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
config.so_axis = "time_of_flight"
# Steps 1-3: Produce a scaled summed dark current dataset
dc_som = dr_lib.scaled_summed_data(config.dkcur, config,
dataset_type="dark_current",
timer=tim)
# Perform Steps 3-6 on black can data
if config.bcan is not None:
b_som1 = dr_lib.calibrate_dgs_data(config.bcan, config, dc_som,
dataset_type="black_can",
inst_geom_dst=inst_geom_dst,
tib_const=config.tib_const,
cwp=config.cwp_bcan,
timer=tim)
else:
b_som1 = None
# Perform Steps 3-6 on empty can data
if config.ecan is not None:
e_som1 = dr_lib.calibrate_dgs_data(config.ecan, config, dc_som,
dataset_type="empty_can",
inst_geom_dst=inst_geom_dst,
tib_const=config.tib_const,
cwp=config.cwp_ecan,
timer=tim)
else:
e_som1 = None
# Perform Steps 3-6 on normalization data
n_som1 = dr_lib.calibrate_dgs_data(config.data, config, dc_som,
dataset_type="normalization",
inst_geom_dst=inst_geom_dst,
tib_const=config.tib_const,
cwp=config.cwp_data,
timer=tim)
# Perform Steps 7-16 on normalization data
if config.norm_trans_coeff is None:
norm_trans_coeff = None
else:
norm_trans_coeff = config.norm_trans_coeff.toValErrTuple()
# Determine if we need to rebin the empty or black can data
if config.ecan is not None and e_som1 is not None:
ecan_cwp = True
else:
ecan_cwp = False
if config.bcan is not None and b_som1 is not None:
bcan_cwp = True
else:
bcan_cwp = False
cwp_used = ecan_cwp or bcan_cwp
n_som2 = dr_lib.process_dgs_data(n_som1, config, b_som1, e_som1,
norm_trans_coeff,
dataset_type="normalization",
cwp_used=cwp_used,
timer=tim)
del n_som1, b_som1, e_som1
# Step 17: Integrate normalization spectra
if config.verbose:
print "Integrating normalization spectra"
if tim is not None:
tim.getTime(False)
if config.norm_int_range is None:
start_val = float("inf")
end_val = float("inf")
else:
if not config.wb_norm:
# Translate energy transfer to final energy
ef_start = config.initial_energy.getValue() - \
config.norm_int_range[0]
ef_end = config.initial_energy.getValue() - \
config.norm_int_range[1]
# Convert final energy to final wavelength
start_val = common_lib.energy_to_wavelength((ef_start, 0.0))[0]
end_val = common_lib.energy_to_wavelength((ef_end, 0.0))[0]
else:
start_val = config.norm_int_range[0]
end_val = config.norm_int_range[1]
n_som3 = dr_lib.integrate_spectra(n_som2, start=start_val,
end=end_val, width=True)
del n_som2
if tim is not None:
tim.getTime(msg="After integrating normalization spectra ")
file_comment = "Normalization Integration range: %0.3fA, %0.3fA" \
% (start_val, end_val)
hlr_utils.write_file(config.output, "text/num-info", n_som3,
output_ext="norm",
data_ext=config.ext_replacement,
path_replacement=config.path_replacement,
verbose=config.verbose,
message="normalization values",
comments=[file_comment],
tag="Integral", units="counts")
if tim is not None:
tim.getTime(False)
if config.verbose:
print "Making mask file"
# Make mask file from threshold
dr_lib.filter_normalization(n_som3, config.lo_threshold,
config.hi_threshold, config)
if tim is not None:
tim.getTime(msg="After making mask file ")
# Write out RMD file
n_som3.attr_list["config"] = config
hlr_utils.write_file(config.output, "text/rmd", n_som3,
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 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 calibrate_dgs_data(datalist, conf, dkcur, **kwargs):
"""
This function combines Steps 3 through 6 in Section 2.1.1 of the data
reduction process for Direct Geometry Spectrometers as specified by the
document at
U{http://neutrons.ornl.gov/asg/projects/SCL/reqspec/DR_Lib_RS.doc}. The
function takes a list of file names, a L{hlr_utils.Configure} object and
processes the data accordingly.
