def plot(data, xlabel, ylabel, filename):
opts = tsg_plot.PlotOptions()
attribute_dict = {
"plot_type":
"line",
"data":
data,
"labels": ["little", "big"],
"legend_ncol":
2,
"file_name":
filename,
"figsize": (3.5, 1.5),
"fontsize":
8,
"xlabel":
xlabel,
"ylabel":
ylabel,
"colors": [
tsg_plot.colors['blue3'][2],
tsg_plot.colors['qualitative_paired'][5]
],
"rotate_labels":
True,
"rotate_labels_angle":
-30,
}
for k, v in attribute_dict.iteritems():
setattr(opts, k, v)
tsg_plot.add_plot(opts)
def plot_em(data, benchmarks, metrics, policies, filename):
"""
Plot energy and deadline misses.
"""
# Common options
opts = plot_common(data, benchmarks, metrics, policies, filename)
# Colors based on number of configurations
if len(policies) == 6:
opts.colors = tsg_plot.colors['blue3'] * 2
opts.hatch = [''] * 3 + ['////'] * 3
elif len(policies) == 2:
opts.colors = [
tsg_plot.colors['blue3'][0], tsg_plot.colors['blue3'][2]
]
else:
raise Exception("Unsuported number of policies = %d" % len(policies))
opts.file_name = filename.split('.')[0] + "_em.pdf"
for metric in ["energy", "deadline_misses"]:
if metric == "energy":
opts.ylabel = "Energy [%]"
elif metric == "deadline_misses":
opts.ylabel = "Misses [%]"
# Restructure data
metric_data = data[metric]
opts.data = []
for policy in policies:
opts.data.append(metric_data[policy])
opts.labels[1].append(policy_to_label(policy))
opts.data = numpy.array(opts.data).T
# Use one legend for both plots
if opts.plot_idx == 1:
opts.legend_enabled = True
else:
opts.legend_enabled = False
tsg_plot.add_plot(opts)
def plot(data):
opts = tsg_plot.PlotOptions()
opts.data = data
attribute_dict = \
{
'labels' : ['line 1', 'line 2', 'line 3'],
'show' : False,
'file_name' : 'example_line.pdf',
'plot_type' : 'line',
'figsize' : (7.0, 3.0),
'legend_ncol' : 3,
'xlabel' : 'x',
'ylabel' : 'f(x)',
'symbols' : ['.']*3,
'linestyles' : ['-', '', '--'],
'markersize' : 8,
}
for name, value in attribute_dict.iteritems():
setattr( opts, name, value )
tsg_plot.add_plot( opts )
def plot_core_counts(data, benchmarks, metrics, policies, filename):
"""
Plot number of core switches and number of jobs on big core.
"""
# Common options
opts = plot_common(data, benchmarks, metrics, policies, filename)
# Colors based on number of configurations
if len(policies) == 6:
opts.colors = tsg_plot.colors['blue3']
opts.hatch = [''] * 3
elif len(policies) == 2:
opts.colors = [tsg_plot.colors['blue3'][2]]
else:
raise Exception("Unsuported number of policies = %d" % len(policies))
opts.file_name = filename.split('.')[0] + "_counts.pdf"
for metric in ["switch_count", "big_count"]:
if metric == "switch_count":
opts.ylabel = "Core Switches [%]"
elif metric == "big_count":
opts.ylabel = "Big Core Jobs [%]"
# Restructure data
metric_data = data[metric]
opts.data = []
for policy in policies:
if "biglittle" in policy:
opts.data.append(metric_data[policy])
opts.labels[1].append(policy_to_label(policy))
opts.data = numpy.array(opts.data).T
# Use one legend for both plots
if opts.plot_idx == 1:
opts.legend_enabled = True
else:
opts.legend_enabled = False
tsg_plot.add_plot(opts)
def plot(data, filename):
# Set up plotting options
opts = tsg_plot.PlotOptions()
# Benchmarks
cat = data['benchmarks']
# Overheads
subcat = data['policies']
opts.data = data['data']
opts.labels = [cat, subcat]
attribute_dict = \
{
'plot_type' : 'bar',
'show' : False,
'file_name' : filename,
'paper_mode' : True,
'figsize' : (3.5, 2.0),
'ylabel' : 'Overhead Difference [%]',
'legend_ncol' : 6,
'legend_handlelength' : 1.0,
'legend_columnspacing' : 0.8,
'legend_handletextpad' : 0.5,
'legend_bbox' : [-0.05, 1.05, 1, 0.1],
'rotate_labels' : True,
'rotate_labels_angle' : -45,
'fontsize' : 8,
'bar_width' : 0.5,
'yrange' : [-50, 50],
