def _iteration_parameters(image_rows,
image_cols,
row_block_size,
col_block_size,
y_overlap=0,
x_overlap=0,
bands=1):
maximum_blocks = 0
for i in range(0, image_rows, row_block_size - y_overlap):
for j in range(0, image_cols, col_block_size - x_overlap):
if bands > 1:
for band in range(1, bands + 1):
maximum_blocks += 1
else:
maximum_blocks += 1
progress_widgets = [
' Percent: ',
widgets.Percentage(), ' ',
widgets.Bar(marker='*', left='[', right=']'), ' ',
widgets.ETA(), ' ',
widgets.FileTransferSpeed()
]
progress_bar = ProgressBar(widgets=progress_widgets, maxval=maximum_blocks)
progress_bar.start()
return 1, progress_bar
def _iteration_parameters_values(value1, value2):
# Set widget and pbar
progress_widgets = [' Perc: ', widgets.Percentage(), ' ', \
widgets.Bar(marker='*', left='[', right=']'), ' ', \
widgets.ETA(), ' ', widgets.FileTransferSpeed()]
progress_bar = ProgressBar(widgets=progress_widgets,
maxval=value1 * value2)
progress_bar.start()
return 1, progress_bar
def main():
decomposer = Decomposer()
print("Loaded %d characters." % len(HANZI_CHARACTERS))
if not is_made_of_radicals_only(u'我'):
sys.exit('ERROR: 我 is not made of radicals, wtf?')
total_characters = len(HANZI_CHARACTERS)
matching_characters = 0
pb = progressbar.ProgressBar(widgets=[PBW.Counter(), PBW.Bar(), PBW.ETA()])
for char in pb(set(HANZI_CHARACTERS)):
decomposition = decomposer.decompose(char)
if is_made_of_radicals_only(decomposition):
matching_characters = 1
print("Matched %d characters out of %d" %
(matching_characters, total_characters))
def __init__(self, prefix, max_value):
ProgressBar.__init__(self,
prefix=prefix,
max_value=max_value,
is_terminal=True,
term_width=200)
self.widgets = [
widgets.Percentage(**self.widget_kwargs),
' ',
widgets.SimpleProgress(format='(%s)' %
widgets.SimpleProgress.DEFAULT_FORMAT,
**self.widget_kwargs),
' ',
widgets.Bar(**self.widget_kwargs),
' ',
widgets.Timer(**self.widget_kwargs),
' ',
widgets.ETA(**self.widget_kwargs),
]
def download(url, filename):
"""Attempts to download url to filename unless the two files have the same size"""
r = requests.get(url, stream=True)
size = int(r.headers.get('Content-Length'))
bname = basename(filename)
if size and isfile(filename) and os.path.getsize(filename) == size:
print('File %s already exists, skipping download' % bname, file=sys.stderr)
return
currdown = 0.0
fmt = [
'Downloading %s: ' % bname, widgets.Bar(), ' ',
widgets.RotatingMarker(), ' ', widgets.Percentage(), ' ',
widgets.FileTransferSpeed(), ' ', widgets.ETA()
]
progress = ProgressBar(maxval=size or 100, widgets=fmt)
progress.start()
# https://stackoverflow.com/a/5137509
mkdir_p(dirname(realpath(filename)))
# https://stackoverflow.com/a/16696317
with open(filename, 'wb') as f:
for chunk in r.iter_content(chunk_size=65536):
if chunk:
f.write(chunk)
currdown += len(chunk)
if size:
progress.update(currdown)
else:
progress.update(random.randint(0, 100))
progress.finish()
from .Logger import Logger
from .analyse.linecoverage import linecoverage
# The widgets used by the process bar
WIDGETS = [
widgets.Percentage(),
' (',
widgets.SimpleProgress(),
')',
' ',
widgets.Bar("="),
' ',
widgets.Timer(),
' ',
widgets.ETA(),
]
class Macke:
"""
Main container for all steps of the MACKE analysis
"""
# static "constants"
SYM_ONLY = 0
FUZZ_ONLY = 1
FLIPPER = 2
def __init__(self,
bitcodefile,
else:
shuffles = []
indices = list(range(len(paired_results)))
for iteration in range(num_tests):
# Select randomly half of the results to shuffle
to_shuffle = random.sample(indices, len(paired_results)/2)
shuffled = [stats2 if i in to_shuffle else stats1
for i,(stats1,stats2) in enumerate(paired_results)]
shuffles.append(shuffled)
# Show a progress bar
if not options.quiet:
pbar = ProgressBar(widgets=["Shuffling: ",
widgets.Percentage(),
" ", widgets.Bar(),
' ', widgets.ETA()],
maxval=num_tests).start()
# Don't update to often
pb_update = max(1000, num_tests/100)
# Do the shuffling
f_matches = 0
r_matches = 0
p_matches = 0
for iteration,shuffled in enumerate(shuffles):
if not options.quiet and (iteration % pb_update) == 0:
pbar.update(iteration)
# Use the shuffled stats to compute an f-score for the pretend model
recall, precision, fscore = _fscore(*zip(*shuffled))