def process_reflp_data(datalist, conf, roi_file, bkg_roi_file=None, no_bkg=False, **kwargs): """ This function combines Steps 1 through 3 in section 2.4.6.1 of the data reduction process for Reduction from TOF to lambda_T 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, region-of-interest (ROI) file for the normalization dataset, a background region-of-interest (ROI) file and an optional flag about background subtractionand 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 roi_file: The file containing the list of pixel IDs for the region of interest. This only applies to normalization data. @type 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: File object that contains instrument geometry information. @type inst_geom_dst: C{DST.GeomDST} @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 import common_lib import dr_lib # Check keywords try: i_geom_dst = kwargs["inst_geom_dst"] except KeyError: i_geom_dst = None try: t = kwargs["timer"] except KeyError: t = None if roi_file is not None: # Normalization dataset_type = "norm" else: # Sample data dataset_type = "data" so_axis = "time_of_flight" # Step 0: Open data files and select ROI (if necessary) 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=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) # Override geometry if necessary if i_geom_dst is not None: i_geom_dst.setGeometry(conf.data_paths.toPath(), d_som1) if dataset_type == "data": # Get TOF bin width conf.delta_TOF = d_som1[0].axis[0].val[1] - d_som1[0].axis[0].val[0] if conf.mon_norm: 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_som1 = 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 ") else: dm_som1 = None # Step 1: Sum all spectra along the low resolution direction # Set sorting for REF_L if conf.verbose: print "Summing over 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 t is not None: t.getTime(False) d_som2 = dr_lib.sum_all_spectra(d_som1, y_sort=y_sort, stripe=True, pixel_fix=cent_pixel) if b_som1 is not None: b_som2 = dr_lib.sum_all_spectra(b_som1, y_sort=y_sort, stripe=True, pixel_fix=cent_pixel) del b_som1 else: b_som2 = b_som1 if t is not None: t.getTime(msg="After summing low resolution direction ") del d_som1 # Determine background spectrum if conf.verbose and not no_bkg: print "Determining %s background" % dataset_type if b_som2 is not None: B = dr_lib.calculate_ref_background(b_som2, no_bkg, conf.inst, None, aobj=d_som2) if t is not None: t.getTime(msg="After background determination") # Subtract background spectrum from data spectra if not no_bkg: d_som3 = dr_lib.subtract_bkg_from_data(d_som2, B, verbose=conf.verbose, timer=t, dataset1="data", dataset2="background") else: d_som3 = d_som2 del d_som2 # Zero the spectra if necessary if roi_file is None and (conf.tof_cut_min is not None or \ conf.tof_cut_max is not None): import utils # Find the indicies for the non zero range if conf.tof_cut_min is None: conf.TOF_min = d_som3[0].axis[0].val[0] start_index = 0 else: start_index = utils.bisect_helper(d_som3[0].axis[0].val, conf.tof_cut_min) if conf.tof_cut_max is None: conf.TOF_max = d_som3[0].axis[0].val[-1] end_index = len(d_som3[0].axis[0].val) - 1 else: end_index = utils.bisect_helper(d_som3[0].axis[0].val, conf.tof_cut_max) nz_list = [] for i in xrange(hlr_utils.get_length(d_som3)): nz_list.append((start_index, end_index)) d_som4 = dr_lib.zero_spectra(d_som3, nz_list, use_bin_index=True) else: conf.TOF_min = d_som3[0].axis[0].val[0] conf.TOF_max = d_som3[0].axis[0].val[-1] d_som4 = d_som3 del d_som3 # Step N: Convert TOF to wavelength if conf.verbose: print "Converting TOF to wavelength" if t is not None: t.getTime(False) d_som5 = common_lib.tof_to_wavelength(d_som4, inst_param="total", units="microsecond") if dm_som1 is not None: dm_som2 = common_lib.tof_to_wavelength(dm_som1, units="microsecond") else: dm_som2 = None del dm_som1 if t is not None: t.getTime(msg="After converting TOF to wavelength ") del d_som4 if conf.mon_norm: dm_som3 = dr_lib.rebin_monitor(dm_som2, d_som5, rtype="frac") else: dm_som3 = None del dm_som2 if not conf.mon_norm: # Step 2: Multiply the spectra by the proton charge if conf.verbose: print "Multiply spectra by proton charge" pc_tag = dataset_type + "-proton_charge" proton_charge = d_som5.attr_list[pc_tag] if t is not None: t.getTime(False) d_som6 = common_lib.div_ncerr(d_som5, (proton_charge.getValue(), 0.0)) if t is not None: t.getTime(msg="After scaling by proton charge ") else: if conf.verbose: print "Normalize by monitor spectrum" if t is not None: t.getTime(False) d_som6 = common_lib.div_ncerr(d_som5, dm_som3) if t is not None: t.getTime(msg="After monitor normalization ") del d_som5, dm_som3 if roi_file is None: return d_som6 else: # Step 3: Make one spectrum for normalization dataset # Need to create a final rebinning axis pathlength = d_som6.attr_list.instrument.get_total_path( det_secondary=True) delta_lambda = common_lib.tof_to_wavelength((conf.delta_TOF, 0.0), pathlength=pathlength) lambda_bins = dr_lib.create_axis_from_data(d_som6, width=delta_lambda[0]) return dr_lib.sum_by_rebin_frac(d_som6, lambda_bins.toNessiList())
def run(config, tim): """ 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: Object that will allow the method to perform timing evaluations. @type tim: C{sns_time.DiffTime} """ import array_manip import common_lib import dr_lib import DST import SOM import math 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 sample data geometry if one is provided if config.data_inst_geom is not None: if config.verbose: print "Reading in sample data instrument geometry file" data_inst_geom_dst = DST.getInstance("application/x-NxsGeom", config.data_inst_geom) else: data_inst_geom_dst = None # Read in normalization data geometry if one is provided if config.norm_inst_geom is not None: if config.verbose: print "Reading in normalization instrument geometry file" norm_inst_geom_dst = DST.getInstance("application/x-NxsGeom", config.norm_inst_geom) else: norm_inst_geom_dst = None # Perform Steps 1-2 on sample data d_som1 = dr_lib.process_reflp_data(config.data, config, None, config.dbkg_roi_file, config.no_bkg, inst_geom_dst=data_inst_geom_dst, timer=tim) # Get the detector angle if config.omega is None: # Make a fake SO so = SOM.SO() try: theta = hlr_utils.get_special(d_som1.attr_list["Theta"], so) except KeyError: theta = (float('nan'), float('nan')) else: theta = config.omega.toFullTuple() if theta[0] is not None: if theta[2] == "degrees" or theta[2] == "degree": theta_rads = (theta[0] * (math.pi / 180.0), 0.0) else: theta_rads = (theta[0], 0.0) else: theta_rads = (float('nan'), float('nan')) d_som1.attr_list["data-theta"] = (theta_rads[0], theta_rads[1], "radians") # Perform Steps 1-3 on normalization data if config.norm is not None: n_som1 = dr_lib.process_reflp_data(config.norm, config, config.norm_roi_file, config.nbkg_roi_file, config.no_norm_bkg, inst_geom_dst=norm_inst_geom_dst, timer=tim) else: n_som1 = None # Closing sample data instrument geometry file if data_inst_geom_dst is not None: data_inst_geom_dst.release_resource() # Closing normalization data instrument geometry file if norm_inst_geom_dst