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=None):
"""
This method is where the data reduction process gets done.
@param config: Object containing the data reduction configuration
information.
@type config: L{hlr_utils.Configure}
@param tim: (OPTIONAL) Object that will allow the method to perform
timing evaluations.
@type tim: C{sns_time.DiffTime}
"""
import common_lib
import dr_lib
import DST
if tim is not None:
tim.getTime(False)
old_time = tim.getOldTime()
if config.data is None:
raise RuntimeError("Need to pass a data filename to the driver "\
+"script.")
# Read in geometry if one is provided
if config.inst_geom is not None:
if config.verbose:
print "Reading in instrument geometry file"
inst_geom_dst = DST.getInstance("application/x-NxsGeom",
config.inst_geom)
else:
inst_geom_dst = None
# Perform early background subtraction if the hwfix flag is used
if config.hwfix:
if not config.mc:
so_axis = "time_of_flight"
else:
so_axis = "Time_of_Flight"
bkg_som0 = dr_lib.add_files(config.back,
Data_Paths=config.data_paths.toPath(),
SO_Axis=so_axis,
Signal_ROI=config.roi_file,
dataset_type="background",
Verbose=config.verbose, Timer=tim)
bkg_som = dr_lib.fix_bin_contents(bkg_som0)
del bkg_som0
else:
bkg_som = None
# Perform Steps 1-15 on sample data
d_som1 = dr_lib.process_igs_data(config.data, config, timer=tim,
inst_geom_dst=inst_geom_dst,
tib_const=config.tib_data_const,
bkg_som=bkg_som)
# Perform Steps 1-15 on empty can data
if config.ecan is not None:
e_som1 = dr_lib.process_igs_data(config.ecan, config, timer=tim,
inst_geom_dst=inst_geom_dst,
dataset_type="empty_can",
tib_const=config.tib_ecan_const,
bkg_som=bkg_som)
else:
e_som1 = None
# Perform Steps 1-15 on normalization data
if config.norm is not None:
n_som1 = dr_lib.process_igs_data(config.norm, config, timer=tim,
inst_geom_dst=inst_geom_dst,
dataset_type="normalization",
tib_const=config.tib_norm_const,
bkg_som=bkg_som)
else:
n_som1 = None
# Perform Steps 1-15 on background data
if config.back is not None and not config.hwfix:
b_som1 = dr_lib.process_igs_data(config.back, config, timer=tim,
inst_geom_dst=inst_geom_dst,
dataset_type="background",
tib_const=config.tib_back_const)
else:
b_som1 = None
# Perform Step 1-15 on direct scattering background data
if config.dsback is not None:
ds_som1 = dr_lib.process_igs_data(config.dsback, config, timer=tim,
inst_geom_dst=inst_geom_dst,
tib_const=config.tib_dsback_const,
dataset_type="dsbackground",
bkg_som=bkg_som)
# Note: time_zero_slope MUST be a tuple
if config.time_zero_slope is not None:
ds_som1.attr_list["Time_zero_slope"] = \
config.time_zero_slope.toValErrTuple()
# Note: time_zero_offset MUST be a tuple
if config.time_zero_offset is not None:
ds_som1.attr_list["Time_zero_offset"] = \
config.time_zero_offset.toValErrTuple()
# Step 16: Linearly interpolate TOF elastic range in direct scattering
# background data
# First convert TOF elastic range to appropriate pixel initial
# wavelengths
if config.verbose:
print "Determining initial wavelength range for elastic line"
if tim is not None:
tim.getTime(False)
if config.tof_elastic is None:
# Units are in microseconds
tof_elastic_range = (140300, 141300)
else:
tof_elastic_range = config.tof_elastic
ctof_elastic_low = dr_lib.convert_single_to_list(\
"tof_to_initial_wavelength_igs_lin_time_zero",
(tof_elastic_range[0], 0.0),
ds_som1)
ctof_elastic_high = dr_lib.convert_single_to_list(\
"tof_to_initial_wavelength_igs_lin_time_zero",
(tof_elastic_range[1], 0.0),
ds_som1)
ctof_elastic_range = [(ctof_elastic_low[i][0], ctof_elastic_high[i][0])
