DNS.py 49.9 KB
Newer Older
1
2
3
4
################################################################################
#                                                                              #
#  Copyright 2015-2019 Max Planck Institute for Dynamics and Self-Organization #
#                                                                              #
5
#  This file is part of TurTLE.                                                #
6
#                                                                              #
7
#  TurTLE is free software: you can redistribute it and/or modify              #
8
9
10
11
#  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.                                      #
#                                                                              #
12
#  TurTLE is distributed in the hope that it will be useful,                   #
13
14
15
16
17
#  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           #
18
#  along with TurTLE.  If not, see <http://www.gnu.org/licenses/>              #
19
20
21
22
#                                                                              #
# Contact: Cristian.Lalescu@ds.mpg.de                                          #
#                                                                              #
################################################################################
Cristian Lalescu's avatar
Cristian Lalescu committed
23
24
25
26
27
28
29
30
31
32
33
34



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

35
import TurTLE
Cristian Lalescu's avatar
Cristian Lalescu committed
36
from ._code import _code
37
from TurTLE import tools
Cristian Lalescu's avatar
Cristian Lalescu committed
38
39
40
41
42
43
44
45

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 = './',
46
47
48
49
50
51
            simname = 'test'):
        _code.__init__(
                self,
                work_dir = work_dir,
                simname = simname)
        self.generate_default_parameters()
Chichi Lalescu's avatar
Chichi Lalescu committed
52
        self.statistics = {}
53
54
55
56
        return None
    def set_precision(
            self,
            fluid_dtype):
Cristian Lalescu's avatar
Cristian Lalescu committed
57
58
59
60
61
62
63
64
65
66
        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)
67
            self.C_field_dtype = 'float'
68
            self.fluid_precision = 'single'
Cristian Lalescu's avatar
Cristian Lalescu committed
69
70
        elif self.rtype == np.float64:
            self.ctype = np.dtype(np.complex128)
71
            self.C_field_dtype = 'double'
72
73
            self.fluid_precision = 'double'
        return None
74
75
    def write_src(
            self):
Cristian Lalescu's avatar
Cristian Lalescu committed
76
77
        self.version_message = (
                '/***********************************************************************\n' +
78
79
                '* this code automatically generated by TurTLE\n' +
                '* version {0}\n'.format(TurTLE.__version__) +
Cristian Lalescu's avatar
Cristian Lalescu committed
80
                '***********************************************************************/\n\n\n')
81
82
83
84
85
        self.include_list = [
                '"base.hpp"',
                '"scope_timer.hpp"',
                '"fftw_interface.hpp"',
                '"full_code/main_code.hpp"',
86
                '<cmath>',
87
88
89
90
91
92
93
94
95
                '<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
96
        self.main = """
97
98
99
            int main(int argc, char *argv[])
            {{
                bool fpe = (
Cristian Lalescu's avatar
Cristian Lalescu committed
100
101
                    (getenv("TURTLE_FPE_OFF") == nullptr) ||
                    (getenv("TURTLE_FPE_OFF") != std::string("TRUE")));
102
103
104
                return main_code< {0} >(argc, argv, fpe);
            }}
            """.format(self.dns_type + '<{0}>'.format(self.C_field_dtype))
105
106
107
108
109
110
111
        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')
            outfile.write(self.main + '\n')
112
113
114
        return None
    def generate_default_parameters(self):
        # these parameters are relevant for all DNS classes
Chichi Lalescu's avatar
Chichi Lalescu committed
115
        self.parameters['fftw_plan_rigor'] = 'FFTW_ESTIMATE'
116
117
118
119
120
121
122
        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)
123
        self.parameters['checkpoints_per_file'] = int(1)
124
