Add v2.calculate / fix v1.calculate

petab/v1/calculate.py

@@ -1,6 +1,7 @@
 """Functions performing various calculations."""
 
 import numbers
+import operator
 from functools import reduce
 
 import numpy as np
@@ -139,19 +140,17 @@ def calculate_residuals_for_table(
             # apply scaling
             observable = observable_df.loc[row[OBSERVABLE_ID]]
             trafo = observable.get(OBSERVABLE_TRANSFORMATION, LIN)
-            simulation = petab.scale(simulation, trafo)
-            measurement = petab.scale(measurement, trafo)
+            scaled_simulation = petab.scale(simulation, trafo)
+            scaled_measurement = petab.scale(measurement, trafo)
 
         # non-normalized residual is just the difference
-        residual = simulation - measurement
+        residual = scaled_measurement - scaled_simulation
 
-        noise_value = 1
         if normalize:
-            # look up noise standard deviation
-            noise_value = evaluate_noise_formula(
+            # divide by standard deviation
+            residual /= evaluate_noise_formula(
                 row, noise_formulas, parameter_df, simulation
             )
-        residual /= noise_value
 
         # fill in value
         residual_df.loc[irow, RESIDUAL] = residual
@@ -169,13 +168,10 @@ def get_symbolic_noise_formulas(observable_df) -> dict[str, sp.Expr]:
     """
     noise_formulas = {}
     # iterate over observables
-    for row in observable_df.itertuples():
-        observable_id = row.Index
-        if NOISE_FORMULA not in observable_df.columns:
-            noise_formula = None
-        else:
-            noise_formula = sympify_petab(row.noiseFormula)
-        noise_formulas[observable_id] = noise_formula
+    for observable_id, row in observable_df.iterrows():
+        noise_formulas[observable_id] = (
+            sympify_petab(row.noiseFormula) if NOISE_FORMULA in row else None
+        )
     return noise_formulas
 
 
@@ -364,7 +360,7 @@ def calculate_llh_for_table(
             (simulation_df[col] == row[col]) | petab.is_empty(row[col])
             for col in compared_cols
         ]
-        mask = reduce(lambda x, y: x & y, masks)
+        mask = reduce(operator.and_, masks)
 
         simulation = simulation_df.loc[mask][SIMULATION].iloc[0]
 
@@ -375,7 +371,7 @@ def calculate_llh_for_table(
 
         # get noise standard deviation
         noise_value = evaluate_noise_formula(
-            row, noise_formulas, parameter_df, petab.scale(simulation, scale)
+            row, noise_formulas, parameter_df, simulation
         )
 
         # get noise distribution