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src/agent/ovs/ofctl_manager.py | SSICLOPS/interconnection-agent | 0 | 173057 | """
BSD 3-Clause License
Copyright (c) 2018, <NAME>, Aalto University, Finland
All rights reserved.
Redistribution and use in source and binary forms, with or without
modification, are permitted provided that the following conditions are met:
* Redistributions of source code must retain the above copyright notice, this
list of conditions and the following disclaimer.
* Redistributions in binary form must reproduce the above copyright notice,
this list of conditions and the following disclaimer in the documentation
and/or other materials provided with the distribution.
* Neither the name of the copyright holder nor the names of its
contributors may be used to endorse or promote products derived from
this software without specific prior written permission.
THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS"
AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE
IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE
DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE LIABLE
FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL
DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR
SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER
CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY,
OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE
OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
"""
import logging
import utils
from helpers_n_wrappers import utils3
TABLE_START = 0
TABLE_IN_MPTCP = 10
TABLE_VNI_SPLIT = 20
TABLE_VLAN_CHECK = 21
TABLE_LEARNING = 22
TABLE_TYPE_SPLIT = 30
TABLE_UNICAST_LEARNT = 31
TABLE_UNICAST_APPLY = 32
TABLE_MULTICAST = 35
TABLE_SPLIT_MPTCP = 40
TABLE_APPLY_MPTCP = 41
TABLE_ROUTING = 42
TABLE_MPTCP_VLAN = 2
TABLE_MPTCP_LEARN = 1
TABLE_MPTCP_LEARNT = 3
TABLE_MPTCP_FORWARD = 4
def add_flow(switch, *args, strict=True):
apply(switch, "mod-flows", *args, strict=strict)
def del_flow(switch, *args, strict=True):
apply(switch, "del-flows", *args, strict=strict)
def apply(switch, action, *args, strict=True):
cmd = ["ovs-ofctl"]
cmd.append(action)
if strict:
cmd.append("--strict")
cmd.append(switch)
if args:
cmd.append(" ".join(args))
utils.execute_list(cmd)
class Ofctl_manager(object):
def __init__(self, **kwargs):
utils3.set_attributes(self, override = True, **kwargs)
def init_flows(self):
self.init_tun()
self.init_in()
self.init_out()
def init_tun(self):
del_flow(self.dp_tun, strict=False)
add_flow(self.dp_tun,
"table={}, priority=0, actions=drop".format(TABLE_START)
)
add_flow(self.dp_tun, "table={}, priority=10,".format(TABLE_START),
"in_port={},".format(self.patch_tun_id),
"actions=goto_table:{}".format(TABLE_TYPE_SPLIT)
)
add_flow(self.dp_tun,"table={}, priority=0,".format(TABLE_VNI_SPLIT),
"actions=goto_table:{}".format(TABLE_ROUTING)
)
add_flow(self.dp_tun, "table={}, priority=10,".format(TABLE_VNI_SPLIT),
"tun_id={}/0xfff,".format(self.self_vni),
"actions=goto_table:{}".format(TABLE_VLAN_CHECK)
)
add_flow(self.dp_tun,"table={}, priority=0,".format(TABLE_VLAN_CHECK),
"actions=drop"
)
add_flow(self.dp_tun,
"table={}, priority=0, actions=learn(".format(TABLE_LEARNING),
"table={}, hard_timeout=300,".format(TABLE_UNICAST_LEARNT),
"priority=10, NXM_OF_VLAN_TCI[0..11]",
"NXM_OF_ETH_DST[]=NXM_OF_ETH_SRC[],",
"load:NXM_NX_REG0[]->NXM_NX_REG0[],",
"load:NXM_NX_TUN_ID[0..11]->NXM_NX_REG2[12..23],",
"load:NXM_NX_TUN_ID[12..23]->NXM_NX_REG2[0..11]),",
"output:{}".format(self.patch_tun_id)
)
add_flow(self.dp_tun, "table={}, priority=10,".format(TABLE_TYPE_SPLIT),
"dl_dst=00:00:00:00:00:00/01:00:00:00:00:00,",
"actions=resubmit(,{}),resubmit(,{})".format(
TABLE_UNICAST_LEARNT, TABLE_UNICAST_APPLY
)
)
add_flow(self.dp_tun,"table={}, priority=10,".format(TABLE_TYPE_SPLIT),
"dl_dst=01:00:00:00:00:00/01:00:00:00:00:00,",
"actions=goto_table:{}".format(TABLE_MULTICAST)
)
add_flow(self.dp_tun, "table={},".format(TABLE_UNICAST_LEARNT),
"priority=0, actions=drop"
)
add_flow(self.dp_tun, "table={},".format(TABLE_UNICAST_APPLY),
"priority=20, reg0=0, actions=goto_table:{}".format(TABLE_MULTICAST)
)
add_flow(self.dp_tun, "table={},".format(TABLE_UNICAST_APPLY),
"priority=10,",
"actions=move:NXM_NX_REG0[0..11]->NXM_OF_VLAN_TCI[0..11],",
"move:NXM_NX_REG2[0..23]->NXM_NX_TUN_ID[0..23],",
"goto_table:{}".format(TABLE_SPLIT_MPTCP)
)
add_flow(self.dp_tun, "table={}, priority=0,".format(TABLE_MULTICAST),
"actions=drop"
)
add_flow(self.dp_tun, "table={}, priority=0,".format(TABLE_SPLIT_MPTCP),
"actions=goto_table:{}".format(TABLE_ROUTING)
)
add_flow(self.dp_tun, "table={}, priority=0,".format(TABLE_APPLY_MPTCP),
"actions=goto_table:{}".format(TABLE_ROUTING)
)
add_flow(self.dp_tun, "table={}, priority=0,".format(TABLE_ROUTING),
"actions=drop"
)
def init_in(self):
del_flow(self.dp_in, strict=False)
add_flow(self.dp_in, "table={}, priority=0,".format(TABLE_START),
"actions=normal"
)
def init_out(self):
del_flow(self.dp_out, strict=False)
add_flow(self.dp_out, "table={}, priority=0,".format(TABLE_START),
"actions=normal"
)
def init_mptcp(self, **kwargs):
utils3.set_attributes(self, override=True, **kwargs)
del_flow(self.dp_mptcp, strict=False)
add_flow(self.dp_mptcp, "table={}, priority=0,".format(TABLE_START),
"actions=goto_table:{}".format(TABLE_MPTCP_VLAN)
)
add_flow(self.dp_mptcp, "table={}, priority=10,".format(TABLE_START),
"in_port={},".format(self.patch_mptcp_port),
"actions=goto_table:{}".format(TABLE_MPTCP_LEARN)
)
add_flow(self.dp_mptcp, "table={},".format(TABLE_MPTCP_LEARN),
"priority=10, tcp,",
"actions=goto_table:{}".format(TABLE_MPTCP_FORWARD)
)
add_flow(self.dp_mptcp, "table={},".format(TABLE_MPTCP_LEARN),
"priority=10, arp,",
"actions=learn(table={}, priority=10,".format(TABLE_MPTCP_LEARNT),
"eth_src=E1:8E:36:8C:F6:0D, eth_type=0x0800,",
"NXM_OF_IP_DST[]=NXM_OF_ARP_SPA[],",
"load:NXM_NX_ARP_SHA[]->NXM_OF_ETH_DST[],",
"output:NXM_OF_IN_PORT[])"
)
add_flow(self.dp_mptcp, "table={},".format(TABLE_MPTCP_LEARN),
"priority=0, actions=drop"
)
add_flow(self.dp_mptcp, "table={},".format(TABLE_MPTCP_VLAN),
"priority=0, actions=drop"
)
add_flow(self.dp_mptcp, "table={},".format(TABLE_MPTCP_LEARNT),
"priority=0, actions=drop"
)
add_flow(self.dp_mptcp, "table={},".format(TABLE_MPTCP_FORWARD),
"priority=0, actions=drop"
)
add_flow(self.dp_tun, "table={}, priority=10,".format(TABLE_START),
"in_port={},".format(self.patch_tun_port_mptcp),
"actions=goto_table:{}".format(TABLE_IN_MPTCP)
)
add_flow(self.dp_tun, "table={}, priority=0,".format(TABLE_IN_MPTCP),
"actions=drop"
)
def add_proxy(self, port, vni, eth_addr):
add_flow(self.dp_mptcp, "table={},".format(TABLE_MPTCP_VLAN),
"priority=10, in_port={}".format(port),
"actions=mod_vlan_vid:{},".format(vni),
"goto_table:{}".format(TABLE_MPTCP_LEARNT)
)
add_flow(self.dp_mptcp, "table={},".format(TABLE_MPTCP_FORWARD),
"priority=10, vlan_vid=0x1{:03x}".format(vni),
"actions=set_field:{}->eth_dst,strip_vlan,".format(eth_addr),
"output:{}".format(port)
)
add_flow(self.dp_tun,"table={}, priority=10,".format(TABLE_APPLY_MPTCP),
"tcp, tun_id=0x{:x}/0xfff".format(vni),
"actions=move:NXM_OF_VLAN_TCI[0..11]->NXM_NX_PKT_MARK[0..11]",
"move:NXM_NX_TUN_ID[0..11]->NXM_OF_VLAN_TCI[0..11]",
"output:{}".format(self.patch_tun_port_mptcp)
)
add_flow(self.dp_tun,"table={}, priority=10,".format(TABLE_APPLY_MPTCP),
"arp, tun_id=0x{:x}/0xfff".format(vni),
"actions=resubmit(,{}),".format(TABLE_ROUTING),
"move:NXM_OF_VLAN_TCI[0..11]->NXM_NX_PKT_MARK[0..11]",
"move:NXM_NX_TUN_ID[0..11]->NXM_OF_VLAN_TCI[0..11]",
"output:{}".format(self.patch_tun_port_mptcp)
)
add_flow(self.dp_tun, "table={}, priority=10,".format(TABLE_IN_MPTCP),
"vlan_vid=0x1{:03x}/0x1fff".format(vni),
"actions=move:NXM_OF_VLAN_TCI[0..11]->NXM_NX_TUN_ID[12..23]",
"move:NXM_NX_PKT_MARK[0..11]->NXM_OF_VLAN_TCI[0..11]",
"load:{}->NXM_NX_TUN_ID[0..11]".format(self.self_vni),
"goto_table:{}".format(TABLE_VLAN_CHECK)
)
def del_proxy(self, port, vni):
del_flow(self.dp_mptcp, "table={},".format(TABLE_MPTCP_VLAN),
"priority=10, in_port={}".format(port)
)
del_flow(self.dp_mptcp, "table={},".format(TABLE_MPTCP_FORWARD),
"priority=10, vlan_vid=0x1{:03x}".format(vni)
)
del_flow(self.dp_tun,"table={}, priority=10,".format(TABLE_APPLY_MPTCP),
"tcp, tun_id=0x{:x}/0xfff".format(vni)
)
del_flow(self.dp_tun,"table={}, priority=10,".format(TABLE_APPLY_MPTCP),
"arp, tun_id=0x{:x}/0xfff".format(vni),
)
del_flow(self.dp_tun, "table={}, priority=10,".format(TABLE_IN_MPTCP),
"vlan_vid=0x1{:03x}/0x1fff".format(vni),
)
def add_tunnel(self, port_id):
add_flow(self.dp_tun, "table={}, priority=10,".format(TABLE_START),
"in_port={}, actions=goto_table:{}".format(port_id, TABLE_VNI_SPLIT)
)
def del_tunnel(self, port_id):
del_flow(self.dp_tun, "table={},".format(TABLE_START),
"priority=10, in_port={}".format(port_id)
)
def add_route(self, vni, port_id):
add_flow(self.dp_tun, "table={}, priority=10,".format(TABLE_ROUTING),
"tun_id={}/0xfff, actions=output:{}".format(vni, port_id)
)
def del_route(self, vni):
del_flow(self.dp_tun, "table={},".format(TABLE_ROUTING),
"priority=10, tun_id={}/0xfff".format(vni)
)
def add_expansion(self, expansion, expansions_list, local_vlan):
add_flow(self.dp_tun, "table={}, priority=10,".format(TABLE_VLAN_CHECK),
"tun_id=0x{:03x}{:03x},".format(expansion["peer_vni"],self.self_vni),
"vlan_vid=0x1{:03x}/0x1fff, ".format(expansion["intercloud_id"]),
"actions=mod_vlan_vid:{},".format(local_vlan),
"load:{}->NXM_NX_REG0[],".format(hex(expansion["intercloud_id"])),
"goto_table:{}".format(TABLE_LEARNING)
)
if expansion["mptcp"]:
add_flow(self.dp_tun, "table={},".format(TABLE_SPLIT_MPTCP),
"priority=10, tun_id=0x{:03x}{:03x},".format(self.self_vni,
expansion["peer_vni"]
),
"vlan_vid=0x1{:03x}/0x1fff,".format(expansion["intercloud_id"]),
"actions=goto_table:{}".format(TABLE_APPLY_MPTCP)
)
actions=[]
for expansion_mult in expansions_list:
actions.append("mod_vlan_vid:{}".format(expansion_mult["intercloud_id"]))
actions.append("set_field:0x{:03x}{:03x}->tun_id".format(self.self_vni,
expansion_mult["peer_vni"]
))
actions.append("resubmit(,{})".format(TABLE_SPLIT_MPTCP))
actions_str = ",".join(actions)
add_flow(self.dp_tun, "table={}, priority=10,".format(TABLE_MULTICAST),
"vlan_vid=0x1{:03x}/0x1fff,".format(local_vlan),
"actions={}".format(actions_str)
)
def del_expansion(self, expansion, expansions_list, local_vlan):
del_flow(self.dp_tun, "table={}, priority=10,".format(TABLE_VLAN_CHECK),
"tun_id=0x{:03x}{:03x},".format(expansion["peer_vni"],self.self_vni),
"vlan_vid=0x1{:03x}/0x1fff, ".format(expansion["intercloud_id"]),
)
if expansion["mptcp"]:
del_flow(self.dp_tun, "table={},".format(TABLE_SPLIT_MPTCP),
"priority=10, tun_id=0x{:03x}{:03x},".format(self.self_vni,
expansion["peer_vni"]
),
"vlan_vid=0x1{:03x}/0x1fff,".format(expansion["intercloud_id"]),
)
actions=[]
for expansion_mult in expansions_list:
actions.append("mod_vlan_vid:{}".format(expansion_mult["intercloud_id"]))
actions.append("set_field:0x{:03x}{:03x}->tun_id".format(self.self_vni,
expansion_mult["peer_vni"]
))
actions.append("resubmit(,{})".format(TABLE_SPLIT_MPTCP))
actions_str = ",".join(actions)
add_flow(self.dp_tun, "table={}, priority=10,".format(TABLE_MULTICAST),
"vlan_vid=0x1{:03x}/0x1fff,".format(local_vlan),
"actions={}".format(actions_str)
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] |
arrowhead/decorators.py | zyga/arrowhead | 2 | 82707 | import functools
import inspect
import types
from arrowhead.core import ErrorArrow
from arrowhead.core import NormalArrow
from arrowhead.core import Step
from arrowhead.core import ValueArrow
def step(func_or_label=None, **kwargs):
"""
Decorator for converting functions to steps.
This decorator can be applied in a number of ways::
@step
def do_stuff(self):
pass
@step
def do_other_stuff(self):
pass
Without any arguments it just creates a step that when executed, will call
the decorated function. Typically steps will also need to be decorated with
the @arrow decorator to form a sensible graph but in very simple cases one
can rely on the top-to-bottom implicit arrows.
With arguments, one can specify several additional details. The most
important are the initial and accepting flags::
@step(initial=True)
def start(self):
pass
@step(accepting=True)
def stop(self):
pass
Within one flow exactly one step must be initial. A ProgrammingError
exception is raised if that is not the case. All exceptions of this type
carry additional information to help diagnose the problem.
All steps have a label. By default, if you don't provide one and the
decorated function doesn't have a docstring then function name is used as a
label. Labels are very important as they can be used to 'address' steps
throughout the graph. Labels can obviously be specified explicitly::
@step(label="Start your engines!")
def start_engines(self):
pass
As a convention, the first line of the docstring becomes the implicit label
of any step, unless overridden with an explicit label. This encourages
documentation and improves the readability of the built-in flow debugger.
@step
def load_all_data(self):
'''
load all data from the disk
This long description is not a part of the label. Luckily!
'''
.. note::
The order of @step and @arrow calls is irrelevant.
"""
if func_or_label is None or isinstance(func_or_label, str):
if func_or_label is not None:
kwargs['label'] = func_or_label
@functools.wraps(func_or_label)
def step(func):
return _convert_to_step(func, **kwargs)
return step
else:
return _convert_to_step(func_or_label)
def arrow(to, **kwargs):
"""
Decorator for attaching arrows between steps.
:param to:
step to go to
:param value:
(optional) value to associate the arrow with
:param value:
(optional) error to associate the arrow with
In the most basic mode the arrow connects two steps together. In the
example below the two steps would create an infinite loop going from the
first step to the second step and back::
@arrow('second')
@step
def first(self):
print("First")
@arrow(to='first')
@step
def second(self):
print("Second")
Arrows can carry a value condition, such arrows are only followed if the
``value`` argument is equal to ``step.value``. This can be used to create
typical flow control structures. In the example below the step will
either go left or right, depending on the outcome of a coin toss::
@arrow('go_left', value='heads')
@arrow('go_right', value='tails')
@step
def fork_in_the_road(self):
self.value = toss_a_coin()
Lastly arrows can carry an error condition. This is useful to structure
abnormal exits so that the program won't crash but instead do something
sensible for the user. Such arrows are followed if the runtime error
instance is a subclass of the ``error`` argument. For a contrived example
let's pretend that the coin can someties land on the side and in that
case we want to just try again::
@arrow('go_left', value='heads')
@arrow('go_right', value='tails')
@arrow('fork_in_the_road', error=CoinLandedOnTheSide)
@step
def fork_in_the_road(self):
self.value = toss_a_coin()
"""
target = _resolve_arrow_target(to)
if 'value' in kwargs:
value = kwargs.pop('value')
arrow = ValueArrow(target, value)
elif 'error' in kwargs:
error = kwargs.pop('error')
arrow = ErrorArrow(target, error)
else:
arrow = NormalArrow(target)
if kwargs:
raise TypeError("stray arguments: {!r}".format(kwargs))
def decorator(decoratee):
if isinstance(decoratee, Step):
step = decoratee
step.Meta.arrows.append(arrow)
elif isinstance(decoratee, types.FunctionType):
func = decoratee
if not hasattr(func, 'arrows'):
func.arrows = []
func.arrows.append(arrow)
else:
raise TypeError(
"unsupported decorated type {}".format(type(decoratee)))
return decoratee
return decorator
def _resolve_arrow_target(target):
"""
Convert arrow target specification to a step name
:param target:
arrow target specification
:returns:
name of the target step
:raises TypeError:
if the target specification is incorrect
The target specification can be one of three things: a Step class, a
string or a function. Step classes use cls.Meta.label, functions use
func.__name__ and strings are used as-is.
"""
if isinstance(target, type) and issubclass(target, Step):
return target.Meta.label
elif isinstance(target, types.FunctionType):
return target.__name__
elif isinstance(target, str):
return target
else:
raise TypeError("unsupported target type: {0}".format(type(target)))
def _convert_to_step(func, label=None, initial=None, accepting=False,
level=None):
"""
Convert a step function to a subclass of :class:`Step`
:param func:
Function to work with
:param goto:
The default step to take next
:param initial:
if True, this step will be the initial step of the flow
:param accepting:
if True, this step will be an accepting step
:param level:
explicit level number for graph layout
"""
if label is None:
if func.__doc__:
label = func.__doc__.lstrip().splitlines()[0]
else:
label = func.__name__
ns = {
'name': func.__name__,
'label': label,
'initial': initial,
'accepting': accepting,
'arrows': func.arrows if hasattr(func, 'arrows') else [],
'needs_flow': 'flow' in inspect.getargspec(func)[0],
'level': level,
'__call__': func,
}
return type(func.__name__, (Step,), ns)
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setup.py | c-rainbow/twitch-chat-analyzer-py | 0 | 184916 | <filename>setup.py
import setuptools
with open("README.md", "r", encoding="utf-8") as fh:
long_description = fh.read()
setuptools.setup(
name="twitch-chat-analyzer", # Replace with your own username
version="0.0.5",
author="Rainbow",
author_email="<EMAIL>",
description="Twitch chat analyzer from past broadcasts, designed for Jupyter notebook",
long_description=long_description,
long_description_content_type="text/markdown",
url="https://github.com/c-rainbow/twitch-chat-analyzer-py",
packages=setuptools.find_packages(),
classifiers=[
"Programming Language :: Python :: 3.5",
"License :: OSI Approved :: MIT License",
"Operating System :: OS Independent",
],
python_requires='>=3.5',
)
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src/pretix/api/serializers/organizer.py | NicsTr/pretix | 0 | 1616795 | <gh_stars>0
from decimal import Decimal
from django.db.models import Q
from django.utils.translation import get_language, gettext_lazy as _
from rest_framework import serializers
from rest_framework.exceptions import ValidationError
from pretix.api.serializers.i18n import I18nAwareModelSerializer
from pretix.api.serializers.order import CompatibleJSONField
from pretix.base.auth import get_auth_backends
from pretix.base.models import (
Device, GiftCard, Organizer, SeatingPlan, Team, TeamAPIToken, TeamInvite,
User,
)
from pretix.base.models.seating import SeatingPlanLayoutValidator
from pretix.base.services.mail import SendMailException, mail
from pretix.helpers.urls import build_absolute_uri
class OrganizerSerializer(I18nAwareModelSerializer):
class Meta:
model = Organizer
fields = ('name', 'slug')
class SeatingPlanSerializer(I18nAwareModelSerializer):
layout = CompatibleJSONField(
validators=[SeatingPlanLayoutValidator()]
)
class Meta:
model = SeatingPlan
fields = ('id', 'name', 'layout')
class GiftCardSerializer(I18nAwareModelSerializer):
value = serializers.DecimalField(max_digits=10, decimal_places=2, min_value=Decimal('0.00'))
def validate(self, data):
data = super().validate(data)
s = data['secret']
qs = GiftCard.objects.filter(
secret=s
).filter(
Q(issuer=self.context["organizer"]) | Q(
issuer__gift_card_collector_acceptance__collector=self.context["organizer"])
)
if self.instance:
qs = qs.exclude(pk=self.instance.pk)
if qs.exists():
raise ValidationError(
{'secret': _(
'A gift card with the same secret already exists in your or an affiliated organizer account.')}
)
return data
class Meta:
model = GiftCard
fields = ('id', 'secret', 'issuance', 'value', 'currency', 'testmode', 'expires', 'conditions')
class EventSlugField(serializers.SlugRelatedField):
def get_queryset(self):
return self.context['organizer'].events.all()
class TeamSerializer(serializers.ModelSerializer):
limit_events = EventSlugField(slug_field='slug', many=True)
class Meta:
model = Team
fields = (
'id', 'name', 'all_events', 'limit_events', 'can_create_events', 'can_change_teams',
'can_change_organizer_settings', 'can_manage_gift_cards', 'can_change_event_settings',
'can_change_items', 'can_view_orders', 'can_change_orders', 'can_view_vouchers',
'can_change_vouchers'
)
def validate(self, data):
full_data = self.to_internal_value(self.to_representation(self.instance)) if self.instance else {}
full_data.update(data)
if full_data.get('limit_events') and full_data.get('all_events'):
raise ValidationError('Do not set both limit_events and all_events.')
return data
class DeviceSerializer(serializers.ModelSerializer):
limit_events = EventSlugField(slug_field='slug', many=True)
device_id = serializers.IntegerField(read_only=True)
unique_serial = serializers.CharField(read_only=True)
hardware_brand = serializers.CharField(read_only=True)
hardware_model = serializers.CharField(read_only=True)
software_brand = serializers.CharField(read_only=True)
software_version = serializers.CharField(read_only=True)
created = serializers.DateTimeField(read_only=True)
revoked = serializers.BooleanField(read_only=True)
initialized = serializers.DateTimeField(read_only=True)
initialization_token = serializers.DateTimeField(read_only=True)
class Meta:
model = Device
fields = (
'device_id', 'unique_serial', 'initialization_token', 'all_events', 'limit_events',
'revoked', 'name', 'created', 'initialized', 'hardware_brand', 'hardware_model',
'software_brand', 'software_version', 'security_profile'
)
class TeamInviteSerializer(serializers.ModelSerializer):
class Meta:
model = TeamInvite
fields = (
'id', 'email'
)
def _send_invite(self, instance):
try:
mail(
instance.email,
_('pretix account invitation'),
'pretixcontrol/email/invitation.txt',
{
'user': self,
'organizer': self.context['organizer'].name,
'team': instance.team.name,
'url': build_absolute_uri('control:auth.invite', kwargs={
'token': instance.token
})
},
event=None,
locale=get_language() # TODO: expose?
)
except SendMailException:
pass # Already logged
def create(self, validated_data):
if 'email' in validated_data:
try:
user = User.objects.get(email__iexact=validated_data['email'])
except User.DoesNotExist:
if self.context['team'].invites.filter(email__iexact=validated_data['email']).exists():
raise ValidationError(_('This user already has been invited for this team.'))
if 'native' not in get_auth_backends():
raise ValidationError('Users need to have a pretix account before they can be invited.')
invite = self.context['team'].invites.create(email=validated_data['email'])
self._send_invite(invite)
invite.team.log_action(
'pretix.team.invite.created',
data={
'email': validated_data['email']
},
**self.context['log_kwargs']
)
return invite
else:
if self.context['team'].members.filter(pk=user.pk).exists():
raise ValidationError(_('This user already has permissions for this team.'))
self.context['team'].members.add(user)
self.context['team'].log_action(
'pretix.team.member.added',
data={
'email': user.email,
'user': user.pk,
},
**self.context['log_kwargs']
)
return TeamInvite(email=user.email)
else:
raise ValidationError('No email address given.')
class TeamAPITokenSerializer(serializers.ModelSerializer):
active = serializers.BooleanField(default=True, read_only=True)
class Meta:
model = TeamAPIToken
fields = (
'id', 'name', 'active'
)
class TeamMemberSerializer(serializers.ModelSerializer):
class Meta:
model = User
fields = (
'id', 'email', 'fullname', 'require_2fa'
)
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] |
cli/loc.py | deb17/moneycare | 0 | 63775 | import os
def file_len(filename, comment):
line_count = 0
with open(filename) as f:
for line in f:
if line.strip().startswith(comment):
continue
if line.strip():
line_count += 1
return line_count
def walk(path='.'):
loc = 0
for root, dirs, files in os.walk(path):
if '.git' in root or 'venv' in root:
continue
for file in files:
filepath = os.path.join(root, file)
if file.endswith('.py'):
loc += file_len(filepath, '#')
elif file.endswith('.html'):
if file in ('privacy_policy.html', 'tac.html'):
continue
loc += file_len(filepath, '<!--')
elif file.endswith('.css'):
if file == 'jquery.tagsinput-revisited.css':
continue
loc += file_len(filepath, '/*')
elif file.endswith('.js'):
if file == 'jquery.tagsinput-revisited.js':
continue
loc += file_len(filepath, '//')
return loc
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736,
1180,
13,
2
] |
package.py | srini009/ascent | 0 | 46731 | <gh_stars>0
#!/bin/env python
###############################################################################
# Copyright (c) Lawrence Livermore National Security, LLC and other Ascent
# Project developers. See top-level LICENSE AND COPYRIGHT files for dates and
# other details. No copyright assignment is required to contribute to Ascent.
###############################################################################
###############################################################################
#
# file: package.py
#
###############################################################################
import subprocess
import sys
import datetime
import os
from os.path import join as pjoin
def create_package(output_file=None):
scripts_dir = pjoin(os.path.abspath(os.path.split(__file__)[0]),"scripts")
pkg_script = pjoin(scripts_dir,"git_archive_all.py");
repo_name = os.path.basename(os.path.dirname(os.path.abspath(__file__)))
if output_file is None:
suffix = "tar"
t = datetime.datetime.now()
output_file = "%s.%04d.%02d.%02d.%s" % (repo_name,t.year,t.month,t.day,suffix)
cmd = "python " + pkg_script + " --prefix=ascent " + output_file
print("[exe: {}]".format(cmd))
subprocess.call(cmd,shell=True)
if __name__ == "__main__":
ofile = None
if len(sys.argv) > 1:
ofile = sys.argv[1]
create_package(ofile)
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] |
nova/pci/stats.py | 10088/nova | 0 | 718 | # Copyright (c) 2013 Intel, Inc.
# Copyright (c) 2013 OpenStack Foundation
# All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License"); you may
# not use this file except in compliance with the License. You may obtain
# a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS, WITHOUT
# WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the
# License for the specific language governing permissions and limitations
# under the License.
import copy
import typing as ty
from oslo_config import cfg
from oslo_log import log as logging
from oslo_utils import strutils
from nova import exception
from nova import objects
from nova.objects import fields
from nova.objects import pci_device_pool
from nova.pci.request import PCI_REMOTE_MANAGED_TAG
from nova.pci import utils
from nova.pci import whitelist
CONF = cfg.CONF
LOG = logging.getLogger(__name__)
# TODO(stephenfin): We might want to use TypedDict here. Refer to
# https://mypy.readthedocs.io/en/latest/kinds_of_types.html#typeddict for
# more information.
Pool = ty.Dict[str, ty.Any]
class PciDeviceStats(object):
"""PCI devices summary information.
According to the PCI SR-IOV spec, a PCI physical function can have up to
256 PCI virtual functions, thus the number of assignable PCI functions in
a cloud can be big. The scheduler needs to know all device availability
information in order to determine which compute hosts can support a PCI
request. Passing individual virtual device information to the scheduler
does not scale, so we provide summary information.
Usually the virtual functions provided by a host PCI device have the same
value for most properties, like vendor_id, product_id and class type.
The PCI stats class summarizes this information for the scheduler.
The pci stats information is maintained exclusively by compute node
resource tracker and updated to database. The scheduler fetches the
information and selects the compute node accordingly. If a compute
node is selected, the resource tracker allocates the devices to the
instance and updates the pci stats information.
This summary information will be helpful for cloud management also.
"""
pool_keys = ['product_id', 'vendor_id', 'numa_node', 'dev_type']
def __init__(
self,
numa_topology: 'objects.NUMATopology',
stats: 'objects.PCIDevicePoolList' = None,
dev_filter: whitelist.Whitelist = None,
) -> None:
self.numa_topology = numa_topology
self.pools = (
[pci_pool.to_dict() for pci_pool in stats] if stats else []
)
self.pools.sort(key=lambda item: len(item))
self.dev_filter = dev_filter or whitelist.Whitelist(
CONF.pci.passthrough_whitelist)
def _equal_properties(
self, dev: Pool, entry: Pool, matching_keys: ty.List[str],
) -> bool:
return all(dev.get(prop) == entry.get(prop)
for prop in matching_keys)
def _find_pool(self, dev_pool: Pool) -> ty.Optional[Pool]:
"""Return the first pool that matches dev."""
for pool in self.pools:
pool_keys = pool.copy()
del pool_keys['count']
del pool_keys['devices']
if (len(pool_keys.keys()) == len(dev_pool.keys()) and
self._equal_properties(dev_pool, pool_keys, list(dev_pool))):
return pool
return None
@staticmethod
def _ensure_remote_managed_tag(
dev: 'objects.PciDevice', pool: Pool):
"""Add a remote_managed tag depending on a device type if needed.
Network devices may be managed remotely, e.g. by a SmartNIC DPU. If
a tag has not been explicitly provided, populate it by assuming that
a device is not remote managed by default.
"""
if dev.dev_type not in (fields.PciDeviceType.SRIOV_VF,
fields.PciDeviceType.SRIOV_PF,
fields.PciDeviceType.VDPA):
return
# A tag is added here rather than at the client side to avoid an
# issue with having objects without this tag specified during an
# upgrade to the first version that supports handling this tag.
if pool.get(PCI_REMOTE_MANAGED_TAG) is None:
# NOTE: tags are compared as strings case-insensitively, see
# pci_device_prop_match in nova/pci/utils.py.
pool[PCI_REMOTE_MANAGED_TAG] = 'false'
def _create_pool_keys_from_dev(
self, dev: 'objects.PciDevice',
) -> ty.Optional[Pool]:
"""Create a stats pool dict that this dev is supposed to be part of
Note that this pool dict contains the stats pool's keys and their
values. 'count' and 'devices' are not included.
"""
# Don't add a device that doesn't have a matching device spec.
# This can happen during initial sync up with the controller
devspec = self.dev_filter.get_devspec(dev)
if not devspec:
return None
tags = devspec.get_tags()
pool = {k: getattr(dev, k) for k in self.pool_keys}
if tags:
pool.update(tags)
# NOTE(gibi): parent_ifname acts like a tag during pci claim but
# not provided as part of the whitelist spec as it is auto detected
# by the virt driver.
# This key is used for match InstancePciRequest backed by neutron ports
# that has resource_request and therefore that has resource allocation
# already in placement.
if dev.extra_info.get('parent_ifname'):
pool['parent_ifname'] = dev.extra_info['parent_ifname']
self._ensure_remote_managed_tag(dev, pool)
return pool
def _get_pool_with_device_type_mismatch(
self, dev: 'objects.PciDevice',
) -> ty.Optional[ty.Tuple[Pool, 'objects.PciDevice']]:
"""Check for device type mismatch in the pools for a given device.
Return (pool, device) if device type does not match or a single None
if the device type matches.
"""
for pool in self.pools:
for device in pool['devices']:
if device.address == dev.address:
if dev.dev_type != pool["dev_type"]:
return pool, device
return None
return None
def update_device(self, dev: 'objects.PciDevice') -> None:
"""Update a device to its matching pool."""
pool_device_info = self._get_pool_with_device_type_mismatch(dev)
if pool_device_info is None:
return None
pool, device = pool_device_info
pool['devices'].remove(device)
self._decrease_pool_count(self.pools, pool)
self.add_device(dev)
def add_device(self, dev: 'objects.PciDevice') -> None:
"""Add a device to its matching pool."""
dev_pool = self._create_pool_keys_from_dev(dev)
if dev_pool:
pool = self._find_pool(dev_pool)
if not pool:
dev_pool['count'] = 0
dev_pool['devices'] = []
self.pools.append(dev_pool)
self.pools.sort(key=lambda item: len(item))
pool = dev_pool
pool['count'] += 1
pool['devices'].append(dev)
@staticmethod
def _decrease_pool_count(
pool_list: ty.List[Pool], pool: Pool, count: int = 1,
) -> int:
"""Decrement pool's size by count.
If pool becomes empty, remove pool from pool_list.
"""
if pool['count'] > count:
pool['count'] -= count
count = 0
else:
count -= pool['count']
pool_list.remove(pool)
return count
def remove_device(self, dev: 'objects.PciDevice') -> None:
"""Remove one device from the first pool that it matches."""
dev_pool = self._create_pool_keys_from_dev(dev)
if dev_pool:
pool = self._find_pool(dev_pool)
if not pool:
raise exception.PciDevicePoolEmpty(
compute_node_id=dev.compute_node_id, address=dev.address)
pool['devices'].remove(dev)
self._decrease_pool_count(self.pools, pool)
def get_free_devs(self) -> ty.List['objects.PciDevice']:
free_devs: ty.List[objects.PciDevice] = []
for pool in self.pools:
free_devs.extend(pool['devices'])
return free_devs
def consume_requests(
self,
pci_requests: 'objects.InstancePCIRequests',
numa_cells: ty.Optional[ty.List['objects.InstanceNUMACell']] = None,
) -> ty.Optional[ty.List['objects.PciDevice']]:
alloc_devices: ty.List[objects.PciDevice] = []
for request in pci_requests:
count = request.count
pools = self._filter_pools(self.pools, request, numa_cells)
# Failed to allocate the required number of devices. Return the
# devices already allocated during previous iterations back to
# their pools
if not pools:
LOG.error("Failed to allocate PCI devices for instance. "
"Unassigning devices back to pools. "
"This should not happen, since the scheduler "
"should have accurate information, and allocation "
"during claims is controlled via a hold "
"on the compute node semaphore.")
for d in range(len(alloc_devices)):
self.add_device(alloc_devices.pop())
return None
for pool in pools:
if pool['count'] >= count:
num_alloc = count
else:
num_alloc = pool['count']
count -= num_alloc
pool['count'] -= num_alloc
for d in range(num_alloc):
pci_dev = pool['devices'].pop()
self._handle_device_dependents(pci_dev)
pci_dev.request_id = request.request_id
alloc_devices.append(pci_dev)
if count == 0:
break
return alloc_devices
def _handle_device_dependents(self, pci_dev: 'objects.PciDevice') -> None:
"""Remove device dependents or a parent from pools.
In case the device is a PF, all of it's dependent VFs should
be removed from pools count, if these are present.
When the device is a VF, or a VDPA device, it's parent PF
pool count should be decreased, unless it is no longer in a pool.
"""
if pci_dev.dev_type == fields.PciDeviceType.SRIOV_PF:
vfs_list = pci_dev.child_devices
if vfs_list:
free_devs = self.get_free_devs()
for vf in vfs_list:
# NOTE(gibi): do not try to remove a device that are
# already removed
if vf in free_devs:
self.remove_device(vf)
elif pci_dev.dev_type in (
fields.PciDeviceType.SRIOV_VF,
fields.PciDeviceType.VDPA,
):
try:
parent = pci_dev.parent_device
# Make sure not to decrease PF pool count if this parent has
# been already removed from pools
if parent in self.get_free_devs():
self.remove_device(parent)
except exception.PciDeviceNotFound:
return
def _filter_pools_for_spec(
self, pools: ty.List[Pool], request: 'objects.InstancePCIRequest',
) -> ty.List[Pool]:
"""Filter out pools that don't match the request's device spec.
Exclude pools that do not match the specified ``vendor_id``,
``product_id`` and/or ``device_type`` field, or any of the other
arbitrary tags such as ``physical_network``, specified in the request.
:param pools: A list of PCI device pool dicts
:param request: An InstancePCIRequest object describing the type,
quantity and required NUMA affinity of device(s) we want.
:returns: A list of pools that can be used to support the request if
this is possible.
"""
request_specs = request.spec
return [
pool for pool in pools
if utils.pci_device_prop_match(pool, request_specs)
]
def _filter_pools_for_numa_cells(
self,
pools: ty.List[Pool],
request: 'objects.InstancePCIRequest',
numa_cells: ty.Optional[ty.List['objects.InstanceNUMACell']],
) -> ty.List[Pool]:
"""Filter out pools with the wrong NUMA affinity, if required.
Exclude pools that do not have *suitable* PCI NUMA affinity.
``numa_policy`` determines what *suitable* means, being one of
PREFERRED (nice-to-have), LEGACY (must-have-if-available) and REQUIRED
(must-have). We iterate through the various policies in order of
strictness. This means that even if we only *prefer* PCI-NUMA affinity,
we will still attempt to provide it if possible.
:param pools: A list of PCI device pool dicts
:param request: An InstancePCIRequest object describing the type,
quantity and required NUMA affinity of device(s) we want.
:param numa_cells: A list of InstanceNUMACell objects whose ``id``
corresponds to the ``id`` of host NUMACells.
:returns: A list of pools that can, together, provide at least
``requested_count`` PCI devices with the level of NUMA affinity
required by ``numa_policy``, else all pools that can satisfy this
policy even if it's not enough.
"""
if not numa_cells:
return pools
# we default to the 'legacy' policy for...of course...legacy reasons
requested_policy = fields.PCINUMAAffinityPolicy.LEGACY
if 'numa_policy' in request:
requested_policy = request.numa_policy or requested_policy
requested_count = request.count
numa_cell_ids = [cell.id for cell in numa_cells]
# filter out pools which numa_node is not included in numa_cell_ids
filtered_pools = [
pool for pool in pools if any(utils.pci_device_prop_match(
pool, [{'numa_node': cell}]) for cell in numa_cell_ids)]
# we can't apply a less strict policy than the one requested, so we
# need to return if we've demanded a NUMA affinity of REQUIRED.
# However, NUMA affinity is a good thing. If we can get enough devices
# with the stricter policy then we will use them.
if requested_policy == fields.PCINUMAAffinityPolicy.REQUIRED or sum(
pool['count'] for pool in filtered_pools) >= requested_count:
return filtered_pools
# the SOCKET policy is a bit of a special case. It's less strict than
# REQUIRED (so REQUIRED will automatically fulfil SOCKET, at least
# with our assumption of never having multiple sockets per NUMA node),
# but not always more strict than LEGACY: a PCI device with no NUMA
# affinity will fulfil LEGACY but not SOCKET. If we have SOCKET,
# process it here and don't continue.
if requested_policy == fields.PCINUMAAffinityPolicy.SOCKET:
return self._filter_pools_for_socket_affinity(pools, numa_cells)
# some systems don't report NUMA node info for PCI devices, in which
# case None is reported in 'pci_device.numa_node'. The LEGACY policy
# allows us to use these devices so we include None in the list of
# suitable NUMA cells.
numa_cell_ids.append(None)
# filter out pools which numa_node is not included in numa_cell_ids
filtered_pools = [
pool for pool in pools if any(utils.pci_device_prop_match(
pool, [{'numa_node': cell}]) for cell in numa_cell_ids)]
# once again, we can't apply a less strict policy than the one
# requested, so we need to return if we've demanded a NUMA affinity of
# LEGACY. Similarly, we will also return if we have enough devices to
# satisfy this somewhat strict policy.
if requested_policy == fields.PCINUMAAffinityPolicy.LEGACY or sum(
pool['count'] for pool in filtered_pools) >= requested_count:
return filtered_pools
# if we've got here, we're using the PREFERRED policy and weren't able
# to provide anything with stricter affinity. Use whatever devices you
# can, folks.
return sorted(
pools, key=lambda pool: pool.get('numa_node') not in numa_cell_ids)
def _filter_pools_for_socket_affinity(
self,
pools: ty.List[Pool],
numa_cells: ty.List['objects.InstanceNUMACell'],
) -> ty.List[Pool]:
host_cells = self.numa_topology.cells
# bail early if we don't have socket information for all host_cells.
# This could happen if we're running on an weird older system with
# multiple sockets per NUMA node, which is a configuration that we
# explicitly chose not to support.
if any(cell.socket is None for cell in host_cells):
LOG.debug('No socket information in host NUMA cell(s).')
return []
# get a set of host sockets that the guest cells are in. Since guest
# cell IDs map to host cell IDs, we can just lookup the latter's
# socket.
socket_ids = set()
for guest_cell in numa_cells:
for host_cell in host_cells:
if guest_cell.id == host_cell.id:
socket_ids.add(host_cell.socket)
# now get a set of host NUMA nodes that are in the above sockets
allowed_numa_nodes = set()
for host_cell in host_cells:
if host_cell.socket in socket_ids:
allowed_numa_nodes.add(host_cell.id)
# filter out pools that are not in one of the correct host NUMA nodes.
return [
pool for pool in pools if any(
utils.pci_device_prop_match(pool, [{'numa_node': numa_node}])
for numa_node in allowed_numa_nodes
)
]
def _filter_pools_for_unrequested_pfs(
self, pools: ty.List[Pool], request: 'objects.InstancePCIRequest',
) -> ty.List[Pool]:
"""Filter out pools with PFs, unless these are required.
This is necessary in cases where PFs and VFs have the same product_id
and generally useful elsewhere.
:param pools: A list of PCI device pool dicts
:param request: An InstancePCIRequest object describing the type,
quantity and required NUMA affinity of device(s) we want.
:returns: A list of pools that can be used to support the request if
this is possible.
"""
if all(
spec.get('dev_type') != fields.PciDeviceType.SRIOV_PF
for spec in request.spec
):
pools = [
pool for pool in pools
if not pool.get('dev_type') == fields.PciDeviceType.SRIOV_PF
]
return pools
def _filter_pools_for_unrequested_vdpa_devices(
self,
pools: ty.List[Pool],
request: 'objects.InstancePCIRequest',
) -> ty.List[Pool]:
"""Filter out pools with VDPA devices, unless these are required.
This is necessary as vdpa devices require special handling and
should not be allocated to generic pci device requests.
:param pools: A list of PCI device pool dicts
:param request: An InstancePCIRequest object describing the type,
quantity and required NUMA affinity of device(s) we want.
:returns: A list of pools that can be used to support the request if
this is possible.
"""
if all(
spec.get('dev_type') != fields.PciDeviceType.VDPA
for spec in request.spec
):
pools = [
pool for pool in pools
if not pool.get('dev_type') == fields.PciDeviceType.VDPA
]
return pools
def _filter_pools_for_unrequested_remote_managed_devices(
self, pools: ty.List[Pool], request: 'objects.InstancePCIRequest',
) -> ty.List[Pool]:
"""Filter out pools with remote_managed devices, unless requested.
Remote-managed devices are not usable for legacy SR-IOV or hardware
offload scenarios and must be excluded from allocation.
:param pools: A list of PCI device pool dicts
:param request: An InstancePCIRequest object describing the type,
quantity and required NUMA affinity of device(s) we want.
:returns: A list of pools that can be used to support the request if
this is possible.
"""
if all(not strutils.bool_from_string(spec.get(PCI_REMOTE_MANAGED_TAG))
for spec in request.spec):
pools = [pool for pool in pools
if not strutils.bool_from_string(
pool.get(PCI_REMOTE_MANAGED_TAG))]
return pools
def _filter_pools(
self,
pools: ty.List[Pool],
request: 'objects.InstancePCIRequest',
numa_cells: ty.Optional[ty.List['objects.InstanceNUMACell']],
) -> ty.Optional[ty.List[Pool]]:
"""Determine if an individual PCI request can be met.
Filter pools, which are collections of devices with similar traits, to
identify those that can support the provided PCI request.
If ``numa_cells`` is provided then NUMA locality may be taken into
account, depending on the value of ``request.numa_policy``.
:param pools: A list of PCI device pool dicts
:param request: An InstancePCIRequest object describing the type,
quantity and required NUMA affinity of device(s) we want.
:param numa_cells: A list of InstanceNUMACell objects whose ``id``
corresponds to the ``id`` of host NUMACell objects.
:returns: A list of pools that can be used to support the request if
this is possible, else None.
"""
# NOTE(vladikr): This code may be open to race conditions.
# Two concurrent requests may succeed when called support_requests
# because this method does not remove related devices from the pools
# Firstly, let's exclude all devices that don't match our spec (e.g.
# they've got different PCI IDs or something)
before_count = sum([pool['count'] for pool in pools])
pools = self._filter_pools_for_spec(pools, request)
after_count = sum([pool['count'] for pool in pools])
if after_count < before_count:
LOG.debug(
'Dropped %d device(s) due to mismatched PCI attribute(s)',
before_count - after_count
)
if after_count < request.count:
LOG.debug('Not enough PCI devices left to satisfy request')
return None
# Next, let's exclude all devices that aren't on the correct NUMA node
# or socket, *assuming* we have devices and care about that, as
# determined by policy
before_count = after_count
pools = self._filter_pools_for_numa_cells(pools, request, numa_cells)
after_count = sum([pool['count'] for pool in pools])
if after_count < before_count:
LOG.debug(
'Dropped %d device(s) as they are on the wrong NUMA node(s)',
before_count - after_count
)
if after_count < request.count:
LOG.debug('Not enough PCI devices left to satisfy request')
return None
# If we're not requesting PFs then we should not use these.
# Exclude them.
before_count = after_count
pools = self._filter_pools_for_unrequested_pfs(pools, request)
after_count = sum([pool['count'] for pool in pools])
if after_count < before_count:
LOG.debug(
'Dropped %d device(s) as they are PFs which we have not '
'requested',
before_count - after_count
)
if after_count < request.count:
LOG.debug('Not enough PCI devices left to satisfy request')
return None
# If we're not requesting VDPA devices then we should not use these
# either. Exclude them.
before_count = after_count
pools = self._filter_pools_for_unrequested_vdpa_devices(pools, request)
after_count = sum([pool['count'] for pool in pools])
if after_count < before_count:
LOG.debug(
'Dropped %d device(s) as they are VDPA devices which we have '
'not requested',
before_count - after_count
)
# If we're not requesting remote_managed devices then we should not
# use these either. Exclude them.
before_count = after_count
pools = self._filter_pools_for_unrequested_remote_managed_devices(
pools, request)
after_count = sum([pool['count'] for pool in pools])
if after_count < before_count:
LOG.debug(
'Dropped %d device(s) as they are remote-managed devices which'
'we have not requested',
before_count - after_count
)
if after_count < request.count:
LOG.debug('Not enough PCI devices left to satisfy request')
return None
return pools
def support_requests(
self,
requests: ty.List['objects.InstancePCIRequest'],
numa_cells: ty.Optional[ty.List['objects.InstanceNUMACell']] = None,
) -> bool:
"""Determine if the PCI requests can be met.
Determine, based on a compute node's PCI stats, if an instance can be
scheduled on the node. **Support does not mean real allocation**.
If ``numa_cells`` is provided then NUMA locality may be taken into
account, depending on the value of ``numa_policy``.
:param requests: A list of InstancePCIRequest object describing the
types, quantities and required NUMA affinities of devices we want.
:type requests: nova.objects.InstancePCIRequests
:param numa_cells: A list of InstanceNUMACell objects whose ``id``
corresponds to the ``id`` of host NUMACells, or None.
:returns: Whether this compute node can satisfy the given request.
"""
# NOTE(yjiang5): this function has high possibility to fail,
# so no exception should be triggered for performance reason.
return all(
self._filter_pools(self.pools, r, numa_cells) for r in requests
)
def _apply_request(
self,
pools: ty.List[Pool],
request: 'objects.InstancePCIRequest',
numa_cells: ty.Optional[ty.List['objects.InstanceNUMACell']] = None,
) -> bool:
"""Apply an individual PCI request.
Apply a PCI request against a given set of PCI device pools, which are
collections of devices with similar traits.
If ``numa_cells`` is provided then NUMA locality may be taken into
account, depending on the value of ``request.numa_policy``.
:param pools: A list of PCI device pool dicts
:param request: An InstancePCIRequest object describing the type,
quantity and required NUMA affinity of device(s) we want.
:param numa_cells: A list of InstanceNUMACell objects whose ``id``
corresponds to the ``id`` of host NUMACell objects.
:returns: True if the request was applied against the provided pools
successfully, else False.
"""
# NOTE(vladikr): This code maybe open to race conditions.
# Two concurrent requests may succeed when called support_requests
# because this method does not remove related devices from the pools
filtered_pools = self._filter_pools(pools, request, numa_cells)
if not filtered_pools:
return False
count = request.count
for pool in filtered_pools:
count = self._decrease_pool_count(pools, pool, count)
if not count:
break
return True
def apply_requests(
self,
requests: ty.List['objects.InstancePCIRequest'],
numa_cells: ty.Optional[ty.List['objects.InstanceNUMACell']] = None,
) -> None:
"""Apply PCI requests to the PCI stats.
This is used in multiple instance creation, when the scheduler has to
maintain how the resources are consumed by the instances.
If ``numa_cells`` is provided then NUMA locality may be taken into
account, depending on the value of ``numa_policy``.
:param requests: A list of InstancePCIRequest object describing the
types, quantities and required NUMA affinities of devices we want.
:type requests: nova.objects.InstancePCIRequests
:param numa_cells: A list of InstanceNUMACell objects whose ``id``
corresponds to the ``id`` of host NUMACells, or None.
:raises: exception.PciDeviceRequestFailed if this compute node cannot
satisfy the given request.
"""
if not all(
self._apply_request(self.pools, r, numa_cells) for r in requests
):
raise exception.PciDeviceRequestFailed(requests=requests)
def __iter__(self) -> ty.Iterator[Pool]:
pools: ty.List[Pool] = []
for pool in self.pools:
pool = copy.deepcopy(pool)
# 'devices' shouldn't be part of stats
if 'devices' in pool:
del pool['devices']
pools.append(pool)
return iter(pools)
def clear(self) -> None:
"""Clear all the stats maintained."""
self.pools = []
def __eq__(self, other: object) -> bool:
if not isinstance(other, PciDeviceStats):
return NotImplemented
return self.pools == other.pools
def to_device_pools_obj(self) -> 'objects.PciDevicePoolList':
"""Return the contents of the pools as a PciDevicePoolList object."""
stats = [x for x in self]
return pci_device_pool.from_pci_stats(stats)
def has_remote_managed_device_pools(self) -> bool:
"""Determine whether remote managed device pools are present on a host.
The check is pool-based, not free device-based and is NUMA cell
agnostic.
