def restart():
try:
shutil.rmtree(HAPI)
except OSError:
pass
os.makedirs(HAPI)
db_begin(HAPI)
def import_hitran_and_populate_particles_table():
start_time = time.time()
'''
#change file to loadable version
with open('/home/toma/Desktop/molecule_properties.txt', 'r') as f:
outfile = open('/home/toma/Desktop/molecule_properties (copy).txt', 'w')
for line in f:
data = line.split()
data.pop(1)
for i in data:
outfile.write("%s " % i)
outfile.write('\n')
outfile.close()
f.close()
'''
version_name = 'HITRAN_2016'
reference_link = r'https://www.sciencedirect.com/science/article/pii/S0022407317301073?via%3Dihub'
#everything is in string though to be noticed
#length of lists are 124
mol_ids, iso_ids, iso_names, iso_abundances, iso_masses, mol_names = \
np.loadtxt('/home/toma/Desktop/molecule_properties (copy).txt', dtype='str', skiprows=1, usecols=(1, 2, 3, 4, 5, 6), unpack=True)
for i in range(len(mol_ids)):
particle_property_query = "INSERT INTO particles VALUES('%s', '%s', '%s', '%s', '%s', null);" % (mol_names[i], iso_names[i], \
iso_abundances[i], iso_masses[i], 1) #this one is temporary
#insert each molecule's properties into particles table
sql_order(particle_property_query)
#then, fetch all the data from HITRAN using HAPI
hapi.db_begin('data')
#becasue cannot choose inifinity as upper limit, use a giant number instead
#gpp is upper state degeneracy
hapi.fetch(mol_names[i], int(mol_ids[i]), int(iso_ids[i]), 0, 1e9, Parameters=['nu', 'a', 'gamma_air', 'n_air', 'delta_air', \
'elower', 'gpp', 'gamma_H2', 'n_H2', 'delta_H2', 'gamma_He', 'n_He', 'delta_He'])
#open the file and use insert_hitran.py to insert all parameters into transitions table
filename = '/home/toma/Desktop/linelists-database/data/{}.data'.format(
mol_names[i])
insert_hitran.insert_hitran(filename, version_name, i + 1,
reference_link)
#delete the files since the files are named by HAPI using mol_name instead of iso_name
#s.t. python wont get confused in the for loop
header_filename = '/home/toma/Desktop/linelists-database/data/{}.header'.format(
mol_names[i])
os.remove(filename)
os.remove(header_filename)
print("Finished in %s seconds" % (time.time() - start_time))
def calc_hapi():
""" Calc spectrum under HAPI """
clean_after_run = not exists(HAPIdb) and False
try:
db_begin(HAPIdb)
if not molecule in tableList(
): # only if data not downloaded already
fetch(
molecule,
mol_id,
iso,
wmin - broadening_max_width / 2,
wmax + broadening_max_width / 2,
)
# HAPI doesnt correct for side effects
# Calculate with HAPI
nu, coef = absorptionCoefficient_Voigt(
SourceTables="CO2",
Environment={
"T": T,
"p": p / 1.01325,
}, # K # atm
WavenumberStep=dnu,
HITRAN_units=False,
GammaL="gamma_self",
)
nu, trans = transmittanceSpectrum(
nu,
coef,
Environment={
"l": L,
},
) # cm
s_hapi = Spectrum.from_array(
nu,
trans,
"transmittance_noslit",
"cm-1",
"1",
conditions={"Tgas": T},
name="HAPI",
)
except:
raise
finally:
if clean_after_run:
shutil.rmtree(HAPIdb)
return s_hapi
def generate_availability_():
Config.data_folder = "__temp_data"
hapi.db_begin(Config.data_folder)
records = dict()
for molecule_id in range(0, 100):
molecule = MoleculeMeta(molecule_id)
if not molecule.is_populated():
continue
records[molecule.id] = check_availability(molecule)
with open(f"res/parameters/availability.json", "w+") as f:
f.write(json.dumps(records, indent=2, sort_keys=True))
print(records)
sys.exit(0)
def test_broadening_vs_hapi(rtol=1e-2,
verbose=True,
plot=False,
*args,
**kwargs):
"""
Test broadening against HAPI and tabulated data
