from __future__ import division
from builtins import range
from past.utils import old_div
import numpy as np
import matplotlib.pyplot as plt
from boututils.file_import import file_import
from matplotlib.ticker import FixedFormatter, FormatStrFormatter, AutoLocator, AutoMinorLocator
g = file_import("uedge.grd_Up_Ni_Tei_2d.nc")
x_temp = g.get("Rxy")
y_temp = g.get("Zxy")
#print x_temp.shape
nR = x_temp.shape[0]
nZ = x_temp.shape[1]
for r in np.arange(nR):
plt.plot(x_temp[r, :], y_temp[r, :], color='black', linewidth=0.2)
for z in np.arange(nZ):
plt.plot(x_temp[:, z], y_temp[:, z], color='black', linewidth=0.2)
plt.savefig('mesh.pdf')
plt.show()
from builtins import range
from past.utils import old_div
import numpy as np
from boututils.file_import import file_import
from boututils.surface_average import surface_average
from boututils.showdata import showdata
from boutdata.collect import collect
from pylab import plot, show, annotate, xlabel, ylabel, figure, xlim, ylim, legend, gca
import os
path='./data'
gfile='../cbm18_dens8.grid_nx68ny64.nc'
g = file_import(gfile)
var=collect("P", path=path)
sol=surface_average(var, g)
#sol=np.mean(var,axis=3)
p0av=collect("P0", path=path)
q=np.zeros(sol.shape)
for i in range(sol.shape[1]):
q[:,i]=sol[:,i]+p0av[:,0]
psixy=g.get('psixy')
2:0.145174416274, #0.143251663975, Changed 25th April 2014, revision fd032da
4:0.226979299615,
8:0.28632661549,
16:0.295998650267,
32:0.271288568509,
64:0.222344013151,
128:0.1716229,
256:0.12957680451} #0.130220286897} Changed 25th April 2014
#zlist = map(lambda x:2**x, range(9))
# Create a directory for the data
shell_safe("mkdir -p data")
# Import the grid file
grid = file_import("uedge.grd_std.cdl")
code = 0 # Return code
for zeff in zlist:
# Create the input file, setting Zeff
# If we get passed Staggered or something like this, use staggered config file
inp='BOUT_stag.inp' if 'stag' in [i.lower()[:4] for i in argv] else 'BOUT.inp'
shell_safe("sed 's/Zeff = 128.0/Zeff = "+str(zeff)+"/g' "+inp+" > data/BOUT.inp")
timestep = 5e3
if zeff < 128:
# reduce time-step. At large times these cases produce noise
timestep = 1e3
# Delete old output files
shell("rm data/BOUT.dmp.*.nc")
import numpy as np
from boututils.moment_xyzt import moment_xyzt
from boututils.file_import import file_import
from boutdata.collect import collect
import os
#Dynamic matplotlib settings
from matplotlib import rcParams
rcParams['font.size'] = 20.
rcParams['legend.fontsize'] = 'small'
rcParams['lines.linewidth'] = 2
if not os.path.exists('image'):
os.makedirs('image')
g = file_import('../cbm18_dens8.grid_nx68ny64.nc')
psi = old_div((g['psixy'][:, 32] - g['psi_axis']),
(g['psi_bndry'] - g['psi_axis']))
path = './data'
plt.figure()
p0 = collect('P0', path=path)
p = collect('P', path=path)
res = moment_xyzt(p, 'RMS', 'DC')
rmsp = res.rms
dcp = res.dc
nt = dcp.shape[2]
from __future__ import division
from builtins import range
from past.utils import old_div
import numpy as np
import matplotlib.pyplot as plt
from boututils.file_import import file_import
from matplotlib.ticker import FixedFormatter, FormatStrFormatter, AutoLocator, AutoMinorLocator
g = file_import("data/cbm18_dens8.grid_nx68ny64.nc")
majorLocator = AutoLocator()
majorFormatter = FormatStrFormatter('%3.0e')
minorLocator = AutoMinorLocator()
Fm = FixedFormatter([
'0', '$1 \\times 10^4$', '$2 \\times 10^4$', '$3 \\times 10^4$',
'$4 \\times 10^4$'
])
Fm2 = FixedFormatter(
['0', '$2 \\times 10^5$', '$4 \\times 10^5$', '$6 \\times 10^5$'])
bxy = g.get('Bxy')
p = g.get('pressure')
jpar0 = g.get('Jpar0')
psixy = g.get('psixy')
btxy = g.get('Btxy')
shiftangle = g.get('ShiftAngle')
nx = g.get('nx')
ny = g.get('ny')
q = np.zeros((nx, ny))
from __future__ import print_function
