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|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
beec5c42019bf790f7eff3d7e22e90a1e3ff9437 | 6,463 | py | Python | tests/test_layer.py | deppen8/prospect | fbde75e57fef967643ca0f4c43fe53004d11da70 | [
"MIT"
] | null | null | null | tests/test_layer.py | deppen8/prospect | fbde75e57fef967643ca0f4c43fe53004d11da70 | [
"MIT"
] | 9 | 2021-02-02T03:40:32.000Z | 2021-09-10T13:35:05.000Z | tests/test_layer.py | deppen8/prospect | fbde75e57fef967643ca0f4c43fe53004d11da70 | [
"MIT"
] | null | null | null | import pytest
from geopandas import GeoDataFrame
import prospect
def test_returns_Layer(a_layer):
assert isinstance(a_layer, prospect.Layer)
def test_has_desired_attributes(a_layer):
for a in ["name", "input_features", "df"]:
assert hasattr(a_layer, a)
def test_name_attribute_str(a_layer):
assert isinstance(a_layer.name, str)
def test_input_features_attribute_List_Features(a_layer):
assert isinstance(a_layer.input_features, list)
assert all(
isinstance(feature, prospect.Feature) for feature in a_layer.input_features
)
def test_df_attribute_gdf(a_layer):
assert isinstance(a_layer.df, GeoDataFrame)
def test_from_shapefile_returns_Layer(a_area_layer_shapefile_path_pair):
area = prospect.Area.from_shapefile(
name="test_area_from_shapefile", path=a_area_layer_shapefile_path_pair[0]
)
layer = prospect.Layer.from_shapefile(
path=a_area_layer_shapefile_path_pair[1],
name="test_layer_from_shapefile",
area=area,
)
assert isinstance(layer, prospect.Layer)
def test_from_shapefile_has_desired_attributes(a_layer_from_shapefile):
for a in ["name", "input_features", "df"]:
assert hasattr(a_layer_from_shapefile, a)
def test_from_shapefile_name_attribute_str(a_layer_from_shapefile):
assert isinstance(a_layer_from_shapefile.name, str)
def test_from_shapefile_input_features_attribute_List_Features(a_layer_from_shapefile):
assert isinstance(a_layer_from_shapefile.input_features, list)
assert all(
isinstance(feature, prospect.Feature)
for feature in a_layer_from_shapefile.input_features
)
def test_from_shapefile_df_attribute_gdf(a_layer_from_shapefile):
assert isinstance(a_layer_from_shapefile.df, GeoDataFrame)
def test_from_pseudorandom_pts_returns_Layer(an_area_from_shapefile):
layer = prospect.Layer.from_pseudorandom_points(
n=25, name="layer_from_pseudorandom_pts", area=an_area_from_shapefile
)
assert isinstance(layer, prospect.Layer)
def test_from_pseudorandom_pts_has_desired_attributes(a_layer_from_pseudorandom_points):
for a in ["name", "input_features", "df"]:
assert hasattr(a_layer_from_pseudorandom_points, a)
def test_from_pseudorandom_pts_name_attribute_str(a_layer_from_pseudorandom_points):
assert isinstance(a_layer_from_pseudorandom_points.name, str)
def test_from_pseudorandom_pts_input_features_attribute_List_Features(
a_layer_from_pseudorandom_points,
):
assert isinstance(a_layer_from_pseudorandom_points.input_features, list)
assert all(
isinstance(feature, prospect.Feature)
for feature in a_layer_from_pseudorandom_points.input_features
)
def test_from_pseudorandom_pts_creates_expected_25_points(
a_layer_from_pseudorandom_points,
):
assert len(a_layer_from_pseudorandom_points.input_features) == 25
assert a_layer_from_pseudorandom_points.df.shape[0] == 25
def test_from_pseudorandom_pts_df_attribute_gdf(a_layer_from_pseudorandom_points):
assert isinstance(a_layer_from_pseudorandom_points.df, GeoDataFrame)
def test_from_poisson_pts_returns_Layer(an_area_from_shapefile):
layer = prospect.Layer.from_poisson_points(
rate=0.001, name="layer_from_poisson_pts", area=an_area_from_shapefile
)
assert isinstance(layer, prospect.Layer)
def test_from_poisson_pts_has_desired_attributes(a_layer_from_poisson_points):
for a in ["name", "input_features", "df"]:
assert hasattr(a_layer_from_poisson_points, a)
def test_from_poisson_pts_name_attribute_str(a_layer_from_poisson_points):
assert isinstance(a_layer_from_poisson_points.name, str)
def test_from_poisson_pts_input_features_attribute_List_Features(
a_layer_from_poisson_points,
):
assert isinstance(a_layer_from_poisson_points.input_features, list)
assert all(
isinstance(feature, prospect.Feature)
for feature in a_layer_from_poisson_points.input_features
)
def test_from_poisson_pts_df_attribute_gdf(a_layer_from_poisson_points):
assert isinstance(a_layer_from_poisson_points.df, GeoDataFrame)
def test_from_thomas_pts_returns_Layer(an_area_from_shapefile):
layer = prospect.Layer.from_thomas_points(
parent_rate=0.001,
child_rate=1,
gauss_var=5,
name="layer_from_thomas_pts",
area=an_area_from_shapefile,
)
assert isinstance(layer, prospect.Layer)
def test_from_thomas_pts_has_desired_attributes(a_layer_from_thomas_points):
for a in ["name", "input_features", "df"]:
assert hasattr(a_layer_from_thomas_points, a)
def test_from_thomas_pts_name_attribute_str(a_layer_from_thomas_points):
assert isinstance(a_layer_from_thomas_points.name, str)
def test_from_thomas_pts_input_features_attribute_List_Features(
a_layer_from_thomas_points,
):
assert isinstance(a_layer_from_thomas_points.input_features, list)
assert all(
isinstance(feature, prospect.Feature)
for feature in a_layer_from_thomas_points.input_features
)
def test_from_thomas_pts_df_attribute_gdf(a_layer_from_thomas_points):
assert isinstance(a_layer_from_thomas_points.df, GeoDataFrame)
def test_from_matern_pts_returns_Layer(an_area_from_shapefile):
layer = prospect.Layer.from_matern_points(
parent_rate=0.001,
child_rate=1,
radius=5,
name="layer_from_matern_pts",
area=an_area_from_shapefile,
)
assert isinstance(layer, prospect.Layer)
def test_from_matern_pts_has_desired_attributes(a_layer_from_matern_points):
for a in ["name", "input_features", "df"]:
assert hasattr(a_layer_from_matern_points, a)
def test_from_matern_pts_name_attribute_str(a_layer_from_matern_points):
assert isinstance(a_layer_from_matern_points.name, str)
def test_from_matern_pts_input_features_attribute_List_Features(
a_layer_from_matern_points,
):
assert isinstance(a_layer_from_matern_points.input_features, list)
assert all(
isinstance(feature, prospect.Feature)
for feature in a_layer_from_matern_points.input_features
)
def test_from_matern_pts_df_attribute_gdf(a_layer_from_matern_points):
assert isinstance(a_layer_from_matern_points.df, GeoDataFrame)
def test_from_rectangles_raises_NotImplementedError(an_area_from_shapefile):
with pytest.raises(NotImplementedError):
prospect.Layer.from_rectangles(an_area_from_shapefile, n=25)
| 31.222222 | 88 | 0.792511 | 911 | 6,463 | 5.107574 | 0.065862 | 0.07608 | 0.103159 | 0.089835 | 0.863959 | 0.762949 | 0.656566 | 0.556845 | 0.526972 | 0.476252 | 0 | 0.005221 | 0.140647 | 6,463 | 206 | 89 | 31.373786 | 0.832553 | 0 | 0 | 0.257353 | 0 | 0 | 0.040229 | 0.021662 | 0 | 0 | 0 | 0 | 0.279412 | 1 | 0.235294 | false | 0 | 0.022059 | 0 | 0.257353 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 3 |
bef3255483f7d18f6ac80553cda527235082821e | 370 | py | Python | sigfeat/base/result.py | SiggiGue/sigfeat | 86bb94200dcd4b33c21de1abc01814bf85f97b38 | [
"BSD-3-Clause"
] | 8 | 2017-01-30T08:26:52.000Z | 2021-02-25T07:00:38.000Z | sigfeat/base/result.py | SiggiGue/sigfeat | 86bb94200dcd4b33c21de1abc01814bf85f97b38 | [
"BSD-3-Clause"
] | 1 | 2017-03-10T16:10:11.000Z | 2017-03-10T16:10:11.000Z | sigfeat/base/result.py | SiggiGue/sigfeat | 86bb94200dcd4b33c21de1abc01814bf85f97b38 | [
"BSD-3-Clause"
] | 5 | 2017-08-05T02:26:00.000Z | 2019-09-11T13:06:28.000Z | class Result(dict):
"""Result dict. Behaves 'immutable' to the Feature.process method.
Just a simple dict to hold the results from features.
"""
__slots__ = ()
def __setitem__(self, *args):
raise TypeError('`Result` object does not support item assignment.')
def _setitem(self, key, value):
dict.__setitem__(self, key, value)
| 26.428571 | 76 | 0.659459 | 46 | 370 | 5.021739 | 0.695652 | 0.142857 | 0.121212 | 0.164502 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.232432 | 370 | 13 | 77 | 28.461538 | 0.81338 | 0.318919 | 0 | 0 | 0 | 0 | 0.207627 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0.333333 | false | 0 | 0 | 0 | 0.666667 | 0 | 0 | 0 | 0 | null | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 3 |
bef4c0a269b4217ad30f786b0205e1ed1a85c404 | 1,431 | py | Python | mak/libs/pyxx/cxx/grammar/expression/compound/unary/general.py | bugengine/BugEngine | 1b3831d494ee06b0bd74a8227c939dd774b91226 | [
"BSD-3-Clause"
] | 4 | 2015-05-13T16:28:36.000Z | 2017-05-24T15:34:14.000Z | mak/libs/pyxx/cxx/grammar/expression/compound/unary/general.py | bugengine/BugEngine | 1b3831d494ee06b0bd74a8227c939dd774b91226 | [
"BSD-3-Clause"
] | null | null | null | mak/libs/pyxx/cxx/grammar/expression/compound/unary/general.py | bugengine/BugEngine | 1b3831d494ee06b0bd74a8227c939dd774b91226 | [
"BSD-3-Clause"
] | 1 | 2017-03-21T08:28:07.000Z | 2017-03-21T08:28:07.000Z | """
unary-expression:
postfix-expression
unary-operator cast-expression
++ cast-expression
-- cast-expression
await-expression
sizeof unary-expression
sizeof ( type-id )
sizeof ... ( identifier )
alignof ( type-id )
noexcept-expression
new-expression
delete-expression
unary-operator: one of
* & + - ! ~
"""
import glrp
from .....parser import cxx98
from be_typing import TYPE_CHECKING
@glrp.rule('unary-expression : postfix-expression')
@glrp.rule('unary-expression : unary-operator cast-expression')
@glrp.rule('unary-expression : "++" cast-expression')
@glrp.rule('unary-expression : "--" cast-expression')
@glrp.rule('unary-expression : await-expression')
@glrp.rule('unary-expression : "sizeof" unary-expression')
@glrp.rule('unary-expression : "sizeof" "(" type-id ")"')
@glrp.rule('unary-expression : "sizeof" "..." "(" identifier ")"')
@glrp.rule('unary-expression : "alignof" "(" type-id ")"')
@glrp.rule('unary-expression : noexcept-expression')
@glrp.rule('unary-expression : new-expression')
@glrp.rule('unary-expression : delete-expression')
@cxx98
def unary_expression(self, p):
# type: (CxxParser, glrp.Production) -> None
pass
@glrp.rule('unary-operator : "*" | "&" | "+" | "-" | "!" | "~"')
@cxx98
def unary_operator(self, p):
# type: (CxxParser, glrp.Production) -> None
pass
if TYPE_CHECKING:
from .....parser import CxxParser
| 27.519231 | 66 | 0.66457 | 159 | 1,431 | 5.949686 | 0.207547 | 0.2537 | 0.178647 | 0.291755 | 0.538055 | 0.345666 | 0.201903 | 0.201903 | 0.117336 | 0.117336 | 0 | 0.004971 | 0.156534 | 1,431 | 51 | 67 | 28.058824 | 0.77879 | 0.310273 | 0 | 0.166667 | 0 | 0 | 0.551125 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0.083333 | false | 0.083333 | 0.166667 | 0 | 0.25 | 0 | 0 | 0 | 0 | null | 1 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 3 |
83081bf6f3aebf340e6cf5d826b30b2617d638d4 | 207 | py | Python | setup.py | Kiikurage/bash_with_display | 614ed6cd7f73e143a8f3aae491eeefdfd230d713 | [
"MIT"
] | 1 | 2017-07-06T23:24:03.000Z | 2017-07-06T23:24:03.000Z | setup.py | Kiikurage/bash_with_display | 614ed6cd7f73e143a8f3aae491eeefdfd230d713 | [
"MIT"
] | null | null | null | setup.py | Kiikurage/bash_with_display | 614ed6cd7f73e143a8f3aae491eeefdfd230d713 | [
"MIT"
] | null | null | null | from setuptools import setup
setup(
name='bash_with_display',
version='0.0.1',
author='Yuichiro Kikura',
author_email='y.kikura@gmail.com',
license='MIT', install_requires=['IPython']
)
| 20.7 | 47 | 0.681159 | 27 | 207 | 5.074074 | 0.851852 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.017341 | 0.164251 | 207 | 9 | 48 | 23 | 0.774566 | 0 | 0 | 0 | 0 | 0 | 0.31401 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | true | 0 | 0.125 | 0 | 0.125 | 0 | 1 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 3 |
832fbb1d2eb003c1aa9ac4462f586cdd3fce574d | 657 | py | Python | testdriven-app/services/users/project/api/users/crud.py | felkost/flask_microservice | 46d8d14fc79bc6cbb457b56cb621c50c066a979b | [
"MIT"
] | null | null | null | testdriven-app/services/users/project/api/users/crud.py | felkost/flask_microservice | 46d8d14fc79bc6cbb457b56cb621c50c066a979b | [
"MIT"
] | 5 | 2021-04-08T21:59:04.000Z | 2022-02-10T14:19:59.000Z | testdriven-app/services/users/project/api/users/crud.py | felkost/flask_microservice | 46d8d14fc79bc6cbb457b56cb621c50c066a979b | [
"MIT"
] | null | null | null | from project import db
from project.api.users.models import User
def get_all_users():
return User.query.all()
def get_user_by_id(user_id):
return User.query.filter_by(id=user_id).first()
def get_user_by_email(email):
return User.query.filter_by(email=email).first()
def add_user(username, email):
user = User(username=username, email=email)
db.session.add(user)
db.session.commit()
return user
def update_user(user, username, email):
user.username = username
user.email = email
db.session.commit()
return user
def delete_user(user):
db.session.delete(user)
db.session.commit()
return user
| 18.771429 | 52 | 0.707763 | 99 | 657 | 4.545455 | 0.242424 | 0.133333 | 0.1 | 0.14 | 0.3 | 0.197778 | 0 | 0 | 0 | 0 | 0 | 0 | 0.179604 | 657 | 34 | 53 | 19.323529 | 0.834879 | 0 | 0 | 0.272727 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0.272727 | false | 0 | 0.090909 | 0.136364 | 0.636364 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 1 | 1 | 0 | 0 | 3 |
8335b3c0f664e05554ef2fd147b47d0f1be8a68a | 139 | py | Python | login/urls.py | Arpppit/data_leakage_detection | 2fa96782feda5b31efd15a25c1366865ccdca952 | [
"BSD-3-Clause"
] | 6 | 2020-05-03T12:03:21.000Z | 2020-09-07T08:33:58.000Z | login/urls.py | Arpppit/data_leakage_detection | 2fa96782feda5b31efd15a25c1366865ccdca952 | [
"BSD-3-Clause"
] | 3 | 2020-04-17T06:50:44.000Z | 2022-01-13T02:16:48.000Z | login/urls.py | shrey-c/DataLeakageDjango | a827c5a09e5501921f9fb97b656755671238dd63 | [
"BSD-3-Clause"
] | null | null | null | from django.conf.urls import url
from . import views
urlpatterns = [
url(r'^$', views.login_form),
url(r'^assign', views.login_assign),
] | 23.166667 | 37 | 0.71223 | 21 | 139 | 4.619048 | 0.571429 | 0.082474 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.122302 | 139 | 6 | 38 | 23.166667 | 0.795082 | 0 | 0 | 0 | 0 | 0 | 0.064286 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | false | 0 | 0.333333 | 0 | 0.333333 | 0 | 1 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 3 |
833cdcf98c09c582e98951d74d4bfe6a5d0d1c16 | 5,483 | py | Python | q13/q13_large_sum.py | vesteny77/proj_euler | d12d6c6a6e6c717b5ab9d0415005945d983eea53 | [
"MIT"
] | 1 | 2021-12-27T22:18:51.000Z | 2021-12-27T22:18:51.000Z | q13/q13_large_sum.py | vesteny77/proj_euler | d12d6c6a6e6c717b5ab9d0415005945d983eea53 | [
"MIT"
] | null | null | null | q13/q13_large_sum.py | vesteny77/proj_euler | d12d6c6a6e6c717b5ab9d0415005945d983eea53 | [
"MIT"
] | null | null | null | # Work out the first ten digits of the sum of
# the following one-hundred 50-digit numbers.
num_str = "37107287533902102798797998220837590246510135740250\
46376937677490009712648124896970078050417018260538\
74324986199524741059474233309513058123726617309629\
91942213363574161572522430563301811072406154908250\
23067588207539346171171980310421047513778063246676\
89261670696623633820136378418383684178734361726757\
28112879812849979408065481931592621691275889832738\
44274228917432520321923589422876796487670272189318\
47451445736001306439091167216856844588711603153276\
70386486105843025439939619828917593665686757934951\
62176457141856560629502157223196586755079324193331\
64906352462741904929101432445813822663347944758178\
92575867718337217661963751590579239728245598838407\
58203565325359399008402633568948830189458628227828\
80181199384826282014278194139940567587151170094390\
35398664372827112653829987240784473053190104293586\
86515506006295864861532075273371959191420517255829\
71693888707715466499115593487603532921714970056938\
54370070576826684624621495650076471787294438377604\
53282654108756828443191190634694037855217779295145\
36123272525000296071075082563815656710885258350721\
45876576172410976447339110607218265236877223636045\
17423706905851860660448207621209813287860733969412\
81142660418086830619328460811191061556940512689692\
51934325451728388641918047049293215058642563049483\
62467221648435076201727918039944693004732956340691\
15732444386908125794514089057706229429197107928209\
55037687525678773091862540744969844508330393682126\
18336384825330154686196124348767681297534375946515\
80386287592878490201521685554828717201219257766954\
78182833757993103614740356856449095527097864797581\
16726320100436897842553539920931837441497806860984\
48403098129077791799088218795327364475675590848030\
87086987551392711854517078544161852424320693150332\
59959406895756536782107074926966537676326235447210\
69793950679652694742597709739166693763042633987085\
41052684708299085211399427365734116182760315001271\
65378607361501080857009149939512557028198746004375\
35829035317434717326932123578154982629742552737307\
94953759765105305946966067683156574377167401875275\
88902802571733229619176668713819931811048770190271\
25267680276078003013678680992525463401061632866526\
36270218540497705585629946580636237993140746255962\
24074486908231174977792365466257246923322810917141\
91430288197103288597806669760892938638285025333403\
34413065578016127815921815005561868836468420090470\
23053081172816430487623791969842487255036638784583\
11487696932154902810424020138335124462181441773470\
63783299490636259666498587618221225225512486764533\
67720186971698544312419572409913959008952310058822\
95548255300263520781532296796249481641953868218774\
76085327132285723110424803456124867697064507995236\
37774242535411291684276865538926205024910326572967\
23701913275725675285653248258265463092207058596522\
29798860272258331913126375147341994889534765745501\
18495701454879288984856827726077713721403798879715\
38298203783031473527721580348144513491373226651381\
34829543829199918180278916522431027392251122869539\
40957953066405232632538044100059654939159879593635\
29746152185502371307642255121183693803580388584903\
41698116222072977186158236678424689157993532961922\
62467957194401269043877107275048102390895523597457\
23189706772547915061505504953922979530901129967519\
86188088225875314529584099251203829009407770775672\
11306739708304724483816533873502340845647058077308\
82959174767140363198008187129011875491310547126581\
97623331044818386269515456334926366572897563400500\
42846280183517070527831839425882145521227251250327\
55121603546981200581762165212827652751691296897789\
32238195734329339946437501907836945765883352399886\
75506164965184775180738168837861091527357929701337\
62177842752192623401942399639168044983993173312731\
32924185707147349566916674687634660915035914677504\
99518671430235219628894890102423325116913619626622\
73267460800591547471830798392868535206946944540724\
76841822524674417161514036427982273348055556214818\
97142617910342598647204516893989422179826088076852\
87783646182799346313767754307809363333018982642090\
10848802521674670883215120185883543223812876952786\
71329612474782464538636993009049310363619763878039\
62184073572399794223406235393808339651327408011116\
66627891981488087797941876876144230030984490851411\
60661826293682836764744779239180335110989069790714\
85786944089552990653640447425576083659976645795096\
66024396409905389607120198219976047599490197230297\
64913982680032973156037120041377903785566085089252\
16730939319872750275468906903707539413042652315011\
94809377245048795150954100921645863754710598436791\
78639167021187492431995700641917969777599028300699\
15368713711936614952811305876380278410754449733078\
40789923115535562561142322423255033685442488917353\
44889911501440648020369068063960672322193204149535\
41503128880339536053299340368006977710650566631954\
81234880673210146739058568557934581403627822703280\
82616570773948327592232845941706525094512325230608\
22918802058777319719839450180888072429661980811197\
77158542502016545090413245809786882778948721859617\
72107838435069186155435662884062257473692284509516\
20849603980134001723930671666823555245252804609722\
53503534226472524250874054075591789781264330331690"
def large_sum():
s = 0
for i in range(100):
s += int(num_str[(i * 50):(i * 50 + 50)])
return str(s)[:10]
if __name__ == "__main__":
print(large_sum())
| 47.678261 | 62 | 0.94255 | 148 | 5,483 | 34.837838 | 0.891892 | 0.001939 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.944256 | 0.031552 | 5,483 | 114 | 63 | 48.096491 | 0.026742 | 0.015867 | 0 | 0 | 0 | 0 | 0.001483 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0.009346 | false | 0 | 0 | 0 | 0.018692 | 0.009346 | 0 | 0 | 1 | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 3 |
8341b3fe0559496b4dde48fd334b03e214b5089a | 224 | py | Python | python/exception.py | gajubadge11/hackerrank-3 | 132a5019b7ed21507bb95b5063fa66c446b0eff7 | [
"MIT"
] | 21 | 2015-02-09T18:08:38.000Z | 2021-11-08T15:00:48.000Z | python/exception.py | gajubadge11/hackerrank-3 | 132a5019b7ed21507bb95b5063fa66c446b0eff7 | [
"MIT"
] | 7 | 2020-04-12T23:00:19.000Z | 2021-01-30T23:44:24.000Z | python/exception.py | gajubadge11/hackerrank-3 | 132a5019b7ed21507bb95b5063fa66c446b0eff7 | [
"MIT"
] | 27 | 2015-07-22T18:08:12.000Z | 2022-02-28T19:50:26.000Z | number_of_test_cases = int(input().strip())
for _ in range(number_of_test_cases):
try:
a, b = map(int, input().strip().split(" "))
print(a // b)
except Exception as e:
print("Error Code:", e)
| 28 | 51 | 0.580357 | 33 | 224 | 3.727273 | 0.666667 | 0.130081 | 0.195122 | 0.276423 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.25 | 224 | 7 | 52 | 32 | 0.732143 | 0 | 0 | 0 | 0 | 0 | 0.053571 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | false | 0 | 0 | 0 | 0 | 0.285714 | 0 | 0 | 0 | null | 0 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 3 |
835d245c3b08e0b280e276fdab31c8205b782158 | 528 | py | Python | pyoptix/__init__.py | juhyeonkim95/PyOptiX | e439e9bfeb1541720d254af2e062fd4940a784bf | [
"MIT"
] | 50 | 2016-06-24T23:48:54.000Z | 2021-11-08T16:10:46.000Z | pyoptix/__init__.py | LongerVision/PyOptiX | c7510ee9d967fe6c22fddcdcdd3b0127e075c8ba | [
"MIT"
] | 4 | 2017-02-04T18:48:49.000Z | 2018-10-02T14:13:45.000Z | pyoptix/__init__.py | LongerVision/PyOptiX | c7510ee9d967fe6c22fddcdcdd3b0127e075c8ba | [
"MIT"
] | 8 | 2018-04-19T11:37:11.000Z | 2021-03-04T19:16:47.000Z | from .acceleration import Acceleration
from .buffer import Buffer
from .compiler import Compiler
from .context import Context, current_context
from .entry_point import EntryPoint
from .geometry import Geometry
from .geometry_group import GeometryGroup
from .geometry_instance import GeometryInstance
from .group import Group
from .material import Material
from .program import Program
from .selector import Selector
from .texture_sampler import TextureSampler
from .transform import Transform
from ._driver import OPTIX_VERSION
| 33 | 47 | 0.854167 | 67 | 528 | 6.626866 | 0.373134 | 0.081081 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.11553 | 528 | 15 | 48 | 35.2 | 0.950749 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | true | 0 | 1 | 0 | 1 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 0 | 1 | 0 | 0 | 3 |
835dfc9afc14a562f07f04330d4af2c585f0bd5f | 1,518 | py | Python | Beat-Ai/BeatSaber-AI/BeatSaber-AI/phase2/createEvents.py | Codingmace/BeatSaber-AI | 1978c68ac983320996eb9161b603ab12be868d0c | [
"MIT"
] | null | null | null | Beat-Ai/BeatSaber-AI/BeatSaber-AI/phase2/createEvents.py | Codingmace/BeatSaber-AI | 1978c68ac983320996eb9161b603ab12be868d0c | [
"MIT"
] | null | null | null | Beat-Ai/BeatSaber-AI/BeatSaber-AI/phase2/createEvents.py | Codingmace/BeatSaber-AI | 1978c68ac983320996eb9161b603ab12be868d0c | [
"MIT"
] | null | null | null | # TODO
# Have not even started one bit but easy once get the other part done
# https://bsmg.wiki/mapping/map-format.html#events-2
# Get the Melograph
# Translate the graph to the events
# Write the results to the file
# Onset Can create a idea of where to put the beats
# https://librosa.org/doc/latest/generated/librosa.beat.beat_track.html?msclkid=bd66166baebe11ecabf9d7d7420bed35
# Pitches used for the movement going through
# https://librosa.org/doc/latest/generated/librosa.decompose.nn_filter.html
# https://github.com/ItsOrius/LiteMapper#readme
'''
You may be wondering, how do we manage to incentivize more creative mapping? Rather than just placing events based on time and location, we run a multitude of different checks to decide on where to place our events.
Beats with a high pace (more than 1 block per beat) receive a red center light, beats with a medium pace (at least 2 block per two beats) receive a blue center light, and beats with a slow pace (one block or less per two beats) receive a fading blue center light.
A change in pace results in a ring zoom.
Timestamps with more than one block at a time results in a ring rotation.
Beats with more than one block per two beats receive a ring light every beat.
Any-direction blocks and bombs result in the back lights turning on and the center lights turning off.
The laser opposite of the last (starting on the left) will flash, but the other laser will deactivate.
Both lasers activate on double notes with two beats or more of padding
''' | 52.344828 | 263 | 0.783926 | 264 | 1,518 | 4.5 | 0.526515 | 0.030303 | 0.025253 | 0.045455 | 0.153199 | 0.107744 | 0.06734 | 0 | 0 | 0 | 0 | 0.014173 | 0.163373 | 1,518 | 29 | 264 | 52.344828 | 0.92126 | 0.974967 | 0 | null | 0 | null | 0 | 0 | null | 0 | 0 | 0.034483 | null | 1 | null | true | 0 | 0 | null | null | null | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | null | 0 | 0 | 1 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 3 |
360ace8875cd29af2d5d4fc3b540dc758c14d22f | 6,741 | py | Python | tests/util/chem/test_chem_core.py | KarrLab/wc_utilities | a4c0e2e8b9bd88356729e38faf5c0d09d61ff921 | [
"MIT"
] | 1 | 2019-12-12T15:49:32.000Z | 2019-12-12T15:49:32.000Z | tests/util/chem/test_chem_core.py | KarrLab/wc_utilities | a4c0e2e8b9bd88356729e38faf5c0d09d61ff921 | [
"MIT"
] | 36 | 2017-03-14T18:59:21.000Z | 2019-12-18T04:08:43.000Z | tests/util/chem/test_chem_core.py | KarrLab/wc_utilities | a4c0e2e8b9bd88356729e38faf5c0d09d61ff921 | [
"MIT"
] | 1 | 2019-10-16T10:30:42.000Z | 2019-10-16T10:30:42.000Z | """ Tests of the chemistry utilities
:Author: Jonathan Karr <jonrkarr@gmail.com>
:Date: 2018-02-07
:Copyright: 2018, Karr Lab
:License: MIT
"""
from wc_utils.util import chem
import attrdict
import openbabel
import unittest
class EmpiricalFormulaTestCase(unittest.TestCase):
def test_EmpiricalFormula_constructor(self):
f = chem.EmpiricalFormula()
self.assertEqual(f, {})
f = chem.EmpiricalFormula('H')
self.assertEqual(f, {'H': 1})
f = chem.EmpiricalFormula('H2')
self.assertEqual(f, {'H': 2})
f = chem.EmpiricalFormula('H2.5')
self.assertEqual(f, {'H': 2.5})
f = chem.EmpiricalFormula('H2.5e3')
self.assertEqual(f, {'H': 2.5e3})
f = chem.EmpiricalFormula('H-2.5e3')
self.assertEqual(f, {'H': -2.5e3})
f = chem.EmpiricalFormula('H2.5e+3')
self.assertEqual(f, {'H': 2.5e3})
f = chem.EmpiricalFormula('H2.5e-3')
self.assertEqual(f, {'H': 2.5e-3})
f = chem.EmpiricalFormula('He2')
self.assertEqual(f, {'He': 2})
f = chem.EmpiricalFormula('He-2')
self.assertEqual(f, {'He': -2})
f = chem.EmpiricalFormula('He-20')
self.assertEqual(f, {'He': -20})
f = chem.EmpiricalFormula('H2O')
self.assertEqual(f, {'H': 2, 'O': 1})
f = chem.EmpiricalFormula('He-20He30')
self.assertEqual(f, {'He': 10})
f = chem.EmpiricalFormula('RaRb')
self.assertEqual(f, {'Ra': 1, 'Rb': 1})
f = chem.EmpiricalFormula(attrdict.AttrDict({'Ra': 1, 'Rb': 1}))
self.assertEqual(f, {'Ra': 1, 'Rb': 1})
f = chem.EmpiricalFormula(attrdict.AttrDefault(int, {'Ra': 1, 'Rb': 1}))
self.assertEqual(f, {'Ra': 1, 'Rb': 1})
f = chem.EmpiricalFormula(chem.EmpiricalFormula('RaRb'))
self.assertEqual(f, {'Ra': 1, 'Rb': 1})
with self.assertRaisesRegex(ValueError, 'not a valid formula'):
chem.EmpiricalFormula('Hee2')
with self.assertRaisesRegex(ValueError, 'not a valid formula'):
chem.EmpiricalFormula('h2')
def test_EmpiricalFormula_get_attr(self):
f = chem.EmpiricalFormula()
self.assertEqual(f.C, 0)
self.assertEqual(f['C'], 0)
def test_EmpiricalFormula___setitem__(self):
f = chem.EmpiricalFormula()
f.C = 0
self.assertEqual(f, {})
self.assertEqual(dict(f), {})
self.assertEqual(str(f), '')
f = chem.EmpiricalFormula()
f.A = 1
self.assertEqual(f, {'A': 1})
f.A = 0
self.assertEqual(f, {})
self.assertEqual(dict(f), {})
self.assertEqual(str(f), '')
f.A = 1.5
self.assertEqual(f, {'A': 1.5})
f = chem.EmpiricalFormula()
with self.assertRaisesRegex(ValueError, 'Coefficient must be a float'):
f.A = 'a'
f = chem.EmpiricalFormula()
with self.assertRaisesRegex(ValueError, 'Element must be a one or two letter string'):
f.Aaa = 1
def test_EmpiricalFormula_get_molecular_weight(self):
f = chem.EmpiricalFormula('H2O')
self.assertAlmostEqual(f.get_molecular_weight(), 18.015)
def test_EmpiricalFormula___add__(self):
f = chem.EmpiricalFormula('H2O')
g = chem.EmpiricalFormula('HO')
self.assertEqual(str(f + g), 'H3O2')
self.assertEqual(str(f + 'HO'), 'H3O2')
def test_EmpiricalFormula___sub__(self):
f = chem.EmpiricalFormula('H2O')
g = chem.EmpiricalFormula('HO')
self.assertEqual(str(f - g), 'H')
self.assertEqual(str(f - 'HO'), 'H')
def test_EmpiricalFormula___mul__(self):
f = chem.EmpiricalFormula('H2O')
self.assertEqual(str(f * 2), 'H4O2')
def test_EmpiricalFormula___truediv__(self):
f = chem.EmpiricalFormula('H4O2')
self.assertEqual(f / 2, chem.EmpiricalFormula({'H': 2, 'O': 1}))
def test_EmpiricalFormula___str__(self):
f = chem.EmpiricalFormula('H2O')
self.assertEqual(str(f), 'H2O')
f = chem.EmpiricalFormula('OH2')
self.assertEqual(str(f), 'H2O')
f = chem.EmpiricalFormula('N0OH2')
self.assertEqual(str(f), 'H2O')
f = chem.EmpiricalFormula('H2O1.1')
self.assertEqual(str(f), 'H2O1.1')
f = chem.EmpiricalFormula('H2O1.1e-3')
self.assertEqual(str(f), 'H2O0.0011')
f = chem.EmpiricalFormula('H2O1.1e+3')
self.assertEqual(str(f), 'H2O1100')
f = chem.EmpiricalFormula('H2O-1.1e+3')
self.assertEqual(str(f), 'H2O-1100')
def test_EmpiricalFormula___contains__(self):
f = chem.EmpiricalFormula('H2O')
self.assertIn('H', f)
self.assertIn('C', f)
self.assertNotIn('Ccc', f)
def test_EmpiricalFormula___hash__(self):
f = chem.EmpiricalFormula('H2O')
g = chem.EmpiricalFormula('H2O')
h = chem.EmpiricalFormula('H')
self.assertIn(f, [g])
self.assertIn(f, set([g]))
self.assertIn(f, {g: True})
self.assertNotIn(f, [h])
self.assertNotIn(f, set([h]))
self.assertNotIn(f, {h: True})
class OpenBabelUtilsTestCase(unittest.TestCase):
def test_get_formula(self):
gly_inchi = 'InChI=1S/C2H5NO2/c3-1-2(4)5/h1,3H2,(H,4,5)'
gly_formula = 'C2H5NO2'
mol = openbabel.OBMol()
conversion = openbabel.OBConversion()
conversion.SetInFormat('inchi')
conversion.ReadString(mol, gly_inchi)
self.assertEqual(chem.OpenBabelUtils.get_formula(mol), chem.EmpiricalFormula('C2H5NO2'))
def test_get_inchi(self):
gly_inchi = 'InChI=1S/C2H5NO2/c3-1-2(4)5/h1,3H2,(H,4,5)'
mol = openbabel.OBMol()
conversion = openbabel.OBConversion()
conversion.SetInFormat('inchi')
conversion.ReadString(mol, gly_inchi)
self.assertEqual(chem.OpenBabelUtils.get_inchi(mol), gly_inchi)
def test_export(self):
gly_smiles = 'C([N+])C([O-])=O'
mol = openbabel.OBMol()
conversion = openbabel.OBConversion()
conversion.SetInFormat('can')
conversion.ReadString(mol, gly_smiles)
self.assertEqual(chem.OpenBabelUtils.export(mol, 'smi'), 'C([N+])C(=O)[O-]')
self.assertEqual(chem.OpenBabelUtils.export(mol, 'smi', options=('c',)), '[O-]C(=O)C[N+]')
gly_inchi = 'InChI=1S/C2H5NO2/c3-1-2(4)5/h1,3H2,(H,4,5)'
mol = openbabel.OBMol()
conversion = openbabel.OBConversion()
conversion.SetInFormat('inchi')
conversion.ReadString(mol, gly_inchi)
self.assertEqual(chem.OpenBabelUtils.export(mol, 'inchi'), gly_inchi)
self.assertTrue(chem.OpenBabelUtils.export(mol, 'mol', options='m').endswith('END'))
| 32.723301 | 98 | 0.601098 | 806 | 6,741 | 4.930521 | 0.171216 | 0.226472 | 0.190237 | 0.066935 | 0.601661 | 0.568445 | 0.529693 | 0.458228 | 0.400856 | 0.356064 | 0 | 0.038551 | 0.238095 | 6,741 | 205 | 99 | 32.882927 | 0.735202 | 0.020175 | 0 | 0.342282 | 0 | 0.020134 | 0.088828 | 0.0191 | 0 | 0 | 0 | 0 | 0.402685 | 1 | 0.09396 | false | 0 | 0.026846 | 0 | 0.134228 | 0 | 0 | 0 | 0 | null | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 3 |
361fcbc7922fe3ede148316d44de3c2b5071f839 | 1,622 | py | Python | tests/test_mappings.py | Naassila/bioptim | 511e7ba315de5ca8c3bdcc85decd43bac30676b9 | [
"MIT"
] | 34 | 2020-12-14T17:09:41.000Z | 2022-03-31T17:03:37.000Z | tests/test_mappings.py | Naassila/bioptim | 511e7ba315de5ca8c3bdcc85decd43bac30676b9 | [
"MIT"
] | 229 | 2020-09-30T16:53:40.000Z | 2022-03-29T21:11:46.000Z | tests/test_mappings.py | fbailly/bioptim | 3a5473ee7c39d645d960611596a45b044e8ccf58 | [
"MIT"
] | 15 | 2020-11-20T12:32:59.000Z | 2022-01-22T22:59:08.000Z | import pytest
import numpy as np
from bioptim import Mapping, BiMapping
def test_mapping():
obj_to_map = np.array([[0, 1, 2], [3, 4, 5], [6, 7, 8], [9, 10, 11]])
np.testing.assert_almost_equal(Mapping([0, 2]).map(obj_to_map), [[0, 1, 2], [6, 7, 8]])
np.testing.assert_almost_equal(Mapping([None, 2, None]).map(obj_to_map), [[0, 0, 0], [6, 7, 8], [0, 0, 0]])
np.testing.assert_almost_equal(
Mapping([None, 2, 1], oppose=[1, 2]).map(obj_to_map), [[0, 0, 0], [-6, -7, -8], [-3, -4, -5]]
)
np.testing.assert_almost_equal(Mapping([None, 0], oppose=1).map(obj_to_map), [[0, 0, 0], [0, -1, -2]])
def test_bidirectional_mapping():
mapping = BiMapping([0, 1, 2], [3, 4, 5])
np.testing.assert_almost_equal(len(mapping.to_first), 3)
np.testing.assert_almost_equal(mapping.to_first.map_idx, [3, 4, 5])
np.testing.assert_almost_equal(mapping.to_second.map_idx, [0, 1, 2])
np.testing.assert_almost_equal(mapping.to_second.map_idx, [0, 1, 2])
mapping_with_oppose = BiMapping([0, 1, 2], [3, 4, 5], 1, [1, 2])
np.testing.assert_almost_equal(mapping_with_oppose.to_second.map_idx, [0, 1, 2])
np.testing.assert_almost_equal(mapping_with_oppose.to_second.oppose, [1, -1, 1])
np.testing.assert_almost_equal(mapping_with_oppose.to_first.map_idx, [3, 4, 5])
np.testing.assert_almost_equal(mapping_with_oppose.to_first.oppose, [1, -1, -1])
with pytest.raises(RuntimeError, match="to_second must be a Mapping class"):
BiMapping(1, [3, 4, 5])
with pytest.raises(RuntimeError, match="to_first must be a Mapping class"):
BiMapping([0, 1, 2], 3)
| 45.055556 | 111 | 0.658446 | 282 | 1,622 | 3.567376 | 0.163121 | 0.107356 | 0.178926 | 0.250497 | 0.769384 | 0.765408 | 0.587475 | 0.50994 | 0.405567 | 0.369781 | 0 | 0.071533 | 0.155364 | 1,622 | 35 | 112 | 46.342857 | 0.662774 | 0 | 0 | 0.076923 | 0 | 0 | 0.040074 | 0 | 0 | 0 | 0 | 0 | 0.461538 | 1 | 0.076923 | false | 0 | 0.115385 | 0 | 0.192308 | 0 | 0 | 0 | 0 | null | 0 | 0 | 1 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 3 |
362ec1f1ef00c47c15c61341104964e23324789f | 566 | py | Python | breadcrumbs/namedobject.py | hgrecco/breadcrumbs | e7fc4f8e9181b8140a0a4e8c533c415dc19e6f3f | [
"BSD-3-Clause"
] | 1 | 2021-11-17T04:04:02.000Z | 2021-11-17T04:04:02.000Z | breadcrumbs/namedobject.py | hgrecco/breadcrumbs | e7fc4f8e9181b8140a0a4e8c533c415dc19e6f3f | [
"BSD-3-Clause"
] | null | null | null | breadcrumbs/namedobject.py | hgrecco/breadcrumbs | e7fc4f8e9181b8140a0a4e8c533c415dc19e6f3f | [
"BSD-3-Clause"
] | null | null | null | """
breadcrumbs.namedobject
~~~~~~~~~~~~~~~~~~~~~~~
Sentinels with good representation.
:copyright: 2021 by breadcrumbs Authors, see AUTHORS for more details.
:license: BSD, see LICENSE for more details.
