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What is the equivalent of Octave's j in NumPy? How can I use j in Python?
In Octave:
octave:1> j ans = 0 + 1i octave:1> j*pi/4 ans = 0.00000 + 0.78540i But in Python:
>>> import numpy as np >>> np.imag <function imag at 0x2368140> >>> np.imag(3) array(0) >>> np.imag(3,2) Traceback (most recent call last): File "<stdin>", line 1, in <module> TypeError: imag() takes exactly 1 argument (2 given) >>> np.imag(32) array(0) >>> >>> 0+np.imag(1) 1 
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Basic Definitions In Python, the symbol j is used to denote the imaginary unit. Furthermore, a coefficient before j is needed. To specify a complex number, one can also use the constructor complex . Python offers the built-in math package for basic processing of complex numbers.
Use the dtype Parameter to Store Imaginary Numbers in NumPy Arrays. Another method of initiating imaginary numbers in arrays is by specifying the dtype parameter in some numpy array functions. As we know, we can use the numpy. zeros() and numpy.
Why does Python uses symbol j to represent imaginary part of a complex number instead of the conventional i ? In Electronics 'i' is used to represent current and hence they use 'j' to represent iota. Python adheres to this same convention.
Therefore the equivalent for NumPy in Java would simply be the standard Java math module This is just not true. Java Math class doesn't provide anything close to NumPy. There is no efficient multidimensional arrays, linear algebra, special functions etc. thanks! I don't think there is a single Java library that covers so much functionality.
Masked array operations Mathematical functions Matrix library ( numpy.matlib ) Miscellaneous routines Padding Arrays Polynomials Random sampling ( numpy.random ) Set routines Sorting, searching, and counting Statistics Test Support ( numpy.testing )
numpy.not_equal Masked array operations Mathematical functions Matrix library ( numpy.matlib ) Miscellaneous routines Padding Arrays Polynomials Random sampling ( numpy.random ) Set routines Sorting, searching, and counting Statistics
True if two arrays have the same shape and elements, False otherwise. Parameters a1, a2array_like Input arrays. equal_nanbool Whether to compare NaN’s as equal. If the dtype of a1 and a2 is complex, values will be considered equal if either the real or the imaginary component of a given value is nan.
In Python, 1j or 0+1j is a literal of complex type. You can broadcast that into an array using expressions, for example
In [17]: 1j * np.arange(5) Out[17]: array([ 0.+0.j, 0.+1.j, 0.+2.j, 0.+3.j, 0.+4.j]) Create an array from literals:
In [18]: np.array([1j]) Out[18]: array([ 0.+1.j]) Note that what Michael9 posted creates a complex, not a complex array:
In [21]: np.complex(0,1) Out[21]: 1j In [22]: type(_) Out[22]: complex You can create one if needed or use 1j which instance of complex class
>>> 1j #complex object 1j >>> type(1j) <class 'complex'> >>> j = np.complex(0,1) #create complex number >>> j 1j If you love us? You can donate to us via Paypal or buy me a coffee so we can maintain and grow! Thank you!
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