def exercise_recycle(space_group_info, anomalous_flag, n_scatterers=8, d_min=2.5, verbose=0):
f_calc = random_f_calc(
space_group_info=space_group_info,
n_scatterers=n_scatterers,
d_min=d_min,
anomalous_flag=anomalous_flag,
verbose=verbose,
)
if f_calc is None:
return
recycle(f_calc, "f_calc", verbose=verbose)
for column_root_label, column_types in [("f_obs", None), ("Ework", "E")]:
if anomalous_flag and column_types == "E":
continue
recycle(
miller_array=abs(f_calc), column_root_label=column_root_label, column_types=column_types, verbose=verbose
)
if not anomalous_flag:
recycle(abs(f_calc), "f_obs", column_types="R", verbose=verbose)
for column_root_label, column_types in [("f_obs", None), ("Ework", "EQ")]:
if anomalous_flag and column_types == "EQ":
continue
recycle(
miller_array=miller.array(
miller_set=f_calc, data=flex.abs(f_calc.data()), sigmas=flex.abs(f_calc.data()) / 10
),
column_root_label=column_root_label,
column_types=column_types,
verbose=verbose,
)
recycle(f_calc.centric_flags(), "cent", verbose=verbose)
recycle(generate_random_hl(miller_set=f_calc), "prob", verbose=verbose)
def exercise_recycle(space_group_info,
anomalous_flag,
n_scatterers=8,
d_min=2.5,
verbose=0):
f_calc = random_f_calc(space_group_info=space_group_info,
n_scatterers=n_scatterers,
d_min=d_min,
anomalous_flag=anomalous_flag,
verbose=verbose)
if (f_calc is None): return
recycle(f_calc, "f_calc", verbose=verbose)
for column_root_label, column_types in [("f_obs", None), ("Ework", "E")]:
if (anomalous_flag and column_types == "E"): continue
recycle(miller_array=abs(f_calc),
column_root_label=column_root_label,
column_types=column_types,
verbose=verbose)
if (not anomalous_flag):
recycle(abs(f_calc), "f_obs", column_types="R", verbose=verbose)
for column_root_label, column_types in [("f_obs", None), ("Ework", "EQ")]:
if (anomalous_flag and column_types == "EQ"): continue
recycle(miller_array=miller.array(miller_set=f_calc,
data=flex.abs(f_calc.data()),
sigmas=flex.abs(f_calc.data()) / 10),
column_root_label=column_root_label,
column_types=column_types,
verbose=verbose)
recycle(f_calc.centric_flags(), "cent", verbose=verbose)
recycle(generate_random_hl(miller_set=f_calc), "prob", verbose=verbose)
def exercise(space_group_info,
n_scatterers=8,
d_min=2.5,
anomalous_flag=False,
verbose=0):
f_calc = random_f_calc(space_group_info=space_group_info,
n_scatterers=n_scatterers,
d_min=d_min,
anomalous_flag=anomalous_flag,
verbose=verbose)
if (f_calc is None): return
data = flex.norm(f_calc.data())
scale_factor = 9999998 / flex.max(data)
data = data * scale_factor + 1
f_calc = miller.array(miller_set=f_calc, data=data, sigmas=data /
10).set_observation_type_xray_intensity()
f_calc = f_calc.select(flex.random_permutation(size=data.size()))
recycle(miller_array=f_calc)
recycle(miller_array=f_calc.f_sq_as_f())
def exercise(space_group_info, n_scatterers=8, d_min=2.5,
anomalous_flag=False, verbose=0):
f_calc = random_f_calc(
space_group_info=space_group_info,
n_scatterers=n_scatterers,
d_min=d_min,
anomalous_flag=anomalous_flag,
verbose=verbose)
if (f_calc is None): return
data = flex.norm(f_calc.data())
scale_factor = 9999998/flex.max(data)
data = data * scale_factor + 1
f_calc = miller.array(
miller_set=f_calc,
data=data,
sigmas=data/10).set_observation_type_xray_intensity()
f_calc = f_calc.select(flex.random_permutation(size=data.size()))
recycle(miller_array=f_calc)
recycle(miller_array=f_calc.f_sq_as_f())
