Repository: projectmesa/mesa
Pull request #445 (2018-01-01 18:03:50)

Datacollector fix
Simplify the `DataCollector` API by allowing the user-provided model- and agent-level reporters to be the names of attributes, in addition to methods. e.g. instead of needing to write

```
agent_reporters={"Wealth": lambda a: a.wealth}
```

you can write
```
agent_reporters={"Wealth":"wealth"}
```

This PR implements this feature, and updates the tests and documentation accordingly.


Patch:
diff --git a/docs/tutorials/intro_tutorial.ipynb b/docs/tutorials/intro_tutorial.ipynb
index 3127a7fd..fc3dfd2e 100644
--- a/docs/tutorials/intro_tutorial.ipynb
+++ b/docs/tutorials/intro_tutorial.ipynb
@@ -96,7 +96,7 @@
    "cell_type": "code",
    "execution_count": 1,
    "metadata": {
-    "collapsed": true
+    "collapsed": false
    },
    "outputs": [],
    "source": [
@@ -183,7 +183,9 @@
   {
    "cell_type": "code",
    "execution_count": 3,
-   "metadata": {},
+   "metadata": {
+    "collapsed": true
+   },
    "outputs": [],
    "source": [
     "empty_model = MoneyModel(10)\n",
@@ -233,7 +235,9 @@
   {
    "cell_type": "code",
    "execution_count": 5,
-   "metadata": {},
+   "metadata": {
+    "collapsed": true
+   },
    "outputs": [],
    "source": [
     "class MoneyAgent(Agent):\n",
@@ -290,13 +294,15 @@
   {
    "cell_type": "code",
    "execution_count": 7,
-   "metadata": {},
+   "metadata": {
+    "collapsed": false
+   },
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "(array([ 4.,  0.,  5.,  0.,  0.,  0.,  0.,  0.,  0.,  1.]),\n",
-       " array([ 0. ,  0.5,  1. ,  1.5,  2. ,  2.5,  3. ,  3.5,  4. ,  4.5,  5. ]),\n",
+       "(array([ 4.,  0.,  0.,  3.,  0.,  0.,  2.,  0.,  0.,  1.]),\n",
+       " array([ 0. ,  0.3,  0.6,  0.9,  1.2,  1.5,  1.8,  2.1,  2.4,  2.7,  3. ]),\n",
        " <a list of 10 Patch objects>)"
       ]
      },
@@ -306,9 +312,9 @@
     },
     {
      "data": {
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+      "image/png": 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TSe7tzh+oqsNJ9iY5BbwB3DPzriVJW9pyWUaStJym/gnV1j/0NOr+kqwleS3J0W57aB59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\n",
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x110782ba8>"
+       "<matplotlib.figure.Figure at 0x107123748>"
       ]
      },
      "metadata": {},
@@ -344,14 +350,16 @@
   {
    "cell_type": "code",
    "execution_count": 8,
-   "metadata": {},
+   "metadata": {
+    "collapsed": false
+   },
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "(array([ 429.,  306.,  160.,   63.,   26.,   16.]),\n",
-       " array([0, 1, 2, 3, 4, 5, 6]),\n",
-       " <a list of 6 Patch objects>)"
+       "(array([ 433.,  311.,  140.,   73.,   30.,    9.,    4.]),\n",
+       " array([0, 1, 2, 3, 4, 5, 6, 7]),\n",
+       " <a list of 7 Patch objects>)"
       ]
      },
      "execution_count": 8,
@@ -360,9 +368,9 @@
     },
     {
      "data": {
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OAh8ArkjyIvD7wL3AoSS3AqeAjwJU1USSQ8AEcBa4rapGedl6SRopGUbmdufgRzPrx8bu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\ngg==\n",
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x11901ae80>"
+       "<matplotlib.figure.Figure at 0x108fc1e10>"
       ]
      },
      "metadata": {},
@@ -594,23 +602,33 @@
   {
    "cell_type": "code",
    "execution_count": 13,
-   "metadata": {},
+   "metadata": {
+    "collapsed": false
+   },
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "<matplotlib.colorbar.Colorbar at 0x119326b00>"
+       "<matplotlib.colorbar.Colorbar at 0x1094697f0>"
       ]
      },
      "execution_count": 13,
      "metadata": {},
      "output_type": "execute_result"
     },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/Users/dmasad/.virtualenvs/mesa/lib/python3.4/site-packages/matplotlib/collections.py:590: FutureWarning: elementwise comparison failed; returning scalar instead, but in the future will perform elementwise comparison\n",
+      "  if self._edgecolors == str('face'):\n"
+     ]
+    },
     {
      "data": {
