def add_files_bg(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 background C{SOM}
(if requested) 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 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 Bkg_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
background C{SOM}
@type Bkg_ROI: 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 dst_type: The type of C{DST} to be created during file read-in.
The default value is I{application/x-NeXus}.
@type dst_type: C{string}
@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 background C{SOM.SOM}
@rtype: C{tuple}
@raise SystemExit: If any file cannot be read
"""
import sys
import common_lib
import DST
import hlr_utils
# 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:
signal_roi = kwargs["Signal_ROI"]
except KeyError:
signal_roi = None
try:
bkg_roi = kwargs["Bkg_ROI"]
except KeyError:
bkg_roi = None
try:
dataset_type = kwargs["dataset_type"]
except KeyError:
dataset_type = "data"
try:
dst_type = kwargs["dst_type"]
except KeyError:
try:
dst_type = hlr_utils.file_peeker(filelist[0])
except RuntimeError:
# Assume it is a NeXus file, since it is not a DR produced file
dst_type = "application/x-NeXus"
try:
verbose = kwargs["Verbose"]
except KeyError:
verbose = False
try:
timer = kwargs["Timer"]
except KeyError:
timer = None
counter = 0
for filename in filelist:
if verbose:
print "File:", filename
try:
if dst_type == "application/x-NeXus":
data_dst = DST.getInstance(dst_type, filename)
else:
resource = open(filename, "r")
data_dst = DST.getInstance(dst_type, resource)
except SystemError:
print "ERROR: Failed to data read file %s" % filename
sys.exit(-1)
if verbose:
print "Reading data file %d" % counter
if counter == 0:
if dst_type == "application/x-NeXus":
d_som1 = data_dst.getSOM(data_paths,
so_axis,
roi_file=signal_roi)
d_som1.rekeyNxPars(dataset_type)
else:
if dst_type != "text/Dave2d":
d_som1 = data_dst.getSOM(data_paths, roi_file=signal_roi)
else:
d_som1 = data_dst.getSOM(data_paths)
if verbose:
print "# Signal SO:", len(d_som1)
if dst_type == "application/x-NeXus":
print "# TOF:", len(d_som1[0])
print "# TOF Axis:", len(d_som1[0].axis[0].val)
elif dst_type != "text/num-info":
print "# Data Size:", len(d_som1[0])
print "# X-Axis:", len(d_som1[0].axis[0].val)
try:
axis_len = len(d_som1[0].axis[1].val)
print "# Y-Axis:", axis_len
except IndexError:
pass
if bkg_roi is not None:
if dst_type == "application/x-NeXus":
b_som1 = data_dst.getSOM(data_paths,
so_axis,
roi_file=bkg_roi)
b_som1.rekeyNxPars(dataset_type)
else:
if dst_type != "text/Dave2d":
b_som1 = data_dst.getSOM(data_paths, roi_file=bkg_roi)
else:
b_som1 = data_dst.getSOM(data_paths)
if verbose:
print "# Background SO:", len(b_som1)
else:
b_som1 = None
if timer is not None:
timer.getTime(msg="After reading data")
else:
if dst_type == "application/x-NeXus":
d_som_t = data_dst.getSOM(data_paths,
so_axis,
roi_file=signal_roi)
d_som_t.rekeyNxPars(dataset_type)
add_nxpars_sig = True
else:
if dst_type != "text/Dave2d":
d_som_t = data_dst.getSOM(data_paths, roi_file=signal_roi)
else:
d_som_t = data_dst.getSOM(data_paths)
add_nxpars_sig = False
if bkg_roi is not None:
if dst_type == "application/x-NeXus":
b_som_t = data_dst.getSOM(data_paths,
so_axis,
roi_file=bkg_roi)
b_som_t.rekeyNxPars(dataset_type)
add_nxpars_bkg = True
else:
if dst_type != "text/Dave2d":
b_som_t = data_dst.getSOM(data_paths, roi_file=bkg_roi)
else:
b_som_t = data_dst.getSOM(data_paths)
add_nxpars_bkg = False
else:
b_som_t = None
if timer is not None:
timer.getTime(msg="After reading data")
d_som1 = common_lib.add_ncerr(d_som_t,
d_som1,
add_nxpars=add_nxpars_sig)
if bkg_roi is not None:
b_som1 = common_lib.add_ncerr(b_som_t,
b_som1,
add_nxpars=add_nxpars_bkg)
if timer is not None:
timer.getTime(msg="After adding spectra")
del d_som_t
if bkg_roi is not None:
del b_som_t
if timer is not None:
timer.getTime(msg="After 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")
if dst_type == "application/x-NeXus":
som_key_parts = [dataset_type, "filename"]
som_key = "-".join(som_key_parts)
d_som1.attr_list[som_key] = filelist
if b_som1 is not None:
b_som1.attr_list[som_key] = filelist
else:
# Previously written files already have this structure imposed
pass
return (d_som1, b_som1)
def process_igs_data(datalist, conf, **kwargs):
"""
This function combines Steps 1 through 8 of the data reduction process for
Inverse Geometry Spectrometers as specified by the documents 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. This function should really only be used in
the context of I{amorphous_reduction} and I{calc_norm_eff}.
