def to_gemmi(self):
mtz = gemmi.Mtz(with_base=False)
mtz.title = self.mtz_title
mtz.history = self.mtz_history
spacegroup = self.spacegroup.to_gemmi()
mtz.spacegroup = spacegroup
unit_cell = self.unit_cell.to_gemmi()
mtz.set_cell_for_all(unit_cell)
for dataset in self.datasets:
mtz.add_dataset(dataset.dataset_name)
ds = mtz.dataset(dataset.id)
ds.project_name = dataset.project_name
ds.crystal_name = dataset.crystal_name
ds.wavelength = dataset.wavelength
for column in self.columns:
mtz.add_column(column.label,
column.column_type,
dataset_id=column.dataset_id)
mtz.set_data(self.array)
mtz.update_reso()
return mtz
def main():
config = Config.from_config()
m = gemmi.read_ccp4_map(str(config.xmap_in))
print(dir(m))
# m.spacegroup = gemmi.find_spacegroup_by_name('P1')
print(m.grid.spacegroup)
m.grid.spacegroup = gemmi.find_spacegroup_by_name('P1')
print(m.grid.spacegroup)
m.setup()
print(m.grid.spacegroup)
m.grid.spacegroup = gemmi.find_spacegroup_by_name('P1')
print(m.grid.spacegroup)
sf = gemmi.transform_map_to_f_phi(m.grid, half_l=True)
print(sf.spacegroup)
# data = sf
print(dir(sf))
data = sf.prepare_asu_data(dmin=config.resolution, with_000=True)
mtz = gemmi.Mtz(with_base=True)
# mtz = gemmi.Mtz()
print(dir(mtz))
mtz.spacegroup = sf.spacegroup
# mtz.spacegroup = gemmi.find_spacegroup_by_name('P1')
# mtz.set_cell_for_all(sf.unit_cell)
mtz.cell = sf.unit_cell
mtz.add_dataset('unknown')
mtz.add_column('FWT', 'F')
mtz.add_column('PHWT', 'P')
mtz.set_data(data)
mtz.write_to_file(str(config.mtz_out))
def __setstate__(self, state):
data = state["data"]
spacegroup = state["spacegroup"]
unit_cell = state["unit_cell"]
mtz = gemmi.Mtz()
mtz.spacegroup = gemmi.find_spacegroup_by_number(spacegroup)
mtz.cell.set(
unit_cell[0],
unit_cell[1],
unit_cell[2],
unit_cell[3],
unit_cell[4],
unit_cell[5],
)
datasets = state["datasets"]
columns = state["columns"]
for dataset_id, dataset_label in datasets.items():
mtz.add_dataset(dataset_label)
dataset_columns = columns[dataset_id]
for column_name, column_type in dataset_columns.items():
mtz.add_column(column_name, column_type)
mtz.set_data(data)
self.mtz = mtz
def combine_mtz(prefix, columns):
"""Combine columns from multiple MTZ files with gemmijoin"""
result = {
"hklout": "%s.mtz" % prefix,
"stdout": "%s.log" % prefix,
"stderr": "%s.err" % prefix,
}
reference = columns[0]
reference_mtz = gemmi.read_mtz_file(reference)
reference_data = numpy.array(reference_mtz, copy=False)
reference_df = pandas.DataFrame(data=reference_data,
columns=reference_mtz.column_labels())
reference_sel_col_df = reference_df[[
'H', 'K', 'L', 'FP', 'SIGFP', 'FREE', 'PHWT', 'FOM'
]]
model = columns[1]
model_mtz = gemmi.read_mtz_file(model)
model_data = numpy.array(model_mtz, copy=False)
model_df = pandas.DataFrame(data=model_data,
columns=model_mtz.column_labels())
model_sel_col_df = model_df[['PHWT', 'FOM']]
model_sel_col_df = model_sel_col_df.rename(columns={
'PHWT': 'PHWT_model',
'FOM': 'FOM_model'
})
combined_df = pandas.concat([reference_sel_col_df, model_sel_col_df],
ignore_index=True,
axis=1)
combined_array = combined_df.to_numpy()
mtz_new = gemmi.Mtz()
mtz_new.add_dataset('combined')
mtz_new.add_column('H', 'H', dataset_id=0)
mtz_new.add_column('K', 'H', dataset_id=0)
mtz_new.add_column('L', 'H', dataset_id=0)