from boututils.file_import import file_import
from bunch import Bunch
import numpy as np
def cbmtogeqdsk(g):
gg=Bunch()
gg.r=g['Rxy']
gg.z=g['Zxy']
gg.psi=g['psi']
gg.pres=g['mu0p']
gg.qpsi=g['qsafe']
gg.fpol=g['f']
gg.nx=g['nx']
gg.ny=g['ny']
i=np.argwhere(g['mu0p']==0)
gg.simagx=gg.psi.min()
gg.sibdry=gg.psi[i[0]]
gg.xlim=0
gg.ylim=0
gg.nlim=0
return gg
if __name__ == '__main__':
gfile='../cbm18_dens8.dskgato.cdl'
g=file_import(gfile)
print(cbmtogeqdsk(g))
path='./data'
t_array=collect('t_array', path=path)
save('t_array.dat', t_array)
p0=collect('P0', path=path)
save('p0.dat', p0)
# n0=collect('n0', path=path)
# save('n0.dat', n0
# ti0=collect('ti0', path=path)
# save('ti0.dat', ti0)
# te0=collect('te0', path=path)
# save('te0.idl.dat', te0)
gfile=file_import('./cbm18_dens8.grid_nx68ny64.nc')
p=collect('P', path=path)
save('p.dat', p)
res=moment_xyzt(p,'RMS','DC')
rmsp=res.rms
dcp=res.dc
save('rmsp.dat', rmsp)
save('dcp.dat', dcp)
elmsp=elm_size(dcp,p0,gfile,yind=32,Bbar=gfile['bmag'])
save('elmsp.dat', elmsp)
figure(0)
plot(t_array,elmsp.s2, 'k-')
xlabel('t/Ta')
ylabel('Elm size')
mayavi.add_source(src)
mayavi.add_module(Outline())
g = GridPlane()
g.grid_plane.axis = 'x'
mayavi.add_module(g)
if __name__ == '__main__':
from boutdata.collect import collect
from boututils.file_import import file_import
path = "/media/449db594-b2fe-4171-9e79-2d9b76ac69b6/runs/data_33/"
#path="/home/ben/run4"
#g = file_import("../cbm18_dens8.grid_nx68ny64.nc")
g = file_import("data/cbm18_8_y064_x516_090309.nc")
#g = file_import("/home/ben/run4/reduced_y064_x256.nc")
data = collect("P", tind=50, path=path)
data = data[0, :, :, :]
s = np.shape(data)
nz = s[2]
bkgd = collect("P0", path=path)
for z in range(nz):
data[:, :, z] += bkgd
# Create a structured grid
sgrid = create_grid(g, data, 10)
w = tvtk.XMLStructuredGridWriter(input=sgrid, file_name='sgrid.vts')
# mayavi.new_scene()
src = VTKDataSource(data=sgrid)
e.add_source(src)
e.add_module(Outline())
g = GridPlane()
g.grid_plane.axis = 'x'
e.add_module(g)
if __name__ == '__main__':
from boutdata.collect import collect
from boututils.file_import import file_import
#path = "/media/449db594-b2fe-4171-9e79-2d9b76ac69b6/runs/data_33/"
path="../data"
g = file_import("../bout.grd.nc")
#g = file_import("../cbm18_8_y064_x516_090309.nc")
#g = file_import("/home/ben/run4/reduced_y064_x256.nc")
data = collect("P", tind=10, path=path)
data = data[0,:,:,:]
s = np.shape(data)
nz = s[2]
#bkgd = collect("P0", path=path)
#for z in range(nz):
# data[:,:,z] += bkgd
# Create a structured grid
sgrid = create_grid(g, data, 1)
from __future__ import division
from builtins import range
from past.utils import old_div
from boutdata.collect import collect
import numpy as np
import matplotlib.pyplot as plt
from boututils.file_import import file_import
from matplotlib.ticker import FixedFormatter, FormatStrFormatter, AutoLocator, AutoMinorLocator
#read grid data
g = file_import('data/slab.grd.nc')
x_temp = g.get("Rxy")
y_temp = g.get("Zxy")
#read simulation data
z_temp = collect("Ni", path="data")
nx = z_temp.shape[1]
ny = z_temp.shape[2]
time = 10
plot = "1d"
fig = plt.figure(figsize=(9, 6), dpi=100)
if plot == "2d":
ax2d = fig.add_subplot(111, aspect='equal')
x = np.zeros((nx, ny))
y = np.zeros((nx, ny))
z = z_temp[10, :, :]
def metadata(inpfile='BOUT.inp', path='.', v=False):
filepath = path + '/' + inpfile
print(filepath)
inp = read_inp(path=path, boutinp=inpfile)
inp = parse_inp(inp) #inp file
print(path)
outinfo = file_import(path + '/BOUT.dmp.0.nc') #output data
try:
print(path)
cxxinfo = no_comment_cxx(path=path, boutcxx='physics_code.cxx.ref')
#evolved = get_evolved_cxx(cxxinfo)
fieldkeys = get_evolved_cxx(cxxinfo)
