iotf single benchmark plotter

plot_benchmark_runtimes.py

import collections
import pandas as pd
import matplotlib.pyplot as plt

logfile = '<path to benchmark output.dat>'

tables = collections.defaultdict(list)

def tryfloat(x):
    # the argument can be a float or
    # an entire ndarray
    # for runtimes, we only want the float
    try:
        return float(x)
    except ValueError:
        return None

for i, line in enumerate(open(logfile)):
    parts = line.split(None, 6)
    if parts[0] == 'row':
        tables[parts[4]].append(dict(
            n=int(parts[2]),                # problem size
            gj=int(parts[3]),               # gap junction count (=10*n)
            dur=float(parts[5]),            # measured duration (in seconds)
            arg=tryfloat(parts[6].rstrip()) # 'argument' usually the run number from 0 to 4
            ))

for k, rows in tables.items():
    tables[k] = pd.DataFrame(rows)
    print('Read table', k)
    print(tables[k].head())
    globals()[k] = tables[k]

def plot_runtimes(df, label):
    df = df[df.arg > 0.5] # skip first run with JIT timings
    df = df.groupby('n')['dur'].min()  # n is problem size, take smallest runtime
    n = df.index
    t = df.values
    print(n)
    print(t)
    plt.plot(n, t, label=label)

plot_runtimes(run_unconnected_perf, label='Unconnected')
plot_runtimes(run_connected_perf, label='Connected')
plt.xscale('log')
plt.yscale('log')
plt.legend()
plt.show()