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()
#!/usr/bin/env python3
from lib.plot import plot
if __name__ == '__main__':
plot()
for j, b in enumerate(values):
if x[j] not in combined:
combined[x[j]] = []
combined[x[j]].append(b)
group_str = get_group_str(options)
xmax = max(combined[sorted(combined.keys())[0]])
axis_limits = [0, xmax, 0, 1]
for xscale in ["linear", "log"]:
ptype = 'latency_cdfs_' + xscale
write_filepath = 'data_vis/merged/%s_%i_to_%i_%s_%s' % (group_str, options.from_start, options.from_end, metric, ptype)
# Assume the loweest-numbered element is the smallest
plot.plot('cdf', combined, COLORS, axis_limits,
metric, xscale, "linear", write_filepath,
options.write,
xlabel = 'optimal ' + metric_fullname(metric) + ' (miles)',
ylabel = 'fraction of topologies',
ext = options.ext,
legend = True)
if options.gen_1ctrl_table:
write_filepath = 'data_vis/merged/%s_%i_to_%i_%s_%s' % (group_str, options.from_start, options.from_end, metric, '1ctrl_table')
dump_1ctrl_latex_table(combined[1], write_filepath, SAFETY_MARGINS, USE_FRACTIONS)
if 'pareto' in options.cdf_plots:
assert 'pareto_max' in metric_data
assert 'base' in metric_data['pareto_max']
topo_data = metric_data['pareto_max']['base']
combined = {} # keys are values for k; values are distribution of optimal latencies
for topo, data_lines in topo_data.iteritems():
x = data_lines['x']
args = parser.parse_args()
if args.work == None:
print('must specify work')
elif args.work == 'preprocess' or args.work == 'pre':
from lib.preprocess import do_preprocess
if args.file != None:
do_preprocess(args.file[0], args.file[1])
else:
do_preprocess(test_exist=True)
os.system('python3 main.py -w train -e 5 -b 256')
elif args.work == 'test':
from lib.test import test
test(args.file[0], args.file[1])
elif args.work == 'train':
from lib.train import train
history = train(model_path=args.model,
epochs=args.epochs,
batch_size=args.batch_size,
seed=args.random_seed)
if args.plot == True:
from lib.plot import plot
plot(history)
elif args.work == 'semi':
from lib.semiParse import semiParse
semiParse()
from ij import IJ
import sys
sys.path.append("/home/albert/lab/scripts/python/imagej/IsoView-GCaMP/")
from lib.plot import plot2DRoiOverZ as plot
imp3D = IJ.getImage()
plot(imp3D,
YaxisLabel="Fluorescence intensity",
XaxisLabel="Time (seconds)",
Zscale=0.75)
