hexsha
string | size
int64 | ext
string | lang
string | max_stars_repo_path
string | max_stars_repo_name
string | max_stars_repo_head_hexsha
string | max_stars_repo_licenses
list | max_stars_count
int64 | max_stars_repo_stars_event_min_datetime
string | max_stars_repo_stars_event_max_datetime
string | max_issues_repo_path
string | max_issues_repo_name
string | max_issues_repo_head_hexsha
string | max_issues_repo_licenses
list | max_issues_count
int64 | max_issues_repo_issues_event_min_datetime
string | max_issues_repo_issues_event_max_datetime
string | max_forks_repo_path
string | max_forks_repo_name
string | max_forks_repo_head_hexsha
string | max_forks_repo_licenses
list | max_forks_count
int64 | max_forks_repo_forks_event_min_datetime
string | max_forks_repo_forks_event_max_datetime
string | content
string | avg_line_length
float64 | max_line_length
int64 | alphanum_fraction
float64 | qsc_code_num_words_quality_signal
int64 | qsc_code_num_chars_quality_signal
float64 | qsc_code_mean_word_length_quality_signal
float64 | qsc_code_frac_words_unique_quality_signal
float64 | qsc_code_frac_chars_top_2grams_quality_signal
float64 | qsc_code_frac_chars_top_3grams_quality_signal
float64 | qsc_code_frac_chars_top_4grams_quality_signal
float64 | qsc_code_frac_chars_dupe_5grams_quality_signal
float64 | qsc_code_frac_chars_dupe_6grams_quality_signal
float64 | qsc_code_frac_chars_dupe_7grams_quality_signal
float64 | qsc_code_frac_chars_dupe_8grams_quality_signal
float64 | qsc_code_frac_chars_dupe_9grams_quality_signal
float64 | qsc_code_frac_chars_dupe_10grams_quality_signal
float64 | qsc_code_frac_chars_replacement_symbols_quality_signal
float64 | qsc_code_frac_chars_digital_quality_signal
float64 | qsc_code_frac_chars_whitespace_quality_signal
float64 | qsc_code_size_file_byte_quality_signal
float64 | qsc_code_num_lines_quality_signal
float64 | qsc_code_num_chars_line_max_quality_signal
float64 | qsc_code_num_chars_line_mean_quality_signal
float64 | qsc_code_frac_chars_alphabet_quality_signal
float64 | qsc_code_frac_chars_comments_quality_signal
float64 | qsc_code_cate_xml_start_quality_signal
float64 | qsc_code_frac_lines_dupe_lines_quality_signal
float64 | qsc_code_cate_autogen_quality_signal
float64 | qsc_code_frac_lines_long_string_quality_signal
float64 | qsc_code_frac_chars_string_length_quality_signal
float64 | qsc_code_frac_chars_long_word_length_quality_signal
float64 | qsc_code_frac_lines_string_concat_quality_signal
float64 | qsc_code_cate_encoded_data_quality_signal
float64 | qsc_code_frac_chars_hex_words_quality_signal
float64 | qsc_code_frac_lines_prompt_comments_quality_signal
float64 | qsc_code_frac_lines_assert_quality_signal
float64 | qsc_codepython_cate_ast_quality_signal
float64 | qsc_codepython_frac_lines_func_ratio_quality_signal
float64 | qsc_codepython_cate_var_zero_quality_signal
bool | qsc_codepython_frac_lines_pass_quality_signal
float64 | qsc_codepython_frac_lines_import_quality_signal
float64 | qsc_codepython_frac_lines_simplefunc_quality_signal
float64 | qsc_codepython_score_lines_no_logic_quality_signal
float64 | qsc_codepython_frac_lines_print_quality_signal
float64 | qsc_code_num_words
int64 | qsc_code_num_chars
int64 | qsc_code_mean_word_length
int64 | qsc_code_frac_words_unique
null | qsc_code_frac_chars_top_2grams
int64 | qsc_code_frac_chars_top_3grams
int64 | qsc_code_frac_chars_top_4grams
int64 | qsc_code_frac_chars_dupe_5grams
int64 | qsc_code_frac_chars_dupe_6grams
int64 | qsc_code_frac_chars_dupe_7grams
int64 | qsc_code_frac_chars_dupe_8grams
int64 | qsc_code_frac_chars_dupe_9grams
int64 | qsc_code_frac_chars_dupe_10grams
int64 | qsc_code_frac_chars_replacement_symbols
int64 | qsc_code_frac_chars_digital
int64 | qsc_code_frac_chars_whitespace
int64 | qsc_code_size_file_byte
int64 | qsc_code_num_lines
int64 | qsc_code_num_chars_line_max
int64 | qsc_code_num_chars_line_mean
int64 | qsc_code_frac_chars_alphabet
int64 | qsc_code_frac_chars_comments
int64 | qsc_code_cate_xml_start
int64 | qsc_code_frac_lines_dupe_lines
int64 | qsc_code_cate_autogen
int64 | qsc_code_frac_lines_long_string
int64 | qsc_code_frac_chars_string_length
int64 | qsc_code_frac_chars_long_word_length
int64 | qsc_code_frac_lines_string_concat
null | qsc_code_cate_encoded_data
int64 | qsc_code_frac_chars_hex_words
int64 | qsc_code_frac_lines_prompt_comments
int64 | qsc_code_frac_lines_assert
int64 | qsc_codepython_cate_ast
int64 | qsc_codepython_frac_lines_func_ratio
int64 | qsc_codepython_cate_var_zero
int64 | qsc_codepython_frac_lines_pass
int64 | qsc_codepython_frac_lines_import
int64 | qsc_codepython_frac_lines_simplefunc
int64 | qsc_codepython_score_lines_no_logic
int64 | qsc_codepython_frac_lines_print
int64 | effective
string | hits
int64 |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
8387849f0cbf70433979ec7eedac71b381a0cd7e
| 3,426
|
py
|
Python
|
ukpsummarizer-be/cplex/python/docplex/docplex/mp/operand.py
|
avineshpvs/vldb2018-sherlock
|
5e116f42f44c50bcb289be3c4b4b76e29b238c18
|
[
"Apache-2.0"
] | 2
|
2019-01-13T08:41:00.000Z
|
2021-03-27T22:55:10.000Z
|
ukpsummarizer-be/cplex/python/docplex/docplex/mp/operand.py
|
AIPHES/vldb2018-sherlock
|
3746efa35c4c1769cc4aaeb15aeb9453564e1226
|
[
"Apache-2.0"
] | null | null | null |
ukpsummarizer-be/cplex/python/docplex/docplex/mp/operand.py
|
AIPHES/vldb2018-sherlock
|
3746efa35c4c1769cc4aaeb15aeb9453564e1226
|
[
"Apache-2.0"
] | 4
|
2018-11-06T16:12:55.000Z
|
2019-08-21T13:22:32.000Z
|
# --------------------------------------------------------------------------
# Source file provided under Apache License, Version 2.0, January 2004,
# http://www.apache.org/licenses/
# (c) Copyright IBM Corp. 2015, 2016
# --------------------------------------------------------------------------
# gendoc: ignore
from docplex.mp.utils import iter_emptyset, is_number
from docplex.mp.constants import ComparisonType
class Operand(object):
__slots__ = ()
def get_constant(self):
return 0
def is_constant(self):
return False
# --- basic subscription api
def notify_used(self, user):
pass
def notify_unsubscribed(self, subscriber):
pass
def is_in_use(self):
return False
def notify_modified(self, event):
pass
# ---
def keep(self):
return self
def resolve(self):
# used for lazy expansions
pass
def get_linear_part(self):
return self
def __le__(self, rhs):
return self._model._qfactory.new_xconstraint(lhs=self, rhs=rhs, comparaison_type=ComparisonType.LE)
def __eq__(self, rhs):
return self._model._qfactory.new_xconstraint(lhs=self, rhs=rhs, comparaison_type=ComparisonType.EQ)
def __ge__(self, rhs):
return self._model._qfactory.new_xconstraint(lhs=self, rhs=rhs, comparaison_type=ComparisonType.GE)
le = __le__
eq = __eq__
ge = __ge__
class LinearOperand(Operand):
# no ctor as used in multiple inheritance
def unchecked_get_coef(self, dvar):
raise NotImplementedError('unchecked_get_coef missing for class: {0}'.format(self.__class__)) # pragma: no cover
def iter_variables(self):
"""
Iterates over all variables in the expression.
Returns:
iterator: An iterator over all variables present in the operand.
"""
for v, k in self.iter_terms():
yield v
def iter_terms(self):
# iterates over alllinear terms, if any
return iter_emptyset()
iter_sorted_terms = iter_terms
def number_of_terms(self):
return sum(1 for _ in self.iter_terms())
def size(self):
return self.number_of_terms()
def iter_quads(self):
return iter_emptyset()
def is_constant(self):
# redefine this for subclasses.
return False # pragma: no cover
def as_variable(self):
# return a variable if the expression is actually one variable, else None
return None
def is_zero(self):
return False
# no strict comparisons
def __lt__(self, e):
self.model.unsupported_relational_operator_error(self, "<", e)
def __gt__(self, e):
self.model.unsupported_relational_operator_error(self, ">", e)
def __contains__(self, dvar):
"""Overloads operator `in` for an expression and a variable.
:param: dvar (:class:`docplex.mp.linear.Var`): A decision variable.
Returns:
Boolean: True if the variable is present in the expression, else False.
"""
return self.contains_var(dvar)
def contains_var(self, dvar):
raise NotImplementedError # pragma: no cover
def lock_discrete(self):
pass
def is_discrete_locked(self):
return False
| 27.190476
| 122
| 0.597782
| 398
| 3,426
| 4.894472
| 0.359296
| 0.056468
| 0.030801
| 0.026181
| 0.189938
| 0.189938
| 0.189938
| 0.189938
| 0.189938
| 0.189938
| 0
| 0.006885
| 0.279335
| 3,426
| 126
| 123
| 27.190476
| 0.782098
| 0.277291
| 0
| 0.25
| 0
| 0
| 0.019043
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0.421875
| false
| 0.078125
| 0.03125
| 0.25
| 0.828125
| 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
| 1
| 0
| 1
| 1
| 0
|
0
| 4
|
8395cc826b6713904055364982e4b1af1c00a2b4
| 7,601
|
py
|
Python
|
newWorld/seleniumDemo/dbshopTestDemo/GoodsManageDemo.py
|
CypHelp/TestNewWorldDemo
|
ee6f73df05756f191c1c56250fa290461fdd1b9a
|
[
"Apache-2.0"
] | null | null | null |
newWorld/seleniumDemo/dbshopTestDemo/GoodsManageDemo.py
|
CypHelp/TestNewWorldDemo
|
ee6f73df05756f191c1c56250fa290461fdd1b9a
|
[
"Apache-2.0"
] | null | null | null |
newWorld/seleniumDemo/dbshopTestDemo/GoodsManageDemo.py
|
CypHelp/TestNewWorldDemo
|
ee6f73df05756f191c1c56250fa290461fdd1b9a
|
[
"Apache-2.0"
] | null | null | null |
# encoding: utf-8
"""
@author: yp
@software: PyCharm
@file: GoodsManageDemo.py
@time: 2019/8/1 0001 16:43
"""
from AutoTestPlatform.web.WebDriver import Driver
driver = Driver()
#登录Dbshop
driver.get("http://192.168.1.16/DBshop/admin")
driver.find_element_by_id_data("user_name", 'admin')
driver.find_element_by_id_data("user_passwd", "123456")
driver.find_element_by_xpath('//*[@id="admin_login_form"]/button').click()
#--------------------------------------------------------------------------------------------
#进入商品管理，添加商品
driver.find_element_by_xpath('/html/body/div[1]/div/ul[1]/li[3]/a').click()
driver.find_element_by_xpath('/html/body/div[1]/div/ul[1]/li[3]/ul/li[1]/a').click()
driver.find_element_by_xpath('/html/body/div[2]/div/div[2]/div/p[2]/a[1]').click()
#商品基本信息
driver.find_element_by_id_data('goods_name',"ipad ")
driver.find_element_by_id_data('goods_extend_name',"mini5")
driver.find_element_by_id_data('goods_item',"0551")
driver.find_element_by_id_data('goods_price',"6000")
driver.find_element_by_id_data('goods_shop_price',"5999")
driver.find_element_by_xpath('//*[@id="goods_a"]/div[2]/div[7]/div/table/tbody/tr/td[2]/input').send_keys("5899")
driver.find_element_by_id_data("virtual_sales","1000")
driver.find_element_by_id_data("goods_weight","15")
driver.switch_to_iframe(driver.find_element_by_id("ueditor_0"))
driver.find_element_by_xpath('/html/body').send_keys("最实用的ipad，你值得拥有")
driver.switch_to_parent_handle()
driver.find_element_by_xpath('//*[@id="sticky_navigation_right"]/button').click()
#对商品进行分类
driver.find_element_by_xpath('/html/body/div[2]/div/div[2]/table/tbody/tr[2]/td[9]/a[1]').click()
driver.find_element_by_xpath('/html/body/div[2]/div/div[2]/div/ul/li[3]/a').click()
driver.find_element_by_id('class_id_14').click()
driver.find_element_by_xpath('//*[@id="sticky_navigation_right"]/button[1]').click()
#goods库存
driver.find_element_by_xpath('/html/body/div[2]/div/div[2]/div/ul/li[5]/a').click()
driver.find_element_by_id_data('goods_stock','1000000')
driver.find_element_by_id_data('goods_out_of_stock_set','250')
driver.find_element_by_id_data('goods_cart_buy_min_num','1')
driver.find_element_by_id_data('goods_cart_buy_max_num','99')
driver.find_element_by_xpath('//*[@id="sticky_navigation_right"]/button[1]').click()
#优惠价格
driver.find_element_by_xpath('/html/body/div[2]/div/div[2]/div/ul/li[6]/a').click()
driver.find_element_by_id_data('goods_preferential_price',"4999")
driver.find_element_by_id_data('goods_preferential_start_time',"2019-08-05 14:25")
driver.find_element_by_id_data('goods_preferential_end_time',"2019-08-09 14:25")
driver.find_element_by_xpath('//*[@id="sticky_navigation_right"]/button[1]').click()
#销售规格
driver.find_element_by_xpath('/html/body/div[2]/div/div[2]/div/ul/li[7]/a').click()
driver.find_element_by_id('ff0000').click()
driver.find_element_by_id('other1').click()
driver.find_element_by_id_data('price_ff0000other1',"6000")
driver.find_element_by_id_data('stock_ff0000other1','100')
driver.find_element_by_id_data('item_ff0000other1','0551-001')
driver.find_element_by_id_data('weight_ff0000other1',"15")
driver.find_element_by_xpath('//*[@id="select_goods_color_size_in"]/tbody/tr/td[8]/table/tbody/tr/td[2]/input').send_keys('2999')
driver.find_element_by_xpath('//*[@id="sticky_navigation_right"]/button[1]').click()
#商品属性
driver.find_element_by_xpath('/html/body/div[2]/div/div[2]/div/ul/li[8]/a').click()
driver.find_element_by_id('attribute_group_id').click()
driver.find_element_by_xpath('//*[@id="attribute_group_id"]/option[2]').click()
driver.find_element_by_xpath('//*[@id="sticky_navigation_right"]/button[1]').click()
#商品标签
driver.find_element_by_xpath('/html/body/div[2]/div/div[2]/div/ul/li[9]/a').click()
driver.find_element_by_xpath('//*[@id="goods_l"]/div[2]/div[2]/div/label[1]/input').click()
driver.find_element_by_xpath('//*[@id="sticky_navigation_right"]/button[1]').click()
#商品自定义
driver.find_element_by_xpath('/html/body/div[2]/div/div[2]/div/ul/li[10]/a').click()
driver.find_element_by_xpath('//*[@id="goods_f"]/div[2]/div[2]/label/input').send_keys('蔡徐坤！！！必备')
driver.find_element_by_xpath('//*[@id="goods_f"]/div[2]/div[2]/div/input').send_keys("你其实不止是会'唱跳rap打篮球'，ipad给你带来新世界的one piece~")
driver.find_element_by_xpath('//*[@id="goods_f"]/div[2]/div[2]/div/label/input').click()
driver.find_element_by_xpath('//*[@id="goods_f"]/div[2]/div[3]/label/input').send_keys("22世纪的大佬们！！！")
driver.find_element_by_xpath('//*[@id="goods_f"]/div[2]/div[3]/div/input').send_keys("大佬无处不在，因为这是22世纪，拥有ipad，你离大佬只是一步之遥")
driver.find_element_by_xpath('//*[@id="goods_f"]/div[2]/div[3]/div/label/input').click()
driver.find_element_by_xpath('//*[@id="sticky_navigation_right"]/button[1]').click()
# #关联商品
#
# driver.find_element_by_xpath('/html/body/div[2]/div/div[2]/div/ul/li[11]/a').click()
# driver.find_element_by_xpath('//*[@id="relationgoods_id"]').execute_script('type="visable"')
# sleep(2)
# driver.find_element_by_id_data('relationgoods_id','2')
# # d = dr.find_element_by_xpath('//*[@id="mainImgclass"]/div[2]/input')
# # dr.execute_script('arguments[0].removeAttribute(\"style\")', d)
# # driver.find_element_by_id('relationgoods_id').set_element_visable('visable')
# # driver.find_element_by_id_data('relationgoods_id',"2")
# # driver.find_element_by_xpath('//*[@id="relation_goods_keyword"]').send_keys('索尼').key_down(Keys.ENTER)
# # driver.find_element_by_id_data('relationgoods_id','2').set_element_visable("type='visable'")
# driver.find_element_by_xpath('//*[@id="goods_n"]/div[2]/div[2]/button').click()
# driver.find_element_by_xpath('//*[@id="sticky_navigation_right"]/button[1]').click()
# #相关商品
# driver.find_element_by_xpath('/html/body/div[2]/div/div[2]/div/ul/li[12]/a').click()
# driver.find_element_by_id_data('related_goods_keyword','苹果(Apple) iPhone X 64GB 深空灰色 移动联通电信全网通4G手机')
# driver.find_element_by_xpath('//*[@id="goods_e"]/div[2]/div[2]/button').click()
# driver.find_element_by_xpath('//*[@id="sticky_navigation_right"]/button[1]').click()
#
# #组合商品
# driver.find_element_by_xpath('/html/body/div[2]/div/div[2]/div/ul/li[13]/a').click()
# driver.find_element_by_id_data('combination_goods_keyword','苹果(Apple) iPhone X 64GB 深空灰色 移动联通电信全网通4G手机')
# driver.find_element_by_xpath('//*[@id="goods_m"]/div[2]/div[2]/button').click()
# driver.find_element_by_xpath('//*[@id="sticky_navigation_right"]/button[1]').click()
#
#
#
# #商品评价
# driver.find_element_by_xpath('/html/body/div[2]/div/div[2]/div/ul/li[14]/a').click()
#
# driver.find_element_by_xpath('//*[@id="sticky_navigation_right"]/button[1]').click()
#
#
#保存商品
driver.find_element_by_xpath('//*[@id="sticky_navigation_right"]/button[2]').click()
#--------------------------------------------------------------------------------------
#商品分类
driver.find_element_by_xpath('/html/body/div[1]/div/ul[1]/li[3]/a').click()
driver.find_element_by_xpath('/html/body/div[1]/div/ul[1]/li[3]/ul/li[2]/a')
driver.find_element_by_xpath('/html/body/div[1]/div/ul[1]/li[3]/ul/li[2]/ul/li[2]/a').click()
#添加侧边信息
driver.find_element_by_xpath('//*[@id="sticky_navigation"]/p[2]/a[1]').click()
driver.find_element_by_id_data('frontside_name','我是你得不到的baba')
driver.find_element_by_id_data('frontside_url','https://ask.csdn.net/questions/664268')
driver.find_element_by_xpath('//*[@id="frontside_class_id"]/option[10]').click()
driver.find_element_by_xpath('//*[@id="sticky_navigation"]/div[2]/button').click()
#-----------------------------------------------------------------------------------------
#退出登录
driver.find_element_by_xpath('/html/body/div[2]/div/div[1]/p[2]/a[2]').click()
driver.close()
| 46.347561
| 129
| 0.719774
| 1,238
| 7,601
| 4.089661
| 0.163166
| 0.186846
| 0.220818
| 0.318981
| 0.752123
| 0.734742
| 0.689512
| 0.584634
| 0.508987
| 0.439068
| 0
| 0.0377
| 0.029864
| 7,601
| 163
| 130
| 46.631902
| 0.648902
| 0.291409
| 0
| 0.126761
| 0
| 0.211268
| 0.455418
| 0.359105
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| false
| 0.014085
| 0.014085
| 0
| 0.014085
| 0
| 0
| 0
| 0
| null | 0
| 1
| 1
| 0
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
|
0
| 4
|
839600f622fa7dfd1a20501060933e3134f8a05e
| 313
|
py
|
Python
|
PyCharm/Exercicios/Aula8/ex020.py
|
fabiodarice/Python
|
15ec1c7428f138be875111ac98ba38cf2eec1a93
|
[
"MIT"
] | null | null | null |
PyCharm/Exercicios/Aula8/ex020.py
|
fabiodarice/Python
|
15ec1c7428f138be875111ac98ba38cf2eec1a93
|
[
"MIT"
] | null | null | null |
PyCharm/Exercicios/Aula8/ex020.py
|
fabiodarice/Python
|
15ec1c7428f138be875111ac98ba38cf2eec1a93
|
[
"MIT"
] | null | null | null |
from random import shuffle
a1 = input('Digite o nome do aluno 1: ')
a2 = input('Digite o nome do aluno 2: ')
a3 = input('Digite o nome do aluno 3: ')
a4 = input('Digite o nome do aluno 4: ')
alunos = [a1, a2, a3, a4]
shuffle(alunos)
print('A ordem de apresentação será a seguinte \033[34m{}\033[m'.format(alunos))
| 39.125
| 80
| 0.683706
| 57
| 313
| 3.754386
| 0.526316
| 0.205607
| 0.224299
| 0.299065
| 0.429907
| 0.429907
| 0
| 0
| 0
| 0
| 0
| 0.07722
| 0.172524
| 313
| 8
| 80
| 39.125
| 0.749035
| 0
| 0
| 0
| 0
| 0
| 0.509554
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| false
| 0
| 0.125
| 0
| 0.125
| 0.125
| 0
| 0
| 0
| null | 1
| 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
| 4
|
839654d15b984574fdcebefa4302cc8d6113a801
| 218
|
py
|
Python
|
apps/software/views.py
|
ggjersund/personal-website
|
c9b0095508c44248e1405925077b4b0ada2f411a
|
[
"MIT"
] | null | null | null |
apps/software/views.py
|
ggjersund/personal-website
|
c9b0095508c44248e1405925077b4b0ada2f411a
|
[
"MIT"
] | 6
|
2019-10-23T15:06:00.000Z
|
2021-09-15T17:52:15.000Z
|
apps/software/views.py
|
ggjersund/personal-website
|
c9b0095508c44248e1405925077b4b0ada2f411a
|
[
"MIT"
] | null | null | null |
"""
Software views
"""
import socket
from django.shortcuts import render
def frontpage(request):
"""
Index view
"""
return render(request, 'software/software.html', {'hostname': socket.gethostname()})
| 18.166667
| 88
| 0.678899
| 23
| 218
| 6.434783
| 0.73913
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.174312
| 218
| 11
| 89
| 19.818182
| 0.822222
| 0.114679
| 0
| 0
| 0
| 0
| 0.176471
| 0.129412
| 0
| 0
| 0
| 0
| 0
| 1
| 0.25
| false
| 0
| 0.5
| 0
| 1
| 0
| 1
| 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
| 1
| 0
| 1
| 0
|
0
| 4
|
83c8d24e7ca2750fca13be129b6914e699ca1eda
| 468
|
py
|
Python
|
{{cookiecutter.project_slug}}/{{cookiecutter.project_slug}}/settings.py
|
nathfroech/cookiecutter-pypackage
|
5b435aa734fbf93a600bc2e88aaa644f9e2df825
|
[
"BSD-3-Clause"
] | null | null | null |
{{cookiecutter.project_slug}}/{{cookiecutter.project_slug}}/settings.py
|
nathfroech/cookiecutter-pypackage
|
5b435aa734fbf93a600bc2e88aaa644f9e2df825
|
[
"BSD-3-Clause"
] | null | null | null |
{{cookiecutter.project_slug}}/{{cookiecutter.project_slug}}/settings.py
|
nathfroech/cookiecutter-pypackage
|
5b435aa734fbf93a600bc2e88aaa644f9e2df825
|
[
"BSD-3-Clause"
] | null | null | null |
"""
Global settings for project.
May be just some literals, or path-related values.
{%- if cookiecutter.use_environment_based_settings %}
All environment-based settings should be declared here too.
