DNS.py 49.9 KB
Newer Older
Cristian Lalescu's avatar
Cristian Lalescu committed
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
#######################################################################
#                                                                     #
#  Copyright 2015 Max Planck Institute                                #
#                 for Dynamics and Self-Organization                  #
#                                                                     #
#  This file is part of bfps.                                         #
#                                                                     #
#  bfps is free software: you can redistribute it and/or modify       #
#  it under the terms of the GNU General Public License as published  #
#  by the Free Software Foundation, either version 3 of the License,  #
#  or (at your option) any later version.                             #
#                                                                     #
#  bfps is distributed in the hope that it will be useful,            #
#  but WITHOUT ANY WARRANTY; without even the implied warranty of     #
#  MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the      #
#  GNU General Public License for more details.                       #
#                                                                     #
#  You should have received a copy of the GNU General Public License  #
#  along with bfps.  If not, see <http://www.gnu.org/licenses/>       #
#                                                                     #
# Contact: Cristian.Lalescu@ds.mpg.de                                 #
#                                                                     #
#######################################################################



import os
import sys
import shutil
import subprocess
import argparse
import h5py
import math
import numpy as np
import warnings

import bfps
from ._code import _code
39
from bfps import tools
Cristian Lalescu's avatar
Cristian Lalescu committed
40
41
42
43
44
45
46
47

class DNS(_code):
    """This class is meant to stitch together the C++ code into a final source file,
    compile it, and handle all job launching.
    """
    def __init__(
            self,
            work_dir = './',
48
49
50
51
52
53
54
55
56
57
58
59
            simname = 'test'):
        _code.__init__(
                self,
                work_dir = work_dir,
                simname = simname)
        self.host_info = {'type'        : 'cluster',
                          'environment' : None,
                          'deltanprocs' : 1,
                          'queue'       : '',
                          'mail_address': '',
                          'mail_events' : None}
        self.generate_default_parameters()
Chichi Lalescu's avatar
Chichi Lalescu committed
60
        self.statistics = {}
61
62
63
64
        return None
    def set_precision(
            self,
            fluid_dtype):
Cristian Lalescu's avatar
Cristian Lalescu committed
65
66
67
68
69
70
71
72
73
74
        if fluid_dtype in [np.float32, np.float64]:
            self.fluid_dtype = fluid_dtype
        elif fluid_dtype in ['single', 'double']:
            if fluid_dtype == 'single':
                self.fluid_dtype = np.dtype(np.float32)
            elif fluid_dtype == 'double':
                self.fluid_dtype = np.dtype(np.float64)
        self.rtype = self.fluid_dtype
        if self.rtype == np.float32:
            self.ctype = np.dtype(np.complex64)
75
            self.C_field_dtype = 'float'
76
            self.fluid_precision = 'single'
Cristian Lalescu's avatar
Cristian Lalescu committed
77
78
        elif self.rtype == np.float64:
            self.ctype = np.dtype(np.complex128)
79
            self.C_field_dtype = 'double'
80
81
            self.fluid_precision = 'double'
        return None
82
83
    def write_src(
            self):
Cristian Lalescu's avatar
Cristian Lalescu committed
84
85
86
87
88
        self.version_message = (
                '/***********************************************************************\n' +
                '* this code automatically generated by bfps\n' +
                '* version {0}\n'.format(bfps.__version__) +
                '***********************************************************************/\n\n\n')
89
90
91
92
93
        self.include_list = [
                '"base.hpp"',
                '"scope_timer.hpp"',
                '"fftw_interface.hpp"',
                '"full_code/main_code.hpp"',
94
                '<cmath>',
95
96
97
98
99
100
101
102
103
                '<iostream>',
                '<hdf5.h>',
                '<string>',
                '<cstring>',
                '<fftw3-mpi.h>',
                '<omp.h>',
                '<cfenv>',
                '<cstdlib>',
                '"full_code/{0}.hpp"\n'.format(self.dns_type)]
Cristian Lalescu's avatar
Cristian Lalescu committed
104
        self.main = """
105
106
107
108
109
110
111
112
            int main(int argc, char *argv[])
            {{
                bool fpe = (
                    (getenv("BFPS_FPE_OFF") == nullptr) ||
                    (getenv("BFPS_FPE_OFF") != std::string("TRUE")));
                return main_code< {0} >(argc, argv, fpe);
            }}
            """.format(self.dns_type + '<{0}>'.format(self.C_field_dtype))
113
114
115
116
117
118
        self.includes = '\n'.join(
                ['#include ' + hh
                 for hh in self.include_list])
        with open(self.name + '.cpp', 'w') as outfile:
            outfile.write(self.version_message + '\n\n')
            outfile.write(self.includes + '\n\n')
119
120
121
122
123
124
125
            outfile.write(
                    self.cread_pars(
                       template_class = '{0}<rnumber>::'.format(self.dns_type),
                        template_prefix = 'template <typename rnumber> ',
                        simname_variable = 'this->simname.c_str()',
                        prepend_this = True) +
                    '\n\n')
126
127
128
129
130
            for rnumber in ['float', 'double']:
                outfile.write(self.cread_pars(
                    template_class = '{0}<{1}>::'.format(self.dns_type, rnumber),