-      "image/png": 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+      "image/png": "iVBORw0KGgoAAAANSUhEUgAAASUAAAEACAYAAAD1BmDyAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nQmCC\n",
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x1191a07b8>"
+       "<matplotlib.figure.Figure at 0x109273400>"
       ]
      },
      "metadata": {},
@@ -708,7 +726,7 @@
     "        \n",
     "        self.datacollector = DataCollector(\n",
     "            model_reporters={\"Gini\": compute_gini},\n",
-    "            agent_reporters={\"Wealth\": lambda a: a.wealth})\n",
+    "            agent_reporters={\"Wealth\": \"wealth\"})\n",
     "\n",
     "    def step(self):\n",
     "        self.datacollector.collect(self)\n",
@@ -727,7 +745,9 @@
   {
    "cell_type": "code",
    "execution_count": 15,
-   "metadata": {},
+   "metadata": {
+    "collapsed": true
+   },
    "outputs": [],
    "source": [
     "model = MoneyModel(50, 10, 10)\n",
@@ -745,12 +765,14 @@
   {
    "cell_type": "code",
    "execution_count": 16,
-   "metadata": {},
+   "metadata": {
+    "collapsed": false
+   },
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "<matplotlib.axes._subplots.AxesSubplot at 0x1192d1518>"
+       "<matplotlib.axes._subplots.AxesSubplot at 0x109428cf8>"
       ]
      },
      "execution_count": 16,
@@ -759,9 +781,9 @@
     },
     {
      "data": {
-      "image/png": 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ArhofSdcAb1buzKzz+lfWp/Lrj3Yxc0hPXrw+rk1DvaY+If7cdlZ/Pt2ZeXJx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\nF/sbEAS8JyI/isjyBk53ipJ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mAT16uAC4Im4oEVEmcLvdcLvdYcuFnKQjIp0B5Kpqtnd7CACPqg71KbMR5QG7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\nICZbftprAAAAAElFTkSuQmCC\n",
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x11934cdd8>"
+       "<matplotlib.figure.Figure at 0x1094b1c88>"
       ]
      },
      "metadata": {},
@@ -783,7 +805,9 @@
   {
    "cell_type": "code",
    "execution_count": 17,
-   "metadata": {},
+   "metadata": {
+    "collapsed": false
+   },
    "outputs": [
     {
      "data": {
@@ -871,12 +895,14 @@
   {
    "cell_type": "code",
    "execution_count": 18,
-   "metadata": {},
+   "metadata": {
+    "collapsed": false
+   },
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "<matplotlib.axes._subplots.AxesSubplot at 0x11af58c18>"
+       "<matplotlib.axes._subplots.AxesSubplot at 0x10adacdd8>"
       ]
      },
      "execution_count": 18,
@@ -885,9 +911,9 @@
     },
     {
      "data": {
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+      "image/png": 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4JyLukfRCdo5JRcThiDjQnD4q6aCkH81NNZ6I+E5z8g1afgylTInYfqukX5b0WdV9WkbJ\n3LZ/UNJ7I+J6afmxuIql3bhY0jfbSluaTXHz4pyesD0naYek+3OTjMf2JtsHJB2RtC8iHsvONIY/\nl/QHkr6XHWRCIekrth+0/aHsMGM6T9Kztm+w/Z+2/9b26dmhJvSbkj4/6sJZFDePdvZAM5PcLOnq\n5p53GRHxvYiYl/RWST9neyE5Uie2f1XSMxGxX0XvtUq6MCJ2SHqfpI8002EVmyW9XdJfRcTbJR2T\ndE1upPHZfoOk90v6p1GfM4vi7vTiHMyO7VMlfVHS30fEl7LzTKr5NfdfJL0zO0tH75F0abMT3yTp\n523fmJxpLBHxdPPns5Ju0fL0WcVTkp6KiK82xzdrucireZ+kh5q/g1azKO4HJf2E7bnmluM3JH15\nBt8HLbz8krHdkh6LiF3ZecZl+yzbW5rTp0n6RUn7c1N1ExGfiIhtEXGeln/V/beI+O3sXF3ZPt32\nG5vTZ0j6JUllnl0VEYclPWn7/OasiyU9mhhpUldo+YZ/pKn/02XVX5xj+yZJF0n6YdtPSvpkRNyQ\nHGscF0q6UtLDto8X3rURcUdipnFslbS3eVR9k6S/i4i7kzNNqtpseLakW5qXi2+W9LmIuDM30tiu\nkvS55k7jNyX9TnKesTQ3mBdLWvPxBV6AAwDF8E+XAUAxFDcAFENxA0AxFDcAFENxA0AxFDcAFENx\nA0AxFDcAFPP/SzzoGCdXjoIAAAAASUVORK5CYII=\n",
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x119132898>"
+       "<matplotlib.figure.Figure at 0x10adf47f0>"
       ]
      },
      "metadata": {},
@@ -909,12 +935,14 @@
   {
    "cell_type": "code",
    "execution_count": 19,
-   "metadata": {},
+   "metadata": {
+    "collapsed": false
+   },
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "<matplotlib.axes._subplots.AxesSubplot at 0x11ae5a588>"
+       "<matplotlib.axes._subplots.AxesSubplot at 0x10b138860>"
       ]
      },
      "execution_count": 19,
@@ -923,9 +951,9 @@
     },
     {
