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def assign_only_product_as_production(db):
"Assign only product as reference product.\n\n Skips datasets that already have a reference product or no production exchanges. Production exchanges must have a ``name`` and an amount.\n\n Will replace the following activity fields, if not already specified:\n\n * 'name' - name of reference product\n * 'unit' - unit of reference product\n * 'production amount' - amount of reference product\n\n "
for ds in db:
if ds.get('reference product'):
continue
products = [x for x in ds.get('exchanges', []) if (x.get('type') == 'production')]
if (len(products) == 1):
product = products[0]
assert product['name']
ds['reference product'] = product['name']
ds['production amount'] = product['amount']
ds['name'] = (ds.get('name') or product['name'])
ds['unit'] = (ds.get('unit') or product.get('unit') or 'Unknown')
return db | -1,332,683,319,952,914,000 | Assign only product as reference product.
Skips datasets that already have a reference product or no production exchanges. Production exchanges must have a ``name`` and an amount.
Will replace the following activity fields, if not already specified:
* 'name' - name of reference product
* 'unit' - unit of reference product
* 'production amount' - amount of reference product | bw2io/strategies/generic.py | assign_only_product_as_production | pjamesjoyce/brightway2-io | python | def assign_only_product_as_production(db):
"Assign only product as reference product.\n\n Skips datasets that already have a reference product or no production exchanges. Production exchanges must have a ``name`` and an amount.\n\n Will replace the following activity fields, if not already specified:\n\n * 'name' - name of reference product\n * 'unit' - unit of reference product\n * 'production amount' - amount of reference product\n\n "
for ds in db:
if ds.get('reference product'):
continue
products = [x for x in ds.get('exchanges', []) if (x.get('type') == 'production')]
if (len(products) == 1):
product = products[0]
assert product['name']
ds['reference product'] = product['name']
ds['production amount'] = product['amount']
ds['name'] = (ds.get('name') or product['name'])
ds['unit'] = (ds.get('unit') or product.get('unit') or 'Unknown')
return db |
def link_technosphere_by_activity_hash(db, external_db_name=None, fields=None):
'Link technosphere exchanges using ``activity_hash`` function.\n\n If ``external_db_name``, link against a different database; otherwise link internally.\n\n If ``fields``, link using only certain fields.'
TECHNOSPHERE_TYPES = {'technosphere', 'substitution', 'production'}
if (external_db_name is not None):
if (external_db_name not in databases):
raise StrategyError("Can't find external database {}".format(external_db_name))
other = (obj for obj in Database(external_db_name) if (obj.get('type', 'process') == 'process'))
internal = False
else:
other = None
internal = True
return link_iterable_by_fields(db, other, internal=internal, kind=TECHNOSPHERE_TYPES, fields=fields) | 4,406,278,707,064,293,400 | Link technosphere exchanges using ``activity_hash`` function.
If ``external_db_name``, link against a different database; otherwise link internally.
If ``fields``, link using only certain fields. | bw2io/strategies/generic.py | link_technosphere_by_activity_hash | pjamesjoyce/brightway2-io | python | def link_technosphere_by_activity_hash(db, external_db_name=None, fields=None):
'Link technosphere exchanges using ``activity_hash`` function.\n\n If ``external_db_name``, link against a different database; otherwise link internally.\n\n If ``fields``, link using only certain fields.'
TECHNOSPHERE_TYPES = {'technosphere', 'substitution', 'production'}
if (external_db_name is not None):
if (external_db_name not in databases):
raise StrategyError("Can't find external database {}".format(external_db_name))
other = (obj for obj in Database(external_db_name) if (obj.get('type', 'process') == 'process'))
internal = False
else:
other = None
internal = True
return link_iterable_by_fields(db, other, internal=internal, kind=TECHNOSPHERE_TYPES, fields=fields) |
def set_code_by_activity_hash(db, overwrite=False):
"Use ``activity_hash`` to set dataset code.\n\n By default, won't overwrite existing codes, but will if ``overwrite`` is ``True``."
for ds in db:
if (('code' not in ds) or overwrite):
ds['code'] = activity_hash(ds)
return db | 8,632,796,420,951,601,000 | Use ``activity_hash`` to set dataset code.
By default, won't overwrite existing codes, but will if ``overwrite`` is ``True``. | bw2io/strategies/generic.py | set_code_by_activity_hash | pjamesjoyce/brightway2-io | python | def set_code_by_activity_hash(db, overwrite=False):
"Use ``activity_hash`` to set dataset code.\n\n By default, won't overwrite existing codes, but will if ``overwrite`` is ``True``."
for ds in db:
if (('code' not in ds) or overwrite):
ds['code'] = activity_hash(ds)
return db |
def drop_unlinked(db):
'This is the nuclear option - use at your own risk!'
for ds in db:
ds['exchanges'] = [obj for obj in ds['exchanges'] if obj.get('input')]
return db | 127,509,395,245,177,260 | This is the nuclear option - use at your own risk! | bw2io/strategies/generic.py | drop_unlinked | pjamesjoyce/brightway2-io | python | def drop_unlinked(db):
for ds in db:
ds['exchanges'] = [obj for obj in ds['exchanges'] if obj.get('input')]
return db |
def normalize_units(db):
'Normalize units in datasets and their exchanges'
for ds in db:
if ('unit' in ds):
ds['unit'] = normalize_units_function(ds['unit'])
for exc in ds.get('exchanges', []):
if ('unit' in exc):
exc['unit'] = normalize_units_function(exc['unit'])
for param in ds.get('parameters', {}).values():
if ('unit' in param):
param['unit'] = normalize_units_function(param['unit'])
return db | 8,029,459,837,467,838 | Normalize units in datasets and their exchanges | bw2io/strategies/generic.py | normalize_units | pjamesjoyce/brightway2-io | python | def normalize_units(db):
for ds in db:
if ('unit' in ds):
ds['unit'] = normalize_units_function(ds['unit'])
for exc in ds.get('exchanges', []):
if ('unit' in exc):
exc['unit'] = normalize_units_function(exc['unit'])
for param in ds.get('parameters', {}).values():
if ('unit' in param):
param['unit'] = normalize_units_function(param['unit'])
return db |
def add_database_name(db, name):
'Add database name to datasets'
for ds in db:
ds['database'] = name
return db | 3,011,635,110,219,210,000 | Add database name to datasets | bw2io/strategies/generic.py | add_database_name | pjamesjoyce/brightway2-io | python | def add_database_name(db, name):
for ds in db:
ds['database'] = name
return db |
def convert_uncertainty_types_to_integers(db):
'Generic number conversion function convert to floats. Return to integers.'
for ds in db:
for exc in ds['exchanges']:
try:
exc['uncertainty type'] = int(exc['uncertainty type'])
except:
pass
return db | 7,904,900,524,443,326,000 | Generic number conversion function convert to floats. Return to integers. | bw2io/strategies/generic.py | convert_uncertainty_types_to_integers | pjamesjoyce/brightway2-io | python | def convert_uncertainty_types_to_integers(db):
for ds in db:
for exc in ds['exchanges']:
try:
exc['uncertainty type'] = int(exc['uncertainty type'])
except:
pass
return db |
def drop_falsey_uncertainty_fields_but_keep_zeros(db):
"Drop fields like '' but keep zero and NaN.\n\n Note that this doesn't strip `False`, which behaves *exactly* like 0.\n\n "
uncertainty_fields = ['minimum', 'maximum', 'scale', 'shape', 'loc']
def drop_if_appropriate(exc):
for field in uncertainty_fields:
if ((field not in exc) or (exc[field] == 0)):
continue
elif (isinstance(exc[field], numbers.Number) and np.isnan(exc[field])):
continue
elif (not exc[field]):
del exc[field]
for ds in db:
for exc in ds['exchanges']:
drop_if_appropriate(exc)
return db | -8,108,741,207,312,171,000 | Drop fields like '' but keep zero and NaN.
Note that this doesn't strip `False`, which behaves *exactly* like 0. | bw2io/strategies/generic.py | drop_falsey_uncertainty_fields_but_keep_zeros | pjamesjoyce/brightway2-io | python | def drop_falsey_uncertainty_fields_but_keep_zeros(db):
"Drop fields like but keep zero and NaN.\n\n Note that this doesn't strip `False`, which behaves *exactly* like 0.\n\n "
uncertainty_fields = ['minimum', 'maximum', 'scale', 'shape', 'loc']
def drop_if_appropriate(exc):
for field in uncertainty_fields:
if ((field not in exc) or (exc[field] == 0)):
continue
elif (isinstance(exc[field], numbers.Number) and np.isnan(exc[field])):
continue
elif (not exc[field]):
del exc[field]
for ds in db:
for exc in ds['exchanges']:
drop_if_appropriate(exc)
return db |
def convert_activity_parameters_to_list(data):
'Convert activity parameters from dictionary to list of dictionaries'
def _(key, value):
dct = deepcopy(value)
dct['name'] = key
return dct
for ds in data:
if ('parameters' in ds):
ds['parameters'] = [_(x, y) for (x, y) in ds['parameters'].items()]
return data | -8,496,173,251,939,245,000 | Convert activity parameters from dictionary to list of dictionaries | bw2io/strategies/generic.py | convert_activity_parameters_to_list | pjamesjoyce/brightway2-io | python | def convert_activity_parameters_to_list(data):
def _(key, value):
dct = deepcopy(value)
dct['name'] = key
return dct
for ds in data:
if ('parameters' in ds):
ds['parameters'] = [_(x, y) for (x, y) in ds['parameters'].items()]
return data |
@property
def Ospfv3PseudoInterface(self):
'\n Returns\n -------\n - obj(uhd_restpy.testplatform.sessions.ixnetwork.topology.ospfv3pseudointerface_bbc932877888c8c8400661ec299754a8.Ospfv3PseudoInterface): An instance of the Ospfv3PseudoInterface class\n\n Raises\n ------\n - ServerError: The server has encountered an uncategorized error condition\n '
from uhd_restpy.testplatform.sessions.ixnetwork.topology.ospfv3pseudointerface_bbc932877888c8c8400661ec299754a8 import Ospfv3PseudoInterface
return Ospfv3PseudoInterface(self) | -7,806,945,575,050,585,000 | Returns
-------
- obj(uhd_restpy.testplatform.sessions.ixnetwork.topology.ospfv3pseudointerface_bbc932877888c8c8400661ec299754a8.Ospfv3PseudoInterface): An instance of the Ospfv3PseudoInterface class
Raises
------
- ServerError: The server has encountered an uncategorized error condition | uhd_restpy/testplatform/sessions/ixnetwork/topology/siminterfaceipv6config_189f3bfbc365f2b105e35cd8b9d542d6.py | Ospfv3PseudoInterface | rfrye-github/ixnetwork_restpy | python | @property
def Ospfv3PseudoInterface(self):
'\n Returns\n -------\n - obj(uhd_restpy.testplatform.sessions.ixnetwork.topology.ospfv3pseudointerface_bbc932877888c8c8400661ec299754a8.Ospfv3PseudoInterface): An instance of the Ospfv3PseudoInterface class\n\n Raises\n ------\n - ServerError: The server has encountered an uncategorized error condition\n '
from uhd_restpy.testplatform.sessions.ixnetwork.topology.ospfv3pseudointerface_bbc932877888c8c8400661ec299754a8 import Ospfv3PseudoInterface
return Ospfv3PseudoInterface(self) |
@property
def Count(self):
'\n Returns\n -------\n - number: Number of elements inside associated multiplier-scaled container object, e.g. number of devices inside a Device Group.\n '
return self._get_attribute(self._SDM_ATT_MAP['Count']) | 9,202,294,428,103,448,000 | Returns
-------
- number: Number of elements inside associated multiplier-scaled container object, e.g. number of devices inside a Device Group. | uhd_restpy/testplatform/sessions/ixnetwork/topology/siminterfaceipv6config_189f3bfbc365f2b105e35cd8b9d542d6.py | Count | rfrye-github/ixnetwork_restpy | python | @property
def Count(self):
'\n Returns\n -------\n - number: Number of elements inside associated multiplier-scaled container object, e.g. number of devices inside a Device Group.\n '
return self._get_attribute(self._SDM_ATT_MAP['Count']) |
@property
def DescriptiveName(self):
"\n Returns\n -------\n - str: Longer, more descriptive name for element. It's not guaranteed to be unique like -name-, but may offer more context.\n "
return self._get_attribute(self._SDM_ATT_MAP['DescriptiveName']) | 6,335,322,004,352,822,000 | Returns
-------
- str: Longer, more descriptive name for element. It's not guaranteed to be unique like -name-, but may offer more context. | uhd_restpy/testplatform/sessions/ixnetwork/topology/siminterfaceipv6config_189f3bfbc365f2b105e35cd8b9d542d6.py | DescriptiveName | rfrye-github/ixnetwork_restpy | python | @property
def DescriptiveName(self):
"\n Returns\n -------\n - str: Longer, more descriptive name for element. It's not guaranteed to be unique like -name-, but may offer more context.\n "
return self._get_attribute(self._SDM_ATT_MAP['DescriptiveName']) |
@property
def EnableIp(self):
'\n Returns\n -------\n - obj(uhd_restpy.multivalue.Multivalue): Enable IPv6\n '
from uhd_restpy.multivalue import Multivalue
return Multivalue(self, self._get_attribute(self._SDM_ATT_MAP['EnableIp'])) | 1,591,239,963,546,353,000 | Returns
-------
- obj(uhd_restpy.multivalue.Multivalue): Enable IPv6 | uhd_restpy/testplatform/sessions/ixnetwork/topology/siminterfaceipv6config_189f3bfbc365f2b105e35cd8b9d542d6.py | EnableIp | rfrye-github/ixnetwork_restpy | python | @property
def EnableIp(self):
'\n Returns\n -------\n - obj(uhd_restpy.multivalue.Multivalue): Enable IPv6\n '
from uhd_restpy.multivalue import Multivalue
return Multivalue(self, self._get_attribute(self._SDM_ATT_MAP['EnableIp'])) |
@property
def FromIP(self):
'\n Returns\n -------\n - obj(uhd_restpy.multivalue.Multivalue): 128 Bits IPv6 address.\n '
from uhd_restpy.multivalue import Multivalue
return Multivalue(self, self._get_attribute(self._SDM_ATT_MAP['FromIP'])) | 619,688,722,874,091,400 | Returns
-------
- obj(uhd_restpy.multivalue.Multivalue): 128 Bits IPv6 address. | uhd_restpy/testplatform/sessions/ixnetwork/topology/siminterfaceipv6config_189f3bfbc365f2b105e35cd8b9d542d6.py | FromIP | rfrye-github/ixnetwork_restpy | python | @property
def FromIP(self):
'\n Returns\n -------\n - obj(uhd_restpy.multivalue.Multivalue): 128 Bits IPv6 address.\n '
from uhd_restpy.multivalue import Multivalue
return Multivalue(self, self._get_attribute(self._SDM_ATT_MAP['FromIP'])) |
@property
def Name(self):
'\n Returns\n -------\n - str: Name of NGPF element, guaranteed to be unique in Scenario\n '
return self._get_attribute(self._SDM_ATT_MAP['Name']) | -1,824,082,867,023,513,900 | Returns
-------
- str: Name of NGPF element, guaranteed to be unique in Scenario | uhd_restpy/testplatform/sessions/ixnetwork/topology/siminterfaceipv6config_189f3bfbc365f2b105e35cd8b9d542d6.py | Name | rfrye-github/ixnetwork_restpy | python | @property
def Name(self):
'\n Returns\n -------\n - str: Name of NGPF element, guaranteed to be unique in Scenario\n '
return self._get_attribute(self._SDM_ATT_MAP['Name']) |
@property
def SubnetPrefixLength(self):
'\n Returns\n -------\n - obj(uhd_restpy.multivalue.Multivalue): Subnet Prefix Length\n '
from uhd_restpy.multivalue import Multivalue
return Multivalue(self, self._get_attribute(self._SDM_ATT_MAP['SubnetPrefixLength'])) | -8,855,745,252,700,621,000 | Returns
-------
- obj(uhd_restpy.multivalue.Multivalue): Subnet Prefix Length | uhd_restpy/testplatform/sessions/ixnetwork/topology/siminterfaceipv6config_189f3bfbc365f2b105e35cd8b9d542d6.py | SubnetPrefixLength | rfrye-github/ixnetwork_restpy | python | @property
def SubnetPrefixLength(self):
'\n Returns\n -------\n - obj(uhd_restpy.multivalue.Multivalue): Subnet Prefix Length\n '
from uhd_restpy.multivalue import Multivalue
return Multivalue(self, self._get_attribute(self._SDM_ATT_MAP['SubnetPrefixLength'])) |
@property
def ToIP(self):
'\n Returns\n -------\n - obj(uhd_restpy.multivalue.Multivalue): 128 Bits IPv6 address.\n '
from uhd_restpy.multivalue import Multivalue
return Multivalue(self, self._get_attribute(self._SDM_ATT_MAP['ToIP'])) | -1,024,568,113,818,389,900 | Returns
-------
- obj(uhd_restpy.multivalue.Multivalue): 128 Bits IPv6 address. | uhd_restpy/testplatform/sessions/ixnetwork/topology/siminterfaceipv6config_189f3bfbc365f2b105e35cd8b9d542d6.py | ToIP | rfrye-github/ixnetwork_restpy | python | @property
def ToIP(self):
'\n Returns\n -------\n - obj(uhd_restpy.multivalue.Multivalue): 128 Bits IPv6 address.\n '
from uhd_restpy.multivalue import Multivalue
return Multivalue(self, self._get_attribute(self._SDM_ATT_MAP['ToIP'])) |
def update(self, Name=None):
'Updates simInterfaceIPv6Config resource on the server.\n\n This method has some named parameters with a type: obj (Multivalue).\n The Multivalue class has documentation that details the possible values for those named parameters.\n\n Args\n ----\n - Name (str): Name of NGPF element, guaranteed to be unique in Scenario\n\n Raises\n ------\n - ServerError: The server has encountered an uncategorized error condition\n '
return self._update(self._map_locals(self._SDM_ATT_MAP, locals())) | 8,011,544,818,928,487,000 | Updates simInterfaceIPv6Config resource on the server.
This method has some named parameters with a type: obj (Multivalue).
The Multivalue class has documentation that details the possible values for those named parameters.
Args
----
- Name (str): Name of NGPF element, guaranteed to be unique in Scenario
Raises
------
- ServerError: The server has encountered an uncategorized error condition | uhd_restpy/testplatform/sessions/ixnetwork/topology/siminterfaceipv6config_189f3bfbc365f2b105e35cd8b9d542d6.py | update | rfrye-github/ixnetwork_restpy | python | def update(self, Name=None):
'Updates simInterfaceIPv6Config resource on the server.\n\n This method has some named parameters with a type: obj (Multivalue).\n The Multivalue class has documentation that details the possible values for those named parameters.\n\n Args\n ----\n - Name (str): Name of NGPF element, guaranteed to be unique in Scenario\n\n Raises\n ------\n - ServerError: The server has encountered an uncategorized error condition\n '
return self._update(self._map_locals(self._SDM_ATT_MAP, locals())) |
def find(self, Count=None, DescriptiveName=None, Name=None):
"Finds and retrieves simInterfaceIPv6Config resources from the server.\n\n All named parameters are evaluated on the server using regex. The named parameters can be used to selectively retrieve simInterfaceIPv6Config resources from the server.\n To retrieve an exact match ensure the parameter value starts with ^ and ends with $\n By default the find method takes no parameters and will retrieve all simInterfaceIPv6Config resources from the server.\n\n Args\n ----\n - Count (number): Number of elements inside associated multiplier-scaled container object, e.g. number of devices inside a Device Group.\n - DescriptiveName (str): Longer, more descriptive name for element. It's not guaranteed to be unique like -name-, but may offer more context.\n - Name (str): Name of NGPF element, guaranteed to be unique in Scenario\n\n Returns\n -------\n - self: This instance with matching simInterfaceIPv6Config resources retrieved from the server available through an iterator or index\n\n Raises\n ------\n - ServerError: The server has encountered an uncategorized error condition\n "
return self._select(self._map_locals(self._SDM_ATT_MAP, locals())) | -1,710,958,394,887,526,400 | Finds and retrieves simInterfaceIPv6Config resources from the server.
All named parameters are evaluated on the server using regex. The named parameters can be used to selectively retrieve simInterfaceIPv6Config resources from the server.
To retrieve an exact match ensure the parameter value starts with ^ and ends with $
By default the find method takes no parameters and will retrieve all simInterfaceIPv6Config resources from the server.
Args
----
- Count (number): Number of elements inside associated multiplier-scaled container object, e.g. number of devices inside a Device Group.
- DescriptiveName (str): Longer, more descriptive name for element. It's not guaranteed to be unique like -name-, but may offer more context.
- Name (str): Name of NGPF element, guaranteed to be unique in Scenario
Returns
-------
- self: This instance with matching simInterfaceIPv6Config resources retrieved from the server available through an iterator or index
Raises
------
- ServerError: The server has encountered an uncategorized error condition | uhd_restpy/testplatform/sessions/ixnetwork/topology/siminterfaceipv6config_189f3bfbc365f2b105e35cd8b9d542d6.py | find | rfrye-github/ixnetwork_restpy | python | def find(self, Count=None, DescriptiveName=None, Name=None):
"Finds and retrieves simInterfaceIPv6Config resources from the server.\n\n All named parameters are evaluated on the server using regex. The named parameters can be used to selectively retrieve simInterfaceIPv6Config resources from the server.\n To retrieve an exact match ensure the parameter value starts with ^ and ends with $\n By default the find method takes no parameters and will retrieve all simInterfaceIPv6Config resources from the server.\n\n Args\n ----\n - Count (number): Number of elements inside associated multiplier-scaled container object, e.g. number of devices inside a Device Group.\n - DescriptiveName (str): Longer, more descriptive name for element. It's not guaranteed to be unique like -name-, but may offer more context.\n - Name (str): Name of NGPF element, guaranteed to be unique in Scenario\n\n Returns\n -------\n - self: This instance with matching simInterfaceIPv6Config resources retrieved from the server available through an iterator or index\n\n Raises\n ------\n - ServerError: The server has encountered an uncategorized error condition\n "
return self._select(self._map_locals(self._SDM_ATT_MAP, locals())) |
def read(self, href):
'Retrieves a single instance of simInterfaceIPv6Config data from the server.\n\n Args\n ----\n - href (str): An href to the instance to be retrieved\n\n Returns\n -------\n - self: This instance with the simInterfaceIPv6Config resources from the server available through an iterator or index\n\n Raises\n ------\n - NotFoundError: The requested resource does not exist on the server\n - ServerError: The server has encountered an uncategorized error condition\n '
return self._read(href) | -3,705,495,493,069,741,000 | Retrieves a single instance of simInterfaceIPv6Config data from the server.
