-
Notifications
You must be signed in to change notification settings - Fork 3
/
kid_1D.py
337 lines (281 loc) · 14.4 KB
/
kid_1D.py
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
215
216
217
218
219
220
221
222
223
224
225
226
227
228
229
230
231
232
233
234
235
236
237
238
239
240
241
242
243
244
245
246
247
248
249
250
251
252
253
254
255
256
257
258
259
260
261
262
263
264
265
266
267
268
269
270
271
272
273
274
275
276
277
278
279
280
281
282
283
284
285
286
287
288
289
290
291
292
293
294
295
296
297
298
299
300
301
302
303
304
305
306
307
308
309
310
311
312
313
314
315
316
317
318
319
320
321
322
323
324
325
326
327
328
329
330
331
332
333
334
335
336
337
#!/usr/bin/python
import numpy as np
import cffi
import traceback
import libcloudphxx as libcl
from libcloudphxx.common import R_v, R_d, c_pd, eps, p_1000
from setup import params, opts
import diagnostics_1D as dg
import os
import json
import pdb
import time
from argparse import ArgumentParser
ptrfname = "/tmp/micro_step-" + str(os.getuid()) + "-" + str(os.getpid()) + ".ptr"
# CFFI stuff
ffi = cffi.FFI()
flib = ffi.dlopen('KiD_1D.so')
clib = ffi.dlopen('ptrutil.so')
# C functions
ffi.cdef("void save_ptr(char*,void*);")
# Fortran functions (_sp_ means single precision)
ffi.cdef("void __main_MOD_main_loop();")
# object storing super-droplet model state (to be initialised)
prtcls = False
arrays = {}
prev_val = {}
prev_val["accr20"] = 0
prev_val["accr25"] = 0
prev_val["accr32"] = 0
prev_val["acnv20"] = 0
prev_val["acnv25"] = 0
prev_val["acnv32"] = 0
prev_val["revp20"] = 0
prev_val["revp25"] = 0
prev_val["revp32"] = 0
prev_val["acc_surf_precip"] = 0
timestep = 0
last_diag = -1
#parser for overriding values from setup.py with command-line arguments
prsr = ArgumentParser(add_help=True, description='1D kidA case')
prsr.add_argument('--n_tot', required=False, type=float, default=params["n_tot"], help='initial aerosol concentation at STP [1/kg_dry_air]')
prsr.add_argument('--spinup_rain', required=False, type=float, default=params["spinup_rain"], help='time, after which coalescence and sedimentation are turned on [s]')
prsr.add_argument('--sd_conc', required=False, type=int, default=params["sd_conc"], help='initial no of SD per cell')
prsr.add_argument('--sstp_cond', required=False, type=int, default=params["sstp_cond"], help='no of cond substeps')
prsr.add_argument('--sstp_coal', required=False, type=int, default=params["sstp_coal"], help='no of coal substeps')
prsr.add_argument('--backend', required=False, type=str, default="None", help='no of coal substeps')
prsr.add_argument('--sd_const_multi', required=False, type=int, default=0, help='should SDs have same multiplicities')
prsr.add_argument('--rng_seed', required=False, type=int, default=-1, help='rng seed, set to -1 to use time in sec as seed')
args = prsr.parse_args()
params["n_tot"] = args.n_tot # * 1.225 / 1. # 1.225 is air density at stp, 1 is the actual density
params["spinup_rain"] = args.spinup_rain
params["sd_conc"] = args.sd_conc
params["sstp_cond"] = args.sstp_cond
params["sstp_coal"] = args.sstp_coal
#savings some parameters from setup.py file and libcl revision number
params_write = params.copy()
# converting numpy objects to lists or strings, so json can save them
for key_ar in ["bins_qc_r20um", "bins_qc_r32um"]:
params_write[key_ar] = params[key_ar].tolist()
for key_str in ["real_t"]:
params_write[key_str] = str(params[key_str])
params_write["libcloudph_git_rev"] = libcl.git_revision
#file_out = open("output/python_setup.txt", "w")
#json.dump(params_write, file_out)
#file_out.close()
def lognormal(lnr):
from math import exp, log, sqrt, pi
return params["n_tot"] * exp(
-pow((lnr - log(params["meanr"])), 2) / 2 / pow(log(params["gstdv"]),2)
) / log(params["gstdv"]) / sqrt(2*pi);
def ptr2np(ptr, size_x, size_z):
numpy_ar = np.frombuffer(
ffi.buffer(ptr, size_x*size_z*np.dtype(params["real_t"]).itemsize),