                    template_prefix = 'template '.format(rnumber),
                    just_declaration = True) + '\n\n')
131
            if self.dns_type in ['NSVEparticles', 'NSVE_no_output', 'NSVEparticles_no_output', 'NSVEcomplex_particles']:
132
133
134
                outfile.write('template <typename rnumber> int NSVE<rnumber>::read_parameters(){return EXIT_SUCCESS;}\n')
                outfile.write('template int NSVE<float>::read_parameters();\n')
                outfile.write('template int NSVE<double>::read_parameters();\n\n')
135
136
137
138
            if self.dns_type in ['NSVEparticles_no_output']:
                outfile.write('template <typename rnumber> int NSVEparticles<rnumber>::read_parameters(){return EXIT_SUCCESS;}\n')
                outfile.write('template int NSVEparticles<float>::read_parameters();\n')
                outfile.write('template int NSVEparticles<double>::read_parameters();\n\n')
139
            outfile.write(self.main + '\n')
140
141
142
        return None
    def generate_default_parameters(self):
        # these parameters are relevant for all DNS classes
143
144
145
146
147
148
149
        self.parameters['dealias_type'] = int(1)
        self.parameters['dkx'] = float(1.0)
        self.parameters['dky'] = float(1.0)
        self.parameters['dkz'] = float(1.0)
        self.parameters['niter_todo'] = int(8)
        self.parameters['niter_stat'] = int(1)
        self.parameters['niter_out'] = int(8)
150
        self.parameters['checkpoints_per_file'] = int(1)
151
        self.parameters['dt'] = float(0.01)
152
        self.parameters['nu'] = float(0.1)
153
        self.parameters['fmode'] = int(1)
154
        self.parameters['famplitude'] = float(0.5)
Chichi Lalescu's avatar
Chichi Lalescu committed
155
        self.parameters['friction_coefficient'] = float(0.5)
Cristian Lalescu's avatar
Cristian Lalescu committed
156
157
        self.parameters['energy'] = float(0.5)
        self.parameters['injection_rate'] = float(0.4)
158
159
        self.parameters['fk0'] = float(2.0)
        self.parameters['fk1'] = float(4.0)
Cristian Lalescu's avatar
Cristian Lalescu committed
160
        self.parameters['forcing_type'] = 'fixed_energy_injection_rate'
161
162
163
164
        self.parameters['histogram_bins'] = int(256)
        self.parameters['max_velocity_estimate'] = float(1)
        self.parameters['max_vorticity_estimate'] = float(1)
        # parameters specific to particle version
165
166
167
168
169
170
        self.NSVEp_extra_parameters = {}
        self.NSVEp_extra_parameters['niter_part'] = int(1)
        self.NSVEp_extra_parameters['nparticles'] = int(10)
        self.NSVEp_extra_parameters['tracers0_integration_steps'] = int(4)
        self.NSVEp_extra_parameters['tracers0_neighbours'] = int(1)
        self.NSVEp_extra_parameters['tracers0_smoothness'] = int(1)
Cristian Lalescu's avatar
Cristian Lalescu committed
171
        #self.extra_parameters = {}
172
        #for key in ['NSVE', 'NSVE_no_output', 'NSVEparticles', 'NSVEparticles_no_output', 'NSVEcomplex_particles']:
Cristian Lalescu's avatar
Cristian Lalescu committed
173
        #    self.extra_parameters[key] = {}
174
        #for key in ['NSVEparticles', 'NSVEparticles_no_output', 'NSVEcomplex_particles']:
Cristian Lalescu's avatar
Cristian Lalescu committed
175
        #    self.extra_parameters[key].update(self.NSVEp_extra_parameters)
Cristian Lalescu's avatar
Cristian Lalescu committed
176
        return None
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
    def get_kspace(self):
        kspace = {}
        if self.parameters['dealias_type'] == 1:
            kMx = self.parameters['dkx']*(self.parameters['nx']//2 - 1)
            kMy = self.parameters['dky']*(self.parameters['ny']//2 - 1)
            kMz = self.parameters['dkz']*(self.parameters['nz']//2 - 1)
        else:
            kMx = self.parameters['dkx']*(self.parameters['nx']//3 - 1)
            kMy = self.parameters['dky']*(self.parameters['ny']//3 - 1)
            kMz = self.parameters['dkz']*(self.parameters['nz']//3 - 1)
        kspace['kM'] = max(kMx, kMy, kMz)
        kspace['dk'] = min(self.parameters['dkx'],
                           self.parameters['dky'],
                           self.parameters['dkz'])
        nshells = int(kspace['kM'] / kspace['dk']) + 2
        kspace['nshell'] = np.zeros(nshells, dtype = np.int64)
        kspace['kshell'] = np.zeros(nshells, dtype = np.float64)
        kspace['kx'] = np.arange( 0,
                                  self.parameters['nx']//2 + 1).astype(np.float64)*self.parameters['dkx']
        kspace['ky'] = np.arange(-self.parameters['ny']//2 + 1,
                                  self.parameters['ny']//2 + 1).astype(np.float64)*self.parameters['dky']
        kspace['ky'] = np.roll(kspace['ky'], self.parameters['ny']//2+1)
        kspace['kz'] = np.arange(-self.parameters['nz']//2 + 1,
                                  self.parameters['nz']//2 + 1).astype(np.float64)*self.parameters['dkz']
        kspace['kz'] = np.roll(kspace['kz'], self.parameters['nz']//2+1)
        return kspace
    def get_data_file_name(self):
        return os.path.join(self.work_dir, self.simname + '.h5')
    def get_data_file(self):
        return h5py.File(self.get_data_file_name(), 'r')
    def get_particle_file_name(self):
        return os.path.join(self.work_dir, self.simname + '_particles.h5')
    def get_particle_file(self):
        return h5py.File(self.get_particle_file_name(), 'r')
211
212
213
214
    def get_cache_file_name(self):
        return os.path.join(self.work_dir, self.simname + '_cache.h5')
    def get_cache_file(self):
        return h5py.File(self.get_cache_file_name(), 'r')
215
    def get_postprocess_file_name(self):
216
        return self.get_cache_file_name()
217
218
219
220
221
222
223
224
225
226
227
228
229
230
231
    def get_postprocess_file(self):
        return h5py.File(self.get_postprocess_file_name(), 'r')
    def compute_statistics(self, iter0 = 0, iter1 = None):
        """Run basic postprocessing on raw data.
        The energy spectrum :math:`E(t, k)` and the enstrophy spectrum
        :math:`\\frac{1}{2}\omega^2(t, k)` are computed from the