"""
dummy_req = objects.InstancePCIRequest(
count=0,
spec=[{'remote_managed': True}]
)
pools = self._filter_pools_for_spec(self.pools, dummy_req)
return bool(pools)
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8426,
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2401,
368,
263,
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13,
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29901,
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565,
278,
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7436,
2750,
278,
4944,
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13,
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7700,
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2000,
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9995,
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routine_qiime2_analyses/post_analyses.py | FranckLejzerowicz/routine_qiime2_analyses | 0 | 52416 | <reponame>FranckLejzerowicz/routine_qiime2_analyses
# ----------------------------------------------------------------------------
# Copyright (c) 2020, <NAME>.
#
# Distributed under the terms of the Modified BSD License.
#
# The full license is in the file LICENSE, distributed with this software.
# ----------------------------------------------------------------------------
import glob
import pandas as pd
from os.path import dirname, isfile, splitext
from skbio.stats.ordination import OrdinationResults
from routine_qiime2_analyses.analyses_prep import AnalysisPrep
from routine_qiime2_analyses._routine_q2_cmds import (
get_case, get_new_meta_pd, run_import, run_export, filter_feature_table,
)
from routine_qiime2_analyses._routine_q2_io_utils import (
read_yaml_file, get_analysis_folder
)
from routine_qiime2_analyses._routine_q2_taxonomy import (
get_split_taxonomy
)
from routine_qiime2_analyses._routine_q2_mmbird import (
get_order_omics, get_pc_sb_correlations, edit_ordi_qzv, get_qzs,
get_biplot_commands, get_xmmvec_commands, get_paired_heatmaps_command
)
class PostAnalysis(object):
def __init__(self, config, project) -> None:
self.config = config
self.project = project
self.cmds = {}
self.mmvec_songbird_pd = pd.DataFrame()
self.taxo_pds = {}
self.metas = {}
self.mmvec_res = {}
self.mmvec_issues = set()
self.xmmvecs = read_yaml_file(config.xmmvec)
self.highlights = read_yaml_file(config.mmvec_highlights)
def merge_mmvec_songbird(self, songbird_pd):
rename_dic1 = dict(
(x, '%s_omic1_songbird_common_fp' % x) for x in songbird_pd.columns)
rename_dic1.update({'pair_omic_subset_filt': 'pair_omic_subset_filt1'})
self.mmvec_songbird_pd = self.mmvec_songbird_pd.merge(
songbird_pd.rename(columns=rename_dic1),
on='pair_omic_subset_filt1', how='left')
rename_dic2 = dict(
(x, '%s_omic2_songbird_common_fp' % x) for x in songbird_pd.columns)
rename_dic2.update({'pair_omic_subset_filt': 'pair_omic_subset_filt2'})
self.mmvec_songbird_pd = self.mmvec_songbird_pd.merge(
songbird_pd.rename(columns=rename_dic2),
on='pair_omic_subset_filt2', how='left')
def prep_mmvec(self, mmvec_pd):
mmvec_pd = mmvec_pd.set_index(mmvec_pd.columns.tolist()[:-1]).unstack()
mmvec_pd.columns = mmvec_pd.columns.droplevel()
mmvec_pd.reset_index(inplace=True)
mmvec_pd['filter1'] = mmvec_pd['pr1'] + '_' + mmvec_pd['ab1']
mmvec_pd['filter2'] = mmvec_pd['pr2'] + '_' + mmvec_pd['ab2']
mmvec_pd['omic_subset_filt1'] = mmvec_pd.apply(
lambda x: '__'.join(x[['omic1', 'subset', 'filter1']]), axis=1)
mmvec_pd['omic_subset_filt2'] = mmvec_pd.apply(
lambda x: '__'.join(x[['omic1', 'subset', 'filter2']]), axis=1)
mmvec_pd['pair_omic_subset_filt1'] = mmvec_pd.apply(
lambda x: '__'.join(x[['pair', 'omic_subset_filt1']]), axis=1)
mmvec_pd['pair_omic_subset_filt2'] = mmvec_pd.apply(
lambda x: '__'.join(x[['pair', 'omic_subset_filt2']]), axis=1)
self.mmvec_songbird_pd = mmvec_pd
@staticmethod
def prep_songbird(songbird_pd):
songbird_pd['pair_omic_subset_filt'] = songbird_pd.apply(
lambda x: '__'.join(x[['pair', 'dataset', 'subset', 'filter']]),
axis=1)
songbird_pd = songbird_pd[
['params', 'pair_omic_subset_filt', 'differentials']
].drop_duplicates().pivot(
columns='params', index='pair_omic_subset_filt')
songbird_pd.columns = songbird_pd.columns.droplevel()
songbird_pd = songbird_pd.reset_index()
return songbird_pd
def get_tax_fp(self, omic):
if isfile(self.project.datasets[omic].tax[-1]):
omic_tax_fp = self.project.datasets[omic].tax[-1]
else:
omic_tax_fp = ''
return omic_tax_fp
def get_taxo_pds(self):
for omicn in ['1', '2']:
for omic in self.mmvec_songbird_pd['omic%s' % omicn].unique():
omic_tax_fp = self.get_tax_fp(omic)
if isfile(omic_tax_fp):
omic_tax_pd = pd.read_csv(
omic_tax_fp, header=0, sep='\t', dtype=str)
omic_tax_pd.rename(
columns={omic_tax_pd.columns[0]: 'Feature ID'},
inplace=True)
else:
omic_tax_pd = pd.DataFrame()
self.taxo_pds[omic] = omic_tax_pd
def get_omics_songbirds_taxa(self):
for omicn in ['1', '2']:
pair_case_omics_filts = ['pair', 'subset', 'omic1',
'filter1', 'omic2', 'filter2']
all_omic_sb = [x for x in self.mmvec_songbird_pd.columns if
x.endswith('omic%s_songbird_common_fp' % omicn)]
omicn_songbirds = self.mmvec_songbird_pd[
(pair_case_omics_filts + all_omic_sb)].set_index(
pair_case_omics_filts).T.to_dict()
for (pair, subset, omic1, filt1, omic2,
filt2), sb_head_diff_fp in omicn_songbirds.items():
if omicn == '1':
omic = omic1
omic_ = omic2
filt = filt1
filt_ = filt2
else:
omic = omic2
omic_ = omic1
filt = filt2
filt_ = filt1
feats_diff_cols = []
cur_mmvec_folder = get_analysis_folder(
self.config.i_datasets_folder,
'mmvec/metadata/%s/%s' % (pair, subset))
omic_diff_list = []
if len(sb_head_diff_fp):
for sb_head, diff_fp in sb_head_diff_fp.items():
model = sb_head.replace(
'_omic%s_songbird_common_fp' % omicn, '')
if str(diff_fp) != 'nan' and isfile(diff_fp):
diff_pd = pd.read_csv(diff_fp, header=0, sep='\t',
dtype=str)
index_header = diff_pd.columns[0]
if diff_pd[index_header][0] == '#q2:types':
diff_pd = diff_pd[1:]
diff_pd = diff_pd.rename(
columns={index_header: 'Feature ID'}).set_index(
'Feature ID')
diff_pd = diff_pd.drop(
columns=[x for x in diff_pd.columns if
'Intercept' in x])
q2s = {}
diff_htmls = glob.glob(
'%s/*/tensorboard.html' % dirname(diff_fp))
if len(diff_htmls):
for diff_html in diff_htmls:
baseline = diff_html.split('/')[-2]
with open(diff_html) as f:
for line in f:
if 'Pseudo Q-squared' in line:
q2 = line.split(
'Pseudo Q-squared:</a></strong> ')[
-1].split('<')[0]
if float(q2) > 0.01:
q2s[baseline] = q2
break
if q2s:
diff_cols = ['%s__%s__%s' % (
model, x, '--'.join(
['%s-Q2=%s' % (b, q) if b else 'noQ2'
for b, q in q2s.items()])
) for x in diff_pd.columns]
diff_pd.columns = diff_cols
feats_diff_cols.extend(diff_cols)
omic_diff_list.append(diff_pd)
if len(omic_diff_list):
omic_songbird_ranks = pd.concat(
omic_diff_list, axis=1, sort=False).reset_index()
omic_songbird_ranks.rename(
columns={omic_songbird_ranks.columns[0]: 'Feature ID'},
inplace=True
)
else:
omic_common_fp = self.mmvec_songbird_pd.loc[
(self.mmvec_songbird_pd['pair'] == pair) &
(self.mmvec_songbird_pd['subset'] == subset) &
(self.mmvec_songbird_pd['omic1'] == omic1) &
(self.mmvec_songbird_pd['filter1'] == filt1) &
(self.mmvec_songbird_pd['omic2'] == omic2) &
(self.mmvec_songbird_pd['filter2'] == filt2),
'omic%s_common_fp' % omicn
].tolist()[0]
omic_tax_list = []
if not isfile(omic_common_fp):
continue
with open(omic_common_fp) as f:
for ldx, line in enumerate(f):
if ldx:
omic_tax_list.append([line.split('\t')[0]])
omic_songbird_ranks = pd.DataFrame(omic_tax_list,
columns=['Feature ID'])
if omic in self.taxo_pds:
omic_tax_pd = self.taxo_pds[omic]
if omic_tax_pd.shape[0]:
if 'Taxon' in omic_tax_pd.columns:
omic_split_taxa_pd = get_split_taxonomy(
omic_tax_pd.Taxon.tolist(), True)
omic_tax_pd = pd.concat(
[omic_tax_pd, omic_split_taxa_pd], axis=1,
sort=False)
omic_songbird_ranks = omic_songbird_ranks.merge(
omic_tax_pd, on='Feature ID',
how='left').drop_duplicates()
meta_omic_fp = '%s/feature_metadata_%s_%s__%s_%s.tsv' % (
cur_mmvec_folder, omic, filt, omic_, filt_)
drop_columns = [col for col in omic_songbird_ranks.columns if
omic_songbird_ranks[col].unique().size == 1]
meta_omic_pd = omic_songbird_ranks.drop(columns=drop_columns)
meta_omic_pd.to_csv(meta_omic_fp, index=False, sep='\t')
meta_omic_pd.set_index('Feature ID', inplace=True)
self.metas[(pair, subset, omic, filt, omic_, filt_)] = (
meta_omic_fp, meta_omic_pd, feats_diff_cols)
def get_mmvec_res(self):
mmvec_out_cols = [x for x in self.mmvec_songbird_pd.columns if
x.startswith('mmvec_out__')]
# for ech row of the main table that also
# contain the mmvec output folders
for r, row in self.mmvec_songbird_pd.iterrows():
pair = row['pair']
subset = row['subset']
omic1 = row['omic1']
omic2 = row['omic2']
filt1 = row['filter1']
filt2 = row['filter2']
omic1_common_fp = row['omic1_common_fp']
if str(omic1_common_fp) == 'nan':
continue
omic2_common_fp = row['omic2_common_fp']
n_common = row['n_common']
meta_fp = row['meta_common_fp']
# for each mmvec-parameters result
for mmvec_out_col in mmvec_out_cols:
# get the current parameters output result folder
mmvec_out = row[mmvec_out_col]
if str(mmvec_out) == 'nan':
continue
# get the ordination file and the ranks file and skip
# + warning if not performed
mmvec_out_ranks = mmvec_out + '/model/ranks.tsv'
mmvec_out_ordi = mmvec_out + '/model/ordination.txt'
if not isfile(mmvec_out_ranks) or not isfile(mmvec_out_ordi):
issue = '\t\t[run mmvec first] %s (%s) %s (%s)' % (
omic1, filt1, omic2, filt2)
self.mmvec_issues.add(issue)
continue
# collect the ranks + ordination
# + songbirds for each pair of omics and parameters
self.mmvec_res[
(pair, subset, omic1, omic2, filt1, filt2, n_common,
mmvec_out_col.replace('mmvec_out__', ''))
] = [mmvec_out_ranks, mmvec_out_ordi, meta_fp,
omic1_common_fp, omic2_common_fp]
def get_pair_cmds(self, omics_pairs):
crowdeds = [0, 1]
pc_sb_correlations = []
for keys, values in self.mmvec_res.items():
pair, case, omic1, omic2, filt1, filt2, sams, mmvec = keys
ranks_fp, ordi_fp, meta_fp, omic1_common, omic2_common = values
order_omics = get_order_omics(omic1, omic2, filt1, filt2, case,
omics_pairs)
omic1 = order_omics[0]
omic2 = order_omics[1]
filt1 = order_omics[2]
filt2 = order_omics[3]
omic_feature = order_omics[4]
omic_sample = order_omics[5]
omic_microbe = order_omics[6]
omic_metabolite = order_omics[7]
# get differentials
meta1, meta_pd1, diff_cols1 = self.metas[(pair, case, omic1,
filt1, omic2, filt2)]
meta2, meta_pd2, diff_cols2 = self.metas[(pair, case, omic2,
filt2, omic1, filt1)]
# features are biplot, samples are dots
ordi = OrdinationResults.read(ordi_fp)
cur_pc_sb_correlations, max_r = get_pc_sb_correlations(
pair, case, ordi, omic1, omic2, filt1, filt2, diff_cols1,
meta_pd1, diff_cols2, meta_pd2, meta_fp, omic1_common,
omic2_common, ranks_fp)
pc_sb_correlations.append(cur_pc_sb_correlations)
cmd = ''
if pair in self.highlights:
pair_highlights = self.highlights[pair]
for highlight, regexes_list in pair_highlights.items():
n_edit, meta_edit, ordi_edit_fp = edit_ordi_qzv(
ordi, ordi_fp, highlight, regexes_list, meta1, meta_pd1)
if n_edit:
qza, qzv = get_qzs(ordi_edit_fp)
cmd += get_biplot_commands(
ordi_edit_fp, qza, qzv, omic_feature, omic_sample,
meta_edit, meta2, n_edit, max_r)
ordi_edit_fp = ordi_fp
qza, qzv = get_qzs(ordi_edit_fp)
for crowded in crowdeds:
if crowded:
n_ordi_feats = ordi.features.shape[0]
qzv = qzv.replace('.qzv', '_crowded.qzv')
else:
n_ordi_feats = 15
# heat_qza, heat_qzv = get_heatmap_qzs(ranks_fp)
# cmd += get_heatmap_commands(
# ranks_fp, heat_qza, heat_qzv, meta1,
# meta2, meta_pd1, meta_pd2)
cmd += get_biplot_commands(
ordi_edit_fp, qza, qzv, omic_feature, omic_sample,
meta1, meta2, n_ordi_feats, max_r)
cmd += get_xmmvec_commands(
ordi_edit_fp, omic1, omic2, meta1, meta2, self.xmmvecs, pair)
topn = 5
features_names = []
if features_names:
heat = '%s_paired_heatmaps_custom.qzv' % splitext(ranks_fp)[0]
else:
heat = '%s_paired_heatmaps_top%s.qzv' % (splitext(ranks_fp)[0],
topn)
cmd += get_paired_heatmaps_command(
ranks_fp, omic1_common, omic2_common, meta1, features_names,
topn, heat)
self.cmds.setdefault(pair, []).append(cmd)
return pc_sb_correlations
def show_mmvec_issues(self):
if self.mmvec_issues:
for mmvec_issue in self.mmvec_issues:
print(mmvec_issue)
def mmbird(self, paired_datasets, differentials):
if not paired_datasets.mmvec_pd.shape[0]:
print('No mmvec output detected...')
return None
self.prep_mmvec(paired_datasets.mmvec_pd)
if differentials.songbird_pd.shape[0]:
songbird_pd = self.prep_songbird(differentials.songbird_pd)
self.merge_mmvec_songbird(songbird_pd)
self.get_taxo_pds()
self.get_omics_songbirds_taxa()
self.get_mmvec_res()
self.show_mmvec_issues()
omics_pairs = [tuple(x) for x in self.mmvec_songbird_pd[
['omic_subset_filt1', 'omic_subset_filt2']].values.tolist()]
pc_sb_correlations = self.get_pair_cmds(omics_pairs)
if len(pc_sb_correlations):
out_folder = get_analysis_folder(self.config.i_datasets_folder,
'mmbird')
out_correlations = '%s/pc_vs_songbird_correlations.tsv' % out_folder
pc_sb_correlations_pd = pd.concat(pc_sb_correlations)
if pc_sb_correlations_pd.shape[0]:
pc_sb_correlations_pd.to_csv(
out_correlations, index=False, sep='\t')
print('\t\t==> Written:', out_correlations)
else:
print('\t\t==> No good songbird model to '
'make correlations with mmvec PCs...')
self.register_command('mmbird')
def register_command(self, analysis):
AnalysisPrep.analyses_commands[analysis] = self.cmds
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12765,
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22724,
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13,
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679,
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4317,
2003,
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690,
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2003,
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22724,
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2003,
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2541,
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1649,
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305,
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310,
278,
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1591,
393,
884,
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1712,
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5654,
2003,
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16495,
13,
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363,
364,
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1948,
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4317,
2003,
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21453,
18513,
29918,
15926,
29889,
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5727,
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13,
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5101,
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1948,
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29918,
9435,
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2003,
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354,
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1958,
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1958,
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13,
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9920,
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679,
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4317,
2003,
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13,
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12700,
29906,
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29889,
29916,
4317,
2003,
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5101,
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13,
13,
9651,
2246,
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353,
29871,
29945,
13,
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2616,
2674,
800,
13,
13,
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822,
1510,
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4317,
2003,
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12175,
29898,
1311,
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13,
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565,
1583,
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4317,
2003,
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13,
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363,
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2003,
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15118,
297,
1583,
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4317,
2003,
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12175,
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13,
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1596,
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2003,
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13,
13,
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18513,
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13,
4706,
565,
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3300,
2859,
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1691,
29889,
4317,
2003,
29918,
15926,
29889,
12181,
29961,
29900,
5387,
13,
9651,
1596,
877,
3782,
5654,
2003,
1962,
17809,
856,
1495,
13,
9651,
736,
6213,
13,
4706,
1583,
29889,
15287,
29918,
4317,
2003,
29898,
3274,
2859,
29918,
14538,
1691,
29889,
4317,
2003,
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] |
fabfile.py | zzz123-tech/zhanzhenblog | 0 | 46113 | <gh_stars>0
from fabric import task
from invoke import Responder
from _credentials import github_username, github_password
def _get_github_auth_responders():
"""
返回 GitHub 用户名密码自动填充器
"""
username_responder = Responder(
pattern="Username for 'https://github.com':",
response='{}\n'.format(github_username)
)
password_responder = Responder(
pattern="Password for 'https://{}@github.com':".format(github_username),
response='{}\n'.format(github_password)
)
return [username_responder, password_responder]
@task()
def deploy(c):
supervisor_conf_path = '~/etc/'
supervisor_program_name = 'zhanzhenblog'
project_root_path = '~/apps/zhanzhenblog/'
# 先停止应用
with c.cd(supervisor_conf_path):
cmd = '/home/zhanzhen/.local/bin/supervisorctl stop {}'.format(supervisor_program_name)
c.run(cmd)
# 进入项目根目录，从 Git 拉取最新代码
with c.cd(project_root_path):
cmd = 'git pull'
responders = _get_github_auth_responders()
c.run(cmd, watchers=responders)
# 安装依赖，迁移数据库，收集静态文件
with c.cd(project_root_path):
c.run('pipenv install --deploy --ignore-pipfile')
c.run('pipenv run python manage.py migrate')
c.run('pipenv run python manage.py collectstatic --noinput')
# 重新启动应用
with c.cd(supervisor_conf_path):
cmd = '/home/zhanzhen/.local/bin/supervisorctl start {}'.format(supervisor_program_name)
c.run(cmd)
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sources/praline/client/project/pipeline/stages/pull_dependencies_test.py | dansandu/praline | 0 | 1615411 | <filename>sources/praline/client/project/pipeline/stages/pull_dependencies_test.py
from os.path import basename, normpath
from praline.client.project.pipeline.stages.pull_dependencies import pull_dependencies
from praline.common.testing.file_system_mock import FileSystemMock
from typing import Any, Dict
from unittest import TestCase
import pickle
class CompilerMock:
def get_name(self):
return 'clang'
def get_architecture(self):
return 'x64'
def get_platform(self):
return 'darwin'
def get_mode(self):
return 'release'
class RemoteProxyMock:
def __init__(self, client_file_system):
self.client_file_system = client_file_system
self.packages = {
'fruit-apple-x64-darwin-clang-release-1.0.0.tar.gz': pickle.dumps({
'headers/fruit/apple/apple.hpp': b'fruit-apple-hpp',
'libraries/libfruit-apple-x64-darwin-clang-release-1.0.0.dylib': b'fruit-apple-dylib'
}),
'fruit-grapes-x64-darwin-clang-release-1.0.0.tar.gz': pickle.dumps({
'headers/fruit/grapes/grapes.hpp': b'new-fruit-grapes-hpp',
'libraries/libfruit-grapes-x64-darwin-clang-release-1.0.0.dylib': b'new-fruit-grapes-dylib'
}),
'fruit-waterlemon-x64-darwin-clang-release-1.0.0.tar.gz': pickle.dumps({
'headers/fruit/waterlemon/waterlemon.hpp': b'fruit-waterlemon-hpp',
'libraries/libfruit-waterlemon-x64-darwin-clang-release-1.0.0.dylib': b'fruit-waterlemon-dylib'
})
}
def pull_package(self, package_path: str) -> None:
package_name = basename(package_path)
with self.client_file_system.open_file(package_path, 'wb') as f:
f.write(self.packages[package_name])
def push_package(self, package_path: str) -> None:
package_name = basename(package_path)
with self.client_file_system.open_file(package_path, 'rb') as f:
self.packages[package_name] = f.read()
def solve_dependencies(self, pralinefile: Dict[str, Any], architecture: str, platform: str, compiler: str, mode: str) -> Dict[str, str]:
return {
'fruit-apple-x64-darwin-clang-release-1.0.0.tar.gz': 'apple-hash',
'fruit-grapes-x64-darwin-clang-release-1.0.0.tar.gz': 'new-grapes-hash',
'fruit-waterlemon-x64-darwin-clang-release-1.0.0.tar.gz': 'waterlemon-hash'
}
class PullDependenciesStageTest(TestCase):
def test_pull_dependencies(self):
pralinefile = b"""\
organization: fruit
artifact: banana
version: 1.0.0
dependencies:
- organization: fruit
artifact: apple
version: 1.0.0
"""
file_system = FileSystemMock({
'project/target/external/headers/fruit/grapes',
'project/target/external/headers/fruit/waterlemon',
'project/target/external/headers/fruit/raspberry',
'project/target/external/libraries',
'project/target/external/packages'
}, {
'project/target/external/headers/fruit/grapes/grapes.hpp': b'fruit-grapes-hpp',
'project/target/external/headers/fruit/waterlemon/waterlemon.hpp': b'fruit-waterlemon-hpp',
'project/target/external/headers/fruit/raspberry/raspberry.hpp': b'fruit-raspberry-hpp',
'project/target/external/libraries/libfruit-grapes-x64-darwin-clang-release-1.0.0.dylib': b'fruit-grapes-dylib',
'project/target/external/libraries/libfruit-waterlemon-x64-darwin-clang-release-1.0.0.dylib': b'fruit-waterlemon-dylib',
'project/target/external/libraries/libfruit-raspberry-x64-darwin-clang-release-1.0.0.dylib': b'fruit-raspberry-dylib',
'project/target/external/packages/fruit-grapes-x64-darwin-clang-release-1.0.0.tar.gz': pickle.dumps({
'headers/fruit/grapes/grapes.hpp': b'fruit-grapes-hpp',
'libraries/libfruit-grapes-x64-darwin-clang-release-1.0.0.dylib': b'fruit-grapes-dylib'
}),
'project/target/external/packages/fruit-waterlemon-x64-darwin-clang-release-1.0.0.tar.gz': pickle.dumps({
'headers/fruit/waterlemon/waterlemon.hpp': b'fruit-waterlemon-hpp',
'libraries/libfruit-waterlemon-x64-darwin-clang-release-1.0.0.dylib': b'fruit-waterlemon-dylib'
}),
'project/target/external/packages/fruit-raspberry-x64-darwin-clang-release-1.0.0.tar.gz': pickle.dumps({
'headers/fruit/raspberry/raspberry.hpp': b'fruit-raspberry-hpp',
'libraries/libfruit-raspberry-x64-darwin-clang-release-1.0.0.dylib': b'fruit-raspberry-dylib'
})
})
resources = {
'project_directory': 'project',
'pralinefile': pralinefile,
'architecture': 'x64',
'platform': 'darwin',
'mode': 'release',
'compiler': CompilerMock()
}
cache = {
'fruit-grapes-x64-darwin-clang-release-1.0.0.tar.gz': 'grapes-hash',
'fruit-waterlemon-x64-darwin-clang-release-1.0.0.tar.gz': 'waterlemon-hash',
'fruit-raspberry-x64-darwin-clang-release-1.0.0.tar.gz': 'raspberry-hash'
}
program_arguments = {
'global': {
'logging_level': 4
}
}
remote_proxy = RemoteProxyMock(file_system)
pull_dependencies(file_system, resources, cache, program_arguments, None, remote_proxy)
expected_files = {
'project/target/external/headers/fruit/apple/apple.hpp': b'fruit-apple-hpp',
'project/target/external/headers/fruit/grapes/grapes.hpp': b'new-fruit-grapes-hpp',
'project/target/external/headers/fruit/waterlemon/waterlemon.hpp': b'fruit-waterlemon-hpp',
'project/target/external/libraries/libfruit-apple-x64-darwin-clang-release-1.0.0.dylib': b'fruit-apple-dylib',
'project/target/external/libraries/libfruit-grapes-x64-darwin-clang-release-1.0.0.dylib': b'new-fruit-grapes-dylib',
'project/target/external/libraries/libfruit-waterlemon-x64-darwin-clang-release-1.0.0.dylib': b'fruit-waterlemon-dylib',
'project/target/external/packages/fruit-apple-x64-darwin-clang-release-1.0.0.tar.gz': pickle.dumps({
'headers/fruit/apple/apple.hpp': b'fruit-apple-hpp',
'libraries/libfruit-apple-x64-darwin-clang-release-1.0.0.dylib': b'fruit-apple-dylib'
}),
'project/target/external/packages/fruit-grapes-x64-darwin-clang-release-1.0.0.tar.gz': pickle.dumps({
'headers/fruit/grapes/grapes.hpp': b'new-fruit-grapes-hpp',
'libraries/libfruit-grapes-x64-darwin-clang-release-1.0.0.dylib': b'new-fruit-grapes-dylib'
}),
'project/target/external/packages/fruit-waterlemon-x64-darwin-clang-release-1.0.0.tar.gz': pickle.dumps({
'headers/fruit/waterlemon/waterlemon.hpp': b'fruit-waterlemon-hpp',
'libraries/libfruit-waterlemon-x64-darwin-clang-release-1.0.0.dylib': b'fruit-waterlemon-dylib'
})
}
self.assertEqual(file_system.files, {normpath(p): d for p, d in expected_files.items()})
expected_directories = {
'project/target/external/packages',
'project/target/external/resources',
'project/target/external/headers/fruit/apple',
'project/target/external/headers/fruit/grapes',
'project/target/external/headers/fruit/waterlemon',
'project/target/external/libraries',
'project/target/external/libraries_interfaces',
'project/target/external/symbols_tables',
'project/target/external/executables'
}
self.assertEqual(file_system.directories, {normpath(p) for p in expected_directories})
self.assertEqual(resources['external_resources_root'], normpath('project/target/external/resources'))
self.assertEqual(resources['external_headers_root'], normpath('project/target/external/headers'))
self.assertEqual(resources['external_libraries_root'], normpath('project/target/external/libraries'))
self.assertEqual(resources['external_libraries_interfaces_root'], normpath('project/target/external/libraries_interfaces'))
self.assertEqual(resources['external_symbols_tables_root'], normpath('project/target/external/symbols_tables'))
self.assertEqual(resources['external_executables_root'], normpath('project/target/external/executables'))
self.assertEqual(resources['external_resources'], [])
expected_headers = {
'project/target/external/headers/fruit/apple/apple.hpp',
'project/target/external/headers/fruit/grapes/grapes.hpp',
'project/target/external/headers/fruit/waterlemon/waterlemon.hpp'
}
self.assertEqual({normpath(p) for p in resources['external_headers']}, {normpath(p) for p in expected_headers})
expected_libraries = {
'project/target/external/libraries/libfruit-apple-x64-darwin-clang-release-1.0.0.dylib',
'project/target/external/libraries/libfruit-grapes-x64-darwin-clang-release-1.0.0.dylib',
'project/target/external/libraries/libfruit-waterlemon-x64-darwin-clang-release-1.0.0.dylib'
}
self.assertEqual({normpath(p) for p in resources['external_libraries']}, {normpath(p) for p in expected_libraries})
self.assertEqual(resources['external_libraries_interfaces'], [])
self.assertEqual(resources['external_symbols_tables'], [])
self.assertEqual(resources['external_executables'], [])
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] |
tests/conftest.py | Peter-Metz/taxdata | 0 | 29588 | import os
import json
import pytest
import pandas as pd
# TODO: revise the following constants when using new or revised CPS/PUF data
CPS_START_YEAR = 2014
PUF_START_YEAR = 2011
PUF_COUNT = 248591
LAST_YEAR = 2027
@pytest.fixture(scope='session')
def test_path():
return os.path.abspath(os.path.dirname(__file__))
@pytest.fixture(scope='session')
def growfactors(test_path):
gf_path = os.path.join(test_path, '../puf_stage1/growfactors.csv')
return pd.read_csv(gf_path, index_col='YEAR')
@pytest.fixture(scope='session')
def metadata(test_path):
md_path = os.path.join(test_path, 'records_metadata.json')
with open(md_path, 'r') as mdf:
return json.load(mdf)
@pytest.fixture(scope='session')
def cps(test_path):
cps_path = os.path.join(test_path, '../cps_data/cps.csv.gz')
return pd.read_csv(cps_path)
@pytest.fixture(scope='session')
def cps_count(test_path):
cps_path = os.path.join(test_path, '../cps_data/cps.csv.gz')
cps_df = pd.read_csv(cps_path)
return cps_df.shape[0]
@pytest.fixture(scope='session')
def cps_start_year():
return CPS_START_YEAR
@pytest.fixture(scope='session')
def puf_path(test_path):
return os.path.join(test_path, '../puf_data/puf.csv')
@pytest.fixture(scope='session')
def puf(puf_path):
if os.path.isfile(puf_path):
return pd.read_csv(puf_path)
else:
return None
@pytest.fixture(scope='session')
def puf_count(puf_path):
if os.path.isfile(puf_path):
puf_df = pd.read_csv(puf_path)
count = puf_df.shape[0]
if count != PUF_COUNT:
msg = 'puf.shape[0] = {} not equal to PUF_COUNT = {}'
raise ValueError(msg.format(count, PUF_COUNT))
else:
count = PUF_COUNT
return count
@pytest.fixture(scope='session')
def puf_start_year():
return PUF_START_YEAR
@pytest.fixture(scope='session')
def last_year():
return LAST_YEAR
@pytest.fixture(scope='session')
def cps_weights(test_path):
cpsw_path = os.path.join(test_path, '../cps_stage2/cps_weights.csv.gz')
return pd.read_csv(cpsw_path)
@pytest.fixture(scope='session')
def puf_weights(test_path):
pufw_path = os.path.join(test_path, '../puf_stage2/puf_weights.csv.gz')
return pd.read_csv(pufw_path)
@pytest.fixture(scope='session')
def cps_ratios(test_path):
# cpsr_path = os.path.join(test_path, '../cps_stage3/cps_ratios.csv')
# return pd.read_csv(cpsr_path, index_col=0)
return None
@pytest.fixture(scope='session')
def puf_ratios(test_path):
pufr_path = os.path.join(test_path, '../puf_stage3/puf_ratios.csv')
return pd.read_csv(pufr_path, index_col=0)
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] |
cli_helpers/templates/bin/renamer.py | ppreeper/ansible_roles | 0 | 108602 | <reponame>ppreeper/ansible_roles
#!/usr/bin/env python3
# -*- coding: utf-8 -*-
import os
import eyed3
import argparse
import re
us = re.compile(r' ')
sq = re.compile(r"'")
dq = re.compile(r'""')
ex = re.compile(r"!")
colon = re.compile(r":")
semicolon = re.compile(r";")
question = re.compile(r'\?')
parser = argparse.ArgumentParser()
parser.add_argument("filename")
args = parser.parse_args()
audiofile = eyed3.load(args.filename)
track_num = "%02d" % audiofile.tag.track_num[0]
disc_num = "%02d" % audiofile.tag.disc_num[0]
# print(disc_num)
title = us.sub('_', audiofile.tag.title)
title = sq.sub('', title)
title = dq.sub('', title)
title = ex.sub('', title)
title = colon.sub('', title)
title = semicolon.sub('', title)
title = question.sub('', title)
print("%s-%s.mp3" % (track_num, title))
new_filename = "{0}-{1}.mp3".format(track_num, title)
os.rename(args.filename, new_filename)
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user_role_mgmt/operations.py | sundarsms1975/user-role-management | 0 | 66932 | <reponame>sundarsms1975/user-role-management
from accounts import User,Role
import ast
import json
import os
import sys
from copy import deepcopy
# Operation List presented when the main program execution starts
def choice_list():
print('\n Choose the operation you would like to perform on User & Role List')
print(' 1. Load User List from INPUT file ')
print(' 2. Load Role List from INPUT file ')
print(' 3. Show Subordinates defined under a Role ID')
print(' 4. Add a new Role to List ')
print(' 5. Add a new User to List ')
print(' 6. Remove a role from List ')
print(' 7. Remove a user from List ')
print(' 8. Show Users with role definition ')
print(' 9. Show Subordinates with role details defined under a Role ID ')
print(' 10. Show Users belongs to a Role_ID ')
print(' 11. Show Users belongs to a Parent_ID')
print(' 12. Show Roles belongs to a Particular Parent_ID')
print(' 14. Exit ')
print('\n')
# Function loads User list from INPUT FILE and returns a list of dictionary objects
def getUserFromFile(filename):
try:
with open(filename) as f:
s = f.read()
users = ast.literal_eval(s)
except OSError as err:
print("OS error: {0}".format(err))
except:
print("Unexpected error:", sys.exc_info()[0])
return users
# Function loads Roles list from INPUT FILE and returns a list of dictionary objects
def getRoleFromFile(filename):
try:
with open(filename) as f:
s = f.read()
roles = ast.literal_eval(s)
except OSError as err:
print("OS error: {0}".format(err))
except:
print("Unexpected error:", sys.exc_info()[0])
return roles
# Function to fetch subordinates defined under a particular Role ID
def getSubordinates(roleid,userList):
filteredUsers = [user for user in userList if int(user["Role"]) > int(roleid) ]
return filteredUsers
# Function checks whether a Role exist in the current List
def isRoleExist(role_id,roles):
for r in roles:
if int(role_id) == int(r["Id"]):
return True
# Function checks whether Role to be added exists in the list before adding a Role to the List
def addRoleToList(role,roleList):
try:
if not isRoleExist(role.role_id,roleList):
roleList.append({"Id":role.role_id,"Name":role.role_name,"Parent":role.parent_id})
del role
else:
print("Role_ID found in the new User object")
except OSError as err:
print("OS error: {0}".format(err))
except:
print("Unexpected error:", sys.exc_info()[0])
return roleList
# Function checks the role to be deleted before removing it
def removeRole(role_id,roleList):
if not isRoleExist(role_id,roleList):
print("Role ID supplied does not exist")
try:
for i in range(len(roleList)):
if int(roleList[i]["Id"]) == int(role_id):
del roleList[i]
break
except:
print("Unexpected error:", sys.exc_info()[0])
# Function checks the role before adding a user to a particular role.
def addUserToList(user,userList,roleList):
try:
if isRoleExist(user.roleId,roleList):
userList.append({"Id":user.user_id,"Name":user.username,"Role":user.roleId})
del user
else:
print("Role_ID not found in the new User object, please add the role and add the user again")
except OSError as err:
print("OS error: {0}".format(err))
except:
print("Unexpected error:", sys.exc_info()[0])
return userList
# Function checks whether a User exists
def isUserExist(user_id,users):
for user in users:
if int(user_id) == int(user["Id"]):
return True
# Function checks whether the user exits in the list before deleting it
def removeUser(user_id,userList):
if not isUserExist(user_id,userList):
print("User ID supplied does not exist")
try:
for i in range(len(userList)):
if int(userList[i]["Id"]) == int(user_id):
del userList[i]
break
except:
print("Unexpected error:", sys.exc_info()[0])
def lineDecorator(ichar):
print(ichar*120)
#Get subordinates based on role_id
def getSubOrdinates(role_id,users):
filteredUsers = [user for user in users if int(user["Role"]) > int(role_id) ]
return filteredUsers
#Get user based on user_id
def getUser(user_id,users):
filteredUsers = [user for user in users if int(user["Id"]) == int(user_id) ]
return filteredUsers
# Decorator funtion
def linespacing():
print("\n")
# Function to flatten User and Role, produces a combined list
# A single list user and role details in one record
def flattenUserRole(userList,roleList):
userList_c = deepcopy(userList)
for user in userList_c:
for role in roleList:
if user["Role"] == role["Id"]:
user.update({"role_name":role["Name"],"parent_id":role["Parent"]})
return userList_c
# Function to output a Role or User List in a nice JSON format
def dispResult(output_list):
lineDecorator("*")
print(json.dumps(output_list,indent=2))
lineDecorator("*")
#Spacing function as decorator
linespacing()
# Function to filter subordinates defined under a particular role.
def outputResult(id,output_list):
lineDecorator("*")
print(json.dumps(getSubOrdinates(id,output_list),indent=2))
lineDecorator("*")
#Spacing function as decorator
linespacing()
# Screen clear function, based on the OS type, it automatically chooses the command
# to clear the screen
def clear():
_ = os.system('clear' if os.name =='posix' else 'cls')
# Pause function waits until user hits ENTER button to move the main Menu
def hitContine():
input('Hit Enter to continue')
# Function to update changes back to User and Role definition INPUT files
# Next run of this program of picks up the latest changes as the starting List of Roles
def dumpListsToFile(Lists,filename):
try:
with open(filename,'w+') as f:
json.dump(Lists,f,indent=2)
except OSError as err:
print("OS error: {0}".format(err))
except:
print("Unexpected error:", sys.exc_info()[0])
#Get subordinates based on role_id
def getUserListForRoleId(role_id,users):
filteredUsers = [user for user in users if int(user["Role"]) == int(role_id) ]
return filteredUsers
#Get All users belongs to a particular parent_id
def getListForParentID(parent_id,users):
filteredUsers = [user for user in users if int(user["parent_id"]) == int(parent_id) ]
return filteredUsers
# Get all roles belongs to a particular parent_id
def getRolesListForParentID(parent_id,roles):
filteredRoles = [role for role in roles if int(role["Parent"]) == int(parent_id) ]
return filteredRoles
# The below functions are defined as decorator to output the result.s
def outputUserList_forRoleId(id,output_list):
lineDecorator("*")
print(json.dumps(getUserListForRoleId(id,output_list),indent=2))
lineDecorator("*")
#Spacing function as decorator
linespacing()
def outputUserList_forParentId(id,output_list):
lineDecorator("*")
print(json.dumps(getListForParentID(id,output_list),indent=2))
lineDecorator("*")
#Spacing function as decorator
linespacing()
def outputRolesList_forParentId(parent_id,roles):
lineDecorator("*")
print(json.dumps(getRolesListForParentID(parent_id,roles),indent=2))
lineDecorator("*")
#Spacing function as decorator
linespacing() | [
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] |
scripts/featuresFromIntermediates.py | mkalte666/sdl2wrap | 0 | 174968 | import sys
import re
ignoreList = [
".*_h_$",
"^SDL_test.*",
"^SDLTest_.*",
"SDLCALL",
"^SDL_$",
".*_$",
"SDL_INLINE",
"^SDL$",
"^SDL_Read.*$",
"^SDL_Write.*$",
]
class ImplInfo:
def __init__(self):
self.implemented = "NO"
self.implName = ""
self.comment = ""
if len(sys.argv) != 5:
print("do not use this directly!!!")
def calcCoverage(knownSymbols):
count = len(knownSymbols)
countYes = 0
for key in knownSymbols:
value = knownSymbols[key]
if value.implemented != "NO":
countYes+=1
return "%.2f %% (%d/%d)" % (float(countYes)/float(count)*100.0, countYes, count)
wrapImplName = sys.argv[1]
wrapWontfixName = sys.argv[2]
sdlGrepName = sys.argv[3]
outfileName = sys.argv[4]
wrapImplFile = open(wrapImplName, "r").read().splitlines(False)
wrapWontfixFile = open(wrapWontfixName, "r").read().splitlines(False)
sdlGrepFile = open(sdlGrepName, "r").read().splitlines(False)
knownSymbols = {}
for name in sdlGrepFile:
needIgnore = False
for ignore in ignoreList:
if re.match(rf"{ignore}", name):
needIgnore = True
break
if needIgnore:
continue
knownSymbols[name] = ImplInfo()
for line in wrapImplFile:
parts = line.split(" ", 2)
what = parts[0]
info = ImplInfo()
info.implemented = "YES"
if len(parts) > 1:
info.implName = parts[1]
if len(parts) > 2:
info.comment = parts[2]
knownSymbols[what] = info
for line in wrapWontfixFile:
parts = line.split(" ", 1)
what = parts[0]
info = ImplInfo()
info.implemented = "WONTFIX"
if len(parts) > 1:
info.comment = parts[1]
knownSymbols[what] = info
outfile = open(outfileName, "w")
outfile.write("This list is generated and might not be reflect the truth to 100%\n")
outfile.write("Estimated coverage: %s\n" % calcCoverage(knownSymbols))
outfile.write("\n")
outfile.write("| SDL Symbol | Implemented? | New Name | Comment |\n")
outfile.write("| ---------- | ------------ | -------- | ------- |\n")
for key in sorted(knownSymbols):
info = knownSymbols[key]
line = "| {} | {} | {} | {} |\n".format(key, info.implemented, info.implName, info.comment)
outfile.write(line)
outfile.close() | [
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] |
examples/examples_interceptor.py | hatamiarash7/PacketTracer | 3 | 126791 | """
Interceptor example using ICMP
Requirements:
iptables -I INPUT 1 -p icmp -j NFQUEUE --queue-balance 0:2
"""
import time
from packetracer import interceptor
from packetracer.layer3 import ip, icmp
# Add iptables rule:
# iptables -I INPUT 1 -p icmp -j NFQUEUE --queue-balance 0:2
# ICMP Echo request intercepting
def verdict_cb(ll_data, ll_proto_id, data, ctx):
ip1 = ip.IP(data)
icmp1 = ip1[icmp.ICMP]
if icmp1 is None or icmp1.type != icmp.ICMP_TYPE_ECHO_REQ:
return data, interceptor.NF_ACCEPT
echo1 = icmp1[icmp.ICMP.Echo]
if echo1 is None:
return data, interceptor.NF_ACCEPT
pp_bts = b"PACKETRACER"
print("changing ICMP echo request packet")
echo1.body_bytes = echo1.body_bytes[:len(pp_bts)] + pp_bts
return ip1.bin(), interceptor.NF_ACCEPT
ictor = interceptor.Interceptor()
ictor.start(verdict_cb, queue_ids=[0, 1, 2])
try:
time.sleep(999)
except KeyboardInterrupt:
pass
ictor.stop()
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TCO2_Dashboard/content_BCT.py | originalpkbims/subgrounds-pkbims | 0 | 117049 | <gh_stars>0
from dash import html
from dash import dcc
import dash_bootstrap_components as dbc
from helpers import (date_manipulations, black_list_manipulations,
filter_carbon_pool)
from Figures_Carbon_Pool import deposited_over_time, redeemed_over_time
from data_related_constants import redeems_rename_map, deposits_rename_map
from import_data import get_data_pool
df_deposited, df_redeemed = get_data_pool()
# rename_columns
df_deposited = df_deposited.rename(columns=deposits_rename_map)
df_redeemed = df_redeemed.rename(columns=redeems_rename_map)
# datetime manipulations
df_deposited = date_manipulations(df_deposited)
df_redeemed = date_manipulations(df_redeemed)
# Blacklist manipulations
df_deposited = black_list_manipulations(df_deposited)
df_redeemed = black_list_manipulations(df_redeemed)
# Carbon pool filter
BCT_deposited, BCT_redeemed = filter_carbon_pool(
df_deposited, df_redeemed, "0x2f800db0fdb5223b3c3f354886d907a671414a7f")
# Figures
fig_deposited_over_time = deposited_over_time(BCT_deposited)
fig_redeemed_over_time = redeemed_over_time(BCT_redeemed)
content_BCT = [
dbc.Row(
dbc.Col(
dbc.Card([
dbc.CardHeader(
html.H1("Toucan Protocol : Base Carbon Tonne Pool", className='page-title'))
]), width=8)
),
dbc.Row([
dbc.Col(
dbc.Card([
html.H5("TCO2 tokens deposited", className="card-title"),
dbc.CardBody("{:,}".format(
int(BCT_deposited["Quantity"].sum())), className="card-text")
]), width=4),
dbc.Col(
dbc.Card([
html.H5("TCO2 tokens redeemed", className="card-title"),
dbc.CardBody("{:,}".format(
int(BCT_redeemed["Quantity"].sum())), className="card-text")
]), width=4),
dbc.Col(
dbc.Card([
html.H5("BCT tokens retired", className="card-title"),
dbc.CardBody("Coming soon", className="card-text")
]), width=4),
], style={'padding-top': '96px'}),
dbc.Row([
dbc.Col([
dbc.Card([
html.H5("TCO2 tokens deposited over time",
className="card-title"),
dbc.CardBody(dcc.Graph(figure=fig_deposited_over_time))
], className="card-graph")
], width=4),
dbc.Col([
dbc.Card([
html.H5("TCO2 tokens redeemed over time",
className="card-title"),
dbc.CardBody(dcc.Graph(figure=fig_redeemed_over_time))
], className="card-graph")
], width=4),
dbc.Col([
dbc.Card([
html.H5("BCT tokens retired over time",
className="card-title"),
dbc.CardBody("Coming soon", className="card-text")
], className="card-graph"),
], width=4)
])
]
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src/nprintml/learn/__init__.py | nprint/nPrintML | 13 | 1604351 | from .automl import AutoML # noqa: F401
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test/__init__.py | shikajiro/picosdk-python-wrappers | 114 | 28921 | #
# Copyright (C) 2018 Pico Technology Ltd. See LICENSE file for terms.
#
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configs/keypoints/cascade_mask_rcnn_x101_3x_coco.py | VGrondin/CBNetV2_mask_remote | 0 | 109190 | _base_ = '../cascade_rcnn/cascade_mask_rcnn_r50_fpn_mstrain_3x_coco.py'
model = dict(
backbone=dict(
type='ResNeXt',
depth=101,
groups=32,
base_width=8,
num_stages=4,
out_indices=(0, 1, 2, 3),
frozen_stages=1,
norm_cfg=dict(type='BN', requires_grad=False),
style='pytorch',
init_cfg=dict(
type='Pretrained',
checkpoint='open-mmlab://detectron2/resnext101_32x8d')),
roi_head=dict(
_delete_=True,
type='KeypointCascadeRoIHead',
output_heatmaps=False,
keypoint_decoder=dict(type='HeatmapDecodeOneKeypoint', upscale=4),
num_keypoints=5,
num_stages=3,
stage_loss_weights=[1, 0.5, 0.25],
bbox_roi_extractor=dict(
type='SingleRoIExtractor',
roi_layer=dict(type='RoIAlign', output_size=7, sampling_ratio=0),
out_channels=256,
featmap_strides=[4, 8, 16, 32]),
bbox_head=[
dict(
type='Shared2FCBBoxHead',
in_channels=256,
fc_out_channels=1024,
roi_feat_size=7,
num_classes=80,
bbox_coder=dict(
type='DeltaXYWHBBoxCoder',
target_means=[0., 0., 0., 0.],
target_stds=[0.1, 0.1, 0.2, 0.2]),
reg_class_agnostic=True,
loss_cls=dict(
type='CrossEntropyLoss',
use_sigmoid=False,
loss_weight=1.0),
loss_bbox=dict(type='SmoothL1Loss', beta=1.0,
loss_weight=1.0)),
dict(
type='Shared2FCBBoxHead',
in_channels=256,
fc_out_channels=1024,
roi_feat_size=7,
num_classes=80,
bbox_coder=dict(
type='DeltaXYWHBBoxCoder',
target_means=[0., 0., 0., 0.],
target_stds=[0.05, 0.05, 0.1, 0.1]),
reg_class_agnostic=True,
loss_cls=dict(
type='CrossEntropyLoss',
use_sigmoid=False,
loss_weight=1.0),
loss_bbox=dict(type='SmoothL1Loss', beta=1.0,
loss_weight=1.0)),
dict(
type='Shared2FCBBoxHead',
in_channels=256,
fc_out_channels=1024,
roi_feat_size=7,
num_classes=80,
bbox_coder=dict(
type='DeltaXYWHBBoxCoder',
target_means=[0., 0., 0., 0.],
target_stds=[0.033, 0.033, 0.067, 0.067]),
reg_class_agnostic=True,
loss_cls=dict(
type='CrossEntropyLoss',
use_sigmoid=False,
loss_weight=1.0),
loss_bbox=dict(type='SmoothL1Loss', beta=1.0, loss_weight=1.0))
],
mask_roi_extractor=dict(
type='SingleRoIExtractor',
roi_layer=dict(type='RoIAlign', output_size=14, sampling_ratio=0),
out_channels=256,
featmap_strides=[4, 8, 16, 32]),
mask_head=dict(
type='FCNMaskHead',
num_convs=4,
in_channels=256,
conv_out_channels=256,
num_classes=80,
loss_mask=dict(
type='CrossEntropyLoss', use_mask=True, loss_weight=1.0)))
)
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geekhours/database.py | YukieK/GeekHours | 3 | 175192 | <gh_stars>1-10
""" database.py is a module to communicate with a database. """
from datetime import datetime
import sqlite3
from typing import List
class Database:
""" Database class initializes and manipulates SQLite3 database. """
def __init__(self, db_name):
self.con = sqlite3.connect(db_name)
self.cur = self.con.cursor()
self.donelist = 'donelist'
self.course = 'course'
def close_db(self):
""" Close the database """
if self.con:
self.con.close()
def create_table(self):
"""
Create table.
donelist: Table for registration of date, course name and duration you studied.
course: Table for validation of course name.
"""
donelist = ("CREATE TABLE IF NOT EXISTS donelist ("
"id INTEGER PRIMARY KEY AUTOINCREMENT, "
"date TEXT NOT NULL, "
"course TEXT NOT NULL, "
"duration TEXT NOT NULL)")
course = ("CREATE TABLE IF NOT EXISTS course ("
"id INTEGER PRIMARY KEY AUTOINCREMENT, "
"name TEXT NOT NULL UNIQUE)")
with self.con:
self.cur.execute(donelist)
self.cur.execute(course)
def get_column(self, table: str):
""" Get column names """
self.con.row_factory = sqlite3.Row
cur = self.con.cursor()
if table == self.course:
course_columns = cur.execute('SELECT * FROM course').fetchone()
columns = course_columns.keys()
elif table == self.donelist:
donelist_columns = cur.execute('SELECT * FROM donelist').fetchone()
columns = donelist_columns.keys()
return columns
def show(self, table: str):
""" Show table """
if table == self.course:
ret = self.cur.execute('SELECT * FROM course').fetchall()
elif table == self.donelist:
ret = self.cur.execute('SELECT * FROM donelist').fetchall()
else:
raise RuntimeError("No such table.")
return ret
def insert_course(self, courses: List[str]):
""" Insert course name.
Insert course name into the 'course' table.
Insertion of the course name which is already registered will be discarded.
"""
for elem in courses:
with self.con:
self.con.execute('INSERT INTO course(name) VALUES (?)', (elem,))
print("Add '{}' in course.".format(elem))
def insert_donelist(self, date: str, course: str, duration: str):
""" Insert donelist.
Insert donelist into the 'donelist' table.
The course name must be a registered name in the 'course' table.
"""
check_course_name = self.con.execute('SELECT name FROM course WHERE name=?', (course,))
check_course_name = check_course_name.fetchall()
check_duplicate = self.con.execute(
'SELECT date course FROM donelist WHERE date=? AND course=?', (
date,
course,
))
check_duplicate = check_duplicate.fetchall()
if not check_course_name:
raise RuntimeError("No such course in 'course' table.")
if check_duplicate:
raise RuntimeError("Record already exists in 'donelist' table.")
if len(date) != 10:
raise ValueError("The date must be in YYYY-MM-DD format.")
is_valid = True
try:
date_dt = datetime.strptime(date, '%Y-%m-%d')
except ValueError:
is_valid = False
if is_valid:
date = date_dt.date()
else:
raise ValueError('Invalid date')
with self.con:
self.con.execute('INSERT INTO donelist(date, course, duration) VALUES (?, ?, ?)', (
date,
course,
duration,
))
print("Add '{} {} {}' in donelist.".format(date, course, duration))
def remove_course(self, course: str):
""" Remove course name from 'course' table. """
ret = self.con.execute('SELECT name FROM course WHERE name=?', (course,))
check = ret.fetchall()
if not check:
raise RuntimeError("No such course in 'course' table.")
with self.con:
self.cur.execute('DELETE FROM course WHERE name=?', (course,))
print("Removed '{}' from course.".format(course))
def remove_donelist(self, date: str, course: str):
""" Remove record from 'donelist' table.
Search record by date and course name and if found matched record, it will be removed.
"""
ret = self.con.execute('SELECT date, course FROM donelist WHERE date=? AND course=?', (
date,
course,
))
check = ret.fetchall()
if not check:
raise RuntimeError("No such record in 'donelist' table.")
with self.con:
self.cur.execute('DELETE FROM donelist WHERE date=? AND course=?', (
date,
course,
))
print('Removed record.')
def get_total_hours(self):
""" Get the total hours
Get the total number of hours and return it.
"""
with self.con:
total = self.cur.execute(
("SELECT 'Total: ', SUM(donelist.duration) FROM donelist")).fetchall()
return total
def get_total_hours_course(self):
""" Get the total hours per course
Get the total number of hours per course and return it.
"""
with self.con:
total = self.cur.execute(("SELECT course.name, SUM(donelist.duration) "
"FROM donelist "
"INNER JOIN course ON course.name = donelist.course "
"GROUP BY course.name")).fetchall()
return total
def get_total_hours_week(self, course: str = None):
""" Get the total hours per week
Get the total number of hours per week and return it.
If the course name is passed as an argument, return the total
hours per week for each course.
Args:
course: Target course name to get the total hours.
"""
with self.con:
if not course:
total = self.cur.execute(
("SELECT strftime('%w', donelist.date) AS week, SUM(donelist.duration) "
"FROM donelist "
"INNER JOIN course ON course.name = donelist.course "
"GROUP BY week")).fetchall()
else:
total = self.cur.execute(
("SELECT strftime('%w', donelist.date) AS week, SUM(donelist.duration) "
"FROM donelist "
"INNER JOIN course ON course.name = donelist.course "
"WHERE course=? "
"GROUP BY week"), (course,)).fetchall()
return total
def get_total_hours_month(self, course=None):
""" Get the total hours per month
Get the total number of hours per month and return it.
If the course name is passed as an argument, return the total
hours per month for each course.
Args:
course: Target course name to get the total hours.
"""
with self.con:
if not course:
total = self.cur.execute(
("SELECT strftime('%m', donelist.date) AS month, SUM(donelist.duration) "
"FROM donelist "
"INNER JOIN course ON course.name = donelist.course "
"GROUP BY month")).fetchall()
else:
total = self.cur.execute(
("SELECT strftime('%m', donelist.date) AS month, SUM(donelist.duration) "
"FROM donelist "
"INNER JOIN course ON course.name = donelist.course "
"WHERE course=? "
"GROUP BY month"), (course,)).fetchall()
return total
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] |
train.py | ostaptan/deeptrans | 0 | 105467 | <reponame>ostaptan/deeptrans<gh_stars>0
import argparse
import math
import os
import dill
from collections import OrderedDict
from tqdm import tqdm
from torchtext.legacy import data
from torchtext import datasets
from torchtext.vocab import Vectors
import torch
import torch.nn as nn
import torch.optim as optim
from options import train_opts
from options import model_opts
from model import Seq2seqAttn
class Trainer(object):
def __init__(
self, model, criterion, optimizer, scheduler, clip):
self.model = model
self.criterion = criterion
self.optimizer = optimizer
self.scheduler = scheduler
self.clip = clip
self.n_updates = 0
def get_lr(self):
return self.optimizer.param_groups[0]['lr']
def step(self, samples, tf_ratio):
self.optimizer.zero_grad()
bsz = samples.src.size(1)
outs = self.model(samples.src, samples.tgt, tf_ratio)
loss = self.criterion(outs.view(-1, outs.size(2)), samples.tgt.view(-1))
if self.model.training:
loss.backward()
nn.utils.clip_grad_norm_(self.model.parameters(), self.clip)
self.optimizer.step()
self.n_updates += 1
return loss
def save_model(save_vars, filename):
model_path = os.path.join(args.savedir, filename)
torch.save(save_vars, model_path)
def save_vocab(savedir, fields):
name, field = fields
save_path = os.path.join(savedir, f'{name}_vocab.txt')
with open(save_path, 'w') as fout:
for w in field.vocab.itos:
fout.write(w + '\n')
def save_field(savedir, fields):
name, field = fields
save_path = os.path.join(savedir, f"{name}.field")
with open(save_path, 'wb') as fout:
dill.dump(field, fout)
def main(args):
device = torch.device('cuda' if args.gpu else 'cpu')
# load data and construct vocabulary dictionary
SRC = data.Field(lower=True)
TGT = data.Field(lower=True, eos_token='<eos>')
fields = [('src', SRC), ('tgt', TGT)]
train_data = data.TabularDataset(
path=args.train,
format='tsv',
fields=fields,
)
valid_data = data.TabularDataset(
path=args.valid,
format='tsv',
fields=fields,
)
SRC.build_vocab(train_data, min_freq=args.src_min_freq)
TGT.build_vocab(train_data, min_freq=args.tgt_min_freq)
if not os.path.exists(args.savedir):
os.mkdir(args.savedir)
# save field and vocabulary
for field in fields:
save_field(args.savedir, field)
save_vocab(args.savedir, field)
# set iterator
train_iter, valid_iter = data.BucketIterator.splits(
(train_data, valid_data),
batch_size=args.batch_size,
sort_within_batch=True,
sort_key= lambda x: len(x.src),
repeat=False,
device=device
)
model = Seq2seqAttn(args, fields, device).to(device)
print(model)
print('')
criterion = nn.CrossEntropyLoss(ignore_index=TGT.vocab.stoi['<pad>'])
optimizer = optim.SGD(model.parameters(), lr=args.lr)
scheduler = optim.lr_scheduler.ReduceLROnPlateau(optimizer, mode='min')
trainer = Trainer(model, criterion, optimizer, scheduler, args.clip)
epoch = 1
max_epoch = args.max_epoch or math.inf
max_update = args.max_update or math.inf
best_loss = math.inf
while epoch < max_epoch and trainer.n_updates < max_update \
and args.min_lr < trainer.get_lr():
# training
with tqdm(train_iter, dynamic_ncols=True) as pbar:
train_loss = 0.0
trainer.model.train()
for samples in pbar:
bsz = samples.src.size(1)
loss = trainer.step(samples, args.tf_ratio)
train_loss += loss.item()
# setting of progressbar
pbar.set_description(f"epoch {str(epoch).zfill(3)}")
progress_state = OrderedDict(
loss=loss.item(),
ppl=math.exp(loss.item()),
bsz=len(samples),
lr=trainer.get_lr(),
clip=args.clip,
num_updates=trainer.n_updates)
pbar.set_postfix(progress_state)
train_loss /= len(train_iter)
print(f"| epoch {str(epoch).zfill(3)} | train ", end="")
print(f"| loss {train_loss:.{4}} ", end="")
print(f"| ppl {math.exp(train_loss):.{4}} ", end="")
print(f"| lr {trainer.get_lr():.1e} ", end="")
print(f"| clip {args.clip} ", end="")
print(f"| num_updates {trainer.n_updates} |")
# validation
valid_loss = 0.0
trainer.model.eval()
for samples in valid_iter:
bsz = samples.src.size(1)
loss = trainer.step(samples, tf_ratio=0.0)
valid_loss += loss.item()
valid_loss /= len(valid_iter)
print(f"| epoch {str(epoch).zfill(3)} | valid ", end="")
print(f"| loss {valid_loss:.{4}} ", end="")
print(f"| ppl {math.exp(valid_loss):.{4}} ", end="")
print(f"| lr {trainer.get_lr():.1e} ", end="")
print(f"| clip {args.clip} ", end="")
print(f"| num_updates {trainer.n_updates} |")
# saving model
save_vars = {"train_args": args,
"state_dict": model.state_dict()}
if valid_loss < best_loss:
best_loss = valid_loss
save_model(save_vars, 'checkpoint_best.pt')
save_model(save_vars, "checkpoint_last.pt")
# update
trainer.scheduler.step(valid_loss)
epoch += 1
if __name__ == '__main__':
parser = argparse.ArgumentParser('')
train_opts(parser)
model_opts(parser)
args = parser.parse_args()
main(args)
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train_medseg_timm-regnetx_002_grid_distortion.py | BrunoKrinski/segtool | 0 | 66955 | <reponame>BrunoKrinski/segtool
import os
ls=["python main.py --configs configs/train_medseg_unetplusplus_timm-regnetx_002_fold0_grid_distortion.yml",
"python main.py --configs configs/train_medseg_unetplusplus_timm-regnetx_002_fold1_grid_distortion.yml",
"python main.py --configs configs/train_medseg_unetplusplus_timm-regnetx_002_fold2_grid_distortion.yml",
"python main.py --configs configs/train_medseg_unetplusplus_timm-regnetx_002_fold3_grid_distortion.yml",
"python main.py --configs configs/train_medseg_unetplusplus_timm-regnetx_002_fold4_grid_distortion.yml",
]
for l in ls:
os.system(l) | [
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examples/test_project/mlpa/models.py | movermeyer/madrona | 9 | 73710 | from django.contrib.gis.db import models
from madrona.features import register
from madrona.features.models import PointFeature, LineFeature, PolygonFeature, FeatureCollection
from madrona.layers.models import PrivateLayerList
from madrona.features.forms import FeatureForm, SpatialFeatureForm
DESIGNATION_CHOICES = (
('R', 'Reserve'),
('P', 'Park'),
('C', 'Conservation Area')
)
###########################
@register
class Mpa(PolygonFeature):
designation = models.CharField(max_length=1, choices=DESIGNATION_CHOICES)
class Options:
verbose_name = 'Marine Protected Area'
form = 'mlpa.models.MpaForm'
manipulators = []
class MpaForm(SpatialFeatureForm):
class Meta(SpatialFeatureForm.Meta):
model = Mpa
###########################
@register
class Array(FeatureCollection):
class Options:
form = 'mlpa.models.ArrayForm'
valid_children = ('mlpa.models.Mpa', )
class ArrayForm(FeatureForm):
class Meta(FeatureForm.Meta):
model = Array
###########################
@register
class Shipwreck(PointFeature):
incident = models.CharField(max_length=100,default='')
class Options:
verbose_name = 'Shipwreck'
form = 'mlpa.models.ShipwreckForm'
class ShipwreckForm(SpatialFeatureForm):
class Meta(SpatialFeatureForm.Meta):
model = Shipwreck
###########################
@register
class Pipeline(LineFeature):
type = models.CharField(max_length=30,default='')
diameter = models.FloatField(null=True)
class Options:
verbose_name = 'Pipeline'
form = 'mlpa.models.PipelineForm'
class PipelineForm(SpatialFeatureForm):
class Meta(SpatialFeatureForm.Meta):
model = Pipeline
###########################
@register
class Folder(FeatureCollection):
class Options:
form = 'mlpa.models.FolderForm'
valid_children = (
'mlpa.models.Mpa',
'mlpa.models.Array',
'mlpa.models.Pipeline',
'mlpa.models.Shipwreck',
'mlpa.models.Folder'
)
class FolderForm(FeatureForm):
class Meta(FeatureForm.Meta):
model = Folder
###########################
@register
class UserKml(PrivateLayerList):
class Options:
form = 'mlpa.models.UserKmlForm'
class UserKmlForm(FeatureForm):
class Meta(FeatureForm.Meta):
model = UserKml
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src/components/convLayer.py | JasonYuJjyu/unsupFlownet | 24 | 1606627 | <filename>src/components/convLayer.py
import tensorflow as tf
def convLayer(x,kernelSize, outMaps, stride): # default caffe style MRSA
with tf.variable_scope(None,default_name="conv"):
inMaps = x.get_shape()[3]
kShape = [kernelSize,kernelSize,inMaps,outMaps]
w = tf.get_variable("weights",shape=kShape,initializer=tf.uniform_unit_scaling_initializer())
b = tf.get_variable("biases",shape=[outMaps],initializer=tf.constant_initializer(0))
tf.add_to_collection("weights",w)
conv = tf.nn.conv2d(x,w,strides=[1,stride,stride,1], padding="SAME",name="conv2d")
return conv + b
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route/recent_changes.py | lsh23/openNAMU | 0 | 195404 | <reponame>lsh23/openNAMU
from .tool.func import *
def recent_changes_2(conn, name, tool):
curs = conn.cursor()
if flask.request.method == 'POST':
return redirect(
'/diff/' + url_pas(name) +
'?first=' + flask.request.form.get('b', '1') +
'&second=' + flask.request.form.get('a', '1')
)
else:
ban = ''
select = ''
sub = ''
div = '''
<table id="main_table_set">
<tbody>
<tr>
'''
num = int(number_check(flask.request.args.get('num', '1')))
if num * 50 > 0:
sql_num = num * 50 - 50
else:
sql_num = 0
if name:
if tool == 'history':
sub += ' (' + load_lang('history') + ')'
div += '''
<td id="main_table_width">''' + load_lang('version') + '''</td>
<td id="main_table_width">''' + load_lang('editor') + '''</td>
<td id="main_table_width">''' + load_lang('time') + '''</td>
'''
tool_select = flask.request.args.get('tool', 'normal')
if tool_select == 'move':
plus_sql = 'where (send like ? or send like ?) and type = "" '
plus_list = ['%(<a>' + name +'</a>%', '%<a>' + name + '</a> move)', sql_num]
sub += ' (' + load_lang('move') + ')'
elif tool_select == 'delete':
plus_sql = 'where (send like "%(delete)") and title = ? and type = "" '
plus_list = [name, sql_num]
sub += ' (' + load_lang('revert') + ')'
elif tool_select == 'revert':
plus_sql = 'where (send like ?) and title = ? and type = "" '
plus_list = ['%(r%)', name, sql_num]
sub += ' (' + load_lang('revert') + ')'
else:
plus_sql = 'where title = ? and type = "" '
plus_list = [name, sql_num]
curs.execute(db_change('' + \
'select id, title, date, ip, send, leng from history ' + \
plus_sql + \
'order by id + 0 desc ' + \
"limit ?, 50" + \
''), plus_list)
else:
div += '''
<td id="main_table_width">''' + load_lang('document_name') + '''</td>
<td id="main_table_width">''' + load_lang('editor') + '''</td>
<td id="main_table_width">''' + load_lang('time') + '''</td>
'''
div = '<a href="/topic_record/' + url_pas(name) + '">(' + load_lang('discussion') + ')</a><hr class=\"main_hr\">' + div
curs.execute(db_change('' + \
'select id, title, date, ip, send, leng from history ' + \
"where ip = ? and type = '' order by date desc limit ?, 50" + \
''), [name, sql_num])
else:
div += '''
<td id="main_table_width">''' + load_lang('document_name') + '''</td>
<td id="main_table_width">''' + load_lang('editor') + '''</td>
<td id="main_table_width">''' + load_lang('time') + '''</td>
'''
set_type = flask.request.args.get('set', 'normal')
if set_type == 'normal':
div = '' + \
'<a href="?set=user">(' + load_lang('user_document') + ')</a> ' + \
'<a href="?set=req">(' + load_lang('edit_req') + ')</a>' + \
'<hr class="main_hr">' + div + \
''
if set_type == 'req':
plus_sql = "where type = 'req' "
elif set_type == 'user':
plus_sql = "where title like 'user:%' and type = '' "
else:
plus_sql = "where not title like 'user:%' and type = '' "
curs.execute(db_change('' + \
'select id, title, date, ip, send, leng from history ' + \
plus_sql + \
'order by date desc ' + \
'limit ?, 50' + \
''), [sql_num])
div += '</tr>'
data_list = curs.fetchall()
for data in data_list:
select += '<option value="' + data[0] + '">' + data[0] + '</option>'
send = '<br>'
if data[4]:
if not re.search("^(?: *)$", data[4]):
send = data[4]
if re.search("\+", data[5]):
leng = '<span style="color:green;">(' + data[5] + ')</span>'
elif re.search("\-", data[5]):
leng = '<span style="color:red;">(' + data[5] + ')</span>'
else:
leng = '<span style="color:gray;">(' + data[5] + ')</span>'
ip = ip_pas(data[3])
if tool == 'history':
m_tool = '<a href="/history_tool/' + url_pas(data[1]) + '?num=' + data[0] + '&type=history">(' + load_lang('tool') + ')</a>'
else:
m_tool = '<a href="/history_tool/' + url_pas(data[1]) + '?num=' + data[0] + '">(' + load_lang('tool') + ')</a>'
style = ['', '']
date = data[2]
curs.execute(db_change('''
select title from history
where title = ? and id = ? and hide = 'O'
'''), [data[1], data[0]])
hide = curs.fetchall()
if admin_check(6) == 1:
if hide:
style[0] = 'id="toron_color_grey"'
style[1] = 'id="toron_color_grey"'
send += ' (' + load_lang('hide') + ')'
elif not hide:
pass
else:
ip = ''
ban = ''
date = ''
send = '(' + load_lang('hide') + ')'
style[0] = 'style="display: none;"'
style[1] = 'id="toron_color_grey"'
if tool == 'history':
title = '<a href="/w/' + url_pas(name) + '?num=' + data[0] + '">r' + data[0] + '</a> '
else:
if not name and set_type == 'req':
title = '<a href="/edit_req/' + url_pas(data[1]) + '?r=' + data[0] + '">' + html.escape(data[1]) + ' (r' + data[0] + ')</a> '
else:
title = '<a href="/w/' + url_pas(data[1]) + '">' + html.escape(data[1]) + '</a> '
title += '<a href="/history/' + url_pas(data[1]) + '">(r' + data[0] + ')</a> '
div += '''
<tr ''' + style[0] + '''>
<td>''' + title + m_tool + ' ' + leng + '''</td>
<td>''' + ip + ban + '''</td>
<td>''' + date + '''</td>
</tr>
<tr ''' + style[1] + '''>
<td colspan="3">''' + send_parser(send) + '''</td>
</tr>
'''
div += '''
</tbody>
</table>
'''
if name:
if tool == 'history':
if not tool_select:
div = '' + \
'<a href="?tool=move">(' + load_lang('move') + ')</a> ' + \
'<a href="?tool=delete">(' + load_lang('delete') + ')</a> ' + \
'<a href="?tool=revert">(' + load_lang('revert') + ')</a>' + \
'<hr class="main_hr">' + div + \
''
menu = [['w/' + url_pas(name), load_lang('document')], ['raw/' + url_pas(name), load_lang('raw')]]
else:
menu = [['history/' + url_pas(name), load_lang('return')]]
div = '''
<form method="post">
<select name="a">''' + select + '''</select> <select name="b">''' + select + '''</select>
<button type="submit">''' + load_lang('compare') + '''</button>
</form>
<hr class=\"main_hr\">
''' + div
title = name
div += next_fix('/history/' + url_pas(name) + '?tool=' + tool_select + '&num=', num, data_list)
else:
title = load_lang('edit_record')
menu = [['other', load_lang('other')], ['user', load_lang('user')], ['count/' + url_pas(name), load_lang('count')]]
div += next_fix('/record/' + url_pas(name) + '?num=', num, data_list)
else:
menu = 0
title = load_lang('recent_change')
div += next_fix('/recent_changes?set=' + set_type + '&num=', num, data_list)
if set_type == 'user':
sub = ' (' + load_lang('user') + ')'
menu = [['recent_changes', load_lang('return')]]
elif set_type == 'req':
sub = ' (' + load_lang('edit_req') + ')'
menu = [['recent_changes', load_lang('return')]]
if sub == '':
sub = 0
return easy_minify(flask.render_template(skin_check(),
imp = [title, wiki_set(), custom(), other2([sub, 0])],
data = div,
menu = menu
)) | [
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] |
nipype/interfaces/semtools/diffusion/diffusion.py | sebastientourbier/nipype | 2 | 71827 | # -*- coding: utf-8 -*-
# -*- coding: utf8 -*-
"""Autogenerated file - DO NOT EDIT
If you spot a bug, please report it on the mailing list and/or change the generator."""
import os
from ...base import (CommandLine, CommandLineInputSpec, SEMLikeCommandLine,
TraitedSpec, File, Directory, traits, isdefined,
InputMultiPath, OutputMultiPath)
class dtiaverageInputSpec(CommandLineInputSpec):
inputs = InputMultiPath(File(exists=True), desc="List of all the tensor fields to be averaged", argstr="--inputs %s...")
tensor_output = traits.Either(traits.Bool, File(), hash_files=False, desc="Averaged tensor volume", argstr="--tensor_output %s")
DTI_double = traits.Bool(desc="Tensor components are saved as doubles (cannot be visualized in Slicer)", argstr="--DTI_double ")
verbose = traits.Bool(desc="produce verbose output", argstr="--verbose ")
class dtiaverageOutputSpec(TraitedSpec):
tensor_output = File(desc="Averaged tensor volume", exists=True)
class dtiaverage(SEMLikeCommandLine):
"""title: DTIAverage (DTIProcess)
category: Diffusion.Diffusion Tensor Images.CommandLineOnly
description: dtiaverage is a program that allows to compute the average of an arbitrary number of tensor fields (listed after the --inputs option) This program is used in our pipeline as the last step of the atlas building processing. When all the tensor fields have been deformed in the same space, to create the average tensor field (--tensor_output) we use dtiaverage.