@param datalist: A list containing the filenames of the data to be
processed.
@type datalist: C{list} of C{string}s
@param conf: Object that contains the current setup of the driver.
@type conf: L{hlr_utils.Configure}
@param dkcur: The object containing the TOF dark current data.
@type dkcur: C{SOM.SOM}
@param kwargs: A list of keyword arguments that the function accepts:
@keyword inst_geom_dst: File object that contains instrument geometry
information.
@type inst_geom_dst: C{DST.GeomDST}
@keyword tib_const: A time-independent background constant to subtract
from every pixel.
@type tib_const: L{hlr_utils.DrParameter}
@keyword dataset_type: The practical name of the dataset being processed.
The default value is I{data}.
@type dataset_type: C{string}
@keyword cwp: A list of chopper phase corrections in units of microseconds.
@type cwp: C{list} of C{float}s
@keyword timer: Timing object so the function can perform timing estimates.
@type timer: C{sns_timer.DiffTime}
@return: Object that has undergone all requested processing steps
@rtype: C{SOM.SOM}
"""
import common_lib
import dr_lib
import hlr_utils
# Check keywords
try:
tib_const = kwargs["tib_const"]
except KeyError:
tib_const = None
try:
dataset_type = kwargs["dataset_type"]
except KeyError:
dataset_type = "data"
try:
t = kwargs["timer"]
except KeyError:
t = None
try:
i_geom_dst = kwargs["inst_geom_dst"]
except KeyError:
i_geom_dst = None
dataset_cwp = kwargs.get("cwp")
# Open the appropriate datafiles
if conf.verbose:
print "Reading %s file" % dataset_type
data_paths = conf.data_paths.toPath()
if conf.no_mon_norm:
mon_paths = None
else:
mon_paths = conf.usmon_path.toPath()
# Check for mask file since normalization drive doesn't understand option
try:
mask_file = conf.mask_file
except AttributeError:
mask_file = None
if t is not None:
oldtime = t.getOldTime()
(dp_som0, dm_som0) = dr_lib.add_files_dm(datalist, Data_Paths=data_paths,
Mon_Paths=mon_paths,
SO_Axis=conf.so_axis,
Signal_ROI=conf.roi_file,
Signal_MASK=mask_file,
dataset_type=dataset_type,
dataset_cwp=dataset_cwp,
Verbose=conf.verbose, Timer=t)
if t is not None:
t.setOldTime(oldtime)
t.getTime(msg="After reading %s file" % dataset_type)
# Cut the spectra if necessary
dp_somA = dr_lib.cut_spectra(dp_som0, conf.tof_cut_min, conf.tof_cut_max)
del dp_som0
dp_somB = dr_lib.fix_bin_contents(dp_somA)
del dp_somA
if dp_somB.attr_list.instrument.get_name() != "CNCS":
if conf.verbose:
print "Cutting spectrum at minimum TOF"
if t is not None:
t.getTime(False)
# Calculate minimum TOF for physical neutrons
if conf.initial_energy is not None:
initial_wavelength = common_lib.energy_to_wavelength(\
conf.initial_energy.toValErrTuple())
initial_velocity = common_lib.wavelength_to_velocity(\
initial_wavelength)
else:
# This should actually calculate it, but don't have a way right now
pass
if conf.time_zero_offset is not None:
time_zero_offset = conf.time_zero_offset.toValErrTuple()
else:
# This should actually calculate it, but don't have a way right now
time_zero_offset = (0.0, 0.0)
ss_length = dp_somB.attr_list.instrument.get_primary()
tof_min = (ss_length[0] / initial_velocity[0]) + time_zero_offset[0]
# Cut all spectra a the minimum TOF
dp_som1 = dr_lib.cut_spectra(dp_somB, tof_min, None)
