def read(self):
'''
Read the MOLECULE_radiat file and set a dictionary for the instance
with all the information.
Done on creation of an instance of the class.
'''
#-- Read the radiat file which is just one long column
radiat = np.loadtxt(self.fn)
#-- If nline wasn't given, find out now.
if self.nline is None:
nline = DataIO.findZero(0,radiat)
#-- Check if this is a good nline. If not, there's likely no zeroes
# (and the above function found the 0-energy level or an ny-0).
if len(radiat) < 4*nline+2*self.ny:
#-- But there might still be zeroes for ny: this is always the
# first non-zero energy level
ienergy = DataIO.findNumber(nline,radiat)
#-- Find the ny-zeroes, but this might be end of file: no zeroes
n0_nyi = DataIO.findZero(ienergy,radiat)
if n0_nyi == len(radiat):
n0_ny = 0
#-- Otherwise, continue. We do have ny-zeroes
else:
n0_nyj = DataIO.findNumber(n0_nyi,radiat)
n0_ny = n0_nyj-n0_nyi
self.nline = (len(radiat)-2*(self.ny+n0_ny))/4
else:
self.nline = nline
self['pars']['nline'] = self.nline
#-- Prep the transition array
dtype = [('index',int),('lup',int),('llow',int),('frequency',float),\
('einsteinA',float)]
self['trans'] = np.empty(shape=(self.nline,),dtype=dtype)
self['trans']['index'] = range(1,self.nline+1)
#-- Prep the level array
dtype = [('index',int),('weight',float),('energy',float)]
self['level'] = np.empty(shape=(self.ny,),dtype=dtype)
self['level']['index'] = range(1,self.ny+1)
#-- The number of values per property is either nline or ny
nvals = [self.nline,self.nline,self.ny,self.ny,self.nline,self.nline]
#-- Numbers are padded with zeroes at end. The amount of padding is
# unknown. if total length is equal to sum of all step sizes, reading
# radiat is very simple.
if sum(nvals) == len(radiat):
n0_nline = 0
n0_ny = 0
#-- Otherwise a more elaborate method must be used to determine the
# amount of zero-padding that is used for each nline and ny.
else:
#-- Einstein A is never 0, so this is easy.
n0_nline = DataIO.findNumber(self.nline,radiat)-self.nline
#-- Two nline-sized blocks, one ny-sized block. Because the first
# energy level might be 0 cm^-1, this is not the real n0_ny
start_i = 2*(self.nline+n0_nline)+self.ny
n0_ny1 = DataIO.findNumber(start_i,radiat)-start_i
#-- Second ny-sized block gives the real n0_ny, since llow can never
# be zero:
start_i = DataIO.findZero(start_i+n0_ny1,radiat)
n0_ny = DataIO.findNumber(start_i,radiat)-start_i
#-- Collect property names, types and number of zero-padding
props = ['einsteinA','frequency','weight','energy','llow','lup']
ptypes = ['trans','trans','level','level','trans','trans']
n0s = [n0_nline,n0_nline,n0_ny,n0_ny,n0_nline,n0_nline]
#-- Save the data into the arrays
for i,(nval,ptype,prop) in enumerate(zip(nvals,ptypes,props)):
#-- Determine starting index for this block
start_i = sum(nvals[:i])+sum(n0s[:i])
#-- Retrieve information
self[ptype][prop] = radiat[start_i:start_i+nval]
def read(self):
'''
Read the collision rates file. Assumes GASTRoNOoM format.
To read ALI/MCP collision rates (which are in the Lamda format), make
use of the LamdaReader, which redefines this method. The other retrieval
methods remain valid.
Each transition is stored as an index, and gives upper and lower level
index as well as the collision rate.
