def tof_to_initial_wavelength_igs(obj, **kwargs):
"""
This function converts a primary axis of a C{SOM} or C{SO} from
time-of-flight to initial_wavelength_igs. The time-of-flight axis for a
C{SOM} must be in units of I{microseconds}. The primary axis of a C{SO} is
assumed to be in units of I{microseconds}. A C{tuple} of C{(tof, tof_err2)}
(assumed to be in units of I{microseconds}) can be converted to
C{(initial_wavelength_igs, initial_wavelength_igs_err2)}.
@param obj: Object to be converted
@type obj: C{SOM.SOM}, C{SOM.SO} or C{tuple}
@param kwargs: A list of keyword arguments that the function accepts:
@keyword lambda_f:The final wavelength and its associated error^2
@type lambda_f: C{tuple}
@keyword time_zero: The time zero offset and its associated error^2
@type time_zero: C{tuple}
@keyword dist_source_sample: The source to sample distance information and
its associated error^2
@type dist_source_sample: C{tuple} or C{list} of C{tuple}s
@keyword dist_sample_detector: The sample to detector distance information
and its associated error^2
@type dist_sample_detector: C{tuple} or C{list} of C{tuple}s
@keyword run_filter: This determines if the filter on the negative
wavelengths is run. The default setting is True.
@type run_filter: C{boolean}
@keyword units: The expected units for this function. The default for this
function is I{microseconds}
@type units: C{string}
@return: Object with a primary axis in time-of-flight converted to
initial_wavelength_igs
@rtype: C{SOM.SOM}, C{SOM.SO} or C{tuple}
@raise TypeError: The incoming object is not a type the function recognizes
@raise RuntimeError: The C{SOM} x-axis units are not I{microseconds}
"""
# import the helper functions
import hlr_utils
# set up for working through data
(result, res_descr) = hlr_utils.empty_result(obj)
o_descr = hlr_utils.get_descr(obj)
# Setup keyword arguments
try:
lambda_f = kwargs["lambda_f"]
except KeyError:
lambda_f = None
try:
time_zero = kwargs["time_zero"]
except KeyError:
time_zero = None
try:
dist_source_sample = kwargs["dist_source_sample"]
except KeyError:
dist_source_sample = None
try:
dist_sample_detector = kwargs["dist_sample_detector"]
except KeyError:
dist_sample_detector = None
try:
units = kwargs["units"]
except KeyError:
units = "microseconds"
try:
run_filter = kwargs["run_filter"]
except KeyError:
run_filter = True
# Primary axis for transformation. If a SO is passed, the function, will
# assume the axis for transformation is at the 0 position
if o_descr == "SOM":
axis = hlr_utils.one_d_units(obj, units)
else:
axis = 0
result = hlr_utils.copy_som_attr(result, res_descr, obj, o_descr)
if res_descr == "SOM":
result = hlr_utils.force_units(result, "Angstroms", axis)
result.setAxisLabel(axis, "wavelength")
result.setYUnits("Counts/A")
result.setYLabel("Intensity")
else:
pass
# Where to get instrument information
if dist_source_sample is None or dist_sample_detector is None:
if o_descr == "SOM":
try:
obj.attr_list.instrument.get_primary()
inst = obj.attr_list.instrument
except RuntimeError:
raise RuntimeError("A detector was not provided!")
else:
if dist_source_sample is None and dist_sample_detector is None:
raise RuntimeError("If a SOM is not passed, the "\
+"source-sample and sample-detector "\
+"distances must be provided.")
elif dist_source_sample is None:
raise RuntimeError("If a SOM is not passed, the "\
+"source-sample distance must be provided.")
elif dist_sample_detector is None:
raise RuntimeError("If a SOM is not passed, the "\
+"sample-detector distance must be "\
+"provided.")
else:
raise RuntimeError("If you get here, see Steve Miller for "\
+"your mug.")
else:
pass
if lambda_f is not None:
l_descr = hlr_utils.get_descr(lambda_f)
else:
if o_descr == "SOM":
try:
som_l_f = obj.attr_list["Wavelength_final"]
except KeyError:
raise RuntimeError("Please provide a final wavelength "\
+"parameter either via the function call "\
+"or the SOM")
else:
raise RuntimeError("You need to provide a final wavelength")
if time_zero is not None:
t_descr = hlr_utils.get_descr(time_zero)
else:
if o_descr == "SOM":
try:
t_0 = obj.attr_list["Time_zero"][0]
t_0_err2 = obj.attr_list["Time_zero"][1]
except KeyError:
raise RuntimeError("Please provide a time-zero "\
+"parameter either via the function call "\
+"or the SOM")
else:
t_0 = 0.0
t_0_err2 = 0.0
if dist_source_sample is not None:
ls_descr = hlr_utils.get_descr(dist_source_sample)
# Do nothing, go on
else:
pass
if dist_sample_detector is not None:
ld_descr = hlr_utils.get_descr(dist_sample_detector)
# Do nothing, go on
else:
pass
# iterate through the values
import axis_manip
for i in xrange(hlr_utils.get_length(obj)):
val = hlr_utils.get_value(obj, i, o_descr, "x", axis)
err2 = hlr_utils.get_err2(obj, i, o_descr, "x", axis)
map_so = hlr_utils.get_map_so(obj, None, i)
if dist_source_sample is None:
(L_s, L_s_err2) = hlr_utils.get_parameter("primary", map_so, inst)
else:
L_s = hlr_utils.get_value(dist_source_sample, i, ls_descr)
L_s_err2 = hlr_utils.get_err2(dist_source_sample, i, ls_descr)
if dist_sample_detector is None:
(L_d, L_d_err2) = hlr_utils.get_parameter("secondary", map_so,
inst)
else:
L_d = hlr_utils.get_value(dist_sample_detector, i, ld_descr)
L_d_err2 = hlr_utils.get_err2(dist_sample_detector, i, ld_descr)
if lambda_f is not None:
l_f = hlr_utils.get_value(lambda_f, i, l_descr)
l_f_err2 = hlr_utils.get_err2(lambda_f, i, l_descr)
else:
l_f_tuple = hlr_utils.get_special(som_l_f, map_so)
l_f = l_f_tuple[0]
l_f_err2 = l_f_tuple[1]
if time_zero is not None:
t_0 = hlr_utils.get_value(time_zero, i, t_descr)
t_0_err2 = hlr_utils.get_err2(time_zero, i, t_descr)
else:
pass
value = axis_manip.tof_to_initial_wavelength_igs(
val, err2, l_f, l_f_err2, t_0, t_0_err2, L_s, L_s_err2, L_d,
L_d_err2)
# Remove all wavelengths < 0
if run_filter:
index = 0
for val in value[0]:
if val >= 0:
break
index += 1
value[0].__delslice__(0, index)
value[1].__delslice__(0, index)
map_so.y.__delslice__(0, index)
map_so.var_y.__delslice__(0, index)
else:
pass
hlr_utils.result_insert(result, res_descr, value, map_so, "x", axis)
return result
def wavelength_to_energy(obj, **kwargs):
"""
This function converts a primary axis of a C{SOM} or C{SO} from wavelength
to energy. The wavelength axis for a C{SOM} must be in units of
I{Angstroms}. The primary axis of a C{SO} is assumed to be in units of
I{Angstroms}. A C{tuple} of C{(wavelength, wavelength_err2)} (assumed to
be in units of I{Angstroms}) can be converted to C{(energy, energy_err)}.
@param obj: Object to be converted
@type obj: C{SOM.SOM}, C{SOM.SO} or C{tuple}
@param kwargs: A list of keyword arguments that the function accepts:
@keyword offset: Energy offset information
@type offset: C{tuple} or C{list} of C{tuple}s
@keyword lojac: A flag that allows one to turn off the calculation of the
linear-order Jacobian. The default action is True for
histogram data.
@type lojac: C{boolean}
@keyword units: The expected units for this function. The default for this
function is I{Angstroms}
@type units: C{string}
@return: Object with a primary axis in wavelength converted to energy
@rtype: C{SOM.SOM}, C{SOM.SO} or C{tuple}
@raise TypeError: The incoming object is not a type the function recognizes
@raise RuntimeError: The C{SOM} x-axis units are not I{Angstroms}
"""
# import the helper functions
import hlr_utils
# set up for working through data
(result, res_descr) = hlr_utils.empty_result(obj)
o_descr = hlr_utils.get_descr(obj)
if o_descr == "list":
raise TypeError("Do not know how to handle given type: %s" % \
o_descr)
else:
pass
# Setup keyword arguments
try:
units = kwargs["units"]
except KeyError:
units = "Angstroms"
try:
offset = kwargs["offset"]
except KeyError:
offset = None
try:
lojac = kwargs["lojac"]
except KeyError:
lojac = hlr_utils.check_lojac(obj)
# Primary axis for transformation. If a SO is passed, the function, will
# assume the axis for transformation is at the 0 position
if o_descr == "SOM":
axis = hlr_utils.one_d_units(obj, units)
else:
axis = 0
result = hlr_utils.copy_som_attr(result, res_descr, obj, o_descr)
if res_descr == "SOM":
result = hlr_utils.force_units(result, "meV", axis)
result.setAxisLabel(axis, "energy")
result.setYUnits("Counts/meV")
result.setYLabel("Intensity")
else:
pass
# iterate through the values
import array_manip
import axis_manip
if lojac:
import utils
for i in xrange(hlr_utils.get_length(obj)):
val = hlr_utils.get_value(obj, i, o_descr, "x", axis)
err2 = hlr_utils.get_err2(obj, i, o_descr, "x", axis)
map_so = hlr_utils.get_map_so(obj, None, i)
value = axis_manip.wavelength_to_energy(val, err2)
if lojac:
y_val = hlr_utils.get_value(obj, i, o_descr, "y")
y_err2 = hlr_utils.get_err2(obj, i, o_descr, "y")
counts = utils.linear_order_jacobian(val, value[0], y_val, y_err2)
else:
pass
if o_descr != "number":
value1 = axis_manip.reverse_array_cp(value[0])
value2 = axis_manip.reverse_array_cp(value[1])
rev_value = (value1, value2)
else:
rev_value = value
if map_so is not None:
if not lojac:
map_so.y = axis_manip.reverse_array_cp(map_so.y)
map_so.var_y = axis_manip.reverse_array_cp(map_so.var_y)
else:
map_so.y = axis_manip.reverse_array_cp(counts[0])
map_so.var_y = axis_manip.reverse_array_cp(counts[1])
else:
pass
if offset is not None:
info = hlr_utils.get_special(offset, map_so)
try:
rev_value = array_manip.add_ncerr(rev_value[0], rev_value[1],
info[0], info[1])
except TypeError:
# Have to do this since add_ncerr does not support
# scalar-scalar operations
value1 = rev_value[0] + info[0]
value2 = rev_value[1] + info[1]
rev_value = (value1, value2)
else:
pass
hlr_utils.result_insert(result, res_descr, rev_value, map_so, "x",
axis)
return result
def tof_to_ref_scalar_Q(obj, **kwargs):
"""
This function converts a primary axis of a C{SOM} or C{SO} from
time-of-flight to reflectometer scalar Q. This means that a single angle
and a single flightpath is used. The time-of-flight axis for a C{SOM} must
be in units of I{microseconds}. The primary axis of a C{SO} is assumed to
be in units of I{microseconds}. A C{tuple} of C{(time-of-flight,
time-of-flight_err2)} (assumed to be in units of I{microseconds}) can be
converted to C{(scalar_Q, scalar_Q_err2)}.
@param obj: Object to be converted
@type obj: C{SOM.SOM}, C{SOM.SO} or C{tuple}
@param kwargs: A list of keyword arguments that the function accepts:
@keyword polar: The polar angle and its associated error^2
@type polar: C{tuple}
@keyword pathlength: The pathlength and its associated error^2
@type pathlength: C{tuple}
@keyword angle_offset: A constant offset for the polar angle and its
associated error^2. The units of the offset should
be in radians.
@type angle_offset: C{tuple}
@keyword lojac: A flag that allows one to turn off the calculation of the
linear-order Jacobian. The default action is True for
histogram data.
@type lojac: C{boolean}
@keyword units: The expected units for this function. The default for this
function is I{microseconds}.
@type units: C{string}
@keyword configure: This is the object containing the driver configuration.
This will signal the function to write out the counts
and fractional area to files.
@type configure: C{Configure}
@return: Object with a primary axis in time-of-flight converted to
reflectometer scalar Q
@rtype: C{SOM.SOM}, C{SOM.SO} or C{tuple}
@raise TypeError: The incoming object is not a type the function recognizes
@raise RuntimeError: A C{SOM} is not passed and no polar angle is provided
@raise RuntimeError: The C{SOM} x-axis units are not I{microseconds}
"""
# import the helper functions
import hlr_utils
# set up for working through data
(result, res_descr) = hlr_utils.empty_result(obj)
o_descr = hlr_utils.get_descr(obj)
if o_descr == "list":
raise TypeError("Do not know how to handle given type: %s" % \
o_descr)
else:
pass
# Setup keyword arguments
polar = kwargs.get("polar")
pathlength = kwargs.get("pathlength")
units = kwargs.get("units", "microseconds")
lojac = kwargs.get("lojac", hlr_utils.check_lojac(obj))
angle_offset = kwargs.get("angle_offset")
config = kwargs.get("configure")
if config is None:
beamdiv_corr = False
else:
beamdiv_corr = config.beamdiv_corr
# Primary axis for transformation. If a SO is passed, the function, will
# assume the axis for transformation is at the 0 position
if o_descr == "SOM":
axis = hlr_utils.one_d_units(obj, units)
else:
axis = 0
result = hlr_utils.copy_som_attr(result, res_descr, obj, o_descr)
if res_descr == "SOM":
result = hlr_utils.force_units(result, "1/Angstroms", axis)
result.setAxisLabel(axis, "scalar wavevector transfer")
result.setYUnits("Counts/A-1")
result.setYLabel("Intensity")
else:
pass
if pathlength is None or polar is None:
if o_descr == "SOM":
try:
obj.attr_list.instrument.get_primary()
inst = obj.attr_list.instrument
except RuntimeError:
raise RuntimeError("A detector was not provided")
else:
if pathlength is None and polar is None:
raise RuntimeError("If no SOM is provided, then pathlength "\
+"and polar angle information must be "\
+"provided")
elif pathlength is None:
raise RuntimeError("If no SOM is provided, then pathlength "\
+"information must be provided")
elif polar is None:
raise RuntimeError("If no SOM is provided, then polar angle "\
+"information must be provided")
else:
raise RuntimeError("If you get here, see Steve Miller for "\
+"your mug.")
else:
pass
if pathlength is None:
(pl, pl_err2) = obj.attr_list.instrument.get_total_path(obj[0].id,
det_secondary=True)
else:
(pl, pl_err2) = pathlength
if polar is None:
angle = hlr_utils.get_special(obj.attr_list["data-theta"], obj[0])[0]
angle_err2 = 0.0
else:
(angle, angle_err2) = polar
if angle_offset is not None:
angle += angle_offset[0]
angle_err2 += angle_offset[1]
# Need to multiply angle by 2.0 in order to make it be Theta to
# underlying conversion function
angle *= 2.0
angle_err2 *= 4.0
# iterate through the values
import axis_manip
if lojac:
import utils
if beamdiv_corr:
import dr_lib
for i in xrange(hlr_utils.get_length(obj)):
skip_pixel = False
val = hlr_utils.get_value(obj, i, o_descr, "x", axis)
err2 = hlr_utils.get_err2(obj, i, o_descr, "x", axis)
map_so = hlr_utils.get_map_so(obj, None, i)
if beamdiv_corr:
dangle = dr_lib.ref_beamdiv_correct(obj.attr_list, map_so.id,
config.det_spat_res,
config.center_pix)
# We subtract due to the inversion of the z coordinates from the
# mirror reflection of the beam at the sample.
if dangle is not None:
pangle = angle - (2.0 * dangle)
else:
pangle = angle
skip_pixel = True
else:
pangle = angle
value = axis_manip.tof_to_scalar_Q(val, err2, pl, pl_err2, pangle,
angle_err2)
if lojac:
y_val = hlr_utils.get_value(obj, i, o_descr, "y")
y_err2 = hlr_utils.get_err2(obj, i, o_descr, "y")
counts = utils.linear_order_jacobian(val, value[0],
y_val, y_err2)
else:
pass
if o_descr != "number":
value1 = axis_manip.reverse_array_cp(value[0])
value2 = axis_manip.reverse_array_cp(value[1])
rev_value = (value1, value2)
else:
rev_value = value
if map_so is not None:
if not lojac:
map_so.y = axis_manip.reverse_array_cp(map_so.y)
map_so.var_y = axis_manip.reverse_array_cp(map_so.var_y)
else:
map_so.y = axis_manip.reverse_array_cp(counts[0])
map_so.var_y = axis_manip.reverse_array_cp(counts[1])
else:
pass
if not skip_pixel:
hlr_utils.result_insert(result, res_descr, rev_value, map_so, "x",
axis)
return result
def initial_wavelength_igs_lin_time_zero_to_tof(obj, **kwargs):
"""
This function converts a primary axis of a C{SOM} or C{SO} from
initial_wavelength_igs_lin_time_zero to time-of-flight. The
initial_wavelength_igs_lin_time_zero axis for a C{SOM} must be in units of
I{Angstroms}. The primary axis of a C{SO} is assumed to be in units of
I{Angstroms}. A C{tuple} of C{(initial_wavelength_igs_lin_time_zero,
initial_wavelength_igs_lin_time_zero_err2)} (assumed to be in units of
I{Angstroms}) can be converted to C{(tof, tof_err2)}.
