def picture(method, SS, flag, vari, Prx, sensorlocations): #,sensorlocations
area_size = myglobals.area_size #[10, 5]
xrange = [0, area_size[0]]
yrange = [0, area_size[1]]
Range = np.array([xrange[0], xrange[1], yrange[0], yrange[1]])
PixelRange = coord2pixel(Range) + [1, 0, 1, 0]
Source_loc = myglobals.loc_source #[3, 4]
Ptx = myglobals.Ptx #-10
#MM = MM
##
# Locations = np.array(
# [[0.5, 4], [3, 5], [7, 2.5], [1, 0.5], [8.5, 0.8], [3, 1.5], [4, 1], [5, 2], [6.2, 1.1], [2, 1.9], [2.5, 2.3],
# [3, 3], [7.5, 3.5], [3.5, 2.75], [0.6, 1.5], [1.25, 3.1], [7.1, 0.3], [8.1, 3.1],
# [4, 3.8], [2, 4.5], [5, 3.1], [6.1, 3.5], [6.2, 4.3], [8, 4.5], [2, 1]])
# for 50 sensors test
# Locations=np.array(
# [[0.67364638, 2.28947991],[9.93341331, 0.95542107],[1.82242923, 1.53087528],[1.04751652, 3.05047091],[3.9480761, 3.5022931 ],
# [6.1083599, 4.75512156],[4.97983297, 0.52770699],[5.32639046, 3.55030126],[6.59182669, 0.51394635],[7.25343255, 4.88115543],
# [3.96248924, 4.6862628 ],[7.52347415, 3.73200227],[4.53042301, 1.61072596],[6.39027678, 2.19262825],[0.199442, 3.7110518 ],
# [3.28389192, 3.423724 ],[9.5403416, 0.25601425],[0.81477858, 0.98104154],[8.34015388, 4.47387084],[8.2669614, 0.0783419 ],
# [9.7288704, 3.7857175 ],[5.62445041, 4.08035627],[5.67375543, 0.66634032],[6.02209598, 2.56838373],[6.78476374, 0.66731677],
# [0.53226241, 0.64590897],[9.73884966, 1.92304957],[3.10276626, 3.4410621 ],[3.58134045, 2.09529237],[7.75843324, 1.37180076],
# [3.22643465, 2.62245479],[2.70272802, 2.81852081],[1.46448928, 4.68585227],[3.14660896, 2.38422047],[8.67692837, 4.99861914],
# [0.0192289, 3.22337307],[7.1758682, 4.05264881],[7.66234889, 0.1793061 ],[3.98083337, 4.56559927],[4.83294335, 0.25021726],
# [3.9727205, 1.28927461],[6.36246273, 0.01184955],[9.20024388, 3.63732518],[3.91988766, 0.23612493],[2.10838329, 0.85399577],
# [7.46888132, 0.85088804],[3.5466248, 3.60073434],[8.05463967, 1.54125835],[4.49369649, 1.3254797 ],[5.47086327, 3.07157279]])
Locations = sensorlocations
# #####
# plt.figure(11)
# plt.plot(2 * Locations[:, 1].T * myglobals.PixelResolution, 2 * Locations[:, 0].T * myglobals.PixelResolution, 'b*')
# plt.xlabel('x/m')
# plt.ylabel('y/m')
# plt.title('simulation environment')
#Locations = Locations[0:MM, :]
tdist, tmean = find_pairwise_distance(Locations)
Dist = tdist
# make sure different methods will process same data
Prx = Prx
# use different interpolation methods to get REM
def estimate_map(Prx, Dist, Locations, method, vari):
Pow = Prx
if vari == 'pbp': # calculate the variogram point by point
rij = np.zeros((Pow.shape[1], Pow.shape[1]))
for i in range(0, Pow.shape[1]):
rij[i, :] = 0.5 * (Pow[0, i] - Pow) * (Pow[0, i] - Pow)
dij = Dist
# # calculate mean-value of variogram that under same distance
# dd = []
# semi = []
# for n in range(0, dij.shape[0]):
# for m in range(0, dij.shape[1]):
# if n == m:
# continue
# else:
# lo = np.argwhere(dij == dij[n, m])
#
# dd.append(dij[n, m])
# se = []
# for l in range(0, lo.shape[0]):
# se.append(rij[lo[l, 0], lo[l, 1]])
#
# semi.append(np.sum(se) / lo.shape[0])
# Bin_bound=dd
# Variogram=semi
# # calculate variogram directly
dd = dij.reshape(1, Pow.shape[1] * Pow.shape[1])
semi = rij.reshape(1, Pow.shape[1] * Pow.shape[1])
Bin_bound = dd[0]
Variogram = semi[0]
elif vari == 'bin': # use equal space method to get variogram
VarResolution = 1
rang = Range
MaxDist = get_distance(np.array([rang[0], rang[2]]),
np.array([rang[1], rang[3]]))
bins = np.arange(0, MaxDist + VarResolution,
VarResolution) + VarResolution
Pow = Pow
Variogram, Bin_bound = find_variogram(Dist, Pow, bins)
#plt.figure(30)
#plt.plot(Bin_bound, Variogram, 'b.-')