dtype=params["real_t"]
).reshape(size_x, size_z)
return numpy_ar.squeeze()
def th_kid2dry(th, rv):
return th * (1 + rv * R_v / R_d)**(R_d/c_pd)
def th_dry2kid(th_d, rv):
return th_d * (1 + rv * R_v / R_d)**(-R_d/c_pd)
def rho_kid2dry(rho, rv):
# KiD seems to define rho as (p_v + p_d) / (R_d T)
return rho / (1 + rv / eps)
@ffi.callback("bool(int, float, int, int, double*, double*, double*, double*, double*, double*, double*, double*, double*, double*, double*, double*, double*, double*, double*, double*)")
def micro_step(it_diag, dt, size_z, size_x, th_ar, qv_ar, rhof_ar, rhoh_ar, exner_ar,
uf_ar, uh_ar, wf_ar, wh_ar, xf_ar, zf_ar, xh_ar, zh_ar, tend_th_ar, tend_qv_ar, rh_ar):
try:
# global should be used for all variables defined in "if first_timestep"
global prtcls, dx, dz, timestep, last_diag
#pdb.set_trace()
# superdroplets: initialisation (done only once)
if timestep == 0:
# first, removing the no-longer-needed pointer file
os.unlink(ptrfname)
arrx = ptr2np(xf_ar, size_x, 1)
arrz = ptr2np(zf_ar, 1, size_z)
# checking if grids are equal
np.testing.assert_almost_equal((arrx[1:]-arrx[:-1]).max(), (arrx[1:]-arrx[:-1]).min(), decimal=7)
np.testing.assert_almost_equal((arrz[1:]-arrz[:-1]).max(), (arrz[1:]-arrz[:-1]).min(), decimal=7)
dx = 1.
dz = arrz[1] - arrz[0]
opts_init = libcl.lgrngn.opts_init_t()
opts_init.dt = dt
opts_init.nx, opts_init.nz = size_x - 2, size_z
opts_init.dx, opts_init.dz = dx, dz
#opts_init.z0 = dz # skipping the first sub-terrain level
opts_init.z0 = 0
opts_init.x1, opts_init.z1 = dx * opts_init.nx, dz * opts_init.nz
opts_init.dry_distros = { params["kappa"] : lognormal }
opts_init.sstp_cond, opts_init.sstp_coal = params["sstp_cond"], params["sstp_coal"]
opts_init.terminal_velocity = libcl.lgrngn.vt_t.beard76
opts_init.kernel = libcl.lgrngn.kernel_t.hall_davis_no_waals
#opts_init.adve_scheme = libcl.lgrngn.as_t.pred_corr
opts_init.adve_scheme = libcl.lgrngn.as_t.euler
opts_init.exact_sstp_cond = 1
if args.sd_const_multi > 0:
opts_init.sd_const_multi = int(args.sd_const_multi)
opts_init.n_sd_max = int(120 * 300000 * 1.2)
opts_init.sd_conc = int(0)
else:
opts_init.sd_conc_large_tail = 1
opts_init.sd_conc = int(params["sd_conc"])
opts_init.n_sd_max = int(1.2 * opts_init.nx*opts_init.nz*opts_init.sd_conc)
opts_init.aerosol_independent_of_rhod = 1 # set to true, because rhod is supposed to be =1, but we cannot pass rhod=1 as it is not in agreement with the values of p and theta and would lead to wrong T,RH,etc...
opts_init.RH_formula = libcl.lgrngn.RH_formula_t.rv_tet
if(args.rng_seed == -1):
opts_init.rng_seed = int(time.time())
else:
opts_init.rng_seed = args.rng_seed
print('rng seed = ', opts_init.rng_seed)
#print "nx = ", opts_init.nx
#print "nz = ", opts_init.nz
#print "dx = ", opts_init.dx
#print "dz = ", opts_init.dz
#print "dt = ", opts_init.dt
if args.backend == "multi_CUDA":
prtcls = libcl.lgrngn.factory(libcl.lgrngn.backend_t.multi_CUDA, opts_init)
elif args.backend == "CUDA":
prtcls = libcl.lgrngn.factory(libcl.lgrngn.backend_t.CUDA, opts_init)
elif args.backend == "OpenMP":
prtcls = libcl.lgrngn.factory(libcl.lgrngn.backend_t.OpenMP, opts_init)
elif args.backend == "serial":
prtcls = libcl.lgrngn.factory(libcl.lgrngn.backend_t.serial, opts_init)
else:
try:
print(("Trying with multi_CUDA backend..."), end=' ')
prtcls = libcl.lgrngn.factory(libcl.lgrngn.backend_t.multi_CUDA, opts_init)
print (" OK!")
except:
print (" KO!")
try:
print(("Trying with CUDA backend..."), end=' ')
prtcls = libcl.lgrngn.factory(libcl.lgrngn.backend_t.CUDA, opts_init)
print (" OK!")
except:
print (" KO!")
try:
print(("Trying with OpenMP backend..."), end=' ')
prtcls = libcl.lgrngn.factory(libcl.lgrngn.backend_t.OpenMP, opts_init)
print (" OK!")
except:
print (" KO!")
print(("Trying with serial backend..."), end=' ')
prtcls = libcl.lgrngn.factory(libcl.lgrngn.backend_t.serial, opts_init)
print (" OK!")
