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"author": "Charles Wang",
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"description": "# Spatial Uncertainty Research Framework\n\n![\"SURF\"](\"https://raw.githubusercontent.com/NHERI-SimCenter/SURF/master/docs/images/surf.png\")
\n\n## What is SURF?\n\nSURF is a Python package for performing spatial uncertainty analysis uisng random fields and machine leaning.\n\n## Install\n\n```sh\npip install pySURF\n```\n\n## Examples\n\nThe example below shows how to train a neural network on a data set, and use it to predict.\n\n```python\n from surf.NN import SpatialNeuralNet\n\n #---------------------------------------\n # 1. Prepare your data\n #---------------------------------------\n\n # ... see SURF.ET-AI.py \n\n #---------------------------------------\n # 2. Train the neural network\n #---------------------------------------\n \n nn = SpatialNeuralNet(rawData = data, numNei = 20)\n nn.build_model()\n nn.train()\n\n #---------------------------------------\n # 3. Predict\n #---------------------------------------\n\n unkown_point = [x, y] # define a point \n predicted = nn.predict(unkown_point) # predict\n\n```\n\nThe example below shows how to define a spatial model for a data set, and insert it into random field to predict.\n\n```python\n from surf.rf import SK\n import surf.spatialModel as m\n\n #---------------------------------------\n # 1. Prepare your data\n #---------------------------------------\n\n # ... see SURF.ET.py \n\n #---------------------------------------\n # 2. Define spatial model \n #---------------------------------------\n\n colLength = 0.1\n sill = np.var( data[:,2] )\n cov = m.cov( m.exponential, ( colLength, sill ) )\n\n #---------------------------------------\n # 3. Predict\n #---------------------------------------\n \n unkown_point = [x, y] # define a point \n predicted_mu, predicted_std = SK( data, cov, unkown_point, N=100 )\n```\n## Documentation\n\n[Online documentation](https://nheri-simcenter.github.io/SURF)\n\n## Application examples\n\n[BRAILS](https://github.com/NHERI-SimCenter/BRAILS)\n\n[Soft-Story Detection](https://github.com/charlesxwang/Soft-Story-Detection)\n\n## License\n\nSURF is distributed under the BSD 3-Clause license.\n\n## Acknowledgement\n\nThis material is based upon work supported by the National Science Foundation under Grant No. 1612843. Any opinions, findings, and conclusions or recommendations expressed in this material are those of the authors and do not necessarily reflect the views of the National Science Foundation.\n\n## Contact\n\nCharles Wang, NHERI SimCenter, University of California, Berkeley, c_w@berkeley.edu",
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