is not None: norm_inst_geom_dst.release_resource() # Step 4: Divide data by normalization if config.verbose and config.norm is not None: print "Scale data by normalization" if tim is not None: tim.getTime(False) if config.norm is not None: # Need to rebin the normalization spectra to the data pixel spectra n_som2 = dr_lib.rebin_monitor(n_som1, d_som1, rtype="frac") # Now divide the spectra d_som2 = common_lib.div_ncerr(d_som1, n_som2) del n_som2 else: d_som2 = d_som1 if tim is not None and config.norm is not None: tim.getTime(msg="After normalizing signal spectra") del d_som1, n_som1 sin_theta_rads = (math.sin(theta_rads[0]), math.sin(theta_rads[1])) if sin_theta_rads[0] < 0.0: sin_theta_rads = (math.fabs(sin_theta_rads[0]), math.fabs(sin_theta_rads[1])) # Step 6: Scale wavelength axis by sin(theta) to make lambda_T if config.verbose: print "Scaling wavelength axis by sin(theta)" if tim is not None: tim.getTime(False) d_som3 = common_lib.div_ncerr(d_som2, sin_theta_rads, axis="x") if tim is not None: tim.getTime(msg="After scaling wavelength axis ") del d_som2 d_som3.setAxisLabel(0, "lambda_T") # Step 7: Rebin to lambda_T axis if config.verbose: print "Rebinning spectra" if config.lambdap_bins is None: # Create a binning scheme pathlength = d_som3.attr_list.instrument.get_total_path( det_secondary=True) delta_lambda = common_lib.tof_to_wavelength((config.delta_TOF, 0.0), pathlength=pathlength) delta_lambdap = array_manip.div_ncerr(delta_lambda[0], delta_lambda[1], sin_theta_rads[0], 0.0) config.lambdap_bins = dr_lib.create_axis_from_data(d_som3, width=delta_lambdap[0]) else: # Do nothing, got the binning scheme pass if tim is not None: tim.getTime(False) d_som4 = common_lib.rebin_axis_1D_frac(d_som3, config.lambdap_bins.toNessiList()) if tim is not None: tim.getTime(msg="After rebinning spectra ") del d_som3 if config.inst == "REF_M": # Clean up spectrum if config.tof_cut_min is not None: tof_cut_min = float(config.tof_cut_min) else: tof_cut_min = config.TOF_min if config.tof_cut_max is not None: tof_cut_max = float(config.tof_cut_max) else: tof_cut_max = config.TOF_max pathlength = d_som4.attr_list.instrument.get_total_path( det_secondary=True) lambda_min = common_lib.tof_to_wavelength((tof_cut_min, 0.0), pathlength=pathlength) lambda_T_min = common_lib.div_ncerr(lambda_min, sin_theta_rads) lambda_max = common_lib.tof_to_wavelength((tof_cut_max, 0.0), pathlength=pathlength) lambda_T_max = common_lib.div_ncerr(lambda_max, sin_theta_rads) nz_list = [] for i in xrange(hlr_utils.get_length(d_som4)): nz_list.append((lambda_T_min[0], lambda_T_max[0])) d_som4A = dr_lib.zero_spectra(d_som4, nz_list) else: d_som4A = d_som4 del d_som4 # Step 8: Write out all spectra to a file hlr_utils.write_file(config.output, "text/Spec", d_som4A, replace_ext=False, replace_path=False, verbose=config.verbose, message="Reflectivity information") if config.dump_twod: d_som5 = dr_lib.create_X_vs_pixpos(d_som4A, config.lambdap_bins.toNessiList(), rebin=False, y_label="R", y_units="", x_label="$\lambda_T$", x_units="$\AA$") hlr_utils.write_file(config.output, "text/Dave2d", d_som5, output_ext="plp", verbose=config.verbose, data_ext=config.ext_replacement, path_replacement=config.path_replacement, message="2D Reflectivity information") d_som4A.attr_list["config"] = config hlr_utils.write_file(config.output, "text/rmd", d_som4A, output_ext="rmd", verbose=config.verbose, data_ext=config.ext_replacement, path_replacement=config.path_replacement, message="metadata") if tim is not None: tim.setOldTime(old_time) tim.getTime(msg="Total Running Time")
