numpy.argmax(a, axis=None, out=None) [source] Returns the indices of the maximum values along an axis. Parameters: a : array_like Input array. axis : int, optional By default, the index is into the flattened array, otherwise along the specified axis. out : array, optional If provided, the result will be inserted into this array. It should be of the appropriate shape and dtype. Returns: index_array : ndarray of ints Array of indices into the array. It has the same shape as a.shape

numpy.arctanh(x[, out]) = Inverse hyperbolic tangent element-wise. Parameters: x : array_like Input array. Returns: out : ndarray Array of the same shape as x. See also emath.arctanh Notes arctanh is a multivalued function: for each x there are infinitely many numbers z such that tanh(z) = x. The convention is to return the z whose imaginary part lies in [-pi/2, pi/2]. For real-valued input data types, arctanh always returns real output. For each value that cannot be expressed as

numpy.arctan2(x1, x2[, out]) = Element-wise arc tangent of x1/x2 choosing the quadrant correctly. The quadrant (i.e., branch) is chosen so that arctan2(x1, x2) is the signed angle in radians between the ray ending at the origin and passing through the point (1,0), and the ray ending at the origin and passing through the point (x2, x1). (Note the role reversal: the ?y-coordinate? is the first function parameter, the ?x-coordinate? is the second.) By IEEE convention, this function is defined

numpy.arctan(x[, out]) = Trigonometric inverse tangent, element-wise. The inverse of tan, so that if y = tan(x) then x = arctan(y). Parameters: x : array_like Input values. arctan is applied to each element of x. Returns: out : ndarray Out has the same shape as x. Its real part is in [-pi/2, pi/2] (arctan(+/-inf) returns +/-pi/2). It is a scalar if x is a scalar. See also arctan2 The ?four quadrant? arctan of the angle formed by (x, y) and the positive x-axis. angle Argument

numpy.arcsinh(x[, out]) = Inverse hyperbolic sine element-wise. Parameters: x : array_like Input array. out : ndarray, optional Array into which the output is placed. Its type is preserved and it must be of the right shape to hold the output. See doc.ufuncs. Returns: out : ndarray Array of of the same shape as x. Notes arcsinh is a multivalued function: for each x there are infinitely many numbers z such that sinh(z) = x. The convention is to return the z whose imaginary part lie

numpy.arcsin(x[, out]) = Inverse sine, element-wise. Parameters: x : array_like y-coordinate on the unit circle. out : ndarray, optional Array of the same shape as x, in which to store the results. See doc.ufuncs (Section ?Output arguments?) for more details. Returns: angle : ndarray The inverse sine of each element in x, in radians and in the closed interval [-pi/2, pi/2]. If x is a scalar, a scalar is returned, otherwise an array. See also sin, cos, arccos, tan, arctan, arctan

numpy.arccosh(x[, out]) = Inverse hyperbolic cosine, element-wise. Parameters: x : array_like Input array. out : ndarray, optional Array of the same shape as x, to store results in. See doc.ufuncs (Section ?Output arguments?) for details. Returns: arccosh : ndarray Array of the same shape as x. See also cosh, arcsinh, sinh, arctanh, tanh Notes arccosh is a multivalued function: for each x there are infinitely many numbers z such that cosh(z) = x. The convention is to return the

numpy.arccos(x[, out]) = Trigonometric inverse cosine, element-wise. The inverse of cos so that, if y = cos(x), then x = arccos(y). Parameters: x : array_like x-coordinate on the unit circle. For real arguments, the domain is [-1, 1]. out : ndarray, optional Array of the same shape as a, to store results in. See doc.ufuncs (Section ?Output arguments?) for more details. Returns: angle : ndarray The angle of the ray intersecting the unit circle at the given x-coordinate in radians [0

numpy.arange([start, ]stop, [step, ]dtype=None) Return evenly spaced values within a given interval. Values are generated within the half-open interval [start, stop) (in other words, the interval including start but excluding stop). For integer arguments the function is equivalent to the Python built-in range function, but returns an ndarray rather than a list. When using a non-integer step, such as 0.1, the results will often not be consistent. It is better to use linspace for these cases.

numpy.apply_over_axes(func, a, axes) [source] Apply a function repeatedly over multiple axes. func is called as res = func(a, axis), where axis is the first element of axes. The result res of the function call must have either the same dimensions as a or one less dimension. If res has one less dimension than a, a dimension is inserted before axis. The call to func is then repeated for each axis in axes, with res as the first argument. Parameters: func : function This function must take tw