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Calibration with Dakota

Build Status

Binder

Under development

About

This repository contains resources used in a clinic on using Dakota for calibration taught by @kbarnhart. If you have any questions, comments, or problems, please make an issue.

Options for running the examples.

  1. Use Binder.
  2. Use Hydroshare.
  3. Install the repo and Dakota yourself.

Install instructions

Make sure you had git and the Anaconda python distribution (recommended version 3.6 or above).

Step 1: Get Dakota and this repository

Option A: Full env with conda.

This option is only available on Linux and on MacOSX. It will not work on Windows.

(1) Open a terminal and navigate to a folder where you want this folder copied.

Then execute the following commands to download the repository, create the specified conda environment, and build/test the heat module. Note that you'll have to replace YOUR_OS_HERE with either osx or linux.

$ git clone https://github.com/kbarnhart/calibration_with_dakota_clinic.git
$ cd calibration_with_dakota_clinic
$ conda env create -f environment_YOUR_OS_HERE.yml
$ conda activate dakota_clinic
$ make install

If you've never made an environment with conda before, you might need to use source instead of conda in the last command. If that command fails, conda will give you an error message that is reasonably helpful. Basically, it will instruct you to run

$ conda init <NAME OF SHELL>

which will make the the conda activate dakota_clinic command will work.

If you want to test your install, type

pytest

This may take ~15 minutes (you are running all of the experiments we will do in the clinic and testing that they work).

Option B: Dakota Binary + conda env

Step 1: Install the Dakota binary for Windows and make a conda environment based on the file environment_everything_but_dakota.yml.

After downloading/installing the Dakota binary, open a terminal and do the following:

$ git clone https://github.com/kbarnhart/calibration_with_dakota_clinic.git
$ conda env create -f environment_everything_but_dakota.yml
$ conda activate dakota_clinic
$ make install

Step 2: Open a notebook.

Assuming that the results of pytest don't include any failures, open Jupyter Notebooks.

$ jupyter notebook

Click Clinic_Notes_and_Tutorial.ipynb.