Starred expressions must be last value for Py < 3.5

nest · Oct 11, 2019 · 7d8ef1d · 7d8ef1d
1 parent ba6397a
commit 7d8ef1d
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Showing 3 changed files with 5 additions and 5 deletions.
diff --git a/pynest/nest/lib/hl_api_topology.py b/pynest/nest/lib/hl_api_topology.py
@@ -1345,8 +1345,8 @@ def PlotProbabilityParameter(source, parameter=None, mask=None, edges=[-0.5, 0.5
 
     if parameter is not None:
         z = np.zeros(shape[::-1])
-        for i, x in enumerate(np.linspace(*edges[:2], shape[0])):
-            positions = [[x, y] for y in np.linspace(*edges[2:], shape[1])]
+        for i, x in enumerate(np.linspace(edges[0], edges[1], shape[0])):
+            positions = [[x, y] for y in np.linspace(edges[2], edges[3], shape[1])]
             values = parameter.apply(source, positions)
             z[:, i] = np.array(values)
         img = ax.imshow(np.minimum(np.maximum(z, 0.0), 1.0), extent=edges,

diff --git a/pynest/nest/tests/test_topology/test_dumping.py b/pynest/nest/tests/test_topology/test_dumping.py
@@ -49,7 +49,7 @@ def test_DumpNodes(self):
         nest.DumpLayerNodes(l, filename)
 
         npa = np.genfromtxt(filename)
-        reference = np.array([[n.get('global_id'), *nest.GetPosition(n)] for n in l])
+        reference = np.array([[n.get('global_id')] + list(nest.GetPosition(n)) for n in l])
         self.assertTrue(np.allclose(npa, reference))
         os.remove(filename)
 

diff --git a/pynest/nest/tests/test_topology/test_plotting.py b/pynest/nest/tests/test_topology/test_plotting.py
@@ -101,8 +101,8 @@ def probability_calculation(distance):
 
         # Calculate reference values
         ref_probability = np.zeros(plot_shape[::-1])
-        for i, x in enumerate(np.linspace(*plot_edges[:2], plot_shape[0])):
-            positions = np.array([[x, y] for y in np.linspace(*plot_edges[2:], plot_shape[1])])
+        for i, x in enumerate(np.linspace(plot_edges[0], plot_edges[1], plot_shape[0])):
+            positions = np.array([[x, y] for y in np.linspace(plot_edges[2], plot_edges[3], plot_shape[1])])
             ref_distances = np.sqrt((positions[:, 0] - source_x)**2 + (positions[:, 1] - source_y)**2)
             values = probability_calculation(ref_distances)
             ref_probability[:, i] = np.maximum(np.minimum(np.array(values), 1.0), 0.0)