def exercise_real_to_complex_3d():
print "real_to_complex_3d"
for n_real,n_repeats in [((100,80,90),8),
((200,160,180),2),
((300,240,320),1)]:
print " dimensions:", n_real
print " repeats:", n_repeats
fft = fftpack.real_to_complex_3d(n_real)
m_real = fft.m_real()
np = n_real[0]*n_real[1]*n_real[2]
mp = m_real[0]*m_real[1]*m_real[2]
d0 = flex.random_double(size=mp)*2-1
d0.reshape(flex.grid(m_real).set_focus(n_real))
#
t0 = time.time()
for i_trial in xrange(n_repeats):
d = d0.deep_copy()
overhead = time.time()-t0
print " overhead: %.2f seconds" % overhead
#
t0 = time.time()
for i_trial in xrange(n_repeats):
d = d0.deep_copy()
c = fftw3tbx.real_to_complex_3d_in_place(data=d)
assert c.all() == fft.n_complex()
assert c.focus() == fft.n_complex()
assert c.id() == d.id()
r = fftw3tbx.complex_to_real_3d_in_place(data=c, n=n_real)
assert r.all() == fft.m_real()
assert r.focus() == fft.n_real()
assert r.id() == d.id()
print " fftw: %.2f seconds" % (time.time()-t0-overhead)
if (maptbx is not None):
maptbx.unpad_in_place(map=d)
rw = d / np
#
t0 = time.time()
for i_trial in xrange(n_repeats):
d = d0.deep_copy()
c = fftpack.real_to_complex_3d(n_real).forward(d)
assert c.all() == fft.n_complex()
assert c.focus() == fft.n_complex()
assert c.id() == d.id()
r = fftpack.real_to_complex_3d(n_real).backward(c)
assert r.all() == fft.m_real()
assert r.focus() == fft.n_real()
assert r.id() == d.id()
print " fftpack: %.2f seconds" % (time.time()-t0-overhead)
sys.stdout.flush()
if (maptbx is not None):
maptbx.unpad_in_place(map=d)
rp = d / np
#
assert flex.max(flex.abs(rw-rp)) < 1.e-6
def one_series(num_threads, n_iterations, quick=False):
if (num_threads > 0):
omptbx.env.num_threads = num_threads
print "working omptbx.env.num_threads:", omptbx.env.num_threads
use_wall_clock = (omptbx.env.num_threads > 1)
print "use_wall_clock:", use_wall_clock
#
if (quick):
dims = (2*3, 3*4, 4*5)
else:
dims = (2*3*5*7,3*4*5*7,3*4*5*5)
rfft = fftpack.real_to_complex_3d(dims)
print "rfft.m_real():", rfft.m_real()
#
t_map = time_log(label="map", use_wall_clock=use_wall_clock)
t_fill = time_log(label="fill", use_wall_clock=use_wall_clock)
t_fft = time_log(label="fft", use_wall_clock=use_wall_clock)
print t_map.legend
sys.stdout.flush()
for i_iteration in xrange(n_iterations):
t_map.start()
map = fftpack.zeros_parallel_double(flex_grid=flex.grid(rfft.m_real()))
print t_map.log()
sys.stdout.flush()
t_fill.start()
for i in xrange(0, map.size(), 97):
map[i] = random.random()
print t_fill.log()
sys.stdout.flush()
t_fft.start()
map = rfft.forward(map)
print t_fft.log()
sys.stdout.flush()
def direct_space_squaring(start, selection_fixed):
map_gridding = miller.index_span(
miller.set.expand_to_p1(start).indices()).map_grid()
if (selection_fixed is None):
fixed = start
var = start
else:
fixed = start.select(selection_fixed)
var = start.select(~selection_fixed)
rfft = fftpack.real_to_complex_3d([n * 3 // 2 for n in map_gridding])
conjugate_flag = True
structure_factor_map = maptbx.structure_factors.to_map(
space_group=fixed.space_group(),
anomalous_flag=fixed.anomalous_flag(),
miller_indices=fixed.indices(),
structure_factors=fixed.data(),
n_real=rfft.n_real(),
map_grid=flex.grid(rfft.n_complex()),
conjugate_flag=conjugate_flag)
real_map = rfft.backward(structure_factor_map.complex_map())
squared_map = flex.pow2(real_map)
squared_sf_map = rfft.forward(squared_map)
allow_miller_indices_outside_map = False
from_map = maptbx.structure_factors.from_map(
anomalous_flag=var.anomalous_flag(),
miller_indices=var.indices(),
complex_map=squared_sf_map,
conjugate_flag=conjugate_flag,
allow_miller_indices_outside_map=allow_miller_indices_outside_map)
if (selection_fixed is None):
return from_map.data()
result = start.data().deep_copy()
result.set_selected(~selection_fixed, from_map.data())
assert result.select(selection_fixed).all_eq(fixed.data())
return result
def run(args):
assert len(args) == 1
timer = time_log("pdb.input").start()
pdb_inp = iotbx.pdb.input(file_name=args[0])
print "number of pdb atoms:", pdb_inp.atoms().size()
print timer.log()
crystal_symmetry = pdb_inp.crystal_symmetry()
assert crystal_symmetry is not None
crystal_symmetry.show_summary()
assert crystal_symmetry.unit_cell() is not None
assert crystal_symmetry.space_group_info() is not None
sites_cart = pdb_inp.atoms().extract_xyz()
site_radii = flex.double(sites_cart.size(), 2.5)
crystal_gridding = maptbx.crystal_gridding(
unit_cell=crystal_symmetry.unit_cell(),
d_min=2,
resolution_factor=1/3)
fft = fftpack.real_to_complex_3d(crystal_gridding.n_real())
print "n_real:", fft.n_real()
print "m_real:", fft.m_real()
timer = time_log("grid_indices_around_sites").start()
grid_indices = maptbx.grid_indices_around_sites(
unit_cell=crystal_symmetry.unit_cell(),
fft_n_real=fft.n_real(),
fft_m_real=fft.m_real(),
sites_cart=sites_cart,
site_radii=site_radii)
print "grid_indices.size():", grid_indices.size()
print timer.log()
print "grid fraction:", \
grid_indices.size() / matrix.col(fft.n_real()).product()
def exercise_f000():
miller_indices = flex.miller_index([(0,0,0)])
data = flex.complex_double([1-2j])
n_real = [1,2,3]
conjugate_flag = True
for hall_symbol in ["P 1", "P 3", "R 3*"]:
for is_centric in [False, True]:
if (not is_centric):
space_group = sgtbx.space_group(hall_symbol)
else:
space_group.expand_smx("-x,-y,-z")
for anomalous_flag in [False, True]:
if (not anomalous_flag):
rfft = fftpack.real_to_complex_3d(n_real)
n_complex = rfft.n_complex()
else:
cfft = fftpack.complex_to_complex_3d(n_real)
n_complex = cfft.n()
for treat_restricted in [False, True]:
map = maptbx.structure_factors.to_map(
space_group=space_group,
anomalous_flag=anomalous_flag,
miller_indices=miller_indices,
structure_factors=data,
n_real=n_real,
map_grid=flex.grid(n_complex),
conjugate_flag=conjugate_flag,
treat_restricted=treat_restricted)
if (treat_restricted):
assert approx_equal(
map.complex_map()[0], data[0])
else:
assert approx_equal(
map.complex_map()[0], data[0]*space_group.order_p())
def exercise_f000():
miller_indices = flex.miller_index([(0, 0, 0)])
data = flex.complex_double([1 - 2j])
n_real = [1, 2, 3]
conjugate_flag = True
for hall_symbol in ["P 1", "P 3", "R 3*"]:
for is_centric in [False, True]:
if (not is_centric):
space_group = sgtbx.space_group(hall_symbol)
else:
space_group.expand_smx("-x,-y,-z")
for anomalous_flag in [False, True]:
if (not anomalous_flag):
rfft = fftpack.real_to_complex_3d(n_real)
n_complex = rfft.n_complex()
else:
cfft = fftpack.complex_to_complex_3d(n_real)
n_complex = cfft.n()
for treat_restricted in [False, True]:
map = maptbx.structure_factors.to_map(
space_group=space_group,
anomalous_flag=anomalous_flag,
miller_indices=miller_indices,
structure_factors=data,
n_real=n_real,
map_grid=flex.grid(n_complex),
conjugate_flag=conjugate_flag,
treat_restricted=treat_restricted)
if (treat_restricted):
assert approx_equal(map.complex_map()[0], data[0])
else:
assert approx_equal(map.complex_map()[0],
data[0] * space_group.order_p())
def test_real_to_complex_3d(verbose):
for nx in [3,4]:
for ny in [4,5]:
for nz in [7,8]:
fft = fftpack.real_to_complex_3d((nx,ny,nz))
m = fft.m_real()
vd = flex.double(flex.grid(m))
vc = flex.complex_double(flex.grid((m[0], m[1], m[2]//2)))
assert vd.size() == 2 * vc.size()
for i in xrange(vd.size()):
vd[i] = random.random()*2-1
vd_orig = vd.deep_copy()
for i in xrange(vc.size()):
vc[i] = complex(vd[2*i], vd[2*i+1])
vdt = fft.forward(vd)
vct = fft.forward(vc)
for t in (vdt, vct):
assert t.origin() == (0,0,0)
assert t.all() == fft.n_complex()
assert t.focus() == fft.n_complex()
if (verbose): show_rseq_3d(vd, fft.m_real(), fft.m_real())
assert_complex_eq_real(vc, vd)
vdt = fft.backward(vd)
vct = fft.backward(vc)
for t in (vdt, vct):
assert t.origin() == (0,0,0)
assert t.all() == fft.m_real()
assert t.focus() == fft.n_real()
if (verbose): show_rseq_3d(vd, fft.m_real(), fft.n_real())
assert_complex_eq_real(vc, vd)
f = nx*ny*nz
for ix in xrange(nx):
for iy in xrange(ny):
for iz in xrange(nz):
assert approx_equal(vdt[(ix,iy,iz)], vd_orig[(ix,iy,iz)]*f)
def run(args):
assert len(args) == 1
timer = time_log("pdb.input").start()
pdb_inp = iotbx.pdb.input(file_name=args[0])
print("number of pdb atoms:", pdb_inp.atoms().size())
print(timer.log())
crystal_symmetry = pdb_inp.crystal_symmetry()
assert crystal_symmetry is not None
crystal_symmetry.show_summary()
assert crystal_symmetry.unit_cell() is not None
assert crystal_symmetry.space_group_info() is not None
sites_cart = pdb_inp.atoms().extract_xyz()
site_radii = flex.double(sites_cart.size(), 2.5)
crystal_gridding = maptbx.crystal_gridding(
unit_cell=crystal_symmetry.unit_cell(),
d_min=2,
resolution_factor=1 / 3)
fft = fftpack.real_to_complex_3d(crystal_gridding.n_real())
print("n_real:", fft.n_real())
print("m_real:", fft.m_real())
timer = time_log("grid_indices_around_sites").start()
grid_indices = maptbx.grid_indices_around_sites(
unit_cell=crystal_symmetry.unit_cell(),
fft_n_real=fft.n_real(),
fft_m_real=fft.m_real(),
sites_cart=sites_cart,
site_radii=site_radii)
print("grid_indices.size():", grid_indices.size())
print(timer.log())
print("grid fraction:", \
grid_indices.size() / matrix.col(fft.n_real()).product())
def map_of_coeff_scaled(mask_map,structure,nxyz):
assert mask_map.is_0_based()
assert not mask_map.is_padded()
fft_manager = fftpack.real_to_complex_3d(mask_map.focus())
padded_data = maptbx.copy(mask_map,
flex.grid(fft_manager.m_real()
).set_focus(fft_manager.n_real()))
map_of_coeff = fft_manager.forward(padded_data)
scale = matrix.col(nxyz).product()/structure.unit_cell().volume()
sc = matrix.col(mask_map.focus()).product()/structure.unit_cell().volume()
assert sc == scale
map_of_coeff /= scale
return map_of_coeff
def map_of_coeff_scaled(mask_map, structure, nxyz):
assert mask_map.is_0_based()
assert not mask_map.is_padded()
fft_manager = fftpack.real_to_complex_3d(mask_map.focus())
padded_data = maptbx.copy(
mask_map,
flex.grid(fft_manager.m_real()).set_focus(fft_manager.n_real()))
map_of_coeff = fft_manager.forward(padded_data)
scale = matrix.col(nxyz).product() / structure.unit_cell().volume()
sc = matrix.col(
mask_map.focus()).product() / structure.unit_cell().volume()
assert sc == scale
map_of_coeff /= scale
return map_of_coeff
def direct_space_squaring(start, selection_fixed):
map_gridding = miller.index_span(miller.set.expand_to_p1(start).indices()).map_grid()
if selection_fixed is None:
fixed = start
var = start
else:
fixed = start.select(selection_fixed)
var = start.select(~selection_fixed)
rfft = fftpack.real_to_complex_3d([n * 3 // 2 for n in map_gridding])
conjugate_flag = True
structure_factor_map = maptbx.structure_factors.to_map(
space_group=fixed.space_group(),
anomalous_flag=fixed.anomalous_flag(),
miller_indices=fixed.indices(),
structure_factors=fixed.data(),
n_real=rfft.n_real(),
map_grid=flex.grid(rfft.n_complex()),
conjugate_flag=conjugate_flag,
)
real_map = rfft.backward(structure_factor_map.complex_map())
squared_map = flex.pow2(real_map)
squared_sf_map = rfft.forward(squared_map)
allow_miller_indices_outside_map = False
from_map = maptbx.structure_factors.from_map(
anomalous_flag=var.anomalous_flag(),
miller_indices=var.indices(),
complex_map=squared_sf_map,
conjugate_flag=conjugate_flag,
