Brett,
i tried a different way to solve the problem, using :
#############
import os
fpath = '/Users/sasha/py/'
input_fp = open( os.path.join(fpath, 'BE3730072600WC20050817.txt'), 'r' )
input_file = input_fp.readlines()
N = 234560.94503118
S = 234482.56929822
E = 921336.53116178
W = 921185.3779625
xL = []
yL = []
zL = []
for index, line in enumerate( input_file ):
if index == 0:
print 'skipping header line...'
else:
x, y, z = line.split(',')
xL.append( float(x) * 0.3048006096012 )
yL.append( float(y) * 0.3048006096012 )
zL.append( float(z) * 0.3048006096012 )
xLr = []
yLr = []
zLr = []
for coords in zip(xL, yL, zL):
if W < coords[0] < E and S < coords[1] < N:
xLr.append( coords[0] )
yLr.append( coords[1] )
zLr.append( coords[2] )
elements = len(xLr)
minZ = min(zLr)
maxZ = max(zLr)
############
using the same input file i posted early,
it give me 966 elements
instead of 158734 elements gived by your "MASK" example
the input file contains 158734 elements, this means the mask code :
> mask |= mydata[:,0] < E
> mask |= mydata[:,0] > W
> mask |= mydata[:,1] < N
> mask |= mydata[:,1] > S
is not working as aspected
have you hints on how to get working the "MASK" code ?
as it is now it pick all the points in the "mydata" array.
thanks!
Massimo.
Il giorno 11/set/2010, alle ore 16.19, Brett Olsen ha scritto:
> On Sat, Sep 11, 2010 at 7:45 AM, Massimo Di Stefano
> <massimodisa...@gmail.com> 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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