# sometimes the last value of Variogram is smaller than the first one.
# Then it can't fit'RuntimeError: Optimal parameters not found:
# The maximum number of function evaluations is exceeded.'
# if Variogram[len(Variogram) - 1] < Variogram[0]:
#
# Variogram = Variogram[0:len(Variogram) - 1]
# Bin_bound = Bin_bound[0:len(Bin_bound) - 1]
# get coefficients for the Gaussian model
#, bounds=([0,0,0],[np.inf,np.inf,np.inf]),method='trf'
pg1, pg2, pg3 = opt.curve_fit(
Exponent_modul,
Bin_bound,
Variogram,
bounds=([0, 0, 0], [np.inf, np.inf, np.inf]),
method='trf')[
0] #bounds=([-np.inf,0,-np.inf,], [np.inf,np.inf,np.inf])
#pg1, pg2, pg3 = opt.curve_fit(Spherical_modul, Bin_bound, Variogram)[0]
p = [pg1, pg2, pg3]
rang = PixelRange
# to get the estimated map
img = np.zeros(
(int(rang[1]) - int(rang[0]) + 1, int(rang[3]) - int(rang[2]) + 1))
for i in range(0, img.shape[0]):
tx = rang[0] + i
for j in range(0, img.shape[1]):
ty = rang[2] + j
pxcoord = np.array([tx, ty])
coord = pixel2coord(pxcoord)
if method == 'kriging':
# Kriging_method
# Pow = Prx
Pos = Locations
A, B = generate_matrices(p, Dist, Pos, coord)
W = solve(A, B)
W = W[0:Pow.shape[1], 0].T
# print(np.sum(np.sum(W)))
Pow_est = np.sum(np.sum(W * Pow))
img[i, j] = Pow_est
elif method == 'shepard':
Pos = Locations
PowerFactor = 2
flag = 1
n = Pow.shape[1]
W = np.zeros((1, n))
for nn in range(0, n):
td = get_distance(Pos[nn, :], coord)
if td == 0:
flag = 0
Pow_est = Pow[0, nn]
break
W[0, nn] = 1 / (td**PowerFactor)
if flag == 1:
Pow_est = sum(sum(W * Pow)) / sum(sum(W))
img[i, j] = Pow_est
elif method == 'neighbour':
Pos = Locations
Pow = Prx
grid_z0 = griddata(Pos, Pow.T, coord, method='nearest')
img[i, j] = grid_z0
Img = img
return Img
## to get true REM
def map_true(PixelRange, Source_loc, Ptx):
rang = PixelRange
img = np.zeros(
(int(rang[1]) - int(rang[0]) + 1, int(rang[3]) - int(rang[2]) + 1))
zone = np.zeros((img.shape[0] * img.shape[1], 2))
ik = 0
for ix in range(0, img.shape[0]):
txx = rang[0] + ix
for jy in range(0, img.shape[1]):
tyy = rang[2] + jy
pxcoord = np.array([txx, tyy])
coord = pixel2coord(pxcoord)
zone[ik, :] = coord
ik = ik + 1
td, tm = find_pairwise_distance(zone)
img0 = data_for_true_cor(td, Source_loc, zone, Ptx)
img0 = img0.reshape(
int(rang[1]) - int(rang[0]) + 1,
int(rang[3]) - int(rang[2]) + 1)
Img = img0
return Img
def map_LS(PixelRange, esSource_loc, esPtx):
rang = PixelRange
