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"
try:
if type(config.mask_file) == type([]):
if len(config.mask_file) > 1:
if config.verbose:
print "Creating combined mask file"
if tim is not None:
tim.getTime(False)
config.mask_file = hlr_utils.merge_roi_files(\
config.mask_file,
config)
if tim is not None:
tim.getTime(msg="After creating combined mask file")
else:
config.mask_file = config.mask_file[0]
else:
# Do nothing since it's already a string
pass
except TypeError:
# No mask files provided, do nothing
pass
# 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 sample data
d_som1 = dr_lib.calibrate_dgs_data(config.data,
config,
dc_som,
inst_geom_dst=inst_geom_dst,
tib_const=config.tib_const,
cwp=config.cwp_data,
timer=tim)
# Perform Steps 7-16 on sample data
if config.data_trans_coeff is None:
data_trans_coeff = None
else:
data_trans_coeff = config.data_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
d_som2 = dr_lib.process_dgs_data(d_som1,
config,
b_som1,
e_som1,
data_trans_coeff,
cwp_used=cwp_used,
timer=tim)
del d_som1
del b_som1, e_som1
# Step 18: Normalize sample data by integrated values
if config.norm is not None:
if config.verbose:
print "Reading normalization information"
norm_int = dr_lib.add_files(config.norm,
Signal_ROI=config.roi_file,
Signal_MASK=config.mask_file,
dataset_type="normalization",
dst_type="text/num-info",
Verbose=config.verbose,
Timer=tim)
# Make the labels and units compatible with a NeXus file based SOM
norm_int.setAllAxisLabels(["wavelength"])
norm_int.setAllAxisUnits(["Angstroms"])
norm_int.setYLabel("Intensity")
norm_int.setYUnits("Counts/Angstroms")
if config.verbose:
print "Normalizing data by normalization data"
if tim is not None:
tim.getTime(False)
d_som3 = common_lib.div_ncerr(d_som2, norm_int)
if tim is not None:
tim.getTime(msg="After normalizing data ")
del norm_int
else:
d_som3 = d_som2
del d_som2
# Step 19: Calculate the initial energy
if config.initial_energy is not None:
d_som3.attr_list["Initial_Energy"] = config.initial_energy
# Steps 20-21: Calculate the energy transfer
if config.verbose:
print "Calculating energy transfer"
if tim is not None:
tim.getTime(False)
d_som4 = dr_lib.energy_transfer(d_som3,
"DGS",
"Initial_Energy",
lojac=True,
scale=config.lambda_ratio)
if tim is not None:
tim.getTime(msg="After calculating energy transfer ")
del d_som3
# Step 22: Rebin energy transfer spectra
if config.verbose:
print "Rebinning to final energy transfer axis"
if tim is not None:
tim.getTime(False)
d_som5 = common_lib.rebin_axis_1D_frac(d_som4, config.E_bins.toNessiList())
if tim is not None:
tim.getTime(msg="After rebinning energy transfer ")
del d_som4
if config.dump_et_comb:
d_som5_1 = dr_lib.sum_all_spectra(d_som5)
hlr_utils.write_file(config.output,
"text/Spec",
d_som5_1,
output_ext="et",
data_ext=config.ext_replacement,
path_replacement=config.path_replacement,
verbose=config.verbose,
message="combined energy transfer information")
del d_som5_1
# Get the corner geometry information
if config.verbose:
print "Reading in corner geometry information"
if tim is not None:
tim.getTime(False)
corner_angles = hlr_utils.get_corner_geometry(config.corner_geom)
if tim is not None:
tim.getTime(msg="After reading in corner geometry information ")
if config.make_spe:
d_som5.attr_list["corner_geom"] = corner_angles
hlr_utils.write_file(config.output,
"text/PHX",
d_som5,
output_ext="phx",
data_ext=config.ext_replacement,
path_replacement=config.path_replacement,
verbose=config.verbose,
message="PHX information")
hlr_utils.write_file(config.output,
"text/PAR",
d_som5,
output_ext="par",
data_ext=config.ext_replacement,
path_replacement=config.path_replacement,
verbose=config.verbose,
message="PAR information")
hlr_utils.write_file(config.output,
"text/SPE",
d_som5,
output_ext="spe",
data_ext=config.ext_replacement,
path_replacement=config.path_replacement,
verbose=config.verbose,
message="SPE information")
# Steps 23-34: Create S(Q, E) distribution
if config.verbose:
print "Creating S(Q, E)"
if tim is not None:
tim.getTime(False)
d_som5_2 = dr_lib.create_E_vs_Q_dgs(d_som5,
config.initial_energy.toValErrTuple(),
config.Q_bins.toNessiList(),
corner_angles=corner_angles,
split=config.split,
configure=config,
timer=tim)
