def exercise_complex_to_complex_3d():
print "complex_to_complex_3d"
for n_complex,n_repeats in [((100,80,90),2), ((200,160,180),1)]:
print " dimensions:", n_complex
print " repeats:", n_repeats
np = n_complex[0]*n_complex[1]*n_complex[2]
d0 = (flex.random_double(size=np)*2-1) * flex.polar(
1, flex.random_double(size=np)*2-1)
d0.reshape(flex.grid(n_complex))
#
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()
fftw3tbx.complex_to_complex_3d_in_place(data=d, exp_sign=-1)
fftw3tbx.complex_to_complex_3d_in_place(data=d, exp_sign=+1)
print " fftw: %.2f seconds" % (time.time()-t0-overhead)
rw = d / np
#
t0 = time.time()
for i_trial in xrange(n_repeats):
d = d0.deep_copy()
fftpack.complex_to_complex_3d(n_complex).forward(d)
fftpack.complex_to_complex_3d(n_complex).backward(d)
print " fftpack: %.2f seconds" % (time.time()-t0-overhead)
sys.stdout.flush()
rp = d / np
#
assert flex.max(flex.abs(rw-rp)) < 1.e-6
def exercise_complex_to_complex_3d():
from scitbx.array_family import flex
from cudatbx import cufft
from scitbx import fftpack
import time
import sys
print ""
print "complex_to_complex_3d"
for n_complex, n_repeats in [((100, 80, 90), 16), ((200, 160, 180), 16)]:
print " dimensions:", n_complex
print " repeats:", n_repeats
np = n_complex[0] * n_complex[1] * n_complex[2]
d0 = flex.polar(
flex.random_double(size=np) * 2 - 1,
flex.random_double(size=np) * 2 - 1)
d0.reshape(flex.grid(n_complex))
#
t0 = time.time()
for i_trial in xrange(n_repeats):
d = d0.deep_copy()
overhead = time.time() - t0
print " overhead: %.2f seconds" % overhead
#
# XXX extra CuFFT to initialize device - can we avoid this somehow?
d = d0.deep_copy()
cufft.complex_to_complex_3d(n_complex).forward(d)
cufft.complex_to_complex_3d(n_complex).backward(d)
# benchmarking run
t0 = time.time()
for i_trial in xrange(n_repeats):
d = d0.deep_copy()
cufft.complex_to_complex_3d(n_complex).forward(d)
cufft.complex_to_complex_3d(n_complex).backward(d)
print " cufft: %6.2f seconds" % (
(time.time() - t0 - overhead) / n_repeats)
rw = d / np
#
t0 = time.time()
for i_trial in xrange(n_repeats):
d = d0.deep_copy()
fftpack.complex_to_complex_3d(n_complex).forward(d)
fftpack.complex_to_complex_3d(n_complex).backward(d)
print " fftpack: %6.2f seconds" % (
(time.time() - t0 - overhead) / n_repeats)
sys.stdout.flush()
rp = d / np
#
print ""
assert flex.max(flex.abs(rw - rp)) < 1.e-6
def test_complex_to_complex_3d(verbose):
fft = fftpack.complex_to_complex_3d((3, 4, 5))
n = fft.n()
vc = flex.complex_double(flex.grid(n))
vd = flex.double(flex.grid((n[0], n[1], 2 * n[2])))
for i in range(vc.size()):
vc[i] = complex(2 * i, 2 * i + 1)
for i in range(vd.size()):
vd[i] = i
vct = fft.forward(vc)
vdt = fft.forward(vd)
for t in (vct, vdt):
assert t.origin() == (0, 0, 0)
assert t.all() == fft.n()
assert t.focus() == fft.n()
if (verbose): show_cseq(vc)
assert_complex_eq_real(vc, vd)
vct = fft.backward(vc)
vdt = fft.backward(vd)
for t in (vct, vdt):
assert t.origin() == (0, 0, 0)
assert t.all() == fft.n()
assert t.focus() == fft.n()
if (verbose): show_cseq(vc)
assert_complex_eq_real(vc, vd)
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 _fft(self, reciprocal_lattice_vectors, d_min):
reciprocal_space_grid, used_in_indexing = self._map_centroids_to_reciprocal_space_grid(
reciprocal_lattice_vectors, d_min)
logger.info("Number of centroids used: %i" %
((reciprocal_space_grid > 0).count(True)))