mtz_new.add_column('FP', 'F', dataset_id=0)
mtz_new.add_column('SIGFP', 'Q', dataset_id=0)
mtz_new.add_column('FREE', 'I', dataset_id=0)
mtz_new.add_column('PHWT_ref', 'P', dataset_id=0)
mtz_new.add_column('FOM_ref', 'W', dataset_id=0)
mtz_new.add_column('PHWT_model', 'P', dataset_id=0)
mtz_new.add_column('FOM_model', 'W', dataset_id=0)
unit_cell = reference_mtz.cell
space_group = reference_mtz.spacegroup
mtz_new.spacegroup = space_group
mtz_new.cell = unit_cell
mtz_new.set_data(combined_array)
mtz_new.write_to_file(result["hklout"])
if not os.path.exists(result["hklout"]):
return {"error": "No reflection data produced"}
return result
def build_emptyMTZ(sg):
"""Build empty gemmi.MTZ"""
m = gemmi.Mtz()
m.spacegroup = sg
m.add_dataset('crystal')
m.add_column('H', 'H')
m.add_column('K', 'H')
m.add_column('L', 'H')
m.add_column('M/ISYM', 'Y')
return m
#!/usr/bin/env python
# Convert CCP4 map to map coefficients in MTZ
import sys
import gemmi
RESOLUTION_LIMIT = 1.5 # set 0 for no limit
if len(sys.argv) != 3:
sys.exit('Usage: map2mtz.py input.ccp4 output.mtz')
m = gemmi.read_ccp4_map(sys.argv[1])
m.setup()
sf = gemmi.transform_map_to_f_phi(m.grid, half_l=True)
data = sf.prepare_asu_data(dmin=RESOLUTION_LIMIT)
mtz = gemmi.Mtz(with_base=True)
mtz.spacegroup = sf.spacegroup
mtz.set_cell_for_all(sf.unit_cell)
mtz.add_dataset('unknown')
mtz.add_column('FWT', 'F')
mtz.add_column('PHWT', 'P')
mtz.set_data(data)
mtz.write_to_file(sys.argv[2])
def to_gemmi(dataset, skip_problem_mtztypes=False):
"""
Construct gemmi.Mtz object from DataSet
If ``dataset.merged == False``, the reflections will be mapped to the
reciprocal space ASU, and a M/ISYM column will be constructed.
If boolean flags with the label ``PARTIAL`` or ``CENTRIC`` are found
in the DataSet, these will be cast to the ``MTZInt`` dtype, and included
in the gemmi.Mtz object.
Parameters
----------
dataset : rs.DataSet
DataSet object to convert to gemmi.Mtz
skip_problem_mtztypes : bool
Whether to skip columns in DataSet that do not have specified
mtz datatypes
Returns
-------
gemmi.Mtz
"""
# Check that cell and spacegroup are defined
if not dataset.cell:
raise AttributeError(
f"Instance of type {dataset.__class__.__name__} has no unit cell information"
)
if not dataset.spacegroup:
raise AttributeError(
f"Instance of type {dataset.__class__.__name__} has no space group information"
)
# Build up a gemmi.Mtz object
mtz = gemmi.Mtz()
mtz.cell = dataset.cell
mtz.spacegroup = dataset.spacegroup
# Handle Unmerged data
if not dataset.merged:
dataset.hkl_to_asu(inplace=True)
# Construct data for Mtz object.
mtz.add_dataset("reciprocalspaceship")
temp = dataset.reset_index()
columns = []
for c in temp.columns:
cseries = temp[c]
if isinstance(cseries.dtype, MTZDtype):
mtzcol = mtz.add_column(label=c, type=cseries.dtype.mtztype)
columns.append(c)
# Special case for CENTRIC and PARTIAL flags
elif cseries.dtype.name == "bool" and c in ["CENTRIC", "PARTIAL"]:
temp[c] = temp[c].astype("MTZInt")
mtzcol = mtz.add_column(label=c, type="I")
columns.append(c)
elif skip_problem_mtztypes:
continue
else:
raise ValueError(
f"column of type {cseries.dtype} cannot be written to an MTZ file. "
f"To skip columns without explicit MTZ dtypes, set skip_problem_mtztypes=True"
)
mtz.set_data(temp[columns].to_numpy(dtype="float32"))
return mtz