for i in xrange(len(ctof_elastic_low))]
if tim is not None:
tim.getTime(msg="After calculating initial wavelength range for "\
+"elastic line ")
del ctof_elastic_low, ctof_elastic_high
# Now interpolate spectra between TOF elastic range (converted to
# initial wavelength)
if config.verbose:
print "Linearly interpolating direct scattering spectra"
if tim is not None:
tim.getTime(False)
ds_som2 = dr_lib.lin_interpolate_spectra(ds_som1, ctof_elastic_range)
if tim is not None:
tim.getTime(msg="After linearly interpolating direct scattering "\
+"spectra ")
if config.dump_dslin:
ds_som2_1 = dr_lib.sum_all_spectra(ds_som2,\
rebin_axis=config.lambda_bins.toNessiList())
hlr_utils.write_file(config.output, "text/Spec", ds_som2_1,
output_ext="lin",
data_ext=config.ext_replacement,
path_replacement=config.path_replacement,
verbose=config.verbose,
message="dsbackground linear interpolation")
del ds_som2_1
del ds_som1
else:
ds_som2 = None
if inst_geom_dst is not None:
inst_geom_dst.release_resource()
# Steps 17-18: Subtract background spectrum from sample spectrum
if config.dsback is None:
back_som = b_som1
bkg_type = "background"
else:
back_som = ds_som2
bkg_type = "dsbackground"
d_som2 = dr_lib.subtract_bkg_from_data(d_som1, back_som,
verbose=config.verbose,
timer=tim,
dataset1="data",
dataset2=bkg_type,
scale=config.scale_bs)
if config.dsback is not None:
del ds_som2
# Step 19: Zero region outside TOF elastic for background for empty can
if config.dsback is None:
bcs_som = b_som1
cs_som = e_som1
else:
if config.verbose and b_som1 is not None:
print "Zeroing background spectra"
if tim is not None and b_som1 is not None:
tim.getTime(False)
bcs_som = dr_lib.zero_spectra(b_som1, ctof_elastic_range)
if tim is not None and b_som1 is not None:
tim.getTime(msg="After zeroing background spectra")
if config.verbose and e_som1 is not None:
print "Zeroing empty can spectra"
if tim is not None and e_som1 is not None:
tim.getTime(False)
cs_som = dr_lib.zero_spectra(e_som1, ctof_elastic_range)
if tim is not None and e_som1 is not None:
tim.getTime(msg="After zeroing empty can spectra")
del ctof_elastic_range
# Steps 20-21: Subtract background spectrum from empty can spectrum
e_som2 = dr_lib.subtract_bkg_from_data(cs_som, bcs_som,
verbose=config.verbose,
timer=tim,
dataset1="empty_can",
dataset2="background",
scale=config.scale_bcs)
# Steps 22-23: Subtract background spectrum from empty can spectrum for
# normalization
e_som3 = dr_lib.subtract_bkg_from_data(e_som1, b_som1,
verbose=config.verbose,
timer=tim,
dataset1="empty_can",
dataset2="background",
scale=config.scale_bcn)
# Steps 24-25: Subtract background spectrum from normalization spectrum
n_som2 = dr_lib.subtract_bkg_from_data(n_som1, b_som1,
verbose=config.verbose,
timer=tim,
dataset1="normalization",
dataset2="background",
scale=config.scale_bn)
del b_som1, e_som1, bcs_som, cs_som
# Steps 26-27: Subtract empty can spectrum from sample spectrum
d_som3 = dr_lib.subtract_bkg_from_data(d_som2, e_som2,
verbose=config.verbose,
timer=tim,
dataset1="data",
dataset2="empty_can",
scale=config.scale_cs)
del d_som2, e_som2
# Steps 28-29: Subtract empty can spectrum from normalization spectrum
n_som3 = dr_lib.subtract_bkg_from_data(n_som2, e_som3,
verbose=config.verbose,
timer=tim,
dataset1="normalization",
dataset2="empty_can",
scale=config.scale_cn)
del n_som2, e_som3
# Step 30-32: Integrate normalization spectra
if config.verbose and n_som3 is not None:
print "Integrating normalization spectra"
norm_int = dr_lib.integrate_spectra(n_som3, start=config.norm_start,
