Merge pull request #2133 from AMICI-dev/release_0.18.1

dweindl · web-flow · commit a12d17306afb · 2023-06-26T12:30:54.000+02:00
Release 0.18.1
diff --git a/.github/workflows/deploy_release.yml b/.github/workflows/deploy_release.yml
@@ -36,11 +36,11 @@ jobs:
         scripts/buildSdist.sh
 
     - name: Publish a Python distribution to PyPI
-      uses: pypa/gh-action-pypi-publish@master
+      uses: pypa/gh-action-pypi-publish@release/v1
       with:
         user: __token__
         password: ${{ secrets.pypi_password }}
-        packages_dir: python/sdist/dist
+        packages-dir: python/sdist/dist
 
   bioSimulatorsUpdateCliAndDockerImage:
     name: Release to BioSimulators
diff --git a/.github/workflows/test_python_cplusplus.yml b/.github/workflows/test_python_cplusplus.yml
@@ -36,7 +36,7 @@ jobs:
     - run: echo "BNGPATH=${GITHUB_WORKSPACE}/ThirdParty/BioNetGen-2.7.0" >> $GITHUB_ENV
 
     # sonar cloud
-    - run: echo "SONAR_SCANNER_VERSION=4.6.2.2472" >> $GITHUB_ENV
+    - run: echo "SONAR_SCANNER_VERSION=4.7.0.2747" >> $GITHUB_ENV
     - run: echo "SONAR_SCANNER_HOME=${HOME}/.sonar/sonar-scanner-$SONAR_SCANNER_VERSION-linux" >> $GITHUB_ENV
     - run: echo "SONAR_SCANNER_OPTS=-server" >> $GITHUB_ENV
     - run: echo "${SONAR_SCANNER_HOME}/bin" >> $GITHUB_PATH
diff --git a/CHANGELOG.md b/CHANGELOG.md
@@ -2,15 +2,35 @@
 
 ## v0.X Series
 
+### v0.18.1 (2023-06-26)
+
+Fixes:
+* Fixed pysb pattern matching during PEtab import
+  by @FFroehlich in https://github.com/AMICI-dev/AMICI/pull/2118
+* Fixed `sp.Matrix` errors with `numpy==1.25`
+  by @dweindl in https://github.com/AMICI-dev/AMICI/pull/2124
+* Readme: added info containers
+  by @dweindl in https://github.com/AMICI-dev/AMICI/pull/2125
+* Fixed deprecation warnings
+  by @dweindl in https://github.com/AMICI-dev/AMICI/pull/2122
+  https://github.com/AMICI-dev/AMICI/pull/2131
+* Fixed logging typo in SBML import
+  by @dilpath in https://github.com/AMICI-dev/AMICI/pull/2126
+* Added minimum version for `pandas`
+  by @dilpath in https://github.com/AMICI-dev/AMICI/pull/2129
+
+**Full Changelog**: https://github.com/AMICI-dev/AMICI/compare/v0.18.0...v0.18.1
+
 ### v0.18.0 (2023-05-26)
+
 Features:
 * More efficient handling of splines in SBML models
   by @paulstapor, @lcontento, @dweindl
   in https://github.com/AMICI-dev/AMICI/pull/1515
 * Partial support of current PEtab2.0 draft, including support for PySB models
   by @dweindl, @FFroehlich in https://github.com/AMICI-dev/AMICI/pull/1800
 
-Fixes
+Fixes:
 * **Fixed incorrect forward sensitivities for models with events with**
   **state-dependent trigger functions**
   by @dweindl in https://github.com/AMICI-dev/AMICI/pull/2084
diff --git a/README.md b/README.md
@@ -82,6 +82,8 @@ To install AMICI, first read the installation instructions for
 [Python](https://amici.readthedocs.io/en/latest/python_installation.html),
 [C++](https://amici.readthedocs.io/en/develop/cpp_installation.html) or
 [Matlab](https://amici.readthedocs.io/en/develop/matlab_installation.html).
+There are also instructions for using AMICI inside
+[containers](https://github.com/AMICI-dev/AMICI/tree/master/container).
 
 To get you started with Python-AMICI, the best way might be checking out this
 [Jupyter notebook](https://github.com/AMICI-dev/AMICI/blob/master/documentation/GettingStarted.ipynb)
diff --git a/pytest.ini b/pytest.ini
@@ -3,11 +3,19 @@
 addopts = -vv --strict-markers
 