if t is not None:
t.getTime(msg="After cutting spectrum at minimum TOF ")
else:
dp_som1 = dp_somB
del dp_somB
if dm_som0 is not None:
dm_som1 = dr_lib.fix_bin_contents(dm_som0)
else:
dm_som1 = dm_som0
del dm_som0
# Override geometry if necessary
if conf.inst_geom is not None:
i_geom_dst.setGeometry(data_paths, dp_som1)
if conf.inst_geom is not None and dm_som1 is not None:
i_geom_dst.setGeometry(mon_paths, dm_som1)
# Step 3: Integrate the upstream monitor
if dm_som1 is not None:
if conf.verbose:
print "Integrating upstream monitor spectrum"
if t is not None:
t.getTime(False)
if conf.mon_int_range is None:
start_val = float("inf")
end_val = float("inf")
else:
start_val = conf.mon_int_range[0]
end_val = conf.mon_int_range[1]
dm_som2 = dr_lib.integrate_spectra(dm_som1, start=start_val,
end=end_val,
width=True)
if t is not None:
t.getTime(msg="After integrating upstream monitor spectrum ")
else:
dm_som2 = dm_som1
del dm_som1
tib_norm_const = None
# Step 4: Divide data set by summed monitor spectrum
if dm_som2 is not None:
if conf.verbose:
print "Normalizing %s by monitor sum" % dataset_type
if t is not None:
t.getTime(False)
dp_som2 = common_lib.div_ncerr(dp_som1, dm_som2, length_one_som=True)
tib_norm_const = dm_som2[0].y
if t is not None:
t.getTime(msg="After normalizing %s by monitor sum" % dataset_type)
elif conf.pc_norm:
if conf.verbose:
print "Normalizing %s by proton charge" % dataset_type
pc_tag = dataset_type+"-proton_charge"
pc = dp_som1.attr_list[pc_tag]
# Scale the proton charge and then set the scale PC back to attributes
if conf.scale_pc is not None:
if conf.verbose:
print "Scaling %s proton charge" % dataset_type
pc = hlr_utils.scale_proton_charge(pc, conf.scale_pc)
dp_som1.attr_list[pc_tag] = pc
tib_norm_const = pc.getValue()
if t is not None:
t.getTime(False)
dp_som2 = common_lib.div_ncerr(dp_som1, (pc.getValue(), 0.0))
if t is not None:
t.getTime(msg="After normalizing %s by proton charge" \
% dataset_type)
else:
dp_som2 = dp_som1
del dp_som1, dm_som2
# Step 5: Scale dark current by data set measurement time
if dkcur is not None:
if conf.verbose:
print "Scaling dark current by %s acquisition time" % dataset_type
if t is not None:
t.getTime(False)
dstime_tag = dataset_type+"-duration"
dstime = dp_som2.attr_list[dstime_tag]
dkcur1 = common_lib.div_ncerr(dkcur, (dstime.getValue(), 0.0))
if t is not None:
t.getTime(msg="After scaling dark current by %s acquisition time" \
% dataset_type)
else:
dkcur1 = dkcur
del dkcur
# Step 6: Subtract scaled dark current from data set
if dkcur1 is not None:
if conf.verbose:
print "Subtracting %s by scaled dark current" % dataset_type
if t is not None:
t.getTime(False)
dp_som3 = common_lib.sub_ncerr(dp_som2, dkcur1)
if t is not None:
t.getTime(msg="After subtracting %s by scaled dark current" \
% dataset_type)
elif tib_const is not None and dkcur1 is None:
if conf.verbose:
print "Subtracting TIB constant from %s" % dataset_type
# Normalize the TIB constant by dividing by the current normalization
# the duration (if necessary) and the conversion from seconds to
# microseconds
tib_c = tib_const.toValErrTuple()
conv_sec_to_usec = 1.0e-6
if tib_norm_const is None:
tib_norm_const = 1