end=config.norm_end, norm=True)
del n_som3
# Step 33: Normalize data by integrated values
if config.verbose and norm_int is not None:
print "Normalizing data by normalization data"
if norm_int is not None:
d_som4 = common_lib.div_ncerr(d_som3, norm_int)
else:
d_som4 = d_som3
if norm_int is not None:
if tim is not None:
tim.getTime(msg="After normalizing data ")
del d_som3, norm_int
# Step 35: Convert initial wavelength to E_initial
if config.verbose:
print "Converting initial wavelength to E_initial"
if tim is not None:
tim.getTime(False)
d_som6 = common_lib.wavelength_to_energy(d_som4)
if tim is not None:
tim.getTime(msg="After converting initial wavelength to E_initial ")
if config.dump_initial_energy:
hlr_utils.write_file(config.output, "text/Spec", d_som6,
output_ext="ixl",
data_ext=config.ext_replacement,
path_replacement=config.path_replacement,
verbose=config.verbose,
message="pixel initial energy information")
del d_som4
# Steps 36-37: Calculate energy transfer
if config.verbose:
print "Calculating energy transfer"
if tim is not None:
tim.getTime(False)
d_som7 = dr_lib.igs_energy_transfer(d_som6)
if tim is not None:
tim.getTime(msg="After calculating energy transfer ")
if config.dump_energy:
hlr_utils.write_file(config.output, "text/Spec", d_som7,
output_ext="exl",
data_ext=config.ext_replacement,
path_replacement=config.path_replacement,
verbose=config.verbose,
message="pixel energy transfer information")
# Write 3-column ASCII file for E_t
d_som7_1 = dr_lib.sum_all_spectra(d_som7,
rebin_axis=config.E_bins.toNessiList())
hlr_utils.write_file(config.output, "text/Spec", d_som7_1,
output_ext="etr",
data_ext=config.ext_replacement,
path_replacement=config.path_replacement,
verbose=config.verbose,
message="combined energy transfer information")
del d_som7_1
# Steps 34,36-37: Calculate scaled energy transfer
if config.verbose:
print "Calculating scaled energy transfer"
d_som9 = dr_lib.igs_energy_transfer(d_som6, scale=True)
if tim is not None:
tim.getTime(msg="After calculating scaled energy transfer ")
if config.dump_energy:
hlr_utils.write_file(config.output, "text/Spec", d_som9,
output_ext="sexl",
data_ext=config.ext_replacement,
path_replacement=config.path_replacement,
verbose=config.verbose,
message="pixel scaled energy transfer "\
+"information")
# Write 3-column ASCII file for scaled E_t
d_som9_1 = dr_lib.sum_all_spectra(d_som9,
rebin_axis=config.E_bins.toNessiList())
hlr_utils.write_file(config.output, "text/Spec", d_som9_1,
output_ext="setr",
data_ext=config.ext_replacement,
path_replacement=config.path_replacement,
verbose=config.verbose,
message="combined scaled energy transfer "\
+"information")
del d_som9_1
del d_som6, d_som7
d_som9.attr_list["config"] = config
hlr_utils.write_file(config.output, "text/rmd", d_som9,
output_ext="rmd",
data_ext=config.ext_replacement,
path_replacement=config.path_replacement,
verbose=config.verbose,
message="metadata")
if tim is not None:
tim.setOldTime(old_time)
tim.getTime(msg="Total Running Time")
def run(config, tim=None):
"""
This method is where the data reduction process gets done.
@param config: Object containing the data reduction configuration
information.
@type config: L{hlr_utils.Configure}
@param tim: (OPTIONAL) Object that will allow the method to perform
timing evaluations.
@type tim: C{sns_time.DiffTime}
"""
import common_lib
import dr_lib
import DST
if tim is not None:
tim.getTime(False)
old_time = tim.getOldTime()
if config.data is None:
raise RuntimeError("Need to pass a data filename to the driver "\
+"script.")
