Merge pull request #1822 from pybamm-team/issue-1810-shortcuts

Issue 1810 shortcuts

CHANGELOG.md

@@ -2,6 +2,7 @@
 
 ## Features
 
+-   The name of a parameter set can be passed to `ParameterValues` as a string, e.g. `ParameterValues("Chen2020")`
 -   Reformatted SEI growth models into a single submodel with conditionals ([#1808](https://github.com/pybamm-team/PyBaMM/pull/1808))
 -   Stress-induced diffusion is now a separate model option instead of being automatically included when using the particle mechanics submodels ([#1797](https://github.com/pybamm-team/PyBaMM/pull/1797))
 -   `Experiment`s with drive cycles can be solved ([#1793](https://github.com/pybamm-team/PyBaMM/pull/1793))
@@ -18,6 +19,7 @@
 
 ## Breaking changes
 
+-   The `chemistry` keyword argument in `ParameterValues` has been deprecated. Use `ParameterValues(chem)` instead of `ParameterValues(chemistry=chem)`
 -   Raise error when trying to convert an `Interpolant` with the "pchip" interpolator to CasADI ([#1791](https://github.com/pybamm-team/PyBaMM/pull/1791))
 -   Raise error if `Concatenation` is used directly with `Variable` objects (`concatenation` should be used instead) ([#1789](https://github.com/pybamm-team/PyBaMM/pull/1789))
 -   Made jax, jaxlib and the PyBaMM JaxSolver optional ([#1767](https://github.com/pybamm-team/PyBaMM/pull/1767))

benchmarks/time_setup_models_and_sims.py

@@ -19,7 +19,7 @@ def compute_discretisation(model, param):
 
 class TimeBuildSPM:
     def __init__(self):
-        self.param = pybamm.ParameterValues(chemistry=pybamm.parameter_sets.Marquis2019)
+        self.param = pybamm.ParameterValues("Marquis2019")
 
     def time_setup_SPM(self):
         self.model = pybamm.lithium_ion.SPM()
@@ -29,7 +29,7 @@ def time_setup_SPM(self):
 
 class TimeBuildSPMe:
     def __init__(self):
-        self.param = pybamm.ParameterValues(chemistry=pybamm.parameter_sets.Marquis2019)
+        self.param = pybamm.ParameterValues("Marquis2019")
 
     def time_setup_SPMe(self):
         self.model = pybamm.lithium_ion.SPMe()
@@ -39,7 +39,7 @@ def time_setup_SPMe(self):
 
 class TimeBuildDFN:
     def __init__(self):
-        self.param = pybamm.ParameterValues(chemistry=pybamm.parameter_sets.Marquis2019)
+        self.param = pybamm.ParameterValues("Marquis2019")
 
     def time_setup_DFN(self):
         self.model = pybamm.lithium_ion.DFN()
@@ -52,7 +52,7 @@ class TimeBuildSPMSimulation:
     params = [False, True]
 
     def __init__(self):
-        self.param = pybamm.ParameterValues(chemistry=pybamm.parameter_sets.Marquis2019)
+        self.param = pybamm.ParameterValues("Marquis2019")
 
     def time_setup_SPM_simulation(self, with_experiment):
         self.model = pybamm.lithium_ion.SPM()
@@ -72,7 +72,7 @@ class TimeBuildSPMeSimulation:
     params = [False, True]
 
     def __init__(self):
-        self.param = pybamm.ParameterValues(chemistry=pybamm.parameter_sets.Marquis2019)
+        self.param = pybamm.ParameterValues("Marquis2019")
 
     def time_setup_SPMe_simulation(self, with_experiment):
         self.model = pybamm.lithium_ion.SPMe()
@@ -92,7 +92,7 @@ class TimeBuildDFNSimulation:
     params = [False, True]
 
     def __init__(self):
-        self.param = pybamm.ParameterValues(chemistry=pybamm.parameter_sets.Marquis2019)
+        self.param = pybamm.ParameterValues("Marquis2019")
 
     def time_setup_DFN_simulation(self, with_experiment):
         self.model = pybamm.lithium_ion.DFN()

benchmarks/time_solve_models.py

@@ -9,22 +9,12 @@ def prepare_model(model):
     geometry = model.default_geometry
 
     # load parameter values and process model and geometry
-    chemistry = pybamm.parameter_sets.Marquis2019
-    param = pybamm.ParameterValues(chemistry=chemistry)
+    param = pybamm.ParameterValues("Marquis2019")
     param.process_model(model)
     param.process_geometry(geometry)
 
     # set mesh
-    var = pybamm.standard_spatial_vars
-    var_pts = {
-        var.x_n: 20,
-        var.x_s: 20,
-        var.x_p: 20,
-        var.r_n: 30,
-        var.r_p: 30,
-        var.y: 10,
-        var.z: 10,
-    }
+    var_pts = {"x_n": 20, "x_s": 20, "x_p": 20, "r_n": 30, "r_p": 30, "y": 10, "z": 10}
     mesh = pybamm.Mesh(geometry, model.default_submesh_types, var_pts)
 
     # discretise model

docs/tutorials/add-parameter-values.rst

@@ -123,7 +123,7 @@ Then, to use these new parameters, use:
 
 .. code-block:: python
 
-    param = pybamm.ParameterValues(chemistry=pybamm.parameter_sets.AuthorYear)
+    param = pybamm.ParameterValues("AuthorYear")
 
 Note that you can re-use existing parameter subsets instead of creating new ones (for example, you could just replace "experiment": "new_experiment_AuthorYear" with "experiment": "1C_discharge_from_full_Marquis2019" in the above dictionary).
 
