Replaced List[Datapoint] objects with DatapointCollections

Toolbar background color now changes with appearance mode

napytau/gui/app.py

@@ -16,6 +16,7 @@
 from napytau.gui.components.Toolbar import Toolbar
 
 from napytau.import_export.model.datapoint import Datapoint
+from napytau.import_export.model.datapoint_collection import DatapointCollection
 from napytau.util.model.value_error_pair import ValueErrorPair
 
 
@@ -34,9 +35,9 @@ def __init__(self) -> None:
         super().__init__()
 
         # Datapoints
-        self.datapoints: List[Datapoint] = []
-        self.datapoints_for_fitting: List[Datapoint] = []
-        self.datapoints_for_calculation: List[Datapoint] = []
+        self.datapoints: DatapointCollection = DatapointCollection([])
+        self.datapoints_for_fitting: DatapointCollection = DatapointCollection([])
+        self.datapoints_for_calculation: DatapointCollection = DatapointCollection([])
 
         # values
         self.tau = tk.IntVar()
@@ -202,11 +203,11 @@ def update_data_checkboxes(self, new_datapoints: List[Datapoint]) -> None:
         Call this method if there are new datapoints.
         :param new_datapoints: The new list of datapoints.
         """
-        self.datapoints = new_datapoints.copy()
+        self.datapoints = DatapointCollection(new_datapoints)
 
         for point in new_datapoints:
-            self.datapoints_for_fitting.append(point)
-            self.datapoints_for_calculation.append(point)
+            self.datapoints_for_fitting.add_datapoint(point)
+            self.datapoints_for_calculation.add_datapoint(point)
 
         self.checkbox_panel.update_data_checkboxes_fitting()
         self.checkbox_panel.update_data_checkboxes_calculation()

napytau/gui/components/Toolbar.py

@@ -1,16 +1,27 @@
-from tkinter import Canvas
 import tkinter as tk
+
 from matplotlib.backends.backend_tkagg import NavigationToolbar2Tk
 
 
 class Toolbar():
     def __init__(self, parent,):
         self.parent = parent
 
+        #Create frame to hold Toolbar
 
         toolbar_frame = tk.Frame(parent)
         toolbar_frame.config(bg="white")
-        toolbar_frame.grid(row=0, column=0, sticky="new")  # Use grid for the frame
-        toolbar = NavigationToolbar2Tk(self.parent.graph.canvas, toolbar_frame)
-        #toolbar.config(bg="white")# Pack inside the frame
+        toolbar_frame.grid(row=0,
+                           column=0,
+                           padx=10,
+                           pady=10,
+                           sticky="new"
+                           )
+
+        #Create Toolbar
+
+        toolbar = NavigationToolbar2Tk(self.parent.graph.canvas,
+                                       toolbar_frame)
+        #Adjust background color
+        toolbar.config(bg=self.parent.graph.main_color)
         toolbar.update()

napytau/gui/components/control_panel.py

@@ -66,6 +66,6 @@ def calc(self) -> None:
         """
         Starts the main calculation.
         """
-        #entry_value = self.parent.tau.get()
+        entry_value = self.parent.tau.get()
 
-        #self.label.configure(text=f"Result: {logic(entry_value)}")
+        self.label.configure(text=f"Result: {entry_value}")

napytau/gui/components/graph.py

@@ -13,9 +13,7 @@
 from napytau.gui.model.marker_factory import generate_error_marker_path
 
 from napytau.import_export.model.datapoint import Datapoint
-from napytau.import_export.model.datapoint import (get_checked_datapoints,
-                                                   get_distances,
-                                                   get_shifted_intensities)
+from napytau.import_export.model.datapoint_collection import DatapointCollection
 
 
 if TYPE_CHECKING:
@@ -85,7 +83,7 @@ def plot(self, appearance: str) -> Canvas:
 
         # draw the fitting curve on the axes
         self.plot_fitting_curve(
-            self.parent.datapoints_for_fitting,
+            DatapointCollection(self.parent.datapoints_for_fitting),
             axes_1)
 
 
@@ -161,7 +159,8 @@ def plot_markers(self,
             )
             index = index + 1
 
-    def plot_fitting_curve(self, datapoints: list[Datapoint],
+    def plot_fitting_curve(self,
+                           datapoints: DatapointCollection,
                            axes: Axes)-> None:
 
         """
@@ -173,15 +172,24 @@ def plot_fitting_curve(self, datapoints: list[Datapoint],
         """
 
         #Extracting distance values / intensities of checked datapoints
-        checked_datapoints: list[Datapoint] = get_checked_datapoints(datapoints)
-
-        checked_distances: list[float] = (
-            get_distances(checked_datapoints)
-        )
-        checked_shifted_intensities: list[float] = (
-            get_shifted_intensities(checked_datapoints)
+        checked_datapoints: DatapointCollection = (
+            datapoints.get_active_datapoints()
         )
 
+
+
+        checked_distances: list[float] = [
+            valueErrorPair.value
+            for valueErrorPair in checked_datapoints.get_distances()
+        ]
+
+
+        checked_shifted_intensities: list[float] = [
+            valueErrorPair.value
+            for valueErrorPair in checked_datapoints.get_shifted_intensities()
+        ]
+
+
         # Calculating coefficients
         coeffs = np.polyfit(checked_distances,
                             checked_shifted_intensities,

napytau/import_export/model/datapoint.py

@@ -89,30 +89,3 @@ def set_active(self, active: bool) -> None:
         self.active = active
 
 
-def get_checked_datapoints( datapoints: list[Datapoint]) -> list[Datapoint]:
-
-    checked_datapoints: list[Datapoint] = []
-    for datapoint in datapoints:
-        if datapoint.active:
-            checked_datapoints.append(datapoint)
-
-    return checked_datapoints
-
-
-
-def get_distances( datapoints: list[Datapoint]) -> list[float]:
-
-    distances = []
-    for datapoint in datapoints:
-        distances.append(datapoint.get_distance().value)
-
-    return distances
-
-def get_shifted_intensities( datapoints: list[Datapoint]) -> list[float]:
-
-    intensities = []
-    for datapoint in datapoints:
-        intensities.append(datapoint.get_intensity()[0].value)
-
-    return intensities
-