def plotNpartDep(Npx, pxlen, N_part_max, N_part_step, R_mean, R_std, R_mu,
R_sigma):
z = mf.genFlat(Npx)
N_part = np.arange(0, N_part_max + 1, N_part_step)
N_est_list = []
for N in N_part:
z, R_part_real = mf.genLogNormSolidSph(z, pxlen, N_part_step, R_mean,
R_std)
N_est_list.append(partNum(z, pxlen, R_mu, R_sigma)[0])
print(N)
mf.plotfalsecol(z, pxlen)
plt.figure()
plt.plot(N_part, np.array(N_est_list) / N_part, color='r', marker='o')
plt.xlabel(r'$N_{part,real}$')
plt.ylabel(r'$N_{est} / N_{real}$')
plt.title(r'$\mu_R = $' + str(R_mu) + r'$nm, \sigma_R = $' + str(R_sigma) +
r'$nm$' + r'$, R_{mean} = $' + str(R_mean) +
r'$nm, R_{std} = $' + str(R_std) + r'$nm$')
plt.grid()
def saveImg(Npx, pxlen, Npart, R_median, R_std, R_mu, R_sigma, R_tip, h,
N_sample, **kwargs):
note = kwargs.get('note', None)
if not os.path.exists('images/Npart=' + str(Npart) + note):
os.makedirs('images/Npart=' + str(Npart) + note)
else:
shutil.rmtree('images/Npart=' + str(Npart) + note)
os.makedirs('images/Npart=' + str(Npart) + note)
for i in range(1, N_sample + 1):
print('i=' + str(i))
z = mf.genFlat(Npx)
z, trash = mf.genLogNormSolidSph(z, pxlen, Npart, R_mu, R_sigma)
mf.plotfalsecol(z, pxlen)
plt.savefig('images/Npart=' + str(Npart) + note + '/r=0' + '_' +
str(i) + '.png')
np.savetxt('images/Npart=' + str(Npart) + note + '/r=0' + '_' +
str(i) + '.txt',
z,
header='Rmedian=' + str(R_median) + ', Rstd=' + str(R_std) +
', mu=' + str(R_mu) + ', sigma=' + str(R_sigma) +
', Npxl=' + str(Npx) + ', pxlen=' + str(pxlen) + ', h=' +
str(h))
plt.close('all')
for r in R_tip:
print('r=' + str(r))
tip = mf.genParabolicTip(pxlen, h, r=r)
img = mph.grey_dilation(z, structure=-tip)
np.savetxt('images/Npart=' + str(Npart) + note + '/r=' + str(r) +
'_' + str(i) + '.txt',
img,
header='Rmedian=' + str(R_median) + ', Rstd=' +
str(R_std) + ', mu=' + str(R_mu) + ', sigma=' +
str(R_sigma) + ', Npxl=' + str(Npx) + ', pxlen=' +
str(pxlen) + ', h=' + str(h))
mf.plotfalsecol(img, pxlen)
plt.savefig('images/Npart=' + str(Npart) + note + '/r=' + str(r) +
'_' + str(i) + '.png')
plt.close('all')
dist = 2 * np.sqrt(r_part_max**2 + 2 * rtip * r_part_max)
#dist=2*(rtip + r_part_max)
#dist*=1.03
z = mf.genFlat(Npx)
z = mf.genHexCap(z,
pxlen,
dist,
r_part_min,
r_part_max,
xmin=dist / 2,
xmax=len(z) * pxlen - dist / 2,
ymin=dist / 2,
ymax=len(z) * pxlen - dist / 2)
mf.plotfalsecol(z, pxlen)
tip = mf.genSemisphTip(pxlen, htip, r=rtip)
img = mph.grey_dilation(z, structure=-tip)
mf.plotfalsecol(img, pxlen)
mf.plotfalsecol(tip, pxlen)
if r_part_max > htip:
print('Warning: possible spikes higher than semisphere tip')
obj = mf.identObj(img, thres)[0]
for o in obj:
# mf.plotfalsecol(o,pxlen)
h, r, A, V, e = par.capPar(o, pxlen, thres)
h_arr = np.append(h_arr, h)
r_arr = np.append(r_arr, r)
A_arr = np.append(A_arr, A)
V_arr = np.append(V_arr, V)
def partDep_mpi(Npx, pxlen, step_sim, N_part_min, N_part_max, N_part_step, R_mu, R_sigma, firstmap='', usefile=False, savefile=False):
N_part = np.linspace(np.log10(N_part_min), np.log10(N_part_max), N_part_step)
N_part = np.round(10**N_part)
N_part.astype(int, copy=False)
N_est = np.array([])
V_est = np.array([])
rms_est = np.array([])
h_est = np.array([])
h_top = np.array([])
# L_corr_est = np.array([])
# L_corr_err = np.array([])
# alfa_est=np.array([])
# alfa_err=np.array([])
C_true=[]
G_true=[]
C2_true=[]
G2_true=[]
C_list=[]
G_list=[]
comm=MPI.COMM_WORLD
size_mpi=comm.Get_size()
for i in range(step_sim/size_mpi):
if firstmap=='': #initialize map
z = mf.genFlat(Npx)
N_prec=0
V_real=0
else:
z = np.loadtxt(firstmap)
dotpos=firstmap.find('.')
