Signature

class pylayers.antprop.signature.Signature(sig)

Bases: pylayers.util.project.PyLayers, object

class Signature

seq : list of interaction point (edges (>0) or vertices (<0) [int] typ : list of interaction type 1-R 2-T 3-D [int] pa : tail point of interaction segment (2xN) ndarray pb : head point of interaction segment (2xN) ndarray pc : center point of interaction segment (2xN) ndarray

Methods Summary

backtrace(tx, rx, M)	backtrace given image, tx, and rx
ev(L)	evaluation of Signature
ev2(L)	evaluation of Signature
evf(L)	evaluation of Signature (fast version)
evtx(L, tx, rx)	evaluate transmitter
image(tx)	compute the tx’s images with respect to the signature segments
info()
show(L, tx, rx, **kwargs)	Parameters
sig2ray(L, pTx, pRx)	convert a signature to a 2D ray
unfold()	unfold a given signature

Methods Documentation

backtrace(tx, rx, M)

backtrace given image, tx, and rx

txndarray (2x1)

transmitter

rxndarray (2x1)

receiver

Mndarray (2xN)

N image points obtained using self.image method

isvalidbool

True if the backtrace ends successfully

Yndarray (2 x (N+2))

sequence of points corresponding to the seek ray

>>> import matplotlib.pyplot as plt
>>> import numpy as np
>>> from pylayers.gis.layout import *
>>> from pylayers.antprop.signature import *
>>> L = Layout('defstr.ini')
>>> s = Signature(seq)
>>> tx = np.array([760,1113])
>>> rx = np.array([762,1114])
>>> s.ev(L)
>>> M = s.image(tx)
>>> isvalid,Y = s.backtrace(tx,rx,M)

>>> fig,ax = L.showG('s',labels=1,aw=1,axes=1)
>>> l1 = ax.plot(tx[0],tx[1],'or')
>>> l2 = ax.plot(rx[0],rx[1],'og')
>>> l3 = ax.plot(M[0,:],M[1,:],'ob')
>>> l4 = ax.plot(Y[0,:],Y[1,:],'xk')
>>> ray = np.hstack((np.hstack((tx.reshape(2,1),Y)),rx.reshape(2,1)))
>>> l5 = ax.plot(ray[0,:],ray[1,:],color='#999999',alpha=0.6,linewidth=0.6)
>>> plt.show()

(Source code)

For mathematical details see :

@INPROCEEDINGS{6546704, author={Laaraiedh, Mohamed and Amiot, Nicolas and Uguen, Bernard}, booktitle={Antennas and Propagation (EuCAP), 2013 7th European Conference on}, title={Efficient ray tracing tool for UWB propagation and

localization modeling},

year={2013}, pages={2307-2311},}

ev(L)

evaluation of Signature

L : Layout

This function converts the sequence of interactions into numpy arrays which contains coordinates of segments extremities involved in the signature.

At that stage coordinates of extremities (tx and rx) is not known yet

members data

pa tail of segment (2xN) pb head of segment (2xN) pc the center of segment (2xN)

norm normal to the segment if segment in case the interaction is a point the normal is undefined and then set to 0.

ev2(L)

evaluation of Signature

L : Layout

This function converts the sequence of interactions into numpy arrays which contains coordinates of segments extremities involved in the signature. At that level the coordinates of extremities (tx and rx) is not known yet.

members data

pa tail of segment (2xN) pb head of segment (2xN) pc the center of segment (2xN)

norm normal to the segment if segment in case the interaction is a point the normal is undefined and then set to 0

evf(L)

evaluation of Signature (fast version)

L : Layout

This function converts the sequence of interactions into numpy arrays which contains coordinates of segments extremities involved in the signature.

members data

pa tail of segment (2xN) pb head of segment (2xN)

evtx(L, tx, rx)

evaluate transmitter

L : Layout tx : np.array (2xN) rx : np.array (2xM)

DEPRECATED

image(tx)

compute the tx’s images with respect to the signature segments

tx : numpy.ndarray

M : numpy.ndarray

info()

show(L, tx, rx, **kwargs)

L : Layout tx : rx : aw

sig2ray(L, pTx, pRx)

convert a signature to a 2D ray

L : Layout pTx : ndarray

2D transmitter position

pRxndarray

2D receiver position

Y : ndarray (2x(N+2))

Signature.image Signature.backtrace

unfold()

unfold a given signature

returns 2 np.ndarray of pta and phe “aligned” reflexion interactions are mirrored

pta : np.array phe : np.array