I am trying to rewrite a matlab code in python27. There is a matlab line as follows:
vector_C = vector_A > vector_B;
If I try to write this in python using numpy it will be the same, but the result will be an array of booleans instead of binaries. I want the result to be in binaries. Is there a way to make it return binary or should I convert manually each time? Is there a quick way of converting it? I am new to python. Thanks.
To convert a Boolean array a to an integer array, use the a. astype(int) method call. The single argument int specifies the desired data type of each array item. NumPy converts on a best-effort basis.
We can also use the Tilde operator (~) also known as bitwise negation operator in computing to invert the given array. It takes the number n as binary number and “flips” all 0 bits to 1 and 1 to 0 to obtain the complement binary number.
A boolean array can be created manually by using dtype=bool when creating the array. Values other than 0 , None , False or empty strings are considered True. Alternatively, numpy automatically creates a boolean array when comparisons are made between arrays and scalars or between arrays of the same shape.
invert() function is used to Compute the bit-wise Inversion of an array element-wise. It computes the bit-wise NOT of the underlying binary representation of the integers in the input arrays. For signed integer inputs, the two's complement is returned.
Even though vector_C
may have dtype=bool
, you can still do operations such as the following:
In [1]: vector_A = scipy.randn(4)
In [2]: vector_B = scipy.zeros(4)
In [3]: vector_A
Out[3]: array([ 0.12515902, -0.53244222, -0.67717936, -0.74164708])
In [4]: vector_B
Out[4]: array([ 0., 0., 0., 0.])
In [5]: vector_C = vector_A > vector_B
In [6]: vector_C
Out[6]: array([ True, False, False, False], dtype=bool)
In [7]: vector_C.sum()
Out[7]: 1
In [8]: vector_C.mean()
Out[8]: 0.25
In [9]: 3 - vector_C
Out[9]: array([2, 3, 3, 3])
So, in short, you probably don't have to do anything extra.
But if you must do a conversion, you may use astype
:
In [10]: vector_C.astype(int)
Out[10]: array([1, 0, 0, 0])
In [11]: vector_C.astype(float)
Out[11]: array([ 1., 0., 0., 0.])
You can force numpy to store the elements as integers. It treats 0 as false and 1 as true.
import numpy
vector_C = numpy.array( vector_A > vector_B, dtype=int) ;
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