fieldkeys = ['[' + elem + ']' for elem in fieldkeys]
except:
print('cant find the cxx file')
#gridoptions = {'grid':grid,'mesh':mesh}
if '[mesh]' in list(inp.keys()):
#IC = outinfo
IC = read_grid(path + '/BOUT.dmp.0.nc') #output data again
elif 'grid' in inp['[main]']:
gridname = inp['[main]']['grid']
try:
IC = read_grid(gridname) #have to be an ansoulte file path for now
print('IC: ', type(IC))
# print IC.variables
# print gridname
except:
#print gridname
print('Fail to load the grid file')
#print IC
#print gridname
#print len(IC)
#print IC
evolved = []
collected = []
ICscale = []
# fieldkeys = ['[Ni]','[Te]','[Ti]','[Vi]','[rho]',
# '[Ajpar]','[Apar]','[vEBx]','[vEBy]','[vEBz]',
# '[jpar]','[phi]']
#construct fieldkeys from cxx info
#fieldkeys = ['['+x+']' for x in evolved]
#fieldkeys = evolved
#just look ahead and see what 3D fields have been output
available = np.array([str(x) for x in outinfo])
a = np.array([(len(outinfo[x].shape) == 4) for x in available])
available = available[a]
defaultIC = float(inp['[All]'].get('scale', 0.0))
# print inp.keys()
#figure out which fields are evolved
print(fieldkeys)
for section in list(inp.keys()): #loop over section keys
print('section: ', section)
if section in fieldkeys: #pick the relevant sections
print(section)
#print inp[section].get('evolve','True')
#rint (inp[section].get('evolve','True')).lower().strip()
if (inp[section].get('evolve', 'True').lower().strip() == 'true'
): # and section[1:-1] in available :
print('ok reading')
evolved.append(section.strip('[]'))
ICscale.append(float(inp[section].get('scale', defaultIC)))
if inp[section].get('collect', 'False').lower().strip() == 'true':
collected.append(section.strip('[]'))
try:
if inp['[physics]'].get('transport',
'False').lower().strip() == 'true':
vEBstr = ['vEBx', 'vEBy', 'vEBz', 'vEBrms']
[collected.append(item) for item in vEBstr]
except:
print('no [physics] key')
meta = OrderedDict()
class ValUnit(object):
def __init__(self, value=0, units=''):
self.u = units
self.v = value
def todict(self):
return {'u': self.u, 'v': self.v}
#def decode_valunit(d):
def ToFloat(metaString):
try:
return float(metaString)
except ValueError:
return metaString
#meta['evolved'] = ValUnit(evolved,'')
meta['evolved'] = evolved
meta['collected'] = collected
meta['IC'] = np.array(ICscale)
d = {}
print('evolved: ', evolved)
# read meta data from .inp file, this is whre most metadata get written
for section in list(inp.keys()):
if (('evolve' not in inp[section]) and ('first' not in inp[section])
): #hacky way to exclude some less relevant metadata
for elem in list(inp[section].keys()):
meta[elem] = ValUnit(ToFloat(inp[section][elem]))
d[elem] = np.array(ToFloat(inp[section][elem]))
#read in some values from the grid(IC) and scale them as needed
norms = {
'Ni0': ValUnit(1.e14, 'cm^-3'),
'bmag': ValUnit(1.0e4, 'gauss'),
'Ni_x': ValUnit(1.e14, 'cm^-3'),
'Te_x': ValUnit(1.0, 'eV'),
'Ti_x': ValUnit(1.0, 'eV'),
'Rxy': ValUnit(1, 'm'),
'Bxy': ValUnit(1.0e4, 'gauss'),
'Bpxy': ValUnit(1.0e4, 'gauss'),
'Btxy': ValUnit(1.0e4, 'gauss'),
'Zxy': ValUnit(1, 'm'),
'dlthe': ValUnit(1, 'm'),
'dx': ValUnit(1, 'm'),
'hthe0': ValUnit(1, 'm')
}
availkeys = np.array([str(x) for x in outinfo])
tmp1 = np.array([x for x in availkeys])
#b = np.array([x if x not in available for x in a])
tmp2 = np.array([x for x in tmp1 if x not in available])
static_fields = np.array([x for x in tmp2 if x in list(norms.keys())])
#static_fields = tmp2
#print availkeys
# print meta.keys()
#print IC.variables.keys()
# print tmp1
# print tmp2
for elem in static_fields:
print('elem: ', elem)
meta[elem] = ValUnit(IC.variables[elem][:] * norms[elem].v,
norms[elem].u)
d[elem] = np.array(IC.variables[elem][:] * norms[elem].v)
