Merge branch 'develop' into dae_sensitivity

.github/workflows/test_petab_test_suite.yml

@@ -43,8 +43,6 @@ jobs:
             libatlas-base-dev \
             python3-venv
 
-      - run: pip3 install pysb petab
-
       - name: Build BNGL
         run: |
           scripts/buildBNGL.sh
@@ -59,6 +57,11 @@ jobs:
         run: |
           scripts/installAmiciSource.sh
 
+      - name: Install petab
+        run: |
+          source ./build/venv/bin/activate \
+            && pip3 install wheel pytest shyaml pytest-cov pysb
+
       # retrieve test models
       - name: Download and install PEtab test suite
         run: |

CMakeLists.txt

@@ -95,6 +95,8 @@ endif()
 
 if(DEFINED ENV{SWIG})
   message(STATUS "Setting SWIG_EXECUTABLE to $ENV{SWIG} ($SWIG)")
+  unset(SWIG_VERSION CACHE)
+  unset(SWIG_DIR CACHE)
   set(SWIG_EXECUTABLE $ENV{SWIG})
 endif()
 
@@ -151,12 +153,18 @@ if(${BLAS} STREQUAL "MKL" OR DEFINED ENV{MKLROOT})
         CACHE STRING "")
   endif()
 elseif(NOT DEFINED ENV{BLAS_LIBS} AND NOT DEFINED ENV{BLAS_CFLAGS})
-  set(BLAS_INCLUDE_DIRS
-      ""
-      CACHE STRING "")
-  set(BLAS_LIBRARIES
-      -lcblas
-      CACHE STRING "")
+  # if nothing is specified via environment variables, let's try FindBLAS
+  find_package(BLAS)
+  if(NOT BLAS_FOUND)
+    # Nothing specified by the user and FindBLAS didn't find anything; let's try
+    # if cblas is available on the system paths.
+    set(BLAS_INCLUDE_DIRS
+        ""
+        CACHE STRING "")
+    set(BLAS_LIBRARIES
+        -lcblas
+        CACHE STRING "")
+  endif()
 endif()
 add_compile_definitions(AMICI_BLAS_${BLAS})
 

cmake/version.cmake

@@ -3,7 +3,7 @@ if(Git_FOUND)
   execute_process(
     COMMAND
       sh -c
-      "${GIT_EXECUTABLE} describe --abbrev=4 --dirty=-dirty --always --tags  | cut -c 2- | tr -d '\n' | sed s/-/./"
+      "'${GIT_EXECUTABLE}' describe --abbrev=4 --dirty=-dirty --always --tags  | cut -c 2- | tr -d '\n' | sed s/-/./"
     WORKING_DIRECTORY ${CMAKE_CURRENT_SOURCE_DIR}
     OUTPUT_VARIABLE PROJECT_VERSION_GIT)
 endif()

documentation/conf.py

@@ -86,7 +86,7 @@ def install_mtocpp():
 
 def install_doxygen():
     """Get a more recent doxygen"""
-    version = "1.9.6"
+    version = "1.9.7"
     doxygen_exe = os.path.join(
         amici_dir, "ThirdParty", f"doxygen-{version}", "bin", "doxygen"
     )

documentation/rtd_requirements.txt

@@ -4,7 +4,7 @@ mock>=5.0.2
 setuptools==67.7.2
 pysb>=1.11.0
 matplotlib==3.7.1
-nbsphinx>=0.9.1
+nbsphinx==0.9.1
 nbformat==5.8.0
 recommonmark>=0.7.1
 sphinx_rtd_theme>=1.2.0

