Update metadata docstrings

.gitignore

@@ -8,6 +8,10 @@
 /docs/generated/gallery/*.py
 /docs/generated/gallery/*.zip
 
+/doc/generated
+/doc/examples/nwb-cache
+/doc/_build
+
 /tests/npzfilestest
 
 # Byte-compiled / optimized / DLL files

doc/api.rst

@@ -8,7 +8,7 @@ Core objects
 
 .. rubric:: Time Series
 
-.. currentmodule:: pynapple.core.time_series
+.. currentmodule:: pynapple
 
 .. autosummary::
     :toctree: generated/
@@ -22,7 +22,7 @@ Core objects
 
 .. rubric:: Intervals
 
-.. currentmodule:: pynapple.core.interval_set
+.. currentmodule:: pynapple
 
 .. autosummary::
     :toctree: generated/
@@ -34,7 +34,7 @@ Core objects
 
 .. rubric:: Timestamps
 
-.. currentmodule:: pynapple.core.time_series
+.. currentmodule:: pynapple
 
 .. autosummary::
     :toctree: generated/
@@ -45,7 +45,7 @@ Core objects
 
 .. rubric:: Group of timestamps
 
-.. currentmodule:: pynapple.core.ts_group
+.. currentmodule:: pynapple
 
 .. autosummary::
     :toctree: generated/
@@ -58,7 +58,7 @@ Core objects
 Input-Ouput
 -----------
 
-.. currentmodule:: pynapple.io.interface_nwb
+.. currentmodule:: pynapple.io
 
 .. rubric:: Neurodata Without Borders (NWB)
 
@@ -70,7 +70,7 @@ Input-Ouput
     NWBFile
 
 
-.. currentmodule:: pynapple.io.interface_npz
+.. currentmodule:: pynapple.io
 
 .. rubric:: Numpy files
 

doc/examples/tutorial_HD_dataset.md

@@ -116,7 +116,7 @@ tuning_curves = nap.compute_1d_tuning_curves(
     group=spikes_adn, 
     feature=angle, 
     nb_bins=61, 
-    ep = epochs['wake'],
+    ep=epochs[epochs.tags == "wake"],
     minmax=(0, 2 * np.pi)
     )
 ```
@@ -199,7 +199,7 @@ To decode the population activity, we will be using a Bayesian Decoder as implem
 decoded, proba_feature = nap.decode_1d(
     tuning_curves=tuning_curves,
     group=spikes_adn,
-    ep=epochs["wake"],
+    ep=epochs[epochs.tags == "wake"],
     bin_size=0.1,  # second
     feature=angle,
 )