Repository: pybamm-team/PyBaMM

Issue:
Add ability to print all parameter values to a csv file
Add a helper function to parameter values that allow them to be quickly printed to a csv file. 


Discussion:

Patch:
diff --git a/docs/source/parameters/index.rst b/docs/source/parameters/index.rst
index 1851dc332..02f6e8202 100644
--- a/docs/source/parameters/index.rst
+++ b/docs/source/parameters/index.rst
@@ -9,5 +9,4 @@ Parameters
   thermal_parameters
   standard_parameters_lithium_ion
   standard_parameters_lead_acid
-  print_parameters
   parameter_sets
\ No newline at end of file
diff --git a/docs/source/parameters/print_parameters.rst b/docs/source/parameters/print_parameters.rst
deleted file mode 100644
index d32df443c..000000000
--- a/docs/source/parameters/print_parameters.rst
+++ /dev/null
@@ -1,6 +0,0 @@
-Print parameters
-================
-
-.. autofunction:: pybamm.print_parameters
-
-.. autofunction:: pybamm.print_evaluated_parameters
diff --git a/examples/notebooks/parameter-values.ipynb b/examples/notebooks/parameter-values.ipynb
index b3ec7afe6..bd6f2ea21 100644
--- a/examples/notebooks/parameter-values.ipynb
+++ b/examples/notebooks/parameter-values.ipynb
@@ -47,7 +47,9 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "parameter values are <pybamm.parameters.parameter_values.ParameterValues object at 0x7ff8c5411650>\n"
+      "parameter values are {'a': 1,\n",
+      " 'b': 2,\n",
+      " 'c': 3}\n"
      ]
     }
    ],
@@ -73,7 +75,9 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "parameter values are <pybamm.parameters.parameter_values.ParameterValues object at 0x7ff8c541d290>\n"
+      "parameter values are {'a': 4,\n",
+      " 'b': 5,\n",
+      " 'c': 6}\n"
      ]
     }
    ],
@@ -109,7 +113,7 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Marquis2019 chemistry set is {'chemistry': 'lithium-ion', 'cell': 'kokam_Marquis2019', 'anode': 'graphite_mcmb2528_Marquis2019', 'separator': 'separator_Marquis2019', 'cathode': 'lico2_Marquis2019', 'electrolyte': 'lipf6_Marquis2019', 'experiment': '1C_discharge_from_full_Marquis2019', 'citation': 'marquis2019asymptotic'}\n",
+      "Marquis2019 chemistry set is {'chemistry': 'lithium-ion', 'cell': 'kokam_Marquis2019', 'anode': 'graphite_mcmb2528_Marquis2019', 'separator': 'separator_Marquis2019', 'cathode': 'lico2_Marquis2019', 'electrolyte': 'lipf6_Marquis2019', 'experiment': '1C_discharge_from_full_Marquis2019', 'sei': 'Example', 'citation': 'marquis2019asymptotic'}\n",
       "Negative current collector thickness is 2.5e-05 m\n"
      ]
     }
