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 create_E_vs_Q_dgs(som, E_i, Q_final, **kwargs):
"""
This function starts with the rebinned energy transfer and turns this
into a 2D spectra with E and Q axes for DGS instruments.
@param som: The input object with initial IGS wavelength axis
@type som: C{SOM.SOM}
@param E_i: The initial energy for the given data.
@type E_i: C{tuple}
@param Q_final: The momentum transfer axis to rebin the data to
@type Q_final: C{nessi_list.NessiList}
@param kwargs: A list of keyword arguments that the function accepts:
@keyword corner_angles: The object that contains the corner geometry
information.
@type corner_angles: C{dict}
@keyword so_id: The identifier represents a number, string, tuple or other
object that describes the resulting C{SO}
@type so_id: C{int}, C{string}, C{tuple}, C{pixel ID}
@keyword y_label: The y axis label
@type y_label: C{string}
@keyword y_units: The y axis units
@type y_units: C{string}
@keyword x_labels: This is a list of names that sets the individual x axis
labels
@type x_labels: C{list} of C{string}s
@keyword x_units: This is a list of names that sets the individual x axis
units
@type x_units: C{list} of C{string}s
@keyword split: This flag causes the counts and the fractional area to
be written out into separate files.
@type split: C{boolean}
@keyword configure: This is the object containing the driver configuration.
@type configure: C{Configure}
@return: Object containing a 2D C{SO} with E and Q axes
@rtype: C{SOM.SOM}
"""
import array_manip
import axis_manip
import common_lib
import hlr_utils
import nessi_list
import SOM
import utils
# Check for keywords
corner_angles = kwargs["corner_angles"]
configure = kwargs.get("configure")
split = kwargs.get("split", False)
# Setup output object
so_dim = SOM.SO(2)
so_dim.axis[0].val = Q_final
so_dim.axis[1].val = som[0].axis[0].val # E_t
# Calculate total 2D array size
N_tot = (len(so_dim.axis[0].val) - 1) * (len(so_dim.axis[1].val) - 1)
# Create y and var_y lists from total 2D size
so_dim.y = nessi_list.NessiList(N_tot)
so_dim.var_y = nessi_list.NessiList(N_tot)
# Create area sum and errors for the area sum lists from total 2D size
area_sum = nessi_list.NessiList(N_tot)
area_sum_err2 = nessi_list.NessiList(N_tot)
# Convert initial energy to initial wavevector
l_i = common_lib.energy_to_wavelength(E_i)
k_i = common_lib.wavelength_to_scalar_k(l_i)
# Since all the data is rebinned to the same energy transfer axis, we can
# calculate the final energy axis once
E_t = som[0].axis[0].val
if som[0].axis[0].var is not None:
E_t_err2 = som[0].axis[0].var
else:
E_t_err2 = nessi_list.NessiList(len(E_t))
# Get the bin width arrays from E_t
(E_t_bw, E_t_bw_err2) = utils.calc_bin_widths(E_t)
E_f = array_manip.sub_ncerr(E_i[0], E_i[1], E_t, E_t_err2)
# Now we can get the final wavevector
l_f = axis_manip.energy_to_wavelength(E_f[0], E_f[1])
