def do_cdfs(options, stats, write_filepath):
if not write_filepath:
write_filepath = get_output_filepath(options.input)
for metric in options.metrics:
print("reformatting data...")
data = {}
for i, g in enumerate(stats['group']):
if options.max and i >= options.max:
break
data[g] = [d[metric] for d in stats['data'][g]["distribution"]]
print("plotting CDFs")
xmax = round(math.ceil(max(data[stats['group'][0]])))
axis_limits = [0, xmax, 0, 1]
if options.minx:
axis_limits[0] = options.minx
if options.maxx:
axis_limits[1] = options.maxx
plot.plot('cdf',
data,
COLORS,
axis_limits,
metric,
"linear",
"linear",
write_filepath + '_' + metric + '_cdfs',
options.write,
xlabel=metric_fullname(metric) + ' (miles)',
ylabel='fraction of \ncontroller placements',
ext=options.ext)
if not options.write:
plot.show()
def do_cdfs(options, stats, write_filepath):
if not write_filepath:
write_filepath = get_output_filepath(options.input)
for metric in options.metrics:
print "reformatting data..."
data = {}
for i, g in enumerate(stats['group']):
if options.max and i >= options.max:
break
data[g] = [d[metric] for d in stats['data'][g]["distribution"]]
print "plotting CDFs"
xmax = round(math.ceil(max(data[stats['group'][0]])))
axis_limits = [0, xmax, 0, 1]
if options.minx:
axis_limits[0] = options.minx
if options.maxx:
axis_limits[1] = options.maxx
plot.plot('cdf', data, COLORS, axis_limits,
metric, "linear", "linear", write_filepath + '_' + metric + '_cdfs',
options.write,
xlabel = metric_fullname(metric) + ' (miles)',
ylabel = 'fraction of \ncontroller placements',
ext = options.ext)
if not options.write:
plot.show()
def do_ranges(options, stats, write_filepath, topo_name):
# Grab topo to enable analysis that requires generating new metric values,
# such as the value of one metric with a combo optimized for another one.
'''
topo_graph returns
@param g: NetworkX Graph
@param usable: boolean: locations on all nodes and connected?
@param note: error or note about mods
'''
print "generating topo for: %s" % topo_name
graph, usable, note = get_topo_graph(topo_name)
if not usable:
raise Exception("unusable graph?")
if not write_filepath:
write_filepath = get_output_filepath(options.input)
for metric in options.metrics:
this_write_filepath = write_filepath + '_' + metric
xlabel = 'number of controllers (k)'
for ptype in options.plots:
if ptype in PLOT_FCNS.keys():
p = PLOT_FCNS[ptype]
#print "plotting %s" % ptype
filepath = this_write_filepath + '_' + ptype
aspect_fcns = get_aspect_fcns(p, stats, metric, graph)
aspects = aspect_fcns.keys()
aspect_colors = p['aspect_colors']
ylabel = p['ylabel'](metric)
min_x = p['min_x'](options) if 'min_x' in p else None
max_x = p['max_x'](options) if 'max_x' in p else None
min_y = p['min_y'](options) if 'min_y' in p else None
max_y = p['max_y'](options) if 'max_y' in p else None
ylabel2 = p['ylabel2'](metric) if 'ylabel2' in p else None
y2_scale_factor = p['y2_scale_factor'] if 'y2_scale_factor' in p else None
hlines = p['hlines'] if 'hlines' in p else None
plot.ranges(stats, metric, aspects, aspect_colors, aspect_fcns,
"linear", "linear", None, None, filepath,
options.write, ext = options.ext,
xlabel = xlabel,
ylabel = ylabel,
min_x = min_x,
max_x = max_x,
min_y = min_y,
max_y = max_y,
ylabel2 = ylabel2,
y2_scale_factor = y2_scale_factor,
hlines = hlines)
else:
raise Exception("undefined ptype: %s" % ptype)
if not options.write:
plot.show()
def do_pareto(options, stats, write_filepath):
assert len(options.metrics) == 2
x_metric, y_metric = options.metrics
print "reformatting data..."
