def get_dano(names, miller_arrays, xray_structure, parameters, out):
miller_array = None
if parameters.input_data is None:
if len(miller_arrays) == 1:
miller_array = miller_arrays[0]
else:
if names.has_key(parameters.input_data):
miller_array = miller_arrays[names[parameters.input_data]]
else:
raise Sorry("Unknown data name.")
if miller_array.is_xray_intensity_array():
miller_array = miller_array.f_sq_as_f()
assert miller_array.is_xray_amplitude_array()
pair_generator = fa_estimation.ano_scaling(miller_array)
plus = pair_generator.x1p.deep_copy()
minus = pair_generator.x1n.deep_copy()
delta_gen = pair_analyses.delta_generator(plus, minus)
deltas = delta_gen.abs_delta_f.deep_copy()
return deltas
def get_diso(names, miller_arrays, xray_structure, parameters, out):
#first scale please
if parameters.native is None:
raise Sorry("Please define native data name")
if parameters.derivative is None:
raise Sorry("Please define derivative data name")
native=None
derivative=None
if names.has_key( parameters.native ):
native = miller_arrays[ names[parameters.native] ].deep_copy()
else:
raise Sorry("Unknown data name: >>%s<<"%(parameters.native) )
if names.has_key( parameters.derivative ):
derivative = miller_arrays[ names[parameters.derivative] ].deep_copy()
else:
raise Sorry("Unknown data name: >>%s<<"%(parameters.derivative) )
scaler = relative_scaling.ls_rel_scale_driver(
miller_native = native,
miller_derivative = derivative,
use_intensities = parameters.use_intensities,
scale_weight = parameters.scale_weight,
use_weights = parameters.use_weights)
#
scaler.show(out=out)
if native.is_xray_intensity_array():
native = native.f_sq_as_f()
if derivative.is_xray_intensity_array():
derivative = derivative.f_sq_as_f()
delta_gen = pair_analyses.delta_generator( derivative,
native )
deltas = delta_gen.delta_f.deep_copy()
return deltas
def get_diso(names, miller_arrays, xray_structure, parameters, out):
#first scale please
if parameters.native is None:
raise Sorry("Please define native data name")
if parameters.derivative is None:
raise Sorry("Please define derivative data name")
native = None
derivative = None
if names.has_key(parameters.native):
native = miller_arrays[names[parameters.native]].deep_copy()
else:
raise Sorry("Unknown data name: >>%s<<" % (parameters.native))
if names.has_key(parameters.derivative):
derivative = miller_arrays[names[parameters.derivative]].deep_copy()
else:
raise Sorry("Unknown data name: >>%s<<" % (parameters.derivative))
scaler = relative_scaling.ls_rel_scale_driver(
miller_native=native,
miller_derivative=derivative,
use_intensities=parameters.use_intensities,
scale_weight=parameters.scale_weight,
use_weights=parameters.use_weights)
#
scaler.show(out=out)
if native.is_xray_intensity_array():
native = native.f_sq_as_f()
if derivative.is_xray_intensity_array():
derivative = derivative.f_sq_as_f()
delta_gen = pair_analyses.delta_generator(derivative, native)
deltas = delta_gen.delta_f.deep_copy()
return deltas
def get_dano(names, miller_arrays, xray_structure, parameters, out ):
miller_array = None
if parameters.input_data is None:
if len(miller_arrays)==1:
miller_array = miller_arrays[0]
else:
if names.has_key( parameters.input_data ):
miller_array = miller_arrays[ names[ parameters.input_data ] ]
else:
raise Sorry("Unknown data name.")
