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_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 create_E_vs_Q_dgs(som, E_i, Q_final, **kwargs):
"""
This function starts with the rebinned energy transfer and turns this
into a 2D spectra with E and Q axes for DGS instruments.
@param som: The input object with initial IGS wavelength axis
@type som: C{SOM.SOM}
@param E_i: The initial energy for the given data.
@type E_i: C{tuple}
@param Q_final: The momentum transfer axis to rebin the data to
@type Q_final: C{nessi_list.NessiList}
@param kwargs: A list of keyword arguments that the function accepts:
@keyword corner_angles: The object that contains the corner geometry
information.
@type corner_angles: C{dict}
@keyword so_id: The identifier represents a number, string, tuple or other
object that describes the resulting C{SO}
@type so_id: C{int}, C{string}, C{tuple}, C{pixel ID}
@keyword y_label: The y axis label
@type y_label: C{string}
@keyword y_units: The y axis units
@type y_units: C{string}
@keyword x_labels: This is a list of names that sets the individual x axis
labels
@type x_labels: C{list} of C{string}s
@keyword x_units: This is a list of names that sets the individual x axis
units
@type x_units: C{list} of C{string}s
@keyword split: This flag causes the counts and the fractional area to
be written out into separate files.
@type split: C{boolean}
@keyword configure: This is the object containing the driver configuration.
@type configure: C{Configure}
@return: Object containing a 2D C{SO} with E and Q axes
@rtype: C{SOM.SOM}
"""
import array_manip
import axis_manip
import common_lib
import hlr_utils
import nessi_list
import SOM
import utils
# Check for keywords
corner_angles = kwargs["corner_angles"]
configure = kwargs.get("configure")
split = kwargs.get("split", False)
# Setup output object
so_dim = SOM.SO(2)
so_dim.axis[0].val = Q_final
so_dim.axis[1].val = som[0].axis[0].val # E_t
# Calculate total 2D array size
N_tot = (len(so_dim.axis[0].val) - 1) * (len(so_dim.axis[1].val) - 1)
# Create y and var_y lists from total 2D size
so_dim.y = nessi_list.NessiList(N_tot)
so_dim.var_y = nessi_list.NessiList(N_tot)
# Create area sum and errors for the area sum lists from total 2D size
area_sum = nessi_list.NessiList(N_tot)
area_sum_err2 = nessi_list.NessiList(N_tot)
# Convert initial energy to initial wavevector
l_i = common_lib.energy_to_wavelength(E_i)
k_i = common_lib.wavelength_to_scalar_k(l_i)
# Since all the data is rebinned to the same energy transfer axis, we can
# calculate the final energy axis once
E_t = som[0].axis[0].val
if som[0].axis[0].var is not None:
E_t_err2 = som[0].axis[0].var
else:
E_t_err2 = nessi_list.NessiList(len(E_t))
# Get the bin width arrays from E_t
(E_t_bw, E_t_bw_err2) = utils.calc_bin_widths(E_t)
E_f = array_manip.sub_ncerr(E_i[0], E_i[1], E_t, E_t_err2)
# Now we can get the final wavevector
l_f = axis_manip.energy_to_wavelength(E_f[0], E_f[1])
k_f = axis_manip.wavelength_to_scalar_k(l_f[0], l_f[1])
# Output position for Q
X = 0
# Iterate though the data
len_som = hlr_utils.get_length(som)
for i in xrange(len_som):
map_so = hlr_utils.get_map_so(som, None, i)
yval = hlr_utils.get_value(som, i, "SOM", "y")
yerr2 = hlr_utils.get_err2(som, i, "SOM", "y")
cangles = corner_angles[str(map_so.id)]
avg_theta1 = (cangles.getPolar(0) + cangles.getPolar(1)) / 2.0
avg_theta2 = (cangles.getPolar(2) + cangles.getPolar(3)) / 2.0
Q1 = axis_manip.init_scatt_wavevector_to_scalar_Q(k_i[0],
k_i[1],
k_f[0][:-1],
k_f[1][:-1],
avg_theta2,
0.0)
Q2 = axis_manip.init_scatt_wavevector_to_scalar_Q(k_i[0],
k_i[1],
k_f[0][:-1],
k_f[1][:-1],
avg_theta1,
0.0)
Q3 = axis_manip.init_scatt_wavevector_to_scalar_Q(k_i[0],
k_i[1],
k_f[0][1:],
k_f[1][1:],
avg_theta1,
0.0)
Q4 = axis_manip.init_scatt_wavevector_to_scalar_Q(k_i[0],
k_i[1],
k_f[0][1:],
k_f[1][1:],
avg_theta2,
0.0)
# Calculate the area of the E,Q polygons
(A, A_err2) = utils.calc_eq_jacobian_dgs(E_t[:-1], E_t[:-1],
