold_ms"] == 2
     assert "snr" in metrics.columns
     assert "isolation_distance" not in metrics.columns
 
@@ -40,7 +40,7 @@ def test_compute_quality_metrics(sorting_analyzer_simple):
     metrics = compute_quality_metrics(
         sorting_analyzer,
         metric_names=None,
-        qm_params=dict(isi_violation=dict(isi_threshold_ms=2)),
+        metric_params=dict(isi_violation=dict(isi_threshold_ms=2)),
         skip_pc_metrics=False,
         seed=2205,
     )
@@ -81,7 +81,7 @@ def test_compute_quality_metrics_recordingless(sorting_analyzer_simple):
     metrics = compute_quality_metrics(
         sorting_analyzer,
         metric_names=None,
-        qm_params=dict(isi_violation=dict(isi_threshold_ms=2)),
+        metric_params=dict(isi_violation=dict(isi_threshold_ms=2)),
         skip_pc_metrics=False,
         seed=2205,
     )
@@ -95,7 +95,7 @@ def test_compute_quality_metrics_recordingless(sorting_analyzer_simple):
     metrics_norec = compute_quality_metrics(
         sorting_analyzer_norec,
         metric_names=None,
-        qm_params=dict(isi_violation=dict(isi_threshold_ms=2)),
+        metric_params=dict(isi_violation=dict(isi_threshold_ms=2)),
         skip_pc_metrics=False,
         seed=2205,
     )
@@ -128,7 +128,7 @@ def test_empty_units(sorting_analyzer_simple):
     metrics_empty = compute_quality_metrics(
         sorting_analyzer_empty,
         metric_names=None,
-        qm_params=dict(isi_violation=dict(isi_threshold_ms=2)),
+        metric_params=dict(isi_violation=dict(isi_threshold_ms=2)),
         skip_pc_metrics=True,
         seed=2205,
     )
diff --git a/src/spikeinterface/sorters/internal/spyking_circus2.py b/src/spikeinterface/sorters/internal/spyking_circus2.py
index eed693b343..6e84cc996f 100644
--- a/src/spikeinterface/sorters/internal/spyking_circus2.py
+++ b/src/spikeinterface/sorters/internal/spyking_circus2.py
@@ -52,7 +52,7 @@ class Spykingcircus2Sorter(ComponentsBasedSorter):
         "matched_filtering": True,
         "cache_preprocessing": {"mode": "memory", "memory_limit": 0.5, "delete_cache": True},
         "multi_units_only": False,
-        "job_kwargs": {"n_jobs": 0.8},
+        "job_kwargs": {"n_jobs": 0.5},
         "debug": False,
     }
 
@@ -115,7 +115,6 @@ def _run_from_folder(cls, sorter_output_folder, params, verbose):
         from spikeinterface.sortingcomponents.matching import find_spikes_from_templates
         from spikeinterface.sortingcomponents.tools import remove_empty_templates
         from spikeinterface.sortingcomponents.tools import get_prototype_spike, check_probe_for_drift_correction
-        from spikeinterface.sortingcomponents.tools import get_prototype_spike
 
         job_kwargs = fix_job_kwargs(params["job_kwargs"])
         job_kwargs.update({"progress_bar": verbose})
@@ -219,7 +218,6 @@ def _run_from_folder(cls, sorter_output_folder, params, verbose):
             clustering_params["radius_um"] = radius_um
             clustering_params["waveforms"]["ms_before"] = ms_before
             clustering_params["waveforms"]["ms_after"] = ms_after
-            clustering_params["job_kwargs"] = job_kwargs
             clustering_params["noise_levels"] = noise_levels
             clustering_params["ms_before"] = exclude_sweep_ms
             clustering_params["ms_after"] = exclude_sweep_ms
@@ -233,7 +231,7 @@ def _run_from_folder(cls, sorter_output_folder, params, verbose):
                 clustering_method = "random_projections"
 
             labels, peak_labels = find_cluster_from_peaks(
-                recording_w, selected_peaks, method=clustering_method, method_kwargs=clustering_params
+                recording_w, selected_peaks, method=clustering_method, method_kwargs=clustering_params, **job_kwargs
             )
 