        .. math::

            \sum_{k \\leq \\|\\mathbf{k}\\| \\leq k+dk}\\hat{u_i} \\hat{u_j}^*, \\hskip .5cm
            \sum_{k \\leq \\|\\mathbf{k}\\| \\leq k+dk}\\hat{\omega_i} \\hat{\\omega_j}^*

        tensors, and the enstrophy spectrum is also used to
        compute the dissipation :math:`\\varepsilon(t)`.
        These basic quantities are stored in a newly created HDF5 file,
232
        ``simname_cache.h5``.
233
234
235
        """
        if len(list(self.statistics.keys())) > 0:
            return None
Cristian Lalescu's avatar
Cristian Lalescu committed
236
237
238
239
240
241
242
243
244
        if not os.path.exists(self.get_data_file_name()):
            if os.path.exists(self.get_cache_file_name()):
                self.read_parameters(fname = self.get_cache_file_name())
                with self.get_cache_file() as pp_file:
                    for k in ['t',
                              'energy(t)',
                              'energy(k)',
                              'enstrophy(t)',
                              'enstrophy(k)',
245
                              'R_ij(t)',
Cristian Lalescu's avatar
Cristian Lalescu committed
246
247
                              'vel_max(t)',
                              'renergy(t)']:
248
                        if k in pp_file.keys():
Cristian Lalescu's avatar
Cristian Lalescu committed
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
                            self.statistics[k] = pp_file[k].value
                    self.statistics['kM'] = pp_file['kspace/kM'].value
                    self.statistics['dk'] = pp_file['kspace/dk'].value
                    self.statistics['kshell'] = pp_file['kspace/kshell'].value
                    self.statistics['nshell'] = pp_file['kspace/nshell'].value
        else:
            self.read_parameters()
            with self.get_data_file() as data_file:
                if 'moments' not in data_file['statistics'].keys():
                    return None
                iter0 = min((data_file['statistics/moments/velocity'].shape[0] *
                             self.parameters['niter_stat']-1),
                            iter0)
                if type(iter1) == type(None):
                    iter1 = data_file['iteration'].value
                else:
                    iter1 = min(data_file['iteration'].value, iter1)
                ii0 = iter0 // self.parameters['niter_stat']
                ii1 = iter1 // self.parameters['niter_stat']
                self.statistics['kshell'] = data_file['kspace/kshell'].value
                self.statistics['nshell'] = data_file['kspace/nshell'].value
                for kk in [-1, -2]:
                    if (self.statistics['kshell'][kk] == 0):
                        self.statistics['kshell'][kk] = np.nan
                self.statistics['kM'] = data_file['kspace/kM'].value
                self.statistics['dk'] = data_file['kspace/dk'].value
                computation_needed = True
                pp_file = h5py.File(self.get_postprocess_file_name(), 'a')
                if not ('parameters' in pp_file.keys()):
                    data_file.copy('parameters', pp_file)
                    data_file.copy('kspace', pp_file)
                if 'ii0' in pp_file.keys():
                    computation_needed =  not (ii0 == pp_file['ii0'].value and
                                               ii1 == pp_file['ii1'].value)
                    if computation_needed:
                        for k in ['t', 'vel_max(t)', 'renergy(t)',
                                  'energy(t)', 'enstrophy(t)',
                                  'energy(k)', 'enstrophy(k)',
                                  'energy(t, k)',
                                  'enstrophy(t, k)',
                                  'R_ij(t)',
                                  'ii0', 'ii1', 'iter0', 'iter1']:
                            if k in pp_file.keys():
                                del pp_file[k]
                if computation_needed:
                    pp_file['iter0'] = iter0
                    pp_file['iter1'] = iter1
                    pp_file['ii0'] = ii0
                    pp_file['ii1'] = ii1
                    pp_file['t'] = (self.parameters['dt']*
                                    self.parameters['niter_stat']*
                                    (np.arange(ii0, ii1+1).astype(np.float)))
                    phi_ij = data_file['statistics/spectra/velocity_velocity'][ii0:ii1+1]
                    pp_file['R_ij(t)'] = np.sum(phi_ij, axis = 1)
                    energy_tk = (
                        phi_ij[:, :, 0, 0] +
                        phi_ij[:, :, 1, 1] +
                        phi_ij[:, :, 2, 2])/2
                    pp_file['energy(t)'] = np.sum(energy_tk, axis = 1)
                    pp_file['energy(k)'] = np.mean(energy_tk, axis = 0)*(4*np.pi*self.statistics['kshell']**2) / (self.statistics['dk']*self.statistics['nshell'])
                    enstrophy_tk = (
                        data_file['statistics/spectra/vorticity_vorticity'][ii0:ii1+1, :, 0, 0] +
                        data_file['statistics/spectra/vorticity_vorticity'][ii0:ii1+1, :, 1, 1] +
                        data_file['statistics/spectra/vorticity_vorticity'][ii0:ii1+1, :, 2, 2])/2
                    pp_file['enstrophy(t)'] = np.sum(enstrophy_tk, axis = 1)
                    pp_file['enstrophy(k)'] = np.mean(enstrophy_tk, axis = 0)*(4*np.pi*self.statistics['kshell']**2) / (self.statistics['dk']*self.statistics['nshell'])
                    pp_file['vel_max(t)'] = data_file['statistics/moments/velocity'][ii0:ii1+1, 9, 3]
                    pp_file['renergy(t)'] = data_file['statistics/moments/velocity'][ii0:ii1+1, 2, 3]/2
        for k in ['t',
                  'energy(t)',
                  'energy(k)',
                  'enstrophy(t)',
                  'enstrophy(k)',
                  'R_ij(t)',
                  'vel_max(t)',
                  'renergy(t)']:
            if k in pp_file.keys():
                self.statistics[k] = pp_file[k].value
        # sanity check --- Parseval theorem check
        assert(np.max(np.abs(
                self.statistics['renergy(t)'] -
                self.statistics['energy(t)']) / self.statistics['energy(t)']) < 1e-5)
        self.compute_time_averages()
332
        return None
333
334
335
336
337
338
339
340
341
342
    def compute_Reynolds_stress_invariants(
            self):
        Rij = self.statistics['R_ij(t)']
        Rij /= (2*self.statistics['energy(t)'][:, None, None])
        Rij[:, 0, 0] -= 1./3
        Rij[:, 1, 1] -= 1./3
        Rij[:, 2, 2] -= 1./3
        self.statistics['I2(t)'] = np.sqrt(np.einsum('...ij,...ij', Rij, Rij, optimize = True) / 6)
        self.statistics['I3(t)'] = np.cbrt(np.einsum('...ij,...jk,...ki', Rij, Rij, Rij, optimize = True) / 6)
        return None
343
344
345
346
    def compute_time_averages(self):
        """Compute easy stats.

        Further computation of statistics based on the contents of
347
        ``simname_cache.h5``.
348
349
350
351
352
353
        Standard quantities are as follows
        (consistent with [Ishihara]_):

        .. math::

            U_{\\textrm{int}}(t) = \\sqrt{\\frac{2E(t)}{3}}, \\hskip .5cm
354
355
356
            L_{\\textrm{int}} = \\frac{\pi}{2U_{int}^2} \\int \\frac{dk}{k} E(k), \\hskip .5cm
            T_{\\textrm{int}} =
            \\frac{L_{\\textrm{int}}}{U_{\\textrm{int}}}
357
358
359
360
361
362
363
364
365
366
367
368
369
370
371
372
373
374
375

            \\eta_K = \\left(\\frac{\\nu^3}{\\varepsilon}\\right)^{1/4}, \\hskip .5cm
            \\tau_K = \\left(\\frac{\\nu}{\\varepsilon}\\right)^{1/2}, \\hskip .5cm
            \\lambda = \\sqrt{\\frac{15 \\nu U_{\\textrm{int}}^2}{\\varepsilon}}

            Re = \\frac{U_{\\textrm{int}} L_{\\textrm{int}}}{\\nu}, \\hskip
            .5cm
            R_{\\lambda} = \\frac{U_{\\textrm{int}} \\lambda}{\\nu}

