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ThermodynamicsDry.pyx
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#!python
#cython: boundscheck=False
#cython: wraparound=False
#cython: initializedcheck=False
#cython: cdivision=True
cimport numpy as np
import numpy as np
cimport ParallelMPI
cimport Grid
cimport ReferenceState
cimport DiagnosticVariables
cimport PrognosticVariables
from NetCDFIO cimport NetCDFIO_Fields, NetCDFIO_Stats
from thermodynamic_functions cimport thetas_c
import cython
from Thermodynamics cimport LatentHeat, ClausiusClapeyron
cdef extern from "entropies.h":
inline double sd_c(double p0, double T) nogil
cdef extern from "thermodynamics_dry.h":
inline double eos_c(double p0, double s) nogil
inline double alpha_c(double p0, double T, double qt, double qv) nogil
void eos_update(Grid.DimStruct *dims, double *pd, double *s, double *T,
double *alpha)
void buoyancy_update(Grid.DimStruct *dims, double *alpha0, double *alpha,double *buoyancy,
double *wt)
void bvf_dry(Grid.DimStruct* dims, double* p0, double* T, double* theta, double* bvf)
cdef class ThermodynamicsDry:
def __init__(self,namelist,LatentHeat LH, ParallelMPI.ParallelMPI Pa):
self.L_fp = LH.L_fp
self.Lambda_fp = LH.Lambda_fp
self.CC = ClausiusClapeyron()
self.CC.initialize(namelist,LH,Pa)
return
cpdef initialize(self,Grid.Grid Gr,PrognosticVariables.PrognosticVariables PV, DiagnosticVariables.DiagnosticVariables DV, NetCDFIO_Stats NS, ParallelMPI.ParallelMPI Pa):
PV.add_variable('s','m/s',"sym","scalar",Pa)
#Initialize class member arrays
DV.add_variables('buoyancy','--','sym',Pa)
DV.add_variables('alpha','--','sym',Pa)
DV.add_variables('temperature','K','sym',Pa)
DV.add_variables('buoyancy_frequency','1/s','sym',Pa)
DV.add_variables('theta','K','sym',Pa)
#Add statistical output
NS.add_profile('thetas_mean',Gr,Pa)
NS.add_profile('thetas_mean2',Gr,Pa)
NS.add_profile('thetas_mean3',Gr,Pa)
NS.add_profile('thetas_max',Gr,Pa)
NS.add_profile('thetas_min',Gr,Pa)
NS.add_ts('thetas_max',Gr,Pa)
NS.add_ts('thetas_min',Gr,Pa)
return
cpdef entropy(self,double p0, double T,double qt, double ql, double qi):
qt = 0.0
ql = 0.0
qi = 0.0
return sd_c(p0,T)
cpdef eos(self,double p0, double s, double qt):
ql = 0.0
qi = 0.0
return eos_c(p0,s), ql, qi
cpdef alpha(self, double p0, double T, double qt, double qv):
qv = 0.0
qt = 0.0
return alpha_c(p0,T,qv,qt)
cpdef update(self, Grid.Grid Gr, ReferenceState.ReferenceState RS,
PrognosticVariables.PrognosticVariables PV, DiagnosticVariables.DiagnosticVariables DV):
cdef Py_ssize_t buoyancy_shift = DV.get_varshift(Gr,'buoyancy')
cdef Py_ssize_t alpha_shift = DV.get_varshift(Gr,'alpha')
cdef Py_ssize_t t_shift = DV.get_varshift(Gr,'temperature')
cdef Py_ssize_t s_shift = PV.get_varshift(Gr,'s')
cdef Py_ssize_t w_shift = PV.get_varshift(Gr,'w')
cdef Py_ssize_t theta_shift = DV.get_varshift(Gr,'theta')
cdef Py_ssize_t bvf_shift = DV.get_varshift(Gr,'buoyancy_frequency')
eos_update(&Gr.dims,&RS.p0_half[0],&PV.values[s_shift],&DV.values[t_shift],&DV.values[alpha_shift])
buoyancy_update(&Gr.dims,&RS.alpha0_half[0],&DV.values[alpha_shift],&DV.values[buoyancy_shift],&PV.tendencies[w_shift])
