def run(self):
dataset, grid, reference_map, args, verbose = self.data
log_file = dataset.file_manager.get_file('dataset_log')
assert reference_map.is_sparse(), 'Reference map is not in sparse form'
# ============================================================================>
# Create map handler in the native coordinate frame
# ============================================================================>
# Extract the map data
fft_map = dataset.data.fft_maps['truncated']
# Scale the map
if args.params.maps.density_scaling == 'none': pass
elif args.params.maps.density_scaling == 'sigma':
fft_map.apply_sigma_scaling()
elif args.params.maps.density_scaling == 'volume':
fft_map.apply_volume_scaling()
# ============================================================================>
# Morph the map to the reference frame
# ============================================================================>
# Extract the map sites from the grid partition
point_mappings_grid = grid.partition.nn_groups[
grid.global_mask().outer_mask_indices()]
assert sum(point_mappings_grid == -1) == 0
sites_cart_map = grid.grid2cart(grid.global_mask().outer_mask(),
origin_shift=True)
# Translate the grid partition mappings to the dataset alignment mappings
mappings_grid2dataset = get_interpolated_mapping_between_coordinates(
query_list=grid.partition.sites_cart,
ref_list=dataset.model.alignment.reference_sites,
tol=0.01)
point_mappings_dataset = numpy.array(
[mappings_grid2dataset[i] for i in point_mappings_grid])
assert sum(point_mappings_dataset == -1) == 0
sites_cart_map_d = dataset.model.alignment.ref2nat(
coordinates=sites_cart_map, mappings=point_mappings_dataset)
# Create and sample from map object
native_map_true = ElectronDensityMap.from_fft_map(fft_map)
morphed_map_data = native_map_true.get_cart_values(sites_cart_map_d)
# Scale map to reference
scale_mask = grid.index_on_other(
query=grid.global_mask().inner_mask_indices(),
other=grid.global_mask().outer_mask_indices())
scaled_map_data = scale_map_to_reference(
ref_vals=reference_map.data,
vals=morphed_map_data,
mask_idxs=flex.size_t(scale_mask))
# Create map holder
morphed_map = reference_map.new_from_template(
map_data=scaled_map_data, sparse=reference_map.is_sparse())
morphed_map.meta.num = dataset.num
morphed_map.meta.tag = dataset.tag
morphed_map.meta.type = 'observed map'
morphed_map.meta.resolution = reference_map.meta.resolution
morphed_map.meta.map_uncertainty = None
morphed_map.meta.obs_map_mean = morphed_map_data.min_max_mean().mean
morphed_map.meta.obs_map_rms = morphed_map_data.standard_deviation_of_the_sample(
)
morphed_map.meta.scl_map_mean = scaled_map_data.min_max_mean().mean
morphed_map.meta.scl_map_rms = scaled_map_data.standard_deviation_of_the_sample(
)
# Print a running row of dots
print('>', end='')
sys.stdout.flush()
return morphed_map.make_sparse()
def __call__(self, dataset, grid, reference_map, map_resolution):
assert reference_map.is_sparse(), 'Reference map is not in sparse form'
# ============================================================================>
# Create map handler in the native coordinate frame
# ============================================================================>
# Extract the map data
# TODO: make sure new version works
# fft_map = dataset.data.fft_maps['truncated']
dataset.data.fft_maps['truncated'] = dataset.data.miller_arrays['truncated'].fft_map(
resolution_factor=float(self.resolution_factor),
d_min=float(map_resolution),
symmetry_flags=cctbx.maptbx.use_space_group_symmetry)
fft_map = dataset.data.fft_maps['truncated']
gc.collect()
# Scale the map
if self.density_scaling == 'none': pass
elif self.density_scaling == 'sigma': fft_map.apply_sigma_scaling()
elif self.density_scaling == 'volume': fft_map.apply_volume_scaling()
# Create map object
# native_map_true = ElectronDensityMap.from_fft_map(fft_map).as_map()
native_map_true = ElectronDensityMap.from_fft_map(fft_map)
# ============================================================================>
# Morph the map to the reference frame
# ============================================================================>
# Extract the map sites from the grid partition
point_mappings_grid = grid.partition.nn_groups[grid.global_mask().outer_mask_indices()]
assert sum(point_mappings_grid == -1) == 0
sites_cart_map = grid.grid2cart(grid.global_mask().outer_mask(),
origin_shift=True,
)
# Translate the grid partition mappings to the dataset alignment mappings
mappings_grid2dataset = get_interpolated_mapping_between_coordinates(query_list=grid.partition.sites_cart,
ref_list=dataset.model.alignment.reference_sites,
tol=0.01,
)
point_mappings_dataset = numpy.array([mappings_grid2dataset[i] for i in point_mappings_grid])
assert sum(point_mappings_dataset == -1) == 0
sites_cart_map_d = dataset.model.alignment.ref2nat(coordinates=sites_cart_map,
mappings=point_mappings_dataset,
)
morphed_map_data = native_map_true.get_cart_values(sites_cart_map_d)
# Scale map to reference
scale_mask = grid.index_on_other(query=grid.global_mask().inner_mask_indices(),
other=grid.global_mask().outer_mask_indices())
scaled_map_data = scale_map_to_reference(ref_vals = reference_map.data,
vals = morphed_map_data,
mask_idxs = flex.size_t(scale_mask))
# Create map holder
morphed_map = reference_map.new_from_template(map_data=scaled_map_data, sparse=reference_map.is_sparse())
morphed_map.meta.num = dataset.num
morphed_map.meta.tag = dataset.tag
morphed_map.meta.type = 'observed map'
morphed_map.meta.resolution = reference_map.meta.resolution
morphed_map.meta.map_uncertainty = None
morphed_map.meta.obs_map_mean = morphed_map_data.min_max_mean().mean
morphed_map.meta.obs_map_rms = morphed_map_data.standard_deviation_of_the_sample()
morphed_map.meta.scl_map_mean = scaled_map_data.min_max_mean().mean
morphed_map.meta.scl_map_rms = scaled_map_data.standard_deviation_of_the_sample()
# Print a running row of dots
print('>', end=''); sys.stdout.flush()
return morphed_map.make_sparse()