def init_ui(data_muggler):
""" Do the enaml import and set up of the UI
Parameters
----------
data_muggler : replay.pipeline.DataMuggler
"""
with enaml.imports():
from pipeline_w2d import PipelineView
histogram_model = HistogramModel()
c_c_combo_fitter = MultiFitController(valid_models=valid_models)
scalar_collection = ScalarCollection()
scalar_collection.data_muggler = data_muggler
scalar_collection.multi_fit_controller = c_c_combo_fitter
cs_model = CrossSectionModel(data_muggler=data_muggler,
histogram_model=histogram_model)
view = PipelineView(histogram_model=histogram_model)
histogram_model.cmap = cs_model.cmap
# provide the pipeline view with its attributes
view.scalar_collection=scalar_collection
view.multi_fit_controller = c_c_combo_fitter
view.cs_model = cs_model
view.start_observation = start_observation
view.stop_observation = stop_observation
view.clear_data = clear_datamuggler
view.reinit_data = init_datamuggler
cmap = cs_model.cmap
cs_model.cmap = 'gray'
cs_model.cmap = cmap
return view
def init_ui(data_muggler):
""" Do the enaml import and set up of the UI
Parameters
----------
data_muggler : replay.pipeline.DataMuggler
"""
with enaml.imports():
from pipeline_watching_PVs import PipelineView
c_c_combo_fitter = MultiFitController(valid_models=valid_models)
scalar_collection = ScalarCollection()
scalar_collection.data_muggler = data_muggler
scalar_collection.multi_fit_controller = c_c_combo_fitter
view = PipelineView()
# provide the pipeline view with its attributes
view.scalar_collection=scalar_collection
view.multi_fit_controller = c_c_combo_fitter
return view
# p2 output -> dm2
p2.source_signal.connect(dm.append_data)
app = QtApplication()
with enaml.imports():
from pipeline import PipelineView
img_seq = DmImgSequence(data_muggler=dm, data_name='img')
cs_model = CrossSectionModel(data_muggler=dm, name='img',
sliceable_data=img_seq)
roi_model = RegionOfInterestModel(callback=roi_callback)
from replay.model.fitting_model import FitController
multi_fit_controller = MultiFitController(valid_models=valid_models)
scalar_collection = ScalarCollection(data_muggler=dm,
multi_fit_controller=multi_fit_controller)
scalar_collection.scalar_models['count'].is_plotting = False
scalar_collection.scalar_models['T'].is_plotting = False
scalar_collection.scalar_models['x'].is_plotting = False
scalar_collection.scalar_models['y'].is_plotting = False
scalar_collection.fit_target = 'max'
view = PipelineView(scalar_collection=scalar_collection,
cs_model=cs_model,
roi_model=roi_model,
multi_fit_controller=multi_fit_controller)
view.show()
frame_source.start()
app.start()