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replaced moldb.MdbExomol and MdbHit to api.MdbExomol and MdbHit #309

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merged 5 commits into from
Nov 8, 2022
Merged

replaced moldb.MdbExomol and MdbHit to api.MdbExomol and MdbHit #309

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03daf12
replaced moldb.MdbExomol and MdbHit to api.MdbExomol and MdbHit
HajimeKawahara Nov 7, 2022
ac713e7
df instrance False as default
HajimeKawahara Nov 7, 2022
da31f16
bugfixed
HajimeKawahara Nov 7, 2022
d91a50c
test done
HajimeKawahara Nov 7, 2022
df99de0
del print
HajimeKawahara Nov 8, 2022
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Binary file modified src/exojax/data/testdata/moldb_h2o_exomol.pickle
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83 changes: 51 additions & 32 deletions src/exojax/spec/api.py
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Original file line number Diff line number Diff line change
Expand Up @@ -71,6 +71,7 @@ def __init__(self,
bkgdatm='H2',
broadf=True,
gpu_transfer=False,
inherit_dataframe=False,
local_databases="./"):
"""Molecular database for Exomol form.

Expand All @@ -82,7 +83,9 @@ def __init__(self,
Ttyp: typical temperature to calculate Sij(T) used in crit
bkgdatm: background atmosphere for broadening. e.g. H2, He,
broadf: if False, the default broadening parameters in .def file is used

gpu_transfer: if True, some instances will be transfered to jnp.array
inherit_dataframe: if True, it makes self.df instance available, which needs more DRAM when pickling.

Note:
The trans/states files can be very large. For the first time to read it, we convert it to HDF/vaex. After the second-time, we use the HDF5 format with vaex instead.
"""
Expand Down Expand Up @@ -122,28 +125,32 @@ def __init__(self,
local_files = [mgr.cache_file(f) for f in self.trans_file]

# data frame instance:
self.df = self.load(
df = self.load(
local_files,
columns=[k for k in self.__slots__ if k not in ["logsij0"]],
#lower_bound=([("nu_lines", wavenum_min)] if wavenum_min else []) +
lower_bound=([("Sij0", 0.0)]),
#upper_bound=([("nu_lines", wavenum_max)] if wavenum_max else []),
output="vaex")

load_mask = self.compute_load_mask()
self.instances_from_dataframes(self.df[load_mask])
load_mask = self.compute_load_mask(df)
self.instances_from_dataframes(df[load_mask])
self.compute_broadening(self.jlower, self.jupper)
self.gamma_natural = gn(self.A)

if gpu_transfer:
self.generate_jnp_arrays()

def compute_load_mask(self):
wavelength_mask = (self.df.nu_lines > self.nurange[0]-self.margin) \
* (self.df.nu_lines < self.nurange[1]+self.margin)
if inherit_dataframe:
self.df = df

def compute_load_mask(self, df):
wavelength_mask = (df.nu_lines > self.nurange[0]-self.margin) \
* (df.nu_lines < self.nurange[1]+self.margin)
intensity_mask = (line_strength_numpy(
self.Ttyp, self.df.Sij0, self.df.nu_lines, self.df.elower,
self.Ttyp, df.Sij0, df.nu_lines, df.elower,
self.QTtyp / self.QTref) > self.crit)

return wavelength_mask * intensity_mask

def instances_from_dataframes(self, df_load_mask):
Expand Down Expand Up @@ -233,7 +240,8 @@ def __init__(self,
crit=0.,
Ttyp=1000.,
isotope=0,
gpu_transfer=False):
gpu_transfer=False,
inherit_dataframe=False):
"""Molecular database for HITRAN/HITEMP form.

Args:
Expand All @@ -244,6 +252,7 @@ def __init__(self,
Ttyp: typical temperature to calculate Sij(T) used in crit
isotope: isotope number, 0 or None = use all isotopes.
gpu_transfer: tranfer data to jnp.array?
inherit_dataframe: if True, it makes self.df instance available, which needs more DRAM when pickling.
"""

self.path = pathlib.Path(path).expanduser()
Expand Down Expand Up @@ -311,7 +320,7 @@ def __init__(self,
output = "vaex"

self.isotope = _convert_proper_isotope(isotope)
self.df = self.load(
df = self.load(
files_loaded, # filter other files,
columns=columns,
within=[("iso", self.isotope)] if self.isotope is not None else [],
Expand All @@ -323,26 +332,30 @@ def __init__(self,
output=output,
)

