def computeAbsorption(pressure, temperature, gasSpecies, concValues, Line_Data, Mol_Data):
profs = [
Profile([0.0], what="altitude", unit="km"),
Profile([pressure], what="pressure", unit="g/cm/s**2"),
Profile([temperature], what="temperature", unit="K"),
]
for i in range(0, len(gasSpecies)):
profs.append(Profile([concValues[i]], what=gasSpecies[i], unit="ppV"))
atmos = Atmos1D(profs)
xsMatrix = []
for i in range(0, len(gasSpecies)):
# compute cross sections for selected line shape, returns a list with npT dictionaries
molCrossSections = lbl_xs(
Line_Data[i],
Mol_Data[i],
atmos.p,
atmos.T,
xLimits,
lineShape,
sampling,
wingExt,
nGrids,
gridRatio,
nWidths,
lagrange,
verbose,
)
xsMatrix.append(molCrossSections)
# compute absorption coefficients, also return uniform, equiidistant wavenumber grid corresponding to most dense cross section
xOut, absCoeffOut = sum_xsTimesDensity(xsMatrix, atmos.densities, interpolate=interpolate, verbose=verbose)
return xOut, absCoeffOut[:, 0]
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!'
def fitErr(pressures):
crossSections = []
sum = np.array([0.0]*len(data['x']))
for i in range(0,len(Line_Data)):
factor = pressures[i]/1013500.0/8.31/temperature*6.022*10**23
crossSections.append(lbl_xs (Line_Data[i], Mol_Data[i], np.array([pressures[i]]), np.array([temperature]), xLimits, lineShape, sampling, wingExt, nGrids, gridRatio, nWidths, lagrange, verbose)[0] )
xarr = np.linspace(xMin,xMax,len(crossSections[-1]['y']))
interp = interp1d(xarr,crossSections[-1]['y']*factor)
sum = sum + interp(data['x(wn)'])
diff = sum - data['alpha']
return diff.dot(diff)
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)
for i in range(0,len(absCoeff[0])):
plt.plot(vGrid,absCoeff[:,i])
plt.show()
from scipy.optimize import minimize
if(fitScale):
minResult = minimize(fitErrScale,initialGuess,method='Nelder-Mead')
else:
minResult = minimize(fitErr,initialGuess,method='Nelder-Mead')
#plot data
plt.plot(data['x(wn)'],data['alpha'],'ro')
crossSections = []
xplt = np.linspace(xMin,xMax,2000)
interps = []
interpFuncs = []
sum = np.array([0.0]*len(xplt))
for i in range(0,len(Line_Data)):
factor = minResult['x'][i]/1013500.0/8.31/temperature*6.022*10**23
crossSections.append(lbl_xs (Line_Data[i], Mol_Data[i], np.array([minResult['x'][i]]), np.array([temperature]), xLimits, lineShape, sampling, wingExt, nGrids, gridRatio, nWidths, lagrange, verbose)[0] )
xarr = np.linspace(xMin,xMax,len(crossSections[-1]['y']))
interp = interp1d(xarr,crossSections[-1]['y']*factor)
interpFuncs.append(interp)
interps.append(interp(data['x(wn)']))
if(fitScale):
A = np.column_stack(interps)
c, resid,rank,sigma = np.linalg.lstsq(A,data['alpha'])
else:
c = [1.0]*len(Line_Data)
for i in range(0,len(Line_Data)):
factor = minResult['x'][i]/1013500.0/8.31/temperature*6.022*10**23
sum = sum + interpFuncs[i](xplt)*c[i]
xarr = np.linspace(xMin,xMax,len(crossSections[i]['y']))
plt.plot(xarr,crossSections[i]['y']*factor*c[i])
#plot total hitran absorbance