class PluginLoader(object):
extension = ".py"
def __init__(self, filepath):
if not _os.path.isfile(filepath):
raise ValueError(
"PluginLoader expects a single filename. '%s' does not point to an existing file"
% filepath)
if not filepath.endswith(self.extension):
raise ValueError(
"PluginLoader expects a filename ending with .py. '%s' does not have a .py extension"
% filepath)
self._filepath = filepath
self._logger = Logger("PluginLoader")
def run(self):
"""
Try and load the module we are pointing at and return
the module object.
Any ImportErrors raised are not caught and are passed
on to the caller
"""
pathname = self._filepath
name = _os.path.basename(pathname) # Including extension
name = _os.path.splitext(name)[0]
self._logger.debug("Loading python plugin %s" % pathname)
return _imp.load_source(name, pathname)
def __init__(self, parent_presenter, beam_centre_model=None):
self._view = None
self._parent_presenter = parent_presenter
self._logger = Logger("SANS")
self._beam_centre_model = BeamCentreModel(
) if not beam_centre_model else beam_centre_model
self._worker = BeamCentreAsync(parent_presenter=self)
def __init__(self,
project_file_name: Union[str, Path],
mode: HidraProjectFileMode = HidraProjectFileMode.READONLY):
"""
Initialization
:param project_file_name: project file name
:param mode: I/O mode
"""
# configure logging for this class
self._log = Logger(__name__)
# convert the mode to the enum
self._io_mode = HidraProjectFileMode.getMode(mode)
# check the file
if not project_file_name:
raise RuntimeError('Must supply a filename')
# force the file to be a Path
self._file_name = to_filepath(
project_file_name,
check_exists=bool(self._io_mode != HidraProjectFileMode.OVERWRITE))
self._checkFileAccess()
# open the file using h5py
self._project_h5 = h5py.File(self._file_name, mode=str(self._io_mode))
if self._io_mode == HidraProjectFileMode.OVERWRITE:
self._init_project()
class PluginLoader(object):
extension = ".py"
def __init__(self, filepath):
if not _os.path.isfile(filepath):
raise ValueError("PluginLoader expects a single filename. '%s' does not point to an existing file" % filepath)
if not filepath.endswith(self.extension):
raise ValueError("PluginLoader expects a filename ending with .py. '%s' does not have a .py extension" % filepath)
self._filepath = filepath
self._logger = Logger("PluginLoader")
def run(self):
"""
Try and load the module we are pointing at and return
the module object.
Any ImportErrors raised are not caught and are passed
on to the caller
"""
pathname = self._filepath
name = _os.path.basename(pathname) # Including extension
name = _os.path.splitext(name)[0]
self._logger.debug("Loading python plugin %s" % pathname)
return _imp.load_source(name, pathname)
def __init__(self, facility, view=None):
super(RunTabPresenter, self).__init__()
self._facility = facility
# Logger
self.sans_logger = Logger("SANS")
# Presenter needs to have a handle on the view since it delegates it
self._view = None
self.set_view(view)
# Models that are being used by the presenter
self._state_model = None
self._table_model = None
# Due to the nature of the DataProcessorWidget we need to provide an algorithm with at least one input
# workspace and at least one output workspace. Our SANS state approach is not compatible with this. Hence
# we provide a dummy workspace which is not used. We keep it invisible on the ADS and delete it when the
# main_presenter is deleted.
# This is not a nice solution but in line with the SANS dummy algorithm approach that we have provided
# for the
self._create_dummy_input_workspace()
# File information for the first input
self._file_information = None
# Settings diagnostic tab presenter
self._settings_diagnostic_tab_presenter = SettingsDiagnosticPresenter(
self)
# Masking table presenter
self._masking_table_presenter = MaskingTablePresenter(self)
# Beam centre presenter
self._beam_centre_presenter = BeamCentrePresenter(self)
def __init__(self, runObj):
self._log = Logger(__name__)
# verify the scan index exists
try:
if runObj['scan_index'].size() == 0:
raise RuntimeError('"scan_index" is empty')
except KeyError as e:
raise RuntimeError('"scan_index" does not exist') from e
# Get the time and value from the run object
scan_index_times = runObj['scan_index'].times # absolute times
scan_index_value = runObj['scan_index'].value
# TODO add final time from pcharge logs + 1s with scan_index=0
if np.unique(scan_index_value).size == 1:
raise RuntimeError('WARNING: only one scan_index value'
) # TODO should be something else
self.times = None
self.subruns = None
self.propertyFilters = list()
self.__generate_sub_run_splitter(scan_index_times, scan_index_value)
self.__correct_starting_scan_index_time(runObj)
self._createPropertyFilters()
def convert(self):
#Always reset these values before conversion.
self.Theta = None
self.flightpath = None
try:
if self.ui.InputVal.text() == "":
raise RuntimeError("Input value is required for conversion")
if float(self.ui.InputVal.text()) <= 0:
raise RuntimeError(
"Input value must be greater than 0 for conversion")
inOption = self.ui.inputUnits.currentText()
outOption = self.ui.outputUnits.currentText()
if self.ui.totalFlightPathInput.text():
self.flightpath = float(self.ui.totalFlightPathInput.text())
else:
self.flightpath = -1.0
if self.ui.scatteringAngleInput.text():
self.Theta = float(
self.ui.scatteringAngleInput.text()) * math.pi / 360.0
else:
self.Theta = -1.0
self.output = TofConverter.convertUnits.doConversion(
self.ui.InputVal.text(), inOption, outOption, self.Theta,
self.flightpath)
self.ui.convertedVal.clear()
self.ui.convertedVal.insert(str(self.output))
except (UnboundLocalError, ArithmeticError, ValueError,
RuntimeError) as err:
QMessageBox.warning(self, "TofConverter", str(err))
return
except Exception as exc:
Logger.error(exc)
return
def __init__(self,
view,
exit_code,
application='mantidplot',
traceback=''):
self.error_log = Logger("error")
self._view = view
self._exit_code = exit_code
self._application = application
self._traceback = traceback
self._view.set_report_callback(self.error_handler)
if not traceback:
traceback_file_path = os.path.join(
ConfigService.getAppDataDirectory(),
'{}_stacktrace.txt'.format(application))
try:
if os.path.isfile(traceback_file_path):
with open(traceback_file_path, 'r') as file:
self._traceback = file.readlines()
new_workspace_name = os.path.join(
ConfigService.getAppDataDirectory(),
'{}_stacktrace_sent.txt'.format(application))
os.rename(traceback_file_path, new_workspace_name)
except OSError:
pass
def PyExec(self):
self.logger = Logger("CentreFinder")
self.logger.notice("Starting centre finder routine...")
self.sample_scatter = self._get_cloned_workspace("SampleScatterWorkspace")
self.sample_scatter_monitor = self._get_cloned_workspace("SampleScatterMonitorWorkspace")
self.sample_transmission = self._get_cloned_workspace("SampleTransmissionWorkspace")
self.sample_direct = self._get_cloned_workspace("SampleDirectWorkspace")
self.can_scatter = self._get_cloned_workspace("CanScatterWorkspace")
self.can_scatter_monitor = self._get_cloned_workspace("CanScatterMonitorWorkspace")
self.can_transmission = self._get_cloned_workspace("CanTransmissionWorkspace")
self.can_direct = self._get_cloned_workspace("CanDirectWorkspace")
self.component = self.getProperty("Component").value
self.r_min = self.getProperty("RMin").value
self.r_max = self.getProperty("RMax").value
self.state = self._get_state()
instrument = self.sample_scatter.getInstrument()
self.scale_1 = 1.0 if instrument.getName() == 'LARMOR' else 1000
self.scale_2 = 1000
centre_1_hold, centre_2_hold = self._find_centres()
self.setProperty("Centre1", centre_1_hold)
self.setProperty("Centre2", centre_2_hold)
self.logger.notice("Centre coordinates updated: [{}, {}]".format(centre_1_hold * self.scale_1,
centre_2_hold * self.scale_2))
def _remove_job(self, trans_id, job_id=None, is_running=False):
"""
Abort job and/or stop transaction
@param trans_id: remote transaction ID
@param job_id: remote job ID
@param is_running: True if the job is currently running
"""
if is_running:
try:
# At this point we are authenticated so just purge
alg = AlgorithmManager.create("AbortRemoteJob", 1)
alg.initialize()
alg.setProperty("ComputeResource",
str(self._settings.compute_resource))
alg.setProperty("JobID", job_id)
alg.execute()
except:
Logger("cluster_status").error("Problem aborting job: %s" %
sys.exc_value)
try:
alg = AlgorithmManager.create("StopRemoteTransaction", 1)
alg.initialize()
alg.setProperty("ComputeResource",
str(self._settings.compute_resource))
alg.setProperty("TransactionID", trans_id)
alg.execute()
except:
Logger("cluster_status").error(
"Project stopping remote transaction: %s" % sys.exc_value)
self._update_content()
def __init__(self, facility, view=None):
super(RunTabPresenter, self).__init__()
self._facility = facility
# Logger
self.sans_logger = Logger("SANS")
# Name of grpah to output to
self.output_graph = 'SANS-Latest'
self.progress = 0
# Models that are being used by the presenter
self._state_model = None
self._table_model = TableModel()
# Presenter needs to have a handle on the view since it delegates it
self._view = None
self.set_view(view)
self._processing = False
self.work_handler = WorkHandler()
self.batch_process_runner = BatchProcessRunner(self.notify_progress, self.on_processing_finished, self.on_processing_error)
# File information for the first input
self._file_information = None
self._clipboard = []
# Settings diagnostic tab presenter
self._settings_diagnostic_tab_presenter = SettingsDiagnosticPresenter(self)
# Masking table presenter
self._masking_table_presenter = MaskingTablePresenter(self)
# Beam centre presenter
self._beam_centre_presenter = BeamCentrePresenter(self, WorkHandler, BeamCentreModel, SANSCentreFinder)
# Workspace Diagnostic page presenter
self._workspace_diagnostic_presenter = DiagnosticsPagePresenter(self, WorkHandler, run_integral, create_state, self._facility)
def __init__(self,
view,
exit_code: str,
application: str,
traceback: Optional[str] = None):
"""
:param view: A reference to the view managed by this presenter
:param exit_code: A string containing the exit_code of the failing application
:param application: A string containing the failing application name
:param traceback: An optional string containing a traceback dumped as JSON-encoded string
"""
self.error_log = Logger("errorreports")
self._view = view
self._exit_code = exit_code
self._application = application
self._traceback = traceback if traceback else ''
self._view.set_report_callback(self.error_handler)
self._view.moreDetailsButton.clicked.connect(self.show_more_details)
if not traceback:
traceback_file_path = os.path.join(
ConfigService.getAppDataDirectory(),
'{}_stacktrace.txt'.format(application))
try:
if os.path.isfile(traceback_file_path):
with open(traceback_file_path, 'r') as file:
self._traceback = file.readlines()
new_workspace_name = os.path.join(
ConfigService.getAppDataDirectory(),
'{}_stacktrace_sent.txt'.format(application))
os.rename(traceback_file_path, new_workspace_name)
except OSError:
pass
def __init__(self, parent_presenter):
super(BeamCentrePresenter, self).__init__()
self._view = None
self._parent_presenter = parent_presenter
self._work_handler = WorkHandler()
self._logger = Logger("SANS")
self._beam_centre_model = BeamCentreModel()
def __init__(self, filepath):
if not _os.path.isfile(filepath):
raise ValueError("PluginLoader expects a single filename. '%s' does not point to an existing file" % filepath)
if not filepath.endswith(self.extension):
raise ValueError("PluginLoader expects a filename ending with .py. '%s' does not have a .py extension" % filepath)
self._filepath = filepath
self._logger = Logger("PluginLoader")
def pre_process(self):
"""
Reduction steps that are meant to be executed only once per set
of data files. After this is executed, all files will go through
the list of reduction steps.
"""
Logger("Reducer").information("Setting up reduction options")
if self.setup_algorithm is not None:
alg = AlgorithmManager.create(self.setup_algorithm)
alg.initialize()
props = [p.name for p in alg.getProperties()]
for key in self.reduction_properties.keys():
if key in props:
try:
alg.setProperty(key, self.reduction_properties[key])
except:
msg = "Error setting %s=%s" % (
key, str(self.reduction_properties[key]))
msg += "\n %s" % sys.exc_value
Logger("Reducer").error(msg)
else:
Logger("Reducer").warning(
"Setup algorithm has no %s property" % key)
if "ReductionProperties" in props:
alg.setPropertyValue("ReductionProperties",
self.get_reduction_table_name())
alg.execute()
def __init__(self, parent_presenter, WorkHandler, BeamCentreModel,
SANSCentreFinder):
self._view = None
self._parent_presenter = parent_presenter
self._work_handler = WorkHandler()
self._logger = Logger("SANS")
self._beam_centre_model = BeamCentreModel(SANSCentreFinder)
class ErrorReporterPresenter(object):
def __init__(self, view, exit_code):
self.error_log = Logger("error")
self._view = view
self._exit_code = exit_code
self._view.action.connect(self.error_handler)
def error_handler(self, continue_working, share, name, email):
if share == 0:
errorReporter = ErrorReporter("mantidplot",
UsageService.getUpTime(),
self._exit_code, True, str(name),
str(email))
errorReporter.sendErrorReport()
elif share == 1:
errorReporter = ErrorReporter("mantidplot",
UsageService.getUpTime(),
self._exit_code, False, str(name),
str(email))
errorReporter.sendErrorReport()
if not continue_working:
self.error_log.error("Terminated by user.")
self._view.quit()
else:
self.error_log.error("Continue working.")