# Writing 2D DAVE file
if not config.split:
hlr_utils.write_file(config.output,
"text/Dave2d",
d_som5_2,
output_ext="sqe",
data_ext=config.ext_replacement,
path_replacement=config.path_replacement,
verbose=config.verbose,
message="S(Q,E)")
hlr_utils.write_file(config.output,
"application/x-RedNxs",
d_som5_2,
output_ext="nxs",
data_ext=config.ext_replacement,
path_replacement=config.path_replacement,
verbose=config.verbose,
extra_tag="sqe",
getsom_kwargs={"entry_name": "sqe"},
message="NeXus S(Q,E)")
if tim is not None:
tim.getTime(msg="After calculating S(Q,E) spectrum ")
del d_som5_2
if config.qmesh:
# Steps 23-27,35-36: Create S(Qvec, E) distribution
if config.verbose:
print "Creating S(Qvec, E)"
if tim is not None:
tim.getTime(False)
dr_lib.create_Qvec_vs_E_dgs(d_som5,
config.initial_energy.toValErrTuple(),
config,
corner_angles=corner_angles,
make_fixed=config.fixed,
output=config.output,
timer=tim)
if tim is not None:
tim.getTime(msg="After calculating final spectrum ")
# Write out RMD file
d_som5.attr_list["config"] = config
hlr_utils.write_file(config.output,
"text/rmd",
d_som5,
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):
"""
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=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"
try:
if type(config.mask_file) == type([]):
if len(config.mask_file) > 1:
if config.verbose:
print "Creating combined mask file"
if tim is not None:
tim.getTime(False)
config.mask_file = hlr_utils.merge_roi_files(\
config.mask_file,
config)
if tim is not None:
tim.getTime(msg="After creating combined mask file")
else:
config.mask_file = config.mask_file[0]
else:
# Do nothing since it's already a string
pass
except TypeError:
# No mask files provided, do nothing
pass
# 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 sample data
d_som1 = dr_lib.calibrate_dgs_data(config.data, config, dc_som,
inst_geom_dst=inst_geom_dst,
tib_const=config.tib_const,
cwp=config.cwp_data,
timer=tim)
# Perform Steps 7-16 on sample data
if config.data_trans_coeff is None:
data_trans_coeff = None
else:
data_trans_coeff = config.data_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
d_som2 = dr_lib.process_dgs_data(d_som1, config, b_som1, e_som1,
data_trans_coeff, cwp_used=cwp_used,
timer=tim)
del d_som1
del b_som1, e_som1
# Step 18: Normalize sample data by integrated values
if config.norm is not None:
if config.verbose:
print "Reading normalization information"
norm_int = dr_lib.add_files(config.norm, Signal_ROI=config.roi_file,
Signal_MASK=config.mask_file,
dataset_type="normalization",
dst_type="text/num-info",
Verbose=config.verbose,
Timer=tim)
# Make the labels and units compatible with a NeXus file based SOM
norm_int.setAllAxisLabels(["wavelength"])
norm_int.setAllAxisUnits(["Angstroms"])
norm_int.setYLabel("Intensity")
norm_int.setYUnits("Counts/Angstroms")
if config.verbose:
print "Normalizing data by normalization data"
if tim is not None:
tim.getTime(False)
d_som3 = common_lib.div_ncerr(d_som2, norm_int)
if tim is not None:
tim.getTime(msg="After normalizing data ")
del norm_int
else:
d_som3 = d_som2
del d_som2
# Step 19: Calculate the initial energy
if config.initial_energy is not None:
d_som3.attr_list["Initial_Energy"] = config.initial_energy
# Steps 20-21: Calculate the energy transfer
if config.verbose:
print "Calculating energy transfer"
if tim is not None:
tim.getTime(False)
d_som4 = dr_lib.energy_transfer(d_som3, "DGS", "Initial_Energy",
lojac=True, scale=config.lambda_ratio)
if tim is not None:
tim.getTime(msg="After calculating energy transfer ")
del d_som3
# Step 22: Rebin energy transfer spectra
if config.verbose:
print "Rebinning to final energy transfer axis"
if tim is not None:
tim.getTime(False)
d_som5 = common_lib.rebin_axis_1D_frac(d_som4, config.E_bins.toNessiList())
if tim is not None:
tim.getTime(msg="After rebinning energy transfer ")
del d_som4
if config.dump_et_comb:
d_som5_1 = dr_lib.sum_all_spectra(d_som5)
hlr_utils.write_file(config.output, "text/Spec", d_som5_1,
output_ext="et",
data_ext=config.ext_replacement,
path_replacement=config.path_replacement,
verbose=config.verbose,
message="combined energy transfer information")
del d_som5_1
# Get the corner geometry information
if config.verbose:
print "Reading in corner geometry information"
if tim is not None:
tim.getTime(False)
corner_angles = hlr_utils.get_corner_geometry(config.corner_geom)
if tim is not None:
tim.getTime(msg="After reading in corner geometry information ")
if config.make_spe:
d_som5.attr_list["corner_geom"] = corner_angles
hlr_utils.write_file(config.output, "text/PHX", d_som5,
output_ext="phx",
data_ext=config.ext_replacement,
path_replacement=config.path_replacement,
verbose=config.verbose,
message="PHX information")
hlr_utils.write_file(config.output, "text/PAR", d_som5,
output_ext="par",
data_ext=config.ext_replacement,
path_replacement=config.path_replacement,
verbose=config.verbose,
message="PAR information")
hlr_utils.write_file(config.output, "text/SPE", d_som5,
output_ext="spe",
data_ext=config.ext_replacement,
path_replacement=config.path_replacement,
verbose=config.verbose,
message="SPE information")
# Steps 23-34: Create S(Q, E) distribution
if config.verbose:
print "Creating S(Q, E)"
if tim is not None:
tim.getTime(False)
d_som5_2 = dr_lib.create_E_vs_Q_dgs(d_som5,
config.initial_energy.toValErrTuple(),
config.Q_bins.toNessiList(),
corner_angles=corner_angles,
split=config.split,
configure=config,
timer=tim)
# Writing 2D DAVE file
if not config.split:
hlr_utils.write_file(config.output, "text/Dave2d", d_som5_2,
output_ext="sqe",
data_ext=config.ext_replacement,
path_replacement=config.path_replacement,
verbose=config.verbose,
message="S(Q,E)")
hlr_utils.write_file(config.output, "application/x-RedNxs", d_som5_2,
output_ext="nxs",
data_ext=config.ext_replacement,
path_replacement=config.path_replacement,
verbose=config.verbose,
extra_tag="sqe",
getsom_kwargs={"entry_name": "sqe"},
message="NeXus S(Q,E)")
if tim is not None:
tim.getTime(msg="After calculating S(Q,E) spectrum ")
del d_som5_2
if config.qmesh:
# Steps 23-27,35-36: Create S(Qvec, E) distribution
if config.verbose:
print "Creating S(Qvec, E)"
if tim is not None:
tim.getTime(False)
dr_lib.create_Qvec_vs_E_dgs(d_som5,
config.initial_energy.toValErrTuple(),
config, corner_angles=corner_angles,
make_fixed=config.fixed,
output=config.output,
timer=tim)
if tim is not None:
tim.getTime(msg="After calculating final spectrum ")
# Write out RMD file
d_som5.attr_list["config"] = config
hlr_utils.write_file(config.output, "text/rmd", d_som5,
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 add_files_dm(filelist, **kwargs):
"""
This function takes a list of U{NeXus<www.nexusformat.org>} files and
various keyword arguments and returns a data C{SOM} and a monitor C{SOM}
that is the sum of all the data from the specified files. B{It is assumed
that the files contain similar data as only crude cross-checks will be
made. You have been warned.}
@param filelist: A list containing the names of the files to sum
@type filelist: C{list}
@param kwargs: A list of keyword arguments that the function accepts:
@keyword SO_Axis: This is the name of the main axis to read from the NeXus
file
@type SO_Axis: C{string}
@keyword Data_Paths: This contains the data paths and signals for the
requested detector banks
@type Data_Paths: C{tuple} of C{tuple}s
@keyword Mon_Paths: This contains the data paths and signals for the
requested monitor banks
@type Mon_Paths: C{tuple} of C{tuple}s
@keyword Signal_ROI: This is the name of a file that contains a list of
pixel IDs that will be read from the data file and
stored as a signal C{SOM}
@type Signal_ROI: C{string}
@keyword Signal_MASK: This is the name of a file that contains a list of
pixel IDs that will be read from the data file and
stored as a signal C{SOM}
@type Signal_MASK: C{string}
@keyword dataset_type: The practical name of the dataset being processed.
The default value is I{data}.
@type dataset_type: C{string}
@keyword dataset_cwp: A set of chopper phase corrections for the dataset.
This will instruct the function to shift the TOF
axes of mulitple datasets and perform rebinning. The
TOF axis for the first dataset is the one that all
other datasets will be rebinned to.
@type dataset_cwp: C{list} of C{float}s
@keyword Verbose: This is a flag to turn on print statments. The default is
I{False}.
@type Verbose: C{boolean}
@keyword Timer: This is an SNS Timer object used for showing the
performance timing in the function.
