Merge pull request #2406 from AMICI-dev/release_0.24.0

Release 0.24.0

CHANGELOG.md

@@ -2,6 +2,48 @@
 
 ## v0.X Series
 
+### v0.24.0 (2024-04-22)
+
+This will be the last release supporting Python 3.9.
+Future releases will require Python 3.10.
+
+**Fixes**
+
+* Fix cmake error `cannot create directory: /cmake/Amici`
+  during model import in cases where BLAS was not found via `FindBLAS`
+  by @dweindl in https://github.com/AMICI-dev/AMICI/pull/2389
+* Added status code `AMICI_CONSTR_FAIL`
+  by @dweindl in https://github.com/AMICI-dev/AMICI/pull/2379
+* Fixed certain initial state issues with PEtab
+  by @dweindl in https://github.com/AMICI-dev/AMICI/pull/2382
+* Fixed Solver `operator==` and copyctor
+  (constraints were not copied correctly)
+  by @dweindl in https://github.com/AMICI-dev/AMICI/pull/2388
+* Avoid confusing warnings about non-finite timepoints
+  by @dweindl in https://github.com/AMICI-dev/AMICI/pull/2395
+* Fixed incorrect exception types / messages for `IDASolver`
+  by @dweindl in https://github.com/AMICI-dev/AMICI/pull/2398
+* cmake: set SUNDIALS path hint for python package to help CMake find
+  the correct SUNDIALS installation
+  by @dweindl in https://github.com/AMICI-dev/AMICI/pull/2397
+
+* **Features**
+
+* Optionally include measurements in `plot_observable_trajectories`
+  by @dweindl in https://github.com/AMICI-dev/AMICI/pull/2381
+* Improved type annotations in swig-wrappers
+  by @dweindl in https://github.com/AMICI-dev/AMICI/pull/2401
+* Additional attributes are accessible directly via `ReturnDataView` and
+  `ExpDataView`, e.g. `ReturnDataView.ny`, `ReturnDataView.nx`
+  by @dweindl in https://github.com/AMICI-dev/AMICI/pull/2405
+* Allow subselection of state variables for convergence check during
+  steady-state simulations via `Model.set_steadystate_mask([1, 0, ..., 1])`
+  (positive value: check; non-positive: don't check).
+  by @dweindl in https://github.com/AMICI-dev/AMICI/pull/2387
+
+**Full Changelog**: https://github.com/AMICI-dev/AMICI/compare/v0.23.1...v0.24.0
+
+
 ### v0.23.1 (2024-03-11)
 
 * Fixes installation issues related to building SuiteSparse on some systems

CMakeLists.txt

@@ -133,9 +133,16 @@ elseif(AMICI_TRY_ENABLE_HDF5)
 endif()
 
 set(VENDORED_SUNDIALS_DIR ${CMAKE_CURRENT_SOURCE_DIR}/ThirdParty/sundials)
-set(VENDORED_SUNDIALS_BUILD_DIR ${VENDORED_SUNDIALS_DIR}/build)
-set(VENDORED_SUNDIALS_INSTALL_DIR ${VENDORED_SUNDIALS_BUILD_DIR})
 set(SUNDIALS_PRIVATE_INCLUDE_DIRS "${VENDORED_SUNDIALS_DIR}/src")
+# Handle different sundials build/install dirs, depending on whether we are
+#  building the Python extension only or the full C++ interface
+if(AMICI_PYTHON_BUILD_EXT_ONLY)
+  set(VENDORED_SUNDIALS_BUILD_DIR ${CMAKE_CURRENT_SOURCE_DIR})
+  set(VENDORED_SUNDIALS_INSTALL_DIR ${VENDORED_SUNDIALS_BUILD_DIR})
+else()
+  set(VENDORED_SUNDIALS_BUILD_DIR ${VENDORED_SUNDIALS_DIR}/build)
+  set(VENDORED_SUNDIALS_INSTALL_DIR ${VENDORED_SUNDIALS_BUILD_DIR})
+endif()
 find_package(
   SUNDIALS REQUIRED PATHS
   "${VENDORED_SUNDIALS_INSTALL_DIR}/${CMAKE_INSTALL_LIBDIR}/cmake/sundials/")

