Switch exporters docs to SortingAnalyzer

doc/modules/exporters.rst

@@ -13,29 +13,31 @@ The :py:func:`~spikeinterface.exporters.export_to_phy` function allows you to us
 results.
 
 **Note** : :py:func:`~spikeinterface.exporters.export_to_phy` speed and the size of the folder will highly depend
-on the sparsity of the :code:`WaveformExtractor` itself or the external specified sparsity.
+on the sparsity of the :code:`SortingAnalyzer` itself or the external specified sparsity.
 The Phy viewer enables one to explore PCA projections, spike amplitudes, waveforms and quality of spike sorting results.
-So if these pieces of information have already been computed as extensions (see :ref:`waveform_extensions`),
+So if these pieces of information have already been computed as extensions (see :ref:`analyzer_extensions`),
 then exporting to Phy should be fast (and the user has better control of the parameters for the extensions).
 If not pre-computed, then the required extensions (e.g., :code:`spike_amplitudes`, :code:`principal_components`)
 can be computed directly at export time.
 
-The input of the :py:func:`~spikeinterface.exporters.export_to_phy` is a :code:`WaveformExtractor` object.
+The input of the :py:func:`~spikeinterface.exporters.export_to_phy` is a :code:`SortingAnalyzer` object.
 
 .. code-block:: python
 
+    import spikeinterface as si # core module only
     from spikeinterface.postprocessing import compute_spike_amplitudes, compute_principal_components
     from spikeinterface.exporters import export_to_phy
 
     # the waveforms are sparse so it is faster to export to phy
-    we = extract_waveforms(recording=recording, sorting=sorting, folder='waveforms')
+    sorting_analyzer = si.create_sorting_analyzer(sorting=sorting, recording=recording)
 
     # some computations are done before to control all options
-    _ = compute_spike_amplitudes(waveform_extractor=we)
-    _ = compute_principal_components(waveform_extractor=we, n_components=3, mode='by_channel_global')
+    sorting_analyzer.compute(['random_spikes', 'waveforms', 'templates', 'noise_levels'])
+    _ = sorting_analyzer.compute('spike_amplitudes')
+    _ = sorting_analyzer.compute('principal_components', n_components = 5, mode="by_channel_local")
 
     # the export process is fast because everything is pre-computed
-    export_to_phy(waveform_extractor=we, output_folder='path/to/phy_folder')
+    export_to_phy(sorting_analyzer=sorting_analyzer, output_folder='path/to/phy_folder')
 
 
 
@@ -54,7 +56,7 @@ depth VS amplitude) as well as unit-specific reports, that include waveforms, te
 ISI distributions, and more.
 
 **Note** : similarly to :py:func:`~spikeinterface.exporters.export_to_phy` the
-:py:func:`~spikeinterface.exporters.export_report` depends on the sparsity of the :code:`WaveformExtractor` itself and
+:py:func:`~spikeinterface.exporters.export_report` depends on the sparsity of the :code:`SortingAnalyzer` itself and
 on which extensions have been computed. For example, :code:`spike_amplitudes` and :code:`correlograms` related plots
 will be automatically included in the report if the associated extensions are computed in advance.
 The function can perform these computations as well, but it is a better practice to compute everything that's needed
@@ -65,18 +67,20 @@ with many units!
 
 .. code-block:: python
 
+    import spikeinterface as si # core module only
     from spikeinterface.postprocessing import compute_spike_amplitudes, compute_correlograms
     from spikeinterface.qualitymetrics import compute_quality_metrics
     from spikeinterface.exporters import export_report
 
 
     # the waveforms are sparse for more interpretable figures
-    we = extract_waveforms(recording=recording, sorting=sorting, folder='path/to/wf',)
+    sorting_analyzer = si.create_sorting_analyzer(sorting=sorting, recording=recording,)
 