@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 inst_geom_dst: File object that contains instrument geometry
information.
@type inst_geom_dst: C{DST.GeomDST}
@keyword dataset_type: The practical name of the dataset being processed.
The default value is I{data}.
@type dataset_type: C{string}
@keyword tib_const: Object providing the time-independent background
constant to subtract.
@type tib_const: L{hlr_utils.DrParameter}
@keyword bkg_som: Object that will be used for early background subtraction
@type bkg_som: C{SOM.SOM}
@keyword timer: Timing object so the function can perform timing estimates.
@type timer: C{sns_timer.DiffTime}
@return: Object that has undergone all requested processing steps
@rtype: C{SOM.SOM}
"""
import hlr_utils
# Check keywords
try:
dataset_type = kwargs["dataset_type"]
except KeyError:
dataset_type = "data"
try:
t = kwargs["timer"]
except KeyError:
t = None
try:
if kwargs["tib_const"] is not None:
tib_const = kwargs["tib_const"].toValErrTuple()
else:
tib_const = None
except KeyError:
tib_const = None
try:
i_geom_dst = kwargs["inst_geom_dst"]
except KeyError:
i_geom_dst = None
try:
bkg_som = kwargs["bkg_som"]
except KeyError:
bkg_som = None
# Step 1: Open appropriate data files
if not conf.mc:
so_axis = "time_of_flight"
else:
so_axis = "Time_of_Flight"
# Add so_axis to Configure object
conf.so_axis = so_axis
if conf.verbose:
print "Reading %s file" % dataset_type
# Special case handling for normalization data. Dynamically trying to
# determine if incoming file is a previously calculated one.
if dataset_type == "normalization":
try:
# Check the first incoming file
dst_type = hlr_utils.file_peeker(datalist[0])
# If file_peeker succeeds, the DST is different than the function
# returns
dst_type = "text/num-info"
# Let ROI file handle filtering
data_paths = None
except RuntimeError:
# It's a NeXus file
dst_type = "application/x-NeXus"
data_paths = conf.data_paths.toPath()
else:
dst_type = "application/x-NeXus"
data_paths = conf.data_paths.toPath()
# The [0] is to get the data SOM and ignore the None background SOM
dp_som0 = dr_lib.add_files(datalist,
Data_Paths=data_paths,
SO_Axis=so_axis,
Signal_ROI=conf.roi_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)
if dst_type == "text/num-info":
# Since we have a pre-calculated normalization dataset, set the flag
# and return the SOM now
conf.pre_norm = True
# Make the labels and units compatible with a NeXus file based SOM
dp_som0.setAxisLabel(0, "wavelength")
dp_som0.setAxisUnits(0, "Angstroms")
dp_som0.setYUnits("Counts/A")
return dp_som0
else:
if dataset_type == "normalization":
# Since we have a NeXus file, we need to continue
conf.pre_norm = False
# 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_som1 = dr_lib.fix_bin_contents(dp_somA)
del dp_somA
if conf.inst_geom is not None:
i_geom_dst.setGeometry(conf.data_paths.toPath(), dp_som1)
if conf.no_mon_norm:
dm_som1 = None
else:
if conf.verbose:
print "Reading in monitor data from %s file" % dataset_type
# The [0] is to get the data SOM and ignore the None background SOM
dm_som0 = dr_lib.add_files(datalist,
Data_Paths=conf.mon_path.toPath(),
SO_Axis=so_axis,
dataset_type=dataset_type,
Verbose=conf.verbose,
Timer=t)
if t is not None:
t.getTime(msg="After reading monitor data ")
dm_som1 = dr_lib.fix_bin_contents(dm_som0)
del dm_som0
if conf.inst_geom is not None:
i_geom_dst.setGeometry(conf.mon_path.toPath(), dm_som1)
if bkg_som is not None:
bkg_pcharge = bkg_som.attr_list["background-proton_charge"].getValue()
data_pcharge = dp_som1.attr_list[dataset_type +
"-proton_charge"].getValue()
ratio = data_pcharge / bkg_pcharge
bkg_som1 = common_lib.mult_ncerr(bkg_som, (ratio, 0.0))
del bkg_som
dp_som2 = dr_lib.subtract_bkg_from_data(dp_som1,
bkg_som1,
verbose=conf.verbose,
timer=t,
dataset1=dataset_type,
dataset2="background")
else:
dp_som2 = dp_som1
del dp_som1
# Step 2: Dead Time Correction
# No dead time correction is being applied to the data yet
# Step 3: Time-independent background determination
if conf.verbose and conf.tib_tofs is not None:
print "Determining time-independent background from data"