{%- endif %}
"""
import pathlib
{%- if cookiecutter.use_environment_based_settings %}
from dotenv import load_dotenv # type: ignore
from environs import Env
load_dotenv()
env = Env()
env.read_env()
{%- endif %}
BASE_DIR = pathlib.Path(__file__).resolve().parent
| 19.5
| 59
| 0.75
| 63
| 468
| 5.349206
| 0.619048
| 0.142433
| 0.21365
| 0.166172
| 0.243323
| 0.243323
| 0
| 0
| 0
| 0
| 0
| 0
| 0.138889
| 468
| 23
| 60
| 20.347826
| 0.836228
| 0.025641
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| null | null | 0
| 0.333333
| null | null | 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
| 1
| 0
| 0
| 0
| 1
| 0
| 0
| 0
|
0
| 4
|
83df53c2b28b5b00d6c2cf99eebbdf55bf1c8eb6
| 91
|
py
|
Python
|
src/example_validate.py
|
RichardOkubo/PythonScripts
|
86090465f739a2fc3f1f8ef22977efd241f97361
|
[
"MIT"
] | null | null | null |
src/example_validate.py
|
RichardOkubo/PythonScripts
|
86090465f739a2fc3f1f8ef22977efd241f97361
|
[
"MIT"
] | null | null | null |
src/example_validate.py
|
RichardOkubo/PythonScripts
|
86090465f739a2fc3f1f8ef22977efd241f97361
|
[
"MIT"
] | null | null | null |
from validate import validate
@validate
def sub(x: int, y: int) -> int:
return x - y
| 13
| 31
| 0.648352
| 15
| 91
| 3.933333
| 0.6
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.241758
| 91
| 6
| 32
| 15.166667
| 0.855072
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0.25
| false
| 0
| 0.25
| 0.25
| 0.75
| 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
| 1
| 0
| 0
| 0
| 1
| 0
| 0
|
0
| 4
|
83e9694e3f620e7e3bb4a9be03aa41f32793f466
| 126
|
py
|
Python
|
users/serializer.py
|
Micahtugi/Awards
|
b01f787686c920c9a765ea212357dc50b079277e
|
[
"Unlicense"
] | null | null | null |
users/serializer.py
|
Micahtugi/Awards
|
b01f787686c920c9a765ea212357dc50b079277e
|
[
"Unlicense"
] | 6
|
2020-02-12T00:50:19.000Z
|
2022-01-13T01:23:26.000Z
|
users/serializer.py
|
Micahtugi/Awards
|
b01f787686c920c9a765ea212357dc50b079277e
|
[
"Unlicense"
] | null | null | null |
from rest_framework import serializers
from .models import Profile
class ProfileSerializer(serializers.ModelSerializer):
| 25.2
| 53
| 0.84127
| 13
| 126
| 8.076923
| 0.769231
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.119048
| 126
| 5
| 54
| 25.2
| 0.945946
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| null | null | 0
| 0.666667
| null | null | 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
| 1
| 0
| 0
| 0
| 1
| 0
| 0
| 0
|
0
| 4
|
83fc7817e7a2c289156666833164c4fe86ea21b3
| 92
|
py
|
Python
|
no_covers/apps.py
|
qiwiGremL1n/blog
|
2ed534c0c62d91603f39da6b1c7e421b1cbf4047
|
[
"MIT"
] | null | null | null |
no_covers/apps.py
|
qiwiGremL1n/blog
|
2ed534c0c62d91603f39da6b1c7e421b1cbf4047
|
[
"MIT"
] | null | null | null |
no_covers/apps.py
|
qiwiGremL1n/blog
|
2ed534c0c62d91603f39da6b1c7e421b1cbf4047
|
[
"MIT"
] | null | null | null |
from django.apps import AppConfig
class NoCoversConfig(AppConfig):
name = 'no_covers'
| 15.333333
| 33
| 0.76087
| 11
| 92
| 6.272727
| 0.909091
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.163043
| 92
| 5
| 34
| 18.4
| 0.896104
| 0
| 0
| 0
| 0
| 0
| 0.097826
| 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
| 1
| 0
|
0
| 4
|
f7c5e836dd01c9374182a548fd89ead3dc36d2aa
| 146
|
py
|
Python
|
api_key.py
|
apreble21/python-api-challenge
|
de9c066473a10cb28976aca38c61d703ff1669c0
|
[
"ADSL"
] | null | null | null |
api_key.py
|
apreble21/python-api-challenge
|
de9c066473a10cb28976aca38c61d703ff1669c0
|
[
"ADSL"
] | null | null | null |
api_key.py
|
apreble21/python-api-challenge
|
de9c066473a10cb28976aca38c61d703ff1669c0
|
[
"ADSL"
] | null | null | null |
# OpenWeatherMap API Key
weather_api_key = "601b4c14f4ddb46a0080bbfb5ca51d3e"
# Google API Key
g_key = "AIzaSyDNUFB01N6sBwZfPznGBiHayHJrON12pYw"
| 24.333333
| 52
| 0.842466
| 13
| 146
| 9.230769
| 0.615385
| 0.15
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.167939
| 0.10274
| 146
| 5
| 53
| 29.2
| 0.748092
| 0.253425
| 0
| 0
| 0
| 0
| 0.669811
| 0.669811
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| false
| 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
| 1
| 1
| null | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
|
0
| 4
|
f7dc475b41f557a908d7125b18175f13969f5b0d
| 95
|
py
|
Python
|
localusers/apps.py
|
SentF/henix
|
960636f4ffa053ef26016a37bd895801ce47c099
|
[
"Unlicense"
] | null | null | null |
localusers/apps.py
|
SentF/henix
|
960636f4ffa053ef26016a37bd895801ce47c099
|
[
"Unlicense"
] | null | null | null |
localusers/apps.py
|
SentF/henix
|
960636f4ffa053ef26016a37bd895801ce47c099
|
[
"Unlicense"
] | null | null | null |
from django.apps import AppConfig
class LocalusersConfig(AppConfig):
name = 'localusers'
| 15.833333
| 34
| 0.768421
| 10
| 95
| 7.3
| 0.9
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.157895
| 95
| 5
| 35
| 19
| 0.9125
| 0
| 0
| 0
| 0
| 0
| 0.105263
| 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
| 1
| 0
|
0
| 4
|
f7e4644e320ce710e993c01363469095dcd3a0ed
| 52
|
py
|
Python
|
code/Dirs.py
|
bradkav/DarkAxionPortal
|
5716e0684cf0f7e84f0a4de00a37734deff71d7b
|
[
"MIT"
] | null | null | null |
code/Dirs.py
|
bradkav/DarkAxionPortal
|
5716e0684cf0f7e84f0a4de00a37734deff71d7b
|
[
"MIT"
] | null | null | null |
code/Dirs.py
|
bradkav/DarkAxionPortal
|
5716e0684cf0f7e84f0a4de00a37734deff71d7b
|
[
"MIT"
] | null | null | null |
axionlimits_dir = "/Users/bradkav/Code/AxionLimits/"
| 52
| 52
| 0.807692
| 6
| 52
| 6.833333
| 0.833333
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.038462
| 52
| 1
| 52
| 52
| 0.82
| 0
| 0
| 0
| 0
| 0
| 0.603774
| 0.603774
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| false
| 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
| 1
| 1
| null | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
|
0
| 4
|
f7e842d55b300343c8ba53e377bfb9d9cef766fb
| 3,239
|
py
|
Python
|
fdrtd/plugins/simon/accumulators/accumulator_statistics_bivariate.py
|
fdrtd/simon
|
46926200c74f17f48d27a0d7b195f14b293dda4a
|
[
"MIT"
] | null | null | null |
fdrtd/plugins/simon/accumulators/accumulator_statistics_bivariate.py
|
fdrtd/simon
|
46926200c74f17f48d27a0d7b195f14b293dda4a
|
[
"MIT"
] | null | null | null |
fdrtd/plugins/simon/accumulators/accumulator_statistics_bivariate.py
|
fdrtd/simon
|
46926200c74f17f48d27a0d7b195f14b293dda4a
|
[
"MIT"
] | null | null | null |
import math as _math
from fdrtd.plugins.simon.accumulators.accumulator import Accumulator
from fdrtd.plugins.simon.accumulators.accumulator_statistics_univariate import AccumulatorStatisticsUnivariate
class AccumulatorStatisticsBivariate(Accumulator):
def __init__(self, _=None):
self.accumulator_x = AccumulatorStatisticsUnivariate()
self.accumulator_y = AccumulatorStatisticsUnivariate()
self.accumulator_xy = AccumulatorStatisticsUnivariate()
def serialize(self):
return {'accumulator_x': self.accumulator_x.serialize(),
'accumulator_y': self.accumulator_y.serialize(),
'accumulator_xy': self.accumulator_xy.serialize()}
@staticmethod
def deserialize(dictionary):
accumulator = AccumulatorStatisticsBivariate()
accumulator.accumulator_x = AccumulatorStatisticsUnivariate.deserialize(dictionary['accumulator_x'])
accumulator.accumulator_y = AccumulatorStatisticsUnivariate.deserialize(dictionary['accumulator_y'])
accumulator.accumulator_xy = AccumulatorStatisticsUnivariate.deserialize(dictionary['accumulator_xy'])
return accumulator
def add(self, other):
self.accumulator_x.add(other.accumulator_x)
self.accumulator_y.add(other.accumulator_y)
self.accumulator_xy.add(other.accumulator_xy)
def update(self, data):
(x, y) = data
self.accumulator_x.update(x)
self.accumulator_y.update(y)
self.accumulator_xy.update(x*y)
def finalize(self):
self.accumulator_x.finalize()
self.accumulator_y.finalize()
self.accumulator_xy.finalize()
def encrypt_data_for_upload(self, nonce):
return {'accumulator_x': self.accumulator_x.encrypt_data_for_upload(nonce),
'accumulator_y': self.accumulator_y.encrypt_data_for_upload(nonce),
'accumulator_xy': self.accumulator_xy.encrypt_data_for_upload(nonce, power=2)}
@staticmethod
def decrypt_result_from_download(encrypted, nonce):
decryption_powers = {'samples': 0, 'covariance_mle': 2, 'covariance': 2, 'correlation_coefficient': 0,
'regression_slope': 0, 'regression_interceipt': 1, 'regression_slope_only': 0}
return nonce.decrypt_dictionary_numerical(encrypted, decryption_powers)
def get_samples(self):
return self.accumulator_xy.get_samples()
def get_covariance_mle(self):
return self.accumulator_xy.get_mean() - self.accumulator_x.get_mean() * self.accumulator_y.get_mean()
def get_covariance(self):
return self.get_covariance_mle() / (1.0 - 1.0 / self.accumulator_xy.get_samples())
def get_correlation_coefficient(self):
return self.get_covariance() / _math.sqrt(self.accumulator_x.get_variance() * self.accumulator_y.get_variance())
def get_regression_slope(self):
return self.get_covariance() / self.accumulator_x.get_variance()
def get_regression_interceipt(self):
return self.accumulator_y.get_mean() - self.get_regression_slope() * self.accumulator_x.get_mean()
def get_regression_slope_only(self):
return self.accumulator_xy.get_mean() / self.accumulator_x.calculate_raw_moment(2)
| 43.77027
| 120
| 0.727694
| 359
| 3,239
| 6.256267
| 0.172702
| 0.200356
| 0.078362
| 0.035619
| 0.345948
| 0.179875
| 0.073909
| 0.044524
| 0.044524
| 0.044524
| 0
| 0.004867
| 0.175363
| 3,239
| 73
| 121
| 44.369863
| 0.836016
| 0
| 0
| 0.036364
| 0
| 0
| 0.071627
| 0.020068
| 0
| 0
| 0
| 0
| 0
| 1
| 0.272727
| false
| 0
| 0.054545
| 0.163636
| 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
| 1
| 1
| 0
|
0
| 4
|
f7eeaa099b83aecb7abbc258520807361c03cb88
| 65
|
py
|
Python
|
src_tf/templates/tf_dataset_template/base/base_model.py
|
ashishpatel26/finch
|
bf2958c0f268575e5d51ad08fbc08b151cbea962
|
[
"MIT"
] | 1
|
2019-02-12T09:22:00.000Z
|
2019-02-12T09:22:00.000Z
|
src_tf/templates/tf_dataset_template/base/base_model.py
|
loopzxl/finch
|
bf2958c0f268575e5d51ad08fbc08b151cbea962
|
[
"MIT"
] | null | null | null |
src_tf/templates/tf_dataset_template/base/base_model.py
|
loopzxl/finch
|
bf2958c0f268575e5d51ad08fbc08b151cbea962
|
[
"MIT"
] | 1
|
2020-10-15T21:34:17.000Z
|
2020-10-15T21:34:17.000Z
|
class BaseModel:
def __init__(self):
self.ops = {}
| 10.833333
| 23
| 0.553846
| 7
| 65
| 4.571429
| 0.857143
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.323077
| 65
| 5
| 24
| 13
| 0.727273
| 0
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| 0
| 0
| 0
| 0
| 0
| 1
| 0.333333
| false
| 0
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| 0.666667
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| 0
| null | 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| 1
| 0
|
0
| 4
|
f7fb97db3220bbc64ca010f0349740e9c79f310a
| 178
|
py
|
Python
|
bus_system/apps/bus_driver/admin.py
|
pygabo/bus_system
|
ffb76d3414e058286799f3df1cb551b26286e7c3
|
[
"MIT"
] | null | null | null |
bus_system/apps/bus_driver/admin.py
|
pygabo/bus_system
|
ffb76d3414e058286799f3df1cb551b26286e7c3
|
[
"MIT"
] | null | null | null |
bus_system/apps/bus_driver/admin.py
|
pygabo/bus_system
|
ffb76d3414e058286799f3df1cb551b26286e7c3
|
[
"MIT"
] | null | null | null |
# Core Django imports
from django.contrib import admin
# Imports from my apps
from bus_system.apps.bus_driver.models import BusDriverModel
admin.site.register(BusDriverModel)
| 22.25
| 60
| 0.825843
| 25
| 178
| 5.8
| 0.64
| 0.151724
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.117978
| 178
| 7
| 61
| 25.428571
| 0.923567
| 0.230337
| 0
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| 0
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| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| true
| 0
| 0.666667
| 0
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| null | 0
| 0
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| 0
| 0
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| 0
| 0
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| 0
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| 1
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| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 1
| 0
| 0
| 0
|
0
| 4
|
7913c04ccb3d3a404fd572f5ba2d25d3927e3c07
| 415
|
py
|
Python
|
WebClassApp/mainpage/serializers.py
|
jesuscol96/WebClassApp
|
092bde4cb16f09f3efafc32af904715fae59773a
|
[
"MIT"
] | null | null | null |
WebClassApp/mainpage/serializers.py
|
jesuscol96/WebClassApp
|
092bde4cb16f09f3efafc32af904715fae59773a
|
[
"MIT"
] | null | null | null |
WebClassApp/mainpage/serializers.py
|
jesuscol96/WebClassApp
|
092bde4cb16f09f3efafc32af904715fae59773a
|
[
"MIT"
] | null | null | null |
from rest_framework import serializers
from django.contrib.auth.models import User
from .models import *
class UserSerializer(serializers.ModelSerializer):
class Meta:
model= User
fields = ['username','first_name','last_name','password','email','is_superuser']
class RolesSerializer(serializers.ModelSerializer):
class Meta:
model= Roles
fields = ['name']
| 25.9375
| 89
| 0.684337
| 43
| 415
| 6.511628
| 0.604651
| 0.085714
| 0.221429
| 0.25
| 0.285714
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.214458
| 415
| 15
| 90
| 27.666667
| 0.858896
| 0
| 0
| 0.181818
| 0
| 0
| 0.14
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| false
| 0.090909
| 0.272727
| 0
| 0.636364
| 0
| 0
| 0
| 0
| null | 0
| 1
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
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| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 1
| 0
|
0
| 4
|
7936dd133e3fdbf46aa9c6c09a12e6cbc0c9cc6a
| 169
|
py
|
Python
|
device_captures/models.py
|
mohbandy/probr-core
|
df152c4fe0d0e5e393f11154db38dc56dcefb636
|
[
"MIT"
] | 45
|
2015-08-11T11:37:46.000Z
|
2022-03-27T19:27:56.000Z
|
device_captures/models.py
|
mohbandy/probr-core
|
df152c4fe0d0e5e393f11154db38dc56dcefb636
|
[
"MIT"
] | 33
|
2015-08-11T10:23:44.000Z
|
2022-03-01T15:57:15.000Z
|
device_captures/models.py
|
mohbandy/probr-core
|
df152c4fe0d0e5e393f11154db38dc56dcefb636
|
[
"MIT"
] | 23
|
2015-10-06T17:07:54.000Z
|
2021-11-12T10:47:17.000Z
|
from captures.models import Capture
from django.db import models
from devices.models import Device
class DeviceCapture(Capture):
device = models.ForeignKey(Device)
| 24.142857
| 38
| 0.810651
| 22
| 169
| 6.227273
| 0.545455
| 0.175182
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.130178
| 169
| 7
| 38
| 24.142857
| 0.931973
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| false
| 0
| 0.6
| 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
| 1
| 0
|
0
| 4
|
f738c8d7c592d77c0c81025d123c1cecf33946f4
| 154
|
py
|
Python
|
pysensors/basis/__init__.py
|
Jimmy-INL/pysensors
|
62b79a233a551ae01125e20e06fde0c96b4dffd2
|
[
"MIT"
] | null | null | null |
pysensors/basis/__init__.py
|
Jimmy-INL/pysensors
|
62b79a233a551ae01125e20e06fde0c96b4dffd2
|
[
"MIT"
] | null | null | null |
pysensors/basis/__init__.py
|
Jimmy-INL/pysensors
|
62b79a233a551ae01125e20e06fde0c96b4dffd2
|
[
"MIT"
] | null | null | null |
from ._identity import Identity
from ._random_projection import RandomProjection
from ._svd import SVD
__all__ = ["Identity", "SVD", "RandomProjection"]
| 25.666667
| 49
| 0.792208
| 17
| 154
| 6.705882
| 0.470588
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.116883
| 154
| 5
| 50
| 30.8
| 0.838235
| 0
| 0
| 0
| 0
| 0
| 0.175325
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| false
| 0
| 0.75
| 0
| 0.75
| 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
| 1
| 0
|
0
| 4
|
f7464013eeebcdb4986304f0e5c49ade64cbb57c
| 113
|
py
|
Python
|
commit_checker/tests/__main__.py
|
hrome/commit_checker
|
b212cb9cf6728b6bb9006097fa8211e9a06537b8
|
[
"MIT"
] | 2
|
2017-11-24T12:28:50.000Z
|
2018-12-28T10:13:40.000Z
|
commit_checker/tests/functional/__main__.py
|
hrome/commit_checker
|
b212cb9cf6728b6bb9006097fa8211e9a06537b8
|
[
"MIT"
] | null | null | null |
commit_checker/tests/functional/__main__.py
|
hrome/commit_checker
|
b212cb9cf6728b6bb9006097fa8211e9a06537b8
|
[
"MIT"
] | null | null | null |
import os
from nose.core import TestProgram
os.chdir(os.path.abspath(os.path.dirname(__file__)))
TestProgram()
| 16.142857
| 52
| 0.787611
| 17
| 113
| 5
| 0.647059
| 0.141176
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.088496
| 113
| 6
| 53
| 18.833333
| 0.825243
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| true
| 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
| 1
| 0
| 1
| 0
| 0
| 0
|
0
| 4
|
f76a3bf1b294256e0e46b5250fdd278b0773cccd
| 1,620
|
py
|
Python
|
cohesity_management_sdk/models/environment_list_protection_sources_enum.py
|
cohesity/management-sdk-python
|
867d8c0c40dd317cdb017902c895527da7ae31c0
|
[
"Apache-2.0"
] | 18
|
2019-09-24T17:35:53.000Z
|
2022-03-25T08:08:47.000Z
|
cohesity_management_sdk/models/environment_list_protection_sources_enum.py
|
cohesity/management-sdk-python
|
867d8c0c40dd317cdb017902c895527da7ae31c0
|
[
"Apache-2.0"
] | 18
|
2019-03-29T19:32:29.000Z
|
2022-01-03T23:16:45.000Z
|
cohesity_management_sdk/models/environment_list_protection_sources_enum.py
|
cohesity/management-sdk-python
|
867d8c0c40dd317cdb017902c895527da7ae31c0
|
[
"Apache-2.0"
] | 16
|
2019-02-27T06:54:12.000Z
|
2021-11-16T18:10:24.000Z
|
# -*- coding: utf-8 -*-
# Copyright 2021 Cohesity Inc.
class EnvironmentListProtectionSourcesEnum(object):
"""Implementation of the 'environment_ListProtectionSources' enum.
TODO: type enum description here.
Attributes:
K_VMWARE: TODO: type description here.
KSQL: TODO: type description here.
KVIEW: TODO: type description here.
KPUPPETEER: TODO: type description here.
KPHYSICAL: TODO: type description here.
KPURE: TODO: type description here.
KNETAPP: TODO: type description here.
KGENERICNAS: TODO: type description here.
K_HYPERV: TODO: type description here.
KACROPOLIS: TODO: type description here.
KAZURE: TODO: type description here.
KKUBERNETES: TODO: type description here.
KCASSANDRA: TODO: type description here.
KMONGODB: TODO: type description here.
KCOUCHBASE: TODO: type description here.
KHDFS: TODO: type description here.
KHIVE: TODO: type description here.
KHBASE: TODO: type description here.
KUDA: TODO: type description here.
"""
K_VMWARE = 'kVMware'
KSQL = 'kSQL'
KVIEW = 'kView'
KPUPPETEER = 'kPuppeteer'
KPHYSICAL = 'kPhysical'
KPURE = 'kPure'
KNETAPP = 'kNetapp'
KGENERICNAS = 'kGenericNas'
K_HYPERV = 'kHyperV'
KACROPOLIS = 'kAcropolis'
KAZURE = 'kAzure'
KKUBERNETES = 'kKubernetes'
KCASSANDRA = 'kCassandra'
KMONGODB = 'kMongoDB'
KCOUCHBASE = 'kCouchbase'
KHDFS = 'kHdfs'
KHIVE = 'kHive'
KHBASE = 'kHBase'
KUDA = 'kUDA'
| 22.816901
| 70
| 0.638889
| 159
| 1,620
| 6.477987
| 0.27044
| 0.15534
| 0.350485
| 0.424272
| 0.046602
| 0
| 0
| 0
| 0
| 0
| 0
| 0.004255
| 0.274691
| 1,620
| 70
| 71
| 23.142857
| 0.87234
| 0.598765
| 0
| 0
| 0
| 0
| 0.253623
| 0
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| 0
| 0
| 0.285714
| 0
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| 0
| false
| 0
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| null | 0
| 1
| 1
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| 0
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| 0
| 0
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| 0
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| null | 0
| 0
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
|
0
| 4
|
f780b53286805d43cf8d3e4449572f04d4dcff75
| 6,693
|
py
|
Python
|
looking_for_group/rpgcollections/utils.py
|
andrlik/looking-for-group
|
0b1cecb37ef0f6d75692fd188130e2c60d09b7d2
|
[
"BSD-3-Clause"
] | null | null | null |
looking_for_group/rpgcollections/utils.py
|
andrlik/looking-for-group
|
0b1cecb37ef0f6d75692fd188130e2c60d09b7d2
|
[
"BSD-3-Clause"
] | null | null | null |
looking_for_group/rpgcollections/utils.py
|
andrlik/looking-for-group
|
0b1cecb37ef0f6d75692fd188130e2c60d09b7d2
|
[
"BSD-3-Clause"
] | null | null | null |
from django.contrib.contenttypes.models import ContentType
from ..game_catalog import models as catalog_models
from . import models
def get_distinct_games(library):
sb_ct = ContentType.objects.get_for_model(catalog_models.SourceBook)
md_ct = ContentType.objects.get_for_model(catalog_models.PublishedModule)
sourcebook_games = catalog_models.PublishedGame.objects.filter(
id__in=[
sb.edition.game.pk
for sb in catalog_models.SourceBook.objects.filter(
id__in=[
b.content_object.pk
for b in models.Book.objects.filter(
library=library, content_type=sb_ct
)
]
).select_related("edition", "edition__game")
]
).order_by("title")
module_games = catalog_models.PublishedGame.objects.filter(
id__in=[
md.parent_game_edition.game.pk
for md in catalog_models.PublishedModule.objects.filter(
id__in=[
b.content_object.pk
for b in models.Book.objects.filter(
library=library, content_type=md_ct
)
]
).select_related("parent_game_edition", "parent_game_edition__game")
]
).order_by("title")
games = sourcebook_games.union(module_games).order_by("title")
return games
def get_distinct_editions(library):
sb_ct = ContentType.objects.get_for_model(catalog_models.SourceBook)
md_ct = ContentType.objects.get_for_model(catalog_models.PublishedModule)
sourcebook_editions = (
catalog_models.GameEdition.objects.filter(
id__in=[
sb.edition.pk
for sb in catalog_models.SourceBook.objects.filter(
id__in=[
b.content_object.pk
for b in models.Book.objects.filter(
library=library, content_type=sb_ct
)
]
).select_related("edition")
]
)
.select_related("game")
.order_by("game__title", "release_date")
)
module_editions = (
catalog_models.GameEdition.objects.filter(
id__in=[
md.parent_game_edition.pk
for md in catalog_models.PublishedModule.objects.filter(
id__in=[
b.content_object.pk
for b in models.Book.objects.filter(
library=library, content_type=md_ct
)
]
).select_related("parent_game_edition")
]
)
.select_related("game")
.order_by("game__title", "release_date")
)
editions = sourcebook_editions.union(module_editions).order_by(
"game__title", "release_date"
)
return editions
def get_distinct_systems(library):
sb_ct = ContentType.objects.get_for_model(catalog_models.SourceBook)
md_ct = ContentType.objects.get_for_model(catalog_models.PublishedModule)
sys_ct = ContentType.objects.get_for_model(catalog_models.GameSystem)
sourcebook_systems = catalog_models.GameSystem.objects.filter(
id__in=[
sb.edition.game_system.pk
for sb in catalog_models.SourceBook.objects.filter(
id__in=[
b.content_object.pk
for b in models.Book.objects.filter(
library=library, content_type=sb_ct
)
],
edition__game_system__isnull=False,
).select_related("edition", "edition__game_system")
]
).order_by("name", "publication_date")
module_systems = catalog_models.GameSystem.objects.filter(
id__in=[
md.parent_game_edition.game_system.pk
for md in catalog_models.PublishedModule.objects.filter(
id__in=[
b.content_object.pk
for b in models.Book.objects.filter(
library=library, content_type=md_ct
)
],
parent_game_edition__game_system__isnull=False,
).select_related("parent_game_edition", "parent_game_edition__game_system")
]
).order_by("name", "publication_date")
system_systems = catalog_models.GameSystem.objects.filter(
id__in=[
b.content_object.pk
for b in models.Book.objects.filter(library=library, content_type=sys_ct)
]
).order_by("name", "publication_date")
sb_and_md = sourcebook_systems.union(module_systems)
systems = sb_and_md.union(system_systems).order_by("name", "publication_date")
return systems
def get_distinct_publishers(library):
sb_ct = ContentType.objects.get_for_model(catalog_models.SourceBook)
md_ct = ContentType.objects.get_for_model(catalog_models.PublishedModule)
sys_ct = ContentType.objects.get_for_model(catalog_models.GameSystem)
sourcebook_publishers = catalog_models.GamePublisher.objects.filter(
id__in=[
sb.publisher.pk
for sb in catalog_models.SourceBook.objects.filter(
id__in=[
b.content_object.pk
for b in models.Book.objects.filter(
library=library, content_type=sb_ct
)
]
)
]
).order_by("name")
module_publishers = catalog_models.GamePublisher.objects.filter(
id__in=[
md.publisher.pk
for md in catalog_models.PublishedModule.objects.filter(
id__in=[
b.content_object.pk
for b in models.Book.objects.filter(
library=library, content_type=md_ct
)
]
).select_related("publisher")
]
).order_by("name")
system_publishers = catalog_models.GamePublisher.objects.filter(
id__in=[
sys.original_publisher.pk
for sys in catalog_models.GameSystem.objects.filter(
id__in=[
b.content_object.pk
for b in models.Book.objects.filter(
library=library, content_type=sys_ct
)
]
).select_related("original_publisher")
]
).order_by("name")
sb_and_md = sourcebook_publishers.union(module_publishers)
publishers = sb_and_md.union(system_publishers).order_by("name")
return publishers
| 38.912791
| 87
| 0.585089
| 700
| 6,693
| 5.245714
| 0.085714
| 0.106209
| 0.077614
| 0.087963
| 0.827342
| 0.784858
| 0.775054
| 0.749183
| 0.609205
| 0.56781
| 0
| 0
| 0.332885
| 6,693
| 171
| 88
| 39.140351
| 0.822396
| 0
| 0
| 0.493827
| 0
| 0
| 0.057224
| 0.008516
| 0
| 0
| 0
| 0
| 0
| 1
| 0.024691
| false
| 0
| 0.018519
| 0
| 0.067901
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 1
| 1
| 1
| 1
| 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
| 4
|
e398c9cb3f7797fc2675aea92c940e9f6259d99d
| 250
|
py
|
Python
|
image_vision/plugins/visualizers/registry.py
|
IvanKosik/ImageVision
|
038b2b3948a16adc4c2abb3bc8c1c32f62aa4319
|
[
"BSD-3-Clause"
] | null | null | null |
image_vision/plugins/visualizers/registry.py
|
IvanKosik/ImageVision
|
038b2b3948a16adc4c2abb3bc8c1c32f62aa4319
|
[
"BSD-3-Clause"
] | null | null | null |
image_vision/plugins/visualizers/registry.py
|
IvanKosik/ImageVision
|
038b2b3948a16adc4c2abb3bc8c1c32f62aa4319
|
[
"BSD-3-Clause"
] | null | null | null |
from core import Plugin
from extensions.visualizers import DataVisualizerRegistry
class DataVisualizerRegistryPlugin(Plugin):
def __init__(self):
super().__init__()
self.visualizers_registry = DataVisualizerRegistry()
| 25
| 61
| 0.744
| 21
| 250
| 8.428571
| 0.666667
| 0.090395
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.192
| 250
| 9
| 62
| 27.777778
| 0.876238
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0.166667
| false
| 0
| 0.333333
| 0
| 0.666667
| 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
| 1
| 0
|
0
| 4
|
e3a2355730a5d5adebb58f1e888f6f8df8743d7e
| 108
|
py
|
Python
|
resource-timing/resources/eventsource.py
|
meyerweb/wpt
|
f04261533819893c71289614c03434c06856c13e
|
[
"BSD-3-Clause"
] | 14,668
|
2015-01-01T01:57:10.000Z
|
2022-03-31T23:33:32.000Z
|
resource-timing/resources/eventsource.py
|
meyerweb/wpt
|
f04261533819893c71289614c03434c06856c13e
|
[
"BSD-3-Clause"
] | 7,642
|
2018-05-28T09:38:03.000Z
|
2022-03-31T20:55:48.000Z
|
resource-timing/resources/eventsource.py
|
meyerweb/wpt
|
f04261533819893c71289614c03434c06856c13e
|
[
"BSD-3-Clause"
] | 5,941
|
2015-01-02T11:32:21.000Z
|
2022-03-31T16:35:46.000Z
|
def main(request, response):
response.headers.set(b"Content-Type", b"text/event-stream")
return u""
| 27
| 63
| 0.694444
| 16
| 108
| 4.6875
| 0.875
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.138889
| 108
| 3
| 64
| 36
| 0.806452
| 0
| 0
| 0
| 0
| 0
| 0.268519
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0.333333
| false
| 0
| 0
| 0
| 0.666667
| 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
| 1
| 0
| 0
| 0
| 0
| 1
| 0
|
0
| 4
|
e3c0bfd415010b834e3a379c979e37202495e059
| 233
|
py
|
Python
|
flaskr/models/user_role.py
|
tuhinpaul/flask-sample-project
|
fb4cae2d00b7c1e1318f44e477f71ed93ecaed52
|
[
"MIT"
] | null | null | null |
flaskr/models/user_role.py
|
tuhinpaul/flask-sample-project
|
fb4cae2d00b7c1e1318f44e477f71ed93ecaed52
|
[
"MIT"
] | null | null | null |
flaskr/models/user_role.py
|
tuhinpaul/flask-sample-project
|
fb4cae2d00b7c1e1318f44e477f71ed93ecaed52
|
[
"MIT"
] | null | null | null |
from sqlalchemy import Column, Integer, String, Float
from .base import Base
class UserRole(Base):
__tablename__ = 'UserRole'
id = Column(Integer, primary_key=True)
userId = Column(Integer)
roleId = Column(Integer)
| 23.3
| 53
| 0.72103
| 28
| 233
| 5.821429
| 0.607143
| 0.319018
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.188841
| 233
| 9
| 54
| 25.888889
| 0.862434
| 0
| 0
| 0
| 0
| 0
| 0.034335
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| false
| 0
| 0.285714
| 0
| 1
| 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
| 1
| 0
|
0
| 4
|
e3e002bcda1c127e55cf91be255d6c11fb96f6c1
| 2,948
|
py
|
Python
|
tests/test_stack_yaml.py
|
AFCYBER-DREAM/piperci-mindflayer
|
bfa1cce9f48563f34b4fd8cc8b92b54018f11be9
|
[
"MIT"
] | null | null | null |
tests/test_stack_yaml.py
|
AFCYBER-DREAM/piperci-mindflayer
|
bfa1cce9f48563f34b4fd8cc8b92b54018f11be9
|
[
"MIT"
] | 2
|
2019-06-05T15:31:41.000Z
|
2019-06-07T17:56:12.000Z
|
tests/test_stack_yaml.py
|
AFCYBER-DREAM/piperci-mindflayer
|
bfa1cce9f48563f34b4fd8cc8b92b54018f11be9
|
[
"MIT"
] | 2
|
2019-05-21T20:33:29.000Z
|
2019-06-05T13:44:41.000Z
|
import os
def test_stack_functions(stack_data):
errMsg = 'Error: \'stack.yml\' file does not contain any functions.'
assert 'functions' in stack_data, errMsg
def test_git_ignore(stack_dir):
path = os.path.join(stack_dir, '.gitignore')
errMsg = f'Error: No \'.gitignore\' file found in {stack_dir}.'
assert os.path.exists(os.path.realpath(path)), errMsg
def test_stack_handlers(stack_function, stack_dir):
path = os.path.join(stack_dir, stack_function['handler'])
errMsg = f'Error: No directory for {stack_function["handler"]}.'
assert os.path.exists(os.path.realpath(path)), errMsg
def test_stack_handlers_file(stack_function, stack_dir):
path = os.path.join(stack_dir, stack_function['handler'], 'handler.py')
errMsg = (f'Error: \'{stack_function["handler"]}\' does not contain '
'\'handler.py\' file.')
assert os.path.exists(os.path.realpath(path)), errMsg
def test_stack_handlers_requires(stack_function, stack_dir):
path = os.path.join(stack_dir, stack_function['handler'],
'requirements.txt')
errMsg = (f'Error: \'{stack_function["handler"]}\' does not contain '
'\'requirements.txt\' file.')
assert os.path.exists(os.path.realpath(path)), errMsg
def test_stack_handlers_init(stack_function, stack_dir):
path = os.path.join(stack_dir, stack_function['handler'],
'__init__.py')
errMsg = (f'Error: \'{stack_function["handler"]}\' does not contain '
'\'__init__.py\' file.')
assert os.path.exists(os.path.realpath(path)), errMsg
def test_stack_langs(stack_function, stack_dir):
path = os.path.join(stack_dir, 'template', stack_function['lang'])
errMsg = (f'Error: No directory for {stack_function["lang"]} is present in '
'\'template\' directory.')
assert os.path.exists(os.path.realpath(path)), errMsg
def test_stack_langs_dockerfile(stack_function, stack_dir):
path = os.path.join(stack_dir, 'template', stack_function['lang'],
'Dockerfile')
errMsg = (f'Error: \'template/{stack_function["lang"]}/\' does not contain '
'\'Dockerfile\'.')
assert os.path.exists(os.path.realpath(path)), errMsg
def test_stack_langs_requires(stack_function, stack_dir):
path = os.path.join(stack_dir, 'template', stack_function['lang'],
'requirements.txt')
errMsg = (f'Error: \'template/{stack_function["lang"]}/\' does not contain '
'\'requirements.txt\' file.')
assert os.path.exists(os.path.realpath(path)), errMsg
def test_only_langs(stack_data, stack_dir):
lang_set = {v['lang'] for k, v in stack_data['functions'].items()}
dir_set = set(os.listdir(os.path.join(stack_dir, 'template')))
warnMsg = ('Warning: Unused language templates are present in \'template\' '
'directory.')
