Update pyplot-notebook.rst

Final edits to Examples.rst pages (4/4)

Author: samanthahamilton

docs/examples/pyplot-notebook.rst

@@ -4,16 +4,16 @@ Control Plots with Sliders
 
 .. note::
     Unfortunately the interactive plots won't work on a website as there is no Python kernel
-    running. So for all the interactive outputs have been replaced by gifs of what you should expect.
+    running. So for this site all the interactive outputs have been replaced by gifs of what you will see.
 
-On this example page all of the outputs will use ipywidgets widgets for controls. However, if you are
-not working in a Jupyter notebook then the examples here will still work with the built-in Matplolitb widgets.
-For examples that that explicitly use matplotlib widgets instead of ipywidgets check out the :doc:`mpl-sliders` page.
+On this example page all of the outputs will use **ipywidgets** widgets for controls. If you are
+not working in a Jupyter Notebook the examples here will still work with the built-in Matplolitb widgets.
+For examples that that explicitly use Matplotlib widgets instead of ipywidgets see the :doc:`mpl-sliders` page.
 
 
 .. jupyter-execute::
 
-    # only run these lines if you are using a jupyter notebook or jupyter lab
+    # only run these lines if you are using a Jupyter Notebook or JupyterLab
     %matplotlib ipympl
     import ipywidgets as widgets
 
@@ -23,11 +23,11 @@ For examples that that explicitly use matplotlib widgets instead of ipywidgets c
     from mpl_interactions import interactive_plot, interactive_plot_factory
 
 
-Simple Example
+Simple example
 --------------
 
-To use the interactive plot function all you need to do is write a function that will
-return a numpy array or a list of numbers. You can provide the parameters that you want
+To use the interactive plot function, write a function that will
+return a NumPy array or a list of numbers. You can provide the parameters you want 
 to vary with sliders as keyword arguments to the :meth:`~mpl_interactions.interactive_plot` function. 
 
 
@@ -39,7 +39,7 @@ to vary with sliders as keyword arguments to the :meth:`~mpl_interactions.intera
     def f(x, tau, beta):
         return np.sin(x*tau)*x**beta
 
-and then to display the plot
+Then to display the plot:
 
 .. code-block:: python
 
@@ -51,24 +51,25 @@ and then to display the plot
 Other ways to set parameter values
 ----------------------------------
 
-You can set parameters with any of the following:
+You can set parameters using any of the following:
 
-- **numpy array/list** - Creates a slider with the values in the array
-- **tuple** - Acts as an argument to linspace. Can have either 2 or 3 items
+- **NumPy array/list** - Creates a slider with the values in the array
+- **tuple** - Acts as an argument to linspace (can have either 2 or 3 items)
 - **set** - Creates a categorical selector (order will not preserved)
 - **set(tuple())** - Categorical selector with order maintained
 - **scalar** - Fixed value
-- **ipywidgets.Widget** any subclass of ``ipywidgets.Widget`` that has a ``value`` attribute can be used
-- **matplotlib.widgets.Slider** or **RadioButton** - Note this cannot be used at the same time as an ipywidgets.Widget
+- **ipywidgets.Widget** - any subclass of ``ipywidgets.Widget`` that has a ``value`` attribute can be used
+- **matplotlib.widgets.Slider** or **RadioButton** - Note this cannot be used at the same time as an ``ipywidgets.Widget``
 
-Here is an example using all of the possibilities with a dummy function. The ``display=False``
-prevent the widgets from being automatically displayed which makes it easier to render them in this webpage,
-but in general you should not need to use that.
+Here is an example using all of the possibilities with a dummy function.
 
 
 .. note::
     The slider labels will not update here as that update requires a Python kernel.
 
