def _createDataWorkspace(self, datawsname):
""" Create data workspace
"""
import math
tof0 = 4900.
delta = 0.001
numpts = 200
vecx = []
vecy = []
vece = []
tof = tof0
for n in range(numpts):
vecx.append(tof)
vecy.append(math.sin(tof0))
vece.append(1.)
tof = tof * (1+delta)
# ENDFOR
vecx.append(tof)
dataws = api.CreateWorkspace(DataX = vecx, DataY = vecy, DataE = vece, NSpec = 1,
UnitX = "TOF")
# Add to data service
AnalysisDataService.addOrReplace(datawsname, dataws)
return dataws
def test_exportFileNew(self):
""" Test to export logs without header file
"""
# Generate the matrix workspace with some logs
ws = self.createTestWorkspace()
AnalysisDataService.addOrReplace("TestMatrixWS", ws)
# Test algorithm
alg_test = run_algorithm("ExportExperimentLog",
InputWorkspace = "TestMatrixWS",
OutputFilename = "TestRecord001.txt",
SampleLogNames = ["run_number", "duration", "proton_charge", "proton_charge", "proton_charge"],
SampleLogTitles = ["RUN", "Duration", "ProtonCharge", "MinPCharge", "MeanPCharge"],
SampleLogOperation = [None, None, "sum", "min", "average"],
FileMode = "new")
# Validate
self.assertTrue(alg_test.isExecuted())
# Locate file
outfilename = alg_test.getProperty("OutputFilename").value
try:
ifile = open(outfilename)
lines = ifile.readlines()
ifile.close()
except IOError as err:
print("Unable to open file {0}.".format(outfilename))
self.assertTrue(False)
return
# Last line cannot be empty, i.e., before EOF '\n' is not allowed
lastline = lines[-1]
self.assertTrue(len(lastline.strip()) > 0)
# Number of lines
self.assertEquals(len(lines), 2)
# Check line
firstdataline = lines[1]
terms = firstdataline.strip().split("\t")
self.assertEquals(len(terms), 5)
# Get property
pchargelog = ws.getRun().getProperty("proton_charge").value
sumpcharge = numpy.sum(pchargelog)
minpcharge = numpy.min(pchargelog)
avgpcharge = numpy.average(pchargelog)
v2 = float(terms[2])
self.assertAlmostEqual(sumpcharge, v2)
v3 = float(terms[3])
self.assertAlmostEqual(minpcharge, v3)
v4 = float(terms[4])
self.assertAlmostEqual(avgpcharge, v4)
# Remove generated files
os.remove(outfilename)
AnalysisDataService.remove("TestMatrixWS")
return
def setUp(self):
self.working_directory = tempfile.mkdtemp()
self.ws1_name = "ws1"
self.project_ext = ".mtdproj"
ADS.addOrReplace(self.ws1_name, CreateSampleWorkspace(OutputWorkspace=self.ws1_name))
project_saver = projectsaver.ProjectSaver(self.project_ext)
project_saver.save_project(workspace_to_save=[self.ws1_name], directory=self.working_directory)
def test_add_raises_error_if_name_exists(self):
data = [1.0,2.0,3.0]
alg = run_algorithm('CreateWorkspace',DataX=data,DataY=data,NSpec=1,UnitX='Wavelength', child=True)
name = "testws"
ws = alg.getProperty("OutputWorkspace").value
AnalysisDataService.addOrReplace(name, ws)
self.assertRaises(RuntimeError, AnalysisDataService.add, name, ws)
def _run_createws(self, wsname):
"""
Run create workspace storing the output in the named workspace
"""
data = [1.0,2.0,3.0]
alg = run_algorithm('CreateWorkspace',DataX=data,DataY=data,NSpec=1,UnitX='Wavelength', child=True)
AnalysisDataService.addOrReplace(wsname, alg.getProperty("OutputWorkspace").value)
def _createTwoCurves(self, datawsname):
""" Create data workspace
"""
E = np.arange(-50, 50, 1.0)
# curve 1
I = 1000 * np.exp(-E**2/10**2)
err = I ** .5
# curve 2
I2 = 1000 * (1+np.sin(E/5*np.pi))
err2 = I ** .5
# workspace
ws = WorkspaceFactory.create(
"Workspace2D", NVectors=2,
XLength = E.size, YLength = I.size
)
# curve1
ws.dataX(0)[:] = E
ws.dataY(0)[:] = I
ws.dataE(0)[:] = err
# curve2
ws.dataX(1)[:] = E
ws.dataY(1)[:] = I2
ws.dataE(1)[:] = err2
# Add to data service
AnalysisDataService.addOrReplace(datawsname, ws)
return E, I, err, I2, err2
def test_exportFileAppend(self):
""" Test to export logs without header file
"""
# Generate the matrix workspace with some logs
ws = self.createTestWorkspace()
AnalysisDataService.addOrReplace("TestMatrixWS", ws)
# Test algorithm
# create new file
alg_test = run_algorithm("ExportExperimentLog",
InputWorkspace = "TestMatrixWS",
OutputFilename = "TestRecord.txt",
SampleLogNames = ["run_number", "duration", "proton_charge"],
SampleLogTitles = ["RUN", "Duration", "ProtonCharge"],
SampleLogOperation = [None, None, "sum"],
FileMode = "new")
# append
alg_test = run_algorithm("ExportExperimentLog",
InputWorkspace = "TestMatrixWS",
OutputFilename = "TestRecord.txt",
SampleLogNames = ["run_number", "duration", "proton_charge"],
SampleLogTitles = ["RUN", "Duration", "ProtonCharge"],
SampleLogOperation = [None, None, "sum"],
FileMode = "fastappend")
# Validate
self.assertTrue(alg_test.isExecuted())
# Locate file
outfilename = alg_test.getProperty("OutputFilename").value
try:
print "Output file is %s. " % (outfilename)
ifile = open(outfilename)
lines = ifile.readlines()
ifile.close()
except IOError as err:
print "Unable to open file %s. " % (outfilename)
self.assertTrue(False)
return
# Last line cannot be empty, i.e., before EOF '\n' is not allowed
lastline = lines[-1]
self.assertTrue(len(lastline.strip()) > 0)
# Number of lines
self.assertEquals(len(lines), 3)
# Check line
firstdataline = lines[1]
terms = firstdataline.strip().split("\t")
self.assertEquals(len(terms), 3)
#
# # Remove generated files
os.remove(outfilename)
AnalysisDataService.remove("TestMatrixWS")
return
def add_to_ads(calibration_workspace, full_file_path):
"""
Add the calibration file to the ADS. The file name is used to publish it to the ADS.
:param calibration_workspace: the calibration file which is to be published.
:param full_file_path: the file path to the calibration file.
