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 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_dgs_data(obj, conf, bcan, ecan, tcoeff, **kwargs): """ This function combines Steps 7 through 16 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 calibrated dataset, a L{hlr_utils.Configure} object and processes the data accordingly. @param obj: A calibrated dataset object. @type obj: C{SOM.SOM} @param conf: Object that contains the current setup of the driver. @type conf: L{hlr_utils.Configure} @param bcan: The object containing the black can data. @type bcan: C{SOM.SOM} @param ecan: The object containing the empty can data. @type ecan: C{SOM.SOM} @param tcoeff: The transmission coefficient appropriate to the given data set. @type tcoeff: C{tuple} @param kwargs: A list of keyword arguments that the function accepts: @keyword dataset_type: The practical name of the dataset being processed. The default value is I{data}. @type dataset_type: C{string} @keyword cwp_used: A flag signalling the use of the chopper phase corrections. @type cwp_used: C{bool} @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 array_manip import common_lib import dr_lib import hlr_utils # Check keywords try: dataset_type = kwargs["dataset_type"] except KeyError: dataset_type = "data" try: t = kwargs["timer"] except KeyError: t = None cwp_used = kwargs.get("cwp_used", False) if conf.verbose: print "Processing %s information" % dataset_type # Step 7: Create black can background contribution if bcan is not None: if conf.verbose: print "Creating black can background contribution for %s" \ % dataset_type if t is not None: t.getTime(False) bccoeff = array_manip.sub_ncerr(1.0, 0.0, tcoeff[0], tcoeff[1]) bcan1 = common_lib.mult_ncerr(bcan, bccoeff) if t is not None: t.getTime(msg="After creating black can background contribution ") del bcan else: bcan1 = None # Step 8: Create empty can background contribution if ecan is not None: if conf.verbose: print "Creating empty can background contribution for %s" \ % dataset_type if t is not None: t.getTime(False) ecan1 = common_lib.mult_ncerr(ecan, tcoeff) if t is not None: t.getTime(msg="After creating empty can background contribution ") del ecan else: ecan1 = None # Step 9: Create background spectra if bcan1 is not None or ecan1 is not None and conf.verbose: print "Creating background spectra for %s" % dataset_type if bcan1 is not None and ecan1 is not None: if cwp_used: if conf.verbose: print "Rebinning empty can to black can axis." ecan2 = common_lib.rebin_axis_1D_frac(ecan1, bcan1[0].axis[0].val) else: ecan2 = ecan1 del ecan1 if t is not None: t.getTime(False) b_som = common_lib.add_ncerr(bcan1, ecan2) if t is not None: t.getTime(msg="After creating background spectra ") elif bcan1 is not None and ecan1 is None: b_som = bcan1 elif bcan1 is None and ecan1 is not None: b_som = ecan1 else: b_som = None del bcan1, ecan1 if cwp_used: if conf.verbose: print "Rebinning background spectra to %s" % dataset_type b_som1 = common_lib.rebin_axis_1D_frac(b_som, obj[0].axis[0].val) else: b_som1 = b_som del b_som if conf.dump_ctof_comb and b_som1 is not None: b_som_1 = dr_lib.sum_all_spectra(b_som1) hlr_utils.write_file(conf.output, "text/Spec", b_som_1, output_ext="ctof", extra_tag="background", data_ext=conf.ext_replacement, path_replacement=conf.path_replacement, verbose=conf.verbose, message="combined background TOF information") del b_som_1 # Step 10: Subtract background from data obj1 = dr_lib.subtract_bkg_from_data(obj, b_som1, verbose=conf.verbose, timer=t, dataset1=dataset_type, dataset2="background") del obj, b_som1 # Step 11: Calculate initial velocity if conf.verbose: print "Calculating initial velocity" if t is not None: t.getTime(False) 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 t is not None: t.getTime(msg="After calculating initial velocity ") # Step 12: Calculate the time-zero offset 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) # Step 13: Convert time-of-flight to final velocity if conf.verbose: print "Converting TOF to final velocity DGS" if t is not None: t.getTime(False) obj2 = common_lib.tof_to_final_velocity_dgs(obj1, initial_velocity, time_zero_offset, units="microsecond") if t is not None: t.getTime(msg="After calculating TOF to final velocity DGS ") del obj1 # Step 14: Convert final velocity to final wavelength if conf.verbose: print "Converting final velocity DGS to final wavelength" if t is not None: t.getTime(False) obj3 = common_lib.velocity_to_wavelength(obj2) if t is not None: t.getTime(msg="After calculating velocity to wavelength ") del obj2 if conf.dump_wave_comb: obj3_1 = dr_lib.sum_all_spectra( obj3, rebin_axis=conf.lambda_bins.toNessiList()) hlr_utils.write_file(conf.output, "text/Spec", obj3_1, output_ext="fwv", extra_tag=dataset_type, data_ext=conf.ext_replacement, path_replacement=conf.path_replacement, verbose=conf.verbose, message="combined final wavelength information") del obj3_1 # Step 15: Create the detector efficiency if conf.det_eff is not None: if conf.verbose: print "Creating detector efficiency spectra" if t is not None: t.getTime(False) det_eff = dr_lib.create_det_eff(obj3) if t is not None: t.getTime(msg="After creating detector efficiency spectra ") else: det_eff = None # Step 16: Divide the detector pixel spectra by the detector efficiency if det_eff is not None: if conf.verbose: print "Correcting %s for detector efficiency" % dataset_type if t is not None: t.getTime(False) obj4 = common_lib.div_ncerr(obj3, det_eff) if t is not None: t.getTime(msg="After correcting %s for detector efficiency" \ % dataset_type) else: obj4 = obj3 del obj3, det_eff return obj4
