"""Create file names

Usage: fname = fname(wdir='.', type='c', time=0)

Returns file names wdir/data(or imgs)/name.time

"""

dtime = "%.4d" % time

datname = wdir + '/data/' + type + '.' + dtime

imgname = wdir + '/imgs/' + type + '.' + dtime

pltname = wdir + '/plts/' + type + '.' + dtime

"""Read a data file

Usage: data = fread(fname)

Returns a float array of dimensions Nx x Ny

"""

fhandler = open(fname)

header = fhandler.readline().split()

data = fhandler.read().split()

Nx = int(header[2])

Ny = int(header[3])

Nz = int(header[4])

fdata = np.array(data, dtype='f')

if (Nz == 1):

fdata.shape = (Nx,Ny)

else:

fdata.shape = (Nx,Ny,Nz)

f = open(fname, 'w')

nx, ny = data.shape

Nx = nx*8

Ny = ny*4

header = '%5d'%Nx+'%5d'%Ny+'%5d'%nx+'%5d'%ny+'%5d'%1+' 0'+' 0.0'

f.write(header)

f.write('\n')

for m in range(nx):

for n in range(ny):

l = ' % .5e'%data[m][n]

f.write(l)

if ((m*ny+n+1)%10 == 0):

f.write('\n')

"name = simulation name"

dtime = "%.4d" % time

if (os.path.isfile(workdir+'/data/'+type+'.'+dtime+'.00') == True):

return

else:

hosts = np.loadtxt(workdir+'/init/hfile.txt',dtype='S3')

for i in range(np.size(hosts)):

rank = "%.2d" % i

" Read array with rank = 0 and read Np and Nq from header file "

name = fname(wdir, type, time)

name0 = name[0] + '.' + "%.2d" % 0

pget(wdir,type,time)

fhandler = open(name0)

header = fhandler.readline().split()

Np = int(header[5])

Nq = int(header[6])

size = Np*Nq

for rank in range(size+1):

if (rank < size):

name_rank = name[0] + '.' + "%.2d" % rank

print name_rank

data = fread(name_rank)

if (rank % Nq == 0):

if (rank == Nq):

datarr = datacol

datacol = data

elif (rank > 0):

datarr = np.concatenate((datarr,datacol),axis=0)

datacol = data

else:

datacol = data

else:

datacol = np.concatenate((datacol,data),axis=1)

name = fname_init(ldir,type)

name0 = name + '.' + "%.2d" % 0

fhandler = open(name0)

header = fhandler.readline().split()

Np = int(header[0])/int(header[2])

Nq = int(header[1])/int(header[3])

size = Np*Nq

for rank in range(size+1):

if (rank < size):

name_rank = name + '.' + "%.2d" % rank

data = fread(name_rank)

if (rank % Nq == 0):

if (rank == Nq):

datarr = datacol

datacol = data

elif (rank > 0):

datarr = np.concatenate((datarr,datacol),axis=0)

datacol = data

else:

datacol = data

else:

datacol = np.concatenate((datacol,data),axis=1)

if (dim == 2):

datarr = datarr.reshape(int(header[0]),int(header[1]))

name = fname_init(ldir,type)

name0 = name + '.' + "%.2d" % 0

fhandler = open(name0)

header = fhandler.readline().split()

hinit = ida.paradata_init(ldir,type,2)

Nx = hinit.shape[0]

Ny = hinit.shape[1]

nx = int(header[2])

ny = int(header[3])

Np = Nx/nx

Nq = Ny/ny

for x in np.arange(Nx):

Ai = A*np.exp(-alp*x)

if (Ai > 1e-6):

for y in np.arange(Ny):