        self.parameters['dt'] = float(0.01)
125
        self.parameters['nu'] = float(0.1)
126
        self.parameters['fmode'] = int(1)
127
        self.parameters['famplitude'] = float(0.5)
Chichi Lalescu's avatar
Chichi Lalescu committed
128
        self.parameters['friction_coefficient'] = float(0.5)
Cristian Lalescu's avatar
Cristian Lalescu committed
129
130
        self.parameters['energy'] = float(0.5)
        self.parameters['injection_rate'] = float(0.4)
131
132
        self.parameters['fk0'] = float(2.0)
        self.parameters['fk1'] = float(4.0)
Cristian Lalescu's avatar
Cristian Lalescu committed
133
        self.parameters['forcing_type'] = 'fixed_energy_injection_rate'
134
135
136
137
        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
138
139
        self.NSVEp_extra_parameters = {}
        self.NSVEp_extra_parameters['niter_part'] = int(1)
140
141
        self.NSVEp_extra_parameters['niter_part_fine_period'] = int(10)
        self.NSVEp_extra_parameters['niter_part_fine_duration'] = int(0)
142
143
144
145
        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)
146
147
148
149
150
        self.NSVEp_extra_parameters['tracers0_enable_p2p'] = int(0)
        self.NSVEp_extra_parameters['tracers0_enable_inner'] = int(0)
        self.NSVEp_extra_parameters['tracers0_enable_vorticity_omega'] = int(0)
        self.NSVEp_extra_parameters['tracers0_cutoff'] = float(1)
        self.NSVEp_extra_parameters['tracers0_inner_v0'] = float(1)
Cristian Lalescu's avatar
Cristian Lalescu committed
151
        self.NSVEp_extra_parameters['tracers0_lambda'] = float(1)
Cristian Lalescu's avatar
Cristian Lalescu committed
152
        #self.extra_parameters = {}
153
        #for key in ['NSVE', 'NSVE_no_output', 'NSVEparticles', 'NSVEparticles_no_output', 'NSVEcomplex_particles']:
Cristian Lalescu's avatar
Cristian Lalescu committed
154
        #    self.extra_parameters[key] = {}
155
        #for key in ['NSVEparticles', 'NSVEparticles_no_output', 'NSVEcomplex_particles']:
Cristian Lalescu's avatar
Cristian Lalescu committed
156
        #    self.extra_parameters[key].update(self.NSVEp_extra_parameters)
Cristian Lalescu's avatar
Cristian Lalescu committed
157
        return None
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
    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')
192
193
194
195
    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')
196
    def get_postprocess_file_name(self):
197
        return self.get_cache_file_name()
198
199
200
201
202
203
204
205
206
207
208
209
210
211
212
    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,
213
        ``simname_cache.h5``.
214
215
216
        """
        if len(list(self.statistics.keys())) > 0:
            return None
Cristian Lalescu's avatar
Cristian Lalescu committed
217
218
219
        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())
Cristian Lalescu's avatar
Cristian Lalescu committed
220
221
222
223
224
225
226
227
                pp_file = self.get_cache_file()
                for k in ['t',
                          'energy(t)',
                          'energy(k)',
                          'enstrophy(t)',
                          'enstrophy(k)',
                          'R_ij(t)',
                          'vel_max(t)',
228
229
                          'renergy(t)',
                          'renstrophy(t)']:
Cristian Lalescu's avatar
Cristian Lalescu committed
230
231
232
233
234
235
                    if k in pp_file.keys():
                        self.statistics[k] = pp_file[k][...]
                self.statistics['kM'] = pp_file['kspace/kM'][...]
                self.statistics['dk'] = pp_file['kspace/dk'][...]
                self.statistics['kshell'] = pp_file['kspace/kshell'][...]
                self.statistics['nshell'] = pp_file['kspace/nshell'][...]
Cristian Lalescu's avatar
Cristian Lalescu committed
236
237
238
239
240
241
242
243
244
        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):
245
                    iter1 = data_file['iteration'][...]
Cristian Lalescu's avatar
Cristian Lalescu committed
246
                else:
247
                    iter1 = min(data_file['iteration'][...], iter1)
Cristian Lalescu's avatar
Cristian Lalescu committed
248
249
                ii0 = iter0 // self.parameters['niter_stat']
                ii1 = iter1 // self.parameters['niter_stat']
250
251
                self.statistics['kshell'] = data_file['kspace/kshell'][...]
                self.statistics['nshell'] = data_file['kspace/nshell'][...]