data = {}
for i, g in enumerate(stats['group']):
if options.max and i >= options.max:
break
data[g] = [d for d in stats['data'][g]["distribution"]]
print "plotting point pareto"
if not write_filepath:
write_filepath = get_output_filepath(options.input)
write_filepath += '_pareto_' + ','.join(options.metrics)
plot.pareto(data, COLORS, None,
"linear", "linear", write_filepath,
options.write,
xlabel = metric_fullname(x_metric) + ' (miles)',
ylabel = metric_fullname(y_metric) + ' (miles)',
ext = options.ext,
x_metric = x_metric,
y_metric = y_metric,
min_x = 0,
min_y = 0)
plot.pareto(data, COLORS, None,
"linear", "linear", write_filepath + '_zoom',
options.write,
xlabel = metric_fullname(x_metric) + ' (miles)',
ylabel = metric_fullname(y_metric) + ' (miles)',
ext = options.ext,
min_x = 0,
min_y = 0,
x_metric = x_metric,
y_metric = y_metric,
loc = "lower right")
plot.pareto(data, COLORS, None,
"linear", "linear", write_filepath + '_norm',
options.write,
xlabel = metric_fullname(x_metric) + ' (ratio)',
ylabel = metric_fullname(y_metric) + ' (ratio)',
ext = options.ext,
x_metric = x_metric,
y_metric = y_metric,
# max_x = 1.2,
# max_y = 1.2,
normalize = True,
legend = True,
loc = "upper right")
if not options.write:
plot.show()
def systemrun():
sys = ising.Wolff(N,T)
initialConfig, delta, flipcount = sys.run(100000)
show = plot.show(initialConfig, delta, flipcount)
#show.saveVideo()
show.showPlot()
def do_cloud(options, stats, write_filepath, ext=None):
assert len(options.metrics) == 2
x_metric, y_metric = options.metrics
print("reformatting data...")
metrics = [x_metric, y_metric]
data = {}
for i, g in enumerate(stats['group']):
if options.max and i >= options.max:
break
data[g] = [d for d in stats['data'][g]["distribution"]]
print("plotting point cloud")
if not write_filepath:
write_filepath = get_output_filepath(options.input)
# Series may not have values that monotonically decrease by the controller
# number, so consider each value when choosing axis extents.
extra_margin = 1.02
maxes = {x_metric: [], y_metric: []}
for c in stats['group']:
for metric in options.metrics:
maxes[metric].append(stats['data'][c][metric]['highest'])
axes = [
0,
max(maxes[x_metric]) * extra_margin, 0,
max(maxes[y_metric]) * extra_margin
]
if not ext:
ext = options.ext
plot.cloud(data,
COLORS,
axes,
"linear",
"linear",
write_filepath + '_cloud_' + ','.join(options.metrics),
options.write,
xlabel=metric_fullname(x_metric) + ' (miles)',
ylabel=metric_fullname(y_metric) + ' (miles)',
ext=ext,
x_metric=x_metric,
y_metric=y_metric,
legend=True)
if not options.write:
plot.show()
def metropolisrun():
N = 400
T = 1.8
sys = ising.Metropolis(N,T)
initialConfig, delta = sys.run(1600000)
show = plot.show(initialConfig, delta, [])
show.showPlot()
def do_cloud(options, stats, write_filepath, ext = None):
assert len(options.metrics) == 2
x_metric, y_metric = options.metrics
print "reformatting data..."
metrics = [x_metric, y_metric]
data = {}
for i, g in enumerate(stats['group']):
if options.max and i >= options.max:
break
data[g] = [d for d in stats['data'][g]["distribution"]]
print "plotting point cloud"
if not write_filepath:
write_filepath = get_output_filepath(options.input)
# Series may not have values that monotonically decrease by the controller
# number, so consider each value when choosing axis extents.
extra_margin = 1.02
maxes = {x_metric: [], y_metric: []}
for c in stats['group']:
for metric in options.metrics:
maxes[metric].append(stats['data'][c][metric]['highest'])
axes = [0, max(maxes[x_metric]) * extra_margin, 0, max(maxes[y_metric]) * extra_margin]
if not ext:
ext = options.ext
plot.cloud(data, COLORS, axes,
"linear", "linear", write_filepath + '_cloud_' + ','.join(options.metrics),
options.write,
xlabel = metric_fullname(x_metric) + ' (miles)',
ylabel = metric_fullname(y_metric) + ' (miles)',
ext = ext,
x_metric = x_metric,
y_metric = y_metric,
legend = True)
if not options.write:
plot.show()
def do_ranges(options, stats, write_filepath, topo_name):
