def process_sas_data(datalist, conf, **kwargs): """ This function combines Steps 1 through 9 of the data reduction process for Small-Angle Scattering section 2.5.1 as specified by the documents at U{http://neutrons.ornl.gov/asg/projects/SCL/reqspec/DR_Lib_RS.doc}. The function takes a list of file names, a L{hlr_utils.Configure} object and processes the data accordingly. This function should really only be used in the context of I{sas_reduction}. @param datalist: A list containing the filenames of the data to be processed. @type datalist: C{list} of C{string}s @param conf: Object that contains the current setup of the driver. @type conf: L{hlr_utils.Configure} @param kwargs: A list of keyword arguments that the function accepts: @keyword inst_geom_dst: File object that contains instrument geometry information. @type inst_geom_dst: C{DST.GeomDST} @keyword dataset_type: The practical name of the dataset being processed. The default value is I{data}. @type dataset_type: C{string} @keyword trans_data: Alternate data for the transmission spectrum. This is used in the absence of transmission monitors. @type trans_data: C{string} @keyword transmission: A flag that signals the function to stop after doing the conversion from TOF to wavelength. The default is I{False}. @type transmission: C{boolean} @keyword bkg_subtract: A list of coefficients that help determine the wavelength dependent background subtraction. @type bkg_subtract: C{list} @keyword get_background: A flag that signals the function to convert the main data to wavelength and exit before normalizing to the beam monitor. @type get_background: C{boolean} @keyword acc_down_time: The information for the accelerator downtime. @type acc_down_time: C{tuple} @keyword bkg_scale: The scaling used for the axis dependent background parameters. @type bkg_scale: C{float} @keyword timer: Timing object so the function can perform timing estimates. @type timer: C{sns_timer.DiffTime} @return: Object that has undergone all requested processing steps @rtype: C{SOM.SOM} """ import common_lib import dr_lib import hlr_utils # Check keywords try: dataset_type = kwargs["dataset_type"] except KeyError: dataset_type = "data" try: i_geom_dst = kwargs["inst_geom_dst"] except KeyError: i_geom_dst = None try: t = kwargs["timer"] except KeyError: t = None try: transmission = kwargs["transmission"] except KeyError: transmission = False try: bkg_subtract = kwargs["bkg_subtract"] except KeyError: bkg_subtract = None try: trans_data = kwargs["trans_data"] except KeyError: trans_data = None try: get_background = kwargs["get_background"] except KeyError: get_background = False acc_down_time = kwargs.get("acc_down_time") bkg_scale = kwargs.get("bkg_scale") # Add so_axis to Configure object conf.so_axis = "time_of_flight" # Step 0: Open appropriate data files # Data if conf.verbose: print "Reading %s file" % dataset_type # The [0] is to get the data SOM and ignore the None background SOM dp_som = dr_lib.add_files(datalist, Data_Paths=conf.data_paths.toPath(), SO_Axis=conf.so_axis, Signal_ROI=conf.roi_file, dataset_type=dataset_type, Verbose=conf.verbose, Timer=t) if t is not None: t.getTime(msg="After reading %s " % dataset_type) dp_som1 = dr_lib.fix_bin_contents(dp_som) del dp_som if conf.inst_geom is not None: i_geom_dst.setGeometry(conf.data_paths.toPath(), dp_som1) if conf.dump_tof_r: dp_som1_1 = dr_lib.create_param_vs_Y(dp_som1, "radius", "param_array", conf.r_bins.toNessiList(), y_label="counts", y_units="counts / (usec * m)", x_labels=["Radius", "TOF"], x_units=["m", "usec"]) hlr_utils.write_file(conf.output, "text/Dave2d", dp_som1_1, output_ext="tvr", extra_tag=dataset_type, verbose=conf.verbose, data_ext=conf.ext_replacement, path_replacement=conf.path_replacement, message="TOF vs radius information") del dp_som1_1 if conf.dump_tof_theta: dp_som1_1 = dr_lib.create_param_vs_Y(dp_som1, "polar", "param_array", conf.theta_bins.toNessiList(), y_label="counts", y_units="counts / (usec * rads)", x_labels=["Polar Angle", "TOF"], x_units=["rads", "usec"]) hlr_utils.write_file(conf.output, "text/Dave2d", dp_som1_1, output_ext="tvt", extra_tag=dataset_type, verbose=conf.verbose, data_ext=conf.ext_replacement, path_replacement=conf.path_replacement, message="TOF vs polar angle information") del dp_som1_1 # Beam monitor if not get_background: if conf.beammon_over is None: if conf.verbose: print "Reading in beam monitor data from %s file" \ % dataset_type # The [0] is to get the data SOM and ignore the None # background SOM dbm_som0 = dr_lib.add_files(datalist, Data_Paths=conf.bmon_path.toPath(), SO_Axis=conf.so_axis, dataset_type=dataset_type, Verbose=conf.verbose, Timer=t) if t is not None: t.getTime(msg="After reading beam monitor data ") if conf.inst_geom is not None: i_geom_dst.setGeometry(conf.bmon_path.toPath(), dbm_som0) else: if conf.verbose: print "Reading in vanadium data" dbm_som0 = dr_lib.add_files(datalist, Data_Paths=conf.data_paths.toPath(), Signal_ROI=conf.roi_file, SO_Axis=conf.so_axis, dataset_type=dataset_type, Verbose=conf.verbose, Timer=t) if t is not None: t.getTime(msg="After reading vanadium data ") if conf.inst_geom is not None: i_geom_dst.setGeometry(conf.data_paths.toPath(), dbm_som0) dbm_som1 = dr_lib.fix_bin_contents(dbm_som0) del dbm_som0 else: dbm_som1 = None # Transmission monitor if trans_data is None: if conf.verbose: print "Reading in transmission monitor data from %s file" \ % dataset_type try: dtm_som0 = dr_lib.add_files(datalist, Data_Paths=conf.tmon_path.toPath(), SO_Axis=conf.so_axis, dataset_type=dataset_type, Verbose=conf.verbose, Timer=t) if t is not None: t.getTime(msg="After reading transmission monitor data ") if conf.inst_geom is not None: i_geom_dst.setGeometry(conf.tmon_path.toPath(), dtm_som0) dtm_som1 = dr_lib.fix_bin_contents(dtm_som0) del dtm_som0 # Transmission monitor cannot be found except KeyError: if conf.verbose: print "Transmission monitor not found" dtm_som1 = None else: dtm_som1 = None # Note: time_zero_offset_det MUST be a tuple if conf.time_zero_offset_det is not None: dp_som1.attr_list["Time_zero_offset_det"] = \ conf.time_zero_offset_det.toValErrTuple() # Note: time_zero_offset_mon MUST be a tuple if conf.time_zero_offset_mon is not None and not get_background and \ conf.beammon_over is None: dbm_som1.attr_list["Time_zero_offset_mon"] = \ conf.time_zero_offset_mon.toValErrTuple() if conf.beammon_over is not None: dbm_som1.attr_list["Time_zero_offset_det"] = \ conf.time_zero_offset_det.toValErrTuple() if trans_data is None and dtm_som1 is not None: dtm_som1.attr_list["Time_zero_offset_mon"] = \ conf.time_zero_offset_mon.toValErrTuple() # Step 1: Convert TOF to wavelength for data and monitor if conf.verbose: print "Converting TOF to wavelength" if t is not None: t.getTime(False) if not get_background: # Convert beam monitor if conf.beammon_over is None: dbm_som2 = common_lib.tof_to_wavelength_lin_time_zero( dbm_som1, units="microsecond", time_zero_offset=conf.time_zero_offset_mon.toValErrTuple()) else: dbm_som2 = common_lib.tof_to_wavelength_lin_time_zero( dbm_som1, units="microsecond", time_zero_offset=conf.time_zero_offset_det.toValErrTuple(), inst_param="total") else: dbm_som2 = None # Convert detector pixels dp_som2 = common_lib.tof_to_wavelength_lin_time_zero( dp_som1, units="microsecond", time_zero_offset=conf.time_zero_offset_det.toValErrTuple(), inst_param="total") if get_background: return dp_som2 if dtm_som1 is not None: # Convert transmission monitor dtm_som2 = common_lib.tof_to_wavelength_lin_time_zero( dtm_som1, units="microsecond", time_zero_offset=conf.time_zero_offset_mon.toValErrTuple()) else: dtm_som2 = dtm_som1 if t is not None: t.getTime(msg="After converting TOF to wavelength ") del dp_som1, dbm_som1, dtm_som1 if conf.verbose and (conf.lambda_low_cut is not None or \ conf.lambda_high_cut is not None): print "Cutting data spectra" if t is not None: t.getTime(False) dp_som3 = dr_lib.cut_spectra(dp_som2, conf.lambda_low_cut, conf.lambda_high_cut) if t is not None: t.getTime(msg="After cutting data spectra ") del dp_som2 if conf.beammon_over is not None: dbm_som2 = dr_lib.cut_spectra(dbm_som2, conf.lambda_low_cut, conf.lambda_high_cut) if conf.dump_wave: hlr_utils.write_file(conf.output, "text/Spec", dp_som3, output_ext="pxl", extra_tag=dataset_type, verbose=conf.verbose, data_ext=conf.ext_replacement, path_replacement=conf.path_replacement, message="pixel wavelength information") if conf.dump_bmon_wave: if conf.beammon_over is None: hlr_utils.write_file(conf.output, "text/Spec", dbm_som2, output_ext="bmxl", extra_tag=dataset_type, verbose=conf.verbose, data_ext=conf.ext_replacement, path_replacement=conf.path_replacement, message="beam monitor wavelength information") else: dbm_som2_1 = dr_lib.sum_by_rebin_frac(dbm_som2, conf.lambda_bins.toNessiList()) hlr_utils.write_file(conf.output, "text/Spec", dbm_som2_1, output_ext="bmxl", extra_tag=dataset_type, verbose=conf.verbose, data_ext=conf.ext_replacement, path_replacement=conf.path_replacement, message="beam monitor override wavelength "\ +"information") del dbm_som2_1 # Step 2: Subtract wavelength dependent background if necessary if conf.verbose and bkg_subtract is not None: print "Subtracting wavelength dependent background" if bkg_subtract is not None: if t is not None: t.getTime(False) duration = dp_som3.attr_list["%s-duration" % dataset_type] scale = duration.getValue() - acc_down_time[0] dp_som4 = dr_lib.subtract_axis_dep_bkg(dp_som3, bkg_subtract, old_scale=bkg_scale, new_scale=scale) if t is not None: t.getTime(msg="After subtracting wavelength dependent background ") else: dp_som4 = dp_som3 del dp_som3 # Step 3: Efficiency correct beam monitor if conf.verbose and conf.mon_effc: print "Efficiency correct beam monitor data" if t is not None: t.getTime(False) if conf.mon_effc: dbm_som3 = dr_lib.feff_correct_mon(dbm_som2, inst_name=conf.inst, eff_const=conf.mon_eff_const) else: dbm_som3 = dbm_som2 if t is not None and conf.mon_effc: t.getTime(msg="After efficiency correcting beam monitor ") if conf.dump_bmon_effc and conf.mon_effc: hlr_utils.write_file(conf.output, "text/Spec", dbm_som3, output_ext="bmel", extra_tag=dataset_type, verbose=conf.verbose, data_ext=conf.ext_replacement, path_replacement=conf.path_replacement, message="beam monitor wavelength information "\ +"(efficiency)") del dbm_som2 # Step 4: Efficiency correct transmission monitor if dtm_som2 is not None: if conf.verbose and conf.mon_effc: print "Efficiency correct transmission monitor data" if t is not None: t.getTime(False) if conf.mon_effc: dtm_som3 = dr_lib.feff_correct_mon(dtm_som2) else: dtm_som3 = dtm_som2 else: dtm_som3 = dtm_som2 if t is not None and conf.mon_effc and dtm_som2 is not None: t.getTime(msg="After efficiency correcting beam monitor ") # Step 5: Efficiency correct detector pixels if conf.det_effc: if conf.verbose: print "Calculating detector efficiency" if t is not None: t.getTime(False) det_eff = dr_lib.create_det_eff(dp_som4, inst_name=conf.inst, eff_scale_const=conf.det_eff_scale_const, eff_atten_const=conf.det_eff_atten_const) if t is not None: t.getTime(msg="After calculating detector efficiency") if conf.verbose: print "Applying detector efficiency" if t is not None: t.getTime(False) dp_som5 = common_lib.div_ncerr(dp_som4, det_eff) if t is not None: t.getTime(msg="After spplying detector efficiency") else: dp_som5 = dp_som4 del dp_som4 # Step 6: Rebin beam monitor axis onto detector pixel axis if conf.beammon_over is None: if not conf.no_bmon_norm: if conf.verbose: print "Rebin beam monitor axis to detector pixel axis" if t is not None: t.getTime(False) dbm_som4 = dr_lib.rebin_monitor(dbm_som3, dp_som5, rtype="frac") if t is not None: t.getTime(msg="After rebinning beam monitor ") else: dbm_som4 = dbm_som3 else: dbm_som4 = dbm_som3 del dbm_som3 if conf.dump_bmon_rebin: hlr_utils.write_file(conf.output, "text/Spec", dbm_som4, output_ext="bmrl", extra_tag=dataset_type, verbose=conf.verbose, data_ext=conf.ext_replacement, path_replacement=conf.path_replacement, message="beam monitor wavelength information "\ +"(rebinned)") # Step 7: Normalize data by beam monitor if not conf.no_bmon_norm: if conf.verbose: print "Normalizing data by beam monitor" if t is not None: t.getTime(False) dp_som6 = common_lib.div_ncerr(dp_som5, dbm_som4) if t is not None: t.getTime(msg="After normalizing data by beam monitor ") else: dp_som6 = dp_som5 del dp_som5 if transmission: return dp_som6 if conf.dump_wave_bmnorm: dp_som6_1 = dr_lib.sum_by_rebin_frac(dp_som6, conf.lambda_bins.toNessiList()) write_message = "combined pixel wavelength information" write_message += " (beam monitor normalized)" hlr_utils.write_file(conf.output, "text/Spec", dp_som6_1, output_ext="pbml", extra_tag=dataset_type, verbose=conf.verbose, data_ext=conf.ext_replacement, path_replacement=conf.path_replacement, message=write_message) del dp_som6_1 if conf.dump_wave_r: dp_som6_1 = dr_lib.create_param_vs_Y(dp_som6, "radius", "param_array", conf.r_bins.toNessiList(), rebin_axis=conf.lambda_bins.toNessiList(), y_label="counts", y_units="counts / (Angstrom * m)", x_labels=["Radius", "Wavelength"], x_units=["m", "Angstrom"]) hlr_utils.write_file(conf.output, "text/Dave2d", dp_som6_1, output_ext="lvr", extra_tag=dataset_type, verbose=conf.verbose, data_ext=conf.ext_replacement, path_replacement=conf.path_replacement, message="wavelength vs radius information") del dp_som6_1 if conf.dump_wave_theta: dp_som6_1 = dr_lib.create_param_vs_Y(dp_som6, "polar", "param_array", conf.theta_bins.toNessiList(), rebin_axis=conf.lambda_bins.toNessiList(), y_label="counts", y_units="counts / (Angstrom * rads)", x_labels=["Polar Angle", "Wavelength"], x_units=["rads", "Angstrom"]) hlr_utils.write_file(conf.output, "text/Dave2d", dp_som6_1, output_ext="lvt", extra_tag=dataset_type, verbose=conf.verbose, data_ext=conf.ext_replacement, path_replacement=conf.path_replacement, message="wavelength vs polar angle information") del dp_som6_1 # Step 8: Rebin transmission monitor axis onto detector pixel axis if trans_data is not None: print "Reading in transmission monitor data from file" dtm_som3 = dr_lib.add_files([trans_data], dataset_type=dataset_type, dst_type="text/Spec", Verbose=conf.verbose, Timer=t) if conf.verbose and dtm_som3 is not None: print "Rebin transmission monitor axis to detector pixel axis" if t is not None: t.getTime(False) dtm_som4 = dr_lib.rebin_monitor(dtm_som3, dp_som6, rtype="frac") if t is not None and dtm_som3 is not None: t.getTime(msg="After rebinning transmission monitor ") del dtm_som3 # Step 9: Normalize data by transmission monitor if conf.verbose and dtm_som4 is not None: print "Normalizing data by transmission monitor" if t is not None: t.getTime(False) if dtm_som4 is not None: # The transmission spectra derived from sas_tranmission does not have # the same y information by convention as sample data or a # tranmission monitor. Therefore, we'll fake it by setting the # y information from the sample data into the transmission if trans_data is not None: dtm_som4.setYLabel(dp_som6.getYLabel()) dtm_som4.setYUnits(dp_som6.getYUnits()) dp_som7 = common_lib.div_ncerr(dp_som6, dtm_som4) else: dp_som7 = dp_som6 if t is not None and dtm_som4 is not None: t.getTime(msg="After normalizing data by transmission monitor ") del dp_som6 # Step 10: Convert wavelength to Q for data if conf.verbose: print "Converting data from wavelength to scalar Q" if t is not None: t.getTime(False) dp_som8 = common_lib.wavelength_to_scalar_Q(dp_som7) if t is not None: t.getTime(msg="After converting wavelength to scalar Q ") del dp_som7 if conf.facility == "LENS": # Step 11: Apply SAS correction factor to data if conf.verbose: print "Applying geometrical correction" if t is not None: t.getTime(False) dp_som9 = dr_lib.apply_sas_correct(dp_som8) if t is not None: t.getTime(msg="After applying geometrical correction ") return dp_som9 else: return dp_som8
def calibrate_dgs_data(datalist, conf, dkcur, **kwargs): """ This function combines Steps 3 through 6 in Section 2.1.1 of the data reduction process for Direct Geometry Spectrometers as specified by the document at U{http://neutrons.ornl.gov/asg/projects/SCL/reqspec/DR_Lib_RS.doc}. The function takes a list of file names, a L{hlr_utils.Configure} object and processes the data accordingly. @param datalist: A list containing the filenames of the data to be processed. @type datalist: C{list} of C{string}s @param conf: Object that contains the current setup of the driver. @type conf: L{hlr_utils.Configure} @param dkcur: The object containing the TOF dark current data. @type dkcur: C{SOM.SOM} @param kwargs: A list of keyword arguments that the function accepts: @keyword inst_geom_dst: File object that contains instrument geometry information. @type inst_geom_dst: C{DST.GeomDST} @keyword tib_const: A time-independent background constant to subtract from every pixel. @type tib_const: L{hlr_utils.DrParameter} @keyword dataset_type: The practical name of the dataset being processed. The default value is I{data}. @type dataset_type: C{string} @keyword cwp: A list of chopper phase corrections in units of microseconds. @type cwp: C{list} of C{float}s @keyword timer: Timing object so the function can perform timing estimates. @type timer: C{sns_timer.DiffTime} @return: Object that has undergone all requested processing steps @rtype: C{SOM.SOM} """ import common_lib import dr_lib import hlr_utils # Check keywords try: tib_const = kwargs["tib_const"] except KeyError: tib_const = None try: dataset_type = kwargs["dataset_type"] except KeyError: dataset_type = "data" try: t = kwargs["timer"] except KeyError: t = None try: i_geom_dst = kwargs["inst_geom_dst"] except KeyError: i_geom_dst = None dataset_cwp = kwargs.get("cwp") # Open the appropriate datafiles if conf.verbose: print "Reading %s file" % dataset_type data_paths = conf.data_paths.toPath() if conf.no_mon_norm: mon_paths = None else: mon_paths = conf.usmon_path.toPath() # Check for mask file since normalization drive doesn't understand option try: mask_file = conf.mask_file except AttributeError: mask_file = None if t is not None: oldtime = t.getOldTime() (dp_som0, dm_som0) = dr_lib.add_files_dm(datalist, Data_Paths=data_paths, Mon_Paths=mon_paths, SO_Axis=conf.so_axis, Signal_ROI=conf.roi_file, Signal_MASK=mask_file, dataset_type=dataset_type, dataset_cwp=dataset_cwp, Verbose=conf.verbose, Timer=t) if t is not None: t.setOldTime(oldtime) t.getTime(msg="After reading %s file" % dataset_type) # Cut the spectra if necessary dp_somA = dr_lib.cut_spectra(dp_som0, conf.tof_cut_min, conf.tof_cut_max) del dp_som0 dp_somB = dr_lib.fix_bin_contents(dp_somA) del dp_somA if dp_somB.attr_list.instrument.get_name() != "CNCS": if conf.verbose: print "Cutting spectrum at minimum TOF" if t is not None: t.getTime(False) # Calculate minimum TOF for physical neutrons if conf.initial_energy is not None: initial_wavelength = common_lib.energy_to_wavelength(\ conf.initial_energy.toValErrTuple()) initial_velocity = common_lib.wavelength_to_velocity(\ initial_wavelength) else: # This should actually calculate it, but don't have a way right now pass if conf.time_zero_offset is not None: time_zero_offset = conf.time_zero_offset.toValErrTuple() else: # This should actually calculate it, but don't have a way right now time_zero_offset = (0.0, 0.0) ss_length = dp_somB.attr_list.instrument.get_primary() tof_min = (ss_length[0] / initial_velocity[0]) + time_zero_offset[0] # Cut all spectra a the minimum TOF dp_som1 = dr_lib.cut_spectra(dp_somB, tof_min, None) if t is not None: t.getTime(msg="After cutting spectrum at minimum TOF ") else: dp_som1 = dp_somB del dp_somB if dm_som0 is not None: dm_som1 = dr_lib.fix_bin_contents(dm_som0) else: dm_som1 = dm_som0 del dm_som0 # Override geometry if necessary if conf.inst_geom is not None: i_geom_dst.setGeometry(data_paths, dp_som1) if conf.inst_geom is not None and dm_som1 is not None: i_geom_dst.setGeometry(mon_paths, dm_som1) # Step 3: Integrate the upstream monitor if dm_som1 is not None: if conf.verbose: print "Integrating upstream monitor spectrum" if t is not None: t.getTime(False) if conf.mon_int_range is None: start_val = float("inf") end_val = float("inf") else: start_val = conf.mon_int_range[0] end_val = conf.mon_int_range[1] dm_som2 = dr_lib.integrate_spectra(dm_som1, start=start_val, end=end_val, width=True) if t is not None: t.getTime(msg="After integrating upstream monitor spectrum ") else: dm_som2 = dm_som1 del dm_som1 tib_norm_const = None # Step 4: Divide data set by summed monitor spectrum if dm_som2 is not None: if conf.verbose: print "Normalizing %s by monitor sum" % dataset_type if t is not None: t.getTime(False) dp_som2 = common_lib.div_ncerr(dp_som1, dm_som2, length_one_som=True) tib_norm_const = dm_som2[0].y if t is not None: t.getTime(msg="After normalizing %s by monitor sum" % dataset_type) elif conf.pc_norm: if conf.verbose: print "Normalizing %s by proton charge" % dataset_type pc_tag = dataset_type+"-proton_charge" pc = dp_som1.attr_list[pc_tag] # Scale the proton charge and then set the scale PC back to attributes if conf.scale_pc is not None: if conf.verbose: print "Scaling %s proton charge" % dataset_type pc = hlr_utils.scale_proton_charge(pc, conf.scale_pc) dp_som1.attr_list[pc_tag] = pc tib_norm_const = pc.getValue() if t is not None: t.getTime(False) dp_som2 = common_lib.div_ncerr(dp_som1, (pc.getValue(), 0.0)) if t is not None: t.getTime(msg="After normalizing %s by proton charge" \ % dataset_type) else: dp_som2 = dp_som1 del dp_som1, dm_som2 # Step 5: Scale dark current by data set measurement time if dkcur is not None: if conf.verbose: print "Scaling dark current by %s acquisition time" % dataset_type if t is not None: t.getTime(False) dstime_tag = dataset_type+"-duration" dstime = dp_som2.attr_list[dstime_tag] dkcur1 = common_lib.div_ncerr(dkcur, (dstime.getValue(), 0.0)) if t is not None: t.getTime(msg="After scaling dark current by %s acquisition time" \ % dataset_type) else: dkcur1 = dkcur del dkcur # Step 6: Subtract scaled dark current from data set if dkcur1 is not None: if conf.verbose: print "Subtracting %s by scaled dark current" % dataset_type if t is not None: t.getTime(False) dp_som3 = common_lib.sub_ncerr(dp_som2, dkcur1) if t is not None: t.getTime(msg="After subtracting %s by scaled dark current" \ % dataset_type) elif tib_const is not None and dkcur1 is None: if conf.verbose: print "Subtracting TIB constant from %s" % dataset_type # Normalize the TIB constant by dividing by the current normalization # the duration (if necessary) and the conversion from seconds to # microseconds tib_c = tib_const.toValErrTuple() conv_sec_to_usec = 1.0e-6 if tib_norm_const is None: tib_norm_const = 1 duration = 1 else: duration_tag = dataset_type+"-duration" duration = dp_som2.attr_list[duration_tag].getValue() norm_const = (duration * conv_sec_to_usec) / tib_norm_const tib_val = tib_c[0] * norm_const tib_err2 = tib_c[1] * (norm_const * norm_const) if t is not None: t.getTime(False) dp_som3 = common_lib.sub_ncerr(dp_som2, (tib_val, tib_err2)) if t is not None: t.getTime(msg="After subtracting TIB constant from %s" \ % dataset_type) elif conf.tib_range is not None and dkcur1 is None: if conf.verbose: print "Determining TIB constant from %s" % dataset_type if t is not None: t.getTime(False) TIB = dr_lib.determine_time_indep_bkg(dp_som2, conf.tib_range, is_range=True) if t is not None: t.getTime(msg="After determining TIB constant from %s" \ % dataset_type) if conf.dump_tib: file_comment = "TIB TOF Range: [%d, %d]" % (conf.tib_range[0], conf.tib_range[1]) hlr_utils.write_file(conf.output, "text/num-info", TIB, output_ext="tib", extra_tag=dataset_type, verbose=conf.verbose, data_ext=conf.ext_replacement, path_replacement=conf.path_replacement, message="time-independent background "\ +"information", tag="Average TIB", units="counts/usec", comments=[file_comment]) if conf.verbose: print "Subtracting TIB constant from %s" % dataset_type if t is not None: t.getTime(False) dp_som3 = common_lib.sub_ncerr(dp_som2, TIB) if t is not None: t.getTime(msg="After subtracting TIB constant from %s" \ % dataset_type) del TIB else: dp_som3 = dp_som2 del dp_som2, dkcur1 if conf.dump_ctof_comb: dp_som3_1 = dr_lib.sum_all_spectra(dp_som3) hlr_utils.write_file(conf.output, "text/Spec", dp_som3_1, output_ext="ctof", extra_tag=dataset_type, data_ext=conf.ext_replacement, path_replacement=conf.path_replacement, verbose=conf.verbose, message="combined calibrated TOF information") del dp_som3_1 return dp_som3
def run(config, tim=None): """ This method is where the data reduction process gets done. @param config: Object containing the data reduction configuration information. @type config: L{hlr_utils.Configure} @param tim: (OPTIONAL) Object that will allow the method to perform timing evaluations. @type tim: C{sns_time.DiffTime} """ import common_lib import dr_lib import DST if tim is not None: tim.getTime(False) old_time = tim.getOldTime() if config.data is None: raise RuntimeError("Need to pass a data filename to the driver "\ +"script.") # Read in geometry if one is provided if config.inst_geom is not None: if config.verbose: print "Reading in instrument geometry file" inst_geom_dst = DST.getInstance("application/x-NxsGeom", config.inst_geom) else: inst_geom_dst = None # Add so_axis to Configure object config.so_axis = "time_of_flight" dataset_type = "background" # Step 0: Open appropriate data files # Data if config.verbose: print "Reading %s file" % dataset_type # The [0] is to get the data SOM and ignore the None background SOM dp_som = dr_lib.add_files(config.data, Data_Paths=config.data_paths.toPath(), SO_Axis=config.so_axis, Signal_ROI=config.roi_file, dataset_type=dataset_type, Verbose=config.verbose, Timer=tim) if tim is not None: tim.getTime(msg="After reading %s " % dataset_type) dp_som0 = dr_lib.fix_bin_contents(dp_som) del dp_som if inst_geom_dst is not None: inst_geom_dst.setGeometry(config.data_paths.toPath(), dp_som0) # Note: time_zero_offset_det MUST be a tuple if config.time_zero_offset_det is not None: dp_som0.attr_list["Time_zero_offset_det"] = \ config.time_zero_offset_det.toValErrTuple() # Step 2: Convert TOF to wavelength for data if config.verbose: print "Converting TOF to wavelength" if tim is not None: tim.getTime(False) # Convert detector pixels dp_som1 = common_lib.tof_to_wavelength_lin_time_zero( dp_som0, units="microsecond", time_zero_offset=config.time_zero_offset_det.toValErrTuple(), inst_param="total") if tim is not None: tim.getTime(msg="After converting TOF to wavelength ") del dp_som0 if config.verbose: print "Cutting spectra" if tim is not None: tim.getTime(False) dp_som2 = dr_lib.cut_spectra(dp_som1, config.lambda_low_cut, config.lambda_high_cut) if tim is not None: tim.getTime(msg="After cutting spectra ") del dp_som1 rebin_axis = config.lambda_bins.toNessiList() # Put the data on the same axis if config.verbose: print "Rebinning data onto specified wavelength axis" if tim is not None: tim.getTime(False) dp_som3 = dr_lib.sum_by_rebin_frac(dp_som2, rebin_axis) if tim is not None: tim.getTime(msg="After rebinning data onto specified wavelength axis ") del dp_som2 data_run_time = dp_som3.attr_list["background-duration"] # Calculate the accelerator on time if config.verbose: print "Calculating accelerator on time" acc_on_time = hlr_utils.DrParameter( data_run_time.getValue() - config.acc_down_time.getValue(), 0.0, "seconds") # Get the number of data bins num_wave_bins = len(rebin_axis) - 1 # Calculate the scaled accelerator uptime if config.verbose: print "Calculating the scaled accelerator uptime" if tim is not None: tim.getTime(False) final_scale = acc_on_time.toValErrTuple()[0] / num_wave_bins if tim is not None: tim.getTime(msg="After calculating the scaled accelerator uptime ") # Create the final background spectrum if config.verbose: print "Creating the background spectrum" if tim is not None: tim.getTime(False) dp_som4 = common_lib.div_ncerr(dp_som3, (final_scale, 0)) dp_som4.attr_list["%s-Scaling" % dataset_type] = final_scale if tim is not None: tim.getTime(msg="After creating background spectrum ") del dp_som3 # Write out the background spectrum hlr_utils.write_file(config.output, "text/Spec", dp_som4, verbose=config.verbose, output_ext="bkg", data_ext=config.ext_replacement, replace_path=False, replace_ext=True, message="background spectrum") dp_som4.attr_list["config"] = config hlr_utils.write_file(config.output, "text/rmd", dp_som4, output_ext="rmd", data_ext=config.ext_replacement, path_replacement=config.path_replacement, verbose=config.verbose, message="metadata") if tim is not None: tim.setOldTime(old_time) tim.getTime(msg="Total Running Time")
