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Copy pathprocess-rel.py
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199 lines (149 loc) · 3.92 KB
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import sys
import matplotlib.pyplot as plt
import numpy
f = open(sys.argv[1])
dataset = {}
min_ts = float('inf')
max_ts = 0
for line in f.readlines():
if "checkpoint" in line: continue
line = line.strip()
line = line.split(',')
lp = int(line[0])
ts = float(line[2])
pr = float(line[3])
abs_str = float(line[4])
rel_str = float(line[5])
er = float('inf')
if pr != 0: er = (pr-rel_str)#/pr
if pr == er: er = 0
if lp not in dataset: dataset[lp] = {"x":[], "y":[]}
dataset[lp]["x"] += [ts]
dataset[lp]["y"] += [abs(er)]
min_ts = min(min_ts, ts)
max_ts = max(max_ts, ts)
r = sys.argv[1].replace(".txt", "-").split("/")[-1]
plt.figure(figsize=(5,4))
plt.xlabel("virtual time")
plt.ylabel("absolute error")
#plt.ylim(0, 10)
for lp in dataset:
plt.plot(dataset[lp]["x"], dataset[lp]["y"])
plt.savefig(f"{r}trace-abs-s2.pdf")
plt.clf()
f = open(sys.argv[1])
dataset = {}
min_ts = float('inf')
max_ts = 0
for line in f.readlines():
if "checkpoint" in line: continue
line = line.strip()
line = line.split(',')
lp = int(line[0])
ts = float(line[2])
pr = float(line[3])
abs_str = float(line[4])
rel_str = float(line[5])
er = float('inf')
if pr != 0: er = (pr-rel_str)/pr
if pr == 0: er = 0.0
if abs(er) > 0.05: print(line, abs(er))
if lp not in dataset: dataset[lp] = {"x":[], "y":[]}
dataset[lp]["x"] += [ts]
dataset[lp]["y"] += [abs(er)*100]
min_ts = min(min_ts, ts)
max_ts = max(max_ts, ts)
r = sys.argv[1].replace(".txt", "-").split("/")[-1]
plt.figure(figsize=(5,4))
plt.xlabel("virtual time")
plt.ylabel("relative error (%)")
for lp in dataset:
plt.plot(dataset[lp]["x"], dataset[lp]["y"])
plt.savefig(f"{r}trace-rel-s2.pdf")
plt.clf()
f = open(sys.argv[1])
dataset = {}
min_ts = float('inf')
max_ts = 0
mean = 0
if "0_1_" in sys.argv[1]: mean = 0.5
if "0_1pI_" in sys.argv[1]: mean = 1.0
if "1_1pI_" in sys.argv[1]: mean = 2.0
if "1_1pC_" in sys.argv[1]: mean = 1.5
for line in f.readlines():
if "checkpoint" in line: continue
line = line.strip()
line = line.split(',')
lp = int(line[0])
ts = float(line[2])
pr = float(line[3])
abs_str = float(line[4])
rel_str = float(line[5])
er = float('inf')
if pr != 0: er = (pr-mean)/pr
if pr == 0 or pr == abs_str: er = 0.0
if lp not in dataset: dataset[lp] = {"x":[], "y":[]}
dataset[lp]["x"] += [ts]
dataset[lp]["y"] += [abs(er)*100]
min_ts = min(min_ts, ts)
max_ts = max(max_ts, ts)
r = sys.argv[1].replace(".txt", "-").split("/")[-1]
plt.figure(figsize=(5,4))
plt.xlabel("virtual time")
plt.ylabel("relative error (%)")
for lp in dataset:
plt.plot(dataset[lp]["x"], dataset[lp]["y"])
plt.savefig(f"{r}trace-rel-ba.pdf")
plt.clf()
f = open(sys.argv[1])
dataset = {}
min_ts = float('inf')
max_ts = 0
mean = 0
if "0_1_" in sys.argv[1]: mean = 0.5
if "0_1pI_" in sys.argv[1]: mean = 1.0
if "1_1pI_" in sys.argv[1]: mean = 2.0
if "1_1pC_" in sys.argv[1]: mean = 1.5
for line in f.readlines():
if "checkpoint" in line: continue
line = line.strip()
line = line.split(',')
lp = int(line[0])
ts = float(line[2])
pr = float(line[3])
abs_str = float(line[4])
rel_str = float(line[5])
er = float('inf')
if pr != 0: er = (pr-mean)
if pr == 0 or pr == abs_str: er = 0.0
if lp not in dataset: dataset[lp] = {"x":[], "y":[]}
dataset[lp]["x"] += [ts]
dataset[lp]["y"] += [abs(er)]
min_ts = min(min_ts, ts)
max_ts = max(max_ts, ts)
plt.figure(figsize=(5,4))
plt.xlabel("virtual time")
plt.ylabel("absolute error")
r = sys.argv[1].replace(".txt", "-").split("/")[-1]
for lp in dataset:
plt.plot(dataset[lp]["x"], dataset[lp]["y"])
plt.savefig(f"{r}trace-abs-ba.pdf")
'''
plt.clf()
#plt.ylim(0, 10)
samples=50000
freq = (max_ts-min_ts)/float(samples)
x = [min_ts+i*freq for i in range(samples)]
fy = None
for lp in dataset:
y = numpy.interp(x, dataset[lp]["x"], dataset[lp]["y"])
if fy is None: fy = y
else: fy += y
plt.plot(x, y)
plt.savefig(f"{r}trace2-abs.pdf")
plt.clf()
if "base" not in r: plt.ylim(0, 50)
plt.plot(x, fy)
plt.savefig(f"{r}sum-abs.pdf")
f.close()
'''