     # some computations are done before to control all options
-    _ = compute_spike_amplitudes(waveform_extractor=we)
-    - = compute_correlograms(waveform_extractor=we)
-    _ = compute_quality_metrics(waveform_extractor=we, metric_names=['snr', 'isi_violation', 'presence_ratio'])
+    sorting_analyzer.compute(['random_spikes', 'waveforms', 'templates', 'noise_levels'])
+    sorting_analyzer.compute(['spike_amplitudes', 'correlograms', 'template_similarity', 'quality_metrics'],
+                             extension_params=dict(quality_metrics=dict(metric_names=['snr', 'isi_violation', 'presence_ratio']))
+                             )
 
     # the export process
-    export_report(waveform_extractor=we, output_folder='path/to/spikeinterface-report-folder')
+    export_report(sorting_analyzer=sorting_analyzer, output_folder='path/to/spikeinterface-report-folder')

src/spikeinterface/exporters/report.py

@@ -60,7 +60,7 @@ def export_report(
     else:
         spike_amplitudes = None
         print(
-            "export_report(): spike_amplitudes will not be exported. Use compute_spike_amplitudes() if you want to include them."
+            "export_report(): spike_amplitudes will not be exported. Use sorting_analyzer.compute('spike_amplitudes') if you want to include them."
         )
 
     # load or compute quality_metrics
@@ -72,7 +72,7 @@ def export_report(
     else:
         metrics = None
         print(
-            "export_report(): quality metrics will not be exported. Use compute_quality_metrics() if you want to include them."
+            "export_report(): quality metrics will not be exported. Use sorting_analyzer.compute('quality_metrics') if you want to include them."
         )
 
     # load or compute correlograms
@@ -83,7 +83,7 @@ def export_report(
     else:
         correlograms = None
         print(
-            "export_report(): correlograms will not be exported. Use compute_correlograms() if you want to include them."
+            "export_report(): correlograms will not be exported. Use sorting_anlyzer.compute('correlograms') if you want to include them."
         )
 
     # pre-compute unit locations if not done
@@ -102,9 +102,9 @@ def export_report(
     units = pd.DataFrame(index=unit_ids)  #  , columns=['max_on_channel_id', 'amplitude'])
     units.index.name = "unit_id"
     units["max_on_channel_id"] = pd.Series(
-        get_template_extremum_channel(sorting_analyzer, peak_sign="neg", outputs="id")
+        get_template_extremum_channel(sorting_analyzer, peak_sign=peak_sign, outputs="id")
     )
-    units["amplitude"] = pd.Series(get_template_extremum_amplitude(sorting_analyzer, peak_sign="neg"))
+    units["amplitude"] = pd.Series(get_template_extremum_amplitude(sorting_analyzer, peak_sign=peak_sign))
     units.to_csv(output_folder / "unit list.csv", sep="\t")
 
     unit_colors = sw.get_unit_colors(sorting)

src/spikeinterface/exporters/to_phy.py

@@ -147,7 +147,7 @@ def export_to_phy(
             rec_path = "None"
     else:  # don't save recording.dat
         if copy_binary:
-            warnings.warn("Recording will not be copied since waveform extractor is recordingless.")
+            warnings.warn("Recording will not be copied since sorting_analyzer is recordingless.")
         rec_path = "None"
 
     dtype_str = np.dtype(dtype).name
@@ -181,7 +181,7 @@ def export_to_phy(
     # export templates/templates_ind/similar_templates
     # shape (num_units, num_samples, max_num_channels)
     templates_ext = sorting_analyzer.get_extension("templates")
-    assert templates_ext is not None, "export_to_phy need SortingAnalyzer with extension 'templates'"
+    assert templates_ext is not None, "export_to_phy requires a SortingAnalyzer with the extension 'templates'"
     max_num_channels = max(len(chan_inds) for chan_inds in sparse_dict.values())
     dense_templates = templates_ext.get_templates(unit_ids=unit_ids, operator=template_mode)
     num_samples = dense_templates.shape[1]