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Description:
se360demo 0.1.2
Stat-Ease 360 and Python demo
This is a collection of a few Python programs that interact with Stat-Ease 360,
initially demonstrated at the 2021 DOE Summit by Hank Anderson. The slides
from that talk are included in this repository as well.
In order to use the data from this repository, you should install the se360demo
Python package by running pip install se360demo.
Multi-response Graph
This uses the examples\multiresponse-graph.py script. Open Stat-Ease 360 and
load this script from the Script Window, then click File -> Run.
It will retrieve the two Analysis objects from SE360 and make predictions for
both of them while varying the A factor. These predictions are plotted on the
same Matplotlib plot.
Streamflow
This example uses the examples\streamflow.py script.
It demonstrates fetching data from a cloud API and inserting it into
Stat-Ease 360. It has a flag in the script to toggle between live data and
a csv file.
The data retrieval and manipulation code are in se360demo\__init__.py.
To use the live data you'll need to set an environment variable called
NOAA_TOKEN with a token generated from https://www.ncdc.noaa.gov/cdo-web/token.
Delivery Time
This example uses the examples\delivery-time.py script.
This is the Delivery Time example from chapter 11.2.3 of Montgomery, Peck and
Vining (2012) on "data splitting" or cross validation. The data are first
segmented and evaluated according to the textbook. The data are then split into
4 segments for a k-fold cross validation. Each split is evaluated and the
results are plotted in a bar chart.
Simulated Cross Validation
This example uses the examples\sim-cv.py script.
This uses two data sets that both have simulated responses. The script compares
two analyses of each response using cross validation. One analysis is done using
Ordinary Least Squares and the other using a Gaussian Process Model.
Streamflow Cross Validation
This example uses the examples\streamflow-cv.py script.
This does a time-based cross validation on the streamflow data. It divides the
data up into years for one cross validation, then does another cross validation
based on the month of the year. The results are output to the console.
License
For personal and professional use. You cannot resell or redistribute these repositories in their original state.
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