Several average method can be used (specified by the --method option): euclidian, log-euclidian and pga. The default being euclidian.
version: 1.0.0
documentation-url: http://www.slicer.org/slicerWiki/index.php/Documentation/Nightly/Extensions/DTIProcess
license: Copyright (c) <NAME>. All rights reserved.
See http://www.ia.unc.edu/dev/Copyright.htm for details.
This software is distributed WITHOUT ANY WARRANTY; without even
the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR
PURPOSE. See the above copyright notices for more information.
contributor: <NAME>
"""
input_spec = dtiaverageInputSpec
output_spec = dtiaverageOutputSpec
_cmd = " dtiaverage "
_outputs_filenames = {'tensor_output': 'tensor_output.nii'}
_redirect_x = False
class dtiestimInputSpec(CommandLineInputSpec):
dwi_image = File(desc="DWI image volume (required)", exists=True, argstr="--dwi_image %s")
tensor_output = traits.Either(traits.Bool, File(), hash_files=False, desc="Tensor OutputImage", argstr="--tensor_output %s")
B0 = traits.Either(traits.Bool, File(), hash_files=False, desc="Baseline image, average of all baseline images", argstr="--B0 %s")
idwi = traits.Either(traits.Bool, File(), hash_files=False, desc="idwi output image. Image with isotropic diffusion-weighted information = geometric mean of diffusion images", argstr="--idwi %s")
B0_mask_output = traits.Either(traits.Bool, File(), hash_files=False, desc="B0 mask used for the estimation. B0 thresholded either with the -t option value or the automatic OTSU value", argstr="--B0_mask_output %s")
brain_mask = File(desc="Brain mask. Image where for every voxel == 0 the tensors are not estimated. Be aware that in addition a threshold based masking will be performed by default. If such an additional threshold masking is NOT desired, then use option -t 0.",
exists=True, argstr="--brain_mask %s")
bad_region_mask = File(desc="Bad region mask. Image where for every voxel > 0 the tensors are not estimated", exists=True, argstr="--bad_region_mask %s")
method = traits.Enum("lls", "wls", "nls", "ml", desc="Esitmation method (lls:linear least squares, wls:weighted least squares, nls:non-linear least squares, ml:maximum likelihood)", argstr="--method %s")
correction = traits.Enum("none", "zero", "abs", "nearest", desc="Correct the tensors if computed tensor is not semi-definite positive", argstr="--correction %s")
threshold = traits.Int(desc="Baseline threshold for estimation. If not specified calculated using an OTSU threshold on the baseline image.", argstr="--threshold %d")
weight_iterations = traits.Int(desc="Number of iterations to recaluate weightings from tensor estimate", argstr="--weight_iterations %d")
step = traits.Float(desc="Gradient descent step size (for nls and ml methods)", argstr="--step %f")
sigma = traits.Float(argstr="--sigma %f")
DTI_double = traits.Bool(desc="Tensor components are saved as doubles (cannot be visualized in Slicer)", argstr="--DTI_double ")
verbose = traits.Bool(desc="produce verbose output", argstr="--verbose ")
defaultTensor = InputMultiPath(traits.Float, desc="Default tensor used if estimated tensor is below a given threshold", sep=",", argstr="--defaultTensor %s")
shiftNeg = traits.Bool(
desc="Shift eigenvalues so all are positive (accounts for bad tensors related to noise or acquisition error). This is the same option as the one available in DWIToDTIEstimation in Slicer (but instead of just adding the minimum eigenvalue to all the eigenvalues if it is smaller than 0, we use a coefficient to have stictly positive eigenvalues", argstr="--shiftNeg ")
shiftNegCoeff = traits.Float(
desc="Shift eigenvalues so all are positive (accounts for bad tensors related to noise or acquisition error). Instead of just adding the minimum eigenvalue to all the eigenvalues if it is smaller than 0, we use a coefficient to have stictly positive eigenvalues. Coefficient must be between 1.0 and 1.001 (included).", argstr="--shiftNegCoeff %f")
class dtiestimOutputSpec(TraitedSpec):
tensor_output = File(desc="Tensor OutputImage", exists=True)
B0 = File(desc="Baseline image, average of all baseline images", exists=True)
idwi = File(desc="idwi output image. Image with isotropic diffusion-weighted information = geometric mean of diffusion images", exists=True)
B0_mask_output = File(desc="B0 mask used for the estimation. B0 thresholded either with the -t option value or the automatic OTSU value", exists=True)
class dtiestim(SEMLikeCommandLine):
"""title: DTIEstim (DTIProcess)
category: Diffusion.Diffusion Weighted Images
description: dtiestim is a tool that takes in a set of DWIs (with --dwi_image option) in nrrd format and estimates a tensor field out of it. The output tensor file name is specified with the --tensor_output option
There are several methods to estimate the tensors which you can specify with the option --method lls|wls|nls|ml . Here is a short description of the different methods:
lls
Linear least squares. Standard estimation technique that recovers the tensor parameters by multiplying the log of the normalized signal intensities by the pseudo-inverse of the gradient matrix. Default option.
wls
Weighted least squares. This method is similar to the linear least squares method except that the gradient matrix is weighted by the original lls estimate. (See <NAME>., <NAME>., <NAME>., <NAME>., <NAME>., and <NAME>. Formal characterization and extension of the linearized diffusion tensor model. Human Brain Mapping 24, 2 (Feb. 2005), 144-155. for more information on this method). This method is recommended for most applications. The weight for each iteration can be specified with the --weight_iterations. It is not currently the default due to occasional matrix singularities.
nls
Non-linear least squares. This method does not take the log of the signal and requires an optimization based on levenberg-marquadt to optimize the parameters of the signal. The lls estimate is used as an initialization. For this method the step size can be specified with the --step option.
ml
Maximum likelihood estimation. This method is experimental and is not currently recommended. For this ml method the sigma can be specified with the option --sigma and the step size can be specified with the --step option.
You can set a threshold (--threshold) to have the tensor estimated to only a subset of voxels. All the baseline voxel value higher than the threshold define the voxels where the tensors are computed. If not specified the threshold is calculated using an OTSU threshold on the baseline image.The masked generated by the -t option or by the otsu value can be saved with the --B0_mask_output option.
dtiestim also can extract a few scalar images out of the DWI set of images:
- the average baseline image (--B0) which is the average of all the B0s.
- the IDWI (--idwi)which is the geometric mean of the diffusion images.
You can also load a mask if you want to compute the tensors only where the voxels are non-zero (--brain_mask) or a negative mask and the tensors will be estimated where the negative mask has zero values (--bad_region_mask)
version: 1.2.0
documentation-url: http://www.slicer.org/slicerWiki/index.php/Documentation/Nightly/Extensions/DTIProcess
license: Copyright (c) <NAME>. All rights reserved.
See http://www.ia.unc.edu/dev/Copyright.htm for details.
This software is distributed WITHOUT ANY WARRANTY; without even
the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR
PURPOSE. See the above copyright notices for more information.
contributor: <NAME>, <NAME>
acknowledgements: <NAME>(1,3,4); <NAME>(1); (1=University of Iowa Department of Psychiatry, 3=University of Iowa Department of Biomedical Engineering, 4=University of Iowa Department of Electrical and Computer Engineering) provided conversions to make DTIProcess compatible with Slicer execution, and simplified the stand-alone build requirements by removing the dependancies on boost and a fortran compiler.
"""
input_spec = dtiestimInputSpec
output_spec = dtiestimOutputSpec
_cmd = " dtiestim "
_outputs_filenames = {'B0': 'B0.nii', 'idwi': 'idwi.nii', 'tensor_output': 'tensor_output.nii', 'B0_mask_output': 'B0_mask_output.nii'}
_redirect_x = False
class dtiprocessInputSpec(CommandLineInputSpec):
dti_image = File(desc="DTI tensor volume", exists=True, argstr="--dti_image %s")
fa_output = traits.Either(traits.Bool, File(), hash_files=False, desc="Fractional Anisotropy output file", argstr="--fa_output %s")
md_output = traits.Either(traits.Bool, File(), hash_files=False, desc="Mean Diffusivity output file", argstr="--md_output %s")
sigma = traits.Float(desc="Scale of gradients", argstr="--sigma %f")
fa_gradient_output = traits.Either(traits.Bool, File(), hash_files=False, desc="Fractional Anisotropy Gradient output file", argstr="--fa_gradient_output %s")
fa_gradmag_output = traits.Either(traits.Bool, File(), hash_files=False, desc="Fractional Anisotropy Gradient Magnitude output file", argstr="--fa_gradmag_output %s")
color_fa_output = traits.Either(traits.Bool, File(), hash_files=False, desc="Color Fractional Anisotropy output file", argstr="--color_fa_output %s")
principal_eigenvector_output = traits.Either(traits.Bool, File(), hash_files=False, desc="Principal Eigenvectors Output", argstr="--principal_eigenvector_output %s")
negative_eigenvector_output = traits.Either(traits.Bool, File(), hash_files=False, desc="Negative Eigenvectors Output: create a binary image where if any of the eigen value is below zero, the voxel is set to 1, otherwise 0.", argstr="--negative_eigenvector_output %s")
frobenius_norm_output = traits.Either(traits.Bool, File(), hash_files=False, desc="Frobenius Norm Output", argstr="--frobenius_norm_output %s")
lambda1_output = traits.Either(traits.Bool, File(), hash_files=False, desc="Axial Diffusivity - Lambda 1 (largest eigenvalue) output", argstr="--lambda1_output %s")
lambda2_output = traits.Either(traits.Bool, File(), hash_files=False, desc="Lambda 2 (middle eigenvalue) output", argstr="--lambda2_output %s")
lambda3_output = traits.Either(traits.Bool, File(), hash_files=False, desc="Lambda 3 (smallest eigenvalue) output", argstr="--lambda3_output %s")
RD_output = traits.Either(traits.Bool, File(), hash_files=False, desc="RD (Radial Diffusivity 1/2*(lambda2+lambda3)) output", argstr="--RD_output %s")
rot_output = traits.Either(traits.Bool, File(), hash_files=False, desc="Rotated tensor output file. Must also specify the dof file.", argstr="--rot_output %s")
affineitk_file = File(desc="Transformation file for affine transformation. ITK format.", exists=True, argstr="--affineitk_file %s")
dof_file = File(desc="Transformation file for affine transformation. This can be ITK format (or the outdated RView).", exists=True, argstr="--dof_file %s")
newdof_file = File(desc="Transformation file for affine transformation. RView NEW format. (txt file output of dof2mat)", exists=True, argstr="--newdof_file %s")
mask = File(desc="Mask tensors. Specify --outmask if you want to save the masked tensor field, otherwise the mask is applied just for the current processing ", exists=True, argstr="--mask %s")
outmask = traits.Either(traits.Bool, File(), hash_files=False, desc="Name of the masked tensor field.", argstr="--outmask %s")
hField = traits.Bool(desc="forward and inverse transformations are h-fields instead of displacement fields", argstr="--hField ")
forward = File(desc="Forward transformation. Assumed to be a deformation field in world coordinates, unless the --h-field option is specified.", exists=True, argstr="--forward %s")
deformation_output = traits.Either(traits.Bool, File(), hash_files=False, desc="Warped tensor field based on a deformation field. This option requires the --forward,-F transformation to be specified.", argstr="--deformation_output %s")
interpolation = traits.Enum("nearestneighbor", "linear", "cubic", desc="Interpolation type (nearestneighbor, linear, cubic)", argstr="--interpolation %s")
reorientation = traits.Enum("fs", "ppd", desc="Reorientation type (fs, ppd)", argstr="--reorientation %s")
correction = traits.Enum("none", "zero", "abs", "nearest", desc="Correct the tensors if computed tensor is not semi-definite positive", argstr="--correction %s")
scalar_float = traits.Bool(desc="Write scalar [FA,MD] as unscaled float (with their actual values, otherwise scaled by 10 000). Also causes FA to be unscaled [0..1].", argstr="--scalar_float ")
DTI_double = traits.Bool(desc="Tensor components are saved as doubles (cannot be visualized in Slicer)", argstr="--DTI_double ")
verbose = traits.Bool(desc="produce verbose output", argstr="--verbose ")
class dtiprocessOutputSpec(TraitedSpec):
fa_output = File(desc="Fractional Anisotropy output file", exists=True)
md_output = File(desc="Mean Diffusivity output file", exists=True)
fa_gradient_output = File(desc="Fractional Anisotropy Gradient output file", exists=True)
fa_gradmag_output = File(desc="Fractional Anisotropy Gradient Magnitude output file", exists=True)
color_fa_output = File(desc="Color Fractional Anisotropy output file", exists=True)
principal_eigenvector_output = File(desc="Principal Eigenvectors Output", exists=True)
negative_eigenvector_output = File(desc="Negative Eigenvectors Output: create a binary image where if any of the eigen value is below zero, the voxel is set to 1, otherwise 0.", exists=True)
frobenius_norm_output = File(desc="Frobenius Norm Output", exists=True)
lambda1_output = File(desc="Axial Diffusivity - Lambda 1 (largest eigenvalue) output", exists=True)
lambda2_output = File(desc="Lambda 2 (middle eigenvalue) output", exists=True)
lambda3_output = File(desc="Lambda 3 (smallest eigenvalue) output", exists=True)
RD_output = File(desc="RD (Radial Diffusivity 1/2*(lambda2+lambda3)) output", exists=True)
rot_output = File(desc="Rotated tensor output file. Must also specify the dof file.", exists=True)
outmask = File(desc="Name of the masked tensor field.", exists=True)
deformation_output = File(desc="Warped tensor field based on a deformation field. This option requires the --forward,-F transformation to be specified.", exists=True)
class dtiprocess(SEMLikeCommandLine):
"""title: DTIProcess (DTIProcess)
category: Diffusion.Diffusion Tensor Images
description: dtiprocess is a tool that handles tensor fields. It takes as an input a tensor field in nrrd format.
It can generate diffusion scalar properties out of the tensor field such as : FA (--fa_output), Gradient FA image (--fa_gradient_output), color FA (--color_fa_output), MD (--md_output), Frobenius norm (--frobenius_norm_output), lbd1, lbd2, lbd3 (--lambda{1,2,3}_output), binary map of voxel where if any of the eigenvalue is negative, the voxel is set to 1 (--negative_eigenvector_output)
It also creates 4D images out of the tensor field such as: Highest eigenvector map (highest eigenvector at each voxel) (--principal_eigenvector_output)
Masking capabilities: For any of the processing done with dtiprocess, it's possible to apply it on a masked region of the tensor field. You need to use the --mask option for any of the option to be applied on that tensor field sub-region only. If you want to save the masked tensor field use the option --outmask and specify the new masked tensor field file name.
dtiprocess also allows a range of transformations on the tensor fields. The transformed tensor field file name is specified with the option --deformation_output. There are 3 resampling interpolation methods specified with the tag --interpolation followed by the type to use (nearestneighbor, linear, cubic) Then you have several transformations possible to apply:
- Affine transformations using as an input
- itk affine transformation file (based on the itkAffineTransform class)
- Affine transformations using rview (details and download at http://www.doc.ic.ac.uk/~dr/software/). There are 2 versions of rview both creating transformation files called dof files. The old version of rview outputs text files containing the transformation parameters. It can be read in with the --dof_file option. The new version outputs binary dof files. These dof files can be transformed into human readable file with the dof2mat tool which is part of the rview package. So you need to save the output of dof2mat into a text file which can then be used with the -- newdof_file option. Usage example: dof2mat mynewdoffile.dof >> mynewdoffile.txt dtiprocess --dti_image mytensorfield.nhdr --newdof_file mynewdoffile.txt --rot_output myaffinetensorfield.nhdr
Non linear transformations as an input: The default transformation file type is d-field (displacement field) in nrrd format. The option to use is --forward with the name of the file. If the transformation file is a h-field you have to add the option --hField.
version: 1.0.1
documentation-url: http://www.slicer.org/slicerWiki/index.php/Documentation/Nightly/Extensions/DTIProcess
license: Copyright (c) <NAME>. All rights reserved.
See http://www.ia.unc.edu/dev/Copyright.htm for details.
This software is distributed WITHOUT ANY WARRANTY; without even
the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR
PURPOSE. See the above copyright notices for more information.
contributor: <NAME>
"""
input_spec = dtiprocessInputSpec
output_spec = dtiprocessOutputSpec
_cmd = " dtiprocess "
_outputs_filenames = {'fa_gradmag_output': 'fa_gradmag_output.nii', 'fa_gradient_output': 'fa_gradient_output.nii', 'lambda1_output': 'lambda1_output.nii', 'lambda2_output': 'lambda2_output.nii', 'color_fa_output': 'color_fa_output.nii', 'fa_output': 'fa_output.nii', 'frobenius_norm_output': 'frobenius_norm_output.nii',
'principal_eigenvector_output': 'principal_eigenvector_output.nii', 'outmask': 'outmask.nii', 'lambda3_output': 'lambda3_output.nii', 'negative_eigenvector_output': 'negative_eigenvector_output.nii', 'md_output': 'md_output.nii', 'RD_output': 'RD_output.nii', 'deformation_output': 'deformation_output.nii', 'rot_output': 'rot_output.nii'}
_redirect_x = False
class DWIConvertInputSpec(CommandLineInputSpec):
conversionMode = traits.Enum("DicomToNrrd", "DicomToFSL", "NrrdToFSL", "FSLToNrrd",
desc="Determine which conversion to perform. DicomToNrrd (default): Convert DICOM series to NRRD DicomToFSL: Convert DICOM series to NIfTI File + gradient/bvalue text files NrrdToFSL: Convert DWI NRRD file to NIfTI File + gradient/bvalue text files FSLToNrrd: Convert NIfTI File + gradient/bvalue text files to NRRD file.", argstr="--conversionMode %s")
inputVolume = File(desc="Input DWI volume -- not used for DicomToNrrd mode.", exists=True, argstr="--inputVolume %s")
outputVolume = traits.Either(traits.Bool, File(), hash_files=False, desc="Output filename (.nhdr or .nrrd)", argstr="--outputVolume %s")
inputDicomDirectory = Directory(desc="Directory holding Dicom series", exists=True, argstr="--inputDicomDirectory %s")
fslNIFTIFile = File(desc="4D NIfTI file containing gradient volumes", exists=True, argstr="--fslNIFTIFile %s")
inputBValues = File(desc="The B Values are stored in FSL .bval text file format", exists=True, argstr="--inputBValues %s")
inputBVectors = File(desc="The Gradient Vectors are stored in FSL .bvec text file format", exists=True, argstr="--inputBVectors %s")
outputBValues = traits.Either(traits.Bool, File(), hash_files=False, desc="The B Values are stored in FSL .bval text file format (defaults to <outputVolume>.bval)", argstr="--outputBValues %s")
outputBVectors = traits.Either(traits.Bool, File(), hash_files=False, desc="The Gradient Vectors are stored in FSL .bvec text file format (defaults to <outputVolume>.bvec)", argstr="--outputBVectors %s")
fMRI = traits.Bool(desc="Output a NRRD file, but without gradients", argstr="--fMRI ")
writeProtocolGradientsFile = traits.Bool(
desc="Write the protocol gradients to a file suffixed by \'.txt\' as they were specified in the procol by multiplying each diffusion gradient direction by the measurement frame. This file is for debugging purposes only, the format is not fixed, and will likely change as debugging of new dicom formats is necessary.", argstr="--writeProtocolGradientsFile ")
useIdentityMeaseurementFrame = traits.Bool(desc="Adjust all the gradients so that the measurement frame is an identity matrix.", argstr="--useIdentityMeaseurementFrame ")
useBMatrixGradientDirections = traits.Bool(
desc="Fill the nhdr header with the gradient directions and bvalues computed out of the BMatrix. Only changes behavior for Siemens data. In some cases the standard public gradients are not properly computed. The gradients can emperically computed from the private BMatrix fields. In some cases the private BMatrix is consistent with the public grandients, but not in all cases, when it exists BMatrix is usually most robust.", argstr="--useBMatrixGradientDirections ")
outputDirectory = traits.Either(traits.Bool, Directory(), hash_files=False, desc="Directory holding the output NRRD file", argstr="--outputDirectory %s")
gradientVectorFile = traits.Either(traits.Bool, File(), hash_files=False, desc="Text file giving gradient vectors", argstr="--gradientVectorFile %s")
smallGradientThreshold = traits.Float(desc="If a gradient magnitude is greater than 0 and less than smallGradientThreshold, then DWIConvert will display an error message and quit, unless the useBMatrixGradientDirections option is set.", argstr="--smallGradientThreshold %f")
allowLossyConversion = traits.Bool(desc="The only supported output type is \'short\'. Conversion from images of a different type may cause data loss due to rounding or truncation. Use with caution!", argstr="--allowLossyConversion ")
transposeInputBVectors = traits.Bool(desc="FSL input BVectors are expected to be encoded in the input file as one vector per line. If it is not the case, use this option to transpose the file as it is read.", argstr="--transposeInputBVectors ")
class DWIConvertOutputSpec(TraitedSpec):
outputVolume = File(desc="Output filename (.nhdr or .nrrd)", exists=True)
outputBValues = File(desc="The B Values are stored in FSL .bval text file format (defaults to <outputVolume>.bval)", exists=True)
outputBVectors = File(desc="The Gradient Vectors are stored in FSL .bvec text file format (defaults to <outputVolume>.bvec)", exists=True)
outputDirectory = Directory(desc="Directory holding the output NRRD file", exists=True)
gradientVectorFile = File(desc="Text file giving gradient vectors", exists=True)
class DWIConvert(SEMLikeCommandLine):
"""title: DWIConverter
category: Diffusion.Diffusion Data Conversion
description: Converts diffusion weighted MR images in dicom series into Nrrd format for analysis in Slicer. This program has been tested on only a limited subset of DTI dicom formats available from Siemens, GE, and Phillips scanners. Work in progress to support dicom multi-frame data. The program parses dicom header to extract necessary information about measurement frame, diffusion weighting directions, b-values, etc, and write out a nrrd image. For non-diffusion weighted dicom images, it loads in an entire dicom series and writes out a single dicom volume in a .nhdr/.raw pair.
version: Version 1.0
documentation-url: http://wiki.slicer.org/slicerWiki/index.php/Documentation/4.1/Modules/DWIConverter
license: https://www.nitrc.org/svn/brains/BuildScripts/trunk/License.txt
contributor: <NAME> (UIowa), <NAME> (UIowa), <NAME> (UIowa), <NAME> (UIowa), <NAME> (Uiowa), <NAME> (GE)
acknowledgements: This work is part of the National Alliance for Medical Image Computing (NAMIC), funded by the National Institutes of Health through the NIH Roadmap for Medical Research, Grant U54 EB005149. Additional support for DTI data produced on Philips scanners was contributed by <NAME> and <NAME> at the University of Iowa.
"""
input_spec = DWIConvertInputSpec
output_spec = DWIConvertOutputSpec
_cmd = " DWIConvert "
_outputs_filenames = {'outputVolume': 'outputVolume.nii', 'outputDirectory': 'outputDirectory', 'outputBValues': 'outputBValues.bval', 'gradientVectorFile': 'gradientVectorFile', 'outputBVectors': 'outputBVectors.bvec'}
_redirect_x = False
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13,
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2319,
25584,
993,
1349,
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353,
1020,
1169,
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11031,
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14273,
543,
3644,
263,
16030,
18497,
338,
7621,
1135,
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29900,
322,
3109,
1135,
2319,
25584,
993,
1349,
12268,
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769,
360,
22119,
18455,
674,
2479,
385,
1059,
2643,
322,
23283,
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543,
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25584,
993,
1349,
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1273,
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13,
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2758,
29931,
2209,
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1168,
3259,
353,
1020,
1169,
29889,
24693,
29898,
14273,
543,
1576,
871,
6969,
1962,
1134,
338,
320,
29915,
12759,
29905,
4286,
1281,
3259,
515,
4558,
310,
263,
1422,
1134,
1122,
4556,
848,
6410,
2861,
304,
4513,
292,
470,
21022,
362,
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4803,
411,
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543,
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9536,
29931,
2209,
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16521,
13,
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1301,
4220,
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29933,
29963,
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353,
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1881,
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526,
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304,
367,
18511,
297,
278,
1881,
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408,
697,
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639,
1196,
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960,
372,
338,
451,
278,
1206,
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671,
445,
2984,
304,
1301,
4220,
278,
934,
408,
372,
338,
1303,
19602,
1852,
710,
543,
489,
3286,
4220,
4290,
29933,
29963,
11142,
16521,
13,
13,
13,
1990,
360,
22119,
18455,
6466,
10299,
29898,
5323,
1573,
10299,
1125,
13,
1678,
1962,
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543,
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10422,
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1962,
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9065,
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350,
2630,
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12750,
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12750,
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2003,
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6820,
16030,
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5574,
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13,
13,
1990,
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1660,
1988,
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6255,
3542,
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13,
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3515,
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23253,
7688,
292,
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29892,
289,
29899,
5975,
29892,
2992,
29892,
322,
2436,
714,
263,
17114,
5499,
1967,
29889,
1152,
1661,
29899,
12765,
3958,
7688,
287,
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290,
4558,
29892,
372,
15376,
297,
385,
4152,
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290,
3652,
322,
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714,
263,
2323,
12124,
290,
7977,
297,
263,
869,
29876,
29882,
7707,
6294,
1610,
5101,
29889,
13,
13,
3259,
29901,
10079,
29871,
29896,
29889,
29900,
13,
13,
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neurodsp/sim/aperiodic.py | JanCBrammer/neurodsp | 154 | 69532 | """Simulating time series, with aperiodic activity."""
import numpy as np
from scipy.stats import zscore
from scipy.linalg import toeplitz, cholesky
from neurodsp.filt import filter_signal, infer_passtype
from neurodsp.filt.fir import compute_filter_length
from neurodsp.filt.checks import check_filter_definition
from neurodsp.utils import remove_nans
from neurodsp.utils.checks import check_param_range
from neurodsp.utils.data import create_times, compute_nsamples
from neurodsp.utils.decorators import normalize
from neurodsp.spectral import rotate_powerlaw
from neurodsp.sim.transients import sim_synaptic_kernel
###################################################################################################
###################################################################################################
@normalize
def sim_poisson_pop(n_seconds, fs, n_neurons=1000, firing_rate=2):
"""Simulate a Poisson population.
Parameters
----------
n_seconds : float
Simulation time, in seconds.
fs : float
Sampling rate of simulated signal, in Hz.
n_neurons : int, optional, default: 1000
Number of neurons in the simulated population.
firing_rate : float, optional, default: 2
Firing rate of individual neurons in the population.
Returns
-------
sig : 1d array
Simulated population activity.
Notes
-----
The simulated signal is essentially white noise, but satisfies the Poisson
property, i.e. mean(X) = var(X).
The lambda parameter of the Poisson process (total rate) is determined as
firing rate * number of neurons, i.e. summation of Poisson processes is still
a Poisson processes.
Note that the Gaussian approximation for a sum of Poisson processes is only
a good approximation for large lambdas.
Examples
--------
Simulate a Poisson population:
>>> sig = sim_poisson_pop(n_seconds=1, fs=500, n_neurons=1000, firing_rate=2)
"""
# Poisson population rate signal scales with # of neurons and individual rate
lam = n_neurons * firing_rate
# Variance is equal to the mean
sig = np.random.normal(loc=lam, scale=lam**0.5, size=compute_nsamples(n_seconds, fs))
# Enforce that sig is non-negative in cases of low firing rate
sig[np.where(sig < 0.)] = 0.
return sig
@normalize
def sim_synaptic_current(n_seconds, fs, n_neurons=1000, firing_rate=2.,
tau_r=0., tau_d=0.01, t_ker=None):
"""Simulate a signal as a synaptic current, which has 1/f characteristics with a knee.
Parameters
----------
n_seconds : float
Simulation time, in seconds.
fs : float
Sampling rate of simulated signal, in Hz.
n_neurons : int, optional, default: 1000
Number of neurons in the simulated population.
firing_rate : float, optional, default: 2
Firing rate of individual neurons in the population.
tau_r : float, optional, default: 0.
Rise time of synaptic kernel, in seconds.
tau_d : float, optional, default: 0.01
Decay time of synaptic kernel, in seconds.
t_ker : float, optional
Length of time of the simulated synaptic kernel, in seconds.
Returns
-------
sig : 1d array
Simulated synaptic current.
Notes
-----
- This simulation is based on the one used in [1]_.
- The resulting signal is most similar to unsigned intracellular current or conductance change.
References
----------
.. [1] <NAME>., <NAME>., & <NAME>. (2017). Inferring synaptic
excitation/inhibition balance from field potentials. NeuroImage, 158, 70–78.
DOI: https://doi.org/10.1016/j.neuroimage.2017.06.078
Examples
--------
Simulate a synaptic current signal:
>>> sig = sim_synaptic_current(n_seconds=1, fs=500)
"""
# If not provided, compute t_ker as a function of decay time constant
if t_ker is None:
t_ker = 5. * tau_d
# Simulate an extra bit because the convolution will trim & turn off normalization
sig = sim_poisson_pop((n_seconds + t_ker), fs, n_neurons, firing_rate,
mean=None, variance=None)
ker = sim_synaptic_kernel(t_ker, fs, tau_r, tau_d)
sig = np.convolve(sig, ker, 'valid')[:compute_nsamples(n_seconds, fs)]
return sig
@normalize
def sim_knee(n_seconds, fs, chi1, chi2, knee):
"""Simulate a signal whose power spectrum has a 1/f structure with a knee.
Parameters
----------
n_seconds : float
Simulation time, in seconds.
fs : float
Sampling rate of simulated signal, in Hz.
chi1 : float
Power law exponent before the knee.
chi2 : float
Power law exponent added to chi1 after the knee.
knee : float
Location of the knee in Hz.
Returns
-------
sig : 1d array
Time series with the desired power spectrum.
Notes
-----
This simulated time series has a power spectrum that follows the Lorentzian equation:
`P(f) = 1 / (f**chi1 * (f**chi2 + knee))`
- This simulation creates this power spectrum shape using a sum of sinusoids.
- The slope of the log power spectrum before the knee is chi1 whereas after the knee it is chi2,
but only when the sign of chi1 and chi2 are the same.
Examples
--------
Simulate a time series with chi1 of -1, chi2 of -2, and knee of 100:
>> sim_knee(n_seconds=10, fs=1000, chi1=-1, chi2=-2, knee=100)
"""
times = create_times(n_seconds, fs)
n_samples = compute_nsamples(n_seconds, fs)
# Create frequencies for the power spectrum, which will be freqs of the summed cosines
freqs = np.linspace(0, fs/2, num=int(n_samples//2 + 1), endpoint=True)
# Drop the DC component
freqs = freqs[1:]
# Map the frequencies under the (square root) Lorentzian
# This will give us the amplitude coefficients for the sinusoids
cosine_coeffs = np.array([np.sqrt(1 / (freq ** -chi1 * (freq ** (-chi2 - chi1) + knee))) \
for freq in freqs])
# Add sinusoids with a random phase shift
sig = np.sum(np.array([cosine_coeffs[ell] * \
np.cos(2 * np.pi * freq * times + 2 * np.pi * np.random.rand()) \
for ell, freq in enumerate(freqs)]), axis=0)
return sig
@normalize
def sim_random_walk(n_seconds, fs, theta=1., mu=0., sigma=5.):
"""Simulate a mean-reverting random walk, as an Ornstein-Uhlenbeck process.
Parameters
----------
n_seconds : float
Simulation time, in seconds.
fs : float
Sampling rate of simulated signal, in Hz.
theta : float, optional, default: 1.0
Memory scale parameter. Larger theta values create faster fluctuations.
mu : float, optional, default: 0.0
Mean of the random walk.
sigma : float, optional, default: 5.0
Standard deviation of the random walk.
Returns
-------
sig : 1d array
Simulated random walk signal.
Notes
-----
The random walk is simulated as a discretized Ornstein-Uhlenbeck process:
`dx = theta*(x-mu)*dt + sigma*dWt`
Where:
- mu : mean
- sigma : standard deviation
- theta : memory scale
- dWt : increments of Wiener process, i.e. white noise
See the wikipedia page [1]_ for the integral solution.
References
----------
.. [1] https://en.wikipedia.org/wiki/Ornstein-Uhlenbeck_process#Formal_solution
Examples
--------
Simulate a Ornstein-Uhlenbeck random walk:
>>> sig = sim_random_walk(n_seconds=1, fs=500, theta=1.)
"""
times = create_times(n_seconds, fs)
x0 = mu
dt = times[1] - times[0]
ws = np.random.normal(size=len(times))
ex = np.exp(-theta * times)
ws[0] = 0.
sig = x0 * ex + mu * (1. - ex) + sigma * ex * \
np.cumsum(np.exp(theta * times) * np.sqrt(dt) * ws)
return sig
@normalize
def sim_powerlaw(n_seconds, fs, exponent=-2.0, f_range=None, **filter_kwargs):
"""Simulate a power law time series, with a specified exponent.
Parameters
----------
n_seconds : float
Simulation time, in seconds.
fs : float
Sampling rate of simulated signal, in Hz.
exponent : float, optional, default: -2
Desired power-law exponent, of the form P(f)=f^exponent.
f_range : list of [float, float] or None, optional
Frequency range to filter simulated data, as [f_lo, f_hi], in Hz.
**filter_kwargs : kwargs, optional
Keyword arguments to pass to `filter_signal`.
Returns
-------
sig : 1d array
Time-series with the desired power law exponent.
Notes
-----
- Powerlaw data with exponents is created by spectrally rotating white noise [1]_.
References
----------
.. [1] <NAME>., & <NAME>. (1995). On Generating Power Law Noise.
Astronomy and Astrophysics, 300, 707–710.
Examples
--------
Simulate a power law signal, with an exponent of -2 (brown noise):
>>> sig = sim_powerlaw(n_seconds=1, fs=500, exponent=-2.0)
Simulate a power law signal, with a highpass filter applied at 2 Hz:
>>> sig = sim_powerlaw(n_seconds=1, fs=500, exponent=-1.5, f_range=(2, None))
"""
# Compute the number of samples for the simulated time series
n_samples = compute_nsamples(n_seconds, fs)
# Get the number of samples to simulate for the signal
# If signal is to be filtered, with FIR, add extra to compensate for edges
if f_range and filter_kwargs.get('filter_type', None) != 'iir':
pass_type = infer_passtype(f_range)
filt_len = compute_filter_length(fs, pass_type,
*check_filter_definition(pass_type, f_range),
n_seconds=filter_kwargs.get('n_seconds', None),
n_cycles=filter_kwargs.get('n_cycles', 3))
n_samples += filt_len + 1
# Simulate the powerlaw data
sig = _create_powerlaw(n_samples, fs, exponent)
if f_range is not None:
sig = filter_signal(sig, fs, infer_passtype(f_range), f_range,
remove_edges=True, **filter_kwargs)
# Drop the edges, that were compensated for, if not using FIR filter
if not filter_kwargs.get('filter_type', None) == 'iir':
sig, _ = remove_nans(sig)
return sig
@normalize
def sim_frac_gaussian_noise(n_seconds, fs, chi=0, hurst=None):
"""Simulate a timeseries as fractional gaussian noise.
Parameters
----------
n_seconds : float
Simulation time, in seconds.
fs : float
Sampling rate of simulated signal, in Hz.
chi: float, optional, default: 0
Desired power law exponent of the spectrum of the signal.
Must be in the range (-1, 1).
hurst : float, optional, default: None
Desired Hurst parameter, which must be in the range (0, 1).
If provided, this value overwrites the `chi` parameter.
Returns
-------
sig: 1d array
Simulated fractional gaussian noise time series.
Notes
-----
The time series can be specified with either a desired power law exponent,
or alternatively with a specified Hurst parameter.
The Hurst parameter is not the Hurst exponent as defined in rescaled range analysis.
The Hurst parameter is defined for self-similar processes such that Y(at) = a^H Y(t)
for all a > 0, where this equality holds in distribution.
The relationship between the power law exponent chi and the Hurst parameter
for fractional gaussian noise is chi = 2 * hurst - 1.
For more information, consult [1]_.
References
----------
.. [1] <NAME>., <NAME>., <NAME>., & <NAME>. (2002). Fractal characterization of
complexity in temporal physiological signals. Physiological Measurement, 23(1), R1–R38.
DOI: https://doi.org/10.1088/0967-3334/23/1/201
Examples
--------
Simulate fractional gaussian noise with a power law decay of 0 (white noise):
>>> sig = sim_frac_gaussian_noise(n_seconds=1, fs=500, chi=0)
Simulate fractional gaussian noise with a Hurst parameter of 0.5 (also white noise):
>>> sig = sim_frac_gaussian_noise(n_seconds=1, fs=500, hurst=0.5)
"""
if hurst is not None:
check_param_range(hurst, 'hurst', (0, 1))
else:
check_param_range(chi, 'chi', (-1, 1))
# Infer the hurst parameter from chi
hurst = (-chi + 1.) / 2
# Compute the number of samples for the simulated time series
n_samples = compute_nsamples(n_seconds, fs)
# Define helper function for computing the auto-covariance
def autocov(hurst):
return lambda k: 0.5 * (np.abs(k - 1) ** (2 * hurst) - 2 * \
k ** (2 * hurst) + (k + 1) ** (2 * hurst))
# Build the autocovariance matrix
gamma = np.arange(0, n_samples)
gamma = np.apply_along_axis(autocov(hurst), 0, gamma)
autocov_matrix = toeplitz(gamma)
# Use the Cholesky factor to transform white noise to get the desired time series
white_noise = np.random.randn(n_samples)
cholesky_factor = cholesky(autocov_matrix, lower=True)
sig = cholesky_factor @ white_noise
return sig
@normalize
def sim_frac_brownian_motion(n_seconds, fs, chi=-2, hurst=None):
"""Simulate a timeseries as fractional brownian motion.
Parameters
----------
n_seconds : float
Simulation time, in seconds.
fs : float
Sampling rate of simulated signal, in Hz.
chi : float, optional, default: -2
Desired power law exponent of the spectrum of the signal.
Must be in the range (-3, -1).
hurst : float, optional, default: None
Desired Hurst parameter, which must be in the range (0, 1).
If provided, this value overwrites the `chi` parameter.
Returns
-------
sig : 1d array
Simulated fractional brownian motion time series.
Notes
-----
The time series can be specified with either a desired power law exponent,
or alternatively with a specified Hurst parameter.
Note that when specifying there can be some bias leading to a steeper than expected
spectrum of the simulated signal. This bias is higher for chi values near to 1,
and may be more severe in shorter signals.
The Hurst parameter is not the Hurst exponent in general. The Hurst parameter
is defined for self-similar processes such that Y(at) = a^H Y(t) for all a > 0,
where this equality holds in distribution.
The relationship between the power law exponent chi and the Hurst parameter
for fractional brownian motion is chi = 2 * hurst + 1
For more information, consult [1]_ and/or [2]_.
References
----------
.. [1] <NAME>., <NAME>., <NAME>., & <NAME>. (2002). Fractal characterization of
complexity in temporal physiological signals. Physiological Measurement, 23(1), R1–R38.
DOI: https://doi.org/10.1088/0967-3334/23/1/201
.. [2] <NAME>. (2004). Simulation of fractional Brownian motion. 77.
Examples
--------
Simulate fractional brownian motion with a power law exponent of -2 (brown noise):
>>> sig = sim_frac_brownian_motion(n_seconds=1, fs=500, chi=-2)
Simulate fractional brownian motion with a Hurst parameter of 0.5 (also brown noise):
>>> sig = sim_frac_brownian_motion(n_seconds=1, fs=500, hurst=0.5)
"""
if hurst is not None:
check_param_range(hurst, 'hurst', (0, 1))
else:
check_param_range(chi, 'chi', (-3, -1))
# Infer the hurst parameter from chi
hurst = (-chi - 1.) / 2
# Fractional brownian motion is the cumulative sum of fractional gaussian noise
fgn = sim_frac_gaussian_noise(n_seconds, fs, hurst=hurst)
sig = np.cumsum(fgn)
return sig
def _create_powerlaw(n_samples, fs, exponent):
"""Create a power law time series.
Parameters
----------
n_samples : int
The number of samples to simulate.
fs : float
Sampling rate of simulated signal, in Hz.
exponent : float
Desired power-law exponent, of the form P(f)=f^exponent.
Returns
-------
sig : 1d array
Time-series with the desired power law exponent.
Notes
-----
This function creates variable power law exponents by spectrally rotating white noise.
"""
# Start with white noise signal, that we will rotate, in frequency space
sig = np.random.randn(n_samples)
# Compute the FFT
fft_output = np.fft.fft(sig)
freqs = np.fft.fftfreq(len(sig), 1. / fs)
# Rotate spectrum and invert back to time series, with a z-score to normalize
# Delta exponent is divided by two, as the FFT output is in units of amplitude not power
fft_output_rot = rotate_powerlaw(freqs, fft_output, -exponent/2)
sig = zscore(np.real(np.fft.ifft(fft_output_rot)))
return sig
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] |
yauber_algo/_algo/tests/test_featurewiz_categorize.py | LongNguyen1012/yauber-algo | 1 | 141031 | import unittest
from yauber_algo.errors import *
class CategorizeTestCase(unittest.TestCase):
def test_categorize(self):
import yauber_algo.sanitychecks as sc
from numpy import array, nan, inf
import os
import sys
import pandas as pd
import numpy as np
from yauber_algo.algo import categorize
#
# Function settings
#
algo = 'categorize'
func = categorize
with sc.SanityChecker(algo) as s:
#
# Check regular algorithm logic
#
s.check_regular(
np.array([0., 0., 0., 0., 1., 1., 1., 2., 2., 2.]),
func,
(
np.array([0, 1, 2, 3, 4, 5, 6, 7, 8, 10]),
[0, 3, 6, 10]
),
suffix='reg'
)
s.check_regular(
np.array([0., 0., 0., 0., 1., 1., 1., 2., 2., 2.]),
func,
(
np.array([0, 1, 2, 3, 4, 5, 6, 7, 8, 10]),
[0.1, 3, 6, 10]
),
suffix='min_not_in_bins',
exception=YaUberAlgoInternalError
)
s.check_regular(
np.array([0., 0., 0., 0., 1., 1., 1., 2., 2., 2.]),
func,
(
np.array([0, 1, 2, 3, 4, 5, 6, 7, 8, 10]),
[0, 3, 6, 9.999]
),
suffix='max_not_in_bins',
exception=YaUberAlgoInternalError
)
s.check_regular(
np.array([0., 0., 0., 0., 0., 0., 0., 0., 0., 0.]),
func,
(
np.array([0, 1, 2, 3, 4, 5, 6, 7, 8, 10]),
[0, 10]
),
suffix='min_max_one_bin',
exception=YaUberAlgoArgumentError
)
s.check_regular(
np.array([0., 0., 0., 0., 0., 0., 0., 0., 0., 0.]),
func,
(
np.array([0, 1, 2, 3, 4, 5, 6, 7, 8, 10]),
[0, 10, 10]
),
suffix='bins_non_unique',
exception=YaUberAlgoArgumentError
)
s.check_regular(
np.array([0., 0., 0., 0., 0., 0., 0., 0., 0., 0.]),
func,
(
np.array([0, 1, 2, 3, 4, 5, 6, 7, 8, 10]),
[0, 10, 5]
),
suffix='bins_not_sorted',
exception=YaUberAlgoArgumentError
)
s.check_regular(
np.array([0., 0., 0., 0., 0., 0., 0., 0., 0., 0.]),
func,
(
np.array([0, 1, 2, 3, 4, 5, 6, 7, 8, 10]),
[0, 5, 'obj']
),
suffix='bins_non_number',
exception=YaUberAlgoArgumentError
)
s.check_regular(
np.array([0., 0., 0., 0., 0., 0., 0., 0., 0., 0.]),
func,
(
np.array([0, 1, 2, 3, 4, 5, 6, 7, 8, 10]),
[0, 5, nan]
),
suffix='bins_nan',
exception=YaUberAlgoArgumentError
)
s.check_regular(
np.array([0., 0., 0., 0., 0., 0., 0., 0., 0., 0.]),
func,
(
np.array([0, 1, 2, 3, 4, 5, 6, 7, 8, 10]),
[0, 5, inf]
),
suffix='bins_inf',
exception=YaUberAlgoArgumentError
)
s.check_naninf(
np.array([0., 0., 0., 0., 1., 1., 1., 2., nan, nan]),
func,
(
np.array([0, 1, 2, 3, 4, 5, 6, 7, inf, nan]),
[0, 3, 6, 10]
),
suffix='reg'
)
s.check_regular(
np.array([0., 0., 0., 0., 1., 1., 1., 2., 2., 2.]),
func,
(
np.array([0, 1, 2, 3, 4, 5, 6, 7, 8, 10]),
np.array([0, 3, 6, 10])
),
suffix='bins_are_np_array'
)
s.check_regular(
np.array([0., 0., 0., 0., 1., 1., 1., 2., 2., 2.]),
func,
(
np.array([0, 1, 2, 3, 4, 5, 6, 7, 8, 10]),
pd.Series([0, 3, 6, 10])
),
suffix='bins_are_series'
)
s.check_series(
pd.Series(np.array([0., 0., 0., 0., 1., 1., 1., 2., 2., 2.])),
func,
(
pd.Series(np.array([0, 1, 2, 3, 4, 5, 6, 7, 8, 10])),
[0, 3, 6, 10]
),
suffix=''
)
s.check_dtype_float(
np.array([0., 0., 0., 0., 1., 1., 1., 2., 2., 2.], dtype=np.float),
func,
(
np.array([0, 1, 2, 3, 4, 5, 6, 7, 8, 10], dtype=np.float),
[0, 3, 6, 10]
),
suffix=''
)
s.check_dtype_int(
np.array([0., 0., 0., 0., 1., 1., 1., 2., 2., 2.], dtype=np.float),
func,
(
np.array([0, 1, 2, 3, 4, 5, 6, 7, 8, 10], dtype=np.int32),
[0, 3, 6, 10]
),
suffix=''
)
s.check_dtype_bool(
np.array([0., 0., 0., 0., 1., 1., 1., 2., 2., 2.], dtype=np.float),
func,
(
np.array([0, 1, 0, 0, 1, 0, 1, 0, 0, 1], dtype=np.bool),
[0, 3, 6, 10]
),
suffix='',
exception=YaUberAlgoDtypeNotSupportedError
)
s.check_dtype_object(
func,
(
np.array([0, 1, 2, 3, 4, 5, 6, 7, 8, 10], dtype=np.object),
[0, 3, 6, 10]
),
suffix=''
)
s.check_futref(5, 1,
func,
(
np.random.random(1000),
[0, 0.33, 0.66, 1.0]
),
)
s.check_window_consistency(5, 1,
func,
(
np.random.random(1000),
[0, 0.33, 0.66, 1.0]
),
) | [
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eda_scripts/pct_cans_interested_in.py | eitrheim/Resume-Screening-and-Selection | 14 | 174827 | <gh_stars>10-100
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
resume_text = pd.read_csv("~/data/Candidate Report_tokenized.csv").fillna('')
job_text = pd.read_csv("~/data/full_requisition_data_tokenized.csv").fillna('')
jobs_reviewed_atleast_once = ['Review',
'Completion',
'Phone Screen',
'Schedule Interview',
'Offer Rejected',
'Schedule interview',
'No Show (Interview / First Day)',
'Offer',
'Second Round Interview',
'Background Check',
'Revise Offer',
'Final Round Interview',
'Voluntary Withdrew', # NEW ADDT
'Salary Expectations too high', # NEW ADDT
'Skills or Abilities'] # NEW ADDT
#how many candidates with NO RESUMES were they interested in?
temp_df = resume_text[resume_text['Resume Text'] == '[\'nan\']']
temp_df = temp_df.merge(job_text, how='left',on='Req ID')
temp_df = temp_df['Latest Recruiting Step'].value_counts()
sum(temp_df[temp_df.index.isin(jobs_reviewed_atleast_once)])
####### 860 CANDIDATES?! #######
#interested in atleast one candidate with a resume
temp_df = resume_text[resume_text['Latest Recruiting Step'].isin(jobs_reviewed_atleast_once)]
#temp_df = temp_df[temp_df['Resume Text'] != '[\'nan\']']
x = temp_df[['Req ID', 'Candidate ID','Resume Text']]
x = x.merge(job_text, how='left',on='Req ID')
x = x['Req ID'].value_counts()
x = x[x >= 1]
jobIDs = x.index
def pct_reviewed(low_lim, high_lim):
temp_df = x[x >= low_lim][x < high_lim].index
jobIDs = temp_df
if len(temp_df) == 0:
output = np.nan
else:
y = resume_text[resume_text['Req ID'].isin(jobIDs)]
y = y['Latest Recruiting Step'].value_counts()
output = sum(y[y.index.isin(jobs_reviewed_atleast_once)])/sum(y)
return output
pcts = []
for i in list(range(0,245,15)):
pcts.append(pct_reviewed(i, i+15))
plot_data = pd.DataFrame(pcts,list(range(0,245,15))).reset_index()
plot_data.columns = ['Number of Candidates Applied', 'Percent that Recruiters Were Interested In']
plt.scatter(x=plot_data.iloc[:,0],y=plot_data.iloc[:,1])
PercentInterestedIn = []
for i in x.index:
y = resume_text[resume_text['Req ID'] == i]
y = y['Latest Recruiting Step'].value_counts()
output = sum(y[y.index.isin(jobs_reviewed_atleast_once)])/sum(y)
# output = round(output*100)
PercentInterestedIn.append(output)
import seaborn as sns
plot_data = pd.DataFrame(x.values,PercentInterestedIn).reset_index()
plot_data.columns = ['Percent that Recruiters Were Interested In', 'Number of Candidates to Apply']
sns.set()
sns.jointplot(x=plot_data[plot_data.columns[1]],y=plot_data[plot_data.columns[0]],s=3).ax_joint.legend_.remove()
#if a role has a lot of candidates apply to it, they are interested in more candidates
#this seems backwards, say if 1000 people apply, they shouldn't be interested in almost 1000
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] |
check_logstash_hot_threads.py | adolci/nagios-plugins | 0 | 50349 | #!/usr/bin/env python
# coding=utf-8
# vim:ts=4:sts=4:sw=4:et
#
# Author: <NAME>
# Date: 2017-11-26 18:55:00 +0100 (Sun, 26 Nov 2017)
#
# https://github.com/harisekhon/nagios-plugins
#
# License: see accompanying Hari Sekhon LICENSE file
#
# If you're using my code you're welcome to connect with me on LinkedIn
# and optionally send me feedback to help steer this or other code I publish
#
# https://www.linkedin.com/in/harisekhon
#
"""
Nagios Plugin to check Logstash hot threads via the Logstash Rest API
Optional thresholds apply to the % CPU time for the busiest hot thread by default, or if using the
--top-3 switch then the total sum of % CPU time for top 3 hot threads combined
The top hot thread CPU % and state is output regardless, and perfdata for the top hot thread CPU % and
the top 3 hot threads total CPU % is output for graphing
API is only available in Logstash 5.x onwards, will get connection refused on older versions
Ensure Logstash options:
--http.host should be set to 0.0.0.0 if querying remotely
--http.port should be set to the same port that you are querying via this plugin's --port switch
Tested on Logstash 5.0, 5.1, 5.2, 5.3, 5.4, 5.5, 5.6, 6.0, 6.1
"""
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
from __future__ import unicode_literals
import os
import sys
import traceback
srcdir = os.path.abspath(os.path.dirname(__file__))
libdir = os.path.join(srcdir, 'pylib')
sys.path.append(libdir)
try:
# pylint: disable=wrong-import-position
#from harisekhon.utils import log
from harisekhon.utils import UnknownError, support_msg_api
from harisekhon.utils import isDict, isList
from harisekhon import RestNagiosPlugin
except ImportError as _:
print(traceback.format_exc(), end='')
sys.exit(4)
__author__ = '<NAME>'
__version__ = '0.2'
class CheckLogstashHotThreads(RestNagiosPlugin):
def __init__(self):
# Python 2.x
super(CheckLogstashHotThreads, self).__init__()
# Python 3.x
# super().__init__()
self.name = 'Logstash'
self.default_port = 9600
# could add pipeline name to end of this endpoint but error would be less good 404 Not Found
# Logstash 5.x /_node/pipeline <= use -5 switch for older Logstash
# Logstash 6.x /_node/pipelines
self.path = '/_node/hot_threads'
self.auth = False
self.json = True
self.msg = 'Logstash hot threads msg not defined yet'
self.plugins = None
def add_options(self):
super(CheckLogstashHotThreads, self).add_options()
self.add_opt('--top-3', action='store_true',
help='Test the total sum cpu percentage of the top 3 hot threads' + \
' instead of the top thread')
self.add_thresholds(default_warning=50)
def process_options(self):
super(CheckLogstashHotThreads, self).process_options()
self.validate_thresholds(percent=True, optional=True)
def parse_json(self, json_data):
if not isDict(json_data):
raise UnknownError('non-dict returned for hot threads. {}'.format(support_msg_api()))
hot_threads = json_data['hot_threads']['threads']
top_3 = self.get_opt('top_3')
sum_percent = 0
last_percent = None
for thread in hot_threads:
thread_percent = thread['percent_of_cpu_time']
if last_percent is None:
last_percent = thread_percent
if thread_percent > last_percent:
raise UnknownError('assertion failure - subsequent thread percent is unexpectedly higher' + \
', out of expected order. {}'.format(support_msg_api()))
sum_percent += thread_percent
self.msg = 'Logstash '
if top_3:
self.msg += 'top 3 hot threads cpu percentage = {}%'.format(sum_percent)
self.check_thresholds(sum_percent)
self.msg += ', '
# they come sorted with highest at top
top_thread = hot_threads[0]
name = top_thread['name']
percent = top_thread['percent_of_cpu_time']
state = top_thread['state']
# not available in 5.0, only later versions such as 6.0
#thread_id = top_thread['thread_id']
self.msg += 'top hot thread \'{}\' cpu percentage = {}%'.format(name, percent)
if not top_3:
self.check_thresholds(percent)
self.msg += ', state = \'{}\''.format(state)
#self.msg += ', id = {}'.format(state, thread_id)
if self.verbose:
if not isList(top_thread['traces']):
raise UnknownError('hot thread\'s trace field is not a list. {}'.format(support_msg_api()))
traces = '\\n'.join(top_thread['traces'])
self.msg += ', traces: {}'.format(traces)
if not top_3:
self.msg += ', top 3 hot threads cpu percentage = {}%'.format(sum_percent)
self.msg += ' | top_hot_thread_cpu_percentage={}%'.format(percent)
if not top_3:
self.msg += '{}'.format(self.get_perf_thresholds())
self.msg += ' top_three_hot_thread_cpu_percentage={}%'.format(sum_percent)
if top_3:
self.msg += '{}'.format(self.get_perf_thresholds())
if __name__ == '__main__':
CheckLogstashHotThreads().main()
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pseudo_dojo/refdata/nist/atomicdata.py | fnaccarato/pseudo_dojo | 0 | 28659 | #!/usr/bin/env python
import os.path
import sys
from pprint import pprint
import nist_database
def sort_dict(d, key=None, reverse=False):
"""
Returns an OrderedDict object whose keys are ordered according to their
value.
Args:
d:
Input dictionary
key:
function which takes an tuple (key, object) and returns a value to
compare and sort by. By default, the function compares the values
of the dict i.e. key = lambda t : t[1]
reverse:
allows to reverse sort order.
"""
import collections
kv_items = [kv for kv in d.items()]
# Sort kv_items according to key.
if key is None:
kv_items.sort(key=lambda t: t[1], reverse=reverse)
else:
kv_items.sort(key=key, reverse=reverse)
# Build ordered dict.
return collections.OrderedDict(kv_items)
#class AtomicData(dict):
#
# _mandatory_keys = [
# "Etot",
# "Ekin",
# "Ecoul",
# "Eenuc",
# "Exc",
# ]
#
# def __init__(self, *args, **kwargs):
# super(AtomicData, self).__init__(*args, **kwargs)
#
# for k in self._mandatory_keys:
# if k not in self:
# raise ValueError("mandatory key %s is missing" % k)
# #self.symbol
# #self.Z = Z
# #self.configurations = configurations
def make_nist_configurations(path):
neutral, cations, symb2Z = parse_nist_configurations(path)
print("# Computer generated code")
print("neutral = ")
pprint(neutral)
print("")
print("# Computer generated code")
print("cations = ")
pprint(cations)
print("")
print("# Computer generated code")
print("symb2Z = ")
pprint(symb2Z)
print("")
def parse_nist_configurations(path):
"""Read and parse the file with the configurations."""