@type Timer: C{sns_timing.Timer}
@return: Signal C{SOM.SOM} and monitor C{SOM.SOM}
@rtype: C{tuple}
@raise SystemExit: If any file cannot be read
@raise RuntimeError: If both a ROI and MASK file are specified
"""
import sys
import common_lib
import DST
# Parse keywords
try:
so_axis = kwargs["SO_Axis"]
except KeyError:
so_axis = "time_of_flight"
try:
data_paths = kwargs["Data_Paths"]
except KeyError:
data_paths = None
try:
mon_paths = kwargs["Mon_Paths"]
except KeyError:
mon_paths = None
try:
signal_roi = kwargs["Signal_ROI"]
except KeyError:
signal_roi = None
try:
signal_mask = kwargs["Signal_MASK"]
except KeyError:
signal_mask = None
try:
dataset_type = kwargs["dataset_type"]
except KeyError:
dataset_type = "data"
try:
verbose = kwargs["Verbose"]
except KeyError:
verbose = False
try:
timer = kwargs["Timer"]
except KeyError:
timer = None
dataset_cwp = kwargs.get("dataset_cwp")
if signal_roi is not None and signal_mask is not None:
raise RuntimeError("Cannot specify both ROI and MASK file! Please "\
+"choose!")
dst_type = "application/x-NeXus"
counter = 0
for filename in filelist:
if verbose:
print "File:", filename
if dataset_cwp is not None:
print "TOF Offset:", dataset_cwp[counter]
if dataset_cwp is not None:
cwp = dataset_cwp[counter]
else:
cwp = None
try:
data_dst = DST.getInstance(dst_type, filename)
except SystemError:
print "ERROR: Failed to data read file %s" % filename
sys.exit(-1)
if timer is not None:
timer.getTime(msg="After parsing file")
if verbose:
print "Reading data file %d" % counter
if counter == 0:
d_som1 = data_dst.getSOM(data_paths,
so_axis,
roi_file=signal_roi,
mask_file=signal_mask,
tof_offset=cwp)
d_som1.rekeyNxPars(dataset_type)
if verbose:
print "# Signal SO:", len(d_som1)
try:
print "# TOF:", len(d_som1[0])
print "# TOF Axis:", len(d_som1[0].axis[0].val)
except IndexError:
# No data is present so say so again
print "information is unavailable since no data "\
+"present. Exiting."
sys.exit(0)
if timer is not None:
timer.getTime(msg="After reading data")
if mon_paths is not None:
if verbose:
print "Reading monitor %d" % counter
if counter == 0:
m_som1 = data_dst.getSOM(mon_paths,
so_axis,
tof_offset=cwp)
m_som1.rekeyNxPars(dataset_type)
if verbose:
print "# Monitor SO:", len(m_som1)
print "# TOF:", len(m_som1[0])
print "# TOF Axis:", len(m_som1[0].axis[0].val)
if timer is not None:
timer.getTime(msg="After reading monitor data")
else:
m_som1 = None
else:
d_som_t0 = data_dst.getSOM(data_paths,
so_axis,
roi_file=signal_roi,
mask_file=signal_mask,
tof_offset=cwp)
d_som_t0.rekeyNxPars(dataset_type)
if timer is not None:
timer.getTime(msg="After reading data")
if dataset_cwp is not None:
d_som_t = common_lib.rebin_axis_1D_frac(
d_som_t0, d_som1[0].axis[0].val)
del d_som_t0
else:
d_som_t = d_som_t0
d_som1 = common_lib.add_ncerr(d_som_t, d_som1, add_nxpars=True)
if timer is not None:
timer.getTime(msg="After adding data spectra")
del d_som_t
if timer is not None:
timer.getTime(msg="After data SOM deletion")
if mon_paths is not None:
m_som_t0 = data_dst.getSOM(mon_paths, so_axis, tof_offset=cwp)
m_som_t0.rekeyNxPars(dataset_type)
if timer is not None:
timer.getTime(msg="After reading monitor data")
if dataset_cwp is not None:
m_som_t = common_lib.rebin_axis_1D_frac(
m_som_t0, m_som1[0].axis[0].val)
del m_som_t0
else:
m_som_t = m_som_t0
m_som1 = common_lib.add_ncerr(m_som_t, m_som1, add_nxpars=True)
if timer is not None:
timer.getTime(msg="After adding monitor spectra")
del m_som_t
if timer is not None:
timer.getTime(msg="After monitor SOM deletion")
data_dst.release_resource()
del data_dst
counter += 1
if timer is not None:
timer.getTime(msg="After resource release and DST deletion")
som_key_parts = [dataset_type, "filename"]
som_key = "-".join(som_key_parts)
d_som1.attr_list[som_key] = filelist
if m_som1 is not None:
m_som1.attr_list[som_key] = filelist
return (d_som1, m_som1)
def process_dgs_data(obj, conf, bcan, ecan, tcoeff, **kwargs):
"""
This function combines Steps 7 through 16 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 calibrated dataset, a L{hlr_utils.Configure} object and
processes the data accordingly.