self.isoid = self.df.iso
self.uniqiso = np.unique(self.df.iso.values)
self.isoid = df.iso
self.uniqiso = np.unique(df.iso.values)
for iso in self.uniqiso:
Q = PartFuncTIPS(self.molecid, iso)
QTref = Q.at(T=Tref)
QTtyp = Q.at(T=Ttyp)
load_mask = self.compute_load_mask(QTtyp / QTref)
self.instances_from_dataframes(self.df[load_mask])
load_mask = self.compute_load_mask(df, QTtyp / QTref)
self.instances_from_dataframes(df[load_mask])
self.gQT, self.T_gQT = hitranapi.get_pf(self.molecid, self.uniqiso)

if gpu_transfer:
self.generate_jnp_arrays()

def compute_load_mask(self, qrtyp):
wav_mask = (self.df.wav > self.nurange[0]-self.margin) \
* (self.df.wav < self.nurange[1]+self.margin)
intensity_mask = (line_strength_numpy(self.Ttyp, self.df.int, self.df.wav, self.df.El,
qrtyp) > self.crit)
if inherit_dataframe:
self.df = df

def compute_load_mask(self, df, qrtyp):
wav_mask = (df.wav > self.nurange[0]-self.margin) \
* (df.wav < self.nurange[1]+self.margin)
intensity_mask = (line_strength_numpy(self.Ttyp, df.int,
df.wav, df.El, qrtyp) >
self.crit)
return wav_mask * intensity_mask

def instances_from_dataframes(self, df_load_mask):
"""generate instances from (usually masked) data farame

Expand Down Expand Up @@ -473,7 +486,8 @@ def __init__(self,
crit=0.,
Ttyp=1000.,
isotope=0,
gpu_transfer=False):
gpu_transfer=False,
inherit_dataframe=False):
"""Molecular database for HITRAN/HITEMP form.

Args:
Expand All @@ -484,6 +498,7 @@ def __init__(self,
Ttyp: typical temperature to calculate Sij(T) used in crit
isotope: isotope number. 0 or None= use all isotopes.
gpu_transfer: tranfer data to jnp.array?
inherit_dataframe: if True, it makes self.df instance available, which needs more DRAM when pickling.
"""

self.path = pathlib.Path(path).expanduser()
Expand Down Expand Up @@ -529,7 +544,7 @@ def __init__(self,

self.isotope = _convert_proper_isotope(isotope)
# Load and return
self.df = self.load(
df = self.load(
local_file,
columns=columns,
within=[("iso", self.isotope)] if self.isotope is not None else [],
Expand All @@ -541,26 +556,30 @@ def __init__(self,
output=output,
)

self.isoid = self.df.iso
self.uniqiso = np.unique(self.df.iso.values)
self.isoid = df.iso
self.uniqiso = np.unique(df.iso.values)
for iso in self.uniqiso:
Q = PartFuncTIPS(self.molecid, iso)
QTref = Q.at(T=Tref)
QTtyp = Q.at(T=Ttyp)
load_mask = self.compute_load_mask(QTtyp / QTref)
self.instances_from_dataframes(self.df[load_mask])
load_mask = self.compute_load_mask(df, QTtyp / QTref)
self.instances_from_dataframes(df[load_mask])
self.gQT, self.T_gQT = hitranapi.get_pf(self.molecid, self.uniqiso)

if gpu_transfer:
self.generate_jnp_arrays()

def compute_load_mask(self, qrtyp):
wav_mask = (self.df.wav > self.nurange[0]-self.margin) \
* (self.df.wav < self.nurange[1]+self.margin)
intensity_mask = (line_strength_numpy(self.Ttyp, self.df.int, self.df.wav, self.df.El,
qrtyp) > self.crit)
if inherit_dataframe:
self.df = df

def compute_load_mask(self, df, qrtyp):
wav_mask = (df.wav > self.nurange[0]-self.margin) \
* (df.wav < self.nurange[1]+self.margin)
intensity_mask = (line_strength_numpy(self.Ttyp, df.int,
df.wav, df.El, qrtyp) >
self.crit)
return wav_mask * intensity_mask

def instances_from_dataframes(self, df_load_mask):
"""generate instances from (usually masked) data farame

Expand Down
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