Merge pull request #1859 from samuelgarcia/refactor_widget_module

Refactor widget module

doc/api.rst

@@ -269,13 +269,14 @@ spikeinterface.widgets
     .. autofunction:: plot_amplitudes
     .. autofunction:: plot_autocorrelograms
     .. autofunction:: plot_crosscorrelograms
+    .. autofunction:: plot_motion
     .. autofunction:: plot_quality_metrics
     .. autofunction:: plot_sorting_summary
     .. autofunction:: plot_spike_locations
     .. autofunction:: plot_spikes_on_traces
     .. autofunction:: plot_template_metrics
     .. autofunction:: plot_template_similarity
-    .. autofunction:: plot_timeseries
+    .. autofunction:: plot_traces
     .. autofunction:: plot_unit_depths
     .. autofunction:: plot_unit_locations
     .. autofunction:: plot_unit_summary

doc/how_to/analyse_neuropixels.rst

@@ -264,7 +264,7 @@ the ipywydgets interactive ploter
 .. code:: python
 
    %matplotlib widget
-   si.plot_timeseries({'filter':rec1, 'cmr': rec4}, backend='ipywidgets')
+   si.plot_traces({'filter':rec1, 'cmr': rec4}, backend='ipywidgets')
 
 Note that using this ipywydgets make possible to explore diffrents
 preprocessing chain wihtout to save the entire file to disk. Everything
@@ -276,9 +276,9 @@ is lazy, so you can change the previsous cell (parameters, step order,
     # here we use static plot using matplotlib backend
     fig, axs = plt.subplots(ncols=3, figsize=(20, 10))
 
-    si.plot_timeseries(rec1, backend='matplotlib',  clim=(-50, 50), ax=axs[0])
-    si.plot_timeseries(rec4, backend='matplotlib',  clim=(-50, 50), ax=axs[1])
-    si.plot_timeseries(rec, backend='matplotlib',  clim=(-50, 50), ax=axs[2])
+    si.plot_traces(rec1, backend='matplotlib',  clim=(-50, 50), ax=axs[0])
+    si.plot_traces(rec4, backend='matplotlib',  clim=(-50, 50), ax=axs[1])
+    si.plot_traces(rec, backend='matplotlib',  clim=(-50, 50), ax=axs[2])
     for i, label in enumerate(('filter', 'cmr', 'final')):
         axs[i].set_title(label)
 
@@ -292,7 +292,7 @@ is lazy, so you can change the previsous cell (parameters, step order,
     # plot some channels
     fig, ax = plt.subplots(figsize=(20, 10))
     some_chans = rec.channel_ids[[100, 150, 200, ]]
-    si.plot_timeseries({'filter':rec1, 'cmr': rec4}, backend='matplotlib', mode='line', ax=ax, channel_ids=some_chans)
+    si.plot_traces({'filter':rec1, 'cmr': rec4}, backend='matplotlib', mode='line', ax=ax, channel_ids=some_chans)
 
 
 

doc/how_to/get_started.rst

@@ -104,7 +104,7 @@ and the raster plots.
 
 .. code:: ipython3
 
-    w_ts = sw.plot_timeseries(recording, time_range=(0, 5))
+    w_ts = sw.plot_traces(recording, time_range=(0, 5))
     w_rs = sw.plot_rasters(sorting_true, time_range=(0, 5))
 
 

doc/modules/widgets.rst

@@ -123,7 +123,7 @@ The :code:`plot_*(..., backend="matplotlib")` functions come with the following
 .. code-block:: python
 
     # matplotlib backend
-    w = plot_timeseries(recording, backend="matplotlib")
+    w = plot_traces(recording, backend="matplotlib")
 
 **Output:**
 
@@ -146,9 +146,9 @@ Each function has the following additional arguments:
 
     from spikeinterface.preprocessing import common_reference
 
-    # ipywidgets backend also supports multiple "layers" for plot_timeseries
+    # ipywidgets backend also supports multiple "layers" for plot_traces
     rec_dict = dict(filt=recording, cmr=common_reference(recording))
-    w = sw.plot_timeseries(rec_dict, backend="ipywidgets")
+    w = sw.plot_traces(rec_dict, backend="ipywidgets")
 