      "data": {
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xwraVzGMXyXCslklvkfyZ2x/CgUe6AIA9x7dx+SXPMxrYmxddmZ+qxDzsvEaE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\nlTKPL7MdEzvPGrWXlEYrWg4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/uKTbJf1U0jckHTbvdZ0VSaskbZH0tc7tNu+LwyR9RdKDkh6Q9I6W749LO++V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\nzCxBLt5mZgn6/7uOt/nNmgUwAAAAAElFTkSuQmCC\n",
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x11afe9a20>"
+       "<matplotlib.figure.Figure at 0x10adeeef0>"
       ]
      },
      "metadata": {},
@@ -991,7 +1019,7 @@
     "        \n",
     "        self.datacollector = DataCollector(\n",
     "            model_reporters={\"Gini\": compute_gini},\n",
-    "            agent_reporters={\"Wealth\": lambda a: a.wealth})\n",
+    "            agent_reporters={\"Wealth\": \"wealth\"})\n",
     "\n",
     "    def step(self):\n",
     "        self.datacollector.collect(self)\n",
@@ -1012,13 +1040,15 @@
   {
    "cell_type": "code",
    "execution_count": 22,
-   "metadata": {},
+   "metadata": {
+    "collapsed": false
+   },
    "outputs": [
     {
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "245it [00:37,  6.58it/s]\n"
+      "245it [00:51,  4.78it/s]\n"
      ]
     }
    ],
@@ -1046,23 +1076,33 @@
   {
    "cell_type": "code",
    "execution_count": 23,
-   "metadata": {},
+   "metadata": {
+    "collapsed": false
+   },
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "<matplotlib.collections.PathCollection at 0x11b28bac8>"
+       "<matplotlib.collections.PathCollection at 0x10af8cba8>"
       ]
      },
      "execution_count": 23,
      "metadata": {},
      "output_type": "execute_result"
     },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/Users/dmasad/.virtualenvs/mesa/lib/python3.4/site-packages/matplotlib/collections.py:590: FutureWarning: elementwise comparison failed; returning scalar instead, but in the future will perform elementwise comparison\n",
+      "  if self._edgecolors == str('face'):\n"
+     ]
+    },
     {
      "data": {
-      "image/png": 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+      "image/png": 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CbgSOjluoJGl6Bnb7ZOaZiLgNOAxsA+7MzGMRsacafwfwTuC3IuIM8AxwczX7K4DPVtuF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\n4S9JBTL8JalAhr8kFcjwl6QC/X/3MB7fFr7gjgAAAABJRU5ErkJggg==\n",
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x11f4bb048>"
+       "<matplotlib.figure.Figure at 0x1094fe390>"
       ]
      },
      "metadata": {},
@@ -1104,10 +1144,11 @@
   }
  ],
  "metadata": {
+  "anaconda-cloud": {},
   "kernelspec": {
-   "display_name": "Python [mesa_dev]",
+   "display_name": "Python 3",
    "language": "python",
-   "name": "Python [mesa_dev]"
+   "name": "python3"
   },
   "language_info": {
    "codemirror_mode": {
@@ -1119,7 +1160,11 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.5.3"
+   "version": "3.4.2"
+  },
+  "widgets": {
+   "state": {},
+   "version": "1.1.2"
   }
  },
  "nbformat": 4,
diff --git a/docs/tutorials/intro_tutorial.rst b/docs/tutorials/intro_tutorial.rst
index 9b3ff29c..e3aa0683 100644
--- a/docs/tutorials/intro_tutorial.rst
+++ b/docs/tutorials/intro_tutorial.rst
@@ -590,7 +590,8 @@ storage for us and make it easier to analyze.
 The data collector stores three categories of data: model-level
 variables, agent-level variables, and tables (which are a catch-all for
 everything else). Model- and agent-level variables are added to the data
-collector along with a function for collecting them. Model-level
+collector along with either the name of an attribute that the agents or
+models have, or a function for collecting some data. Model-level
 collection functions take a model object as an input, while agent-level
 collection functions take an agent object as an input. Both then return
 a value computed from the model or each agent at their current state.
@@ -640,8 +641,8 @@ measure of wealth inequality.
                 self.grid.place_agent(a, (x, y))
             