assert len(dir_set - lang_set) == 0, warnMsg
| 39.837838
| 80
| 0.662144
| 387
| 2,948
| 4.834625
| 0.149871
| 0.080171
| 0.062533
| 0.072154
| 0.7814
| 0.734367
| 0.72047
| 0.72047
| 0.662747
| 0.645644
| 0
| 0.000418
| 0.187924
| 2,948
| 73
| 81
| 40.383562
| 0.781119
| 0
| 0
| 0.403846
| 0
| 0
| 0.213026
| 0.017639
| 0
| 0
| 0
| 0
| 0.192308
| 1
| 0.192308
| false
| 0
| 0.019231
| 0
| 0.211538
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 1
| 1
| 1
| 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
| 4
|
e3e7b6608fbc1741aa7a35fb7da3577023ac131b
| 147
|
py
|
Python
|
fatd/measure/accountability/data/tools.py
|
AnthropocentricAI/fat-dummy
|
08fc3e6c55e11f664c541283dde5a1cc6fd40298
|
[
"BSD-3-Clause"
] | null | null | null |
fatd/measure/accountability/data/tools.py
|
AnthropocentricAI/fat-dummy
|
08fc3e6c55e11f664c541283dde5a1cc6fd40298
|
[
"BSD-3-Clause"
] | null | null | null |
fatd/measure/accountability/data/tools.py
|
AnthropocentricAI/fat-dummy
|
08fc3e6c55e11f664c541283dde5a1cc6fd40298
|
[
"BSD-3-Clause"
] | null | null | null |
import numpy as np
def class_count(data_holder):
unique, counts = np.unique(data_holder.target, return_counts=True)
return unique, counts
| 24.5
| 70
| 0.761905
| 22
| 147
| 4.909091
| 0.636364
| 0.185185
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.156463
| 147
| 5
| 71
| 29.4
| 0.870968
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0.25
| false
| 0
| 0.25
| 0
| 0.75
| 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
| 1
| 0
| 0
| 0
| 0
| 1
| 0
|
0
| 4
|
540ad43bc746e83d4ed301ab8b904082adcde0b2
| 88
|
py
|
Python
|
text.py
|
Ravindra-14/python-01
|
189d19b8987eb01ed3b1f6261ad1da24e0d338be
|
[
"Apache-2.0"
] | null | null | null |
text.py
|
Ravindra-14/python-01
|
189d19b8987eb01ed3b1f6261ad1da24e0d338be
|
[
"Apache-2.0"
] | null | null | null |
text.py
|
Ravindra-14/python-01
|
189d19b8987eb01ed3b1f6261ad1da24e0d338be
|
[
"Apache-2.0"
] | null | null | null |
print("hello")
count=1
if count<1:
print("yes")
else:
print("no")
| 12.571429
| 22
| 0.488636
| 12
| 88
| 3.583333
| 0.666667
| 0.27907
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.033898
| 0.329545
| 88
| 6
| 23
| 14.666667
| 0.694915
| 0
| 0
| 0
| 0
| 0
| 0.121951
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| false
| 0
| 0
| 0
| 0
| 0.5
| 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
| 1
|
0
| 4
|
5412ff2bd0dae926fd1fbf97be2d9ce144344885
| 61
|
py
|
Python
|
django_version.py
|
Muflhi01/flow-dashboard
|
993320e2eb0f86d89b9904a3d5415c7479c5918e
|
[
"MIT"
] | 1,623
|
2017-03-11T11:49:48.000Z
|
2022-03-30T06:44:11.000Z
|
django_version.py
|
Muflhi01/flow-dashboard
|
993320e2eb0f86d89b9904a3d5415c7479c5918e
|
[
"MIT"
] | 136
|
2017-03-11T17:08:57.000Z
|
2022-03-09T21:38:46.000Z
|
django_version.py
|
Muflhi01/flow-dashboard
|
993320e2eb0f86d89b9904a3d5415c7479c5918e
|
[
"MIT"
] | 217
|
2017-05-06T14:28:36.000Z
|
2022-03-29T16:56:01.000Z
|
import os
os.environ["DJANGO_SETTINGS_MODULE"] = "settings"
| 15.25
| 49
| 0.770492
| 8
| 61
| 5.625
| 0.75
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.098361
| 61
| 3
| 50
| 20.333333
| 0.818182
| 0
| 0
| 0
| 0
| 0
| 0.5
| 0.366667
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| true
| 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
| 1
| 0
| 1
| 0
| 0
| 0
|
0
| 4
|
54275bb038f023a711df21868a4e7e60e27c8dbb
| 152
|
py
|
Python
|
PYTHON/Tuples.py
|
MatheusKlebson/Programming-in-English
|
36b1746378802cfe169b6138d036fcb9a140eaad
|
[
"MIT"
] | null | null | null |
PYTHON/Tuples.py
|
MatheusKlebson/Programming-in-English
|
36b1746378802cfe169b6138d036fcb9a140eaad
|
[
"MIT"
] | null | null | null |
PYTHON/Tuples.py
|
MatheusKlebson/Programming-in-English
|
36b1746378802cfe169b6138d036fcb9a140eaad
|
[
"MIT"
] | null | null | null |
firt_tuple = (5,5,4,6,1,2,3)
new_list = list(firt_tuple)
new_tuple = tuple(new_list)
print(len(firt_tuple))
print(max(new_list))
print(min(new_tuple))
| 19
| 28
| 0.736842
| 31
| 152
| 3.354839
| 0.451613
| 0.259615
| 0.230769
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.05036
| 0.085526
| 152
| 8
| 29
| 19
| 0.697842
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| false
| 0
| 0
| 0
| 0
| 0.5
| 0
| 0
| 0
| null | 1
| 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
| 1
|
0
| 4
|
588a7cff1907ee5869e3abd8472d79cde6f0e56c
| 165
|
py
|
Python
|
probability/gamma_test2.py
|
peterhogan/python
|
bc6764f7794a862ff0d138bad80f1d6313984dcd
|
[
"MIT"
] | null | null | null |
probability/gamma_test2.py
|
peterhogan/python
|
bc6764f7794a862ff0d138bad80f1d6313984dcd
|
[
"MIT"
] | null | null | null |
probability/gamma_test2.py
|
peterhogan/python
|
bc6764f7794a862ff0d138bad80f1d6313984dcd
|
[
"MIT"
] | null | null | null |
from scipy.integrate import quad
def integrand(t,n,x):
return exp(-x*t)/t**n
def expint(n,x):
return quad(integrand, 0, Inf, args(n,x))[0]
print(expint(2,1.0))
| 16.5
| 45
| 0.672727
| 34
| 165
| 3.264706
| 0.558824
| 0.054054
| 0.144144
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.034722
| 0.127273
| 165
| 9
| 46
| 18.333333
| 0.736111
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0.333333
| false
| 0
| 0.166667
| 0.333333
| 0.833333
| 0.166667
| 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
| 1
| 0
| 0
| 0
| 1
| 1
| 0
|
0
| 4
|
589a4e8832a1c82348910df930db979cfb479cc5
| 6,084
|
py
|
Python
|
src/dewloosh/solid/fem/cells/gen/b3.py
|
dewloosh/dewloosh-solid
|
dbd6757ddd1373df870ccd99f5ee791c08d342cb
|
[
"MIT"
] | null | null | null |
src/dewloosh/solid/fem/cells/gen/b3.py
|
dewloosh/dewloosh-solid
|
dbd6757ddd1373df870ccd99f5ee791c08d342cb
|
[
"MIT"
] | null | null | null |
src/dewloosh/solid/fem/cells/gen/b3.py
|
dewloosh/dewloosh-solid
|
dbd6757ddd1373df870ccd99f5ee791c08d342cb
|
[
"MIT"
] | null | null | null |
# -*- coding: utf-8 -*-
import numpy as np
from numpy import ndarray
from numba import njit, prange
__cache = True
@njit(nogil=True, cache=__cache)
def shape_function_values(x, L):
"""
Evaluates the shape functions at a point x in the range [-1, 1].
"""
return np.array([
[
0.5 * x * (x - 1),
x**2 * (0.75 * x + 1.0) * (x - 1)**2,
x**2 * (0.75 * x + 1.0) * (x - 1)**2,
0.5 * x * (x - 1),
-0.125 * L * x**2 * (x - 1)**2 * (x + 1),
0.125 * L * x**2 * (x - 1)**2 * (x + 1)
],
[
1.0 - 1.0 * x**2,
1.0 * (x - 1)**2 * (x + 1)**2,
1.0 * (x - 1)**2 * (x + 1)**2,
1.0 - 1.0 * x**2,
L * x * (-0.5 * x**4 + 1.0 * x**2 - 0.5),
0.5 * L * x * (x - 1)**2 * (x + 1)**2
],
[
0.5 * x * (x + 1),
x**2 * (1.0 - 0.75 * x) * (x + 1)**2,
x**2 * (1.0 - 0.75 * x) * (x + 1)**2,
0.5 * x * (x + 1),
-0.125 * L * x**2 * (x - 1) * (x + 1)**2,
0.125 * L * x**2 * (x - 1) * (x + 1)**2
]
])
@njit(nogil=True, cache=__cache)
def shape_function_derivatives_1(x, L):
"""
Evaluates the first derivatives of the shape
functions at a point x in the range [-1, 1].
"""
return np.array([
[
1.0 * x - 0.5,
3.75 * x * (x - 1) * (1.0 * x - 0.533333333333333) * (x + 1),
3.75 * x * (x - 1) * (1.0 * x - 0.533333333333333) * (x + 1),
1.0 * x - 0.5,
L * x * (-0.625 * x**3 + 0.5 * x**2 + 0.375 * x - 0.25),
0.625 * L * x * (x - 1) * (1.0 * x**2 + 0.2 * x - 0.4)
],
[
-2.0 * x,
4.0 * x * (x**2 - 1),
4.0 * x * (x**2 - 1),
-2.0 * x,
L * (-2.5 * x**4 + 3.0 * x**2 - 0.5),
L * (2.5 * x**4 - 3.0 * x**2 + 0.5)
],
[
1.0 * x + 0.5,
-3.75 * x * (x - 1) * (1.0 * x + 0.533333333333333) * (x + 1),
-3.75 * x * (x - 1) * (1.0 * x + 0.533333333333333) * (x + 1),
1.0 * x + 0.5,
0.625 * L * x * (x + 1) * (-1.0 * x**2 + 0.2 * x + 0.4),
L * x * (0.625 * x**3 + 0.5 * x**2 - 0.375 * x - 0.25)
]
])
@njit(nogil=True, cache=__cache)
def shape_function_derivatives_2(x, L):
"""
Evaluates the second derivatives of the shape
functions at a point x in the range [-1, 1].
"""
return np.array([
[
1.00000000000000,
15.0 * x**3 - 6.0 * x**2 - 7.5 * x + 2.0,
15.0 * x**3 - 6.0 * x**2 - 7.5 * x + 2.0,
1.00000000000000,
L * (-2.5 * x**3 + 1.5 * x**2 + 0.75 * x - 0.25),
L * (2.5 * x**3 - 1.5 * x**2 - 0.75 * x + 0.25)
],
[
-2.00000000000000,
12.0 * x**2 - 4.0,
12.0 * x**2 - 4.0,
-2.00000000000000,
L * x * (6.0 - 10.0 * x**2),
L * x * (10.0 * x**2 - 6.0)
],
[
1.00000000000000,
-15.0 * x**3 - 6.0 * x**2 + 7.5 * x + 2.0,
-15.0 * x**3 - 6.0 * x**2 + 7.5 * x + 2.0,
1.00000000000000,
L * (-2.5 * x**3 - 1.5 * x**2 + 0.75 * x + 0.25),
L * (2.5 * x**3 + 1.5 * x**2 - 0.75 * x - 0.25)
]
])
@njit(nogil=True, cache=__cache)
def shape_function_derivatives_3(x, L):
"""
Evaluates the third derivatives of the shape
functions at a point x in the range [-1, 1].
"""
return np.array([
[
0,
45.0 * x**2 - 12.0 * x - 7.5,
45.0 * x**2 - 12.0 * x - 7.5,
0,
L * (-7.5 * x**2 + 3.0 * x + 0.75),
L * (7.5 * x**2 - 3.0 * x - 0.75)
],
[
0,
24.0 * x,
24.0 * x,
0,
L * (6.0 - 30.0 * x**2),
L * (30.0 * x**2 - 6.0)
],
[
0,
-45.0 * x**2 - 12.0 * x + 7.5,
-45.0 * x**2 - 12.0 * x + 7.5,
0,
L * (-7.5 * x**2 - 3.0 * x + 0.75),
L * (7.5 * x**2 + 3.0 * x - 0.75)
]
])
@njit(nogil=True, parallel=True, cache=__cache)
def shape_function_values_bulk(x: ndarray, L: ndarray):
"""
Evaluates the shape functions at several points
in the range [-1, 1].
Parameters
----------
x : 1d numpy float array
The points of interest in the range [-1, -1]
Returns
-------
numpy float array of shape (nE, nP, nNE, nDOF=6)
"""
nP = x.shape[0]
nE = L.shape[0]
res = np.zeros((nE, nP, 3, 6), dtype=x.dtype)
for iE in prange(nE):
for iP in prange(nP):
res[iE, iP] = shape_function_values(x[iP], L[iE])
return res
@njit(nogil=True, cache=__cache)
def shape_function_derivatives(x, L):
"""
Evaluates the derivatives of the shape
functions at a point x in the range [-1, 1].
Parameters
----------
x : float
The point of interest in the range [-1, -1]
djac : float
Determinant of the Jacobi matrix of local-global transformation
between the master elment and the actual element.
Default is 1.0.
Returns
-------
numpy float array of shape (nNE, nDOF=6, 3)
"""
res = np.zeros((3, 6, 3))
res[:, :, 0] = shape_function_derivatives_1(x, L)
res[:, :, 1] = shape_function_derivatives_2(x, L)
res[:, :, 2] = shape_function_derivatives_3(x, L)
return res
@njit(nogil=True, parallel=True, cache=__cache)
def shape_function_derivatives_bulk(x: ndarray, L: ndarray):
"""
Evaluates the derivatives of the shape
functions at several points in the range [-1, 1].
Returns
-------
dshp (nE, nP, nNE, nDOF=6, 3)
"""
nP = x.shape[0]
nE = L.shape[0]
res = np.zeros((nE, nP, 3, 6, 3), dtype=x.dtype)
for iE in prange(nE):
for iP in prange(nP):
res[iE, iP] = shape_function_derivatives(x[iP], L[iE])
return res
| 28.834123
| 74
| 0.406969
| 987
| 6,084
| 2.45998
| 0.092199
| 0.04201
| 0.025947
| 0.016474
| 0.826606
| 0.799423
| 0.707578
| 0.649094
| 0.614498
| 0.573311
| 0
| 0.177583
| 0.40023
| 6,084
| 210
| 75
| 28.971429
| 0.487805
| 0.182281
| 0
| 0.58156
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0.049645
| false
| 0
| 0.021277
| 0
| 0.120567
| 0
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| 0
| null | 0
| 0
| 0
| 1
| 1
| 1
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| 0
| 1
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| 0
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| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
|
0
| 4
|
544d6ab3c93e8ea09e3212e798c6ae35d747cb10
| 121
|
py
|
Python
|
maml_trpo/policies/base_policy.py
|
adityabingi/maml-trpo-metaworld
|
9247018e72563a5cbf3df9ce7c384aef9812d18b
|
[
"MIT"
] | null | null | null |
maml_trpo/policies/base_policy.py
|
adityabingi/maml-trpo-metaworld
|
9247018e72563a5cbf3df9ce7c384aef9812d18b
|
[
"MIT"
] | null | null | null |
maml_trpo/policies/base_policy.py
|
adityabingi/maml-trpo-metaworld
|
9247018e72563a5cbf3df9ce7c384aef9812d18b
|
[
"MIT"
] | null | null | null |
import torch
import torch.nn as nn
class BasePolicy(nn.Module):
def__init__(self):
def update_params(self):
| 8.066667
| 28
| 0.710744
| 18
| 121
| 4.5
| 0.666667
| 0.271605
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.198347
| 121
| 15
| 29
| 8.066667
| 0.835052
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| null | null | 0
| 0.4
| null | null | 0
| 1
| 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
| 1
| 0
| 0
| 0
| 1
| 0
| 0
| 0
|
0
| 4
|
545a7d273c29c5e2b2386ded130c6883b5c4bd98
| 384
|
py
|
Python
|
hearthstone/simulator/replay/observer.py
|
JDBumgardner/stone_ground_hearth_battles
|
9fe095651fab60e8ddbf563f0b9b7f3e723d5f4f
|
[
"Apache-2.0"
] | 20
|
2020-08-01T03:14:57.000Z
|
2021-12-19T11:47:50.000Z
|
hearthstone/simulator/replay/observer.py
|
JDBumgardner/stone_ground_hearth_battles
|
9fe095651fab60e8ddbf563f0b9b7f3e723d5f4f
|
[
"Apache-2.0"
] | 48
|
2020-08-01T03:06:43.000Z
|
2022-02-27T10:03:47.000Z
|
hearthstone/simulator/replay/observer.py
|
JDBumgardner/stone_ground_hearth_battles
|
9fe095651fab60e8ddbf563f0b9b7f3e723d5f4f
|
[
"Apache-2.0"
] | 3
|
2020-06-28T01:23:37.000Z
|
2021-11-11T23:09:36.000Z
|
from typing import Any
from hearthstone.simulator.agent.actions import Action
from hearthstone.simulator.core.tavern import Tavern
Annotation = Any
class Observer:
def name(self) -> str:
pass
def on_action(self, tavern: 'Tavern', player: str, action: 'Action') -> Annotation:
pass
def on_game_over(self, tavern: 'Tavern') -> Annotation:
pass
| 21.333333
| 87
| 0.690104
| 48
| 384
| 5.458333
| 0.479167
| 0.114504
| 0.183206
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.213542
| 384
| 17
| 88
| 22.588235
| 0.86755
| 0
| 0
| 0.272727
| 0
| 0
| 0.046875
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0.272727
| false
| 0.272727
| 0.272727
| 0
| 0.636364
| 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
| 1
| 0
|
0
| 4
|
5467aa342ec4b4d507a2adfb89432d7d8d59b569
| 1,094
|
py
|
Python
|
dunner/preprocess_helper.py
|
ebegen/Dunner
|
36e3ab6edb3692a9713cdca02badf45da8153ce8
|
[
"MIT"
] | null | null | null |
dunner/preprocess_helper.py
|
ebegen/Dunner
|
36e3ab6edb3692a9713cdca02badf45da8153ce8
|
[
"MIT"
] | null | null | null |
dunner/preprocess_helper.py
|
ebegen/Dunner
|
36e3ab6edb3692a9713cdca02badf45da8153ce8
|
[
"MIT"
] | null | null | null |
import numpy as np
import pandas as pd
from multiprocessing import cpu_count, Pool
import dask.dataframe as ddf
import swifter
class PreprocessHelper(object):
def __init__(self):
pass
def parallelize(self, data, func):
# cores = cpu_count() # Number of CPU cores on your system
# partitions = cores-2 # Define as many partitions as you want
#
# data_split = np.array_split(data, partitions)
# pool = Pool(cores)
# data = pd.concat(pool.map(func, data_split))
# pool.close()
# pool.join()
#df_dask = ddf.from_pandas(data, npartitions=cpu_count()-2)
#new_data = df_dask.apply(lambda x: func(x), meta=('str'), axis=1).compute(scheduler='multiprocessing')
new_data = data.swifter.apply(lambda row: func(row), axis=1)
return new_data
def pipeline(self, function_list=None):
'''
serial function executer method
:param function_list: functions dictionary
:return:
'''
for name, func in function_list.items():
func()
| 29.567568
| 111
| 0.626143
| 139
| 1,094
| 4.791367
| 0.510791
| 0.036036
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.005006
| 0.269653
| 1,094
| 36
| 112
| 30.388889
| 0.828536
| 0.454296
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0.214286
| false
| 0.071429
| 0.357143
| 0
| 0.714286
| 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
| 1
| 1
| 0
| 1
| 0
|
0
| 4
|
548157d1356804fbe2837dd6f05690a62424edf1
| 320
|
py
|
Python
|
dteenergybridge/exceptions.py
|
kylehendricks/dteenergybridge
|
26b9b280ca16c7c86e679d5dc30c0faa0cbcf6eb
|
[
"MIT"
] | 2
|
2018-09-28T01:55:30.000Z
|
2020-05-26T02:54:46.000Z
|
dteenergybridge/exceptions.py
|
kylehendricks/dteenergybridge
|
26b9b280ca16c7c86e679d5dc30c0faa0cbcf6eb
|
[
"MIT"
] | 3
|
2019-01-09T19:51:57.000Z
|
2021-11-15T18:24:11.000Z
|
dteenergybridge/exceptions.py
|
kylehendricks/dteenergybridge
|
26b9b280ca16c7c86e679d5dc30c0faa0cbcf6eb
|
[
"MIT"
] | null | null | null |
"""DTE Energy Bridge Exceptions."""
class DteEnergyBridgeError(Exception):
"""Base class for all DTE Energy Bridge exceptions"""
class InvalidResponseError(DteEnergyBridgeError):
"""Response from DTE Energy Bridge was invalid"""
class InvalidArgumentError(DteEnergyBridgeError):
"""Invalid argument"""
| 22.857143
| 57
| 0.753125
| 30
| 320
| 8.033333
| 0.566667
| 0.112033
| 0.186722
| 0.207469
| 0.248963
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.140625
| 320
| 13
| 58
| 24.615385
| 0.876364
| 0.43125
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| true
| 0
| 0
| 0
| 1
| 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
| 1
| 0
| 0
| 0
| 1
| 0
|
0
| 4
|
54832c4e5cb7953181c83261c693adc1034cbabd
| 203
|
py
|
Python
|
backend/clients/api/urls.py
|
gitdevstar/tikatok
|
78729028f20eda822d9ef36634685feb69d5a3a5
|
[
"Apache-2.0"
] | null | null | null |
backend/clients/api/urls.py
|
gitdevstar/tikatok
|
78729028f20eda822d9ef36634685feb69d5a3a5
|
[
"Apache-2.0"
] | null | null | null |
backend/clients/api/urls.py
|
gitdevstar/tikatok
|
78729028f20eda822d9ef36634685feb69d5a3a5
|
[
"Apache-2.0"
] | null | null | null |
from clients.api.views import ClientsViewSet
from rest_framework.routers import DefaultRouter
router = DefaultRouter()
router.register(r'', ClientsViewSet, basename='clients')
urlpatterns = router.urls
| 29
| 56
| 0.82266
| 23
| 203
| 7.217391
| 0.695652
| 0.228916
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.08867
| 203
| 6
| 57
| 33.833333
| 0.897297
| 0
| 0
| 0
| 0
| 0
| 0.034483
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| false
| 0
| 0.4
| 0
| 0.4
| 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
| 1
| 0
| 0
| 0
|
0
| 4
|
54862f003a93a7d1a8a92841d4d25ad2e37e1c02
| 1,769
|
py
|
Python
|
src/BSL_IHI/variables.py
|
juhuntenburg/pipelines
|
9904065cccb8e316cece5451f595a24774f07bd5
|
[
"MIT"
] | 13
|
2019-03-10T23:13:06.000Z
|
2022-02-08T08:49:28.000Z
|
src/BSL_IHI/variables.py
|
juhuntenburg/pipelines
|
9904065cccb8e316cece5451f595a24774f07bd5
|
[
"MIT"
] | 1
|
2015-03-31T20:42:08.000Z
|
2015-04-03T23:58:58.000Z
|
src/BSL_IHI/variables.py
|
NeuroanatomyAndConnectivity/pipelines
|
9904065cccb8e316cece5451f595a24774f07bd5
|
[
"MIT"
] | 18
|
2015-01-08T13:27:40.000Z
|
2021-06-22T03:35:45.000Z
|
'''
Created on Feb 20, 2013
@author: gorgolewski, steele
'''
#import os
#subjects = os.listdir("/scr/namibia1/baird/MPI_Project/Neuroimaging_Data/")
working_dir = "/scr/alaska1/steele/BSL_IHI/processing/cmt"
results_dir = "/scr/alaska1/steele/BSL_IHI/processing/cmt/results"
freesurfer_dir = '/scr/alaska1/steele/BSL_IHI/processing/freesurfer/'
subjects_M = ['KCDT100819_T1.TRIO',
'JA7T100824_T1.TRIO',
'17230.95_20111026_T1.TRIO',
'SJAT_100416_T1.TRIO',
'DM6T100909_T1.TRIO',
'NS5T090217_T1.TRIO',
'11530.56_090910_T1.TRIO',
'15205.bb_20110818_T1.TRIO',
'BSLT100916__T1.TRIO',
'SF8T100916_T1.TRIO',
'SAST_100421_T1.TRIO',
'SMXT100805_T1.TRIO',
'MN3T090909_T1.TRIO',
'ED2T101126_T1.TRIO',
'LP4T091026_T1.TRIO',
'DS9T101110_T1.TRIO',
'GD4T100909_T1.TRIO',
'AS3T100715_T1.TRIO',
'SL6T101119_T1.TRIO',
'UF1T100824_T1.TRIO',
'12522.80_20110818_T1.TRIO',
'GC6T100805_T1.TRIO',
'15832.a8_20110616_T1.TRIO',
'KAHT101103_T1.TRIO',
'16833.de_20111025_T1.TRIO',
'SCMT101110_T1.TRIO',
'KE5T100909_T1.TRIO',
'14841.b6_20111026_T1.TRIO']
subjects_NM= ['MMJT100420_T1.TRIO',
'RMFT100708_T1.TRIO',
'KG6T100708_T1.TRIO',
'GMOT100628_T1.TRIO',
'STCT090817_T1.TRIO',
'DH2T100420_T1.TRIO',
'12612.9b_20090318_T1_TRIO',
'RSET090817_T1.TRIO',
'LC7T100629_T1.TRIO',
'14102.d1_20111024_T1.TRIO',
'DA5T110620_T1.TRIO',
'16687.41_20111025_T1.TRIO',
'01212.43_20090617_T1.TRIO',
'BC9T100831_T1.TRIO',
'NC3T090721_T1.TRIO',
'WMCT090817_T1.TRIO',
'WSFT100322_T1.TRIO',
'BSGT081016_T1.TRIO',
'10060.70_20111025_T1.TRIO',
'HCBT060321_T1.DTI',
'11401.38_111025_T1.TRIO',
'10576.44_20091217_T1.TRIO',
'15510.c9_20111110_T1.TRIO',
'JR1T090216_T1.TRIO',
'WF5T091110_T1.TRIO',
'WT6T090807_T1.TRIO',
'HN3T090610_T1.TRIO',
'SU3T090819_T1.TRIO']
| 24.232877
| 76
| 0.751837
| 252
| 1,769
| 4.940476
| 0.464286
| 0.26506
| 0.031325
| 0.045783
| 0.100402
| 0.100402
| 0.100402
| 0.072289
| 0.072289
| 0
| 0
| 0.337846
| 0.081402
| 1,769
| 72
| 77
| 24.569444
| 0.428308
| 0.07801
| 0
| 0
| 0
| 0
| 0.773935
| 0.316862
| 0
| 0
| 0
| 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
| 1
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
|
0
| 4
|
548d396c2b5196bb71f8c11e8c2e71544047cb42
| 604
|
py
|
Python
|
python/mixer_shortcode/errors.py
|
AlinGhinoiu/shortcode-oauth
|
9fc4a44ee3a96ec743bd94efb1715cf1acee0996
|
[
"MIT"
] | 10
|
2018-09-19T20:13:35.000Z
|
2020-05-23T22:38:52.000Z
|
python/mixer_shortcode/errors.py
|
AlinGhinoiu/shortcode-oauth
|
9fc4a44ee3a96ec743bd94efb1715cf1acee0996
|
[
"MIT"
] | 7
|
2018-09-21T18:03:31.000Z
|
2020-04-25T18:26:56.000Z
|
python/mixer_shortcode/errors.py
|
AlinGhinoiu/shortcode-oauth
|
9fc4a44ee3a96ec743bd94efb1715cf1acee0996
|
[
"MIT"
] | 9
|
2019-01-27T04:08:22.000Z
|
2020-01-18T20:43:35.000Z
|
class ShortCodeError(Exception):
"""Base exception raised when some unexpected event occurs in the shortcode
OAuth flow."""
pass
class UnknownShortCodeError(ShortCodeError):
"""Exception raised when an unknown error happens while running shortcode
OAuth.
