passing simple single condition fit

multidms/model.py

@@ -1030,7 +1030,9 @@ def fit(
             reference-condition latent offset (TODO math? beta0[ref]) are locked.
         admm_niter : int
             Number of iterations to perform during the ADMM optimization.
-            Defaults to 50.
+            Defaults to 50. Note that in the case of single-condition models,
+            This is set to zero as the generalized
+            lasso ADMM optimization is not used.
         admm_tau : float
             ADMM step size. Defaults to 1.0.
         warn_unconverged : bool
@@ -1106,7 +1108,10 @@ def fit(
 
         admm_mu = 0.99 * admm_tau / eig
 
-        assert 0 < admm_mu < admm_tau / eig
+        if len(self.data.conditions) > 1:
+            assert 0 < admm_mu < admm_tau / eig
+
+        admm_niter = 0 if len(self.data.conditions) == 1 else admm_niter
 
         lock_params[("beta0", self.data.reference)] = 0.0
 

tests/test_data.py

@@ -255,20 +255,20 @@ def test_difference_matrix():
 """
 
 
-# def test_single_condition_fit():
-#     """
-#     Simple test to see that the single-condition model
-#     fits without error.
-#     """
-#     data = multidms.Data(
-#         TEST_FUNC_SCORES.query("condition == 'a'"),
-#         alphabet=multidms.AAS_WITHSTOP,
-#         reference="a",
-#         assert_site_integrity=False,
-#     )
-#     model = multidms.Model(data, PRNGKey=23)
+def test_single_condition_fit():
+    """
+    Simple test to see that the single-condition model
+    fits without error.
+    """
+    data = multidms.Data(
+        TEST_FUNC_SCORES.query("condition == 'a'"),
+        alphabet=multidms.AAS_WITHSTOP,
+        reference="a",
+        assert_site_integrity=False,
+    )
+    model = multidms.Model(data, PRNGKey=23)
 
-#     model.fit(maxiter=2, warn_unconverged=False)
+    model.fit(maxiter=2, warn_unconverged=False)
 
 
 def test_fit_simple():