add plotter for the results of qdp which turns them into a donut-graph

qdp_project/plotter.py

@@ -0,0 +1,77 @@
+import os
+import csv
+import numpy as np
+import seaborn as sns
+import matplotlib.pyplot as plt
+
+output_path = "./plots"
+prefetch_result = "./evaluation-results/qdp-xeonmax-simple-prefetch-tca4-tcb1-tcj2-tmul8-wl4294967296-cs8388608.csv"
+dram_result = "./evaluation-results/qdp-xeonmax-simple-dram-tca2-tcb0-tcj1-tmul16-wl4294967296-cs2097152.csv"
+
+tt_name = "rt-ns"
+function_names = [ "scana-run", "scana-load", "scanb-run", "aggrj-run", "aggrj-load" ]
+fn_nice = [ "Scan A, Filter", "Scan A, Load", "Scan B", "Aggregate, Project + Sum", "Aggregate, Load" ]
+
+def read_timings_from_csv(fname) -> tuple[list[float], list[str]]:
+    t = {}
+    total_time = 0
+
+    # Read data from CSV file
+    with open(fname, newline='') as csvfile:
+        reader = csv.DictReader(csvfile, delimiter=';')
+        for row in reader:
+            total_time += int(row[tt_name])
+            for i in range(len(function_names)):
+                t[fn_nice[i]] = t.get(fn_nice[i], 0) + int(row[function_names[i]])
+
+    t = {key: value * 100 / total_time for key, value in t.items() if value != 0}
+
+    total = sum(list(t.values()))
+    if total < 100.0:
+        t["Waiting / Other"] = 100.0 - total
+
+    return list(t.values()), list(t.keys())
+
+
+def get_data_prefetch_cache_access() -> tuple[list[float], list[str]]:
+    total = 1.14
+    data = [ 0.05, 0.02, 0.17, 0.40, 0.36, 0.13 ]
+    data = [ x * 100 / total for x in data ]
+    keys = ["Cache::GetCacheNode", "Cache::GetFromCache", "dml::handler::constructor", "Cache::AllocOnNode", "dml::make_task", "dml::submit"]
+
+    return data,keys
+
+def get_data_prefetch_total() -> tuple[list[float], list[str]]:
+    return read_timings_from_csv(prefetch_result)
+
+def get_data_dram_total() -> tuple[list[float], list[str]]:
+    return read_timings_from_csv(dram_result)
+
+# loops over all possible configuration combinations and calls
+# process_file_to_dataset for them in order to build a dataframe
+# which is then displayed and saved
+def main(data: tuple[list[float], list[str]], fname):
+    palette_color = sns.color_palette('mako') 
+    fig, ax = plt.subplots(figsize=(6, 3), subplot_kw=dict(aspect="equal"))
+
+    wedges, texts = ax.pie(data[0], wedgeprops=dict(width=0.5), startangle=-40, colors=palette_color)
+
+    bbox_props = dict(boxstyle="square,pad=0.3", fc="w", ec="k", lw=0.72)
+    kw = dict(arrowprops=dict(arrowstyle="-"), bbox=bbox_props, zorder=0, va="center")
+
+    for i, p in enumerate(wedges):
+        ang = (p.theta2 - p.theta1)/2. + p.theta1
+        y = np.sin(np.deg2rad(ang))
+        x = np.cos(np.deg2rad(ang))
+        horizontalalignment = {-1: "right", 1: "left"}[int(np.sign(x))]
+        connectionstyle = f"angle,angleA=0,angleB={ang}"
+        kw["arrowprops"].update({"connectionstyle": connectionstyle})
+        ax.annotate(f"{data[1][i]} - {data[0][i]:2.1f}%", xy=(x, y), xytext=(1.35*np.sign(x), 1.4*y), horizontalalignment=horizontalalignment, **kw)    
+    
+    fig.savefig(os.path.join(output_path, fname), bbox_inches='tight')
+
+
+if __name__ == "__main__":
+    main(get_data_prefetch_cache_access(), "plot-timing-prefetch-cacheaccess.pdf")
+    main(get_data_prefetch_total(), "plot-timing-prefetch-totalexec.pdf")
+    main(get_data_dram_total(), "plot-timing-dram-totalexec.pdf")