        .. [Ishihara] T. Ishihara et al,
                      *Small-scale statistics in high-resolution direct numerical
                      simulation of turbulence: Reynolds number dependence of
                      one-point velocity gradient statistics*.
                      J. Fluid Mech.,
                      **592**, 335-366, 2007
        """
        self.statistics['Uint(t)'] = np.sqrt(2*self.statistics['energy(t)'] / 3)
        for key in ['energy',
                    'enstrophy',
376
377
                    'mean_trS2',
                    'Uint']:
378
379
            if key + '(t)' in self.statistics.keys():
                self.statistics[key] = np.average(self.statistics[key + '(t)'], axis = 0)
380
        self.statistics['vel_max'] = np.max(self.statistics['vel_max(t)'])
381
382
383
384
385
386
387
388
389
390
391
392
393
394
395
396
397
        for suffix in ['', '(t)']:
            self.statistics['diss'    + suffix] = (self.parameters['nu'] *
                                                   self.statistics['enstrophy' + suffix]*2)
            self.statistics['etaK'    + suffix] = (self.parameters['nu']**3 /
                                                   self.statistics['diss' + suffix])**.25
            self.statistics['tauK'    + suffix] =  (self.parameters['nu'] /
                                                    self.statistics['diss' + suffix])**.5
            self.statistics['lambda' + suffix] = (15 * self.parameters['nu'] *
                                                  self.statistics['Uint' + suffix]**2 /
                                                  self.statistics['diss' + suffix])**.5
            self.statistics['Rlambda' + suffix] = (self.statistics['Uint' + suffix] *
                                                   self.statistics['lambda' + suffix] /
                                                   self.parameters['nu'])
            self.statistics['kMeta' + suffix] = (self.statistics['kM'] *
                                                 self.statistics['etaK' + suffix])
            if self.parameters['dealias_type'] == 1:
                self.statistics['kMeta' + suffix] *= 0.8
Cristian Lalescu's avatar
Cristian Lalescu committed
398
        self.statistics['Lint'] = ((np.pi /
399
400
401
402
403
404
                                    (2*self.statistics['Uint']**2)) *
                                   np.nansum(self.statistics['energy(k)'] /
                                                self.statistics['kshell']))
        self.statistics['Re'] = (self.statistics['Uint'] *
                                 self.statistics['Lint'] /
                                 self.parameters['nu'])
405
406
407
408
409
410
411
412
413
414
415
416
417
418
419
420
421
422
423
424
425
426
427
428
429
430
431
432
433
434
435
436
437
438
439
440
441
        self.statistics['Tint'] = self.statistics['Lint'] / self.statistics['Uint']
        self.statistics['Taylor_microscale'] = self.statistics['lambda']
        return None
    def set_plt_style(
            self,
            style = {'dashes' : (None, None)}):
        self.style.update(style)
        return None
    def convert_complex_from_binary(
            self,
            field_name = 'vorticity',
            iteration = 0,
            file_name = None):
        """read the Fourier representation of a vector field.

        Read the binary file containing iteration ``iteration`` of the
        field ``field_name``, and write it in a ``.h5`` file.
        """
        data = np.memmap(
                os.path.join(self.work_dir,
                             self.simname + '_{0}_i{1:0>5x}'.format('c' + field_name, iteration)),
                dtype = self.ctype,
                mode = 'r',
                shape = (self.parameters['ny'],
                         self.parameters['nz'],
                         self.parameters['nx']//2+1,
                         3))
        if type(file_name) == type(None):
            file_name = self.simname + '_{0}_i{1:0>5x}.h5'.format('c' + field_name, iteration)
            file_name = os.path.join(self.work_dir, file_name)
        f = h5py.File(file_name, 'a')
        f[field_name + '/complex/{0}'.format(iteration)] = data
        f.close()
        return None
    def write_par(
            self,
            iter0 = 0,
442
443
            particle_ic = None,
            particles_off = False):
444
445
446
        assert (self.parameters['niter_todo'] % self.parameters['niter_stat'] == 0)
        assert (self.parameters['niter_todo'] % self.parameters['niter_out']  == 0)
        assert (self.parameters['niter_out']  % self.parameters['niter_stat'] == 0)
447
        if self.dns_type in ['NSVEparticles_no_output', 'NSVEcomplex_particles', 'NSVEparticles']:
448
449
            assert (self.parameters['niter_todo'] % self.parameters['niter_part'] == 0)
            assert (self.parameters['niter_out']  % self.parameters['niter_part'] == 0)
450
451
452
453
454
455
456
457
458
459
460
461
462
463
464
465
466
467
468
469
470
471
472
473
474
475
476
477
478
479
480
481
482
483
484
485
486
487
488
489
        _code.write_par(self, iter0 = iter0)
        with h5py.File(self.get_data_file_name(), 'r+') as ofile:
            ofile['bfps_info/exec_name'] = self.name
            kspace = self.get_kspace()
            for k in kspace.keys():
                ofile['kspace/' + k] = kspace[k]
            nshells = kspace['nshell'].shape[0]
            kspace = self.get_kspace()
            nshells = kspace['nshell'].shape[0]
            vec_stat_datasets = ['velocity', 'vorticity']
            scal_stat_datasets = []
            for k in vec_stat_datasets:
                time_chunk = 2**20//(8*3*3*nshells)
                time_chunk = max(time_chunk, 1)
                ofile.create_dataset('statistics/spectra/' + k + '_' + k,
                                     (1, nshells, 3, 3),
                                     chunks = (time_chunk, nshells, 3, 3),
                                     maxshape = (None, nshells, 3, 3),
                                     dtype = np.float64)
                time_chunk = 2**20//(8*4*10)
                time_chunk = max(time_chunk, 1)
                a = ofile.create_dataset('statistics/moments/' + k,
                                     (1, 10, 4),
                                     chunks = (time_chunk, 10, 4),
                                     maxshape = (None, 10, 4),
                                     dtype = np.float64)
                time_chunk = 2**20//(8*4*self.parameters['histogram_bins'])
                time_chunk = max(time_chunk, 1)
                ofile.create_dataset('statistics/histograms/' + k,
                                     (1,
                                      self.parameters['histogram_bins'],
                                      4),
                                     chunks = (time_chunk,
                                               self.parameters['histogram_bins'],
                                               4),
                                     maxshape = (None,
                                                 self.parameters['histogram_bins'],
                                                 4),
                                     dtype = np.int64)
            ofile['checkpoint'] = int(0)
490
        if (self.dns_type in ['NSVE', 'NSVE_no_output']) or particles_off:
491
492
493
494
495
496
497
498
499
500
501
            return None

