"""Conversion class defined for 'direct' excitations conversions
"""
import ConvertToEnergy
import CommonFunctions as common
from mantidsimple import *
#-------------- ElasticConversion class -------------
class DirectEnergyConversion(ConvertToEnergy.EnergyConversion):
'''
Performs a convert to energy assuming the provided instrument is an elastic instrument
'''
def __init__(self, prefix):
super(self.__class__, self).__init__(prefix)
def init_params(self):
'''
Attach analysis arguments that are particular to the ElasticConversion
'''
self.save_formats = ['.spe','.nxs']
self.fix_ei=False
self.energy_bins = None
self.background = False
self.normalise_method = 'monitor-1'
# self.normalise_method = 'current'
self.map_file = None
if (self.file_prefix == "CNCS" or self.file_prefix == "ARCS" or self.file_prefix == "SEQUOIA"):
self.facility = "SNS"
self.normalise_method = 'current'
self.file_ext = '.dat'
self.time_bins = None
# Detector diagnosis
self.spectra_masks = None
# Absolute normalisation
self.abs_map_file = None
self.abs_spectra_masks = None
self.abs_mass = 1.0
self.abs_rmm = 1.0
self.applyDetectorEfficiency = True
def init_idf_params(self):
'''
Initialise the parameters from the IDF file
'''
self.ei_mon_spectra = [int(self.get_default_parameter("ei-mon1-spec")), int(self.get_default_parameter("ei-mon2-spec"))]
self.scale_factor = self.get_default_parameter("scale-factor")
self.wb_scale_factor = self.get_default_parameter("wb-scale-factor")
self.wb_integr_range = [self.get_default_parameter("wb-integr-min"), self.get_default_parameter("wb-integr-max")]
self.mon1_norm_spec = int(self.get_default_parameter("norm-mon1-spec"))
self.mon1_norm_range = [self.get_default_parameter("norm-mon1-min"), self.get_default_parameter("norm-mon1-max")]
self.background_range = [self.get_default_parameter("bkgd-range-min"), self.get_default_parameter("bkgd-range-max")]
self.monovan_integr_range = [self.get_default_parameter("monovan-integr-min"), self.get_default_parameter("monovan-integr-max")]
self.van_mass = self.get_default_parameter("vanadium-mass")
self.van_rmm = self.get_default_parameter("vanadium-rmm")
self.abs_min_value = self.get_default_parameter('abs-average-min')
self.abs_max_value = self.get_default_parameter('abs-average-max')
self.abs_median_lbound = self.get_default_parameter('abs-median-lbound')
self.abs_median_ubound = self.get_default_parameter('abs-median-ubound')
self.abs_median_frac_low = self.get_default_parameter('abs-median-lo-frac')
self.abs_median_frac_hi = self.get_default_parameter('abs-median-hi-frac')
self.abs_median_sig = self.get_default_parameter('abs-median-signif')
def get_default_parameter(self, name):
if self.instrument is None:
raise ValueError("Cannot init default parameter, instrument has not been loaded.")
values = self.instrument.getNumberParameter(name)
if len(values) != 1:
raise ValueError('Instrument parameter file does not contain a definition for "%s". Cannot continue' % name)
return values[0]
def convert_to_energy(self, mono_run, ei, white_run=None, abs_mono_run=None, abs_white_run=None, abs_ei=None, save_path=None, Tzero=None):
'''
Convert mono-chromatic run to deltaE
'''
# Check if we need to perform the absolute normalisation first
if not abs_mono_run is None:
if abs_ei is None:
abs_ei = ei
mapping_file = self.abs_map_file
spectrum_masks = self.spectra_masks
abs_norm_wkspace = self.do_conversion(abs_mono_run, abs_white_run, abs_ei,mapping_file, spectrum_masks)
absnorm_factor = (self.van_rmm/self.van_mass) * self.abs_average(abs_norm_wkspace)
# Scale by vanadium cross-section which is energy dependent up to a point
ei_value = float(abs_norm_wkspace.getSampleDetails().getLogData('Ei').value())
if ei_value >= 200.0:
xsection = 421.0
else:
xsection = 400.0 + (ei_value/10.0)
absnorm_factor /= xsection
mtd.deleteWorkspace(abs_norm_wkspace.getName())
else:
absnorm_factor = None
# Figure out what to call the workspace
resultws_name = save_path
if not resultws_name is None:
if os.path.isdir(save_path):
resultws_name = None
else:
resultws_name = os.path.split(save_path)[1]
if resultws_name == '':
resultws_name = None
# Main run file conversion
sample_wkspace = self.do_conversion(mono_run, ei, white_run, self.map_file, self.spectra_masks, resultws_name, Tzero)
if not absnorm_factor is None:
absnorm_factor *= (self.abs_mass/self.abs_rmm)
sample_wkspace /= absnorm_factor
if save_path is None:
save_path = sample_wkspace.getName()
elif os.path.isdir(save_path):
save_path = os.path.join(save_path, sample_wkspace.getName())
self.save_results(sample_wkspace, save_path)
return sample_wkspace
def do_conversion(self, mono_run, ei_guess, white_run=None, map_file=None, spectra_masks=None, resultws_name = None, Tzero=None):
"""
Convert units of a mono-chromatic run to deltaE, including normalisation to a white-beam vanadium run.