Cristian Lalescu's avatar
Cristian Lalescu committed
252
253
254
                for kk in [-1, -2]:
                    if (self.statistics['kshell'][kk] == 0):
                        self.statistics['kshell'][kk] = np.nan
255
256
                self.statistics['kM'] = data_file['kspace/kM'][...]
                self.statistics['dk'] = data_file['kspace/dk'][...]
Cristian Lalescu's avatar
Cristian Lalescu committed
257
258
259
260
261
262
                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():
263
264
                    computation_needed =  not (ii0 == pp_file['ii0'][...] and
                                               ii1 == pp_file['ii1'][...])
Cristian Lalescu's avatar
Cristian Lalescu committed
265
                    if computation_needed:
266
267
268
                        for k in ['t', 'vel_max(t)',
                                  'renergy(t)',
                                  'renstrophy(t)',
Cristian Lalescu's avatar
Cristian Lalescu committed
269
270
271
272
273
274
275
276
277
                                  '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:
Cristian Lalescu's avatar
Cristian Lalescu committed
278
                    #TODO figure out whether normalization is sane or not
Cristian Lalescu's avatar
Cristian Lalescu committed
279
280
281
282
283
284
285
                    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)))
Cristian Lalescu's avatar
Cristian Lalescu committed
286
287
288
                    # we have an extra division by shell_width because of the Dirac delta restricting integration to the shell
                    phi_ij = data_file['statistics/spectra/velocity_velocity'][ii0:ii1+1] / self.statistics['dk']
                    pp_file['R_ij(t)'] = np.sum(phi_ij*self.statistics['dk'], axis = 1)
Cristian Lalescu's avatar
Cristian Lalescu committed
289
290
291
292
                    energy_tk = (
                        phi_ij[:, :, 0, 0] +
                        phi_ij[:, :, 1, 1] +
                        phi_ij[:, :, 2, 2])/2
Cristian Lalescu's avatar
Cristian Lalescu committed
293
294
295
296
297
                    pp_file['energy(t)'] = np.sum(energy_tk*self.statistics['dk'], axis = 1)
                    # normalization factor is (4 pi * shell_width * kshell^2) / (nmodes in shell * dkx*dky*dkz)
                    norm_factor = (4*np.pi*self.statistics['dk']*self.statistics['kshell']**2) / (self.parameters['dkx']*self.parameters['dky']*self.parameters['dkz']*self.statistics['nshell'])
                    pp_file['energy(k)'] = np.mean(energy_tk, axis = 0)*norm_factor
                    phi_vorticity_ij = data_file['statistics/spectra/vorticity_vorticity'][ii0:ii1+1] / self.statistics['dk']
Cristian Lalescu's avatar
Cristian Lalescu committed
298
                    enstrophy_tk = (
299
300
301
                        phi_vorticity_ij[:, :, 0, 0] +
                        phi_vorticity_ij[:, :, 1, 1] +
                        phi_vorticity_ij[:, :, 2, 2])/2
Cristian Lalescu's avatar
Cristian Lalescu committed
302
303
                    pp_file['enstrophy(t)'] = np.sum(enstrophy_tk*self.statistics['dk'], axis = 1)
                    pp_file['enstrophy(k)'] = np.mean(enstrophy_tk, axis = 0)*norm_factor
Cristian Lalescu's avatar
Cristian Lalescu committed
304
305
                    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
306
                    pp_file['renstrophy(t)'] = data_file['statistics/moments/vorticity'][ii0:ii1+1, 2, 3]/2
Cristian Lalescu's avatar
Cristian Lalescu committed
307
308
309
310
311
312
313
        for k in ['t',
                  'energy(t)',
                  'energy(k)',
                  'enstrophy(t)',
                  'enstrophy(k)',
                  'R_ij(t)',
                  'vel_max(t)',
314
315
                  'renergy(t)',
                  'renstrophy(t)']:
Cristian Lalescu's avatar
Cristian Lalescu committed
316
            if k in pp_file.keys():
317
                self.statistics[k] = pp_file[k][...]
Cristian Lalescu's avatar
Cristian Lalescu committed
318
319
320
321
        # sanity check --- Parseval theorem check
        assert(np.max(np.abs(
                self.statistics['renergy(t)'] -
                self.statistics['energy(t)']) / self.statistics['energy(t)']) < 1e-5)
322
323
324
        assert(np.max(np.abs(
                self.statistics['renstrophy(t)'] -
                self.statistics['enstrophy(t)']) / self.statistics['enstrophy(t)']) < 1e-5)
Cristian Lalescu's avatar
Cristian Lalescu committed
325
        self.compute_time_averages()
326
        return None
327
328
    def compute_Reynolds_stress_invariants(
            self):
Cristian Lalescu's avatar
Cristian Lalescu committed
329
330
331
        """
        see Choi and Lumley, JFM v436 p59 (2001)
        """
332
333
334
335
336
337
338
339
        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
340
341
342
343
    def compute_time_averages(self):
        """Compute easy stats.

        Further computation of statistics based on the contents of
344
        ``simname_cache.h5``.
345
346
347
348
349
350
        Standard quantities are as follows
        (consistent with [Ishihara]_):

        .. math::

            U_{\\textrm{int}}(t) = \\sqrt{\\frac{2E(t)}{3}}, \\hskip .5cm
351
352
353
            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}}}
354
355
356
357
358
359
360
361
362
363
364
365
366
367
368
369
370
371
372

            \\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',
373
374
                    'mean_trS2',
                    'Uint']:
375
376
            if key + '(t)' in self.statistics.keys():
                self.statistics[key] = np.average(self.statistics[key + '(t)'], axis = 0)
377
        self.statistics['vel_max'] = np.max(self.statistics['vel_max(t)'])
378
379
380
381
382
383
384
385
386
387
388
389
390
391
392
393
394
        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
395
        self.statistics['Lint'] = ((np.pi /
396
                                    (2*self.statistics['Uint']**2)) *
397
398
                                   np.sum(self.statistics['energy(k)'][1:-2] /
                                          self.statistics['kshell'][1:-2]))
399
400
401
        self.statistics['Re'] = (self.statistics['Uint'] *
                                 self.statistics['Lint'] /
                                 self.parameters['nu'])
402
403
404
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
        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,
Cristian Lalescu's avatar
Cristian Lalescu committed
438
            iter0 = 0):
439
440
441
        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)
442
        if self.dns_type in ['NSVEparticles_no_output', 'NSVEcomplex_particles', 'NSVEparticles', 'static_field']:
443
444
            assert (self.parameters['niter_todo'] % self.parameters['niter_part'] == 0)
            assert (self.parameters['niter_out']  % self.parameters['niter_part'] == 0)
445
446
        _code.write_par(self, iter0 = iter0)
        with h5py.File(self.get_data_file_name(), 'r+') as ofile:
447
            ofile['code_info/exec_name'] = self.name
448
449
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
            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)
Cristian Lalescu's avatar
Cristian Lalescu committed
485
        if (self.dns_type in ['NSVE', 'NSVE_no_output']):
486
487
            return None
        return None
488
    def job_parser_arguments(
489
490
            self,
            parser):
491
492
493
494
495
496
497
498
499
500
501
502
503
504
505
506
507
508
509
        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)
510
511
512
513
        parser.add_argument(
                '--no-debug',
                action = 'store_true',
                dest = 'no_debug')
514
515
516
517
518
519
520
521
522
523
524
525
526
527
528
529
530
531
532
533
534
535
536
537
538
539
540
541
        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(
542
               '-n', '--grid-size',
543
544
545
546
547
               type = int,
               dest = 'n',
               default = 32,
               metavar = 'N',
               help = 'code is run by default in a grid of NxNxN')
548
549
550
551
552
553
554
555
        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))
556
557
558
559
560
561
562
563
564
        parser.add_argument(