"""
class NamedObject(object):
"""A class to construct named sentinels."""
def __init__(self, name):
self.name = name
def __repr__(self):
return self.name
def __str__(self):
return self.name
def __hash__(self):
return id(self)
def __deepcopy__(self, memo):
return self
| 19.517241 | 74 | 0.609541 | 63 | 566 | 5.15873 | 0.52381 | 0.098462 | 0.086154 | 0.110769 | 0.129231 | 0 | 0 | 0 | 0 | 0 | 0 | 0.009592 | 0.263251 | 566 | 28 | 75 | 20.214286 | 0.769784 | 0.422261 | 0 | 0.181818 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0.454545 | false | 0 | 0 | 0.363636 | 0.909091 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 1 | 1 | 0 | 0 | 3 |
364a72b4e08e4de25012926b7cc0b093581ceb33 | 275 | py | Python | lfs/utils/views.py | michael-hahn/django-lfs | 26c3471a8f8d88269c84f714f507b952dfdb6397 | [
"BSD-3-Clause"
] | 345 | 2015-01-03T19:19:27.000Z | 2022-03-20T11:00:50.000Z | lfs/utils/views.py | michael-hahn/django-lfs | 26c3471a8f8d88269c84f714f507b952dfdb6397 | [
"BSD-3-Clause"
] | 73 | 2015-01-06T14:54:02.000Z | 2022-03-11T23:11:34.000Z | lfs/utils/views.py | michael-hahn/django-lfs | 26c3471a8f8d88269c84f714f507b952dfdb6397 | [
"BSD-3-Clause"
] | 148 | 2015-01-07T16:30:08.000Z | 2022-03-25T21:20:58.000Z | from django.http import HttpResponse
from django.shortcuts import render
def test(request):
return render(request, "test.html")
def upload_test(request):
if request.method == "GET":
return render(request, "testuploadform.html")
return HttpResponse()
| 19.642857 | 53 | 0.72 | 33 | 275 | 5.969697 | 0.515152 | 0.101523 | 0.192893 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.178182 | 275 | 13 | 54 | 21.153846 | 0.871681 | 0 | 0 | 0 | 0 | 0 | 0.112727 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0.25 | false | 0 | 0.25 | 0.125 | 0.875 | 0 | 0 | 0 | 0 | null | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 3 |
365398e1f12ba599573c90a12216ab43d7f69f9b | 419 | py | Python | gramat/expressions/_true.py | gramat-lang/python-gramat | c3edb7cf045b109596bb4cfdf43d58e04763ac19 | [
"MIT"
] | null | null | null | gramat/expressions/_true.py | gramat-lang/python-gramat | c3edb7cf045b109596bb4cfdf43d58e04763ac19 | [
"MIT"
] | null | null | null | gramat/expressions/_true.py | gramat-lang/python-gramat | c3edb7cf045b109596bb4cfdf43d58e04763ac19 | [
"MIT"
] | null | null | null | from __future__ import annotations
from typing import List
from ._expression import Expression
from ._expression import EvalContext
class TrueExp(Expression):
def eval(self, ctx: EvalContext) -> bool:
return True
@property
def children(self) -> List[Expression]:
return []
def optimize(self) -> Expression:
return self
def __str__(self) -> str:
return 'True'
| 18.217391 | 45 | 0.668258 | 46 | 419 | 5.869565 | 0.456522 | 0.103704 | 0.148148 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.250597 | 419 | 22 | 46 | 19.045455 | 0.859873 | 0 | 0 | 0 | 0 | 0 | 0.009547 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0.285714 | false | 0 | 0.285714 | 0.285714 | 0.928571 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 1 | 1 | 0 | 0 | 3 |
36580042b222fa33af5ee3a3ea888b71f5ab157b | 73 | py | Python | src/lib/HTMLParser.py | timmartin/skulpt | 2e3a3fbbaccc12baa29094a717ceec491a8a6750 | [
"MIT"
] | 10 | 2015-11-13T17:02:40.000Z | 2021-02-09T23:21:05.000Z | src/lib/HTMLParser.py | timmartin/skulpt | 2e3a3fbbaccc12baa29094a717ceec491a8a6750 | [
"MIT"
] | 43 | 2015-06-03T17:59:23.000Z | 2021-09-17T10:45:21.000Z | src/lib/HTMLParser.py | timmartin/skulpt | 2e3a3fbbaccc12baa29094a717ceec491a8a6750 | [
"MIT"
] | 13 | 2017-07-02T03:16:46.000Z | 2021-07-05T14:53:56.000Z | raise NotImplementedError("HTMLParser is not yet implemented in Skulpt")
| 36.5 | 72 | 0.835616 | 9 | 73 | 6.777778 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.109589 | 73 | 1 | 73 | 73 | 0.938462 | 0 | 0 | 0 | 0 | 0 | 0.589041 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | true | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 3 |
366620816f8f19ebd63ab581b51dc9672e6d07ec | 332 | py | Python | output/models/nist_data/atomic/non_negative_integer/schema_instance/nistschema_sv_iv_atomic_non_negative_integer_max_exclusive_2_xsd/__init__.py | tefra/xsdata-w3c-tests | b6b6a4ac4e0ab610e4b50d868510a8b7105b1a5f | [
"MIT"
] | 1 | 2021-08-14T17:59:21.000Z | 2021-08-14T17:59:21.000Z | output/models/nist_data/atomic/non_negative_integer/schema_instance/nistschema_sv_iv_atomic_non_negative_integer_max_exclusive_2_xsd/__init__.py | tefra/xsdata-w3c-tests | b6b6a4ac4e0ab610e4b50d868510a8b7105b1a5f | [
"MIT"
] | 4 | 2020-02-12T21:30:44.000Z | 2020-04-15T20:06:46.000Z | output/models/nist_data/atomic/non_negative_integer/schema_instance/nistschema_sv_iv_atomic_non_negative_integer_max_exclusive_2_xsd/__init__.py | tefra/xsdata-w3c-tests | b6b6a4ac4e0ab610e4b50d868510a8b7105b1a5f | [
"MIT"
] | null | null | null | from output.models.nist_data.atomic.non_negative_integer.schema_instance.nistschema_sv_iv_atomic_non_negative_integer_max_exclusive_2_xsd.nistschema_sv_iv_atomic_non_negative_integer_max_exclusive_2 import NistschemaSvIvAtomicNonNegativeIntegerMaxExclusive2
__all__ = [
"NistschemaSvIvAtomicNonNegativeIntegerMaxExclusive2",
]
| 55.333333 | 257 | 0.918675 | 36 | 332 | 7.722222 | 0.583333 | 0.097122 | 0.183453 | 0.258993 | 0.366906 | 0.366906 | 0.366906 | 0.366906 | 0.366906 | 0.366906 | 0 | 0.012579 | 0.042169 | 332 | 5 | 258 | 66.4 | 0.861635 | 0 | 0 | 0 | 0 | 0 | 0.153614 | 0.153614 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | false | 0 | 0.25 | 0 | 0.25 | 0 | 0 | 0 | 0 | null | 0 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 3 |
3679edd926a02ae42dcea31726e5e217b8265ea2 | 401 | py | Python | ProgramsToRead/ExercisesFromClasses/ex001agosto26.py | ItanuRomero/PythonStudyPrograms | 2b784b2af068b34e65ddf817ca8d99c1ca3a710e | [
"MIT"
] | null | null | null | ProgramsToRead/ExercisesFromClasses/ex001agosto26.py | ItanuRomero/PythonStudyPrograms | 2b784b2af068b34e65ddf817ca8d99c1ca3a710e | [
"MIT"
] | null | null | null | ProgramsToRead/ExercisesFromClasses/ex001agosto26.py | ItanuRomero/PythonStudyPrograms | 2b784b2af068b34e65ddf817ca8d99c1ca3a710e | [
"MIT"
] | null | null | null | vogais = {
'a': 0,
'e': 0,
'i': 0,
'o': 0,
'u': 0
}
texto = str(input('insira um texto: ')).strip().lower()
for letra in texto:
if letra in 'a':
vogais['a'] += 1
elif letra in 'e':
vogais['e'] += 1
elif letra in 'i':
vogais['i'] += 1
elif letra in 'o':
vogais['o'] += 1
elif letra in 'u':
vogais['u'] += 1
print(vogais)
| 18.227273 | 55 | 0.438903 | 60 | 401 | 2.933333 | 0.35 | 0.238636 | 0.227273 | 0.272727 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.038462 | 0.351621 | 401 | 21 | 56 | 19.095238 | 0.638462 | 0 | 0 | 0 | 0 | 0 | 0.08 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | false | 0 | 0 | 0 | 0 | 0.05 | 0 | 0 | 0 | null | 1 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 3 |
367c7da870575b7776f56633757247388d4b4b70 | 794 | py | Python | film_details_searcher/tests/scrappers/test_filmweb_movie_service.py | tomaszkyc/film_details_searcher | e8e12bc739990324f6ab4110fdd363e6ef207f1a | [
"MIT"
] | null | null | null | film_details_searcher/tests/scrappers/test_filmweb_movie_service.py | tomaszkyc/film_details_searcher | e8e12bc739990324f6ab4110fdd363e6ef207f1a | [
"MIT"
] | null | null | null | film_details_searcher/tests/scrappers/test_filmweb_movie_service.py | tomaszkyc/film_details_searcher | e8e12bc739990324f6ab4110fdd363e6ef207f1a | [
"MIT"
] | null | null | null | import pytest
from film_details_searcher.models.movie import Movie
from film_details_searcher.scrappers.filmweb_movie_service import FilmwebMovieService
from film_details_searcher.scrappers.movie_service import MovieService
@pytest.fixture
def movie_service():
return FilmwebMovieService()
@pytest.fixture
def valid_movie_link():
return r'https://www.filmweb.pl/film/Green+Book-2018-809630'
def test_should_find_film_by_correct_link(valid_movie_link, movie_service: MovieService):
movie: Movie = movie_service.get_movie(valid_movie_link)
assert movie is not None
assert len(movie.details) > 0
def test_should_raise_exception_if_movie_link_has_incorrect_type(movie_service: MovieService):
with pytest.raises(TypeError) as e:
movie_service.get_movie(None) | 30.538462 | 94 | 0.81864 | 111 | 794 | 5.522523 | 0.441441 | 0.137031 | 0.073409 | 0.112561 | 0.104405 | 0 | 0 | 0 | 0 | 0 | 0 | 0.015603 | 0.112091 | 794 | 26 | 95 | 30.538462 | 0.853901 | 0 | 0 | 0.117647 | 0 | 0 | 0.062893 | 0 | 0 | 0 | 0 | 0 | 0.117647 | 1 | 0.235294 | false | 0 | 0.235294 | 0.117647 | 0.588235 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 1 | 1 | 0 | 0 | 3 |
368215c6fa360572b047c6c68b30aa2130b61391 | 2,623 | py | Python | src/web/backend/__init__.py | SubOptimal/seascope | 8a669e0e333801477c4624883bd1ab1823b71bbb | [
"BSD-3-Clause"
] | 8 | 2015-03-29T01:29:17.000Z | 2020-05-10T18:45:43.000Z | src/web/backend/__init__.py | SubOptimal/seascope | 8a669e0e333801477c4624883bd1ab1823b71bbb | [
"BSD-3-Clause"
] | 5 | 2015-12-04T10:53:35.000Z | 2021-01-03T05:39:55.000Z | src/web/backend/__init__.py | SubOptimal/seascope | 8a669e0e333801477c4624883bd1ab1823b71bbb | [
"BSD-3-Clause"
] | 9 | 2016-09-06T15:53:51.000Z | 2020-12-15T16:32:48.000Z | # Copyright (c) 2010 Anil Kumar
# All rights reserved.
#
# License: BSD
import os
import sys
import re
backend_plugins = []
backend_dict = {}
def _load_plugins(module, directory):
pluginImports = __import__(module, globals(), locals())
print('Scanning for backend plugins...')
plist = []
pdict = {}
for i in sorted(os.listdir(directory)):
path = os.path.join( directory, i, '__init__.py' )
if os.path.isfile( path ):
p = __import__( '%s.%s' % (module, i), globals(), locals(), ['*'] )
plist.append(p)
pdict[p.name()] = p
if not hasattr(p, 'priority'):
p.priority = 0
plist = sorted(plist, key=lambda p: p.priority, reverse=True)
for p in plist:
print('\t', p.name())
return (plist, pdict)
def load_plugins():
global backend_plugins, backend_dict
(backend_plugins, backend_dict) = _load_plugins('backend.plugins', 'backend/plugins')
def plugin_list():
return backend_plugins
def plugin_guess(proj_path):
bi = []
for p in backend_plugins:
if p.is_your_prj(proj_path):
bi.append(p.name())
return bi
class BProject:
def __init__(self):
self.prj = None
def proj_new_open_app_cb(self):
self.prj.prj_feature_setup()
def proj_close_app_cb(self):
pass
def _proj_new_open(self):
self.proj_new_open_app_cb()
def proj_new(self, bname, proj_args):
b = backend_dict[bname]
assert not self.prj
prj = b.project_class().prj_new(proj_args)
if prj:
_proj_new_open()
return self.prj != None
def proj_open(self, proj_path, proj_type):
b = backend_dict[proj_type]
self.prj = b.project_class().prj_open(proj_path)
if self.prj:
self._proj_new_open()
return self.prj != None
def proj_close(self):
self.prj.prj_close()
self.prj = None
from .plugins import CtagsCache
CtagsCache.flush()
self.proj_close_app_cb()
def proj_is_open(self):
return self.prj != None
def proj_name(self):
return self.prj.prj_name() if self.prj else None
def proj_dir(self):
return self.prj.prj_dir() if self.prj else None
def proj_src_files(self):
return self.prj.prj_src_files()
def proj_conf(self):
return self.prj.prj_conf()
def proj_settings_get(self):
return self.prj.prj_settings_get()
def proj_settings_update(proj_args):
return self.prj.prj_settings_update(proj_args)
def proj_is_ready(self):
return self.prj.prj_is_ready()
def proj_query(self, rquery):
return self.prj.prj_query(rquery)
def proj_rebuild(self):
return self.prj.prj_rebuild()
def proj_query_fl(self):
return self.prj.prj_query_fl()
def proj_type(self):
return self.prj.prj_type()
def proj_feature(self):
return self.prj.prj_feature()
| 20.984 | 86 | 0.711399 | 416 | 2,623 | 4.211538 | 0.230769 | 0.09589 | 0.085616 | 0.109589 | 0.276256 | 0.08105 | 0.067352 | 0.039954 | 0.039954 | 0 | 0 | 0.002269 | 0.159741 | 2,623 | 124 | 87 | 21.153226 | 0.79265 | 0.024018 | 0 | 0.057471 | 0 | 0 | 0.034456 | 0 | 0 | 0 | 0 | 0 | 0.011494 | 1 | 0.275862 | false | 0.011494 | 0.068966 | 0.16092 | 0.563218 | 0.022989 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 1 | 1 | 0 | 0 | 3 |
36bc098a6276572944d43f25a44dfbc1c45b71d5 | 179 | py | Python | settings.py | dmitriypru/VK2TL | f662c9478928ca8d793592b69ade5a0d07e9b1de | [
"MIT"
] | null | null | null | settings.py | dmitriypru/VK2TL | f662c9478928ca8d793592b69ade5a0d07e9b1de | [
"MIT"
] | null | null | null | settings.py | dmitriypru/VK2TL | f662c9478928ca8d793592b69ade5a0d07e9b1de | [
"MIT"
] | null | null | null | class SETTINGS:
__slots__ = ("TOKEN","DB_NAME")
def __init__(self):
self.TOKEN = '415193750:AAFNBxqmF5ow24TwzuJlzYKpYSPmt_K5p_A'
self.DB_NAME = 'vk2tl.db'
| 29.833333 | 68 | 0.670391 | 20 | 179 | 5.4 | 0.7 | 0.111111 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.098592 | 0.206704 | 179 | 5 | 69 | 35.8 | 0.661972 | 0 | 0 | 0 | 0 | 0 | 0.363128 | 0.251397 | 0 | 0 | 0 | 0 | 0 | 1 | 0.2 | false | 0 | 0 | 0 | 0.6 | 0 | 1 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 3 |
36ec2dc9e70d8727f25924d1ea377ed7fd2a3ac7 | 2,409 | py | Python | talleres_y_practicas/TAREA02-Patrones_estructurales/decorator.py | GabrielJHR/UADER_ISII_RAMOS | 380aac6759750330a18d114d213d717a898538d0 | [
"MIT"
] | null | null | null | talleres_y_practicas/TAREA02-Patrones_estructurales/decorator.py | GabrielJHR/UADER_ISII_RAMOS | 380aac6759750330a18d114d213d717a898538d0 | [
"MIT"
] | null | null | null | talleres_y_practicas/TAREA02-Patrones_estructurales/decorator.py | GabrielJHR/UADER_ISII_RAMOS | 380aac6759750330a18d114d213d717a898538d0 | [
"MIT"
] | null | null | null | #*--------------------------------------------------
#* decorator.py
#* excerpt from https://refactoring.guru/design-patterns/decorator/python/example
#* ejemplo obtenido desde el canal de youtube BettaTech: https://www.youtube.com/watch?v=Ab9HxiPLryg
#*--------------------------------------------------
# COMPONENTE
class Enemy():
def take_damage(self) -> float:
pass
def movement_speed(self) -> float:
pass
# Componente concreto enemigo base
class BaseEnemy(Enemy):
def take_damage(self) -> float:
return 10
def movement_speed(self) -> float:
return 10
# Componente enemigo invencible (enemigo que no recibe daño)
class InvencibleEnemy(Enemy):
def take_damage(self) -> float:
return 0
def movement_speed(self):
return 0
# Clase decorator
class EnemyDecorator(Enemy):
_enemy: Enemy = None
def __init__(self, enemy: Enemy) -> None:
self._enemy = enemy
@property
def enemy(self) -> Enemy:
return self._enemy
def take_damage(self) -> float:
return self._enemy.take_damage()
def movement_speed(self) -> float:
return self._enemy.movement_speed()
# Concrete decorators
class HelmetDecorator(EnemyDecorator):
def take_damage(self) -> float:
return self.enemy.take_damage() * 0.5
# Concrete decorators
class BootsDecorator(EnemyDecorator):
def take_damage(self) -> float:
return self._enemy.take_damage() * 0.2
def movement_speed(self) -> float:
return self._enemy.movement_speed() * 2
if __name__ == "__main__":
# Crear un enemigo base
base_enemy = BaseEnemy()
print(f"Damage dealt to base enemy: ", base_enemy.take_damage())
print(f"Movement speed of the base enemy: ", enemy_with_helmet.movement_speed())
print()
# Le agrega un casco al enemigo
enemy_with_helmet = HelmetDecorator(base_enemy)
print(f"Damage dealt to the enemy with helmet: ", enemy_with_helmet.take_damage())
print(f"Movement speed of the enemy with helmet and boots: ", enemy_with_helmet.movement_speed())
print()
# Le agrega botas al enemigo
enemy_with_boots_helmet = BootsDecorator(enemy_with_helmet)
print(f"Damage dealt to the enemy with boots and helmet: ", enemy_with_boots_helmet.take_damage())
print(f"Movement speed of the enemy with helmet and boots: ", enemy_with_boots_helmet.movement_speed())
| 30.884615 | 107 | 0.669572 | 302 | 2,409 | 5.13245 | 0.261589 | 0.109032 | 0.077419 | 0.065806 | 0.492258 | 0.463871 | 0.426452 | 0.380645 | 0.271613 | 0.271613 | 0 | 0.006189 | 0.195102 | 2,409 | 77 | 108 | 31.285714 | 0.793192 | 0.221254 | 0 | 0.391304 | 0 | 0 | 0.139635 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0.282609 | false | 0.043478 | 0 | 0.217391 | 0.652174 | 0.173913 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 1 | 1 | 0 | 0 | 3 |
7feda00daae9b8843ed2775a8157d87f8ef8c221 | 6,088 | py | Python | PackGrid/categoryAdjacent/T_C_AmmBeen.py | whitegreen/PackingDone | b7f1d2966f1d62a052ca9c96ce70b314d694c9f1 | [
"MIT"
] | null | null | null | PackGrid/categoryAdjacent/T_C_AmmBeen.py | whitegreen/PackingDone | b7f1d2966f1d62a052ca9c96ce70b314d694c9f1 | [
"MIT"
] | null | null | null | PackGrid/categoryAdjacent/T_C_AmmBeen.py | whitegreen/PackingDone | b7f1d2966f1d62a052ca9c96ce70b314d694c9f1 | [
"MIT"
] | null | null | null | # the same results by categoryAdjacent/T_C_AmmBeen.java
import gurobipy as gp
from gurobipy import GRB, LinExpr
import M
DM = 4
P = [(-3, -3, -2, 0), (-3, -3, -2, 1), (-3, -3, -1, 1), (-3, -3, -1, 2), (-3, -3, 0, 2), (-3, -2, -1, 1),
(-3, -2, -1, 2), (-3, -2, 0, 1), (-3, -2, 0, 2), (-3, -2, 0, 3), (-3, -2, 1, 2), (-3, -2, 1, 3), (-3, -1, 0, 2),
(-3, -1, 1, 2), (-3, -1, 1, 3), (-3, -1, 2, 3), (-3, 0, 2, 3), (-2, -3, -3, -1), (-2, -3, -3, 0),
(-2, -3, -2, -1), (-2, -3, -2, 0), (-2, -3, -2, 1), (-2, -3, -1, 0), (-2, -3, -1, 1), (-2, -2, -2, 0),
(-2, -2, -1, 0), (-2, -2, -1, 1), (-2, -2, 0, 1), (-2, -2, 0, 2), (-2, -1, -1, 1), (-2, -1, 0, 1), (-2, -1, 0, 2),
(-2, -1, 1, 1), (-2, -1, 1, 2), (-2, -1, 1, 3), (-2, -1, 2, 3), (-2, 0, 1, 2), (-2, 0, 1, 3), (-2, 0, 2, 2),
(-2, 0, 2, 3), (-2, 0, 3, 3), (-2, 1, 2, 3), (-2, 1, 3, 3), (-1, -3, -3, -2), (-1, -3, -3, -1), (-1, -3, -2, -1),
(-1, -3, -2, 0), (-1, -2, -3, -2), (-1, -2, -3, -1), (-1, -2, -2, -1), (-1, -2, -2, 0), (-1, -2, -1, -1),
(-1, -2, -1, 0), (-1, -2, -1, 1), (-1, -1, -2, -1), (-1, -1, -1, -1), (-1, -1, -1, 0), (-1, -1, -1, 1),
(-1, -1, 0, 0), (-1, -1, 0, 1), (-1, -1, 1, 1), (-1, -1, 1, 2), (-1, 0, 0, 0), (-1, 0, 0, 1), (-1, 0, 1, 1),
(-1, 0, 1, 2), (-1, 0, 2, 2), (-1, 0, 2, 3), (-1, 1, 1, 1), (-1, 1, 1, 2), (-1, 1, 2, 1), (-1, 1, 2, 2),
(-1, 1, 2, 3), (-1, 1, 3, 2), (-1, 1, 3, 3), (-1, 2, 3, 2), (-1, 2, 3, 3), (0, -3, -3, -2), (0, -2, -3, -3),
(0, -2, -3, -2), (0, -2, -3, -1), (0, -2, -2, -2), (0, -2, -2, -1), (0, -1, -3, -2), (0, -1, -2, -2),
(0, -1, -2, -1), (0, -1, -1, -1), (0, -1, -1, 0), (0, -1, 0, 0), (0, 0, -1, -1), (0, 0, -1, 0), (0, 0, 0, -1),
(0, 0, 0, 0), (0, 0, 0, 1), (0, 0, 1, 0), (0, 0, 1, 1), (0, 1, 0, 0), (0, 1, 1, 0), (0, 1, 1, 1), (0, 1, 2, 1),
(0, 1, 2, 2), (0, 1, 3, 2), (0, 2, 2, 1), (0, 2, 2, 2), (0, 2, 3, 1), (0, 2, 3, 2), (0, 2, 3, 3), (0, 3, 3, 2),
(1, -2, -3, -3), (1, -2, -3, -2), (1, -1, -3, -3), (1, -1, -3, -2), (1, -1, -2, -3), (1, -1, -2, -2),
(1, -1, -2, -1), (1, -1, -1, -2), (1, -1, -1, -1), (1, 0, -2, -3), (1, 0, -2, -2), (1, 0, -1, -2), (1, 0, -1, -1),
(1, 0, 0, -1), (1, 0, 0, 0), (1, 1, -1, -2), (1, 1, -1, -1), (1, 1, 0, -1), (1, 1, 0, 0), (1, 1, 1, -1),
(1, 1, 1, 0), (1, 1, 1, 1), (1, 1, 2, 1), (1, 2, 1, -1), (1, 2, 1, 0), (1, 2, 1, 1), (1, 2, 2, 0), (1, 2, 2, 1),
(1, 2, 3, 1), (1, 2, 3, 2), (1, 3, 2, 0), (1, 3, 2, 1), (1, 3, 3, 1), (1, 3, 3, 2), (2, -1, -3, -3),
(2, -1, -2, -3), (2, 0, -3, -3), (2, 0, -2, -3), (2, 0, -2, -2), (2, 0, -1, -3), (2, 0, -1, -2), (2, 1, -2, -3),
(2, 1, -1, -3), (2, 1, -1, -2), (2, 1, -1, -1), (2, 1, 0, -2), (2, 1, 0, -1), (2, 1, 1, -1), (2, 2, 0, -2),
(2, 2, 0, -1), (2, 2, 1, -1), (2, 2, 1, 0), (2, 2, 2, 0), (2, 3, 1, -1), (2, 3, 1, 0), (2, 3, 2, -1),
(2, 3, 2, 0), (2, 3, 2, 1), (2, 3, 3, 0), (2, 3, 3, 1), (3, 0, -2, -3), (3, 1, -2, -3), (3, 1, -1, -3),
(3, 1, -1, -2), (3, 1, 0, -2), (3, 2, -1, -3), (3, 2, -1, -2), (3, 2, 0, -3), (3, 2, 0, -2), (3, 2, 0, -1),
(3, 2, 1, -2), (3, 2, 1, -1), (3, 3, 0, -2), (3, 3, 1, -2), (3, 3, 1, -1), (3, 3, 2, -1), (3, 3, 2, 0)]
assert (185 == len(P)) # en=7
# ************************************************************************************
K = DM + 3 * DM + 4
A = []
A.append([(-1, 0, 0, 0), (0, 0, 0, 0), (1, 0, 0, 0)])
A.append([(0, -1, 0, 0), (0, 0, 0, 0), (0, 1, 0, 0)])
A.append([(0, 0, -1, 0), (0, 0, 0, 0), (0, 0, 1, 0)])
A.append([(0, 0, 0, -1), (0, 0, 0, 0), (0, 0, 0, 1)])
for i in range(DM):
for j in range(3):
A.append([A[i][0], (0, 0, 0, 0), A[(i + j + 1) % DM][2]])
A.append([(0, 0, 0, 0), (1, 0, 0, 0)]) # 2 - straight
A.append([(0, 0, 0, 0), (0, 1, 0, 0)])
A.append([(0, 0, 0, 0), (0, 0, 1, 0)])
A.append([(0, 0, 0, 0), (0, 0, 0, 1)])
assert (K == len(A))
# for tmp in A:
# st = ''
# for p in tmp:
# st = st + str(p[0])+ ','+str(p[1])+','+str(p[2])+ ','+str(p[3])+" "
# print(st)
_P_ = M.expand(P, DM)
print(len(P), len(_P_))
keyType = 0
def optimize():
try:
m = gp.Model("Type-Category Adjacent")
X = [m.addVars(P, vtype=GRB.BINARY) for k in range(K)]
oe = LinExpr()
for k in range(K):
oe.add(X[k].sum(), len(A[k]))
m.setObjective(oe, GRB.MAXIMIZE)
for v in P:
le = LinExpr()
for k in range(K):
Akv = [M.sub(v, tp) for tp in A[k]]
for u in Akv:
if u in P:
le.add(X[k][u])
m.addConstr(le <= 1)
for v in _P_:
for k in range(K):
le = LinExpr()
Akv = [M.sub(v, tp) for tp in A[k]]
for u in Akv:
if u in P:
le.add(X[k][u])
m.addConstr(le == 0)
le = LinExpr()
for k in range(16, 20):
le.add(X[k].sum())
m.addConstr(le <= 20) # sum of type 16-19 <= threshold
# *********************************** adjacency ***********************************
tj = keyType
for v in P:
le = LinExpr()
for ti in range(4, 16):
AA = M.calAA4((ti, tj), A)
for pa in AA:
u = M.add(v, pa)
if u in P:
le.add(X[ti][u])
m.addConstr(le >= X[tj][v]) # given a keyType patch, at least a (4,15)-patch around
ti = keyType
for tj in range(4, 16):
AA = M.calAA4((ti, tj), A)
for v in P:
le = LinExpr()
for pa in AA:
u = M.add(v, pa)
if u in P:
le.add(X[ti][u])
m.addConstr(le >= X[tj][v]) # given a (4,15)-patch, at least one keyType patch around
m.optimize()
for k in range(K):
for i in range(len(P)):
if 0.5 < X[k][P[i]].x:
print(k, i)
except gp.GurobiError as er:
print(' ****** error ******')
optimize()
| 47.937008 | 119 | 0.315867 | 1,222 | 6,088 | 1.567103 | 0.074468 | 0.125326 | 0.086162 | 0.071018 | 0.667363 | 0.648564 | 0.606266 | 0.54047 | 0.476762 | 0.398433 | 0 | 0.215046 | 0.336235 | 6,088 | 126 | 120 | 48.31746 | 0.258847 | 0.084428 | 0 | 0.304762 | 0 | 0 | 0.007554 | 0 | 0 | 0 | 0 | 0 | 0.019048 | 1 | 0.009524 | false | 0 | 0.028571 | 0 | 0.038095 | 0.028571 | 0 | 0 | 1 | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 3 |
3d0c488ae9755a8d7e9479fe1e8f67fdbd016c84 | 121 | py | Python | 30_days_of_code/Day-7: Arrays.py | haneefnani/hackerrank | 795e4d1db0637baf610632aea5e9dd6b44777365 | [
"MIT"
] | null | null | null | 30_days_of_code/Day-7: Arrays.py | haneefnani/hackerrank | 795e4d1db0637baf610632aea5e9dd6b44777365 | [
"MIT"
] | null | null | null | 30_days_of_code/Day-7: Arrays.py | haneefnani/hackerrank | 795e4d1db0637baf610632aea5e9dd6b44777365 | [
"MIT"
] | null | null | null | n = int(input())
arr = list(map(int, input().rstrip().split()))
arr.reverse()
for num in arr:
print(num , end=" ")
| 15.125 | 46 | 0.578512 | 19 | 121 | 3.684211 | 0.736842 | 0.228571 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.181818 | 121 | 7 | 47 | 17.285714 | 0.707071 | 0 | 0 | 0 | 0 | 0 | 0.008264 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | false | 0 | 0 | 0 | 0 | 0.2 | 1 | 0 | 0 | null | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 3 |
3d10757799c62ba9c65059f2ccc61b3e9a6e6a56 | 87 | py | Python | warmup_scheduler/__init__.py | seo-95/pytorch-gradual-warmup-lr | 99ce139a003d117b256fe72b0d5f33f0597f771a | [
"MIT"
] | 803 | 2019-01-16T09:59:08.000Z | 2022-03-31T03:36:51.000Z | warmup_scheduler/__init__.py | seo-95/pytorch-gradual-warmup-lr | 99ce139a003d117b256fe72b0d5f33f0597f771a | [
"MIT"
] | 20 | 2019-03-09T04:04:48.000Z | 2021-11-24T09:10:00.000Z | warmup_scheduler/__init__.py | seo-95/pytorch-gradual-warmup-lr | 99ce139a003d117b256fe72b0d5f33f0597f771a | [
"MIT"
] | 120 | 2019-01-18T13:39:35.000Z | 2022-03-30T11:40:24.000Z |
from warmup_scheduler.scheduler import GradualWarmupScheduler
__version__ = '0.3.2'
| 14.5 | 61 | 0.816092 | 10 | 87 | 6.6 | 0.9 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.038961 | 0.114943 | 87 | 5 | 62 | 17.4 | 0.818182 | 0 | 0 | 0 | 0 | 0 | 0.05814 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | false | 0 | 0.5 | 0 | 0.5 | 0 | 1 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 3 |
3d1839f7928cc8f1097ec5b2cf25dbbab7da65bb | 701 | py | Python | Bite_77/uncommon.py | alehpineda/bitesofpy | bfd319a606cd0b7b9bfb85a3e8942872a2d43c48 | [
"MIT"
] | null | null | null | Bite_77/uncommon.py | alehpineda/bitesofpy | bfd319a606cd0b7b9bfb85a3e8942872a2d43c48 | [
"MIT"
] | 2 | 2020-09-24T11:25:29.000Z | 2021-06-25T15:43:35.000Z | Bite_77/uncommon.py | alehpineda/bitesofpy | bfd319a606cd0b7b9bfb85a3e8942872a2d43c48 | [
"MIT"
] | null | null | null | """
You want to find people who have as much exposure to different cultures as yourself.
Complete the uncommon_cities helper that takes the cities you have visited (my_cities) and the cities the other person has visited (other_cities) and returns the number of cities that both sequences do NOT have in common.
So given [A B C] and [B C D] it should return 2 because only A and D are different.
You can loop through both sequences but maybe there is a more concise way to do it?
"""
def uncommon_cities(my_cities, other_cities):
"""Compare my_cities and other_cities and return the number of different
cities between the two"""
return len(list(set(my_cities) ^ set(other_cities)))
| 43.8125 | 221 | 0.761769 | 122 | 701 | 4.295082 | 0.532787 | 0.061069 | 0.041985 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.001751 | 0.185449 | 701 | 15 | 222 | 46.733333 | 0.915937 | 0.813124 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0.5 | false | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 3 |
3d369882a105ff68b47b9d8f2e1286596bee8328 | 134 | py | Python | src/zbdb.py | ZigAnon/babadook | c27ba32681d2d6933408952b0496faf37a3eda58 | [
"MIT"
] | null | null | null | src/zbdb.py | ZigAnon/babadook | c27ba32681d2d6933408952b0496faf37a3eda58 | [
"MIT"
] | null | null | null | src/zbdb.py | ZigAnon/babadook | c27ba32681d2d6933408952b0496faf37a3eda58 | [
"MIT"
] | null | null | null | #!/usr/local/bin/python3.6
def is_ztest(m):
if int(m.author.id) == int(zigID):
return True
else:
return False
| 19.142857 | 38 | 0.58209 | 21 | 134 | 3.666667 | 0.857143 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.020408 | 0.268657 | 134 | 6 | 39 | 22.333333 | 0.765306 | 0.186567 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0.2 | false | 0 | 0 | 0 | 0.6 | 0 | 1 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 3 |
3d4403a2a1ba241e034f26570268a6d4a611eb8b | 228 | py | Python | ProcessingReviewAnalyse/viewInBoxSizes.py | RubberDuckDestroyer/enron | 748df96ee63bce98d9b566ed797a7cb7e32a1714 | [
"MIT"
] | 2 | 2020-04-27T11:48:26.000Z | 2020-04-27T11:58:57.000Z | ProcessingReviewAnalyse/viewInBoxSizes.py | RubberDuckDestroyer/enron | 748df96ee63bce98d9b566ed797a7cb7e32a1714 | [
"MIT"
] | null | null | null | ProcessingReviewAnalyse/viewInBoxSizes.py | RubberDuckDestroyer/enron | 748df96ee63bce98d9b566ed797a7cb7e32a1714 | [
"MIT"
] | null | null | null | import os
from email.parser import Parser
rootdir = "C:\\Users\\maxfr\\Desktop\\2020-Enron-Journal\\maildir\\lay-k"
for directory, subdirectory, filenames in os.walk(rootdir):
print(directory, subdirectory, len(filenames)) | 32.571429 | 73 | 0.758772 | 31 | 228 | 5.580645 | 0.774194 | 0.242775 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.019512 | 0.100877 | 228 | 7 | 74 | 32.571429 | 0.82439 | 0 | 0 | 0 | 0 | 0 | 0.266376 | 0.266376 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | false | 0 | 0.4 | 0 | 0.4 | 0.2 | 0 | 0 | 0 | null | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 3 |
e9e47d30f365685a79b97e14ad9f2e66a2c6b956 | 1,357 | py | Python | ravenframework/Databases/__init__.py | dgarrett622/raven | f36cc108f7500b0e2717df4832b69b801b43960d | [
"Apache-2.0"
] | null | null | null | ravenframework/Databases/__init__.py | dgarrett622/raven | f36cc108f7500b0e2717df4832b69b801b43960d | [
"Apache-2.0"
] | null | null | null | ravenframework/Databases/__init__.py | dgarrett622/raven | f36cc108f7500b0e2717df4832b69b801b43960d | [
"Apache-2.0"
] | null | null | null | # Copyright 2017 Battelle Energy Alliance, LLC
#
# 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.
"""
The Databases module includes efficient ways to serialize data to file.
"""
from __future__ import absolute_import
from ..utils import InputData
from .Database import DateBase as Database
from .HDF5 import HDF5
from .NetCDF import NetCDF
from .Factory import factory
class DatabasesCollection(InputData.ParameterInput):
"""
Class for reading in a collection of databases
"""
DatabasesCollection.createClass("Databases")
DatabasesCollection.addSub(HDF5.getInputSpecification())
DatabasesCollection.addSub(NetCDF.getInputSpecification())
def returnInputParameter():
"""
Returns the input specs for the desired classes
@ In, None
@ Out, returnInputParameter, InputData.ParameterInput, parsing class
"""
return DatabasesCollection()
| 30.840909 | 74 | 0.775239 | 173 | 1,357 | 6.052023 | 0.589595 | 0.057307 | 0.024833 | 0.030564 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.009582 | 0.154016 | 1,357 | 43 | 75 | 31.55814 | 0.902439 | 0.596905 | 0 | 0 | 0 | 0 | 0.01833 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0.083333 | true | 0 | 0.5 | 0 | 0.75 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 0 | 1 | 0 | 0 | 3 |
e9e63e975777f45769bdc25dfb5de68dccd19186 | 70 | py | Python | titanic data.py | ransherraj/Predictions-using-Titanic-Dataset- | 0bc175d9f85615489beab134fde039bda1c335b8 | [
"Apache-2.0"
] | null | null | null | titanic data.py | ransherraj/Predictions-using-Titanic-Dataset- | 0bc175d9f85615489beab134fde039bda1c335b8 | [
"Apache-2.0"
] | null | null | null | titanic data.py | ransherraj/Predictions-using-Titanic-Dataset- | 0bc175d9f85615489beab134fde039bda1c335b8 | [
"Apache-2.0"
] | null | null | null | import pandas as pd
df = pd.read_csv('titanic.csv')
print(df.head()) | 23.333333 | 32 | 0.7 | 13 | 70 | 3.692308 | 0.769231 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.128571 | 70 | 3 | 33 | 23.333333 | 0.786885 | 0 | 0 | 0 | 0 | 0 | 0.15942 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | false | 0 | 0.333333 | 0 | 0.333333 | 0.333333 | 1 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 3 |
e9fe5fa1b2d2c6cc461959149d8a65a6435a5779 | 145 | py | Python | app/data/schema/pydantic/token.py | lokaimoma/Bugza | 93ffe344cb0be7dc4c45965f52798e02d05d320b | [
"Unlicense"
] | 2 | 2022-02-14T23:53:00.000Z | 2022-03-24T12:19:49.000Z | app/data/schema/pydantic/token.py | lokaimoma/Bugza | 93ffe344cb0be7dc4c45965f52798e02d05d320b | [
"Unlicense"
] | null | null | null | app/data/schema/pydantic/token.py | lokaimoma/Bugza | 93ffe344cb0be7dc4c45965f52798e02d05d320b | [
"Unlicense"
] | null | null | null | # Created by Kelvin_Clark on 1/31/2022, 12:42 PM
from pydantic import BaseModel
class TokenData(BaseModel):
username: str
user_id: int
| 18.125 | 48 | 0.737931 | 23 | 145 | 4.565217 | 0.956522 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.094017 | 0.193103 | 145 | 7 | 49 | 20.714286 | 0.803419 | 0.317241 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | true | 0 | 0.25 | 0 | 1 | 0 | 1 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 3 |
180194eb7dce0c8168d47f447cf4eb4f25c278ca | 253 | py | Python | server/configure.py | shayanray/Memoirs | c0377b3467e79b7c653e04f47aca099ec53ac8a5 | [
"CNRI-Python"
] | 1 | 2018-09-15T14:57:57.000Z | 2018-09-15T14:57:57.000Z | server/configure.py | shayanray/Memoirs | c0377b3467e79b7c653e04f47aca099ec53ac8a5 | [
"CNRI-Python"
] | null | null | null | server/configure.py | shayanray/Memoirs | c0377b3467e79b7c653e04f47aca099ec53ac8a5 | [
"CNRI-Python"
] | null | null | null | from flask import Flask
import os
FLASK_NAME = os.environ.get("FLASK_NAME")
FLASK_SECRET_KEY = os.environ.get("FLASK_SECRET_KEY")
def create_app():
app = Flask(FLASK_NAME)
app.secret_key = FLASK_SECRET_KEY
return app
app = create_app()
| 16.866667 | 53 | 0.735178 | 40 | 253 | 4.35 | 0.325 | 0.206897 | 0.241379 | 0.195402 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.166008 | 253 | 14 | 54 | 18.071429 | 0.824645 | 0 | 0 | 0 | 0 | 0 | 0.102767 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0.111111 | false | 0 | 0.222222 | 0 | 0.444444 | 0 | 0 | 0 | 0 | null | 1 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 3 |
1822d9592112b5a3c272c252e66ed9f1db7d8db6 | 98 | py | Python | app.py | ijafri26/dashi | 2b21f166b0897ac25b2b4127391db17f9d33b80c | [
"MIT"
] | null | null | null | app.py | ijafri26/dashi | 2b21f166b0897ac25b2b4127391db17f9d33b80c | [
"MIT"
] | 7 | 2019-06-06T13:42:08.000Z | 2019-06-06T17:43:08.000Z | dashi/app.py | slazicoicr/dashi | ede0cebbd2c3490e9f8c4b56ba3f2e04b6576997 | [
"MIT"
] | null | null | null | import dash
app = dash.Dash()
server = app.server
app.config.suppress_callback_exceptions = True
| 16.333333 | 46 | 0.785714 | 14 | 98 | 5.357143 | 0.642857 | 0.24 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.122449 | 98 | 5 | 47 | 19.6 | 0.872093 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | false | 0 | 0.25 | 0 | 0.25 | 0 | 1 | 0 | 0 | null | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 3 |
1823214ec67111fb5e46a3d97c7acfe6d290c211 | 363 | py | Python | 2020/05/16/Adding Extra Fields On Many-To-Many Relationships in Django/many_to_many_extra/many_to_many_extra/example/migrations/0003_auto_20200516_1759.py | kenjitagawa/youtube_video_code | ef3c48b9e136b3745d10395d94be64cb0a1f1c97 | [
"Unlicense"
] | 492 | 2019-06-25T12:54:31.000Z | 2022-03-30T12:38:28.000Z | 2020/05/16/Adding Extra Fields On Many-To-Many Relationships in Django/many_to_many_extra/many_to_many_extra/example/migrations/0003_auto_20200516_1759.py | kenjitagawa/youtube_video_code | ef3c48b9e136b3745d10395d94be64cb0a1f1c97 | [
"Unlicense"
] | 23 | 2019-10-01T01:36:08.000Z | 2022-02-10T12:46:16.000Z | 2020/05/16/Adding Extra Fields On Many-To-Many Relationships in Django/many_to_many_extra/many_to_many_extra/example/migrations/0003_auto_20200516_1759.py | kenjitagawa/youtube_video_code | ef3c48b9e136b3745d10395d94be64cb0a1f1c97 | [
"Unlicense"
] | 1,734 | 2019-06-03T06:25:13.000Z | 2022-03-31T23:57:53.000Z | # Generated by Django 3.0.6 on 2020-05-16 17:59
from django.db import migrations
class Migration(migrations.Migration):
dependencies = [
('example', '0002_course_students'),
]
operations = [
migrations.DeleteModel(
name='Course',
),
migrations.DeleteModel(
name='Student',
),
]
| 18.15 | 47 | 0.570248 | 35 | 363 | 5.857143 | 0.771429 | 0.204878 | 0.243902 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.076613 | 0.316804 | 363 | 19 | 48 | 19.105263 | 0.75 | 0.123967 | 0 | 0.307692 | 1 | 0 | 0.126582 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | false | 0 | 0.076923 | 0 | 0.307692 | 0 | 0 | 0 | 0 | null | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 3 |
182e06cef46a285d9a2d1ce99a77ccd40f9009ad | 156 | py | Python | config/api_keys.py | rupakc/Positron | 99c8a34d565b812411e5f26b3a60baffb512a1f2 | [
"Unlicense"
] | null | null | null | config/api_keys.py | rupakc/Positron | 99c8a34d565b812411e5f26b3a60baffb512a1f2 | [
"Unlicense"
] | null | null | null | config/api_keys.py | rupakc/Positron | 99c8a34d565b812411e5f26b3a60baffb512a1f2 | [
"Unlicense"
] | null | null | null | CURRENT_NEWS_API_KEY = '' # Replace with your news.org API keys
news = {
'api_key': '',
'base_everything_url': 'https://newsapi.org/v2/everything'
} | 31.2 | 63 | 0.679487 | 22 | 156 | 4.545455 | 0.681818 | 0.14 | 0.2 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.007634 | 0.160256 | 156 | 5 | 64 | 31.2 | 0.755725 | 0.224359 | 0 | 0 | 0 | 0 | 0.491667 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | false | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | null | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 3 |
1845f462a7da2df21023bb7b8ed75fa7a5a9ee3b | 323 | py | Python | AutoDL_sample_code_submission/at_speech/__init__.py | dianjixz/AutoDL | 48db4eb04d55ce69e93d4a3bdc24592bdb34a868 | [
"Apache-2.0"
] | 1,044 | 2020-04-19T04:48:24.000Z | 2022-03-24T07:38:43.000Z | AutoDL_sample_code_submission/at_speech/__init__.py | dianjixz/AutoDL | 48db4eb04d55ce69e93d4a3bdc24592bdb34a868 | [
"Apache-2.0"
] | 39 | 2020-05-02T01:19:20.000Z | 2021-09-11T21:32:12.000Z | AutoDL_sample_code_submission/at_speech/__init__.py | dianjixz/AutoDL | 48db4eb04d55ce69e93d4a3bdc24592bdb34a868 | [
"Apache-2.0"
] | 203 | 2020-04-07T11:06:39.000Z | 2022-03-11T02:49:06.000Z | import os
from at_speech.data_space import DNpAugPreprocessor, MixupGenerator, TTAGenerator
from at_speech.backbones.thinresnet34 import build_tr34_model
from at_speech.data_space.examples_gen_maker import DataGenerator as Tr34DataGenerator
from at_speech.classifier import SLLRLiblinear, SLLRSag, ThinResnet34Classifier
| 40.375 | 86 | 0.888545 | 40 | 323 | 6.925 | 0.625 | 0.086643 | 0.173285 | 0.115523 | 0.151625 | 0 | 0 | 0 | 0 | 0 | 0 | 0.026936 | 0.080495 | 323 | 7 | 87 | 46.142857 | 0.905724 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | true | 0 | 1 | 0 | 1 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 0 | 0 | 0 | 0 | 3 |
185a2863dde4a3d7e7f7d3ac618b13d805eda0f1 | 166 | py | Python | config.py | bandham-manikanta/latest-ipo-subscription-details-streamlit | 51ec999dcc6db807543b5576f9ec44fe770f68f2 | [
"MIT"
] | null | null | null | config.py | bandham-manikanta/latest-ipo-subscription-details-streamlit | 51ec999dcc6db807543b5576f9ec44fe770f68f2 | [
"MIT"
] | null | null | null | config.py | bandham-manikanta/latest-ipo-subscription-details-streamlit | 51ec999dcc6db807543b5576f9ec44fe770f68f2 | [
"MIT"
] | null | null | null | ENV='development'
DEBUG=True
SQLALCHEMY_DATABASE_URI='sqlite:///data_base.db'
#SQLALCHEMY_ECHO=True
SQLALCHEMY_TRACK_MODIFICATIONS=False
SCHEDULER_API_ENABLED = True
| 23.714286 | 48 | 0.855422 | 22 | 166 | 6.090909 | 0.818182 | 0.208955 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.048193 | 166 | 6 | 49 | 27.666667 | 0.848101 | 0.120482 | 0 | 0 | 0 | 0 | 0.227586 | 0.151724 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | false | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | null | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 3 |
185d42a8695b7744f91ba6169ec2fe7bdc4fb73e | 2,931 | py | Python | scripts/cli_rebalance.py | ramalingam-cb/testrunner | 81cea7a5a493cf0c67fca7f97c667cd3c6ad2142 | [
"Apache-2.0"
] | null | null | null | scripts/cli_rebalance.py | ramalingam-cb/testrunner | 81cea7a5a493cf0c67fca7f97c667cd3c6ad2142 | [
"Apache-2.0"
] | null | null | null | scripts/cli_rebalance.py | ramalingam-cb/testrunner | 81cea7a5a493cf0c67fca7f97c667cd3c6ad2142 | [
"Apache-2.0"
] | null | null | null |
import getopt
import copy
import logging
import sys
from threading import Thread
from datetime import datetime
import socket
import Queue
sys.path = [".", "lib"] + sys.path
import testconstants
import time
from builds.build_query import BuildQuery
import logging.config
from membase.api.exception import ServerUnavailableException
from membase.api.rest_client import RestConnection, RestHelper
from remote.remote_util import RemoteMachineShellConnection, RemoteUtilHelper
from membase.helper.cluster_helper import ClusterOperationHelper
from testconstants import MV_LATESTBUILD_REPO
from testconstants import SHERLOCK_BUILD_REPO
from testconstants import COUCHBASE_REPO
import TestInput
logging.config.fileConfig("scripts.logging.conf")
log = logging.getLogger()
def usage():
print "Please provide ini file"
def main():
log_install_failed = "some nodes were not install successfully!"
try:
(opts, args) = getopt.getopt(sys.argv[1:], 'hi:p:', [])
for o, a in opts:
if o == "-h":
usage()
if len(sys.argv) <= 1:
usage()
input = TestInput.TestInputParser.get_test_input(sys.argv)
if not input.servers:
usage("ERROR: no servers specified. Please use the -i parameter.")
