def get_conv_factors(self, spcdb_dir):
"""
Gets conversion factors used in budget computations
Arguments:
spcdb_dir : str
Path to the species_database.yml file
"""
# Read the species database
path = os.path.join(spcdb_dir, "species_database.yml")
spcdb = yaml_load_file(open(path))
# Molecular weights [kg mol-1], as taken from the species database
self.mw = {}
self.mw["O3"] = spcdb["O3"]["MW_g"] * 1.0e-3
self.mw["Ox"] = self.mw["O3"]
self.mw["Air"] = constants.MW_AIR_g * 1.0e-3
# kg/s --> Tg/d
self.kg_s_to_tg_a_value = 86400.0 * self.d_per_a * 1e-9
def __init__(self, devstr, devdir, plotsdir, year, overwrite):
"""
Initializes the _GlobVars class.
Args:
-----
devstr : str
Label denoting the "Dev" version.
devdir : str
Directory where benchmark diagnostic files are found.
plotsdir : str
Directory where plots & tables will be created.
year : int
Year of the benchmark simulation.
overwrite : bool
Denotes whether to ovewrite existing budget tables.
""" """
Initializes the _GlobVars class.
"""
# ------------------------------
# Arguments from outside
# ------------------------------
self.devstr = devstr
self.devdir = devdir
self.plotsdir = plotsdir
self.overwrite = overwrite
# ------------------------------
# Benchmark year
# ------------------------------
self.y0 = year
self.y1 = self.y0 + 1
self.y0_str = "{}".format(self.y0)
self.y1_str = "{}".format(self.y1)
# ------------------------------
# Species info
# ------------------------------
# Directory where diagnostic files are found
datadir = join(devstr, "OutputDir")
# List of species (and subsets for the trop & strat)
self.species_list = ["BCPI", "OCPI", "SO4", "DST1", "SALA", "SALC" ]
# Read the species database
try:
path = join(datadir, "species_database.yml")
spcdb = yaml_load_file(open(path))
except FileNotFoundError:
path = join(os.path.dirname(__file__), "species_database.yml")
spcdb = yaml_load_file(open(path))
# Molecular weights [g mol-1], as taken from the species database
self.mw = {}
for v in self.species_list:
self.mw[v] = spcdb[v]["MW_g"]
self.mw["Air"] = constants.MW_AIR * 1.0e3
# Get the list of relevant AOD diagnostics from a YAML file
path = join(os.path.dirname(__file__), "aod_species.yml")
aod = yaml_load_file(open(path))
self.aod_list = [v for v in aod.keys() if "Dust" in v or "Hyg" in v]
# Descriptive names
self.spc2name = {"BCPI": "Black Carbon",
"DST1": "Dust",
"OCPI": "Organic Carbon",
"SO4" : "Sulfate",
"SALA": "Sea Salt (accum)",
"SALC": "Sea Salt (coarse)"}
# ------------------------------
# Collection file lists
# ------------------------------
Aerosols = join(datadir,
"*.Aerosols.{}*.nc4".format(self.y0_str))
StateMet = join(datadir,
"*.StateMet.{}*.nc4".format(self.y0_str))
SpeciesConc = join(datadir,
"*.SpeciesConc.{}*.nc4".format(self.y0_str))
# ------------------------------
# Read data collections
# ------------------------------
# Diagnostics
self.ds_aer = xr.open_mfdataset(Aerosols, data_vars=self.aod_list)
self.ds_cnc = xr.open_mfdataset(SpeciesConc)
self.ds_met = xr.open_mfdataset(StateMet)
# Troposphere mask
self.tropmask = get_troposphere_mask(self.ds_met)
# Number of vertical levels
self.N_LEVS = self.ds_cnc.dims["lev"]
# Set a flag to denote if this data is from GCHP
self.is_gchp = "nf" in self.ds_cnc.dims.keys()
# ------------------------------
# Months and days
# ------------------------------
self.N_MONTHS = 12
self.N_MONTHS_FLOAT = self.N_MONTHS * 1.0
# Days per month in the benchmark year
self.d_per_mon = np.zeros(self.N_MONTHS)
for t in range(self.N_MONTHS):
self.d_per_mon[t] = monthrange(self.y0, t+1)[1] * 1.0