        if type(particle_ic) == type(None):
            pbase_shape = (self.parameters['nparticles'],)
            number_of_particles = self.parameters['nparticles']
        else:
            pbase_shape = particle_ic.shape[:-1]
            assert(particle_ic.shape[-1] == 3)
            number_of_particles = 1
            for val in pbase_shape[1:]:
                number_of_particles *= val
502
        ncomponents = 3
503
        if self.dns_type in ['NSVEcomplex_particles']:
504
            ncomponents = 6
505
506
507
508
509
510
511
512
513
514
        with h5py.File(self.get_checkpoint_0_fname(), 'a') as ofile:
            s = 0
            ofile.create_group('tracers{0}'.format(s))
            ofile.create_group('tracers{0}/rhs'.format(s))
            ofile.create_group('tracers{0}/state'.format(s))
            ofile['tracers{0}/rhs'.format(s)].create_dataset(
                    '0',
                    shape = (
                        (self.parameters['tracers{0}_integration_steps'.format(s)],) +
                        pbase_shape +
515
                        (ncomponents,)),
516
517
518
519
520
                    dtype = np.float)
            ofile['tracers{0}/state'.format(s)].create_dataset(
                    '0',
                    shape = (
                        pbase_shape +
521
                        (ncomponents,)),
522
523
                    dtype = np.float)
        return None
524
    def job_parser_arguments(
525
526
            self,
            parser):
527
528
529
530
531
532
533
534
535
536
537
538
539
540
541
542
543
544
545
546
547
548
549
550
551
552
553
554
555
556
557
558
559
560
561
562
563
564
565
566
567
568
569
570
571
572
573
        parser.add_argument(
                '--ncpu',
                type = int,
                dest = 'ncpu',
                default = -1)
        parser.add_argument(
                '--np', '--nprocesses',
                metavar = 'NPROCESSES',
                help = 'number of mpi processes to use',
                type = int,
                dest = 'nb_processes',
                default = 4)
        parser.add_argument(
                '--ntpp', '--nthreads-per-process',
                type = int,
                dest = 'nb_threads_per_process',
                metavar = 'NTHREADS_PER_PROCESS',
                help = 'number of threads to use per MPI process',
                default = 1)
        parser.add_argument(
                '--no-submit',
                action = 'store_true',
                dest = 'no_submit')
        parser.add_argument(
                '--environment',
                type = str,
                dest = 'environment',
                default = None)
        parser.add_argument(
                '--minutes',
                type = int,
                dest = 'minutes',
                default = 5,
                help = 'If environment supports it, this is the requested wall-clock-limit.')
        parser.add_argument(
               '--njobs',
               type = int, dest = 'njobs',
               default = 1)
        return None
    def simulation_parser_arguments(
            self,
            parser):
        parser.add_argument(
                '--simname',
                type = str, dest = 'simname',
                default = 'test')
        parser.add_argument(
574
               '-n', '--grid-size',
575
576
577
578
579
               type = int,
               dest = 'n',
               default = 32,
               metavar = 'N',
               help = 'code is run by default in a grid of NxNxN')
580
581
582
583
584
585
586
587
        for coord in ['x', 'y', 'z']:
            parser.add_argument(
                   '--L{0}'.format(coord), '--box-length-{0}'.format(coord),
                   type = float,
                   dest = 'L{0}'.format(coord),
                   default = 2.0,
                   metavar = 'length{0}'.format(coord),
                   help = 'length of the box in the {0} direction will be `length{0} x pi`'.format(coord))
588
589
590
591
592
593
594
595
596
        parser.add_argument(
                '--wd',
                type = str, dest = 'work_dir',
                default = './')
        parser.add_argument(
                '--precision',
                choices = ['single', 'double'],
                type = str,
                default = 'single')
597
598
599
600
601
602
603
604
605
606
607
608
609
610
611
612
613
614
615
616
617
618
619
620
621
622
        parser.add_argument(
                '--src-wd',
                type = str,
                dest = 'src_work_dir',
                default = '')
        parser.add_argument(
                '--src-simname',
                type = str,
                dest = 'src_simname',
                default = '')
        parser.add_argument(
                '--src-iteration',
                type = int,
                dest = 'src_iteration',
                default = 0)
        parser.add_argument(
               '--kMeta',
               type = float,
               dest = 'kMeta',
               default = 2.0)
        parser.add_argument(
               '--dtfactor',
               type = float,
               dest = 'dtfactor',
               default = 0.5,
               help = 'dt is computed as DTFACTOR / N')
623
624
625
626
        return None
    def particle_parser_arguments(
            self,
            parser):
627
628
629
630
631
632
633
634
635
636
637
638
639
640
641
642
643
644
645
646
647
648
649
650
651
        parser.add_argument(
               '--particle-rand-seed',
               type = int,
               dest = 'particle_rand_seed',
               default = None)
        parser.add_argument(
               '--pclouds',
               type = int,
               dest = 'pclouds',
               default = 1,
               help = ('number of particle clouds. Particle "clouds" '
                       'consist of particles distributed according to '
                       'pcloud-type.'))
        parser.add_argument(
                '--pcloud-type',
                choices = ['random-cube',
                           'regular-cube'],
                dest = 'pcloud_type',
                default = 'random-cube')
        parser.add_argument(
               '--particle-cloud-size',
               type = float,
               dest = 'particle_cloud_size',
               default = 2*np.pi)
        return None
652
653
654
655
656
657
658
659
660
661
662
663
664
665
    def add_parser_arguments(
            self,
            parser):
        subparsers = parser.add_subparsers(
                dest = 'DNS_class',
                help = 'type of simulation to run')
        subparsers.required = True
        parser_NSVE = subparsers.add_parser(
                'NSVE',
                help = 'plain Navier-Stokes vorticity formulation')
        self.simulation_parser_arguments(parser_NSVE)
        self.job_parser_arguments(parser_NSVE)
        self.parameters_to_parser_arguments(parser_NSVE)

