def run():
show_times = libtbx.utils.show_times()
from scitbx.python_utils import command_line
flags = command_line.parse_options(sys.argv[1:], [
"RandomSeed",
"Verbose",
])
if (not flags.RandomSeed): random.seed(0)
assert fftpack.adjust_gridding(13, 5) == 15
assert fftpack.adjust_gridding(13, 5, 6) == 18
assert fftpack.adjust_gridding_triple((13, 22, 34), 5) == (15, 24, 36)
assert fftpack.adjust_gridding_triple((13, 22, 34), 5,
(6, 10, 8)) == (18, 30, 40)
f = fftpack.factorization(30, False)
assert f.n() == 30
assert tuple(f.factors()) == (2, 3, 5)
test_complex_to_complex(flags.Verbose)
test_real_to_complex(flags.Verbose)
test_complex_to_complex_2d(flags.Verbose)
test_complex_to_complex_3d(flags.Verbose)
test_real_to_complex_3d(flags.Verbose)
exercise_real_to_complex_padding_area()
max_transform_size = 300
test_comprehensive_cc_1d(max_transform_size)
test_comprehensive_rc_1d(max_transform_size)
show_times()
def run():
show_times = libtbx.utils.show_times()
from scitbx.python_utils import command_line
flags = command_line.parse_options(sys.argv[1:], [
"RandomSeed",
"Verbose",
])
if (not flags.RandomSeed): random.seed(0)
assert fftpack.adjust_gridding(13, 5) == 15
assert fftpack.adjust_gridding(13, 5, 6) == 18
assert fftpack.adjust_gridding_triple((13,22,34), 5) == (15,24,36)
assert fftpack.adjust_gridding_triple((13,22,34), 5, (6,10,8)) == (18,30,40)
f = fftpack.factorization(30, False)
assert f.n() == 30
assert tuple(f.factors()) == (2, 3, 5)
test_complex_to_complex(flags.Verbose)
test_real_to_complex(flags.Verbose)
test_complex_to_complex_2d(flags.Verbose)
test_complex_to_complex_3d(flags.Verbose)
test_real_to_complex_3d(flags.Verbose)
exercise_real_to_complex_padding_area()
max_transform_size = 300
test_comprehensive_cc_1d(max_transform_size)
test_comprehensive_rc_1d(max_transform_size)
show_times()
def __init__(self, max_cell, min_cell=3, params=None, *args, **kwargs):
"""Construct an FFT3D object.
Args:
max_cell (float): An estimate of the maximum cell dimension of the primitive
cell.
n_points (int): The size of the fft3d grid.
d_min (float): The high resolution limit in Angstrom for spots to include in
the initial indexing. If `Auto` then calculated as
`d_min = 5 * max_cell/n_points`.
b_iso (float): Apply an isotropic b_factor weight to the points when doing
the FFT. If `Auto` then calculated as
`b_iso = -4 * d_min ** 2 * math.log(0.05)`.
rmsd_cutoff (float): RMSD cutoff applied to the transformed real-space map
prior to performing the peak search.
peak_volume_cutoff (float): Only include peaks that are larger than this
fraction of the volume of the largest peak in the transformed real-space
map.
min_cell (float): A conservative lower bound on the minimum possible
primitive unit cell dimension.
"""
super(FFT3D, self).__init__(max_cell, params=params, *args, **kwargs)
n_points = self._params.reciprocal_space_grid.n_points
self._gridding = fftpack.adjust_gridding_triple(
(n_points, n_points, n_points), max_prime=5)
self._n_points = self._gridding[0]
self._min_cell = min_cell
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 find_lattices(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)))
self.fft()
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()
if self.params.multiple_lattice_search.cluster_analysis_search:
self.find_basis_vector_combinations_cluster_analysis()
self.debug_show_candidate_basis_vectors()
if self.params.debug_plots:
self.debug_plot_candidate_basis_vectors()
crystal_models = self.candidate_crystal_models
if self.params.multiple_lattice_search.max_lattices is not None:
crystal_models = \
crystal_models[:self.params.multiple_lattice_search.max_lattices]
else:
self.find_candidate_basis_vectors()
self.debug_show_candidate_basis_vectors()
if self.params.debug_plots:
self.debug_plot_candidate_basis_vectors()
self.candidate_crystal_models = self.find_candidate_orientation_matrices(
self.candidate_basis_vectors,
max_combinations=self.params.basis_vector_combinations.max_try)
crystal_model, n_indexed = self.choose_best_orientation_matrix(
self.candidate_crystal_models)
if crystal_model is not None:
crystal_models = [crystal_model]
else:
crystal_models = []
experiments = ExperimentList()
for cm in crystal_models:
for imageset in self.imagesets:
experiments.append(Experiment(imageset=imageset,
beam=imageset.get_beam(),
detector=imageset.get_detector(),
goniometer=imageset.get_goniometer(),
scan=imageset.get_scan(),
crystal=cm))
return experiments
