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mri2d_test.py
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import matplotlib
import numpy as np
import matplotlib.pyplot as plt
from matplotlib import ticker
import athena_read as ath
import sys
from scipy import integrate
def read_all_hdf5(fname,level=0,subsample=False):
time,data=ath.athdf(fname,level=level,subsample=subsample)
bx = data['Bcc1'][0]
#time,data=ath.athdf(fname,quantities=['Bcc2'])
by = data['Bcc2'][0]
#time,data=ath.athdf(fname,quantities=['Bcc3'])
bz = data['Bcc3'][0]
#time,data=ath.athdf(fname,quantities=['vel1'])
vx = data['vel1'][0]
#time,data=ath.athdf(fname,quantities=['vel2'])
vy = data['vel2'][0]
#time,data=ath.athdf(fname,quantities=['vel3'])
vz = data['vel3'][0]
rho = data['rho'][0]
T = data['press'][0]/rho
x = data['x1f'];y = data['x2f']; z = data['x3f']
x = (x + 0.5*(x[1]-x[0]))[:-1]
y = (y + 0.5*(y[1]-y[0]))[:-1]
z = (z + 0.5*(z[1]-z[0]))[:-1]
return time,x,y,z,rho,T,bx,by,bz,vx,vy,vz
def read_all_vtk(fname):
time,x,y,z,data=ath.vtk(fname)
bx = data['Bcc'][0,...,0]
#time,data=ath.athdf(fname,quantities=['Bcc2'])
by = data['Bcc'][0,...,1]
#time,data=ath.athdf(fname,quantities=['Bcc3'])
bz = data['Bcc'][0,...,2]
#time,data=ath.athdf(fname,quantities=['vel1'])
vx = data['vel'][0,...,0]
#time,data=ath.athdf(fname,quantities=['vel2'])
vy = data['vel'][0,...,1]
#time,data=ath.athdf(fname,quantities=['vel3'])
vz = data['vel'][0,...,2]
rho = data['rho'][0]
T = data['press'][0]/rho
x = (x + 0.5*(x[1]-x[0]))[:-1]
y = (y + 0.5*(y[1]-y[0]))[:-1]
if len(z) > 1:
z = (z + 0.5*(z[1]-z[0]))[:-1]
else:
z = z
return time,x,y,z,rho,T,bx,by,bz,vx,vy,vz
def read_bz_hdf5(fname,level=0,subsample=False):
time,data=ath.athdf(fname,level=level,subsample=subsample,quantities=['Bcc3'])
bz = data['Bcc3'][0]
x = data['x1f'];y = data['x2f']; z = data['x3f']
x = (x + 0.5*(x[1]-x[0]))[:-1]
y = (y + 0.5*(y[1]-y[0]))[:-1]
z = (z + 0.5*(z[1]-z[0]))[:-1]
return time,x,y,z,bz
def plot_all_hdf5(time,x,y,z,rho,T,bx,by,bz,vx,vy,vz,cmap='RdBu_r',to_png=False,pname=None):
matplotlib.rcParams['figure.figsize'] = (6,12)
nrow,ncol,count = 4,2,8
fig,axes = plt.subplots(nrows=nrow,ncols=ncol)#,sharey=True,sharex=True)
for i in np.arange(nrow*ncol):
if (i==0):
label=r'$v_r$'
dmin,dmax = -0.14,0.14
var = vx
if (i==1):
label=r'$B_r$'
var = bx
dmin,dmax = -0.18,0.18
dmin,dmax = -0.3,0.3
if (i==2):
label=r'$v_z$'
var = vy
dmin,dmax = -0.12,0.12
if (i==3):
label=r'$B_z$'
var = by
dmin,dmax = -0.1,0.1
dmin,dmax = -0.3,0.3
if (i==4):
label=r'$v_{\phi}$'
var = vz
dmin,dmax = -0.4,0.4
dmin,dmax = -0.6,0.6
if (i==5):
label=r'$B_{\phi}$'
var = bz
dmin,dmax = -0.22,0.22
dmin,dmax = -0.6,0.6
if (i==6):
label=r'$log \rho$'
var = np.log10(rho)
dmin,dmax = -0.012,0.012
if (i==7):
label=r'$\delta T$'
var = T-np.average(T) #np.log10(T/10.0)
dmin,dmax = -0.05,0.05