666
667
668
669
670
671
672
673
674
675
        parser_NSVE_no_output = subparsers.add_parser(
                'NSVE_no_output',
                help = 'plain Navier-Stokes vorticity formulation, checkpoints are NOT SAVED')
        self.simulation_parser_arguments(parser_NSVE_no_output)
        self.job_parser_arguments(parser_NSVE_no_output)
        self.parameters_to_parser_arguments(parser_NSVE_no_output)

        parser_NSVEparticles_no_output = subparsers.add_parser(
                'NSVEparticles_no_output',
                help = 'plain Navier-Stokes vorticity formulation, with basic fluid tracers, checkpoints are NOT SAVED')
676
677
678
679

        parser_NSVEp2 = subparsers.add_parser(
                'NSVEparticles',
                help = 'plain Navier-Stokes vorticity formulation, with basic fluid tracers')
680
681

        parser_NSVEp2p = subparsers.add_parser(
682
683
                'NSVEcomplex_particles',
                help = 'plain Navier-Stokes vorticity formulation, with oriented active particles')
Cristian Lalescu's avatar
Cristian Lalescu committed
684
685
686
687
688
689
690
691
692

        for parser in ['NSVEparticles_no_output', 'NSVEp2', 'NSVEp2p']:
            eval('self.simulation_parser_arguments({0})'.format('parser_' + parser))
            eval('self.job_parser_arguments({0})'.format('parser_' + parser))
            eval('self.particle_parser_arguments({0})'.format('parser_' + parser))
            eval('self.parameters_to_parser_arguments({0})'.format('parser_' + parser))
            eval('self.parameters_to_parser_arguments('
                    'parser_{0},'
                    'self.NSVEp_extra_parameters)'.format(parser))
693
        return None
694
695
    def prepare_launch(
            self,
696
697
            args = [],
            extra_parameters = None):
698
699
700
701
702
703
704
705
706
707
708
709
710
711
712
713
714
715
716
717
718
719
720
721
722
723
        """Set up reasonable parameters.

        With the default Lundgren forcing applied in the band [2, 4],
        we can estimate the dissipation, therefore we can estimate
        :math:`k_M \\eta_K` and constrain the viscosity.

        In brief, the command line parameter :math:`k_M \\eta_K` is
        used in the following formula for :math:`\\nu` (:math:`N` is the
        number of real space grid points per coordinate):

        .. math::

            \\nu = \\left(\\frac{2 k_M \\eta_K}{N} \\right)^{4/3}

        With this choice, the average dissipation :math:`\\varepsilon`
        will be close to 0.4, and the integral scale velocity will be
        close to 0.77, yielding the approximate value for the Taylor
        microscale and corresponding Reynolds number:

        .. math::

            \\lambda \\approx 4.75\\left(\\frac{2 k_M \\eta_K}{N} \\right)^{4/6}, \\hskip .5in
            R_\\lambda \\approx 3.7 \\left(\\frac{N}{2 k_M \\eta_K} \\right)^{4/6}

        """
        opt = _code.prepare_launch(self, args = args)
724
725
726
727
        self.set_precision(opt.precision)
        self.dns_type = opt.DNS_class
        self.name = self.dns_type + '-' + self.fluid_precision + '-v' + bfps.__version__
        # merge parameters if needed
728
        if self.dns_type in ['NSVEparticles', 'NSVEcomplex_particles', 'NSVEparticles_no_output']:
729
730
            for k in self.NSVEp_extra_parameters.keys():
                self.parameters[k] = self.NSVEp_extra_parameters[k]
731
732
733
734
        if type(extra_parameters) != type(None):
            if self.dns_type in extra_parameters.keys():
                for k in extra_parameters[self.dns_type].keys():
                    self.parameters[k] = extra_parameters[self.dns_type][k]
735
736
737
738
        if ((self.parameters['niter_todo'] % self.parameters['niter_out']) != 0):
            self.parameters['niter_out'] = self.parameters['niter_todo']
        if len(opt.src_work_dir) == 0:
            opt.src_work_dir = os.path.realpath(opt.work_dir)
739
740
741
742
        if type(opt.dkx) == type(None):
            opt.dkx = 2. / opt.Lx
        if type(opt.dky) == type(None):
            opt.dky = 2. / opt.Ly
Cristian Lalescu's avatar
Cristian Lalescu committed
743
        if type(opt.dkz) == type(None):
744
            opt.dkz = 2. / opt.Lz
745
746
747
748
749
750
        if type(opt.nx) == type(None):
            opt.nx = opt.n
        if type(opt.ny) == type(None):
            opt.ny = opt.n
        if type(opt.nz) == type(None):
            opt.nz = opt.n
751
752
753
754
755
756
        if type(opt.fk0) == type(None):
            opt.fk0 = self.parameters['fk0']
        if type(opt.fk1) == type(None):
            opt.fk1 = self.parameters['fk1']
        if type(opt.injection_rate) == type(None):
            opt.injection_rate = self.parameters['injection_rate']
Cristian Lalescu's avatar
Cristian Lalescu committed
757
        if type(opt.dealias_type) == type(None):
758
            opt.dealias_type = self.parameters['dealias_type']
759
760
761
762
763
        if (opt.nx > opt.n or
            opt.ny > opt.n or
            opt.nz > opt.n):
            opt.n = min(opt.nx, opt.ny, opt.nz)
            print("Warning: '-n' parameter changed to minimum of nx, ny, nz. This affects the computation of nu.")
