Merge pull request #3993 from zm711/deps

First pass of deprecation removals for 0.103.0

Author: alejoe91

doc/get_started/quickstart.rst

@@ -336,7 +336,7 @@ Alternatively we can pass a full dictionary containing the parameters:
 
     # parameters set by params dictionary
     sorting_TDC_2 = ss.run_sorter(
-        sorter_name="tridesclous", recording=recording_preprocessed, output_folder="tdc_output2", **other_params
+        sorter_name="tridesclous", recording=recording_preprocessed, folder="tdc_output2", **other_params
     )
     print(sorting_TDC_2)
 

doc/how_to/analyze_neuropixels.rst

@@ -567,7 +567,7 @@ In this example:
     # run kilosort2.5 without drift correction
     params_kilosort2_5 = {'do_correction': False}
 
-    sorting = si.run_sorter('kilosort2_5', rec, output_folder=base_folder / 'kilosort2.5_output',
+    sorting = si.run_sorter('kilosort2_5', rec, folder=base_folder / 'kilosort2.5_output',
                             docker_image=True, verbose=True, **params_kilosort2_5)
 
 .. code:: ipython3

doc/how_to/process_by_channel_group.rst

@@ -160,7 +160,7 @@ sorting objects in a dictionary for later use.
         sorting = run_sorter(
             sorter_name='kilosort2',
             recording=split_preprocessed_recording,
-            output_folder=f"folder_KS2_group{group}"
+            folder=f"folder_KS2_group{group}"
             )
         sortings[group] = sorting
 

doc/modules/sorters.rst

@@ -55,15 +55,15 @@ to easily run spike sorters:
     from spikeinterface.sorters import run_sorter
 
     # run Tridesclous
-    sorting_TDC = run_sorter(sorter_name="tridesclous", recording=recording, output_folder="/folder_TDC")
+    sorting_TDC = run_sorter(sorter_name="tridesclous", recording=recording, folder="/folder_TDC")
     # run Kilosort2.5
-    sorting_KS2_5 = run_sorter(sorter_name="kilosort2_5", recording=recording, output_folder="/folder_KS2_5")
+    sorting_KS2_5 = run_sorter(sorter_name="kilosort2_5", recording=recording, folder="/folder_KS2_5")
     # run IronClust
-    sorting_IC = run_sorter(sorter_name="ironclust", recording=recording, output_folder="/folder_IC")
+    sorting_IC = run_sorter(sorter_name="ironclust", recording=recording, folder="/folder_IC")
     # run pyKilosort
-    sorting_pyKS = run_sorter(sorter_name="pykilosort", recording=recording, output_folder="/folder_pyKS")
+    sorting_pyKS = run_sorter(sorter_name="pykilosort", recording=recording, folder="/folder_pyKS")
     # run SpykingCircus
-    sorting_SC = run_sorter(sorter_name="spykingcircus", recording=recording, output_folder="/folder_SC")
+    sorting_SC = run_sorter(sorter_name="spykingcircus", recording=recording, folder="/folder_SC")
 
 
 Then the output, which is a :py:class:`~spikeinterface.core.BaseSorting` object, can be easily
@@ -87,10 +87,10 @@ Spike-sorter-specific parameters can be controlled directly from the
 
 .. code-block:: python
 
-    sorting_TDC = run_sorter(sorter_name='tridesclous', recording=recording, output_folder="/folder_TDC",
+    sorting_TDC = run_sorter(sorter_name='tridesclous', recording=recording, folder="/folder_TDC",
                              detect_threshold=8.)
 
-    sorting_KS2_5 = run_sorter(sorter_name="kilosort2_5", recording=recording, output_folder="/folder_KS2_5"
+    sorting_KS2_5 = run_sorter(sorter_name="kilosort2_5", recording=recording, folder="/folder_KS2_5"
                                do_correction=False, preclust_threshold=6, freq_min=200.)
 
 
@@ -193,7 +193,7 @@ The following code creates a test recording and runs a containerized spike sorte
 
     sorting = ss.run_sorter(sorter_name='kilosort3',
         recording=test_recording,
-        output_folder="kilosort3",
+        folder="kilosort3",
         singularity_image=True)
 
     print(sorting)
@@ -208,7 +208,7 @@ To run in Docker instead of Singularity, use ``docker_image=True``.
 .. code-block:: python
 
     sorting = run_sorter(sorter_name='kilosort3', recording=test_recording,
-                         output_folder="/tmp/kilosort3", docker_image=True)
+                         folder="/tmp/kilosort3", docker_image=True)
 
 To use a specific image, set either ``docker_image`` or ``singularity_image`` to a string,
 e.g. ``singularity_image="spikeinterface/kilosort3-compiled-base:0.1.0"``.
@@ -217,7 +217,7 @@ e.g. ``singularity_image="spikeinterface/kilosort3-compiled-base:0.1.0"``.
 
     sorting = run_sorter(sorter_name="kilosort3",
         recording=test_recording,
-        output_folder="kilosort3",
+        folder="kilosort3",
         singularity_image="spikeinterface/kilosort3-compiled-base:0.1.0")
 
 
@@ -301,10 +301,10 @@ an :code:`engine` that supports parallel processing (such as :code:`joblib` or :
     another_recording = ...
 
     job_list = [
-      {'sorter_name': 'tridesclous', 'recording': recording, 'output_folder': 'folder1','detect_threshold': 5.},
-      {'sorter_name': 'tridesclous', 'recording': another_recording, 'output_folder': 'folder2', 'detect_threshold': 5.},
-      {'sorter_name': 'herdingspikes', 'recording': recording, 'output_folder': 'folder3', 'clustering_bandwidth': 8., 'docker_image': True},
-      {'sorter_name': 'herdingspikes', 'recording': another_recording, 'output_folder': 'folder4', 'clustering_bandwidth': 8., 'docker_image': True},
+      {'sorter_name': 'tridesclous', 'recording': recording, 'folder': 'folder1','detect_threshold': 5.},
+      {'sorter_name': 'tridesclous', 'recording': another_recording, 'folder': 'folder2', 'detect_threshold': 5.},
+      {'sorter_name': 'herdingspikes', 'recording': recording, 'folder': 'folder3', 'clustering_bandwidth': 8., 'docker_image': True},
+      {'sorter_name': 'herdingspikes', 'recording': another_recording, 'folder': 'folder4', 'clustering_bandwidth': 8., 'docker_image': True},
     ]
 
     # run in loop
@@ -380,7 +380,7 @@ In this example, we create a 16-channel recording with 4 tetrodes:
     # here the result is a dict of a sorting object
     sortings = {}
     for group, sub_recording in recordings.items():
-        sorting = run_sorter(sorter_name='kilosort2', recording=recording, output_folder=f"folder_KS2_group{group}")
+        sorting = run_sorter(sorter_name='kilosort2', recording=recording, folder=f"folder_KS2_group{group}")
         sortings[group] = sorting
 
 **Option 2 : Automatic splitting**
@@ -390,7 +390,7 @@ In this example, we create a 16-channel recording with 4 tetrodes:
     # here the result is one sorting that aggregates all sub sorting objects
     aggregate_sorting = run_sorter_by_property(sorter_name='kilosort2', recording=recording_4_tetrodes,
                                                grouping_property='group',
-                                               working_folder='working_path')
+                                               folder='working_path')
 
 
 Handling multi-segment recordings
@@ -546,7 +546,7 @@ From the user's perspective, they behave exactly like the external sorters:
 
 .. code-block:: python
 
-    sorting = run_sorter(sorter_name="spykingcircus2", recording=recording, output_folder="/tmp/folder")
+    sorting = run_sorter(sorter_name="spykingcircus2", recording=recording, folder="/tmp/folder")
 
 
 Contributing

examples/tutorials/core/plot_1_recording_extractor.py

@@ -122,7 +122,7 @@
 ##############################################################################
 # You can also get a recording with a subset of channels (i.e. a channel slice):
 
-recording4 = recording3.channel_slice(channel_ids=["a", "c", "e"])
+recording4 = recording3.select_channels(channel_ids=["a", "c", "e"])
 print(recording4)
 print(recording4.get_channel_ids())
 

examples/tutorials/qualitymetrics/plot_3_quality_metrics.py

@@ -9,12 +9,10 @@
 
 import spikeinterface.core as si
 import spikeinterface.extractors as se
-from spikeinterface.postprocessing import compute_principal_components
 from spikeinterface.qualitymetrics import (
     compute_snrs,
     compute_firing_rates,
     compute_isi_violations,
-    calculate_pc_metrics,
     compute_quality_metrics,
 )
 
@@ -70,7 +68,7 @@
 
 ##############################################################################
 # Some metrics are based on the principal component scores, so the exwtension
-# need to be computed before. For instance:
+# must be computed before. For instance:
 
 analyzer.compute("principal_components", n_components=3, mode="by_channel_global", whiten=True)
 

installation_tips/check_your_install.py

@@ -21,7 +21,7 @@ def _run_one_sorter_and_analyzer(sorter_name):
     job_kwargs = dict(n_jobs=-1, progress_bar=True, chunk_duration="1s")
     import spikeinterface.full as si
     recording = si.load_extractor('./toy_example_recording')
-    sorting = si.run_sorter(sorter_name, recording, output_folder=f'./sorter_with_{sorter_name}', verbose=False)
+    sorting = si.run_sorter(sorter_name, recording, folder=f'./sorter_with_{sorter_name}', verbose=False)
 
     sorting_analyzer = si.create_sorting_analyzer(sorting, recording,
                                                 format="binary_folder", folder=f"./analyzer_with_{sorter_name}",

src/spikeinterface/benchmark/benchmark_motion_interpolation.py

@@ -53,7 +53,7 @@ def run(self, **job_kwargs):
         sorting = run_sorter(
             sorter_name,
             recording,
-            output_folder=self.sorter_folder,
+            folder=self.sorter_folder,
             **sorter_params,
             delete_output_folder=False,
         )

src/spikeinterface/core/baserecording.py

@@ -369,21 +369,6 @@ def get_traces(
                 traces = traces.astype("float32", copy=False) * gains + offsets
         return traces
 
