Merge branch 'main' into ks4

Author: zm711

doc/development/development.rst

@@ -397,3 +397,52 @@ After this you need to add a block in `Install Sorters <https://github.com/Spike
 to describe your sorter.
 
 Finally, make a pull request so we can review the code and incorporate into the sorters module of SpikeInterface!
+
+
+
+How to make a release
+---------------------
+
+Checklist
+^^^^^^^^^
+* pyproject.toml: check that the version is ahead of current release. Also, comment out the @ (git dependencies)
+* In the top level ``__init__`` (located at ``src/spikeinterface/__init__.py``) set ``DEV_MODE`` to ``False`` (this is used for the docker installations)
+* Create a new release note for the appropriate version on doc/releases/new_version_tag.
+
+There can be large releases like:
+
+``doc/releases/0.101.0.rst``
+
+Which contain a section called "Main Changes" and minor releases which include only bug fixes like:
+
+``doc/releases/0.101.2.rst``
+
+To collect all the PRs and bug fixes we have a script in:
+``doc/scripts/``
+called ``auto-release-notes.sh``. Run it with ``bash auto-release-notes.sh`` and it will create the release notes for the module specific changes.
+
+The first time you run the script, GitHub will guide you through an authorization process if you've not already done so.
+
+The signature of the script is:
+
+.. code-block:: bash
+
+    bash auto-release-notes.sh <start_date> <end_date>
+
+Where the start date is the date of the last release and the end date is the current date. Dates are in YYYY-MM-DD format
+
+The date of the last release can be found on `PyPI <https://pypi.org/project/spikeinterface/>`_.
+
+
+As a specific example:
+.. code-block:: bash
+
+    bash auto-release-notes.sh 2025-02-19 2025-03-24
+
+* Finish the release notes and merge
+* Locally tag the main branch with the newly merged release notes with the new version
+* Push the tag to the remote repository which will trigger the release action (.github/workflows/publish-to-pypi.yml)
+* Do an after-release `PR <https://github.com/SpikeInterface/spikeinterface/pull/3828/files>`_:
+    - Uncomment the git installs in pyproject
+    - Set ``DEV_MODE`` to ``True`` in the top level ``__init__`` (located at ``src/spikeinterface/__init__.py``)
+    - Update `pyproject.toml` version one patch ahead or one minor if it is larger one.

doc/how_to/customize_a_plot.rst

@@ -0,0 +1,136 @@
+Customize a plot
+================
+
+The ``SpikeInterface`` widgets are designed to have reasonable default
+plotting options, but sometimes you’ll want to make adjustments to the
+plots. The plotting functions all return a ``Widget`` object. These
+contain and give you access to the underlying matplotlib figure and
+axis, which you can apply any matplotlib machinery to. Let’s see how to
+do this in an example, by first making some synthetic data and computing
+extensions which can be used for plotting.
+
+.. code::
+
+    import spikeinterface.full as si
+    import matplotlib.pyplot as plt
+
+    recording, sorting = si.generate_ground_truth_recording(seed=1205)
+    sorting_analyzer = si.create_sorting_analyzer(sorting=sorting, recording=recording)
+    sorting_analyzer.compute({"random_spikes": {'seed': 1205}, "templates": {}, "unit_locations": {}})
+
+    unit_locations = sorting_analyzer.get_extension("unit_locations").get_data()
+
+
+
+.. parsed-literal::
+