             self.datacollector = DataCollector(
-                model_reporters={"Gini": compute_gini},
-                agent_reporters={"Wealth": lambda a: a.wealth})
+                model_reporters={"Gini": compute_gini},  # A function to call
+                agent_reporters={"Wealth": "wealth"})  # An agent attribute
     
         def step(self):
             self.datacollector.collect(self)
diff --git a/examples/Schelling/model.py b/examples/Schelling/model.py
index fe9a6915..b53f04c3 100644
--- a/examples/Schelling/model.py
+++ b/examples/Schelling/model.py
@@ -56,7 +56,7 @@ class SchellingModel(Model):
 
         self.happy = 0
         self.datacollector = DataCollector(
-            {"happy": lambda m: m.happy},  # Model-level count of happy agents
+            {"happy": "happy"},  # Model-level count of happy agents
             # For testing purposes, agent's individual x and y
             {"x": lambda a: a.pos[0], "y": lambda a: a.pos[1]})
 
diff --git a/examples/boltzmann_wealth_model/wealth_model/model.py b/examples/boltzmann_wealth_model/wealth_model/model.py
index 89a16a84..ad083a5c 100644
--- a/examples/boltzmann_wealth_model/wealth_model/model.py
+++ b/examples/boltzmann_wealth_model/wealth_model/model.py
@@ -29,7 +29,7 @@ class MoneyModel(Model):
         self.schedule = RandomActivation(self)
         self.datacollector = DataCollector(
             model_reporters={"Gini": compute_gini},
-            agent_reporters={"Wealth": lambda a: a.wealth}
+            agent_reporters={"Wealth": "wealth"}
         )
         # Create agents
         for i in range(self.num_agents):
diff --git a/mesa/datacollection.py b/mesa/datacollection.py
index b5aa6f28..dfa4fa92 100644
--- a/mesa/datacollection.py
+++ b/mesa/datacollection.py
@@ -8,13 +8,15 @@ generated by a Mesa model. It collects three types of data: model-level data,
 agent-level data, and tables.
 
