def createImgFLEXPART(inputPath):
driver = gdal.GetDriverByName('GTiff')
H = rh.Header(inputPath)
#H.fill_backward(nspec=(0,1))
lower_left = [H.outlon0, H.outlat0]
upper_right = [
int(H.outlon0 + (H.dxout * H.numxgrid)),
int(H.outlat0 + (H.dyout * H.numygrid))
]
heightLevels = H.outheight
timestamps = H.available_dates
outFileList = []
for t in timestamps:
G = rg.read_grid(H, date=t)
grid = G[(0, t)]
volume = grid.grid[:, :] * 112 / 35
xSize = volume.shape[0]
ySize = volume.shape[1]
numBands = volume.shape[2]
filename = 'FLEXPART_SO2_' + t[:8] + '.' + t[8:] + '.tif'
dst_ds = driver.Create(filename, xSize, ySize, numBands, GDT_Float32)
for i in range(numBands):
band = volume[:, :, i].squeeze().transpose()
band = np.flipud(band)
dst_ds.GetRasterBand(i + 1).WriteArray(band)
dst_ds.GetRasterBand(i + 1).SetNoDataValue(-9999)
dst_ds.GetRasterBand(i + 1).ComputeStatistics(False)
pixelWidth = abs((lower_left[0] - upper_right[0]) / xSize)
pixelHeight = abs((lower_left[1] - upper_right[1]) / ySize)
dst_ds.SetGeoTransform(
[lower_left[0], pixelWidth, 0, upper_right[1], 0, -pixelHeight])
srs = osr.SpatialReference()
srs.SetWellKnownGeogCS('WGS84')
dst_ds.SetProjection(srs.ExportToWkt())
time = t[:4] + '-' + t[4:6] + '-' + t[6:8] + 'T' + t[8:10] + ':' + t[
10:12] + ':' + t[12:14] + 'Z'
dst_ds.SetMetadataItem('TIME_START', time)
dst_ds.SetMetadataItem('TIME_END', time)
dst_ds.SetMetadataItem('GLOBAL_MIN', str(np.amin(volume[:])))
dst_ds.SetMetadataItem('GLOBAL_MAX', str(np.amax(volume[:])))
levels = str()
for l in heightLevels:
levels += str(l) + ','
levels = levels[:-1]
dst_ds.SetMetadataItem('VERTICAL_LEVELS', levels)
dst_ds.SetMetadataItem('VERTICAL_LEVELS_NUMBER',
str(len(heightLevels)))
dst_ds = None
outFileList.append(filename)
logging.debug('Finished processing for ' + t)
return outFileList
import eof_reconstruction as eof_r
import matplotlib.pyplot as plt
import noisegen as ng
# Open displacement maps
path = '/home/hipperta/Documents/img/serie_temp/argentiere/s1a/'
im = []
n_i = 0
for filename in sorted(os.listdir(path)):
fichier = gampy.ouvrir(path + filename, 's')
im.append(gampy.reshape(fichier))
n_i += 1
# Create mask with shape defined in text file (Gamma)
polygon = rg.read_grid('argentiere_mask.txt', 0, 1, False, False)
width = im[0].shape[1]
height = im[0].shape[0]
img = Image.new('L', (width, height), 0)
ImageDraw.Draw(img).polygon(polygon, outline=1, fill=1)
mask = np.array(img)
im_i = copy.deepcopy(im)
n_pts = len(im[0][mask == True])
datai = np.zeros([n_i, n_pts])
dataii = np.zeros([n_i, n_pts])
# Generate cross validation points
mask_gaps = np.zeros([n_i, n_pts], dtype=bool)
mask_cv = np.zeros([n_i, n_pts], dtype=bool)
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 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 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 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 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 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 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 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(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(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(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(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(d['Te_x']/d['Ni_x'])
meta['L_i_inrt'] = 2.28e7*np.sqrt(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'] = (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']*(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'] = 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*((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'] = 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 metadata(inpfile='BOUT.inp',path ='.',v=False):
filepath = path+'/'+inpfile
print filepath
inp = read_inp(path=path,boutinp=inpfile)
inp = parse_inp(inp)
try:
gridname = inp['[main]']['grid']
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 gridname
#print len(IC)
#print IC
evolved = []
ICscale = []
fieldkeys = ['[Ni]','[jpar]','[Te]','[Ti]','[Vi]','[rho]']
defaultIC = float(inp['[All]'].get('scale',0.0))
for section in inp.keys(): #loop over section keys
#print section
if section in fieldkeys: #pick the relevant sections
print section
#print type(inp[section].get('evolve','True'))
print (inp[section].get('evolve','True')).lower().strip()
if inp[section].get('evolve','True').lower().strip() == 'true':
print 'ok reading'
evolved.append(section.strip('[]'))
ICscale.append(float(inp[section].get('scale',defaultIC)))
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['IC']= np.array(ICscale)
d = {}
# read meta data from .inp file
for section in 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 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')}
for elem in norms.keys():
#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)
#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}
diff = set(default.keys()).difference(set(d.keys()))
for elem in diff:
meta[elem] = default[elem]
d[elem] = np.array(default[elem])
nx,ny = d['Rxy'].shape
#compute some quantities that are usefull
print meta['AA'].v
meta['nx'] = nx
meta['ny']= ny
meta['dt'] = meta['TIMESTEP']
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(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'] = 9.58e3*d['ZZ']*d['bmag']/d['AA'] # ion gyrofrteq
meta['wpi'] = 1.32e3*d['ZZ']*np.sqrt(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(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(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(d['Te_x']/d['Ni_x'])
meta['L_i_inrt'] = 2.28e7*np.sqrt(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'] = (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']*(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'] = 1e2*dx*(meta['Ni0'].v)/np.gradient(meta['Ni0'].v)[0]/meta['rho_s'].v
meta['w_Ln'] = 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*((d['Bxy']/d['Bpxy'])*d['dlthe']).sum(1)/meta['rho_s'].v #-[normed], average over flux surfaces, parallel length
#meta['lpar']=1e2*(d['Bxy']/d['Bpxy']).mean(1)*d['dlthe'].mean(1) #function of x
meta['sig_par'] = 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 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(1.0e14, "cm^-3"),
"bmag": ValUnit(1.0e4, "gauss"),
"Ni_x": ValUnit(1.0e14, "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(1.0 / 1836.2 / d["AA"])
meta["lambda_ei"] = 24.0 - np.log(old_div(np.sqrt(d["Ni_x"]), d["Te_x"]))
meta["lambda_ii"] = 23.0 - np.log(d["ZZ"] ** 3 * np.sqrt(2.0 * 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"] ** 4.0 * 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