-    def has_scaled_traces(self) -> bool:
-        """Checks if the recording has scaled traces
-
-        Returns
-        -------
-        bool
-            True if the recording has scaled traces, False otherwise
-        """
-        warnings.warn(
-            "`has_scaled_traces` is deprecated and will be removed in 0.103.0. Use has_scaleable_traces() instead",
-            category=DeprecationWarning,
-            stacklevel=2,
-        )
-        return self.has_scaled()
-
     def get_time_info(self, segment_index=None) -> dict:
         """
         Retrieves the timing attributes for a given segment index. As with
@@ -725,17 +710,6 @@ def rename_channels(self, new_channel_ids: list | np.array | tuple) -> "BaseReco
 
         return ChannelSliceRecording(self, renamed_channel_ids=new_channel_ids)
 
-    def _channel_slice(self, channel_ids, renamed_channel_ids=None):
-        from .channelslice import ChannelSliceRecording
-
-        warnings.warn(
-            "Recording.channel_slice will be removed in version 0.103, use `select_channels` or `rename_channels` instead.",
-            DeprecationWarning,
-            stacklevel=2,
-        )
-        sub_recording = ChannelSliceRecording(self, channel_ids, renamed_channel_ids=renamed_channel_ids)
-        return sub_recording
-
     def _remove_channels(self, remove_channel_ids):
         from .channelslice import ChannelSliceRecording
 
@@ -878,8 +852,6 @@ def binary_compatible_with(
         time_axis=None,
         file_paths_length=None,
         file_offset=None,
-        file_suffix=None,
-        file_paths_lenght=None,
     ):
         """
         Check is the recording is binary compatible with some constrain on
@@ -891,14 +863,6 @@ def binary_compatible_with(
           * file_suffix
         """
 
-        # spelling typo need to fix
-        if file_paths_lenght is not None:
-            warnings.warn(
-                "`file_paths_lenght` is deprecated and will be removed in 0.103.0 please use `file_paths_length`"
-            )
-            if file_paths_length is None:
-                file_paths_length = file_paths_lenght
-
         if not self.is_binary_compatible():
             return False
 

src/spikeinterface/core/baserecordingsnippets.py

@@ -51,14 +51,6 @@ def has_scaleable_traces(self) -> bool:
         else:
             return True
 
-    def has_scaled(self):
-        warn(
-            "`has_scaled` has been deprecated and will be removed in 0.103.0. Please use `has_scaleable_traces()`",
-            category=DeprecationWarning,
-            stacklevel=2,
-        )
-        return self.has_scaleable_traces()
-
     def has_probe(self) -> bool:
         return "contact_vector" in self.get_property_keys()
 
@@ -69,9 +61,6 @@ def is_filtered(self):
         # the is_filtered is handle with annotation
         return self._annotations.get("is_filtered", False)
 
-    def _channel_slice(self, channel_ids, renamed_channel_ids=None):
-        raise NotImplementedError
-
     def set_probe(self, probe, group_mode="by_probe", in_place=False):
         """
         Attach a list of Probe object to a recording.
@@ -234,21 +223,6 @@ def _set_probes(self, probe_or_probegroup, group_mode="by_probe", in_place=False
 
         return sub_recording
 
-    def set_probes(self, probe_or_probegroup, group_mode="by_probe", in_place=False):
-
-        warning_msg = (
-            "`set_probes` is now a private function and the public function will be "
-            "removed in 0.103.0. Please use `set_probe` or `set_probegroup` instead"
-        )
-
-        warn(warning_msg, category=DeprecationWarning, stacklevel=2)
-
-        sub_recording = self._set_probes(
-            probe_or_probegroup=probe_or_probegroup, group_mode=group_mode, in_place=in_place
-        )
-
-        return sub_recording
-
     def get_probe(self):
         probes = self.get_probes()
         assert len(probes) == 1, "there are several probe use .get_probes() or get_probegroup()"
@@ -441,25 +415,6 @@ def planarize(self, axes: str = "xy"):
 
         return recording2d
 
-    # utils
-    def channel_slice(self, channel_ids, renamed_channel_ids=None):
-        """
-        Returns a new object with sliced channels.
-
-        Parameters
-        ----------
-        channel_ids : np.array or list
-            The list of channels to keep
-        renamed_channel_ids : np.array or list, default: None
-            A list of renamed channels
-
-        Returns
-        -------
-        BaseRecordingSnippets
-            The object with sliced channels
-        """
-        return self._channel_slice(channel_ids, renamed_channel_ids=renamed_channel_ids)
-
     def select_channels(self, channel_ids):
         """
         Returns a new object with sliced channels.