start=dotpos-1
for i in range(dotpos-1):
if firstmap[start]=='_': break
start-=1
N_prec=int(firstmap[start+1:dotpos])
out=open(firstmap)
head=out.readline()
out.close()
start=head.find('V=')+2
V_real=float(head[start:head.find(';', start)])
for N in N_part:
print('Sim.',i+1,'; N=',str(N)[:len(str(N))-2], end=' ')
if usefile and isfile('maps/lognorm_'+str(Npx)+'_'+str(pxlen)+'_'+str(N)[:len(str(N))-2]+'.dat'):
print('map from file ...', end=' ')
z= np.loadtxt('maps/lognorm_'+str(Npx)+'_'+str(pxlen)+'_'+str(N)[:len(str(N))-2]+'.dat')
out=open('maps/lognorm_'+str(Npx)+'_'+str(pxlen)+'_'+str(N)[:len(str(N))-2]+'.dat')
head=out.readline()
out.close()
start=head.find('V=')+2
V_real=float(head[start:head.find(';', start)])
else:
print('generating map ...', end=' ')
z, R_part_real = mf.genLogNormSolidSph(z,pxlen,int(N-N_prec),R_mu,R_sigma)
mf.plotfalsecol(z,pxlen)
V_real += np.sum(4/3 * np.pi * R_part_real**3)
if savefile: np.savetxt('maps/lognorm_'+str(Npx)+'_'+str(pxlen)+'_'+str(N)[:len(str(N))-2]+'.dat', z, header='V='+str(V_real)+'; Npx,pxlen,Npart in filename')
N_prec=N
print('computing parameters ...')
N_est=np.append(N_est, partNum(z,pxlen,R_mu,R_sigma)[0]/N)
V_est=np.append(V_est, par.V(z,pxlen)/V_real)
rms_est=np.append(rms_est, np.std(z))
h_est=np.append(h_est, np.mean(z))
h_top=np.append(h_top, np.amax(z))
#print('computing correlations ...')
l,C=par.C_profile(z, 2, 800)
C_list.append(C)
l,C=par.G_profile(z, 2, 800)
G_list.append(C)
if i==0:
for el in C_list:
C_true.append(el)
C2_true.append(el**2)
for el in G_list:
G_true.append(el)
G2_true.append(el**2)
else:
for i in range(len(C_true)):
C_true[i]=C_true[i]+C_list[i]
C2_true[i]=C2_true[i]+C_list[i]**2
for i in range(len(G_true)):
G_true[i]=G_true[i]+G_list[i]
G2_true[i]=G2_true[i]+G_list[i]**2
C_list.clear()
G_list.clear()
est=[N_est, V_est, rms_est, h_est, h_top]
rank=comm.Get_rank()
if rank!=0: comm.Send(est, dest=0)
if rank==0:
rec=[]
for i in range(1, size_mpi):
comm.Recv(rec, source=i)
for k in range(len(est)):
est[k]=np.append(est[k], rec[k])
rec.clear()
C_true=np.array(C_true)/(step_sim/size_mpi)
G_true=np.array(G_true)/(step_sim/size_mpi)
C2_true=np.array(C2_true)/(step_sim/size_mpi) - C_true**2
G2_true=np.array(G2_true)/(step_sim/size_mpi) - G_true**2
corr=[C_true, G_true, C2_true, G2_true]
if rank!=0: comm.Send(corr, dest=0)
if rank==0:
for i in range(1, size_mpi):
comm.Recv(rec, source=i)
for k in range(4):
corr[k]=np.append(corr[k], rec[k])
rec.clear()
corr[0]=np.array(corr[0])/size_mpi
corr[1]=np.array(corr[1])/size_mpi
corr[2]=np.array(corr[2])/size_mpi - corr[0]**2
corr[3]=np.array(corr[3])/size_mpi - corr[1]**2
filename=['N_relVsN.dat', 'V_relVsN.dat', 'rmsVsN.dat', 'hVsN.dat', 'maxhVsN.dat']
for j in range(len(est)):
err=np.array([])
for i in range(len(N_part)):
if step_sim==1: err=np.append(err, 0)
else: err=np.append(err, np.std(est[j][i::len(N_part)]))
est[j][i]=np.mean(est[j][i::len(N_part)])
np.savetxt(filename[j], np.array([ est[j][:len(N_part)], N_part, err ]),
header=str(R_mu) + ' ' + str(R_sigma) + ' ' + str(Npx) + ' ' + str(pxlen) + '\n' +
r'$\mu _R$ $\sigma _R$ $N_{px}$ $L_{px}$')
print('data saved in '+ filename[j] +' and in folder maps/')
np.savetxt('correlations.dat', np.array([ l*pxlen, corr[0], corr[1], corr[2], corr[3]]), fmt='%s')
height.append(np.amax(obj[0:,x])/R)
if maxh<height[-1]:
maxh=height[-1]
posmax[-1]=x
profiles.append(np.array(height))
height.clear()
for rtip in R_tip:
for i in range(len(Npx)): #iterazione su risoluzione
print('R_tip=', rtip , ' Npx=', int(Npx[i]))
z=mf.genFlat(int(Npx[i]))
z=mf.genSphere(z,pxlen[i],np.array([L/2, L/2]),np.array([R]))
tip=mf.genSemisphTip(pxlen[i],htip,r=rtip)
z = mph.grey_dilation(z, structure=-tip)
if rtip==R_tip[-1] and i==0: mf.plotfalsecol(z,pxlen[i])
obj = mf.identObj(z,thres)[0]
V_red_dil.append(par.V(obj, pxlen[i]) / volsphere(R))
if i==len(Npx)-1:
maxh=0
posmax.append(0)
for x in range(np.shape(obj)[0]):
height.append(np.amax(obj[0:,x])/R)
if maxh<height[-1]:
maxh=height[-1]
posmax[-1]=x
profiles.append(np.array(height))
height.clear()
R_tip=np.append(R_tip, R)