# hinit[x][y] += Ai*(np.sin(2*n*np.pi*y/Ny))

hinit[x][y] += Ai*np.exp(-b*((y-Ny/2)**2))

for i in range(Np):

for j in range(Nq):

rank = i*Nq+j

data = hinit[i*nx:(i+1)*nx]

data = np.transpose(data)

data = data[j*ny:(j+1)*ny]

data = np.transpose(data)

name_seed = name+'-seed.'+"%.2d" % rank

#name_seed = ldir+'/seed'+num+'/'+type+'.dat'+'.'+"%.2d" % rank

f = open(name_seed, 'w')

header = '%5d'%Nx+'%5d'%Ny+'%5d'%nx+'%5d'%ny+'%5d'%1+' 0'+' 0.0'

f.write(header)

f.write('\n')

for m in range(nx):

for n in range(ny):

l = ' % .5e'%data[m][n]

f.write(l)

if ((m*ny+n+1)%10 == 0):

f.write('\n')

f.close();

sd = ldir+'/seed.dat'

f = open(sd, 'w')

f.write('A alp b\n')

l = '% 2.2f'%A+'% 2.2f'%alp+'% 2.4f'%b

f.write(l)

f.close()

nx, ny, nz = data.shape[0], data.shape[1], data.shape[2]

f = open(name, 'wb')

f.write("# vtk DataFile Version\n")

f.write("3D visualization of data\n")

f.write("ASCII\n\n")

f.write("DATASET STRUCTURED_POINTS\n")

f.write("DIMENSIONS %1d %1d %1d\n" % (nx, ny, nz))

f.write("ORIGIN 0 0 0\n")

f.write("SPACING 1.00000 1.00000 1.00000\n\n")

f.write("POINT_DATA %1d\n" % (nx*ny*nz))

f.write("SCALARS Data float\n")

f.write("LOOKUP_TABLE default\n")

for k in range(nz):

for j in range(ny):

for i in range(nx):

line = []

line = np.str(data[i][j][k])

f.write(line)

f.write("\n")

"""Make a color contour plot of data

Usage: cplot(data, limits=[None,None], fname='')

If no filename is specified a plot is displayed

File format is ext (default is png)

"""

SIZE = 12

DPI = 100

nx, ny = data.shape[0], data.shape[1]

data = data.reshape(nx,ny)

scale = SIZE/float(max(nx,ny))

plt.figure(figsize=(scale*nx, scale*ny+1.0))

plt.clf()

c = plt.imshow(np.transpose(data), cmap=CM)

plt.clim(limits)

plt.axis([0,nx,0,ny])

#cbar = plt.colorbar(c, ticks=np.arange(0.831,0.835,0.001), aspect = 20, orientation='vertical', shrink=0.72, extend='neither', spacing='proportional')

#cbar = plt.colorbar(c, aspect = 40, orientation='vertical', shrink=0.72, extend='neither', spacing='proportional')

#cbar = plt.colorbar(c, orientation='horizontal', shrink=1.0)

cbar = plt.colorbar(c, orientation='vertical', shrink=0.72, extend='neither', spacing='proportional')

cbar.ax.tick_params(labelsize=21,size=10)

#cbar.ax.yaxis.set_ticks_position('left')

#c.cmap.set_under(color='black')

if len(fname) == 0:

plt.show()

else:

plt.savefig(fname+'.'+ ext, format=ext, dpi=DPI, bbox_inches='tight', pad_inches=0.1)

loc='upper right', ext='png'):

"""Make an axial plot of a funtion of data

Usage: xplot(data, limits=[None,None], fname='', loc='upper left', ext='png')

Possible functions to plot are max, min, avg

If no filename is specified a plot is displayed

The special filename 'M' turns "hold" on (for multiplots)

File format is ext (default is png)

"""

nx,ny = data.shape[0],data.shape[1]

z = np.zeros([3,nx])

for x in range(nx):

z[0,x] = data[x,:].min()

z[1,x] = data[x,:].max()

z[2,x] = data[x,:].sum()/float(ny)

#plt.plot(z[0], label=label+' min')