def show_view(self):
self._view.show()
def beam_center_gravitational_drop(beam_center_file, sdd=1.13):
'''
This method is used for correcting for gravitational drop
@param beam_center_file :: file where the beam center was found
@param sdd :: sample detector distance to apply the beam center
'''
def calculate_neutron_drop(path_length, wavelength):
'''
Calculate the gravitational drop of the neutrons
path_length in meters
wavelength in Angstrom
'''
wavelength *= 1e-10
neutron_mass = 1.674927211e-27
gravity = 9.80665
h_planck = 6.62606896e-34
l_2 = (gravity * neutron_mass**2 / (2.0 * h_planck**2 )) * path_length**2
return wavelength**2 * l_2
# Get beam center used in the previous reduction
pm = mantid.PropertyManagerDataService[ReductionSingleton().property_manager]
beam_center_x = pm['LatestBeamCenterX'].value
beam_center_y = pm['LatestBeamCenterY'].value
Logger("CommandInterface").information("Beam Center before: [%.2f, %.2f] pixels" % (beam_center_x, beam_center_y))
try:
# check if the workspace still exists
wsname = "__beam_finder_" + os.path.splitext(beam_center_file)[0]
ws = mantid.mtd[wsname]
Logger("CommandInterface").debug("Using Workspace: %s." % (wsname))
except KeyError:
# Let's try loading the file. For some reason the beamcenter ws is not there...
try:
ws = Load(beam_center_file)
Logger("CommandInterface").debug("Using filename %s." % (beam_center_file))
except IOError:
Logger("CommandInterface").error("Cannot read input file %s." % beam_center_file)
return
i = ws.getInstrument()
y_pixel_size_mm = i.getNumberParameter('y-pixel-size')[0]
Logger("CommandInterface").debug("Y Pixel size = %.2f mm" % y_pixel_size_mm)
y_pixel_size = y_pixel_size_mm * 1e-3 # In meters
distance_detector1 = i.getComponentByName("detector1").getPos()[2]
path_length = distance_detector1 - sdd
Logger("CommandInterface").debug("SDD detector1 = %.3f meters. SDD for wing = %.3f meters." % (distance_detector1, sdd))
Logger("CommandInterface").debug("Path length for gravitational drop = %.3f meters." % (path_length))
r = ws.run()
wavelength = r.getProperty("wavelength").value
Logger("CommandInterface").debug("Wavelength = %.2f A." % (wavelength))
drop = calculate_neutron_drop(path_length, wavelength)
Logger("CommandInterface").debug("Gravitational drop = %.6f meters." % (drop))
# 1 pixel -> y_pixel_size
# x pixel -> drop
drop_in_pixels = drop / y_pixel_size
new_beam_center_y = beam_center_y + drop_in_pixels
Logger("CommandInterface").information("Beam Center after: [%.2f, %.2f] pixels" % (beam_center_x, new_beam_center_y))
return beam_center_x, new_beam_center_y
def cluster_submit(self,
output_dir,
user,
pwd,
resource=None,
nodes=4,
cores_per_node=4,
job_name=None):
"""
Submit the reduction job to a cluster
@param output_dir: directory where the output data will be written
@param user: name of the user on the cluster
@param pwd: password of the user on the cluster
"""
Logger("scripter").notice("Preparing remote reduction job submission")
if HAS_MANTID:
# Generate reduction script and write it to file
scripts = self.to_batch()
for i in range(len(scripts)):
script = scripts[i]
script_name = "job_submission_%s.py" % i
lower_case_instr = self.instrument_name.lower()
job_name_lower = job_name.lower()
_job_name = job_name
if job_name is None or len(job_name) == 0:
_job_name = lower_case_instr
elif job_name_lower.find(lower_case_instr) >= 0:
_job_name = job_name.strip()
else:
_job_name = "%s_%s" % (lower_case_instr, job_name.strip())
# Make sure we have unique job names
if len(scripts) > 1:
_job_name += "_%s" % i
# Submit the job
# Note: keeping version 1 for now. See comment about
# versions in cluster_status.py
submit_cmd = "Authenticate(Version=1, ComputeResource='%s', " % resource
submit_cmd += "UserName='******', Password='******')\n" % (user, pwd)
# Note: keeping version 1 for now. See comment about
# versions in cluster_status.py
submit_cmd += "id=StartRemoteTransaction(Version=1, ComputeResource='%s')\n" % resource
# Note: keeping version 1 for now. See comment about
# versions in cluster_status.py
submit_cmd += "SubmitRemoteJob(Version=1, ComputeResource='%s', " % resource
submit_cmd += "TaskName='%s'," % _job_name
submit_cmd += "NumNodes=%s, CoresPerNode=%s, " % (
nodes, cores_per_node)
submit_cmd += "TransactionID=id, "
submit_cmd += "PythonScript=\"\"\"%s\"\"\", " % script
submit_cmd += "ScriptName='%s')" % script_name
mantidplot.runPythonScript(submit_cmd, True)
else:
Logger("scripter").error(
"Mantid is unavailable to submit a reduction job")
def find_beam_centre(self, state):
"""
This is called from the GUI and runs the find beam centre algorithm given a state model and a beam_centre_model object.
:param state: A SANS state object
:param beam_centre_model: An instance of the BeamCentreModel class.
:returns: The centre position found.
"""
centre_finder = self.SANSCentreFinder()
find_direction = None
if self.up_down and self.left_right:
find_direction = FindDirectionEnum.All
elif self.up_down:
find_direction = FindDirectionEnum.Up_Down
elif self.left_right:
find_direction = FindDirectionEnum.Left_Right
else:
logger = Logger("CentreFinder")
logger.notice("Have chosen no find direction exiting early")
return {"pos1": self.lab_pos_1, "pos2": self.lab_pos_2}
if self.q_min:
state.convert_to_q.q_min = self.q_min
if self.q_max:
state.convert_to_q.q_max = self.q_max
if self.COM:
centre = centre_finder(state,
r_min=self.r_min,
r_max=self.r_max,
max_iter=self.max_iterations,
x_start=self.lab_pos_1,
y_start=self.lab_pos_2,
tolerance=self.tolerance,
find_direction=find_direction,
reduction_method=False)
centre = centre_finder(state,
r_min=self.r_min,
r_max=self.r_max,
max_iter=self.max_iterations,
x_start=centre['pos1'],
y_start=centre['pos2'],
tolerance=self.tolerance,
find_direction=find_direction,
reduction_method=True,
verbose=self.verbose)
else:
centre = centre_finder(state,
r_min=self.r_min,
r_max=self.r_max,
max_iter=self.max_iterations,
x_start=self.lab_pos_1,
y_start=self.lab_pos_2,
tolerance=self.tolerance,
find_direction=find_direction,
reduction_method=True,
verbose=self.verbose)
return centre
def __init__(self, notify_progress, notify_done, notify_error):
super(BatchProcessRunner, self).__init__()
self.row_processed_signal.connect(notify_progress)
self.row_failed_signal.connect(notify_error)
self.notify_done = notify_done
self.batch_processor = SANSBatchReduction()
self._logger = Logger("SANS")
self._worker = None
def set_startTime(self):
""" Set the starting time and left slide bar
"""
inps = str(self.ui.lineEdit_3.text())
info_msg = "Starting time = %s" % (inps)
Logger("Filter_Events").information(info_msg)
xlim = self.ui.mainplot.get_xlim()
if inps == "":
# Empty. Use default
newtime0 = xlim[0]
else:
newtime0 = float(inps)
# Convert to integer slide value
ileftvalue = int((newtime0 - xlim[0]) / (xlim[1] - xlim[0]) * 100)
debug_msg = "iLeftSlide = %s" % str(ileftvalue)
Logger("Filter_Events").debug(debug_msg)
# Skip if same as origina
if ileftvalue == self._leftSlideValue:
return
# Set the value if out of range
resetT = True
if ileftvalue < 0:
# Minimum value as 0
ileftvalue = 0
elif ileftvalue > self._rightSlideValue:
# Maximum value as right slide value
ileftvalue = self._rightSlideValue
else:
resetT = False
if resetT is True:
newtime0 = xlim[0] + ileftvalue * (xlim[1] - xlim[0]) * 0.01
info_msg = "Corrected iLeftSlide = %s (vs. right = %s)" % (
str(ileftvalue), str(self._rightSlideValue))
Logger("Filter_Events").information(info_msg)
# Move the slide bar (left)
self._leftSlideValue = ileftvalue
# Move the vertical line
leftx = [newtime0, newtime0]
lefty = self.ui.mainplot.get_ylim()
setp(self.leftslideline, xdata=leftx, ydata=lefty)
self.ui.graphicsView.draw()
# Set the value to left slider
self.ui.horizontalSlider.setValue(self._leftSlideValue)
# Reset the value of line edit
if resetT is True:
self.ui.lineEdit_3.setText(str(newtime0))
return
def __init__(self, guess_centre):
"""
Takes a loaded reducer (sample information etc.) and the initial guess of the centre
position that are required for all later iterations
@param guess_centre: the starting position that the trial x and y are relative to
"""
self.logger = Logger("CentreFinder")
self._last_pos = guess_centre
self.detector = None
def SaveIqAscii(reducer=None, process=''):
""" Old command for backward compatibility """
output_dir = os.path.expanduser('~')
msg = "SaveIqAscii is not longer used:\n "
msg += "Please use 'SaveIq' instead\n "
msg += "Your output files are currently in %s" % output_dir
Logger.get("CommandInterface").warning(msg)
ReductionSingleton().reduction_properties["OutputDirectory"] = output_dir
ReductionSingleton().reduction_properties["ProcessInfo"] = str(process)
def find_data(file, instrument='', allow_multiple=False):
"""
Finds a file path for the specified data set, which can either be:
- a run number
- an absolute path
- a file name
@param file: file name or part of a file name
@param instrument: if supplied, FindNeXus will be tried as a last resort
"""
# First, assume a file name
file = str(file).strip()
# If we allow multiple files, users may use ; as a separator,
# which is incompatible with the FileFinder
n_files = 1
if allow_multiple:
file=file.replace(';',',')
toks = file.split(',')
n_files = len(toks)
instrument = str(instrument)
file_path = FileFinder.getFullPath(file)
if os.path.isfile(file_path):
return file_path
# Second, assume a run number and pass the instrument name as a hint
try:
# FileFinder doesn't like dashes...
instrument=instrument.replace('-','')
f = FileFinder.findRuns(instrument+file)
if os.path.isfile(f[0]):
if allow_multiple:
# Mantid returns its own list object type, so make a real list out if it
if len(f)==n_files:
return [i for i in f]
else: return f[0]
except:
# FileFinder couldn't make sense of the the supplied information
pass
# Third, assume a run number, without instrument name to take care of list of full paths
try:
f = FileFinder.findRuns(file)
if os.path.isfile(f[0]):
if allow_multiple:
# Mantid returns its own list object type, so make a real list out if it
if len(f)==n_files:
return [i for i in f]
else: return f[0]
except:
# FileFinder couldn't make sense of the the supplied information
pass
# If we didn't find anything, raise an exception
Logger.get('find_data').error("\n\nCould not find a file for %s: check your reduction parameters\n\n" % str(file))
raise RuntimeError, "Could not find a file for %s" % str(file)
def _loadFile(self, filename):
""" Load file or run
File will be loaded to a workspace shown in MantidPlot
"""
config = ConfigService
# Check input file name and output workspace name
if filename.isdigit() is True:
# Construct a file name from run number
runnumber = int(filename)
if runnumber <= 0:
error_msg = "Run number cannot be less or equal to zero. User gives %s. " % (
filename)
Logger("Filter_Events").error(error_msg)
return None
else:
ishort = config.getInstrument(self._instrument).shortName()
filename = "%s_%s" % (ishort, filename)
wsname = filename + "_event"
elif filename.count(".") > 0:
# A proper file name
wsname = os.path.splitext(os.path.split(filename)[1])[0]
elif filename.count("_") == 1:
# A short one as instrument_runnumber
iname = filename.split("_")[0]
str_runnumber = filename.split("_")[1]
if str_runnumber.isdigit() is True and int(str_runnumber) > 0:
# Acccepted format
ishort = config.getInstrument(iname).shortName()
wsname = "%s_%s_event" % (ishort, str_runnumber)
else:
# Non-supported
error_msg = "File name / run number in such format %s is not supported. " % (
filename)
Logger("Filter_Events").error(error_msg)
return None
else:
# Unsupported format
error_msg = "File name / run number in such format %s is not supported. " % (
filename)
Logger("Filter_Events").error(error_msg)
return None
# Load
try:
ws = api.Load(Filename=filename, OutputWorkspace=wsname)
except RuntimeError as e:
ws = None
return str(e)
return ws
def set_stopTime(self):
""" Set the starting time and left slide bar
"""
inps = str(self.ui.lineEdit_4.text())
info_msg = "Stopping time = %s" % (inps)
Logger("Filter_Events").information(info_msg)
xlim = self.ui.mainplot.get_xlim()
if inps == "":
# Empty. Use default
newtimef = xlim[1]
else:
# Parse
newtimef = float(inps)
# Convert to integer slide value
irightvalue = int((newtimef - xlim[0]) / (xlim[1] - xlim[0]) * 100)
info_msg = "iRightSlide = %s" % str(irightvalue)
Logger("Filter_Events").information(info_msg)
# Return if no change
if irightvalue == self._rightSlideValue:
return
# Correct value
resetT = True
if irightvalue > 100:
irightvalue = 100
elif irightvalue < self._leftSlideValue:
irightvalue = self._leftSlideValue
else:
resetT = False
if resetT is True:
newtimef = xlim[0] + irightvalue * (xlim[1] - xlim[0]) * 0.01
# Move the slide bar (right)
self._rightSlideValue = irightvalue
# Move the vertical line
rightx = [newtimef, newtimef]
righty = self.ui.mainplot.get_ylim()
setp(self.rightslideline, xdata=rightx, ydata=righty)
self.ui.graphicsView.draw()
# Set the value to left slider
self.ui.horizontalSlider_2.setValue(self._rightSlideValue)
# Reset to line edit
if resetT:
self.ui.lineEdit_4.setText(str(newtimef))
return
def __init__(self, ws, parent=None, window_flags=Qt.Window, model=None, view=None, conf=None):
"""
Create a presenter for controlling the slice display for a workspace
:param ws: Workspace containing data to display and slice
:param parent: An optional parent widget
:param window_flags: An optional set of window flags
:param model: A model to define slicing operations. If None uses SliceViewerModel
:param view: A view to display the operations. If None uses SliceViewerView
"""
model: SliceViewerModel = model if model else SliceViewerModel(ws)
self.view = view if view else SliceViewerView(self,
Dimensions.get_dimensions_info(ws),
model.can_normalize_workspace(), parent,
window_flags, conf)
super().__init__(ws, self.view.data_view, model)
self._logger = Logger("SliceViewer")