# Z Symbol Neutral Positive ion
# 1 H 1s^1 -
# 2 He 1s^2 1s^1
neutral, cations, symb2Z = {}, {}, {}
count = 1
with open(os.path.join(path, "configurations"), "r") as fh:
for line in fh:
if not (len(line) > 1 and line[1].isdigit()):
continue
head, catio = line[:44], line[44:].strip()
toks = head.split()
Z, symbol, neutr = int(toks[0]), toks[1], " ".join(t for t in toks[2:])
assert Z == count
count += 1
neutr = neutr.replace("^", "")
catio = catio.replace("^", "")
neutral[symbol] = neutr
cations[symbol] = catio if catio != "-" else None
symb2Z[symbol] = Z
return neutral, cations, symb2Z
def occupations_from_symbol(symbol):
Z = symb2Z[symbol]
configuration = neutral[Z]
if configuration[0][0] == '[':
occupations = occupations_from_symbol(configuration[0][1:-1])
configuration = configuration[1:]
else:
occupations = []
for s in configuration:
occupations.append((s[:2], int(s[3:])))
return occupations
def extract_nistdata(path, nist_xctype, iontype):
# http://www.physics.nist.gov/PhysRefData/DFTdata/
# Read and parse the configurations file
# Z Symbol Neutral Positive ion
# 1 H 1s^1 -
# 2 He 1s^2 1s^1
Ztable = {}
configurations = [['X', '']]
Z = 1
with open(os.path.join(path, "configurations"), "r") as fh:
for line in fh:
if len(line) > 1 and line[1].isdigit():
line = line[:44].split()
symbol = line[1]
Ztable[symbol] = Z
assert int(line[0]) == Z
configurations.append(line[2:])
Z += 1
def occupations_from_symbol(symbol):
Z = Ztable[symbol]
configuration = configurations[Z]
if configuration[0][0] == '[':
occupations = occupations_from_symbol(configuration[0][1:-1])
configuration = configuration[1:]
else:
occupations = []
for s in configuration:
occupations.append((s[:2], int(s[3:])))
return occupations
# Ex of file with AE results:
# Etot = -675.742283
# Ekin = 674.657334
# Ecoul = 285.206130
# Eenuc = -1601.463209
# Exc = -34.142538
# 1s -143.935181
# 2s -15.046905
# 2p -12.285376
# 3s -1.706331
# 3p -1.030572
# 4s -0.141411
nistdata = {}
spdf = {'s': 0, 'p': 1, 'd': 2, 'f': 3}
for (symbol, Z) in Ztable.items():
#print("in symbol %s" % symbol)
occupations = occupations_from_symbol(symbol)
fname = os.path.join(path, nist_xctype, iontype, '%02d%s' % (Z, symbol))
energies, eigens = {}, {}
with open(fname, 'r') as fh:
for n in range(5):
ename, evalue = fh.readline().split("=")
energies[ename.strip()] = float(evalue)
for line in fh:
state, eig = line.split()
eigens[state] = float(eig)
nloe = []
for (state, occ) in occupations:
n = int(state[0])
l = spdf[state[1]]
eig = eigens[state]
nloe.append((n, l, occ, eig))
nistdata[symbol] = (Z, nloe, energies)
return nistdata
def make_nistmodule(path, nist_xctype):
#for iontype in ["neutrals", "cations",]:
for iontype in ["neutrals",]:
print('# Computer generated code: nist_xctype = %s, iontype = %s' % (nist_xctype, iontype))
print("format:\n\t element: (atomic number, [(n, l, occ, energy), ...]")
print('%s = ' % iontype)
data = extract_nistdata(path, nist_xctype, iontype)
pprint(data)
#print(json.dumps(data, indent=4))
if __name__ == '__main__':
import sys
from qatom import states_from_string, AtomicConfiguration
for symbol in nist_database.symbols:
aconf = AtomicConfiguration.neutral_from_symbol(symbol)
print(aconf)
sys.exit(1)
path = sys.argv[1]
#neutral = {}
#for (symbol, confstr) in nist_database.neutral.items():
# states = states_from_string(confstr)
# print(confstr)
# neutral[symbol] = (nist_database.symb2Z[symbol], states)
#for s in states:
# pprint(tuple(s))
#aconf = AtomicConfiguration.from_string(confstr, symb2Z[symbol])
#make_nist_configurations(path)
#xctypes = ["LDA", "ScRLDA"] #, "LSD", "RLDA",]
nist_xctypes = ["LDA",]
for xctype in nist_xctypes:
print("xctype: %s" % xctype)
make_nistmodule(path, xctype)
#iontype = "neutrals"
#dft_data = extract_nistdata(sys.argv[1], nist_xctype, iontype)
#make_nistmodule(sys.argv[1], "LDA")
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] |
mumu/feature/_woe.py | mingminyu/mumu | 1 | 143814 | <filename>mumu/feature/_woe.py
# coding: utf-8
# ================================================
# Project: mumu
# File: feature/_woe.py
# Author: <NAME>
# Email: <EMAIL>
# Date: 2021/6/24 18:24
# Description:
# ================================================
import os
import string
import logging
import math
import xlsxwriter
import pandas as pd
import numpy as np
import scipy.stats
import matplotlib.pyplot as plt
from sklearn import tree
from matplotlib.font_manager import FontProperties
from ._contingency import chi2_contingency
def __woe_calc(bad=None, good=None, bad_freq=None, good_freq=None):
"""Calculate woe
:param bad: int or float,
count of samples(target=1) in single bin
:param good: int or float
count of samples(target=0) in single bin
:param bad_freq: int or float
count of samples(target=1) in all samples
:param good_freq: int or float
count of samples(target=0) in all samples
:return:
"""
target_rt = bad / float(bad_freq)
non_target_rt = good / float(good_freq)
if float(bad) != 0.0 and bad / float(bad + good) != 1.0:
woe = math.log(float(target_rt / non_target_rt))
elif target_rt == 0.0:
woe = -99999999.0
elif bad / float(bad + good) == 1.0:
woe = 99999999.0
else:
woe = -99999999.0
return woe
def __iv_calc(ds=None):
"""Calculate iv value of the variable
:param ds: DataFrame
:return: iv, float
"""
bad_dist = ds['1'] / float(ds['1'].sum())
good_dist = ds['0'] / float(ds['0'].sum())
bad_dist = bad_dist.apply(lambda x: 0.0001 if x == 0 else x)
ds['iv'] = (bad_dist - good_dist) * np.log(bad_dist / good_dist)
iv = ds['iv'].sum()
return iv
def __target_check(df_master=None, target="target"):
"""Check target is right, target column only includes 0 and 1 in theory.
:param df_master: DataFrame
:param target: str
:return:
"""
# 检查target是否只有0和1两个取值
if set(df_master[target].unique()) != {0, 1}:
raise ValueError('Target are not only 0 and 1!')
def init_binning(df_master=None, var_name=None, target=None, max_bin_num=200, missing=False, cut_points=None):
"""Cut bins for numerical variables(`var_name`) and describe distribution of target in each bin.
:param df_master: DataFrame
:param var_name: str
:param target: str
:param max_bin_num: int, default 200,
max num of bins cut
:param missing: bool, default False
whether if the variable has missing value.
:param cut_points: list, 指定的切分点，若不指定则根据分位点分bin
specified cut points, if not, then cut bins according to cut points.
:return: ds: DataFrame,
statistical information for bins cut of the variable.
"""
df_tmp = df_master[[var_name, target]].copy()
# Initialization cut points
if cut_points is not None:
if len(cut_points) == 0:
raise ValueError('wrong cut points: {0}'.format(var_name))
if np.max(df_tmp[var_name]) >= cut_points[-1]:
# last value of bins is inf
cut_points[-1] = np.inf
if np.min(df_tmp[var_name]) < cut_points[0]:
# # last value of bins is minimum
cut_points[0] = np.min(df_tmp[var_name]) # bins第一个值改为min value, 防止忘记填最小值
# If #(unique value) < max_bin_num, then each value will be a bin.
elif len(df_tmp[var_name].unique()) < max_bin_num:
cut_points = np.sort(df_tmp[var_name].unique())
cut_points = np.append(cut_points, np.inf)
# If #(unique value) >= max_bin_num, then cut `max_bin_num` bins according to cut points.
else:
pct = np.arange(max_bin_num + 1) / max_bin_num
# calculate cut points and drop duplicates.
cut_points = df_tmp[var_name].quantile(pct, interpolation='higher').unique() # 计算分位点并去重
cut_points[-1] = np.inf
# when missing is true, put `-1.0` into a single bin
if missing:
if cut_points[0] == -1.0:
tmp_ary1 = np.asarray([-1.0, 0.0])
tmp_ary2 = np.asarray(cut_points[2:])
cut_points = np.concatenate((tmp_ary1, tmp_ary2), axis=0)
else:
logging.warning('Expect variable has missing value but actually no missing')
# cut bins according to cut points and calculate distribution of target in each bin.
# 按切分点分bin，并计算每个bin中target的分布
df_tmp[var_name + '_bin'] = np.digitize(df_tmp[var_name], bins=cut_points, right=False)
ds = df_tmp.groupby(var_name + '_bin')[target].value_counts().unstack().fillna(value=0)
ds['total'] = ds[0] + ds[1]
ds['bin'] = [[cut_points[i - 1], cut_points[i]] for i in list(ds.index)]
ds['bin_lb'] = [cut_points[i - 1] for i in list(ds.index)]
ds = ds.sort_values(by='bin_lb', axis=0, ascending=True).reset_index(drop=True) # 根据bin的下界进行排序
ds.columns = ['0', '1', 'total', 'bin', 'bin_lb']
return ds
def __value_match(map_dict=None, key_list=None):
"""Return corresponding key of the value according to `map_dict`
example: [1,3,4,5] → ['本科','大专','高中','研究生']
:param map_dict: dict
:param key_list: list
:return: list,
"""
result = []
for key in key_list:
if key in map_dict:
result.append(map_dict[key])
else:
result.append('base')
return result
def __mergebin(ds=None, idx_list=None, idx=None, var_type=None):
"""Merge adjacent bins, and calculate statistical information of the merged bin and chi value
:param ds: DataFrame
:param idx_list: list
:param idx: int
index of bins in list will be merged
:param var_type: str, options ['numerical', 'categorical']
type of the variable.
:return: ds: DataFrame
"""
# merge two bins, and recalculate distribution of target.
ds.at[idx_list[idx], ['0', '1']] = ds.loc[idx_list[idx], ['0', '1']] + ds.loc[idx_list[idx + 1], ['0', '1']]
ds.at[idx_list[idx], 'total'] = ds.loc[idx_list[idx], 'total'] + ds.loc[idx_list[idx + 1], 'total']
# recalculate range of the merged bin
if var_type == 'numerical':
ds.at[idx_list[idx], 'bin'] = [ds.loc[idx_list[idx], 'bin'][0], ds.loc[idx_list[idx + 1], 'bin'][1]]
elif var_type == 'categorical':
ds.at[idx_list[idx], 'bin'] = ds.loc[idx_list[idx:idx + 2], 'bin'].sum()
# drop original bin after merged
ds = ds.drop(idx_list[idx + 1], axis=0)
# drop the index of original bins after merged
idx_list.pop(idx + 1)
# recalculate chi values of the merged bin, previous bin and later bin
# if the merged bin is not first, then don't need to calculate the chi value of the previous bin
if idx != 0:
ds.at[idx_list[idx - 1], 'chisq'] = chi2_contingency(ds.loc[idx_list[(idx - 1):(idx + 1)], ['0', '1']])[0]
# if the merged bin is not last, then don't need to calculate the chi value of the later bin
if idx < ds.shape[0] - 1:
ds.at[idx_list[idx], 'chisq'] = chi2_contingency(ds.loc[idx_list[idx:idx + 2], ['0', '1']])[0]
else:
ds.at[idx_list[idx], 'chisq'] = 9999999.0
return ds
def generate_reference(ds=None, var_name=None, var_type=None):
"""generate reference table of woe of the variable.
:param ds: DataFrame
:param var_name: str
:param var_type: str, options ['numerical', 'categorical']
:return: DataFrame, reference table
"""
# calculate woe and iv value for each bin
good_freq = ds['0'].sum()
bad_freq = ds['1'].sum()
ds['woe_value'] = ds.apply(lambda x: __woe_calc(x['1'], x['0'], bad_freq, good_freq), axis=1)
iv = __iv_calc(ds)
# generate reference table
df_ref_table = pd.DataFrame(columns=['Var_Name', 'Var_Type', 'Bin_No', 'Var_Value', 'Ref_Value',
'Count_0', 'Count_1', 'Total', 'Target_Rate', 'Proportion', 'IV'])
df_ref_table['Bin_No'] = range(1, ds.shape[0] + 1) # Bin的编号，从1开始
df_ref_table['Var_Value'] = ds['bin'].astype(str) # 将list转成字符串
df_ref_table['Ref_Value'] = ds['woe_value']
df_ref_table['Count_0'] = ds['0']
df_ref_table['Count_1'] = ds['1']
df_ref_table['Total'] = ds['total']
df_ref_table['Target_Rate'] = 1.0 * df_ref_table['Count_1'] / df_ref_table['Total']
df_ref_table['Proportion'] = 1.0 * df_ref_table['Total'] / ds['total'].sum()
df_ref_table['IV'] = iv
df_ref_table['Var_Name'] = var_name
df_ref_table['Var_Type'] = var_type
return df_ref_table
def __get_list_str(x):
"""Get value of the categorical variable, and put 3 value in one line
:param x: str
:return: list
"""
str_list = x.split('\001')
s = ''
for i in range(len(str_list)):
s += str_list[i] + ','
if (i + 1) % 3 == 0 and i + 1 != len(str_list):
s += '\n'
return s[:-1]
def plot_reference(df_ref=None, save_path=None, figsize=(10, 4)):
"""Woe plotting according to reference table
:param df_ref: DataFrame
:param save_path: str, the path of image saved
:param figsize: tuple, size of the figure, default (10, 4)
:return:
"""
x = np.arange(df_ref.shape[0])
y = df_ref['Ref_Value'].values
z = df_ref['Target_Rate'].values
var_name = df_ref['Var_Name'].iloc[0]
iv = round(df_ref['IV'].iloc[0], 5)
plt.figure(figsize=figsize, dpi=200)
plt.bar(x, df_ref['Proportion'], color='royalblue', label='0', align='center')
plt.bar(x, df_ref['Proportion'] * df_ref['Target_Rate'], color='firebrick', label='1', align='center')
# draw label of x axis
if df_ref['Var_Type'].iloc[0] == 'numerical':
xticks_list = df_ref['Var_Value'].values
xticks_list = [tuple([float(j) for j in i.strip('([] ').split(',')]) for i in xticks_list]
xticks_list = [[round(i[0], 4), round(i[1], 4)] for i in xticks_list]
plt.xticks(x, xticks_list)
package_path = os.path.abspath(os.path.dirname(os.path.dirname(__file__)))
font_path = os.path.join(package_path, "resources", "fonts", "simsun.ttc")
# noinspection PyBroadException
try:
zh_font = FontProperties(fname=font_path)
except Exception:
zh_font = FontProperties()
if df_ref['Var_Type'].iloc[0] == 'categorical':
xticks_list = df_ref['Var_Value'].apply(__get_list_str).tolist()
plt.xticks(x, xticks_list, fontproperties=zh_font)
plt.ylabel('proportion')
plt.legend(loc=1, fontsize=9)
ax2 = plt.twinx()
plt.plot(x, y, '.-k', lw=2, markersize=8)
for i, j, k in zip(x, y, z):
ax2.annotate('%.2f(%.2f%%)' % (j, k * 100), xy=(i, j), va='center', ha='center',
bbox={'boxstyle': 'round', 'fc': 'w'})
plt.ylabel('Woe value(Target rate)')
plt.title('{0}: IV={1}'.format(var_name, iv), fontproperties=zh_font, fontsize=15)
# save image
if save_path is not None:
if save_path.endswith('.png') or save_path.endswith('.jpg'):
plt.savefig(save_path, bbox_inches='tight')
elif os.path.isdir(save_path):
plt.savefig(os.path.join(save_path, '{0}.png'.format(var_name)), bbox_inches='tight')
else:
raise ValueError('No such file or directory: {0}'.format(save_path))
plt.show()
plt.close()
def numwoe_autobinning(df_master=None, var_name=None, target='target', max_bins=6,
min_prop_in_bin=0.05, missing=True, max_bin_init=200,
method='chisq', to_plot=True, save_path=None):
"""Cut bins for numerical variable automatically, and calculate woe, iv value
:param df_master: DataFrame
:param var_name:
:param target:
:param max_bins: int, default 6
:param min_prop_in_bin: float, default 0.05
minimum sample ratio in each bin
:param missing: bool, default True
if missing is true, only support that cast `-1.0` into missing value now
:param max_bin_init: int, default 200
max num of bin when initialization cut, determined by the size of samples and unique value
:param method: str, option ['chisq', 'entropy']
methods of cut bins
:param to_plot: bool, default True
whether to plot reference table
:param save_path: str, the path of image saved
:return: df_ref_table: DataFrame, woe reference table
"""
# calculate size of samples for each bin
min_samples_in_bin = int(df_master.shape[0] * min_prop_in_bin)
if method == 'chisq':
ds = init_binning(df_master, var_name=var_name, target=target, max_bin_num=max_bin_init, missing=missing)
# calculate chi value of adjacent bins
chisq = []
for i in range(ds.shape[0] - 1):
chisq.append(chi2_contingency(ds.iloc[[i, i + 1], [0, 1]])[0])
chisq.append(9999999.0)
ds['chisq'] = chisq
# cut missing value into a single bin
if missing:
if ds[ds['bin_lb'] == -1.0].shape[0] > 0:
ds_miss = ds[ds['bin_lb'] == -1.0].copy()
ds = ds[ds['bin_lb'] != -1.0]
else:
ds_miss = pd.DataFrame()
# merge two bins with small chi value
ds_idx_list = list(ds.index)
while (ds.shape[0] > max_bins) | (ds['chisq'].min() <= scipy.stats.chi2.ppf(0.95, 1)):
# 找到卡方值最小的bin的index在index list中的位置
k = ds_idx_list.index(ds['chisq'].idxmin())
ds = __mergebin(ds=ds, idx_list=ds_idx_list, idx=k, var_type='numerical')
# limit size of samples in each bin
while (ds['total'].min() < min_samples_in_bin) & (ds.shape[0] > 2):
# find the bin with minimum size of samples
k = ds_idx_list.index(ds['total'].idxmin())
# if chi value of previous bin < later bin, choose previous bin to merge
if (k == len(ds_idx_list) - 1) | (
ds.loc[ds_idx_list[k], 'chisq'] > ds.loc[ds_idx_list[k - 1], 'chisq']):
k -= 1
ds = __mergebin(ds, idx_list=ds_idx_list, idx=k, var_type='numerical')
elif method == 'entropy':
max_depth = int(math.log(max_bins, 2))
if missing:
df_miss = df_master.loc[df_master[var_name] == -1.0, [var_name, target]].reset_index(drop=True)
df_no_miss = df_master.loc[df_master[var_name] != -1.0, [var_name, target]].reset_index(drop=True)
if df_miss.shape[0] > 0:
ds_miss = init_binning(df_master=df_miss, var_name=var_name, target=target,
cut_points=[-1.0, -0.5], missing=False)
else:
ds_miss = pd.DataFrame()
min_value = -0.5
else:
df_no_miss = df_master
min_value = df_no_miss[var_name].min()
clf = tree.DecisionTreeClassifier(criterion='entropy', max_depth=max_depth,
min_samples_leaf=min_samples_in_bin)
clf.fit(df_no_miss[var_name].values.reshape(-1, 1), df_no_miss[target])
cut_points = np.sort(clf.tree_.threshold[clf.tree_.threshold != -2.0])
cut_points = np.append([min_value], cut_points)
cut_points = np.append(cut_points, df_no_miss[var_name].max())
ds = init_binning(df_no_miss, var_name=var_name, target=target, cut_points=cut_points, missing=False)
else:
raise ValueError('wrong method, only choose "chisq" or "entropy"!')
# generate final reference table
if missing:
ds = pd.concat([ds_miss, ds])
ds = ds.reset_index(drop=True)
df_ref_table = generate_reference(ds=ds, var_name=var_name, var_type='numerical')
# plotting
if to_plot:
plot_reference(df_ref_table, save_path=save_path)
return df_ref_table
def numwoe_aptbinning(df_master=None, var_name=None, target=None, bins=None, to_plot=True, save_path=None):
"""Cut bins of numerical variables according to specified cut points, and calculate woe, iv value
:param df_master: DataFrame
:param var_name: str, variable name
:param target: str, target name
:param bins: list,
specified cut points, eg. [0.0, 1.0, 3.0, 10.0]
:param to_plot: bool, default True
whether to plot reference table
:param save_path: str, the path of image saved
:return: df_ref_table: DataFrame, woe reference table
"""
ds = init_binning(df_master, var_name=var_name, target=target, cut_points=bins, missing=False)
df_ref_table = generate_reference(ds, var_name=var_name, var_type='numerical')
if to_plot:
plot_reference(df_ref_table, save_path=save_path)
return df_ref_table
def catwoe_autobinning(df_master=None, var_name=None, target=None,
max_bins=6, min_prop_in_bin=0.05, min_samples_init=1,
missing_value=None, to_plot=True, save_path=None):
"""Cut bins of categorical variables according to specified cut points, and calculate woe, iv value
:param: df_master: DataFrame
:param: var_name: str, variable name
:param: target: str, target name
:param: max_bins: int
:param: min_samples_init: int, default 1
minimum size of cut bins on initialization, merge bins with small size of samples
:param: min_prop_in_bin: float, default None
minimum sample ratio in each bin
:param: missing_value: str, default None
if missing is true, will put the value into a single bin
:param: to_plot: bool, default True
whether to plot reference table
:param: save_path: str, default None
str, the path of image saved
:return: df_ref_table: DataFrame, woe reference table
"""
min_samples_in_bin = int(df_master.shape[0] * min_prop_in_bin)
ds = pd.crosstab(df_master[var_name], df_master[target]).fillna(value=0).reset_index(drop=False)
ds['total'] = ds[1] + ds[0]
# index for each bin
ds['bin'] = [[i] for i in ds.index]
ds.columns = ['value', '0', '1', 'total', 'bin']
# generate a mapping dict of index and real value
map_dict = dict(zip(ds.index, ds['value']))
# whether to put missing value into a single bin
if missing_value is not None:
ds_miss = ds[ds['value'] == missing_value].copy()
ds = ds[ds['value'] != missing_value]
# sort the bin order by size of samples in each bin
ds = ds.sort_values(by=['total'], ascending=True)
# merge bins with small size of samples
idx_small_bin = list(ds[ds['total'] < min_samples_init].index)
if len(idx_small_bin) >= 2:
ds.at[idx_small_bin[0], ['0', '1']] = ds.loc[idx_small_bin, ['0', '1']].sum()
ds.at[idx_small_bin[0], 'total'] = ds.loc[idx_small_bin, 'total'].sum()
ds.at[idx_small_bin[0], 'bin'] = idx_small_bin
ds = ds.drop(idx_small_bin[1:], axis=0)
# calculate target rate for each bin
ds['target_rt'] = ds['1'] / (ds['0'] + ds['1'])
# sort order by target rate of each bin
ds = ds.sort_values(by='target_rt', ascending=True)
# calculate chi value of two adjacent bins
chisq = []
for i in range(ds.shape[0] - 1):
# noinspection PyBroadException
try:
chisq.append(chi2_contingency(ds.iloc[[i, i + 1], [1, 2]])[0])
except Exception:
chisq.append(chi2_contingency(ds.iloc[[i, i + 1], [0, 1]])[0])
chisq.append(9999999.0)
ds['chisq'] = chisq
# loop of merging two adjacent bins, recalculate chi value of previous and later bin
ds_idx_list = list(ds.index)
while (ds.shape[0] > max_bins) | (ds.chisq.min() <= scipy.stats.chi2.ppf(0.95, 1)):
k = ds_idx_list.index(ds['chisq'].idxmin())
ds = __mergebin(ds, idx_list=ds_idx_list, idx=k, var_type='categorical')
# limit size of samples for each bin
while (ds['total'].min() < min_samples_in_bin) & (ds.shape[0] > 2):
# find the bin with minimum size of samples
k = ds_idx_list.index(ds['total'].idxmin())
if (k == len(ds_idx_list) - 1) | (ds.loc[ds_idx_list[k], 'chisq'] > ds.loc[ds_idx_list[k - 1], 'chisq']):
k -= 1
ds = __mergebin(ds, idx_list=ds_idx_list, idx=k, var_type='categorical')
# generate reference table
if missing_value is not None:
ds = pd.concat([ds_miss, ds])
ds = ds.reset_index(drop=True)
ds['bin'] = ds['bin'].apply(lambda x: __value_match(map_dict, x)) # 将索引还原成变量原本的取值
ds['bin'] = ds['bin'].apply(lambda x: '\001'.join(x)) # 用特殊符号'\001'拼接value，防止出现value中有标点符号
df_ref_table = generate_reference(ds, var_name=var_name, var_type='categorical')
# plotting
if to_plot:
plot_reference(df_ref_table, save_path=save_path)
return df_ref_table
def catwoe_aptbinning(df_master=None, var_name=None, target=None, bins=None, to_plot=True, save_path=None):
"""Cut bins of categorical variables according to specified cut points, and calculate woe, iv value
:param: df_master: DataFrame
:param: var_name: str
:param: target: str
:param: bins: list
rules of grouping, eg. [['初中', '高中'], ['大专', '本科', '硕士研究生'], ['博士研究生']]
:param: to_plot: bool, default True
whether to plot reference table
:param: save_path: str, the path of image saved
:return: df_ref_table: DataFrame, woe reference table
"""
unique_values = set(sum(bins, []))
if len(unique_values) != len(sum(bins, [])):
raise ValueError('Value is repetitive, please check bins is correct')
ds = pd.crosstab(df_master[var_name], df_master[target]).fillna(value=0).reset_index(drop=False)
ds['total'] = ds[1] + ds[0]
ds.columns = ['bin', '0', '1', 'total']
# cut bins according to the specified method
for bin in bins:
idx_list = []
for value in bin:
idx_list.append(int(ds[ds['bin'] == value].index.values))
ds.at[idx_list[0], ['0', '1']] = ds.loc[idx_list, ['0', '1']].sum()
ds.at[idx_list[0], 'total'] = ds.loc[idx_list, 'total'].sum()
ds.at[idx_list[0], 'bin'] = bin
ds = ds.drop(idx_list[1:], axis=0)
ds = ds.reset_index(drop=True)
# generate reference table
ds['bin'] = ds['bin'].apply(lambda x: '\001'.join(x)) # 用特殊符号'\001'拼接value，防止出现value中有标点符号
df_ref_table = generate_reference(ds, var_name=var_name, var_type='categorical')
# plotting
if to_plot:
plot_reference(df_ref_table, save_path=save_path)
return df_ref_table
def __str_convert(x):
"""Cast types of variable into str
:param x:
:return:
"""
if type(x) in [int, float, np.float64]:
return str(int(x))
elif type(x) is str:
return x
else:
return x
def __restore_list(s=None, value_type=None):
"""Restore list with str format into original list
eg:'[1, 2]' → [1,2]
:param s: str, need to restored
:param value_type: str, options ['numerical', 'categorical']
type of value
:return:
"""
if value_type == 'numerical':
return [np.float(i.strip('[] ')) for i in s.split(',')]
elif value_type == 'categorical':
return s.split('\001')
else:
raise ValueError('Wrong value type!')
def __cvlookup(value=None, map_dict=None):
"""Find corresponding woe value of the variable
:param value: str,
:param map_dict: dict, mapping dict
:return: woe_value: float, woe value
"""
if value in map_dict.keys():
woe_value = map_dict[value]
else:
woe_value = 0.0
return woe_value
def numwoe_apply(df_master=None, ref_table=None, var_name=None):
"""Replace original value with woe value for single numerical variable
if `bin_lb[i] ≤ X ＜ bin_ub[i]` is meet, replace X with `ref_value[i]`
:param: df_master: DataFrame
:param: ref_table: DataFrame, reference table
:param: var_name: str, variable name
"""
interval = ref_table['Var_Value'].apply(lambda x: __restore_list(x, 'numerical')).values
# minimum
bin_lb = np.asarray([i[0] for i in interval]).reshape((1, -1)) # bin的下界
bin_lb[0][0] = -np.Infinity # 把第一个bin的下界替换成负无穷
base_woe = ref_table['Ref_Value'].iloc[0] # 取第一个值作为baseline
x = (df_master[var_name].values.reshape((-1, 1)) >= bin_lb) * 1.0
w = ref_table['Ref_Value'].diff(periods=1).fillna(base_woe).values # 进行1阶差分
df_master['nwoe_' + var_name] = np.dot(x, w)
def catwoe_apply(df_master=None, ref_table=None, var_name=None):
"""Replace original value with woe value for single categorical variable
:param: df_master: DataFrame
:param: ref_table: DataFrame, reference table
:param: var_name: str, variable name
"""
var_value = ref_table['Var_Value'].apply(lambda x: __restore_list(x, 'categorical')).values
df_master[var_name] = df_master[var_name].apply(lambda x: __str_convert(x))
# build a dict of values
value_list = []
ref_value_list = []
for i, lst in enumerate(var_value):
value_list += lst
ref_value_list += [ref_table['Ref_Value'].iloc[i]] * len(lst)
value_dict = dict(zip(value_list, ref_value_list))
df_master['cwoe_' + var_name] = df_master[var_name].apply(lambda x: __cvlookup(x, value_dict))
def woeref_old2new(ref_woe=None):
"""Replace old woe reference table with the new
:param ref_woe: DataFrame
old woe reference table
:return: ref_new_woe: DataFrame
new woe reference table
"""
ref_woe_copy = ref_woe.copy()
ref_woe_copy = ref_woe_copy.loc[ref_woe_copy['Var_Value'] != 'base']
ref_woe_copy['Total'] = ref_woe_copy['Count_0'] + ref_woe_copy['Count_1']
ref_woe_copy = ref_woe_copy.rename(columns={'Ratio_1': 'Target_Rate', 'Ratio_All': 'Proportion'})
ref_woe_copy['Var_Value'] = ref_woe_copy['Var_Value'].apply(lambda x: str(x).split('_'))
ref_new_woe = ref_woe_copy.groupby(['Var_Name', 'Var_Type', 'IV', 'Ref_Value'], as_index=False, ) \
.agg({'Var_Value': 'sum', 'Count_0': 'sum', 'Count_1': 'sum', 'Total': 'sum', 'Proportion': 'sum', })
ref_new_woe['Target_Rate'] = ref_new_woe['Count_1'] / ref_new_woe['Total']
ref_new_woe['Var_Value'].loc[ref_new_woe['Var_Type'] == 'categorical'] = ref_new_woe['Var_Value'].loc[
ref_new_woe['Var_Type'] == 'categorical'].apply(lambda x: "\001".join(x))
ref_new_woe['Bin_No'] = ref_new_woe[['Var_Name', 'Ref_Value']].groupby(['Var_Name']).rank(method='dense',
ascending=True)
ref_new_woe['Var_Value'] = ref_new_woe['Var_Value'].apply(lambda x: str(x).replace("'", ""))
return ref_new_woe
def iv_extract(woe_ref=None, save_path=None):
"""Extract iv value according to woe reference table, order by desc
:param woe_ref: DataFrame
woe reference table
:param save_path: str, default None
the path of csv file saved
:return: df_iv: DataFrame
"""
iv = []
for var in woe_ref.Var_Name.unique():
iv.append([var, woe_ref['IV'][woe_ref['Var_Name'] == var].iloc[0]])
df_iv = pd.DataFrame(iv, columns=['Var_Name', 'IV'])
df_iv = df_iv.sort_values(by='IV', ascending=False)
if not save_path:
df_iv.to_csv(save_path, index=False)
return df_iv
def replace_var_woe_ref(woe_ref_all=None, woe_ref_var=None):
"""Replace the specified reference table of variable in all with the new
:param: woe_ref_all: DataFrame
reference tables of all variables
:param: woe_ref_var: DataFrame
reference table of the specified variable
:return: woe_ref_new: DataFrame
new reference table
"""
replace_var = woe_ref_var['Var_Name'].unique()[0]
woe_ref_new = woe_ref_all.loc[woe_ref_all['Var_Name'] != replace_var, :].copy()
woe_ref_new = pd.concat([woe_ref_new, woe_ref_var], axis=0, ignore_index=True)
return woe_ref_new
def save_pictures_in_excel(file_name=None, img_loc=None, var_list=None, columns_num=1, img_rows=20, sheet_name='images'):
"""Save the image in excel
:param file_name: str, default xlsx format
:param img_loc: str, path of the image
:param var_list: list, default None
variables will saved in excel, if None, will embed all images in excel
:param columns_num: int, default 1
columns for the image
:param img_rows: int, default 20
rows for the image
:param sheet_name：str, default 'images'
excel sheet name
:return:
"""
if not img_loc.endswith('/'):
img_loc = img_loc + '/'
book = xlsxwriter.Workbook(file_name)
sheet = book.add_worksheet(sheet_name)
# generate a list of images
if var_list is not None:
img_list = ['{0}.png'.format(var) for var in var_list]
else:
img_list = [img for img in os.listdir(img_loc) if img.split(".")[-1] in ['jpg', 'png', 'bmp']]
img_list.sort(reverse=True)
# list of A - Z
location_list = [i for i in string.ascii_uppercase]
for i, img in zip(range(len(img_list)), img_list):
location_x = location_list[(i % columns_num) * 2 + 1]
if i < columns_num:
# set 90px of width
sheet.set_column('{0}:{0}'.format(location_x), 90)
location_y = img_rows * (i // columns_num) + 1
sheet.write('{0}{1}'.format(location_x, location_y), img)
sheet.insert_image('{0}{1}'.format(location_x, location_y + 1), img_loc + img,
options={'x_scale': 0.8, 'y_scale': 0.88})
book.close()
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projects/SalsaNext/salsa/crf.py | Gorilla-Lab-SCUT/gorilla-3d | 6 | 1617692 | <gh_stars>1-10
#!/usr/bin/env python3
# This file is covered by the LICENSE file in the root of this project.
import gorilla
import numpy as np
import torch
import torch.nn as nn
import torch.nn.functional as F
class LocallyConnectedXYZLayer(nn.Module):
def __init__(self,
h: int,
w: int,
sigma: float,
nclasses: int):
super().__init__()
# size of window
self.h = h
self.padh = h//2
self.w = w
self.padw = w//2
assert(self.h % 2 == 1 and self.w % 2 == 1) # window must be odd
self.sigma = sigma
self.gauss_den = 2 * self.sigma**2
self.nclasses = nclasses
def forward(self, xyz, softmax, mask):
# softmax size
N, C, H, W = softmax.shape
# make sofmax zero everywhere input is invalid
softmax = softmax * mask.unsqueeze(1).float()
# get x,y,z for distance (shape N,1,H,W)
x = xyz[:, 0].unsqueeze(1)
y = xyz[:, 1].unsqueeze(1)
z = xyz[:, 2].unsqueeze(1)
# im2col in size of window of input (x,y,z separately)
window_x = F.unfold(x, kernel_size=(self.h, self.w),
padding=(self.padh, self.padw))
center_x = F.unfold(x, kernel_size=(1, 1),
padding=(0, 0))
window_y = F.unfold(y, kernel_size=(self.h, self.w),
padding=(self.padh, self.padw))
center_y = F.unfold(y, kernel_size=(1, 1),
padding=(0, 0))
window_z = F.unfold(z, kernel_size=(self.h, self.w),
padding=(self.padh, self.padw))
center_z = F.unfold(z, kernel_size=(1, 1),
padding=(0, 0))
# sq distance to center (center distance is zero)
unravel_dist2 = (window_x - center_x)**2 + \
(window_y - center_y)**2 + \
(window_z - center_z)**2
# weight input distance by gaussian weights
unravel_gaussian = torch.exp(- unravel_dist2 / self.gauss_den)
# im2col in size of window of softmax to reweight by gaussian weights from input
cloned_softmax = softmax.clone()
for i in range(self.nclasses):
# get the softmax for this class
c_softmax = softmax[:, i].unsqueeze(1)
# unfold this class to weigh it by the proper gaussian weights
unravel_softmax = F.unfold(c_softmax,
kernel_size=(self.h, self.w),
padding=(self.padh, self.padw))
unravel_w_softmax = unravel_softmax * unravel_gaussian
# add dimenssion 1 to obtain the new softmax for this class
unravel_added_softmax = unravel_w_softmax.sum(dim=1).unsqueeze(1)
# fold it and put it in new tensor
added_softmax = unravel_added_softmax.view(N, H, W)
cloned_softmax[:, i] = added_softmax
return cloned_softmax
@gorilla.MODULES.register_module()
class CRF(nn.Module):
def __init__(self,
nclasses: int,
iter: int,
height: int,
width: int,
xyz_coef: float,
xyz_sigma: float):
super().__init__()
self.iter = torch.nn.Parameter(torch.tensor(iter),
requires_grad=False)
self.lcn_size = torch.nn.Parameter(torch.tensor([height, width]),
requires_grad=False)
self.xyz_coef = torch.nn.Parameter(torch.tensor(xyz_coef), requires_grad=False).float()
self.xyz_sigma = torch.nn.Parameter(torch.tensor(xyz_sigma), requires_grad=False).float()
self.nclasses = nclasses
print("Using CRF!")
# define layers here
# compat init
self.compat_kernel_init = np.reshape(np.ones((self.nclasses, self.nclasses)) -
np.identity(self.nclasses),
[self.nclasses, self.nclasses, 1, 1])
# bilateral compatibility matrixes
self.compat_conv = nn.Conv2d(self.nclasses, self.nclasses, 1)
self.compat_conv.weight = torch.nn.Parameter(torch.from_numpy(
self.compat_kernel_init).float() * self.xyz_coef, requires_grad=True)
# locally connected layer for message passing
self.local_conn_xyz = LocallyConnectedXYZLayer(height, width, xyz_coef, self.nclasses)
def forward(self, input, softmax, mask):
# use xyz
xyz = input[:, 1:4]
# iteratively
for iter in range(self.iter):
# message passing as locally connected layer
locally_connected = self.local_conn_xyz(xyz, softmax, mask)
# reweigh with the 1x1 convolution
reweight_softmax = self.compat_conv(locally_connected)
# add the new values to the original softmax
reweight_softmax = reweight_softmax + softmax
# lastly, renormalize
softmax = F.softmax(reweight_softmax, dim=1)
return softmax
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] |
setup.py | rako233/rako-pyads | 1 | 52090 | from setuptools import setup, find_packages
exec(open('counsyl_pyads/version.py').read())
setup(
name='counsyl-pyads',
version=__version__,
packages=find_packages(),
scripts=['bin/twincat_plc_info.py'],
include_package_data=True,
zip_safe=False,
author='<NAME>.',
author_email='<EMAIL>',
description='A library for directly interacting with a Twincat PLC.',
url='https://github.com/counsyl/counsyl-pyads/',
classifiers=[
'Development Status :: 5 - Production/Stable',
'License :: OSI Approved :: MIT License',
'Programming Language :: Python',
'Topic :: Scientific/Engineering :: Interface Engine/Protocol Translator',
'Topic :: Software Development :: Libraries :: Python Modules',
],
)
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2433,
991,
597,
3292,
29889,
510,
29914,
29883,
1309,
29879,
2904,
29914,
29883,
1309,
29879,
2904,
29899,
2272,
7925,
29914,
742,
13,
1678,
770,
14903,
11759,
13,
4706,
525,
21956,
358,
16034,
4761,
29871,
29945,
448,
19561,
29914,
855,
519,
742,
13,
4706,
525,
29931,
293,
1947,
4761,
438,
5425,
28268,
1490,
4761,
341,
1806,
19245,
742,
13,
4706,
525,
9283,
4056,
17088,
4761,
5132,
742,
13,
4706,
525,
7031,
293,
4761,
23753,
928,
29914,
12412,
3241,
4761,
25796,
10863,
29914,
17830,
4103,
29880,
1061,
742,
13,
4706,
525,
7031,
293,
4761,
18540,
14650,
4761,
365,
4626,
4314,
4761,
5132,
3382,
2540,
742,
13,
1678,
21251,
13,
29897,
13,
2
] |
example/wallets/fetch_payment_address_by_id.py | oyeolamilekan/quidax-python | 1 | 111752 | from ..base import quidax
response = quidax.wallets.fetch_payment_address("<address_id>", "<currency>")
print(response)
| [
1,
515,
6317,
3188,
1053,
439,
333,
1165,
13,
13,
5327,
353,
439,
333,
1165,
29889,
14625,
10376,
29889,
9155,
29918,
27825,
29918,
7328,
28945,
7328,
29918,
333,
28341,
9872,
26095,
29958,
1159,
13,
13,
2158,
29898,
5327,
29897,
13,
2
] |
SVHN/core.py | IMBINGO95/FairMOT | 0 | 194818 | import threading, queue, time
from queue import Queue
from threading import Thread, currentThread
import os
from CalibrateTransfer.img_operation import ScreenSHot_batch
from CalibrateTransfer.data_preprocess import write_data_to_json_file, read_data_from_json_file_v2
import numpy as np
import torch.utils.data as data
import torch
import json
import shutil
from ReID_model.modeling import ReID_Model
from utils_BINGO.K_Means import k_means
from ReID_model.utils.dataset_loader import ReID_imgs_load_by_home_and_away
import logging
from utils.log import Log
from utils.timer import Timer
from utils.dir_related_operation import makedir_v1
import cv2
from SVHN.svhn import load_in_Svhn_model
from torchvision import transforms
from PIL import Image
from utils_BINGO.Number_Rectifier import Number_Rectifier
class SVHN_Predict():
def __init__(self, opt, ReIDCfg, Num_Pred_opt, Pose_output_queue, S_Number_Predict,
vis=False, save_results=False, queueSize=1024):
self.opt = opt
self.dir_name = opt.dir_name
self.root_path = os.path.join(opt.data_root, '{}'.format(opt.dir_name))
# logger.info('目标文件夹是{}'.format(self.root_path))
self.file_name = opt.file_name
self.file_save_name_before_Number_Rectify = 'Step1_'
self.file_save_name_after_Number_Rectify = 'Step2_'
# 本来就是要载入两次视频，分开读亦可以
self.Videoparameters, \
self.setting_parameter, \
self.action_datas, \
self.channel_list, \
self.parameter = read_data_from_json_file_v2(self.root_path, self.file_name, self.opt)
# 号码纠正器， 根据四官报告来修改参数
self.Number_Rectifier = Number_Rectifier
self.datalen = len(self.action_datas)
self.batch_size = 60
self.Num_Pred_opt = Num_Pred_opt # 用来设置号码识别模型的参数。
self.SVHN_predictor = load_in_Svhn_model(self.Num_Pred_opt)
self.input_Q = Pose_output_queue # 骨骼关键节点检测后的输入结果
self.PreProcess_Q = Queue(maxsize=queueSize) # 在号码识别前，对输入图片进行预处理。
self.SVHN_Q = Queue(maxsize=queueSize)
self.transform = transforms.Compose([
transforms.Resize([54, 54]),
transforms.ToTensor(),
transforms.Normalize([0.5, 0.5, 0.5], [0.5, 0.5, 0.5])
])
self.vis = vis
if self.vis == True:
self.vis_path = os.path.join(self.root_path, 'vis')
os.makedirs(self.vis_path, exist_ok=True)
self.S_Number_Predict = S_Number_Predict
self.S_Final = max( S_Number_Predict - 1, 0)
self.height_threshold = 21
self.width_threshold = 12
self.save_results = save_results
if self.save_results == True:
self.intermediate_results_dir = os.path.join(self.root_path, 'intermediate_results', 'SVHN_Predict')
os.makedirs(self.intermediate_results_dir, exist_ok=True)
self.main_imgs_dir = os.path.join(self.root_path, 'intermediate_results', 'main_imgs')
self.FMLoader_dir = os.path.join(self.root_path, 'intermediate_results', 'FMLoader')
# 加载 ReID 模型
self.ReIDCfg = ReIDCfg
self.num_cls = 4 # 场上有几种类型的人
self.logger = Log(__name__, 'SVHN_Predict').getlog()
def Read_From_Cache(self):
'''
从文件把之前计算过的结果提取出来
'''
self.logger.debug( 'The pid of SVHN_Predict.Read_From_Cache() : {}'.format(os.getpid()))
self.logger.debug( 'The thread of SVHN_Predict.Read_From_Cache() : {}'.format(currentThread()))
self.load_intermediate_resutls(self.S_Final)
self.logger.log(24, ' SVHN_Predict loads action {} from Cache file '.format(self.S_Final))
def PreProcess_(self):
self.t_PreProcess = Thread(target=self.PreProcess, args=())
self.t_PreProcess.daemon = True
self.t_PreProcess.start()
def PreProcess(self):
'''
对需要号码识别的图片进行预处理。
'''
self.logger.debug('The pid of SVHN_Predict.PreProcess() : {}'.format(os.getpid()))
self.logger.debug('The thread of SVHN_Predict.PreProcess() : {}'.format(currentThread()))
PreProcess_timer = Timer()
for action_index in range(self.S_Number_Predict, self.datalen):
PreProcess_timer.tic() # 开始计时
self.logger.debug('PreProcess() ======================================== action {}'.format(action_index))
Flag, input_results = self.input_Q.get()
if Flag == False:
# Flag == False 的话，直接就不要了
self.PreProcess_Q.put((False, (action_index, [])))
continue
#输入的数据有意义，可以接着处理
[input_index, sub_imgs_out, target_regions] = input_results
if input_index != action_index:
self.logger.log(31, '---——————————————————————————————————index does match')
raise Exception(
'SVHN_Predict.PreProcess action_index_update {} != input_index {} '.format(action_index,
input_index))
# 对数据进行预处理。
rectangle_imgs,original_imgs = self.img_pre_for_SVHN(sub_imgs_out,target_regions)
if type(rectangle_imgs) != torch.Tensor:
self.PreProcess_Q.put((False, (action_index, [])))
else:
self.PreProcess_Q.put((True, (action_index, rectangle_imgs, original_imgs)))
# self.logger.info('Calibrate_transfer.sub_img_generate() action {} consums {}s'.format(action_index,sub_img_generate_timer.toc()))
self.logger.log(24, 'SVHN_Predict.PreProcess() action {} consums {}s'.format(action_index, PreProcess_timer.toc()))
def img_pre_for_SVHN(self,sub_imgs_out,target_regions):
'''
对需要 SVHN 的图片进行 数据预处理的 具体操作
'''
rectangle_imgs = []
original_imgs = []
for target_index in range(len(target_regions)) :
sub_img = sub_imgs_out[target_index]
[xmin, xmax, ymin, ymax] = target_regions[target_index]
i_height, i_weight, i_channel = sub_img.shape
crop_img = sub_img[max(ymin, 0):min(i_height, ymax), max(xmin, 0):min(xmax, i_weight)]
h_i, w_i, _ = crop_img.shape
if h_i < self.height_threshold or w_i < self.width_threshold:
# 如果背部区域太小了，也要舍弃。
continue
crop_image = Image.fromarray(crop_img)
crop_image = self.transform(crop_image)
rectangle_imgs.append(crop_image)
original_imgs.append(sub_img)
# 如果都不符合条件的话。
if len(rectangle_imgs) == 0:
return None, None
rectangle_imgs = torch.stack(rectangle_imgs, dim=0)
return rectangle_imgs,original_imgs
def Predict_(self):
self.t_Predict = Thread(target=self.Predict, args=())
self.t_Predict.daemon = True
self.t_Predict.start()
def Predict(self):
'''
使用 SVHN 对完成预处理的图片进行号码预测
'''
Predict_timer = Timer()
self.logger.debug( 'The pid of SVHN_Predict.Predict() : {}'.format(os.getpid()))
self.logger.debug( 'The thread of SVHN_Predict.Predict() : {}'.format(currentThread()))
for action_index in range(self.S_Number_Predict, self.datalen):
Predict_timer.tic() # 开始计时
PreProcess_Flag, PreResults = self.PreProcess_Q.get()
self.logger.debug('Predict() ======================================== action {}'.format(action_index))
if PreProcess_Flag == False:
# 输入的数据无意义
preNum = -1
self.action_datas[action_index]['predicted_nums'] = []
else:
# 输入的数据有意义， 读取数据
_, rectangle_imgs,original_imgs = PreResults
imgs_length = rectangle_imgs.size(0)
leftover = 0
if (imgs_length) % self.batch_size:
leftover = 1
num_batches = imgs_length // self.batch_size + leftover
if self.vis == True:
vis_dir = os.path.join(self.vis_path,'{}'.format(action_index),'SVHN_Predict')
makedir_v1(vis_dir)
vis_dir_0 = os.path.join(self.vis_path, '{}'.format(action_index), 'SVHN_Predict_Minus_one')
makedir_v1(vis_dir_0)
NumsArray = []
for j in range(num_batches):
input_imgs_j = rectangle_imgs[j*self.batch_size:min((j+1)*self.batch_size , imgs_length)]
length_logits_j, digits_logits_j = self.SVHN_predictor(input_imgs_j.cuda())
'''This max function return two column, the first row is value, and the second row is index '''
length_predictions_j = length_logits_j.max(1)[1].cpu().tolist()
digits_predictions_j = [digits_logits_j.max(1)[1].cpu().tolist() for digits_logits_j in digits_logits_j]
NumsArray_j = []
for Num_i in range(len(length_predictions_j)):
Number_len = length_predictions_j[Num_i]
if Number_len == 1:
Num = digits_predictions_j[0][Num_i]
NumsArray_j.append(Num)
elif Number_len == 2:
Num = digits_predictions_j[0][Num_i] * 10 + digits_predictions_j[1][Num_i]
NumsArray_j.append(Num)
elif Number_len == 0:
Num = -1
if self.vis == True:
cv2.imwrite(os.path.join(vis_dir_0, '{}_P{}.jpg'.format(num_batches*j + Num_i, Num)), original_imgs[Num_i])
continue
else:
continue
if self.vis == True:
cv2.imwrite(os.path.join(vis_dir, '{}_P{}.jpg'.format(num_batches*j + Num_i, Num)), original_imgs[Num_i])
NumsArray.extend(NumsArray_j)
# 将数据保存下来
self.action_datas[action_index]['predicted_nums'] = NumsArray
if len(NumsArray) > 1:
# NumberArray range from 0 to 99.
# We need to count how many times does each number appear!
NumsArray = np.histogram(NumsArray, bins=100, range=(0, 100))[0]
preNum = np.argmax(NumsArray)
# if preNum == 10:
# print('wrong value')
preNum_count = NumsArray[preNum]
if np.where(NumsArray == preNum_count)[0].size > 1:
# if there are more than one number have the maximun counts, then return -1
# can sort by number classification scores.
preNum = -1
else:
preNum = -1
# 保存数据
True_num = self.action_datas[action_index]['num']
self.action_datas[action_index]['num'] = '{}'.format(preNum)
self.logger.log(24, 'SVHN_Predict.Predict action {} consums {}s'.format(action_index, Predict_timer.toc()))
self.logger.log(24,'action {} ====================== True num = {}, Predict num = {} ============='.format(
action_index,True_num,preNum))
if self.save_results == True:
self.save_intermediate_resutls(action_index)
self.logger.log(24, '-----------------------------Finished SVHN_Predict.Predict() datalen = {}-----------------------------'.format(self.datalen))
# Finished 完成了所有的计算，保存最终结果,未进行号码矫正
write_data_to_json_file(self.root_path, self.file_name, self.action_datas, self.parameter, file_save_name=self.file_save_name_before_Number_Rectify)
# 根据四官报告修改最终结果
self.action_datas = self.Number_Rectifier(os.path.join(self.root_path,self.file_save_name_before_Number_Rectify + self.file_name)).rectify()
self.logger.log(24, 'Successfully Rectify numbers according to four officials report')
write_data_to_json_file(self.root_path, self.file_name, self.action_datas, self.parameter, file_save_name=self.file_save_name_after_Number_Rectify)
# 并根据ReID特征划分主图片。
self.cluster_main_imgs()
def save_intermediate_resutls(self, action_index):
'''将每一次计算的结果保存下来。'''
intermediate_resutls_path = os.path.join(self.intermediate_results_dir,'{}'.format(action_index))
os.makedirs(intermediate_resutls_path,exist_ok=True)
json_file = os.path.join(intermediate_resutls_path, '{}_action_data.json'.format(action_index))
with open(json_file,'w') as f:
json.dump(self.action_datas,f)
def load_intermediate_resutls(self, action_index):
'''将中间结果读取出来'''
intermediate_resutls_path = os.path.join(self.intermediate_results_dir, '{}'.format(action_index))
os.makedirs(intermediate_resutls_path, exist_ok=True)
json_file = os.path.join(intermediate_resutls_path, '{}_action_data.json'.format(action_index))
with open(json_file, 'r') as f:
self.action_datas = json.load(f)
def mk_cluster_dirs(self, save_dir, num_cls):
'''
save_dir : 保存分类结果的根目录
num_cls : 分类的数量，种类数
'''
for i in range(num_cls):
sub_dir = os.path.join(save_dir, str(i))
if os.path.exists(sub_dir):
shutil.rmtree(sub_dir)
os.makedirs(sub_dir, exist_ok=True)
def generate_main_imgs(self):
'''在追踪结果的基础之上，生成各个动作的主图片。'''
if os.path.exists(self.main_imgs_dir):
shutil.rmtree(self.main_imgs_dir)
os.makedirs(self.main_imgs_dir)
FMLoader = self.FMLoader_dir
if os.path.exists(FMLoader):
print('{} exists'.format(FMLoader))
action_indexes = os.listdir(FMLoader)
action_indexes = sorted(action_indexes, key=lambda x: int(x))
for action_index in action_indexes:
action_dir = os.path.join(FMLoader, '{}'.format(action_index))
if os.path.exists(action_dir):
target_read_path = os.path.join(action_dir, '0.jpg')
target_save_path = os.path.join(self.main_imgs_dir, '{}.jpg'.format(action_index))
shutil.copy(target_read_path, target_save_path)
self.logger.log(24, 'SVHN_Predict.generate_main_imgs() Finished')
def cluster_main_imgs(self):
'''
:param ReID: ReID model
:param ReIDCfg: ReID configure
:param main_img_dir: The dir save the imgs which the programme what to cluster.
:param action_datas:
:param save_dir:
:param num_cls: how many classes that the programme want !
:return:
'''
# 计时器
cluster_main_imgs_timer = Timer()
cluster_main_imgs_timer.tic()
'''在追踪结果的基础之上，生成各个动作的主图片。'''
self.generate_main_imgs()
# 创建ReID模型
self.ReID = ReID_Model(self.ReIDCfg)
self.ReID.cuda()
# make directories to save the clustered imgs.
action_datas = self.action_datas
# 场上有四类目标人物，创建四个子文件夹
save_dir = self.main_imgs_dir
self.mk_cluster_dirs(save_dir, self.num_cls)
'''Preprocess the imgs before ReID'''
if not os.path.exists(self.main_imgs_dir):
raise ValueError("The main_img_dir is not exits")
'''对要输入ReID网络的图片进行预处理'''
imgs_arrays_all, img_names_all = ReID_imgs_load_by_home_and_away(self.ReIDCfg, self.main_imgs_dir, self.action_datas)
# 分成主客两队
cls_res_all = {'Home': 0, 'Away': 2} # 主队保存在前两个文件夹 0 和 1， 客队保存在后两个文件夹 2 和 3
for TeanIndex, TeamType in enumerate(['Home', 'Away']):
imgs_arrays = imgs_arrays_all[TeamType]
img_names = img_names_all[TeamType]
cls_res = cls_res_all[TeamType]
all_feats = [] # 用来存储各个动作主图片的ReID特征
with torch.no_grad():
for imgs_array in imgs_arrays:
imgs_array = imgs_array.to('cuda')
feats = self.ReID(imgs_array).cpu().numpy().tolist()
all_feats.extend(feats)
length = len(all_feats)
self.logger.log(24, ' ReID models ,there are {} actions of TeamType {} want to be delt with.'.format(length,TeamType))
'''根据ReID特征，进行分类，分成num_cls类, 门将和球员'''
assignments, dataset = k_means(all_feats, 2)
'''根据分类结果，将图片按文件夹分类'''
for index, cls in enumerate(assignments):
cls += cls_res # 所要保存的文件夹的序号
# 是否有识别成功以号码检测为准。
if int(action_datas[int(img_names[index])]['num']) == -1 or \
action_datas[int(img_names[index])]['num'] == None:
shutil.copyfile(os.path.join(self.main_imgs_dir, img_names[index] + '.jpg'),
os.path.join(save_dir,'{}'.format(cls),'{}_.jpg'.format(img_names[index])))
else:
shutil.copyfile(os.path.join(self.main_imgs_dir, img_names[index] + '.jpg'),
os.path.join(save_dir,'{}'.format(cls),'{}_{}.jpg'.format(img_names[index], action_datas[int(img_names[index])]['num'])))
action_datas[int(img_names[index])]['team'] = str(cls)
self.action_datas = action_datas
self.logger.log(24, 'SVHN_Predict.cluster_main_imgs() Finished， consums {}s'.format(cluster_main_imgs_timer.toc()))
| [
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tests/test_binary_sensor.py | dermotduffy/hass-motioneye | 13 | 71879 | """Tests for the motionEye binary sensor platform."""
from __future__ import annotations
import datetime
from datetime import timedelta
import logging
from unittest.mock import patch
from pytest_homeassistant_custom_component.common import async_fire_time_changed
from custom_components.motioneye import get_motioneye_device_identifier
from custom_components.motioneye.const import (
CONF_EVENT_DURATION,
DOMAIN,
EVENT_FILE_STORED,
EVENT_MOTION_DETECTED,
TYPE_MOTIONEYE_MOTION_BINARY_SENSOR,
)
from homeassistant.components.binary_sensor import (
DEVICE_CLASS_MOTION,
DOMAIN as BINARY_SENSOR_DOMAIN,
)
from homeassistant.const import CONF_DEVICE_ID
from homeassistant.core import HomeAssistant
from homeassistant.helpers import device_registry as dr, entity_registry as er
import homeassistant.util.dt as dt_util
from . import (
TEST_BINARY_SENSOR_FILE_STORED_ENTITY_ID,
TEST_BINARY_SENSOR_MOTION_ENTITY_ID,
TEST_CAMERA_ID,
create_mock_motioneye_client,
register_test_entity,
setup_mock_motioneye_config_entry,
)
_LOGGER = logging.getLogger(__name__)
async def test_binary_sensor_events(hass: HomeAssistant) -> None:
"""Test the actions sensor."""
async def fire_event(
now: datetime.datetime, event_type: str, device_id: str | None = None
) -> None:
"""Fire an event."""
with patch("homeassistant.helpers.event.dt_util.utcnow", return_value=now):
hass.bus.async_fire(
f"{DOMAIN}.{event_type}",
{CONF_DEVICE_ID: device_id} if device_id else {},
)
await hass.async_block_till_done()
register_test_entity(
hass,
BINARY_SENSOR_DOMAIN,
TEST_CAMERA_ID,
TYPE_MOTIONEYE_MOTION_BINARY_SENSOR,
TEST_BINARY_SENSOR_MOTION_ENTITY_ID,
)
device_registry = dr.async_get(hass)
client = create_mock_motioneye_client()
config_entry = await setup_mock_motioneye_config_entry(hass, client=client)
device_identifer = get_motioneye_device_identifier(
config_entry.entry_id, TEST_CAMERA_ID
)
device = device_registry.async_get_device({device_identifer})
assert device
now = dt_util.utcnow()
# Ensure it starts off...
entity_state = hass.states.get(TEST_BINARY_SENSOR_MOTION_ENTITY_ID)
assert entity_state
assert entity_state.state == "off"
# ... motion is detected ...
await fire_event(now, EVENT_MOTION_DETECTED, device.id)
# ... state should now be on ...
entity_state = hass.states.get(TEST_BINARY_SENSOR_MOTION_ENTITY_ID)
assert entity_state
assert entity_state.state == "on"
# ... 10 seconds elapses ...
now += timedelta(seconds=10)
async_fire_time_changed(hass, now)
await hass.async_block_till_done()
# ... still should still be on ...
entity_state = hass.states.get(TEST_BINARY_SENSOR_MOTION_ENTITY_ID)
assert entity_state
assert entity_state.state == "on"
# ... motion is detected again ...
await fire_event(now, EVENT_MOTION_DETECTED, device.id)
# ... 21 seconds later it should still be on ...
now += timedelta(seconds=21)
async_fire_time_changed(hass, now)
await hass.async_block_till_done()
entity_state = hass.states.get(TEST_BINARY_SENSOR_MOTION_ENTITY_ID)
assert entity_state
assert entity_state.state == "on"
# ... but after a further 10 seconds it should be back off.
now += timedelta(seconds=10)
async_fire_time_changed(hass, now)
await hass.async_block_till_done()
entity_state = hass.states.get(TEST_BINARY_SENSOR_MOTION_ENTITY_ID)
assert entity_state
assert entity_state.state == "off"
# Now a different device detects motion.
await fire_event(now, EVENT_MOTION_DETECTED, "different-id")
entity_state = hass.states.get(TEST_BINARY_SENSOR_MOTION_ENTITY_ID)
assert entity_state
assert entity_state.state == "off"
# Test storing a file.
await fire_event(now, EVENT_FILE_STORED, device.id)
entity_state = hass.states.get(TEST_BINARY_SENSOR_MOTION_ENTITY_ID)
assert entity_state
assert entity_state.state == "off"
entity_state = hass.states.get(TEST_BINARY_SENSOR_FILE_STORED_ENTITY_ID)
assert entity_state
assert entity_state.state == "on"
now += timedelta(seconds=31)
async_fire_time_changed(hass, now)
await hass.async_block_till_done()
entity_state = hass.states.get(TEST_BINARY_SENSOR_FILE_STORED_ENTITY_ID)
assert entity_state
assert entity_state.state == "off"
# Test missing device id.
await fire_event(now, EVENT_FILE_STORED)
entity_state = hass.states.get(TEST_BINARY_SENSOR_MOTION_ENTITY_ID)
assert entity_state
assert entity_state.state == "off"
entity_state = hass.states.get(TEST_BINARY_SENSOR_FILE_STORED_ENTITY_ID)
assert entity_state
assert entity_state.state == "off"
# Reload with a larger event duration.
with patch(
"custom_components.motioneye.MotionEyeClient",
return_value=client,
):
hass.config_entries.async_update_entry(
config_entry, options={CONF_EVENT_DURATION: 600}
)
await hass.async_block_till_done()
await fire_event(now, EVENT_MOTION_DETECTED, device.id)
now += timedelta(seconds=32)
async_fire_time_changed(hass, now)
await hass.async_block_till_done()
entity_state = hass.states.get(TEST_BINARY_SENSOR_MOTION_ENTITY_ID)
assert entity_state
assert entity_state.state == "on"
async def test_binary_sensor_device_info(hass: HomeAssistant) -> None:
"""Verify device information includes expected details."""
config_entry = await setup_mock_motioneye_config_entry(hass)
device_identifer = get_motioneye_device_identifier(
config_entry.entry_id, TEST_CAMERA_ID
)
device_registry = dr.async_get(hass)
device = device_registry.async_get_device({device_identifer})
assert device
entity_registry = await er.async_get_registry(hass)
entities_from_device = [
entry.entity_id
for entry in er.async_entries_for_device(entity_registry, device.id)
]
assert TEST_BINARY_SENSOR_MOTION_ENTITY_ID in entities_from_device
async def test_binary_sensor_device_class(hass: HomeAssistant) -> None:
"""Verify device information includes expected details."""
await setup_mock_motioneye_config_entry(hass)
entity_state = hass.states.get(TEST_BINARY_SENSOR_MOTION_ENTITY_ID)
assert entity_state
assert entity_state.attributes.get("device_class") == DEVICE_CLASS_MOTION
entity_state = hass.states.get(TEST_BINARY_SENSOR_FILE_STORED_ENTITY_ID)
assert entity_state
assert "device_class" not in entity_state.attributes
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3919,
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1367,
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4974,
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29918,
1990,
29908,
451,
297,
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29918,
3859,
29889,
15697,
13,
2
] |
datadog_checks_dev/datadog_checks/dev/tooling/commands/meta/windows/__init__.py | tdimnet/integrations-core | 663 | 86764 | <reponame>tdimnet/integrations-core
# (C) Datadog, Inc. 2021-present
# All rights reserved
# Licensed under a 3-clause BSD style license (see LICENSE)
import click
from ...console import CONTEXT_SETTINGS
from .pdh import pdh
ALL_COMMANDS = [pdh]
@click.group(context_settings=CONTEXT_SETTINGS, short_help='Windows utilities')
def windows():
pass
for command in ALL_COMMANDS:
windows.add_command(command)
| [
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2
] |
accounts/tests/test_views.py | EliasOPrado/tour-project | 7 | 88347 | <filename>accounts/tests/test_views.py
from django.test import TestCase, Client
from django.shortcuts import redirect
from django.urls import reverse
from tour_store.models import Destinations
from django.utils import timezone
from django.contrib.auth.models import User
from django.contrib import messages
from django.contrib.messages import get_messages
class TestView(TestCase):
def test_get_register_customer_page(self):
#test if the register view is working
page = self.client.get("/accounts/register/")
self.assertEqual(page.status_code, 200)
self.assertTemplateUsed(page, "register.html")
def test_registering_a_new_user(self):
# test if the register form is working for user
page = self.client.get('/accounts/register/',
data={'username': 'test',
'email': '<EMAIL>',
'password1': <PASSWORD>',
'password2': <PASSWORD>'},
)
self.assertEqual(page.status_code, 200)
self.assertTemplateUsed(page, "register.html")
def test_get_login_page(self):
# test login url directs to login.html
# url to login
page = self.client.get("/accounts/login/")
# check for a status code 200
self.assertEqual(page.status_code, 200)
# check Template Used is login.html
self.assertTemplateUsed(page, "login.html")
def test_login_view_when_a_user_is_log_in(self):
# create a user
user = User.objects.create_user('username',
'<EMAIL>',
'password')
# login
self.client.login(username='username',
password='password')
# url to the login page
page = self.client.get("/accounts/login/")
# check for a status code 200
self.assertEqual(page.status_code, 200)
# check Template Used is profile.html
self.assertTemplateUsed(page, 'login.html')
def test_logout_view_after_user_already_logged_in(self):
#test logout view
# create a user
user = User.objects.create_user('username',
'<EMAIL>',
<PASSWORD>')
# login
self.client.login(username='username',
password='<PASSWORD>')
# url to the logout page
page = self.client.get("/accounts/logout/", follow=True)
# check for a status code 302
self.assertEqual(page.status_code, 200)
# check Template Used is main.html page
self.assertTemplateUsed(page, 'main.html')
# check the message stored is equal to the expected message
messages = list(get_messages(page.wsgi_request))
self.assertEqual(str(messages[0]),
'You have successfully logged out')
| [
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501,
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338,
1667,
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1420,
1813,
13,
4706,
1583,
29889,
9294,
6733,
29965,
8485,
29898,
3488,
29892,
525,
3396,
29889,
1420,
1495,
13,
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396,
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278,
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304,
278,
3806,
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7191,
353,
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5652,
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29918,
3827,
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1583,
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9294,
9843,
29898,
710,
29898,
19158,
29961,
29900,
11724,
13,
462,
308,
525,
3492,
505,
8472,
13817,
714,
1495,
13,
2
] |
z2z_metadata/__init__.py | Mitchellpkt/z2z-metadata | 0 | 23832 | <gh_stars>0
"""Top-level package for z2z Metadata analysis."""
