def fit_variogram(self): # calculate semivariogram using bin_bound ##
VarResolution = 1
range = self.Clust.Range
MaxDist = get_distance(np.array([range[0], range[2]]), np.array([range[1], range[3]]))
bins=np.arange(0,MaxDist+VarResolution,VarResolution)+VarResolution
Pow=self.SensorPowers
self.update_pairwise_distance()
Variogram, Bin_bound = find_variogram(self.PairwiseDist, Pow, bins)
self.Variogram=Variogram
pg1, pg2, pg3 = \
opt.curve_fit(Exponent_modul, Bin_bound, Variogram, bounds=([0, 0, 0], [np.inf, np.inf, np.inf]), method='trf')[
0]
self.p=[pg1,pg2,pg3]
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
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 = np.sort(dd)
# Variogram = np.sort(semi)
Bin_bound = dd
Variogram = semi
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.-')
pg1, pg2, pg3 = opt.curve_fit(Gaussian_modul, Bin_bound, Variogram)[0]
# pg1, pg2, pg3 = opt.curve_fit(Spherical_modul, Bin_bound, Variogram)[0]
p = [pg1, pg2, pg3]
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 = 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 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]
plt.figure(30)
plt.plot(Bin_bound, Variogram, 'b.-')
plt.xlabel('distance/m')
plt.ylabel('semivariogram')
plt.title('equal space method')
plt.show()
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