"""
calibration_workspace_name = get_expected_calibration_workspace_name(full_file_path)
AnalysisDataService.addOrReplace(calibration_workspace_name, calibration_workspace)
def test_addOrReplace_replaces_workspace_with_existing_name(self):
data = [1.0,2.0,3.0]
alg = run_algorithm('CreateWorkspace',DataX=data,DataY=data,NSpec=1,UnitX='Wavelength', child=True)
name = "testws"
ws = alg.getProperty("OutputWorkspace").value
AnalysisDataService.add(name, ws)
len_before = len(AnalysisDataService)
AnalysisDataService.addOrReplace(name, ws)
len_after = len(AnalysisDataService)
self.assertEquals(len_after, len_before)
def test_saving_single_workspace(self):
ws_saver = workspacesaver.WorkspaceSaver(self.working_directory)
ws1 = CreateSampleWorkspace()
ws1_name = "ws1"
ADS.addOrReplace(ws1_name, ws1)
ws_saver.save_workspaces([ws1_name])
list_of_files = listdir(self.working_directory)
self.assertEqual(len(list_of_files), 1)
self.assertTrue(ws1_name + ".nxs" in list_of_files)
def test_add_raises_error_if_name_exists(self):
data = [1.0, 2.0, 3.0]
alg = run_algorithm('CreateWorkspace',
DataX=data,
DataY=data,
NSpec=1,
UnitX='Wavelength',
child=True)
name = "testws"
ws = alg.getProperty("OutputWorkspace").value
AnalysisDataService.addOrReplace(name, ws)
self.assertRaises(RuntimeError, AnalysisDataService.add, name, ws)
def test_exportUTC(self):
""" Test to export logs without header file
"""
import os
import os.path
# Generate the matrix workspace with some logs
ws = self.createTestWorkspace()
AnalysisDataService.addOrReplace("TestMatrixWS", ws)
# Test algorithm
alg_test = run_algorithm(
"ExportSampleLogsToCSVFile",
InputWorkspace="TestMatrixWS",
OutputFilename="furnace20339utc.txt",
SampleLogNames=["SensorA", "SensorB", "SensorC"],
WriteHeaderFile=True,
TimeZone="UTC",
Header="SensorA[K]\t SensorB[K]\t SensorC[K]",
)
# Validate
self.assertTrue(alg_test.isExecuted())
# Locate file
outfilename = alg_test.getProperty("OutputFilename").value
filepath = os.path.dirname(outfilename)
basename = os.path.basename(outfilename)
baseheadername = basename.split(".")[0] + "_header.txt"
headerfilename = os.path.join(filepath, baseheadername)
try:
ifile = open(headerfilename)
lines = ifile.readlines()
ifile.close()
except IOError as err:
errmsg = "Unable to open header file %s. " % (headerfilename)
self.assertEquals(errmsg, "")
return
# Count lines in the file
goodlines = 0
for line in lines:
line = line.strip()
if len(line) > 0:
goodlines += 1
self.assertEquals(goodlines, 3)
# Clean
os.remove(outfilename)
os.remove(headerfilename)
AnalysisDataService.remove("TestMatrixWS")
return
def test_that_x_limits_of_current_dataset_will_return_the_x_limits_of_the_workspace(
self):
self.model.dataset_names = self.dataset_names
workspace = CreateSampleWorkspace()
AnalysisDataService.addOrReplace("EMU20884; Group; fwd; Asymmetry",
workspace)
x_lower, x_upper = self.model.x_limits_of_workspace(
self.model.current_dataset_name)
self.assertEqual(x_lower, 0.0 - X_OFFSET)
self.assertEqual(x_upper, 20000.0 + X_OFFSET)
def test_exportFileNewCSV(self):
""" Test to export logs without header file in csv format
and with a name not endind with .csv
"""
# Generate the matrix workspace with some logs
ws = self.createTestWorkspace()
AnalysisDataService.addOrReplace("TestMatrixWS", ws)
# Test algorithm
alg_test = run_algorithm(
"ExportExperimentLog",
InputWorkspace="TestMatrixWS",
OutputFilename="TestRecord.txt",
SampleLogNames=["run_number", "duration", "proton_charge"],
SampleLogTitles=["RUN", "Duration", "ProtonCharge"],
SampleLogOperation=[None, None, "sum"],
FileMode="new",
FileFormat="comma (csv)")
# Validate
self.assertTrue(alg_test.isExecuted())
# Locate file
outfilename = alg_test.getProperty("OutputFilename").value.split(
".txt")[0] + ".csv"
try:
print "Output file is %s. " % (outfilename)
ifile = open(outfilename)
lines = ifile.readlines()
ifile.close()
except IOError as err:
print "Unable to open file %s. " % (outfilename)
self.assertTrue(False)
return
# Last line cannot be empty, i.e., before EOF '\n' is not allowed
lastline = lines[-1]
self.assertTrue(len(lastline.strip()) > 0)
# Number of lines
self.assertEquals(len(lines), 2)
# Check line
firstdataline = lines[1]
terms = firstdataline.strip().split(",")
self.assertEquals(len(terms), 3)
#
# # Remove generated files
os.remove(outfilename)
AnalysisDataService.remove("TestMatrixWS")
return
def _createOneQCurve(self, datawsname):
""" Create data workspace
"""
Q = np.arange(0, 13, 1.0)
I = 1000 * np.exp(-Q**2/10**2)
err = I ** .5
# create workspace
dataws = api.CreateWorkspace(
DataX = Q, DataY = I, DataE = err, NSpec = 1,
UnitX = "Momentum")
# Add to data service
AnalysisDataService.addOrReplace(datawsname, dataws)
return Q, I, err
def _createOneCurve(self, datawsname):
""" Create data workspace
"""
E = np.arange(-50, 50, 10.0)
I = 1000 * np.exp(-E**2/10**2)
err = I ** .5
# create workspace
dataws = api.CreateWorkspace(
DataX = E, DataY = I, DataE = err, NSpec = 1,
UnitX = "Energy")
# Add to data service
AnalysisDataService.addOrReplace(datawsname, dataws)
return E, I, err
def _createOneCurve(self, datawsname):
""" Create data workspace
"""
E = np.arange(-50, 50, 10.0)
I = 1000 * np.exp(-E**2/10**2)
err = I ** .5
# create workspace
dataws = api.CreateWorkspace(
DataX = E, DataY = I, DataE = err, NSpec = 1,
UnitX = "Energy")
# Add to data service
AnalysisDataService.addOrReplace(datawsname, dataws)
return E, I, err
def __init__(self):
super(FitScriptGeneratorStartupTest, self).__init__()
self._app = get_application()
self.ws_name = "WorkspaceName"
test_workspace = CreateWorkspace(DataX=[1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16],
DataY=[1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12], NSpec=4, UnitX="Wavelength")
AnalysisDataService.addOrReplace(self.ws_name, test_workspace)
self.fsg_model = FitScriptGeneratorModel()
self.fsg_view = FitScriptGeneratorView(None, FittingMode.SIMULTANEOUS, {"Minimizer": "Levenberg-Marquardt"})
self.fsg_presenter = FitScriptGeneratorPresenter(self.fsg_view, self.fsg_model, [self.ws_name], 1.0, 3.0)
def test_exportUTC(self):
""" Test to export logs without header file
"""
import os
import os.path
# Generate the matrix workspace with some logs
ws = self.createTestWorkspace()
AnalysisDataService.addOrReplace("TestMatrixWS", ws)
# Test algorithm
alg_test = run_algorithm(
"ExportSampleLogsToCSVFile",
InputWorkspace="TestMatrixWS",
OutputFilename="furnace20339utc.txt",
SampleLogNames=["SensorA", "SensorB", "SensorC"],
WriteHeaderFile=True,
TimeZone='UTC',
Header="SensorA[K]\t SensorB[K]\t SensorC[K]")
# Validate
self.assertTrue(alg_test.isExecuted())
# Locate file
outfilename = alg_test.getProperty("OutputFilename").value
filepath = os.path.dirname(outfilename)
basename = os.path.basename(outfilename)
baseheadername = basename.split(".")[0] + "_header.txt"
headerfilename = os.path.join(filepath, baseheadername)
try:
ifile = open(headerfilename)
lines = ifile.readlines()
ifile.close()
except IOError as err:
errmsg = "Unable to open header file %s. " % (headerfilename)
self.assertEquals(errmsg, "")
return
# Count lines in the file
goodlines = 0
for line in lines:
line = line.strip()
if len(line) > 0:
goodlines += 1
self.assertEquals(goodlines, 3)
# Clean
os.remove(outfilename)
os.remove(headerfilename)
AnalysisDataService.remove("TestMatrixWS")
return
def _run_createws(self, wsname):
"""
Run create workspace storing the output in the named workspace
"""
data = [1.0, 2.0, 3.0]
alg = run_algorithm('CreateWorkspace',
DataX=data,
DataY=data,
NSpec=1,
UnitX='Wavelength',
child=True)
AnalysisDataService.addOrReplace(
wsname,
alg.getProperty("OutputWorkspace").value)
def _create_data_workspace(data_ws_name, num_spec, tof0=None, delta=None, num_pts=None):
"""
Create a multiple spectra data workspace
:param data_ws_name:
:param num_spec:
:return:
"""
# get base data sets for the workspace as Histograms
tof0 = 10000.
