def avgQ(qx,qy,h): 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() return Q
def cline(qx,qy,h):
"""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()
return ymax2
def chline(h, hcrit=2.0):
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
return ch
def lpdata(h):
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
return lp,tx,ty
def interp(data, h):
"""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()
return ymax2
def Bplot(inputs):
"""Batch contour plots
Usage: python bplot 'wdir', 'times'
Examples: wdir = '/work/dissoltest'
times = '0,10,2' (t=0, t=2, t=4, t=6, t=8)
Makes a contour plot from wdir/name_time.dat.
Output (png) file wdir/imgs/name_num-c.png
Optional args (must be entered in order):
'lim', 'names', 'types', 'func', 'xval'
Limits for contour plots are: h [1,lim], qy [-lim,lim]; else [0,lim]
"""
import os
import ida
import matplotlib as plt
# Defaults
names = ['c1'] # concentration
types = ['C'] # contour plot
lim = None # limit on contours
xval = '0.2,0.4,0.6,0.8' # list for yplot
# Mandatory arguments
args = len(inputs) - 1 # Argument count
if (args < 2):
print "Usage: python bplot 'sim', 'times'"
return
sim = inputs[1]
times = inputs[2].split(',')
times = range(int(times[0]), int(times[1])+1, int(times[2]))
# Optional arguments
if (args > 2): lim = [float(x) for x in inputs[3].split(',')]
if (args > 3): names = inputs[4].split(',')
if (args > 4): types = inputs[5].split(',')
if (args > 5): xval = [float(x) for x in inputs[6].split(',')]
# Make directories
wdir = '/work/viratu/'+sim
ldir = '/home/ladd/viratu/code/porous/'+sim
os.system('mkdir -p '+wdir+'/imgs '+wdir+'/plts')
#wdir = ldir
#print(wdir)
# Make plots
n = 0
Tip = []
N = []
for name in names:
if (lim == None):
clim = lim
else:
clim = lim[n]
n = n + 1
for time in times:
print name, time
#if ('N' in types):
# wdir = ldir
if ('A' not in types):
data = ida.paradata(wdir, name, time)
Nm = ida.fname(wdir, name, time)
#if time == 0:
# data = data+1
if ('N' in types):
Nact = ida.chactive(data,0.1,40)
N = np.append(N,Nact)
print Nact
if ('D' in types):
dc = ida.difference(wdir, name, time)
nd = name + '-diff'
plotc(dc, wdir, nd, time, clim)
if ('V' in types):
tipp = ida.tipcoord(data)
Tip = np.append(Tip,tipp)
if ('C' in types): plotc(data, wdir, name, time, clim)
if ('X' in types): plotx(data, wdir, name, time)
if ('Y' in types): ploty(data, wdir, name, time, xval)
if ('A' in types):
dx = clim
ida.plotxx(wdir, name, dx, time)
if ('Z' in types):
os.system('mkdir -p '+ldir+'/vtk')
tm = np.str(time)
znm = ldir+'/vtk/'+name + tm + '.vtk'
ida.vtk_format(data,znm)
#if ('N' not in types):
#os.system('rm '+ Nm[0] + '*') #Remove the files from work directory
if ('D' in types):
Nmd = ida.fname(wdir+'-seed', name, time)
os.system('rm '+ Nmd[0] + '*')
if ('V' in types):
np.savetxt(wdir+"/Tip.dat",Tip)
np.savetxt(ldir+"/Tip.dat",Tip)
if ('N' in types):
np.savetxt(ldir+"/Nact.dat",N)
os.system('cp -r '+wdir+'/imgs '+ldir+'/')
os.system('cp -r '+wdir+'/plts '+ldir+'/')
return