img = np.zeros(
(int(rang[1]) - int(rang[0]) + 1, int(rang[3]) - int(rang[2]) + 1))
for ix in range(0, img.shape[0]):
txx = rang[0] + ix
for jy in range(0, img.shape[1]):
tyy = rang[2] + jy
pxcoord = np.array([txx, tyy])
coord = pixel2coord(pxcoord)
dd = get_distance(coord, esSource_loc)
if dd == 0:
esPrx = esPtx
else:
esPrx = esPtx - myglobals.Pl0 - 10 * myglobals.alpha * np.log10(
dd)
img[ix, jy] = esPrx
return img
#
if flag == 1:
Img = estimate_map(Prx, Dist, Locations, method, vari)
elif flag == 0:
Img = estimate_map_nearst_method(SS, Locations, Prx, PixelRange)
elif flag == -1:
Img = map_true(PixelRange, Source_loc, Ptx)
elif flag == -2:
# for locating, don't need here
sour_loc, es_ptx = LS_loc(Locations, Prx)
Img = map_LS(PixelRange, sour_loc, es_ptx)
# Img=neighbour(Locations,Prx,PixelRange)
IMG = Img
# #IMG = np.where(IMG < -80., -100, IMG)
#
# lo = np.argwhere(IMG == np.max(IMG))
# print(lo)
# print(np.max(IMG))
return IMG, Prx
def guitest(MM,xcor,ycor):
area_size = myglobals.area_size # [10, 5]
xrange = [0, area_size[0]]
yrange = [0, area_size[1]]
Range = np.array([xrange[0], xrange[1], yrange[0], yrange[1]])
PixelRange = coord2pixel(Range) + [1, 0, 1, 0]
Source_loc = [xcor, ycor]
Ptx = -10
MM = MM
flag = 1
method = 'kriging'
# method='shepard'
# method='neighbour'
vari = 'bin'
##
# Locations = np.array(
# [[0.5, 4], [3, 5], [7, 2.5], [1, 0.5], [8.5, 0.8], [3, 1.5], [4, 1], [5, 2], [6.2, 1.1], [2, 1.9], [2.5, 2.3],
# [3, 3], [7.5, 3.5], [3.5, 2.75], [0.6, 1.5], [1.25, 3.1], [7.1, 0.3], [8.1, 3.1],
# [4, 3.8], [2, 4.5], [5, 3.1], [6.1, 3.5], [6.2, 4.3], [8, 4.5], [2, 1]])
##for 50 sensors test
# Locations=np.array(
# [[0.67364638, 2.28947991],[9.93341331, 0.95542107],[1.82242923, 1.53087528],[1.04751652, 3.05047091],[3.9480761, 3.5022931 ],
# [6.1083599, 4.75512156],[4.97983297, 0.52770699],[5.32639046, 3.55030126],[6.59182669, 0.51394635],[7.25343255, 4.88115543],
# [3.96248924, 4.6862628 ],[7.52347415, 3.73200227],[4.53042301, 1.61072596],[6.39027678, 2.19262825],[0.199442, 3.7110518 ],
# [3.28389192, 3.423724 ],[9.5403416, 0.25601425],[0.81477858, 0.98104154],[8.34015388, 4.47387084],[8.2669614, 0.0783419 ],
# [9.7288704, 3.7857175 ],[5.62445041, 4.08035627],[5.67375543, 0.66634032],[6.02209598, 2.56838373],[6.78476374, 0.66731677],
# [0.53226241, 0.64590897],[9.73884966, 1.92304957],[3.10276626, 3.4410621 ],[3.58134045, 2.09529237],[7.75843324, 1.37180076],