def calibrate_dgs_data(datalist, conf, dkcur, **kwargs): """ This function combines Steps 3 through 6 in Section 2.1.1 of the data reduction process for Direct Geometry Spectrometers as specified by the document at U{http://neutrons.ornl.gov/asg/projects/SCL/reqspec/DR_Lib_RS.doc}. The function takes a list of file names, a L{hlr_utils.Configure} object and processes the data accordingly. @param datalist: A list containing the filenames of the data to be processed. @type datalist: C{list} of C{string}s @param conf: Object that contains the current setup of the driver. @type conf: L{hlr_utils.Configure} @param dkcur: The object containing the TOF dark current data. @type dkcur: C{SOM.SOM} @param kwargs: A list of keyword arguments that the function accepts: @keyword inst_geom_dst: File object that contains instrument geometry information. @type inst_geom_dst: C{DST.GeomDST} @keyword tib_const: A time-independent background constant to subtract from every pixel. @type tib_const: L{hlr_utils.DrParameter} @keyword dataset_type: The practical name of the dataset being processed. The default value is I{data}. @type dataset_type: C{string} @keyword cwp: A list of chopper phase corrections in units of microseconds. @type cwp: C{list} of C{float}s @keyword timer: Timing object so the function can perform timing estimates. @type timer: C{sns_timer.DiffTime} @return: Object that has undergone all requested processing steps @rtype: C{SOM.SOM} """ import common_lib import dr_lib import hlr_utils # Check keywords try: tib_const = kwargs["tib_const"] except KeyError: tib_const = None try: dataset_type = kwargs["dataset_type"] except KeyError: dataset_type = "data" try: t = kwargs["timer"] except KeyError: t = None try: i_geom_dst = kwargs["inst_geom_dst"] except KeyError: i_geom_dst = None dataset_cwp = kwargs.get("cwp") # Open the appropriate datafiles if conf.verbose: print "Reading %s file" % dataset_type data_paths = conf.data_paths.toPath() if conf.no_mon_norm: mon_paths = None else: mon_paths = conf.usmon_path.toPath() # Check for mask file since normalization drive doesn't understand option try: mask_file = conf.mask_file except AttributeError: mask_file = None if t is not None: oldtime = t.getOldTime() (dp_som0, dm_som0) = dr_lib.add_files_dm(datalist, Data_Paths=data_paths, Mon_Paths=mon_paths, SO_Axis=conf.so_axis, Signal_ROI=conf.roi_file, Signal_MASK=mask_file, dataset_type=dataset_type, dataset_cwp=dataset_cwp, Verbose=conf.verbose, Timer=t) if t is not None: t.setOldTime(oldtime) t.getTime(msg="After reading %s file" % dataset_type) # Cut the spectra if necessary dp_somA = dr_lib.cut_spectra(dp_som0, conf.tof_cut_min, conf.tof_cut_max) del dp_som0 dp_somB = dr_lib.fix_bin_contents(dp_somA) del dp_somA if dp_somB.attr_list.instrument.get_name() != "CNCS": if conf.verbose: print "Cutting spectrum at minimum TOF" if t is not None: t.getTime(False) # Calculate minimum TOF for physical neutrons if conf.initial_energy is not None: initial_wavelength = common_lib.energy_to_wavelength(\ conf.initial_energy.toValErrTuple()) initial_velocity = common_lib.wavelength_to_velocity(\ initial_wavelength) else: # This should actually calculate it, but don't have a way right now pass if conf.time_zero_offset is not None: time_zero_offset = conf.time_zero_offset.toValErrTuple() else: # This should actually calculate it, but don't have a way right now time_zero_offset = (0.0, 0.0) ss_length = dp_somB.attr_list.instrument.get_primary() tof_min = (ss_length[0] / initial_velocity[0]) + time_zero_offset[0] # Cut all spectra a the minimum TOF dp_som1 = dr_lib.cut_spectra(dp_somB, tof_min, None) if t is not None: t.getTime(msg="After cutting spectrum at minimum TOF ") else: dp_som1 = dp_somB del dp_somB if dm_som0 is not None: dm_som1 = dr_lib.fix_bin_contents(dm_som0) else: dm_som1 = dm_som0 del dm_som0 # Override geometry if necessary if conf.inst_geom is not None: i_geom_dst.setGeometry(data_paths, dp_som1) if conf.inst_geom is not None and dm_som1 is not None: i_geom_dst.setGeometry(mon_paths, dm_som1) # Step 3: Integrate the upstream monitor if dm_som1 is not None: if conf.verbose: print "Integrating upstream monitor spectrum" if t is not None: t.getTime(False) if conf.mon_int_range is None: start_val = float("inf") end_val = float("inf") else: start_val = conf.mon_int_range[0] end_val = conf.mon_int_range[1] dm_som2 = dr_lib.integrate_spectra(dm_som1, start=start_val, end=end_val, width=True) if t is not None: t.getTime(msg="After integrating upstream monitor spectrum ") else: dm_som2 = dm_som1 del dm_som1 tib_norm_const = None # Step 4: Divide data set by summed monitor spectrum if dm_som2 is not None: if conf.verbose: print "Normalizing %s by monitor sum" % dataset_type if t is not None: t.getTime(False) dp_som2 = common_lib.div_ncerr(dp_som1, dm_som2, length_one_som=True) tib_norm_const = dm_som2[0].y if t is not None: t.getTime(msg="After normalizing %s by monitor sum" % dataset_type) elif conf.pc_norm: if conf.verbose: print "Normalizing %s by proton charge" % dataset_type pc_tag = dataset_type + "-proton_charge" pc = dp_som1.attr_list[pc_tag] # Scale the proton charge and then set the scale PC back to attributes if conf.scale_pc is not None: if conf.verbose: print "Scaling %s proton charge" % dataset_type pc = hlr_utils.scale_proton_charge(pc, conf.scale_pc) dp_som1.attr_list[pc_tag] = pc tib_norm_const = pc.getValue() if t is not None: t.getTime(False) dp_som2 = common_lib.div_ncerr(dp_som1, (pc.getValue(), 0.0)) if t is not None: t.getTime(msg="After normalizing %s by proton charge" \ % dataset_type) else: dp_som2 = dp_som1 del dp_som1, dm_som2 # Step 5: Scale dark current by data set measurement time if dkcur is not None: if conf.verbose: print "Scaling dark current by %s acquisition time" % dataset_type if t is not None: t.getTime(False) dstime_tag = dataset_type + "-duration" dstime = dp_som2.attr_list[dstime_tag] dkcur1 = common_lib.div_ncerr(dkcur, (dstime.getValue(), 0.0)) if t is not None: t.getTime(msg="After scaling dark current by %s acquisition time" \ % dataset_type) else: dkcur1 = dkcur del dkcur # Step 6: Subtract scaled dark current from data set if dkcur1 is not None: if conf.verbose: print "Subtracting %s by scaled dark current" % dataset_type if t is not None: t.getTime(False) dp_som3 = common_lib.sub_ncerr(dp_som2, dkcur1) if t is not None: t.getTime(msg="After subtracting %s by scaled dark current" \ % dataset_type) elif tib_const is not None and dkcur1 is None: if conf.verbose: print "Subtracting TIB constant from %s" % dataset_type # Normalize the TIB constant by dividing by the current normalization # the duration (if necessary) and the conversion from seconds to # microseconds tib_c = tib_const.toValErrTuple() conv_sec_to_usec = 1.0e-6 if tib_norm_const is None: tib_norm_const = 1 duration = 1 else: duration_tag = dataset_type + "-duration" duration = dp_som2.attr_list[duration_tag].getValue() norm_const = (duration * conv_sec_to_usec) / tib_norm_const tib_val = tib_c[0] * norm_const tib_err2 = tib_c[1] * (norm_const * norm_const) if t is not None: t.getTime(False) dp_som3 = common_lib.sub_ncerr(dp_som2, (tib_val, tib_err2)) if t is not None: t.getTime(msg="After subtracting TIB constant from %s" \ % dataset_type) elif conf.tib_range is not None and dkcur1 is None: if conf.verbose: print "Determining TIB constant from %s" % dataset_type if t is not None: t.getTime(False) TIB = dr_lib.determine_time_indep_bkg(dp_som2, conf.tib_range, is_range=True) if t is not None: t.getTime(msg="After determining TIB constant from %s" \ % dataset_type) if conf.dump_tib: file_comment = "TIB TOF Range: [%d, %d]" % (conf.tib_range[0], conf.tib_range[1]) hlr_utils.write_file(conf.output, "text/num-info", TIB, output_ext="tib", extra_tag=dataset_type, verbose=conf.verbose, data_ext=conf.ext_replacement, path_replacement=conf.path_replacement, message="time-independent background "\ +"information", tag="Average TIB", units="counts/usec", comments=[file_comment]) if conf.verbose: print "Subtracting TIB constant from %s" % dataset_type if t is not None: t.getTime(False) dp_som3 = common_lib.sub_ncerr(dp_som2, TIB) if t is not None: t.getTime(msg="After subtracting TIB constant from %s" \ % dataset_type) del TIB else: dp_som3 = dp_som2 del dp_som2, dkcur1 if conf.dump_ctof_comb: dp_som3_1 = dr_lib.sum_all_spectra(dp_som3) hlr_utils.write_file(conf.output, "text/Spec", dp_som3_1, output_ext="ctof", extra_tag=dataset_type, data_ext=conf.ext_replacement, path_replacement=conf.path_replacement, verbose=conf.verbose, message="combined calibrated TOF information") del dp_som3_1 return dp_som3