k_f = axis_manip.wavelength_to_scalar_k(l_f[0], l_f[1])
# Output position for Q
X = 0
# Iterate though the data
len_som = hlr_utils.get_length(som)
for i in xrange(len_som):
map_so = hlr_utils.get_map_so(som, None, i)
yval = hlr_utils.get_value(som, i, "SOM", "y")
yerr2 = hlr_utils.get_err2(som, i, "SOM", "y")
cangles = corner_angles[str(map_so.id)]
avg_theta1 = (cangles.getPolar(0) + cangles.getPolar(1)) / 2.0
avg_theta2 = (cangles.getPolar(2) + cangles.getPolar(3)) / 2.0
Q1 = axis_manip.init_scatt_wavevector_to_scalar_Q(k_i[0],
k_i[1],
k_f[0][:-1],
k_f[1][:-1],
avg_theta2,
0.0)
Q2 = axis_manip.init_scatt_wavevector_to_scalar_Q(k_i[0],
k_i[1],
k_f[0][:-1],
k_f[1][:-1],
avg_theta1,
0.0)
Q3 = axis_manip.init_scatt_wavevector_to_scalar_Q(k_i[0],
k_i[1],
k_f[0][1:],
k_f[1][1:],
avg_theta1,
0.0)
Q4 = axis_manip.init_scatt_wavevector_to_scalar_Q(k_i[0],
k_i[1],
k_f[0][1:],
k_f[1][1:],
avg_theta2,
0.0)
# Calculate the area of the E,Q polygons
(A, A_err2) = utils.calc_eq_jacobian_dgs(E_t[:-1], E_t[:-1],
E_t[1:], E_t[1:],
Q1[X], Q2[X], Q3[X], Q4[X])
# Apply the Jacobian: C/dE_t * dE_t / A(EQ) = C/A(EQ)
(jac_ratio, jac_ratio_err2) = array_manip.div_ncerr(E_t_bw,
E_t_bw_err2,
A, A_err2)
(counts, counts_err2) = array_manip.mult_ncerr(yval, yerr2,
jac_ratio,
jac_ratio_err2)
try:
(y_2d, y_2d_err2,
area_new,
bin_count) = axis_manip.rebin_2D_quad_to_rectlin(Q1[X], E_t[:-1],
Q2[X], E_t[:-1],
Q3[X], E_t[1:],
Q4[X], E_t[1:],
counts,
counts_err2,
so_dim.axis[0].val,
so_dim.axis[1].val)
del bin_count
except IndexError, e:
# Get the offending index from the error message
index = int(str(e).split()[1].split('index')[-1].strip('[]'))
print "Id:", map_so.id
print "Index:", index
print "Verticies: %f, %f, %f, %f, %f, %f, %f, %f" % (Q1[X][index],
E_t[:-1][index],
Q2[X][index],
E_t[:-1][index],
Q3[X][index],
E_t[1:][index],
Q4[X][index],
E_t[1:][index])
raise IndexError(str(e))
# Add in together with previous results
(so_dim.y, so_dim.var_y) = array_manip.add_ncerr(so_dim.y,
so_dim.var_y,
y_2d, y_2d_err2)
(area_sum, area_sum_err2) = array_manip.add_ncerr(area_sum,
area_sum_err2,
area_new,
area_sum_err2)
def create_Qvec_vs_E_dgs(som, E_i, conf, **kwargs):
"""
This function starts with the energy transfer axis from DGS reduction and
turns this into a 4D spectra with Qx, Qy, Qz and Et axes.
@param som: The input object with initial IGS wavelength axis
@type som: C{SOM.SOM}
@param E_i: The initial energy for the given data.
@type E_i: C{tuple}
@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 timer: Timing object so the function can perform timing estimates.
@type timer: C{sns_timer.DiffTime}
@keyword corner_angles: The object that contains the corner geometry
information.
@type corner_angles: C{dict}
@keyword make_fixed: A flag that turns on writing the fixed grid mesh
information to a file.
@type make_fixed: C{boolean}
@keyword output: The output filename and or directory.