# Days in the benchmark year
self.d_per_yr = np.sum(self.d_per_mon)
# Fraction of year occupied by each month
self.frac_of_yr = np.zeros(self.N_MONTHS)
for t in range(self.N_MONTHS):
self.frac_of_yr[t] = self.d_per_mon[t] / self.d_per_yr
# --------------------------------
# Surface area
# (kludgey but it works)
# --------------------------------
if self.is_gchp:
area = self.ds_met["Met_AREAM2"].values
a = area.shape
self.area_m2 = np.zeros([a[0], N_LEVS, a[1], a[2], a[3]])
for t in range(self.N_MONTHS):
for k in range(self.N_LEVS):
self.area_m2[t,k,:,:,:] = area[t,:,:,:]
self.total_area_m2 = np.sum(self.area_m2[0,0,:,:,:])
else:
area = self.ds_met["AREA"].values
a = area.shape
self.area_m2 = np.zeros([a[0], self.N_LEVS, a[1], a[2]])
for t in range(self.N_MONTHS):
for k in range(self.N_LEVS):
self.area_m2[t,k,:,:] = area[t,:,:]
self.total_area_m2 = np.sum(self.area_m2[0,0,:,:])
# ------------------------------
# Conversion factors
# ------------------------------
# v/v dry --> Tg
self.vv_to_Tg = {}
for spc in self.species_list:
self.vv_to_Tg[spc] = self.ds_met["Met_AD"].values \
* (self.mw[spc] / self.mw["Air"]) * 1e-9
def __init__(self, devstr, devdir, devrstdir, year, dst, is_gchp,
overwrite, spcdb_dir):
"""
Initializes the _GlobVars class.
Args:
-----
devstr : str
Label denoting the "Dev" version.
devdir : str
Directory where diagnostic files are found.
devrstdir : str
Directory where restart files are found.
dst : str
Directory where plots & tables will be created.
year : int
Year of the benchmark simulation.
is_gchp : bool
Denotes if this is GCHP (True) or GCC (False) data.
overwrite : bool
Denotes whether to ovewrite existing budget tables.
spcdb_dir : str
Directory where species_database.yml is stored.
"""
# ------------------------------
# Arguments from outside
# ------------------------------
self.devstr = devstr
self.devdir = devdir
self.devrstdir = devrstdir
self.dst = dst
self.is_gchp = is_gchp
self.overwrite = overwrite
# ------------------------------
# Benchmark year
# ------------------------------
self.y0 = year
self.y1 = self.y0 + 1
self.y0_str = "{}".format(self.y0)
self.y1_str = "{}".format(self.y1)
# ------------------------------
# Collection file lists
# ------------------------------
# Restarts
if self.is_gchp:
RstInit = join(self.devrstdir,
"initial_GEOSChem_rst.c48_TransportTracers.nc")
RstFinal = join(
self.devrstdir,
"gcchem_internal_checkpoint.restart.{}*.nc4".format(
self.y1_str))
else:
RstInit = join(self.devrstdir,
"GEOSChem.Restart.{}*nc4".format(self.y0_str))
RstFinal = join(self.devrstdir,
"GEOSChem.Restart.{}*.nc4".format(self.y1_str))
# Diagnostics
HemcoDiag = join(self.devdir,
"HEMCO_diagnostics.{}*.nc".format(self.y0_str))
DryDep = join(self.devdir, "*.DryDep.{}*.nc4".format(self.y0_str))
RadioNucl = join(self.devdir,
"*.RadioNuclide.{}*.nc4".format(self.y0_str))
if is_gchp:
StateMetAvg = join(self.devdir,
"*.StateMet_avg.{}*.nc4".format(self.y0_str))
StateMetInst = join(self.devdir,
"*.StateMet_inst.{}*.nc4".format(self.y0_str))
else:
StateMet = join(self.devdir,
"*.StateMet.{}*.nc4".format(self.y0_str))
SpeciesConc = join(self.devdir,
"*.SpeciesConc.{}*.nc4".format(self.y0_str))
WetLossConv = join(self.devdir,
"*.WetLossConv.{}*.nc4".format(self.y0_str))
WetLossLS = join(self.devdir,
"*.WetLossLS.{}*.nc4".format(self.y0_str))
GCHPEmiss = join(self.devdir,
"GCHP.Emissions.{}*.nc4".format(self.y0_str))
# ------------------------------
# Read data collections
# ------------------------------
# Restarts
skip_vars = constants.skip_these_vars
self.ds_ini = xr.open_mfdataset(RstInit, drop_variables=skip_vars)