"""
pass
class ShortCodeAccessDeniedError(ShortCodeError):
"""Exception raised when the user denies access to the client in shortcode
OAuth."""
pass
class ShortCodeTimeoutError(ShortCodeError):
"""Exception raised when the shortcode expires without being accepted."""
pass
| 26.26087
| 80
| 0.710265
| 62
| 604
| 6.919355
| 0.548387
| 0.214452
| 0.177156
| 0.230769
| 0.167832
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.22351
| 604
| 22
| 81
| 27.454545
| 0.914712
| 0.511589
| 0
| 0.5
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| true
| 0.5
| 0
| 0
| 0.5
| 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
| 1
| 1
| 0
| 0
| 0
| 0
|
0
| 4
|
5490c7251251b33877c0edf654cc2f79d064ab79
| 55
|
py
|
Python
|
Python/Input/Input.py
|
sachinprabhu007/HackerRank-Solutions
|
f42d3c1e989b288e42b4674a926d007aa22940a1
|
[
"MIT"
] | null | null | null |
Python/Input/Input.py
|
sachinprabhu007/HackerRank-Solutions
|
f42d3c1e989b288e42b4674a926d007aa22940a1
|
[
"MIT"
] | 1
|
2019-01-16T12:13:29.000Z
|
2019-01-16T14:57:57.000Z
|
Python/Input/Input.py
|
sachinprabhu007/HackerRank-Solutions
|
f42d3c1e989b288e42b4674a926d007aa22940a1
|
[
"MIT"
] | null | null | null |
x,k = map(int,input().split())
print(k==eval(input()))
| 27.5
| 31
| 0.6
| 10
| 55
| 3.3
| 0.8
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.072727
| 55
| 2
| 32
| 27.5
| 0.647059
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| true
| 0
| 0
| 0
| 0
| 0.5
| 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
| 1
|
0
| 4
|
54b5c79d63bf4c07865a1e26472a55bfc9ea5e90
| 399
|
py
|
Python
|
test1/forms.py
|
djsaeedkhan/django-ip-validator
|
99af5285e1e0ef74d49aae2dc93693fdbb9b8628
|
[
"Apache-2.0"
] | null | null | null |
test1/forms.py
|
djsaeedkhan/django-ip-validator
|
99af5285e1e0ef74d49aae2dc93693fdbb9b8628
|
[
"Apache-2.0"
] | null | null | null |
test1/forms.py
|
djsaeedkhan/django-ip-validator
|
99af5285e1e0ef74d49aae2dc93693fdbb9b8628
|
[
"Apache-2.0"
] | null | null | null |
from django import forms
from django.core.validators import RegexValidator, EmailValidator, validate_ipv46_address
validate_hostname = RegexValidator(regex=r'[a-zA-Z0-9-_]*\.[a-zA-Z]{2,6}')
my_validator = RegexValidator(r'[a-zA-Z0-9-_]*\.[a-zA-Z]{2,6}', "Your string should contain letter A in it.")
class CacheCheck(forms.Form):
ip = forms.CharField(max_length=100,validators=[my_validator])
| 44.333333
| 109
| 0.75188
| 62
| 399
| 4.709677
| 0.612903
| 0.041096
| 0.027397
| 0.041096
| 0.089041
| 0.089041
| 0.089041
| 0.089041
| 0.089041
| 0.089041
| 0
| 0.035714
| 0.087719
| 399
| 8
| 110
| 49.875
| 0.766484
| 0
| 0
| 0
| 0
| 0.333333
| 0.250627
| 0.145363
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 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
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 1
| 0
|
0
| 4
|
54c4542000e5d54e1971578a0e73c7e34feca16d
| 6,860
|
py
|
Python
|
src/model/activation.py
|
Ziems/OBST
|
e31f460616d8bc29931f069843e4f94b7f38e260
|
[
"BSD-2-Clause"
] | 15
|
2021-06-26T10:03:07.000Z
|
2021-12-04T12:56:36.000Z
|
src/model/activation.py
|
Ziems/OBST
|
e31f460616d8bc29931f069843e4f94b7f38e260
|
[
"BSD-2-Clause"
] | null | null | null |
src/model/activation.py
|
Ziems/OBST
|
e31f460616d8bc29931f069843e4f94b7f38e260
|
[
"BSD-2-Clause"
] | 2
|
2021-06-24T14:15:31.000Z
|
2021-12-09T16:11:40.000Z
|
import mesh_tensorflow as mtf
import numpy as np
import tensorflow as tf
from .. import tf_wrapper as tfw
from ..dataclass import BlockArgs
from ..mtf_wrapper import relu as _relu, multiply, einsum, constant, sigmoid as _sigmoid, tanh as _tanh, softplus
from ..utils_core import random_name, scoped
tf1 = tf.compat.v1
class MishForward(mtf.Operation):
def __init__(self, x: mtf.Tensor):
super().__init__([x], name=random_name("mish_forward"))
self._outputs = [mtf.Tensor(self, x.shape, x.dtype)]
def gradient(self, grad_ys):
return MishBackward(self.inputs[0], grad_ys[0]).outputs
def lower(self, lowering):
mesh_impl = lowering.mesh_impl(self)
def slicewise_fn(x):
return tfw.multiply(x, tfw.tanh(tfw.softplus(x)))
y = mesh_impl.slicewise(slicewise_fn, lowering.tensors[self.inputs[0]])
lowering.set_tensor_lowering(self.outputs[0], y)
class MishBackward(mtf.Operation):
def __init__(self, x: mtf.Tensor, dy: mtf.Tensor):
super().__init__([x, dy], name=random_name("mish_backward"))
self._outputs = [mtf.Tensor(self, x.shape, x.dtype)]
def lower(self, lowering):
mesh_impl = lowering.mesh_impl(self)
def slicewise_fn(x, dy):
gte = tfw.tanh(tfw.softplus(x))
gte += 1. - tfw.square(gte) * x * tfw.sigmoid(x)
return tfw.multiply(dy, gte)
y = mesh_impl.slicewise(slicewise_fn, lowering.tensors[self.inputs[0]], lowering.tensors[self.inputs[1]])
lowering.set_tensor_lowering(self.outputs[0], y)
class SiluForward(mtf.Operation):
def __init__(self, x: mtf.Tensor):
super().__init__([x], name=random_name("silu_forward"))
self._outputs = [mtf.Tensor(self, x.shape, x.dtype)]
def gradient(self, grad_ys):
return SiluBackward(self.inputs[0], grad_ys[0]).outputs
def lower(self, lowering):
mesh_impl = lowering.mesh_impl(self)
def slicewise_fn(x):
return tfw.multiply(x, tfw.sigmoid(x))
y = mesh_impl.slicewise(slicewise_fn, lowering.tensors[self.inputs[0]])
lowering.set_tensor_lowering(self.outputs[0], y)
class SiluBackward(mtf.Operation):
def __init__(self, x: mtf.Tensor, dy: mtf.Tensor):
super().__init__([x, dy], name=random_name("silu_backward"))
self._outputs = [mtf.Tensor(self, x.shape, x.dtype)]
def lower(self, lowering):
mesh_impl = lowering.mesh_impl(self)
def slicewise_fn(x, dy):
gte = tfw.sigmoid(x)
return dy * ((x - 1) * gte + 1)
y = mesh_impl.slicewise(slicewise_fn, lowering.tensors[self.inputs[0]], lowering.tensors[self.inputs[1]])
lowering.set_tensor_lowering(self.outputs[0], y)
class LeCunTanhForward(mtf.Operation):
def __init__(self, x: mtf.Tensor):
super().__init__([x], name=random_name("lecun_tanh_forward"))
self._outputs = [mtf.Tensor(self, x.shape, x.dtype)]
def gradient(self, grad_ys):
return LeCunTanhBackward(self.inputs[0], grad_ys[0]).outputs
def lower(self, lowering):
mesh_impl = lowering.mesh_impl(self)
def slicewise_fn(x):
return tfw.tanh(x) + x * 0.1
y = mesh_impl.slicewise(slicewise_fn, lowering.tensors[self.inputs[0]])
lowering.set_tensor_lowering(self.outputs[0], y)
class LeCunTanhBackward(mtf.Operation):
def __init__(self, x: mtf.Tensor, dy: mtf.Tensor):
super().__init__([x, dy], name=random_name("lecun_tanh_backward"))
self._outputs = [mtf.Tensor(self, x.shape, x.dtype)]
def lower(self, lowering):
mesh_impl = lowering.mesh_impl(self)
def slicewise_fn(x, dy):
return tfw.multiply(dy, tfw.subtract(1.1, tfw.square(tfw.tanh(x))))
y = mesh_impl.slicewise(slicewise_fn, lowering.tensors[self.inputs[0]], lowering.tensors[self.inputs[1]])
lowering.set_tensor_lowering(self.outputs[0], y)
class SoftsignForward(mtf.Operation):
def __init__(self, x: mtf.Tensor):
super().__init__([x], name=random_name("softsign_forward"))
self._outputs = [mtf.Tensor(self, x.shape, x.dtype)]
def gradient(self, grad_ys):
return SoftsignBackward(self.inputs[0], grad_ys[0]).outputs
def lower(self, lowering):
mesh_impl = lowering.mesh_impl(self)
def slicewise_fn(x):
return x / (1. + tfw.abs(x))
y = mesh_impl.slicewise(slicewise_fn, lowering.tensors[self.inputs[0]])
lowering.set_tensor_lowering(self.outputs[0], y)
class SoftsignBackward(mtf.Operation):
def __init__(self, x: mtf.Tensor, dy: mtf.Tensor):
super().__init__([x, dy], name=random_name("softsign_backward"))
self._outputs = [mtf.Tensor(self, x.shape, x.dtype)]
def lower(self, lowering):
mesh_impl = lowering.mesh_impl(self)
def slicewise_fn(x, dy):
return dy / tfw.square(1. + tfw.abs(x))
y = mesh_impl.slicewise(slicewise_fn, lowering.tensors[self.inputs[0]], lowering.tensors[self.inputs[1]])
lowering.set_tensor_lowering(self.outputs[0], y)
def _output0(op):
if not issubclass(op, mtf.Operation):
raise ValueError
def _wrapped(args: BlockArgs):
return op(args.tensor).outputs[0]
return _wrapped
def _gelu(params, tensor: mtf.Tensor):
return einsum([tensor, _tanh(einsum([tensor, tensor, tensor, constant(params, 0.044715)],
output_shape=tensor.shape) + tensor * np.sqrt(2 / np.pi)) + 1.0,
constant(params, 0.5)], output_shape=tensor.shape)
def gelu(args: BlockArgs):
return scoped("gelu", _gelu, args.params, args.tensor)
def relu(args: BlockArgs):
return _relu(args.tensor)
def sigmoid(args: BlockArgs):
return _sigmoid(args.tensor)
def tanh(args: BlockArgs):
return _tanh(args.tensor)
def _mtf_mish(tensor: mtf.Tensor):
return multiply(_tanh(softplus(tensor)), tensor)
def mtf_mish(args: BlockArgs):
return scoped("mtf_mish", _mtf_mish, args.tensor)
ACTIVATIONS = {'relu': relu,
'sigmoid': sigmoid,
'tanh': tanh,
'gelu': gelu,
'lecun_tanh': _output0(LeCunTanhForward),
'silu': _output0(SiluForward),
'mish': _output0(MishForward),
"mtf_mish": mtf_mish,
'softsign': _output0(SoftsignForward)
}
def activate(args: BlockArgs) -> mtf.Tensor:
"""
Call activation function on mtf.Tensor.
"""
for fn_name in args:
if fn_name not in ACTIVATIONS:
continue
return scoped(fn_name, ACTIVATIONS[fn_name], args)
print(f'No activation function found for "{args.name_extras}". Falling back to identity. '
f'Known functions: {list(ACTIVATIONS.keys())}')
return args.tensor
| 32.511848
| 113
| 0.645918
| 912
| 6,860
| 4.648026
| 0.122807
| 0.050955
| 0.060392
| 0.070771
| 0.615711
| 0.604152
| 0.604152
| 0.604152
| 0.604152
| 0.604152
| 0
| 0.010648
| 0.219679
| 6,860
| 210
| 114
| 32.666667
| 0.781244
| 0.005685
| 0
| 0.431655
| 0
| 0
| 0.045408
| 0.006907
| 0
| 0
| 0
| 0
| 0
| 1
| 0.273381
| false
| 0
| 0.05036
| 0.129496
| 0.546763
| 0.007194
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 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
| 4
|
54c61fc4e587efdfd99182b2383a171bf6c66796
| 332
|
py
|
Python
|
ui.py
|
fuckTextBooks/fuckTextBooks
|
41571f61cb201003057060657546d2ac0065b4bf
|
[
"MIT"
] | null | null | null |
ui.py
|
fuckTextBooks/fuckTextBooks
|
41571f61cb201003057060657546d2ac0065b4bf
|
[
"MIT"
] | null | null | null |
ui.py
|
fuckTextBooks/fuckTextBooks
|
41571f61cb201003057060657546d2ac0065b4bf
|
[
"MIT"
] | null | null | null |
from getpass import getpass
def get_username() -> str:
u = input("UTORID: ")
return u
def get_password() -> str:
return getpass("Password: ")
"""Deprecated
def get_download_location() -> str:
loc = ""
while loc == "":
loc = input("Download Location \n(i.e. /Documents/textbooks): ")
return loc
"""
| 20.75
| 72
| 0.608434
| 40
| 332
| 4.95
| 0.55
| 0.090909
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.231928
| 332
| 16
| 73
| 20.75
| 0.776471
| 0
| 0
| 0
| 0
| 0
| 0.113924
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0.333333
| false
| 0.5
| 0.166667
| 0.166667
| 0.833333
| 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
| 1
| 0
| 1
| 1
| 0
|
0
| 4
|
49a82fb3e8e8ae062a605eb2938b8e761eb96123
| 1,010
|
py
|
Python
|
TripScheduling/Passenger.py
|
StevenBryceLee/TripScheduling
|
1bf2513c2fec54ce4f7c44433529e7d1b37e0ff0
|
[
"MIT"
] | null | null | null |
TripScheduling/Passenger.py
|
StevenBryceLee/TripScheduling
|
1bf2513c2fec54ce4f7c44433529e7d1b37e0ff0
|
[
"MIT"
] | null | null | null |
TripScheduling/Passenger.py
|
StevenBryceLee/TripScheduling
|
1bf2513c2fec54ce4f7c44433529e7d1b37e0ff0
|
[
"MIT"
] | null | null | null |
class Passenger:
def __init__(self, name):
self.name = name
def selectTrip(self, tripOptions):
'''
Given a list of trip options, a passenger may select a trip
This trip is then added to the trip queue, which allows for later return pricing
tripOptions: the queue of given trips available to the passenger
This should come from TripPlanner.offerReturnPrices()
returns the index of the desired trip
'''
for idx, trip in enumerate(tripOptions):
print(f'Trip Number: {idx}\n{repr(trip)}')
trip_num = 100
while trip_num < 0 and trip_num > len(tripOptions):
trip_num = int(input('enter desired trip number: '))
return trip_num
def stateSource(self):
return input('enter desired source: ')
def stateDestination(self):
return input('enter desired destination: ')
def stateHour(self):
return int(input('enter desired hour to leave: '))
| 32.580645
| 88
| 0.628713
| 127
| 1,010
| 4.929134
| 0.496063
| 0.055911
| 0.108626
| 0.063898
| 0.086262
| 0
| 0
| 0
| 0
| 0
| 0
| 0.005602
| 0.293069
| 1,010
| 31
| 89
| 32.580645
| 0.871148
| 0.29604
| 0
| 0
| 0
| 0
| 0.208841
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0.3125
| false
| 0.0625
| 0
| 0.1875
| 0.625
| 0.0625
| 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
| 1
| 0
| 1
| 1
| 0
|
0
| 4
|
49b7b1a4153828f8b04ac3f0caadd6d320d0253a
| 211
|
py
|
Python
|
setup.py
|
difince/kinney
|
31bf5a51a1378f4c9e2284739ac353a4f5aa12d3
|
[
"Apache-2.0"
] | 27
|
2020-05-08T20:45:26.000Z
|
2022-01-12T02:50:07.000Z
|
setup.py
|
difince/kinney
|
31bf5a51a1378f4c9e2284739ac353a4f5aa12d3
|
[
"Apache-2.0"
] | 26
|
2020-02-25T22:02:45.000Z
|
2021-12-13T20:52:29.000Z
|
setup.py
|
difince/kinney
|
31bf5a51a1378f4c9e2284739ac353a4f5aa12d3
|
[
"Apache-2.0"
] | 7
|
2020-02-14T23:11:50.000Z
|
2020-09-25T02:34:41.000Z
|
"""Setuptools script for the "kinney" Python package."""
import setuptools
# Package configuration exists solely in `setup.cfg` in order to constrain it to
# be simpler and more declarative.
setuptools.setup()
| 30.142857
| 80
| 0.772512
| 29
| 211
| 5.62069
| 0.793103
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.14218
| 211
| 6
| 81
| 35.166667
| 0.900552
| 0.772512
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| true
| 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
| 1
| 0
| 1
| 0
| 0
| 0
|
0
| 4
|
49d6e22c5e68c3af10504ef2b0bc23b157b185d4
| 188
|
py
|
Python
|
send_key_explame/scan_code/test.py
|
Llona/hotkey
|
ec40ccf8212dd166b54bf1e7a462f889fa905424
|
[
"Apache-2.0"
] | null | null | null |
send_key_explame/scan_code/test.py
|
Llona/hotkey
|
ec40ccf8212dd166b54bf1e7a462f889fa905424
|
[
"Apache-2.0"
] | null | null | null |
send_key_explame/scan_code/test.py
|
Llona/hotkey
|
ec40ccf8212dd166b54bf1e7a462f889fa905424
|
[
"Apache-2.0"
] | null | null | null |
import json
import utils
import keyboard
print(keyboard.normalize_name('DIK_F1'))
# with open("test.cfg", 'r', ) as load_f:
# load_dict = json.load(load_f)
# print(load_dict)
| 14.461538
| 41
| 0.68617
| 29
| 188
| 4.241379
| 0.62069
| 0.081301
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.006452
| 0.175532
| 188
| 12
| 42
| 15.666667
| 0.787097
| 0.5
| 0
| 0
| 0
| 0
| 0.066667
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| true
| 0
| 0.75
| 0
| 0.75
| 0.25
| 1
| 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
| 4
|
49f53dac99049066cb6783b071671edb71ff6bab
| 53
|
py
|
Python
|
one.py
|
bryanseah234/python-crash-code
|
a1440ef74fac212e494253e4144d85cbb626228d
|
[
"MIT"
] | 1
|
2020-11-03T07:52:33.000Z
|
2020-11-03T07:52:33.000Z
|
one.py
|
bryanseah234/python-crash-code
|
a1440ef74fac212e494253e4144d85cbb626228d
|
[
"MIT"
] | null | null | null |
one.py
|
bryanseah234/python-crash-code
|
a1440ef74fac212e494253e4144d85cbb626228d
|
[
"MIT"
] | null | null | null |
from itertools import count as crash
list(crash(0))
| 17.666667
| 36
| 0.773585
| 9
| 53
| 4.555556
| 0.888889
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.022222
| 0.150943
| 53
| 2
| 37
| 26.5
| 0.888889
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| true
| 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
| 1
| 0
| 1
| 0
| 0
| 0
|
0
| 4
|
49f613d6ef4cc82539b647b5337f8ea848263940
| 237
|
py
|
Python
|
classes/models/ModelEncoder.py
|
canary-for-cognition/merge-datasets-gan
|
435d62a2cc281e25b9616db0ee6bacd13a12fa9c
|
[
"MIT"
] | null | null | null |
classes/models/ModelEncoder.py
|
canary-for-cognition/merge-datasets-gan
|
435d62a2cc281e25b9616db0ee6bacd13a12fa9c
|
[
"MIT"
] | null | null | null |
classes/models/ModelEncoder.py
|
canary-for-cognition/merge-datasets-gan
|
435d62a2cc281e25b9616db0ee6bacd13a12fa9c
|
[
"MIT"
] | null | null | null |
from classes.core.Model import Model
from classes.modules.Encoder import Encoder
class ModelEncoder(Model):
def __init__(self):
super().__init__()
self._network = Encoder()
def optimize(self, x):
pass
| 18.230769
| 43
| 0.666667
| 28
| 237
| 5.321429
| 0.607143
| 0.147651
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.236287
| 237
| 12
| 44
| 19.75
| 0.823204
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0.25
| false
| 0.125
| 0.25
| 0
| 0.625
| 0
| 1
| 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
| 1
| 0
| 0
| 1
| 0
|
0
| 4
|
49f6bad05021ff76b2769ec7d9b34a66ceec5733
| 711
|
py
|
Python
|
nonebot/adapters/onebot/v12/__init__.py
|
nonebot/adapter-onebot
|
65f1a1d906c1291099a23c02acc9450814e9d42a
|
[
"MIT"
] | 13
|
2021-12-21T10:33:32.000Z
|
2022-02-26T08:40:14.000Z
|
nonebot/adapters/onebot/v12/__init__.py
|
nonebot/adapter-onebot
|
65f1a1d906c1291099a23c02acc9450814e9d42a
|
[
"MIT"
] | 7
|
2022-01-13T05:25:25.000Z
|
2022-03-25T17:58:45.000Z
|
nonebot/adapters/onebot/v12/__init__.py
|
nonebot/adapter-onebot
|
65f1a1d906c1291099a23c02acc9450814e9d42a
|
[
"MIT"
] | 3
|
2022-01-11T11:28:37.000Z
|
2022-01-20T02:54:20.000Z
|
"""OneBot v12 协议适配。
协议详情请看: [OneBot V12](https://12.1bot.dev/)
FrontMatter:
sidebar_position: 0
description: onebot.v12 模块
"""
from nonebot.adapters.onebot.exception import ActionFailed as ActionFailed
from nonebot.adapters.onebot.exception import NetworkError as NetworkError
from nonebot.adapters.onebot.exception import ApiNotAvailable as ApiNotAvailable
from nonebot.adapters.onebot.exception import (
OneBotAdapterException as OneBotAdapterException,
)
from .event import *
from .permission import *
from .bot import Bot as Bot
from .log import log as log
from .adapter import Adapter as Adapter
from .message import Message as Message
from .message import MessageSegment as MessageSegment
| 29.625
| 80
| 0.804501
| 90
| 711
| 6.344444
| 0.344444
| 0.077058
| 0.1331
| 0.175131
| 0.28021
| 0.28021
| 0
| 0
| 0
| 0
| 0
| 0.016181
| 0.130802
| 711
| 23
| 81
| 30.913043
| 0.907767
| 0.181435
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| true
| 0
| 0.846154
| 0
| 0.846154
| 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
| 1
| 0
| 1
| 0
|
0
| 4
|
b71afda126c5e7a9b0edef54a60b8144558e516b
| 28
|
py
|
Python
|
test/__init__.py
|
finkbeiner-lab/tidyML
|
3738b89ca5b40b0b19adcf635f95875e2e20017d
|
[
"MIT"
] | null | null | null |
test/__init__.py
|
finkbeiner-lab/tidyML
|
3738b89ca5b40b0b19adcf635f95875e2e20017d
|
[
"MIT"
] | null | null | null |
test/__init__.py
|
finkbeiner-lab/tidyML
|
3738b89ca5b40b0b19adcf635f95875e2e20017d
|
[
"MIT"
] | null | null | null |
"""
Exported unit tests
"""
| 7
| 19
| 0.607143
| 3
| 28
| 5.666667
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.178571
| 28
| 3
| 20
| 9.333333
| 0.73913
| 0.678571
| 0
| null | 0
| null | 0
| 0
| null | 0
| 0
| 0
| null | 1
| null | true
| 0
| 0
| null | null | null | 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
| 1
| 0
| 0
| 0
| 0
| 0
|
0
| 4
|
b732b9748d0fa245275041a3ee4752143530d8af
| 114
|
py
|
Python
|
python/day01/part1.py
|
ijanos/advent2015
|
6f7fda5ed67957e087fadd9638d620f1687484f3
|
[
"MIT"
] | null | null | null |
python/day01/part1.py
|
ijanos/advent2015
|
6f7fda5ed67957e087fadd9638d620f1687484f3
|
[
"MIT"
] | null | null | null |
python/day01/part1.py
|
ijanos/advent2015
|
6f7fda5ed67957e087fadd9638d620f1687484f3
|
[
"MIT"
] | null | null | null |
#!/bin/env python3
import fileinput
for line in fileinput.input():
print(line.count("(") - line.count(")"))
| 19
| 44
| 0.649123
| 15
| 114
| 4.933333
| 0.733333
| 0.243243
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.010309
| 0.149123
| 114
| 6
| 44
| 19
| 0.752577
| 0.149123
| 0
| 0
| 0
| 0
| 0.020833
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| false
| 0
| 0.333333
| 0
| 0.333333
| 0.333333
| 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
| 1
| 0
| 0
| 0
|
0
| 4
|
b7340a5c6e187ea41de2eb1af71ccaab6eee53e0
| 17
|
py
|
Python
|
data/studio21_generated/introductory/4142/starter_code.py
|
vijaykumawat256/Prompt-Summarization
|
614f5911e2acd2933440d909de2b4f86653dc214
|
[
"Apache-2.0"
] | null | null | null |
data/studio21_generated/introductory/4142/starter_code.py
|
vijaykumawat256/Prompt-Summarization
|
614f5911e2acd2933440d909de2b4f86653dc214
|
[
"Apache-2.0"
] | null | null | null |
data/studio21_generated/introductory/4142/starter_code.py
|
vijaykumawat256/Prompt-Summarization
|
614f5911e2acd2933440d909de2b4f86653dc214
|
[
"Apache-2.0"
] | null | null | null |
def solve(arr):
| 8.5
| 15
| 0.647059
| 3
| 17
| 3.666667
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.176471
| 17
| 2
| 16
| 8.5
| 0.785714
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| null | null | 0
| 0
| null | null | 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
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
|
0
| 4
|
3f898af703590cf04e115b48656029dbb5657680
| 200
|
py
|
Python
|
tests/test_version.py
|
LucaCappelletti94/transpose_dict
|
d3ce6fcf23ad7fddb654ecf89f618c59d62a7b35
|
[
"MIT"
] | 1
|
2021-10-11T18:19:14.000Z
|
2021-10-11T18:19:14.000Z
|
tests/test_version.py
|
LucaCappelletti94/transpose_dict
|
d3ce6fcf23ad7fddb654ecf89f618c59d62a7b35
|
[
"MIT"
] | null | null | null |
tests/test_version.py
|
LucaCappelletti94/transpose_dict
|
d3ce6fcf23ad7fddb654ecf89f618c59d62a7b35
|
[
"MIT"
] | null | null | null |
"""Test for version file syntax."""