@type output: C{string}
"""
import array_manip
import axis_manip
import common_lib
import hlr_utils
import os
# Check keywords
try:
t = kwargs["timer"]
except KeyError:
t = None
corner_angles = kwargs["corner_angles"]
try:
make_fixed = kwargs["make_fixed"]
except KeyError:
make_fixed = False
try:
output = kwargs["output"]
except KeyError:
output = None
# Convert initial energy to initial wavevector
l_i = common_lib.energy_to_wavelength(E_i)
k_i = common_lib.wavelength_to_scalar_k(l_i)
# Since all the data is rebinned to the same energy transfer axis, we can
# calculate the final energy axis once
E_t = som[0].axis[0].val
if som[0].axis[0].var is not None:
E_t_err2 = som[0].axis[0].var
else:
import nessi_list
E_t_err2 = nessi_list.NessiList(len(E_t))
E_f = array_manip.sub_ncerr(E_i[0], E_i[1], E_t, E_t_err2)
# Check for negative final energies which will cause problems with
# wavelength conversion due to square root
if E_f[0][-1] < 0:
E_f[0].reverse()
E_f[1].reverse()
index = 0
for E in E_f[0]:
if E >= 0:
break
index += 1
E_f[0].__delslice__(0, index)
E_f[1].__delslice__(0, index)
E_f[0].reverse()
E_f[1].reverse()
len_E = len(E_f[0]) - 1
# Now we can get the final wavevector
l_f = axis_manip.energy_to_wavelength(E_f[0], E_f[1])
k_f = axis_manip.wavelength_to_scalar_k(l_f[0], l_f[1])
# Grab the instrument from the som
inst = som.attr_list.instrument
if make_fixed:
import SOM
fixed_grid = {}
for key in corner_angles:
so_id = SOM.NeXusId.fromString(key).toTuple()
try:
pathlength = inst.get_secondary(so_id)[0]
points = []
for j in range(4):
points.extend(
__calc_xyz(pathlength, corner_angles[key].getPolar(j),
corner_angles[key].getAzimuthal(j)))
fixed_grid[key] = points
except KeyError:
# Pixel ID is not in instrument geometry
pass
CNT = {}
ERR2 = {}
V1 = {}
V2 = {}
V3 = {}
V4 = {}
# Output positions for Qx, Qy, Qz coordinates
X = 0
Y = 2
Z = 4
if t is not None:
t.getTime(False)
# Iterate though the data
len_som = hlr_utils.get_length(som)
for i in xrange(len_som):
map_so = hlr_utils.get_map_so(som, None, i)
yval = hlr_utils.get_value(som, i, "SOM", "y")
yerr2 = hlr_utils.get_err2(som, i, "SOM", "y")
CNT[str(map_so.id)] = yval
ERR2[str(map_so.id)] = yerr2
cangles = corner_angles[str(map_so.id)]
Q1 = axis_manip.init_scatt_wavevector_to_Q(k_i[0], k_i[1], k_f[0],
k_f[1],
cangles.getAzimuthal(0),
0.0, cangles.getPolar(0),
0.0)
V1[str(map_so.id)] = {}
V1[str(map_so.id)]["x"] = Q1[X]
V1[str(map_so.id)]["y"] = Q1[Y]
V1[str(map_so.id)]["z"] = Q1[Z]
Q2 = axis_manip.init_scatt_wavevector_to_Q(k_i[0], k_i[1], k_f[0],
k_f[1],
cangles.getAzimuthal(1),
0.0, cangles.getPolar(1),
0.0)
V2[str(map_so.id)] = {}
V2[str(map_so.id)]["x"] = Q2[X]
V2[str(map_so.id)]["y"] = Q2[Y]
V2[str(map_so.id)]["z"] = Q2[Z]
Q3 = axis_manip.init_scatt_wavevector_to_Q(k_i[0], k_i[1], k_f[0],
k_f[1],
cangles.getAzimuthal(2),
0.0, cangles.getPolar(2),
0.0)
V3[str(map_so.id)] = {}
V3[str(map_so.id)]["x"] = Q3[X]
V3[str(map_so.id)]["y"] = Q3[Y]
V3[str(map_so.id)]["z"] = Q3[Z]
Q4 = axis_manip.init_scatt_wavevector_to_Q(k_i[0], k_i[1], k_f[0],
k_f[1],
cangles.getAzimuthal(3),
0.0, cangles.getPolar(3),
0.0)
V4[str(map_so.id)] = {}
V4[str(map_so.id)]["x"] = Q4[X]
V4[str(map_so.id)]["y"] = Q4[Y]
V4[str(map_so.id)]["z"] = Q4[Z]
if t is not None:
t.getTime(msg="After calculating verticies ")
# Form the messages
if t is not None:
t.getTime(False)
jobstr = 'MR' + hlr_utils.create_binner_string(conf) + 'JH'
num_lines = len(CNT) * len_E
linestr = str(num_lines)
