Better readme, plots.

manufacturing/__init__.py

@@ -3,4 +3,4 @@
 
 __all__ = ['calc_cpk', 'show_cpk']
 
-__version__ = '0.1.0'
+__version__ = '0.1.1'

manufacturing/visual.py

@@ -81,15 +81,18 @@ def show_cpk(data: (List[int], List[float], pd.Series, np.array),
 
     strings = [f'Cpk = {cpk:.02f}']
 
+    strings.append(f'$\mu = {mean:.3g}$')
+    strings.append(f'$\sigma = {std:.3g}$')
+
     if lower_percent_text:
         strings.append(lower_percent_text)
     if higher_percent_text:
         strings.append(higher_percent_text)
 
     props = dict(boxstyle='round', facecolor='white', alpha=0.75, edgecolor='grey')
-    ax.text(right - (right - left) * 0.05, 0.8 * top, '\n'.join(strings), bbox=props, ha='right')
+    ax.text(right - (right - left) * 0.05, 0.85 * top, '\n'.join(strings), bbox=props, ha='right', va='top')
 
-    ax.legend(loc='center right')
+    ax.legend(loc='lower right')
 
     if show:
         plt.show()

readme.md

@@ -2,9 +2,51 @@
 
 To provide analysis tools and metrics useful in manufacturing environments.
 
+# Installation
+
+## From `setup.py`
+
+Clone this repository and install into your python environment using `setuptools`:
+
+    $ git clone https://github.com/slightlynybbled/manufacturing
+    $ cd manufacturing
+    $ python setup.py install
+
+## From Wheel File
+
+Download the wheel file and install from pip:
+
+    ~/downloads $ python -m pip install manufacturing-X.X.X-py3-none-any.whl
+
+Of course, the `X.X.X` will be replaced with the version that you are looking at.
+
 # Usage
 
-todo
+## Visualizations with Jupyter Notebooks
+
+Visualizations will return a `matplotlib.Figure`; therefore, if you want to embed 
+an image into a jupyter notebook, you will need to use a semicolon at the end of
+the function call in order to not have a double-image.
+
+    data = np.random.normal(0, 1, size=30)  # generate some data
+    manufacturing.show_cpk(data, lower_spec_limit=-2, upper_spec_limit=2);  # show the plot
+
+Note the trailing semicolon on the second statement!
+
+## Cpk Visualization
+
+The most useful feature of the `manufacturing` package is the visualization of Cpk.
+As hinted previously, the `show_cpk()` function is the primary method for display of
+Cpk visual information.  First, get your data into a `list`, `numpy.array`, or 
+`pandas.Series`; then supply that data, along with the `lower_spec_limit` and 
+`upper_spec_limit` into the `show_cpk()` function.
+
+    manufacturing.show_cpk(data, lower_spec_limit=-2, upper_spec_limit=2)
+
+![Screenshot](images/example3.png)
+
+In this example, it appears that the manufacturing processes are not up to the task of 
+making consistent product within the specified limits.
 
 # Features