def __calc_x4(*args):
"""
This function calculates the x4 coeffiecient to the S(Q,E) Jacobian
@param args: A list of parameters used to calculate the x4 coefficient
The following is a list of the arguments needed in there expected order
1. Initial Energy
2. Initial Time-of-Flight
3. Time-zero Slope Correction
4. Vector of Zeros
@type args: C{list}
@return: The calculated x4 coefficient
@rtype: (C{nessi_list.NessiList}, C{nessi_list.NessiList})
"""
# Settle out the arguments to sensible names
E_i = args[0]
T_i = args[1]
t_0_s_corr = args[2]
z_vec = args[3]
# E_i / T_i
temp1 = array_manip.div_ncerr(E_i, z_vec, T_i, z_vec)
# -2 * (E_i / T_i)
temp2 = array_manip.mult_ncerr(temp1[0], temp1[1], -2.0, 0.0)
# -2 * (E_i / T_i) * (1 / 1 + ((h / m) * (t0_s / L_i)))
return array_manip.mult_ncerr(temp2[0], temp2[1], t_0_s_corr, 0.0)
def __calc_x4(*args):
"""
This function calculates the x4 coeffiecient to the S(Q,E) Jacobian
@param args: A list of parameters used to calculate the x4 coefficient
The following is a list of the arguments needed in there expected order
1. Initial Energy
2. Initial Time-of-Flight
3. Time-zero Slope Correction
4. Vector of Zeros
@type args: C{list}
@return: The calculated x4 coefficient
@rtype: (C{nessi_list.NessiList}, C{nessi_list.NessiList})
"""
# Settle out the arguments to sensible names
E_i = args[0]
T_i = args[1]
t_0_s_corr = args[2]
z_vec = args[3]
# E_i / T_i
temp1 = array_manip.div_ncerr(E_i, z_vec, T_i, z_vec)
# -2 * (E_i / T_i)
temp2 = array_manip.mult_ncerr(temp1[0], temp1[1], -2.0, 0.0)
# -2 * (E_i / T_i) * (1 / 1 + ((h / m) * (t0_s / L_i)))
return array_manip.mult_ncerr(temp2[0], temp2[1], t_0_s_corr, 0.0)
def calc_deltat_over_t(axis, axis_err2=None):
"""
This function takes a TOF axis and calculates the quantity Delta t / t
for every element.
@param axis: The TOF axis from which Delta t / t will be calculated
@type axis: C{nessi_list.NessiList}
@param axis_err2: (OPTIONAL) The error^2 on the incoming TOF axis
@type axis_err2: C{nessi_list.NessiList}
@return: The calculated Delta t / t
@rtype: C{SOM.SOM}
"""
import nessi_list
# Check to see if incoming is really a NessiList
try:
axis.__type__
except AttributeError:
raise RuntimeError("The object passed to this function needs to be a "\
+"NessiList. Do not understand how to deal with "\
+"%s" % type(axis))
len_axis = len(axis)
if axis_err2 is None:
axis_err2 = nessi_list.NessiList(len_axis)
deltat = nessi_list.NessiList()
deltat_err2 = nessi_list.NessiList()
# Calculate bin deltas, assume axis in ascending order
for i in xrange(len_axis - 1):
deltat.append(axis[i + 1] - axis[i])
deltat_err2.append(axis_err2[i + 1] - axis_err2[i])
# Calculate bin centers
import utils
(binc, binc_err2) = utils.calc_bin_centers(axis, axis_err2)
# Calculate delta t / t
import array_manip
dtot = array_manip.div_ncerr(deltat, deltat_err2, binc, binc_err2)
import SOM
som = SOM.SOM()
so = SOM.SO()
so.y = dtot[0]
so.var_y = dtot[1]
som.append(so)
som.setDataSetType("density")
som.setYLabel("deltat_over_t")
return som
def calc_deltat_over_t(axis, axis_err2=None):
"""
This function takes a TOF axis and calculates the quantity Delta t / t
for every element.
@param axis: The TOF axis from which Delta t / t will be calculated
@type axis: C{nessi_list.NessiList}
@param axis_err2: (OPTIONAL) The error^2 on the incoming TOF axis
@type axis_err2: C{nessi_list.NessiList}
@return: The calculated Delta t / t
@rtype: C{SOM.SOM}
"""
import nessi_list
# Check to see if incoming is really a NessiList
try:
axis.__type__
except AttributeError:
raise RuntimeError("The object passed to this function needs to be a "\
+"NessiList. Do not understand how to deal with "\
+"%s" % type(axis))
len_axis = len(axis)
if axis_err2 is None:
axis_err2 = nessi_list.NessiList(len_axis)
deltat = nessi_list.NessiList()
deltat_err2 = nessi_list.NessiList()
# Calculate bin deltas, assume axis in ascending order
for i in xrange(len_axis - 1):
deltat.append(axis[i+1] - axis[i])
deltat_err2.append(axis_err2[i+1] - axis_err2[i])
# Calculate bin centers
import utils
(binc, binc_err2) = utils.calc_bin_centers(axis, axis_err2)
# Calculate delta t / t
import array_manip
dtot = array_manip.div_ncerr(deltat, deltat_err2, binc, binc_err2)
import SOM
som = SOM.SOM()
so = SOM.SO()
so.y = dtot[0]
so.var_y = dtot[1]
som.append(so)
som.setDataSetType("density")
som.setYLabel("deltat_over_t")
return som
def __calc_x2(*args):
"""
This function calculates the x2 coeffiecient to the S(Q,E) Jacobian
@param args: A list of parameters used to calculate the x2 coefficient
The following is a list of the arguments needed in there expected order
1. Initial Wavevector
2. Momentum Transfer
3. Initial Time-of-Flight
4. Wavevector Final x Cos(polar)
5. Time-zero Slope Correction
6. Vector of Zeros
@type args: C{list}
@return: The calculated x2 coefficient
@rtype: (C{nessi_list.NessiList}, C{nessi_list.NessiList})
"""
# Settle out the arguments to sensible names
k_i = args[0]
Q = args[1]
T_i = args[2]
k_f_cos_pol = args[3]
t_0_s_corr = args[4]
z_vec = args[5]
# k_f * cos(pol) - k_i
temp1 = array_manip.sub_ncerr(k_f_cos_pol, 0.0, k_i, z_vec)
# (k_f * cos(pol) - k_i) / Q
temp2 = array_manip.div_ncerr(temp1[0], temp1[1], Q, z_vec)
# k_i / T_i
temp3 = array_manip.div_ncerr(k_i, z_vec, T_i, z_vec)
# (k_i / T_i) * ((k_f * cos(pol) - k_i) / Q)
temp4 = array_manip.mult_ncerr(temp2[0], temp2[1], temp3[0], temp3[1])
# (k_i / T_i) * ((k_f * cos(pol) - k_i) / Q) *
# (1 / 1 + ((h / m) * (t0_s / L_i)))
return array_manip.mult_ncerr(temp4[0], temp4[1], t_0_s_corr, 0.0)
def __calc_x2(*args):
"""
This function calculates the x2 coeffiecient to the S(Q,E) Jacobian
@param args: A list of parameters used to calculate the x2 coefficient
The following is a list of the arguments needed in there expected order
1. Initial Wavevector
2. Momentum Transfer
3. Initial Time-of-Flight
4. Wavevector Final x Cos(polar)
5. Time-zero Slope Correction
6. Vector of Zeros
@type args: C{list}
@return: The calculated x2 coefficient
@rtype: (C{nessi_list.NessiList}, C{nessi_list.NessiList})
"""
# Settle out the arguments to sensible names
k_i = args[0]
Q = args[1]
T_i = args[2]
k_f_cos_pol = args[3]
t_0_s_corr = args[4]
z_vec = args[5]
# k_f * cos(pol) - k_i
temp1 = array_manip.sub_ncerr(k_f_cos_pol, 0.0, k_i, z_vec)
# (k_f * cos(pol) - k_i) / Q
temp2 = array_manip.div_ncerr(temp1[0], temp1[1], Q, z_vec)
# k_i / T_i
temp3 = array_manip.div_ncerr(k_i, z_vec, T_i, z_vec)
# (k_i / T_i) * ((k_f * cos(pol) - k_i) / Q)
temp4 = array_manip.mult_ncerr(temp2[0], temp2[1], temp3[0], temp3[1])
# (k_i / T_i) * ((k_f * cos(pol) - k_i) / Q) *
# (1 / 1 + ((h / m) * (t0_s / L_i)))
return array_manip.mult_ncerr(temp4[0], temp4[1], t_0_s_corr, 0.0)
def fix_bin_contents(obj, **kwargs):
"""
This function takes a SOM or SO and goes through the individual spectra
adjusting the bin contents by either multiplying or dividing by the
bin widths or the bin centers taken from the individual spectra.
@param obj: The data object to be scaled
@type obj: C{SOM.SOM} or C{SOM.SO}
@param kwargs: A list of keyword arguments that the function accepts:
@keyword scale: A flag that signals multiplication by the required bin
quantity. The default is I{False} (divide).
@type scale: C{bool}
@keyword width: A flag that signals that the adjusting quantity is the
bin width. The default is I{True}. If I{False}, the bin
center is used.
@type width: C{bool}
@keyword units: The expected units for this function. The default for this
function is I{microsecond}.
@type units: C{string}
@return: The object with the individual spectrum scaled
@rtype: C{SOM.SOM} or C{SOM.SO}
"""
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:
scale = kwargs["scale"]
except KeyError:
scale = False
try:
width = kwargs["width"]
except KeyError:
width = True
try:
units = kwargs["units"]
except KeyError:
units = "microsecond"
# 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_pos = hlr_utils.one_d_units(obj, units)
else:
axis_pos = 0
result = hlr_utils.copy_som_attr(result, res_descr, obj, o_descr)
# iterate through the values
import array_manip
import utils
for i in xrange(hlr_utils.get_length(obj)):
val = hlr_utils.get_value(obj, i, o_descr, "y")
err2 = hlr_utils.get_err2(obj, i, o_descr, "y")
axis = hlr_utils.get_value(obj, i, o_descr, "x", axis_pos)
axis_err2 = hlr_utils.get_err2(obj, i, o_descr, "x", axis_pos)
map_so = hlr_utils.get_map_so(obj, None, i)
if width:
(bin_const,
bin_const_err2) = utils.calc_bin_widths(axis, axis_err2)
else:
(bin_const,
bin_const_err2) = utils.calc_bin_centers(axis, axis_err2)
if scale:
value = array_manip.mult_ncerr(val, err2, bin_const,
bin_const_err2)
else:
value = array_manip.div_ncerr(val, err2, bin_const, bin_const_err2)
hlr_utils.result_insert(result, res_descr, value, map_so, "y")
return result
def div_ncerr(left, right, **kwargs):
"""
This function divides two objects (C{SOM}, C{SO} or {tuple(val,val_err2)})
and returns the result of the division in an C{SOM}, C{SO} or C{tuple}.
@param left: Object on the left of the division sign
@type left: C{SOM.SOM} or C{SOM.SO} or C{tuple}
@param right: Object on the right of the division sign
@type right: C{SOM.SOM} or C{SOM.SO} or C{tuple}
@param kwargs: A list of keyword arguments that the function accepts:
@keyword axis: This is the axis one wishes to manipulate. If no argument
is given the default value is y
@type axis: C{string}=<y or x>
@keyword axis_pos: This is position of the axis in the axis array. If no
argument is given, the default value is 0
@type axis_pos: C{int}
@keyword length_one_som: This is a flag that lets the function know it is
dealing with a length 1 C{SOM} so that attributes
may be passed along. The length 1 C{SOM} will be
turned into a C{SO}. The default value is False.
@type length_one_som: C{boolean}
@keyword length_one_som_pos: This is the argument position of the length 1
C{SOM} since division order is not
commutative. The default value is 2.
