def create_test_workspace(ws_name=None,
time_series_logs=None,
string_value_logs=None):
"""
Create a test workspace.
:param ws_name: An optional name for the workspace
:param time_series_logs: A set of (name, (values,...))
:param string_value_logs: A set of (name, value) pairs
:return: The new workspace
"""
fake_ws = WorkspaceFactory.create('Workspace2D', 1, 1, 1)
run = fake_ws.run()
if time_series_logs is not None:
for name, values in time_series_logs:
tsp = FloatTimeSeriesProperty(name)
for item in values:
try:
time, value = item[0], item[1]
except TypeError:
time, value = "2000-05-01T12:00:00", item
tsp.addValue(time, value)
run.addProperty(name, tsp, replace=True)
if string_value_logs is not None:
for name, value in string_value_logs:
run.addProperty(name,
StringPropertyWithValue(name, value),
replace=True)
ws_name = ws_name if ws_name is not None else 'fitting_context_model_test'
AnalysisDataService.Instance().addOrReplace(ws_name, fake_ws)
return fake_ws
def add_metadata(self, ws, metadata, data):
"""Adds metadata to the workspace"""
run = ws.getRun()
# Just copy all metadata in the file
for key in metadata.keys():
run.addProperty(key, str(metadata[key]), True)
# Add correct start and end time
start_time = np.datetime64(
datetime.datetime.strptime(
metadata['time'] + ' ' + metadata['date'],
'%I:%M:%S %p %m/%d/%Y'))
run.addProperty('start_time', str(start_time), True)
# Create time array for time series logs
time_array = start_time + np.cumsum(
data['time'], dtype=np.int64) * np.timedelta64(1, 's')
run.addProperty('end_time', str(time_array[-1]), True)
run.addProperty(
'duration',
float((time_array[-1] - time_array[0]) / np.timedelta64(1, 's')),
True)
# Create time series logs for the scan variables
for name in data.dtype.names:
if 'anode' not in name:
log = FloatTimeSeriesProperty(name)
for t, v in zip(time_array, data[name]):
log.addValue(t, v)
run[name] = log
def test_timestd(self):
"""
"""
run = Run()
start_time = DateAndTime("2008-12-18T17:58:38")
nanosec = 1000000000
# === Float type ===
temp1 = FloatTimeSeriesProperty("TEMP1")
vals = np.arange(10) * 2.
for i in range(10):
temp1.addValue(start_time + i * nanosec, vals[i])
run.addProperty(temp1.name, temp1, True)
# ignore the last value
expected = vals[:-1].std()
self.assertEqual(run.getTimeAveragedStd("TEMP1"), expected)
def setUp(self):
if self._test_ws is not None:
return
alg = run_algorithm('CreateWorkspace',
DataX=[1, 2, 3, 4, 5],
DataY=[1, 2, 3, 4, 5],
NSpec=1,
child=True)
ws = alg.getProperty("OutputWorkspace").value
run = ws.run()
start_time = DateAndTime("2008-12-18T17:58:38")
nanosec = 1000000000
# === Float type ===
temp1 = FloatTimeSeriesProperty("TEMP1")
tempvalue = -0.00161
for i in range(self._ntemp):
temp1.addValue(start_time + i * nanosec, tempvalue)
run.addProperty(temp1.name, temp1, True)
# === Int type ===
raw_frames = Int64TimeSeriesProperty("raw_frames")
values = [17, 1436, 2942, 4448, 5955, 7461]
for value in values:
raw_frames.addValue(start_time + i * nanosec, value)
run.addProperty(raw_frames.name, raw_frames, True)
# === String type ===
icp_event = temp1 = StringTimeSeriesProperty("icp_event")
values = [
'CHANGE_PERIOD 1',
'START_COLLECTION PERIOD 1 GF 0 RF 0 GUAH 0.000000', 'BEGIN',
'STOP_COLLECTION PERIOD 1 GF 1053 RF 1053 GUAH 0.000000 DUR 22'
]
for value in values:
icp_event.addValue(start_time + i * nanosec, value)
run.addProperty(icp_event.name, icp_event, True)
