HPPT
The software is organized in a Model-View-Presenter pattern. The main (Tavi) model, view and presenter classes and their components interactions are described here.
Tavi Model
The TaviModel encapsulates the backend functionality of data calculations. The object fields are updated every time there are new valid values received from the user (front end).
classDiagram
TaviModel "1" -->"1" CrosshairParameters
CrosshairParameters "1" -->"1" SingleCrystalParameters
class TaviModel{
+float incident_energy_ei
+float detector_tank_angle_s2
+float polarization_direction_angle_p
+enum 'PlotType' plot_type
+CrosshairParameters crosshair_parameters
+set_single_crystal_parameters(params: dict[str, float])
+get_single_crystal_parameters()
+set_crosshair(current_experiment_type: str, DeltaE: float = None, modQ: float = None)
+get_crosshair()
+get_ang_Q_beam()
+set_experiment_parameters(Ei: float, S2: float, alpha_p: float, plot_type: str)
+get_experiment_parameters()
+check_plot_update(deltaE)
+calculate_graph_data()
}
class CrosshairParameters{
+float crosshair_delta_e
+float crosshair_mod_q
+experiment_type: str
+SingleCrystalParameters single_crystal_parameters
+set_crosshair(current_experiment_type: str, DeltaE: float = None, modQ: float = None)
+get_crosshair()
+get_experiment_type()
}
class SingleCrystalParameters{
+float lattice_a
+float lattice_b
+float lattice_c
+float lattice_alpha
+float lattice_beta
+float lattice_gamma
+float q_rlu_h
+float q_rlu_k
+float q_rlu_l
+set_single_crystal_parameters(params: dict[str, float])
+get_single_crystal_parameters()
+calculate_crosshair_mod_q()
}
Tavi View
classDiagram
TaviView "1" -->"1" ExperimentWidget
TaviView "1" -->"1" CrosshairWidget
TaviView "1" -->"1" SingleCrystalWidget
TaviView "1" -->"1" SelectorWidget
TaviView "1" -->"1" PlotWidget
class TaviView{
+ExperimentWidget:experiment_widget
+SingleCrystalWidget:sc_widget
+CrosshairWidget:crosshair_widget
+SelectorWidget:selection_widget
+PlotWidget:plot_widget
}
class PlotWidget{
+matplotlib.Figure: figure
+matplotlib.FigureCanvas: static_canvas
+matplotlib.NavigationToolbar: toolbar
+matplotlib.colorbar.Colorbar: heatmap
+float:eline_data
+float:qline_data
+matplotlib.lines.Line2D:eline
+matplotlib.lines.Line2D:qline
+update_plot_crosshair(crosshair_data: dict)
+update_crosshair(eline: float, qline: float)
+update_plot(q_min: list[float], q_max: list[float], energy_transfer: list[float], q2d: list[list[float]], e2d: list[list[float]], scharpf_angle: list[list[float]],plot_label: str)
+set_axes_meta_and_draw_plot()
}
class SelectorWidget{
+str:powder_label
+QRadioButton: powder_rb
+str:sc_label
+QRadioButton: sc_rb
+selector_init(selected_label: str)
+sc_toggle()
+get_selected_mode_label()
}
class ExperimentWidget{
+QLabel:incident_energy_ei_label
+QLineEdit:incident_energy_ei_edit
+QLabel:detector_tank_angle_s2_label
+QLineEdit:detector_tank_angle_s2_edit
+QLabel:polarization_direction_angle_p_label
+QLineEdit:polarization_direction_angle_p_edit
+QLabel:plot_type_label
+QComboBox:plot_type_combobox
+initializeCombo(options: list[str])
+validate_inputs(*_, **__)
+validate_all_inputs()
+set_values(values: dict[str, Union[float, str]])
}
class CrosshairWidget{
+QLabel:crosshair_delta_e_label
+QLineEdit:crosshair_delta_e_edit
+QLabel:crosshair_mod_q_label
+QLineEdit:crosshair_mod_q_edit
+QLabel:angle_q_z_label
+QLineEdit:angle_q_z_edit
+set_mod_q_enabled(state: bool)
+set_values(values: dict[str, float])
+validate_inputs(*_, **__)
+validation_status_all_inputs()
+validate_all_inputs()
+set_QZ_values(angle: float)
}
class SingleCrystalWidget{
+QLabel:lattice_a_label
+QLineEdit:lattice_a_edit
+QLabel:latticeb_label
+QLineEdit:lattice_b_edit
+QLabel:lattice_c_label
+QLineEdit:lattice_c_edit
+QLabel:lattice_alpha_label
+QLineEdit:lattice_alpha_edit
+QLabel:lattice_beta_label
+QLineEdit:lattice_beta_edit
+QLabel:lattice_gamma_label
+QLineEdit:lattice_gamma_edit
+QLabel:q_rlu_h_label
+QLineEdit:q_rlu_h_edit
+QLabel:q_rlu_k_label
+QLineEdit:q_rlu_k_edit
+QLabel:q_rlu_l_label
+QLineEdit:q_rlu_l_edit
+set_values(values: dict[str, float])
+validate_inputs(*_, **__)
+validate_angles()
+validate_all_inputs()
}
Tavi Presenter
classDiagram
TaviPresenter "1" -->"1" TaviModel
TaviPresenter "1" -->"1" TaviView
class TaviPresenter{
-TaviModel:model
-TaviView:view
+handle_field_values_update()
+handle_switch_to_powder()
+handle_switch_to_sc()
+handle_QZ_angle()
}
class TaviModel{
#from above
}
class TaviView{
#from above
}