@type length_one_som_pos: C{int}=<1 or 2>
@return: Object containing the results of the division
@rtype: C{SOM.SOM}, C{SOM.SO} or C{tuple}
@raise IndexError: The two C{SOM}s do not contain the same number of
spectra
@raise RunTimeError: The x-axis units of the C{SOM}s do not match
@raise RunTimeError: The y-axis units of the C{SOM}s do not match
@raise RunTimeError: The x-axes of the two C{SO}s are not equal
"""
# import the helper functions
import hlr_utils
# Check to see if we are working with a length 1 SOM
try:
length_one_som = kwargs["length_one_som"]
except KeyError:
length_one_som = False
try:
length_one_som_pos = kwargs["length_one_som_pos"]
if length_one_som_pos != 1 or length_one_som_pos != 2:
raise RuntimeError("length_one_som_pos must be either 1 or 2 and "\
+"%d" % length_one_som_pos)
except KeyError:
length_one_som_pos = 2
if length_one_som:
if length_one_som_pos == 1:
som_copy = left
left = left[0]
else:
som_copy = right
right = right[0]
else:
# Not working with a length 1 SOM, do nothing
pass
# set up for working through data
(result, res_descr) = hlr_utils.empty_result(left, right)
(l_descr, r_descr) = hlr_utils.get_descr(left, right)
is_number = False
# error check information
if r_descr == "SOM" and l_descr == "SOM":
hlr_utils.math_compatible(left, l_descr, right, r_descr)
elif l_descr == "number" and r_descr == "number":
is_number = True
else:
pass
# Check for axis keyword argument
try:
axis = kwargs["axis"]
except KeyError:
axis = "y"
# Check for axis_pos keyword argument
try:
axis_pos = kwargs["axis_pos"]
except KeyError:
axis_pos = 0
if length_one_som:
if length_one_som_pos == 1:
result = hlr_utils.copy_som_attr(result, res_descr, som_copy,
"SOM", right, r_descr)
else:
result = hlr_utils.copy_som_attr(result, res_descr, left, l_descr,
som_copy, "SOM")
else:
result = hlr_utils.copy_som_attr(result, res_descr, left, l_descr,
right, r_descr)
# iterate through the values
import array_manip
for i in xrange(hlr_utils.get_length(left, right)):
val1 = hlr_utils.get_value(left, i, l_descr, axis, axis_pos)
err2_1 = hlr_utils.get_err2(left, i, l_descr, axis, axis_pos)
val2 = hlr_utils.get_value(right, i, r_descr, axis, axis_pos)
err2_2 = hlr_utils.get_err2(right, i, r_descr, axis, axis_pos)
(descr_1, descr_2) = hlr_utils.get_descr(val1, val2)
hlr_utils.math_compatible(val1, descr_1, val2, descr_2)
value = array_manip.div_ncerr(val1, err2_1, val2, err2_2)
map_so = hlr_utils.get_map_so(left, right, i)
hlr_utils.result_insert(result, res_descr, value, map_so, axis,
axis_pos)
if is_number:
return tuple(result)
else:
return result
def div_ncerr(left, right, **kwargs):
"""
This function divides two objects (C{SOM}, C{SO} or {tuple(val,val_err2)})
and returns the result of the division in an C{SOM}, C{SO} or C{tuple}.
@param left: Object on the left of the division sign
@type left: C{SOM.SOM} or C{SOM.SO} or C{tuple}
@param right: Object on the right of the division sign
@type right: C{SOM.SOM} or C{SOM.SO} or C{tuple}
@param kwargs: A list of keyword arguments that the function accepts:
@keyword axis: This is the axis one wishes to manipulate. If no argument
is given the default value is y
@type axis: C{string}=<y or x>
@keyword axis_pos: This is position of the axis in the axis array. If no
argument is given, the default value is 0
@type axis_pos: C{int}
@keyword length_one_som: This is a flag that lets the function know it is
dealing with a length 1 C{SOM} so that attributes
may be passed along. The length 1 C{SOM} will be
turned into a C{SO}. The default value is False.
@type length_one_som: C{boolean}
@keyword length_one_som_pos: This is the argument position of the length 1
C{SOM} since division order is not
commutative. The default value is 2.
@type length_one_som_pos: C{int}=<1 or 2>
@return: Object containing the results of the division
@rtype: C{SOM.SOM}, C{SOM.SO} or C{tuple}
@raise IndexError: The two C{SOM}s do not contain the same number of
spectra
@raise RunTimeError: The x-axis units of the C{SOM}s do not match
@raise RunTimeError: The y-axis units of the C{SOM}s do not match
@raise RunTimeError: The x-axes of the two C{SO}s are not equal
"""
# import the helper functions
import hlr_utils
# Check to see if we are working with a length 1 SOM
try:
length_one_som = kwargs["length_one_som"]
except KeyError:
length_one_som = False
try:
length_one_som_pos = kwargs["length_one_som_pos"]
if length_one_som_pos != 1 or length_one_som_pos != 2:
raise RuntimeError("length_one_som_pos must be either 1 or 2 and "\
+"%d" % length_one_som_pos)
except KeyError:
length_one_som_pos = 2
if length_one_som:
if length_one_som_pos == 1:
som_copy = left
left = left[0]
else:
som_copy = right
right = right[0]
else:
# Not working with a length 1 SOM, do nothing
pass
# set up for working through data
(result, res_descr) = hlr_utils.empty_result(left, right)
(l_descr, r_descr) = hlr_utils.get_descr(left, right)
is_number = False
# error check information
if r_descr == "SOM" and l_descr == "SOM":
hlr_utils.math_compatible(left, l_descr, right, r_descr)
elif l_descr == "number" and r_descr == "number":
is_number = True
else:
pass
# Check for axis keyword argument
try:
axis = kwargs["axis"]
except KeyError:
axis = "y"
# Check for axis_pos keyword argument
try:
axis_pos = kwargs["axis_pos"]
except KeyError:
axis_pos = 0
if length_one_som:
if length_one_som_pos == 1:
result = hlr_utils.copy_som_attr(result, res_descr,
som_copy, "SOM",
right, r_descr)
else:
result = hlr_utils.copy_som_attr(result, res_descr, left, l_descr,
som_copy, "SOM")
else:
result = hlr_utils.copy_som_attr(result, res_descr, left, l_descr,
right, r_descr)
# iterate through the values
import array_manip
for i in xrange(hlr_utils.get_length(left, right)):
val1 = hlr_utils.get_value(left, i, l_descr, axis, axis_pos)
err2_1 = hlr_utils.get_err2(left, i, l_descr, axis, axis_pos)
val2 = hlr_utils.get_value(right, i, r_descr, axis, axis_pos)
err2_2 = hlr_utils.get_err2(right, i, r_descr, axis, axis_pos)
(descr_1, descr_2)=hlr_utils.get_descr(val1, val2)
hlr_utils.math_compatible(val1, descr_1, val2, descr_2)
value = array_manip.div_ncerr(val1, err2_1, val2, err2_2)
map_so = hlr_utils.get_map_so(left, right, i)
hlr_utils.result_insert(result, res_descr, value, map_so, axis,
axis_pos)
if is_number:
return tuple(result)
else:
return result
def fix_bin_contents(obj, **kwargs):
"""
This function takes a SOM or SO and goes through the individual spectra
adjusting the bin contents by either multiplying or dividing by the
bin widths or the bin centers taken from the individual spectra.
@param obj: The data object to be scaled
@type obj: C{SOM.SOM} or C{SOM.SO}
@param kwargs: A list of keyword arguments that the function accepts:
@keyword scale: A flag that signals multiplication by the required bin
quantity. The default is I{False} (divide).
@type scale: C{bool}
@keyword width: A flag that signals that the adjusting quantity is the
bin width. The default is I{True}. If I{False}, the bin
center is used.
@type width: C{bool}
@keyword units: The expected units for this function. The default for this
function is I{microsecond}.
@type units: C{string}
@return: The object with the individual spectrum scaled
@rtype: C{SOM.SOM} or C{SOM.SO}
"""
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:
scale = kwargs["scale"]
except KeyError:
scale = False
try:
width = kwargs["width"]
except KeyError:
width = True
try:
units = kwargs["units"]
except KeyError:
units = "microsecond"
# 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_pos = hlr_utils.one_d_units(obj, units)
else:
axis_pos = 0
result = hlr_utils.copy_som_attr(result, res_descr, obj, o_descr)
# iterate through the values
import array_manip
import utils
for i in xrange(hlr_utils.get_length(obj)):
val = hlr_utils.get_value(obj, i, o_descr, "y")
err2 = hlr_utils.get_err2(obj, i, o_descr, "y")
axis = hlr_utils.get_value(obj, i, o_descr, "x", axis_pos)
axis_err2 = hlr_utils.get_err2(obj, i, o_descr, "x", axis_pos)
map_so = hlr_utils.get_map_so(obj, None, i)
if width:
(bin_const, bin_const_err2) = utils.calc_bin_widths(axis,
axis_err2)
else:
(bin_const, bin_const_err2) = utils.calc_bin_centers(axis,
axis_err2)
if scale:
value = array_manip.mult_ncerr(val, err2, bin_const,
bin_const_err2)
else:
value = array_manip.div_ncerr(val, err2, bin_const, bin_const_err2)
hlr_utils.result_insert(result, res_descr, value, map_so, "y")
return result
# Replace counts data with fractional area. The axes remain the same
comb_som[0].y = area_sum
comb_som[0].var_y = area_sum_err2
# Write out summed counts into file
hlr_utils.write_file(configure.output, "text/Dave2d", comb_som,
output_ext="fra",
verbose=configure.verbose,
data_ext=configure.ext_replacement,
path_replacement=configure.path_replacement,
message="fractional area")
else:
# Divide summed fractional counts by the sum of the fractional areas
(so_dim.y, so_dim.var_y) = array_manip.div_ncerr(so_dim.y,
so_dim.var_y,
area_sum,
area_sum_err2)
if configure.scale_sqe:
(so_dim.y, so_dim.var_y) = array_manip.div_ncerr(so_dim.y,
so_dim.var_y,
bin_count,
bin_count_err2)
comb_som.append(so_dim)
del so_dim
return comb_som
def __set_som_attributes(tsom, inst_name, **kwargs):
def create_E_vs_Q_dgs(som, E_i, Q_final, **kwargs):
"""
This function starts with the rebinned energy transfer and turns this
into a 2D spectra with E and Q axes for DGS instruments.
@param som: The input object with initial IGS wavelength axis
@type som: C{SOM.SOM}
@param E_i: The initial energy for the given data.
@type E_i: C{tuple}
@param Q_final: The momentum transfer axis to rebin the data to
@type Q_final: C{nessi_list.NessiList}
@param kwargs: A list of keyword arguments that the function accepts:
@keyword corner_angles: The object that contains the corner geometry
information.
@type corner_angles: C{dict}
@keyword so_id: The identifier represents a number, string, tuple or other
object that describes the resulting C{SO}
@type so_id: C{int}, C{string}, C{tuple}, C{pixel ID}
@keyword y_label: The y axis label
@type y_label: C{string}
@keyword y_units: The y axis units
@type y_units: C{string}
@keyword x_labels: This is a list of names that sets the individual x axis
labels
@type x_labels: C{list} of C{string}s
@keyword x_units: This is a list of names that sets the individual x axis
units
@type x_units: C{list} of C{string}s
@keyword split: This flag causes the counts and the fractional area to
be written out into separate files.
@type split: C{boolean}
@keyword configure: This is the object containing the driver configuration.
