This repository has been archived by the owner on Jul 29, 2024. It is now read-only.
-
Notifications
You must be signed in to change notification settings - Fork 69
/
ThermodynamicsDry.pyx
216 lines (172 loc) · 8.45 KB
/
ThermodynamicsDry.pyx
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
171
172
173
174
175
176
177
178
179
180
181
182
183
184
185
186
187
188
189
190
191
192
193
194
195
196
197
198
199
200
201
202
203
204
205
206
207
208
209
210
211
212
213
214
#!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
cimport Thermodynamics
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":
double sd_c(double p0, double T) nogil
cdef extern from "thermodynamics_dry.h":
double eos_c(double p0, double s) nogil
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', 'J kg^-1 K^-1', 's', 'specific entropy', "sym", "scalar", Pa)
#Initialize class member arrays
DV.add_variables('buoyancy' ,r'ms^{-1}', r'b', 'buoyancy','sym', Pa)
DV.add_variables('alpha', r'm^3kg^-2', r'\alpha', 'specific volume', 'sym', Pa)
DV.add_variables('temperature', r'K', r'T', r'temperature', 'sym', Pa)
DV.add_variables('buoyancy_frequency', r's^-1', r'N', 'buoyancy frequencyt', 'sym', Pa)
DV.add_variables('theta', r'K', r'\theta','potential tremperature', 'sym', Pa)
#Add statistical output
units = r'K'
nice_name = r'\overline{\theta_{s}}'
desc = r'horizontal mean entropy potential temperature'
NS.add_profile('thetas_mean' ,Gr ,Pa, units=units, nice_name = nice_name, desc=desc)
units = r'K^2'
nice_name = r'\overline{\theta_{s}^2}'
desc = r'horizontal mean squared entropy potential temperature'
NS.add_profile('thetas_mean2', Gr, Pa, units=units, nice_name = nice_name, desc=desc)
units = r'K^3'
nice_name = r'\overline{\theta_{s}^3}'
desc = r'horizontal mean cubed entropy potential temperature'
NS.add_profile('thetas_mean3', Gr, Pa, units=units, nice_name = nice_name, desc=desc)
units = r'K'
nice_name = r'\max\left(\theta_s\right)'
desc = r'horizontal max entropy potential temperature'
NS.add_profile('thetas_max', Gr, Pa, units=units, nice_name = nice_name, desc=desc)
units = r'K'
nice_name = r'\min\left(\theta_s\right)'
desc = r'horizontal min entropy potential temperature'
NS.add_profile('thetas_min',Gr,Pa, units=units, nice_name = nice_name, desc=desc)
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