self._peaks_presenter: PeaksViewerCollectionPresenter = None
self._cutviewer_presenter = None
self.conf = conf
# Acts as a 'time capsule' to the properties of the model at this
# point in the execution. By the time the ADS observer calls self.replace_workspace,
# the workspace associated with self.model has already been changed.
self.initial_model_properties = model.get_properties()
self._new_plot_method, self.update_plot_data = self._decide_plot_update_methods()
self.view.setWindowTitle(self.model.get_title())
self.view.data_view.create_axes_orthogonal(
redraw_on_zoom=not WorkspaceInfo.can_support_dynamic_rebinning(self.model.ws))
if self.model.can_normalize_workspace():
self.view.data_view.set_normalization(ws)
self.view.data_view.norm_opts.currentTextChanged.connect(self.normalization_changed)
if not self.model.can_support_peaks_overlays():
self.view.data_view.disable_tool_button(ToolItemText.OVERLAY_PEAKS)
# check whether to enable non-orthog view
# don't know whether can always assume init with display indices (0,1) - so get sliceinfo
sliceinfo = self.get_sliceinfo()
if not sliceinfo.can_support_nonorthogonal_axes():
self.view.data_view.disable_tool_button(ToolItemText.NONORTHOGONAL_AXES)
if not self.model.can_support_non_axis_cuts():
self.view.data_view.disable_tool_button(ToolItemText.NONAXISALIGNEDCUTS)
self.view.data_view.help_button.clicked.connect(self.action_open_help_window)
self.refresh_view()
# Start the GUI with zoom selected.
self.view.data_view.activate_tool(ToolItemText.ZOOM)
self.ads_observer = SliceViewerADSObserver(self.replace_workspace, self.rename_workspace,
self.ADS_cleared, self.delete_workspace)
def __init__(self,
parent_presenter,
SANSCentreFinder,
work_handler=None,
beam_centre_model=None):
self._view = None
self._parent_presenter = parent_presenter
self._work_handler = WorkHandler(
) if not work_handler else work_handler
self._logger = Logger("SANS")
self._beam_centre_model = BeamCentreModel(
SANSCentreFinder) if not beam_centre_model else beam_centre_model
def set_maxLogValue(self):
""" Set maximum log value from line-edit
"""
inps = str(self.ui.lineEdit_6.text())
debug_msg = "Maximum Log Value = %s" % (inps)
Logger("Filter_Events").debug(debug_msg)
ylim = self.ui.mainplot.get_ylim()
if inps == "":
# Empty. Default to minY
newmaxY = ylim[1]
else:
# Parse
newmaxY = float(inps)
# Convert to integer slide value
imaxlogval = int((newmaxY - ylim[0]) / (ylim[1] - ylim[0]) * 100)
debug_msg = "iUpperSlide = %s" % str(imaxlogval)
Logger("Filter_Events").debug(debug_msg)
# Return if no change
if imaxlogval == self._upperSlideValue:
return
# Set to default if out of range
resetL = True
# if imaxlogval >= 100:
# imaxlogval = 100
if imaxlogval < self._lowerSlideValue:
imaxlogval = self._lowerSlideValue + 1
else:
resetL = False
# Set newmaxY if necessary
if resetL is True:
newmaxY = ylim[0] + imaxlogval * (ylim[1] - ylim[0]) * 0.01
# Move the vertical line
upperx = self.ui.mainplot.get_xlim()
uppery = [newmaxY, newmaxY]
setp(self.upperslideline, xdata=upperx, ydata=uppery)
self.ui.graphicsView.draw()
# Set the value to upper slider
self._upperSlideValue = imaxlogval
self.ui.verticalSlider.setValue(self._upperSlideValue)
# Set the value to editor if necessary
if resetL is True:
self.ui.lineEdit_6.setText(str(newmaxY))
return
def set_minLogValue(self):
""" Set the starting time and left slide bar
"""
debug_msg = "Minimum Log Value = %s" % (str(self.ui.lineEdit_5.text()))
Logger("Filter_Events").debug(debug_msg)
ylim = self.ui.mainplot.get_ylim()
if str(self.ui.lineEdit_5.text()) == "":
# Empty. Default to minY
newminY = ylim[0]
else:
# Non empty. Parse
newminY = float(self.ui.lineEdit_5.text())
# Convert to integer slide value
iminlogval = int((newminY - ylim[0]) / (ylim[1] - ylim[0]) * 100)
debug_msg = "ilowerSlide = %s" % str(iminlogval)
Logger("Filter_Events").debug(debug_msg)
# Return if no change
if iminlogval == self._lowerSlideValue:
return
# Set value if out of range
resetL = True
if iminlogval >= self._upperSlideValue:
iminlogval = self._upperSlideValue - 1
else:
resetL = False
if resetL is True:
newminY = ylim[0] + iminlogval * (ylim[1] - ylim[0]) * 0.01
# Move the vertical line
lowerx = self.ui.mainplot.get_xlim()
lowery = [newminY, newminY]
setp(self.lowerslideline, xdata=lowerx, ydata=lowery)
self.ui.graphicsView.draw()
# Move the slide bar (lower)
self._lowerSlideValue = iminlogval
debug_msg = "LineEdit5 set slide to %s" % str(self._lowerSlideValue)
Logger("Filter_Events").debug(debug_msg)
self.ui.verticalSlider_2.setValue(self._lowerSlideValue)
# Reset line Edit if using default
if resetL is True:
self.ui.lineEdit_5.setText(str(newminY))
return
def __init__(self, name="", facility=""):
self.instrument_name = name
self.facility_name = facility
self._observers = []
self._output_directory = os.path.expanduser('~')
if HAS_MANTID:
config = ConfigService.Instance()
try:
head, tail = os.path.split(config.getUserFilename())
if os.path.isdir(head):
self._output_directory = head
except:
Logger.get("scripter").debug("Could not get user filename")
def _check_data_list(data_list, scale):
if not isinstance(data_list, list):
error_msg = "The data_list parameter should be a list"
Logger("data_stitching").error(error_msg)
raise RuntimeError(error_msg)
if len(data_list) < 2:
error_msg = "The data_list parameter should contain at least two data sets"
Logger("data_stitching").error(error_msg)
raise RuntimeError(error_msg)
if isinstance(scale, list) and len(scale) != len(data_list):
error_msg = "If the scale parameter is provided as a list, it should have the same length as data_list"
Logger("data_stitching").error(error_msg)
raise RuntimeError(error_msg)
def __init__(self, filepath):
if not _os.path.isfile(filepath):
raise ValueError("PluginLoader expects a single filename. '%s' does not point to an existing file" % filepath)
if not filepath.endswith(self.extension):
raise ValueError("PluginLoader expects a filename ending with .py. '%s' does not have a .py extension" % filepath)
self._filepath = filepath
self._logger = Logger.get("PluginLoader")
def test_logger_creation_does_not_raise_an_error(self):
logger = Logger.get("LoggerTest")
self.assertTrue(isinstance(logger, Logger))
attrs = ['fatal', 'error','warning','notice', 'information', 'debug']
for att in attrs:
if not hasattr(logger, att):
self.fail("Logger object does not have the required attribute '%s'" % att)
def __init__(self, parent_presenter, WorkHandler, BeamCentreModel, SANSCentreFinder):
super(BeamCentrePresenter, self).__init__()
self._view = None
self._parent_presenter = parent_presenter
self._work_handler = WorkHandler()
self._logger = Logger("SANS")
self._beam_centre_model = BeamCentreModel(SANSCentreFinder)
def load_File(self):
""" Load the file by file name or run number
"""
# Get file name from line editor
filename = str(self.ui.lineEdit.text())
# Find out it is relative path or absolute path
#if os.path.abspath(filename) == filename:
# isabspath = True
#else:
# isabspath = False
dataws = self._loadFile(str(filename))
if dataws is None:
error_msg = "Unable to locate run %s in default directory %s." % (
filename, self._defaultdir)
Logger("Filter_Events").error(error_msg)
self._setErrorMsg(error_msg)
else:
self._importDataWorkspace(dataws)
self._defaultdir = os.path.dirname(str(filename))
# Reset GUI
self._resetGUI(resetfilerun=False)
return
def __init__(self, guess_centre):
"""
Takes a loaded reducer (sample information etc.) and the initial guess of the centre
position that are required for all later iterations
@param guess_centre: the starting position that the trial x and y are relative to
"""
self.logger = Logger("CentreFinder")
self._last_pos = guess_centre
self.detector = None
def find_beam_centre(self, state):
"""
This is called from the GUI and runs the find beam centre algorithm given a state model and a beam_centre_model object.
:param state: A SANS state object
:param beam_centre_model: An instance of the BeamCentreModel class.
:returns: The centre position found.
"""
centre_finder = self.SANSCentreFinder()
find_direction = None
if self.up_down and self.left_right:
find_direction = FindDirectionEnum.All
elif self.up_down:
find_direction = FindDirectionEnum.Up_Down
elif self.left_right:
find_direction = FindDirectionEnum.Left_Right
else:
logger = Logger("CentreFinder")
logger.notice("Have chosen no find direction exiting early")
return {"pos1": self.lab_pos_1, "pos2": self.lab_pos_2}
if self.COM:
centre = centre_finder(state, r_min=self.r_min, r_max=self.r_max,
max_iter=self.max_iterations,
x_start=self.lab_pos_1, y_start=self.lab_pos_2,
tolerance=self.tolerance,
find_direction=find_direction, reduction_method=False, component=self.component)
centre = centre_finder(state, r_min=self.r_min, r_max=self.r_max,
max_iter=self.max_iterations,
x_start=centre['pos1'], y_start=centre['pos2'],
tolerance=self.tolerance,
find_direction=find_direction, reduction_method=True,
verbose=self.verbose, component=self.component)
else:
centre = centre_finder(state, r_min=self.r_min, r_max=self.r_max,
max_iter=self.max_iterations, x_start=self.lab_pos_1,
y_start=self.lab_pos_2, tolerance=self.tolerance,
find_direction=find_direction, reduction_method=True,
verbose=self.verbose, component=self.component)
return centre
def __init__(self, data_file, workspace_name=None):
self.errors = []
if HAS_MANTID:
try:
if workspace_name is None:
self.data_ws = "__raw_data_file"
else:
self.data_ws = str(workspace_name)
api.HFIRLoad(Filename=str(data_file), OutputWorkspace=self.data_ws)
ws = AnalysisDataService.retrieve(self.data_ws)
x = ws.dataX(0)
self.wavelength = (x[0]+x[1])/2.0
self.wavelength_spread = x[1]-x[0]
self.sample_detector_distance = ws.getRun().getProperty("sample_detector_distance").value
self.sample_thickness = ws.getRun().getProperty("sample-thickness").value
self.beam_diameter = ws.getRun().getProperty("beam-diameter").value
Logger.get("hfir_data_proxy").information("Loaded data file: %s" % data_file)
except:
Logger.get("hfir_data_proxy").error("Error loading data file:\n%s" % sys.exc_value)
self.errors.append("Error loading data file:\n%s" % sys.exc_value)
def pre_process(self):
"""
Reduction steps that are meant to be executed only once per set
of data files. After this is executed, all files will go through
the list of reduction steps.
"""
Logger.get("Reducer").information("Setting up reduction options")
if self.setup_algorithm is not None:
alg = AlgorithmManager.create(self.setup_algorithm)
alg.initialize()
props = [p.name for p in alg.getProperties()]
for key in self.reduction_properties.keys():
if key in props:
try:
alg.setProperty(key, self.reduction_properties[key])
except:
msg = "Error setting %s=%s" % (key, str(self.reduction_properties[key]))
msg += "\n %s" % sys.exc_value
Logger.get("Reducer").error(msg)
else:
Logger.get("Reducer").warning("Setup algorithm has no %s property" % key)
if "ReductionProperties" in props:
alg.setPropertyValue("ReductionProperties",
self.get_reduction_table_name())
alg.execute()
def __init__(self, reducer, coord1_scale_factor, coord2_scale_factor):
super(BeamCenterLogger, self).__init__()
self.logger = Logger("CentreFinder")
self.using_angle = False
if is_workspace_which_requires_angle(reducer):
self.coord1_scale_factor = 1.
self.using_angle = True
# Find the bench rotation. Only supply the bench rotation if it is really needed. If we supply an offset
# through a bench rotation we need to take into account that the directionality of the angles is not
# the same as in Mantid. We need to reverse the sign of the bench rotation to get the correct rotation.
self.offset_coord1 = -1*get_bench_rotation(reducer)
else:
self.coord1_scale_factor = coord1_scale_factor
self.offset_coord1 = 0.0
self.coord2_scale_factor = coord2_scale_factor
self.offset_coord2 = 0.0
def __init__(self, guess_centre, sign_policy, find_direction = FindDirectionEnum.ALL):
"""
Takes a loaded reducer (sample information etc.) and the initial guess of the centre
position that are required for all later iterations
@param guess_centre: the starting position that the trial x and y are relative to
@param sign_policy: sets the sign for the move operation.