@param obj: Object to be converted
@type obj: C{SOM.SOM}, C{SOM.SO} or C{tuple}
@param kwargs: A list of keyword arguments that the function accepts:
@keyword lambda_f:The final wavelength and its associated error^2
@type lambda_f: C{tuple}
@keyword time_zero_slope: The time zero slope and its associated error^2
@type time_zero_slope: C{tuple}
@keyword time_zero_offset: The time zero offset and its associated error^2
@type time_zero_offset: C{tuple}
@keyword dist_source_sample: The source to sample distance information and
its associated error^2
@type dist_source_sample: C{tuple} or C{list} of C{tuple}s
@keyword dist_sample_detector: The sample to detector distance information
and its associated error^2
@type dist_sample_detector: C{tuple} or C{list} of C{tuple}s
@keyword lojac: A flag that allows one to turn off the calculation of the
linear-order Jacobian. The default action is True for
histogram data.
@type lojac: C{boolean}
@keyword units: The expected units for this function. The default for this
function is I{Angstroms}
@type units: C{string}
@return: Object with a primary axis in initial_wavelength_igs converted to
time-of-flight
@rtype: C{SOM.SOM}, C{SOM.SO} or C{tuple}
@raise TypeError: The incoming object is not a type the function recognizes
@raise RuntimeError: The C{SOM} x-axis units are not I{Angstroms}
"""
# import the helper functions
import hlr_utils
# set up for working through data
(result, res_descr) = hlr_utils.empty_result(obj)
o_descr = hlr_utils.get_descr(obj)
# Setup keyword arguments
try:
lambda_f = kwargs["lambda_f"]
except KeyError:
lambda_f = None
try:
time_zero_slope = kwargs["time_zero_slope"]
except KeyError:
time_zero_slope = None
# Current constants for Time Zero Slope
TIME_ZERO_SLOPE = (float(0.0), float(0.0))
try:
time_zero_offset = kwargs["time_zero_offset"]
except KeyError:
time_zero_offset = None
# Current constants for Time Zero Offset
TIME_ZERO_OFFSET = (float(0.0), float(0.0))
try:
dist_source_sample = kwargs["dist_source_sample"]
except KeyError:
dist_source_sample = None
try:
dist_sample_detector = kwargs["dist_sample_detector"]
except KeyError:
dist_sample_detector = None
try:
lojac = kwargs["lojac"]
except KeyError:
lojac = hlr_utils.check_lojac(obj)
try:
units = kwargs["units"]
except KeyError:
units = "Angstroms"
# Primary axis for transformation. If a SO is passed, the function, will
# assume the axis for transformation is at the 0 position
if o_descr == "SOM":
axis = hlr_utils.one_d_units(obj, units)
else:
axis = 0
result = hlr_utils.copy_som_attr(result, res_descr, obj, o_descr)
if res_descr == "SOM":
result = hlr_utils.force_units(result, "Microseconds", axis)
result.setAxisLabel(axis, "time-of-flight")
result.setYUnits("Counts/uS")
result.setYLabel("Intensity")
else:
pass
# Where to get instrument information
if dist_source_sample is None or dist_sample_detector is None:
if o_descr == "SOM":
try:
obj.attr_list.instrument.get_primary()
inst = obj.attr_list.instrument
except RuntimeError:
raise RuntimeError("A detector was not provided!")
else:
if dist_source_sample is None and dist_sample_detector is None:
raise RuntimeError("If a SOM is not passed, the "\
+"source-sample and sample-detector "\
+"distances must be provided.")
elif dist_source_sample is None:
raise RuntimeError("If a SOM is not passed, the "\
+"source-sample distance must be provided.")
elif dist_sample_detector is None:
raise RuntimeError("If a SOM is not passed, the "\
+"sample-detector distance must be "\
+"provided.")
else:
raise RuntimeError("If you get here, see Steve Miller for "\
+"your mug.")
else:
pass
if lambda_f is not None:
l_descr = hlr_utils.get_descr(lambda_f)
else:
if o_descr == "SOM":
try:
som_l_f = obj.attr_list["Wavelength_final"]
except KeyError:
raise RuntimeError("Please provide a final wavelength "\
+"parameter either via the function call "\
+"or the SOM")
else:
raise RuntimeError("You need to provide a final wavelength")
if time_zero_slope is not None:
t_0_slope_descr = hlr_utils.get_descr(time_zero_slope)
else:
if o_descr == "SOM":
try:
t_0_slope = obj.attr_list["Time_zero_slope"][0]
t_0_slope_err2 = obj.attr_list["Time_zero_slope"][1]
except KeyError:
t_0_slope = TIME_ZERO_SLOPE[0]
t_0_slope_err2 = TIME_ZERO_SLOPE[1]
else:
t_0_slope = TIME_ZERO_SLOPE[0]
t_0_slope_err2 = TIME_ZERO_SLOPE[1]
if time_zero_offset is not None:
t_0_offset_descr = hlr_utils.get_descr(time_zero_offset)
else:
if o_descr == "SOM":
try:
t_0_offset = obj.attr_list["Time_zero_offset"][0]
t_0_offset_err2 = obj.attr_list["Time_zero_offset"][1]
except KeyError:
t_0_offset = TIME_ZERO_OFFSET[0]
t_0_offset_err2 = TIME_ZERO_OFFSET[1]
else:
t_0_offset = TIME_ZERO_OFFSET[0]
t_0_offset_err2 = TIME_ZERO_OFFSET[1]
if dist_source_sample is not None:
ls_descr = hlr_utils.get_descr(dist_source_sample)
# Do nothing, go on
else:
pass
if dist_sample_detector is not None:
ld_descr = hlr_utils.get_descr(dist_sample_detector)
# Do nothing, go on
else:
pass
# iterate through the values
len_obj = hlr_utils.get_length(obj)
MNEUT_OVER_H = 1.0 / 0.003956034
MNEUT_OVER_H2 = MNEUT_OVER_H * MNEUT_OVER_H
for i in xrange(len_obj):
val = hlr_utils.get_value(obj, i, o_descr, "x", axis)
err2 = hlr_utils.get_err2(obj, i, o_descr, "x", axis)
map_so = hlr_utils.get_map_so(obj, None, i)
if dist_source_sample is None:
(L_s, L_s_err2) = hlr_utils.get_parameter("primary", map_so, inst)
else:
L_s = hlr_utils.get_value(dist_source_sample, i, ls_descr)
L_s_err2 = hlr_utils.get_err2(dist_source_sample, i, ls_descr)
if dist_sample_detector is None:
(L_d, L_d_err2) = hlr_utils.get_parameter("secondary", map_so,
inst)
else:
L_d = hlr_utils.get_value(dist_sample_detector, i, ld_descr)
L_d_err2 = hlr_utils.get_err2(dist_sample_detector, i, ld_descr)
if lambda_f is not None:
l_f = hlr_utils.get_value(lambda_f, i, l_descr)
l_f_err2 = hlr_utils.get_err2(lambda_f, i, l_descr)
else:
l_f_tuple = hlr_utils.get_special(som_l_f, map_so)
l_f = l_f_tuple[0]
l_f_err2 = l_f_tuple[1]
if time_zero_slope is not None:
t_0_slope = hlr_utils.get_value(time_zero_slope, i,
t_0_slope_descr)
t_0_slope_err2 = hlr_utils.get_err2(time_zero_slope, i,
t_0_slope_descr)
else:
pass
if time_zero_offset is not None:
t_0_offset = hlr_utils.get_value(time_zero_offset, i,
t_0_offset_descr)
t_0_offset_err2 = hlr_utils.get_err2(time_zero_offset, i,
t_0_offset_descr)
else:
pass
# Going to violate rules since the current usage is with a single
# number. When an SCL equivalent function arises, this code can be
# fixed.
front_const = MNEUT_OVER_H * L_s + t_0_slope
term2 = MNEUT_OVER_H * l_f * L_d
tof = (front_const * val) + term2 + t_0_offset
front_const2 = front_const * front_const
eterm1 = l_f * l_f * L_d_err2
eterm2 = L_d * L_d * l_f_err2
eterm3 = MNEUT_OVER_H2 * L_s_err2
tof_err2 = (front_const2 * err2) + (val * val) * \
(eterm3 + t_0_slope_err2) + (MNEUT_OVER_H2 * \
(eterm1 + eterm2)) + \
t_0_offset_err2
hlr_utils.result_insert(result, res_descr, (tof, tof_err2), None,
"all")
return result
def tof_to_initial_wavelength_igs_lin_time_zero(obj, **kwargs):
"""
This function converts a primary axis of a C{SOM} or C{SO} from
time-of-flight to initial_wavelength_igs_lin_time_zero. The time-of-flight
axis for a C{SOM} must be in units of I{microseconds}. The primary axis of
a C{SO} is assumed to be in units of I{microseconds}. A C{tuple} of
C{(tof, tof_err2)} (assumed to be in units of I{microseconds}) can be
converted to C{(initial_wavelength_igs, initial_wavelength_igs_err2)}.
@param obj: Object to be converted
@type obj: C{SOM.SOM}, C{SOM.SO} or C{tuple}
@param kwargs: A list of keyword arguments that the function accepts:
@keyword lambda_f:The final wavelength and its associated error^2
@type lambda_f: C{tuple}
@keyword time_zero_slope: The time zero slope and its associated error^2
@type time_zero_slope: C{tuple}
@keyword time_zero_offset: The time zero offset and its associated error^2
@type time_zero_offset: C{tuple}
@keyword dist_source_sample: The source to sample distance information and
its associated error^2
@type dist_source_sample: C{tuple} or C{list} of C{tuple}s
@keyword dist_sample_detector: The sample to detector distance information
and its associated error^2
@type dist_sample_detector: C{tuple} or C{list} of C{tuple}s
@keyword run_filter: This determines if the filter on the negative
wavelengths is run. The default setting is True.
@type run_filter: C{boolean}
@keyword lojac: A flag that allows one to turn off the calculation of the
linear-order Jacobian. The default action is True for
histogram data.
@type lojac: C{boolean}
@keyword units: The expected units for this function. The default for this
function is I{microseconds}
@type units: C{string}
@return: Object with a primary axis in time-of-flight converted to
initial_wavelength_igs
@rtype: C{SOM.SOM}, C{SOM.SO} or C{tuple}
@raise TypeError: The incoming object is not a type the function recognizes
@raise RuntimeError: The C{SOM} x-axis units are not I{microseconds}
"""
# import the helper functions
import hlr_utils
# set up for working through data
(result, res_descr) = hlr_utils.empty_result(obj)
o_descr = hlr_utils.get_descr(obj)
# Setup keyword arguments
try:
lambda_f = kwargs["lambda_f"]
except KeyError:
lambda_f = None
try:
time_zero_slope = kwargs["time_zero_slope"]
except KeyError:
time_zero_slope = None
# Current constants for Time Zero Slope
TIME_ZERO_SLOPE = (float(0.0), float(0.0))
try:
time_zero_offset = kwargs["time_zero_offset"]
except KeyError:
time_zero_offset = None
# Current constants for Time Zero Offset
TIME_ZERO_OFFSET = (float(0.0), float(0.0))
try:
dist_source_sample = kwargs["dist_source_sample"]
except KeyError:
dist_source_sample = None
try:
dist_sample_detector = kwargs["dist_sample_detector"]
except KeyError:
dist_sample_detector = None
try:
lojac = kwargs["lojac"]
except KeyError:
lojac = hlr_utils.check_lojac(obj)
try:
units = kwargs["units"]
except KeyError:
units = "microseconds"
try:
run_filter = kwargs["run_filter"]
except KeyError:
run_filter = True
try:
iobj = kwargs["iobj"]
except KeyError:
iobj = None
# Primary axis for transformation. If a SO is passed, the function, will
# assume the axis for transformation is at the 0 position
if o_descr == "SOM":
axis = hlr_utils.one_d_units(obj, units)
else:
axis = 0
result = hlr_utils.copy_som_attr(result, res_descr, obj, o_descr)
if res_descr == "SOM":
result = hlr_utils.force_units(result, "Angstroms", axis)
result.setAxisLabel(axis, "wavelength")
result.setYUnits("Counts/A")
result.setYLabel("Intensity")
else:
pass
# Where to get instrument information
if dist_source_sample is None or dist_sample_detector is None:
if o_descr == "SOM":
try:
obj.attr_list.instrument.get_primary()
inst = obj.attr_list.instrument
mobj = obj
except RuntimeError:
raise RuntimeError("A detector was not provided!")
else:
if iobj is None:
if dist_source_sample is None and dist_sample_detector is None:
raise RuntimeError("If a SOM is not passed, the "\
+"source-sample and sample-detector "\
+"distances must be provided.")
elif dist_source_sample is None:
raise RuntimeError("If a SOM is not passed, the "\
+"source-sample distance must be "\
+"provided.")
elif dist_sample_detector is None:
raise RuntimeError("If a SOM is not passed, the "\
+"sample-detector distance must be "\
+"provided.")
else:
raise RuntimeError("If you get here, see Steve Miller "\
+"for your mug.")
else:
inst = iobj.attr_list.instrument
mobj = iobj
else:
mobj = obj
if lambda_f is not None:
l_descr = hlr_utils.get_descr(lambda_f)
else:
if o_descr == "SOM":
try:
som_l_f = obj.attr_list["Wavelength_final"]
except KeyError:
raise RuntimeError("Please provide a final wavelength "\
+"parameter either via the function call "\
+"or the SOM")
else:
if iobj is None:
raise RuntimeError("You need to provide a final wavelength")
else:
som_l_f = iobj.attr_list["Wavelength_final"]
if time_zero_slope is not None:
t_0_slope_descr = hlr_utils.get_descr(time_zero_slope)
else:
if o_descr == "SOM":
try:
t_0_slope = obj.attr_list["Time_zero_slope"][0]
t_0_slope_err2 = obj.attr_list["Time_zero_slope"][1]
except KeyError:
t_0_slope = TIME_ZERO_SLOPE[0]
t_0_slope_err2 = TIME_ZERO_SLOPE[1]
else:
t_0_slope = TIME_ZERO_SLOPE[0]
t_0_slope_err2 = TIME_ZERO_SLOPE[1]
if time_zero_offset is not None:
t_0_offset_descr = hlr_utils.get_descr(time_zero_offset)
else:
if o_descr == "SOM":
try:
t_0_offset = obj.attr_list["Time_zero_offset"][0]
t_0_offset_err2 = obj.attr_list["Time_zero_offset"][1]
except KeyError:
t_0_offset = TIME_ZERO_OFFSET[0]
t_0_offset_err2 = TIME_ZERO_OFFSET[1]
else:
t_0_offset = TIME_ZERO_OFFSET[0]
t_0_offset_err2 = TIME_ZERO_OFFSET[1]
if dist_source_sample is not None:
ls_descr = hlr_utils.get_descr(dist_source_sample)
# Do nothing, go on
else:
pass
if dist_sample_detector is not None:
ld_descr = hlr_utils.get_descr(dist_sample_detector)
# Do nothing, go on
else:
pass
# iterate through the values
import axis_manip
if lojac:
import utils
for i in xrange(hlr_utils.get_length(obj)):
val = hlr_utils.get_value(obj, i, o_descr, "x", axis)
err2 = hlr_utils.get_err2(obj, i, o_descr, "x", axis)
map_so = hlr_utils.get_map_so(mobj, None, i)
if dist_source_sample is None:
(L_s, L_s_err2) = hlr_utils.get_parameter("primary", map_so, inst)
else:
L_s = hlr_utils.get_value(dist_source_sample, i, ls_descr)
L_s_err2 = hlr_utils.get_err2(dist_source_sample, i, ls_descr)
if dist_sample_detector is None:
(L_d, L_d_err2) = hlr_utils.get_parameter("secondary", map_so,
inst)
else:
L_d = hlr_utils.get_value(dist_sample_detector, i, ld_descr)
L_d_err2 = hlr_utils.get_err2(dist_sample_detector, i, ld_descr)
if lambda_f is not None:
l_f = hlr_utils.get_value(lambda_f, i, l_descr)
l_f_err2 = hlr_utils.get_err2(lambda_f, i, l_descr)
else:
l_f_tuple = hlr_utils.get_special(som_l_f, map_so)
l_f = l_f_tuple[0]
l_f_err2 = l_f_tuple[1]
if time_zero_slope is not None:
t_0_slope = hlr_utils.get_value(time_zero_slope, i,
t_0_slope_descr)
t_0_slope_err2 = hlr_utils.get_err2(time_zero_slope, i,
t_0_slope_descr)
else:
pass
if time_zero_offset is not None:
t_0_offset = hlr_utils.get_value(time_zero_offset, i,
t_0_offset_descr)
t_0_offset_err2 = hlr_utils.get_err2(time_zero_offset, i,
t_0_offset_descr)
else:
pass
value = axis_manip.tof_to_initial_wavelength_igs_lin_time_zero(
val, err2, l_f, l_f_err2, t_0_slope, t_0_slope_err2, t_0_offset,
t_0_offset_err2, L_s, L_s_err2, L_d, L_d_err2)
# Remove all wavelengths < 0
if run_filter:
index = 0
for valx in value[0]:
if valx >= 0:
break
index += 1
value[0].__delslice__(0, index)
value[1].__delslice__(0, index)
map_so.y.__delslice__(0, index)
map_so.var_y.__delslice__(0, index)
if lojac:
val.__delslice__(0, index)
err2.__delslice__(0, index)
else:
pass
if lojac:
try:
counts = utils.linear_order_jacobian(val, value[0], map_so.y,
map_so.var_y)
except Exception, e:
# Lets us know offending pixel ID
raise Exception(str(map_so.id) + " " + str(e))
hlr_utils.result_insert(result, res_descr, counts, map_so, "all",
axis, [value[0]])
else:
hlr_utils.result_insert(result, res_descr, value, map_so, "x",
axis)
def convert_single_to_list(funcname, number, som, **kwargs):
"""
This function retrieves a function object from the L{common_lib} set of
functions that provide axis transformations and converts the provided
number based on that function. Instrument geometry information needs to be
provided via the C{SOM}. The following is the list of functions supported
by this one.
