def convertToRST(algName, tempDir, convertedDir, version):
#Set input name and change working directory
inputN = algName+'.txt'
outputN = algName+'.rst'
cwd = os.getcwd()
os.chdir(tempDir)
args = ['pandoc', '-f', 'mediawiki', '-t', 'rst', inputN, '-o', outputN]
#run pandoc and wait for output
obj = subprocess.Popen(args)
obj.wait()
os.chdir(cwd)
#move completed rst file into completed folder for tidyness
mvFile = tempDir+algName+'.rst'
destname = None
if AlgorithmFactory.exists(algName):
destname = convertedDir + "/algorithms/" + algName + "-v" + str(version) + ".rst"
elif AlgorithmFactory.exists(algName[:-1]):
destname = convertedDir + "/algorithms/" + algName[:-1] + "-v" + str(version) + ".rst"
elif algName in FUNC_NAMES:
destname = convertedDir + "/functions/" + algName + ".rst"
elif "USAGE" in algName:
destname = convertedDir + algName + ".rst"
else:
unknowns_file = open("unknowns.txt", 'a')
unknowns_file.write("Unknown object type %s\n" % algName)
unknowns_file.close()
return
shutil.move(mvFile, destname)
def create(algName, convertedDir):
#read the rst content in
with open (convertedDir+algName+'.rst', 'r') as rstFile:
rst=rstFile.read()
#remove extra whitespace and newlines from the end of the documentation
rst = rst.rstrip()
template_dir = os.path.dirname(__file__)
if AlgorithmFactory.exists(algName):
template_file = os.path.join(template_dir,'algorithm_template.rst')
elif AlgorithmFactory.exists(algName[:-3]):
template_file = os.path.join(template_dir,'algorithm_template.rst')
elif algName in FUNC_NAMES:
template_file = os.path.join(template_dir,'function_template.rst')
elif "USAGE" in algName:
raise RuntimeError("Usage section rst encountered in directive insertion: %s" % algName)
else:
raise RuntimeError("Unknown object in directive insertion: %s" % algName)
with open (template_file, 'r') as templateFile:
template=templateFile.read()
built = template.replace('[TITLE]', "="*len(algName) + "\n" + algName + "\n" + "="*len(algName) + "\n")
built = built.replace('[DOCUMENTATION]', rst)
builtFile = open(convertedDir+algName+'.rst', 'w')
builtFile.write(built)
def test_getDescriptors(self):
from mantid import AlgorithmFactory
descriptors = AlgorithmFactory.getDescriptors(True)
self.assertGreater(len(descriptors), 0)
d = descriptors[0]
self.assertFalse(isinstance(d, AlgorithmDescriptorMock))
self.assertTrue(hasattr(d, 'name'))
self.assertTrue(hasattr(d, 'alias'))
self.assertTrue(hasattr(d, 'category'))
self.assertTrue(hasattr(d, 'version'))
def get_algorithm_data(self):
"""
Prepare the algorithm description data for displaying in the view.
:return: A tuple of two elements. The first is a list of all algorithm names.
The second is a tree of nested dicts where keys either category names
or self.algorithm_key. Values of the category names are sub-trees
and those of self.algorithm_key have dicts mapping algorithm names
to lists of their versions. For example (as yaml):
Arithmetic:
Errors:
_:
PoissonErrors: [1]
SetUncertainties: [1]
SignalOverError: [1]
FFT:
_:
ExtractFFTSpectrum: [1]
FFT: [1]
FFTDerivative: [1]
FFTSmooth: [1, 2]
_:
CrossCorrelate: [1]
Divide: [1]
Exponential: [1]
GeneralisedSecondDifference: [1]
Here self.algorithm_key == '_'
"""
descriptors = AlgorithmFactory.getDescriptors(self.include_hidden)
algorithm_names = []
data = {}
for descriptor in descriptors:
# descriptor is a class with data fields: name, alias, category, version
if descriptor.name not in algorithm_names:
algorithm_names.append(descriptor.name)
categories = descriptor.category.split('\\')
# Create nested dictionaries in which the key is a category and the value
# is a similar dictionary with sub-categories as keys
d = data
for cat in categories:
if cat not in d:
d[cat] = {}
d = d[cat]
# Entry with key == '' contains a dict with algorithm names as keys
# The values are lists of version numbers
if self.algorithm_key not in d:
d[self.algorithm_key] = {}
d = d[self.algorithm_key]
if descriptor.name not in d:
d[descriptor.name] = []
d[descriptor.name].append(descriptor.version)
return algorithm_names, data
ties = []
kwargs = {}
num_spectra = mtd[input_ws].getNumberHistograms()
for i in range(0, num_spectra):
multi += comp
kwargs['WorkspaceIndex_' + str(i)] = i
if i > 0:
kwargs['InputWorkspace_' + str(i)] = input_ws
# tie beta for every spectrum
for stretched_index in stretched_indices:
ties.append('f{0}.f{1}.Stretching=f0.f{1}.Stretching'.format(i, stretched_index))
ties = ','.join(ties)
multi += 'ties=(' + ties + ')'
return multi, kwargs
def _find_indices_of_stretched_exponentials(composite):
indices = []
for index in range(0, len(composite)):
if composite.getFunction(index).name() == "StretchExp":
indices.append(index)
return indices
AlgorithmFactory.subscribe(IqtFitMultiple)
'fit_type': self._fit_type,