#plt.plot(z[1], label=label+' max')

plt.plot(z[2], label=label+' avg')

if (fname == 'M'):

plt.hold=True

else:

plt.legend(loc=loc)

plt.axis([0,data.shape[0]-1,limits[0],limits[1]])

plt.hold=False

if len(fname) == 0:

plt.show()

else:

plt.savefig(fname+'-x.'+ext, format=ext)

loc='upper left', ext='png'):

"""Make transverse plots of data

Usage: yplot(data, limits=[None,None], fname='', xval=[0.5], label='',

loc='upper left', ext='ext')

xval is a list of axial distances in units of nx

If no filename is specified a plot is displayed

The special filename 'M' turns "hold" on (for multiplots)

File format is ext (default is png)

"""

nx, ny = data.shape[0], data.shape[1]

y = np.array(range(ny)) + 0.5

for x in xval:

ix = int(x*nx)

plt.plot(y, data[ix,:], label=label+" : "+"x="+str(x))

if (fname == 'M'):

plt.hold=True

else:

plt.axis([0,data.shape[1],limits[0],limits[1]])

plt.legend(loc=loc)

plt.hold=False

if len(fname) == 0:

plt.show()

else:

plt.savefig(fname+'-y.'+ext, format=ext)

nx, ny = h.shape[0], h.shape[1]

c = measure.find_contours(h, hcrit)

n = np.shape(c)[0]

X = []

Y = []

for i in range(n):

cx0 = c[i][0,0]

cy0 = c[i][0,1]

cx1 = c[i][(np.shape(c[i])[0]-1),0]

cy1 = c[i][(np.shape(c[i])[0]-1),1]

if (cx0 != cx1):

if (cy0 != cy1):

xi = c[i][:,0]

yi = c[i][:,1]

xi = xi.tolist()

yi = yi.tolist()

X = X + xi #Appending the list

Y = Y + yi #Appending the list

X = np.array(X)

Y = np.array(Y)

X1 = np.roll(X,1)

nch = np.zeros(int(max(X)))

for k in range(xlim):

I = np.logical_xor(X <= k, X1 > k)

iI = [i for i, j in enumerate(I) if j == False and i != 0]

nch[k] = np.size(iI)/2

tx = np.zeros(int(nch[xlim-1]))

ty = []

if X[iI[0]] > (xlim-1): # If 1st peak is within the fracture

for i in range(int(nch[xlim-1])-1):

peak = X[iI[2*i]:iI[(2*i+1)]]

tx[i] = max(peak)

ty = ty + [Y[(j+iI[2*i])] for j,k in enumerate(peak) if k == max(peak)]

else: # If 1st peak is periodic at the end

peak0 = X[0:iI[0]]

tx[0] = max(peak0)

ty = [Y[j] for j,k in enumerate(peak0) if k == max(peak0)]

for i in range(int(nch[xlim-1])-1):

peak = X[iI[2*i+1]:iI[2*(i+1)]]

tx[i+1] = max(peak)

ty = ty + [Y[(j+iI[2*i])] for j,k in enumerate(peak) if k == max(peak)]

for i in range(xlim,int(max(X))):

tips = np.logical_not(tx[0:len(tx)] < i)

tips = tips.tolist()

nch[i] = tips.count(True)

ty = np.array(ty)

nx, ny = c.shape[0], c.shape[1]

ch = c[xlim]

nch = np.size(ch)

N = 0

for i in range(1,nch-1):

if ch[i-1]<ch[i] and ch[i+1]<ch[i]:

if ch[i] > ccrit:

N = N+1

N = np.size(nch)

x = np.arange(N)

A = [x,nch]

A = np.array(A)

A = np.transpose(A)

A = np.transpose(np.genfromtxt(fname)) # Read N(L) for each x

x = A[0] # L values

N = A[1] # N values

X = np.arange(1,3,0.02) # x = 10-1000 with equidistant points in log-scale

Xe = np.ceil(np.power(10,X)) # Find the nearest integer

n = np.size(Xe)