@param obj: A calibrated dataset object.
@type obj: C{SOM.SOM}
@param conf: Object that contains the current setup of the driver.
@type conf: L{hlr_utils.Configure}
@param bcan: The object containing the black can data.
@type bcan: C{SOM.SOM}
@param ecan: The object containing the empty can data.
@type ecan: C{SOM.SOM}
@param tcoeff: The transmission coefficient appropriate to the given data
set.
@type tcoeff: C{tuple}
@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 cwp_used: A flag signalling the use of the chopper phase
corrections.
@type cwp_used: C{bool}
@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 array_manip
import common_lib
import dr_lib
import hlr_utils
# Check keywords
try:
dataset_type = kwargs["dataset_type"]
except KeyError:
dataset_type = "data"
try:
t = kwargs["timer"]
except KeyError:
t = None
cwp_used = kwargs.get("cwp_used", False)
if conf.verbose:
print "Processing %s information" % dataset_type
# Step 7: Create black can background contribution
if bcan is not None:
if conf.verbose:
print "Creating black can background contribution for %s" \
% dataset_type
if t is not None:
t.getTime(False)
bccoeff = array_manip.sub_ncerr(1.0, 0.0, tcoeff[0], tcoeff[1])
bcan1 = common_lib.mult_ncerr(bcan, bccoeff)
if t is not None:
t.getTime(msg="After creating black can background contribution ")
del bcan
else:
bcan1 = None
# Step 8: Create empty can background contribution
if ecan is not None:
if conf.verbose:
print "Creating empty can background contribution for %s" \
% dataset_type
if t is not None:
t.getTime(False)
ecan1 = common_lib.mult_ncerr(ecan, tcoeff)
if t is not None:
t.getTime(msg="After creating empty can background contribution ")
del ecan
else:
ecan1 = None
# Step 9: Create background spectra
if bcan1 is not None or ecan1 is not None and conf.verbose:
print "Creating background spectra for %s" % dataset_type
if bcan1 is not None and ecan1 is not None:
if cwp_used:
if conf.verbose:
print "Rebinning empty can to black can axis."
ecan2 = common_lib.rebin_axis_1D_frac(ecan1, bcan1[0].axis[0].val)
else:
ecan2 = ecan1
del ecan1
if t is not None:
t.getTime(False)
b_som = common_lib.add_ncerr(bcan1, ecan2)
if t is not None:
t.getTime(msg="After creating background spectra ")
elif bcan1 is not None and ecan1 is None:
b_som = bcan1
elif bcan1 is None and ecan1 is not None:
b_som = ecan1
else:
b_som = None
del bcan1, ecan1
if cwp_used:
if conf.verbose:
print "Rebinning background spectra to %s" % dataset_type
b_som1 = common_lib.rebin_axis_1D_frac(b_som, obj[0].axis[0].val)
else:
b_som1 = b_som
del b_som
if conf.dump_ctof_comb and b_som1 is not None:
b_som_1 = dr_lib.sum_all_spectra(b_som1)
hlr_utils.write_file(conf.output,
"text/Spec",
b_som_1,
output_ext="ctof",
extra_tag="background",
data_ext=conf.ext_replacement,
path_replacement=conf.path_replacement,
verbose=conf.verbose,
message="combined background TOF information")
del b_som_1
# Step 10: Subtract background from data
obj1 = dr_lib.subtract_bkg_from_data(obj,
b_som1,
verbose=conf.verbose,
timer=t,
dataset1=dataset_type,
dataset2="background")
del obj, b_som1
# Step 11: Calculate initial velocity
if conf.verbose:
print "Calculating initial velocity"
if t is not None:
t.getTime(False)
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 t is not None:
t.getTime(msg="After calculating initial velocity ")
# Step 12: Calculate the time-zero offset
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)
# Step 13: Convert time-of-flight to final velocity
if conf.verbose:
print "Converting TOF to final velocity DGS"
if t is not None:
t.getTime(False)
obj2 = common_lib.tof_to_final_velocity_dgs(obj1,
initial_velocity,
time_zero_offset,
units="microsecond")
if t is not None:
t.getTime(msg="After calculating TOF to final velocity DGS ")
del obj1
# Step 14: Convert final velocity to final wavelength
if conf.verbose:
print "Converting final velocity DGS to final wavelength"
if t is not None:
t.getTime(False)
obj3 = common_lib.velocity_to_wavelength(obj2)
if t is not None:
t.getTime(msg="After calculating velocity to wavelength ")
del obj2
if conf.dump_wave_comb:
obj3_1 = dr_lib.sum_all_spectra(
obj3, rebin_axis=conf.lambda_bins.toNessiList())
hlr_utils.write_file(conf.output,
"text/Spec",
obj3_1,
output_ext="fwv",
extra_tag=dataset_type,
data_ext=conf.ext_replacement,
path_replacement=conf.path_replacement,
verbose=conf.verbose,
message="combined final wavelength information")
del obj3_1
# Step 15: Create the detector efficiency
if conf.det_eff is not None:
if conf.verbose:
print "Creating detector efficiency spectra"
if t is not None:
t.getTime(False)
det_eff = dr_lib.create_det_eff(obj3)
if t is not None:
t.getTime(msg="After creating detector efficiency spectra ")
else:
det_eff = None
# Step 16: Divide the detector pixel spectra by the detector efficiency
if det_eff is not None:
if conf.verbose:
print "Correcting %s for detector efficiency" % dataset_type
if t is not None:
t.getTime(False)
obj4 = common_lib.div_ncerr(obj3, det_eff)
if t is not None:
t.getTime(msg="After correcting %s for detector efficiency" \
% dataset_type)
else:
obj4 = obj3
del obj3, det_eff
return obj4
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_dgs_data(obj, conf, bcan, ecan, tcoeff, **kwargs):
"""
This function combines Steps 7 through 16 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 calibrated dataset, a L{hlr_utils.Configure} object and
processes the data accordingly.
@param obj: A calibrated dataset object.
@type obj: C{SOM.SOM}
@param conf: Object that contains the current setup of the driver.
@type conf: L{hlr_utils.Configure}
@param bcan: The object containing the black can data.
@type bcan: C{SOM.SOM}
@param ecan: The object containing the empty can data.
@type ecan: C{SOM.SOM}
@param tcoeff: The transmission coefficient appropriate to the given data
set.
@type tcoeff: C{tuple}
@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 cwp_used: A flag signalling the use of the chopper phase
corrections.
@type cwp_used: C{bool}
@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 array_manip
import common_lib
import dr_lib
import hlr_utils
# Check keywords
try:
dataset_type = kwargs["dataset_type"]
except KeyError:
dataset_type = "data"
try:
t = kwargs["timer"]
except KeyError:
t = None
cwp_used = kwargs.get("cwp_used", False)
if conf.verbose:
print "Processing %s information" % dataset_type
# Step 7: Create black can background contribution
if bcan is not None:
if conf.verbose:
print "Creating black can background contribution for %s" \
% dataset_type
if t is not None:
t.getTime(False)
bccoeff = array_manip.sub_ncerr(1.0, 0.0, tcoeff[0], tcoeff[1])
bcan1 = common_lib.mult_ncerr(bcan, bccoeff)
if t is not None:
t.getTime(msg="After creating black can background contribution ")
del bcan
else:
bcan1 = None
# Step 8: Create empty can background contribution
if ecan is not None:
if conf.verbose:
print "Creating empty can background contribution for %s" \
% dataset_type
if t is not None:
t.getTime(False)
ecan1 = common_lib.mult_ncerr(ecan, tcoeff)
if t is not None:
t.getTime(msg="After creating empty can background contribution ")
del ecan
else:
ecan1 = None
# Step 9: Create background spectra
if bcan1 is not None or ecan1 is not None and conf.verbose:
print "Creating background spectra for %s" % dataset_type
if bcan1 is not None and ecan1 is not None:
if cwp_used:
if conf.verbose:
print "Rebinning empty can to black can axis."
