v2: unscaled obs for noise

Author: dweindl

petab/v2/calculate.py

@@ -151,11 +151,11 @@ def calculate_residuals_for_table(
 
             # scale simulation and measurement
 
-            simulation = scale(simulation, trafo)
-            measurement = scale(measurement, trafo)
+            scaled_simulation = scale(simulation, trafo)
+            scaled_measurement = scale(measurement, trafo)
 
         # non-normalized residual is just the difference
-        residual = measurement - simulation
+        residual = scaled_measurement - scaled_simulation
 
         if normalize:
             # divide by standard deviation
@@ -373,7 +373,6 @@ def calculate_llh_for_table(
     """Calculate log-likelihood for one set of tables. For the arguments, see
     `calculate_llh`.
     """
-    from petab.v1 import scale
 
     llhs = []
 
@@ -415,7 +414,7 @@ def calculate_llh_for_table(
             row,
             noise_formulas,
             parameter_df,
-            scale(simulation, obs_scale),
+            simulation,
             observable,
         )
 

tests/v2/test_calculate.py

@@ -204,8 +204,8 @@ def model_non_numeric_overrides():
     simulation_df[SIMULATION] = [2, 3]
 
     expected_residuals = {
-        (np.log(0.5) - np.log(2)) / (2 * 7 + 8 + 4 + np.log(2)),
-        (np.log(1) - np.log(3)) / (2 * 2 + 3 + 4 + np.log(3)),
+        (np.log(0.5) - np.log(2)) / (2 * 7 + 8 + 4 + 2),
+        (np.log(1) - np.log(3)) / (2 * 2 + 3 + 4 + 3),
     }
     expected_residuals_nonorm = {
         np.log(0.5) - np.log(2),
@@ -217,8 +217,7 @@ def model_non_numeric_overrides():
         * np.log(
             2
             * np.pi
-            * np.array([2 * 7 + 8 + 4 + np.log(2), 2 * 2 + 3 + 4 + np.log(3)])
-            ** 2
+            * np.array([2 * 7 + 8 + 4 + 2, 2 * 2 + 3 + 4 + 3]) ** 2
             * np.array([0.5, 1]) ** 2
         ).sum()
     )