Merge pull request #279 from pynapple-org/dev

Fixing #273

docs/HISTORY.md

@@ -9,6 +9,13 @@ In 2018, Francesco started neuroseries, a Python package built on Pandas. It was
 In 2021, Guillaume and other trainees in Adrien's lab decided to fork from neuroseries and started *pynapple*. The core of pynapple is largely built upon neuroseries. Some of the original changes to TSToolbox made by Luke were included in this package, especially the *time_support* property of all ts/tsd objects.
 
 
+0.6.5 (Soon)
+------------------
+
+- Full `pynajax` backend compatibility
+- `get` method can update time support
+
+
 0.6.4 (2024-04-18)
 ------------------
 

pynapple/__init__.py

@@ -1,4 +1,4 @@
-__version__ = "0.6.4"
+__version__ = "0.6.5"
 from .core import (
     IntervalSet,
     Ts,

pynapple/core/time_series.py

@@ -955,10 +955,13 @@ def __getitem__(self, key, *args, **kwargs):
         ):
             return self.loc[key]
         else:
-            # return super().__getitem__(key, *args, **kwargs)
             output = self.values.__getitem__(key)
+            columns = self.columns
+
             if isinstance(key, tuple):
                 index = self.index.__getitem__(key[0])
+                if len(key) == 2:
+                    columns = self.columns.__getitem__(key[1])
             else:
                 index = self.index.__getitem__(key)
 
@@ -967,6 +970,11 @@ def __getitem__(self, key, *args, **kwargs):
 
             if all(is_array_like(a) for a in [index, output]):
                 if output.shape[0] == index.shape[0]:
+
+                    if isinstance(columns, pd.Index):
+                        if not columns.is_integer():
+                            kwargs["columns"] = columns
+
                     return _get_class(output)(
                         t=index, d=output, time_support=self.time_support, **kwargs
                     )

pyproject.toml

@@ -4,7 +4,7 @@ build-backend = "setuptools.build_meta"
 
 [project]
 name = "pynapple"
-version = "0.6.4"
+version = "0.6.5"
 description = "PYthon Neural Analysis Package Pour Laboratoires d’Excellence"
 readme = "README.md"
 authors = [{ name = "Guillaume Viejo", email = "[email protected]" }]

setup.py

@@ -59,7 +59,7 @@
     test_suite='tests',
     tests_require=test_requirements,
     url='https://github.com/pynapple-org/pynapple',
-    version='v0.6.4',
+    version='v0.6.5',
     zip_safe=False,
     long_description_content_type='text/markdown',
     download_url='https://github.com/pynapple-org/pynapple/archive/refs/tags/v0.6.0.tar.gz'

tests/test_time_series.py

@@ -7,7 +7,7 @@
 
 
 # tsd1 = nap.Tsd(t=np.arange(100), d=np.random.rand(100), time_units="s")
-# tsd2 = nap.TsdFrame(t=np.arange(100), d=np.random.rand(100, 10), time_units="s")
+# tsd2 = nap.TsdFrame(t=np.arange(100), d=np.random.rand(100, 3), columns = ['a', 'b', 'c'])
 # tsd3 = nap.TsdTensor(t=np.arange(100), d=np.random.rand(100, 5, 4), time_units="s")
 # tsd4 = nap.Ts(t=np.arange(100), time_units="s")