'''
#-- Read the collis file which is just one long column. Assumes there is
# at least one zero value for padding!
collis = np.loadtxt(self.fn)
self['pars'] = dict()
#-- The number of transitions is given by the number of non-zero values
# in the beginning of the file.
ntrans = DataIO.findZero(0,collis)
n0 = DataIO.findNumber(ntrans,collis)-ntrans
#-- The number of temperatures in the file can be calculated now
ntemp = (len(collis)-2*(ntrans+n0))/(ntrans+n0+1)
#-- Prep the coll_trans array and add indices for coll_trans
dtype = [('index',int),('lup',int),('llow',int),('rates',np.ndarray)]
self['coll_trans'] = np.empty(shape=(ntrans,),dtype=dtype)
self['coll_trans']['index'] = range(1,ntrans+1)
#-- Add the level indices
self['coll_trans']['lup'] = collis[:ntrans]
self['coll_trans']['llow'] = collis[ntrans+n0:2*ntrans+n0]
#-- Retrieve the temperatures.
start_i = 2*(ntrans+n0)
Tgrid = [collis[start_i+i*(ntrans + n0 + 1)] for i in range(ntemp)]
#-- Check if any of them is zero, and
# readjust ntemp (sometimes a 0 temp with 0 rates is present in file)
Tgrid_real = [Ti for Ti in Tgrid if Ti != 0.]
ntemp_real = len(Tgrid_real)
#-- Retrieve rates and insert into array. Loop for ntemp, and ignore
# rates if T is 0 there.
rates = np.empty(shape=(ntrans,ntemp_real))
start_i = start_i + 1
for i,Ti in enumerate(Tgrid):
if Ti == 0.0: continue
this_i = start_i+i*(ntrans + n0 + 1)
rates[:,i] = collis[this_i:this_i+ntrans]
#-- Save into coll_trans array
for i in range(ntrans):
self['coll_trans']['rates'][i] = rates[i,:]
#-- Save additional information
self['pars']['ncoll_trans'] = ntrans
self['pars']['ncoll_temp'] = ntemp_real
self['coll_temp'] = np.array(Tgrid_real)
def read(self):
'''
Read the collision rates file. Assumes GASTRoNOoM format.
To read ALI/MCP collision rates (which are in the Lamda format), make
use of the LamdaReader, which redefines this method. The other retrieval
methods remain valid.
Each transition is stored as an index, and gives upper and lower level
index as well as the collision rate.
'''
#-- Read the collis file which is just one long column. Assumes there is
# at least one zero value for padding!
collis = np.loadtxt(self.fn)
self['pars'] = dict()
#-- The number of transitions is given by the number of non-zero values
# in the beginning of the file.
ntrans = DataIO.findZero(0, collis)
n0 = DataIO.findNumber(ntrans, collis) - ntrans
#-- The number of temperatures in the file can be calculated now
ntemp = (len(collis) - 2 * (ntrans + n0)) / (ntrans + n0 + 1)
#-- Prep the coll_trans array and add indices for coll_trans
dtype = [('index', int), ('lup', int), ('llow', int),
('rates', np.ndarray)]
self['coll_trans'] = np.empty(shape=(ntrans, ), dtype=dtype)
self['coll_trans']['index'] = range(1, ntrans + 1)
#-- Add the level indices
self['coll_trans']['lup'] = collis[:ntrans]
self['coll_trans']['llow'] = collis[ntrans + n0:2 * ntrans + n0]
#-- Retrieve the temperatures.
start_i = 2 * (ntrans + n0)
Tgrid = [collis[start_i + i * (ntrans + n0 + 1)] for i in range(ntemp)]
#-- Check if any of them is zero, and
# readjust ntemp (sometimes a 0 temp with 0 rates is present in file)
Tgrid_real = [Ti for Ti in Tgrid if Ti != 0.]
ntemp_real = len(Tgrid_real)
#-- Retrieve rates and insert into array. Loop for ntemp, and ignore
# rates if T is 0 there.
rates = np.empty(shape=(ntrans, ntemp_real))
start_i = start_i + 1
for i, Ti in enumerate(Tgrid):
if Ti == 0.0: continue
this_i = start_i + i * (ntrans + n0 + 1)
rates[:, i] = collis[this_i:this_i + ntrans]
#-- Save into coll_trans array
for i in range(ntrans):
self['coll_trans']['rates'][i] = rates[i, :]
#-- Save additional information
self['pars']['ncoll_trans'] = ntrans
self['pars']['ncoll_temp'] = ntemp_real
self['coll_temp'] = np.array(Tgrid_real)
