Merge pull request projectmesa#928 from tpike3/master

Batchrunner_redux fixes

docs/tutorials/intro_tutorial.rst

@@ -295,13 +295,13 @@ to see the distribution of the agent's wealth. We can get the wealth
 values with list comprehension, and then use matplotlib (or another
 graphics library) to visualize the data in a histogram.
 
-If you are running from a text editor or IDE, you'll also need to add
-this line, to make the graph appear.
-
 .. code:: python
 
    plt.show()
 
+If you are running from a text editor or IDE, you'll also need to add
+this line, to make the graph appear.
+
 .. code:: ipython3
 
     # For a jupyter notebook add the following line:

mesa/batchrunner.py

@@ -116,7 +116,6 @@ def __init__(
 
         self.display_progress = display_progress
 
-    @property
     def _make_model_args(self):
         """Prepare all combinations of parameter values for `run_all`
 
@@ -128,21 +127,21 @@ def _make_model_args(self):
         all_kwargs = []
         all_param_values = []
 
-        _count = len(self.parameters_list)
-        if _count:
+        count = len(self.parameters_list)
+        if count:
             for params in self.parameters_list:
                 kwargs = params.copy()
                 kwargs.update(self.fixed_parameters)
                 all_kwargs.append(kwargs)
                 all_param_values.append(list(params.values()))
 
         elif len(self.fixed_parameters):
-            _count = 1
+            count = 1
             kwargs = self.fixed_parameters.copy()
             all_kwargs.append(kwargs)
             all_param_values.append(list(kwargs.values()))
 
-        total_iterations *= _count
+        total_iterations *= count
 
         return total_iterations, all_kwargs, all_param_values
 
@@ -178,7 +177,7 @@ def _make_model_args_mp(self):
     def run_all(self):
         """ Run the model at all parameter combinations and store results. """
         run_count = count()
-        total_iterations, all_kwargs, all_param_values = self._make_model_args
+        total_iterations, all_kwargs, all_param_values = self._make_model_args()
 
         with tqdm(total_iterations, disable=not self.display_progress) as pbar:
             for i, kwargs in enumerate(all_kwargs):
@@ -188,8 +187,7 @@ def run_all(self):
                     pbar.update()
 
     def run_iteration(self, kwargs, param_values, run_count):
-        kwargs_copy = copy.deepcopy(kwargs)
-        model = self.model_cls(**kwargs_copy)
+        model = self.model_cls(**kwargs)
         results = self.run_model(model)
         if param_values is not None:
             model_key = tuple(param_values) + (run_count,)
@@ -215,7 +213,7 @@ def run_iteration(self, kwargs, param_values, run_count):
                 getattr(self, "datacollector_agent_reporters", None))
 
     @staticmethod
-    def run_wrappermp(iter_args):
+    def _run_wrappermp(iter_args):
         """
         Based on requirement of Python multiprocessing requires @staticmethod decorator;
         this is primarily to ensure functionality on Windows OS and doe not impact MAC or Linux distros
@@ -498,7 +496,7 @@ def __init__(self, model_cls, nr_processes=None, **kwargs):
         super().__init__(model_cls, **kwargs)
         self.pool = Pool(self.processes)
 
-    def result_prep_mp(self, results):
+    def _result_prep_mp(self, results):
         """
         Helper Function
         :param results: Takes results dictionary from Processpool and single processor debug run and fixes format to
@@ -539,18 +537,18 @@ def run_all(self):
 
         if self.processes > 1:
             with tqdm(total_iterations, disable=not self.display_progress) as pbar:
-                for params, model in self.pool.imap_unordered(self.run_wrappermp, run_iter_args):
+                for params, model in self.pool.imap_unordered(self._run_wrappermp, run_iter_args):
                     results[params] = model
                     pbar.update()
 
-                self.result_prep_mp(results)
+                self._result_prep_mp(results)
         # For debugging model due to difficulty of getting errors during multiprocessing
         else:
             for run in run_iter_args:
-                params, model_data = self.run_wrappermp(run)
+                params, model_data = self._run_wrappermp(run)
                 results[params] = model_data
 
-            self.result_prep_mp(results)
+            self._result_prep_mp(results)
 
         # Close multi-processing
         self.pool.close()

mesa/datacollection.py

@@ -85,14 +85,14 @@ def __init__(self, model_reporters=None, agent_reporters=None, tables=None):
             If your model includes a large number of agents, you should *only*
             use attribute names for the agent reporter, it will be much faster.
 
-            Model reporters can take three types of arguments:
+            Model reporters can take four types of arguments:
             lambda like above:
             {"agent_count": lambda m: m.schedule.get_agent_count() }
             method with @property decorators
             {"agent_count": schedule.get_agent_count()
             class attributes of model
             {"model_attribute": "model_attribute"}
-            functions with paramters that have placed in a list
+            functions with parameters that have placed in a list
             {"Model_Function":[function, [param_1, param_2]]}
 
         """
@@ -186,14 +186,7 @@ def collect(self, model):
                 elif isinstance(reporter, list):
                     self.model_vars[var].append(reporter[0](*reporter[1]))
                 else:
-                    try:
-                        self.model_vars[var].append(reporter)
-                    except TypeError:
-                        print("Reporters must be in dictionary in one of the following forms: /n\
-                        - key_name: <class attribute> /n \
-                        - key_name: function (e.g schedule.get_agent_count) /n \
-                        - key_name: lambda function /n \
-                        - key_name: [function, [<arguments for function>]")
+                    self.model_vars[var].append(reporter)
 
         if self.agent_reporters:
             agent_records = self._record_agents(model)