if miller_array.is_xray_intensity_array():
miller_array = miller_array.f_sq_as_f()
assert miller_array.is_xray_amplitude_array()
pair_generator = fa_estimation.ano_scaling( miller_array )
plus = pair_generator.x1p.deep_copy()
minus = pair_generator.x1n.deep_copy()
delta_gen = pair_analyses.delta_generator( plus,
minus )
deltas = delta_gen.abs_delta_f.deep_copy()
return deltas
def run(args):
if len(args) == 0:
master_params.show(expert_level=0)
elif ("--help" in args):
print("no help available")
elif ("--h" in args):
print("no help available")
elif ("--show_defaults" in args):
master_params.show(expert_level=0)
elif ("--show_defaults_all" in args):
master_params.show(expert_level=10)
else:
log = multi_out()
if (not "--quiet" in args):
log.register(label="stdout", file_object=sys.stdout)
string_buffer = StringIO()
string_buffer_plots = StringIO()
log.register(label="log_buffer", file_object=string_buffer)
log_plots = StringIO()
print("#phil __OFF__", file=log)
print(file=log)
print(date_and_time(), file=log)
print(file=log)
print(file=log)
phil_objects = []
argument_interpreter = master_params.command_line_argument_interpreter(
home_scope="scaling")
reflection_file = None
for arg in args:
command_line_params = None
arg_is_processed = False
if arg == '--quiet':
arg_is_processed = True
## The associated action with this keyword is implemented above
if (os.path.isfile(arg)): ## is this a file name?
## Check if this is a phil file
try:
command_line_params = iotbx.phil.parse(file_name=arg)
except KeyboardInterrupt:
raise
except Exception:
pass
if command_line_params is not None:
phil_objects.append(command_line_params)
arg_is_processed = True
## Check if this file is a reflection file
if command_line_params is None:
reflection_file = reflection_file_reader.any_reflection_file(
file_name=arg, ensure_read_access=False)
if (reflection_file is not None):
reflection_file = arg
arg_is_processed = True
## If it is not a file, it must be a phil command
else:
try:
command_line_params = argument_interpreter.process(arg=arg)
if command_line_params is not None:
phil_objects.append(command_line_params)
arg_is_processed = True
except KeyboardInterrupt:
raise
except Exception:
pass
if not arg_is_processed:
print("##----------------------------------------------##",
file=log)
print("## Unknown phil-file or phil-command:", arg, file=log)
print("##----------------------------------------------##",
file=log)
print(file=log)
raise Sorry("Unknown file format or phil command: %s" % arg)
effective_params = master_params.fetch(sources=phil_objects)
params = effective_params.extract()
## Now please read in the reflections files
## get symmetry and cell data first please
## By default, the native cell and symmetry are used
## as reference
crystal_symmetry_nat = None
crystal_symmetry_nat = crystal_symmetry_from_any.extract_from(
file_name=params.scaling.input.xray_data.native.file_name)
if params.scaling.input.xray_data.space_group is None:
params.scaling.input.xray_data.space_group =\
crystal_symmetry_nat.space_group_info()
print("Using symmetry of native data", file=log)
if params.scaling.input.xray_data.unit_cell is None:
params.scaling.input.xray_data.unit_cell =\
crystal_symmetry_nat.unit_cell()
print("Using cell of native data", file=log)
## Check if a unit cell is defined
if params.scaling.input.xray_data.space_group is None:
raise Sorry("No space group defined")
if params.scaling.input.xray_data.unit_cell is None:
raise Sorry("No unit cell defined")
crystal_symmetry = crystal_symmetry = crystal.symmetry(
unit_cell=params.scaling.input.xray_data.unit_cell,
space_group_symbol=str(params.scaling.input.xray_data.space_group))
effective_params = master_params.fetch(sources=phil_objects)
new_params = master_params.format(python_object=params)
print("Effective parameters", file=log)
print("#phil __ON__", file=log)
new_params.show(out=log,
expert_level=params.scaling.input.expert_level)
print("#phil __END__", file=log)
print(file=log)
## define a xray data server
xray_data_server = reflection_file_utils.reflection_file_server(
crystal_symmetry=crystal_symmetry,
force_symmetry=True,
reflection_files=[])
## Read in native data and make appropriatre selections
miller_array_native = None
miller_array_native = xray_data_server.get_xray_data(
file_name=params.scaling.input.xray_data.native.file_name,
labels=params.scaling.input.xray_data.native.labels,
ignore_all_zeros=True,
parameter_scope='scaling.input.SIR_scale.xray_data.native')
info_native = miller_array_native.info()
miller_array_native = miller_array_native.map_to_asu().select(
miller_array_native.indices() != (0, 0, 0))
miller_array_native = miller_array_native.select(
