Merge branch 'develop' into startpoint_kwargs

.github/workflows/ci.yml

@@ -172,7 +172,7 @@ jobs:
       run: .github/workflows/install_deps.sh
 
     - name: Install PEtabJL dependencies
-      run: julia -e 'using Pkg; Pkg.add(name="PEtab", version="1.4.2"); Pkg.add("OrdinaryDiffEq"), Pkg.add("Sundials")'
+      run: julia -e 'using Pkg; Pkg.add("PEtab"); Pkg.add("OrdinaryDiffEq"), Pkg.add("Sundials")'
 
     - name: Run tests
       timeout-minutes: 25

doc/example/conversion_reaction/PEtabJl_module.jl

@@ -5,15 +5,15 @@ using Sundials
 using PEtab
 
 pathYaml = "/Users/pauljonasjost/Documents/GitHub_Folders/pyPESTO/test/julia/../../doc/example/conversion_reaction/conversion_reaction.yaml"
-petabModel = readPEtabModel(pathYaml, verbose=true)
+petabModel = PEtabModel(pathYaml, verbose=true)
 
-# A full list of options for createPEtabODEProblem can be found at https://sebapersson.github.io/PEtab.jl/dev/API_choosen/#PEtab.setupPEtabODEProblem
-petabProblem = createPEtabODEProblem(
+# A full list of options for PEtabODEProblem can be found at https://sebapersson.github.io/PEtab.jl/stable/API_choosen/
+petabProblem = PEtabODEProblem(
 	petabModel,
-	odeSolverOptions=ODESolverOptions(Rodas5P(), abstol=1e-08, reltol=1e-08, maxiters=Int64(1e4)),
-	gradientMethod=:ForwardDiff,
-	hessianMethod=:ForwardDiff,
-	sparseJacobian=nothing,
+	ode_solver=ODESolver(Rodas5P(), abstol=1e-08, reltol=1e-08, maxiters=Int64(1e4)),
+	gradient_method=:ForwardDiff,
+	hessian_method=:ForwardDiff,
+	sparse_jacobian=nothing,
 	verbose=true
 )
 

pypesto/objective/julia/petabJl.py

@@ -61,10 +61,10 @@ def __init__(
         self.petab_jl_problem = petab_jl_problem
 
         # get functions
-        fun = self.petab_jl_problem.computeCost
-        grad = self.petab_jl_problem.computeGradient
-        hess = self.petab_jl_problem.computeHessian
-        x_names = np.asarray(self.petab_jl_problem.θ_estNames)
+        fun = self.petab_jl_problem.compute_cost
+        grad = self.petab_jl_problem.compute_gradient
+        hess = self.petab_jl_problem.compute_hessian
+        x_names = np.asarray(self.petab_jl_problem.θ_names)
 
         # call the super super super constructor
         super(JuliaObjective, self).__init__(
@@ -102,10 +102,10 @@ def __setstate__(self, state):
         self.petab_jl_problem = petab_jl_problem
 
         # get functions
-        fun = self.petab_jl_problem.computeCost
-        grad = self.petab_jl_problem.computeGradient
-        hess = self.petab_jl_problem.computeHessian
-        x_names = np.asarray(self.petab_jl_problem.θ_estNames)
+        fun = self.petab_jl_problem.compute_cost
+        grad = self.petab_jl_problem.compute_gradient
+        hess = self.petab_jl_problem.compute_hessian
+        x_names = np.asarray(self.petab_jl_problem.θ_names)
 
         # call the super super constructor
         super(JuliaObjective, self).__init__(fun, grad, hess, x_names)

pypesto/objective/julia/petab_jl_importer.py

@@ -50,10 +50,10 @@ def __init__(
     @staticmethod
     def from_yaml(
         yaml_file: str,
-        odeSolverOptions: Optional[dict] = None,
-        gradientMethod: Optional[str] = None,
-        hessianMethod: Optional[str] = None,
-        sparseJacobian: Optional[bool] = None,
+        ode_solver_options: Optional[dict] = None,
+        gradient_method: Optional[str] = None,
+        hessian_method: Optional[str] = None,
+        sparse_jacobian: Optional[bool] = None,
         verbose: Optional[bool] = None,
         directory: Optional[str] = None,
     ) -> PetabJlImporter:
@@ -67,11 +67,11 @@ def from_yaml(
         ----------
         yaml_file:
             The yaml file of the PEtab problem
-        odeSolverOptions:
+        ode_solver_options:
             Dictionary like options for the ode solver in julia
-        gradientMethod, hessianMethod:
+        gradient_method, hessian_method:
             Julia methods to compute gradient and hessian
-        sparseJacobian:
+        sparse_jacobian:
             Whether to compute sparse Jacobians
         verbose:
             Whether to have a more informative log.
@@ -81,10 +81,10 @@ def from_yaml(
         """
         # get default values
         options = _get_default_options(
-            odeSolverOptions=odeSolverOptions,
-            gradientMethod=gradientMethod,
-            hessianMethod=hessianMethod,
-            sparseJacobian=sparseJacobian,
+            ode_solver_options=ode_solver_options,
+            gradient_method=gradient_method,
+            hessian_method=hessian_method,
+            sparse_jacobian=sparse_jacobian,
             verbose=verbose,
         )
 