@param find_direction: Find beam centre for directions, ie if all or only up/down
or only left right
"""
self.logger = Logger("CentreFinder")
self._last_pos = guess_centre
self.detector = None
self.coord1_scale_factor = 1.0
self.coord2_scale_factor = 1.0
self.find_direction = find_direction
self.sign_coord1 = -1.
self.sign_coord2 = -1.
if sign_policy is not None and len(sign_policy) == 2:
self.sign_coord1 = sign_policy[0]
self.sign_coord2 = sign_policy[1]
# whether to use default wavelength range for transmissions
# Allows over-riding of following from user (mask) file:
# radial integration limits
# wavelength limits, bin size and binning
# q limits, q bin size and binning
# qxy limits, qxy bin size and binning
# The save directory must currently be specified in the Mantid.user.properties file
#Make the reduction module available
from ISISCommandInterface import *
from mantid.simpleapi import *
from mantid.api import WorkspaceGroup
from mantid.kernel import Logger
sanslog = Logger("SANS")
import copy
import sys
import re
from reduction_settings import REDUCTION_SETTINGS_OBJ_NAME
################################################################################
# Avoid a bug with deepcopy in python 2.6, details and workaround here:
# http://bugs.python.org/issue1515
if sys.version_info[0] == 2 and sys.version_info[1] == 6:
import types
def _deepcopy_method(x, memo):
return type(x)(x.im_func, copy.deepcopy(x.im_self, memo), x.im_class)
copy._deepcopy_dispatch[types.MethodType] = _deepcopy_method
################################################################################
class BeamCentrePresenter(object):
class ConcreteBeamCentreListener(BeamCentre.BeamCentreListener):
def __init__(self, presenter):
self._presenter = presenter
def on_run_clicked(self):
self._presenter.on_run_clicked()
class CentreFinderListener(WorkHandler.WorkListener):
def __init__(self, presenter):
super(BeamCentrePresenter.CentreFinderListener, self).__init__()
self._presenter = presenter
def on_processing_finished(self, result):
self._presenter.on_processing_finished_centre_finder(result)
def on_processing_error(self, error):
self._presenter.on_processing_error_centre_finder(error)
def __init__(self, parent_presenter, WorkHandler, BeamCentreModel, SANSCentreFinder):
super(BeamCentrePresenter, self).__init__()
self._view = None
self._parent_presenter = parent_presenter
self._work_handler = WorkHandler()
self._logger = Logger("SANS")
self._beam_centre_model = BeamCentreModel(SANSCentreFinder)
def set_view(self, view):
if view:
self._view = view
# Set up run listener
listener = BeamCentrePresenter.ConcreteBeamCentreListener(self)
self._view.add_listener(listener)
# Set the default gui
self._view.set_options(self._beam_centre_model)
def on_update_instrument(self, instrument):
self._beam_centre_model.set_scaling(instrument)
self._view.on_update_instrument(instrument)
def on_update_rows(self):
file_information = self._parent_presenter._table_model.get_file_information_for_row(0)
if file_information:
self._beam_centre_model.reset_to_defaults_for_instrument(file_information=file_information)
self._view.set_options(self._beam_centre_model)
def on_processing_finished_centre_finder(self, result):
# Enable button
self._view.set_run_button_to_normal()
# Update Centre Positions in model and GUI
if self._beam_centre_model.update_lab:
self._beam_centre_model.lab_pos_1 = result['pos1']
self._beam_centre_model.lab_pos_2 = result['pos2']
self._view.lab_pos_1 = self._beam_centre_model.lab_pos_1 * self._beam_centre_model.scale_1
self._view.lab_pos_2 = self._beam_centre_model.lab_pos_2 * self._beam_centre_model.scale_2
if self._beam_centre_model.update_hab:
self._beam_centre_model.hab_pos_1 = result['pos1']
self._beam_centre_model.hab_pos_2 = result['pos2']
self._view.hab_pos_1 = self._beam_centre_model.hab_pos_1 * self._beam_centre_model.scale_1
self._view.hab_pos_2 = self._beam_centre_model.hab_pos_2 * self._beam_centre_model.scale_2
def on_processing_error_centre_finder(self, error):
self._logger.warning("There has been an error. See more: {}".format(error))
self._view.set_run_button_to_normal()
def on_processing_error(self, error):
self._view.set_run_button_to_normal()
def on_run_clicked(self):
# Get the state information for the first row.
state = self._parent_presenter.get_state_for_row(0)
if not state:
self._logger.information("You can only calculate the beam centre if a user file has been loaded and there"
"valid sample scatter entry has been provided in the selected row.")
return
# Disable the button
self._view.set_run_button_to_processing()
#Update model
self._update_beam_model_from_view()
# Run the task
listener = BeamCentrePresenter.CentreFinderListener(self)
state_copy = copy.copy(state)
self._work_handler.process(listener, self._beam_centre_model.find_beam_centre, 0, state_copy)
def _update_beam_model_from_view(self):
self._beam_centre_model.r_min = self._view.r_min
self._beam_centre_model.r_max = self._view.r_max
self._beam_centre_model.max_iterations = self._view.max_iterations
self._beam_centre_model.tolerance = self._view.tolerance
self._beam_centre_model.left_right = self._view.left_right
self._beam_centre_model.verbose = self._view.verbose
self._beam_centre_model.COM = self._view.COM
self._beam_centre_model.up_down = self._view.up_down
self._beam_centre_model.lab_pos_1 = self._view.lab_pos_1 / self._beam_centre_model.scale_1
self._beam_centre_model.lab_pos_2 = self._view.lab_pos_2 / self._beam_centre_model.scale_2
self._beam_centre_model.hab_pos_1 = self._view.hab_pos_1 / self._beam_centre_model.scale_1
self._beam_centre_model.hab_pos_2 = self._view.hab_pos_2 / self._beam_centre_model.scale_2
self._beam_centre_model.q_min = self._view.q_min
self._beam_centre_model.q_max = self._view.q_max
self._beam_centre_model.component = self._view.component
self._beam_centre_model.update_hab = self._view.update_hab
self._beam_centre_model.update_lab = self._view.update_lab
def update_centre_positions(self, state_model):
lab_pos_1 = getattr(state_model, 'lab_pos_1')
lab_pos_2 = getattr(state_model, 'lab_pos_2')
hab_pos_1 = getattr(state_model, 'hab_pos_1') if getattr(state_model, 'hab_pos_1') else lab_pos_1
hab_pos_2 = getattr(state_model, 'hab_pos_2') if getattr(state_model, 'hab_pos_2') else lab_pos_2
self._view.lab_pos_1 = lab_pos_1
self._view.lab_pos_2 = lab_pos_2
self._view.hab_pos_1 = hab_pos_1
self._view.hab_pos_2 = hab_pos_2
def set_on_state_model(self, attribute_name, state_model):
attribute = getattr(self._view, attribute_name)
if attribute or isinstance(attribute, bool):
setattr(state_model, attribute_name, attribute)
def set_on_view(self, attribute_name, state_model):
attribute = getattr(state_model, attribute_name)
if attribute or isinstance(attribute, bool): # We need to be careful here. We don't want to set empty strings, or None, but we want to set boolean values. # noqa
setattr(self._view, attribute_name, attribute)
class SettingsDiagnosticPresenter(object):
class ConcreteSettingsDiagnosticTabListener(SettingsDiagnosticTab.SettingsDiagnosticTabListener):
def __init__(self, presenter):
super(SettingsDiagnosticPresenter.ConcreteSettingsDiagnosticTabListener, self).__init__()
self._presenter = presenter
def on_row_changed(self):
self._presenter.on_row_changed()
def on_update_rows(self):
self._presenter.on_update_rows()
def on_collapse(self):
self._presenter.on_collapse()
def on_expand(self):
self._presenter.on_expand()
def on_save_state_to_file(self):
self._presenter.on_save_state()
def __init__(self, parent_presenter):
super(SettingsDiagnosticPresenter, self).__init__()
self._view = None
self._parent_presenter = parent_presenter
# Logger
self.gui_logger = Logger("SANS GUI LOGGER")
def on_collapse(self):
self._view.collapse()
def on_expand(self):
self._view.expand()
def on_row_changed(self):
try:
row_index = self._view.get_current_row()
state = self.get_state(row_index)
if state:
self.display_state_diagnostic_tree(state)
except RuntimeError as e:
self.gui_logger.error(str(e))
self._parent_presenter.display_warning_box('Warning', 'Unable to find files.', str(e))
def on_update_rows(self):
"""
Update the row selection in the combobox
"""
current_row_index = self._view.get_current_row()
valid_row_indices = self._parent_presenter.get_row_indices()
new_row_index = -1
if current_row_index in valid_row_indices:
new_row_index = current_row_index
elif len(valid_row_indices) > 0:
new_row_index = valid_row_indices[0]
self._view.update_rows(valid_row_indices)
if new_row_index != -1:
self.set_row(new_row_index)
self.on_row_changed()
def set_row(self, index):
self._view.set_row(index)
def set_view(self, view):
if view:
self._view = view
# Set up row selection listener
listener = SettingsDiagnosticPresenter.ConcreteSettingsDiagnosticTabListener(self)
self._view.add_listener(listener)
# Set the default gui
self._set_default_gui()
def _set_default_gui(self):
self._view.update_rows([])
self.display_state_diagnostic_tree(state=None)
def get_state(self, index):
return self._parent_presenter.get_state_for_row(index)
def display_state_diagnostic_tree(self, state):
# Convert to dict before passing the state to the view
if state is not None:
state = state.property_manager
self._view.set_tree(state)
def on_save_state(self):
# Get the save location
save_location = self._view.get_save_location()
# Check if it exists
path_dir = os.path.dirname(save_location)
if not path_dir:
self.gui_logger.warning("The provided save location for the SANS state does not seem to exist. "
"Please provide a validate path")
return
file_name, _ = os.path.splitext(save_location)
full_file_path = file_name + JSON_SUFFIX
row_index = self._view.get_current_row()
state = self.get_state(row_index)
serialized_state = state.property_manager
with open(full_file_path, 'w') as f:
json.dump(serialized_state, f, sort_keys=True, indent=4)
self.gui_logger.information("The state for row {} has been saved to: {} ".format(row_index, full_file_path))
# Update the file name in the UI
self._view.set_save_location(full_file_path)
def __init__(self, parent_presenter):
super(SettingsDiagnosticPresenter, self).__init__()
self._view = None
self._parent_presenter = parent_presenter
# Logger
self.gui_logger = Logger("SANS GUI LOGGER")
def __init__(self, parent_presenter):
self._view = None
self._parent_presenter = parent_presenter
# Logger
self.gui_logger = Logger("SANS GUI LOGGER")
def reduce(self):
"""
Go through the list of reduction steps
"""
t_0 = time.time()
self.output_workspaces = []
# Log text
self.log_text = "%s reduction - %s\n" % (self.instrument_name, time.ctime())
self.log_text += "Mantid Python API v2\n"
# Go through the list of steps that are common to all data files
self.pre_process()
if self.reduction_algorithm is None:
Logger.get("Reducer").error("A reduction algorithm wasn't set: stopping")
return
for ws in self._data_files.keys():
alg = AlgorithmManager.create(self.reduction_algorithm)
alg.initialize()
props = [p.name for p in alg.getProperties()]
# Check whether the data is already available or needs to be loaded
if self._data_files[ws] is not None:
datafile = self._data_files[ws]
if type(datafile)==list:
datafile=','.join(datafile)
if "Filename" in props:
alg.setPropertyValue("Filename", datafile)
else:
msg = "Can't set the Filename property on %s" % self.reduction_algorithm
Logger.get("Reducer").error(msg)
else:
if "InputWorkspace" in props:
alg.setPropertyValue("InputWorkspace", ws)
else:
msg = "Can't set the InputWorkspace property on %s" % self.reduction_algorithm
Logger.get("Reducer").error(msg)
if "ReductionProperties" in props:
alg.setPropertyValue("ReductionProperties",
self.get_reduction_table_name())
if "OutputWorkspace" in props:
alg.setPropertyValue("OutputWorkspace", ws)
alg.execute()
if "OutputMessage" in props:
self.log_text += alg.getProperty("OutputMessage").value+'\n'
#any clean up, possibly removing workspaces
self.post_process()
# Determine which directory to use
output_dir = self._data_path
if self._output_path is not None:
if os.path.isdir(self._output_path):
output_dir = self._output_path
else:
output_dir = os.path.expanduser('~')
self.log_text += "Reduction completed in %g sec\n" % (time.time()-t_0)
log_path = os.path.join(output_dir,"%s_reduction.log" % self.instrument_name)
self.log_text += "Log saved to %s" % log_path
# Write the log to file
f = open(log_path, 'a')
f.write("\n-------------------------------------------\n")
f.write(self.log_text)
f.close()
return self.log_text
class CentreFinder(object):
"""
Aids estimating the effective centre of the particle beam by calculating Q in four
quadrants and using the asymmetry to calculate the direction to the beam centre. A
better estimate for the beam centre position can hence be calculated iteratively
"""
QUADS = ['Left', 'Right', 'Up', 'Down']
def __init__(self, guess_centre, sign_policy, find_direction = FindDirectionEnum.ALL):
"""
Takes a loaded reducer (sample information etc.) and the initial guess of the centre
position that are required for all later iterations
@param guess_centre: the starting position that the trial x and y are relative to
@param sign_policy: sets the sign for the move operation.
@param find_direction: Find beam centre for directions, ie if all or only up/down
or only left right
"""
self.logger = Logger("CentreFinder")
self._last_pos = guess_centre
self.detector = None
self.coord1_scale_factor = 1.0
self.coord2_scale_factor = 1.0
self.find_direction = find_direction
self.sign_coord1 = -1.
self.sign_coord2 = -1.
