tests and docs build.

MPoL-dev · Dec 28, 2023 · c2e5370 · c2e5370
1 parent 94ab743
commit c2e5370
Showing 1 changed file with 7 additions and 13 deletions.
diff --git a/test/losses_test.py b/test/losses_test.py
@@ -67,15 +67,9 @@ def test_chi_squared_evaluation(
     data = torch.tensor(data_re + 1.0j * data_im)
     weight = torch.tensor(weight)
 
-    chi_squared = losses.chi_squared(loose_visibilities, data, weight)
+    chi_squared = losses._chi_squared(loose_visibilities, data, weight)
     print("loose chi_squared", chi_squared)
 
-    # calculate the gridded chi^2
-    chi_squared_gridded = losses.chi_squared_gridded(gridded_visibilities, dataset)
-    print("gridded chi_squared", chi_squared_gridded)
-
-    # it's OK that the values are different
-
 
 def test_log_likelihood_evaluation(
     loose_visibilities, mock_visibility_data, gridded_visibilities, dataset
@@ -102,7 +96,7 @@ def test_nll_hermitian_pairs(loose_visibilities, mock_visibility_data):
     data = torch.tensor(data_re + 1.0j * data_im)
     weight = torch.tensor(weight)
 
-    log_like = losses.reduced_chi_squared(loose_visibilities, data, weight)
+    log_like = losses.r_chi_squared(loose_visibilities, data, weight)
     print("loose nll", log_like)
 
     # calculate it with Hermitian pairs
@@ -116,7 +110,7 @@ def test_nll_hermitian_pairs(loose_visibilities, mock_visibility_data):
     data = torch.cat([data, torch.conj(data)], axis=1)
     weight = torch.cat([weight, weight], axis=1)
 
-    log_like = losses.reduced_chi_squared(loose_visibilities, data, weight)
+    log_like = losses.r_chi_squared(loose_visibilities, data, weight)
     print("loose nll w/ Hermitian", log_like)
 
 
@@ -156,7 +150,7 @@ def test_nll_1D_zero():
     data_im = model_im
     data_vis = torch.complex(data_re, data_im)
 
-    loss = losses.reduced_chi_squared(model_vis, data_vis, weights)
+    loss = losses.r_chi_squared(model_vis, data_vis, weights)
     assert loss.item() == 0.0
 
 
@@ -175,7 +169,7 @@ def test_nll_1D_random():
     data_im = torch.randn_like(weights)
     data_vis = torch.complex(data_re, data_im)
 
-    losses.reduced_chi_squared(model_vis, data_vis, weights)
+    losses.r_chi_squared(model_vis, data_vis, weights)
 
 
 def test_nll_2D_zero():
@@ -195,7 +189,7 @@ def test_nll_2D_zero():
     data_im = model_im
     data_vis = torch.complex(data_re, data_im)
 
-    loss = losses.reduced_chi_squared(model_vis, data_vis, weights)
+    loss = losses.r_chi_squared(model_vis, data_vis, weights)
     assert loss.item() == 0.0
 
 
@@ -215,7 +209,7 @@ def test_nll_2D_random():
     data_im = torch.randn_like(weights)
     data_vis = torch.complex(data_re, data_im)
 
-    losses.reduced_chi_squared(model_vis, data_vis, weights)
+    losses.r_chi_squared(model_vis, data_vis, weights)
 
 
 def test_entropy_raise_error_negative():