Prepare dataframe format for Latex plots

pim_plots.py

@@ -3,33 +3,46 @@ import seaborn as sns
 import pandas as pd
 import numpy as np
 
-df = pd.read_csv("pim_results.csv")
+from pathlib import Path
 
-workloads = df["workload"].unique()
+df = pd.read_csv("pim_results.csv")
 
 sns.set_theme()
 
 def calc_speedup(x):
     return x.iat[0] / x.iat[1]
 
-for workload in df["workload"].unique():
-    workload_filter = df["workload"] == workload
+workload_sets = [["vadd", "vmul", "haxpy"], ["gemv", "gemv_layers"]]
 
-    filtered_df = df[workload_filter]
-    preprocessed_df = filtered_df.groupby(["workload", "level", "frequency"], as_index=False).agg({"ticks": calc_speedup}).rename(columns={"ticks":"speedup"})
+for workload_set in workload_sets:
+    workload_filter = df["workload"].isin(workload_set)
 
-    # print(preprocessed_df)
-    # preprocessed_df.to_csv("plot.csv", index=False)
+    for frequency in df["frequency"].unique():
+        frequency_filter = df["frequency"] == frequency
 
-    g = sns.catplot(
-        data=preprocessed_df, kind="bar",
-        x="level", y="speedup", hue="frequency",
-        palette="dark", alpha=.6, height=6
-    )
+        filtered_df = df[workload_filter & frequency_filter]
+        print(filtered_df)
+        preprocessed_df = filtered_df.groupby(["workload", "level", "frequency"], as_index=False).agg({"ticks": calc_speedup}).rename(columns={"ticks":"speedup"})
 
-    g.despine(left=True)
-    g.set_axis_labels("", "Speedup")
-    g.set(title=workload)
-    g.legend.set_title("")
+        print(preprocessed_df)
+        # preprocessed_df.to_csv("plot.csv", index=False)
+
+        g = sns.catplot(
+            data=preprocessed_df, kind="bar",
+            x="level", y="speedup", hue="workload",
+            palette="dark", alpha=.6, height=6
+        )
+
+        g.despine(left=True)
+        g.set_axis_labels("", "Speedup")
+        g.set(title=frequency)
+        g.legend.set_title("")
+
+        for workload in workload_set:
+            export_df = preprocessed_df[preprocessed_df["workload"] == workload]
+
+            filename = f"{workload}_{frequency}.csv"
+            directory = Path("plots_out")
+            export_df.to_csv(directory / filename, index=False)
 
 plt.show()

simulation_script.py

@@ -49,7 +49,7 @@ workloads = [
     "vmul",
     "haxpy",
     "gemv",
-    # "gemv_layers",
+    "gemv_layers",
 ]
 
 systems = [
@@ -63,9 +63,6 @@ for frequency in ["3GHz", "100GHz"]:
     for level in ["X1", "X2", "X3", "X4"]:
         for system in systems:
             for workload in workloads:                
-                if workload == "gemv_layers" and level != "X4":
-                    continue
-                
                 executable = workload
 
                 if system == "HBM":