We're looking at CO(0->1) line 'R1' at 2150.86 cm-1
"""
from hapi import absorptionCoefficient_Voigt, db_begin, fetch, tableList
if plot: # Make sure matplotlib is interactive so that test are not stuck in pytest
plt.ion()
setup_test_line_databases() # add HITRAN-CO-TEST in ~/.radis if not there
# Conditions
T = 3000
p = 0.0001
wstep = 0.001
wmin = 2150 # cm-1
wmax = 2152 # cm-1
broadening_max_width = 10 # cm-1
# %% HITRAN calculation
# -----------
# Generate HAPI database locally
hapi_data_path = join(dirname(__file__),
__file__.replace(".py", "_HAPIdata"))
db_begin(hapi_data_path)
if not "CO" in tableList(): # only if data not downloaded already
fetch("CO", 5, 1, wmin - broadening_max_width / 2,
wmax + broadening_max_width / 2)
# HAPI doesnt correct for side effects
# Calculate with HAPI
nu, coef = absorptionCoefficient_Voigt(
SourceTables="CO",
Environment={
"T": T,
"p": p / 1.01325,
}, # K # atm
WavenumberStep=wstep,
HITRAN_units=False,
)
s_hapi = Spectrum.from_array(nu,
coef,
"abscoeff",
"cm-1",
"cm-1",
conditions={"Tgas": T},
name="HAPI")
# %% Calculate with RADIS
# ----------
sf = SpectrumFactory(
wavenum_min=wmin,
wavenum_max=wmax,
mole_fraction=1,
path_length=1, # doesnt change anything
wstep=wstep,
pressure=p,
broadening_max_width=broadening_max_width,
isotope=[1],
warnings={
"MissingSelfBroadeningWarning": "ignore",
"NegativeEnergiesWarning": "ignore",
"HighTemperatureWarning": "ignore",
"GaussianBroadeningWarning": "ignore",
},
) # 0.2)
sf.load_databank(path=join(hapi_data_path, "CO.data"),
format="hitran",
parfuncfmt="hapi")
# s = pl.non_eq_spectrum(Tvib=T, Trot=T, Ttrans=T)
s = sf.eq_spectrum(Tgas=T, name="RADIS")
if plot: # plot broadening of line of largest linestrength
sf.plot_broadening(i=sf.df1.S.idxmax())
# Plot and compare
res = abs(get_residual_integral(s, s_hapi, "abscoeff"))
if plot:
plot_diff(
s,
s_hapi,
var="abscoeff",
title="{0} bar, {1} K (residual {2:.2g}%)".format(p, T, res * 100),
show_points=False,
)
plt.xlim((wmin, wmax))
if verbose:
printm("residual:", res)
assert res < rtol
s.name = "RADIS ({0:.1f}s)".format(s.conditions["calculation_time"])
# Print our HWHM for comparison (a posteriori)
print(("HWHM max {0:.2f} cm-1".format(sf.df1.hwhm_voigt.max())))
print((
"WavenumberWingHW/HWHM",
int(sf.params.broadening_max_width / (sf.df1.hwhm_voigt.max())),
))
assert (int(sf.params.broadening_max_width /
(sf.df1.hwhm_voigt.max()))) == benchmark_line_brd_ratio
# %% Run HAPI
print("Now calculating with HAPI")
# Generate HAPI database locally
db_begin(join(dirname(__file__), __file__.replace(".py", "_HAPIdata")))
if not molecule in tableList(): # only if data not downloaded already
mol_iso_ids = [k for k in ISO_ID if ISO_ID[k][-1] == molecule]
mol_iso_ids = [k for k in mol_iso_ids if ISO_ID[k][1] in isotopes]
fetch_by_ids(molecule, mol_iso_ids, wavenum_min, wavenum_max)
# Calculate with HAPI
def calc_hapi():
nu, coef = absorptionCoefficient_Voigt(
SourceTables=molecule,
Environment={
"T": T,
"p": pressure_bar / 1.01315,
}, # K # atm
GammaL="gamma_self",
WavenumberStep=dnu,
plt.ylabel("Absorption Coefficient")
plt.subplot(2, 2, 3)
plt.plot(wave_numbers, datasets["NO"]["absorption_coefficient"], color="g")
plt.title(f"NO Absorption")
plt.xlabel("Wavenumber")
plt.ylabel("Absorption Coefficient")
plt.subplot(2, 2, 4)
plt.plot(wave_numbers, mixture["absorption_coefficient"], color="k")
plt.title(f"{mix[0]}%CO2-{mix[1]}%CH4-{mix[2]}%NO, Absorption")
plt.xlabel("Wavenumber")
plt.ylabel("Absorption Coefficient")
plt.tight_layout()
plt.savefig(f"plots/mixture_absorption.png")