@type configure: C{Configure}
@return: Object containing a 2D C{SO} with E and Q axes
@rtype: C{SOM.SOM}
"""
import array_manip
import axis_manip
import common_lib
import hlr_utils
import nessi_list
import SOM
import utils
# Check for keywords
corner_angles = kwargs["corner_angles"]
configure = kwargs.get("configure")
split = kwargs.get("split", False)
# Setup output object
so_dim = SOM.SO(2)
so_dim.axis[0].val = Q_final
so_dim.axis[1].val = som[0].axis[0].val # E_t
# Calculate total 2D array size
N_tot = (len(so_dim.axis[0].val) - 1) * (len(so_dim.axis[1].val) - 1)
# Create y and var_y lists from total 2D size
so_dim.y = nessi_list.NessiList(N_tot)
so_dim.var_y = nessi_list.NessiList(N_tot)
# Create area sum and errors for the area sum lists from total 2D size
area_sum = nessi_list.NessiList(N_tot)
area_sum_err2 = nessi_list.NessiList(N_tot)
# Convert initial energy to initial wavevector
l_i = common_lib.energy_to_wavelength(E_i)
k_i = common_lib.wavelength_to_scalar_k(l_i)
# Since all the data is rebinned to the same energy transfer axis, we can
# calculate the final energy axis once
E_t = som[0].axis[0].val
if som[0].axis[0].var is not None:
E_t_err2 = som[0].axis[0].var
else:
E_t_err2 = nessi_list.NessiList(len(E_t))
# Get the bin width arrays from E_t
(E_t_bw, E_t_bw_err2) = utils.calc_bin_widths(E_t)
E_f = array_manip.sub_ncerr(E_i[0], E_i[1], E_t, E_t_err2)
# Now we can get the final wavevector
l_f = axis_manip.energy_to_wavelength(E_f[0], E_f[1])
k_f = axis_manip.wavelength_to_scalar_k(l_f[0], l_f[1])
# Output position for Q
X = 0
# Iterate though the data
len_som = hlr_utils.get_length(som)
for i in xrange(len_som):
map_so = hlr_utils.get_map_so(som, None, i)
yval = hlr_utils.get_value(som, i, "SOM", "y")
yerr2 = hlr_utils.get_err2(som, i, "SOM", "y")
cangles = corner_angles[str(map_so.id)]
avg_theta1 = (cangles.getPolar(0) + cangles.getPolar(1)) / 2.0
avg_theta2 = (cangles.getPolar(2) + cangles.getPolar(3)) / 2.0
Q1 = axis_manip.init_scatt_wavevector_to_scalar_Q(k_i[0],
k_i[1],
k_f[0][:-1],
k_f[1][:-1],
avg_theta2,
0.0)
Q2 = axis_manip.init_scatt_wavevector_to_scalar_Q(k_i[0],
k_i[1],
k_f[0][:-1],
k_f[1][:-1],
avg_theta1,
0.0)
Q3 = axis_manip.init_scatt_wavevector_to_scalar_Q(k_i[0],
k_i[1],
k_f[0][1:],
k_f[1][1:],
avg_theta1,
0.0)
Q4 = axis_manip.init_scatt_wavevector_to_scalar_Q(k_i[0],
k_i[1],
k_f[0][1:],
k_f[1][1:],
avg_theta2,
0.0)
# Calculate the area of the E,Q polygons
(A, A_err2) = utils.calc_eq_jacobian_dgs(E_t[:-1], E_t[:-1],
E_t[1:], E_t[1:],
Q1[X], Q2[X], Q3[X], Q4[X])
# Apply the Jacobian: C/dE_t * dE_t / A(EQ) = C/A(EQ)
(jac_ratio, jac_ratio_err2) = array_manip.div_ncerr(E_t_bw,
E_t_bw_err2,
A, A_err2)
(counts, counts_err2) = array_manip.mult_ncerr(yval, yerr2,
jac_ratio,
jac_ratio_err2)
try:
(y_2d, y_2d_err2,
area_new,
bin_count) = axis_manip.rebin_2D_quad_to_rectlin(Q1[X], E_t[:-1],
Q2[X], E_t[:-1],
Q3[X], E_t[1:],
Q4[X], E_t[1:],
counts,
counts_err2,
so_dim.axis[0].val,
so_dim.axis[1].val)
del bin_count
except IndexError, e:
# Get the offending index from the error message
index = int(str(e).split()[1].split('index')[-1].strip('[]'))
print "Id:", map_so.id
print "Index:", index
print "Verticies: %f, %f, %f, %f, %f, %f, %f, %f" % (Q1[X][index],
E_t[:-1][index],
Q2[X][index],
E_t[:-1][index],
Q3[X][index],
E_t[1:][index],
Q4[X][index],
E_t[1:][index])
raise IndexError(str(e))
# Add in together with previous results
(so_dim.y, so_dim.var_y) = array_manip.add_ncerr(so_dim.y,
so_dim.var_y,
y_2d, y_2d_err2)
(area_sum, area_sum_err2) = array_manip.add_ncerr(area_sum,
area_sum_err2,
area_new,
area_sum_err2)
def rebin_axis_1D_frac(obj, axis_out):
"""
This function rebins the primary axis for a C{SOM} or a C{SO} based on the
given C{NessiList} axis.
@param obj: Object to be rebinned
@type obj: C{SOM.SOM} or C{SOM.SO}
@param axis_out: The axis to rebin the C{SOM} or C{SO} to
@type axis_out: C{NessiList}
@return: Object that has been rebinned according to the provided axis
@rtype: C{SOM.SOM} or C{SOM.SO}
@raise TypeError: The rebinning axis given is not a C{NessiList}
@raise TypeError: The object being rebinned is not a C{SOM} or a C{SO}
"""
# import the helper functions
import hlr_utils
# set up for working through data
try:
axis_out.__type__
except AttributeError:
raise TypeError("Rebinning axis must be a NessiList!")
o_descr = hlr_utils.get_descr(obj)
if o_descr == "number" or o_descr == "list":
raise TypeError("Do not know how to handle given type: %s" % \
o_descr)
else:
pass
(result, res_descr) = hlr_utils.empty_result(obj)
result = hlr_utils.copy_som_attr(result, res_descr, obj, o_descr)
# iterate through the values
import array_manip
import axis_manip
for i in xrange(hlr_utils.get_length(obj)):
axis_in = hlr_utils.get_value(obj, i, o_descr, "x", 0)
val = hlr_utils.get_value(obj, i, o_descr)
err2 = hlr_utils.get_err2(obj, i, o_descr)
value = axis_manip.rebin_axis_1D_frac(axis_in, val, err2, axis_out)
frac_err = nessi_list.NessiList(len(value[2]))
value1 = array_manip.div_ncerr(value[0], value[1], value[2], frac_err)
xvals = []
xvals.append(axis_out)
map_so = hlr_utils.get_map_so(obj, None, i)
hlr_utils.result_insert(result, res_descr, value1, map_so, "all", 0,
xvals)
return result
def __calc_x1(*args):
"""
This function calculates the x1 coeffiecient to the S(Q,E) Jacobian
@param args: A list of parameters used to calculate the x1 coefficient
The following is a list of the arguments needed in there expected order
1. Initial Wavevector
2. Momentum Transfer
3. Length Ratio (L_f / L_i)
4. Final Wavevector
5. Wavevector Final x Cos(polar)
6. Wavevector Final x Sin(polar)
7. Lambda Constant (2*pi/l_f^2)(dlf/dh)
8. Derivative dazi_dh
9. Derivative dpol_dh
10. Derivative dpol_dtd
11. Derivative dazi_dtd
12. Cos(polar)
13. Time-zero Slope Correction
14. Vector of Zeros
@type args: C{list}
@return: The calculated x1 coefficient
@rtype: (C{nessi_list.NessiList}, C{nessi_list.NessiList})
"""
# Settle out the arguments to sensible names
k_i = args[0]
Q = args[1]
len_ratio = args[2]
k_f = args[3]
k_f_cos_pol = args[4]
k_f_sin_pol = args[5]
lam_const = args[6]
dpol_dh = args[7]
dpol_dtd = args[8]
dtd_over_dh = args[9]
cos_pol = args[10]
t_0_s_corr = args[11]
z_vec = args[12]
# (L_f / L_i) * (1 / k_f)^2 * (1 / 1 + ((h / m) * (t0_s / L_i)))
const1 = (len_ratio * t_0_s_corr) / (k_f * k_f)
# (dpol/dh + (dpol/dtd * dtd/dh)) * k_f * sin(pol)
const2 = (dpol_dh + (dpol_dtd * dtd_over_dh)) * k_f_sin_pol
# k_i^2
temp1 = array_manip.mult_ncerr(k_i, z_vec, k_i, z_vec)
# (L_f / L_i) * (k_i / k_f)^2 * (1 / 1 + ((h / m) * (t0_s / L_i)))
temp2 = array_manip.mult_ncerr(temp1[0], temp1[1], const1, 0.0)
# k_i - k_f * cos(pol)
temp3 = array_manip.sub_ncerr(k_i, z_vec, k_f_cos_pol, 0.0)
# (k_i - k_f * cos(pol)) * (L_f / L_i) * (k_i / k_f)^2
temp4 = array_manip.mult_ncerr(temp2[0], temp2[1], temp3[0], temp3[1])
# k_i * cos(pol)
temp5 = array_manip.mult_ncerr(k_i, z_vec, cos_pol, 0.0)
# k_f - k_i * cos(pol)
temp6 = array_manip.sub_ncerr(k_f, 0.0, temp5[0], temp5[1])
# (k_i - k_f * cos(pol)) * (L_f / L_i) * (k_i / k_f)^2 -
# (k_f - k_i * cos(pol))
temp7 = array_manip.sub_ncerr(temp4[0], temp4[1], temp6[0], temp6[1])
# ((k_i - k_f * cos(pol)) * (L_f / L_i) * (k_i / k_f)^2 -
# (k_f - k_i * cos(pol))) * (2 * pi / l_f^2) * dlf/dh
temp8 = array_manip.mult_ncerr(temp7[0], temp7[1], lam_const, 0.0)
# k_i * k_f * sin(pol) * (dpol/dh + (dpol/dtd * dtd/dh))
temp9 = array_manip.mult_ncerr(k_i, z_vec, const2, 0.0)
# ((k_i - k_f * cos(pol)) * (L_f / L_i) * (k_i / k_f)^2 -
# (k_f - k_i * cos(pol))) * (2 * pi / l_f^2) * dlf/dh +
# k_i * k_f * sin(pol) * (dpol/dh + (dpol/dtd * dtd/dh))
temp10 = array_manip.add_ncerr(temp8[0], temp8[1], temp9[0], temp9[1])
# (((k_i - k_f * cos(pol)) * (L_f / L_i) * (k_i / k_f)^2 -
# (k_f - k_i * cos(pol))) * (2 * pi / l_f^2) * dlf/dh +
# k_i * k_f * sin(pol) * (dpol/dh - (dpol/dtd dazi/dh / dazi/dtd))
return array_manip.div_ncerr(temp10[0], temp10[1], Q, z_vec)
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 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 create_E_vs_Q_dgs(som, E_i, Q_final, **kwargs):
"""
This function starts with the rebinned energy transfer and turns this
into a 2D spectra with E and Q axes for DGS instruments.
@param som: The input object with initial IGS wavelength axis
@type som: C{SOM.SOM}
@param E_i: The initial energy for the given data.
@type E_i: C{tuple}
@param Q_final: The momentum transfer axis to rebin the data to
@type Q_final: C{nessi_list.NessiList}
@param kwargs: A list of keyword arguments that the function accepts:
@keyword corner_angles: The object that contains the corner geometry
information.
@type corner_angles: C{dict}
@keyword so_id: The identifier represents a number, string, tuple or other
object that describes the resulting C{SO}
@type so_id: C{int}, C{string}, C{tuple}, C{pixel ID}
@keyword y_label: The y axis label
@type y_label: C{string}
@keyword y_units: The y axis units
@type y_units: C{string}
@keyword x_labels: This is a list of names that sets the individual x axis
labels
@type x_labels: C{list} of C{string}s
@keyword x_units: This is a list of names that sets the individual x axis
units
@type x_units: C{list} of C{string}s
@keyword split: This flag causes the counts and the fractional area to
be written out into separate files.
@type split: C{boolean}
@keyword configure: This is the object containing the driver configuration.
@type configure: C{Configure}
@return: Object containing a 2D C{SO} with E and Q axes
@rtype: C{SOM.SOM}
"""
import array_manip
import axis_manip
import common_lib
import hlr_utils
import nessi_list
import SOM
import utils
# Check for keywords
corner_angles = kwargs["corner_angles"]
configure = kwargs.get("configure")
split = kwargs.get("split", False)
# Setup output object
so_dim = SOM.SO(2)
so_dim.axis[0].val = Q_final
so_dim.axis[1].val = som[0].axis[0].val # E_t
# Calculate total 2D array size
N_tot = (len(so_dim.axis[0].val) - 1) * (len(so_dim.axis[1].val) - 1)
# Create y and var_y lists from total 2D size
so_dim.y = nessi_list.NessiList(N_tot)
so_dim.var_y = nessi_list.NessiList(N_tot)
# Create area sum and errors for the area sum lists from total 2D size
area_sum = nessi_list.NessiList(N_tot)
area_sum_err2 = nessi_list.NessiList(N_tot)
# Convert initial energy to initial wavevector
l_i = common_lib.energy_to_wavelength(E_i)
k_i = common_lib.wavelength_to_scalar_k(l_i)
# Since all the data is rebinned to the same energy transfer axis, we can
# calculate the final energy axis once
E_t = som[0].axis[0].val
if som[0].axis[0].var is not None:
E_t_err2 = som[0].axis[0].var
else:
E_t_err2 = nessi_list.NessiList(len(E_t))
# Get the bin width arrays from E_t
(E_t_bw, E_t_bw_err2) = utils.calc_bin_widths(E_t)
E_f = array_manip.sub_ncerr(E_i[0], E_i[1], E_t, E_t_err2)
# Now we can get the final wavevector
l_f = axis_manip.energy_to_wavelength(E_f[0], E_f[1])
k_f = axis_manip.wavelength_to_scalar_k(l_f[0], l_f[1])
# Output position for Q
X = 0
# Iterate though the data
len_som = hlr_utils.get_length(som)
for i in xrange(len_som):
map_so = hlr_utils.get_map_so(som, None, i)
yval = hlr_utils.get_value(som, i, "SOM", "y")
yerr2 = hlr_utils.get_err2(som, i, "SOM", "y")
cangles = corner_angles[str(map_so.id)]
avg_theta1 = (cangles.getPolar(0) + cangles.getPolar(1)) / 2.0
avg_theta2 = (cangles.getPolar(2) + cangles.getPolar(3)) / 2.0
Q1 = axis_manip.init_scatt_wavevector_to_scalar_Q(
k_i[0], k_i[1], k_f[0][:-1], k_f[1][:-1], avg_theta2, 0.0)
Q2 = axis_manip.init_scatt_wavevector_to_scalar_Q(
k_i[0], k_i[1], k_f[0][:-1], k_f[1][:-1], avg_theta1, 0.0)
Q3 = axis_manip.init_scatt_wavevector_to_scalar_Q(
k_i[0], k_i[1], k_f[0][1:], k_f[1][1:], avg_theta1, 0.0)
Q4 = axis_manip.init_scatt_wavevector_to_scalar_Q(
k_i[0], k_i[1], k_f[0][1:], k_f[1][1:], avg_theta2, 0.0)
# Calculate the area of the E,Q polygons
(A, A_err2) = utils.calc_eq_jacobian_dgs(E_t[:-1], E_t[:-1], E_t[1:],
E_t[1:], Q1[X], Q2[X], Q3[X],
Q4[X])
# Apply the Jacobian: C/dE_t * dE_t / A(EQ) = C/A(EQ)
(jac_ratio,
jac_ratio_err2) = array_manip.div_ncerr(E_t_bw, E_t_bw_err2, A,
A_err2)
(counts, counts_err2) = array_manip.mult_ncerr(yval, yerr2, jac_ratio,
jac_ratio_err2)
try:
(y_2d, y_2d_err2, area_new,
bin_count) = axis_manip.rebin_2D_quad_to_rectlin(
Q1[X], E_t[:-1], Q2[X], E_t[:-1], Q3[X], E_t[1:], Q4[X],
E_t[1:], counts, counts_err2, so_dim.axis[0].val,
so_dim.axis[1].val)
del bin_count
except IndexError, e:
# Get the offending index from the error message
index = int(str(e).split()[1].split('index')[-1].strip('[]'))
print "Id:", map_so.id
print "Index:", index
print "Verticies: %f, %f, %f, %f, %f, %f, %f, %f" % (
Q1[X][index], E_t[:-1][index], Q2[X][index], E_t[:-1][index],
Q3[X][index], E_t[1:][index], Q4[X][index], E_t[1:][index])
raise IndexError(str(e))
# Add in together with previous results
(so_dim.y,
so_dim.var_y) = array_manip.add_ncerr(so_dim.y, so_dim.var_y, y_2d,
y_2d_err2)
(area_sum,
area_sum_err2) = array_manip.add_ncerr(area_sum, area_sum_err2,
area_new, area_sum_err2)
comb_som[0].var_y = area_sum_err2
# Write out summed counts into file
hlr_utils.write_file(configure.output,
"text/Dave2d",
comb_som,
output_ext="fra",
verbose=configure.verbose,
data_ext=configure.ext_replacement,
path_replacement=configure.path_replacement,
message="fractional area")
else:
# Divide summed fractional counts by the sum of the fractional areas
(so_dim.y,
so_dim.var_y) = array_manip.div_ncerr(so_dim.y, so_dim.var_y,
area_sum, area_sum_err2)
comb_som.append(so_dim)
del so_dim
return comb_som
def __set_som_attributes(tsom, **kwargs):
"""
This is a helper function that sets attributes for the final S(Q,E)
C{SOM.SOM}.
@param tsom: The input object for attribute setting
@type tsom: C{SOM.SOM}
def dimensionless_mon(obj, min_ext, max_ext, **kwargs):
"""
This function takes monitor spectra and converts them to dimensionless
spectra by dividing each spectrum by the total number of counts within the
range [min_ext, max_ext]. Then, each spectrum is multiplied by the quantity
max_ext - min_ext. The units of min_ext and max_ext are assumed to be the
same as the monitor spectra axis.
@param obj: Object containing monitor spectra
@type obj: C{SOM.SOM} or C{SOM.SO}
@param min_ext: Minimium range and associated error^2 for integrating total
counts.
@type min_ext: C{tuple}
@param max_ext: Maximium range and associated error^2 for integrating total
counts.
@type max_ext: C{tuple}
@param kwargs: A list of keyword arguments that the function accepts:
@keyword units: The expected units for this function. The default for this
function is I{Angstroms}.
@type units: C{string}
@return: Dimensionless monitor spectra
@rtype: C{SOM.SOM} or C{SOM.SO}
"""
# import the helper functions
import hlr_utils
if obj is None:
return obj
# 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:
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)
import array_manip
import dr_lib
import utils
for i in xrange(hlr_utils.get_length(obj)):
val = hlr_utils.get_value(obj, i, o_descr, "y")
err2 = hlr_utils.get_err2(obj, i, o_descr, "y")
x_axis = hlr_utils.get_value(obj, i, o_descr, "x", axis)
x_err2 = hlr_utils.get_err2(obj, i, o_descr, "x", axis)
map_so = hlr_utils.get_map_so(obj, None, i)
bin_widths = utils.calc_bin_widths(x_axis, x_err2)
# Scale bin contents by bin width
value0 = array_manip.mult_ncerr(val, err2, bin_widths[0],
bin_widths[1])
# Find bin range for extents
min_index = utils.bisect_helper(x_axis, min_ext[0])
max_index = utils.bisect_helper(x_axis, max_ext[0])
# Integrate axis using bin width multiplication
(asum, asum_err2) = dr_lib.integrate_axis_py(map_so,
start=min_index,
end=max_index,
width=True)
# Get the number of bins in the integration range
num_bins = max_index - min_index + 1
asum /= num_bins
asum_err2 /= (num_bins * num_bins)
# Divide by sum
value1 = array_manip.div_ncerr(value0[0], value0[1], asum, asum_err2)
hlr_utils.result_insert(result, res_descr, value1, map_so, "y")
return result
def run(config, tim):
"""
This method is where the data reduction process gets done.
@param config: Object containing the data reduction configuration
information.
@type config: L{hlr_utils.Configure}
@param tim: Object that will allow the method to perform timing
evaluations.
@type tim: C{sns_time.DiffTime}
"""
import array_manip
import common_lib
import dr_lib
import DST
import SOM
import math
if tim is not None:
tim.getTime(False)
old_time = tim.getOldTime()
if config.data is None:
raise RuntimeError("Need to pass a data filename to the driver "\
+"script.")
# Read in sample data geometry if one is provided
if config.data_inst_geom is not None:
if config.verbose:
print "Reading in sample data instrument geometry file"
data_inst_geom_dst = DST.getInstance("application/x-NxsGeom",
config.data_inst_geom)
else:
data_inst_geom_dst = None
# Read in normalization data geometry if one is provided
if config.norm_inst_geom is not None:
if config.verbose:
print "Reading in normalization instrument geometry file"
norm_inst_geom_dst = DST.getInstance("application/x-NxsGeom",
config.norm_inst_geom)
else:
norm_inst_geom_dst = None
# Perform Steps 1-2 on sample data
d_som1 = dr_lib.process_reflp_data(config.data, config, None,
config.dbkg_roi_file,
config.no_bkg,
inst_geom_dst=data_inst_geom_dst,
timer=tim)
# Get the detector angle
if config.omega is None:
# Make a fake SO
so = SOM.SO()
try:
theta = hlr_utils.get_special(d_som1.attr_list["Theta"], so)
except KeyError:
theta = (float('nan'), float('nan'))
else:
theta = config.omega.toFullTuple()
if theta[0] is not None:
if theta[2] == "degrees" or theta[2] == "degree":
theta_rads = (theta[0] * (math.pi / 180.0), 0.0)
else:
theta_rads = (theta[0], 0.0)
else:
theta_rads = (float('nan'), float('nan'))
d_som1.attr_list["data-theta"] = (theta_rads[0], theta_rads[1], "radians")
# Perform Steps 1-3 on normalization data
if config.norm is not None:
n_som1 = dr_lib.process_reflp_data(config.norm, config,
config.norm_roi_file,
config.nbkg_roi_file,
config.no_norm_bkg,
inst_geom_dst=norm_inst_geom_dst,
timer=tim)
else:
n_som1 = None
# Closing sample data instrument geometry file
if data_inst_geom_dst is not None:
data_inst_geom_dst.release_resource()
# Closing normalization data instrument geometry file
if norm_inst_geom_dst is not None:
norm_inst_geom_dst.release_resource()
# Step 4: Divide data by normalization
if config.verbose and config.norm is not None:
print "Scale data by normalization"
if tim is not None:
tim.getTime(False)
if config.norm is not None:
# Need to rebin the normalization spectra to the data pixel spectra
n_som2 = dr_lib.rebin_monitor(n_som1, d_som1, rtype="frac")
# Now divide the spectra
d_som2 = common_lib.div_ncerr(d_som1, n_som2)
del n_som2
else:
d_som2 = d_som1
if tim is not None and config.norm is not None:
tim.getTime(msg="After normalizing signal spectra")
del d_som1, n_som1
sin_theta_rads = (math.sin(theta_rads[0]), math.sin(theta_rads[1]))
if sin_theta_rads[0] < 0.0:
sin_theta_rads = (math.fabs(sin_theta_rads[0]),
math.fabs(sin_theta_rads[1]))
# Step 6: Scale wavelength axis by sin(theta) to make lambda_T
if config.verbose:
print "Scaling wavelength axis by sin(theta)"
if tim is not None:
tim.getTime(False)
d_som3 = common_lib.div_ncerr(d_som2, sin_theta_rads, axis="x")
if tim is not None:
tim.getTime(msg="After scaling wavelength axis ")
del d_som2
d_som3.setAxisLabel(0, "lambda_T")
# Step 7: Rebin to lambda_T axis
if config.verbose:
print "Rebinning spectra"
if config.lambdap_bins is None:
# Create a binning scheme
pathlength = d_som3.attr_list.instrument.get_total_path(
det_secondary=True)
delta_lambda = common_lib.tof_to_wavelength((config.delta_TOF, 0.0),
pathlength=pathlength)
delta_lambdap = array_manip.div_ncerr(delta_lambda[0], delta_lambda[1],
sin_theta_rads[0], 0.0)
config.lambdap_bins = dr_lib.create_axis_from_data(d_som3,
width=delta_lambdap[0])
else:
# Do nothing, got the binning scheme
pass
if tim is not None:
tim.getTime(False)
d_som4 = common_lib.rebin_axis_1D_frac(d_som3,
config.lambdap_bins.toNessiList())
if tim is not None:
tim.getTime(msg="After rebinning spectra ")
del d_som3
if config.inst == "REF_M":
# Clean up spectrum
if config.tof_cut_min is not None:
tof_cut_min = float(config.tof_cut_min)
else:
tof_cut_min = config.TOF_min
if config.tof_cut_max is not None:
tof_cut_max = float(config.tof_cut_max)
else:
tof_cut_max = config.TOF_max
pathlength = d_som4.attr_list.instrument.get_total_path(
det_secondary=True)
lambda_min = common_lib.tof_to_wavelength((tof_cut_min, 0.0),
pathlength=pathlength)
lambda_T_min = common_lib.div_ncerr(lambda_min, sin_theta_rads)
lambda_max = common_lib.tof_to_wavelength((tof_cut_max, 0.0),
pathlength=pathlength)
lambda_T_max = common_lib.div_ncerr(lambda_max, sin_theta_rads)
nz_list = []
for i in xrange(hlr_utils.get_length(d_som4)):
nz_list.append((lambda_T_min[0], lambda_T_max[0]))
d_som4A = dr_lib.zero_spectra(d_som4, nz_list)
else:
d_som4A = d_som4
del d_som4
# Step 8: Write out all spectra to a file
hlr_utils.write_file(config.output, "text/Spec", d_som4A,
replace_ext=False,
replace_path=False,
verbose=config.verbose,
message="Reflectivity information")
if config.dump_twod:
d_som5 = dr_lib.create_X_vs_pixpos(d_som4A,
config.lambdap_bins.toNessiList(),
rebin=False,
y_label="R",
y_units="",
x_label="$\lambda_T$",
x_units="$\AA$")
hlr_utils.write_file(config.output, "text/Dave2d", d_som5,
output_ext="plp", verbose=config.verbose,
data_ext=config.ext_replacement,
path_replacement=config.path_replacement,
message="2D Reflectivity information")
d_som4A.attr_list["config"] = config
hlr_utils.write_file(config.output, "text/rmd", d_som4A,
output_ext="rmd", verbose=config.verbose,
data_ext=config.ext_replacement,
path_replacement=config.path_replacement,
message="metadata")
if tim is not None:
tim.setOldTime(old_time)
tim.getTime(msg="Total Running Time")
def run(config, tim):
"""
This method is where the data reduction process gets done.
@param config: Object containing the data reduction configuration
information.
@type config: L{hlr_utils.Configure}
@param tim: Object that will allow the method to perform timing
evaluations.
@type tim: C{sns_time.DiffTime}
"""
import array_manip
import common_lib
import dr_lib
import DST
import SOM
import math
if tim is not None:
tim.getTime(False)
old_time = tim.getOldTime()
if config.data is None:
raise RuntimeError("Need to pass a data filename to the driver "\
+"script.")
# Read in sample data geometry if one is provided
if config.data_inst_geom is not None:
if config.verbose:
print "Reading in sample data instrument geometry file"
data_inst_geom_dst = DST.getInstance("application/x-NxsGeom",
config.data_inst_geom)
else:
data_inst_geom_dst = None
# Read in normalization data geometry if one is provided
if config.norm_inst_geom is not None:
if config.verbose:
print "Reading in normalization instrument geometry file"
norm_inst_geom_dst = DST.getInstance("application/x-NxsGeom",
config.norm_inst_geom)
else:
norm_inst_geom_dst = None
# Perform Steps 1-2 on sample data
d_som1 = dr_lib.process_reflp_data(config.data,
config,
None,
config.dbkg_roi_file,
config.no_bkg,
inst_geom_dst=data_inst_geom_dst,
timer=tim)
# Get the detector angle
if config.omega is None:
# Make a fake SO
so = SOM.SO()
try:
theta = hlr_utils.get_special(d_som1.attr_list["Theta"], so)
except KeyError:
theta = (float('nan'), float('nan'))
else:
theta = config.omega.toFullTuple()
if theta[0] is not None:
if theta[2] == "degrees" or theta[2] == "degree":
theta_rads = (theta[0] * (math.pi / 180.0), 0.0)
else:
theta_rads = (theta[0], 0.0)
else:
theta_rads = (float('nan'), float('nan'))
d_som1.attr_list["data-theta"] = (theta_rads[0], theta_rads[1], "radians")
# Perform Steps 1-3 on normalization data
if config.norm is not None:
n_som1 = dr_lib.process_reflp_data(config.norm,
config,
config.norm_roi_file,
config.nbkg_roi_file,
config.no_norm_bkg,
inst_geom_dst=norm_inst_geom_dst,
timer=tim)
else:
n_som1 = None
# Closing sample data instrument geometry file
if data_inst_geom_dst is not None:
data_inst_geom_dst.release_resource()
# Closing normalization data instrument geometry file
if norm_inst_geom_dst is not None:
norm_inst_geom_dst.release_resource()
# Step 4: Divide data by normalization
if config.verbose and config.norm is not None:
print "Scale data by normalization"
if tim is not None:
tim.getTime(False)
if config.norm is not None:
# Need to rebin the normalization spectra to the data pixel spectra
n_som2 = dr_lib.rebin_monitor(n_som1, d_som1, rtype="frac")
# Now divide the spectra
d_som2 = common_lib.div_ncerr(d_som1, n_som2)
del n_som2
else:
d_som2 = d_som1
if tim is not None and config.norm is not None:
tim.getTime(msg="After normalizing signal spectra")
del d_som1, n_som1
sin_theta_rads = (math.sin(theta_rads[0]), math.sin(theta_rads[1]))
if sin_theta_rads[0] < 0.0:
sin_theta_rads = (math.fabs(sin_theta_rads[0]),
math.fabs(sin_theta_rads[1]))
# Step 6: Scale wavelength axis by sin(theta) to make lambda_T
if config.verbose:
print "Scaling wavelength axis by sin(theta)"
if tim is not None:
tim.getTime(False)
d_som3 = common_lib.div_ncerr(d_som2, sin_theta_rads, axis="x")
if tim is not None:
tim.getTime(msg="After scaling wavelength axis ")
del d_som2
d_som3.setAxisLabel(0, "lambda_T")
# Step 7: Rebin to lambda_T axis
if config.verbose:
print "Rebinning spectra"
if config.lambdap_bins is None:
# Create a binning scheme
pathlength = d_som3.attr_list.instrument.get_total_path(
det_secondary=True)
delta_lambda = common_lib.tof_to_wavelength((config.delta_TOF, 0.0),
pathlength=pathlength)
delta_lambdap = array_manip.div_ncerr(delta_lambda[0], delta_lambda[1],
sin_theta_rads[0], 0.0)
config.lambdap_bins = dr_lib.create_axis_from_data(
d_som3, width=delta_lambdap[0])
else:
# Do nothing, got the binning scheme
pass
if tim is not None:
tim.getTime(False)
d_som4 = common_lib.rebin_axis_1D_frac(d_som3,
config.lambdap_bins.toNessiList())
if tim is not None:
tim.getTime(msg="After rebinning spectra ")
del d_som3
if config.inst == "REF_M":
# Clean up spectrum
if config.tof_cut_min is not None:
tof_cut_min = float(config.tof_cut_min)
else:
tof_cut_min = config.TOF_min
if config.tof_cut_max is not None:
tof_cut_max = float(config.tof_cut_max)
else:
tof_cut_max = config.TOF_max
pathlength = d_som4.attr_list.instrument.get_total_path(
det_secondary=True)
lambda_min = common_lib.tof_to_wavelength((tof_cut_min, 0.0),
pathlength=pathlength)
lambda_T_min = common_lib.div_ncerr(lambda_min, sin_theta_rads)
lambda_max = common_lib.tof_to_wavelength((tof_cut_max, 0.0),
pathlength=pathlength)
lambda_T_max = common_lib.div_ncerr(lambda_max, sin_theta_rads)
nz_list = []
for i in xrange(hlr_utils.get_length(d_som4)):
nz_list.append((lambda_T_min[0], lambda_T_max[0]))
d_som4A = dr_lib.zero_spectra(d_som4, nz_list)
else:
d_som4A = d_som4
del d_som4
# Step 8: Write out all spectra to a file
hlr_utils.write_file(config.output,
"text/Spec",
d_som4A,
replace_ext=False,
replace_path=False,
verbose=config.verbose,
message="Reflectivity information")
if config.dump_twod:
d_som5 = dr_lib.create_X_vs_pixpos(d_som4A,
config.lambdap_bins.toNessiList(),
rebin=False,
y_label="R",
y_units="",
x_label="$\lambda_T$",
x_units="$\AA$")
hlr_utils.write_file(config.output,
"text/Dave2d",
d_som5,
output_ext="plp",
verbose=config.verbose,
data_ext=config.ext_replacement,
path_replacement=config.path_replacement,
message="2D Reflectivity information")
d_som4A.attr_list["config"] = config
hlr_utils.write_file(config.output,
"text/rmd",
d_som4A,
output_ext="rmd",
verbose=config.verbose,
data_ext=config.ext_replacement,
path_replacement=config.path_replacement,
message="metadata")
if tim is not None:
tim.setOldTime(old_time)
tim.getTime(msg="Total Running Time")
def sum_by_rebin_frac(obj, axis_out, **kwargs):
"""
This function uses the C{axis_manip.rebin_axis_1D_frac} function from the
SCL to perform the rebinning. The function tracks the counts and fractional
area from all spectra separately. The counts and fractional area are
divided after all spectra have been parsed.
@param obj: Object to be rebinned and summed
@type obj: C{SOM.SOM} or C{SOM.SO}
@param axis_out: The axis to rebin the C{SOM} or C{SO} to
@type axis_out: C{NessiList}
@param kwargs: A list of keyword arguments that the function accepts:
@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 that has been rebinned and summed according to the
provided axis
@rtype: C{SOM.SOM} or C{SOM.SO}
@raise TypeError: The rebinning axis given is not a C{NessiList}
@raise TypeError: The object being rebinned is not a C{SOM} or a C{SO}
@raise TypeError: The dimension of the input object is not 1D
"""
# import the helper functions
import hlr_utils
# set up for working through data
try:
axis_out.__type__
except AttributeError:
raise TypeError("Rebinning axis must be a NessiList!")
o_descr = hlr_utils.get_descr(obj)
if o_descr == "number" or o_descr == "list":
raise TypeError("Do not know how to handle given type: %s" % \
o_descr)
else:
pass
try:
if obj.getDimension() != 1:
raise TypeError("The input object must be 1D!. This one is "\
+"%dD." % obj.getDimension())
except AttributeError:
# obj is a SO
if obj.dim() != 1:
raise TypeError("The input object must be 1D!. This one is "\
+"%dD." % obj.dim())
# Check for keywords
try:
config = kwargs["configure"]
except KeyError:
config = None
(result, res_descr) = hlr_utils.empty_result(obj)
result = hlr_utils.copy_som_attr(result, res_descr, obj, o_descr)
import array_manip
import axis_manip
len_data = len(axis_out) - 1
counts = nessi_list.NessiList(len_data)
counts_err2 = nessi_list.NessiList(len_data)
frac_area = nessi_list.NessiList(len_data)
frac_area_err2 = nessi_list.NessiList(len_data)
for i in xrange(hlr_utils.get_length(obj)):
axis_in = hlr_utils.get_value(obj, i, o_descr, "x", 0)
val = hlr_utils.get_value(obj, i, o_descr)
err2 = hlr_utils.get_err2(obj, i, o_descr)
value = axis_manip.rebin_axis_1D_frac(axis_in, val, err2, axis_out)
(counts, counts_err2) = array_manip.add_ncerr(counts, counts_err2,
value[0], value[1])
(frac_area, frac_area_err2) = array_manip.add_ncerr(frac_area,
frac_area_err2,
value[2],
frac_area_err2)
# Divide the total counts by the total fractional area
value1 = array_manip.div_ncerr(counts, counts_err2, frac_area,
frac_area_err2)
xvals = []
xvals.append(axis_out)
map_so = hlr_utils.get_map_so(obj, None, 0)
hlr_utils.result_insert(result, res_descr, value1, map_so, "all",
0, xvals)
if config is not None:
if o_descr == "SOM":
import SOM
o_som = SOM.SOM()
o_som.copyAttributes(obj)
so = hlr_utils.get_map_so(obj, None, 0)
so.axis[0].val = axis_out
so.y = counts
so.var_y = counts_err2
o_som.append(so)
# Write out summed counts into file
hlr_utils.write_file(config.output, "text/Spec", o_som,
output_ext="cnt",
verbose=config.verbose,
data_ext=config.ext_replacement,
path_replacement=config.path_replacement,
message="summed counts")
# Replace counts data with fractional area. The axes remain the
# same
o_som[0].y = frac_area
o_som[0].var_y = frac_area_err2
# Write out summed fractional area into file
hlr_utils.write_file(config.output, "text/Spec", o_som,
output_ext="fra",
verbose=config.verbose,
data_ext=config.ext_replacement,
path_replacement=config.path_replacement,
message="fractional area")
return result
# Replace counts data with fractional area. The axes remain the same
comb_som[0].y = area_sum
comb_som[0].var_y = area_sum_err2
# Write out summed counts into file
hlr_utils.write_file(configure.output, "text/Dave2d", comb_som,
output_ext="fra",
verbose=configure.verbose,
data_ext=configure.ext_replacement,
path_replacement=configure.path_replacement,
message="fractional area")
else:
# Divide summed fractional counts by the sum of the fractional areas
(so_dim.y, so_dim.var_y) = array_manip.div_ncerr(so_dim.y,
so_dim.var_y,
area_sum,
area_sum_err2)
comb_som.append(so_dim)
del so_dim
return comb_som
def __set_som_attributes(tsom, **kwargs):
"""
This is a helper function that sets attributes for the final S(Q,E)
C{SOM.SOM}.