             ## We get the labels for our peaks
@@ -284,11 +282,10 @@ def _run_from_folder(cls, sorter_output_folder, params, verbose):
             matching_method = params["matching"].pop("method")
             matching_params = params["matching"].copy()
             matching_params["templates"] = templates
-            matching_job_params = job_kwargs.copy()
 
             if matching_method is not None:
                 spikes = find_spikes_from_templates(
-                    recording_w, matching_method, method_kwargs=matching_params, **matching_job_params
+                    recording_w, matching_method, method_kwargs=matching_params, **job_kwargs
                 )
 
                 if params["debug"]:
diff --git a/src/spikeinterface/sortingcomponents/clustering/circus.py b/src/spikeinterface/sortingcomponents/clustering/circus.py
index 99c59f493e..243c854bba 100644
--- a/src/spikeinterface/sortingcomponents/clustering/circus.py
+++ b/src/spikeinterface/sortingcomponents/clustering/circus.py
@@ -18,7 +18,6 @@
 from spikeinterface.core.waveform_tools import estimate_templates
 from .clustering_tools import remove_duplicates_via_matching
 from spikeinterface.core.recording_tools import get_noise_levels, get_channel_distances
-from spikeinterface.core.job_tools import fix_job_kwargs
 from spikeinterface.sortingcomponents.peak_selection import select_peaks
 from spikeinterface.sortingcomponents.waveforms.temporal_pca import TemporalPCAProjection
 from spikeinterface.core.template import Templates
@@ -63,16 +62,13 @@ class CircusClustering:
         "rank": 5,
         "noise_levels": None,
         "tmp_folder": None,
-        "job_kwargs": {},
         "verbose": True,
     }
 
     @classmethod
-    def main_function(cls, recording, peaks, params):
+    def main_function(cls, recording, peaks, params, job_kwargs=dict()):
         assert HAVE_HDBSCAN, "random projections clustering needs hdbscan to be installed"
 
-        job_kwargs = fix_job_kwargs(params["job_kwargs"])
-
         d = params
         verbose = d["verbose"]
 
@@ -248,8 +244,10 @@ def main_function(cls, recording, peaks, params):
             probe=recording.get_probe(),
             is_scaled=False,
         )
+
         if params["noise_levels"] is None:
-            params["noise_levels"] = get_noise_levels(recording, return_scaled=False)
+            params["noise_levels"] = get_noise_levels(recording, return_scaled=False, **job_kwargs)
+
         sparsity = compute_sparsity(templates, noise_levels=params["noise_levels"], **params["sparsity"])
         templates = templates.to_sparse(sparsity)
         empty_templates = templates.sparsity_mask.sum(axis=1) == 0
@@ -260,13 +258,12 @@ def main_function(cls, recording, peaks, params):
         if verbose:
             print("We found %d raw clusters, starting to clean with matching..." % (len(templates.unit_ids)))
 
-        cleaning_matching_params = params["job_kwargs"].copy()
-        cleaning_matching_params["n_jobs"] = 1
-        cleaning_matching_params["progress_bar"] = False
+        cleaning_job_kwargs = job_kwargs.copy()
+        cleaning_job_kwargs["progress_bar"] = False
         cleaning_params = params["cleaning_kwargs"].copy()
 
         labels, peak_labels = remove_duplicates_via_matching(
-            templates, peak_labels, job_kwargs=cleaning_matching_params, **cleaning_params
+            templates, peak_labels, job_kwargs=cleaning_job_kwargs, **cleaning_params
         )
 
         if verbose:
diff --git a/src/spikeinterface/sortingcomponents/clustering/clean.py b/src/spikeinterface/sortingcomponents/clustering/clean.py
index c7d57b14e4..e8bc5a1d49 100644
--- a/src/spikeinterface/sortingcomponents/clustering/clean.py
+++ b/src/spikeinterface/sortingcomponents/clustering/clean.py
@@ -32,7 +32,6 @@ def clean_clusters(
     count = np.zeros(n, dtype="int64")
     for i, label in enumerate(labels_set):
         count[i] = np.sum(peak_labels == label)
-    print(count)
 
     templates = compute_template_from_sparse(peaks, peak_labels, labels_set, sparse_wfs, sparse_mask, total_channels)
 