Ne = np.zeros(n) # N(L) at that value of x

for i in range(n):

Ne[i] = N[Xe[i]]

nx, ny = data.shape[0], data.shape[1]

avg = np.zeros(nx)

Max = np.zeros(nx)

for i in range(nx):

avg[i] = sum(data[i,:])/ny

Max[i] = max(data[i,:])

nx,ny = h.shape[0], h.shape[1]

tx,ty = ida.tipcoord(h)

ch=[]

lp=0

avgd = np.zeros(tx)

for x in range(tx):

vec = h[x,:]

ch.append([x for i in vec if i>2.0])

if(np.size(ch[x])==ny):

lp=x

nx,ny = h.shape[0], h.shape[1]

tx,ty = ida.tipcoord(h)

ch = np.zeros(nx*ny)

ch.shape = (nx,ny)

for x in range(tx):

for y in range(ny):

if(h[x][y])<hcrit:

ch[x][y]=0

else:

ch[x][y]=1

"""Finds the coordinate of tip of the longest channel based on the aperture data

Usage: tipcoord(h_data)

"""

nx, ny = hdata.shape[0], hdata.shape[1]

z = np.zeros(nx)

tx=0

for x in range(nx):

z[x] = hdata[x,:].max()

if z[x] > 2.0:

tx = x

else:

break

for y in range(ny):

if hdata[tx][y] == hdata[tx].max():

ty = y

"""Finds the center-line flow in the channel, i.e. the path of the maximum flow rate in a channel. It uses quadratic interpolation to find the exact location where the flow rate is max imum between two pixels.

Usage: cline(qx_data, qy_data, h_data)

"""

tx,ty = ida.tipcoord(h)

print tx,ty

nx, ny = qx.shape[0], qx.shape[1]

Q = np.sqrt(np.matrix(qx**2.0 + qy**2.0))

Qmax = np.zeros(tx)

ymax = np.zeros(tx)

ymax2 = np.zeros(tx)

for x in range(tx):

Qmax[x] = Q[x,:].max()

for y in range(ny):

if Q[x,y] == Qmax[x]:

ymax[x] = y

A = np.matrix([[(ymax[x]-1)**2,ymax[x]-1,1],[(ymax[x])**2,ymax[x],1],[(ymax[x]+1)**2,ymax[x]+1,1]])

B = np.matrix([[(Q[x,(ymax[x]-1)])],[(Q[x,(ymax[x])])],[(Q[x,(ymax[x]+1)])]])

X = np.linalg.solve(A,B)

ymax2[x] = (-X[1]/(2*X[0]))

plt.plot(ymax2,Qmax)

#plt.axis([0,h.shape[0],ymax2[0]-5,ymax2[0]+5])

plt.show()

"""Quadratic Interpolation of data

"""

nx, ny = data.shape[0], data.shape[1]

#data = np.matrix(data)

tx,ty = ida.tipcoord(h)

ymax = np.zeros(tx)

ymax2 = np.zeros(tx)

datam = np.zeros(tx)

for x in range(tx):

datam[x] = data[x,:].max()

for y in range(ny):

if data[x,y] == datam[x]:

ymax[x] = y

A = np.matrix([[(ymax[x]-1)**2,ymax[x]-1,1],[(ymax[x])**2,ymax[x],1],[(ymax[x]+1)**2,ymax[x]+1,1]])

B = np.matrix([[(data[x,(ymax[x]-1)])],[(data[x,(ymax[x])])],[(data[x,(ymax[x]+1)])]])

X = np.linalg.solve(A,B)

ymax2[x] = (-X[1]/(2*X[0]))

#plt.plot(ymax2)

#plt.axis([0,nx,0,ny])

#plt.show()

nx,ny = h.shape[0], h.shape[1]

tx,ty = ida.tipcoord(h)

ch=[]

lp=0

Q = np.zeros(tx)

for x in range(tx):

vec = h[x,:]

ch.append([x for i in vec if i>2.0])

if(np.size(ch[x])==ny):

lp=x

#ch = np.array(ch)

for x in range(tx):

for y in range(ny):

if x <= lp:

Q[x] += (math.sqrt((qx[x][y]**2.0+qy[x][y]**2.0)))/ny

#Q[x] += p[x][y]/ny

else:

break

for x in range(lp+1,tx):

for y in range(ty-200,ty+200):

if h[x][y] > 2.0:

Q[x] += (math.sqrt((qx[x][y]**2.0+qy[x][y]**2.0)))#/np.size(ch[x])

# Q[x] += p[x][y]/np.size(ch[x])

#plt.plot(Q)

#plt.show()

cn = ida.paradata(wdir,type,time)

ws = wdir+'-seed'

cs = ida.paradata(ws,type,time)

dc = cs - cn

l = 0

for i in range(np.size(data)-1):

l += ((data[i+1]-data[i])**2+1)**0.5

q = 0

for y in range(ty-chd,ty+chd):

q += np.sqrt(qx[lp+94][y]**2+qy[lp+94][y]**2)

A = np.genfromtxt("../PCON/"+simname+"/data/avg.csv")

A = np.transpose(A)

qavg = A[3][1:np.size(A[3])]

t = A[1][1:np.size(A[1])]

f1 = ida.paradata(wdir,'f1',time)

f2 = ida.paradata(wdir,'f2',time)

c2 = ida.paradata(wdir,'c2',time)

c1 = ida.paradata(wdir,'c1',time)

nx,ny = c1.shape[0],c1.shape[1]

f1x = np.zeros(nx)

f2x = np.zeros(nx)

c1x = np.zeros(nx)

c2x = np.zeros(nx)

X = np.zeros(nx)

for x in range(nx):

f1x[x] = f1[x,:].sum()/float(ny)

f2x[x] = f2[x,:].sum()/float(ny)

c1x[x] = c1[x,:].sum()/float(ny)

c2x[x] = c2[x,:].sum()/float(ny)

X[x] = x*dx

cx = c1x+c2x

dcx = np.diff(cx)

flux = c1x[0:nx-1]+c2x[0:nx-1]-dcx/dx

plt.plot(X,c1x,label=r'Coupling ion $X$ concentration $c_X$',color='red')

plt.hold=True

plt.plot(X,c2x,label=r'Secondary ion $A_S$ concentration $c_S$',color='green')

plt.hold=True

plt.plot(X,f2x,label=r'Primary specific volume $f_P$',color='blue')

plt.hold=True

plt.plot(X,f1x,'o',markersize=3, markeredgecolor='#9502f9', label=r'Secondary specific volume $f_S$',color='#9502f9')

#plt.hold=True

#plt.plot(X,cx,'o',color='black',markersize=3,markeredgecolor='black',label=r'$(c_X+c_S)$')

#plt.hold=True

#plt.plot(X[0:nx-1],flux,'o',color='m',markersize=3,markeredgecolor='m',label=r'$c_S+c_X-Pe^{-1}d(c_S+c_X)/dx$')

plt.axis([0,20,-0.1,2])

plt.legend(loc='upper right',prop={'size':10})

plt.grid(True)

fn = ida.fname(wdir,type,time)

plt.savefig(fn[2]+'-x.png',format='png')

plt.close()

nx = 128*(0.2/dx)

ny = 64

lx = 10*(0.2/dx)

f1 = np.zeros(nx*ny)

f2 = np.zeros(nx*ny)

f1 = f1.reshape(nx,ny)

f2 = f2.reshape(nx,ny)

f1[0:lx] = 0.99

f2[lx:nx] = 0.99

f1 = f1.reshape(nx,ny,1)

f2 = f2.reshape(nx,ny,1)

f1name = '../'+dir+'/init/f1.dat.00'

f2name = '../'+dir+'/init/f2.dat.00'

ida.fwrite(f1,f1name)