def test_file_output(unicode_filename, verbose=False):
data = open(unicode_filename, 'rb').read().decode('utf-8')
handle, filename = tempfile.mkstemp()
os.close(handle)
try:
stream = StringIO.StringIO()
yaml.dump(data, stream, allow_unicode=True)
data1 = stream.getvalue()
stream = open(filename, 'wb')
yaml.dump(data, stream, allow_unicode=True)
stream.close()
data2 = open(filename, 'rb').read()
stream = open(filename, 'wb')
yaml.dump(data, stream, encoding='utf-16-le', allow_unicode=True)
stream.close()
data3 = open(filename, 'rb').read().decode('utf-16-le')[1:].encode('utf-8')
stream = _unicode_open(open(filename, 'wb'), 'utf-8')
yaml.dump(data, stream, allow_unicode=True)
stream.close()
data4 = open(filename, 'rb').read()
assert data1 == data2, (data1, data2)
assert data1 == data3, (data1, data3)
assert data1 == data4, (data1, data4)
finally:
if os.path.exists(filename):
os.unlink(filename)
def test_representer_types(code_filename, verbose=False):
test_constructor._make_objects()
for allow_unicode in [False, True]:
for encoding in ['utf-8', 'utf-16-be', 'utf-16-le']:
native1 = test_constructor._load_code(open(code_filename, 'rb').read())
native2 = None
try:
output = yaml.dump(native1, Dumper=test_constructor.MyDumper,
allow_unicode=allow_unicode, encoding=encoding)
native2 = yaml.load(output, Loader=test_constructor.MyLoader)
try:
if native1 == native2:
continue
except TypeError:
pass
value1 = test_constructor._serialize_value(native1)
value2 = test_constructor._serialize_value(native2)
if verbose:
print "SERIALIZED NATIVE1:"
print value1
print "SERIALIZED NATIVE2:"
print value2
assert value1 == value2, (native1, native2)
finally:
if verbose:
print "NATIVE1:"
pprint.pprint(native1)
print "NATIVE2:"
pprint.pprint(native2)
print "OUTPUT:"
print output
def test_recursive(recursive_filename, verbose=False):
exec open(recursive_filename, "rb").read()
value1 = value
output1 = None
value2 = None
output2 = None
try:
output1 = yaml.dump(value1)
value2 = yaml.load(output1)
output2 = yaml.dump(value2)
assert output1 == output2, (output1, output2)
finally:
if verbose:
# print "VALUE1:", value1
# print "VALUE2:", value2
print "OUTPUT1:"
print output1
print "OUTPUT2:"
print output2
def main(file):
stream = open(file, "r")
docs = yaml.load(stream)
dirname = os.path.dirname(sys.argv[0])
for doc in docs:
id = doc['id']
newfile = open(dirname + "/../_papers/" + id + ".html", "w")
#for item in doc.
doc['layout'] = 'singlepaper'
doc['picture'] = 'paco2'
string = yaml.dump(doc,
explicit_start=True,
default_flow_style=False,
allow_unicode=True)
newfile.write(string)
newfile.write("---\n\n")
newfile.write("{% include singlepaper.html paper=page %}")
newfile.close()
def test_unicode_output(unicode_filename, verbose=False):
data = open(unicode_filename, 'rb').read().decode('utf-8')
value = ' '.join(data.split())
for allow_unicode in [False, True]:
data1 = yaml.dump(value, allow_unicode=allow_unicode)
for encoding in [None, 'utf-8', 'utf-16-be', 'utf-16-le']:
stream = StringIO.StringIO()
yaml.dump(value, _unicode_open(stream, 'utf-8'), encoding=encoding, allow_unicode=allow_unicode)
data2 = stream.getvalue()
data3 = yaml.dump(value, encoding=encoding, allow_unicode=allow_unicode)
stream = StringIO.StringIO()
yaml.dump(value, stream, encoding=encoding, allow_unicode=allow_unicode)
data4 = stream.getvalue()
for copy in [data1, data2, data3, data4]:
if allow_unicode:
try:
copy[4:].encode('ascii')
except (UnicodeDecodeError, UnicodeEncodeError), exc:
if verbose:
print exc
else:
raise AssertionError("expected an exception")
else:
copy[4:].encode('ascii')
assert isinstance(data1, str), (type(data1), encoding)
data1.decode('utf-8')
assert isinstance(data2, str), (type(data2), encoding)
data2.decode('utf-8')
if encoding is None:
assert isinstance(data3, unicode), (type(data3), encoding)
assert isinstance(data4, unicode), (type(data4), encoding)
else:
assert isinstance(data3, str), (type(data3), encoding)
data3.decode(encoding)
assert isinstance(data4, str), (type(data4), encoding)
data4.decode(encoding)
def save(self):
simpleyaml.dump(self.config['var'], open(os.path.join('/var/tmp', self.configfilename), 'w'))
proximity_counts = get_proximity_counts(map_list, edge_class, directions, proximity_stats=save_prox)
proximity_prob = calculate_probabilities(proximity_counts, min_n_size, edge_class)
if class_prob_csv:
classprobs = classify_byprob(map_list, proximity_prob, directions, edge_class)
with open(class_prob_csv, 'w') as f:
w = csv.writer(f)
w.writerow(["i", "j"] + proximity_prob.keys() + ["intended"])
for r in classprobs:
w.writerow(r)
prob_headers = csv_probs(proximity_prob, map_classifcation_type)
header_names = ["i", "j", map_classifcation_type] + get_header_from_prox_prob(prob_headers)
writer.writerow(header_names)
for i in range(0, len(map_list)):
for j in range(0,len(map_list[i])):
cell_class = get_class(j, i, map_list)
writer.writerow([i, j, cell_class]+prob_headers[cell_class].values())
if __name__ == "__main__":
args = parse_args()
if args.output:
out_fd = open(args.output, "w")
else:
out_fd = sys.stdout
prox=dict()
if args.load:
prox=yaml.load(open(args.load))
main(args.map_csv, out_fd, args.edge_class, args.min_n_size, args.map_classification_type, save_prox=prox, class_prob_csv=args.probs)
if args.save:
open(args.save, 'w').write(yaml.dump(prox))
image=blend_images(imgs),
n=len(imgs))
# update the meta_data and global means
for name, newdata in img_by_class.items():
if name in meta_data:
data = meta_data[name]
data['n'] += newdata['n']
cell = newdata['image']
if not args.noalign:
cell = align_images.align_to(data['template'], cell)
img = data['image']
data['image'] = Image.blend(img, cell, newdata['n'] / data['n'])
else:
meta_data[name] = dict(
image=newdata['image'],
n=newdata['n'],
path=name + '_mean.png',
template_path=name + '_temp_mean.png',
template=newdata['template'])
# write out images and remove PIL data
for name, data in meta_data.items():
data['image'].save(data['path'])
data['template'].save(data['template_path'])
del data['image']
del data['template']
with open(args.output, 'w') as out:
out.write(yaml.dump(meta_data))