@@ -42,6 +41,5 @@ def clean_clusters(
         max_values = -np.min(templates, axis=(1, 2))
     elif peak_sign == "pos":
         max_values = np.max(templates, axis=(1, 2))
-    print(max_values)
 
     return clean_labels
diff --git a/src/spikeinterface/sortingcomponents/clustering/dummy.py b/src/spikeinterface/sortingcomponents/clustering/dummy.py
index c1032ee6c6..b5761ad5cf 100644
--- a/src/spikeinterface/sortingcomponents/clustering/dummy.py
+++ b/src/spikeinterface/sortingcomponents/clustering/dummy.py
@@ -13,7 +13,7 @@ class DummyClustering:
     _default_params = {}
 
     @classmethod
-    def main_function(cls, recording, peaks, params):
+    def main_function(cls, recording, peaks, params, job_kwargs=dict()):
         labels = np.arange(recording.get_num_channels(), dtype="int64")
         peak_labels = peaks["channel_index"]
         return labels, peak_labels
diff --git a/src/spikeinterface/sortingcomponents/clustering/main.py b/src/spikeinterface/sortingcomponents/clustering/main.py
index 99881f2f34..ba0fe6f9ac 100644
--- a/src/spikeinterface/sortingcomponents/clustering/main.py
+++ b/src/spikeinterface/sortingcomponents/clustering/main.py
@@ -41,7 +41,7 @@ def find_cluster_from_peaks(recording, peaks, method="stupid", method_kwargs={},
     params = method_class._default_params.copy()
     params.update(**method_kwargs)
 
-    outputs = method_class.main_function(recording, peaks, params)
+    outputs = method_class.main_function(recording, peaks, params, job_kwargs=job_kwargs)
 
     if extra_outputs:
         return outputs
diff --git a/src/spikeinterface/sortingcomponents/clustering/position.py b/src/spikeinterface/sortingcomponents/clustering/position.py
index ae772206bb..dc76d787f6 100644
--- a/src/spikeinterface/sortingcomponents/clustering/position.py
+++ b/src/spikeinterface/sortingcomponents/clustering/position.py
@@ -25,18 +25,17 @@ class PositionClustering:
         "hdbscan_kwargs": {"min_cluster_size": 20, "allow_single_cluster": True, "core_dist_n_jobs": -1},
         "debug": False,
         "tmp_folder": None,
-        "job_kwargs": {"n_jobs": -1, "chunk_memory": "10M"},
     }
 
     @classmethod
-    def main_function(cls, recording, peaks, params):
+    def main_function(cls, recording, peaks, params, job_kwargs=dict()):
         assert HAVE_HDBSCAN, "position clustering need hdbscan to be installed"
         d = params
 
         if d["peak_locations"] is None:
             from spikeinterface.sortingcomponents.peak_localization import localize_peaks
 
-            peak_locations = localize_peaks(recording, peaks, **d["peak_localization_kwargs"], **d["job_kwargs"])
+            peak_locations = localize_peaks(recording, peaks, **d["peak_localization_kwargs"], **job_kwargs)
         else:
             peak_locations = d["peak_locations"]
 
diff --git a/src/spikeinterface/sortingcomponents/clustering/position_and_features.py b/src/spikeinterface/sortingcomponents/clustering/position_and_features.py
index d23eb26239..20067a2eec 100644
--- a/src/spikeinterface/sortingcomponents/clustering/position_and_features.py
+++ b/src/spikeinterface/sortingcomponents/clustering/position_and_features.py
@@ -42,23 +42,14 @@ class PositionAndFeaturesClustering:
         "ms_before": 1.5,
         "ms_after": 1.5,
         "cleaning_method": "dip",
-        "job_kwargs": {"n_jobs": -1, "chunk_memory": "10M", "progress_bar": True},
     }
 
     @classmethod
-    def main_function(cls, recording, peaks, params):
+    def main_function(cls, recording, peaks, params, job_kwargs=dict()):
         from sklearn.preprocessing import QuantileTransformer
 
         assert HAVE_HDBSCAN, "twisted clustering needs hdbscan to be installed"
 