miller_array_native.data() > 0)
## Convert to amplitudes
if (miller_array_native.is_xray_intensity_array()):
miller_array_native = miller_array_native.f_sq_as_f()
elif (miller_array_native.is_complex_array()):
miller_array_native = abs(miller_array_native)
if not miller_array_native.is_real_array():
raise Sorry("miller_array_native is not a real array")
miller_array_native.set_info(info=info_native)
## Read in derivative data and make appropriate selections
miller_array_derivative = None
miller_array_derivative = xray_data_server.get_xray_data(
file_name=params.scaling.input.xray_data.derivative.file_name,
labels=params.scaling.input.xray_data.derivative.labels,
ignore_all_zeros=True,
parameter_scope='scaling.input.SIR_scale.xray_data.derivative')
info_derivative = miller_array_derivative.info()
miller_array_derivative = miller_array_derivative.map_to_asu().select(
miller_array_derivative.indices() != (0, 0, 0))
miller_array_derivative = miller_array_derivative.select(
miller_array_derivative.data() > 0)
## Convert to amplitudes
if (miller_array_derivative.is_xray_intensity_array()):
miller_array_derivative = miller_array_derivative.f_sq_as_f()
elif (miller_array_derivative.is_complex_array()):
miller_array_derivative = abs(miller_array_derivative)
if not miller_array_derivative.is_real_array():
raise Sorry("miller_array_derivative is not a real array")
miller_array_derivative.set_info(info=info_derivative)
## As this is a SIR case, we will remove any anomalous pairs
if miller_array_derivative.anomalous_flag():
miller_array_derivative = miller_array_derivative.average_bijvoet_mates()\
.set_observation_type( miller_array_derivative )
if miller_array_native.anomalous_flag():
miller_array_native = miller_array_native.average_bijvoet_mates()\
.set_observation_type( miller_array_native )
## Print info
print(file=log)
print("Native data", file=log)
print("===========", file=log)
miller_array_native.show_comprehensive_summary(f=log)
print(file=log)
native_pre_scale = pre_scale.pre_scaler(
miller_array_native,
params.scaling.input.scaling_strategy.pre_scaler_protocol,
params.scaling.input.basic)
miller_array_native = native_pre_scale.x1.deep_copy()
del native_pre_scale
print(file=log)
print("Derivative data", file=log)
print("===============", file=log)
miller_array_derivative.show_comprehensive_summary(f=log)
print(file=log)
derivative_pre_scale = pre_scale.pre_scaler(
miller_array_derivative,
params.scaling.input.scaling_strategy.pre_scaler_protocol,
params.scaling.input.basic)
miller_array_derivative = derivative_pre_scale.x1.deep_copy()
del derivative_pre_scale
scaler = fa_estimation.combined_scaling(
miller_array_native, miller_array_derivative,
params.scaling.input.scaling_strategy.iso_protocol)
miller_array_native = scaler.x1.deep_copy()
miller_array_derivative = scaler.x2.deep_copy()
del scaler
print(file=log)
print("Making delta f's", file=log)
print("----------------", file=log)
print(file=log)
delta_gen = pair_analyses.delta_generator(miller_array_native,
miller_array_derivative)
print(file=log)
print("writing mtz file", file=log)
print("----------------", file=log)
print(file=log)
## some assertions to make sure nothing went weerd
assert miller_array_native.observation_type() is not None
assert miller_array_derivative.observation_type() is not None
assert delta_gen.abs_delta_f.observation_type() is not None
## Please write out the abs_delta_f array
mtz_dataset = delta_gen.abs_delta_f.as_mtz_dataset(
column_root_label='F' + params.scaling.input.output.outlabel)
mtz_dataset.mtz_object().write(
file_name=params.scaling.input.output.hklout)
def run(args):
if len(args)==0:
master_params.show(expert_level=0)
elif ( "--help" in args ):
print "no help available"
elif ( "--h" in args ):
print "no help available"
elif ( "--show_defaults" in args ):
master_params.show(expert_level=0)
elif ( "--show_defaults_all" in args ):
master_params.show(expert_level=10)
else:
log = multi_out()
if (not "--quiet" in args):
log.register(label="stdout", file_object=sys.stdout)
string_buffer = StringIO()
string_buffer_plots = StringIO()
log.register(label="log_buffer", file_object=string_buffer)
log_plots = StringIO()
print >> log,"#phil __OFF__"
print >> log
print >> log, date_and_time()
print >> log
print >> log
phil_objects = []
argument_interpreter = master_params.command_line_argument_interpreter(
home_scope="scaling")
reflection_file = None
for arg in args:
command_line_params = None
arg_is_processed = False
if arg == '--quiet':
arg_is_processed = True
## The associated action with this keyword is implemented above
if (os.path.isfile(arg)): ## is this a file name?
