Merge pull request #1497 from pybamm-team/other-changes-julia-branch

"Other" changes from the julia branch

.github/workflows/test_on_push.yml

@@ -40,7 +40,7 @@ jobs:
       uses: actions/setup-python@v1
       with:
         python-version: ${{ matrix.python-version }}
-    
+
     - name: Install Linux system dependencies
       if: matrix.os == 'ubuntu-latest'
       run: |

.gitignore

@@ -110,8 +110,5 @@ test.c
 # tox
 .tox/
 
-# julia
-Manifest.toml
-
 # vcpkg
-vcpkg_installed/
+vcpkg_installed/

CHANGELOG.md

@@ -2,6 +2,7 @@
 
 ## Features
 
+-   Added `print_name` to some symbols ([#1495](https://github.com/pybamm-team/PyBaMM/pull/1497), [#1495](https://github.com/pybamm-team/PyBaMM/pull/1497))
 -   Added Base Parameters class and SymPy in dependencies ([#1495](https://github.com/pybamm-team/PyBaMM/pull/1495))
 -   Added a new "reaction-driven" model for LAM from Reniers et al (2019) ([#1490](https://github.com/pybamm-team/PyBaMM/pull/1490))
 -   Some features ("loss of active material" and "particle mechanics") can now be specified separately for the negative electrode and positive electrode by passing a 2-tuple ([#1490](https://github.com/pybamm-team/PyBaMM/pull/1490))

pybamm/__init__.py

@@ -95,16 +95,16 @@ def version(formatted=False):
 from .expression_tree.exceptions import *
 
 # Operations
-from .expression_tree.operations.evaluate import (
+from .expression_tree.operations.evaluate_python import (
     find_symbols,
     id_to_python_variable,
     to_python,
     EvaluatorPython,
 )
 
 if system() != "Windows":
-    from .expression_tree.operations.evaluate import EvaluatorJax
-    from .expression_tree.operations.evaluate import JaxCooMatrix
+    from .expression_tree.operations.evaluate_python import EvaluatorJax
+    from .expression_tree.operations.evaluate_python import JaxCooMatrix
 
 from .expression_tree.operations.jacobian import Jacobian
 from .expression_tree.operations.convert_to_casadi import CasadiConverter
@@ -115,7 +115,7 @@ def version(formatted=False):
 # Model classes
 #
 from .models.base_model import BaseModel
-from .models import standard_variables
+from .models.standard_variables import standard_variables
 from .models.event import Event
 from .models.event import EventType
 

pybamm/expression_tree/concatenations.py

@@ -349,6 +349,16 @@ def __init__(self, *children):
             np.min([child.bounds[1] for child in children]),
         )
 
+        if not any(c.print_name is None for c in children):
+            print_name = intersect(children[0].print_name, children[1].print_name)
+            for child in children[2:]:
+                print_name = intersect(print_name, child.print_name)
+            if print_name.endswith("_"):
+                print_name = print_name[:-1]
+        else:
+            print_name = None
+        self.print_name = print_name
+
 
 def substrings(s):
     for i in range(len(s)):

pybamm/expression_tree/functions.py

@@ -358,20 +358,34 @@ def log10(child):
     return log(child, base=10)
 
 
+class Max(SpecificFunction):
+    """ Max function """
+
+    def __init__(self, child):
+        super().__init__(np.max, child)
+
+
 def max(child):
     """
     Returns max function of child. Not to be confused with :meth:`pybamm.maximum`, which
     returns the larger of two objects.
     """
-    return pybamm.simplify_if_constant(Function(np.max, child))
+    return pybamm.simplify_if_constant(Max(child))
+
+
+class Min(SpecificFunction):
+    """ Min function """
+
+    def __init__(self, child):
+        super().__init__(np.min, child)
 
 
 def min(child):
     """
     Returns min function of child. Not to be confused with :meth:`pybamm.minimum`, which
     returns the smaller of two objects.
     """
-    return pybamm.simplify_if_constant(Function(np.min, child))
+    return pybamm.simplify_if_constant(Min(child))
 
 
 def sech(child):

pybamm/expression_tree/operations/evaluate.py → pybamm/expression_tree/operations/evaluate_python.py

@@ -313,23 +313,23 @@ def find_symbols(symbol, constant_symbols, variable_symbols, output_jax=False):
 
     elif isinstance(symbol, pybamm.Concatenation):
 
-        # don't bother to concatenate if there is only a single child
+        # no need to concatenate if there is only a single child
         if isinstance(symbol, pybamm.NumpyConcatenation):
-            if len(children_vars) > 1:
-                symbol_str = "np.concatenate(({}))".format(",".join(children_vars))
+            if len(children_vars) == 1:
+                symbol_str = children_vars[0]
             else:
-                symbol_str = "{}".format(",".join(children_vars))
+                symbol_str = "np.concatenate(({}))".format(",".join(children_vars))
 
         elif isinstance(symbol, pybamm.SparseStack):
-            if len(children_vars) > 1:
+            if len(children_vars) == 1:
+                symbol_str = children_vars[0]
+            else:
                 if output_jax:
                     raise NotImplementedError
                 else:
                     symbol_str = "scipy.sparse.vstack(({}))".format(
                         ",".join(children_vars)
                     )
-            else:
-                symbol_str = "{}".format(",".join(children_vars))
 