def process_igs_data(datalist, conf, **kwargs): """ This function combines Steps 1 through 8 of the data reduction process for Inverse Geometry Spectrometers as specified by the documents at U{http://neutrons.ornl.gov/asg/projects/SCL/reqspec/DR_Lib_RS.doc}. The function takes a list of file names, a L{hlr_utils.Configure} object and processes the data accordingly. This function should really only be used in the context of I{amorphous_reduction} and I{calc_norm_eff}. @param datalist: A list containing the filenames of the data to be processed. @type datalist: C{list} of C{string}s @param conf: Object that contains the current setup of the driver. @type conf: L{hlr_utils.Configure} @param kwargs: A list of keyword arguments that the function accepts: @keyword inst_geom_dst: File object that contains instrument geometry information. @type inst_geom_dst: C{DST.GeomDST} @keyword dataset_type: The practical name of the dataset being processed. The default value is I{data}. @type dataset_type: C{string} @keyword tib_const: Object providing the time-independent background constant to subtract. @type tib_const: L{hlr_utils.DrParameter} @keyword bkg_som: Object that will be used for early background subtraction @type bkg_som: C{SOM.SOM} @keyword timer: Timing object so the function can perform timing estimates. @type timer: C{sns_timer.DiffTime} @return: Object that has undergone all requested processing steps @rtype: C{SOM.SOM} """ import hlr_utils # Check keywords try: dataset_type = kwargs["dataset_type"] except KeyError: dataset_type = "data" try: t = kwargs["timer"] except KeyError: t = None try: if kwargs["tib_const"] is not None: tib_const = kwargs["tib_const"].toValErrTuple() else: tib_const = None except KeyError: tib_const = None try: i_geom_dst = kwargs["inst_geom_dst"] except KeyError: i_geom_dst = None try: bkg_som = kwargs["bkg_som"] except KeyError: bkg_som = None # Step 1: Open appropriate data files if not conf.mc: so_axis = "time_of_flight" else: so_axis = "Time_of_Flight" # Add so_axis to Configure object conf.so_axis = so_axis if conf.verbose: print "Reading %s file" % dataset_type # Special case handling for normalization data. Dynamically trying to # determine if incoming file is a previously calculated one. if dataset_type == "normalization": try: # Check the first incoming file dst_type = hlr_utils.file_peeker(datalist[0]) # If file_peeker succeeds, the DST is different than the function # returns dst_type = "text/num-info" # Let ROI file handle filtering data_paths = None except RuntimeError: # It's a NeXus file dst_type = "application/x-NeXus" data_paths = conf.data_paths.toPath() else: dst_type = "application/x-NeXus" data_paths = conf.data_paths.toPath() # The [0] is to get the data SOM and ignore the None background SOM dp_som0 = dr_lib.add_files(datalist, Data_Paths=data_paths, SO_Axis=so_axis, Signal_ROI=conf.roi_file, dataset_type=dataset_type, dst_type=dst_type, Verbose=conf.verbose, Timer=t) if t is not None: t.getTime(msg="After reading %s " % dataset_type) if dst_type == "text/num-info": # Since we have a pre-calculated normalization dataset, set the flag # and return the SOM now conf.pre_norm = True # Make the labels and units compatible with a NeXus file based SOM dp_som0.setAxisLabel(0, "wavelength") dp_som0.setAxisUnits(0, "Angstroms") dp_som0.setYUnits("Counts/A") return dp_som0 else: if dataset_type == "normalization": # Since we have a NeXus file, we need to continue conf.pre_norm = False # Cut the spectra if necessary dp_somA = dr_lib.cut_spectra(dp_som0, conf.tof_cut_min, conf.tof_cut_max) del dp_som0 dp_som1 = dr_lib.fix_bin_contents(dp_somA) del dp_somA if conf.inst_geom is not None: i_geom_dst.setGeometry(conf.data_paths.toPath(), dp_som1) if conf.no_mon_norm: dm_som1 = None else: if conf.verbose: print "Reading in monitor data from %s file" % dataset_type # The [0] is to get the data SOM and ignore the None background SOM dm_som0 = dr_lib.add_files(datalist, Data_Paths=conf.mon_path.toPath(), SO_Axis=so_axis, dataset_type=dataset_type, Verbose=conf.verbose, Timer=t) if t is not None: t.getTime(msg="After reading monitor data ") dm_som1 = dr_lib.fix_bin_contents(dm_som0) del dm_som0 if conf.inst_geom is not None: i_geom_dst.setGeometry(conf.mon_path.toPath(), dm_som1) if bkg_som is not None: bkg_pcharge = bkg_som.attr_list["background-proton_charge"].getValue() data_pcharge = dp_som1.attr_list[dataset_type + "-proton_charge"].getValue() ratio = data_pcharge / bkg_pcharge bkg_som1 = common_lib.mult_ncerr(bkg_som, (ratio, 0.0)) del bkg_som dp_som2 = dr_lib.subtract_bkg_from_data(dp_som1, bkg_som1, verbose=conf.verbose, timer=t, dataset1=dataset_type, dataset2="background") else: dp_som2 = dp_som1 del dp_som1 # Step 2: Dead Time Correction # No dead time correction is being applied to the data yet # Step 3: Time-independent background determination if conf.verbose and conf.tib_tofs is not None: print "Determining time-independent background from data" if t is not None and conf.tib_tofs is not None: t.getTime(False) B = dr_lib.determine_time_indep_bkg(dp_som2, conf.tib_tofs) if t is not None and B is not None: t.getTime(msg="After determining time-independent background ") if conf.dump_tib and B is not None: file_comment = "TOFs: %s" % conf.tib_tofs hlr_utils.write_file(conf.output, "text/num-info", B, output_ext="tib", extra_tag=dataset_type, verbose=conf.verbose, data_ext=conf.ext_replacement, path_replacement=conf.path_replacement, message="time-independent background "\ +"information", tag="Average", units="counts", comments=[file_comment]) # Step 4: Subtract time-independent background if conf.verbose and B is not None: print "Subtracting time-independent background from data" if t is not None: t.getTime(False) if B is not None: dp_som3 = common_lib.sub_ncerr(dp_som2, B) else: dp_som3 = dp_som2 if B is not None and t is not None: t.getTime(msg="After subtracting time-independent background ") del dp_som2, B # Step 5: Subtract time-independent background constant if conf.verbose and tib_const is not None: print "Subtracting time-independent background constant from data" if t is not None and tib_const is not None: t.getTime(False) if tib_const is not None: dp_som4 = common_lib.sub_ncerr(dp_som3, tib_const) else: dp_som4 = dp_som3 if t is not None and tib_const is not None: t.getTime(msg="After subtracting time-independent background "\ +"constant ") del dp_som3 # Provide override capability for final wavelength, time-zero slope and # time-zero offset if conf.wavelength_final is not None: dp_som4.attr_list["Wavelength_final"] = \ conf.wavelength_final.toValErrTuple() # Note: time_zero_slope MUST be a tuple if conf.time_zero_slope is not None: dp_som4.attr_list["Time_zero_slope"] = \ conf.time_zero_slope.toValErrTuple() if dm_som1 is not None: dm_som1.attr_list["Time_zero_slope"] = \ conf.time_zero_slope.toValErrTuple() # Note: time_zero_offset MUST be a tuple if conf.time_zero_offset is not None: dp_som4.attr_list["Time_zero_offset"] = \ conf.time_zero_offset.toValErrTuple() if dm_som1 is not None: dm_som1.attr_list["Time_zero_offset"] = \ conf.time_zero_offset.toValErrTuple() # Step 6: Convert TOF to wavelength for data and monitor if conf.verbose: print "Converting TOF to wavelength" if t is not None: t.getTime(False) # Convert monitor if dm_som1 is not None: dm_som2 = common_lib.tof_to_wavelength_lin_time_zero( dm_som1, units="microsecond") else: dm_som2 = None # Convert detector pixels dp_som5 = common_lib.tof_to_initial_wavelength_igs_lin_time_zero( dp_som4, units="microsecond", run_filter=conf.filter) if t is not None: t.getTime(msg="After converting TOF to wavelength ") if conf.dump_wave: hlr_utils.write_file(conf.output, "text/Spec", dp_som5, output_ext="pxl", extra_tag=dataset_type, verbose=conf.verbose, data_ext=conf.ext_replacement, path_replacement=conf.path_replacement, message="pixel wavelength information") if conf.dump_mon_wave and dm_som2 is not None: hlr_utils.write_file(conf.output, "text/Spec", dm_som2, output_ext="mxl", extra_tag=dataset_type, verbose=conf.verbose, data_ext=conf.ext_replacement, path_replacement=conf.path_replacement, message="monitor wavelength information") del dp_som4, dm_som1 # Step 7: Efficiency correct monitor if conf.verbose and dm_som2 is not None and not conf.no_mon_effc: print "Efficiency correct monitor data" if t is not None: t.getTime(False) if not conf.no_mon_effc: dm_som3 = dr_lib.feff_correct_mon(dm_som2) else: dm_som3 = dm_som2 if t is not None and dm_som2 is not None and not conf.no_mon_effc: t.getTime(msg="After efficiency correcting monitor ") if conf.dump_mon_effc and not conf.no_mon_effc and dm_som3 is not None: hlr_utils.write_file(conf.output, "text/Spec", dm_som3, output_ext="mel", extra_tag=dataset_type, verbose=conf.verbose, data_ext=conf.ext_replacement, path_replacement=conf.path_replacement, message="monitor wavelength information "\ +"(efficiency)") del dm_som2 # Step 8: Rebin monitor axis onto detector pixel axis if conf.verbose and dm_som3 is not None: print "Rebin monitor axis to detector pixel axis" if t is not None: t.getTime(False) dm_som4 = dr_lib.rebin_monitor(dm_som3, dp_som5) if t is not None and dm_som4 is