-        if "n_jobs" in params["job_kwargs"]:
-            if params["job_kwargs"]["n_jobs"] == -1:
-                params["job_kwargs"]["n_jobs"] = os.cpu_count()
-
-        if "core_dist_n_jobs" in params["hdbscan_kwargs"]:
-            if params["hdbscan_kwargs"]["core_dist_n_jobs"] == -1:
-                params["hdbscan_kwargs"]["core_dist_n_jobs"] = os.cpu_count()
-
         d = params
 
         peak_dtype = [("sample_index", "int64"), ("unit_index", "int64"), ("segment_index", "int64")]
@@ -80,7 +71,7 @@ def main_function(cls, recording, peaks, params):
         }
 
         features_data = compute_features_from_peaks(
-            recording, peaks, features_list, features_params, ms_before=1, ms_after=1, **params["job_kwargs"]
+            recording, peaks, features_list, features_params, ms_before=1, ms_after=1, **job_kwargs
         )
 
         hdbscan_data = np.zeros((len(peaks), 3), dtype=np.float32)
@@ -150,10 +141,10 @@ def main_function(cls, recording, peaks, params):
                 dtype=recording.get_dtype(),
                 sparsity_mask=None,
                 copy=True,
-                **params["job_kwargs"],
+                **job_kwargs,
             )
 
-            noise_levels = get_noise_levels(recording, return_scaled=False)
+            noise_levels = get_noise_levels(recording, return_scaled=False, **job_kwargs)
             labels, peak_labels = remove_duplicates(
                 wfs_arrays, noise_levels, peak_labels, num_samples, num_chans, **params["cleaning_kwargs"]
             )
@@ -181,7 +172,7 @@ def main_function(cls, recording, peaks, params):
                 nbefore,
                 nafter,
                 return_scaled=False,
-                **params["job_kwargs"],
+                **job_kwargs,
             )
             templates = Templates(
                 templates_array=templates_array,
@@ -193,7 +184,7 @@ def main_function(cls, recording, peaks, params):
             )
 
             labels, peak_labels = remove_duplicates_via_matching(
-                templates, peak_labels, job_kwargs=params["job_kwargs"], **params["cleaning_kwargs"]
+                templates, peak_labels, job_kwargs=job_kwargs, **params["cleaning_kwargs"]
             )
             shutil.rmtree(tmp_folder)
 
diff --git a/src/spikeinterface/sortingcomponents/clustering/position_and_pca.py b/src/spikeinterface/sortingcomponents/clustering/position_and_pca.py
index 4dfe3c960c..c4f372fc21 100644
--- a/src/spikeinterface/sortingcomponents/clustering/position_and_pca.py
+++ b/src/spikeinterface/sortingcomponents/clustering/position_and_pca.py
@@ -38,7 +38,6 @@ class PositionAndPCAClustering:
         "ms_after": 2.5,
         "n_components_by_channel": 3,
         "n_components": 5,
-        "job_kwargs": {"n_jobs": -1, "chunk_memory": "10M", "progress_bar": True},
         "hdbscan_global_kwargs": {"min_cluster_size": 20, "allow_single_cluster": True, "core_dist_n_jobs": -1},
         "hdbscan_local_kwargs": {"min_cluster_size": 20, "allow_single_cluster": True, "core_dist_n_jobs": -1},
         "waveform_mode": "shared_memory",
@@ -73,7 +72,7 @@ def _check_params(cls, recording, peaks, params):
         return params2
 
     @classmethod
-    def main_function(cls, recording, peaks, params):
+    def main_function(cls, recording, peaks, params, job_kwargs=dict()):
         # res = PositionClustering(recording, peaks, params)
 
         assert HAVE_HDBSCAN, "position_and_pca clustering need hdbscan to be installed"
@@ -85,9 +84,7 @@ def main_function(cls, recording, peaks, params):
         if params["peak_locations"] is None:
             from spikeinterface.sortingcomponents.peak_localization import localize_peaks
 
-            peak_locations = localize_peaks(
-                recording, peaks, **params["peak_localization_kwargs"], **params["job_kwargs"]
-            )
+            peak_locations = localize_peaks(recording, peaks, **params["peak_localization_kwargs"], **job_kwargs)
         else:
             peak_locations = params["peak_locations"]
 