# === Boolean type ===
period_1 = temp1 = BoolTimeSeriesProperty("period 1")
values = [True]
for value in values:
period_1.addValue(start_time + i * nanosec, value)
run.addProperty(period_1.name, period_1, True)
self.__class__._test_ws = ws
def add_logs(workspace_name, logs):
"""
Add a list of logs to a workspace
:param workspace_name: A workspace to contain the logs
:param logs: A list of logs and values
:return: The workspace reference
"""
workspace = create_test_workspace(workspace_name)
run = workspace.run()
# populate with log data
dt_format = "%Y-%m-%dT%H:%M:%S"
for name, values in logs:
tsp = FloatTimeSeriesProperty(name)
time = datetime.datetime.strptime("2019-05-30T09:00:00", dt_format)
for value in values:
tsp.addValue(time.strftime(dt_format), float(value))
time += datetime.timedelta(seconds=5)
run.addProperty(name, tsp, replace=True)
return workspace
def setUp(self):
if self._test_ws is not None:
return
alg = run_algorithm('CreateWorkspace', DataX=[1,2,3,4,5], DataY=[1,2,3,4,5],NSpec=1, child=True)
ws = alg.getProperty("OutputWorkspace").value
run = ws.run()
start_time = DateAndTime("2008-12-18T17:58:38")
nanosec = 1000000000
# === Float type ===
temp1 = FloatTimeSeriesProperty("TEMP1")
tempvalue = -0.00161
for i in range(self._ntemp):
temp1.addValue(start_time + i*nanosec, tempvalue)
run.addProperty(temp1.name, temp1,True)
# === Int type ===
raw_frames = Int64TimeSeriesProperty("raw_frames")
values = [17,1436,2942,4448,5955,7461]
for value in values:
raw_frames.addValue(start_time + i*nanosec, value)
run.addProperty(raw_frames.name, raw_frames,True)
# === String type ===
icp_event = temp1 = StringTimeSeriesProperty("icp_event")
values = ['CHANGE_PERIOD 1','START_COLLECTION PERIOD 1 GF 0 RF 0 GUAH 0.000000',
'BEGIN','STOP_COLLECTION PERIOD 1 GF 1053 RF 1053 GUAH 0.000000 DUR 22']
for value in values:
icp_event.addValue(start_time + i*nanosec, value)
run.addProperty(icp_event.name, icp_event,True)
# === Boolean type ===
period_1 = temp1 = BoolTimeSeriesProperty("period 1")
values = [True]
for value in values:
period_1.addValue(start_time + i*nanosec, value)
run.addProperty(period_1.name, period_1,True)
self.__class__._test_ws = ws
def add_metadata(self, ws, metadata, data):
"""Adds metadata to the workspace"""
run = ws.getRun()
# Just copy all metadata in the file
for key in metadata.keys():
run.addProperty(key, str(metadata[key]), True)
# Add correct start and end time
start_time = np.datetime64(datetime.datetime.strptime(metadata['time']+' '+metadata['date'], '%I:%M:%S %p %m/%d/%Y'))
run.addProperty('start_time', str(start_time), True)
# Create time array for time series logs
time_array = start_time + np.cumsum(data['time'], dtype=np.int64)*np.timedelta64(1,'s')
run.addProperty('end_time', str(time_array[-1]), True)
run.addProperty('duration', float((time_array[-1]-time_array[0])/np.timedelta64(1, 's')), True)
# Create time series logs for the scan variables
for name in data.dtype.names:
if 'anode' not in name:
log = FloatTimeSeriesProperty(name)
for t, v in zip(time_array, data[name]):
log.addValue(t, v)
run[name]=log
def setUp(self):
if self.__class__._source is not None:
return
height = FloatTimeSeriesProperty("height")
height.addValue("2007-11-30T16:17:00", 1)
height.addValue("2007-11-30T16:17:10", 2)
height.addValue("2007-11-30T16:17:20", 3)
height.addValue("2007-11-30T16:17:30", 4)
height.addValue("2007-11-30T16:17:40", 5)
