Final addition of p_chart, including documentation.

docs/api.rst

@@ -37,7 +37,7 @@ Plotting & Charting
 
 ----
 
-.. autofunction:: manufacturing.control_plot
+.. autofunction:: manufacturing.p_chart
 
 Import & Export
 ---------------

docs/plotting.rst

@@ -136,3 +136,10 @@ that warrant further explanation:
  * lower control limit - the lower control limit as calculated from the data on the plot
 
 .. image:: _static/images/x_bar_s-anatomy.png
+
+Additional Chart Types
+----------------------
+
+One may also utilize the :meth:`manufacturing.p_chart()`, which requires a `pandas.DataFrame` rather than a `pandas.Series`.
+
+.. image:: _static/images/p_chart.png

manufacturing/visual.py

@@ -398,8 +398,12 @@ def control_chart_base(
 
     ax.plot(data, marker=".")
 
-    # ax.axhline(spec_center, linestyle="--", color="red", alpha=0.2)
-    ax.plot(spec_center, linestyle='--', color='red', alpha=0.2)
+    try:
+        iter(spec_center)
+        ax.plot(spec_center, linestyle='--', color='red', alpha=0.2)
+    except TypeError:
+        ax.axhline(spec_center, linestyle="--", color="red", alpha=0.2)
+
     ax.axhline(mean, linestyle="--", color="blue", alpha=0.3, zorder=-10)
 
     texts = [
@@ -1080,16 +1084,16 @@ def p_chart(
 
       - `pass`, which contains a `True`/`False` or `1`/`0` indication of pass/fail \
       status of a test sequence
-      - `lotid` or `datetime`, either of which will be used to create subgroups; if `lotid` is
-      provided, then data will be subgrouped into the defined lots; if `datetime` is provided,
-      then lot sizes will be based on time units (hour, day, week, year) and will automatically
+      - `lotid` or `datetime`, either of which will be used to create subgroups; if `lotid` is \
+      provided, then data will be subgrouped into the defined lots; if `datetime` is provided, \
+      then lot sizes will be based on time units (hour, day, week, year) and will automatically \
       be chosen to ensure that some defects are present in each lot size
 
     :param data: a dataframe containing two columns, `pass` and `lotid` or `datetime`
-    :param parameter_name:
-    :param highlight_beyond_limits:
+    :param parameter_name: a string representing the parameter name
+    :param highlight_beyond_limits: `True` or `False`
     :param figure: instance of `matplotlib.figure.Figure` on which to create the plot
-    :return:
+    :return: an instance of `matplotlib.figure.Figure` on which the plot has been created
     """
     if not isinstance(data, pd.DataFrame):
         raise ValueError('data must be of type `pandas.Dataframe`')

pyproject.toml

@@ -1,6 +1,6 @@
 [tool.poetry]
 name = "manufacturing"
-version = "1.1.25"
+version = "1.2.0"
 description = "Six-Sigma based analysis of manufacturing data"
 readme = 'readme.md'
 authors = ["Jason R. Jones <[email protected]>"]

readme.md

@@ -10,9 +10,7 @@ Project is currently undergoing frequent updates for documentation and to add
 functionality and update documentation!!  Screenshots and features that you see
 on here may be out of date, but are in progress.
 
-Current focus is to add test coverage and shore up functionality that has 
-been implemented.  All versions before v1.2.0 will be minor bugfixes or minor 
-functionality as it is revealed in test development.
+Current focus is to add more plot types.
 
 # Installation
 
@@ -54,6 +52,7 @@ the dataset requires.  However, if you wish to call a specific type of control c
  - `x_mr_chart`
  - `xbar_r_chart`
  - `xbar_s_chart`
+ - `p_chart`
 
 # Contributions
 
@@ -70,7 +69,7 @@ Items marked out were added most recently.
  - ~~Add `Xbar-R Chart` (subgroups between 2 and 10)~~
  - ~~Add `Xbar-S Chart` (subgroups of 11 or more)~~
  - ~~Update documentation to reflect recent API changes~~
- - Add `p chart`
+ - ~~Add `p chart`~~
  - Add `np chart`
  - Add `u chart`
  - Add `c chart`