def parseProfile(self):
'''
Parse the sphinx file 2, which includes all line profile info.
The output is stored in dict self['sph2'].
'''
self['sph2'] = dict()
self['sph2']['nobeam'] = dict()
self['sph2']['beam'] = dict()
self['sph2']['nobeam_cont'] = dict()
self['sph2']['beam_cont'] = dict()
data = self.getFile(wildcard='2',delimiter=' ')
data_col_1 = [d[0] for d in data]
data_i = 6
data_j = DataIO.findString(data_i,data_col_1)
self['sph2']['nobeam']['velocity'] = array([float(line[0])
for line in data[data_i:data_j]])
#-- Reverse this flux grid. Sphinx output files give the mirrored
# flux grid for the associated velocity grid.
self['sph2']['nobeam']['flux'] = array([DataIO.convertFloat(line[-1],\
nans=1)
for line in data[data_i:data_j]])
self['sph2']['nobeam']['flux'] = self['sph2']['nobeam']['flux'][::-1]
data_k = data_j + 4
data_l = DataIO.findString(data_k,data_col_1)
self['sph2']['beam']['velocity'] = array([float(line[0])
for line in data[data_k:data_l]])
self['sph2']['beam']['flux'] = array([float(line[-1])
for line in data[data_k:data_l]])
self['sph2']['beam']['norm_flux'] = array([float(line[1])
for line in data[data_k:data_l]])
self['sph2']['beam']['tmb'] = array([float(line[2])
for line in data[data_k:data_l]])
#-- Set the continuum value for the different profiles
self.setContinuum('nobeam','flux')
for lp in ['flux','norm_flux','tmb']:
self.setContinuum('beam',lp)
#-- Check if the velocity is correctly monotonously increasing
if self['sph2']['beam']['velocity'][0] > self['sph2']['beam']['velocity'][-1]:
self['sph2']['beam']['velocity'] = self['sph2']['beam']['velocity'][::-1]
self['sph2']['beam']['flux'] = self['sph2']['beam']['flux'][::-1]
self['sph2']['beam']['norm_flux'] = self['sph2']['beam']['norm_flux'][::-1]
self['sph2']['beam']['tmb'] = self['sph2']['beam']['tmb'][::-1]
if self['sph2']['nobeam']['velocity'][0] > self['sph2']['nobeam']['velocity'][-1]:
self['sph2']['nobeam']['velocity'] = self['sph2']['nobeam']['velocity'][::-1]
self['sph2']['nobeam']['flux'] = self['sph2']['nobeam']['flux'][::-1]
#-- Check for NaNs in the profile.
if True in list(isnan(self['sph2']['nobeam']['flux'])):
self.nans_present = True
print "WARNING! There are NaN's in the intrinsic line profile " + \
"with model id %s:"\
%(os.path.split(os.path.split(self.fn)[0])[1])
print os.path.split(self.fn.replace('sph*','sph2'))[1]
def parseProfile(self):
'''
Parse the sphinx file 2, which includes all line profile info.
The output is stored in dict self.sph2.
'''
self.sph2 = dict()
self.contents['sph2'] = self.sph2
self.sph2['nobeam'] = dict()
self.sph2['beam'] = dict()
self.sph2['nobeam_cont'] = dict()
self.sph2['beam_cont'] = dict()
data = self.getFile(self.filename.replace('*', '2'))
data_col_1 = [d[0] for d in data]
data_i = 6
data_j = DataIO.findString(data_i, data_col_1)
self.sph2['nobeam']['velocity'] = array(
[float(line[0]) for line in data[data_i:data_j]])
#-- Reverse this flux grid. Sphinx output files give the mirrored
# flux grid for the associated velocity grid.
self.sph2['nobeam']['flux'] = array([DataIO.convertFloat(line[-1],\
nans=1)
for line in data[data_i:data_j]])
self.sph2['nobeam']['flux'] = self.sph2['nobeam']['flux'][::-1]
data_k = data_j + 4
data_l = DataIO.findString(data_k, data_col_1)
self.sph2['beam']['velocity'] = array(
[float(line[0]) for line in data[data_k:data_l]])
self.sph2['beam']['flux'] = array(
[float(line[-1]) for line in data[data_k:data_l]])
self.sph2['beam']['norm_flux'] = array(
[float(line[1]) for line in data[data_k:data_l]])
self.sph2['beam']['tmb'] = array(
[float(line[2]) for line in data[data_k:data_l]])
self.setContinuum('nobeam', 'flux')
for lp in ['flux', 'norm_flux', 'tmb']:
self.setContinuum('beam', lp)
if self.sph2['beam']['velocity'][0] > self.sph2['beam']['velocity'][-1]:
self.sph2['beam']['velocity'] = self.sph2['beam']['velocity'][::-1]
self.sph2['beam']['flux'] = self.sph2['beam']['flux'][::-1]
self.sph2['beam']['norm_flux'] = self.sph2['beam'][
'norm_flux'][::-1]
self.sph2['beam']['tmb'] = self.sph2['beam']['tmb'][::-1]
if self.sph2['nobeam']['velocity'][0] > self.sph2['nobeam'][
'velocity'][-1]:
self.sph2['nobeam']['velocity'] = self.sph2['nobeam'][
'velocity'][::-1]
self.sph2['nobeam']['flux'] = self.sph2['nobeam']['flux'][::-1]
if True in list(isnan(self.sph2['nobeam']['flux'])):
self.nans_present = True
print "WARNING! There are NaN's in the intrinsic line profile " + \
"with model id %s:"\
%(os.path.split(os.path.split(self.filename)[0])[1])
print os.path.split(self.filename.replace('sph*', 'sph2'))[1]
def parseProfile(self):
"""
Parse the sphinx file 2, which includes all line profile info.
The output is stored in dict self.sph2.
"""
self.sph2 = dict()
self.contents["sph2"] = self.sph2
self.sph2["nobeam"] = dict()
self.sph2["beam"] = dict()
self.sph2["nobeam_cont"] = dict()
self.sph2["beam_cont"] = dict()
data = self.getFile(self.filename.replace("*", "2"))
data_col_1 = [d[0] for d in data]
data_i = 6
data_j = DataIO.findString(data_i, data_col_1)
self.sph2["nobeam"]["velocity"] = array([float(line[0]) for line in data[data_i:data_j]])
# -- Reverse this flux grid. Sphinx output files give the mirrored
# flux grid for the associated velocity grid.
self.sph2["nobeam"]["flux"] = array([DataIO.convertFloat(line[-1], nans=1) for line in data[data_i:data_j]])
self.sph2["nobeam"]["flux"] = self.sph2["nobeam"]["flux"][::-1]
data_k = data_j + 4
data_l = DataIO.findString(data_k, data_col_1)
self.sph2["beam"]["velocity"] = array([float(line[0]) for line in data[data_k:data_l]])
self.sph2["beam"]["flux"] = array([float(line[-1]) for line in data[data_k:data_l]])
self.sph2["beam"]["norm_flux"] = array([float(line[1]) for line in data[data_k:data_l]])
self.sph2["beam"]["tmb"] = array([float(line[2]) for line in data[data_k:data_l]])
self.setContinuum("nobeam", "flux")
for lp in ["flux", "norm_flux", "tmb"]:
self.setContinuum("beam", lp)
if self.sph2["beam"]["velocity"][0] > self.sph2["beam"]["velocity"][-1]:
self.sph2["beam"]["velocity"] = self.sph2["beam"]["velocity"][::-1]
self.sph2["beam"]["flux"] = self.sph2["beam"]["flux"][::-1]
self.sph2["beam"]["norm_flux"] = self.sph2["beam"]["norm_flux"][::-1]
self.sph2["beam"]["tmb"] = self.sph2["beam"]["tmb"][::-1]
if self.sph2["nobeam"]["velocity"][0] > self.sph2["nobeam"]["velocity"][-1]:
self.sph2["nobeam"]["velocity"] = self.sph2["nobeam"]["velocity"][::-1]
self.sph2["nobeam"]["flux"] = self.sph2["nobeam"]["flux"][::-1]
if True in list(isnan(self.sph2["nobeam"]["flux"])):
self.nans_present = True
print "WARNING! There are NaN's in the intrinsic line profile " + "with model id %s:" % (
os.path.split(os.path.split(self.filename)[0])[1]
)
print os.path.split(self.filename.replace("sph*", "sph2"))[1]
def convertMCMaxDatabase(path_mcmax):
"""
Convert MCMax database to the dict format.
This change was made to speed up the use of the database and makes use of
the Database() class.
@param path_mcmax: the name of the MCMac subfolder.
@type path_mcmax: string
"""
print "** Converting MCMax database to dictionary format..."
# -- Convenience path
cc.path.mout = os.path.join(cc.path.mcmax, path_mcmax)
db_path = os.path.join(cc.path.mout, "MCMax_models.db")
i = 0
backup_file = "%s_backup%i" % (db_path, i)
while os.path.isfile(backup_file):
i += 1
backup_file = "%s_backup%i" % (db_path, i)
subprocess.call(["mv %s %s" % (db_path, backup_file)], shell=True, stdout=subprocess.PIPE)
mcmax_db = open(backup_file, "r")
old_db = []
try:
while True:
model = cPickle.load(mcmax_db)
old_db.append(model)
except EOFError:
print "** End of old database reached."
finally:
mcmax_db.close()
db = Database(db_path)
print "** Inserting entries into the new database."
for commands, model_id in old_db:
photon_count = DataIO.convertFloat(commands.pop(0), convert_int=1)
commands = [c.split("=") for c in commands]
commanddict = dict(
[
(k, DataIO.convertFloat(v, convert_int=1))
for k, v in commands
if k[0:4] not in ["opac", "part", "abun", "Tdes", "minr", "maxr"]
]
)
# - Correcting erroneous input entries in the old version
if commanddict.has_key("densfile"):
commanddict["densfile"] = "'%s'" % commanddict["densfile"]
if not commanddict.has_key("FLD"):
commanddict["FLD"] = ".false."
if not commanddict.has_key("randomwalk"):
commanddict["randomwalk"] = ".true."
commanddict["photon_count"] = photon_count
speciespars = [
(k, DataIO.convertFloat(v, convert_int=1))
for k, v in commands
if k[0:4] in ["opac", "part", "abun", "Tdes", "minr", "maxr"]
]
commanddict["dust_species"] = dict()
i = 0
while speciespars:
i += 1
this_species = dict()
for k, v in speciespars:
if int(k[-2:]) == i:
if k[:4] == "part" or k[:4] == "opac":
speciesfile = v.strip("'")
else:
this_species[k.strip("%.2i" % i)] = v
commanddict["dust_species"][os.path.split(speciesfile)[1]] = this_species
speciespars = [(k, v) for k, v in speciespars if int(k[-2:]) != i]
db[model_id] = commanddict
db.sync()
print "** Done!"
def convertMCMaxDatabase(path_mcmax):
'''
Convert MCMax database to the dict format.
This change was made to speed up the use of the database and makes use of
the Database() class.
@param path_mcmax: the name of the MCMac subfolder.
@type path_mcmax: string
'''
print '** Converting MCMax database to dictionary format...'
#-- Convenience path
cc.path.mout = os.path.join(cc.path.mcmax,path_mcmax)
db_path = os.path.join(cc.path.mout,'MCMax_models.db')
i = 0
backup_file = '%s_backup%i'%(db_path,i)
while os.path.isfile(backup_file):
i += 1
backup_file = '%s_backup%i'%(db_path,i)
subprocess.call(['mv %s %s'%(db_path,backup_file)],\
shell=True,stdout=subprocess.PIPE)
mcmax_db = open(backup_file,'r')
old_db = []
try:
while True:
model = cPickle.load(mcmax_db)
old_db.append(model)
except EOFError:
print '** End of old database reached.'
finally:
mcmax_db.close()
db = Database(db_path)
print '** Inserting entries into the new database.'
for commands,model_id in old_db:
photon_count = DataIO.convertFloat(commands.pop(0),convert_int=1)
commands = [c.split('=') for c in commands]
commanddict = dict([(k,DataIO.convertFloat(v,convert_int=1))
for k,v in commands
if k[0:4] not in ['opac','part','abun',\
'Tdes','minr','maxr']])
#- Correcting erroneous input entries in the old version
if commanddict.has_key('densfile'):
commanddict['densfile'] = "'%s'"%commanddict['densfile']
if not commanddict.has_key('FLD'):
commanddict['FLD'] = '.false.'
if not commanddict.has_key('randomwalk'):
commanddict['randomwalk'] = '.true.'
commanddict['photon_count'] = photon_count
speciespars = [(k,DataIO.convertFloat(v,convert_int=1))
for k,v in commands
if k[0:4] in ['opac','part','abun',\
'Tdes','minr','maxr']]
commanddict['dust_species'] = dict()
i = 0
while speciespars:
i += 1
this_species = dict()
for k,v in speciespars:
if int(k[-2:]) == i:
if k[:4] == 'part' or k[:4] == 'opac':
speciesfile = v.strip("'")
else:
this_species[k.strip('%.2i'%i)] = v
commanddict['dust_species'][os.path.split(speciesfile)[1]] \
= this_species
speciespars = [(k,v) for k,v in speciespars
if int(k[-2:]) != i]
db[model_id] = commanddict
db.sync()
print '** Done!'