filter = BoolTimeSeriesProperty("filter")
filter.addValue("2007-11-30T16:16:50", False)
filter.addValue("2007-11-30T16:17:25", True)
filter.addValue("2007-11-30T16:17:39", False)
self.__class__._source = height
self.__class__._filter = filter
def setUp(self):
if self.__class__._source is not None:
return
height = FloatTimeSeriesProperty("height")
height.addValue("2007-11-30T16:17:00",1)
height.addValue("2007-11-30T16:17:10",2)
height.addValue("2007-11-30T16:17:20",3)
height.addValue("2007-11-30T16:17:30",4)
height.addValue("2007-11-30T16:17:40",5)
filter = BoolTimeSeriesProperty("filter")
filter.addValue("2007-11-30T16:16:50",False)
filter.addValue("2007-11-30T16:17:25",True)
filter.addValue("2007-11-30T16:17:39",False)
self.__class__._source = height
self.__class__._filter = filter
def test_addFilter_filters_log(self):
height_log = FloatTimeSeriesProperty("height_log")
height_log.addValue("2008-Jun-17 11:10:44", -0.86526)
height_log.addValue("2008-Jun-17 11:10:45", -1.17843)
height_log.addValue("2008-Jun-17 11:10:47", -1.27995)
height_log.addValue("2008-Jun-17 11:20:15", -1.38216)
height_log.addValue("2008-Jun-17 11:20:16", -1.87435)
height_log.addValue("2008-Jun-17 11:20:17", -2.70547)
height_log.addValue("2008-Jun-17 11:20:19", -2.99125)
height_log.addValue("2008-Jun-17 11:20:20", -3)
height_log.addValue("2008-Jun-17 11:20:27", -2.98519)
height_log.addValue("2008-Jun-17 11:20:29", -2.68904)
period_log = BoolTimeSeriesProperty("period 7")
period_log.addValue("2008-Jun-17 11:11:13", False)
period_log.addValue("2008-Jun-17 11:11:13", False)
period_log.addValue("2008-Jun-17 11:11:18", False)
period_log.addValue("2008-Jun-17 11:11:30", False)
period_log.addValue("2008-Jun-17 11:11:42", False)
period_log.addValue("2008-Jun-17 11:11:52", False)
period_log.addValue("2008-Jun-17 11:12:01", False)
period_log.addValue("2008-Jun-17 11:12:11", False)
period_log.addValue("2008-Jun-17 11:12:21", True)
period_log.addValue("2008-Jun-17 11:12:32", False)
self.assertEquals(height_log.size(), 10)
filter = LogFilter(height_log)
filter.addFilter(period_log)
filtered = filter.data()
self.assertEquals(filtered.size(), 1)
def test_addFilter_filters_log(self):
height_log = FloatTimeSeriesProperty("height_log");
height_log.addValue("2008-Jun-17 11:10:44", -0.86526)
height_log.addValue("2008-Jun-17 11:10:45", -1.17843)
height_log.addValue("2008-Jun-17 11:10:47", -1.27995)
height_log.addValue("2008-Jun-17 11:20:15", -1.38216)
height_log.addValue("2008-Jun-17 11:20:16", -1.87435)
height_log.addValue("2008-Jun-17 11:20:17", -2.70547)
height_log.addValue("2008-Jun-17 11:20:19", -2.99125)
height_log.addValue("2008-Jun-17 11:20:20", -3);
height_log.addValue("2008-Jun-17 11:20:27", -2.98519)
height_log.addValue("2008-Jun-17 11:20:29", -2.68904)
period_log = BoolTimeSeriesProperty("period 7")
period_log.addValue("2008-Jun-17 11:11:13", False)
period_log.addValue("2008-Jun-17 11:11:13", False)
period_log.addValue("2008-Jun-17 11:11:18", False)
period_log.addValue("2008-Jun-17 11:11:30", False)
period_log.addValue("2008-Jun-17 11:11:42", False)
period_log.addValue("2008-Jun-17 11:11:52", False)
period_log.addValue("2008-Jun-17 11:12:01", False)
period_log.addValue("2008-Jun-17 11:12:11", False)
period_log.addValue("2008-Jun-17 11:12:21", True)
period_log.addValue("2008-Jun-17 11:12:32", False)
self.assertEquals(height_log.size(), 10);
filter = LogFilter(height_log)
filter.addFilter(period_log)
