Modified visualization code to wread metadata

sw/plot_results.py

@@ -3,41 +3,39 @@ import matplotlib.pyplot as plt
 import seaborn as sns
 import pandas as pd
 import os
-from utility import visualization
+from utility import visualization, simulation
 
 
 def plot_results():
-    graph_names = {"96.3.965": "n=96, k=48 - 965",
-                   "204.3.486": "n=204, k=102 - 486",
-                   "204.55.187": "n=204, k=102 - 187",
-                   "408.33.844": "n=408, k=204 - 844",
-                   "816.1A4.845": "n=816, k=272 - 843",
-                   "999.111.3.5543": "n=999, k=888 - 5543",
-                   "999.111.3.5565": "n=999, k=888 - 5565",
-                   "PEGReg252x504": "n=504, k=252 - PEGReg"}
 
-    line_labels = {"BER_ML": "ML",
+    sim_names = [
-                   "BER_prox_0_15": "$\gamma = 0.15$",
+        "96.3965",
-                   "BER_prox_0_05": "$\gamma = 0.05$",
+        "204.3.486",
-                   "BER_prox_0_01": "$\gamma = 0.01$"}
+        "204.55.187",
+        "408.33.844",
+        "816.1A4.845",
+        "999.111.3.5543",
+        "999.111.3.5565",
+        "PEGReg252x504"
+    ]
 
-    # Read data from files
+    deserializer = simulation.SimulationDeSerializer(save_dir="sim_saves", results_dir="sim_results")
-    results_dir = "sim_results"
 
-    data = {}
+    data = []
-    for file in os.listdir(results_dir):
+    for sim_name in sim_names:
-        if file.endswith(".csv"):
+        df, metadata = deserializer.read_results(sim_name)
-            filename = os.path.splitext(file)[0]
+        df = df.loc[:, ~df.columns.str.contains('^Unnamed')]
 
-            df = pd.read_csv(os.path.join(results_dir, file))
+        graph_title = sim_name
-            df = df.loc[:, ~df.columns.str.contains('^Unnamed')]
+        line_labels = metadata["labels"]
-            data[graph_names[filename]] = df
 
-    # Create and show graphs
+        graph_tuple = (graph_title, df, line_labels)
+        data.append(graph_tuple)
 
     sns.set_theme()
-    fig = visualization.show_BER_curves("Bit-Error-Rates of proximal decoder for different codes",
+    fig = visualization.plot_BERs(title="Bit-Error-Rates of proximal decoder for different codes",
-                                        data, num_cols=4, line_labels=line_labels)
+                                  data=data, num_cols=4)
+
     plt.show()
 
 

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,
+def plot_BERs(title: str,
-                    data: typing.Dict[str, pd.DataFrame],
+              data: typing.Sequence[typing.Tuple[str, pd.DataFrame, typing.Sequence[str]]],
-                    line_labels: typing.Dict[str, str],
+              num_cols: int = 3) -> plt.figure:
-                    num_cols: int = 3) -> plt.figure:
+    """This function creates a matplotlib figure containing a number of plots.
-    """This function creates a matplotlib figure containing a number of BER curves.
+
+    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
+    :param data: Sequence of tuples. Each tuple corresponds to a new plot and
-        containing the data to be plotted. Each dataframe is assumed to contain a column named "SNR" which is used
+        is of the following form: [graph_title, pd.Dataframe, [line_label_1, line_label2, ...]].
-        as the x-axis
+        Each dataframe is assumed to have an "SNR" column that is used as the x axis.
-    :param line_labels: Dictionary mapping column names to proper labels
     :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())):
+    # Populate axes
-        graph_name, df = name_data_pair
+
+    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:
+        for column, label in zip(column_names, labels):
-            sns.lineplot(ax=axis, data=df, x="SNR", y=column, label=line_labels[column])
+            sns.lineplot(ax=axis, data=df, x="SNR", y=column, label=label)
 
-        axis.set_title(graph_name)
+            axis.set_title(graph_title)
-        axis.set(yscale="log")
+            axis.set(yscale="log")
-        axis.set_xlabel("SNR")
+            axis.set_xlabel("SNR")
-        axis.set_ylabel("BER")
+            axis.set_ylabel("BER")
-        axis.set_yticks([10e-5, 10e-4, 10e-3, 10e-2, 10e-1, 10e0])
+            axis.set_yticks([10e-5, 10e-4, 10e-3, 10e-2, 10e-1, 10e0])
-        axis.legend()
+            axis.legend()
 
     return fig