if t is not None and conf.tib_tofs is not None:
t.getTime(False)
B = dr_lib.determine_time_indep_bkg(dp_som2, conf.tib_tofs)
if t is not None and B is not None:
t.getTime(msg="After determining time-independent background ")
if conf.dump_tib and B is not None:
file_comment = "TOFs: %s" % conf.tib_tofs
hlr_utils.write_file(conf.output, "text/num-info", B,
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",
units="counts",
comments=[file_comment])
# Step 4: Subtract time-independent background
if conf.verbose and B is not None:
print "Subtracting time-independent background from data"
if t is not None:
t.getTime(False)
if B is not None:
dp_som3 = common_lib.sub_ncerr(dp_som2, B)
else:
dp_som3 = dp_som2
if B is not None and t is not None:
t.getTime(msg="After subtracting time-independent background ")
del dp_som2, B
# Step 5: Subtract time-independent background constant
if conf.verbose and tib_const is not None:
print "Subtracting time-independent background constant from data"
if t is not None and tib_const is not None:
t.getTime(False)
if tib_const is not None:
dp_som4 = common_lib.sub_ncerr(dp_som3, tib_const)
else:
dp_som4 = dp_som3
if t is not None and tib_const is not None:
t.getTime(msg="After subtracting time-independent background "\
+"constant ")
del dp_som3
# Provide override capability for final wavelength, time-zero slope and
# time-zero offset
if conf.wavelength_final is not None:
dp_som4.attr_list["Wavelength_final"] = \
conf.wavelength_final.toValErrTuple()
# Note: time_zero_slope MUST be a tuple
if conf.time_zero_slope is not None:
dp_som4.attr_list["Time_zero_slope"] = \
conf.time_zero_slope.toValErrTuple()
if dm_som1 is not None:
dm_som1.attr_list["Time_zero_slope"] = \
conf.time_zero_slope.toValErrTuple()
# Note: time_zero_offset MUST be a tuple
if conf.time_zero_offset is not None:
dp_som4.attr_list["Time_zero_offset"] = \
conf.time_zero_offset.toValErrTuple()
if dm_som1 is not None:
dm_som1.attr_list["Time_zero_offset"] = \
conf.time_zero_offset.toValErrTuple()
# Step 6: Convert TOF to wavelength for data and monitor
if conf.verbose:
print "Converting TOF to wavelength"
if t is not None:
t.getTime(False)
# Convert monitor
if dm_som1 is not None:
dm_som2 = common_lib.tof_to_wavelength_lin_time_zero(
dm_som1, units="microsecond")
else:
dm_som2 = None
# Convert detector pixels
dp_som5 = common_lib.tof_to_initial_wavelength_igs_lin_time_zero(
dp_som4, units="microsecond", run_filter=conf.filter)
if t is not None:
t.getTime(msg="After converting TOF to wavelength ")
if conf.dump_wave:
hlr_utils.write_file(conf.output,
"text/Spec",
dp_som5,
output_ext="pxl",
extra_tag=dataset_type,
verbose=conf.verbose,
data_ext=conf.ext_replacement,
path_replacement=conf.path_replacement,
message="pixel wavelength information")
if conf.dump_mon_wave and dm_som2 is not None:
hlr_utils.write_file(conf.output,
"text/Spec",
dm_som2,
output_ext="mxl",
extra_tag=dataset_type,
verbose=conf.verbose,
data_ext=conf.ext_replacement,
path_replacement=conf.path_replacement,
message="monitor wavelength information")
del dp_som4, dm_som1
# Step 7: Efficiency correct monitor
if conf.verbose and dm_som2 is not None and not conf.no_mon_effc:
print "Efficiency correct monitor data"
if t is not None:
t.getTime(False)
if not conf.no_mon_effc:
dm_som3 = dr_lib.feff_correct_mon(dm_som2)
else:
dm_som3 = dm_som2
if t is not None and dm_som2 is not None and not conf.no_mon_effc:
t.getTime(msg="After efficiency correcting monitor ")
if conf.dump_mon_effc and not conf.no_mon_effc and dm_som3 is not None:
hlr_utils.write_file(conf.output, "text/Spec", dm_som3,
output_ext="mel",
extra_tag=dataset_type,
verbose=conf.verbose,
data_ext=conf.ext_replacement,
path_replacement=conf.path_replacement,
message="monitor wavelength information "\
+"(efficiency)")
del dm_som2
# Step 8: Rebin monitor axis onto detector pixel axis
if conf.verbose and dm_som3 is not None:
print "Rebin monitor axis to detector pixel axis"
if t is not None:
t.getTime(False)
dm_som4 = dr_lib.rebin_monitor(dm_som3, dp_som5)
if t is not None and dm_som4 is not None:
t.getTime(msg="After rebinning monitor ")
del dm_som3
if conf.dump_mon_rebin and dm_som4 is not None:
hlr_utils.write_file(conf.output, "text/Spec", dm_som4,
output_ext="mrl",
extra_tag=dataset_type,
verbose=conf.verbose,
data_ext=conf.ext_replacement,
path_replacement=conf.path_replacement,
message="monitor wavelength information "\
+"(rebinned)")
# The lambda-dependent background is only done on sample data (aka data)
# for the BSS instrument at the SNS
if conf.inst == "BSS" and conf.ldb_const is not None and \
dataset_type == "data":
# Step 9: Convert chopper center wavelength to TOF center
if conf.verbose:
print "Converting chopper center wavelength to TOF"
if t is not None:
t.getTime(False)
tof_center = dr_lib.convert_single_to_list(\
"initial_wavelength_igs_lin_time_zero_to_tof",
conf.chopper_lambda_cent.toValErrTuple(), dp_som5)
# Step 10: Calculate beginning and end of detector TOF spectrum
if conf.verbose:
print "Calculating beginning and ending TOF ranges"
half_inv_chop_freq = 0.5 / conf.chopper_freq.toValErrTuple()[0]
# Above is in seconds, need microseconds
half_inv_chop_freq *= 1.0e6
tof_begin = common_lib.sub_ncerr(tof_center, (half_inv_chop_freq, 0.0))
tof_end = common_lib.add_ncerr(tof_center, (half_inv_chop_freq, 0.0))
# Step 11: Convert TOF_begin and TOF_end to wavelength
if conf.verbose:
print "Converting TOF_begin and TOF_end to wavelength"
# Check for time-zero slope information
try:
tz_slope = conf.time_zero_slope.toValErrTuple()
except AttributeError:
tz_slope = (0.0, 0.0)
# Check for time-zero offset information
try:
tz_offset = conf.time_zero_offset.toValErrTuple()
except AttributeError:
tz_offset = (0.0, 0.0)
l_begin = common_lib.tof_to_initial_wavelength_igs_lin_time_zero(\
tof_begin, time_zero_slope=tz_slope, time_zero_offset=tz_offset,
iobj=dp_som5, run_filter=False)
l_end = common_lib.tof_to_initial_wavelength_igs_lin_time_zero(\
tof_end, time_zero_slope=tz_slope, time_zero_offset=tz_offset,
iobj=dp_som5, run_filter=False)
# Step 12: tof-least-bkg to lambda-least-bkg
if conf.verbose:
print "Converting TOF least background to wavelength"
lambda_least_bkg = dr_lib.convert_single_to_list(\
"tof_to_initial_wavelength_igs_lin_time_zero",
conf.tof_least_bkg.toValErrTuple(), dp_som5)
if t is not None:
t.getTime(msg="After converting boundary positions ")
# Step 13: Create lambda-dependent background spectrum
if conf.verbose:
print "Creating lambda-dependent background spectra"
if t is not None:
t.getTime(False)
ldb_som = dr_lib.shift_spectrum(dm_som4, lambda_least_bkg, l_begin,
l_end, conf.ldb_const.getValue())
if t is not None:
t.getTime(msg="After creating lambda-dependent background "\
+"spectra ")
# Step 14: Subtract lambda-dependent background from sample data
if conf.verbose:
print "Subtracting lambda-dependent background from data"
if t is not None:
t.getTime(False)
dp_som6 = common_lib.sub_ncerr(dp_som5, ldb_som)
if t is not None:
t.getTime(msg="After subtracting lambda-dependent background "\
+"from data ")
else:
dp_som6 = dp_som5
del dp_som5
# Step 15: Normalize data by monitor
if conf.verbose and dm_som4 is not None:
print "Normalizing data by monitor"
if t is not None:
t.getTime(False)
if dm_som4 is not None:
dp_som7 = common_lib.div_ncerr(dp_som6, dm_som4)
if t is not None:
t.getTime(msg="After normalizing data by monitor ")
else:
dp_som7 = dp_som6
if conf.dump_wave_mnorm:
dp_som7_1 = dr_lib.sum_all_spectra(dp_som7,\
rebin_axis=conf.lambda_bins.toNessiList())
write_message = "combined pixel wavelength information"
if dm_som4 is not None:
write_message += " (monitor normalized)"
hlr_utils.write_file(conf.output,
"text/Spec",
dp_som7_1,
output_ext="pml",
extra_tag=dataset_type,
verbose=conf.verbose,
data_ext=conf.ext_replacement,
path_replacement=conf.path_replacement,
message=write_message)
del dp_som7_1
del dm_som4, dp_som6
return dp_som7
def add_files(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 background C{SOM}
(if requested) 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 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 dst_type: The type of C{DST} to be created during file read-in.