for elem in IC.variables:
if elem not in meta:
if elem in list(norms.keys()):
meta[elem] = ValUnit(IC.variables[elem][:] * norms[elem].v,
norms[elem].u)
d[elem] = np.array(IC.variables[elem][:] * norms[elem].v)
else:
meta[elem] = IC.variables[elem][:]
d[elem] = IC.variables[elem][:]
#print d.keys()
#if case some values are missing
default = {
'bmag': 1,
'Ni_x': 1,
'NOUT': 100,
'TIMESTEP': 1,
'MZ': 32,
'AA': 1,
'Zeff': ValUnit(1, ''),
'ZZ': 1,
'zlowpass': 0.0,
'transport': False
}
diff = set(default.keys()).difference(set(d.keys()))
for elem in diff:
#print 'diff: ',elem
meta[elem] = default[elem]
d[elem] = np.array(default[elem])
#print meta.keys()
#print d.keys()
#print meta['zlowpass']
if meta['zlowpass'] != 0:
print(meta['MZ'].v, meta['zlowpass'].v)
meta['maxZ'] = int(np.floor(meta['MZ'].v * meta['zlowpass'].v))
else:
meta['maxZ'] = 5
#meta['nx'] = nx
#meta['ny']= ny
meta['dt'] = meta['TIMESTEP']
#nx,ny = d['Rxy'].shape
#print meta['AA'].v
meta['rho_s'] = ValUnit(1.02e2 * np.sqrt(d['AA'] * d['Te_x']) /
(d['ZZ'] * d['bmag']),
'cm') # ion gyrorad at T_e, in cm
meta['rho_i'] = ValUnit(
1.02e2 * np.sqrt(d['AA'] * d['Ti_x']) / (d['ZZ'] * d['bmag']), 'cm')
meta['rho_e'] = ValUnit(2.38 * np.sqrt(d['Te_x']) / (d['bmag']), 'cm')
meta['fmei'] = ValUnit(1. / 1836.2 / d['AA'])
meta['lambda_ei'] = 24. - np.log(old_div(np.sqrt(d['Ni_x']), d['Te_x']))
meta['lambda_ii'] = 23. - np.log(d['ZZ']**3 * np.sqrt(2. * d['Ni_x']) /
(d['Ti_x']**1.5)) #
meta['wci'] = 1.0 * 9.58e3 * d['ZZ'] * d['bmag'] / d['AA'] # ion gyrofrteq
meta['wpi'] = 1.32e3 * d['ZZ'] * np.sqrt(old_div(
d['Ni_x'], d['AA'])) # ion plasma freq
meta['wce'] = 1.78e7 * d['bmag'] #electron gyrofreq
meta['wpe'] = 5.64e4 * np.sqrt(d['Ni_x']) #electron plasma freq
meta['v_the'] = 4.19e7 * np.sqrt(d['Te_x']) #cm/s
meta['v_thi'] = 9.79e5 * np.sqrt(old_div(d['Ti_x'], d['AA'])) #cm/s
meta['c_s'] = 9.79e5 * np.sqrt(
5.0 / 3.0 * d['ZZ'] * d['Te_x'] / d['AA']) #
meta['v_A'] = 2.18e11 * np.sqrt(old_div(1.0, (d['AA'] * d['Ni_x'])))
meta['nueix'] = 2.91e-6 * d['Ni_x'] * meta['lambda_ei'] / d['Te_x']**1.5 #
meta['nuiix'] = 4.78e-8 * d['ZZ']**4. * d['Ni_x'] * meta['lambda_ii'] / d[
'Ti_x']**1.5 / np.sqrt(d['AA']) #
meta['nu_hat'] = meta['Zeff'].v * meta['nueix'] / meta['wci']
meta['L_d'] = 7.43e2 * np.sqrt(old_div(d['Te_x'], d['Ni_x']))
meta['L_i_inrt'] = 2.28e7 * np.sqrt(old_div(
d['AA'], d['Ni_x'])) / d['ZZ'] #ion inertial length in cm
meta['L_e_inrt'] = 5.31e5 * np.sqrt(d['Ni_x']) #elec inertial length in cm
meta['Ve_x'] = 4.19e7 * d['Te_x']
meta['R0'] = old_div((d['Rxy'].max() + d['Rxy'].min()), 2.0)
print(d['Rxy'].mean(1))
print(d['ZMAX'])
print(d['ZMIN'])
meta['L_z'] = 1e2 * 2 * np.pi * d['Rxy'].mean(1) * (
d['ZMAX'] - d['ZMIN']) # in cm toroidal range
meta['dz'] = (d['ZMAX'] - d['ZMIN'])
#meta['lbNorm']=meta['L_z']*(d['Bpxy']/d['Bxy']).mean(1) #-binormal coord range [cm]
meta['lbNorm'] = meta['L_z'] * (old_div(d['Bxy'], d['Bpxy'])).mean(1)
#meta['zPerp']=np.array(meta['lbNorm']).mean*np.array(range(d['MZ']))/(d['MZ']-1)
#let's calculate some profile properties
dx = np.gradient(d['Rxy'])[0]
meta['L'] = 1.0 * 1e2 * dx * (meta['Ni0'].v) / np.gradient(
meta['Ni0'].v)[0] / meta['rho_s'].v
meta['w_Ln'] = old_div(meta['c_s'], (np.min(abs(meta['L'])) * meta['wci'] *
meta['rho_s'].v)) #normed to wci
AA = meta['AA'].v
ZZ = d['ZZ']
Te_x = d['Te_x']
Ti_x = d['Ti_x']
fmei = meta['fmei'].v
meta['lpar'] = 1e2 * (
(old_div(d['Bxy'], d['Bpxy'])) * d['dlthe']).sum(1) / meta[
'rho_s'].v #-[normed], average over flux surfaces, parallel length
#yes dlthe is always the vertical displacement
#dlthe = (hthe0*2 pi)/nz
#meta['lpar']=1e2*(d['Bxy']/d['Bpxy']).mean(1)*d['dlthe'].mean(1) #function of x
meta['sig_par'] = old_div(1.0, (fmei * 0.51 * meta['nu_hat']))