E_t[1:], E_t[1:],
Q1[X], Q2[X], Q3[X], Q4[X])
# Apply the Jacobian: C/dE_t * dE_t / A(EQ) = C/A(EQ)
(jac_ratio, jac_ratio_err2) = array_manip.div_ncerr(E_t_bw,
E_t_bw_err2,
A, A_err2)
(counts, counts_err2) = array_manip.mult_ncerr(yval, yerr2,
jac_ratio,
jac_ratio_err2)
try:
(y_2d, y_2d_err2,
area_new,
bin_count) = axis_manip.rebin_2D_quad_to_rectlin(Q1[X], E_t[:-1],
Q2[X], E_t[:-1],
Q3[X], E_t[1:],
Q4[X], E_t[1:],
counts,
counts_err2,
so_dim.axis[0].val,
so_dim.axis[1].val)
del bin_count
except IndexError, e:
# Get the offending index from the error message
index = int(str(e).split()[1].split('index')[-1].strip('[]'))
print "Id:", map_so.id
print "Index:", index
print "Verticies: %f, %f, %f, %f, %f, %f, %f, %f" % (Q1[X][index],
E_t[:-1][index],
Q2[X][index],
E_t[:-1][index],
Q3[X][index],
E_t[1:][index],
Q4[X][index],
E_t[1:][index])
raise IndexError(str(e))
# Add in together with previous results
(so_dim.y, so_dim.var_y) = array_manip.add_ncerr(so_dim.y,
so_dim.var_y,
y_2d, y_2d_err2)
(area_sum, area_sum_err2) = array_manip.add_ncerr(area_sum,
area_sum_err2,
area_new,
area_sum_err2)
def create_E_vs_Q_igs(som, *args, **kwargs):
"""
This function starts with the initial IGS wavelength axis and turns this
into a 2D spectra with E and Q axes.
@param som: The input object with initial IGS wavelength axis
@type som: C{SOM.SOM}
@param args: A mandatory list of axes for rebinning. There is a particular
order to them. They should be present in the following order:
Without errors
1. Energy transfer
2. Momentum transfer
With errors
1. Energy transfer
2. Energy transfer error^2
3. Momentum transfer
4. Momentum transfer error ^2
@type args: C{nessi_list.NessiList}s
@param kwargs: A list of keyword arguments that the function accepts:
@keyword withXVar: Flag for whether the function should be expecting the
associated axes to have errors. The default value will
be I{False}.
@type withXVar: C{boolean}
@keyword data_type: Name of the data type which can be either I{histogram},
I{density} or I{coordinate}. The default value will be
I{histogram}
@type data_type: C{string}
@keyword Q_filter: Flag to turn on or off Q filtering. The default behavior
is I{True}.
@type Q_filter: C{boolean}
@keyword so_id: The identifier represents a number, string, tuple or other
object that describes the resulting C{SO}
@type so_id: C{int}, C{string}, C{tuple}, C{pixel ID}
@keyword y_label: The y axis label
@type y_label: C{string}
@keyword y_units: The y axis units
@type y_units: C{string}
@keyword x_labels: This is a list of names that sets the individual x axis
labels
@type x_labels: C{list} of C{string}s
@keyword x_units: This is a list of names that sets the individual x axis
units
@type x_units: C{list} of C{string}s
@keyword split: This flag causes the counts and the fractional area to
be written out into separate files.
@type split: C{boolean}
@keyword configure: This is the object containing the driver configuration.
@type configure: C{Configure}
@return: Object containing a 2D C{SO} with E and Q axes
@rtype: C{SOM.SOM}
@raise RuntimeError: Anything other than a C{SOM} is passed to the function
@raise RuntimeError: An instrument is not contained in the C{SOM}
"""
import nessi_list
# Setup some variables
dim = 2
N_y = []
N_tot = 1
N_args = len(args)
# Get T0 slope in order to calculate dT = dT_i + dT_0
try:
t_0_slope = som.attr_list["Time_zero_slope"][0]
t_0_slope_err2 = som.attr_list["Time_zero_slope"][1]
except KeyError:
t_0_slope = float(0.0)
t_0_slope_err2 = float(0.0)
# Check withXVar keyword argument and also check number of given args.
# Set xvar to the appropriate value
try:
value = kwargs["withXVar"]
if value.lower() == "true":
if N_args != 4:
raise RuntimeError("Since you have requested x errors, 4 x "\
+"axes must be provided.")
else:
xvar = True
elif value.lower() == "false":
if N_args != 2:
raise RuntimeError("Since you did not requested x errors, 2 "\
+"x axes must be provided.")