@@ -138,7 +142,10 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "parameter values are <pybamm.parameters.parameter_values.ParameterValues object at 0x7ff8c541d290>\n"
+      "parameter values are {'a': 4,\n",
+      " 'b': 5,\n",
+      " 'c': 6,\n",
+      " 'cube function': <function cubed at 0x13be91c20>}\n"
      ]
     }
    ],
@@ -165,7 +172,11 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "parameter values are <pybamm.parameters.parameter_values.ParameterValues object at 0x7ff8c541d290>\n"
+      "parameter values are {'a': 4,\n",
+      " 'b': 5,\n",
+      " 'c': 6,\n",
+      " 'cube function': <function cubed at 0x13be91c20>,\n",
+      " 'square function': <function squared at 0x13be91ef0>}\n"
      ]
     }
    ],
@@ -329,7 +340,7 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
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\n",
       "text/plain": [
        "<Figure size 936x288 with 2 Axes>"
       ]
@@ -401,7 +412,7 @@
     {
      "data": {
       "text/plain": [
-       "{Variable(-0x2cdf9e1c15ea083, u, children=[], domain=[], auxiliary_domains={}): Multiplication(0x1d65a94f24de5058, *, children=['-a', 'y[0:1]'], domain=[], auxiliary_domains={})}"
+       "{Variable(0x695ab8a4e3770753, u, children=[], domain=[], auxiliary_domains={}): Multiplication(-0x36aade29331c92f3, *, children=['-a', 'y[0:1]'], domain=[], auxiliary_domains={})}"
       ]
      },
      "execution_count": 12,
@@ -445,7 +456,7 @@
     "b = pybamm.Parameter(\"b\")\n",
     "parameter_values = pybamm.ParameterValues({\"a\": 4, \"b\": 3})\n",
     "parameters = {\"a\": a, \"b\": b, \"a + b\": a + b, \"a * b\": a * b}\n",
-    "param_eval = pybamm.print_parameters(parameters, parameter_values)\n",
+    "param_eval = parameter_values.print_parameters(parameters)\n",
     "for name, (value,C_dependence) in param_eval.items():\n",
     "    print(\"{}: {}\".format(name, value))"
    ]
@@ -481,9 +492,9 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.7.5"
+   "version": "3.7.7"
   }
  },
  "nbformat": 4,
  "nbformat_minor": 2
-}
\ No newline at end of file
+}
diff --git a/pybamm/__init__.py b/pybamm/__init__.py
index 44033ec2d..7a134c377 100644
--- a/pybamm/__init__.py
+++ b/pybamm/__init__.py
@@ -165,7 +165,6 @@ from .parameters import electrical_parameters
 from .parameters import thermal_parameters
 from .parameters import standard_parameters_lithium_ion, standard_parameters_lead_acid
 from .parameters import sei_parameters
-from .parameters.print_parameters import print_parameters, print_evaluated_parameters
 from .parameters import parameter_sets
 