@@ -173,7 +173,7 @@ To test this, add something like the following test to one of the model test fil
 
     def test_my_new_parameters(self):
         model = pybamm.lithium_ion.DFN()
-        parameter_values = pybamm.ParameterValues(chemistry=pybamm.parameter_sets.AuthorYear)
+        parameter_values = pybamm.ParameterValues("AuthorYear")
         modeltest = tests.StandardModelTest(model, parameter_values=parameter_values)
         modeltest.test_all()
 

examples/notebooks/Creating Models/4-comparing-full-and-reduced-order-models.ipynb

@@ -52,8 +52,6 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "\u001b[33mWARNING: You are using pip version 21.0.1; however, version 21.1.2 is available.\n",
-      "You should consider upgrading via the '/home/user/Documents/PyBaMM/env/bin/python3.8 -m pip install --upgrade pip' command.\u001b[0m\n",
       "Note: you may need to restart the kernel to use updated packages.\n"
      ]
     }
@@ -251,7 +249,7 @@
    "outputs": [],
    "source": [
     "# mesh\n",
-    "submesh_types = {\"negative particle\": pybamm.MeshGenerator(pybamm.Uniform1DSubMesh)}\n",
+    "submesh_types = {\"negative particle\": pybamm.Uniform1DSubMesh}\n",
     "var_pts = {r: 20}\n",
     "mesh = pybamm.Mesh(geometry, submesh_types, var_pts)\n",
     "\n",
@@ -294,14 +292,14 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "2021-05-27 10:31:08,618 - [WARNING] processed_variable.get_spatial_scale(471): No length scale set for negative particle. Using default of 1 [m].\n",
-      "2021-05-27 10:31:08,729 - [WARNING] processed_variable.get_spatial_scale(471): No length scale set for negative particle. Using default of 1 [m].\n"
+      "2021-11-19 15:29:38,512 - [WARNING] processed_variable.get_spatial_scale(520): No length scale set for negative particle. Using default of 1 [m].\n",
+      "2021-11-19 15:29:38,541 - [WARNING] processed_variable.get_spatial_scale(520): No length scale set for negative particle. Using default of 1 [m].\n"
      ]
     },
     {
      "data": {
       "application/vnd.jupyter.widget-view+json": {
-       "model_id": "28d9f238e5364c4588a39b0984631dee",
+       "model_id": "c7ec6d3f5bf14206a6ec219f10583c46",
        "version_major": 2,
        "version_minor": 0
       },
@@ -397,7 +395,7 @@
      "text": [
       "[1] Joel A. E. Andersson, Joris Gillis, Greg Horn, James B. Rawlings, and Moritz Diehl. CasADi – A software framework for nonlinear optimization and optimal control. Mathematical Programming Computation, 11(1):1–36, 2019. doi:10.1007/s12532-018-0139-4.\n",
       "[2] Charles R. Harris, K. Jarrod Millman, Stéfan J. van der Walt, Ralf Gommers, Pauli Virtanen, David Cournapeau, Eric Wieser, Julian Taylor, Sebastian Berg, Nathaniel J. Smith, and others. Array programming with NumPy. Nature, 585(7825):357–362, 2020. doi:10.1038/s41586-020-2649-2.\n",
-      "[3] Valentin Sulzer, Scott G. Marquis, Robert Timms, Martin Robinson, and S. Jon Chapman. Python Battery Mathematical Modelling (PyBaMM). ECSarXiv. February, 2020. doi:10.1149/osf.io/67ckj.\n",
+      "[3] Valentin Sulzer, Scott G. Marquis, Robert Timms, Martin Robinson, and S. Jon Chapman. Python Battery Mathematical Modelling (PyBaMM). Journal of Open Research Software, 9(1):14, 2021. doi:10.5334/jors.309.\n",
       "[4] Pauli Virtanen, Ralf Gommers, Travis E. Oliphant, Matt Haberland, Tyler Reddy, David Cournapeau, Evgeni Burovski, Pearu Peterson, Warren Weckesser, Jonathan Bright, and others. SciPy 1.0: fundamental algorithms for scientific computing in Python. Nature Methods, 17(3):261–272, 2020. doi:10.1038/s41592-019-0686-2.\n",
       "\n"
      ]
@@ -410,7 +408,7 @@
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": "Python 3",
+   "display_name": "Python 3 (ipykernel)",
    "language": "python",
    "name": "python3"
   },
@@ -424,7 +422,20 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.8.10"
+   "version": "3.8.12"
+  },
+  "toc": {
+   "base_numbering": 1,
+   "nav_menu": {},
+   "number_sections": true,
+   "sideBar": true,
+   "skip_h1_title": false,
+   "title_cell": "Table of Contents",
+   "title_sidebar": "Contents",
+   "toc_cell": false,
+   "toc_position": {},
+   "toc_section_display": true,
+   "toc_window_display": true
   }
  },
  "nbformat": 4,