not None: t.getTime(msg="After rebinning monitor ") del dm_som3 if conf.dump_mon_rebin and dm_som4 is not None: hlr_utils.write_file(conf.output, "text/Spec", dm_som4, output_ext="mrl", extra_tag=dataset_type, verbose=conf.verbose, data_ext=conf.ext_replacement, path_replacement=conf.path_replacement, message="monitor wavelength information "\ +"(rebinned)") # The lambda-dependent background is only done on sample data (aka data) # for the BSS instrument at the SNS if conf.inst == "BSS" and conf.ldb_const is not None and \ dataset_type == "data": # Step 9: Convert chopper center wavelength to TOF center if conf.verbose: print "Converting chopper center wavelength to TOF" if t is not None: t.getTime(False) tof_center = dr_lib.convert_single_to_list(\ "initial_wavelength_igs_lin_time_zero_to_tof", conf.chopper_lambda_cent.toValErrTuple(), dp_som5) # Step 10: Calculate beginning and end of detector TOF spectrum if conf.verbose: print "Calculating beginning and ending TOF ranges" half_inv_chop_freq = 0.5 / conf.chopper_freq.toValErrTuple()[0] # Above is in seconds, need microseconds half_inv_chop_freq *= 1.0e6 tof_begin = common_lib.sub_ncerr(tof_center, (half_inv_chop_freq, 0.0)) tof_end = common_lib.add_ncerr(tof_center, (half_inv_chop_freq, 0.0)) # Step 11: Convert TOF_begin and TOF_end to wavelength if conf.verbose: print "Converting TOF_begin and TOF_end to wavelength" # Check for time-zero slope information try: tz_slope = conf.time_zero_slope.toValErrTuple() except AttributeError: tz_slope = (0.0, 0.0) # Check for time-zero offset information try: tz_offset = conf.time_zero_offset.toValErrTuple() except AttributeError: tz_offset = (0.0, 0.0) l_begin = common_lib.tof_to_initial_wavelength_igs_lin_time_zero(\ tof_begin, time_zero_slope=tz_slope, time_zero_offset=tz_offset, iobj=dp_som5, run_filter=False) l_end = common_lib.tof_to_initial_wavelength_igs_lin_time_zero(\ tof_end, time_zero_slope=tz_slope, time_zero_offset=tz_offset, iobj=dp_som5, run_filter=False) # Step 12: tof-least-bkg to lambda-least-bkg if conf.verbose: print "Converting TOF least background to wavelength" lambda_least_bkg = dr_lib.convert_single_to_list(\ "tof_to_initial_wavelength_igs_lin_time_zero", conf.tof_least_bkg.toValErrTuple(), dp_som5) if t is not None: t.getTime(msg="After converting boundary positions ") # Step 13: Create lambda-dependent background spectrum if conf.verbose: print "Creating lambda-dependent background spectra" if t is not None: t.getTime(False) ldb_som = dr_lib.shift_spectrum(dm_som4, lambda_least_bkg, l_begin, l_end, conf.ldb_const.getValue()) if t is not None: t.getTime(msg="After creating lambda-dependent background "\ +"spectra ") # Step 14: Subtract lambda-dependent background from sample data if conf.verbose: print "Subtracting lambda-dependent background from data" if t is not None: t.getTime(False) dp_som6 = common_lib.sub_ncerr(dp_som5, ldb_som) if t is not None: t.getTime(msg="After subtracting lambda-dependent background "\ +"from data ") else: dp_som6 = dp_som5 del dp_som5 # Step 15: Normalize data by monitor if conf.verbose and dm_som4 is not None: print "Normalizing data by monitor" if t is not None: t.getTime(False) if dm_som4 is not None: dp_som7 = common_lib.div_ncerr(dp_som6, dm_som4) if t is not None: t.getTime(msg="After normalizing data by monitor ") else: dp_som7 = dp_som6 if conf.dump_wave_mnorm: dp_som7_1 = dr_lib.sum_all_spectra(dp_som7,\ rebin_axis=conf.lambda_bins.toNessiList()) write_message = "combined pixel wavelength information" if dm_som4 is not None: write_message += " (monitor normalized)" hlr_utils.write_file(conf.output, "text/Spec", dp_som7_1, output_ext="pml", extra_tag=dataset_type, verbose=conf.verbose, data_ext=conf.ext_replacement, path_replacement=conf.path_replacement, message=write_message) del dp_som7_1 del dm_som4, dp_som6 return dp_som7
def process_igs_data(datalist, conf, **kwargs): """ This function combines Steps 1 through 8 of the data reduction process for Inverse Geometry Spectrometers as specified by the documents at U{http://neutrons.ornl.gov/asg/projects/SCL/reqspec/DR_Lib_RS.doc}. The function takes a list of file names, a L{hlr_utils.Configure} object and processes the data accordingly. This function should really only be used in the context of I{amorphous_reduction} and I{calc_norm_eff}. @param datalist: A list containing the filenames of the data to be processed. @type datalist: C{list} of C{string}s @param conf: Object that contains the current setup of the driver. @type conf: L{hlr_utils.Configure} @param kwargs: A list of keyword arguments that the function accepts: @keyword inst_geom_dst: File object that contains instrument geometry information. @type inst_geom_dst: C{DST.GeomDST} @keyword dataset_type: The practical name of the dataset being processed. The default value is I{data}. @type dataset_type: C{string} @keyword tib_const: Object providing the time-independent background constant to subtract. @type tib_const: L{hlr_utils.DrParameter} @keyword bkg_som: Object that will be used for early background subtraction @type bkg_som: C{SOM.SOM} @keyword timer: Timing object so the function can perform timing estimates. @type timer: C{sns_timer.DiffTime} @return: Object that has undergone all requested processing steps @rtype: C{SOM.SOM} """ import hlr_utils # Check keywords try: dataset_type = kwargs["dataset_type"] except KeyError: dataset_type = "data" try: t = kwargs["timer"] except KeyError: t = None try: if kwargs["tib_const"] is not None: tib_const = kwargs["tib_const"].toValErrTuple() else: tib_const = None except KeyError: tib_const = None try: i_geom_dst = kwargs["inst_geom_dst"] except KeyError: i_geom_dst = None try: bkg_som = kwargs["bkg_som"] except KeyError: bkg_som = None # Step 1: Open appropriate data files if not conf.mc: so_axis = "time_of_flight" else: so_axis = "Time_of_Flight" # Add so_axis to Configure object conf.so_axis = so_axis if conf.verbose: print "Reading %s file" % dataset_type # Special case handling for normalization data. Dynamically trying to # determine if incoming file is a previously calculated one. if dataset_type == "normalization": try: # Check the first incoming file dst_type = hlr_utils.file_peeker(datalist[0]) # If file_peeker succeeds, the DST is different than the function # returns dst_type = "text/num-info" # Let ROI file handle filtering data_paths = None except RuntimeError: # It's a NeXus file dst_type = "application/x-NeXus" data_paths = conf.data_paths.toPath() else: dst_type = "application/x-NeXus" data_paths = conf.data_paths.toPath() # The [0] is to get the data SOM and ignore the None background SOM dp_som0 = dr_lib.add_files( datalist, Data_Paths=data_paths, SO_Axis=so_axis, Signal_ROI=conf.roi_file, dataset_type=dataset_type, dst_type=dst_type, Verbose=conf.verbose, Timer=t, ) if t is not None: t.getTime(msg="After reading %s " % dataset_type) if dst_type == "text/num-info": # Since we have a pre-calculated normalization dataset, set the flag # and return the SOM now conf.pre_norm = True # Make the labels and units compatible with a NeXus file based SOM dp_som0.setAxisLabel(0, "wavelength") dp_som0.setAxisUnits(0, "Angstroms") dp_som0.setYUnits("Counts/A") return dp_som0 else: if dataset_type == "normalization": # Since we have a NeXus file, we need to continue conf.pre_norm = False # Cut the spectra if necessary dp_somA = dr_lib.cut_spectra(dp_som0, conf.tof_cut_min, conf.tof_cut_max) del dp_som0 dp_som1 = dr_lib.fix_bin_contents(dp_somA) del dp_somA if conf.inst_geom is not None: i_geom_dst.setGeometry(conf.data_paths.toPath(), dp_som1) if conf.no_mon_norm: dm_som1 = None else: if conf.verbose: print "Reading in monitor data from %s file" % dataset_type # The [0] is to get the data SOM and ignore the None background SOM dm_som0 = dr_lib.add_files( datalist, Data_Paths=conf.mon_path.toPath(), SO_Axis=so_axis, dataset_type=dataset_type, Verbose=conf.verbose, Timer=t, ) if t is not None: t.getTime(msg="After reading monitor data ") dm_som1 = dr_lib.fix_bin_contents(dm_som0) del dm_som0 if conf.inst_geom is not None: i_geom_dst.setGeometry(conf.mon_path.toPath(), dm_som1) if bkg_som is not None: bkg_pcharge = bkg_som.attr_list["background-proton_charge"].getValue() data_pcharge = dp_som1.attr_list[dataset_type + "-proton_charge"].getValue() ratio = data_pcharge / bkg_pcharge bkg_som1 = common_lib.mult_ncerr(bkg_som, (ratio, 0.0)) del bkg_som dp_som2 = dr_lib.subtract_bkg_from_data( dp_som1, bkg_som1, verbose=conf.verbose, timer=t, dataset1=dataset_type, dataset2="background" ) else: dp_som2 = dp_som1 del dp_som1 # Step 2: Dead Time Correction # No dead time correction is being applied to the data yet # Step 3: Time-independent background determination if conf.verbose and conf.tib_tofs is not None: print "Determining time-independent background from data" if t is not None and conf.tib_tofs is not None: t.getTime(False) B = dr_lib.determine_time_indep_bkg(dp_som2, conf.tib_tofs) if t is not None and B is not None: t.getTime(msg="After determining time-independent background ") if conf.dump_tib and B is not None: file_comment = "TOFs: %s" % conf.tib_tofs hlr_utils.write_file( conf.output, "text/num-info", B, output_ext="tib", extra_tag=dataset_type, verbose=conf.verbose, data_ext=conf.ext_replacement, path_replacement=conf.path_replacement, message="time-independent