def __init__(self,path,field="n",meta=None,fast_center=True,get_Xc=True,
get_lambda=True,debug=False,fast = True):
self.path=path
self.field=field
#print 'read the inp'
f=open(path+'/BOUT.log.0', 'r')
self.md5 = hashlib.md5(f.read()).hexdigest()
f.close()
defaults = {'dx':1,'x0':0,'dy':1,'y0':0,'log_n':False}
for key,val in defaults.items():
if not hasattr(self,key):
#print 'setting: ',key,val
setattr(self, key, val)
#read all metadata from BOUT.inp
#print 'read the inp'
inp = read_inp(path=path,boutinp='BOUT.inp')
inp = parse_inp(inp) #inp file
for k, v in inp.items():
setattr(self, k, v)
for k,v in (getattr(self,'[main]')).items():
#print k,v
try:
setattr(self, str.lower(k), float(v))
except:
setattr(self, str.lower(k), v)
for k,v in (getattr(self,'[physics]')).items():
#print k,v
try:
setattr(self, str.lower(k), float(v))
except:
try:
setattr(self, str.bool(k), v)
except:
setattr(self, str.lower(k), v)
if meta is not None:
for k, v in meta.items():
setattr(self, k, v)
self.mz = self.mz-1
self.nz = self.mz #self.nz = self.np.squeeze(collect("MZ",xind=[0,0],path=path,info=False))-1
nxpe = self.nxpe# np.squeeze(collect("NXPE",xind=[0,0],path=path,info=False))
mxg = self.mxg #np.squeeze(collect("MXG",xind=[0,0],path=path,info=False))
self.mxsub = np.squeeze(collect("MXSUB",xind=[0,0],path=path,info=False)) #gaurds
self.nx = int(self.mxsub*nxpe + 2*mxg) #including gaurds
nx,ny,nz = self.nx,self.nz,self.nz
self.dx = np.squeeze(collect("dx",path=path,xind=[0,0]))
self.dy = np.squeeze(collect("dz",path=path,xind=[0,0]))
self.zmax = np.squeeze(collect("ZMAX",path=path))
self.time = np.array(np.squeeze(collect("t_array",path=path,xind=[0,0])))
nt = self.nt = len(self.time)
try:
self.nave = np.squeeze(collect("nave",path=path))
self.nave = self.nave/nt
have_nave = True
except:
have_nave = False
try:
self.G = np.squeeze(collect("gamma",path=path))
self.G = self.G/nt
have_flux = True
except:
havex_flux = False
self.debug = debug
if debug:
t_chunk = 20
t_stop = np.max(self.nt)
t1 = np.int(self.nt*.90)
else:
t_chunk = 30
t_stop = np.max(self.nt)
t1 = 0 #self.time[0]
self.t_chunk = t_chunk
self.t1 = t1
self.t_stop = t_stop
tarray = np.array(np.arange(nt - nt/2))
t2 = np.min([t1+t_chunk,t_stop])
self.xmoment = {'1':[],'2':[]}
self.ymoment = {'1':[],'2':[]}
self.max_val = []
y0,x0 = (self.y0,self.x0)
dx,dy = (self.dx,self.dy)
ymin = y0
xmin = x0
xmax = nx*dx + xmin
ymax = ny*dy + ymin
self.kx_max = nx
self.ky_max = ny
kxmax,kymax = nx,ny
kxmin = 0.
kymin = 0.
dky = self.dky = 1.0/self.zmax
dkx = self.dkx = 2.*np.pi/self.dx
self.kx = np.arange(0,nx)*self.dkx
self.ky = np.arange(0,ny)*self.dky
#print nx,nx*self.mxsub
self.pos_i = np.mgrid[0:nx:1,0:ny:1]
self.x = np.mgrid[xmin:xmax:self.dx]
self.y = np.mgrid[xmin:xmax:self.dy]
sys.stdout = mystdout = StringIO()
a = np.squeeze(collect("alpha",path=path))
sys.stdout = old_stdout
#self.a_ave
a_ave = self.a_ave = np.average(a,axis=1)
# popt, pcov= curve_fit(fit_tanh,xnew,a_ave,p0=p0)
# w = run['w'] = popt[0]*dx
try:
xstart = np.min(np.where((a_ave-.1*a_ave.max()>0)) )
self.xstart = xstart
xstop = np.int(xstart+ nx/2.)