If multiple run files are passed to this function, they are summed into a run and then processed
"""
if type(mono_run) == list:
result_ws, det_info_file = self.load_data(mono_run[0], resultws_name)
if len(mono_run) > 1:
del mono_run[0]
common.sum_files(result_ws, mono_run, self.file_prefix)
elif type(str):
result_ws, det_info_file = self.load_data(mono_run, resultws_name)
else:
raise TypeError("Run number must be a list or a string")
# Special load monitor stuff.
if (self.file_prefix == "CNCS"):
#self.log("--- CNCS ---")
self.fix_ei = True
ei_value = ei_guess
if (Tzero is None):
tzero = (0.1982*(1+ei_value)**(-0.84098))*1000.0
else:
tzero = Tzero
ChangeBinOffset(result_ws, result_ws, -tzero)
mon1_peak = 0.0
self.applyDetectorEfficiency = False
elif (self.file_prefix == "ARCS" or self.file_prefix == "SEQUOIA"):
#self.log("***** ARCS/SEQUOIA *****")
self.log(mono_run)
InfoFilename = mono_run.replace("_neutron_event.dat", "_runinfo.xml")
loader=LoadPreNeXusMonitors(RunInfoFilename=InfoFilename,OutputWorkspace="monitor_ws")
monitor_ws = loader.workspace()
alg = GetEi(monitor_ws, int(self.ei_mon_spectra[0]), int(self.ei_mon_spectra[1]), ei_guess, False)
ei_value = float(monitor_ws.getSampleDetails().getLogData("Ei").value())
if (self.fix_ei):
ei_value = ei_guess
if (Tzero is None):
# TODO: Calculate T0
#tzero = float(alg.getPropertyValue("Tzero"))
tzero = 0.0
else:
tzero = Tzero
mon1_peak = 0.0
ChangeBinOffset(result_ws, result_ws, -tzero)
self.applyDetectorEfficiency = False
else:
# Do ISIS stuff for Ei
ei_value, mon1_peak = self.get_ei(result_ws, ei_guess)
# For event mode, we are going to histogram in energy first, then go back to TOF
if (self.file_prefix == "ARCS" or self.file_prefix == "SEQUOIA" or self.file_prefix == "CNCS"):
# Convert to Et
ConvertUnits(result_ws, "_tmp_energy_ws", Target="DeltaE",EMode="Direct", EFixed=ei_value)
RenameWorkspace("_tmp_energy_ws", result_ws)
mtd.deleteWorkspace("_tmp_energy_ws")
# Histogram
Rebin(result_ws, "_tmp_rebin_ws", self.energy_bins)
RenameWorkspace("_tmp_rebin_ws", result_ws)
mtd.deleteWorkspace("_tmp_rebin_ws")
# Convert back to TOF
ConvertUnits(result_ws, result_ws, Target="TOF",EMode="Direct", EFixed=ei_value)
bin_offset = -mon1_peak
if self.background == True:
# Remove the count rate seen in the regions of the histograms defined as the background regions, if the user defined a region
ConvertToDistribution(result_ws)
FlatBackground(result_ws, result_ws, self.background_range[0] + bin_offset, self.background_range[1] + bin_offset, '', 'Mean')
ConvertFromDistribution(result_ws)
self.normalise(result_ws, self.normalise_method, range_offset=bin_offset)
LoadDetectorInfo(result_ws, det_info_file)
ConvertUnits(result_ws, result_ws, Target="DeltaE",EMode='Direct', EFixed=ei_value)
if not self.energy_bins is None:
Rebin(result_ws, result_ws, self.energy_bins)
if self.applyDetectorEfficiency:
DetectorEfficiencyCor(result_ws, result_ws, ei_value)
self.apply_masking(result_ws, spectra_masks, map_file)
ConvertToDistribution(result_ws)
if white_run != None:
white_ws = self.convert_white(white_run, spectra_masks, map_file)
result_ws /= white_ws
mtd.deleteWorkspace(white_ws.getName())