#'colors' : ['#deebf7', '#9acae1', '#3182bd', '#deebf7', '#9ecae1', '#3182bd'],
'colors' : ['#deebf7', '#3182bd'],
}
for name, value in attribute_dict.iteritems():
setattr(opts, name, value)
# Plot
tsg_plot.add_plot(opts)
def plot(data, labels, kk):
opts.labels = labels
attribute_dict = \
{
'bar_width' : 0.7,
'figsize' : (7.0, 2),
'fontsize' : 4,
'labels_fontsize' : 4,
'colors' : ["#a6cee3", "#1f78b4", "#b2df8a", "#33a02c", "#fb9a99", "#e31a1c", "#ff7f00", "#cab2d6", "#6a3d9a", "#ffff99", "#b15928", "#fdbf6f"],
'paper_mode' : True,
'show' : False,
'paper_mode' : True,
'legend_ncol' : 2,
'rotate_labels' : True,
'rotate_labels_angle' : -45,
'num_rows' : 1,
'num_cols' : 3
}
opts.xlabel = big_little
if kk == 0 :
opts.ylabel = "energy [%]"
opts.legend_enabled = True
elif kk == 1 :
opts.ylabel = "deadline_misses [%]"
opts.legend_enabled = False
#opts.file_name = ""+big_little+"_"+benchmarks+".pdf"
elif kk == 2:
opts.ylabel = "tardiness [us]"
opts.legend_enabled = False
opts.file_name = ""+big_little+"_"+benchmarks+".pdf"
opts.data = data
for name, value in attribute_dict.iteritems():
setattr( opts, name, value )
# Plot
tsg_plot.add_plot( opts )
def plot(data, labels):
opts = tsg_plot.PlotOptions()
opt_dict = \
{
"data" : data,
"labels" : labels,
"plot_type" : "line",
"figsize" : (7, 2.5),
"fontsize" : 8,
"file_name" : "plot_prediction.pdf",
"symbols" : ['']*10,
"legend_ncol" : 4,
"xlabel" : "Job",
"ylabel" : "Execution Time [us]",
#"colors" : ["#000000", "#e31a1c"]
"colors" : tsg_plot.colors["qualitative"],
"linestyles" : ['-', '-', '--', '--'],
}
for k, v in opt_dict.iteritems():
setattr(opts, k, v)
tsg_plot.add_plot(opts)
def plot(data):
opts = tsg_plot.PlotOptions()
opts.data = []
opts.labels = [[], []]
opts.colors = []
color_keys = []
for (k, v) in data['data'].iteritems():
opts.data.append(v)
opts.labels[1].append(k)
# Use same colors based on key
# color_key = k.split("_")[-2]
# if not color_key in color_keys:
# color_keys.append(color_key)
# opts.colors.append(colors[color_keys.index(color_key)])
opts.colors = colors
attribute_dict = \
{
'show' : False,
'file_name' : 'scatter.pdf',
'plot_type' : 'scatter',
'paper_mode' : True,
'figsize' : (7.0, 3.5),
'legend_ncol' : 3,
'ylabel' : 'Deadline Misses [%]',
'xlabel' : 'Energy [%]',
'fontsize' : 8,
'title' : '',
#'yrange' : [0, 3],
'symbols' : ['o']*15
}
for name, value in attribute_dict.iteritems():
setattr(opts, name, value)
tsg_plot.add_plot(opts)
f = open(filename, 'r')
data = []
subplot_index = 1
for line in f:
if line != "\n":
data.append(line.strip())
# End of block of data, plot it
elif data:
# Parse data
metric = data[0]
benchmarks = data[1].split(',')[1:]
policies = [d.split(',')[0] for d in data[2:]]
data = [d.split(',')[1:] for d in data[2:]]
data = [[100 * float(dd) for dd in d] for d in data]
data = numpy.array(data).transpose()
# Add to options
opts.data = data
opts.labels = [benchmarks, map(policy_to_label, policies)]
opts.ylabel = metric.replace('_', ' ') + " [%]"
# Only show legend in middle plot
#if opts.plot_idx == 1:
# opts.legend_enabled = True
#else:
# opts.legend_enabled = False
# Plot
tsg_plot.add_plot(opts)
# Reset for next metric
data = []
f.close()
cat = []
data = []
for line in f:
ls = line.split(',')
cat.append(ls[0])
data.append([float(x) for x in ls[1:]])
f.close()
# Set up plotting options
opts = tsg_plot.PlotOptions()
opts.data = data
opts.labels = [cat, subcat]
attribute_dict = \
{
'show' : False,
'file_name' : 'example_stacked_bar.pdf',
'plot_type' : 'stacked_bar',
'figsize' : (3.5, 2.5),
'xlabel' : 'Functions',
'ylabel' : 'Value',
'legend_ncol' : 3,
'rotate_labels' : True,
'rotate_labels_angle' : -30
}
for name, value in attribute_dict.iteritems():
setattr( opts, name, value )
# Plot
tsg_plot.add_plot( opts )