- d_spacing_to_tof_focused_det
- energy_to_wavelength
- frequency_to_energy
- initial_wavelength_igs_lin_time_zero_to_tof
- init_scatt_wavevector_to_scalar_Q
- tof_to_initial_wavelength_igs_lin_time_zero
- tof_to_initial_wavelength_igs
- tof_to_scalar_Q
- tof_to_wavelength_lin_time_zero
- tof_to_wavelength
- wavelength_to_d_spacing
- wavelength_to_energy
- wavelength_to_scalar_k
- wavelength_to_scalar_Q
@param funcname: The name of the axis conversion function to use
@type funcname: C{string}
@param number: The value and error^2 to convert
@type number: C{tuple}
@param som: The object containing geometry and other special information
@type som: C{SOM.SOM}
@param kwargs: A list of keyword arguments that the function accepts:
@keyword inst_param: The type of parameter requested from an associated
instrument. For this function the acceptable
parameters are I{primary}, I{secondary} and I{total}.
Default is I{primary}.
@type inst_param: C{string}
@keyword pixel_id: The pixel ID from which the geometry information will
be retrieved from the instrument
@type pixel_id: C{tuple}=(\"bankN\", (x, y))
@return: A converted number for every unique spectrum
@rtype: C{list} of C{tuple}s
@raise AttributeError: The requested function is not in the approved
list
"""
# Setup supported function list and check to see if funcname is available
function_list = []
function_list.append("d_spacing_to_tof_focused_det")
function_list.append("energy_to_wavelength")
function_list.append("frequency_to_energy")
function_list.append("initial_wavelength_igs_lin_time_zero_to_tof")
function_list.append("init_scatt_wavevector_to_scalar_Q")
function_list.append("tof_to_initial_wavelength_igs_lin_time_zero")
function_list.append("tof_to_initial_wavelength_igs")
function_list.append("tof_to_scalar_Q")
function_list.append("tof_to_wavelength_lin_time_zero")
function_list.append("tof_to_wavelength")
function_list.append("wavelength_to_d_spacing")
function_list.append("wavelength_to_energy")
function_list.append("wavelength_to_scalar_k")
function_list.append("wavelength_to_scalar_Q")
if funcname not in function_list:
raise AttributeError("Function %s is not supported by "\
+"convert_single_to_list" % funcname)
import common_lib
# Get the common_lib function object
func = common_lib.__getattribute__(funcname)
# Setup inclusive dictionary containing the requested keywords for all
# common_lib axis conversion functions
fkwds = {}
fkwds["pathlength"] = ()
fkwds["polar"] = ()
fkwds["lambda_f"] = ()
try:
lambda_final = som.attr_list["Wavelength_final"]
except KeyError:
lambda_final = None
try:
fkwds["time_zero_slope"] = som.attr_list["Time_zero_slope"]
except KeyError:
pass
try:
fkwds["time_zero_offset"] = som.attr_list["Time_zero_offset"]
except KeyError:
pass
try:
fkwds["time_zero"] = som.attr_list["Time_zero"]
except KeyError:
pass
fkwds["dist_source_sample"] = ()
fkwds["dist_sample_detector"] = ()
try:
fkwds["inst_param"] = kwargs["inst_param"]
except KeyError:
fkwds["inst_param"] = "primary"
try:
fkwds["pixel_id"] = kwargs["pixel_id"]
except KeyError:
fkwds["pixel_id"] = None
fkwds["run_filter"] = False
# Set up for working through data
# This time highest object in the hierarchy is NOT what we need
result = []
res_descr = "list"
inst = som.attr_list.instrument
import hlr_utils
# iterate through the values
for i in xrange(hlr_utils.get_length(som)):
map_so = hlr_utils.get_map_so(som, None, i)
fkwds["pathlength"] = hlr_utils.get_parameter(fkwds["inst_param"],
map_so, inst)
fkwds["dist_source_sample"] = hlr_utils.get_parameter("primary",
map_so, inst)
fkwds["dist_sample_detector"] = hlr_utils.get_parameter("secondary",
map_so, inst)
fkwds["polar"] = hlr_utils.get_parameter("polar", map_so, inst)
fkwds["lambda_f"] = hlr_utils.get_special(lambda_final, map_so)
value = tuple(func(number, **fkwds))
hlr_utils.result_insert(result, res_descr, value, None, "all")
return result
def wavelength_to_energy(obj, **kwargs):
"""
This function converts a primary axis of a C{SOM} or C{SO} from wavelength
to energy. The wavelength axis for a C{SOM} must be in units of
I{Angstroms}. The primary axis of a C{SO} is assumed to be in units of
I{Angstroms}. A C{tuple} of C{(wavelength, wavelength_err2)} (assumed to
be in units of I{Angstroms}) can be converted to C{(energy, energy_err)}.
@param obj: Object to be converted
@type obj: C{SOM.SOM}, C{SOM.SO} or C{tuple}
@param kwargs: A list of keyword arguments that the function accepts:
@keyword offset: Energy offset information
@type offset: C{tuple} or C{list} of C{tuple}s
@keyword lojac: A flag that allows one to turn off the calculation of the
linear-order Jacobian. The default action is True for
histogram data.
@type lojac: C{boolean}
@keyword units: The expected units for this function. The default for this
function is I{Angstroms}
@type units: C{string}
@return: Object with a primary axis in wavelength converted to energy
@rtype: C{SOM.SOM}, C{SOM.SO} or C{tuple}
@raise TypeError: The incoming object is not a type the function recognizes
@raise RuntimeError: The C{SOM} x-axis units are not I{Angstroms}
"""
# import the helper functions
import hlr_utils
# set up for working through data
(result, res_descr) = hlr_utils.empty_result(obj)
o_descr = hlr_utils.get_descr(obj)
if o_descr == "list":
raise TypeError("Do not know how to handle given type: %s" % \
o_descr)
else:
pass
# Setup keyword arguments
try:
units = kwargs["units"]
except KeyError:
units = "Angstroms"
try:
offset = kwargs["offset"]
except KeyError:
offset = None
try:
lojac = kwargs["lojac"]
except KeyError:
lojac = hlr_utils.check_lojac(obj)
# Primary axis for transformation. If a SO is passed, the function, will
# assume the axis for transformation is at the 0 position
if o_descr == "SOM":
axis = hlr_utils.one_d_units(obj, units)
else:
axis = 0
result = hlr_utils.copy_som_attr(result, res_descr, obj, o_descr)
if res_descr == "SOM":
result = hlr_utils.force_units(result, "meV", axis)
result.setAxisLabel(axis, "energy")
result.setYUnits("Counts/meV")
result.setYLabel("Intensity")
else:
pass
# iterate through the values
import array_manip
import axis_manip
if lojac:
import utils
for i in xrange(hlr_utils.get_length(obj)):
val = hlr_utils.get_value(obj, i, o_descr, "x", axis)
err2 = hlr_utils.get_err2(obj, i, o_descr, "x", axis)
map_so = hlr_utils.get_map_so(obj, None, i)
value = axis_manip.wavelength_to_energy(val, err2)
if lojac:
y_val = hlr_utils.get_value(obj, i, o_descr, "y")
y_err2 = hlr_utils.get_err2(obj, i, o_descr, "y")
counts = utils.linear_order_jacobian(val, value[0],
y_val, y_err2)
else:
pass
if o_descr != "number":
value1 = axis_manip.reverse_array_cp(value[0])
value2 = axis_manip.reverse_array_cp(value[1])
rev_value = (value1, value2)
else:
rev_value = value
if map_so is not None:
if not lojac:
map_so.y = axis_manip.reverse_array_cp(map_so.y)
map_so.var_y = axis_manip.reverse_array_cp(map_so.var_y)
else:
map_so.y = axis_manip.reverse_array_cp(counts[0])
map_so.var_y = axis_manip.reverse_array_cp(counts[1])
else:
pass
if offset is not None:
info = hlr_utils.get_special(offset, map_so)
try:
rev_value = array_manip.add_ncerr(rev_value[0], rev_value[1],
info[0], info[1])
except TypeError:
# Have to do this since add_ncerr does not support
# scalar-scalar operations
value1 = rev_value[0] + info[0]
value2 = rev_value[1] + info[1]
rev_value = (value1, value2)
else:
pass
hlr_utils.result_insert(result, res_descr, rev_value, map_so, "x",
axis)
return result
def igs_energy_transfer(obj, **kwargs):
"""
@depricated: This function will eventually disappear when the full S(Q,E)
transformation for IGS detectors is completed and verified.
This function takes a SOM or a SO and calculates the energy transfer for
the IGS class of instruments. It is different from
common_lib.energy_transfer in that the final wavelength is provided in a
SOM.Information, SOM.CompositeInformation or a tuple, then converted to
energy in place before being given to the common_lib.energy_transfer
function.
Parameters:
----------
-> obj
-> kwargs is a list of key word arguments that the function accepts:
units= a string containing the expected units for this function.
The default for this function is meV
lambda_f= a SOM.Information, SOM.CompositeInformation or a tuple
containing the final wavelength information
offset= a SOM.Information or SOM.CompositeInformation containing
the final energy offsets
scale=<boolean> is a flag that determines if the energy transfer
results are scaled by the ratio of lambda_f/lambda_i.
The default is False
Returns:
-------
<- A SOM or SO with the energy transfer calculated in units of THz
Exceptions:
----------
<- RuntimeError is raised if the x-axis units are not meV
<- RuntimeError is raised if a SOM or SO is not given to the function
<- RuntimeError is raised if the final wavelength is not provided to the
function
"""
# import the helper functions
import hlr_utils
# set up for working through data
(result, res_descr) = hlr_utils.empty_result(obj)
o_descr = hlr_utils.get_descr(obj)
if o_descr == "number" or o_descr == "list":
raise RuntimeError, "Must provide a SOM of a SO to the function."
# Go on
else:
pass
# Setup keyword arguments
try:
units = kwargs["units"]
except KeyError:
units = "meV"
try:
lambda_f = kwargs["lambda_f"]
except KeyError:
lambda_f = None
try:
offset = kwargs["offset"]
except KeyError:
offset = None
try:
scale = kwargs["scale"]
except KeyError:
scale = False
# Primary axis for transformation. If a SO is passed, the function, will
# assume the axis for transformation is at the 0 position
if o_descr == "SOM":
axis = hlr_utils.one_d_units(obj, units)
else:
axis = 0
if lambda_f is None:
if o_descr == "SOM":
try:
lambda_f = obj.attr_list["Wavelength_final"]
except KeyError:
raise RuntimeError("Must provide a final wavelength via the "\
+"incoming SOM or the lambda_f keyword")
else:
raise RuntimeError("Must provide a final wavelength via the "\
+"lambda_f keyword")
else:
pass
result = hlr_utils.copy_som_attr(result, res_descr, obj, o_descr)
if res_descr == "SOM":
result = hlr_utils.force_units(result, "ueV", axis)
result.setAxisLabel(axis, "energy_transfer")
result.setYUnits("Counts/ueV")
result.setYLabel("Intensity")
else:
pass
# iterate through the values
import array_manip
import axis_manip
import utils
for i in xrange(hlr_utils.get_length(obj)):
val = hlr_utils.get_value(obj, i, o_descr, "x", axis)
err2 = hlr_utils.get_err2(obj, i, o_descr, "x", axis)
y_val = hlr_utils.get_value(obj, i, o_descr, "y", axis)
y_err2 = hlr_utils.get_err2(obj, i, o_descr, "y", axis)
map_so = hlr_utils.get_map_so(obj, None, i)
l_f = hlr_utils.get_special(lambda_f, map_so)
(E_f, E_f_err2) = axis_manip.wavelength_to_energy(l_f[0], l_f[1])
if offset is not None:
info = hlr_utils.get_special(offset, map_so)
try:
E_f_new = array_manip.add_ncerr(E_f, E_f_err2, info[0],
info[1])
except TypeError:
# Have to do this since add_ncerr does not support
# scalar-scalar operations
value1 = E_f + info[0]
value2 = E_f_err2 + info[1]
E_f_new = (value1, value2)
else:
E_f_new = (E_f, E_f_err2)
# Scale counts by lambda_f / lambda_i
if scale:
l_i = axis_manip.energy_to_wavelength(val, err2)
l_i_bc = utils.calc_bin_centers(l_i[0], l_i[1])
ratio = array_manip.div_ncerr(l_f[0], l_f[1], l_i_bc[0], l_i_bc[1])
scale_y = array_manip.mult_ncerr(y_val, y_err2, ratio[0], ratio[1])
else:
scale_y = (y_val, y_err2)
value = array_manip.sub_ncerr(val, err2, E_f_new[0], E_f_new[1])
# Convert from meV to ueV
value2 = array_manip.mult_ncerr(value[0], value[1], 1000.0, 0.0)
value3 = array_manip.mult_ncerr(scale_y[0], scale_y[1], 1.0 / 1000.0,
0.0)
hlr_utils.result_insert(result, res_descr, value3, map_so, "all", 0,
[value2[0]])
return result
def energy_transfer(obj, itype, axis_const, **kwargs):
"""
This function takes a SOM with a wavelength axis (initial for IGS and
final for DGS) and calculates the energy transfer.
@param obj: The object containing the wavelength axis
@type obj: C{SOM.SOM}
@param itype: The instrument class type. The choices are either I{IGS} or
I{DGS}.
@type itype: C{string}
@param axis_const: The attribute name for the axis constant which is the
final wavelength for I{IGS} and the initial energy for
I{DGS}.
@type axis_const: C{string}
@param kwargs: A list of keyword arguments that the function accepts:
@keyword units: The units for the incoming axis. The default is
I{Angstroms}.
@type units: C{string}
@keyword change_units: A flag that signals the function to convert from
I{meV} to I{ueV}. The default is I{False}.
@type change_units: C{boolean}
@keyword scale: A flag to scale the y-axis by lambda_f/lambda_i for I{IGS}
and lambda_i/lambda_f for I{DGS}. The default is I{False}.
@type scale: C{boolean}
@keyword lojac: A flag that turns on the calculation and application of
the linear-order Jacobian. The default is I{False}.
@type lojac: C{boolean}
@keyword sa_norm: A flag to turn on solid angle normlaization.
@type sa_norm: C{boolean}
@return: Object with the energy transfer calculated in units of I{meV} or
I{ueV}. The default is I{meV}.
@rtype: C{SOM.SOM}
@raise RuntimeError: The instrument class type is not recognized
@raise RuntimeError: The x-axis units are not Angstroms
@raise RuntimeError: A SOM is not given to the function
"""
# Check the instrument class type to make sure its allowed
allowed_types = ["DGS", "IGS"]
if itype not in allowed_types:
raise RuntimeError("The instrument class type %s is not known. "\
+"Please use DGS or IGS" % itype)
# import the helper functions
import hlr_utils
# set up for working through data
(result, res_descr) = hlr_utils.empty_result(obj)
o_descr = hlr_utils.get_descr(obj)
if o_descr != "SOM":
raise RuntimeError("Must provide a SOM to the function.")