# [3.22643465, 2.62245479],[2.70272802, 2.81852081],[1.46448928, 4.68585227],[3.14660896, 2.38422047],[8.67692837, 4.99861914],
# [0.0192289, 3.22337307],[7.1758682, 4.05264881],[7.66234889, 0.1793061 ],[3.98083337, 4.56559927],[4.83294335, 0.25021726],
# [3.9727205, 1.28927461],[6.36246273, 0.01184955],[9.20024388, 3.63732518],[3.91988766, 0.23612493],[2.10838329, 0.85399577],
# [7.46888132, 0.85088804],[3.5466248, 3.60073434],[8.05463967, 1.54125835],[4.49369649, 1.3254797 ],[5.47086327, 3.07157279]])
xmax = myglobals.area_size[0]
ymax = myglobals.area_size[1]
# Locations=np.zeros(((Nmx-1)*(Nmy-1),2))
# xy=0
# for ii in range(0, Nmx - 1):
# xx = xi[ii] + xmax / (2 * (Nmx - 1))
#
# for jj in range(0, Nmy - 1):
# yy = yi[jj] + ymax / (2 * (Nmy - 1))
# Locations[xy,:]=np.array([xx,yy])
# xy=xy+1
Locations = generate_sensor_locations(MM, [0, xmax, 0, ymax])
# Locations = Locations[0:MM, :]
# plt.figure(11)
# plt.plot(2 * Locations[:, 1].T * myglobals.PixelResolution, 2 * Locations[:, 0].T * myglobals.PixelResolution, 'b*')
# plt.xlabel('x/m')
# plt.ylabel('y/m')
# plt.title('simulation environment')
tdist, tmean = find_pairwise_distance(Locations)
Dist = tdist
Prx = data_log(Dist, Source_loc, Locations, Ptx)
# for locating #####################################
# sour_loc, es_ptx = LS_loc(Locations, Prx)
def estimate_map(Prx, Dist, Locations, method, vari):
Pow = Prx
if vari == 'pbp':
rij = np.zeros((Pow.shape[1], Pow.shape[1]))
for i in range(0, Pow.shape[1]):
rij[i, :] = 0.5 * (Pow[0, i] - Pow) * (Pow[0, i] - Pow)
dij = Dist
# # # calculate mean-value of variogram that under same distance
# dd = []
# semi = []
# for n in range(0, dij.shape[0]):
# for m in range(0, dij.shape[1]):
# if n == m:
# continue
# else:
# lo = np.argwhere(dij == dij[n, m])
#
# dd.append(dij[n, m])
# se = []
# for l in range(0, lo.shape[0]):
# se.append(rij[lo[l, 0], lo[l, 1]])
#
# semi.append(np.sum(se) / lo.shape[0])
# Bin_bound=dd
# Variogram=semi
# # calculate variogram directly
dd = dij.reshape(1, Pow.shape[1] * Pow.shape[1])
semi = rij.reshape(1, Pow.shape[1] * Pow.shape[1])
Bin_bound = dd[0]
Variogram = semi[0]
elif vari == 'bin':
VarResolution = 1
rang = Range
MaxDist = get_distance(np.array([rang[0], rang[2]]), np.array([rang[1], rang[3]]))
bins = np.arange(0, MaxDist + VarResolution, VarResolution) + VarResolution
Pow = Pow
Variogram, Bin_bound = find_variogram(Dist, Pow, bins)