# Read in geometry if one is provided
if config.inst_geom is not None:
if config.verbose:
print "Reading in instrument geometry file"
inst_geom_dst = DST.getInstance("application/x-NxsGeom",
config.inst_geom)
else:
inst_geom_dst = None
# Perform early background subtraction if the hwfix flag is used
if config.hwfix:
if not config.mc:
so_axis = "time_of_flight"
else:
so_axis = "Time_of_Flight"
bkg_som0 = dr_lib.add_files(config.back,
Data_Paths=config.data_paths.toPath(),
SO_Axis=so_axis,
Signal_ROI=config.roi_file,
dataset_type="background",
Verbose=config.verbose, Timer=tim)
bkg_som = dr_lib.fix_bin_contents(bkg_som0)
del bkg_som0
else:
bkg_som = None
# Perform Steps 1-15 on sample data
d_som1 = dr_lib.process_igs_data(config.data, config, timer=tim,
inst_geom_dst=inst_geom_dst,
tib_const=config.tib_data_const,
bkg_som=bkg_som)
# Perform Steps 1-15 on empty can data
if config.ecan is not None:
e_som1 = dr_lib.process_igs_data(config.ecan, config, timer=tim,
inst_geom_dst=inst_geom_dst,
dataset_type="empty_can",
tib_const=config.tib_ecan_const,
bkg_som=bkg_som)
else:
e_som1 = None
# Perform Steps 1-15 on normalization data
if config.norm is not None:
n_som1 = dr_lib.process_igs_data(config.norm, config, timer=tim,
inst_geom_dst=inst_geom_dst,
dataset_type="normalization",
tib_const=config.tib_norm_const,
bkg_som=bkg_som)
else:
n_som1 = None
# Perform Steps 1-15 on background data
if config.back is not None:
b_som1 = dr_lib.process_igs_data(config.back, config, timer=tim,
inst_geom_dst=inst_geom_dst,
dataset_type="background",
tib_const=config.tib_back_const,
bkg_som=bkg_som)
else:
b_som1 = None
# Perform Step 1-15 on direct scattering background data
if config.dsback is not None:
ds_som1 = dr_lib.process_igs_data(config.dsback, config, timer=tim,
inst_geom_dst=inst_geom_dst,
tib_const=config.tib_dsback_const,
dataset_type="dsbackground",
bkg_som=bkg_som)
# Note: time_zero_slope MUST be a tuple
if config.time_zero_slope is not None:
ds_som1.attr_list["Time_zero_slope"] = \
config.time_zero_slope.toValErrTuple()
# Note: time_zero_offset MUST be a tuple
if config.time_zero_offset is not None:
ds_som1.attr_list["Time_zero_offset"] = \
config.time_zero_offset.toValErrTuple()
# Step 16: Linearly interpolate TOF elastic range in direct scattering
# background data
# First convert TOF elastic range to appropriate pixel initial
# wavelengths
if config.verbose:
print "Determining initial wavelength range for elastic line"
if tim is not None:
tim.getTime(False)
if config.tof_elastic is None:
# Units are in microseconds
tof_elastic_range = (140300, 141300)
else:
tof_elastic_range = config.tof_elastic
ctof_elastic_low = dr_lib.convert_single_to_list(\
"tof_to_initial_wavelength_igs_lin_time_zero",
(tof_elastic_range[0], 0.0),
ds_som1)
ctof_elastic_high = dr_lib.convert_single_to_list(\
"tof_to_initial_wavelength_igs_lin_time_zero",
(tof_elastic_range[1], 0.0),
ds_som1)
ctof_elastic_range = [(ctof_elastic_low[i][0], ctof_elastic_high[i][0])
for i in xrange(len(ctof_elastic_low))]
if tim is not None:
tim.getTime(msg="After calculating initial wavelength range for "\
+"elastic line ")
del ctof_elastic_low, ctof_elastic_high
if config.split:
lambda_filter = [(d_som1[i].axis[0].val[0],
d_som1[i].axis[0].val[-1])
for i in xrange(len(d_som1))]
else:
lambda_filter = None
# Now interpolate spectra between TOF elastic range (converted to
# initial wavelength)
if config.verbose:
print "Linearly interpolating direct scattering spectra"
if tim is not None:
tim.getTime(False)
ds_som2 = dr_lib.lin_interpolate_spectra(ds_som1, ctof_elastic_range,
filter_axis=lambda_filter)
if tim is not None:
tim.getTime(msg="After linearly interpolating direct scattering "\
+"spectra ")
if config.dump_dslin:
ds_som2_1 = dr_lib.sum_all_spectra(ds_som2,\
rebin_axis=config.lambda_bins.toNessiList())
hlr_utils.write_file(config.output, "text/Spec", ds_som2_1,
output_ext="lin",
data_ext=config.ext_replacement,