Args
----
- href (str): An href to the instance to be retrieved
Returns
-------
- self: This instance with the simInterfaceIPv6Config resources from the server available through an iterator or index
Raises
------
- NotFoundError: The requested resource does not exist on the server
- ServerError: The server has encountered an uncategorized error condition | uhd_restpy/testplatform/sessions/ixnetwork/topology/siminterfaceipv6config_189f3bfbc365f2b105e35cd8b9d542d6.py | read | rfrye-github/ixnetwork_restpy | python | def read(self, href):
'Retrieves a single instance of simInterfaceIPv6Config data from the server.\n\n Args\n ----\n - href (str): An href to the instance to be retrieved\n\n Returns\n -------\n - self: This instance with the simInterfaceIPv6Config resources from the server available through an iterator or index\n\n Raises\n ------\n - NotFoundError: The requested resource does not exist on the server\n - ServerError: The server has encountered an uncategorized error condition\n '
return self._read(href) |
def get_device_ids(self, PortNames=None, EnableIp=None, FromIP=None, SubnetPrefixLength=None, ToIP=None):
'Base class infrastructure that gets a list of simInterfaceIPv6Config device ids encapsulated by this object.\n\n Use the optional regex parameters in the method to refine the list of device ids encapsulated by this object.\n\n Args\n ----\n - PortNames (str): optional regex of port names\n - EnableIp (str): optional regex of enableIp\n - FromIP (str): optional regex of fromIP\n - SubnetPrefixLength (str): optional regex of subnetPrefixLength\n - ToIP (str): optional regex of toIP\n\n Returns\n -------\n - list(int): A list of device ids that meets the regex criteria provided in the method parameters\n\n Raises\n ------\n - ServerError: The server has encountered an uncategorized error condition\n '
return self._get_ngpf_device_ids(locals()) | -2,168,971,069,518,069,800 | Base class infrastructure that gets a list of simInterfaceIPv6Config device ids encapsulated by this object.
Use the optional regex parameters in the method to refine the list of device ids encapsulated by this object.
Args
----
- PortNames (str): optional regex of port names
- EnableIp (str): optional regex of enableIp
- FromIP (str): optional regex of fromIP
- SubnetPrefixLength (str): optional regex of subnetPrefixLength
- ToIP (str): optional regex of toIP
Returns
-------
- list(int): A list of device ids that meets the regex criteria provided in the method parameters
Raises
------
- ServerError: The server has encountered an uncategorized error condition | uhd_restpy/testplatform/sessions/ixnetwork/topology/siminterfaceipv6config_189f3bfbc365f2b105e35cd8b9d542d6.py | get_device_ids | rfrye-github/ixnetwork_restpy | python | def get_device_ids(self, PortNames=None, EnableIp=None, FromIP=None, SubnetPrefixLength=None, ToIP=None):
'Base class infrastructure that gets a list of simInterfaceIPv6Config device ids encapsulated by this object.\n\n Use the optional regex parameters in the method to refine the list of device ids encapsulated by this object.\n\n Args\n ----\n - PortNames (str): optional regex of port names\n - EnableIp (str): optional regex of enableIp\n - FromIP (str): optional regex of fromIP\n - SubnetPrefixLength (str): optional regex of subnetPrefixLength\n - ToIP (str): optional regex of toIP\n\n Returns\n -------\n - list(int): A list of device ids that meets the regex criteria provided in the method parameters\n\n Raises\n ------\n - ServerError: The server has encountered an uncategorized error condition\n '
return self._get_ngpf_device_ids(locals()) |
def Abort(self):
'Executes the abort operation on the server.\n\n Abort CPF control plane (equals to demote to kUnconfigured state).\n\n Raises\n ------\n - NotFoundError: The requested resource does not exist on the server\n - ServerError: The server has encountered an uncategorized error condition\n '
payload = {'Arg1': self}
return self._execute('abort', payload=payload, response_object=None) | 2,809,284,878,408,633,000 | Executes the abort operation on the server.
Abort CPF control plane (equals to demote to kUnconfigured state).
Raises
------
- NotFoundError: The requested resource does not exist on the server
- ServerError: The server has encountered an uncategorized error condition | uhd_restpy/testplatform/sessions/ixnetwork/topology/siminterfaceipv6config_189f3bfbc365f2b105e35cd8b9d542d6.py | Abort | rfrye-github/ixnetwork_restpy | python | def Abort(self):
'Executes the abort operation on the server.\n\n Abort CPF control plane (equals to demote to kUnconfigured state).\n\n Raises\n ------\n - NotFoundError: The requested resource does not exist on the server\n - ServerError: The server has encountered an uncategorized error condition\n '
payload = {'Arg1': self}
return self._execute('abort', payload=payload, response_object=None) |
def Start(self):
'Executes the start operation on the server.\n\n Start CPF control plane (equals to promote to negotiated state).\n\n Raises\n ------\n - NotFoundError: The requested resource does not exist on the server\n - ServerError: The server has encountered an uncategorized error condition\n '
payload = {'Arg1': self}
return self._execute('start', payload=payload, response_object=None) | 6,849,918,185,653,348,000 | Executes the start operation on the server.
Start CPF control plane (equals to promote to negotiated state).
Raises
------
- NotFoundError: The requested resource does not exist on the server
- ServerError: The server has encountered an uncategorized error condition | uhd_restpy/testplatform/sessions/ixnetwork/topology/siminterfaceipv6config_189f3bfbc365f2b105e35cd8b9d542d6.py | Start | rfrye-github/ixnetwork_restpy | python | def Start(self):
'Executes the start operation on the server.\n\n Start CPF control plane (equals to promote to negotiated state).\n\n Raises\n ------\n - NotFoundError: The requested resource does not exist on the server\n - ServerError: The server has encountered an uncategorized error condition\n '
payload = {'Arg1': self}
return self._execute('start', payload=payload, response_object=None) |
def Stop(self):
'Executes the stop operation on the server.\n\n Stop CPF control plane (equals to demote to PreValidated-DoDDone state).\n\n Raises\n ------\n - NotFoundError: The requested resource does not exist on the server\n - ServerError: The server has encountered an uncategorized error condition\n '
payload = {'Arg1': self}
return self._execute('stop', payload=payload, response_object=None) | 328,731,283,898,110,100 | Executes the stop operation on the server.
Stop CPF control plane (equals to demote to PreValidated-DoDDone state).
Raises
------
- NotFoundError: The requested resource does not exist on the server
- ServerError: The server has encountered an uncategorized error condition | uhd_restpy/testplatform/sessions/ixnetwork/topology/siminterfaceipv6config_189f3bfbc365f2b105e35cd8b9d542d6.py | Stop | rfrye-github/ixnetwork_restpy | python | def Stop(self):
'Executes the stop operation on the server.\n\n Stop CPF control plane (equals to demote to PreValidated-DoDDone state).\n\n Raises\n ------\n - NotFoundError: The requested resource does not exist on the server\n - ServerError: The server has encountered an uncategorized error condition\n '
payload = {'Arg1': self}
return self._execute('stop', payload=payload, response_object=None) |
@logargs
@ValidateAndDefault({'account_name': (OptionalValueType(StrType), None), 'account_id': (OptionalValueType(SolidFireIDType), None), 'by_id': (BoolType, False), 'mvip': (IPv4AddressType, sfdefaults.mvip), 'username': (StrType, sfdefaults.username), 'password': (StrType, sfdefaults.password), 'output_format': (OptionalValueType(SelectionType(sfdefaults.all_output_formats)), None)})
def AccountListVolumes(account_name, account_id, by_id, mvip, username, password, output_format):
'\n Show the list of volumes for an account\n\n Args:\n \n account_name: the name of the account\n account_id: the ID of the account\n by_id: show volume IDs instead of names\n mvip: the management IP of the cluster\n username: the admin user of the cluster\n password: the admin password of the cluster\n output_format: the format to display the information\n '
log = GetLogger()
NameOrID(account_name, account_id, 'account')
log.info('Searching for accounts')
try:
account = SFCluster(mvip, username, password).FindAccount(accountName=account_name, accountID=account_id)
except UnknownObjectError:
log.error('Account does not exists')
return False
except SolidFireError as e:
log.error('Could not search for accounts: {}'.format(e))
return False
log.info('Searching for volumes')
try:
all_volumes = SFCluster(mvip, username, password).ListActiveVolumes()
all_volumes += SFCluster(mvip, username, password).ListDeletedVolumes()
except SolidFireError as e:
log.error('Could not search for volumes: {}'.format(e))
return False
all_volumes = {vol['volumeID']: vol for vol in all_volumes}
attr = 'name'
if by_id:
attr = 'volumeID'
account_volumes = [all_volumes[vid][attr] for vid in account.volumes]
if (output_format and (output_format == 'bash')):
sys.stdout.write((' '.join([str(item) for item in account_volumes]) + '\n'))
sys.stdout.flush()
elif (output_format and (output_format == 'json')):
sys.stdout.write((json.dumps({'volumes': account_volumes}) + '\n'))
sys.stdout.flush()
else:
log.info('{} volumes in account {}'.format(len(account.volumes), account.username))
if account.volumes:
log.info(' {}'.format(', '.join([str(item) for item in account_volumes])))
return True | -4,768,445,726,859,416,000 | Show the list of volumes for an account
Args:
account_name: the name of the account
account_id: the ID of the account
by_id: show volume IDs instead of names
mvip: the management IP of the cluster
username: the admin user of the cluster
password: the admin password of the cluster
output_format: the format to display the information | account_list_volumes.py | AccountListVolumes | cseelye/sfauto | python | @logargs
@ValidateAndDefault({'account_name': (OptionalValueType(StrType), None), 'account_id': (OptionalValueType(SolidFireIDType), None), 'by_id': (BoolType, False), 'mvip': (IPv4AddressType, sfdefaults.mvip), 'username': (StrType, sfdefaults.username), 'password': (StrType, sfdefaults.password), 'output_format': (OptionalValueType(SelectionType(sfdefaults.all_output_formats)), None)})
def AccountListVolumes(account_name, account_id, by_id, mvip, username, password, output_format):
'\n Show the list of volumes for an account\n\n Args:\n \n account_name: the name of the account\n account_id: the ID of the account\n by_id: show volume IDs instead of names\n mvip: the management IP of the cluster\n username: the admin user of the cluster\n password: the admin password of the cluster\n output_format: the format to display the information\n '
log = GetLogger()
NameOrID(account_name, account_id, 'account')
log.info('Searching for accounts')
try:
account = SFCluster(mvip, username, password).FindAccount(accountName=account_name, accountID=account_id)
except UnknownObjectError:
log.error('Account does not exists')
return False
except SolidFireError as e:
log.error('Could not search for accounts: {}'.format(e))
return False
log.info('Searching for volumes')
try:
all_volumes = SFCluster(mvip, username, password).ListActiveVolumes()
all_volumes += SFCluster(mvip, username, password).ListDeletedVolumes()
except SolidFireError as e:
log.error('Could not search for volumes: {}'.format(e))
return False
all_volumes = {vol['volumeID']: vol for vol in all_volumes}
attr = 'name'
if by_id:
attr = 'volumeID'
account_volumes = [all_volumes[vid][attr] for vid in account.volumes]
if (output_format and (output_format == 'bash')):
sys.stdout.write((' '.join([str(item) for item in account_volumes]) + '\n'))
sys.stdout.flush()
elif (output_format and (output_format == 'json')):
sys.stdout.write((json.dumps({'volumes': account_volumes}) + '\n'))
sys.stdout.flush()
else:
log.info('{} volumes in account {}'.format(len(account.volumes), account.username))
if account.volumes:
log.info(' {}'.format(', '.join([str(item) for item in account_volumes])))
return True |
@cached_property
def openapi_types():
'\n This must be a method because a model may have properties that are\n of type self, this must run after the class is loaded\n\n Returns\n openapi_types (dict): The key is attribute name\n and the value is attribute type.\n '
lazy_import()
return {'class_id': (str,), 'object_type': (str,), 'array_controller': (StorageNetAppNodeRelationship,)} | 4,470,210,284,486,104,600 | This must be a method because a model may have properties that are
of type self, this must run after the class is loaded
Returns
openapi_types (dict): The key is attribute name
and the value is attribute type. | intersight/model/storage_net_app_sensor_all_of.py | openapi_types | CiscoDevNet/intersight-python | python | @cached_property
def openapi_types():
'\n This must be a method because a model may have properties that are\n of type self, this must run after the class is loaded\n\n Returns\n openapi_types (dict): The key is attribute name\n and the value is attribute type.\n '
lazy_import()
return {'class_id': (str,), 'object_type': (str,), 'array_controller': (StorageNetAppNodeRelationship,)} |
@convert_js_args_to_python_args
def __init__(self, *args, **kwargs):
'StorageNetAppSensorAllOf - a model defined in OpenAPI\n\n Args:\n\n Keyword Args:\n class_id (str): The fully-qualified name of the instantiated, concrete type. This property is used as a discriminator to identify the type of the payload when marshaling and unmarshaling data.. defaults to "storage.NetAppSensor", must be one of ["storage.NetAppSensor", ] # noqa: E501\n object_type (str): The fully-qualified name of the instantiated, concrete type. The value should be the same as the \'ClassId\' property.. defaults to "storage.NetAppSensor", must be one of ["storage.NetAppSensor", ] # noqa: E501\n _check_type (bool): if True, values for parameters in openapi_types\n will be type checked and a TypeError will be\n raised if the wrong type is input.\n Defaults to True\n _path_to_item (tuple/list): This is a list of keys or values to\n drill down to the model in received_data\n when deserializing a response\n _spec_property_naming (bool): True if the variable names in the input data\n are serialized names, as specified in the OpenAPI document.\n False if the variable names in the input data\n are pythonic names, e.g. snake case (default)\n _configuration (Configuration): the instance to use when\n deserializing a file_type parameter.\n If passed, type conversion is attempted\n If omitted no type conversion is done.\n _visited_composed_classes (tuple): This stores a tuple of\n classes that we have traveled through so that\n if we see that class again we will not use its\n discriminator again.\n When traveling through a discriminator, the\n composed schema that is\n is traveled through is added to this set.\n For example if Animal has a discriminator\n petType and we pass in "Dog", and the class Dog\n allOf includes Animal, we move through Animal\n once using the discriminator, and pick Dog.\n Then in Dog, we will make an instance of the\n Animal class but this time we won\'t travel\n through its discriminator because we passed in\n _visited_composed_classes = (Animal,)\n array_controller (StorageNetAppNodeRelationship): [optional] # noqa: E501\n '
class_id = kwargs.get('class_id', 'storage.NetAppSensor')
object_type = kwargs.get('object_type', 'storage.NetAppSensor')
_check_type = kwargs.pop('_check_type', True)
_spec_property_naming = kwargs.pop('_spec_property_naming', False)
_path_to_item = kwargs.pop('_path_to_item', ())
_configuration = kwargs.pop('_configuration', None)
_visited_composed_classes = kwargs.pop('_visited_composed_classes', ())
if args:
raise ApiTypeError(('Invalid positional arguments=%s passed to %s. Remove those invalid positional arguments.' % (args, self.__class__.__name__)), path_to_item=_path_to_item, valid_classes=(self.__class__,))
self._data_store = {}
self._check_type = _check_type
self._spec_property_naming = _spec_property_naming
self._path_to_item = _path_to_item
self._configuration = _configuration
self._visited_composed_classes = (_visited_composed_classes + (self.__class__,))
self.class_id = class_id
self.object_type = object_type
for (var_name, var_value) in kwargs.items():
if ((var_name not in self.attribute_map) and (self._configuration is not None) and self._configuration.discard_unknown_keys and (self.additional_properties_type is None)):
continue
setattr(self, var_name, var_value) | -5,255,135,685,495,371,000 | StorageNetAppSensorAllOf - a model defined in OpenAPI
Args:
Keyword Args:
class_id (str): The fully-qualified name of the instantiated, concrete type. This property is used as a discriminator to identify the type of the payload when marshaling and unmarshaling data.. defaults to "storage.NetAppSensor", must be one of ["storage.NetAppSensor", ] # noqa: E501
object_type (str): The fully-qualified name of the instantiated, concrete type. The value should be the same as the 'ClassId' property.. defaults to "storage.NetAppSensor", must be one of ["storage.NetAppSensor", ] # noqa: E501
_check_type (bool): if True, values for parameters in openapi_types
will be type checked and a TypeError will be
raised if the wrong type is input.
Defaults to True
_path_to_item (tuple/list): This is a list of keys or values to
drill down to the model in received_data
when deserializing a response
_spec_property_naming (bool): True if the variable names in the input data
are serialized names, as specified in the OpenAPI document.
False if the variable names in the input data
are pythonic names, e.g. snake case (default)
_configuration (Configuration): the instance to use when
deserializing a file_type parameter.
If passed, type conversion is attempted
If omitted no type conversion is done.
_visited_composed_classes (tuple): This stores a tuple of
classes that we have traveled through so that
if we see that class again we will not use its
discriminator again.
When traveling through a discriminator, the
composed schema that is
is traveled through is added to this set.
For example if Animal has a discriminator
petType and we pass in "Dog", and the class Dog
allOf includes Animal, we move through Animal
once using the discriminator, and pick Dog.
Then in Dog, we will make an instance of the
Animal class but this time we won't travel
through its discriminator because we passed in
_visited_composed_classes = (Animal,)
array_controller (StorageNetAppNodeRelationship): [optional] # noqa: E501 | intersight/model/storage_net_app_sensor_all_of.py | __init__ | CiscoDevNet/intersight-python | python | @convert_js_args_to_python_args
def __init__(self, *args, **kwargs):
'StorageNetAppSensorAllOf - a model defined in OpenAPI\n\n Args:\n\n Keyword Args:\n class_id (str): The fully-qualified name of the instantiated, concrete type. This property is used as a discriminator to identify the type of the payload when marshaling and unmarshaling data.. defaults to "storage.NetAppSensor", must be one of ["storage.NetAppSensor", ] # noqa: E501\n object_type (str): The fully-qualified name of the instantiated, concrete type. The value should be the same as the \'ClassId\' property.. defaults to "storage.NetAppSensor", must be one of ["storage.NetAppSensor", ] # noqa: E501\n _check_type (bool): if True, values for parameters in openapi_types\n will be type checked and a TypeError will be\n raised if the wrong type is input.\n Defaults to True\n _path_to_item (tuple/list): This is a list of keys or values to\n drill down to the model in received_data\n when deserializing a response\n _spec_property_naming (bool): True if the variable names in the input data\n are serialized names, as specified in the OpenAPI document.\n False if the variable names in the input data\n are pythonic names, e.g. snake case (default)\n _configuration (Configuration): the instance to use when\n deserializing a file_type parameter.\n If passed, type conversion is attempted\n If omitted no type conversion is done.\n _visited_composed_classes (tuple): This stores a tuple of\n classes that we have traveled through so that\n if we see that class again we will not use its\n discriminator again.\n When traveling through a discriminator, the\n composed schema that is\n is traveled through is added to this set.\n For example if Animal has a discriminator\n petType and we pass in "Dog", and the class Dog\n allOf includes Animal, we move through Animal\n once using the discriminator, and pick Dog.\n Then in Dog, we will make an instance of the\n Animal class but this time we won\'t travel\n through its discriminator because we passed in\n _visited_composed_classes = (Animal,)\n array_controller (StorageNetAppNodeRelationship): [optional] # noqa: E501\n '
class_id = kwargs.get('class_id', 'storage.NetAppSensor')
object_type = kwargs.get('object_type', 'storage.NetAppSensor')
_check_type = kwargs.pop('_check_type', True)
_spec_property_naming = kwargs.pop('_spec_property_naming', False)
_path_to_item = kwargs.pop('_path_to_item', ())
_configuration = kwargs.pop('_configuration', None)
_visited_composed_classes = kwargs.pop('_visited_composed_classes', ())
if args:
raise ApiTypeError(('Invalid positional arguments=%s passed to %s. Remove those invalid positional arguments.' % (args, self.__class__.__name__)), path_to_item=_path_to_item, valid_classes=(self.__class__,))
self._data_store = {}
self._check_type = _check_type
self._spec_property_naming = _spec_property_naming
self._path_to_item = _path_to_item
self._configuration = _configuration
self._visited_composed_classes = (_visited_composed_classes + (self.__class__,))
self.class_id = class_id
self.object_type = object_type
for (var_name, var_value) in kwargs.items():
if ((var_name not in self.attribute_map) and (self._configuration is not None) and self._configuration.discard_unknown_keys and (self.additional_properties_type is None)):
continue
setattr(self, var_name, var_value) |
@export
def dbcluster_events(data: Any, cluster_columns: List[Any]=None, verbose: bool=False, normalize: bool=True, time_column: str='TimeCreatedUtc', max_cluster_distance: float=0.01, min_cluster_samples: int=2, **kwargs) -> Tuple[(pd.DataFrame, DBSCAN, np.ndarray)]:
"\n Cluster data set according to cluster_columns features.\n\n Parameters\n ----------\n data : Any\n Input data as a pandas DataFrame or numpy array\n cluster_columns : List[Any], optional\n List of columns to use for features\n - for DataFrame this is a list of column names\n - for numpy array this is a list of column indexes\n verbose : bool, optional\n Print additional information about clustering results (the default is False)\n normalize : bool, optional\n Normalize the input data (should probably always be True)\n time_column : str, optional\n If there is a time column the output data will be ordered by this\n (the default is 'TimeCreatedUtc')\n max_cluster_distance : float, optional\n DBSCAN eps (max cluster member distance) (the default is 0.01)\n min_cluster_samples : int, optional\n DBSCAN min_samples (the minimum cluster size) (the default is 2)\n\n Other Parameters\n ----------------\n kwargs: Other arguments are passed to DBSCAN constructor\n\n Returns\n -------\n Tuple[pd.DataFrame, DBSCAN, np.ndarray]\n Output dataframe with clustered rows\n DBSCAN model\n Normalized data set\n\n "
allowed_types = [np.ndarray, pd.DataFrame]
x_input = None
if isinstance(data, pd.DataFrame):
if (cluster_columns is None):
x_input = data.values
else:
x_input = data[cluster_columns].values
elif isinstance(data, np.ndarray):
x_input = (data if (cluster_columns is None) else data[:, cluster_columns].values)
if (x_input is None):
mssg = 'Input data not in expected format.\n{} is not one of allowed types {}'
type_list = ', '.join((str(t) for t in allowed_types))
mssg = mssg.format(str(type(data)), type_list)
raise ValueError(mssg)
db_cluster = DBSCAN(eps=max_cluster_distance, min_samples=min_cluster_samples, **kwargs)
x_norm = (Normalizer().fit_transform(x_input) if normalize else x_input)
db_cluster.fit(x_norm)
labels = db_cluster.labels_
(cluster_set, counts) = np.unique(labels, return_counts=True)
if verbose:
print('Clustering for set size ', len(x_norm), ' - ', len(cluster_set), ' clusters')
print('Individual cluster sizes: ', ', '.join((str(c) for c in counts)))
clustered_events = _merge_clustered_items(cluster_set, labels, data, time_column, counts)
if verbose:
print('Cluster output rows: ', len(clustered_events))
return (clustered_events, db_cluster, x_norm) | -3,809,816,684,162,627,600 | Cluster data set according to cluster_columns features.