The default value is I{application/x-NeXus}.
@type dst_type: C{string}
@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 background 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
import hlr_utils
# 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:
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:
dst_type = kwargs["dst_type"]
except KeyError:
try:
dst_type = hlr_utils.file_peeker(filelist[0])
except RuntimeError:
# Assume it is a NeXus file, since it is not a DR produced file
dst_type = "application/x-NeXus"
try:
verbose = kwargs["Verbose"]
except KeyError:
verbose = False
try:
timer = kwargs["Timer"]
except KeyError:
timer = None
if signal_roi is not None and signal_mask is not None:
raise RuntimeError("Cannot specify both ROI and MASK file! Please " + "choose!")
counter = 0
for filename in filelist:
if verbose:
print "File:", filename
try:
if dst_type == "application/x-NeXus":
data_dst = DST.getInstance(dst_type, filename)
else:
resource = open(filename, "r")
data_dst = DST.getInstance(dst_type, resource)
except SystemError:
print "ERROR: Failed to data read file %s" % filename
sys.exit(-1)
if verbose:
print "Reading data file %d" % counter
if counter == 0:
if dst_type == "application/x-NeXus":
d_som1 = data_dst.getSOM(data_paths, so_axis, roi_file=signal_roi, mask_file=signal_mask)
d_som1.rekeyNxPars(dataset_type)
else:
if dst_type != "text/Dave2d":
d_som1 = data_dst.getSOM(data_paths, roi_file=signal_roi, mask_file=signal_mask)
else:
d_som1 = data_dst.getSOM(data_paths)
if verbose:
len_data = len(d_som1)
print "# Signal SO:", len_data
if len_data == 0:
print "All data has been filtered. Program exiting."
sys.exit(0)
if dst_type == "application/x-NeXus":
print "# TOF:", len(d_som1[0])
print "# TOF Axis:", len(d_som1[0].axis[0].val)
elif dst_type != "text/num-info":
print "# Data Size:", len(d_som1[0])
print "# X-Axis:", len(d_som1[0].axis[0].val)
try:
axis_len = len(d_som1[0].axis[1].val)
print "# Y-Axis:", axis_len
except IndexError:
pass
if timer is not None:
timer.getTime(msg="After reading data")
else:
if dst_type == "application/x-NeXus":
d_som_t = data_dst.getSOM(data_paths, so_axis, roi_file=signal_roi, mask_file=signal_mask)
d_som_t.rekeyNxPars(dataset_type)
add_nxpars_sig = True
else:
if dst_type != "text/Dave2d":
d_som_t = data_dst.getSOM(data_paths, roi_file=signal_roi, mask_file=signal_mask)
else:
d_som_t = data_dst.getSOM(data_paths)
add_nxpars_sig = False
if timer is not None:
timer.getTime(msg="After reading data")
d_som1 = common_lib.add_ncerr(d_som_t, d_som1, add_nxpars=add_nxpars_sig)
if timer is not None:
timer.getTime(msg="After adding spectra")
del d_som_t
if timer is not None:
timer.getTime(msg="After 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")
if dst_type == "application/x-NeXus":
som_key_parts = [dataset_type, "filename"]
som_key = "-".join(som_key_parts)
d_som1.attr_list[som_key] = filelist
else:
# Previously written files already have this structure imposed
pass
return d_som1
def process_igs_data(datalist, conf, **kwargs):
"""
This function combines Steps 1 through 8 of the data reduction process for
Inverse Geometry Spectrometers as specified by the documents 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. This function should really only be used in
the context of I{amorphous_reduction} and I{calc_norm_eff}.
@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 inst_geom_dst: File object that contains instrument geometry
information.
@type inst_geom_dst: C{DST.GeomDST}
@keyword dataset_type: The practical name of the dataset being processed.
