def update_data(self, dataset_control, min_control, max_control,
scale_control):
"""
Update a data set
@param dataset_control: combo box with the file path or workspace name
@param min_control: text widget containing the minimum Q of the overlap region
@param max_control: text widget containing the maximum Q of the overlap region
@param scale_control: text widget containing the scale (can be input or output)
"""
data_object = None
file_in = str(dataset_control.lineEdit().text())
if len(file_in.strip()) == 0:
data_object = None
elif os.path.isfile(file_in) or AnalysisDataService.doesExist(file_in):
data_object = DataSet(file_in)
try:
data_object.load(True)
except (AttributeError, ImportError, NameError, TypeError,
ValueError, Warning):
data_object = None
util.set_valid(dataset_control.lineEdit(), False)
QtGui.QMessageBox.warning(
self, "Error loading file",
"Could not load %s.\nMake sure you pick the XML output from the reduction."
% file_in)
return
if min_control is not None and max_control is not None \
and (len(min_control.text()) == 0 or len(max_control.text()) == 0):
minx, maxx = data_object.get_range()
min_control.setText("%-6.3g" % minx)
max_control.setText("%-6.3g" % maxx)
# Set the reference scale, unless we just loaded the data
if len(scale_control.text()) == 0:
scale_control.setText("1.0")
else:
scale = util._check_and_get_float_line_edit(scale_control)
data_object.set_scale(scale)
util.set_valid(dataset_control.lineEdit(), True)
else:
data_object = None
util.set_valid(dataset_control.lineEdit(), False)
self._plotted = False
return data_object
def stitch_data(self, input_file, output_dir, q_min, q_step):
from LargeScaleStructures.data_stitching import DataSet, Stitcher #, RangeSelector
# Identify the data sets to stitch and order them
workspace_list = []
_list_name = []
_list_ts = []
ws_list = AnalysisDataService.getObjectNames()
for item in ws_list:
if item.endswith('ts'):
(_name, _ts) = item.split('_#')
_list_name.append(item)
_list_ts.append(_ts)
_name_ts = zip(_list_ts, _list_name)
_name_ts.sort()
_ts_sorted, workspace_list = zip(*_name_ts)
# Stitch the data
s = Stitcher()
q_max = 0
for item in workspace_list:
data = DataSet(item)
data.load(True, True)
dummy_x_min, x_max = data.get_range()
if x_max > q_max:
q_max = x_max
s.append(data)
s.set_reference(0)
s.compute()
# Apply the scaling factors
for data in s._data_sets:
Scale(InputWorkspace=str(data),
OutputWorkspace=data._ws_scaled,
Operation="Multiply",
Factor=data.get_scale())
SaveAscii(InputWorkspace=str(data),
Filename=os.path.join(output_dir, '%s.txt' % str(data)))
output_file = input_file.replace('.xml', '_reprocessed.txt')
Logger("REFLReprocess").notice("Saving to %s" % output_file)
output_ws = _average_y_of_same_x_(q_min, q_step, q_max)
SaveAscii(InputWorkspace=output_ws, Filename=output_file)
def update_data(self, dataset_control, min_control, max_control,
scale_control):
"""
Update a data set
@param dataset_control: combo box with the file path or workspace name
@param min_control: text widget containing the minimum Q of the overlap region
@param max_control: text widget containing the maximum Q of the overlap region
@param scale_control: text widget containing the scale (can be input or output)
"""
data_object = None
file_in = str(dataset_control.lineEdit().text())
if len(file_in.strip()) == 0:
data_object = None
elif os.path.isfile(file_in) or AnalysisDataService.doesExist(file_in):
data_object = DataSet(file_in)
try:
data_object.load(True)
except (StandardError, Warning):
data_object = None
util.set_valid(dataset_control.lineEdit(), False)
QtGui.QMessageBox.warning(self, "Error loading file",
"Could not load %s.\nMake sure you pick the XML output from the reduction." % file_in)
return
if min_control is not None and max_control is not None \
and (len(min_control.text()) == 0 or len(max_control.text()) == 0):
minx, maxx = data_object.get_range()
min_control.setText("%-6.3g" % minx)
max_control.setText("%-6.3g" % maxx)
# Set the reference scale, unless we just loaded the data
if len(scale_control.text()) == 0:
scale_control.setText("1.0")
else:
scale = util._check_and_get_float_line_edit(scale_control)
data_object.set_scale(scale)
_npts = data_object.get_number_of_points()
util.set_valid(dataset_control.lineEdit(), True)
else:
data_object = None
util.set_valid(dataset_control.lineEdit(), False)
self._plotted = False
return data_object
def stitch_data(self, input_file, output_dir, q_min, q_step):
from LargeScaleStructures.data_stitching import DataSet, Stitcher, RangeSelector
# Identify the data sets to stitch and order them
workspace_list = []
_list_name = []
_list_ts = []
ws_list = AnalysisDataService.getObjectNames()
for item in ws_list:
if item.endswith('ts'):
(_name,_ts) = item.split('_#')
_list_name.append(item)
_list_ts.append(_ts)
_name_ts = zip(_list_ts, _list_name)
_name_ts.sort()
_ts_sorted, workspace_list = zip(*_name_ts)
# Stitch the data
s = Stitcher()
q_max = 0
for item in workspace_list:
data = DataSet(item)
data.load(True, True)
x_min, x_max = data.get_range()
if x_max > q_max:
q_max = x_max
s.append(data)
s.set_reference(0)
s.compute()
# Apply the scaling factors
for data in s._data_sets:
Scale(InputWorkspace=str(data), OutputWorkspace=data._ws_scaled,
Operation="Multiply", Factor=data.get_scale())
SaveAscii(InputWorkspace=str(data), Filename=os.path.join(output_dir, '%s.txt' % str(data)))
output_file = input_file.replace('.xml', '_reprocessed.txt')
Logger("REFLReprocess").notice("Saving to %s" % output_file)
output_ws = _average_y_of_same_x_(q_min, q_step, q_max)
SaveAscii(InputWorkspace=output_ws, Filename=output_file)