@@ -155,7 +152,7 @@ def main_function(cls, recording, peaks, params):
             dtype=recording.get_dtype(),
             sparsity_mask=sparsity_mask,
             copy=(params["waveform_mode"] == "shared_memory"),
-            **params["job_kwargs"],
+            **job_kwargs,
         )
 
         noise = get_random_data_chunks(
@@ -222,7 +219,7 @@ def main_function(cls, recording, peaks, params):
             dtype=recording.get_dtype(),
             sparsity_mask=sparsity_mask3,
             copy=(params["waveform_mode"] == "shared_memory"),
-            **params["job_kwargs"],
+            **job_kwargs,
         )
 
         clean_peak_labels, peak_sample_shifts = auto_clean_clustering(
diff --git a/src/spikeinterface/sortingcomponents/clustering/position_ptp_scaled.py b/src/spikeinterface/sortingcomponents/clustering/position_ptp_scaled.py
index 788addf1e6..0f7390d7ac 100644
--- a/src/spikeinterface/sortingcomponents/clustering/position_ptp_scaled.py
+++ b/src/spikeinterface/sortingcomponents/clustering/position_ptp_scaled.py
@@ -26,7 +26,6 @@ class PositionPTPScaledClustering:
         "ptps": None,
         "scales": (1, 1, 10),
         "peak_localization_kwargs": {"method": "center_of_mass"},
-        "job_kwargs": {"n_jobs": -1, "chunk_memory": "10M", "progress_bar": True},
         "hdbscan_kwargs": {
             "min_cluster_size": 20,
             "min_samples": 20,
@@ -38,7 +37,7 @@ class PositionPTPScaledClustering:
     }
 
     @classmethod
-    def main_function(cls, recording, peaks, params):
+    def main_function(cls, recording, peaks, params, job_kwargs=dict()):
         assert HAVE_HDBSCAN, "position clustering need hdbscan to be installed"
         d = params
 
@@ -60,7 +59,7 @@ def main_function(cls, recording, peaks, params):
 
         if d["ptps"] is None:
             (ptps,) = compute_features_from_peaks(
-                recording, peaks, ["ptp"], feature_params={"ptp": {"all_channels": True}}, **d["job_kwargs"]
+                recording, peaks, ["ptp"], feature_params={"ptp": {"all_channels": True}}, **job_kwargs
             )
         else:
             ptps = d["ptps"]
diff --git a/src/spikeinterface/sortingcomponents/clustering/random_projections.py b/src/spikeinterface/sortingcomponents/clustering/random_projections.py
index f7ca999d53..484a7376c1 100644
--- a/src/spikeinterface/sortingcomponents/clustering/random_projections.py
+++ b/src/spikeinterface/sortingcomponents/clustering/random_projections.py
@@ -16,8 +16,7 @@
 from spikeinterface.core.basesorting import minimum_spike_dtype
 from spikeinterface.core.waveform_tools import estimate_templates
 from .clustering_tools import remove_duplicates_via_matching
-from spikeinterface.core.recording_tools import get_noise_levels, get_channel_distances
-from spikeinterface.core.job_tools import fix_job_kwargs
+from spikeinterface.core.recording_tools import get_noise_levels
 from spikeinterface.sortingcomponents.waveforms.savgol_denoiser import SavGolDenoiser
 from spikeinterface.sortingcomponents.features_from_peaks import RandomProjectionsFeature
 from spikeinterface.core.template import Templates
@@ -55,16 +54,13 @@ class RandomProjectionClustering:
         "noise_levels": None,
         "smoothing_kwargs": {"window_length_ms": 0.25},
         "tmp_folder": None,
-        "job_kwargs": {},
         "verbose": True,
     }
 
     @classmethod
-    def main_function(cls, recording, peaks, params):
+    def main_function(cls, recording, peaks, params, job_kwargs=dict()):
         assert HAVE_HDBSCAN, "random projections clustering need hdbscan to be installed"
 
-        job_kwargs = fix_job_kwargs(params["job_kwargs"])
-
         d = params
         verbose = d["verbose"]
 