def run(config, tim): """ 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: Object that will allow the method to perform timing evaluations. @type tim: C{sns_time.DiffTime} """ import array_manip import common_lib import dr_lib import DST import SOM import math 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 sample data geometry if one is provided if config.data_inst_geom is not None: if config.verbose: print "Reading in sample data instrument geometry file" data_inst_geom_dst = DST.getInstance("application/x-NxsGeom", config.data_inst_geom) else: data_inst_geom_dst = None # Read in normalization data geometry if one is provided if config.norm_inst_geom is not None: if config.verbose: print "Reading in normalization instrument geometry file" norm_inst_geom_dst = DST.getInstance("application/x-NxsGeom", config.norm_inst_geom) else: norm_inst_geom_dst = None # Perform Steps 1-2 on sample data d_som1 = dr_lib.process_reflp_data(config.data, config, None, config.dbkg_roi_file, config.no_bkg, inst_geom_dst=data_inst_geom_dst, timer=tim) # Get the detector angle if config.omega is None: # Make a fake SO so = SOM.SO() try: theta = hlr_utils.get_special(d_som1.attr_list["Theta"], so) except KeyError: theta = (float('nan'), float('nan')) else: theta = config.omega.toFullTuple() if theta[0] is not None: if theta[2] == "degrees" or theta[2] == "degree": theta_rads = (theta[0] * (math.pi / 180.0), 0.0) else: theta_rads = (theta[0], 0.0) else: theta_rads = (float('nan'), float('nan')) d_som1.attr_list["data-theta"] = (theta_rads[0], theta_rads[1], "radians") # Perform Steps 1-3 on normalization data if config.norm is not None: n_som1 = dr_lib.process_reflp_data(config.norm, config, config.norm_roi_file, config.nbkg_roi_file, config.no_norm_bkg, inst_geom_dst=norm_inst_geom_dst, timer=tim) else: n_som1 = None # Closing sample data instrument geometry file if data_inst_geom_dst is not None: data_inst_geom_dst.release_resource() # Closing normalization data instrument geometry file if norm_inst_geom_dst is not None: norm_inst_geom_dst.release_resource() # Step 4: Divide data by normalization if config.verbose and config.norm is not None: print "Scale data by normalization" if tim is not None: tim.getTime(False) if config.norm is not None: # Need to rebin the normalization spectra to the data pixel spectra n_som2 = dr_lib.rebin_monitor(n_som1, d_som1, rtype="frac") # Now divide the spectra d_som2 = common_lib.div_ncerr(d_som1, n_som2) del n_som2 else: d_som2 = d_som1 if tim is not None and config.norm is not None: tim.getTime(msg="After normalizing signal spectra") del d_som1, n_som1 sin_theta_rads = (math.sin(theta_rads[0]), math.sin(theta_rads[1])) if sin_theta_rads[0] < 0.0: sin_theta_rads = (math.fabs(sin_theta_rads[0]), math.fabs(sin_theta_rads[1])) # Step 6: Scale wavelength axis by sin(theta) to make lambda_T if config.verbose: print "Scaling wavelength axis by sin(theta)" if tim is not None: tim.getTime(False) d_som3 = common_lib.div_ncerr(d_som2, sin_theta_rads, axis="x") if tim is not None: tim.getTime(msg="After scaling wavelength axis ") del d_som2 d_som3.setAxisLabel(0, "lambda_T") # Step 7: Rebin to lambda_T axis if config.verbose: print "Rebinning spectra" if config.lambdap_bins is None: # Create a binning scheme pathlength = d_som3.attr_list.instrument.get_total_path( det_secondary=True) delta_lambda = common_lib.tof_to_wavelength((config.delta_TOF, 0.0), pathlength=pathlength) delta_lambdap = array_manip.div_ncerr(delta_lambda[0], delta_lambda[1], sin_theta_rads[0], 0.0) config.lambdap_bins = dr_lib.create_axis_from_data( d_som3, width=delta_lambdap[0]) else: # Do nothing, got the binning scheme pass if tim is not None: tim.getTime(False) d_som4 = common_lib.rebin_axis_1D_frac(d_som3, config.lambdap_bins.toNessiList()) if tim is not None: tim.getTime(msg="After rebinning spectra ") del d_som3 if config.inst == "REF_M": # Clean up spectrum if config.tof_cut_min is not None: tof_cut_min = float(config.tof_cut_min) else: tof_cut_min = config.TOF_min if config.tof_cut_max is not None: tof_cut_max = float(config.tof_cut_max) else: tof_cut_max = config.TOF_max pathlength = d_som4.attr_list.instrument.get_total_path( det_secondary=True) lambda_min = common_lib.tof_to_wavelength((tof_cut_min, 0.0), pathlength=pathlength) lambda_T_min = common_lib.div_ncerr(lambda_min, sin_theta_rads) lambda_max = common_lib.tof_to_wavelength((tof_cut_max, 0.0), pathlength=pathlength) lambda_T_max = common_lib.div_ncerr(lambda_max, sin_theta_rads) nz_list = [] for i in xrange(hlr_utils.get_length(d_som4)): nz_list.append((lambda_T_min[0], lambda_T_max[0])) d_som4A = dr_lib.zero_spectra(d_som4, nz_list) else: d_som4A = d_som4 del d_som4 # Step 8: Write out all spectra to a file hlr_utils.write_file(config.output, "text/Spec", d_som4A, replace_ext=False, replace_path=False, verbose=config.verbose, message="Reflectivity information") if config.dump_twod: d_som5 = dr_lib.create_X_vs_pixpos(d_som4A, config.lambdap_bins.toNessiList(), rebin=False, y_label="R", y_units="", x_label="$\lambda_T$", x_units="$\AA$") hlr_utils.write_file(config.output, "text/Dave2d", d_som5, output_ext="plp", verbose=config.verbose, data_ext=config.ext_replacement, path_replacement=config.path_replacement, message="2D Reflectivity information") d_som4A.attr_list["config"] = config hlr_utils.write_file(config.output, "text/rmd", d_som4A, output_ext="rmd", verbose=config.verbose, data_ext=config.ext_replacement, path_replacement=config.path_replacement, message="metadata") if tim is not None: tim.setOldTime(old_time) tim.getTime(msg="Total Running Time")
def process_reflp_data(datalist, conf, roi_file, bkg_roi_file=None, no_bkg=False, **kwargs): """ This function combines Steps 1 through 3 in section 2.4.6.1 of the data reduction process for Reduction from TOF to lambda_T 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, region-of-interest (ROI) file for the normalization dataset, a background region-of-interest (ROI) file and an optional flag about background subtractionand 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 roi_file: The file containing the list of pixel IDs for the region of interest. This only applies to normalization data. @type 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: File object that contains instrument geometry information. @type inst_geom_dst: C{DST.GeomDST} @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 import common_lib import dr_lib # Check keywords try: i_geom_dst = kwargs["inst_geom_dst"] except KeyError: i_geom_dst = None try: t = kwargs["timer"] except KeyError: t = None if roi_file is not None: # Normalization dataset_type = "norm" else: # Sample data dataset_type = "data" so_axis = "time_of_flight" # Step 0: Open data files and select ROI (if necessary) 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=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) # Override geometry if necessary if i_geom_dst is not None: i_geom_dst.setGeometry(conf.data_paths.toPath(), d_som1) if dataset_type == "data": # Get TOF bin width conf.delta_TOF = d_som1[0].axis[0].val[1] - d_som1[0].axis[0].val[0] if conf.mon_norm: 