Chichi Lalescu's avatar
Chichi Lalescu committed
764
        self.parameters['dt'] = (opt.dtfactor / opt.n)
765
        self.parameters['nu'] = (opt.kMeta * 2 / opt.n)**(4./3)
Cristian Lalescu's avatar
Cristian Lalescu committed
766
767
768
769
770
        # check value of kMax
        kM = opt.n * 0.5
        if opt.dealias_type == 1:
            kM *= 0.8
        # tweak forcing/viscosity based on forcint type
Cristian Lalescu's avatar
Cristian Lalescu committed
771
        if opt.forcing_type == 'linear':
772
773
774
775
776
            # custom famplitude for 288 and 576
            if opt.n == 288:
                self.parameters['famplitude'] = 0.45
            elif opt.n == 576:
                self.parameters['famplitude'] = 0.47
Cristian Lalescu's avatar
Cristian Lalescu committed
777
        elif opt.forcing_type == 'fixed_energy_injection_rate':
778
779
            # use the fact that mean dissipation rate is equal to injection rate
            self.parameters['nu'] = (
Cristian Lalescu's avatar
Cristian Lalescu committed
780
                    opt.injection_rate *
781
                    (opt.kMeta / kM)**4)**(1./3)
782
        elif opt.forcing_type == 'fixed_energy':
Cristian Lalescu's avatar
Cristian Lalescu committed
783
784
            kf = 1. / (1./opt.fk0 +
                       1./opt.fk1)
785
786
787
788
            self.parameters['nu'] = (
                    (opt.kMeta / kM)**(4./3) *
                    (np.pi / kf)**(1./3) *
                    (2*self.parameters['energy'] / 3)**0.5)
789
790
791
792
        if type(opt.checkpoints_per_file) == type(None):
            # hardcoded FFTW complex representation size
            field_size = 3*(opt.nx+2)*opt.ny*opt.nz*self.fluid_dtype.itemsize
            checkpoint_size = field_size
793
            if self.dns_type in ['NSVEparticles', 'NSVEcomplex_particles', 'NSVEparticles_no_output']:
794
795
796
                rhs_size = self.parameters['tracers0_integration_steps']
                if type(opt.tracers0_integration_steps) != type(None):
                    rhs_size = opt.tracers0_integration_steps
797
798
799
800
                nparticles = opt.nparticles
                if type(nparticles) == type(None):
                    nparticles = self.NSVEp_extra_parameters['nparticles']
                particle_size = (1+rhs_size)*3*nparticles*8
801
802
803
                checkpoint_size += particle_size
            if checkpoint_size < 1e9:
                opt.checkpoints_per_file = int(1e9 / checkpoint_size)
804
805
806
807
808
809
810
811
812
813
814
815
816
817
818
819
        self.pars_from_namespace(opt)
        return opt
    def launch(
            self,
            args = [],
            **kwargs):
        opt = self.prepare_launch(args = args)
        self.launch_jobs(opt = opt, **kwargs)
        return None
    def get_checkpoint_0_fname(self):
        return os.path.join(
                    self.work_dir,
                    self.simname + '_checkpoint_0.h5')
    def generate_tracer_state(
            self,
            rseed = None,
820
821
822
823
            species = 0):
        with h5py.File(self.get_checkpoint_0_fname(), 'a') as data_file:
            dset = data_file[
                'tracers{0}/state/0'.format(species)]
824
825
            if not type(rseed) == type(None):
                np.random.seed(rseed)
826
827
828
            nn = self.parameters['nparticles']
            cc = int(0)
            batch_size = int(1e6)
829
830
831
832
833
834
            def get_random_phases(npoints):
                return np.random.random(
                            (npoints, 3))*2*np.pi
            def get_random_versors(npoints):
                bla = np.random.normal(
                        size = (npoints, 3))
835
                bla  /= np.sum(bla**2, axis = 1)[:, None]**.5
836
                return bla
837
838
            while nn > 0:
                if nn > batch_size:
839
840
841
                    dset[cc*batch_size:(cc+1)*batch_size, :3] = get_random_phases(batch_size)
                    if dset.shape[1] == 6:
                        dset[cc*batch_size:(cc+1)*batch_size, 3:] = get_random_versors(batch_size)
842
843
                    nn -= batch_size
                else:
844
845
846
                    dset[cc*batch_size:cc*batch_size+nn, :3] = get_random_phases(nn)
                    if dset.shape[1] == 6:
                        dset[cc*batch_size:cc*batch_size+nn, 3:] = get_random_versors(nn)
847
848
849
                    nn = 0
                cc += 1
        return None
850
851
852
853
854
855
856
857
858
859
860
861
862
863
864
865
866
867
868
869
870
871
872
873
874
875
876
877
878
879
880
881
882
883
884
885
886
887
888
889
890
891
892
    def generate_vector_field(
            self,
            rseed = 7547,
            spectra_slope = 1.,
            amplitude = 1.,
            iteration = 0,
            field_name = 'vorticity',
            write_to_file = False,
            # to switch to constant field, use generate_data_3D_uniform
            # for scalar_generator
            scalar_generator = tools.generate_data_3D):
        """generate vector field.