except IndexError:
usage()
except getopt.GetoptError, err:
usage("ERROR: " + str(err))
print input
cli_command = "rebalance"
if "rebalance_in" in input.test_params:
# add upgraded nodes in the cluster
# Assumption 4 nodes in ini file and add nodes from last node upwards
remote_client = RemoteMachineShellConnection(input.servers[0])
for server in input.servers[2:]:
print server.ip
options = "--server-add={0}:8091".format(server.ip) + " --server-add-username=Administrator --server-add-password=password"
output, error = remote_client.execute_couchbase_cli(cli_command, options=options, cluster_host=input.servers[0].ip, cluster_port=server.port, user=server.rest_username, password=server.rest_password)
print output, error
time.sleep(5)
if "rebalance_out" in input.test_params:
# remove old build nodes out from the cluster
# Assumption 4 nodes and remove nodes from the top
cli_command = "rebalance"
remote_client = RemoteMachineShellConnection(input.servers[2])
for server in input.servers[:2]:
print server.ip
options = "--server-remove={0}:8091".format(server.ip) + " --server-add-username=Administrator --server-add-password=password"
output, error = remote_client.execute_couchbase_cli(cli_command, options=options, cluster_host=input.servers[2].ip, cluster_port=server.port, user=server.rest_username, password=server.rest_password)
print output, error
time.sleep(5)
if __name__ == "__main__":
main()
| 36.185185 | 211 | 0.700102 | 364 | 2,931 | 5.513736 | 0.346154 | 0.041854 | 0.025909 | 0.026906 | 0.38864 | 0.310912 | 0.310912 | 0.310912 | 0.310912 | 0.310912 | 0 | 0.009475 | 0.207779 | 2,931 | 80 | 212 | 36.6375 | 0.854866 | 0.066189 | 0 | 0.177419 | 0 | 0 | 0.142439 | 0.064079 | 0 | 0 | 0 | 0 | 0 | 0 | null | null | 0.064516 | 0.322581 | null | null | 0.096774 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 1 | 1 | 0 | 0 | 0 | 0 | 3 |
185f1f449d6c6421dfa20d0eb34750c98ef71817 | 24,420 | py | Python | adversarial regularization/training_code/purchase_defended.py | inspire-group/membership-inference-evaluation | daa4b0c88a7eda36536abe5b9a2650623243a3c5 | [
"MIT"
] | 66 | 2020-03-25T06:10:23.000Z | 2022-03-29T13:27:32.000Z | adversarial regularization/training_code/purchase_defended.py | wjw950224/membership-inference-evaluation | daa4b0c88a7eda36536abe5b9a2650623243a3c5 | [
"MIT"
] | 5 | 2020-06-04T12:53:19.000Z | 2021-11-24T15:57:24.000Z | adversarial regularization/training_code/purchase_defended.py | wjw950224/membership-inference-evaluation | daa4b0c88a7eda36536abe5b9a2650623243a3c5 | [
"MIT"
] | 11 | 2020-09-02T22:16:05.000Z | 2022-02-08T08:34:12.000Z |
# coding: utf-8
# In[8]:
from __future__ import print_function
import argparse
import os
os.environ['CUDA_DEVICE_ORDER']="PCI_BUS_ID"
os.environ['CUDA_VISIBLE_DEVICES']='7'
import shutil
import time
import random
import torch.nn.functional as F
import torch
import pickle
import torch.nn as nn
import torch.nn.parallel
import torch.backends.cudnn as cudnn
import torch.optim as optim
import torch.utils.data as data
import torchvision.transforms as transforms
import torchvision.datasets as datasets
from utils import Logger, AverageMeter, accuracy, mkdir_p, savefig
import numpy as np
import tarfile
from sklearn.cluster import KMeans
from sklearn import datasets
import urllib
use_cuda = torch.cuda.is_available()
DATASET_PATH='../datasets/purchase'
DATASET_NAME= 'dataset_purchase'
if not os.path.isdir(DATASET_PATH):
mkdir_p(DATASET_PATH)
DATASET_FILE = os.path.join(DATASET_PATH,DATASET_NAME)
if not os.path.isfile(DATASET_FILE):
print("Dowloading the dataset...")
urllib.request.urlretrieve("https://www.comp.nus.edu.sg/~reza/files/dataset_purchase.tgz",os.path.join(DATASET_PATH,'tmp.tgz'))
print('Dataset Dowloaded')
tar = tarfile.open(os.path.join(DATASET_PATH,'tmp.tgz'))
tar.extractall(path=DATASET_PATH)
data_set =np.genfromtxt(DATASET_FILE,delimiter=',')
X = data_set[:,1:].astype(np.float64)
Y = (data_set[:,0]).astype(np.int32)-1
print(X.shape, Y.shape)
class PurchaseClassifier(nn.Module):
def __init__(self,num_classes=100):
super(PurchaseClassifier, self).__init__()
self.features = nn.Sequential(
nn.Linear(600,1024),
nn.Tanh(),
nn.Linear(1024,512),
nn.Tanh(),
nn.Linear(512,256),
nn.Tanh(),
nn.Linear(256,128),
nn.Tanh(),
)
self.classifier = nn.Linear(128,num_classes)
# for key in self.state_dict():
# if key.split('.')[-1] == 'weight':
# nn.init.normal(self.state_dict()[key], std=0.01)
# print (key)
# elif key.split('.')[-1] == 'bias':
# self.state_dict()[key][...] = 0
def forward(self,x):
hidden_out = self.features(x)
return self.classifier(hidden_out),hidden_out
# In[16]:
class InferenceAttack_HZ(nn.Module):
def __init__(self,num_classes):
self.num_classes=num_classes
super(InferenceAttack_HZ, self).__init__()
self.features=nn.Sequential(
nn.Linear(100,1024),
nn.ReLU(),
nn.Linear(1024,512),
nn.ReLU(),
nn.Linear(512,64),
nn.ReLU(),
)
self.labels=nn.Sequential(
nn.Linear(num_classes,128),
nn.ReLU(),
nn.Linear(128,64),
nn.ReLU(),
)
self.combine=nn.Sequential(
nn.Linear(64*2,512),
nn.ReLU(),
nn.Linear(512,256),
nn.ReLU(),
nn.Linear(256,128),
nn.ReLU(),
nn.Linear(128,64),
nn.ReLU(),
nn.Linear(64,1),
)
for key in self.state_dict():
print (key)
if key.split('.')[-1] == 'weight':
nn.init.normal(self.state_dict()[key], std=0.01)
print (key)
elif key.split('.')[-1] == 'bias':
self.state_dict()[key][...] = 0
self.output= nn.Sigmoid()
def forward(self,x1,x2,l):
#print (l.size(),x.size())
out_x1 = self.features(x1)
out_l = self.labels(l)
is_member =self.combine( torch.cat((out_x1,out_l),1))
return self.output(is_member)
# In[17]:
len_train =len(X)
###################################################################
###################################################################
r = np.load('../dataset_shuffle/random_r_purchase100.npy')
X=X[r]
Y=Y[r]
train_classifier_ratio, train_attack_ratio = 0.1,0.15
train_classifier_data = X[:int(train_classifier_ratio*len_train)]
train_attack_data = X[int(train_classifier_ratio*len_train):int((train_classifier_ratio+train_attack_ratio)*len_train)]
test_data = X[int((train_classifier_ratio+train_attack_ratio)*len_train):]
train_classifier_label = Y[:int(train_classifier_ratio*len_train)]
train_attack_label = Y[int(train_classifier_ratio*len_train):int((train_classifier_ratio+train_attack_ratio)*len_train)]
test_label = Y[int((train_classifier_ratio+train_attack_ratio)*len_train):]
# In[18]:
def train(train_data,labels, model, criterion, optimizer, epoch, use_cuda,num_batchs=999999):
# switch to train mode
model.train()
batch_time = AverageMeter()
data_time = AverageMeter()
losses = AverageMeter()
top1 = AverageMeter()
top5 = AverageMeter()
end = time.time()
len_t = (len(train_data)//batch_size)-1
for ind in range(len_t):
if ind > num_batchs:
break
# measure data loading time
inputs = train_data[ind*batch_size:(ind+1)*batch_size]
targets = labels[ind*batch_size:(ind+1)*batch_size]
data_time.update(time.time() - end)
if use_cuda:
inputs, targets = inputs.cuda(), targets.cuda(async=True)
inputs, targets = torch.autograd.Variable(inputs), torch.autograd.Variable(targets)
# compute output
outputs,_ = model(inputs)
loss = criterion(outputs, targets)
# measure accuracy and record loss
prec1, prec5 = accuracy(outputs.data, targets.data, topk=(1, 5))
losses.update(loss.data, inputs.size()[0])
top1.update(prec1, inputs.size()[0])
top5.update(prec5, inputs.size()[0])
# compute gradient and do SGD step
optimizer.zero_grad()
loss.backward()
optimizer.step()
# measure elapsed time
batch_time.update(time.time() - end)
end = time.time()
# plot progress
if ind%100==0:
print ('({batch}/{size}) Data: {data:.3f}s | Batch: {bt:.3f}s | | Loss: {loss:.4f} | top1: {top1: .4f} | top5: {top5: .4f}'.format(
batch=ind + 1,
size=len_t,
data=data_time.avg,
bt=batch_time.avg,
loss=losses.avg,
top1=top1.avg,
top5=top5.avg,
))
return (losses.avg, top1.avg)
return
# In[19]:
def test(test_data,labels, model, criterion, epoch, use_cuda):
batch_time = AverageMeter()
data_time = AverageMeter()
losses = AverageMeter()
top1 = AverageMeter()
top5 = AverageMeter()
# switch to evaluate mode
model.eval()
end = time.time()
len_t = (len(test_data)//batch_size)-1
for ind in range(len_t):
# measure data loading time
inputs = test_data[ind*batch_size:(ind+1)*batch_size]
targets = labels[ind*batch_size:(ind+1)*batch_size]
data_time.update(time.time() - end)
if use_cuda:
inputs, targets = inputs.cuda(), targets.cuda()
inputs, targets = torch.autograd.Variable(inputs, volatile=True), torch.autograd.Variable(targets)
# compute output
outputs,_ = model(inputs)
loss = criterion(outputs, targets)
# measure accuracy and record loss
prec1, prec5 = accuracy(outputs.data, targets.data, topk=(1, 5))
losses.update(loss.data, inputs.size()[0])
top1.update(prec1, inputs.size()[0])
top5.update(prec5, inputs.size()[0])
# measure elapsed time
batch_time.update(time.time() - end)
end = time.time()
# plot progress
# if ind % 100==0:
# print ('({batch}/{size}) Data: {data:.3f}s | Batch: {bt:.3f}s | Loss: {loss:.4f} | top1: {top1: .4f} | top5: {top5: .4f}'.format(
# batch=ind + 1,
# size=len(test_data),
# data=data_time.avg,
# bt=batch_time.avg,
# loss=losses.avg,
# top1=top1.avg,
# top5=top5.avg,
# ))
return (losses.avg, top1.avg)
# In[20]:
def train_privatly(train_data,labels, model,inference_model, criterion, optimizer, epoch, use_cuda,num_batchs=10000,skip_batch=0,alpha=0.5):
# switch to train mode
model.train()
inference_model.eval()
batch_time = AverageMeter()
data_time = AverageMeter()
losses = AverageMeter()
top1 = AverageMeter()
top5 = AverageMeter()
end = time.time()
len_t = (len(train_data)//batch_size)-1
for ind in range(skip_batch,len_t):
if ind >= skip_batch+num_batchs:
break
# measure data loading time
#print (ind)
inputs = train_data[ind*batch_size:(ind+1)*batch_size]
targets = labels[ind*batch_size:(ind+1)*batch_size]
data_time.update(time.time() - end)
if use_cuda:
inputs, targets = inputs.cuda(), targets.cuda(async=True)
inputs, targets = torch.autograd.Variable(inputs), torch.autograd.Variable(targets)
# compute output
outputs,h_layer = model(inputs)
one_hot_tr = torch.from_numpy((np.zeros((outputs.size()[0],outputs.size(1))))).cuda().type(torch.cuda.FloatTensor)
target_one_hot_tr = one_hot_tr.scatter_(1, targets.type(torch.cuda.LongTensor).view([-1,1]).data,1)
infer_input_one_hot = torch.autograd.Variable(target_one_hot_tr)
inference_output = inference_model ( outputs,h_layer,infer_input_one_hot)
#print (inference_output.mean())
relu = nn.ReLU()
loss = criterion(outputs, targets) + ((alpha)*(torch.mean((inference_output ))-0.5))
# measure accuracy and record loss
prec1, prec5 = accuracy(outputs.data, targets.data, topk=(1, 5))
losses.update(loss.data, inputs.size()[0])
top1.update(prec1, inputs.size()[0])
top5.update(prec5, inputs.size()[0])
# compute gradient and do SGD step
optimizer.zero_grad()
loss.backward()
optimizer.step()
# measure elapsed time
batch_time.update(time.time() - end)
end = time.time()
# plot progress
if ind%100==0:
print (alpha, '({batch}/{size}) Data: {data:.3f}s | Batch: {bt:.3f}s | | Loss: {loss:.4f} | top1: {top1: .4f} | top5: {top5: .4f}'.format(
batch=ind + 1,
size=len_t,
data=data_time.avg,
bt=batch_time.avg,
loss=losses.avg,
top1=top1.avg,
top5=top5.avg,
))
return (losses.avg, top1.avg)
# In[ ]:
# In[21]:
def save_checkpoint(state, is_best, checkpoint='./models/purchase_defended', filename='checkpoint.pth.tar'):
if not os.path.isdir(checkpoint):
mkdir_p(checkpoint)
filepath = os.path.join(checkpoint, filename)
torch.save(state, filepath)
if is_best:
shutil.copyfile(filepath, os.path.join(checkpoint, 'model_best.pth.tar'))
# In[22]:
def train_attack(train_data,labels,attack_data,attack_label, model,attack_model, criterion,attack_criterion, optimizer,attack_optimizer, epoch, use_cuda,num_batchs=100000,skip_batch=0):
# switch to train mode
model.eval()
attack_model.train()
batch_time = AverageMeter()
data_time = AverageMeter()
losses = AverageMeter()
top1 = AverageMeter()
top5 = AverageMeter()
end = time.time()
len_t = min((len(attack_data)//batch_size) ,(len(train_data)//batch_size))-1
#print (skip_batch, len_t)
for ind in range(skip_batch, len_t):
if ind >= skip_batch+num_batchs:
break
# measure data loading time
inputs = train_data[ind*batch_size:(ind+1)*batch_size]
targets = labels[ind*batch_size:(ind+1)*batch_size]
inputs_attack = attack_data[ind*batch_size:(ind+1)*batch_size]
targets_attack = attack_label[ind*batch_size:(ind+1)*batch_size]
#print ( len(targets_attack), len(targets))
data_time.update(time.time() - end)
if use_cuda:
inputs, targets = inputs.cuda(), targets.cuda(async=True)
inputs_attack , targets_attack = inputs_attack.cuda(), targets_attack.cuda(async=True)
inputs, targets = torch.autograd.Variable(inputs), torch.autograd.Variable(targets)
inputs_attack , targets_attack = torch.autograd.Variable(inputs_attack), torch.autograd.Variable(targets_attack)
# compute output
outputs, h_layer = model(inputs)
outputs_non, h_layer_non = model(inputs_attack)
classifier_input = torch.cat((inputs,inputs_attack))
comb_inputs_h = torch.cat((h_layer,h_layer_non))
comb_inputs = torch.cat((outputs,outputs_non))
if use_cuda:
comb_targets= torch.cat((targets,targets_attack)).view([-1,1]).type(torch.cuda.FloatTensor)
else:
comb_targets= torch.cat((targets,targets_attack)).view([-1,1]).type(torch.FloatTensor)
#print (comb_inputs.size(),comb_targets.size())
attack_input = comb_inputs #torch.cat((comb_inputs,comb_targets),1)
one_hot_tr = torch.from_numpy((np.zeros((attack_input.size()[0],outputs.size(1))))).cuda().type(torch.cuda.FloatTensor)
target_one_hot_tr = one_hot_tr.scatter_(1, torch.cat((targets,targets_attack)).type(torch.cuda.LongTensor).view([-1,1]).data,1)
infer_input_one_hot = torch.autograd.Variable(target_one_hot_tr)
# sf= nn.Softmax(dim=0)
# att_inp=torch.stack([attack_input, infer_input_one_hot],1)
# att_inp = att_inp.view([attack_input.size()[0],1,2,attack_input.size(1)])
#attack_output = attack_model(att_inp).view([-1])
attack_output = attack_model(attack_input,comb_inputs_h,infer_input_one_hot).view([-1])
#attack_output = attack_model(attack_input).view([-1])
att_labels = np.zeros((inputs.size()[0]+inputs_attack.size()[0]))
att_labels [:inputs.size()[0]] =1.0
att_labels [inputs.size()[0]:] =0.0
is_member_labels = torch.from_numpy(att_labels).type(torch.FloatTensor)
if use_cuda:
is_member_labels = is_member_labels.cuda()
v_is_member_labels = torch.autograd.Variable(is_member_labels)
classifier_targets = comb_targets.clone().view([-1]).type(torch.cuda.LongTensor)
loss_attack = attack_criterion(attack_output, v_is_member_labels)
# measure accuracy and record loss
#prec1,p5 = accuracy(attack_output.data, v_is_member_labels.data, topk=(1,2))
prec1=np.mean(np.equal((attack_output.data.cpu().numpy() >0.5),(v_is_member_labels.data.cpu().numpy()> 0.5)))
losses.update(loss_attack.data, attack_input.size()[0])
top1.update(prec1, attack_input.size()[0])
#print ( attack_output.data.cpu().numpy(),v_is_member_labels.data.cpu().numpy() ,attack_input.data.cpu().numpy())
#raise
# compute gradient and do SGD step
attack_optimizer.zero_grad()
loss_attack.backward()
attack_optimizer.step()
# measure elapsed time
batch_time.update(time.time() - end)
end = time.time()
# plot progress
if ind%100==0:
print ('({batch}/{size}) Data: {data:.3f}s | Batch: {bt:.3f}s | | Loss: {loss:.4f} | top1: {top1: .4f} '.format(
batch=ind + 1,
size=len_t,
data=data_time.avg,
bt=batch_time.avg,
loss=losses.avg,
top1=top1.avg,
))
return (losses.avg, top1.avg)
# In[23]:
def test_attack(train_data,labels,attack_data,attack_label, model,attack_model, criterion,attack_criterion, optimizer,attack_optimizer, epoch, use_cuda):
model.eval()
attack_model.eval()
batch_time = AverageMeter()
data_time = AverageMeter()
losses = AverageMeter()
top1 = AverageMeter()
top5 = AverageMeter()
end = time.time()
len_t = min((len(attack_data)//batch_size) ,(len(train_data)//batch_size))-1
member_prob = np.zeros((len_t+1)*batch_size)
nonmember_prob = np.zeros((len_t+1)*batch_size)
for ind in range(len_t):
# measure data loading time
inputs = train_data[ind*batch_size:(ind+1)*batch_size]
targets = labels[ind*batch_size:(ind+1)*batch_size]
inputs_attack = attack_data[ind*batch_size:(ind+1)*batch_size]
targets_attack = attack_label[ind*batch_size:(ind+1)*batch_size]
#print ( len(targets_attack), len(targets))
data_time.update(time.time() - end)
if use_cuda:
inputs, targets = inputs.cuda(), targets.cuda(async=True)
inputs_attack , targets_attack = inputs_attack.cuda(), targets_attack.cuda(async=True)
inputs, targets = torch.autograd.Variable(inputs), torch.autograd.Variable(targets)
inputs_attack , targets_attack = torch.autograd.Variable(inputs_attack), torch.autograd.Variable(targets_attack)
# compute output
outputs,h_layer = model(inputs)
outputs_non,h_layer_non = model(inputs_attack)
comb_inputs_h = torch.cat((h_layer,h_layer_non))
comb_inputs = torch.cat((outputs,outputs_non))
if use_cuda:
comb_targets= torch.cat((targets,targets_attack)).view([-1,1]).type(torch.cuda.FloatTensor)
else:
comb_targets= torch.cat((targets,targets_attack)).view([-1,1]).type(torch.FloatTensor)
#print (comb_inputs.size(),comb_targets.size())
attack_input = comb_inputs #torch.cat((comb_inputs,comb_targets),1)
one_hot_tr = torch.from_numpy((np.zeros((attack_input.size()[0],outputs.size(1))))).cuda().type(torch.cuda.FloatTensor)
target_one_hot_tr = one_hot_tr.scatter_(1, torch.cat((targets,targets_attack)).type(torch.cuda.LongTensor).view([-1,1]).data,1)
infer_input_one_hot = torch.autograd.Variable(target_one_hot_tr)
#attack_output = attack_model(att_inp).view([-1])
attack_output = attack_model(attack_input,comb_inputs_h,infer_input_one_hot).view([-1])
#attack_output = attack_model(attack_input).view([-1])
att_labels = np.zeros((inputs.size()[0]+inputs_attack.size()[0]))
att_labels [:inputs.size()[0]] =1.0
att_labels [inputs.size()[0]:] =0.0
is_member_labels = torch.from_numpy(att_labels).type(torch.FloatTensor)
if use_cuda:
is_member_labels = is_member_labels.cuda()
v_is_member_labels = torch.autograd.Variable(is_member_labels)
loss = attack_criterion(attack_output, v_is_member_labels)
# measure accuracy and record loss
#prec1,p5 = accuracy(attack_output.data, v_is_member_labels.data, topk=(1,2))
member_prob[ind*batch_size:(ind+1)*batch_size]= attack_output.data.cpu().numpy()[:batch_size]
nonmember_prob[ind*batch_size:(ind+1)*batch_size]= attack_output.data.cpu().numpy()[batch_size:]
prec1=np.mean(np.equal((attack_output.data.cpu().numpy() >0.5),(v_is_member_labels.data.cpu().numpy()> 0.5)))
losses.update(loss.data, attack_input.size()[0])
top1.update(prec1, attack_input.size()[0])
#raise
# compute gradient and do SGD step
# measure elapsed time
batch_time.update(time.time() - end)
end = time.time()
# plot progress
if ind%100==0:
print ('({batch}/{size}) Data: {data:.3f}s | Batch: {bt:.3f}s | | Loss: {loss:.4f} | top1: {top1: .4f} '.format(
batch=ind + 1,
size=len_t,
data=data_time.avg,
bt=batch_time.avg,
loss=losses.avg,
top1=top1.avg,
))
return (losses.avg, top1.avg,member_prob,nonmember_prob)
return
# In[24]:
def find_alpha(acc):
return 3.0
# In[29]:
best_acc = 0.0
epochs=20
batch_size=128
# In[36]:
attack_model = InferenceAttack_HZ(100)
attack_model = torch.nn.DataParallel(attack_model).cuda()
attack_criterion = nn.MSELoss()
attack_optimizer = optim.Adam(attack_model.parameters(),lr=0.0001)
model = PurchaseClassifier()
model = torch.nn.DataParallel(model).cuda()
criterion = nn.CrossEntropyLoss()
optimizer = optim.Adam(model.parameters(), lr=0.001)
# In[37]:
for epoch in range(epochs):
r= np.arange(len(train_classifier_data))
np.random.shuffle(r)
train_classifier_data = train_classifier_data[r]
train_classifier_label = train_classifier_label[r]
train_classifier_data_tensor = torch.from_numpy(train_classifier_data).type(torch.FloatTensor)
train_classifier_label_tensor = torch.from_numpy(train_classifier_label).type(torch.LongTensor)
r= np.arange(len(train_attack_data))
np.random.shuffle(r)
train_attack_data = train_attack_data[r]
train_attack_label = train_attack_label[r]
train_attack_data_tensor = torch.from_numpy(train_attack_data).type(torch.FloatTensor)
train_attack_label_tensor = torch.from_numpy(train_attack_label).type(torch.LongTensor)
test_data_tensor = torch.from_numpy(test_data).type(torch.FloatTensor)
test_label_tensor = torch.from_numpy(test_label).type(torch.LongTensor)
test_loss, test_acc = test(test_data_tensor,test_label_tensor, model, criterion, epoch, use_cuda)
#privacy_loss, privacy_acc = privacy_train(trainloader,testloader,model,inferenece_model,criterion_attack,optimizer_mem,epoch,use_cuda)
print('\nEpoch: [%d | %d]' % (epoch + 1, epochs))
if epoch == 0:
train_loss, train_acc = train(train_classifier_data_tensor,train_classifier_label_tensor, model, criterion, optimizer, epoch, use_cuda)
for i in range(5):
train_attack(train_classifier_data_tensor,train_classifier_label_tensor
,train_attack_data_tensor,train_attack_label_tensor,model,attack_model,criterion,attack_criterion,optimizer,attack_optimizer,epoch,use_cuda)
else:
for i in range(76):
at_loss,at_acc = train_attack(train_classifier_data_tensor,train_classifier_label_tensor
,train_attack_data_tensor,train_attack_label_tensor,model,attack_model,criterion,attack_criterion,optimizer,attack_optimizer,epoch,use_cuda,52,(i*52)%150)
tr_loss,tr_acc=train_privatly(train_classifier_data_tensor,train_classifier_label_tensor, model,attack_model, criterion, optimizer, epoch, use_cuda,2,(2*i)%152,3.0)
test_loss, test_acc = test(test_data_tensor,test_label_tensor, model, criterion, epoch, use_cuda)
#privacy_loss, privacy_acc = privacy_train(trainloader,testloader,model,inferenece_model,criterion_attack,optimizer_mem,epoch,use_cuda)
print ('test acc',test_acc, at_acc,at_loss,best_acc)
# append logger file
# save model
is_best = test_acc>best_acc
best_acc = max(test_acc, best_acc)
save_checkpoint({
'epoch': epoch + 1,
'state_dict': model.state_dict(),
'acc': test_acc,
'best_acc': best_acc,
'optimizer' : optimizer.state_dict(),
}, is_best,filename='Depoch%d'%epoch)
| 31.632124 | 203 | 0.6043 | 3,071 | 24,420 | 4.577662 | 0.098339 | 0.03137 | 0.028382 | 0.017072 | 0.750747 | 0.718025 | 0.678688 | 0.663181 | 0.638071 | 0.619434 | 0 | 0.02468 | 0.264947 | 24,420 | 771 | 204 | 31.673152 | 0.758496 | 0.12068 | 0 | 0.576355 | 0 | 0.009852 | 0.037888 | 0.003248 | 0 | 0 | 0 | 0 | 0 | 0 | null | null | 0 | 0.054187 | null | null | 0.029557 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 3 |
1865e2523008915367fdfb329906864ecba89d3d | 283 | py | Python | Commander-Rank/DAY-6/88A.py | rohansaini886/Peer-Programming-Hub-CP-Winter_Camp | d27fb6aa7e726e6d2cb95270c9e644d38d64dd1c | [
"MIT"
] | 2 | 2021-12-09T18:07:46.000Z | 2022-01-26T16:51:18.000Z | Commander-Rank/DAY-6/88A.py | rohansaini886/Peer-Programming-Hub-CP-Winter_Camp | d27fb6aa7e726e6d2cb95270c9e644d38d64dd1c | [
"MIT"
] | null | null | null | Commander-Rank/DAY-6/88A.py | rohansaini886/Peer-Programming-Hub-CP-Winter_Camp | d27fb6aa7e726e6d2cb95270c9e644d38d64dd1c | [
"MIT"
] | null | null | null | n = ['C', 'C#', 'D', 'D#', 'E', 'F', 'F#', 'G', 'G#', 'A', 'B', 'H']
i = sorted(n.index(x) for x in input().split())
c = (i[1] - i[0], i[2] - i[1])
if c in ((4, 3), (3, 5), (5, 4)):
print('major')
elif c in ((3, 4), (4, 5), (5, 3)):
print('minor')
else:
print('strange')
| 28.3 | 68 | 0.39576 | 57 | 283 | 1.964912 | 0.526316 | 0.035714 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.072727 | 0.222615 | 283 | 9 | 69 | 31.444444 | 0.436364 | 0 | 0 | 0 | 0 | 0 | 0.116608 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | false | 0 | 0 | 0 | 0 | 0.333333 | 0 | 0 | 1 | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 3 |
187791d447dccbca890d596b06b38a232e1237e1 | 905 | py | Python | lifepath/__init__.py | jyurkiw/py_cyberpunk_2020_rest_api | f423761622c11b6f91556560a309697144a166a5 | [
"MIT"
] | null | null | null | lifepath/__init__.py | jyurkiw/py_cyberpunk_2020_rest_api | f423761622c11b6f91556560a309697144a166a5 | [
"MIT"
] | null | null | null | lifepath/__init__.py | jyurkiw/py_cyberpunk_2020_rest_api | f423761622c11b6f91556560a309697144a166a5 | [
"MIT"
] | null | null | null | # __all__
from .lifepath import LifepathRandomOriginsApi
from .lifepath import LifepathRandomFamilyApi
from .lifepath import LifepathRandomMotivationsApi
from .lifepath import LifepathRandomLifeEventsApi
from .lifepath import LifepathRandomCompleteApi
from .lifepath import LifepathRandomStyleAndMotivationsApi
from .lifepath import LifepathRandomFamilyAndEventsApi
__all__ = [
"LifepathRandomOriginsApi",
"LifepathRandomFamilyApi",
"LifepathRandomMotivationsApi",
"LifepathRandomLifeEventsApi",
"LifepathRandomCompleteApi",
"LifepathRandomStyleAndMotivationsApi",
"LifepathRandomFamilyAndEventsApi",
]
# Remainder
from .lifepath_util import getLifepath
from .lifepath_util import originsAndPersonalStyleStart
from .lifepath_util import familyBackgroundStart
from .lifepath_util import motivationsStart
from .lifepath_util import lifeEventsStart
| 32.321429 | 59 | 0.823204 | 63 | 905 | 11.619048 | 0.285714 | 0.196721 | 0.172131 | 0.150273 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.134807 | 905 | 27 | 60 | 33.518519 | 0.934866 | 0.018785 | 0 | 0 | 0 | 0 | 0.227273 | 0.227273 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | false | 0 | 0.571429 | 0 | 0.571429 | 0 | 0 | 0 | 1 | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 0 | 0 | 3 |
188376b6c2ef959e2c76d0f0fa90c2fd56f7318b | 284 | py | Python | ex14plus.py | Allwillcome/LearnPythontheHardWay | e974cd5dec37b9819fb324f4e66166160518277a | [
"PSF-2.0"
] | null | null | null | ex14plus.py | Allwillcome/LearnPythontheHardWay | e974cd5dec37b9819fb324f4e66166160518277a | [
"PSF-2.0"
] | null | null | null | ex14plus.py | Allwillcome/LearnPythontheHardWay | e974cd5dec37b9819fb324f4e66166160518277a | [
"PSF-2.0"
] | null | null | null | from sys import argv
# import moudle
script, user_name = argv
prompt = '>'
# define prompt
print "Hi %s, I am the %s script." %(user_name, script) #import user_name
print "I'd like to ask you a few questions."
print "Do you like me %s?" % user_name
likes = raw_input(prompt)
| 28.4 | 74 | 0.68662 | 49 | 284 | 3.877551 | 0.591837 | 0.168421 | 0.147368 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.204225 | 284 | 9 | 75 | 31.555556 | 0.840708 | 0.151408 | 0 | 0 | 0 | 0 | 0.355263 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | null | null | 0 | 0.142857 | null | null | 0.428571 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 3 |
43f00c97acf7c2d65ccdd1e74ab76a1a2caa016d | 16 | py | Python | constants.py | tomwhross/budget | c6382d1e03d2264df6306145563fc390c8def30f | [
"MIT"
] | null | null | null | constants.py | tomwhross/budget | c6382d1e03d2264df6306145563fc390c8def30f | [
"MIT"
] | 2 | 2020-11-10T00:50:30.000Z | 2020-11-10T14:35:52.000Z | constants.py | tomwhross/budget | c6382d1e03d2264df6306145563fc390c8def30f | [
"MIT"
] | null | null | null | DB = "budget.db" | 16 | 16 | 0.625 | 3 | 16 | 3.333333 | 0.666667 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.125 | 16 | 1 | 16 | 16 | 0.714286 | 0 | 0 | 0 | 0 | 0 | 0.529412 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | false | 0 | 0 | 0 | 0 | 0 | 1 | 1 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 3 |
43f60a6723df437e84bd0266edfd5a7f5557c4be | 369 | py | Python | dynamicserialize/dstypes/com/raytheon/uf/common/auth/resp/UserNotAuthorized.py | srcarter3/python-awips | d981062662968cf3fb105e8e23d955950ae2497e | [
"BSD-3-Clause"
] | 33 | 2016-03-17T01:21:18.000Z | 2022-02-08T10:41:06.000Z | dynamicserialize/dstypes/com/raytheon/uf/common/auth/resp/UserNotAuthorized.py | srcarter3/python-awips | d981062662968cf3fb105e8e23d955950ae2497e | [
"BSD-3-Clause"
] | 15 | 2016-04-19T16:34:08.000Z | 2020-09-09T19:57:54.000Z | dynamicserialize/dstypes/com/raytheon/uf/common/auth/resp/UserNotAuthorized.py | Unidata/python-awips | 8459aa756816e5a45d2e5bea534d23d5b1dd1690 | [
"BSD-3-Clause"
] | 20 | 2016-03-12T01:46:58.000Z | 2022-02-08T06:53:22.000Z | from dynamicserialize.dstypes.com.raytheon.uf.common.auth.resp import AbstractFailedResponse
class UserNotAuthorized(AbstractFailedResponse):
def __init__(self):
super(UserNotAuthorized, self).__init__()
self.message = None
def getMessage(self):
return self.message
def setMessage(self, message):
self.message = message
| 24.6 | 92 | 0.720867 | 37 | 369 | 6.972973 | 0.594595 | 0.170543 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.195122 | 369 | 14 | 93 | 26.357143 | 0.868687 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0.333333 | false | 0 | 0.111111 | 0.111111 | 0.666667 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 1 | 1 | 0 | 0 | 3 |
43fda8679ba7e215e4c59c298f4fddbf8481bf91 | 6,075 | py | Python | fillinblankgame.py | jaewrek/Fill-In-The-Blanks | ccd06dca48d9444459fe233b316dcdfadc2d934a | [
"Unlicense"
] | null | null | null | fillinblankgame.py | jaewrek/Fill-In-The-Blanks | ccd06dca48d9444459fe233b316dcdfadc2d934a | [
"Unlicense"
] | null | null | null | fillinblankgame.py | jaewrek/Fill-In-The-Blanks | ccd06dca48d9444459fe233b316dcdfadc2d934a | [
"Unlicense"
] | null | null | null | # IPND Stage 2 Final Project
# Jerrik Neri
# Intro to Programming ND
# Udacity
# Strings for different quiz levels
easy = """Heroes of the Storm is a game made by __1__ Entertainment. __1__ Entertainment is owned by their parent company __2__. Heroes of the Storm,
or HotS for short, is a __3__ type game, like DOTA2 and League of Legends. __3__s actually started in WC3 as user made map mods, that eventually gained
popularity and became their own game. __1__ Entertainment makes various other games like the Starcraft series, the (for now) Diablo trilogy, and their most
well-known game __4__, the most successful MMORPG of all time. """
medium = """World of Warcraft, or WoW for short, is Blizzard Entertainment's most popular and successful game. The two main factions that are
constantly at war are the __1__, consisting of the Humans, Night Elves, Dranei, Gnomes, Worgen, and Dwarves, and the __2__, made up of the
war-driven Orcs, displaced Trolls, nature-seeking Tauren, magic-enriched Blood Elves, and money-hungry Goblins. There is also a newly added
netural race known as the __3__. Fun fact, the __3__ were actually subtetly joked at in WC3 in an April Fool's video and gained enough popularity
to become reality! This game released in 2004 and at it's peak had over 12 million players. The game itself takes place in the world of __4__ a few
years after thelore of WarCraft 3 The Frozen Throne. Throughout the years, Blizzard Entertainment has added several expansions, including more levels, zones, and
even other planets!"""
hard = """Blizzard Entertainment has not released a new IP for almost over a decade. They have continued on with their Starcraft, Diablo, and Warcraft series.
They have just recently stepped foot into the FPS scene with their latest and extremely successful game, __1__. __1__ is set in a futuristic world where
Humans and robots known as Omnics, were once at war. A team of heroes named __1__ was created to help win the war against the Omnics, and eventually
find peace for the world. One of the most well known heroes from this game is __2__, with her ability to bend time at her will, flashing forward, slowing
it down, or recalling back in time altogether. This game was released in 2016 and in just 2 weeks, gained over 10 million players and generated over 280
million dollars of revenue. It quickly knocked out League of Legends as the most played game in __3__, a country well known for dominating the competitive
electronic gaming market.
__1__ in some ways is the redemption game for Blizzard Entertainment. It is not well-known that for almost a decade they were working on another IP called
__4__ that never came to fruition. In fact, __2__'s abilities are taken from one of the characters meant to be in __4__. In the height of success from WoW,
they believed they could create another very successful MOBA, the never released __4__. It is both fortunate and unfortunate that they were able to fail
and yet persever through and create another wildly successful game like __1__!"""
answer_key = ["__1__", "__2__", "__3__", "__4__"]
easy_answers = ["Blizzard", "Activision", "MOBA", "World of Warcraft"]
medium_answers = ["Alliance", "Horde", "Pandaren", "Azeroth" ]
hard_answers = ["Overwatch", "Tracer", "South Korea", "Titan"]
"""def correct_answer takes in which string difficulty to use as string difficulty, the list of answers, and what index quiz currently is in
loops through every word changing blanks to correct answer and returning string """
def correct_answer(stringdifficulty, answers_list, index):
splitquiz = stringdifficulty.split()
replaced = []
# for every word in splitquiz check if answer key index, ex. __1__ is in that word
# if it is equal, replace with answer of corresponding index
for word in splitquiz:
if answer_key[index] in word:
word = word.replace(answer_key[index], answers_list[index])
replaced.append(word)
else:
replaced.append(word)
replaced = " ".join(replaced)
return replaced
def show_intro_message():
print "\nSo you think you're a " + game_difficulty +"?!?! EH?!\n"
print "You get 5 lives ADVENTURER, I hope you know your games!\n"
# Runs the game taking in the string and answer set as parameters
# Prints out to user to interact with them
# Answer Key index to ensure the blanks and answer set are coordinated
def play_game(quizstring, answers):
lives, answer_key_index = 5, 0
show_intro_message()
while lives > 0:
print quizstring + "\n"
guess = raw_input("ADVENTURER, what is the answer for "+answer_key[answer_key_index]+"? \n")
if guess == answers[answer_key_index]:
print "\nGRATZ ADVENTURER! That's the correct answer. You advance! DING!\n"
lives = 5
quizstring = correct_answer(quizstring, answers, answer_key_index)
answer_key_index+=1
else:
lives-=1
print "\nIncorrect ADVENTURER, try again! You have "+str(lives)+" lives remaining! Tread carefully!"
if lives == 0:
print "\nGAME OVER ADVENTURER!!! You've run out of lives.\n"
if answer_key_index == len(answer_key):
print quizstring + "\n\nCONGRATULATONS ADVENTURER. GG YOU'VE WON!\n"
break
print "\nHELLO, welcome to my game about GAMES!\n"
game_difficulty = "unselected"
#Loop through until difficulty level typed appropriately
while game_difficulty == "unselected":
game_difficulty = raw_input("What kind of adventurer are you?! Please enter below: (NOVICE, INTERMEDIATE, VETERAN) \n").lower()
if game_difficulty == "novice":
play_game(easy, easy_answers)
break
if game_difficulty == "intermediate":
play_game(medium, medium_answers)
break
if game_difficulty == "veteran":
play_game(hard, hard_answers)
break
else:
print "\nAdventurer! Please provide correct difficulty level!\n"
game_difficulty = "unselected"
| 55.733945 | 162 | 0.727572 | 907 | 6,075 | 4.699008 | 0.401323 | 0.027452 | 0.032848 | 0.011262 | 0.013139 | 0 | 0 | 0 | 0 | 0 | 0 | 0.01184 | 0.207572 | 6,075 | 108 | 163 | 56.25 | 0.873494 | 0.07786 | 0 | 0.146667 | 0 | 0.24 | 0.677293 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | null | null | 0 | 0 | null | null | 0.12 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 1 | 0 | null | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 3 |
a101f1fa346a8ed0cfbbf1810589145730e11a83 | 120 | py | Python | Python Fundamentals/Functions/Lab/Task07.py | IvanTodorovBG/SoftUni | 7b667f6905d9f695ab1484efbb02b6715f6d569e | [
"MIT"
] | 1 | 2022-03-16T10:23:04.000Z | 2022-03-16T10:23:04.000Z | Python Fundamentals/Functions/Lab/Task07.py | IvanTodorovBG/SoftUni | 7b667f6905d9f695ab1484efbb02b6715f6d569e | [
"MIT"
] | null | null | null | Python Fundamentals/Functions/Lab/Task07.py | IvanTodorovBG/SoftUni | 7b667f6905d9f695ab1484efbb02b6715f6d569e | [
"MIT"
] | null | null | null | def rounding(numbers):
num = [round(float(x)) for x in numbers]
return num
print(rounding(input().split(" "))) | 20 | 44 | 0.641667 | 17 | 120 | 4.529412 | 0.764706 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.183333 | 120 | 6 | 45 | 20 | 0.785714 | 0 | 0 | 0 | 0 | 0 | 0.008264 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0.25 | false | 0 | 0 | 0 | 0.5 | 0.25 | 1 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 3 |
a10f47d5cede993d17b9822d0ccfb27a8574f423 | 61 | py | Python | sykepic/__init__.py | veot/syke-pic | c2bbf5f87b64348122fb7014ab4e19294ee90009 | [
"MIT"
] | null | null | null | sykepic/__init__.py | veot/syke-pic | c2bbf5f87b64348122fb7014ab4e19294ee90009 | [
"MIT"
] | null | null | null | sykepic/__init__.py | veot/syke-pic | c2bbf5f87b64348122fb7014ab4e19294ee90009 | [
"MIT"
] | null | null | null | from pathlib import Path
APP_DIR = Path.home() / ".sykepic"
| 15.25 | 34 | 0.704918 | 9 | 61 | 4.666667 | 0.888889 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.163934 | 61 | 3 | 35 | 20.333333 | 0.823529 | 0 | 0 | 0 | 0 | 0 | 0.131148 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | false | 0 | 0.5 | 0 | 0.5 | 0 | 1 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 3 |
a114b10892cb4660be2dd372938e1b6346ae6a14 | 302 | py | Python | guanabara/Exercicios/mundo 1 _ aulas 01 a 12/006.py | pbittencourt/datasciencestudies | 85f0b2a4366fe7c6daa5628ed4bd2994355963c0 | [
"MIT"
] | null | null | null | guanabara/Exercicios/mundo 1 _ aulas 01 a 12/006.py | pbittencourt/datasciencestudies | 85f0b2a4366fe7c6daa5628ed4bd2994355963c0 | [
"MIT"
] | null | null | null | guanabara/Exercicios/mundo 1 _ aulas 01 a 12/006.py | pbittencourt/datasciencestudies | 85f0b2a4366fe7c6daa5628ed4bd2994355963c0 | [
"MIT"
] | null | null | null | # DOBRO, TRIPLO E RAIZ QUADRADA
"""Lê um número e exibe seu dobro, seu triplo e sua raiz quadrada"""
n = float(input('Digite um número: _ '))
print('O dobro de {} é {:.2f}'.format(n, n * 2))
print('O triplo de {} é {:.2f}'.format(n, n * 3))
print('A raiz quadrada de {} é {:.2f}'.format(n, n ** 0.5))
| 37.75 | 68 | 0.60596 | 56 | 302 | 3.25 | 0.464286 | 0.197802 | 0.082418 | 0.181319 | 0.214286 | 0.214286 | 0 | 0 | 0 | 0 | 0 | 0.02834 | 0.182119 | 302 | 7 | 69 | 43.142857 | 0.708502 | 0.307947 | 0 | 0 | 0 | 0 | 0.46798 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | false | 0 | 0 | 0 | 0 | 0.75 | 0 | 0 | 0 | null | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 3 |
a145f86b68f18596fb9ef4335d0248cddab180eb | 402 | py | Python | bot/entry.py | shauncameron/ChatBot | 0b3168204788e3e44369c0a96f8e0321f74790ad | [
"Unlicense"
] | null | null | null | bot/entry.py | shauncameron/ChatBot | 0b3168204788e3e44369c0a96f8e0321f74790ad | [
"Unlicense"
] | null | null | null | bot/entry.py | shauncameron/ChatBot | 0b3168204788e3e44369c0a96f8e0321f74790ad | [
"Unlicense"
] | null | null | null | import datetime
class Entry:
def __repr__(self):
return f'Entry @ {self.created}: {self.message}'
def __init__(self, message):
self.__message__ = message
self.__created__ = datetime.datetime.now()
self.read = False
@property
def message(self):
return self.__message__
@property
def created(self):
return self.__created__ | 16.75 | 56 | 0.621891 | 43 | 402 | 5.255814 | 0.372093 | 0.19469 | 0.123894 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.283582 | 402 | 24 | 57 | 16.75 | 0.784722 | 0 | 0 | 0.142857 | 0 | 0 | 0.094293 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0.285714 | false | 0 | 0.071429 | 0.214286 | 0.642857 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 1 | 1 | 0 | 0 | 3 |
a151a2b3a001b7432600b86d8fb1602274c6796a | 124 | py | Python | codeforces/a2oj/evenOdd.py | atpk/CP | 0eee3af02bb0466c85aeb8dd86cf3620567a354c | [
"MIT"
] | null | null | null | codeforces/a2oj/evenOdd.py | atpk/CP | 0eee3af02bb0466c85aeb8dd86cf3620567a354c | [
"MIT"
] | null | null | null | codeforces/a2oj/evenOdd.py | atpk/CP | 0eee3af02bb0466c85aeb8dd86cf3620567a354c | [
"MIT"
] | null | null | null | s=input()
s=s.split(" ")
n=int(s[0])
k=int(s[1])
m=n//2
if n%2==1:
m+=1
if k<=m:
print(2*k-1)
else:
k=k-m
print(2*k) | 10.333333 | 14 | 0.491935 | 36 | 124 | 1.694444 | 0.361111 | 0.131148 | 0.229508 | 0.262295 | 0.295082 | 0 | 0 | 0 | 0 | 0 | 0 | 0.088235 | 0.177419 | 124 | 12 | 15 | 10.333333 | 0.509804 | 0 | 0 | 0 | 0 | 0 | 0.008 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | false | 0 | 0 | 0 | 0 | 0.166667 | 0 | 0 | 1 | null | 0 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 3 |
a1669e10646a8341826e9f3515292f93a953b835 | 566 | py | Python | python/0013. romanToInt.py | whtahy/leetcode | a2955123d203b155455ceefe38514fd0077d6db9 | [
"CC0-1.0"
] | 1 | 2017-12-09T05:37:51.000Z | 2017-12-09T05:37:51.000Z | python/0013. romanToInt.py | whtahy/leetcode | a2955123d203b155455ceefe38514fd0077d6db9 | [
"CC0-1.0"
] | null | null | null | python/0013. romanToInt.py | whtahy/leetcode | a2955123d203b155455ceefe38514fd0077d6db9 | [
"CC0-1.0"
] | null | null | null | class Solution:
d = {
'I': 1,
'V': 5,
'X': 10,
'L': 50,
'C': 100,
'D': 500,
'M': 1000,
'IV': 4,
'IX': 9,
'XL': 40,
'XC': 90,
'CD': 400,
'CM': 900
}
def romanToInt(self, s):
n,i = 0,0
while i < len(s):
if i + 1 < len(s) and s[i: i + 2] in Solution.d:
n += Solution.d[s[i: i + 2]]
i += 2
else:
n += Solution.d.get(s[i], 0)
i += 1
return n
| 20.214286 | 60 | 0.289753 | 75 | 566 | 2.186667 | 0.573333 | 0.219512 | 0.036585 | 0.04878 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.136882 | 0.535336 | 566 | 27 | 61 | 20.962963 | 0.486692 | 0 | 0 | 0 | 0 | 0 | 0.033569 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0.038462 | false | 0 | 0 | 0 | 0.153846 | 0 | 0 | 0 | 0 | null | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 3 |
a18dda3f1d90d769fc71afd09617c5546df88079 | 3,684 | py | Python | misc/spathy_run_calibration.py | LukeEcomod/SpaFHy_v1_Pallas | bc8937a6aa72683a765506fc8f967916f81e0f12 | [
"MIT"
] | 3 | 2019-04-26T02:43:06.000Z | 2020-10-10T21:49:53.000Z | misc/spathy_run_calibration.py | LukeEcomod/SpaFHy_v1_Pallas | bc8937a6aa72683a765506fc8f967916f81e0f12 | [
"MIT"
] | null | null | null | misc/spathy_run_calibration.py | LukeEcomod/SpaFHy_v1_Pallas | bc8937a6aa72683a765506fc8f967916f81e0f12 | [
"MIT"
] | 6 | 2019-06-19T12:12:29.000Z | 2022-01-14T22:05:03.000Z | #!/usr/bin/env python2
# -*- coding: utf-8 -*-
"""
Created on Wed Mar 22 13:48:49 2017
@author: MG
"""
import os
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import seaborn as sns
from spathy_calibration import sve_calibrations
# import matplotlib.lines as mlines
# import matplotlib.gridspec as gridspec
# from src import analysis as at
# from scipy.stats import lognorm
# from datetime import datetime, timedelta
# from iotools import read_setup, create_catchment, read_SVE_runoff,read_FMI_weather,read_AsciiGrid, read_climate_prj
# spathy_path = os.path.join(os.path.expanduser('~'),'projects','spathy') #path to spathy folder
eps = np.finfo(float).eps
spathy_path = os.path.join('c:', 'c:\datat\spathydata')
results_path = os.path.join(spathy_path, 'Results', 'Cal')
setupfile = os.path.join(r'c:\repositories\spathy\ini', 'spathy_default.ini')
Nreps = 50
chm=[['1', '2013-01-01', '2015-12-31', '2013-12-31'], # lompolojanganoja 514 ha
['2', '2006-01-01', '2009-12-31', '2006-12-31'], # liuhapuro 170 ha
['3', '2008-01-01', '2015-12-31', '2008-12-31'], # porkkavaara 72 ha
['10', '2011-01-01', '2013-12-31', '2011-12-31'], # kelopuro 74 ha. 2014 gappy, 2015 runoff is low
['11', '2014-01-01', '2015-12-31', '2014-12-31'], # hauklammenoja 137 ha
['13', '2014-01-01', '2015-12-31', '2014-12-31'], # rudbacken 436 ha
['14', '2011-01-01', '2015-12-31', '2011-12-31'], # paunulanpuro 154 ha
['16', '2011-01-01', '2015-12-31', '2011-12-31'], # huhtisuonoja 500 ha. very flat, large fraction is drained peatlands
['17', '2006-01-01', '2009-12-31', '2006-12-31'], # kesselinpuro 2100 ha
# ['18','2011-01-01', '2015-12-31', '2011-12-31'], # korpijoki, area 12200 ha so not suitable
['19', '2011-01-01', '2015-12-31', '2011-12-31'], # pahkaoja 2344 ha
['20', '2011-01-01', '2015-12-31', '2011-12-31'], # vaarajoki 1900 ha
['21', '2011-01-01', '2015-12-31', '2011-12-31'], # myllypuro 1053 ha
['22', '2011-01-01', '2015-12-31', '2011-12-31'], # vaha-askanjoki 1600 ha
# [ '23','2011-01-01', '2015-12-31', '2011-12-31'], # ylijoki 5600 ha, very large and slow
['24', '2011-01-01', '2015-12-31', '2011-12-31'], # kotioja 1800 ha
['25', '2011-01-01', '2015-12-31', '2011-12-31'], # kohisevanpuro 1070 ha
['26', '2011-01-01', '2015-12-31', '2011-12-31'], # iittovuoma 1160 ha
['27', '2011-01-01', '2015-12-31', '2011-12-31'], # laanioja 1362 ha
['28', '2013-01-01', '2015-12-31', '2013-12-31'], # kroopinsuo 179 ha
['29', '2012-01-01', '2015-12-31', '2012-12-31'], # surnui 71 ha, poor data quality
['30', '2011-01-01', '2015-12-31', '2011-12-31'], # pakopirtti 795 ha, uncertain catchment boundaries
['31', '2011-01-01', '2015-12-31', '2011-12-31'], # ojakorpi 33 ha
['32', '2011-01-01', '2015-12-31', '2011-12-31'], # rantainrahka 38 ha
['33', '2011-01-01', '2012-12-31', '2011-12-31'], # kivipuro 54 ha
]
#subset = [0,1,2,3,4,5,6,7,8,11,13,14,15,16,17,20,21,22]#
subset = [9, 10, 12, 18, 19] # these were missing!