# Go on
else:
pass
# Setup keyword arguments
try:
units = kwargs["units"]
except KeyError:
units = "Angstroms"
try:
change_units = kwargs["change_units"]
except KeyError:
change_units = False
try:
scale = kwargs["scale"]
except KeyError:
scale = False
try:
sa_norm = kwargs["sa_norm"]
except KeyError:
sa_norm = False
if sa_norm:
inst = obj.attr_list.instrument
try:
lojac = kwargs["lojac"]
except KeyError:
lojac = False
# Primary axis for transformation.
axis = hlr_utils.one_d_units(obj, units)
# Get the subtraction constant
try:
axis_c = obj.attr_list[axis_const]
except KeyError:
raise RuntimeError("Must provide a final wavelength (IGS) or initial "\
+"energy (DGS) via the incoming SOM")
result = hlr_utils.copy_som_attr(result, res_descr, obj, o_descr)
if change_units:
unit_str = "ueV"
else:
unit_str = "meV"
result = hlr_utils.force_units(result, unit_str, axis)
result.setAxisLabel(axis, "energy_transfer")
result.setYUnits("Counts/" + unit_str)
result.setYLabel("Intensity")
# iterate through the values
import array_manip
import axis_manip
import dr_lib
import utils
for i in xrange(hlr_utils.get_length(obj)):
if itype == "IGS":
l_i = hlr_utils.get_value(obj, i, o_descr, "x", axis)
l_i_err2 = hlr_utils.get_err2(obj, i, o_descr, "x", axis)
else:
l_f = hlr_utils.get_value(obj, i, o_descr, "x", axis)
l_f_err2 = hlr_utils.get_err2(obj, i, o_descr, "x", axis)
y_val = hlr_utils.get_value(obj, i, o_descr, "y", axis)
y_err2 = hlr_utils.get_err2(obj, i, o_descr, "y", axis)
map_so = hlr_utils.get_map_so(obj, None, i)
if itype == "IGS":
(E_i, E_i_err2) = axis_manip.wavelength_to_energy(l_i, l_i_err2)
l_f = hlr_utils.get_special(axis_c, map_so)[:2]
(E_f, E_f_err2) = axis_manip.wavelength_to_energy(l_f[0], l_f[1])
if lojac:
(y_val, y_err2) = utils.linear_order_jacobian(l_i, E_i,
y_val, y_err2)
else:
(E_i, E_i_err2) = axis_c.toValErrTuple()
(E_f, E_f_err2) = axis_manip.wavelength_to_energy(l_f, l_f_err2)
if lojac:
(y_val, y_err2) = utils.linear_order_jacobian(l_f, E_f,
y_val, y_err2)
if scale:
# Scale counts by lambda_f / lambda_i
if itype == "IGS":
(l_n, l_n_err2) = l_f
(l_d, l_d_err2) = utils.calc_bin_centers(l_i, l_i_err2)
else:
(l_n, l_n_err2) = utils.calc_bin_centers(l_f, l_f_err2)
(l_d, l_d_err2) = axis_manip.energy_to_wavelength(E_i,
E_i_err2)
ratio = array_manip.div_ncerr(l_n, l_n_err2, l_d, l_d_err2)
scale_y = array_manip.mult_ncerr(y_val, y_err2, ratio[0], ratio[1])
else:
scale_y = (y_val, y_err2)
value = array_manip.sub_ncerr(E_i, E_i_err2, E_f, E_f_err2)
if change_units:
# Convert from meV to ueV
value2 = array_manip.mult_ncerr(value[0], value[1], 1000.0, 0.0)
scale_y = array_manip.mult_ncerr(scale_y[0], scale_y[1],
1.0/1000.0, 0.0)
else:
value2 = value
if sa_norm:
if inst.get_name() == "BSS":
dOmega = dr_lib.calc_BSS_solid_angle(map_so, inst)
scale_y = array_manip.div_ncerr(scale_y[0], scale_y[1],
dOmega, 0.0)
else:
raise RuntimeError("Do not know how to get solid angle from "\
+"%s" % inst.get_name())
if itype == "IGS":
# Reverse the values due to the conversion
value_y = axis_manip.reverse_array_cp(scale_y[0])
value_var_y = axis_manip.reverse_array_cp(scale_y[1])
value_x = axis_manip.reverse_array_cp(value2[0])
else:
value_y = scale_y[0]
value_var_y = scale_y[1]
value_x = value2[0]
hlr_utils.result_insert(result, res_descr, (value_y, value_var_y),
map_so, "all", 0, [value_x])
return result
def calc_delta_theta_over_theta(som, dataset_type="data"):
"""
This function takes a C{SOM} that contains reflectometer slit and angle
information and calculates the parameter delta theta / theta. The function
will also store the calculated prameter as well as other slit and angle
information into the C{SOM}s attribute list. The function will not return
the C{SOM} as the core data values were not changed.
@param som: The object that contains the slit and angle information
@type som: C{SOM.SOM}
@param dataset_type: The practical name of the dataset being processed.
The default value is I{data}.
@type dataset_type: C{string}
@raise TypeError: Anything other than a C{SOM} is given
"""
import hlr_utils
import SOM
o_descr = hlr_utils.get_descr(som)
if o_descr != "SOM":
raise TypeError("Function argument must be a SOM")
# Have a SOM, go on
else:
pass
import math
# Create a dummy SO
so = SOM.SO()
# Create a dummy information tuple
no_info = (None, None, None)
# Set instrument specific strings
inst_name = som.attr_list["instrument_name"]
if inst_name == "REF_L":
first_slit_ext1 = "Slit1_top"
first_slit_ext2 = "Slit1_bottom"
last_slit_ext1 = "Slit2_top"
last_slit_ext2 = "Slit2_bottom"
last_slit_dis = "Slit2_distance"
last_slit = "slit2"
last_slit_distot = "slit12"
slit_ext1 = "top"
slit_ext2 = "bottom"
elif inst_name == "REF_M":
first_slit_ext1 = "Slit1_left"
first_slit_ext2 = "Slit1_right"
last_slit_ext1 = "Slit3_left"
last_slit_ext2 = "Slit3_right"
last_slit_dis = "Slit3_distance"
last_slit = "slit3"
last_slit_distot = "slit13"
slit_ext1 = "left"
slit_ext2 = "right"
else:
raise RuntimeError("Do not know how to handle instrument %s" \
% inst_name)
# Get slit information
try:
slit1_ext1 = hlr_utils.get_special(som.attr_list[first_slit_ext1], so)
except KeyError:
slit1_ext1 = no_info
try:
slit1_ext2 = hlr_utils.get_special(som.attr_list[first_slit_ext2], so)
except KeyError:
slit1_ext2 = no_info
try:
slit1_dist = hlr_utils.get_special(som.attr_list["Slit1_distance"], so)
except KeyError:
slit1_dist = no_info
if slit1_ext1[0] is None or slit1_ext2[0] is None:
slit1_size_ok = False
else:
slit1_size_ok = True
try:
slit2_ext1 = hlr_utils.get_special(som.attr_list[last_slit_ext1], so)
except KeyError:
slit2_ext1 = no_info
try:
slit2_ext2 = hlr_utils.get_special(som.attr_list[last_slit_ext2], so)
except KeyError:
slit2_ext2 = no_info
try:
slit2_dist = hlr_utils.get_special(som.attr_list[last_slit_dis], so)
except KeyError:
slit2_dist = no_info
if slit2_ext1[0] is None or slit2_ext2[0] is None:
slit2_size_ok = False
else:
slit2_size_ok = True
if slit1_dist[0] is None or slit2_dist[0] is None:
slit12_dist_ok = False
else:
slit12_dist_ok = True
# Unit checks
if slit1_size_ok and slit2_size_ok:
if slit1_ext1[2] != slit2_ext1[2]:
raise ValueError("Slit %s opening distances are not in the same "\
+"units. slit1 (%s), %s (%s)" % (slit_ext1,
slit1_ext1[2],
last_slit,
slit2_ext1[2]))
if slit1_ext2[2] != slit2_ext2[2]:
raise ValueError("Slit %s opening distances are not in the "\
+"same units. slit1 (%s), %s (%s)" \
% (slit_ext2,
slit1_ext2[2],
last_slit,
slit2_ext2[2]))
if slit1_dist[2] != slit2_dist[2] and slit12_dist_ok:
raise ValueError("Slit distances are not in the same units. "\
+"slit1 (%s), %s (%s)" % (slit1_dist[2],
last_slit,
slit2_dist[2]))
# Calculate intermediate slit parameters
if slit1_size_ok:
slit1_size = math.fabs(slit1_ext1[0] - slit1_ext2[0])
if slit1_ext1[2] == "millimetre":
slit1_size /= 1000.0
slit1_size_units = "metre"
else:
slit1_size_units = slit1_ext1[2]
else:
slit1_size = float('nan')
slit1_size_units = None
if slit2_size_ok:
slit2_size = math.fabs(slit2_ext1[0] - slit2_ext2[0])
if slit2_ext1[2] == "millimetre":
slit2_size /= 1000.0
slit2_size_units = "metre"
else:
slit2_size_units = slit2_ext1[2]
else:
slit2_size = float('nan')
slit2_size_units = None
if slit12_dist_ok:
slit12_distance = math.fabs(slit1_dist[0] - slit2_dist[0])
slit2_distance = math.fabs(slit2_dist[0])
else:
slit12_distance = float('nan')
slit2_distance = float('nan')
# Calculate delta theta
if slit1_size_ok and not slit2_size_ok:
dtheta = slit1_size / slit12_distance
elif slit2_size_ok and not slit1_size_ok:
dtheta = slit2_size / slit12_distance
else:
dtheta = max(slit1_size, slit2_size) / slit12_distance
# Calculate delta theta over theta
try:
theta = hlr_utils.get_special(som.attr_list["Theta"], so)
except KeyError:
theta = no_info
if theta[0] is not None:
if theta[2] == "degrees" or theta[2] == "degree":
theta_rads = theta[0] * (math.pi / 180.0)
else:
theta_rads = theta[0]
else:
theta_rads = float('nan')
dtheta_over_theta = dtheta / theta_rads
# Add parameters to attribute list
som.attr_list[dataset_type + "-slit1_size"] = (slit1_size,
slit1_size_units)
last_slit_tag = "-%s_size" % last_slit
som.attr_list[dataset_type + last_slit_tag] = (slit2_size,
slit2_size_units)
last_slit_dist_tag = "-%s_distance" % last_slit_distot
som.attr_list[dataset_type + last_slit_dist_tag] = (slit12_distance,
slit1_dist[2])
som.attr_list[dataset_type + "-" +
last_slit_dis.lower()] = (slit2_distance, slit1_dist[2])
som.attr_list[dataset_type + "-delta_theta"] = (dtheta, "radians")
som.attr_list[dataset_type + "-theta"] = (theta_rads, "radians")
som.attr_list[dataset_type + "-dtheta_over_theta"] = dtheta_over_theta
def convert_single_to_list(funcname, number, som, **kwargs):
"""
This function retrieves a function object from the L{common_lib} set of
functions that provide axis transformations and converts the provided
number based on that function. Instrument geometry information needs to be
provided via the C{SOM}. The following is the list of functions supported
by this one.
- d_spacing_to_tof_focused_det
- energy_to_wavelength
- frequency_to_energy
- initial_wavelength_igs_lin_time_zero_to_tof
- init_scatt_wavevector_to_scalar_Q
- tof_to_initial_wavelength_igs_lin_time_zero
- tof_to_initial_wavelength_igs
- tof_to_scalar_Q
- tof_to_wavelength_lin_time_zero
- tof_to_wavelength
- wavelength_to_d_spacing
- wavelength_to_energy
- wavelength_to_scalar_k
- wavelength_to_scalar_Q
@param funcname: The name of the axis conversion function to use
@type funcname: C{string}
@param number: The value and error^2 to convert
@type number: C{tuple}
@param som: The object containing geometry and other special information
@type som: C{SOM.SOM}
@param kwargs: A list of keyword arguments that the function accepts:
@keyword inst_param: The type of parameter requested from an associated
instrument. For this function the acceptable
parameters are I{primary}, I{secondary} and I{total}.
Default is I{primary}.
@type inst_param: C{string}
@keyword pixel_id: The pixel ID from which the geometry information will
be retrieved from the instrument
@type pixel_id: C{tuple}=(\"bankN\", (x, y))
@return: A converted number for every unique spectrum
@rtype: C{list} of C{tuple}s
@raise AttributeError: The requested function is not in the approved
list
"""
# Setup supported function list and check to see if funcname is available
function_list = []
function_list.append("d_spacing_to_tof_focused_det")
function_list.append("energy_to_wavelength")
function_list.append("frequency_to_energy")
function_list.append("initial_wavelength_igs_lin_time_zero_to_tof")
function_list.append("init_scatt_wavevector_to_scalar_Q")
function_list.append("tof_to_initial_wavelength_igs_lin_time_zero")
function_list.append("tof_to_initial_wavelength_igs")
function_list.append("tof_to_scalar_Q")
function_list.append("tof_to_wavelength_lin_time_zero")
function_list.append("tof_to_wavelength")
function_list.append("wavelength_to_d_spacing")
function_list.append("wavelength_to_energy")
function_list.append("wavelength_to_scalar_k")
function_list.append("wavelength_to_scalar_Q")
if funcname not in function_list:
raise AttributeError("Function %s is not supported by "\
+"convert_single_to_list" % funcname)
import common_lib
# Get the common_lib function object
func = common_lib.__getattribute__(funcname)
# Setup inclusive dictionary containing the requested keywords for all
# common_lib axis conversion functions
fkwds = {}
fkwds["pathlength"] = ()
fkwds["polar"] = ()
fkwds["lambda_f"] = ()
try:
lambda_final = som.attr_list["Wavelength_final"]
except KeyError:
lambda_final = None
try:
fkwds["time_zero_slope"] = som.attr_list["Time_zero_slope"]
except KeyError:
pass
try:
fkwds["time_zero_offset"] = som.attr_list["Time_zero_offset"]
except KeyError:
pass
try:
fkwds["time_zero"] = som.attr_list["Time_zero"]
except KeyError:
pass
fkwds["dist_source_sample"] = ()
fkwds["dist_sample_detector"] = ()
try:
fkwds["inst_param"] = kwargs["inst_param"]
except KeyError:
fkwds["inst_param"] = "primary"
try:
fkwds["pixel_id"] = kwargs["pixel_id"]
except KeyError:
fkwds["pixel_id"] = None
fkwds["run_filter"] = False
# Set up for working through data
# This time highest object in the hierarchy is NOT what we need
result = []
res_descr = "list"
inst = som.attr_list.instrument
import hlr_utils
# iterate through the values
for i in xrange(hlr_utils.get_length(som)):
map_so = hlr_utils.get_map_so(som, None, i)
fkwds["pathlength"] = hlr_utils.get_parameter(fkwds["inst_param"],
map_so, inst)
fkwds["dist_source_sample"] = hlr_utils.get_parameter(
"primary", map_so, inst)
fkwds["dist_sample_detector"] = hlr_utils.get_parameter(
"secondary", map_so, inst)
fkwds["polar"] = hlr_utils.get_parameter("polar", map_so, inst)
fkwds["lambda_f"] = hlr_utils.get_special(lambda_final, map_so)
value = tuple(func(number, **fkwds))
hlr_utils.result_insert(result, res_descr, value, None, "all")
return result
def tof_to_initial_wavelength_igs(obj, **kwargs):
"""
This function converts a primary axis of a C{SOM} or C{SO} from
time-of-flight to initial_wavelength_igs. The time-of-flight axis for a
C{SOM} must be in units of I{microseconds}. The primary axis of a C{SO} is
assumed to be in units of I{microseconds}. A C{tuple} of C{(tof, tof_err2)}
(assumed to be in units of I{microseconds}) can be converted to
C{(initial_wavelength_igs, initial_wavelength_igs_err2)}.
@param obj: Object to be converted
@type obj: C{SOM.SOM}, C{SOM.SO} or C{tuple}
@param kwargs: A list of keyword arguments that the function accepts:
@keyword lambda_f:The final wavelength and its associated error^2
@type lambda_f: C{tuple}
@keyword time_zero: The time zero offset and its associated error^2
@type time_zero: C{tuple}
@keyword dist_source_sample: The source to sample distance information and
its associated error^2
@type dist_source_sample: C{tuple} or C{list} of C{tuple}s
@keyword dist_sample_detector: The sample to detector distance information
and its associated error^2
@type dist_sample_detector: C{tuple} or C{list} of C{tuple}s
@keyword run_filter: This determines if the filter on the negative
wavelengths is run. The default setting is True.