        The generated field is not divergence free, but it has the proper
        shape.

        :param rseed: seed for random number generator
        :param spectra_slope: spectrum of field will look like k^(-p)
        :param amplitude: all amplitudes are multiplied with this value
        :param iteration: the field is written at this iteration
        :param field_name: the name of the field being generated
        :param write_to_file: should we write the field to file?
        :param scalar_generator: which function to use for generating the
            individual components.
            Possible values: bfps.tools.generate_data_3D,
            bfps.tools.generate_data_3D_uniform
        :type rseed: int
        :type spectra_slope: float
        :type amplitude: float
        :type iteration: int
        :type field_name: str
        :type write_to_file: bool
        :type scalar_generator: function

        :returns: ``Kdata``, a complex valued 4D ``numpy.array`` that uses the
            transposed FFTW layout.
            Kdata[ky, kz, kx, i] is the amplitude of mode (kx, ky, kz) for
            the i-th component of the field.
            (i.e. x is the fastest index and z the slowest index in the
            real-space representation).
        """
        np.random.seed(rseed)
        Kdata00 = scalar_generator(
893
894
895
                self.parameters['nz'],
                self.parameters['ny'],
                self.parameters['nx'],
896
897
898
                p = spectra_slope,
                amplitude = amplitude).astype(self.ctype)
        Kdata01 = scalar_generator(
899
900
901
                self.parameters['nz'],
                self.parameters['ny'],
                self.parameters['nx'],
902
903
904
                p = spectra_slope,
                amplitude = amplitude).astype(self.ctype)
        Kdata02 = scalar_generator(
905
906
907
                self.parameters['nz'],
                self.parameters['ny'],
                self.parameters['nx'],
908
909
910
911
912
913
914
915
916
917
918
919
920
921
922
923
924
925
                p = spectra_slope,
                amplitude = amplitude).astype(self.ctype)
        Kdata0 = np.zeros(
                Kdata00.shape + (3,),
                Kdata00.dtype)
        Kdata0[..., 0] = Kdata00
        Kdata0[..., 1] = Kdata01
        Kdata0[..., 2] = Kdata02
        Kdata1 = tools.padd_with_zeros(
                Kdata0,
                self.parameters['nz'],
                self.parameters['ny'],
                self.parameters['nx'])
        if write_to_file:
            Kdata1.tofile(
                    os.path.join(self.work_dir,
                                 self.simname + "_c{0}_i{1:0>5x}".format(field_name, iteration)))
        return Kdata1
926
927
928
929
930
931
932
933
934
935
936
    def copy_complex_field(
            self,
            src_file_name,
            src_dset_name,
            dst_file,
            dst_dset_name,
            make_link = True):
        # I define a min_shape thingie, but for now I only trust this method for
        # the case of increasing/decreasing by the same factor in all directions.
        # in principle we could write something more generic, but i'm not sure
        # how complicated that would be
937
938
        dst_shape = (self.parameters['ny'],
                     self.parameters['nz'],
939
940
941
942
943
944
945
946
947
948
949
950
951
952
953
954
955
956
957
958
959
                     (self.parameters['nx']+2) // 2,
                     3)
        src_file = h5py.File(src_file_name, 'r')
        if (src_file[src_dset_name].shape == dst_shape):
            if make_link and (src_file[src_dset_name].dtype == self.ctype):
                dst_file[dst_dset_name] = h5py.ExternalLink(
                        src_file_name,
                        src_dset_name)
            else:
                dst_file.create_dataset(
                        dst_dset_name,
                        shape = dst_shape,
                        dtype = self.ctype,
                        fillvalue = 0.0)
                for kz in range(src_file[src_dset_name].shape[0]):
                    dst_file[dst_dset_name][kz] = src_file[src_dset_name][kz]
        else:
            min_shape = (min(dst_shape[0], src_file[src_dset_name].shape[0]),
                         min(dst_shape[1], src_file[src_dset_name].shape[1]),
                         min(dst_shape[2], src_file[src_dset_name].shape[2]),
                         3)
960
            src_shape = src_file[src_dset_name].shape
961
962
963
            dst_file.create_dataset(
                    dst_dset_name,
                    shape = dst_shape,
964
965
                    dtype = np.dtype(self.ctype),
                    fillvalue = complex(0))
966
967
968
969
970
971
972
973
974
975
976
977
978
979
980
981
982
983
            for kz in range(min_shape[0]//2):
                dst_file[dst_dset_name][kz,:min_shape[1]//2, :min_shape[2]] = \
                        src_file[src_dset_name][kz, :min_shape[1]//2, :min_shape[2]]
                dst_file[dst_dset_name][kz,
                                        dst_shape[1] - min_shape[1]//2+1:,
                                        :min_shape[2]] = \
                        src_file[src_dset_name][kz,
                                                src_shape[1] - min_shape[1]//2+1,
                                                :min_shape[2]]
                if kz > 0:
                    dst_file[dst_dset_name][-kz,:min_shape[1]//2, :min_shape[2]] = \
                            src_file[src_dset_name][-kz, :min_shape[1]//2, :min_shape[2]]
                    dst_file[dst_dset_name][-kz,
                                            dst_shape[1] - min_shape[1]//2+1:,
                                            :min_shape[2]] = \
                            src_file[src_dset_name][-kz,
                                                    src_shape[1] - min_shape[1]//2+1,
                                                    :min_shape[2]]
984
        return None
985
986
987
988
989
990
991
992
993
    def generate_particle_data(
            self,
            opt = None):
        if self.parameters['nparticles'] > 0:
            self.generate_tracer_state(
                    species = 0,
                    rseed = opt.particle_rand_seed)
            if not os.path.exists(self.get_particle_file_name()):
                with h5py.File(self.get_particle_file_name(), 'w') as particle_file:
Cristian Lalescu's avatar
Cristian Lalescu committed
994
                    particle_file.create_group('tracers0/position')
995
996
                    particle_file.create_group('tracers0/velocity')
                    particle_file.create_group('tracers0/acceleration')
997
                    if self.dns_type in ['NSVEcomplex_particles']:
998
                        particle_file.create_group('tracers0/orientation')
999
                        particle_file.create_group('tracers0/velocity_gradient')
1000
        return None
1001
1002
1003
1004
1005
1006
1007
1008
1009
1010
1011
1012
1013
1014
1015
1016
1017
1018
1019
1020
    def launch_jobs(
            self,
            opt = None,
            particle_initial_condition = None):
        if not os.path.exists(os.path.join(self.work_dir, self.simname + '.h5')):
            # take care of fields' initial condition
            if not os.path.exists(self.get_checkpoint_0_fname()):
                f = h5py.File(self.get_checkpoint_0_fname(), 'w')
                if len(opt.src_simname) > 0:
                    source_cp = 0
                    src_file = 'not_a_file'
                    while True:
                        src_file = os.path.join(
                            os.path.realpath(opt.src_work_dir),
                            opt.src_simname + '_checkpoint_{0}.h5'.format(source_cp))
                        f0 = h5py.File(src_file, 'r')
                        if '{0}'.format(opt.src_iteration) in f0['vorticity/complex'].keys():
                            f0.close()
                            break
                        source_cp += 1
1021
                    self.copy_complex_field(
1022
                            src_file,
1023
1024
1025
                            'vorticity/complex/{0}'.format(opt.src_iteration),
                            f,
                            'vorticity/complex/{0}'.format(0))
1026
1027
1028
1029
1030
1031
1032
                else:
                    data = self.generate_vector_field(
                           write_to_file = False,
                           spectra_slope = 2.0,
                           amplitude = 0.05)
                    f['vorticity/complex/{0}'.format(0)] = data
                f.close()
1033
            ## take care of particles' initial condition
1034
            #if self.dns_type in ['NSVEparticles', 'NSVEparticles_no_output']:
1035
1036
1037
1038
1039
1040
1041
1042
1043
1044
1045
1046
1047
1048
1049
1050
1051
1052
1053
1054
1055
1056
            #    if opt.pclouds > 1:
            #        np.random.seed(opt.particle_rand_seed)
            #        if opt.pcloud_type == 'random-cube':
            #            particle_initial_condition = (
            #                np.random.random((opt.pclouds, 1, 3))*2*np.pi +
            #                np.random.random((1, self.parameters['nparticles'], 3))*opt.particle_cloud_size)
            #        elif opt.pcloud_type == 'regular-cube':
            #            onedarray = np.linspace(
            #                    -opt.particle_cloud_size/2,
            #                    opt.particle_cloud_size/2,
            #                    self.parameters['nparticles'])
            #            particle_initial_condition = np.zeros(
            #                    (opt.pclouds,
            #                     self.parameters['nparticles'],
            #                     self.parameters['nparticles'],
            #                     self.parameters['nparticles'], 3),
            #                    dtype = np.float64)
            #            particle_initial_condition[:] = \
            #                np.random.random((opt.pclouds, 1, 1, 1, 3))*2*np.pi
            #            particle_initial_condition[..., 0] += onedarray[None, None, None, :]
            #            particle_initial_condition[..., 1] += onedarray[None, None, :, None]
            #            particle_initial_condition[..., 2] += onedarray[None, :, None, None]
1057
            self.write_par(
1058
                    particle_ic = None)
1059
            if self.dns_type in ['NSVEparticles', 'NSVEcomplex_particles', 'NSVEparticles_no_output']:
1060
                self.generate_particle_data(opt = opt)
1061
1062
1063
1064
1065
1066
1067
1068
        self.run(
                nb_processes = opt.nb_processes,
                nb_threads_per_process = opt.nb_threads_per_process,
                njobs = opt.njobs,
                hours = opt.minutes // 60,
                minutes = opt.minutes % 60,
                no_submit = opt.no_submit)
        return None
Cristian Lalescu's avatar
Cristian Lalescu committed
1069