# marker = ['o','.',',','v','>','*','h','s','D','p','o','.',',','v','>','*','h','s','D','p']
# ids = np.empty(len(subset))
# subset = [1]# , 2, 3, 4, 6]
#subset = [0, 1, 2, 3, 4, 5, 6, 7, 17, 18, 20, 21, 22]
#subset = range(0, len(chm))
for k in subset:
print chm[k]
cid = chm[k][0]
start = chm[k][1]
end = chm[k][2]
spinup_end = chm[k][3]
# run full calibration
spot, res = sve_calibrations(setupfile, cid, start, end, spinup_end, reps=Nreps)
# # run only topmodel calibration
# _, _ = sve_topmodel_calibration(fn, cid, start, end, spinup_end, reps=Nreps)
| 46.05 | 125 | 0.604235 | 617 | 3,684 | 3.568882 | 0.358185 | 0.090827 | 0.076294 | 0.095368 | 0.313352 | 0.273388 | 0.267938 | 0.241599 | 0.241599 | 0 | 0 | 0.266667 | 0.169381 | 3,684 | 79 | 126 | 46.632911 | 0.452941 | 0.435668 | 0 | 0 | 0 | 0 | 0.409736 | 0.013185 | 0 | 0 | 0 | 0 | 0 | 0 | null | null | 0 | 0.139535 | null | null | 0.023256 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 3 |
a1af0a5ad52f745ee4051a899c71c2dfdbddf09b | 153 | py | Python | pcdet/models/backbones_3d/__init__.py | TillBeemelmanns/OpenPCDet | b7553c879d0ba36477931efe07a55adbc39823b9 | [
"Apache-2.0"
] | 184 | 2021-03-09T12:19:49.000Z | 2022-03-31T09:19:05.000Z | pcdet/models/backbones_3d/__init__.py | TillBeemelmanns/OpenPCDet | b7553c879d0ba36477931efe07a55adbc39823b9 | [
"Apache-2.0"
] | 36 | 2021-03-23T08:42:38.000Z | 2022-03-31T09:14:41.000Z | pcdet/models/backbones_3d/__init__.py | TillBeemelmanns/OpenPCDet | b7553c879d0ba36477931efe07a55adbc39823b9 | [
"Apache-2.0"
] | 22 | 2021-03-10T09:32:27.000Z | 2022-03-28T05:01:45.000Z | from .spconv_backbone import VoxelBackBone8x
from .spconv_unet import UNetV2
__all__ = {
'VoxelBackBone8x': VoxelBackBone8x,
'UNetV2': UNetV2
}
| 19.125 | 44 | 0.75817 | 15 | 153 | 7.333333 | 0.533333 | 0.181818 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.046875 | 0.163399 | 153 | 7 | 45 | 21.857143 | 0.8125 | 0 | 0 | 0 | 0 | 0 | 0.137255 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | false | 0 | 0.333333 | 0 | 0.333333 | 0 | 1 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 3 |
a1b70102540f2ab980982842d6c947c41c36a7ec | 5,059 | py | Python | tests/test_csv.py | philippeitis/jc | d96b3a65a98bc135d21d4feafc0a43317b5a11fa | [
"MIT"
] | null | null | null | tests/test_csv.py | philippeitis/jc | d96b3a65a98bc135d21d4feafc0a43317b5a11fa | [
"MIT"
] | null | null | null | tests/test_csv.py | philippeitis/jc | d96b3a65a98bc135d21d4feafc0a43317b5a11fa | [
"MIT"
] | null | null | null | import os
import json
import unittest
import jc.parsers.csv
THIS_DIR = os.path.dirname(os.path.abspath(__file__))
class MyTests(unittest.TestCase):
def setUp(self):
# input
with open(os.path.join(THIS_DIR, os.pardir, 'tests/fixtures/generic/csv-biostats.csv'), 'r') as f:
self.generic_csv_biostats = f.read()
with open(os.path.join(THIS_DIR, os.pardir, 'tests/fixtures/generic/csv-cities.csv'), 'r') as f:
self.generic_csv_cities = f.read()
with open(os.path.join(THIS_DIR, os.pardir, 'tests/fixtures/generic/csv-deniro.csv'), 'r') as f:
self.generic_csv_deniro = f.read()
with open(os.path.join(THIS_DIR, os.pardir, 'tests/fixtures/generic/csv-example.csv'), 'r') as f:
self.generic_csv_example = f.read()
with open(os.path.join(THIS_DIR, os.pardir, 'tests/fixtures/generic/csv-flyrna.tsv'), 'r') as f:
self.generic_csv_flyrna = f.read()
with open(os.path.join(THIS_DIR, os.pardir, 'tests/fixtures/generic/csv-flyrna2.tsv'), 'r') as f:
self.generic_csv_flyrna2 = f.read()
with open(os.path.join(THIS_DIR, os.pardir, 'tests/fixtures/generic/csv-homes-pipe.csv'), 'r') as f:
self.generic_csv_homes_pipe = f.read()
with open(os.path.join(THIS_DIR, os.pardir, 'tests/fixtures/generic/csv-homes.csv'), 'r') as f:
self.generic_csv_homes = f.read()
with open(os.path.join(THIS_DIR, os.pardir, 'tests/fixtures/generic/csv-insurance.csv'), 'r') as f:
self.generic_csv_insurance = f.read()
# output
with open(os.path.join(THIS_DIR, os.pardir, 'tests/fixtures/generic/csv-biostats.json'), 'r') as f:
self.generic_csv_biostats_json = json.loads(f.read())
with open(os.path.join(THIS_DIR, os.pardir, 'tests/fixtures/generic/csv-cities.json'), 'r') as f:
self.generic_csv_cities_json = json.loads(f.read())
with open(os.path.join(THIS_DIR, os.pardir, 'tests/fixtures/generic/csv-deniro.json'), 'r') as f:
self.generic_csv_deniro_json = json.loads(f.read())
with open(os.path.join(THIS_DIR, os.pardir, 'tests/fixtures/generic/csv-example.json'), 'r') as f:
self.generic_csv_example_json = json.loads(f.read())
with open(os.path.join(THIS_DIR, os.pardir, 'tests/fixtures/generic/csv-flyrna.json'), 'r') as f:
self.generic_csv_flyrna_json = json.loads(f.read())
with open(os.path.join(THIS_DIR, os.pardir, 'tests/fixtures/generic/csv-flyrna2.json'), 'r') as f:
self.generic_csv_flyrna2_json = json.loads(f.read())
with open(os.path.join(THIS_DIR, os.pardir, 'tests/fixtures/generic/csv-homes-pipe.json'), 'r') as f:
self.generic_csv_homes_pipe_json = json.loads(f.read())
with open(os.path.join(THIS_DIR, os.pardir, 'tests/fixtures/generic/csv-homes.json'), 'r') as f:
self.generic_csv_homes_json = json.loads(f.read())
with open(os.path.join(THIS_DIR, os.pardir, 'tests/fixtures/generic/csv-insurance.json'), 'r') as f:
self.generic_csv_insurance_json = json.loads(f.read())
def test_csv_biostats(self):
"""
Test 'biostats.csv' file
"""
self.assertEqual(jc.parsers.csv.parse(self.generic_csv_biostats, quiet=True), self.generic_csv_biostats_json)
def test_csv_cities(self):
"""
Test 'cities.csv' file
"""
self.assertEqual(jc.parsers.csv.parse(self.generic_csv_cities, quiet=True), self.generic_csv_cities_json)
def test_csv_deniro(self):
"""
Test 'deniro.csv' file
"""
self.assertEqual(jc.parsers.csv.parse(self.generic_csv_deniro, quiet=True), self.generic_csv_deniro_json)
def test_csv_example(self):
"""
Test 'example.csv' file
"""
self.assertEqual(jc.parsers.csv.parse(self.generic_csv_example, quiet=True), self.generic_csv_example_json)
def test_csv_flyrna(self):
"""
Test 'flyrna.tsv' file
"""
self.assertEqual(jc.parsers.csv.parse(self.generic_csv_flyrna, quiet=True), self.generic_csv_flyrna_json)
def test_csv_flyrna2(self):
"""
Test 'flyrna2.tsv' file
"""
self.assertEqual(jc.parsers.csv.parse(self.generic_csv_flyrna2, quiet=True), self.generic_csv_flyrna2_json)
def test_csv_homes_pipe(self):
"""
Test 'homes-pipe.csv' file
"""
self.assertEqual(jc.parsers.csv.parse(self.generic_csv_homes_pipe, quiet=True), self.generic_csv_homes_pipe_json)
def test_csv_homes(self):
"""
Test 'homes.csv' file
"""
self.assertEqual(jc.parsers.csv.parse(self.generic_csv_homes, quiet=True), self.generic_csv_homes_json)
def test_csv_insurance(self):
"""
Test 'insurance.csv' file
"""
self.assertEqual(jc.parsers.csv.parse(self.generic_csv_insurance, quiet=True), self.generic_csv_insurance_json)
if __name__ == '__main__':
unittest.main()
| 40.472 | 121 | 0.650721 | 734 | 5,059 | 4.288828 | 0.069482 | 0.171537 | 0.160102 | 0.080051 | 0.850064 | 0.756671 | 0.736341 | 0.609593 | 0.57338 | 0.57338 | 0 | 0.001988 | 0.204388 | 5,059 | 124 | 122 | 40.798387 | 0.780124 | 0.045464 | 0 | 0 | 0 | 0 | 0.156061 | 0.150433 | 0 | 0 | 0 | 0 | 0.142857 | 1 | 0.15873 | false | 0 | 0.063492 | 0 | 0.238095 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 1 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 3 |
a1ba4b682f35bf2e560b067d04b8487c386bd186 | 417 | py | Python | okl4_kernel/okl4_2.1.1-patch.9/tools/magpie/magpie/targets/nicta/generic_biguuid_n2_noabi_compat_target.py | CyberQueenMara/baseband-research | e1605537e10c37e161fff1a3416b908c9894f204 | [
"MIT"
] | 77 | 2018-12-31T22:12:09.000Z | 2021-12-31T22:56:13.000Z | okl4_kernel/okl4_2.1.1-patch.9/tools/magpie/magpie/targets/nicta/generic_biguuid_n2_noabi_compat_target.py | CyberQueenMara/baseband-research | e1605537e10c37e161fff1a3416b908c9894f204 | [
"MIT"
] | null | null | null | okl4_kernel/okl4_2.1.1-patch.9/tools/magpie/magpie/targets/nicta/generic_biguuid_n2_noabi_compat_target.py | CyberQueenMara/baseband-research | e1605537e10c37e161fff1a3416b908c9894f204 | [
"MIT"
] | 24 | 2019-01-20T15:51:52.000Z | 2021-12-25T18:29:13.000Z | from magpie.targets.nicta import generic_n2_target as nictageneric_n2
from magpie.targets.idl4 import generic_biguuid_l4v4_target as idl4_biguuid
Generator = nictageneric_n2.Generator
class Templates(nictageneric_n2.Templates, idl4_biguuid.Templates):
client_function_body = 'v4nicta_generic/client_function_body_noabi.template.c'
service_function = 'v4nicta_generic/service_function_backcompat_noabi.template.h'
| 46.333333 | 82 | 0.8753 | 56 | 417 | 6.142857 | 0.464286 | 0.122093 | 0.098837 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.028278 | 0.067146 | 417 | 8 | 83 | 52.125 | 0.856041 | 0 | 0 | 0 | 0 | 0 | 0.270983 | 0.270983 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | false | 0 | 0.333333 | 0 | 0.833333 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 0 | 0 | 3 |
a1d3396357adf0e73d684e9ffb2d4777cb1bffe8 | 43,705 | py | Python | bindings/python/capstone/x86_const.py | zchee/capstone | f278de39c1e8a9fca977b8dfeed99d6d1f8b82bf | [
"BSD-3-Clause"
] | 127 | 2017-08-29T17:32:53.000Z | 2022-03-31T16:58:44.000Z | bindings/python/capstone/x86_const.py | zchee/capstone | f278de39c1e8a9fca977b8dfeed99d6d1f8b82bf | [
"BSD-3-Clause"
] | 90 | 2017-08-31T04:44:38.000Z | 2022-03-31T15:58:29.000Z | bindings/python/capstone/x86_const.py | zchee/capstone | f278de39c1e8a9fca977b8dfeed99d6d1f8b82bf | [
"BSD-3-Clause"
] | 48 | 2017-08-29T11:34:01.000Z | 2022-03-31T15:39:28.000Z | # For Capstone Engine. AUTO-GENERATED FILE, DO NOT EDIT [x86_const.py]
X86_REG_INVALID = 0
X86_REG_AH = 1
X86_REG_AL = 2
X86_REG_AX = 3
X86_REG_BH = 4
X86_REG_BL = 5
X86_REG_BP = 6
X86_REG_BPL = 7
X86_REG_BX = 8
X86_REG_CH = 9
X86_REG_CL = 10
X86_REG_CS = 11
X86_REG_CX = 12
X86_REG_DH = 13
X86_REG_DI = 14
X86_REG_DIL = 15
X86_REG_DL = 16
X86_REG_DS = 17
X86_REG_DX = 18
X86_REG_EAX = 19
X86_REG_EBP = 20
X86_REG_EBX = 21
X86_REG_ECX = 22
X86_REG_EDI = 23
X86_REG_EDX = 24
X86_REG_EFLAGS = 25
X86_REG_EIP = 26
X86_REG_EIZ = 27
X86_REG_ES = 28
X86_REG_ESI = 29
X86_REG_ESP = 30
X86_REG_FPSW = 31
X86_REG_FS = 32
X86_REG_GS = 33
X86_REG_IP = 34
X86_REG_RAX = 35
X86_REG_RBP = 36
X86_REG_RBX = 37
X86_REG_RCX = 38
X86_REG_RDI = 39
X86_REG_RDX = 40
X86_REG_RIP = 41
X86_REG_RIZ = 42
X86_REG_RSI = 43
X86_REG_RSP = 44
X86_REG_SI = 45
X86_REG_SIL = 46
X86_REG_SP = 47
X86_REG_SPL = 48
X86_REG_SS = 49
X86_REG_CR0 = 50
X86_REG_CR1 = 51
X86_REG_CR2 = 52
X86_REG_CR3 = 53
X86_REG_CR4 = 54
X86_REG_CR5 = 55
X86_REG_CR6 = 56
X86_REG_CR7 = 57
X86_REG_CR8 = 58
X86_REG_CR9 = 59
X86_REG_CR10 = 60
X86_REG_CR11 = 61
X86_REG_CR12 = 62
X86_REG_CR13 = 63
X86_REG_CR14 = 64
X86_REG_CR15 = 65
X86_REG_DR0 = 66
X86_REG_DR1 = 67
X86_REG_DR2 = 68
X86_REG_DR3 = 69
X86_REG_DR4 = 70
X86_REG_DR5 = 71
X86_REG_DR6 = 72
X86_REG_DR7 = 73
X86_REG_DR8 = 74
X86_REG_DR9 = 75
X86_REG_DR10 = 76
X86_REG_DR11 = 77
X86_REG_DR12 = 78
X86_REG_DR13 = 79
X86_REG_DR14 = 80
X86_REG_DR15 = 81
X86_REG_FP0 = 82
X86_REG_FP1 = 83
X86_REG_FP2 = 84
X86_REG_FP3 = 85
X86_REG_FP4 = 86
X86_REG_FP5 = 87
X86_REG_FP6 = 88
X86_REG_FP7 = 89
X86_REG_K0 = 90
X86_REG_K1 = 91
X86_REG_K2 = 92
X86_REG_K3 = 93
X86_REG_K4 = 94
X86_REG_K5 = 95
X86_REG_K6 = 96
X86_REG_K7 = 97
X86_REG_MM0 = 98
X86_REG_MM1 = 99
X86_REG_MM2 = 100
X86_REG_MM3 = 101
X86_REG_MM4 = 102
X86_REG_MM5 = 103
X86_REG_MM6 = 104
X86_REG_MM7 = 105
X86_REG_R8 = 106
X86_REG_R9 = 107
X86_REG_R10 = 108
X86_REG_R11 = 109
X86_REG_R12 = 110
X86_REG_R13 = 111
X86_REG_R14 = 112
X86_REG_R15 = 113
X86_REG_ST0 = 114
X86_REG_ST1 = 115
X86_REG_ST2 = 116
X86_REG_ST3 = 117
X86_REG_ST4 = 118
X86_REG_ST5 = 119
X86_REG_ST6 = 120
X86_REG_ST7 = 121
X86_REG_XMM0 = 122
X86_REG_XMM1 = 123
X86_REG_XMM2 = 124
X86_REG_XMM3 = 125
X86_REG_XMM4 = 126
X86_REG_XMM5 = 127
X86_REG_XMM6 = 128
X86_REG_XMM7 = 129
X86_REG_XMM8 = 130
X86_REG_XMM9 = 131
X86_REG_XMM10 = 132
X86_REG_XMM11 = 133
X86_REG_XMM12 = 134
X86_REG_XMM13 = 135
X86_REG_XMM14 = 136
X86_REG_XMM15 = 137
X86_REG_XMM16 = 138
X86_REG_XMM17 = 139
X86_REG_XMM18 = 140
X86_REG_XMM19 = 141
X86_REG_XMM20 = 142
X86_REG_XMM21 = 143
X86_REG_XMM22 = 144
X86_REG_XMM23 = 145
X86_REG_XMM24 = 146
X86_REG_XMM25 = 147
X86_REG_XMM26 = 148
X86_REG_XMM27 = 149
X86_REG_XMM28 = 150
X86_REG_XMM29 = 151
X86_REG_XMM30 = 152
X86_REG_XMM31 = 153
X86_REG_YMM0 = 154
X86_REG_YMM1 = 155
X86_REG_YMM2 = 156
X86_REG_YMM3 = 157
X86_REG_YMM4 = 158
X86_REG_YMM5 = 159
X86_REG_YMM6 = 160
X86_REG_YMM7 = 161
X86_REG_YMM8 = 162
X86_REG_YMM9 = 163
X86_REG_YMM10 = 164
X86_REG_YMM11 = 165
X86_REG_YMM12 = 166
X86_REG_YMM13 = 167
X86_REG_YMM14 = 168
X86_REG_YMM15 = 169
X86_REG_YMM16 = 170
X86_REG_YMM17 = 171
X86_REG_YMM18 = 172
X86_REG_YMM19 = 173
X86_REG_YMM20 = 174
X86_REG_YMM21 = 175
X86_REG_YMM22 = 176
X86_REG_YMM23 = 177
X86_REG_YMM24 = 178
X86_REG_YMM25 = 179
X86_REG_YMM26 = 180
X86_REG_YMM27 = 181
X86_REG_YMM28 = 182
X86_REG_YMM29 = 183
X86_REG_YMM30 = 184
X86_REG_YMM31 = 185
X86_REG_ZMM0 = 186
X86_REG_ZMM1 = 187
X86_REG_ZMM2 = 188
X86_REG_ZMM3 = 189
X86_REG_ZMM4 = 190
X86_REG_ZMM5 = 191
X86_REG_ZMM6 = 192
X86_REG_ZMM7 = 193
X86_REG_ZMM8 = 194
X86_REG_ZMM9 = 195
X86_REG_ZMM10 = 196
X86_REG_ZMM11 = 197
X86_REG_ZMM12 = 198
X86_REG_ZMM13 = 199
X86_REG_ZMM14 = 200
X86_REG_ZMM15 = 201
X86_REG_ZMM16 = 202
X86_REG_ZMM17 = 203
X86_REG_ZMM18 = 204
X86_REG_ZMM19 = 205
X86_REG_ZMM20 = 206
X86_REG_ZMM21 = 207
X86_REG_ZMM22 = 208
X86_REG_ZMM23 = 209
X86_REG_ZMM24 = 210
X86_REG_ZMM25 = 211
X86_REG_ZMM26 = 212
X86_REG_ZMM27 = 213
X86_REG_ZMM28 = 214
X86_REG_ZMM29 = 215
X86_REG_ZMM30 = 216
X86_REG_ZMM31 = 217
X86_REG_R8B = 218
X86_REG_R9B = 219
X86_REG_R10B = 220
X86_REG_R11B = 221
X86_REG_R12B = 222
X86_REG_R13B = 223
X86_REG_R14B = 224
X86_REG_R15B = 225
X86_REG_R8D = 226
X86_REG_R9D = 227
X86_REG_R10D = 228
X86_REG_R11D = 229
X86_REG_R12D = 230
X86_REG_R13D = 231
X86_REG_R14D = 232
X86_REG_R15D = 233
X86_REG_R8W = 234
X86_REG_R9W = 235
X86_REG_R10W = 236
X86_REG_R11W = 237
X86_REG_R12W = 238
X86_REG_R13W = 239
X86_REG_R14W = 240
X86_REG_R15W = 241
X86_REG_BND0 = 242
X86_REG_BND1 = 243
X86_REG_BND2 = 244
X86_REG_BND3 = 245
X86_REG_ENDING = 246
X86_EFLAGS_MODIFY_AF = 1<<0
X86_EFLAGS_MODIFY_CF = 1<<1
X86_EFLAGS_MODIFY_SF = 1<<2
X86_EFLAGS_MODIFY_ZF = 1<<3
X86_EFLAGS_MODIFY_PF = 1<<4
X86_EFLAGS_MODIFY_OF = 1<<5
X86_EFLAGS_MODIFY_TF = 1<<6
X86_EFLAGS_MODIFY_IF = 1<<7
X86_EFLAGS_MODIFY_DF = 1<<8
X86_EFLAGS_MODIFY_NT = 1<<9
X86_EFLAGS_MODIFY_RF = 1<<10
X86_EFLAGS_PRIOR_OF = 1<<11
X86_EFLAGS_PRIOR_SF = 1<<12
X86_EFLAGS_PRIOR_ZF = 1<<13
X86_EFLAGS_PRIOR_AF = 1<<14
X86_EFLAGS_PRIOR_PF = 1<<15
X86_EFLAGS_PRIOR_CF = 1<<16
X86_EFLAGS_PRIOR_TF = 1<<17
X86_EFLAGS_PRIOR_IF = 1<<18
X86_EFLAGS_PRIOR_DF = 1<<19
X86_EFLAGS_PRIOR_NT = 1<<20
X86_EFLAGS_RESET_OF = 1<<21
X86_EFLAGS_RESET_CF = 1<<22
X86_EFLAGS_RESET_DF = 1<<23
X86_EFLAGS_RESET_IF = 1<<24
X86_EFLAGS_RESET_SF = 1<<25
X86_EFLAGS_RESET_AF = 1<<26
X86_EFLAGS_RESET_TF = 1<<27
X86_EFLAGS_RESET_NT = 1<<28
X86_EFLAGS_RESET_PF = 1<<29
X86_EFLAGS_SET_CF = 1<<30
X86_EFLAGS_SET_DF = 1<<31
X86_EFLAGS_SET_IF = 1<<32
X86_EFLAGS_TEST_OF = 1<<33
X86_EFLAGS_TEST_SF = 1<<34
X86_EFLAGS_TEST_ZF = 1<<35
X86_EFLAGS_TEST_PF = 1<<36
X86_EFLAGS_TEST_CF = 1<<37
X86_EFLAGS_TEST_NT = 1<<38
X86_EFLAGS_TEST_DF = 1<<39
X86_EFLAGS_UNDEFINED_OF = 1<<40
X86_EFLAGS_UNDEFINED_SF = 1<<41
X86_EFLAGS_UNDEFINED_ZF = 1<<42
X86_EFLAGS_UNDEFINED_PF = 1<<43
X86_EFLAGS_UNDEFINED_AF = 1<<44
X86_EFLAGS_UNDEFINED_CF = 1<<45
X86_EFLAGS_RESET_RF = 1<<46
X86_EFLAGS_TEST_RF = 1<<47
X86_EFLAGS_TEST_IF = 1<<48
X86_EFLAGS_TEST_TF = 1<<49
X86_EFLAGS_TEST_AF = 1<<50
X86_EFLAGS_RESET_ZF = 1<<51
X86_EFLAGS_SET_OF = 1<<52
X86_EFLAGS_SET_SF = 1<<53
X86_EFLAGS_SET_ZF = 1<<54
X86_EFLAGS_SET_AF = 1<<55
X86_EFLAGS_SET_PF = 1<<56
X86_EFLAGS_RESET_0F = 1<<57
X86_EFLAGS_RESET_AC = 1<<58
X86_FPU_FLAGS_MODIFY_C0 = 1<<0
X86_FPU_FLAGS_MODIFY_C1 = 1<<1
X86_FPU_FLAGS_MODIFY_C2 = 1<<2
X86_FPU_FLAGS_MODIFY_C3 = 1<<3
X86_FPU_FLAGS_RESET_C0 = 1<<4
X86_FPU_FLAGS_RESET_C1 = 1<<5
X86_FPU_FLAGS_RESET_C2 = 1<<6
X86_FPU_FLAGS_RESET_C3 = 1<<7
X86_FPU_FLAGS_SET_C0 = 1<<8
X86_FPU_FLAGS_SET_C1 = 1<<9
X86_FPU_FLAGS_SET_C2 = 1<<10
X86_FPU_FLAGS_SET_C3 = 1<<11
X86_FPU_FLAGS_UNDEFINED_C0 = 1<<12
X86_FPU_FLAGS_UNDEFINED_C1 = 1<<13
X86_FPU_FLAGS_UNDEFINED_C2 = 1<<14
X86_FPU_FLAGS_UNDEFINED_C3 = 1<<15
X86_FPU_FLAGS_TEST_C0 = 1<<16
X86_FPU_FLAGS_TEST_C1 = 1<<17
X86_FPU_FLAGS_TEST_C2 = 1<<18
X86_FPU_FLAGS_TEST_C3 = 1<<19
X86_OP_INVALID = 0
X86_OP_REG = 1
X86_OP_IMM = 2
X86_OP_MEM = 3
X86_XOP_CC_INVALID = 0
X86_XOP_CC_LT = 1
X86_XOP_CC_LE = 2
X86_XOP_CC_GT = 3
X86_XOP_CC_GE = 4
X86_XOP_CC_EQ = 5
X86_XOP_CC_NEQ = 6
X86_XOP_CC_FALSE = 7
X86_XOP_CC_TRUE = 8
X86_AVX_BCAST_INVALID = 0
X86_AVX_BCAST_2 = 1
X86_AVX_BCAST_4 = 2
X86_AVX_BCAST_8 = 3
X86_AVX_BCAST_16 = 4
X86_SSE_CC_INVALID = 0
X86_SSE_CC_EQ = 1
X86_SSE_CC_LT = 2
X86_SSE_CC_LE = 3
X86_SSE_CC_UNORD = 4
X86_SSE_CC_NEQ = 5
X86_SSE_CC_NLT = 6
X86_SSE_CC_NLE = 7
X86_SSE_CC_ORD = 8
X86_AVX_CC_INVALID = 0
X86_AVX_CC_EQ = 1
X86_AVX_CC_LT = 2
X86_AVX_CC_LE = 3
X86_AVX_CC_UNORD = 4
X86_AVX_CC_NEQ = 5
X86_AVX_CC_NLT = 6
X86_AVX_CC_NLE = 7
X86_AVX_CC_ORD = 8
X86_AVX_CC_EQ_UQ = 9
X86_AVX_CC_NGE = 10
X86_AVX_CC_NGT = 11
X86_AVX_CC_FALSE = 12
X86_AVX_CC_NEQ_OQ = 13
X86_AVX_CC_GE = 14
X86_AVX_CC_GT = 15
X86_AVX_CC_TRUE = 16
X86_AVX_CC_EQ_OS = 17
X86_AVX_CC_LT_OQ = 18
X86_AVX_CC_LE_OQ = 19
X86_AVX_CC_UNORD_S = 20
X86_AVX_CC_NEQ_US = 21
X86_AVX_CC_NLT_UQ = 22
X86_AVX_CC_NLE_UQ = 23
X86_AVX_CC_ORD_S = 24
X86_AVX_CC_EQ_US = 25
X86_AVX_CC_NGE_UQ = 26
X86_AVX_CC_NGT_UQ = 27
X86_AVX_CC_FALSE_OS = 28
X86_AVX_CC_NEQ_OS = 29
X86_AVX_CC_GE_OQ = 30
X86_AVX_CC_GT_OQ = 31
X86_AVX_CC_TRUE_US = 32
X86_AVX_RM_INVALID = 0
X86_AVX_RM_RN = 1
X86_AVX_RM_RD = 2
X86_AVX_RM_RU = 3
X86_AVX_RM_RZ = 4
X86_PREFIX_LOCK = 0xf0
X86_PREFIX_REP = 0xf3
X86_PREFIX_REPE = 0xf3
X86_PREFIX_REPNE = 0xf2
X86_PREFIX_CS = 0x2e
X86_PREFIX_SS = 0x36
X86_PREFIX_DS = 0x3e
X86_PREFIX_ES = 0x26
X86_PREFIX_FS = 0x64
X86_PREFIX_GS = 0x65
X86_PREFIX_OPSIZE = 0x66
X86_PREFIX_ADDRSIZE = 0x67
X86_INS_INVALID = 0
X86_INS_AAA = 1
X86_INS_AAD = 2
X86_INS_AAM = 3
X86_INS_AAS = 4
X86_INS_FABS = 5
X86_INS_ADC = 6
X86_INS_ADCX = 7
X86_INS_ADD = 8
X86_INS_ADDPD = 9
X86_INS_ADDPS = 10
X86_INS_ADDSD = 11
X86_INS_ADDSS = 12
X86_INS_ADDSUBPD = 13
X86_INS_ADDSUBPS = 14
X86_INS_FADD = 15
X86_INS_FIADD = 16
X86_INS_ADOX = 17
X86_INS_AESDECLAST = 18
X86_INS_AESDEC = 19
X86_INS_AESENCLAST = 20
X86_INS_AESENC = 21
X86_INS_AESIMC = 22
X86_INS_AESKEYGENASSIST = 23
X86_INS_AND = 24
X86_INS_ANDN = 25
X86_INS_ANDNPD = 26
X86_INS_ANDNPS = 27
X86_INS_ANDPD = 28
X86_INS_ANDPS = 29
X86_INS_ARPL = 30
X86_INS_BEXTR = 31
X86_INS_BLCFILL = 32
X86_INS_BLCI = 33
X86_INS_BLCIC = 34
X86_INS_BLCMSK = 35
X86_INS_BLCS = 36
X86_INS_BLENDPD = 37
X86_INS_BLENDPS = 38
X86_INS_BLENDVPD = 39
X86_INS_BLENDVPS = 40
X86_INS_BLSFILL = 41
X86_INS_BLSI = 42
X86_INS_BLSIC = 43
X86_INS_BLSMSK = 44
X86_INS_BLSR = 45
X86_INS_BNDCL = 46
X86_INS_BNDCN = 47
X86_INS_BNDCU = 48
X86_INS_BNDLDX = 49
X86_INS_BNDMK = 50
X86_INS_BNDMOV = 51
X86_INS_BNDSTX = 52
X86_INS_BOUND = 53
X86_INS_BSF = 54
X86_INS_BSR = 55
X86_INS_BSWAP = 56
X86_INS_BT = 57
X86_INS_BTC = 58
X86_INS_BTR = 59
X86_INS_BTS = 60
X86_INS_BZHI = 61
X86_INS_CALL = 62
X86_INS_CBW = 63
X86_INS_CDQ = 64
X86_INS_CDQE = 65
X86_INS_FCHS = 66
X86_INS_CLAC = 67
X86_INS_CLC = 68
X86_INS_CLD = 69
X86_INS_CLDEMOTE = 70
X86_INS_CLFLUSH = 71
X86_INS_CLFLUSHOPT = 72
X86_INS_CLGI = 73
X86_INS_CLI = 74
X86_INS_CLRSSBSY = 75
X86_INS_CLTS = 76
X86_INS_CLWB = 77
X86_INS_CLZERO = 78
X86_INS_CMC = 79
X86_INS_CMOVA = 80
X86_INS_CMOVAE = 81
X86_INS_CMOVB = 82
X86_INS_CMOVBE = 83
X86_INS_FCMOVBE = 84
X86_INS_FCMOVB = 85
X86_INS_CMOVE = 86
X86_INS_FCMOVE = 87
X86_INS_CMOVG = 88
X86_INS_CMOVGE = 89
X86_INS_CMOVL = 90
X86_INS_CMOVLE = 91
X86_INS_FCMOVNBE = 92
X86_INS_FCMOVNB = 93
X86_INS_CMOVNE = 94
X86_INS_FCMOVNE = 95
X86_INS_CMOVNO = 96
X86_INS_CMOVNP = 97
X86_INS_FCMOVNU = 98
X86_INS_FCMOVNP = 99
X86_INS_CMOVNS = 100
X86_INS_CMOVO = 101
X86_INS_CMOVP = 102
X86_INS_FCMOVU = 103
X86_INS_CMOVS = 104
X86_INS_CMP = 105
X86_INS_CMPPD = 106
X86_INS_CMPPS = 107
X86_INS_CMPSB = 108
X86_INS_CMPSD = 109
X86_INS_CMPSQ = 110
X86_INS_CMPSS = 111
X86_INS_CMPSW = 112
X86_INS_CMPXCHG16B = 113
X86_INS_CMPXCHG = 114
X86_INS_CMPXCHG8B = 115
X86_INS_COMISD = 116
X86_INS_COMISS = 117
X86_INS_FCOMP = 118
X86_INS_FCOMPI = 119
X86_INS_FCOMI = 120
X86_INS_FCOM = 121
X86_INS_FCOS = 122
X86_INS_CPUID = 123
X86_INS_CQO = 124
X86_INS_CRC32 = 125
X86_INS_CVTDQ2PD = 126
X86_INS_CVTDQ2PS = 127
X86_INS_CVTPD2DQ = 128
X86_INS_CVTPD2PS = 129
X86_INS_CVTPS2DQ = 130
X86_INS_CVTPS2PD = 131
X86_INS_CVTSD2SI = 132
X86_INS_CVTSD2SS = 133
X86_INS_CVTSI2SD = 134
X86_INS_CVTSI2SS = 135
X86_INS_CVTSS2SD = 136
X86_INS_CVTSS2SI = 137
X86_INS_CVTTPD2DQ = 138
X86_INS_CVTTPS2DQ = 139
X86_INS_CVTTSD2SI = 140
X86_INS_CVTTSS2SI = 141
X86_INS_CWD = 142
X86_INS_CWDE = 143
X86_INS_DAA = 144
X86_INS_DAS = 145
X86_INS_DATA16 = 146
X86_INS_DEC = 147
X86_INS_DIV = 148
X86_INS_DIVPD = 149
X86_INS_DIVPS = 150
X86_INS_FDIVR = 151
X86_INS_FIDIVR = 152
X86_INS_FDIVRP = 153
X86_INS_DIVSD = 154
X86_INS_DIVSS = 155
X86_INS_FDIV = 156
X86_INS_FIDIV = 157
X86_INS_FDIVP = 158
X86_INS_DPPD = 159
X86_INS_DPPS = 160
X86_INS_ENCLS = 161
X86_INS_ENCLU = 162
X86_INS_ENCLV = 163
X86_INS_ENDBR32 = 164
X86_INS_ENDBR64 = 165
X86_INS_ENTER = 166
X86_INS_EXTRACTPS = 167
X86_INS_EXTRQ = 168
X86_INS_F2XM1 = 169
X86_INS_LCALL = 170
X86_INS_LJMP = 171
X86_INS_JMP = 172
X86_INS_FBLD = 173
X86_INS_FBSTP = 174
X86_INS_FCOMPP = 175
X86_INS_FDECSTP = 176
X86_INS_FDISI8087_NOP = 177
X86_INS_FEMMS = 178
X86_INS_FENI8087_NOP = 179
X86_INS_FFREE = 180
X86_INS_FFREEP = 181
X86_INS_FICOM = 182
X86_INS_FICOMP = 183
X86_INS_FINCSTP = 184
X86_INS_FLDCW = 185
X86_INS_FLDENV = 186
X86_INS_FLDL2E = 187
X86_INS_FLDL2T = 188
X86_INS_FLDLG2 = 189
X86_INS_FLDLN2 = 190
X86_INS_FLDPI = 191
X86_INS_FNCLEX = 192
X86_INS_FNINIT = 193
X86_INS_FNOP = 194
X86_INS_FNSTCW = 195
X86_INS_FNSTSW = 196
X86_INS_FPATAN = 197
X86_INS_FSTPNCE = 198
X86_INS_FPREM = 199
X86_INS_FPREM1 = 200
X86_INS_FPTAN = 201
X86_INS_FRNDINT = 202
X86_INS_FRSTOR = 203
X86_INS_FNSAVE = 204
X86_INS_FSCALE = 205
X86_INS_FSETPM = 206
X86_INS_FSINCOS = 207
X86_INS_FNSTENV = 208
X86_INS_FXAM = 209
X86_INS_FXRSTOR = 210
X86_INS_FXRSTOR64 = 211
X86_INS_FXSAVE = 212
X86_INS_FXSAVE64 = 213
X86_INS_FXTRACT = 214
X86_INS_FYL2X = 215
X86_INS_FYL2XP1 = 216
X86_INS_GETSEC = 217
X86_INS_GF2P8AFFINEINVQB = 218
X86_INS_GF2P8AFFINEQB = 219
X86_INS_GF2P8MULB = 220
X86_INS_HADDPD = 221
X86_INS_HADDPS = 222
X86_INS_HLT = 223
X86_INS_HSUBPD = 224
X86_INS_HSUBPS = 225
X86_INS_IDIV = 226
X86_INS_FILD = 227
X86_INS_IMUL = 228
X86_INS_IN = 229
X86_INS_INC = 230
X86_INS_INCSSPD = 231
X86_INS_INCSSPQ = 232
X86_INS_INSB = 233
X86_INS_INSERTPS = 234
X86_INS_INSERTQ = 235
X86_INS_INSD = 236
X86_INS_INSW = 237
X86_INS_INT = 238
X86_INS_INT1 = 239
X86_INS_INT3 = 240
X86_INS_INTO = 241
X86_INS_INVD = 242
X86_INS_INVEPT = 243
X86_INS_INVLPG = 244
X86_INS_INVLPGA = 245
X86_INS_INVPCID = 246
X86_INS_INVVPID = 247
X86_INS_IRET = 248
X86_INS_IRETD = 249
X86_INS_IRETQ = 250
X86_INS_FISTTP = 251
X86_INS_FIST = 252
X86_INS_FISTP = 253
X86_INS_JAE = 254
X86_INS_JA = 255
X86_INS_JBE = 256
X86_INS_JB = 257
X86_INS_JCXZ = 258
X86_INS_JECXZ = 259
X86_INS_JE = 260
X86_INS_JGE = 261
X86_INS_JG = 262
X86_INS_JLE = 263
X86_INS_JL = 264
X86_INS_JNE = 265
X86_INS_JNO = 266
X86_INS_JNP = 267
X86_INS_JNS = 268
X86_INS_JO = 269
X86_INS_JP = 270
X86_INS_JRCXZ = 271
X86_INS_JS = 272
X86_INS_KADDB = 273
X86_INS_KADDD = 274
X86_INS_KADDQ = 275
X86_INS_KADDW = 276
X86_INS_KANDB = 277
X86_INS_KANDD = 278
X86_INS_KANDNB = 279
X86_INS_KANDND = 280
X86_INS_KANDNQ = 281
X86_INS_KANDNW = 282
X86_INS_KANDQ = 283
X86_INS_KANDW = 284
X86_INS_KMOVB = 285
X86_INS_KMOVD = 286
X86_INS_KMOVQ = 287
X86_INS_KMOVW = 288
X86_INS_KNOTB = 289
X86_INS_KNOTD = 290
X86_INS_KNOTQ = 291
X86_INS_KNOTW = 292
X86_INS_KORB = 293
X86_INS_KORD = 294
X86_INS_KORQ = 295
X86_INS_KORTESTB = 296
X86_INS_KORTESTD = 297
X86_INS_KORTESTQ = 298
X86_INS_KORTESTW = 299
X86_INS_KORW = 300
X86_INS_KSHIFTLB = 301
X86_INS_KSHIFTLD = 302
X86_INS_KSHIFTLQ = 303
X86_INS_KSHIFTLW = 304
X86_INS_KSHIFTRB = 305
X86_INS_KSHIFTRD = 306
X86_INS_KSHIFTRQ = 307
X86_INS_KSHIFTRW = 308
X86_INS_KTESTB = 309
X86_INS_KTESTD = 310
X86_INS_KTESTQ = 311
X86_INS_KTESTW = 312
X86_INS_KUNPCKBW = 313
X86_INS_KUNPCKDQ = 314
X86_INS_KUNPCKWD = 315
X86_INS_KXNORB = 316
X86_INS_KXNORD = 317
X86_INS_KXNORQ = 318
X86_INS_KXNORW = 319
X86_INS_KXORB = 320
X86_INS_KXORD = 321
X86_INS_KXORQ = 322
X86_INS_KXORW = 323
X86_INS_LAHF = 324
X86_INS_LAR = 325
X86_INS_LDDQU = 326
X86_INS_LDMXCSR = 327
X86_INS_LDS = 328
X86_INS_FLDZ = 329
X86_INS_FLD1 = 330
X86_INS_FLD = 331
X86_INS_LEA = 332
X86_INS_LEAVE = 333
X86_INS_LES = 334
X86_INS_LFENCE = 335
X86_INS_LFS = 336
X86_INS_LGDT = 337
X86_INS_LGS = 338
X86_INS_LIDT = 339
X86_INS_LLDT = 340
X86_INS_LLWPCB = 341
X86_INS_LMSW = 342
X86_INS_LOCK = 343
X86_INS_LODSB = 344
X86_INS_LODSD = 345
X86_INS_LODSQ = 346
X86_INS_LODSW = 347
X86_INS_LOOP = 348
X86_INS_LOOPE = 349
X86_INS_LOOPNE = 350
X86_INS_RETF = 351
X86_INS_RETFQ = 352
X86_INS_LSL = 353
X86_INS_LSS = 354
X86_INS_LTR = 355
X86_INS_LWPINS = 356
X86_INS_LWPVAL = 357
X86_INS_LZCNT = 358
X86_INS_MASKMOVDQU = 359
X86_INS_MAXPD = 360
X86_INS_MAXPS = 361
X86_INS_MAXSD = 362
X86_INS_MAXSS = 363
X86_INS_MFENCE = 364
X86_INS_MINPD = 365
X86_INS_MINPS = 366
X86_INS_MINSD = 367
X86_INS_MINSS = 368
X86_INS_CVTPD2PI = 369
X86_INS_CVTPI2PD = 370
X86_INS_CVTPI2PS = 371
X86_INS_CVTPS2PI = 372
X86_INS_CVTTPD2PI = 373
X86_INS_CVTTPS2PI = 374
X86_INS_EMMS = 375
X86_INS_MASKMOVQ = 376
X86_INS_MOVD = 377
X86_INS_MOVQ = 378
X86_INS_MOVDQ2Q = 379
X86_INS_MOVNTQ = 380
X86_INS_MOVQ2DQ = 381
X86_INS_PABSB = 382
X86_INS_PABSD = 383
X86_INS_PABSW = 384
X86_INS_PACKSSDW = 385
X86_INS_PACKSSWB = 386
X86_INS_PACKUSWB = 387
X86_INS_PADDB = 388
X86_INS_PADDD = 389
X86_INS_PADDQ = 390
X86_INS_PADDSB = 391
X86_INS_PADDSW = 392
X86_INS_PADDUSB = 393
X86_INS_PADDUSW = 394
X86_INS_PADDW = 395
X86_INS_PALIGNR = 396
X86_INS_PANDN = 397
X86_INS_PAND = 398
X86_INS_PAVGB = 399
X86_INS_PAVGW = 400
X86_INS_PCMPEQB = 401
X86_INS_PCMPEQD = 402
X86_INS_PCMPEQW = 403
X86_INS_PCMPGTB = 404
X86_INS_PCMPGTD = 405
X86_INS_PCMPGTW = 406
X86_INS_PEXTRW = 407
X86_INS_PHADDD = 408
X86_INS_PHADDSW = 409
X86_INS_PHADDW = 410
X86_INS_PHSUBD = 411
X86_INS_PHSUBSW = 412
X86_INS_PHSUBW = 413
X86_INS_PINSRW = 414
X86_INS_PMADDUBSW = 415
X86_INS_PMADDWD = 416
X86_INS_PMAXSW = 417
X86_INS_PMAXUB = 418
X86_INS_PMINSW = 419
X86_INS_PMINUB = 420
X86_INS_PMOVMSKB = 421
X86_INS_PMULHRSW = 422
X86_INS_PMULHUW = 423
X86_INS_PMULHW = 424
X86_INS_PMULLW = 425
X86_INS_PMULUDQ = 426
X86_INS_POR = 427
X86_INS_PSADBW = 428
X86_INS_PSHUFB = 429
X86_INS_PSHUFW = 430
X86_INS_PSIGNB = 431
X86_INS_PSIGND = 432
X86_INS_PSIGNW = 433
X86_INS_PSLLD = 434
X86_INS_PSLLQ = 435
X86_INS_PSLLW = 436
X86_INS_PSRAD = 437
X86_INS_PSRAW = 438
X86_INS_PSRLD = 439
X86_INS_PSRLQ = 440
X86_INS_PSRLW = 441
X86_INS_PSUBB = 442
X86_INS_PSUBD = 443
X86_INS_PSUBQ = 444
X86_INS_PSUBSB = 445
X86_INS_PSUBSW = 446
X86_INS_PSUBUSB = 447
X86_INS_PSUBUSW = 448
X86_INS_PSUBW = 449
X86_INS_PUNPCKHBW = 450
X86_INS_PUNPCKHDQ = 451
X86_INS_PUNPCKHWD = 452
X86_INS_PUNPCKLBW = 453
X86_INS_PUNPCKLDQ = 454
X86_INS_PUNPCKLWD = 455
X86_INS_PXOR = 456
X86_INS_MONITORX = 457
X86_INS_MONITOR = 458
X86_INS_MONTMUL = 459
X86_INS_MOV = 460
X86_INS_MOVABS = 461
X86_INS_MOVAPD = 462
X86_INS_MOVAPS = 463
X86_INS_MOVBE = 464
X86_INS_MOVDDUP = 465
X86_INS_MOVDIR64B = 466
X86_INS_MOVDIRI = 467
X86_INS_MOVDQA = 468