self.ds_end = xr.open_mfdataset(RstFinal, drop_variables=skip_vars)
# Change the restart datasets into format similar to GCC, and flip vertical axis
if is_gchp:
self.ds_ini = rename_and_flip_gchp_rst_vars(self.ds_ini)
self.ds_end = rename_and_flip_gchp_rst_vars(self.ds_end)
# Diagnostics
self.ds_dcy = xr.open_mfdataset(RadioNucl, drop_variables=skip_vars)
self.ds_dry = xr.open_mfdataset(DryDep, drop_variables=skip_vars)
self.ds_cnc = xr.open_mfdataset(SpeciesConc, drop_variables=skip_vars)
self.ds_wcv = xr.open_mfdataset(WetLossConv, drop_variables=skip_vars)
self.ds_wls = xr.open_mfdataset(WetLossLS, drop_variables=skip_vars)
# Met fields
if is_gchp:
self.ds_met = xr.open_mfdataset(StateMetAvg,
drop_variables=skip_vars)
# For now, don't read restarts for GCHP (bmy, 3/16/20)
#ds_met_inst = xr.open_mfdataset(StateMetInst,
# drop_variables=skip_vars)
#
# Add the initial met fields to the restart file collections
#self.ds_ini = xr.merge([self.ds_ini, ds_met_inst.isel(time=0)])
#self.ds_end = xr.merge([self.ds_end, ds_met_inst.isel(time=11)])
else:
self.ds_met = xr.open_mfdataset(StateMet, drop_variables=skip_vars)
# Emissions
if self.is_gchp:
self.ds_hco = xr.open_mfdataset(GCHPEmiss,
drop_variables=skip_vars)
else:
self.ds_hco = xr.open_mfdataset(HemcoDiag,
drop_variables=skip_vars)
# Area and troposphere mask
# For gchp, get area in m2 from restart for use in calculating initial
# and final mass from restart. Get area in cm2 from diagnostic
# file for format compatibility with diagnostic output.
if self.is_gchp:
self.area_m2 = self.ds_ini["AREA"]
self.area_cm2 = self.ds_met["Met_AREAM2"] * 1.0e4
else:
self.area_m2 = self.ds_met["AREA"].isel(time=0)
self.area_cm2 = self.area_m2 * 1.0e4
self.tropmask = get_troposphere_mask(self.ds_met)
# ------------------------------
# Months and days
# ------------------------------
self.N_MONTHS = 12
self.N_MONTHS_FLOAT = self.N_MONTHS * 1.0
# Days per month in the benchmark year
self.d_per_mon = np.zeros(self.N_MONTHS)
for t in range(self.N_MONTHS):
self.d_per_mon[t] = monthrange(self.y0, t + 1)[1] * 1.0
# Days in the benchmark year
self.d_per_yr = np.sum(self.d_per_mon)
# Fraction of year occupied by each month
self.frac_of_yr = np.zeros(self.N_MONTHS)
for t in range(self.N_MONTHS):
self.frac_of_yr[t] = self.d_per_mon[t] / self.d_per_yr
# ------------------------------
# Species info
# ------------------------------
# List of species (and subsets for the trop & strat)
self.species_list = ["Pb210", "Be7", "Be10"]
# Read the species database
path = join(spcdb_dir, "species_database.yml")
spcdb = yaml_load_file(open(path))
# Molecular weights [g mol-1], as taken from the species database
self.mw = {}
for v in self.species_list:
self.mw[v] = spcdb[v]["MW_g"]
self.mw["Air"] = constants.MW_AIR * 1.0e3
# kg/s --> g/day
self.kg_s_to_g_d_value = 86400.0 * 1000.0
# ------------------------------
# Conversion factors
# ------------------------------
self.kg_per_mol = {}
self.vv_to_g = {}
self.mcm2s_to_g_d = {}
self.kg_s_to_g_d = {}
for spc in self.species_list:
# kg/s --> g/day (same for all species)
self.kg_s_to_g_d[spc] = self.kg_s_to_g_d_value
# kg/mole for each species
self.kg_per_mol[spc] = constants.AVOGADRO / (self.mw[spc] * 1e-3)
# v/v dry --> g
self.vv_to_g[spc] = self.ds_met["Met_AD"].values \
* (self.mw[spc] / self.mw["Air"]) * 1000.0
# molec/cm2/s --> g/day
self.mcm2s_to_g_d[spc] = self.area_cm2.values \
/ self.kg_per_mol[spc] \
* self.kg_s_to_g_d[spc]
def __init__(self, devstr, maindir, plotsdir, year, overwrite):
"""
Initializes the _GlobVars class.