__author__ = """Isthmus // <NAME>"""
__email__ = '<EMAIL>'
__version__ = '0.0.1'
| [
1,
529,
12443,
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29920,
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525,
29900,
29889,
29900,
29889,
29896,
29915,
13,
2
] |
Python/Fibonacci.py | kennethsequeira/Hello-world | 1 | 1762 | #Doesn't work.
import time
fibonacci = [1, 1]
n = int(input())
while len(fibonacci) < n:
fibonacci.append(fibonacci[-1] + fibonacci[-2])
for i in range(n):
print(fibonacci[i], end=' ')
| [
1,
396,
25125,
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22993,
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21566,
29961,
29875,
1402,
1095,
2433,
525,
8443,
13,
2
] |
DynamicProgramming/solution.py | ylyzty/leecode_study | 0 | 80022 | from typing import List
class Solution:
@staticmethod
def fib(n: int) -> int:
"""
Offer 10-I 斐波那契数列
:param n:
:return:
"""
a, b = 0, 1
for _ in range(n):
a, b = b, a + b
return a % 1000000007
@staticmethod
def fib_2(n: int) -> int:
a = [0, 1]
for i in range(1, n):
a.append(a[i - 1] + a[i])
print(a)
return a[n] % 100000007
@staticmethod
def num_ways(n: int) -> int:
"""
Offer 10-II 青蛙跳台阶问题
:param n:
:return:
"""
a, b = 1, 1
for _ in range(n):
a, b = b, a + b
return a
@staticmethod
def max_profit(prices: List[int]) -> int:
"""
Offer63 股票最大利润
:param prices:
:return:
"""
if not prices:
return 0
res = [0]
cheapest = prices[0]
for i in range(1, len(prices)):
res.append(max(prices[i] - cheapest, res[i - 1]))
cheapest = min(cheapest, prices[i])
return res[len(prices) - 1]
@staticmethod
def max_sub_array(nums: List[int]) -> int:
"""
Offer 42. 连续子数组的最大和
:param nums:
:return:
"""
if not len(nums):
return 0
res = [nums[0]]
for i in range(1, len(nums)):
res.append(max(res[i - 1] + nums[i], nums[i]))
return max(res)
@staticmethod
def max_value(grid: List[List[int]]) -> int:
"""
Offer 47. 礼物的最大价值
:param grid:
:return:
"""
row, col = len(grid), len(grid(0))
values = [[0 for x in range(col)] for y in range(row)]
values[0][0] = grid[0][0]
for i in range(1, col):
values[0][i] = values[0][i - 1] + grid[0][i]
for j in range(1, row):
values[i][0] = values[i - 1][0] + grid[i][0]
for i in range(1, row):
for j in range(1, col):
values[i][j] = max(values[i - 1][j], values[i][j - 1]) + grid[i][j]
return values[row - 1][col - 1]
@staticmethod
def translate_num(num: int) -> int:
"""
Offer 46. 把数字翻译成字符串
:param num:
:return:
"""
# dp[i]只和dp[i-1]有关, 所以可以省略dp列表
s_num = str(num)
a, b = 1, 1
for i in range(1, len(s_num)):
a, b = a, (a + b if "10" <= s_num[i - 1:i + 1] <= "25" else b)
return b
@staticmethod
def length_of_longest_substring(s: str) -> int:
"""
Offer 48. 最长不含重复字符的子字符串
:param s:
:return:
"""
if len(s) <= 1:
return len(s)
dic = {}
dp = [0 for _ in range(len(s))]
dp[0], dic[s[0]] = 1, 0
for i in range(1, len(s)):
index = dic.get(s[i], -1)
if dp[i - 1] < i - index:
dp[i] = dp[i - 1] + 1
else:
dp[i] = i - index
dic[s[i]] = i # 更新哈希表
return max(dp)
@staticmethod
def length_of_longest_substring_2(s: str) -> int:
"""
节省 O(N)的 dp 空间
:param s:
:return:
"""
dic = {}
res, temp = 0, 0
for i in range(len(s)):
index = dic.get(s[i], -1)
if temp < i - index:
temp += 1
else:
temp = i - index
dic[s[i]] = [i]
res = max(res, temp)
return res
@staticmethod
def last_remaining(n: int, m: int):
"""
Offer 62. 圆圈中最后剩下的数字
"""
x = 0
for i in range(2, n + 1):
x = (x + m) % i
return x
@staticmethod
def dices_probability(n: int) -> List[float]:
dp = [[0 for j in range(n)] for i in range(6 * n)]
for i in range(6):
dp[i][0] = 1
for j in range(1, n):
for i in range(j, 6 * (j + 1)):
count = 1
while i - count >= 0 and count <= 6:
dp[i][j] += dp[i - count][j - 1]
count += 1
res = []
for i in range(n-1, 6*n):
res.append(dp[i][n-1])
for i in range(len(res)):
res[i] /= pow(6, n)
return res
def nth_ugly_number(n: int) -> int:
"""
动态规划
Offer 49. 丑数
初始状态：dp[0] = 1
转移方程：dp[i] = min(n2, n3, n5)
"""
dp = [1 for _ in range(n)]
a, b, c = 0, 0, 0
for i in range(1, n):
n2, n3, n5 = dp[a] * 2, dp[b] * 3, dp[c] * 5
dp[i] = min(n2, n3, n5)
if dp[i] == n2:
a += 1
if dp[i] == n3:
b += 1
if dp[i] == n5:
c += 1
return dp[-1]
if __name__ == "__main__":
print(Solution.dices_probability(2))
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app/models.py | marknesh/Blogging-website | 0 | 184165 | <gh_stars>0
from . import db
from werkzeug.security import generate_password_hash,check_password_hash
from flask_login import UserMixin
from . import login_manager
from datetime import datetime
class User(UserMixin,db.Model):
__tablename__ = 'person'
id = db.Column(db.Integer,primary_key = True)
email = db.Column(db.String(255), unique=True, index=True)
username = db.Column(db.String(255))
secure_password=db.Column(db.String(255))
@property
def password(self):
raise AttributeError('you cannot this attribute password')
@password.setter
def password(self,password):
self.secure_password=generate_password_hash(password)
def verify_password(self,password):
return check_password_hash(self.secure_password,password)
class Blog(db.Model):
__tablename__='blogs'
id = db.Column(db.Integer, primary_key=True)
title=db.Column(db.String(255))
content = db.Column(db.String(255))
posted=db.Column(db.DateTime,default=datetime.utcnow)
comments=db.relationship('Comments',backref='blogr',lazy="dynamic")
def save_blog(self):
db.session.add(self)
db.session.commit()
@classmethod
def get_blog(cls,id):
eka=Blog.query.filter_by().all()
return eka
@login_manager.user_loader
def load_user(user_id):
return User.query.get(int(user_id))
class Comments(db.Model):
__tablename__='comments'
id =db.Column(db.Integer, primary_key=True)
comment=db.Column(db.String(255))
blogr_id=db.Column(db.Integer,db.ForeignKey('blogs.id'))
def save_comment(self):
db.session.add(self)
db.session.commit()
class Subscriber(db.Model):
__tablename__ = 'subscriber'
id = db.Column(db.Integer,primary_key = True)
email = db.Column(db.String(255), unique=True, index=True)
class Qoutes:
def __init__(self,id,author,quote):
self.id = id
self.author=author
self.quote=quote
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salty_listener.py | BatedUrGonnaDie/salty_bot | 12 | 70380 | <reponame>BatedUrGonnaDie/salty_bot
#! /usr/bin/env python2.7
# -*- coding: utf-8 -*-
import socket
import urlparse
import psycopg2
import psycopg2.extras
class WebRetrieve:
def __init__(self, dev, db_url, web_ip, web_port, web_secret):
self.development = dev
self.db_url = db_url
self.web_secret = ""
self.web_secret = web_secret
self.web_port = web_port
self.web_host = web_ip
self.web_s = socket.socket()
self.web_s.bind((self.web_host, self.web_port))
self.disect_url()
def disect_url(self):
urlparse.uses_netloc.append("postgres")
url_parts = urlparse.urlparse(self.db_url)
self.db_name = url_parts.path[1:]
self.db_user = url_parts.username
self.db_password = url_parts.password
self.db_host = url_parts.hostname
self.db_port = url_parts.port
def db_connect(self):
conn = psycopg2.connect(database=self.db_name, user=self.db_user, host=self.db_host, password=self.db_password, port=self.db_port)
return conn
def db_close(self, connection):
connection.close()
def setup_cursor(self, connection_inst, cf_type = psycopg2.extras.RealDictCursor):
cursor = connection_inst.cursor(cursor_factory=cf_type)
return cursor
def close_cursor(self, cursor):
cursor.close()
def execute_one(self, cursor, query, parameters = None):
cursor.execute(query, parameters)
return cursor.fetchone()
def execute_all(self, cursor, query, parameters = None):
cursor.execute(query, parameters)
return cursor.fetchall()
def initial_retrieve(self):
channels_dict = {}
conn = self.db_connect()
cur = self.setup_cursor(conn)
if not self.development:
users = self.execute_all(cur, """SELECT * FROM users AS u JOIN settings AS s on u.id=s.user_id WHERE s.active=true""")
commands = self.execute_all(cur, """SELECT * FROM commands AS c WHERE c.user_id in (SELECT s.user_id FROM Settings AS s WHERE s.active=true)""")
custom_commands = self.execute_all(cur, """SELECT * FROM custom_commands AS c WHERE c.user_id in (SELECT s.user_id FROM Settings AS s WHERE s.active=true)""")
else:
users = self.execute_all(cur, """SELECT * FROM users AS u JOIN settings AS s on u.id=1 AND u.id=s.user_id WHERE s.active=true""")
commands = self.execute_all(cur, """SELECT * FROM commands AS c WHERE c.user_id=1""")
custom_commands = self.execute_all(cur, """SELECT * FROM custom_commands AS c WHERE c.user_id=1""")
self.close_cursor(cur)
self.db_close(conn)
users_dict = {}
for i in users:
i["id"] = i["user_id"]
users_dict[i["id"]] = i
users_dict[i["id"]]["commands"] = []
users_dict[i["id"]]["custom_commands"] = []
for i in commands:
users_dict[i["user_id"]]["commands"].append(i)
for i in custom_commands:
users_dict[i["user_id"]]["custom_commands"].append(i)
for v in users_dict.values():
channels_dict[v["twitch_name"]] = v
return channels_dict
def update_retrieve(self, user_id):
user_dict = {}
conn = self.db_connect()
cur = self.setup_cursor(conn)
user = self.execute_one(cur, """SELECT u.*, s.* FROM users AS u JOIN settings AS s on s.user_id=%s WHERE u.id=%s""", parameters=(user_id, user_id))
commands = self.execute_all(cur, """SELECT c.* FROM commands AS c WHERE c.user_id=%s""", parameters=(user_id,))
custom_commands = self.execute_all(cur, """SELECT cc.* FROM custom_commands AS cc WHERE cc.user_id=%s""", parameters=(user_id,))
name = user["twitch_name"]
user_dict[name] = user
user_dict[name]["id"] = user_dict[name]["user_id"]
user_dict[name]["commands"] = commands
user_dict[name]["custom_commands"] = custom_commands
return user_dict
def main_listen(self):
self.web_s.listen(1)
connection, address = self.web_s.accept()
secret = connection.recv(128)
if secret != self.web_secret:
connection.close()
print address
raise ValueError
else:
to_update = connection.recv(128)
connection.close()
return to_update
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] |
tests/test_helper.py | xlsdnx/myambra | 0 | 125709 | <reponame>xlsdnx/myambra
import requests
import logging
import sys
import os
import pytest
import shlex
from singledispatch import singledispatch
from subprocess import call
import subprocess
from retry import retry
# TODO: perhaps this file should be moved to envoy?
logging.basicConfig(stream=sys.stderr )
logging.getLogger("log").setLevel( logging.DEBUG )
log=logging.getLogger("log")
@singledispatch
def assert_status(req, status_code=200):
log.error('input type unmatched')
assert False # since input type unmatched
@assert_status.register(str)
def _(url, status_code=200):
req = requests.get(url)
assert req.status_code == status_code, req.text
return req
@assert_status.register(requests.Response)
def _(req, status_code=200):
assert req.status_code == status_code, req.text
return req
def docker_compose(compose_config, command):
compose = 'docker-compose -f /dockerfiles/configurations/'+compose_config+'.yml'
log.debug("RUN: " + compose + ' ' + command)
subprocess.check_output(shlex.split(compose + ' ' + command))
@retry(tries=30, delay=3)
def wait_for_web_service(url):
log.debug("Trying to reach " + url + " ...")
req = requests.get(url)
if req.status_code != 200:
raise Exception('status=' + str(req.status_code))
return req
@pytest.fixture(scope="class")
def stack(request):
compose_config = getattr(request.module, "compose_config", "unknown")
wait_urls = getattr(request.module, "wait_urls", "http://unknown:123")
log.debug('compose_config: ' + compose_config)
docker_compose(compose_config, 'kill')
docker_compose(compose_config, 'rm -f -v')
docker_compose(compose_config, 'up -d')
for url in wait_urls:
wait_for_web_service(url)
yield
docker_compose(compose_config, 'kill')
docker_compose(compose_config, 'rm -f -v')
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photobooth/1-web-base-layout/app/views.py | albertoSoto/raspberry-tic-projects | 0 | 37274 | <filename>photobooth/1-web-base-layout/app/views.py<gh_stars>0
from flask import render_template, flash, redirect
from app import app
@app.route('/')
@app.route('/index')
def index():
user = {'nickname': 'Berto'}
posts = [
{
'author': {'nickname': 'Alum Post 1'},
'body': 'This is the body of the first post.'
},
{
'author': {'nickname': 'Teacher Post 2'},
'body': 'This is the body of the second post.'
}
]
return render_template('index.html',
title='Home',
user=user,
posts=posts)
@app.route('/about')
def about():
return render_template('about.html',
title='About') | [
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] |
comms.py | kajusz/ufscreenadsclient | 0 | 16103 | <reponame>kajusz/ufscreenadsclient<filename>comms.py
import zmq
context = zmq.Context()
socket = context.socket(zmq.PAIR)
address = "tcp://127.0.0.1:5000"
def client(address):
socket.connect(address)
def server(address):
socket.bind(address)
def send(data):
socket.send_string(data)
def recv():
try:
return socket.recv(flags=zmq.NOBLOCK)
except zmq.Again as e:
return None
# print("No message received yet")
| [
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nmfTools/NMFtoolbox/NMFdiag.py | avinashpujala/av_segmentation | 2 | 137603 | <gh_stars>1-10
"""
Name: NMFdiag
Date of Revision: Jun 2019
Programmer: <NAME>, <NAME>
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
If you use the 'NMF toolbox' please refer to:
[1] <NAME>, <NAME>, <NAME>, and <NAME>
NMF Toolbox: Music Processing Applications of Nonnegative Matrix
Factorization
In Proceedings of the International Conference on Digital Audio Effects
(DAFx), 2019.
License:
This file is part of 'NMF toolbox'.
https://www.audiolabs-erlangen.de/resources/MIR/NMFtoolbox/
'NMF toolbox' is free software: you can redistribute it and/or modify it
under the terms of the GNU General Public License as published by the
the Free Software Foundation, either version 3 of the License, or (at
your option) any later version.
'NMF toolbox' is distributed in the hope that it will be useful, but
WITHOUT ANY WARRANTY; without even the implied warranty of
MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU General
Public License for more details.
You should have received a copy of the GNU General Public License along
with 'NMF toolbox'. If not, see http://www.gnu.org/licenses/.
"""
from copy import deepcopy
import numpy as np
from scipy.signal import convolve2d
from scipy.ndimage import maximum_filter
import matplotlib.pyplot as plt
from tqdm import tnrange
from NMFtoolbox.utils import EPS
def NMFdiag(V, W0, H0, parameter=None):
"""Given a non-negative matrix V, find non-negative matrix factors W and H
such that V ~ WH. Possibly also enforce continuity constraints.
References
----------
[2] Lee, DD & Seung, HS. "Algorithms for Non-negative Matrix Factorization"
<NAME>. & Mueller, M. "Using Score-Informed Constraints For NMF-Based
Source Separation"
Parameters
----------
V: array-like
NxM matrix to be factorized
W0: array-like
Initialized W matrix
H0: array-like
Initialized H matrix
parameter: dict
distMeas Distance measure which is used for the optimization.
Values are 'euclidean' for Euclidean, or 'divergence'
for KL-divergence.
numOfIter Number of iterations the algorithm will run.
fixW Set to 1 if Templates W should be fixed during the
update process.
divergence cost function update rules # TODO: ?
continuity Set of parameters related to the enforced continuity
constraints.
length Number of templates which should be activated
successively.
decay Parameter for specifying the decaying gain of successively
activated templates.
grid This value indicates in wich iterations of the NMF update
procedure the continuity constraints should be enforced.
Returns
-------
W: array-like
NxK non-negative matrix factor
H: array-like
KxM non-negative matrix factor
"""
N, M = V.shape # V matrix dimensions
parameter = init_parameters(parameter)
assertions(V, W0, H0)
numOfSimulAct = parameter['continuity']['polyphony']
vis = parameter['vis']
# V matrix factorization
# initialization of W and H
W = deepcopy(W0)
H = deepcopy(H0)
fixW = parameter['fixW']
energyInW = np.sum(W**2, axis=0).reshape(-1, 1)
energyScaler = np.tile(energyInW, (1, H.shape[1]))
# prepare the max neighborhood kernel
s = np.array(parameter['continuity']['sparsen'])
assert np.mod(s[0], 2) == 1 and np.mod(s[1], 2) == 1, 'Sparsity parameter needs to be odd!'
maxFiltKernel = np.zeros(s)
maxFiltKernel[:, np.ceil(s[0] / 2).astype(int) - 1] = 1
maxFiltKernel[np.ceil(s[0] / 2).astype(int) - 1, :] = 1
for k in tnrange(parameter['numOfIter'], desc='Processing'):
if vis:
fig, ax = plt.subplots(figsize=(15, 10))
ax.imshow(H, aspect='auto', cmap='gray_r')
ax.set_title('Activation Matrix H in Iteration {}'.format(k+1))
# in every 'grid' iteration of the update...
if np.mod(k, parameter['continuity']['grid']) == 0:
# sparsen the activations
if s.max() > 1:
# should in principle also include the energyScaler...
H_filt = maximum_filter(H, footprint=maxFiltKernel, mode='constant') # find max values in neighborhood
cond = np.array(H != np.array(H_filt))
H = np.where(cond, H * (1 - (k + 1) / parameter['numOfIter']), H)
# ...restrict polyphony...
if numOfSimulAct < H.shape[1]:
sortVec = np.argsort(np.multiply(-H, energyScaler), axis=0)
for j in range(H.shape[1]):
H[sortVec[numOfSimulAct:, j], j] *= (1 - (k + 1) / parameter['numOfIter'])
# ... and enforce continuity
filt = np.eye(parameter['continuity']['length'])
H = convolve2d(H, filt, 'same')
if parameter['distMeas'] == 'euclidean': # euclidean update rules
H *= (W.T @ V) / (W.T @ W @ H + EPS)
if not fixW:
W *= (V @ H.T / ((W @ H @ H.T) + EPS))
elif parameter['distMeas'] == 'divergence': # divergence update rules
H *= (W.T @ (V / (W @ H + EPS))) / (np.sum(W, axis=0).T.reshape(-1, 1) @ np.ones((1, M)) + EPS)
if not fixW:
W *= ((V / (W @ H + EPS)) @ H.T) / (np.ones((N, 1)) @ np.sum(H, axis=1).reshape(1, -1) + EPS)
else:
raise ValueError('Unknown distance measure')
if vis:
_, ax2 = plt.subplots(figsize=(15, 10))
ax2.imshow(H, aspect='auto', cmap='gray_r')
ax2.set_title('Final Activation Matrix H')
return W, H
def init_parameters(parameter):
"""Auxiliary function to set the parameter dictionary
Parameters
----------
parameter: dict
See the above function NMFdiag for further information
Returns
-------
parameter: dict
"""
parameter = dict() if parameter is None else parameter
parameter['distMeas'] = 'divergence' if 'distMeas' not in parameter else parameter['distMeas']
parameter['numOfIter'] = 50 if 'fixW' not in parameter else parameter['numOfIter']
parameter['fixW'] = False if 'fixW' not in parameter else parameter['fixW']
parameter['continuity'] = {'length': 10,
'grid': 5,
'sparsen': [1, 1],
'polyphony': 5} if 'continuity' not in parameter else parameter['continuity']
parameter['vis'] = False if 'vis' not in parameter else parameter['vis']
return parameter
def assertions(V, W0, H0):
"""
Auxiliary function to throw assertion errors, if needed
Parameters
----------
V: array-like
NxM matrix to be factorized
W0: array-like
Initialized W matrix
H0: array-like
Initialized H matrix
"""
# matrices dimensions consistency check
N, M = V.shape # V matrix dimensions
WN, WK = W0.shape # W matrix dimensions
HK, HM = H0.shape # H matrix dimensions
K = deepcopy(WK)
# check if W matrix dimensions are consistent
assert WN == N, 'W matrix has inconsistent dimensions.'
# check if H matrix dimensions are consistent
assert HK == K and HM == M, 'H matrix has inconsistent dimensions.'
# matrices non-negativity check
# check V matrix is non-negative
assert not np.any(V < 0), 'V matrix must not contain negative values.'
# check W0 matrix is non-negative
assert not np.any(W0 < 0), 'W0 matrix must not contain negative values.'
# check H0 matrix is non-negative
assert not np.any(H0 < 0), 'H0 matrix must not contain negative values.'
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] |
functest/opnfv_tests/openstack/shaker/shaker.py | opnfv-poc/functest | 0 | 7959 | <filename>functest/opnfv_tests/openstack/shaker/shaker.py
#!/usr/bin/env python
# Copyright (c) 2018 Orange and others.
#
# All rights reserved. This program and the accompanying materials
# are made available under the terms of the Apache License, Version 2.0
# which accompanies this distribution, and is available at
# http://www.apache.org/licenses/LICENSE-2.0
"""
Shaker_ wraps around popular system network testing tools like iperf, iperf3
and netperf (with help of flent). Shaker is able to deploy OpenStack instances
and networks in different topologies. Shaker scenario specifies the deployment
and list of tests to execute.
.. _Shaker: http://pyshaker.readthedocs.io/en/latest/
"""
import logging
import os
import json
import scp
from functest.core import singlevm
from functest.utils import env
class Shaker(singlevm.SingleVm2):
"""Run shaker full+perf l2 and l3"""
# pylint: disable=too-many-instance-attributes
__logger = logging.getLogger(__name__)
filename = '/home/opnfv/functest/images/shaker-image-1.3.0+stretch.qcow2'
flavor_ram = 512
flavor_vcpus = 1
flavor_disk = 3
username = 'debian'
port = 9000
ssh_connect_loops = 12
create_server_timeout = 300
shaker_timeout = '3600'
quota_instances = -1
quota_cores = -1
def __init__(self, **kwargs):
super(Shaker, self).__init__(**kwargs)
self.role = None
def check_requirements(self):
if self.count_hypervisors() < 2:
self.__logger.warning("Shaker requires at least 2 hypervisors")
self.is_skipped = True
self.project.clean()
def prepare(self):
super(Shaker, self).prepare()
self.cloud.create_security_group_rule(
self.sec.id, port_range_min=self.port, port_range_max=self.port,
protocol='tcp', direction='ingress')
def execute(self):
"""
Returns:
- 0 if success
- 1 on operation error
"""
assert self.ssh
endpoint = self.get_public_auth_url(self.orig_cloud)
self.__logger.debug("keystone endpoint: %s", endpoint)
if self.orig_cloud.get_role("admin"):
role_name = "admin"
elif self.orig_cloud.get_role("Admin"):
role_name = "Admin"
else:
raise Exception("Cannot detect neither admin nor Admin")
self.orig_cloud.grant_role(
role_name, user=self.project.user.id,
project=self.project.project.id,
domain=self.project.domain.id)
if not self.orig_cloud.get_role("heat_stack_owner"):
self.role = self.orig_cloud.create_role("heat_stack_owner")
self.orig_cloud.grant_role(
"heat_stack_owner", user=self.project.user.id,
project=self.project.project.id,
domain=self.project.domain.id)
self.orig_cloud.set_compute_quotas(
self.project.project.name,
instances=self.quota_instances,
cores=self.quota_cores)
scpc = scp.SCPClient(self.ssh.get_transport())
scpc.put('/home/opnfv/functest/conf/env_file', remote_path='~/')
if os.environ.get('OS_CACERT'):
scpc.put(os.environ.get('OS_CACERT'), remote_path='~/os_cacert')
(_, stdout, stderr) = self.ssh.exec_command(
'source ~/env_file && '
'export OS_INTERFACE=public && '
'export OS_AUTH_URL={} && '
'export OS_USERNAME={} && '
'export OS_PROJECT_NAME={} && '
'export OS_PROJECT_ID={} && '
'unset OS_TENANT_NAME && '
'unset OS_TENANT_ID && '
'unset OS_ENDPOINT_TYPE && '
'export OS_PASSWORD="{}" && '
'{}'
'env && '
'timeout {} shaker --debug --image-name {} --flavor-name {} '
'--server-endpoint {}:9000 --external-net {} --dns-nameservers {} '
'--scenario openstack/full_l2,'
'openstack/full_l3_east_west,'
'openstack/full_l3_north_south,'
'openstack/perf_l3_north_south '
'--report report.html --output report.json'.format(
endpoint, self.project.user.name, self.project.project.name,
self.project.project.id, self.project.password,
'export OS_CACERT=~/os_cacert && ' if os.environ.get(
'OS_CACERT') else '',
self.shaker_timeout, self.image.name, self.flavor.name,
self.fip.floating_ip_address, self.ext_net.id,
env.get('NAMESERVER')))
self.__logger.info("output:\n%s", stdout.read().decode("utf-8"))
self.__logger.info("error:\n%s", stderr.read().decode("utf-8"))
if not os.path.exists(self.res_dir):
os.makedirs(self.res_dir)
try:
scpc.get('report.json', self.res_dir)
scpc.get('report.html', self.res_dir)
except scp.SCPException:
self.__logger.exception("cannot get report files")
return 1
with open(os.path.join(self.res_dir, 'report.json')) as json_file:
data = json.load(json_file)
for value in data["records"].values():
if value["status"] != "ok":
self.__logger.error(
"%s failed\n%s", value["scenario"], value["stderr"])
return 1
return stdout.channel.recv_exit_status()
def clean(self):
super(Shaker, self).clean()
if self.role:
self.orig_cloud.delete_role(self.role.id)
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] |
sa/profiles/Zyxel/ZyNOS/get_switchport.py | xUndero/noc | 1 | 108678 | <reponame>xUndero/noc
# -*- coding: utf-8 -*-
# ---------------------------------------------------------------------
# Zyxel.ZyNOS.get_switchport
# ---------------------------------------------------------------------
# Copyright (C) 2007-2015 The NOC Project
# See LICENSE for details
# ---------------------------------------------------------------------
# Python modules
import re
# NOC modules
from noc.core.script.base import BaseScript
from noc.sa.interfaces.igetswitchport import IGetSwitchport
class Script(BaseScript):
name = "Zyxel.ZyNOS.get_switchport"
interface = IGetSwitchport
rx_portinfo = re.compile(
r"Port No\s+:(?P<interface>\d+)\n"
r"\s*Active\s+:(?P<admin>\S+)\n"
r"\s*Name\s+:(?P<description>.+)?\n"
r"\s*PVID\s+:(?P<untag>\d+)\s+Flow Control\s+:\S+$",
re.MULTILINE,
)
rx_vlan_stack = re.compile(r"^(?P<interface>\d+)\s+(?P<role>\S+).+$", re.MULTILINE)
rx_vlan_stack_global = re.compile(r"^Operation:\s+(?P<status>active)$")
rx_vlan_ports = re.compile(
r"\s+\d+\s+(?P<vid>\d+)\s+\S+\s+\S+\s+"
r"Untagged\s+:(?P<untagged>[0-9,\-]*)."
r"\s+Tagged\s+:(?P<tagged>[0-9,\-]*)",
re.MULTILINE | re.DOTALL,
)
def execute(self):
# Get portchannels
portchannel_members = []
portchannels = self.scripts.get_portchannel()
for p in portchannels:
portchannel_members += p["members"]
# Get interafces' status
interface_status = {}
for s in self.scripts.get_interface_status():
interface_status[s["interface"]] = s["status"]
# Get 802.1ad status if supported
vlan_stack_status = {}
try:
cmd = self.cli("show vlan-stacking")
match = self.rx_vlan_stack_global.match(cmd)
if match:
for match in self.rx_vlan_stack.finditer(cmd):
if match.group("role").lower() == "tunnel":
vlan_stack_status[int(match.group("interface"))] = True
except self.CLISyntaxError:
pass
# Get ports in vlans
vlan_ports = []
for match in self.rx_vlan_ports.finditer(self.cli("show vlan")):
vlan_ports += [
{
"vid": match.group("vid"),
"tagged": self.expand_rangelist(match.group("tagged")),
"untagged": self.expand_rangelist(match.group("untagged")),
}
]
# Make a list of tags for each port
port_tags = {}
for port in interface_status:
tags = []
untag = []
for vlan in vlan_ports:
if int(port) in vlan["tagged"]:
tags += [vlan["vid"]]
elif int(port) in vlan["untagged"]:
untag = vlan["vid"]
port_tags[port] = {"tags": tags, "untag": untag}
# Get switchport data and overall result
r = []
swp = {}
for match in self.rx_portinfo.finditer(self.cli("show interface config *")):
name = match.group("interface")
swp = {
"status": interface_status.get(name, False),
"802.1Q Enabled": len(port_tags[name].get("tags", None)) > 0,
"802.1ad Tunnel": vlan_stack_status.get(int(name), False),
"tagged": port_tags[name]["tags"],
}
if match.group("description"):
swp["description"] = match.group("description")
if port_tags[name]["untag"]:
swp["untagged"] = port_tags[name]["untag"]
if name not in portchannel_members:
swp["interface"] = name
swp["members"] = []
r += [swp]
else:
for p in portchannels:
if name in p["members"]:
swp["interface"] = p["interface"]
swp["members"] = p["members"]
r += [swp]
st = False
for m in p["members"]:
st = interface_status.get(name, False)
if st:
break
swp["status"] = st
portchannels.remove(p)
break
return r
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src/python/dart/engine/emr/add_sub_graphs.py | RetailMeNotSandbox/dart | 18 | 1603302 | import logging
import os
from dart.client.python.dart_client import Dart
from dart.config.config import configuration
from dart.engine.emr.metadata import EmrActionTypes
from dart.model.action import Action, ActionData, ActionState
from dart.model.graph import SubGraphDefinition, EntityType, Relationship, Ref, SubGraphDefinitionData
from dart.model.subscription import Subscription, SubscriptionData
from dart.model.trigger import Trigger, TriggerData
from dart.model.workflow import Workflow, WorkflowData
from dart.trigger.subscription import subscription_batch_trigger
_logger = logging.getLogger(__name__)
def add_emr_engine_sub_graphs(config):
engine_config = config['engines']['emr_engine']
opts = engine_config['options']
dart = Dart(opts['dart_host'], opts['dart_port'], opts['dart_api_version'])
assert isinstance(dart, Dart)
_logger.info('saving emr_engine sub_graphs')
engine_id = None
for e in dart.get_engines():
if e.data.name == 'emr_engine':
engine_id = e.id
if not engine_id:
raise
subgraph_definitions = [
SubGraphDefinition(data=SubGraphDefinitionData(
name='consume_subscription_workflow',
description='Add to a datastore to create entities for loading a dataset on an ongoing basis',
engine_name='emr_engine',
related_type=EntityType.datastore,
related_is_a=Relationship.PARENT,
workflows=[
Workflow(id=Ref.workflow(1), data=WorkflowData(
name='emr-workflow-consume_subscription',
datastore_id=Ref.parent(),
engine_name='emr_engine',
)),
],
subscriptions=[
Subscription(id=Ref.subscription(1), data=SubscriptionData(
name='emr-subscription',
dataset_id=''
)),
],
triggers=[
Trigger(id=Ref.trigger(1), data=TriggerData(
name='emr-trigger-subscription-1G-batch',
trigger_type_name=subscription_batch_trigger.name,
workflow_ids=[Ref.workflow(1)],
args={
'subscription_id': Ref.subscription(1),
'unconsumed_data_size_in_bytes': 1000*1000*1000
}
)),
],
actions=[
Action(id=Ref.action(1), data=ActionData(
name='emr-action-consume_subscription',
action_type_name=EmrActionTypes.consume_subscription.name,
engine_name='emr_engine',
workflow_id=Ref.workflow(1),
state=ActionState.TEMPLATE,
args={'subscription_id': Ref.subscription(1)}
)),
]
))
]
for e in subgraph_definitions:
s = dart.save_subgraph_definition(e, engine_id)
_logger.info('created subgraph_definition: %s' % s.id)
if __name__ == '__main__':
add_emr_engine_sub_graphs(configuration(os.environ['DART_CONFIG']))
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w2v_flask_app_run.py | dataquanty/Word2Vec-Search-API | 0 | 147625 | #!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
Created on Thu Mar 22 16:12:00 2018
@author: dataquanty
"""
from flask import Flask, request, redirect, url_for, flash, jsonify
from textsearch import TextSearch
import json
"""
Flask API that returns the n closest match of a text query
Takes as input a json query in the following format :
{
"0":"search query number one",
"1":"search query number two",
"2":"search query number three",
...
}
Returns a json, see below an example query :
{
"0":"screw 12mm",
"1":"WEIL SCREW 14MM",
"2":"PLASTIC RULER"
}
And the API response :
"0": {
"486254": {
"PrimaryDI": "5055662908169",
"deviceDescription": "WEIL SCREW 15MM",
"score": 0.9808971874473267
},
"486255": {
"PrimaryDI": "5055662908145",
"deviceDescription": "WEIL SCREW 14MM",
"score": 0.9832867778975147
},
"486256": {
"PrimaryDI": "5055662908107",
"deviceDescription": "WEIL SCREW 12MM",
"score": 0.9984706088591793
},
"486271": {
"PrimaryDI": "5055662907803",
"deviceDescription": "SCARF SCREW 14MM",
"score": 0.9830988843545456
},
"486272": {
"PrimaryDI": "5055662907780",
"deviceDescription": "SCARF SCREW 12MM",
"score": 0.9991383984663418
}
},
"1": {
"486255": {
"PrimaryDI": "5055662908145",
"deviceDescription": "WEIL SCREW 14MM",
"score": 1.0
},
"486256": {
"PrimaryDI": "5055662908107",
"deviceDescription": "WEIL SCREW 12MM",
"score": 0.9837789865449079
},
"486270": {
"PrimaryDI": "5055662907827",
"deviceDescription": "SCARF SCREW 16MM",
"score": 0.9807465993142839
},
"486271": {
"PrimaryDI": "5055662907803",
"deviceDescription": "SCARF SCREW 14MM",
"score": 0.9984709553907667
},
"486272": {
"PrimaryDI": "5055662907780",
"deviceDescription": "SCARF SCREW 12MM",
"score": 0.9833732279886143
}
},
"2": {
"128236": {
"PrimaryDI": "10887488232799",
"deviceDescription": "PLASTIC WORKMAN",
"score": 0.9935797830217448
},
"166500": {
"PrimaryDI": "10885425323524",
"deviceDescription": "PLASTIC RULER",
"score": 1.0
},
"817102": {
"PrimaryDI": "J011774560000",
"deviceDescription": "Dentalastics\u00ae personal plastic ligatures, orange",
"score": 0.8884202724654189
},
"817105": {
"PrimaryDI": "J011774557000",
"deviceDescription": "Dentalastics\u00ae personal plastic ligatures, purple",
"score": 0.8872598116029575
},
"920321": {
"PrimaryDI": "817573023578",
"deviceDescription": "REL-PLASTIC CONDITIONER 10CC",
"score": 0.905961617774754
}
}
}
"""
app = Flask(__name__)
@app.route('/api/makecalc/', methods=['POST'])
def makecalc():
"""
Function run at each API call
TODO: add inputs checks and cleaning
"""
jsonfile = request.get_json()
res = dict()
for key in jsonfile.keys():
# TODO: remove prints after debug
print(key, jsonfile[key])
res[key] = textSearch.searchTop(jsonfile[key], 5)
return jsonify(res)
# used for debug
# return jsonify({"uuid":jsonfile['0']})
if __name__ == '__main__':
"""
The TextSearch class instance is loaded at the init of the flask
API for performance reasons, meaning that all the inputs matrices are
stored as in-memory objects
Then at each call of the API, the searchTop method is called and uses
the in-memory matrices to make the proximity calculation
The largest file - document matrix - is
(n documents) x (m vect components) 64 bit float
64 bits is not necessary, this can be optimized.
"""
with open("params.json", 'r') as stream:
params = json.load(stream)
modelfile = params['modelfile']
docmatrixfile = params['docmatrixfile']
textfile = params['textfile']
textSearch = TextSearch(modelfile, docmatrixfile, textfile)
app.run(debug=True) | [
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] |
authInterlink/authInterlink.py | danielSilva21/interlinker-service-augmenter | 0 | 169262 | from flask import Blueprint, jsonify, flash
import requests, math
from urllib.parse import urljoin, urlparse
import datetime
import uuid
from flask import current_app, g
from flask_babel import format_number,gettext,format_decimal, format_currency, format_percent
from flask import Flask, render_template, redirect, request, url_for, session
from flask_login import (
LoginManager,
current_user,
login_required,
login_user,
logout_user,
)
from annotator.survey import Survey
from authInterlink.user import User
from annotator.annotation import Annotation
from annotator.description import Description
from annotator.notification import Notification
from website.languages import getLanguagesList
authInterlink = Blueprint('authInterlink', __name__,template_folder="./gui/templates")
#Genero Secretos para los estados:
tok1 = uuid.uuid4()
tok2 = uuid.uuid4()
APP_STATE = tok1.hex
NONCE = tok2.hex
@authInterlink.route("/login")
def login():
# get request params
query_params = {'client_id': current_app.config["CLIENT_ID"],
'redirect_uri': current_app.config["REDIRECT_URI"],
'scope': "openid email profile",
'state': APP_STATE,
'nonce': NONCE,
'response_type': 'code',
'response_mode': 'query'}
# build request_uri
request_uri = "{base_url}?{query_params}".format(
base_url=current_app.config["AUTH_URI"],
query_params=requests.compat.urlencode(query_params)
)
return redirect(request_uri)
@authInterlink.route("/logout", methods=["GET", "POST"])
@login_required
def logout():
logout_user()
#response = redirect(config["end_session_endpoint"])
payload = {'id_token_hint': session['id_token'],
'post_logout_redirect_uri': "http://127.0.0.1:5000/home",
'state': APP_STATE}
#headers = {'Content-Type': 'application/x-www-form-urlencoded'}
r = requests.get(
current_app.config["END_SESSION_ENDPOINT"],
params=payload,
)
r.url
r.text
session.clear()
#return response
#return render_template("home.html")
return redirect(current_app.config["END_SESSION_ENDPOINT"]) #Por ahora queda asi.
@authInterlink.route("/about")
def about():
return render_template("about.html")
@authInterlink.route("/dashboard")
@login_required
def dashboard():
#Cargo los combos:
vectorUrls=Description._get_uniqueValuesUrl()
urlList=[]
for urls in vectorUrls:
key=urls["key"]
if(key!=""):
domain = urlparse(key).netloc
if not (domain in urlList):
urlList.append(domain)
print(urlList)
vectorPAs=Description._get_uniqueValues(campo="padministration")
paList=[]
for pas in vectorPAs:
key=pas["key"]
if key=="":
key='Unassigned'
paList.append(key)
print(paList)
textoABuscar=request.args.get("searchText")
padministration=request.args.get("padministration")
domain=request.args.get("domain")
page=request.args.get("page",1)
registroInicial=(int(page)-1)*10
totalRegistros=0
if(textoABuscar==None or textoABuscar==''):
res= Description.search(offset=registroInicial)
totalRegistros= Description.count()
else:
res= Description._get_Descriptions(textoABuscar=textoABuscar,padministration=padministration,url=domain,offset=registroInicial)
totalRegistros= Description._get_DescriptionsCounts(textoABuscar=textoABuscar,padministration=padministration,url=domain)
#Cargo los números de anotaciones por categoria
for itemDesc in res:
#Obtengo los Urls:
listUrl=[]
for url in itemDesc['urls']:
listUrl.append(url['url'])
#Cargo datos estadisticos de las descripciones
resCategory=Annotation.descriptionStats(Annotation,uris=listUrl)
nroFeedbacks=0
nroQuestions=0
nroTerms=0
nroFeedProgress=0
nroFeedApproved=0
nroQuesProgress=0
nroQuesApproved=0
nroTermProgress=0
nroTermApproved=0
#Obtengo la informacion estadistica:
if(len(resCategory)>0):
for itemCategory in resCategory:
cateGroup=itemCategory['key']
if(cateGroup=='feedback'):
nroFeedbacks=itemCategory['doc_count']
listStates=itemCategory['group_state']['buckets']
for itemState in listStates:
cateState=itemState['key']
nroState=itemState['doc_count']
if(cateState==0):#In Progress
nroFeedProgress=nroState
if(cateState==2):#In Approved
nroFeedApproved=nroState
if(cateGroup=='question'):
nroQuestions=itemCategory['doc_count']
listStates=itemCategory['group_state']['buckets']
for itemState in listStates:
cateState=itemState['key']
nroState=itemState['doc_count']
if(cateState==0):#In Progress
nroQuesProgress=nroState
if(cateState==2):#In Approved
nroQuesApproved=nroState
if(cateGroup=='term'):
nroTerms=itemCategory['doc_count']
listStates=itemCategory['group_state']['buckets']
for itemState in listStates:
cateState=itemState['key']
nroState=itemState['doc_count']
if(cateState==0):#In Progress
nroTermProgress=nroState
if(cateState==2):#In Approved
nroTermApproved=nroState
#Cargo los valores totales
itemDesc['nroTerms']=nroTerms
itemDesc['nroQuest']=nroQuestions
itemDesc['nroFeeds']=nroFeedbacks
#Cargo los progressBar con valores por estados.
# Progreso Total (%) = Approved * 100 / (InProgress + Approved)
#Feedback Progress:
#Incluyo validacion de la division x / 0 (if statement)
progressFeed= ( (nroFeedApproved * 100) / ( nroFeedProgress + nroFeedApproved ) ) if ( nroFeedProgress + nroFeedApproved ) != 0 else 0
progressTerm= ( (nroTermApproved * 100) / ( nroTermProgress + nroTermApproved ) ) if ( nroTermProgress + nroTermApproved ) != 0 else 0
progressQues= ( (nroQuesApproved * 100) / ( nroQuesProgress + nroQuesApproved ) ) if ( nroQuesProgress + nroQuesApproved ) != 0 else 0
itemDesc['progressFeed']=progressFeed
itemDesc['progressTerm']=progressTerm
itemDesc['progressQues']=progressQues
textoStats=("<b>Feedback ("+str(nroFeedApproved)+"/"+str(nroFeedApproved+nroFeedProgress)+")</b> : "+str(round(progressFeed))+"% <br>"+
"<b>Terms ("+str(nroTermApproved)+"/"+str(nroTermApproved+nroTermProgress)+")</b>: "+str(round(progressTerm))+"% <br>"+
"<b>Questions ("+str(nroQuesApproved)+"/"+str(nroQuesApproved+nroQuesProgress)+")</b>: "+str(round(progressQues))+"% <br>")
itemDesc['textoStats']=textoStats
progressTotalApproved = nroFeedApproved + nroTermApproved + nroQuesApproved
progressTotalInProgress = nroFeedProgress + nroTermProgress + nroQuesProgress
progressTotal= ( (progressTotalApproved * 100) / ( progressTotalInProgress + progressTotalApproved ) ) if ( progressTotalInProgress + progressTotalApproved ) != 0 else 0
itemDesc['progressTotal']=round(progressTotal)
pagesNumbers=math.ceil(totalRegistros/10)
paginacion={'page':page,'pagesNumbers':pagesNumbers,'totalRegisters':totalRegistros,'searchBox':textoABuscar,'padministration':padministration,'url':domain}
listNotifications,numRes=cargarNotifications()
return render_template("dashboard.html",descriptions=res,urls=urlList,publicsa=paList,paginacion=paginacion,notifications=listNotifications,notificationNum=numRes)
@authInterlink.route("/moderate")
@login_required
def moderate():
#Cargo los combos:
vectorUrls=Description._get_uniqueValues(campo="url")
urlList=[]
for urls in vectorUrls:
key=urls["key"]
if(key!=""):
domain = urlparse(key).netloc
if not (domain in urlList):
urlList.append(domain)
print(urlList)
vectorPAs=Description._get_uniqueValues(campo="padministration")
paList=[]
for pas in vectorPAs:
key=pas["key"]
if key=="":
key='Unassigned'
paList.append(key)
print(paList)
textoABuscar=request.args.get("searchText")
padministration=request.args.get("padministration")
domain=request.args.get("domain")
page=request.args.get("page",1)
registroInicial=(int(page)-1)*10
totalRegistros=0
if(textoABuscar==None or textoABuscar==''):
res= Description.search(offset=registroInicial)
totalRegistros= Description.count()
else:
res= Description._get_Descriptions(textoABuscar=textoABuscar,padministration=padministration,url=domain,offset=registroInicial)
totalRegistros= Description._get_DescriptionsCounts(textoABuscar=textoABuscar,padministration=padministration,url=domain)
res=Description._get_Descript_byModerEmail(email=current_user.email)
totalRegistros= Description._get_Descript_byModerEmailCounts(email=current_user.email)
pagesNumbers=math.ceil(totalRegistros/10)
paginacion={'page':page,'pagesNumbers':pagesNumbers,'totalRegisters':totalRegistros,'searchBox':textoABuscar,'padministration':padministration,'url':domain}
#Cargo las Notificaciones
listNotifications,numRes=cargarNotifications()
return render_template("moderate.html",descriptions=res,urls=urlList,publicsa=paList,paginacion=paginacion,notifications=listNotifications,notificationNum=numRes)
@authInterlink.route("/survey")
@login_required
def survey():
textoABuscar=request.args.get("searchText")
page=request.args.get("page",1)
registroInicial=(int(page)-1)*10
totalRegistros=0
#Searchs:
""" if(textoABuscar==None or textoABuscar==''):
res= Survey.search(offset=registroInicial)
totalRegistros= Survey.count()
else:
res= Survey._get_Surveys(textoABuscar=textoABuscar,offset=registroInicial) """
resTemp=Survey._get_all()
res=resTemp['surveys']
totalRegistros= resTemp['numRes']
pagesNumbers=math.ceil(totalRegistros/10)
paginacion={'page':page,'pagesNumbers':pagesNumbers,'totalRegisters':totalRegistros,'searchBox':textoABuscar}
#Cargo las Notificaciones
listNotifications,numRes=cargarNotifications()
#Defino la direccion del SurveyHost
surveyHost=current_app.config['SURVEYINTERLINK_URL']
surveyApiVersion=current_app.config['SURVEYAPI_VERSION']
return render_template("surveys.html",surveys=res,paginacion=paginacion,notifications=listNotifications,notificationNum=numRes,surveyHost=surveyHost,surveyApiVersion=surveyApiVersion)
@authInterlink.route("/surveyInstantiator",methods=["POST"])
def surveyInstantiator():
#Redirecciono al editor:
return redirect(current_app.config['SURVEYINTERLINK_URL']+"/assets/"+"instantiate")
def obtainUsersEmail(listItemsBucket=[]):
listUsers=[]
for itemBucket in listItemsBucket:
userEmail=itemBucket['key']
if userEmail!='Anonymous' and userEmail!='Annonymous' :
listUsers.append(userEmail)
return listUsers
@authInterlink.route("/lauchSurvey",methods=["POST"])
def surveyLauchProcess():
#Obtengo los valores del Survey:
selTargetUsers=request.form.get("selTargetList")
listUsersArea=request.form.get("listUsersArea")
is_optional=request.form.get("is_optional")
ini_date=request.form.get("ini_date")
fin_date=request.form.get("fin_date")
selEvent=request.form.get("selEvent")
mandatory=True
if is_optional =='on':
mandatory=False
#Creo la notification:
idAsset=request.form.get('assetId')
title=request.form.get('surveyTitle')
description= request.form.get('surveyDesc')
#Defino the users:
listUsersEmails=[]
if selTargetUsers=="everybody":
listUsersWhoAnnotated=obtainUsersEmail(Annotation.currentActiveUsers())
listUsersWhoModerate= obtainUsersEmail(Description.currentActiveUsersModerators())
listUsersEmails=list(set(listUsersWhoAnnotated+listUsersWhoModerate))
else:
listUsersEmails= listUsersArea.split(";")
for userEmail in listUsersEmails:
email=userEmail
target_url=current_app.config['SURVEYINTERLINK_URL']+ "/assets/"+idAsset+"/view/"
newNotification=Notification(
title=title,
email=email,
description=description,
target_url=target_url,
resolved=False,
category="survey",
idAsset=idAsset,
triggerEvent=selEvent,
triggerDate=ini_date,
isMandatory=mandatory
)
newNotification.save(index="notification")
#Se ha lanzado exitosamente el suvey:
flash("The survey has been lauched.","info")
#Redirecciono al editor:
return redirect("/survey")
@authInterlink.route('/advanceSearch',)
def advanceSearch():
res = Annotation.search(query={'user': current_user.email})
return render_template("advanceSearch.html", user=current_user, anotations=res)
@authInterlink.route('/description/<string:descriptionId>',)
@login_required
def description(descriptionId=None):
description = Description._get_Descriptions_byId(id=descriptionId)[0]
urlMainPage = [url['url'] for url in description['urls'] if url['isMain'] == True][0]
categoria=request.args.get('category')
page=request.args.get("page",1)
registroInicial=(int(page)-1)*10
if(categoria == None or categoria=='all' ):
categoria=''
res=[]
stats=[]
numRes=0
listUrlsPages=[]
for itemUrl in description['urls']:
url=itemUrl['url']
listUrlsPages.append(url)
# Cargo las replies de cada annotacion:
stats=stats+Annotation.annotationStats(Annotation,uri=itemUrl['url'])
res= Annotation._get_by_multiple(Annotation,textoABuscar='',estados={'InProgress':True,'Archived':True,'Approved':True},urls=listUrlsPages,category=categoria,notreply=True,page=page)
numRes= res['numRes']
res=res['annotations']
dictStats={}
for itemStat in stats:
clave=itemStat['key']
val=itemStat['doc_count']
dictStats[clave]=val
for itemRes in res:
if itemRes['id'] in dictStats.keys():
itemRes['nroReplies']=dictStats[itemRes['id']]
else:
itemRes['nroReplies']=0
page=request.args.get("page",1)
pagesNumbers=math.ceil(numRes/10)
paginacion={'page':page,'pagesNumbers':pagesNumbers,'totalRegisters':numRes}
#Cargo las Notificaciones
listNotifications,numRes=cargarNotifications()
return render_template("description.html", user=current_user, description=description,anotations=res,categoryLabel=categoria,paginacion=paginacion,urlMainPage=urlMainPage,notifications=listNotifications,notificationNum=numRes)
# return 'la desc: '+category+'lauri is'+str(uri)
@authInterlink.route('/description/<string:descriptionId>/<string:option>',)
@login_required
def editDescription(descriptionId=None,option='Edit'):
vectorPAs=Description._get_uniqueValues(campo="padministration")
paList=[]
for pas in vectorPAs:
key=pas["key"]
if key=="":
key='Unassigned'
paList.append(key)
print(paList)
description = Description._get_Descriptions_byId(id=descriptionId)[0]
for itemUrl in description['urls']:
if itemUrl['language']!='Undefined':
itemUrl['langText']=getLanguagesList()[itemUrl['language']]
else:
itemUrl['langText']="Undefined"
#Cargo las Notificaciones
listNotifications,numRes=cargarNotifications()
return render_template("descriptionDetail.html", user=current_user, description=description,option=option,publicsa=paList,notifications=listNotifications,notificationNum=numRes)
@authInterlink.route('/subjectPage/<string:descriptionId>/<string:annotatorId>',)
@login_required
def subjectPage(descriptionId=None,annotatorId=None):
description = Description._get_Descriptions_byId(id=descriptionId)[0]
urlMainPage = [url['url'] for url in description['urls'] if url['isMain'] == True][0]
annotation = Annotation._get_Annotation_byId(id=annotatorId)[0]
nroReplies = Annotation.count(query={ 'idReplyRoot': annotatorId ,'category':'reply' })
replies = Annotation.search(query={ 'idReplyRoot': annotatorId ,'category':'reply' },limit=nroReplies)
nroRepliesOfAnnotation=nroReplies
#nroRepliesOfAnnotation = Annotation.count(query={ '_id': description['id'] ,'category':'reply','idReplyRoot':annotatorId })
#Cargo las Notificaciones
listNotifications,numRes=cargarNotifications()
return render_template("subjectPage.html", user=current_user, annotation=annotation,description=description,categoryLabel=annotation['category'],replies=replies,nroReplies=nroRepliesOfAnnotation,urlMainPage=urlMainPage,notifications=listNotifications,notificationNum=numRes)
# return 'la desc: '+category+'lauri is'+str(uri)
@authInterlink.route('/subjectPage/<string:descriptionId>/<string:annotatorId>/<string:option>', methods=["GET", "POST"])
@login_required
def changeAnnotation(descriptionId=None,annotatorId=None,option=None):
if option == 'state':
argumentos=request.json
newstate=argumentos.pop('stateToChange')
commentsChangeState=argumentos.pop('commentsChangeState')
objtype=argumentos.pop('objtype')
annotationRootId=''
losvalores=argumentos.keys()
if "annotationRootId" in losvalores:
annotationRootId=argumentos.pop('annotationRootId')
annotation = Annotation._get_Annotation_byId(id=annotatorId)[0]
#Registro el cambio y quien lo hizo
if(len(annotation['statechanges'])==0):
annotation['statechanges']=[]
#Si el estado inicial es prohibido y el estado final es prohibido
#Se resetea el estado a in progress.
if(annotation['state']==3 & int(newstate)==3):
newstate=0
annotation['statechanges'].append({
"initstate": annotation['state'],
"endstate": int(newstate),
"text": commentsChangeState,
"objtype" : objtype,
"date": datetime.datetime.now().replace(microsecond=0).isoformat(),
"user": current_user.email
})
annotation['state']=int(newstate)
annotation.updateState()
return jsonify(annotation)
elif option=='like':
argumentos=request.json
vote=int(argumentos.pop('stateToChange'))
newstate=vote
commentsChangeState=argumentos.pop('commentsChangeState')
objtype=argumentos.pop('objtype')
annotationRootId=''
losvalores=argumentos.keys()
if "annotationRootId" in losvalores:
annotationRootId=argumentos.pop('annotationRootId')
#Verifico si anteriormente este usuario ha realizado una anotacion
annotation = Annotation._get_Annotation_byId(id=annotatorId)[0]
nroLikes=annotation.userAlreadyLike(email=current_user.email,id=annotatorId)
#Un usuario solo puede votar una vez
if nroLikes==0:
#Registro el cambio y quien lo hizo
if(len(annotation['statechanges'])==0):
annotation['statechanges']=[]
#Registro el cambio y quien lo hizo
initState=0
if(vote==1):
initState=int(annotation['like'])
annotation['like']=initState+1
objtype='annotation_like'
elif vote==-1:
initState=int(annotation['dislike'])
annotation['dislike']=initState+1
objtype='annotation_like'
#Registro el cambio de estado
annotation['statechanges'].append({
"initstate": initState,
"endstate": initState+1,
"text": commentsChangeState,
"objtype" : objtype,
"date": datetime.datetime.now().replace(microsecond=0).isoformat(),
"user": current_user.email
})
annotation.updateLike()
annotation.updateState()
return jsonify(annotation)
@authInterlink.route("/descriptionDetail")
@login_required
def descriptionDetail():
vectorPAs=Description._get_uniqueValues(campo="padministration")
paList=[]
for pas in vectorPAs:
key=pas["key"]
if key=="":
key='Unassigned'
paList.append(key)
print(paList)
res = Annotation.search(query={'user': current_user.email})
return render_template("descriptionDetail.html", user=current_user, anotations=res,publicsa=paList)
@authInterlink.route("/profile")
@login_required
def profile():
#Cargo las Notificaciones
listNotifications,numRes=cargarNotifications()
return render_template("profile.html", user=current_user,notifications=listNotifications,notificationNum=numRes)
@authInterlink.route("/settings")
@login_required
def settings():
results=[]
anthony =gettext('Anthony')
us_num=format_number(1099)
results.append(us_num)
us_num=format_currency(1099.98, 'USD')
results.append(us_num)
us_num=format_decimal(1.2346)
results.append(us_num)
#Cargo las Notificaciones
listNotifications,numRes=cargarNotifications()
return render_template("settings.html", user=current_user,results=results,anthony=anthony,notifications=listNotifications,notificationNum=numRes)
@authInterlink.route("/oidc_callback")
def callback():
headers = {'Content-Type': 'application/x-www-form-urlencoded'}
code = request.args.get("code")
#la pagina que se pretende ingresar es:
paginaNext=''
if 'next' in session.keys():
paginaNext=session['next']
if not code:
return "The code was not returned or is not accessible", 403
query_params = {'grant_type': 'authorization_code',
'code': code,
'redirect_uri': request.base_url
}
query_params = requests.compat.urlencode(query_params)
exchange = requests.post(
current_app.config["TOKEN_URI"],
headers=headers,
data=query_params,
auth=(current_app.config["CLIENT_ID"], current_app.config["CLIENT_SECRET"]),
).json()
# Get tokens and validate
if not exchange.get("token_type"):
return "Unsupported token type. Should be 'Bearer'.", 403
access_token = exchange["access_token"]
id_token = exchange["id_token"]
session['id_token']=id_token
#if not is_access_token_valid(access_token, config["issuer"], config["client_id"]):
# return "Access token is invalid", 403
#if not is_id_token_valid(id_token, config["issuer"], config["client_id"], NONCE):
# return "ID token is invalid", 403
# Authorization flow successful, get userinfo and login user
userinfo_response = requests.get(current_app.config["USERINFO_URI"],
headers={'Authorization': f'Bearer {access_token}'}).json()
unique_id = userinfo_response["sub"]
user_email = userinfo_response["email"]
user_name = userinfo_response["given_name"]
user = User(
id_=unique_id, name=user_name, email=user_email
)
if not User.get(unique_id):
User.create(unique_id, user_name, user_email)
login_user(user)
# g.user =user
session.pop('_flashes', None)
if paginaNext!="":
return redirect(paginaNext)
else:
return redirect(url_for("authInterlink.dashboard"))
def cargarNotifications():
#Cargo las Notificaciones
listNotifications=Notification._get_Notification_byModerCategory(category="survey")
#listNotifications.append(Notification._get_Notification_byModerCategory(category="survey"))
numRes=listNotifications['numRes']
listNotifications=listNotifications['notifications']
return listNotifications,numRes
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] |
Graphy.py | rasmdc/Graphy | 0 | 125467 | <gh_stars>0
# -*- coding: utf-8 -*-
"""
Created on Sat Apr 11 17:47:37 2020
@author: Eriel
"""
from tkinter import *
from matplotlib import *
from PIL import Image, ImageTk
def menu():
# Menu Window
menu = Tk()
menu.geometry('500x500')
menu.title("Menu")
menu.iconbitmap(r'graphy.ico')
menu['bg'] = '#161616'
# Image Header
canvas = Canvas(menu, height = 244, width = 365, bg = '#161616' )
canvas.pack(pady= 20)
wel_img = PhotoImage(file='...\src\img\welcome.png')
canvas.create_image(0, 0, anchor = NW, image = wel_img)
# Selection Frame
btn_frame = Frame(menu, bg = '#161616')
btn_frame.pack()
graph_btn = PhotoImage(file = r"graph_btn.png")
exit_btn = PhotoImage(file = r"exit_btn.png")
btn = Button(btn_frame, image = graph_btn, command = graphy, bg = '#161616')
btn.pack(pady = 10)
btn = Button(btn_frame, image = exit_btn, bg = '#161616')
btn.pack(pady = 10)
menu.mainloop() # Runs menu in a loop
def graphy():
# Graphy Window
graph = Tk()
graph.geometry('1500x500')
graph.title("Graphy")
graph.iconbitmap(r'graphy.ico')
graph['bg'] = '#161616'
def change(*args):
print("running change")
OPTIONS = [
"Linear",
"Squared",
"Cubic",
"Squared root",
"Cubic root",
"Cardioid",
"Lemniscate",
"Spiral",
"Roses"
]
var = StringVar(graph)
var.set("Select One")
var.trace("w", change)
down_menu = OptionMenu(graph, var, OPTIONS[0], OPTIONS[1], OPTIONS[2])
down_menu.pack()
menu() | [
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test_phyton/pages/func.py | gergo1001/conduit | 0 | 1606210 | import random
import string
webpage_visible = False
def random_email():
return "".join([random.choice(string.ascii_lowercase) for _ in range(8)]) + '@testemail.hu'
def random_user():
return "".join([random.choice(string.ascii_letters) for _ in range(6)])
def random_pass():
return "".join([random.choice(string.ascii_lowercase) for _ in range(6)]) + "".join(
[random.choice(string.ascii_uppercase) for _ in range(1)]) + "".join(
[random.choice(string.digits) for _ in range(1)]) + '!'
def random_title():
return "".join([random.choice(string.ascii_letters) for _ in range(10)])
def inputelement(driver, typeofelementpath, path):
if typeofelementpath == 'xpath':
return driver.find_element_by_xpath(path)
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users/tests/test_models.py | City-of-Helsinki/apartment-application-service | 1 | 126879 | import pytest
from django.core.exceptions import ValidationError
from users.models import Profile
from users.tests.factories import ProfileFactory
@pytest.mark.django_db
def test_profile_exist_after_user_is_deleted():
profile = ProfileFactory()
profile.user.delete()
assert Profile.objects.all().count() == 1
assert Profile.objects.first().user is None
@pytest.mark.django_db
def test_profile_national_identification_number_validation():
profile = ProfileFactory()
# Invalid format
profile.national_identification_number = "123456-1234"
with pytest.raises(ValidationError, match="The number has an invalid format."):
profile.clean()
# Invalid checksum
profile.national_identification_number = "131052-308U"
with pytest.raises(
ValidationError, match="The number's checksum or check digit is invalid."
