An example

[charles]

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]

Code: [Select]

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()