import re
from transpose_dict.__version__ import __version__
def test_version():
pattern = re.compile(r"\d+\.\d+\.\d+")
assert pattern.match(__version__)
| 22.222222
| 50
| 0.71
| 27
| 200
| 4.740741
| 0.62963
| 0.03125
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.14
| 200
| 9
| 51
| 22.222222
| 0.744186
| 0.145
| 0
| 0
| 0
| 0
| 0.078313
| 0
| 0
| 0
| 0
| 0
| 0.2
| 1
| 0.2
| false
| 0
| 0.4
| 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
| 1
| 0
| 1
| 0
|
0
| 4
|
3f945b029203aa02cb32771307a6bd5412afc839
| 173
|
py
|
Python
|
transport/tests/__init__.py
|
zkdev/cc-utils
|
042c6632ca6f61a484bc0a71f85957aeba7f7278
|
[
"BSD-3-Clause"
] | 15
|
2018-04-18T13:25:30.000Z
|
2022-03-04T09:25:41.000Z
|
transport/tests/__init__.py
|
zkdev/cc-utils
|
042c6632ca6f61a484bc0a71f85957aeba7f7278
|
[
"BSD-3-Clause"
] | 221
|
2018-04-12T06:29:43.000Z
|
2022-03-27T03:01:40.000Z
|
transport/tests/__init__.py
|
zkdev/cc-utils
|
042c6632ca6f61a484bc0a71f85957aeba7f7278
|
[
"BSD-3-Clause"
] | 29
|
2018-04-11T14:42:23.000Z
|
2021-11-09T16:26:32.000Z
|
import sys
import os
own_dir = os.path.abspath(os.path.dirname(__name__))
repo_root = os.path.abspath(os.path.join(own_dir, os.path.pardir))
sys.path.insert(1, repo_root)
| 21.625
| 66
| 0.763006
| 32
| 173
| 3.875
| 0.46875
| 0.241935
| 0.129032
| 0.193548
| 0.306452
| 0
| 0
| 0
| 0
| 0
| 0
| 0.006329
| 0.086705
| 173
| 7
| 67
| 24.714286
| 0.778481
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| false
| 0
| 0.4
| 0
| 0.4
| 0
| 0
| 0
| 0
| null | 1
| 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
| 1
| 0
| 0
| 0
|
0
| 4
|
3f9df0f3a0a6016739ed445e549fc615e528ff14
| 112
|
py
|
Python
|
Case7.py
|
ciracheta99/TestLinkPython
|
563bf24dac6c2309bd5989767c30a2e70e6c0f68
|
[
"Apache-2.0"
] | 1
|
2022-01-18T07:48:24.000Z
|
2022-01-18T07:48:24.000Z
|
Case7.py
|
ciracheta99/TestLinkPython
|
563bf24dac6c2309bd5989767c30a2e70e6c0f68
|
[
"Apache-2.0"
] | null | null | null |
Case7.py
|
ciracheta99/TestLinkPython
|
563bf24dac6c2309bd5989767c30a2e70e6c0f68
|
[
"Apache-2.0"
] | null | null | null |
print ('Inside Case 7')
self.logResult("Just checking if a log file gets generated")
self.reportTCResults("p")
| 37.333333
| 61
| 0.75
| 17
| 112
| 4.941176
| 0.941176
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.010204
| 0.125
| 112
| 3
| 62
| 37.333333
| 0.846939
| 0
| 0
| 0
| 1
| 0
| 0.504505
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| true
| 0
| 0
| 0
| 0
| 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
| 1
| 0
| 0
| 0
| null | 0
| 0
| 0
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| 0
| 0
| 0
|
0
| 4
|
3fb7ed37a63870d807d69c796bf9cacea7ad9b72
| 89
|
py
|
Python
|
okcupid/apps.py
|
ealmuina/statsproject
|
43186bdb213202ff846b18e677b89abfc233bca3
|
[
"MIT"
] | null | null | null |
okcupid/apps.py
|
ealmuina/statsproject
|
43186bdb213202ff846b18e677b89abfc233bca3
|
[
"MIT"
] | null | null | null |
okcupid/apps.py
|
ealmuina/statsproject
|
43186bdb213202ff846b18e677b89abfc233bca3
|
[
"MIT"
] | null | null | null |
from django.apps import AppConfig
class OkcupidConfig(AppConfig):
name = 'okcupid'
| 14.833333
| 33
| 0.752809
| 10
| 89
| 6.7
| 0.9
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| 1
| 0
|
0
| 4
|
3fbd235306ae4985b02f3fa36eaca50647a0e931
| 210
|
py
|
Python
|
scieio/viscometers/apps.py
|
arnelimperial/scieio
|
279a25766f20d074a3df824c0fbc8b2d8e35f272
|
[
"MIT"
] | null | null | null |
scieio/viscometers/apps.py
|
arnelimperial/scieio
|
279a25766f20d074a3df824c0fbc8b2d8e35f272
|
[
"MIT"
] | 8
|
2021-03-19T01:56:44.000Z
|
2022-03-12T00:24:21.000Z
|
scieio/viscometers/apps.py
|
arnelimperial/scieio
|
279a25766f20d074a3df824c0fbc8b2d8e35f272
|
[
"MIT"
] | null | null | null |
from django.apps import AppConfig
from django.utils.translation import gettext_lazy as _
class ViscometersConfig(AppConfig):
name = 'scieio.viscometers'
verbose_name = _("Viscometers and Rheometers")
| 26.25
| 54
| 0.785714
| 24
| 210
| 6.708333
| 0.75
| 0.124224
| 0
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| 0
| 0
| 0
| 0
| 0
| 0.142857
| 210
| 7
| 55
| 30
| 0.894444
| 0
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| 0
| 0.209524
| 0
| 0
| 0
| 0
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| 1
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| false
| 0
| 0.4
| 0
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| 1
| 0
| 1
| 0
|
0
| 4
|
3fd5d933fbabb187873f90255cf0154903357ea7
| 9,513
|
py
|
Python
|
TwoThreeTree/test_two_three_tree.py
|
alexander-dejeu/CodeForMediumArticles
|
186048463fbf8d8e7095cff3f91c27bfceeec3a4
|
[
"MIT"
] | null | null | null |
TwoThreeTree/test_two_three_tree.py
|
alexander-dejeu/CodeForMediumArticles
|
186048463fbf8d8e7095cff3f91c27bfceeec3a4
|
[
"MIT"
] | null | null | null |
TwoThreeTree/test_two_three_tree.py
|
alexander-dejeu/CodeForMediumArticles
|
186048463fbf8d8e7095cff3f91c27bfceeec3a4
|
[
"MIT"
] | null | null | null |
from starter_two_three_tree import TwoThreeTree, Node
import unittest
class NodeTest(unittest.TestCase):
def test_init(self):
data = 1
node = Node(data)
assert node.data[0] == data
assert len(node.data) == 1
assert len(node.children) == 0
assert node.parent is None
def test_init_list_of_data(self):
data = [2, 3, 4]
node = Node(*data)
assert node.data[2] == data[2]
assert len(node.data) == 3
assert len(node.children) == 0
assert node.parent is None
def test_init_with_args(self):
node = Node(1, 2, 3)
assert node.data[2] == 3
assert len(node.data) == 3
assert len(node.children) == 0
assert node.parent is None
def test_init_full(self):
parent = Node(3)
data = 1
node_children = [Node(2), Node(4)]
node = Node(data, children=node_children, parent=parent)
parent.children.append(node)
assert node.data[0] == 1
assert node.parent == parent
assert parent.children[0] == node
assert len(parent.children) == 1
assert len(node.children) == 2
assert len(node.children[0].children) == 0
class TwoThreeTreeTest(unittest.TestCase):
def test_init(self):
ttt = TwoThreeTree()
assert ttt.root is None
def test_first_root(self):
ttt = TwoThreeTree()
ttt.insert(4)
assert ttt.root is not None
assert ttt.root.data[0] == 4
assert len(ttt.root.data) == 1
assert ttt.root.parent is None
assert len(ttt.root.children) == 0
def test_first_split(self):
ttt = TwoThreeTree()
ttt.insert(4)
ttt.insert(30)
# It is important that the values remain sorted so going to Check
assert ttt.root.data[0] == 4
assert ttt.root.data[1] == 30
ttt.insert(7)
assert len(ttt.root.data) == 1
assert ttt.root.data[0] == 7
assert len(ttt.root.children) == 2
assert ttt.root.children[0].data[0] == 4
assert ttt.root.children[0].parent is ttt.root
assert ttt.root.children[1].data[0] == 30
assert ttt.root.children[1].parent is ttt.root
def test_split_leaf(self):
ttt = TwoThreeTree()
ttt.insert(4)
ttt.insert(30)
ttt.insert(7)
ttt.insert(5)
ttt.insert(3)
assert len(ttt.root.children[0].data) == 1
assert len(ttt.root.children[1].data) == 1
assert len(ttt.root.children[2].data) == 1
assert ttt.root.children[0].parent is ttt.root
assert ttt.root.children[1].parent is ttt.root
assert ttt.root.children[2].parent is ttt.root
assert len(ttt.root.data) == 2
assert ttt.root.data[0] == 4
assert ttt.root.data[1] == 7
assert len(ttt.root.children[0].data) == 1
assert len(ttt.root.children[1].data) == 1
assert len(ttt.root.children[2].data) == 1
assert ttt.root.children[0].data[0] == 3
assert ttt.root.children[1].data[0] == 5
assert ttt.root.children[2].data[0] == 30
def test_full_two_level(self):
ttt = TwoThreeTree()
ttt.insert(4)
ttt.insert(30)
ttt.insert(7)
ttt.insert(5)
ttt.insert(3)
ttt.insert(6)
ttt.insert(2)
ttt.insert(36)
assert len(ttt.root.children[0].data) == 2
assert len(ttt.root.children[1].data) == 2
assert len(ttt.root.children[2].data) == 2
assert ttt.root.children[0].parent is ttt.root
assert ttt.root.children[1].parent is ttt.root
assert ttt.root.children[2].parent is ttt.root
assert ttt.root.data[0] == 4
assert ttt.root.data[1] == 7
def test_full_two_level_split(self):
ttt = TwoThreeTree()
ttt.insert(4)
ttt.insert(30)
ttt.insert(7)
ttt.insert(5)
ttt.insert(3)
ttt.insert(6)
ttt.insert(2)
ttt.insert(36)
ttt.insert(1)
assert len(ttt.root.children[0].children[0].data) == 1
assert len(ttt.root.children[0].children[1].data) == 1
assert len(ttt.root.children[1].children[0].data) == 2
assert len(ttt.root.children[1].children[1].data) == 2
assert len(ttt.root.data) == 1
assert len(ttt.root.children) == 2
assert len(ttt.root.children[1].data) == 1
assert ttt.root.data[0] == 4
assert ttt.root.children[0].data[0] == 2
assert ttt.root.children[1].data[0] == 7
assert ttt.root.children[0].parent is ttt.root
assert ttt.root.children[1].parent is ttt.root
assert ttt.root.children[0].children[0].parent is ttt.root.children[0]
assert ttt.root.children[0].children[1].parent is ttt.root.children[0]
assert ttt.root.children[1].children[0].parent is ttt.root.children[1]
assert ttt.root.children[1].children[1].parent is ttt.root.children[1]
def test_middle_split(self):
ttt = TwoThreeTree()
ttt.insert(10)
ttt.insert(20)
ttt.insert(30)
ttt.insert(60)
ttt.insert(70)
ttt.insert(50)
ttt.insert(40)
assert len(ttt.root.children) == 2
assert ttt.root.children[0].data[0] == 20
assert ttt.root.children[1].data[0] == 60
assert len(ttt.root.children[0].children) == 2
assert ttt.root.children[0].children[0].data[0] == 10
assert ttt.root.children[0].children[1].data[0] == 30
assert len(ttt.root.children[1].children) == 2
assert ttt.root.children[1].children[0].data[0] == 50
assert ttt.root.children[1].children[1].data[0] == 70
def test_fill_full_three_level(self):
ttt = TwoThreeTree()
ttt.insert(4)
ttt.insert(30)
ttt.insert(7)
ttt.insert(5)
ttt.insert(3)
ttt.insert(6)
ttt.insert(2)
ttt.insert(36)
ttt.insert(1)
ttt.insert(3)
ttt.insert(0)
ttt.insert(1)
ttt.insert(40)
ttt.insert(0)
ttt.insert(2)
ttt.insert(25)
ttt.insert(41)
assert len(ttt.root.children[0].children[0].data) == 2
assert len(ttt.root.children[0].children[1].data) == 2
assert len(ttt.root.children[1].children[0].data) == 2
assert len(ttt.root.children[1].children[1].data) == 2
assert len(ttt.root.data) == 1
assert len(ttt.root.children) == 2
assert len(ttt.root.children[1].data) == 2
assert ttt.root.children[0].data[0] == 1
assert ttt.root.children[0].data[1] == 2
assert ttt.root.children[0].children[0].parent == ttt.root.children[0]
# At this point we are feeling better with the tests because we know
# our split function can split a few times up the tree without fail
def test_full_three_level_split(self):
ttt = TwoThreeTree()
ttt.insert(4)
ttt.insert(30)
ttt.insert(7)
ttt.insert(5)
ttt.insert(3)
ttt.insert(6)
ttt.insert(2)
ttt.insert(36)
ttt.insert(1)
ttt.insert(3)
ttt.insert(0)
ttt.insert(1)
ttt.insert(40)
ttt.insert(0)
ttt.insert(2)
ttt.insert(25)
ttt.insert(41)
ttt.insert(45)
# Pushed data up to the root correctly
assert len(ttt.root.data) == 2
assert ttt.root.data[0] == 4
assert ttt.root.data[1] == 36
# Make sure the 2nd layer data looks right
assert len(ttt.root.children[0].data) == 2
assert ttt.root.children[1].data[0] == 7
assert ttt.root.children[2].data[0] == 41
# Make sure the childen's parent relationships are correct
assert ttt.root.children[2].parent is ttt.root
assert ttt.root.children[2].children[0].parent is ttt.root.children[2]
assert ttt.root.children[2].children[1].parent is ttt.root.children[2]
assert ttt.root.children[1].children[0].parent is ttt.root.children[1]
assert ttt.root.children[1].children[1].parent is ttt.root.children[1]
def test_search(self):
ttt = TwoThreeTree()
ttt.insert(4)
ttt.insert(30)
ttt.insert(7)
ttt.insert(5)
ttt.insert(3)
ttt.insert(6)
ttt.insert(2)
ttt.insert(36)
ttt.insert(1)
ttt.insert(3)
ttt.insert(0)
ttt.insert(1)
ttt.insert(40)
ttt.insert(0)
ttt.insert(2)
ttt.insert(25)
ttt.insert(41)
ttt.insert(45)
assert ttt.search(4) is True
assert ttt.search(30) is True
assert ttt.search(7) is True
assert ttt.search(5) is True
assert ttt.search(3) is True
assert ttt.search(6) is True
assert ttt.search(2) is True
assert ttt.search(36) is True
assert ttt.search(1) is True
assert ttt.search(0) is True
assert ttt.search(40) is True
assert ttt.search(25) is True
assert ttt.search(41) is True
assert ttt.search(45) is True
assert ttt.search(12) is False
assert ttt.search(-4) is False
assert ttt.search(49) is False
assert ttt.search(57) is False
assert ttt.search(101) is False
assert ttt.search(124) is False
def test_search_empty_tree(self):
ttt = TwoThreeTree()
if __name__ == '__main__':
unittest.main()
| 31.922819
| 78
| 0.582151
| 1,400
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| 3.919286
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| 0
| 0
|
0
| 4
|
3fe4e8215c8616ad5d38caffdbec7d30e6ddf83b
| 123
|
py
|
Python
|
src/ingest-pipeline/md/type_base.py
|
AustinHartman/ingest-pipeline
|
788d9310792c9396a38650deda3dad11483b368c
|
[
"MIT"
] | 6
|
2020-02-18T19:09:59.000Z
|
2021-10-07T20:38:46.000Z
|
src/ingest-pipeline/md/type_base.py
|
AustinHartman/ingest-pipeline
|
788d9310792c9396a38650deda3dad11483b368c
|
[
"MIT"
] | 324
|
2020-02-06T22:08:50.000Z
|
2022-03-24T20:44:33.000Z
|
src/ingest-pipeline/md/type_base.py
|
AustinHartman/ingest-pipeline
|
788d9310792c9396a38650deda3dad11483b368c
|
[
"MIT"
] | 2
|
2020-07-20T14:43:49.000Z
|
2021-10-29T18:24:36.000Z
|
#! /usr/bin/env python
"""
Some type definitions for metadata extraction
"""
class MetadataError(RuntimeError):
pass
| 13.666667
| 45
| 0.723577
| 14
| 123
| 6.357143
| 1
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| 0.162602
| 123
| 8
| 46
| 15.375
| 0.864078
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| 1
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| 0
| 0
| 0
|
0
| 4
|
b74750d5a71d32dc52804691456ca2ca5ef2fabf
| 21
|
py
|
Python
|
psslib/__init__.py
|
bunyk/pss
|
d903f187b69ea2282b79b730454a041dd0c5f007
|
[
"Unlicense"
] | null | null | null |
psslib/__init__.py
|
bunyk/pss
|
d903f187b69ea2282b79b730454a041dd0c5f007
|
[
"Unlicense"
] | null | null | null |
psslib/__init__.py
|
bunyk/pss
|
d903f187b69ea2282b79b730454a041dd0c5f007
|
[
"Unlicense"
] | null | null | null |
__version__ = '1.42'
| 10.5
| 20
| 0.666667
| 3
| 21
| 3.333333
| 1
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| 0.166667
| 0.142857
| 21
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| 0.388889
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| null | 0
| 0
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| 0
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| 0
| 0
| 0
| 0
|
0
| 4
|
b750844f1bfaec19347572eeff54e7e6aa632c72
| 165
|
py
|
Python
|
binaryCycleSearch.py
|
Case-y/algorithms
|
48e3c183269bf6108b6d2c0c4dece687ecf01e90
|
[
"WTFPL"
] | null | null | null |
binaryCycleSearch.py
|
Case-y/algorithms
|
48e3c183269bf6108b6d2c0c4dece687ecf01e90
|
[
"WTFPL"
] | null | null | null |
binaryCycleSearch.py
|
Case-y/algorithms
|
48e3c183269bf6108b6d2c0c4dece687ecf01e90
|
[
"WTFPL"
] | null | null | null |
def uncycle(list):
if len(list) <= 3:
return max(list)
m = int(len(list) / 2)
if list[0] < list[m]:
return uncycle(list[m:])
else:
return uncycle(list[:m])
| 20.625
| 26
| 0.612121
| 29
| 165
| 3.482759
| 0.448276
| 0.19802
| 0.336634
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| 0.022388
| 0.187879
| 165
| 8
| 27
| 20.625
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| 0
| 0
|
0
| 4
|
b7517d25347560d5452deac6a15e237b69af37f9
| 199
|
py
|
Python
|
api/__init__.py
|
nosoyyo/blox
|
f42913759a96d7ba387accc31dd3255b32b68f14
|
[
"MIT"
] | null | null | null |
api/__init__.py
|
nosoyyo/blox
|
f42913759a96d7ba387accc31dd3255b32b68f14
|
[
"MIT"
] | null | null | null |
api/__init__.py
|
nosoyyo/blox
|
f42913759a96d7ba387accc31dd3255b32b68f14
|
[
"MIT"
] | null | null | null |
#!/usr/bin/env python
# -*- coding: utf-8 -*-
# @absurdity v0.3 app
__author__ = 'nosoyyo'
from .block import *
from .task import *
from .note import *
from .command import *
from .control import *
| 18.090909
| 23
| 0.673367
| 28
| 199
| 4.642857
| 0.714286
| 0.307692
| 0
| 0
| 0
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| 0
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| 11
| 24
| 18.090909
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| 0
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| 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
| 0
| 0
| 0
| 1
| 0
| 1
| 0
|
0
| 4
|
b7535c0e8e2eddb01bcc755085fa38f94c8efe2a
| 610
|
py
|
Python
|
arrp/utils/paths.py
|
LucaCappelletti94/arrp_dataset
|
bcea455a504e8ff718458ce12623c63e0314badb
|
[
"MIT"
] | null | null | null |
arrp/utils/paths.py
|
LucaCappelletti94/arrp_dataset
|
bcea455a504e8ff718458ce12623c63e0314badb
|
[
"MIT"
] | null | null | null |
arrp/utils/paths.py
|
LucaCappelletti94/arrp_dataset
|
bcea455a504e8ff718458ce12623c63e0314badb
|
[
"MIT"
] | null | null | null |
def _build_csv_path(target:str, directory:str, cell_line:str):
return "{target}/{directory}/{cell_line}.csv".format(
target=target,
directory=directory,
cell_line=cell_line
)
def get_raw_epigenomic_data_path(target:str, cell_line:str):
return _build_csv_path(target, "epigenomic_data", cell_line)
def get_raw_nucleotides_sequences_path(target:str, cell_line:str):
return _build_csv_path(target, "one_hot_encoded_expanded_regions", cell_line)
def get_raw_classes_path(target:str, cell_line:str):
return _build_csv_path(target, "one_hot_encoded_classes", cell_line)
| 40.666667
| 81
| 0.770492
| 90
| 610
| 4.766667
| 0.255556
| 0.18648
| 0.111888
| 0.167832
| 0.561772
| 0.39627
| 0.39627
| 0.39627
| 0.39627
| 0.39627
| 0
| 0
| 0.12623
| 610
| 15
| 82
| 40.666667
| 0.804878
| 0
| 0
| 0
| 0
| 0
| 0.173486
| 0.148936
| 0
| 0
| 0
| 0
| 0
| 1
| 0.333333
| false
| 0
| 0
| 0.333333
| 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
| 1
| 1
| 0
|
0
| 4
|
b75874a29a2a0ced6c1e6a4e8c1e13651744c6ad
| 1,769
|
py
|
Python
|
imgaug/augmenters/overlay.py
|
fmder/imgaug
|
4c81c7a7503b64f54d76144385ea4330fd7c8a84
|
[
"MIT"
] | 1
|
2019-10-27T19:17:18.000Z
|
2019-10-27T19:17:18.000Z
|
imgaug/augmenters/overlay.py
|
fmder/imgaug
|
4c81c7a7503b64f54d76144385ea4330fd7c8a84
|
[
"MIT"
] | null | null | null |
imgaug/augmenters/overlay.py
|
fmder/imgaug
|
4c81c7a7503b64f54d76144385ea4330fd7c8a84
|
[
"MIT"
] | null | null | null |
"""Alias for module blend.
Deprecated module. Original name for module blend.py. Was changed in 0.2.8.
"""
from __future__ import print_function, division, absolute_import
import imgaug as ia
from . import blend
@ia.deprecated(alt_func="imgaug.augmenters.blend.blend_alpha()",
comment="It has the exactly same interface.")
def blend_alpha(*args, **kwargs):
"""See :func:`imgaug.augmenters.blend.blend_alpha`."""
# pylint: disable=invalid-name
return blend.blend_alpha(*args, **kwargs)
@ia.deprecated(alt_func="imgaug.augmenters.blend.Alpha",
comment="It has the exactly same interface.")
def Alpha(*args, **kwargs):
"""See :func:`imgaug.augmenters.blend.Alpha`."""
# pylint: disable=invalid-name
return blend.Alpha(*args, **kwargs)
@ia.deprecated(alt_func="imgaug.augmenters.blend.AlphaElementwise",
comment="It has the exactly same interface.")
def AlphaElementwise(*args, **kwargs):
"""See :func:`imgaug.augmenters.blend.AlphaElementwise`."""
# pylint: disable=invalid-name
return blend.AlphaElementwise(*args, **kwargs)
@ia.deprecated(alt_func="imgaug.augmenters.blend.SimplexNoiseAlpha",
comment="It has the exactly same interface.")
def SimplexNoiseAlpha(*args, **kwargs):
"""See :func:`imgaug.augmenters.blend.SimplexNoiseAlpha`."""
# pylint: disable=invalid-name
return blend.SimplexNoiseAlpha(*args, **kwargs)
@ia.deprecated(alt_func="imgaug.augmenters.blend.FrequencyNoiseAlpha",
comment="It has the exactly same interface.")
def FrequencyNoiseAlpha(*args, **kwargs):
"""See :func:`imgaug.augmenters.blend.FrequencyNoiseAlpha`."""
# pylint: disable=invalid-name
return blend.FrequencyNoiseAlpha(*args, **kwargs)
| 35.38
| 75
| 0.705483
| 207
| 1,769
| 5.956522
| 0.222222
| 0.081103
| 0.162206
| 0.202758
| 0.773723
| 0.689376
| 0.604217
| 0.479319
| 0.25223
| 0.171127
| 0
| 0.001999
| 0.151498
| 1,769
| 49
| 76
| 36.102041
| 0.819454
| 0.284907
| 0
| 0.217391
| 0
| 0
| 0.29316
| 0.154723
| 0
| 0
| 0
| 0
| 0
| 1
| 0.217391
| true
| 0
| 0.130435
| 0
| 0.565217
| 0.043478
| 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
| 1
| 0
| 0
| 0
| 1
| 0
|
0
| 4
|
b75ddff876b51793954a78860e9a7cceb1c1264e
| 558
|
py
|
Python
|
src/utilities/database/delete.py
|
smccaffrey/plex-etl
|
341e86f22ae17fa0b48a7ca50ff0a1fc14b6578a
|
[
"Apache-2.0"
] | 7
|
2020-03-08T05:52:26.000Z
|
2022-03-18T12:32:50.000Z
|
src/utilities/database/delete.py
|
smccaffrey/plex-etl
|
341e86f22ae17fa0b48a7ca50ff0a1fc14b6578a
|
[
"Apache-2.0"
] | 5
|
2020-03-15T05:12:08.000Z
|
2020-03-15T18:18:38.000Z
|
src/utilities/database/delete.py
|
smccaffrey/plex-etl
|
341e86f22ae17fa0b48a7ca50ff0a1fc14b6578a
|
[
"Apache-2.0"
] | 1
|
2020-10-05T14:08:21.000Z
|
2020-10-05T14:08:21.000Z
|
import logging
from src.utilities.database.models import db
from src.utilities.database.database import create_or_update
from src.utilities.database.models import ExtractedMovies
from src.utilities.database.models import TransformedMovies
from src.utilities.database.models import LoadMovies
class Delete:
def __init__(self):
return
@staticmethod
@create_or_update
def all():
db.session.query(ExtractedMovies).delete()
db.session.query(TransformedMovies).delete()
db.session.query(LoadMovies).delete()
| 23.25
| 60
| 0.758065
| 66
| 558
| 6.287879
| 0.363636
| 0.084337
| 0.192771
| 0.289157
| 0.346988
| 0.346988
| 0
| 0
| 0
| 0
| 0
| 0
| 0.16129
| 558
| 23
| 61
| 24.26087
| 0.886752
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0.133333
| false
| 0
| 0.4
| 0.066667
| 0.666667
| 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
| 1
| 0
| 1
| 0
|
0
| 4
|
b77f59eb8155d255dff90f7bed8a4535d43c2c14
| 157
|
py
|
Python
|
tests/__init__.py
|
razor-1/aiociscospark
|
69cfe41a8f1fda788cbd5b3a77e84ce04e6d562d
|
[
"MIT"
] | 8
|
2017-12-29T19:03:27.000Z
|
2020-08-17T06:53:58.000Z
|
tests/__init__.py
|
razor-1/aiociscospark
|
69cfe41a8f1fda788cbd5b3a77e84ce04e6d562d
|
[
"MIT"
] | 119
|
2017-10-28T10:27:38.000Z
|
2020-03-16T05:19:45.000Z
|
tests/__init__.py
|
razor-1/aiociscospark
|
69cfe41a8f1fda788cbd5b3a77e84ce04e6d562d
|
[
"MIT"
] | 5
|
2018-05-08T17:49:32.000Z
|
2019-06-05T17:34:29.000Z
|
# Ideally "tests" directory should not be treated as a package (no __init__.py file).
# Read more: http://docs.python-guide.org/en/latest/writing/structure/
| 52.333333
| 85
| 0.757962
| 25
| 157
| 4.6
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.11465
| 157
| 2
| 86
| 78.5
| 0.827338
| 0.968153
| 0
| null | 0
| null | 0
| 0
| null | 0
| 0
| 0
| null | 1
| null | true
| 0
| 0
| null | null | null | 1
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| 0
|
0
| 4
|
4d0c2e92e8f10e7a88c0af94da460e2ccd1489e1
| 40
|
py
|
Python
|
buddy/__init__.py
|
ucbrise/buddy
|
24908db7fba651f2b586a2c2b03e02b805a1f1b2
|
[
"Apache-2.0"
] | 1
|
2020-11-29T19:42:59.000Z
|
2020-11-29T19:42:59.000Z
|
buddy/__init__.py
|
ucbrise/buddy
|
24908db7fba651f2b586a2c2b03e02b805a1f1b2
|
[
"Apache-2.0"
] | null | null | null |
buddy/__init__.py
|
ucbrise/buddy
|
24908db7fba651f2b586a2c2b03e02b805a1f1b2
|
[
"Apache-2.0"
] | null | null | null |
from . import study
__all__ = ['study']
| 13.333333
| 19
| 0.675
| 5
| 40
| 4.6
| 0.8
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.175
| 40
| 3
| 20
| 13.333333
| 0.69697
| 0
| 0
| 0
| 0
| 0
| 0.121951
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| false
| 0
| 0.5
| 0
| 0.5
| 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
| 1
| 0
| 0
| 0
|
0
| 4
|
4d5056441d8c778aff8a5c0cacb5ef954568872e
| 65
|
py
|
Python
|
src/cobra/apps/auditlog/__init__.py
|
lyoniionly/django-cobra
|
2427e5cf74b7739115b1224da3306986b3ee345c
|
[
"Apache-2.0"
] | 1
|
2015-01-27T08:56:46.000Z
|
2015-01-27T08:56:46.000Z
|
src/cobra/apps/auditlog/__init__.py
|
lyoniionly/django-cobra
|
2427e5cf74b7739115b1224da3306986b3ee345c
|
[
"Apache-2.0"
] | null | null | null |
src/cobra/apps/auditlog/__init__.py
|
lyoniionly/django-cobra
|
2427e5cf74b7739115b1224da3306986b3ee345c
|
[
"Apache-2.0"
] | null | null | null |
default_app_config = 'cobra.apps.auditlog.config.AuditLogConfig'
| 32.5
| 64
| 0.846154
| 8
| 65
| 6.625
| 0.875
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.046154
| 65
| 1
| 65
| 65
| 0.854839
| 0
| 0
| 0
| 0
| 0
| 0.630769
| 0.630769
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| false
| 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
| 1
| 1
| null | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
|
0
| 4
|
4d6a7c1564593425f064066473b88a96d2d41539
| 159
|
py
|
Python
|
packages/auto-nlp-deployment/src/common/runtimes/__init__.py
|
fhswf/tagflip-autonlp
|
f94abb35ed06198567e5d9cbb7abb7e112149d6c
|
[
"MIT"
] | 4
|
2021-10-05T17:34:02.000Z
|
2022-03-23T07:33:19.000Z
|
packages/auto-nlp-deployment/src/common/runtimes/__init__.py
|
fhswf/tagflip-autonlp
|
f94abb35ed06198567e5d9cbb7abb7e112149d6c
|
[
"MIT"
] | 11
|
2022-03-01T14:37:52.000Z
|
2022-03-31T05:11:23.000Z
|
packages/auto-nlp-deployment/src/common/runtimes/__init__.py
|
fhswf/tagflip-autonlp
|
f94abb35ed06198567e5d9cbb7abb7e112149d6c
|
[
"MIT"
] | 1
|
2022-01-29T13:32:22.000Z
|
2022-01-29T13:32:22.000Z
|
from .runtime import Runtime
from .runtime_config import RuntimeConfig
from .ssh_config import SSHConfig
from .parameter_definition import ParameterDefinition
| 31.8
| 53
| 0.874214
| 19
| 159
| 7.157895
| 0.526316
| 0.161765
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.100629
| 159
| 4
| 54
| 39.75
| 0.951049
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| true
| 0
| 1
| 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
| 1
| 0
| 0
| 0
|
0
| 4
|
4d7eb180ca04b15ec5785a17f92b4f888e11fc9b
| 116
|
py
|
Python
|
src/project/cli/__init__.py
|
DeviaVir/super-secret-project
|
cae0ac6be32b28a8d3cebdb023d2984d60433b3a
|
[
"MIT"
] | null | null | null |
src/project/cli/__init__.py
|
DeviaVir/super-secret-project
|
cae0ac6be32b28a8d3cebdb023d2984d60433b3a
|
[
"MIT"
] | null | null | null |
src/project/cli/__init__.py
|
DeviaVir/super-secret-project
|
cae0ac6be32b28a8d3cebdb023d2984d60433b3a
|
[
"MIT"
] | null | null | null |
import click
from .memcache import memcache_cmd
CMDS = (memcache_cmd,)
CLI = click.CommandCollection(sources=CMDS)
| 19.333333
| 43
| 0.801724
| 15
| 116
| 6.066667
| 0.6
| 0.241758
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.112069
| 116
| 5
| 44
| 23.2
| 0.883495
| 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 | 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
| 4
|
4d9031a690d7da5262992f455e71525186ac63f4
| 3,322
|
py
|
Python
|
web/kwmo/kwmo/lib/uimessage.py
|
tmbx/kas
|
0d4e74d0a8ec0e0f85ba574eb01d389530bdeecc
|
[
"BSD-3-Clause"
] | null | null | null |
web/kwmo/kwmo/lib/uimessage.py
|
tmbx/kas
|
0d4e74d0a8ec0e0f85ba574eb01d389530bdeecc
|
[
"BSD-3-Clause"
] | null | null | null |
web/kwmo/kwmo/lib/uimessage.py
|
tmbx/kas
|
0d4e74d0a8ec0e0f85ba574eb01d389530bdeecc
|
[
"BSD-3-Clause"
] | null | null | null |
from pylons import session, tmpl_context as c
from strings import message_codes_map
# Show an information message in the web interface.
def ui_info(code=None, message=None, hide_after_ms=None):
uim = UIMessage.info(code=code, message=message, hide_after_ms=hide_after_ms)
c.glob_messages.append(uim)
# Show a warning message in the web interface.
def ui_warn(code=None, message=None, hide_after_ms=None):
uim = UIMessage.warn(code=code, message=message, hide_after_ms=hide_after_ms)
c.glob_messages.append(uim)
# Show an error message in the web interface.
def ui_error(code=None, message=None, hide_after_ms=None):
uim = UIMessage.error(code=code, message=message, hide_after_ms=hide_after_ms)
c.glob_messages.append(uim)
# Show an information message in the web interface (at next request).
def ui_flash_info(code=None, message=None, hide_after_ms=None):
uim = UIMessage.info(code=code, message=message, hide_after_ms=hide_after_ms)
session['uimessage'] = uim
session.save()
# Show a warning message in the web interface (at next request).
def ui_flash_warn(code=None, message=None, hide_after_ms=None):
uim = UIMessage.warn(code=code, message=message, hide_after_ms=hide_after_ms)
session['uimessage'] = uim
session.save()
# Show an error message in the web interface (at next request).
def ui_flash_error(code=None, message=None, hide_after_ms=None):
uim = UIMessage.error(code=code, message=message, hide_after_ms=hide_after_ms)
session['uimessage'] = uim
session.save()
# UIMessage object.
class UIMessage(object):
def __init__(self):
self.reset()
def reset(self):
self.type = None
self.message = None
self.hide_after_ms = None
def from_dict(self, d):
self.reset()
if d.has_key('type'): self.type = d['type']
if d.has_key('message'): self.message = d['message']
if d.has_key('hide_after_ms'): self.hide_after_ms = d['hide_after_ms']
# Return self, although changes happen in place too.
return self
def to_dict(self):
return {'type' : self.type, 'message' : self.message, 'hide_after_ms' : self.hide_after_ms}
def set_code(self, code):
if message_codes_map.has_key(code): self.message = message_codes_map[code]
else: self.message = message_codes_map['unknown_code']
def __repr__(self):
return "<%s type='%s' message='%s' hide_after_ms='%s'>" % ( self.__class__.__name__, self.type, self.message, str(self.hide_after_ms) )
@staticmethod
def info(code=None, message=None, hide_after_ms=None):
uim = UIMessage()
uim.type = 'info'
uim.set_code(code)
if message: uim.message = message
uim.hide_after_ms = hide_after_ms
return uim
@staticmethod
def warn(code=None, message=None, hide_after_ms=None):
uim = UIMessage()
uim.type = 'warn'
uim.set_code(code)
if message: uim.message = message
uim.hide_after_ms = hide_after_ms
return uim
@staticmethod
def error(code=None, message=None, hide_after_ms=None):
uim = UIMessage()
uim.type = 'error'
uim.set_code(code)
if message: uim.message = message
uim.hide_after_ms = hide_after_ms
return uim
| 35.340426
| 143
| 0.685129
| 493
| 3,322
| 4.377282
| 0.135903
| 0.145968
| 0.178406
| 0.069509
| 0.734013
| 0.709917
| 0.709917
| 0.678869
| 0.619555
| 0.619555
| 0
| 0
| 0.204696
| 3,322
| 93
| 144
| 35.72043
| 0.816805
| 0.120409
| 0
| 0.522388
| 0
| 0
| 0.058379
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0.223881
| false
| 0
| 0.029851
| 0.029851
| 0.358209
| 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
| 1
| 0
| 0
| 0
| 0
| 0
| 0
|
0
| 4
|
4db0b8fe5069365ed7cdc97e5964b3b83c49f23d
| 114
|
py
|
Python
|
settings.py
|
rchien6191/demo
|
10ed245b06539d53f1ac108e2077a896f05a6439
|
[
"MIT"
] | null | null | null |
settings.py
|
rchien6191/demo
|
10ed245b06539d53f1ac108e2077a896f05a6439
|
[
"MIT"
] | null | null | null |
settings.py
|
rchien6191/demo
|
10ed245b06539d53f1ac108e2077a896f05a6439
|
[
"MIT"
] | null | null | null |
TITLE = "Jump game"
WIDTH = 480
HEIGHT = 600
FPS = 30
WHITE = (255, 255, 255)
BLACK = (0,0,0)
RED = (240, 55, 66)
| 14.25
| 23
| 0.587719
| 21
| 114
| 3.190476
| 0.809524
| 0.179104
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.306818
| 0.22807
| 114
| 8
| 24
| 14.25
| 0.454545
| 0
| 0
| 0
| 0
| 0
| 0.078261
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| false
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 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
| 4
|
4dd0c97dc8bbee9310ebc5f011355bc0d891d28f
| 99
|
py
|
Python
|
vehicles/models/tools.py
|
kackey0-1/drf-sample
|
914907320bc317240b4d7c07968b6d4ea80b4511
|
[
"MIT"
] | null | null | null |
vehicles/models/tools.py
|
kackey0-1/drf-sample
|
914907320bc317240b4d7c07968b6d4ea80b4511
|
[
"MIT"
] | 6
|
2021-03-30T12:05:07.000Z
|
2021-04-05T14:21:46.000Z
|
vehicles/models/tools.py
|
kackey0-1/drf-sample
|
914907320bc317240b4d7c07968b6d4ea80b4511
|
[
"MIT"
] | null | null | null |
class Tool:
def __init__(self, name, make):
self.name = name
self.make = make
| 16.5
| 35
| 0.565657
| 13
| 99
| 4
| 0.538462
| 0.307692
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.333333
| 99
| 5
| 36
| 19.8
| 0.787879
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0.25
| false
| 0
| 0
| 0
| 0.5
| 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
| 1
| 0
| 0
| 0
| 0
| 0
| 0
|
0
| 4
|
1283e72d54accb0165e3a7fdf82827960ace0957
| 907
|
py
|
Python
|
note/models.py
|
pjkui/notepad
|
99f106c27e584709e5ac583e3cc64a506c4eacc7
|
[
"MIT"
] | 1
|
2015-10-16T15:55:00.000Z
|
2015-10-16T15:55:00.000Z
|
note/models.py
|
pjkui/notepad
|
99f106c27e584709e5ac583e3cc64a506c4eacc7
|
[
"MIT"
] | null | null | null |
note/models.py
|
pjkui/notepad
|
99f106c27e584709e5ac583e3cc64a506c4eacc7
|
[
"MIT"