@param tsom: The input object for attribute setting
def feff_correct_mon(obj, **kwargs):
"""
This function takes in a monitor spectra, calculates efficiencies based on
the montior's wavelength axis and divides the monitor counts by the
calculated efficiencies. The function is a M{constant * wavelength}.
@param obj: Object containing monitor spectra
@type obj: C{SOM.SOM} or C{SOM.SO}
@param kwargs: A list of keyword arguments that the function accepts:
@keyword units: The expected units for this function. The default for this
function is I{Angstroms}.
@type units: C{string}
@keyword eff_const: Use this provided effieciency constant. The default is
(0.00000085 / 1.8) Angstroms^-1.
@type eff_const: L{hlr_utils.DrParameter}
@keyword inst_name: The short name of an instrument.
@type inst_name: C{string}
@return: Efficiency corrected monitor spectra
@rtype: C{SOM.SOM} or C{SOM.SO}
"""
# import the helper functions
import hlr_utils
if obj is None:
return obj
# 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:
units = kwargs["units"]
except KeyError:
units = "Angstroms"
try:
eff_const = kwargs["eff_const"]
except KeyError:
# This is for SNS (specifically BASIS) monitors
eff_const = hlr_utils.DrParameter((0.00000085 / 1.8), 0.0,
"Angstroms^-1") # A^-1
inst_name = kwargs.get("inst_name")
# 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)
# iterate through the values
import array_manip
import nessi_list
import dr_lib
for i in xrange(hlr_utils.get_length(obj)):
val = hlr_utils.get_value(obj, i, o_descr, "x", axis)
map_so = hlr_utils.get_map_so(obj, None, i)
if inst_name is None:
eff = nessi_list.NessiList()
for j in xrange(len(val)-1):
bin_center = (val[j+1] + val[j]) / 2.0
eff.append(eff_const.getValue() * bin_center)
eff_err2 = nessi_list.NessiList(len(eff))
else:
if inst_name == "SANS":
(eff, eff_err2) = dr_lib.subexp_eff(eff_const, val)
else:
raise RuntimeError("Do not know how to handle %s instrument" \
% inst_name)
y_val = hlr_utils.get_value(obj, i, o_descr, "y")
y_err2 = hlr_utils.get_err2(obj, i, o_descr, "y")
value = array_manip.div_ncerr(y_val, y_err2, eff, eff_err2)
hlr_utils.result_insert(result, res_descr, value, map_so, "y")
return result
def rebin_axis_1D_frac(obj, axis_out):
"""
This function rebins the primary axis for a C{SOM} or a C{SO} based on the
given C{NessiList} axis.
@param obj: Object to be rebinned
@type obj: C{SOM.SOM} or C{SOM.SO}
@param axis_out: The axis to rebin the C{SOM} or C{SO} to
@type axis_out: C{NessiList}
@return: Object that has been rebinned according to the provided axis
@rtype: C{SOM.SOM} or C{SOM.SO}
@raise TypeError: The rebinning axis given is not a C{NessiList}
@raise TypeError: The object being rebinned is not a C{SOM} or a C{SO}
"""
# import the helper functions
import hlr_utils
# set up for working through data
try:
axis_out.__type__
except AttributeError:
raise TypeError("Rebinning axis must be a NessiList!")
o_descr = hlr_utils.get_descr(obj)
if o_descr == "number" or o_descr == "list":
raise TypeError("Do not know how to handle given type: %s" % \
o_descr)
else:
pass
(result, res_descr) = hlr_utils.empty_result(obj)
result = hlr_utils.copy_som_attr(result, res_descr, obj, o_descr)
# iterate through the values
import array_manip
import axis_manip
for i in xrange(hlr_utils.get_length(obj)):
axis_in = hlr_utils.get_value(obj, i, o_descr, "x", 0)
val = hlr_utils.get_value(obj, i, o_descr)
err2 = hlr_utils.get_err2(obj, i, o_descr)
value = axis_manip.rebin_axis_1D_frac(axis_in, val, err2, axis_out)
frac_err = nessi_list.NessiList(len(value[2]))
value1 = array_manip.div_ncerr(value[0], value[1], value[2], frac_err)
xvals = []
xvals.append(axis_out)
map_so = hlr_utils.get_map_so(obj, None, i)
hlr_utils.result_insert(result, res_descr, value1, map_so, "all",
0, xvals)
return result
def dimensionless_mon(obj, min_ext, max_ext, **kwargs):
"""
This function takes monitor spectra and converts them to dimensionless
spectra by dividing each spectrum by the total number of counts within the
range [min_ext, max_ext]. Then, each spectrum is multiplied by the quantity
max_ext - min_ext. The units of min_ext and max_ext are assumed to be the
same as the monitor spectra axis.
@param obj: Object containing monitor spectra
@type obj: C{SOM.SOM} or C{SOM.SO}
@param min_ext: Minimium range and associated error^2 for integrating total
counts.
@type min_ext: C{tuple}
@param max_ext: Maximium range and associated error^2 for integrating total
counts.
@type max_ext: C{tuple}
@param kwargs: A list of keyword arguments that the function accepts:
@keyword units: The expected units for this function. The default for this
function is I{Angstroms}.
@type units: C{string}
@return: Dimensionless monitor spectra
@rtype: C{SOM.SOM} or C{SOM.SO}
"""
# import the helper functions
import hlr_utils
if obj is None:
return obj
# 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:
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)
import array_manip
import dr_lib
import utils
for i in xrange(hlr_utils.get_length(obj)):
val = hlr_utils.get_value(obj, i, o_descr, "y")
err2 = hlr_utils.get_err2(obj, i, o_descr, "y")
x_axis = hlr_utils.get_value(obj, i, o_descr, "x", axis)
x_err2 = hlr_utils.get_err2(obj, i, o_descr, "x", axis)
map_so = hlr_utils.get_map_so(obj, None, i)
bin_widths = utils.calc_bin_widths(x_axis, x_err2)
# Scale bin contents by bin width
value0 = array_manip.mult_ncerr(val, err2,
bin_widths[0], bin_widths[1])
# Find bin range for extents
min_index = utils.bisect_helper(x_axis, min_ext[0])
max_index = utils.bisect_helper(x_axis, max_ext[0])
# Integrate axis using bin width multiplication
(asum, asum_err2) = dr_lib.integrate_axis_py(map_so, start=min_index,
end=max_index, width=True)
# Get the number of bins in the integration range
num_bins = max_index - min_index + 1
asum /= num_bins
asum_err2 /= (num_bins * num_bins)
# Divide by sum
value1 = array_manip.div_ncerr(value0[0], value0[1], asum, asum_err2)
hlr_utils.result_insert(result, res_descr, value1, map_so, "y")
return result
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_param_vs_Y(som, param, param_func, param_axis, **kwargs):
"""
This function takes a group of single spectrum with any given axes
(wavelength, energy etc.). The function can optionally rebin those axes to
a given axis. It then creates a 2D spectrum by using a parameter,
parameter functiona and a given axis for the lookup locations and places
each original spectrum in the found location.
@param som: The input object with arbitrary (but same) axis spectra
@type som: C{SOM.SOM}
@param param: The parameter that will be used for creating the lookups.
@type param: C{string}
@param param_func: The function that will convert the parameter into the
values for lookups.
@type param_func: C{string}
@param param_axis: The axis that will be searched for the lookup values.
@type param_axis: C{nessi_list.NessiList}
@param kwargs: A list of keyword arguments that the function accepts:
@keyword rebin_axis: An axis to rebin the given spectra to.
@type rebin_axis: C{nessi_list.NessiList}
@keyword data_type: The 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 pixnorm: A flag to track the number of pixels that contribute to
a bin and then normalize the bin by that number.
@type pixnorm: C{boolean}
@keyword prnorm: A parameter to track and determine a range (max - min)
for each bin the requested parameter axis. The range will
then be divided into the final summed spectrum for the
given bin.
@type prnorm: C{string}
@keyword binnorm: A flag that turns on the scaling of each stripe of the
y-axis by the individual bins widths from the y-axis.
@type binnorm: 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 dependent axis label
@type y_label: C{string}
@keyword y_units: The dependent axis units
@type y_units: C{string}
@keyword x_labels: The two independent axis labels
@type x_labels: C{list} of C{string}s
@keyword x_units: The two independent axis units
@type x_units: C{list} of C{string}s
@return: A two dimensional spectrum with the parameter as the x-axis and
the given spectra axes as the y-axis.