duration = 1
else:
duration_tag = dataset_type+"-duration"
duration = dp_som2.attr_list[duration_tag].getValue()
norm_const = (duration * conv_sec_to_usec) / tib_norm_const
tib_val = tib_c[0] * norm_const
tib_err2 = tib_c[1] * (norm_const * norm_const)
if t is not None:
t.getTime(False)
dp_som3 = common_lib.sub_ncerr(dp_som2, (tib_val, tib_err2))
if t is not None:
t.getTime(msg="After subtracting TIB constant from %s" \
% dataset_type)
elif conf.tib_range is not None and dkcur1 is None:
if conf.verbose:
print "Determining TIB constant from %s" % dataset_type
if t is not None:
t.getTime(False)
TIB = dr_lib.determine_time_indep_bkg(dp_som2, conf.tib_range,
is_range=True)
if t is not None:
t.getTime(msg="After determining TIB constant from %s" \
% dataset_type)
if conf.dump_tib:
file_comment = "TIB TOF Range: [%d, %d]" % (conf.tib_range[0],
conf.tib_range[1])
hlr_utils.write_file(conf.output, "text/num-info", TIB,
output_ext="tib",
extra_tag=dataset_type,
verbose=conf.verbose,
data_ext=conf.ext_replacement,
path_replacement=conf.path_replacement,
message="time-independent background "\
+"information",
tag="Average TIB",
units="counts/usec",
comments=[file_comment])
if conf.verbose:
print "Subtracting TIB constant from %s" % dataset_type
if t is not None:
t.getTime(False)
dp_som3 = common_lib.sub_ncerr(dp_som2, TIB)
if t is not None:
t.getTime(msg="After subtracting TIB constant from %s" \
% dataset_type)
del TIB
else:
dp_som3 = dp_som2
del dp_som2, dkcur1
if conf.dump_ctof_comb:
dp_som3_1 = dr_lib.sum_all_spectra(dp_som3)
hlr_utils.write_file(conf.output, "text/Spec", dp_som3_1,
output_ext="ctof",
extra_tag=dataset_type,
data_ext=conf.ext_replacement,
path_replacement=conf.path_replacement,
verbose=conf.verbose,
message="combined calibrated TOF information")
del dp_som3_1
return dp_som3
def 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 calibrate_dgs_data(datalist, conf, dkcur, **kwargs):
"""
This function combines Steps 3 through 6 in Section 2.1.1 of the data
reduction process for Direct Geometry Spectrometers as specified by the
document at
U{http://neutrons.ornl.gov/asg/projects/SCL/reqspec/DR_Lib_RS.doc}. The
function takes a list of file names, a L{hlr_utils.Configure} object and
processes the data accordingly.
@param datalist: A list containing the filenames of the data to be
processed.
@type datalist: C{list} of C{string}s
@param conf: Object that contains the current setup of the driver.
@type conf: L{hlr_utils.Configure}
@param dkcur: The object containing the TOF dark current data.
@type dkcur: C{SOM.SOM}
@param kwargs: A list of keyword arguments that the function accepts:
@keyword inst_geom_dst: File object that contains instrument geometry
information.
@type inst_geom_dst: C{DST.GeomDST}
@keyword tib_const: A time-independent background constant to subtract
from every pixel.
@type tib_const: L{hlr_utils.DrParameter}
@keyword dataset_type: The practical name of the dataset being processed.
The default value is I{data}.
@type dataset_type: C{string}
@keyword cwp: A list of chopper phase corrections in units of microseconds.
@type cwp: C{list} of C{float}s
@keyword timer: Timing object so the function can perform timing estimates.
