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())