background " + "information", tag="Average", units="counts", comments=[file_comment], ) # Step 4: Subtract time-independent background if conf.verbose and B is not None: print "Subtracting time-independent background from data" if t is not None: t.getTime(False) if B is not None: dp_som3 = common_lib.sub_ncerr(dp_som2, B) else: dp_som3 = dp_som2 if B is not None and t is not None: t.getTime(msg="After subtracting time-independent background ") del dp_som2, B # Step 5: Subtract time-independent background constant if conf.verbose and tib_const is not None: print "Subtracting time-independent background constant from data" if t is not None and tib_const is not None: t.getTime(False) if tib_const is not None: dp_som4 = common_lib.sub_ncerr(dp_som3, tib_const) else: dp_som4 = dp_som3 if t is not None and tib_const is not None: t.getTime(msg="After subtracting time-independent background " + "constant ") del dp_som3 # Provide override capability for final wavelength, time-zero slope and # time-zero offset if conf.wavelength_final is not None: dp_som4.attr_list["Wavelength_final"] = conf.wavelength_final.toValErrTuple() # Note: time_zero_slope MUST be a tuple if conf.time_zero_slope is not None: dp_som4.attr_list["Time_zero_slope"] = conf.time_zero_slope.toValErrTuple() if dm_som1 is not None: dm_som1.attr_list["Time_zero_slope"] = conf.time_zero_slope.toValErrTuple() # Note: time_zero_offset MUST be a tuple if conf.time_zero_offset is not None: dp_som4.attr_list["Time_zero_offset"] = conf.time_zero_offset.toValErrTuple() if dm_som1 is not None: dm_som1.attr_list["Time_zero_offset"] = conf.time_zero_offset.toValErrTuple() # Step 6: Convert TOF to wavelength for data and monitor if conf.verbose: print "Converting TOF to wavelength" if t is not None: t.getTime(False) # Convert monitor if dm_som1 is not None: dm_som2 = common_lib.tof_to_wavelength_lin_time_zero(dm_som1, units="microsecond") else: dm_som2 = None # Convert detector pixels dp_som5 = common_lib.tof_to_initial_wavelength_igs_lin_time_zero( dp_som4, units="microsecond", run_filter=conf.filter ) if t is not None: t.getTime(msg="After converting TOF to wavelength ") if conf.dump_wave: hlr_utils.write_file( conf.output, "text/Spec", dp_som5, output_ext="pxl", extra_tag=dataset_type, verbose=conf.verbose, data_ext=conf.ext_replacement, path_replacement=conf.path_replacement, message="pixel wavelength information", ) if conf.dump_mon_wave and dm_som2 is not None: hlr_utils.write_file( conf.output, "text/Spec", dm_som2, output_ext="mxl", extra_tag=dataset_type, verbose=conf.verbose, data_ext=conf.ext_replacement, path_replacement=conf.path_replacement, message="monitor wavelength information", ) del dp_som4, dm_som1 # Step 7: Efficiency correct monitor if conf.verbose and dm_som2 is not None and not conf.no_mon_effc: print "Efficiency correct monitor data" if t is not None: t.getTime(False) if not conf.no_mon_effc: dm_som3 = dr_lib.feff_correct_mon(dm_som2) else: dm_som3 = dm_som2 if t is not None and dm_som2 is not None and not conf.no_mon_effc: t.getTime(msg="After efficiency correcting monitor ") if conf.dump_mon_effc and not conf.no_mon_effc and dm_som3 is not None: hlr_utils.write_file( conf.output, "text/Spec", dm_som3, output_ext="mel", extra_tag=dataset_type, verbose=conf.verbose, data_ext=conf.ext_replacement, path_replacement=conf.path_replacement, message="monitor wavelength information " + "(efficiency)", ) del dm_som2 # Step 8: Rebin monitor axis onto detector pixel axis if conf.verbose and dm_som3 is not None: print "Rebin monitor axis to detector pixel axis" if t is not None: t.getTime(False) dm_som4 = dr_lib.rebin_monitor(dm_som3, dp_som5) if t is not None and dm_som4 is not None: t.getTime(msg="After rebinning monitor ") del dm_som3 if conf.dump_mon_rebin and dm_som4 is not None: hlr_utils.write_file( conf.output, "text/Spec", dm_som4, output_ext="mrl", extra_tag=dataset_type, verbose=conf.verbose, data_ext=conf.ext_replacement, path_replacement=conf.path_replacement, message="monitor wavelength information " + "(rebinned)", ) # The lambda-dependent background is only done on sample data (aka data) # for the BSS instrument at the SNS if conf.inst == "BSS" and conf.ldb_const is not None and dataset_type == "data": # Step 9: Convert chopper center wavelength to TOF center if conf.verbose: print "Converting chopper center wavelength to TOF" if t is not None: t.getTime(False) tof_center = dr_lib.convert_single_to_list( "initial_wavelength_igs_lin_time_zero_to_tof", conf.chopper_lambda_cent.toValErrTuple(), dp_som5 ) # Step 10: Calculate beginning and end of detector TOF spectrum if conf.verbose: print "Calculating beginning and ending TOF ranges" half_inv_chop_freq = 0.5 / conf.chopper_freq.toValErrTuple()[0] # Above is in seconds, need microseconds half_inv_chop_freq *= 1.0e6 tof_begin = common_lib.sub_ncerr(tof_center, (half_inv_chop_freq, 0.0)) tof_end = common_lib.add_ncerr(tof_center, (half_inv_chop_freq, 0.0)) # Step 11: Convert TOF_begin and TOF_end to wavelength if conf.verbose: print "Converting TOF_begin and TOF_end to wavelength" # Check for time-zero slope information try: tz_slope = conf.time_zero_slope.toValErrTuple() except AttributeError: tz_slope = (0.0, 0.0) # Check for time-zero offset information try: tz_offset = conf.time_zero_offset.toValErrTuple() except AttributeError: tz_offset = (0.0, 0.0) l_begin = common_lib.tof_to_initial_wavelength_igs_lin_time_zero( tof_begin, time_zero_slope=tz_slope, time_zero_offset=tz_offset, iobj=dp_som5, run_filter=False ) l_end = common_lib.tof_to_initial_wavelength_igs_lin_time_zero( tof_end, time_zero_slope=tz_slope, time_zero_offset=tz_offset, iobj=dp_som5, run_filter=False ) # Step 12: tof-least-bkg to lambda-least-bkg if conf.verbose: print "Converting TOF least background to wavelength" lambda_least_bkg = dr_lib.convert_single_to_list( "tof_to_initial_wavelength_igs_lin_time_zero", conf.tof_least_bkg.toValErrTuple(), dp_som5 ) if t is not None: t.getTime(msg="After converting boundary positions ") # Step 13: Create lambda-dependent background spectrum if conf.verbose: print "Creating lambda-dependent background spectra" if t is not None: t.getTime(False) ldb_som = dr_lib.shift_spectrum(dm_som4, lambda_least_bkg, l_begin, l_end, conf.ldb_const.getValue()) if t is not None: t.getTime(msg="After creating lambda-dependent background " + "spectra ") # Step 14: Subtract lambda-dependent background from sample data if conf.verbose: print "Subtracting lambda-dependent background from data" if t is not None: t.getTime(False) dp_som6 = common_lib.sub_ncerr(dp_som5, ldb_som) if t is not None: t.getTime(msg="After subtracting lambda-dependent background " + "from data ") else: dp_som6 = dp_som5 del dp_som5 # Step 15: Normalize data by monitor if conf.verbose and dm_som4 is not None: print "Normalizing data by monitor" if t is not None: t.getTime(False) if dm_som4 is not None: dp_som7 = common_lib.div_ncerr(dp_som6, dm_som4) if t is not None: t.getTime(msg="After normalizing data by monitor ") else: dp_som7 = dp_som6 if conf.dump_wave_mnorm: dp_som7_1 = dr_lib.sum_all_spectra(dp_som7, rebin_axis=conf.lambda_bins.toNessiList()) write_message = "combined pixel wavelength information" if dm_som4 is not None: write_message += " (monitor normalized)" hlr_utils.write_file( conf.output, "text/Spec", dp_som7_1, output_ext="pml", extra_tag=dataset_type, verbose=conf.verbose, data_ext=conf.ext_replacement, path_replacement=conf.path_replacement, message=write_message, ) del dp_som7_1 del dm_som4, dp_som6 return dp_som7
def process_ref_data(datalist, conf, signal_roi_file, bkg_roi_file=None, no_bkg=False, **kwargs): """ This function combines Steps 1 through 6 in section 2.4.5 of the data reduction process for Reflectometers (without Monitors) as specified by the document at U{http://neutrons.ornl.gov/asg/projects/SCL/reqspec/DR_Lib_RS.doc}. The function takes a list of file names, a L{hlr_utils.Configure} object, signal and background region-of-interest (ROI) files and an optional flag about background subtraction and processes the data accordingly. @param datalist: The filenames of the data to be processed @type datalist: C{list} of C{string}s @param conf: Object that contains the current setup of the driver @type conf: L{hlr_utils.Configure} @param signal_roi_file: The file containing the list of pixel IDs for the signal region of interest. @type signal_roi_file: C{string} @param bkg_roi_file: The file containing the list of pixel IDs for the (possible) background region of interest. @type bkg_roi_file: C{string} @param no_bkg: (OPTIONAL) Flag which determines if the background will be calculated and subtracted. @type no_bkg: C{boolean} @param kwargs: A list of keyword arguments that the function accepts: @keyword inst_geom_dst: Object that contains the instrument geometry information. @type inst_geom_dst: C{DST.getInstance()} @keyword dataset_type: The practical name of the dataset being processed. The default value is I{data}. @type dataset_type: C{string} @keyword tof_cuts: Time-of-flight bins to remove (zero) from the data @type tof_cuts: C{list} of C{string}s @keyword no_tof_cuts: Flag to stop application of the TOF cuts @type no_tof_cuts: C{boolean} @keyword timer: Timing object