filtered = filter.data()
self.assertEquals(filtered.size(), 1)
ws = LoadMD('HB3A_exp0724_scan0182.nxs')
SetGoniometer(ws, Axis0='omega,0,1,0,-1', Axis1='chi,0,0,1,-1', Axis2='phi,0,1,0,-1', Average=False)
r = ws.getExperimentInfo(0).run()
for i in range(r.getNumGoniometers()):
print(i,r.getGoniometer(i).getEulerAngles('YZY'))
ws = LoadILLDiffraction(Filename='ILL/D20/000017.nxs')
SetGoniometer(ws, Axis0='omega.position,0,1,0,1', Average=False)
for i in range(ws.run().getNumGoniometers()):
print(f'{i} omega = {ws.run().getGoniometer(i).getEulerAngles("YZY")[0]:.1f}')
SetGoniometer(ws, Axis0='omega.position,0,1,0,1')
for i in range(ws.run().getNumGoniometers()):
print(f'{i} omega = {ws.run().getGoniometer(i).getEulerAngles("YZY")[0]:.1f}')
ws=LoadMD('ExternalData/Testing/Data/SystemTest/HB2C_WANDSCD_data.nxs')
s1 = ws.getExperimentInfo(0).run().getLogData('s1').value
s1_log = FloatTimeSeriesProperty('s1')
for n, v in enumerate(s1):
s1_log.addValue(n*1e6,v)
ws.getExperimentInfo(0).run()['s1'] = s1_log
ws.getExperimentInfo(0).run().getProperty('s1').units = 'deg'
SetGoniometer(ws, Axis0='s1,0,1,0,1', Average=False)
r = ws.getExperimentInfo(0).run()
for i in range(r.getNumGoniometers()):
print(f'{i} omega = {r.getGoniometer(i).getEulerAngles("YZY")[0]:.1f}')
def add_time_series_property(name, run, times, values):
log = FloatTimeSeriesProperty(name)
for t, v in zip(times, values):
log.addValue(t, v)
run[name] = log
def runTest(self):
S = np.random.random(32 * 240 * 100)
ConvertWANDSCDtoQTest_data = CreateMDHistoWorkspace(
Dimensionality=3,
Extents='0.5,32.5,0.5,240.5,0.5,100.5',
SignalInput=S.ravel('F'),
ErrorInput=np.sqrt(S.ravel('F')),
NumberOfBins='32,240,100',
Names='y,x,scanIndex',
Units='bin,bin,number')
ConvertWANDSCDtoQTest_dummy = CreateSingleValuedWorkspace()
LoadInstrument(ConvertWANDSCDtoQTest_dummy,
InstrumentName='WAND',
RewriteSpectraMap=False)
ConvertWANDSCDtoQTest_data.addExperimentInfo(
ConvertWANDSCDtoQTest_dummy)
log = FloatTimeSeriesProperty('s1')
for t, v in zip(range(100), np.arange(0, 50, 0.5)):
log.addValue(t, v)
ConvertWANDSCDtoQTest_data.getExperimentInfo(0).run()['s1'] = log
ConvertWANDSCDtoQTest_data.getExperimentInfo(0).run().addProperty(
'duration', [60.] * 100, True)
ConvertWANDSCDtoQTest_data.getExperimentInfo(0).run().addProperty(
'monitor_count', [120000.] * 100, True)
ConvertWANDSCDtoQTest_data.getExperimentInfo(0).run().addProperty(
'twotheta', list(np.linspace(np.pi * 2 / 3, 0, 240).repeat(32)),
True)
ConvertWANDSCDtoQTest_data.getExperimentInfo(0).run().addProperty(
'azimuthal', list(np.tile(np.linspace(-0.15, 0.15, 32), 240)),
True)
peaks = CreatePeaksWorkspace(NumberOfPeaks=0,
OutputType='LeanElasticPeak')
SetUB(ConvertWANDSCDtoQTest_data,
5,
5,
7,
90,
90,
120,
u=[-1, 0, 1],
v=[1, 0, 1])
SetGoniometer(ConvertWANDSCDtoQTest_data,
Axis0='s1,0,1,0,1',
Average=False)
CopySample(InputWorkspace=ConvertWANDSCDtoQTest_data,
OutputWorkspace=peaks,
CopyName=False,
CopyMaterial=False,
CopyEnvironment=False,
CopyShape=False,
CopyLattice=True)
Q = ConvertWANDSCDtoQ(InputWorkspace=ConvertWANDSCDtoQTest_data,
UBWorkspace=peaks,
Wavelength=1.486,
Frame='HKL',
Uproj='1,1,0',
Vproj='-1,1,0',
BinningDim0='-6.04,6.04,151',
BinningDim1='-6.04,6.04,151',
BinningDim2='-6.04,6.04,151')
data_norm = ConvertHFIRSCDtoMDE(ConvertWANDSCDtoQTest_data,
Wavelength=1.486,
MinValues='-6.04,-6.04,-6.04',
MaxValues='6.04,6.04,6.04')