bvf_dry(&Gr.dims,&RS.p0_half[0],&DV.values[t_shift],&DV.values[theta_shift],&DV.values[bvf_shift])
return
cpdef get_pv_star(self,t):
return self.CC.LT.fast_lookup(t)
cpdef get_lh(self,t):
cdef double lam = self.Lambda_fp(t)
return self.L_fp(lam,t)
cpdef write_fields(self, Grid.Grid Gr, ReferenceState.ReferenceState RS,
PrognosticVariables.PrognosticVariables PV, DiagnosticVariables.DiagnosticVariables DV, NetCDFIO_Fields NF, ParallelMPI.ParallelMPI Pa):
cdef:
Py_ssize_t i,j,k, ijk, ishift, jshift
Py_ssize_t istride = Gr.dims.nlg[1] * Gr.dims.nlg[2]
Py_ssize_t jstride = Gr.dims.nlg[2]
Py_ssize_t imin = Gr.dims.gw
Py_ssize_t jmin = Gr.dims.gw
Py_ssize_t kmin = Gr.dims.gw
Py_ssize_t imax = Gr.dims.nlg[0] - Gr.dims.gw
Py_ssize_t jmax = Gr.dims.nlg[1] - Gr.dims.gw
Py_ssize_t kmax = Gr.dims.nlg[2] - Gr.dims.gw
Py_ssize_t count
Py_ssize_t s_shift = PV.get_varshift(Gr,'s')
double [:] data = np.empty((Gr.dims.npl,),dtype=np.double,order='c')
#Add entropy potential temperature to 3d fields
with nogil:
count = 0
for i in xrange(imin,imax):
ishift = i * istride
for j in xrange(jmin,jmax):
jshift = j * jstride
for k in xrange(kmin,kmax):
ijk = ishift + jshift + k
data[count] = thetas_c(PV.values[s_shift+ijk],0.0)
count += 1
NF.add_field('thetas')
NF.write_field('thetas',data)
print(np.amax(data),np.amin(data))
return
cpdef stats_io(self, Grid.Grid Gr, ReferenceState.ReferenceState RS, PrognosticVariables.PrognosticVariables PV,
DiagnosticVariables.DiagnosticVariables DV, NetCDFIO_Stats NS, ParallelMPI.ParallelMPI Pa):
cdef:
Py_ssize_t i,j,k, ijk, ishift, jshift
Py_ssize_t istride = Gr.dims.nlg[1] * Gr.dims.nlg[2]
Py_ssize_t jstride = Gr.dims.nlg[2]
Py_ssize_t imin = 0
Py_ssize_t jmin = 0
Py_ssize_t kmin = 0
Py_ssize_t imax = Gr.dims.nlg[0]
Py_ssize_t jmax = Gr.dims.nlg[1]
Py_ssize_t kmax = Gr.dims.nlg[2]
Py_ssize_t count
Py_ssize_t s_shift = PV.get_varshift(Gr,'s')
double [:] data = np.empty((Gr.dims.npg,),dtype=np.double,order='c')
double [:] tmp
#Add entropy potential temperature to 3d fields
with nogil:
count = 0
for i in xrange(imin,imax):
ishift = i * istride
for j in xrange(jmin,jmax):
jshift = j * jstride
for k in xrange(kmin,kmax):
ijk = ishift + jshift + k
data[count] = thetas_c(PV.values[s_shift+ijk],0.0)
count += 1
#Compute and write mean
tmp = Pa.HorizontalMean(Gr,&data[0])
NS.write_profile('thetas_mean',tmp[Gr.dims.gw:-Gr.dims.gw],Pa)
#Compute and write mean of squres
tmp = Pa.HorizontalMeanofSquares(Gr,&data[0],&data[0])
NS.write_profile('thetas_mean2',tmp[Gr.dims.gw:-Gr.dims.gw],Pa)
#Compute and write mean of cubes
tmp = Pa.HorizontalMeanofCubes(Gr,&data[0],&data[0],&data[0])
NS.write_profile('thetas_mean3',tmp[Gr.dims.gw:-Gr.dims.gw],Pa)
#Compute and write maxes
tmp = Pa.HorizontalMaximum(Gr,&data[0])
NS.write_profile('thetas_max',tmp[Gr.dims.gw:-Gr.dims.gw],Pa)
NS.write_ts('thetas_max',np.amax(tmp[Gr.dims.gw:-Gr.dims.gw]),Pa)
#Compute and write mins
tmp = Pa.HorizontalMinimum(Gr,&data[0])
NS.write_profile('thetas_min',tmp[Gr.dims.gw:-Gr.dims.gw],Pa)
NS.write_ts('thetas_min',np.amin(tmp[Gr.dims.gw:-Gr.dims.gw]),Pa)
return