if xstop > nx:
xstop = nx -1;
except:
xstart= np.int(nx/3.)
xstop = np.int(xstart+ nx/2.)
self.fast = fast
#will return 'real n' not 'log n' statistics and profiles, spasly sampled in x, dense in t
moving_min_n, moving_max_n, moving_min, moving_max, nave_net, nrms_net ,nmin_net,nmax_net = self.compute_profile()
pdf_x = []
df_x = []
self.nbins = nbins = 300
for x in xrange(nx):
#print moving_min_n[x]
pdf_x.append(np.mgrid[moving_min_n[x]-.01:moving_max_n[x] +.01:complex(0,nbins)])
df_x.append(np.mgrid[moving_min[x]-.01:moving_max[x] +.01:complex(0,nbins)])
self.pdf_x = pdf_x
self.df_x = df_x
pdf_y=nx*[None]
df_y=nx*[None]
flux_pdf_y=nx*[None]
flux_df_y =nx*[None]
pdf_y_net = 0
df_y_net = 0
# self.moving_min_n= moving_min_n
# self.moving_max_n= moving_max_n
# self.moving_min= moving_min
# self.moving_max= moving_max
for i in range(nx):
#print "some unique object %d" % ( i, )
pdf_y[i]= 0*pdf_x[i]
df_y[i]= 0*pdf_x[i]
flux_pdf_y[i]= 0*pdf_x[i]
flux_df_y[i]= 0*pdf_x[i]
xstart = np.min(np.where(a_ave - 0.1 * a_ave.max() > 0))
xstop = np.int(xstart + nx / 2.0)
x1 = np.int(xstart)
x2 = np.int(xstart + nx/5.0)
# x1 = np.min(np.where((a_ave-.1*a_ave.max()>0)) )
# x2 = np.min(np.where((a_ave-.9*a_ave.max()>0)) )
df_x_start = np.array(df_x[x1:x2]).min()
df_x_stop = np.array(df_x[x1:x2]).max()
pdf_x_start = np.array(pdf_x[x1:x2]).min()
pdf_x_stop = np.array(pdf_x[x1:x2]).max()
self.df_x_start = df_x_start
self.df_x_stop = df_x_stop
self.pdf_x_start = pdf_x_start
self.pdf_x_stop = pdf_x_stop
if xstop > nx:
xstop = nx - 1
j = 0
print 't2,t_stop:', t2, t_stop
self.nave = []
self.flux = []
self.v = []
while t2 < t_stop:
print 'computing statistical measures'
(nave, dn), (flux, flux_max, d_flux), (vx,vy),data = self.dens_and_flux(t1, t2)
self.save_linlam(np.log(nave), np.log(dn))
self.nave.append([nave, dn])
self.flux.append([flux, flux_max, d_flux])
self.v.append([vx,vy])
#print len(self.flux)
ntt, nxx, nyy = data.shape
#print 'data.shape: ', data.shape, t1, t_stop
if t1 > np.int(2*t_stop)/3:
j = j + 1
for x in xrange(nx):
if not fast:
p = np.polyfit(np.arange(ntt), np.squeeze(data[:, x, :]).mean(axis=1), 1)
nave2d = p[1] + p[0] * np.arange(ntt)
p = np.polyfit(np.arange(ntt), np.squeeze(flux[:, x, :]).mean(axis=1), 1)
flux2d_x = p[1] + p[0] * np.arange(ntt)
if self.log_n:
cond = np.exp(data[:, x, :]) - np.exp(np.reshape(np.repeat(nave2d, ny), [ntt, ny]))
cond_flux = np.exp(data[:, x, :])*vy - np.reshape(np.repeat(flux2d_x, ny), [ntt, ny])
else:
cond = data[:, x, :] - np.reshape(np.repeat(nave2d, ny), [ntt, ny])
elif self.log_n:
#print nave.shape,data.shape
cond = np.exp(data[:, x, :]) - nave[x]
else:
cond = data[:, x, :] - nave[x]
nrms = cond.std()
datamax = data[:, x, :].max()
if np.isfinite(datamax):
dfadd = np.squeeze(np.histogram(cond, bins=nbins, normed=False, range=(df_x[x].min(), df_x[x].max()))[0])
