Repository: projectmesa/mesa

Issue:
Minor docstring clean-up needed on time.py and batchrunner.py
A couple of typos and minor content edit items for the two files.

Minor docstring clean-up needed on time.py and batchrunner.py
A couple of typos and minor content edit items for the two files.



Discussion:

Resolving patch:
diff --git a/mesa/batchrunner.py b/mesa/batchrunner.py
--- a/mesa/batchrunner.py
+++ b/mesa/batchrunner.py
@@ -12,7 +12,7 @@
 
 class BatchRunner:
     """ This class is instantiated with a model class, and model parameters
-    associated with one or more values. It is also instantiated with model- and
+    associated with one or more values. It is also instantiated with model and
     agent-level reporters, dictionaries mapping a variable name to a function
     which collects some data from the model or its agents at the end of the run
     and stores it.
@@ -34,11 +34,11 @@ def __init__(self, model_cls, parameter_values, iterations=1,
                     {"param_1": range(5),
                      "param_2": [1, 5, 10],
                       "const_param": 100}
-            iterations: How many times to run the model at each combination of
-                parameters.
-            max_steps: After how many steps to halt each run if it hasn't
-                halted on its own.
-            model_reporters: Dictionary of variables to collect on each run at
+            iterations: The total number of times to run the model for each
+                combination of parameters.
+            max_steps: The upper limit of steps above which each run will be halted
+                if it hasn't halted on its own.
+            model_reporters: The dictionary of variables to collect on each run at
                 the end, with variable names mapped to a function to collect
                 them. For example:
                     {"agent_count": lambda m: m.schedule.get_agent_count()}
@@ -111,8 +111,7 @@ def collect_agent_vars(self, model):
         return agent_vars
 
     def get_model_vars_dataframe(self):
-        """ Generate a pandas DataFrame from the model-level collected
-        variables.
+        """ Generate a pandas DataFrame from the model-level variables collected.
 
         """
         index_col_names = list(self.parameter_values.keys())
diff --git a/mesa/time.py b/mesa/time.py
--- a/mesa/time.py
+++ b/mesa/time.py
@@ -8,7 +8,7 @@
 which controls when agents are called upon to act, and when.
 
 The activation order can have a serious impact on model behavior, so it's
-important to specify it explicity. Example simple activation regimes include
+important to specify it explicitly. Example simple activation regimes include
 activating all agents in the same order every step, shuffling the activation
 order every time, activating each agent *on average* once per step, and more.
 
@@ -162,7 +162,7 @@ def __init__(self, model, stage_list=["step"], shuffle=False,
         self.stage_time = 1 / len(self.stage_list)
 
     def step(self):
-        """ Executes all the stages of all agents. """
+        """ Executes all the stages for all agents. """
         if self.shuffle:
             random.shuffle(self.agents)
         for stage in self.stage_list:


Test patch: