forked from qrefine/qrefine
/
calculator.py
540 lines (499 loc) · 19.1 KB
/
calculator.py
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
171
172
173
174
175
176
177
178
179
180
181
182
183
184
185
186
187
188
189
190
191
192
193
194
195
196
197
198
199
200
201
202
203
204
205
206
207
208
209
210
211
212
213
214
215
216
217
218
219
220
221
222
223
224
225
226
227
228
229
230
231
232
233
234
235
236
237
238
239
240
241
242
243
244
245
246
247
248
249
250
251
252
253
254
255
256
257
258
259
260
261
262
263
264
265
266
267
268
269
270
271
272
273
274
275
276
277
278
279
280
281
282
283
284
285
286
287
288
289
290
291
292
293
294
295
296
297
298
299
300
301
302
303
304
305
306
307
308
309
310
311
312
313
314
315
316
317
318
319
320
321
322
323
324
325
326
327
328
329
330
331
332
333
334
335
336
337
338
339
340
341
342
343
344
345
346
347
348
349
350
351
352
353
354
355
356
357
358
359
360
361
362
363
364
365
366
367
368
369
370
371
372
373
374
375
376
377
378
379
380
381
382
383
384
385
386
387
388
389
390
391
392
393
394
395
396
397
398
399
400
401
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
438
439
440
441
442
443
444
445
446
447
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
485
486
487
488
489
490
491
492
493
494
495
496
497
498
499
500
501
502
503
504
505
506
507
508
509
510
511
512
513
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
"""This module handles the weight factors and scaling.
An adaptive restraints weight factor calculator is implemented, whereby the
weight factor is doubled if a sufficiently large bond-RMSD is observed.
Conversely, if a sufficiently small bond-RMSD is observed, then the weight
factor is halved.
"""
from __future__ import division
from __future__ import print_function
from __future__ import absolute_import
import random, time
from cctbx import xray
from libtbx import adopt_init_args
from scitbx.array_family import flex
import cctbx.maptbx.real_space_refinement_simple
import scitbx.lbfgs
from libtbx import group_args
from . import refine
from mmtbx.validation.ramalyze import ramalyze
from mmtbx.validation.cbetadev import cbetadev
from mmtbx.validation.rotalyze import rotalyze
from mmtbx.validation.clashscore import clashscore
from libtbx.utils import null_out
from cctbx import maptbx
def get_bonds_rmsd(restraints_manager, xrs):
hd_sel = xrs.hd_selection()
energies_sites = \
restraints_manager.select(~hd_sel).energies_sites(
sites_cart = xrs.sites_cart().select(~hd_sel),
compute_gradients = False)
return energies_sites.bond_deviations()[2]
class weights(object):
def __init__(self,
shake_sites = True,
restraints_weight = None,
data_weight = None,
restraints_weight_scale = 1.0):
adopt_init_args(self, locals())
if(self.data_weight is not None):
self.weight_was_provided = True
else:
self.weight_was_provided = False
self.restraints_weight_scales = flex.double([self.restraints_weight_scale])
self.r_frees = []
self.r_works = []
def scale_restraints_weight(self):
if(self.weight_was_provided): return
self.restraints_weight_scale *= 4.0
def adjust_restraints_weight_scale(
self,
fmodel,
geometry_rmsd_manager,
max_bond_rmsd,
scale):
adjusted = None
if(self.weight_was_provided): return adjusted
rw = fmodel.r_work()
rf = fmodel.r_free()
cctbx_rm_bonds_rmsd = get_bonds_rmsd(
restraints_manager = geometry_rmsd_manager.geometry,
xrs = fmodel.xray_structure)
####
adjusted = False
if(cctbx_rm_bonds_rmsd>max_bond_rmsd):
self.restraints_weight_scale *= scale
adjusted = True
if(not adjusted and rf<rw):
self.restraints_weight_scale /= scale
adjusted = True
if(not adjusted and cctbx_rm_bonds_rmsd<max_bond_rmsd and rf>rw and
abs(rf-rw)*100.<5.):
self.restraints_weight_scale /= scale
adjusted = True
if(not adjusted and cctbx_rm_bonds_rmsd<max_bond_rmsd and rf>rw and
abs(rf-rw)*100.>5.):
self.restraints_weight_scale *= scale
adjusted = True
####
self.r_frees.append(round(rf,4))
self.r_works.append(round(rw,4))
return adjusted
def add_restraints_weight_scale_to_restraints_weight_scales(self):
if(self.weight_was_provided): return
self.restraints_weight_scales.append(self.restraints_weight_scale)
def compute_weight(self, fmodel, rm, verbose=False):
if(self.weight_was_provided): return
random.seed(1)
flex.set_random_seed(1)
#
fmodel_dc = fmodel.deep_copy()
xrs = fmodel_dc.xray_structure.deep_copy_scatterers()
if(self.shake_sites):
xrs.shake_sites_in_place(mean_distance=0.2)
fmodel_dc.update_xray_structure(xray_structure=xrs, update_f_calc=True)
x_target_functor = fmodel_dc.target_functor()
tgx = x_target_functor(compute_gradients=True)
gx = flex.vec3_double(tgx.\
gradients_wrt_atomic_parameters(site=True).packed())
tc, gc = rm.target_and_gradients(sites_cart=xrs.sites_cart())
x = gc.norm()
y = gx.norm()
if verbose: print('>>> gradient norms c,x %0.2f %0.2f' % (x, y))
# filter out large contributions
gx_d = flex.sqrt(gx.dot())
sel = gx_d>flex.mean(gx_d)*6
y = gx.select(~sel).norm()
#
gc_d = flex.sqrt(gc.dot())
sel = gc_d>flex.mean(gc_d)*6
x = gc.select(~sel).norm()
################
if(y != 0.0): self.data_weight = x/y
else: self.data_weight = 1.0 # ad hoc default fallback
if verbose: print('>>> data_weight %0.2f' % self.data_weight)
class calculator(object):
def __init__(self,
fmodel=None,
xray_structure=None,
restraints_weight_scale = 1.0):
assert [fmodel, xray_structure].count(None)==1
self.fmodel=None
self.xray_structure=None
if(fmodel is not None):
self.fmodel = fmodel
if(xray_structure is not None):
self.xray_structure = xray_structure
self.restraints_weight_scale = restraints_weight_scale
def update_fmodel(self):
if(self.fmodel is not None):
self.fmodel.xray_structure.tidy_us()
self.fmodel.xray_structure.apply_symmetry_sites()
self.fmodel.update_xray_structure(
xray_structure = self.fmodel.xray_structure,
update_f_calc = True,
update_f_mask = True)
self.fmodel.update_all_scales(remove_outliers=False)
else:
self.xray_structure.tidy_us()
self.xray_structure.apply_symmetry_sites()
class sites_opt(object):
"""
General calculator for model geometry optimization. For native CCTBX
restraints, restraints_manager and model.restraints_manager are the same
things.
However, restraints_manager can be an external entity, such as coming from
external packeges (eg., QM).
Ideally, and this is probably a TODO item for the future, any
restraints_manager should always be in the model.
dump_gradients is used for debugging.