else:
xvar = False
else:
raise RuntimeError("Do not understand given parameter %s" % \
value)
except KeyError:
if N_args != 2:
raise RuntimeError("Since you did not requested x errors, 2 "\
+"x axes must be provided.")
else:
xvar = False
# Check dataType keyword argument. An offset will be set to 1 for the
# histogram type and 0 for either density or coordinate
try:
data_type = kwargs["data_type"]
if data_type.lower() == "histogram":
offset = 1
elif data_type.lower() == "density" or \
data_type.lower() == "coordinate":
offset = 0
else:
raise RuntimeError("Do not understand data type given: %s" % \
data_type)
# Default is offset for histogram
except KeyError:
offset = 1
try:
Q_filter = kwargs["Q_filter"]
except KeyError:
Q_filter = True
# Check for split keyword
try:
split = kwargs["split"]
except KeyError:
split = False
# Check for configure keyword
try:
configure = kwargs["configure"]
except KeyError:
configure = None
so_dim = SOM.SO(dim)
for i in range(dim):
# Set the x-axis arguments from the *args list into the new SO
if not xvar:
# Axis positions are 1 (Q) and 0 (E)
position = dim - i - 1
so_dim.axis[i].val = args[position]
else:
# Axis positions are 2 (Q), 3 (eQ), 0 (E), 1 (eE)
position = dim - 2 * i
so_dim.axis[i].val = args[position]
so_dim.axis[i].var = args[position + 1]
# Set individual value axis sizes (not x-axis size)
N_y.append(len(args[position]) - offset)
# Calculate total 2D array size
N_tot = N_tot * N_y[-1]
# Create y and var_y lists from total 2D size
so_dim.y = nessi_list.NessiList(N_tot)
so_dim.var_y = nessi_list.NessiList(N_tot)
# Create area sum and errors for the area sum lists from total 2D size
area_sum = nessi_list.NessiList(N_tot)
area_sum_err2 = nessi_list.NessiList(N_tot)
# Create area sum and errors for the area sum lists from total 2D size
bin_count = nessi_list.NessiList(N_tot)
bin_count_err2 = nessi_list.NessiList(N_tot)
inst = som.attr_list.instrument
lambda_final = som.attr_list["Wavelength_final"]
inst_name = inst.get_name()
import bisect
import math
import dr_lib
import utils
arr_len = 0
#: Vector of zeros for function calculations
zero_vec = None
for j in xrange(hlr_utils.get_length(som)):
# Get counts
counts = hlr_utils.get_value(som, j, "SOM", "y")
counts_err2 = hlr_utils.get_err2(som, j, "SOM", "y")
arr_len = len(counts)
zero_vec = nessi_list.NessiList(arr_len)
# Get mapping SO
map_so = hlr_utils.get_map_so(som, None, j)
# Get lambda_i
l_i = hlr_utils.get_value(som, j, "SOM", "x")
l_i_err2 = hlr_utils.get_err2(som, j, "SOM", "x")
# Get lambda_f from instrument information
l_f_tuple = hlr_utils.get_special(lambda_final, map_so)
l_f = l_f_tuple[0]
l_f_err2 = l_f_tuple[1]
# Get source to sample distance
(L_s, L_s_err2) = hlr_utils.get_parameter("primary", map_so, inst)
# Get sample to detector distance
L_d_tuple = hlr_utils.get_parameter("secondary", map_so, inst)
L_d = L_d_tuple[0]
# Get polar angle from instrument information
(angle, angle_err2) = hlr_utils.get_parameter("polar", map_so, inst)
# Get the detector pixel height
dh_tuple = hlr_utils.get_parameter("dh", map_so, inst)
dh = dh_tuple[0]
# Need dh in units of Angstrom
dh *= 1e10
# Calculate T_i
(T_i, T_i_err2) = axis_manip.wavelength_to_tof(l_i, l_i_err2,
L_s, L_s_err2)
# Scale counts by lambda_f / lambda_i
(l_i_bc, l_i_bc_err2) = utils.calc_bin_centers(l_i, l_i_err2)
(ratio, ratio_err2) = array_manip.div_ncerr(l_f, l_f_err2,
l_i_bc, l_i_bc_err2)
(counts, counts_err2) = array_manip.mult_ncerr(counts, counts_err2,
ratio, ratio_err2)
# Calculate E_i
(E_i, E_i_err2) = axis_manip.wavelength_to_energy(l_i, l_i_err2)
# Calculate E_f
(E_f, E_f_err2) = axis_manip.wavelength_to_energy(l_f, l_f_err2)
# Calculate E_t
(E_t, E_t_err2) = array_manip.sub_ncerr(E_i, E_i_err2, E_f, E_f_err2)
if inst_name == "BSS":
# Convert E_t from meV to ueV
(E_t, E_t_err2) = array_manip.mult_ncerr(E_t, E_t_err2,
1000.0, 0.0)
(counts, counts_err2) = array_manip.mult_ncerr(counts, counts_err2,
1.0/1000.0, 0.0)
# Convert lambda_i to k_i
(k_i, k_i_err2) = axis_manip.wavelength_to_scalar_k(l_i, l_i_err2)
# Convert lambda_f to k_f
(k_f, k_f_err2) = axis_manip.wavelength_to_scalar_k(l_f, l_f_err2)
# Convert k_i and k_f to Q
(Q, Q_err2) = axis_manip.init_scatt_wavevector_to_scalar_Q(k_i,
k_i_err2,
k_f,
k_f_err2,
angle,
angle_err2)
# Calculate dT = dT_0 + dT_i
dT_i = utils.calc_bin_widths(T_i, T_i_err2)
(l_i_bw, l_i_bw_err2) = utils.calc_bin_widths(l_i, l_i_err2)
dT_0 = array_manip.mult_ncerr(l_i_bw, l_i_bw_err2,
t_0_slope, t_0_slope_err2)
dT_tuple = array_manip.add_ncerr(dT_i[0], dT_i[1], dT_0[0], dT_0[1])
dT = dT_tuple[0]
# Calculate Jacobian
if inst_name == "BSS":
(x_1, x_2,
x_3, x_4) = dr_lib.calc_BSS_coeffs(map_so, inst, (E_i, E_i_err2),
(Q, Q_err2), (k_i, k_i_err2),
(T_i, T_i_err2), dh, angle,
E_f, k_f, l_f, L_s, L_d,
t_0_slope, zero_vec)
else:
raise RuntimeError("Do not know how to calculate x_i "\
+"coefficients for instrument %s" % inst_name)
(A, A_err2) = dr_lib.calc_EQ_Jacobian(x_1, x_2, x_3, x_4, dT, dh,
zero_vec)
# Apply Jacobian: C/dlam * dlam / A(EQ) = C/EQ
(jac_ratio, jac_ratio_err2) = array_manip.div_ncerr(l_i_bw,
l_i_bw_err2,
A, A_err2)
(counts, counts_err2) = array_manip.mult_ncerr(counts, counts_err2,
jac_ratio,
jac_ratio_err2)
# Reverse counts, E_t, k_i and Q
E_t = axis_manip.reverse_array_cp(E_t)
E_t_err2 = axis_manip.reverse_array_cp(E_t_err2)
Q = axis_manip.reverse_array_cp(Q)
Q_err2 = axis_manip.reverse_array_cp(Q_err2)
counts = axis_manip.reverse_array_cp(counts)
counts_err2 = axis_manip.reverse_array_cp(counts_err2)
k_i = axis_manip.reverse_array_cp(k_i)
x_1 = axis_manip.reverse_array_cp(x_1)
x_2 = axis_manip.reverse_array_cp(x_2)
x_3 = axis_manip.reverse_array_cp(x_3)
x_4 = axis_manip.reverse_array_cp(x_4)
dT = axis_manip.reverse_array_cp(dT)
# Filter for duplicate Q values
if Q_filter:
k_i_cutoff = k_f * math.cos(angle)
k_i_cutbin = bisect.bisect(k_i, k_i_cutoff)
counts.__delslice__(0, k_i_cutbin)
counts_err2.__delslice__(0, k_i_cutbin)
Q.__delslice__(0, k_i_cutbin)
Q_err2.__delslice__(0, k_i_cutbin)
E_t.__delslice__(0, k_i_cutbin)
E_t_err2.__delslice__(0, k_i_cutbin)
x_1.__delslice__(0, k_i_cutbin)
x_2.__delslice__(0, k_i_cutbin)
x_3.__delslice__(0, k_i_cutbin)
x_4.__delslice__(0, k_i_cutbin)
dT.__delslice__(0, k_i_cutbin)
zero_vec.__delslice__(0, k_i_cutbin)
try:
if inst_name == "BSS":
((Q_1, E_t_1),
(Q_2, E_t_2),
(Q_3, E_t_3),
(Q_4, E_t_4)) = dr_lib.calc_BSS_EQ_verticies((E_t, E_t_err2),
(Q, Q_err2), x_1,
x_2, x_3, x_4,
dT, dh, zero_vec)
else:
raise RuntimeError("Do not know how to calculate (Q_i, "\
+"E_t_i) verticies for instrument %s" \
% inst_name)
except IndexError:
# All the data got Q filtered, move on
continue
try:
(y_2d, y_2d_err2,
area_new,
bin_count_new) = axis_manip.rebin_2D_quad_to_rectlin(Q_1, E_t_1,
Q_2, E_t_2,
Q_3, E_t_3,
Q_4, E_t_4,
counts,
counts_err2,
so_dim.axis[0].val,
so_dim.axis[1].val)
except IndexError, e:
# Get the offending index from the error message
index = int(str(e).split()[1].split('index')[-1].strip('[]'))
print "Id:", map_so.id
print "Index:", index
print "Verticies: %f, %f, %f, %f, %f, %f, %f, %f" % (Q_1[index],
E_t_1[index],
Q_2[index],
E_t_2[index],
Q_3[index],
E_t_3[index],
Q_4[index],
E_t_4[index])
raise IndexError(str(e))
# Add in together with previous results
(so_dim.y, so_dim.var_y) = array_manip.add_ncerr(so_dim.y,
so_dim.var_y,
y_2d, y_2d_err2)
(area_sum, area_sum_err2) = array_manip.add_ncerr(area_sum,
area_sum_err2,
area_new,
area_sum_err2)
if configure.dump_pix_contrib or configure.scale_sqe:
if inst_name == "BSS":
dOmega = dr_lib.calc_BSS_solid_angle(map_so, inst)
(bin_count_new,
bin_count_err2) = array_manip.mult_ncerr(bin_count_new,
bin_count_err2,
dOmega, 0.0)
(bin_count,
bin_count_err2) = array_manip.add_ncerr(bin_count,
bin_count_err2,
bin_count_new,
bin_count_err2)
else:
del bin_count_new
def create_E_vs_Q_igs(som, *args, **kwargs):
"""
This function starts with the initial IGS wavelength axis and turns this
into a 2D spectra with E and Q axes.
@param som: The input object with initial IGS wavelength axis
@type som: C{SOM.SOM}
@param args: A mandatory list of axes for rebinning. There is a particular
order to them. They should be present in the following order:
Without errors
1. Energy transfer
2. Momentum transfer
With errors
1. Energy transfer
2. Energy transfer error^2
3. Momentum transfer
4. Momentum transfer error ^2
@type args: C{nessi_list.NessiList}s
@param kwargs: A list of keyword arguments that the function accepts:
@keyword withXVar: Flag for whether the function should be expecting the
associated axes to have errors. The default value will
be I{False}.
@type withXVar: C{boolean}
@keyword data_type: Name of the data type which can be either I{histogram},
I{density} or I{coordinate}. The default value will be
I{histogram}
@type data_type: C{string}
@keyword Q_filter: Flag to turn on or off Q filtering. The default behavior
is I{True}.
@type Q_filter: C{boolean}
@keyword so_id: The identifier represents a number, string, tuple or other
object that describes the resulting C{SO}
@type so_id: C{int}, C{string}, C{tuple}, C{pixel ID}
@keyword y_label: The y axis label
@type y_label: C{string}
@keyword y_units: The y axis units
@type y_units: C{string}
@keyword x_labels: This is a list of names that sets the individual x axis
labels
@type x_labels: C{list} of C{string}s
@keyword x_units: This is a list of names that sets the individual x axis
units
@type x_units: C{list} of C{string}s
@keyword split: This flag causes the counts and the fractional area to
be written out into separate files.
@type split: C{boolean}
@keyword configure: This is the object containing the driver configuration.
@type configure: C{Configure}
@return: Object containing a 2D C{SO} with E and Q axes
@rtype: C{SOM.SOM}
@raise RuntimeError: Anything other than a C{SOM} is passed to the function
@raise RuntimeError: An instrument is not contained in the C{SOM}
"""
import nessi_list
# Setup some variables
dim = 2
N_y = []
N_tot = 1
N_args = len(args)
# Get T0 slope in order to calculate dT = dT_i + dT_0
try:
t_0_slope = som.attr_list["Time_zero_slope"][0]
t_0_slope_err2 = som.attr_list["Time_zero_slope"][1]
except KeyError:
t_0_slope = float(0.0)
t_0_slope_err2 = float(0.0)
# Check withXVar keyword argument and also check number of given args.
# Set xvar to the appropriate value
try:
value = kwargs["withXVar"]
if value.lower() == "true":
if N_args != 4:
raise RuntimeError("Since you have requested x errors, 4 x "\
+"axes must be provided.")
else:
xvar = True
elif value.lower() == "false":
if N_args != 2:
raise RuntimeError("Since you did not requested x errors, 2 "\
+"x axes must be provided.")
else:
xvar = False
else:
raise RuntimeError("Do not understand given parameter %s" % \
value)
except KeyError:
if N_args != 2:
raise RuntimeError("Since you did not requested x errors, 2 "\
+"x axes must be provided.")
else:
xvar = False
# Check dataType keyword argument. An offset will be set to 1 for the
# histogram type and 0 for either density or coordinate
try:
data_type = kwargs["data_type"]
if data_type.lower() == "histogram":
offset = 1
elif data_type.lower() == "density" or \
data_type.lower() == "coordinate":
offset = 0
else:
raise RuntimeError("Do not understand data type given: %s" % \
data_type)
# Default is offset for histogram
except KeyError:
offset = 1
try:
Q_filter = kwargs["Q_filter"]
except KeyError:
Q_filter = True
# Check for split keyword
try:
split = kwargs["split"]
except KeyError:
split = False
# Check for configure keyword
try:
configure = kwargs["configure"]
except KeyError:
configure = None
so_dim = SOM.SO(dim)
for i in range(dim):
# Set the x-axis arguments from the *args list into the new SO
if not xvar:
# Axis positions are 1 (Q) and 0 (E)
position = dim - i - 1
so_dim.axis[i].val = args[position]
else:
# Axis positions are 2 (Q), 3 (eQ), 0 (E), 1 (eE)
position = dim - 2 * i
so_dim.axis[i].val = args[position]
so_dim.axis[i].var = args[position + 1]
# Set individual value axis sizes (not x-axis size)
N_y.append(len(args[position]) - offset)
# Calculate total 2D array size
N_tot = N_tot * N_y[-1]
# Create y and var_y lists from total 2D size
so_dim.y = nessi_list.NessiList(N_tot)
so_dim.var_y = nessi_list.NessiList(N_tot)
# Create area sum and errors for the area sum lists from total 2D size
area_sum = nessi_list.NessiList(N_tot)
area_sum_err2 = nessi_list.NessiList(N_tot)
# Create area sum and errors for the area sum lists from total 2D size
bin_count = nessi_list.NessiList(N_tot)
bin_count_err2 = nessi_list.NessiList(N_tot)
inst = som.attr_list.instrument
lambda_final = som.attr_list["Wavelength_final"]
inst_name = inst.get_name()
import bisect
import math
import dr_lib
import utils
arr_len = 0
#: Vector of zeros for function calculations
zero_vec = None
for j in xrange(hlr_utils.get_length(som)):
# Get counts
counts = hlr_utils.get_value(som, j, "SOM", "y")
counts_err2 = hlr_utils.get_err2(som, j, "SOM", "y")
arr_len = len(counts)
zero_vec = nessi_list.NessiList(arr_len)
# Get mapping SO
map_so = hlr_utils.get_map_so(som, None, j)
# Get lambda_i
l_i = hlr_utils.get_value(som, j, "SOM", "x")
l_i_err2 = hlr_utils.get_err2(som, j, "SOM", "x")
# Get lambda_f from instrument information
l_f_tuple = hlr_utils.get_special(lambda_final, map_so)
l_f = l_f_tuple[0]
l_f_err2 = l_f_tuple[1]
# Get source to sample distance
(L_s, L_s_err2) = hlr_utils.get_parameter("primary", map_so, inst)
# Get sample to detector distance
L_d_tuple = hlr_utils.get_parameter("secondary", map_so, inst)
L_d = L_d_tuple[0]
# Get polar angle from instrument information
(angle, angle_err2) = hlr_utils.get_parameter("polar", map_so, inst)
# Get the detector pixel height
dh_tuple = hlr_utils.get_parameter("dh", map_so, inst)
dh = dh_tuple[0]
# Need dh in units of Angstrom
dh *= 1e10
# Calculate T_i
(T_i, T_i_err2) = axis_manip.wavelength_to_tof(l_i, l_i_err2, L_s,
L_s_err2)
# Scale counts by lambda_f / lambda_i
(l_i_bc, l_i_bc_err2) = utils.calc_bin_centers(l_i, l_i_err2)
(ratio, ratio_err2) = array_manip.div_ncerr(l_f, l_f_err2, l_i_bc,
l_i_bc_err2)
(counts, counts_err2) = array_manip.mult_ncerr(counts, counts_err2,
ratio, ratio_err2)
# Calculate E_i
(E_i, E_i_err2) = axis_manip.wavelength_to_energy(l_i, l_i_err2)
# Calculate E_f
(E_f, E_f_err2) = axis_manip.wavelength_to_energy(l_f, l_f_err2)
# Calculate E_t
(E_t, E_t_err2) = array_manip.sub_ncerr(E_i, E_i_err2, E_f, E_f_err2)
if inst_name == "BSS":
# Convert E_t from meV to ueV
(E_t, E_t_err2) = array_manip.mult_ncerr(E_t, E_t_err2, 1000.0,
0.0)
(counts,
counts_err2) = array_manip.mult_ncerr(counts, counts_err2,
1.0 / 1000.0, 0.0)
# Convert lambda_i to k_i
(k_i, k_i_err2) = axis_manip.wavelength_to_scalar_k(l_i, l_i_err2)
# Convert lambda_f to k_f
(k_f, k_f_err2) = axis_manip.wavelength_to_scalar_k(l_f, l_f_err2)
# Convert k_i and k_f to Q
(Q, Q_err2) = axis_manip.init_scatt_wavevector_to_scalar_Q(
k_i, k_i_err2, k_f, k_f_err2, angle, angle_err2)
# Calculate dT = dT_0 + dT_i
dT_i = utils.calc_bin_widths(T_i, T_i_err2)
(l_i_bw, l_i_bw_err2) = utils.calc_bin_widths(l_i, l_i_err2)
dT_0 = array_manip.mult_ncerr(l_i_bw, l_i_bw_err2, t_0_slope,
t_0_slope_err2)
dT_tuple = array_manip.add_ncerr(dT_i[0], dT_i[1], dT_0[0], dT_0[1])
dT = dT_tuple[0]
# Calculate Jacobian
if inst_name == "BSS":
(x_1, x_2, x_3, x_4) = dr_lib.calc_BSS_coeffs(
map_so, inst, (E_i, E_i_err2), (Q, Q_err2), (k_i, k_i_err2),
(T_i, T_i_err2), dh, angle, E_f, k_f, l_f, L_s, L_d, t_0_slope,
zero_vec)
else:
raise RuntimeError("Do not know how to calculate x_i "\
+"coefficients for instrument %s" % inst_name)
(A, A_err2) = dr_lib.calc_EQ_Jacobian(x_1, x_2, x_3, x_4, dT, dh,
zero_vec)
# Apply Jacobian: C/dlam * dlam / A(EQ) = C/EQ
(jac_ratio,
jac_ratio_err2) = array_manip.div_ncerr(l_i_bw, l_i_bw_err2, A,
A_err2)
(counts, counts_err2) = array_manip.mult_ncerr(counts, counts_err2,
jac_ratio,
jac_ratio_err2)
# Reverse counts, E_t, k_i and Q
E_t = axis_manip.reverse_array_cp(E_t)
E_t_err2 = axis_manip.reverse_array_cp(E_t_err2)
Q = axis_manip.reverse_array_cp(Q)
Q_err2 = axis_manip.reverse_array_cp(Q_err2)
counts = axis_manip.reverse_array_cp(counts)
counts_err2 = axis_manip.reverse_array_cp(counts_err2)
k_i = axis_manip.reverse_array_cp(k_i)
x_1 = axis_manip.reverse_array_cp(x_1)
x_2 = axis_manip.reverse_array_cp(x_2)
x_3 = axis_manip.reverse_array_cp(x_3)
x_4 = axis_manip.reverse_array_cp(x_4)
dT = axis_manip.reverse_array_cp(dT)
# Filter for duplicate Q values
if Q_filter:
k_i_cutoff = k_f * math.cos(angle)
k_i_cutbin = bisect.bisect(k_i, k_i_cutoff)
counts.__delslice__(0, k_i_cutbin)
counts_err2.__delslice__(0, k_i_cutbin)
Q.__delslice__(0, k_i_cutbin)
Q_err2.__delslice__(0, k_i_cutbin)
E_t.__delslice__(0, k_i_cutbin)
E_t_err2.__delslice__(0, k_i_cutbin)
x_1.__delslice__(0, k_i_cutbin)
x_2.__delslice__(0, k_i_cutbin)
x_3.__delslice__(0, k_i_cutbin)
x_4.__delslice__(0, k_i_cutbin)
dT.__delslice__(0, k_i_cutbin)
zero_vec.__delslice__(0, k_i_cutbin)
try:
if inst_name == "BSS":
((Q_1, E_t_1), (Q_2, E_t_2), (Q_3, E_t_3),
(Q_4, E_t_4)) = dr_lib.calc_BSS_EQ_verticies(
(E_t, E_t_err2), (Q, Q_err2), x_1, x_2, x_3, x_4, dT, dh,
zero_vec)
else:
raise RuntimeError("Do not know how to calculate (Q_i, "\
+"E_t_i) verticies for instrument %s" \
% inst_name)
except IndexError:
# All the data got Q filtered, move on
continue
try:
(y_2d, y_2d_err2, area_new,
bin_count_new) = axis_manip.rebin_2D_quad_to_rectlin(
Q_1, E_t_1, Q_2, E_t_2, Q_3, E_t_3, Q_4, E_t_4, counts,
counts_err2, so_dim.axis[0].val, so_dim.axis[1].val)
except IndexError, e:
# Get the offending index from the error message
index = int(str(e).split()[1].split('index')[-1].strip('[]'))
print "Id:", map_so.id
print "Index:", index
print "Verticies: %f, %f, %f, %f, %f, %f, %f, %f" % (
Q_1[index], E_t_1[index], Q_2[index], E_t_2[index], Q_3[index],
E_t_3[index], Q_4[index], E_t_4[index])
raise IndexError(str(e))
# Add in together with previous results
(so_dim.y,
so_dim.var_y) = array_manip.add_ncerr(so_dim.y, so_dim.var_y, y_2d,
y_2d_err2)
(area_sum,
area_sum_err2) = array_manip.add_ncerr(area_sum, area_sum_err2,
area_new, area_sum_err2)
if configure.dump_pix_contrib or configure.scale_sqe:
if inst_name == "BSS":
dOmega = dr_lib.calc_BSS_solid_angle(map_so, inst)
(bin_count_new, bin_count_err2) = array_manip.mult_ncerr(
bin_count_new, bin_count_err2, dOmega, 0.0)