# Overall scale factor
result_ws *= self.scale_factor
return result_ws
def load_data(self, run_num, output_name):
'''
Load a run number or sum a set of run numbers
'''
data = common.load_run(self.file_prefix, run_num, output_name, self.file_ext)
self.setup_mtd_instrument(data[0])
return data
def get_ei(self, input_ws, ei_guess):
"""
Calculate incident energy of neutrons
"""
fix_ei = str(self.fix_ei).lower()
if fix_ei == 'true':
fix_ei = True
elif fix_ei == 'false':
fix_ei = False
elif fix_ei == 'fixei':
fix_ei = True
else:
raise TypeError('Unknown option passed to get_ei "%s"' % fix_ei)
# Calculate the incident energy
alg = GetEi(input_ws, int(self.ei_mon_spectra[0]), int(self.ei_mon_spectra[1]), ei_guess, fix_ei)
mon1_peak = float(alg.getPropertyValue("FirstMonitorPeak"))
mon1_index = int(alg.getPropertyValue("FirstMonitorIndex"))
ei = float(input_ws.getSampleDetails().getLogData("Ei").value())
# Adjust the TOF such that the first monitor peak is at t=0
ChangeBinOffset(input_ws, input_ws, -mon1_peak)
mon1_det = input_ws.getDetector(mon1_index)
mon1_pos = mon1_det.getPos()
src_name = input_ws.getInstrument().getSource().getName()
MoveInstrumentComponent(input_ws, src_name, X=mon1_pos.getX(), Y=mon1_pos.getY(), Z=mon1_pos.getZ(), RelativePosition=False)
return ei, mon1_peak
def apply_masking(self, result_ws, spec_masks, map_file):
'''
Mask and group detectors based on input parameters
'''
if not spec_masks is None:
MaskDetectors(result_ws, SpectraList=spec_masks)
if not map_file is None:
GroupDetectors(result_ws, result_ws, map_file, KeepUngroupedSpectra=0)
def convert_white(self, white_run, spectra_masks, map_file):
'''
Normalise to a specified white-beam run
'''
white_ws = common.load_run(self.file_prefix, white_run, '_tmp_white_', self.file_ext)[0]
self.normalise(white_ws, self.normalise_method)
ConvertUnits(white_ws, white_ws, "Energy", AlignBins=0)
# This both integrates the workspace into one bin spectra and sets up common bin boundaries for all spectra
low = self.wb_integr_range[0]
upp = self.wb_integr_range[1]
if low > upp:
raise ValueError("White beam integration range is inconsistent. low=%d, upp=%d" % (low,upp))
delta = 2.0*(upp - low)
Rebin(white_ws, white_ws, [low, delta, upp])
self.apply_masking(white_ws, spectra_masks, map_file)
# White beam scale factor
white_ws *= self.wb_scale_factor
return white_ws
def normalise(self, data_ws, method, range_offset=0.0):
'''
Apply normalisation using specified source
'''
method = method.lower()
if method == 'monitor-1':
range_min = self.mon1_norm_range[0] + range_offset
range_max = self.mon1_norm_range[1] + range_offset
NormaliseToMonitor(InputWorkspace=data_ws, OutputWorkspace=data_ws, MonitorSpectrum=int(self.mon1_norm_spec), IntegrationRangeMin=range_min, IntegrationRangeMax=range_max,IncludePartialBins=True)
elif method == 'current':
NormaliseByCurrent(InputWorkspace=data_ws, OutputWorkspace=data_ws)
elif method == 'none':
return
else:
raise RuntimeError('Normalisation scheme ' + reference + ' not found. It must be one of monitor-1, current, peak or none')
def abs_average(self, data_ws):
'''
Compute the average Y value of a workspace.