):
profile.clean()
# Correct checksum
profile.national_identification_number = "131052-308T"
profile.save()
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main.py | hashc/Toward_Reuse | 24 | 156198 | '''Train CIFAR10 with PyTorch.'''
from __future__ import print_function
import sys
import torch
import torch.nn as nn
import torch.optim as optim
import torch.nn.functional as F
import torch.backends.cudnn as cudnn
import config as cf
import torchvision
import torchvision.transforms as transforms
import os
import argparse
from models import *
from utils import progress_bar
import numpy as np
parser = argparse.ArgumentParser(description='PyTorch CIFAR10 Training')
parser.add_argument('--lr', default=0.1, type=float, help='learning rate')
parser.add_argument('--batch_size', default=256, type=int, help='batch size')
parser.add_argument('--dataset', default='cifar10', type=str, help='dataset = [cifar10, cifar100, fashion-mnist]')
parser.add_argument('--model', default='lrunet', type=str, help='model = [lrunet, shufflenet, shufflenetv2, mobilenet, mobilenetv2]')
parser.add_argument('--layer_reuse', default=8, type=int, help='layer reuse')
parser.add_argument('--width_mult', default=1.0, type=float, help='width multiplier')
parser.add_argument('--drop', default=0.5, type=float, help='applied dropout')
parser.add_argument('--groups', default=3, type=int, help='The number of groups at group convolution at ShuffleNet')
parser.add_argument('--resume_path', default='', type=str, help='Save data (.pth) of previous training')
args = parser.parse_args()
device = 'cuda' if torch.cuda.is_available() else 'cpu'
best_acc = 0 # best test accuracy
start_epoch = 0 # start from epoch 0 or last checkpoint epoch
# Data
print('==> Preparing data..')
transform_train = transforms.Compose([
transforms.RandomCrop(32, padding=4),
transforms.RandomRotation(degrees=10),
transforms.RandomHorizontalFlip(),
transforms.ToTensor(),
# transforms.Normalize(cf.mean[args.dataset], cf.std[args.dataset]),
])
transform_test = transforms.Compose([
transforms.ToTensor(),
# transforms.Normalize(cf.mean[args.dataset], cf.std[args.dataset]),
])
if(args.dataset == 'cifar10'):
print("| Preparing CIFAR-10 dataset...")
sys.stdout.write("| ")
trainset = torchvision.datasets.CIFAR10(root='./data', train=True, download=True, transform=transform_train)
testset = torchvision.datasets.CIFAR10(root='./data', train=False, download=False, transform=transform_test)
num_classes = 10
init_ch = 3 # Number of channels for the first conv layer
elif(args.dataset == 'cifar100'):
print("| Preparing CIFAR-100 dataset...")
sys.stdout.write("| ")
trainset = torchvision.datasets.CIFAR100(root='./data', train=True, download=True, transform=transform_train)
testset = torchvision.datasets.CIFAR100(root='./data', train=False, download=False, transform=transform_test)
num_classes = 100
init_ch = 3 # Number of channels for the first conv layer
elif(args.dataset == 'fashionmnist'):
print("| Preparing FashionMNIST dataset...")
sys.stdout.write("| ")
trainset = torchvision.datasets.FashionMNIST(root='./data', train=True, download=True, transform=transform_train)
testset = torchvision.datasets.FashionMNIST(root='./data', train=False, download=True, transform=transform_test)
num_classes = 10
init_ch = 1 # Number of channels for the first conv layer
trainloader = torch.utils.data.DataLoader(trainset, batch_size=args.batch_size, shuffle=True, num_workers=2)
testloader = torch.utils.data.DataLoader(testset, batch_size=100, shuffle=False, num_workers=2)
# Model
print('==> Building model..')
if args.model == 'lrunet':
net = LruNet(num_classes, args.width_mult, args.layer_reuse, args.drop, init_ch)
elif args.model == 'mobilenet':
net = MobileNet(num_classes, args.width_mult, init_ch)
elif args.model == 'mobilenetv2':
net = MobileNetV2(num_classes, args.width_mult, init_ch)
elif args.model == 'shufflenet':
net = ShuffleNet(num_classes, args.width_mult, args.groups, init_ch)
elif args.model == 'shufflenetv2':
net = ShuffleNetV2(num_classes, args.width_mult, init_ch)
net = net.to(device)
if device == 'cuda':
net = torch.nn.DataParallel(net)
cudnn.benchmark = True
print(net)
conv_params = 0
for key in net.modules():
if (isinstance(key, nn.Conv2d) | isinstance(key, nn.Linear)):
#print(key)
conv_params += sum(p.numel() for p in key.parameters() if p.requires_grad)
print("Total number of convolution parameters: ", conv_params)
pytorch_total_params = sum(p.numel() for p in net.parameters() if p.requires_grad)
print("Total number of trainable parameters: ", pytorch_total_params)
if args.resume_path:
# Load checkpoint.
print('==> Resuming from checkpoint..')
checkpoint = torch.load(args.resume_path)
net.load_state_dict(checkpoint['net'])
best_acc = checkpoint['acc']
start_epoch = checkpoint['epoch']
criterion = nn.CrossEntropyLoss()
optimizer = optim.SGD(net.parameters(), lr=args.lr, momentum=0.9, weight_decay=5e-4)
# Training
def train(epoch):
print('Epoch: %d' % epoch)
net.train()
train_loss = 0
correct = 0
total = 0
for batch_idx, (inputs, targets) in enumerate(trainloader):
inputs, targets = inputs.to(device), targets.to(device)
optimizer.zero_grad()
outputs = net(inputs)
loss = criterion(outputs, targets)
loss.backward()
optimizer.step()
train_loss += loss.item()
_, predicted = outputs.max(1)
total += targets.size(0)
correct += predicted.eq(targets).sum().item()
progress_bar(batch_idx, len(trainloader), 'Loss: %.3f | Acc: %.3f%% (%d/%d)'
% (train_loss/(batch_idx+1), 100.*correct/total, correct, total))
def test(epoch):
global best_acc
net.eval()
test_loss = 0
correct = 0
total = 0
with torch.no_grad():
for batch_idx, (inputs, targets) in enumerate(testloader):
inputs, targets = inputs.to(device), targets.to(device)
outputs = net(inputs)
loss = criterion(outputs, targets)
test_loss += loss.item()
_, predicted = outputs.max(1)
total += targets.size(0)
correct += predicted.eq(targets).sum().item()
progress_bar(batch_idx, len(testloader), 'Loss: %.3f | Acc: %.3f%% (%d/%d)'
% (test_loss/(batch_idx+1), 100.*correct/total, correct, total))
# Save checkpoint.
acc = 100.*correct/total
if acc > best_acc:
print('Saving..')
state = {
'net': net.state_dict(),
'acc': acc,
'epoch': epoch,
}
if not os.path.isdir('checkpoint'):
os.mkdir('checkpoint')
torch.save(state, './checkpoint/ckpt.t7')
best_acc = acc
for epoch in range(start_epoch, start_epoch+275):
train(epoch)
test(epoch)
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tests/test_no_inputenc.py | vikorbit/PyLaTeX | 4 | 114398 | <filename>tests/test_no_inputenc.py
# -*- coding: utf-8 -*-
r"""A test to make sure the document compiles with inputenc set to `None`."""
from pylatex.base_classes import Arguments
from pylatex import Document
doc = Document('no_inputenc', inputenc=None)
doc.append('test text')
# Make sure inputenc isn't used
assert not any([p.arguments == Arguments('inputenc') for p in doc.packages])
doc.generate_pdf(clean=True, clean_tex=False, silent=False)
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calculator.py | rupen4678/botique_management_system | 0 | 5980 | <reponame>rupen4678/botique_management_system
from tkinter import *
import random
import time
from PIL import Image
from datetime import datetime
from tinydb import *
import os
import pickle
#from database1 import *
from random import randint
root = Tk()
root.geometry("1600x800+0+0")
root.title("Suman_dai_ko_DHOKAN")
root.configure(bg="goldenrod4")
text_Input = StringVar()
operator =""
yes =""
no=""
Tops = Frame(root, width=1600 ,height=50,bg="goldenrod4", relief=RIDGE)
Tops.pack(side=TOP)
f1 = Frame(root, width = 800 ,height=500,bg="goldenrod4",relief=SUNKEN)
f1.pack(side=LEFT)
f2 = Frame(root, width = 300,height = 700,bg="dark slate blue",relief=SUNKEN)
f2.pack(side=RIGHT)
#f3= Frame(root,width=1600,height=300,fg="blue", bg="powder blue", relief=SUNKEN).pack(side=Bottom)
#==========================================================Time=======================================
localtime=time.asctime(time.localtime(time.time()))
#datetime=Label(Tops,font("arial",20,"bold"),text=nowTime,bd=10 ,bg="black", #fg="white", anchor="w").pack()
#====================================debugged========================
shirt = IntVar()
pant = IntVar()
sale = IntVar()
buy = IntVar()
deposite = IntVar()
withdraw = IntVar()
coat = IntVar()
order = IntVar()
total = IntVar()
out = IntVar()
before = IntVar() #order before the 60
stock = IntVar()
delivery = IntVar()
#########################main_gate######################
def _calculation():
shirt_mm = shirt.get()
pant_mm = pant.get()
sale_mm = sale.get()
buy_mm = buy.get()
deposite_mm = deposite.get()
withdraw_mm = withdraw.get()
coat_mm = coat.get()
order_mm = order.get()
total_mm = total.get()
time = datetime.now()
day = time.day
month = time.month
hour = time.hour
second = time.second
year = time.year
minute = time.minute
#setting the filename using the loop
#file = open("1{}".format())
'''for i in range(5):
if os.path.isfile(i):
pass
else:
file = open("{}.txt".format(i+1), "w+")
created with name {}".format(file))'''
#creating the filenames with append =1 if the name already existed
file_name = "r.txt"
if os.path.isfile(file_name):
expand = 1
while True:
expand += 1
new_file_name = file_name.split(".txt")[0] + str(expand) + ".txt"
if os.path.isfile(new_file_name): #if the newfilename exists
print("using the file {}".format(new_file_name))
#file = open("{}".format(new_file_name), "w+")
continue
else:
file_name = open(new_file_name, "w+")
print("creating the file {}".format(file_name))
#file = open("{}".format(file_name), "w+")
break
file_name = "fil.txt"
file = open("{}".format(file_name),"w+")
totalx = shirt_mm+pant_mm+sale_mm+buy_mm+deposite_mm+withdraw_mm+coat_mm+order_mm
file.write("Total:-{}".format(totalx))
file.write("shirt:-{}".format(shirt_mm))
file.write("pant_mm:-{}".format(pant_mm))
file.write("sale_mm:-{}".format(sale_mm))
file.write("buy_mm:-{}".format(buy_mm))
file.write("deposite_mm:-{}".format(deposite_mm))
file.write("withdraw_mm:-{}".format(withdraw_mm))
file.write("coat:-{}".format(coat_mm))
file.write("order:-{}".format(order_mm))
reading = file.readlines()
file.close()
#after wards set the total from here total.set
#++++++++++++++++++++++++++++++Varibales_inset+++++++++++++++++++++++++++++++++
order_bef = IntVar()
stock_full = IntVar()
shrting = IntVar()
pant = IntVar()
sari = IntVar()
order_info = IntVar()
delivery_report = IntVar()
daily_info = IntVar()
sales = IntVar()
buy = IntVar()
total_bank = IntVar()
bank_deposite = IntVar()
bank_withdraw = IntVar()
due_amount = IntVar()
order_info = IntVar()
daily_cash = IntVar()
cus_name = IntVar()
cus_no = IntVar()
employee = IntVar()
###############################class of algoriths#########################
class __main():
def __init__(self):
self.order = order
def __order_info(self):
self.now = datetime()
self.hour = now.hour
self.minute = now.minute
self.second = now.second
self.year = now.year
self.month = now.month
self.day = now.day
self.record_time = record_time
if self.hour == self.record_timeD:
print("the time for the product is actually %s left" %(self.hour-self.record_timeD))
#++++++++++++++++++++++++++++++++++++++++tinydb example++++++++++++++++++++++
#db = TinyDB("/databse/d4ta.json")
#db.insert({"cus_number":"98938232", "cus_name":"rupen"})
#def no_y():
# lis = db.all()
################Info===============
lblInfo = Label(Tops, font=("arial",60, "italic bold"),text="Botique Management Systewm",fg="white", bg="dark slate blue", bd=10, anchor="w", relief=RIDGE)
lblInfo.pack()
lblInfo = Label(Tops, font=("arial",30, "bold"),text=localtime,fg="white",bg="black", bd=10, anchor="w", relief=RIDGE)
lblInfo.pack()
#===========================================================Calculator==================================
"""def current_dir():
import os
import sys
DIR = os.getcwd()
print(DIR)
lblInfo = Label(Tops, font=("arial",60, "italic"),text=current_dir,fg="black",bg="powder blue",bd=10, anchor="W")
lblInfo.pack()
#DIR = dir
#return dir
"""
#randomBtn=Button(f1,pady=16,padx=16,bd=8,bg="powder blue", text="C_dir", command=lambda: current_dir(dir)).pack(side=TOP)
def btnClick(numbers):
global operator
operator = operator + str(numbers)
text_Input.set(operator)
def btnClearDisplay():
global operator
operator=""
text_Input.set("")
def btnEqualsInput():
global operator
sumup=str(eval(operator))
text_Input.set(sumup)
operator=""
def bill_entry():
global bill_in
global bill_out
bill_out = ""
bill_in = ""
def rupen():
global rupen
rupen = rupen
ronley = StringVar()
'''def malware_activate():
global cmd_active
if "rupen" in cmd_active:
if "rupen" in cmd_active[1]:
if "ronley" in cmd_active[2]:'''
#==============================another windows about me=====================
def ano_win1():
win1 = Toplevel()
#this is going to be the window in which there is nothing in the function
#of the system on the support in teh main loop
#there is no limit in the system of teh
win1.title("this is the owner window:")
win1.geometry("1600x800+0+0")
#win1.configure(bg="silver")
my_info = Frame(win1, width=600, height=700,bg="RoyalBlue4",relief=GROOVE)
my_info.pack(side=LEFT)
customer_info = Frame(win1, width=600, height=500,bg="RoyalBlue4", relief=GROOVE)
customer_info.pack(side=RIGHT)
others_info = Frame(win1, width=100, height=100,bg="RoyalBlue4",relief=GROOVE)
others_info.pack(side=BOTTOM)
all_info = Frame(win1, width=50, height=50,bg="RoyalBlue4",relief=RAISED)
all_info.pack()
lblname=Label(my_info,font=("arial",20,"italic"),text="<NAME>",bg="powder blue", fg="green", bd=10, relief=SUNKEN).pack(side=TOP)
lblpro=Label(my_info,font=("arial", 20,"bold"),text="Software Engineer",bg="powder blue", fg="green",bd=10, relief=RAISED).pack()
ima = StringVar()
imageloc=Entry(win1,font=("arial",16,"italic"),bg="black",fg="white",bd=5,insertwidth=1,relief=GROOVE,textvariable=ima).pack()
imageButt=Button(win1,font=("arial",20, "bold"),bd=5,bg="white",fg="white",command= lambda: _image(image)).pack()
'''def _image(image):
image = image.set(imageloc)
return image
#image = Image.open("/root/Desktop/Desktop/anonymous/5.png")
imae = Label(win1,font=("arial", 20,"italic"),width=300, height=168,bg="black",fg="white", text=image,relief=FLAT).pack()
win1.mainloop()'''
#=============================getting all the infos ========================
def _price_inputs():
win2 = Toplevel()
win2.title("This is going to the section for the price inputs")
win2.geometry("1600x800")
framex = Frame(win2,width=1600,bg="RoyalBlue4",height=100,relief=GROOVE).pack(side=TOP)
frame1 = Frame(win2,width=775, height=750,bg="white", relief=SUNKEN).pack()
frame2 = Frame(win2, width=775,height=750,bg="black", relief=FLAT).pack()
#==++++===========================title=============================
llb1 = Label(framex,font=("arial", 20,"italic"),bg="powder blue",fg="green",text="INPUT THE PRICES",relief=GROOVE).pack()
win2.mainloop()
###########################sending emails############################
def __send_email():
'''import smtplib
gmail = smtplib.SMTP("smtp.gmail.com", 587)
gmail.starttls()
_file = open("/root/Desktop/Desktop/python/")
gmail.login("username", "password")
msg = "YOUR MESSAGE"
gmail.sendmail("your email adress", "the")
gmail.quit()'''
dialog = Tk()
dialog.title("Send emails")
dialog.geometry("800x800")
dframe = Frame(dialog,width=800,height=800,bg="white",relief=SUNKEN).pack()
email = StringVar()
password = StringVar()
semail = StringVar()
spassword = StringVar()
label = Label(dframe, font=("arial",16, "bold"), fg="white", bg="black", text="your_email").pack(side=LEFT)
entry1 = Entry(dframe, font=("arial",16,"bold"), fg="white",bg="black", textvariable=email,insertwidth=1,bd=5).pack(side=RIGHT)
label1 = Label(dframe, font=("arial",16, "bold"), fg="white", bg="black", text="password", relief=SUNKEN).pack()
entry2 = Entry(dframe,font=("arial", 16 ,"bold"),textvariable=password, insertwidth=1,bd=5).pack(side=RIGHT)
Label2 =Label(dframe,font=("arial",16, "bold"),fg="white",bg="black", text="sender_email",relief=SUNKEN).pack(side=LEFT)
entry2 = Entry(dframe,font=("arial",16, "bold"),bd=5,fg="white",bg="black",textvariable=semail,insertwidth=1).pack(side=LEFT)
label3 = Label(dframe,font=("arial",16,"bold"),fg="white",bg="black",text="sender_password", relief=SUNKEN).pack(side=LEFT)
entry3= Entry(dframe,font=("arial",16,"bold"),fg="white",textvariable=spassword,insertwidth=1,relief=SUNKEN).pack()
dialog.mainloop()
#btnEmail = Button(root,font=("arial", 16, "bold"), bg="black",fg="white",text="email",command=lambda: __send_email(),relief=GROOVE).pack()
#================================next section===========================
fix = Button(root, bd=10,bg="black",fg="white",command=_price_inputs,relief=GROOVE).pack(side=BOTTOM)
btnru = Button(root, font=("arial 20 bold"),bd=20, bg="black",fg="white",text="click",command=ano_win1,relief=GROOVE).pack(side=BOTTOM)
#fucking mazing yr coding
def column(col):
for coll in col:
call=cal+1
return call
#def yes_y():
# rupe = Toplevel(root)
# rupe.title("this is second window")
# return
#def no_y():
#nos = Toplevel(root)
#nos.title("this is nos window")
#return
a = Entry(f2,font=("arial", 20,"bold"), textvariable=text_Input, bd=30, insertwidth=4,
bg="dark slate blue",fg="white", justify="right").grid(columnspan=4)
btn7=Button(f2,padx=16,pady=16,bd=8, fg="black", font=("arial",20,"bold"),
text="7",bg="dim gray", command=lambda: btnClick(7)).grid(row=2,column=0)
btn8=Button(f2,padx=16,pady=16,bd=8, fg="black", font=("arial",20,"bold"),
text="8",bg="dim gray", command=lambda: btnClick(8)).grid(row=2,column=1)
btn9=Button(f2,padx=16,pady=16,bd=8, fg="black", font=("arial",20,"bold"),
text="9",bg="dim gray", command=lambda: btnClick(9)).grid(row=2,column=2)
#!!!!!!!!!!!!!!!!!!!!!!additions!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!
Addition=Button(f2,padx=16,pady=16,bd=8,text="+",fg="black",bg="dim gray", command=lambda: btnClick("+")).grid(row=2,column=3)
btn6=Button(f2,padx=16,pady=16,bd=8, fg="black", font=("arial",20,"bold"),text="4", bg="dim gray", command=lambda: btnClick(4)).grid(row=3,column=0)
btn5=Button(f2,padx=16,pady=16,bd=8, fg="black", font=("arial",20,"bold"),text="5", bg="dim gray", command=lambda: btnClick(5)).grid(row=3,column=1)
btn4=Button(f2,padx=16,pady=16,bd=8, fg="black", font=("arial",20,"bold"),text="6",bg="dim gray", command=lambda: btnClick(6)).grid(row=3,column=2)
Subtract=Button(f2,padx=16,pady=16,bd=8,text="-", bg="dim gray", command=lambda: btnClick("-")).grid(row=3,column=3)
btn3=Button(f2,padx=16,pady=16,bd=8,text="3",font=("arial", 20, "bold") ,bg="dim gray", command=lambda: btnClick(3)).grid(row=4,column=0)
btn2=Button(f2,padx=16,pady=16,bd=8,text="2",font=("arial", 20, "bold"), bg="dim gray", command=lambda: btnClick(2)).grid(row=4,column=1)
btn1=Button(f2,padx=16,pady=16,bd=8,text="1",font=("arial", 20, "bold") ,bg="dim gray", command=lambda: btnClick(1)).grid(row=4,column=2)
Multiply=Button(f2,padx=16,pady=16,bd=8,text="*", bg="dim gray", command=lambda: btnClick("X")).grid(row=4,column=3)
#+++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++
btn0=Button(f2,padx=16,pady=16,bd=8,bg="dim gray",text="0",fg="black",font=("arial", 20, "bold"), command=lambda: btnClick(0)).grid(row=5,column=0)
btnClear=Button(f2,pady=16,padx=16,bd=8, fg="black",font=("arial", 20, "bold"),text="C",bg="dim gray", command=btnClearDisplay).grid(row=5,column=1)
btnEquals=Button(f2,padx=16,pady=16,fg="black",bd=8,text="=",bg="dim gray", font=("arial", 20,"bold"), command=btnEqualsInput).grid(row=5,column=2)
#btn2=Button(f2,padx=16,pady=16,bd=8,fg="black",text="2",bg="dim gray", command=lambda: btnClick(2)).grid(row=5,column=3)
division=Button(f2,padx=16,pady=16,bd=8,fg="black", text="/", bg="dim gray", command=lambda: btnClick("/")).grid(row=5,column=3)
#!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!
rand = StringVar()
#lblReference = Label(f1,font=("arial", 16,"bold"), text="Reference",bd=16,fg="red",bg="red",anchor="w",relief=RIDGE).grid(row=0,column=0)
#txtReference=Entry(f1,font=("arial", 16, "bold"), textvariable=rand, bd=10,insertwidth=4,bg="red",fg="white", justify = "right").grid(row=0,column=1)
#!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!
lblReference = Label(f1,font=("arial", 16,"bold"), text="Reference",bd=16,fg="white",bg="green",anchor="w", relief=RIDGE)
lblReference.grid(row=0,column=0)
b=Entry(f1,font=("arial", 16, "bold"), textvariable=rand, bd=10,insertwidth=4,fg="white",bg="black", justify = "left")
b.grid(row=0,column=1)
#img = "/root/Desktop/Desktop/python/projects/prj1_Botik/1.jpg"
#root.ima = Image.open(img)
#Label (root,bg="white",width=120,height=120, image=ima).pack()
bill_in = StringVar()
bill_out = StringVar()
shrting=Label(f1,font=("arial", 20, "bold"), text="Shirting:",bg="powder blue", fg="black",anchor="w",relief=GROOVE).grid(row=1,column=0)
shirts=Entry(f1,font=("arial", 16, "italic"), bd=10, textvariable=shirt, insertwidth=1,bg="black",fg="white", justify="left").grid(row=2,column=0)
owner=Button(root,padx=16,pady=16, font=("arial",12, "bold"),text="info", bd=8,bg="black",command=ano_win1,fg="white",relief=RAISED).pack(side=LEFT)
yes=Button(root,padx=16,pady=16,font=("arial",12, "bold"),text="Done",bd=8,bg="black", fg="white", command=_calculation(),relief=RAISED).pack(side=RIGHT)
panting=Label(f1,font=("arial",20, "bold"), text="pant_mm:", bg="powder blue",fg="black",anchor="w",relief=GROOVE).grid(row=1,column=1)
pantx=Entry(f1,font=("arial",16, "bold"), textvariable=pant, insertwidth=1, bd=10,bg="black",fg="white", justify="left").grid(row=2,column=1)
sales=Label(f1,font=("arial",16, "bold"), text="sales_total:",bg="powder blue",fg="black",anchor="w",bd=8,relief=GROOVE).grid(row=1,column=2)
salex=Entry(f1,font=("arial",16, "bold"),bg="black",fg="white",textvariable=sale,insertwidth=1,bd=10,justify="left").grid(row=2,column=2)
buying=Label(f1,font=("arial",16, "bold"), text="buying_something: ",bg="powder blue",fg="black", anchor="e", relief=GROOVE).grid(row=3,column=0)
buyx=Entry(f1,font=("arial", 16, "bold"), textvariable=buy, insertwidth=1, bd=10,bg="black", fg="white", justify="left").grid(row=4,column=0)
Bank_Total=Label(f1,font=("arial",16,"bold"),text="Bank_Deposite: ", bg="powder blue", fg="black", anchor="e",relief=GROOVE).grid(row=3, column=1)
depositex=Entry(f1,font=("arial",16,"bold"),bd=10, textvariable=deposite, bg="black", fg="white", justify="left").grid(row=4, column=1)
lblBankwith=Label(f1, font=("arial", 16, "bold"),fg="black",bg="powder blue",text="Bank_Withdraw", anchor="e",relief=GROOVE).grid(row=3,column=2)
withdrawx=Entry(f1,font=("arial",16, "bold"),bd=10, fg="white",bg="black", textvariable=withdraw, insertwidth=1).grid(row=4,column=2)
coating=Label(f1, font=("arial", 16, "bold"),text="coat_mm:", bg="powder blue",fg="black",anchor="e").grid(row=5,column=0)
coatx=Entry(f1, font=("arial", 16, "bold"), bg="black", fg="white",
textvariable=coat, insertwidth=1, justify="left",bd=10).grid(row=6,column=0)
lablsari=Label(f1,font=("arial", 16, "bold"), bg="powder blue",text="sari mm:", fg="black",anchor="e",relief=GROOVE).grid(row=5,column=1)
sarix=Entry(f1, font=("arial", 16, "bold"), bg="black",bd=10, fg="white",textvariable=sari, insertwidth=1).grid(row=6,column=1)
buying=Label(f1,font=("arial", 16, "bold"), bg="powder blue",text="buy_info:",fg="black",anchor="e",relief=GROOVE).grid(row=7,column=0)
buyx=Entry(f1,font=("arial",16, "bold"),bd=8, fg="white",bg="black",textvariable=buy,insertwidth=1).grid(row=8,column=0)
outgoing =Label(f1, font=("arial", 16, "bold"), bg="powder blue", text="outgoing:", fg="black",anchor="e",relief=GROOVE).grid(row=7,column=1)
outx=Entry(f1,font=("arial", 16, "bold"),textvariable=out, bd=8,fg="white",bg="black",insertwidth=1).grid(row=8,column=1)
ordering=Label(f1,font=("arial",16,"bold"),bg="powder blue",text="order_info:",fg="black",anchor="e",relief=GROOVE).grid(row=9,column=0)
orderx=Entry(f1,font=("arial",16,"bold"),insertwidth=1, textvariable=order,bd=8,fg="white",bg="black").grid(row=10,column=0)
lblcustomer=Label(f1,font=("arial",16,"bold"),bg="powder blue",text="cus_name:",fg="black",anchor="e",relief=GROOVE).grid(row=9,column=1)
no=Entry(f1,font=("arial",16, "bold"),bd=8,bg="black",fg="white",insertwidth=1, textvariable=cus_name).grid(row=10,column=1)
lblmonthly=Label(f1, font=("arial",16,"bold"),bg="powder blue",text="monthly:",fg="black",anchor="e",relief=GROOVE).grid(row=5,column=2)
monthly=StringVar()
monthx=Entry(f1,font=("arial",16,"bold"),show="blank",bg="black",textvariable=monthly,insertwidth=1,fg="white",bd=10).grid(row=6,column=2)
lbltotal=Label(f1, font=("arial", 16, "bold"),bg="powder blue",text="Total:",fg="black").grid(row=7,column=2)
totalx=Entry(f1, font=("arial", 16, "bold"),bg="black",textvariable=total,fg="white",insertwidth=1,bd=10).grid(row=8,column=2)
lblemployee = Label(f1,font=("arial", 16, "bold"),bg="powder blue",text="employee name:",fg="black",anchor="e",relief=GROOVE).grid(row=9,column=2)
employx= Entry(f1,font=("arial", 16,"bold"),textvariable=employee,insertwidth=1,bg="black",fg="white",bd=10).grid(row=10,column=2)
###############################database for the project######################
'''def __database():
db = TinyDB("/records.json")
#print(monthly)
#print(b)
#fuck = c.get()
a = order_bef.get()
b = stock_full.get()
c = shrting.get()
d = pant.get()
e = sari.get()
f = order_info.get()
g = delivery_report.get()
h = daily_info.get()
i = sales.get()
j = buy.get()
k = total_bank.get()
l = bank_deposite.get()
m = bank_withdraw.get()
n = due_amount.get()
o = order_info.get()
p = daily_cash.get()
q = cus_name.get()
r = cus_no.get()
s = employee.get()
files = {"a": "", "b": "", "c": "", "d": "", "e": "", "f": "", "g": "", "h": "", "i": "", "j": ""
, "k": "", "l": "", "m": "", "n": "", "o": "", "p": "", "q": "", "r": "", "s": ""}
db.insert({"total": a }),
db.insert({"regrds":"reference"}),
db.insert({"day_income":"billion"}),
db.insert({"day_outgoing":"billout"}),
db.insert({"bankdeposit":"bankdepo"}),
db.insert({"full_stock":"stock"}),
db.insert({"shirt_mm":"shirt"}),
db.insert({"bankwithdraw":"bankwith"}),
db.insert({"pantmm":"pant"}),
db.insert({"sarimm":"sari"}),
db.insert({"orderday":"orderinfo"}),
db.insert({"salling":"sales"}),
db.insert({"buying":"buy"}),
db.insert({"customern":"customer"}),
db.insert({"monthly_info":"monthly"}),
db.insert({"totaldy":"total"}),
db.insert({"employeid":"employee"})
for db in range(1):
print(db)
files = list(files)
file = open("/file.txt", "wb")
da = ""
for data in files:
if len(data) != 0:
print("this is are the files written in python\\n check the file.txt for debug ")
da += data
print(data)
da = int(da)
file.write(da)
try:
file = open("/records.txt", "r")
except:
print("creating the file from script {}".format(__file__))
file = open("/records.txt","w")
finally:
pass
check = os.path.isfile("/records.txt")
if check:
for item in db:
data = open("/records.txt","wb")
#with open("/records.txt","wb") as file:
#pickle.dump(item, data)
#file.close()
#file1 = pickle.load(file)
if len(item) == len(file1):
break
if item != file:
#item = str(item)
file.write("%s" %(item))
time.sleep(1)
print("done writing to the file")
#for item in db:
with open("/records.txt", "rb") as file:
reading = file1
if len(reading) != None:
print("its printed")
print(reading)
file.close()
#db.insert({"name":"<NAME>"})
name = Query()
#db(name.type == "changed")
d = datetime.now()
month = str(d.month)
day = str(d.day)
year = str(d.year)
hour = str(d.hour)
minute = str(d.minute)
second = str(d.second)
between = str(":")'''
'''def __time(infos):
time = datetime.now()
day = str(time.day)
month = str(time.month)
hour = str(time.hour)
second = str(time.second)
year = str(time.year)
minute = str(time.minute)
#assuming the infos as the order taken that will be notified before the
#60 hours
#changing all the formats to the seconds that will be easy for the #calculation
#first calculating seconds in one day that will ease all the further operations
daysec = (24*60) * 60 * 60
###
##this is will be easy now
yearSec = daysec * 365
month = daysec * 30
daySec = daysec
hourSec = 60 * 60 * 60
minuteSec = 60 * 60
files = {"a":"", "b":"","c":"","d":"","e":"","f":"","g":"","h":"","i":"","j":""
,"k":"","l":"","m":"","n":"","o":"","p":"","q":"","r":"","s":""}'''
#files = list(files)
'''for data in files:
if len(data) != 0:
print(data)'''
#lenght = len(db)
##this will show the recorded bill numbers
def bill_in():
##assuming the variable as bill number .get var
bill = bill_in.get()
billo = bill_out.get()
bills = tinydb.TinyDb("/bills.json")
while bill or billo != None:
bills.insert({"billInput": bill, "billOutput": billo})
win = Toplevel()
win.title("bills")
winF = Frame(win, bg="black",relief=SUNKEN).pack()
winE = Entry(winF, insertwidth=10,insertheight=10,fg="white",bg="black",textvariable=bills).pack()
win.mainloop()
#l
# command=bill_in).pack(anchor=NE)
root.mainloop()
#__database()
#add1=Button(f2,padx=16,pady=16,bd=8, fg="black", font=("arial",20,"bold"),
#text="+",bg="powder blue", command=lambda: btnClick("+")).grid(row=3,column=6)
#btn10=Button(f2,padx=16,padx=16, fg="blue", font("arial",5,"bold"),
# text="rupen",bg="powder blue", command=rupen).grid(row=3,column=5)
#def function():
# pass():
# pass main():
# root.mainloop()
#for the revies of the follow in the sorry of the same of the tkinter in the main function of the sollow
#main()
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Server/manifest_server.py | llmay98/WSI-online-analysis-framework | 2 | 121806 | <gh_stars>1-10
from flask import render_template, request, redirect
from flask import jsonify
from flask_login import login_required
from Controller import manifest_controller
from Controller import thread_controller
from Controller import image_processing
from Controller import dataset_controller
from Controller import annotation_project_controller
import os
import uuid
icon_root = 'static/data/slide_icon/'
def add_manifest_server(app):
@app.route('/slide_table')
@login_required
def slide_table():
page_no = request.args.get('page_no', default=1, type=int)
item_per_page = request.args.get('item_per_page', default=15, type=int)
total_page = (manifest_controller.get_total_number() + item_per_page - 1) // item_per_page
if total_page == 0:
page_no = 1
elif page_no <= 0:
page_no = 1
elif page_no > total_page:
page_no = total_page
return render_template('slide_table.html', page_no=page_no, total_page=total_page, item_per_page=item_per_page)
@app.route('/manifest_table_data')
@login_required
def table_data():
page_no = request.args.get('page_no', default=1, type=int)
item_per_page = request.args.get('item_per_page', default=15, type=int)
return jsonify(
manifest_controller.get_table(page_no * item_per_page - item_per_page, page_no * item_per_page, 0))
@app.route('/dzi_list_data')
@login_required
def dzi_list_data():
page_no = request.args.get('page_no', default=1, type=int)
item_per_page = request.args.get('item_per_page', default=15, type=int)
return jsonify(
manifest_controller.get_dzi_path(page_no * item_per_page - item_per_page, page_no * item_per_page))
@app.route('/refresh_manifest_table_data')
@login_required
def refresh_manifest_table_data():
thread_controller.BackgroundThread(manifest_controller.get_table).start()
return redirect('/manifest_table')
@app.route('/continue_slide_id')
@login_required
def continue_slide_id():
manifest_controller.continue_slide_id()
thread_controller.BackgroundThread(annotation_project_controller.refresh_npy).start()
return jsonify({"mas": 'None'})
@app.route('/remove_wsi')
@login_required
def remove_wsi():
slide_id = request.args.get('slide_id', type=int)
manifest_controller.remove_wsi_by_id(slide_id)
return jsonify({"info": "Removed Successfully !", "time": "1"})
@app.route('/uploader', methods=['GET', 'POST'])
@login_required
def uploader_file():
slide_uuid = request.form['uuid']
file = request.files['file']
print(file.filename[-3:])
if file.filename[-3:] == "txt":
if not os.path.exists('manifest_temp/'):
os.mkdir('manifest_temp/')
file.save('manifest_temp/' + file.filename)
dataset_controller.import_manifest('manifest_temp/' + file.filename)
return render_template('warning.html', info='file uploaded successfully')
else:
if slide_uuid == "":
slide_uuid = str(uuid.uuid4())
if not os.path.exists('Data/Original_data/' + slide_uuid):
os.mkdir('Data/Original_data/' + slide_uuid)
try:
file.save('Data/Original_data/' + slide_uuid + '/' + file.filename)
manifest_controller.add_wsi(slide_uuid, file.filename)
except Exception as e:
print(e)
return render_template('warning.html', info='file uploaded fail')
try:
if not os.path.exists(icon_root + slide_uuid + '/'):
os.mkdir(icon_root + slide_uuid + '/')
icon_file_path = icon_root + slide_uuid + '/' + 'icon.png'
image_processing.generate_icon_image_from_svs_file(
'Data/Original_data/' + slide_uuid + '/' + file.filename,
icon_file_path)
except Exception as e:
print(e)
return render_template('warning.html', info='file uploaded successfully')
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] |
lib/EXP_label.py | devsecops-SRC/CVE-Flow | 0 | 169242 | <reponame>devsecops-SRC/CVE-Flow
import requests
import json
import math
import re
from bs4 import BeautifulSoup
import codecs
import os
import random
from multiprocessing import Process,Pool
import pandas
import configparser
from sqlite3_operate import SQLite,create_table
from utils import path,time_delta,get_ua,list2tuple_dict
from seebug_crawler import seebug_headers
def cve_details_parser(url):
"""
单次解析，返回cve id的exp标记
:param url:
:return label:
:type:int
:value:1不存在，2存在且无exp，3存在且有msf exp标记，4存在且有public exploit,7存在且有msf和pub
"""
try:
r=requests.get(url,timeout=5).text
if re.search(r'(\d) public exploit',r):
if re.search(r'(\d) Metasploit modules',r):
label=7
else:
label=4
elif re.search(r'(\d) Metasploit modules',r):
label=3
elif re.search(r'Unknown CVE ID',r):
label=1
else:
label=2
return label
except Exception as e:
print("[!] Error %s %s in function `cve_details_parser`" %(url,str(e)))
return False
def cve_exists(db='nvd',table='nvd_cve',key='CVE_Items_cve_CVE_data_meta_ID'):
"""
取本地数据库已有的CVE id等单字段
:return exist_cid:
:type:list
:value:['1','2','3']
"""
so=SQLite('../data/{db}.db'.format(db=db))
sql='select {key} from {table}'.format(table=table,key=key)
exist_cid=so.query(sql)
if exist_cid:
exist_cid=[i[0] for i in exist_cid]
else:
exist_cid=[]
return so,exist_cid
def cve_details_exp():
"""
从cvedetails.com爬cve详情和exp label:public/msf
"""
create_table(db='cvedetail',table='cvedetail_exp',key=['cve_id','label'],primary_key='cve_id')
so,exist_cid=cve_exists(db='cvedetail',table='cvedetail_exp',key='cve_id')
so1,all_cid=cve_exists(db='nvd',table='nvd_cve',key='CVE_Items_cve_CVE_data_meta_ID')
add_cid=list(set(all_cid)^set(exist_cid))
cve_exp=dict()
for cid in add_cid:
url="https://www.cvedetails.com/cve/{}/".format(cid)
label=cve_details_parser(url)
print(url,label)
if label:
cve_exp[cid]=(cid,label)
sql='replace INTO cvedetail_exp (cve_id, label) VALUES (?, ?)'
so.executemany(sql,cve_exp.values())
return 'cvedetails.com',cve_exp
def nvd_nist_exp():
"""
从nvd.nist中reference source中提取CVE exp label
打标策略之一：exploit-db verified or verified
"""
so=SQLite('../data/nvd.db')
sql='select CVE_Items_cve_CVE_data_meta_ID from nvd_cve where CVE_Items_cve_references_reference_data_tags like "%Exploit%"'
r=so.query(sql)
ids=[i[0] for i in r]
cve_exp=dict()
for cid in ids:
url='https://nvd.nist.gov/vuln/detail/{}'.format(cid)
cve_exp[cid]=url
return 'nvd.nist.gov',cve_exp
def mitre_expdb_exp():
"""
从cve.mitre.org中提取CVE exp label，弥补nvd.nist中Resource中Exploit标记的不足
更新策略：全量更新，返回全部数据
:return cve_exp:
:type:dict
:value:{'cve-id':'edb-id'}
"""
hfile=path('../data/nvd',time_delta()+'source-EXPLOIT-DB.html')
if not os.path.exists(hfile):
r=requests.get('https://cve.mitre.org/data/refs/refmap/source-EXPLOIT-DB.html')
html=r.content
with codecs.open(hfile,'wb') as f:
f.write(html)
cve_exp=dict()
if os.path.exists(hfile):
with codecs.open(hfile,'rb') as f:
soup=BeautifulSoup(f,'html.parser')
for tr in soup.find_all('tr'):
exp_db=''
cve=''
for td in tr.find_all('td'):
t=str(td)
if re.search(r'EXPLOIT-DB:(\d+)',t):
r=re.search(r'EXPLOIT-DB:(\d+)',t)
exp_db=r.group(1)
elif re.search(r'(CVE-[\d]+-[\d]+)',t):
r=re.findall(r'(CVE-[\d]+-[\d]+)',t)
cve=[]
for c in r:
cve.append(c)
else:
continue
if exp_db and cve:
if isinstance(cve,list):
for c in cve:
cve_exp[c]=exp_db
else:
cve_exp[cve]=exp_db
return 'cve.mitre.org',cve_exp
def expdb_parser(eid):
"""
单次解析，返回单条数据
:param eid:
:return exp_values:
:type:dict
:value:{'EDB-ID:': '37074', 'CVE:': '2015-4039;2015-4038', 'Author:': '<NAME>', 'Type:': 'webapps', 'Platform:': 'PHP', 'Date:': '2015-05-21'}:
"""
exp_values=dict()
url='https://www.exploit-db.com/exploits/'+str(eid)
r=requests.get(url,headers={'User-Agent':get_ua()},timeout=10)
html=r.content
if r.status_code==200 and b'404 Page Not Found' not in html:
soup=BeautifulSoup(html,'html.parser')
for div in soup.find_all('div',class_='col-6 text-center'):
exp_value=list(div.stripped_strings)
if len(exp_value)>=2:
exp_values[exp_value[0]]=";".join(exp_value[1:])
else:
exp_values[exp_value[0]]=''
return exp_values
else:
return False
def offensive_download():
try:
url='https://raw.githubusercontent.com/offensive-security/exploitdb/master/files_exploits.csv'
r=requests.get(url)
with codecs.open('../data/exploit-db/offensive_expfile.csv','wb') as f:
f.write(r.content)
print("[+] DOWNLOAD NEW EXPLOIT-DB")
except Exception as e:
print("[!] DOWNLOAD %s error" %url)
def expdb_exists():
"""
从offensive Security github file exploits取最新的全量edb-id
"""
offensive_download()
if os.path.exists('../data/exploit-db/offensive_expfile.csv'):
exps=pandas.read_csv('../data/exploit-db/offensive_expfile.csv')
return list(exps['id'].values)
else:
return False
def expdb_exp(batch=False,batch_size=10):
"""
从exploit-db.com中提取CVE exp label，弥补不足
更新策略：增量覆盖
"""
create_table(db='exp2',table='expdb',key=['edb_id','cve_id','author','type','platform','date'],primary_key='edb_id')
so,exist_eid=cve_exists(db='exp2',table='expdb',key='edb_id')
all_eid=expdb_exists()
all_eid=[str(i) for i in all_eid]
add_eid=list(set(all_eid)^set(exist_eid))
print(add_eid)
j=0
cve_exp=dict()
for eid in add_eid:
try:
exp_values=expdb_parser(eid)
if exp_values:
cve_exp[eid]=tuple(exp_values.values())
print(cve_exp)
j=j+1
if batch:
if j%batch_size==0: ### todo
sql='replace INTO expdb (edb_id, cve_id, author, type, platform, date) VALUES (?, ?, ?, ?, ?, ?)'
so.executemany(sql,cve_exp.values())
else:
sql='replace INTO expdb (edb_id, cve_id, author, type, platform, date) VALUES (?, ?, ?, ?, ?, ?)'
so.execute(sql,tuple(exp_values.values()))
else:
print("[!] exp-values Not Found")
except Exception as e:
print("[!] DOWNLOAD ERROR %s error:%s" %(eid,repr(e)))
so.close()
class GitHub(object):
def __init__(self, **config_options):
self.__dict__.update(**config_options)
self.session = requests.Session()
if hasattr(self, 'api_token'):
self.session.headers['Authorization'] = 'token %s' % self.api_token
elif hasattr(self, 'username') and hasattr(self, 'password'):
self.session.auth = (self.username, self.password)
def call_to_the_api(self, *args):
try:
res=self.session.get(*args)
except Exception as e:
return
# do stuff with args
return res
def single_parser(gh,items):
"""
解析单个仓库：仓库名，描述，目录名，获取CVE和exp标记
"""
cve_list=[]
for item in items:
name=item['name']
description=item['description']
content_url=item['contents_url']
name=re.search(r'CVE-(\d+)-(\d+)',name)
description=re.search(r'CVE-(\d+)-(\d+)',description)
if name:
cve_list.append(name.group(0))
continue
elif description:
cve_list.append(description.group(0))
continue
elif content_url:
r=gh.call_to_the_api(content_url)
content=r.text
match=re.findall(r'(CVE-[\d]+-[\d]+)',content)
if match:
match=list(set(match))
cve_list.extend(match)
cve_list=list(set(cve_list))
return cve_list
def single_request(gh,kname='CVE exploit',page=1,per_page=50):
"""
解析单页仓库数据，获取CVE和exp标记
"""
cve_list=[]
url="https://api.github.com/search/repositories?q={key_name}&sort=updated&order=desc&page={page}&per_page={per_page}".format(key_name=kname,page=page,per_page=per_page)
r=gh.call_to_the_api(url)
if r:
content=r.text
js=json.loads(content)
items=js['items']
cve_list.extend(single_parser(gh,items))
total_count=js['total_count']
return total_count,cve_list
else:
return False,False
def max_total_count(gh,kname='CVE exploit',page=1,per_page=50):
"""
一次解析，返回检索结果总条数
"""
cve_list=[]
url="https://api.github.com/search/repositories?q={key_name}&sort=updated&order=desc&page={page}&per_page={per_page}".format(key_name=kname,page=page,per_page=per_page)
r=gh.call_to_the_api(url)
if r:
content=r.text
js=json.loads(content)
total_count=js['total_count']
return total_count
else:
return
def github_exp(api_token,per_page=100):
"""
根据(多组)关键词搜索github上cve和exp标记
更新策略：支持存量和增量更新
"""
# 本地和远程对比->差集
create_table(db='github',table='github_exp',key=['cve_id','label'],primary_key='cve_id') ## 选择sqlite3 而不是json存储的原因在易于扩展和检索
so,exist_cid=cve_exists(db='github',table='github_exp',key='cve_id')
conf=configparser.ConfigParser()
conf.read('conf/info.conf')
local_count=conf.get('CVE_Label','total_count')
key_name=conf.get('CVE_Label','search_key')
gh=GitHub(api_token=api_token)
total_count=max_total_count(gh=gh,kname=key_name,per_page=per_page)
cve_list=[]
if total_count:
if total_count>1000:
total_count=1000
add_count=total_count-int(local_count)
print("local num:%s,total num:%s,add num:%s" %(local_count,total_count,add_count))
# 补全差集
page_count=math.ceil(add_count/per_page)
if page_count>0:
for page in range(page_count,-1,-1):
t,c=single_request(gh=gh,kname=key_name,per_page=per_page,page=page)
if t:
print(c)
cve_list.extend(c)
else:
print("[!] Error in call_to_the_api")
else:
print("[!] WARNING:No Updated in function github_cve")
# 插入sqlite3
if page_count>0:
cve_tuple=list2tuple_dict(cve_list,label=1)
sql='replace INTO github_exp (cve_id, label) VALUES (?, ?)'
so.executemany(sql,cve_tuple.values())
conf.set('CVE_Label','total_count',str(total_count))
with open('conf/info.conf', 'w') as configfile:
conf.write(configfile)
else:
pass
else:
print("[!] Error in call_to_the_api")
return
return cve_list
def seebug_page_parser(url,headers):
"""
解析seebug单页，获取CVE exp label,返回多条数据
:return cve_page:
:type:dict
:value:{'cve_id1':0,'cve_id2':1,'cve_idN':0 or 1}
"""
r=requests.get(url,headers=headers)
soup=BeautifulSoup(r.content,'html.parser')
cve_page=dict()
for td_date in soup.find_all('td',class_='text-center datetime hidden-sm hidden-xs'):
date=td_date.text
td_status=td_date.find_next('td',class_='text-center hidden-sm hidden-xs ')
i=td_status.find_all('i')[0]
cve_id=re.search(r'(CVE-[\d]+-[\d]+)',str(i))
if cve_id:
cid=cve_id.group(0)
if " ".join(td_status.find_all('i')[1].get('class'))=='fa fa-rocket text-muted ':
label=0
elif " ".join(td_status.find_all('i')[1].get('class'))=='fa fa-rocket ':
label=1
else:
print("[!] WARNING poc_value is not 0 or 1")
cve_page[cid]=(cid,label,date)
else:
# 非cve的漏洞
pass
return cve_page
def seebug_max_page(url='https://www.seebug.org/vuldb/vulnerabilities'):
"""
获取seebug漏洞数据的最大页面数
:return max_page:
:type:int
:value:N
:return headers:
:type:dict
:value:浏览器头
"""
headers=seebug_headers(url)
r=requests.get(url,headers=headers)
regex=re.compile(r'page=(\d+)')
num=re.findall(regex,r.text)
num=[int(i) for i in num]
max_page=max(num)
print("[+] Got max page:%d" %max_page)
return max_page,headers
def seebug_exp(stock=True):
"""
爬取seebug全部cve数据
:return source:
:type:str
:value:'seebug'
:return cve_seebug:
:type:dict
:value:seebug全部cve数据，形如{'CVE-2020-0001':0}
"""
create_table(db='seebug',table='seebug_exp',key=['cve_id','label','date'],primary_key='cve_id')
so,exist_cid=cve_exists(db='seebug',table='seebug_exp',key='cve_id')
max_page,headers=seebug_max_page()
cve_seebug=dict()
if stock:
for i in range(1,max_page):
vul_url='https://www.seebug.org/vuldb/vulnerabilities?page={}'.format(str(i))
cve_page=seebug_page_parser(url=vul_url,headers=headers)
print("[+] Got page:%d" %(i))
cve_seebug={**cve_seebug,**cve_page}
sql='replace INTO seebug_exp (cve_id, label, date) VALUES (?, ?, ?)'
so.executemany(sql,cve_seebug.values())
else:
page=1
et=0
while(page>0):
vul_url='https://www.seebug.org/vuldb/vulnerabilities?page={}'.format(str(page))
cve_page=seebug_page_parser(url=vul_url,headers=headers)
for i in cve_page.values():
if i[0] in exist_cid:
et=et+1
if et>0:
sql='replace INTO seebug_exp (cve_id, label, date) VALUES (?, ?, ?)'
so.executemany(sql,cve_page.values())
cve_seebug={**cve_seebug,**cve_page}
break
else:
page=page+1
return 'seebug',cve_seebug
def securityfocus_exp():
"""
security_focus exp label
"""
def symantic_exp():
"""
symantic exp label
"""
if __name__=='__main__':
#b,nexp=nvd_nist_exp()
#c,mexp=mitre_expdb_exp()
#print(set(list(mitre_expdb_exp()[1].keys()))^set(list(nvd_nist_exp()[1].keys())))
#expdb_exp()
#cve_details_exp()
# github:res/code/commits/issues->cve exp label and github link
#cve_list=github_exp(api_token='<PASSWORD>')
#print(cve_list)
# other vul db, such as seebug, 0day.today etc.
#print(seebug_exp(stock=False))
# SecurityFocus
# in the wild
pass
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] |
launch_jobs/ecs_control.py | rhodricusack/docker-hcp | 0 | 107000 | <reponame>rhodricusack/docker-hcp
import boto3
import os
import time
def register_task(client):
response = client.register_task_definition(
executionRoleArn='arn:aws:iam::807820536621:role/ecsTaskExecutionRole',
family='roi-extract',
taskRoleArn='arn:aws:iam::807820536621:role/cusacklab_elasticcontainer_service_task',
networkMode='awsvpc',
containerDefinitions=[
{
'name': 'roi-extract',
'image': "807820536621.dkr.ecr.eu-west-1.amazonaws.com/roi-extract:latest",
'essential': True,
'command': [
'echo testing',
],
'logConfiguration': {
'logDriver': 'awslogs',
'options': {
"awslogs-group": "/ecs/roi-extract",
"awslogs-region": "eu-west-1",
"awslogs-stream-prefix": "ecs"
},
},
'secrets': [
{
"valueFrom": "arn:aws:secretsmanager:eu-west-1:807820536621:secret:aws/hcp-B9P9EV",
"name": "HCP_KEYS"
}
]
},
],
requiresCompatibilities=[
'FARGATE',
],
cpu='1024',
memory='4096',
tags=[
{
'key': 'PrimaryUser',
'value': 'rhodricusack'
},
{
'key': 'SystemComponent',
'value': 'cusacklab'
},
],
)
return response
def run_task(client=None, command=None):
response = client.run_task(
cluster='cusacklab-fargate-cluster',
count=1,
launchType='FARGATE',
networkConfiguration={
'awsvpcConfiguration': {
'subnets': [ "subnet-0e1d25cdf492ff98b", "subnet-06311e579ae22eae0"],
'securityGroups': ['sg-01743b5e88320d81a'],
'assignPublicIp': 'ENABLED'
}
},
overrides={
'containerOverrides': [
{
'name': 'roi-extract',
'command': command
},
],
'executionRoleArn': 'arn:aws:iam::807820536621:role/ecsTaskExecutionRole',
'taskRoleArn': 'arn:aws:iam::807820536621:role/cusacklab_elasticcontainer_service_task'
},
platformVersion='LATEST',
startedBy='neurana-python',
tags=[
{
'key': 'PrimaryUser',
'value': 'chiaracaldinelli' # put your username in here!
