Adding few changes for metadata

pynapple/core/time_series.py

@@ -1218,11 +1218,15 @@ def __getitem__(self, key, *args, **kwargs):
                     "When indexing with a Tsd, it must contain boolean values"
                 )
             key = key.d
-        elif (
-            isinstance(key, str)
-            or hasattr(key, "__iter__")
-            and all([isinstance(k, str) for k in key])
-        ):
+        elif isinstance(key, str):
+            if key in self.columns:
+                with warnings.catch_warnings():
+                    # ignore deprecated warning for loc
+                    warnings.simplefilter("ignore")
+                    return self.loc[key]
+            else:
+                return _MetadataMixin.__getitem__(self, key)
+        elif hasattr(key, "__iter__") and all([isinstance(k, str) for k in key]):
             if all(k in self.columns for k in key):
                 with warnings.catch_warnings():
                     # ignore deprecated warning for loc
@@ -1272,8 +1276,6 @@ def __getitem__(self, key, *args, **kwargs):
                 return _get_class(output)(
                     t=index, d=output, time_support=self.time_support, **kwargs
                 )
-            # else:
-            #     return output
             else:
                 return output
 

pynapple/core/ts_group.py

@@ -345,7 +345,7 @@ def __getitem__(self, key):
                 return _MetadataMixin.__getitem__(self, key)
             else:
                 raise KeyError(r"Key {} not in group index.".format(key))
-        elif isinstance(key, list) and all(isinstance(k, str) for k in key):
+        elif isinstance(key, list) and len(key) and all(isinstance(k, str) for k in key):
             # index multiple metadata columns
             return _MetadataMixin.__getitem__(self, key)
 
@@ -389,11 +389,17 @@ def __repr__(self):
         max_rows = np.maximum(rows - 10, 2)
 
         # By default, the first three columns should always show.
-        col_names = self._metadata.columns.drop("rate")
+        col_names = self._metadata.columns
+        if "rate" in col_names:
+            col_names = col_names.drop("rate")
+
         headers = ["Index", "rate"] + [c for c in col_names][0:max_cols]
         end = ["..."] if len(headers) > max_cols else []
         headers += end
 
+        if len(self) == 0:
+            return tabulate(tabular_data=[], headers=headers)
+
         if len(self) > max_rows:
             n_rows = max_rows // 2
             ends = np.array([end] * n_rows)
@@ -530,7 +536,7 @@ def restrict(self, ep):
         cols = self._metadata.columns.drop("rate")
 
         return TsGroup(
-            newgr, time_support=ep, bypass_check=True, **self._metadata[cols]
+            newgr, time_support=ep, bypass_check=True, metadata=self._metadata[cols]
         )
 
     def value_from(self, tsd, ep=None):
@@ -576,7 +582,7 @@ def value_from(self, tsd, ep=None):
             newgr[k] = self.data[k].value_from(tsd, ep)
 
         cols = self._metadata.columns.drop("rate")
-        return TsGroup(newgr, time_support=ep, **self._metadata[cols])
+        return TsGroup(newgr, time_support=ep, metadata=self._metadata[cols])
 
     def count(self, *args, dtype=None, **kwargs):
         """
@@ -867,7 +873,7 @@ def get(self, start, end=None, time_units="s"):
             newgr,
             time_support=self.time_support,
             bypass_check=True,
-            **self._metadata[cols],
+            metadata=self._metadata[cols],
         )
 
     #################################

pynapple/io/interface_nwb.py

@@ -279,7 +279,7 @@ def _make_tsgroup(obj, **kwargs):
                 else:
                     pass
 
-    tsgroup = nap.TsGroup(tsgroup, **metainfo)
+    tsgroup = nap.TsGroup(tsgroup, metadata=metainfo)
 
     return tsgroup
 

pynapple/process/decoding.py

@@ -63,6 +63,7 @@ def decode_1d(tuning_curves, group, ep, bin_size, time_units="s", feature=None):
 
     # Bin spikes
     count = newgroup.count(bin_size, ep, time_units)
+    count = count.smooth(bin_size*3)
 
     # Occupancy
     if feature is None: