def create_X_vs_pixpos(som, *args, **kwargs):
"""
This function takes a group of single spectrum with any given axes
(wavelength, energy etc.) and rebins those axes to the given axis and
converts the spectra into a single I{I(X, pixel)} spectrum.
@param som: The input object with arbitrary (but same) axis spectra
@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. Axis
With errors
1. Axis
2. Axis error^2
@type args: C{nessi_list.NessiList}s
@param kwargs: A list of keyword arguments that the function accepts:
@keyword withXVar: A 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: 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 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_label: The second primary axis label
@type x_label: C{string}
@keyword x_units: The second primary axis units
@type x_units: C{string}
@keyword rebin: A flag for turning rebin on or off. The default is I{True}.
@type rebin: C{boolean}
@return: Object with a single 2D C{SO} with the given axis and global pixel
position
@rtype: C{SOM.SOM}
@raise RuntimeError: The parameter given to the keyword argument withXVar
is not I{True} or I{False}
@raise RuntimeError: The parameter given to the keyword argument data_type
is not I{histogram} or I{density} or I{coordinate}.
@raise RuntimeError: The number of given arguments (x-axes) is not either 2
(no errors) or 4 (with errors)
"""
import common_lib
import nessi_list
# Setup some variables
dim = 2
N_y = []
N_tot = 1
N_args = len(args)
# 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 != 2:
raise RuntimeError("Since you have requested x errors, 2 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 != 1:
raise RuntimeError("Since you did not requested x errors, 1 "\
+"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
so_dim = SOM.SO(dim)
arb_axis = 1
pixel_axis = 0
# Set the x-axis arguments from the *args list into the new SO
if not xvar:
so_dim.axis[arb_axis].val = args[0]
else:
so_dim.axis[arb_axis].val = args[0]
so_dim.axis[arb_axis].var = args[1]
# Set individual value axis sizes (not x-axis size)
N_y.append(len(args[0]) - offset)
# Calculate total 2D array size
N_som = len(som)
N_tot = N_som * N_y[-1]
# Make second axis on total number of pixels
so_dim.axis[pixel_axis].val = hlr_utils.make_axis(0, N_som, 1)
if xvar:
so_dim.axis[pixel_axis].var = nessi_list.NessiList(N_som+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)
# Check for rebinning
try:
rebin = kwargs["rebin"]
except KeyError:
rebin = True
# Rebin data to X axis
if rebin:
som_1 = common_lib.rebin_axis_1D(som, args[0])
else:
som_1 = som
del som
import array_manip
for i in xrange(hlr_utils.get_length(som_1)):
val = hlr_utils.get_value(som_1, i, "SOM", "y")
err2 = hlr_utils.get_err2(som_1, i, "SOM", "y")
start = i * N_y[0]
(so_dim.y, so_dim.var_y) = array_manip.add_ncerr(so_dim.y,
so_dim.var_y,
val,
err2,
a_start=start)
# Check for so_id keyword argument
try:
so_dim.id = kwargs["so_id"]
except KeyError:
so_dim.id = 0
comb_som = SOM.SOM()
comb_som.copyAttributes(som_1)
del som_1
# 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(["Pixel Number", kwargs["x_label"]])
except KeyError:
comb_som.setAllAxisLabels(["Pixel Number", "Arbitrary"])
# Check for x_units keyword argument
try:
comb_som.setAllAxisUnits(["Pixel#", kwargs["x_units"]])
except KeyError:
comb_som.setAllAxisUnits(["Pixel#", "Arb"])
comb_som.append(so_dim)
del so_dim
return comb_som
result.attr_list["summed_ids"] = so_id_list
return result
if __name__ == "__main__":
import hlr_test
import SOM
som1 = SOM.SOM()
so1 = hlr_test.generate_so("histogram", 0, 5, 1, 1)
so1.id = 1
som1.append(so1)
so2 = hlr_test.generate_so("histogram", 0, 5, 1, 3)
so2.id = 2
som1.append(so2)
so3 = hlr_test.generate_so("histogram", 0, 5, 1, 2)
so3.id = 3
som1.append(so3)
axis_new = hlr_utils.make_axis(0, 5, 2.5)
print "********** SOM1"
print "* ", som1[0]
print "* ", som1[1]
print "* ", som1[2]
print "********** sum_all_spectra"
print "* som:", sum_all_spectra(som1)
print "* som (rebin):", sum_all_spectra(som1, rebin_axis=axis_new)
# Check for x_units keyword argument
try:
comb_som.setAllAxisUnits(["Pixel#", kwargs["x_units"]])
except KeyError:
comb_som.setAllAxisUnits(["Pixel#", "Arb"])
comb_som.append(so_dim)
del so_dim
return comb_som
if __name__ == "__main__":
import hlr_test
som1 = hlr_test.generate_som("histogram", 1, 3)
som1.attr_list.instrument = SOM.ASG_Instrument()
x_axis_err = hlr_utils.make_axis(0, 1, 0.25)
print "********** SOM1"
print "* ", som1[0]
print "* ", som1[1]
print "* ", som1[2]
print "********** create_X_vs_pixpos"
print " som :", create_X_vs_pixpos(som1, som1[0].axis[0].val, x_axis_err,
withXVar="True")
result.attr_list["summed_ids"] = so_id_list
return result
if __name__ == "__main__":
import hlr_test
import SOM
som1 = SOM.SOM()
so1 = hlr_test.generate_so("histogram", 0, 5, 1, 1)
so1.id = 1
som1.append(so1)
so2 = hlr_test.generate_so("histogram", 0, 5, 1, 3)
so2.id = 2
som1.append(so2)
so3 = hlr_test.generate_so("histogram", 0, 5, 1, 2)
so3.id = 3
som1.append(so3)
axis_new = hlr_utils.make_axis(0, 5, 2.5)
print "********** SOM1"
print "* ", som1[0]
print "* ", som1[1]
print "* ", som1[2]
print "********** sum_all_spectra"
print "* som:", sum_all_spectra(som1)
print "* som (rebin):", sum_all_spectra(som1, rebin_axis=axis_new)
if options.d_bins is None and options.Q_bins is None:
parser.error("Must specify at least one type of binning")
else:
pass
# set the verbosity
configure.verbose = options.verbose
# set the data paths
configure.data_paths = hlr_utils.create_data_paths(options.data_paths)
# set the d bins
if options.d_bins is not None:
dfacts = options.d_bins.split(',')
configure.d_bins = hlr_utils.make_axis(float(dfacts[0]),
float(dfacts[1]),
float(dfacts[2]))
else:
configure.d_bins = options.d_bins
# set the Q bins
if options.Q_bins is not None:
qfacts = options.Q_bins.split(',')
configure.Q_bins = hlr_utils.make_axis(float(qfacts[0]),
float(qfacts[1]),
float(qfacts[2]))
else:
configure.Q_bins = options.Q_bins
# set the tof bins
if options.tof_bins is not None:
if options.d_bins is None and options.Q_bins is None:
parser.error("Must specify at least one type of binning")
else:
pass
# set the verbosity
configure.verbose = options.verbose
# set the data paths
configure.data_paths = hlr_utils.create_data_paths(options.data_paths)
# set the d bins
if options.d_bins is not None:
dfacts = options.d_bins.split(',')
configure.d_bins = hlr_utils.make_axis(float(dfacts[0]),
float(dfacts[1]),
float(dfacts[2]))
else:
configure.d_bins = options.d_bins
# set the Q bins
if options.Q_bins is not None:
qfacts = options.Q_bins.split(',')
configure.Q_bins = hlr_utils.make_axis(float(qfacts[0]),
float(qfacts[1]),
float(qfacts[2]))
else:
configure.Q_bins = options.Q_bins
# set the tof bins
if options.tof_bins is not None:
def create_X_vs_pixpos(som, *args, **kwargs):
"""
This function takes a group of single spectrum with any given axes
(wavelength, energy etc.) and rebins those axes to the given axis and
converts the spectra into a single I{I(X, pixel)} spectrum.
@param som: The input object with arbitrary (but same) axis spectra
@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. Axis
With errors
1. Axis
2. Axis error^2
@type args: C{nessi_list.NessiList}s
@param kwargs: A list of keyword arguments that the function accepts:
@keyword withXVar: A 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: 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 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_label: The second primary axis label
@type x_label: C{string}
@keyword x_units: The second primary axis units
@type x_units: C{string}
@keyword rebin: A flag for turning rebin on or off. The default is I{True}.
@type rebin: C{boolean}
@return: Object with a single 2D C{SO} with the given axis and global pixel
position
@rtype: C{SOM.SOM}
@raise RuntimeError: The parameter given to the keyword argument withXVar
is not I{True} or I{False}
@raise RuntimeError: The parameter given to the keyword argument data_type
is not I{histogram} or I{density} or I{coordinate}.
@raise RuntimeError: The number of given arguments (x-axes) is not either 2
(no errors) or 4 (with errors)
"""
import common_lib
import nessi_list
# Setup some variables
dim = 2
N_y = []
N_tot = 1
N_args = len(args)
# 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 != 2:
raise RuntimeError("Since you have requested x errors, 2 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 != 1:
raise RuntimeError("Since you did not requested x errors, 1 "\
+"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
so_dim = SOM.SO(dim)
arb_axis = 1
pixel_axis = 0
# Set the x-axis arguments from the *args list into the new SO
if not xvar:
so_dim.axis[arb_axis].val = args[0]
else:
so_dim.axis[arb_axis].val = args[0]
so_dim.axis[arb_axis].var = args[1]
# Set individual value axis sizes (not x-axis size)
N_y.append(len(args[0]) - offset)
# Calculate total 2D array size
N_som = len(som)
N_tot = N_som * N_y[-1]
# Make second axis on total number of pixels
so_dim.axis[pixel_axis].val = hlr_utils.make_axis(0, N_som, 1)
if xvar:
so_dim.axis[pixel_axis].var = nessi_list.NessiList(N_som + 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)
# Check for rebinning
try:
rebin = kwargs["rebin"]
except KeyError:
rebin = True
# Rebin data to X axis
if rebin:
som_1 = common_lib.rebin_axis_1D(som, args[0])
else:
som_1 = som
del som
import array_manip
for i in xrange(hlr_utils.get_length(som_1)):
val = hlr_utils.get_value(som_1, i, "SOM", "y")
err2 = hlr_utils.get_err2(som_1, i, "SOM", "y")
start = i * N_y[0]
(so_dim.y, so_dim.var_y) = array_manip.add_ncerr(so_dim.y,
so_dim.var_y,
val,
err2,
a_start=start)
# Check for so_id keyword argument
try:
so_dim.id = kwargs["so_id"]
except KeyError:
so_dim.id = 0
comb_som = SOM.SOM()
comb_som.copyAttributes(som_1)
del som_1
# 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(["Pixel Number", kwargs["x_label"]])
except KeyError:
comb_som.setAllAxisLabels(["Pixel Number", "Arbitrary"])
# Check for x_units keyword argument
try:
comb_som.setAllAxisUnits(["Pixel#", kwargs["x_units"]])
except KeyError:
comb_som.setAllAxisUnits(["Pixel#", "Arb"])
comb_som.append(so_dim)
del so_dim
return comb_som
try:
comb_som.setAllAxisUnits(["Pixel#", kwargs["x_units"]])
except KeyError:
comb_som.setAllAxisUnits(["Pixel#", "Arb"])
comb_som.append(so_dim)
del so_dim
return comb_som
if __name__ == "__main__":
import hlr_test
som1 = hlr_test.generate_som("histogram", 1, 3)
som1.attr_list.instrument = SOM.ASG_Instrument()
x_axis_err = hlr_utils.make_axis(0, 1, 0.25)
print "********** SOM1"
print "* ", som1[0]
print "* ", som1[1]
print "* ", som1[2]
print "********** create_X_vs_pixpos"
print " som :", create_X_vs_pixpos(som1,
som1[0].axis[0].val,
x_axis_err,
withXVar="True")