delta = 0.001
num_pts = 200
list_x = list()
list_y = list()
list_e = list()
tof = tof0
for n in range(num_pts):
list_x.append(tof)
list_y.append(math.sin(tof0))
list_e.append(1.)
tof *= 1+delta
# END-FOR
list_x.append(tof)
vec_x = numpy.array(list_x)
vec_y = numpy.array(list_y)
vec_e = numpy.array(list_e)
# expand to multiple spectra
if num_spec > 1:
vec_x_orig = vec_x[:]
vec_y_orig = vec_y[:]
vec_e_orig = vec_e[:]
for spec_index in range(1, num_spec):
vec_x = numpy.append(vec_x, vec_x_orig)
vec_i = vec_y_orig[:]
vec_i *= 2 * (spec_index + 1)
vec_y = numpy.append(vec_y, vec_i)
vec_e = numpy.append(vec_e, vec_e_orig)
# END-FOR
data_ws = api.CreateWorkspace(DataX=vec_x, DataY=vec_y, DataE=vec_e, NSpec=num_spec, UnitX="TOF")
# Add to data service
AnalysisDataService.addOrReplace(data_ws_name, data_ws)
return data_ws
def test_when_MDWorkspace_is_in_ADS(self):
ws_saver = workspacesaver.WorkspaceSaver(self.working_directory)
ws1 = CreateMDHistoWorkspace(SignalInput='1,2,3,4,5,6,7,8,9', ErrorInput='1,1,1,1,1,1,1,1,1',
Dimensionality='2', Extents='-1,1,-1,1', NumberOfBins='3,3', Names='A,B',
Units='U,T')
ws1_name = "ws1"
ADS.addOrReplace(ws1_name, ws1)
ws_saver.save_workspaces([ws1_name])
list_of_files = listdir(self.working_directory)
self.assertEqual(len(list_of_files), 1)
self.assertTrue(ws1_name + ".nxs" in list_of_files)
self._load_MDWorkspace_and_test_it(ws1_name)
def _create_data_workspace(data_ws_name, num_spec, tof0=None, delta=None, num_pts=None):
"""
Create a multiple spectra data workspace
:param data_ws_name:
:param num_spec:
:return:
"""
# get base data sets for the workspace as Histograms
tof0 = 10000.
delta = 0.001
num_pts = 200
list_x = list()
list_y = list()
list_e = list()
tof = tof0
for n in range(num_pts):
list_x.append(tof)
list_y.append(math.sin(tof0))
list_e.append(1.)
tof *= 1+delta
# END-FOR
list_x.append(tof)
vec_x = numpy.array(list_x)
vec_y = numpy.array(list_y)
vec_e = numpy.array(list_e)
# expand to multiple spectra
if num_spec > 1:
vec_x_orig = vec_x[:]
vec_y_orig = vec_y[:]
vec_e_orig = vec_e[:]
for spec_index in range(1, num_spec):
vec_x = numpy.append(vec_x, vec_x_orig)
vec_i = vec_y_orig[:]
vec_i *= 2 * (spec_index + 1)
vec_y = numpy.append(vec_y, vec_i)
vec_e = numpy.append(vec_e, vec_e_orig)
# END-FOR
data_ws = api.CreateWorkspace(DataX=vec_x, DataY=vec_y, DataE=vec_e, NSpec=num_spec, UnitX="TOF")
# Add to data service
AnalysisDataService.addOrReplace(data_ws_name, data_ws)
return data_ws
def test_when_MDWorkspace_is_in_ADS(self):
ws_saver = workspacesaver.WorkspaceSaver(self.working_directory)
ws1 = CreateMDHistoWorkspace(SignalInput='1,2,3,4,5,6,7,8,9', ErrorInput='1,1,1,1,1,1,1,1,1',
Dimensionality='2', Extents='-1,1,-1,1', NumberOfBins='3,3', Names='A,B',
Units='U,T')
ws1_name = "ws1"
ADS.addOrReplace(ws1_name, ws1)
ws_saver.save_workspaces([ws1_name])
list_of_files = listdir(self.working_directory)
self.assertEqual(len(list_of_files), 1)
self.assertTrue(ws1_name + ".nxs" in list_of_files)
self._load_MDWorkspace_and_test_it(ws1_name)
def _createOneHistogram(self, datawsname):
""" Create data workspace
"""
E = np.arange(-50.5, 50, 1.0)
Ecenters = (E[:-1] + E[1:]) / 2
I = 1000 * np.exp(-Ecenters**2/10**2)
err = I ** .5
# create workspace
dataws = api.CreateWorkspace(
DataX = E, DataY = I, DataE = err, NSpec = 1,
UnitX = "Energy(meV)")
# Add to data service
AnalysisDataService.addOrReplace(datawsname, dataws)
return E, I, err
def test_exportFileMissingLog(self):
""" Test to export logs without header file
"""
# Generate the matrix workspace with some logs
ws = self.createTestWorkspace()
AnalysisDataService.addOrReplace("TestMatrixWS", ws)
# Test algorithm
alg_test = run_algorithm(
"ExportSampleLogsToCSVFile",
InputWorkspace="TestMatrixWS",
OutputFilename="furnace20335.txt",
SampleLogNames=["SensorA", "SensorB", "SensorX", "SensorC"],
WriteHeaderFile=False,
)
# Validate
self.assertTrue(alg_test.isExecuted())
# Locate file
outfilename = alg_test.getProperty("OutputFilename").value
try:
ifile = open(outfilename)
lines = ifile.readlines()
ifile.close()
except IOError as err:
print "Unable to open file %s. " % (outfilename)
self.assertTrue(False)
return
# Count lines in the file
goodlines = 0
for line in lines:
line = line.strip()
if len(line) > 0:
goodlines += 1
self.assertEquals(goodlines, 25)
# Check values
line0 = lines[0]
terms = line0.split()
self.assertEquals(len(terms), 6)
value2 = float(terms[4])
self.assertEquals(value2, 0.0)
# Clean
os.remove(outfilename)
AnalysisDataService.remove("TestMatrixWS")
return
def setUp(self):
if self._test_ws is None:
self.__class__._test_ws = WorkspaceFactory.Instance().create(
"Workspace2D", NVectors=2, YLength=5, XLength=5)
if self._test_ws_2 is None:
self.__class__._test_ws_2 = WorkspaceFactory.Instance().create(
"Workspace2D", NVectors=2, YLength=5, XLength=5)
AnalysisDataService.addOrReplace('test_ws', self._test_ws)
AnalysisDataService.addOrReplace('test_ws_2', self._test_ws_2)
self.get_spectra_selection_patcher = mock.patch('mantidqt.plotting.functions.get_spectra_selection')
self.addCleanup(self.get_spectra_selection_patcher.stop)
self.get_spectra_selection_mock = self.get_spectra_selection_patcher.start()
def _createOneHistogram(self, datawsname):
""" Create data workspace
"""
E = np.arange(-50.5, 50, 1.0)
Ecenters = (E[:-1] + E[1:]) / 2
I = 1000 * np.exp(-Ecenters**2/10**2)
err = I ** .5
# create workspace
dataws = api.CreateWorkspace(
DataX = E, DataY = I, DataE = err, NSpec = 1,
UnitX = "Energy(meV)")
# Add to data service
AnalysisDataService.addOrReplace(datawsname, dataws)
return E, I, err
def test_exportFileMissingLog(self):
""" Test to export logs without header file
"""
# Generate the matrix workspace with some logs
ws = self.createTestWorkspace()
AnalysisDataService.addOrReplace("TestMatrixWS", ws)
# Test algorithm
alg_test = run_algorithm(
"ExportSampleLogsToCSVFile",
InputWorkspace="TestMatrixWS",
OutputFilename="furnace20335.txt",
SampleLogNames=["SensorA", "SensorB", "SensorX", "SensorC"],
WriteHeaderFile=False)
# Validate
self.assertTrue(alg_test.isExecuted())
# Locate file
outfilename = alg_test.getProperty("OutputFilename").value
try:
ifile = open(outfilename)
lines = ifile.readlines()
ifile.close()
except IOError as err:
print("Unable to open file {0}.".format(outfilename))
self.assertTrue(False)
return
# Count lines in the file
goodlines = 0
for line in lines:
line = line.strip()
if len(line) > 0:
goodlines += 1
self.assertEquals(goodlines, 25)
# Check values
line0 = lines[0]
terms = line0.split()
self.assertEquals(len(terms), 6)
value2 = float(terms[4])
self.assertEquals(value2, 0.)