Args:
-----
devstr : str
Label denoting the "Dev" version.
maindir : str
Top-level benchmark run directory.
plotsdir : str
Directory where plots & tables will be created.
year : int
Year of the benchmark simulation.
overwrite : bool
Denotes whether to ovewrite existing budget tables.
"""
# ------------------------------
# Arguments from outside
# ------------------------------
self.devstr = devstr
self.maindir = maindir
self.plotsdir = plotsdir
self.overwrite = overwrite
# ------------------------------
# Benchmark year
# ------------------------------
self.y0 = year
self.y1 = self.y0 + 1
self.y0_str = "{}".format(self.y0)
self.y1_str = "{}".format(self.y1)
# ------------------------------
# Collection file lists
# ------------------------------
rstdir = join(self.maindir, self.devstr, "restarts")
RstInit = join(rstdir, "GEOSChem.Restart.{}*nc4".format(self.y0_str))
RstFinal = join(rstdir, "GEOSChem.Restart.{}*.nc4".format(self.y1_str))
datadir = join(self.maindir, self.devstr, "OutputDir")
HemcoDiag = join(datadir,
"HEMCO_diagnostics.{}*.nc".format(self.y0_str))
DryDep = join(datadir,
"*.DryDep.{}*.nc4".format(self.y0_str))
RadioNucl = join(datadir,
"*.RadioNuclide.{}*.nc4".format(self.y0_str))
StateMet = join(datadir,
"*.StateMet.{}*.nc4".format(self.y0_str))
SpeciesConc = join(datadir,
"*.SpeciesConc.{}*.nc4".format(self.y0_str))
WetLossConv = join(datadir,
"*.WetLossConv.{}*.nc4".format(self.y0_str))
WetLossLS = join(datadir,
"*.WetLossLS.{}*.nc4".format(self.y0_str))
GCHPEmiss = join(datadir,
"GCHP.Emissions.{}*.nc4".format(self.y0_str))
# ------------------------------
# Read data collections
# ------------------------------
# Restarts
self.ds_ini = xr.open_mfdataset(RstInit)
self.ds_end = xr.open_mfdataset(RstFinal)
# Diagnostics
self.ds_dcy = xr.open_mfdataset(RadioNucl)
self.ds_dry = xr.open_mfdataset(DryDep)
self.ds_met = xr.open_mfdataset(StateMet)
self.ds_cnc = xr.open_mfdataset(SpeciesConc)
self.ds_wcv = xr.open_mfdataset(WetLossConv)
self.ds_wls = xr.open_mfdataset(WetLossLS)
# Set a flag if this data is from GCHP
self.is_gchp = "nf" in self.ds_cnc.dims.keys()
# Emissions
if self.is_gchp:
self.ds_hco = xr.open_mfdataset(GCHPEmiss)
else:
self.ds_hco = xr.open_mfdataset(HemcoDiag)
# Area and troposphere mask
if self.is_gchp:
self.area_m2 = self.ds_met["Met_AREAM2"].isel(time=0)
else:
self.area_m2 = self.ds_met["AREA"].isel(time=0)
self.area_cm2 = self.area_m2 * 1.0e4
self.tropmask = get_troposphere_mask(self.ds_met)
# ------------------------------
# Months and days
# ------------------------------
self.N_MONTHS = 12
self.N_MONTHS_FLOAT = self.N_MONTHS * 1.0
# Days per month in the benchmark year
self.d_per_mon = np.zeros(self.N_MONTHS)
for t in range(self.N_MONTHS):
self.d_per_mon[t] = monthrange(self.y0, t+1)[1] * 1.0
# Days in the benchmark year
self.d_per_yr = np.sum(self.d_per_mon)
# Fraction of year occupied by each month
self.frac_of_yr = np.zeros(self.N_MONTHS)
for t in range(self.N_MONTHS):
self.frac_of_yr[t] = self.d_per_mon[t] / self.d_per_yr
# ------------------------------
# Species info
# ------------------------------
# List of species (and subsets for the trop & strat)
self.species_list = ["Pb210", "Be7", "Be10" ]
# Read the species database
try:
path = join(datadir, "species_database.yml")
spcdb = yaml_load_file(open(path))
tmp = spcdb["Pb210"]
except KeyError or FileNotFoundError:
path = join(os.path.dirname(__file__), "species_database.yml")
spcdb = yaml_load_file(open(path))
# Molecular weights [g mol-1], as taken from the species database
self.mw = {}
for v in self.species_list:
self.mw[v] = spcdb[v]["MW_g"]
self.mw["Air"] = constants.MW_AIR * 1.0e3
# kg/s --> g/day
self.kg_s_to_g_d_value= 86400.0 * 1000.0
# ------------------------------
# Conversion factors
# ------------------------------
self.kg_per_mol = {}
self.vv_to_g = {}
self.mcm2s_to_g_d = {}
self.kg_s_to_g_d = {}
for spc in self.species_list:
# kg/s --> g/day (same for all species)
self.kg_s_to_g_d[spc] = self.kg_s_to_g_d_value
# kg/mole for each species
self.kg_per_mol[spc] = constants.AVOGADRO / (self.mw[spc] * 1e-3)
# v/v dry --> g
self.vv_to_g[spc] = self.ds_met["Met_AD"].values \
* (self.mw[spc] / self.mw["Air"]) * 1000.0
# molec/cm2/s --> g/day
self.mcm2s_to_g_d[spc] = self.area_cm2.values \
/ self.kg_per_mol[spc] \
* self.kg_s_to_g_d[spc]
def __init__(self, devstr, devdir, devrstdir, year, dst, is_gchp,
overwrite, spcdb_dir):
"""
Initializes the _GlobVars class.