The Hppt Model and View are unaware of one another. The Presenter is the connecting link that has a direct access and interacts with both. The Presenter describes the main workflows that require communication and coordination between the Model and View through the Presenter. Additionally, the widgets’ data initialization come from the model initialization and passed to the View. Any value processing and/or filtering to match the requirements and logic of the View and Model side should happen on the Presenter.
Application Start - TaviView Initialization. All default values are retrieved from the settings file.
sequenceDiagram participant View participant Presenter participant Model Note over View,Model: TaviView Initialization Presenter->>Model: A. Get Experiment parameters Presenter->>View: Set Experiment parameters (experiment_widget.set_values) Note left of View: Display Experiment parameters values Note left of View: experiment_parameters_update is triggered Presenter->>Model: B. Get SingleCrystal parameters Note left of Presenter: Get the available plot types from the experiment_settings file Presenter->>View: Set SingleCrystal parameters (singlecrystal_widget.set_parameters) Note left of View: Display SingleCrystal parameters values Note left of View: handle_field_values_update is triggered Presenter->>Model: C. Get default experiment mode (Single Crystal) Presenter->>View: Set experiment mode (selection_widget.selector_init) Note left of View: Workflow continues for selecting experiment type = Single Crystal Note left of View: handle_field_values_update is triggeredThis describes the sequence of events happening among M-V-P when CrosshairWidget parameters are updated in order to see a new plot : handle_field_values_update()
Valid Status with Replot:
sequenceDiagram participant View participant Presenter participant Model Note over View,Model: Plot draw due to any CrosshairWidget parameter update Note left of View: User updates a parameter at CrosshairWidget Note left of View: Check the validation status of all CrosshairWidget parameters (CrosshairWidget.validate_all_inputs) View->>Presenter: Emit the valid signal and pass the crosshair parameters Presenter->>View: Get the experiment type Presenter->>Model: Send crosshair_delta_e to decide on replot Note right of Model: Calculate replot based on new delta_e value, and previous Emin, delta_e values Model->>Presenter: Returns the replot to True Presenter->>Model: Set crosshair data (set_crosshair_data) Note right of Model: Store the crosshair data Presenter->>Model: Get momentum transfer angle Model->>Presenter: Return momentum transfer angle Presenter->>Model: Calculate plot data (calculate_graph_data) Note right of Model: Calculate plot dictionary data Model->>Presenter: Return graph data dictionary Presenter->>View: Return graph data (plot_widget.update_plot) Note left of View: Draw the (colormap) heatmap Presenter->>View: Return graph data (plot_widget.update_crosshair) Note left of View: Draw the crosshair Presenter->>View: Display momentum transfer angle Note left of View: Update momentum transfer angleValid Status without Replot:
sequenceDiagram participant View participant Presenter participant Model Note over View,Model: Plot draw due to any CrosshairWidget parameter update Note left of View: User updates a parameter at CrosshairWidget Note left of View: Check the validation status of all CrosshairWidget parameters (CrosshairWidget.validate_all_inputs) View->>Presenter: Emit the valid signal and pass the crosshair parameters Presenter->>Model: Send crosshair_delta_e to decide on replot Note right of Model: Calculate replot based on new delta_e value, and previous Emin, delta_e values Model->>Presenter: Returns the replot to False Presenter->>Model: Set crosshair data (set_crosshair_data) Note right of Model: Store the crosshair data Presenter->>Model: Get momentum transfer angle Model->>Presenter: Return momentum transfer angle Presenter->>View: Return graph data (plot_widget.update_crosshair) Note left of View: Draw the crosshair Presenter->>View: Display momentum transfer angle Note left of View: Update momentum transfer angleInvalid Status:
sequenceDiagram participant View participant Presenter participant Model Note over View,Model: CrosshairWidget parameter update Note left of View: User updates a parameter at CrosshairWidget Note Left of View: Check the validation status of all CrosshairWidget parameters (CrosshairWidget.validate_all_inputs) Note Left of View: Red borders appear (validate_inputs) no signal is emittedThis describes the sequence of events happening among M-V-P when ExperimentWidget parameters are updated in order to see a new plot : handle_field_values_update()