cmake/AmiciFindBLAS.cmake

@@ -83,13 +83,14 @@ if(NOT TARGET BLAS::BLAS)
     INTERFACE_INCLUDE_DIRECTORIES "${BLAS_INCLUDE_DIRS}"
     INTERFACE_LINK_LIBRARIES "${BLAS_LIBRARIES}")
   add_library(BLAS::BLAS ALIAS BLAS)
-  install(TARGETS BLAS EXPORT BLAS)
-  export(EXPORT BLAS NAMESPACE BLAS::)
-  install(
-    EXPORT BLAS
-    DESTINATION "${CMAKE_INSTALL_LIBDIR}/cmake/Amici"
-    NAMESPACE BLAS::)
-
+  if("${PROJECT_NAME}" STREQUAL "amici")
+      install(TARGETS BLAS EXPORT BLAS)
+      export(EXPORT BLAS NAMESPACE BLAS::)
+      install(
+        EXPORT BLAS
+        DESTINATION "${CMAKE_INSTALL_LIBDIR}/cmake/Amici"
+        NAMESPACE BLAS::)
+  endif()
 
   # legacy python package environment variables:
   if(DEFINED ENV{BLAS_CFLAGS})

documentation/amici_refs.bib

@@ -1275,6 +1275,37 @@ @Article{SluijsZho2024
   publisher        = {Springer Science and Business Media LLC},
 }
 
+@Article{KissVen2024,
+  author           = {Kiss, Anna E and Venkatasubramani, Anuroop V and Pathirana, Dilan and Krause, Silke and Sparr, Aline Campos and Hasenauer, Jan and Imhof, Axel and Müller, Marisa and Becker, Peter B},
+  journal          = {Nucleic Acids Research},
+  title            = {{Processivity and specificity of histone acetylation by the male-specific lethal complex}},
+  year             = {2024},
+  issn             = {0305-1048},
+  month            = {02},
+  pages            = {gkae123},
+  abstract         = {{Acetylation of lysine 16 of histone H4 (H4K16ac) stands out among the histone modifications, because it decompacts the chromatin fiber. The metazoan acetyltransferase MOF (KAT8) regulates transcription through H4K16 acetylation. Antibody-based studies had yielded inconclusive results about the selectivity of MOF to acetylate the H4 N-terminus. We used targeted mass spectrometry to examine the activity of MOF in the male-specific lethal core (4-MSL) complex on nucleosome array substrates. This complex is part of the Dosage Compensation Complex (DCC) that activates X-chromosomal genes in male Drosophila. During short reaction times, MOF acetylated H4K16 efficiently and with excellent selectivity. Upon longer incubation, the enzyme progressively acetylated lysines 12, 8 and 5, leading to a mixture of oligo-acetylated H4. Mathematical modeling suggests that MOF recognizes and acetylates H4K16 with high selectivity, but remains substrate-bound and continues to acetylate more N-terminal H4 lysines in a processive manner. The 4-MSL complex lacks non-coding roX RNA, a critical component of the DCC. Remarkably, addition of RNA to the reaction non-specifically suppressed H4 oligo-acetylation in favor of specific H4K16 acetylation. Because RNA destabilizes the MSL-nucleosome interaction in vitro we speculate that RNA accelerates enzyme-substrate turn-over in vivo, thus limiting the processivity of MOF, thereby increasing specific H4K16 acetylation.}},
+  creationdate     = {2024-02-28T18:25:06},
+  doi              = {10.1093/nar/gkae123},
+  eprint           = {https://academic.oup.com/nar/advance-article-pdf/doi/10.1093/nar/gkae123/56756494/gkae123.pdf},
+  modificationdate = {2024-02-28T18:25:06},
+  url              = {https://doi.org/10.1093/nar/gkae123},
+}
+
+@Article{DoresicGre2024,
+  author           = {Domagoj Dore{\v s}i{\'c} and Stephan Grein and Jan Hasenauer},
+  journal          = {bioRxiv},
+  title            = {Efficient parameter estimation for ODE models of cellular processes using semi-quantitative data},
+  year             = {2024},
+  abstract         = {Quantitative dynamical models facilitate the understanding of biological processes and the prediction of their dynamics. The parameters of these models are commonly estimated from experimental data. Yet, experimental data generated from different techniques do not provide direct information about the state of the system but a non-linear (monotonic) transformation of it. For such semi-quantitative data, when this transformation is unknown, it is not apparent how the model simulations and the experimental data can be compared. Here, we propose a versatile spline-based approach for the integration of a broad spectrum of semi-quantitative data into parameter estimation. We derive analytical formulas for the gradients of the hierarchical objective function and show that this substantially increases the estimation efficiency. Subsequently, we demonstrate that the method allows for the reliable discovery of unknown measurement transformations. Furthermore, we show that this approach can significantly improve the parameter inference based on semi-quantitative data in comparison to available methods. Modelers can easily apply our method by using our implementation in the open-source Python Parameter EStimation TOolbox (pyPESTO).Competing Interest StatementThe authors have declared no competing interest.},
+  creationdate     = {2024-04-20T13:05:06},
+  doi              = {10.1101/2024.01.26.577371},
+  elocation-id     = {2024.01.26.577371},
+  eprint           = {https://www.biorxiv.org/content/early/2024/01/30/2024.01.26.577371.full.pdf},
+  modificationdate = {2024-04-20T13:05:06},
+  publisher        = {Cold Spring Harbor Laboratory},
+  url              = {https://www.biorxiv.org/content/early/2024/01/30/2024.01.26.577371},
+}
+
 @Comment{jabref-meta: databaseType:bibtex;}
 