         # DomainConcatenation specifies a particular ordering for the concatenation,
         # which we must follow
@@ -376,7 +376,9 @@ def find_symbols(symbol, constant_symbols, variable_symbols, output_jax=False):
 
     else:
         raise NotImplementedError(
-            "Not implemented for a symbol of type '{}'".format(type(symbol))
+            "Conversion to python not implemented for a symbol of type '{}'".format(
+                type(symbol)
+            )
         )
 
     variable_symbols[symbol.id] = symbol_str

pybamm/expression_tree/parameter.py

@@ -4,6 +4,7 @@
 import numbers
 import numpy as np
 import pybamm
+import sys
 
 
 class Parameter(pybamm.Symbol):
@@ -62,13 +63,17 @@ class FunctionParameter(pybamm.Symbol):
         if diff_variable is specified, the FunctionParameter node will be replaced by a
         :class:`pybamm.Function` and then differentiated with respect to diff_variable.
         Default is None.
+    print_name : str, optional
+        The name to show when printing. Default is 'calculate', in which case the name
+        is calculated using sys._getframe().
     """
 
     def __init__(
         self,
         name,
         inputs,
         diff_variable=None,
+        print_name="calculate",
     ):
         # assign diff variable
         self.diff_variable = diff_variable
@@ -90,6 +95,23 @@ def __init__(
 
         self.input_names = list(inputs.keys())
 
+        # Use the inspect module to find the function's "short name" from the
+        # Parameters module that called it
+        if print_name != "calculate":
+            self.print_name = print_name
+        else:
+            frame = sys._getframe().f_back
+            print_name = frame.f_code.co_name
+            if print_name.startswith("_"):
+                self.print_name = None
+            else:
+                if print_name.endswith("_dimensional"):
+                    self.print_name = print_name[: -len("_dimensional")]
+                elif print_name.endswith("_dim"):
+                    self.print_name = print_name[: -len("_dim")]
+                else:
+                    self.print_name = print_name
+
     @property
     def input_names(self):
         return self._input_names
@@ -159,9 +181,14 @@ def diff(self, variable):
 
     def new_copy(self):
         """ See :meth:`pybamm.Symbol.new_copy()`. """
-        return self._function_parameter_new_copy(self._input_names, self.orphans)
+        out = self._function_parameter_new_copy(
+            self._input_names, self.orphans, print_name=self.print_name
+        )
+        return out
 
-    def _function_parameter_new_copy(self, input_names, children):
+    def _function_parameter_new_copy(
+        self, input_names, children, print_name="calculate"
+    ):
         """Returns a new copy of the function parameter.
 
         Inputs
@@ -173,14 +200,17 @@ def _function_parameter_new_copy(self, input_names, children):
 
         Returns
         -------
-            : :pybamm.FunctionParameter
+        :class:`pybamm.FunctionParameter`
             A new copy of the function parameter
         """
 
         input_dict = {input_names[i]: children[i] for i in range(len(input_names))}
 
         return FunctionParameter(
-            self.name, input_dict, diff_variable=self.diff_variable
+            self.name,
+            input_dict,
+            diff_variable=self.diff_variable,
+            print_name=print_name,
         )
 
     def _evaluate_for_shape(self):

pybamm/expression_tree/scalar.py

@@ -31,6 +31,9 @@ def __init__(self, value, name=None, domain=[]):
 
         super().__init__(name, domain=domain)
 
+    def __str__(self):
+        return str(self.value)
+
     @property
     def value(self):
         """the value returned by the node when evaluated"""

pybamm/expression_tree/variable.py

@@ -47,13 +47,16 @@ def __init__(self, name, domain=None, auxiliary_domains=None, bounds=None):
                     + "Lower bound should be strictly less than upper bound."
                 )
         self.bounds = bounds
+        self.print_name = None
 
     def new_copy(self):
         """ See :meth:`pybamm.Symbol.new_copy()`. """
 
-        return self.__class__(
+        out = self.__class__(
             self.name, self.domain, self.auxiliary_domains, self.bounds
         )
+        out.print_name = self.print_name
+        return out
 
     def _evaluate_for_shape(self):
         """ See :meth:`pybamm.Symbol.evaluate_for_shape_using_domain()` """

pybamm/models/base_model.py

@@ -774,6 +774,22 @@ def info(self, symbol_name):
 
         print(div)
 
+    def check_discretised_or_discretise_inplace_if_0D(self):
+        """
+        Discretise model if it isn't already discretised
+        This only works with purely 0D models, as otherwise the mesh and spatial
+        method should be specified by the user
+        """
+        if self.is_discretised is False:
+            try:
+                disc = pybamm.Discretisation()
+                disc.process_model(self)
+            except pybamm.DiscretisationError as e:
+                raise pybamm.DiscretisationError(
+                    "Cannot automatically discretise model, model should be "
+                    "discretised before exporting casadi functions ({})".format(e)
+                )
+
     def export_casadi_objects(self, variable_names, input_parameter_order=None):
         """
         Export the constituent parts of the model (rhs, algebraic, initial conditions,
@@ -793,18 +809,7 @@ def export_casadi_objects(self, variable_names, input_parameter_order=None):
             Dictionary of {str: casadi object} pairs representing the model in casadi
             format
         """
-        # Discretise model if it isn't already discretised
-        # This only works with purely 0D models, as otherwise the mesh and spatial
-        # method should be specified by the user
-        if self.is_discretised is False:
-            try:
-                disc = pybamm.Discretisation()
-                disc.process_model(self)
-            except pybamm.DiscretisationError as e:
-                raise pybamm.DiscretisationError(
-                    "Cannot automatically discretise model, model should be "
-                    "discretised before exporting casadi functions ({})".format(e)
-                )
+        self.check_discretised_or_discretise_inplace_if_0D()
 
         # Create casadi functions for the model
         t_casadi = casadi.MX.sym("t")