                '--wd',
                type = str, dest = 'work_dir',
                default = './')
        parser.add_argument(
                '--precision',
                choices = ['single', 'double'],
                type = str,
                default = 'single')
565
566
567
568
569
570
571
572
573
574
575
576
577
578
579
580
581
582
583
584
585
586
587
588
589
590
        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')
591
592
593
594
        return None
    def particle_parser_arguments(
            self,
            parser):
595
596
597
598
599
600
601
602
603
604
605
606
607
608
609
610
611
612
613
614
615
616
617
618
619
        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
620
621
622
623
624
625
626
627
628
629
630
631
632
633
    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)

634
635
636
637
638
639
640
641
642
643
        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')
644

645
646
647
648
        parser_static_field = subparsers.add_parser(
                'static_field',
                help = 'static field with basic fluid tracers')

649
650
651
        parser_NSVEp2 = subparsers.add_parser(
                'NSVEparticles',
                help = 'plain Navier-Stokes vorticity formulation, with basic fluid tracers')
652
653

        parser_NSVEp2p = subparsers.add_parser(
654
655
                'NSVEcomplex_particles',
                help = 'plain Navier-Stokes vorticity formulation, with oriented active particles')
Cristian Lalescu's avatar
Cristian Lalescu committed
656

657
658
659
        parser_NSVEp_extra = subparsers.add_parser(
                'NSVEp_extra_sampling',
                help = 'plain Navier-Stokes vorticity formulation, with basic fluid tracers, that sample velocity gradient, as well as pressure and its derivatives.')
sniklas142's avatar
sniklas142 committed
660
661
662
        parser_NSVE_ou = subparsers.add_parser(
                'NSVE_ou_forcing',
                help = 'plain Navier-Stokes vorticity formulation, with ornstein-uhlenbeck forcing')
663
        for parser in ['NSVEparticles_no_output', 'NSVEp2', 'NSVEp2p', 'NSVEp_extra', 'static_field']:
Cristian Lalescu's avatar
Cristian Lalescu committed
664
665
666
667
668
669
670
            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))
671
        return None
672
673
    def prepare_launch(
            self,
674
675
            args = [],
            extra_parameters = None):
676
677
678
679
680
681
682
683
684
685
686
687
688
689
690
691
692
693
694
695
696
697
698
699
700
701
        """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)
702
703
        self.set_precision(opt.precision)
        self.dns_type = opt.DNS_class
704
        self.name = self.dns_type + '-' + self.fluid_precision + '-v' + TurTLE.__version__
705
        # merge parameters if needed
706
        if self.dns_type in ['NSVEparticles', 'NSVEcomplex_particles', 'NSVEparticles_no_output', 'NSVEp_extra_sampling', 'static_field']:
707
708
            for k in self.NSVEp_extra_parameters.keys():
                self.parameters[k] = self.NSVEp_extra_parameters[k]
709
710
711
712
        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]
713
714
715
716
        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)
717
718
719
720
        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
721
        if type(opt.dkz) == type(None):
722
            opt.dkz = 2. / opt.Lz
723
724
725
726
727
728
        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
729
730
731
732
733
734
        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
735
        if type(opt.dealias_type) == type(None):
736
            opt.dealias_type = self.parameters['dealias_type']
737
738
739
740
741
        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
742
        self.parameters['dt'] = (opt.dtfactor / opt.n)
743
        self.parameters['nu'] = (opt.kMeta * 2 / opt.n)**(4./3)
Cristian Lalescu's avatar
Cristian Lalescu committed
744
745
746
747
748
        # 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
749
        if opt.forcing_type == 'linear':
750
751
752
753
754
            # 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
755
        elif opt.forcing_type == 'fixed_energy_injection_rate':
756
757
            # use the fact that mean dissipation rate is equal to injection rate
            self.parameters['nu'] = (
Cristian Lalescu's avatar