# Clean
os.remove(outfilename)
AnalysisDataService.remove("TestMatrixWS")
return
def test_addOrReplace_replaces_workspace_with_existing_name(self):
data = [1.0, 2.0, 3.0]
alg = run_algorithm('CreateWorkspace',
DataX=data,
DataY=data,
NSpec=1,
UnitX='Wavelength',
child=True)
name = "testws"
ws = alg.getProperty("OutputWorkspace").value
AnalysisDataService.add(name, ws)
len_before = len(AnalysisDataService)
AnalysisDataService.addOrReplace(name, ws)
len_after = len(AnalysisDataService)
self.assertEquals(len_after, len_before)
def test_saving_multiple_workspaces(self):
ws_saver = workspacesaver.WorkspaceSaver(self.working_directory)
ws1 = CreateSampleWorkspace()
ws1_name = "ws1"
ws2 = CreateSampleWorkspace()
ws2_name = "ws2"
ADS.addOrReplace(ws1_name, ws1)
ADS.addOrReplace(ws2_name, ws2)
ws_saver.save_workspaces([ws1_name, ws2_name])
list_of_files = listdir(self.working_directory)
self.assertEqual(len(list_of_files), 2)
self.assertTrue(ws2_name + ".nxs" in list_of_files)
self.assertTrue(ws1_name + ".nxs" in list_of_files)
def calculate_resolution(input_data, mass, index=0):
"""
Run the VesuvioResolution function to produce a workspace
with the value of the Vesuvio resolution.
@param input_data The original TOF data
@param mass The mass defining the recoil peak in AMU
@param index An optional index to specify the spectrum to use
"""
from mantid.api import AlgorithmManager, AnalysisDataService
from mantid.kernel.funcreturns import lhs_info
# Grab the name of the variable that this function call is assigned to
try:
output_name = lhs_info("names")[0]
except IndexError:
# No variable specified
name_stem = str(input_data)
output_name = name_stem + "_res" + str(index)
function = "name=VesuvioResolution, Mass=%f" % mass
# execute the resolution function using fit.
# functions can't currently be executed as stand alone objects,
# so for now we will run fit with zero iterations to achieve the same result.
fit = mantid.api.AlgorithmManager.createUnmanaged('Fit')
fit.initialize()
fit.setChild(True)
fit.setLogging(False)
mantid.simpleapi._set_properties(fit, function, input_data, MaxIterations=0,
CreateOutput=True, Output=output_name,WorkspaceIndex=index)
fit.execute()
values_ws = fit.getProperty("OutputWorkspace").value
# extract just the function values
extract = mantid.api.AlgorithmManager.createUnmanaged('ExtractSingleSpectrum')
extract.initialize()
extract.setChild(True)
extract.setLogging(False)
extract.setProperty("InputWorkspace", values_ws)
extract.setProperty("OutputWorkspace", "__unused_for_child")
extract.setProperty("WorkspaceIndex", 1)
extract.execute()
calculated = extract.getProperty("OutputWorkspace").value
AnalysisDataService.addOrReplace(output_name, calculated)
return calculated
def _determine_factors(self, q_high_angle, q_low_angle, mode, scale, shift):
# We need to make suret that the fitting only occurs in the y direction
constant_x_shift_and_scale = ', f0.Shift=0.0, f0.XScaling=1.0'
# Determine the StartQ and EndQ values
q_min, q_max = self._get_start_q_and_end_q_values(rear_data=q_low_angle, front_data=q_high_angle)
# We need to transfer the errors from the front data to the rear data, as we are using the the front data as a model, but
# we want to take into account the errors of both workspaces.
error_correction = ErrorTransferFromModelToData()
front_data_corrected, rear_data_corrected = error_correction.get_error_corrected(rear_data=q_low_angle,
front_data=q_high_angle,
q_min=q_min, q_max=q_max)
fit = self.createChildAlgorithm('Fit')
# We currently have to put the front_data into the ADS so that the TabulatedFunction has access to it
front_data_corrected = AnalysisDataService.addOrReplace('front_data_corrected', front_data_corrected)
front_in_ads = AnalysisDataService.retrieve('front_data_corrected')
function = 'name=TabulatedFunction, Workspace="' + str(
front_in_ads.name()) + '"' + ";name=FlatBackground"
fit.setProperty('Function', function)
fit.setProperty('InputWorkspace', rear_data_corrected)
constant_x_shift_and_scale = 'f0.Shift=0.0, f0.XScaling=1.0'
if mode == Mode.BothFit:
fit.setProperty('Ties', constant_x_shift_and_scale)
elif mode == Mode.ShiftOnly:
fit.setProperty('Ties', 'f0.Scaling=' + str(scale) + ',' + constant_x_shift_and_scale)
elif mode == Mode.ScaleOnly:
fit.setProperty('Ties', 'f1.A0=' + str(shift) + '*f0.Scaling,' + constant_x_shift_and_scale)
else:
raise RuntimeError('Unknown fitting mode requested.')
fit.setProperty('StartX', q_min)
fit.setProperty('EndX', q_max)
fit.setProperty('CreateOutput', True)
fit.execute()
param = fit.getProperty('OutputParameters').value
AnalysisDataService.remove(front_in_ads.name())
# The outparameters are:
# 1. Scaling in y direction
# 2. Shift in x direction
# 3. Scaling in x direction
# 4. Shift in y direction
scale = param.row(0)['Value']
if scale == 0.0:
raise RuntimeError('Fit scaling as part of stitching evaluated to zero')
# In order to determine the shift, we need to remove the scale factor
shift = param.row(3)['Value'] / scale
return (shift, scale)
def test_exportFile2(self):
""" Get a partial of real load frame log values, and set them to
different logs
"""
# Generate the matrix workspace with some logs
ws = self.createTestWorkspace2()
AnalysisDataService.addOrReplace("TestMatrixWS2", ws)
# Test algorithm
alg_test = run_algorithm(
"ExportSampleLogsToCSVFile",
InputWorkspace="TestMatrixWS2",
OutputFilename="furnace20334.txt",
SampleLogNames=["SensorA", "SensorB", "SensorC", "SensorD"],
WriteHeaderFile=False,
TimeTolerance=1.0)
# Validate
self.assertTrue(alg_test.isExecuted())
# Locate file
outfilename = alg_test.getProperty("OutputFilename").value
try:
ifile = open(outfilename)
lines = ifile.readlines()
ifile.close()
except IOError as err:
print("Unable to open file {0}.".format(outfilename))
self.assertTrue(False)
return
# Count lines in the file
goodlines = 0
for line in lines:
line = line.strip()
if len(line) > 0 and len(
line.split()) == 6 and line.startswith('76130'):
goodlines += 1
self.assertEquals(goodlines, 64)
# Remove generated files
os.remove(outfilename)
AnalysisDataService.remove("TestMatrixWS2")
return
def test_exportFile2(self):
""" Get a partial of real load frame log values, and set them to
different logs
"""
# Generate the matrix workspace with some logs
ws = self.createTestWorkspace2()
AnalysisDataService.addOrReplace("TestMatrixWS2", ws)
# Test algorithm
alg_test = run_algorithm(
"ExportSampleLogsToCSVFile",
InputWorkspace="TestMatrixWS2",
OutputFilename="furnace20334.txt",
SampleLogNames=["SensorA", "SensorB", "SensorC", "SensorD"],
WriteHeaderFile=False,
TimeTolerance=1.0,
)
# Validate
self.assertTrue(alg_test.isExecuted())
# Locate file
outfilename = alg_test.getProperty("OutputFilename").value
try:
ifile = open(outfilename)
lines = ifile.readlines()
ifile.close()
except IOError as err:
print "Unable to open file %s. " % (outfilename)
self.assertTrue(False)
return
# Count lines in the file
goodlines = 0
for line in lines:
line = line.strip()
if len(line) > 0:
goodlines += 1
self.assertEquals(goodlines, 64)
# Remove generated files
os.remove(outfilename)
AnalysisDataService.remove("TestMatrixWS2")
return
def add_workspaces_to_analysis_data_service(workspaces, workspace_names, is_monitor):
"""
Adds a list of workspaces to the ADS.