# Grab topo to enable analysis that requires generating new metric values,
# such as the value of one metric with a combo optimized for another one.
'''
topo_graph returns
@param g: NetworkX Graph
@param usable: boolean: locations on all nodes and connected?
@param note: error or note about mods
'''
print "generating topo for: %s" % topo_name
graph, usable, note = get_topo_graph(topo_name)
if not usable:
raise Exception("unusable graph?")
if not write_filepath:
write_filepath = get_output_filepath(options.input)
for metric in options.metrics:
this_write_filepath = write_filepath + '_' + metric
xlabel = 'number of controllers (k)'
for ptype in options.plots:
if ptype in PLOT_FCNS.keys():
p = PLOT_FCNS[ptype]
#print "plotting %s" % ptype
filepath = this_write_filepath + '_' + ptype
aspect_fcns = get_aspect_fcns(p, stats, metric, graph)
aspects = aspect_fcns.keys()
aspect_colors = p['aspect_colors']
ylabel = p['ylabel'](metric)
min_x = p['min_x'](options) if 'min_x' in p else None
max_x = p['max_x'](options) if 'max_x' in p else None
min_y = p['min_y'](options) if 'min_y' in p else None
max_y = p['max_y'](options) if 'max_y' in p else None
ylabel2 = p['ylabel2'](metric) if 'ylabel2' in p else None
y2_scale_factor = p[
'y2_scale_factor'] if 'y2_scale_factor' in p else None
hlines = p['hlines'] if 'hlines' in p else None
plot.ranges(stats,
metric,
aspects,
aspect_colors,
aspect_fcns,
"linear",
"linear",
None,
None,
filepath,
options.write,
ext=options.ext,
xlabel=xlabel,
ylabel=ylabel,
min_x=min_x,
max_x=max_x,
min_y=min_y,
max_y=max_y,
ylabel2=ylabel2,
y2_scale_factor=y2_scale_factor,
hlines=hlines)
else:
raise Exception("undefined ptype: %s" % ptype)
if not options.write:
plot.show()
def do_pareto(options, stats, write_filepath):
assert len(options.metrics) == 2
x_metric, y_metric = options.metrics
print "reformatting data..."
data = {}
for i, g in enumerate(stats['group']):
if options.max and i >= options.max:
break
data[g] = [d for d in stats['data'][g]["distribution"]]
print "plotting point pareto"
if not write_filepath:
write_filepath = get_output_filepath(options.input)
write_filepath += '_pareto_' + ','.join(options.metrics)
plot.pareto(data,
COLORS,
None,
"linear",
"linear",
write_filepath,
options.write,
xlabel=metric_fullname(x_metric) + ' (miles)',
ylabel=metric_fullname(y_metric) + ' (miles)',
ext=options.ext,
x_metric=x_metric,
y_metric=y_metric,
min_x=0,
min_y=0)
plot.pareto(data,
COLORS,
None,
"linear",
"linear",
write_filepath + '_zoom',
options.write,
xlabel=metric_fullname(x_metric) + ' (miles)',
ylabel=metric_fullname(y_metric) + ' (miles)',
ext=options.ext,
min_x=0,
min_y=0,
x_metric=x_metric,
y_metric=y_metric,
loc="lower right")
plot.pareto(
data,
COLORS,
None,
"linear",
"linear",
write_filepath + '_norm',
options.write,
xlabel=metric_fullname(x_metric) + ' (ratio)',
ylabel=metric_fullname(y_metric) + ' (ratio)',
ext=options.ext,
x_metric=x_metric,
y_metric=y_metric,
# max_x = 1.2,
# max_y = 1.2,
normalize=True,
legend=True,
loc="upper right")
if not options.write:
plot.show()
