Fixing formatting error.

Author: jackiekazil

docs/intro-tutorial.rst

@@ -239,7 +239,7 @@ This outcome might be surprising. Despite the fact that all agents, on average,
 Adding space
 ~~~~~~~~~~~~~
 
-Many ABMs have a spatial element, with agents moving around and interacting with nearby neighbors. Mesa currently supports two overall kinds of spaces: grid, and continuous. Grids are divided into cells, and agents can only be on a particular cell, like pieces on a chess board. Continuous space, in contrast, allows agents to have any arbitrary position. Both grids and continuous spaces are frequently `toroidalhttps://en.wikipedia.org/wiki/Toroidal_graph`_, meaning that the edges wrap around, with cells on the right edge connected to those on the left edge, and the top to the bottom. This prevents some cells having fewer neighbors than others, or agents being able to go off the edge of the environment.
+Many ABMs have a spatial element, with agents moving around and interacting with nearby neighbors. Mesa currently supports two overall kinds of spaces: grid, and continuous. Grids are divided into cells, and agents can only be on a particular cell, like pieces on a chess board. Continuous space, in contrast, allows agents to have any arbitrary position. Both grids and continuous spaces are frequently `toroidal <https://en.wikipedia.org/wiki/Toroidal_graph>`_, meaning that the edges wrap around, with cells on the right edge connected to those on the left edge, and the top to the bottom. This prevents some cells having fewer neighbors than others, or agents being able to go off the edge of the environment.
 
 Let's add a simple spatial element to our model by putting our agents on a grid and make them walk around at random. Instead of giving their unit of money to any random agent, they'll give it to an agent on the same cell.
 
@@ -407,7 +407,7 @@ Collecting Data
 
 So far, at the end of every model run, we've had to go and write our own code to get the data out of the model. This has two problems: it isn't very efficient, and it only gives us end results. If we wanted to know the wealth of each agent at each step, we'd have to add that to the loop of executing steps, and figure out some way to store the data.
 
-Since one of the main goals of agent-based modeling is generating data for analysis, Mesa provides a  class which can handle data collection and storage for us and make it easier to analyze.
+Since one of the main goals of agent-based modeling is generating data for analysis, Mesa provides a class which can handle data collection and 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 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. When the data collector’s ``collect`` method is called, with a model object as its argument, it applies each model-level collection function to the model, and stores the results in a dictionary, associating the current value with the current step of the model. Similarly, the method applies each agent-level collection function to each agent currently in the schedule, associating the resulting value with the step of the model, and the agent’s ``unique_id``.