Signatures

class pylayers.antprop.signature.Signatures(L, source, target, cutoff=3, threshold=0.6)

Bases: pylayers.util.project.PyLayers, dict

set of Signature given 2 Gt cycle (convex) indices

L : gis.Layout source : int

source convex cycle

targetint

target convex cycle

Methods Summary

backtrace(tx, rx, M)	backtracing betwen tx and rx
calsig(G[, dia, cutoff])	calculates signature
check()	check signature
compl(lint, L)	completion from lint
exist(seq)	verifies if seq exists in signatures
image([tx])	Warning :
image2(tx)	determine rays from images (second implementation)
info()
load([filename])	load signatures
loadh5([filename])	load signatures hdf5 format
num()	determine the number of signatures
plot_cones(L[, i, s, fig, ax, figsize])	display cones of an unfolded signature
pltunfold(L[, i, s])
rays([ptx, prx])	from signatures dict to 2D rays
raysv([ptx, prx])	transform dict of signatures into 2D rays - default vectorized version
run(**kwargs)	evaluate signatures between cycle of tx and cycle of rx
save()	save signatures
saveh5()	save signatures in hdf5 format
show(L, **kwargs)	plot signatures in the simulated environment
showi([uni, us])	interactive show
sig2inter(L[, lsi])	convert signature to corresponding list of interactions in Gi
sig2prob(L, lsi)	get signatures probability
sp(G, source, target[, cutoff])	algorithm for signature determination
unfold(L[, i, s])	unfold a given signature

Methods Documentation

backtrace(tx, rx, M)

backtracing betwen tx and rx

txndarray

position of tx (2,)

rxndarray

position of tx (2,)

Mdict

position of intermediate points obtained from self.image()

rayp : dict key = number_of_interactions value =ndarray positions of interactions for creating rays

dictionnary of intermediate coordinated : key = number_of_interactions value = nd array M with shape : (2,nb_signatures,nb_interactions) and 2 represent x and y coordinates

pylayers.antprop.signature.image

calsig(G, dia={}, cutoff=None)

calculates signature

G : graph dia : dictionnary of interactions cutoff : integer

check()

check signature

OK : np.array KO : np.array

compl(lint, L)

completion from lint

lintlist

list of interactions

>>> Si.compl([(6220,3),(6262,3),(6241,3)],DL.L)

exist(seq)

verifies if seq exists in signatures

seqlist of tuple

[(2,2),(5,3),(7,2)]

1 : Diffraction 2 : Reflexion 3 : Diffraction

>>> DL=DLink()
>>> DL.eval()
>>> seq = [(2,3)] # transmission through segment 2
>>> DL.Si.exist(seq)

image(tx=array([ 2.7, 12.5]))

Warning :

txndarray

position of tx (2,)

M : dictionnary

dictionnary of intermediate coordinates key = number_of_interactions value = nd array M with shape : (2,nb_signatures,nb_interactions) and 2 represent x and y coordinates

image2(tx)

determine rays from images (second implementation)

tx : point

info()

load(filename=[])

load signatures

loadh5(filename=[])

load signatures hdf5 format

num()

determine the number of signatures

plot_cones(L, i=0, s=0, fig=[], ax=[], figsize=(10, 10))

display cones of an unfolded signature

L : Layout i : int

the interaction block

sint

the signature number in the block

fig : ax : figsize :

pltunfold(L, i=0, s=0)

rays(ptx=0, prx=1)

from signatures dict to 2D rays

ptxnumpy.array or int

Tx coordinates is the center of gravity of the cycle number if type(tx)=int

prxnumpy.array or int

Rx coordinates is the center of gravity of the cycle number if sigtype(rx)=int

rays : Rays

In the same time the signature of the ray is stored in the Rays object

Todo : Find the best memory implementation

Signature.sig2ray Signature.raysv

raysv(ptx=0, prx=1)

transform dict of signatures into 2D rays - default vectorized version

ptxnumpy.array or int

Tx coordinates is the center of gravity of the cycle ptx if type(ptx)=int

prxnumpy.array or int

Rx coordinates is the center of gravity of the cycle prx if type(prx)=int

rays : Rays

This is a vectorized version of Signatures.rays. This implementation takes advantage of the np.ndarray and calculates images and backtrace for block of signatures. A block of signatures gathers all signatures with the same number of interactions.

For mathematical details see :

@phdthesis{amiot:tel-00971809,

TITLE = {{Design of simulation platform joigning site specific radio propagation and human mobility for localization applications}}, AUTHOR = {Amiot, Nicolas}, URL = {https://tel.archives-ouvertes.fr/tel-00971809}, NUMBER = {2013REN1S125}, SCHOOL = {{Universit{‘e} Rennes 1}}, YEAR = {2013}, MONTH = Dec, TYPE = {Theses}, HAL_ID = {tel-00971809}, HAL_VERSION = {v1},

}

Signatures.image Signatures.backtrace

run(**kwargs)

evaluate signatures between cycle of tx and cycle of rx

cutoffint

limit the exploration of all_simple_path

btboolean

backtrace (allow to visit already visited nodes in simple path algorithm)

progressboolean

display the time passed in the loop

diffractionboolean

activate diffraction

thresholdfloat

for reducing calculation time

animations : boolean nD : int

maximum number of diffraction

nRint

maximum number of reflection

nTint

maximum number of transmission

pylayers.simul.link.Dlink.eval

save()

save signatures

saveh5()

save signatures in hdf5 format

show(L, **kwargs)

plot signatures in the simulated environment

L : Layout i : list or -1 (default = all groups)

list of interaction group numbers

slist or -1 (default = all sig)

list of indices of signature in interaction group

ctx : cycle of tx (optional) crx : cycle of rx (optional) graph : type of graph to be displayed color : string alphasig : float widthsig : float colsig : string ms : int ctx : int crx :int

showi(uni=0, us=0)

interactive show

press n to visit signatures sequentially

uni : index of interaction dictionnary keys us : signature index

sig2inter(L, lsi=[])

convert signature to corresponding list of interactions in Gi

L : Layout lsi : nd.array

signature (2xnb_sig,sig_length)

>>> lsi = DL.Si[3]
>>> DL.Si.sig2inter(DL.L,lsi)

sig2prob(L, lsi)

get signatures probability

L : Layout lsi : nd.array

signature (2xnb_sig,sig_length)

tlprobalist (nb_sig,sig_length-2)

output proba of each triplet of interaction

sp(G, source, target, cutoff=None)

algorithm for signature determination

G : Graph source : tuple or int target : tuple or int cutoff : int

pylayers.antprop.signature.run3

unfold(L, i=0, s=0)

unfold a given signature

return 2 np.ndarray of pta and phe “aligned” (reflexion interaction are mirrored)

L : Layout i : int

the interaction block

sint

the signature number in the block

pta,phe

Signature.unfold