# plt.figure(30)
# plt.plot(Bin_bound, Variogram, 'b.-')
# plt.xlabel('distance/m')
# plt.ylabel('semivariogram')
# plt.title('equal space method')
# plt.show()
# sometimes the last value of Variogram is smaller than the first one.
# Then it can't fit'RuntimeError: Optimal parameters not found:
# The maximum number of function evaluations is exceeded.'
# if Variogram[len(Variogram)-1]<Variogram[0]:
# print(Variogram)
# print(Bin_bound)
# Variogram=Variogram[0:len(Variogram)-1]
# Bin_bound=Bin_bound[0:len(Bin_bound)-1]
pg1, pg2, pg3 = opt.curve_fit(Exponent_modul, Bin_bound, Variogram, bounds=([0, 0, 0], [np.inf, np.inf, np.inf]), method='trf')[0] # bounds=([-np.inf,0,-np.inf,], [np.inf,np.inf,np.inf])
# pg1, pg2, pg3 = opt.curve_fit(Spherical_modul, Bin_bound, Variogram)[0]
#pg1, pg2, pg3 = opt.curve_fit(Exponent_modul, Bin_bound, Variogram)[0]
p = [pg1, pg2, pg3]
# print(p)
rang = PixelRange
img = np.zeros((int(rang[1]) - int(rang[0]) + 1, int(rang[3]) - int(rang[2]) + 1))
for i in range(0, img.shape[0]):
tx = rang[0] + i
for j in range(0, img.shape[1]):
ty = rang[2] + j
pxcoord = np.array([tx, ty])
coord = pixel2coord(pxcoord)
if method == 'kriging':
# Kriging_method
# Pow = Prx
Pos = Locations
A, B = generate_matrices(p, Dist, Pos, coord)
W = np.linalg.solve(A, B)
W = W[0:Pow.shape[1], 0].T
Pow_est = sum(sum(W * Pow))
img[i, j] = Pow_est
elif method == 'shepard':
Pos = Locations
PowerFactor = 2
flag = 1
n = Pow.shape[1]
W = np.zeros((1, n))
for nn in range(0, n):
td = get_distance(Pos[nn, :], coord)
if td == 0:
flag = 0
Pow_est = Pow[0, nn]
break
W[0, nn] = 1 / (td ** PowerFactor)
if flag == 1:
Pow_est = sum(sum(W * Pow)) / sum(sum(W))
img[i, j] = Pow_est
elif method == 'neighbour':
Pos = Locations
Pow = Prx
grid_z0 = griddata(Pos, Pow.T, coord, method='nearest')
# grid_z0 = naturalneighbor.griddata(Pos, Pow.T, coord)
img[i, j] = grid_z0
Img = img
return Img
##
def map_true():
rang = PixelRange
img = np.zeros((int(rang[1]) - int(rang[0]) + 1, int(rang[3]) - int(rang[2]) + 1))
zone = np.zeros((img.shape[0] * img.shape[1], 2))
ik = 0
for ix in range(0, img.shape[0]):
txx = rang[0] + ix
for jy in range(0, img.shape[1]):
tyy = rang[2] + jy
pxcoord = np.array([txx, tyy])
coord = pixel2coord(pxcoord)
zone[ik, :] = coord
ik = ik + 1
td, tm = find_pairwise_distance(zone)
img0 = data_for_true_cor(td, Source_loc, zone, Ptx)
img0 = img0.reshape(int(rang[1]) - int(rang[0]) + 1, int(rang[3]) - int(rang[2]) + 1)
print(img0.shape)
Img = img0
return Img
#
if flag == 1:
Img = estimate_map(Prx, Dist, Locations, method, vari)
elif flag == 0:
Img = estimate_map_nearst_method(SS, Locations, Prx, PixelRange)
elif flag == -1:
Img = map_true()
# Img=neighbour(Locations,Prx,PixelRange)
IMG = Img
# # IMG = np.where(IMG < -80., -100, IMG)
#
# lo = np.argwhere(IMG == np.max(IMG))
# lo = (lo + np.array([1, 1])) * myglobals.PixelResolution
# print(lo)
# print(np.max(IMG))
# # IMG = np.where(IMG < -60, -100, IMG)
# plt.figure(2)
# plt.imshow(IMG, interpolation='nearest')
#
# # nn=nn+1
# # plt.pause(0.5)
# plt.colorbar()
# plt.show()
#
# # fig = plt.figure(2)
# # ax = fig.gca(projection='3d')
# # nx=IMG.shape[0]
# # ny = IMG.shape[1]
# # Y = np.linspace(0,nx-1,nx)
# # X = np.linspace(0,ny-1,ny)
# # X, Y = np.meshgrid(X, Y)
# #
# #
# # surf=ax.plot_surface(X, Y, IMG, cmap=cm.coolwarm, linewidth=0, antialiased=False)
# # plt.xlabel('x[m]')
# # plt.ylabel('y[m]')
# # plt.title('Estimated REM')
# # plt.show()
return IMG
