def print_to_image(path):
grid = grid_to_metrix(path)
if algo.solve(grid) :
# algo.print_board(grid)
t=0
for i in range(9):
for j in range(9):
if(t<len(temp1)):
if(i==temp1[t] and j==temp2[t]):
x = j*50+10
y = i*50+40
cv2.putText(image_final,str(grid[i][j]), (x, y), cv2.FONT_HERSHEY_SIMPLEX, 1, (0, 0, 255),2)
t+=1
return image_final
else:
return "Detection Error!"
print('done. time:', tconv, flush=True)
print('solving...', end=' ', flush=True)
tsolve = time()
WWhat = la.solve(mat, Vhat)
tsolve = time() - tsolve
print('done. time:', tsolve, flush=True)
An += phi(tj) * WWhat
Bn += phi(tj) * psi(tj) * WWhat
if its == True:
print('its solving...', end=' ', flush=True)
What = np.zeros((m, 1), dtype=np.complex)
for ii in range(l):
v = Vhat[:, ii]
tsolve = time()
x = solve(met, v)
What = np.concatenate((What, x.reshape(m, 1)), axis=1)
What = What[:, 1:]
tsolve = time() - tsolve
print('done. time:', tsolve, flush=True)
AN += phi(tj) * What
BN += phi(tj) * psi(tj) * What
AN, BN = 1/(1j*N) * AN, 1/(1j*N) * BN
An, Bn = 1/(1j*N) * An, 1/(1j*N) * Bn
A, B = An, Bn
print(' ')
print('shape(MET)=', met.shape)
listeVal = []
valEntree = 10.0
valSortie = 0.0
listE = []
listS = []
listePression = []
nbN = len(listeNoeuds)
posE = 0
posS = nbN - 1
[A, b] = data.generateMatriceReseau(listeNoeuds, listeArcs, posE,
valEntree, posS, valSortie)
if (choixMeth == "1"):
listePression = algo.cramer(A, b)
if (choixMeth == "2"):
listePression = algo.solve(A, b)
listE = [posE]
listS = [posS]
verif = algo.verifReseau(listeNoeuds, listeArcs, listePression, listE,
listS)
affichage.afficherReseauResultat(listeNoeuds, listeArcs, listePression,
listE, listS)
##----------------------------------
##----------------------------------
# Detection dans le reseau de toute les positions de source et de sortie admissibles :
# Decommenter et completer ICI :
if (butPrgm == "3"):
listeNoeuds = []
xtf = xTF(kRef, n)
xtf.setRHS(dir_data, neu_data)
space = xtf.space
shape = xtf.shape
fd, fn = xtf.getDir(), xtf.getNeu()
fdir, fneu = xtf.getGFdir(), xtf.getGFneu()
STF, MTF = STF(xtf), MTF(xtf)
stf, rhs_stf = STF.get(), STF.rhs()
mtf, rhs_mtf = MTF.get(), MTF.rhs()
x_stf = solve(stf, rhs_stf)
xd_stf, xn_stf = x_stf[0:shape], x_stf[shape:]
x_mtf = solve(mtf, rhs_mtf)
xd_mtf, xn_mtf = x_mtf[0:shape], x_mtf[shape:2 * shape]
yd_mtf, yn_mtf = x_mtf[2 * shape:3 * shape], x_mtf[3 * shape:4 * shape]
print('')
print('l2 norm (relative)')
print(la.norm(xd_mtf - xd_stf), la.norm(xn_mtf - xn_stf))
print(
la.norm(xd_mtf - yd_mtf - fd) / la.norm(xd_mtf),
la.norm(-xn_mtf - yn_mtf - fn) / la.norm(xn_mtf))
gd_mtf = bem.GridFunction(space, coefficients=xd_mtf)
gn_mtf = bem.GridFunction(space, coefficients=xn_mtf)
import input
from algo import solve
a_file = "files/a_an_example.in.txt"
b_file = "files/b_better_start_small.in.txt"
c_file = "files/c_collaboration.in.txt"
d_file = "files/d_dense_schedule.in.txt"
e_file = "files/e_exceptional_skills.in.txt"
f_file = "files/f_find_great_mentors.in.txt"
file = a_file
sim = input.SimulationData(file)
sim.print_info()
output = solve(sim.contributor_to_skills_dict, sim.projects_info_dict,
sim.projects_skills_dict)
print('done. time:', tconv, flush=True)
print('solving...', end=' ', flush=True)
tsolve = time()
WWhat = la.solve(mat, Vhat)
tsolve = time() - tsolve
print('done. time:', tsolve, flush=True)
An += phi(tj) * WWhat
Bn += phi(tj) * psi(tj) * WWhat
if its == True:
print('its solving...', end=' ', flush=True)
What = np.zeros((m, 1), dtype=np.complex)
for ii in range(l):
v = Vhat[:, ii]
tsolve = time()
x = solve(met, v)
What = np.concatenate((What, x.reshape(m, 1)), axis=1)
What = What[:, 1:]
tsolve = time() - tsolve
print('done. time:', tsolve, flush=True)
AN += phi(tj) * What
BN += phi(tj) * psi(tj) * What
AN, BN = 1 / (1j * N) * AN, 1 / (1j * N) * BN
An, Bn = 1 / (1j * N) * An, 1 / (1j * N) * Bn
A, B = An, Bn
print(' ')
print('shape(MET)=', met.shape)
result[0] = -1j * normal[1] * kRef * np.exp( 1j * kRef * x[1])
xtf = xTF(kRef, n)
xtf.setRHS(dir_data, neu_data)
space = xtf.space
shape = xtf.shape
fd, fn = xtf.getDir(), xtf.getNeu()
fdir, fneu = xtf.getGFdir(), xtf.getGFneu()
STF, MTF = STF(xtf), MTF(xtf)
stf, rhs_stf = STF.get(), STF.rhs()
mtf, rhs_mtf = MTF.get(), MTF.rhs()
x_stf = solve(stf, rhs_stf)
xd_stf, xn_stf = x_stf[0:shape], x_stf[shape:]
x_mtf = solve(mtf, rhs_mtf)
xd_mtf, xn_mtf = x_mtf[0:shape], x_mtf[shape:2*shape]
yd_mtf, yn_mtf = x_mtf[2*shape:3*shape], x_mtf[3*shape:4*shape]
print('')
print('l2 norm (relative)')
print(la.norm(xd_mtf - xd_stf), la.norm(xn_mtf - xn_stf))
print(la.norm(xd_mtf - yd_mtf - fd)/la.norm(xd_mtf),
la.norm(-xn_mtf - yn_mtf -fn)/la.norm(xn_mtf))
gd_mtf = bem.GridFunction(space, coefficients=xd_mtf)
gn_mtf = bem.GridFunction(space, coefficients=xn_mtf)
ggd_mtf = bem.GridFunction(space, coefficients=yd_mtf)