Parameters
----------
data : Any
Input data as a pandas DataFrame or numpy array
cluster_columns : List[Any], optional
List of columns to use for features
- for DataFrame this is a list of column names
- for numpy array this is a list of column indexes
verbose : bool, optional
Print additional information about clustering results (the default is False)
normalize : bool, optional
Normalize the input data (should probably always be True)
time_column : str, optional
If there is a time column the output data will be ordered by this
(the default is 'TimeCreatedUtc')
max_cluster_distance : float, optional
DBSCAN eps (max cluster member distance) (the default is 0.01)
min_cluster_samples : int, optional
DBSCAN min_samples (the minimum cluster size) (the default is 2)
Other Parameters
----------------
kwargs: Other arguments are passed to DBSCAN constructor
Returns
-------
Tuple[pd.DataFrame, DBSCAN, np.ndarray]
Output dataframe with clustered rows
DBSCAN model
Normalized data set | msticpy/analysis/eventcluster.py | dbcluster_events | 2xyo/msticpy | python | @export
def dbcluster_events(data: Any, cluster_columns: List[Any]=None, verbose: bool=False, normalize: bool=True, time_column: str='TimeCreatedUtc', max_cluster_distance: float=0.01, min_cluster_samples: int=2, **kwargs) -> Tuple[(pd.DataFrame, DBSCAN, np.ndarray)]:
"\n Cluster data set according to cluster_columns features.\n\n Parameters\n ----------\n data : Any\n Input data as a pandas DataFrame or numpy array\n cluster_columns : List[Any], optional\n List of columns to use for features\n - for DataFrame this is a list of column names\n - for numpy array this is a list of column indexes\n verbose : bool, optional\n Print additional information about clustering results (the default is False)\n normalize : bool, optional\n Normalize the input data (should probably always be True)\n time_column : str, optional\n If there is a time column the output data will be ordered by this\n (the default is 'TimeCreatedUtc')\n max_cluster_distance : float, optional\n DBSCAN eps (max cluster member distance) (the default is 0.01)\n min_cluster_samples : int, optional\n DBSCAN min_samples (the minimum cluster size) (the default is 2)\n\n Other Parameters\n ----------------\n kwargs: Other arguments are passed to DBSCAN constructor\n\n Returns\n -------\n Tuple[pd.DataFrame, DBSCAN, np.ndarray]\n Output dataframe with clustered rows\n DBSCAN model\n Normalized data set\n\n "
allowed_types = [np.ndarray, pd.DataFrame]
x_input = None
if isinstance(data, pd.DataFrame):
if (cluster_columns is None):
x_input = data.values
else:
x_input = data[cluster_columns].values
elif isinstance(data, np.ndarray):
x_input = (data if (cluster_columns is None) else data[:, cluster_columns].values)
if (x_input is None):
mssg = 'Input data not in expected format.\n{} is not one of allowed types {}'
type_list = ', '.join((str(t) for t in allowed_types))
mssg = mssg.format(str(type(data)), type_list)
raise ValueError(mssg)
db_cluster = DBSCAN(eps=max_cluster_distance, min_samples=min_cluster_samples, **kwargs)
x_norm = (Normalizer().fit_transform(x_input) if normalize else x_input)
db_cluster.fit(x_norm)
labels = db_cluster.labels_
(cluster_set, counts) = np.unique(labels, return_counts=True)
if verbose:
print('Clustering for set size ', len(x_norm), ' - ', len(cluster_set), ' clusters')
print('Individual cluster sizes: ', ', '.join((str(c) for c in counts)))
clustered_events = _merge_clustered_items(cluster_set, labels, data, time_column, counts)
if verbose:
print('Cluster output rows: ', len(clustered_events))
return (clustered_events, db_cluster, x_norm) |
def _merge_clustered_items(cluster_set: np.array, labels: np.array, data: Union[(pd.DataFrame, np.array)], time_column: str, counts: np.array) -> pd.DataFrame:
'\n Merge outliers and core clusters into single DataFrame.\n\n Parameters\n ----------\n cluster_set : np.array\n The set of clusters\n labels : np.array\n The cluster labels\n data : Union[pd.DataFrame, np.array]\n The source data\n time_column : str\n Name of the Time column\n counts : np.array\n The counts of members in each cluster\n\n Returns\n -------\n pd.DataFrame\n Merged dataframe\n\n '
tz_aware = data.iloc[0][time_column].tz
ts_type = ('datetime64[ns, UTC]' if (tz_aware is not None) else 'datetime64[ns]')
cluster_list = []
for idx in range(len(cluster_set)):
cluster_id = cluster_set[idx]
class_members = (labels == cluster_id)
if isinstance(data, pd.DataFrame):
time_ordered = data[class_members].sort_values(time_column, ascending=True)
first_event_time = time_ordered[0:][time_column].iat[0]
last_event_time = time_ordered[(- 1):][time_column].iat[0]
else:
first_event_time = None
last_event_time = None
if (cluster_id == (- 1)):
cluster_list.append(data[class_members].assign(Clustered=False, ClusterId=cluster_id, ClusterSize=1, TimeGenerated=first_event_time, FirstEventTime=first_event_time, LastEventTime=last_event_time).astype(dtype={'TimeGenerated': ts_type, 'FirstEventTime': ts_type, 'LastEventTime': ts_type}))
else:
cluster_list.append(data[class_members].assign(Clustered=True, ClusterId=cluster_id, ClusterSize=counts[idx], TimeGenerated=first_event_time, FirstEventTime=first_event_time, LastEventTime=last_event_time)[0:1].astype(dtype={'TimeGenerated': ts_type, 'FirstEventTime': ts_type, 'LastEventTime': ts_type}))
return pd.concat(cluster_list) | 413,818,286,560,348,900 | Merge outliers and core clusters into single DataFrame.
Parameters
----------
cluster_set : np.array
The set of clusters
labels : np.array
The cluster labels
data : Union[pd.DataFrame, np.array]
The source data
time_column : str
Name of the Time column
counts : np.array
The counts of members in each cluster
Returns
-------
pd.DataFrame
Merged dataframe | msticpy/analysis/eventcluster.py | _merge_clustered_items | 2xyo/msticpy | python | def _merge_clustered_items(cluster_set: np.array, labels: np.array, data: Union[(pd.DataFrame, np.array)], time_column: str, counts: np.array) -> pd.DataFrame:
'\n Merge outliers and core clusters into single DataFrame.\n\n Parameters\n ----------\n cluster_set : np.array\n The set of clusters\n labels : np.array\n The cluster labels\n data : Union[pd.DataFrame, np.array]\n The source data\n time_column : str\n Name of the Time column\n counts : np.array\n The counts of members in each cluster\n\n Returns\n -------\n pd.DataFrame\n Merged dataframe\n\n '
tz_aware = data.iloc[0][time_column].tz
ts_type = ('datetime64[ns, UTC]' if (tz_aware is not None) else 'datetime64[ns]')
cluster_list = []
for idx in range(len(cluster_set)):
cluster_id = cluster_set[idx]
class_members = (labels == cluster_id)
if isinstance(data, pd.DataFrame):
time_ordered = data[class_members].sort_values(time_column, ascending=True)
first_event_time = time_ordered[0:][time_column].iat[0]
last_event_time = time_ordered[(- 1):][time_column].iat[0]
else:
first_event_time = None
last_event_time = None
if (cluster_id == (- 1)):
cluster_list.append(data[class_members].assign(Clustered=False, ClusterId=cluster_id, ClusterSize=1, TimeGenerated=first_event_time, FirstEventTime=first_event_time, LastEventTime=last_event_time).astype(dtype={'TimeGenerated': ts_type, 'FirstEventTime': ts_type, 'LastEventTime': ts_type}))
else:
cluster_list.append(data[class_members].assign(Clustered=True, ClusterId=cluster_id, ClusterSize=counts[idx], TimeGenerated=first_event_time, FirstEventTime=first_event_time, LastEventTime=last_event_time)[0:1].astype(dtype={'TimeGenerated': ts_type, 'FirstEventTime': ts_type, 'LastEventTime': ts_type}))
return pd.concat(cluster_list) |
@export
def add_process_features(input_frame: pd.DataFrame, path_separator: str=None, force: bool=False) -> pd.DataFrame:
'\n Add numerical features based on patterns of command line and process name.\n\n Parameters\n ----------\n input_frame : pd.DataFrame\n The input dataframe\n path_separator : str, optional\n Path separator. If not supplied, try to determine\n from \'NewProcessName\' column of first 10 rows\n (the default is None)\n force : bool, optional\n Forces re-calculation of feature columns even if they\n already exist (the default is False)\n\n Returns\n -------\n pd.DataFrame\n Copy of the dataframe with the additional numeric features\n\n Notes\n -----\n Features added:\n\n - processNameLen: length of process file name (inc path)\n - processNameTokens: the number of elements in the path\n - processName: the process file name (minus path)\n - commandlineTokens: number of space-separated tokens in the command line\n - commandlineLen: length of the command line\n - commandlineLogLen: log10 length of commandline\n - isSystemSession: 1 if session Id is 0x3e7 for Windows or -1 for Linux\n - commandlineTokensFull: counts number of token separators in commandline\n [\\\\s\\-\\\\/\\.,"\\\'\\|&:;%$()]\n - pathScore: sum of ord() value of characters in path\n - pathLogScore: log10 of pathScore\n - commandlineScore: sum of ord() value of characters in commandline\n - commandlineLogScore: log10 of commandlineScore\n\n '
output_df = input_frame.copy()
if (('NewProcessName' in output_df) and ('CommandLine' in output_df)):
output_df[['NewProcessName', 'CommandLine']] = output_df[['NewProcessName', 'CommandLine']].fillna(value='')
if (path_separator is None):
sample_df = output_df.head(10)
lx_path = len(sample_df[sample_df['NewProcessName'].str.contains('/')])
path_separator = ('/' if lx_path else '\\')
if ('NewProcessName' in output_df):
_add_processname_features(output_df, force, path_separator)
if ('CommandLine' in output_df):
_add_commandline_features(output_df, force)
if (('SubjectLogonId' in output_df) and (('isSystemSession' not in output_df) or force)):
output_df['isSystemSession'] = output_df['SubjectLogonId'].isin(['0x3e7', '-1'])
return output_df | -5,266,310,471,347,284,000 | Add numerical features based on patterns of command line and process name.
Parameters
----------
input_frame : pd.DataFrame
The input dataframe
path_separator : str, optional
Path separator. If not supplied, try to determine
from 'NewProcessName' column of first 10 rows
(the default is None)
force : bool, optional
Forces re-calculation of feature columns even if they
already exist (the default is False)
Returns
-------
pd.DataFrame
Copy of the dataframe with the additional numeric features
Notes
-----
Features added:
- processNameLen: length of process file name (inc path)
- processNameTokens: the number of elements in the path
- processName: the process file name (minus path)
- commandlineTokens: number of space-separated tokens in the command line
- commandlineLen: length of the command line
- commandlineLogLen: log10 length of commandline
- isSystemSession: 1 if session Id is 0x3e7 for Windows or -1 for Linux
- commandlineTokensFull: counts number of token separators in commandline
[\\s\-\\/\.,"\'\|&:;%$()]
- pathScore: sum of ord() value of characters in path
- pathLogScore: log10 of pathScore
- commandlineScore: sum of ord() value of characters in commandline
- commandlineLogScore: log10 of commandlineScore | msticpy/analysis/eventcluster.py | add_process_features | 2xyo/msticpy | python | @export
def add_process_features(input_frame: pd.DataFrame, path_separator: str=None, force: bool=False) -> pd.DataFrame:
'\n Add numerical features based on patterns of command line and process name.\n\n Parameters\n ----------\n input_frame : pd.DataFrame\n The input dataframe\n path_separator : str, optional\n Path separator. If not supplied, try to determine\n from \'NewProcessName\' column of first 10 rows\n (the default is None)\n force : bool, optional\n Forces re-calculation of feature columns even if they\n already exist (the default is False)\n\n Returns\n -------\n pd.DataFrame\n Copy of the dataframe with the additional numeric features\n\n Notes\n -----\n Features added:\n\n - processNameLen: length of process file name (inc path)\n - processNameTokens: the number of elements in the path\n - processName: the process file name (minus path)\n - commandlineTokens: number of space-separated tokens in the command line\n - commandlineLen: length of the command line\n - commandlineLogLen: log10 length of commandline\n - isSystemSession: 1 if session Id is 0x3e7 for Windows or -1 for Linux\n - commandlineTokensFull: counts number of token separators in commandline\n [\\\\s\\-\\\\/\\.,"\\\'\\|&:;%$()]\n - pathScore: sum of ord() value of characters in path\n - pathLogScore: log10 of pathScore\n - commandlineScore: sum of ord() value of characters in commandline\n - commandlineLogScore: log10 of commandlineScore\n\n '
output_df = input_frame.copy()
if (('NewProcessName' in output_df) and ('CommandLine' in output_df)):
output_df[['NewProcessName', 'CommandLine']] = output_df[['NewProcessName', 'CommandLine']].fillna(value=)
if (path_separator is None):
sample_df = output_df.head(10)
lx_path = len(sample_df[sample_df['NewProcessName'].str.contains('/')])
path_separator = ('/' if lx_path else '\\')
if ('NewProcessName' in output_df):
_add_processname_features(output_df, force, path_separator)
if ('CommandLine' in output_df):
_add_commandline_features(output_df, force)
if (('SubjectLogonId' in output_df) and (('isSystemSession' not in output_df) or force)):
output_df['isSystemSession'] = output_df['SubjectLogonId'].isin(['0x3e7', '-1'])
return output_df |
def _add_processname_features(output_df: pd.DataFrame, force: bool, path_separator: str):
'\n Add process name default features.\n\n Parameters\n ----------\n output_df : pd.DataFrame\n The dataframe to add features to\n force : bool\n If True overwrite existing feature columns\n path_separator : str\n Path separator for OS\n\n '
if (('processName' not in output_df) or force):
output_df['processName'] = output_df.apply((lambda x: x.NewProcessName.split(path_separator)[(- 1)]), axis=1)
if (('pathScore' not in output_df) or force):
output_df['pathScore'] = output_df.apply((lambda x: char_ord_score(x.NewProcessName)), axis=1)
if (('pathLogScore' not in output_df) or force):
output_df['pathLogScore'] = output_df.apply((lambda x: (log10(x.pathScore) if x.pathScore else 0)), axis=1)
if (('pathHash' not in output_df) or force):
output_df['pathHash'] = output_df.apply((lambda x: crc32_hash(x.NewProcessName)), axis=1) | -2,476,954,572,613,136,000 | Add process name default features.
Parameters
----------
output_df : pd.DataFrame
The dataframe to add features to
force : bool
If True overwrite existing feature columns
path_separator : str
Path separator for OS | msticpy/analysis/eventcluster.py | _add_processname_features | 2xyo/msticpy | python | def _add_processname_features(output_df: pd.DataFrame, force: bool, path_separator: str):
'\n Add process name default features.\n\n Parameters\n ----------\n output_df : pd.DataFrame\n The dataframe to add features to\n force : bool\n If True overwrite existing feature columns\n path_separator : str\n Path separator for OS\n\n '
if (('processName' not in output_df) or force):
output_df['processName'] = output_df.apply((lambda x: x.NewProcessName.split(path_separator)[(- 1)]), axis=1)
if (('pathScore' not in output_df) or force):
output_df['pathScore'] = output_df.apply((lambda x: char_ord_score(x.NewProcessName)), axis=1)
if (('pathLogScore' not in output_df) or force):
output_df['pathLogScore'] = output_df.apply((lambda x: (log10(x.pathScore) if x.pathScore else 0)), axis=1)
if (('pathHash' not in output_df) or force):
output_df['pathHash'] = output_df.apply((lambda x: crc32_hash(x.NewProcessName)), axis=1) |
def _add_commandline_features(output_df: pd.DataFrame, force: bool):
'\n Add commandline default features.\n\n Parameters\n ----------\n output_df : pd.DataFrame\n The dataframe to add features to\n force : bool\n If True overwrite existing feature columns\n\n '
if (('commandlineLen' not in output_df) or force):
output_df['commandlineLen'] = output_df.apply((lambda x: len(x.CommandLine)), axis=1)
if (('commandlineLogLen' not in output_df) or force):
output_df['commandlineLogLen'] = output_df.apply((lambda x: (log10(x.commandlineLen) if x.commandlineLen else 0)), axis=1)
if (('commandlineTokensFull' not in output_df) or force):
output_df['commandlineTokensFull'] = output_df[['CommandLine']].apply((lambda x: delim_count(x.CommandLine)), axis=1)
if (('commandlineScore' not in output_df) or force):
output_df['commandlineScore'] = output_df.apply((lambda x: char_ord_score(x.CommandLine)), axis=1)
if (('commandlineTokensHash' not in output_df) or force):
output_df['commandlineTokensHash'] = output_df.apply((lambda x: delim_hash(x.CommandLine)), axis=1) | 9,055,919,210,541,722,000 | Add commandline default features.
Parameters
----------
output_df : pd.DataFrame
The dataframe to add features to
force : bool
If True overwrite existing feature columns | msticpy/analysis/eventcluster.py | _add_commandline_features | 2xyo/msticpy | python | def _add_commandline_features(output_df: pd.DataFrame, force: bool):
'\n Add commandline default features.\n\n Parameters\n ----------\n output_df : pd.DataFrame\n The dataframe to add features to\n force : bool\n If True overwrite existing feature columns\n\n '
if (('commandlineLen' not in output_df) or force):
output_df['commandlineLen'] = output_df.apply((lambda x: len(x.CommandLine)), axis=1)
if (('commandlineLogLen' not in output_df) or force):
output_df['commandlineLogLen'] = output_df.apply((lambda x: (log10(x.commandlineLen) if x.commandlineLen else 0)), axis=1)
if (('commandlineTokensFull' not in output_df) or force):
output_df['commandlineTokensFull'] = output_df[['CommandLine']].apply((lambda x: delim_count(x.CommandLine)), axis=1)
if (('commandlineScore' not in output_df) or force):
output_df['commandlineScore'] = output_df.apply((lambda x: char_ord_score(x.CommandLine)), axis=1)
if (('commandlineTokensHash' not in output_df) or force):
output_df['commandlineTokensHash'] = output_df.apply((lambda x: delim_hash(x.CommandLine)), axis=1) |
@export
@lru_cache(maxsize=1024)
def delim_count(value: str, delim_list: str='[\\s\\-\\\\/\\.,"\\\'|&:;%$()]') -> int:
'\n Count the delimiters in input column.\n\n Parameters\n ----------\n value : str\n Data to process\n delim_list : str, optional\n delimiters to use. (the default is r\'[\\\\s\\\\\\\\-\\\\\\\\\\\\\\\\/\\.,"\\\\\\\\\'|&:;%$()]\')\n\n Returns\n -------\n int\n Count of delimiters in the string.\n\n '
return len(re.findall(delim_list, value)) | -5,264,490,467,521,285,000 | Count the delimiters in input column.
Parameters
----------
value : str
Data to process
delim_list : str, optional
delimiters to use. (the default is r'[\\s\\\\-\\\\\\\\/\.,"\\\\'|&:;%$()]')
Returns
-------
int
Count of delimiters in the string. | msticpy/analysis/eventcluster.py | delim_count | 2xyo/msticpy | python | @export
@lru_cache(maxsize=1024)
def delim_count(value: str, delim_list: str='[\\s\\-\\\\/\\.,"\\\'|&:;%$()]') -> int:
'\n Count the delimiters in input column.\n\n Parameters\n ----------\n value : str\n Data to process\n delim_list : str, optional\n delimiters to use. (the default is r\'[\\\\s\\\\\\\\-\\\\\\\\\\\\\\\\/\\.,"\\\\\\\\\'|&:;%$()]\')\n\n Returns\n -------\n int\n Count of delimiters in the string.\n\n '
return len(re.findall(delim_list, value)) |
@export
@lru_cache(maxsize=1024)
def delim_hash(value: str, delim_list: str='[\\s\\-\\\\/\\.,"\\\'|&:;%$()]') -> int:
'\n Return a hash (CRC32) of the delimiters from input column.\n\n Parameters\n ----------\n value : str\n Data to process\n delim_list : str, optional\n delimiters to use. (the default is r\'[\\\\s\\\\\\\\-\\\\\\\\\\\\\\\\/\\.,"\\\\\\\\\'|&:;%$()]\')\n\n Returns\n -------\n int\n Hash of delimiter set in the string.\n\n '
return crc32(bytes(''.join(re.findall(delim_list, value)), 'utf-8')) | -8,977,949,746,261,767,000 | Return a hash (CRC32) of the delimiters from input column.