 
diff --git a/pybamm/parameters/parameter_values.py b/pybamm/parameters/parameter_values.py
index 5c782ec34..7cd10f1b6 100644
--- a/pybamm/parameters/parameter_values.py
+++ b/pybamm/parameters/parameter_values.py
@@ -6,6 +6,7 @@ import pandas as pd
 import os
 import numbers
 from pprint import pformat
+from collections import defaultdict
 
 
 class ParameterValues:
@@ -602,3 +603,137 @@ class ParameterValues:
 
     def _ipython_key_completions_(self):
         return list(self._dict_items.keys())
+
+    def export_csv(self, filename):
+
+        # process functions and data to output
+        # like they appear in inputs csv files
+        parameter_output = {}
+        for key, val in self.items():
+            if callable(val):
+                val = "[function]" + val.__name__
+            elif isinstance(val, tuple):
+                val = "[data]" + val[0]
+            parameter_output[key] = [val]
+
+        df = pd.DataFrame(parameter_output)
+        df = df.transpose()
+        df.to_csv(filename, header=None)
+
+    def print_parameters(self, parameters, output_file=None):
+        """
+        Return dictionary of evaluated parameters, and optionally print these evaluated
+        parameters to an output file.
+        For dimensionless parameters that depend on the C-rate, the value is given as a
+        function of the C-rate (either x * Crate or x / Crate depending on the
+        dependence)
+
+        Parameters
+        ----------
+        parameters : class or dict containing :class:`pybamm.Parameter` objects
+            Class or dictionary containing all the parameters to be evaluated
+        output_file : string, optional
+            The file to print parameters to. If None, the parameters are not printed,
+            and this function simply acts as a test that all the parameters can be
+            evaluated, and returns the dictionary of evaluated parameters.
+
+        Returns
+        -------
+        evaluated_parameters : defaultdict
+            The evaluated parameters, for further processing if needed
+
+        Notes
+        -----
+        A C-rate of 1 C is the current required to fully discharge the battery in 1
+        hour, 2 C is current to discharge the battery in 0.5 hours, etc
+        """
+        # Set list of attributes to ignore, for when we are evaluating parameters from
+        # a class of parameters
+        ignore = [
+            "__name__",
+            "__doc__",
+            "__package__",
+            "__loader__",
+            "__spec__",
+            "__file__",
+            "__cached__",
+            "__builtins__",
+            "absolute_import",
+            "division",
+            "print_function",
+            "unicode_literals",
+            "pybamm",
+            "constants",
+            "np",
+        ]
+
+        # If 'parameters' is a class, extract the dict
+        if not isinstance(parameters, dict):
+            parameters = {
+                k: v for k, v in parameters.__dict__.items() if k not in ignore
+            }
+
+        evaluated_parameters = defaultdict(list)
+        # Calculate parameters for each C-rate
+        for Crate in [1, 10]:
+            # Update Crate
+            capacity = self.get("Cell capacity [A.h]")
+            if capacity is not None:
+                self.update(
+                    {"Current function [A]": Crate * capacity},
+                    check_already_exists=False,
+                )
+            for name, symbol in parameters.items():
+                if not callable(symbol):
+                    proc_symbol = self.process_symbol(symbol)
+                    if not (
+                        callable(proc_symbol)
+                        or proc_symbol.has_symbol_of_classes(
+                            (pybamm.Concatenation, pybamm.Broadcast)
+                        )
+                    ):
+                        evaluated_parameters[name].append(proc_symbol.evaluate(t=0))
+
+        # Calculate C-dependence of the parameters based on the difference between the
+        # value at 1C and the value at C / 10
+        for name, values in evaluated_parameters.items():
+            if values[1] == 0 or abs(values[0] / values[1] - 1) < 1e-10:
+                C_dependence = ""
+            elif abs(values[0] / values[1] - 10) < 1e-10:
+                C_dependence = " * Crate"
+            elif abs(values[0] / values[1] - 0.1) < 1e-10:
+                C_dependence = " / Crate"
+            evaluated_parameters[name] = (values[0], C_dependence)
+        # Print the evaluated_parameters dict to output_file
+        if output_file:
+            self.print_evaluated_parameters(evaluated_parameters, output_file)
+
+        return evaluated_parameters
+
+    def print_evaluated_parameters(self, evaluated_parameters, output_file):
+        """
+        Print a dictionary of evaluated parameters to an output file
+
+        Parameters
+        ----------
+        evaluated_parameters : defaultdict
+            The evaluated parameters, for further processing if needed
+        output_file : string, optional
+            The file to print parameters to. If None, the parameters are not printed,
+            and this function simply acts as a test that all the parameters can be
+            evaluated
+
+        """
+        # Get column width for pretty printing