"""
def __init__(self, model, max_shift, restraints_manager, shift_eval,
dump_gradients=False, convergence_threshold=1.e-3,
convergence_reached_times=3):
self.model = model
self.restraints_manager = restraints_manager
self.dump_gradients = dump_gradients
self.convergence_threshold = convergence_threshold
self.convergence_reached_times = convergence_reached_times
self.meat_convergence_criteria = 0
self.x = flex.double(self.model.size()*3, 0)
self.n = self.x.size()
self.f = None
self.g = None
self.f_start = None
self.max_shift_between_resets = 0
self.sites_cart = self.model.get_sites_cart()
self.sites_cart_start = self.sites_cart.deep_copy()
self.bound_flags = flex.int(self.n, 2)
self.lower_bound = flex.double([-1*max_shift]*self.n)
self.upper_bound = flex.double([ max_shift]*self.n)
self.shift_eval = shift_eval
assert self.shift_eval in ["max", "mean"]
if(self.shift_eval == "mean"): self.shift_eval_func = flex.mean
else: self.shift_eval_func = flex.max
def target_and_gradients(self):
sites_plus_x = self.sites_cart+flex.vec3_double(self.x)
self.f, self.g = self.restraints_manager.target_and_gradients(
sites_cart = sites_plus_x)
self.g = self.g.as_double()
# For tests
if(self.dump_gradients):
from libtbx import easy_pickle
easy_pickle.dump(self.dump_gradients, self.g)
STOP()
#
if(self.f_start is None):
self.f_start = self.f
self.max_shift_between_resets = self.shift_eval_func(flex.sqrt((
self.sites_cart - sites_plus_x).dot()))
return self.f, self.g
def compute_functional_and_gradients(self):
return self.target_and_gradients()
def apply_x(self):
self.f_start = self.f
self.model.set_sites_cart(
sites_cart = self.sites_cart+flex.vec3_double(self.x))
self.x = flex.double(self.model.size()*3, 0)
self.sites_cart = self.model.get_sites_cart()
if(self.max_shift_between_resets < self.convergence_threshold):
self.meat_convergence_criteria += 1
def converged(self):
if(self.meat_convergence_criteria >= self.convergence_reached_times):
return True
return False
def mean_shift_from_start(self):
# Assumes apply_x has been called before so that self.sites_cart are updated
return flex.mean(flex.sqrt((
self.sites_cart - self.sites_cart_start).dot()))
def __call__(self):
f, g = self.target_and_gradients()
return self.x, f, g
class sites(calculator):
def __init__(self,
fmodel=None,
restraints_manager=None,
weights=None,
dump_gradients=None):
adopt_init_args(self, locals())
self.x = None
self.x_target_functor = None
self.not_hd_selection = None # XXX UGLY
self.initialize(fmodel = self.fmodel)
def initialize(self, fmodel=None):
self.not_hd_selection = ~self.fmodel.xray_structure.hd_selection() # XXX UGLY
assert fmodel is not None
self.fmodel = fmodel
self.fmodel.xray_structure.scatterers().flags_set_grads(state=False)
xray.set_scatterer_grad_flags(
scatterers = self.fmodel.xray_structure.scatterers(),
site = True)
self.x = self.fmodel.xray_structure.sites_cart().as_double()
self.x_target_functor = self.fmodel.target_functor()
def calculate_weight(self, verbose=False):
self.weights.compute_weight(
fmodel = self.fmodel,
rm = self.restraints_manager,
verbose = verbose)
def reset_fmodel(self, fmodel=None):
if(fmodel is not None):
self.initialize(fmodel=fmodel)
self.fmodel = fmodel
self.update_fmodel()
def update_restraints_weight_scale(self, restraints_weight_scale):
self.weights.restraints_weight_scale = restraints_weight_scale
def update(self, x):
self.x = flex.vec3_double(x)
self.fmodel.xray_structure.set_sites_cart(sites_cart = self.x)
self.fmodel.update_xray_structure(
xray_structure = self.fmodel.xray_structure,
update_f_calc = True)
def target_and_gradients(self, x):
self.update(x = x)
rt, rg = self.restraints_manager.target_and_gradients(sites_cart = self.x)
tgx = self.x_target_functor(compute_gradients=True)
dt = tgx.target_work()
dg = flex.vec3_double(tgx.\
gradients_wrt_atomic_parameters(site=True).packed())
t = dt*self.weights.data_weight + \
self.weights.restraints_weight*rt*self.weights.restraints_weight_scale
g = dg*self.weights.data_weight + \
self.weights.restraints_weight*rg*self.weights.restraints_weight_scale
if(self.dump_gradients is not None):
from libtbx import easy_pickle
easy_pickle.dump(self.dump_gradients+"_dg", dg.as_double())
easy_pickle.dump(self.dump_gradients+"_rg", rg.as_double())
easy_pickle.dump(self.dump_gradients+"_g", g.as_double())
STOP()
return t, g.as_double()
class adp(calculator):
def __init__(self,
fmodel=None,
restraints_manager=None,
restraints_weight=None,
data_weight=None,
restraints_weight_scale=None):
adopt_init_args(self, locals())
self.x = None
self.x_target_functor = None
self.initialize(fmodel = self.fmodel)
def initialize(self, fmodel=None):
assert fmodel is not None
self.fmodel = fmodel
self.fmodel.xray_structure.scatterers().flags_set_grads(state=False)
assert self.fmodel.xray_structure.scatterers().size() == \
self.fmodel.xray_structure.use_u_iso().count(True)
sel = flex.bool(
self.fmodel.xray_structure.scatterers().size(), True).iselection()
self.fmodel.xray_structure.scatterers().flags_set_grad_u_iso(iselection=sel)
self.x = fmodel.xray_structure.extract_u_iso_or_u_equiv()
self.x_target_functor = self.fmodel.target_functor()
def calculate_weight(self):
raise RuntimeError("Not implemented.")
