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())