:param workspaces: list of workspaces
:param workspace_names: the names under which they are to be published
:param is_monitor: if the workspace is a monitor or not
"""
if is_monitor:
workspace_names = [workspace_name + MONITOR_SUFFIX for workspace_name in workspace_names]
if len(workspaces) != len(workspace_names):
raise RuntimeError("SANSLoad: There is a mismatch between the generated names and the length of"
" the WorkspaceGroup. The workspace has {0} entries and there are {1} "
"workspace names".format(len(workspaces), len(workspace_names)))
for index in range(0, len(workspaces)):
if not AnalysisDataService.doesExist(workspace_names[index]):
AnalysisDataService.addOrReplace(workspace_names[index], workspaces[index])
def add_workspaces_to_analysis_data_service(workspaces, workspace_names, is_monitor):
"""
Adds a list of workspaces to the ADS.
:param workspaces: list of workspaces
:param workspace_names: the names under which they are to be published
:param is_monitor: if the workspace is a monitor or not
"""
if is_monitor:
workspace_names = [workspace_name + MONITOR_SUFFIX for workspace_name in workspace_names]
if len(workspaces) != len(workspace_names):
raise RuntimeError("SANSLoad: There is a mismatch between the generated names and the length of"
" the WorkspaceGroup. The workspace has {0} entries and there are {1} "
"workspace names".format(len(workspaces), len(workspace_names)))
for index in range(0, len(workspaces)):
if not AnalysisDataService.doesExist(workspace_names[index]):
AnalysisDataService.addOrReplace(workspace_names[index], workspaces[index])
def test_exportFileOnly(self):
""" Test to export logs without header file
"""
# Generate the matrix workspace with some logs
ws = self.createTestWorkspace()
AnalysisDataService.addOrReplace("TestMatrixWS", ws)
# Test algorithm
alg_test = run_algorithm(
"ExportSampleLogsToCSVFile",
InputWorkspace="TestMatrixWS",
OutputFilename="furnace20333.txt",
SampleLogNames=["SensorA", "SensorB", "SensorC"],
WriteHeaderFile=False)
# Validate
self.assertTrue(alg_test.isExecuted())
# Locate file
outfilename = alg_test.getProperty("OutputFilename").value
try:
ifile = open(outfilename)
lines = ifile.readlines()
ifile.close()
except IOError as err:
print("Unable to open file {0}.".format(outfilename))
self.fail()
return
# Count lines in the file
goodlines = 0
for line in lines:
line = line.strip()
if len(line) > 0:
goodlines += 1
# ENDIF
# ENDFOR
self.assertEqual(goodlines, 25)
# Remove generated files
os.remove(outfilename)
AnalysisDataService.remove("TestMatrixWS")
return
def test_exportFileOnly(self):
""" Test to export logs without header file
"""
# Generate the matrix workspace with some logs
ws = self.createTestWorkspace()
AnalysisDataService.addOrReplace("TestMatrixWS", ws)
# Test algorithm
alg_test = run_algorithm("ExportSampleLogsToCSVFile",
InputWorkspace = "TestMatrixWS",
OutputFilename = "furnace20333.txt",
SampleLogNames = ["SensorA", "SensorB", "SensorC"],
WriteHeaderFile = False)
# Validate
self.assertTrue(alg_test.isExecuted())
# Locate file
outfilename = alg_test.getProperty("OutputFilename").value
try:
ifile = open(outfilename)
lines = ifile.readlines()
ifile.close()
except IOError as err:
print("Unable to open file {0}.".format(outfilename))
self.assertTrue(False)
return
# Count lines in the file
goodlines = 0
for line in lines:
line = line.strip()
if len(line) > 0:
goodlines += 1
# ENDIF
# ENDFOR
self.assertEquals(goodlines, 25)
# Remove generated files
os.remove(outfilename)
AnalysisDataService.remove("TestMatrixWS")
return
def set_output_workspaces(self, reduction_mode_vs_output_workspaces,
reduction_mode_vs_workspace_names):
"""
Sets the output workspaces which can be HAB, LAB or Merged.
At this step we also provide a workspace name to the sample logs which can be used later on for saving
:param reduction_mode_vs_output_workspaces: map from reduction mode to output workspace
:param reduction_mode_vs_workspace_names: map from reduction mode to output workspace name
"""
workspace_group_merged = WorkspaceGroup()
workspace_group_lab = WorkspaceGroup()
workspace_group_hab = WorkspaceGroup()
# Note that this breaks the flexibility that we have established with the reduction mode. We have not hardcoded
# HAB or LAB anywhere which means that in the future there could be other detectors of relevance. Here we
# reference HAB and LAB directly since we currently don't want to rely on dynamic properties. See also in PyInit
for reduction_mode, output_workspaces in list(
reduction_mode_vs_output_workspaces.items()):
workspace_names = reduction_mode_vs_workspace_names[reduction_mode]
for output_workspace, output_name in zip(output_workspaces,
workspace_names):
# In an MPI reduction output_workspace is produced on the master rank, skip others.
if output_workspace is None:
continue
else:
AnalysisDataService.addOrReplace(output_name,
output_workspace)
if reduction_mode is ReductionMode.Merged:
workspace_group_merged.addWorkspace(output_workspace)
elif reduction_mode is ISISReductionMode.LAB:
workspace_group_lab.addWorkspace(output_workspace)
elif reduction_mode is ISISReductionMode.HAB:
workspace_group_hab.addWorkspace(output_workspace)
else:
raise RuntimeError(
"SANSSingleReduction: Cannot set the output workspace. "
"The selected reduction mode {0} is unknown.".format(
reduction_mode))
if workspace_group_merged.size() > 0:
self.setProperty("OutputWorkspaceMerged", workspace_group_merged)
if workspace_group_lab.size() > 0:
self.setProperty("OutputWorkspaceLAB", workspace_group_lab)
if workspace_group_hab.size() > 0:
self.setProperty("OutputWorkspaceHAB", workspace_group_hab)
def set_reduced_can_workspace_on_output(self, output_bundles):
"""
Sets the reduced can group workspaces on the output properties.