The average is computed by collapsing the workspace to a single bin per spectra then masking
masking out detectors given by the FindDetectorsOutsideLimits and MedianDetectorTest algorithms.
The average is then the computed as the using the remainder and factoring in their errors as weights, i.e.
average = sum(Yvalue[i]*weight[i]) / sum(weights)
where only those detectors that are unmasked are used and the weight[i] = 1/errorValue[i].
'''
e_low = self.monovan_integr_range[0]
e_upp = self.monovan_integr_range[1]
if e_low > e_upp:
raise ValueError("Inconsistent mono-vanadium integration range defined!")
Rebin(data_ws, data_ws, [e_low, 2.*(e_upp-e_low), e_upp])
min_value = self.abs_min_value
max_value = self.abs_max_value
median_lbound = self.abs_median_lbound
median_ubound = self.abs_median_ubound
median_frac_low = self.abs_median_frac_low
median_frac_hi = self.abs_median_frac_hi
median_sig = self.abs_median_sig
self.mask_detectors_outside_range(data_ws, min_value, max_value,median_lbound, median_ubound, median_frac_low, median_frac_hi, median_sig)
ConvertFromDistribution(data_ws)
nhist = data_ws.getNumberHistograms()
average_value = 0.0
weight_sum = 0.0
for i in range(nhist):
try:
det = data_ws.getDetector(i)
except Exception:
continue
if det.isMasked():
continue
y_value = data_ws.dataY(i)[0]
if y_value != y_value:
continue
weight = 1.0/data_ws.dataE(i)[0]
average_value += y_value * weight
weight_sum += weight
average_value /= weight_sum
return average_value
def mask_detectors_outside_range(self, data_ws, min_value, max_value,median_lbound, median_ubound, median_frac_lo, median_frac_hi, median_sig):
'''
Masks detecrors on the given workspace according the ranges given where:
min_value - lower bound of meaningful value;
max_value - upper bound of meaningful value;
median_lbound - lower bound defining outliers as fraction of median value;
median_ubound - upper bound defining outliers as fraction of median value;
median_frac_lo - lower acceptable bound as fraction of median value;
median_frac_hi - upper acceptable bound as fraction of median value;
media_sig - error criterion as a multiple of error bar i.e. to fail the test, the magnitude of the
difference with respect to the median value must also exceed this number of error bars.
'''
# Limit test
median_tests_ws = '_tmp_abs_median_tests'
fdol_alg = FindDetectorsOutsideLimits(data_ws, median_tests_ws, HighThreshold=max_value, LowThreshold=min_value)
MaskDetectors(data_ws, SpectraList=fdol_alg.getPropertyValue('BadSpectraNums'))
# Median tests
median_test_alg = MedianDetectorTest(data_ws, median_tests_ws, LowThreshold=median_lbound, HighThreshold=median_ubound)
MaskDetectors(data_ws, SpectraList=median_test_alg.getPropertyValue('BadSpectraNums'))
median_test_alg = MedianDetectorTest(data_ws, median_tests_ws, SignificanceTest=median_sig, LowThreshold=median_frac_lo, HighThreshold=median_frac_hi)
MaskDetectors(data_ws, SpectraList=median_test_alg.getPropertyValue('BadSpectraNums'))
mtd.deleteWorkspace(median_tests_ws)
def save_results(self, workspace, save_path, formats = None):
'''
Save the result workspace to the specfied filename using the list of formats specified in
self.save_formats
'''
if save_path == '':
raise ValueError('Empty filename is not allowed for saving')
if formats is None:
formats = self.save_formats
if type(formats) == str:
formats = [formats]
#Make sure we just have a file stem
save_path = os.path.splitext(save_path)[0]
for ext in formats:
filename = save_path + ext
if ext == '.spe':
SaveSPE(workspace, filename)
elif ext == '.nxs':
SaveNexus(workspace, filename)
elif ext == '.nxspe':
SaveNXSPE(workspace, filename)
else:
self.log('Unknown file format "%s" encountered while saving results.')
#-----------------------------------------------------------------