+    estimate_sparsity (no parallelization):   0%|          | 0/10 [00:00<?, ?it/s]
+
+
+
+.. parsed-literal::
+
+    estimate_templates_with_accumulator (no parallelization):   0%|          | 0/10 [00:00<?, ?it/s]
+
+
+Now we can plot the ``unit_locations`` and ``unit_templates`` using the
+appropriate widgets (see the `full list of
+widgets <https://spikeinterface.readthedocs.io/en/stable/modules/widgets.html#available-plotting-functions>`__
+for more!). These functions output a ``Widget object``. We’ll assign the
+unit locations widget to ``fig_units``.
+
+.. code::
+
+    fig_units = si.plot_unit_locations(sorting_analyzer)
+
+    # Each widget contains a `matplotlib` figure and axis:
+    print(type(fig_units.figure))
+    print(type(fig_units.ax))
+
+
+.. parsed-literal::
+
+    <class 'matplotlib.figure.Figure'>
+    <class 'matplotlib.axes._axes.Axes'>
+
+
+
+.. image:: customize_a_plot_files/customize_a_plot_4_1.png
+
+
+By gaining access to the matplotlib objects, we are able to utilize the
+full ``matplotlib`` machinery: adding custom titles, axis labels, ticks,
+more plots etc. Let’s customize our unit locations plot. (Note: the
+``SpikeInterface`` Team does not endorse the following style
+conventions):
+
+.. code::
+
+    # Get the widget
+    fig_units = si.plot_unit_locations(sorting_analyzer)
+
+    # Modify the widget's `axis`` to set the title and axes labels
+    fig_units.ax.set_title("My favorite units", fontname = "Comic Sans MS")
+    fig_units.ax.set_xlabel("x probe location (um)")
+    fig_units.ax.set_ylabel("y probe location (um)")
+
+    # You can also set custom ticks
+    fig_units.ax.set_xticks([-60,-30,unit_locations[0,0],30,60])
+    fig_units.ax.set_xticklabels([-60,-30,"unit_0_x",30,60])
+    fig_units.ax.set_yticks([-40,-20,0,unit_locations[0,1],40])
+    fig_units.ax.set_yticklabels([-40,-20,0,"unit_0_y",40])
+
+    # Change the limits of the plot
+    fig_units.ax.set_xlim((-30,50))
+    fig_units.ax.set_ylim((-50,50))
+
+    # And add extra information on the plot
+    fig_units.ax.text(unit_locations[6,0], unit_locations[6,1]+5, s="UNIT 6!!!", fontname="Courier")
+
+    fig_units
+
+
+
+
+.. parsed-literal::
+
+    <spikeinterface.widgets.unit_locations.UnitLocationsWidget at 0x147a81520>
+
+
+
+
+.. image:: customize_a_plot_files/customize_a_plot_6_1.png
+
+
+Beautiful!!!
+
+You can also combine figures into a multi-figure plot. The easiest way
+to do this is to set up your figure and axes first, then tell
+``SpikeInterface`` which axes it should attach the widget plot to.
+Here’s an example of making a unit summary plot.
+
+.. code::
+
+    import matplotlib.pyplot as plt
+    fig, axs = plt.subplots(ncols=2, nrows=1)
+
+    unit_id=8
+    si.plot_unit_locations(sorting_analyzer=sorting_analyzer, ax=axs[0])
+    si.plot_unit_templates(sorting_analyzer, axes=[axs[1]], unit_ids=[f'{unit_id}'])
+
+    axs[0].plot([unit_locations[8,0], unit_locations[8,0]+50], [unit_locations[8,1], unit_locations[8,1]+50])
+    axs[0].text(unit_locations[8,0]+52, unit_locations[8,1]+52, s=f"Unit {unit_id}")
+    axs[0].set_title("Unit location", fontsize=10)
+
+    fig.suptitle(f"Unit {unit_id} summary", fontfamily="Comic Sans MS", fontsize=20)
+
+    fig.tight_layout()
+
+
+
+
+.. image:: customize_a_plot_files/customize_a_plot_8_1.png
+
+
+For more details on what you can do using matplotlib, check out their
+`extensive documentation <https://matplotlib.org/stable/>`__