path_replacement=config.path_replacement,
verbose=config.verbose,
message="dsbackground linear interpolation")
del ds_som2_1
del ds_som1
else:
ds_som2 = None
if inst_geom_dst is not None:
inst_geom_dst.release_resource()
# Steps 17-18: Subtract background spectrum from sample spectrum
if config.dsback is None:
back_som = b_som1
bkg_type = "background"
else:
back_som = ds_som2
bkg_type = "dsbackground"
d_som2 = dr_lib.subtract_bkg_from_data(d_som1, back_som,
verbose=config.verbose,
timer=tim,
dataset1="data",
dataset2=bkg_type,
scale=config.scale_bs)
if config.dsback is not None:
del ds_som2
# Step 19: Zero region outside TOF elastic for background for empty can
if config.dsback is None:
bcs_som = b_som1
cs_som = e_som1
else:
if config.verbose and b_som1 is not None:
print "Zeroing background spectra"
if tim is not None and b_som1 is not None:
tim.getTime(False)
bcs_som = dr_lib.zero_spectra(b_som1, ctof_elastic_range)
if tim is not None and b_som1 is not None:
tim.getTime(msg="After zeroing background spectra")
if config.verbose and e_som1 is not None:
print "Zeroing empty can spectra"
if tim is not None and e_som1 is not None:
tim.getTime(False)
cs_som = dr_lib.zero_spectra(e_som1, ctof_elastic_range)
if tim is not None and e_som1 is not None:
tim.getTime(msg="After zeroing empty can spectra")
del ctof_elastic_range
# Steps 20-21: Subtract background spectrum from empty can spectrum
e_som2 = dr_lib.subtract_bkg_from_data(cs_som, bcs_som,
verbose=config.verbose,
timer=tim,
dataset1="data-empty_can",
dataset2="background",
scale=config.scale_bcs)
# Steps 22-23: Subtract background spectrum from empty can spectrum for
# normalization
try:
config.pre_norm
except AttributeError:
config.pre_norm = False
if not config.pre_norm:
e_som3 = dr_lib.subtract_bkg_from_data(e_som1, b_som1,
verbose=config.verbose,
timer=tim,
dataset1="norm-empty_can",
dataset2="background",
scale=config.scale_bcn)
else:
e_som3 = None
# Steps 24-25: Subtract background spectrum from normalization spectrum
if not config.pre_norm:
n_som2 = dr_lib.subtract_bkg_from_data(n_som1, b_som1,
verbose=config.verbose,
timer=tim,
dataset1="normalization",
dataset2="background",
scale=config.scale_bn)
else:
n_som2 = n_som1
del b_som1, e_som1, bcs_som, cs_som
# Steps 26-27: Subtract empty can spectrum from sample spectrum
d_som3 = dr_lib.subtract_bkg_from_data(d_som2, e_som2,
verbose=config.verbose,
timer=tim,
dataset1="data",
dataset2="empty_can",
scale=config.scale_cs)
del d_som2, e_som2
# Steps 28-29: Subtract empty can spectrum from normalization spectrum
if not config.pre_norm:
n_som3 = dr_lib.subtract_bkg_from_data(n_som2, e_som3,
verbose=config.verbose,
timer=tim,
dataset1="normalization",
dataset2="empty_can",
scale=config.scale_cn)
else:
n_som3 = n_som2
del n_som2, e_som3
# Step 30-31: Integrate normalization spectra
if config.verbose and n_som3 is not None and not config.pre_norm:
print "Integrating normalization spectra"
if not config.pre_norm:
norm_int = dr_lib.integrate_spectra(n_som3, start=config.norm_start,
end=config.norm_end, norm=True)
else:
norm_int = n_som3
del n_som3
# Step 32: Normalize data by integrated values
if config.verbose and norm_int is not None:
print "Normalizing data by normalization data"
if norm_int is not None:
d_som4 = common_lib.div_ncerr(d_som3, norm_int)
else:
d_som4 = d_som3
if norm_int is not None:
if tim is not None:
tim.getTime(msg="After normalizing data ")
del d_som3, norm_int
if config.dump_norm:
if tim is not None:
tim.getTime(False)
hlr_utils.write_file(config.output, "text/Spec", d_som4,
output_ext="wvn",
data_ext=config.ext_replacement,
path_replacement=config.path_replacement,
verbose=config.verbose,
message="wavelength (vanadium norm) information")
if tim is not None:
tim.getTime(msg="After writing wavelength (vanadium norm) info ")
# Steps 33 to end: Creating S(Q,E)
if config.Q_bins is not None:
if config.verbose:
print "Creating 2D spectrum"
if tim is not None:
tim.getTime(False)
d_som5 = dr_lib.create_E_vs_Q_igs(d_som4,
config.E_bins.toNessiList(),
config.Q_bins.toNessiList(),
so_id="Full Detector",
y_label="counts",
y_units="counts / (ueV * A^-1)",
x_labels=["Q transfer",
"energy transfer"],
x_units=["1/Angstroms","ueV"],
split=config.split,
Q_filter=False,
configure=config)
if tim is not None:
tim.getTime(msg="After creation of final spectrum ")
del d_som4
# Steps 33 to 36: Create S(-cos(polar), E)
elif config.ncospol_bins is not None:
if config.verbose:
print "Convert wavelength to energy transfer"
if tim is not None:
tim.getTime(False)
d_som4a = dr_lib.energy_transfer(d_som4, "IGS", "Wavelength_final",
sa_norm=True, scale=True,
change_units=True)
if tim is not None:
tim.getTime(msg="After wavelength to energy transfer conversion ")
del d_som4
if config.verbose:
print "Creating 2D spectrum"
if tim is not None:
tim.getTime(False)
d_som5 = dr_lib.create_param_vs_Y(d_som4a, "polar",
"negcos_param_array",
config.ncospol_bins.toNessiList(),
rebin_axis=config.E_bins.toNessiList(),
y_label="counts",
y_units="counts / ueV",
x_labels=["-cos(polar)",
"Energy Transfer"],
x_units=["", "ueV"])
if tim is not None:
tim.getTime(msg="After creation of final spectrum ")
# If rescaling factor present, rescale the data
if config.rescale_final is not None and not config.split:
d_som6 = common_lib.mult_ncerr(d_som5, (config.rescale_final, 0.0))
else:
d_som6 = d_som5
if tim is None:
old_time = None
if not __name__ == "amorphous_reduction_sqe":
del d_som5
__write_output(d_som6, config, tim, old_time)
else:
if config.create_output:
del d_som5
__write_output(d_som6, config, tim, old_time)
else:
return d_som6
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())
def run(config, tim=None):
"""
This method is where the data reduction process gets done.
@param config: Object containing the data reduction configuration
information.
@type config: L{hlr_utils.Configure}
@param tim: (OPTIONAL) Object that will allow the method to perform
timing evaluations.
@type tim: C{sns_time.DiffTime}
"""
import common_lib
import dr_lib
import DST
if tim is not None:
tim.getTime(False)
old_time = tim.getOldTime()
if config.data is None:
raise RuntimeError("Need to pass a data filename to the driver "\
+"script.")
# Read in geometry if one is provided
if config.inst_geom is not None:
if config.verbose:
print "Reading in instrument geometry file"
inst_geom_dst = DST.getInstance("application/x-NxsGeom",
config.inst_geom)
else:
inst_geom_dst = None
# Perform early background subtraction if the hwfix flag is used
if config.hwfix:
if not config.mc:
so_axis = "time_of_flight"
else:
so_axis = "Time_of_Flight"
bkg_som0 = dr_lib.add_files(config.back,
Data_Paths=config.data_paths.toPath(),
SO_Axis=so_axis,
Signal_ROI=config.roi_file,
dataset_type="background",
Verbose=config.verbose,
Timer=tim)
bkg_som = dr_lib.fix_bin_contents(bkg_som0)
del bkg_som0
else:
bkg_som = None
# Perform Steps 1-15 on sample data
d_som1 = dr_lib.process_igs_data(config.data,
config,
timer=tim,
inst_geom_dst=inst_geom_dst,
tib_const=config.tib_data_const,
bkg_som=bkg_som)
# Perform Steps 1-15 on empty can data
if config.ecan is not None:
e_som1 = dr_lib.process_igs_data(config.ecan,
config,
timer=tim,
inst_geom_dst=inst_geom_dst,
dataset_type="empty_can",
tib_const=config.tib_ecan_const,
bkg_som=bkg_som)
else:
e_som1 = None
# Perform Steps 1-15 on normalization data
if config.norm is not None:
n_som1 = dr_lib.process_igs_data(config.norm,
config,
timer=tim,
inst_geom_dst=inst_geom_dst,
dataset_type="normalization",
tib_const=config.tib_norm_const,
bkg_som=bkg_som)
else:
n_som1 = None
# Perform Steps 1-15 on background data
if config.back is not None:
b_som1 = dr_lib.process_igs_data(config.back,
config,
timer=tim,
inst_geom_dst=inst_geom_dst,
dataset_type="background",
tib_const=config.tib_back_const,
bkg_som=bkg_som)
else:
b_som1 = None
# Perform Step 1-15 on direct scattering background data
if config.dsback is not None:
ds_som1 = dr_lib.process_igs_data(config.dsback,
config,
timer=tim,
inst_geom_dst=inst_geom_dst,
tib_const=config.tib_dsback_const,
dataset_type="dsbackground",
bkg_som=bkg_som)
# Note: time_zero_slope MUST be a tuple
if config.time_zero_slope is not None:
ds_som1.attr_list["Time_zero_slope"] = \
config.time_zero_slope.toValErrTuple()
# Note: time_zero_offset MUST be a tuple
if config.time_zero_offset is not None:
ds_som1.attr_list["Time_zero_offset"] = \
config.time_zero_offset.toValErrTuple()
# Step 16: Linearly interpolate TOF elastic range in direct scattering
# background data
# First convert TOF elastic range to appropriate pixel initial
# wavelengths
if config.verbose:
print "Determining initial wavelength range for elastic line"
if tim is not None:
tim.getTime(False)
if config.tof_elastic is None:
# Units are in microseconds
tof_elastic_range = (140300, 141300)
else:
tof_elastic_range = config.tof_elastic
ctof_elastic_low = dr_lib.convert_single_to_list(\
"tof_to_initial_wavelength_igs_lin_time_zero",
(tof_elastic_range[0], 0.0),
ds_som1)
ctof_elastic_high = dr_lib.convert_single_to_list(\
"tof_to_initial_wavelength_igs_lin_time_zero",
(tof_elastic_range[1], 0.0),
ds_som1)
ctof_elastic_range = [(ctof_elastic_low[i][0], ctof_elastic_high[i][0])
for i in xrange(len(ctof_elastic_low))]
if tim is not None:
tim.getTime(msg="After calculating initial wavelength range for "\
+"elastic line ")
del ctof_elastic_low, ctof_elastic_high
if config.split:
lambda_filter = [(d_som1[i].axis[0].val[0],
d_som1[i].axis[0].val[-1])
for i in xrange(len(d_som1))]
else:
lambda_filter = None
# Now interpolate spectra between TOF elastic range (converted to
# initial wavelength)
if config.verbose:
print "Linearly interpolating direct scattering spectra"
if tim is not None:
tim.getTime(False)
ds_som2 = dr_lib.lin_interpolate_spectra(ds_som1,
ctof_elastic_range,
filter_axis=lambda_filter)
if tim is not None:
tim.getTime(msg="After linearly interpolating direct scattering "\
+"spectra ")
if config.dump_dslin:
ds_som2_1 = dr_lib.sum_all_spectra(ds_som2,\
rebin_axis=config.lambda_bins.toNessiList())
hlr_utils.write_file(config.output,
"text/Spec",
ds_som2_1,
output_ext="lin",
data_ext=config.ext_replacement,
path_replacement=config.path_replacement,
verbose=config.verbose,
message="dsbackground linear interpolation")
del ds_som2_1
del ds_som1
else:
ds_som2 = None
if inst_geom_dst is not None:
inst_geom_dst.release_resource()
# Steps 17-18: Subtract background spectrum from sample spectrum
if config.dsback is None:
back_som = b_som1
bkg_type = "background"
else:
back_som = ds_som2
bkg_type = "dsbackground"
d_som2 = dr_lib.subtract_bkg_from_data(d_som1,
back_som,
verbose=config.verbose,
timer=tim,
dataset1="data",
dataset2=bkg_type,
scale=config.scale_bs)
if config.dsback is not None:
del ds_som2
# Step 19: Zero region outside TOF elastic for background for empty can
if config.dsback is None:
bcs_som = b_som1
cs_som = e_som1
else:
if config.verbose and b_som1 is not None:
print "Zeroing background spectra"
if tim is not None and b_som1 is not None:
tim.getTime(False)
bcs_som = dr_lib.zero_spectra(b_som1, ctof_elastic_range)
if tim is not None and b_som1 is not None:
tim.getTime(msg="After zeroing background spectra")
if config.verbose and e_som1 is not None:
print "Zeroing empty can spectra"
if tim is not None and e_som1 is not None:
tim.getTime(False)
cs_som = dr_lib.zero_spectra(e_som1, ctof_elastic_range)
if tim is not None and e_som1 is not None:
tim.getTime(msg="After zeroing empty can spectra")
del ctof_elastic_range
# Steps 20-21: Subtract background spectrum from empty can spectrum
e_som2 = dr_lib.subtract_bkg_from_data(cs_som,
bcs_som,
verbose=config.verbose,
timer=tim,
dataset1="data-empty_can",
dataset2="background",
scale=config.scale_bcs)
# Steps 22-23: Subtract background spectrum from empty can spectrum for
# normalization
try:
config.pre_norm
except AttributeError:
config.pre_norm = False
if not config.pre_norm:
e_som3 = dr_lib.subtract_bkg_from_data(e_som1,
b_som1,
verbose=config.verbose,
timer=tim,
dataset1="norm-empty_can",
dataset2="background",
scale=config.scale_bcn)
else:
e_som3 = None
# Steps 24-25: Subtract background spectrum from normalization spectrum
if not config.pre_norm:
n_som2 = dr_lib.subtract_bkg_from_data(n_som1,
b_som1,
verbose=config.verbose,
timer=tim,
dataset1="normalization",
dataset2="background",
scale=config.scale_bn)
else:
n_som2 = n_som1
del b_som1, e_som1, bcs_som, cs_som
# Steps 26-27: Subtract empty can spectrum from sample spectrum
d_som3 = dr_lib.subtract_bkg_from_data(d_som2,
e_som2,
verbose=config.verbose,
timer=tim,
dataset1="data",
dataset2="empty_can",
scale=config.scale_cs)
del d_som2, e_som2
# Steps 28-29: Subtract empty can spectrum from normalization spectrum
if not config.pre_norm:
n_som3 = dr_lib.subtract_bkg_from_data(n_som2,
e_som3,
verbose=config.verbose,
timer=tim,
dataset1="normalization",
dataset2="empty_can",
scale=config.scale_cn)
else:
n_som3 = n_som2
del n_som2, e_som3
# Step 30-31: Integrate normalization spectra
if config.verbose and n_som3 is not None and not config.pre_norm:
print "Integrating normalization spectra"
if not config.pre_norm:
norm_int = dr_lib.integrate_spectra(n_som3,
start=config.norm_start,
end=config.norm_end,
norm=True)
else:
norm_int = n_som3
del n_som3
# Step 32: Normalize data by integrated values
if config.verbose and norm_int is not None:
print "Normalizing data by normalization data"
if norm_int is not None:
d_som4 = common_lib.div_ncerr(d_som3, norm_int)
else:
d_som4 = d_som3
if norm_int is not None:
if tim is not None:
tim.getTime(msg="After normalizing data ")
del d_som3, norm_int
if config.dump_norm:
if tim is not None:
tim.getTime(False)
hlr_utils.write_file(config.output,
"text/Spec",
d_som4,
output_ext="wvn",
data_ext=config.ext_replacement,
path_replacement=config.path_replacement,
verbose=config.verbose,
message="wavelength (vanadium norm) information")
if tim is not None:
tim.getTime(msg="After writing wavelength (vanadium norm) info ")
# Steps 33 to end: Creating S(Q,E)
if config.Q_bins is not None:
if config.verbose:
print "Creating 2D spectrum"
if tim is not None:
tim.getTime(False)
d_som5 = dr_lib.create_E_vs_Q_igs(
d_som4,
config.E_bins.toNessiList(),
config.Q_bins.toNessiList(),
so_id="Full Detector",
y_label="counts",
y_units="counts / (ueV * A^-1)",
x_labels=["Q transfer", "energy transfer"],
x_units=["1/Angstroms", "ueV"],
split=config.split,
Q_filter=False,
configure=config)
if tim is not None:
tim.getTime(msg="After creation of final spectrum ")
del d_som4
# Steps 33 to 36: Create S(-cos(polar), E)
elif config.ncospol_bins is not None:
if config.verbose:
print "Convert wavelength to energy transfer"
if tim is not None:
tim.getTime(False)
d_som4a = dr_lib.energy_transfer(d_som4,
"IGS",
"Wavelength_final",
sa_norm=True,
scale=True,
change_units=True)
if tim is not None:
tim.getTime(msg="After wavelength to energy transfer conversion ")
del d_som4
if config.verbose:
print "Creating 2D spectrum"
if tim is not None:
tim.getTime(False)
d_som5 = dr_lib.create_param_vs_Y(
d_som4a,
"polar",
"negcos_param_array",
config.ncospol_bins.toNessiList(),
rebin_axis=config.E_bins.toNessiList(),
y_label="counts",
y_units="counts / ueV",
x_labels=["-cos(polar)", "Energy Transfer"],
x_units=["", "ueV"])
if tim is not None:
tim.getTime(msg="After creation of final spectrum ")
# If rescaling factor present, rescale the data
if config.rescale_final is not None and not config.split:
d_som6 = common_lib.mult_ncerr(d_som5, (config.rescale_final, 0.0))
else:
d_som6 = d_som5
if tim is None:
old_time = None
if not __name__ == "amorphous_reduction_sqe":
del d_som5
__write_output(d_som6, config, tim, old_time)
else:
if config.create_output:
del d_som5
__write_output(d_som6, config, tim, old_time)
else:
return d_som6