 A DataCollector is instantiated with two dictionaries of reporter names and
-associated functions for each, one for model-level data and one for
-agent-level data; a third dictionary provides table names and columns.
+associated variable names or functions for each, one for model-level data and
+one for agent-level data; a third dictionary provides table names and columns.
+Variable names are converted into functions which retrieve attributes of that
+name.
 
 When the collect() method is called, each model-level function is called, with
 the model as the argument, and the results associated with the relevant
-variable. Then the agent-level functions are called on each
-agent in the model scheduler.
+variable. Then the agent-level functions are called on each agent in the model
+scheduler.
 
 Additionally, other objects can write directly to tables by passing in an
 appropriate dictionary object for a table row.
@@ -42,9 +44,10 @@ class DataCollector:
     """ Class for collecting data generated by a Mesa model.
 
     A DataCollector is instantiated with dictionaries of names of model- and
-    agent-level variables to collect, associated with functions which actually
-    collect them. When the collect(...) method is called, it executes these
-    functions one by one and stores the results.
+    agent-level variables to collect, associated with attribute names or
+    functions which actually collect them. When the collect(...) method is
+    called, it collects these attributes and executes these functions one by
+    one and stores the results.
 
     """
 
@@ -54,13 +57,15 @@ class DataCollector:
         """ Instantiate a DataCollector with lists of model and agent reporters.
 
         Both model_reporters and agent_reporters accept a dictionary mapping a
-        variable name to a method used to collect it.
+        variable name to either an attribute name, or a method.
         For example, if there was only one model-level reporter for number of
         agents, it might look like:
             {"agent_count": lambda m: m.schedule.get_agent_count() }
         If there was only one agent-level reporter (e.g. the agent's energy),
         it might look like this:
             {"energy": lambda a: a.energy}
+        or like this:
+            {"energy": "energy"}
 
         The tables arg accepts a dictionary mapping names of tables to lists of
         columns. For example, if we want to allow agents to write their age
@@ -69,8 +74,9 @@ class DataCollector:
             {"Lifespan": ["unique_id", "age"]}
 
         Args:
-            model_reporters: Dictionary of reporter names and functions.
-            agent_reporters: Dictionary of reporter names and functions.
+            model_reporters: Dictionary of reporter names and attributes/funcs
+            agent_reporters: Dictionary of reporter names and attributes/funcs.
+            tables: Dictionary of table names to lists of column names.
 
         """
         self.model_reporters = {}
@@ -81,40 +87,43 @@ class DataCollector:
         self.tables = {}
 
         if model_reporters is not None:
-            for name, func in model_reporters.items():
-                self._new_model_reporter(name, func)
+            for name, reporter in model_reporters.items():
+                self._new_model_reporter(name, reporter)
 
         if agent_reporters is not None:
-            for name, func in agent_reporters.items():
-                self._new_agent_reporter(name, func)
+            for name, reporter in agent_reporters.items():
+                self._new_agent_reporter(name, reporter)
 
         if tables is not None:
             for name, columns in tables.items():
                 self._new_table(name, columns)
 
-    def _new_model_reporter(self, reporter_name, reporter_function):
+    def _new_model_reporter(self, name, reporter):
         """ Add a new model-level reporter to collect.
 
         Args:
-            reporter_name: Name of the model-level variable to collect.
-            reporter_function: Function object that returns the variable when
-                               given a model instance.
-
+            name: Name of the model-level variable to collect.
+            reporter: Attribute string, or function object that returns the
+                      variable when given a model instance.
         """
-        self.model_reporters[reporter_name] = reporter_function
-        self.model_vars[reporter_name] = []
+        if type(reporter) is str:
+            reporter = self._make_attribute_collector(reporter)
+        self.model_reporters[name] = reporter
+        self.model_vars[name] = []
 
-    def _new_agent_reporter(self, reporter_name, reporter_function):
+    def _new_agent_reporter(self, name, reporter):
         """ Add a new agent-level reporter to collect.
 