# allocating arrays for those variables that are not ready to use
# (i.e. either different size or value conversion needed)
for name in ("thetad", "qv", "p_d", "T_kid", "rhod_kid"):
arrays[name] = np.empty((opts_init.nx, opts_init.nz))
arrays["rhod"] = np.empty((opts_init.nz,))
arrays["Cx"] = np.empty((opts_init.nx+4+1, opts_init.nz))
arrays["Cz"] = np.empty((opts_init.nx+4, opts_init.nz+1))
arrays["RH_lib_ante_cond"] = np.empty((opts_init.nx, opts_init.nz))
arrays["pressure_lib_ante_cond"] = np.empty((opts_init.nx, opts_init.nz))
arrays["T_lib_ante_cond"] = np.empty((opts_init.nx, opts_init.nz))
arrays["psat_lib_formula_ante_cond"] = np.empty((opts_init.nx, opts_init.nz))
arrays["RH_kid"] = np.empty((opts_init.nx, opts_init.nz))
# defining qv and thetad (in every timestep)
arrays["qv"][:,:] = ptr2np(qv_ar, size_x, size_z)[1:-1, :]
#arrays["thetad"][:,:] = th_kid2dry(ptr2np(th_ar, size_x, size_z)[1:-1, :], arrays["qv"][:,:])
# it seems (c.f. src/test_cases.f90:863 which suggest that exner=(p_d / p_0)^(R_d/c_p)) that theta, p and rhod calculated in KiD are actually dry air values
# so there's no need to make them dry one more time; BTW: this makes the RH calculated in KiD not correct, because
# it should be calculated using total pressure and not dry pressure
# arrays["thetad"][:,:] = ptr2np(th_ar, size_x, size_z)[1:-1, :]
arrays["thetad"][:,:] = ptr2np(th_ar, size_x, size_z).copy()[1:-1, :]
# arrays["thetad"][:,:] -= 0.1
arrays["p_d"][:,:] = (ptr2np(exner_ar, size_x, size_z)[1:-1, :])**(c_pd/R_d) * p_1000# * eps / (eps + arrays["qv"])
arrays["T_kid"][:,:] = ptr2np(exner_ar, size_x, size_z)[1:-1, :] * ptr2np(th_ar, size_x, size_z)[1:-1, :]
arrays["rhod_kid"] = arrays["p_d"] / arrays["T_kid"] / R_d
arrays["RH_kid"][:,:] = ptr2np(rh_ar, size_x, size_z)[1:-1, :]
#pdb.set_trace()
# finalising initialisation
# density in KiD (rhof) is overriden to 1, but it is inconsistent with exner pressure and temperature
# and this leads to wrong RH calculations in libcloud
# therefore we should use the rhod_kid density and multiply diags by rhod_kid ?!
if timestep == 0:
#arrays["rhod"][:] = arrays["rhod_kid"][0,:] # set rhod to be in agreement with other profiles and exner function
#arrays["rhod"][:] = rho_kid2dry(ptr2np(rhof_ar, 1, size_z)[:], arrays["qv"][0,:]) # pass rhod from KiD, i.e. rhod=1
arrays["rhod"][:] = ptr2np(rhof_ar, 1, size_z)[:] # pass rhod from KiD, i.e. rhod=1
#arrays["Cx"][:,:] = ptr2np(uh_ar, size_x, size_z)[:-1, :] * dt / dx
arrays["Cx"][:,:] = 0
assert (arrays["Cx"][0,:] == arrays["Cx"][-1,:]).all()
# putting meaningful values to the sub-terain level (to avoid segfault from library)
#arrays["Cz"][:, 0] = 0.
