Metadata-Version: 2.1
Name: se360demo
Version: 0.1.0
Summary:  Demo code for SE360 
Home-page: https://github.com/statease/se360-python-demo
Author: Hank Anderson
Author-email: hank@statease.com
License: Other/Proprietary License
Description: # 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.
        
Platform: UNKNOWN
Classifier: Development Status :: 4 - Beta
Classifier: Intended Audience :: Developers
Classifier: Intended Audience :: Manufacturing
Classifier: Intended Audience :: End Users/Desktop
Classifier: Intended Audience :: Science/Research
Classifier: Operating System :: OS Independent
Classifier: Natural Language :: English
Classifier: License :: Other/Proprietary License
Classifier: Programming Language :: Python
Classifier: Programming Language :: Python :: 3
Classifier: Topic :: Scientific/Engineering
Classifier: Topic :: Scientific/Engineering :: Mathematics
Classifier: Topic :: Scientific/Engineering :: Information Analysis
Description-Content-Type: text/markdown