@@ -148,7 +144,7 @@ def main_function(cls, recording, peaks, params):
             is_scaled=False,
         )
         if params["noise_levels"] is None:
-            params["noise_levels"] = get_noise_levels(recording, return_scaled=False)
+            params["noise_levels"] = get_noise_levels(recording, return_scaled=False, **job_kwargs)
         sparsity = compute_sparsity(templates, params["noise_levels"], **params["sparsity"])
         templates = templates.to_sparse(sparsity)
         templates = remove_empty_templates(templates)
@@ -156,18 +152,12 @@ def main_function(cls, recording, peaks, params):
         if verbose:
             print("We found %d raw clusters, starting to clean with matching..." % (len(templates.unit_ids)))
 
-        cleaning_matching_params = job_kwargs.copy()
-        for value in ["chunk_size", "chunk_memory", "total_memory", "chunk_duration"]:
-            if value in cleaning_matching_params:
-                cleaning_matching_params[value] = None
-        cleaning_matching_params["chunk_duration"] = "100ms"
-        cleaning_matching_params["n_jobs"] = 1
-        cleaning_matching_params["progress_bar"] = False
-
+        cleaning_job_kwargs = job_kwargs.copy()
+        cleaning_job_kwargs["progress_bar"] = False
         cleaning_params = params["cleaning_kwargs"].copy()
 
         labels, peak_labels = remove_duplicates_via_matching(
-            templates, peak_labels, job_kwargs=cleaning_matching_params, **cleaning_params
+            templates, peak_labels, job_kwargs=cleaning_job_kwargs, **cleaning_params
         )
 
         if verbose:
diff --git a/src/spikeinterface/sortingcomponents/clustering/sliding_hdbscan.py b/src/spikeinterface/sortingcomponents/clustering/sliding_hdbscan.py
index 8b9acbc92d..5f8ac99848 100644
--- a/src/spikeinterface/sortingcomponents/clustering/sliding_hdbscan.py
+++ b/src/spikeinterface/sortingcomponents/clustering/sliding_hdbscan.py
@@ -23,7 +23,7 @@
     get_random_data_chunks,
     extract_waveforms_to_buffers,
 )
-from .clustering_tools import auto_clean_clustering, auto_split_clustering
+from .clustering_tools import auto_clean_clustering
 
 
 class SlidingHdbscanClustering:
@@ -55,18 +55,17 @@ class SlidingHdbscanClustering:
         "auto_merge_quantile_limit": 0.8,
         "ratio_num_channel_intersect": 0.5,
         # ~ 'auto_trash_misalignment_shift' : 4,
-        "job_kwargs": {"n_jobs": -1, "chunk_memory": "10M", "progress_bar": True},
     }
 
     @classmethod
-    def main_function(cls, recording, peaks, params):
+    def main_function(cls, recording, peaks, params, job_kwargs=dict()):
         assert HAVE_HDBSCAN, "sliding_hdbscan clustering need hdbscan to be installed"
         params = cls._check_params(recording, peaks, params)
         wfs_arrays, sparsity_mask, noise = cls._initialize_folder(recording, peaks, params)
         peak_labels = cls._find_clusters(recording, peaks, wfs_arrays, sparsity_mask, noise, params)
 
         wfs_arrays2, sparsity_mask2 = cls._prepare_clean(
-            recording, peaks, wfs_arrays, sparsity_mask, peak_labels, params
+            recording, peaks, wfs_arrays, sparsity_mask, peak_labels, params, job_kwargs
         )
 
         clean_peak_labels, peak_sample_shifts = cls._clean_cluster(
@@ -100,7 +99,7 @@ def _check_params(cls, recording, peaks, params):
         return params2
 
     @classmethod
-    def _initialize_folder(cls, recording, peaks, params):
+    def _initialize_folder(cls, recording, peaks, params, job_kwargs=dict()):
         d = params
         tmp_folder = params["tmp_folder"]
 
@@ -145,7 +144,7 @@ def _initialize_folder(cls, recording, peaks, params):
             dtype=dtype,
             sparsity_mask=sparsity_mask,
             copy=(d["waveform_mode"] == "shared_memory"),
-            **d["job_kwargs"],
+            **job_kwargs,
         )
 
         # noise
@@ -401,7 +400,7 @@ def _find_clusters(cls, recording, peaks, wfs_arrays, sparsity_mask, noise, d):
         return peak_labels
 