] | null | null | null |
from django.db import models
# Create your models here.
class Note(models.Model):
title = models.CharField(max_length=60)
color = models.CharField(max_length=10)
authorID = models.IntegerField()
noteType = models.IntegerField()
createTime = models.DateTimeField()
content = models.TextField()
def __unicode__(self):
return self.title
class User(models.Model):
username = models.CharField(max_length=40)
password = models.CharField(max_length=40)
nickname = models.CharField(max_length=40)
loginIP = models.CharField(max_length=12)
def __unicode__(self):
return str(self.id) + " " + self.username + " " + self.password
class NoteAuthor(models.Model):
userID = models.ForeignKey(User)
noteID = models.ForeignKey(Note)
class Tag(models.Model):
tagName = models.CharField(max_length=20)
noteID = models.ForeignKey(Note)
| 25.194444
| 71
| 0.702315
| 108
| 907
| 5.759259
| 0.416667
| 0.16881
| 0.202572
| 0.270096
| 0.125402
| 0
| 0
| 0
| 0
| 0
| 0
| 0.018945
| 0.185226
| 907
| 35
| 72
| 25.914286
| 0.822733
| 0.026461
| 0
| 0.173913
| 0
| 0
| 0.00227
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0.086957
| false
| 0.086957
| 0.043478
| 0.086957
| 1
| 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
| 1
| 0
| 0
| 1
| 0
|
0
| 4
|
12c5c48fe468909ab3820c3dcd375b372e264a69
| 607
|
py
|
Python
|
src/pyomexmeta/__init__.py
|
aram148/libOmexMeta
|
331a0a48570a212aaa2b6cb3fe72b9f43ac828af
|
[
"Apache-2.0"
] | null | null | null |
src/pyomexmeta/__init__.py
|
aram148/libOmexMeta
|
331a0a48570a212aaa2b6cb3fe72b9f43ac828af
|
[
"Apache-2.0"
] | null | null | null |
src/pyomexmeta/__init__.py
|
aram148/libOmexMeta
|
331a0a48570a212aaa2b6cb3fe72b9f43ac828af
|
[
"Apache-2.0"
] | null | null | null |
from .pyomexmeta import PersonalInformation
from .pyomexmeta import EnergyDiff
from .pyomexmeta import PhysicalProcess
from .pyomexmeta import RDF, Editor, PhysicalEntity
from .pyomexmeta import SingularAnnotation
from .pyomexmeta import OmexMetaException
from .pyomexmeta_api import PyOmexMetaAPI, get_version, eUriType, eXmlType
__version__ = get_version()
def run_tests():
import os
import unittest
loader = unittest.TestLoader()
start_dir = os.path.abspath(os.path.dirname(__file__))
suite = loader.discover(start_dir)
runner = unittest.TextTestRunner()
runner.run(suite)
| 30.35
| 74
| 0.789127
| 68
| 607
| 6.838235
| 0.5
| 0.210753
| 0.258065
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.14168
| 607
| 19
| 75
| 31.947368
| 0.892514
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0.0625
| false
| 0
| 0.5625
| 0
| 0.625
| 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
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 1
| 0
|
0
| 4
|
12d33f0e116da12db1d49faebe3c24c699cd7bd8
| 255
|
py
|
Python
|
flask/desktop-host.py
|
iarecrazy/AIris
|
b6f316a46b6d567a62712e2844e4baffddb8bbfe
|
[
"MIT"
] | null | null | null |
flask/desktop-host.py
|
iarecrazy/AIris
|
b6f316a46b6d567a62712e2844e4baffddb8bbfe
|
[
"MIT"
] | 3
|
2020-06-05T18:08:55.000Z
|
2021-06-10T20:14:27.000Z
|
flask/desktop-host.py
|
iarecrazy/AIris
|
b6f316a46b6d567a62712e2844e4baffddb8bbfe
|
[
"MIT"
] | null | null | null |
import os
from flask import Flask
from app import *
app.config['root_dir'] = 'X:\\'
app.config['tmp_dir'] = 'C:\\Temp'
app.config['path_to_index'] = os.path.join(app.config['tmp_dir'], 'index.json')
if __name__ == '__main__':
app.run(host='0.0.0.0')
| 19.615385
| 79
| 0.662745
| 44
| 255
| 3.545455
| 0.522727
| 0.230769
| 0.153846
| 0.192308
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.017778
| 0.117647
| 255
| 12
| 80
| 21.25
| 0.675556
| 0
| 0
| 0
| 0
| 0
| 0.282353
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| true
| 0
| 0.375
| 0
| 0.375
| 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
| 1
| 0
| 1
| 0
| 0
| 0
|
0
| 4
|
12f54719fe5c8a4055d1bfedf9e84f1ab5157eff
| 90
|
py
|
Python
|
semana_01/exercicios/exercicio_01.py
|
luispaulojr/cursoPython
|
24aaa73741508986d7f747be8f3822889be81025
|
[
"MIT"
] | null | null | null |
semana_01/exercicios/exercicio_01.py
|
luispaulojr/cursoPython
|
24aaa73741508986d7f747be8f3822889be81025
|
[
"MIT"
] | null | null | null |
semana_01/exercicios/exercicio_01.py
|
luispaulojr/cursoPython
|
24aaa73741508986d7f747be8f3822889be81025
|
[
"MIT"
] | null | null | null |
print('O numero informado foi: {}'.format(
int(input('Informe um número inteiro: '))))
| 45
| 47
| 0.666667
| 12
| 90
| 5
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.144444
| 90
| 2
| 47
| 45
| 0.779221
| 0
| 0
| 0
| 0
| 0
| 0.582418
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| true
| 0
| 0
| 0
| 0
| 0.5
| 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
| 1
|
0
| 4
|
12ff02b885037c933f8b02121472062d8be1bc93
| 181
|
py
|
Python
|
pymilldb/__main__.py
|
Toka-Taka/mill-db
|
1edf390f2ce89d9232ba91d722cb4b104c398078
|
[
"MIT"
] | 2
|
2019-11-05T06:24:59.000Z
|
2020-03-06T09:04:38.000Z
|
pymilldb/__main__.py
|
bmstu-iu9/mill-db
|
a3725b11fcd995953dabc21f7fe6f4d5f5d38815
|
[
"MIT"
] | 2
|
2019-05-22T09:40:51.000Z
|
2020-03-03T12:17:12.000Z
|
pymilldb/__main__.py
|
Toka-Taka/mill-db
|
1edf390f2ce89d9232ba91d722cb4b104c398078
|
[
"MIT"
] | 6
|
2018-05-03T16:04:13.000Z
|
2019-12-01T11:01:07.000Z
|
import sys
from .main import generate
if __name__ == '__main__':
if len(sys.argv) != 2:
raise Exception('Insert only filename')
else:
generate(sys.argv[1])
| 20.111111
| 47
| 0.629834
| 24
| 181
| 4.416667
| 0.708333
| 0.132075
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.014706
| 0.248619
| 181
| 8
| 48
| 22.625
| 0.764706
| 0
| 0
| 0
| 1
| 0
| 0.154696
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| true
| 0
| 0.285714
| 0
| 0.285714
| 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
| 1
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| 0
|
0
| 4
|
420e28b2a60a5915fbdb6541b8329035905ca2eb
| 717
|
py
|
Python
|
tests/testtask.py
|
MichaelDoyle/riemann-sumd
|
06f7599d4f93e16e0f37de29e3f9afad45c7f92d
|
[
"MIT"
] | 31
|
2015-02-04T07:17:24.000Z
|
2021-08-20T21:20:58.000Z
|
tests/testtask.py
|
MichaelDoyle/riemann-sumd
|
06f7599d4f93e16e0f37de29e3f9afad45c7f92d
|
[
"MIT"
] | 17
|
2015-03-08T14:33:14.000Z
|
2018-10-25T21:02:47.000Z
|
tests/testtask.py
|
MichaelDoyle/riemann-sumd
|
06f7599d4f93e16e0f37de29e3f9afad45c7f92d
|
[
"MIT"
] | 5
|
2015-06-29T11:06:47.000Z
|
2018-10-27T05:46:25.000Z
|
import sys
sys.path.append("lib")
import unittest
class TestTask(unittest.TestCase):
def setUp(self):
pass
def test_add_tag(self):
pass
def test_add_timing(self):
pass
def test_skew(self):
pass
def test_start(self):
pass
def test_draing(self):
pass
class TestJSONTask(TestTask):
def setUp(self):
pass
class TestHTTPJSONTask(TestTask):
def setUp(self):
# Perhaps start a simple http server with JSON output?
# Or replay a captured copy of actual JSON output?
pass
class TestNagiosTask(TestTask):
def setUp(self):
# Scaffold up a nagios task that does something silly
pass
| 16.674419
| 62
| 0.627615
| 91
| 717
| 4.868132
| 0.494505
| 0.126411
| 0.124154
| 0.1693
| 0.081264
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.301255
| 717
| 42
| 63
| 17.071429
| 0.884232
| 0.213389
| 0
| 0.52
| 0
| 0
| 0.005357
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0.36
| false
| 0.36
| 0.08
| 0
| 0.6
| 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
| 1
| 0
| 0
| 1
| 0
|
0
| 4
|
4224b369742a74c14c6de9afaaea5afafb05a9c6
| 2,121
|
py
|
Python
|
usaspending_api/disaster/tests/integration/test_disaster_def_code_count.py
|
g4brielvs/usaspending-api
|
bae7da2c204937ec1cdf75c052405b13145728d5
|
[
"CC0-1.0"
] | 1
|
2020-08-14T04:14:32.000Z
|
2020-08-14T04:14:32.000Z
|
usaspending_api/disaster/tests/integration/test_disaster_def_code_count.py
|
g4brielvs/usaspending-api
|
bae7da2c204937ec1cdf75c052405b13145728d5
|
[
"CC0-1.0"
] | null | null | null |
usaspending_api/disaster/tests/integration/test_disaster_def_code_count.py
|
g4brielvs/usaspending-api
|
bae7da2c204937ec1cdf75c052405b13145728d5
|
[
"CC0-1.0"
] | null | null | null |
import pytest
from rest_framework import status
url = "/api/v2/disaster/def_code/count/"
@pytest.mark.django_db
def test_def_code_count_success(client, monkeypatch, disaster_account_data, helpers):
helpers.patch_datetime_now(monkeypatch, 2022, 12, 31)
helpers.reset_dabs_cache()
resp = helpers.post_for_count_endpoint(client, url, ["L", "M", "N", "O", "P"])
assert resp.status_code == status.HTTP_200_OK
assert resp.data["count"] == 5
resp = helpers.post_for_count_endpoint(client, url, ["N", "O"])
assert resp.status_code == status.HTTP_200_OK
assert resp.data["count"] == 2
resp = helpers.post_for_count_endpoint(client, url, ["P"])
assert resp.status_code == status.HTTP_200_OK
assert resp.data["count"] == 1
resp = helpers.post_for_count_endpoint(client, url, ["9"])
assert resp.status_code == status.HTTP_200_OK
assert resp.data["count"] == 0
@pytest.mark.django_db
def test_def_code_count_invalid_defc(client, monkeypatch, disaster_account_data, helpers):
helpers.patch_datetime_now(monkeypatch, 2022, 12, 31)
resp = helpers.post_for_count_endpoint(client, url, ["ZZ"])
assert resp.status_code == status.HTTP_400_BAD_REQUEST
assert resp.data["detail"] == "Field 'filter|def_codes' is outside valid values ['9', 'L', 'M', 'N', 'O', 'P']"
@pytest.mark.django_db
def test_def_code_count_invalid_defc_type(client, monkeypatch, disaster_account_data, helpers):
helpers.patch_datetime_now(monkeypatch, 2022, 12, 31)
resp = helpers.post_for_count_endpoint(client, url, "100")
assert resp.status_code == status.HTTP_400_BAD_REQUEST
assert resp.data["detail"] == "Invalid value in 'filter|def_codes'. '100' is not a valid type (array)"
@pytest.mark.django_db
def test_def_code_count_missing_defc(client, monkeypatch, disaster_account_data, helpers):
helpers.patch_datetime_now(monkeypatch, 2022, 12, 31)
resp = helpers.post_for_count_endpoint(client, url)
assert resp.status_code == status.HTTP_422_UNPROCESSABLE_ENTITY
assert resp.data["detail"] == "Missing value: 'filter|def_codes' is a required field"
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| 115
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| 0.095109
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| 0.136728
| 2,121
| 51
| 116
| 41.588235
| 0.767886
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| 0
| 0.378378
| 0
| 0.027027
| 0.134842
| 0.015087
| 0
| 0
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| 0.378378
| 1
| 0.108108
| false
| 0
| 0.054054
| 0
| 0.162162
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 1
| 1
| 1
| 0
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
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| null | 0
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0
| 4
|
42366cfa4ee1d3d101bd1c0152db5a64fe7833f6
| 33,030
|
py
|
Python
|
enaml/qt/docking/dock_resources.py
|
xtuzy/enaml
|
a1b5c0df71c665b6ef7f61d21260db92d77d9a46
|
[
"BSD-3-Clause-Clear"
] | 1,080
|
2015-01-04T14:29:34.000Z
|
2022-03-29T05:44:51.000Z
|
enaml/qt/docking/dock_resources.py
|
xtuzy/enaml
|
a1b5c0df71c665b6ef7f61d21260db92d77d9a46
|
[
"BSD-3-Clause-Clear"
] | 308
|
2015-01-05T22:44:13.000Z
|
2022-03-30T21:19:18.000Z
|
enaml/qt/docking/dock_resources.py
|
xtuzy/enaml
|
a1b5c0df71c665b6ef7f61d21260db92d77d9a46
|
[
"BSD-3-Clause-Clear"
] | 123
|
2015-01-25T16:33:48.000Z
|
2022-02-25T19:57:10.000Z
|
# -*- coding: utf-8 -*-
# Resource object code
#
# Created: Tue Jul 2 13:23:21 2013
# by: The Resource Compiler for PyQt (Qt v4.8.3)
#
# WARNING! All changes made in this file will be lost!
# this line manually edited
from enaml.qt import QtCore
qt_resource_data = b"\
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def qInitResources():
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def qCleanupResources():
QtCore.qUnregisterResourceData(0x01, qt_resource_struct, qt_resource_name, qt_resource_data)
qInitResources()
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0
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424e802f4dbcaee8a96d6d1b8408c135132d6dce
| 203
|
py
|
Python
|
0x03-python-data_structures/5-no_c.py
|
darkares23/holbertonschool-higher_level_programming
|
931b1b701d8a1d990b7cd931486496c0b5502e21
|
[
"MIT"
] | null | null | null |
0x03-python-data_structures/5-no_c.py
|
darkares23/holbertonschool-higher_level_programming
|
931b1b701d8a1d990b7cd931486496c0b5502e21
|
[
"MIT"
] | null | null | null |
0x03-python-data_structures/5-no_c.py
|
darkares23/holbertonschool-higher_level_programming
|
931b1b701d8a1d990b7cd931486496c0b5502e21
|
[
"MIT"
] | null | null | null |
#!/usr/bin/python3
def no_c(my_string):
newStr = ""
for i in range(len(my_string)):
if my_string[i] != "c" and my_string[i] != "C":
newStr += my_string[i]
return (newStr)
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0
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|
424ec4b4bdb2aecf125f957628d8c4ad4964ec90
| 139
|
py
|
Python
|
week3/user/urls.py
|
RomulusGwelt/advanced-django2019
|
6b8d4ce2f829456da1e00ddb8c357608d001aad2
|
[
"MIT"
] | null | null | null |
week3/user/urls.py
|
RomulusGwelt/advanced-django2019
|
6b8d4ce2f829456da1e00ddb8c357608d001aad2
|
[
"MIT"
] | null | null | null |
week3/user/urls.py
|
RomulusGwelt/advanced-django2019
|
6b8d4ce2f829456da1e00ddb8c357608d001aad2
|
[
"MIT"
] | null | null | null |
from django.urls import path
from rest_framework_jwt.views import obtain_jwt_token
urlpatterns = [
path('login/', obtain_jwt_token),
]
| 23.166667
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0
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425f3c9dc68cbe9337f3205d8269bee68f00d246
| 201
|
py
|
Python
|
app/api/serializers/time_entry_serializers.py
|
nabaz/projecttracker
|
c6b326592f7a6925b2fbc0924350dd0951beca0f
|
[
"MIT"
] | null | null | null |
app/api/serializers/time_entry_serializers.py
|
nabaz/projecttracker
|
c6b326592f7a6925b2fbc0924350dd0951beca0f
|
[
"MIT"
] | null | null | null |
app/api/serializers/time_entry_serializers.py
|
nabaz/projecttracker
|
c6b326592f7a6925b2fbc0924350dd0951beca0f
|
[
"MIT"
] | null | null | null |
from api.models import TimeEntry
from rest_framework import serializers
class TimeEntrySerializers(serializers.ModelSerializer):
class Meta:
model = TimeEntry
fields = '__all__'
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0
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4277eacc148a7b98a23d9c3d7372e795895a0156
| 1,725
|
py
|
Python
|
tests/test_default_ssl.py
|
aboedo/sqlalchemy-redshift
|
bf81bb81e42987bb81345845fede560d9184302f
|
[
"MIT"
] | 1
|
2019-06-04T21:01:13.000Z
|
2019-06-04T21:01:13.000Z
|
tests/test_default_ssl.py
|
aboedo/sqlalchemy-redshift
|
bf81bb81e42987bb81345845fede560d9184302f
|
[
"MIT"
] | 1
|
2020-05-23T10:54:44.000Z
|
2020-05-23T10:54:44.000Z
|
tests/test_default_ssl.py
|
aboedo/sqlalchemy-redshift
|
bf81bb81e42987bb81345845fede560d9184302f
|
[
"MIT"
] | 1
|
2020-12-24T10:20:24.000Z
|
2020-12-24T10:20:24.000Z
|
import sqlalchemy as sa
CERT = b"""-----BEGIN CERTIFICATE-----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-----END CERTIFICATE-----
"""
def test_ssl_args():
engine = sa.create_engine('redshift+psycopg2://test')
dialect = engine.dialect
url = engine.url
cargs, cparams = dialect.create_connect_args(url)
assert cargs == []
assert cparams.pop('host') == 'test'
assert cparams.pop('sslmode') == 'verify-full'
with open(cparams.pop('sslrootcert'), 'rb') as cert:
assert cert.read() == CERT
assert cparams == {}
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| 1,725
| 16.228261
| 0.695652
| 0.026122
| 0.021433
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.088474
| 0.069565
| 1,725
| 40
| 65
| 43.125
| 0.841745
| 0
| 0
| 0
| 0
| 0
| 0.769855
| 0.704928
| 0
| 1
| 0
| 0
| 0.147059
| 1
| 0.029412
| false
| 0
| 0.029412
| 0
| 0.058824
| 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
| 1
| 1
| null | 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
|
0
| 4
|
428957763c1aa9d60abef794d1033af5d2c14d70
| 1,108
|
py
|
Python
|
typedcollections/test.py
|
dhilst/typedcollections
|
23a7307f2aaa0ec6e20b8d6ea8b217f3aa8d3a50
|
[
"Apache-2.0"
] | 1
|
2019-09-26T18:18:06.000Z
|
2019-09-26T18:18:06.000Z
|
typedcollections/test.py
|
dhilst/typedcollections
|
23a7307f2aaa0ec6e20b8d6ea8b217f3aa8d3a50
|
[
"Apache-2.0"
] | null | null | null |
typedcollections/test.py
|
dhilst/typedcollections
|
23a7307f2aaa0ec6e20b8d6ea8b217f3aa8d3a50
|
[
"Apache-2.0"
] | null | null | null |
import unittest
from . import MultiTypedDict, MultiTypedList, TypedList, TypedDict
class MyMTD(MultiTypedDict):
i = int,
s = str,
class MyTD(TypedDict):
value_type = int,
class MyMTL(MultiTypedList):
type = int,int,int
class MyTL(TypedList):
type = int,
class Test(unittest.TestCase):
def test(self):
mtd = MyMTD(i=1, s='hello')
mtl = MyMTL(1,2,3)
td = MyTD(a=1,b=1,c=1)
tl = MyTL(1,2)
if __debug__:
self.assertRaises(TypeError, MyMTD,1,2)
self.assertRaises(TypeError, MyMTL,'str')
self.assertRaises(TypeError, lambda: MyTD(a='str'))
self.assertRaises(TypeError, MyTL,'str')
def raiseTE(): mtd['i'] = 'not an int'
self.assertRaises(TypeError, raiseTE)
def raiseTE(): mtl[0] = 'str'
self.assertRaises(TypeError, raiseTE)
def raiseTE(): td['a'] = 'str'
self.assertRaises(TypeError, raiseTE)
def raiseTE(): tl[0] = 'str'
self.assertRaises(TypeError, raiseTE)
| 22.16
| 66
| 0.563177
| 126
| 1,108
| 4.912698
| 0.333333
| 0.206785
| 0.323102
| 0.226171
| 0.321486
| 0.273021
| 0.145396
| 0
| 0
| 0
| 0
| 0.016971
| 0.308664
| 1,108
| 49
| 67
| 22.612245
| 0.791123
| 0
| 0
| 0.133333
| 0
| 0
| 0.031617
| 0
| 0
| 0
| 0
| 0
| 0.266667
| 1
| 0.166667
| false
| 0
| 0.066667
| 0
| 0.566667
| 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
| 1
| 0
|
0
| 4
|
35ef0ae9adbcecb9e8a08f50d55b218f1dd4ebd9
| 576
|
py
|
Python
|
sfa/admin.py
|
yashiki-takajin/sfa-next
|
049058a37b9ee45b58be5f4393a0b3191362043c
|
[
"MIT"
] | 19
|
2018-11-23T10:13:14.000Z
|
2022-03-26T11:57:55.000Z
|
sfa/admin.py
|
yashiki-takajin/sfa-next
|
049058a37b9ee45b58be5f4393a0b3191362043c
|
[
"MIT"
] | 3
|
2020-06-05T19:25:20.000Z
|
2021-06-10T20:59:30.000Z
|
sfa/admin.py
|
yashiki-takajin/sfa-next
|
049058a37b9ee45b58be5f4393a0b3191362043c
|
[
"MIT"
] | 8
|
2019-04-21T11:08:22.000Z
|
2021-12-08T09:38:30.000Z
|
from django.contrib import admin
from django.conf import settings
from django.contrib.auth.admin import UserAdmin
from django.contrib.auth.forms import UserChangeForm, UserCreationForm
from django.utils.translation import ugettext_lazy as _
# Register your models here.
from .models import CustomerInfo, ContactInfo, AddressInfo
@admin.register(CustomerInfo)
class CustomerInfoAdmin(admin.ModelAdmin):
pass
@admin.register(ContactInfo)
class ContactInfoAdmin(admin.ModelAdmin):
pass
@admin.register(AddressInfo)
class AddressInfoAdmin(admin.ModelAdmin):
pass
| 26.181818
| 70
| 0.821181
| 67
| 576
| 7.029851
| 0.447761
| 0.106157
| 0.10828
| 0.089172
| 0.135881
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.109375
| 576
| 21
| 71
| 27.428571
| 0.918129
| 0.045139
| 0
| 0.2
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| true
| 0.2
| 0.4
| 0
| 0.6
| 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
| 1
| 1
| 0
| 1
| 0
|
0
| 4
|
35f2b583bdde280d0c378ff7523999bf83cc0747
| 1,011
|
py
|
Python
|
source/rpg.py
|
chowdaryprasaad/Python-Random-Password-Generator
|
e810715f3358dcaec159765dc71b95559430e6bb
|
[
"MIT"
] | 10
|
2019-10-15T07:38:53.000Z
|
2022-03-01T17:21:50.000Z
|
source/rpg.py
|
chowdaryprasaad/Python-Random-Password-Generator
|
e810715f3358dcaec159765dc71b95559430e6bb
|
[
"MIT"
] | 3
|
2019-07-18T19:42:09.000Z
|
2019-08-06T07:17:21.000Z
|
source/rpg.py
|
ismailtasdelen/Python-Randon-Password-Generator
|
b62f1833ea326d7ad934e77924f83bfa9d2481ba
|
[
"MIT"
] | 17
|
2020-01-17T20:07:42.000Z
|
2022-02-20T16:27:02.000Z
|
#!/usr/bin/env python
# -*- coding:utf-8 -*-
import string
import random
def random_password_generator():
chars = string.ascii_uppercase + string.ascii_lowercase + string.digits
size = 8
return ''.join(random.choice(chars) for x in range(size, 20))
def random_password_generator_ico():
random_password_generator_ico = """
#############################################################
# PYTHON - Random Password Generetor (RPG) - GH0ST S0FTWARE #
#############################################################
# CONTACT #
#############################################################
# DEVELOPER : İSMAİL TAŞDELEN #
# Mail Address : pentestdatabase@gmail.com #
# LINKEDIN : https://www.linkedin.com/in/ismailtasdelen #
# Whatsapp : + 90 534 295 94 31 #
#############################################################
"""
print(random_password_generator_ico)
| 38.884615
| 75
| 0.447082
| 84
| 1,011
| 5.25
| 0.654762
| 0.15873
| 0.208617
| 0.176871
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.023286
| 0.23541
| 1,011
| 25
| 76
| 40.44
| 0.544631
| 0.040554
| 0
| 0.2
| 0
| 0
| 0.652893
| 0.277893
| 0
| 0
| 0
| 0
| 0
| 1
| 0.1
| false
| 0.25
| 0.1
| 0
| 0.25
| 0.05
| 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
| 1
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 0
|
0
| 4
|
c4277ffdfccc49eb138855098ba686d211931bf9
| 520
|
py
|
Python
|
tests/unit/test_diffusion2d_functions.py
|
sab-inf/testing-python-exercise
|
83938966322b37f417d8f76e83c42851f856e7b2
|
[
"CC-BY-4.0"
] | null | null | null |
tests/unit/test_diffusion2d_functions.py
|
sab-inf/testing-python-exercise
|
83938966322b37f417d8f76e83c42851f856e7b2
|
[
"CC-BY-4.0"
] | 1
|
2022-01-20T06:10:30.000Z
|
2022-01-20T06:10:30.000Z
|
tests/unit/test_diffusion2d_functions.py
|
sab-inf/testing-python-exercise
|
83938966322b37f417d8f76e83c42851f856e7b2
|
[
"CC-BY-4.0"
] | 16
|
2022-01-13T13:31:15.000Z
|
2022-01-19T17:42:42.000Z
|
"""
Tests for functions in class SolveDiffusion2D
"""
from diffusion2d import SolveDiffusion2D
def test_initialize_domain():
"""
Check function SolveDiffusion2D.initialize_domain
"""
solver = SolveDiffusion2D()
def test_initialize_physical_parameters():
"""
Checks function SolveDiffusion2D.initialize_domain
"""
solver = SolveDiffusion2D()
def test_set_initial_condition():
"""
Checks function SolveDiffusion2D.get_initial_function
"""
solver = SolveDiffusion2D()
| 19.259259
| 57
| 0.726923
| 46
| 520
| 7.956522
| 0.5
| 0.155738
| 0.188525
| 0.180328
| 0.377049
| 0.377049
| 0.377049
| 0.377049
| 0
| 0
| 0
| 0.021277
| 0.186538
| 520
| 26
| 58
| 20
| 0.843972
| 0.384615
| 0
| 0.428571
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0.428571
| false
| 0
| 0.142857
| 0
| 0.571429
| 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
| 1
| 0
| 0
| 0
| 0
| 1
| 0
|
0
| 4
|
c428e6856a3c08cda06817e9a636e40f9bb36701
| 28
|
py
|
Python
|
tdclient/version.py
|
minchuang/td-client-python
|
6cf6dfbb60119f400274491d3e942d4f9fbcebd6
|
[
"Apache-2.0"
] | 2
|
2019-02-22T11:56:17.000Z
|
2019-02-25T10:09:46.000Z
|
tdclient/version.py
|
minchuang/td-client-python
|
6cf6dfbb60119f400274491d3e942d4f9fbcebd6
|
[
"Apache-2.0"
] | null | null | null |
tdclient/version.py
|
minchuang/td-client-python
|
6cf6dfbb60119f400274491d3e942d4f9fbcebd6
|
[
"Apache-2.0"
] | null | null | null |
__version__ = "0.12.1.dev0"
| 14
| 27
| 0.678571
| 5
| 28
| 3
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.2
| 0.107143
| 28
| 1
| 28
| 28
| 0.4
| 0
| 0
| 0
| 0
| 0
| 0.392857
| 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
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
|
0
| 4
|
c47aa102369a24662f4ad636e755afa0f84d0b0d
| 272
|
py
|
Python
|
Models/__init__.py
|
Sijiu/Xbill
|
b4f18d3e7db3b17ed4ccddc6a8971c25931428eb
|
[
"MIT"
] | 14
|
2020-03-15T13:40:02.000Z
|
2021-06-15T18:04:08.000Z
|
Models/__init__.py
|
Sijiu/Xbill
|
b4f18d3e7db3b17ed4ccddc6a8971c25931428eb
|
[
"MIT"
] | 1
|
2020-05-24T13:14:46.000Z
|
2020-05-24T13:14:46.000Z
|
Models/__init__.py
|
Sijiu/Xbill
|
b4f18d3e7db3b17ed4ccddc6a8971c25931428eb
|
[
"MIT"
] | 3
|
2020-05-05T00:23:36.000Z
|
2021-06-10T01:18:16.000Z
|
from .BaseModel import database, BillModel
from .AlipayBill import AlipayBill
from .WeChatBill import WeChatBill
from .ICBCBill import ICBCBill
from .XBill import XBill
def create_table():
database.create_tables([AlipayBill, WeChatBill, ICBCBill, XBill], safe=True)
| 27.2
| 80
| 0.805147
| 33
| 272
| 6.575758
| 0.454545
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.125
| 272
| 9
| 81
| 30.222222
| 0.911765
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0.142857
| true
| 0
| 0.714286
| 0
| 0.857143
| 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
| 1
| 0
|
0
| 4
|
67135b947e455619e93cbb7468d877d070fe3de2
| 52
|
py
|
Python
|
homeassistant/components/hp_ilo/__init__.py
|
domwillcode/home-assistant
|
f170c80bea70c939c098b5c88320a1c789858958
|
[
"Apache-2.0"
] | 30,023
|
2016-04-13T10:17:53.000Z
|
2020-03-02T12:56:31.000Z
|
homeassistant/components/hp_ilo/__init__.py
|
jagadeeshvenkatesh/core
|
1bd982668449815fee2105478569f8e4b5670add
|
[
"Apache-2.0"
] | 31,101
|
2020-03-02T13:00:16.000Z
|
2022-03-31T23:57:36.000Z
|
homeassistant/components/hp_ilo/__init__.py
|
jagadeeshvenkatesh/core
|
1bd982668449815fee2105478569f8e4b5670add
|
[
"Apache-2.0"
] | 11,956
|
2016-04-13T18:42:31.000Z
|
2020-03-02T09:32:12.000Z
|
"""The HP Integrated Lights-Out (iLO) component."""
