Gave user the option to not visualise the variance with the 'visualize_variance' argument

Author: deniselezi

truelearn/utils/visualisations/_bar_plotter.py

@@ -41,13 +41,15 @@ def __init__(
             ylabel: The default y label of the visualization
         """
         super().__init__(title, xlabel, ylabel)
-
+    
+    # pylint: disable=too-many-arguments
     def plot(
         self,
         content: Knowledge,
         topics: Optional[Iterable[str]] = None,
         top_n: Optional[int] = None,
         history: bool = False,
+        visualize_variance: bool = True,
     ) -> Self:
         """Plot the graph based on the given data.
 
@@ -68,6 +70,8 @@ def plot(
                 Whether to utilize history information in the visualisation.
                 If this is set to True, an attribute called history must be
                 present in all knowledge components.
+            visualize_variance:
+                Whether to visualise variance.
         """
         content_dict, _ = self._standardise_data(content, history, topics)
         content_dict = content_dict[:top_n]
@@ -106,7 +110,7 @@ def plot(
                     "color": "black",
                     "thickness": 4,
                     "width": 3,
-                    "visible": True,
+                    "visible": visualize_variance,
                 },
                 customdata=np.transpose(
                     [variances, number_of_videos, last_video_watched]  # type: ignore

truelearn/utils/visualisations/_dot_plotter.py

@@ -42,12 +42,14 @@ def __init__(
         """
         super().__init__(title, xlabel, ylabel)
 
+    # pylint: disable=too-many-arguments
     def plot(
         self,
         content: Knowledge,
         topics: Optional[Iterable[str]] = None,
         top_n: Optional[int] = None,
         history: bool = False,
+        visualize_variance: bool = True,
     ) -> Self:
         """Plot the graph based on the given data.
 
@@ -68,6 +70,8 @@ def plot(
                 Whether to utilize history information in the visualisation.
                 If this is set to True, an attribute called history must be
                 present in all knowledge components.
+            visualize_variance:
+                Whether to visualise variance.
         """
         content_dict, _ = self._standardise_data(content, history, topics)
         content_dict = content_dict[:top_n]
@@ -106,7 +110,7 @@ def plot(
                     "color": "black",
                     "thickness": 4,
                     "width": 3,
-                    "visible": True,
+                    "visible": visualize_variance,
                 },
                 customdata=np.transpose(
                     [variances, number_of_videos, last_video_watched]  # type: ignore

truelearn/utils/visualisations/_line_plotter.py

@@ -82,7 +82,7 @@ def plot(
         content: Union[List[Knowledge], Knowledge],
         topics: Optional[Iterable[str]] = None,
         top_n: Optional[int] = None,
-        variance: bool = False,
+        visualize_variance: bool = False,
     ) -> Self:
         """Plot the graph based on the given data.
 
@@ -100,7 +100,7 @@ def plot(
             top_n:
                 The number of topics to visualise. E.g. if top_n is 5, then the
                 top 5 topics ranked by mean will be visualised.
-            variance:
+            visualize_variance:
                 Whether to visualise variance.
         """
         if isinstance(content, list):
@@ -111,7 +111,7 @@ def plot(
         if not content_dict:
             return self
 
-        traces = [self._trace(tr_data, variance) for tr_data in content_dict]
+        traces = [self._trace(tr_data, visualize_variance) for tr_data in content_dict]
 
         self.figure.add_traces(traces)
 
@@ -152,7 +152,7 @@ def _content_for_multiple(
     def _trace(
         self,
         tr_data: Tuple[Iterable[float], Iterable[float], str, Iterable[float]],
-        visualise_variance: bool,
+        visualize_variance: bool,
     ) -> go.Scatter:
         """Return the Scatter object representing a single line.
 
@@ -161,7 +161,7 @@ def _trace(
                 The data used to plot the line. A tuple containing the mean
                 and variance of each point, the name of the line (the topic or
                 the learner it represents) and the time when the learning event happens.
-            visualise_variance:
+            visualize_variance:
                 Whether to make the error bars at each point visible.
         """
         means, variances, name, timestamps = tr_data
@@ -175,7 +175,7 @@ def _trace(
             line={"width": 2},
             error_y={
                 "array": variances,
-                "visible": visualise_variance,
+                "visible": visualize_variance,
             },
         )
 

truelearn/utils/visualisations/_radar_plotter.py

@@ -33,6 +33,7 @@ def plot(
         content: Knowledge,
         topics: Optional[Iterable[str]] = None,
         top_n: Optional[int] = None,
+        visualize_variance: bool = True,
     ) -> Self:
         """Plot the graph based on the given data.
 
@@ -49,6 +50,8 @@ def plot(
             top_n:
                 The number of topics to visualise. E.g. if top_n is 5, then the
                 top 5 topics ranked by mean will be visualised.
+            visualize_variance:
+                Whether to visualise variance.
         """
         content_dict, _ = self._standardise_data(content, False, topics)
         content_dict = content_dict[:top_n]
@@ -65,13 +68,15 @@ def plot(
         variances.append(variances[0])
         titles.append(titles[0])
 
-        self.figure.add_traces(
-            [
-                self._trace(means, titles, "Means"),
-                self._trace(variances, titles, "Variances"),
-            ],
+        self.figure.add_trace(
+            self._trace(means, titles, "Means"),
         )
 
+        if visualize_variance:
+            self.figure.add_trace(
+                self._trace(variances, titles, "Variances")
+            )
+
         self.figure.update_layout(
             polar={
                 "radialaxis": {