[Numpy-discussion] scan array to extract min-max values (with if condition)

[Massimo Di Stefano]

That's awesome!

masked array are defintley what i need!

thanks to point my attention on it!

best regards,

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
> <massimodisasha at 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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