def save(path='/home/cryosphere/BOUT/examples/Q3/data_short'):
print 'in post_bout.save'
boutpath = path
print path
print sys.path
meta = metadata(path=path)
print 'ok got meta'
output = OrderedDict()
data = OrderedDict()
con = sql.connect('test.db')
all_modes = []
print meta['evolved']['v'] #evolved fields for the given run
for i,active in enumerate(meta['evolved']['v']): #loop over the fields
print path, active
data[active] = boutdata.collect(active,path=path)
modes,ave = basic_info(data[active],meta)
import os
path=sys.argv[1]
key=sys.argv[2]
cache='/scratch/01523/meyerson/'+key
if os.path.exists(cache):
os.rmdir(cache)
os.makedirs(cache)
print path
meta = metadata(path=path)
from boutdata import collect
from boututils import savemovie
gamma = collect("Gammax",yind=[5,5],path=path)
ni = collect("Ni",yind=[5,5],path=path)
rho = collect("rho",yind=[5,5],path=path)
#one movie per cpu
print ni.shape, gamma.shape
savemovie(gamma[:,:,0,:],ni[:,:,0,:],moviename='movie_'+key+'.avi',
meta=meta,cache=cache+"/",overcontour=True)
savemovie(rho[:,:,0,:],ni[:,:,0,:],moviename='movie_phi_ni'+key+'.avi',
meta=meta,cache=cache+"/",overcontour=True)
def save(path='/home/cryosphere/BOUT/examples/Q3/data_short',
savemovie=False,IConly=0,transform=False,fast = False,
debug = False):
#lets collect the data
print 'path :', path
print 'in post_bout/post_bout.save'
boutpath = path
print path
#print sys.path
meta = metadata(path=path)
#ICmodes =
print 'ok got meta'
#return meta
output = OrderedDict()
data = OrderedDict()
data_r = OrderedDict()
all_modes = []
print meta['evolved']['v']
for i,active in enumerate(meta['evolved']['v']): #loop over the fields
print path, active
data[active] = collect(active,path=path)
if savemovie:
movie(data,meta)
if transform:
#if you take the time to rotate the data go ahead and save it
cpydata = data[active]
data_r[active] = rotate(cpydata,meta) # keep it simple for now
if debug:
return data[active],data_r[active]
minIC = np.array([np.max(data[x][0,:,:,:]) for x in meta['evolved']['v']])
minIC = min(minIC[np.array(meta['IC']).nonzero()])
ICmodes =[]
if(meta['ys_opt']['v']==2 and meta['zs_opt']['v']==2): #if BOUT.inp IC indicates a single mode
ICmodes.append([meta['ys_mode']['v'],meta['zs_mode']['v']])
elif (meta['ys_opt']['v']==3 and meta['zs_opt']['v']==2):
[ICmodes.append([p+1,meta['zs_mode']['v']]) for p in range(5)]
elif(meta['ys_opt']['v']==2 and meta['zs_opt']['v']==3):
[ICmodes.append([meta['ys_mode']['v'],p+1]) for p in range(8)]
elif(meta['ys_opt']['v']==3 and meta['zs_opt']['v']==3):
[ICmodes.append([p+1,q+1]) for p in range(7) for q in range(8)]
elif(meta['ys_opt']['v']==0 and meta['zs_opt']['v']==3):
[ICmodes.append([0,p+1]) for p in range(4)]
#some attemps to automate peak finding
for i,active in enumerate(meta['evolved']['v']):
modes_db,ave = basic_info(data[active],meta) #basic_info does not correct for incomplete runs
#print modes[0]['gamma']
output[active] = {'ave':ave}
#for x in output[active]['modes']: #keep track of relevant harmonics
print 'debug:', len(modes_db),type(modes_db),len(modes_db)
for x in modes_db:
print x['mn'],all_modes
if x['mn'] not in all_modes:
#don't append if the mode is weak . .
##minIC = np.array(meta['IC'])
#minIC = min(minIC[minIC.nonzero()])
print 'minIC: ',minIC, x['amp'][-10:,:].max()
#if x['amp'][-10:,:].max()>=(minIC/1.0):
all_modes.append(x['mn'])
if IConly or len(all_modes)==0:
all_modes = ICmodes
else:
z = []
[z.append(x) for x in all_modes]
[z.append(x) for x in ICmodes]
all_modes = z
print 'len(all_modes):',len(all_modes)
#rebuild with all the relevant modes
output = OrderedDict()
output['meta'] = meta
output['ave'] = {}
allmodes_db =[]
for i,active in enumerate(meta['evolved']['v']):
print 'once again', active
print all_modes
print meta['ys_opt']['v'], meta['zs_opt']['v']
modes_db,ave = basic_info(data[active],meta,
user_peak = all_modes)
if transform:
print all_modes
modes_db_r,ave_r = basic_info(data_r[active],meta,
user_peak = all_modes) #for now data_r must maintain same shape as data
# if debug:
# return modes_db_r,modes_db
#print modes_db_r.__class__,len(modes_db_r)
# if transform:
# for j,x in enumerate(modes_db_r):
# x['amp_r'] = modes_db_r[j]['amp']
# x['phase_r'] = modes_db_r[j]['phase']
# x['k_r']= modes_db_r[j]['k']
# x['freq_r']= modes_db_r[j]['freq']
# x['gamma_r'] =modes_db_r[j]['gamma']
# allmodes_db.append(modes_db_r)
for j,x in enumerate(modes_db):
print 'j: ',j
x['field'] = active
x['dz']=meta['dz']
x['IC']=meta['IC']
x['L']=meta['L']
x['Rxy']= meta['Rxy']['v']
x['nfields'] = len(meta['evolved']['v'])
x['meta'] = meta
x['nx']= meta['nx']
x['ny'] = meta['ny']
x['nz'] = meta['MZ']['v']-1
x['dt'] = meta['dt']['v']
x['Rxynorm'] = 100*x['Rxy']/meta['rho_s']['v']
x['rho_s'] = meta['rho_s']['v']
#for now if there was rotation just loop a few keys
x['transform'] = transform
if transform:
try:
x['amp_r'] = modes_db_r[j]['amp']
x['phase_r'] = modes_db_r[j]['phase']
x['k_r']= modes_db_r[j]['k']
x['freq_r']= modes_db_r[j]['freq']
x['gamma_r'] =modes_db_r[j]['gamma']
except:
print "FAIL TO ADD TRANSFORMED VALUES"
ntt = x['nt'] #actual number of steps
nt = meta['NOUT']['v'] #requested number of steps
print j,' out of ',len(modes_db)
print nt,ntt
#for incomplete runs
if nt > ntt: #if the run did not finish then rescale to keep data dims the same
xx = np.array(range(0,ntt)) #dep var to interp FROM
xxnew = np.linspace(0,ntt-1,nt+1) #new dep var grid, nt points up to ntt-1
print ntt/nt,x['dt']*float(ntt/nt),nt,ntt
x['dt'] = x['dt']*float(ntt/nt) #smaller dt if we are interp tp more points . .