def process_dgs_data(obj, conf, bcan, ecan, tcoeff, **kwargs): """ This function combines Steps 7 through 16 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 calibrated dataset, a L{hlr_utils.Configure} object and processes the data accordingly. @param obj: A calibrated dataset object. @type obj: C{SOM.SOM} @param conf: Object that contains the current setup of the driver. @type conf: L{hlr_utils.Configure} @param bcan: The object containing the black can data. @type bcan: C{SOM.SOM} @param ecan: The object containing the empty can data. @type ecan: C{SOM.SOM} @param tcoeff: The transmission coefficient appropriate to the given data set. @type tcoeff: C{tuple} @param kwargs: A list of keyword arguments that the function accepts: @keyword dataset_type: The practical name of the dataset being processed. The default value is I{data}. @type dataset_type: C{string} @keyword cwp_used: A flag signalling the use of the chopper phase corrections. @type cwp_used: C{bool} @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 array_manip import common_lib import dr_lib import hlr_utils # Check keywords try: dataset_type = kwargs["dataset_type"] except KeyError: dataset_type = "data" try: t = kwargs["timer"] except KeyError: t = None cwp_used = kwargs.get("cwp_used", False) if conf.verbose: print "Processing %s information" % dataset_type # Step 7: Create black can background contribution if bcan is not None: if conf.verbose: print "Creating black can background contribution for %s" \ % dataset_type if t is not None: t.getTime(False) bccoeff = array_manip.sub_ncerr(1.0, 0.0, tcoeff[0], tcoeff[1]) bcan1 = common_lib.mult_ncerr(bcan, bccoeff) if t is not None: t.getTime(msg="After creating black can background contribution ") del bcan else: bcan1 = None # Step 8: Create empty can background contribution if ecan is not None: if conf.verbose: print "Creating empty can background contribution for %s" \ % dataset_type if t is not None: t.getTime(False) ecan1 = common_lib.mult_ncerr(ecan, tcoeff) if t is not None: t.getTime(msg="After creating empty can background contribution ") del ecan else: ecan1 = None # Step 9: Create background spectra if bcan1 is not None or ecan1 is not None and conf.verbose: print "Creating background spectra for %s" % dataset_type if bcan1 is not None and ecan1 is not None: if cwp_used: if conf.verbose: print "Rebinning empty can to black can axis." ecan2 = common_lib.rebin_axis_1D_frac(ecan1, bcan1[0].axis[0].val) else: ecan2 = ecan1 del ecan1 if t is not None: t.getTime(False) b_som = common_lib.add_ncerr(bcan1, ecan2) if t is not None: t.getTime(msg="After creating background spectra ") elif bcan1 is not None and ecan1 is None: b_som = bcan1 elif bcan1 is None and ecan1 is not None: b_som = ecan1 else: b_som = None del bcan1, ecan1 if cwp_used: if conf.verbose: print "Rebinning background spectra to %s" % dataset_type b_som1 = common_lib.rebin_axis_1D_frac(b_som, obj[0].axis[0].val) else: b_som1 = b_som del b_som if conf.dump_ctof_comb and b_som1 is not None: b_som_1 = dr_lib.sum_all_spectra(b_som1) hlr_utils.write_file(conf.output, "text/Spec", b_som_1, output_ext="ctof", extra_tag="background", data_ext=conf.ext_replacement, path_replacement=conf.path_replacement, verbose=conf.verbose, message="combined background TOF information") del b_som_1 # Step 10: Subtract background from data obj1 = dr_lib.subtract_bkg_from_data(obj, b_som1, verbose=conf.verbose, timer=t, dataset1=dataset_type, dataset2="background") del obj, b_som1 # Step 11: Calculate initial velocity if conf.verbose: print "Calculating initial velocity" if t is not None: t.getTime(False) 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 t is not None: t.getTime(msg="After calculating initial velocity ") # Step 12: Calculate the time-zero offset 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) # Step 13: Convert time-of-flight to final velocity if conf.verbose: print "Converting TOF to final velocity DGS" if t is not None: t.getTime(False) obj2 = common_lib.tof_to_final_velocity_dgs(obj1, initial_velocity, time_zero_offset, units="microsecond") if t is not None: t.getTime(msg="After calculating TOF to final velocity DGS ") del obj1 # Step 14: Convert final velocity to final wavelength if conf.verbose: print "Converting final velocity DGS to final wavelength" if t is not None: t.getTime(False) obj3 = common_lib.velocity_to_wavelength(obj2) if t is not None: t.getTime(msg="After calculating velocity to wavelength ") del obj2 if conf.dump_wave_comb: obj3_1 = dr_lib.sum_all_spectra(obj3, rebin_axis=conf.lambda_bins.toNessiList()) hlr_utils.write_file(conf.output, "text/Spec", obj3_1, output_ext="fwv", extra_tag=dataset_type, data_ext=conf.ext_replacement, path_replacement=conf.path_replacement, verbose=conf.verbose, message="combined final wavelength information") del obj3_1 # Step 15: Create the detector efficiency if conf.det_eff is not None: if conf.verbose: print "Creating detector efficiency spectra" if t is not None: t.getTime(False) det_eff = dr_lib.create_det_eff(obj3) if t is not None: t.getTime(msg="After creating detector efficiency spectra ") else: det_eff = None # Step 16: Divide the detector pixel spectra by the detector efficiency if det_eff is not None: if conf.verbose: print "Correcting %s for detector efficiency" % dataset_type if t is not None: t.getTime(False) obj4 = common_lib.div_ncerr(obj3, det_eff) if t is not None: t.getTime(msg="After correcting %s for detector efficiency" \ % dataset_type) else: obj4 = obj3 del obj3, det_eff return obj4