if sign_policy is not None and len(sign_policy) == 2:
self.sign_coord1 = sign_policy[0]
self.sign_coord2 = sign_policy[1]
def SeekCentre(self, setup, trial):
"""
Does four calculations of Q to estimate a better centre location than the one passed
to it
@param setup: the reduction chain object that contains information about the reduction
@param trial: the coordinates of the location to test as a list in the form [x, y]
@return: the asymmetry in the calculated Q in the x and y directions
"""
self.detector = setup.instrument.cur_detector().name()
# populate the x and y scale factor values at this point for the text box
self.coord1_scale_factor = setup.instrument.beam_centre_scale_factor1
self.coord2_scale_factor = setup.instrument.beam_centre_scale_factor2
# We are looking only at the differnce between the old position and the trial.
self.move(setup, trial[0]-self._last_pos[0], trial[1]-self._last_pos[1])
#phi masking will remove areas of the detector that we need
setup.mask.mask_phi = False
setup.pre_process()
setup.output_wksp = 'centre'
steps = setup._conv_Q
steps = steps[0:len(steps)-1]
setup._reduce(init=False, post=False, steps=steps)
self._group_into_quadrants(setup, 'centre', suffix='_tmp')
if setup.get_can():
#reduce the can here
setup.reduce_can('centre_can', run_Q=False)
self._group_into_quadrants(setup, 'centre_can', suffix='_can')
Minus(LHSWorkspace='Left_tmp',RHSWorkspace= 'Left_can',OutputWorkspace= 'Left_tmp')
Minus(LHSWorkspace='Right_tmp',RHSWorkspace= 'Right_can',OutputWorkspace= 'Right_tmp')
Minus(LHSWorkspace='Up_tmp',RHSWorkspace= 'Up_can',OutputWorkspace= 'Up_tmp')
Minus(LHSWorkspace='Down_tmp',RHSWorkspace= 'Down_can',OutputWorkspace= 'Down_tmp')
DeleteWorkspace(Workspace='Left_can')
DeleteWorkspace(Workspace='Right_can')
DeleteWorkspace(Workspace='Up_can')
DeleteWorkspace(Workspace='Down_can')
DeleteWorkspace(Workspace='centre_can')
DeleteWorkspace(Workspace='centre')
self._last_pos = trial
# prepare the workspaces for "publication", after they have their
# standard names calculations will be done on them and they will be plotted
for out_wksp in self.QUADS:
in_wksp = out_wksp+'_tmp'
ReplaceSpecialValues(InputWorkspace=in_wksp,OutputWorkspace=in_wksp,NaNValue=0,InfinityValue=0)
rem_nans = StripEndNans()
rem_nans.execute(setup, in_wksp)
RenameWorkspace(InputWorkspace=in_wksp,OutputWorkspace= out_wksp)
return self._calculate_residue()
def move(self, setup, x, y):
"""
Move the selected detector in both the can and sample workspaces, remembering the
that ISIS SANS team see the detector from the other side
@param setup: the reduction chain object that contains information about the reduction
@param x: the distance to move in the x (-x) direction in metres
@param y: the distance to move in the y (-y) direction in metres
"""
# Displacing the beam by +5 is equivalent to displacing the isntrument by -5. Hence we change
# the sign here. LARMOR does this correction in the instrument itself, while for the others
# we don't
x = self.sign_coord1*x
y = self.sign_coord2*y
setup.instrument.elementary_displacement_of_single_component(workspace=setup.get_sample().wksp_name,
component_name=self.detector,
coord1 = x,
coord2 = y,
coord1_scale_factor = 1.,
coord2_scale_factor = 1.,
relative_displacement = True)
if setup.get_can():
setup.instrument.elementary_displacement_of_single_component(workspace=setup.get_can().wksp_name,
component_name=self.detector,
coord1 = x,
coord2 = y,
coord1_scale_factor = 1.,
coord2_scale_factor = 1.,
relative_displacement = True)
# Create a workspace with a quadrant value in it
def _create_quadrant(self, setup, reduced_ws, quadrant, r_min, r_max, suffix):
out_ws = quadrant+suffix
# Need to create a copy because we're going to mask 3/4 out and that's a one-way trip
CloneWorkspace(InputWorkspace=reduced_ws,OutputWorkspace= out_ws)
objxml = SANSUtility.QuadrantXML([0, 0, 0.0], r_min, r_max, quadrant)
# Mask out everything outside the quadrant of interest
MaskDetectorsInShape(Workspace=out_ws,ShapeXML= objxml)
setup.to_Q.execute(setup, out_ws)
#Q1D(output,rawcount_ws,output,q_bins,AccountForGravity=GRAVITY)
# Create 4 quadrants for the centre finding algorithm and return their names
def _group_into_quadrants(self, setup, input_value, suffix=''):
r_min = setup.CENT_FIND_RMIN
r_max = setup.CENT_FIND_RMAX
for q in self.QUADS:
self._create_quadrant(setup, input_value, q, r_min, r_max, suffix)
def _calculate_residue(self):
"""
Calculate the sum squared difference between pairs of workspaces named Left, Right, Up
and Down. This assumes that a workspace with one spectrum for each of the quadrants
@return: difference left to right, difference up down
"""
residueX = 0
if self.find_direction == FindDirectionEnum.ALL or self.find_direction == FindDirectionEnum.LEFT_RIGHT:
yvalsAX = mtd['Left'].readY(0)
yvalsBX = mtd['Right'].readY(0)
qvalsAX = mtd['Left'].readX(0)
qvalsBX = mtd['Right'].readX(0)
qrangeX = [len(yvalsAX), len(yvalsBX)]
nvalsX = min(qrangeX)
id1X = "LR1"
id2X = "LR2"
residueX = self._residual_calculation_for_single_direction(yvalsA = yvalsAX,
yvalsB = yvalsBX,
qvalsA = qvalsAX,
qvalsB = qvalsBX,
qrange = qrangeX,
nvals = nvalsX,
id1 = id1X,
id2 = id2X)
residueY = 0
if self.find_direction == FindDirectionEnum.ALL or self.find_direction == FindDirectionEnum.UP_DOWN:
yvalsAY = mtd['Up'].readY(0)
yvalsBY = mtd['Down'].readY(0)
qvalsAY = mtd['Up'].readX(0)
qvalsBY = mtd['Down'].readX(0)
qrangeY = [len(yvalsAY), len(yvalsBY)]
nvalsY = min(qrangeY)
id1Y = "UD1"
id2Y = "UD2"
residueY = self._residual_calculation_for_single_direction(yvalsA = yvalsAY,
yvalsB = yvalsBY,
qvalsA = qvalsAY,
qvalsB = qvalsBY,
qrange = qrangeY,
nvals = nvalsY,
id1 = id1Y,
id2 = id2Y)
return residueX, residueY
def _residual_calculation_for_single_direction(self, yvalsA, yvalsB, qvalsA, qvalsB, qrange, nvals, id1, id2):
dummy_1 = qrange
residue = 0
indexB = 0
for indexA in range(0, nvals):
if qvalsA[indexA] < qvalsB[indexB]:
self.logger.notice(id1 + " " +str(indexA)+" "+str(indexB))
continue
elif qvalsA[indexA] > qvalsB[indexB]:
while qvalsA[indexA] > qvalsB[indexB]:
self.logger(id2 + " " +str(indexA)+" "+str(indexB))
indexB += 1
if indexA > nvals - 1 or indexB > nvals - 1:
break
residue += pow(yvalsA[indexA] - yvalsB[indexB], 2)
indexB += 1
return residue
def _get_cylinder_direction(self, workspace):
'''
Get the direction that the masking clyinder needs to point at. This should be the normal
of the tilted detector bench. The original normal is along the beam axis as defined in
the instrument definition file.
@param workspace: the workspace with the tilted detector bench
@returns the required direction of the cylinder axis
'''
ws = mtd[workspace]
instrument = ws.getInstrument()
quat = instrument.getComponentByName(self.detector).getRotation()
cylinder_direction = instrument.getReferenceFrame().vecPointingAlongBeam()
quat.rotate(cylinder_direction)
return cylinder_direction.X(), cylinder_direction.Y(), cylinder_direction.Z()
def __init__(self, parent_presenter):
super(MaskingTablePresenter, self).__init__()
self._view = None
self._parent_presenter = parent_presenter
self._work_handler = WorkHandler()
self._logger = Logger("SANS")
class MaskingTablePresenter(object):
DISPLAY_WORKSPACE_NAME = "__sans_mask_display_dummy_workspace"
class ConcreteMaskingTableListener(MaskingTable.MaskingTableListener):
def __init__(self, presenter):
super(MaskingTablePresenter.ConcreteMaskingTableListener, self).__init__()
self._presenter = presenter
def on_row_changed(self):
self._presenter.on_row_changed()
def on_update_rows(self):
self._presenter.on_update_rows()
def on_display(self):
self._presenter.on_display()
class DisplayMaskListener(WorkHandler.WorkListener):
def __init__(self, presenter):
super(MaskingTablePresenter.DisplayMaskListener, self).__init__()
self._presenter = presenter
def on_processing_finished(self, result):
self._presenter.on_processing_finished_masking_display(result)
def on_processing_error(self, error):
self._presenter.on_processing_error_masking_display(error)
def __init__(self, parent_presenter):
super(MaskingTablePresenter, self).__init__()
self._view = None
self._parent_presenter = parent_presenter
self._work_handler = WorkHandler()
self._logger = Logger("SANS")
def on_row_changed(self):
row_index = self._view.get_current_row()
state = self.get_state(row_index, file_lookup=False)
if state:
self.display_masking_information(state)
def on_display(self):
# Get the state information for the selected row.
# Disable the button
self._view.set_display_mask_button_to_processing()
try:
row_index = self._view.get_current_row()
state = self.get_state(row_index)
except Exception as e:
self.on_processing_error_masking_display(e)
raise Exception(str(e)) # propagate errors for run_tab_presenter to deal with
else:
if not state:
self._logger.information("You can only show a masked workspace if a user file has been loaded and there"
"valid sample scatter entry has been provided in the selected row.")
return
# Run the task
listener = MaskingTablePresenter.DisplayMaskListener(self)
state_copy = copy.copy(state)
self._work_handler.process(listener, load_and_mask_workspace, 0, state_copy, self.DISPLAY_WORKSPACE_NAME)
def on_processing_finished_masking_display(self, result):
# Enable button
self._view.set_display_mask_button_to_normal()
# Display masked workspace
self._display(result)
def on_processing_error_masking_display(self, error):
self._logger.warning("There has been an error. See more: {}".format(error))
# Enable button
self._view.set_display_mask_button_to_normal()
def on_processing_error(self, error):
pass
def on_update_rows(self):
"""
Update the row selection in the combobox
"""
current_row_index = self._view.get_current_row()
valid_row_indices = self._parent_presenter.get_row_indices()
new_row_index = -1
if current_row_index in valid_row_indices:
new_row_index = current_row_index
elif len(valid_row_indices) > 0:
new_row_index = valid_row_indices[0]
self._view.update_rows(valid_row_indices)
if new_row_index != -1:
self.set_row(new_row_index)
self.on_row_changed()
def set_row(self, index):
self._view.set_row(index)
def set_view(self, view):
if view:
self._view = view
# Set up row selection listener
listener = MaskingTablePresenter.ConcreteMaskingTableListener(self)
self._view.add_listener(listener)
# Set the default gui
self._set_default_gui()
def _set_default_gui(self):
self._view.update_rows([])
self.display_masking_information(state=None)
def get_state(self, index, file_lookup=True):
return self._parent_presenter.get_state_for_row(index, file_lookup=file_lookup)
@staticmethod
def _append_single_spectrum_mask(spectrum_mask, container, detector_name, prefix):
if spectrum_mask:
for item in spectrum_mask:
detail = prefix + str(item)
container.append(masking_information(first="Spectrum", second=detector_name, third=detail))
@staticmethod
def _append_strip_spectrum_mask(strip_mask_start, strip_mask_stop, container, detector_name, prefix):
if strip_mask_start and strip_mask_stop:
for start, stop in zip(strip_mask_start, strip_mask_stop):
detail = prefix + str(start) + ">" + prefix + str(stop)
container.append(masking_information(first="Strip", second=detector_name, third=detail))
@staticmethod
def _append_block_spectrum_mask(horizontal_mask_start, horizontal_mask_stop, vertical_mask_start,
vertical_mask_stop, container, detector_name):
if horizontal_mask_start and horizontal_mask_stop and vertical_mask_start and vertical_mask_stop:
for h_start, h_stop, v_start, v_stop in zip(horizontal_mask_start, horizontal_mask_stop,
vertical_mask_start, vertical_mask_stop):
detail = "H{}>H{}+V{}>V{}".format(h_start, h_stop, v_start, v_stop)
container.append(masking_information(first="Strip", second=detector_name, third=detail))
@staticmethod
def _append_spectrum_block_cross_mask(horizontal_mask, vertical_mask, container, detector_name):
if horizontal_mask and vertical_mask:
for h, v in zip(horizontal_mask, vertical_mask):
detail = "H{}+V{}".format(h, v)
container.append(masking_information(first="Strip", second=detector_name, third=detail))
@staticmethod
def _get_spectrum_masks(mask_detector_info):
detector_name = mask_detector_info.detector_name
spectrum_masks = []
# -------------------------------
# Get the vertical spectrum masks
# -------------------------------
single_vertical_strip_mask = mask_detector_info.single_vertical_strip_mask
MaskingTablePresenter._append_single_spectrum_mask(single_vertical_strip_mask, spectrum_masks,
detector_name, "V")
range_vertical_strip_start = mask_detector_info.range_vertical_strip_start
range_vertical_strip_stop = mask_detector_info.range_vertical_strip_stop
MaskingTablePresenter._append_strip_spectrum_mask(range_vertical_strip_start,
range_vertical_strip_stop,
spectrum_masks, detector_name, "V")
# ---------------------------------
# Get the horizontal spectrum masks
# ---------------------------------
single_horizontal_strip_mask = mask_detector_info.single_horizontal_strip_mask
MaskingTablePresenter._append_single_spectrum_mask(single_horizontal_strip_mask, spectrum_masks,
detector_name, "H")
range_horizontal_strip_start = mask_detector_info.range_horizontal_strip_start
range_horizontal_strip_stop = mask_detector_info.range_horizontal_strip_stop
MaskingTablePresenter._append_strip_spectrum_mask(range_horizontal_strip_start,
range_horizontal_strip_stop,
spectrum_masks, detector_name, "H")
# ---------------------------------
# Get the block masks
# ---------------------------------
block_horizontal_start = mask_detector_info.block_horizontal_start
block_horizontal_stop = mask_detector_info.block_horizontal_stop
block_vertical_start = mask_detector_info.block_vertical_start
block_vertical_stop = mask_detector_info.block_vertical_stop
MaskingTablePresenter._append_block_spectrum_mask(block_horizontal_start, block_horizontal_stop,
block_vertical_start, block_vertical_stop,
spectrum_masks, detector_name)
block_cross_horizontal = mask_detector_info.block_cross_horizontal
block_cross_vertical = mask_detector_info.block_cross_vertical
MaskingTablePresenter._append_spectrum_block_cross_mask(block_cross_horizontal, block_cross_vertical,
spectrum_masks, detector_name)
# ---------------------------------
# Get spectrum masks
# ---------------------------------
single_spectra = mask_detector_info.single_spectra
MaskingTablePresenter._append_single_spectrum_mask(single_spectra, spectrum_masks,
detector_name, "S")
spectrum_range_start = mask_detector_info.spectrum_range_start
spectrum_range_stop = mask_detector_info.spectrum_range_stop
MaskingTablePresenter._append_strip_spectrum_mask(spectrum_range_start,
spectrum_range_stop,
spectrum_masks, detector_name, "S")
return spectrum_masks
@staticmethod
def _get_time_masks_general(mask_info):
container = []
bin_mask_general_start = mask_info.bin_mask_general_start
bin_mask_general_stop = mask_info.bin_mask_general_stop
if bin_mask_general_start and bin_mask_general_stop:
for start, stop in zip(bin_mask_general_start, bin_mask_general_stop):
detail = "{}-{}".format(start, stop)
container.append(masking_information(first="Time", second="", third=detail))
return container
@staticmethod
def _get_time_masks(mask_info):
container = []
bin_mask_start = mask_info.bin_mask_start
bin_mask_stop = mask_info.bin_mask_stop
detector_name = mask_info.detector_name
if bin_mask_start and bin_mask_stop:
for start, stop in zip(bin_mask_start, bin_mask_stop):
detail = "{}-{}".format(start, stop)
container.append(masking_information(first="Time", second=detector_name, third=detail))
return container
@staticmethod
def _get_arm_mask(mask_info):
container = []
beam_stop_arm_width = mask_info.beam_stop_arm_width
beam_stop_arm_angle = mask_info.beam_stop_arm_angle
beam_stop_arm_pos1 = mask_info.beam_stop_arm_pos1 if mask_info.beam_stop_arm_pos1 else 0.