},
{
'key': 'SystemComponent',
'value': 'cusacklab'
},
],
taskDefinition='roi-extract'
)
return response
def wait_for_completion(client=None, taskresponses=None, waitfor = 3600, delay=6.0):
stages = ['PROVISIONING', 'PENDING', 'RUNNING', 'DEPROVISIONING', 'STOPPED']
for attempt in range(int(waitfor/delay)): # Twelve hours max
all_status=[]
for taskresponse in taskresponses:
if taskresponse['tasks']:
cluster = taskresponse['tasks'][0]['clusterArn']
tasks =[x['taskArn'] for x in taskresponse['tasks']]
status = client.describe_tasks(cluster = cluster, tasks=tasks)
for task in status['tasks']:
for container in task['containers']:
all_status.append({x:container[x] for x in ('lastStatus', 'exitCode', 'reason') if x in container})
all_status[-1]['task_lastStatus']=task['lastStatus']
njobs = len(all_status)
stage_count = {x:0 for x in stages}
for task in all_status:
stage_count[task['lastStatus']] +=1
exit0 = sum([x['exitCode']==0 for x in all_status if 'exitCode' in x])
for stage in stages:
print(f'{stage}:{stage_count[stage]} ', end = ' ')
print(f'{exit0}/{njobs} completed successfully')
if njobs == stage_count['STOPPED']:
print(f'All jobs have stopped, {exit0} successfully and {len(all_status)-exit0} with errors')
return
time.sleep(delay)
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] |
ucf_sub_catkin_ros/src/sub_states/src/qual/test.py | RoboticsClubatUCF/RoboSub | 0 | 16780 | #!/usr/bin/env python
import rospy
import smach
import gate
import pole
class SubStates:
def __init__(self):
rospy.loginfo("State Machine has started.")
self.gate = smach.StateMachine(outcomes=['preempted', 'POLE', 'GATE'])
self.pole = smach.StateMachine(outcomes=['preempted', 'GATE', 'POLE'])
self.tasks = smach.StateMachine(outcomes=['POLE', 'GATE', 'preempted', self.gate, self.pole])
with self.tasks:
smach.StateMachine.add('Start', self.pole, transitions={'POLE':self.pole, 'GATE':self.gate})
with self.gate:
smach.StateMachine.add('LOCATE', gate.locate(),
transitions={'preempted':'preempted',
'success': 'ALIGN',
'failure': 'LOCATE'})
smach.StateMachine.add('ALIGN', gate.align(),
transitions={'preempted':'preempted',
'success': 'THROUGH',
'failure': 'LOCATE'})
smach.StateMachine.add('THROUGH', gate.through(),
transitions={'preempted':'preempted',
'success': 'POLE',
'failure':'LOCATE'})
with self.pole:
smach.StateMachine.add('LOCATE', pole.locate(),
transitions={'preempted':'preempted',
'success': 'ALIGN',
'failure': 'LOCATE'})
smach.StateMachine.add('ALIGN', pole.align(),
transitions={'preempted':'preempted',
'success': 'DRIFT',
'failure': 'LOCATE'})
smach.StateMachine.add('DRIFT', pole.drift(),
transitions={'preempted':'preempted',
'success': 'GATE',
'failure': 'LOCATE'})
if __name__ == '__main__':
rospy.init_node('hippo_sm')
sm = SubStates()
outcome = sm.tasks.execute()
rospy.spin()
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] |
stamper/hashes.py | ISTU-Labs/stumper | 0 | 199286 | import bcrypt
import mmh3 as mmh3
def get_hashed_password(plain_text_password):
# Хэширует пароль в фазе его задания
# Используется модуль bcrypt, соль записывается в сам хэш
plain_text_password = as_bytes(plain_text_password)
return bcrypt.hashpw(plain_text_password, bcrypt.gensalt()).decode("utf-8")
def check_password(plain_text_password, hashed_password):
# Проверка пароля. В модуле bcrypt соль записана в самом хеше
plain_text_password = as_bytes(plain_text_password)
print(type(plain_text_password), type(hashed_password))
return bcrypt.checkpw(plain_text_password, hashed_password.encode("utf-8"))
def as_bytes(s):
if type(s) == str:
return s.encode('utf-8')
return s
def hexdigest(digest):
"""Convert byte digest to
hex digest
Arguments:
- `digest`: Byte array representing
digest
"""
if type(digest) in (tuple, list):
digest = joindigest(digest)
if type(digest) == str:
return digest # implied, that string is a digest already
if type(digest) == int:
digest = bindigest(digest)
return ''.join(["{:02x}".format(b) for b in digest])
def bindigest(digest, bs=16):
if type(digest) in (tuple, list):
digest = joindigest(digest)
if type(digest) == str:
return bytearray.fromhex(digest)
if type(digest) == int:
digest = digest.to_bytes(bs, byteorder='little')
return digest
def intdigest(digest):
if type(digest) in (tuple, list):
digest = joindigest(digest)
if type(digest) == int:
return digest
if type(digest) == str:
digest = bytearray.fromhex(digest)
return int.from_bytes(digest, byteorder='little')
def hash128(content):
return mmh3.hash_bytes(content)
def hash128_int(content):
return intdigest(hash128(content))
def splitdigest(digest):
"""Splits 128bit hash into two
64bit numbers."""
d = bindigest(digest)
l, h = intdigest(d[:8]), intdigest(d[8:])
return l, h
two64 = 1 << 64
def joindigest(digest):
l, h = digest
if l < 0:
l = two64 - l
if h < 0:
h = two64 - h
l = bindigest(l, bs=8)
h = bindigest(h, bs=8)
return l + h
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] |
hub_module/tests/unittests/test_ernie_skep_sentiment_analysis.py | 18621579069/PaddleHub-yu | 4 | 1612336 | <reponame>18621579069/PaddleHub-yu<filename>hub_module/tests/unittests/test_ernie_skep_sentiment_analysis.py
# Copyright (c) 2020 PaddlePaddle Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import os
from unittest import TestCase, main
os.environ['CUDA_VISIBLE_DEVICES'] = '1'
import numpy as np
import paddlehub as hub
class ErnieSkepSentimentAnalysisTestCase(TestCase):
def setUp(self):
self.module = hub.Module(name='ernie_skep_sentiment_analysis')
self.test_text = [[
'飞桨（PaddlePaddle）是国内开源产业级深度学习平台', 'PaddleHub是飞桨生态的预训练模型应用工具'
], ["飞浆PaddleHub"]]
self.test_data = ['你不是不聪明，而是不认真', '虽然小明很努力，但是他还是没有考100分']
self.results = [{
'text': '你不是不聪明，而是不认真',
'sentiment_label': 'negative',
'positive_probs': 0.10738213360309601,
'negative_probs': 0.8926178216934204
},
{
'text': '虽然小明很努力，但是他还是没有考100分',
'sentiment_label': 'negative',
'positive_probs': 0.053915347903966904,
'negative_probs': 0.9460846185684204
}]
def test_predict_sentiment(self):
results_1 = self.module.predict_sentiment(self.test_data, use_gpu=False)
results_2 = self.module.predict_sentiment(self.test_data, use_gpu=True)
for index, res in enumerate(results_1):
self.assertEqual(res['text'], self.results[index]['text'])
self.assertEqual(res['sentiment_label'],
self.results[index]['sentiment_label'])
self.assertTrue(
abs(res['positive_probs'] -
self.results[index]['positive_probs']) < 1e-6)
self.assertTrue(
abs(res['negative_probs'] -
self.results[index]['negative_probs']) < 1e-6)
self.assertEqual(res['text'], results_2[index]['text'])
self.assertEqual(res['sentiment_label'],
results_2[index]['sentiment_label'])
self.assertTrue(
abs(res['positive_probs'] -
results_2[index]['positive_probs']) < 1e-6)
self.assertTrue(
abs(res['negative_probs'] -
results_2[index]['negative_probs']) < 1e-6)
def test_get_embedding(self):
# test batch_size
max_seq_len = 128
results = self.module.get_embedding(
texts=self.test_text,
use_gpu=False,
batch_size=1,
max_seq_len=max_seq_len)
results_2 = self.module.get_embedding(
texts=self.test_text,
use_gpu=False,
batch_size=10,
max_seq_len=max_seq_len)
# 2 sample results
self.assertEqual(len(results), 2)
self.assertEqual(len(results_2), 2)
# sequence embedding and token embedding results per sample
self.assertEqual(len(results[0]), 2)
self.assertEqual(len(results_2[0]), 2)
# sequence embedding shape
self.assertEqual(results[0][0].shape, (1024, ))
self.assertEqual(results_2[0][0].shape, (1024, ))
# token embedding shape
self.assertEqual(results[0][1].shape, (max_seq_len, 1024))
self.assertEqual(results_2[0][1].shape, (max_seq_len, 1024))
# test gpu
results_3 = self.module.get_embedding(
texts=self.test_text,
use_gpu=True,
batch_size=1,
max_seq_len=max_seq_len)
diff = np.abs(results[0][0] - results_3[0][0])
self.assertTrue((diff < 1e-6).all)
diff = np.abs(results[0][1] - results_3[0][1])
self.assertTrue((diff < 1e-6).all)
diff = np.abs(results[1][0] - results_3[1][0])
self.assertTrue((diff < 1e-6).all)
diff = np.abs(results[1][1] - results_3[1][1])
self.assertTrue((diff < 1e-6).all)
def test_get_params_layer(self):
self.module.context()
layers = self.module.get_params_layer()
layers = list(set(layers.values()))
true_layers = [i for i in range(24)]
self.assertEqual(layers, true_layers)
def test_get_spm_path(self):
self.assertEqual(self.module.get_spm_path(), None)
def test_get_word_dict_path(self):
self.assertEqual(self.module.get_word_dict_path(), None)
def test_get_vocab_path(self):
vocab_path = self.module.get_vocab_path()
true_vocab_path = os.path.join(self.module.directory, "assets",
"ernie_1.0_large_ch.vocab.txt")
self.assertEqual(vocab_path, true_vocab_path)
if __name__ == '__main__':
main()
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] |
hatsploit/utils/string.py | sunmughan/rat | 0 | 142802 | #!/usr/bin/env python3
#
# MIT License
#
# Copyright (c) 2020-2021 EntySec
#
# Permission is hereby granted, free of charge, to any person obtaining a copy
# of this software and associated documentation files (the "Software"), to deal
# in the Software without restriction, including without limitation the rights
# to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
# copies of the Software, and to permit persons to whom the Software is
# furnished to do so, subject to the following conditions:
#
# The above copyright notice and this permission notice shall be included in all
# copies or substantial portions of the Software.
#
# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
# IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
# FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
# AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
# LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
# OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
# SOFTWARE.
#
import re
import base64
import random
import string
##############################################################
# Lempel-Ziv-Stac decompression
# BitReader and RingList classes
#
# Copyright (C) 2011 <NAME> - FiloSottile
# filosottile.wiki gmail.com - www.pytux.it
#
# This program is free software: you can redistribute it and/or modify
# it under the terms of the GNU General Public License as published by
# the Free Software Foundation, either version 3 of the License, or
# (at your option) any later version.
#
# This program is distributed in the hope that it will be useful,
# but WITHOUT ANY WARRANTY; without even the implied warranty of
# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
# GNU General Public License for more details.
#
# You should have received a copy of the GNU General Public License
# along with this program. If not, see <http://www.gnu.org/licenses/>.
#
##############################################################
import collections
class BitReader:
def __init__(self, data_bytes):
self._bits = collections.deque()
for byte in data_bytes:
for n in range(8):
self._bits.append(bool((byte >> (7 - n)) & 1))
def getBit(self):
return self._bits.popleft()
def getBits(self, num):
res = 0
for i in range(num):
res += self.getBit() << num - 1 - i
return res
def getByte(self):
return self.getBits(8)
def __len__(self):
return len(self._bits)
class RingList:
def __init__(self, length):
self.__data__ = collections.deque()
self.__full__ = False
self.__max__ = length
def append(self, x):
if self.__full__:
self.__data__.popleft()
self.__data__.append(x)
if self.size() == self.__max__:
self.__full__ = True
def get(self):
return self.__data__
def size(self):
return len(self.__data__)
def maxsize(self):
return self.__max__
def __getitem__(self, n):
if n >= self.size():
return None
return self.__data__[n]
def LZSDecompress(data, window=RingList(2048)):
reader = BitReader(data)
result = ''
while True:
bit = reader.getBit()
if not bit:
char = reader.getByte()
result += chr(char)
window.append(char)
else:
bit = reader.getBit()
if bit:
offset = reader.getBits(7)
if offset == 0:
# EOF
break
else:
offset = reader.getBits(11)
lenField = reader.getBits(2)
if lenField < 3:
length = lenField + 2
else:
lenField <<= 2
lenField += reader.getBits(2)
if lenField < 15:
length = (lenField & 0x0f) + 5
else:
lenCounter = 0
lenField = reader.getBits(4)
while lenField == 15:
lenField = reader.getBits(4)
lenCounter += 1
length = 15 * lenCounter + 8 + lenField
for i in range(length):
char = window[-offset]
result += chr(char)
window.append(char)
return result, window
class StringTools:
@staticmethod
def extract_strings(binary_data):
strings = re.findall("[^\x00-\x1F\x7F-\xFF]{4,}", binary_data)
return strings
@staticmethod
def xor_string(string):
result = ""
for c in string:
result += chr(ord(c) ^ len(string))
return result
@staticmethod
def base64_string(string):
base64.b64encode(string.encode())
return string.decode()
@staticmethod
def random_string(length=16, alphabet=string.ascii_letters + string.digits):
return "".join(random.choice(alphabet) for _ in range(length))
@staticmethod
def lzs_decompress(data, window=RingList(2048)):
result, window = LZSDecompress(data, window)
return result
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] |
Curso_de_Python_ Curso_em_Video/PythonExercicios/ex113.py | DanilooSilva/Cursos_de_Python | 0 | 71162 | def leiaInt(mgn):
while True:
try:
n = int(input(mgn))
except (ValueError, TypeError):
print('\033[031mErro: por favor, digite um número interio válido.\033[m')
else:
return n
break
def leiaFloat(mgn):
while True:
try:
n = float(input(mgn))
except (ValueError, TypeError):
print('\033[031mErro: por favor, digite um número real válido.\033[m')
else:
return f'{n:.2f}'
break
nu = leiaInt('Digite um valor inteiro: ')
fl = leiaFloat('Digite um número real: ')
print(f'O valor Inteiro é {nu} e o número real é {fl}') | [
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1579,
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2
] |
prog/python/python tuto/snapper/Snapper.py | gribdesbois/prog-backup | 0 | 1606068 |
#handle text display
def display_scoreboard_data(scoreboard_text, alignment):
display_text = font.render(scoreboard_text, True, WHITE)
text_rect = display_text.get_rect()
text_loc = [0, 0]
if alignment == 'Left':
text_loc = [SCOREBOARD_MARGIN, SCREEN_HEIGHT - SCOREBOARD_HEIGHT]
elif alignment == 'Centre':
text_loc = [(SCREEN_WIDTH - text_rect.width) / 2, SCREEN_HEIGHT - SCOREBOARD_HEIGHT]
game_screen.blit(display_text, text_loc)
#display end of game message
def display_game_over():
game_over_rect = (3 * SCOREBOARD_HEIGHT, 8 * SCOREBOARD_HEIGHT, SCREEN_WIDTH - SCOREBOARD_HEIGHT * 6, SCOREBOARD_HEIGHT * 5)
pygame.draw.rect(game_screen, DARK_GREEN, game_over_rect)
text_line_1 = font.render('GAME OVER', True, WHITE)
text_rect_1 = text_line_1.get_rect()
text_line_1_loc = [(SCREEN_WIDTH - text_rect_1.width) / 2, (SCREEN_HEIGHT / 2) - 16]
text_line_2 = font.render('HIT RETURN FOR A NEW GAME', True, WHITE)
text_rect_2 = text_line_2.get_rect()
text_line_2_loc = [(SCREEN_WIDTH - text_rect_2.width) / 2, (SCREEN_HEIGHT / 2) + 16]
game_screen.blit(text_line_1, text_line_1_loc)
game_screen.blit(text_line_2, text_line_2_loc)
if __name__ == '__main__':
main()
| [
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utopian.py | tolgahanuzun/curation | 0 | 132104 | <reponame>tolgahanuzun/curation
import requests
from bs4 import BeautifulSoup
from utils import avaible_link
utopian_link = 'https://utopian.rocks/queue'
exclude = 'https://steemit.com/utopian-io/@amosbastian/developing-the-new-utopian-bot'
def run():
r = requests.get(utopian_link)
data = r.text
soup = BeautifulSoup(data)
contributions = soup.find('div', {'class' :'contributions'})
data = []
for link in soup.find_all('a'):
link = link.get('href')
if avaible_link(link):
data.append(link)
if exclude in data:
data.remove(exclude)
return data
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pysal/explore/pointpats/centrography.py | ocefpaf/pysal | 1 | 72736 | """
Centrographic measures for point patterns
TODO
- testing
- documentation
"""
__author__ = "<NAME> <EMAIL>"
__all__ = ['mbr', 'hull', 'mean_center', 'weighted_mean_center',
'manhattan_median', 'std_distance', 'euclidean_median', 'ellipse',
'skyum', 'dtot',"_circle"]
import sys
import numpy as np
import warnings
import copy
from math import pi as PI
from scipy.spatial import ConvexHull
from pysal.lib.cg import get_angle_between, Ray, is_clockwise
from scipy.spatial import distance as dist
from scipy.optimize import minimize
not_clockwise = lambda x: not is_clockwise(x)
MAXD = sys.float_info.max
MIND = sys.float_info.min
def mbr(points):
"""
Find minimum bounding rectangle of a point array.
Parameters
----------
points : arraylike
(n,2), (x,y) coordinates of a series of event points.
Returns
-------
min_x : float
leftmost value of the vertices of minimum bounding rectangle.
min_y : float
downmost value of the vertices of minimum bounding rectangle.
max_x : float
rightmost value of the vertices of minimum bounding rectangle.
max_y : float
upmost value of the vertices of minimum bounding rectangle.
"""
points = np.asarray(points)
min_x = min_y = MAXD
max_x = max_y = MIND
for point in points:
x, y = point
if x > max_x:
max_x = x
if x < min_x:
min_x = x
if y > max_y:
max_y = y
if y < min_y:
min_y = y
return min_x, min_y, max_x, max_y
def hull(points):
"""
Find convex hull of a point array.
Parameters
----------
points: arraylike
(n,2), (x,y) coordinates of a series of event points.
Returns
-------
_ : array
(h,2), points defining the hull in counterclockwise order.
"""
points = np.asarray(points)
h = ConvexHull(points)
return points[h.vertices]
def mean_center(points):
"""
Find mean center of a point array.
Parameters
----------
points: arraylike
(n,2), (x,y) coordinates of a series of event points.
Returns
-------
_ : array
(2,), (x,y) coordinates of the mean center.
"""
points = np.asarray(points)
return points.mean(axis=0)
def weighted_mean_center(points, weights):
"""
Find weighted mean center of a marked point pattern.
Parameters
----------
points : arraylike
(n,2), (x,y) coordinates of a series of event points.
weights : arraylike
a series of attribute values of length n.
Returns
-------
_ : array
(2,), (x,y) coordinates of the weighted mean center.
"""
points, weights = np.asarray(points), np.asarray(weights)
w = weights * 1. / weights.sum()
w.shape = (1, len(points))
return np.dot(w, points)[0]
def manhattan_median(points):
"""
Find manhattan median of a point array.
Parameters
----------
points : arraylike
(n,2), (x,y) coordinates of a series of event points.
Returns
-------
_ : array
(2,), (x,y) coordinates of the manhattan median.
"""
points = np.asarray(points)
if not len(points) % 2:
s = "Manhattan Median is not unique for even point patterns."
warnings.warn(s)
return np.median(points, axis=0)
def std_distance(points):
"""
Calculate standard distance of a point array.
Parameters
----------
points : arraylike
(n,2), (x,y) coordinates of a series of event points.
Returns
-------
_ : float
standard distance.
"""
points = np.asarray(points)
n, p = points.shape
m = points.mean(axis=0)
return np.sqrt(((points*points).sum(axis=0)/n - m*m).sum())
def ellipse(points):
"""
Calculate parameters of standard deviational ellipse for a point pattern.
Parameters
----------
points : arraylike
(n,2), (x,y) coordinates of a series of event points.
Returns
-------
_ : float
semi-major axis.
_ : float
semi-minor axis.
theta : float
clockwise rotation angle of the ellipse.
Notes
-----
Implements approach from:
https://www.icpsr.umich.edu/CrimeStat/files/CrimeStatChapter.4.pdf
"""
points = np.asarray(points)
n, k = points.shape
x = points[:, 0]
y = points[:, 1]
xd = x - x.mean()
yd = y - y.mean()
xss = (xd * xd).sum()
yss = (yd * yd).sum()
cv = (xd * yd).sum()
num = (xss - yss) + np.sqrt((xss - yss)**2 + 4 * (cv)**2)
den = 2 * cv
theta = np.arctan(num / den)
cos_theta = np.cos(theta)
sin_theta = np.sin(theta)
n_2 = n - 2
sd_x = (2 * (xd * cos_theta - yd * sin_theta)**2).sum() / n_2
sd_y = (2 * (xd * sin_theta - yd * cos_theta)**2).sum() / n_2
return np.sqrt(sd_x), np.sqrt(sd_y), theta
def dtot(coord, points):
"""
Sum of Euclidean distances between event points and a selected point.
Parameters
----------
coord : arraylike
(x,y) coordinates of a point.
points : arraylike
(n,2), (x,y) coordinates of a series of event points.
Returns
-------
d : float
sum of Euclidean distances.
"""
points = np.asarray(points)
xd = points[:, 0] - coord[0]
yd = points[:, 1] - coord[1]
d = np.sqrt(xd*xd + yd*yd).sum()
return d
def euclidean_median(points):
"""
Calculate the Euclidean median for a point pattern.
Parameters
----------
points: arraylike
(n,2), (x,y) coordinates of a series of event points.
Returns
-------
_ : array
(2,), (x,y) coordinates of the Euclidean median.
"""
points = np.asarray(points)
start = mean_center(points)
res = minimize(dtot, start, args=(points,))
return res['x']
def skyum(points, not_hull=True):
"""
Implements Skyum (1990)'s algorithm for the minimum bounding circle in R^2.
0. Store points clockwise.
1. Find p in S that maximizes angle(prec(p), p, succ(p) THEN radius(prec(p),
p, succ(p)). This is also called the lexicographic maximum, and is the last
entry of a list of (radius, angle) in lexicographical order.
2a. If angle(prec(p), p, succ(p)) <= 90 degrees, then finish.
2b. If not, remove p from set.
"""
points = hull(points).tolist()
if not_clockwise(points):
points.reverse()
if not_clockwise(points):
raise Exception('Points are neither clockwise nor counterclockwise')
POINTS = copy.deepcopy(points)
removed = []
i=0
while True:
angles = [_angle(_prec(p, points), p, _succ(p, points)) for p in points]
circles = [_circle(_prec(p, points), p, _succ(p, points)) for p in points]
radii = [c[0] for c in circles]
lexord = np.lexsort((radii, angles)) #confusing as hell defaults...
lexmax = lexord[-1]
candidate = (_prec(points[lexmax], points),
points[lexmax],
_succ(points[lexmax], points))
if angles[lexmax] <= PI/2.0:
#print("Constrained by points: {}".format(candidate))
return _circle(*candidate), points, removed, candidate
else:
try:
removed.append((points.pop(lexmax), i))
except IndexError:
raise Exception("Construction of Minimum Bounding Circle failed!")
i+=1
def _angle(p,q,r):
"""
compute the positive angle formed by PQR
"""
return np.abs(get_angle_between(Ray(q,p),Ray(q,r)))
def _prec(p,l):
"""
retrieve the predecessor of p in list l
"""
pos = l.index(p)
if pos-1 < 0:
return l[-1]
else:
return l[pos-1]
def _succ(p,l):
"""
retrieve the successor of p in list l
"""
pos = l.index(p)
if pos+1 >= len(l):
return l[0]
else:
return l[pos+1]
def _circle(p,q,r, dmetric=dist.euclidean):
"""
Returns (radius, (center_x, center_y)) of the circumscribed circle by the
triangle pqr.
note, this does not assume that p!=q!=r
"""
px,py = p
qx,qy = q
rx,ry = r
if np.allclose(np.abs(_angle(p,q,r)), PI):
radius = dmetric(p,r)/2.
center_x = (px + rx)/2.
center_y = (py + ry)/2.
elif np.allclose(np.abs(_angle(p,q,r)), 0):
radius = dmetric(p,q)/2.
center_x = (px + qx)/2.
center_y = (py + qy)/2.
else:
D = 2*(px*(qy - ry) + qx*(ry - py) + rx*(py - qy))
center_x = ((px**2 + py**2)*(qy-ry) + (qx**2 + qy**2)*(ry-py)
+ (rx**2 + ry**2)*(py-qy)) / float(D)
center_y = ((px**2 + py**2)*(rx-qx) + (qx**2 + qy**2)*(px-rx)
+ (rx**2 + ry**2)*(qx-px)) / float(D)
radius = dmetric((center_x, center_y), p)
return radius, (center_x, center_y)
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toutiao-backend/toutiao/resources/user/profile.py | weiyunfei520/toutiao | 0 | 34324 | from flask import current_app
from flask import g
from flask import request
from flask_restful.reqparse import RequestParser
from flask_restful import Resource
from models import db
from models.user import User
from utils.decorators import login_required
from utils.parser import image_file
from utils.storage import upload_image
class PhotoResource(Resource):
# 使用装饰器验证用户
method_decorators = [login_required]
def patch(self):
print(request.__dict__)
# 接收请求的参数,并做检查
rp = RequestParser()
rp.add_argument('photo', type=image_file, required=True, location='files')
args_dict = rp.parse_args()
# 文件对象
photo = args_dict['photo']
# 上传图片到七牛云,获取图片key,就是图片的url名称
file_name = upload_image(photo.read())
# 把图片的名字保存到数据库
User.query.filter(User.id==g.user_id).update({'profile_photo': file_name})
db.session.commit()
# 把图片的完整url返回
ret_dict = {
'photo_url': '{}/{}'.format(current_app.config['QINIU_DOMAIN'], file_name)
}
return ret_dict
from cache.user import UserProfileCache
class CurrentUserResource(Resource):
# 检查登录
method_decorators = [login_required]
# 请求钩子 utils.middlewares.jwt_authentication已经注册生效了：把token中的user_id写入g对象中
def get(self):
# 返回当前用户信息
# 从缓存和持久化存储中获取
# 代码执行到这里时，就应该已经有g.user_id
ret = UserProfileCache(user_id=g.user_id).get()
print('=')
print(ret)
ret_dict = {
'user_id': g.user_id,
'user_name': ret['name'],
'user_mobile': ret['mobile'],
'user_photo': ret['profile_photo'],
'certificate': ret['certificate'],
'introduction': ret['introduction'],
'arts_count': 0,
'following_count': 0
}
return ret_dict
def delete(self):
ret = UserProfileCache(user_id=g.user_id).exists()
if ret:
UserProfileCache(user_id=g.user_id).clear()
return {'message': 'ok'} | [
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code/4chan-scraper-api/4chan_catalog_scraper_custom.py | interimblue/idp | 0 | 135159 |
"""
Created on 26/05/2020
4chan-scraper v0.2
@author: <NAME>
"""
import requests
import logging
import json
import os
import datetime as dt
import sys
import time
#===============================================================================
# Setup
#===============================================================================
board = 'pol'
url = "https://a.4cdn.org/" + board + "/catalog.json"
datetime = dt.datetime.now().strftime("%Y-%m-%d_%H-%M-%S")
dateMonth = dt.datetime.now().strftime("%Y-%m")
path = '4chan-data/' + dateMonth
filename = path +'/' + datetime + '_' + board + '.json'
### Logging
logging.basicConfig(
format='%(asctime)s %(levelname)-8s %(message)s',
datefmt='%Y-%m-%d_T_%H:%M:%S',
filename='4chan_catalog_scraper_custom.log',
level=logging.DEBUG
)
start = time.time()
### Thread attributes that we wish to preserve - image and file related attributes are excluded to save space
### For full list of attributes, see:
### https://github.com/4chan/4chan-API/blob/master/pages/Catalog.md
### https://github.com/4chan/4chan-API/blob/master/pages/Threads.md
desiredAttributes = [
'no',
'resto',
'sticky',
'closed',
'now',
'time',
'name',
'trip',
'id',
'capcode',
'country',
'country_name',
'sub',
'com',
'tim',
'replies',
'images',
'last_modified',
'tag',
'semantic_url',
'unique_ips',
'archived',
'archived_on',
'last_replies'
]
if os.path.exists(path):
logging.info("Directory %s exists, using existing directory" % path)
else:
try:
logging.info("Directory %s not found, attepting to create directory" % path)
os.makedirs(path)
except OSError:
logging.error("Failed to create directory: %s" % path)
else:
logging.info("Successfully created the directory %s. Proceeding to scrape..." % path)
#===============================================================================
# Request
#===============================================================================
### Iterate through the catalog (list of page objects containing thread arrays) and
### rebuild the thread objects containing only the attributes we are interested in
def filterCatalog(boardCatalog):
logging.info("Filtering 4chan response...")
customThreadCatalog = []
customPage = {}
### Loop through each page
for page in boardCatalog:
customThreads = []
### Loop through each thread in page and create a new thread object containing
### exclusively the attributes we wish to collect
for thread in page['threads']:
customThread = {}
for attribute in desiredAttributes:
if attribute in thread:
customThread[attribute] = thread[attribute]
customThreads.append(customThread)
### Create a page with the new thread list
customPage = {
'page': page['page'],
'threads': customThreads
}
### Add the new page to our customized catalog
customThreadCatalog.append(customPage)
return customThreadCatalog
### Get the 4chan board catalog
try:
boardCatalog = requests.get(url).json()
except Exception as e:
logging.error("Fatal error when attempting to fetch 4chan data")
logging.error(e)
### The thread JSON object contains many attributes we do not require. Therefore we filter these out
try:
customThreadCatalogJsonArray = filterCatalog(boardCatalog)
except Exception as e:
logging.error("Fatal error when filtering JSON response from 4chan")
#===============================================================================
# Storage
#===============================================================================
### Append serialized submission object to the end of the JSON file
try:
logging.info("Writing filtered 4chan response to file...")
with open(filename, 'w') as f:
json.dump(customThreadCatalogJsonArray, f)
except Exception as e:
logging.error("Fatal error when attempting to write filtered JSON response from 4chan to file")
logging.error(e)
### Logging: Timing consolidation
end = time.time()
timeElasped = end-start
logging.info('=============== Time elasped: %s ==============='% timeElasped) | [
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src/cosmic_ray/tools/filters/filter_app.py | XD-DENG/cosmic-ray | 1 | 27937 | <gh_stars>1-10
"""A simple base for creating common types of work-db filters.
"""
import argparse
import logging
import sys
from exit_codes import ExitCode
from cosmic_ray.work_db import use_db
class FilterApp:
"""Base class for simple WorkDB filters.
This provides command-line handling for common filter options like
the session and verbosity level. Subclasses can add their own arguments
as well. This provides a `main()` function that open the session's WorkDB
and passes it to the subclass's `filter()` function.
"""
def add_args(self, parser: argparse.ArgumentParser):
"""Add any arguments that the subclass needs to the parser.
Args:
parser: The ArgumentParser for command-line processing.
"""
def description(self):
"""The description of the filter.
This is used for the command-line help message.
"""
return None
def main(self, argv=None):
"""The main function for the app.
Args:
argv: Command line argument list of parse.
"""
if argv is None:
argv = sys.argv[1:]
parser = argparse.ArgumentParser(
description=self.description(),
)
parser.add_argument(
'session', help="Path to the session on which to operate")
parser.add_argument(
'--verbosity', help='Verbosity level for logging', default='WARNING')
self.add_args(parser)
args = parser.parse_args(argv)
logging.basicConfig(level=getattr(logging, args.verbosity))
with use_db(args.session) as db:
self.filter(db, args)
return ExitCode.OK
def filter(self, work_db, args):
"""Apply this filter to a WorkDB.
This should modify the WorkDB in place.
Args:
work_db: An open WorkDB instance.
args: The argparse Namespace for the command line.
"""
raise NotImplementedError()
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FaceSwapper/faceLandmarks.py | rahulsingh50/Computer-Vision-Projects | 1 | 71412 | import cv2
import dlib
import numpy as np
# Path to shape predictor file
PATH = 'shape_predictor_68_face_landmarks.dat'
# Our landpoints' predictor and detector objects
predictor = dlib.shape_predictor(PATH)
detector = dlib.get_frontal_face_detector()
# Reading our image
img = cv2.imread('images/mr_robot.jpg')
# Defining classes for some exception
class TooManyFaces(Exception):
pass
class NoFaces(Exception):
pass
# Detect landpoints' on input image
def get_landmarks(image):
points = detector(image, 1)
if len(points) > 1:
raise TooManyFaces
if len(points) == 0:
raise NoFaces
return np.matrix([[t.x, t.y] for t in predictor(image, points[0]).parts()])
# Mark and point landmarks' on input image using numbers
def mark_landmarks(image, landmarks):
image = image.copy()
for i, point in enumerate(landmarks):
position = (point[0,0], point[0,1])
cv2.putText(image, str(i), (position), fontFace=cv2.FONT_ITALIC, fontScale=0.4, color=(0,0,0))
cv2.circle(image, position, 3, color=(0,255,0))
return image
l = get_landmarks(img)
marked_image = mark_landmarks(img, l)
cv2.imshow("Landmarks", marked_image)
cv2.waitKey(0)
cv2.destroyAllWindows()
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2
] |
python/1931.py | zheedong/BaekJoon | 0 | 2785 | <filename>python/1931.py
n = int(input())
conf_set = []
for _ in range(n):
conf_set.append(tuple(map(int, input().split())))
conf_set.sort(key=lambda x : (x[1], x[0]))
# 끝나는 시간을 기준으로 정렬
# 시작과 종료가 같은 경우를 포함하기 위해선, 시작 시간도 오름차순으로 정렬해 줘야 한다
solution_list = [conf_set[0]]
# Greedy Algorithm
for conf in conf_set[1:]:
last_conf = solution_list[-1]
_, last_end_time = last_conf
new_start_time, _ = conf
# 정렬된 회의의 list의 마지막 값의 시작 시간과, 정답 list 마지막의 종료 시간을 비교한다
if new_start_time >= last_end_time:
solution_list.append(conf)
print(len(solution_list)) | [
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2
] |
pulsar/apps/data/redis/store.py | goodboy/pulsar | 1 | 5857 | from functools import partial
from pulsar import Connection, Pool, get_actor
from pulsar.utils.pep import to_string
from pulsar.apps.data import RemoteStore
from pulsar.apps.ds import redis_parser
from .client import RedisClient, Pipeline, Consumer, ResponseError
from .pubsub import RedisPubSub, RedisChannels
class RedisStoreConnection(Connection):
def __init__(self, *args, **kw):
super().__init__(*args, **kw)
self.parser = self._producer._parser_class()
async def execute(self, *args, **options):
consumer = self.current_consumer()
await consumer.start((args, options))
result = await consumer.on_finished
if isinstance(result, ResponseError):
raise result.exception
return result
async def execute_pipeline(self, commands, raise_on_error=True):
consumer = self.current_consumer()
consumer.start((commands, raise_on_error, []))
result = await consumer.on_finished
if isinstance(result, ResponseError):
raise result.exception
return result
class RedisStore(RemoteStore):
'''Redis :class:`.Store` implementation.
'''
protocol_factory = partial(RedisStoreConnection, Consumer)
supported_queries = frozenset(('filter', 'exclude'))
def _init(self, namespace=None, parser_class=None, pool_size=50,
decode_responses=False, **kwargs):
self._decode_responses = decode_responses
if not parser_class:
actor = get_actor()
pyparser = actor.cfg.redis_py_parser if actor else False
parser_class = redis_parser(pyparser)
self._parser_class = parser_class
if namespace:
self._urlparams['namespace'] = namespace
self._pool = Pool(self.connect, pool_size=pool_size, loop=self._loop)
if self._database is None:
self._database = 0
self._database = int(self._database)
self.loaded_scripts = set()
@property
def pool(self):
return self._pool
@property
def namespace(self):
'''The prefix namespace to append to all transaction on keys
'''
n = self._urlparams.get('namespace')
return '%s:' % n if n else ''
def key(self):
return (self._dns, self._encoding)
def client(self):
'''Get a :class:`.RedisClient` for the Store'''
return RedisClient(self)
def pipeline(self):
'''Get a :class:`.Pipeline` for the Store'''
return Pipeline(self)
def pubsub(self, protocol=None):
return RedisPubSub(self, protocol=protocol)
def channels(self, protocol=None, **kw):
return RedisChannels(self.pubsub(protocol=protocol), **kw)
def ping(self):
return self.client().ping()
async def execute(self, *args, **options):
connection = await self._pool.connect()
with connection:
result = await connection.execute(*args, **options)
return result
async def execute_pipeline(self, commands, raise_on_error=True):
conn = await self._pool.connect()
with conn:
result = await conn.execute_pipeline(commands, raise_on_error)
return result
async def connect(self, protocol_factory=None):
protocol_factory = protocol_factory or self.create_protocol
if isinstance(self._host, tuple):
host, port = self._host
transport, connection = await self._loop.create_connection(
protocol_factory, host, port)
else:
raise NotImplementedError('Could not connect to %s' %
str(self._host))
if self._password:
await connection.execute('AUTH', self._password)
if self._database:
await connection.execute('SELECT', self._database)
return connection
def flush(self):
return self.execute('flushdb')
def close(self):
'''Close all open connections.'''
return self._pool.close()
def has_query(self, query_type):
return query_type in self.supported_queries
def basekey(self, meta, *args):
key = '%s%s' % (self.namespace, meta.table_name)
postfix = ':'.join((to_string(p) for p in args if p is not None))
return '%s:%s' % (key, postfix) if postfix else key
def meta(self, meta):
'''Extract model metadata for lua script stdnet/lib/lua/odm.lua'''
# indices = dict(((idx.attname, idx.unique) for idx in meta.indices))
data = meta.as_dict()
data['namespace'] = self.basekey(meta)
return data
class CompiledQuery:
def __init__(self, pipe, query):
self.pipe = pipe
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] |
script/rpi3-crowdmodel/init_reactions.py | ctuning/ck-rpi-optimization-results | 22 | 87058 | <reponame>ctuning/ck-rpi-optimization-results
#
# Collective Knowledge script
#
# See CK LICENSE.txt for licensing details
# See CK COPYRIGHT.txt for copyright details
#
# Developer: <NAME>, <EMAIL>, http://fursin.net, 2017
#
import ck.kernel as ck
import os
import json
improvement_threshold=1.05
r=ck.access({'action':'search',
'repo_uoa':'d0714418b5886022',
'module_uoa':'experiment',
'data_uoa':'rpi3-*',
'add_meta':'yes'})
if r['return']>0: ck.err(r)
lst=r['lst']
im=len(lst)
iq=0
ck.out('Found '+str(im)+' experiments ...')
# Get unique values for all keys
umeta={}
for q in lst:
meta=q['meta']['meta']
for k in meta:
v=meta[k]
x=umeta.get(k,[])
if v not in x:
x.append(v)
umeta[k]=x
# Get optimizations per compiler
compilers=umeta['compiler']
uopts={}
iuopts={}
classes={}
improvements={}
bench_index={}
individual_flags={}
ck.out('')
ck.out('Trying to pre-load optimization numbers from previous analysis ...')
for q in compilers:
s='rpi3-snapshot-'+q.lower().replace(' ','-')+'-autotuning'
# Find entry
r=ck.access({'action':'load',
'module_uoa':'slide',
'data_uoa':s})
if r['return']>0: ck.err(r)
p=r['path']
d=r['dict']
d1=d['slides'][0]
p1=os.path.join(p,d1+'.json')
r=ck.load_json_file({'json_file':p1})
if r['return']>0: ck.err(r)
d2=r['dict']
classes[q]={'-O3':[]}
uopts[q]={'-O3':0}
improvements[q]={}
individual_flags[q]={}
for t in d2['table']:
num=t['solution_num']
flags=t['best_flags'].strip()
classes[q][flags]=[]
uopts[q][flags]=num
#for q in compilers:
# classes[q]={'-O3':[]}
#
# uopts[q]={'-O3':0}
# iuopts[q]=1
for comp in compilers:
heatmap=[]
iq=0
for q in sorted(lst, key=lambda x: x['data_uoa']):
meta=q['meta']['meta']
compiler=meta['compiler']
if compiler==comp:
iq+=1
duoa=q['data_uoa']
p=q['path']
cmd_key=meta['cmd_key']
ds_uoa=meta['dataset_uoa']
p_uoa=meta['program_uoa']
ck.out('')
ind=p_uoa+' ; '+cmd_key #+' ; '+duoa
ck.out('* '+compiler+' ; '+ind)
bench_index[str(iq)]=ind
# Process reactions (and calcluate speedup based on min values to see what hardware can achieve, later can use expected values)
ld=os.listdir(p)
reactions={}
default_time=None
for df in ld:
if df.endswith('.flat.json'):
# Load file
pdf=os.path.join(p,df)
r=ck.load_json_file({'json_file':pdf})
if r['return']>0: ck.err(r)
d=r['dict']
opt=d['##characteristics#compile#joined_compiler_flags#min'].strip()
if opt not in uopts[compiler]:
continue
# uopts[compiler][opt]=iuopts[compiler]
# iuopts[compiler]+=1
t=d.get('##characteristics#run#execution_time_kernel_0#min',None)
if t!=None:
if opt=='-O3':
default_time=t
else:
reactions[opt]=t
# Calculate improvements/degradation
if default_time!=None and default_time!=0.0:
for k in reactions:
t=reactions[k]
if t==0.0:
reactions[k]=0.0
else:
s=default_time/t
if t<0.5:
s=0
reactions[k]=s
# Sort by improvements
opts=sorted(reactions, key=lambda v: reactions[v], reverse=True)
ck.out('')
for k in opts:
ck.out((' %.2f' % reactions[k])+' : '+k)
# Get highest improvement
ohi=opts[0]
hi=reactions[ohi]
if hi>=improvement_threshold:
if ohi in classes[compiler]:
x=classes[compiler].get(ohi,[])
if ind not in x:
x.append({"name":ind,"improvement":hi})
classes[compiler][ohi]=x
improvements[compiler][ind]=hi
else:
classes[compiler]['-O3'].append({"name":ind,"improvement":1.0})
# Prepare heat map (unsorted)
for opt in uopts[compiler]:
if opt!='-O3':
iopt=uopts[compiler][opt]
x=[iq, iopt, reactions.get(opt,0.0)]
heatmap.append(x)
# Record heat map
dd={'table':{"0":heatmap}}
xx=[]
iq=0
for k in uopts[comp]:
xx.append(iq)
iq+=1
dd['axis_y_labels']=xx
r=ck.save_json_to_file({'json_file':'init_reactions_tmp_heatmap_'+comp.replace(' ','_')+'.json','dict':dd,'sort_keys':'yes'})
if r['return']>0: ck.err(r)
# Sort classes
for o in classes[comp]:
b=classes[comp][o]
b1=sorted(b, key=lambda k: k.get('improvement',0.0), reverse=True)
classes[comp][o]=b1
# Prepare individual flags to predict (YES/NO)
for opt in classes[comp]:
benchs=classes[comp][opt]
sopt=opt.split(' ')
for o in sopt:
ot=o.strip()
x=individual_flags[comp].get(ot,[])
for b in benchs:
if b not in x:
x.append(b)
individual_flags[comp][ot]=x
# Save info
info={'opts':uopts, 'classes':classes, 'improvements':improvements, 'bench_index':bench_index, 'individual_flags':individual_flags}
r=ck.save_json_to_file({'json_file':'init_reactions_tmp_info.json','dict':info,'sort_keys':'yes'})
if r['return']>0: ck.err(r)
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] |
bender_service/bender_service/urls.py | Dreem-Organization/bender-api | 0 | 83776 | <reponame>Dreem-Organization/bender-api
"""bender URL Configuration
The `urlpatterns` list routes URLs to views. For more information please see:
https://docs.djangoproject.com/en/1.10/topics/http/urls/
Examples:
Function views
1. Add an import: from my_app import views
2. Add a URL to urlpatterns: url(r'^$', views.home, name='home')
Class-based views
1. Add an import: from other_app.views import Home
2. Add a URL to urlpatterns: url(r'^$', Home.as_view(), name='home')
Including another URLconf
1. Import the include() function: from django.conf.urls import url, include
2. Add a URL to urlpatterns: url(r'^blog/', include('blog.urls'))
"""
from django.conf.urls import url, include
from django.contrib import admin
from bender_service.social_login import FacebookLogin, GoogleLogin
from django.views.generic import TemplateView
from django.views.generic import RedirectView
urlpatterns = [
url(r'^admin/', admin.site.urls),
# Rest auth (login signup etc.)
url(r'^', include('rest_auth.urls')),
# This is not meant to be used, it just allows allauth to send email of reset
url(r'^password/reset/(?P<uidb64>[0-9A-Za-z_\-]+)/(?P<token>[0-9A-Za-z]{1,13}-[0-9A-Za-z]{1,20})/$',
TemplateView.as_view(),
name='password_reset_confirm'), # https://github.com/Tivix/django-rest-auth/issues/63
# Registration
url(r'^registration/', include('rest_auth.registration.urls')),
url(r'^facebook/', FacebookLogin.as_view(), name='facebook_login'),
url(r'^google/', GoogleLogin.as_view(), name='google_login'),
# Necessary imports (profile seems useless for now.)
url(r'^accounts/profile/$',
RedirectView.as_view(url='/', permanent=True),
name='profile-redirect'),
url(r'^account/', include('allauth.urls')),
# Bender import
url(r'^api/', include('bender.urls')),
]
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investing_algorithm_framework/core/models/__init__.py | coding-kitties/investing-algorithm-framework | 9 | 11277 | from flask import Flask
from flask_sqlalchemy import SQLAlchemy
db = SQLAlchemy()
def create_all_tables():
db.create_all()
def initialize_db(app: Flask):
db.init_app(app)
db.app = app
from investing_algorithm_framework.core.models.order_status import OrderStatus
from investing_algorithm_framework.core.models.order_type import OrderType
from investing_algorithm_framework.core.models.order_side import OrderSide
from investing_algorithm_framework.core.models.time_unit import TimeUnit
from investing_algorithm_framework.core.models.order import Order
from investing_algorithm_framework.core.models.portfolio import Portfolio
from investing_algorithm_framework.core.models.position import Position
__all__ = [
"db",
"Portfolio",
"Position",
'Order',
"OrderType",
'OrderSide',
"TimeUnit",
"create_all_tables",
"initialize_db",
"OrderStatus"
]
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fudge-domain.py | jordantrc/domain-fudgery | 0 | 27748 | <filename>fudge-domain.py
#!/usr/bin/env python3
#
# fudge-domain.py
#
# Finds potentially useful domains
# which are visually similar to the
# target domain and ascertains whether
# these domains are currently available
# (not registered). Also checks if any TLDs
# are not registered for the domain.
#
# Usage:
# domain-fudgery.py [options] [domain]
# Only works with second-level domain names,
# e.g. google.com, amazon.co.uk
#
# OPTIONS:
#
# TLD Options:
# --country-code-tlds check country code TLDs
# --original-tlds check original TLDs (.com, .net, .org)
# --custom-tlds check additional list of TLDS, comma separated
#
# General Options:
# --file load domains from the given filename
# one domain per line
# --no-active use only passive checks
# --no-whois do not perform whois checks
#
import argparse
import itertools
import os
import sys
from dns import name
from dns import message
CHARACTER_LOOK_ALIKES = {
'a': ['d'],
'A': ['4'],
'b': ['1o', 'lo'],
'B': ['8'],
'd': ['ol', 'o1'],
'E': ['3'],
'i': ['1', 'l'],
'I': ['1', 'l'],
'l': ['1', 'i'],
'm': ['rn'],
'o': ['0'],
'O': ['0'],
'Q': ['O'],
's': ['5'],
'S': ['5'],
'T': ['7'],
'w': ['vv'],
'W': ['VV'],
'z': ['2'],
'Z': ['2'],
'0': ['O'],
'1': ['l'],
'2': ['Z'],
'4': ['A'],
'5': ['S'],
'7': ['T'],
'8': ['B']
}
# original TLDs, does not include restricted-use
# TLDs .edu, .gov, .mil, .int
TLDS_ORIGINAL = ['.com', '.net', '.org']
# country code TLDs
TLDS_COUNTRY_CODE = [
'.ac','.ad','.ae','.af','.ag','.ai','.al','.am','.ao','.aq','.ar','.as','.at','.au','.aw','.ax',
'.az','.ba','.bb','.bd','.be','.bf','.bg','.bh','.bi','.bj','.bm','.bn','.bo','.bq','.br','.bs',
'.bt','.bw','.by','.bz','.ca','.cc','.cd','.cf','.cg','.ch','.ci','.ck','.cl','.cm','.cn','.co',
'.cr','.cu','.cv','.cw','.cx','.cy','.cz','.de','.dj','.dk','.dm','.do','.dz','.ec','.ee','.eg',
'.eh','.er','.es','.et','.eu','.fi','.fj','.fk','.fm','.fo','.fr','.ga','.gd','.ge','.gf','.gg',
'.gh','.gi','.gl','.gm','.gn','.gp','.gq','.gr','.gs','.gt','.gu','.gw','.gy','.hk','.hm','.hn',
'.hr','.ht','.hu','.id','.ie','.il','.im','.in','.io','.iq','.ir','.is','.it','.je','.jm','.jo',
'.jp','.ke','.kg','.kh','.ki','.km','.kn','.kp','.kr','.kw','.ky','.kz','.la','.lb','.lc','.li',
'.lk','.lr','.ls','.lt','.lu','.lv','.ly','.ma','.mc','.md','.me','.mg','.mh','.mk','.ml','.mm',
'.mn','.mo','.mp','.mq','.mr','.ms','.mt','.mu','.mv','.mw','.mx','.my','.mz','.na','.nc','.ne',
'.nf','.ng','.ni','.nl','.no','.np','.nr','.nu','.nz','.om','.pa','.pe','.pf','.pg','.ph','.pk',
'.pl','.pm','.pn','.pr','.ps','.pt','.pw','.py','.qa','.re','.ro','.rs','.ru','.rw','.sa','.sb',
'.sc','.sd','.se','.sg','.sh','.si','.sk','.sl','.sm','.sn','.so','.sr','.ss','.st','.su','.sv',
'.sx','.sy','.sz','.tc','.td','.tf','.tg','.th','.tj','.tk','.tl','.tm','.tn','.to','.tr','.tt',
'.tv','.tw','.tz','.ua','.ug','.uk','.us','.uy','.uz','.va','.vc','.ve','.vg','.vi','.vn','.vu',
'.wf','.ws','.ye','.yt','.za','.zm'
]
# country codes with restricted second level domains (individuals or companies can
# only register third level domains)
TLDS_COUNTRY_CODE_RESTRICTED_LVL2 = [
'.au','.bn','.bt','.cy','.et','.fk','.gh','.gn','.gu','.jm','.ke','.kh','.kp','.kw','.lb','.lr',
'.ls','.mm','.mq','.mt','.mz','.ni','.np','.pa','.pg','.py','.qa','.sb','.sv','.sz','.th','.tz',
'.ve','.ye'
]
# the second level domains for those domains above that can be used
# for third level domains
TLDS_COUNTRY_CODE_UNRESTRICTED_LVL2 = [
'.com.au','.net.au','.org.au','.asn.au','.id.au','.com.bn','.edu.bn','.net.bn','.org.bn','.bt',
'.com.bt','.edu.bt','.net.bt','.org.bt','.ac.cy','.net.cy','.org.cy','.pro.cy','.name.cy',
'.ekloges.cy','.tm.cy','.ltd.cy','.biz.cy','.press.cy','.parliament.cy','.com.cy',
'.centralbank.cy','.com.et','.org.et','.edu.et','.net.et','.name.et','.co.fk','.org.fk',
'.ac.fk','.nom.fk','.net.fk','.com.gh','.edu.gh','.com.gn','.ac.gn','.org.gn','.net.gn',
'.com.gu','.net.gu','.org.gu','.edu.gu','.com.jm','.net.jm','.org.jm','.edu.jm','.co.ke',
'.or.ke','.ne.ke','.go.ke','.ac.ke','.sc.ke','.me.ke','.mobi.ke','.info.ke','.per.kh','.com.kh',
'.edu.kh','.net.kh','.org.kh','.aca.kp','.com.kp','.edu.kp','.law.kp','.org.kp','.rep.kp',
'.net.kp','.sca.kp','.com.kw','.ind.kw','.net.kw','.org.kw','.emb.kw','.edu.kw','.com.lb',
'.edu.lb','.net.lb','.org.lb','.com.lr','.edu.lr','.org.lr','.net.lr','.ac.ls','.co.ls',
'.net.ls','.nul.ls','.org.ls','.sc.ls','.net.mm','.com.mm','.edu.mm','.org.mm','.edu.mt',
'.com.mt','.net.mt','.org.mt','.co.mz','.net.mz','.org.mz','.ac.mz','.edu.mz','.gob.ni',
'.co.ni','.com.ni','.ac.ni','.edu.ni','.org.ni','.nom.ni','.net.ni','.edu.np','.com.np',
'.org.np','.net.np','.aero.np','.asia.np','.biz.np','.coop.np','.info.np','.jobs.np','.mobi.np',
'.museum.np','.name.np','.pro.np','.services.np','.travel.np','.net.pa','.com.pa','.ac.pa',
'.sld.pa','.edu.pa','.org.pa','.abo.pa','.ing.pa','.med.pa','.nom.pa','.com.pg','.net.pg',
'.ac.pg','.org.pg','.com.py','.coop.py','.edu.py','.org.py','.net.py','.una.py','.com.qa',
'.edu.qa','.sch.qa','.net.qa','.org.qa','.com.sb','.net.sb','.edu.sv','.com.sv','.org.sv',
'.red.sv','.co.sz','.ac.sz','.org.sz','.ac.th','.co.th','.or.th','.net.th','.in.th','.co.tz',
'.ac.tz','.or.tz','.ne.tz','.hotel.tz','.mobi.tz','.tv.tz','.info.tz','.me.tz','.arts.ve',
'.co.ve','.com.ve','.info.ve','.net.ve','.org.ve','.radio.ve','.web.ve','.com.ye','.co.ye',
'.ltd.ye','.me.ye','.net.ye','.org.ye','.plc.ye'
]
def replacement_combinations(indices):
"""returns a list of all possible replacement combinations for count
instances of a character in a string"""
result = []
for i in range(1, len(indices) + 1):
for c in itertools.combinations(indices, i):
result.append(c)
return result
def permutate_domain(domain, character, replacements):
"""returns all permutations of character replacements"""
new_domains = []
indices = [ i for i, ltr in enumerate(domain) if ltr == character ]
combinations = replacement_combinations(indices)
for c in combinations:
new_domain = domain
for i in c:
for r in replacements:
new_domain = new_domain[:i] + r + new_domain[i + 1:]
new_domains.append(new_domain)
return new_domains
def domain_permutations(domain, orig_tld, country_code_tlds=False, original_tlds=False, custom_tlds=[]):
"""returns a list of domains to check"""
result = []
domains = [domain, domain.upper()]
# character replacement
for c in CHARACTER_LOOK_ALIKES.keys():
for d in domains:
count = d.count(c)
if count > 0:
permutated_domains = permutate_domain(d, c, CHARACTER_LOOK_ALIKES[c])
for p in permutated_domains:
print(p + orig_tld)
def main():
"""Main function."""
parser = argparse.ArgumentParser(description="Finds fudged domains.")
parser.add_argument("--country-code-tlds", action='store_true', dest="country_code_tld", help="look for unregistered country code TLDs")
parser.add_argument("--original-tlds", action='store_true', dest="original_tld", help="look for unregistered original TLDs")
parser.add_argument("--custom-tlds", dest="custom_tld", help="look for custom list of TLDs")
parser.add_argument("--no-whois", action='store_true', dest="no_whois", help="disable whois queries")
parser.add_argument("--file", dest="file", help="file containing DNS names to load")
parser.add_argument("--no-active", action='store_true', dest="no_active", help="disable active checks")
parser.add_argument("domain", nargs='*', help="domain to fudge")
args = parser.parse_args()
# ensure at least one domain was provided
if not args.file and not args.domain:
print("[-] must provide a domain as argument or a file containing domains")
sys.exit(1)
domains = []
if args.file:
if os.path.isfile(args.file):
with open(args.file, "r") as fd:
domains = fd.readlines()
else:
print("[-] file not found or permission denied")
sys.exit(1)
if args.domain is not None:
domains.append(args.domain[0])
# for each domain, determine TLDs for domain
for d in domains:
domain_parts = d.split(".")
domain = domain_parts[0]
tld = "." + ".".join(domain_parts[1:])
domain_permutations(domain, tld)
if __name__ == "__main__":
main() | [
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] |
common/doLoginOut.py | TamCharlene/automatic_testing | 0 | 178747 | import os
import json
import requests
from common.doConfig import GetInfo
class DoLoginOut:
def getUrl(self):
"""
从配置文件中接口url
:return: 接口url
"""
cf = GetInfo(os.path.abspath(os.path.dirname(os.path.dirname(__file__))) + "/config/user_info.cfg")
base_interface = cf.get_info_for_public('INTERFACE')
return base_interface
def getHeaders(self):
"""
:return:
"""
cf = GetInfo(os.path.abspath(os.path.dirname(os.path.dirname(__file__))) + "/config/user_info.cfg")
base_headers = cf.get_info_for_public('HEADERS')
return base_headers
def getUser(self):
"""
:return:
"""
cf = GetInfo(os.path.abspath(os.path.dirname(os.path.dirname(__file__))) + "/config/user_info.cfg")
user_info = cf.get_info_for_public('USER_INFO')
return user_info
def getBody(self):
"""
:return:
"""
cf = GetInfo(os.path.abspath(os.path.dirname(os.path.dirname(__file__))) + "/config/user_info.cfg")
body = cf.get_info_for_public('BODY')
return body
def login(self):
"""
1.获取验证码
2.登录
:return:返回json
"""
# 从配置文件中读取host
cf = GetInfo(os.path.abspath(os.path.dirname(os.path.dirname(__file__))) + "/config/env_info.cfg")
base_url = cf.get_info_for_public('TEST_HOST')
base_url_re = base_url.get('fcb_app_host_c')
#url
data_code = {
"phone": DoLoginOut().getUser().get('phone'),
"type": DoLoginOut().getBody().get('type'),
"by_voice_code": DoLoginOut().getBody().get('by_voice_code')}
data_login = {
"code": DoLoginOut().getUser().get('password'),
"phone": DoLoginOut().getUser().get('phone'),
"registerFrom": DoLoginOut().getBody().get('registerFrom'),
"terminalVersion":DoLoginOut().getBody().get('terminalVersion'),
"os_type": DoLoginOut().getBody().get('os_type'),
"clientType": DoLoginOut().getBody().get('clientType')
}
res_code = requests.post(base_url_re+DoLoginOut().getUrl().get('getCode'), data=json.dumps(data_code), headers=DoLoginOut().getHeaders())
res_login = requests.post(base_url_re+DoLoginOut().getUrl().get('login'), data=json.dumps(data_login), headers=DoLoginOut().getHeaders())
return res_login.json()
class TokenSet:
def getToken(self):
"""
获取全局变量 access_token
:return:
"""
re = DoLoginOut().login()
access_token = re['data'].get('access_token')
return access_token
def getCustomerId(self):
"""
获取全局变量 customerId
:return:
"""
re = DoLoginOut().login()
customerId = re['data'].get('customerId')
return customerId
def getUnionId(self):
"""
获取全局变量 union_id
:return:
"""
re = DoLoginOut().login()
union_id = re['data'].get('union_id')
return union_id
class GetToken:
"""
设置三个全局变量，供所有class使用
"""
global access_token, customerId, union_id
access_token = TokenSet().getToken()
customerId = TokenSet().getCustomerId()
union_id = TokenSet().getUnionId()
print(access_token)
print(customerId)
print(union_id)
if __name__ == '__main__':
re = DoLoginOut().login()
print(re)
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tests/test_experiments/test_n3_base_no_slash_after_colon.py | octadocs/octadocs | 1 | 75197 | """
Test for GitHub issue: https://github.com/RDFLib/rdflib/issues/1216
"""
import pytest
import rdflib
from octadocs.types import LOCAL
DOCUMENT = f"""
@base <{LOCAL}> .
@prefix rdfs: <http://www.w3.org/2000/01/rdf-schema#> .
<class_to_class>
a
rdfs:Class ,
<Category> ;
<color> "blue" ;
<priority> 4 .