if output is not None:
outdir = os.path.dirname(output)
if outdir != '':
if outdir.rfind('.') != -1:
outdir = ""
else:
outdir = ""
value = str(som.attr_list["data-run_number"].getValue()).split('/')
topdir = os.path.join(outdir, value[0].strip() + "-mesh")
try:
os.mkdir(topdir)
except OSError:
pass
outtag = os.path.basename(output)
if outtag.rfind('.') == -1:
outtag = ""
else:
outtag = outtag.split('.')[0]
if outtag != "":
filehead = outtag + "_bmesh"
if make_fixed:
filehead1 = outtag + "_fgrid"
filehead2 = outtag + "_conf"
else:
filehead = "bmesh"
if make_fixed:
filehead1 = "fgrid"
filehead2 = "conf"
hfile = open(os.path.join(topdir, "%s.in" % filehead2), "w")
print >> hfile, jobstr
print >> hfile, linestr
hfile.close()
import utils
use_zero_supp = not conf.no_zero_supp
for k in xrange(len_E):
ofile = open(os.path.join(topdir, "%s%04d.in" % (filehead, k)), "w")
if make_fixed:
ofile1 = open(os.path.join(topdir, "%s%04d.in" % (filehead1, k)),
"w")
for pid in CNT:
if use_zero_supp:
write_value = not utils.compare(CNT[pid][k], 0.0) == 0
else:
write_value = True
if write_value:
result = []
result.append(str(k))
result.append(str(E_t[k]))
result.append(str(E_t[k + 1]))
result.append(str(CNT[pid][k]))
result.append(str(ERR2[pid][k]))
__get_coords(V1, pid, k, result)
__get_coords(V2, pid, k, result)
__get_coords(V3, pid, k, result)
__get_coords(V4, pid, k, result)
__get_coords(V1, pid, k + 1, result)
__get_coords(V2, pid, k + 1, result)
__get_coords(V3, pid, k + 1, result)
__get_coords(V4, pid, k + 1, result)
print >> ofile, " ".join(result)
if make_fixed:
result1 = []
result1.append(str(k))
result1.append(str(E_t[k]))
result1.append(str(E_t[k + 1]))
result1.append(str(CNT[pid][k]))
result1.append(str(ERR2[pid][k]))
result1.extend([str(x) for x in fixed_grid[pid]])
print >> ofile1, " ".join(result1)
ofile.close()
if make_fixed:
ofile1.close()
if t is not None:
t.getTime(msg="After creating messages ")
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 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 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 create_E_vs_Q_dgs(som, E_i, Q_final, **kwargs):
"""
This function starts with the rebinned energy transfer and turns this
into a 2D spectra with E and Q axes for DGS instruments.
@param som: The input object with initial IGS wavelength axis
@type som: C{SOM.SOM}
@param E_i: The initial energy for the given data.
@type E_i: C{tuple}
@param Q_final: The momentum transfer axis to rebin the data to
@type Q_final: C{nessi_list.NessiList}
@param kwargs: A list of keyword arguments that the function accepts:
@keyword corner_angles: The object that contains the corner geometry
information.
@type corner_angles: C{dict}
@keyword so_id: The identifier represents a number, string, tuple or other
object that describes the resulting C{SO}
@type so_id: C{int}, C{string}, C{tuple}, C{pixel ID}
@keyword y_label: The y axis label
@type y_label: C{string}
@keyword y_units: The y axis units
@type y_units: C{string}
@keyword x_labels: This is a list of names that sets the individual x axis
labels
@type x_labels: C{list} of C{string}s
@keyword x_units: This is a list of names that sets the individual x axis
units
@type x_units: C{list} of C{string}s
@keyword split: This flag causes the counts and the fractional area to
be written out into separate files.
@type split: C{boolean}
@keyword configure: This is the object containing the driver configuration.