## Check if this is a phil file
try:
command_line_params = iotbx.phil.parse(file_name=arg)
except KeyboardInterrupt: raise
except Exception : pass
if command_line_params is not None:
phil_objects.append(command_line_params)
arg_is_processed = True
## Check if this file is a reflection file
if command_line_params is None:
reflection_file = reflection_file_reader.any_reflection_file(
file_name=arg, ensure_read_access=False)
if (reflection_file is not None):
reflection_file = arg
arg_is_processed = True
## If it is not a file, it must be a phil command
else:
try:
command_line_params = argument_interpreter.process(arg=arg)
if command_line_params is not None:
phil_objects.append(command_line_params)
arg_is_processed = True
except KeyboardInterrupt: raise
except Exception : pass
if not arg_is_processed:
print >> log, "##----------------------------------------------##"
print >> log, "## Unknown phil-file or phil-command:", arg
print >> log, "##----------------------------------------------##"
print >> log
raise Sorry("Unknown file format or phil command: %s" % arg)
effective_params = master_params.fetch(sources=phil_objects)
params = effective_params.extract()
## Now please read in the reflections files
## get symmetry and cell data first please
## By default, the native cell and symmetry are used
## as reference
crystal_symmetry_nat = None
crystal_symmetry_nat = crystal_symmetry_from_any.extract_from(
file_name=params.scaling.input.xray_data.after_burn.file_name)
if params.scaling.input.xray_data.space_group is None:
params.scaling.input.xray_data.space_group =\
crystal_symmetry_nat.space_group_info()
print >> log, "Using symmetry of after_burn data"
if params.scaling.input.xray_data.unit_cell is None:
params.scaling.input.xray_data.unit_cell =\
crystal_symmetry_nat.unit_cell()
print >> log, "Using cell of after_burn data"
## Check if a unit cell is defined
if params.scaling.input.xray_data.space_group is None:
raise Sorry("No space group defined")
if params.scaling.input.xray_data.unit_cell is None:
raise Sorry("No unit cell defined")
crystal_symmetry = crystal_symmetry = crystal.symmetry(
unit_cell = params.scaling.input.xray_data.unit_cell,
space_group_symbol = str(
params.scaling.input.xray_data.space_group) )
effective_params = master_params.fetch(sources=phil_objects)
new_params = master_params.format(python_object=params)
print >> log, "Effective parameters"
print >> log, "#phil __ON__"
new_params.show(out=log,
expert_level=params.scaling.input.expert_level )
print >> log, "#phil __END__"
print >> log
## define a xray data server
xray_data_server = reflection_file_utils.reflection_file_server(
crystal_symmetry = crystal_symmetry,
force_symmetry = True,
reflection_files=[])
## Read in native data and make appropriatre selections
miller_array_native = None
miller_array_native = xray_data_server.get_xray_data(
file_name = params.scaling.input.xray_data.after_burn.file_name,
labels = params.scaling.input.xray_data.after_burn.labels,
ignore_all_zeros = True,
parameter_scope = 'scaling.input.SIR_scale.xray_data.after_burn'
)
info_native = miller_array_native.info()
miller_array_native=miller_array_native.map_to_asu().select(
miller_array_native.indices()!=(0,0,0) )
miller_array_native = miller_array_native.select(
miller_array_native.data() > 0 )
## Convert to amplitudes
if (miller_array_native.is_xray_intensity_array()):
miller_array_native = miller_array_native.f_sq_as_f()