 @Comment{jabref-meta: grouping:

documentation/conf.py

@@ -9,19 +9,33 @@
 import subprocess
 import sys
 from enum import EnumType
-
-# need to import before setting typing.TYPE_CHECKING=True, fails otherwise
-import amici
 import exhale.deploy
-import exhale_multiproject_monkeypatch
 from unittest import mock
-import pandas as pd
 import sphinx
-import sympy as sp
 from exhale import configs as exhale_configs
 from sphinx.transforms.post_transforms import ReferencesResolver
 
-exhale_multiproject_monkeypatch, pd, sp  # to avoid removal of unused import
+try:
+    import exhale_multiproject_monkeypatch  # noqa: F401
+except ModuleNotFoundError:
+    # for unclear reasons, the import of exhale_multiproject_monkeypatch
+    #  fails on some systems, because the the location of the editable install
+    #  is not automatically added to sys.path ¯\_(ツ)_/¯
+    from importlib.metadata import Distribution
+    import json
+    from urllib.parse import unquote_plus, urlparse
+
+    dist = Distribution.from_name("sphinx-contrib-exhale-multiproject")
+    url = json.loads(dist.read_text("direct_url.json"))["url"]
+    package_dir = unquote_plus(urlparse(url).path)
+    sys.path.append(package_dir)
+    import exhale_multiproject_monkeypatch  # noqa: F401
+
+# need to import before setting typing.TYPE_CHECKING=True, fails otherwise
+import amici
+import pandas as pd  # noqa: F401
+import sympy as sp  # noqa: F401
+
 
 # BEGIN Monkeypatch exhale
 from exhale.deploy import _generate_doxygen as exhale_generate_doxygen

documentation/references.md

@@ -1,6 +1,6 @@
 # References
 
-List of publications using AMICI. Total number is 83.
+List of publications using AMICI. Total number is 85.
 
 If you applied AMICI in your work and your publication is missing, please let us know via a new GitHub issue.
 
