def _lbl2od_ (atmFile, lineFiles, outFile=None, commentChar='#', zToA=None,
lineShape='Voigt',
xLimits=None, airWidth=0.1, sampling=5.0, wingExt=5.0, interpolate='3', nGrids=2, gridRatio=0, nWidths=25.0,
mode='d', quad='T', nanometer=False, yOnly=False, flipUpDown=False, plot=False, verbose=False):
# parse file header and retrieve molecule names
species = [grep_from_header(file,'molecule') for file in lineFiles]
#print 'lbl2od for', len(species), ' molecules:', join(species)
# read atmospheric data from file, returns an instance
atmos = Atmos1D(get_profile_list (atmFile, commentChar, ['z', 'p', 'T']+species, verbose))
# optinally remove high altitudes
if zToA: atmos=reGridAtmos1d(atmos,zToA)
#print '\n', atmFile, ' ==> atmos\n', atmos
# vertical column densities
vcd = atmos.vcd()
#print str(' vcd [molec/cm**2]' + 20*' '+len(atmos.gases)*'%10.4g') % tuple(vcd)
# interpolation method used in multigrid algorithms: only Lagrange!
if interpolate in 'bBsScC': lagrange=4
elif interpolate in 'qQ': lagrange=3
elif interpolate in 'lL': lagrange=2
else: lagrange=int(interpolate)
# loop over molecules: read its line parameters (and some other data like mass) and compute cross sections for all p,T
xsMatrix = []
for file in lineFiles:
# read a couple of lines and convert to actual pressure and temperature
Line_Data, Mol_Data = get_lbl_data (file, xLimits, airWidth, wingExt, commentChar=commentChar)
# compute cross sections for selected line shape, returns a list with npT dictionaries
molCrossSections = lbl_xs (Line_Data, Mol_Data, atmos.p, atmos.T, xLimits, lineShape,
sampling, wingExt, nGrids, gridRatio, nWidths, lagrange, verbose)
xsMatrix.append(molCrossSections)
# for xss in xsMatrix: print 'xs', type(xss), len(xss), [(xs.get('molecule'),len(xs.get('y'))) for xs in xss]
# compute absorption coefficients, also return uniform, equiidistant wavenumber grid corresponding to most dense cross section
vGrid, absCoeff = sum_xsTimesDensity (xsMatrix, atmos.densities, interpolate=interpolate, verbose=verbose)
# integrate absorption coefficients along line of sight
optDepth = integral_acTimesDistance (absCoeff, atmos.z, quad, mode, verbose)
# save optical depth
if outFile and os.path.splitext(outFile)[1].lower() in ('.nc', '.ncdf', '.netcdf'):
save_optDepth_netcdf (vGrid, optDepth, outFile, atmos, nanometer)
else:
save_optDepth_xy (vGrid, optDepth, outFile, atmos, nanometer, yOnly, flipUpDown, commentChar)
if plot:
try:
from pylab import plot, semilogy, show
if mode in 'cdr':
for j in range(optDepth.shape[1]): semilogy (vGrid, optDepth[:,j])
show()
else:
plot (vGrid, optDepth); show()
except ImportError:
raise SystemExit, 'ERROR --- lbl2od: matplotlib not available, no quicklook!'
if len(molData) > 0:
moles[mol] = molData
mol = split[0]
molData = {}
if len(split) == 5:
molData[split[0]] = {
"abundance": float(split[1]),
"Q(296K)": float(split[2]),
"gj": float(split[3]),
"mass": float(split[4]),
}
moles[mol] = molData
# parse file header and retrieve molecule names
species = [grep_from_header(file, "molecule") for file in lineFiles]
print "lbl2od for", len(species), " molecules:", join(species)
# interpolation method used in multigrid algorithms: only Lagrange!
if interpolate in "bBsScC":
lagrange = 4
elif interpolate in "qQ":
lagrange = 3
elif interpolate in "lL":
lagrange = 2
else:
lagrange = int(interpolate)
# loop over molecules: read its line parameters (and some other data like mass) and compute cross sections for all p,T