@type run_filter: C{boolean}
@keyword units: The expected units for this function. The default for this
function is I{microseconds}
@type units: C{string}
@return: Object with a primary axis in time-of-flight converted to
initial_wavelength_igs
@rtype: C{SOM.SOM}, C{SOM.SO} or C{tuple}
@raise TypeError: The incoming object is not a type the function recognizes
@raise RuntimeError: The C{SOM} x-axis units are not I{microseconds}
"""
# import the helper functions
import hlr_utils
# set up for working through data
(result, res_descr) = hlr_utils.empty_result(obj)
o_descr = hlr_utils.get_descr(obj)
# Setup keyword arguments
try:
lambda_f = kwargs["lambda_f"]
except KeyError:
lambda_f = None
try:
time_zero = kwargs["time_zero"]
except KeyError:
time_zero = None
try:
dist_source_sample = kwargs["dist_source_sample"]
except KeyError:
dist_source_sample = None
try:
dist_sample_detector = kwargs["dist_sample_detector"]
except KeyError:
dist_sample_detector = None
try:
units = kwargs["units"]
except KeyError:
units = "microseconds"
try:
run_filter = kwargs["run_filter"]
except KeyError:
run_filter = True
# Primary axis for transformation. If a SO is passed, the function, will
# assume the axis for transformation is at the 0 position
if o_descr == "SOM":
axis = hlr_utils.one_d_units(obj, units)
else:
axis = 0
result = hlr_utils.copy_som_attr(result, res_descr, obj, o_descr)
if res_descr == "SOM":
result = hlr_utils.force_units(result, "Angstroms", axis)
result.setAxisLabel(axis, "wavelength")
result.setYUnits("Counts/A")
result.setYLabel("Intensity")
else:
pass
# Where to get instrument information
if dist_source_sample is None or dist_sample_detector is None:
if o_descr == "SOM":
try:
obj.attr_list.instrument.get_primary()
inst = obj.attr_list.instrument
except RuntimeError:
raise RuntimeError("A detector was not provided!")
else:
if dist_source_sample is None and dist_sample_detector is None:
raise RuntimeError("If a SOM is not passed, the "\
+"source-sample and sample-detector "\
+"distances must be provided.")
elif dist_source_sample is None:
raise RuntimeError("If a SOM is not passed, the "\
+"source-sample distance must be provided.")
elif dist_sample_detector is None:
raise RuntimeError("If a SOM is not passed, the "\
+"sample-detector distance must be "\
+"provided.")
else:
raise RuntimeError("If you get here, see Steve Miller for "\
+"your mug.")
else:
pass
if lambda_f is not None:
l_descr = hlr_utils.get_descr(lambda_f)
else:
if o_descr == "SOM":
try:
som_l_f = obj.attr_list["Wavelength_final"]
except KeyError:
raise RuntimeError("Please provide a final wavelength "\
+"parameter either via the function call "\
+"or the SOM")
else:
raise RuntimeError("You need to provide a final wavelength")
if time_zero is not None:
t_descr = hlr_utils.get_descr(time_zero)
else:
if o_descr == "SOM":
try:
t_0 = obj.attr_list["Time_zero"][0]
t_0_err2 = obj.attr_list["Time_zero"][1]
except KeyError:
raise RuntimeError("Please provide a time-zero "\
+"parameter either via the function call "\
+"or the SOM")
else:
t_0 = 0.0
t_0_err2 = 0.0
if dist_source_sample is not None:
ls_descr = hlr_utils.get_descr(dist_source_sample)
# Do nothing, go on
else:
pass
if dist_sample_detector is not None:
ld_descr = hlr_utils.get_descr(dist_sample_detector)
# Do nothing, go on
else:
pass
# iterate through the values
import axis_manip
for i in xrange(hlr_utils.get_length(obj)):
val = hlr_utils.get_value(obj, i, o_descr, "x", axis)
err2 = hlr_utils.get_err2(obj, i, o_descr, "x", axis)
map_so = hlr_utils.get_map_so(obj, None, i)
if dist_source_sample is None:
(L_s, L_s_err2) = hlr_utils.get_parameter("primary", map_so, inst)
else:
L_s = hlr_utils.get_value(dist_source_sample, i, ls_descr)
L_s_err2 = hlr_utils.get_err2(dist_source_sample, i, ls_descr)
if dist_sample_detector is None:
(L_d, L_d_err2) = hlr_utils.get_parameter("secondary", map_so,
inst)
else:
L_d = hlr_utils.get_value(dist_sample_detector, i, ld_descr)
L_d_err2 = hlr_utils.get_err2(dist_sample_detector, i, ld_descr)
if lambda_f is not None:
l_f = hlr_utils.get_value(lambda_f, i, l_descr)
l_f_err2 = hlr_utils.get_err2(lambda_f, i, l_descr)
else:
l_f_tuple = hlr_utils.get_special(som_l_f, map_so)
l_f = l_f_tuple[0]
l_f_err2 = l_f_tuple[1]
if time_zero is not None:
t_0 = hlr_utils.get_value(time_zero, i, t_descr)
t_0_err2 = hlr_utils.get_err2(time_zero, i, t_descr)
else:
pass
value = axis_manip.tof_to_initial_wavelength_igs(val, err2,
l_f, l_f_err2,
t_0, t_0_err2,
L_s, L_s_err2,
L_d, L_d_err2)
# Remove all wavelengths < 0
if run_filter:
index = 0
for val in value[0]:
if val >= 0:
break
index += 1
value[0].__delslice__(0, index)
value[1].__delslice__(0, index)
map_so.y.__delslice__(0, index)
map_so.var_y.__delslice__(0, index)
else:
pass
hlr_utils.result_insert(result, res_descr, value, map_so, "x", axis)
return result
def tof_to_initial_wavelength_igs_lin_time_zero(obj, **kwargs):
"""
This function converts a primary axis of a C{SOM} or C{SO} from
time-of-flight to initial_wavelength_igs_lin_time_zero. The time-of-flight
axis for a C{SOM} must be in units of I{microseconds}. The primary axis of
a C{SO} is assumed to be in units of I{microseconds}. A C{tuple} of
C{(tof, tof_err2)} (assumed to be in units of I{microseconds}) can be
converted to C{(initial_wavelength_igs, initial_wavelength_igs_err2)}.
@param obj: Object to be converted
@type obj: C{SOM.SOM}, C{SOM.SO} or C{tuple}
@param kwargs: A list of keyword arguments that the function accepts:
@keyword lambda_f:The final wavelength and its associated error^2
@type lambda_f: C{tuple}
@keyword time_zero_slope: The time zero slope and its associated error^2
@type time_zero_slope: C{tuple}
@keyword time_zero_offset: The time zero offset and its associated error^2
@type time_zero_offset: C{tuple}
@keyword dist_source_sample: The source to sample distance information and
its associated error^2
@type dist_source_sample: C{tuple} or C{list} of C{tuple}s
@keyword dist_sample_detector: The sample to detector distance information
and its associated error^2
@type dist_sample_detector: C{tuple} or C{list} of C{tuple}s
@keyword run_filter: This determines if the filter on the negative
wavelengths is run. The default setting is True.
@type run_filter: C{boolean}
@keyword lojac: A flag that allows one to turn off the calculation of the
linear-order Jacobian. The default action is True for
histogram data.
@type lojac: C{boolean}
@keyword units: The expected units for this function. The default for this
function is I{microseconds}
@type units: C{string}
@return: Object with a primary axis in time-of-flight converted to
initial_wavelength_igs
@rtype: C{SOM.SOM}, C{SOM.SO} or C{tuple}
@raise TypeError: The incoming object is not a type the function recognizes
@raise RuntimeError: The C{SOM} x-axis units are not I{microseconds}
"""
# import the helper functions
import hlr_utils
# set up for working through data
(result, res_descr) = hlr_utils.empty_result(obj)
o_descr = hlr_utils.get_descr(obj)
# Setup keyword arguments
try:
lambda_f = kwargs["lambda_f"]
except KeyError:
lambda_f = None
try:
time_zero_slope = kwargs["time_zero_slope"]
except KeyError:
time_zero_slope = None
# Current constants for Time Zero Slope
TIME_ZERO_SLOPE = (float(0.0), float(0.0))
try:
time_zero_offset = kwargs["time_zero_offset"]
except KeyError:
time_zero_offset = None
# Current constants for Time Zero Offset
TIME_ZERO_OFFSET = (float(0.0), float(0.0))
try:
dist_source_sample = kwargs["dist_source_sample"]
except KeyError:
dist_source_sample = None
try:
dist_sample_detector = kwargs["dist_sample_detector"]
except KeyError:
dist_sample_detector = None
try:
lojac = kwargs["lojac"]
except KeyError:
lojac = hlr_utils.check_lojac(obj)
try:
units = kwargs["units"]
except KeyError:
units = "microseconds"
try:
run_filter = kwargs["run_filter"]
except KeyError:
run_filter = True
try:
iobj = kwargs["iobj"]
except KeyError:
iobj = None
# Primary axis for transformation. If a SO is passed, the function, will
# assume the axis for transformation is at the 0 position
if o_descr == "SOM":
axis = hlr_utils.one_d_units(obj, units)
else:
axis = 0
result = hlr_utils.copy_som_attr(result, res_descr, obj, o_descr)
if res_descr == "SOM":
result = hlr_utils.force_units(result, "Angstroms", axis)
result.setAxisLabel(axis, "wavelength")
result.setYUnits("Counts/A")
result.setYLabel("Intensity")
else:
pass
# Where to get instrument information
if dist_source_sample is None or dist_sample_detector is None:
if o_descr == "SOM":
try:
obj.attr_list.instrument.get_primary()
inst = obj.attr_list.instrument
mobj = obj
except RuntimeError:
raise RuntimeError("A detector was not provided!")
else:
if iobj is None:
if dist_source_sample is None and dist_sample_detector is None:
raise RuntimeError("If a SOM is not passed, the "\
+"source-sample and sample-detector "\
+"distances must be provided.")
elif dist_source_sample is None:
raise RuntimeError("If a SOM is not passed, the "\
+"source-sample distance must be "\
+"provided.")
elif dist_sample_detector is None:
raise RuntimeError("If a SOM is not passed, the "\
+"sample-detector distance must be "\
+"provided.")
else:
raise RuntimeError("If you get here, see Steve Miller "\
+"for your mug.")
else:
inst = iobj.attr_list.instrument
mobj = iobj
else:
mobj = obj
if lambda_f is not None:
l_descr = hlr_utils.get_descr(lambda_f)
else:
if o_descr == "SOM":
try:
som_l_f = obj.attr_list["Wavelength_final"]
except KeyError:
raise RuntimeError("Please provide a final wavelength "\
+"parameter either via the function call "\
+"or the SOM")
else:
if iobj is None:
raise RuntimeError("You need to provide a final wavelength")
else:
som_l_f = iobj.attr_list["Wavelength_final"]
if time_zero_slope is not None:
t_0_slope_descr = hlr_utils.get_descr(time_zero_slope)
else:
if o_descr == "SOM":
try:
t_0_slope = obj.attr_list["Time_zero_slope"][0]
t_0_slope_err2 = obj.attr_list["Time_zero_slope"][1]
except KeyError:
t_0_slope = TIME_ZERO_SLOPE[0]
t_0_slope_err2 = TIME_ZERO_SLOPE[1]
else:
t_0_slope = TIME_ZERO_SLOPE[0]
t_0_slope_err2 = TIME_ZERO_SLOPE[1]
if time_zero_offset is not None:
t_0_offset_descr = hlr_utils.get_descr(time_zero_offset)
else:
if o_descr == "SOM":
try:
t_0_offset = obj.attr_list["Time_zero_offset"][0]
t_0_offset_err2 = obj.attr_list["Time_zero_offset"][1]
except KeyError:
t_0_offset = TIME_ZERO_OFFSET[0]
t_0_offset_err2 = TIME_ZERO_OFFSET[1]
else:
t_0_offset = TIME_ZERO_OFFSET[0]
t_0_offset_err2 = TIME_ZERO_OFFSET[1]
if dist_source_sample is not None:
ls_descr = hlr_utils.get_descr(dist_source_sample)
# Do nothing, go on
else:
pass
if dist_sample_detector is not None:
ld_descr = hlr_utils.get_descr(dist_sample_detector)
# Do nothing, go on
else:
pass
# iterate through the values
import axis_manip
if lojac:
import utils
for i in xrange(hlr_utils.get_length(obj)):
val = hlr_utils.get_value(obj, i, o_descr, "x", axis)
err2 = hlr_utils.get_err2(obj, i, o_descr, "x", axis)
map_so = hlr_utils.get_map_so(mobj, None, i)
if dist_source_sample is None:
(L_s, L_s_err2) = hlr_utils.get_parameter("primary", map_so, inst)
else:
L_s = hlr_utils.get_value(dist_source_sample, i, ls_descr)
L_s_err2 = hlr_utils.get_err2(dist_source_sample, i, ls_descr)
if dist_sample_detector is None:
(L_d, L_d_err2) = hlr_utils.get_parameter("secondary", map_so,
inst)
else:
L_d = hlr_utils.get_value(dist_sample_detector, i, ld_descr)
L_d_err2 = hlr_utils.get_err2(dist_sample_detector, i, ld_descr)
if lambda_f is not None:
l_f = hlr_utils.get_value(lambda_f, i, l_descr)
l_f_err2 = hlr_utils.get_err2(lambda_f, i, l_descr)
else:
l_f_tuple = hlr_utils.get_special(som_l_f, map_so)
l_f = l_f_tuple[0]
l_f_err2 = l_f_tuple[1]
if time_zero_slope is not None:
t_0_slope = hlr_utils.get_value(time_zero_slope, i,
t_0_slope_descr)
t_0_slope_err2 = hlr_utils.get_err2(time_zero_slope, i,
t_0_slope_descr)
else:
pass
if time_zero_offset is not None:
t_0_offset = hlr_utils.get_value(time_zero_offset, i,
t_0_offset_descr)
t_0_offset_err2 = hlr_utils.get_err2(time_zero_offset, i,
t_0_offset_descr)
else:
pass
value = axis_manip.tof_to_initial_wavelength_igs_lin_time_zero(
val, err2,
l_f, l_f_err2,
t_0_slope, t_0_slope_err2,
t_0_offset, t_0_offset_err2,
L_s, L_s_err2,
L_d, L_d_err2)
# Remove all wavelengths < 0
if run_filter:
index = 0
for valx in value[0]:
if valx >= 0:
break
index += 1
value[0].__delslice__(0, index)
value[1].__delslice__(0, index)
map_so.y.__delslice__(0, index)
map_so.var_y.__delslice__(0, index)
if lojac:
val.__delslice__(0, index)
err2.__delslice__(0, index)
else:
pass
if lojac:
try:
counts = utils.linear_order_jacobian(val, value[0],
map_so.y, map_so.var_y)
except Exception, e:
# Lets us know offending pixel ID
raise Exception(str(map_so.id) + " " + str(e))
hlr_utils.result_insert(result, res_descr, counts, map_so,
"all", axis, [value[0]])
else:
hlr_utils.result_insert(result, res_descr, value, map_so,
"x", axis)
def calc_delta_theta_over_theta(som, dataset_type="data"):
"""
This function takes a C{SOM} that contains reflectometer slit and angle
information and calculates the parameter delta theta / theta. The function
will also store the calculated prameter as well as other slit and angle
information into the C{SOM}s attribute list. The function will not return
the C{SOM} as the core data values were not changed.
@param som: The object that contains the slit and angle information
@type som: C{SOM.SOM}
@param dataset_type: The practical name of the dataset being processed.
The default value is I{data}.
@type dataset_type: C{string}
@raise TypeError: Anything other than a C{SOM} is given
"""
import hlr_utils
import SOM
o_descr = hlr_utils.get_descr(som)
if o_descr != "SOM":
raise TypeError("Function argument must be a SOM")
# Have a SOM, go on
else:
pass
import math
# Create a dummy SO
so = SOM.SO()
# Create a dummy information tuple
no_info = (None, None, None)
# Set instrument specific strings
inst_name = som.attr_list["instrument_name"]
if inst_name == "REF_L":
first_slit_ext1 = "Slit1_top"
first_slit_ext2 = "Slit1_bottom"
last_slit_ext1 = "Slit2_top"
last_slit_ext2 = "Slit2_bottom"
last_slit_dis = "Slit2_distance"
last_slit = "slit2"
last_slit_distot = "slit12"
slit_ext1 = "top"
slit_ext2 = "bottom"
elif inst_name == "REF_M":
first_slit_ext1 = "Slit1_left"
first_slit_ext2 = "Slit1_right"
last_slit_ext1 = "Slit3_left"
last_slit_ext2 = "Slit3_right"
last_slit_dis = "Slit3_distance"
last_slit = "slit3"
last_slit_distot = "slit13"
slit_ext1 = "left"
slit_ext2 = "right"
else:
raise RuntimeError("Do not know how to handle instrument %s" % inst_name)
# Get slit information
try:
slit1_ext1 = hlr_utils.get_special(som.attr_list[first_slit_ext1], so)
except KeyError:
slit1_ext1 = no_info
try:
slit1_ext2 = hlr_utils.get_special(som.attr_list[first_slit_ext2], so)
except KeyError:
slit1_ext2 = no_info
try:
slit1_dist = hlr_utils.get_special(som.attr_list["Slit1_distance"], so)
except KeyError:
slit1_dist = no_info
if slit1_ext1[0] is None or slit1_ext2[0] is None:
slit1_size_ok = False
else:
slit1_size_ok = True
try:
slit2_ext1 = hlr_utils.get_special(som.attr_list[last_slit_ext1], so)
except KeyError:
slit2_ext1 = no_info
try:
slit2_ext2 = hlr_utils.get_special(som.attr_list[last_slit_ext2], so)
except KeyError:
slit2_ext2 = no_info
try:
slit2_dist = hlr_utils.get_special(som.attr_list[last_slit_dis], so)
except KeyError:
slit2_dist = no_info
if slit2_ext1[0] is None or slit2_ext2[0] is None:
slit2_size_ok = False
else:
slit2_size_ok = True
if slit1_dist[0] is None or slit2_dist[0] is None:
slit12_dist_ok = False
else:
slit12_dist_ok = True
# Unit checks
if slit1_size_ok and slit2_size_ok:
if slit1_ext1[2] != slit2_ext1[2]:
raise ValueError(
"Slit %s opening distances are not in the same "
+ "units. slit1 (%s), %s (%s)" % (slit_ext1, slit1_ext1[2], last_slit, slit2_ext1[2])
)
if slit1_ext2[2] != slit2_ext2[2]:
raise ValueError(
"Slit %s opening distances are not in the "
+ "same units. slit1 (%s), %s (%s)" % (slit_ext2, slit1_ext2[2], last_slit, slit2_ext2[2])
)
if slit1_dist[2] != slit2_dist[2] and slit12_dist_ok:
raise ValueError(
"Slit distances are not in the same units. "
+ "slit1 (%s), %s (%s)" % (slit1_dist[2], last_slit, slit2_dist[2])
)
# Calculate intermediate slit parameters
if slit1_size_ok:
slit1_size = math.fabs(slit1_ext1[0] - slit1_ext2[0])
if slit1_ext1[2] == "millimetre":
slit1_size /= 1000.0
slit1_size_units = "metre"
else:
slit1_size_units = slit1_ext1[2]
else:
slit1_size = float("nan")
slit1_size_units = None
if slit2_size_ok:
slit2_size = math.fabs(slit2_ext1[0] - slit2_ext2[0])
if slit2_ext1[2] == "millimetre":
slit2_size /= 1000.0
slit2_size_units = "metre"
else:
slit2_size_units = slit2_ext1[2]
else:
slit2_size = float("nan")
slit2_size_units = None
if slit12_dist_ok:
slit12_distance = math.fabs(slit1_dist[0] - slit2_dist[0])
slit2_distance = math.fabs(slit2_dist[0])
else:
slit12_distance = float("nan")
slit2_distance = float("nan")
# Calculate delta theta
if slit1_size_ok and not slit2_size_ok:
dtheta = slit1_size / slit12_distance
elif slit2_size_ok and not slit1_size_ok:
dtheta = slit2_size / slit12_distance
else:
dtheta = max(slit1_size, slit2_size) / slit12_distance
# Calculate delta theta over theta
try:
theta = hlr_utils.get_special(som.attr_list["Theta"], so)
except KeyError:
theta = no_info
if theta[0] is not None:
if theta[2] == "degrees" or theta[2] == "degree":
theta_rads = theta[0] * (math.pi / 180.0)
else:
theta_rads = theta[0]
else:
theta_rads = float("nan")
dtheta_over_theta = dtheta / theta_rads
# Add parameters to attribute list
som.attr_list[dataset_type + "-slit1_size"] = (slit1_size, slit1_size_units)
last_slit_tag = "-%s_size" % last_slit
som.attr_list[dataset_type + last_slit_tag] = (slit2_size, slit2_size_units)
last_slit_dist_tag = "-%s_distance" % last_slit_distot
som.attr_list[dataset_type + last_slit_dist_tag] = (slit12_distance, slit1_dist[2])
som.attr_list[dataset_type + "-" + last_slit_dis.lower()] = (slit2_distance, slit1_dist[2])
som.attr_list[dataset_type + "-delta_theta"] = (dtheta, "radians")
som.attr_list[dataset_type + "-theta"] = (theta_rads, "radians")
som.attr_list[dataset_type + "-dtheta_over_theta"] = dtheta_over_theta
def create_E_vs_Q_igs(som, *args, **kwargs):
"""
This function starts with the initial IGS wavelength axis and turns this
into a 2D spectra with E and Q axes.
