Merge pull request #295 from pynapple-org/dev

Bumping 0.6.6.

docs/HISTORY.md

@@ -8,7 +8,7 @@ Around 2016-2017, Luke Sjulson started *TSToolbox2*, still in Matlab and which i
 In 2018, Francesco started neuroseries, a Python package built on Pandas. It was quickly adopted in Adrien's lab, especially by Guillaume Viejo, a postdoc in the lab. Gradually, the majority of the lab was using it and new functions were constantly added.
 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.
 
-0.6.6 (Soon)
+0.6.6 (2024-05-28)
 ------------------
 
 - Full lazy-loading for NWB file.

docs/api_guide/tutorial_pynapple_nwb.py

@@ -49,9 +49,13 @@
 print(data['z'])
 
 # %%
+#
 # Internally, the `NWBClass` has replaced the pointer to the data with the actual data.
 #
 # While it looks like pynapple has loaded the data, in fact it did not. By default, calling the NWB object will return an HDF5 dataset.
+# !!! warning
+# 
+#     New in `0.6.6`
 
 print(type(z.values))
 

docs/pynajax.md

@@ -1,5 +1,10 @@
 ### Motivation
 
+
+!!! warning
+
+    New in `0.6.6`
+
 Multiple python packages exist for high-performance computing. Internally, pynapple makes extensive use of [numba](https://numba.pydata.org/) for accelerating some functions. Numba is a stable package that provide speed gains with minimal installation issues when running on CPUs.
 
 Another high-performance toolbox for numerical analysis is 

pynapple/__init__.py

@@ -1,4 +1,4 @@
-__version__ = "0.6.5"
+__version__ = "0.6.6"
 from .core import (
     IntervalSet,
     Ts,

pyproject.toml

@@ -4,7 +4,7 @@ build-backend = "setuptools.build_meta"
 
 [project]
 name = "pynapple"
-version = "0.6.5"
+version = "0.6.6"
 description = "PYthon Neural Analysis Package Pour Laboratoires d’Excellence"
 readme = "README.md"
 authors = [{ name = "Guillaume Viejo", email = "[email protected]" }]

setup.py

@@ -59,8 +59,8 @@
     test_suite='tests',
     tests_require=test_requirements,
     url='https://github.com/pynapple-org/pynapple',
-    version='v0.6.5',
+    version='v0.6.6',
     zip_safe=False,
     long_description_content_type='text/markdown',
-    download_url='https://github.com/pynapple-org/pynapple/archive/refs/tags/v0.6.5.tar.gz'
+    download_url='https://github.com/pynapple-org/pynapple/archive/refs/tags/v0.6.6.tar.gz'
 )

tests/test_lazy_loading.py

@@ -13,6 +13,8 @@
 import pynapple as nap
 
 
+
+
 @pytest.mark.parametrize(
     "time, data, expectation",
     [
@@ -224,11 +226,14 @@ def test_lazy_load_nwb_no_warnings(data):
             nwbfile.add_acquisition(time_series)
             nwb = nap.NWBFile(nwbfile)
 
-            with warnings.catch_warnings():
-                warnings.simplefilter("error")
+            with warnings.catch_warnings(record=True) as w:
                 tsd = nwb["TimeSeries"]
                 tsd.count(0.1)
                 assert isinstance(tsd.d, h5py.Dataset)
+
+            if len(w):
+                if not str(w[0].message).startswith("Converting 'd' to"):
+                    raise RuntimeError
 
     finally:
         if file_path.exists():
@@ -242,8 +247,7 @@ def test_tsgroup_no_warnings():
             file_path = Path(f'data_{k}.h5')
             with h5py.File(file_path, 'w') as f:
                 f.create_dataset('spks', data=np.sort(np.random.uniform(0, 10, size=20)))
-        with warnings.catch_warnings():
-            warnings.simplefilter("error")
+        with warnings.catch_warnings(record=True) as w:            
 
             nwbfile = mock_NWBFile()
 
@@ -255,9 +259,15 @@ def test_tsgroup_no_warnings():
             nwb = nap.NWBFile(nwbfile)
             tsgroup = nwb["units"]
             tsgroup.count(0.1)
+
+        if len(w):
+            if not str(w[0].message).startswith("Converting 'd' to"):
+                raise RuntimeError
+
 
     finally:
         for k in range(n_units):
             file_path = Path(f'data_{k}.h5')
             if file_path.exists():
                 file_path.unlink()
+