@type configure: C{Configure}
@return: Object containing a 2D C{SO} with E and Q axes
@rtype: C{SOM.SOM}
"""
import array_manip
import axis_manip
import common_lib
import hlr_utils
import nessi_list
import SOM
import utils
# Check for keywords
corner_angles = kwargs["corner_angles"]
configure = kwargs.get("configure")
split = kwargs.get("split", False)
# Setup output object
so_dim = SOM.SO(2)
so_dim.axis[0].val = Q_final
so_dim.axis[1].val = som[0].axis[0].val # E_t
# Calculate total 2D array size
N_tot = (len(so_dim.axis[0].val) - 1) * (len(so_dim.axis[1].val) - 1)
# Create y and var_y lists from total 2D size
so_dim.y = nessi_list.NessiList(N_tot)
so_dim.var_y = nessi_list.NessiList(N_tot)
# Create area sum and errors for the area sum lists from total 2D size
area_sum = nessi_list.NessiList(N_tot)
area_sum_err2 = nessi_list.NessiList(N_tot)
# Convert initial energy to initial wavevector
l_i = common_lib.energy_to_wavelength(E_i)
k_i = common_lib.wavelength_to_scalar_k(l_i)
# Since all the data is rebinned to the same energy transfer axis, we can
# calculate the final energy axis once
E_t = som[0].axis[0].val
if som[0].axis[0].var is not None:
E_t_err2 = som[0].axis[0].var
else:
E_t_err2 = nessi_list.NessiList(len(E_t))
# Get the bin width arrays from E_t
(E_t_bw, E_t_bw_err2) = utils.calc_bin_widths(E_t)
E_f = array_manip.sub_ncerr(E_i[0], E_i[1], E_t, E_t_err2)
# Now we can get the final wavevector
l_f = axis_manip.energy_to_wavelength(E_f[0], E_f[1])
k_f = axis_manip.wavelength_to_scalar_k(l_f[0], l_f[1])
# Output position for Q
X = 0
# Iterate though the data
len_som = hlr_utils.get_length(som)
for i in xrange(len_som):
map_so = hlr_utils.get_map_so(som, None, i)
yval = hlr_utils.get_value(som, i, "SOM", "y")
yerr2 = hlr_utils.get_err2(som, i, "SOM", "y")
cangles = corner_angles[str(map_so.id)]
avg_theta1 = (cangles.getPolar(0) + cangles.getPolar(1)) / 2.0
avg_theta2 = (cangles.getPolar(2) + cangles.getPolar(3)) / 2.0
Q1 = axis_manip.init_scatt_wavevector_to_scalar_Q(
k_i[0], k_i[1], k_f[0][:-1], k_f[1][:-1], avg_theta2, 0.0)
Q2 = axis_manip.init_scatt_wavevector_to_scalar_Q(
k_i[0], k_i[1], k_f[0][:-1], k_f[1][:-1], avg_theta1, 0.0)
Q3 = axis_manip.init_scatt_wavevector_to_scalar_Q(
k_i[0], k_i[1], k_f[0][1:], k_f[1][1:], avg_theta1, 0.0)
Q4 = axis_manip.init_scatt_wavevector_to_scalar_Q(
k_i[0], k_i[1], k_f[0][1:], k_f[1][1:], avg_theta2, 0.0)
# Calculate the area of the E,Q polygons
(A, A_err2) = utils.calc_eq_jacobian_dgs(E_t[:-1], E_t[:-1], E_t[1:],
E_t[1:], Q1[X], Q2[X], Q3[X],
Q4[X])
# Apply the Jacobian: C/dE_t * dE_t / A(EQ) = C/A(EQ)
(jac_ratio,
jac_ratio_err2) = array_manip.div_ncerr(E_t_bw, E_t_bw_err2, A,
A_err2)
(counts, counts_err2) = array_manip.mult_ncerr(yval, yerr2, jac_ratio,
jac_ratio_err2)
try:
(y_2d, y_2d_err2, area_new,
bin_count) = axis_manip.rebin_2D_quad_to_rectlin(
Q1[X], E_t[:-1], Q2[X], E_t[:-1], Q3[X], E_t[1:], Q4[X],
E_t[1:], counts, counts_err2, so_dim.axis[0].val,
so_dim.axis[1].val)
del bin_count
except IndexError, e:
# Get the offending index from the error message
index = int(str(e).split()[1].split('index')[-1].strip('[]'))
print "Id:", map_so.id
print "Index:", index
print "Verticies: %f, %f, %f, %f, %f, %f, %f, %f" % (
Q1[X][index], E_t[:-1][index], Q2[X][index], E_t[:-1][index],
Q3[X][index], E_t[1:][index], Q4[X][index], E_t[1:][index])
raise IndexError(str(e))
# Add in together with previous results
(so_dim.y,
so_dim.var_y) = array_manip.add_ncerr(so_dim.y, so_dim.var_y, y_2d,
y_2d_err2)
(area_sum,
area_sum_err2) = array_manip.add_ncerr(area_sum, area_sum_err2,
area_new, area_sum_err2)
def create_Qvec_vs_E_dgs(som, E_i, conf, **kwargs):
"""
This function starts with the energy transfer axis from DGS reduction and
turns this into a 4D spectra with Qx, Qy, Qz and Et axes.