beam_stop_arm_pos2 = mask_info.beam_stop_arm_pos2 if mask_info.beam_stop_arm_pos2 else 0.
if beam_stop_arm_width and beam_stop_arm_angle:
detail = "LINE {}, {}, {}, {}".format(beam_stop_arm_width, beam_stop_arm_angle,
beam_stop_arm_pos1, beam_stop_arm_pos2)
container.append(masking_information(first="Arm", second="", third=detail))
return container
@staticmethod
def _get_phi_mask(mask_info):
container = []
phi_min = mask_info.phi_min
phi_max = mask_info.phi_max
use_mask_phi_mirror = mask_info.use_mask_phi_mirror
if phi_min and phi_max:
if use_mask_phi_mirror:
detail = "L/PHI {} {}".format(phi_min, phi_max)
else:
detail = "L/PHI/NOMIRROR{} {}".format(phi_min, phi_max)
container.append(masking_information(first="Phi", second="", third=detail))
return container
@staticmethod
def _get_mask_files(mask_info):
container = []
mask_files = mask_info.mask_files
if mask_files:
for mask_file in mask_files:
container.append(masking_information(first="Mask file", second="", third=mask_file))
return container
@staticmethod
def _get_radius(mask_info):
container = []
radius_min = mask_info.radius_min
radius_max = mask_info.radius_max
if radius_min:
detail = "infinite-cylinder, r = {}".format(radius_min)
container.append(masking_information(first="Beam stop", second="", third=detail))
if radius_max:
detail = "infinite-cylinder, r = {}".format(radius_max)
container.append(masking_information(first="Corners", second="", third=detail))
return container
def _generate_masking_information(self, state):
if state is None:
return []
mask_info = state.mask
masks = []
mask_info_lab = mask_info.detectors[DetectorType.to_string(DetectorType.LAB)]
mask_info_hab = mask_info.detectors[DetectorType.to_string(DetectorType.HAB)] if DetectorType.to_string(DetectorType.HAB) in mask_info.detectors else None # noqa
# Add the radius mask
radius_mask = self._get_radius(mask_info)
masks.extend(radius_mask)
# Add the spectrum masks for LAB
spectrum_masks_lab = self._get_spectrum_masks(mask_info_lab)
masks.extend(spectrum_masks_lab)
# Add the spectrum masks for HAB
if mask_info_hab:
spectrum_masks_hab = self._get_spectrum_masks(mask_info_hab)
masks.extend(spectrum_masks_hab)
# Add the general time mask
time_masks_general = self._get_time_masks_general(mask_info)
masks.extend(time_masks_general)
# Add the time masks for LAB
time_masks_lab = self._get_time_masks(mask_info_lab)
masks.extend(time_masks_lab)
# Add the time masks for HAB
if mask_info_hab:
time_masks_hab = self._get_time_masks(mask_info_hab)
masks.extend(time_masks_hab)
# Add arm mask
arm_mask = self._get_arm_mask(mask_info)
masks.extend(arm_mask)
# Add phi mask
phi_mask = self._get_phi_mask(mask_info)
masks.extend(phi_mask)
# Add mask files
mask_files = self._get_mask_files(mask_info)
masks.extend(mask_files)
return masks
def get_masking_information(self, state):
table_entries = []
if state is not None:
table_entries = self._generate_masking_information(state)
return table_entries
def display_masking_information(self, state):
table_entries = self.get_masking_information(state)
self._view.set_table(table_entries)
@staticmethod
def _display(masked_workspace):
if masked_workspace and AnalysisDataService.doesExist(masked_workspace.name()):
instrument_win = mantidplot.getInstrumentView(masked_workspace.name())
instrument_win.show()
def __init__(self, view, exit_code):
self.error_log = Logger("error")
self._view = view
self._exit_code = exit_code
self._view.set_report_callback(self.error_handler)
class ErrorReporterPresenter(object):
def __init__(self, view, exit_code):
self.error_log = Logger("error")
self._view = view
self._exit_code = exit_code
self._view.set_report_callback(self.error_handler)
def do_not_share(self, continue_working=True):
self.error_log.notice("No information shared")
self._handle_exit(continue_working)
return -1
def share_non_identifiable_information(self, continue_working):
uptime = UsageService.getUpTime()
status = self._send_report_to_server(share_identifiable=False, uptime=uptime)
self.error_log.notice("Sent non-identifiable information")
self._handle_exit(continue_working)
return status
def share_all_information(self, continue_working, name, email, text_box):
uptime = UsageService.getUpTime()
try:
recovery_archive, file_hash = zip_recovery_directory()
except Exception as exc:
self.error_log.information("Error creating recovery archive: {}. No recovery information will be sent")
recovery_archive, file_hash = None, ""
status = self._send_report_to_server(share_identifiable=True, uptime=uptime, name=name, email=email, file_hash=file_hash,
text_box=text_box)
self.error_log.notice("Sent full information")
if status == 201 and recovery_archive:
self._upload_recovery_file(recovery_archive=recovery_archive)
try:
os.remove(recovery_archive)
except OSError as exc:
self.error_log.information("Unable to remove zipped recovery information: {}".format(str(exc)))
self._handle_exit(continue_working)
return status
def error_handler(self, continue_working, share, name, email, text_box):
if share == 0:
status = self.share_all_information(continue_working, name, email, text_box)
elif share == 1:
status = self.share_non_identifiable_information(continue_working)
elif share == 2:
status = self.do_not_share(continue_working)
else:
self.error_log.error("Unrecognised signal in errorreporter exiting")
self._handle_exit(continue_working)
status = -2
return status
def _handle_exit(self, continue_working):
if not continue_working:
self.error_log.error("Terminated by user.")
self._view.quit()
else:
self.error_log.error("Continue working.")
def _upload_recovery_file(self, recovery_archive):
url = ConfigService['errorreports.rooturl']
url = '{}/api/recovery'.format(url)
files = {'file': open('{}'.format(recovery_archive), 'rb')}
response = requests.post(url, files=files)
if response.status_code == 201:
self.error_log.notice("Uploaded recovery file to server. HTTP response {}".format(response.status_code))
else:
self.error_log.error("Failed to send recovery data HTTP response {}".format(response.status_code))
def _send_report_to_server(self, share_identifiable=False, name='', email='', file_hash='', uptime='', text_box=''):
errorReporter = ErrorReporter(
"mantidplot", uptime, self._exit_code, share_identifiable, str(name), str(email), str(text_box),
str(file_hash))
status = errorReporter.sendErrorReport()
if status != 201:
self._view.display_message_box('Error contacting server', 'There was an error when sending the report.'
'Please contact [email protected] directly',
'http request returned with status {}'.format(status))
self.error_log.error("Failed to send error report http request returned status {}".format(status))
return status
def show_view(self):
self._view.show()
def test_unicode_logger(self):
logger = Logger("LoggerTest")
self.assertTrue(isinstance(logger, Logger))
for att in ['fatal', 'error', 'warning', 'notice', 'information', 'debug']:
if not hasattr(logger, att):
self.fail("Logger object does not have the required attribute '%s'" % att)
logger.fatal('This is a test')
logger.error('This is a test')
logger.warning('This is a test')
logger.notice('This is a test')
logger.information('This is a test')
logger.debug('This is a test')
from mantid import *
import numpy as np
import os, csv, math
from mantid.kernel import Logger
import BilbyCustomFunctions_Reduction
reload (BilbyCustomFunctions_Reduction)
ansto_logger = Logger("AnstoDataReduction")
# ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ #
# INPUT - mandatory from a USER - START
###########################################################################################
red_settings = FileFinder.getFullPath('settings_csv_5779.csv')
#mantid_reduction_input_latex_shp.csv
#settings_csv_peptides
# INPUT - index of a line with reduction parameters
index_reduction_settings = ["2"] # INDEX OF THE LINE WITH REDUCTION SETTINGS
if len(index_reduction_settings) > 1: # must be single choice
raise ValueError('Please check your choice of reduction settigns; only single value is allowed')
# ID to evaluate - INPUT, in any combination of 'a-b' or ',c', or empty line; empty line means evaluate all files listed in csv
index_files_to_reduce = "98" # as per csv_files_to_reduce_list file - LINES' INDEXES FOR FILES TO BE REDUCED
# Data file with numbers
path_tube_shift_correction = FileFinder.getFullPath('shift_assembled.csv')
###########################################################################################
class BilbySANSDataProcessor(DataProcessorAlgorithm):
def __init__(self):
DataProcessorAlgorithm.__init__(self)
self.sanslog = Logger("ANSTO SANS Data reduction")
def category(self):
return "Workflow\\SANS"
def seeAlso(self):
return ["Q1D", "TOFSANSResolutionByPixel", "SANSWideAngleCorrection"]
def name(self):
return "BilbySANSDataProcessor"
def summary(self):
return "BILBY SANS data reduction. Converts a workspace in wavelength into a 1D or 2D workspace of" \
" momentum transfer, assuming elastic scattering."
def PyInit(self):
# input
self.declareProperty(MatrixWorkspaceProperty('InputWorkspace', '',
direction=Direction.Input,
optional=PropertyMode.Mandatory),
doc='Particle counts as a function of wavelength')
self.declareProperty(MatrixWorkspaceProperty('InputMaskingWorkspace', '',
direction=Direction.Input,
optional=PropertyMode.Optional),
doc='Mask for the scattering data')
# blocked beam, beam shape and detector corrections
self.declareProperty(MatrixWorkspaceProperty('BlockedBeamWorkspace', '',
direction=Direction.Input,
optional=PropertyMode.Optional),
doc='Blocked beam scattering')
self.declareProperty(MatrixWorkspaceProperty('EmptyBeamSpectrumShapeWorkspace', '',
direction=Direction.Input,
optional=PropertyMode.Mandatory,
validator=WorkspaceUnitValidator("Wavelength")),
doc='Empty beam transmission, where only a given wavelength slice is considered')
self.declareProperty(MatrixWorkspaceProperty('SensitivityCorrectionMatrix', '',
direction=Direction.Input,
optional=PropertyMode.Optional),
doc='Detector sensitivity calibration data set')
self.declareProperty(MatrixWorkspaceProperty('TransmissionWorkspace', '',
direction=Direction.Input,
optional=PropertyMode.Mandatory),
doc='Sample transmission workspace')
self.declareProperty(MatrixWorkspaceProperty('TransmissionEmptyBeamWorkspace', '',
direction=Direction.Input,
optional=PropertyMode.Mandatory),
doc='Empty beam transmission workspace')
self.declareProperty(MatrixWorkspaceProperty('TransmissionMaskingWorkspace', '',
direction=Direction.Input,
optional=PropertyMode.Mandatory),
doc='Mask for the transmission data')
self.declareProperty(name='FitMethod',
defaultValue='log', doc='Function to use to fit transmission; can be Linear,'
' Log, Polynomial (first letter shall be capital)')
self.declareProperty(name='PolynomialOrder',
defaultValue='3',
doc='Used only for Polynomial function, but needed as an input parameter anyway')
self.declareProperty(name='ScalingFactor',
defaultValue=1.0,
validator=FloatBoundedValidator(lower=0.0),
doc='Attenuating factor')
self.declareProperty(name='SampleThickness',
defaultValue=1.0,
validator=FloatBoundedValidator(lower=0.0),
doc='Thickness of sample')
self.declareProperty(FloatArrayProperty('BinningWavelength',
direction=Direction.Input,
validator=FloatArrayMandatoryValidator()),
doc='Wavelength boundaries for reduction: a comma separated list of first bin boundary,'
' width, last bin boundary')
self.declareProperty(FloatArrayProperty('BinningWavelengthTransm',
direction=Direction.Input,
validator=FloatArrayMandatoryValidator()),
doc='Wavelengths boundaries for transmission binning: a comma separated list of first bin'
' boundary, width, last bin')
self.declareProperty(FloatArrayProperty('BinningQ',
direction=Direction.Input,
validator=FloatArrayMandatoryValidator()),
doc='Output Q-boundaries: a comma separated list of first bin boundary,'
' width, last bin boundary')
self.declareProperty(name='Timemode',
defaultValue=True,
doc='If data collected in ToF or monochromatic mode')
self.declareProperty(name='AccountForGravity',
defaultValue=True,
doc='Whether to correct for the effects of gravity')
self.declareProperty(name='SolidAngleWeighting',
defaultValue=True,
doc='If True, pixels will be weighted by their solid angle')
self.declareProperty(name='RadiusCut',
defaultValue=1.0,
validator=FloatBoundedValidator(lower=0.0),
doc='To increase resolution some wavelengths are excluded within this distance from the'
' beam center (mm). Note that RadiusCut and WaveCut both need to be larger than 0 to'
' affect the effective cutoff. See the algorithm description for a detailed'
' explanation of the cutoff.')
self.declareProperty(name='WaveCut',
defaultValue=1.0,
validator=FloatBoundedValidator(lower=0.0),
doc='To increase resolution by starting to remove some wavelengths below this threshold'
' (angstrom). Note that WaveCut and RadiusCut both need to be larger than 0 to affect'
' on the effective cutoff. See the algorithm description for a detailed explanation'
' of the cutoff.')
self.declareProperty(name='WideAngleCorrection',
defaultValue=True,
doc='If true, the wide angle correction for transmissions will be applied')
self.declareProperty(name='Reduce2D',
defaultValue=False,
doc='If true, 2D data reduction will be performed')
self.declareProperty(MatrixWorkspaceProperty('OutputWorkspace', '', direction=Direction.Output),
doc='Name of the workspace that contains the result of the calculation. '
'Created automatically.')