#arrays["qv"][:, 0] = arrays["qv"][:, 1]
#arrays["thetad"][:,0] = arrays["thetad"][:,1]
#arrays["rhod"][0] = arrays["rhod"][1]
arrays["Cz"][:, 1:] = ptr2np(wh_ar, size_x, size_z)[1:-1, :] * dt / dz
arrays["Cz"][:, 0] = arrays["Cz"][:, 1];
if timestep == 0:
prtcls.init(arrays["thetad"], arrays["qv"], arrays["rhod"], arrays["p_d"], Cx = arrays["Cx"], Cz = arrays["Cz"])
dg.diagnostics(prtcls, arrays, prev_val, 1, size_x, size_z, timestep == 0) # writing down state at t=0
# spinup period logic
opts.sedi = opts.coal = timestep >= params["spinup_rain"]
# if timestep >= params["spinup_smax"]: opts.RH_max = 44
# saving RH for the output file
prtcls.diag_all()
prtcls.diag_RH()
arrays["RH_lib_ante_cond"] = np.frombuffer(prtcls.outbuf()).copy().reshape(size_x - 2, size_z) * 100
prtcls.diag_all()
prtcls.diag_pressure()
arrays["pressure_lib_ante_cond"] = np.frombuffer(prtcls.outbuf()).copy().reshape(size_x - 2, size_z) * 100
prtcls.diag_all()
prtcls.diag_temperature()
arrays["T_lib_ante_cond"] = np.frombuffer(prtcls.outbuf()).copy().reshape(size_x - 2, size_z)
# if timestep == 0:
# arrays["rhod"][:] = ptr2np(rhof_ar, 1, size_z)[:]
# print "rhof_ar: ", arrays["rhod"]
# arrays["rhod"][:] = rho_kid2dry(ptr2np(rhof_ar, 1, size_z)[:], arrays["qv"][0,:])
# print "rhod: ", arrays["rhod"]
# print "rhod_kid: ", arrays["rhod_kid"]
# for i in range(0, prtcls.opts_init.nx):
# for j in range(0, prtcls.opts_init.nz):
# arrays["T_lib_ante_cond"][i,j] = libcl.common.T(arrays["thetad"][i,j], arrays["rhod_kid"][i,j])
# print "T_lib_ante_cond with rhod_kid: ", arrays["T_lib_ante_cond"]
# for i in range(0, prtcls.opts_init.nx):
# for j in range(0, prtcls.opts_init.nz):
# arrays["T_lib_ante_cond"][i,j] = libcl.common.T(arrays["thetad"][i,j], arrays["rhod"][j])
for i in range(0, prtcls.opts_init.nx):
for j in range(0, prtcls.opts_init.nz):
arrays["psat_lib_formula_ante_cond"][i,j] = libcl.common.p_vs(arrays["T_lib_ante_cond"][i,j])
# if timestep == 0:
# print "rhod: ", arrays["rhod"]
# print "thetad: ", arrays["thetad"]
# print "qv: ", arrays["qv"]
# print "RH_lib_ante_cond: ", arrays["RH_lib_ante_cond"]
# print "T_lib_ante_cond: ", arrays["T_lib_ante_cond"]
# print "T_kid: ", arrays["T_kid"]
# superdroplets: all what have to be done within a timestep
prtcls.step_sync(opts, arrays["thetad"], arrays["qv"], Cx = arrays["Cx"], Cz = arrays["Cz"])
#prtcls.step_sync(opts, arrays["thetad"], arrays["qv"], Cx = arrays["Cx"], Cz = arrays["Cz"], RH = arrays["RH_kid"])
#prtcls.step_sync(opts, arrays["thetad"], arrays["qv"], Cx = arrays["Cx"], Cz = arrays["Cz"], T = arrays["T_kid"])
#prtcls.step_sync(opts, arrays["thetad"], arrays["qv"], Cx = arrays["Cx"], Cz = arrays["Cz"], RH = arrays["RH_kid"], T = arrays["T_kid"])
prtcls.step_async(opts)
# calculating tendency for theta (first converting back to non-dry theta
ptr2np(tend_th_ar, size_x, size_z)[1:-1, :] = - (
ptr2np(th_ar, size_x, size_z)[1:-1, :] - # old
arrays["thetad"] # new
# th_dry2kid(arrays["thetad"], arrays["qv"]) # new
) / dt #TODO: check if dt needed
# calculating tendency for qv
ptr2np(tend_qv_ar, size_x, size_z)[1:-1, :] = - (
ptr2np(qv_ar, size_x, size_z)[1:-1, :] - # old
arrays["qv"] # new
) / dt #TODO: check if dt needed
# diagnostics
if last_diag < it_diag:
dg.diagnostics(prtcls, arrays, prev_val, it_diag, size_x, size_z, timestep == 0)
last_diag = it_diag
timestep += 1
except:
traceback.print_exc()
return False
else:
return True
# storing pointers to Python functions
clib.save_ptr(ptrfname.encode('ascii'), micro_step)
# running Fortran stuff
# note: not using command line arguments, namelist name hardcoded in
# kid_a_setup/namelists/SC_2D_input.nml
flib.__main_MOD_main_loop()