Valid Status:
sequenceDiagram participant View participant Presenter participant Model Note over View,Model: Plot draw due to any ExperimentWidget parameter update Note left of View: User updates a parameter at ExperimentWidget Note left of View: Check the validation status of all ExperimentWidget parameters (ExperimentWidget.validate_all_inputs) View->>Presenter: Emit the valid signal and pass the experiment parameters Presenter->>Model: Set the parameters (set_experiment_data) Presenter->>Model: Calculate plot data (calculate_graph_data) Note right of Model: Calculate plot dictionary data Model->>Presenter: Return graph data dictionary Presenter->>View: Return graph data (plot_widget.update_plot) Presenter->>Model: Get momentum transfer angle Model->>Presenter: Return momentum transfer angle Note left of View: Draw the (colormap) heatmap Presenter->>View: Display momentum transfer angle Note left of View: Update momentum transfer angleInvalid Status:
sequenceDiagram participant View participant Presenter participant Model Note over View,Model: ExperimentWidget parameter update Note left of View: User updates a parameter at ExperimentWidget Note left of View: Check the validation status of all ExperimentWidget parameters (ExperimentWidget.validate_all_inputs) Note Left of View: Red borders appear (validate_inputs) no signal is emittedThis describes the sequence of events happening among M-V-P when Single Crystal parameters are updated in order to see a new plot : handle_field_values_update()
Valid Status:
sequenceDiagram participant View participant Presenter participant Model Note over View,Model: Plot draw due to any SingleCrystalWidget parameter update Note left of View: User updates a parameter at SingleCrystalWidget Note left of View: Check the validation status of all SingleCrystalWidget parameters (SingleCrystalWidget.validate_all_inputs) View->>Presenter: Emit the valid signal and pass the single crystal parameters Presenter->>Model: Set the parameters (set_single_crystal_data) Presenter->>Model: Get the new crosshair data (get_crosshair_data) Presenter->>Model: Get momentum transfer angle Model->>Presenter: Return momentum transfer angle Presenter->>View: Display the crosshair data (crosshair_widget.set_values) Note left of Presenter: Check the validation status of all crosshair_widget parameters (CrosshairWidget.validation_status_all_inputs) is valid Presenter->>View: Return graph data (plot_widget.update_crosshair) Note left of View: Draw the crosshair Presenter->>View: Display momentum transfer angle Note left of View: Update momentum transfer angleInvalid Status:
sequenceDiagram participant View participant Presenter participant Model Note over View,Model: Plot draw due to any SingleCrystalWidget parameter update Note left of View: User updates a parameter at SingleCrystalWidget Note left of View: Check the validation status of all SingleCrystalWidget parameters (SingleCrystalWidget.validate_all_inputs) View->>Presenter: Emit the valid signal and pass the single crystal parameters Presenter->>Model: Set the parameters (set_single_crystal_data) Presenter->>Model: Get the new crosshair data (get_crosshair_data) Presenter->>View: Display the crosshair data (crosshair_widget.set_values) Note left of Presenter: Check the validation status of all crosshair_widget parameters (CrosshairWidget.validation_status_all_inputs) is invalid Note left of Presenter: Nothing
This describes the sequence of events happening among M-V-P when user selects the “Powder” mode : handle_switch_to_powder()
sequenceDiagram participant View participant Presenter participant Model Note over View,Model: Updates due to switching to Powder Mode Note left of View: User selects the Powder radio button View->>Presenter: Trigger the update Presenter->>View: Update fields' visibility for powder case(field_visibility_in_Powder) Note left of View: Hide the SingleCrystalWidget Note left of View: Make crosshair_mod_q_edit field editable Presenter->>Model: Set crosshair parameters with the experiment_type="powder" (set_crosshair_data) Note right of Model: Store the crosshair data Presenter->>Model: Get crosshair parameters for the experiment_type="powder"(get_crosshair_data) Note right of Model: Return the mod_q and the delta_e values Model->>Presenter: Return the crosshair data Presenter->>View: Return the crosshair data Note left of View: Display the data in the crosshair_widget Presenter->>View: Return graph data (plot_widget.update_crosshair) Note left of View: Draw the crosshair Presenter->>Model: Get experiment parameters (get_experiment_data) Presenter->>View: Set experiment parameters (experiment_widget.set_values) Note left of View: Display experiment parameters values Note left of View: handle_field_values_update is triggeredThis describes the sequence of events happening among M-V-P when user selects the “Single Crystal” mode : handle_switch_to_sc()
