mwavepy.transmissionLine.genericTEM | index /home/alex/docs/python/path/mwavepy/transmissionLine/genericTEM.py

`general class for TEM transmission lines`

Modules
`` |  |
mwavepy.mathFunctions | numpy |  |

Classes
`` |  | __builtin__.object GenericTEM
class GenericTEM(__builtin__.object)
`` | `This is a general super-class for TEM transmission lines. The structure behind the methods dependencies is a results of the physics. a brief summary is given below. a TEM transmission line is defined by its: distributed Capacitance, C' distributed Inductance, I' distributed Resistance, R' distributed Conductance, G' from these the following quantities may be calculated, which are functions of angular frequency (w): distributed Impedance, Z'(w) = R' + jwI' distributed Admittance, Y'(w) = G' + jwC' from these we can get to properties which define their wave behavoir characteristic Impedance, Z0(w) = sqrt(Z'(w)/Y'(w)) [ohms] propagation Constant, gamma(w) = sqrt(Z'(w)*Y'(w)) and then finnally produce methods which we use electrical Length input Impedance relfection Coefficient`
| Methods defined here: __init__(self, distributed_capacitance, distributed_inductance, distributed_resistance, distributed_conductance) `constructor. takes: distributed_capacitance, C' distributed_inductance, I' distributed_resistance, R' distributed_conductance, G' fBand: a mwavepy.fb.frequencyBand object. this provides all the methods of the transmissionLine, with frequency info. this is solely for convinience, because frequently many calculations are done over a given range of frequencies.` characteristic_impedance(self, f) `The characteristic impedance at a given angular frequency. Z0(w) = sqrt(Z'(w)/Y'(w)) takes: f: frequency returns: Z0: characteristic impedance ohms` distributed_admittance(self, f) `distributed Admittance, Y'(w) = G' + jwC' takes: f: frequency [Hz]` distributed_impedance(self, f) `distributed Impedance, Z'(w) = R' + jwI' takes: f: frequency [Hz]` electrical_length(self, f, d, deg=False) propagation_constant(self, f) `the propagation constant gamma(w) = sqrt(Z'(w)*Y'(w)) takes: f: frequency [Hz] returns: gamma: possibly complex propagation constant, [jrad/m+]` Data descriptors defined here: __dict__ `dictionary for instance variables (if defined)` __weakref__ `list of weak references to the object (if defined)`

Functions
`` |  | array(...) `array(object, dtype=None, copy=True, order=None, subok=False, ndmin=True) 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 : {tuple of ints, int} 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 -------- numpy.zeros_like : Return an array of zeros with shape and type of input. numpy.ones_like : Return an array of ones with shape and type of input. numpy.empty_like : Return an empty array with shape and type of input. numpy.ones : Return a new array setting values to one. numpy.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')]) array([(0, 0), (0, 0)], dtype=[('x', '<i4'), ('y', '<i4')])`

Data
`` |  | INF = 9.9999999999999997e+98 ONE = 1.00000000000001 c = 299792458.0 cos = <ufunc 'cos'> epsilon_0 = 8.8541878176203892e-12 exp = <ufunc 'exp'> inf = inf log = <ufunc 'log'> mil = 2.5399999999999997e-05 mu_0 = 1.2566370614359173e-06 pi = 3.1415926535897931 sin = <ufunc 'sin'> sqrt = <ufunc 'sqrt'> tan = <ufunc 'tan'>