ecan2 = common_lib.rebin_axis_1D_frac(ecan1, bcan1[0].axis[0].val)
else:
ecan2 = ecan1
del ecan1
if t is not None:
t.getTime(False)
b_som = common_lib.add_ncerr(bcan1, ecan2)
if t is not None:
t.getTime(msg="After creating background spectra ")
elif bcan1 is not None and ecan1 is None:
b_som = bcan1
elif bcan1 is None and ecan1 is not None:
b_som = ecan1
else:
b_som = None
del bcan1, ecan1
if cwp_used:
if conf.verbose:
print "Rebinning background spectra to %s" % dataset_type
b_som1 = common_lib.rebin_axis_1D_frac(b_som, obj[0].axis[0].val)
else:
b_som1 = b_som
del b_som
if conf.dump_ctof_comb and b_som1 is not None:
b_som_1 = dr_lib.sum_all_spectra(b_som1)
hlr_utils.write_file(conf.output, "text/Spec", b_som_1,
output_ext="ctof",
extra_tag="background",
data_ext=conf.ext_replacement,
path_replacement=conf.path_replacement,
verbose=conf.verbose,
message="combined background TOF information")
del b_som_1
# Step 10: Subtract background from data
obj1 = dr_lib.subtract_bkg_from_data(obj, b_som1, verbose=conf.verbose,
timer=t,
dataset1=dataset_type,
dataset2="background")
del obj, b_som1
# Step 11: Calculate initial velocity
if conf.verbose:
print "Calculating initial velocity"
if t is not None:
t.getTime(False)
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 t is not None:
t.getTime(msg="After calculating initial velocity ")
# Step 12: Calculate the time-zero offset
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)
# Step 13: Convert time-of-flight to final velocity
if conf.verbose:
print "Converting TOF to final velocity DGS"
if t is not None:
t.getTime(False)
obj2 = common_lib.tof_to_final_velocity_dgs(obj1, initial_velocity,
time_zero_offset,
units="microsecond")
if t is not None:
t.getTime(msg="After calculating TOF to final velocity DGS ")
del obj1
# Step 14: Convert final velocity to final wavelength
if conf.verbose:
print "Converting final velocity DGS to final wavelength"
if t is not None:
t.getTime(False)
obj3 = common_lib.velocity_to_wavelength(obj2)
if t is not None:
t.getTime(msg="After calculating velocity to wavelength ")
del obj2
if conf.dump_wave_comb:
obj3_1 = dr_lib.sum_all_spectra(obj3,
rebin_axis=conf.lambda_bins.toNessiList())
hlr_utils.write_file(conf.output, "text/Spec", obj3_1,
output_ext="fwv",
extra_tag=dataset_type,
data_ext=conf.ext_replacement,
path_replacement=conf.path_replacement,
verbose=conf.verbose,
message="combined final wavelength information")
del obj3_1
# Step 15: Create the detector efficiency
if conf.det_eff is not None:
if conf.verbose:
print "Creating detector efficiency spectra"
if t is not None:
t.getTime(False)
det_eff = dr_lib.create_det_eff(obj3)
if t is not None:
t.getTime(msg="After creating detector efficiency spectra ")
else:
det_eff = None
# Step 16: Divide the detector pixel spectra by the detector efficiency
if det_eff is not None:
if conf.verbose:
print "Correcting %s for detector efficiency" % dataset_type
if t is not None:
t.getTime(False)
obj4 = common_lib.div_ncerr(obj3, det_eff)
if t is not None:
t.getTime(msg="After correcting %s for detector efficiency" \
% dataset_type)
else:
obj4 = obj3
del obj3, det_eff
return obj4
def add_files_dm(filelist, **kwargs):
"""
This function takes a list of U{NeXus<www.nexusformat.org>} files and
various keyword arguments and returns a data C{SOM} and a monitor C{SOM}
that is the sum of all the data from the specified files. B{It is assumed
that the files contain similar data as only crude cross-checks will be
made. You have been warned.}
@param filelist: A list containing the names of the files to sum
@type filelist: C{list}
@param kwargs: A list of keyword arguments that the function accepts:
@keyword SO_Axis: This is the name of the main axis to read from the NeXus
file
@type SO_Axis: C{string}
@keyword Data_Paths: This contains the data paths and signals for the
requested detector banks
@type Data_Paths: C{tuple} of C{tuple}s
@keyword Mon_Paths: This contains the data paths and signals for the
requested monitor banks
@type Mon_Paths: C{tuple} of C{tuple}s
@keyword Signal_ROI: This is the name of a file that contains a list of
pixel IDs that will be read from the data file and
stored as a signal C{SOM}
@type Signal_ROI: C{string}
@keyword Signal_MASK: This is the name of a file that contains a list of
pixel IDs that will be read from the data file and
stored as a signal C{SOM}
@type Signal_MASK: C{string}
@keyword dataset_type: The practical name of the dataset being processed.
The default value is I{data}.
@type dataset_type: C{string}
@keyword dataset_cwp: A set of chopper phase corrections for the dataset.
This will instruct the function to shift the TOF
axes of mulitple datasets and perform rebinning. The
TOF axis for the first dataset is the one that all
other datasets will be rebinned to.
@type dataset_cwp: C{list} of C{float}s
@keyword Verbose: This is a flag to turn on print statments. The default is
I{False}.
@type Verbose: C{boolean}
@keyword Timer: This is an SNS Timer object used for showing the
performance timing in the function.
