Modified visualization code to wread metadata

sw/utility/visualization.py

@@ -18,20 +18,22 @@ def _get_num_rows(num_graphs: int, num_cols: int) -> int:
 
 
 # TODO: Handle number of graphs not nicely fitting into rows and columns
-def show_BER_curves(title: str,
-                    data: typing.Dict[str, pd.DataFrame],
-                    line_labels: typing.Dict[str, str],
-                    num_cols: int = 3) -> plt.figure:
-    """This function creates a matplotlib figure containing a number of BER curves.
+def plot_BERs(title: str,
+              data: typing.Sequence[typing.Tuple[str, pd.DataFrame, typing.Sequence[str]]],
+              num_cols: int = 3) -> plt.figure:
+    """This function creates a matplotlib figure containing a number of plots.
+
+    The plots created are logarithmic and the scaling is adjusted to be sensible for BER plots.
 
     :param title: Title of the figure
-    :param data: Dictionary where each key corresponds to the name of a new graph and the value is a pandas Dataframe
-        containing the data to be plotted. Each dataframe is assumed to contain a column named "SNR" which is used
-        as the x-axis
-    :param line_labels: Dictionary mapping column names to proper labels
+    :param data: Sequence of tuples. Each tuple corresponds to a new plot and
+        is of the following form: [graph_title, pd.Dataframe, [line_label_1, line_label2, ...]].
+        Each dataframe is assumed to have an "SNR" column that is used as the x axis.
     :param num_cols: Number of columns in which the graphs should be arranged in the resulting figure
     :return: Matplotlib figure
     """
+    # Determine layout and create figure
+
     num_graphs = len(data)
     num_rows = _get_num_rows(num_graphs, num_cols)
 
@@ -47,18 +49,19 @@ def show_BER_curves(title: str,
 
     axes = list(chain.from_iterable(axes))[:num_graphs]  # Flatten the 2d axes array
 
-    for axis, name_data_pair in zip(axes, sorted(data.items())):
-        graph_name, df = name_data_pair
+    # Populate axes
+
+    for axis, (graph_title, df, labels) in zip(axes, data):
         column_names = [column for column in df.columns.values.tolist() if not column == "SNR"]
 
-        for column in column_names:
-            sns.lineplot(ax=axis, data=df, x="SNR", y=column, label=line_labels[column])
+        for column, label in zip(column_names, labels):
+            sns.lineplot(ax=axis, data=df, x="SNR", y=column, label=label)
 
-        axis.set_title(graph_name)
-        axis.set(yscale="log")
-        axis.set_xlabel("SNR")
-        axis.set_ylabel("BER")
-        axis.set_yticks([10e-5, 10e-4, 10e-3, 10e-2, 10e-1, 10e0])
-        axis.legend()
+            axis.set_title(graph_title)
+            axis.set(yscale="log")
+            axis.set_xlabel("SNR")
+            axis.set_ylabel("BER")
+            axis.set_yticks([10e-5, 10e-4, 10e-3, 10e-2, 10e-1, 10e0])
+            axis.legend()
 
     return fig