The reduced can workspaces can be:
LAB Can or
HAB Can
:param output_bundles: a list of output bundles
"""
workspace_group_lab_can = WorkspaceGroup()
workspace_group_hab_can = WorkspaceGroup()
# Find the LAB Can and HAB Can entries if they exist
for component_bundle in output_bundles:
for output_bundle in component_bundle:
if output_bundle.data_type is DataType.Can:
reduction_mode = output_bundle.reduction_mode
output_workspace = output_bundle.output_workspace
# Make sure that the output workspace is not None which can be the case if there has never been a
# can set for the reduction.
if output_workspace is not None and not does_can_workspace_exist_on_ads(
output_workspace):
name = self._get_output_workspace_name(
output_bundle.state,
output_bundle.reduction_mode,
can=True)
AnalysisDataService.addOrReplace(
name, output_workspace)
if reduction_mode is ISISReductionMode.LAB:
workspace_group_lab_can.addWorkspace(
output_workspace)
elif reduction_mode is ISISReductionMode.HAB:
workspace_group_hab_can.addWorkspace(
output_workspace)
else:
raise RuntimeError(
"SANSSingleReduction: The reduction mode {0} should not"
" be set with a can.".format(reduction_mode))
if workspace_group_lab_can.size() > 0:
# LAB group workspace is non-empty, so we want to set it as output
self.setProperty("OutputWorkspaceLABCan", workspace_group_lab_can)
if workspace_group_hab_can.size() > 0:
self.setProperty("OutputWorkspaceHABCan", workspace_group_hab_can)
def test_that_get_workspace_names_to_display_from_context_will_attempt_to_get_runs_and_groups_for_single_fit_mode(self):
workspace_names = ["Name"]
workspace = CreateSampleWorkspace()
AnalysisDataService.addOrReplace("Name", workspace)
runs = "All"
group_or_pair = ["long"]
self.model.simultaneous_fitting_mode = False
self.model._get_selected_groups_and_pairs = mock.MagicMock(return_value=(group_or_pair))
self.model.context.get_workspace_names_for = mock.MagicMock(return_value=workspace_names)
self.model.context.get_workspace_names_of_fit_data_with_run = mock.MagicMock()
self.assertEqual(self.model.get_workspace_names_to_display_from_context(), workspace_names)
self.model._get_selected_groups_and_pairs.assert_called_with()
self.model.context.get_workspace_names_for.assert_called_with(runs, group_or_pair, True)
self.assertEqual(1, self.model._get_selected_groups_and_pairs.call_count)
self.assertEqual(1, self.model.context.get_workspace_names_for.call_count)
def test_only_one_workspace_saving(self):
ws1_name = "ws1"
ADS.addOrReplace(ws1_name, CreateSampleWorkspace(OutputWorkspace=ws1_name))
project_saver = projectsaver.ProjectSaver(project_file_ext)
file_name = working_directory + "/" + os.path.basename(working_directory) + project_file_ext
workspaces_string = "\"workspaces\": [\"ws1\"]"
project_saver.save_project(workspace_to_save=[ws1_name], directory=working_directory)
# Check project file is saved correctly
f = open(file_name, "r")
file_string = f.read()
self.assertTrue(workspaces_string in file_string)
# Check workspace is saved
list_of_files = os.listdir(working_directory)
self.assertEqual(len(list_of_files), 2)
self.assertTrue(os.path.basename(working_directory) + project_file_ext in list_of_files)
self.assertTrue(ws1_name + ".nxs" in list_of_files)
def _fill_s_2d_workspace(self,
s_points=None,
workspace=None,
protons_number=None,
nucleons_number=None):
from mantid.api import NumericAxis
from abins.constants import MILLI_EV_TO_WAVENUMBER
if protons_number is not None:
s_points = s_points * self.get_cross_section(
scattering=self._scale_by_cross_section,
protons_number=protons_number,
nucleons_number=nucleons_number)
n_q_values, n_freq_bins = s_points.shape
n_q_bins = self._q_bins.size
assert n_q_values + 1 == n_q_bins
if self._energy_units == 'meV':
energy_bins = self._bins / MILLI_EV_TO_WAVENUMBER
else:
energy_bins = self._bins
wrk = WorkspaceFactory.create("Workspace2D",
NVectors=n_freq_bins,
XLength=n_q_bins,
YLength=n_q_values)
freq_axis = NumericAxis.create(n_freq_bins)
freq_offset = (energy_bins[1] - energy_bins[0]) / 2
for i, freq in enumerate(energy_bins[1:]):
wrk.setX(i, self._q_bins)
wrk.setY(i, s_points[:, i].T)
freq_axis.setValue(i, freq + freq_offset)
wrk.replaceAxis(1, freq_axis)
AnalysisDataService.addOrReplace(workspace, wrk)
self.set_workspace_units(workspace,
layout="2D",
energy_units=self._energy_units)
def show(self, name=''):
"""
Show the workspace in the ADS inside the WorkspaceGroup structure specified in name
name = dirs/../dirs/workspace_name
"""
if not name and not self.name:
raise ValueError("Cannot store workspace in ADS with name : ",
str(name))
self.name = str(name)
if len(self.name) > 0:
# add workspace to ADS
if self.is_hidden:
AnalysisDataService.addOrReplace(self._workspace_name, self.workspace)
self._workspace = None
self._is_in_ads = True
else:
raise ValueError("Cannot store workspace in ADS with name : ",
str(name))
def _prepare_workspaces(number_of_workspaces, tagged_workspace_names=None, state=None, reduction_mode=None):
create_name = "CreateSampleWorkspace"
create_options = {"OutputWorkspace": "test",
"NumBanks": 1,
"BankPixelWidth": 2,
"XMin": 1,
"XMax": 10,
"BinWidth": 2}
create_alg = create_unmanaged_algorithm(create_name, **create_options)
for index in range(number_of_workspaces):
create_alg.execute()
workspace = create_alg.getProperty("OutputWorkspace").value
workspace_name = "test" + "_" + str(index)
AnalysisDataService.addOrReplace(workspace_name, workspace)
if tagged_workspace_names is not None:
for key, value in list(tagged_workspace_names.items()):
create_alg.execute()
workspace = create_alg.getProperty("OutputWorkspace").value
AnalysisDataService.addOrReplace(value, workspace)
write_hash_into_reduced_can_workspace(state, workspace, reduction_mode, key)
def test_only_one_workspace_saving(self):
ws1_name = "ws1"
ADS.addOrReplace(ws1_name, CreateSampleWorkspace(OutputWorkspace=ws1_name))
project_saver = projectsaver.ProjectSaver(project_file_ext)
file_name = working_directory + "/" + os.path.basename(working_directory) + project_file_ext
workspaces_string = "\"workspaces\": [\"ws1\"]"
plots_string = "\"plots\": []"
project_saver.save_project(workspace_to_save=[ws1_name], directory=working_directory)
# Check project file is saved correctly
f = open(file_name, "r")
file_string = f.read()
self.assertTrue(workspaces_string in file_string)
self.assertTrue(plots_string in file_string)
# Check workspace is saved
list_of_files = os.listdir(working_directory)
self.assertEqual(len(list_of_files), 2)
self.assertTrue(os.path.basename(working_directory) + project_file_ext in list_of_files)
self.assertTrue(ws1_name + ".nxs" in list_of_files)
def _prepare_workspaces(number_of_workspaces, tagged_workspace_names=None, state=None):
create_name = "CreateSampleWorkspace"
create_options = {"OutputWorkspace": "test",
"NumBanks": 1,
"BankPixelWidth": 2,
"XMin": 1,
"XMax": 10,
"BinWidth": 2}
create_alg = create_unmanaged_algorithm(create_name, **create_options)
for index in range(number_of_workspaces):
create_alg.execute()
workspace = create_alg.getProperty("OutputWorkspace").value
workspace_name = "test" + "_" + str(index)
AnalysisDataService.addOrReplace(workspace_name, workspace)
if tagged_workspace_names is not None:
for key, value in tagged_workspace_names.items():
create_alg.execute()
workspace = create_alg.getProperty("OutputWorkspace").value
AnalysisDataService.addOrReplace(value, workspace)
write_hash_into_reduced_can_workspace(state, workspace, key)
def _loadRun(self, run, isTrans):
"""Load a run as an event workspace if slicing is requested, or a histogram
workspace otherwise. Transmission runs are always loaded as histogram workspaces."""
event_mode = not isTrans and self._slicingEnabled()
args = {'InputRunList': [run], 'EventMode': event_mode}
alg = self.createChildAlgorithm('ReflectometryISISPreprocess', **args)
alg.setRethrows(True)
alg.execute()
ws = alg.getProperty('OutputWorkspace').value
monitor_ws = alg.getProperty('MonitorWorkspace').value
workspace_name = self._prefixedName(_getRunNumberAsString(ws), isTrans)
AnalysisDataService.addOrReplace(workspace_name, ws)
if monitor_ws:
AnalysisDataService.addOrReplace(_monitorWorkspace(workspace_name),
monitor_ws)
if event_mode:
_throwIfNotValidReflectometryEventWorkspace(workspace_name)
self.log().information('Loaded event workspace ' + workspace_name)
else:
self.log().information('Loaded workspace ' + workspace_name)
return workspace_name
def _fill_s_1d_workspace(self, s_points=None, workspace=None, protons_number=None, nucleons_number=None):
"""
Puts 1D S into workspace.