 filterwarnings =
+    error
+    # amici
+    ignore:Conservation laws for non-constant species in models with RateRules are currently not supported and will be turned off.:UserWarning
+    ignore:Conservation laws for non-constant species in models with Species-AssignmentRules are currently not supported and will be turned off.:UserWarning
+    ignore:Conservation laws for non-constant species in combination with parameterized stoichiometric coefficients are not currently supported and will be turned off.:UserWarning
+    ignore:Support for PEtab2.0 is experimental!:UserWarning
     # hundreds of SBML <=5.17 warnings
     ignore:.*inspect.getargspec\(\) is deprecated.*:DeprecationWarning
     # pysb warnings
     ignore:the imp module is deprecated in favour of importlib.*:DeprecationWarning:pysb\.core
     ignore:Using or importing the ABCs from 'collections' instead of from 'collections.abc' is deprecated since Python 3.3,and in 3.9 it will stop working.*:DeprecationWarning:pysb\.core
     ignore:Model.initial_conditions will be removed in a future version. Instead, you can get a list of Initial objects with Model.initials.:DeprecationWarning:pysb\.core
+    # https://github.com/pytest-dev/pytest-xdist/issues/825#issuecomment-1292283870
+    ignore:The --rsyncdir command line argument and rsyncdirs config variable are deprecated.:DeprecationWarning
 
 norecursedirs = .git amici_models build doc documentation matlab models ThirdParty amici sdist examples
diff --git a/python/sdist/amici/petab_util.py b/python/sdist/amici/petab_util.py
@@ -58,6 +58,11 @@ def get_states_in_condition_table(
             return states
         import pysb.pattern
 
+        if not petab_problem.model.model.species:
+            import pysb.bng
+
+            pysb.bng.generate_equations(petab_problem.model.model)
+
         try:
             spm = pysb.pattern.SpeciesPatternMatcher(
                 model=petab_problem.model.model
diff --git a/python/sdist/amici/sbml_import.py b/python/sdist/amici/sbml_import.py
@@ -18,6 +18,7 @@
 import libsbml as sbml
 import numpy as np
 import sympy as sp
+from sympy.logic.boolalg import BooleanFalse, BooleanTrue
 
 from . import has_clibs
 from .constants import SymbolId
@@ -214,7 +215,7 @@ def _process_document(self) -> None:
             conversion_properties.addOption("performValidation", False)
             conversion_properties.addOption("abortIfUnflattenable", "none")
             if (
-                log_execution_time("converting SBML local parameters", logger)(
+                log_execution_time("flattening hierarchical SBML", logger)(
                     self.sbml_doc.convert
                 )(conversion_properties)
                 != sbml.LIBSBML_OPERATION_SUCCESS
@@ -1047,7 +1048,12 @@ def _process_reactions(self):
                     reaction.getKineticLaw() or sp.Float(0)
                 )
 
-            self.flux_vector[reaction_index] = sym_math
+            self.flux_vector[reaction_index] = sym_math.subs(
+                {
+                    BooleanTrue(): sp.Float(1.0),
+                    BooleanFalse(): sp.Float(0.0),
+                }
+            )
             if any(
                 str(symbol) in reaction_ids
                 for symbol in self.flux_vector[reaction_index].free_symbols
diff --git a/python/sdist/setup.cfg b/python/sdist/setup.cfg
@@ -35,7 +35,7 @@ install_requires =
     numpy; python_version>='3.12'
     python-libsbml
     h5py
-    pandas
+    pandas>=2.0.2
     wurlitzer
     toposort
     setuptools>=48
diff --git a/python/tests/splines_utils.py b/python/tests/splines_utils.py
@@ -47,7 +47,6 @@ def integrate_spline(
     params: Union[Dict, None],
     tt: Sequence[float],
     initial_value: float = 0,
-    **kwargs,
 ):
     """
     Integrate the `AbstractSpline` `spline` at timepoints `tt`
@@ -56,7 +55,7 @@ def integrate_spline(
     ispline = [initial_value + spline.integrate(0, t) for t in tt]
     if params is not None:
         ispline = [x.subs(params) for x in ispline]
-    return np.asarray(ispline, **kwargs)
+    return ispline
 
 
 def create_condition_table() -> pd.DataFrame:
@@ -213,10 +212,13 @@ def create_petab_problem(
     dt /= measure_upsample
     n_obs = math.ceil(T / dt) + 1
     tt_obs = np.linspace(0, float(T), n_obs)
-    zz_true = [
-        integrate_spline(spline, params_true, tt_obs, iv, dtype=float)
-        for (spline, iv) in zip(splines, initial_values)
-    ]
+    zz_true = np.array(
+        [
+            integrate_spline(spline, params_true, tt_obs, iv)
+            for (spline, iv) in zip(splines, initial_values)
+        ],
+        dtype=float,
+    )
     zz_obs = [zz + sigma * np.random.randn(len(zz)) for zz in zz_true]
 