X86_INS_MOVDQU = 469
X86_INS_MOVHLPS = 470
X86_INS_MOVHPD = 471
X86_INS_MOVHPS = 472
X86_INS_MOVLHPS = 473
X86_INS_MOVLPD = 474
X86_INS_MOVLPS = 475
X86_INS_MOVMSKPD = 476
X86_INS_MOVMSKPS = 477
X86_INS_MOVNTDQA = 478
X86_INS_MOVNTDQ = 479
X86_INS_MOVNTI = 480
X86_INS_MOVNTPD = 481
X86_INS_MOVNTPS = 482
X86_INS_MOVNTSD = 483
X86_INS_MOVNTSS = 484
X86_INS_MOVSB = 485
X86_INS_MOVSD = 486
X86_INS_MOVSHDUP = 487
X86_INS_MOVSLDUP = 488
X86_INS_MOVSQ = 489
X86_INS_MOVSS = 490
X86_INS_MOVSW = 491
X86_INS_MOVSX = 492
X86_INS_MOVSXD = 493
X86_INS_MOVUPD = 494
X86_INS_MOVUPS = 495
X86_INS_MOVZX = 496
X86_INS_MPSADBW = 497
X86_INS_MUL = 498
X86_INS_MULPD = 499
X86_INS_MULPS = 500
X86_INS_MULSD = 501
X86_INS_MULSS = 502
X86_INS_MULX = 503
X86_INS_FMUL = 504
X86_INS_FIMUL = 505
X86_INS_FMULP = 506
X86_INS_MWAITX = 507
X86_INS_MWAIT = 508
X86_INS_NEG = 509
X86_INS_NOP = 510
X86_INS_NOT = 511
X86_INS_OR = 512
X86_INS_ORPD = 513
X86_INS_ORPS = 514
X86_INS_OUT = 515
X86_INS_OUTSB = 516
X86_INS_OUTSD = 517
X86_INS_OUTSW = 518
X86_INS_PACKUSDW = 519
X86_INS_PAUSE = 520
X86_INS_PAVGUSB = 521
X86_INS_PBLENDVB = 522
X86_INS_PBLENDW = 523
X86_INS_PCLMULQDQ = 524
X86_INS_PCMPEQQ = 525
X86_INS_PCMPESTRI = 526
X86_INS_PCMPESTRM = 527
X86_INS_PCMPGTQ = 528
X86_INS_PCMPISTRI = 529
X86_INS_PCMPISTRM = 530
X86_INS_PCONFIG = 531
X86_INS_PDEP = 532
X86_INS_PEXT = 533
X86_INS_PEXTRB = 534
X86_INS_PEXTRD = 535
X86_INS_PEXTRQ = 536
X86_INS_PF2ID = 537
X86_INS_PF2IW = 538
X86_INS_PFACC = 539
X86_INS_PFADD = 540
X86_INS_PFCMPEQ = 541
X86_INS_PFCMPGE = 542
X86_INS_PFCMPGT = 543
X86_INS_PFMAX = 544
X86_INS_PFMIN = 545
X86_INS_PFMUL = 546
X86_INS_PFNACC = 547
X86_INS_PFPNACC = 548
X86_INS_PFRCPIT1 = 549
X86_INS_PFRCPIT2 = 550
X86_INS_PFRCP = 551
X86_INS_PFRSQIT1 = 552
X86_INS_PFRSQRT = 553
X86_INS_PFSUBR = 554
X86_INS_PFSUB = 555
X86_INS_PHMINPOSUW = 556
X86_INS_PI2FD = 557
X86_INS_PI2FW = 558
X86_INS_PINSRB = 559
X86_INS_PINSRD = 560
X86_INS_PINSRQ = 561
X86_INS_PMAXSB = 562
X86_INS_PMAXSD = 563
X86_INS_PMAXUD = 564
X86_INS_PMAXUW = 565
X86_INS_PMINSB = 566
X86_INS_PMINSD = 567
X86_INS_PMINUD = 568
X86_INS_PMINUW = 569
X86_INS_PMOVSXBD = 570
X86_INS_PMOVSXBQ = 571
X86_INS_PMOVSXBW = 572
X86_INS_PMOVSXDQ = 573
X86_INS_PMOVSXWD = 574
X86_INS_PMOVSXWQ = 575
X86_INS_PMOVZXBD = 576
X86_INS_PMOVZXBQ = 577
X86_INS_PMOVZXBW = 578
X86_INS_PMOVZXDQ = 579
X86_INS_PMOVZXWD = 580
X86_INS_PMOVZXWQ = 581
X86_INS_PMULDQ = 582
X86_INS_PMULHRW = 583
X86_INS_PMULLD = 584
X86_INS_POP = 585
X86_INS_POPAW = 586
X86_INS_POPAL = 587
X86_INS_POPCNT = 588
X86_INS_POPF = 589
X86_INS_POPFD = 590
X86_INS_POPFQ = 591
X86_INS_PREFETCH = 592
X86_INS_PREFETCHNTA = 593
X86_INS_PREFETCHT0 = 594
X86_INS_PREFETCHT1 = 595
X86_INS_PREFETCHT2 = 596
X86_INS_PREFETCHW = 597
X86_INS_PREFETCHWT1 = 598
X86_INS_PSHUFD = 599
X86_INS_PSHUFHW = 600
X86_INS_PSHUFLW = 601
X86_INS_PSLLDQ = 602
X86_INS_PSRLDQ = 603
X86_INS_PSWAPD = 604
X86_INS_PTEST = 605
X86_INS_PTWRITE = 606
X86_INS_PUNPCKHQDQ = 607
X86_INS_PUNPCKLQDQ = 608
X86_INS_PUSH = 609
X86_INS_PUSHAW = 610
X86_INS_PUSHAL = 611
X86_INS_PUSHF = 612
X86_INS_PUSHFD = 613
X86_INS_PUSHFQ = 614
X86_INS_RCL = 615
X86_INS_RCPPS = 616
X86_INS_RCPSS = 617
X86_INS_RCR = 618
X86_INS_RDFSBASE = 619
X86_INS_RDGSBASE = 620
X86_INS_RDMSR = 621
X86_INS_RDPID = 622
X86_INS_RDPKRU = 623
X86_INS_RDPMC = 624
X86_INS_RDRAND = 625
X86_INS_RDSEED = 626
X86_INS_RDSSPD = 627
X86_INS_RDSSPQ = 628
X86_INS_RDTSC = 629
X86_INS_RDTSCP = 630
X86_INS_REPNE = 631
X86_INS_REP = 632
X86_INS_RET = 633
X86_INS_REX64 = 634
X86_INS_ROL = 635
X86_INS_ROR = 636
X86_INS_RORX = 637
X86_INS_ROUNDPD = 638
X86_INS_ROUNDPS = 639
X86_INS_ROUNDSD = 640
X86_INS_ROUNDSS = 641
X86_INS_RSM = 642
X86_INS_RSQRTPS = 643
X86_INS_RSQRTSS = 644
X86_INS_RSTORSSP = 645
X86_INS_SAHF = 646
X86_INS_SAL = 647
X86_INS_SALC = 648
X86_INS_SAR = 649
X86_INS_SARX = 650
X86_INS_SAVEPREVSSP = 651
X86_INS_SBB = 652
X86_INS_SCASB = 653
X86_INS_SCASD = 654
X86_INS_SCASQ = 655
X86_INS_SCASW = 656
X86_INS_SETAE = 657
X86_INS_SETA = 658
X86_INS_SETBE = 659
X86_INS_SETB = 660
X86_INS_SETE = 661
X86_INS_SETGE = 662
X86_INS_SETG = 663
X86_INS_SETLE = 664
X86_INS_SETL = 665
X86_INS_SETNE = 666
X86_INS_SETNO = 667
X86_INS_SETNP = 668
X86_INS_SETNS = 669
X86_INS_SETO = 670
X86_INS_SETP = 671
X86_INS_SETSSBSY = 672
X86_INS_SETS = 673
X86_INS_SFENCE = 674
X86_INS_SGDT = 675
X86_INS_SHA1MSG1 = 676
X86_INS_SHA1MSG2 = 677
X86_INS_SHA1NEXTE = 678
X86_INS_SHA1RNDS4 = 679
X86_INS_SHA256MSG1 = 680
X86_INS_SHA256MSG2 = 681
X86_INS_SHA256RNDS2 = 682
X86_INS_SHL = 683
X86_INS_SHLD = 684
X86_INS_SHLX = 685
X86_INS_SHR = 686
X86_INS_SHRD = 687
X86_INS_SHRX = 688
X86_INS_SHUFPD = 689
X86_INS_SHUFPS = 690
X86_INS_SIDT = 691
X86_INS_FSIN = 692
X86_INS_SKINIT = 693
X86_INS_SLDT = 694
X86_INS_SLWPCB = 695
X86_INS_SMSW = 696
X86_INS_SQRTPD = 697
X86_INS_SQRTPS = 698
X86_INS_SQRTSD = 699
X86_INS_SQRTSS = 700
X86_INS_FSQRT = 701
X86_INS_STAC = 702
X86_INS_STC = 703
X86_INS_STD = 704
X86_INS_STGI = 705
X86_INS_STI = 706
X86_INS_STMXCSR = 707
X86_INS_STOSB = 708
X86_INS_STOSD = 709
X86_INS_STOSQ = 710
X86_INS_STOSW = 711
X86_INS_STR = 712
X86_INS_FST = 713
X86_INS_FSTP = 714
X86_INS_SUB = 715
X86_INS_SUBPD = 716
X86_INS_SUBPS = 717
X86_INS_FSUBR = 718
X86_INS_FISUBR = 719
X86_INS_FSUBRP = 720
X86_INS_SUBSD = 721
X86_INS_SUBSS = 722
X86_INS_FSUB = 723
X86_INS_FISUB = 724
X86_INS_FSUBP = 725
X86_INS_SWAPGS = 726
X86_INS_SYSCALL = 727
X86_INS_SYSENTER = 728
X86_INS_SYSEXIT = 729
X86_INS_SYSEXITQ = 730
X86_INS_SYSRET = 731
X86_INS_SYSRETQ = 732
X86_INS_T1MSKC = 733
X86_INS_TEST = 734
X86_INS_TPAUSE = 735
X86_INS_FTST = 736
X86_INS_TZCNT = 737
X86_INS_TZMSK = 738
X86_INS_UCOMISD = 739
X86_INS_UCOMISS = 740
X86_INS_FUCOMPI = 741
X86_INS_FUCOMI = 742
X86_INS_FUCOMPP = 743
X86_INS_FUCOMP = 744
X86_INS_FUCOM = 745
X86_INS_UD0 = 746
X86_INS_UD1 = 747
X86_INS_UD2 = 748
X86_INS_UMONITOR = 749
X86_INS_UMWAIT = 750
X86_INS_UNPCKHPD = 751
X86_INS_UNPCKHPS = 752
X86_INS_UNPCKLPD = 753
X86_INS_UNPCKLPS = 754
X86_INS_V4FMADDPS = 755
X86_INS_V4FMADDSS = 756
X86_INS_V4FNMADDPS = 757
X86_INS_V4FNMADDSS = 758
X86_INS_VADDPD = 759
X86_INS_VADDPS = 760
X86_INS_VADDSD = 761
X86_INS_VADDSS = 762
X86_INS_VADDSUBPD = 763
X86_INS_VADDSUBPS = 764
X86_INS_VAESDECLAST = 765
X86_INS_VAESDEC = 766
X86_INS_VAESENCLAST = 767
X86_INS_VAESENC = 768
X86_INS_VAESIMC = 769
X86_INS_VAESKEYGENASSIST = 770
X86_INS_VALIGND = 771
X86_INS_VALIGNQ = 772
X86_INS_VANDNPD = 773
X86_INS_VANDNPS = 774
X86_INS_VANDPD = 775
X86_INS_VANDPS = 776
X86_INS_VBLENDMPD = 777
X86_INS_VBLENDMPS = 778
X86_INS_VBLENDPD = 779
X86_INS_VBLENDPS = 780
X86_INS_VBLENDVPD = 781
X86_INS_VBLENDVPS = 782
X86_INS_VBROADCASTF128 = 783
X86_INS_VBROADCASTF32X2 = 784
X86_INS_VBROADCASTF32X4 = 785
X86_INS_VBROADCASTF32X8 = 786
X86_INS_VBROADCASTF64X2 = 787
X86_INS_VBROADCASTF64X4 = 788
X86_INS_VBROADCASTI128 = 789
X86_INS_VBROADCASTI32X2 = 790
X86_INS_VBROADCASTI32X4 = 791
X86_INS_VBROADCASTI32X8 = 792
X86_INS_VBROADCASTI64X2 = 793
X86_INS_VBROADCASTI64X4 = 794
X86_INS_VBROADCASTSD = 795
X86_INS_VBROADCASTSS = 796
X86_INS_VCMP = 797
X86_INS_VCMPPD = 798
X86_INS_VCMPPS = 799
X86_INS_VCMPSD = 800
X86_INS_VCMPSS = 801
X86_INS_VCOMISD = 802
X86_INS_VCOMISS = 803
X86_INS_VCOMPRESSPD = 804
X86_INS_VCOMPRESSPS = 805
X86_INS_VCVTDQ2PD = 806
X86_INS_VCVTDQ2PS = 807
X86_INS_VCVTPD2DQ = 808
X86_INS_VCVTPD2PS = 809
X86_INS_VCVTPD2QQ = 810
X86_INS_VCVTPD2UDQ = 811
X86_INS_VCVTPD2UQQ = 812
X86_INS_VCVTPH2PS = 813
X86_INS_VCVTPS2DQ = 814
X86_INS_VCVTPS2PD = 815
X86_INS_VCVTPS2PH = 816
X86_INS_VCVTPS2QQ = 817
X86_INS_VCVTPS2UDQ = 818
X86_INS_VCVTPS2UQQ = 819
X86_INS_VCVTQQ2PD = 820
X86_INS_VCVTQQ2PS = 821
X86_INS_VCVTSD2SI = 822
X86_INS_VCVTSD2SS = 823
X86_INS_VCVTSD2USI = 824
X86_INS_VCVTSI2SD = 825
X86_INS_VCVTSI2SS = 826
X86_INS_VCVTSS2SD = 827
X86_INS_VCVTSS2SI = 828
X86_INS_VCVTSS2USI = 829
X86_INS_VCVTTPD2DQ = 830
X86_INS_VCVTTPD2QQ = 831
X86_INS_VCVTTPD2UDQ = 832
X86_INS_VCVTTPD2UQQ = 833
X86_INS_VCVTTPS2DQ = 834
X86_INS_VCVTTPS2QQ = 835
X86_INS_VCVTTPS2UDQ = 836
X86_INS_VCVTTPS2UQQ = 837
X86_INS_VCVTTSD2SI = 838
X86_INS_VCVTTSD2USI = 839
X86_INS_VCVTTSS2SI = 840
X86_INS_VCVTTSS2USI = 841
X86_INS_VCVTUDQ2PD = 842
X86_INS_VCVTUDQ2PS = 843
X86_INS_VCVTUQQ2PD = 844
X86_INS_VCVTUQQ2PS = 845
X86_INS_VCVTUSI2SD = 846
X86_INS_VCVTUSI2SS = 847
X86_INS_VDBPSADBW = 848
X86_INS_VDIVPD = 849
X86_INS_VDIVPS = 850
X86_INS_VDIVSD = 851
X86_INS_VDIVSS = 852
X86_INS_VDPPD = 853
X86_INS_VDPPS = 854
X86_INS_VERR = 855
X86_INS_VERW = 856
X86_INS_VEXP2PD = 857
X86_INS_VEXP2PS = 858
X86_INS_VEXPANDPD = 859
X86_INS_VEXPANDPS = 860
X86_INS_VEXTRACTF128 = 861
X86_INS_VEXTRACTF32X4 = 862
X86_INS_VEXTRACTF32X8 = 863
X86_INS_VEXTRACTF64X2 = 864
X86_INS_VEXTRACTF64X4 = 865
X86_INS_VEXTRACTI128 = 866
X86_INS_VEXTRACTI32X4 = 867
X86_INS_VEXTRACTI32X8 = 868
X86_INS_VEXTRACTI64X2 = 869
X86_INS_VEXTRACTI64X4 = 870
X86_INS_VEXTRACTPS = 871
X86_INS_VFIXUPIMMPD = 872
X86_INS_VFIXUPIMMPS = 873
X86_INS_VFIXUPIMMSD = 874
X86_INS_VFIXUPIMMSS = 875
X86_INS_VFMADD132PD = 876
X86_INS_VFMADD132PS = 877
X86_INS_VFMADD132SD = 878
X86_INS_VFMADD132SS = 879
X86_INS_VFMADD213PD = 880
X86_INS_VFMADD213PS = 881
X86_INS_VFMADD213SD = 882
X86_INS_VFMADD213SS = 883
X86_INS_VFMADD231PD = 884
X86_INS_VFMADD231PS = 885
X86_INS_VFMADD231SD = 886
X86_INS_VFMADD231SS = 887
X86_INS_VFMADDPD = 888
X86_INS_VFMADDPS = 889
X86_INS_VFMADDSD = 890
X86_INS_VFMADDSS = 891
X86_INS_VFMADDSUB132PD = 892
X86_INS_VFMADDSUB132PS = 893
X86_INS_VFMADDSUB213PD = 894
X86_INS_VFMADDSUB213PS = 895
X86_INS_VFMADDSUB231PD = 896
X86_INS_VFMADDSUB231PS = 897
X86_INS_VFMADDSUBPD = 898
X86_INS_VFMADDSUBPS = 899
X86_INS_VFMSUB132PD = 900
X86_INS_VFMSUB132PS = 901
X86_INS_VFMSUB132SD = 902
X86_INS_VFMSUB132SS = 903
X86_INS_VFMSUB213PD = 904
X86_INS_VFMSUB213PS = 905
X86_INS_VFMSUB213SD = 906
X86_INS_VFMSUB213SS = 907
X86_INS_VFMSUB231PD = 908
X86_INS_VFMSUB231PS = 909
X86_INS_VFMSUB231SD = 910
X86_INS_VFMSUB231SS = 911
X86_INS_VFMSUBADD132PD = 912
X86_INS_VFMSUBADD132PS = 913
X86_INS_VFMSUBADD213PD = 914
X86_INS_VFMSUBADD213PS = 915
X86_INS_VFMSUBADD231PD = 916
X86_INS_VFMSUBADD231PS = 917
X86_INS_VFMSUBADDPD = 918
X86_INS_VFMSUBADDPS = 919
X86_INS_VFMSUBPD = 920
X86_INS_VFMSUBPS = 921
X86_INS_VFMSUBSD = 922
X86_INS_VFMSUBSS = 923
X86_INS_VFNMADD132PD = 924
X86_INS_VFNMADD132PS = 925
X86_INS_VFNMADD132SD = 926
X86_INS_VFNMADD132SS = 927
X86_INS_VFNMADD213PD = 928
X86_INS_VFNMADD213PS = 929
X86_INS_VFNMADD213SD = 930
X86_INS_VFNMADD213SS = 931
X86_INS_VFNMADD231PD = 932
X86_INS_VFNMADD231PS = 933
X86_INS_VFNMADD231SD = 934
X86_INS_VFNMADD231SS = 935
X86_INS_VFNMADDPD = 936
X86_INS_VFNMADDPS = 937
X86_INS_VFNMADDSD = 938
X86_INS_VFNMADDSS = 939
X86_INS_VFNMSUB132PD = 940
X86_INS_VFNMSUB132PS = 941
X86_INS_VFNMSUB132SD = 942
X86_INS_VFNMSUB132SS = 943
X86_INS_VFNMSUB213PD = 944
X86_INS_VFNMSUB213PS = 945
X86_INS_VFNMSUB213SD = 946
X86_INS_VFNMSUB213SS = 947
X86_INS_VFNMSUB231PD = 948
X86_INS_VFNMSUB231PS = 949
X86_INS_VFNMSUB231SD = 950
X86_INS_VFNMSUB231SS = 951
X86_INS_VFNMSUBPD = 952
X86_INS_VFNMSUBPS = 953
X86_INS_VFNMSUBSD = 954
X86_INS_VFNMSUBSS = 955
X86_INS_VFPCLASSPD = 956
X86_INS_VFPCLASSPS = 957
X86_INS_VFPCLASSSD = 958
X86_INS_VFPCLASSSS = 959
X86_INS_VFRCZPD = 960
X86_INS_VFRCZPS = 961
X86_INS_VFRCZSD = 962
X86_INS_VFRCZSS = 963
X86_INS_VGATHERDPD = 964
X86_INS_VGATHERDPS = 965
X86_INS_VGATHERPF0DPD = 966
X86_INS_VGATHERPF0DPS = 967
X86_INS_VGATHERPF0QPD = 968
X86_INS_VGATHERPF0QPS = 969
X86_INS_VGATHERPF1DPD = 970
X86_INS_VGATHERPF1DPS = 971
X86_INS_VGATHERPF1QPD = 972
X86_INS_VGATHERPF1QPS = 973
X86_INS_VGATHERQPD = 974
X86_INS_VGATHERQPS = 975
X86_INS_VGETEXPPD = 976
X86_INS_VGETEXPPS = 977
X86_INS_VGETEXPSD = 978
X86_INS_VGETEXPSS = 979
X86_INS_VGETMANTPD = 980
X86_INS_VGETMANTPS = 981
X86_INS_VGETMANTSD = 982
X86_INS_VGETMANTSS = 983
X86_INS_VGF2P8AFFINEINVQB = 984
X86_INS_VGF2P8AFFINEQB = 985
X86_INS_VGF2P8MULB = 986
X86_INS_VHADDPD = 987
X86_INS_VHADDPS = 988
X86_INS_VHSUBPD = 989
X86_INS_VHSUBPS = 990
X86_INS_VINSERTF128 = 991
X86_INS_VINSERTF32X4 = 992
X86_INS_VINSERTF32X8 = 993
X86_INS_VINSERTF64X2 = 994
X86_INS_VINSERTF64X4 = 995
X86_INS_VINSERTI128 = 996
X86_INS_VINSERTI32X4 = 997
X86_INS_VINSERTI32X8 = 998
X86_INS_VINSERTI64X2 = 999
X86_INS_VINSERTI64X4 = 1000
X86_INS_VINSERTPS = 1001
X86_INS_VLDDQU = 1002
X86_INS_VLDMXCSR = 1003
X86_INS_VMASKMOVDQU = 1004
X86_INS_VMASKMOVPD = 1005
X86_INS_VMASKMOVPS = 1006
X86_INS_VMAXPD = 1007
X86_INS_VMAXPS = 1008
X86_INS_VMAXSD = 1009
X86_INS_VMAXSS = 1010
X86_INS_VMCALL = 1011
X86_INS_VMCLEAR = 1012
X86_INS_VMFUNC = 1013
X86_INS_VMINPD = 1014
X86_INS_VMINPS = 1015
X86_INS_VMINSD = 1016
X86_INS_VMINSS = 1017
X86_INS_VMLAUNCH = 1018
X86_INS_VMLOAD = 1019
X86_INS_VMMCALL = 1020
X86_INS_VMOVQ = 1021
X86_INS_VMOVAPD = 1022
X86_INS_VMOVAPS = 1023
X86_INS_VMOVDDUP = 1024
X86_INS_VMOVD = 1025
X86_INS_VMOVDQA32 = 1026
X86_INS_VMOVDQA64 = 1027
X86_INS_VMOVDQA = 1028
X86_INS_VMOVDQU16 = 1029
X86_INS_VMOVDQU32 = 1030
X86_INS_VMOVDQU64 = 1031
X86_INS_VMOVDQU8 = 1032
X86_INS_VMOVDQU = 1033
X86_INS_VMOVHLPS = 1034
X86_INS_VMOVHPD = 1035
X86_INS_VMOVHPS = 1036
X86_INS_VMOVLHPS = 1037
X86_INS_VMOVLPD = 1038
X86_INS_VMOVLPS = 1039
X86_INS_VMOVMSKPD = 1040
X86_INS_VMOVMSKPS = 1041
X86_INS_VMOVNTDQA = 1042
X86_INS_VMOVNTDQ = 1043
X86_INS_VMOVNTPD = 1044
X86_INS_VMOVNTPS = 1045
X86_INS_VMOVSD = 1046
X86_INS_VMOVSHDUP = 1047
X86_INS_VMOVSLDUP = 1048
X86_INS_VMOVSS = 1049
X86_INS_VMOVUPD = 1050
X86_INS_VMOVUPS = 1051
X86_INS_VMPSADBW = 1052
X86_INS_VMPTRLD = 1053
X86_INS_VMPTRST = 1054
X86_INS_VMREAD = 1055
X86_INS_VMRESUME = 1056
X86_INS_VMRUN = 1057
X86_INS_VMSAVE = 1058
X86_INS_VMULPD = 1059
X86_INS_VMULPS = 1060
X86_INS_VMULSD = 1061
X86_INS_VMULSS = 1062
X86_INS_VMWRITE = 1063
X86_INS_VMXOFF = 1064
X86_INS_VMXON = 1065
X86_INS_VORPD = 1066
X86_INS_VORPS = 1067
X86_INS_VP4DPWSSDS = 1068
X86_INS_VP4DPWSSD = 1069
X86_INS_VPABSB = 1070
X86_INS_VPABSD = 1071
X86_INS_VPABSQ = 1072
X86_INS_VPABSW = 1073
X86_INS_VPACKSSDW = 1074
X86_INS_VPACKSSWB = 1075
X86_INS_VPACKUSDW = 1076
X86_INS_VPACKUSWB = 1077
X86_INS_VPADDB = 1078
X86_INS_VPADDD = 1079
X86_INS_VPADDQ = 1080
X86_INS_VPADDSB = 1081
X86_INS_VPADDSW = 1082
X86_INS_VPADDUSB = 1083
X86_INS_VPADDUSW = 1084
X86_INS_VPADDW = 1085
X86_INS_VPALIGNR = 1086
X86_INS_VPANDD = 1087
X86_INS_VPANDND = 1088
X86_INS_VPANDNQ = 1089
X86_INS_VPANDN = 1090
X86_INS_VPANDQ = 1091
X86_INS_VPAND = 1092
X86_INS_VPAVGB = 1093
X86_INS_VPAVGW = 1094
X86_INS_VPBLENDD = 1095
X86_INS_VPBLENDMB = 1096
X86_INS_VPBLENDMD = 1097
X86_INS_VPBLENDMQ = 1098
X86_INS_VPBLENDMW = 1099
X86_INS_VPBLENDVB = 1100
X86_INS_VPBLENDW = 1101
X86_INS_VPBROADCASTB = 1102
X86_INS_VPBROADCASTD = 1103
X86_INS_VPBROADCASTMB2Q = 1104
X86_INS_VPBROADCASTMW2D = 1105
X86_INS_VPBROADCASTQ = 1106
X86_INS_VPBROADCASTW = 1107
X86_INS_VPCLMULQDQ = 1108
X86_INS_VPCMOV = 1109
X86_INS_VPCMP = 1110
X86_INS_VPCMPB = 1111
X86_INS_VPCMPD = 1112
X86_INS_VPCMPEQB = 1113
X86_INS_VPCMPEQD = 1114
X86_INS_VPCMPEQQ = 1115
X86_INS_VPCMPEQW = 1116
X86_INS_VPCMPESTRI = 1117
X86_INS_VPCMPESTRM = 1118
X86_INS_VPCMPGTB = 1119
X86_INS_VPCMPGTD = 1120
X86_INS_VPCMPGTQ = 1121
X86_INS_VPCMPGTW = 1122
X86_INS_VPCMPISTRI = 1123
X86_INS_VPCMPISTRM = 1124
X86_INS_VPCMPQ = 1125
X86_INS_VPCMPUB = 1126
X86_INS_VPCMPUD = 1127
X86_INS_VPCMPUQ = 1128
X86_INS_VPCMPUW = 1129
X86_INS_VPCMPW = 1130
X86_INS_VPCOM = 1131
X86_INS_VPCOMB = 1132
X86_INS_VPCOMD = 1133
X86_INS_VPCOMPRESSB = 1134
X86_INS_VPCOMPRESSD = 1135
X86_INS_VPCOMPRESSQ = 1136
X86_INS_VPCOMPRESSW = 1137
X86_INS_VPCOMQ = 1138
X86_INS_VPCOMUB = 1139
X86_INS_VPCOMUD = 1140
X86_INS_VPCOMUQ = 1141
X86_INS_VPCOMUW = 1142
X86_INS_VPCOMW = 1143
X86_INS_VPCONFLICTD = 1144
X86_INS_VPCONFLICTQ = 1145
X86_INS_VPDPBUSDS = 1146
X86_INS_VPDPBUSD = 1147
X86_INS_VPDPWSSDS = 1148
X86_INS_VPDPWSSD = 1149
X86_INS_VPERM2F128 = 1150
X86_INS_VPERM2I128 = 1151
X86_INS_VPERMB = 1152
X86_INS_VPERMD = 1153
X86_INS_VPERMI2B = 1154
X86_INS_VPERMI2D = 1155
X86_INS_VPERMI2PD = 1156
X86_INS_VPERMI2PS = 1157
X86_INS_VPERMI2Q = 1158
X86_INS_VPERMI2W = 1159
X86_INS_VPERMIL2PD = 1160
X86_INS_VPERMILPD = 1161
X86_INS_VPERMIL2PS = 1162
X86_INS_VPERMILPS = 1163
X86_INS_VPERMPD = 1164
X86_INS_VPERMPS = 1165
X86_INS_VPERMQ = 1166
X86_INS_VPERMT2B = 1167
X86_INS_VPERMT2D = 1168
X86_INS_VPERMT2PD = 1169
X86_INS_VPERMT2PS = 1170
X86_INS_VPERMT2Q = 1171
X86_INS_VPERMT2W = 1172
X86_INS_VPERMW = 1173
X86_INS_VPEXPANDB = 1174
X86_INS_VPEXPANDD = 1175
X86_INS_VPEXPANDQ = 1176
X86_INS_VPEXPANDW = 1177
X86_INS_VPEXTRB = 1178
X86_INS_VPEXTRD = 1179
X86_INS_VPEXTRQ = 1180
X86_INS_VPEXTRW = 1181
X86_INS_VPGATHERDD = 1182
X86_INS_VPGATHERDQ = 1183
X86_INS_VPGATHERQD = 1184
X86_INS_VPGATHERQQ = 1185
X86_INS_VPHADDBD = 1186
X86_INS_VPHADDBQ = 1187
X86_INS_VPHADDBW = 1188
X86_INS_VPHADDDQ = 1189
X86_INS_VPHADDD = 1190
X86_INS_VPHADDSW = 1191
X86_INS_VPHADDUBD = 1192
X86_INS_VPHADDUBQ = 1193
X86_INS_VPHADDUBW = 1194
X86_INS_VPHADDUDQ = 1195
X86_INS_VPHADDUWD = 1196
X86_INS_VPHADDUWQ = 1197
X86_INS_VPHADDWD = 1198
X86_INS_VPHADDWQ = 1199
X86_INS_VPHADDW = 1200
X86_INS_VPHMINPOSUW = 1201
X86_INS_VPHSUBBW = 1202
X86_INS_VPHSUBDQ = 1203
X86_INS_VPHSUBD = 1204
X86_INS_VPHSUBSW = 1205
X86_INS_VPHSUBWD = 1206
X86_INS_VPHSUBW = 1207
X86_INS_VPINSRB = 1208
X86_INS_VPINSRD = 1209
X86_INS_VPINSRQ = 1210
X86_INS_VPINSRW = 1211
X86_INS_VPLZCNTD = 1212
X86_INS_VPLZCNTQ = 1213
X86_INS_VPMACSDD = 1214
X86_INS_VPMACSDQH = 1215
X86_INS_VPMACSDQL = 1216
X86_INS_VPMACSSDD = 1217
X86_INS_VPMACSSDQH = 1218
X86_INS_VPMACSSDQL = 1219
X86_INS_VPMACSSWD = 1220
X86_INS_VPMACSSWW = 1221
X86_INS_VPMACSWD = 1222
X86_INS_VPMACSWW = 1223
X86_INS_VPMADCSSWD = 1224
X86_INS_VPMADCSWD = 1225
X86_INS_VPMADD52HUQ = 1226
X86_INS_VPMADD52LUQ = 1227
X86_INS_VPMADDUBSW = 1228
X86_INS_VPMADDWD = 1229
X86_INS_VPMASKMOVD = 1230
X86_INS_VPMASKMOVQ = 1231
X86_INS_VPMAXSB = 1232
X86_INS_VPMAXSD = 1233
X86_INS_VPMAXSQ = 1234
X86_INS_VPMAXSW = 1235
X86_INS_VPMAXUB = 1236
X86_INS_VPMAXUD = 1237
X86_INS_VPMAXUQ = 1238
X86_INS_VPMAXUW = 1239
X86_INS_VPMINSB = 1240
X86_INS_VPMINSD = 1241
X86_INS_VPMINSQ = 1242
X86_INS_VPMINSW = 1243
X86_INS_VPMINUB = 1244
X86_INS_VPMINUD = 1245
X86_INS_VPMINUQ = 1246
X86_INS_VPMINUW = 1247
X86_INS_VPMOVB2M = 1248
X86_INS_VPMOVD2M = 1249
X86_INS_VPMOVDB = 1250
X86_INS_VPMOVDW = 1251
X86_INS_VPMOVM2B = 1252
X86_INS_VPMOVM2D = 1253
X86_INS_VPMOVM2Q = 1254
X86_INS_VPMOVM2W = 1255
X86_INS_VPMOVMSKB = 1256
X86_INS_VPMOVQ2M = 1257
X86_INS_VPMOVQB = 1258
X86_INS_VPMOVQD = 1259
X86_INS_VPMOVQW = 1260
X86_INS_VPMOVSDB = 1261
X86_INS_VPMOVSDW = 1262
X86_INS_VPMOVSQB = 1263
X86_INS_VPMOVSQD = 1264
X86_INS_VPMOVSQW = 1265
X86_INS_VPMOVSWB = 1266
X86_INS_VPMOVSXBD = 1267
X86_INS_VPMOVSXBQ = 1268
X86_INS_VPMOVSXBW = 1269
X86_INS_VPMOVSXDQ = 1270
X86_INS_VPMOVSXWD = 1271
X86_INS_VPMOVSXWQ = 1272
X86_INS_VPMOVUSDB = 1273
X86_INS_VPMOVUSDW = 1274
X86_INS_VPMOVUSQB = 1275
X86_INS_VPMOVUSQD = 1276
X86_INS_VPMOVUSQW = 1277
X86_INS_VPMOVUSWB = 1278
X86_INS_VPMOVW2M = 1279
X86_INS_VPMOVWB = 1280
X86_INS_VPMOVZXBD = 1281
X86_INS_VPMOVZXBQ = 1282
X86_INS_VPMOVZXBW = 1283
X86_INS_VPMOVZXDQ = 1284
X86_INS_VPMOVZXWD = 1285
X86_INS_VPMOVZXWQ = 1286
X86_INS_VPMULDQ = 1287
X86_INS_VPMULHRSW = 1288
X86_INS_VPMULHUW = 1289
X86_INS_VPMULHW = 1290
X86_INS_VPMULLD = 1291
X86_INS_VPMULLQ = 1292
X86_INS_VPMULLW = 1293
X86_INS_VPMULTISHIFTQB = 1294
X86_INS_VPMULUDQ = 1295
X86_INS_VPOPCNTB = 1296
X86_INS_VPOPCNTD = 1297
X86_INS_VPOPCNTQ = 1298
X86_INS_VPOPCNTW = 1299
X86_INS_VPORD = 1300
X86_INS_VPORQ = 1301
X86_INS_VPOR = 1302
X86_INS_VPPERM = 1303
X86_INS_VPROLD = 1304
X86_INS_VPROLQ = 1305
X86_INS_VPROLVD = 1306
X86_INS_VPROLVQ = 1307
X86_INS_VPRORD = 1308
X86_INS_VPRORQ = 1309
X86_INS_VPRORVD = 1310
X86_INS_VPRORVQ = 1311
X86_INS_VPROTB = 1312
X86_INS_VPROTD = 1313
X86_INS_VPROTQ = 1314
X86_INS_VPROTW = 1315
X86_INS_VPSADBW = 1316
X86_INS_VPSCATTERDD = 1317
X86_INS_VPSCATTERDQ = 1318
X86_INS_VPSCATTERQD = 1319
X86_INS_VPSCATTERQQ = 1320
X86_INS_VPSHAB = 1321
X86_INS_VPSHAD = 1322
X86_INS_VPSHAQ = 1323
X86_INS_VPSHAW = 1324
X86_INS_VPSHLB = 1325
X86_INS_VPSHLDD = 1326
X86_INS_VPSHLDQ = 1327
X86_INS_VPSHLDVD = 1328
X86_INS_VPSHLDVQ = 1329
X86_INS_VPSHLDVW = 1330
X86_INS_VPSHLDW = 1331
X86_INS_VPSHLD = 1332
X86_INS_VPSHLQ = 1333
X86_INS_VPSHLW = 1334
X86_INS_VPSHRDD = 1335
X86_INS_VPSHRDQ = 1336
X86_INS_VPSHRDVD = 1337
X86_INS_VPSHRDVQ = 1338
X86_INS_VPSHRDVW = 1339
X86_INS_VPSHRDW = 1340
X86_INS_VPSHUFBITQMB = 1341
X86_INS_VPSHUFB = 1342
X86_INS_VPSHUFD = 1343
X86_INS_VPSHUFHW = 1344
X86_INS_VPSHUFLW = 1345
X86_INS_VPSIGNB = 1346
X86_INS_VPSIGND = 1347
X86_INS_VPSIGNW = 1348
X86_INS_VPSLLDQ = 1349
X86_INS_VPSLLD = 1350
X86_INS_VPSLLQ = 1351
X86_INS_VPSLLVD = 1352
X86_INS_VPSLLVQ = 1353
X86_INS_VPSLLVW = 1354
X86_INS_VPSLLW = 1355
X86_INS_VPSRAD = 1356
X86_INS_VPSRAQ = 1357
X86_INS_VPSRAVD = 1358
X86_INS_VPSRAVQ = 1359
X86_INS_VPSRAVW = 1360
X86_INS_VPSRAW = 1361
X86_INS_VPSRLDQ = 1362
X86_INS_VPSRLD = 1363
X86_INS_VPSRLQ = 1364
X86_INS_VPSRLVD = 1365
X86_INS_VPSRLVQ = 1366
X86_INS_VPSRLVW = 1367
X86_INS_VPSRLW = 1368
X86_INS_VPSUBB = 1369
X86_INS_VPSUBD = 1370
X86_INS_VPSUBQ = 1371
X86_INS_VPSUBSB = 1372
X86_INS_VPSUBSW = 1373
X86_INS_VPSUBUSB = 1374
X86_INS_VPSUBUSW = 1375
X86_INS_VPSUBW = 1376
X86_INS_VPTERNLOGD = 1377
X86_INS_VPTERNLOGQ = 1378
X86_INS_VPTESTMB = 1379
X86_INS_VPTESTMD = 1380
X86_INS_VPTESTMQ = 1381
X86_INS_VPTESTMW = 1382
X86_INS_VPTESTNMB = 1383
X86_INS_VPTESTNMD = 1384
X86_INS_VPTESTNMQ = 1385
X86_INS_VPTESTNMW = 1386
X86_INS_VPTEST = 1387
X86_INS_VPUNPCKHBW = 1388
X86_INS_VPUNPCKHDQ = 1389
X86_INS_VPUNPCKHQDQ = 1390
X86_INS_VPUNPCKHWD = 1391
X86_INS_VPUNPCKLBW = 1392
X86_INS_VPUNPCKLDQ = 1393
X86_INS_VPUNPCKLQDQ = 1394
X86_INS_VPUNPCKLWD = 1395
X86_INS_VPXORD = 1396
X86_INS_VPXORQ = 1397
X86_INS_VPXOR = 1398
X86_INS_VRANGEPD = 1399
X86_INS_VRANGEPS = 1400
X86_INS_VRANGESD = 1401
X86_INS_VRANGESS = 1402
X86_INS_VRCP14PD = 1403
X86_INS_VRCP14PS = 1404
X86_INS_VRCP14SD = 1405
X86_INS_VRCP14SS = 1406
X86_INS_VRCP28PD = 1407
X86_INS_VRCP28PS = 1408
X86_INS_VRCP28SD = 1409
X86_INS_VRCP28SS = 1410
X86_INS_VRCPPS = 1411
X86_INS_VRCPSS = 1412
X86_INS_VREDUCEPD = 1413
X86_INS_VREDUCEPS = 1414
X86_INS_VREDUCESD = 1415
X86_INS_VREDUCESS = 1416
X86_INS_VRNDSCALEPD = 1417
X86_INS_VRNDSCALEPS = 1418
X86_INS_VRNDSCALESD = 1419
X86_INS_VRNDSCALESS = 1420
X86_INS_VROUNDPD = 1421
X86_INS_VROUNDPS = 1422
X86_INS_VROUNDSD = 1423
X86_INS_VROUNDSS = 1424
X86_INS_VRSQRT14PD = 1425
X86_INS_VRSQRT14PS = 1426
X86_INS_VRSQRT14SD = 1427
X86_INS_VRSQRT14SS = 1428
X86_INS_VRSQRT28PD = 1429
X86_INS_VRSQRT28PS = 1430
X86_INS_VRSQRT28SD = 1431
X86_INS_VRSQRT28SS = 1432
X86_INS_VRSQRTPS = 1433
X86_INS_VRSQRTSS = 1434
X86_INS_VSCALEFPD = 1435
X86_INS_VSCALEFPS = 1436
X86_INS_VSCALEFSD = 1437
X86_INS_VSCALEFSS = 1438
X86_INS_VSCATTERDPD = 1439
X86_INS_VSCATTERDPS = 1440
X86_INS_VSCATTERPF0DPD = 1441
X86_INS_VSCATTERPF0DPS = 1442
X86_INS_VSCATTERPF0QPD = 1443
X86_INS_VSCATTERPF0QPS = 1444
X86_INS_VSCATTERPF1DPD = 1445
X86_INS_VSCATTERPF1DPS = 1446
X86_INS_VSCATTERPF1QPD = 1447
X86_INS_VSCATTERPF1QPS = 1448
X86_INS_VSCATTERQPD = 1449
X86_INS_VSCATTERQPS = 1450
X86_INS_VSHUFF32X4 = 1451
X86_INS_VSHUFF64X2 = 1452
X86_INS_VSHUFI32X4 = 1453
X86_INS_VSHUFI64X2 = 1454
X86_INS_VSHUFPD = 1455
X86_INS_VSHUFPS = 1456
X86_INS_VSQRTPD = 1457
X86_INS_VSQRTPS = 1458
X86_INS_VSQRTSD = 1459
X86_INS_VSQRTSS = 1460
X86_INS_VSTMXCSR = 1461
X86_INS_VSUBPD = 1462
X86_INS_VSUBPS = 1463
X86_INS_VSUBSD = 1464
X86_INS_VSUBSS = 1465
X86_INS_VTESTPD = 1466
X86_INS_VTESTPS = 1467
X86_INS_VUCOMISD = 1468
X86_INS_VUCOMISS = 1469
X86_INS_VUNPCKHPD = 1470
X86_INS_VUNPCKHPS = 1471
X86_INS_VUNPCKLPD = 1472
X86_INS_VUNPCKLPS = 1473
X86_INS_VXORPD = 1474
X86_INS_VXORPS = 1475
X86_INS_VZEROALL = 1476
X86_INS_VZEROUPPER = 1477
X86_INS_WAIT = 1478
X86_INS_WBINVD = 1479
X86_INS_WBNOINVD = 1480
X86_INS_WRFSBASE = 1481
X86_INS_WRGSBASE = 1482
X86_INS_WRMSR = 1483
X86_INS_WRPKRU = 1484
X86_INS_WRSSD = 1485
X86_INS_WRSSQ = 1486
X86_INS_WRUSSD = 1487
X86_INS_WRUSSQ = 1488
X86_INS_XABORT = 1489
X86_INS_XACQUIRE = 1490
X86_INS_XADD = 1491
X86_INS_XBEGIN = 1492
X86_INS_XCHG = 1493
X86_INS_FXCH = 1494
X86_INS_XCRYPTCBC = 1495
X86_INS_XCRYPTCFB = 1496
X86_INS_XCRYPTCTR = 1497
X86_INS_XCRYPTECB = 1498
X86_INS_XCRYPTOFB = 1499
X86_INS_XEND = 1500
X86_INS_XGETBV = 1501
X86_INS_XLATB = 1502
X86_INS_XOR = 1503
X86_INS_XORPD = 1504
X86_INS_XORPS = 1505
X86_INS_XRELEASE = 1506
X86_INS_XRSTOR = 1507
X86_INS_XRSTOR64 = 1508
X86_INS_XRSTORS = 1509
X86_INS_XRSTORS64 = 1510
X86_INS_XSAVE = 1511
X86_INS_XSAVE64 = 1512
X86_INS_XSAVEC = 1513
X86_INS_XSAVEC64 = 1514
X86_INS_XSAVEOPT = 1515
X86_INS_XSAVEOPT64 = 1516
X86_INS_XSAVES = 1517
X86_INS_XSAVES64 = 1518
X86_INS_XSETBV = 1519
X86_INS_XSHA1 = 1520
X86_INS_XSHA256 = 1521
X86_INS_XSTORE = 1522
X86_INS_XTEST = 1523
X86_INS_ENDING = 1524
X86_GRP_INVALID = 0
X86_GRP_JUMP = 1
X86_GRP_CALL = 2
X86_GRP_RET = 3
X86_GRP_INT = 4
X86_GRP_IRET = 5
X86_GRP_PRIVILEGE = 6
X86_GRP_BRANCH_RELATIVE = 7
X86_GRP_VM = 128
X86_GRP_3DNOW = 129
X86_GRP_AES = 130
X86_GRP_ADX = 131
X86_GRP_AVX = 132
X86_GRP_AVX2 = 133
X86_GRP_AVX512 = 134
X86_GRP_BMI = 135
X86_GRP_BMI2 = 136
X86_GRP_CMOV = 137
X86_GRP_F16C = 138
X86_GRP_FMA = 139
X86_GRP_FMA4 = 140
X86_GRP_FSGSBASE = 141
X86_GRP_HLE = 142
X86_GRP_MMX = 143
X86_GRP_MODE32 = 144
X86_GRP_MODE64 = 145
X86_GRP_RTM = 146
X86_GRP_SHA = 147
X86_GRP_SSE1 = 148
X86_GRP_SSE2 = 149
X86_GRP_SSE3 = 150
X86_GRP_SSE41 = 151
X86_GRP_SSE42 = 152
X86_GRP_SSE4A = 153
X86_GRP_SSSE3 = 154
X86_GRP_PCLMUL = 155
X86_GRP_XOP = 156
X86_GRP_CDI = 157
X86_GRP_ERI = 158
X86_GRP_TBM = 159
X86_GRP_16BITMODE = 160
X86_GRP_NOT64BITMODE = 161
X86_GRP_SGX = 162
X86_GRP_DQI = 163
X86_GRP_BWI = 164
X86_GRP_PFI = 165
X86_GRP_VLX = 166
X86_GRP_SMAP = 167
X86_GRP_NOVLX = 168
X86_GRP_FPU = 169
X86_GRP_ENDING = 170
| 21.962312 | 70 | 0.814667 | 8,188 | 43,705 | 3.842819 | 0.418051 | 0.290799 | 0.00839 | 0.002161 | 0.00089 | 0.00089 | 0 | 0 | 0 | 0 | 0 | 0.290392 | 0.136277 | 43,705 | 1,989 | 71 | 21.973353 | 0.54314 | 0.001556 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0.0011 | 0 | 0 | 1 | 0 | false | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | null | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 3 |
a1e78a56fa6aa6ef2c6170fad57b77f5ea9d8689 | 66 | py | Python | Tensor.py | robbierobinette/rcv-tensorflow | 984852902f465bb6f61ba863e4b76092249911d0 | [
"MIT"
] | null | null | null | Tensor.py | robbierobinette/rcv-tensorflow | 984852902f465bb6f61ba863e4b76092249911d0 | [
"MIT"
] | null | null | null | Tensor.py | robbierobinette/rcv-tensorflow | 984852902f465bb6f61ba863e4b76092249911d0 | [
"MIT"
] | null | null | null | import tensorflow as tf
Tensor = tf.types.experimental.TensorLike
| 22 | 41 | 0.833333 | 9 | 66 | 6.111111 | 0.888889 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.106061 | 66 | 2 | 42 | 33 | 0.932203 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | false | 0 | 0.5 | 0 | 0.5 | 0 | 1 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 3 |
a1eab458ad0c6e5268522cdacc0dd3d8014517ef | 319 | py | Python | spec/construct/test_zlib_surrounded.py | generalmimon/kaitai_struct_tests | d6baf92e1e47404fcb904c698d627450ebe9b314 | [
"MIT"
] | null | null | null | spec/construct/test_zlib_surrounded.py | generalmimon/kaitai_struct_tests | d6baf92e1e47404fcb904c698d627450ebe9b314 | [
"MIT"
] | null | null | null | spec/construct/test_zlib_surrounded.py | generalmimon/kaitai_struct_tests | d6baf92e1e47404fcb904c698d627450ebe9b314 | [
"MIT"
] | null | null | null | # Autogenerated from KST: please remove this line if doing any edits by hand!
