Merge branch 'develop' into docker_hub_gha

.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

CHANGELOG.rst

@@ -6,6 +6,25 @@ Release notes
 ..........
 
 
+0.3.3 (2023-10-19)
+-------------------
+
+* Visualize:
+    * Get optimization result by id (#1116)
+* Storage:
+    * allow "{id}" in history storage filename (#1118)
+* Objective:
+    * adjusted PEtab.jl syntax to new release (#1128, #1131)
+    * Documentation on PEtab importer updated (#1126)
+* Ensembles
+    * Additional option for cutoff calculation (#1124)
+    * Ensembles from optimization endpoints now only takes free parameters (#1130)
+* General
+    * Added How to Cite (#1125)
+    * Additional summary option (#1134)
+    * Speed up base tests (#1127)
+
+
 0.3.2 (2023-10-02)
 -------------------
 

README.md

@@ -59,8 +59,29 @@ We are happy about any contributions. For more information on how to contribute
 to pyPESTO check out
 <https://pypesto.readthedocs.io/en/latest/contribute.html>
 
+## Publications
+
+**Citeable DOI for the latest pyPESTO release:**
+[![DOI](https://zenodo.org/badge/DOI/10.5281/zenodo.2553546.svg)](https://doi.org/10.5281/zenodo.2553546)
+
+There is a list of [publications using pyPESTO](https://pypesto.readthedocs.io/en/latest/references.html).
+If you used pyPESTO in your work, we are happy to include
+your project, please let us know via a GitHub issue.
+
+When using pyPESTO in your project, please cite
+* Schälte, Y., Fröhlich, F., Jost, P. J., Vanhoefer, J., Pathirana, D., Stapor, P.,
+  Lakrisenko, P., Wang, D., Raimúndez, E., Merkt, S., Schmiester, L., Städter, P.,
+  Grein, S., Dudkin, E., Doresic, D., Weindl, D., & Hasenauer, J. (2023). pyPESTO: A
+  modular and scalable tool for parameter estimation for dynamic models [(arXiv:2305.01821)](https://doi.org/10.48550/arXiv.2305.01821).
+
+When presenting work that employs pyPESTO, feel free to use one of the icons in
+[doc/logo/](https://github.com/ICB-DCM/pyPESTO/tree/main/doc/logo):
+
+<p align="center">
+  <img src="https://raw.githubusercontent.com/ICB-DCM/pyPESTO/master/doc/logo/logo.png" height="75" alt="AMICI Logo">
+</p>
+
 ## References
 
-[**PESTO**](https://github.com/ICB-DCM/PESTO/):
-Parameter estimation toolbox for MATLAB. Development is discontinued, but PESTO
-comes with additional features waiting to be ported to pyPESTO.
+pyPESTO supersedes [**PESTO**](https://github.com/ICB-DCM/PESTO/) a parameter estimation
+toolbox for MATLAB, whose development is discontinued.

doc/api.rst

@@ -20,9 +20,11 @@ API reference
    pypesto.predict
    pypesto.problem
    pypesto.profile
+   pypesto.profile.profile_next_guess
    pypesto.result
    pypesto.sample
    pypesto.select
+   pypesto.select.postprocessors
    pypesto.startpoint
    pypesto.store
    pypesto.visualize

doc/conf.py

@@ -81,6 +81,7 @@
         None,
     ),
     'amici': ('https://amici.readthedocs.io/en/latest/', None),
+    'fides': ('https://fides-optimizer.readthedocs.io/en/latest/', None),
 }
 
 bibtex_bibfiles = ["using_pypesto.bib"]

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
 )
 

doc/example/model_selection.ipynb

@@ -57,7 +57,7 @@
     "\n",
     "| model_subspace_id | petab_yaml                             | $\\theta_1$ | $\\theta_2$ | $\\theta_3$ |\n",
     "|:---------|:----------------------------------|:----|:----|:----|\n",
-    "| M1_0\t| example_modelSelection.yaml\t| 0\t  | 0   |\t0 | \n",
+    "| M1_0\t| example_modelSelection.yaml\t| 0\t  | 0   |\t0 |\n",
     "| M1_1\t| example_modelSelection.yaml\t| 0\t  | 0\t| estimate |\n",
     "| M1_2\t| example_modelSelection.yaml\t| 0\t  | estimate |\t0 |\n",
     "| M1_3\t| example_modelSelection.yaml\t| estimate |\t0\t| 0 |\n",
@@ -249,7 +249,6 @@
     "import pypesto.logging\n",
     "\n",
     "pypesto.logging.log(level=logging.WARNING, name=\"pypesto.petab\", console=True)\n",
-    "import petab\n",
     "\n",
     "pypesto_select_problem_1 = pypesto.select.Problem(\n",
     "    petab_select_problem=petab_select_problem\n",
@@ -260,7 +259,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "Models can be selected with a model selection algorithm (here: [forward](https://en.wikipedia.org/wiki/Stepwise_regression)) and a comparison criterion (here: [AIC](https://en.wikipedia.org/wiki/Akaike_information_criterion)). The forward method start with the smallest model. Within each following iteration it tests all models with one additional estimated parameter.\n",
+    "Models can be selected with a model selection algorithm (here: [forward](https://en.wikipedia.org/wiki/Stepwise_regression)) and a comparison criterion (here: [AIC](https://en.wikipedia.org/wiki/Akaike_information_criterion)). The forward method starts with the smallest model. Within each following iteration it tests all models with one additional estimated parameter.\n",
     "\n",
     "To perform a single iteration, use `select` as shown below. Later in the notebook, `select_to_completion` is demonstrated, which performs multiple consecutive iterations automatically.\n",
     "\n",
@@ -303,7 +302,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "To search more of the model space, hence modls with more parameters, the algorithm can be repeated. As models with no estimated parameters have already been tested, subsequent `select` calls will begin with the next simplest model (in this case, models with exactly 1 estimated parameter, if they exist in the model space), and move on to more complex models.\n",
+    "To search more of the model space, hence models with more parameters, the algorithm can be repeated. As models with no estimated parameters have already been tested, subsequent `select` calls will begin with the next simplest model (in this case, models with exactly 1 estimated parameter, if they exist in the model space), and move on to more complex models.\n",
     "\n",
     "The best model from the first iteration is supplied as the predecessor (initial) model here."
    ]
@@ -443,8 +442,6 @@
     }
    ],
    "source": [
-    "from pprint import pprint\n",
-    "\n",
     "import numpy as np\n",
     "from petab_select import Model\n",
     "\n",