+        column_width = max(len(name) for name in evaluated_parameters.keys())
+        s = "{{:>{}}}".format(column_width)
+        with open(output_file, "w") as file:
+            for name, (value, C_dependence) in sorted(evaluated_parameters.items()):
+                if 0.001 < abs(value) < 1000:
+                    file.write(
+                        (s + " : {:10.4g}{!s}\n").format(name, value, C_dependence)
+                    )
+                else:
+                    file.write(
+                        (s + " : {:10.3E}{!s}\n").format(name, value, C_dependence)
+                    )
diff --git a/pybamm/parameters/print_parameters.py b/pybamm/parameters/print_parameters.py
deleted file mode 100644
index 7f51aa028..000000000
--- a/pybamm/parameters/print_parameters.py
+++ /dev/null
@@ -1,118 +0,0 @@
-#
-# Print parameters
-#
-import pybamm
-from collections import defaultdict
-
-# Set list of attributes to ignore, for when we are evaluating parameters from a class
-# of parameters
-ignore = [
-    "__name__",
-    "__doc__",
-    "__package__",
-    "__loader__",
-    "__spec__",
-    "__file__",
-    "__cached__",
-    "__builtins__",
-    "absolute_import",
-    "division",
-    "print_function",
-    "unicode_literals",
-    "pybamm",
-    "constants",
-    "np",
-]
-
-
-def print_parameters(parameters, parameter_values, output_file=None):
-    """
-    Return dictionary of evaluated parameters, and optionally print these evaluated
-    parameters to an output file.
-    For dimensionless parameters that depend on the C-rate, the value is given as a
-    function of the C-rate (either x * Crate or x / Crate depending on the dependence)
-
-    Parameters
-    ----------
-    parameters : class or dict containing :class:`pybamm.Parameter` objects
-        Class or dictionary containing all the parameters to be evaluated
-    parameter_values : :class:`pybamm.ParameterValues`
-        The class of parameter values
-    output_file : string, optional
-        The file to print parameters to. If None, the parameters are not printed, and
-        this function simply acts as a test that all the parameters can be evaluated,
-        and returns the dictionary of evaluated parameters.
-
-    Returns
-    -------
-    evaluated_parameters : defaultdict
-        The evaluated parameters, for further processing if needed
-
-    Notes
-    -----
-    A C-rate of 1 C is the current required to fully discharge the battery in 1 hour,
-    2 C is current to discharge the battery in 0.5 hours, etc
-    """
-    # If 'parameters' is a class, extract the dict
-    if not isinstance(parameters, dict):
-        parameters = {k: v for k, v in parameters.__dict__.items() if k not in ignore}
-
-    evaluated_parameters = defaultdict(list)
-    # Calculate parameters for each C-rate
-    for Crate in [1, 10]:
-        # Update Crate
-        capacity = parameter_values.get("Cell capacity [A.h]")
-        if capacity is not None:
-            parameter_values.update(
-                {"Current function [A]": Crate * capacity}, check_already_exists=False
-            )
-        for name, symbol in parameters.items():
-            if not callable(symbol):
-                proc_symbol = parameter_values.process_symbol(symbol)
-                if not (
-                    callable(proc_symbol)
-                    or proc_symbol.has_symbol_of_classes(
-                        (pybamm.Concatenation, pybamm.Broadcast)
-                    )
-                ):
-                    evaluated_parameters[name].append(proc_symbol.evaluate(t=0))
-
-    # Calculate C-dependence of the parameters based on the difference between the
-    # value at 1C and the value at C / 10
-    for name, values in evaluated_parameters.items():
-        if values[1] == 0 or abs(values[0] / values[1] - 1) < 1e-10:
-            C_dependence = ""
-        elif abs(values[0] / values[1] - 10) < 1e-10:
-            C_dependence = " * Crate"
-        elif abs(values[0] / values[1] - 0.1) < 1e-10:
-            C_dependence = " / Crate"
-        evaluated_parameters[name] = (values[0], C_dependence)
-    # Print the evaluated_parameters dict to output_file
-    if output_file:
-        print_evaluated_parameters(evaluated_parameters, output_file)
-
-    return evaluated_parameters
-
-
-def print_evaluated_parameters(evaluated_parameters, output_file):
-    """
-    Print a dictionary of evaluated parameters to an output file
-
-    Parameters
-    ----------
-    evaluated_parameters : defaultdict
-        The evaluated parameters, for further processing if needed
-    output_file : string, optional
-        The file to print parameters to. If None, the parameters are not printed, and
-        this function simply acts as a test that all the parameters can be evaluated
-
-    """
-    # Get column width for pretty printing
-    column_width = max(len(name) for name in evaluated_parameters.keys())
-    s = "{{:>{}}}".format(column_width)
-    with open(output_file, "w") as file:
-        for name, (value, C_dependence) in sorted(evaluated_parameters.items()):
-            if 0.001 < abs(value) < 1000:
-                file.write((s + " : {:10.4g}{!s}\n").format(name, value, C_dependence))
-            else:
-                file.write((s + " : {:10.3E}{!s}\n").format(name, value, C_dependence))