elif (miller_array_native.is_complex_array()):
miller_array_native = abs(miller_array_native)
if not miller_array_native.is_real_array():
raise Sorry("miller_array_native is not a real array")
miller_array_native.set_info(info = info_native)
## Read in derivative data and make appropriate selections
miller_array_derivative = None
miller_array_derivative = xray_data_server.get_xray_data(
file_name = params.scaling.input.xray_data.before_burn.file_name,
labels = params.scaling.input.xray_data.before_burn.labels,
ignore_all_zeros = True,
parameter_scope = 'scaling.input.SIR_scale.xray_data.before_burn'
)
info_derivative = miller_array_derivative.info()
miller_array_derivative=miller_array_derivative.map_to_asu().select(
miller_array_derivative.indices()!=(0,0,0) )
miller_array_derivative = miller_array_derivative.select(
miller_array_derivative.data() > 0 )
## Convert to amplitudes
if (miller_array_derivative.is_xray_intensity_array()):
miller_array_derivative = miller_array_derivative.f_sq_as_f()
elif (miller_array_derivative.is_complex_array()):
miller_array_derivative = abs(miller_array_derivative)
if not miller_array_derivative.is_real_array():
raise Sorry("miller_array_derivative is not a real array")
miller_array_derivative.set_info(info = info_derivative)
## As this is a SIR case, we will remove any anomalous pairs
if miller_array_derivative.anomalous_flag():
miller_array_derivative = miller_array_derivative.average_bijvoet_mates()\
.set_observation_type( miller_array_derivative )
if miller_array_native.anomalous_flag():
miller_array_native = miller_array_native.average_bijvoet_mates()\
.set_observation_type( miller_array_native )
## Print info
print >> log
print >> log, "Native data"
print >> log, "==========="
miller_array_native.show_comprehensive_summary(f=log)
print >> log
native_pre_scale = pre_scale.pre_scaler(
miller_array_native,
params.scaling.input.scaling_strategy.pre_scaler_protocol,
params.scaling.input.basic)
miller_array_native = native_pre_scale.x1.deep_copy()
del native_pre_scale
print >> log
print >> log, "Derivative data"
print >> log, "==============="
miller_array_derivative.show_comprehensive_summary(f=log)
print >> log
derivative_pre_scale = pre_scale.pre_scaler(
miller_array_derivative,
params.scaling.input.scaling_strategy.pre_scaler_protocol,
params.scaling.input.basic)
miller_array_derivative = derivative_pre_scale.x1.deep_copy()
del derivative_pre_scale
scaler = fa_estimation.combined_scaling(
miller_array_native,
miller_array_derivative,
params.scaling.input.scaling_strategy.iso_protocol)
miller_array_native = scaler.x1.deep_copy()
miller_array_derivative = scaler.x2.deep_copy()
del scaler
print >> log
print >> log, "Making delta f's"
print >> log, "----------------"
print >> log
delta_gen = pair_analyses.delta_generator( miller_array_native,
miller_array_derivative,
params.scaling.input.scaling_strategy.iso_protocol.nsr_bias )
print >> log
print >> log, "writing mtz file"
print >> log, "----------------"
print >> log
## some assertions to make sure nothing went weerd
assert miller_array_native.observation_type() is not None
assert miller_array_derivative.observation_type() is not None
assert delta_gen.abs_delta_f.observation_type() is not None
## Please write out the abs_delta_f array
mtz_dataset = delta_gen.abs_delta_f.as_mtz_dataset(
column_root_label='F'+params.scaling.input.output.outlabel)
mtz_dataset.mtz_object().write(
file_name=params.scaling.input.output.hklout)
