def load_xmap_from_ccp4(event_map_path):
xmap = clipper.Xmap_float()
ccp4_file = clipper.CCP4MAPfile()
ccp4_file.open_read(event_map_path)
ccp4_file.import_xmap_float(xmap)
ccp4_file.close_read()
return xmap
def transform_fft(hkl_info, hkl_data):
grid = clipper.Grid_sampling(hkl_info.spacegroup, hkl_info.cell,
hkl_info.resolution)
xmap = clipper.Xmap_float(hkl_info.spacegroup, hkl_info.cell, grid)
xmap.fft_from(hkl_data)
return xmap, grid
def b_factor_smoothing_real_space(
reference_reflections: Reflections,
reflections: Reflections,
):
print("Reference_res: {}; Res: {}".format(
reference_reflections.hkl_info.resolution.limit,
reflections.hkl_info.resolution.limit,
))
if reference_reflections.hkl_info.resolution.limit < reflections.hkl_info.resolution.limit:
raise AssertionError(
"Reference reflections MUST be lower than reflections resolution")
spacegroup = reference_reflections.hkl_info.spacegroup
cell = reference_reflections.hkl_info.cell
sample_res = reference_reflections.hkl_info.resolution
grid = clipper_python.Grid_sampling(
spacegroup,
cell,
sample_res,
)
xmap_reference = clipper_python.Xmap_float(
spacegroup,
cell,
grid,
)
xmap_reference.fft_from(reference_reflections.hkl_data)
xmap = clipper_python.Xmap_float(
spacegroup,
cell,
grid,
)
xmap.fft_from(reflections.hkl_data)
xmap_reference_np = xmap_reference.export_numpy()
xmap_np = xmap.export_numpy()
print(xmap_reference_np.shape)
print(xmap_np.shape)
# optimise blur of xmap
# target_func =
ndimage.gaussian_filter()
def load_ccp4_map(ccp4_path):
print("Opening XMAP")
xmap = clipper_python.Xmap_float()
print("starting map file")
mapout = clipper_python.CCP4MAPfile()
print("opening read")
mapout.open_read(str(ccp4_path))
print("importing xmap")
mapout.import_xmap_float(xmap)
print("closing read")
mapout.close_read()
return xmap
def transform_fft(hkl_info, hkl_data):
grid = clipper.Grid_sampling(hkl_info.spacegroup, hkl_info.cell,
hkl_info.resolution)
# print("Making sampling grid")
xmap = clipper.Xmap_float(hkl_info.spacegroup, hkl_info.cell, grid)
xmap.fft_from(hkl_data)
# print("fft'd")
# map_numpy = np.zeros((xmap.grid_asu.nu(), xmap.grid_asu.nv(), xmap.grid_asu.nw()), dtype='double')
#
# xmap.export_numpy(map_numpy)
# return map_numpy
return xmap, grid
def align_structures(ref_structure, mobile_structure, mobile_xmap):
# translation_mobile_to_ref = ref_structure.atoms.center_of_mass() - mobile_structure.atoms.center_of_mass()
#
#
# mobile0 = mobile_structure.select_atoms('name CA').positions - mobile_structure.atoms.center_of_mass()
# ref0 = ref_structure.select_atoms('name CA').positions - ref_structure.atoms.center_of_mass()
# rotation_mobile_to_ref, rmsd = align.rotation_matrix(mobile0, ref0)
ref_atoms = []
alt_atoms = []
for (ref_model, alt_model) in zip(ref_structure, mobile_structure):
for (ref_chain, alt_chain) in zip(ref_model, alt_model):
for ref_res, alt_res in zip(ref_chain, alt_chain):
# CA = alpha carbon
try:
# print("\tModel: {}".format(ref_model))
# print("\tChain: {}".format(ref_chain))
# print("\tResidue: {}".format(ref_res))
# print(ref_res)
# print(dir(ref_res))
# print([x for x in ref_res.get_atoms()])
ref_atoms.append(ref_res['CA'])
alt_atoms.append(alt_res['CA'])
except:
pass
super_imposer = PDB.Superimposer()
super_imposer.set_atoms(
ref_atoms,
alt_atoms,
)
translation_mobile_to_ref = super_imposer.rotran[1]
rotation_mobile_to_ref = super_imposer.rotran[0]
# print("\tTranslation is: {}".format(translation_mobile_to_ref))
# print("\tRotation is: {}".format(rotation_mobile_to_ref))
# xmap_np = interpolate_uniform_grid(mobile_xmap,
# translation_mobile_to_ref,
# np.transpose(rotation_mobile_to_ref),
# )
rtop = clipper_python.RTop_orth(
clipper_python.Mat33_double(np.transpose(rotation_mobile_to_ref)),
clipper_python.Vec3_double(translation_mobile_to_ref),
)
xmap_new = clipper_python.Xmap_float(
mobile_xmap.xmap.spacegroup,
mobile_xmap.xmap.cell,
mobile_xmap.xmap.grid_sampling,
)
clipper_python.rotate_translate(
mobile_xmap.xmap,
xmap_new,
rtop,
)
return xmap_new.export_numpy()