Valid Status:
sequenceDiagram participant View participant Presenter participant Model Note over View,Model: Updates due to switching to Single Crystal Mode Note left of View: User selects the Single Crystal radio button View->>Presenter: Trigger the update Presenter->>View: Update fields' visibility for single crystal case(field_visibility_in_SC) Note left of View: Display the SingleCrystalWidget Note left of View: Make crosshair_mod_q_edit field readonly Presenter->>Model: Set crosshair parameters with the experiment_type="SingleCrystal" (set_crosshair_data) Note right of Model: Store the crosshair data Presenter->>Model: Get crosshair parameters with the experiment_type="SingleCrystal"(get_crosshair_data) Note right of Model: Calculate the mod_q from the single crystal parameters and return it with the delta_e value Model->>Presenter: Return the crosshair data Presenter->>View: Return the crosshair data Note left of View: Display the data in the crosshair_widget Presenter->>Model: Get experiment parameters (get_experiment_data) Presenter->>View: Set experiment parameters (experiment_widget.set_values) Note left of View: Display experiment parameters values Note left of View: handle_field_values_update is triggered Presenter->>Model: Get single crystal parameters (get_single_crystal_data) Presenter->>Model: Get momentum transfer angle Model->>Presenter: Return momentum transfer angle Presenter->>View: Set single crystal parameters (singlecrystal_widget.set_parameters) Note left of View: Display SingleCrystal parameters values Note left of View: handle_field_values_update is triggered Presenter->>View: Display momentum transfer angle Note left of View: Update momentum transfer angleInvalid Status:
sequenceDiagram participant View participant Presenter participant Model Note over View,Model: Updates due to switching to Single Crystal Mode Note left of View: User selects the Single Crystal radio button View->>Presenter: Triggers the update Presenter->>View: Update fields' visibility for single crystal case (field_visibility_in_SC) Note left of View: Display the SingleCrystalWidget Note left of View: Make crosshair_mod_q_edit field readonly Presenter->>Model: Set crosshair parameters with the experiment_type="SingleCrystal" (set_crosshair_data) Note right of Model: Store the crosshair data Presenter->>Model: Get crosshair parameters with the experiment_type="SingleCrystal"(get_crosshair_data) Note right of Model: Calculate the mod_q from the single crystal parameters and return it with the delta_e value Model->>Presenter: Return the crosshair data Presenter->>View: Return the crosshair data Presenter->>Model: Get experiment parameters (get_experiment_data) Presenter->>View: Set experiment parameters (experiment_widget.set_values) Note left of View: Display experiment parameters values Note left of View: handle_field_values_update is triggered Presenter->>Model: Get single crystal parameters (get_single_crystal_data) Presenter->>View: Set single crystal parameters (singlecrystal_widget.set_parameters) Note left of View: Display SingleCrystal parameters values Note left of View: handle_field_values_update is triggered
Experiment Settings
The parameters’ default values for the application are stored in a file, experiment_settings.py, next to the model file. They are imported in the Tavi Model file and used during the Experiment object’s initialization and data calculations. The options for experiment and plot types are used in Tavi Model and View files. More specifically the parameters with their values are:
- Experiment type options
- plot type options
- DEFAULT_MODE:dict =
experiment_type=”single_crystal”
- DEFAULT_CROSSHAIR: dict =
delta_e = 0
mod_q = 0
- DEFAULT_EXPERIMENT:dict =
plot_type = PlotType.COS_2_ALPHA_S
incident_energy_e = 20
detector_tank_angle_s = 30
polarization_direction_angle_p = 0
- DEFAULT_LATTICE:dict =
a = 1
b = 1
c = 1
alpha = 90
beta = 90
gamma = 90
h = 0
k = 0
l = 0
MAX_MODQ = 15 – maximum momentum transfer
N_POINTS = 200 – number of points in the plot
TANK_HALF_WIDTH = 30.0 – tank half-width