@type timer: C{sns_timer.DiffTime}
@return: Object that has undergone all requested processing steps
@rtype: C{SOM.SOM}
"""
import common_lib
import dr_lib
import hlr_utils
# Check keywords
try:
tib_const = kwargs["tib_const"]
except KeyError:
tib_const = None
try:
dataset_type = kwargs["dataset_type"]
except KeyError:
dataset_type = "data"
try:
t = kwargs["timer"]
except KeyError:
t = None
try:
i_geom_dst = kwargs["inst_geom_dst"]
except KeyError:
i_geom_dst = None
dataset_cwp = kwargs.get("cwp")
# Open the appropriate datafiles
if conf.verbose:
print "Reading %s file" % dataset_type
data_paths = conf.data_paths.toPath()
if conf.no_mon_norm:
mon_paths = None
else:
mon_paths = conf.usmon_path.toPath()
# Check for mask file since normalization drive doesn't understand option
try:
mask_file = conf.mask_file
except AttributeError:
mask_file = None
if t is not None:
oldtime = t.getOldTime()
(dp_som0, dm_som0) = dr_lib.add_files_dm(datalist,
Data_Paths=data_paths,
Mon_Paths=mon_paths,
SO_Axis=conf.so_axis,
Signal_ROI=conf.roi_file,
Signal_MASK=mask_file,
dataset_type=dataset_type,
dataset_cwp=dataset_cwp,
Verbose=conf.verbose,
Timer=t)
if t is not None:
t.setOldTime(oldtime)
t.getTime(msg="After reading %s file" % dataset_type)
# Cut the spectra if necessary
dp_somA = dr_lib.cut_spectra(dp_som0, conf.tof_cut_min, conf.tof_cut_max)
del dp_som0
dp_somB = dr_lib.fix_bin_contents(dp_somA)
del dp_somA
if dp_somB.attr_list.instrument.get_name() != "CNCS":
if conf.verbose:
print "Cutting spectrum at minimum TOF"
if t is not None:
t.getTime(False)
# Calculate minimum TOF for physical neutrons
if conf.initial_energy is not None:
initial_wavelength = common_lib.energy_to_wavelength(\
conf.initial_energy.toValErrTuple())
initial_velocity = common_lib.wavelength_to_velocity(\
initial_wavelength)
else:
# This should actually calculate it, but don't have a way right now
pass
if conf.time_zero_offset is not None:
time_zero_offset = conf.time_zero_offset.toValErrTuple()
else:
# This should actually calculate it, but don't have a way right now
time_zero_offset = (0.0, 0.0)
ss_length = dp_somB.attr_list.instrument.get_primary()
tof_min = (ss_length[0] / initial_velocity[0]) + time_zero_offset[0]
# Cut all spectra a the minimum TOF
dp_som1 = dr_lib.cut_spectra(dp_somB, tof_min, None)
if t is not None:
t.getTime(msg="After cutting spectrum at minimum TOF ")
else:
dp_som1 = dp_somB
del dp_somB
if dm_som0 is not None:
dm_som1 = dr_lib.fix_bin_contents(dm_som0)
else:
dm_som1 = dm_som0
del dm_som0
# Override geometry if necessary
if conf.inst_geom is not None:
i_geom_dst.setGeometry(data_paths, dp_som1)
if conf.inst_geom is not None and dm_som1 is not None:
i_geom_dst.setGeometry(mon_paths, dm_som1)
# Step 3: Integrate the upstream monitor
if dm_som1 is not None:
if conf.verbose:
print "Integrating upstream monitor spectrum"
if t is not None:
t.getTime(False)
if conf.mon_int_range is None:
start_val = float("inf")
end_val = float("inf")
else:
start_val = conf.mon_int_range[0]
end_val = conf.mon_int_range[1]
dm_som2 = dr_lib.integrate_spectra(dm_som1,
start=start_val,
end=end_val,
width=True)
if t is not None:
t.getTime(msg="After integrating upstream monitor spectrum ")
else:
dm_som2 = dm_som1
del dm_som1
tib_norm_const = None
# Step 4: Divide data set by summed monitor spectrum
if dm_som2 is not None:
if conf.verbose:
print "Normalizing %s by monitor sum" % dataset_type
if t is not None:
t.getTime(False)
dp_som2 = common_lib.div_ncerr(dp_som1, dm_som2, length_one_som=True)
tib_norm_const = dm_som2[0].y
if t is not None:
t.getTime(msg="After normalizing %s by monitor sum" % dataset_type)
elif conf.pc_norm:
if conf.verbose:
print "Normalizing %s by proton charge" % dataset_type
pc_tag = dataset_type + "-proton_charge"
pc = dp_som1.attr_list[pc_tag]