# gb_to_bytes = 1073741824
# bytes_to_gb = 1/gb_to_bytes
# (128**3)*8*2*bytes_to_gb
# 0.03125
# (256**3)*8*2*bytes_to_gb
# 0.25
# (512**3)*8*2*bytes_to_gb
# 2.0
fft = fftpack.complex_to_complex_3d(self._gridding)
grid_complex = flex.complex_double(
reals=reciprocal_space_grid,
imags=flex.double(reciprocal_space_grid.size(), 0),
)
grid_transformed = fft.forward(grid_complex)
grid_real = flex.pow2(flex.real(grid_transformed))
del grid_transformed
return grid_real, used_in_indexing
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_complex_to_complex_3d(verbose):
fft = fftpack.complex_to_complex_3d((3,4,5))
n = fft.n()
vc = flex.complex_double(flex.grid(n))
vd = flex.double(flex.grid((n[0], n[1], 2 * n[2])))
for i in xrange(vc.size()):
vc[i] = complex(2*i, 2*i+1)
for i in xrange(vd.size()):
vd[i] = i
vct = fft.forward(vc)
vdt = fft.forward(vd)
for t in (vct, vdt):
assert t.origin() == (0,0,0)
assert t.all() == fft.n()
assert t.focus() == fft.n()
if (verbose): show_cseq(vc)
assert_complex_eq_real(vc, vd)
vct = fft.backward(vc)
vdt = fft.backward(vd)
for t in (vct, vdt):
assert t.origin() == (0,0,0)
assert t.all() == fft.n()
assert t.focus() == fft.n()
if (verbose): show_cseq(vc)
assert_complex_eq_real(vc, vd)
def get_3D_transform():
#with mrcfile.open("cropout_bin4.mrc") as mrc: # small example
with mrcfile.open("cropout.mrc") as mrc:
imax = 512 #take small subset, optimized for prime factorization
# coerce to double
from scitbx.array_family import flex
realpart = flex.double(mrc.data.astype(np.float64))
# in-place resize to a good multiple of 2
realpart.resize(flex.grid(imax, imax, imax))
complpart = flex.double(flex.grid(imax, imax, imax))
#from IPython import embed; embed()
C3D = flex.complex_double(realpart, complpart)
from matplotlib import pyplot as plt
from scitbx import fftpack
#plt.imshow(S2D)
#plt.show()
#from IPython import embed; embed()
print "C3Dfocus", C3D.focus()
print C3D
FFT = fftpack.complex_to_complex_3d(
(C3D.focus()[0], C3D.focus()[1], C3D.focus()[2]))
c = FFT.forward(C3D)
print c.focus()
print c
return c
def exercise_complex_to_complex_3d () :
from scitbx.array_family import flex
from cudatbx import cufft
from scitbx import fftpack
import time
import sys
print ""
print "complex_to_complex_3d"
for n_complex,n_repeats in [((100,80,90),16), ((200,160,180),16)]:
print " dimensions:", n_complex
print " repeats:", n_repeats
np = n_complex[0]*n_complex[1]*n_complex[2]
d0 = flex.polar(
flex.random_double(size=np)*2-1,
flex.random_double(size=np)*2-1)
d0.reshape(flex.grid(n_complex))
#
t0 = time.time()
for i_trial in xrange(n_repeats):
d = d0.deep_copy()
overhead = time.time()-t0
print " overhead: %.2f seconds" % overhead
#
# XXX extra CuFFT to initialize device - can we avoid this somehow?
d = d0.deep_copy()
cufft.complex_to_complex_3d(n_complex).forward(d)
cufft.complex_to_complex_3d(n_complex).backward(d)
# benchmarking run
t0 = time.time()
for i_trial in xrange(n_repeats):
d = d0.deep_copy()
cufft.complex_to_complex_3d(n_complex).forward(d)
cufft.complex_to_complex_3d(n_complex).backward(d)
print " cufft: %6.2f seconds" % ((time.time()-t0-overhead)/n_repeats)
rw = d / np
#
t0 = time.time()
for i_trial in xrange(n_repeats):
d = d0.deep_copy()
fftpack.complex_to_complex_3d(n_complex).forward(d)
fftpack.complex_to_complex_3d(n_complex).backward(d)
print " fftpack: %6.2f seconds" % ((time.time()-t0-overhead)/n_repeats)
sys.stdout.flush()
rp = d / np
#
print ""
assert flex.max(flex.abs(rw-rp)) < 1.e-6
def fft(self):
if self.params.fft3d.reciprocal_space_grid.d_min is libtbx.Auto:
# rough calculation of suitable d_min based on max cell
# see also Campbell, J. (1998). J. Appl. Cryst., 31(3), 407-413.
# fft_cell should be greater than twice max_cell, so say:
# fft_cell = 2.5 * max_cell
# then:
# fft_cell = n_points * d_min/2
# 2.5 * max_cell = n_points * d_min/2
# a little bit of rearrangement:
# d_min = 5 * max_cell/n_points
max_cell = self.params.max_cell
d_min = (5 * max_cell /
self.params.fft3d.reciprocal_space_grid.n_points)
d_spacings = 1 / self.reflections['rlp'].norms()
self.params.fft3d.reciprocal_space_grid.d_min = max(
d_min, min(d_spacings))
logger.info("Setting d_min: %.2f" %
self.params.fft3d.reciprocal_space_grid.d_min)
n_points = self.params.fft3d.reciprocal_space_grid.n_points
self.gridding = fftpack.adjust_gridding_triple(
(n_points, n_points, n_points), max_prime=5)
n_points = self.gridding[0]
self.map_centroids_to_reciprocal_space_grid()
self.d_min = self.params.fft3d.reciprocal_space_grid.d_min
logger.info("Number of centroids used: %i" %
((self.reciprocal_space_grid > 0).count(True)))
#gb_to_bytes = 1073741824
#bytes_to_gb = 1/gb_to_bytes
#(128**3)*8*2*bytes_to_gb
#0.03125
#(256**3)*8*2*bytes_to_gb
#0.25
#(512**3)*8*2*bytes_to_gb
#2.0
fft = fftpack.complex_to_complex_3d(self.gridding)
grid_complex = flex.complex_double(
reals=self.reciprocal_space_grid,
imags=flex.double(self.reciprocal_space_grid.size(), 0))
grid_transformed = fft.forward(grid_complex)
#self.grid_real = flex.pow2(flex.abs(grid_transformed))
self.grid_real = flex.pow2(flex.real(grid_transformed))
#self.grid_real = flex.pow2(flex.imag(self.grid_transformed))
del grid_transformed
if self.params.debug:
self.debug_write_ccp4_map(map_data=self.grid_real,
file_name="fft3d.map")
if self.params.fft3d.peak_search == 'flood_fill':
self.find_peaks()
elif self.params.fft3d.peak_search == 'clean':
self.find_peaks_clean()
def fft(self):
fft = fftpack.complex_to_complex_3d(self.gridding)
grid_complex = flex.complex_double(
reals=self.reciprocal_space_grid,
imags=flex.double(self.reciprocal_space_grid.size(), 0))
grid_transformed = fft.forward(grid_complex)
#self.grid_real = flex.pow2(flex.abs(grid_transformed))
self.grid_real = flex.abs(grid_transformed)
#self.grid_real = flex.pow2(flex.real(grid_transformed))
#self.grid_real = flex.pow2(flex.imag(self.grid_transformed))
del grid_transformed
def fft(self):
fft = fftpack.complex_to_complex_3d(self.gridding)
grid_complex = flex.complex_double(
reals=self.reciprocal_space_grid,
imags=flex.double(self.reciprocal_space_grid.size(), 0),
)
grid_transformed = fft.forward(grid_complex)
# self.grid_real = flex.pow2(flex.abs(grid_transformed))
self.grid_real = flex.abs(grid_transformed)
# self.grid_real = flex.pow2(flex.real(grid_transformed))
# self.grid_real = flex.pow2(flex.imag(self.grid_transformed))
del grid_transformed
def main(filenames,
map_file,
npoints=192,
max_resolution=6,
reverse_phi=False):
rec_range = 1 / max_resolution
image = ImageFactory(filenames[0])
panel = image.get_detector()[0]
beam = image.get_beam()
s0 = beam.get_s0()
pixel_size = panel.get_pixel_size()
xlim, ylim = image.get_raw_data().all()
xy = recviewer.get_target_pixels(panel, s0, xlim, ylim, max_resolution)
s1 = panel.get_lab_coord(xy * pixel_size[0]) # FIXME: assumed square pixel
s1 = s1 / s1.norms() * (1 / beam.get_wavelength()) # / is not supported...
S = s1 - s0
grid = flex.double(flex.grid(npoints, npoints, npoints), 0)
cnts = flex.int(flex.grid(npoints, npoints, npoints), 0)
for filename in filenames:
print "Processing image", filename
try:
fill_voxels(ImageFactory(filename), grid, cnts, S, xy, reverse_phi,
rec_range)
except:
print " Failed to process. Skipped this."
recviewer.normalize_voxels(grid, cnts)
uc = uctbx.unit_cell((npoints, npoints, npoints, 90, 90, 90))
ccp4_map.write_ccp4_map(map_file, uc, sgtbx.space_group("P1"), (0, 0, 0),
grid.all(), grid,
flex.std_string(["cctbx.miller.fft_map"]))
return
from scitbx import fftpack
fft = fftpack.complex_to_complex_3d(grid.all())
grid_complex = flex.complex_double(reals=flex.pow2(grid),
imags=flex.double(grid.size(), 0))
grid_transformed = flex.abs(fft.backward(grid_complex))
print flex.max(grid_transformed), flex.min(
grid_transformed), grid_transformed.all()
ccp4_map.write_ccp4_map(map_file, uc, sgtbx.space_group("P1"), (0, 0, 0),
grid.all(), grid_transformed,
flex.std_string(["cctbx.miller.fft_map"]))
def load_fft(filename):
with mrcfile.open(filename) as mrc:
# coerce to double
realpart = flex.double(mrc.data.astype(np.float64))
complpart = flex.double(flex.grid(realpart.focus()))
C3D = flex.complex_double(realpart, complpart)
print "C3Dfocus", C3D.focus()
print C3D
FFT = fftpack.complex_to_complex_3d(
(C3D.focus()[0], C3D.focus()[1], C3D.focus()[2]))
complex_flex = FFT.forward(C3D)
print complex_flex.focus()
print complex_flex
return complex_flex
def main(filenames, map_file, npoints=192, max_resolution=6, reverse_phi=False):
rec_range = 1 / max_resolution
image = ImageFactory(filenames[0])
panel = image.get_detector()[0]
beam = image.get_beam()
s0 = beam.get_s0()
pixel_size = panel.get_pixel_size()
xlim, ylim = image.get_raw_data().all()
xy = recviewer.get_target_pixels(panel, s0, xlim, ylim, max_resolution)
s1 = panel.get_lab_coord(xy * pixel_size[0]) # FIXME: assumed square pixel
s1 = s1 / s1.norms() * (1 / beam.get_wavelength()) # / is not supported...
S = s1 - s0
grid = flex.double(flex.grid(npoints, npoints, npoints), 0)
cnts = flex.int(flex.grid(npoints, npoints, npoints), 0)
for filename in filenames:
print "Processing image", filename
try:
fill_voxels(ImageFactory(filename), grid, cnts, S, xy, reverse_phi, rec_range)
except:
print " Failed to process. Skipped this."
recviewer.normalize_voxels(grid, cnts)
uc = uctbx.unit_cell((npoints, npoints, npoints, 90, 90, 90))
ccp4_map.write_ccp4_map(map_file, uc, sgtbx.space_group("P1"),
(0, 0, 0), grid.all(), grid,
flex.std_string(["cctbx.miller.fft_map"]))
return
from scitbx import fftpack
fft = fftpack.complex_to_complex_3d(grid.all())
grid_complex = flex.complex_double(
reals=flex.pow2(grid),
imags=flex.double(grid.size(), 0))
grid_transformed = flex.abs(fft.backward(grid_complex))
print flex.max(grid_transformed), flex.min(grid_transformed), grid_transformed.all()
ccp4_map.write_ccp4_map(map_file, uc, sgtbx.space_group("P1"),
(0, 0, 0), grid.all(), grid_transformed,
flex.std_string(["cctbx.miller.fft_map"]))
def fft(self):
#gb_to_bytes = 1073741824
#bytes_to_gb = 1/gb_to_bytes
#(128**3)*8*2*bytes_to_gb
#0.03125
#(256**3)*8*2*bytes_to_gb
#0.25
#(512**3)*8*2*bytes_to_gb
#2.0
fft = fftpack.complex_to_complex_3d(self.gridding)
grid_complex = flex.complex_double(
reals=self.reciprocal_space_grid,
imags=flex.double(self.reciprocal_space_grid.size(), 0))
grid_transformed = fft.forward(grid_complex)
#self.grid_real = flex.pow2(flex.abs(grid_transformed))
self.grid_real = flex.pow2(flex.real(grid_transformed))
#self.grid_real = flex.pow2(flex.imag(self.grid_transformed))
del grid_transformed
def load_fft(filename, Tukey):
print "Opening file"
with mrcfile.open(filename) as mrc:
print "Converting to flex"
# coerce to double
if Tukey is True:
mod_vol = apply_tukey_3d(mrc.data.astype(np.float64))
realpart = flex.double(mod_vol)
else:
realpart = flex.double(mrc.data.astype(np.float64))