dmin,dmax = -0.1,0.1
#plt.subplot(nrow,ncol,i+1)
#im = plt.imshow(np.transpose(var),vmin=dmin,vmax=dmax,extent=[0,1,-0.5,0.5],cmap='RdBu_r',origin='lower')
# plt.xlabel('z')
# plt.ylabel('r')
# plt.text(1-0.15,0.5-0.1,label,fontsize=15)
# cb = plt.colorbar(orientation='horizontal',pad=0.02)
# tick_locator = ticker.MaxNLocator(nbins=5)
# cb.locator = tick_locator
# cb.update_ticks()
# if i==0:
# plt.text(1.05,0.6,'t='+str(time),fontsize=10)
idn,jdn = i/ncol,i%ncol
ax = axes[idn,jdn]
im = ax.imshow(np.transpose(var),vmin=dmin,vmax=dmax,extent=[0,1,-0.5,0.5],cmap=cmap,origin='lower')
ax.text(1-0.15,0.5-0.1,label,fontsize=15)
cb = fig.colorbar(im,ax=ax,orientation='horizontal',pad=0.02)
tick_locator = ticker.MaxNLocator(nbins=5)
cb.locator = tick_locator
cb.update_ticks()
if i==0:
ax.set_title('t='+str(time),fontsize=10)
#ax.text(0.5,0.6,'t='+str(time),fontsize=10)
if i==7:
ax.set_xlabel('z')
ax.set_ylabel('r')
plt.tight_layout()
# export to png image
if to_png:
if pname == None:
pname = fname+'.png'
plt.savefig(pname, format='png', dpi=300)
plt.close(fig)
def plot_one_panel(time,x,y,z,var,label='default',cmap='RdBu_r',to_png=False,pname=None):
matplotlib.rcParams['figure.figsize'] = (6,12)
nrow,ncol,count = 1,1,1
#fig,ax = plt.subplots(nrows=nrow,ncols=ncol)#,sharey=True,sharex=True)
dmin,dmax = np.min(var),np.max(var)
print dmin,dmax
if dmin < 0.0:
print np.abs(dmin),np.abs(dmax)
smax = np.maximum(np.abs(dmin),np.abs(dmax))
dmin,dmax = -smax,smax
matplotlib.rcParams['figure.figsize'] = (6,6)
fig = plt.figure()
ax = fig.add_subplot(111)
im = ax.imshow(np.transpose(var),vmin=dmin,vmax=dmax,extent=[0,1,-0.5,0.5],cmap=cmap,origin='lower')
ax.text(1-0.15,0.5-0.1,label,fontsize=15)
cb = fig.colorbar(im,ax=ax,orientation='horizontal',pad=0.062,fraction=0.045)
tick_locator = ticker.MaxNLocator(nbins=5)
cb.locator = tick_locator
#cb.ax.xaxis.set_ticks_position('top')
cb.update_ticks()
ax.set_title('t='+str(time),fontsize=10)
ax.set_xlabel('z')
ax.set_ylabel('r')
# export to png image
if to_png:
if pname == None:
pname = fname+'.png'
plt.savefig(pname, format='png', dpi=300)
plt.close(fig)
def plot_aphi_contour(b_z,x,y,nlev=40,t=0,vmin=5e-5,vmax=2e-3,cmap='RdBu_r',to_png=False,pname=None):
ndim= np.shape(b_z)
A_phi = np.zeros(ndim)
for k in np.arange(ndim[0]):
yint = b_z[k,:]
xint = x
phi = integrate.cumtrapz(yint, xint, initial=0)
A_phi[k,:]=phi[:]
matplotlib.rcParams['figure.figsize'] = (6,6)
fig = plt.figure()
ax = fig.add_subplot(111)
cs = ax.contour(x,y,A_phi,nlev,vmin=5e-5,vmax=2e-3,cmap=cmap,antialias=True)
# export to png image
if to_png:
if pname == None:
pname = 'Aphi0000.png'
plt.savefig(pname, format='png', dpi=300)
plt.close(fig)
def curl3d(vx,vy,vz,dx,dy,dz):
[dzvx,dyvx,dxvx] = np.gradient(vx)
[dzvy,dyvy,dxvy] = np.gradient(vy)
[dzvz,dyvz,dxvz] = np.gradient(vz)
cx = dyvz/dy-dzvy/dz