 **Output:**
 
@@ -171,7 +171,7 @@ The functions have the following additional arguments:
 .. code-block:: python
 
     # sortingview backend
-    w_ts = sw.plot_timeseries(recording, backend="ipywidgets")
+    w_ts = sw.plot_traces(recording, backend="ipywidgets")
     w_ss = sw.plot_sorting_summary(recording, backend="sortingview")
 
 

examples/how_to/analyse_neuropixels.py

@@ -82,7 +82,7 @@
 #
 # ```python
 # # %matplotlib widget
-# si.plot_timeseries({'filter':rec1, 'cmr': rec4}, backend='ipywidgets')
+# si.plot_traces({'filter':rec1, 'cmr': rec4}, backend='ipywidgets')
 # ```
 #
 # Note that using this ipywidgets make possible to explore different preprocessing chains without saving the entire file to disk.
@@ -94,17 +94,17 @@
 # here we use a static plot using matplotlib backend
 fig, axs = plt.subplots(ncols=3, figsize=(20, 10))
 
-si.plot_timeseries(rec1, backend='matplotlib',  clim=(-50, 50), ax=axs[0])
-si.plot_timeseries(rec4, backend='matplotlib',  clim=(-50, 50), ax=axs[1])
-si.plot_timeseries(rec, backend='matplotlib',  clim=(-50, 50), ax=axs[2])
+si.plot_traces(rec1, backend='matplotlib',  clim=(-50, 50), ax=axs[0])
+si.plot_traces(rec4, backend='matplotlib',  clim=(-50, 50), ax=axs[1])
+si.plot_traces(rec, backend='matplotlib',  clim=(-50, 50), ax=axs[2])
 for i, label in enumerate(('filter', 'cmr', 'final')):
     axs[i].set_title(label)
 # -
 
 # plot some channels
 fig, ax = plt.subplots(figsize=(20, 10))
 some_chans = rec.channel_ids[[100, 150, 200, ]]
-si.plot_timeseries({'filter':rec1, 'cmr': rec4}, backend='matplotlib', mode='line', ax=ax, channel_ids=some_chans)
+si.plot_traces({'filter':rec1, 'cmr': rec4}, backend='matplotlib', mode='line', ax=ax, channel_ids=some_chans)
 
 
 # ### Should we save the preprocessed data to a binary file?

examples/how_to/get_started.py

@@ -92,7 +92,7 @@
 #
 # Let's use the `spikeinterface.widgets` module to visualize the traces and the raster plots.
 
-w_ts = sw.plot_timeseries(recording, time_range=(0, 5))
+w_ts = sw.plot_traces(recording, time_range=(0, 5))
 w_rs = sw.plot_rasters(sorting_true, time_range=(0, 5))
 
 # This is how you retrieve info from a `BaseRecording`...

examples/modules_gallery/extractors/plot_1_read_various_formats.py

@@ -87,7 +87,7 @@
 
 import spikeinterface.widgets as sw
 
-w_ts = sw.plot_timeseries(recording, time_range=(0, 5))
+w_ts = sw.plot_traces(recording, time_range=(0, 5))
 w_rs = sw.plot_rasters(sorting, time_range=(0, 5))
 
 plt.show()

examples/modules_gallery/widgets/plot_1_rec_gallery.py

@@ -15,22 +15,22 @@
 recording, sorting = se.toy_example(duration=10, num_channels=4, seed=0, num_segments=1)
 
 ##############################################################################
-# plot_timeseries()
+# plot_traces()
 # ~~~~~~~~~~~~~~~~~
 
-w_ts = sw.plot_timeseries(recording)
+w_ts = sw.plot_traces(recording)
 
 ##############################################################################
 # We can select time range
 