The default value is I{data}.
@type dataset_type: C{string}
@keyword tib_const: Object providing the time-independent background
constant to subtract.
@type tib_const: L{hlr_utils.DrParameter}
@keyword bkg_som: Object that will be used for early background subtraction
@type bkg_som: C{SOM.SOM}
@keyword timer: Timing object so the function can perform timing estimates.
@type timer: C{sns_timer.DiffTime}
@return: Object that has undergone all requested processing steps
@rtype: C{SOM.SOM}
"""
import hlr_utils
# Check keywords
try:
dataset_type = kwargs["dataset_type"]
except KeyError:
dataset_type = "data"
try:
t = kwargs["timer"]
except KeyError:
t = None
try:
if kwargs["tib_const"] is not None:
tib_const = kwargs["tib_const"].toValErrTuple()
else:
tib_const = None
except KeyError:
tib_const = None
try:
i_geom_dst = kwargs["inst_geom_dst"]
except KeyError:
i_geom_dst = None
try:
bkg_som = kwargs["bkg_som"]
except KeyError:
bkg_som = None
# Step 1: Open appropriate data files
if not conf.mc:
so_axis = "time_of_flight"
else:
so_axis = "Time_of_Flight"
# Add so_axis to Configure object
conf.so_axis = so_axis
if conf.verbose:
print "Reading %s file" % dataset_type
# Special case handling for normalization data. Dynamically trying to
# determine if incoming file is a previously calculated one.
if dataset_type == "normalization":
try:
# Check the first incoming file
dst_type = hlr_utils.file_peeker(datalist[0])
# If file_peeker succeeds, the DST is different than the function
# returns
dst_type = "text/num-info"
# Let ROI file handle filtering
data_paths = None
except RuntimeError:
# It's a NeXus file
dst_type = "application/x-NeXus"
data_paths = conf.data_paths.toPath()
else:
dst_type = "application/x-NeXus"
data_paths = conf.data_paths.toPath()
# The [0] is to get the data SOM and ignore the None background SOM
dp_som0 = dr_lib.add_files(
datalist,
Data_Paths=data_paths,
SO_Axis=so_axis,
Signal_ROI=conf.roi_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)
if dst_type == "text/num-info":
# Since we have a pre-calculated normalization dataset, set the flag
# and return the SOM now
conf.pre_norm = True
# Make the labels and units compatible with a NeXus file based SOM
dp_som0.setAxisLabel(0, "wavelength")
dp_som0.setAxisUnits(0, "Angstroms")
dp_som0.setYUnits("Counts/A")
return dp_som0
else:
if dataset_type == "normalization":
# Since we have a NeXus file, we need to continue
conf.pre_norm = False
# 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_som1 = dr_lib.fix_bin_contents(dp_somA)
del dp_somA
if conf.inst_geom is not None:
i_geom_dst.setGeometry(conf.data_paths.toPath(), dp_som1)
if conf.no_mon_norm:
dm_som1 = None
else:
if conf.verbose:
print "Reading in monitor data from %s file" % dataset_type
# The [0] is to get the data SOM and ignore the None background SOM
dm_som0 = dr_lib.add_files(
datalist,
Data_Paths=conf.mon_path.toPath(),
SO_Axis=so_axis,
dataset_type=dataset_type,
Verbose=conf.verbose,
Timer=t,
)
if t is not None:
t.getTime(msg="After reading monitor data ")
dm_som1 = dr_lib.fix_bin_contents(dm_som0)
del dm_som0
if conf.inst_geom is not None:
i_geom_dst.setGeometry(conf.mon_path.toPath(), dm_som1)
if bkg_som is not None:
bkg_pcharge = bkg_som.attr_list["background-proton_charge"].getValue()
data_pcharge = dp_som1.attr_list[dataset_type + "-proton_charge"].getValue()
ratio = data_pcharge / bkg_pcharge
bkg_som1 = common_lib.mult_ncerr(bkg_som, (ratio, 0.0))
del bkg_som
dp_som2 = dr_lib.subtract_bkg_from_data(
dp_som1, bkg_som1, verbose=conf.verbose, timer=t, dataset1=dataset_type, dataset2="background"
)
else:
dp_som2 = dp_som1
del dp_som1
# Step 2: Dead Time Correction
# No dead time correction is being applied to the data yet
# Step 3: Time-independent background determination
if conf.verbose and conf.tib_tofs is not None:
print "Determining time-independent background from data"
if t is not None and conf.tib_tofs is not None:
t.getTime(False)