'intensities_constrained': self._intensities_constrained,
'beta_constrained': False
}
copy_log_alg = self.createChildAlgorithm("CopyLogs")
copy_log_alg.setProperty("InputWorkspace", self._input_ws)
copy_log_alg.setProperty("OutputWorkspace", self._fit_group_name)
copy_log_alg.execute()
copy_log_alg.setProperty("InputWorkspace", self._input_ws)
copy_log_alg.setProperty("OutputWorkspace", self._result_ws.getName())
copy_log_alg.execute()
log_names = [item for item in sample_logs]
log_values = [sample_logs[item] for item in sample_logs]
add_sample_log_multi = self.createChildAlgorithm(
"AddSampleLogMultiple")
add_sample_log_multi.setProperty("Workspace",
self._result_ws.getName())
add_sample_log_multi.setProperty("LogNames", log_names)
add_sample_log_multi.setProperty("LogValues", log_values)
add_sample_log_multi.execute()
add_sample_log_multi.setProperty("Workspace", self._fit_group_name)
add_sample_log_multi.setProperty("LogNames", log_names)
add_sample_log_multi.setProperty("LogValues", log_values)
add_sample_log_multi.execute()
AlgorithmFactory.subscribe(IqtFitSequential)
self.declareProperty(name="TimeMax", defaultValue=0.2, validator=FloatMandatoryValidator(),
doc="Multiplicative scale factor")
self.declareProperty(name='Plot', defaultValue='None', validator=StringListValidator(['None', 'Intensity', 'Tau', 'Beta', 'All']),
doc='Switch Plot Off/On')
def PyExec(self):
from IndirectDataAnalysis import furyfitMult
workdir = config['defaultsave.directory']
inType = self.getProperty('InputType').value
prefix = self.getProperty('Instrument').value
ana = self.getProperty('Analyser').value
RunNumb = self.getProperty('RunNumber').value
xMax = self.getProperty('TimeMax').value
filename = prefix + str(RunNumb) + '_' + ana
plotOp = self.getProperty('Plot').value
inWS = filename + '_iqt'
if inType == 'File':
spath = os.path.join(workdir, inWS + '.nxs') # path name for sample nxs file
logger.notice('Input from File : ' + spath)
LoadNexusProcessed(Filename=spath, OutputWorkspace=inWS)
else:
logger.notice('Input from Workspace : ' + inWS)
CropWorkspace(InputWorkspace=inWS, OutputWorkspace=inWS, XMin=0.0, XMax=xMax)
function = 'name=LinearBackground,A0=0.02,A1=0,ties=(A1=0);'
function += 'name=UserFunction,Formula=Intensity*exp(-(x/Tau)^Beta),Intensity=0.98,Tau=0.02,Beta=0.8'
furyfitMult(inWS, function, 'SSSS', 0.0, 0.2, False, plotOp, True)
AlgorithmFactory.subscribe(FuryFitMultiple)
"""
Copy the sample logs from the input workspace and add them to the output workspaces
"""
sample_logs = {'start_x': self._start_x, 'end_x': self._end_x, 'fit_type': self._fit_type,
'intensities_constrained': self._intensities_constrained, 'beta_constrained': False}
copy_log_alg = self.createChildAlgorithm("CopyLogs", enableLogging=False)
copy_log_alg.setProperty("InputWorkspace", self._input_ws)
copy_log_alg.setProperty("OutputWorkspace", self._fit_group_name)
copy_log_alg.execute()
copy_log_alg.setProperty("InputWorkspace", self._input_ws)
copy_log_alg.setProperty("OutputWorkspace", self._result_ws.name())
copy_log_alg.execute()
log_names = [item for item in sample_logs]
log_values = [sample_logs[item] for item in sample_logs]
add_sample_log_multi = self.createChildAlgorithm("AddSampleLogMultiple", enableLogging=False)
add_sample_log_multi.setProperty("Workspace", self._result_ws.name())
add_sample_log_multi.setProperty("LogNames", log_names)
add_sample_log_multi.setProperty("LogValues", log_values)
add_sample_log_multi.execute()
add_sample_log_multi.setProperty("Workspace", self._fit_group_name)
add_sample_log_multi.setProperty("LogNames", log_names)
add_sample_log_multi.setProperty("LogValues", log_values)
add_sample_log_multi.execute()
AlgorithmFactory.subscribe(IqtFitSequential)
def PyExec(self):
from IndirectDataAnalysis import furyfitMult
workdir = config['defaultsave.directory']
inType = self.getProperty('InputType').value
prefix = self.getProperty('Instrument').value
ana = self.getProperty('Analyser').value
RunNumb = self.getProperty('RunNumber').value
xMax = self.getProperty('TimeMax').value
filename = prefix + str(RunNumb) + '_' + ana
plotOp = self.getProperty('Plot').value
inWS = filename + '_iqt'
if inType == 'File':
spath = os.path.join(workdir, inWS +
'.nxs') # path name for sample nxs file
logger.notice('Input from File : ' + spath)
LoadNexusProcessed(Filename=spath, OutputWorkspace=inWS)
else:
logger.notice('Input from Workspace : ' + inWS)
CropWorkspace(InputWorkspace=inWS,
OutputWorkspace=inWS,
XMin=0.0,
XMax=xMax)
function = 'name=LinearBackground,A0=0.02,A1=0,ties=(A1=0);'
function += 'name=UserFunction,Formula=Intensity*exp(-(x/Tau)^Beta),Intensity=0.98,Tau=0.02,Beta=0.8'
furyfitMult(inWS, function, 'SSSS', 0.0, 0.2, False, plotOp, True)
AlgorithmFactory.subscribe(FuryFitMultiple)