SimCenter Forum

Hello,

I tried to run my data on designsafe, but got this error...
problems with the setup?

Thanks.

In the document there is an example showing how to use SURF to learn the pattern in a dataset, and use the learned knowledge to enhance the original dataset.

The example codes are as follows, but you can also run it in Golab notebook: [https://colab.research.google.com/github/NHERI-SimCenter/SURF/blob/master/examples/demo-NN.ipynb]

import os
import wget
import zipfile
import shutil
import csv
import numpy as np

import matplotlib.cm as cm
import matplotlib.pyplot as plt
import matplotlib.colors as colors

#---------------------------------------------------------------------------------------
# 0. Download data
#---------------------------------------------------------------------------------------

url = 'https://berkeley.box.com/shared/static/jbvyu0ng0zrt75il3ps1qygwellpr8c5.zip'
workDir = 'tmp'
fileName = 'AnchorageBuildings.zip'
print('Beginning file download with wget module')
wget.download(url, fileName)
print('\nFile downloaded. \n')

with zipfile.ZipFile(fileName, 'r') as zip_ref:
    zip_ref.extractall('tmp')

try:
    dest = shutil.move(fileName, workDir+'/')
except:
    os.remove(workDir+'/'+fileName)
    dest = shutil.move(fileName, workDir+'/')


#---------------------------------------------------------------------------------------
# 1. Prepare data
#---------------------------------------------------------------------------------------

BIMFileName = 'tmp/AnchorageBuildings.csv'

# read raw data
data = []
with open(BIMFileName, newline='') as csvfile:
    reader = csv.DictReader(csvfile)
    for row in reader:
        lat = float(row['Latitude'])
        lon = float(row['Longitude'])
        yearbuilt = int(float(row['Year Built']) )
        if lat>61.05 and lat<61.2 and lon>-150. and lon<-149.7:
            data.append([lon, lat, yearbuilt])

# data is a numpy matrix with columns: [x,y,value]
data = np.array(data)


#---------------------------------------------------------------------------------------
# 2. Train a neural network
#---------------------------------------------------------------------------------------
from surf.NN import SpatialNeuralNet


# define a spatial neural network
# numNei is the number of nearest neighbors to be considered
# writeTmpData is true so that temporary files are written down in ./tmp
nn = SpatialNeuralNet(rawData = data, numNei = 2, writeTmpData=True)
nn.build_model()
nn.train()


#---------------------------------------------------------------------------------------
# 3. Test the neural network
#---------------------------------------------------------------------------------------

nn.test()


We recently released a building inventory dataset generated by BRAILS.

It is formatted in HAZUS style and can be found in this link: https://github.com/NHERI-SimCenter/BRAILS/tree/master/docs/datarelease

You can always export your analysis configuration and share it with others so that they can repeat your analysis!

To export:
Go to menu and click "File", then click "Save as", then select a local location where you'll save the configuration file.
Give it a name (for example, analysis.json), then click "Save".

Now you can share it with people!