Parameters
----------
value : str
Data to process
delim_list : str, optional
delimiters to use. (the default is r'[\\s\\\\-\\\\\\\\/\.,"\\\\'|&:;%$()]')
Returns
-------
int
Hash of delimiter set in the string. | msticpy/analysis/eventcluster.py | delim_hash | 2xyo/msticpy | python | @export
@lru_cache(maxsize=1024)
def delim_hash(value: str, delim_list: str='[\\s\\-\\\\/\\.,"\\\'|&:;%$()]') -> int:
'\n Return a hash (CRC32) of the delimiters from input column.\n\n Parameters\n ----------\n value : str\n Data to process\n delim_list : str, optional\n delimiters to use. (the default is r\'[\\\\s\\\\\\\\-\\\\\\\\\\\\\\\\/\\.,"\\\\\\\\\'|&:;%$()]\')\n\n Returns\n -------\n int\n Hash of delimiter set in the string.\n\n '
return crc32(bytes(.join(re.findall(delim_list, value)), 'utf-8')) |
@export
@lru_cache(maxsize=1024)
def char_ord_score(value: str, scale: int=1) -> int:
'\n Return sum of ord values of characters in string.\n\n Parameters\n ----------\n value : str\n Data to process\n scale : int, optional\n reduce the scale of the feature (reducing the\n influence of variations this feature on the clustering\n algorithm (the default is 1)\n\n Returns\n -------\n int\n [description]\n\n Notes\n -----\n This function sums the ordinal value of each character in the\n input string. Two strings with minor differences will result in\n a similar score. However, for strings with highly variable content\n (e.g. command lines or http requests containing GUIDs) this may result\n in too much variance to be useful when you are trying to detect\n similar patterns. You can use the scale parameter to reduce the\n influence of features using this function on clustering and anomaly\n algorithms.\n\n '
return floor((sum((ord(x) for x in value)) / scale)) | -2,230,151,292,892,541,200 | Return sum of ord values of characters in string.
Parameters
----------
value : str
Data to process
scale : int, optional
reduce the scale of the feature (reducing the
influence of variations this feature on the clustering
algorithm (the default is 1)
Returns
-------
int
[description]
Notes
-----
This function sums the ordinal value of each character in the
input string. Two strings with minor differences will result in
a similar score. However, for strings with highly variable content
(e.g. command lines or http requests containing GUIDs) this may result
in too much variance to be useful when you are trying to detect
similar patterns. You can use the scale parameter to reduce the
influence of features using this function on clustering and anomaly
algorithms. | msticpy/analysis/eventcluster.py | char_ord_score | 2xyo/msticpy | python | @export
@lru_cache(maxsize=1024)
def char_ord_score(value: str, scale: int=1) -> int:
'\n Return sum of ord values of characters in string.\n\n Parameters\n ----------\n value : str\n Data to process\n scale : int, optional\n reduce the scale of the feature (reducing the\n influence of variations this feature on the clustering\n algorithm (the default is 1)\n\n Returns\n -------\n int\n [description]\n\n Notes\n -----\n This function sums the ordinal value of each character in the\n input string. Two strings with minor differences will result in\n a similar score. However, for strings with highly variable content\n (e.g. command lines or http requests containing GUIDs) this may result\n in too much variance to be useful when you are trying to detect\n similar patterns. You can use the scale parameter to reduce the\n influence of features using this function on clustering and anomaly\n algorithms.\n\n '
return floor((sum((ord(x) for x in value)) / scale)) |
@export
@lru_cache(maxsize=1024)
def token_count(value: str, delimiter: str=' ') -> int:
"\n Return count of delimiter-separated tokens pd.Series column.\n\n Parameters\n ----------\n value : str\n Data to process\n delimiter : str, optional\n Delimiter used to split the column string.\n (the default is ' ')\n\n Returns\n -------\n int\n count of tokens\n\n "
return len(value.split(delimiter)) | -6,788,009,994,740,111,000 | Return count of delimiter-separated tokens pd.Series column.
Parameters
----------
value : str
Data to process
delimiter : str, optional
Delimiter used to split the column string.
(the default is ' ')
Returns
-------
int
count of tokens | msticpy/analysis/eventcluster.py | token_count | 2xyo/msticpy | python | @export
@lru_cache(maxsize=1024)
def token_count(value: str, delimiter: str=' ') -> int:
"\n Return count of delimiter-separated tokens pd.Series column.\n\n Parameters\n ----------\n value : str\n Data to process\n delimiter : str, optional\n Delimiter used to split the column string.\n (the default is ' ')\n\n Returns\n -------\n int\n count of tokens\n\n "
return len(value.split(delimiter)) |
def _string_score(input_str):
'Sum the ord(c) for characters in a string.'
return sum((ord(x) for x in input_str)) | 2,873,044,902,948,387,000 | Sum the ord(c) for characters in a string. | msticpy/analysis/eventcluster.py | _string_score | 2xyo/msticpy | python | def _string_score(input_str):
return sum((ord(x) for x in input_str)) |
@export
@lru_cache(maxsize=1024)
def crc32_hash(value: str) -> int:
'\n Return the CRC32 hash of the input column.\n\n Parameters\n ----------\n value : str\n Data to process\n\n Returns\n -------\n int\n CRC32 hash\n\n '
return crc32(bytes(value.encode('utf-8'))) | 4,065,869,139,596,183,000 | Return the CRC32 hash of the input column.
Parameters
----------
value : str
Data to process
Returns
-------
int
CRC32 hash | msticpy/analysis/eventcluster.py | crc32_hash | 2xyo/msticpy | python | @export
@lru_cache(maxsize=1024)
def crc32_hash(value: str) -> int:
'\n Return the CRC32 hash of the input column.\n\n Parameters\n ----------\n value : str\n Data to process\n\n Returns\n -------\n int\n CRC32 hash\n\n '
return crc32(bytes(value.encode('utf-8'))) |
@export
def delim_count_df(data: pd.DataFrame, column: str, delim_list: str='[\\s\\-\\\\/\\.,"\\\'|&:;%$()]') -> pd.Series:
'\n Count the delimiters in input column.\n\n Parameters\n ----------\n data : pd.DataFrame\n The DataFrame to process\n column : str\n The name of the column to process\n delim_list : str, optional\n delimiters to use. (the default is r\\\'[\\\\s\\\\\\\\-\\\\\\\\\\\\\\\\/\\.,"\\\\\\\\\'|&:;%$()]\\\')\n\n Returns\n -------\n pd.Series\n Count of delimiters in the string in `column`.\n\n '
return data[column].str.count(delim_list) | 1,464,786,969,080,396,500 | Count the delimiters in input column.
Parameters
----------
data : pd.DataFrame
The DataFrame to process
column : str
The name of the column to process
delim_list : str, optional
delimiters to use. (the default is r\'[\\s\\\\-\\\\\\\\/\.,"\\\\'|&:;%$()]\')
Returns
-------
pd.Series
Count of delimiters in the string in `column`. | msticpy/analysis/eventcluster.py | delim_count_df | 2xyo/msticpy | python | @export
def delim_count_df(data: pd.DataFrame, column: str, delim_list: str='[\\s\\-\\\\/\\.,"\\\'|&:;%$()]') -> pd.Series:
'\n Count the delimiters in input column.\n\n Parameters\n ----------\n data : pd.DataFrame\n The DataFrame to process\n column : str\n The name of the column to process\n delim_list : str, optional\n delimiters to use. (the default is r\\\'[\\\\s\\\\\\\\-\\\\\\\\\\\\\\\\/\\.,"\\\\\\\\\'|&:;%$()]\\\')\n\n Returns\n -------\n pd.Series\n Count of delimiters in the string in `column`.\n\n '
return data[column].str.count(delim_list) |
@export
def char_ord_score_df(data: pd.DataFrame, column: str, scale: int=1) -> pd.Series:
'\n Return sum of ord values of characters in string.\n\n Parameters\n ----------\n data : pd.DataFrame\n The DataFrame to process\n column : str\n Column name to process\n scale : int, optional\n reduce the scale of the feature (reducing the\n influence of variations this feature on the clustering\n algorithm (the default is 1)\n\n Returns\n -------\n pd.Series\n The sum of the ordinal values of the characters\n in `column`.\n\n Notes\n -----\n This function sums the ordinal value of each character in the\n input string. Two strings with minor differences will result in\n a similar score. However, for strings with highly variable content\n (e.g. command lines or http requests containing GUIDs) this may result\n in too much variance to be useful when you are trying to detect\n similar patterns. You can use the scale parameter to reduce the\n influence of features using this function on clustering and anomaly\n algorithms.\n\n '
return data.apply((lambda x: (sum((ord(char) for char in x[column])) / scale)), axis=1) | 8,791,648,072,038,837,000 | Return sum of ord values of characters in string.
Parameters
----------
data : pd.DataFrame
The DataFrame to process
column : str
Column name to process
scale : int, optional
reduce the scale of the feature (reducing the
influence of variations this feature on the clustering
algorithm (the default is 1)
Returns
-------
pd.Series
The sum of the ordinal values of the characters
in `column`.
Notes
-----
This function sums the ordinal value of each character in the
input string. Two strings with minor differences will result in
a similar score. However, for strings with highly variable content
(e.g. command lines or http requests containing GUIDs) this may result
in too much variance to be useful when you are trying to detect
similar patterns. You can use the scale parameter to reduce the
influence of features using this function on clustering and anomaly
algorithms. | msticpy/analysis/eventcluster.py | char_ord_score_df | 2xyo/msticpy | python | @export
def char_ord_score_df(data: pd.DataFrame, column: str, scale: int=1) -> pd.Series:
'\n Return sum of ord values of characters in string.\n\n Parameters\n ----------\n data : pd.DataFrame\n The DataFrame to process\n column : str\n Column name to process\n scale : int, optional\n reduce the scale of the feature (reducing the\n influence of variations this feature on the clustering\n algorithm (the default is 1)\n\n Returns\n -------\n pd.Series\n The sum of the ordinal values of the characters\n in `column`.\n\n Notes\n -----\n This function sums the ordinal value of each character in the\n input string. Two strings with minor differences will result in\n a similar score. However, for strings with highly variable content\n (e.g. command lines or http requests containing GUIDs) this may result\n in too much variance to be useful when you are trying to detect\n similar patterns. You can use the scale parameter to reduce the\n influence of features using this function on clustering and anomaly\n algorithms.\n\n '
return data.apply((lambda x: (sum((ord(char) for char in x[column])) / scale)), axis=1) |
@export
def token_count_df(data: pd.DataFrame, column: str, delimiter: str=' ') -> pd.Series:
"\n Return count of delimiter-separated tokens pd.Series column.\n\n Parameters\n ----------\n data : pd.DataFrame\n The DataFrame to process\n column : str\n Column name to process\n delimiter : str, optional\n Delimiter used to split the column string.\n (the default is ' ')\n\n Returns\n -------\n pd.Series\n count of tokens in strings in `column`\n\n "
return data.apply((lambda x: len(x[column].split(delimiter))), axis=1) | 6,190,990,618,236,187,000 | Return count of delimiter-separated tokens pd.Series column.
Parameters
----------
data : pd.DataFrame
The DataFrame to process
column : str
Column name to process
delimiter : str, optional
Delimiter used to split the column string.
(the default is ' ')
Returns
-------
pd.Series
count of tokens in strings in `column` | msticpy/analysis/eventcluster.py | token_count_df | 2xyo/msticpy | python | @export
def token_count_df(data: pd.DataFrame, column: str, delimiter: str=' ') -> pd.Series:
"\n Return count of delimiter-separated tokens pd.Series column.\n\n Parameters\n ----------\n data : pd.DataFrame\n The DataFrame to process\n column : str\n Column name to process\n delimiter : str, optional\n Delimiter used to split the column string.\n (the default is ' ')\n\n Returns\n -------\n pd.Series\n count of tokens in strings in `column`\n\n "
return data.apply((lambda x: len(x[column].split(delimiter))), axis=1) |
@export
def crc32_hash_df(data: pd.DataFrame, column: str) -> pd.Series:
'\n Return the CRC32 hash of the input column.\n\n Parameters\n ----------\n data : pd.DataFrame\n The DataFrame to process\n column : str\n Column name to process\n\n Returns\n -------\n pd.Series\n CRC32 hash of input column\n\n '
return data.apply((lambda x: crc32(bytes(x[column].encode('utf-8')))), axis=1) | 3,132,224,339,412,920,300 | Return the CRC32 hash of the input column.
Parameters
----------
data : pd.DataFrame
The DataFrame to process
column : str
Column name to process
Returns
-------
pd.Series
CRC32 hash of input column | msticpy/analysis/eventcluster.py | crc32_hash_df | 2xyo/msticpy | python | @export
def crc32_hash_df(data: pd.DataFrame, column: str) -> pd.Series:
'\n Return the CRC32 hash of the input column.\n\n Parameters\n ----------\n data : pd.DataFrame\n The DataFrame to process\n column : str\n Column name to process\n\n Returns\n -------\n pd.Series\n CRC32 hash of input column\n\n '
return data.apply((lambda x: crc32(bytes(x[column].encode('utf-8')))), axis=1) |
@export
def plot_cluster(db_cluster: DBSCAN, data: pd.DataFrame, x_predict: np.ndarray, plot_label: str=None, plot_features: Tuple[(int, int)]=(0, 1), verbose: bool=False, cut_off: int=3, xlabel: str=None, ylabel: str=None):
'\n Plot clustered data as scatter chart.\n\n Parameters\n ----------\n db_cluster : DBSCAN\n DBScan Cluster (from SkLearn DBSCAN).\n data : pd.DataFrame\n Dataframe containing original data.\n x_predict : np.ndarray\n The DBSCAN predict numpy array\n plot_label : str, optional\n If set the column to use to label data points\n (the default is None)\n plot_features : Tuple[int, int], optional\n Which two features in x_predict to plot (the default is (0, 1))\n verbose : bool, optional\n Verbose execution with some extra info\n (the default is False)\n cut_off : int, optional\n The cluster size below which items are considered outliers\n (the default is 3)\n xlabel : str, optional\n x-axis label (the default is None)\n ylabel : str, optional\n y-axis label (the default is None)\n\n '
max_idx = (x_predict.shape[1] - 1)
if (plot_features[0] >= x_predict.shape[1]):
raise ValueError('plot_features[0] index must be a value from 0 to {}.'.format(max_idx))
if (plot_features[1] >= x_predict.shape[1]):
raise ValueError('plot_features[1] index must be a value from 0 to {}.'.format(max_idx))
if (plot_features[0] == plot_features[1]):
mssg = 'plot_features indexes must be 2 different values in range 0 to'
raise ValueError((mssg + f' {max_idx}.'))
labels = db_cluster.labels_
core_samples_mask = np.zeros_like(labels, dtype=bool)
core_samples_mask[db_cluster.core_sample_indices_] = True
unique_labels = set(labels)
colors = [cm.Spectral(each) for each in np.linspace(0, 1, len(unique_labels))]
n_clusters_ = (len(set(labels)) - (1 if ((- 1) in labels) else 0))
n_noise_ = list(labels).count((- 1))
(_, counts) = np.unique(labels, return_counts=True)
if verbose:
print(('Estimated number of clusters: %d' % n_clusters_))
print(('Estimated number of noise points: %d' % n_noise_))
if ((not isinstance(data, pd.DataFrame)) or ((plot_label is not None) and (plot_label not in data))):
plot_label = None
p_label = None
for (cluster_id, color) in zip(unique_labels, colors):
if (cluster_id == (- 1)):
color = [0, 0, 0, 1]
class_member_mask = (labels == cluster_id)
cluster_size = counts[cluster_id]
marker_size = cluster_size
marker = 'o'
font_size = 'small'
alpha = 0.4
if (cluster_size < cut_off):
marker = '+'
marker_size = 10
font_size = 'large'
alpha = 1.0
xy_pos = x_predict[(class_member_mask & core_samples_mask)]
plt.plot(xy_pos[:, plot_features[0]], xy_pos[:, plot_features[1]], marker, markerfacecolor=tuple(color), markersize=marker_size)
if plot_label:
first_row = data[class_member_mask].iloc[0]
if ((not first_row.empty) and (plot_label in first_row)):
p_label = first_row[plot_label]
try:
plt.annotate(p_label, xy=(xy_pos[(0, plot_features[0])], xy_pos[(0, plot_features[1])]), fontsize=font_size, alpha=alpha)
except IndexError:
pass
plt.xlabel(xlabel)
plt.ylabel(ylabel)
plt.title(('Estimated number of clusters: %d' % n_clusters_))
plt.show()
return plt | 463,275,847,204,298,500 | Plot clustered data as scatter chart.
Parameters
----------
db_cluster : DBSCAN
DBScan Cluster (from SkLearn DBSCAN).
data : pd.DataFrame
Dataframe containing original data.
x_predict : np.ndarray
The DBSCAN predict numpy array
plot_label : str, optional
If set the column to use to label data points
(the default is None)
plot_features : Tuple[int, int], optional
Which two features in x_predict to plot (the default is (0, 1))
verbose : bool, optional
Verbose execution with some extra info
(the default is False)
cut_off : int, optional
The cluster size below which items are considered outliers
(the default is 3)
xlabel : str, optional
x-axis label (the default is None)
ylabel : str, optional
y-axis label (the default is None) | msticpy/analysis/eventcluster.py | plot_cluster | 2xyo/msticpy | python | @export
def plot_cluster(db_cluster: DBSCAN, data: pd.DataFrame, x_predict: np.ndarray, plot_label: str=None, plot_features: Tuple[(int, int)]=(0, 1), verbose: bool=False, cut_off: int=3, xlabel: str=None, ylabel: str=None):
'\n Plot clustered data as scatter chart.\n\n Parameters\n ----------\n db_cluster : DBSCAN\n DBScan Cluster (from SkLearn DBSCAN).\n data : pd.DataFrame\n Dataframe containing original data.\n x_predict : np.ndarray\n The DBSCAN predict numpy array\n plot_label : str, optional\n If set the column to use to label data points\n (the default is None)\n plot_features : Tuple[int, int], optional\n Which two features in x_predict to plot (the default is (0, 1))\n verbose : bool, optional\n Verbose execution with some extra info\n (the default is False)\n cut_off : int, optional\n The cluster size below which items are considered outliers\n (the default is 3)\n xlabel : str, optional\n x-axis label (the default is None)\n ylabel : str, optional\n y-axis label (the default is None)\n\n '
max_idx = (x_predict.shape[1] - 1)
if (plot_features[0] >= x_predict.shape[1]):
raise ValueError('plot_features[0] index must be a value from 0 to {}.'.format(max_idx))
if (plot_features[1] >= x_predict.shape[1]):
raise ValueError('plot_features[1] index must be a value from 0 to {}.'.format(max_idx))
if (plot_features[0] == plot_features[1]):
mssg = 'plot_features indexes must be 2 different values in range 0 to'
raise ValueError((mssg + f' {max_idx}.'))
labels = db_cluster.labels_
core_samples_mask = np.zeros_like(labels, dtype=bool)
core_samples_mask[db_cluster.core_sample_indices_] = True
unique_labels = set(labels)
colors = [cm.Spectral(each) for each in np.linspace(0, 1, len(unique_labels))]
n_clusters_ = (len(set(labels)) - (1 if ((- 1) in labels) else 0))
n_noise_ = list(labels).count((- 1))
(_, counts) = np.unique(labels, return_counts=True)
if verbose:
print(('Estimated number of clusters: %d' % n_clusters_))
print(('Estimated number of noise points: %d' % n_noise_))
if ((not isinstance(data, pd.DataFrame)) or ((plot_label is not None) and (plot_label not in data))):
plot_label = None
p_label = None
for (cluster_id, color) in zip(unique_labels, colors):
if (cluster_id == (- 1)):
color = [0, 0, 0, 1]
class_member_mask = (labels == cluster_id)
cluster_size = counts[cluster_id]
marker_size = cluster_size
marker = 'o'
font_size = 'small'
alpha = 0.4
if (cluster_size < cut_off):
marker = '+'
marker_size = 10
font_size = 'large'
alpha = 1.0
xy_pos = x_predict[(class_member_mask & core_samples_mask)]
plt.plot(xy_pos[:, plot_features[0]], xy_pos[:, plot_features[1]], marker, markerfacecolor=tuple(color), markersize=marker_size)
if plot_label:
first_row = data[class_member_mask].iloc[0]
if ((not first_row.empty) and (plot_label in first_row)):
p_label = first_row[plot_label]
try:
plt.annotate(p_label, xy=(xy_pos[(0, plot_features[0])], xy_pos[(0, plot_features[1])]), fontsize=font_size, alpha=alpha)
except IndexError:
pass
plt.xlabel(xlabel)
plt.ylabel(ylabel)
plt.title(('Estimated number of clusters: %d' % n_clusters_))
plt.show()
return plt |
def get_store(factory=AuthStore, key=_store_registry_key, app=None):
"Returns an instance of :class:`AuthStore` from the app registry.\n\n It'll try to get it from the current app registry, and if it is not\n registered it'll be instantiated and registered. A second call to this\n function will return the same instance.\n\n :param factory:\n The callable used to build and register the instance if it is not yet\n registered. The default is the class :class:`AuthStore` itself.\n :param key:\n The key used to store the instance in the registry. A default is used\n if it is not set.\n :param app:\n A :class:`webapp2.WSGIApplication` instance used to store the instance.\n The active app is used if it is not set.\n "
app = (app or webapp2.get_app())
store = app.registry.get(key)
if (not store):
store = app.registry[key] = factory(app)
return store | 6,624,406,828,909,451,000 | Returns an instance of :class:`AuthStore` from the app registry.
It'll try to get it from the current app registry, and if it is not
registered it'll be instantiated and registered. A second call to this
function will return the same instance.
:param factory:
The callable used to build and register the instance if it is not yet
registered. The default is the class :class:`AuthStore` itself.
:param key:
The key used to store the instance in the registry. A default is used
if it is not set.
:param app:
A :class:`webapp2.WSGIApplication` instance used to store the instance.