self.data_weight = compute_weight(
fmodel = self.fmodel,
rm = self.restraints_manager)
def reset_fmodel(self, fmodel=None):
if(fmodel is not None):
self.initialize(fmodel=fmodel)
self.fmodel = fmodel
def update(self, x):
self.x = x
self.fmodel.xray_structure.set_u_iso(values = self.x)
self.fmodel.update_xray_structure(
xray_structure = self.fmodel.xray_structure,
update_f_calc = True)
def target_and_gradients(self, x):
self.update(x = x)
tgx = self.x_target_functor(compute_gradients=True)
f = tgx.target_work()
g = tgx.gradients_wrt_atomic_parameters(u_iso=True)
return f, g
class sites_real_space(object):
def __init__(self,
model,
geometry_rmsd_manager,
max_bond_rmsd,
stpmax,
gradient_only,
line_search,
data_weight,
refine_cycles,
skip_weight_search,
log,
map_data=None,
restraints_manager=None,
max_iterations=None):
adopt_init_args(self, locals())
self.gradient_only = True
self.max_iterations = 100
self.weight = data_weight
self.sites_cart_start = self.model.get_xray_structure().sites_cart()
self.show(model=self.model, weight = data_weight)
#
self.rama_fav_best = None
self.cbeta_best = None
self.rota_best = None
self.clash_best = None
#
if(self.weight is None):
self.weight = 1.
self.refine_cycles = refine_cycles
self.skip_weight_search = skip_weight_search
self.lbfgs_termination_params = scitbx.lbfgs.termination_parameters(
max_iterations = self.max_iterations)
self.lbfgs_core_params = scitbx.lbfgs.core_parameters(
stpmin = 1.e-9,
stpmax = stpmax)
self.lbfgs_exception_handling_params = scitbx.lbfgs.\
exception_handling_parameters(
ignore_line_search_failed_step_at_lower_bound = False,
ignore_line_search_failed_step_at_upper_bound = False,
ignore_line_search_failed_maxfev = False)
self.sites_cart_refined = None
self.cctbx_rm_bonds_rmsd = get_bonds_rmsd(
restraints_manager = self.geometry_rmsd_manager.geometry,
xrs = self.model.get_xray_structure())
def get_shift(self, other):
s1 = self.sites_cart_start
s2 = other.sites_cart()
return flex.mean(flex.sqrt((s1 - s2).dot()))
def get_shift2(self, m1, m2):
s1 = m1.get_sites_cart()
s2 = m2.get_sites_cart()
return flex.mean(flex.sqrt((s1 - s2).dot()))
#def ready_to_stop(self, sc):
# return (sc.rama_fav < self.rama_fav_best and
# abs(sc.rama_fav-self.rama_fav_best)>1.) or \
# sc.cbeta > self.cbeta_best or \
# sc.rota > self.rota_best or \
# (sc.clash > self.clash_best and
# abs(sc.clash-self.clash_best)>1.)
def geometry_is_good(self, stats):
b, a = stats.bond().mean, stats.angle().mean
#return round(b, 2) <= 0.01 or round(a, 1) <= 1.5
return round(b, 2) <= 0.01 and round(a, 1) <= 1.5
def macro_cycle(self, weight):
def stalled(x):
for it in x:
if x.count(it) > 3: return True
return False
up = 0
down = 0
slow_down = 0
previous_good = None
bond_rmsds = []
cntr = 0
while True: # Endless loop!
