def add_files_bg(filelist, **kwargs): """ This function takes a list of U{NeXus<www.nexusformat.org>} files and various keyword arguments and returns a data C{SOM} and a background C{SOM} (if requested) that is the sum of all the data from the specified files. B{It is assumed that the files contain similar data as only crude cross-checks will be made. You have been warned.} @param filelist: A list containing the names of the files to sum @type filelist: C{list} @param kwargs: A list of keyword arguments that the function accepts: @keyword SO_Axis: This is the name of the main axis to read from the NeXus file @type SO_Axis: C{string} @keyword Data_Paths: This contains the data paths and signals for the requested detector banks @type Data_Paths: C{tuple} of C{tuple}s @keyword Signal_ROI: This is the name of a file that contains a list of pixel IDs that will be read from the data file and stored as a signal C{SOM} @type Signal_ROI: C{string} @keyword Bkg_ROI: This is the name of a file that contains a list of pixel IDs that will be read from the data file and stored as a background C{SOM} @type Bkg_ROI: C{string} @keyword dataset_type: The practical name of the dataset being processed. The default value is I{data}. @type dataset_type: C{string} @keyword dst_type: The type of C{DST} to be created during file read-in. The default value is I{application/x-NeXus}. @type dst_type: C{string} @keyword Verbose: This is a flag to turn on print statments. The default is I{False}. @type Verbose: C{boolean} @keyword Timer: This is an SNS Timer object used for showing the performance timing in the function. @type Timer: C{sns_timing.Timer} @return: Signal C{SOM.SOM} and background C{SOM.SOM} @rtype: C{tuple} @raise SystemExit: If any file cannot be read """ import sys import common_lib import DST import hlr_utils # Parse keywords try: so_axis = kwargs["SO_Axis"] except KeyError: so_axis = "time_of_flight" try: data_paths = kwargs["Data_Paths"] except KeyError: data_paths = None try: signal_roi = kwargs["Signal_ROI"] except KeyError: signal_roi = None try: bkg_roi = kwargs["Bkg_ROI"] except KeyError: bkg_roi = None try: dataset_type = kwargs["dataset_type"] except KeyError: dataset_type = "data" try: dst_type = kwargs["dst_type"] except KeyError: try: dst_type = hlr_utils.file_peeker(filelist[0]) except RuntimeError: # Assume it is a NeXus file, since it is not a DR produced file dst_type = "application/x-NeXus" try: verbose = kwargs["Verbose"] except KeyError: verbose = False try: timer = kwargs["Timer"] except KeyError: timer = None counter = 0 for filename in filelist: if verbose: print "File:", filename try: if dst_type == "application/x-NeXus": data_dst = DST.getInstance(dst_type, filename) else: resource = open(filename, "r") data_dst = DST.getInstance(dst_type, resource) except SystemError: print "ERROR: Failed to data read file %s" % filename sys.exit(-1) if verbose: print "Reading data file %d" % counter if counter == 0: if dst_type == "application/x-NeXus": d_som1 = data_dst.getSOM(data_paths, so_axis, roi_file=signal_roi) d_som1.rekeyNxPars(dataset_type) else: if dst_type != "text/Dave2d": d_som1 = data_dst.getSOM(data_paths, roi_file=signal_roi) else: d_som1 = data_dst.getSOM(data_paths) if verbose: print "# Signal SO:", len(d_som1) if dst_type == "application/x-NeXus": print "# TOF:", len(d_som1[0]) print "# TOF Axis:", len(d_som1[0].axis[0].val) elif dst_type != "text/num-info": print "# Data Size:", len(d_som1[0]) print "# X-Axis:", len(d_som1[0].axis[0].val) try: axis_len = len(d_som1[0].axis[1].val) print "# Y-Axis:", axis_len except IndexError: pass if bkg_roi is not None: if dst_type == "application/x-NeXus": b_som1 = data_dst.getSOM(data_paths, so_axis, roi_file=bkg_roi) b_som1.rekeyNxPars(dataset_type) else: if dst_type != "text/Dave2d": b_som1 = data_dst.getSOM(data_paths, roi_file=bkg_roi) else: b_som1 = data_dst.getSOM(data_paths) if verbose: print "# Background SO:", len(b_som1) else: b_som1 = None if timer is not None: timer.getTime(msg="After reading data") else: if dst_type == "application/x-NeXus": d_som_t = data_dst.getSOM(data_paths, so_axis, roi_file=signal_roi) d_som_t.rekeyNxPars(dataset_type) add_nxpars_sig = True else: if dst_type != "text/Dave2d": d_som_t = data_dst.getSOM(data_paths, roi_file=signal_roi) else: d_som_t = data_dst.getSOM(data_paths) add_nxpars_sig = False if bkg_roi is not None: if dst_type == "application/x-NeXus": b_som_t = data_dst.getSOM(data_paths, so_axis, roi_file=bkg_roi) b_som_t.rekeyNxPars(dataset_type) add_nxpars_bkg = True else: if dst_type != "text/Dave2d": b_som_t = data_dst.getSOM(data_paths, roi_file=bkg_roi) else: b_som_t = data_dst.getSOM(data_paths) add_nxpars_bkg = False else: b_som_t = None if timer is not None: timer.getTime(msg="After reading data") d_som1 = common_lib.add_ncerr(d_som_t, d_som1, add_nxpars=add_nxpars_sig) if bkg_roi is not None: b_som1 = common_lib.add_ncerr(b_som_t, b_som1, add_nxpars=add_nxpars_bkg) if timer is not None: timer.getTime(msg="After adding spectra") del d_som_t if bkg_roi is not None: del b_som_t if timer is not None: timer.getTime(msg="After SOM deletion") data_dst.release_resource() del data_dst counter += 1 if timer is not None: timer.getTime(msg="After resource release and DST deletion") if dst_type == "application/x-NeXus": som_key_parts = [dataset_type, "filename"] som_key = "-".join(som_key_parts) d_som1.attr_list[som_key] = filelist if b_som1 is not None: b_som1.attr_list[som_key] = filelist else: # Previously written files already have this structure imposed pass return (d_som1, b_som1)