"""
@pytest.mark.skip('https://github.com/RDFLib/rdflib/issues/1216')
def test_no_slash_after_colon():
"""Test @base directive with no slash after colon."""
graph = rdflib.ConjunctiveGraph()
graph.parse(
data=DOCUMENT,
format='n3',
)
# Raises ValueError:
# Base <LOCAL> has no slash after colon - with relative 'class_to_class'.
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backend/plugins/bs4_spacer/models.py | Guanch1/rep1 | 20 | 170409 | from cms.models.pluginmodel import CMSPlugin
from django.db import models
class VerticalSpacerPlugin(CMSPlugin):
smart_space = models.PositiveIntegerField(
"Default Space",
default=0,
help_text="in px, for desktop, height on other devices is calculated automatically",
)
space_xs = models.PositiveIntegerField(
"All screens (default value)",
default=0,
help_text="in px for extra small screens and above",
blank=True,
null=True,
)
space_sm = models.PositiveIntegerField(
"small screens and above",
help_text="in px for small screens and above",
blank=True,
null=True,
)
space_md = models.PositiveIntegerField(
"medium screens and above",
help_text="in px for medium screens and above",
blank=True,
null=True,
)
space_lg = models.PositiveIntegerField(
"large screens and above",
help_text="in px for large screens and above",
blank=True,
null=True,
)
space_xl = models.PositiveIntegerField(
"very large screens", help_text="in px for extra large screens", blank=True, null=True
)
def has_advanced_settings(self):
# 0 doesnt count
return self.space_xs or self.space_sm or self.space_md or self.space_lg or self.space_xl
def __str__(self):
return "smart {}, xs {}, sm {}, md {}, lg {}, xl {}".format(
self.smart_space,
self.space_xs,
self.space_sm,
self.space_md,
self.space_lg,
self.space_xl,
)
| [
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] |
polyaxon/scheduler/job_scheduler.py | wbuchwalter/polyaxon | 0 | 78544 | <filename>polyaxon/scheduler/job_scheduler.py<gh_stars>0
import logging
from kubernetes.client.rest import ApiException
from django.conf import settings
from constants.jobs import JobLifeCycle
from docker_images.image_info import get_image_info
from libs.paths.exceptions import VolumeNotFoundError
from polyaxon.config_manager import config
from scheduler.spawners.job_spawner import JobSpawner
from scheduler.spawners.utils import get_job_definition
_logger = logging.getLogger('polyaxon.scheduler.notebook')
def start_job(job):
# Update job status to show that its started
job.set_status(JobLifeCycle.SCHEDULED)
try:
image_name, image_tag = get_image_info(build_job=job.build_job)
except ValueError as e:
_logger.warning('Could not start the job, %s', e)
job.set_status(JobLifeCycle.FAILED, message='External git repo was note found.')
return
job_docker_image = '{}:{}'.format(image_name, image_tag)
_logger.info('Start job with built image `%s`', job_docker_image)
spawner = JobSpawner(
project_name=job.project.unique_name,
project_uuid=job.project.uuid.hex,
job_name=job.unique_name,
job_uuid=job.uuid.hex,
spec=job.specification,
k8s_config=settings.K8S_CONFIG,
namespace=settings.K8S_NAMESPACE,
job_docker_image=job_docker_image,
in_cluster=True,
use_sidecar=True,
sidecar_config=config.get_requested_params(to_str=True))
try:
results = spawner.start_job(persistence_data=job.persistence_data,
persistence_outputs=job.persistence_outputs,
outputs_refs_jobs=job.outputs_refs_jobs,
outputs_refs_experiments=job.outputs_refs_experiments,
resources=job.resources,
node_selectors=job.node_selectors)
except ApiException as e:
_logger.warning('Could not start job, please check your polyaxon spec %s', e)
job.set_status(
JobLifeCycle.FAILED,
message='Could not start job, encountered a Kubernetes ApiException.')
return
except VolumeNotFoundError as e:
_logger.warning('Could not start the job, please check your volume definitions %s', e)
job.set_status(
JobLifeCycle.FAILED,
message='Could not start the job, '
'encountered a volume definition problem. %s' % e)
return False
except Exception as e:
_logger.warning('Could not start job, please check your polyaxon spec %s', e)
job.set_status(
JobLifeCycle.FAILED,
message='Could not start job encountered an {} exception.'.format(
e.__class__.__name__
))
return
job.definition = get_job_definition(results)
job.save()
def stop_job(project_name, project_uuid, job_name, job_uuid, specification):
spawner = JobSpawner(
project_name=project_name,
project_uuid=project_uuid,
job_name=job_name,
job_uuid=job_uuid,
spec=specification,
k8s_config=settings.K8S_CONFIG,
namespace=settings.K8S_NAMESPACE,
in_cluster=True)
spawner.stop_job()
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580,
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2
] |
backend/medico/urls.py | danizavtz/desafiodrf | 0 | 95319 | <filename>backend/medico/urls.py
from django.urls import include, path
from rest_framework import routers
from .views import MedicoViewSet, EspecialidadeViewSet
router = routers.DefaultRouter()
router.register(r'medicos', MedicoViewSet, basename='medicos')
router.register(r'especialidades', EspecialidadeViewSet, basename='especialidades')
urlpatterns = [
path(r'', include(router.urls)),
] | [
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2697,
13,
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15140,
353,
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29915,
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742,
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29892,
2362,
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2433,
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4869,
1495,
13,
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29889,
9573,
29898,
29878,
29915,
267,
412,
1455,
7305,
742,
3423,
412,
1455,
5558,
1043,
2697,
29892,
2362,
3871,
2433,
267,
412,
1455,
7305,
1495,
13,
13,
2271,
11037,
29879,
353,
518,
13,
1678,
2224,
29898,
29878,
29915,
742,
3160,
29898,
15140,
29889,
26045,
8243,
13,
29962,
2
] |
features/steps/running.py | lstephen/construi | 27 | 94355 | import contextlib
import os
import re
import shlex
import subprocess
from behave import *
ANSI_ESCAPE = re.compile(r"\x1B\[[0-?]*[ -/]*[@-~]")
def strip_ansi_codes(inp):
return ANSI_ESCAPE.sub("", inp)
@contextlib.contextmanager
def pushd(new_dir):
old_dir = os.getcwd()
os.chdir(new_dir)
yield
os.chdir(old_dir)
@given("a {file_name} file")
def create_file(context, file_name):
with pushd(context.working_directory):
with open(file_name, "w") as f:
f.write(context.text)
@when("running {command}")
def running(context, command):
with pushd(context.working_directory):
try:
output = subprocess.check_output(
shlex.split(command), stderr=subprocess.STDOUT
)
context.exit_code = 0
except subprocess.CalledProcessError as err:
output = err.output
context.exit_code = err.returncode
if isinstance(output, bytes):
output = output.decode("utf-8")
context.output = output
@then("it has an exit code of {exit_code:d}")
def exits_with(context, exit_code):
assert context.exit_code == exit_code, "Expected: %d. Got %d. Output was: %s" % (
exit_code,
context.exit_code,
context.output,
)
@then("it outputs the version")
def outputs_version(context):
with open("VERSION") as v:
expected_version = v.read()
assert context.output == expected_version, "Expected: '%s'. Got: '%s'." % (
expected_version,
context.output,
)
@then("the output is")
@then("it outputs")
def outputs(context):
output = strip_ansi_codes(context.output)
assert output == context.text, "Expected: '%s'. Got: '%s'." % (context.text, output)
@then("the output contains")
def output_contains(context):
output = strip_ansi_codes(context.output)
assert context.text in output, "Expected '%s' to contain '%s'." % (
output,
context.text,
)
@then("the output contains only once")
def output_contains(context):
output = strip_ansi_codes(context.output)
needle = context.text
idx = output.find(needle)
assert idx > 0, "Expected '%s' to contain '%s'." % (output, needle)
assert (
output[idx + 1 :].find(needle) < 0
), "Expected '%s' to contain only once '%s'." % (output, needle)
| [
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304,
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817,
280,
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13,
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4974,
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584,
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313,
4905,
29892,
817,
280,
29897,
13,
2
] |
exercicios/ex 061 a 070/ex063.py | CarlosWillian/python | 0 | 36319 | print('Sequência de Fibonacci')
print('='*24)
t = int(input('Número de termos da sequência: '))
print('='*24)
c = 3
termo1 = 0
termo2 = 1
print('A sequência é ({}, {}, '.format(termo1, termo2), end='')
while c <= t:
termo3 = termo1 + termo2
print('{}'.format(termo3), end='')
print(', ' if c < t else '', end='')
c += 1
termo1 = termo2
termo2 = termo3
print(')')
| [
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2
] |
xpanse/api/assets/v2/ip_range.py | PaloAltoNetworks/cortex-xpanse-python-sdk | 3 | 12173 | <reponame>PaloAltoNetworks/cortex-xpanse-python-sdk<gh_stars>1-10
from typing import Any, Dict, List
from xpanse.const import V2_PREFIX
from xpanse.endpoint import ExEndpoint
from xpanse.iterator import ExResultIterator
class IpRangeEndpoint(ExEndpoint):
"""
Part of the Assets v2 API for handling IP Ranges.
See: https://api.expander.expanse.co/api/v1/docs/
"""
def list(self, **kwargs: Any) -> ExResultIterator:
"""
Returns the list of IP Ranges. Arguments should be passed as keyword args using
the names below.
Args:
limit (int, optional):
Returns at most this many results in a single api call.
Default is 100, max is 10000.
offset (int, optional):
Returns results starting at this offset.
Default is 0.
sort (str, optional):
Comma-separated string; orders results by the given fields. If the field name is
prefixed by a -, then the ordering will be descending for that field.
Use a dotted notation to order by fields that are nested.
business_units (str, optional):
Comma-separated string; Returns only results whose Business Unit's ID falls in the provided list.
NOTE: If omitted, API will return results for all Business Units the user has permissions to view.
Also, cannot be used with the business-unit-names parameter.
business_unit_names (str, optional):
Comma-separated string; Returns only results whose Business Unit's name falls in the provided list.
NOTE: If omitted, API will return results for all Business Units the user has permissions to view.
Also, cannot be used with the business-units parameter.
inet (str, optional):
Search for given IP/CIDR block using a single IP (d.d.d.d), a dashed IP range (d.d.d.d-d.d.d.d),
a CIDR block (d.d.d.d/m), a partial CIDR (d.d.), or a wildcard (d.d.*.d).
Returns only results whose [startAddress, endAddress] range overlap with the given IP Address or CIDR.
tags (str, optional):
Comma-separated string; Returns only results who are associated with the provided Tag IDs.
Cannot be used with the tag-names parameter.
tag_names (str, optional):
Comma-separated string; Returns only results who are associated with the provided Tag names.
Cannot be used with the tags parameter.
include (str, optional):
Comma-separated string; Include the provided fields as part of the serialized result. Allowed values are
`annotations`, `severityCounts`, `attributionReasons`, `relatedRegistrationInformation`, `certDetails`, and `locationInformation`
Returns:
:obj:`ExResultIterator`:
An iterator containing all of the ip_range results. Results can be iterated
or called by page using `<iterator>.next()`.
Examples:
>>> # Return all ip ranges and print each range:
>>> for res in client.assets.ip_range.v2.list():
... for ip_r in res:
... print(ip_r)
"""
return ExResultIterator(self._api, f"{V2_PREFIX}/ip-range", kwargs)
def get(self, id: str, **kwargs: Any) -> Dict[str, Any]:
"""
Returns the details for a given IP Range. Arguments should be passed as keyword args using
the names below.
Args:
id (str):
ID for the ip-range. Should be a UUID.
include (str, optional):
Comma-separated string; Include the provided fields as part of the serialized result.
Returns:
:obj:`dict`:
A dictionary containing all of the details about an IP Range.
Examples:
>>> # Return IP Range with severity counts
>>> my_range = client.assets.ip_range.v2.get(<id>, include="severityCounts")
"""
return self._api.get(f"{V2_PREFIX}/ip-range/{id}", params=kwargs).json()
def create(
self, startAddress: str, endAddress: str, parentId: str, **kwargs: Any
) -> Dict[str, Any]:
"""
Creates a new custom IP Range.
NOTE: A validation error will be returned if the start and end addresses of the custom range do not fit within a top level range defined by Xpanse.
Args:
startAddress (str):
Start address of custom ip-range.
endAddress (str):
End address of custom ip-range.
parentId (str):
Id of parent ip-range.
tags (list, optional):
A list of tag annotation names.
additionalNotes (str, optional):
Any additional notes about the custom ip-range.
pointOfContactIds (list, optional):
A lost of point-of-contact annotation ids.
Returns:
:obj:`dict`:
A dictionary containing all of the details about the newly created, custom IP Range.
Examples:
>>> # Create a new ip-range under a parent range
>>> new_range = client.assets.ip_range.v2.create("172.16.31.10", "172.16.17.32", "43a5a569-27b0-39b5-98f4-22b9885546d7", additionalNotes="Business Unit X - Marketing website hosts")
"""
payload: Dict[str, Any] = {
"startAddress": startAddress,
"endAddress": endAddress,
"parentId": parentId,
"annotations": {},
}
if "tags" in kwargs:
payload["annotations"]["tags"] = kwargs["tags"]
if "additionalNotes" in kwargs:
payload["annotations"]["additionalNotes"] = kwargs["additionalNotes"]
if "pointOfContactIds" in kwargs:
payload["annotations"]["pointOfContactIds"] = kwargs["pointOfContactIds"]
return self._api.post(f"{V2_PREFIX}/ip-range", json=payload).json()
def delete(self, id: str) -> bool:
"""
Delete the given IP Range, and all connections to other data.
NOTE: This will only work for user-defined IP Ranges.
Args:
id (str):
ID for the ip-range. Should be a UUID.
Returns:
:obj:`boolean`:
`True` if the range was successfully deleted, otherwise `False`.
Examples:
>>> # Deletes a user defined range
>>> client.assets.ip_range.v2.delete("43a5a569-27b0-39b5-98f4-22b9885546d7")
"""
return (
True
if self._api.delete(f"{V2_PREFIX}/ip-range/{id}").status_code == 204
else False
)
def update(self, id: str, **kwargs: Any) -> Dict[str, Any]:
"""
Allows the partial update of the given IP Range.
Args:
id (str):
ID for the ip-range. Should be a UUID.
startAddress (str, optional):
Start address of custom ip-range.
endAddress (str, optional):
End address of custom ip-range.
parentId (str, optional):
Id of parent ip-range.
tags (list, optional):
A list of tag annotation ids.
additionalNotes (str, optional):
Any additional notes about the custom ip-range.
pointOfContactIds (list, optional):
A lost of point-of-contact annotation ids.
Returns:
:obj:`dict`:
A dictionary containing all of the details about the updated, custom IP Range.
Examples:
>>> # Update an ip-range under a parent range
>>> new_range = client.assets.ip_range.v2.update("43a5a569-27b0-39b5-98f4-22b9885546d7", additionalNotes="Business Unit X - Development Environment")
"""
payload = {}
if "startAddress" in kwargs:
payload["startAddress"] = kwargs["startAddress"]
if "endAddress" in kwargs:
payload["endAddress"] = kwargs["endAddress"]
if "parentId" in kwargs:
payload["parentId"] = kwargs["parentId"]
if any(
arg in ("tags", "additionalNotes", "pointOfContactIds") for arg in kwargs
):
payload["annotations"] = {}
if "tags" in kwargs:
payload["annotations"]["tags"] = kwargs["tags"]
if "additionalNotes" in kwargs:
payload["annotations"]["additionalNotes"] = kwargs["additionalNotes"]
if "pointOfContactIds" in kwargs:
payload["annotations"]["pointOfContactIds"] = kwargs["pointOfContactIds"]
return self._api.patch(f"{V2_PREFIX}/ip-range/{id}", json=payload).json()
def tag(self, ranges: List[str], tags: List[str]) -> bool:
"""
Adds the provided tags to all of the specified ip ranges.
If the any of the provided tags do not exist, they will be created.
Args:
ranges (list):
A list of ip-range IDs. Should be UUIDs.
tags (list):
A list of tag annotation names to add to an ip-range.
Returns:
:obj:`boolean`:
`True` if the ranges were tagged successfully, otherwise `False`.
"""
payload = {"ipRangeIds": ranges, "tags": tags}
return self._api.post(f"{V2_PREFIX}/ip-range/tag", json=payload)
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] |
bluebottle/events/serializers.py | jayvdb/bluebottle | 0 | 141982 | <filename>bluebottle/events/serializers.py<gh_stars>0
from builtins import object
from rest_framework.validators import UniqueTogetherValidator
from rest_framework import serializers
from rest_framework_json_api.relations import ResourceRelatedField
from bluebottle.activities.utils import (
BaseActivitySerializer, BaseContributionSerializer,
BaseActivityListSerializer, BaseTinyActivitySerializer
)
from bluebottle.events.filters import ParticipantListFilter
from bluebottle.events.models import Event, Participant
from bluebottle.utils.utils import reverse_signed
from bluebottle.fsm.serializers import TransitionSerializer
from bluebottle.utils.serializers import ResourcePermissionField, FilteredRelatedField
from bluebottle.utils.serializers import NoCommitMixin
class ParticipantListSerializer(BaseContributionSerializer):
class Meta(BaseContributionSerializer.Meta):
model = Participant
fields = BaseContributionSerializer.Meta.fields + ('time_spent', )
class JSONAPIMeta(BaseContributionSerializer.JSONAPIMeta):
resource_name = 'contributions/participants'
included_resources = [
'user',
'activity'
]
included_serializers = {
'activity': 'bluebottle.events.serializers.TinyEventSerializer',
'user': 'bluebottle.initiatives.serializers.MemberSerializer',
}
class ParticipantSerializer(BaseContributionSerializer):
class Meta(BaseContributionSerializer.Meta):
model = Participant
fields = BaseContributionSerializer.Meta.fields + ('time_spent', )
validators = [
UniqueTogetherValidator(
queryset=Participant.objects.all(),
fields=('activity', 'user')
)
]
class JSONAPIMeta(BaseContributionSerializer.JSONAPIMeta):
resource_name = 'contributions/participants'
included_resources = [
'user',
'activity'
]
included_serializers = {
'activity': 'bluebottle.events.serializers.EventListSerializer',
'user': 'bluebottle.initiatives.serializers.MemberSerializer',
}
class ParticipantTransitionSerializer(TransitionSerializer):
resource = ResourceRelatedField(queryset=Participant.objects.all())
field = 'states'
included_serializers = {
'resource': 'bluebottle.events.serializers.ParticipantSerializer',
'resource.activity': 'bluebottle.events.serializers.EventSerializer',
}
class JSONAPIMeta(object):
resource_name = 'contributions/participant-transitions'
included_resources = [
'resource',
'resource.activity'
]
class EventListSerializer(BaseActivityListSerializer):
permissions = ResourcePermissionField('event-detail', view_args=('pk',))
class Meta(BaseActivityListSerializer.Meta):
model = Event
fields = BaseActivityListSerializer.Meta.fields + (
'capacity',
'start',
'local_start',
'duration',
'is_online',
'location',
'location_hint',
'permissions',
'registration_deadline',
)
class JSONAPIMeta(BaseActivityListSerializer.JSONAPIMeta):
included_resources = [
'location',
]
resource_name = 'activities/events'
included_serializers = dict(
BaseActivitySerializer.included_serializers,
**{
'location': 'bluebottle.geo.serializers.GeolocationSerializer',
}
)
class EventSerializer(NoCommitMixin, BaseActivitySerializer):
permissions = ResourcePermissionField('event-detail', view_args=('pk',))
contributions = FilteredRelatedField(many=True, filter_backend=ParticipantListFilter)
links = serializers.SerializerMethodField()
def get_links(self, instance):
return {
'ical': reverse_signed('event-ical', args=(instance.pk, )),
'google': instance.google_calendar_link,
'outlook': instance.outlook_link,
}
def get_fields(self):
fields = super(EventSerializer, self).get_fields()
user = self.context['request'].user
if (
not user.is_authenticated or (
self.instance and (
user not in [
self.instance.owner,
self.instance.initiative.owner,
self.instance.initiative.activity_manager
] and
not len(self.instance.participants.filter(user=user))
)
)
):
del fields['online_meeting_url']
return fields
class Meta(BaseActivitySerializer.Meta):
model = Event
fields = BaseActivitySerializer.Meta.fields + (
'capacity',
'start',
'local_start',
'duration',
'is_online',
'online_meeting_url',
'location',
'location_hint',
'permissions',
'registration_deadline',
'contributions',
'links'
)
class JSONAPIMeta(BaseActivitySerializer.JSONAPIMeta):
included_resources = BaseActivitySerializer.JSONAPIMeta.included_resources + [
'location',
'contributions',
'contributions.user'
]
resource_name = 'activities/events'
included_serializers = dict(
BaseActivitySerializer.included_serializers,
**{
'location': 'bluebottle.geo.serializers.GeolocationSerializer',
'contributions': 'bluebottle.events.serializers.ParticipantSerializer',
}
)
class TinyEventSerializer(BaseTinyActivitySerializer):
class Meta(BaseTinyActivitySerializer.Meta):
model = Event
fields = BaseTinyActivitySerializer.Meta.fields + ('start', 'local_start', 'duration')
class JSONAPIMeta(BaseTinyActivitySerializer.JSONAPIMeta):
resource_name = 'activities/events'
class EventTransitionSerializer(TransitionSerializer):
resource = ResourceRelatedField(queryset=Event.objects.all())
field = 'states'
included_serializers = {
'resource': 'bluebottle.events.serializers.EventSerializer',
}
class JSONAPIMeta(object):
included_resources = ['resource', ]
resource_name = 'event-transitions'
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] |
classes_and_testsTest/DocumentationFromUnitTestsTestData/DataSet1_py_ClassFile.py | anconaesselmann/LiveUnit | 0 | 44045 | <reponame>anconaesselmann/LiveUnit<filename>classes_and_testsTest/DocumentationFromUnitTestsTestData/DataSet1_py_ClassFile.py
class DataClassFile():
def functionName1(self):
""" One Function (without parameters) with one test function (with single line comments)
where the documentation had not been generated.
"""
say = "say"
fu = "fu"
return say + " " + fu | [
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src/complete_analysis.py | joigno/cloudlight | 3 | 58184 | #!/usr/bin/python
import sys
from cloudlight.classes.graph import Graph
from cloudlight.algorithms.plot import Plot
import cloudlight.tests.data_enc1
graph = Graph()
graph.debug = True
graph.input_debug_links = 200000
graph.output_debug_nodes = 100
graph.max_links_input = 5*10**7
graph.max_nodes_analysis = 10000
filename = len(sys.argv) > 1 and sys.argv[1] or None
filename = '/media/truecrypt1/temp/livejournal-links.txt-symmetric.snowball.10k'
if not filename:
print 'Error: first argument missing, input filename with space separated graph!'
exit(-1)
folder = len(sys.argv) > 2 and sys.argv[2] or None
folder = '/media/truecrypt1/temp/lj/'
if not folder:
print 'Error: second argument missing, output folder name!'
exit(-1)
user_params = len(sys.argv) > 3 and sys.argv[3] or None
if not user_params:
user_params = 'degree,clustering,knn'
graph.load_edgelist(open(filename), num=True)
graph.create_index_degree()
p = Plot()
p.debug = True
sample_size = 1000
bins = 8
compute_data = True
params = {
'degree' : 10, #17,
'clustering' : 10,#15,
'knn' : 10,#12,
'kcore' : 5,#10,
'eccentricity' : 10,#15,
'path_len' : 15,#30,
'scaling' : 8,#10,
'connectivity' : 8,#10,
}
params = dict([(param,params[param]) for param in user_params.split(',')])
p.init_complete_analysis(graph, folder, sample_size, bins, 666, params )
if compute_data:
p.complete_analysis(graph)
p.plot_graph_params()
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lagom/core/transform/centralize.py | dkorduban/lagom | 0 | 46269 | <filename>lagom/core/transform/centralize.py
import numpy as np
from .base_transform import BaseTransform
class Centralize(BaseTransform):
r"""Centralize the input data to zero-centered.
Let :math:`x_1, \dots, x_N` be :math:`N` samples, the centralization does the following:
.. math::
\hat{x}_i = x_i - \frac{1}{N}\sum_{j=1}^{N} x_j, \forall i\in \{ 1, \dots, N \}
.. warning::
The mean is calculated over the first dimension. This allows to deal with batched data with
shape ``[N, ...]`` where ``N`` is the batch size. However, be careful when you want to centralize
a multidimensional array with mean over all elements. This is not supported.
Example::
>>> centralize = Centralize()
>>> centralize([1, 2, 3, 4])
array([-1.5, -0.5, 0.5, 1.5], dtype=float32)
>>> centralize([[1, 3], [2, 11]])
array([[-0.5, -4. ],
[ 0.5, 4. ]], dtype=float32)
"""
def __call__(self, x, mean=None):
r"""Centralize the input data.
Args:
x (object): input data
mean (ndarray): If not ``None``, then use this specific mean to centralize the input.
Returns
-------
out : ndarray
centralized data
"""
assert not np.isscalar(x), 'does not support scalar value !'
# Convert input to ndarray
x = self.to_numpy(x, np.float32)
# Get mean
if mean is None: # compute the mean
# keepdims=True very important ! otherwise wrong value
mean = x.mean(0, keepdims=True) # over first dimension e.g. batch dim
else: # use provided mean
mean = np.asarray(mean).astype(x.dtype)
# Centralize the data
out = x - mean
return out
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tests/test_schwefel26_1981.py | kyawlin/smlb | 0 | 78304 | """Schefel26 1981 dataset tests.
Scientific Machine Learning Benchmark:
A benchmark of regression models in chem- and materials informatics.
(c) <NAME> 2020, Citrine Informatics.
"""
import pytest
import numpy as np
import smlb
def test_schwefel26_1981_examples():
"""Tests instantiating and evaluating Schwefel26 (1981) datasets."""
from datasets.synthetic.schwefel26_1981.schwefel26_1981 import Schwefel261981Data
s1 = Schwefel261981Data(dimensions=1)
s2 = Schwefel261981Data(dimensions=2)
s9 = Schwefel261981Data(dimensions=9)
assert (s1.dimensions, s2.dimensions, s9.dimensions) == (1, 2, 9)
# results from Mathematica reference implementation
# minima
inp = np.asfarray([[420.9687] * 9])
assert np.allclose(s1.labels(inp[:,:1]), [0], atol=1e-4)
assert np.allclose(s2.labels(inp[:,:2]), [0], atol=1e-4)
assert np.allclose(s9.labels(inp[:,:9]), [0], atol=1e-3)
inp = np.asfarray(
[
[0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9],
[0.5, 0.4, 0.3, 0.2, 0.1, 0.9, 0.8, 0.7, 0.6],
]
)
assert np.allclose(s1.labels(inp[:,:1]), [418.9518016407093, 418.6580815304599], atol=1e-6)
assert np.allclose(s2.labels(inp[:,:2]), [837.8482106729184, 837.4045306835739], atol=1e-6)
assert np.allclose(s9.labels(inp[:,:9]), [3767.716410053263, 3767.716410053263], atol=1e-6)
# invalid inputs
with pytest.raises(smlb.InvalidParameterError):
s2.labels(inp)
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demos/wqztemplate/app.py | 13871433675/helloflask | 0 | 127877 | from flask import Flask, render_template
app = Flask(__name__)
namelist = ['chengli', 'qizhi', 'zhangsan', 'wangqizhi']
name2 = 'wangqizhi'
user = {
'username': 'Grey Li',
'bio': 'A boy who loves movies and music.',
}
movies = [
{'name': 'My Neighbor Totoro', 'year': '1988'},
{'name': 'Three Colours trilogy', 'year': '1993'},
{'name': 'Forrest Gump', 'year': '1994'},
{'name': 'Perfect Blue', 'year': '1997'},
{'name': 'The Matrix', 'year': '1999'},
{'name': 'Memento', 'year': '2000'},
{'name': 'The Bucket list', 'year': '2007'},
{'name': 'Black Swan', 'year': '2010'},
{'name': 'Gone Girl', 'year': '2014'},
{'name': 'CoCo', 'year': '2017'},
]
mylist = ['list01', 'list02']
mydic = {'name': 'dicname'}
@app.route('/')
def index():
return render_template('index.html', movies=movies, name2=name2, mylist=mylist, mydic=mydic)
@app.template_global()
def test():
return "method test!"
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setup.py | BerkeleyGW/BGWpy | 27 | 71104 | from __future__ import print_function
import sys
import os
from glob import glob
import shutil
try:
import setuptools
except ImportError:
sys.stderr.write(
"Please install setuptools before running this script. Exiting.")
sys.exit(1)
from setuptools import setup, find_packages
# --------------------------------------------------------------------------- #
# Basic project information
# --------------------------------------------------------------------------- #
name = 'BGWpy'
description = 'Interface BerkeleyGW flows in python.'
license = 'BSD'
url = 'https://github.com/BerkeleyGW/BGWpy'
__version__ = '3.1.4'
# author and author_email should be a single string, not a list, but we can put
# multiple authors / emails by separating them by commas inside the string.
# If you contributed to this package, add your name and email. Don't be shy!
author = '<NAME>'
author_email = '<EMAIL>'
# Requirements
install_requires = [
'numpy >=1.6',
'pymatgen >=2020',
]
# --------------------------------------------------------------------------- #
# Helper functions
# --------------------------------------------------------------------------- #
def find_package_data():
package_data={'BGWpy': ['data/structures/*', 'data/pseudos/*']}
return package_data
def find_data_files():
return [('config', ['config/BGWpyrc'])]
def find_scripts():
scripts = []
scripts.extend(glob(os.path.join('BGWpy', 'scripts', "*.py")))
return scripts
def cleanup():
print('Cleaning up')
shutil.rmtree('BGWpy.egg-info', ignore_errors=True)
shutil.rmtree('build', ignore_errors=True)
shutil.rmtree('__pycache__', ignore_errors=True)
# --------------------------------------------------------------------------- #
# Setup
# --------------------------------------------------------------------------- #
setup_args = dict(
name = name,
version = __version__,
description = description,
author = author,
author_email = author_email,
license = license,
url = url,
install_requires = install_requires,
packages = find_packages(),
package_data = find_package_data(),
data_files = find_data_files(),
scripts = find_scripts(),
)
if __name__ == "__main__":
setup(**setup_args)
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data/ebmnlp/stream.py | bepnye/tf_ner | 0 | 19647 | import os
import data_utils
from pathlib import Path
top_path = Path(os.path.dirname(os.path.abspath(__file__)))
EBM_NLP = Path('/Users/ben/Desktop/ebm_nlp/repo/ebm_nlp_2_00/')
NO_LABEL = '0'
def overwrite_tags(new_tags, tags):
for i, t in enumerate(new_tags):
if t != NO_LABEL:
tags[i] = t
def get_tags(d):
pmid_tags = {}
for e in ['participants', 'interventions', 'outcomes']:
for a in (EBM_NLP / 'annotations' / 'aggregated' / 'starting_spans' / e / d).glob('*.ann'):
pmid = a.stem.split('.')[0]
tags = a.open().read().split()
tags = [e[0] if t == '1' else NO_LABEL for t in tags]
if pmid not in pmid_tags:
pmid_tags[pmid] = tags
else:
overwrite_tags(tags, pmid_tags[pmid])
return pmid_tags
def get_words(pmids):
return { pmid: (EBM_NLP / 'documents' / '{}.tokens'.format(pmid)).open().read().split() for pmid in pmids }
def get_seqs(tag_d, word_d, keys):
tag_seqs = []
word_seqs = []
for k in keys:
words, tags = data_utils.generate_seqs(word_d[k], tag_d[k])
tag_seqs += tags
word_seqs += words
return word_seqs, tag_seqs
TRAIN_TAG_D = get_tags(Path('train/'))
TRAIN_PMIDS = sorted(TRAIN_TAG_D.keys())
TRAIN_WORD_D = get_words(TRAIN_PMIDS)
TRAIN_WORDS, TRAIN_TAGS = get_seqs(TRAIN_TAG_D, TRAIN_WORD_D, TRAIN_PMIDS)
TEST_TAG_D = get_tags(Path('test/gold/'))
TEST_PMIDS = sorted(TEST_TAG_D.keys())
TEST_WORD_D = get_words(TEST_PMIDS)
TEST_WORDS, TEST_TAGS = get_seqs(TEST_TAG_D, TEST_WORD_D, TEST_PMIDS)
def train_words():
return TRAIN_WORDS
def train_tags():
return TRAIN_TAGS
def test_words():
return TEST_WORDS
def test_tags():
return TEST_TAGS
def word_embeddings():
return ((top_path / '..' / 'embeddings' / 'glove.840B.300d.txt').open(), 300)
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noxfile.py | Juh10/pytd | 16 | 191865 | <filename>noxfile.py
import nox
@nox.session
def lint(session):
lint_tools = ["black", "isort", "flake8"]
targets = ["pytd", "setup.py", "noxfile.py"]
session.install(*lint_tools)
session.run("flake8", *targets)
session.run("black", "--diff", "--check", *targets)
session.run("isort", "--diff", "--check", *targets)
@nox.session
@nox.parametrize("pandas", ["0.25.3", "1.0.1"])
def tests(session, pandas):
session.install(".[test,spark]")
session.install(f"pandas=={pandas}")
session.run("pytest", "-v")
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BinaryImageRec/2-class+Demo.py | apur27/public | 1 | 109063 | <filename>BinaryImageRec/2-class+Demo.py<gh_stars>1-10
# coding: utf-8
# In[ ]:
# In[5]:
# Building the CNN
from keras.models import Sequential
from keras.layers import Convolution2D
from keras.layers import MaxPooling2D
from keras.layers import Flatten
from keras.layers import Dense
# Initializing the CNN
classifier = Sequential()
# Step 1 - Convolution
classifier.add(Convolution2D(32, 3, 3, input_shape = (64, 64, 3),activation = 'relu'))
# Step 2 - Max Pooling
classifier.add(MaxPooling2D(pool_size = (2,2)))
# Adding a second convolution layer
classifier.add(Convolution2D(32, 3, 3, activation = 'relu'))
classifier.add(MaxPooling2D(pool_size = (2,2)))
# Adding a third convolution layer
classifier.add(Convolution2D(64, 3, 3, activation = 'relu'))
classifier.add(MaxPooling2D(pool_size = (2,2)))
# Step 3 - Flattening
classifier.add(Flatten())
# Step 4 - Full Connection
classifier.add(Dense(output_dim = 128, activation= 'relu'))
classifier.add(Dense(output_dim = 1, activation= 'sigmoid'))
# Compiling the CNN
classifier.compile(optimizer = 'adam', loss = 'binary_crossentropy', metrics = ['accuracy'])
# Part 2 - Fitting the CNN to the image
from keras.preprocessing.image import ImageDataGenerator
train_datagen = ImageDataGenerator(
rescale=1./255,
shear_range=0.2,
zoom_range=0.2,
horizontal_flip=True)
test_datagen = ImageDataGenerator(rescale=1./255)
train_generator = train_datagen.flow_from_directory(
'/home/demo/TrainingData',
target_size=(64, 64),
batch_size=10,
class_mode='binary')
validation_generator = test_datagen.flow_from_directory(
'/home/demo/ValidationData',
target_size=(64, 64),
batch_size=5,
class_mode='binary')
classifier.fit_generator(
train_generator,
samples_per_epoch=2000,
epochs=5,
validation_data=validation_generator,
validation_steps=800)
import numpy as np
from keras.preprocessing import image
test_image = image.load_img( '/home/Prediction/325109432-4_3_Q25_00200559_000009.jpg',target_size=(64, 64))
test_image = image.img_to_array(test_image)
test_image = np.expand_dims(test_image, axis = 0)
result = classifier.predict(test_image)
print (result)
print (train_generator.class_indices)
# In[6]:
test_image = image.load_img( '/home/demo/Prediction/325047821-3_3_Q39_00204704_000015.jpg',target_size=(64, 64))
test_image = image.img_to_array(test_image)
test_image = np.expand_dims(test_image, axis = 0)
result = classifier.predict(test_image)
print (result)
print (train_generator.class_indices)
# In[7]:
test_image = image.load_img( '/home/demo/Prediction/325047821-3_3_Q39_00204704_000015.jpg',target_size=(64, 64))
test_image = image.img_to_array(test_image)
test_image = np.expand_dims(test_image, axis = 0)
result = classifier.predict(test_image)
print (result)
print (train_generator.class_indices)
# In[8]:
test_image = image.load_img( '/home/demo/Prediction/325109299-1_3_Q25_00205691_000031.jpg',target_size=(64, 64))
test_image = image.img_to_array(test_image)
test_image = np.expand_dims(test_image, axis = 0)
result = classifier.predict(test_image)
print (result)
print (train_generator.class_indices)
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2
] |
teacher/admin.py | Swarda6/TCS-Project | 0 | 153135 | <reponame>Swarda6/TCS-Project
from django.contrib import admin
from .models import Teacher
admin.site.register(Teacher)
# Register your models here.
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src/geni.py | hhugo/General | 7 | 138473 | <reponame>hhugo/General
import itertools
import functools
import os
import sys
import textwrap
max_arity = 6
def generate(*element, **kwds):
p = functools.partial(print, file=kwds.get("file"))
previous_line_was_end = False
for line in indent(element, kwds.get("indent", 0)):
line_is_end = line.strip() == "end"
if previous_line_was_end and not line_is_end:
p("")
p(line)
previous_line_was_end = line_is_end
def indent(element, levels=1):
if isinstance(element, str):
yield f"{' ' * levels}{element}"
else:
for sub in element:
yield from indent(sub, levels)
class Facets:
def __init__(
self, *,
prefix, name,
variadic,
bases, values, extensions,
test_examples, test_requirements,
):
self.prefix = prefix
self.name = name
self.bases = list(bases)
self.max_arity = min(itertools.chain([max_arity], (i.max_arity for i in self.bases))) if variadic else 1
self.arities = list(range(self.max_arity))
self.non_zero_arities = list(range(1, self.max_arity))
self.values = list(values)
self.extensions = list(extensions)
self.all_items = list(itertools.chain(
self.values,
itertools.chain.from_iterable(extension.members for extension in self.extensions),
))
self.operators = [item for item in self.all_items if item.operator is not None]
self.test_examples = list(test_examples)
self.test_requirements = list(test_requirements)
@property
def graphviz_label(self):
parts = [self.name]
if len(self.values) > 0:
parts.append("")
parts += [val.name for val in self.values]
exts = [val.name for extension in self.extensions for val in extension.members]
if len(exts) > 0:
parts.append("")
parts += exts
return "\\n".join(parts)
@property
def specification(self):
return mod_spec(self.name, self.specification_items)
@property
def implementation(self):
return mod_impl(self.name, self.implementation_items)
@property
def specification_items(self):
if self.__has_operators():
yield self.__operators_specification()
if self.__is_basic():
yield self.__basic_specification_items()
else:
yield self.__extended_specification_items()
yield self.__extension_makers_specification_items()
yield self.__tests_specification()
@property
def implementation_items(self):
if self.__has_operators():
yield self.__operators_implementation()
if self.__is_basic():
yield self.__basic_implementation_items()
else:
yield self.__extended_implementation_items()
yield self.__extensions_makers_implementation_items()
yield self.__tests_implementation()
def __contextualized_name(self, prefix):
if prefix == self.prefix:
return self.name
else:
return f"{self.prefix}.{self.name}"
# Operators
def __has_operators(self):
return self.__has_own_operators() or self.__inherits_operators()
def __has_own_operators(self):
return len(self.operators) > 0
def __inherits_operators(self):
return any(base.__has_operators() for base in self.bases)
def __operators_specification(self):
return mod_spec("Operators", self.__operators_specification_items())
def __operators_implementation(self):
return mod_impl("Operators", self.__operators_implementation_items())
def __operators_specification_items(self):
yield self.__operators_s0_mod_type()
if len(self.operators) > 0:
yield self.__operators_make0_specification()
def __operators_implementation_items(self):
yield self.__operators_s0_mod_type()
if len(self.operators) > 0:
yield self.__operators_make0_implementation()
def __operators_s0_mod_type(self):
return mod_type("S0", self.__operators_s0_mod_type_items())
def __operators_s0_mod_type_items(self):
yield "type t"
for base in self.bases:
if base.__has_operators():
yield f"include {base.__contextualized_name(self.prefix)}.Operators.S0 with type t := t"
for operator in self.operators:
yield f"val ( {operator.operator} ): { operator.value_type(0, 't')}"
def __operators_make0_specification(self):
yield "module Make0(M: sig"
yield " type t"
for operator in self.operators:
yield f" val {operator.name}: {operator.value_type(0, 't')}"
yield "end): sig"
for operator in self.operators:
yield f" val ( {operator.operator} ): {operator.value_type(0, 'M.t')}"
yield "end"
def __operators_make0_implementation(self):
yield "module Make0(M: sig"
yield " type t"
for operator in self.operators:
yield f" val {operator.name}: {operator.value_type(0, f'{type_params(0)}t')}"
yield "end) = struct"
for operator in self.operators:
yield f" let ( {operator.operator} ) = M.{operator.name}"
yield "end"
# Core contents: basic
def __is_basic(self):
return len(list(itertools.chain.from_iterable(extension.members for extension in self.extensions))) == 0
def __basic_specification_items(self):
yield self.__basic_signature_mod_types()
yield self.__basic_specialize_specifications()
def __basic_implementation_items(self):
yield self.__basic_signature_mod_types()
yield self.__basic_specialize_implementations()
def __basic_signature_mod_types(self):
for arity in self.arities:
yield mod_type(f"S{arity}", self.__basic_signature_mod_type_items(arity))
def __basic_signature_mod_type_items(self, arity):
t = f"{type_params(arity)}t"
yield f"type {t}"
if arity == 0 and self.__has_operators() and self.__is_basic():
yield "module O: Operators.S0 with type t := t"
for base in self.bases:
if arity == 0 and base.__has_operators():
operators_constraint = " and module O := O"
else:
operators_constraint = ""
yield f"include {base.__contextualized_name(self.prefix)}.S{arity} with type {t} := {t}{operators_constraint}"
for value in self.values:
yield f"val {value.name}: {value.value_type(arity, t)}"
def __basic_specialize_specifications(self):
for arity in self.non_zero_arities:
functor_params = "".join(f"({a.upper()}: S0)" for a in abcd(arity))
yield mod_spec(f"Specialize{arity}(M: S{arity}){functor_params}", self.__specialize_specification_items(arity))
def __basic_specialize_implementations(self):
for arity in self.non_zero_arities:
functor_params = "".join(f"({a.upper()}: S0)" for a in abcd(arity))
yield mod_impl(f"Specialize{arity}(M: S{arity}){functor_params}", self.__basic_specialize_implementation_items(arity))
def __specialize_specification_items(self, arity):
yield f"type t = {type_args(arity)}M.t",
yield "include S0 with type t := t",
def __basic_specialize_implementation_items(self, arity):
yield f"type t = {type_args(arity)}M.t"
functor_args = "".join(f"({a.upper()})" for a in abcd(arity))
for base in self.bases:
yield f"module {base.name}_ = {base.__contextualized_name(self.prefix)}.Specialize{arity}(M){functor_args}"
if self.__inherits_operators():
yield mod_impl("O", (f"include {base.name}_.O" for base in self.bases if base.__has_operators()))
for base in self.bases:
if base.__has_operators():
operators_constraint = " and module O := O"
else:
operators_constraint = ""
yield f"include ({base.name}_: {base.__contextualized_name(self.prefix)}.S0 with type t := t{operators_constraint})"
for value in self.values:
yield value.value_specialization(arity)
# Core contents: extended
def __extended_specification_items(self):
yield mod_spec("Basic", self.__basic_specification_items())
yield self.__extended_signature_mod_types()
yield self.__extended_specialize_specifications()
def __extended_implementation_items(self):
yield mod_impl("Basic", self.__basic_implementation_items())
yield self.__extended_signature_mod_types()
yield self.__extended_specialize_implementations()
def __extended_signature_mod_types(self):
for arity in self.arities:
yield mod_type(f"S{arity}", self.__extended_signature_mod_type_items(arity))
def __extended_signature_mod_type_items(self, arity):
yield f"include Basic.S{arity}"
if arity == 0:
yield "module O: Operators.S0 with type t := t"
for extension in self.extensions:
for value in extension.members:
yield f"val {value.name}: {value.value_type(arity, f'{type_params(arity)}t')}"
def __extended_specialize_specifications(self):
for arity in self.non_zero_arities:
functor_params = "".join(f"({a.upper()}: Basic.S0)" for a in abcd(arity))
yield mod_spec(f"Specialize{arity}(M: S{arity}){functor_params}", self.__specialize_specification_items(arity))
def __extended_specialize_implementations(self):
for arity in self.non_zero_arities:
functor_params = "".join(f"({a.upper()}: Basic.S0)" for a in abcd(arity))
yield mod_impl(f"Specialize{arity}(M: S{arity}){functor_params}", self.__extended_specialize_implementation_items(arity))
def __extended_specialize_implementation_items(self, arity):
functor_args = "".join(f"({a.upper()})" for a in abcd(arity))
yield mod_impl("Self",
f"include Basic.Specialize{arity}(M){functor_args}",
(value.value_specialization(arity) for extension in self.extensions for value in extension.members)
)
yield "module O = Operators.Make0(Self)"
yield "include Self"
# Extension makers
def __extension_makers_specification_items(self):
for extension in self.extensions:
yield mod_spec(extension.name, extension.extension_makers_specification(self.arities))
def __extensions_makers_implementation_items(self):
for extension in self.extensions:
yield mod_impl(f"{extension.name}_", extension.extension_makers_implementation(self.arities))
# Tests
def __tests_specification(self):
yield mod_spec("Tests", self.__tests_specification_items())
def __tests_implementation(self):
yield mod_impl("Tests_", self.__tests_implementation_items())
def __tests_specification_items(self):
yield self.__tests_examples_specification()
yield mod_spec("Testable", self.__tests_testable_items())
yield self.__tests_makers_specifications()
def __tests_implementation_items(self):
yield self.__tests_examples_implementation()
yield mod_impl("Testable", self.__tests_testable_items())
yield self.__tests_makers_implementations()
def __tests_examples_specification(self):
yield mod_spec("Examples", self.__tests_examples_items())
def __tests_examples_implementation(self):
yield mod_impl("Examples", self.__tests_examples_items())
def __tests_examples_items(self):
if self.max_arity > 1:
yield mod_type("Element", self.__tests_examples_element_mod_type_items())
for arity in self.arities:
yield mod_type(f"S{arity}", self.__tests_examples_mod_type_items(arity))
def __tests_examples_element_mod_type_items(self):
yield "type t"
basic = "" if self.__is_basic() else "Basic."
yield f"include {basic}S0 with type t := t"
for req in self.test_requirements:
basic = "" if req.__is_basic() else "Basic."
yield f"include {req.name}.{basic}S0 with type t := t"
def __tests_examples_mod_type_items(self, arity):
yield f"type {type_params(arity)}t"
for a in abcd(arity):
yield f"module {a.upper()}: Element"
for base in self.bases:
yield (
f"include {base.__contextualized_name(self.prefix)}.Tests.Examples.S{arity} with type {type_params(arity)}t := {type_params(arity)}t"
+ "".join(f" and module {a.upper()} := {a.upper()}" for a in abcd(arity))
)
t = f"{type_args(arity)}t"
for item in self.test_examples:
yield f"val {item.name}: {item.value_type(0, t)}"
def __tests_testable_items(self):
for arity in self.arities:
yield mod_type(f"S{arity}", self.__tests_testable_mod_type_items(arity))
def __tests_testable_mod_type_items(self, arity):
yield f"include S{arity}"
for req in self.test_requirements:
basic = "" if req.__is_basic() else "Basic."
yield f"include {req.__contextualized_name(self.prefix)}.{basic}S{arity} with type {type_params(arity)}t := {type_params(arity)}t"
def __tests_makers_specifications(self):
for arity in self.arities:
yield mod_spec(
f"Make{arity}(M: Testable.S{arity})(E: Examples.S{arity} with type {type_params(arity)}t := {type_params(arity)}M.t)",
"val test: Test.t",
)
def __tests_makers_implementations(self):
yield mod_impl(
"MakeMakers(MakeExamples: functor (M: Testable.S0) -> functor (E: Examples.S0 with type t := M.t) -> Examples.S0 with type t := M.t)(MakeTests: functor (M: Testable.S0) -> functor (E: Examples.S0 with type t := M.t) -> sig val tests: Test.t list end)",
self.__tests_makers_implementations_items(),
)
def __tests_makers_implementations_items(self):
yield mod_impl(
"Make0(M: Testable.S0)(E: Examples.S0 with type t := M.t)",
self.__tests_make0_implementation_items(),
)
for arity in self.non_zero_arities:
yield mod_impl(
f"Make{arity}(M: Testable.S{arity})(E: Examples.S{arity} with type {type_params(arity)}t := {type_params(arity)}M.t)",
self.__tests_maker_implementation_items(arity),
)
def __tests_make0_implementation_items(self):
yield "open Testing"
yield "module E = MakeExamples(M)(E)"
yield f'let test = "{self.name}" >:: ['
for base in self.bases:
yield f" (let module T = {base.__contextualized_name(self.prefix)}.Tests.Make0(M)(E) in T.test);"
yield "] @ (let module T = MakeTests(M)(E) in T.tests)"
def __tests_maker_implementation_items(self, arity):
yield "include Make0(struct",
functor_args = "".join(f"(E.{a.upper()})" for a in abcd(arity))
yield indent(f"include Specialize{arity}(M){functor_args}"),
for req in self.test_requirements:
basic = "" if req.__is_basic() else "Basic."
yield indent(f"include ({req.__contextualized_name(self.prefix)}.{basic}Specialize{arity}(M){functor_args}: {req.__contextualized_name(self.prefix)}.{basic}S0 with type t := t)")
yield "end)(E)",
def mod_spec(name, *items):
yield f"module {name}: sig"
yield indent(items)
yield "end"
def mod_impl(name, *items):
yield f"module {name} = struct"
yield indent(items)
yield "end"
def mod_type(name, *items):
yield f"module type {name} = sig"
yield indent(items)
yield "end"
def type_params(arity):
if arity == 0:
return ""
elif arity == 1:
return "'a "
else:
return "({}) ".format(', '.join(f"'{a}" for a in abcd(arity)))
def type_args(arity):
if arity == 0:
return ""
elif arity == 1:
return "A.t "
else:
return "({}) ".format(', '.join(f"{a.upper()}.t" for a in abcd(arity)))
def abcd(arity):
return list("abcdefghijkl"[:arity])
class Extension:
def __init__(self, *, name, members, requirements, basic_production):
self.__name = name
self.__members = list(members)
self.__requirements = list(requirements)
self.__basic_production = list(basic_production)
@property
def name(self):
return self.__name
@property
def members(self):
return list(self.__members)
def extension_makers_specification(self, arities):
for arity in arities:
yield f"module Make{arity}(M: sig"
yield f" type {type_params(arity)}t"
for requirement in self.__requirements:
yield f" val {requirement.name}: {requirement.value_type(arity, f'{type_params(arity)}t')}"
yield "end): sig"
for value in itertools.chain(self.members, self.__basic_production):
yield f" val {value.name}: {value.value_type(arity, f'{type_params(arity)}M.t')}"
yield "end"
def extension_makers_implementation(self, arities):
yield "module MakeMakers(Implementation: sig"
additional_prefix_params = ""
for requirement in self.__requirements:
additional_prefix_params += LabelledParameter(requirement.name, requirement.value_type(0, "'a")).param_type(0, "t") + " -> "
t = "'a"
for value in itertools.chain(self.members, self.__basic_production):
yield f" val {value.name}: {additional_prefix_params}{value.value_type(0, t)}"
yield "end) = struct"
for arity in arities:
yield f" module Make{arity}(M: sig"
yield f" type {type_params(arity)}t"
requirement_names = []
for requirement in self.__requirements:
yield f" val {requirement.name}: {requirement.value_type(arity, f'{type_params(arity)}t')}"
requirement_names.append(requirement.name)
yield " end) = struct"
for value in itertools.chain(self.members, self.__basic_production):
yield indent(value.value_extension(requirement_names, arity), levels=2)
yield " end"
yield "end"
variadic_type_marker = "THIS_VARIADIC_TYPE_MARKER_SHOULD_NEVER_APPEAR_IN_GENERATED_CODE"
class UnlabelledParameter:
def __init__(self, type_):
self.__type = type_
def param_type(self, _arity, t):
return self.__type.replace(variadic_type_marker, t)
def param_pattern(self, _arity, index):
return "xyzuvw"[index]
param_specialization = param_pattern
param_extension = param_pattern
class LabelledParameter:
def __init__(self, label, type_):
self.__label = label
self.__type = type_
def param_type(self, _arity, t):
return f"{self.__label}:({self.__type.replace(variadic_type_marker, t)})"
def param_pattern(self, _arity, _index):
return f"~{self.__label}"
param_specialization = param_pattern
param_extension = param_pattern
class DelegateParameter:
def __init__(self, values, delegate_name):
self.__values = values
self.__delegate_name = delegate_name
@property
def name(self):
return self.__delegate_name
def param_type(self, arity, t):
if arity == 0:
return None
else:
return " -> ".join(
f"{self.name}_{a}:(" + self.__values[self.__delegate_name].value_type(0, f"'{a}") + ")"
for a in abcd(arity)
)
def param_pattern(self, arity, _index):
if arity == 0:
return None
else:
return " ".join(f"~{self.name}_{a}" for a in abcd(arity))
def param_specialization(self, arity, _index):
if arity == 0:
return None
else:
return " ".join(f"~{self.name}_{a}:{a.upper()}.{self.name}" for a in abcd(arity))
def param_extension(self, _arity, _index):
return None
class Value:
def __init__(self, *, name, type_chain, operator):
self.__name = name
self.__type_chain = list(type_chain)
assert len(self.__type_chain) > 0
self.__parameters = self.__type_chain[:-1]
self.__operator = operator
@property
def name(self):
return self.__name
@property
def operator(self):
return self.__operator
def value_type(self, arity, t):
return " -> ".join(filter(None, (param.param_type(arity, t) for param in self.__type_chain)))
def value_specialization(self, arity):
yield (
f"let {self.name} "
+ " ".join(filter(None, (p.param_pattern(0, i) for (i, p) in enumerate(self.__parameters))))
+ f" = M.{self.name} "
+ " ".join(p.param_specialization(arity, i) for (i, p) in enumerate(self.__parameters))
)
def value_extension(self, requirements, arity):
for param in self.__parameters:
if isinstance(param, DelegateParameter):
delegate_ = param
break
else:
delegate_ = None
yield (
f"let {self.name} "
+ " ".join(filter(None, (p.param_pattern(arity, i) for (i, p) in enumerate(self.__parameters))))
+ f" = Implementation.{self.name} "
+ "".join(
f"~{req}:(M.{req}" + "".join([] if delegate_ is None else (f" ~{delegate_.name}_{a}" for a in abcd(arity))) + ") "
for req in requirements
)
+ " ".join(filter(None, (p.param_extension(arity, i) for (i, p) in enumerate(self.__parameters))))
)
values = {}
def val(name, *type_chain, operator=None):
def make_param(param):
if isinstance(param, str):
return UnlabelledParameter(param)
elif isinstance(param, dict):
[(label, type_)] = param.items()
return LabelledParameter(label, type_)
elif isinstance(param, tuple):
if param[0] is DelegateParameter:
return DelegateParameter(values, param[1])
else:
assert False
else:
assert False
value = Value(
name=name,
type_chain=(make_param(param) for param in type_chain),
operator=operator,
)
assert name not in values
values[name] = value
return value
def ext(name, *, members, requirements):
def make_requirement(req):
if isinstance(req, str):
return values[req]
elif isinstance(req, Value):
return req
else:
assert False
members = list(members)
return Extension(
name=name,
members=(member for member in members if isinstance(member, Value)),
requirements=(make_requirement(requirement) for requirement in requirements),
basic_production=(values[member] for member in members if isinstance(member, str)),
)
def deleg(name):
return (DelegateParameter, name)
t = variadic_type_marker
traits = []
def trait(
name,
variadic=True,
values=[],
extensions=[],
test_examples=[],
test_requirements=[],
):
trait = Facets(
prefix="Traits",
name=name,
variadic=variadic,
bases=[],
values=values,
extensions=extensions,
test_examples=test_examples,
test_requirements=test_requirements,
)
traits.append(trait)
return trait
concepts = []
def concept(
name,
bases,
values=[],
test_requirements=[],
):
concept = Facets(
prefix="Concepts",
name=name,
variadic=True,
bases=bases,
values=values,
extensions=[],
test_examples=[],
test_requirements=test_requirements,
)
concepts.append(concept)
return concept
###### TRAITS ######
# @feature (?) Add trait Testable with val test: Test.t
# A *representation* is a string representing a value for a software developer audience.
# When possible, it should be a valid OCaml expression for the value.
representable = trait(
"Representable",
values=[val("repr", t, deleg("repr"), "string")],
test_examples=[val("representations", f"({t} * string) list")],
)
equalities = val("equalities", f"{t} list list")
equatable = trait(
"Equatable",
values=[val("equal", t, t, deleg("equal"), "bool", operator="=")],
extensions=[
ext(
"Different",
members=[val("different", t, t, deleg("equal"), "bool", operator="<>")],
requirements=["equal"],
),
],
test_examples=[
equalities,
val("differences", f"({t} * {t}) list"),
],
test_requirements=[representable],
)
displayable = trait(
"Displayable",
variadic=False,
values=[val("to_string", t, "string")],
test_examples=[val("displays", f"({t} * string) list")],
)
# @feature Traits.Hashable with val hash: t -> int, Poly using Hashtbl.hash
parsable = trait(
"Parsable",
variadic=False,
values=[
val("try_of_string", "string", f"{t} option"),
val("of_string", "string", t),
],
test_examples=[val("literals", f"(string * {t}) list")],
test_requirements=[equatable, representable],
)
comparable = trait(
"Comparable",
values=[val("compare", t, t, deleg("compare"), "Compare.t")],
extensions=[
ext(
"GreaterLessThan",
members=[
val("less_than", t, t, deleg("compare"), "bool", operator="<"),
val("less_or_equal", t, t, deleg("compare"), "bool", operator="<="),
val("greater_than", t, t, deleg("compare"), "bool", operator=">"),
val("greater_or_equal", t, t, deleg("compare"), "bool", operator=">="),
],
requirements=["compare"],
),
ext(
"Between",
members=[
val("between", t, {"low": t}, {"high": t}, deleg("compare"), "bool"),
val("between_or_equal", t, {"low": t}, {"high": t}, deleg("compare"), "bool")
],
requirements=["less_than", "less_or_equal", "greater_than", "greater_or_equal"],
),
ext(
"MinMax",
members=[
val("min", t, t, deleg("compare"), t),
val("max", t, t, deleg("compare"), t),
val("min_max", t, t, deleg("compare"), f"{t} * {t}"),
],
requirements=["compare"],
),
],
test_examples=[
val("orders", f"{t} list list"),
equalities,
],
test_requirements=[equatable, representable],
)
ringoid = trait(
"Ringoid",
variadic=False,
values=[
val("zero", t),
val("one", t),
# val("posate", t, t, operator="~+"),
val("negate", t, t, operator="~-"),
val("add", t, t, t, operator="+"),
val("subtract", t, t, t, operator="-"),
val("multiply", t, t, t, operator="*"),
val("divide", t, t, t, operator="/"),
],
extensions=[
ext(
"Subtract",
members=["subtract"],
requirements=["negate", "add"],
),
ext(
"Square",
members=[val("square", t, t)],
requirements=["multiply"],
),
ext(
"Exponentiate",
members=[val("exponentiate", t, "int", t, operator="**")],
requirements=[
"one",
"square",
"multiply",
val("exponentiate_negative_exponent", {"exponentiate": f"{t} -> int -> {t}"}, t, "int", t),
],
),
],
test_examples=[
val("additions", f"({t} * {t} * {t}) list"),
val("negations", f"({t} * {t}) list"),
val("multiplications", f"({t} * {t} * {t}) list"),
val("divisions", f"({t} * {t} * {t}) list"),
val("exponentiations", f"({t} * int * {t}) list"),
],
test_requirements=[equatable, representable],
)
of_standard_numbers = trait(
"OfStandardNumbers",
variadic=False,
values=[
val("of_int", "int", t),
val("of_float", "float", t),
],
)
to_standard_numbers = trait(
"ToStandardNumbers",
variadic=False,
values=[
val("to_int", t, "int"),
val("to_float", t, "float"),
],
)
pred_succ = trait(
"PredSucc",
variadic=False,
values=[
val("pred", t, t),
val("succ", t, t),
],
extensions=[
ext(
"PredSucc",
members=["pred", "succ"],
requirements=["one", "add", "subtract"],
),
],
test_examples=[val("successions", f"({t} * {t}) list")],
test_requirements=[equatable, representable],
)
###### CONCEPTS ######
# @feature Concepts for iterables and collections. Something like Collection, Container, MonoBag, MultiBag, LinearContainer
identifiable = concept(
"Identifiable",
bases=[equatable, representable],
)
able = concept(
"Able",
bases=[identifiable, comparable],
)
stringable = concept(
"Stringable",
bases=[displayable, parsable],
test_requirements=[representable, equatable], # @todo Deduce from parsable's test requirements
)
number = concept(
"Number",
bases=[identifiable, stringable, ringoid, of_standard_numbers],
)
real_number = concept(
"RealNumber",
# @feature sign
bases=[number, comparable, to_standard_numbers],
values=[
val("abs", t, t),
val("modulo", t, t, t, operator="mod"),
],
)
integer = concept(
"Integer",
# @feature Bitwise?
# @feature gcd, lcm, quomod
# @feature width: like Sys.int_size
bases=[real_number, pred_succ],
)
if __name__ == "__main__":
destination = sys.argv[1]
assert os.path.isdir(destination)
def gen(name, *items):
with open(os.path.join(destination, name), "w") as f:
generate(items, file = f)
gen(
"Facets.dot",
'digraph {',
' rankdir="BT"',
' node [shape="box"]',
' subgraph cluster_Traits {',
' label="Traits";',
(f' {trait.name.lower()} [label="{trait.graphviz_label}"];' for trait in traits),
' }',
' subgraph cluster_Concepts {',
' label="Concepts";',
(f' {concept.name.lower()} [label="{concept.graphviz_label}"];' for concept in concepts),
' }',
(f' {concept.name.lower()} -> {base.name.lower()}' for concept in concepts for base in concept.bases),
'}',
)
gen("Traits.mli", (trait.specification for trait in traits))
gen("Concepts.mli", (concept.specification for concept in concepts))
destination = os.path.join(sys.argv[1], "Traits")
os.mkdir(destination)
for trait in traits:
gen(f"{trait.name}.ml", trait.implementation_items)
destination = os.path.join(sys.argv[1], "Concepts")
os.mkdir(destination)
for concept in concepts:
gen(f"{concept.name}.ml", concept.implementation_items)
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2
] |
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