/
reuse_heatmap.py
199 lines (163 loc) · 5.86 KB
/
reuse_heatmap.py
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"""
plot reuse time heatmap
usage:
1. run traceAnalyzer: `./traceAnalyzer /path/trace trace_format --common`,
this will generate some output, including reuse distribution result per-window, trace.reuseWindow_w300_rt
2. plot reuse heatmap using this script:
`python3 reuse_heatmap.py trace.reuseWindow_w300_rt`
"""
import os, sys
import re
import numpy as np
import copy
import numpy.ma as ma
import matplotlib.pyplot as plt
from matplotlib.ticker import FuncFormatter
from typing import List, Dict, Tuple
import logging
sys.path.append(os.path.dirname(os.path.abspath(__file__)))
from utils.trace_utils import extract_dataname
from utils.plot_utils import FIG_DIR, FIG_TYPE
logger = logging.getLogger("reuse_heatmap")
def _load_reuse_heatmap_data(datapath: str) -> Tuple[np.ndarray, int, int, float]:
"""load reuse heatmap plot data from C++ computation
Args:
datapath (str): the path of reuse heatmap data file
Returns:
Tuple[np.ndarray, int, int, float]: plot_data, time_granularity, time_window, log_base
"""
ifile = open(datapath)
data_line = ifile.readline()
desc_line = ifile.readline()
time_granularity, log_base = 0, 0
if "real time" in desc_line:
m = re.search(
r"# reuse real time distribution per window \(time granularity (?P<tg>\d+), time window (?P<tw>\d+)\)",
desc_line,
)
assert m is not None, (
"the input file might not be reuse heatmap data file, desc line "
+ desc_line
+ "data "
+ datapath
)
time_granularity = int(m.group("tg"))
time_window = int(m.group("tw"))
elif "virtual time" in desc_line:
m = re.search(
r"# reuse virtual time distribution per window \(log base (?P<lb>\d+\.?\d*), time window (?P<tw>\d+)\)",
desc_line,
)
assert m is not None, (
"the input file might not be reuse heatmap data file, desc line "
+ desc_line
+ "data "
+ datapath
)
log_base = float(m.group("lb"))
time_window = int(m.group("tw"))
else:
raise RuntimeError(
"the input file might not be reuse heatmap data file, desc line "
+ desc_line
+ "data "
+ datapath
)
reuse_time_distribution_list = []
for line in ifile:
if len(line.strip()) == 0:
continue
else:
reuse_time_distribution_list.append(
[int(i) for i in line.strip(",\n").split(",")]
)
ifile.close()
dim = max([len(l) for l in reuse_time_distribution_list])
plot_data = np.ones((len(reuse_time_distribution_list), dim))
for idx, l in enumerate(reuse_time_distribution_list):
plot_data[idx][: len(l)] = np.cumsum(np.array(l) / sum(l))
# plot_data = ma.array(plot_data, mask=plot_data<1e-12).T
# print(np.sum(plot_data, axis=1))
plot_data = plot_data.T
# print(plot_data)
# print(plot_data.shape)
# time_granularity is for real_time, log_base is for virtual time
# the time in real_time data is time_granularity * time, the time in virtual_time is log_base ** time
return plot_data, time_granularity, time_window, log_base
def plot_reuse_heatmap(datapath: str, figname_prefix: str = "") -> None:
"""
plot reuse heatmap
Args:
datapath (str): the path of reuse heatmap data file
Returns:
None
"""
if len(figname_prefix) == 0:
figname_prefix = extract_dataname(datapath)
plot_data_rt, time_granularity, time_window, log_base = _load_reuse_heatmap_data(
datapath + "_rt"
)
assert log_base == 0
# plot heatmap
cmap = copy.copy(plt.cm.jet)
# cmap = copy.copy(plt.cm.viridis)
cmap.set_bad(color="white", alpha=1.0)
img = plt.imshow(plot_data_rt, origin="lower", cmap=cmap, aspect="auto")
cb = plt.colorbar(img)
plt.gca().xaxis.set_major_formatter(
FuncFormatter(lambda x, pos: "{:.0f}".format(x * time_window / 3600))
)
plt.gca().yaxis.set_major_formatter(
FuncFormatter(lambda x, pos: "{:.0f}".format(x * time_granularity / 3600))
)
plt.xlabel("Time (hour)")
plt.ylabel("Reuse time (hour)")
plt.savefig(
"{}/{}_reuse_heatmap_rt.{}".format(FIG_DIR, figname_prefix, FIG_TYPE),
bbox_inches="tight",
)
plt.clf()
logger.info(
"plot saved to {}/{}_reuse_heatmap_rt.{}".format(
FIG_DIR, figname_prefix, FIG_TYPE
)
)
plot_data_vt, time_granularity, time_window, log_base = _load_reuse_heatmap_data(
datapath + "_vt"
)
assert time_granularity == 0
# plot heatmap
cmap = copy.copy(plt.cm.jet)
# cmap = copy.copy(plt.cm.viridis)
cmap.set_bad(color="white", alpha=1.0)
img = plt.imshow(plot_data_vt, origin="lower", cmap=cmap, aspect="auto")
cb = plt.colorbar(img)
plt.gca().xaxis.set_major_formatter(
FuncFormatter(lambda x, pos: "{:.0f}".format(x * time_window / 3600))
)
plt.gca().yaxis.set_major_formatter(
FuncFormatter(lambda x, pos: "{:.0f}".format(log_base**x))
)
plt.xlabel("Time (hour)")
plt.ylabel("Reuse time (# request)")
plt.savefig(
"{}/{}_reuse_heatmap_vt.{}".format(FIG_DIR, figname_prefix, FIG_TYPE),
bbox_inches="tight",
)
plt.clf()
logger.info(
"plot saved to {}/{}_reuse_heatmap_vt.{}".format(
FIG_DIR, figname_prefix, FIG_TYPE
)
)
if __name__ == "__main__":
import argparse
ap = argparse.ArgumentParser()
ap.add_argument("datapath", type=str, help="data path")
ap.add_argument(
"--figname-prefix", type=str, default="", help="the prefix of figname"
)
p = ap.parse_args()
if p.datapath.endswith("_rt") or p.datapath.endswith("_vt"):
p.datapath = p.datapath[:-3]
plot_reuse_heatmap(p.datapath, p.figname_prefix)