doc/how_to/customize_a_plot_files/customize_a_plot_4_1.png

@@ -17,3 +17,4 @@ Guides on how to solve specific, short problems in SpikeInterface. Learn how to.
     drift_with_lfp
     auto_curation_training
     auto_curation_prediction
+    customize_a_plot

doc/how_to/customize_a_plot_files/customize_a_plot_6_1.png

@@ -14,7 +14,7 @@ with `nbconvert`. Here are the steps (in this example for the `get_started`):
 
 ```
 >>>  jupytext --to notebook get_started.py
->>>  jupytext --set-formats ipynb,py get_started.ipynb
+>>>  jupytext --set-formats ipynb.py get_started.ipynb
 ```
 
 2. Run the notebook

doc/how_to/customize_a_plot_files/customize_a_plot_8_1.png

@@ -0,0 +1,100 @@
+# ---
+# jupyter:
+#   jupytext:
+#     cell_metadata_filter: -all
+#     formats: py:light,ipynb
+#     text_representation:
+#       extension: .py
+#       format_name: light
+#       format_version: '1.5'
+#       jupytext_version: 1.17.0
+#   kernelspec:
+#     display_name: .venv
+#     language: python
+#     name: python3
+# ---
+
+# # Customize a plot
+
+# The `SpikeInterface` widgets are designed to have reasonable default plotting options, but
+# sometimes you'll want to make adjustments to the plots. The plotting functions all return
+# a `Widget` object. These contain and give you access to the underlying matplotlib figure
+# and axis, which you can apply any matplotlib machinery to. Let's see how to do this in an
+# example, by first making some synthetic data and computing extensions which can be used for plotting.
+
+# +
+import spikeinterface.full as si
+import matplotlib.pyplot as plt
+
+recording, sorting = si.generate_ground_truth_recording(seed=1205)
+sorting_analyzer = si.create_sorting_analyzer(sorting=sorting, recording=recording)
+sorting_analyzer.compute({"random_spikes": {'seed': 1205}, "templates": {}, "unit_locations": {}})
+
+unit_locations = sorting_analyzer.get_extension("unit_locations").get_data()
+# -
+
+# Now we can plot the `unit_locations` and `unit_templates` using the appropriate widgets
+# (see the  [full list of widgets](https://spikeinterface.readthedocs.io/en/stable/modules/widgets.html#available-plotting-functions)
+# for more!). These functions output a `Widget object`.  We'll assign the unit locations widget to `fig_units`.
+
+# +
+fig_units = si.plot_unit_locations(sorting_analyzer)
+
+# Each widget contains a `matplotlib` figure and axis:
+print(type(fig_units.figure))
+print(type(fig_units.ax))
+# -
+
+# By gaining access to the matplotlib objects, we are able to utilize the full `matplotlib`
+# machinery: adding custom titles, axis labels, ticks, more plots etc. Let's customize
+# our unit locations plot. (Note: the `SpikeInterface` Team does not endorse the following style conventions):
+
+# +
+# Get the widget
+fig_units = si.plot_unit_locations(sorting_analyzer)
+
+# Modify the widget's `axis`` to set the title and axes labels
+fig_units.ax.set_title("My favorite units", fontname = "Comic Sans MS")
+fig_units.ax.set_xlabel("x probe location (um)")
+fig_units.ax.set_ylabel("y probe location (um)")
+
+# You can also set custom ticks
+fig_units.ax.set_xticks([-60,-30,unit_locations[0,0],30,60])
+fig_units.ax.set_xticklabels([-60,-30,"unit_0_x",30,60])
+fig_units.ax.set_yticks([-40,-20,0,unit_locations[0,1],40])
+fig_units.ax.set_yticklabels([-40,-20,0,"unit_0_y",40])
+
+# Change the limits of the plot
+fig_units.ax.set_xlim((-30,50))
+fig_units.ax.set_ylim((-50,50))
+
+# And add extra information on the plot
+fig_units.ax.text(unit_locations[6,0], unit_locations[6,1]+5, s="UNIT 6!!!", fontname="Courier")
+
+fig_units
+# -
+
+# Beautiful!!!
+#
+# You can also combine figures into a multi-figure plot. The easiest way to do this is to set up your
+# figure and axes first, then tell `SpikeInterface` which axes it should attach the widget plot to.
+# Here's an example of making a unit summary plot.
+
+# +
+import matplotlib.pyplot as plt
+fig, axs = plt.subplots(ncols=2, nrows=1)
+
+unit_id=8
+si.plot_unit_locations(sorting_analyzer=sorting_analyzer, ax=axs[0])
+si.plot_unit_templates(sorting_analyzer, axes=[axs[1]], unit_ids=[f'{unit_id}'])
+
+axs[0].plot([unit_locations[8,0], unit_locations[8,0]+50], [unit_locations[8,1], unit_locations[8,1]+50])
+axs[0].text(unit_locations[8,0]+52, unit_locations[8,1]+52, s=f"Unit {unit_id}")
+axs[0].set_title("Unit location", fontsize=10)
+
+fig.suptitle(f"Unit {unit_id} summary", fontfamily="Comic Sans MS", fontsize=20)
+
+fig.tight_layout()
+# -
+
+# For more details on what you can do using matplotlib, check out their [extensive documentation](https://matplotlib.org/stable/)

doc/how_to/index.rst

@@ -709,7 +709,7 @@ class ComputeNoiseLevels(AnalyzerExtension):
     depend_on = []
     need_recording = True
     use_nodepipeline = False
-    need_job_kwargs = False
+    need_job_kwargs = True
     need_backward_compatibility_on_load = True
 
     def __init__(self, sorting_analyzer):
@@ -729,9 +729,12 @@ def _merge_extension_data(
         # this does not depend on units
         return self.data.copy()
 
-    def _run(self, verbose=False):
+    def _run(self, verbose=False, **job_kwargs):
         self.data["noise_levels"] = get_noise_levels(
-            self.sorting_analyzer.recording, return_scaled=self.sorting_analyzer.return_scaled, **self.params
+            self.sorting_analyzer.recording,
+            return_scaled=self.sorting_analyzer.return_scaled,
+            **self.params,
+            **job_kwargs,
         )
 
     def _get_data(self):

examples/how_to/README.md

@@ -148,6 +148,18 @@ def get_binary_description(self):
         )
         return d
 
+    def __del__(self):
+        """
+        Ensures that all segment resources are properly cleaned up when this recording extractor is deleted.
+        Closes any open file handles in the recording segments.
+        """
+        # Close all recording segments
+        if hasattr(self, "_recording_segments"):
+            for segment in self._recording_segments:
+                # This will trigger the __del__ method of the BinaryRecordingSegment
+                # which will close the file handle
+                del segment
+
 
 BinaryRecordingExtractor.write_recording.__doc__ = BinaryRecordingExtractor.write_recording.__doc__.format(
     _shared_job_kwargs_doc
@@ -223,6 +235,15 @@ def get_traces(
 
         return traces
 
+    def __del__(self):
+        # Ensure that the file handle is closed when the segment is garbage-collected
+        try:
+            if hasattr(self, "file") and self.file and not self.file.closed:
+                self.file.close()
+        except Exception as e:
+            warnings.warn(f"Error closing file handle in BinaryRecordingSegment: {e}")
+            pass
+
 
 # For backward compatibility (old good time)
 BinDatRecordingExtractor = BinaryRecordingExtractor