To integrate along the given axis using the composite trapezoidal rule, use the numpy.trapz() method. If x is provided, the integration happens in sequence along its elements - they are not sorted. The method returns the definite integral of ‘y’ = n-dimensional array as approximated along a single axis by ... Read More

To integrate along the given axis using the composite trapezoidal rule, use the numpy.trapz() method. If x is provided, the integration happens in sequence along its elements - they are not sorted. The method returns the definite integral of ‘y’ = n-dimensional array as approximated along a single axis by ... Read More

To compute the cross product of two vectors, use the numpy.cross() method in Python Numpy. The method returns c, the Vector cross product(s). The 1st parameter is a, the components of the first vector(s). The 2nd parameter is b, the components of the second vector(s). The 3rd parameter is axisa, ... Read More

To return the scaled companion matrix of a 1-D array of polynomial coefficients, return the hermite.hermcompanion() method in Python Numpy. The basis polynomials are scaled so that the companion matrix is symmetric when c is an Hermite basis polynomial. This provides better eigenvalue estimates than the unscaled case and for ... Read More

To generate a pseudo Vandermonde matrix of the Hermite polynomial and x, y, z sample points, use the hermite.hermvander3d() in Python Numpy. The method returns the pseudo-Vandermonde matrix. The parameter, x, y, z are arrays of point coordinates, all of the same shape. The dtypes will be converted to either ... Read More

The gradient is computed using second order accurate central differences in the interior points and either first or second order accurate one-sides (forward or backwards) differences at the boundaries. The returned gradient hence has the same shape as the input array. The 1st parameter, f is an Ndimensional array containing ... Read More

To calculate the n-th discrete difference, use the numpy.diff() method. The first difference is given by out[i] = a[i+1] - a[i] along the given axis, higher differences are calculated by using diff recursively. The 1st parameter is the input array. The 2nd parameter is n, i.e. the number of times ... Read More

To return the real part of the complex argument, use the numpy.real() method. The method returns the real component of the complex argument. If val is real, the type of val is used for the output. If val has complex elements, the returned type is float. The 1st parameter, val ... Read More

To compute the condition number of a matrix in linear algebra, use the numpy.linalg.cond() method in Python. This method is capable of returning the condition number using one of seven different norms, depending on the value of p. Returns the condition number of the matrix. May be infinite.The condition number ... Read More

To return the Norm of the matrix or vector in Linear Algebra, use the LA.norm() method in Python Numpy. The 1st parameter, x is an input array. If axis is None, x must be 1-D or 2-D, unless ord is None. If both axis and ord are None, the 2-norm ... Read More