         Args:
-            reporter_name: Name of the agent-level variable to collect.
-            reporter_function: Function object that returns the variable when
-                               given an agent object.
+            name: Name of the agent-level variable to collect.
+            reporter: Attribute string, or function object that returns the
+                      variable when given a model instance.
 
         """
-        self.agent_reporters[reporter_name] = reporter_function
-        self.agent_vars[reporter_name] = []
+        if type(reporter) is str:
+            reporter = self._make_attribute_collector(reporter)
+        self.agent_reporters[name] = reporter
+        self.agent_vars[name] = []
 
     def _new_table(self, table_name, table_columns):
         """ Add a new table that objects can write to.
@@ -161,6 +170,17 @@ class DataCollector:
             else:
                 raise Exception("Could not insert row with missing column")
 
+    @staticmethod
+    def _make_attribute_collector(attr):
+        '''
+        Create a function which collects the value of a named attribute
+        '''
+
+        def attr_collector(obj):
+            return getattr(obj, attr)
+
+        return attr_collector
+
     def get_model_vars_dataframe(self):
         """ Create a pandas DataFrame from the model variables.
 


Test patch:
diff --git a/tests/test_datacollector.py b/tests/test_datacollector.py
index 3c62f19c..645712d5 100644
--- a/tests/test_datacollector.py
+++ b/tests/test_datacollector.py
@@ -15,12 +15,14 @@ class MockAgent(Agent):
     def __init__(self, unique_id, model, val=0):
         super().__init__(unique_id, model)
         self.val = val
+        self.val2 = val
 
     def step(self):
         '''
-        Increment val by 1.
+        Increment vals by 1.
         '''
         self.val += 1
+        self.val2 += 1
 
     def write_final_values(self):
         '''
@@ -39,12 +41,15 @@ class MockModel(Model):
 
     def __init__(self):
         self.schedule = BaseScheduler(self)
+        self.model_val = 100
+
         for i in range(10):
             a = MockAgent(i, self, val=i)
             self.schedule.add(a)
         self.datacollector = DataCollector(
-            {"total_agents": lambda m: m.schedule.get_agent_count()},
-            {"value": lambda a: a.val},
+            {"total_agents": lambda m: m.schedule.get_agent_count(),
+             "model_value": "model_val"},
+            {"value": lambda a: a.val, "value2": "val2"},
             {"Final_Values": ["agent_id", "final_value"]})
 
     def step(self):
@@ -70,9 +75,13 @@ class TestDataCollector(unittest.TestCase):
         '''
         data_collector = self.model.datacollector
         assert "total_agents" in data_collector.model_vars
+        assert "model_value" in data_collector.model_vars
         assert len(data_collector.model_vars["total_agents"]) == 7
+        assert len(data_collector.model_vars["model_value"]) == 7
         for element in data_collector.model_vars["total_agents"]:
             assert element == 10
+        for element in data_collector.model_vars["model_value"]:
+            assert element == 100
 
     def test_agent_vars(self):
         '''
@@ -80,10 +89,12 @@ class TestDataCollector(unittest.TestCase):
         '''
         data_collector = self.model.datacollector
         assert len(data_collector.agent_vars["value"]) == 7
-        for step in data_collector.agent_vars["value"]:
-            assert len(step) == 10
-            for record in step:
-                assert len(record) == 2
+        assert len(data_collector.agent_vars["value2"]) == 7
+        for var in ["value", "value2"]:
+            for step in data_collector.agent_vars[var]:
+                assert len(step) == 10
+                for record in step:
+                    assert len(record) == 2
 
     def test_table_rows(self):
         '''
@@ -111,8 +122,8 @@ class TestDataCollector(unittest.TestCase):
         agent_vars = data_collector.get_agent_vars_dataframe()
         table_df = data_collector.get_table_dataframe("Final_Values")
 
-        assert model_vars.shape == (7, 1)
-        assert agent_vars.shape == (70, 1)
+        assert model_vars.shape == (7, 2)
+        assert agent_vars.shape == (70, 2)
         assert table_df.shape == (10, 2)
 
         with self.assertRaises(Exception):