import unittest
from zlib_surrounded import _schema
class TestZlibSurrounded(unittest.TestCase):
def test_zlib_surrounded(self):
r = _schema.parse_file('src/zlib_surrounded.bin')
self.assertEqual(r.zlib.inflated, -1)
| 26.583333 | 77 | 0.755486 | 43 | 319 | 5.44186 | 0.744186 | 0.179487 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.003759 | 0.166144 | 319 | 11 | 78 | 29 | 0.87594 | 0.23511 | 0 | 0 | 1 | 0 | 0.095041 | 0.095041 | 0 | 0 | 0 | 0 | 0.166667 | 1 | 0.166667 | false | 0 | 0.333333 | 0 | 0.666667 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 0 | 0 | 3 |
a1fc1fd7c54c70c42b70a8ca154892bcd2ec8bdc | 99 | py | Python | tests/test_find_forks/__init__.py | ivan2kh/find_forks | 409251282a85da48445afc03c5a1797df393ca95 | [
"MIT"
] | 41 | 2015-05-15T14:37:42.000Z | 2022-02-05T01:52:00.000Z | tests/test_find_forks/__init__.py | ivan2kh/find_forks | 409251282a85da48445afc03c5a1797df393ca95 | [
"MIT"
] | 12 | 2015-05-15T22:10:36.000Z | 2021-12-05T14:21:58.000Z | tests/test_find_forks/__init__.py | ivan2kh/find_forks | 409251282a85da48445afc03c5a1797df393ca95 | [
"MIT"
] | 16 | 2015-05-15T14:44:33.000Z | 2020-11-18T00:54:18.000Z | # coding: utf-8
"""Dummy."""
from os import path
BASEPATH = path.abspath(path.dirname(__file__))
| 14.142857 | 47 | 0.69697 | 14 | 99 | 4.642857 | 0.857143 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.011628 | 0.131313 | 99 | 6 | 48 | 16.5 | 0.744186 | 0.212121 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | false | 0 | 0.5 | 0 | 0.5 | 0 | 1 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 3 |
a1fe67272a9d8d60a3dd0a101ed388dbc262f480 | 171 | py | Python | yatsm/algorithms/__init__.py | bullocke/yatsm_nrt | b0ded56032bf9f9dcdf6b7b749f6554ade56de1e | [
"MIT"
] | 2 | 2018-04-25T02:10:30.000Z | 2021-07-30T03:57:49.000Z | yatsm/algorithms/__init__.py | bullocke/yatsm_nrt | b0ded56032bf9f9dcdf6b7b749f6554ade56de1e | [
"MIT"
] | null | null | null | yatsm/algorithms/__init__.py | bullocke/yatsm_nrt | b0ded56032bf9f9dcdf6b7b749f6554ade56de1e | [
"MIT"
] | 1 | 2017-04-01T16:11:52.000Z | 2017-04-01T16:11:52.000Z | """ Submodule for YATSM algorithms
Algorithms currently include:
- :py:class:`ccdc.CCDCesque`
"""
from .ccdc import CCDCesque # noqa
available = ['CCDCesque'] #:
| 17.1 | 35 | 0.690058 | 18 | 171 | 6.555556 | 0.777778 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.175439 | 171 | 9 | 36 | 19 | 0.836879 | 0.590643 | 0 | 0 | 0 | 0 | 0.15 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | false | 0 | 0.5 | 0 | 0.5 | 0 | 1 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 3 |
b8062423f11b39f515f2542a845f269ad75bd7c2 | 99 | py | Python | gentelaadmin/apps.py | xuhairmeer/school-management | 36394c841a61e46bc00e1dc21bcfcdd5fa6f6918 | [
"bzip2-1.0.6"
] | null | null | null | gentelaadmin/apps.py | xuhairmeer/school-management | 36394c841a61e46bc00e1dc21bcfcdd5fa6f6918 | [
"bzip2-1.0.6"
] | 9 | 2021-03-19T08:15:07.000Z | 2022-03-12T00:13:19.000Z | gentelaadmin/apps.py | muhammadzuhair95/school-management | 36394c841a61e46bc00e1dc21bcfcdd5fa6f6918 | [
"bzip2-1.0.6"
] | null | null | null | from django.apps import AppConfig
class GentelaadminConfig(AppConfig):
name = 'gentelaadmin'
| 16.5 | 36 | 0.777778 | 10 | 99 | 7.7 | 0.9 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.151515 | 99 | 5 | 37 | 19.8 | 0.916667 | 0 | 0 | 0 | 0 | 0 | 0.121212 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | false | 0 | 0.333333 | 0 | 1 | 0 | 1 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 3 |
b809b4a12bddd7da35b7380d74eb18575ad6ea9f | 14,479 | py | Python | res/res/pub/database/models.py | onap/vfc-gvnfm-vnfres | 2ff32469650ac5b6dc6b65d99cc27f3f7aab4161 | [
"Apache-2.0"
] | 1 | 2021-10-15T15:26:31.000Z | 2021-10-15T15:26:31.000Z | res/res/pub/database/models.py | onap/vfc-gvnfm-vnfres | 2ff32469650ac5b6dc6b65d99cc27f3f7aab4161 | [
"Apache-2.0"
] | null | null | null | res/res/pub/database/models.py | onap/vfc-gvnfm-vnfres | 2ff32469650ac5b6dc6b65d99cc27f3f7aab4161 | [
"Apache-2.0"
] | null | null | null | # Copyright 2017 ZTE Corporation.
#
# 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.
from django.db import models
class NfInstModel(models.Model):
class Meta:
db_table = 'NFINST'
nfinstid = models.CharField(db_column='NFINSTID', max_length=200, primary_key=True)
nf_name = models.CharField(db_column='NFNAME', max_length=100, blank=True, null=True) # CreateVnfRequest.vnfInstanceName
package_id = models.CharField(db_column='PACKAGEID', max_length=200, blank=True, null=True)
status = models.CharField(db_column='STATUS', max_length=20, blank=True, null=True)
flavour_id = models.CharField(db_column='FLAVOURID', max_length=200, blank=True, null=True) # InstantiateVnfRequest.flavourId
location = models.CharField(db_column='LOCATION', max_length=200, blank=True, null=True)
version = models.CharField(db_column='VERSION', max_length=255, null=True)
vendor = models.CharField(db_column='VENDOR', max_length=255, null=True, blank=True)
netype = models.CharField(db_column='NETYPE', max_length=255, null=True)
vnfd_model = models.TextField(db_column='VNFDMODEL', max_length=20000, blank=True, null=True)
input_params = models.TextField(db_column='INPUTPARAMS', max_length=2000, blank=True, null=True) # InstantiateVnfRequest.additionalParams
create_time = models.CharField(db_column='CREATETIME', max_length=200, null=True, blank=True)
lastuptime = models.CharField(db_column='LASTUPTIME', max_length=200, blank=True, null=True)
nf_desc = models.CharField(db_column='VNFINSTANCEDESC', max_length=200, blank=True, null=True)
vnfdid = models.CharField(db_column='VNFDID', max_length=200, blank=True, null=True)
vnfSoftwareVersion = models.CharField(db_column='VNFSOFTWAREVER', max_length=200, blank=True, null=True)
vnfConfigurableProperties = models.TextField(db_column='VNFCONFIGURABLEPROPERTIES', max_length=20000, blank=True, null=True)
localizationLanguage = models.CharField(db_column='LOCALIZATIONLANGUAGE', max_length=255, null=True)
class CPInstModel(models.Model):
class Meta:
db_table = 'CPINST'
cpinstanceid = models.CharField(db_column='CPINSTANCEID', max_length=255, primary_key=True)
cpdid = models.CharField(db_column='CPDID', max_length=255)
cpinstancename = models.CharField(db_column='CPINSTANCENAME', max_length=255)
vlinstanceid = models.CharField(db_column='VLINSTANCEID', max_length=255)
ownertype = models.IntegerField(db_column='OWNERTYPE')
ownerid = models.CharField(db_column='OWNERID', max_length=255)
relatedtype = models.IntegerField(db_column='RELATEDTYPE')
relatedvl = models.CharField(db_column='RELATEDVL', max_length=255, blank=True, null=True)
relatedcp = models.CharField(db_column='RELATEDCP', max_length=255, blank=True, null=True)
relatedport = models.CharField(db_column='RELATEDPORT', max_length=255, blank=True, null=True)
class StorageInstModel(models.Model):
class Meta:
db_table = 'STORAGEINST'
storageid = models.CharField(db_column='STORAGEID', primary_key=True, max_length=255)
vimid = models.CharField(db_column='VIMID', max_length=255)
resouceid = models.CharField(db_column='RESOURCEID', max_length=255)
insttype = models.IntegerField(db_column='INSTTYPE')
instid = models.CharField(db_column='INSTID', max_length=255)
name = models.CharField(db_column='NAME', max_length=255, null=True)
storagetype = models.CharField(db_column='STORAGETYPE', max_length=255)
size = models.CharField(db_column='SIZE', max_length=255)
# rdmaenabled = models.IntegerField(db_column='RDMAENABLED', null=True)
# disktype = models.CharField(db_column='DISKTYPE', max_length=255)
# ownerid = models.CharField(db_column='OWNERID', max_length=255, null=True)
# zoneid = models.CharField(db_column='ZONEID', max_length=255, null=True)
# hostid = models.CharField(db_column='HOSTID', max_length=255, null=True)
# operationalstate = models.CharField(db_column='OPERATIONALSTATE', max_length=255, null=True)
tenant = models.CharField(db_column='TENANT', max_length=50, null=True)
is_predefined = models.IntegerField(db_column='ISPREDEFINED', default=0, null=True)
create_time = models.CharField(db_column='CREATETIME', max_length=200, null=True, blank=True)
nodeId = models.CharField(db_column='NODEID', max_length=255, null=True)
class NetworkInstModel(models.Model):
class Meta:
db_table = 'NETWORKINST'
networkid = models.CharField(db_column='NETWORKID', primary_key=True, max_length=255)
vimid = models.CharField(db_column='VIMID', max_length=255)
resouceid = models.CharField(db_column='RESOURCEID', max_length=255)
insttype = models.IntegerField(db_column='INSTTYPE')
instid = models.CharField(db_column='INSTID', max_length=255)
name = models.CharField(db_column='NAME', max_length=255)
tenant = models.CharField(db_column='TENANT', max_length=255, null=True)
is_predefined = models.IntegerField(db_column='ISPREDEFINED', default=0, null=True)
desc = models.CharField(db_column='DESC', max_length=255, null=True)
vendor = models.CharField(db_column='VENDOR', max_length=255, null=True)
bandwidth = models.IntegerField(db_column='BANDWIDTH', null=True)
mtu = models.IntegerField(db_column='MTU', null=True)
network_type = models.CharField(db_column='NETWORKTYPE', max_length=255, null=True)
segmentid = models.CharField(db_column='SEGMENTID', max_length=255, null=True)
networkqos = models.CharField(db_column='NETWORKQOS', max_length=255, null=True)
create_time = models.CharField(db_column='CREATETIME', max_length=200, null=True, blank=True)
physicalNetwork = models.CharField(db_column='PHYNETWORK', max_length=255, null=True)
is_shared = models.IntegerField(db_column='ISSHARED', default=0, null=True)
vlantrans = models.IntegerField(db_column='VLANTRANS', null=True)
routerExternal = models.IntegerField(db_column='ROUTEREXTERNAL', default=0, null=True)
nodeId = models.CharField(db_column='NODEID', max_length=255, null=True)
class SubNetworkInstModel(models.Model):
class Meta:
db_table = 'SUBNETWORKINST'
subnetworkid = models.CharField(db_column='SUBNETWORKID', primary_key=True, max_length=255)
vimid = models.CharField(db_column='VIMID', max_length=255)
resouceid = models.CharField(db_column='RESOURCEID', max_length=255)
networkid = models.CharField(db_column='NETWORKID', max_length=255)
insttype = models.IntegerField(db_column='INSTTYPE')
instid = models.CharField(db_column='INSTID', max_length=255)
name = models.CharField(db_column='NAME', max_length=255)
ipversion = models.IntegerField(db_column='IPVERSION', null=True)
gatewayip = models.CharField(db_column='GATEWAYIP', max_length=255, null=True)
isdhcpenabled = models.IntegerField(db_column='ISDHCPENABLED', null=True)
cidr = models.CharField(db_column='CIDR', max_length=255)
vdsname = models.CharField(db_column='VDSNAME', max_length=255, null=True)
operationalstate = models.CharField(db_column='OPERATIONALSTATE', max_length=255, null=True)
tenant = models.CharField(db_column='TENANT', max_length=255, null=True)
is_predefined = models.IntegerField(db_column='ISPREDEFINED', default=0, null=True)
create_time = models.CharField(db_column='CREATETIME', max_length=200, null=True, blank=True)
dnsNameservers = models.TextField(db_column='DNSNAMESERVERS', max_length=1024)
hostRoutes = models.TextField(db_column='HOSTROUTES', max_length=1024)
allocationPools = models.TextField(db_column='ALLOCATIONPOOLS', max_length=1024)
class PortInstModel(models.Model):
class Meta:
db_table = 'PORTINST'
portid = models.CharField(db_column='PORTID', primary_key=True, max_length=255)
networkid = models.CharField(db_column='NETWORKID', max_length=255)
subnetworkid = models.CharField(db_column='SUBNETWORKID', max_length=255, null=True)
vimid = models.CharField(db_column='VIMID', max_length=255)
resouceid = models.CharField(db_column='RESOURCEID', max_length=255)
name = models.CharField(db_column='NAME', max_length=255, null=True)
insttype = models.IntegerField(db_column='INSTTYPE')
instid = models.CharField(db_column='INSTID', max_length=255)
cpinstanceid = models.CharField(db_column='CPINSTANCEID', max_length=255, null=True)
bandwidth = models.CharField(db_column='BANDWIDTH', max_length=255, null=True)
operationalstate = models.CharField(db_column='OPERATIONALSTATE', max_length=255, null=True)
ipaddress = models.CharField(db_column='IPADDRESS', max_length=255)
macaddress = models.CharField(db_column='MACADDRESS', max_length=255)
nicorder = models.CharField(db_column='NICORDER', max_length=255)
floatipaddress = models.CharField(db_column='FLOATIPADDRESS', max_length=255, null=True)
serviceipaddress = models.CharField(db_column='SERVICEIPADDRESS', max_length=255, null=True)
typevirtualnic = models.CharField(db_column='TYPEVIRTUALNIC', max_length=255, null=True)
sfcencapsulation = models.CharField(db_column='SFCENCAPSULATION', max_length=255, null=True)
direction = models.CharField(db_column='DIRECTION', max_length=255, null=True)
tenant = models.CharField(db_column='TENANT', max_length=255, null=True)
interfacename = models.CharField(db_column='INTERFACENAME', max_length=255, blank=True, null=True)
vmid = models.CharField(db_column='VMID', max_length=255, blank=True, null=True)
create_time = models.CharField(db_column='CREATETIME', max_length=200, null=True, blank=True)
securityGroups = models.CharField(db_column='SECURITYGROUPS', max_length=255)
is_predefined = models.IntegerField(db_column='ISPREDEFINED', default=0, null=True)
nodeId = models.CharField(db_column='NODEID', max_length=255, null=True)
class VmInstModel(models.Model):
class Meta:
db_table = 'VMINST'
vmid = models.CharField(db_column='VMID', primary_key=True, max_length=255)
vimid = models.CharField(db_column='VIMID', max_length=255)
tenant = models.CharField(db_column='TENANT', max_length=255, null=True)
resouceid = models.CharField(db_column='RESOURCEID', max_length=255)
vmname = models.CharField(db_column='VMNAME', max_length=255)
nic_array = models.CharField(db_column='NICARRAY', max_length=255)
metadata = models.CharField(db_column='METADATA', max_length=255)
volume_array = models.CharField(db_column='VOLUMEARRAY', max_length=255)
server_group = models.CharField(db_column='SERVERGROUP', max_length=255)
availability_zone = models.CharField(db_column='AVAILABILITYZONE', max_length=255)
flavor_id = models.CharField(db_column='FLAVORID', max_length=255)
security_groups = models.CharField(db_column='SECURITYGROUPS', max_length=255)
operationalstate = models.CharField(db_column='OPERATIONALSTATE', max_length=255, null=True)
insttype = models.IntegerField(db_column='INSTTYPE')
is_predefined = models.IntegerField(db_column='ISPREDEFINED', default=0, null=True)
create_time = models.CharField(db_column='CREATETIME', max_length=200, null=True, blank=True)
instid = models.CharField(db_column='INSTID', max_length=255)
nodeId = models.CharField(db_column='NODEID', max_length=255, null=True)
class VLInstModel(models.Model):
class Meta:
db_table = 'VLINST'
vlinstanceid = models.CharField(db_column='VLINSTANCEID', max_length=255, primary_key=True)
vldid = models.CharField(db_column='VLDID', max_length=255)
vlinstancename = models.CharField(db_column='VLINSTANCENAME', max_length=255, blank=True, null=True)
ownertype = models.IntegerField(db_column='OWNERTYPE')
ownerid = models.CharField(db_column='OWNERID', max_length=255)
relatednetworkid = models.CharField(db_column='RELATEDNETWORKID', max_length=255, blank=True, null=True)
relatedsubnetworkid = models.CharField(db_column='RELATEDSUBNETWORKID', max_length=255, blank=True, null=True)
vltype = models.IntegerField(db_column='VLTYPE', default=0)
vimid = models.CharField(db_column='VIMID', max_length=255)
tenant = models.CharField(db_column='TENANT', max_length=50)
class VNFCInstModel(models.Model):
class Meta:
db_table = 'VNFCINST'
vnfcinstanceid = models.CharField(db_column='VNFCINSTANCEID', max_length=255, primary_key=True)
vduid = models.CharField(db_column='VDUID', max_length=255)
vdutype = models.CharField(db_column='VDUTYPE', max_length=255)
instid = models.CharField(db_column='NFINSTID', max_length=255)
vmid = models.CharField(db_column='VMID', max_length=255)
is_predefined = models.IntegerField(db_column='ISPREDEFINED', default=0, null=True)
class FlavourInstModel(models.Model):
class Meta:
db_table = 'FLAVOURINST'
flavourid = models.CharField(db_column='FLAVOURID', max_length=255, primary_key=True)
vimid = models.CharField(db_column='VIMID', max_length=255)
resouceid = models.CharField(db_column='RESOURCEID', max_length=255)
name = models.CharField(db_column='NAME', max_length=255)
tenant = models.CharField(db_column='TENANT', max_length=255, null=True)
vcpu = models.IntegerField(db_column='VCPU', null=True)
memory = models.IntegerField(db_column='MEMORY', null=True)
disk = models.IntegerField(db_column='DISK', null=True)
ephemeral = models.IntegerField(db_column='EPHEMERAL', null=True)
swap = models.IntegerField(db_column='SWAP', null=True)
isPublic = models.IntegerField(db_column='ISPUBLIC', null=True)
extraspecs = models.TextField(db_column='EXTRASPECS', max_length=4096)
instid = models.CharField(db_column='INSTID', max_length=255)
create_time = models.CharField(db_column='CREATETIME', max_length=200, null=True, blank=True)
is_predefined = models.IntegerField(db_column='ISPREDEFINED', default=0, null=True)
def __unicode__(self):
return '%s' % self.name
| 60.078838 | 142 | 0.754541 | 1,865 | 14,479 | 5.673458 | 0.131367 | 0.121728 | 0.199225 | 0.26954 | 0.575749 | 0.53133 | 0.478027 | 0.414895 | 0.406767 | 0.358473 | 0 | 0.032775 | 0.121279 | 14,479 | 240 | 143 | 60.329167 | 0.798868 | 0.076732 | 0 | 0.37766 | 0 | 0 | 0.111727 | 0.001873 | 0 | 0 | 0 | 0 | 0 | 1 | 0.005319 | false | 0 | 0.005319 | 0.005319 | 0.946809 | 0 | 0 | 0 | 0 | null | 0 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 3 |
62adceeecfdd77a88bff6b8d206582291a0569cc | 8,407 | py | Python | branches/old/python/ansi-C-generator/build_executables_a4d.py | jeffhammond/spaghetty | e2dbe2dd2621110b899b21dff590906a579e8bf5 | [
"BSD-2-Clause"
] | 1 | 2018-01-05T16:13:08.000Z | 2018-01-05T16:13:08.000Z | branches/old/python/ansi-C-generator/build_executables_a4d.py | jeffhammond/spaghetty | e2dbe2dd2621110b899b21dff590906a579e8bf5 | [
"BSD-2-Clause"
] | null | null | null | branches/old/python/ansi-C-generator/build_executables_a4d.py | jeffhammond/spaghetty | e2dbe2dd2621110b899b21dff590906a579e8bf5 | [
"BSD-2-Clause"
] | null | null | null | import fileinput
import string
import sys
import os
c_compiler = 'icc'
c_link_flags = '-g -O1 -xT -march=core2 -mtune=core2 -align -strict-ansi'
c_opt_flags = '-g -O3 -xT -march=core2 -mtune=core2 -funroll-loops -align -strict-ansi'
#fortran_compiler = 'ifort'
#fortran_link_flags = '-g -O1 -xT -march=core2 -mtune=core2 -align '
#fortran_opt_flags = '-g -O3 -xT -march=core2 -mtune=core2 -funroll-loops -align '
src_dir = '/home/jeff/code/spaghetty/trunk/source/ansi-C/'
exe_dir = '/home/jeff/code/spaghetty/trunk/binary/ansi-C/'
modlabel = 'a4d'
lib_name = 'tce_sort_'+modlabel+'.a'
count = '20'
rank = '8'
ranks = [rank,rank,rank,rank]
size = int(ranks[0])*int(ranks[1])*int(ranks[2])*int(ranks[3])
sizechar = str(size)
cind = ' '
ctab = ' '
def perm(l):
sz = len(l)
if sz <= 1:
return [l]
return [p[:i]+[l[0]]+p[i:] for i in xrange(sz) for p in perm(l[1:])]
indices = ['1','2','3','4']
#indices = ['4','3','2','1']
transpose_list = [indices]
#transpose_list = perm(indices)
loop_list = [indices]
#loop_list = perm(indices)
for transpose_order in transpose_list:
dummy = 0
A = transpose_order[0]
B = transpose_order[1]
C = transpose_order[2]
D = transpose_order[3]
driver_name = 'trans_a4d_'+A+B+C+D+'_'+modlabel
print driver_name
source_name = driver_name+'_driver.c'
source_file = open(source_name,'w')
source_file.write(cind+'#include <stdio.h>\n')
source_file.write(cind+'#include <stdlib.h>\n')
source_file.write(cind+'#include <math.h>\n')
source_file.write(cind+'#include <time.h>\n\n')
#source_file.write(cind+'#define MIN(X,Y) ((X) < (Y) ? : (X) : (Y))\n\n')
#source_file.write(cind+'void main(){\n\n')
source_file.write(cind+'int main(int argc, char **argv){\n\n')
source_file.write(cind+'double before['+ranks[0]+']['+ranks[0]+']['+ranks[0]+']['+ranks[0]+'];\n')
source_file.write(cind+'double after_jeff['+ranks[0]+']['+ranks[0]+']['+ranks[0]+']['+ranks[0]+'];\n')
source_file.write(cind+'double after_hirata['+ranks[0]+']['+ranks[0]+']['+ranks[0]+']['+ranks[0]+'];\n')
source_file.write(cind+'double factor;\n')
source_file.write(cind+'clock_t Tstart,Tfinish;\n')
source_file.write(cind+'double Thirata,Tjeff,Tspeedup,Tbest;\n')
source_file.write(cind+'unsigned int i,j,k,l;\n')
source_file.write(cind+'unsigned int aSize[4];\n')
source_file.write(cind+'unsigned int perm[4];\n')
source_file.write(cind+'unsigned int fastest[4];\n\n')
source_file.write(cind+'aSize[0] = '+ranks[0]+';\n')
source_file.write(cind+'aSize[1] = '+ranks[1]+';\n')
source_file.write(cind+'aSize[2] = '+ranks[2]+';\n')
source_file.write(cind+'aSize[3] = '+ranks[3]+';\n\n')
source_file.write(cind+'perm[0] = '+A+';\n')
source_file.write(cind+'perm[1] = '+B+';\n')
source_file.write(cind+'perm[2] = '+C+';\n')
source_file.write(cind+'perm[3] = '+D+';\n\n')
source_file.write(cind+0*ctab+'for( i=0; i<'+ranks[0]+'; i++) {\n')
source_file.write(cind+1*ctab+'for( j=0; j<'+ranks[1]+'; j++) {\n')
source_file.write(cind+2*ctab+'for( k=0; k<'+ranks[2]+'; k++) {\n')
source_file.write(cind+3*ctab+'for( l=0; l<'+ranks[3]+'; l++) {\n')
source_file.write(cind+4*ctab+'before[i][j][k][l] = (double)(l + k*10 + j*100 + i*1000);\n')
source_file.write(cind+3*ctab+'}\n')
source_file.write(cind+2*ctab+'}\n')
source_file.write(cind+1*ctab+'}\n')
source_file.write(cind+0*ctab+'}\n\n')
source_file.write(cind+0*ctab+'for( i=0; i<'+ranks[0]+'; i++) {\n')
source_file.write(cind+1*ctab+'for( j=0; j<'+ranks[1]+'; j++) {\n')
source_file.write(cind+2*ctab+'for( k=0; k<'+ranks[2]+'; k++) {\n')
source_file.write(cind+3*ctab+'for( l=0; l<'+ranks[3]+'; l++) {\n')
source_file.write(cind+4*ctab+'printf("before[i][j][k][l] = %f\\n",before[i][j][k][l]);\n')
source_file.write(cind+3*ctab+'}\n')
source_file.write(cind+2*ctab+'}\n')
source_file.write(cind+1*ctab+'}\n')
source_file.write(cind+0*ctab+'}\n\n')
source_file.write(cind+'factor = 1.0;\n')
source_file.write(cind+'Tbest=999999.0;\n')
source_file.write(cind+'Tstart=clock();\n\n')
source_file.write(cind+'for( i=0; i<'+count+'; i++) {\n')
#source_file.write(cind+' CALL tce_sort_4(before, after_hirata,\n')
#source_file.write(cind+' aSize(1), aSize(2), aSize(3), aSize(4),\n')
#source_file.write(cind+' perm(1), perm(2), perm(3), perm(4), factor)\n')
source_file.write(cind+'}\n\n')
source_file.write(cind+'Tfinish=clock();\n')
source_file.write(cind+'Thirata=(double)(Tfinish-Tstart);\n\n')
source_file.write(cind+'printf("TESTING TRANPOSE TYPE '+A+B+C+D+'\\n");\n')
source_file.write(cind+'printf("===================\\n");\n')
source_file.write(cind+'printf("The compilation flags were:\\n");\n')
for option in range(0,len(c_opt_flags.split())):
source_file.write(cind+'printf("'+c_opt_flags.split()[option]+'\\n");\n')
source_file.write(cind+'printf("===================\\n");\n\n')
source_file.write(cind+'printf("Hirata Reference = %f seconds\\n",Thirata);\n')
source_file.write(cind+'printf("Algorithm Jeff Speedup Best Best Speedup\\n");\n')
for loop_order in loop_list:
dummy = dummy+1
a = loop_order[0]
b = loop_order[1]
c = loop_order[2]
d = loop_order[3]
subroutine_name = 'trans_a4d_'+A+B+C+D+'_loop_'+a+b+c+d+'_'
source_file.write(cind+'Tstart=clock();\n\n')
#source_file.write(cind+'printf("*** START TIMING THE SUBROUTINE ***\\n");\n')
source_file.write(cind+'for( i=0; i<'+count+'; i++) {\n')
source_file.write(cind+1*ctab+subroutine_name+'(before, after_jeff,\n')
source_file.write(cind+7*ctab+'aSize[1], aSize[1], aSize[2], aSize[3],\n')
source_file.write(cind+7*ctab+'&factor);\n')
source_file.write(cind+'}\n\n')
#source_file.write(cind+'printf("*** FINISHED TIMING THE SUBROUTINE ***\\n");\n')
source_file.write(cind+'Tfinish=clock();\n')
source_file.write(cind+'Tjeff=(double)(Tfinish-Tstart);\n')
source_file.write(cind+'Tspeedup=Thirata/Tjeff;\n\n')
#source_file.write(cind+'Tbest=MIN(Tjeff,Tbest);\n')
source_file.write(cind+'if(Tjeff == Tbest){ \n')
source_file.write(cind+' fastest[0]='+a+';\n')
source_file.write(cind+' fastest[1]='+b+';\n')
source_file.write(cind+' fastest[2]='+c+';\n')
source_file.write(cind+' fastest[3]='+d+';\n')
source_file.write(cind+'}\n\n')
if 0 < dummy < 10:
nice_dummy=cind+''+str(dummy)
if 9 < dummy < 100:
nice_dummy=' '+str(dummy)
if 99 < dummy < 999:
nice_dummy=''+str(dummy)
source_file.write(cind+'printf("'+nice_dummy+' Loop '+a+b+c+d+' %f %f %f %f\\n",Tjeff,Tspeedup,Tbest,Thirata/Tbest);\n\n')
###source_file.write(cind+0*ctab+'for( i=0; i<'+ranks[0]+'; i++) {\n')
###source_file.write(cind+1*ctab+'for( j=0; j<'+ranks[1]+'; j++) {\n')
###source_file.write(cind+2*ctab+'for( k=0; k<'+ranks[2]+'; k++) {\n')
###source_file.write(cind+3*ctab+'for( l=0; l<'+ranks[3]+'; l++) {\n')
###source_file.write(cind+4*ctab+'if (after_jeff[i][j][k][l] != after_hirata[i][j][k][l]) {\n')
###source_file.write(cind+5*ctab+'printf("jeff error %d %d %d %d %f %f\\n",i,j,k,l,after_jeff[i][j][k][l], after_hirata[i][j][k][l]);\n')
###source_file.write(cind+4*ctab+'}\n')
###source_file.write(cind+3*ctab+'}\n')
###source_file.write(cind+2*ctab+'}\n')
###source_file.write(cind+1*ctab+'}\n')
###source_file.write(cind+0*ctab+'}\n\n')
source_file.write(cind+'printf("The best loop order is: %d%d%d%d\\n",fastest[0],fastest[1],fastest[2],fastest[3]);\n')
source_file.write(cind+'printf("The best time is: %f\\n",Tbest);\n')
source_file.write(cind+'printf("The best speedup is: %f\\n",Thirata/Tbest);\n\n')
source_file.write(cind+'return 0;\n\n')
source_file.write(cind+'}\n')
source_file.close()
print c_compiler+' '+c_link_flags+' '+' '+source_name+' '+lib_name+' '+' -o '+exe_dir+driver_name+'.x'
os.system(c_compiler+' '+c_link_flags+' '+' '+source_name+' '+lib_name+' '+' -o '+exe_dir+driver_name+'.x')
os.system('mv '+source_name+' '+src_dir)
| 49.163743 | 145 | 0.59843 | 1,367 | 8,407 | 3.549378 | 0.114119 | 0.199918 | 0.293693 | 0.372012 | 0.692498 | 0.663232 | 0.564303 | 0.448681 | 0.396538 | 0.370981 | 0 | 0.025479 | 0.15499 | 8,407 | 170 | 146 | 49.452941 | 0.657517 | 0.177709 | 0 | 0.207692 | 0 | 0.038462 | 0.310706 | 0.07327 | 0 | 0 | 0 | 0 | 0 | 0 | null | null | 0 | 0.030769 | null | null | 0.107692 | 0 | 0 | 0 | null | 0 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 3 |
62b112bfb912a435311d45858a5ef39209bd771c | 100 | py | Python | countdigit.py | Hrushabhs/My-Pycode | c27549b89a7848827117e42d324d383732adfac5 | [
"Apache-2.0"
] | null | null | null | countdigit.py | Hrushabhs/My-Pycode | c27549b89a7848827117e42d324d383732adfac5 | [
"Apache-2.0"
] | null | null | null | countdigit.py | Hrushabhs/My-Pycode | c27549b89a7848827117e42d324d383732adfac5 | [
"Apache-2.0"
] | null | null | null | n=int(input("Enetr number: "))
count=0
while(n>0):
dig=n%10
count+=1
n=n/10
print count
| 12.5 | 30 | 0.59 | 20 | 100 | 2.95 | 0.6 | 0.101695 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.089744 | 0.22 | 100 | 7 | 31 | 14.285714 | 0.666667 | 0 | 0 | 0 | 0 | 0 | 0.14 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | null | null | 0 | 0 | null | null | 0.142857 | 1 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 3 |
62da902e73beced86891224544cc228868991ddd | 315 | py | Python | src/restfx/util/md5.py | hyjiacan/restfx | 8ba70bc099e6ace0c9b3afe8909ea61a5ff82dec | [
"MIT",
"BSD-3-Clause"
] | 5 | 2021-01-25T11:09:41.000Z | 2021-04-28T07:17:21.000Z | src/restfx/util/md5.py | mgbin088/restfx | 86a499a9a4396829e2c40428feb8b2ee13406d52 | [
"MIT",
"BSD-3-Clause"
] | null | null | null | src/restfx/util/md5.py | mgbin088/restfx | 86a499a9a4396829e2c40428feb8b2ee13406d52 | [
"MIT",
"BSD-3-Clause"
] | 1 | 2021-01-28T00:53:37.000Z | 2021-01-28T00:53:37.000Z | import hashlib
def hash_bytes(s: (str, bytes)) -> bytes:
if isinstance(s, str):
s = s.encode(encoding='utf8')
return hashlib.md5(s).digest()
def hash_str(s: (str, bytes)) -> str:
if isinstance(s, str):
s = s.encode(encoding='utf8')
return hashlib.md5(s).hexdigest()
| 22.5 | 42 | 0.584127 | 44 | 315 | 4.136364 | 0.363636 | 0.087912 | 0.098901 | 0.175824 | 0.582418 | 0.582418 | 0.582418 | 0.582418 | 0.582418 | 0.582418 | 0 | 0.016949 | 0.250794 | 315 | 13 | 43 | 24.230769 | 0.754237 | 0 | 0 | 0.444444 | 0 | 0 | 0.02649 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0.222222 | false | 0 | 0.111111 | 0 | 0.555556 | 0 | 0 | 0 | 0 | null | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 3 |
62ff9078ea75c4139cf4fc59de6c0cad0c865a2d | 714 | py | Python | python/403.py | kylekanos/project-euler-1 | af7089356a4cea90f8ef331cfdc65e696def6140 | [
"BSD-2-Clause-FreeBSD"
] | null | null | null | python/403.py | kylekanos/project-euler-1 | af7089356a4cea90f8ef331cfdc65e696def6140 | [
"BSD-2-Clause-FreeBSD"
] | null | null | null | python/403.py | kylekanos/project-euler-1 | af7089356a4cea90f8ef331cfdc65e696def6140 | [
"BSD-2-Clause-FreeBSD"
] | 1 | 2019-09-17T00:55:58.000Z | 2019-09-17T00:55:58.000Z | #!/usr/bin/env python
N=10**12
R=int(N**.5)
mod = 10**8
def s(r,h):
return (h**3 + 3*h*r**2 + r**3 + (3*h**2 + 5)*r + 5*h + 6)//6%mod
def f(r,ph,qh):
return (-4*(ph - qh - 1)*r**3 - ph**4 + 2*ph**3 - 11*ph**2 + \
qh**4 - 6*(ph**2 - qh**2 - ph - qh)*r**2 + 2*qh**3 - 2*(2*ph**3 - 3*ph**2 - \
2*qh**3 - 3*qh**2 + 11*ph - 11*qh - 10)*r + 11*qh**2 - 14*ph + 34*qh + 24)//24 % mod
total = (f(0,1,N) + f(-1,0,N-1))%mod
for r in xrange(1,R):
total = (total + f(r,r+1,N//r))%mod
for r in xrange(-R,-1):
total = (total + f(r, -r+1, N//abs(r)))%mod
total = (total + s(r,-r)%mod)%mod
total = 2*total%mod
for r in xrange(0,R+1):
total = (total + s(r,r)%mod)%mod
print total
| 28.56 | 96 | 0.462185 | 169 | 714 | 1.952663 | 0.207101 | 0.024242 | 0.063636 | 0.081818 | 0.342424 | 0.206061 | 0.206061 | 0 | 0 | 0 | 0 | 0.132597 | 0.239496 | 714 | 24 | 97 | 29.75 | 0.475138 | 0.028011 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | null | null | 0 | 0 | null | null | 0.052632 | 0 | 0 | 1 | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 3 |
1a2694058b0cd5e5a084ac2d4c13b8e15e9cc4d1 | 2,062 | py | Python | tests/test_single_file.py | alepuzio/listfiles | cddc329cf773016a4703946c338a6e16d81380f5 | [
"MIT"
] | null | null | null | tests/test_single_file.py | alepuzio/listfiles | cddc329cf773016a4703946c338a6e16d81380f5 | [
"MIT"
] | null | null | null | tests/test_single_file.py | alepuzio/listfiles | cddc329cf773016a4703946c338a6e16d81380f5 | [
"MIT"
] | 1 | 2021-06-13T12:44:31.000Z | 2021-06-13T12:44:31.000Z | import os
import unittest
from os.path import splitext
from list_files.physical_data import PhysicalData
from list_files.physical_data import PhysicalDataFake
from .test_filename import Filename
class SingleFile:
def __init__(self, new_physical_data):
self.physical = new_physical_data
self.filename = Filename(new_physical_data)
def directory(self):
"""
@return the path of the file
"""
dirs = self.physical.path().split(os.sep)
#print(">SingleFile.directory")
value = str( os.sep.join(dirs[0:len(dirs) -1 ] ) )
#print("<SingleFile.directory")
return value
def dimension(self):
"""
@return the number of bytes of the file
"""
return self.physical.data().st_size
def timestamp(self):
"""
@return the timestamp of the last modifiy or creation of the file
"""
return self.physical.data().st_atime
def name(self):
"""
@return the name of the file
"""
return self.filename
def __iter__(self):
return iter(self.name)
def __lt__(self, other):
return len(self.filename) > len(other.filename)
def __eq__(self, other):
return self.name().name() == other.name().name() and self.dimension() == other.dimension()
def __hash__(self):
return hash(self.filename)
def __str__(self):
return "SingleFile:{0};{1}-{2}".format ( self.name().name(), self.name().extension(), str(self.dimension()) )
def __repr__(self):
return "SingleFile:{0};{1}".format (self.physical, self.filename)
def test_eq():
one = SingleFile ( PhysicalDataFake( "nome.txt", "C:\\path\\") )
two = SingleFile ( PhysicalDataFake( "nome.txt", "C:\\path\\") )
print (one)
print (two)
assert(one.name() == two.name())
def test_not_eq():
one = SingleFile ( PhysicalDataFake( "nome.txt", "C:\\path\\") )
two = SingleFile ( PhysicalDataFake( "nome1.txt", "C:\\path\\") )
assert(one != two)
| 27.493333 | 117 | 0.608632 | 249 | 2,062 | 4.863454 | 0.261044 | 0.066061 | 0.04294 | 0.037159 | 0.28654 | 0.234517 | 0.17341 | 0.17341 | 0.11891 | 0.11891 | 0 | 0.005171 | 0.249758 | 2,062 | 74 | 118 | 27.864865 | 0.777634 | 0.108632 | 0 | 0.047619 | 0 | 0 | 0.064868 | 0.012629 | 0 | 0 | 0 | 0 | 0.047619 | 1 | 0.309524 | false | 0 | 0.142857 | 0.142857 | 0.714286 | 0.047619 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 1 | 1 | 0 | 0 | 3 |
1a289619d08881edaf8f3c451d2b461d1329acc8 | 224 | py | Python | pages/themes/beginners/unicodeTopics/examples/unicode_escapes_in_strings.py | ProgressBG-Python-Course/ProgressBG-VC2-Python | 03b892a42ee1fad3d4f97e328e06a4b1573fd356 | [
"MIT"
] | null | null | null | pages/themes/beginners/unicodeTopics/examples/unicode_escapes_in_strings.py | ProgressBG-Python-Course/ProgressBG-VC2-Python | 03b892a42ee1fad3d4f97e328e06a4b1573fd356 | [
"MIT"
] | null | null | null | pages/themes/beginners/unicodeTopics/examples/unicode_escapes_in_strings.py | ProgressBG-Python-Course/ProgressBG-VC2-Python | 03b892a42ee1fad3d4f97e328e06a4b1573fd356 | [
"MIT"
] | null | null | null | # Unicode symbol in string:
print("Ѣ")
# Using the character name:
print("\N{Cyrillic Capital Letter Yat}")
# Using a 16-bit hex value code point:
print("\u0462")
# Using a 32-bit hex value code point:
print("\U00000462") | 20.363636 | 40 | 0.705357 | 36 | 224 | 4.388889 | 0.694444 | 0.075949 | 0.139241 | 0.189873 | 0.316456 | 0.316456 | 0 | 0 | 0 | 0 | 0 | 0.084656 | 0.15625 | 224 | 11 | 41 | 20.363636 | 0.751323 | 0.558036 | 0 | 0 | 0 | 0 | 0.505263 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | true | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | null | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 1 | 0 | 3 |
a7e30c19b82191d01543661cf87f800e6cda8984 | 2,244 | py | Python | tests/test_walk.py | asherf/flake8-os-walk | ba76c88783d3cb01bc25b497a2413ae2c8366790 | [
"MIT"
] | 1 | 2020-06-04T05:12:12.000Z | 2020-06-04T05:12:12.000Z | tests/test_walk.py | asherf/flake8-os-walk | ba76c88783d3cb01bc25b497a2413ae2c8366790 | [
"MIT"
] | null | null | null | tests/test_walk.py | asherf/flake8-os-walk | ba76c88783d3cb01bc25b497a2413ae2c8366790 | [
"MIT"
] | null | null | null | from ast import parse
from sys import version_info
from textwrap import dedent
from flake8_os_walk.checker import OsWalkVisitor
def test_os_walk_func_bad_usage():
tree = parse(
dedent(
"""\
import os
def hello_jerry():
for dir in os.walk('this-is-bad'):
print(dir)
"""
)
)
visitor = OsWalkVisitor()
visitor.visit(tree)
violations = visitor.violations
assert len(violations) == 1
assert (
violations[0][1] == "OW100 usage of os.walk() without an onerror param detected"
)
node = violations[0][0]
assert node.lineno == 4
assert node.col_offset == 13
def test_os_walk_func_onerror_none():
tree = parse(
dedent(
"""\
import os
def hello_jerry():
for dir in os.walk('this-is-bad', onerror=None):
print(dir)
"""
)
)
visitor = OsWalkVisitor()
visitor.visit(tree)
violations = visitor.violations
assert len(violations) == 1
assert (
violations[0][1] == "OW100 usage of os.walk() without an onerror param detected"
)
node = violations[0][0]
assert node.lineno == 4
assert node.col_offset == 13
def test_os_walk_func():
tree = parse(
dedent(
"""\
import os
def _handle_error(error):
raise Exception("no soup for you.")