-w_ts1 = sw.plot_timeseries(recording, time_range=(5, 8))
+w_ts1 = sw.plot_traces(recording, time_range=(5, 8))
 
 ##############################################################################
 # We can color with groups
 
 recording2 = recording.clone()
 recording2.set_channel_groups(channel_ids=recording.get_channel_ids(), groups=[0, 0, 1, 1])
-w_ts2 = sw.plot_timeseries(recording2, time_range=(5, 8), color_groups=True)
+w_ts2 = sw.plot_traces(recording2, time_range=(5, 8), color_groups=True)
 
 ##############################################################################
 # **Note**: each function returns a widget object, which allows to access the figure and axis.
@@ -41,7 +41,7 @@
 ##############################################################################
 # We can also use the 'map' mode useful for high channel count
 
-w_ts = sw.plot_timeseries(recording, mode='map', time_range=(5, 8),
+w_ts = sw.plot_traces(recording, mode='map', time_range=(5, 8),
         show_channel_ids=True, order_channel_by_depth=True)
 
 ##############################################################################

src/spikeinterface/extractors/tests/test_cbin_ibl_extractors.py

@@ -22,7 +22,7 @@ class CompressedBinaryIblExtractorTest(RecordingCommonTestSuite, unittest.TestCa
 # ~ import matplotlib.pyplot as plt
 # ~ import spikeinterface.widgets as sw
 # ~ from probeinterface.plotting import plot_probe
-# ~ sw.plot_timeseries(rec)
+# ~ sw.plot_traces(rec)
 # ~ plot_probe(rec.get_probe())
 # ~ plt.show()
 

src/spikeinterface/preprocessing/tests/test_filter.py

@@ -105,10 +105,10 @@ def test_filter_opencl():
     # rec2_cached0 = rec2.save(chunk_size=1000,verbose=False, progress_bar=True, n_jobs=4)
 
     # import matplotlib.pyplot as plt
-    # from spikeinterface.widgets import plot_timeseries
-    # plot_timeseries(rec, segment_index=0)
-    # plot_timeseries(rec_filtered, segment_index=0)
-    # plot_timeseries(rec2_cached0, segment_index=0)
+    # from spikeinterface.widgets import plot_traces
+    # plot_traces(rec, segment_index=0)
+    # plot_traces(rec_filtered, segment_index=0)
+    # plot_traces(rec2_cached0, segment_index=0)
     # plt.show()
 
 

src/spikeinterface/preprocessing/tests/test_normalize_scale.py

@@ -30,7 +30,7 @@ def test_normalize_by_quantile():
     rec2.save(verbose=False)
 
     # import matplotlib.pyplot as plt
-    # from spikeinterface.widgets import plot_timeseries
+    # from spikeinterface.widgets import plot_traces
     # fig, ax = plt.subplots()
     # ax.plot(rec.get_traces(segment_index=0)[:, 0], color='g')
     # ax.plot(rec2.get_traces(segment_index=0)[:, 0], color='r')

src/spikeinterface/preprocessing/tests/test_phase_shift.py

@@ -104,9 +104,9 @@ def test_phase_shift():
 
     # ~ import matplotlib.pyplot as plt
     # ~ import spikeinterface.full as si
-    # ~ si.plot_timeseries(rec, segment_index=0, time_range=[0, 10])
-    # ~ si.plot_timeseries(rec2, segment_index=0, time_range=[0, 10])
-    # ~ si.plot_timeseries(rec3, segment_index=0, time_range=[0, 10])
+    # ~ si.plot_traces(rec, segment_index=0, time_range=[0, 10])
+    # ~ si.plot_traces(rec2, segment_index=0, time_range=[0, 10])
+    # ~ si.plot_traces(rec3, segment_index=0, time_range=[0, 10])
     # ~ plt.show()
 
