Merge pull request #2886 from jvesely/devel

requirements: Add scipy to requirements file

psyneulink/core/components/functions/nonstateful/distributionfunctions.py

@@ -26,6 +26,7 @@
 
 import numpy as np
 from beartype import beartype
+from scipy.special import erfinv
 
 from psyneulink._typing import Optional
 
@@ -371,11 +372,6 @@ def _function(self,
                  params=None,
                  ):
 
-        try:
-            from scipy.special import erfinv
-        except:
-            raise FunctionError("The UniformToNormalDist function requires the SciPy package.")
-
         mean = self._get_current_parameter_value(DIST_MEAN, context)
         standard_deviation = self._get_current_parameter_value(STANDARD_DEVIATION, context)
         random_state = self.parameters.random_state._get(context)

requirements.txt

@@ -18,5 +18,6 @@ pillow<10.3.0
 pint<0.22.0
 protobuf<3.20.4
 rich>=10.1, <10.13
+scipy<1.12
 toposort<1.11
 torch>=1.10.0, <2.2.0; (platform_machine == 'AMD64' or platform_machine == 'x86_64' or platform_machine == 'arm64' or platform_machine == 'aarch64') and platform_python_implementation == 'CPython' and implementation_name == 'cpython'

tests/mechanisms/test_transfer_mechanism.py

@@ -299,33 +299,19 @@ def test_transfer_mech_exponential_noise(self):
     @pytest.mark.mechanism
     @pytest.mark.transfer_mechanism
     def test_transfer_mech_uniform_to_normal_noise(self):
-        try:
-            import scipy
-        except ModuleNotFoundError:
-            with pytest.raises(FunctionError) as error_text:
-                T = TransferMechanism(
-                    name='T',
-                    default_variable=[0, 0, 0, 0],
-                    function=Linear(),
-                    noise=UniformToNormalDist(),
-                    integration_rate=1.0
-                )
-            assert "The UniformToNormalDist function requires the SciPy package." in str(error_text.value)
-        else:
-            T = TransferMechanism(
-                name='T',
-                default_variable=[0, 0, 0, 0],
-                function=Linear(),
-                noise=UniformToNormalDist(),
-                integration_rate=1.0
-            )
-            # This is equivalent to
-            # T.noise.base.parameters.random_state.get(None).seed([22])
-            T.noise.parameters.seed.set(22, None)
-            val = T.execute([0, 0, 0, 0])
-            np.testing.assert_allclose(val, [[1.73027452, -1.07866481, -1.98421126,  2.99564032]])
-
 
+        T = TransferMechanism(
+            name='T',
+            default_variable=[0, 0, 0, 0],
+            function=Linear(),
+            noise=UniformToNormalDist(),
+            integration_rate=1.0
+        )
+        # This is equivalent to
+        # T.noise.base.parameters.random_state.get(None).seed([22])
+        T.noise.parameters.seed.set(22, None)
+        val = T.execute([0, 0, 0, 0])
+        np.testing.assert_allclose(val, [[1.73027452, -1.07866481, -1.98421126,  2.99564032]])
 
     @pytest.mark.mechanism
     @pytest.mark.transfer_mechanism