def plot(self, directory, entries, samples, nr_samples, verbose=False):
from matplotlib import use as muse
muse('Agg')
from matplotlib.backends.backend_agg import FigureCanvasAgg as FigureCanvas
from matplotlib.figure import Figure
from matplotlib.ticker import FuncFormatter, FixedFormatter, LinearLocator
from matplotlib.mlab import std as std_deviation
from matplotlib.mlab import mean
from time import asctime
yfont = {
'fontname': 'Bitstream Vera Sans',
'color': 'r',
'fontsize': 8
}
xfont = {
'fontname': 'Bitstream Vera Sans',
'color': 'b',
'fontsize': 8
}
titlefont = {
'fontname': 'Bitstream Vera Sans',
'color': 'g',
'fontweight': 'bold',
'fontsize': 10
}
inches = 0.00666667
width = 950 * inches
height = 680 * inches
fig = Figure(figsize=(width, height))
canvas = FigureCanvas(fig)
ax = fig.add_subplot(111)
ax.grid(False)
xtickfontsize = 5
ytickfontsize = 5
plot_type = 'b-'
field_mean = mean(samples)
yaxis_plot_fmt = FuncFormatter(pylab_formatter_ms)
ax.plot(entries, samples, "b-")
ax.set_xlabel("samples", xfont)
ax.set_ylabel("time", yfont)
for label in ax.get_xticklabels():
label.set(fontsize=xtickfontsize)
for label in ax.get_yticklabels():
label.set(fontsize=ytickfontsize)
ax.yaxis.set_major_formatter(yaxis_plot_fmt)
ax.set_title("%d %s samples (%s)" % (nr_samples, self.name, asctime()),
titlefont)
canvas.print_figure("%s/methods/%s.png" % (directory, self.name))
del fig, canvas, ax
def plot(self, directory, entries, samples, nr_samples, verbose = False):
from matplotlib import use as muse
muse('Agg')
from matplotlib.backends.backend_agg import FigureCanvasAgg as FigureCanvas
from matplotlib.figure import Figure
from matplotlib.ticker import FuncFormatter, FixedFormatter, LinearLocator
from matplotlib.mlab import std as std_deviation
from matplotlib.mlab import mean
from time import asctime
yfont = { 'fontname' : 'Bitstream Vera Sans',
'color' : 'r',
'fontsize' : 8 }
xfont = { 'fontname' : 'Bitstream Vera Sans',
'color' : 'b',
'fontsize' : 8 }
titlefont = { 'fontname' : 'Bitstream Vera Sans',
'color' : 'g',
'fontweight' : 'bold',
'fontsize' : 10 }
inches = 0.00666667
width = 950 * inches
height = 680 * inches
fig = Figure(figsize = (width, height))
canvas = FigureCanvas(fig)
ax = fig.add_subplot(111)
ax.grid(False)
xtickfontsize = 5
ytickfontsize = 5
plot_type = 'b-'
field_mean = mean(samples)
yaxis_plot_fmt = FuncFormatter(pylab_formatter_ms)
ax.plot(entries, samples, "b-")
ax.set_xlabel("samples", xfont)
ax.set_ylabel("time", yfont)
for label in ax.get_xticklabels():
label.set(fontsize = xtickfontsize)
for label in ax.get_yticklabels():
label.set(fontsize = ytickfontsize)
ax.yaxis.set_major_formatter(yaxis_plot_fmt)
ax.set_title("%d %s samples (%s)" % (nr_samples, self.name, asctime()), titlefont)
canvas.print_figure("%s/methods/%s.png" % (directory, self.name))
del fig, canvas, ax
def plot_field(name,
directory,
tstamps,
samples,
nr_samples,
plot_fmt=None,
table=None,
verbose=False):
global current_plot_fmt
from matplotlib import use as muse
muse('Agg')
from matplotlib.backends.backend_agg import FigureCanvasAgg as FigureCanvas
from matplotlib.figure import Figure
from matplotlib.ticker import FuncFormatter, FixedFormatter, LinearLocator
from matplotlib.mlab import std as std_deviation
from matplotlib.mlab import mean
from time import asctime
yfont = {'fontname': 'Bitstream Vera Sans', 'color': 'r', 'fontsize': 8}
xfont = {'fontname': 'Bitstream Vera Sans', 'color': 'b', 'fontsize': 8}
titlefont = {
'fontname': 'Bitstream Vera Sans',
'color': 'g',
'fontweight': 'bold',
'fontsize': 10
}
inches = 0.00666667
width = 950 * inches
height = 680 * inches
fig = Figure(figsize=(width, height))
canvas = FigureCanvas(fig)
ax = fig.add_subplot(111)
ax.grid(False)
xtickfontsize = 5
ytickfontsize = 5
current_plot_fmt = plot_fmt
field_mean = None
plot_type = 'b-'
if current_plot_fmt == "filter_dev":
std = std_deviation(samples) * 2
if verbose:
print("filter_dev(%s) std=%d" % (name, std))
for i in range(nr_samples):
if samples[i] > std:
if verbose:
print("%s: filtering out %d" % (name, samples[i]))
samples[i] = 0
field_mean = mean(samples)
yaxis_plot_fmt = FuncFormatter(pylab_formatter)
elif current_plot_fmt == "table":
ax.grid(True)
plot_type = 'bo-'
max_value = max(samples)
without_zero = 1
if table.has_key(0):
without_zero = 0
max_value += 1
ax.yaxis.set_major_locator(LinearLocator(max_value))
tstamps = range(nr_samples)
seq = [" "] * max_value
for key in table.keys():
if key in samples:
seq[key - without_zero] = "%s(%d)" % (table[key], key)
ytickfontsize = 4
yaxis_plot_fmt = FixedFormatter(seq)
else:
field_mean = mean(samples)
yaxis_plot_fmt = FuncFormatter(pylab_formatter)
ax.plot(tstamps, samples, plot_type)
ax.set_xlabel("time", xfont)
yname = name
if field_mean:
yname += " (mean=%s)" % pylab_formatter(field_mean)
ax.set_ylabel(yname, yfont)
for label in ax.get_xticklabels():
label.set(fontsize=xtickfontsize)
for label in ax.get_yticklabels():
label.set(fontsize=ytickfontsize)
ax.yaxis.set_major_formatter(yaxis_plot_fmt)
ax.set_title("%d %s samples (%s)" % (nr_samples, name, asctime()),
titlefont)
canvas.print_figure("%s/%s.png" % (directory, name))
del fig, canvas, ax
# Description: # Generate dot graph from gdb backtracet data # Author: # Tarun Sharma ([email protected]) # Date: # 2019-01-19 import os from matplotlib import use as muse from copy import deepcopy muse('Agg') import sys import pydotplus import argparse import pdb from pprint import pprint as pp import logging logging.basicConfig(format='%(asctime)s %(message)s', datefmt='%m/%d/%Y %I:%M:%S %p') logger = logging.getLogger(__name__) logger.setLevel(logging.INFO) HTML_SEP = ' ' # Make resulting SVG interactive js_string = ''' <script> <![CDATA[ class Node {
def plot_field(name, directory, tstamps, samples, nr_samples, plot_fmt = None,
table = None, verbose = False):
global current_plot_fmt
from matplotlib import use as muse
muse('Agg')
from matplotlib.backends.backend_agg import FigureCanvasAgg as FigureCanvas
from matplotlib.figure import Figure
from matplotlib.ticker import FuncFormatter, FixedFormatter, LinearLocator
from matplotlib.mlab import std as std_deviation
from matplotlib.mlab import mean
from time import asctime
yfont = { 'fontname' : 'Bitstream Vera Sans',
'color' : 'r',
'fontsize' : 8 }
xfont = { 'fontname' : 'Bitstream Vera Sans',
'color' : 'b',
'fontsize' : 8 }
titlefont = { 'fontname' : 'Bitstream Vera Sans',
'color' : 'g',
'fontweight' : 'bold',
'fontsize' : 10 }
inches = 0.00666667
width = 950 * inches
height = 680 * inches
fig = Figure(figsize = (width, height))
canvas = FigureCanvas(fig)
ax = fig.add_subplot(111)
ax.grid(False)
xtickfontsize = 5
ytickfontsize = 5
current_plot_fmt = plot_fmt
field_mean = None
plot_type = 'b-'
if current_plot_fmt == "filter_dev":
std = std_deviation(samples) * 2
if verbose:
print "filter_dev(%s) std=%d" % (name, std)
for i in range(nr_samples):
if samples[i] > std:
if verbose:
print "%s: filtering out %d" % (name, samples[i])
samples[i] = 0
field_mean = mean(samples)
yaxis_plot_fmt = FuncFormatter(pylab_formatter)
elif current_plot_fmt == "table":
ax.grid(True)
plot_type = 'bo-'
max_value = max(samples)
without_zero = 1
if table.has_key(0):
without_zero = 0
max_value += 1
ax.yaxis.set_major_locator(LinearLocator(max_value))
tstamps = range(nr_samples)
seq = [ " " ] * max_value
for key in table.keys():
if key in samples:
seq[key - without_zero] = "%s(%d)" % (table[key], key)
ytickfontsize = 4
yaxis_plot_fmt = FixedFormatter(seq)
else:
field_mean = mean(samples)
yaxis_plot_fmt = FuncFormatter(pylab_formatter)
ax.plot(tstamps, samples, plot_type)
ax.set_xlabel("time", xfont)
yname = name
if field_mean:
yname += " (mean=%s)" % pylab_formatter(field_mean)
ax.set_ylabel(yname, yfont)
for label in ax.get_xticklabels():
label.set(fontsize = xtickfontsize)
for label in ax.get_yticklabels():
label.set(fontsize = ytickfontsize)
ax.yaxis.set_major_formatter(yaxis_plot_fmt)
ax.set_title("%d %s samples (%s)" % (nr_samples, name, asctime()), titlefont)
canvas.print_figure("%s/%s.png" % (directory, name))
del fig, canvas, ax