@rtype: C{SOM.SOM}
"""
import array_manip
import dr_lib
import hlr_utils
import nessi_list
import SOM
import utils
# Check for rebinning axis
try:
rebin_axis = kwargs["rebin_axis"]
except KeyError:
rebin_axis = None
# Check for pixnorm flag
try:
pixnorm = kwargs["pixnorm"]
except KeyError:
pixnorm = False
try:
binnorm = kwargs["binnorm"]
except KeyError:
binnorm = False
# Check for prnorm flag
try:
prpar = kwargs["prnorm"]
prnorm = True
except KeyError:
prnorm = 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
# Setup some variables
dim = 2
N_tot = 1
# Create 2D spectrum object
so_dim = SOM.SO(dim)
# Set the axis locations
param_axis_loc = 0
arb_axis_loc = 1
# Rebin original data to rebin_axis if necessary
if rebin_axis is not None:
(som1, som2) = dr_lib.rebin_axis_1D_frac(som, rebin_axis)
len_arb_axis = len(rebin_axis) - offset
so_dim.axis[arb_axis_loc].val = rebin_axis
else:
som1 = som
len_arb_axis = len(som[0].axis[0].val) - offset
so_dim.axis[arb_axis_loc].val = som[0].axis[0].val
del som
# Get parameter axis information
len_param_axis = len(param_axis) - offset
so_dim.axis[param_axis_loc].val = param_axis
if pixnorm:
pixarr = nessi_list.NessiList(len_param_axis)
if prnorm:
prarr = []
for i in xrange(len_param_axis):
prarr.append(nessi_list.NessiList())
# Get the parameters for all the spectra
ppfunc = hlr_utils.__getattribute__("param_array")
prarr_lookup = ppfunc(som1, prpar)
# Get the parameter lookup array
pfunc = hlr_utils.__getattribute__(param_func)
lookup_array = pfunc(som1, param)
# Create y and var_y lists from total 2D size
N_tot = len_param_axis * len_arb_axis
so_dim.y = nessi_list.NessiList(N_tot)
so_dim.var_y = nessi_list.NessiList(N_tot)
if rebin_axis is not None:
frac_area = nessi_list.NessiList(N_tot)
frac_area_err2 = nessi_list.NessiList(N_tot)
# Loop through data and create 2D spectrum
len_som = hlr_utils.get_length(som1)
for i in xrange(len_som):
val = hlr_utils.get_value(som1, i, "SOM", "y")
err2 = hlr_utils.get_err2(som1, i, "SOM", "y")
bin_index = utils.bisect_helper(param_axis, lookup_array[i])
start = bin_index * len_arb_axis
if pixnorm:
pixarr[bin_index] += 1
if prnorm:
prarr[bin_index].append(prarr_lookup[i])
(so_dim.y, so_dim.var_y) = array_manip.add_ncerr(so_dim.y,
so_dim.var_y,
val,
err2,
a_start=start)
if rebin_axis is not None:
val1 = hlr_utils.get_value(som2, i, "SOM", "y")
err1_2 = hlr_utils.get_err2(som2, i, "SOM", "y")
(frac_area, frac_area_err2) = array_manip.add_ncerr(frac_area,
frac_area_err2,
val1,
err1_2,
a_start=start)
if rebin_axis is not None:
(so_dim.y,
so_dim.var_y) = array_manip.div_ncerr(so_dim.y, so_dim.var_y,
frac_area, frac_area_err2)
# If parameter range normalization enabled, find the range for the
# parameter
if prnorm:
import math
prrange = nessi_list.NessiList(len_param_axis)
for i in xrange(len(prrange)):
try:
max_val = max(prarr[i])
except ValueError:
max_val = 0.0
try:
min_val = min(prarr[i])
except ValueError:
min_val = 0.0
prrange[i] = math.fabs(max_val - min_val)
# If pixel normalization tracking enabled, divided slices by pixel counts
if pixnorm or prnorm:
tmp_y = nessi_list.NessiList(N_tot)
tmp_var_y = nessi_list.NessiList(N_tot)
for i in range(len_param_axis):
start = i * len_arb_axis
end = (i + 1) * len_arb_axis
slice_y = so_dim.y[start:end]
slice_var_y = so_dim.var_y[start:end]
divconst = 1.0
if pixnorm:
divconst *= pixarr[i]
# Scale division constant if parameter range normalization enabled
if prnorm:
divconst *= prrange[i]
(dslice_y,
dslice_var_y) = array_manip.div_ncerr(slice_y, slice_var_y,
divconst, 0.0)
(tmp_y, tmp_var_y) = array_manip.add_ncerr(tmp_y,
tmp_var_y,
dslice_y,
dslice_var_y,
a_start=start)
so_dim.y = tmp_y
so_dim.var_y = tmp_var_y
if binnorm:
tmp_y = nessi_list.NessiList(N_tot)
tmp_var_y = nessi_list.NessiList(N_tot)
if rebin_axis is not None:
bin_const = utils.calc_bin_widths(rebin_axis)
else:
bin_const = utils.calc_bin_widths(som1[0].axis[1].val)
for i in range(len_param_axis):
start = i * len_arb_axis
end = (i + 1) * len_arb_axis
slice_y = so_dim.y[start:end]
slice_var_y = so_dim.var_y[start:end]
(dslice_y,
dslice_var_y) = array_manip.mult_ncerr(slice_y, slice_var_y,
bin_const[0], bin_const[1])
(tmp_y, tmp_var_y) = array_manip.add_ncerr(tmp_y,
tmp_var_y,
dslice_y,
dslice_var_y,
a_start=start)
so_dim.y = tmp_y
so_dim.var_y = tmp_var_y
# Create final 2D spectrum object container
comb_som = SOM.SOM()
comb_som.copyAttributes(som1)
del som1
# Check for so_id keyword argument
try:
so_dim.id = kwargs["so_id"]
except KeyError:
so_dim.id = 0
# Check for y_label keyword argument
try:
comb_som.setYLabel(kwargs["y_label"])
except KeyError:
comb_som.setYLabel("Counts")
# Check for y_units keyword argument
try:
comb_som.setYUnits(kwargs["y_units"])
except KeyError:
comb_som.setYUnits("Counts / Arb")
# Check for x_label keyword argument
try:
comb_som.setAllAxisLabels(kwargs["x_labels"])
except KeyError:
comb_som.setAllAxisLabels(["Parameter", "Arbitrary"])
# Check for x_units keyword argument
try:
comb_som.setAllAxisUnits(kwargs["x_units"])
except KeyError:
comb_som.setAllAxisUnits(["Arb", "Arb"])
comb_som.append(so_dim)
del so_dim
return comb_som
def __calc_x1(*args):
"""
This function calculates the x1 coeffiecient to the S(Q,E) Jacobian
@param args: A list of parameters used to calculate the x1 coefficient
The following is a list of the arguments needed in there expected order
1. Initial Wavevector
2. Momentum Transfer
3. Length Ratio (L_f / L_i)
4. Final Wavevector
5. Wavevector Final x Cos(polar)
6. Wavevector Final x Sin(polar)
7. Lambda Constant (2*pi/l_f^2)(dlf/dh)
8. Derivative dazi_dh
9. Derivative dpol_dh
10. Derivative dpol_dtd
11. Derivative dazi_dtd
12. Cos(polar)
13. Time-zero Slope Correction
14. Vector of Zeros
@type args: C{list}
@return: The calculated x1 coefficient
@rtype: (C{nessi_list.NessiList}, C{nessi_list.NessiList})
"""
# Settle out the arguments to sensible names
k_i = args[0]
Q = args[1]
len_ratio = args[2]
k_f = args[3]
k_f_cos_pol = args[4]
k_f_sin_pol = args[5]
lam_const = args[6]
dpol_dh = args[7]
dpol_dtd = args[8]
dtd_over_dh = args[9]
cos_pol = args[10]
t_0_s_corr = args[11]
z_vec = args[12]
# (L_f / L_i) * (1 / k_f)^2 * (1 / 1 + ((h / m) * (t0_s / L_i)))
const1 = (len_ratio * t_0_s_corr) / (k_f * k_f)
# (dpol/dh + (dpol/dtd * dtd/dh)) * k_f * sin(pol)
const2 = (dpol_dh + (dpol_dtd * dtd_over_dh)) * k_f_sin_pol
# k_i^2
temp1 = array_manip.mult_ncerr(k_i, z_vec, k_i, z_vec)
# (L_f / L_i) * (k_i / k_f)^2 * (1 / 1 + ((h / m) * (t0_s / L_i)))
temp2 = array_manip.mult_ncerr(temp1[0], temp1[1], const1, 0.0)
# k_i - k_f * cos(pol)
temp3 = array_manip.sub_ncerr(k_i, z_vec, k_f_cos_pol, 0.0)
# (k_i - k_f * cos(pol)) * (L_f / L_i) * (k_i / k_f)^2
temp4 = array_manip.mult_ncerr(temp2[0], temp2[1], temp3[0], temp3[1])
# k_i * cos(pol)
temp5 = array_manip.mult_ncerr(k_i, z_vec, cos_pol, 0.0)
# k_f - k_i * cos(pol)
temp6 = array_manip.sub_ncerr(k_f, 0.0, temp5[0], temp5[1])
# (k_i - k_f * cos(pol)) * (L_f / L_i) * (k_i / k_f)^2 -
# (k_f - k_i * cos(pol))
temp7 = array_manip.sub_ncerr(temp4[0], temp4[1], temp6[0], temp6[1])
# ((k_i - k_f * cos(pol)) * (L_f / L_i) * (k_i / k_f)^2 -
# (k_f - k_i * cos(pol))) * (2 * pi / l_f^2) * dlf/dh
temp8 = array_manip.mult_ncerr(temp7[0], temp7[1], lam_const, 0.0)
# k_i * k_f * sin(pol) * (dpol/dh + (dpol/dtd * dtd/dh))
temp9 = array_manip.mult_ncerr(k_i, z_vec, const2, 0.0)
# ((k_i - k_f * cos(pol)) * (L_f / L_i) * (k_i / k_f)^2 -
# (k_f - k_i * cos(pol))) * (2 * pi / l_f^2) * dlf/dh +
# k_i * k_f * sin(pol) * (dpol/dh + (dpol/dtd * dtd/dh))
temp10 = array_manip.add_ncerr(temp8[0], temp8[1], temp9[0], temp9[1])
# (((k_i - k_f * cos(pol)) * (L_f / L_i) * (k_i / k_f)^2 -
# (k_f - k_i * cos(pol))) * (2 * pi / l_f^2) * dlf/dh +
# k_i * k_f * sin(pol) * (dpol/dh - (dpol/dtd dazi/dh / dazi/dtd))
return array_manip.div_ncerr(temp10[0], temp10[1], Q, z_vec)
comb_som[0].var_y = area_sum_err2
# Write out summed counts into file
hlr_utils.write_file(configure.output,
"text/Dave2d",
comb_som,
output_ext="fra",
verbose=configure.verbose,
data_ext=configure.ext_replacement,
path_replacement=configure.path_replacement,
message="fractional area")
else:
# Divide summed fractional counts by the sum of the fractional areas
(so_dim.y,
so_dim.var_y) = array_manip.div_ncerr(so_dim.y, so_dim.var_y,
area_sum, area_sum_err2)
if configure.scale_sqe:
(so_dim.y,
so_dim.var_y) = array_manip.div_ncerr(so_dim.y, so_dim.var_y,
bin_count, bin_count_err2)
comb_som.append(so_dim)
del so_dim
return comb_som
def __set_som_attributes(tsom, inst_name, **kwargs):
"""
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 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 sum_by_rebin_frac(obj, axis_out, **kwargs):
"""
This function uses the C{axis_manip.rebin_axis_1D_frac} function from the
SCL to perform the rebinning. The function tracks the counts and fractional
area from all spectra separately. The counts and fractional area are
divided after all spectra have been parsed.
@param obj: Object to be rebinned and summed
@type obj: C{SOM.SOM} or C{SOM.SO}
@param axis_out: The axis to rebin the C{SOM} or C{SO} to
@type axis_out: C{NessiList}
@param kwargs: A list of keyword arguments that the function accepts:
@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 that has been rebinned and summed according to the
provided axis
@rtype: C{SOM.SOM} or C{SOM.SO}
@raise TypeError: The rebinning axis given is not a C{NessiList}
@raise TypeError: The object being rebinned is not a C{SOM} or a C{SO}
@raise TypeError: The dimension of the input object is not 1D
"""
# import the helper functions
import hlr_utils
# set up for working through data
try:
axis_out.__type__
except AttributeError:
raise TypeError("Rebinning axis must be a NessiList!")
o_descr = hlr_utils.get_descr(obj)
if o_descr == "number" or o_descr == "list":
raise TypeError("Do not know how to handle given type: %s" % \
o_descr)
else:
pass
try:
if obj.getDimension() != 1:
raise TypeError("The input object must be 1D!. This one is "\
+"%dD." % obj.getDimension())
except AttributeError:
# obj is a SO
if obj.dim() != 1:
raise TypeError("The input object must be 1D!. This one is "\
+"%dD." % obj.dim())
# Check for keywords
try:
config = kwargs["configure"]
except KeyError:
config = None
(result, res_descr) = hlr_utils.empty_result(obj)
result = hlr_utils.copy_som_attr(result, res_descr, obj, o_descr)
import array_manip
import axis_manip
len_data = len(axis_out) - 1
counts = nessi_list.NessiList(len_data)
counts_err2 = nessi_list.NessiList(len_data)
frac_area = nessi_list.NessiList(len_data)
frac_area_err2 = nessi_list.NessiList(len_data)
for i in xrange(hlr_utils.get_length(obj)):
axis_in = hlr_utils.get_value(obj, i, o_descr, "x", 0)
val = hlr_utils.get_value(obj, i, o_descr)
err2 = hlr_utils.get_err2(obj, i, o_descr)
value = axis_manip.rebin_axis_1D_frac(axis_in, val, err2, axis_out)
(counts, counts_err2) = array_manip.add_ncerr(counts, counts_err2,
value[0], value[1])
(frac_area,
frac_area_err2) = array_manip.add_ncerr(frac_area, frac_area_err2,
value[2], frac_area_err2)
# Divide the total counts by the total fractional area
value1 = array_manip.div_ncerr(counts, counts_err2, frac_area,
frac_area_err2)
xvals = []
xvals.append(axis_out)
map_so = hlr_utils.get_map_so(obj, None, 0)
hlr_utils.result_insert(result, res_descr, value1, map_so, "all", 0, xvals)
if config is not None:
if o_descr == "SOM":
import SOM
o_som = SOM.SOM()
o_som.copyAttributes(obj)
so = hlr_utils.get_map_so(obj, None, 0)
so.axis[0].val = axis_out
so.y = counts
so.var_y = counts_err2
o_som.append(so)
# Write out summed counts into file
hlr_utils.write_file(config.output,
"text/Spec",
o_som,
output_ext="cnt",
verbose=config.verbose,
data_ext=config.ext_replacement,
path_replacement=config.path_replacement,
message="summed counts")
# Replace counts data with fractional area. The axes remain the
# same
o_som[0].y = frac_area
o_som[0].var_y = frac_area_err2
# Write out summed fractional area into file
hlr_utils.write_file(config.output,
"text/Spec",
o_som,
output_ext="fra",
verbose=config.verbose,
data_ext=config.ext_replacement,
path_replacement=config.path_replacement,
message="fractional area")
return result
def feff_correct_mon(obj, **kwargs):
"""
This function takes in a monitor spectra, calculates efficiencies based on
the montior's wavelength axis and divides the monitor counts by the
calculated efficiencies. The function is a M{constant * wavelength}.