:param protons_number: number of protons in the given type fo atom
:param nucleons_number: number of nucleons in the given type of atom
:param s_points: dynamical factor for the given atom
:param workspace: workspace to be filled with S
"""
if protons_number is not None:
s_points = s_points * self._scale * self._get_cross_section(protons_number=protons_number,
nucleons_number=nucleons_number)
dim = 1
length = s_points.size
wrk = WorkspaceFactory.create("Workspace2D", NVectors=dim, XLength=length + 1, YLength=length)
for i in range(dim):
wrk.getSpectrum(i).setDetectorID(i + 1)
wrk.setX(0, self._bins)
wrk.setY(0, s_points)
AnalysisDataService.addOrReplace(workspace, wrk)
# Set correct units on workspace
self._set_workspace_units(wrk=workspace)
def _fill_s_1d_workspace(self, s_points=None, workspace=None, protons_number=None, nucleons_number=None):
"""
Puts 1D S into workspace.
:param protons_number: number of protons in the given type fo atom
:param nucleons_number: number of nucleons in the given type of atom
:param s_points: dynamical factor for the given atom
:param workspace: workspace to be filled with S
"""
if protons_number is not None:
s_points = s_points * self._scale * self._get_cross_section(protons_number=protons_number,
nucleons_number=nucleons_number)
dim = 1
length = s_points.size
wrk = WorkspaceFactory.create("Workspace2D", NVectors=dim, XLength=length + 1, YLength=length)
for i in range(dim):
wrk.getSpectrum(i).setDetectorID(i + 1)
wrk.setX(0, self._bins)
wrk.setY(0, s_points)
AnalysisDataService.addOrReplace(workspace, wrk)
# Set correct units on workspace
self._set_workspace_units(wrk=workspace)
def _group_workspaces(self, workspaces, output_ws_name):
"""
Groups all the given workspaces into a group with the given name. If the group
already exists it will add them to that group.
"""
if not self.getProperty(Prop.GROUP_TOF).value:
return
if AnalysisDataService.doesExist(output_ws_name):
ws_group = AnalysisDataService.retrieve(output_ws_name)
if not isinstance(ws_group, WorkspaceGroup):
raise RuntimeError('Cannot group TOF workspaces, a workspace called TOF already exists')
else:
for ws in workspaces:
if ws not in ws_group:
ws_group.add(ws)
else:
alg = self.createChildAlgorithm("GroupWorkspaces")
alg.setProperty("InputWorkspaces", workspaces)
alg.setProperty("OutputWorkspace", output_ws_name)
alg.execute()
ws_group = alg.getProperty("OutputWorkspace").value
AnalysisDataService.addOrReplace(output_ws_name, ws_group)
return ws_group
def test_exportFileAppend2(self):
""" Test to export file in appending mode
In this case, the original file will be renamed and a new file will
be creatd
"""
import datetime
import time
# Generate the matrix workspace with some logs
ws = self.createTestWorkspace()
AnalysisDataService.addOrReplace("TestMatrixWS", ws)
# Test algorithm
# create new file
alg_test = run_algorithm("ExportExperimentLog",
InputWorkspace = "TestMatrixWS",
OutputFilename = "TestRecord.txt",
SampleLogNames = ["run_number", "duration", "proton_charge"],
SampleLogTitles = ["RUN", "Duration", "ProtonCharge"],
SampleLogOperation = [None, None, "sum"],
FileMode = "new")
# append
alg_test = run_algorithm("ExportExperimentLog",
InputWorkspace = "TestMatrixWS",
OutputFilename = "TestRecord.txt",
SampleLogNames = ["run_number", "duration", "proton_charge", "SensorA"],
SampleLogTitles = ["RUN", "Duration", "ProtonCharge", "SensorA"],
SampleLogOperation = [None, None, "sum", "0"],
FileMode = "append")
# Validate
self.assertTrue(alg_test.isExecuted())
# Locate file
outfilename = alg_test.getProperty("OutputFilename").value
try:
print "Output file is %s. " % (outfilename)
ifile = open(outfilename)
lines = ifile.readlines()
ifile.close()
except IOError as err:
print "Unable to open file %s. " % (outfilename)
self.assertTrue(False)
return
# Last line cannot be empty, i.e., before EOF '\n' is not allowed
lastline = lines[-1]
self.assertTrue(len(lastline.strip()) > 0)
# Number of lines
self.assertEquals(len(lines), 2)
# Check line
firstdataline = lines[1]
terms = firstdataline.strip().split("\t")
self.assertEquals(len(terms), 4)
# Locate the previos file
# Rename old file and reset the file mode
# Rename the old one: split path from file, new name, and rename
fileName, fileExtension = os.path.splitext(outfilename)
now = datetime.datetime.now()
nowstr = time.strftime("%Y_%B_%d_%H_%M")
oldfilename = fileName + "_" + nowstr + fileExtension
print "Saved old file is %s. " % (oldfilename)
self.assertTrue(os.path.exists(oldfilename))
# Remove generated files
os.remove(outfilename)
os.remove(oldfilename)
AnalysisDataService.remove("TestMatrixWS")
return
def PyExec(self):
fn = self.getPropertyValue("Filename")
wsn = self.getPropertyValue("OutputWorkspace")
monitor_workspace_name = self.getPropertyValue("OutputMonitorWorkspace")
if monitor_workspace_name == "":
self.setPropertyValue("OutputMonitorWorkspace", wsn+'_Monitors')
#print (fn, wsn)
self.override_angle = self.getPropertyValue("AngleOverride")
self.fxml = self.getPropertyValue("InstrumentXML")
#load data
parms_dict, det_udet, det_count, det_tbc, data = self.read_file(fn)
nrows=int(parms_dict['NDET'])
#nbins=int(parms_dict['NTC'])
xdata = np.array(det_tbc)
xdata_mon = np.linspace(xdata[0],xdata[-1], len(xdata))
ydata=data.astype(np.float)
ydata=ydata.reshape(nrows,-1)
edata=np.sqrt(ydata)
#CreateWorkspace(OutputWorkspace=wsn,DataX=xdata,DataY=ydata,DataE=edata,
# NSpec=nrows,UnitX='TOF',WorkspaceTitle='Data',YUnitLabel='Counts')
nr,nc=ydata.shape
ws = WorkspaceFactory.create("Workspace2D", NVectors=nr,
XLength=nc+1, YLength=nc)
for i in range(nrows):
ws.setX(i, xdata)
ws.setY(i, ydata[i])
ws.setE(i, edata[i])
ws.getAxis(0).setUnit('tof')
AnalysisDataService.addOrReplace(wsn,ws)
#self.setProperty("OutputWorkspace", wsn)
#print ("ws:", wsn)
#ws=mtd[wsn]
# fix the x values for the monitor
for i in range(nrows-2,nrows):
ws.setX(i,xdata_mon)
self.log().information("set detector IDs")
#set detetector IDs
for i in range(nrows):
ws.getSpectrum(i).setDetectorID(det_udet[i])
#Sample_logs the header values are written into the sample logs
log_names=[str(sl.encode('ascii','ignore').decode()) for sl in parms_dict.keys()]
log_values=[str(sl.encode('ascii','ignore').decode()) if isinstance(sl,UnicodeType) else str(sl) for sl in parms_dict.values()]
for i in range(len(log_values)):
if ('nan' in log_values[i]) or ('NaN' in log_values[i]):
log_values[i] = '-1.0'
AddSampleLogMultiple(Workspace=wsn, LogNames=log_names,LogValues=log_values)
SetGoniometer(Workspace=wsn, Goniometers='Universal')
if (self.fxml == ""):
LoadInstrument(Workspace=wsn, InstrumentName = "Exed", RewriteSpectraMap= True)
else:
LoadInstrument(Workspace=wsn, Filename = self.fxml, RewriteSpectraMap= True)
try:
RotateInstrumentComponent(Workspace=wsn,
ComponentName='Tank',
Y=1,
Angle=-float(parms_dict['phi'].encode('ascii','ignore')),
RelativeRotation=False)
except:
self.log().warning("The instrument does not contain a 'Tank' component. "
"This means that you are using a custom XML instrument definition. "
"OMEGA_MAG will be ignored.")