The active app is used if it is not set. | Webapp2_samplesite/webapp2_extras/auth.py | get_store | Rockfish/PythonCourse | python | def get_store(factory=AuthStore, key=_store_registry_key, app=None):
"Returns an instance of :class:`AuthStore` from the app registry.\n\n It'll try to get it from the current app registry, and if it is not\n registered it'll be instantiated and registered. A second call to this\n function will return the same instance.\n\n :param factory:\n The callable used to build and register the instance if it is not yet\n registered. The default is the class :class:`AuthStore` itself.\n :param key:\n The key used to store the instance in the registry. A default is used\n if it is not set.\n :param app:\n A :class:`webapp2.WSGIApplication` instance used to store the instance.\n The active app is used if it is not set.\n "
app = (app or webapp2.get_app())
store = app.registry.get(key)
if (not store):
store = app.registry[key] = factory(app)
return store |
def set_store(store, key=_store_registry_key, app=None):
'Sets an instance of :class:`AuthStore` in the app registry.\n\n :param store:\n An instance of :class:`AuthStore`.\n :param key:\n The key used to retrieve the instance from the registry. A default\n is used if it is not set.\n :param request:\n A :class:`webapp2.WSGIApplication` instance used to retrieve the\n instance. The active app is used if it is not set.\n '
app = (app or webapp2.get_app())
app.registry[key] = store | 2,798,940,574,467,385,300 | Sets an instance of :class:`AuthStore` in the app registry.
:param store:
An instance of :class:`AuthStore`.
:param key:
The key used to retrieve the instance from the registry. A default
is used if it is not set.
:param request:
A :class:`webapp2.WSGIApplication` instance used to retrieve the
instance. The active app is used if it is not set. | Webapp2_samplesite/webapp2_extras/auth.py | set_store | Rockfish/PythonCourse | python | def set_store(store, key=_store_registry_key, app=None):
'Sets an instance of :class:`AuthStore` in the app registry.\n\n :param store:\n An instance of :class:`AuthStore`.\n :param key:\n The key used to retrieve the instance from the registry. A default\n is used if it is not set.\n :param request:\n A :class:`webapp2.WSGIApplication` instance used to retrieve the\n instance. The active app is used if it is not set.\n '
app = (app or webapp2.get_app())
app.registry[key] = store |
def get_auth(factory=Auth, key=_auth_registry_key, request=None):
"Returns an instance of :class:`Auth` from the request registry.\n\n It'll try to get it from the current request registry, and if it is not\n registered it'll be instantiated and registered. A second call to this\n function will return the same instance.\n\n :param factory:\n The callable used to build and register the instance if it is not yet\n registered. The default is the class :class:`Auth` itself.\n :param key:\n The key used to store the instance in the registry. A default is used\n if it is not set.\n :param request:\n A :class:`webapp2.Request` instance used to store the instance. The\n active request is used if it is not set.\n "
request = (request or webapp2.get_request())
auth = request.registry.get(key)
if (not auth):
auth = request.registry[key] = factory(request)
return auth | 2,269,344,620,877,514,200 | Returns an instance of :class:`Auth` from the request registry.
It'll try to get it from the current request registry, and if it is not
registered it'll be instantiated and registered. A second call to this
function will return the same instance.
:param factory:
The callable used to build and register the instance if it is not yet
registered. The default is the class :class:`Auth` itself.
:param key:
The key used to store the instance in the registry. A default is used
if it is not set.
:param request:
A :class:`webapp2.Request` instance used to store the instance. The
active request is used if it is not set. | Webapp2_samplesite/webapp2_extras/auth.py | get_auth | Rockfish/PythonCourse | python | def get_auth(factory=Auth, key=_auth_registry_key, request=None):
"Returns an instance of :class:`Auth` from the request registry.\n\n It'll try to get it from the current request registry, and if it is not\n registered it'll be instantiated and registered. A second call to this\n function will return the same instance.\n\n :param factory:\n The callable used to build and register the instance if it is not yet\n registered. The default is the class :class:`Auth` itself.\n :param key:\n The key used to store the instance in the registry. A default is used\n if it is not set.\n :param request:\n A :class:`webapp2.Request` instance used to store the instance. The\n active request is used if it is not set.\n "
request = (request or webapp2.get_request())
auth = request.registry.get(key)
if (not auth):
auth = request.registry[key] = factory(request)
return auth |
def set_auth(auth, key=_auth_registry_key, request=None):
'Sets an instance of :class:`Auth` in the request registry.\n\n :param auth:\n An instance of :class:`Auth`.\n :param key:\n The key used to retrieve the instance from the registry. A default\n is used if it is not set.\n :param request:\n A :class:`webapp2.Request` instance used to retrieve the instance. The\n active request is used if it is not set.\n '
request = (request or webapp2.get_request())
request.registry[key] = auth | -940,054,351,290,193,800 | Sets an instance of :class:`Auth` in the request registry.
:param auth:
An instance of :class:`Auth`.
:param key:
The key used to retrieve the instance from the registry. A default
is used if it is not set.
:param request:
A :class:`webapp2.Request` instance used to retrieve the instance. The
active request is used if it is not set. | Webapp2_samplesite/webapp2_extras/auth.py | set_auth | Rockfish/PythonCourse | python | def set_auth(auth, key=_auth_registry_key, request=None):
'Sets an instance of :class:`Auth` in the request registry.\n\n :param auth:\n An instance of :class:`Auth`.\n :param key:\n The key used to retrieve the instance from the registry. A default\n is used if it is not set.\n :param request:\n A :class:`webapp2.Request` instance used to retrieve the instance. The\n active request is used if it is not set.\n '
request = (request or webapp2.get_request())
request.registry[key] = auth |
def __init__(self, app, config=None):
'Initializes the session store.\n\n :param app:\n A :class:`webapp2.WSGIApplication` instance.\n :param config:\n A dictionary of configuration values to be overridden. See\n the available keys in :data:`default_config`.\n '
self.app = app
self.config = app.config.load_config(self.config_key, default_values=default_config, user_values=config) | -9,101,138,287,151,809,000 | Initializes the session store.
:param app:
A :class:`webapp2.WSGIApplication` instance.
:param config:
A dictionary of configuration values to be overridden. See
the available keys in :data:`default_config`. | Webapp2_samplesite/webapp2_extras/auth.py | __init__ | Rockfish/PythonCourse | python | def __init__(self, app, config=None):
'Initializes the session store.\n\n :param app:\n A :class:`webapp2.WSGIApplication` instance.\n :param config:\n A dictionary of configuration values to be overridden. See\n the available keys in :data:`default_config`.\n '
self.app = app
self.config = app.config.load_config(self.config_key, default_values=default_config, user_values=config) |
@webapp2.cached_property
def session_attributes(self):
'The list of attributes stored in a session.\n\n This must be an ordered list of unique elements.\n '
seen = set()
attrs = (self._session_attributes + self.user_attributes)
return [a for a in attrs if ((a not in seen) and (not seen.add(a)))] | -3,796,023,426,019,284,000 | The list of attributes stored in a session.
This must be an ordered list of unique elements. | Webapp2_samplesite/webapp2_extras/auth.py | session_attributes | Rockfish/PythonCourse | python | @webapp2.cached_property
def session_attributes(self):
'The list of attributes stored in a session.\n\n This must be an ordered list of unique elements.\n '
seen = set()
attrs = (self._session_attributes + self.user_attributes)
return [a for a in attrs if ((a not in seen) and (not seen.add(a)))] |
@webapp2.cached_property
def user_attributes(self):
'The list of attributes retrieved from the user model.\n\n This must be an ordered list of unique elements.\n '
seen = set()
attrs = self.config['user_attributes']
return [a for a in attrs if ((a not in seen) and (not seen.add(a)))] | -1,450,585,386,066,368,500 | The list of attributes retrieved from the user model.
This must be an ordered list of unique elements. | Webapp2_samplesite/webapp2_extras/auth.py | user_attributes | Rockfish/PythonCourse | python | @webapp2.cached_property
def user_attributes(self):
'The list of attributes retrieved from the user model.\n\n This must be an ordered list of unique elements.\n '
seen = set()
attrs = self.config['user_attributes']
return [a for a in attrs if ((a not in seen) and (not seen.add(a)))] |
@webapp2.cached_property
def user_model(self):
'Configured user model.'
cls = self.config['user_model']
if isinstance(cls, str):
cls = self.config['user_model'] = webapp2.import_string(cls)
return cls | -9,142,937,729,856,513,000 | Configured user model. | Webapp2_samplesite/webapp2_extras/auth.py | user_model | Rockfish/PythonCourse | python | @webapp2.cached_property
def user_model(self):
cls = self.config['user_model']
if isinstance(cls, str):
cls = self.config['user_model'] = webapp2.import_string(cls)
return cls |
def user_to_dict(self, user):
"Returns a dictionary based on a user object.\n\n Extra attributes to be retrieved must be set in this module's\n configuration.\n\n :param user:\n User object: an instance the custom user model.\n :returns:\n A dictionary with user data.\n "
if (not user):
return None
user_dict = dict(((a, getattr(user, a)) for a in self.user_attributes))
user_dict['user_id'] = user.key.id()
return user_dict | 395,166,271,577,502,900 | Returns a dictionary based on a user object.
Extra attributes to be retrieved must be set in this module's
configuration.
:param user:
User object: an instance the custom user model.
:returns:
A dictionary with user data. | Webapp2_samplesite/webapp2_extras/auth.py | user_to_dict | Rockfish/PythonCourse | python | def user_to_dict(self, user):
"Returns a dictionary based on a user object.\n\n Extra attributes to be retrieved must be set in this module's\n configuration.\n\n :param user:\n User object: an instance the custom user model.\n :returns:\n A dictionary with user data.\n "
if (not user):
return None
user_dict = dict(((a, getattr(user, a)) for a in self.user_attributes))
user_dict['user_id'] = user.key.id()
return user_dict |
def get_user_by_auth_password(self, auth_id, password, silent=False):
'Returns a user dict based on auth_id and password.\n\n :param auth_id:\n Authentication id.\n :param password:\n User password.\n :param silent:\n If True, raises an exception if auth_id or password are invalid.\n :returns:\n A dictionary with user data.\n :raises:\n ``InvalidAuthIdError`` or ``InvalidPasswordError``.\n '
try:
user = self.user_model.get_by_auth_password(auth_id, password)
return self.user_to_dict(user)
except (InvalidAuthIdError, InvalidPasswordError):
if (not silent):
raise
return None | 3,215,861,248,836,423,000 | Returns a user dict based on auth_id and password.
:param auth_id:
Authentication id.
:param password:
User password.
:param silent:
If True, raises an exception if auth_id or password are invalid.
:returns:
A dictionary with user data.
:raises:
``InvalidAuthIdError`` or ``InvalidPasswordError``. | Webapp2_samplesite/webapp2_extras/auth.py | get_user_by_auth_password | Rockfish/PythonCourse | python | def get_user_by_auth_password(self, auth_id, password, silent=False):
'Returns a user dict based on auth_id and password.\n\n :param auth_id:\n Authentication id.\n :param password:\n User password.\n :param silent:\n If True, raises an exception if auth_id or password are invalid.\n :returns:\n A dictionary with user data.\n :raises:\n ``InvalidAuthIdError`` or ``InvalidPasswordError``.\n '
try:
user = self.user_model.get_by_auth_password(auth_id, password)
return self.user_to_dict(user)
except (InvalidAuthIdError, InvalidPasswordError):
if (not silent):
raise
return None |
def get_user_by_auth_token(self, user_id, token):
'Returns a user dict based on user_id and auth token.\n\n :param user_id:\n User id.\n :param token:\n Authentication token.\n :returns:\n A tuple ``(user_dict, token_timestamp)``. Both values can be None.\n The token timestamp will be None if the user is invalid or it\n is valid but the token requires renewal.\n '
(user, ts) = self.user_model.get_by_auth_token(user_id, token)
return (self.user_to_dict(user), ts) | -490,687,262,392,920,260 | Returns a user dict based on user_id and auth token.
:param user_id:
User id.
:param token:
Authentication token.
:returns:
A tuple ``(user_dict, token_timestamp)``. Both values can be None.
The token timestamp will be None if the user is invalid or it
is valid but the token requires renewal. | Webapp2_samplesite/webapp2_extras/auth.py | get_user_by_auth_token | Rockfish/PythonCourse | python | def get_user_by_auth_token(self, user_id, token):
'Returns a user dict based on user_id and auth token.\n\n :param user_id:\n User id.\n :param token:\n Authentication token.\n :returns:\n A tuple ``(user_dict, token_timestamp)``. Both values can be None.\n The token timestamp will be None if the user is invalid or it\n is valid but the token requires renewal.\n '
(user, ts) = self.user_model.get_by_auth_token(user_id, token)
return (self.user_to_dict(user), ts) |
def create_auth_token(self, user_id):
'Creates a new authentication token.\n\n :param user_id:\n Authentication id.\n :returns:\n A new authentication token.\n '
return self.user_model.create_auth_token(user_id) | 8,363,434,732,694,904,000 | Creates a new authentication token.
:param user_id:
Authentication id.
:returns:
A new authentication token. | Webapp2_samplesite/webapp2_extras/auth.py | create_auth_token | Rockfish/PythonCourse | python | def create_auth_token(self, user_id):
'Creates a new authentication token.\n\n :param user_id:\n Authentication id.\n :returns:\n A new authentication token.\n '
return self.user_model.create_auth_token(user_id) |
def delete_auth_token(self, user_id, token):
'Deletes an authentication token.\n\n :param user_id:\n User id.\n :param token:\n Authentication token.\n '
return self.user_model.delete_auth_token(user_id, token) | 7,380,778,098,418,366,000 | Deletes an authentication token.
:param user_id:
User id.
:param token:
Authentication token. | Webapp2_samplesite/webapp2_extras/auth.py | delete_auth_token | Rockfish/PythonCourse | python | def delete_auth_token(self, user_id, token):
'Deletes an authentication token.\n\n :param user_id:\n User id.\n :param token:\n Authentication token.\n '
return self.user_model.delete_auth_token(user_id, token) |
def get_session(self, request):
'Returns an auth session.\n\n :param request:\n A :class:`webapp2.Request` instance.\n :returns:\n A session dict.\n '
store = sessions.get_store(request=request)
return store.get_session(self.config['cookie_name'], backend=self.config['session_backend']) | -5,181,140,631,074,181,000 | Returns an auth session.
:param request:
A :class:`webapp2.Request` instance.
:returns:
A session dict. | Webapp2_samplesite/webapp2_extras/auth.py | get_session | Rockfish/PythonCourse | python | def get_session(self, request):
'Returns an auth session.\n\n :param request:\n A :class:`webapp2.Request` instance.\n :returns:\n A session dict.\n '
store = sessions.get_store(request=request)
return store.get_session(self.config['cookie_name'], backend=self.config['session_backend']) |
def serialize_session(self, data):
'Serializes values for a session.\n\n :param data:\n A dict with session data.\n :returns:\n A list with session data.\n '
assert (len(data) == len(self.session_attributes))
return [data.get(k) for k in self.session_attributes] | 4,561,535,077,663,560,000 | Serializes values for a session.
:param data:
A dict with session data.
:returns:
A list with session data. | Webapp2_samplesite/webapp2_extras/auth.py | serialize_session | Rockfish/PythonCourse | python | def serialize_session(self, data):
'Serializes values for a session.\n\n :param data:\n A dict with session data.\n :returns:\n A list with session data.\n '
assert (len(data) == len(self.session_attributes))
return [data.get(k) for k in self.session_attributes] |
def deserialize_session(self, data):
'Deserializes values for a session.\n\n :param data:\n A list with session data.\n :returns:\n A dict with session data.\n '
assert (len(data) == len(self.session_attributes))
return dict(list(zip(self.session_attributes, data))) | 6,139,114,933,462,788,000 | Deserializes values for a session.
:param data:
A list with session data.
:returns:
A dict with session data. | Webapp2_samplesite/webapp2_extras/auth.py | deserialize_session | Rockfish/PythonCourse | python | def deserialize_session(self, data):
'Deserializes values for a session.\n\n :param data:\n A list with session data.\n :returns:\n A dict with session data.\n '
assert (len(data) == len(self.session_attributes))
return dict(list(zip(self.session_attributes, data))) |
def set_password_validator(self, func):
'Sets the function used to perform password validation.\n\n :param func:\n A function that receives ``(store, auth_id, password)``\n and returns a user dict or None.\n '
self.validate_password = func.__get__(self, self.__class__) | -5,540,569,157,499,997,000 | Sets the function used to perform password validation.
:param func:
A function that receives ``(store, auth_id, password)``
and returns a user dict or None. | Webapp2_samplesite/webapp2_extras/auth.py | set_password_validator | Rockfish/PythonCourse | python | def set_password_validator(self, func):
'Sets the function used to perform password validation.\n\n :param func:\n A function that receives ``(store, auth_id, password)``\n and returns a user dict or None.\n '
self.validate_password = func.__get__(self, self.__class__) |
def set_token_validator(self, func):
'Sets the function used to perform token validation.\n\n :param func:\n A function that receives ``(store, user_id, token, token_ts)``\n and returns a tuple ``(user_dict, token)``.\n '
self.validate_token = func.__get__(self, self.__class__) | -8,153,688,014,519,468,000 | Sets the function used to perform token validation.
:param func:
A function that receives ``(store, user_id, token, token_ts)``
and returns a tuple ``(user_dict, token)``. | Webapp2_samplesite/webapp2_extras/auth.py | set_token_validator | Rockfish/PythonCourse | python | def set_token_validator(self, func):
'Sets the function used to perform token validation.\n\n :param func:\n A function that receives ``(store, user_id, token, token_ts)``\n and returns a tuple ``(user_dict, token)``.\n '
self.validate_token = func.__get__(self, self.__class__) |
def default_password_validator(self, auth_id, password, silent=False):
'Validates a password.\n\n Passwords are used to log-in using forms or to request auth tokens\n from services.\n\n :param auth_id:\n Authentication id.\n :param password:\n Password to be checked.\n :param silent:\n If True, raises an exception if auth_id or password are invalid.\n :returns:\n user or None\n :raises:\n ``InvalidAuthIdError`` or ``InvalidPasswordError``.\n '
return self.get_user_by_auth_password(auth_id, password, silent=silent) | -5,114,930,920,428,402,000 | Validates a password.
Passwords are used to log-in using forms or to request auth tokens
from services.
:param auth_id:
Authentication id.
:param password:
Password to be checked.
:param silent:
If True, raises an exception if auth_id or password are invalid.
:returns:
user or None
:raises:
``InvalidAuthIdError`` or ``InvalidPasswordError``. | Webapp2_samplesite/webapp2_extras/auth.py | default_password_validator | Rockfish/PythonCourse | python | def default_password_validator(self, auth_id, password, silent=False):
'Validates a password.\n\n Passwords are used to log-in using forms or to request auth tokens\n from services.\n\n :param auth_id:\n Authentication id.\n :param password:\n Password to be checked.\n :param silent:\n If True, raises an exception if auth_id or password are invalid.\n :returns:\n user or None\n :raises:\n ``InvalidAuthIdError`` or ``InvalidPasswordError``.\n '
return self.get_user_by_auth_password(auth_id, password, silent=silent) |
def default_token_validator(self, user_id, token, token_ts=None):
'Validates a token.\n\n Tokens are random strings used to authenticate temporarily. They are\n used to validate sessions or service requests.\n\n :param user_id:\n User id.\n :param token:\n Token to be checked.\n :param token_ts:\n Optional token timestamp used to pre-validate the token age.\n :returns:\n A tuple ``(user_dict, token)``.\n '
now = int(time.time())
delete = (token_ts and ((now - token_ts) > self.config['token_max_age']))
create = False
if (not delete):
(user, ts) = self.get_user_by_auth_token(user_id, token)
if user:
delete = ((now - ts) > self.config['token_max_age'])
create = ((now - ts) > self.config['token_new_age'])
if (delete or create or (not user)):
if (delete or create):
self.delete_auth_token(user_id, token)
if delete:
user = None
token = None
return (user, token) | -311,591,043,904,925,200 | Validates a token.
Tokens are random strings used to authenticate temporarily. They are
used to validate sessions or service requests.
:param user_id:
User id.
:param token:
Token to be checked.
:param token_ts:
Optional token timestamp used to pre-validate the token age.
:returns:
A tuple ``(user_dict, token)``. | Webapp2_samplesite/webapp2_extras/auth.py | default_token_validator | Rockfish/PythonCourse | python | def default_token_validator(self, user_id, token, token_ts=None):
'Validates a token.\n\n Tokens are random strings used to authenticate temporarily. They are\n used to validate sessions or service requests.\n\n :param user_id:\n User id.\n :param token:\n Token to be checked.\n :param token_ts:\n Optional token timestamp used to pre-validate the token age.\n :returns:\n A tuple ``(user_dict, token)``.\n '
now = int(time.time())
delete = (token_ts and ((now - token_ts) > self.config['token_max_age']))
create = False
if (not delete):
(user, ts) = self.get_user_by_auth_token(user_id, token)
if user:
delete = ((now - ts) > self.config['token_max_age'])
create = ((now - ts) > self.config['token_new_age'])
if (delete or create or (not user)):
if (delete or create):
self.delete_auth_token(user_id, token)
if delete:
user = None
token = None
return (user, token) |
def __init__(self, request):
'Initializes the auth provider for a request.\n\n :param request:\n A :class:`webapp2.Request` instance.\n '
self.request = request
self.store = get_store(app=request.app) | 5,832,942,312,021,142,000 | Initializes the auth provider for a request.
:param request:
A :class:`webapp2.Request` instance. | Webapp2_samplesite/webapp2_extras/auth.py | __init__ | Rockfish/PythonCourse | python | def __init__(self, request):
'Initializes the auth provider for a request.\n\n :param request:\n A :class:`webapp2.Request` instance.\n '
self.request = request
self.store = get_store(app=request.app) |
def get_user_by_session(self, save_session=True):
'Returns a user based on the current session.\n\n :param save_session:\n If True, saves the user in the session if authentication succeeds.\n :returns:\n A user dict or None.\n '
if (self._user is None):
data = self.get_session_data(pop=True)
if (not data):
self._user = _anon
else:
self._user = self.get_user_by_token(user_id=data['user_id'], token=data['token'], token_ts=data['token_ts'], cache=data, cache_ts=data['cache_ts'], remember=data['remember'], save_session=save_session)
return self._user_or_none() | 3,667,674,903,363,847,000 | Returns a user based on the current session.
:param save_session:
If True, saves the user in the session if authentication succeeds.
