Question

I have a numpy ndarray that I made using numpy.loadtxt. I want to pull an entire row from it based on a condition in the third column. Something like : if array[2][i] is meeting my conditions, then get array[0][i] and array [1][i] as well. I’m new to python, and all of the numpy features, so I’m looking for the best way to do this. Ideally, I’d like to pull 2 rows at a time, but I wont always have an even number of rows, so I imagine that is a problem

import numpy as np

'''
Created on Jan 27, 2013

@author:
'''
class Volume:

    f ='/Users/Documents/workspace/findMinMax/crapc.txt'
    m = np.loadtxt(f, unpack=True, usecols=(1,2,3), ndmin = 2)


    maxZ = max(m[2])
    minZ = min(m[2])
    print("Maximum Z value: " + str(maxZ))
    print("Minimum Z value: " + str(minZ))

    zIncrement = .5
    steps = maxZ/zIncrement
    currentStep = .5
    b = []

    for i in m[2]:#here is my problem
         while currentStep < steps: 
            if m[2][i] < currentStep and m[2][i] > currentStep - zIncrement:
                b.append(m[2][i]) 
            if len(b) < 2:
                currentStep + zIncrement

                print(b)

Here is some code that I did in java that is the general idea of what I want:

while( e < a.length - 1){
for(int i = 0; i < a.length - 1; i++){
        if(a[i][2] < stepSize && a[i][2] > stepSize - 2){

            x.add(a[i][0]);
            y.add(a[i][1]);
            z.add(a[i][2]);
        }
        if(x.size()  < 1){
            stepSize += 1;
        }
    }
}

Answer 1

First of all, you probably don’t want to put your code in that class definition…

import numpy as np


def main():
    m = np.random.random((3, 4))
    mask = (m[2] > 0.5) & (m[2] < 0.8)  # put your conditions here
                                        # instead of 0.5 and 0.8 you can use
                                        # an array if you like
    m[:, mask]

if __name__ == '__main__':
    main()

mask is a boolean array, m[:, mask] is the array you want

m[2] is the third row of m. If you type m[2] + 2 you get a new array with the old values + 2. m[2] > 0.5 creates an array with boolean values. It is best to try this stuff out with ipython (www.ipython.org)

In the expression m[:, mask] the : means “take all rows”, mask describes which columns should be included.

Update

Next try 🙂

for i in range(0, len(m), 2):
    two_rows = m[i:i+2]