self.log().warning("Please make sure that the detector positions in the instrument definition are correct.")
# Separate monitors into seperate workspace
__temp_monitors = ExtractSpectra(InputWorkspace = wsn, WorkspaceIndexList = ','.join([str(s) for s in range(nrows-2, nrows)]),
OutputWorkspace = self.getPropertyValue("OutputMonitorWorkspace"))
# ExtractSpectra(InputWorkspace = wsn, WorkspaceIndexList = ','.join([str(s) for s in range(nrows-2, nrows)]),
# OutputWorkspace = wsn + '_Monitors')
MaskDetectors(Workspace = wsn, WorkspaceIndexList = ','.join([str(s) for s in range(nrows-2, nrows)]))
RemoveMaskedSpectra(InputWorkspace = wsn, OutputWorkspace = wsn)
self.setProperty("OutputWorkspace", wsn)
self.setProperty("OutputMonitorWorkspace", __temp_monitors)
def create_absorption_input( # noqa: C901
filename,
props=None,
num_wl_bins=1000,
material={},
geometry={},
environment={},
opt_wl_min=0,
opt_wl_max=Property.EMPTY_DBL,
metaws=None,
):
"""
Create an input workspace for carpenter or other absorption corrections
:param filename: Input file to retrieve properties from the sample log
:param props: PropertyManager of run characterizations, obtained from PDDetermineCharacterizations
:param num_wl_bins: The number of wavelength bins used for absorption correction
:param material: Optional material to use in SetSample
:param geometry: Optional geometry to use in SetSample
:param environment: Optional environment to use in SetSample
:param opt_wl_min: Optional minimum wavelength. If specified, this is used instead of from the props
:param opt_wl_max: Optional maximum wavelength. If specified, this is used instead of from the props
:param metaws: Optional workspace name with metadata to use for donor workspace instead of reading from filename
:return: Name of the donor workspace created
"""
def confirmProps(props):
'''This function will throw an exception if the PropertyManager
is not defined correctly. It should only be called if the value
is needed.'''
if props is None:
raise ValueError(
"props is required to create donor workspace, props is None")
if not isinstance(props, PropertyManager):
raise ValueError("props must be a PropertyManager object")
log = Logger('CreateAbsorptionInput')
# Load from file if no workspace with metadata has been given, otherwise avoid a duplicate load with the metaws
absName = metaws
if metaws is None:
absName = '__{}_abs'.format(_getBasename(filename))
allowed_log = ",".join([
'SampleFormula', 'SampleDensity',
"BL11A:CS:ITEMS:HeightInContainerUnits", "SampleContainer",
"SampleMass"
])
Load(Filename=filename,
OutputWorkspace=absName,
MetaDataOnly=True,
AllowList=allowed_log)
# attempt to get the wavelength from the function parameters
if opt_wl_min > 0.:
wl_min = opt_wl_min
else:
# or get it from the PropertyManager
confirmProps(props)
wl_min = props['wavelength_min'].value
if opt_wl_max != Property.EMPTY_DBL:
wl_max = opt_wl_max
else:
# or get it from the PropertyManager
confirmProps(props)
wl_max = props['wavelength_max'].value # unset value is 0.
# if it isn't found by this point, guess it from the time-of-flight range
if wl_min == 0. or wl_max == 0.:
confirmProps(props)
tof_min = props['tof_min'].value
tof_max = props['tof_max'].value
if tof_min >= 0. and tof_max > tof_min:
log.information('TOF range is {} to {} microseconds'.format(
tof_min, tof_max))
# determine L1
instr = mtd[absName].getInstrument()
L1 = instr.getSource().getDistance(instr.getSample())
# determine L2 range
PreprocessDetectorsToMD(InputWorkspace=absName,
OutputWorkspace=absName + '_dets',
GetMaskState=False)
L2 = mtd[absName + '_dets'].column('L2')
Lmin = np.min(L2) + L1
Lmax = np.max(L2) + L1
DeleteWorkspace(Workspace=absName + '_dets')
log.information('Distance range is {} to {} meters'.format(
Lmin, Lmax))
# wavelength is h*TOF / m_n * L values copied from Kernel/PhysicalConstants.h
usec_to_sec = 1.e-6
meter_to_angstrom = 1.e10
h_m_n = meter_to_angstrom * usec_to_sec * 6.62606896e-34 / 1.674927211e-27
if wl_min == 0.:
wl_min = h_m_n * tof_min / Lmax
if wl_max == 0.:
wl_max = h_m_n * tof_max / Lmin
# there isn't a good way to guess it so error out
if wl_max <= wl_min:
DeleteWorkspace(Workspace=absName) # no longer needed
raise RuntimeError('Invalid wavelength range min={}A max={}A'.format(
wl_min, wl_max))
log.information('Using wavelength range min={}A max={}A'.format(
wl_min, wl_max))
absorptionWS = WorkspaceFactory.create(
mtd[absName],
NVectors=mtd[absName].getNumberHistograms(),
XLength=num_wl_bins + 1,
YLength=num_wl_bins)
xaxis = np.arange(0., float(num_wl_bins + 1)) * (wl_max - wl_min) / (
num_wl_bins) + wl_min
for i in range(absorptionWS.getNumberHistograms()):
absorptionWS.setX(i, xaxis)
absorptionWS.getAxis(0).setUnit('Wavelength')
# this effectively deletes the metadata only workspace
AnalysisDataService.addOrReplace(absName, absorptionWS)
# cleanup inputs before delegating work
if not material:
material = {}
if not geometry:
geometry = {}
if not environment:
environment = {}
# Make sure one is set before calling SetSample
if material or geometry or environment:
mantid.simpleapi.SetSampleFromLogs(InputWorkspace=absName,
Material=material,
Geometry=geometry,
Environment=environment)
return absName
def store_in_ads_as_hidden(workspace_name, workspace):
AnalysisDataService.addOrReplace(workspace_name, workspace)
def store_in_ads_as_hidden(workspace_name, workspace):
AnalysisDataService.addOrReplace(workspace_name, workspace)
def test_confirm_all_workspaces_loaded(self):
ws1_name = "ws1"
ADS.addOrReplace(ws1_name, CreateSampleWorkspace(OutputWorkspace=ws1_name))
self.assertTrue(projectloader._confirm_all_workspaces_loaded(workspaces_to_confirm=[ws1_name]))
def setUp(self):
ws1_name = "ws1"
ADS.addOrReplace(ws1_name, CreateSampleWorkspace(OutputWorkspace=ws1_name))
project_saver = projectsaver.ProjectSaver(project_file_ext)
project_saver.save_project(workspace_to_save=[ws1_name], file_name=working_project_file)