@param som: The input object with initial IGS wavelength axis
@type som: C{SOM.SOM}
@param args: A mandatory list of axes for rebinning. There is a particular
order to them. They should be present in the following order:
Without errors
1. Energy transfer
2. Momentum transfer
With errors
1. Energy transfer
2. Energy transfer error^2
3. Momentum transfer
4. Momentum transfer error ^2
@type args: C{nessi_list.NessiList}s
@param kwargs: A list of keyword arguments that the function accepts:
@keyword withXVar: Flag for whether the function should be expecting the
associated axes to have errors. The default value will
be I{False}.
@type withXVar: C{boolean}
@keyword data_type: Name of the data type which can be either I{histogram},
I{density} or I{coordinate}. The default value will be
I{histogram}
@type data_type: C{string}
@keyword Q_filter: Flag to turn on or off Q filtering. The default behavior
is I{True}.
@type Q_filter: C{boolean}
@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}
@raise RuntimeError: Anything other than a C{SOM} is passed to the function
@raise RuntimeError: An instrument is not contained in the C{SOM}
"""
import nessi_list
# Setup some variables
dim = 2
N_y = []
N_tot = 1
N_args = len(args)
# Get T0 slope in order to calculate dT = dT_i + dT_0
try:
t_0_slope = som.attr_list["Time_zero_slope"][0]
t_0_slope_err2 = som.attr_list["Time_zero_slope"][1]
except KeyError:
t_0_slope = float(0.0)
t_0_slope_err2 = float(0.0)
# Check withXVar keyword argument and also check number of given args.
# Set xvar to the appropriate value
try:
value = kwargs["withXVar"]
if value.lower() == "true":
if N_args != 4:
raise RuntimeError("Since you have requested x errors, 4 x "\
+"axes must be provided.")
else:
xvar = True
elif value.lower() == "false":
if N_args != 2:
raise RuntimeError("Since you did not requested x errors, 2 "\
+"x axes must be provided.")
else:
xvar = False
else:
raise RuntimeError("Do not understand given parameter %s" % \
value)
except KeyError:
if N_args != 2:
raise RuntimeError("Since you did not requested x errors, 2 "\
+"x axes must be provided.")
else:
xvar = False
# Check dataType keyword argument. An offset will be set to 1 for the
# histogram type and 0 for either density or coordinate
try:
data_type = kwargs["data_type"]
if data_type.lower() == "histogram":
offset = 1
elif data_type.lower() == "density" or \
data_type.lower() == "coordinate":
offset = 0
else:
raise RuntimeError("Do not understand data type given: %s" % \
data_type)
# Default is offset for histogram
except KeyError:
offset = 1
try:
Q_filter = kwargs["Q_filter"]
except KeyError:
Q_filter = True
# Check for split keyword
try:
split = kwargs["split"]
except KeyError:
split = False
# Check for configure keyword
try:
configure = kwargs["configure"]
except KeyError:
configure = None
so_dim = SOM.SO(dim)
for i in range(dim):
# Set the x-axis arguments from the *args list into the new SO
if not xvar:
# Axis positions are 1 (Q) and 0 (E)
position = dim - i - 1
so_dim.axis[i].val = args[position]
else:
# Axis positions are 2 (Q), 3 (eQ), 0 (E), 1 (eE)
position = dim - 2 * i
so_dim.axis[i].val = args[position]
so_dim.axis[i].var = args[position + 1]
# Set individual value axis sizes (not x-axis size)
N_y.append(len(args[position]) - offset)
# Calculate total 2D array size
N_tot = N_tot * N_y[-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)
# Create area sum and errors for the area sum lists from total 2D size
bin_count = nessi_list.NessiList(N_tot)
bin_count_err2 = nessi_list.NessiList(N_tot)
inst = som.attr_list.instrument
lambda_final = som.attr_list["Wavelength_final"]
inst_name = inst.get_name()
import bisect
import math
import dr_lib
import utils
arr_len = 0
#: Vector of zeros for function calculations
zero_vec = None
for j in xrange(hlr_utils.get_length(som)):
# Get counts
counts = hlr_utils.get_value(som, j, "SOM", "y")
counts_err2 = hlr_utils.get_err2(som, j, "SOM", "y")
arr_len = len(counts)
zero_vec = nessi_list.NessiList(arr_len)
# Get mapping SO
map_so = hlr_utils.get_map_so(som, None, j)
# Get lambda_i
l_i = hlr_utils.get_value(som, j, "SOM", "x")
l_i_err2 = hlr_utils.get_err2(som, j, "SOM", "x")
# Get lambda_f from instrument information
l_f_tuple = hlr_utils.get_special(lambda_final, map_so)
l_f = l_f_tuple[0]
l_f_err2 = l_f_tuple[1]
# Get source to sample distance
(L_s, L_s_err2) = hlr_utils.get_parameter("primary", map_so, inst)
# Get sample to detector distance
L_d_tuple = hlr_utils.get_parameter("secondary", map_so, inst)
L_d = L_d_tuple[0]
# Get polar angle from instrument information
(angle, angle_err2) = hlr_utils.get_parameter("polar", map_so, inst)
# Get the detector pixel height
dh_tuple = hlr_utils.get_parameter("dh", map_so, inst)
dh = dh_tuple[0]
# Need dh in units of Angstrom
dh *= 1e10
# Calculate T_i
(T_i, T_i_err2) = axis_manip.wavelength_to_tof(l_i, l_i_err2,
L_s, L_s_err2)
# Scale counts by lambda_f / lambda_i
(l_i_bc, l_i_bc_err2) = utils.calc_bin_centers(l_i, l_i_err2)
(ratio, ratio_err2) = array_manip.div_ncerr(l_f, l_f_err2,
l_i_bc, l_i_bc_err2)
(counts, counts_err2) = array_manip.mult_ncerr(counts, counts_err2,
ratio, ratio_err2)
# Calculate E_i
(E_i, E_i_err2) = axis_manip.wavelength_to_energy(l_i, l_i_err2)
# Calculate E_f
(E_f, E_f_err2) = axis_manip.wavelength_to_energy(l_f, l_f_err2)
# Calculate E_t
(E_t, E_t_err2) = array_manip.sub_ncerr(E_i, E_i_err2, E_f, E_f_err2)
if inst_name == "BSS":
# Convert E_t from meV to ueV
(E_t, E_t_err2) = array_manip.mult_ncerr(E_t, E_t_err2,
1000.0, 0.0)
(counts, counts_err2) = array_manip.mult_ncerr(counts, counts_err2,
1.0/1000.0, 0.0)
# Convert lambda_i to k_i
(k_i, k_i_err2) = axis_manip.wavelength_to_scalar_k(l_i, l_i_err2)
# Convert lambda_f to k_f
(k_f, k_f_err2) = axis_manip.wavelength_to_scalar_k(l_f, l_f_err2)
# Convert k_i and k_f to Q
(Q, Q_err2) = axis_manip.init_scatt_wavevector_to_scalar_Q(k_i,
k_i_err2,
k_f,
k_f_err2,
angle,
angle_err2)
# Calculate dT = dT_0 + dT_i
dT_i = utils.calc_bin_widths(T_i, T_i_err2)
(l_i_bw, l_i_bw_err2) = utils.calc_bin_widths(l_i, l_i_err2)
dT_0 = array_manip.mult_ncerr(l_i_bw, l_i_bw_err2,
t_0_slope, t_0_slope_err2)
dT_tuple = array_manip.add_ncerr(dT_i[0], dT_i[1], dT_0[0], dT_0[1])
dT = dT_tuple[0]
# Calculate Jacobian
if inst_name == "BSS":
(x_1, x_2,
x_3, x_4) = dr_lib.calc_BSS_coeffs(map_so, inst, (E_i, E_i_err2),
(Q, Q_err2), (k_i, k_i_err2),
(T_i, T_i_err2), dh, angle,
E_f, k_f, l_f, L_s, L_d,
t_0_slope, zero_vec)
else:
raise RuntimeError("Do not know how to calculate x_i "\
+"coefficients for instrument %s" % inst_name)
(A, A_err2) = dr_lib.calc_EQ_Jacobian(x_1, x_2, x_3, x_4, dT, dh,
zero_vec)
# Apply Jacobian: C/dlam * dlam / A(EQ) = C/EQ
(jac_ratio, jac_ratio_err2) = array_manip.div_ncerr(l_i_bw,
l_i_bw_err2,
A, A_err2)
(counts, counts_err2) = array_manip.mult_ncerr(counts, counts_err2,
jac_ratio,
jac_ratio_err2)
# Reverse counts, E_t, k_i and Q
E_t = axis_manip.reverse_array_cp(E_t)
E_t_err2 = axis_manip.reverse_array_cp(E_t_err2)
Q = axis_manip.reverse_array_cp(Q)
Q_err2 = axis_manip.reverse_array_cp(Q_err2)
counts = axis_manip.reverse_array_cp(counts)
counts_err2 = axis_manip.reverse_array_cp(counts_err2)
k_i = axis_manip.reverse_array_cp(k_i)
x_1 = axis_manip.reverse_array_cp(x_1)
x_2 = axis_manip.reverse_array_cp(x_2)
x_3 = axis_manip.reverse_array_cp(x_3)
x_4 = axis_manip.reverse_array_cp(x_4)
dT = axis_manip.reverse_array_cp(dT)
# Filter for duplicate Q values
if Q_filter:
k_i_cutoff = k_f * math.cos(angle)
k_i_cutbin = bisect.bisect(k_i, k_i_cutoff)
counts.__delslice__(0, k_i_cutbin)
counts_err2.__delslice__(0, k_i_cutbin)
Q.__delslice__(0, k_i_cutbin)
Q_err2.__delslice__(0, k_i_cutbin)
E_t.__delslice__(0, k_i_cutbin)
E_t_err2.__delslice__(0, k_i_cutbin)
x_1.__delslice__(0, k_i_cutbin)
x_2.__delslice__(0, k_i_cutbin)
x_3.__delslice__(0, k_i_cutbin)
x_4.__delslice__(0, k_i_cutbin)
dT.__delslice__(0, k_i_cutbin)
zero_vec.__delslice__(0, k_i_cutbin)
try:
if inst_name == "BSS":
((Q_1, E_t_1),
(Q_2, E_t_2),
(Q_3, E_t_3),
(Q_4, E_t_4)) = dr_lib.calc_BSS_EQ_verticies((E_t, E_t_err2),
(Q, Q_err2), x_1,
x_2, x_3, x_4,
dT, dh, zero_vec)
else:
raise RuntimeError("Do not know how to calculate (Q_i, "\
+"E_t_i) verticies for instrument %s" \
% inst_name)
except IndexError:
# All the data got Q filtered, move on
continue
try:
(y_2d, y_2d_err2,
area_new,
bin_count_new) = axis_manip.rebin_2D_quad_to_rectlin(Q_1, E_t_1,
Q_2, E_t_2,
Q_3, E_t_3,
Q_4, E_t_4,
counts,
counts_err2,
so_dim.axis[0].val,
so_dim.axis[1].val)
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" % (Q_1[index],
E_t_1[index],
Q_2[index],
E_t_2[index],
Q_3[index],
E_t_3[index],
Q_4[index],
E_t_4[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)
if configure.dump_pix_contrib or configure.scale_sqe:
if inst_name == "BSS":
dOmega = dr_lib.calc_BSS_solid_angle(map_so, inst)
(bin_count_new,
bin_count_err2) = array_manip.mult_ncerr(bin_count_new,
bin_count_err2,
dOmega, 0.0)
(bin_count,
bin_count_err2) = array_manip.add_ncerr(bin_count,
bin_count_err2,
bin_count_new,
bin_count_err2)
else:
del bin_count_new
def create_E_vs_Q_igs(som, *args, **kwargs):
"""
This function starts with the initial IGS wavelength axis and turns this
into a 2D spectra with E and Q axes.
@param som: The input object with initial IGS wavelength axis
@type som: C{SOM.SOM}
@param args: A mandatory list of axes for rebinning. There is a particular
order to them. They should be present in the following order:
Without errors
1. Energy transfer
2. Momentum transfer
With errors
1. Energy transfer
2. Energy transfer error^2
3. Momentum transfer
4. Momentum transfer error ^2
@type args: C{nessi_list.NessiList}s
@param kwargs: A list of keyword arguments that the function accepts:
@keyword withXVar: Flag for whether the function should be expecting the
associated axes to have errors. The default value will
be I{False}.
@type withXVar: C{boolean}
@keyword data_type: Name of the data type which can be either I{histogram},
I{density} or I{coordinate}. The default value will be
I{histogram}
@type data_type: C{string}
@keyword Q_filter: Flag to turn on or off Q filtering. The default behavior
is I{True}.