     @classmethod
-    def _prepare_clean(cls, recording, peaks, wfs_arrays, sparsity_mask, peak_labels, d):
+    def _prepare_clean(cls, recording, peaks, wfs_arrays, sparsity_mask, peak_labels, d, job_kwargs=dict()):
         tmp_folder = d["tmp_folder"]
         if tmp_folder is None:
             wf_folder = None
@@ -465,7 +464,7 @@ def _prepare_clean(cls, recording, peaks, wfs_arrays, sparsity_mask, peak_labels
             dtype=recording.get_dtype(),
             sparsity_mask=sparsity_mask2,
             copy=(d["waveform_mode"] == "shared_memory"),
-            **d["job_kwargs"],
+            **job_kwargs,
         )
 
         return wfs_arrays2, sparsity_mask2
diff --git a/src/spikeinterface/sortingcomponents/clustering/sliding_nn.py b/src/spikeinterface/sortingcomponents/clustering/sliding_nn.py
index a6ffa5fdc2..40cedacdc5 100644
--- a/src/spikeinterface/sortingcomponents/clustering/sliding_nn.py
+++ b/src/spikeinterface/sortingcomponents/clustering/sliding_nn.py
@@ -71,11 +71,10 @@ class SlidingNNClustering:
         "tmp_folder": None,
         "verbose": False,
         "tmp_folder": None,
-        "job_kwargs": {"n_jobs": -1},
     }
 
     @classmethod
-    def _initialize_folder(cls, recording, peaks, params):
+    def _initialize_folder(cls, recording, peaks, params, job_kwargs=dict()):
         assert HAVE_NUMBA, "SlidingNN needs numba to work"
         assert HAVE_TORCH, "SlidingNN needs torch to work"
         assert HAVE_NNDESCENT, "SlidingNN needs pynndescent to work"
@@ -126,16 +125,16 @@ def _initialize_folder(cls, recording, peaks, params):
             dtype=dtype,
             sparsity_mask=sparsity_mask,
             copy=(d["waveform_mode"] == "shared_memory"),
-            **d["job_kwargs"],
+            **job_kwargs,
         )
 
         return wfs_arrays, sparsity_mask
 
     @classmethod
-    def main_function(cls, recording, peaks, params):
+    def main_function(cls, recording, peaks, params, job_kwargs=dict()):
         d = params
 
-        # wfs_arrays, sparsity_mask, noise = cls._initialize_folder(recording, peaks, params)
+        # wfs_arrays, sparsity_mask, noise = cls._initialize_folder(recording, peaks, params, job_kwargs)
 
         # prepare neighborhood parameters
         fs = recording.get_sampling_frequency()
@@ -228,7 +227,7 @@ def main_function(cls, recording, peaks, params):
                 n_channel_neighbors=d["n_channel_neighbors"],
                 low_memory=d["low_memory"],
                 knn_verbose=d["verbose"],
-                n_jobs=d["job_kwargs"]["n_jobs"],
+                n_jobs=job_kwargs["n_jobs"],
             )
             # remove the first nearest neighbor (which should be self)
             knn_distances = knn_distances[:, 1:]
@@ -297,7 +296,7 @@ def main_function(cls, recording, peaks, params):
                     # TODO HDBSCAN can be done on GPU with NVIDIA RAPIDS for speed
                     clusterer = hdbscan.HDBSCAN(
                         prediction_data=True,
-                        core_dist_n_jobs=d["job_kwargs"]["n_jobs"],
+                        core_dist_n_jobs=job_kwargs["n_jobs"],
                         **d["hdbscan_kwargs"],
                     ).fit(embeddings_chunk)
 
diff --git a/src/spikeinterface/sortingcomponents/clustering/tdc.py b/src/spikeinterface/sortingcomponents/clustering/tdc.py
index 13af5b0fab..59472d1374 100644
--- a/src/spikeinterface/sortingcomponents/clustering/tdc.py
+++ b/src/spikeinterface/sortingcomponents/clustering/tdc.py
@@ -9,27 +9,21 @@
 
 from spikeinterface.core import (
     get_channel_distances,
-    Templates,
-    compute_sparsity,
     get_global_tmp_folder,
 )
 
 from spikeinterface.core.node_pipeline import (
     run_node_pipeline,
-    ExtractDenseWaveforms,
     ExtractSparseWaveforms,
     PeakRetriever,
 )
 