HKL = ConvertQtoHKLMDHisto(data_norm,
PeaksWorkspace=peaks,
Uproj='1,1,0',
Vproj='-1,1,0',
Extents='-6.04,6.04,-6.04,6.04,-6.04,6.04',
Bins='151,151,151')
for i in range(HKL.getNumDims()):
print(HKL.getDimension(i).getUnits(), Q.getDimension(i).getUnits())
np.testing.assert_equal(
HKL.getDimension(i).getUnits(),
Q.getDimension(i).getUnits())
hkl_data = mtd["HKL"].getSignalArray()
Q_data = mtd["Q"].getSignalArray()
print(np.isnan(Q_data).sum())
print(np.isclose(hkl_data, 0).sum())
xaxis = mtd["HKL"].getXDimension()
yaxis = mtd["HKL"].getYDimension()
zaxis = mtd["HKL"].getZDimension()
x, y, z = np.meshgrid(
np.linspace(xaxis.getMinimum(), xaxis.getMaximum(),
xaxis.getNBins()),
np.linspace(yaxis.getMinimum(), yaxis.getMaximum(),
yaxis.getNBins()),
np.linspace(zaxis.getMinimum(), zaxis.getMaximum(),
zaxis.getNBins()),
indexing="ij",
copy=False,
)
print(
x[~np.isnan(Q_data)].mean(),
y[~np.isnan(Q_data)].mean(),
z[~np.isnan(Q_data)].mean(),
)
print(
x[~np.isclose(hkl_data, 0)].mean(),
y[~np.isclose(hkl_data, 0)].mean(),
z[~np.isclose(hkl_data, 0)].mean(),
)
np.testing.assert_almost_equal(x[~np.isnan(Q_data)].mean(),
x[~np.isclose(hkl_data, 0)].mean(),
decimal=2)
np.testing.assert_almost_equal(y[~np.isnan(Q_data)].mean(),
y[~np.isclose(hkl_data, 0)].mean(),
decimal=2)
np.testing.assert_almost_equal(z[~np.isnan(Q_data)].mean(),
z[~np.isclose(hkl_data, 0)].mean(),
decimal=1)
def setUpClass(cls):
def gaussian(x, y, z, x0, y0, z0, ox, oy, oz, A):
return A * np.exp(-(x - x0)**2 / (2 * ox**2) - (y - y0)**2 /
(2 * oy**2) - (z - z0)**2 / (2 * oz**2))
def peaks(i, j, k):
return gaussian(i, j, k, 16, 100, 50, 2, 2, 2, 20) + gaussian(
i, j, k, 16, 150, 50, 1, 1, 1, 10)
S = np.fromfunction(peaks, (32, 240, 100))
ConvertWANDSCDtoQTest_data = CreateMDHistoWorkspace(
Dimensionality=3,
Extents='0.5,32.5,0.5,240.5,0.5,100.5',
SignalInput=S.ravel('F'),
ErrorInput=np.sqrt(S.ravel('F')),
NumberOfBins='32,240,100',
Names='y,x,scanIndex',
Units='bin,bin,number')
ConvertWANDSCDtoQTest_dummy = CreateSingleValuedWorkspace()
ConvertWANDSCDtoQTest_data.addExperimentInfo(
ConvertWANDSCDtoQTest_dummy)
log = FloatTimeSeriesProperty('s1')
for t, v in zip(range(100), np.arange(0, 50, 0.5)):
log.addValue(t, v)
ConvertWANDSCDtoQTest_data.getExperimentInfo(0).run()['s1'] = log
ConvertWANDSCDtoQTest_data.getExperimentInfo(0).run().addProperty(
'duration', [60.] * 100, True)
ConvertWANDSCDtoQTest_data.getExperimentInfo(0).run().addProperty(
'monitor_count', [120000.] * 100, True)
ConvertWANDSCDtoQTest_data.getExperimentInfo(0).run().addProperty(
'twotheta', list(np.linspace(np.pi * 2 / 3, 0, 240).repeat(32)),
True)
ConvertWANDSCDtoQTest_data.getExperimentInfo(0).run().addProperty(
'azimuthal', list(np.tile(np.linspace(-0.15, 0.15, 32), 240)),
True)
SetUB(ConvertWANDSCDtoQTest_data,
5,
5,
7,
90,
90,
120,
u=[-1, 0, 1],
v=[1, 0, 1])
SetGoniometer(ConvertWANDSCDtoQTest_data,
Axis0='s1,0,1,0,1',
Average=False)
# Create Normalisation workspace
S = np.ones((32, 240, 1))
ConvertWANDSCDtoQTest_norm = CreateMDHistoWorkspace(
Dimensionality=3,
Extents='0.5,32.5,0.5,240.5,0.5,1.5',
SignalInput=S,
ErrorInput=S,
NumberOfBins='32,240,1',
Names='y,x,scanIndex',
Units='bin,bin,number')
ConvertWANDSCDtoQTest_dummy2 = CreateSingleValuedWorkspace()
ConvertWANDSCDtoQTest_norm.addExperimentInfo(
ConvertWANDSCDtoQTest_dummy2)
ConvertWANDSCDtoQTest_norm.getExperimentInfo(0).run().addProperty(
'monitor_count', [100000.], True)