B = dr_lib.determine_time_indep_bkg(dp_som2, conf.tib_tofs)
if t is not None and B is not None:
t.getTime(msg="After determining time-independent background ")
if conf.dump_tib and B is not None:
file_comment = "TOFs: %s" % conf.tib_tofs
hlr_utils.write_file(
conf.output,
"text/num-info",
B,
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",
units="counts",
comments=[file_comment],
)
# Step 4: Subtract time-independent background
if conf.verbose and B is not None:
print "Subtracting time-independent background from data"
if t is not None:
t.getTime(False)
if B is not None:
dp_som3 = common_lib.sub_ncerr(dp_som2, B)
else:
dp_som3 = dp_som2
if B is not None and t is not None:
t.getTime(msg="After subtracting time-independent background ")
del dp_som2, B
# Step 5: Subtract time-independent background constant
if conf.verbose and tib_const is not None:
print "Subtracting time-independent background constant from data"
if t is not None and tib_const is not None:
t.getTime(False)
if tib_const is not None:
dp_som4 = common_lib.sub_ncerr(dp_som3, tib_const)
else:
dp_som4 = dp_som3
if t is not None and tib_const is not None:
t.getTime(msg="After subtracting time-independent background " + "constant ")
del dp_som3
# Provide override capability for final wavelength, time-zero slope and
# time-zero offset
if conf.wavelength_final is not None:
dp_som4.attr_list["Wavelength_final"] = conf.wavelength_final.toValErrTuple()
# Note: time_zero_slope MUST be a tuple
if conf.time_zero_slope is not None:
dp_som4.attr_list["Time_zero_slope"] = conf.time_zero_slope.toValErrTuple()
if dm_som1 is not None:
dm_som1.attr_list["Time_zero_slope"] = conf.time_zero_slope.toValErrTuple()
# Note: time_zero_offset MUST be a tuple
if conf.time_zero_offset is not None:
dp_som4.attr_list["Time_zero_offset"] = conf.time_zero_offset.toValErrTuple()
if dm_som1 is not None:
dm_som1.attr_list["Time_zero_offset"] = conf.time_zero_offset.toValErrTuple()
# Step 6: Convert TOF to wavelength for data and monitor
if conf.verbose:
print "Converting TOF to wavelength"
if t is not None:
t.getTime(False)
# Convert monitor
if dm_som1 is not None:
dm_som2 = common_lib.tof_to_wavelength_lin_time_zero(dm_som1, units="microsecond")
else:
dm_som2 = None
# Convert detector pixels
dp_som5 = common_lib.tof_to_initial_wavelength_igs_lin_time_zero(
dp_som4, units="microsecond", run_filter=conf.filter
)
if t is not None:
t.getTime(msg="After converting TOF to wavelength ")
if conf.dump_wave:
hlr_utils.write_file(
conf.output,
"text/Spec",
dp_som5,
output_ext="pxl",
extra_tag=dataset_type,
verbose=conf.verbose,
data_ext=conf.ext_replacement,
path_replacement=conf.path_replacement,
message="pixel wavelength information",
)
if conf.dump_mon_wave and dm_som2 is not None:
hlr_utils.write_file(
conf.output,
"text/Spec",
dm_som2,
output_ext="mxl",
extra_tag=dataset_type,
verbose=conf.verbose,
data_ext=conf.ext_replacement,
path_replacement=conf.path_replacement,
message="monitor wavelength information",
)
del dp_som4, dm_som1
# Step 7: Efficiency correct monitor
if conf.verbose and dm_som2 is not None and not conf.no_mon_effc:
print "Efficiency correct monitor data"
if t is not None:
t.getTime(False)
if not conf.no_mon_effc:
dm_som3 = dr_lib.feff_correct_mon(dm_som2)
else:
dm_som3 = dm_som2
if t is not None and dm_som2 is not None and not conf.no_mon_effc:
t.getTime(msg="After efficiency correcting monitor ")
if conf.dump_mon_effc and not conf.no_mon_effc and dm_som3 is not None:
hlr_utils.write_file(
conf.output,
"text/Spec",
dm_som3,
output_ext="mel",
extra_tag=dataset_type,
verbose=conf.verbose,
data_ext=conf.ext_replacement,
path_replacement=conf.path_replacement,
message="monitor wavelength information " + "(efficiency)",
)
del dm_som2
# Step 8: Rebin monitor axis onto detector pixel axis
if conf.verbose and dm_som3 is not None:
print "Rebin monitor axis to detector pixel axis"
if t is not None:
t.getTime(False)