@type Timer: C{sns_timing.Timer}
@return: Signal C{SOM.SOM} and monitor C{SOM.SOM}
@rtype: C{tuple}
@raise SystemExit: If any file cannot be read
@raise RuntimeError: If both a ROI and MASK file are specified
"""
import sys
import common_lib
import DST
# Parse keywords
try:
so_axis = kwargs["SO_Axis"]
except KeyError:
so_axis = "time_of_flight"
try:
data_paths = kwargs["Data_Paths"]
except KeyError:
data_paths = None
try:
mon_paths = kwargs["Mon_Paths"]
except KeyError:
mon_paths = None
try:
signal_roi = kwargs["Signal_ROI"]
except KeyError:
signal_roi = None
try:
signal_mask = kwargs["Signal_MASK"]
except KeyError:
signal_mask = None
try:
dataset_type = kwargs["dataset_type"]
except KeyError:
dataset_type = "data"
try:
verbose = kwargs["Verbose"]
except KeyError:
verbose = False
try:
timer = kwargs["Timer"]
except KeyError:
timer = None
dataset_cwp = kwargs.get("dataset_cwp")
if signal_roi is not None and signal_mask is not None:
raise RuntimeError("Cannot specify both ROI and MASK file! Please "\
+"choose!")
dst_type = "application/x-NeXus"
counter = 0
for filename in filelist:
if verbose:
print "File:", filename
if dataset_cwp is not None:
print "TOF Offset:", dataset_cwp[counter]
if dataset_cwp is not None:
cwp = dataset_cwp[counter]
else:
cwp = None
try:
data_dst = DST.getInstance(dst_type, filename)
except SystemError:
print "ERROR: Failed to data read file %s" % filename
sys.exit(-1)
if timer is not None:
timer.getTime(msg="After parsing file")
if verbose:
print "Reading data file %d" % counter
if counter == 0:
d_som1 = data_dst.getSOM(data_paths, so_axis, roi_file=signal_roi,
mask_file=signal_mask, tof_offset=cwp)
d_som1.rekeyNxPars(dataset_type)
if verbose:
print "# Signal SO:", len(d_som1)
try:
print "# TOF:", len(d_som1[0])
print "# TOF Axis:", len(d_som1[0].axis[0].val)
except IndexError:
# No data is present so say so again
print "information is unavailable since no data "\
+"present. Exiting."
sys.exit(0)
if timer is not None:
timer.getTime(msg="After reading data")
if mon_paths is not None:
if verbose:
print "Reading monitor %d" % counter
if counter == 0:
m_som1 = data_dst.getSOM(mon_paths, so_axis,
tof_offset=cwp)
m_som1.rekeyNxPars(dataset_type)
if verbose:
print "# Monitor SO:", len(m_som1)
print "# TOF:", len(m_som1[0])
print "# TOF Axis:", len(m_som1[0].axis[0].val)
if timer is not None:
timer.getTime(msg="After reading monitor data")
else:
m_som1 = None
else:
d_som_t0 = data_dst.getSOM(data_paths, so_axis,
roi_file=signal_roi,
mask_file=signal_mask, tof_offset=cwp)
d_som_t0.rekeyNxPars(dataset_type)
if timer is not None:
timer.getTime(msg="After reading data")
if dataset_cwp is not None:
d_som_t = common_lib.rebin_axis_1D_frac(d_som_t0,
d_som1[0].axis[0].val)
del d_som_t0
else:
d_som_t = d_som_t0
d_som1 = common_lib.add_ncerr(d_som_t, d_som1, add_nxpars=True)
if timer is not None:
timer.getTime(msg="After adding data spectra")
del d_som_t
if timer is not None:
timer.getTime(msg="After data SOM deletion")
if mon_paths is not None:
m_som_t0 = data_dst.getSOM(mon_paths, so_axis, tof_offset=cwp)
m_som_t0.rekeyNxPars(dataset_type)
if timer is not None:
timer.getTime(msg="After reading monitor data")
if dataset_cwp is not None:
m_som_t = common_lib.rebin_axis_1D_frac(m_som_t0,
m_som1[0].axis[0].val)
del m_som_t0
else:
m_som_t = m_som_t0
m_som1 = common_lib.add_ncerr(m_som_t, m_som1, add_nxpars=True)
if timer is not None:
timer.getTime(msg="After adding monitor spectra")
del m_som_t
if timer is not None:
timer.getTime(msg="After monitor SOM deletion")
data_dst.release_resource()
del data_dst
counter += 1
if timer is not None:
timer.getTime(msg="After resource release and DST deletion")
som_key_parts = [dataset_type, "filename"]
som_key = "-".join(som_key_parts)
d_som1.attr_list[som_key] = filelist
if m_som1 is not None:
m_som1.attr_list[som_key] = filelist
return (d_som1, m_som1)