@@ -166,8 +166,8 @@ def create_problem(
             multistart optimization.
         """
         obj = self.create_objective(precompile=precompile)
-        lb = np.asarray(self.petab_jl_problem.lowerBounds)
-        ub = np.asarray(self.petab_jl_problem.upperBounds)
+        lb = np.asarray(self.petab_jl_problem.lower_bounds)
+        ub = np.asarray(self.petab_jl_problem.upper_bounds)
 
         return Problem(
             objective=obj,
@@ -181,10 +181,10 @@ def create_problem(
 
 
 def _get_default_options(
-    odeSolverOptions: Union[dict, None] = None,
-    gradientMethod: Union[str, None] = None,
-    hessianMethod: Union[str, None] = None,
-    sparseJacobian: Union[str, None] = None,
+    ode_solver_options: Union[dict, None] = None,
+    gradient_method: Union[str, None] = None,
+    hessian_method: Union[str, None] = None,
+    sparse_jacobian: Union[str, None] = None,
     verbose: Union[str, None] = None,
 ) -> dict:
     """
@@ -194,13 +194,13 @@ def _get_default_options(
 
     Parameters
     ----------
-    odeSolverOptions:
+    ode_solver_options:
         Options for the ODE solver.
-    gradientMethod:
+    gradient_method:
         Method for gradient calculation.
-    hessianMethod:
+    hessian_method:
         Method for hessian calculation.
-    sparseJacobian:
+    sparse_jacobian:
         Whether the jacobian should be sparse.
     verbose:
         Whether to print verbose output.
@@ -211,51 +211,51 @@ def _get_default_options(
         The options.
     """
     # get default values
-    if odeSolverOptions is None:
-        odeSolverOptions = {
+    if ode_solver_options is None:
+        ode_solver_options = {
             "solver": "Rodas5P",
             "abstol": 1e-8,
             "reltol": 1e-8,
             "maxiters": "Int64(1e4)",
         }
-    if not odeSolverOptions["solver"].endswith("()"):
-        odeSolverOptions["solver"] += "()"  # add parentheses
-    if gradientMethod is None:
-        gradientMethod = "nothing"
-    if hessianMethod is None:
-        hessianMethod = "nothing"
-    if sparseJacobian is None:
-        sparseJacobian = "nothing"
+    if not ode_solver_options["solver"].endswith("()"):
+        ode_solver_options["solver"] += "()"  # add parentheses
+    if gradient_method is None:
+        gradient_method = "nothing"
+    if hessian_method is None:
+        hessian_method = "nothing"
+    if sparse_jacobian is None:
+        sparse_jacobian = "nothing"
     if verbose is None:
         verbose = "true"
 
-    # check values for gradientMethod and hessianMethod
+    # check values for gradient_method and hessian_method
     allowed_gradient_methods = [
         "ForwardDiff",
         "ForwardEquations",
         "Adjoint",
         "Zygote",
     ]
-    if gradientMethod not in allowed_gradient_methods:
+    if gradient_method not in allowed_gradient_methods:
         logger.warning(
-            f"gradientMethod {gradientMethod} is not in "
+            f"gradient_method {gradient_method} is not in "
             f"{allowed_gradient_methods}. Defaulting to ForwardDiff."
         )
-        gradientMethod = "ForwardDiff"
+        gradient_method = "ForwardDiff"
     allowed_hessian_methods = ["ForwardDiff", "BlocForwardDiff", "GaussNewton"]
-    if hessianMethod not in allowed_hessian_methods:
+    if hessian_method not in allowed_hessian_methods:
         logger.warning(
-            f"hessianMethod {hessianMethod} is not in "
+            f"hessian_method {hessian_method} is not in "
             f"{allowed_hessian_methods}. Defaulting to ForwardDiff."
         )
-        hessianMethod = "ForwardDiff"
+        hessian_method = "ForwardDiff"
 
     # fill options
     options = {
-        "odeSolverOptions": odeSolverOptions,
-        "gradientMethod": gradientMethod,
-        "hessianMethod": hessianMethod,
-        "sparseJacobian": sparseJacobian,
+        "ode_solver_options": ode_solver_options,
+        "gradient_method": gradient_method,
+        "hessian_method": hessian_method,
+        "sparse_jacobian": sparse_jacobian,
         "verbose": verbose,
     }
     return options
@@ -293,7 +293,7 @@ def _write_julia_file(
         "PEtab.jl/dev/API_choosen/#PEtab.setupPEtabODEProblem"
     )
     odeSolvOpt_str = ", ".join(
-        [f"{k}={v}" for k, v in options["odeSolverOptions"].items()]
+        [f"{k}={v}" for k, v in options["ode_solver_options"].items()]
     )
     # delete "solver=" from string
     odeSolvOpt_str = odeSolvOpt_str.replace("solver=", "")
@@ -304,15 +304,15 @@ def _write_julia_file(
         f"using Sundials\n"
         f"using PEtab\n\n"
         f"pathYaml = \"{yaml_file}\"\n"
-        f"petabModel = readPEtabModel(pathYaml, verbose=true)\n\n"
-        f"# A full list of options for createPEtabODEProblem can be "
+        f"petabModel = PEtabModel(pathYaml, verbose=true)\n\n"
+        f"# A full list of options for PEtabODEProblem can be "
         f"found at {link_to_options}\n"
-        f"petabProblem = createPEtabODEProblem(\n\t"
+        f"petabProblem = PEtabODEProblem(\n\t"
         f"petabModel,\n\t"
-        f"odeSolverOptions=ODESolverOptions({odeSolvOpt_str}),\n\t"
-        f"gradientMethod=:{options['gradientMethod']},\n\t"
-        f"hessianMethod=:{options['hessianMethod']},\n\t"
-        f"sparseJacobian={options['sparseJacobian']},\n\t"
+        f"ode_solver=ODESolver({odeSolvOpt_str}),\n\t"
+        f"gradient_method=:{options['gradient_method']},\n\t"
+        f"hessian_method=:{options['hessian_method']},\n\t"
+        f"sparse_jacobian={options['sparse_jacobian']},\n\t"
         f"verbose={options['verbose']}\n)\n\nend\n"
     )
     # write file