| 26
| 51
| 0.692308
| 7
| 52
| 5.142857
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.115385
| 52
| 1
| 52
| 52
| 0.782609
| 0.865385
| 0
| null | 0
| null | 0
| 0
| null | 0
| 0
| 0
| null | 1
| null | true
| 0
| 0
| null | null | null | 1
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| 0
|
0
| 4
|
673c943da50db2408c849b762d87686654ba51ac
| 457
|
py
|
Python
|
tests/unit/test_trustar.py
|
trustar/trustar-sdk2-proto
|
cd0840ad494b95de3f687327da354ad9bdec2040
|
[
"Apache-2.0"
] | 2
|
2021-07-23T15:36:41.000Z
|
2021-11-08T07:37:42.000Z
|
tests/unit/test_trustar.py
|
trustar/trustar-sdk2-proto
|
cd0840ad494b95de3f687327da354ad9bdec2040
|
[
"Apache-2.0"
] | 5
|
2021-01-25T19:34:35.000Z
|
2021-07-15T21:51:27.000Z
|
tests/unit/test_trustar.py
|
trustar/trustar-sdk2-proto
|
cd0840ad494b95de3f687327da354ad9bdec2040
|
[
"Apache-2.0"
] | 1
|
2021-11-11T21:26:59.000Z
|
2021-11-11T21:26:59.000Z
|
import pytest
from trustar2.trustar import TruStar
proxy = {"https": "https://user:pass@le.proxy.com", "http":None}
@pytest.fixture
def trustar_with_proxy():
return TruStar(api_key="xxxx", api_secret="xxx", client_metatag="test_env", proxy=proxy)
def test_trustar_proxy(ts):
assert ts.get_proxy() == {}
def test_trustar_with_proxy(trustar_with_proxy):
assert trustar_with_proxy.get_proxy() == {"https": "https://user:pass@le.proxy.com"}
| 24.052632
| 92
| 0.728665
| 67
| 457
| 4.716418
| 0.432836
| 0.139241
| 0.202532
| 0.120253
| 0.208861
| 0.208861
| 0.208861
| 0.208861
| 0
| 0
| 0
| 0.002469
| 0.113786
| 457
| 18
| 93
| 25.388889
| 0.777778
| 0
| 0
| 0
| 0
| 0
| 0.194748
| 0
| 0
| 0
| 0
| 0
| 0.2
| 1
| 0.3
| false
| 0.2
| 0.2
| 0.1
| 0.6
| 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
| 1
| 0
|
0
| 4
|
6748eb432f0ce02b2ea331f12ad9566ed68138c5
| 193
|
py
|
Python
|
masrt_files/emarald/secret.py
|
Masrt200/Glimpse-of-ISM
|
6d7e33e77bcb0f7fb92b4dc0e2d93a892032385e
|
[
"MIT"
] | 2
|
2021-04-24T15:02:09.000Z
|
2021-04-24T15:04:54.000Z
|
masrt_files/emarald/secret.py
|
Masrt200/Glimpse-of-ISM
|
6d7e33e77bcb0f7fb92b4dc0e2d93a892032385e
|
[
"MIT"
] | null | null | null |
masrt_files/emarald/secret.py
|
Masrt200/Glimpse-of-ISM
|
6d7e33e77bcb0f7fb92b4dc0e2d93a892032385e
|
[
"MIT"
] | null | null | null |
flag="OYE, jaa 4 plate emarald se cheese maggi aur patties le kar aa... aur bolna kharcha mere khate mie likh dene!! ISM: TOH_SANDEEP_KO_BULANA_PADTA_HAI"
binary_password="C(uiICD@CADDEBNEEDD"
| 64.333333
| 154
| 0.797927
| 34
| 193
| 4.352941
| 0.970588
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.005917
| 0.124352
| 193
| 2
| 155
| 96.5
| 0.869822
| 0
| 0
| 0
| 0
| 0.5
| 0.860104
| 0.160622
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| false
| 0.5
| 0
| 0
| 0
| 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
| 1
| 1
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 0
|
0
| 4
|
676eb843df977f69a7e25324ba95618804a09b08
| 87
|
py
|
Python
|
test/jar_lister.py
|
xingao267/rules_scala
|
f2647ed38afc845eb09cc656e7e98cc2b6b6f3d7
|
[
"Apache-2.0"
] | 1
|
2021-04-28T21:40:28.000Z
|
2021-04-28T21:40:28.000Z
|
test/jar_lister.py
|
xingao267/rules_scala
|
f2647ed38afc845eb09cc656e7e98cc2b6b6f3d7
|
[
"Apache-2.0"
] | null | null | null |
test/jar_lister.py
|
xingao267/rules_scala
|
f2647ed38afc845eb09cc656e7e98cc2b6b6f3d7
|
[
"Apache-2.0"
] | null | null | null |
import zipfile
import sys
for n in zipfile.ZipFile(sys.argv[1]).namelist():
print n
| 14.5
| 49
| 0.735632
| 15
| 87
| 4.266667
| 0.666667
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.013514
| 0.149425
| 87
| 5
| 50
| 17.4
| 0.851351
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| null | null | 0
| 0.5
| null | null | 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
| 1
| 0
| 0
| 0
| 1
| 0
| 0
| 0
|
0
| 4
|
677342d9d6104157815cbedb182c63d8e9332527
| 207
|
py
|
Python
|
backend/forum/poll/apps.py
|
karolyi/forum-django
|
a498be3123deb836e0108258c493b88c645b2163
|
[
"MIT"
] | 7
|
2016-09-20T11:49:49.000Z
|
2017-06-24T23:51:56.000Z
|
backend/forum/poll/apps.py
|
karolyi/forum-django
|
a498be3123deb836e0108258c493b88c645b2163
|
[
"MIT"
] | 17
|
2019-12-22T10:41:48.000Z
|
2021-11-17T10:58:50.000Z
|
backend/forum/poll/apps.py
|
karolyi/forum-django
|
a498be3123deb836e0108258c493b88c645b2163
|
[
"MIT"
] | 1
|
2016-09-20T11:50:57.000Z
|
2016-09-20T11:50:57.000Z
|
from django.apps import AppConfig
from django.utils.translation import ugettext_lazy as _
class PollConfig(AppConfig):
name = 'forum.poll'
verbose_name = _('Forum: Polls')
label = 'forum_poll'
| 23
| 55
| 0.7343
| 26
| 207
| 5.653846
| 0.692308
| 0.136054
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.173913
| 207
| 8
| 56
| 25.875
| 0.859649
| 0
| 0
| 0
| 0
| 0
| 0.154589
| 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
| 1
| 0
|
0
| 4
|
677efba8e008a0bb2388dded6d554bd064972009
| 6,808
|
py
|
Python
|
torch/nn/modules/instancenorm.py
|
UmaTaru/run
|
be29e4d41a4de3dee27cd6796801bfe51382d294
|
[
"MIT"
] | null | null | null |
torch/nn/modules/instancenorm.py
|
UmaTaru/run
|
be29e4d41a4de3dee27cd6796801bfe51382d294
|
[
"MIT"
] | null | null | null |
torch/nn/modules/instancenorm.py
|
UmaTaru/run
|
be29e4d41a4de3dee27cd6796801bfe51382d294
|
[
"MIT"
] | null | null | null |
from .batchnorm import _BatchNorm
from .. import functional as F
class _InstanceNorm(_BatchNorm):
def __init__(self, num_features, eps=1e-5, momentum=0.1, affine=False):
super(_InstanceNorm, self).__init__(
num_features, eps, momentum, affine)
def forward(self, input):
self._check_input_dim(input)
b, c = input.size(0), input.size(1)
# Repeat stored stats and affine transform params
running_mean = self.running_mean.repeat(b)
running_var = self.running_var.repeat(b)
weight, bias = None, None
if self.affine:
weight = self.weight.repeat(b)
bias = self.bias.repeat(b)
# Apply instance norm
input_reshaped = input.contiguous().view(1, b * c, *input.size()[2:])
out = F.batch_norm(
input_reshaped, running_mean, running_var, weight, bias,
self.training, self.momentum, self.eps)
# Reshape back
self.running_mean.copy_(running_mean.view(b, c).mean(0))
self.running_var.copy_(running_var.view(b, c).mean(0))
return out.view(b, c, *input.size()[2:])
def eval(self):
return self
class InstanceNorm1d(_InstanceNorm):
r"""Applies Instance Normalization over a 2d or 3d input that is seen as a mini-batch.
.. math::
y = \frac{x - mean[x]}{ \sqrt{Var[x]} + \epsilon} * gamma + beta
The mean and standard-deviation are calculated per-dimension separately
for each object in a mini-batch. Gamma and beta are learnable parameter vectors
of size C (where C is the input size).
During training, this layer keeps a running estimate of its computed mean
and variance. The running sum is kept with a default momentum of 0.1.
At evaluation time (`.eval()`), the default behaviour of the InstanceNorm module stays the same
i.e. running mean/variance is NOT used for normalization. One can force using stored
mean and variance with `.train(False)` method.
Args:
num_features: num_features from an expected input of size `batch_size x num_features x width`
eps: a value added to the denominator for numerical stability. Default: 1e-5
momentum: the value used for the running_mean and running_var computation. Default: 0.1
affine: a boolean value that when set to true, gives the layer learnable affine parameters.
Shape:
- Input: :math:`(N, C, L)`
- Output: :math:`(N, C, L)` (same shape as input)
Examples:
>>> # Without Learnable Parameters
>>> m = nn.InstanceNorm1d(100)
>>> # With Learnable Parameters
>>> m = nn.InstanceNorm1d(100, affine=True)
>>> input = autograd.Variable(torch.randn(20, 100))
>>> output = m(input)
"""
def _check_input_dim(self, input):
if input.dim() != 3:
raise ValueError('expected 2D or 3D input (got {}D input)'
.format(input.dim()))
super(InstanceNorm1d, self)._check_input_dim(input)
class InstanceNorm2d(_InstanceNorm):
r"""Applies Instance Normalization over a 4d input that is seen as a mini-batch of 3d inputs
.. math::
y = \frac{x - mean[x]}{ \sqrt{Var[x]} + \epsilon} * gamma + beta
The mean and standard-deviation are calculated per-dimension separately
for each object in a mini-batch. Gamma and beta are learnable parameter vectors
of size C (where C is the input size).
During training, this layer keeps a running estimate of its computed mean
and variance. The running sum is kept with a default momentum of 0.1.
At evaluation time (`.eval()`), the default behaviour of the InstanceNorm module stays the same
i.e. running mean/variance is NOT used for normalization. One can force using stored
mean and variance with `.train(False)` method.
Args:
num_features: num_features from an expected input of size batch_size x num_features x height x width
eps: a value added to the denominator for numerical stability. Default: 1e-5
momentum: the value used for the running_mean and running_var computation. Default: 0.1
affine: a boolean value that when set to true, gives the layer learnable affine parameters.
Shape:
- Input: :math:`(N, C, H, W)`
- Output: :math:`(N, C, H, W)` (same shape as input)
Examples:
>>> # Without Learnable Parameters
>>> m = nn.InstanceNorm2d(100)
>>> # With Learnable Parameters
>>> m = nn.InstanceNorm2d(100, affine=True)
>>> input = autograd.Variable(torch.randn(20, 100, 35, 45))
>>> output = m(input)
"""
def _check_input_dim(self, input):
if input.dim() != 4:
raise ValueError('expected 4D input (got {}D input)'
.format(input.dim()))
super(InstanceNorm2d, self)._check_input_dim(input)
class InstanceNorm3d(_InstanceNorm):
r"""Applies Instance Normalization over a 5d input that is seen as a mini-batch of 4d inputs
.. math::
y = \frac{x - mean[x]}{ \sqrt{Var[x]} + \epsilon} * gamma + beta
The mean and standard-deviation are calculated per-dimension separately for each object in a mini-batch.
Gamma and beta are learnable parameter vectors
of size C (where C is the input size).
During training, this layer keeps a running estimate of its computed mean
and variance. The running sum is kept with a default momentum of 0.1.
At evaluation time (`.eval()`), the default behaviour of the InstanceNorm module stays the same
i.e. running mean/variance is NOT used for normalization. One can force using stored
mean and variance with `.train(False)` method.
Args:
num_features: num_features from an expected input of size batch_size x num_features x depth x height x width
eps: a value added to the denominator for numerical stability. Default: 1e-5
momentum: the value used for the running_mean and running_var computation. Default: 0.1
affine: a boolean value that when set to true, gives the layer learnable affine parameters.
Shape:
- Input: :math:`(N, C, D, H, W)`
- Output: :math:`(N, C, D, H, W)` (same shape as input)
Examples:
>>> # Without Learnable Parameters
>>> m = nn.InstanceNorm3d(100)
>>> # With Learnable Parameters
>>> m = nn.InstanceNorm3d(100, affine=True)
>>> input = autograd.Variable(torch.randn(20, 100, 35, 45, 10))
>>> output = m(input)
"""
def _check_input_dim(self, input):
if input.dim() != 5:
raise ValueError('expected 5D input (got {}D input)'
.format(input.dim()))
super(InstanceNorm3d, self)._check_input_dim(input)
| 39.581395
| 116
| 0.649236
| 953
| 6,808
| 4.563484
| 0.182581
| 0.023914
| 0.020924
| 0.030352
| 0.805932
| 0.785238
| 0.726604
| 0.694872
| 0.6659
| 0.652564
| 0
| 0.019094
| 0.253819
| 6,808
| 171
| 117
| 39.812866
| 0.837008
| 0.660693
| 0
| 0.133333
| 0
| 0
| 0.053111
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0.133333
| false
| 0
| 0.044444
| 0.022222
| 0.311111
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 1
| 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
| 4
|
67945fc4b1d6bafe6d5374d497cb5e491444e1ee
| 284
|
py
|
Python
|
dnachisel/DnaOptimizationProblem/__init__.py
|
simone-pignotti/DnaChisel
|
b7f0f925c9daefcc5fec903a13cfa74c3b726a7a
|
[
"MIT"
] | 124
|
2017-11-14T14:42:25.000Z
|
2022-03-31T08:02:07.000Z
|
dnachisel/DnaOptimizationProblem/__init__.py
|
simone-pignotti/DnaChisel
|
b7f0f925c9daefcc5fec903a13cfa74c3b726a7a
|
[
"MIT"
] | 65
|
2017-11-15T07:25:38.000Z
|
2022-01-31T10:38:45.000Z
|
dnachisel/DnaOptimizationProblem/__init__.py
|
simone-pignotti/DnaChisel
|
b7f0f925c9daefcc5fec903a13cfa74c3b726a7a
|
[
"MIT"
] | 31
|
2018-10-18T12:59:47.000Z
|
2022-02-11T16:54:43.000Z
|
from .NoSolutionError import NoSolutionError
from .DnaOptimizationProblem import DnaOptimizationProblem
from .CircularDnaOptimizationProblem import CircularDnaOptimizationProblem
__all__ = [
"NoSolutionError",
"DnaOptimizationProblem",
"CircularDnaOptimizationProblem"
]
| 28.4
| 74
| 0.838028
| 16
| 284
| 14.625
| 0.375
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.112676
| 284
| 9
| 75
| 31.555556
| 0.928571
| 0
| 0
| 0
| 0
| 0
| 0.235915
| 0.183099
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| false
| 0
| 0.375
| 0
| 0.375
| 0
| 1
| 0
| 1
| 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
| 4
|
67a8e93a9f23b908a8d8906475339104f28301c6
| 64,778
|
py
|
Python
|
1-XD_XD/code/v9s.py
|
sethahrenbach/BuildingDetectors_Round2
|
19545b6babd176bcca76ce36df4c34ce9fe98056
|
[
"Apache-2.0"
] | 196
|
2017-07-30T12:51:00.000Z
|
2022-03-22T12:16:23.000Z
|
1-XD_XD/code/v9s.py
|
sethahrenbach/BuildingDetectors_Round2
|
19545b6babd176bcca76ce36df4c34ce9fe98056
|
[
"Apache-2.0"
] | 7
|
2017-10-23T06:21:50.000Z
|
2022-03-10T10:17:42.000Z
|
1-XD_XD/code/v9s.py
|
sethahrenbach/BuildingDetectors_Round2
|
19545b6babd176bcca76ce36df4c34ce9fe98056
|
[
"Apache-2.0"
] | 81
|
2017-07-30T14:10:25.000Z
|
2021-11-15T04:15:06.000Z
|
# -*- coding: utf-8 -*-
"""
v9s model
* Input: v5_im
Author: Kohei <i@ho.lc>
"""
from logging import getLogger, Formatter, StreamHandler, INFO, FileHandler
from pathlib import Path
import subprocess
import argparse
import math
import glob
import sys
import json
import re
import warnings
import scipy
import tqdm
import click
import tables as tb
import pandas as pd
import numpy as np
from keras.models import Model
from keras.engine.topology import merge as merge_l
from keras.layers import (
Input, Convolution2D, MaxPooling2D, UpSampling2D,
Reshape, core, Dropout,
Activation, BatchNormalization)
from keras.optimizers import Adam
from keras.callbacks import ModelCheckpoint, EarlyStopping, History
from keras import backend as K
import skimage.transform
import skimage.morphology
import rasterio.features
import shapely.wkt
import shapely.ops
import shapely.geometry
MODEL_NAME = 'v9s'
ORIGINAL_SIZE = 650
INPUT_SIZE = 256
LOGFORMAT = '%(asctime)s %(levelname)s %(message)s'
BASE_DIR = "/data/train"
WORKING_DIR = "/data/working"
IMAGE_DIR = "/data/working/images/{}".format('v5')
MODEL_DIR = "/data/working/models/{}".format(MODEL_NAME)
FN_SOLUTION_CSV = "/data/output/{}.csv".format(MODEL_NAME)
# Parameters
MIN_POLYGON_AREA = 30
# Input files
FMT_TRAIN_SUMMARY_PATH = str(
Path(BASE_DIR) /
Path("{prefix:s}_Train/") /
Path("summaryData/{prefix:s}_Train_Building_Solutions.csv"))
FMT_TRAIN_RGB_IMAGE_PATH = str(
Path(BASE_DIR) /
Path("{prefix:s}_Train/") /
Path("RGB-PanSharpen/RGB-PanSharpen_{image_id:s}.tif"))
FMT_TEST_RGB_IMAGE_PATH = str(
Path(BASE_DIR) /
Path("{prefix:s}_Test_public/") /
Path("RGB-PanSharpen/RGB-PanSharpen_{image_id:s}.tif"))
FMT_TRAIN_MSPEC_IMAGE_PATH = str(
Path(BASE_DIR) /
Path("{prefix:s}_Train/") /
Path("MUL-PanSharpen/MUL-PanSharpen_{image_id:s}.tif"))
FMT_TEST_MSPEC_IMAGE_PATH = str(
Path(BASE_DIR) /
Path("{prefix:s}_Test_public/") /
Path("MUL-PanSharpen/MUL-PanSharpen_{image_id:s}.tif"))
# Preprocessing result
FMT_BANDCUT_TH_PATH = IMAGE_DIR + "/bandcut{}.csv"
FMT_MUL_BANDCUT_TH_PATH = IMAGE_DIR + "/mul_bandcut{}.csv"
# Image list, Image container and mask container
FMT_VALTRAIN_IMAGELIST_PATH = IMAGE_DIR + "/{prefix:s}_valtrain_ImageId.csv"
FMT_VALTEST_IMAGELIST_PATH = IMAGE_DIR + "/{prefix:s}_valtest_ImageId.csv"
FMT_VALTRAIN_IM_STORE = IMAGE_DIR + "/valtrain_{}_im.h5"
FMT_VALTEST_IM_STORE = IMAGE_DIR + "/valtest_{}_im.h5"
FMT_VALTRAIN_MASK_STORE = IMAGE_DIR + "/valtrain_{}_mask.h5"
FMT_VALTEST_MASK_STORE = IMAGE_DIR + "/valtest_{}_mask.h5"
FMT_VALTRAIN_MUL_STORE = IMAGE_DIR + "/valtrain_{}_mul.h5"
FMT_VALTEST_MUL_STORE = IMAGE_DIR + "/valtest_{}_mul.h5"
FMT_TRAIN_IMAGELIST_PATH = IMAGE_DIR + "/{prefix:s}_train_ImageId.csv"
FMT_TEST_IMAGELIST_PATH = IMAGE_DIR + "/{prefix:s}_test_ImageId.csv"
FMT_TRAIN_IM_STORE = IMAGE_DIR + "/train_{}_im.h5"
FMT_TEST_IM_STORE = IMAGE_DIR + "/test_{}_im.h5"
FMT_TRAIN_MASK_STORE = IMAGE_DIR + "/train_{}_mask.h5"
FMT_TRAIN_MUL_STORE = IMAGE_DIR + "/train_{}_mul.h5"
FMT_TEST_MUL_STORE = IMAGE_DIR + "/test_{}_mul.h5"
FMT_IMMEAN = IMAGE_DIR + "/{}_immean.h5"
FMT_MULMEAN = IMAGE_DIR + "/{}_mulmean.h5"
# Model files
FMT_VALMODEL_PATH = MODEL_DIR + "/{}_val_weights.h5"
FMT_FULLMODEL_PATH = MODEL_DIR + "/{}_full_weights.h5"
FMT_VALMODEL_HIST = MODEL_DIR + "/{}_val_hist.csv"
FMT_VALMODEL_EVALHIST = MODEL_DIR + "/{}_val_evalhist.csv"
FMT_VALMODEL_EVALTHHIST = MODEL_DIR + "/{}_val_evalhist_th.csv"
# Prediction & polygon result
FMT_TESTPRED_PATH = MODEL_DIR + "/{}_pred.h5"
FMT_VALTESTPRED_PATH = MODEL_DIR + "/{}_eval_pred.h5"
FMT_VALTESTPOLY_PATH = MODEL_DIR + "/{}_eval_poly.csv"
FMT_VALTESTTRUTH_PATH = MODEL_DIR + "/{}_eval_poly_truth.csv"
FMT_VALTESTPOLY_OVALL_PATH = MODEL_DIR + "/eval_poly.csv"
FMT_VALTESTTRUTH_OVALL_PATH = MODEL_DIR + "/eval_poly_truth.csv"
FMT_TESTPOLY_PATH = MODEL_DIR + "/{}_poly.csv"
# Model related files (others)
FMT_VALMODEL_LAST_PATH = MODEL_DIR + "/{}_val_weights_last.h5"
FMT_FULLMODEL_LAST_PATH = MODEL_DIR + "/{}_full_weights_last.h5"
# Logger
warnings.simplefilter("ignore", UserWarning)
handler = StreamHandler()
handler.setLevel(INFO)
handler.setFormatter(Formatter(LOGFORMAT))
fh_handler = FileHandler(".{}.log".format(MODEL_NAME))
fh_handler.setFormatter(Formatter(LOGFORMAT))
logger = getLogger('spacenet2')
logger.setLevel(INFO)
if __name__ == '__main__':
logger.addHandler(handler)
logger.addHandler(fh_handler)
# Fix seed for reproducibility
np.random.seed(1145141919)
def directory_name_to_area_id(datapath):
"""
Directory name to AOI number
Usage:
>>> directory_name_to_area_id("/data/test/AOI_2_Vegas")
2
"""
dir_name = Path(datapath).name
if dir_name.startswith('AOI_2_Vegas'):
return 2
elif dir_name.startswith('AOI_3_Paris'):
return 3
elif dir_name.startswith('AOI_4_Shanghai'):
return 4
elif dir_name.startswith('AOI_5_Khartoum'):
return 5
else:
raise RuntimeError("Unsupported city id is given.")