self.declareProperty(MatrixWorkspaceProperty('OutputWorkspaceTransmissionFit', '', direction=Direction.Output),
# This works only when transmission is True. Problems starts when it is not...
doc='Counts vs wavelength, fit for the sample transmission')
def validateInputs(self):
inputs = dict()
ws_sam = self.getProperty("InputWorkspace").value
ws_samMsk = self.getProperty("InputMaskingWorkspace").value
ws_blk = self.getProperty("BlockedBeamWorkspace").value
ws_emp = self.getProperty("EmptyBeamSpectrumShapeWorkspace").value
ws_sen = self.getProperty("SensitivityCorrectionMatrix").value
ws_tranSam = self.getProperty("TransmissionWorkspace").value
ws_tranEmp = self.getProperty("TransmissionEmptyBeamWorkspace").value
ws_tranMsk = self.getProperty("TransmissionMaskingWorkspace").value
# -- Validation --
sam_histograms = ws_sam.getNumberHistograms()
if sam_histograms <= 0:
inputs["InputWorkspace"] = "has to contain at least one spectrum"
elif not ws_sam.isHistogramData():
inputs["InputWorkspace"] = "has to be a histogram"
if ws_samMsk:
isinstance(ws_samMsk, IMaskWorkspace)
if ws_blk:
if not ws_blk.isHistogramData():
inputs["BlockedBeamWorkspace"] = "has to be a histogram"
elif ws_blk.blocksize() != 1:
inputs["BlockedBeamWorkspace"] = "each spectrum must contain only one y value"
if ws_emp.getNumberHistograms() != 1:
inputs["EmptyBeamSpectrumShapeWorkspace"] = "has to contain only one spectrum"
elif not ws_emp.isHistogramData():
inputs["EmptyBeamSpectrumShapeWorkspace"] = "has to be a histogram"
if ws_sen:
if ws_sen.getNumberHistograms() != sam_histograms:
inputs["SensitivityCorrectionMatrix"] = "must have same number of spectra as the InputWorkspace"
elif not ws_sen.isHistogramData():
inputs["SensitivityCorrectionMatrix"] = "has to be a histogram"
elif ws_sen.getAxis(0).getUnit().symbol():
inputs["SensitivityCorrectionMatrix"] = "has to be unitless"
tran_histograms = ws_tranSam.getNumberHistograms()
if tran_histograms <= 0:
inputs["TransmissionWorkspace"] = "has to contain at least one spectrum"
elif not ws_tranSam.isHistogramData():
inputs["TransmissionWorkspace"] = "has to be a histogram"
if ws_tranEmp.getNumberHistograms() != tran_histograms:
inputs["TransmissionEmptyBeamWorkspace"] = "must have same number of spectra as the TransmissionWorkspace"
elif not ws_tranEmp.isHistogramData():
inputs["TransmissionEmptyBeamWorkspace"] = "has to be a histogram"
if ws_tranMsk:
isinstance(ws_tranMsk, IMaskWorkspace)
inputs = self.check_geometry_and_cuts(inputs)
return inputs
def check_geometry_and_cuts(self, inputs):
scale = self.getProperty("ScalingFactor").value
thickness = self.getProperty("SampleThickness").value
radiuscut = self.getProperty("RadiusCut").value
wavecut = self.getProperty("WaveCut").value
if scale <= 0.0:
inputs["ScalingFactor"] = "has to be greater than zero"
if thickness <= 0.0:
inputs["SampleThickness"] = "has to be greater than zero"
if radiuscut < 0.0:
inputs["radiuscut"] = "has to be equal or greater than zero"
if wavecut < 0.0:
inputs["wavecut"] = "has to be equal or greater than zero"
return inputs
def PyExec(self):
self.sanslog.warning(
"SANSDataProcessing is in the beta phase of development. Properties may change without notice.")
self.sanslog.warning("Log on the changes is recorded in the body of SANSDataProcessor.py file")
# -- Get Arguments --
ws_sam = self.getProperty("InputWorkspace").value
ws_samMsk = self.getProperty("InputMaskingWorkspace").value
ws_blk = self.getProperty("BlockedBeamWorkspace").value
ws_emp = self.getProperty("EmptyBeamSpectrumShapeWorkspace").value
ws_sen = self.getProperty("SensitivityCorrectionMatrix").value
ws_tranSam = self.getProperty("TransmissionWorkspace").value
ws_tranEmp = self.getProperty("TransmissionEmptyBeamWorkspace").value
ws_tranMsk = self.getProperty("TransmissionMaskingWorkspace").value
scale = self.getProperty("ScalingFactor").value
thickness = self.getProperty("SampleThickness").value
binning_wavelength = self.getProperty("BinningWavelength").value
binning_q = self.getProperty("BinningQ").value
wavecut = self.getProperty("WaveCut").value
radiuscut = self.getProperty("RadiusCut").value
binning_wavelength_transm = self.getProperty("BinningWavelengthTransm").value
fitmethod = self.getProperty("FitMethod").value
polynomialorder = self.getProperty("PolynomialOrder").value
time_mode = self.getProperty(
"TimeMode").value
# True if External time frame (i.e. choppers), False if Internal time frames (Neutron Velocity Selector)
account_for_gravity = self.getProperty("AccountForGravity").value
solid_angle_weighting = self.getProperty("SolidAngleWeighting").value
wide_angle_correction = self.getProperty("WideAngleCorrection").value
reduce_2d = self.getProperty("Reduce2D").value
# -- Masking --
if ws_samMsk:
self._apply_mask(ws_sam, ws_samMsk)
if ws_tranMsk:
self._apply_mask(ws_tranSam, ws_tranMsk)
self._apply_mask(ws_tranEmp, ws_tranMsk)
# -- Convert to Wavelength -- Only for the External time mode - choppers
if time_mode:
ws_sam = self._convert_units(ws_sam, "Wavelength")
ws_tranSam = self._convert_units(ws_tranSam, "Wavelength")
ws_tranEmp = self._convert_units(ws_tranEmp, "Wavelength")
# -- Transmission --
# Intuitively one would think rebin for NVS data is not needed, but it is required;
# not perfect match in binning leads to error like "not matching intervals for calculate_transmission"
ws_sam = self._rebin(ws_sam, binning_wavelength, preserveevents=False)
ws_tranSam = self._rebin(ws_tranSam, binning_wavelength_transm, preserveevents=False)
ws_tranEmp = self._rebin(ws_tranEmp, binning_wavelength_transm, preserveevents=False)
ws_tranroi = self._mask_to_roi(ws_tranMsk)
self.sanslog.information("FitMethod " + fitmethod)
self.sanslog.information("PolynomialOrder " + polynomialorder)
ws_tran = self._calculate_transmission(ws_tranSam, ws_tranEmp, ws_tranroi, fitmethod, polynomialorder,
binning_wavelength_transm)
ws_tranemp_scale = self._get_frame_count(ws_tranEmp)
ws_transam_scale = self._get_frame_count(ws_tranSam)
f = self._single_valued_ws(ws_tranemp_scale / ws_transam_scale)
ws_tran = self._multiply(ws_tran, f)
transmission_fit = ws_tran
self.setProperty("OutputWorkspaceTransmissionFit", transmission_fit)
# -- Blocked Beam Subtraction -- only if blk workspace has been provided (obviously)
if ws_blk:
ws_sam_time = self._get_frame_count(ws_sam)
ws_blk_time = self._get_frame_count(ws_blk)
ws_blk_scaling = self._single_valued_ws(ws_sam_time / ws_blk_time)
# remove estimated blk counts from sample workspace
self._apply_mask(ws_blk, ws_samMsk) # masking blocked beam the same way as sample data
if time_mode:
ws_blk = self._convert_units(ws_blk, "Wavelength")
ws_blk = self._rebin(ws_blk, binning_wavelength, preserveevents=False)
# estimated blk counts for given measurement time and bin width
ws_blk_est = self._multiply(ws_blk, ws_blk_scaling)
ws_sam = self._subtract(ws_sam, ws_blk_est)
# sensitivity
pixeladj = ws_sen
ws_tran = self._emp_shape_adjustment(ws_tran,
ws_emp) # swap arrays; ws_emp will always be shorter or equal to ws_tran
wavelengthadj = self._multiply(ws_emp, ws_tran)
# calculate the wide angle correction for sample transmission
if wide_angle_correction:
wavepixeladj = self._wide_angle_correction(ws_sam, ws_tran)
else:
wavepixeladj = None
# distance to maximum of parabolic motion of neutrons
real_l1 = self._get_l1(ws_sam) # distance from the end of the last guide to the sample
extralength = 0.5 * real_l1 # neutrons following parabolic trajectory with maximum at the middle of the L1
# normalize vector to counting time of sample & long empty beam run
ws_emp_time = self._get_frame_count(ws_emp)
ws_sam_time = self._get_frame_count(ws_sam)
scale_full = scale * (ws_emp_time / ws_sam_time)
# extra multiplier is needed because measured transmission is ~5% lower;
# we need to divide result by lower number, hence need to lowering the final result, i.e. divide by 1.05
f = self._single_valued_ws(scale_full / (thickness * 1.05))
if reduce_2d:
q_max = binning_q[2]
q_delta = binning_q[1]
qxy = self._qxy(ws_sam, q_max, q_delta, pixeladj, wavelengthadj, account_for_gravity, solid_angle_weighting,
extralength)
qxy = self._multiply(qxy, f)
self.setProperty("OutputWorkspace", qxy)
else:
if (ws_sam.run().getProperty("source_aperture").value):
sourceapertureradius = float(ws_sam.run().getProperty("source_aperture").value) / 2.0
if sourceapertureradius > SOURCE_APERTURE_RADIUS_MAX:
sourceapertureradius = SOURCE_APERTURE_RADIUS
print("sourceapertureradius value cannot be retrieved; generic value of 20mm taken")
else:
sourceapertureradius = SOURCE_APERTURE_RADIUS # radius in mm
print("sourceapertureradius value cannot be retrieved; generic value of 20mm taken")
if (ws_sam.run().getProperty("sample_aperture").value):
sampleapertureradius = float(ws_sam.run().getProperty("source_aperture").value) / 2.0
if sampleapertureradius > SAMPLE_APERTURE_RADIUS_MAX:
sampleapertureradius = SAMPLE_APERTURE_RADIUS
print("sampleapertureradius value cannot be retrieved; generic value of 6.25mm taken")
else:
sampleapertureradius = SAMPLE_APERTURE_RADIUS # radius in mm
print("sampleapertureradius value cannot be retrieved; generic value of 6.25mm taken")
# creating empty array for SigmaModerator
# SigmaModerator is a mandatory parameter for ISIS, but not needed for the reactor facility
number_of_bins = NUMBER_OF_BINS
number_of_spectra = NUMBER_OF_SPECTRA
delta_wavelength = DELTA_WAVELENGTH
data_x = np.zeros(number_of_bins + 1)
data_y = np.zeros(number_of_bins)
x_value = 0.5
y_value = 0.0
for index in range(number_of_bins):
data_x[index] = x_value
data_y[index] = y_value
x_value += delta_wavelength
data_x[number_of_bins] = x_value
units = "Wavelength"
sigmamoderator = self._create_empty_ws(data_x, data_y, number_of_spectra, units)
# Call TOFSANSResolutionByPixel
ws_sam = self._multiply(ws_sam, f)
qresolution = self._tofsansresolutionbypixel(ws_sam, sampleapertureradius, sourceapertureradius,
sigmamoderator, real_l1, account_for_gravity, extralength)
# Call Q1D, now with resolution
q1d = self._q1d(ws_sam, binning_q, pixeladj, wavelengthadj, wavepixeladj, account_for_gravity,
solid_angle_weighting, radiuscut, wavecut, extralength, qresolution)
self.setProperty("OutputWorkspace", q1d) # set output, file 1D pattern
def _get_time_span(self, ws):
run = ws.getRun()
duration = run.endTime() - run.starme()
return float(duration.total_microseconds())
def _get_bm_counts(self, ws):
return float(ws.run().getProperty("bm_counts").value)
def _get_frame_count(self, ws):
return float(ws.run().getProperty("frame_count").value)
def _get_period(self, ws):
return float(ws.run().getProperty("period").value)
def _get_l1(self, ws):
return float(ws.run().getProperty("L1").value)
def _apply_mask(self, ws, mask):
alg = self.createChildAlgorithm("MaskDetectors")
alg.setProperty("Workspace", ws)
alg.setProperty("MaskedWorkspace", mask)
alg.execute()
def _convert_units(self, ws, unit):
alg = self.createChildAlgorithm("ConvertUnits")
alg.setProperty("InputWorkspace", ws)
alg.setProperty("Target", unit)
alg.execute()
return alg.getProperty("OutputWorkspace").value
def _rebin(self, ws, binning, preserveevents):
alg = self.createChildAlgorithm("Rebin")
alg.setProperty("InputWorkspace", ws)
alg.setProperty("Params", binning)
alg.setProperty("PreserveEvents", preserveevents)
alg.execute()
return alg.getProperty("OutputWorkspace").value
def _multiply(self, a, b):
alg = self.createChildAlgorithm("Multiply")
alg.setProperty("LHSWorkspace", a)
alg.setProperty("RHSWorkspace", b)
alg.execute()
return alg.getProperty("OutputWorkspace").value
def _scale_mult(self, ws_input, factor, operation):
alg = self.createChildAlgorithm("Scale")
alg.setProperty("InputWorkspace", ws_input)
alg.setProperty("Factor", factor)
alg.setProperty("Operation", operation)
alg.execute()
return alg.getProperty("OutputWorkspace").value
def _subtract(self, a, b):
alg = self.createChildAlgorithm("Minus")
alg.setProperty("LHSWorkspace", a)
alg.setProperty("RHSWorkspace", b)
alg.execute()
return alg.getProperty("OutputWorkspace").value
def _single_valued_ws(self, value):
alg = self.createChildAlgorithm("CreateSingleValuedWorkspace")
alg.setProperty("DataValue", value)
alg.execute()
return alg.getProperty("OutputWorkspace").value
def _mask_to_roi(self, ws_mask):
alg = self.createChildAlgorithm("InvertMask")
alg.setProperty("InputWorkspace", ws_mask)
alg.execute()
ws_tranmskinv = alg.getProperty("OutputWorkspace").value
alg = self.createChildAlgorithm("ExtractMask")
alg.setProperty("InputWorkspace", ws_tranmskinv)
alg.execute()
return alg.getProperty("DetectorList").value
def _calculate_transmission(self, ws_tranSam, ws_tranEmp, ws_tranroi, fitmethod, polynomialorder, binning):