#meta['heat_nue'] = ((2*np.pi/meta['lpar'])**2)/(fmei*meta['nu_hat'])
#kz_e = kz_i*(rho_e/rho_i)
# kz_s = kz_i*(rho_s/rho_i)
# kz_i = (TWOPI/L_z)*(indgen((*current_str).fft.nz+1))*rho_i
# knorm = (TWOPI/lbNorm)*(indgen((*current_str).fft.nz+1))*rho_s
# for now just translate
for elem in meta:
if type(meta[elem]).__name__ == 'ValUnit':
meta[elem] = {'u': meta[elem].u, 'v': meta[elem].v}
print('meta: ', type(meta))
return meta
def plotpolslice(var3d,gridfile,period=1,zangle=0.0, rz=1, fig=0):
""" data2d = plotpolslice(data3d, 'gridfile' , period=1, zangle=0.0, rz:return (r,z) grid also=1, fig: to do the graph, set to 1 ) """
g=file_import(gridfile)
nx=var3d.shape[0]
ny=var3d.shape[1]
nz=var3d.shape[2]
zShift=g.get('zShift')
rxy=g.get('Rxy')
zxy=g.get('Zxy')
dz = 2.0*np.pi / float(period*nz)
ny2=ny
nskip=np.zeros(ny-1)
for i in range(ny-1):
ip=(i+1)%ny
nskip[i]=0
for x in range(nx):
ns=old_div(np.max(np.abs(zShift[x,ip]-zShift[x,i])),dz)-1
if ns > nskip[i] : nskip[i] = ns
nskip = np.int_(np.round(nskip))
ny2 = np.int_(ny2 + np.sum(nskip))
print("Number of poloidal points in output:" + str(ny2))
var2d = np.zeros((nx, ny2))
r = np.zeros((nx, ny2))
z = np.zeros((nx, ny2))
ypos = 0
for y in range (ny-1) :
# put in the original points
for x in range (nx):
zind = old_div((zangle - zShift[x,y]),dz)
var2d[x,ypos] = zinterp(var3d[x,y,:], zind)
# IF KEYWORD_SET(profile) THEN var2d[x,ypos] = var2d[x,ypos] + profile[x,y]
r[x,ypos] = rxy[x,y]
z[x,ypos] = zxy[x,y]
ypos = ypos + 1
print((y, ypos))
# and the extra points
for x in range (nx):
zi0 = old_div((zangle - zShift[x,y]),dz)
zip1 = old_div((zangle - zShift[x,y+1]),dz)
dzi = old_div((zip1 - zi0), (nskip[y] + 1))
for i in range (nskip[y]):
zi = zi0 + float(i+1)*dzi # zindex
w = old_div(float(i+1),float(nskip[y]+1)) # weighting
var2d[x,ypos+i] = w*zinterp(var3d[x,y+1,:], zi) + (1.0-w)*zinterp(var3d[x,y,:], zi)
# IF KEYWORD_SET(profile) THEN var2d[x,ypos+i] = var2d[x,ypos+i] + w*profile[x,y+1] + (1.0-w)*profile[x,y]
r[x,ypos+i] = w*rxy[x,y+1] + (1.0-w)*rxy[x,y]
z[x,ypos+i] = w*zxy[x,y+1] + (1.0-w)*zxy[x,y]
ypos = ypos + nskip[y]
# FINAL POINT
for x in range(nx):
zind = old_div((zangle - zShift[x,ny-1]),dz)
var2d[x,ypos] = zinterp(var3d[x,ny-1,:], zind)
# IF KEYWORD_SET(profile) THEN var2d[x,ypos] = var2d[x,ypos] + profile[x,ny-1]
r[x,ypos] = rxy[x,ny-1]
z[x,ypos] = zxy[x,ny-1]
if(fig==1):
f = mlab.figure(size=(600,600))
# Tell visual to use this as the viewer.
visual.set_viewer(f)
s = mlab.mesh(r,z,var2d, colormap='PuOr')#, wrap_scale='true')#, representation='wireframe')
s.enable_contours=True
s.contour.filled_contours=True
mlab.view(0,0)
else:
# return according to opt
if rz==1 :
return r,z,var2d
else:
return var2d
mayavi.add_source(src)
mayavi.add_module(Outline())
g = GridPlane()
g.grid_plane.axis = "x"
mayavi.add_module(g)
if __name__ == "__main__":
from boutdata.collect import collect
from boututils.file_import import file_import
path = "/media/449db594-b2fe-4171-9e79-2d9b76ac69b6/runs/data_33/"
# path="/home/ben/run4"
# g = file_import("../cbm18_dens8.grid_nx68ny64.nc")
g = file_import("data/cbm18_8_y064_x516_090309.nc")
# g = file_import("/home/ben/run4/reduced_y064_x256.nc")
data = collect("P", tind=50, path=path)
data = data[0, :, :, :]
s = np.shape(data)
nz = s[2]
bkgd = collect("P0", path=path)
for z in range(nz):
data[:, :, z] += bkgd
# Create a structured grid
sgrid = create_grid(g, data, 10)
w = tvtk.XMLStructuredGridWriter(input=sgrid, file_name="sgrid.vts")
def plotpolslice(var3d, gridfile, period=1, zangle=0.0, rz=1, fig=0):
""" data2d = plotpolslice(data3d, 'gridfile' , period=1, zangle=0.0, rz:return (r,z) grid also=1, fig: to do the graph, set to 1 ) """