(bin_count, bin_count_err2) = array_manip.add_ncerr(
bin_count, bin_count_err2, bin_count_new, bin_count_err2)
else:
del bin_count_new
def 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 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 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 integrate_axis_py(obj, **kwargs):
"""
This function takes a spectrum and integrates the given axis. The function
assumes that the incoming data is in the histogram form.
@param obj: Spectrum to be integrated
@type obj: C{SOM.SOM} or C{SOM.SO}
@param kwargs: A list of keyword arguments that the function accepts:
@keyword start: Index of the starting bin
@type start: C{int}
@keyword end: Index of the ending bin. This index is made inclusive by the
function.
@type end: C{int}
@keyword axis: This is the axis one wishes to manipulate. If no argument is
given the default value is I{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 I{0}.
@type axis_pos: C{int}
@keyword avg: This allows the function to calculate a geometrical average.
The default value is I{False}.
@type avg: C{boolean}
@keyword width: This is a flag to turn on the multiplication of the
individual bin contents with the bins corresponding width.
@type width: C{boolean}
@keyword width_pos: This is position of the x-axis in the axis array from
which to calculate the bin widths in support of the
width flag. If no argument is given, the default value
is I{0}.
@type width_pos: C{int}
@return: The integration value and its associated error
@rtype: C{tuple}
@raise RuntimError: A C{SOM} or C{SO} is not given to the function.
@raise RuntimeError: The width keyword is used with x-axis integration.
"""
# import the helper functions
import hlr_utils
# set up for working through data
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
# Check for starting bin
try:
start = kwargs["start"]
except KeyError:
start = 0
# Check for ending bin
try:
end = kwargs["end"]
if end != -1:
end += 1
else:
pass
except KeyError:
end = -1
# 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
# Check for avg keyword argument
try:
avg = kwargs["avg"]
except KeyError:
avg = False
# Check for width keyword argument
try:
width = kwargs["width"]
except KeyError:
width = False
# Check for width_pos keyword argument
try:
width_pos = kwargs["width_pos"]
except KeyError:
width_pos = 0
integration = float(0)
integration_error2 = float(0)
import itertools
if width:
import utils
bad_values = ["nan", "inf", "-inf"]
for i in xrange(hlr_utils.get_length(obj)):
counter = 0
value = hlr_utils.get_value(obj, i, o_descr, axis, axis_pos)
error = hlr_utils.get_err2(obj, i, o_descr, axis, axis_pos)
if end == -1:
value = value[start:]
error = error[start:]
else:
value = value[start:end]
error = error[start:end]
if not width:
for val, err2 in itertools.izip(value, error):
if str(val) in bad_values or str(err2) in bad_values:
continue
else:
integration += val
integration_error2 += err2
counter += 1
else:
if axis == "y":
x_axis = hlr_utils.get_value(obj, i, o_descr, "x", width_pos)
x_err2 = hlr_utils.get_err2(obj, i, o_descr, "x", width_pos)
elif axis == "x":
raise RuntimeError("Cannot use width flag with x-axis "\
+"integration")
bin_widths = utils.calc_bin_widths(x_axis, x_err2)
for val, err2, delta in itertools.izip(value, error,
bin_widths[0]):
if str(val) in bad_values or str(err2) in bad_values:
continue
else:
integration += (delta * val)
integration_error2 += (delta * delta * err2)
counter += 1
if avg:
return (integration / float(counter),
integration_error2 / float(counter))
else:
return (integration, integration_error2)
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 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