allow_miller_indices_outside_map=allow_miller_indices_outside_map,
)
if selection_fixed is None:
return from_map.data()
result = start.data().deep_copy()
result.set_selected(~selection_fixed, from_map.data())
assert result.select(selection_fixed).all_eq(fixed.data())
return result
def test_real_to_complex_3d(verbose):
for nx in [3, 4]:
for ny in [4, 5]:
for nz in [7, 8]:
fft = fftpack.real_to_complex_3d((nx, ny, nz))
m = fft.m_real()
vd = flex.double(flex.grid(m))
vc = flex.complex_double(flex.grid((m[0], m[1], m[2] // 2)))
assert vd.size() == 2 * vc.size()
for i in range(vd.size()):
vd[i] = random.random() * 2 - 1
vd_orig = vd.deep_copy()
for i in range(vc.size()):
vc[i] = complex(vd[2 * i], vd[2 * i + 1])
vdt = fft.forward(vd)
vct = fft.forward(vc)
for t in (vdt, vct):
assert t.origin() == (0, 0, 0)
assert t.all() == fft.n_complex()
assert t.focus() == fft.n_complex()
if (verbose): show_rseq_3d(vd, fft.m_real(), fft.m_real())
assert_complex_eq_real(vc, vd)
vdt = fft.backward(vd)
vct = fft.backward(vc)
for t in (vdt, vct):
assert t.origin() == (0, 0, 0)
assert t.all() == fft.m_real()
assert t.focus() == fft.n_real()
if (verbose): show_rseq_3d(vd, fft.m_real(), fft.n_real())
assert_complex_eq_real(vc, vd)
f = nx * ny * nz
for ix in range(nx):
for iy in range(ny):
for iz in range(nz):
assert approx_equal(vdt[(ix, iy, iz)],
vd_orig[(ix, iy, iz)] * f)
def _exercise_real_to_complex_3d(sizes, benchmark=True):
from cctbx import maptbx
from scitbx.array_family import flex
from cudatbx import cufft
from scitbx import fftpack
from libtbx.test_utils import approx_equal
import time
for n_real, n_repeats, eps in sizes:
nx, ny, nz = n_real
fft = fftpack.real_to_complex_3d((nx, ny, nz))
mt = flex.mersenne_twister(seed=1)
g = flex.grid(fft.m_real()).set_focus(fft.n_real())
map = mt.random_double(size=g.size_1d())
map.reshape(g)
sfs = fft.forward(map.deep_copy())
map2 = fft.backward(sfs)
fft_cuda = cufft.real_to_complex_3d((nx, ny, nz))
sfs_cuda = fft_cuda.forward(
map) #cufft.real_to_complex_3d_in_place(map)
map2_cuda = fft_cuda.backward(
sfs_cuda) #cufft.complex_to_real_3d_in_place(sfs_cuda, n_real)
maptbx.unpad_in_place(map=map2)
maptbx.unpad_in_place(map=map2_cuda)
map2_values = map2.as_1d()
map2_cuda_values = map2_cuda.as_1d()
mmm = map2_values.min_max_mean()
mmm_cuda = map2_cuda_values.min_max_mean()
assert (map2.size() == map2_cuda.size())
assert approx_equal(mmm.min, mmm_cuda.min, eps=eps)
assert approx_equal(mmm.max, mmm_cuda.max, eps=eps)
assert approx_equal(mmm.mean, mmm_cuda.mean, eps=eps)
if (benchmark):
map_bak = map.deep_copy()
r2c = [fft.forward, fft_cuda.forward]
c2r = [fft.backward, fft_cuda.backward]
modules = ["fftpack:", "cufft: "]
last_real = [None, None]
print " dimensions:", n_real
print " repeats:", n_repeats
k = 0
for (r2c_fn, c2r_fn, name) in zip(r2c, c2r, modules):
t_forward = 0
t_backward = 0
map = map_bak.deep_copy()
for i in range(n_repeats):
t1 = time.time()
sfs = r2c_fn(map)
t2 = time.time()
map2 = c2r_fn(sfs)
t3 = time.time()
t_forward += t2 - t1
t_backward += t3 - t2
if (i == n_repeats - 1):
last_real[k] = map2.deep_copy()
k += 1
print " %s %7.3fs (forward) %7.3fs (backward)" % (
name, t_forward / n_repeats, t_backward / n_repeats)
last_fftpack, last_cufft = last_real
maptbx.unpad_in_place(map=last_fftpack)
maptbx.unpad_in_place(map=last_cufft)
mmm = last_fftpack.as_1d().min_max_mean()
mmm_cuda = last_cufft.as_1d().min_max_mean()
# FIXME why doesn't this work?
#assert approx_equal(mmm.min, mmm_cuda.min, eps=eps)
assert approx_equal(mmm.max, mmm_cuda.max, eps=eps)
assert approx_equal(mmm.mean, mmm_cuda.mean, eps=eps)
print ""
def _exercise_real_to_complex_3d (sizes, benchmark=True) :
from cctbx import maptbx
from scitbx.array_family import flex
from cudatbx import cufft
from scitbx import fftpack
from libtbx.test_utils import approx_equal
import time
for n_real, n_repeats, eps in sizes :
nx, ny, nz = n_real
fft = fftpack.real_to_complex_3d((nx,ny,nz))
mt = flex.mersenne_twister(seed=1)
g = flex.grid(fft.m_real()).set_focus(fft.n_real())
map = mt.random_double(size=g.size_1d())
map.reshape(g)
sfs = fft.forward(map.deep_copy())
map2 = fft.backward(sfs)
fft_cuda = cufft.real_to_complex_3d((nx,ny,nz))
sfs_cuda = fft_cuda.forward(map)#cufft.real_to_complex_3d_in_place(map)
map2_cuda = fft_cuda.backward(sfs_cuda)#cufft.complex_to_real_3d_in_place(sfs_cuda, n_real)
maptbx.unpad_in_place(map=map2)
maptbx.unpad_in_place(map=map2_cuda)
map2_values = map2.as_1d()
map2_cuda_values = map2_cuda.as_1d()
mmm = map2_values.min_max_mean()
mmm_cuda = map2_cuda_values.min_max_mean()
assert (map2.size() == map2_cuda.size())
assert approx_equal(mmm.min, mmm_cuda.min, eps=eps)
assert approx_equal(mmm.max, mmm_cuda.max, eps=eps)
assert approx_equal(mmm.mean, mmm_cuda.mean, eps=eps)
if (benchmark) :
map_bak = map.deep_copy()
r2c = [ fft.forward, fft_cuda.forward ]
c2r = [ fft.backward, fft_cuda.backward ]
modules = ["fftpack:", "cufft: "]
last_real = [None, None]
print " dimensions:", n_real
print " repeats:", n_repeats
k = 0
for (r2c_fn, c2r_fn, name) in zip(r2c, c2r, modules) :
t_forward = 0
t_backward = 0
map = map_bak.deep_copy()
for i in range(n_repeats) :
t1 = time.time()
sfs = r2c_fn(map)
t2 = time.time()
map2 = c2r_fn(sfs)
t3 = time.time()
t_forward += t2 - t1
t_backward += t3 - t2
if (i == n_repeats - 1) :
last_real[k] = map2.deep_copy()
k += 1
print " %s %7.3fs (forward) %7.3fs (backward)" % (name,
t_forward / n_repeats, t_backward / n_repeats)
last_fftpack,last_cufft = last_real
maptbx.unpad_in_place(map=last_fftpack)
maptbx.unpad_in_place(map=last_cufft)
mmm = last_fftpack.as_1d().min_max_mean()
mmm_cuda = last_cufft.as_1d().min_max_mean()
# FIXME why doesn't this work?
#assert approx_equal(mmm.min, mmm_cuda.min, eps=eps)
assert approx_equal(mmm.max, mmm_cuda.max, eps=eps)
assert approx_equal(mmm.mean, mmm_cuda.mean, eps=eps)
print ""
def rfft(self):
if (self._rfft is None):
self._rfft = fftpack.real_to_complex_3d(self.crystal_gridding().n_real())
return self._rfft
def exercise(space_group_info, redundancy_counter=0):
n_real = (12, 12, 12)
miller_max = (2, 2, 2)
gt = maptbx.grid_tags(n_real)
uc = space_group_info.any_compatible_unit_cell(volume=1000)
fl = sgtbx.search_symmetry_flags(use_space_group_symmetry=True)
gt.build(space_group_info.type(), fl)
fft = fftpack.real_to_complex_3d(n_real)
map0 = flex.double(flex.grid(fft.m_real()).set_focus(fft.n_real()), 0)
weight_map = map0.deep_copy()
map = map0.deep_copy()
ta = gt.tag_array()
order_z = space_group_info.group().order_z()
problems_expected = (redundancy_counter != 0)
for ijk in flex.nested_loop(n_real):
t = ta[ijk]
if (t < 0):
xyz = [i / n for i, n in zip(ijk, n_real)]
ss = sgtbx.site_symmetry(unit_cell=uc,
space_group=space_group_info.group(),
original_site=xyz,
min_distance_sym_equiv=1e-5)
m = space_group_info.group().multiplicity(
site=boost.rational.vector(ijk, n_real))
assert m == ss.multiplicity()
w = m / order_z
weight_map[ijk] = w
map[ijk] = w
elif (redundancy_counter != 0):
redundancy_counter -= 1
ijk_asu = n_dim_index_from_one_dim(i1d=t, sizes=n_real)
assert ta.accessor()(ijk_asu) == t
map[ijk] = map[ijk_asu]
sf_map = fft.forward(map)
del map
mi = miller.index_generator(space_group_info.type(), False,
miller_max).to_array()
assert mi.size() != 0
from_map = maptbx.structure_factors.from_map(
space_group=space_group_info.group(),
anomalous_flag=False,
miller_indices=mi,
complex_map=sf_map,
conjugate_flag=True)
sf = [iround(abs(f)) for f in from_map.data()]
if (sf != [0] * len(sf)):
assert problems_expected
return
else:
not problems_expected
#
map_p1 = map0.deep_copy()
map_sw = map0.deep_copy()
for ijk in flex.nested_loop(n_real):
t = ta[ijk]
if (t < 0):
v = random.random() * 2 - 1
map_p1[ijk] = v
map_sw[ijk] = v * weight_map[ijk]
else:
ijk_asu = n_dim_index_from_one_dim(i1d=t, sizes=n_real)
assert ta.accessor()(ijk_asu) == t
assert map_p1[ijk_asu] != 0
map_p1[ijk] = map_p1[ijk_asu]
#
# fft followed by symmetry summation in reciprocal space
sf_map_sw = fft.forward(map_sw)
del map_sw
sf_sw = maptbx.structure_factors.from_map(
space_group=space_group_info.group(),
anomalous_flag=False,
miller_indices=mi,
complex_map=sf_map_sw,
conjugate_flag=True).data()
del sf_map_sw
#
# symmetry expansion in real space (done above already) followed fft
sf_map_p1 = fft.forward(map_p1)
del map_p1
sf_p1 = maptbx.structure_factors.from_map(space_group=sgtbx.space_group(),
anomalous_flag=False,
miller_indices=mi,
complex_map=sf_map_p1,
conjugate_flag=True).data()
del sf_map_p1
#
corr = flex.linear_correlation(x=flex.abs(sf_sw), y=flex.abs(sf_p1))
assert corr.is_well_defined
assert approx_equal(corr.coefficient(), 1)
def exercise(space_group_info,
const_gaussian,
negative_gaussian,
anomalous_flag,
allow_mix,
use_u_iso,
use_u_aniso,
d_min=1.,
resolution_factor=1. / 3,
max_prime=5,
quality_factor=100,
wing_cutoff=1.e-6,
exp_table_one_over_step_size=-100,
force_complex=False,
verbose=0):
if (const_gaussian):
elements = ["const"] * 8
elif (negative_gaussian):
elements = ["H"] * 8
else:
elements = ["N", "C", "C", "O", "N", "C", "C", "O"]
if (random.random() < 0.5):
random_f_prime_scale = 0.6
else:
random_f_prime_scale = 0
structure = random_structure.xray_structure(
space_group_info,
elements=elements,
random_f_prime_d_min=1,
random_f_prime_scale=random_f_prime_scale,
random_f_double_prime=anomalous_flag,
use_u_aniso=True,
use_u_iso=False,
random_u_cart_scale=0.3,
random_u_iso=True,
random_occupancy=True)
random_structure.random_modify_adp_and_adp_flags_2(
scatterers=structure.scatterers(),
use_u_iso=use_u_iso,
use_u_aniso=use_u_aniso,
allow_mix=allow_mix,
random_u_iso_scale=0.3,
random_u_iso_min=0.0)
sampled_density_must_be_positive = True
if (negative_gaussian):
reg = structure.scattering_type_registry(
custom_dict={"H": eltbx.xray_scattering.gaussian(-1)})
assert reg.gaussian("H").n_terms() == 0
assert reg.gaussian("H").c() == -1
sampled_density_must_be_positive = False
elif (not const_gaussian and random.random() < 0.5):
if (random.random() < 0.5):
sampled_density_must_be_positive = False
assign_custom_gaussians(
structure, negative_a=not sampled_density_must_be_positive)
f_direct = structure.structure_factors(anomalous_flag=anomalous_flag,
d_min=d_min,
algorithm="direct").f_calc()
crystal_gridding = f_direct.crystal_gridding(
resolution_factor=resolution_factor, d_min=d_min, max_prime=max_prime)
assert crystal_gridding.symmetry_flags() is None
rfft = fftpack.real_to_complex_3d(crystal_gridding.n_real())
u_base = xray.calc_u_base(d_min, resolution_factor, quality_factor)
omptbx.env.num_threads = libtbx.introspection.number_of_processors()
sampled_density = xray.sampled_model_density(
unit_cell=structure.unit_cell(),
scatterers=structure.scatterers(),
scattering_type_registry=structure.scattering_type_registry(),
fft_n_real=rfft.n_real(),
fft_m_real=rfft.m_real(),
u_base=u_base,
wing_cutoff=wing_cutoff,
exp_table_one_over_step_size=exp_table_one_over_step_size,
force_complex=force_complex,
sampled_density_must_be_positive=sampled_density_must_be_positive,
tolerance_positive_definite=1.e-5,
use_u_base_as_u_extra=False)
focus = sampled_density.real_map_unpadded().focus()
all = sampled_density.real_map_unpadded().all()
last = sampled_density.real_map_unpadded().last()
assert approx_equal(focus, last)
assert approx_equal(all, last)
assert sampled_density.anomalous_flag() == (anomalous_flag
or force_complex)
if (0 or verbose):
print("const_gaussian:", const_gaussian)
print("negative_gaussian:", negative_gaussian)
print("number of scatterers passed:", \
sampled_density.n_scatterers_passed())
print("number of contributing scatterers:", \
sampled_density.n_contributing_scatterers())
print("number of anomalous scatterers:", \
sampled_density.n_anomalous_scatterers())
print("wing_cutoff:", sampled_density.wing_cutoff())
print("exp_table_one_over_step_size:", \
sampled_density.exp_table_one_over_step_size())
print("exp_table_size:", sampled_density.exp_table_size())
print("max_sampling_box_edges:",
sampled_density.max_sampling_box_edges(),
end=' ')
print("(%.4f, %.4f, %.4f)" %
sampled_density.max_sampling_box_edges_frac())
if (not sampled_density.anomalous_flag()):
print("map min:", flex.min(sampled_density.real_map()))
print("map max:", flex.max(sampled_density.real_map()))
else:
print("map min:",