g = file_import(gridfile)
nx = var3d.shape[0]
ny = var3d.shape[1]
nz = var3d.shape[2]
zShift = g.get('zShift')
rxy = g.get('Rxy')
zxy = g.get('Zxy')
dz = 2.0 * np.pi / float(period * nz)
ny2 = ny
nskip = np.zeros(ny - 1)
for i in range(ny - 1):
ip = (i + 1) % ny
nskip[i] = 0
for x in range(nx):
ns = old_div(np.max(np.abs(zShift[x, ip] - zShift[x, i])), dz) - 1
if ns > nskip[i]: nskip[i] = ns
nskip = np.int_(np.round(nskip))
ny2 = np.int_(ny2 + np.sum(nskip))
print("Number of poloidal points in output:" + str(ny2))
var2d = np.zeros((nx, ny2))
r = np.zeros((nx, ny2))
z = np.zeros((nx, ny2))
ypos = 0
for y in range(ny - 1):
# put in the original points
for x in range(nx):
zind = old_div((zangle - zShift[x, y]), dz)
var2d[x, ypos] = zinterp(var3d[x, y, :], zind)
# IF KEYWORD_SET(profile) THEN var2d[x,ypos] = var2d[x,ypos] + profile[x,y]
r[x, ypos] = rxy[x, y]
z[x, ypos] = zxy[x, y]
ypos = ypos + 1
print((y, ypos))
# and the extra points
for x in range(nx):
zi0 = old_div((zangle - zShift[x, y]), dz)
zip1 = old_div((zangle - zShift[x, y + 1]), dz)
dzi = old_div((zip1 - zi0), (nskip[y] + 1))
for i in range(nskip[y]):
zi = zi0 + float(i + 1) * dzi # zindex
w = old_div(float(i + 1), float(nskip[y] + 1)) # weighting
var2d[x, ypos + i] = w * zinterp(var3d[x, y + 1, :], zi) + (
1.0 - w) * zinterp(var3d[x, y, :], zi)
# IF KEYWORD_SET(profile) THEN var2d[x,ypos+i] = var2d[x,ypos+i] + w*profile[x,y+1] + (1.0-w)*profile[x,y]
r[x, ypos + i] = w * rxy[x, y + 1] + (1.0 - w) * rxy[x, y]
z[x, ypos + i] = w * zxy[x, y + 1] + (1.0 - w) * zxy[x, y]
ypos = ypos + nskip[y]
# FINAL POINT
for x in range(nx):
zind = old_div((zangle - zShift[x, ny - 1]), dz)
var2d[x, ypos] = zinterp(var3d[x, ny - 1, :], zind)
# IF KEYWORD_SET(profile) THEN var2d[x,ypos] = var2d[x,ypos] + profile[x,ny-1]
r[x, ypos] = rxy[x, ny - 1]
z[x, ypos] = zxy[x, ny - 1]
if (fig == 1):
f = mlab.figure(size=(600, 600))
# Tell visual to use this as the viewer.
visual.set_viewer(f)
s = mlab.mesh(r, z, var2d, colormap='PuOr'
) #, wrap_scale='true')#, representation='wireframe')
s.enable_contours = True
s.contour.filled_contours = True
mlab.view(0, 0)
else:
# return according to opt
if rz == 1:
return r, z, var2d
else:
return var2d
from __future__ import division
from builtins import range
from past.utils import old_div
import numpy as np
import matplotlib.pyplot as plt
from boututils.file_import import file_import
from matplotlib.ticker import FixedFormatter, FormatStrFormatter, AutoLocator, AutoMinorLocator
g = file_import("./cbm18_dens8.grid_nx68ny64.nc")
majorLocator = AutoLocator()
majorFormatter = FormatStrFormatter("%3.0e")
minorLocator = AutoMinorLocator()
Fm = FixedFormatter(["0", "$1 \\times 10^4$", "$2 \\times 10^4$", "$3 \\times 10^4$", "$4 \\times 10^4$"])
Fm2 = FixedFormatter(["0", "$2 \\times 10^5$", "$4 \\times 10^5$", "$6 \\times 10^5$"])
bxy = g.get("Bxy")
p = g.get("pressure")
jpar0 = g.get("Jpar0")
psixy = g.get("psixy")
btxy = g.get("Btxy")
shiftangle = g.get("ShiftAngle")
nx = g.get("nx")
ny = g.get("ny")
q = np.zeros((nx, ny))
for i in range(ny):
q[:, i] = old_div(-shiftangle, (2 * np.pi))
def metadata(inpfile='BOUT.inp',path ='.',v=False):
filepath = path+'/'+inpfile
print(filepath)
inp = read_inp(path=path,boutinp=inpfile)
inp = parse_inp(inp) #inp file
print(path)
outinfo = file_import(path+'/BOUT.dmp.0.nc') #output data
try:
print(path)
cxxinfo = no_comment_cxx(path=path,boutcxx='physics_code.cxx.ref')
#evolved = get_evolved_cxx(cxxinfo)
fieldkeys = get_evolved_cxx(cxxinfo)
fieldkeys = ['['+elem+']' for elem in fieldkeys]
except:
print('cant find the cxx file')
#gridoptions = {'grid':grid,'mesh':mesh}
if '[mesh]' in list(inp.keys()):
#IC = outinfo
IC = read_grid(path+'/BOUT.dmp.0.nc') #output data again