-from spikeinterface.sortingcomponents.tools import extract_waveform_at_max_channel, cache_preprocessing
-from spikeinterface.sortingcomponents.peak_detection import detect_peaks, DetectPeakLocallyExclusive
+from spikeinterface.sortingcomponents.tools import extract_waveform_at_max_channel
 from spikeinterface.sortingcomponents.peak_selection import select_peaks
-from spikeinterface.sortingcomponents.peak_localization import LocalizeCenterOfMass, LocalizeGridConvolution
 from spikeinterface.sortingcomponents.waveforms.temporal_pca import TemporalPCAProjection
 
 from spikeinterface.sortingcomponents.clustering.split import split_clusters
 from spikeinterface.sortingcomponents.clustering.merge import merge_clusters
-from spikeinterface.sortingcomponents.clustering.tools import compute_template_from_sparse
 
 
 class TdcClustering:
@@ -50,15 +44,12 @@ class TdcClustering:
             "merge_radius_um": 40.0,
             "threshold_diff": 1.5,
         },
-        "job_kwargs": {},
     }
 
     @classmethod
-    def main_function(cls, recording, peaks, params):
+    def main_function(cls, recording, peaks, params, job_kwargs=dict()):
         import hdbscan
 
-        job_kwargs = params["job_kwargs"]
-
         if params["folder"] is None:
             randname = "".join(random.choices(string.ascii_uppercase + string.digits, k=6))
             clustering_folder = get_global_tmp_folder() / f"tdcclustering_{randname}"
diff --git a/src/spikeinterface/sortingcomponents/clustering/tools.py b/src/spikeinterface/sortingcomponents/clustering/tools.py
index e2a0d273d6..64cc0f39c4 100644
--- a/src/spikeinterface/sortingcomponents/clustering/tools.py
+++ b/src/spikeinterface/sortingcomponents/clustering/tools.py
@@ -172,8 +172,6 @@ def apply_waveforms_shift(waveforms, peak_shifts, inplace=False):
 
     """
 
-    print("apply_waveforms_shift")
-
     if inplace:
         aligned_waveforms = waveforms
     else:
@@ -193,6 +191,4 @@ def apply_waveforms_shift(waveforms, peak_shifts, inplace=False):
         else:
             aligned_waveforms[mask, -shift:, :] = wfs[:, :-shift, :]
 
-    print("apply_waveforms_shift DONE")
-
     return aligned_waveforms
diff --git a/src/spikeinterface/sortingcomponents/tests/common.py b/src/spikeinterface/sortingcomponents/tests/common.py
index 01e4445a13..d5e5b6be1b 100644
--- a/src/spikeinterface/sortingcomponents/tests/common.py
+++ b/src/spikeinterface/sortingcomponents/tests/common.py
@@ -21,4 +21,10 @@ def make_dataset():
         noise_kwargs=dict(noise_levels=5.0, strategy="on_the_fly"),
         seed=2205,
     )
+
+    channel_ids_as_integers = [id for id in range(recording.get_num_channels())]
+    unit_ids_as_integers = [id for id in range(sorting.get_num_units())]
+    recording = recording.rename_channels(new_channel_ids=channel_ids_as_integers)
+    sorting = sorting.rename_units(new_unit_ids=unit_ids_as_integers)
+
     return recording, sorting
diff --git a/src/spikeinterface/widgets/unit_waveforms.py b/src/spikeinterface/widgets/unit_waveforms.py
index c593836061..3b31eacee5 100644
--- a/src/spikeinterface/widgets/unit_waveforms.py
+++ b/src/spikeinterface/widgets/unit_waveforms.py
@@ -565,7 +565,7 @@ def _update_plot(self, change):
             channel_locations = self.sorting_analyzer.get_channel_locations()
         else:
             unit_indices = [list(self.templates.unit_ids).index(unit_id) for unit_id in unit_ids]
-            templates = self.templates.templates_array[unit_indices]
+            templates = self.templates.get_dense_templates()[unit_indices]
             templates_shadings = None
             channel_locations = self.templates.get_channel_locations()