print("cycle:", cntr)
cntr+=1
stats = self.show(model = self.model, weight = weight, prefix=" start:")
model = self.run_one(weight = weight)
stats = self.show(model = model, weight = weight, prefix=" final:")
b = stats.bond().mean
bond_rmsds.append(round(b,3))
if(stalled(bond_rmsds)):
print("<<<<< weight optimization stalled: quitting >>>>>")
break
if(self.geometry_is_good(stats)):
up += 1
previous_good = weight
if(b<0.03 or stalled(bond_rmsds)): weight = weight*2
else:
weight = weight + 0.3*weight
self.model.set_sites_cart(sites_cart = model.get_sites_cart())
else:
down += 1
if(b>0.03):
weight = weight/2
else:
slow_down += 1
if(slow_down<3):
weight = weight - 0.3*weight
else:
weight = weight/2
slow_down = 0
if(up>0 and down>0):
print("<<<<< weight optimization oscillates: quitting >>>>>")
break
print()
if(previous_good is None): return
for it in [1,2,3,4,5]:
stats = self.show(model = self.model, weight = previous_good, prefix=" start:")
model = self.run_one(weight = previous_good)
stats = self.show(model = model, weight = previous_good, prefix=" final:")
if(self.geometry_is_good(stats)):
shift = self.get_shift2(model, self.model)
self.model.set_sites_cart(sites_cart = model.get_sites_cart())
if(shift<0.01):
print("<<<<< shift fell below 0.01: quitting >>>>>")
break
else:
print("<<<<< geometry got worse: quitting >>>>>")
break
def show(self, model, weight, prefix=""):
stats = model.geometry_statistics(use_hydrogens=False)
s = stats.show_short()
s = s.split()
s = " ".join(s)
dist = self.get_shift(other=model.get_xray_structure())
if(weight is not None): w = "%8.4f"%weight
else: w = "%5s"%str(None)
cc_mask = refine.show_cc(
map_data=self.map_data, xray_structure=model.get_xray_structure())
print(prefix, "weight=%s"%w, s, "shift=%6.4f"%dist, \
"cc_mask=%6.4f"%cc_mask)
return stats
def get_weight(self):
o = maptbx.target_and_gradients_simple(
unit_cell = self.model.crystal_symmetry().unit_cell(),
map_target = self.map_data,
sites_cart = self.model.get_sites_cart(),
selection = flex.bool(self.model.size(), True),
interpolation = "tricubic"
)
hd_sel = self.model.get_xray_structure().hd_selection()
g_map = o.gradients().select(~hd_sel)
_, g_geo = self.restraints_manager.target_and_gradients(
sites_cart = self.model.get_sites_cart())
g_geo = g_geo.select(~hd_sel)
#
g_map_d = flex.sqrt(g_map.dot())
sel = g_map_d>flex.mean(g_map_d)*3
y = g_map.select(~sel).norm()
#-
g_geo_d = flex.sqrt(g_geo.dot())
sel = g_geo_d>flex.mean(g_geo_d)*3
x = g_geo.select(~sel).norm()
#
#y = g_map.norm()
#
#x = g_geo.norm()
################
if(y != 0.0): return x/y
else: return 1.0 # ad hoc default fallback
def run(self):
weight = self.get_weight()
print("Initial weight estimate from ratio of grad norms:", weight)
self.macro_cycle(weight = weight)
return self.model
def run_one(self, weight):
model = self.model.deep_copy()
xrs = model.get_xray_structure()
uc = xrs.crystal_symmetry().unit_cell()
not_hd_sel = ~model.selection(string = "element H or element D")
refined = cctbx.maptbx.real_space_refinement_simple.lbfgs(
unit_cell = uc,
gradients_method = "tricubic",
sites_cart = xrs.sites_cart(),
density_map = self.map_data,
geometry_restraints_manager = self.restraints_manager,
selection_variable_real_space = not_hd_sel,
real_space_target_weight = weight,
real_space_gradients_delta = 0.25,
gradient_only = self.gradient_only,
line_search = self.line_search,
lbfgs_core_params = self.lbfgs_core_params,
lbfgs_termination_params = self.lbfgs_termination_params,
lbfgs_exception_handling_params = self.lbfgs_exception_handling_params)
model.set_sites_cart(sites_cart=refined.sites_cart)
return model