# Scale the proton charge and then set the scale PC back to attributes
if conf.scale_pc is not None:
if conf.verbose:
print "Scaling %s proton charge" % dataset_type
pc = hlr_utils.scale_proton_charge(pc, conf.scale_pc)
dp_som1.attr_list[pc_tag] = pc
tib_norm_const = pc.getValue()
if t is not None:
t.getTime(False)
dp_som2 = common_lib.div_ncerr(dp_som1, (pc.getValue(), 0.0))
if t is not None:
t.getTime(msg="After normalizing %s by proton charge" \
% dataset_type)
else:
dp_som2 = dp_som1
del dp_som1, dm_som2
# Step 5: Scale dark current by data set measurement time
if dkcur is not None:
if conf.verbose:
print "Scaling dark current by %s acquisition time" % dataset_type
if t is not None:
t.getTime(False)
dstime_tag = dataset_type + "-duration"
dstime = dp_som2.attr_list[dstime_tag]
dkcur1 = common_lib.div_ncerr(dkcur, (dstime.getValue(), 0.0))
if t is not None:
t.getTime(msg="After scaling dark current by %s acquisition time" \
% dataset_type)
else:
dkcur1 = dkcur
del dkcur
# Step 6: Subtract scaled dark current from data set
if dkcur1 is not None:
if conf.verbose:
print "Subtracting %s by scaled dark current" % dataset_type
if t is not None:
t.getTime(False)
dp_som3 = common_lib.sub_ncerr(dp_som2, dkcur1)
if t is not None:
t.getTime(msg="After subtracting %s by scaled dark current" \
% dataset_type)
elif tib_const is not None and dkcur1 is None:
if conf.verbose:
print "Subtracting TIB constant from %s" % dataset_type
# Normalize the TIB constant by dividing by the current normalization
# the duration (if necessary) and the conversion from seconds to
# microseconds
tib_c = tib_const.toValErrTuple()
conv_sec_to_usec = 1.0e-6
if tib_norm_const is None:
tib_norm_const = 1
duration = 1
else:
duration_tag = dataset_type + "-duration"
duration = dp_som2.attr_list[duration_tag].getValue()
norm_const = (duration * conv_sec_to_usec) / tib_norm_const
tib_val = tib_c[0] * norm_const
tib_err2 = tib_c[1] * (norm_const * norm_const)
if t is not None:
t.getTime(False)
dp_som3 = common_lib.sub_ncerr(dp_som2, (tib_val, tib_err2))
if t is not None:
t.getTime(msg="After subtracting TIB constant from %s" \
% dataset_type)
elif conf.tib_range is not None and dkcur1 is None:
if conf.verbose:
print "Determining TIB constant from %s" % dataset_type
if t is not None:
t.getTime(False)
TIB = dr_lib.determine_time_indep_bkg(dp_som2,
conf.tib_range,
is_range=True)
if t is not None:
t.getTime(msg="After determining TIB constant from %s" \
% dataset_type)
if conf.dump_tib:
file_comment = "TIB TOF Range: [%d, %d]" % (conf.tib_range[0],
conf.tib_range[1])
hlr_utils.write_file(conf.output, "text/num-info", TIB,
output_ext="tib",
extra_tag=dataset_type,
verbose=conf.verbose,
data_ext=conf.ext_replacement,
path_replacement=conf.path_replacement,
message="time-independent background "\
+"information",
tag="Average TIB",
units="counts/usec",
comments=[file_comment])
if conf.verbose:
print "Subtracting TIB constant from %s" % dataset_type
if t is not None:
t.getTime(False)
dp_som3 = common_lib.sub_ncerr(dp_som2, TIB)
if t is not None:
t.getTime(msg="After subtracting TIB constant from %s" \
% dataset_type)
del TIB
else:
dp_som3 = dp_som2
del dp_som2, dkcur1
if conf.dump_ctof_comb:
dp_som3_1 = dr_lib.sum_all_spectra(dp_som3)
hlr_utils.write_file(conf.output,
"text/Spec",
dp_som3_1,
output_ext="ctof",
extra_tag=dataset_type,
data_ext=conf.ext_replacement,
path_replacement=conf.path_replacement,
verbose=conf.verbose,
message="combined calibrated TOF information")
del dp_som3_1
return dp_som3
def 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 scaled_summed_data(datalist, conf, **kwargs):
"""
This function takes a list of TOF datafiles, creates the dataset
representation, integrates each pixel spectrum and then scales those
integrations with the acquisition time.
