mwavepy | index /home/alex/docs/python/path/mwavepy/__init__.py

`mwavepy is an object-oriented approach to microwave engineering,
implemented in the Python programming language. It provides a set of
objects and features which can be used to build powerful solutions to
specific problems. mwavepy's abilities are; touchstone file manipulation,
calibration, VNA data acquisition, circuit design and much more.
This is the main module file for mwavepy. it simply imports classes and
methods. It does this in two ways; import all into the current namespace,
and import modules themselves for coherent structured referencing`

Package Contents
`` |  |
beta (package) calibration (package) convenience | frequency mathFunctions media (package) | network plotting testCases (package) | tlineFunctions touchstone virtualInstruments (package)

Functions
`` |  | array(...) `array(object, dtype=None, copy=True, order=None, subok=False, ndmin=0) Create an array. Parameters ---------- object : array_like An array, any object exposing the array interface, an object whose __array__ method returns an array, or any (nested) sequence. dtype : data-type, optional The desired data-type for the array. If not given, then the type will be determined as the minimum type required to hold the objects in the sequence. This argument can only be used to 'upcast' the array. For downcasting, use the .astype(t) method. copy : bool, optional If true (default), then the object is copied. Otherwise, a copy will only be made if __array__ returns a copy, if obj is a nested sequence, or if a copy is needed to satisfy any of the other requirements (`dtype`, `order`, etc.). order : {'C', 'F', 'A'}, optional Specify the order of the array. If order is 'C' (default), then the array will be in C-contiguous order (last-index varies the fastest). If order is 'F', then the returned array will be in Fortran-contiguous order (first-index varies the fastest). If order is 'A', then the returned array may be in any order (either C-, Fortran-contiguous, or even discontiguous). subok : bool, optional If True, then sub-classes will be passed-through, otherwise the returned array will be forced to be a base-class array (default). ndmin : int, optional Specifies the minimum number of dimensions that the resulting array should have. Ones will be pre-pended to the shape as needed to meet this requirement. Examples -------- >>> np.array([1, 2, 3]) array([1, 2, 3]) Upcasting: >>> np.array([1, 2, 3.0]) array([ 1., 2., 3.]) More than one dimension: >>> np.array([[1, 2], [3, 4]]) array([[1, 2], [3, 4]]) Minimum dimensions 2: >>> np.array([1, 2, 3], ndmin=2) array([[1, 2, 3]]) Type provided: >>> np.array([1, 2, 3], dtype=complex) array([ 1.+0.j, 2.+0.j, 3.+0.j]) Data-type consisting of more than one element: >>> x = np.array([(1,2),(3,4)],dtype=[('a','<i4'),('b','<i4')]) >>> x['a'] array([1, 3]) Creating an array from sub-classes: >>> np.array(np.mat('1 2; 3 4')) array([[1, 2], [3, 4]]) >>> np.array(np.mat('1 2; 3 4'), subok=True) matrix([[1, 2], [3, 4]])` zeros(...) `zeros(shape, dtype=float, order='C') Return a new array of given shape and type, filled with zeros. Parameters ---------- shape : int or sequence of ints Shape of the new array, e.g., ``(2, 3)`` or ``2``. dtype : data-type, optional The desired data-type for the array, e.g., `numpy.int8`. Default is `numpy.float64`. order : {'C', 'F'}, optional Whether to store multidimensional data in C- or Fortran-contiguous (row- or column-wise) order in memory. Returns ------- out : ndarray Array of zeros with the given shape, dtype, and order. See Also -------- zeros_like : Return an array of zeros with shape and type of input. ones_like : Return an array of ones with shape and type of input. empty_like : Return an empty array with shape and type of input. ones : Return a new array setting values to one. empty : Return a new uninitialized array. Examples -------- >>> np.zeros(5) array([ 0., 0., 0., 0., 0.]) >>> np.zeros((5,), dtype=numpy.int) array([0, 0, 0, 0, 0]) >>> np.zeros((2, 1)) array([[ 0.], [ 0.]]) >>> s = (2,2) >>> np.zeros(s) array([[ 0., 0.], [ 0., 0.]]) >>> np.zeros((2,), dtype=[('x', 'i4'), ('y', 'i4')]) # custom dtype array([(0, 0), (0, 0)], dtype=[('x', '<i4'), ('y', '<i4')])`

Data
`` |  | ALMOST_ZER0 = 1e-06 INF = 1e+99 ONE = 1.00000000000001 all = ['vna', 'stages', 'generalSocketReader'] cos = <ufunc 'cos'> exp = <ufunc 'exp'> f_wr1 = <mwavepy.frequency.Frequency object> f_wr10 = <mwavepy.frequency.Frequency object> f_wr1p5 = <mwavepy.frequency.Frequency object> f_wr2p2 = <mwavepy.frequency.Frequency object> f_wr3 = <mwavepy.frequency.Frequency object> inf = inf log = <ufunc 'log'> mil = 2.5399999999999997e-05 mu_0 = 1.2566370614359173e-06 pi = 3.141592653589793 sin = <ufunc 'sin'> sqrt = <ufunc 'sqrt'> tan = <ufunc 'tan'> wr1 = Rectangular Waveguide Media. 750-1100 GHz. 201 points a= 2.54e-04m, b= 1.27e-04m wr10 = Rectangular Waveguide Media. 75-110 GHz. 201 points a= 2.54e-03m, b= 1.27e-03m wr1p5 = Rectangular Waveguide Media. 500-750 GHz. 201 points a= 3.81e-04m, b= 1.90e-04m wr2p2 = Rectangular Waveguide Media. 330-500 GHz. 201 points a= 5.59e-04m, b= 2.79e-04m wr3 = Rectangular Waveguide Media. 220-325 GHz. 201 points a= 7.62e-04m, b= 3.81e-04m