:returns:
A user dict or None. | Webapp2_samplesite/webapp2_extras/auth.py | get_user_by_session | Rockfish/PythonCourse | python | def get_user_by_session(self, save_session=True):
'Returns a user based on the current session.\n\n :param save_session:\n If True, saves the user in the session if authentication succeeds.\n :returns:\n A user dict or None.\n '
if (self._user is None):
data = self.get_session_data(pop=True)
if (not data):
self._user = _anon
else:
self._user = self.get_user_by_token(user_id=data['user_id'], token=data['token'], token_ts=data['token_ts'], cache=data, cache_ts=data['cache_ts'], remember=data['remember'], save_session=save_session)
return self._user_or_none() |
def get_user_by_token(self, user_id, token, token_ts=None, cache=None, cache_ts=None, remember=False, save_session=True):
'Returns a user based on an authentication token.\n\n :param user_id:\n User id.\n :param token:\n Authentication token.\n :param token_ts:\n Token timestamp, used to perform pre-validation.\n :param cache:\n Cached user data (from the session).\n :param cache_ts:\n Cache timestamp.\n :param remember:\n If True, saves permanent sessions.\n :param save_session:\n If True, saves the user in the session if authentication succeeds.\n :returns:\n A user dict or None.\n '
if (self._user is not None):
assert ((self._user is not _anon) and (self._user['user_id'] == user_id) and (self._user['token'] == token))
return self._user_or_none()
if (cache and cache_ts):
now = int(time.time())
valid = ((now - cache_ts) < self.store.config['token_cache_age'])
if (valid and token_ts):
valid2 = ((now - token_ts) < self.store.config['token_max_age'])
valid3 = ((now - token_ts) < self.store.config['token_new_age'])
valid = (valid2 and valid3)
if valid:
self._user = cache
else:
cache_ts = None
if (self._user is None):
(self._user, token) = self.store.validate_token(user_id, token, token_ts=token_ts)
if (self._user is None):
self._user = _anon
elif save_session:
if (not token):
token_ts = None
self.set_session(self._user, token=token, token_ts=token_ts, cache_ts=cache_ts, remember=remember)
return self._user_or_none() | -5,872,988,609,794,356,000 | Returns a user based on an authentication token.
:param user_id:
User id.
:param token:
Authentication token.
:param token_ts:
Token timestamp, used to perform pre-validation.
:param cache:
Cached user data (from the session).
:param cache_ts:
Cache timestamp.
:param remember:
If True, saves permanent sessions.
:param save_session:
If True, saves the user in the session if authentication succeeds.
:returns:
A user dict or None. | Webapp2_samplesite/webapp2_extras/auth.py | get_user_by_token | Rockfish/PythonCourse | python | def get_user_by_token(self, user_id, token, token_ts=None, cache=None, cache_ts=None, remember=False, save_session=True):
'Returns a user based on an authentication token.\n\n :param user_id:\n User id.\n :param token:\n Authentication token.\n :param token_ts:\n Token timestamp, used to perform pre-validation.\n :param cache:\n Cached user data (from the session).\n :param cache_ts:\n Cache timestamp.\n :param remember:\n If True, saves permanent sessions.\n :param save_session:\n If True, saves the user in the session if authentication succeeds.\n :returns:\n A user dict or None.\n '
if (self._user is not None):
assert ((self._user is not _anon) and (self._user['user_id'] == user_id) and (self._user['token'] == token))
return self._user_or_none()
if (cache and cache_ts):
now = int(time.time())
valid = ((now - cache_ts) < self.store.config['token_cache_age'])
if (valid and token_ts):
valid2 = ((now - token_ts) < self.store.config['token_max_age'])
valid3 = ((now - token_ts) < self.store.config['token_new_age'])
valid = (valid2 and valid3)
if valid:
self._user = cache
else:
cache_ts = None
if (self._user is None):
(self._user, token) = self.store.validate_token(user_id, token, token_ts=token_ts)
if (self._user is None):
self._user = _anon
elif save_session:
if (not token):
token_ts = None
self.set_session(self._user, token=token, token_ts=token_ts, cache_ts=cache_ts, remember=remember)
return self._user_or_none() |
def get_user_by_password(self, auth_id, password, remember=False, save_session=True, silent=False):
'Returns a user based on password credentials.\n\n :param auth_id:\n Authentication id.\n :param password:\n User password.\n :param remember:\n If True, saves permanent sessions.\n :param save_session:\n If True, saves the user in the session if authentication succeeds.\n :param silent:\n If True, raises an exception if auth_id or password are invalid.\n :returns:\n A user dict or None.\n :raises:\n ``InvalidAuthIdError`` or ``InvalidPasswordError``.\n '
if save_session:
self.unset_session()
self._user = self.store.validate_password(auth_id, password, silent=silent)
if (not self._user):
self._user = _anon
elif save_session:
self.set_session(self._user, remember=remember)
return self._user_or_none() | 9,061,094,405,721,935,000 | Returns a user based on password credentials.
:param auth_id:
Authentication id.
:param password:
User password.
:param remember:
If True, saves permanent sessions.
:param save_session:
If True, saves the user in the session if authentication succeeds.
:param silent:
If True, raises an exception if auth_id or password are invalid.
:returns:
A user dict or None.
:raises:
``InvalidAuthIdError`` or ``InvalidPasswordError``. | Webapp2_samplesite/webapp2_extras/auth.py | get_user_by_password | Rockfish/PythonCourse | python | def get_user_by_password(self, auth_id, password, remember=False, save_session=True, silent=False):
'Returns a user based on password credentials.\n\n :param auth_id:\n Authentication id.\n :param password:\n User password.\n :param remember:\n If True, saves permanent sessions.\n :param save_session:\n If True, saves the user in the session if authentication succeeds.\n :param silent:\n If True, raises an exception if auth_id or password are invalid.\n :returns:\n A user dict or None.\n :raises:\n ``InvalidAuthIdError`` or ``InvalidPasswordError``.\n '
if save_session:
self.unset_session()
self._user = self.store.validate_password(auth_id, password, silent=silent)
if (not self._user):
self._user = _anon
elif save_session:
self.set_session(self._user, remember=remember)
return self._user_or_none() |
@webapp2.cached_property
def session(self):
'Auth session.'
return self.store.get_session(self.request) | 5,353,006,842,283,151,000 | Auth session. | Webapp2_samplesite/webapp2_extras/auth.py | session | Rockfish/PythonCourse | python | @webapp2.cached_property
def session(self):
return self.store.get_session(self.request) |
def set_session(self, user, token=None, token_ts=None, cache_ts=None, remember=False, **session_args):
'Saves a user in the session.\n\n :param user:\n A dictionary with user data.\n :param token:\n A unique token to be persisted. If None, a new one is created.\n :param token_ts:\n Token timestamp. If None, a new one is created.\n :param cache_ts:\n Token cache timestamp. If None, a new one is created.\n :remember:\n If True, session is set to be persisted.\n :param session_args:\n Keyword arguments to set the session arguments.\n '
now = int(time.time())
token = (token or self.store.create_auth_token(user['user_id']))
token_ts = (token_ts or now)
cache_ts = (cache_ts or now)
if remember:
max_age = self.store.config['token_max_age']
else:
max_age = None
session_args.setdefault('max_age', max_age)
user.update({'token': token, 'token_ts': token_ts, 'cache_ts': cache_ts, 'remember': int(remember)})
self.set_session_data(user, **session_args)
self._user = user | -2,310,134,152,950,392,300 | Saves a user in the session.
:param user:
A dictionary with user data.
:param token:
A unique token to be persisted. If None, a new one is created.
:param token_ts:
Token timestamp. If None, a new one is created.
:param cache_ts:
Token cache timestamp. If None, a new one is created.
:remember:
If True, session is set to be persisted.
:param session_args:
Keyword arguments to set the session arguments. | Webapp2_samplesite/webapp2_extras/auth.py | set_session | Rockfish/PythonCourse | python | def set_session(self, user, token=None, token_ts=None, cache_ts=None, remember=False, **session_args):
'Saves a user in the session.\n\n :param user:\n A dictionary with user data.\n :param token:\n A unique token to be persisted. If None, a new one is created.\n :param token_ts:\n Token timestamp. If None, a new one is created.\n :param cache_ts:\n Token cache timestamp. If None, a new one is created.\n :remember:\n If True, session is set to be persisted.\n :param session_args:\n Keyword arguments to set the session arguments.\n '
now = int(time.time())
token = (token or self.store.create_auth_token(user['user_id']))
token_ts = (token_ts or now)
cache_ts = (cache_ts or now)
if remember:
max_age = self.store.config['token_max_age']
else:
max_age = None
session_args.setdefault('max_age', max_age)
user.update({'token': token, 'token_ts': token_ts, 'cache_ts': cache_ts, 'remember': int(remember)})
self.set_session_data(user, **session_args)
self._user = user |
def unset_session(self):
'Removes a user from the session and invalidates the auth token.'
self._user = None
data = self.get_session_data(pop=True)
if data:
self.store.delete_auth_token(data['user_id'], data['token']) | 4,615,528,405,671,208,000 | Removes a user from the session and invalidates the auth token. | Webapp2_samplesite/webapp2_extras/auth.py | unset_session | Rockfish/PythonCourse | python | def unset_session(self):
self._user = None
data = self.get_session_data(pop=True)
if data:
self.store.delete_auth_token(data['user_id'], data['token']) |
def get_session_data(self, pop=False):
'Returns the session data as a dictionary.\n\n :param pop:\n If True, removes the session.\n :returns:\n A deserialized session, or None.\n '
func = (self.session.pop if pop else self.session.get)
rv = func('_user', None)
if rv:
return self.store.deserialize_session(rv) | 3,557,656,058,955,620,000 | Returns the session data as a dictionary.
:param pop:
If True, removes the session.
:returns:
A deserialized session, or None. | Webapp2_samplesite/webapp2_extras/auth.py | get_session_data | Rockfish/PythonCourse | python | def get_session_data(self, pop=False):
'Returns the session data as a dictionary.\n\n :param pop:\n If True, removes the session.\n :returns:\n A deserialized session, or None.\n '
func = (self.session.pop if pop else self.session.get)
rv = func('_user', None)
if rv:
return self.store.deserialize_session(rv) |
def set_session_data(self, data, **session_args):
'Sets the session data as a list.\n\n :param data:\n Deserialized session data.\n :param session_args:\n Extra arguments for the session.\n '
self.session['_user'] = self.store.serialize_session(data)
self.session.container.session_args.update(session_args) | 7,625,603,847,048,672,000 | Sets the session data as a list.
:param data:
Deserialized session data.
:param session_args:
Extra arguments for the session. | Webapp2_samplesite/webapp2_extras/auth.py | set_session_data | Rockfish/PythonCourse | python | def set_session_data(self, data, **session_args):
'Sets the session data as a list.\n\n :param data:\n Deserialized session data.\n :param session_args:\n Extra arguments for the session.\n '
self.session['_user'] = self.store.serialize_session(data)
self.session.container.session_args.update(session_args) |
@pytest.fixture(scope='module')
def setup(duthosts, ptfhost, rand_selected_dut, rand_unselected_dut, tbinfo, ptfadapter):
'Gather all required test information from DUT and tbinfo.\n\n Args:\n duthosts: All DUTs belong to the testbed.\n rand_one_dut_hostname: hostname of a random chosen dut to run test.\n tbinfo: A fixture to gather information about the testbed.\n\n Yields:\n A Dictionary with required test information.\n\n '
mg_facts = rand_selected_dut.get_extended_minigraph_facts(tbinfo)
topo = tbinfo['topo']['type']
vlan_ports = []
vlan_mac = None
if (topo == 't0'):
vlan_ports = [mg_facts['minigraph_ptf_indices'][ifname] for ifname in mg_facts['minigraph_vlans'].values()[0]['members']]
config_facts = rand_selected_dut.get_running_config_facts()
vlan_table = config_facts['VLAN']
vlan_name = list(vlan_table.keys())[0]
if ('mac' in vlan_table[vlan_name]):
vlan_mac = vlan_table[vlan_name]['mac']
downstream_ports = defaultdict(list)
upstream_ports = defaultdict(list)
downstream_port_ids = []
upstream_port_ids = []
upstream_port_id_to_router_mac_map = {}
downstream_port_id_to_router_mac_map = {}
downlink_dst_mac = (vlan_mac if (vlan_mac is not None) else rand_selected_dut.facts['router_mac'])
for (interface, neighbor) in mg_facts['minigraph_neighbors'].items():
port_id = mg_facts['minigraph_ptf_indices'][interface]
if (((topo == 't1') and ('T0' in neighbor['name'])) or ((topo == 't0') and ('Server' in neighbor['name']))):
downstream_ports[neighbor['namespace']].append(interface)
downstream_port_ids.append(port_id)
downstream_port_id_to_router_mac_map[port_id] = downlink_dst_mac
elif (((topo == 't1') and ('T2' in neighbor['name'])) or ((topo == 't0') and ('T1' in neighbor['name']))):
upstream_ports[neighbor['namespace']].append(interface)
upstream_port_ids.append(port_id)
upstream_port_id_to_router_mac_map[port_id] = rand_selected_dut.facts['router_mac']
if ('dualtor' not in tbinfo['topo']['name']):
logging.info('Stopping GARP service on single tor')
ptfhost.shell('supervisorctl stop garp_service', module_ignore_errors=True)
if (('dualtor' in tbinfo['topo']['name']) and (rand_unselected_dut is not None)):
peer_mg_facts = rand_unselected_dut.get_extended_minigraph_facts(tbinfo)
for (interface, neighbor) in peer_mg_facts['minigraph_neighbors'].items():
if (((topo == 't1') and ('T2' in neighbor['name'])) or ((topo == 't0') and ('T1' in neighbor['name']))):
port_id = peer_mg_facts['minigraph_ptf_indices'][interface]
upstream_port_ids.append(port_id)
upstream_port_id_to_router_mac_map[port_id] = rand_unselected_dut.facts['router_mac']
port_channels = mg_facts['minigraph_portchannels']
acl_table_ports = defaultdict(list)
if ((topo == 't0') or (tbinfo['topo']['name'] in ('t1', 't1-lag'))):
for (namespace, port) in downstream_ports.iteritems():
acl_table_ports[namespace] += port
if namespace:
acl_table_ports[''] += port
if ((topo == 't0') or (tbinfo['topo']['name'] in ('t1-lag', 't1-64-lag', 't1-64-lag-clet'))):
for (k, v) in port_channels.iteritems():
acl_table_ports[v['namespace']].append(k)
if v['namespace']:
acl_table_ports[''].append(k)
else:
for (namespace, port) in upstream_ports.iteritems():
acl_table_ports[namespace] += port
if namespace:
acl_table_ports[''] += port
dest_mac_mapping = {'downlink->uplink': downstream_port_id_to_router_mac_map, 'uplink->downlink': upstream_port_id_to_router_mac_map}
setup_information = {'destination_mac': dest_mac_mapping, 'downstream_port_ids': downstream_port_ids, 'upstream_port_ids': upstream_port_ids, 'acl_table_ports': acl_table_ports, 'vlan_ports': vlan_ports, 'topo': topo, 'vlan_mac': vlan_mac}
logger.info('Gathered variables for ACL test:\n{}'.format(pprint.pformat(setup_information)))
logger.info('Creating temporary folder "{}" for ACL test'.format(DUT_TMP_DIR))
for duthost in duthosts:
duthost.command('mkdir -p {}'.format(DUT_TMP_DIR))
(yield setup_information)
logger.info('Removing temporary directory "{}"'.format(DUT_TMP_DIR))
for duthost in duthosts:
duthost.command('rm -rf {}'.format(DUT_TMP_DIR)) | 2,646,736,912,230,296,000 | Gather all required test information from DUT and tbinfo.
Args:
duthosts: All DUTs belong to the testbed.
rand_one_dut_hostname: hostname of a random chosen dut to run test.
tbinfo: A fixture to gather information about the testbed.
Yields:
A Dictionary with required test information. | tests/acl/test_acl.py | setup | KostiantynYarovyiBf/sonic-mgmt | python | @pytest.fixture(scope='module')
def setup(duthosts, ptfhost, rand_selected_dut, rand_unselected_dut, tbinfo, ptfadapter):
'Gather all required test information from DUT and tbinfo.\n\n Args:\n duthosts: All DUTs belong to the testbed.\n rand_one_dut_hostname: hostname of a random chosen dut to run test.\n tbinfo: A fixture to gather information about the testbed.\n\n Yields:\n A Dictionary with required test information.\n\n '
mg_facts = rand_selected_dut.get_extended_minigraph_facts(tbinfo)
topo = tbinfo['topo']['type']
vlan_ports = []
vlan_mac = None
if (topo == 't0'):
vlan_ports = [mg_facts['minigraph_ptf_indices'][ifname] for ifname in mg_facts['minigraph_vlans'].values()[0]['members']]
config_facts = rand_selected_dut.get_running_config_facts()
vlan_table = config_facts['VLAN']
vlan_name = list(vlan_table.keys())[0]
if ('mac' in vlan_table[vlan_name]):
vlan_mac = vlan_table[vlan_name]['mac']
downstream_ports = defaultdict(list)
upstream_ports = defaultdict(list)
downstream_port_ids = []
upstream_port_ids = []
upstream_port_id_to_router_mac_map = {}
downstream_port_id_to_router_mac_map = {}
downlink_dst_mac = (vlan_mac if (vlan_mac is not None) else rand_selected_dut.facts['router_mac'])
for (interface, neighbor) in mg_facts['minigraph_neighbors'].items():
port_id = mg_facts['minigraph_ptf_indices'][interface]
if (((topo == 't1') and ('T0' in neighbor['name'])) or ((topo == 't0') and ('Server' in neighbor['name']))):
downstream_ports[neighbor['namespace']].append(interface)
downstream_port_ids.append(port_id)
downstream_port_id_to_router_mac_map[port_id] = downlink_dst_mac
elif (((topo == 't1') and ('T2' in neighbor['name'])) or ((topo == 't0') and ('T1' in neighbor['name']))):
upstream_ports[neighbor['namespace']].append(interface)
upstream_port_ids.append(port_id)
upstream_port_id_to_router_mac_map[port_id] = rand_selected_dut.facts['router_mac']
if ('dualtor' not in tbinfo['topo']['name']):
logging.info('Stopping GARP service on single tor')
ptfhost.shell('supervisorctl stop garp_service', module_ignore_errors=True)
if (('dualtor' in tbinfo['topo']['name']) and (rand_unselected_dut is not None)):
peer_mg_facts = rand_unselected_dut.get_extended_minigraph_facts(tbinfo)
for (interface, neighbor) in peer_mg_facts['minigraph_neighbors'].items():
if (((topo == 't1') and ('T2' in neighbor['name'])) or ((topo == 't0') and ('T1' in neighbor['name']))):
port_id = peer_mg_facts['minigraph_ptf_indices'][interface]
upstream_port_ids.append(port_id)
upstream_port_id_to_router_mac_map[port_id] = rand_unselected_dut.facts['router_mac']
port_channels = mg_facts['minigraph_portchannels']
acl_table_ports = defaultdict(list)
if ((topo == 't0') or (tbinfo['topo']['name'] in ('t1', 't1-lag'))):
for (namespace, port) in downstream_ports.iteritems():
acl_table_ports[namespace] += port
if namespace:
acl_table_ports[] += port
if ((topo == 't0') or (tbinfo['topo']['name'] in ('t1-lag', 't1-64-lag', 't1-64-lag-clet'))):
for (k, v) in port_channels.iteritems():
acl_table_ports[v['namespace']].append(k)
if v['namespace']:
acl_table_ports[].append(k)
else:
for (namespace, port) in upstream_ports.iteritems():
acl_table_ports[namespace] += port
if namespace:
acl_table_ports[] += port
dest_mac_mapping = {'downlink->uplink': downstream_port_id_to_router_mac_map, 'uplink->downlink': upstream_port_id_to_router_mac_map}
setup_information = {'destination_mac': dest_mac_mapping, 'downstream_port_ids': downstream_port_ids, 'upstream_port_ids': upstream_port_ids, 'acl_table_ports': acl_table_ports, 'vlan_ports': vlan_ports, 'topo': topo, 'vlan_mac': vlan_mac}
logger.info('Gathered variables for ACL test:\n{}'.format(pprint.pformat(setup_information)))
logger.info('Creating temporary folder "{}" for ACL test'.format(DUT_TMP_DIR))
for duthost in duthosts:
duthost.command('mkdir -p {}'.format(DUT_TMP_DIR))
(yield setup_information)
logger.info('Removing temporary directory "{}"'.format(DUT_TMP_DIR))
for duthost in duthosts:
duthost.command('rm -rf {}'.format(DUT_TMP_DIR)) |
@pytest.fixture(scope='module')
def populate_vlan_arp_entries(setup, ptfhost, duthosts, rand_one_dut_hostname, ip_version):
'Set up the ARP responder utility in the PTF container.'
duthost = duthosts[rand_one_dut_hostname]
if (setup['topo'] != 't0'):
def noop():
pass
(yield noop)
return
addr_list = [DOWNSTREAM_DST_IP[ip_version], DOWNSTREAM_IP_TO_ALLOW[ip_version], DOWNSTREAM_IP_TO_BLOCK[ip_version]]
vlan_host_map = defaultdict(dict)
for i in range(len(addr_list)):
mac = VLAN_BASE_MAC_PATTERN.format(i)
port = random.choice(setup['vlan_ports'])
addr = addr_list[i]
vlan_host_map[port][str(addr)] = mac
DOWNSTREAM_IP_PORT_MAP[addr] = port
arp_responder_conf = {}
for port in vlan_host_map:
arp_responder_conf['eth{}'.format(port)] = vlan_host_map[port]
with open('/tmp/from_t1.json', 'w') as ar_config:
json.dump(arp_responder_conf, ar_config)
ptfhost.copy(src='/tmp/from_t1.json', dest='/tmp/from_t1.json')
ptfhost.host.options['variable_manager'].extra_vars.update({'arp_responder_args': '-e'})
ptfhost.template(src='templates/arp_responder.conf.j2', dest='/etc/supervisor/conf.d/arp_responder.conf')
ptfhost.shell('supervisorctl reread && supervisorctl update')
ptfhost.shell('supervisorctl restart arp_responder')
def populate_arp_table():
for dut in duthosts:
dut.command('sonic-clear fdb all')
dut.command('sonic-clear arp')
time.sleep(20)
for addr in addr_list:
dut.command('ping {} -c 3'.format(addr), module_ignore_errors=True)
populate_arp_table()
(yield populate_arp_table)
logging.info('Stopping ARP responder')
ptfhost.shell('supervisorctl stop arp_responder')
duthost.command('sonic-clear fdb all')
duthost.command('sonic-clear arp') | -6,976,798,631,610,011,000 | Set up the ARP responder utility in the PTF container. | tests/acl/test_acl.py | populate_vlan_arp_entries | KostiantynYarovyiBf/sonic-mgmt | python | @pytest.fixture(scope='module')
def populate_vlan_arp_entries(setup, ptfhost, duthosts, rand_one_dut_hostname, ip_version):
duthost = duthosts[rand_one_dut_hostname]
if (setup['topo'] != 't0'):
def noop():
pass
(yield noop)
return
addr_list = [DOWNSTREAM_DST_IP[ip_version], DOWNSTREAM_IP_TO_ALLOW[ip_version], DOWNSTREAM_IP_TO_BLOCK[ip_version]]
vlan_host_map = defaultdict(dict)
for i in range(len(addr_list)):
mac = VLAN_BASE_MAC_PATTERN.format(i)
port = random.choice(setup['vlan_ports'])
addr = addr_list[i]
vlan_host_map[port][str(addr)] = mac
DOWNSTREAM_IP_PORT_MAP[addr] = port
arp_responder_conf = {}
for port in vlan_host_map:
arp_responder_conf['eth{}'.format(port)] = vlan_host_map[port]
with open('/tmp/from_t1.json', 'w') as ar_config:
json.dump(arp_responder_conf, ar_config)
ptfhost.copy(src='/tmp/from_t1.json', dest='/tmp/from_t1.json')
ptfhost.host.options['variable_manager'].extra_vars.update({'arp_responder_args': '-e'})
ptfhost.template(src='templates/arp_responder.conf.j2', dest='/etc/supervisor/conf.d/arp_responder.conf')
ptfhost.shell('supervisorctl reread && supervisorctl update')
ptfhost.shell('supervisorctl restart arp_responder')
def populate_arp_table():
for dut in duthosts:
dut.command('sonic-clear fdb all')
dut.command('sonic-clear arp')
time.sleep(20)
for addr in addr_list:
dut.command('ping {} -c 3'.format(addr), module_ignore_errors=True)
populate_arp_table()
(yield populate_arp_table)
logging.info('Stopping ARP responder')
ptfhost.shell('supervisorctl stop arp_responder')
duthost.command('sonic-clear fdb all')
duthost.command('sonic-clear arp') |
@pytest.fixture(scope='module', params=['ingress', 'egress'])
def stage(request, duthosts, rand_one_dut_hostname):
'Parametrize tests for Ingress/Egress stage testing.\n\n Args:\n request: A fixture to interact with Pytest data.\n duthosts: All DUTs belong to the testbed.\n rand_one_dut_hostname: hostname of a random chosen dut to run test.\n\n Returns:\n str: The ACL stage to be tested.\n\n '
duthost = duthosts[rand_one_dut_hostname]
pytest_require(((request.param == 'ingress') or (duthost.facts['asic_type'] not in 'broadcom')), 'Egress ACLs are not currently supported on "{}" ASICs'.format(duthost.facts['asic_type']))
return request.param | -784,252,154,036,044,000 | Parametrize tests for Ingress/Egress stage testing.
