prepare release 1.5.0

.github/workflows/tests.yaml

@@ -14,7 +14,7 @@ jobs:
       fail-fast: false
       matrix:
         os: ["ubuntu-latest"]
-        python-version: ["3.9", "3.10"]
+        python-version: ["3.10", "3.13"]
     steps:
       - name: Checkout ibl-neuropixel repo
         uses: actions/checkout@v3

README.md

@@ -3,6 +3,7 @@ Collection of tools to handle Neuropixel 1.0 and 2.0 data
 (documentation coming soon...)
 
 ## Installation
+Minimum Python version supported is 3.10
 `pip install ibl-neuropixel`
 
 

release_notes.md

@@ -1,3 +1,9 @@
+# 1.5
+
+## 1.5.0 2024-10-24
+- Automatic sorting per shank / row / col
+- Minimum Python version is 3.10
+
 # 1.4
 
 ## 1.4.0 2024-10-05

setup.py

@@ -8,7 +8,7 @@
 
 setuptools.setup(
     name="ibl-neuropixel",
-    version="1.4.0",
+    version="1.5.0",
     author="The International Brain Laboratory",
     description="Collection of tools for Neuropixel 1.0 and 2.0 probes data",
     long_description=long_description,
@@ -27,5 +27,5 @@
     packages=setuptools.find_packages(where="src", exclude=["tests"]),
     include_package_data=True,
     py_modules=["spikeglx", "neuropixel"],
-    python_requires=">=3.8",
+    python_requires=">=3.10",
 )

src/spikeglx.py

@@ -265,7 +265,7 @@ def range_volts(self):
         :return: [nc, ] array of float32 (V)
         """
         if not self.meta:
-            return self.sample2volts * np.NaN
+            return self.sample2volts * np.nan
         maxint = _get_max_int_from_meta(self.meta)
         return self.sample2volts * maxint
 

src/tests/unit/cpu/test_waveforms.py

@@ -118,7 +118,7 @@ def test_dist_chanel_from_peak():
     xyz_testd = np.stack(
         (
             np.array([[0, 0, 0], [0, 1, 0], [1, 0, 0], [1, 1, 0]]),
-            np.array([[4, 0, 0], [2, 0, 0], [np.NaN, np.NaN, np.NaN], [1, 0, 0]]),
+            np.array([[4, 0, 0], [2, 0, 0], [np.nan, np.nan, np.nan], [1, 0, 0]]),
         ),
         axis=2,
     )
@@ -132,7 +132,7 @@ def test_dist_chanel_from_peak():
     sp_spread = waveforms.spatial_spread_weighted(eu_dist, weights)
 
     np.testing.assert_almost_equal(eu_dist[0, :], np.array([0, 1, 1, np.sqrt(2)]))
-    np.testing.assert_equal(eu_dist[1, :], np.array([2, 0, np.NaN, 1]))
+    np.testing.assert_equal(eu_dist[1, :], np.array([2, 0, np.nan, 1]))
     np.testing.assert_equal(sp_spread, np.array([1, 0.5]))