flex.min(flex.real(sampled_density.complex_map())),
end=' ')
print(flex.min(flex.imag(sampled_density.complex_map())))
print("map max:",
flex.max(flex.real(sampled_density.complex_map())),
end=' ')
print(flex.max(flex.imag(sampled_density.complex_map())))
if (not sampled_density.anomalous_flag() and negative_gaussian):
assert flex.min(sampled_density.real_map()) < 0
assert flex.max(sampled_density.real_map()) == 0
if (not sampled_density.anomalous_flag()):
map = sampled_density.real_map()
assert map.all() == rfft.m_real()
assert map.focus() == rfft.n_real()
sf_map = rfft.forward(map)
assert sf_map.all() == rfft.n_complex()
assert sf_map.focus() == rfft.n_complex()
collect_conj = True
else:
cfft = fftpack.complex_to_complex_3d(rfft.n_real())
map = sampled_density.complex_map()
assert map.all() == cfft.n()
assert map.focus() == cfft.n()
sf_map = cfft.backward(map)
assert sf_map.all() == cfft.n()
assert sf_map.focus() == cfft.n()
collect_conj = False
f_fft_data = maptbx.structure_factors.from_map(
space_group=f_direct.space_group(),
anomalous_flag=sampled_density.anomalous_flag(),
miller_indices=f_direct.indices(),
complex_map=sf_map,
conjugate_flag=collect_conj).data()
sampled_density.eliminate_u_extra_and_normalize(f_direct.indices(),
f_fft_data)
structure_factor_utils.check_correlation("direct/fft_regression",
f_direct.indices(),
0,
f_direct.data(),
f_fft_data,
min_corr_ampl=1 * 0.99,
max_mean_w_phase_error=1 * 3.,
verbose=verbose)
f_fft = xray.structure_factors.from_scatterers(
miller_set=f_direct,
grid_resolution_factor=resolution_factor,
quality_factor=quality_factor,
wing_cutoff=wing_cutoff,
exp_table_one_over_step_size=exp_table_one_over_step_size,
sampled_density_must_be_positive=sampled_density_must_be_positive,
max_prime=max_prime)(xray_structure=structure,
miller_set=f_direct,
algorithm="fft").f_calc()
structure_factor_utils.check_correlation("direct/fft_xray",
f_direct.indices(),
0,
f_direct.data(),
f_fft.data(),
min_corr_ampl=1 * 0.99,
max_mean_w_phase_error=1 * 3.,
verbose=verbose)
def exercise(space_group_info, const_gaussian, negative_gaussian,
anomalous_flag,
allow_mix,
use_u_iso,
use_u_aniso,
d_min=1., resolution_factor=1./3, max_prime=5,
quality_factor=100, wing_cutoff=1.e-6,
exp_table_one_over_step_size=-100,
force_complex=False,
verbose=0):
if (const_gaussian):
elements=["const"]*8
elif (negative_gaussian):
elements=["H"]*8
else:
elements=["N", "C", "C", "O", "N", "C", "C", "O"]
if (random.random() < 0.5):
random_f_prime_scale=0.6
else:
random_f_prime_scale=0
structure = random_structure.xray_structure(
space_group_info,
elements=elements,
random_f_prime_d_min=1,
random_f_prime_scale=random_f_prime_scale,
random_f_double_prime=anomalous_flag,
use_u_aniso= True,
use_u_iso= False,
random_u_cart_scale=0.3,
random_u_iso=True,
random_occupancy=True)
random_structure.random_modify_adp_and_adp_flags_2(
scatterers = structure.scatterers(),
use_u_iso = use_u_iso,
use_u_aniso = use_u_aniso,
allow_mix = allow_mix,
random_u_iso_scale = 0.3,
random_u_iso_min = 0.0)
sampled_density_must_be_positive = True
if (negative_gaussian):
reg = structure.scattering_type_registry(
custom_dict={"H": eltbx.xray_scattering.gaussian(-1)})
assert reg.gaussian("H").n_terms() == 0
assert reg.gaussian("H").c() == -1
sampled_density_must_be_positive = False
elif (not const_gaussian and random.random() < 0.5):
if (random.random() < 0.5):
sampled_density_must_be_positive = False
assign_custom_gaussians(
structure,
negative_a=not sampled_density_must_be_positive)
f_direct = structure.structure_factors(
anomalous_flag=anomalous_flag,
d_min=d_min,
algorithm="direct").f_calc()
crystal_gridding = f_direct.crystal_gridding(
resolution_factor=resolution_factor,
d_min=d_min,
max_prime=max_prime)
assert crystal_gridding.symmetry_flags() is None
rfft = fftpack.real_to_complex_3d(crystal_gridding.n_real())
u_base = xray.calc_u_base(d_min, resolution_factor, quality_factor)
omptbx.env.num_threads = libtbx.introspection.number_of_processors()
sampled_density = xray.sampled_model_density(
unit_cell=structure.unit_cell(),
scatterers=structure.scatterers(),
scattering_type_registry=structure.scattering_type_registry(),
fft_n_real=rfft.n_real(),
fft_m_real=rfft.m_real(),
u_base=u_base,
wing_cutoff=wing_cutoff,
exp_table_one_over_step_size=exp_table_one_over_step_size,
force_complex=force_complex,
sampled_density_must_be_positive=sampled_density_must_be_positive,
tolerance_positive_definite=1.e-5,
use_u_base_as_u_extra=False)
focus = sampled_density.real_map_unpadded().focus()
all = sampled_density.real_map_unpadded().all()
last = sampled_density.real_map_unpadded().last()
assert approx_equal(focus, last)
assert approx_equal(all, last)
assert sampled_density.anomalous_flag() == (anomalous_flag or force_complex)
if (0 or verbose):
print "const_gaussian:", const_gaussian
print "negative_gaussian:", negative_gaussian
print "number of scatterers passed:", \
sampled_density.n_scatterers_passed()
print "number of contributing scatterers:", \
sampled_density.n_contributing_scatterers()
print "number of anomalous scatterers:", \
sampled_density.n_anomalous_scatterers()
print "wing_cutoff:", sampled_density.wing_cutoff()
print "exp_table_one_over_step_size:", \
sampled_density.exp_table_one_over_step_size()
print "exp_table_size:", sampled_density.exp_table_size()
print "max_sampling_box_edges:", sampled_density.max_sampling_box_edges(),
print "(%.4f, %.4f, %.4f)" % sampled_density.max_sampling_box_edges_frac()
if (not sampled_density.anomalous_flag()):
print "map min:", flex.min(sampled_density.real_map())
print "map max:", flex.max(sampled_density.real_map())
else:
print "map min:", flex.min(flex.real(sampled_density.complex_map())),
print flex.min(flex.imag(sampled_density.complex_map()))
print "map max:", flex.max(flex.real(sampled_density.complex_map())),
print flex.max(flex.imag(sampled_density.complex_map()))
if (not sampled_density.anomalous_flag() and negative_gaussian):
assert flex.min(sampled_density.real_map()) < 0
assert flex.max(sampled_density.real_map()) == 0
if (not sampled_density.anomalous_flag()):
map = sampled_density.real_map()
assert map.all() == rfft.m_real()
assert map.focus() == rfft.n_real()
sf_map = rfft.forward(map)
assert sf_map.all() == rfft.n_complex()
assert sf_map.focus() == rfft.n_complex()
collect_conj = True
else:
cfft = fftpack.complex_to_complex_3d(rfft.n_real())
map = sampled_density.complex_map()
assert map.all() == cfft.n()
assert map.focus() == cfft.n()
sf_map = cfft.backward(map)
assert sf_map.all() == cfft.n()
assert sf_map.focus() == cfft.n()
collect_conj = False
f_fft_data = maptbx.structure_factors.from_map(