@param obj: Object containing monitor spectra
@type obj: C{SOM.SOM} or C{SOM.SO}
@param kwargs: A list of keyword arguments that the function accepts:
@keyword units: The expected units for this function. The default for this
function is I{Angstroms}.
@type units: C{string}
@keyword eff_const: Use this provided effieciency constant. The default is
(0.00000085 / 1.8) Angstroms^-1.
@type eff_const: L{hlr_utils.DrParameter}
@keyword inst_name: The short name of an instrument.
@type inst_name: C{string}
@return: Efficiency corrected monitor spectra
@rtype: C{SOM.SOM} or C{SOM.SO}
"""
# import the helper functions
import hlr_utils
if obj is None:
return obj
# 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:
units = kwargs["units"]
except KeyError:
units = "Angstroms"
try:
eff_const = kwargs["eff_const"]
except KeyError:
# This is for SNS (specifically BASIS) monitors
eff_const = hlr_utils.DrParameter((0.00000085 / 1.8), 0.0,
"Angstroms^-1") # A^-1
inst_name = kwargs.get("inst_name")
# 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)
# iterate through the values
import array_manip
import nessi_list
import dr_lib
for i in xrange(hlr_utils.get_length(obj)):
val = hlr_utils.get_value(obj, i, o_descr, "x", axis)
map_so = hlr_utils.get_map_so(obj, None, i)
if inst_name is None:
eff = nessi_list.NessiList()
for j in xrange(len(val) - 1):
bin_center = (val[j + 1] + val[j]) / 2.0
eff.append(eff_const.getValue() * bin_center)
eff_err2 = nessi_list.NessiList(len(eff))
else:
if inst_name == "SANS":
(eff, eff_err2) = dr_lib.subexp_eff(eff_const, val)
else:
raise RuntimeError("Do not know how to handle %s instrument" \
% inst_name)
y_val = hlr_utils.get_value(obj, i, o_descr, "y")
y_err2 = hlr_utils.get_err2(obj, i, o_descr, "y")
value = array_manip.div_ncerr(y_val, y_err2, eff, eff_err2)
hlr_utils.result_insert(result, res_descr, value, map_so, "y")
return result
def create_param_vs_Y(som, param, param_func, param_axis, **kwargs):
"""
This function takes a group of single spectrum with any given axes
(wavelength, energy etc.). The function can optionally rebin those axes to
a given axis. It then creates a 2D spectrum by using a parameter,
parameter functiona and a given axis for the lookup locations and places
each original spectrum in the found location.
@param som: The input object with arbitrary (but same) axis spectra
@type som: C{SOM.SOM}
@param param: The parameter that will be used for creating the lookups.
@type param: C{string}
@param param_func: The function that will convert the parameter into the
values for lookups.
@type param_func: C{string}
@param param_axis: The axis that will be searched for the lookup values.
@type param_axis: C{nessi_list.NessiList}
@param kwargs: A list of keyword arguments that the function accepts:
@keyword rebin_axis: An axis to rebin the given spectra to.
@type rebin_axis: C{nessi_list.NessiList}
@keyword data_type: The 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 pixnorm: A flag to track the number of pixels that contribute to
a bin and then normalize the bin by that number.
@type pixnorm: C{boolean}
@keyword prnorm: A parameter to track and determine a range (max - min)
for each bin the requested parameter axis. The range will
then be divided into the final summed spectrum for the
given bin.
@type prnorm: C{string}
@keyword binnorm: A flag that turns on the scaling of each stripe of the
y-axis by the individual bins widths from the y-axis.
@type binnorm: 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 dependent axis label
@type y_label: C{string}
@keyword y_units: The dependent axis units
@type y_units: C{string}
@keyword x_labels: The two independent axis labels
@type x_labels: C{list} of C{string}s
@keyword x_units: The two independent axis units
@type x_units: C{list} of C{string}s
@return: A two dimensional spectrum with the parameter as the x-axis and
the given spectra axes as the y-axis.
@rtype: C{SOM.SOM}
"""
import array_manip
import dr_lib
import hlr_utils
import nessi_list
import SOM
import utils
# Check for rebinning axis
try:
rebin_axis = kwargs["rebin_axis"]
except KeyError:
rebin_axis = None
# Check for pixnorm flag
try:
pixnorm = kwargs["pixnorm"]
except KeyError:
pixnorm = False
try:
binnorm = kwargs["binnorm"]
except KeyError:
binnorm = False
# Check for prnorm flag
try:
prpar = kwargs["prnorm"]
prnorm = True
except KeyError:
prnorm = 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
# Setup some variables
dim = 2
N_tot = 1
# Create 2D spectrum object
so_dim = SOM.SO(dim)
# Set the axis locations
param_axis_loc = 0
arb_axis_loc = 1
# Rebin original data to rebin_axis if necessary
if rebin_axis is not None:
(som1, som2) = dr_lib.rebin_axis_1D_frac(som, rebin_axis)
len_arb_axis = len(rebin_axis) - offset
so_dim.axis[arb_axis_loc].val = rebin_axis
else:
som1 = som
len_arb_axis = len(som[0].axis[0].val) - offset
so_dim.axis[arb_axis_loc].val = som[0].axis[0].val
del som
# Get parameter axis information
len_param_axis = len(param_axis) - offset
so_dim.axis[param_axis_loc].val = param_axis
if pixnorm:
pixarr = nessi_list.NessiList(len_param_axis)
if prnorm:
prarr = []
for i in xrange(len_param_axis):
prarr.append(nessi_list.NessiList())
# Get the parameters for all the spectra
ppfunc = hlr_utils.__getattribute__("param_array")
prarr_lookup = ppfunc(som1, prpar)
# Get the parameter lookup array
pfunc = hlr_utils.__getattribute__(param_func)
lookup_array = pfunc(som1, param)
# Create y and var_y lists from total 2D size
N_tot = len_param_axis * len_arb_axis
so_dim.y = nessi_list.NessiList(N_tot)
so_dim.var_y = nessi_list.NessiList(N_tot)
if rebin_axis is not None:
frac_area = nessi_list.NessiList(N_tot)
frac_area_err2 = nessi_list.NessiList(N_tot)
# Loop through data and create 2D spectrum
len_som = hlr_utils.get_length(som1)
for i in xrange(len_som):
val = hlr_utils.get_value(som1, i, "SOM", "y")
err2 = hlr_utils.get_err2(som1, i, "SOM", "y")
bin_index = utils.bisect_helper(param_axis, lookup_array[i])
start = bin_index * len_arb_axis
if pixnorm:
pixarr[bin_index] += 1
if prnorm:
prarr[bin_index].append(prarr_lookup[i])
(so_dim.y, so_dim.var_y) = array_manip.add_ncerr(so_dim.y,
so_dim.var_y,
val,
err2,
a_start=start)
if rebin_axis is not None:
val1 = hlr_utils.get_value(som2, i, "SOM", "y")
err1_2 = hlr_utils.get_err2(som2, i, "SOM", "y")
(frac_area, frac_area_err2) = array_manip.add_ncerr(frac_area,
frac_area_err2,
val1,
err1_2,
a_start=start)
if rebin_axis is not None:
(so_dim.y, so_dim.var_y) = array_manip.div_ncerr(so_dim.y,
so_dim.var_y,
frac_area,
frac_area_err2)
# If parameter range normalization enabled, find the range for the
# parameter
if prnorm:
import math
prrange = nessi_list.NessiList(len_param_axis)
for i in xrange(len(prrange)):
try:
max_val = max(prarr[i])
except ValueError:
max_val = 0.0
try:
min_val = min(prarr[i])
except ValueError:
min_val = 0.0
prrange[i] = math.fabs(max_val - min_val)
# If pixel normalization tracking enabled, divided slices by pixel counts
if pixnorm or prnorm:
tmp_y = nessi_list.NessiList(N_tot)
tmp_var_y = nessi_list.NessiList(N_tot)
for i in range(len_param_axis):
start = i * len_arb_axis
end = (i + 1) * len_arb_axis
slice_y = so_dim.y[start:end]
slice_var_y = so_dim.var_y[start:end]
divconst = 1.0
if pixnorm:
divconst *= pixarr[i]
# Scale division constant if parameter range normalization enabled
if prnorm:
divconst *= prrange[i]
(dslice_y, dslice_var_y) = array_manip.div_ncerr(slice_y,
slice_var_y,
divconst,
0.0)
(tmp_y, tmp_var_y) = array_manip.add_ncerr(tmp_y,
tmp_var_y,
dslice_y,
dslice_var_y,
a_start=start)
so_dim.y = tmp_y
so_dim.var_y = tmp_var_y
if binnorm:
tmp_y = nessi_list.NessiList(N_tot)
tmp_var_y = nessi_list.NessiList(N_tot)
if rebin_axis is not None:
bin_const = utils.calc_bin_widths(rebin_axis)
else:
bin_const = utils.calc_bin_widths(som1[0].axis[1].val)
for i in range(len_param_axis):
start = i * len_arb_axis
end = (i + 1) * len_arb_axis
slice_y = so_dim.y[start:end]
slice_var_y = so_dim.var_y[start:end]
(dslice_y, dslice_var_y) = array_manip.mult_ncerr(slice_y,
slice_var_y,
bin_const[0],
bin_const[1])
(tmp_y, tmp_var_y) = array_manip.add_ncerr(tmp_y,
tmp_var_y,
dslice_y,
dslice_var_y,
a_start=start)
so_dim.y = tmp_y
so_dim.var_y = tmp_var_y
# Create final 2D spectrum object container
comb_som = SOM.SOM()
comb_som.copyAttributes(som1)
del som1
# Check for so_id keyword argument
try:
so_dim.id = kwargs["so_id"]
except KeyError:
so_dim.id = 0
# Check for y_label keyword argument
try:
comb_som.setYLabel(kwargs["y_label"])
except KeyError:
comb_som.setYLabel("Counts")
# Check for y_units keyword argument
try:
comb_som.setYUnits(kwargs["y_units"])
except KeyError:
comb_som.setYUnits("Counts / Arb")
# Check for x_label keyword argument
try:
comb_som.setAllAxisLabels(kwargs["x_labels"])
except KeyError:
comb_som.setAllAxisLabels(["Parameter", "Arbitrary"])
# Check for x_units keyword argument
try:
comb_som.setAllAxisUnits(kwargs["x_units"])
except KeyError:
comb_som.setAllAxisUnits(["Arb", "Arb"])
comb_som.append(so_dim)
del so_dim
return comb_som
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