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 add_files(filelist, **kwargs): """ This function takes a list of U{NeXus<www.nexusformat.org>} files and various keyword arguments and returns a data C{SOM} and a background C{SOM} (if requested) that is the sum of all the data from the specified files. B{It is assumed that the files contain similar data as only crude cross-checks will be made. You have been warned.} @param filelist: A list containing the names of the files to sum @type filelist: C{list} @param kwargs: A list of keyword arguments that the function accepts: @keyword SO_Axis: This is the name of the main axis to read from the NeXus file @type SO_Axis: C{string} @keyword Data_Paths: This contains the data paths and signals for the requested detector banks @type Data_Paths: C{tuple} of C{tuple}s @keyword Signal_ROI: This is the name of a file that contains a list of pixel IDs that will be read from the data file and stored as a signal C{SOM} @type Signal_ROI: C{string} @keyword Signal_MASK: This is the name of a file that contains a list of pixel IDs that will be read from the data file and stored as a signal C{SOM} @type Signal_MASK: C{string} @keyword dataset_type: The practical name of the dataset being processed. The default value is I{data}. @type dataset_type: C{string} @keyword dst_type: The type of C{DST} to be created during file read-in. The default value is I{application/x-NeXus}. @type dst_type: C{string} @keyword Verbose: This is a flag to turn on print statments. The default is I{False}. @type Verbose: C{boolean} @keyword Timer: This is an SNS Timer object used for showing the performance timing in the function. @type Timer: C{sns_timing.Timer} @return: Signal C{SOM.SOM} and background C{SOM.SOM} @rtype: C{tuple} @raise SystemExit: If any file cannot be read @raise RuntimeError: If both a ROI and MASK file are specified """ import sys import common_lib import DST import hlr_utils # Parse keywords try: so_axis = kwargs["SO_Axis"] except KeyError: so_axis = "time_of_flight" try: data_paths = kwargs["Data_Paths"] except KeyError: data_paths = None try: signal_roi = kwargs["Signal_ROI"] except KeyError: signal_roi = None try: signal_mask = kwargs["Signal_MASK"] except KeyError: signal_mask = None try: dataset_type = kwargs["dataset_type"] except KeyError: dataset_type = "data" try: dst_type = kwargs["dst_type"] except KeyError: try: dst_type = hlr_utils.file_peeker(filelist[0]) except RuntimeError: # Assume it is a NeXus file, since it is not a DR produced file dst_type = "application/x-NeXus" try: verbose = kwargs["Verbose"] except KeyError: verbose = False try: timer = kwargs["Timer"] except KeyError: timer = None if signal_roi is not None and signal_mask is not None: raise RuntimeError("Cannot specify both ROI and MASK file! Please " + "choose!") counter = 0 for filename in filelist: if verbose: print "File:", filename try: if dst_type == "application/x-NeXus": data_dst = DST.getInstance(dst_type, filename) else: resource = open(filename, "r") data_dst = DST.getInstance(dst_type, resource) except SystemError: print "ERROR: Failed to data read file %s" % filename sys.exit(-1) if verbose: print "Reading data file %d" % counter if counter == 0: if dst_type == "application/x-NeXus": d_som1 = data_dst.getSOM(data_paths, so_axis, roi_file=signal_roi, mask_file=signal_mask) d_som1.rekeyNxPars(dataset_type) else: if dst_type != "text/Dave2d": d_som1 = data_dst.getSOM(data_paths, roi_file=signal_roi, mask_file=signal_mask) else: d_som1 = data_dst.getSOM(data_paths) if verbose: len_data = len(d_som1) print "# Signal SO:", len_data if len_data == 0: print "All data has been filtered. Program exiting." sys.exit(0) if dst_type == "application/x-NeXus": print "# TOF:", len(d_som1[0]) print "# TOF Axis:", len(d_som1[0].axis[0].val) elif dst_type != "text/num-info": print "# Data Size:", len(d_som1[0]) print "# X-Axis:", len(d_som1[0].axis[0].val) try: axis_len = len(d_som1[0].axis[1].val) print "# Y-Axis:", axis_len except IndexError: pass if timer is not None: timer.getTime(msg="After reading data") else: if dst_type == "application/x-NeXus": d_som_t = data_dst.getSOM(data_paths, so_axis, roi_file=signal_roi, mask_file=signal_mask) d_som_t.rekeyNxPars(dataset_type) add_nxpars_sig = True else: if dst_type != "text/Dave2d": d_som_t = data_dst.getSOM(data_paths, roi_file=signal_roi, mask_file=signal_mask) else: d_som_t = data_dst.getSOM(data_paths) add_nxpars_sig = False if timer is not None: timer.getTime(msg="After reading data") d_som1 = common_lib.add_ncerr(d_som_t, d_som1, add_nxpars=add_nxpars_sig) if timer is not None: timer.getTime(msg="After adding spectra") del d_som_t if timer is not None: timer.getTime(msg="After SOM deletion") data_dst.release_resource() del data_dst counter += 1 if timer is not None: timer.getTime(msg="After resource release and DST deletion") if dst_type == "application/x-NeXus": som_key_parts = [dataset_type, "filename"] som_key = "-".join(som_key_parts) d_som1.attr_list[som_key] = filelist else: # Previously written files already have this structure imposed pass return d_som1
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