space_group=f_direct.space_group(),
anomalous_flag=sampled_density.anomalous_flag(),
miller_indices=f_direct.indices(),
complex_map=sf_map,
conjugate_flag=collect_conj).data()
sampled_density.eliminate_u_extra_and_normalize(
f_direct.indices(),
f_fft_data)
structure_factor_utils.check_correlation(
"direct/fft_regression", f_direct.indices(), 0,
f_direct.data(), f_fft_data,
min_corr_ampl=1*0.99, max_mean_w_phase_error=1*3.,
verbose=verbose)
f_fft = xray.structure_factors.from_scatterers(
miller_set=f_direct,
grid_resolution_factor=resolution_factor,
quality_factor=quality_factor,
wing_cutoff=wing_cutoff,
exp_table_one_over_step_size=exp_table_one_over_step_size,
sampled_density_must_be_positive=sampled_density_must_be_positive,
max_prime=max_prime)(
xray_structure=structure,
miller_set=f_direct,
algorithm="fft").f_calc()
structure_factor_utils.check_correlation(
"direct/fft_xray", f_direct.indices(), 0,
f_direct.data(), f_fft.data(),
min_corr_ampl=1*0.99, max_mean_w_phase_error=1*3.,
verbose=verbose)
def exercise_shannon_sampled(space_group_info,
anomalous_flag,
conjugate_flag,
d_min=3.,
resolution_factor=0.5,
max_prime=5,
verbose=0):
structure = random_structure.xray_structure(
space_group_info,
elements=("N", "C", "C", "O"),
random_f_prime_d_min=1,
random_f_double_prime=anomalous_flag,
use_u_aniso=True,
random_u_iso=True,
random_occupancy=True)
f_calc = structure.structure_factors(anomalous_flag=anomalous_flag,
d_min=d_min,
algorithm="direct").f_calc()
n_real = f_calc.crystal_gridding(resolution_factor=resolution_factor,
d_min=d_min,
max_prime=max_prime).n_real()
if (not anomalous_flag):
rfft = fftpack.real_to_complex_3d(n_real)
n_complex = rfft.n_complex()
else:
cfft = fftpack.complex_to_complex_3d(n_real)
n_complex = cfft.n()
map = maptbx.structure_factors.to_map(space_group=f_calc.space_group(),
anomalous_flag=anomalous_flag,
miller_indices=f_calc.indices(),
structure_factors=f_calc.data(),
n_real=n_real,
map_grid=flex.grid(n_complex),
conjugate_flag=conjugate_flag)
f_calc_p1 = f_calc.expand_to_p1()
map_p1 = maptbx.structure_factors.to_map(
space_group=f_calc_p1.space_group(),
anomalous_flag=anomalous_flag,
miller_indices=f_calc_p1.indices(),
structure_factors=f_calc_p1.data(),
n_real=n_real,
map_grid=flex.grid(n_complex),
conjugate_flag=conjugate_flag)
assert flex.max(
flex.abs(map_p1.complex_map() - map.complex_map())) < 1.e-10
if (not anomalous_flag):
real_map = rfft.backward(map.complex_map())
assert real_map.all() == rfft.m_real()
complex_map = rfft.forward(real_map)
else:
real_map = cfft.backward(map.complex_map())
assert not real_map.is_padded()
complex_map = cfft.forward(real_map)
complex_map /= n_real[0] * n_real[1] * n_real[2]
assert real_map.focus() == n_real
assert complex_map.focus() == n_complex
from_map = maptbx.structure_factors.from_map(
unit_cell=f_calc.unit_cell(),
space_group_type=f_calc.space_group_info().type(),
anomalous_flag=anomalous_flag,
d_min=d_min,
complex_map=complex_map,
conjugate_flag=conjugate_flag)
from_map = f_calc.customized_copy(indices=from_map.miller_indices(),
data=from_map.data())
lone_sets = f_calc.lone_sets(from_map)
for lone_set in lone_sets:
if (lone_set.indices().size() > 0):
flex.max(lone_set.d_spacings().data() - d_min) < 1.e-5
common_sets = f_calc.common_sets(from_map)
assert flex.max(
flex.abs(common_sets[0].data() - common_sets[1].data())) < 1.e-10
from_map = maptbx.structure_factors.from_map(
anomalous_flag=anomalous_flag,
miller_indices=f_calc.indices(),
complex_map=complex_map,
conjugate_flag=conjugate_flag)
assert from_map.miller_indices().size() == 0
assert flex.max(flex.abs(f_calc.data() - from_map.data())) < 1.e-10
structure_p1 = structure.asymmetric_unit_in_p1()
f_calc_p1 = f_calc_p1.structure_factors_from_scatterers(
xray_structure=structure_p1, algorithm="direct").f_calc()
map = maptbx.structure_factors.to_map(space_group=f_calc_p1.space_group(),
anomalous_flag=anomalous_flag,
miller_indices=f_calc_p1.indices(),
structure_factors=f_calc_p1.data(),
n_real=n_real,
map_grid=flex.grid(n_complex),
conjugate_flag=conjugate_flag)
from_map = maptbx.structure_factors.from_map(
space_group=f_calc.space_group(),
anomalous_flag=anomalous_flag,
miller_indices=f_calc.indices(),
complex_map=map.complex_map(),
conjugate_flag=conjugate_flag)
assert from_map.miller_indices().size() == 0
assert flex.max(flex.abs(f_calc.data() - from_map.data())) < 1.e-10
def exercise_under_sampled(space_group_info, anomalous_flag, conjugate_flag,
under_sampling,
d_min=2., resolution_factor=0.5, max_prime=5,
verbose=0):
structure_factors = random_structure.xray_structure(
space_group_info,
elements=("N", "C", "C", "O"),
random_f_prime_d_min=1,
random_f_double_prime=anomalous_flag,
use_u_aniso=True,
random_u_iso=True,
random_occupancy=True
).structure_factors(
anomalous_flag=anomalous_flag, d_min=d_min, algorithm="direct")
f_calc = structure_factors.f_calc()
n_real = maptbx.crystal_gridding(
unit_cell=f_calc.unit_cell(),
d_min=d_min,
resolution_factor=resolution_factor,
max_prime=max_prime,
mandatory_factors=(under_sampling,)*3).n_real()
if (not anomalous_flag):
rfft = fftpack.real_to_complex_3d(n_real)
n_complex = rfft.n_complex()
else:
cfft = fftpack.complex_to_complex_3d(n_real)
n_complex = cfft.n()
map = maptbx.structure_factors.to_map(
space_group=f_calc.space_group(),
anomalous_flag=anomalous_flag,
miller_indices=f_calc.indices(),
structure_factors=f_calc.data(),
n_real=n_real,
map_grid=flex.grid(n_complex),
conjugate_flag=conjugate_flag)
f_calc_p1 = f_calc.expand_to_p1()
map_p1 = maptbx.structure_factors.to_map(
space_group=f_calc_p1.space_group(),
anomalous_flag=anomalous_flag,
miller_indices=f_calc_p1.indices(),
structure_factors=f_calc_p1.data(),
n_real=n_real,
map_grid=flex.grid(n_complex),
conjugate_flag=conjugate_flag)
assert flex.max(flex.abs(map_p1.complex_map() - map.complex_map())) < 1.e-10
if (not anomalous_flag):
real_map = rfft.backward(map.complex_map())
assert real_map.all() == rfft.m_real()
else:
real_map = cfft.backward(map.complex_map())
assert not real_map.is_padded()
if (0 or verbose):
if (not anomalous_flag):
maptbx.statistics(real_map).show_summary()
maptbx.statistics(real_map).show_summary()
else:
maptbx.statistics(flex.real(real_map)).show_summary()
maptbx.statistics(flex.imag(real_map)).show_summary()
n_real_under_sampled = [n//under_sampling for n in n_real]
if (not anomalous_flag):
rfft = fftpack.real_to_complex_3d(n_real_under_sampled)
n_complex_under_sampled = rfft.n_complex()
else:
cfft = fftpack.complex_to_complex_3d(n_real_under_sampled)
n_complex_under_sampled = cfft.n()
under_sampled_map = maptbx.structure_factors.to_map(