Args:
devstr: str
Label denoting the "Dev" version.
devdir: str
Directory where diagnostic files are found.
devrstdir: str
Directory where restart files are found.
dst: str
Directory where plots & tables will be created.
year: int
Year of the benchmark simulation.
is_gchp: bool
Denotes if this is GCHP (True) or GCC (False) data.
overwrite: bool
Denotes whether to ovewrite existing budget tables.
spcdb_dir: str
Directory where species_database.yml is stored.
"""
# ------------------------------
# Arguments from outside
# ------------------------------
self.devstr = devstr
self.devdir = devdir
self.devrstdir = devrstdir
self.dst = dst
self.is_gchp = is_gchp
self.overwrite = overwrite
# ------------------------------
# Benchmark year
# ------------------------------
self.y0 = year
self.y1 = self.y0 + 1
self.y0_str = "{}".format(self.y0)
self.y1_str = "{}".format(self.y1)
# ------------------------------
# Collection file lists
# ------------------------------
# Restarts
if self.is_gchp:
RstInit = join(self.devrstdir,
"initial_GEOSChem_rst.c48_TransportTracers.nc")
RstFinal = join(
self.devrstdir,
"gcchem_internal_checkpoint.restart.{}*.nc4".format(
self.y1_str))
else:
RstInit = join(self.devrstdir,
"GEOSChem.Restart.{}*nc4".format(self.y0_str))
RstFinal = join(self.devrstdir,
"GEOSChem.Restart.{}*.nc4".format(self.y1_str))
# Diagnostics
HemcoDiag = join(self.devdir,
"HEMCO_diagnostics.{}*.nc".format(self.y0_str))
DryDep = join(self.devdir, "*.DryDep.{}*.nc4".format(self.y0_str))
RadioNucl = join(self.devdir,
"*.RadioNuclide.{}*.nc4".format(self.y0_str))
if is_gchp:
StateMetAvg = join(self.devdir,
"*.StateMet_avg.{}*.nc4".format(self.y0_str))
StateMet = join(self.devdir,
"*.StateMet.{}*.nc4".format(self.y0_str))
# Set a logical if we need to read StateMet_avg or StateMet
gchp_use_statemet_avg = os.path.exists(StateMetAvg)
else:
StateMet = join(self.devdir,
"*.StateMet.{}*.nc4".format(self.y0_str))
SpeciesConc = join(self.devdir,
"*.SpeciesConc.{}*.nc4".format(self.y0_str))
WetLossConv = join(self.devdir,
"*.WetLossConv.{}*.nc4".format(self.y0_str))
WetLossLS = join(self.devdir,
"*.WetLossLS.{}*.nc4".format(self.y0_str))
GCHPEmiss = join(self.devdir,
"*.Emissions.{}*.nc4".format(self.y0_str))
