On Sat, Sep 11, 2010 at 7:45 AM, Massimo Di Stefano
<[email protected]> wrote:
> Hello All,
>
> i need to extract data from an array, that are inside a
> rectangle area defined as :
>
> N, S, E, W = 234560.94503118, 234482.56929822, 921336.53116178, 921185.3779625
>
> the data are in a csv (comma delimited text file, with 3 columns X,Y,Z)
>
> #X,Y,Z
> 3020081.5500,769999.3100,0.0300
> 3020086.2000,769991.6500,0.4600
> 3020099.6600,769996.2700,0.9000
> ...
> ...
>
> i read it using " numpy.loadtxt "
>
> data :
>
> http://www.geofemengineering.it/data/csv.txt 5,3 mb (158735 rows)
>
> to extract data that are inside the boundy-box area (N, S, E, W) i'm using a
> loop
> inside a function like :
>
> import numpy as np
>
> def getMinMaxBB(data, N, S, E, W):
> mydata = data * 0.3048006096012
> for i in range(len(mydata)):
> if mydata[i,0] < E or mydata[i,0] > W or mydata[i,1] < N or
> mydata[i,1] > S :
> if i == 0:
> newdata =
> np.array((mydata[i,0],mydata[i,1],mydata[i,2]), float)
> else :
> newdata = np.vstack((newdata,(mydata[i,0],
> mydata[i,1], mydata[i,2])))
> results = {}
> results['Max_Z'] = newdata.max(0)[2]
> results['Min_Z'] = newdata.min(0)[2]
> results['Num_P'] = len(newdata)
> return results
>
>
> N, S, E, W = 234560.94503118, 234482.56929822, 921336.53116178, 921185.3779625
> data = '/Users/sasha/csv.txt'
> mydata = np.loadtxt(data, comments='#', delimiter=',')
> out = getMinMaxBB(mydata, N, S, E, W)
>
> print out
Use boolean arrays to index the parts of your array that you want to look at:
def newGetMinMax(data, N, S, E, W):
mydata = data * 0.3048006096012
mask = np.zeros(mydata.shape[0], dtype=bool)
mask |= mydata[:,0] < E
mask |= mydata[:,0] > W
mask |= mydata[:,1] < N
mask |= mydata[:,1] > S
results = {}
results['Max_Z'] = mydata[mask,2].max()
results['Min_Z'] = mydata[mask,2].min()
results['Num_P'] = mask.sum()
return results
This runs about 5000 times faster on my machine.
Brett
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