@type Q_filter: C{boolean}
@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}
@raise RuntimeError: Anything other than a C{SOM} is passed to the function
@raise RuntimeError: An instrument is not contained in the C{SOM}
"""
import nessi_list
# Setup some variables
dim = 2
N_y = []
N_tot = 1
N_args = len(args)
# Get T0 slope in order to calculate dT = dT_i + dT_0
try:
t_0_slope = som.attr_list["Time_zero_slope"][0]
t_0_slope_err2 = som.attr_list["Time_zero_slope"][1]
except KeyError:
t_0_slope = float(0.0)
t_0_slope_err2 = float(0.0)
# Check withXVar keyword argument and also check number of given args.
# Set xvar to the appropriate value
try:
value = kwargs["withXVar"]
if value.lower() == "true":
if N_args != 4:
raise RuntimeError("Since you have requested x errors, 4 x "\
+"axes must be provided.")
else:
xvar = True
elif value.lower() == "false":
if N_args != 2:
raise RuntimeError("Since you did not requested x errors, 2 "\
+"x axes must be provided.")
else:
xvar = False
else:
raise RuntimeError("Do not understand given parameter %s" % \
value)
except KeyError:
if N_args != 2:
raise RuntimeError("Since you did not requested x errors, 2 "\
+"x axes must be provided.")
else:
xvar = False
# Check dataType keyword argument. An offset will be set to 1 for the
# histogram type and 0 for either density or coordinate
try:
data_type = kwargs["data_type"]
if data_type.lower() == "histogram":
offset = 1
elif data_type.lower() == "density" or \
data_type.lower() == "coordinate":
offset = 0
else:
raise RuntimeError("Do not understand data type given: %s" % \
data_type)
# Default is offset for histogram
except KeyError:
offset = 1
try:
Q_filter = kwargs["Q_filter"]
except KeyError:
Q_filter = True
# Check for split keyword
try:
split = kwargs["split"]
except KeyError:
split = False
# Check for configure keyword
try:
configure = kwargs["configure"]
except KeyError:
configure = None
so_dim = SOM.SO(dim)
for i in range(dim):
# Set the x-axis arguments from the *args list into the new SO
if not xvar:
# Axis positions are 1 (Q) and 0 (E)
position = dim - i - 1
so_dim.axis[i].val = args[position]
else:
# Axis positions are 2 (Q), 3 (eQ), 0 (E), 1 (eE)
position = dim - 2 * i
so_dim.axis[i].val = args[position]
so_dim.axis[i].var = args[position + 1]
# Set individual value axis sizes (not x-axis size)
N_y.append(len(args[position]) - offset)
# Calculate total 2D array size
N_tot = N_tot * N_y[-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)
# Create area sum and errors for the area sum lists from total 2D size
bin_count = nessi_list.NessiList(N_tot)
bin_count_err2 = nessi_list.NessiList(N_tot)
inst = som.attr_list.instrument
lambda_final = som.attr_list["Wavelength_final"]
inst_name = inst.get_name()
import bisect
import math
import dr_lib
import utils
arr_len = 0
#: Vector of zeros for function calculations
zero_vec = None
for j in xrange(hlr_utils.get_length(som)):
# Get counts
counts = hlr_utils.get_value(som, j, "SOM", "y")
counts_err2 = hlr_utils.get_err2(som, j, "SOM", "y")
arr_len = len(counts)
zero_vec = nessi_list.NessiList(arr_len)
# Get mapping SO
map_so = hlr_utils.get_map_so(som, None, j)
# Get lambda_i
l_i = hlr_utils.get_value(som, j, "SOM", "x")
l_i_err2 = hlr_utils.get_err2(som, j, "SOM", "x")
# Get lambda_f from instrument information
l_f_tuple = hlr_utils.get_special(lambda_final, map_so)
l_f = l_f_tuple[0]
l_f_err2 = l_f_tuple[1]
# Get source to sample distance
(L_s, L_s_err2) = hlr_utils.get_parameter("primary", map_so, inst)
# Get sample to detector distance
L_d_tuple = hlr_utils.get_parameter("secondary", map_so, inst)
L_d = L_d_tuple[0]
# Get polar angle from instrument information
(angle, angle_err2) = hlr_utils.get_parameter("polar", map_so, inst)
# Get the detector pixel height
dh_tuple = hlr_utils.get_parameter("dh", map_so, inst)
dh = dh_tuple[0]
# Need dh in units of Angstrom
dh *= 1e10
# Calculate T_i
(T_i, T_i_err2) = axis_manip.wavelength_to_tof(l_i, l_i_err2, L_s,
L_s_err2)
# Scale counts by lambda_f / lambda_i
(l_i_bc, l_i_bc_err2) = utils.calc_bin_centers(l_i, l_i_err2)
(ratio, ratio_err2) = array_manip.div_ncerr(l_f, l_f_err2, l_i_bc,
l_i_bc_err2)
(counts, counts_err2) = array_manip.mult_ncerr(counts, counts_err2,
ratio, ratio_err2)
# Calculate E_i
(E_i, E_i_err2) = axis_manip.wavelength_to_energy(l_i, l_i_err2)
# Calculate E_f
(E_f, E_f_err2) = axis_manip.wavelength_to_energy(l_f, l_f_err2)
# Calculate E_t
(E_t, E_t_err2) = array_manip.sub_ncerr(E_i, E_i_err2, E_f, E_f_err2)
if inst_name == "BSS":
# Convert E_t from meV to ueV
(E_t, E_t_err2) = array_manip.mult_ncerr(E_t, E_t_err2, 1000.0,
0.0)
(counts,
counts_err2) = array_manip.mult_ncerr(counts, counts_err2,
1.0 / 1000.0, 0.0)
# Convert lambda_i to k_i
(k_i, k_i_err2) = axis_manip.wavelength_to_scalar_k(l_i, l_i_err2)
# Convert lambda_f to k_f
(k_f, k_f_err2) = axis_manip.wavelength_to_scalar_k(l_f, l_f_err2)
# Convert k_i and k_f to Q
(Q, Q_err2) = axis_manip.init_scatt_wavevector_to_scalar_Q(
k_i, k_i_err2, k_f, k_f_err2, angle, angle_err2)
# Calculate dT = dT_0 + dT_i
dT_i = utils.calc_bin_widths(T_i, T_i_err2)
(l_i_bw, l_i_bw_err2) = utils.calc_bin_widths(l_i, l_i_err2)
dT_0 = array_manip.mult_ncerr(l_i_bw, l_i_bw_err2, t_0_slope,
t_0_slope_err2)
dT_tuple = array_manip.add_ncerr(dT_i[0], dT_i[1], dT_0[0], dT_0[1])
dT = dT_tuple[0]
# Calculate Jacobian
if inst_name == "BSS":
(x_1, x_2, x_3, x_4) = dr_lib.calc_BSS_coeffs(
map_so, inst, (E_i, E_i_err2), (Q, Q_err2), (k_i, k_i_err2),
(T_i, T_i_err2), dh, angle, E_f, k_f, l_f, L_s, L_d, t_0_slope,
zero_vec)
else:
raise RuntimeError("Do not know how to calculate x_i "\
+"coefficients for instrument %s" % inst_name)
(A, A_err2) = dr_lib.calc_EQ_Jacobian(x_1, x_2, x_3, x_4, dT, dh,
zero_vec)
# Apply Jacobian: C/dlam * dlam / A(EQ) = C/EQ
(jac_ratio,
jac_ratio_err2) = array_manip.div_ncerr(l_i_bw, l_i_bw_err2, A,
A_err2)
(counts, counts_err2) = array_manip.mult_ncerr(counts, counts_err2,
jac_ratio,
jac_ratio_err2)
# Reverse counts, E_t, k_i and Q
E_t = axis_manip.reverse_array_cp(E_t)
E_t_err2 = axis_manip.reverse_array_cp(E_t_err2)
Q = axis_manip.reverse_array_cp(Q)
Q_err2 = axis_manip.reverse_array_cp(Q_err2)
counts = axis_manip.reverse_array_cp(counts)
counts_err2 = axis_manip.reverse_array_cp(counts_err2)
k_i = axis_manip.reverse_array_cp(k_i)
x_1 = axis_manip.reverse_array_cp(x_1)
x_2 = axis_manip.reverse_array_cp(x_2)
x_3 = axis_manip.reverse_array_cp(x_3)
x_4 = axis_manip.reverse_array_cp(x_4)
dT = axis_manip.reverse_array_cp(dT)
# Filter for duplicate Q values
if Q_filter:
k_i_cutoff = k_f * math.cos(angle)
k_i_cutbin = bisect.bisect(k_i, k_i_cutoff)
counts.__delslice__(0, k_i_cutbin)
counts_err2.__delslice__(0, k_i_cutbin)
Q.__delslice__(0, k_i_cutbin)
Q_err2.__delslice__(0, k_i_cutbin)
E_t.__delslice__(0, k_i_cutbin)
E_t_err2.__delslice__(0, k_i_cutbin)
x_1.__delslice__(0, k_i_cutbin)
x_2.__delslice__(0, k_i_cutbin)
x_3.__delslice__(0, k_i_cutbin)
x_4.__delslice__(0, k_i_cutbin)
dT.__delslice__(0, k_i_cutbin)
zero_vec.__delslice__(0, k_i_cutbin)
try:
if inst_name == "BSS":
((Q_1, E_t_1), (Q_2, E_t_2), (Q_3, E_t_3),
(Q_4, E_t_4)) = dr_lib.calc_BSS_EQ_verticies(
(E_t, E_t_err2), (Q, Q_err2), x_1, x_2, x_3, x_4, dT, dh,
zero_vec)
else:
raise RuntimeError("Do not know how to calculate (Q_i, "\
+"E_t_i) verticies for instrument %s" \
% inst_name)
except IndexError:
# All the data got Q filtered, move on
continue
try:
(y_2d, y_2d_err2, area_new,
bin_count_new) = axis_manip.rebin_2D_quad_to_rectlin(
Q_1, E_t_1, Q_2, E_t_2, Q_3, E_t_3, Q_4, E_t_4, counts,
counts_err2, so_dim.axis[0].val, so_dim.axis[1].val)
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" % (
Q_1[index], E_t_1[index], Q_2[index], E_t_2[index], Q_3[index],
E_t_3[index], Q_4[index], E_t_4[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)
if configure.dump_pix_contrib or configure.scale_sqe:
if inst_name == "BSS":
dOmega = dr_lib.calc_BSS_solid_angle(map_so, inst)
(bin_count_new, bin_count_err2) = array_manip.mult_ncerr(
bin_count_new, bin_count_err2, dOmega, 0.0)
(bin_count, bin_count_err2) = array_manip.add_ncerr(
bin_count, bin_count_err2, bin_count_new, bin_count_err2)
else:
del bin_count_new
def tof_to_ref_scalar_Q(obj, **kwargs):
"""
This function converts a primary axis of a C{SOM} or C{SO} from
time-of-flight to reflectometer scalar Q. This means that a single angle
and a single flightpath is used. The time-of-flight axis for a C{SOM} must
be in units of I{microseconds}. The primary axis of a C{SO} is assumed to
be in units of I{microseconds}. A C{tuple} of C{(time-of-flight,
time-of-flight_err2)} (assumed to be in units of I{microseconds}) can be
converted to C{(scalar_Q, scalar_Q_err2)}.
@param obj: Object to be converted
@type obj: C{SOM.SOM}, C{SOM.SO} or C{tuple}
@param kwargs: A list of keyword arguments that the function accepts:
@keyword polar: The polar angle and its associated error^2
@type polar: C{tuple}
@keyword pathlength: The pathlength and its associated error^2
@type pathlength: C{tuple}
@keyword angle_offset: A constant offset for the polar angle and its
associated error^2. The units of the offset should
be in radians.
@type angle_offset: C{tuple}
@keyword lojac: A flag that allows one to turn off the calculation of the
linear-order Jacobian. The default action is True for
histogram data.
@type lojac: C{boolean}
@keyword units: The expected units for this function. The default for this
function is I{microseconds}.
@type units: C{string}
@keyword configure: This is the object containing the driver configuration.
This will signal the function to write out the counts
and fractional area to files.
@type configure: C{Configure}
@return: Object with a primary axis in time-of-flight converted to
reflectometer scalar Q
@rtype: C{SOM.SOM}, C{SOM.SO} or C{tuple}
@raise TypeError: The incoming object is not a type the function recognizes
@raise RuntimeError: A C{SOM} is not passed and no polar angle is provided
@raise RuntimeError: The C{SOM} x-axis units are not I{microseconds}
"""
# import the helper functions
import hlr_utils
# set up for working through data
(result, res_descr) = hlr_utils.empty_result(obj)
o_descr = hlr_utils.get_descr(obj)
if o_descr == "list":
raise TypeError("Do not know how to handle given type: %s" % \
o_descr)
else:
pass
# Setup keyword arguments
polar = kwargs.get("polar")
pathlength = kwargs.get("pathlength")
units = kwargs.get("units", "microseconds")
lojac = kwargs.get("lojac", hlr_utils.check_lojac(obj))
angle_offset = kwargs.get("angle_offset")
config = kwargs.get("configure")
if config is None:
beamdiv_corr = False
else:
beamdiv_corr = config.beamdiv_corr
# Primary axis for transformation. If a SO is passed, the function, will
# assume the axis for transformation is at the 0 position
if o_descr == "SOM":
axis = hlr_utils.one_d_units(obj, units)
else:
axis = 0
result = hlr_utils.copy_som_attr(result, res_descr, obj, o_descr)
if res_descr == "SOM":
result = hlr_utils.force_units(result, "1/Angstroms", axis)
result.setAxisLabel(axis, "scalar wavevector transfer")
result.setYUnits("Counts/A-1")
result.setYLabel("Intensity")
else:
pass
if pathlength is None or polar is None:
if o_descr == "SOM":
try:
obj.attr_list.instrument.get_primary()
inst = obj.attr_list.instrument
except RuntimeError:
raise RuntimeError("A detector was not provided")
else:
if pathlength is None and polar is None:
raise RuntimeError("If no SOM is provided, then pathlength "\
+"and polar angle information must be "\
+"provided")
elif pathlength is None:
raise RuntimeError("If no SOM is provided, then pathlength "\
+"information must be provided")
elif polar is None:
raise RuntimeError("If no SOM is provided, then polar angle "\
+"information must be provided")
else:
raise RuntimeError("If you get here, see Steve Miller for "\
+"your mug.")
else:
pass
if pathlength is None:
(pl,
pl_err2) = obj.attr_list.instrument.get_total_path(obj[0].id,
det_secondary=True)
else:
(pl, pl_err2) = pathlength
if polar is None:
angle = hlr_utils.get_special(obj.attr_list["data-theta"], obj[0])[0]
angle_err2 = 0.0
else:
(angle, angle_err2) = polar
if angle_offset is not None:
angle += angle_offset[0]
angle_err2 += angle_offset[1]
# Need to multiply angle by 2.0 in order to make it be Theta to
# underlying conversion function
angle *= 2.0
angle_err2 *= 4.0
# iterate through the values
import axis_manip
if lojac:
import utils
if beamdiv_corr:
import dr_lib
for i in xrange(hlr_utils.get_length(obj)):
skip_pixel = False
val = hlr_utils.get_value(obj, i, o_descr, "x", axis)
err2 = hlr_utils.get_err2(obj, i, o_descr, "x", axis)
map_so = hlr_utils.get_map_so(obj, None, i)
if beamdiv_corr:
dangle = dr_lib.ref_beamdiv_correct(obj.attr_list, map_so.id,
config.det_spat_res,
config.center_pix)
# We subtract due to the inversion of the z coordinates from the
# mirror reflection of the beam at the sample.
if dangle is not None:
pangle = angle - (2.0 * dangle)
else:
pangle = angle
skip_pixel = True
else:
pangle = angle
value = axis_manip.tof_to_scalar_Q(val, err2, pl, pl_err2, pangle,
angle_err2)
if lojac:
y_val = hlr_utils.get_value(obj, i, o_descr, "y")
y_err2 = hlr_utils.get_err2(obj, i, o_descr, "y")
counts = utils.linear_order_jacobian(val, value[0], y_val, y_err2)
else:
pass
if o_descr != "number":
value1 = axis_manip.reverse_array_cp(value[0])
value2 = axis_manip.reverse_array_cp(value[1])
rev_value = (value1, value2)
else:
rev_value = value
if map_so is not None:
if not lojac:
map_so.y = axis_manip.reverse_array_cp(map_so.y)
map_so.var_y = axis_manip.reverse_array_cp(map_so.var_y)
else:
map_so.y = axis_manip.reverse_array_cp(counts[0])
map_so.var_y = axis_manip.reverse_array_cp(counts[1])
else:
pass
if not skip_pixel:
hlr_utils.result_insert(result, res_descr, rev_value, map_so, "x",
axis)
return result
def igs_energy_transfer(obj, **kwargs):
"""
@depricated: This function will eventually disappear when the full S(Q,E)
transformation for IGS detectors is completed and verified.
This function takes a SOM or a SO and calculates the energy transfer for
the IGS class of instruments. It is different from
common_lib.energy_transfer in that the final wavelength is provided in a
SOM.Information, SOM.CompositeInformation or a tuple, then converted to
energy in place before being given to the common_lib.energy_transfer
function.
Parameters:
----------
-> obj
-> kwargs is a list of key word arguments that the function accepts:
units= a string containing the expected units for this function.
The default for this function is meV
lambda_f= a SOM.Information, SOM.CompositeInformation or a tuple
containing the final wavelength information
offset= a SOM.Information or SOM.CompositeInformation containing
the final energy offsets
scale=<boolean> is a flag that determines if the energy transfer
results are scaled by the ratio of lambda_f/lambda_i.
The default is False
Returns:
-------
<- A SOM or SO with the energy transfer calculated in units of THz
Exceptions:
----------
<- RuntimeError is raised if the x-axis units are not meV
<- RuntimeError is raised if a SOM or SO is not given to the function
<- RuntimeError is raised if the final wavelength is not provided to the
function
"""
# import the helper functions
import hlr_utils
# set up for working through data
(result, res_descr) = hlr_utils.empty_result(obj)
o_descr = hlr_utils.get_descr(obj)
if o_descr == "number" or o_descr == "list":
raise RuntimeError, "Must provide a SOM of a SO to the function."