@@ -14,6 +14,21 @@ If you applied AMICI in your work and your publication is missing, please let us
 <h1 class="unnumbered" id="section">2024</h1>
 <div id="refs" class="references csl-bib-body hanging-indent"
 role="list">
+<div id="ref-DoresicGre2024" class="csl-entry" role="listitem">
+Dorešić, Domagoj, Stephan Grein, and Jan Hasenauer. 2024.
+<span>“Efficient Parameter Estimation for ODE Models of Cellular
+Processes Using Semi-Quantitative Data.”</span> <em>bioRxiv</em>. <a
+href="https://doi.org/10.1101/2024.01.26.577371">https://doi.org/10.1101/2024.01.26.577371</a>.
+</div>
+<div id="ref-KissVen2024" class="csl-entry" role="listitem">
+Kiss, Anna E, Anuroop V Venkatasubramani, Dilan Pathirana, Silke Krause,
+Aline Campos Sparr, Jan Hasenauer, Axel Imhof, Marisa Müller, and Peter
+B Becker. 2024. <span>“<span class="nocase">Processivity and specificity
+of histone acetylation by the male-specific lethal
+complex</span>.”</span> <em>Nucleic Acids Research</em>, February,
+gkae123. <a
+href="https://doi.org/10.1093/nar/gkae123">https://doi.org/10.1093/nar/gkae123</a>.
+</div>
 <div id="ref-LangPen2024" class="csl-entry" role="listitem">
 Lang, Paul F., David R. Penas, Julio R. Banga, Daniel Weindl, and Bela
 Novak. 2024. <span>“Reusable Rule-Based Cell Cycle Model Explains

include/amici/defines.h

@@ -71,6 +71,7 @@ constexpr int AMICI_CONV_FAILURE=             -4;
 constexpr int AMICI_LSETUP_FAIL=              -6;
 constexpr int AMICI_RHSFUNC_FAIL=             -8;
 constexpr int AMICI_FIRST_RHSFUNC_ERR=        -9;
+constexpr int AMICI_CONSTR_FAIL=             -15;
 constexpr int AMICI_ILL_INPUT=               -22;
 constexpr int AMICI_ERROR=                   -99;
 constexpr int AMICI_NO_STEADY_STATE=         -81;

include/amici/model.h

@@ -1481,6 +1481,40 @@ class Model : public AbstractModel, public ModelDimensions {
      */
     virtual std::vector<double> get_trigger_timepoints() const;
 
+    /**
+     * @brief Get steady-state mask as std::vector.
+     *
+     * See `set_steadystate_mask` for details.
+     *
+     * @return Steady-state mask
+     */
+    std::vector<double> get_steadystate_mask() const {
+        return steadystate_mask_.getVector();
+    };
+
+    /**
+     * @brief Get steady-state mask as AmiVector.
+     *
+     * See `set_steadystate_mask` for details.
+     * @return Steady-state mask
+     */
+    AmiVector const& get_steadystate_mask_av() const {
+        return steadystate_mask_;
+    };
+
+    /**
+     * @brief Set steady-state mask.
+     *
+     * The mask is used to exclude certain state variables from the steady-state
+     * convergence check. Positive values indicate that the corresponding state
+     * variable should be included in the convergence check, while non-positive
+     * values indicate that the corresponding state variable should be excluded.
+     * An empty mask is interpreted as including all state variables.
+     *
+     * @param mask Mask of length `nx_solver`.
+     */
+    void set_steadystate_mask(std::vector<double> const& mask);
+
     /**
      * Flag indicating whether for
      * `amici::Solver::sensi_` == `amici::SensitivityOrder::second`
@@ -2087,6 +2121,15 @@ class Model : public AbstractModel, public ModelDimensions {
 
     /** Simulation parameters, initial state, etc. */
     SimulationParameters simulation_parameters_;
+
+    /**
+     * Mask for state variables that should be checked for steady state
+     * during pre-/post-equilibration. Positive values indicate that the
+     * corresponding state variable should be checked for steady state.
+     * Negative values indicate that the corresponding state variable should
+     * be ignored.
+     */
+    AmiVector steadystate_mask_;
 };
 
 bool operator==(Model const& a, Model const& b);

include/amici/serialization.h

@@ -150,6 +150,7 @@ void serialize(Archive& ar, amici::Model& m, unsigned int const /*version*/) {
     ar & m.steadystate_computation_mode_;
     ar & m.steadystate_sensitivity_mode_;
     ar & m.state_independent_events_;
+    ar & m.steadystate_mask_;
 }
 