def hello_jerry():
for dir in os.walk('this-is-bad', onerror=_handle_error):
print(dir)
"""
)
)
visitor = OsWalkVisitor()
visitor.visit(tree)
assert len(visitor.violations) == 0
def test_os_walk_method_bad_usage():
tree = parse(
dedent(
"""\
import os
class Festivus:
def tinsel(self):
for dir in os.walk('this-is-bad'):
print(dir)
"""
)
)
visitor = OsWalkVisitor()
visitor.visit(tree)
violations = visitor.violations
assert len(violations) == 1
assert (
violations[0][1] == "OW100 usage of os.walk() without an onerror param detected"
)
node = violations[0][0]
assert node.lineno == 5
assert node.col_offset == 19
| 21.786408 | 88 | 0.561497 | 262 | 2,244 | 4.69084 | 0.240458 | 0.058584 | 0.029292 | 0.042311 | 0.780309 | 0.766477 | 0.745321 | 0.677787 | 0.677787 | 0.677787 | 0 | 0.023256 | 0.329323 | 2,244 | 102 | 89 | 22 | 0.793355 | 0 | 0 | 0.614035 | 0 | 0 | 0.110196 | 0 | 0 | 0 | 0 | 0 | 0.22807 | 1 | 0.070175 | false | 0 | 0.070175 | 0 | 0.140351 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 1 | 1 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 3 |
a7e725954870b4ba9822c55d980736a0d2de89f2 | 36 | py | Python | tests/__init__.py | mosquito/python-lsm | a46cf4b6c661a4944dd61470e76fb6442352e501 | [
"Apache-2.0"
] | 6 | 2021-06-09T09:35:37.000Z | 2021-11-24T23:09:57.000Z | tests/__init__.py | mosquito/python-lsm | a46cf4b6c661a4944dd61470e76fb6442352e501 | [
"Apache-2.0"
] | 1 | 2021-11-15T15:52:10.000Z | 2021-11-16T16:40:56.000Z | tests/__init__.py | mosquito/python-lsm | a46cf4b6c661a4944dd61470e76fb6442352e501 | [
"Apache-2.0"
] | 1 | 2021-07-25T21:30:22.000Z | 2021-07-25T21:30:22.000Z | comp_algo = ["none", "lz4", "zstd"]
| 18 | 35 | 0.555556 | 5 | 36 | 3.8 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.032258 | 0.138889 | 36 | 1 | 36 | 36 | 0.580645 | 0 | 0 | 0 | 0 | 0 | 0.305556 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | false | 0 | 0 | 0 | 0 | 0 | 1 | 1 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 3 |
a7ef665f1ec6c10f7fa4fbcb59e0f9eef71b4aff | 646 | py | Python | dalme_app/utils/__init__.py | DALME/dalme | 46f9a0011fdb75c5098b552104fc73b1062e16e9 | [
"BSD-3-Clause"
] | 6 | 2019-05-07T01:06:04.000Z | 2021-02-19T20:45:09.000Z | dalme_app/utils/__init__.py | DALME/dalme | 46f9a0011fdb75c5098b552104fc73b1062e16e9 | [
"BSD-3-Clause"
] | 23 | 2018-09-14T18:01:42.000Z | 2021-12-29T17:25:18.000Z | dalme_app/utils/__init__.py | DALME/dalme | 46f9a0011fdb75c5098b552104fc73b1062e16e9 | [
"BSD-3-Clause"
] | 1 | 2020-02-10T16:20:57.000Z | 2020-02-10T16:20:57.000Z | from .async_middleware import AsyncMiddleware # NOQA
from .dalme_saml_processor import SAMLProcessor # NOQA
from .database_router import ModelDatabaseRouter # NOQA
from .date_time_helpers import FormatDalmeDate, round_timesince # NOQA
from .domain_middleware import SubdomainRedirectMiddleware # NOQA
from .dynamic_preferences import JSONPreferenceSerializer, JSONPreference # NOQA
from .menu_compiler import DALMEMenus # NOQA
from .messaging import send_message # NOQA
from .offline_context_generator import offline_context_generator # NOQA
from .paginated_formsets import formset_factory # NOQA
from .search import Search, SearchContext # NOQA
| 53.833333 | 80 | 0.854489 | 75 | 646 | 7.133333 | 0.533333 | 0.149533 | 0.085981 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.106811 | 646 | 11 | 81 | 58.727273 | 0.92721 | 0.083591 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | true | 0 | 1 | 0 | 1 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 0 | 1 | 0 | 0 | 3 |
a7f6da8a7e6cc5f49f760883f09f74d397fe571c | 2,444 | py | Python | Modules/text.py | DaMuffinDev/ezruh | 7c57b8b77f01674c1f46c0adad661f234584e024 | [
"MIT"
] | null | null | null | Modules/text.py | DaMuffinDev/ezruh | 7c57b8b77f01674c1f46c0adad661f234584e024 | [
"MIT"
] | null | null | null | Modules/text.py | DaMuffinDev/ezruh | 7c57b8b77f01674c1f46c0adad661f234584e024 | [
"MIT"
] | null | null | null | from Resources.required_modules import pymodules
pymodules.install(pymodules.presets.modules("text"))
from pyautogui import typewrite, press
from keyboard import is_pressed
from time import sleep as wait
import random
inf = -1
def enter(): press("enter")
def spam_text_file_contents(text_file, delay, repeat_delay, repeat_amount=inf, hotkey="q"):
"""
:param text - The sentence or word to repeat
:param delay - The delay before the word spamming happens
:param repeat_delay - The delay between each loop call
:param repeat_amount - The amount of times before the scripts
(If inf it will stop when the for loop reaches the end of the file's contents)
:param hotkey - The key to stop the script
"""
wait(delay)
with open(text_file, "r") as file:
for index, word in enumerate(file):
if is_pressed(hotkey) or index >= repeat_amount:
break
typewrite(word)
enter()
wait(repeat_delay)
def repeat_random_text_file_contents(text_file, delay, repeat_delay, repeat_amount=inf, hotkey="q"):
"""
:param text - The sentence or word to repeat
:param delay - The delay before the word spamming happens
:param repeat_delay - The delay between each loop call
:param repeat_amount - The amount of times before the scripts
(If inf it will stop when the for loop reaches the end of the file's contents)
:param hotkey - The key to stop the script
"""
with open(text_file, "r") as file:
file_words = []
for word in file:
file_words.append(word)
wait(delay)
for index in range(0, repeat_amount, 1):
if is_pressed(hotkey):
break
typewrite(random.choice(file_words))
enter()
wait(repeat_delay)
def repeat_sentence(text, delay, repeat_delay, repeat_amount=inf, hotkey="q"):
"""
:param text - The sentence or word to repeat
:param delay - The delay before the word spamming happens
:param repeat_delay - The delay between each loop call
:param repeat_amount - The amount of times before the scripts
(If inf it will stop when the for loop reaches the end of the file's contents)
:param hotkey - The key to stop the script
"""
wait(delay)
for index in range(0, repeat_amount, 1):
if is_pressed(hotkey): break
typewrite(text)
enter()
wait(repeat_delay) | 35.941176 | 100 | 0.671031 | 354 | 2,444 | 4.525424 | 0.200565 | 0.061798 | 0.048689 | 0.041199 | 0.752185 | 0.752185 | 0.71598 | 0.687266 | 0.687266 | 0.687266 | 0 | 0.002746 | 0.25491 | 2,444 | 68 | 101 | 35.941176 | 0.876991 | 0.425532 | 0 | 0.416667 | 0 | 0 | 0.010778 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0.111111 | false | 0 | 0.138889 | 0 | 0.25 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 1 | 1 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 3 |
c50d9d31149a7b960c17e4041c2ab4d64c738cff | 1,332 | py | Python | Algorithms_easy/0944. Delete Columns to Make Sorted.py | VinceW0/Leetcode_Python_solutions | 09e9720afce21632372431606ebec4129eb79734 | [
"Xnet",
"X11"
] | 4 | 2020-08-11T20:45:15.000Z | 2021-03-12T00:33:34.000Z | Algorithms_easy/0944. Delete Columns to Make Sorted.py | VinceW0/Leetcode_Python_solutions | 09e9720afce21632372431606ebec4129eb79734 | [
"Xnet",
"X11"
] | null | null | null | Algorithms_easy/0944. Delete Columns to Make Sorted.py | VinceW0/Leetcode_Python_solutions | 09e9720afce21632372431606ebec4129eb79734 | [
"Xnet",
"X11"
] | null | null | null | """
0944. Delete Columns to Make Sorted
Easy
We are given an array A of N lowercase letter strings, all of the same length.
Now, we may choose any set of deletion indices, and for each string, we delete all the characters in those indices.
For example, if we have an array A = ["abcdef","uvwxyz"] and deletion indices {0, 2, 3}, then the final array after deletions is ["bef", "vyz"], and the remaining columns of A are ["b","v"], ["e","y"], and ["f","z"]. (Formally, the c-th column is [A[0][c], A[1][c], ..., A[A.length-1][c]]).
Suppose we chose a set of deletion indices D such that after deletions, each remaining column in A is in non-decreasing sorted order.
Return the minimum possible value of D.length.
Example 1:
Input: A = ["cba","daf","ghi"]
Output: 1
Explanation:
After choosing D = {1}, each column ["c","d","g"] and ["a","f","i"] are in non-decreasing sorted order.
If we chose D = {}, then a column ["b","a","h"] would not be in non-decreasing sorted order.
Example 2:
Input: A = ["a","b"]
Output: 0
Explanation: D = {}
Example 3:
Input: A = ["zyx","wvu","tsr"]
Output: 3
Explanation: D = {0, 1, 2}
Constraints:
1 <= A.length <= 100
1 <= A[i].length <= 1000
"""
class Solution:
def minDeletionSize(self, A: List[str]) -> int:
return sum(list(col) != sorted(col) for col in zip(*A))
| 27.75 | 291 | 0.65015 | 233 | 1,332 | 3.716738 | 0.450644 | 0.051963 | 0.051963 | 0.072748 | 0.090069 | 0 | 0 | 0 | 0 | 0 | 0 | 0.026777 | 0.186937 | 1,332 | 47 | 292 | 28.340426 | 0.772853 | 0.891141 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0.333333 | false | 0 | 0 | 0.333333 | 1 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 3 |
c5262761e5f3b58f60b2ce79889d60ea4397312d | 64 | py | Python | wrds2postgres/__init__.py | seanxwzhang/wrds2pg | 770bf6df20e3fbec8101f0dfc3da69a512683b0f | [
"MIT"
] | null | null | null | wrds2postgres/__init__.py | seanxwzhang/wrds2pg | 770bf6df20e3fbec8101f0dfc3da69a512683b0f | [
"MIT"
] | null | null | null | wrds2postgres/__init__.py | seanxwzhang/wrds2pg | 770bf6df20e3fbec8101f0dfc3da69a512683b0f | [
"MIT"
] | null | null | null | name = "wrds2postgres"
from wrds2postgres import wrds2postgres
| 16 | 39 | 0.828125 | 6 | 64 | 8.833333 | 0.666667 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.053571 | 0.125 | 64 | 3 | 40 | 21.333333 | 0.892857 | 0 | 0 | 0 | 0 | 0 | 0.203125 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | false | 0 | 0.5 | 0 | 0.5 | 0 | 1 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 3 |
c52700780a73e91f57ce0e59ed0294f200ccc532 | 158 | py | Python | Python/parking.py | mimseyedi/Kattis | a99ea2112544e89cc466feb7d81ffe6eb017f7e2 | [
"MIT"
] | null | null | null | Python/parking.py | mimseyedi/Kattis | a99ea2112544e89cc466feb7d81ffe6eb017f7e2 | [
"MIT"
] | null | null | null | Python/parking.py | mimseyedi/Kattis | a99ea2112544e89cc466feb7d81ffe6eb017f7e2 | [
"MIT"
] | null | null | null | n = int(input())
for _ in range(n):
store = int(input())
loc = list(map(int, input().split()))
dist = 2 * (max(loc) - min(loc))
print(dist)
| 17.555556 | 41 | 0.525316 | 24 | 158 | 3.416667 | 0.666667 | 0.292683 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.008475 | 0.253165 | 158 | 8 | 42 | 19.75 | 0.686441 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | false | 0 | 0 | 0 | 0 | 0.166667 | 1 | 0 | 0 | null | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 3 |
c53c4261965061481b197c48ebf397cf3c40f347 | 551 | py | Python | core/test/rule/naming_task.py | bogonets/answer | 57f892a9841980bcbc35fa1e27521b34cd94bc25 | [
"MIT"
] | 3 | 2021-06-20T02:24:10.000Z | 2022-01-26T23:55:33.000Z | core/test/rule/naming_task.py | bogonets/answer | 57f892a9841980bcbc35fa1e27521b34cd94bc25 | [
"MIT"
] | null | null | null | core/test/rule/naming_task.py | bogonets/answer | 57f892a9841980bcbc35fa1e27521b34cd94bc25 | [
"MIT"
] | null | null | null | # -*- coding: utf-8 -*-
from unittest import TestCase, main
from recc.rule.naming_task import naming_task, naming_task_volume, naming_task_network
class NamingTaskTestCase(TestCase):
def test_naming_task(self):
self.assertEqual("answer.container", naming_task("", "", ""))
def test_naming_task_volume(self):
self.assertEqual("answer.volume", naming_task_volume("", ""))
def test_naming_task_network(self):
self.assertEqual("answer.network", naming_task_network("", ""))
if __name__ == "__main__":
main()
| 27.55 | 86 | 0.702359 | 66 | 551 | 5.454545 | 0.363636 | 0.277778 | 0.133333 | 0.141667 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.002151 | 0.15608 | 551 | 19 | 87 | 29 | 0.772043 | 0.038113 | 0 | 0 | 0 | 0 | 0.096591 | 0 | 0 | 0 | 0 | 0 | 0.272727 | 1 | 0.272727 | false | 0 | 0.181818 | 0 | 0.545455 | 0 | 0 | 0 | 0 | null | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 3 |
c53c66e62fc2199bd0a8605bbf2edf22ea4026c6 | 152 | py | Python | locale/pot/api/utilities/_autosummary/pyvista-lines_from_points-1.py | tkoyama010/pyvista-doc-translations | 23bb813387b7f8bfe17e86c2244d5dd2243990db | [
"MIT"
] | 4 | 2020-08-07T08:19:19.000Z | 2020-12-04T09:51:11.000Z | locale/pot/api/utilities/_autosummary/pyvista-lines_from_points-1.py | tkoyama010/pyvista-doc-translations | 23bb813387b7f8bfe17e86c2244d5dd2243990db | [
"MIT"
] | 19 | 2020-08-06T00:24:30.000Z | 2022-03-30T19:22:24.000Z | locale/pot/api/core/helpers-3.py | tkoyama010/pyvista-doc-translations | 23bb813387b7f8bfe17e86c2244d5dd2243990db | [
"MIT"
] | 1 | 2021-03-09T07:50:40.000Z | 2021-03-09T07:50:40.000Z | import numpy as np
import pyvista
points = np.array([[0, 0, 0], [1, 0, 0], [1, 1, 0]])
poly = pyvista.lines_from_points(points)
poly.plot(line_width=5)
| 25.333333 | 52 | 0.677632 | 29 | 152 | 3.448276 | 0.551724 | 0.06 | 0.06 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.076336 | 0.138158 | 152 | 5 | 53 | 30.4 | 0.687023 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | false | 0 | 0.4 | 0 | 0.4 | 0 | 1 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 3 |
c5416b818e6ca2407ccaa04271bad3a8b35151a4 | 971 | py | Python | utils/checks.py | Proxymiity/Alter | 9e31a5f89f5b7ad464255399de9312691c895635 | [
"Apache-2.0"
] | 3 | 2020-11-18T11:33:27.000Z | 2020-12-24T01:51:03.000Z | utils/checks.py | Proxymiity/Alter | 9e31a5f89f5b7ad464255399de9312691c895635 | [
"Apache-2.0"
] | 5 | 2021-02-16T13:00:22.000Z | 2021-03-05T23:42:12.000Z | utils/checks.py | Proxymiity/Alter | 9e31a5f89f5b7ad464255399de9312691c895635 | [
"Apache-2.0"
] | null | null | null | import discord
from discord.ext import commands
from utils.dataIO import dataIO
bot_owner_id = dataIO.load_json("data/config.json")["owner"]
def bot_owner():
return commands.check(bot_owner_raw)
def bot_owner_raw(ctx):
return ctx.message.author.id == bot_owner_id
def server_owner():
return commands.check(server_owner_raw)
def server_owner_raw(ctx):
if ctx.message.guild is None:
return False
guild = ctx.message.guild
if ctx.message.author.id == guild.owner_id:
return True
else:
return False
def permissions(**perms):
def check(ctx):
return server_perms(ctx, discord.Permissions(**perms))
return commands.check(check)
def server_perms(ctx, perms):
if bot_owner_raw(ctx):
return True
elif server_owner_raw(ctx):
return True
elif not perms:
return False
resolved = ctx.message.channel.permissions_for(ctx.message.author)
return perms <= resolved
| 22.068182 | 70 | 0.697219 | 136 | 971 | 4.808824 | 0.264706 | 0.073395 | 0.067278 | 0.077982 | 0.111621 | 0.076453 | 0 | 0 | 0 | 0 | 0 | 0 | 0.209063 | 971 | 43 | 71 | 22.581395 | 0.851563 | 0 | 0 | 0.193548 | 0 | 0 | 0.021627 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0.225806 | false | 0 | 0.096774 | 0.129032 | 0.709677 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 1 | 1 | 0 | 0 | 3 |
c549d98d89c1141a2a21b7216219b1baeff55407 | 1,490 | py | Python | addon/import_vcap/amulet_nbt/amulet_nbt_py/nbt_types/string.py | Sam54123/mc-world-export | b82632d76f012121de389276880ddb2eed9e6661 | [
"MIT"
] | 2 | 2021-11-30T20:59:36.000Z | 2022-03-09T18:43:23.000Z | addon/import_vcap/amulet_nbt/amulet_nbt_py/nbt_types/string.py | Sam54123/mc-world-export | b82632d76f012121de389276880ddb2eed9e6661 | [
"MIT"
] | 2 | 2021-11-04T03:45:42.000Z | 2022-03-27T00:23:16.000Z | addon/import_vcap/amulet_nbt/amulet_nbt_py/nbt_types/string.py | Sam54123/mc-world-export | b82632d76f012121de389276880ddb2eed9e6661 | [
"MIT"
] | null | null | null | from __future__ import annotations
from typing import ClassVar, BinaryIO
from ..const import SNBTType
from .value import BaseImmutableTag
class TAG_String(BaseImmutableTag):
tag_id: ClassVar[int] = 8
_value: str
_data_type: ClassVar = str
@classmethod
def load_from(cls, context: BinaryIO, little_endian: bool) -> TAG_String:
return cls(cls.load_string(context, little_endian))
def write_value(self, buffer: BinaryIO, little_endian=False):
self.write_string(buffer, self._value, little_endian)
def _to_snbt(self) -> SNBTType:
return f'"{self.escape(self._value)}"'
@staticmethod
def escape(string: str):
return string.replace("\\", "\\\\").replace('"', '\\"')
@staticmethod
def unescape(string: str):
return string.replace('\\"', '"').replace("\\\\", "\\")
def __len__(self) -> int:
return len(self._value)
def __getitem__(self, item):
return self._value.__getitem__(item)
def __add__(self, other):
return self._value + self.get_primitive(other)
def __radd__(self, other):
return self.get_primitive(other) + self._value
def __iadd__(self, other):
return self.__class__(self + other)
def __mul__(self, other):
return self._value * self.get_primitive(other)
def __rmul__(self, other):
return self.get_primitive(other) * self._value
def __imul__(self, other):
return self.__class__(self * other)
| 27.090909 | 77 | 0.658389 | 177 | 1,490 | 5.112994 | 0.293785 | 0.079558 | 0.099448 | 0.125967 | 0.362431 | 0.362431 | 0.285083 | 0.212155 | 0.212155 | 0.212155 | 0 | 0.000855 | 0.215436 | 1,490 | 54 | 78 | 27.592593 | 0.773311 | 0 | 0 | 0.054054 | 0 | 0 | 0.032215 | 0.018792 | 0 | 0 | 0 | 0 | 0 | 1 | 0.351351 | false | 0 | 0.108108 | 0.324324 | 0.891892 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 1 | 1 | 0 | 0 | 3 |
c556fa6151eae3eacaa2d66a7bfce7ab898ba4d3 | 472 | py | Python | checkov/terraform/checks/data/aws/IAMCredentialsExposure.py | antonblr/checkov | 9415c6593c537945c08f7a19f28bdd8b96966f67 | [
"Apache-2.0"
] | 3 | 2021-04-19T17:17:21.000Z | 2021-09-06T06:31:09.000Z | checkov/terraform/checks/data/aws/IAMCredentialsExposure.py | antonblr/checkov | 9415c6593c537945c08f7a19f28bdd8b96966f67 | [
"Apache-2.0"
] | 16 | 2021-03-09T07:38:38.000Z | 2021-06-09T03:53:55.000Z | checkov/terraform/checks/data/aws/IAMCredentialsExposure.py | antonblr/checkov | 9415c6593c537945c08f7a19f28bdd8b96966f67 | [
"Apache-2.0"
] | 1 | 2022-01-06T08:04:56.000Z | 2022-01-06T08:04:56.000Z | from checkov.terraform.checks.data.BaseCloudsplainingIAMCheck import BaseCloudsplainingIAMCheck
class CloudSplainingCredentialsExposure(BaseCloudsplainingIAMCheck):
def __init__(self):
name = "Ensure IAM policies does not allow credentials exposure"
id = "CKV_AWS_107"
super().__init__(name=name, id=id)
def cloudsplaining_analysis(self, policy):
return policy.credentials_exposure
check = CloudSplainingCredentialsExposure()
| 29.5 | 95 | 0.769068 | 44 | 472 | 7.977273 | 0.704545 | 0.108262 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.007595 | 0.163136 | 472 | 15 | 96 | 31.466667 | 0.881013 | 0 | 0 | 0 | 0 | 0 | 0.139831 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0.222222 | false | 0 | 0.111111 | 0.111111 | 0.555556 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 1 | 1 | 0 | 0 | 3 |
c55e0eda4e3e64446f0960b972543e6cf72327d9 | 102 | py | Python | Python Fundamentals/8. Text Processing/Exercise/05. Emoticon Finder.py | a-shiro/SoftUni-Courses | 7d0ca6401017a28b5ff7e7fa3e5df8bba8ddbe77 | [
"MIT"
] | null | null | null | Python Fundamentals/8. Text Processing/Exercise/05. Emoticon Finder.py | a-shiro/SoftUni-Courses | 7d0ca6401017a28b5ff7e7fa3e5df8bba8ddbe77 | [
"MIT"
] | null | null | null | Python Fundamentals/8. Text Processing/Exercise/05. Emoticon Finder.py | a-shiro/SoftUni-Courses | 7d0ca6401017a28b5ff7e7fa3e5df8bba8ddbe77 | [
"MIT"
] | null | null | null | text = input()
for i in range(len(text)):
if text[i] == ":":
print(text[i] + text[i + 1]) | 20.4 | 36 | 0.490196 | 17 | 102 | 2.941176 | 0.588235 | 0.3 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.013514 | 0.27451 | 102 | 5 | 36 | 20.4 | 0.662162 | 0 | 0 | 0 | 0 | 0 | 0.009709 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | false | 0 | 0 | 0 | 0 | 0.25 | 1 | 0 | 0 | null | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 3 |
3d7be59d3294497e1fe93058b832653c3e9260cd | 433 | py | Python | Inventario/cmp/migrations/0029_auto_20190926_2044.py | SYS0xWord/InventarioDjango_Udemy | 073b3edbf3495289251d3edf4d0437a813f5cfb4 | [
"MIT"
] | null | null | null | Inventario/cmp/migrations/0029_auto_20190926_2044.py | SYS0xWord/InventarioDjango_Udemy | 073b3edbf3495289251d3edf4d0437a813f5cfb4 | [
"MIT"
] | null | null | null | Inventario/cmp/migrations/0029_auto_20190926_2044.py | SYS0xWord/InventarioDjango_Udemy | 073b3edbf3495289251d3edf4d0437a813f5cfb4 | [
"MIT"
] | null | null | null | # Generated by Django 2.2.2 on 2019-09-27 02:44
from django.db import migrations
class Migration(migrations.Migration):
dependencies = [
('cmp', '0028_registro_lote'),
]
operations = [
migrations.RemoveField(
model_name='lote',
name='loteventa',
),
migrations.RemoveField(
model_name='registro_lote',
name='loteventa',
),
]
| 19.681818 | 47 | 0.561201 | 42 | 433 | 5.666667 | 0.619048 | 0.016807 | 0.218487 | 0.252101 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.065068 | 0.325635 | 433 | 21 | 48 | 20.619048 | 0.75 | 0.103926 | 0 | 0.4 | 1 | 0 | 0.145078 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | false | 0 | 0.066667 | 0 | 0.266667 | 0 | 0 | 0 | 0 | null | 0 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 3 |
3da9bd10811a51cfe8654f9d77c60db67a78ca6f | 23 | py | Python | Lib/idlelib/idlever.py | deadsnakes/python2.3 | 0b4a6871ca57123c10aa48cc2a5d2b7c0ee3c849 | [
"PSF-2.0"
] | null | null | null | Lib/idlelib/idlever.py | deadsnakes/python2.3 | 0b4a6871ca57123c10aa48cc2a5d2b7c0ee3c849 | [
"PSF-2.0"
] | null | null | null | Lib/idlelib/idlever.py | deadsnakes/python2.3 | 0b4a6871ca57123c10aa48cc2a5d2b7c0ee3c849 | [
"PSF-2.0"
] | null | null | null | IDLE_VERSION = "1.0.7"
| 11.5 | 22 | 0.652174 | 5 | 23 | 2.8 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.15 | 0.130435 | 23 | 1 | 23 | 23 | 0.55 | 0 | 0 | 0 | 0 | 0 | 0.217391 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | false | 0 | 0 | 0 | 0 | 0 | 1 | 1 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 3 |
3dbb319a62a4ca69b922a3261d5a6e7a46d792fd | 687 | py | Python | 20170111.py | JaeGyu/PythonEx_1 | e67053db6ca7431c3dd66351c190c53229e3f141 | [
"MIT"
] | null | null | null | 20170111.py | JaeGyu/PythonEx_1 | e67053db6ca7431c3dd66351c190c53229e3f141 | [
"MIT"
] | null | null | null | 20170111.py | JaeGyu/PythonEx_1 | e67053db6ca7431c3dd66351c190c53229e3f141 | [
"MIT"
] | null | null | null |
import re
p = re.compile("[a-z]")
m = p.match("zaa")
m2 = p.match("4")
print(m)
print(m2)
if m:
print("매치 됨 : ", m.group())
else:
print("매치 안됨")
p = re.compile("[abc]")
print(p.match("a"))
print(p.match("before"))
print(p.match("dude"))
print("-" * 60)
p = re.compile("[1-3]")
print(p.match("aa"))
print(p.match("345"))
print("-" * 60)
p = re.compile("[^0-9]")
print(p.match("123"))
print(p.match("for"))
print("-"*60)
p=re.compile("[\d]") #숫자
print(p.match("123"))
print("-"*60)
p = re.compile("[\D]") #숫자가 아닌거
print(p.match("123"))
print("-"*60)
p = re.compile("a.b")
print(p.match("a123b"))
print("-"*60)
p = re.compile("a.b")
print(p.match("asbc"))
print("-"*60)
| 14.3125 | 31 | 0.55313 | 123 | 687 | 3.089431 | 0.300813 | 0.205263 | 0.318421 | 0.157895 | 0.465789 | 0.326316 | 0.276316 | 0.276316 | 0.276316 | 0.157895 | 0 | 0.06 | 0.126638 | 687 | 47 | 32 | 14.617021 | 0.573333 | 0.0131 | 0 | 0.342857 | 0 | 0 | 0.14095 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | false | 0 | 0.028571 | 0 | 0.028571 | 0.628571 | 0 | 0 | 0 | null | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 3 |
3dc509f4fba3a63a57bd67d52b225b53eff175b5 | 1,123 | py | Python | glhe/profiles/constant_temp.py | stianchris/GLHE | 80c3eecca81ffd50d5077f87027c9441292452f5 | [
"MIT"
] | 2 | 2018-11-06T08:04:04.000Z | 2020-10-09T14:52:36.000Z | glhe/profiles/constant_temp.py | stianchris/GLHE | 80c3eecca81ffd50d5077f87027c9441292452f5 | [
"MIT"
] | 68 | 2018-03-27T01:43:22.000Z | 2019-09-09T12:05:44.000Z | glhe/profiles/constant_temp.py | mitchute/GLHE | 80c3eecca81ffd50d5077f87027c9441292452f5 | [
"MIT"
] | 4 | 2018-05-24T03:02:44.000Z | 2021-08-16T13:54:09.000Z | from glhe.input_processor.component_types import ComponentTypes
from glhe.input_processor.input_processor import InputProcessor
from glhe.interface.entry import SimulationEntryPoint
from glhe.interface.response import SimulationResponse
from glhe.output_processor.output_processor import OutputProcessor
from glhe.output_processor.report_types import ReportTypes
class ConstantTemp(SimulationEntryPoint):
Type = ComponentTypes.ConstantTemp
def __init__(self, inputs: dict, ip: InputProcessor, op: OutputProcessor):
SimulationEntryPoint.__init__(self, inputs)
self.temperature = inputs['value']
self.ip = ip
self.op = op
self.inlet_temperature = ip.init_temp()
def simulate_time_step(self, inputs: SimulationResponse):
return SimulationResponse(inputs.time, inputs.time_step, inputs.flow_rate, self.temperature)
def report_outputs(self):
return {'{:s}:{:s}:{:s}'.format(self.Type, self.name, ReportTypes.InletTemp): self.inlet_temperature,
'{:s}:{:s}:{:s}'.format(self.Type, self.name, ReportTypes.OutletTemp): self.temperature}
| 41.592593 | 109 | 0.753339 | 128 | 1,123 | 6.421875 | 0.34375 | 0.058394 | 0.03163 | 0.053528 | 0.087591 | 0.087591 | 0.087591 | 0.087591 | 0.087591 | 0 | 0 | 0 | 0.147818 | 1,123 | 26 | 110 | 43.192308 | 0.858934 | 0 | 0 | 0 | 0 | 0 | 0.029386 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0.157895 | false | 0 | 0.315789 | 0.105263 | 0.684211 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 1 | 1 | 0 | 0 | 3 |
3dd3eeed55c5c54386f3543577c68e3b155b1308 | 321 | py | Python | dit/utils/__init__.py | Ejjaffe/dit | c9d206f03d1de5a0a298b1d0ea9d79ea5e789ee1 | [
"BSD-3-Clause"
] | 1 | 2020-03-13T10:30:11.000Z | 2020-03-13T10:30:11.000Z | dit/utils/__init__.py | Ejjaffe/dit | c9d206f03d1de5a0a298b1d0ea9d79ea5e789ee1 | [
"BSD-3-Clause"
] | null | null | null | dit/utils/__init__.py | Ejjaffe/dit | c9d206f03d1de5a0a298b1d0ea9d79ea5e789ee1 | [
"BSD-3-Clause"
] | null | null | null | """
Module providing miscellaneous functionality.
"""
from .bindargs import bindcallargs
from .context import cd, named_tempfile, tempdir
from .misc import *
from .latexarray import to_latex as pmf_to_latex, to_pdf as pmf_to_pdf
from .logger import basic_logger
from .table import build_table
from .units import unitful
| 26.75 | 70 | 0.813084 | 47 | 321 | 5.361702 | 0.553191 | 0.055556 | 0.055556 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.130841 | 321 | 11 | 71 | 29.181818 | 0.903226 | 0.140187 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | true | 0 | 1 | 0 | 1 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 0 | 1 | 0 | 0 | 3 |
3ddded32bf52b633e3b0fc82177ada2e0b5635e2 | 27,727 | py | Python | plug-ins/_mpynode/_openmaya.py | mpynode/node-designer | ec620f1388337bc0a4e5664d80e1ea6ade8b410e | [
"Unlicense"
] | 63 | 2019-03-18T03:44:28.000Z | 2021-12-31T07:51:09.000Z | plug-ins/_mpynode/_openmaya.py | mpynode/node-designer | ec620f1388337bc0a4e5664d80e1ea6ade8b410e | [
"Unlicense"
] | 2 | 2019-03-19T10:39:02.000Z | 2020-02-25T06:39:12.000Z | plug-ins/_mpynode/_openmaya.py | mpynode/node-designer | ec620f1388337bc0a4e5664d80e1ea6ade8b410e | [
"Unlicense"
] | 19 | 2019-03-18T08:05:05.000Z | 2022-01-11T08:37:45.000Z | """
This module is for adding functionality to existing Maya API classes
"""
import maya.api.OpenMaya as om
class MAngle(om.MAngle):
"""
Override of the Maya API class of the same name to make
the data within the class 'picklable'
"""
def __init__(self, *args):
super(MAngle, self).__init__(*args)
def __reduce__(self):
return (MAngle, (self.asRadians(), MAngle.kRadians))
def __reduce_ex__(self, protocol):
return self.__reduce__()
def __repr__(self):
return super(MAngle, self).__repr__().replace("maya.api.OpenMaya.", "MPyNode.")
class MColor(om.MColor):
"""
Override of the Maya API class of the same name to make
the data within the class 'picklable'
"""
def __init__(self, clr=None, model=om.MColor.kRGB, dataType=om.MColor.kFloat):
self._model = model
self._data_type = dataType
if clr:
super(MColor, self).__init__(clr, model=model, dataType=dataType)
else:
super(MColor, self).__init__()
def __reduce__(self):
return (MColor, ((self.r, self.g, self.b, self.a), self._model, self._data_type))
def __reduce_ex__(self, protocol):
return self.__reduce__()
def __add__(self, y):
return MColor(super(MColor, self).__add__(y))
def __radd__(self, y):
return MColor(super(MColor, self).__radd__(y))
def __iadd__(self, y):
return MColor(super(MColor, self).__iadd__(y))
def __mul__(self, y):
return MColor(super(MColor, self).__mul__(y))
def __rmul__(self, y):
return MColor(super(MColor, self).__rmul__(y))
def __imul__(self, y):
return MColor(super(MColor, self).__imul__(y))
def __div__(self, y):
return MColor(super(MVector, self).__div__(y))
def __idiv__(self, y):
return MColor(super(MVector, self).__idiv__(y))
def __rdiv__(self, y):
return MColor(super(MVector, self).__rdiv__(y))
def __str__(self):
return str((self.r, self.g, self.b, self.a))
def __repr__(self):
return super(MColor, self).__repr__().replace("maya.api.OpenMaya.", "MPyNode.")
def setColor(self, clr, model=om.MColor.kRGB, dataType=om.MColor.kFloat):
"""
Override of the base class version to add functionality
"""
self._model = model
self._data_type = dataType
super(MColor, self).setColor(clr, model=model, dataType=dataType)
def getColorModel(self):
"""
Returns the color model used by this MColor object
*RETURNS* *int* representing the color model type such as MColor.kRGB
"""
return self._model
def getDataType(self):
"""
Returns the color data type used by this MColor object
*RETURNS* *int* representing the color data type such as MColor.kFloat
"""
return self._data_type
class MColorArray(om.MColorArray):
"""
Override of the Maya API class of the same name to make
the data within the class 'picklable'
"""
def __init__(self, *args):
super(MColorArray, self).__init__(*args)
def __reduce__(self):
return (MColorArray, (tuple([tuple(clr) for clr in self]),))
def __reduce_ex__(self, protocol):
return self.__reduce__()
def __getitem__(self, i):
return MColor(super(MColorArray, self).__getitem__(i))
def __getslice__(self, i, j):
return MColorArray(super(MVectorArray, self).__getslice__(i, j))
def __repr__(self):
return super(MColorArray, self).__repr__().replace("maya.api.OpenMaya.", "MPyNode.")
def __str__(self):
return super(MColorArray, self).__str__().replace("maya.api.OpenMaya.", "MPyNode.")
class MEulerRotation(om.MEulerRotation):
"""
Override of the Maya API class of the same name to make
the data within the class 'picklable'
"""
def __init__(self, *args, **kargs):
super(MEulerRotation, self).__init__(*args, **kargs)
def __reduce__(self):
return (MEulerRotation, (self.x, self.y, self.z, self.order))
def __reduce_ex__(self, protocol):
return self.__reduce__()
def __str__(self):
return str((self.x, self.y, self.z))
def __repr__(self):
return super(MEulerRotation, self).__repr__().replace("maya.api.OpenMaya.", "MPyNode.")
def __add__(self, y):
return MEulerRotation(super(MEulerRotation, self).__add__(y))
def __radd__(self, y):
return MEulerRotation(super(MEulerRotation, self).__radd__(y))
def __iadd__(self, y):
return MEulerRotation(super(MEulerRotation, self).__iadd__(y))
def __sub__(self, y):
return MEulerRotation(super(MEulerRotation, self).__sub__(y))
def __rsub__(self, y):
return MEulerRotation(super(MEulerRotation, self).__rsub__(y))
def __isub__(self, y):
return MEulerRotation(super(MEulerRotation, self).__isub__(y))
def __mul__(self, y):
return MEulerRotation(super(MEulerRotation, self).__mul__(y))
def __rmul__(self, y):
return MEulerRotation(super(MEulerRotation, self).__rmul__(y))
def __imul__(self, y):
return MEulerRotation(super(MEulerRotation, self).__imul__(y))
def alternateSolution(self):
return MEulerRotation(super(MEulerRotation, self).alternateSolution())
def asMatrix(self):
return MMatrix(super(MEulerRotation, self).asMatrix())
def asQuaternion(self):
return MQuaternion(super(MEulerRotation, self).asQuaternion())
def asVector(self):
return MVector(super(MEulerRotation, self).asVector())
def bound(self):
return MEulerRotation(super(MEulerRotation, self).bound())
def closestCut(self, target):
return MEulerRotation(super(MEulerRotation, self).closestCut(target))
def closestSolution(self, target):
return MEulerRotation(super(MEulerRotation, self).closestSolution(target))
@staticmethod
def computeAlternateSolution(rot):
return MEulerRotation(om.MEulerRotation.computeAlternateSolution(rot))
@staticmethod
def computeBound(rot):
return MEulerRotation(om.MEulerRotation.computeBound(rot))
@staticmethod
def computeClosestCut(src, target):
return MEulerRotation(om.MEulerRotation.computeClosestCut(src, target))
@staticmethod
def computeClosestSolution(src, target):
return MEulerRotation(om.MEulerRotation.computeClosestSolution(src, target))
@staticmethod
def decompose(matrix, order):
return MEulerRotation(om.MEulerRotation.decompose(matrix, order))
def inverse(self):
return MEulerRotation(super(MEulerRotation, self).inverse())
def reorder(self, order):
return MEulerRotation(super(MEulerRotation, self).reorder(order))
class MFnMesh(om.MFnMesh):
"""
Override of the Maya API class of the same name to make
the data within the class 'picklable'
"""
def __init__(self, *args, **kargs):
super(MFnMesh, self).__init__(*args, **kargs)
def getPoints(self, space=om.MSpace.kObject):
return MPointArray(super(MFnMesh, self).getPoints(space))
class MFnNurbsCurve(om.MFnNurbsCurve):
"""
Override of the Maya API class of the same name to make
the data within the class 'picklable'
"""
def __init__(self, *args, **kargs):
super(MFnNurbsCurve, self).__init__(*args, **kargs)
def closestPoint(self, test_pnt, guess=None, tolerance=om.MFnNurbsCurve.kPointTolerance, space=om.MSpace.kObject):
pnt, param = super(MFnNurbsCurve, self).closestPoint(test_pnt, guess, tolerance, space)
return MPoint(pnt), param
def cvPosition(self, index, space=om.MSpace.kObject):
return MPoint(super(MFnNurbsCurve, self).cvPosition(index, space))
def cvPositions(self, space=om.MSpace.kObject):
return MPointArray(super(MFnNurbsCurve, self).cvPositions(space))
def getDerivativesAtParam(self, param, space=om.MSpace.kObject, dUU=False):
result = super(MFnNurbsCurve, self).getDerivativesAtParam(param, space=space, dUU=dUU)
if len(result) < 3:
return MPoint(result[0]), MVector(result[1])
else:
return MPoint(result[0]), MVector(result[1]), MVector(result[2])
def getPointAtParam(self, param, space=om.MSpace.kObject):
return MPoint(super(MFnNurbsCurve, self).getPointAtParam(param, space))
def normal(self, param, space=om.MSpace.kObject):
return MVector(super(MFnNurbsCurve, self).normal(param, space))
def tangent(self, param, space=om.MSpace.kObject):
return MVector(super(MFnNurbsCurve, self).tangent(param, space))
class MMatrix(om.MMatrix):
"""
Override of the Maya API class of the same name to make
the data within the class 'picklable'
"""
def __init__(self, *args):
super(MMatrix, self).__init__(*args)
def __reduce__(self):
return (MMatrix, (tuple(self),))
def __reduce_ex__(self, protocol):
return self.__reduce__()
def __add__(self, y):
return MMatrix(super(MMatrix, self).__add__(y))
def __radd__(self, y):
return MMatrix(super(MMatrix, self).__radd__(y))
def __iadd__(self, y):
return MMatrix(super(MMatrix, self).__iadd__(y))
def __sub__(self, y):
return MMatrix(super(MMatrix, self).__sub__(y))
def __rsub__(self, y):
return MMatrix(super(MMatrix, self).__rsub__(y))
def __isub__(self, y):
return MMatrix(super(MMatrix, self).__isub__(y))
def __mul__(self, y):
result = super(MMatrix, self).__mul__(y)
if result is NotImplemented:
return NotImplemented
return MMatrix(result)
def __rmul__(self, y):
return MMatrix(super(MMatrix, self).__rmul__(y))
def __imul__(self, y):
return MMatrix(super(MMatrix, self).__imul__(y))
def adjoint(self):
"""
Returns a new matrix containing this matrix's adjoint.
"""
return MMatrix(super(MMatrix, self).adjoint())
def homogenize(self):
"""
Returns a new matrix containing the homogenized version of this matrix.
"""
return MMatrix(super(MMatrix, self).homogenize())
def inverse(self):
"""
Returns a new matrix containing this matrix's inverse.
"""
return MMatrix(super(MMatrix, self).inverse())
def transpose(self):
"""
Returns a new matrix containing this matrix's inverse.