 

src/spikeinterface/preprocessing/tests/test_rectify.py

@@ -27,7 +27,7 @@ def test_rectify():
     assert traces.shape[1] == 1
 
     # import matplotlib.pyplot as plt
-    # from spikeinterface.widgets import plot_timeseries
+    # from spikeinterface.widgets import plot_traces
     # fig, ax = plt.subplots()
     # ax.plot(rec.get_traces(segment_index=0)[:, 0], color='g')
     # ax.plot(rec2.get_traces(segment_index=0)[:, 0], color='r')

src/spikeinterface/sortingcomponents/benchmark/benchmark_peak_localization.py

@@ -455,7 +455,7 @@ def plot_figure_1(benchmark, mode="average", cell_ind="auto"):
     )
 
     print(benchmark.recording)
-    # si.plot_timeseries(benchmark.recording, mode='line', time_range=(times[0]-0.01, times[0] + 0.1), channel_ids=benchmark.recording.channel_ids[:20], ax=axs[0, 1])
+    # si.plot_traces(benchmark.recording, mode='line', time_range=(times[0]-0.01, times[0] + 0.1), channel_ids=benchmark.recording.channel_ids[:20], ax=axs[0, 1])
     # axs[0, 1].set_ylabel('Neurons')
 
     # si.plot_spikes_on_traces(benchmark.waveforms, unit_ids=[unit_id], time_range=(times[0]-0.01, times[0] + 0.1), unit_colors={unit_id : 'r'}, ax=axs[0, 1],

src/spikeinterface/widgets/__init__.py

@@ -1,37 +1,3 @@
-# check if backend are available
-try:
-    import matplotlib
-
-    HAVE_MPL = True
-except:
-    HAVE_MPL = False
-
-try:
-    import sortingview
-
-    HAVE_SV = True
-except:
-    HAVE_SV = False
-
-try:
-    import ipywidgets
-
-    HAVE_IPYW = True
-except:
-    HAVE_IPYW = False
-
-
-# theses import make the Widget.resgister() at import time
-if HAVE_MPL:
-    import spikeinterface.widgets.matplotlib
-
-if HAVE_SV:
-    import spikeinterface.widgets.sortingview
-
-if HAVE_IPYW:
-    import spikeinterface.widgets.ipywidgets
-
-# when importing widget list backend are already registered
 from .widget_list import *
 
 # general functions

src/spikeinterface/widgets/_legacy_mpl_widgets/__init__.py

@@ -1,30 +1,20 @@
 # basics
-# from .timeseries import plot_timeseries, TimeseriesWidget
+# from .timeseries import plot_timeseries, TracesWidget
 from .rasters import plot_rasters, RasterWidget
 from .probemap import plot_probe_map, ProbeMapWidget
 
 # isi/ccg/acg
 from .isidistribution import plot_isi_distribution, ISIDistributionWidget
 
-# from .correlograms import (plot_crosscorrelograms, CrossCorrelogramsWidget,
-#                            plot_autocorrelograms, AutoCorrelogramsWidget)
-
 # peak activity
 from .activity import plot_peak_activity_map, PeakActivityMapWidget
 
 # waveform/PC related
-# from .unitwaveforms import plot_unit_waveforms, plot_unit_templates
-# from .unitwaveformdensitymap import plot_unit_waveform_density_map, UnitWaveformDensityMapWidget
-# from .amplitudes import plot_amplitudes_distribution
 from .principalcomponent import plot_principal_component
 
-# from .unitlocalization import plot_unit_localization, UnitLocalizationWidget
-
 # units on probe
 from .unitprobemap import plot_unit_probe_map, UnitProbeMapWidget
 
-# from .depthamplitude import plot_units_depth_vs_amplitude
-
 # comparison related
 from .confusionmatrix import plot_confusion_matrix, ConfusionMatrixWidget
 from .agreementmatrix import plot_agreement_matrix, AgreementMatrixWidget
@@ -77,8 +67,6 @@
     ComparisonPerformancesByTemplateSimilarity,
 )
 
-# unit summary
-# from .unitsummary import plot_unit_summary, UnitSummaryWidget
 
 # unit presence
 from .presence import plot_presence, PresenceWidget