print x['dt']
x['nt'] = nt
amp = x['amp'] #2d array ntt x nx
phase = x['phase']
ampnew=[] #will be a nx x nt 2d array
phasenew =[]
for mode_r in np.transpose(amp):
f = interp1d(xx,mode_r)
ampnew.append(f(xxnew))
for mode_r in np.transpose(phase):
f = interp1d(xx,mode_r)
phasenew.append(f(xxnew))
x['amp'] = np.transpose(ampnew)
x['phase'] = np.transpose(phasenew)
print x['phase'].shape
output['ave'][active] = {'ave':ave}
allmodes_db.append(modes_db)
allmodes_db = sum(allmodes_db,[])
filename_db = path+'/post_bout.db'
pickle_db = open(filename_db,'wb')
pickle.dump(allmodes_db,pickle_db)
pickle_db.close()
# let pickle the results
# filename = path+'/post_bout.jsn'
# json_out = open(filename,'wb')
# pickle.dump(output,json_out)
# json_out.close()
print filename_db
def read(path='.',filename='post_bout.pkl',trysave=True,
gamma_updt=False):
print 'in post_bout.read()'
filepath = path+'/'+filename
filepath_db = path+'/post_bout.db'
datafile = path+'/BOUT.dmp.0.nc'
print 'is file present: ', os.path.isfile(filepath_db)
if trysave and not(os.path.isfile
(filepath_db)) and os.path.isfile(datafile):
save(path=path)
if os.path.isfile(filepath_db):
#pkl_file = open(filepath, 'rb')
pkl_file_db = open(filepath_db, 'rb')
#output = pickle.load(pkl_file)
output_db = pickle.load(pkl_file_db)
meta = metadata(path=path)
#update the meta data incase read_inp was altered . . .
for i,x in enumerate(output_db):
x['dz']=meta['dz']
x['IC']=meta['IC']
x['L']=meta['L']
x['Rxy']= meta['Rxy']['v']
x['nfields'] = len(meta['evolved']['v'])
x['meta'] = meta
x['nx'] = meta['nx']
x['ny'] = meta['ny']
x['nz'] = meta['MZ']['v']-1
x['path'] = path
#print x['mn']
x['MN'] = list([1,2])
x['MN'][0] = x['mn'][0]
x['MN'][1] = x['mn'][1]/x['dz']
x['modeid'] = [x['dz'],x['modeid'],x['mn'][1]]
# print x['mn']
#x['MN'] = x['mn']
#x['MN'][1] = x['MN'][1]/x['dz']
x['Rxynorm'] = 100*x['Rxy']/meta['rho_s']['v']
if 'dt' not in x:
x['dt'] = meta['dt']['v'] #this one gets modified no read .
if gamma_updt: #experimental, recalculates gamma from amp, but for a while amp was
#wrong for incomplete runs s0 . . . dont' run for now
lnamp = np.log(x['amp']) #nt x nx
t = x['dt']*np.array(range(int(x['nt'])))
r = np.polyfit(t[x['nt']/2:],lnamp[x['nt']/2:,2:-2],1,full=True)
gamma_est = r[0][0] #nx
f0 = np.exp(r[0][1]) #nx
res = r[1]
pad =[0,0]
gamma_est = np.concatenate([pad,gamma_est,pad])
f0 = np.concatenate([pad,f0,pad])
res = np.concatenate([pad,res,pad])
#sig = res/np.sqrt((x['nt']-2))
sig = np.sqrt(res/(x['nt']-2))
#sig0 = sig*np.sqrt(1/(x['nt'])+ ) # who cares
sig1 = sig*np.sqrt(1.0/(x['nt'] * t.var()))
res = 1 - res/(x['nt']*lnamp.var(0)) #nx
res[0:2] = 0
res[-2:] = 0
x['gamma'] = [gamma_est,f0,sig1,res]
pkl_file_db.close()
return output_db
else:
return 0