cy = dzvx/dz-dxvz/dx
cz = dxvy/dx-dyvx/dy
return cx,cy,cz
def curl2d(vx,vy,vz,dx,dz):
[dzvx,dxvx] = np.gradient(vx)
[dzvy,dxvy] = np.gradient(vy)
[dzvz,dxvz] = np.gradient(vz)
cx = -dzvy/dz
cy = dzvx/dz-dxvz/dx
cz = dxvy/dx
return cx,cy,cz
def plot_jcurrent(time,x,y,bx,by,bz,ext1=[-50,50],ext2=[-150,150],ext3=[-80,80],ext4=None,cmap='RdBu_r',to_png=False,pname=None):
dx,dz = x[1]-x[0],y[1]-y[0]
jx,jy,jz = curl2d(bx,by,bz,dx,dz)
matplotlib.rcParams['figure.figsize'] = (6,6)
nrow,ncol,count = 2,2,4
fig,axes = plt.subplots(nrows=nrow,ncols=ncol)#,sharey=True,sharex=True)
for i in np.arange(4):
if i==0:
var = jx
label=r'$j_r$'
dmin,dmax = ext1[0],ext1[1]
if i==1:
var = jy
label=r'$j_{\phi}$'
dmin,dmax = ext2[0],ext2[1]
if i==2:
var = jz
label=r'$j_z$'
dmin,dmax = ext3[0],ext3[1]
if i==3:
var = np.sqrt(jx**2+jy**2+jz**2)
label=r'$|j|$'
if ext4 == None:
ext4=[0,0]
ext4 = np.sqrt(np.array(ext1)**2+np.array(ext2)**2+np.array(ext3)**2)
dmin,dmax = 0,np.max(ext4)
idn,jdn = i/ncol,i%ncol
ax = axes[idn,jdn]
im = ax.imshow(np.transpose(var),vmin=dmin,vmax=dmax,extent=[0,1,-0.5,0.5],cmap=cmap,origin='lower')
ax.text(1-0.15,0.5-0.1,label,fontsize=15)
cb = fig.colorbar(im,ax=ax,orientation='horizontal',pad=0.02)
tick_locator = ticker.MaxNLocator(nbins=5)
cb.locator = tick_locator
cb.update_ticks()
if i==0:
ax.set_title('t='+str(time),fontsize=10)
#ax.text(0.5,0.6,'t='+str(time),fontsize=10)
if i==3:
ax.set_xlabel('z')
ax.set_ylabel('r')
#plt.tight_layout()
plt.show
# export to png image
if to_png:
if pname == None:
pname = fname+'.png'
plt.savefig(pname, format='png', dpi=300)
plt.close(fig)
def batch_png(fdir,ts=0,te=1,tstride=1):
for i in np.arange(ts,te+1,tstride):
fname=fdir+'/mri2d.out2.'+str(i).zfill(5)+'.athdf'
time,x,y,z,rho,T,bx,by,bz,vx,vy,vz = read_all_hdf5(fname)
plot_all_hdf5(time,x,y,z,rho,T,bx,by,bz,vx,vy,vz,to_png=True,pname=fdir+'/mri2d_t='+str(i).zfill(4)+'.png')
plot_aphi_contour(by,x,y,nlev=40,t=time,vmin=5e-5,vmax=2e-3,to_png=True,pname=fdir+'/mri2d.aphi.'+str(i).zfill(5)+'.png')
plot_jcurrent(time,x,y,bx,by,bz,ext1=[-30,30],ext2=[-80,80],ext3=[-40,40],ext4=[0,80],to_png=True,pname=fdir+'/mri2d.jcurr.'+str(i).zfill(5)+'.png')
if __name__=='__main__':
if len( sys.argv ) < 4:
print "Please specify input targ,ts,te,tstride"
exit( )
targ = sys.argv[1]
ts,te,tstride = int(sys.argv[2]),int(sys.argv[3]),int(sys.argv[4])
fdir = '/tigress/jiming/athena/bin/for_daniel/data/'+targ
#batch_png(fdir,ts,te,tstride)
for i in np.arange(ts,te,tstride):
fname=fdir+'/mri2d.out2.'+str(i).zfill(5)+'.athdf'
#time,x,y,z,rho,T,bx,by,bz,vx,vy,vz = read_all_hdf5(fname,level=2,subsample=True)
time,x,y,z,bz = read_bz_hdf5(fname) #,level=2,subsample=True)
plot_one_panel(time,x,y,z,bz,label=r'$B_{\phi}$',to_png=True,pname=fdir+'/bphi.'+str(i).zfill(5)+'.png')
#ideal
#1024x1024_nu=1e-6'
#512x512_nu=1e-5'