Test patch:
diff --git a/tests/unit/test_parameters/test_parameter_values.py b/tests/unit/test_parameters/test_parameter_values.py
index 5c82a57ed..66d6fe7b9 100644
--- a/tests/unit/test_parameters/test_parameter_values.py
+++ b/tests/unit/test_parameters/test_parameter_values.py
@@ -8,6 +8,8 @@ import numpy as np
 import unittest
 import tests.shared as shared
 
+import pandas as pd
+
 
 class TestParameterValues(unittest.TestCase):
     def test_read_parameters_csv(self):
@@ -587,6 +589,26 @@ class TestParameterValues(unittest.TestCase):
         with self.assertRaises(ValueError):
             parameter_values.evaluate(array)
 
+    def test_export_csv(self):
+        def some_function(self):
+            return None
+
+        example_data = ("some_data", [0, 1, 2])
+
+        parameter_values = pybamm.ParameterValues(
+            {"a": 0.1, "b": some_function, "c": example_data}
+        )
+
+        filename = "parameter_values_test.csv"
+
+        parameter_values.export_csv(filename)
+
+        df = pd.read_csv(filename, index_col=0, header=None)
+
+        self.assertEqual(df[1]["a"], "0.1")
+        self.assertEqual(df[1]["b"], "[function]some_function")
+        self.assertEqual(df[1]["c"], "[data]some_data")
+
 
 if __name__ == "__main__":
     print("Add -v for more debug output")
diff --git a/tests/unit/test_parameters/test_standard_parameters_lead_acid.py b/tests/unit/test_parameters/test_standard_parameters_lead_acid.py
index fa4da125d..e1d3f7617 100644
--- a/tests/unit/test_parameters/test_standard_parameters_lead_acid.py
+++ b/tests/unit/test_parameters/test_standard_parameters_lead_acid.py
@@ -18,20 +18,20 @@ class TestStandardParametersLeadAcid(unittest.TestCase):
         parameters = pybamm.standard_parameters_lead_acid
         parameter_values = pybamm.lead_acid.BaseModel().default_parameter_values
         output_file = "lead_acid_parameters.txt"
-        pybamm.print_parameters(parameters, parameter_values, output_file)
+        parameter_values.print_parameters(parameters, output_file)
         # test print_parameters with dict and without C-rate
         del parameter_values["Cell capacity [A.h]"]
         parameters = {
             "C_e": pybamm.standard_parameters_lead_acid.C_e,
             "sigma_n": pybamm.standard_parameters_lead_acid.sigma_n,
         }
-        pybamm.print_parameters(parameters, parameter_values)
+        parameter_values.print_parameters(parameters)
 
     def test_parameters_defaults_lead_acid(self):
         # Load parameters to be tested
         parameters = pybamm.standard_parameters_lead_acid
         parameter_values = pybamm.lead_acid.BaseModel().default_parameter_values
-        param_eval = pybamm.print_parameters(parameters, parameter_values)
+        param_eval = parameter_values.print_parameters(parameters)
         param_eval = {k: v[0] for k, v in param_eval.items()}
 
         # Diffusional C-rate should be smaller than C-rate
@@ -131,7 +131,7 @@ class TestStandardParametersLeadAcid(unittest.TestCase):
         parameter_values = pybamm.ParameterValues(
             chemistry=pybamm.parameter_sets.Sulzer2019
         )
-        param_eval = pybamm.print_parameters(parameters, parameter_values)
+        param_eval = parameter_values.print_parameters(parameters)
         param_eval = {k: v[0] for k, v in param_eval.items()}
 
         # Known values for dimensionless functions
@@ -146,12 +146,12 @@ class TestStandardParametersLeadAcid(unittest.TestCase):
         # Load parameters to be tested
         parameters = pybamm.standard_parameters_lead_acid
         parameter_values = pybamm.lead_acid.BaseModel().default_parameter_values
-        param_eval = pybamm.print_parameters(parameters, parameter_values)
+        param_eval = parameter_values.print_parameters(parameters)
         param_eval = {k: v[0] for k, v in param_eval.items()}
 
         # Update initial state of charge
         parameter_values.update({"Initial State of Charge": 0.2})
-        param_eval_update = pybamm.print_parameters(parameters, parameter_values)
+        param_eval_update = parameter_values.print_parameters(parameters)
         param_eval_update = {k: v[0] for k, v in param_eval_update.items()}
 
         # Test that relevant parameters have changed as expected