Cristian Lalescu committed
758
                    opt.injection_rate *
759
                    (opt.kMeta / kM)**4)**(1./3)
760
        elif opt.forcing_type == 'fixed_energy':
Cristian Lalescu's avatar
Cristian Lalescu committed
761
762
            kf = 1. / (1./opt.fk0 +
                       1./opt.fk1)
763
764
765
766
            self.parameters['nu'] = (
                    (opt.kMeta / kM)**(4./3) *
                    (np.pi / kf)**(1./3) *
                    (2*self.parameters['energy'] / 3)**0.5)
767
768
769
770
        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
771
            if self.dns_type in ['static_field', 'NSVEparticles', 'NSVEcomplex_particles', 'NSVEparticles_no_output', 'NSVEp_extra_sampling']:
772
773
774
                rhs_size = self.parameters['tracers0_integration_steps']
                if type(opt.tracers0_integration_steps) != type(None):
                    rhs_size = opt.tracers0_integration_steps
775
776
777
778
                nparticles = opt.nparticles
                if type(nparticles) == type(None):
                    nparticles = self.NSVEp_extra_parameters['nparticles']
                particle_size = (1+rhs_size)*3*nparticles*8
779
780
781
                checkpoint_size += particle_size
            if checkpoint_size < 1e9:
                opt.checkpoints_per_file = int(1e9 / checkpoint_size)
782
783
784
785
786
787
788
789
790
791
792
793
794
        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')
795
    def get_checkpoint_fname(self, iteration = 0):
796
        checkpoint = (iteration // self.parameters['niter_out']) // self.parameters['checkpoints_per_file']
797
798
799
        return os.path.join(
                    self.work_dir,
                    self.simname + '_checkpoint_{0}.h5'.format(checkpoint))
800
801
802
    def generate_tracer_state(
            self,
            rseed = None,
803
804
805
            species = 0,
            integration_steps = None,
            ncomponents = 3):
806
        try:
807
808
809
810
811
            if type(integration_steps) == type(None):
                integration_steps = self.NSVEp_extra_parameters['tracers0_integration_steps']
            if 'tracers{0}_integration_steps'.format(species) in self.parameters.keys():
                integration_steps = self.parameters['tracers{0}_integration_steps'.format(species)]
            if self.dns_type == 'NSVEcomplex_particles' and species == 0:
812
813
814
                ncomponents = 6
            with h5py.File(self.get_checkpoint_0_fname(), 'a') as data_file:
                nn = self.parameters['nparticles']
815
816
817
818
                if not 'tracers{0}'.format(species) in data_file.keys():
                    data_file.create_group('tracers{0}'.format(species))
                    data_file.create_group('tracers{0}/rhs'.format(species))
                    data_file.create_group('tracers{0}/state'.format(species))
819
820
                data_file['tracers{0}/rhs'.format(species)].create_dataset(
                        '0',
821
                        shape = (integration_steps, nn, ncomponents,),
822
                        dtype = np.float)
823
                dset = data_file['tracers{0}/state'.format(species)].create_dataset(
824
825
826
827
828
829
830
831
832
833
834
835
836
837
838
839
840
841
842
843
844
845
846
847
848
849
850
851
852
                        '0',
                        shape = (nn, ncomponents,),
                        dtype = np.float)
                if not type(rseed) == type(None):
                    np.random.seed(rseed)
                cc = int(0)
                batch_size = int(1e6)
                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))
                    bla  /= np.sum(bla**2, axis = 1)[:, None]**.5
                    return bla
                while nn > 0:
                    if nn > batch_size:
                        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)
                        nn -= batch_size
                    else:
                        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)
                        nn = 0
                    cc += 1
        except Exception as e:
            print(e)
853
        return None
854
855
856
857
858
859
860
861
862
863
864
865
866
867
868
869
870
871
872
873
874
875
876
877
    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.