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_som1 = 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 ") else: dm_som1 = None # Step 1: Sum all spectra along the low resolution direction # Set sorting for REF_L if conf.verbose: print "Summing over 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 t is not None: t.getTime(False) d_som2 = dr_lib.sum_all_spectra(d_som1, y_sort=y_sort, stripe=True, pixel_fix=cent_pixel) if b_som1 is not None: b_som2 = dr_lib.sum_all_spectra(b_som1, y_sort=y_sort, stripe=True, pixel_fix=cent_pixel) del b_som1 else: b_som2 = b_som1 if t is not None: t.getTime(msg="After summing low resolution direction ") del d_som1 # Determine background spectrum if conf.verbose and not no_bkg: print "Determining %s background" % dataset_type if b_som2 is not None: B = dr_lib.calculate_ref_background(b_som2, no_bkg, conf.inst, None, aobj=d_som2) if t is not None: t.getTime(msg="After background determination") # Subtract background spectrum from data spectra if not no_bkg: d_som3 = dr_lib.subtract_bkg_from_data(d_som2, B, verbose=conf.verbose, timer=t, dataset1="data", dataset2="background") else: d_som3 = d_som2 del d_som2 # Zero the spectra if necessary if roi_file is None and (conf.tof_cut_min is not None or \ conf.tof_cut_max is not None): import utils # Find the indicies for the non zero range if conf.tof_cut_min is None: conf.TOF_min = d_som3[0].axis[0].val[0] start_index = 0 else: start_index = utils.bisect_helper(d_som3[0].axis[0].val, conf.tof_cut_min) if conf.tof_cut_max is None: conf.TOF_max = d_som3[0].axis[0].val[-1] end_index = len(d_som3[0].axis[0].val) - 1 else: end_index = utils.bisect_helper(d_som3[0].axis[0].val, conf.tof_cut_max) nz_list = [] for i in xrange(hlr_utils.get_length(d_som3)): nz_list.append((start_index, end_index)) d_som4 = dr_lib.zero_spectra(d_som3, nz_list, use_bin_index=True) else: conf.TOF_min = d_som3[0].axis[0].val[0] conf.TOF_max = d_som3[0].axis[0].val[-1] d_som4 = d_som3 del d_som3 # Step N: Convert TOF to wavelength if conf.verbose: print "Converting TOF to wavelength" if t is not None: t.getTime(False) d_som5 = common_lib.tof_to_wavelength(d_som4, inst_param="total", units="microsecond") if dm_som1 is not None: dm_som2 = common_lib.tof_to_wavelength(dm_som1, units="microsecond") else: dm_som2 = None del dm_som1 if t is not None: t.getTime(msg="After converting TOF to wavelength ") del d_som4 if conf.mon_norm: dm_som3 = dr_lib.rebin_monitor(dm_som2, d_som5, rtype="frac") else: dm_som3 = None del dm_som2 if not conf.mon_norm: # Step 2: Multiply the spectra by the proton charge if conf.verbose: print "Multiply spectra by proton charge" pc_tag = dataset_type + "-proton_charge" proton_charge = d_som5.attr_list[pc_tag] if t is not None: t.getTime(False) d_som6 = common_lib.div_ncerr(d_som5, (proton_charge.getValue(), 0.0)) if t is not None: t.getTime(msg="After scaling by proton charge ") else: if conf.verbose: print "Normalize by monitor spectrum" if t is not None: t.getTime(False) d_som6 = common_lib.div_ncerr(d_som5, dm_som3) if t is not None: t.getTime(msg="After monitor normalization ") del d_som5, dm_som3 if roi_file is None: return d_som6 else: # Step 3: Make one spectrum for normalization dataset # Need to create a final rebinning axis pathlength = d_som6.attr_list.instrument.get_total_path( det_secondary=True) delta_lambda = common_lib.tof_to_wavelength((conf.delta_TOF, 0.0), pathlength=pathlength) lambda_bins = dr_lib.create_axis_from_data(d_som6, width=delta_lambda[0]) return dr_lib.sum_by_rebin_frac(d_som6, lambda_bins.toNessiList())