alg = self.createChildAlgorithm("CalculateTransmission")
alg.setProperty("SampleRunWorkspace", ws_tranSam)
alg.setProperty("DirectRunWorkspace", ws_tranEmp)
alg.setProperty("TransmissionROI", ws_tranroi)
alg.setProperty("RebinParams", binning)
alg.setProperty("FitMethod", fitmethod) # new
alg.setProperty("PolynomialOrder", polynomialorder) # new
# FitMethod = 'Polynomial', PolynomialOrder = '4'
alg.execute()
return alg.getProperty("OutputWorkspace").value
def _wide_angle_correction(self, ws_sam, ws_tranSam):
alg = self.createChildAlgorithm("SANSWideAngleCorrection")
alg.setProperty("SampleData", ws_sam)
alg.setProperty("TransmissionData", ws_tranSam)
alg.execute()
return alg.getProperty("OutputWorkspace").value
def _emp_shape_adjustment(self, ws_emp, ws_tran):
if ws_emp.getNumberHistograms() != 1:
raise ValueError
if ws_tran.getNumberHistograms() != 1:
raise ValueError
ws_emp_bins = ws_emp.readX(0)
ws_tran_bins = ws_tran.readX(0)
if np.array_equal(ws_emp_bins, ws_tran_bins): # check that bins match
return ws_emp # if they match keep them as they are
self.sanslog.warning(
"EmptyBeamSpectrumShapeWorkspace did not have expected wavelength binning and has to be rebinned")
alg = self.createChildAlgorithm("RebinToWorkspace")
alg.setProperty("WorkspaceToRebin", ws_emp)
alg.setProperty("WorkspaceToMatch", ws_tran)
alg.setProperty("PreserveEvents", False)
alg.execute()
return alg.getProperty("OutputWorkspace").value
def _tofsansresolutionbypixel(self, ws_sam, sampleapertureradius, sourceapertureradius, sigmamoderator,
collimationlength, accountforgravity, extralength, deltar=5.0):
alg = self.createChildAlgorithm("TOFSANSResolutionByPixel")
alg.setProperty("InputWorkspace", ws_sam)
alg.setProperty("DeltaR", deltar)
alg.setProperty("SampleApertureRadius", sampleapertureradius)
alg.setProperty("SourceApertureRadius", sourceapertureradius)
alg.setProperty("SigmaModerator", sigmamoderator)
alg.setProperty("CollimationLength", collimationlength)
alg.setProperty("AccountForGravity", accountforgravity)
alg.setProperty("ExtraLength", extralength)
alg.execute()
return alg.getProperty("OutputWorkspace").value
def _q1d(self, ws_sam, binning_q, pixeladj, wavelengthadj, wavepixeladj, accountforgravity, solidangleweighting,
radiuscut, wavecut, extralength, qresolution):
alg = self.createChildAlgorithm("Q1D")
alg.setProperty("DetBankWorkspace", ws_sam)
alg.setProperty("OutputBinning", binning_q)
alg.setProperty("AccountForGravity", accountforgravity)
alg.setProperty("SolidAngleWeighting", solidangleweighting)
alg.setProperty("RadiusCut", radiuscut)
alg.setProperty("WaveCut", wavecut)
alg.setProperty("ExtraLength", extralength)
alg.setProperty("QResolution", qresolution)
# transmission and beam shape correction
if wavelengthadj:
alg.setProperty("WavelengthAdj", wavelengthadj)
# wide angle correction
if wavepixeladj:
alg.setProperty("wavePixelAdj", wavepixeladj)
# pixel sensitivity correction
if pixeladj:
alg.setProperty("PixelAdj", pixeladj)
alg.execute()
return alg.getProperty("OutputWorkspace").value
def _qxy(self, ws_sam, q_max, q_delta, pixeladj, wavelengthadj, accountforgravity, solidangleweighting,
extralength):
alg = self.createChildAlgorithm("Qxy")
alg.setProperty("InputWorkspace", ws_sam)
alg.setProperty("MaxQxy", q_max)
alg.setProperty("DeltaQ", q_delta)
alg.setProperty("AccountForGravity", accountforgravity)
alg.setProperty("SolidAngleWeighting", solidangleweighting)
alg.setProperty("ExtraLength", extralength)
# pixel sensitivity correction
if pixeladj:
alg.setProperty("PixelAdj", pixeladj)
# transmission and beam shape correction
if wavelengthadj:
alg.setProperty("WavelengthAdj", wavelengthadj)
alg.execute()
return alg.getProperty("OutputWorkspace").value
def _create_empty_ws(self, data_x, data_y, number_of_spectra, unitx):
# empty output workspace in case 2D reduction is not happening
alg = self.createChildAlgorithm("CreateWorkspace")
alg.setProperty('DataX', data_x)
alg.setProperty('DataY', data_y)
alg.setProperty('NSpec', number_of_spectra)
alg.setProperty('UnitX', unitx)
alg.execute()
return alg.getProperty("OutputWorkspace").value
class BeamCenterLogger(object):
'''
Logger during the beam centre operation. The logging will
depend partially on the type of the first coordinate, ie [m, m] or [degree, m].
It will also perform a correction for potential offsets like bench rotations.
'''
def __init__(self, reducer, coord1_scale_factor, coord2_scale_factor):
super(BeamCenterLogger, self).__init__()
self.logger = Logger("CentreFinder")
self.using_angle = False
if is_workspace_which_requires_angle(reducer):
self.coord1_scale_factor = 1.
self.using_angle = True
# Find the bench rotation. Only supply the bench rotation if it is really needed. If we supply an offset
# through a bench rotation we need to take into account that the directionality of the angles is not
# the same as in Mantid. We need to reverse the sign of the bench rotation to get the correct rotation.
self.offset_coord1 = -1*get_bench_rotation(reducer)
else:
self.coord1_scale_factor = coord1_scale_factor
self.offset_coord1 = 0.0
self.coord2_scale_factor = coord2_scale_factor
self.offset_coord2 = 0.0
def report_init(self, coord1, coord2):
'''
Report the initial setup
@param coord1: the first coordinate
@param coord2: the second coordinate
'''
if self.using_angle:
initial_msg = "beta_start"
else:
initial_msg = "x_start"
# We need to substract the offset from the coordinate, since we do not want to display the offset
# which is on the data
val1 = (coord1 - self.offset_coord1)*self.coord1_scale_factor
val2 = (coord2 - self.offset_coord2)*self.coord2_scale_factor
msg = initial_msg + ",ystart= %s %s" % (str(val1), str(val2))
self.logger.notice(msg)
self.logger.notice("Starting centre finding routine ...")
def report_status(self, iteration, coord1, coord2, resid1, resid2): #pylint: disable=too-many-arguments
'''
Report the status of a beam finder iteration
@param iteration: the number of the iteration
@param coord1: the first coordinate
@param coord2: the second coordinate
@param resid1: the residual of the first coordinate
@param resid2: the residual of the second coordinate
'''
msg = self.get_status_message(iteration, coord1, coord2, resid1, resid2)
self.logger.notice(msg)
def get_status_message(self, iteration, coord1, coord2, resid1, resid2): #pylint: disable=too-many-arguments
'''
Report the status of a beam finder iteration
@param iteration: the number of the iteration
@param coord1: the first coordinate
@param coord2: the second coordinate
@param resid1: the residual of the first coordinate
@param resid2: the residual of the second coordinate
'''
# We need to substract the offset from the coordinate, since we do not want to display the offset
# which is on the data
val1 = (coord1 - self.offset_coord1)* self.coord1_scale_factor
val2 = (coord2 - self.offset_coord2)* self.coord2_scale_factor
coord1str = str(val1).ljust(10)[0:9]
coord2str = str(val2).ljust(10)[0:9]
res1str = str(resid1).ljust(7)[0:6]
res2str = str(resid2).ljust(7)[0:6]
msg = "Itr %i: (%s, %s) SX=%s SY=%s" %(iteration, coord1str, coord2str, res1str, res2str)
return msg
def report(self, msg):
'''
Report a general message
@param msg: the message to report
'''
self.logger.notice(msg)
def report_final(self, coord1, coord2):
'''
Report the final coordinates which are set in the reducer
@param coord1: the first coordinate
@param coord2: the second coordinate
'''
# We shouldn't need an offset correction at this point as a possible.
# Also we need to multiply both entries with the same (1000) scaling,
# Because the first coordinate should have been corrected before
# being passed into this method. For reporting purposes we revert this
# correction. Also we don't need to remove the offset, since it the input
# already has it removed.
general_scale = self.coord2_scale_factor
val1 = (coord1)*general_scale
val2 = (coord2)*general_scale
msg = "Centre coordinates updated: [ %f, %f ]" %(val1, val2)
self.logger.notice(msg)
def __init__(self):
DataProcessorAlgorithm.__init__(self)
self.sanslog = Logger("ANSTO SANS Data reduction")
def PyExec(self):
state = self._get_state()
state_serialized = state.property_manager
logger = Logger("CentreFinder")
logger.notice("Starting centre finder routine...")
progress = self._get_progress()
self.scale_1 = 1000
self.scale_2 = 1000
verbose = self.getProperty('Verbose').value
x_start = self.getProperty("Position1Start").value
y_start = self.getProperty("Position2Start").value
sample_scatter = self._get_cloned_workspace("SampleScatterWorkspace")
sample_scatter_monitor = self._get_cloned_workspace("SampleScatterMonitorWorkspace")
sample_transmission = self._get_cloned_workspace("SampleTransmissionWorkspace")
sample_direct = self._get_cloned_workspace("SampleDirectWorkspace")
instrument = sample_scatter.getInstrument()
if instrument.getName() == 'LARMOR':
self.scale_1 = 1.0
can_scatter = self._get_cloned_workspace("CanScatterWorkspace")
can_scatter_monitor = self._get_cloned_workspace("CanScatterMonitorWorkspace")
can_transmission = self._get_cloned_workspace("CanTransmissionWorkspace")
can_direct = self._get_cloned_workspace("CanDirectWorkspace")
component = self.getProperty("Component").value
tolerance = self.getProperty("Tolerance").value
max_iterations = self.getProperty("Iterations").value
r_min = self.getProperty("RMin").value
r_max = self.getProperty("RMax").value
instrument_file = get_instrument_paths_for_sans_file(state.data.sample_scatter)
position_1_step = get_named_elements_from_ipf_file(
instrument_file[1], ["centre-finder-step-size"], float)['centre-finder-step-size']
try:
position_2_step = get_named_elements_from_ipf_file(
instrument_file[1], ["centre-finder-step-size2"], float)['centre-finder-step-size2']
except:
position_2_step = position_1_step
find_direction = self.getProperty("Direction").value
if find_direction == FindDirectionEnum.to_string(FindDirectionEnum.Left_Right):
position_2_step = 0.0
elif find_direction == FindDirectionEnum.to_string(FindDirectionEnum.Up_Down):
position_1_step = 0.0
centre1 = x_start
centre2 = y_start
residueLR = []
residueTB = []
centre_1_hold = x_start
centre_2_hold = y_start
for j in range(0, max_iterations + 1):
if(j != 0):
centre1 += position_1_step
centre2 += position_2_step
progress.report("Reducing ... Pos1 " + str(centre1) + " Pos2 " + str(centre2))
sample_quartiles = self._run_quartile_reduction(sample_scatter, sample_transmission, sample_direct,
"Sample", sample_scatter_monitor, component,
state_serialized, centre1, centre2, r_min, r_max)
if can_scatter:
can_quartiles = self._run_quartile_reduction(can_scatter, can_transmission, can_direct, "Can",
can_scatter_monitor, component, state_serialized, centre1,
centre2, r_min, r_max)
for key in sample_quartiles:
sample_quartiles[key] = perform_can_subtraction(sample_quartiles[key], can_quartiles[key], self)
if mantidplot:
output_workspaces = self._publish_to_ADS(sample_quartiles)
if verbose:
self._rename_and_group_workspaces(j, output_workspaces)
residueLR.append(self._calculate_residuals(sample_quartiles[MaskingQuadrant.Left],
sample_quartiles[MaskingQuadrant.Right]))
residueTB.append(self._calculate_residuals(sample_quartiles[MaskingQuadrant.Top],
sample_quartiles[MaskingQuadrant.Bottom]))
if(j == 0):
logger.notice("Itr {0}: ( {1}, {2} ) SX={3:.5g} SY={4:.5g}".
format(j, self.scale_1 * centre1, self.scale_2 * centre2, residueLR[j], residueTB[j]))
if mantidplot:
self._plot_quartiles(output_workspaces, state.data.sample_scatter)
else:
# have we stepped across the y-axis that goes through the beam center?
if residueLR[j] > residueLR[j-1]:
# yes with stepped across the middle, reverse direction and half the step size
position_1_step = - position_1_step / 2
if residueTB[j] > residueTB[j-1]:
position_2_step = - position_2_step / 2
logger.notice("Itr {0}: ( {1}, {2} ) SX={3:.5g} SY={4:.5g}".
format(j, self.scale_1 * centre1, self.scale_2 * centre2, residueLR[j], residueTB[j]))
if (residueLR[j]+residueTB[j]) < (residueLR[j-1]+residueTB[j-1]) or state.compatibility.use_compatibility_mode:
centre_1_hold = centre1
centre_2_hold = centre2
if abs(position_1_step) < tolerance and abs(position_2_step) < tolerance:
# this is the success criteria, we've close enough to the center
logger.notice("Converged - check if stuck in local minimum! ")
break
if j == max_iterations:
logger.notice("Out of iterations, new coordinates may not be the best")
self.setProperty("Centre1", centre_1_hold)
self.setProperty("Centre2", centre_2_hold)
logger.notice("Centre coordinates updated: [{}, {}]".format(centre_1_hold*self.scale_1, centre_2_hold*self.scale_2))