Args:
request: A fixture to interact with Pytest data.
duthosts: All DUTs belong to the testbed.
rand_one_dut_hostname: hostname of a random chosen dut to run test.
Returns:
str: The ACL stage to be tested. | tests/acl/test_acl.py | stage | KostiantynYarovyiBf/sonic-mgmt | python | @pytest.fixture(scope='module', params=['ingress', 'egress'])
def stage(request, duthosts, rand_one_dut_hostname):
'Parametrize tests for Ingress/Egress stage testing.\n\n Args:\n request: A fixture to interact with Pytest data.\n duthosts: All DUTs belong to the testbed.\n rand_one_dut_hostname: hostname of a random chosen dut to run test.\n\n Returns:\n str: The ACL stage to be tested.\n\n '
duthost = duthosts[rand_one_dut_hostname]
pytest_require(((request.param == 'ingress') or (duthost.facts['asic_type'] not in 'broadcom')), 'Egress ACLs are not currently supported on "{}" ASICs'.format(duthost.facts['asic_type']))
return request.param |
@pytest.fixture(scope='module')
def acl_table(duthosts, rand_one_dut_hostname, setup, stage, ip_version):
'Apply ACL table configuration and remove after tests.\n\n Args:\n duthosts: All DUTs belong to the testbed.\n rand_one_dut_hostname: hostname of a random chosen dut to run test.\n setup: Parameters for the ACL tests.\n stage: The ACL stage under test.\n ip_version: The IP version under test\n\n Yields:\n The ACL table configuration.\n\n '
table_name = 'DATA_{}_{}_TEST'.format(stage.upper(), ip_version.upper())
acl_table_config = {'table_name': table_name, 'table_ports': ','.join(setup['acl_table_ports']['']), 'table_stage': stage, 'table_type': ('L3' if (ip_version == 'ipv4') else 'L3V6')}
logger.info('Generated ACL table configuration:\n{}'.format(pprint.pformat(acl_table_config)))
dut_to_analyzer_map = {}
for duthost in duthosts:
loganalyzer = LogAnalyzer(ansible_host=duthost, marker_prefix='acl')
loganalyzer.load_common_config()
dut_to_analyzer_map[duthost] = loganalyzer
try:
loganalyzer.expect_regex = [LOG_EXPECT_ACL_TABLE_CREATE_RE]
with loganalyzer:
create_or_remove_acl_table(duthost, acl_table_config, setup, 'add')
except LogAnalyzerError as err:
logger.error('ACL table creation failed, attempting to clean-up...')
create_or_remove_acl_table(duthost, acl_table_config, setup, 'remove')
raise err
try:
(yield acl_table_config)
finally:
for (duthost, loganalyzer) in dut_to_analyzer_map.items():
loganalyzer.expect_regex = [LOG_EXPECT_ACL_TABLE_REMOVE_RE]
with loganalyzer:
create_or_remove_acl_table(duthost, acl_table_config, setup, 'remove') | -3,063,090,470,390,482,000 | Apply ACL table configuration and remove after tests.
Args:
duthosts: All DUTs belong to the testbed.
rand_one_dut_hostname: hostname of a random chosen dut to run test.
setup: Parameters for the ACL tests.
stage: The ACL stage under test.
ip_version: The IP version under test
Yields:
The ACL table configuration. | tests/acl/test_acl.py | acl_table | KostiantynYarovyiBf/sonic-mgmt | python | @pytest.fixture(scope='module')
def acl_table(duthosts, rand_one_dut_hostname, setup, stage, ip_version):
'Apply ACL table configuration and remove after tests.\n\n Args:\n duthosts: All DUTs belong to the testbed.\n rand_one_dut_hostname: hostname of a random chosen dut to run test.\n setup: Parameters for the ACL tests.\n stage: The ACL stage under test.\n ip_version: The IP version under test\n\n Yields:\n The ACL table configuration.\n\n '
table_name = 'DATA_{}_{}_TEST'.format(stage.upper(), ip_version.upper())
acl_table_config = {'table_name': table_name, 'table_ports': ','.join(setup['acl_table_ports'][]), 'table_stage': stage, 'table_type': ('L3' if (ip_version == 'ipv4') else 'L3V6')}
logger.info('Generated ACL table configuration:\n{}'.format(pprint.pformat(acl_table_config)))
dut_to_analyzer_map = {}
for duthost in duthosts:
loganalyzer = LogAnalyzer(ansible_host=duthost, marker_prefix='acl')
loganalyzer.load_common_config()
dut_to_analyzer_map[duthost] = loganalyzer
try:
loganalyzer.expect_regex = [LOG_EXPECT_ACL_TABLE_CREATE_RE]
with loganalyzer:
create_or_remove_acl_table(duthost, acl_table_config, setup, 'add')
except LogAnalyzerError as err:
logger.error('ACL table creation failed, attempting to clean-up...')
create_or_remove_acl_table(duthost, acl_table_config, setup, 'remove')
raise err
try:
(yield acl_table_config)
finally:
for (duthost, loganalyzer) in dut_to_analyzer_map.items():
loganalyzer.expect_regex = [LOG_EXPECT_ACL_TABLE_REMOVE_RE]
with loganalyzer:
create_or_remove_acl_table(duthost, acl_table_config, setup, 'remove') |
@abstractmethod
def setup_rules(self, dut, acl_table, ip_version):
'Setup ACL rules for testing.\n\n Args:\n dut: The DUT having ACLs applied.\n acl_table: Configuration info for the ACL table.\n\n '
pass | -7,690,963,436,585,185,000 | Setup ACL rules for testing.
Args:
dut: The DUT having ACLs applied.
acl_table: Configuration info for the ACL table. | tests/acl/test_acl.py | setup_rules | KostiantynYarovyiBf/sonic-mgmt | python | @abstractmethod
def setup_rules(self, dut, acl_table, ip_version):
'Setup ACL rules for testing.\n\n Args:\n dut: The DUT having ACLs applied.\n acl_table: Configuration info for the ACL table.\n\n '
pass |
def post_setup_hook(self, dut, localhost, populate_vlan_arp_entries, tbinfo):
'Perform actions after rules have been applied.\n\n Args:\n dut: The DUT having ACLs applied.\n localhost: The host from which tests are run.\n populate_vlan_arp_entries: A function to populate ARP/FDB tables for VLAN interfaces.\n\n '
pass | 5,227,020,109,855,199,000 | Perform actions after rules have been applied.
Args:
dut: The DUT having ACLs applied.
localhost: The host from which tests are run.
populate_vlan_arp_entries: A function to populate ARP/FDB tables for VLAN interfaces. | tests/acl/test_acl.py | post_setup_hook | KostiantynYarovyiBf/sonic-mgmt | python | def post_setup_hook(self, dut, localhost, populate_vlan_arp_entries, tbinfo):
'Perform actions after rules have been applied.\n\n Args:\n dut: The DUT having ACLs applied.\n localhost: The host from which tests are run.\n populate_vlan_arp_entries: A function to populate ARP/FDB tables for VLAN interfaces.\n\n '
pass |
def teardown_rules(self, dut):
'Tear down ACL rules once the tests have completed.\n\n Args:\n dut: The DUT having ACLs applied.\n\n '
logger.info('Finished with tests, removing all ACL rules...')
dut.copy(src=os.path.join(FILES_DIR, ACL_REMOVE_RULES_FILE), dest=DUT_TMP_DIR)
remove_rules_dut_path = os.path.join(DUT_TMP_DIR, ACL_REMOVE_RULES_FILE)
logger.info('Applying "{}"'.format(remove_rules_dut_path))
dut.command('config acl update full {}'.format(remove_rules_dut_path)) | -7,491,526,164,275,526,000 | Tear down ACL rules once the tests have completed.
Args:
dut: The DUT having ACLs applied. | tests/acl/test_acl.py | teardown_rules | KostiantynYarovyiBf/sonic-mgmt | python | def teardown_rules(self, dut):
'Tear down ACL rules once the tests have completed.\n\n Args:\n dut: The DUT having ACLs applied.\n\n '
logger.info('Finished with tests, removing all ACL rules...')
dut.copy(src=os.path.join(FILES_DIR, ACL_REMOVE_RULES_FILE), dest=DUT_TMP_DIR)
remove_rules_dut_path = os.path.join(DUT_TMP_DIR, ACL_REMOVE_RULES_FILE)
logger.info('Applying "{}"'.format(remove_rules_dut_path))
dut.command('config acl update full {}'.format(remove_rules_dut_path)) |
@pytest.fixture(scope='class', autouse=True)
def acl_rules(self, duthosts, localhost, setup, acl_table, populate_vlan_arp_entries, tbinfo, ip_version):
'Setup/teardown ACL rules for the current set of tests.\n\n Args:\n duthosts: All DUTs belong to the testbed.\n rand_one_dut_hostname: hostname of a random chosen dut to run test.\n localhost: The host from which tests are run.\n setup: Parameters for the ACL tests.\n acl_table: Configuration info for the ACL table.\n populate_vlan_arp_entries: A function to populate ARP/FDB tables for VLAN interfaces.\n\n '
dut_to_analyzer_map = {}
for duthost in duthosts:
loganalyzer = LogAnalyzer(ansible_host=duthost, marker_prefix='acl_rules')
loganalyzer.load_common_config()
dut_to_analyzer_map[duthost] = loganalyzer
try:
loganalyzer.expect_regex = [LOG_EXPECT_ACL_RULE_CREATE_RE]
with loganalyzer:
self.setup_rules(duthost, acl_table, ip_version)
self.post_setup_hook(duthost, localhost, populate_vlan_arp_entries, tbinfo)
assert self.check_rule_counters(duthost), 'Rule counters should be ready!'
except LogAnalyzerError as err:
logger.error('ACL rule application failed, attempting to clean-up...')
self.teardown_rules(duthost)
raise err
try:
(yield)
finally:
for (duthost, loganalyzer) in dut_to_analyzer_map.items():
loganalyzer.expect_regex = [LOG_EXPECT_ACL_RULE_REMOVE_RE]
with loganalyzer:
logger.info('Removing ACL rules')
self.teardown_rules(duthost) | -2,370,236,786,902,345,000 | Setup/teardown ACL rules for the current set of tests.
Args:
duthosts: All DUTs belong to the testbed.
rand_one_dut_hostname: hostname of a random chosen dut to run test.
localhost: The host from which tests are run.
setup: Parameters for the ACL tests.
acl_table: Configuration info for the ACL table.
populate_vlan_arp_entries: A function to populate ARP/FDB tables for VLAN interfaces. | tests/acl/test_acl.py | acl_rules | KostiantynYarovyiBf/sonic-mgmt | python | @pytest.fixture(scope='class', autouse=True)
def acl_rules(self, duthosts, localhost, setup, acl_table, populate_vlan_arp_entries, tbinfo, ip_version):
'Setup/teardown ACL rules for the current set of tests.\n\n Args:\n duthosts: All DUTs belong to the testbed.\n rand_one_dut_hostname: hostname of a random chosen dut to run test.\n localhost: The host from which tests are run.\n setup: Parameters for the ACL tests.\n acl_table: Configuration info for the ACL table.\n populate_vlan_arp_entries: A function to populate ARP/FDB tables for VLAN interfaces.\n\n '
dut_to_analyzer_map = {}
for duthost in duthosts:
loganalyzer = LogAnalyzer(ansible_host=duthost, marker_prefix='acl_rules')
loganalyzer.load_common_config()
dut_to_analyzer_map[duthost] = loganalyzer
try:
loganalyzer.expect_regex = [LOG_EXPECT_ACL_RULE_CREATE_RE]
with loganalyzer:
self.setup_rules(duthost, acl_table, ip_version)
self.post_setup_hook(duthost, localhost, populate_vlan_arp_entries, tbinfo)
assert self.check_rule_counters(duthost), 'Rule counters should be ready!'
except LogAnalyzerError as err:
logger.error('ACL rule application failed, attempting to clean-up...')
self.teardown_rules(duthost)
raise err
try:
(yield)
finally:
for (duthost, loganalyzer) in dut_to_analyzer_map.items():
loganalyzer.expect_regex = [LOG_EXPECT_ACL_RULE_REMOVE_RE]
with loganalyzer:
logger.info('Removing ACL rules')
self.teardown_rules(duthost) |
@pytest.yield_fixture(scope='class', autouse=True)
def counters_sanity_check(self, duthosts, acl_rules, acl_table):
'Validate that the counters for each rule in the rules list increased as expected.\n\n This fixture yields a list of rule IDs. The test case should add on to this list if\n it is required to check the rule for increased counters.\n\n After the test cases pass, the fixture will wait for the ACL counters to update and then\n check if the counters for each rule in the list were increased.\n\n Args:\n duthosts: All DUTs belong to the testbed.\n rand_one_dut_hostname: hostname of a random chosen dut to run test.\n acl_rules: Fixture that sets up the ACL rules.\n acl_table: Fixture that sets up the ACL table.\n\n '
acl_facts = defaultdict(dict)
table_name = acl_table['table_name']
for duthost in duthosts:
acl_facts[duthost]['before'] = duthost.acl_facts()['ansible_facts']['ansible_acl_facts'][table_name]['rules']
rule_list = []
(yield rule_list)
if (not rule_list):
return
time.sleep(self.ACL_COUNTERS_UPDATE_INTERVAL_SECS)
for duthost in duthosts:
acl_facts[duthost]['after'] = duthost.acl_facts()['ansible_facts']['ansible_acl_facts'][table_name]['rules']
for duthost in duthosts:
assert (len(acl_facts[duthost]['before']) == len(acl_facts[duthost]['after']))
for rule in rule_list:
rule = 'RULE_{}'.format(rule)
counters_before = {PACKETS_COUNT: 0, BYTES_COUNT: 0}
for duthost in duthosts:
counters_before[PACKETS_COUNT] += acl_facts[duthost]['before'][rule][PACKETS_COUNT]
counters_before[BYTES_COUNT] += acl_facts[duthost]['before'][rule][BYTES_COUNT]
logger.info('Counters for ACL rule "{}" before traffic:\n{}'.format(rule, pprint.pformat(counters_before)))
counters_after = {PACKETS_COUNT: 0, BYTES_COUNT: 0}
for duthost in duthosts:
counters_after[PACKETS_COUNT] += acl_facts[duthost]['after'][rule][PACKETS_COUNT]
counters_after[BYTES_COUNT] += acl_facts[duthost]['after'][rule][BYTES_COUNT]
logger.info('Counters for ACL rule "{}" after traffic:\n{}'.format(rule, pprint.pformat(counters_after)))
assert (counters_after[PACKETS_COUNT] > counters_before[PACKETS_COUNT])
assert (counters_after[BYTES_COUNT] > counters_before[BYTES_COUNT]) | 3,925,598,669,342,153,700 | Validate that the counters for each rule in the rules list increased as expected.
This fixture yields a list of rule IDs. The test case should add on to this list if
it is required to check the rule for increased counters.
After the test cases pass, the fixture will wait for the ACL counters to update and then
check if the counters for each rule in the list were increased.
Args:
duthosts: All DUTs belong to the testbed.
rand_one_dut_hostname: hostname of a random chosen dut to run test.
acl_rules: Fixture that sets up the ACL rules.
acl_table: Fixture that sets up the ACL table. | tests/acl/test_acl.py | counters_sanity_check | KostiantynYarovyiBf/sonic-mgmt | python | @pytest.yield_fixture(scope='class', autouse=True)
def counters_sanity_check(self, duthosts, acl_rules, acl_table):
'Validate that the counters for each rule in the rules list increased as expected.\n\n This fixture yields a list of rule IDs. The test case should add on to this list if\n it is required to check the rule for increased counters.\n\n After the test cases pass, the fixture will wait for the ACL counters to update and then\n check if the counters for each rule in the list were increased.\n\n Args:\n duthosts: All DUTs belong to the testbed.\n rand_one_dut_hostname: hostname of a random chosen dut to run test.\n acl_rules: Fixture that sets up the ACL rules.\n acl_table: Fixture that sets up the ACL table.\n\n '
acl_facts = defaultdict(dict)
table_name = acl_table['table_name']
for duthost in duthosts:
acl_facts[duthost]['before'] = duthost.acl_facts()['ansible_facts']['ansible_acl_facts'][table_name]['rules']
rule_list = []
(yield rule_list)
if (not rule_list):
return
time.sleep(self.ACL_COUNTERS_UPDATE_INTERVAL_SECS)
for duthost in duthosts:
acl_facts[duthost]['after'] = duthost.acl_facts()['ansible_facts']['ansible_acl_facts'][table_name]['rules']
for duthost in duthosts:
assert (len(acl_facts[duthost]['before']) == len(acl_facts[duthost]['after']))
for rule in rule_list:
rule = 'RULE_{}'.format(rule)
counters_before = {PACKETS_COUNT: 0, BYTES_COUNT: 0}
for duthost in duthosts:
counters_before[PACKETS_COUNT] += acl_facts[duthost]['before'][rule][PACKETS_COUNT]
counters_before[BYTES_COUNT] += acl_facts[duthost]['before'][rule][BYTES_COUNT]
logger.info('Counters for ACL rule "{}" before traffic:\n{}'.format(rule, pprint.pformat(counters_before)))
counters_after = {PACKETS_COUNT: 0, BYTES_COUNT: 0}
for duthost in duthosts:
counters_after[PACKETS_COUNT] += acl_facts[duthost]['after'][rule][PACKETS_COUNT]
counters_after[BYTES_COUNT] += acl_facts[duthost]['after'][rule][BYTES_COUNT]
logger.info('Counters for ACL rule "{}" after traffic:\n{}'.format(rule, pprint.pformat(counters_after)))
assert (counters_after[PACKETS_COUNT] > counters_before[PACKETS_COUNT])
assert (counters_after[BYTES_COUNT] > counters_before[BYTES_COUNT]) |
@pytest.fixture(params=['downlink->uplink', 'uplink->downlink'])
def direction(self, request):
'Parametrize test based on direction of traffic.'
return request.param | 3,840,651,992,981,872,000 | Parametrize test based on direction of traffic. | tests/acl/test_acl.py | direction | KostiantynYarovyiBf/sonic-mgmt | python | @pytest.fixture(params=['downlink->uplink', 'uplink->downlink'])
def direction(self, request):
return request.param |
@pytest.fixture(autouse=True)
def get_src_port(self, setup, direction):
'Get a source port for the current test.'
src_ports = (setup['downstream_port_ids'] if (direction == 'downlink->uplink') else setup['upstream_port_ids'])
src_port = random.choice(src_ports)
logger.info('Selected source port {}'.format(src_port))
self.src_port = src_port | -910,017,347,846,818,700 | Get a source port for the current test. | tests/acl/test_acl.py | get_src_port | KostiantynYarovyiBf/sonic-mgmt | python | @pytest.fixture(autouse=True)
def get_src_port(self, setup, direction):
src_ports = (setup['downstream_port_ids'] if (direction == 'downlink->uplink') else setup['upstream_port_ids'])
src_port = random.choice(src_ports)
logger.info('Selected source port {}'.format(src_port))
self.src_port = src_port |
def get_dst_ports(self, setup, direction):
'Get the set of possible destination ports for the current test.'
