gradient no backflow

NLESC-JCER · Nov 28, 2023 · 9ab7ae8 · 9ab7ae8
1 parent 9784ea1
commit 9ab7ae8
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Showing 4 changed files with 19 additions and 5 deletions.
diff --git a/tests/wavefunction/base_test_cases.py b/tests/wavefunction/base_test_cases.py
@@ -34,8 +34,13 @@ class WaveFunctionBaseTest(unittest.TestCase):
 
         def setUp(self):
             """Init the base test"""
+
+            def wf_placeholder(pos):
+                """Callable for wf"""
+                return None
+
             self.pos = None
-            self.wf = None
+            self.wf = wf_placeholder
             self.nbatch = None
 
         def test_forward(self):

diff --git a/tests/wavefunction/jastrows/elec_elec/base_elec_elec_jastrow_test.py b/tests/wavefunction/jastrows/elec_elec/base_elec_elec_jastrow_test.py
@@ -34,7 +34,12 @@ class ElecElecJastrowBaseTest(unittest.TestCase):
 
         def setUp(self) -> None:
             """Init the test case"""
-            self.jastrow = None
+
+            def jastrow_callable(pos):
+                """Empty callable for jastrow"""
+                return None 
+
+            self.jastrow = jastrow_callable
             self.nbatch = None
             self.pos = None
 

diff --git a/tests/wavefunction/orbitals/base_test_ao.py b/tests/wavefunction/orbitals/base_test_ao.py
@@ -72,7 +72,11 @@ class BaseTestAO:
     class BaseTestAOderivatives(unittest.TestCase):
 
         def setUp(self):
-            self.ao = None
+
+            def ao_callable(pos):
+                """Callable for the AO"""
+                return None
+            self.ao = ao_callable
             self.pos = None
 
         def test_ao_deriv(self):

diff --git a/tests/wavefunction/test_slater_mgcn_graph_jastrow.py b/tests/wavefunction/test_slater_mgcn_graph_jastrow.py
@@ -169,7 +169,7 @@ def test_kinetic_energy(self):
 
     def test_gradients_wf(self):
 
-        grads = self.wf.gradients_jacobi(
+        grads = self.wf.gradients_jacobi_no_backflow(
             self.pos, sum_grad=False).squeeze()
         grad_auto = self.wf.gradients_autograd(self.pos)
 
@@ -181,7 +181,7 @@ def test_gradients_wf(self):
 
     def test_gradients_pdf(self):
 
-        grads_pdf = self.wf.gradients_jacobi(self.pos, pdf=True)
+        grads_pdf = self.wf.gradients_jacobi_no_backflow(self.pos, pdf=True)
         grads_auto = self.wf.gradients_autograd(self.pos, pdf=True)
 
         assert torch.allclose(grads_pdf.sum(), grads_auto.sum())