plt.clf()
if __name__ == "__main__":
db_begin(HAPI)
for molecule, spec in MOLECULES.items():
properties(molecule, spec)
folder = f"{HEAT}/plots/{molecule}"
try:
os.makedirs(folder)
except OSError:
pass
sticks(molecule, folder)
lineshapes(folder)
absorptions(molecule, spec, folder)
spectrums(molecule, spec, folder)
hires(molecule, spec, folder)
from lib.ReadComputeFunc import ReadData
import matplotlib.pyplot as plt
Molecule = "CO"
TempValue = 1000.0
P_Value = 0.1 #0.00001
#TempValue = 1000.0
#P_Value = 10.0
OmegaWingValue = 500.0
OmegaRangeValue = [2086.32193, 2093.410992]
WaveNumber = np.arange(OmegaRangeValue[0], OmegaRangeValue[1] + 0.01, 0.01)
Env = {'T': TempValue, 'p': P_Value}
#StartTime = time.time()
hapi.db_begin('TestData')
nu_hapi_Doppler, abs_hapi_Doppler = hapi.absorptionCoefficient_Doppler(
SourceTables=Molecule,
OmegaGrid=WaveNumber,
HITRAN_units=True,
Environment=Env,
GammaL='gamma_self',
OmegaWing=OmegaWingValue,
OmegaWingHW=0.0,
LineShift=False) #, OmegaStep=0.01)
nu_hapi_Voigt, abs_hapi_Voigt = hapi.absorptionCoefficient_Voigt(
SourceTables=Molecule,
OmegaGrid=WaveNumber,
HITRAN_units=True,
Environment=Env,
GammaL='gamma_air',
html.H2([
u'ИС моделирования спектров\n коэфициентов поглощения смеси газов'
]),
dcc.Link([
html.Img(src='/assets/help.svg'),
],
href='/about',
className='about-link'),
],
style={'background': '#33b5ff'},
),
html.Div(id='page-content'),
dcc.ConfirmDialog(id='confirm', ),
], )
db_begin('data')
@app.callback(Output('page-content', 'children'), [Input('url', 'pathname')])
def display_page(pathname):
if pathname == '/':
return main
elif pathname == '/about':
return about
else:
return '404'
if __name__ == '__main__':
app.run_server(debug=True, dev_tools_ui=True)
D_startWavelength = control['delta start wavelength']
D_endWavelength = control['delta end wavelength']
D_minTH = control['delta min tangent height']
D_maxTH = control['delta max tangent height']
D_w1_step = control['delta w1 step']
D_n_nO2 = control['delta number of loosen O2 layers']
if control['if A band']:
S_startWavelength = control['sigma start wavelength']
S_endWavelength = control['sigma end wavelength']
S_minTH = control['sigma min tangent height']
S_maxTH = control['sigma max tangent height']
S_w1_step = control['sigma w1 step']
S_n_nO2 = control['sigma number of loosen O2 layers']
# load hitran database
db_begin(control['hitran database path'])
if not os.path.exists(
os.path.join(
control['hitran database path'], 'O2_{:.1f}-{:.1f}.data'.format(
D_startWavelength, D_endWavelength))):
fetch('O2_{:.1f}-{:.1f}'.format(D_startWavelength, D_endWavelength), 7, 1,
1e7 / D_endWavelength, 1e7 / D_startWavelength)
if control['if A band']:
if not os.path.exists(
os.path.join(
control['hitran database path'],
'O2_{:.1f}-{:.1f}.data'.format(S_startWavelength,
S_endWavelength))):
fetch('O2_{:.1f}-{:.1f}'.format(S_startWavelength, S_endWavelength), 7,
1, 1e7 / S_endWavelength, 1e7 / S_startWavelength)
def load_hitran(self,
v0,
vf,
threshhold=1e-28,
hit_path='../inputs/HITRAN/'):
"""
Load hitran data file into dictionary
Relies on hapi.py from hitran.org
# To download new hitran data and get table summary:
fetch_by_ids('H2O',[1,2],9000,20000,ParameterGroups=['Voigt_Air'])) # first two isotopologues
fetch_by_ids('O2',[36,37],9000,20000,ParameterGroups=['Voigt_Air'])) # first two isotopologues
tableList()
describeTable('H2O')
"""
# Define parameters to include
pnames = ['nu', 'sw', 'gamma_air', 'n_air', 'delta_air', 'a']
hitparams = {
} # dictionary of hitran parameters, keys by molecule then pnames