+    Also, ``display=False`` prevents the widgets from being automatically displayed, making it easier to render 
+    them on this webpage. In general you should not need to use it.
+
 .. jupyter-execute::
 
     def foo(x, **kwargs):
@@ -82,12 +83,12 @@ but in general you should not need to use that.
     f = widgets.Checkbox(value=True, description='A checkbox!!')
     display(interactive_plot(foo, x=x, a=a, b=b, c=c, d=d, e=e, f_=f, display=False)[-1])
 
-Multiple Functions
+Multiple functions
 ------------------
 
 To plot multiple functions simply pass a list of functions as the first argument ``interactive_plot([f1, f2],...)``.
-Also, whenever you add a legend to the resulting plot the names of the functions will be used as the labels, unless you
-override that using the :ref:`plot_kwargs <plot-kwargs-section>` argument.
+When you add a legend to the resulting plot, the function names will be used as the labels unless overriden 
+using the :ref:`plot_kwargs <plot-kwargs-section>` argument.
 
 .. code-block:: python
 
@@ -102,18 +103,18 @@ override that using the :ref:`plot_kwargs <plot-kwargs-section>` argument.
 
 Styling
 -------
-Calling ``interactive_plot`` will create and display a new figure for you. After that you can
-use standard ``pyplot`` command to continue to modify the plot or you can use the references to the ``figure`` and ``axis``
-that are returned by interactive_plot. Though be careful, anything you add will not be affected by the sliders.
+Calling ``interactive_plot`` will create and display a new figure. Then you can either 
+use the standard ``pyplot`` command to continue modifying the plot, or you can use the references to the ``figure`` and ``axis``
+that are returned by ``interactive_plot``. Though be careful, anything you add will not be affected by the sliders.
 
 
 
-Slider Precision
+Slider precision
 ^^^^^^^^^^^^^^^^
 
-You can change the precision of individual slider displays by passing slider_format_string as a dictionary. 
-The below example will give the tau slider 99 decimal points of precision and use scientific notation to display it. The
-beta slider will use the default 1 decimal point of precision
+You can change the precision of individual slider displays by passing ``slider_format_string`` as a dictionary. 
+The example below gives the tau slider 99 decimal points of precision and uses scientific notation to display it. The
+beta slider uses the default 1 decimal point of precision.
 
 .. code-block:: python
 
@@ -126,26 +127,24 @@ Axis limits
 You can control how the ``xlim/ylim`` behaves using the ``xlim/ylim`` arguments.
 The options are:
 
-1. ``'stretch'`` - The default, allow the x/y axes to expand but never shrink
-2. ``'auto'`` - autoscale the limits for every plot update
-3. ``'fixed'`` - never automatically update the limits
+1. ``'stretch'`` - The default; allows the x/y axes to expand but never shrink
+2. ``'auto'`` - Autoscales the limits for every plot update
+3. ``'fixed'`` - Never automatically update the limits
 4. [``float``, ``float``] - This value will be passed through to ``plt.xlim`` or ``plt.ylim``
 
 Reference parameter values in the Title
 ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
-You can make the title auto update with information about the values by using ``title`` argument.
-Just use the name of one of the parameters as in a format specifier in the string.
-e.g. to put the value of `tau` in the title and round it to two decimals use the following
-title string: ``{'tau:.2f}'``
+You can make the Title automatically update with information about the values by using ``title`` argument.
+Use the name of one of the parameters as a format specifier in the string. For example use the following title string  
+to put the value of `tau` in the title and round it to two decimalsg: ``{'tau:.2f}'``
 
 .. _plot-kwargs-section:
 
 Matplolitb keyword arguments
 ^^^^^^^^^^^^^^^^^^^^^^^^^^^^
 
-You can pass keyword arguments (kwargs) through to the ``plt.plot`` calls using the ``plot_kwargs``
-argument to ``interactive_plot``. For example to add a label and some styling to one of the functions you
-can do the following:
+You can pass keyword arguments (*kwargs*) through to the ``plt.plot`` calls using the ``plot_kwargs``
+argument to ``interactive_plot``. For example, to add a label and some styling to one of the functions try the following:
 
 .. code-block:: python