complpart = flex.double(flex.grid(realpart.focus()))
C3D = flex.complex_double(realpart, complpart)
print "C3Dfocus", C3D.focus()
print C3D
FFT = fftpack.complex_to_complex_3d(
(C3D.focus()[0], C3D.focus()[1], C3D.focus()[2]))
complex_flex = FFT.forward(C3D)
print complex_flex.focus()
print complex_flex
return complex_flex
def get_3D_transform():
with mrcfile.open("new.mrc") as mrc:
# coerce to double
realpart = flex.double(mrc.data.astype(np.float64))
# in-place resize to a good multiple of 2
realpart.resize(flex.grid(imax, imax, imax))
complpart = flex.double(flex.grid(imax, imax, imax))
#from IPython import embed; embed()
C3D = flex.complex_double(realpart, complpart)
from matplotlib import pyplot as plt
from scitbx import fftpack
#plt.imshow(S2D)
#plt.show()
#from IPython import embed; embed()
print "C3Dfocus", C3D.focus()
print C3D
FFT = fftpack.complex_to_complex_3d(
(C3D.focus()[0], C3D.focus()[1], C3D.focus()[2]))
c = FFT.forward(C3D)
print c.focus()
print c
return c
def cfft(self):
if (self._cfft is None):
self._cfft = fftpack.complex_to_complex_3d(
self.crystal_gridding().n_real())
return self._cfft
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_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 cfft(self):
if (self._cfft is None):
self._cfft = fftpack.complex_to_complex_3d(
self.crystal_gridding().n_real())
return self._cfft
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 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 find_peaks_clean(self):
import omptbx
# doesn't seem to be any benefit to using more than say 8 threads
num_threads = min(8, omptbx.omp_get_num_procs(), self.params.nproc)
omptbx.omp_set_num_threads(num_threads)
d_min = self.params.fft3d.reciprocal_space_grid.d_min
rlgrid = 2 / (d_min * self.gridding[0])
frame_number = self.reflections['xyzobs.px.value'].parts()[2]
scan_range_min = max(
int(math.floor(flex.min(frame_number))),
self.imagesets[0].get_array_range()[0]) # XXX what about multiple imagesets?
scan_range_max = min(
int(math.ceil(flex.max(frame_number))),
self.imagesets[0].get_array_range()[1]) # XXX what about multiple imagesets?
scan_range = self.params.scan_range
if not len(scan_range):
scan_range = [[scan_range_min, scan_range_max]]
scan = self.imagesets[0].get_scan() # XXX what about multiple imagesets?
angle_ranges = [
[scan.get_angle_from_array_index(i, deg=False) for i in range_]
for range_ in scan_range]
grid = flex.double(flex.grid(self.gridding), 0)
sampling_volume_map(grid, flex.vec2_double(angle_ranges),
self.imagesets[0].get_beam().get_s0(),
self.imagesets[0].get_goniometer().get_rotation_axis(),
rlgrid, d_min, self.params.b_iso)
fft = fftpack.complex_to_complex_3d(self.gridding)
grid_complex = flex.complex_double(
reals=grid,
imags=flex.double(grid.size(), 0))
grid_transformed = fft.forward(grid_complex)
grid_real = flex.pow2(flex.real(grid_transformed))
gamma = 1
peaks = flex.vec3_double()
#n_peaks = 200
n_peaks = 100 # XXX how many do we need?
dirty_beam = grid_real
dirty_map = self.grid_real.deep_copy()
import time
t0 = time.time()
peaks = clean_3d(dirty_beam, dirty_map, n_peaks, gamma=gamma)
t1 = time.time()
#print "clean_3d took %.2f s" %(t1-t0)
reciprocal_lattice_points = self.reflections['rlp'].select(
self.reflections_used_for_indexing)
peaks = self.optimise_peaks(peaks, reciprocal_lattice_points)
peaks_frac = flex.vec3_double()
for p in peaks:
peaks_frac.append((p[0]/self.gridding[0],
p[1]/self.gridding[1],
p[2]/self.gridding[2]))
#print p, peaks_frac[-1]
if self.params.debug:
self.debug_write_ccp4_map(grid, "sampling_volume.map")
self.debug_write_ccp4_map(grid_real, "sampling_volume_FFT.map")
self.debug_write_ccp4_map(dirty_map, "clean.map")
self.sites = peaks_frac
# we don't really know the "volume"" of the peaks, but this method should
# find the peaks in order of their intensity (strongest first)
self.volumes = flex.double(range(len(self.sites), 0, -1))
return