@param som: The input object with initial IGS wavelength axis
@type som: C{SOM.SOM}
@param E_i: The initial energy for the given data.
@type E_i: C{tuple}
@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 timer: Timing object so the function can perform timing estimates.
@type timer: C{sns_timer.DiffTime}
@keyword corner_angles: The object that contains the corner geometry
information.
@type corner_angles: C{dict}
@keyword make_fixed: A flag that turns on writing the fixed grid mesh
information to a file.
@type make_fixed: C{boolean}
@keyword output: The output filename and or directory.
@type output: C{string}
"""
import array_manip
import axis_manip
import common_lib
import hlr_utils
import os
# Check keywords
try:
t = kwargs["timer"]
except KeyError:
t = None
corner_angles = kwargs["corner_angles"]
try:
make_fixed = kwargs["make_fixed"]
except KeyError:
make_fixed = False
try:
output = kwargs["output"]
except KeyError:
output = None
# Convert initial energy to initial wavevector
l_i = common_lib.energy_to_wavelength(E_i)
k_i = common_lib.wavelength_to_scalar_k(l_i)
# Since all the data is rebinned to the same energy transfer axis, we can
# calculate the final energy axis once
E_t = som[0].axis[0].val
if som[0].axis[0].var is not None:
E_t_err2 = som[0].axis[0].var
else:
import nessi_list
E_t_err2 = nessi_list.NessiList(len(E_t))
E_f = array_manip.sub_ncerr(E_i[0], E_i[1], E_t, E_t_err2)
# Check for negative final energies which will cause problems with
# wavelength conversion due to square root
if E_f[0][-1] < 0:
E_f[0].reverse()
E_f[1].reverse()
index = 0
for E in E_f[0]:
if E >= 0:
break
index += 1
E_f[0].__delslice__(0, index)
E_f[1].__delslice__(0, index)
E_f[0].reverse()
E_f[1].reverse()
len_E = len(E_f[0]) - 1
# Now we can get the final wavevector
l_f = axis_manip.energy_to_wavelength(E_f[0], E_f[1])
k_f = axis_manip.wavelength_to_scalar_k(l_f[0], l_f[1])
# Grab the instrument from the som
inst = som.attr_list.instrument
if make_fixed:
import SOM
fixed_grid = {}
for key in corner_angles:
so_id = SOM.NeXusId.fromString(key).toTuple()
try:
pathlength = inst.get_secondary(so_id)[0]
points = []
for j in range(4):
points.extend(__calc_xyz(pathlength,
corner_angles[key].getPolar(j),
corner_angles[key].getAzimuthal(j)))
fixed_grid[key] = points
except KeyError:
# Pixel ID is not in instrument geometry
pass
CNT = {}
ERR2 = {}
V1 = {}
V2 = {}
V3 = {}
V4 = {}
# Output positions for Qx, Qy, Qz coordinates
X = 0
Y = 2
Z = 4
if t is not None:
t.getTime(False)
# Iterate though the data
len_som = hlr_utils.get_length(som)
for i in xrange(len_som):
map_so = hlr_utils.get_map_so(som, None, i)
yval = hlr_utils.get_value(som, i, "SOM", "y")
yerr2 = hlr_utils.get_err2(som, i, "SOM", "y")
CNT[str(map_so.id)] = yval
ERR2[str(map_so.id)] = yerr2
cangles = corner_angles[str(map_so.id)]
Q1 = axis_manip.init_scatt_wavevector_to_Q(k_i[0], k_i[1],
k_f[0], k_f[1],
cangles.getAzimuthal(0),
0.0,
cangles.getPolar(0),
0.0)
V1[str(map_so.id)] = {}
V1[str(map_so.id)]["x"] = Q1[X]
V1[str(map_so.id)]["y"] = Q1[Y]