def estimate_map_nearst_method(
N, Locations, Prx, PixelRange): # for each point, use nearst N sensors
Pos = Locations
flag = 0
Pow = Prx
rang = PixelRange
img = np.zeros(
(int(rang[1]) - int(rang[0]) + 1, int(rang[3]) - int(rang[2]) + 1))
for i in range(0, img.shape[0]):
tx = rang[0] + i
for j in range(0, img.shape[1]):
ty = rang[2] + j
pxcoord = np.array([tx, ty])
coord = pixel2coord(pxcoord)
sen_dis = []
for ii in range(0, Pos.shape[0]):
dis = get_distance(coord, Pos[ii, :])
# if dis==0:
# flag=1
# img[i,j]=Pow[0,ii]
# break
# if dis==0:
# dis=0.00000000001
sen_dis.append(dis)
# if flag==1:
# continue
sen_dis = np.array(sen_dis)
num = np.argsort(sen_dis)
pos = np.zeros((N, 2))
pow = np.zeros((1, N))
for kk in range(0, N):
pos[kk, :] = Pos[num[kk], :]
pow[0, kk] = Pow[0, num[kk]]
dij, tmean = find_pairwise_distance(pos)
rij = np.zeros((pow.shape[1], pow.shape[1]))
for k in range(0, pow.shape[1]):
rij[k, :] = 0.5 * (pow[0, k] - pow) * (pow[0, k] - pow)
# # # calculate mean-value of variogram that under same distance
# dd = []
# semi = []
# for n in range(0, dij.shape[0]):
# for m in range(0, dij.shape[1]):
# if n == m:
# continue
# else:
# lo = np.argwhere(dij == dij[n, m])
# dd.append(dij[n, m])
# se = []
# for l in range(0, lo.shape[0]):
# se.append(rij[lo[l, 0], lo[l, 1]])
# semi.append(np.sum(se) / lo.shape[0])
# Bin_bound = dd
# Variogram = semi
# # calculate variogram directly
dd = dij.reshape(1, pow.shape[1] * pow.shape[1])
semi = rij.reshape(1, pow.shape[1] * pow.shape[1])
Bin_bound = dd[0]
Variogram = semi[0]
###########
# if Variogram[len(Variogram) - 1] < Variogram[0]:
# Variogram = Variogram[0:len(Variogram) - 1]
# Bin_bound = Bin_bound[0:len(Bin_bound) - 1]
###########
pg1, pg2, pg3 = opt.curve_fit(Exponent_modul,
Bin_bound,
Variogram,
bounds=([0, 0, 0],
[np.inf, np.inf, np.inf]),
method='trf')[0]
# pg1, pg2, pg3 = opt.curve_fit(Spherical_modul, Bin_bound, Variogram)[0]
p = [pg1, pg2, pg3]
A, B = generate_matrices(p, dij, pos, coord)
W = solve(A, B)
W = W[0:pow.shape[1], 0].T
img[i, j] = sum(sum(W * pow))
return img
[3.22643465, 2.62245479], [2.70272802, 2.81852081],
[1.46448928, 4.68585227], [3.14660896, 2.38422047],
[8.67692837, 4.99861914], [0.0192289, 3.22337307],
[7.1758682, 4.05264881], [7.66234889, 0.1793061],
[3.98083337, 4.56559927], [4.83294335, 0.25021726],
[3.9727205, 1.28927461], [6.36246273, 0.01184955],
[9.20024388, 3.63732518], [3.91988766, 0.23612493],
[2.10838329, 0.85399577], [7.46888132, 0.85088804],
[3.5466248, 3.60073434], [8.05463967, 1.54125835],
[4.49369649, 1.3254797], [5.47086327, 3.07157279]])
Locations = Locations[0:MM, :]
plt.figure(11)
plt.plot(2 * Locations[:, 1].T, 2 * Locations[:, 0].T, 'b*')
tdist, tmean = find_pairwise_distance(Locations)
Dist = tdist
Prx = data_log(Dist, Source_loc, Locations, Ptx)
# for locating #####################################
#sour_loc, es_ptx = LS_loc(Locations, Prx)
def estimate_map(Prx, Dist, Locations, method):
Pow = Prx
rij = np.zeros((Pow.shape[1], Pow.shape[1]))
for i in range(0, Pow.shape[1]):
rij[i, :] = 0.5 * (Pow[0, i] - Pow) * (Pow[0, i] - Pow)
dij = Dist
dd = []