space_group=f_calc.space_group(),
anomalous_flag=anomalous_flag,
miller_indices=f_calc.indices(),
structure_factors=f_calc.data(),
n_real=n_real_under_sampled,
map_grid=flex.grid(n_complex_under_sampled),
conjugate_flag=conjugate_flag)
under_sampled_map_p1 = maptbx.structure_factors.to_map(
space_group=f_calc_p1.space_group(),
anomalous_flag=anomalous_flag,
miller_indices=f_calc_p1.indices(),
structure_factors=f_calc_p1.data(),
n_real=n_real_under_sampled,
map_grid=flex.grid(n_complex_under_sampled),
conjugate_flag=conjugate_flag)
assert flex.max(flex.abs(under_sampled_map_p1.complex_map()
- under_sampled_map.complex_map())) < 1.e-10
if (not anomalous_flag):
under_sampled_map_before_fft = under_sampled_map.complex_map().deep_copy()
under_sampled_real_map = rfft.backward(under_sampled_map.complex_map())
assert under_sampled_real_map.all() == rfft.m_real()
else:
under_sampled_real_map = cfft.backward(under_sampled_map.complex_map())
assert not under_sampled_real_map.is_padded()
if (0 or verbose):
if (not anomalous_flag):
maptbx.statistics(under_sampled_real_map).show_summary()
maptbx.statistics(under_sampled_real_map).show_summary()
else:
maptbx.statistics(flex.real(under_sampled_real_map)).show_summary()
maptbx.statistics(flex.imag(under_sampled_real_map)).show_summary()
if (0 or verbose):
print real_map.all(), n_complex
print under_sampled_real_map.all(), n_complex_under_sampled
if (not anomalous_flag):
x_source = real_map
y_source = under_sampled_real_map
else:
x_source = flex.real(real_map)
y_source = flex.real(under_sampled_real_map)
x = flex.double()
n = x_source.focus()
for i in xrange(0, n[0], under_sampling):
for j in xrange(0, n[1], under_sampling):
for k in xrange(0, n[2], under_sampling):
x.append(x_source[(i,j,k)])
y = maptbx.copy(y_source, flex.grid(y_source.focus())).as_1d()
if (0 or verbose):
print "x:", tuple(x)
print "y:", tuple(y)
assert flex.max(flex.abs(x-y)) \
< (flex.max(flex.abs(x))+flex.max(flex.abs(y)))/2*1.e-6
if (under_sampling == 1):
x = maptbx.copy(x_source, flex.grid(x_source.focus())).as_1d()
c = flex.linear_correlation(x, y)
assert c.coefficient() >= 0.9999
def exercise_under_sampled(space_group_info,
anomalous_flag,
conjugate_flag,
under_sampling,
d_min=2.,
resolution_factor=0.5,
max_prime=5,
verbose=0):
structure_factors = random_structure.xray_structure(
space_group_info,
elements=("N", "C", "C", "O"),
random_f_prime_d_min=1,
random_f_double_prime=anomalous_flag,
use_u_aniso=True,
random_u_iso=True,
random_occupancy=True).structure_factors(anomalous_flag=anomalous_flag,
d_min=d_min,
algorithm="direct")
f_calc = structure_factors.f_calc()
n_real = maptbx.crystal_gridding(unit_cell=f_calc.unit_cell(),
d_min=d_min,
resolution_factor=resolution_factor,
max_prime=max_prime,
mandatory_factors=(under_sampling, ) *
3).n_real()
if (not anomalous_flag):
rfft = fftpack.real_to_complex_3d(n_real)
n_complex = rfft.n_complex()
else:
cfft = fftpack.complex_to_complex_3d(n_real)
n_complex = cfft.n()
map = maptbx.structure_factors.to_map(space_group=f_calc.space_group(),
anomalous_flag=anomalous_flag,
miller_indices=f_calc.indices(),
structure_factors=f_calc.data(),
n_real=n_real,
map_grid=flex.grid(n_complex),
conjugate_flag=conjugate_flag)
f_calc_p1 = f_calc.expand_to_p1()
map_p1 = maptbx.structure_factors.to_map(
space_group=f_calc_p1.space_group(),
anomalous_flag=anomalous_flag,
miller_indices=f_calc_p1.indices(),
structure_factors=f_calc_p1.data(),
n_real=n_real,
map_grid=flex.grid(n_complex),
conjugate_flag=conjugate_flag)
assert flex.max(
flex.abs(map_p1.complex_map() - map.complex_map())) < 1.e-10
if (not anomalous_flag):
real_map = rfft.backward(map.complex_map())
assert real_map.all() == rfft.m_real()
else:
real_map = cfft.backward(map.complex_map())
assert not real_map.is_padded()
if (0 or verbose):
if (not anomalous_flag):
maptbx.statistics(real_map).show_summary()
maptbx.statistics(real_map).show_summary()
else:
maptbx.statistics(flex.real(real_map)).show_summary()
maptbx.statistics(flex.imag(real_map)).show_summary()
n_real_under_sampled = [n // under_sampling for n in n_real]
if (not anomalous_flag):
rfft = fftpack.real_to_complex_3d(n_real_under_sampled)
n_complex_under_sampled = rfft.n_complex()
else:
cfft = fftpack.complex_to_complex_3d(n_real_under_sampled)
n_complex_under_sampled = cfft.n()
under_sampled_map = maptbx.structure_factors.to_map(
space_group=f_calc.space_group(),
anomalous_flag=anomalous_flag,
miller_indices=f_calc.indices(),
structure_factors=f_calc.data(),
n_real=n_real_under_sampled,
map_grid=flex.grid(n_complex_under_sampled),
conjugate_flag=conjugate_flag)
under_sampled_map_p1 = maptbx.structure_factors.to_map(
space_group=f_calc_p1.space_group(),
anomalous_flag=anomalous_flag,
miller_indices=f_calc_p1.indices(),
structure_factors=f_calc_p1.data(),
n_real=n_real_under_sampled,
map_grid=flex.grid(n_complex_under_sampled),
conjugate_flag=conjugate_flag)
assert flex.max(
flex.abs(under_sampled_map_p1.complex_map() -
under_sampled_map.complex_map())) < 1.e-10
if (not anomalous_flag):
under_sampled_map_before_fft = under_sampled_map.complex_map(
).deep_copy()
under_sampled_real_map = rfft.backward(under_sampled_map.complex_map())
assert under_sampled_real_map.all() == rfft.m_real()
else:
under_sampled_real_map = cfft.backward(under_sampled_map.complex_map())
assert not under_sampled_real_map.is_padded()
if (0 or verbose):
if (not anomalous_flag):
maptbx.statistics(under_sampled_real_map).show_summary()
maptbx.statistics(under_sampled_real_map).show_summary()
else:
maptbx.statistics(flex.real(under_sampled_real_map)).show_summary()
maptbx.statistics(flex.imag(under_sampled_real_map)).show_summary()
if (0 or verbose):
print(real_map.all(), n_complex)
print(under_sampled_real_map.all(), n_complex_under_sampled)
if (not anomalous_flag):
x_source = real_map
y_source = under_sampled_real_map
else:
x_source = flex.real(real_map)
y_source = flex.real(under_sampled_real_map)
x = flex.double()
n = x_source.focus()
for i in range(0, n[0], under_sampling):
for j in range(0, n[1], under_sampling):
for k in range(0, n[2], under_sampling):
x.append(x_source[(i, j, k)])
y = maptbx.copy(y_source, flex.grid(y_source.focus())).as_1d()
if (0 or verbose):
print("x:", tuple(x))
print("y:", tuple(y))
assert flex.max(flex.abs(x-y)) \
< (flex.max(flex.abs(x))+flex.max(flex.abs(y)))/2*1.e-6
if (under_sampling == 1):
x = maptbx.copy(x_source, flex.grid(x_source.focus())).as_1d()
c = flex.linear_correlation(x, y)
assert c.coefficient() >= 0.9999
def exercise_shannon_sampled(space_group_info, anomalous_flag, conjugate_flag,
d_min=3., resolution_factor=0.5, max_prime=5,
verbose=0):
