Datasets:
thisisanshgupta
/
Pycode

Dataset card Data Studio Files
xet
 Community
text
stringlengths
0
828
print(' * Serving Flask app ""%s""' % info.app_import_path)
if debug is not None:
print(' * Forcing debug mode %s' % (debug and 'on' or 'off'))
reloader_path = '.'
if info.app_import_path:
if os.path.isdir(info.app_import_path):
reloader_path = info.app_import_path
elif os.path.isfile(info.app_import_path):
reloader_path = os.path.dirname(info.app_import_path)
extra_files = get_reloader_extra_files(reloader_path)
run_simple(host, port, app, use_reloader=reload, extra_files=extra_files,
use_debugger=debugger, threaded=with_threads)"
1200,"def load_calls(self, call_type='jsonrpc'):
""""""Loads the KvasirAPI calls into API.call based on the call_type variable. Utilizes the `Calls` class to
establish an attribute-based access method. For instance a configuration with an instance called 'internal'
will create an API.call that can be used like this:
API.call.internal.hosts.list() # return all hosts from Kvasir instance 'internal'
:param call_type: string of 'jsonrpc' or 'restapi'
:return: self.call dictionary
""""""
valid = False
if call_type == 'jsonrpc':
#from jsonrpc import Hosts, Services, Accounts, Vulns, OS, NetBIOS, Evidence
import jsonrpc as api_calls
self.api_calls = api_calls
valid = True
#if call_type == 'rest'
# TODO: Implement restful API functions
#from restapi import hosts, services, accounts, vulns, os, netbios, evidence
if valid:
# if kvasir configuration is valid, go through the instances and build the self.call dict
for instance, values in self.configuration.instances_dict.items():
self.call[instance] = Calls()
self.call[instance].call_type = call_type
self.call[instance].hosts = self.api_calls.Hosts(values, self.configuration.web2py_dir)
self.call[instance].services = self.api_calls.Services(values, self.configuration.web2py_dir)
self.call[instance].accounts = self.api_calls.Accounts(values, self.configuration.web2py_dir)
self.call[instance].vulns = self.api_calls.Vulns(values, self.configuration.web2py_dir)
self.call[instance].os = self.api_calls.OpSys(values, self.configuration.web2py_dir)
self.call[instance].snmp = self.api_calls.SNMP(values, self.configuration.web2py_dir)
self.call[instance].netbios = self.api_calls.NetBIOS(values, self.configuration.web2py_dir)
self.call[instance].evidence = self.api_calls.Evidence(values, self.configuration.web2py_dir)
self.call[instance].stats = self.api_calls.Stats(values, self.configuration.web2py_dir)"
1201,"def install_brew(target_path):
"""""" Install brew to the target path """"""
if not os.path.exists(target_path):
try:
os.makedirs(target_path)
except OSError:
logger.warn(""Unable to create directory %s for brew."" % target_path)
logger.warn(""Skipping..."")
return
extract_targz(HOMEBREW_URL, target_path, remove_common_prefix=True)"
1202,"def scales(self, image):
""""""scales(image) -> scale, shape
Computes the all possible scales for the given image and yields a tuple of the scale and the scaled image shape as an iterator.
**Parameters::**
``image`` : array_like(2D or 3D)
The image, for which the scales should be computed
**Yields:**
``scale`` : float
The next scale of the image to be considered
``shape`` : (int, int) or (int, int, int)
The shape of the image, when scaled with the current ``scale``
""""""
# compute the minimum scale so that the patch size still fits into the given image
minimum_scale = max(self.m_patch_box.size_f[0] / image.shape[-2], self.m_patch_box.size_f[1] / image.shape[-1])
if self.m_lowest_scale:
maximum_scale = min(minimum_scale / self.m_lowest_scale, 1.)
else:
maximum_scale = 1.
current_scale_power = 0.
# iterate over all possible scales
while True:
# scale the image
scale = minimum_scale * math.pow(self.m_scale_factor, current_scale_power)
if scale > maximum_scale:
# image is smaller than the requested minimum size
break
current_scale_power -= 1.
scaled_image_shape = bob.ip.base.scaled_output_shape(image, scale)
# return both the scale and the scaled image size
yield scale, scaled_image_shape"
1203,"def sample_scaled(self, shape):
""""""sample_scaled(shape) -> bounding_box
Yields an iterator that iterates over all sampled bounding boxes in the given (scaled) image shape.