istr = {
} # dictionary of indices of lines that are strong enough to fit
iwk = {}
# Load subset of hitran data already downloaded
self.subhitpath = hit_path + 'v0_%s_vf_%s/' % (int(v0), int(vf))
# Load subfile if exists else make directory to store file
if os.path.exists(self.subhitpath):
# Load pre downloaded sub table
pass
else:
os.system('mkdir ' + self.subhitpath)
db_begin(hit_path)
table = tableList()
for i, molecule in enumerate(self.species):
if molecule in table:
subhitname = '%s_v0_%s_vf_%s.par' % (molecule, int(v0),
int(vf))
subhitfile = self.subhitpath + subhitname
# Save sub table into list
select(molecule,
Conditions=('AND', ('between', 'nu', v0 - 5.0,
vf + 5.0), ('>=', 'sw',
threshhold)),
DestinationTableName=subhitname,
File=subhitfile)
# db_commit() # dont think i need this anymore
else:
# Download so can work offline ..no idea where this stores shit and i htink it fails so just d/l it yourself
print 'Downloading Hitran data for ' + molecule
fetch_by_ids(molecule, [1, 2, 3],
9000,
20000,
ParameterGroups=['Voigt_Air'
]) # first two isotopologues
db_begin(self.subhitpath)
table = tableList()
# with table loaded, Loop through molecules again and save parameters into dics
for i, molecule in enumerate(self.species):
subhitname = '%s_v0_%s_vf_%s.par' % (molecule, int(v0), int(vf))
# Get columns need for Voigt parameters
hitparams[molecule] = {}
istr[molecule] = {}
iwk[molecule] = {}
for parameter in pnames:
hitparams[molecule][parameter] = getColumn(
subhitname.strip('.par'), parameter)
istr[molecule] = np.where(
np.array(hitparams[molecule]['sw']) > self.fit_threshholds[i]
)[0]
iwk[molecule] = np.where((np.array(hitparams[molecule]['sw']) < self.fit_threshholds[i])\
& (np.array(hitparams[molecule]['sw']) > 0.1*self.fit_threshholds[i]))[0]
self.hitran_params = hitparams # save hitran parameters
self.istr = istr
self.iwk = iwk
#!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
Created on Wed Nov 1 11:54:59 2017
@author: grosskc
"""
import numpy as np
import hapi as ht
# import pylab as plt
ht.db_begin('data')
# Set up calculation parameters
nu1 = 400
nu2 = 7100
dnu = 0.0025
wnW = 350
nX = round((nu2 - nu1) / dnu) + 1
X = np.linspace(nu1, nu2, nX)
T1 = 275
T2 = 320
dT = 5.0
nT = round((T2 - T1) / dT) + 1
T = np.linspace(T1, T2, nT)
P1 = 0.85
P2 = 1.05
dP = 0.05
nP = round((P2 - P1) / dP) + 1
P = np.linspace(P1, P2, nP)
molec = [1, 2]
########################
import numpy as np
import os,sys,copy
import hapi
from spectro import wn2freq,wl2wn
from calculator import *
########################
url_jpl = 'http://spec.jpl.nasa.gov'
dir = 'JPL/'
dir_hitran = 'HITRAN'
file_catdir = dir + 'catdir.cat'
file_molecules = dir + 'c%06i.cat'
########################
# prepare for using HITRAN database
hapi.db_begin(dir_hitran)
hitran_db_table = hapi.getTableList()
def read_catdir():
format = 'I6,X, A13, I6, 7F7.4, I2'
f = open(file_catdir,mode='r')
flines = f.readlines()
out = {}
for fl in flines:
tag = np.int(fl[:7])
namefactors = fl[7:13+7].split()
if len(namefactors) > 1:
for i in xrange(len(namefactors)-1):
namefactors[i+1] = ' ' + namefactors[i+1]
name = ''.join(namefactors)
others = fl[13+7:].split()
import hapi as hp
import matplotlib.pyplot as plt
hp.db_begin("data")
#hp.fetch("H2O",1,1,3400,4100)
hp.select('H2O',
ParameterNames=('nu', 'sw'),
Conditions=('between', 'nu', 4000, 4100))
nu, coef = hp.absorptionCoefficient_Voigt(((1, 1), ),
'H2O',
OmegaStep=1,
HITRAN_units=False,
GammaL='gamma_self',
Environment={
'p': 1,
'T': 296.
})
plt.plot(nu, coef)
plt.show()