elif 'grid' in inp['[main]']:
gridname = inp['[main]']['grid']
try:
IC = read_grid(gridname) #have to be an ansoulte file path for now
print('IC: ',type(IC))
# print IC.variables
# print gridname
except:
#print gridname
print('Fail to load the grid file')
#print IC
#print gridname
#print len(IC)
#print IC
evolved = []
collected =[]
ICscale = []
# fieldkeys = ['[Ni]','[Te]','[Ti]','[Vi]','[rho]',
# '[Ajpar]','[Apar]','[vEBx]','[vEBy]','[vEBz]',
# '[jpar]','[phi]']
#construct fieldkeys from cxx info
#fieldkeys = ['['+x+']' for x in evolved]
#fieldkeys = evolved
#just look ahead and see what 3D fields have been output
available = np.array([str(x) for x in outinfo])
a = np.array([(len(outinfo[x].shape) ==4) for x in available])
available = available[a]
defaultIC = float(inp['[All]'].get('scale',0.0))
# print inp.keys()
#figure out which fields are evolved
print(fieldkeys)
for section in list(inp.keys()): #loop over section keys
print('section: ', section)
if section in fieldkeys: #pick the relevant sections
print(section)
#print inp[section].get('evolve','True')
#rint (inp[section].get('evolve','True')).lower().strip()
if (inp[section].get('evolve','True').lower().strip() == 'true'):# and section[1:-1] in available :
print('ok reading')
evolved.append(section.strip('[]'))
ICscale.append(float(inp[section].get('scale',defaultIC)))
if inp[section].get('collect','False').lower().strip() == 'true':
collected.append(section.strip('[]'))
try:
if inp['[physics]'].get('transport','False').lower().strip() == 'true':
vEBstr = ['vEBx','vEBy','vEBz','vEBrms']
[collected.append(item) for item in vEBstr]
except:
print('no [physics] key')
meta = OrderedDict()
class ValUnit(object):
def __init__(self,value=0,units=''):
self.u = units
self.v = value
def todict(self):
return {'u':self.u,'v':self.v}
#def decode_valunit(d):
def ToFloat(metaString):
try:
return float(metaString)
except ValueError:
return metaString
#meta['evolved'] = ValUnit(evolved,'')
meta['evolved'] = evolved
meta['collected'] = collected
meta['IC']= np.array(ICscale)
d = {}
print('evolved: ',evolved)
# read meta data from .inp file, this is whre most metadata get written
for section in list(inp.keys()):
if (('evolve' not in inp[section]) and ('first' not in inp[section])): #hacky way to exclude some less relevant metadata
for elem in list(inp[section].keys()):
meta[elem] = ValUnit(ToFloat(inp[section][elem]))
d[elem] = np.array(ToFloat(inp[section][elem]))
#read in some values from the grid(IC) and scale them as needed
norms = {'Ni0':ValUnit(1.e14,'cm^-3'),'bmag':ValUnit(1.0e4,'gauss'),
'Ni_x':ValUnit(1.e14,'cm^-3'),
'Te_x':ValUnit(1.0,'eV'),'Ti_x':ValUnit(1.0,'eV'),'Rxy':ValUnit(1,'m'),
'Bxy':ValUnit(1.0e4,'gauss'),'Bpxy':ValUnit(1.0e4,'gauss'),
'Btxy':ValUnit(1.0e4,'gauss'),'Zxy':ValUnit(1,'m'),
'dlthe':ValUnit(1,'m'),'dx':ValUnit(1,'m'),'hthe0':ValUnit(1,'m')}
availkeys = np.array([str(x) for x in outinfo])
tmp1 = np.array([x for x in availkeys])
#b = np.array([x if x not in available for x in a])
tmp2 = np.array([x for x in tmp1 if x not in available])
static_fields = np.array([x for x in tmp2 if x in list(norms.keys())])
#static_fields = tmp2
#print availkeys
# print meta.keys()
#print IC.variables.keys()
# print tmp1
# print tmp2
for elem in static_fields:
print('elem: ',elem)
meta[elem] = ValUnit(IC.variables[elem][:]*norms[elem].v,norms[elem].u)
d[elem] = np.array(IC.variables[elem][:]*norms[elem].v)
for elem in IC.variables:
if elem not in meta:
if elem in list(norms.keys()):
meta[elem] = ValUnit(IC.variables[elem][:]*norms[elem].v,norms[elem].u)
d[elem] = np.array(IC.variables[elem][:]*norms[elem].v)
else:
meta[elem] = IC.variables[elem][:]
d[elem] = IC.variables[elem][:]