# ------------------------------
# Read data collections
# ------------------------------
# Restarts
skip_vars = constants.skip_these_vars
extra_kwargs = {}
self.ds_ini = xr.open_mfdataset(RstInit,
drop_variables=skip_vars,
**extra_kwargs)
self.ds_end = xr.open_mfdataset(RstFinal,
drop_variables=skip_vars,
**extra_kwargs)
# Change the restart datasets into format similar to GCC, and flip
# vertical axis. Also test if the restart files have the BXHEIGHT
# variable contained within them.
if is_gchp:
self.ds_ini = rename_and_flip_gchp_rst_vars(self.ds_ini)
self.ds_end = rename_and_flip_gchp_rst_vars(self.ds_end)
# Diagnostics
self.ds_dcy = xr.open_mfdataset(RadioNucl,
drop_variables=skip_vars,
**extra_kwargs)
self.ds_dry = xr.open_mfdataset(DryDep,
drop_variables=skip_vars,
**extra_kwargs)
self.ds_cnc = xr.open_mfdataset(SpeciesConc,
drop_variables=skip_vars,
**extra_kwargs)
self.ds_wcv = xr.open_mfdataset(WetLossConv,
drop_variables=skip_vars,
**extra_kwargs)
self.ds_wls = xr.open_mfdataset(WetLossLS,
drop_variables=skip_vars,
**extra_kwargs)
# Met fields
# For GCHP: Read from StateMet_avg if present (otherwise StateMet)
if is_gchp and gchp_use_statemet_avg:
self.ds_met = xr.open_mfdataset(StateMetAvg,
drop_variables=skip_vars,
**extra_kwargs)
else:
self.ds_met = xr.open_mfdataset(StateMet,
drop_variables=skip_vars,
**extra_kwargs)
# Emissions
if self.is_gchp:
self.ds_hco = xr.open_mfdataset(GCHPEmiss,
drop_variables=skip_vars,
**extra_kwargs)
else:
self.ds_hco = xr.open_mfdataset(HemcoDiag,
drop_variables=skip_vars,
**extra_kwargs)
# Area and troposphere mask
# The area in m2 is on the restart file grid
# The area in cm2 is on the History diagnostic grid
# Both grids are identical in GCClassic but differ in GCHP
if self.is_gchp:
if 'Met_AREAM2' not in self.ds_met.data_vars.keys():
msg = 'Could not find Met_AREAM2 in StateMet_avg collection!'
raise ValueError(msg)
area_m2 = self.ds_met["Met_AREAM2"].isel(time=0)
area_m2 = reshape_MAPL_CS(area_m2)
self.area_m2 = area_m2
self.area_cm2 = self.ds_met["Met_AREAM2"] * 1.0e4
else:
self.area_m2 = self.ds_met["AREA"].isel(time=0)
self.area_cm2 = self.area_m2 * 1.0e4
self.tropmask = get_troposphere_mask(self.ds_met)
# ------------------------------
# Months and days
# ------------------------------
self.N_MONTHS = 12
self.N_MONTHS_FLOAT = self.N_MONTHS * 1.0
# Days per month in the benchmark year
self.d_per_mon = np.zeros(self.N_MONTHS)
for t in range(self.N_MONTHS):
self.d_per_mon[t] = monthrange(self.y0, t + 1)[1] * 1.0
# Days in the benchmark year
self.d_per_yr = np.sum(self.d_per_mon)
# Fraction of year occupied by each month
self.frac_of_yr = np.zeros(self.N_MONTHS)
for t in range(self.N_MONTHS):
self.frac_of_yr[t] = self.d_per_mon[t] / self.d_per_yr
# ------------------------------
# Species info
# ------------------------------
# List of species (and subsets for the trop & strat)
self.species_list = ["Pb210", "Be7", "Be10"]
# Read the species database
path = join(spcdb_dir, "species_database.yml")
spcdb = yaml_load_file(open(path))
# Molecular weights [g mol-1], as taken from the species database
self.mw = {}
for v in self.species_list:
self.mw[v] = spcdb[v]["MW_g"]
self.mw["Air"] = constants.MW_AIR_g
# kg/s --> g/day
self.kg_s_to_g_d_value = 86400.0 * 1000.0
# ------------------------------
# Conversion factors
# ------------------------------
self.kg_per_mol = {}
self.vv_to_g = {}
self.mcm2s_to_g_d = {}
self.kg_s_to_g_d = {}
for spc in self.species_list:
# kg/s --> g/day (same for all species)
self.kg_s_to_g_d[spc] = self.kg_s_to_g_d_value
# kg/mole for each species
self.kg_per_mol[spc] = constants.AVOGADRO / (self.mw[spc] * 1e-3)
# v/v dry --> g
self.vv_to_g[spc] = self.ds_met["Met_AD"].values \
* (self.mw[spc] / self.mw["Air"]) * 1000.0
# molec/cm2/s --> g/day
self.mcm2s_to_g_d[spc] = self.area_cm2.values \
/ self.kg_per_mol[spc] \
* self.kg_s_to_g_d[spc]