structure = random_structure.xray_structure(
space_group_info,
elements=("N", "C", "C", "O"),
random_f_prime_d_min=1,
random_f_double_prime=anomalous_flag,
use_u_aniso=True,
random_u_iso=True,
random_occupancy=True)
f_calc = structure.structure_factors(
anomalous_flag=anomalous_flag,
d_min=d_min,
algorithm="direct").f_calc()
n_real = f_calc.crystal_gridding(
resolution_factor=resolution_factor,
d_min=d_min,
max_prime=max_prime).n_real()
if (not anomalous_flag):
rfft = fftpack.real_to_complex_3d(n_real)
n_complex = rfft.n_complex()
else:
cfft = fftpack.complex_to_complex_3d(n_real)
n_complex = cfft.n()
map = maptbx.structure_factors.to_map(
space_group=f_calc.space_group(),
anomalous_flag=anomalous_flag,
miller_indices=f_calc.indices(),
structure_factors=f_calc.data(),
n_real=n_real,
map_grid=flex.grid(n_complex),
conjugate_flag=conjugate_flag)
f_calc_p1 = f_calc.expand_to_p1()
map_p1 = maptbx.structure_factors.to_map(
space_group=f_calc_p1.space_group(),
anomalous_flag=anomalous_flag,
miller_indices=f_calc_p1.indices(),
structure_factors=f_calc_p1.data(),
n_real=n_real,
map_grid=flex.grid(n_complex),
conjugate_flag=conjugate_flag)
assert flex.max(flex.abs(map_p1.complex_map() - map.complex_map())) < 1.e-10
if (not anomalous_flag):
real_map = rfft.backward(map.complex_map())
assert real_map.all() == rfft.m_real()
complex_map = rfft.forward(real_map)
else:
real_map = cfft.backward(map.complex_map())
assert not real_map.is_padded()
complex_map = cfft.forward(real_map)
complex_map /= n_real[0] * n_real[1] * n_real[2]
assert real_map.focus() == n_real
assert complex_map.focus() == n_complex
from_map = maptbx.structure_factors.from_map(
unit_cell=f_calc.unit_cell(),
space_group_type=f_calc.space_group_info().type(),
anomalous_flag=anomalous_flag,
d_min=d_min,
complex_map=complex_map,
conjugate_flag=conjugate_flag)
from_map = f_calc.customized_copy(
indices=from_map.miller_indices(),
data=from_map.data())
lone_sets = f_calc.lone_sets(from_map)
for lone_set in lone_sets:
if (lone_set.indices().size() > 0):
flex.max(lone_set.d_spacings().data()-d_min) < 1.e-5
common_sets = f_calc.common_sets(from_map)
assert flex.max(flex.abs(common_sets[0].data()
- common_sets[1].data())) < 1.e-10
from_map = maptbx.structure_factors.from_map(
anomalous_flag=anomalous_flag,
miller_indices=f_calc.indices(),
complex_map=complex_map,
conjugate_flag=conjugate_flag)
assert from_map.miller_indices().size() == 0
assert flex.max(flex.abs(f_calc.data()-from_map.data())) < 1.e-10
structure_p1 = structure.asymmetric_unit_in_p1()
f_calc_p1 = f_calc_p1.structure_factors_from_scatterers(
xray_structure=structure_p1,
algorithm="direct").f_calc()
map = maptbx.structure_factors.to_map(
space_group=f_calc_p1.space_group(),
anomalous_flag=anomalous_flag,
miller_indices=f_calc_p1.indices(),
structure_factors=f_calc_p1.data(),
n_real=n_real,
map_grid=flex.grid(n_complex),
conjugate_flag=conjugate_flag)
from_map = maptbx.structure_factors.from_map(
space_group=f_calc.space_group(),
anomalous_flag=anomalous_flag,
miller_indices=f_calc.indices(),
complex_map=map.complex_map(),
conjugate_flag=conjugate_flag)
assert from_map.miller_indices().size() == 0
assert flex.max(flex.abs(f_calc.data()-from_map.data())) < 1.e-10
def exercise(space_group_info, redundancy_counter=0):
n_real = (12,12,12)
miller_max = (2,2,2)
gt = maptbx.grid_tags(n_real)
uc = space_group_info.any_compatible_unit_cell(volume=1000)
fl = sgtbx.search_symmetry_flags(use_space_group_symmetry=True)
gt.build(space_group_info.type(), fl)
fft = fftpack.real_to_complex_3d(n_real)
map0 = flex.double(flex.grid(fft.m_real()).set_focus(fft.n_real()), 0)
weight_map = map0.deep_copy()
map = map0.deep_copy()
ta = gt.tag_array()
order_z = space_group_info.group().order_z()
problems_expected = (redundancy_counter != 0)
for ijk in flex.nested_loop(n_real):
t = ta[ijk]
if (t < 0):
xyz = [i/n for i,n in zip(ijk, n_real)]
ss = sgtbx.site_symmetry(
unit_cell=uc,
space_group=space_group_info.group(),
original_site=xyz,
min_distance_sym_equiv=1e-5)
m = space_group_info.group().multiplicity(
site=boost.rational.vector(ijk, n_real))
assert m == ss.multiplicity()
w = m / order_z
weight_map[ijk] = w
map[ijk] = w
elif (redundancy_counter != 0):
redundancy_counter -= 1
ijk_asu = n_dim_index_from_one_dim(i1d=t, sizes=n_real)
assert ta.accessor()(ijk_asu) == t
map[ijk] = map[ijk_asu]
sf_map = fft.forward(map)
del map
mi = miller.index_generator(
space_group_info.type(), False, miller_max).to_array()
assert mi.size() != 0
from_map = maptbx.structure_factors.from_map(
space_group=space_group_info.group(),
anomalous_flag=False,
miller_indices=mi,
complex_map=sf_map,
conjugate_flag=True)
sf = [iround(abs(f)) for f in from_map.data()]
if (sf != [0]*len(sf)):
assert problems_expected
return
else:
not problems_expected
#
map_p1 = map0.deep_copy()
map_sw = map0.deep_copy()
for ijk in flex.nested_loop(n_real):
t = ta[ijk]
if (t < 0):
v = random.random()*2-1
map_p1[ijk] = v
map_sw[ijk] = v * weight_map[ijk]
else:
ijk_asu = n_dim_index_from_one_dim(i1d=t, sizes=n_real)
assert ta.accessor()(ijk_asu) == t
assert map_p1[ijk_asu] != 0
map_p1[ijk] = map_p1[ijk_asu]
#
# fft followed by symmetry summation in reciprocal space
sf_map_sw = fft.forward(map_sw)
del map_sw
sf_sw = maptbx.structure_factors.from_map(
space_group=space_group_info.group(),
anomalous_flag=False,
miller_indices=mi,
complex_map=sf_map_sw,
conjugate_flag=True).data()
del sf_map_sw
#
# symmetry expansion in real space (done above already) followed fft
sf_map_p1 = fft.forward(map_p1)
del map_p1
sf_p1 = maptbx.structure_factors.from_map(
space_group=sgtbx.space_group(),
anomalous_flag=False,
miller_indices=mi,
complex_map=sf_map_p1,
conjugate_flag=True).data()
del sf_map_p1
#
corr = flex.linear_correlation(x=flex.abs(sf_sw), y=flex.abs(sf_p1))
assert corr.is_well_defined
assert approx_equal(corr.coefficient(), 1)
# coerce to double
from scitbx.array_family import flex
C3D = flex.double(mrc.data.astype(np.float64))
# in-place resize to a good multiple of 2
C3D.resize(flex.grid(imax,imax,imax))
from matplotlib import pyplot as plt
from scitbx import fftpack
#plt.imshow(S2D)
#plt.show()
#from IPython import embed; embed()
print C3D.focus()
print C3D
import copy
FFT = fftpack.real_to_complex_3d((C3D.focus()[0],C3D.focus()[1],C3D.focus()[2]-2))
c = FFT.forward(C3D)
print c.focus()
print c
cum = None
for sec in xrange((imax//4)-25,(imax//4)+25):
print sec
D = c[:,:,sec:sec+1]
#D = C[:,:,sec]
parts = D.parts()
magnitude = flex.sqrt(parts[0]*parts[0] + parts[1]*parts[1])
mag2d = magnitude.reshape(flex.grid(128,128))
n = magnitude.as_numpy_array()
n.reshape(128,128)