Trending for Process Control Data Analysis
Project description
proc_plot
Quick interactive trending of time series data for process control data analysis.
Usage
To start, read process data into a pandas Dataframe. It will work best if the column names in the dataframe are DCS tagnames e.g. FIC101.SP. Next, let proc_plot know which dataframe to use and call the show() function to show the main window.
%matplotlib qt
import matplotlib.pyplot as plt
import proc_plot
import pandas
df = pandas.read_excel('data.xlsx',parse_dates=True)
proc_plot.set_dataframe(df)
proc_plot.show()
Grouping Rules
proc_plot uses regular expression rules to group tags that should be plotted on the same axis.
See help(proc_plot.add_grouping_rule)
for examples if you want to customise grouping rules.
Since v1.4, the function load_grouping_template() makes it easy to load preconfigured grouping rules for different kinds of data. v1.4 includes templates 'ProfCon' and 'DMC'.
%matplotlib magic
The intended use of the tool is to call it from a jupyter notebook. The way the qt gui loop runs in jupyter is tricky. proc_plot checks the current backend (plt.get_backend) to tell if the notebook is using %matplotlib qt
or %matplotlib notebook
.
It is possible to switch the backend after the %matplotlib magic, if the backend is switched before proc_plot is imported then proc_plot could break the qt gui loop. I recommend using %matplotlib qt and then switching the backend with plt.switch_backend('nbagg')
after importing proc_plot if you want interactive notebook plots.
Show Me
The tool has a button "Show Me" that will show you python code to generate the current trend. The code assumes your dataframe is called df
and that you imported matplotlib.pyplot as plt
.
Project details
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