so the function can perform timing estimates. @type timer: C{sns_timer.DiffTime} @return: Object that has undergone all requested processing steps @rtype: C{SOM.SOM} """ import common_lib import dr_lib import hlr_utils # Check keywords try: dataset_type = kwargs["dataset_type"] except KeyError: dataset_type = "data" if dataset_type != "data" and dataset_type != "norm": raise RuntimeError("Please use data or norm to specify the dataset "\ +"type. Do not understand how to handle %s." \ % dataset_type) try: t = kwargs["timer"] except KeyError: t = None try: i_geom_dst = kwargs["inst_geom_dst"] except KeyError: i_geom_dst = None try: tof_cuts = kwargs["tof_cuts"] except KeyError: tof_cuts = None no_tof_cuts = kwargs.get("no_tof_cuts", False) so_axis = "time_of_flight" # Step 0: Open data files and select signal (and possible background) ROIs if conf.verbose: print "Reading %s file" % dataset_type if len(conf.norm_data_paths) and dataset_type == "norm": data_path = conf.norm_data_paths.toPath() else: data_path = conf.data_paths.toPath() (d_som1, b_som1) = dr_lib.add_files_bg(datalist, Data_Paths=data_path, SO_Axis=so_axis, dataset_type=dataset_type, Signal_ROI=signal_roi_file, Bkg_ROI=bkg_roi_file, Verbose=conf.verbose, Timer=t) if t is not None: t.getTime(msg="After reading %s " % dataset_type) if i_geom_dst is not None: i_geom_dst.setGeometry(conf.data_paths.toPath(), d_som1) # Calculate delta t over t if conf.verbose: print "Calculating delta t over t" dtot = dr_lib.calc_deltat_over_t(d_som1[0].axis[0].val) # Calculate delta theta over theta if conf.verbose: print "Calculating delta theta over theta" dr_lib.calc_delta_theta_over_theta(d_som1, dataset_type) # Step 1: Sum all spectra along the low resolution direction # Set sorting (y_sort, cent_pixel) = hlr_utils.get_ref_integration_direction( conf.int_dir, conf.inst, d_som1.attr_list.instrument) if dataset_type == "data": d_som1.attr_list["ref_sort"] = y_sort d_som1A = dr_lib.sum_all_spectra(d_som1, y_sort=y_sort, stripe=True, pixel_fix=cent_pixel) del d_som1 if b_som1 is not None: b_som1A = dr_lib.sum_all_spectra(b_som1, y_sort=y_sort, stripe=True, pixel_fix=cent_pixel) del b_som1 else: b_som1A = b_som1 # Set the TOF cuts if no_tof_cuts: tof_cut_min = None tof_cut_max = None else: tof_cut_min = conf.tof_cut_min tof_cut_max = conf.tof_cut_max # Cut the spectra if necessary d_som2 = dr_lib.cut_spectra(d_som1A, tof_cut_min, tof_cut_max) del d_som1A if b_som1A is not None: b_som2 = dr_lib.cut_spectra(b_som1A, tof_cut_min, tof_cut_max) del b_som1A else: b_som2 = b_som1A # Fix TOF cuts to make them list of integers try: tof_cuts = [int(x) for x in tof_cuts] # This will trigger if tof_cuts is None except TypeError: pass d_som3 = dr_lib.zero_bins(d_som2, tof_cuts) del d_som2 if b_som2 is not None: b_som3 = dr_lib.zero_bins(b_som2, tof_cuts) del b_som2 else: b_som3 = b_som2 if conf.dump_specular: if no_tof_cuts: d_som3_1 = dr_lib.cut_spectra(d_som3, conf.tof_cut_min, conf.tof_cut_max) else: d_som3_1 = d_som3 hlr_utils.write_file(conf.output, "text/Spec", d_som3_1, output_ext="sdc", extra_tag=dataset_type, verbose=conf.verbose, data_ext=conf.ext_replacement, path_replacement=conf.path_replacement, message="specular TOF information") del d_som3_1 # Steps 2-4: Determine background spectrum if conf.verbose and not no_bkg: print "Determining %s background" % dataset_type if dataset_type == "data": peak_excl = conf.data_peak_excl elif dataset_type == "norm": peak_excl = conf.norm_peak_excl if b_som3 is not None: B = dr_lib.calculate_ref_background(b_som3, no_bkg, conf.inst, None, aobj=d_som3) else: B = dr_lib.calculate_ref_background(d_som3, no_bkg, conf.inst, peak_excl) if t is not None: t.getTime(msg="After background determination") if not no_bkg and conf.dump_bkg: if no_tof_cuts: B_1 = dr_lib.cut_spectra(B, conf.tof_cut_min, conf.tof_cut_max) else: B_1 = B hlr_utils.write_file(conf.output, "text/Spec", B_1, output_ext="bkg", extra_tag=dataset_type, verbose=conf.verbose, data_ext=conf.ext_replacement, path_replacement=conf.path_replacement, message="background TOF information") del B_1 # Step 5: Subtract background spectrum from data spectra if not no_bkg: d_som4 = dr_lib.subtract_bkg_from_data(d_som3, B, verbose=conf.verbose, timer=t, dataset1="data", dataset2="background") else: d_som4 = d_som3 del d_som3 if not no_bkg and conf.dump_sub: if no_tof_cuts: d_som4_1 = dr_lib.cut_spectra(d_som4, conf.tof_cut_min, conf.tof_cut_max) else: d_som4_1 = d_som4 hlr_utils.write_file(conf.output, "text/Spec", d_som4_1, output_ext="sub", extra_tag=dataset_type, verbose=conf.verbose, data_ext=conf.ext_replacement, path_replacement=conf.path_replacement, message="subtracted TOF information") del d_som4_1 dtot_int = dr_lib.integrate_axis_py(dtot, avg=True) param_key = dataset_type + "-dt_over_t" d_som4.attr_list[param_key] = dtot_int[0] if conf.store_dtot: d_som4.attr_list["extra_som"] = dtot # Step 6: Scale by proton charge pc = d_som4.attr_list[dataset_type + "-proton_charge"] pc_new = hlr_utils.scale_proton_charge(pc, "C") d_som5 = common_lib.div_ncerr(d_som4, (pc_new.getValue(), 0.0)) del d_som4 return d_som5
def run(config, tim=None): """ This method is where the data reduction process gets done. @param config: Object containing the data reduction configuration information. @type config: L{hlr_utils.Configure} @param tim: (OPTIONAL) Object that will allow the method to perform timing evaluations. @type tim: C{sns_time.DiffTime} """ import common_lib import dr_lib import DST if tim is not None: tim.getTime(False) old_time = tim.getOldTime() if config.data is None: raise RuntimeError("Need to pass a data filename to the driver " + "script.") # Read in geometry if one is provided if config.inst_geom is not None: if config.verbose: print "Reading in instrument geometry file" inst_geom_dst = DST.getInstance("application/x-NxsGeom", config.inst_geom) else: inst_geom_dst = None # Add so_axis to Configure object config.so_axis = "time_of_flight" dataset_type = "background" # Step 0: Open appropriate data files # Data if config.verbose: print "Reading %s file" % dataset_type # The [0] is to get the data SOM and ignore the None background SOM dp_som = dr_lib.add_files( config.data, Data_Paths=config.data_paths.toPath(), SO_Axis=config.so_axis, Signal_ROI=config.roi_file, dataset_type=dataset_type, Verbose=config.verbose, Timer=tim, ) if tim is not None: tim.getTime(msg="After reading %s " % dataset_type) dp_som0 = dr_lib.fix_bin_contents(dp_som) del dp_som if inst_geom_dst is not None: inst_geom_dst.setGeometry(config.data_paths.toPath(), dp_som0) # Note: time_zero_offset_det MUST be a tuple if config.time_zero_offset_det is not None: dp_som0.attr_list["Time_zero_offset_det"] = config.time_zero_offset_det.toValErrTuple() # Step 2: Convert TOF to wavelength for data if config.verbose: print "Converting TOF to wavelength" if tim is not None: tim.getTime(False) # Convert detector pixels dp_som1 = common_lib.tof_to_wavelength_lin_time_zero( dp_som0, units="microsecond", time_zero_offset=config.time_zero_offset_det.toValErrTuple(), inst_param="total" ) if tim is not None: tim.getTime(msg="After converting TOF to wavelength ") del dp_som0 if config.verbose: print "Cutting spectra" if tim is not None: tim.getTime(False) dp_som2 = dr_lib.cut_spectra(dp_som1, config.lambda_low_cut, config.lambda_high_cut) if tim is not None: tim.getTime(msg="After cutting spectra ") del dp_som1 rebin_axis = config.lambda_bins.toNessiList() # Put the data on the same axis if config.verbose: print "Rebinning data onto specified wavelength axis" if tim is not None: tim.getTime(False) dp_som3 = dr_lib.sum_by_rebin_frac(dp_som2, rebin_axis) if tim is not None: tim.getTime(msg="After rebinning data onto specified wavelength axis ") del dp_som2 data_run_time = dp_som3.attr_list["background-duration"] # Calculate the accelerator on time if config.verbose: print "Calculating accelerator on time" acc_on_time = hlr_utils.DrParameter(data_run_time.getValue() - config.acc_down_time.getValue(), 0.0, "seconds") # Get the number of data bins num_wave_bins = len(rebin_axis) - 1 # Calculate the scaled accelerator uptime if config.verbose: print "Calculating the scaled accelerator uptime" if tim is not None: tim.getTime(False) final_scale = acc_on_time.toValErrTuple()[0] / num_wave_bins if tim is not None: tim.getTime(msg="After calculating the scaled accelerator uptime ") # Create the final background spectrum if config.verbose: print "Creating the background spectrum" if tim is not None: tim.getTime(False) dp_som4 = common_lib.div_ncerr(dp_som3, (final_scale, 0)) dp_som4.attr_list["%s-Scaling" % dataset_type] = final_scale if tim is not None: tim.getTime(msg="After creating background spectrum ") del dp_som3 # Write out the background spectrum hlr_utils.write_file( config.output, "text/Spec", dp_som4, verbose=config.verbose, output_ext="bkg", data_ext=config.ext_replacement, replace_path=False, replace_ext=True, message="background spectrum", ) dp_som4.attr_list["config"] = config hlr_utils.write_file( config.output, "text/rmd", dp_som4, output_ext="rmd", data_ext=config.ext_replacement, path_replacement=config.path_replacement, verbose=config.verbose, message="metadata", ) if tim is not None: tim.setOldTime(old_time) tim.getTime(msg="Total Running Time")