     # Create PEtab tables
@@ -263,7 +265,7 @@ def create_petab_problem(
     folder = os.path.abspath(folder)
     os.makedirs(folder, exist_ok=True)
     problem.to_files(
-        sbml_file=os.path.join(folder, f"{model_name}_model.xml"),
+        model_file=os.path.join(folder, f"{model_name}_model.xml"),
         condition_file=os.path.join(folder, f"{model_name}_conditions.tsv"),
         measurement_file=os.path.join(folder, f"{model_name}_measurements.tsv"),
         parameter_file=os.path.join(folder, f"{model_name}_parameters.tsv"),
diff --git a/python/tests/test_events.py b/python/tests/test_events.py
@@ -164,7 +164,7 @@ def get_early_x(t):
                 tmp_x = expm(event_1_time * A)
                 x1 = np.matmul(tmp_x, x0)
                 # apply bolus
-                delta_x = np.array([[float(-x1[2, :] / 2)], [0], [0]])
+                delta_x = np.array([[float(-x1[2, 0] / 2)], [0], [0]])
                 x1 += delta_x
                 # "simulate" on
                 tmp_x = expm((t - event_1_time) * A)
@@ -425,7 +425,11 @@ def model_definition_event_state_dep_ddeltax_dtpx():
     timepoints = np.linspace(0.0, 5.0, 100)
     events = {
         # state-dependent ddeltaxdt
-        "event_1": {"trigger": "time > alpha", "target": "x_1", "assignment": "x_1 * time"},
+        "event_1": {
+            "trigger": "time > alpha",
+            "target": "x_1",
+            "assignment": "x_1 * time",
+        },
         # state-dependent ddeltaxdp
         "event_2": {
             "trigger": "time > beta",
@@ -453,7 +457,9 @@ def x_expected(t, x_1_0, alpha, beta, gamma, delta):
             x = ((x_1_0 + alpha) * alpha + (beta - alpha)) * delta + (t - beta)
         else:
             # after third event triggered
-            x = (((x_1_0 + alpha) * alpha + (beta - alpha)) * delta + (gamma - beta)) ** 2 * 2 + (t - gamma)
+            x = (
+                ((x_1_0 + alpha) * alpha + (beta - alpha)) * delta + (gamma - beta)
+            ) ** 2 * 2 + (t - gamma)
 
         return np.array((x,))
 
diff --git a/python/tests/test_pysb.py b/python/tests/test_pysb.py
@@ -341,6 +341,7 @@ def test_heavyside_and_special_symbols():
 
 @skip_on_valgrind
 def test_energy():
+    pysb.SelfExporter.cleanup()
     model_pysb = pysb.Model("energy")
     pysb.Monomer("A", ["a", "b"])
     pysb.Monomer("B", ["a"])
diff --git a/python/tests/valgrind-python.supp b/python/tests/valgrind-python.supp
@@ -761,3 +761,17 @@
    Memcheck:Value8
    fun:dictkeys_get_index
 }
+
+{
+   Python os_stat / PyFloat_FromDouble
+   Memcheck:Leak
+   match-leak-kinds: definite
+   fun:malloc
+   fun:PyFloat_FromDouble
+   fun:fill_time
+   fun:_pystat_fromstructstat
+   fun:posix_do_stat.constprop.0
+   fun:os_stat_impl
+   fun:os_stat
+   ...
+}
diff --git a/scripts/run-valgrind-py.sh b/scripts/run-valgrind-py.sh
@@ -24,4 +24,8 @@ PYTHONMALLOC=malloc valgrind \
   --leak-check=full \
   --gen-suppressions=all \
   -v \
-  python -m pytest -vv --ignore-glob=*petab*
+  python -m pytest -vv --ignore-glob=*petab* -W "ignore:Signature "
+#                                               ^ ignores the following warning that occurs only under valgrind,
+# e.g. `valgrind python -c "import h5py"`:
+# UserWarning: Signature b'\x00\xd0\xcc\xcc\xcc\xcc\xcc\xcc\xfb\xbf\x00\x00\x00\x00\x00\x00'
+# for <class 'numpy.longdouble'> does not match any known type: falling back to type probe function.
diff --git a/tests/benchmark-models/benchmark_models.yaml b/tests/benchmark-models/benchmark_models.yaml
@@ -23,7 +23,7 @@ Borghans_BiophysChem1997:
   llh: 83.3237191357272
   t_sim: 0.005
   t_fwd: 0.5
-  t_adj: 0.1
+  t_adj: 0.2
   note: benchmark collection reference value matches up to sign when applying log10-correction +sum(log(meas*log(10)) / 2
 
 Brannmark_JBC2010:
diff --git a/tests/benchmark-models/test_petab_benchmark.py b/tests/benchmark-models/test_petab_benchmark.py
@@ -50,6 +50,7 @@
     debug_path.mkdir(exist_ok=True, parents=True)
 
 
+@pytest.mark.filterwarnings("ignore:divide by zero encountered in log10")
 @pytest.mark.parametrize("scale", (True, False))
 @pytest.mark.parametrize("model", models)
 def test_benchmark_gradient(model, scale):
diff --git a/tests/benchmark-models/test_petab_model.py b/tests/benchmark-models/test_petab_model.py
@@ -148,7 +148,7 @@ def main():
         ]
         res = res_repeats[0]
 
-        times[label] = np.mean(
+        times[label] = np.min(
             [
                 sum(r.cpu_time + r.cpu_timeB for r in res[RDATAS]) / 1000
                 # only forwards/backwards simulation
diff --git a/version.txt b/version.txt
@@ -1 +1 @@
-0.18.0
+0.18.1