878
879
            Possible values: TurTLE.tools.generate_data_3D,
            TurTLE.tools.generate_data_3D_uniform
880
881
882
883
884
885
886
887
888
889
890
891
892
893
894
895
896
        :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(
897
898
899
                self.parameters['nz'],
                self.parameters['ny'],
                self.parameters['nx'],
900
901
902
                p = spectra_slope,
                amplitude = amplitude).astype(self.ctype)
        Kdata01 = scalar_generator(
903
904
905
                self.parameters['nz'],
                self.parameters['ny'],
                self.parameters['nx'],
906
907
908
                p = spectra_slope,
                amplitude = amplitude).astype(self.ctype)
        Kdata02 = scalar_generator(
909
910
911
                self.parameters['nz'],
                self.parameters['ny'],
                self.parameters['nx'],
912
913
914
915
916
917
918
919
920
921
922
923
924
925
926
927
928
929
                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
930
931
932
933
934
935
936
937
938
939
940
    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
941
942
        dst_shape = (self.parameters['ny'],
                     self.parameters['nz'],
943
944
945
946
                     (self.parameters['nx']+2) // 2,
                     3)
        src_file = h5py.File(src_file_name, 'r')
        if (src_file[src_dset_name].shape == dst_shape):
947
948
949
            dst_file[dst_dset_name] = h5py.ExternalLink(
                    src_file_name,
                    src_dset_name)
950
951
952
953
954
        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)
955
            src_shape = src_file[src_dset_name].shape
956
957
958
            dst_file.create_dataset(
                    dst_dset_name,
                    shape = dst_shape,
959
960
                    dtype = np.dtype(self.ctype),
                    fillvalue = complex(0))
961
962
963
964
965
966
967
968
969
970
971
972
973
974
975
976
977
978
            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]]
979
        return None
980
981
982
983
984
985
986
987
988
    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
989
                    particle_file.create_group('tracers0/position')
990
991
                    particle_file.create_group('tracers0/velocity')
                    particle_file.create_group('tracers0/acceleration')
992
                    if self.dns_type in ['NSVEcomplex_particles']:
993
                        particle_file.create_group('tracers0/orientation')
994
                        particle_file.create_group('tracers0/velocity_gradient')
995
996
997
998
999
                    if self.dns_type in ['NSVEp_extra_sampling']:
                        particle_file.create_group('tracers0/velocity_gradient')
                        particle_file.create_group('tracers0/pressure')
                        particle_file.create_group('tracers0/pressure_gradient')
                        particle_file.create_group('tracers0/pressure_Hessian')
1000
        return None
Cristian Lalescu's avatar
Cristian Lalescu committed
1001
    def generate_initial_condition(
1002
            self,
Cristian Lalescu's avatar
Cristian Lalescu committed
1003
1004
1005
1006
1007
1008
1009
1010
1011
1012
1013
1014
1015
1016
1017
            opt = None):
        # take care of fields' initial condition
        # first, check if initial field exists
        need_field = False
        if not os.path.exists(self.get_checkpoint_0_fname()):
            need_field = True
        else:
            f = h5py.File(self.get_checkpoint_0_fname(), 'r')
            try:
                dset = f['vorticity/complex/0']
                need_field = (dset.shape == (self.parameters['ny'],
                                             self.parameters['nz'],
                                             self.parameters['nx']//2+1,
                                             3))
            except:
1018
                need_field = True
Cristian Lalescu's avatar
Cristian Lalescu committed
1019
1020
1021
1022
1023
1024
1025
1026
1027
1028
1029
1030
1031
1032
1033
1034
1035
1036
1037
1038
            f.close()
        if need_field:
            f = h5py.File(self.get_checkpoint_0_fname(), 'a')
            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
                self.copy_complex_field(
                        src_file,
                        'vorticity/complex/{0}'.format(opt.src_iteration),
                        f,
                        'vorticity/complex/{0}'.format(0))
1039
            else:
Cristian Lalescu's avatar
Cristian Lalescu committed
1040
1041
1042
1043
1044
1045
1046
                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()
        # now take care of particles' initial condition
1047
        if self.dns_type in ['static_field', 'NSVEparticles', 'NSVEcomplex_particles', 'NSVEparticles_no_output', 'NSVEp_extra_sampling']:
Cristian Lalescu's avatar
Cristian Lalescu committed
1048
1049
1050
1051
1052
1053
            self.generate_particle_data(opt = opt)
        return None
    def launch_jobs(
            self,
            opt = None):
        if not os.path.exists(self.get_data_file_name()):
1054
            self.generate_initial_condition(opt = opt)
Cristian Lalescu's avatar
Cristian Lalescu committed
1055
            self.write_par()
1056
1057
1058
1059
1060
1061
        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,
1062
1063
                no_submit = opt.no_submit,
                no_debug = opt.no_debug)
1064
        return None