 /**

include/amici/steadystateproblem.h

@@ -246,14 +246,16 @@ class SteadystateProblem {
      * w_i = 1 / ( rtol * x_i + atol )
      * @param x current state (sx[ip] for sensitivities)
      * @param xdot current rhs (sxdot[ip] for sensitivities)
+     * @param mask mask for state variables to include in WRMS norm.
+     * Positive value: include; non-positive value: exclude; empty: include all.
      * @param atol absolute tolerance
      * @param rtol relative tolerance
      * @param ewt error weight vector
      * @return root-mean-square norm
      */
     realtype getWrmsNorm(
-        AmiVector const& x, AmiVector const& xdot, realtype atol, realtype rtol,
-        AmiVector& ewt
+        AmiVector const& x, AmiVector const& xdot, AmiVector const& mask,
+        realtype atol, realtype rtol, AmiVector& ewt
     ) const;
 
     /**

include/amici/vector.h

@@ -430,8 +430,8 @@ inline span<realtype> make_span(N_Vector nv) {
 }
 
 /**
- * @brief Create span from N_Vector
- * @param nv
+ * @brief Create span from AmiVector
+ * @param av
  *
  */
 inline span<realtype const> make_span(amici::AmiVector const& av) {

python/sdist/amici/numpy.py

@@ -55,15 +55,18 @@ def __getitem__(self, item: str) -> Union[np.ndarray, float]:
         if item in self._cache:
             return self._cache[item]
 
-        if item == "id":
-            return getattr(self._swigptr, item)
+        if item in self._field_names:
+            value = _field_as_numpy(
+                self._field_dimensions, item, self._swigptr
+            )
+            self._cache[item] = value
 
-        if item not in self._field_names:
-            self.__missing__(item)
+            return value
+
+        if not item.startswith("_") and hasattr(self._swigptr, item):
+            return getattr(self._swigptr, item)
 
-        value = _field_as_numpy(self._field_dimensions, item, self._swigptr)
-        self._cache[item] = value
-        return value
+        self.__missing__(item)
 
     def __missing__(self, key: str) -> None:
         """

python/sdist/amici/petab/parameter_mapping.py

@@ -486,12 +486,11 @@ def create_parameter_mapping_for_condition(
                     condition_scale_map_preeq,
                     preeq_value,
                 )
-            else:
-                # need to set dummy value for preeq parameter anyways, as it
-                #  is expected below (set to 0, not nan, because will be
-                #  multiplied with indicator variable in initial assignment)
-                condition_map_sim[init_par_id] = 0.0
-                condition_scale_map_sim[init_par_id] = LIN
+            # need to set dummy value for preeq parameter anyways, as it
+            #  is expected below (set to 0, not nan, because will be
+            #  multiplied with indicator variable in initial assignment)
+            condition_map_sim[init_par_id] = 0.0
+            condition_scale_map_sim[init_par_id] = LIN
 
             # for simulation
             condition_id = condition[SIMULATION_CONDITION_ID]
@@ -505,6 +504,10 @@ def create_parameter_mapping_for_condition(
                 condition_scale_map_sim,
                 value,
             )
+            # set dummy value as above
+            if condition_map_preeq:
+                condition_map_preeq[init_par_id] = 0.0
+                condition_scale_map_preeq[init_par_id] = LIN
 
     ##########################################################################
     # separate fixed and variable AMICI parameters, because we may have

python/sdist/amici/petab/sbml_import.py

@@ -300,6 +300,8 @@ def import_model_sbml(
             init_par = sbml_model.createParameter()
             init_par.setId(init_par_id)
             init_par.setName(init_par_id)
+            # must be a fixed parameter in any case to allow reinitialization
+            fixed_parameters.append(init_par_id)
         assignment = sbml_model.getInitialAssignment(assignee_id)
         if assignment is None:
             assignment = sbml_model.createInitialAssignment()
@@ -535,7 +537,7 @@ def _get_fixed_parameters_sbml(
                     ia.getMath(), parser_settings
                 )
             )
-            if not sym_math.is_Number:
+            if not sym_math.evalf().is_Number:
                 fixed_parameters.remove(fixed_parameter)
                 continue