return (setup['upstream_port_ids'] if (direction == 'downlink->uplink') else setup['downstream_port_ids']) | -7,723,670,512,636,729,000 | Get the set of possible destination ports for the current test. | tests/acl/test_acl.py | get_dst_ports | KostiantynYarovyiBf/sonic-mgmt | python | def get_dst_ports(self, setup, direction):
return (setup['upstream_port_ids'] if (direction == 'downlink->uplink') else setup['downstream_port_ids']) |
def get_dst_ip(self, direction, ip_version):
'Get the default destination IP for the current test.'
return (UPSTREAM_DST_IP[ip_version] if (direction == 'downlink->uplink') else DOWNSTREAM_DST_IP[ip_version]) | 7,964,747,422,421,615,000 | Get the default destination IP for the current test. | tests/acl/test_acl.py | get_dst_ip | KostiantynYarovyiBf/sonic-mgmt | python | def get_dst_ip(self, direction, ip_version):
return (UPSTREAM_DST_IP[ip_version] if (direction == 'downlink->uplink') else DOWNSTREAM_DST_IP[ip_version]) |
def tcp_packet(self, setup, direction, ptfadapter, ip_version, src_ip=None, dst_ip=None, proto=None, sport=17185, dport=81, flags=None):
'Generate a TCP packet for testing.'
src_ip = (src_ip or DEFAULT_SRC_IP[ip_version])
dst_ip = (dst_ip or self.get_dst_ip(direction, ip_version))
if (ip_version == 'ipv4'):
pkt = testutils.simple_tcp_packet(eth_dst=setup['destination_mac'][direction][self.src_port], eth_src=ptfadapter.dataplane.get_mac(0, 0), ip_dst=dst_ip, ip_src=src_ip, tcp_sport=sport, tcp_dport=dport, ip_ttl=64)
if proto:
pkt['IP'].proto = proto
else:
pkt = testutils.simple_tcpv6_packet(eth_dst=setup['destination_mac'][direction][self.src_port], eth_src=ptfadapter.dataplane.get_mac(0, 0), ipv6_dst=dst_ip, ipv6_src=src_ip, tcp_sport=sport, tcp_dport=dport, ipv6_hlim=64)
if proto:
pkt['IPv6'].nh = proto
if flags:
pkt['TCP'].flags = flags
return pkt | 7,023,518,864,380,091,000 | Generate a TCP packet for testing. | tests/acl/test_acl.py | tcp_packet | KostiantynYarovyiBf/sonic-mgmt | python | def tcp_packet(self, setup, direction, ptfadapter, ip_version, src_ip=None, dst_ip=None, proto=None, sport=17185, dport=81, flags=None):
src_ip = (src_ip or DEFAULT_SRC_IP[ip_version])
dst_ip = (dst_ip or self.get_dst_ip(direction, ip_version))
if (ip_version == 'ipv4'):
pkt = testutils.simple_tcp_packet(eth_dst=setup['destination_mac'][direction][self.src_port], eth_src=ptfadapter.dataplane.get_mac(0, 0), ip_dst=dst_ip, ip_src=src_ip, tcp_sport=sport, tcp_dport=dport, ip_ttl=64)
if proto:
pkt['IP'].proto = proto
else:
pkt = testutils.simple_tcpv6_packet(eth_dst=setup['destination_mac'][direction][self.src_port], eth_src=ptfadapter.dataplane.get_mac(0, 0), ipv6_dst=dst_ip, ipv6_src=src_ip, tcp_sport=sport, tcp_dport=dport, ipv6_hlim=64)
if proto:
pkt['IPv6'].nh = proto
if flags:
pkt['TCP'].flags = flags
return pkt |
def udp_packet(self, setup, direction, ptfadapter, ip_version, src_ip=None, dst_ip=None, sport=1234, dport=80):
'Generate a UDP packet for testing.'
src_ip = (src_ip or DEFAULT_SRC_IP[ip_version])
dst_ip = (dst_ip or self.get_dst_ip(direction, ip_version))
if (ip_version == 'ipv4'):
return testutils.simple_udp_packet(eth_dst=setup['destination_mac'][direction][self.src_port], eth_src=ptfadapter.dataplane.get_mac(0, 0), ip_dst=dst_ip, ip_src=src_ip, udp_sport=sport, udp_dport=dport, ip_ttl=64)
else:
return testutils.simple_udpv6_packet(eth_dst=setup['destination_mac'][direction][self.src_port], eth_src=ptfadapter.dataplane.get_mac(0, 0), ipv6_dst=dst_ip, ipv6_src=src_ip, udp_sport=sport, udp_dport=dport, ipv6_hlim=64) | 7,555,449,266,937,661,000 | Generate a UDP packet for testing. | tests/acl/test_acl.py | udp_packet | KostiantynYarovyiBf/sonic-mgmt | python | def udp_packet(self, setup, direction, ptfadapter, ip_version, src_ip=None, dst_ip=None, sport=1234, dport=80):
src_ip = (src_ip or DEFAULT_SRC_IP[ip_version])
dst_ip = (dst_ip or self.get_dst_ip(direction, ip_version))
if (ip_version == 'ipv4'):
return testutils.simple_udp_packet(eth_dst=setup['destination_mac'][direction][self.src_port], eth_src=ptfadapter.dataplane.get_mac(0, 0), ip_dst=dst_ip, ip_src=src_ip, udp_sport=sport, udp_dport=dport, ip_ttl=64)
else:
return testutils.simple_udpv6_packet(eth_dst=setup['destination_mac'][direction][self.src_port], eth_src=ptfadapter.dataplane.get_mac(0, 0), ipv6_dst=dst_ip, ipv6_src=src_ip, udp_sport=sport, udp_dport=dport, ipv6_hlim=64) |
def icmp_packet(self, setup, direction, ptfadapter, ip_version, src_ip=None, dst_ip=None, icmp_type=8, icmp_code=0):
'Generate an ICMP packet for testing.'
src_ip = (src_ip or DEFAULT_SRC_IP[ip_version])
dst_ip = (dst_ip or self.get_dst_ip(direction, ip_version))
if (ip_version == 'ipv4'):
return testutils.simple_icmp_packet(eth_dst=setup['destination_mac'][direction][self.src_port], eth_src=ptfadapter.dataplane.get_mac(0, 0), ip_dst=dst_ip, ip_src=src_ip, icmp_type=icmp_type, icmp_code=icmp_code, ip_ttl=64)
else:
return testutils.simple_icmpv6_packet(eth_dst=setup['destination_mac'][direction][self.src_port], eth_src=ptfadapter.dataplane.get_mac(0, 0), ipv6_dst=dst_ip, ipv6_src=src_ip, icmp_type=icmp_type, icmp_code=icmp_code, ipv6_hlim=64) | -4,280,413,460,718,121,000 | Generate an ICMP packet for testing. | tests/acl/test_acl.py | icmp_packet | KostiantynYarovyiBf/sonic-mgmt | python | def icmp_packet(self, setup, direction, ptfadapter, ip_version, src_ip=None, dst_ip=None, icmp_type=8, icmp_code=0):
src_ip = (src_ip or DEFAULT_SRC_IP[ip_version])
dst_ip = (dst_ip or self.get_dst_ip(direction, ip_version))
if (ip_version == 'ipv4'):
return testutils.simple_icmp_packet(eth_dst=setup['destination_mac'][direction][self.src_port], eth_src=ptfadapter.dataplane.get_mac(0, 0), ip_dst=dst_ip, ip_src=src_ip, icmp_type=icmp_type, icmp_code=icmp_code, ip_ttl=64)
else:
return testutils.simple_icmpv6_packet(eth_dst=setup['destination_mac'][direction][self.src_port], eth_src=ptfadapter.dataplane.get_mac(0, 0), ipv6_dst=dst_ip, ipv6_src=src_ip, icmp_type=icmp_type, icmp_code=icmp_code, ipv6_hlim=64) |
def expected_mask_routed_packet(self, pkt, ip_version):
'Generate the expected mask for a routed packet.'
exp_pkt = pkt.copy()
exp_pkt = mask.Mask(exp_pkt)
exp_pkt.set_do_not_care_scapy(packet.Ether, 'dst')
exp_pkt.set_do_not_care_scapy(packet.Ether, 'src')
if (ip_version == 'ipv4'):
exp_pkt.set_do_not_care_scapy(packet.IP, 'chksum')
exp_pkt.set_do_not_care_scapy(packet.IP, 'ttl')
else:
exp_pkt.set_do_not_care_scapy(packet.IPv6, 'hlim')
return exp_pkt | -3,256,258,984,992,967,000 | Generate the expected mask for a routed packet. | tests/acl/test_acl.py | expected_mask_routed_packet | KostiantynYarovyiBf/sonic-mgmt | python | def expected_mask_routed_packet(self, pkt, ip_version):
exp_pkt = pkt.copy()
exp_pkt = mask.Mask(exp_pkt)
exp_pkt.set_do_not_care_scapy(packet.Ether, 'dst')
exp_pkt.set_do_not_care_scapy(packet.Ether, 'src')
if (ip_version == 'ipv4'):
exp_pkt.set_do_not_care_scapy(packet.IP, 'chksum')
exp_pkt.set_do_not_care_scapy(packet.IP, 'ttl')
else:
exp_pkt.set_do_not_care_scapy(packet.IPv6, 'hlim')
return exp_pkt |
def test_ingress_unmatched_blocked(self, setup, direction, ptfadapter, ip_version, stage):
'Verify that unmatched packets are dropped for ingress.'
if (stage == 'egress'):
pytest.skip('Only run for ingress')
pkt = self.tcp_packet(setup, direction, ptfadapter, ip_version)
self._verify_acl_traffic(setup, direction, ptfadapter, pkt, True, ip_version) | 2,178,950,070,650,520,800 | Verify that unmatched packets are dropped for ingress. | tests/acl/test_acl.py | test_ingress_unmatched_blocked | KostiantynYarovyiBf/sonic-mgmt | python | def test_ingress_unmatched_blocked(self, setup, direction, ptfadapter, ip_version, stage):
if (stage == 'egress'):
pytest.skip('Only run for ingress')
pkt = self.tcp_packet(setup, direction, ptfadapter, ip_version)
self._verify_acl_traffic(setup, direction, ptfadapter, pkt, True, ip_version) |
def test_egress_unmatched_forwarded(self, setup, direction, ptfadapter, ip_version, stage):
'Verify that default egress rule allow all traffics'
if (stage == 'ingress'):
pytest.skip('Only run for egress')
pkt = self.tcp_packet(setup, direction, ptfadapter, ip_version)
self._verify_acl_traffic(setup, direction, ptfadapter, pkt, False, ip_version) | 5,269,488,178,567,495,000 | Verify that default egress rule allow all traffics | tests/acl/test_acl.py | test_egress_unmatched_forwarded | KostiantynYarovyiBf/sonic-mgmt | python | def test_egress_unmatched_forwarded(self, setup, direction, ptfadapter, ip_version, stage):
if (stage == 'ingress'):
pytest.skip('Only run for egress')
pkt = self.tcp_packet(setup, direction, ptfadapter, ip_version)
self._verify_acl_traffic(setup, direction, ptfadapter, pkt, False, ip_version) |
def test_source_ip_match_forwarded(self, setup, direction, ptfadapter, counters_sanity_check, ip_version):
'Verify that we can match and forward a packet on source IP.'
src_ip = ('0.0.0.0' if (ip_version == 'ipv4') else '0000:0000:0000:0000:0000:0000:0000:0000')
pkt = self.tcp_packet(setup, direction, ptfadapter, ip_version, src_ip=src_ip)
self._verify_acl_traffic(setup, direction, ptfadapter, pkt, False, ip_version)
counters_sanity_check.append(1) | 5,713,231,115,088,048,000 | Verify that we can match and forward a packet on source IP. | tests/acl/test_acl.py | test_source_ip_match_forwarded | KostiantynYarovyiBf/sonic-mgmt | python | def test_source_ip_match_forwarded(self, setup, direction, ptfadapter, counters_sanity_check, ip_version):
src_ip = ('0.0.0.0' if (ip_version == 'ipv4') else '0000:0000:0000:0000:0000:0000:0000:0000')
pkt = self.tcp_packet(setup, direction, ptfadapter, ip_version, src_ip=src_ip)
self._verify_acl_traffic(setup, direction, ptfadapter, pkt, False, ip_version)
counters_sanity_check.append(1) |
def test_rules_priority_forwarded(self, setup, direction, ptfadapter, counters_sanity_check, ip_version):
'Verify that we respect rule priorites in the forwarding case.'
src_ip = ('0.0.0.0' if (ip_version == 'ipv4') else '0000:0000:0000:0000:0000:0000:0000:0000')
pkt = self.tcp_packet(setup, direction, ptfadapter, ip_version, src_ip=src_ip)
self._verify_acl_traffic(setup, direction, ptfadapter, pkt, False, ip_version)
counters_sanity_check.append(20) | -8,105,410,999,960,000,000 | Verify that we respect rule priorites in the forwarding case. | tests/acl/test_acl.py | test_rules_priority_forwarded | KostiantynYarovyiBf/sonic-mgmt | python | def test_rules_priority_forwarded(self, setup, direction, ptfadapter, counters_sanity_check, ip_version):
src_ip = ('0.0.0.0' if (ip_version == 'ipv4') else '0000:0000:0000:0000:0000:0000:0000:0000')
pkt = self.tcp_packet(setup, direction, ptfadapter, ip_version, src_ip=src_ip)
self._verify_acl_traffic(setup, direction, ptfadapter, pkt, False, ip_version)
counters_sanity_check.append(20) |
def test_rules_priority_dropped(self, setup, direction, ptfadapter, counters_sanity_check, ip_version):
'Verify that we respect rule priorites in the drop case.'
src_ip = ('0.0.0.0' if (ip_version == 'ipv4') else '0000:0000:0000:0000:0000:0000:0000:0000')
pkt = self.tcp_packet(setup, direction, ptfadapter, ip_version, src_ip=src_ip)
self._verify_acl_traffic(setup, direction, ptfadapter, pkt, True, ip_version)
counters_sanity_check.append(7) | 959,052,328,267,444,600 | Verify that we respect rule priorites in the drop case. | tests/acl/test_acl.py | test_rules_priority_dropped | KostiantynYarovyiBf/sonic-mgmt | python | def test_rules_priority_dropped(self, setup, direction, ptfadapter, counters_sanity_check, ip_version):
src_ip = ('0.0.0.0' if (ip_version == 'ipv4') else '0000:0000:0000:0000:0000:0000:0000:0000')
pkt = self.tcp_packet(setup, direction, ptfadapter, ip_version, src_ip=src_ip)
self._verify_acl_traffic(setup, direction, ptfadapter, pkt, True, ip_version)
counters_sanity_check.append(7) |
def test_dest_ip_match_forwarded(self, setup, direction, ptfadapter, counters_sanity_check, ip_version):
'Verify that we can match and forward a packet on destination IP.'
dst_ip = (DOWNSTREAM_IP_TO_ALLOW[ip_version] if (direction == 'uplink->downlink') else UPSTREAM_IP_TO_ALLOW[ip_version])
pkt = self.tcp_packet(setup, direction, ptfadapter, ip_version, dst_ip=dst_ip)
self._verify_acl_traffic(setup, direction, ptfadapter, pkt, False, ip_version)
counters_sanity_check.append((2 if (direction == 'uplink->downlink') else 3)) | 7,955,340,746,633,166,000 | Verify that we can match and forward a packet on destination IP. | tests/acl/test_acl.py | test_dest_ip_match_forwarded | KostiantynYarovyiBf/sonic-mgmt | python | def test_dest_ip_match_forwarded(self, setup, direction, ptfadapter, counters_sanity_check, ip_version):
dst_ip = (DOWNSTREAM_IP_TO_ALLOW[ip_version] if (direction == 'uplink->downlink') else UPSTREAM_IP_TO_ALLOW[ip_version])
pkt = self.tcp_packet(setup, direction, ptfadapter, ip_version, dst_ip=dst_ip)
self._verify_acl_traffic(setup, direction, ptfadapter, pkt, False, ip_version)
counters_sanity_check.append((2 if (direction == 'uplink->downlink') else 3)) |
def test_dest_ip_match_dropped(self, setup, direction, ptfadapter, counters_sanity_check, ip_version):
'Verify that we can match and drop a packet on destination IP.'
dst_ip = (DOWNSTREAM_IP_TO_BLOCK[ip_version] if (direction == 'uplink->downlink') else UPSTREAM_IP_TO_BLOCK[ip_version])
pkt = self.tcp_packet(setup, direction, ptfadapter, ip_version, dst_ip=dst_ip)
self._verify_acl_traffic(setup, direction, ptfadapter, pkt, True, ip_version)
counters_sanity_check.append((15 if (direction == 'uplink->downlink') else 16)) | -6,354,044,041,571,471,000 | Verify that we can match and drop a packet on destination IP. | tests/acl/test_acl.py | test_dest_ip_match_dropped | KostiantynYarovyiBf/sonic-mgmt | python | def test_dest_ip_match_dropped(self, setup, direction, ptfadapter, counters_sanity_check, ip_version):
dst_ip = (DOWNSTREAM_IP_TO_BLOCK[ip_version] if (direction == 'uplink->downlink') else UPSTREAM_IP_TO_BLOCK[ip_version])
pkt = self.tcp_packet(setup, direction, ptfadapter, ip_version, dst_ip=dst_ip)
self._verify_acl_traffic(setup, direction, ptfadapter, pkt, True, ip_version)
counters_sanity_check.append((15 if (direction == 'uplink->downlink') else 16)) |
def test_source_ip_match_dropped(self, setup, direction, ptfadapter, counters_sanity_check, ip_version):
'Verify that we can match and drop a packet on source IP.'
src_ip = ('0.0.0.0' if (ip_version == 'ipv4') else '0000:0000:0000:0000:0000:0000:0000:0000')
pkt = self.tcp_packet(setup, direction, ptfadapter, ip_version, src_ip=src_ip)
self._verify_acl_traffic(setup, direction, ptfadapter, pkt, True, ip_version)
counters_sanity_check.append(14) | 5,289,088,510,342,193,000 | Verify that we can match and drop a packet on source IP. | tests/acl/test_acl.py | test_source_ip_match_dropped | KostiantynYarovyiBf/sonic-mgmt | python | def test_source_ip_match_dropped(self, setup, direction, ptfadapter, counters_sanity_check, ip_version):
src_ip = ('0.0.0.0' if (ip_version == 'ipv4') else '0000:0000:0000:0000:0000:0000:0000:0000')
pkt = self.tcp_packet(setup, direction, ptfadapter, ip_version, src_ip=src_ip)
self._verify_acl_traffic(setup, direction, ptfadapter, pkt, True, ip_version)
counters_sanity_check.append(14) |
def test_udp_source_ip_match_forwarded(self, setup, direction, ptfadapter, counters_sanity_check, ip_version):
'Verify that we can match and forward a UDP packet on source IP.'
src_ip = ('0.0.0.0' if (ip_version == 'ipv4') else '0000:0000:0000:0000:0000:0000:0000:0000')
pkt = self.udp_packet(setup, direction, ptfadapter, ip_version, src_ip=src_ip)
self._verify_acl_traffic(setup, direction, ptfadapter, pkt, False, ip_version)
counters_sanity_check.append(13) | -7,716,854,753,495,035,000 | Verify that we can match and forward a UDP packet on source IP. | tests/acl/test_acl.py | test_udp_source_ip_match_forwarded | KostiantynYarovyiBf/sonic-mgmt | python | def test_udp_source_ip_match_forwarded(self, setup, direction, ptfadapter, counters_sanity_check, ip_version):
src_ip = ('0.0.0.0' if (ip_version == 'ipv4') else '0000:0000:0000:0000:0000:0000:0000:0000')
pkt = self.udp_packet(setup, direction, ptfadapter, ip_version, src_ip=src_ip)
self._verify_acl_traffic(setup, direction, ptfadapter, pkt, False, ip_version)
counters_sanity_check.append(13) |
def test_udp_source_ip_match_dropped(self, setup, direction, ptfadapter, counters_sanity_check, ip_version):
'Verify that we can match and drop a UDP packet on source IP.'
src_ip = ('0.0.0.0' if (ip_version == 'ipv4') else '0000:0000:0000:0000:0000:0000:0000:0000')
pkt = self.udp_packet(setup, direction, ptfadapter, ip_version, src_ip=src_ip)
self._verify_acl_traffic(setup, direction, ptfadapter, pkt, True, ip_version)
counters_sanity_check.append(26) | 3,425,280,445,662,462,500 | Verify that we can match and drop a UDP packet on source IP. | tests/acl/test_acl.py | test_udp_source_ip_match_dropped | KostiantynYarovyiBf/sonic-mgmt | python | def test_udp_source_ip_match_dropped(self, setup, direction, ptfadapter, counters_sanity_check, ip_version):
src_ip = ('0.0.0.0' if (ip_version == 'ipv4') else '0000:0000:0000:0000:0000:0000:0000:0000')
pkt = self.udp_packet(setup, direction, ptfadapter, ip_version, src_ip=src_ip)
self._verify_acl_traffic(setup, direction, ptfadapter, pkt, True, ip_version)
counters_sanity_check.append(26) |
def test_icmp_source_ip_match_dropped(self, setup, direction, ptfadapter, counters_sanity_check, ip_version):
'Verify that we can match and drop an ICMP packet on source IP.'
src_ip = ('0.0.0.0' if (ip_version == 'ipv4') else '0000:0000:0000:0000:0000:0000:0000:0000')
pkt = self.icmp_packet(setup, direction, ptfadapter, ip_version, src_ip=src_ip)
self._verify_acl_traffic(setup, direction, ptfadapter, pkt, True, ip_version)
counters_sanity_check.append(25) | -7,805,632,420,765,763,000 | Verify that we can match and drop an ICMP packet on source IP. | tests/acl/test_acl.py | test_icmp_source_ip_match_dropped | KostiantynYarovyiBf/sonic-mgmt | python | def test_icmp_source_ip_match_dropped(self, setup, direction, ptfadapter, counters_sanity_check, ip_version):
src_ip = ('0.0.0.0' if (ip_version == 'ipv4') else '0000:0000:0000:0000:0000:0000:0000:0000')
pkt = self.icmp_packet(setup, direction, ptfadapter, ip_version, src_ip=src_ip)
self._verify_acl_traffic(setup, direction, ptfadapter, pkt, True, ip_version)
counters_sanity_check.append(25) |
Subsets and Splits