@param datalist: A list containing the filenames of the data to be
processed.
@type datalist: C{list} of C{string}s
@param conf: Object that contains the current setup of the driver.
@type conf: L{hlr_utils.Configure}
@param kwargs: A list of keyword arguments that the function accepts:
@keyword dataset_type: The practical name of the dataset being processed.
The default value is I{data}.
@type dataset_type: C{string}
@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}
"""
# Kick-out is no data list is present
if datalist is None:
return None
import common_lib
import dr_lib
# Check keywords
try:
dataset_type = kwargs["dataset_type"]
except KeyError:
dataset_type = "data"
try:
t = kwargs["timer"]
except KeyError:
t = None
# Open the appropriate datafiles
if conf.verbose:
print "Reading %s file" % dataset_type
dst_type = "application/x-NeXus"
data_paths = conf.data_paths.toPath()
dp_som0 = dr_lib.add_files(datalist, Data_Paths=data_paths,
SO_Axis=conf.so_axis, Signal_ROI=conf.roi_file,
Signal_MASK=conf.mask_file,
dataset_type=dataset_type,
dst_type=dst_type,
Verbose=conf.verbose, Timer=t)
if t is not None:
t.getTime(msg="After reading %s " % dataset_type)
dp_som1 = dr_lib.fix_bin_contents(dp_som0)
del dp_som0
if conf.verbose:
print "Integrating %s data" % dataset_type
if t is not None:
t.getTime(False)
dp_som2 = dr_lib.integrate_spectra(dp_som1, width=True)
if t is not None:
t.getTime(msg="After integrating %s data " % dataset_type)
del dp_som1
# Make dataset duration tag
duration_tag = dataset_type+"-duration"
duration = dp_som2.attr_list[duration_tag]
if conf.verbose:
print "Scaling %s integration by acquisition duration " % dataset_type
if t is not None:
t.getTime(False)
dp_som3 = common_lib.div_ncerr(dp_som2, (duration.getValue(), 0.0))
if t is not None:
t.getTime("After scaling %s integration by acquisition duration " \
% dataset_type)
del dp_som2
return dp_som3
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
config.so_axis = "time_of_flight"
# Steps 1-3: Produce a scaled summed dark current dataset
dc_som = dr_lib.scaled_summed_data(config.dkcur, config, dataset_type="dark_current", timer=tim)
# Perform Steps 3-6 on black can data
if config.bcan is not None:
b_som1 = dr_lib.calibrate_dgs_data(
config.bcan,
config,
dc_som,
dataset_type="black_can",
inst_geom_dst=inst_geom_dst,
tib_const=config.tib_const,
cwp=config.cwp_bcan,
timer=tim,
)
else:
b_som1 = None
# Perform Steps 3-6 on empty can data
if config.ecan is not None:
e_som1 = dr_lib.calibrate_dgs_data(
config.ecan,
config,
dc_som,
dataset_type="empty_can",
inst_geom_dst=inst_geom_dst,
tib_const=config.tib_const,
cwp=config.cwp_ecan,
timer=tim,
)
else:
e_som1 = None
# Perform Steps 3-6 on normalization data
n_som1 = dr_lib.calibrate_dgs_data(
config.data,
config,
dc_som,
dataset_type="normalization",
inst_geom_dst=inst_geom_dst,
tib_const=config.tib_const,
cwp=config.cwp_data,
timer=tim,
)
# Perform Steps 7-16 on normalization data
if config.norm_trans_coeff is None:
norm_trans_coeff = None
else:
norm_trans_coeff = config.norm_trans_coeff.toValErrTuple()
# Determine if we need to rebin the empty or black can data
if config.ecan is not None and e_som1 is not None:
ecan_cwp = True
else:
ecan_cwp = False
if config.bcan is not None and b_som1 is not None:
bcan_cwp = True
else:
bcan_cwp = False
cwp_used = ecan_cwp or bcan_cwp
n_som2 = dr_lib.process_dgs_data(
n_som1, config, b_som1, e_som1, norm_trans_coeff, dataset_type="normalization", cwp_used=cwp_used, timer=tim
)
del n_som1, b_som1, e_som1
# Step 17: Integrate normalization spectra
if config.verbose:
print "Integrating normalization spectra"
if tim is not None:
tim.getTime(False)
if config.norm_int_range is None:
start_val = float("inf")
end_val = float("inf")
else:
if not config.wb_norm:
# Translate energy transfer to final energy
ef_start = config.initial_energy.getValue() - config.norm_int_range[0]
ef_end = config.initial_energy.getValue() - config.norm_int_range[1]
# Convert final energy to final wavelength
start_val = common_lib.energy_to_wavelength((ef_start, 0.0))[0]
end_val = common_lib.energy_to_wavelength((ef_end, 0.0))[0]
else:
start_val = config.norm_int_range[0]
end_val = config.norm_int_range[1]
n_som3 = dr_lib.integrate_spectra(n_som2, start=start_val, end=end_val, width=True)
del n_som2
if tim is not None:
tim.getTime(msg="After integrating normalization spectra ")
file_comment = "Normalization Integration range: %0.3fA, %0.3fA" % (start_val, end_val)
hlr_utils.write_file(
config.output,
"text/num-info",
n_som3,
output_ext="norm",
data_ext=config.ext_replacement,
path_replacement=config.path_replacement,
verbose=config.verbose,
message="normalization values",
comments=[file_comment],
tag="Integral",
units="counts",
)
if tim is not None:
tim.getTime(False)
if config.verbose:
print "Making mask file"
# Make mask file from threshold
dr_lib.filter_normalization(n_som3, config.lo_threshold, config.hi_threshold, config)
if tim is not None:
tim.getTime(msg="After making mask file ")
# Write out RMD file
n_som3.attr_list["config"] = config
hlr_utils.write_file(
config.output,
"text/rmd",
n_som3,
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 scaled_summed_data(datalist, conf, **kwargs):
"""
This function takes a list of TOF datafiles, creates the dataset
representation, integrates each pixel spectrum and then scales those
integrations with the acquisition time.
@param datalist: A list containing the filenames of the data to be
processed.
@type datalist: C{list} of C{string}s
@param conf: Object that contains the current setup of the driver.
@type conf: L{hlr_utils.Configure}
@param kwargs: A list of keyword arguments that the function accepts:
@keyword dataset_type: The practical name of the dataset being processed.
The default value is I{data}.
@type dataset_type: C{string}
@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}
"""
# Kick-out is no data list is present
if datalist is None:
return None
import common_lib
import dr_lib
# Check keywords
try:
dataset_type = kwargs["dataset_type"]
except KeyError:
dataset_type = "data"
try:
t = kwargs["timer"]
except KeyError:
t = None
# Open the appropriate datafiles
if conf.verbose:
print "Reading %s file" % dataset_type
dst_type = "application/x-NeXus"
data_paths = conf.data_paths.toPath()
dp_som0 = dr_lib.add_files(datalist,
Data_Paths=data_paths,
SO_Axis=conf.so_axis,
Signal_ROI=conf.roi_file,
Signal_MASK=conf.mask_file,
dataset_type=dataset_type,
dst_type=dst_type,
Verbose=conf.verbose,
Timer=t)
if t is not None:
t.getTime(msg="After reading %s " % dataset_type)
dp_som1 = dr_lib.fix_bin_contents(dp_som0)
del dp_som0
if conf.verbose:
print "Integrating %s data" % dataset_type
if t is not None:
t.getTime(False)
dp_som2 = dr_lib.integrate_spectra(dp_som1, width=True)
if t is not None:
t.getTime(msg="After integrating %s data " % dataset_type)
del dp_som1
# Make dataset duration tag
duration_tag = dataset_type + "-duration"
duration = dp_som2.attr_list[duration_tag]
if conf.verbose:
print "Scaling %s integration by acquisition duration " % dataset_type
if t is not None:
t.getTime(False)
dp_som3 = common_lib.div_ncerr(dp_som2, (duration.getValue(), 0.0))
if t is not None:
t.getTime("After scaling %s integration by acquisition duration " \
% dataset_type)
del dp_som2
return dp_som3