"""
return MMatrix(super(MMatrix, self).transpose())
class MMatrixArray(om.MMatrixArray):
"""
Override of the Maya API class of the same name to make
the data within the class 'picklable'
"""
def __init__(self, *args):
super(MMatrixArray, self).__init__(*args)
def __reduce__(self):
return (MMatrixArray, (tuple([tuple(mat) for mat in self]),))
def __reduce_ex__(self, protocol):
return self.__reduce__()
def __getitem__(self, i):
return MMatrix(super(MMatrixArray, self).__getitem__(i))
def __getslice__(self, i, j):
return MMatrixArray(super(MMatrixArray, self).__getslice__(i, j))
def __repr__(self):
return super(MMatrixArray, self).__repr__().replace("maya.api.OpenMaya.", "MPyNode.")
def __str__(self):
return super(MMatrixArray, self).__str__().replace("maya.api.OpenMaya.", "MPyNode.")
class MPoint(om.MPoint):
"""
Override of the Maya API class of the same name to make
the data within the class 'picklable'
"""
def __init__(self, *args):
super(MPoint, self).__init__(*args)
def __reduce__(self):
return (MPoint, (self.x, self.y, self.z, self.w))
def __reduce_ex__(self, protocol):
return self.__reduce__()
def __add__(self, y):
return MPoint(super(MPoint, self).__add__(y))
def __radd__(self, y):
return MPoint(super(MPoint, self).__radd__(y))
def __iadd__(self, y):
return MPoint(super(MPoint, self).__iadd__(y))
def __mul__(self, y):
return MPoint(super(MPoint, self).__mul__(y))
def __rmul__(self, y):
return MPoint(super(MPoint, self).__rmul__(y))
def __imul__(self, y):
return MPoint(super(MPoint, self).__imul__(y))
def __div__(self, y):
return MPoint(super(MPoint, self).__div__(y))
def __rdiv__(self, y):
return MPoint(super(MPoint, self).__rdiv__(y))
def __sub__(self, y):
diff = super(MPoint, self).__sub__(y)
if type(diff) == om.MVector:
return MVector(diff)
return MPoint(diff)
def __rsub__(self, y):
diff = super(MPoint, self).__rsub__(y)
if type(diff) == om.MVector:
return MVector(diff)
return MPoint(diff)
def __isub__(self, y):
"""
In-place subtract
"""
return MPoint(super(MPoint, self).__isub__(y))
def __str__(self):
return str((self.x, self.y, self.z, self.w))
def __repr__(self):
return super(MPoint, self).__repr__().replace("maya.api.OpenMaya.", "MPyNode.")
class MPointArray(om.MPointArray):
"""
Override of the Maya API class of the same name to make
the data within the class 'picklable'
"""
def __init__(self, *args):
super(MPointArray, self).__init__(*args)
def __reduce__(self):
return (MPointArray, (tuple([tuple(pnt) for pnt in self]),))
def __reduce_ex__(self, protocol):
return self.__reduce__()
def __add__(self, y):
##---some reason calling __add__ directly on the parent class causes a crash (WTF?)----##
return MPointArray(om.MPointArray(self) + y)
def __iadd__(self, y):
return MPointArray(super(MVectorArray, self).__iadd__(y))
def __getitem__(self, i):
return MPoint(super(MPointArray, self).__getitem__(i))
def __getslice__(self, i, j):
return MPointArray(super(MPointArray, self).__getslice__(i, j))
def __repr__(self):
return super(MPointArray, self).__repr__().replace("maya.api.OpenMaya.", "MPyNode.")
def __str__(self):
return super(MPointArray, self).__str__().replace("maya.api.OpenMaya.", "MPyNode.")
class MQuaternion(om.MQuaternion):
"""
Override of the Maya API class of the same name to make
the data within the class 'picklable'
"""
def __init__(self, *args):
super(MQuaternion, self).__init__(*args)
def __reduce__(self):
return (MQuaternion, (self.x, self.y, self.z, self.w))
def __reduce_ex__(self, protocol):
return self.__reduce__()
def __str__(self):
return str((self.x, self.y, self.z, self.w))
def __repr__(self):
return super(MQuaternion, self).__repr__().replace("maya.api.OpenMaya.", "MPyNode.")
def __add__(self, y):
return MQuaternion(super(MQuaternion, self).__add__(y))
def __radd__(self, y):
return MQuaternion(super(MQuaternion, self).__radd__(y))
def __sub__(self, y):
return MQuaternion(super(MQuaternion, self).__sub__(y))
def __rsub__(self, y):
return MQuaternion(super(MQuaternion, self).__rsub__(y))
def __mul__(self, y):
return MQuaternion(super(MQuaternion, self).__mul__(y))
def __rmul__(self, y):
return MQuaternion(super(MQuaternion, self).__rmul__(y))
def __imul__(self, y):
return MQuaternion(super(MQuaternion, self).__imul__(y))
def __neg__(self):
"""
Component-by-component negation
"""
return MQuaternion(super(MQuaternion, self).__neg__())
def asAxisAngle(self):
"""
Returns the rotation as a tuple containing an axis vector and an angle in radians about that axis.
"""
vect, angle = super(MQuaternion, self).asAxisAngle()
return MVector(vect), angle
def asEulerRotation(self):
"""
Returns the rotation as an equivalent MEulerRotation.
"""
return MEulerRotation(super(MQuaternion, self).asEulerRotation())
def asMatrix(self):
"""
Returns the rotation as an equivalent rotation matrix.
"""
return MMatrix(super(MQuaternion, self).asMatrix())
def conjugate(self):
"""
Returns the conjugate of this quaternion (i.e. x, y and z components negated).
"""
return MQuaternion(super(MQuaternion, self).conjugate())
def exp(self):
"""
Returns a new quaternion containing the exponent of this one.
"""
return MQuaternion(super(MQuaternion, self).exp())
def inverse(self):
"""
Returns a new quaternion containing the inverse of this one.
"""
return MQuaternion(super(MQuaternion, self).inverse())
def log(self):
"""
Returns a new quaternion containing the log of this one.
"""
return MQuaternion(super(MQuaternion, self).log())
def normal(self):
"""
Returns a new quaternion containing the normalized version of this one (i.e. scaled to unit length).
"""
return MQuaternion(super(MQuaternion, self).normal())
@staticmethod
def slerp(p, q, t, spin):
"""
Spherical interpolation of unit quaternions. Returns a quaternion along the shortest path (in quaternion space)
between p and q, at interpolation value t. Thus a value of 0.0 will return p while a value of 1.0 will return q.
spin gives the number of complete rotations about the axis which must occur when going from p to q.
"""
return MQuaternion(om.MQuaternion.slerp(p, q, t, spin))
@staticmethod
def squad(p, a, b, q, t, spin=0):
"""
Interpolation along a cubic curve segment in quaternion space. Returns a quaternion along the cubic curve
segment which interpolates p and q, at interpolation value t. Thus a value of 0.0 will return p while a value
of 1.0 will return q. The curve is C1 continuous with a and b as intermediate points. spins gives the number of
complete rotations about the axis which must occur when going from p to q.
"""
return MQuaternion(om.MQuaternion.squad(p, a, b, q, t, spin))
@staticmethod
def squadPt(q0, q1, q2):
"""
Returns a new quaternion representing an intermediate point (in quaternion space) which when used with squad()
will produce a C1 continuous spline.
"""
return MQuaternion(om.MQuaternion.squadPt(q0, q1, q2))
class MTime(om.MTime):
"""
Override of the Maya API class of the same name to make
the data within the class 'picklable'
"""
def __init__(self, *args, **kargs):
super(MTime, self).__init__(*args, **kargs)
def __reduce__(self):
return (MTime, (self.value, self.unit))
def __reduce_ex__(self, protocol):
return self.__reduce__()
def __str__(self):
return super(MTime, self).__str__()
def __repr__(self):
return super(MTime, self).__repr__().replace("maya.api.OpenMaya.", "MPyNode.")
class MTimeArray(om.MTimeArray):
"""
Override of the Maya API class of the same name to make
the data within the class 'picklable'
"""
def __init__(self, *args):
in_times = None
if args:
##---init from anoth MtimeArray---##
if type(args[0]) in (list, tuple):
in_times = args[0]
args = ()
super(MTimeArray, self).__init__(*args)
if in_times:
for time_args in in_times:
if isinstance(time_args, MTime):
self.append(MTime(time_args))
else:
self.append(MTime(*time_args))
def __reduce__(self):
return (MTimeArray, (tuple([(time.value, time.unit) for time in self]),))
def __reduce_ex__(self, protocol):
return self.__reduce__()
def __getitem__(self, i):
return MTime(super(MTimeArray, self).__getitem__(i))
def __getslice__(self, i, j):
return MTimeArray(super(MTimeArray, self).__getslice__(i, j))
def __str__(self):
return super(MTimeArray, self).__str__().replace("maya.api.OpenMaya.", "MPyNode.")
def __repr__(self):
return super(MTimeArray, self).__repr__().replace("maya.api.OpenMaya.", "MPyNode.")
class MVector(om.MVector):
"""
Override of the Maya API class of the same name to make
the data within the class 'picklable'
"""
def __init__(self, *args):
super(MVector, self).__init__(*args)
def __reduce__(self):
return (MVector, (self.x, self.y, self.z))
def __reduce_ex__(self, protocol):
return self.__reduce__()
def __add__(self, y):
return MVector(super(MVector, self).__add__(y))
def __radd__(self, y):
return MVector(super(MVector, self).__radd__(y))
def __iadd__(self, y):
return MVector(super(MVector, self).__iadd__(y))
def __sub__(self, y):
return MVector(super(MVector, self).__sub__(y))
def __rsub__(self, y):
return MVector(super(MVector, self).__rsub__(y))
def __isub__(self, y):
"""
In-place subtract
"""
return MVector(super(MVector, self).__isub__(y))
def __xor__(self, y):
"""
Cross product
"""
return MVector(super(MVector, self).__xor__(y))
def __rxor__(self, y):
"""
Reverse cross product
"""
return MVector(super(MVector, self).__rxor__(y))
def __mul__(self, y):
result = super(MVector, self).__mul__(y)
if type(result) == om.MVector:
return MVector(result)
return result
def __rmul__(self, y):
result = super(MVector, self).__rmul__(y)
if type(result) == om.MVector:
return MVector(result)
return result
def __imul__(self, y):
result = super(MVector, self).__imul__(y)
if type(result) == om.MVector:
return MVector(result)
return result
def __div__(self, y):
return MVector(super(MVector, self).__div__(y))
def __idiv__(self, y):
return MVector(super(MVector, self).__idiv__(y))
def __rdiv__(self, y):
return MVector(super(MVector, self).__rdiv__(y))
def __neg__(self):
"""
New vector which is the negative if the given vector.
"""
return MVector(super(MVector, self).__neg__())
def __str__(self):
return str((self.x, self.y, self.z))
def __repr__(self):
return super(MVector, self).__repr__().replace("maya.api.OpenMaya.", "MPyNode.")
def normal(self):
"""
Returns a new vector containing the normalized version of this one.
**RETURNS** *MVector*
>>> new_v = v.normal()
"""
return MVector(super(MVector, self).normal())
def normalize(self):
"""
Normalizes this vector in-place and returns a new reference to it.
**RETURNS** *MVector*
>>> v.normalize()
"""
return MVector(super(MVector, self).normalize())
def rotateBy(self, *args):
"""
Returns the vector resulting from rotating this one by the given amount.
**args** single *MQuaternion* or *MEulerRotation*, or an axis identifier constant and a *float* angle
**RETURNS** *MVector*
>>> ##---rotate by MQuaternion or MEulerRotation---##
>>> new_v = v.rotateBy(rot)
>>>
>>> ##---rotate by angle radians about the specified axis---##
>>> new_v = v.rotateBy(MVector.kXaxis, 5.0)
"""
return MVector(super(MVector, self).rotateBy(*args))
def rotateTo(self, target):
"""
Returns the quaternion which will rotate this vector into another.
**target** *MVector*
**RETURNS** *MQuaternion*
>>> q = v.rotateTo(MVector(1.0, 0.0, 0.0))
"""
return MQuaternion(super(MVector, self).rotateTo(target))
def transformAsNormal(self, matrix):
"""
Returns a new vector which is calculated by postmultiplying this vector by the transpose of the given matrix's inverse and then normalizing the result.
**matrix** *MMatrix*
**RETURNS** *MVector*
>>> matrix = MMatrix()
>>> new_v = v.transformAsNormal(matrix)
"""
return MVector(super(MVector, self).transformAsNormal(matrix))
class MVectorArray(om.MVectorArray):
"""
Override of the Maya API class of the same name to make
the data within the class 'picklable'
The MVectorArray class provides an array of MVector elements using a common array interface and reference
semantics. See Working with M*Array Classes for more details.
Trying to override muliply functionality causes Maya to crash. If user multiplys this array by a int value
it will return base version of OpenMaya.MVectorArray as a result.
"""
def __init__(self, *args):
super(MVectorArray, self).__init__(*args)
def __reduce__(self):
return (MVectorArray, (tuple([tuple(vect) for vect in self]),))
def __reduce_ex__(self, protocol):
return self.__reduce__()
def __getitem__(self, i):
return MVector(super(MVectorArray, self).__getitem__(i))
def __getslice__(self, i, j):
return MVectorArray(super(MVectorArray, self).__getslice__(i, j))
def __repr__(self):
return super(MVectorArray, self).__repr__().replace("maya.api.OpenMaya.", "MPyNode.")
def __str__(self):
return super(MVectorArray, self).__str__().replace("maya.api.OpenMaya.", "MPyNode.")
def __add__(self, y):
##---some reason calling __add__ directly on the parent class causes a crash (WTF?)----##
return MVectorArray(om.MVectorArray(self) + y)
def __iadd__(self, y):
return MVectorArray(super(MVectorArray, self).__iadd__(y))
| 22.306516 | 160 | 0.598298 | 3,150 | 27,727 | 4.92254 | 0.095238 | 0.023539 | 0.03618 | 0.02412 | 0.66155 | 0.586483 | 0.555011 | 0.410422 | 0.296272 | 0.266091 | 0 | 0.001672 | 0.288203 | 27,727 | 1,242 | 161 | 22.324477 | 0.783999 | 0.190681 | 0 | 0.437788 | 0 | 0 | 0.022022 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0.423963 | false | 0 | 0.002304 | 0.304147 | 0.864055 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 1 | 1 | 0 | 0 | 3 |
3dfce6795d8180bb46d58bc36fe37426104cb5c5 | 551 | py | Python | bdi/result.py | keishinkickback/batch-document-inference | 3529810b02d596dd3cb76df2068682919f2f1857 | [
"MIT"
] | null | null | null | bdi/result.py | keishinkickback/batch-document-inference | 3529810b02d596dd3cb76df2068682919f2f1857 | [
"MIT"
] | null | null | null | bdi/result.py | keishinkickback/batch-document-inference | 3529810b02d596dd3cb76df2068682919f2f1857 | [
"MIT"
] | null | null | null | import abc
from typing import TypeVar
Result = TypeVar("Result")
class Result(metaclass=abc.ABCMeta):
@abc.abstractmethod
def __add__(self, new_result: Result) -> Result:
pass
@abc.abstractmethod
def add(self) -> None:
pass
@abc.abstractmethod
def to_dict(self) -> dict:
pass
def __repr__(self) -> str:
lines = [f" {k}={v}" for k, v in self.to_dict().items()]
str_lines = ",\n".join(lines)
string = f"""{type(self).__name__}(\n{str_lines}\n)"""
return string
| 21.192308 | 65 | 0.591652 | 71 | 551 | 4.352113 | 0.464789 | 0.165049 | 0.194175 | 0.148867 | 0.174757 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.261343 | 551 | 25 | 66 | 22.04 | 0.759214 | 0 | 0 | 0.333333 | 0 | 0 | 0.101633 | 0.068966 | 0 | 0 | 0 | 0 | 0 | 1 | 0.222222 | false | 0.166667 | 0.111111 | 0 | 0.444444 | 0 | 0 | 0 | 0 | null | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 3 |
9a7d1f2922512aacf7b45f9c182c5653fdfccfe4 | 4,476 | py | Python | tests/test__replacer.py | vsvandelik/cubbitt-fixer | eacab30975b9087b7f1a987402dc70bedb16c4bf | [
"MIT"
] | null | null | null | tests/test__replacer.py | vsvandelik/cubbitt-fixer | eacab30975b9087b7f1a987402dc70bedb16c4bf | [
"MIT"
] | null | null | null | tests/test__replacer.py | vsvandelik/cubbitt-fixer | eacab30975b9087b7f1a987402dc70bedb16c4bf | [
"MIT"
] | null | null | null | from fixer._finder import NumberUnitFinderResult
from fixer._languages import Languages
from fixer._replacer import Replacer
from fixer._units import units
def test_replace_unit_after_space():
result = Replacer.replace_unit(
"I went over the handlebars and flew a good 300 yards on the ground.",
NumberUnitFinderResult(300, units.get_unit_by_word("metrů", Languages.CS), False, "300 metrů"),
NumberUnitFinderResult(300, units.get_unit_by_word("yards", Languages.EN), False, "300 yards"),
units.get_unit_by_word("metres", Languages.EN),
Languages.EN)
assert result == "I went over the handlebars and flew a good 300 metres on the ground."
def test_replace_unit_after_no_space():
result = Replacer.replace_unit(
"Abc def 300°F ghch ijk.",
NumberUnitFinderResult(300, units.get_unit_by_word("°C", Languages.CS), False, "300°C"),
NumberUnitFinderResult(300, units.get_unit_by_word("°F", Languages.EN), False, "300°F"),
units.get_unit_by_word("°C", Languages.EN),
Languages.EN)
assert result == "Abc def 300°C ghch ijk."
def test_replace_unit_before():
result = Replacer.replace_unit(
"Abc def 300 dollars ghch ijk.",
NumberUnitFinderResult(300, units.get_unit_by_word("korun", Languages.CS), False, "300 korun"),
NumberUnitFinderResult(300, units.get_unit_by_word("dollars", Languages.EN), False, "300 dollars"),
units.get_unit_by_word("CZK", Languages.EN),
Languages.EN)
assert result == "Abc def CZK 300 ghch ijk."
def test_replace_unit_before_no_space():
result = Replacer.replace_unit(
"Abc def 300 dollars ghch ijk.",
NumberUnitFinderResult(300, units.get_unit_by_word("korun", Languages.CS), False, "300 korun"),
NumberUnitFinderResult(300, units.get_unit_by_word("dollars", Languages.EN), False, "300 dollars"),
units.get_unit_by_word("$", Languages.EN),
Languages.EN)
assert result == "Abc def $300 ghch ijk."
def test_replace_number():
result = Replacer.replace_number(
"Abc def 300 dollars ghch ijk.",
NumberUnitFinderResult(500, units.get_unit_by_word("dolarů", Languages.CS), False, "500 dolarů"),
NumberUnitFinderResult(300, units.get_unit_by_word("dollars", Languages.EN), False, "300 dollars"),
Languages.EN,
"300"
)
assert result == "Abc def 500 dollars ghch ijk."
def test_replace_number_with_scaling():
n = NumberUnitFinderResult(123456.789, units.get_unit_by_word("dolarů", Languages.CS), False, "123 456 789 dolarů")
n.add_scaling(1000)
result = Replacer.replace_number(
"Abc def 500.1 dollars ghch ijk.",
n,
NumberUnitFinderResult(500.1, units.get_unit_by_word("dollars", Languages.EN), False, "500.1 dollars"),
Languages.EN,
"500.1"
)
assert result == "Abc def 123,456.789 thousand dollars ghch ijk."
def test_replace_unit_number_cs():
result = Replacer.replace_unit_number(
"Abc def 500,1 korun ghch ijk.",
NumberUnitFinderResult(500.1, units.get_unit_by_word("crowns", Languages.EN), False, "500.1 crowns"),
NumberUnitFinderResult(500.1, units.get_unit_by_word("korun", Languages.CS), False, "500,1 korun"),
1234.123,
units.get_unit_by_word("dolarů", Languages.CS),
Languages.CS
)
assert result == "Abc def 1 234,1 dolarů ghch ijk."
def test_replace_unit_number():
result = Replacer.replace_unit_number(
"Abc def 500.1 crowns ghch ijk.",
NumberUnitFinderResult(500.1, units.get_unit_by_word("korun", Languages.CS), False, "500,1 korun"),
NumberUnitFinderResult(500.1, units.get_unit_by_word("crowns", Languages.EN), False, "500.1 crowns"),
123456789,
units.get_unit_by_word("dollars", Languages.EN),
Languages.EN
)
assert result == "Abc def 123,460,000 dollars ghch ijk."
def test_replace_unit_number_scaling():
n = NumberUnitFinderResult(500.1, units.get_unit_by_word("korun", Languages.CS), False, "500,1 korun")
n.add_scaling(1)
result = Replacer.replace_unit_number(
"Abc def 500.1 crowns ghch ijk.",
n,
NumberUnitFinderResult(500.1, units.get_unit_by_word("crowns", Languages.EN), False, "500.1 crowns"),
123456789,
units.get_unit_by_word("dollars", Languages.EN),
Languages.EN
)
assert result == "Abc def 123.46 million dollars ghch ijk."
| 41.444444 | 119 | 0.685433 | 615 | 4,476 | 4.793496 | 0.113821 | 0.067843 | 0.101764 | 0.118725 | 0.817503 | 0.77578 | 0.722185 | 0.646201 | 0.569539 | 0.457938 | 0 | 0.06798 | 0.194817 | 4,476 | 107 | 120 | 41.831776 | 0.748058 | 0 | 0 | 0.431818 | 0 | 0 | 0.211126 | 0 | 0 | 0 | 0 | 0 | 0.102273 | 1 | 0.102273 | false | 0 | 0.045455 | 0 | 0.147727 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 1 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 3 |
9a96e88736adeb2502586e8f473b85cf6acfc011 | 895 | py | Python | userauth/models.py | houston-igpp/pdsppi-review-website | 49a40d7b3b866008195259e8ccd27b62fd0210da | [
"Apache-2.0"
] | null | null | null | userauth/models.py | houston-igpp/pdsppi-review-website | 49a40d7b3b866008195259e8ccd27b62fd0210da | [
"Apache-2.0"
] | null | null | null | userauth/models.py | houston-igpp/pdsppi-review-website | 49a40d7b3b866008195259e8ccd27b62fd0210da | [
"Apache-2.0"
] | null | null | null | from django.db import models
from django.contrib.auth.models import AbstractUser
from django.core.validators import RegexValidator
from django.utils.translation import gettext_lazy as _
from django.urls import reverse
# Create your models here.
class CustomUser (AbstractUser):
display_name = models.CharField(verbose_name=_("Display name"), max_length=30, help_text=_("Will be shown e.g. when commenting"), default=(""))
addtl_info = models.CharField(verbose_name=_("Additional information"), max_length=4096, blank=True, null=True)
photo = models.ImageField(verbose_name=_("Photo"), upload_to='photos/', default='photos/default-user-avatar.png')
class Meta:
ordering = ['last_name']
def get_absolute_url(self):
return reverse('account_profile')
def __str__(self):
return f"{self.username}: {self.first_name} {self.last_name}"
| 37.291667 | 148 | 0.731844 | 116 | 895 | 5.431034 | 0.612069 | 0.079365 | 0.069841 | 0.08254 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.007905 | 0.151955 | 895 | 23 | 149 | 38.913043 | 0.822134 | 0.026816 | 0 | 0 | 0 | 0 | 0.213134 | 0.034562 | 0 | 0 | 0 | 0 | 0 | 1 | 0.133333 | false | 0 | 0.333333 | 0.133333 | 0.933333 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 1 | 1 | 0 | 0 | 3 |
9a9c4aadc145f60bf2228e12490f7b8739f7d96b | 1,240 | py | Python | bitboards/masks.py | martinogden/chessval | 8e50f247e0b620bf1d216ab3865074d32575aba9 | [
"MIT"
] | null | null | null | bitboards/masks.py | martinogden/chessval | 8e50f247e0b620bf1d216ab3865074d32575aba9 | [
"MIT"
] | null | null | null | bitboards/masks.py | martinogden/chessval | 8e50f247e0b620bf1d216ab3865074d32575aba9 | [
"MIT"
] | null | null | null | FULL = 0xFFFFFFFFFFFFFFFFL
A1A8 = 0x00000000000000FFL
A1H1 = 0x0101010101010101L
A8H8 = 0x8080808080808080L
A1H8 = 0x8040201008040201L
A8H1 = 0x0102040810204080L
B1B8 = 0x0202020202020202L
C2H7 = 0x0080402010080400L
C7H2 = 0x0004081020408000L
A4H4 = 0x00000000FF000000L
A5H5 = 0x000000FF00000000L
RANK_MASK = (
A1A8,
A1A8 << (8 * 1),
A1A8 << (8 * 2),
A1A8 << (8 * 3),
A1A8 << (8 * 4),
A1A8 << (8 * 5),
A1A8 << (8 * 6),
A1A8 << (8 * 7),
)
FILE_MASK = (
A1H1,
A1H1 << 1,
A1H1 << 2,
A1H1 << 3,
A1H1 << 4,
A1H1 << 5,
A1H1 << 6,
A1H1 << 7,
)
DIAG_MASK = (
A1H8 >> (8 * 7),
A1H8 >> (8 * 6),
A1H8 >> (8 * 5),
A1H8 >> (8 * 4),
A1H8 >> (8 * 3),
A1H8 >> (8 * 2),
A1H8 >> (8 * 1),
A1H8,
A1H8 << (8 * 1),
A1H8 << (8 * 2),
A1H8 << (8 * 3),
A1H8 << (8 * 4),
A1H8 << (8 * 5),
A1H8 << (8 * 6),
A1H8 << (8 * 7),
)
ADIAG_MASK = (
A8H1 >> (8 * 7),
A8H1 >> (8 * 6),
A8H1 >> (8 * 5),
A8H1 >> (8 * 4),
A8H1 >> (8 * 3),
A8H1 >> (8 * 2),
A8H1 >> (8 * 1),
A8H1,
A8H1 << (8 * 1),
A8H1 << (8 * 2),
A8H1 << (8 * 3),
A8H1 << (8 * 4),
A8H1 << (8 * 5),
A8H1 << (8 * 6),
A8H1 << (8 * 7),
)
| 17.222222 | 26 | 0.444355 | 153 | 1,240 | 3.575163 | 0.228758 | 0.127971 | 0.021938 | 0.036563 | 0.255942 | 0 | 0 | 0 | 0 | 0 | 0 | 0.437577 | 0.347581 | 1,240 | 71 | 27 | 17.464789 | 0.238566 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | null | null | 0 | 0 | null | null | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 3 |
9aa2dedfd56beaa17f7c8f96a2692ad82fb51a04 | 101 | gyp | Python | binding.gyp | digitalbazaar/node-posix | 33b25a191135872a52169ebc710390793e20a3bf | [
"MIT"
] | null | null | null | binding.gyp | digitalbazaar/node-posix | 33b25a191135872a52169ebc710390793e20a3bf | [
"MIT"
] | null | null | null | binding.gyp | digitalbazaar/node-posix | 33b25a191135872a52169ebc710390793e20a3bf | [
"MIT"
] | null | null | null | {
"targets": [
{
"target_name": "posix",
"sources": [ "src/posix.cc" ]
}
]
}
| 11.222222 | 35 | 0.39604 | 8 | 101 | 4.875 | 0.875 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.366337 | 101 | 8 | 36 | 12.625 | 0.609375 | 0 | 0 | 0 | 0 | 0 | 0.415842 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | true | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 3 |
9ad1b69f509e21f739495a7ac1a27711e7f5afdc | 413 | py | Python | mock/employees/static.py | vyahello/fake-data-parser | be04dedca8239282aa216e4d8ab927d708d0d250 | [
"MIT"
] | 1 | 2021-04-20T05:25:22.000Z | 2021-04-20T05:25:22.000Z | mock/employees/static.py | vyahello/fake-data-parser | be04dedca8239282aa216e4d8ab927d708d0d250 | [
"MIT"
] | 2 | 2019-09-19T10:09:22.000Z | 2020-11-07T08:51:54.000Z | mock/employees/static.py | vyahello/fake-data-parser | be04dedca8239282aa216e4d8ab927d708d0d250 | [
"MIT"
] | null | null | null | from dataclasses import dataclass
@dataclass(frozen=True)
class Endpoint:
"""The class represents WEB endpoint for API."""
address: str = "0.0.0.0"
port: int = 7777
debug: bool = False
@dataclass(frozen=True)
class Route:
"""The class represents API routes."""
home: str = "/"
search_by_keyword: str = "/api/search/{keyword}"
search_by_id: str = "/api/employee/{identifier}"
| 20.65 | 52 | 0.656174 | 54 | 413 | 4.944444 | 0.574074 | 0.022472 | 0.142322 | 0.179775 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.024316 | 0.20339 | 413 | 19 | 53 | 21.736842 | 0.787234 | 0.181598 | 0 | 0.181818 | 0 | 0 | 0.168196 | 0.143731 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | true | 0 | 0.090909 | 0 | 0.818182 | 0 | 0 | 0 | 0 | null | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 1 | 0 | 0 | 3 |
9ae0be019efe0fc3ae9e36d5d9749e28afaf23d7 | 1,048 | py | Python | stardist/__init__.py | mmvih/stardist | 810dec4727e8e8bf05bd9620f91a3a0dd70de289 | [
"BSD-3-Clause"
] | null | null | null | stardist/__init__.py | mmvih/stardist | 810dec4727e8e8bf05bd9620f91a3a0dd70de289 | [
"BSD-3-Clause"
] | null | null | null | stardist/__init__.py | mmvih/stardist | 810dec4727e8e8bf05bd9620f91a3a0dd70de289 | [
"BSD-3-Clause"
] | null | null | null | from __future__ import absolute_import, print_function
import warnings
import pathlib
def format_Warning(message, category, filename, lineno, line=''):
return f"{pathlib.Path(filename).name} ({lineno}): {message}\n"
warnings.formatwarning = format_Warning
from .version import __version__
# TODO: which functions to expose here? all?
from .nms import non_maximum_suppression, non_maximum_suppression_3d, non_maximum_suppression_3d_sparse
from .utils import edt_prob, fill_label_holes, sample_points, calculate_extents, export_imagej_rois, gputools_available
from .geometry import star_dist, polygons_to_label, relabel_image_stardist, ray_angles, dist_to_coord
from .geometry import star_dist3D, polyhedron_to_label, relabel_image_stardist3D
from .plot.plot import random_label_cmap, draw_polygons, _draw_polygons
from .plot.render import render_label, render_label_pred
from .rays3d import rays_from_json, Rays_Cartesian, Rays_SubDivide, Rays_Tetra, Rays_Octo, Rays_GoldenSpiral, Rays_Explicit
from .sample_patches import sample_patches
| 52.4 | 123 | 0.842557 | 146 | 1,048 | 5.636986 | 0.547945 | 0.036452 | 0.076549 | 0.055893 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.005269 | 0.094466 | 1,048 | 19 | 124 | 55.157895 | 0.86196 | 0.040076 | 0 | 0 | 0 | 0 | 0.052789 | 0.028884 | 0 | 0 | 0 | 0.052632 | 0 | 1 | 0.066667 | false | 0 | 0.8 | 0.066667 | 0.933333 | 0.066667 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | null | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 0 | 0 | 3 |
b1074e35726499c0f9eeb395c227e1f0ebd034da | 143 | py | Python | server/server/chat/routing.py | Shadowsych/matchus | b66241a873ae6bf12b8b7380c42c9920de9c2110 | [
"AML"
] | 1 | 2021-01-10T04:46:12.000Z | 2021-01-10T04:46:12.000Z | server/server/chat/routing.py | Shadowsych/matchus | b66241a873ae6bf12b8b7380c42c9920de9c2110 | [
"AML"
] | null | null | null | server/server/chat/routing.py | Shadowsych/matchus | b66241a873ae6bf12b8b7380c42c9920de9c2110 | [
"AML"
] | null | null | null | from django.urls import path
from . import consumers
websocket_urlpatterns = [
path('ws/chat-room/<int:id>', consumers.ChatRoomConsumer)
] | 23.833333 | 61 | 0.755245 | 18 | 143 | 5.944444 | 0.777778 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.125874 | 143 | 6 | 62 | 23.833333 | 0.856 | 0 | 0 | 0 | 0 | 0 | 0.145833 | 0.145833 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | false | 0 | 0.4 | 0 | 0.4 | 0 | 1 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 3 |
b110d1f21969520b70132fce580dfcc89fcfd27f | 430 | py | Python | opendata/tests/test_flotteurs.py | entrepreneur-interet-general/predisauvetage | 4d985ee79355652709da322db48daffb3e5a895a | [
"MIT"
] | 6 | 2018-02-16T15:07:17.000Z | 2020-10-09T09:34:29.000Z | opendata/tests/test_flotteurs.py | entrepreneur-interet-general/predisauvetage | 4d985ee79355652709da322db48daffb3e5a895a | [
"MIT"
] | 107 | 2018-03-29T14:55:33.000Z | 2021-12-13T19:44:50.000Z | opendata/tests/test_flotteurs.py | entrepreneur-interet-general/predisauvetage | 4d985ee79355652709da322db48daffb3e5a895a | [
"MIT"
] | 1 | 2021-03-03T21:02:33.000Z | 2021-03-03T21:02:33.000Z | # -*- coding: utf-8 -*-
from transformers.flotteurs import FlotteursTransformer
from base import BaseTest
class TestFlotteursTransformer(BaseTest):
def test_basic_file(self):
in_file = self.filepath("tests/files/flotteurs.csv")
expected_file = self.filepath("tests/files/expected_flotteurs.csv")
self.run_for_files(in_file, expected_file)
def subject(self):
return FlotteursTransformer
| 28.666667 | 75 | 0.734884 | 50 | 430 | 6.14 | 0.54 | 0.078176 | 0.104235 | 0.136808 | 0.169381 | 0 | 0 | 0 | 0 | 0 | 0 | 0.002801 | 0.169767 | 430 | 14 | 76 | 30.714286 | 0.857143 | 0.048837 | 0 | 0 | 0 | 0 | 0.144963 | 0.144963 | 0 | 0 | 0 | 0 | 0 | 1 | 0.222222 | false | 0 | 0.222222 | 0.111111 | 0.666667 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 1 | 1 | 0 | 0 | 3 |
b116784531827f5854ca5c5b2d99d3b281cf88e4 | 447 | py | Python | tool/models.py | zzattack/ccmaps-web | b431074219a82a4bf51336568aa0589ffa914169 | [
"MIT",
"Unlicense"
] | null | null | null | tool/models.py | zzattack/ccmaps-web | b431074219a82a4bf51336568aa0589ffa914169 | [
"MIT",
"Unlicense"
] | null | null | null | tool/models.py | zzattack/ccmaps-web | b431074219a82a4bf51336568aa0589ffa914169 | [
"MIT",
"Unlicense"
] | null | null | null | from django.db import models
class ProgramVersion(models.Model):
version = models.CharField(max_length=100, unique=True)
release_date = models.DateField(auto_now_add=True)
release_notes = models.CharField(max_length=100, unique=True)
version = models.CharField(max_length=100, unique=True)
file = models.FileField(upload_to='versions')
def __unicode__(self):
return 'Version ' + self.version.__str__()
class Admin: pass
| 29.8 | 63 | 0.753915 | 59 | 447 | 5.440678 | 0.576271 | 0.140187 | 0.168224 | 0.224299 | 0.389408 | 0.389408 | 0.389408 | 0.274143 | 0 | 0 | 0 | 0.023316 | 0.136465 | 447 | 14 | 64 | 31.928571 | 0.80829 | 0 | 0 | 0.2 | 0 | 0 | 0.036952 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0.1 | false | 0.1 | 0.1 | 0.1 | 1 | 0 | 0 | 0 | 0 | null | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 1 | 0 | 0 | 3 |
b116a1087ef968005cd6487abec57ccc007cb80d | 3,081 | py | Python | AS2101_Labwork/3.Trials-References/Task 3/Submission Codes/Bisection Method/bsm_plot/bsm_plot.py | kirtan2605/Coursework_Codes | 3455496e8ec0ae3a576cb3fc3b2ed01a055149c5 | [
"MIT"
] | null | null | null | AS2101_Labwork/3.Trials-References/Task 3/Submission Codes/Bisection Method/bsm_plot/bsm_plot.py | kirtan2605/Coursework_Codes | 3455496e8ec0ae3a576cb3fc3b2ed01a055149c5 | [
"MIT"
] | null | null | null | AS2101_Labwork/3.Trials-References/Task 3/Submission Codes/Bisection Method/bsm_plot/bsm_plot.py | kirtan2605/Coursework_Codes | 3455496e8ec0ae3a576cb3fc3b2ed01a055149c5 | [
"MIT"
] | null | null | null | import numpy as np
import matplotlib.pyplot as plt
from values_bsm import *
"""
1. f : The function whose roots we want to find
2. a : lower bound on the root
3. b : upper bound on the root
4. e : permissible error in the value of root obtained
5. N_max : maximum number of iterations after which function stops.
"""
"""Function 1"""
# for range 1 : {-3,0}, error = 0.0001
root = 0
f1_N_R1 = []
f1_Roots_R1 = []
for i in range(1,11):
#print()
#print("N_Max = {}".format(i))
root = bisection(func_num = 1,a = -3,b = 0,e = 0.001,N_Max = i)
f1_N_R1.append(i)
f1_Roots_R1.append(root)
# for range 2 : {-5,5}, error = 0.0001
root = 0
f1_N_R2 = []
f1_Roots_R2 = []
for i in range(1,11):
#print()
#print("N_Max = {}".format(i))
root = bisection(func_num = 1, a = -5,b = 5,e = 0.001,N_Max = i)
f1_N_R2.append(i)
f1_Roots_R2.append(root)
#print(f1_N_R2)
#print(f1_Roots_R2)
#print()
"""Function 2"""
# for range 1 : {-3,0}, error = 0.0001
root = 0
f2_N_R1 = []
f2_Roots_R1 = []
for i in range(1,11):
#print()
#print("N_Max = {}".format(i))
root = bisection(func_num = 2, a = -3,b = 0,e = 0.001,N_Max = i)
f2_N_R1.append(i)
f2_Roots_R1.append(root)
# for range 2 : {-5,5}, error = 0.0001
root = 0
f2_N_R2 = []
f2_Roots_R2 = []
for i in range(1,11):
#print()
#print("N_Max = {}".format(i))
root = bisection(func_num = 2, a = -5,b = 5,e = 0.001,N_Max = i)
f2_N_R2.append(i)
f2_Roots_R2.append(root)
func = np.vectorize(f)
f1_value_at_roots_R1 = func(1,f1_Roots_R1)
f1_value_at_roots_R2 = func(1,f1_Roots_R2)
f2_value_at_roots_R1 = func(2,f2_Roots_R1)
f2_value_at_roots_R2 = func(2,f2_Roots_R2)
"""
# change the data and use the following code
# to plot and save for both the Functions
plt.plot(f2_N_R1,f2_value_at_roots_R1,'.-',label = 'initial Limit : {-3,0}')
plt.plot(f2_N_R2,f2_value_at_roots_R2,'.-',label = 'initial Limit : {-5,5}')
plt.xlabel('N',fontsize = 10)
plt.ylabel('Function Value at Root',fontsize = 10)
plt.xticks(fontsize = 8)
plt.yticks(fontsize = 8)
plt.title('Bisection Method - Function2 - permissible error : 0.0001')
plt.grid(linestyle = '--')
plt.legend()
plt.savefig('bsm-f2.png')
plt.show()
"""
# Plotting Absolute Values
f1_value_at_roots_R1_ABS = [abs(ele) for ele in f1_value_at_roots_R1]
f1_value_at_roots_R2_ABS = [abs(ele) for ele in f1_value_at_roots_R2]
f2_value_at_roots_R1_ABS = [abs(ele) for ele in f2_value_at_roots_R1]
f2_value_at_roots_R2_ABS = [abs(ele) for ele in f2_value_at_roots_R2]
# change the data and use the following code
# to plot and save for both the Functions ABSOLUTE VALUES
plt.plot(f2_N_R1,f2_value_at_roots_R1_ABS,'.-',label = 'initial Limit : {-3,0}')
plt.plot(f2_N_R2,f2_value_at_roots_R2_ABS,'.-',label = 'initial Limit : {-5,5}')
plt.xlabel('N',fontsize = 10)
plt.ylabel('Function Value at Root',fontsize = 10)
plt.xticks(fontsize = 8)
plt.yticks(fontsize = 8)
plt.title('Bisection Method - Function2 Absolute Value - permissible error : 0.001')
plt.grid(linestyle = '--')
plt.legend()
plt.savefig('bsm-f2-abs.png')
plt.show()
| 27.508929 | 84 | 0.676728 | 577 | 3,081 | 3.377816 | 0.169844 | 0.064649 | 0.098512 | 0.071832 | 0.735762 | 0.718317 | 0.715239 | 0.667522 | 0.661365 | 0.620318 | 0 | 0.079408 | 0.16618 | 3,081 | 111 | 85 | 27.756757 | 0.679253 | 0.147355 | 0 | 0.156863 | 0 | 0 | 0.087341 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | false | 0 | 0.058824 | 0 | 0.058824 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 1 | 1 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 3 |
b12ecc6608a4386c8f3b148789042e9e19e1b1ca | 152 | py | Python | py_pivot/pivotexample.py | mutazag/misc | dfef362cdd835ef4efd1f2d02e13ff5297ccfc0f | [
"MIT"
] | null | null | null | py_pivot/pivotexample.py | mutazag/misc | dfef362cdd835ef4efd1f2d02e13ff5297ccfc0f | [
"MIT"
] | null | null | null | py_pivot/pivotexample.py | mutazag/misc | dfef362cdd835ef4efd1f2d02e13ff5297ccfc0f | [
"MIT"
] | null | null | null | #%%
import pandas as pd
df = pd.read_csv('df1.csv')
# %%
df.melt(id_vars=['Index']).value.notna()
# %%
df.melt(id_vars=['Index']).value.dropna()
# %%
| 13.818182 | 41 | 0.598684 | 24 | 152 | 3.666667 | 0.625 | 0.136364 | 0.181818 | 0.272727 | 0.5 | 0.5 | 0 | 0 | 0 | 0 | 0 | 0.007463 | 0.118421 | 152 | 10 | 42 | 15.2 | 0.649254 | 0.072368 | 0 | 0 | 0 | 0 | 0.124088 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | false | 0 | 0.25 | 0 | 0.25 | 0 | 1 | 0 | 0 | null | 0 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 3 |
b150bc49db0ae27fc279c250bb282c701190234e | 4,789 | py | Python | design/migrations/0016_customerorderinfo_customerorderproduct.py | pincoin/iclover | 890fcbd836ebffa0de8cf9fbabee55f068b3bc8b | [
"MIT"
] | 1 | 2019-07-20T09:51:53.000Z | 2019-07-20T09:51:53.000Z | design/migrations/0016_customerorderinfo_customerorderproduct.py | pincoin/iclover | 890fcbd836ebffa0de8cf9fbabee55f068b3bc8b | [
"MIT"
] | 11 | 2019-07-26T02:23:52.000Z | 2022-03-11T23:41:09.000Z | design/migrations/0016_customerorderinfo_customerorderproduct.py | pincoin/iclover | 890fcbd836ebffa0de8cf9fbabee55f068b3bc8b | [
"MIT"
] | 1 | 2019-07-26T02:16:49.000Z | 2019-07-26T02:16:49.000Z | # Generated by Django 2.1.7 on 2019-09-30 06:52
from django.conf import settings
from django.db import migrations, models
import django.db.models.deletion
import django.utils.timezone
import model_utils.fields
class Migration(migrations.Migration):
dependencies = [
migrations.swappable_dependency(settings.AUTH_USER_MODEL),
('design', '0015_auto_20190927_1846'),
]
operations = [
migrations.CreateModel(
name='CustomerOrderInfo',
fields=[
('id', models.AutoField(auto_created=True, primary_key=True, serialize=False, verbose_name='ID')),
('created', model_utils.fields.AutoCreatedField(default=django.utils.timezone.now, editable=False, verbose_name='created')),
('modified', model_utils.fields.AutoLastModifiedField(default=django.utils.timezone.now, editable=False, verbose_name='modified')),
('name', models.CharField(blank=True, max_length=255, null=True, verbose_name='회사명')),
('uuid', models.CharField(blank=True, max_length=255, null=True, verbose_name='uuid')),
('code', models.IntegerField(blank=True, null=True, verbose_name='사업자번호')),
('phone', models.CharField(blank=True, max_length=255, verbose_name='폰 번호')),
('address', models.CharField(blank=True, max_length=255, verbose_name='주소')),
('address2', models.CharField(blank=True, max_length=255, verbose_name='주소2')),
('address_detail', models.CharField(blank=True, max_length=255, verbose_name='상세주소')),
('address_option', models.CharField(blank=True, max_length=255, verbose_name='주소 참고')),
('bill_select', models.IntegerField(blank=True, choices=[(0, '세금계산서'), (1, '사업자 지출증빙'), (2, '현금 영수증'), (3, '미발행')], null=True, verbose_name='사업자 상태')),
('user', models.ForeignKey(blank=True, null=True, on_delete=django.db.models.deletion.SET_NULL, to=settings.AUTH_USER_MODEL, verbose_name='고객')),
],
options={
'verbose_name': '고객 주문 기본 정보',
'verbose_name_plural': '고객 주문 기본 정보',
},
),
migrations.CreateModel(
name='CustomerOrderProduct',
fields=[
('id', models.AutoField(auto_created=True, primary_key=True, serialize=False, verbose_name='ID')),
('created', model_utils.fields.AutoCreatedField(default=django.utils.timezone.now, editable=False, verbose_name='created')),
('modified', model_utils.fields.AutoLastModifiedField(default=django.utils.timezone.now, editable=False, verbose_name='modified')),
('size', models.CharField(blank=True, max_length=255, null=True, verbose_name='규격 size')),
('size_value', models.CharField(blank=True, max_length=255, null=True, verbose_name='규격 size value')),
('paper', models.CharField(blank=True, max_length=255, null=True, verbose_name='용지 paper')),
('paper_value', models.CharField(blank=True, max_length=255, null=True, verbose_name='용지 paper value')),
('side', models.CharField(blank=True, max_length=255, null=True, verbose_name='인쇄 side')),
('side_value', models.CharField(blank=True, max_length=255, null=True, verbose_name='인쇄 side value')),
('deal', models.CharField(blank=True, max_length=255, null=True, verbose_name='수량 deal')),
('deal_value', models.CharField(blank=True, max_length=255, null=True, verbose_name='수량 deal value')),
('option1', models.CharField(blank=True, max_length=255, null=True, verbose_name='옵션1 option1')),
('option1_value', models.CharField(blank=True, max_length=255, null=True, verbose_name='옵션1 option1 value')),
('option2', models.CharField(blank=True, max_length=255, null=True, verbose_name='옵션1 option2')),
('option2_value', models.CharField(blank=True, max_length=255, null=True, verbose_name='옵션1 option2 value')),
('supplier', models.CharField(blank=True, max_length=255, null=True, verbose_name='매입')),
('sell', models.DecimalField(blank=True, decimal_places=4, default=0, max_digits=11, verbose_name='sell')),
('sell_opposition', models.DecimalField(blank=True, decimal_places=4, default=0, max_digits=11, verbose_name='buy')),
('sectors_category', models.ForeignKey(blank=True, null=True, on_delete=django.db.models.deletion.SET_NULL, to='design.CustomerOrderInfo', verbose_name='고객 주문 정보')),
],
options={
'verbose_name': '고객 주문 품목',
'verbose_name_plural': '고객 주문 품목',
},
),
]
| 69.405797 | 181 | 0.638755 | 569 | 4,789 | 5.210896 | 0.221441 | 0.133558 | 0.134907 | 0.161889 | 0.716358 | 0.687352 | 0.687352 | 0.687352 | 0.687352 | 0.639798 | 0 | 0.031034 | 0.212779 | 4,789 | 68 | 182 | 70.426471 | 0.755438 | 0.009397 | 0 | 0.258065 | 1 | 0 | 0.145297 | 0.009911 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | false | 0 | 0.080645 | 0 | 0.129032 | 0 | 0 | 0 | 0 | null | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 3 |
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