dm_som4 = dr_lib.rebin_monitor(dm_som3, dp_som5)
if t is not None and dm_som4 is not None:
t.getTime(msg="After rebinning monitor ")
del dm_som3
if conf.dump_mon_rebin and dm_som4 is not None:
hlr_utils.write_file(
conf.output,
"text/Spec",
dm_som4,
output_ext="mrl",
extra_tag=dataset_type,
verbose=conf.verbose,
data_ext=conf.ext_replacement,
path_replacement=conf.path_replacement,
message="monitor wavelength information " + "(rebinned)",
)
# The lambda-dependent background is only done on sample data (aka data)
# for the BSS instrument at the SNS
if conf.inst == "BSS" and conf.ldb_const is not None and dataset_type == "data":
# Step 9: Convert chopper center wavelength to TOF center
if conf.verbose:
print "Converting chopper center wavelength to TOF"
if t is not None:
t.getTime(False)
tof_center = dr_lib.convert_single_to_list(
"initial_wavelength_igs_lin_time_zero_to_tof", conf.chopper_lambda_cent.toValErrTuple(), dp_som5
)
# Step 10: Calculate beginning and end of detector TOF spectrum
if conf.verbose:
print "Calculating beginning and ending TOF ranges"
half_inv_chop_freq = 0.5 / conf.chopper_freq.toValErrTuple()[0]
# Above is in seconds, need microseconds
half_inv_chop_freq *= 1.0e6
tof_begin = common_lib.sub_ncerr(tof_center, (half_inv_chop_freq, 0.0))
tof_end = common_lib.add_ncerr(tof_center, (half_inv_chop_freq, 0.0))
# Step 11: Convert TOF_begin and TOF_end to wavelength
if conf.verbose:
print "Converting TOF_begin and TOF_end to wavelength"
# Check for time-zero slope information
try:
tz_slope = conf.time_zero_slope.toValErrTuple()
except AttributeError:
tz_slope = (0.0, 0.0)
# Check for time-zero offset information
try:
tz_offset = conf.time_zero_offset.toValErrTuple()
except AttributeError:
tz_offset = (0.0, 0.0)
l_begin = common_lib.tof_to_initial_wavelength_igs_lin_time_zero(
tof_begin, time_zero_slope=tz_slope, time_zero_offset=tz_offset, iobj=dp_som5, run_filter=False
)
l_end = common_lib.tof_to_initial_wavelength_igs_lin_time_zero(
tof_end, time_zero_slope=tz_slope, time_zero_offset=tz_offset, iobj=dp_som5, run_filter=False
)
# Step 12: tof-least-bkg to lambda-least-bkg
if conf.verbose:
print "Converting TOF least background to wavelength"
lambda_least_bkg = dr_lib.convert_single_to_list(
"tof_to_initial_wavelength_igs_lin_time_zero", conf.tof_least_bkg.toValErrTuple(), dp_som5
)
if t is not None:
t.getTime(msg="After converting boundary positions ")
# Step 13: Create lambda-dependent background spectrum
if conf.verbose:
print "Creating lambda-dependent background spectra"
if t is not None:
t.getTime(False)
ldb_som = dr_lib.shift_spectrum(dm_som4, lambda_least_bkg, l_begin, l_end, conf.ldb_const.getValue())
if t is not None:
t.getTime(msg="After creating lambda-dependent background " + "spectra ")
# Step 14: Subtract lambda-dependent background from sample data
if conf.verbose:
print "Subtracting lambda-dependent background from data"
if t is not None:
t.getTime(False)
dp_som6 = common_lib.sub_ncerr(dp_som5, ldb_som)
if t is not None:
t.getTime(msg="After subtracting lambda-dependent background " + "from data ")
else:
dp_som6 = dp_som5
del dp_som5
# Step 15: Normalize data by monitor
if conf.verbose and dm_som4 is not None:
print "Normalizing data by monitor"
if t is not None:
t.getTime(False)
if dm_som4 is not None:
dp_som7 = common_lib.div_ncerr(dp_som6, dm_som4)
if t is not None:
t.getTime(msg="After normalizing data by monitor ")
else:
dp_som7 = dp_som6
if conf.dump_wave_mnorm:
dp_som7_1 = dr_lib.sum_all_spectra(dp_som7, rebin_axis=conf.lambda_bins.toNessiList())
write_message = "combined pixel wavelength information"
if dm_som4 is not None:
write_message += " (monitor normalized)"
hlr_utils.write_file(
conf.output,
"text/Spec",
dp_som7_1,
output_ext="pml",
extra_tag=dataset_type,
verbose=conf.verbose,
data_ext=conf.ext_replacement,
path_replacement=conf.path_replacement,
message=write_message,
)
del dp_som7_1
del dm_som4, dp_som6
return dp_som7