python/examples/example_splines_swameye/ExampleSplinesSwameye2003.ipynb

@@ -105,7 +105,7 @@
    "source": [
     "# If running as a Github action, just do the minimal amount of work required to check whether the code is working\n",
     "if os.getenv('GITHUB_ACTIONS') is not None:\n",
-    "    n_starts = 10\n",
+    "    n_starts = 15\n",
     "    pypesto_optimizer = pypesto.optimize.FidesOptimizer(verbose=logging.WARNING, options=dict(maxiter=10))\n",
     "    pypesto_engine = pypesto.engine.SingleCoreEngine()"
    ]
@@ -362,7 +362,7 @@
     "### Maximum Likelihood estimation\n",
     "Using pyPESTO we can optimize for the parameter vector that maximizes the probability of observing the experimental data (maximum likelihood estimation).\n",
     "\n",
-    "A multistart method with local gradient-based optimization is used and the results of each multistart can be visualized in a waterfall plot. "
+    "A multistart method with local gradient-based optimization is used and the results of each multistart can be visualized in a waterfall plot."
    ]
   },
   {
@@ -908,15 +908,15 @@
     "        np.log10(regstrength) # parameter is specified as log10 scale in PEtab\n",
     "    )\n",
     "    regproblem = copy.deepcopy(pypesto_problem)\n",
-    "    \n",
+    "\n",
     "    # Load existing results if available\n",
     "    if os.path.exists(f'{name}.h5'):\n",
     "        regresult = pypesto.store.read_result(f'{name}.h5', problem=regproblem)\n",
     "    else:\n",
     "        regresult = None\n",
     "    # Overwrite\n",
     "    # regresult = None\n",
-    "    \n",
+    "\n",
     "    # Parallel multistart optimization with pyPESTO and FIDES\n",
     "    if n_starts > 0:\n",
     "        if regresult is None:\n",
@@ -935,10 +935,10 @@
     "        regresult.optimize_result.sort()\n",
     "        if regresult.optimize_result.x[0] is None:\n",
     "            raise Exception(\"All multistarts failed (n_starts is probably too small)! If this error occurred during CI, just run the workflow again.\")\n",
-    "    \n",
+    "\n",
     "    # Save results to disk\n",
     "    # pypesto.store.write_result(regresult, f'{name}.h5', overwrite=True)\n",
-    "    \n",
+    "\n",
     "    # Store result\n",
     "    regproblems[regstrength] = regproblem\n",
     "    regresults[regstrength] = regresult"
@@ -1469,15 +1469,15 @@
     "        np.log10(regstrength) # parameter is specified as log10 scale in PEtab\n",
     "    )\n",
     "    regproblem = copy.deepcopy(pypesto_problem)\n",
-    "    \n",
+    "\n",
     "    # Load existing results if available\n",
     "    if os.path.exists(f'{name}.h5'):\n",
     "        regresult = pypesto.store.read_result(f'{name}.h5', problem=regproblem)\n",
     "    else:\n",
     "        regresult = None\n",
     "    # Overwrite\n",
     "    # regresult = None\n",
-    "    \n",
+    "\n",
     "    # Parallel multistart optimization with pyPESTO and FIDES\n",
     "    if n_starts > 0:\n",
     "        if regresult is None:\n",
@@ -1496,10 +1496,10 @@
     "        regresult.optimize_result.sort()\n",
     "        if regresult.optimize_result.x[0] is None:\n",
     "            raise Exception(\"All multistarts failed (n_starts is probably too small)! If this error occurred during CI, just run the workflow again.\")\n",
-    "    \n",
+    "\n",
     "    # Save results to disk\n",
     "    # pypesto.store.write_result(regresult, f'{name}.h5', overwrite=True)\n",
-    "    \n",
+    "\n",
     "    # Store result\n",
     "    regproblems[regstrength] = regproblem\n",
     "    regresults[regstrength] = regresult"
@@ -1934,7 +1934,7 @@
     "## Bibliography\n",
     "Schelker, M. et al. (2012). “Comprehensive estimation of input signals and dynamics in biochemical reaction networks”. In: Bioinformatics 28.18, pp. i529–i534. doi: [10.1093/bioinformatics/bts393](https://doi.org/10.1093/bioinformatics/bts393).\n",
     "\n",
-    "Swameye, I. et al. (2003). “Identification of nucleocytoplasmic cycling as a remote sensor in cellular signaling by databased modeling”. In: Proceedings of the National Academy of Sciences 100.3, pp. 1028–1033. doi: [10.1073/pnas.0237333100](https://doi.org/10.1073/pnas.0237333100)."
+    "Swameye, I. et al. (2003). “Identification of nucleocytoplasmic cycling as a remote sensor in cellular signaling by databased modeling”. In: Proceedings of the National Academy of Sciences 100.3, pp. 1028–1033. doi: [10.1073/pnas.0237333100](https://doi.org/10.1073/pnas.0237333100).\n"
    ]
   }
  ],

python/sdist/amici/de_export.py

@@ -1999,9 +1999,10 @@ def _compute_equation(self, name: str) -> None:
 
                 # need to check if equations are zero since we are using
                 # symbols
-                if not smart_is_zero_matrix(self.eq("stau")[ie]):
+                if not smart_is_zero_matrix(self.eq("stau")[ie]) \
+                        and not smart_is_zero_matrix(self.eq("xdot")):
                     tmp_eq += smart_multiply(
-                        (self.sym("xdot_old") - self.sym("xdot")),
+                        self.sym("xdot_old") - self.sym("xdot"),
                         self.sym("stau").T,
                     )
 

python/sdist/amici/parameter_mapping.py

@@ -248,6 +248,9 @@ def _get_par(model_par, value, mapping):
                 # condition table overrides must have been handled already,
                 # e.g. by the PEtab parameter mapping, but parameters from
                 # InitialAssignments may still be present.
+                if mapping[value] == model_par:
+                    # prevent infinite recursion
+                    raise
                 return _get_par(value, mapping[value], mapping)
         if model_par in problem_parameters:
             # user-provided