#print d.keys()
#if case some values are missing
default = {'bmag':1,'Ni_x':1,'NOUT':100,'TIMESTEP':1,
'MZ':32,'AA':1,'Zeff':ValUnit(1,''),'ZZ':1,
'zlowpass':0.0,'transport':False}
diff = set(default.keys()).difference(set(d.keys()))
for elem in diff:
#print 'diff: ',elem
meta[elem] = default[elem]
d[elem] = np.array(default[elem])
#print meta.keys()
#print d.keys()
#print meta['zlowpass']
if meta['zlowpass'] != 0:
print(meta['MZ'].v, meta['zlowpass'].v)
meta['maxZ'] = int(np.floor(meta['MZ'].v*meta['zlowpass'].v))
else:
meta['maxZ'] = 5
#meta['nx'] = nx
#meta['ny']= ny
meta['dt'] = meta['TIMESTEP']
#nx,ny = d['Rxy'].shape
#print meta['AA'].v
meta['rho_s'] = ValUnit(1.02e2*np.sqrt(d['AA']*d['Te_x'])/(d['ZZ']* d['bmag']),'cm') # ion gyrorad at T_e, in cm
meta['rho_i'] = ValUnit(1.02e2*np.sqrt(d['AA']*d['Ti_x'])/(d['ZZ']* d['bmag']),'cm')
meta['rho_e'] = ValUnit(2.38*np.sqrt(d['Te_x'])/(d['bmag']),'cm')
meta['fmei'] = ValUnit(1./1836.2/d['AA'])
meta['lambda_ei'] = 24.-np.log(old_div(np.sqrt(d['Ni_x']),d['Te_x'])) ;
meta['lambda_ii'] = 23.-np.log(d['ZZ']**3 * np.sqrt(2.*d['Ni_x'])/(d['Ti_x']**1.5)) #
meta['wci'] = 1.0*9.58e3*d['ZZ']*d['bmag']/d['AA'] # ion gyrofrteq
meta['wpi'] = 1.32e3*d['ZZ']*np.sqrt(old_div(d['Ni_x'],d['AA'])) # ion plasma freq
meta['wce'] = 1.78e7*d['bmag'] #electron gyrofreq
meta['wpe'] = 5.64e4*np.sqrt(d['Ni_x'])#electron plasma freq
meta['v_the'] = 4.19e7*np.sqrt(d['Te_x'])#cm/s
meta['v_thi'] = 9.79e5*np.sqrt(old_div(d['Ti_x'],d['AA'])) #cm/s
meta['c_s'] = 9.79e5*np.sqrt(5.0/3.0 * d['ZZ'] * d['Te_x']/d['AA'])#
meta['v_A'] = 2.18e11*np.sqrt(old_div(1.0,(d['AA'] * d['Ni_x'])))
meta['nueix'] = 2.91e-6*d['Ni_x']*meta['lambda_ei']/d['Te_x']**1.5 #
meta['nuiix'] = 4.78e-8*d['ZZ']**4.*d['Ni_x']*meta['lambda_ii']/d['Ti_x']**1.5/np.sqrt(d['AA']) #
meta['nu_hat'] = meta['Zeff'].v*meta['nueix']/meta['wci']
meta['L_d'] = 7.43e2*np.sqrt(old_div(d['Te_x'],d['Ni_x']))
meta['L_i_inrt'] = 2.28e7*np.sqrt(old_div(d['AA'],d['Ni_x']))/ d['ZZ'] #ion inertial length in cm
meta['L_e_inrt'] = 5.31e5*np.sqrt(d['Ni_x']) #elec inertial length in cm
meta['Ve_x'] = 4.19e7*d['Te_x']
meta['R0'] = old_div((d['Rxy'].max()+d['Rxy'].min()),2.0)
print(d['Rxy'].mean(1))
print(d['ZMAX'])
print(d['ZMIN'])
meta['L_z'] = 1e2 * 2*np.pi * d['Rxy'].mean(1) *(d['ZMAX'] - d['ZMIN']) # in cm toroidal range
meta['dz'] = (d['ZMAX'] - d['ZMIN'])
#meta['lbNorm']=meta['L_z']*(d['Bpxy']/d['Bxy']).mean(1) #-binormal coord range [cm]
meta['lbNorm']=meta['L_z']*(old_div(d['Bxy'],d['Bpxy'])).mean(1)
#meta['zPerp']=np.array(meta['lbNorm']).mean*np.array(range(d['MZ']))/(d['MZ']-1)
#let's calculate some profile properties
dx = np.gradient(d['Rxy'])[0]
meta['L'] = 1.0*1e2*dx*(meta['Ni0'].v)/np.gradient(meta['Ni0'].v)[0]/meta['rho_s'].v
meta['w_Ln'] = old_div(meta['c_s'],(np.min(abs(meta['L']))*meta['wci'] *meta['rho_s'].v)) #normed to wci
AA = meta['AA'].v
ZZ = d['ZZ']
Te_x = d['Te_x']
Ti_x = d['Ti_x']
fmei = meta['fmei'].v
meta['lpar'] =1e2*((old_div(d['Bxy'],d['Bpxy']))*d['dlthe']).sum(1)/meta['rho_s'].v #-[normed], average over flux surfaces, parallel length
#yes dlthe is always the vertical displacement
#dlthe = (hthe0*2 pi)/nz
#meta['lpar']=1e2*(d['Bxy']/d['Bpxy']).mean(1)*d['dlthe'].mean(1) #function of x
meta['sig_par'] = old_div(1.0,(fmei*0.51*meta['nu_hat']))
#meta['heat_nue'] = ((2*np.pi/meta['lpar'])**2)/(fmei*meta['nu_hat'])
#kz_e = kz_i*(rho_e/rho_i)
# kz_s = kz_i*(rho_s/rho_i)
# kz_i = (TWOPI/L_z)*(indgen((*current_str).fft.nz+1))*rho_i
# knorm = (TWOPI/lbNorm)*(indgen((*current_str).fft.nz+1))*rho_s
# for now just translate
for elem in meta:
if type(meta[elem]).__name__ =='ValUnit':
meta[elem] = {'u':meta[elem].u,'v':meta[elem].v}
print('meta: ', type(meta))
return meta