V1[str(map_so.id)]["z"] = Q1[Z]
Q2 = axis_manip.init_scatt_wavevector_to_Q(k_i[0], k_i[1],
k_f[0], k_f[1],
cangles.getAzimuthal(1),
0.0,
cangles.getPolar(1),
0.0)
V2[str(map_so.id)] = {}
V2[str(map_so.id)]["x"] = Q2[X]
V2[str(map_so.id)]["y"] = Q2[Y]
V2[str(map_so.id)]["z"] = Q2[Z]
Q3 = axis_manip.init_scatt_wavevector_to_Q(k_i[0], k_i[1],
k_f[0], k_f[1],
cangles.getAzimuthal(2),
0.0,
cangles.getPolar(2),
0.0)
V3[str(map_so.id)] = {}
V3[str(map_so.id)]["x"] = Q3[X]
V3[str(map_so.id)]["y"] = Q3[Y]
V3[str(map_so.id)]["z"] = Q3[Z]
Q4 = axis_manip.init_scatt_wavevector_to_Q(k_i[0], k_i[1],
k_f[0], k_f[1],
cangles.getAzimuthal(3),
0.0,
cangles.getPolar(3),
0.0)
V4[str(map_so.id)] = {}
V4[str(map_so.id)]["x"] = Q4[X]
V4[str(map_so.id)]["y"] = Q4[Y]
V4[str(map_so.id)]["z"] = Q4[Z]
if t is not None:
t.getTime(msg="After calculating verticies ")
# Form the messages
if t is not None:
t.getTime(False)
jobstr = 'MR' + hlr_utils.create_binner_string(conf) + 'JH'
num_lines = len(CNT) * len_E
linestr = str(num_lines)
if output is not None:
outdir = os.path.dirname(output)
if outdir != '':
if outdir.rfind('.') != -1:
outdir = ""
else:
outdir = ""
value = str(som.attr_list["data-run_number"].getValue()).split('/')
topdir = os.path.join(outdir, value[0].strip() + "-mesh")
try:
os.mkdir(topdir)
except OSError:
pass
outtag = os.path.basename(output)
if outtag.rfind('.') == -1:
outtag = ""
else:
outtag = outtag.split('.')[0]
if outtag != "":
filehead = outtag + "_bmesh"
if make_fixed:
filehead1 = outtag + "_fgrid"
filehead2 = outtag + "_conf"
else:
filehead = "bmesh"
if make_fixed:
filehead1 = "fgrid"
filehead2 = "conf"
hfile = open(os.path.join(topdir, "%s.in" % filehead2), "w")
print >> hfile, jobstr
print >> hfile, linestr
hfile.close()
import utils
use_zero_supp = not conf.no_zero_supp
for k in xrange(len_E):
ofile = open(os.path.join(topdir, "%s%04d.in" % (filehead, k)), "w")
if make_fixed:
ofile1 = open(os.path.join(topdir, "%s%04d.in" % (filehead1, k)),
"w")
for pid in CNT:
if use_zero_supp:
write_value = not utils.compare(CNT[pid][k], 0.0) == 0
else:
write_value = True
if write_value:
result = []
result.append(str(k))
result.append(str(E_t[k]))
result.append(str(E_t[k+1]))
result.append(str(CNT[pid][k]))
result.append(str(ERR2[pid][k]))
__get_coords(V1, pid, k, result)
__get_coords(V2, pid, k, result)
__get_coords(V3, pid, k, result)
__get_coords(V4, pid, k, result)
__get_coords(V1, pid, k+1, result)
__get_coords(V2, pid, k+1, result)
__get_coords(V3, pid, k+1, result)
__get_coords(V4, pid, k+1, result)
print >> ofile, " ".join(result)
if make_fixed:
result1 = []
result1.append(str(k))
result1.append(str(E_t[k]))
result1.append(str(E_t[k+1]))
result1.append(str(CNT[pid][k]))
result1.append(str(ERR2[pid][k]))
result1.extend([str(x) for x in fixed_grid[pid]])
print >> ofile1, " ".join(result1)
ofile.close()
if make_fixed:
ofile1.close()
if t is not None:
t.getTime(msg="After creating messages ")
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 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