pdfadd = np.squeeze(np.histogram(cond / nrms, bins=nbins, normed=False, range=(pdf_x[x].min(), pdf_x[x].max()))[0])
if np.isfinite(np.sum(pdfadd)):
pdf_y[x] = pdf_y[x] + pdfadd
df_y[x] = df_y[x] + dfadd
if abs(x - xstart) < nx / 20.0:
pdf_y_net = pdf_y_net + np.squeeze(np.histogram(cond / nrms, bins=nbins, normed=False, range=(pdf_x_start, pdf_x_stop))[0])
#print x1, x2
df_y_net = df_y_net + np.squeeze(np.histogram(cond, bins=nbins, normed=False, range=(df_x_start, df_x_stop))[0])
self.pdf_y = pdf_y
self.pdf_y_net = pdf_y_net
self.df_y = df_y
self.df_y_net = df_y_net
self.t.append(t1 + 0.5 * (t2 - t1))
t1 = t2 + 1
t2 = np.min([t1 + t_chunk - 1, t_stop + 1])
self.nave = np.array(self.nave)
self.flux = np.array(self.flux)
self.pdf_stats = []
self.df_stats = []
print 'almost done'
for x in xrange(nx):
if np.mean(nrms_net[:, x]) != 0:
self.pdf_y[x] = pdf_y[x] / sum(pdf_y[x])
self.df_y[x] = df_y[x] / sum(df_y[x])
else:
self.pdf_x[x] = pdf_x[x - 1]
if hasattr(self, 'pdf_y_net'):
self.pdf_y_net = pdf_y_net / np.float(sum(pdf_y_net))
self.df_y_net = df_y_net / np.float(sum(df_y_net))
# inp = read_inp(path=path,boutinp='BOUT.inp')
# inp = parse_inp(inp)
# inp2 = {}
# for k, v in inp.items():
# inp2[k] = v
# print inp2
#read the data and process
t1= 0
t2 = 200
try:
inp = read_inp(path=path,boutinp='BOUT.inp')
inp = parse_inp(inp)
dx = np.double(inp['[mesh]']['dx'])
#print 'dx',dx
zmax = np.double(inp['[main]']['ZMAX'])
print dx,zmax
n = (np.squeeze(collect("n",path=path,tind =[t1,t2])))
print dx,zmax
n0 = (np.squeeze(collect("n0",path=path,tind =[t1,t2])))
u = np.squeeze(collect("u",path=path,tind =[t1,t2]))
phi = np.squeeze(collect("phi",path=path,tind =[t1,t2]))
time = np.squeeze(collect("t_array",path=path,xind=[0,0]))
#dx = np.squeeze(collect("dx",path=path))
#zmax = np.squeeze(collect("ZMAX",path=path))
cache=path+'_movie'
nfile = path+ 'nfile.dat'
ufile = path+ 'ufile.dat'
phifile = path+ 'phifile.dat'
Akfiile = path+'Akfile.dat'
from boutdata import collect
from boutdata import collect2
from collect2 import collect2
from boututils import savemovie
import numpy as np
inp = read_inp(path=path)
meta = parse_inp(inp)
#print meta
nz = np.double(meta['[main]']['MZ'])
nxpe = np.double(meta['[main]']['NXPE'])
nype = np.double(meta['[main]']['NYPE'])
mxg = np.double(meta['[main]']['MXG'])
dx = np.double(meta['[mesh]']['dx'])
zmax = np.double(meta['[main]']['ZMAX'])
#mxsub = meta['[main]']['']
#nz = np.squeeze(collect("MZ",xind=[0,0],path=path,info=False))
#nxpe= np.squeeze(collect("NXPE",xind=[0,0],path=path,info=False))
mxsub = 15
#mxsub = np.squeeze(collect("MXSUB",xind=[0,0],path=path,info=True)) #without gaurds
#mxg = np.squeeze(collect("MXG",xind=[0,0],path=path,info=False))
nx = mxsub*nxpe + 2*mxg