# Go on
else:
pass
# Setup keyword arguments
try:
units = kwargs["units"]
except KeyError:
units = "meV"
try:
lambda_f = kwargs["lambda_f"]
except KeyError:
lambda_f = None
try:
offset = kwargs["offset"]
except KeyError:
offset = None
try:
scale = kwargs["scale"]
except KeyError:
scale = False
# Primary axis for transformation. If a SO is passed, the function, will
# assume the axis for transformation is at the 0 position
if o_descr == "SOM":
axis = hlr_utils.one_d_units(obj, units)
else:
axis = 0
if lambda_f is None:
if o_descr == "SOM":
try:
lambda_f = obj.attr_list["Wavelength_final"]
except KeyError:
raise RuntimeError("Must provide a final wavelength via the "\
+"incoming SOM or the lambda_f keyword")
else:
raise RuntimeError("Must provide a final wavelength via the "\
+"lambda_f keyword")
else:
pass
result = hlr_utils.copy_som_attr(result, res_descr, obj, o_descr)
if res_descr == "SOM":
result = hlr_utils.force_units(result, "ueV", axis)
result.setAxisLabel(axis, "energy_transfer")
result.setYUnits("Counts/ueV")
result.setYLabel("Intensity")
else:
pass
# iterate through the values
import array_manip
import axis_manip
import utils
for i in xrange(hlr_utils.get_length(obj)):
val = hlr_utils.get_value(obj, i, o_descr, "x", axis)
err2 = hlr_utils.get_err2(obj, i, o_descr, "x", axis)
y_val = hlr_utils.get_value(obj, i, o_descr, "y", axis)
y_err2 = hlr_utils.get_err2(obj, i, o_descr, "y", axis)
map_so = hlr_utils.get_map_so(obj, None, i)
l_f = hlr_utils.get_special(lambda_f, map_so)
(E_f, E_f_err2) = axis_manip.wavelength_to_energy(l_f[0], l_f[1])
if offset is not None:
info = hlr_utils.get_special(offset, map_so)
try:
E_f_new = array_manip.add_ncerr(E_f, E_f_err2,
info[0], info[1])
except TypeError:
# Have to do this since add_ncerr does not support
# scalar-scalar operations
value1 = E_f + info[0]
value2 = E_f_err2 + info[1]
E_f_new = (value1, value2)
else:
E_f_new = (E_f, E_f_err2)
# Scale counts by lambda_f / lambda_i
if scale:
l_i = axis_manip.energy_to_wavelength(val, err2)
l_i_bc = utils.calc_bin_centers(l_i[0], l_i[1])
ratio = array_manip.div_ncerr(l_f[0], l_f[1],
l_i_bc[0], l_i_bc[1])
scale_y = array_manip.mult_ncerr(y_val, y_err2, ratio[0], ratio[1])
else:
scale_y = (y_val, y_err2)
value = array_manip.sub_ncerr(val, err2, E_f_new[0], E_f_new[1])
# Convert from meV to ueV
value2 = array_manip.mult_ncerr(value[0], value[1], 1000.0, 0.0)
value3 = array_manip.mult_ncerr(scale_y[0], scale_y[1],
1.0/1000.0, 0.0)
hlr_utils.result_insert(result, res_descr, value3, map_so, "all",
0, [value2[0]])
return result
def initial_wavelength_igs_lin_time_zero_to_tof(obj, **kwargs):
"""
This function converts a primary axis of a C{SOM} or C{SO} from
initial_wavelength_igs_lin_time_zero to time-of-flight. The
initial_wavelength_igs_lin_time_zero axis for a C{SOM} must be in units of
I{Angstroms}. The primary axis of a C{SO} is assumed to be in units of
I{Angstroms}. A C{tuple} of C{(initial_wavelength_igs_lin_time_zero,
initial_wavelength_igs_lin_time_zero_err2)} (assumed to be in units of
I{Angstroms}) can be converted to C{(tof, tof_err2)}.
@param obj: Object to be converted
@type obj: C{SOM.SOM}, C{SOM.SO} or C{tuple}
@param kwargs: A list of keyword arguments that the function accepts:
@keyword lambda_f:The final wavelength and its associated error^2
@type lambda_f: C{tuple}
@keyword time_zero_slope: The time zero slope and its associated error^2
@type time_zero_slope: C{tuple}
@keyword time_zero_offset: The time zero offset and its associated error^2
@type time_zero_offset: C{tuple}
@keyword dist_source_sample: The source to sample distance information and
its associated error^2
@type dist_source_sample: C{tuple} or C{list} of C{tuple}s
@keyword dist_sample_detector: The sample to detector distance information
and its associated error^2
@type dist_sample_detector: C{tuple} or C{list} of C{tuple}s
@keyword lojac: A flag that allows one to turn off the calculation of the
linear-order Jacobian. The default action is True for
histogram data.
@type lojac: C{boolean}
@keyword units: The expected units for this function. The default for this
function is I{Angstroms}
@type units: C{string}
@return: Object with a primary axis in initial_wavelength_igs converted to
time-of-flight
@rtype: C{SOM.SOM}, C{SOM.SO} or C{tuple}
@raise TypeError: The incoming object is not a type the function recognizes
@raise RuntimeError: The C{SOM} x-axis units are not I{Angstroms}
"""
# import the helper functions
import hlr_utils
# set up for working through data
(result, res_descr) = hlr_utils.empty_result(obj)
o_descr = hlr_utils.get_descr(obj)
# Setup keyword arguments
try:
lambda_f = kwargs["lambda_f"]
except KeyError:
lambda_f = None
try:
time_zero_slope = kwargs["time_zero_slope"]
except KeyError:
time_zero_slope = None
# Current constants for Time Zero Slope
TIME_ZERO_SLOPE = (float(0.0), float(0.0))
try:
time_zero_offset = kwargs["time_zero_offset"]
except KeyError:
time_zero_offset = None
# Current constants for Time Zero Offset
TIME_ZERO_OFFSET = (float(0.0), float(0.0))
try:
dist_source_sample = kwargs["dist_source_sample"]
except KeyError:
dist_source_sample = None
try:
dist_sample_detector = kwargs["dist_sample_detector"]
except KeyError:
dist_sample_detector = None
try:
lojac = kwargs["lojac"]
except KeyError:
lojac = hlr_utils.check_lojac(obj)
try:
units = kwargs["units"]
except KeyError:
units = "Angstroms"
# Primary axis for transformation. If a SO is passed, the function, will
# assume the axis for transformation is at the 0 position
if o_descr == "SOM":
axis = hlr_utils.one_d_units(obj, units)
else:
axis = 0
result = hlr_utils.copy_som_attr(result, res_descr, obj, o_descr)
if res_descr == "SOM":
result = hlr_utils.force_units(result, "Microseconds", axis)
result.setAxisLabel(axis, "time-of-flight")
result.setYUnits("Counts/uS")
result.setYLabel("Intensity")
else:
pass
# Where to get instrument information
if dist_source_sample is None or dist_sample_detector is None:
if o_descr == "SOM":
try:
obj.attr_list.instrument.get_primary()
inst = obj.attr_list.instrument
except RuntimeError:
raise RuntimeError("A detector was not provided!")
else:
if dist_source_sample is None and dist_sample_detector is None:
raise RuntimeError("If a SOM is not passed, the "\
+"source-sample and sample-detector "\
+"distances must be provided.")
elif dist_source_sample is None:
raise RuntimeError("If a SOM is not passed, the "\
+"source-sample distance must be provided.")
elif dist_sample_detector is None:
raise RuntimeError("If a SOM is not passed, the "\
+"sample-detector distance must be "\
+"provided.")
else:
raise RuntimeError("If you get here, see Steve Miller for "\
+"your mug.")
else:
pass
if lambda_f is not None:
l_descr = hlr_utils.get_descr(lambda_f)
else:
if o_descr == "SOM":
try:
som_l_f = obj.attr_list["Wavelength_final"]
except KeyError:
raise RuntimeError("Please provide a final wavelength "\
+"parameter either via the function call "\
+"or the SOM")
else:
raise RuntimeError("You need to provide a final wavelength")
if time_zero_slope is not None:
t_0_slope_descr = hlr_utils.get_descr(time_zero_slope)
else:
if o_descr == "SOM":
try:
t_0_slope = obj.attr_list["Time_zero_slope"][0]
t_0_slope_err2 = obj.attr_list["Time_zero_slope"][1]
except KeyError:
t_0_slope = TIME_ZERO_SLOPE[0]
t_0_slope_err2 = TIME_ZERO_SLOPE[1]
else:
t_0_slope = TIME_ZERO_SLOPE[0]
t_0_slope_err2 = TIME_ZERO_SLOPE[1]
if time_zero_offset is not None:
t_0_offset_descr = hlr_utils.get_descr(time_zero_offset)
else:
if o_descr == "SOM":
try:
t_0_offset = obj.attr_list["Time_zero_offset"][0]
t_0_offset_err2 = obj.attr_list["Time_zero_offset"][1]
except KeyError:
t_0_offset = TIME_ZERO_OFFSET[0]
t_0_offset_err2 = TIME_ZERO_OFFSET[1]
else:
t_0_offset = TIME_ZERO_OFFSET[0]
t_0_offset_err2 = TIME_ZERO_OFFSET[1]
if dist_source_sample is not None:
ls_descr = hlr_utils.get_descr(dist_source_sample)
# Do nothing, go on
else:
pass
if dist_sample_detector is not None:
ld_descr = hlr_utils.get_descr(dist_sample_detector)
# Do nothing, go on
else:
pass
# iterate through the values
len_obj = hlr_utils.get_length(obj)
MNEUT_OVER_H = 1.0 / 0.003956034
MNEUT_OVER_H2 = MNEUT_OVER_H * MNEUT_OVER_H
for i in xrange(len_obj):
val = hlr_utils.get_value(obj, i, o_descr, "x", axis)
err2 = hlr_utils.get_err2(obj, i, o_descr, "x", axis)
map_so = hlr_utils.get_map_so(obj, None, i)
if dist_source_sample is None:
(L_s, L_s_err2) = hlr_utils.get_parameter("primary", map_so, inst)
else:
L_s = hlr_utils.get_value(dist_source_sample, i, ls_descr)
L_s_err2 = hlr_utils.get_err2(dist_source_sample, i, ls_descr)
if dist_sample_detector is None:
(L_d, L_d_err2) = hlr_utils.get_parameter("secondary", map_so,
inst)
else:
L_d = hlr_utils.get_value(dist_sample_detector, i, ld_descr)
L_d_err2 = hlr_utils.get_err2(dist_sample_detector, i, ld_descr)
if lambda_f is not None:
l_f = hlr_utils.get_value(lambda_f, i, l_descr)
l_f_err2 = hlr_utils.get_err2(lambda_f, i, l_descr)
else:
l_f_tuple = hlr_utils.get_special(som_l_f, map_so)
l_f = l_f_tuple[0]
l_f_err2 = l_f_tuple[1]
if time_zero_slope is not None:
t_0_slope = hlr_utils.get_value(time_zero_slope, i,
t_0_slope_descr)
t_0_slope_err2 = hlr_utils.get_err2(time_zero_slope, i,
t_0_slope_descr)
else:
pass
if time_zero_offset is not None:
t_0_offset = hlr_utils.get_value(time_zero_offset, i,
t_0_offset_descr)
t_0_offset_err2 = hlr_utils.get_err2(time_zero_offset, i,
t_0_offset_descr)
else:
pass
# Going to violate rules since the current usage is with a single
# number. When an SCL equivalent function arises, this code can be
# fixed.
front_const = MNEUT_OVER_H * L_s + t_0_slope
term2 = MNEUT_OVER_H * l_f * L_d
tof = (front_const * val) + term2 + t_0_offset
front_const2 = front_const * front_const
eterm1 = l_f * l_f * L_d_err2
eterm2 = L_d * L_d * l_f_err2
eterm3 = MNEUT_OVER_H2 * L_s_err2
tof_err2 = (front_const2 * err2) + (val * val) * \
(eterm3 + t_0_slope_err2) + (MNEUT_OVER_H2 * \
(eterm1 + eterm2)) + \
t_0_offset_err2
hlr_utils.result_insert(result, res_descr, (tof, tof_err2), None,
"all")
return result
def energy_transfer(obj, itype, axis_const, **kwargs):
"""
This function takes a SOM with a wavelength axis (initial for IGS and
final for DGS) and calculates the energy transfer.
@param obj: The object containing the wavelength axis
@type obj: C{SOM.SOM}
@param itype: The instrument class type. The choices are either I{IGS} or
I{DGS}.
@type itype: C{string}
@param axis_const: The attribute name for the axis constant which is the
final wavelength for I{IGS} and the initial energy for
I{DGS}.
@type axis_const: C{string}
@param kwargs: A list of keyword arguments that the function accepts:
@keyword units: The units for the incoming axis. The default is
I{Angstroms}.
@type units: C{string}
@keyword change_units: A flag that signals the function to convert from
I{meV} to I{ueV}. The default is I{False}.
@type change_units: C{boolean}
@keyword scale: A flag to scale the y-axis by lambda_f/lambda_i for I{IGS}
and lambda_i/lambda_f for I{DGS}. The default is I{False}.
@type scale: C{boolean}
@keyword lojac: A flag that turns on the calculation and application of
the linear-order Jacobian. The default is I{False}.
@type lojac: C{boolean}
@keyword sa_norm: A flag to turn on solid angle normlaization.
@type sa_norm: C{boolean}
@return: Object with the energy transfer calculated in units of I{meV} or
I{ueV}. The default is I{meV}.
@rtype: C{SOM.SOM}
@raise RuntimeError: The instrument class type is not recognized
@raise RuntimeError: The x-axis units are not Angstroms
@raise RuntimeError: A SOM is not given to the function
"""
# Check the instrument class type to make sure its allowed
allowed_types = ["DGS", "IGS"]
if itype not in allowed_types:
raise RuntimeError("The instrument class type %s is not known. "\
+"Please use DGS or IGS" % itype)
# import the helper functions
import hlr_utils
# set up for working through data
(result, res_descr) = hlr_utils.empty_result(obj)
o_descr = hlr_utils.get_descr(obj)
if o_descr != "SOM":
raise RuntimeError("Must provide a SOM to the function.")
# Go on
else:
pass
# Setup keyword arguments
try:
units = kwargs["units"]
except KeyError:
units = "Angstroms"
try:
change_units = kwargs["change_units"]
except KeyError:
change_units = False
try:
scale = kwargs["scale"]
except KeyError:
scale = False
try:
sa_norm = kwargs["sa_norm"]
except KeyError:
sa_norm = False
if sa_norm:
inst = obj.attr_list.instrument
try:
lojac = kwargs["lojac"]
except KeyError:
lojac = False
# Primary axis for transformation.
axis = hlr_utils.one_d_units(obj, units)
# Get the subtraction constant
try:
axis_c = obj.attr_list[axis_const]
except KeyError:
raise RuntimeError("Must provide a final wavelength (IGS) or initial "\
+"energy (DGS) via the incoming SOM")
result = hlr_utils.copy_som_attr(result, res_descr, obj, o_descr)
if change_units:
unit_str = "ueV"
else:
unit_str = "meV"
result = hlr_utils.force_units(result, unit_str, axis)
result.setAxisLabel(axis, "energy_transfer")
result.setYUnits("Counts/" + unit_str)
result.setYLabel("Intensity")
# iterate through the values
import array_manip
import axis_manip
import dr_lib
import utils
for i in xrange(hlr_utils.get_length(obj)):
if itype == "IGS":
l_i = hlr_utils.get_value(obj, i, o_descr, "x", axis)
l_i_err2 = hlr_utils.get_err2(obj, i, o_descr, "x", axis)
else:
l_f = hlr_utils.get_value(obj, i, o_descr, "x", axis)
l_f_err2 = hlr_utils.get_err2(obj, i, o_descr, "x", axis)
y_val = hlr_utils.get_value(obj, i, o_descr, "y", axis)
y_err2 = hlr_utils.get_err2(obj, i, o_descr, "y", axis)
map_so = hlr_utils.get_map_so(obj, None, i)
if itype == "IGS":
(E_i, E_i_err2) = axis_manip.wavelength_to_energy(l_i, l_i_err2)
l_f = hlr_utils.get_special(axis_c, map_so)[:2]
(E_f, E_f_err2) = axis_manip.wavelength_to_energy(l_f[0], l_f[1])
if lojac:
(y_val,
y_err2) = utils.linear_order_jacobian(l_i, E_i, y_val, y_err2)
else:
(E_i, E_i_err2) = axis_c.toValErrTuple()
(E_f, E_f_err2) = axis_manip.wavelength_to_energy(l_f, l_f_err2)
if lojac:
(y_val,
y_err2) = utils.linear_order_jacobian(l_f, E_f, y_val, y_err2)
if scale:
# Scale counts by lambda_f / lambda_i
if itype == "IGS":
(l_n, l_n_err2) = l_f
(l_d, l_d_err2) = utils.calc_bin_centers(l_i, l_i_err2)
else:
(l_n, l_n_err2) = utils.calc_bin_centers(l_f, l_f_err2)
(l_d,
l_d_err2) = axis_manip.energy_to_wavelength(E_i, E_i_err2)
ratio = array_manip.div_ncerr(l_n, l_n_err2, l_d, l_d_err2)
scale_y = array_manip.mult_ncerr(y_val, y_err2, ratio[0], ratio[1])
else:
scale_y = (y_val, y_err2)
value = array_manip.sub_ncerr(E_i, E_i_err2, E_f, E_f_err2)
if change_units:
# Convert from meV to ueV
value2 = array_manip.mult_ncerr(value[0], value[1], 1000.0, 0.0)
scale_y = array_manip.mult_ncerr(scale_y[0], scale_y[1],
1.0 / 1000.0, 0.0)
else:
value2 = value
if sa_norm:
if inst.get_name() == "BSS":
dOmega = dr_lib.calc_BSS_solid_angle(map_so, inst)
scale_y = array_manip.div_ncerr(scale_y[0], scale_y[1], dOmega,
0.0)
else:
raise RuntimeError("Do not know how to get solid angle from "\
+"%s" % inst.get_name())
if itype == "IGS":
# Reverse the values due to the conversion
value_y = axis_manip.reverse_array_cp(scale_y[0])
value_var_y = axis_manip.reverse_array_cp(scale_y[1])
value_x = axis_manip.reverse_array_cp(value2[0])
else:
value_y = scale_y[0]
value_var_y = scale_y[1]
value_x = value2[0]
hlr_utils.result_insert(result, res_descr, (value_y, value_var_y),
map_so, "all", 0, [value_x])
return result