def _remove_interiors(line):
if "), (" in line:
line_prefix = line.split('), (')[0]
line_terminate = line.split('))",')[-1]
line = (
line_prefix +
'))",' +
line_terminate
)
return line
def __load_band_cut_th(band_fn, bandsz=3):
df = pd.read_csv(band_fn, index_col='area_id')
all_band_cut_th = {area_id: {} for area_id in range(2, 6)}
for area_id, row in df.iterrows():
for chan_i in range(bandsz):
all_band_cut_th[area_id][chan_i] = dict(
min=row['chan{}_min'.format(chan_i)],
max=row['chan{}_max'.format(chan_i)],
)
return all_band_cut_th
def _calc_fscore_per_aoi(area_id):
prefix = area_id_to_prefix(area_id)
truth_file = FMT_VALTESTTRUTH_PATH.format(prefix)
poly_file = FMT_VALTESTPOLY_PATH.format(prefix)
cmd = [
'java',
'-jar',
'/root/visualizer-2.0/visualizer.jar',
'-truth',
truth_file,
'-solution',
poly_file,
'-no-gui',
'-band-triplets',
'/root/visualizer-2.0/data/band-triplets.txt',
'-image-dir',
'pass',
]
proc = subprocess.Popen(
cmd,
stdout=subprocess.PIPE,
stderr=subprocess.PIPE,
)
stdout_data, stderr_data = proc.communicate()
lines = [line for line in stdout_data.decode('utf8').split('\n')[-10:]]
"""
Overall F-score : 0.85029
AOI_2_Vegas:
TP : 27827
FP : 4999
FN : 4800
Precision: 0.847712
Recall : 0.852883
F-score : 0.85029
"""
if stdout_data.decode('utf8').strip().endswith("Overall F-score : 0"):
overall_fscore = 0
tp = 0
fp = 0
fn = 0
precision = 0
recall = 0
fscore = 0
elif len(lines) > 0 and lines[0].startswith("Overall F-score : "):
assert lines[0].startswith("Overall F-score : ")
assert lines[2].startswith("AOI_")
assert lines[3].strip().startswith("TP")
assert lines[4].strip().startswith("FP")
assert lines[5].strip().startswith("FN")
assert lines[6].strip().startswith("Precision")
assert lines[7].strip().startswith("Recall")
assert lines[8].strip().startswith("F-score")
overall_fscore = float(re.findall("([\d\.]+)", lines[0])[0])
tp = int(re.findall("(\d+)", lines[3])[0])
fp = int(re.findall("(\d+)", lines[4])[0])
fn = int(re.findall("(\d+)", lines[5])[0])
precision = float(re.findall("([\d\.]+)", lines[6])[0])
recall = float(re.findall("([\d\.]+)", lines[7])[0])
fscore = float(re.findall("([\d\.]+)", lines[8])[0])
else:
logger.warn("Unexpected data >>> " + stdout_data.decode('utf8'))
raise RuntimeError("Unsupported format")
return {
'overall_fscore': overall_fscore,
'tp': tp,
'fp': fp,
'fn': fn,
'precision': precision,
'recall': recall,
'fscore': fscore,
}
def prefix_to_area_id(prefix):
area_dict = {
'AOI_2_Vegas': 2,
'AOI_3_Paris': 3,
'AOI_4_Shanghai': 4,
'AOI_5_Khartoum': 5,
}
return area_dict[area_id]
def area_id_to_prefix(area_id):
area_dict = {
2: 'AOI_2_Vegas',
3: 'AOI_3_Paris',
4: 'AOI_4_Shanghai',
5: 'AOI_5_Khartoum',
}
return area_dict[area_id]
# ---------------------------------------------------------
# main
def _get_model_parameter(area_id):
prefix = area_id_to_prefix(area_id)
fn_hist = FMT_VALMODEL_EVALTHHIST.format(prefix)
best_row = pd.read_csv(fn_hist).sort_values(
by='fscore',
ascending=False,
).iloc[0]
param = dict(
fn_epoch=int(best_row['zero_base_epoch']),
min_poly_area=int(best_row['min_area_th']),
)
return param
def get_resized_raster_3chan_image(image_id, band_cut_th=None):
fn = train_image_id_to_path(image_id)
with rasterio.open(fn, 'r') as f:
values = f.read().astype(np.float32)
for chan_i in range(3):
min_val = band_cut_th[chan_i]['min']
max_val = band_cut_th[chan_i]['max']
values[chan_i] = np.clip(values[chan_i], min_val, max_val)
values[chan_i] = (values[chan_i] - min_val) / (max_val - min_val)
values = np.swapaxes(values, 0, 2)
values = np.swapaxes(values, 0, 1)
values = skimage.transform.resize(values, (INPUT_SIZE, INPUT_SIZE))
return values
def get_resized_raster_3chan_image_test(image_id, band_cut_th=None):
fn = test_image_id_to_path(image_id)
with rasterio.open(fn, 'r') as f:
values = f.read().astype(np.float32)
for chan_i in range(3):
min_val = band_cut_th[chan_i]['min']
max_val = band_cut_th[chan_i]['max']
values[chan_i] = np.clip(values[chan_i], min_val, max_val)
values[chan_i] = (values[chan_i] - min_val) / (max_val - min_val)
values = np.swapaxes(values, 0, 2)
values = np.swapaxes(values, 0, 1)
values = skimage.transform.resize(values, (INPUT_SIZE, INPUT_SIZE))
return values
def image_mask_resized_from_summary(df, image_id):
im_mask = np.zeros((650, 650))
for idx, row in df[df.ImageId == image_id].iterrows():
shape_obj = shapely.wkt.loads(row.PolygonWKT_Pix)
if shape_obj.exterior is not None:
coords = list(shape_obj.exterior.coords)
x = [round(float(pp[0])) for pp in coords]
y = [round(float(pp[1])) for pp in coords]
yy, xx = skimage.draw.polygon(y, x, (650, 650))
im_mask[yy, xx] = 1
interiors = shape_obj.interiors
for interior in interiors:
coords = list(interior.coords)
x = [round(float(pp[0])) for pp in coords]
y = [round(float(pp[1])) for pp in coords]
yy, xx = skimage.draw.polygon(y, x, (650, 650))
im_mask[yy, xx] = 0
im_mask = skimage.transform.resize(im_mask, (INPUT_SIZE, INPUT_SIZE))
im_mask = (im_mask > 0.5).astype(np.uint8)
return im_mask
def train_test_image_prep(area_id):
prefix = area_id_to_prefix(area_id)
df_train = pd.read_csv(
FMT_TRAIN_IMAGELIST_PATH.format(prefix=prefix),
index_col='ImageId')
df_test = pd.read_csv(
FMT_TEST_IMAGELIST_PATH.format(prefix=prefix),
index_col='ImageId')
band_cut_th = __load_band_cut_th(
FMT_BANDCUT_TH_PATH.format(prefix))[area_id]
df_summary = _load_train_summary_data(area_id)
fn = FMT_TRAIN_IM_STORE.format(prefix)
logger.info("Prepare image container: {}".format(fn))
with tb.open_file(fn, 'w') as f:
for image_id in tqdm.tqdm(df_train.index, total=len(df_train)):
im = get_resized_raster_3chan_image(image_id, band_cut_th)
atom = tb.Atom.from_dtype(im.dtype)
filters = tb.Filters(complib='blosc', complevel=9)
ds = f.create_carray(f.root, image_id, atom, im.shape,
filters=filters)
ds[:] = im
fn = FMT_TEST_IM_STORE.format(prefix)
logger.info("Prepare image container: {}".format(fn))
with tb.open_file(fn, 'w') as f:
for image_id in tqdm.tqdm(df_test.index, total=len(df_test)):
im = get_resized_raster_3chan_image_test(image_id, band_cut_th)
atom = tb.Atom.from_dtype(im.dtype)
filters = tb.Filters(complib='blosc', complevel=9)
ds = f.create_carray(f.root, image_id, atom, im.shape,
filters=filters)
ds[:] = im
fn = FMT_TRAIN_MASK_STORE.format(prefix)
logger.info("Prepare image container: {}".format(fn))
with tb.open_file(fn, 'w') as f:
for image_id in tqdm.tqdm(df_train.index, total=len(df_train)):
im_mask = image_mask_resized_from_summary(df_summary, image_id)
atom = tb.Atom.from_dtype(im_mask.dtype)
filters = tb.Filters(complib='blosc', complevel=9)
ds = f.create_carray(f.root, image_id, atom, im_mask.shape,
filters=filters)
ds[:] = im_mask
def valtrain_test_image_prep(area_id):
prefix = area_id_to_prefix(area_id)
logger.info("valtrain_test_image_prep for {}".format(prefix))
df_train = pd.read_csv(
FMT_VALTRAIN_IMAGELIST_PATH.format(prefix=prefix),
index_col='ImageId')
df_test = pd.read_csv(
FMT_VALTEST_IMAGELIST_PATH.format(prefix=prefix),
index_col='ImageId')
band_cut_th = __load_band_cut_th(
FMT_BANDCUT_TH_PATH.format(prefix))[area_id]
df_summary = _load_train_summary_data(area_id)
fn = FMT_VALTRAIN_IM_STORE.format(prefix)
logger.info("Prepare image container: {}".format(fn))
with tb.open_file(fn, 'w') as f:
for image_id in tqdm.tqdm(df_train.index, total=len(df_train)):
im = get_resized_raster_3chan_image(image_id, band_cut_th)
atom = tb.Atom.from_dtype(im.dtype)
filters = tb.Filters(complib='blosc', complevel=9)
ds = f.create_carray(f.root, image_id, atom, im.shape,
filters=filters)
ds[:] = im
fn = FMT_VALTEST_IM_STORE.format(prefix)
logger.info("Prepare image container: {}".format(fn))
with tb.open_file(fn, 'w') as f:
for image_id in tqdm.tqdm(df_test.index, total=len(df_test)):
im = get_resized_raster_3chan_image(image_id, band_cut_th)
atom = tb.Atom.from_dtype(im.dtype)
filters = tb.Filters(complib='blosc', complevel=9)
ds = f.create_carray(f.root, image_id, atom, im.shape,
filters=filters)
ds[:] = im
fn = FMT_VALTRAIN_MASK_STORE.format(prefix)
logger.info("Prepare image container: {}".format(fn))
with tb.open_file(fn, 'w') as f:
for image_id in tqdm.tqdm(df_train.index, total=len(df_train)):
im_mask = image_mask_resized_from_summary(df_summary, image_id)
atom = tb.Atom.from_dtype(im_mask.dtype)
filters = tb.Filters(complib='blosc', complevel=9)
ds = f.create_carray(f.root, image_id, atom, im_mask.shape,
filters=filters)
ds[:] = im_mask
fn = FMT_VALTEST_MASK_STORE.format(prefix)
logger.info("Prepare image container: {}".format(fn))
with tb.open_file(fn, 'w') as f:
for image_id in tqdm.tqdm(df_test.index, total=len(df_test)):
im_mask = image_mask_resized_from_summary(df_summary, image_id)
atom = tb.Atom.from_dtype(im_mask.dtype)
filters = tb.Filters(complib='blosc', complevel=9)
ds = f.create_carray(f.root, image_id, atom, im_mask.shape,
filters=filters)
ds[:] = im_mask
def train_test_mul_image_prep(area_id):
prefix = area_id_to_prefix(area_id)
df_train = pd.read_csv(
FMT_TRAIN_IMAGELIST_PATH.format(prefix=prefix),
index_col='ImageId')
df_test = pd.read_csv(
FMT_TEST_IMAGELIST_PATH.format(prefix=prefix),
index_col='ImageId')
band_rgb_th = __load_band_cut_th(
FMT_BANDCUT_TH_PATH.format(prefix))[area_id]
band_mul_th = __load_band_cut_th(
FMT_MUL_BANDCUT_TH_PATH.format(prefix), bandsz=8)[area_id]
df_summary = _load_train_summary_data(area_id)
fn = FMT_TRAIN_MUL_STORE.format(prefix)
logger.info("Prepare image container: {}".format(fn))
with tb.open_file(fn, 'w') as f:
for image_id in tqdm.tqdm(df_train.index, total=len(df_train)):
im = get_resized_raster_8chan_image(
image_id, band_rgb_th, band_mul_th)
atom = tb.Atom.from_dtype(im.dtype)
filters = tb.Filters(complib='blosc', complevel=9)
ds = f.create_carray(f.root, image_id, atom, im.shape,
filters=filters)
ds[:] = im
fn = FMT_TEST_MUL_STORE.format(prefix)
logger.info("Prepare image container: {}".format(fn))
with tb.open_file(fn, 'w') as f:
for image_id in tqdm.tqdm(df_test.index, total=len(df_test)):
im = get_resized_raster_8chan_image_test(
image_id, band_rgb_th, band_mul_th)
atom = tb.Atom.from_dtype(im.dtype)
filters = tb.Filters(complib='blosc', complevel=9)
ds = f.create_carray(f.root, image_id, atom, im.shape,
filters=filters)
ds[:] = im
def valtrain_test_mul_image_prep(area_id):
prefix = area_id_to_prefix(area_id)
logger.info("valtrain_test_image_prep for {}".format(prefix))
df_train = pd.read_csv(
FMT_VALTRAIN_IMAGELIST_PATH.format(prefix=prefix),
index_col='ImageId')
df_test = pd.read_csv(
FMT_VALTEST_IMAGELIST_PATH.format(prefix=prefix),
index_col='ImageId')
band_rgb_th = __load_band_cut_th(
FMT_BANDCUT_TH_PATH.format(prefix))[area_id]
band_mul_th = __load_band_cut_th(
FMT_MUL_BANDCUT_TH_PATH.format(prefix), bandsz=8)[area_id]
df_summary = _load_train_summary_data(area_id)
fn = FMT_VALTRAIN_MUL_STORE.format(prefix)
logger.info("Prepare image container: {}".format(fn))
with tb.open_file(fn, 'w') as f:
for image_id in tqdm.tqdm(df_train.index, total=len(df_train)):
im = get_resized_raster_8chan_image(
image_id, band_rgb_th, band_mul_th)
atom = tb.Atom.from_dtype(im.dtype)
filters = tb.Filters(complib='blosc', complevel=9)
ds = f.create_carray(f.root, image_id, atom, im.shape,
filters=filters)
ds[:] = im
fn = FMT_VALTEST_MUL_STORE.format(prefix)
logger.info("Prepare image container: {}".format(fn))
with tb.open_file(fn, 'w') as f:
for image_id in tqdm.tqdm(df_test.index, total=len(df_test)):
im = get_resized_raster_8chan_image(
image_id, band_rgb_th, band_mul_th)
atom = tb.Atom.from_dtype(im.dtype)
filters = tb.Filters(complib='blosc', complevel=9)
ds = f.create_carray(f.root, image_id, atom, im.shape,
filters=filters)
ds[:] = im
def _load_train_summary_data(area_id):
prefix = area_id_to_prefix(area_id)
fn = FMT_TRAIN_SUMMARY_PATH.format(prefix=prefix)
df = pd.read_csv(fn)
return df
def split_val_train_test(area_id):
prefix = area_id_to_prefix(area_id)
df = _load_train_summary_data(area_id)
df_agg = df.groupby('ImageId').agg('first')
image_id_list = df_agg.index.tolist()
np.random.shuffle(image_id_list)
sz_valtrain = int(len(image_id_list) * 0.7)
sz_valtest = len(image_id_list) - sz_valtrain
pd.DataFrame({'ImageId': image_id_list[:sz_valtrain]}).to_csv(
FMT_VALTRAIN_IMAGELIST_PATH.format(prefix=prefix),
index=False)
pd.DataFrame({'ImageId': image_id_list[sz_valtrain:]}).to_csv(
FMT_VALTEST_IMAGELIST_PATH.format(prefix=prefix),
index=False)
def train_image_id_to_mspec_path(image_id):
prefix = image_id_to_prefix(image_id)
fn = FMT_TRAIN_MSPEC_IMAGE_PATH.format(
prefix=prefix,
image_id=image_id)
return fn
def test_image_id_to_mspec_path(image_id):
prefix = image_id_to_prefix(image_id)
fn = FMT_TEST_MSPEC_IMAGE_PATH.format(
prefix=prefix,
image_id=image_id)
return fn
def train_image_id_to_path(image_id):
prefix = image_id_to_prefix(image_id)
fn = FMT_TRAIN_RGB_IMAGE_PATH.format(
prefix=prefix,
image_id=image_id)
return fn
def test_image_id_to_path(image_id):
prefix = image_id_to_prefix(image_id)
fn = FMT_TEST_RGB_IMAGE_PATH.format(
prefix=prefix,
image_id=image_id)
return fn
def image_id_to_prefix(image_id):
prefix = image_id.split('img')[0][:-1]
return prefix
def calc_multiband_cut_threshold(area_id):
rows = []
band_cut_th = __calc_multiband_cut_threshold(area_id)
prefix = area_id_to_prefix(area_id)
row = dict(prefix=area_id_to_prefix(area_id))
row['area_id'] = area_id
for chan_i in band_cut_th.keys():
row['chan{}_max'.format(chan_i)] = band_cut_th[chan_i]['max']
row['chan{}_min'.format(chan_i)] = band_cut_th[chan_i]['min']
rows.append(row)
pd.DataFrame(rows).to_csv(FMT_BANDCUT_TH_PATH.format(prefix), index=False)
def __calc_multiband_cut_threshold(area_id):
prefix = area_id_to_prefix(area_id)
band_values = {k: [] for k in range(3)}
band_cut_th = {k: dict(max=0, min=0) for k in range(3)}
image_id_list = pd.read_csv(FMT_VALTRAIN_IMAGELIST_PATH.format(
prefix=prefix)).ImageId.tolist()
for image_id in tqdm.tqdm(image_id_list[:500]):
image_fn = train_image_id_to_path(image_id)
with rasterio.open(image_fn, 'r') as f:
values = f.read().astype(np.float32)
for i_chan in range(3):
values_ = values[i_chan].ravel().tolist()
values_ = np.array(
[v for v in values_ if v != 0]
) # Remove sensored mask
band_values[i_chan].append(values_)
image_id_list = pd.read_csv(FMT_VALTEST_IMAGELIST_PATH.format(
prefix=prefix)).ImageId.tolist()
for image_id in tqdm.tqdm(image_id_list[:500]):
image_fn = train_image_id_to_path(image_id)
with rasterio.open(image_fn, 'r') as f:
values = f.read().astype(np.float32)
for i_chan in range(3):
values_ = values[i_chan].ravel().tolist()
values_ = np.array(
[v for v in values_ if v != 0]
) # Remove sensored mask
band_values[i_chan].append(values_)
for i_chan in range(3):
band_values[i_chan] = np.concatenate(
band_values[i_chan]).ravel()
band_cut_th[i_chan]['max'] = scipy.percentile(
band_values[i_chan], 98)
band_cut_th[i_chan]['min'] = scipy.percentile(
band_values[i_chan], 2)
return band_cut_th
def calc_mul_multiband_cut_threshold(area_id):
rows = []
band_cut_th = __calc_mul_multiband_cut_threshold(area_id)
prefix = area_id_to_prefix(area_id)
row = dict(prefix=area_id_to_prefix(area_id))
row['area_id'] = area_id
for chan_i in band_cut_th.keys():
row['chan{}_max'.format(chan_i)] = band_cut_th[chan_i]['max']
row['chan{}_min'.format(chan_i)] = band_cut_th[chan_i]['min']
rows.append(row)
pd.DataFrame(rows).to_csv(
FMT_MUL_BANDCUT_TH_PATH.format(prefix),
index=False)
def __calc_mul_multiband_cut_threshold(area_id):
prefix = area_id_to_prefix(area_id)
band_values = {k: [] for k in range(8)}
band_cut_th = {k: dict(max=0, min=0) for k in range(8)}
image_id_list = pd.read_csv(FMT_VALTRAIN_IMAGELIST_PATH.format(
prefix=prefix)).ImageId.tolist()
for image_id in tqdm.tqdm(image_id_list[:500]):
image_fn = train_image_id_to_mspec_path(image_id)
with rasterio.open(image_fn, 'r') as f:
values = f.read().astype(np.float32)
for i_chan in range(8):
values_ = values[i_chan].ravel().tolist()
values_ = np.array(
[v for v in values_ if v != 0]
) # Remove sensored mask
band_values[i_chan].append(values_)
image_id_list = pd.read_csv(FMT_VALTEST_IMAGELIST_PATH.format(
prefix=prefix)).ImageId.tolist()
for image_id in tqdm.tqdm(image_id_list[:500]):
image_fn = train_image_id_to_mspec_path(image_id)
with rasterio.open(image_fn, 'r') as f:
values = f.read().astype(np.float32)
for i_chan in range(8):
values_ = values[i_chan].ravel().tolist()
values_ = np.array(
[v for v in values_ if v != 0]
) # Remove sensored mask
band_values[i_chan].append(values_)
for i_chan in range(8):
band_values[i_chan] = np.concatenate(
band_values[i_chan]).ravel()
band_cut_th[i_chan]['max'] = scipy.percentile(
band_values[i_chan], 98)
band_cut_th[i_chan]['min'] = scipy.percentile(
band_values[i_chan], 2)
return band_cut_th
def get_unet():
conv_params = dict(activation='relu', border_mode='same')
merge_params = dict(mode='concat', concat_axis=1)
inputs = Input((8, 256, 256))
conv1 = Convolution2D(32, 3, 3, **conv_params)(inputs)
conv1 = Convolution2D(32, 3, 3, **conv_params)(conv1)
pool1 = MaxPooling2D(pool_size=(2, 2))(conv1)
conv2 = Convolution2D(64, 3, 3, **conv_params)(pool1)
conv2 = Convolution2D(64, 3, 3, **conv_params)(conv2)
pool2 = MaxPooling2D(pool_size=(2, 2))(conv2)
conv3 = Convolution2D(128, 3, 3, **conv_params)(pool2)
conv3 = Convolution2D(128, 3, 3, **conv_params)(conv3)
pool3 = MaxPooling2D(pool_size=(2, 2))(conv3)
conv4 = Convolution2D(256, 3, 3, **conv_params)(pool3)
conv4 = Convolution2D(256, 3, 3, **conv_params)(conv4)
pool4 = MaxPooling2D(pool_size=(2, 2))(conv4)
conv5 = Convolution2D(512, 3, 3, **conv_params)(pool4)
conv5 = Convolution2D(512, 3, 3, **conv_params)(conv5)
up6 = merge_l([UpSampling2D(size=(2, 2))(conv5), conv4], **merge_params)
conv6 = Convolution2D(256, 3, 3, **conv_params)(up6)
conv6 = Convolution2D(256, 3, 3, **conv_params)(conv6)
up7 = merge_l([UpSampling2D(size=(2, 2))(conv6), conv3], **merge_params)
conv7 = Convolution2D(128, 3, 3, **conv_params)(up7)
conv7 = Convolution2D(128, 3, 3, **conv_params)(conv7)
up8 = merge_l([UpSampling2D(size=(2, 2))(conv7), conv2], **merge_params)
conv8 = Convolution2D(64, 3, 3, **conv_params)(up8)
conv8 = Convolution2D(64, 3, 3, **conv_params)(conv8)
up9 = merge_l([UpSampling2D(size=(2, 2))(conv8), conv1], **merge_params)
conv9 = Convolution2D(32, 3, 3, **conv_params)(up9)
conv9 = Convolution2D(32, 3, 3, **conv_params)(conv9)
conv10 = Convolution2D(1, 1, 1, activation='sigmoid')(conv9)
adam = Adam()
model = Model(input=inputs, output=conv10)
model.compile(optimizer=adam,
loss='binary_crossentropy',
metrics=['accuracy', jaccard_coef, jaccard_coef_int])
return model
def jaccard_coef(y_true, y_pred):
smooth = 1e-12
intersection = K.sum(y_true * y_pred, axis=[0, -1, -2])
sum_ = K.sum(y_true + y_pred, axis=[0, -1, -2])
jac = (intersection + smooth) / (sum_ - intersection + smooth)
return K.mean(jac)
def jaccard_coef_int(y_true, y_pred):
smooth = 1e-12
y_pred_pos = K.round(K.clip(y_pred, 0, 1))
intersection = K.sum(y_true * y_pred_pos, axis=[0, -1, -2])
sum_ = K.sum(y_true + y_pred_pos, axis=[0, -1, -2])
jac = (intersection + smooth) / (sum_ - intersection + smooth)
return K.mean(jac)
def generate_test_batch(area_id,
batch_size=64,
immean=None,
enable_tqdm=False):
prefix = area_id_to_prefix(area_id)
df_test = pd.read_csv(FMT_TEST_IMAGELIST_PATH.format(prefix=prefix))
fn_im = FMT_TEST_MUL_STORE.format(prefix)
image_id_list = df_test.ImageId.tolist()
if enable_tqdm:
pbar = tqdm.tqdm(total=len(image_id_list))
while 1:
total_sz = len(image_id_list)
n_batch = int(math.floor(total_sz / batch_size) + 1)
with tb.open_file(fn_im, 'r') as f_im:
for i_batch in range(n_batch):
target_image_ids = image_id_list[
i_batch*batch_size:(i_batch+1)*batch_size
]
if len(target_image_ids) == 0:
continue
X_test = []
y_test = []
for image_id in target_image_ids:
im = np.array(f_im.get_node('/' + image_id))
im = np.swapaxes(im, 0, 2)
im = np.swapaxes(im, 1, 2)
X_test.append(im)
mask = np.zeros((INPUT_SIZE, INPUT_SIZE)).astype(np.uint8)
y_test.append(mask)
X_test = np.array(X_test)
y_test = np.array(y_test)
y_test = y_test.reshape((-1, 1, INPUT_SIZE, INPUT_SIZE))
if immean is not None:
X_test = X_test - immean
if enable_tqdm:
pbar.update(y_test.shape[0])
yield (X_test, y_test)
if enable_tqdm:
pbar.close()
def get_resized_raster_8chan_image_test(image_id, band_rgb_th, band_mul_th):
"""
RGB + multispectral (total: 8 channels)
"""
im = []
fn = test_image_id_to_path(image_id)
with rasterio.open(fn, 'r') as f:
values = f.read().astype(np.float32)
for chan_i in range(3):
min_val = band_rgb_th[chan_i]['min']
max_val = band_rgb_th[chan_i]['max']
values[chan_i] = np.clip(values[chan_i], min_val, max_val)
values[chan_i] = (values[chan_i] - min_val) / (max_val - min_val)
im.append(skimage.transform.resize(
values[chan_i],
(INPUT_SIZE, INPUT_SIZE)))
fn = test_image_id_to_mspec_path(image_id)
with rasterio.open(fn, 'r') as f:
values = f.read().astype(np.float32)
usechannels = [1, 2, 5, 6, 7]
for chan_i in usechannels:
min_val = band_mul_th[chan_i]['min']
max_val = band_mul_th[chan_i]['max']
values[chan_i] = np.clip(values[chan_i], min_val, max_val)
values[chan_i] = (values[chan_i] - min_val) / (max_val - min_val)
im.append(skimage.transform.resize(
values[chan_i],
(INPUT_SIZE, INPUT_SIZE)))
im = np.array(im) # (ch, w, h)
im = np.swapaxes(im, 0, 2) # -> (h, w, ch)
im = np.swapaxes(im, 0, 1) # -> (w, h, ch)
return im
def get_resized_raster_8chan_image(image_id, band_rgb_th, band_mul_th):
"""
RGB + multispectral (total: 8 channels)
"""
im = []
fn = train_image_id_to_path(image_id)
with rasterio.open(fn, 'r') as f:
values = f.read().astype(np.float32)
for chan_i in range(3):
min_val = band_rgb_th[chan_i]['min']
max_val = band_rgb_th[chan_i]['max']
values[chan_i] = np.clip(values[chan_i], min_val, max_val)
values[chan_i] = (values[chan_i] - min_val) / (max_val - min_val)
im.append(skimage.transform.resize(
values[chan_i],
(INPUT_SIZE, INPUT_SIZE)))
fn = train_image_id_to_mspec_path(image_id)
with rasterio.open(fn, 'r') as f:
values = f.read().astype(np.float32)
usechannels = [1, 2, 5, 6, 7]
for chan_i in usechannels:
min_val = band_mul_th[chan_i]['min']
max_val = band_mul_th[chan_i]['max']
values[chan_i] = np.clip(values[chan_i], min_val, max_val)
values[chan_i] = (values[chan_i] - min_val) / (max_val - min_val)
im.append(skimage.transform.resize(
values[chan_i],
(INPUT_SIZE, INPUT_SIZE)))
im = np.array(im) # (ch, w, h)
im = np.swapaxes(im, 0, 2) # -> (h, w, ch)
im = np.swapaxes(im, 0, 1) # -> (w, h, ch)
return im
def _get_train_mul_data(area_id):
"""
RGB + multispectral (total: 8 channels)
"""
prefix = area_id_to_prefix(area_id)
fn_train = FMT_TRAIN_IMAGELIST_PATH.format(prefix=prefix)
df_train = pd.read_csv(fn_train)
X_train = []
fn_im = FMT_TRAIN_MUL_STORE.format(prefix)
with tb.open_file(fn_im, 'r') as f:
for idx, image_id in enumerate(df_train.ImageId.tolist()):
im = np.array(f.get_node('/' + image_id))
im = np.swapaxes(im, 0, 2)
im = np.swapaxes(im, 1, 2)
X_train.append(im)
X_train = np.array(X_train)
y_train = []
fn_mask = FMT_TRAIN_MASK_STORE.format(prefix)
with tb.open_file(fn_mask, 'r') as f:
for idx, image_id in enumerate(df_train.ImageId.tolist()):
mask = np.array(f.get_node('/' + image_id))
mask = (mask > 0.5).astype(np.uint8)
y_train.append(mask)
y_train = np.array(y_train)
y_train = y_train.reshape((-1, 1, INPUT_SIZE, INPUT_SIZE))
return X_train, y_train
def _get_test_mul_data(area_id):
"""
RGB + multispectral (total: 8 channels)
"""
prefix = area_id_to_prefix(area_id)
fn_test = FMT_TEST_IMAGELIST_PATH.format(prefix=prefix)
df_test = pd.read_csv(fn_test)
X_test = []
fn_im = FMT_TEST_MUL_STORE.format(prefix)
with tb.open_file(fn_im, 'r') as f:
for idx, image_id in enumerate(df_test.ImageId.tolist()):
im = np.array(f.get_node('/' + image_id))
im = np.swapaxes(im, 0, 2)
im = np.swapaxes(im, 1, 2)
X_test.append(im)
X_test = np.array(X_test)
return X_test
def _get_valtest_mul_data(area_id):
prefix = area_id_to_prefix(area_id)
fn_test = FMT_VALTEST_IMAGELIST_PATH.format(prefix=prefix)
df_test = pd.read_csv(fn_test)
X_val = []
fn_im = FMT_VALTEST_MUL_STORE.format(prefix)
with tb.open_file(fn_im, 'r') as f:
for idx, image_id in enumerate(df_test.ImageId.tolist()):
im = np.array(f.get_node('/' + image_id))
im = np.swapaxes(im, 0, 2)
im = np.swapaxes(im, 1, 2)
X_val.append(im)
X_val = np.array(X_val)
y_val = []
fn_mask = FMT_VALTEST_MASK_STORE.format(prefix)
with tb.open_file(fn_mask, 'r') as f:
for idx, image_id in enumerate(df_test.ImageId.tolist()):
mask = np.array(f.get_node('/' + image_id))
mask = (mask > 0.5).astype(np.uint8)
y_val.append(mask)
y_val = np.array(y_val)
y_val = y_val.reshape((-1, 1, INPUT_SIZE, INPUT_SIZE))
return X_val, y_val
def _get_valtrain_mul_data(area_id):
prefix = area_id_to_prefix(area_id)
fn_train = FMT_VALTRAIN_IMAGELIST_PATH.format(prefix=prefix)
df_train = pd.read_csv(fn_train)
X_val = []
fn_im = FMT_VALTRAIN_MUL_STORE.format(prefix)
with tb.open_file(fn_im, 'r') as f:
for idx, image_id in enumerate(df_train.ImageId.tolist()):
im = np.array(f.get_node('/' + image_id))
im = np.swapaxes(im, 0, 2)
im = np.swapaxes(im, 1, 2)
X_val.append(im)
X_val = np.array(X_val)
y_val = []
fn_mask = FMT_VALTRAIN_MASK_STORE.format(prefix)
with tb.open_file(fn_mask, 'r') as f:
for idx, image_id in enumerate(df_train.ImageId.tolist()):
mask = np.array(f.get_node('/' + image_id))
mask = (mask > 0.5).astype(np.uint8)
y_val.append(mask)
y_val = np.array(y_val)
y_val = y_val.reshape((-1, 1, INPUT_SIZE, INPUT_SIZE))
return X_val, y_val
def get_mul_mean_image(area_id):
prefix = area_id_to_prefix(area_id)
with tb.open_file(FMT_MULMEAN.format(prefix), 'r') as f:
im_mean = np.array(f.get_node('/mulmean'))
return im_mean
def preproc_stage3(area_id):
prefix = area_id_to_prefix(area_id)
if not Path(FMT_VALTEST_MUL_STORE.format(prefix)).exists():
valtrain_test_mul_image_prep(area_id)
if not Path(FMT_TEST_MUL_STORE.format(prefix)).exists():
train_test_mul_image_prep(area_id)
# mean image for subtract preprocessing
X1, _ = _get_train_mul_data(area_id)
X2 = _get_test_mul_data(area_id)
X = np.vstack([X1, X2])
print(X.shape)
X_mean = X.mean(axis=0)
fn = FMT_MULMEAN.format(prefix)
logger.info("Prepare mean image: {}".format(fn))
with tb.open_file(fn, 'w') as f:
atom = tb.Atom.from_dtype(X_mean.dtype)
filters = tb.Filters(complib='blosc', complevel=9)
ds = f.create_carray(f.root, 'mulmean', atom, X_mean.shape,
filters=filters)
ds[:] = X_mean
def _internal_test_predict_best_param(area_id,
save_pred=True):
prefix = area_id_to_prefix(area_id)
param = _get_model_parameter(area_id)
epoch = param['fn_epoch']
min_th = param['min_poly_area']
# Prediction phase
logger.info("Prediction phase: {}".format(prefix))
X_mean = get_mul_mean_image(area_id)
# Load model weights
# Predict and Save prediction result
fn = FMT_TESTPRED_PATH.format(prefix)
fn_model = FMT_VALMODEL_PATH.format(prefix + '_{epoch:02d}')
fn_model = fn_model.format(epoch=epoch)
model = get_unet()
model.load_weights(fn_model)
fn_test = FMT_TEST_IMAGELIST_PATH.format(prefix=prefix)
df_test = pd.read_csv(fn_test, index_col='ImageId')
y_pred = model.predict_generator(
generate_test_batch(
area_id,
batch_size=64,
immean=X_mean,
enable_tqdm=True,
),
val_samples=len(df_test),
)
del model
# Save prediction result
if save_pred:
with tb.open_file(fn, 'w') as f:
atom = tb.Atom.from_dtype(y_pred.dtype)
filters = tb.Filters(complib='blosc', complevel=9)
ds = f.create_carray(f.root, 'pred', atom, y_pred.shape,
filters=filters)
ds[:] = y_pred
return y_pred
def _internal_test(area_id, enable_tqdm=False):
prefix = area_id_to_prefix(area_id)
y_pred = _internal_test_predict_best_param(area_id, save_pred=False)
param = _get_model_parameter(area_id)
min_th = param['min_poly_area']
# Postprocessing phase
logger.info("Postprocessing phase")
fn_test = FMT_TEST_IMAGELIST_PATH.format(prefix=prefix)
df_test = pd.read_csv(fn_test, index_col='ImageId')
fn_out = FMT_TESTPOLY_PATH.format(prefix)
with open(fn_out, 'w') as f:
f.write("ImageId,BuildingId,PolygonWKT_Pix,Confidence\n")
test_image_list = df_test.index.tolist()
for idx, image_id in tqdm.tqdm(enumerate(test_image_list),
total=len(test_image_list)):
df_poly = mask_to_poly(y_pred[idx][0], min_polygon_area_th=min_th)
if len(df_poly) > 0:
for i, row in df_poly.iterrows():
line = "{},{},\"{}\",{:.6f}\n".format(
image_id,
row.bid,
row.wkt,
row.area_ratio)
line = _remove_interiors(line)
f.write(line)
else:
f.write("{},{},{},0\n".format(
image_id,
-1,
"POLYGON EMPTY"))
def validate_score(area_id):
"""
Calc competition score
"""
prefix = area_id_to_prefix(area_id)
# Prediction phase
if not Path(FMT_VALTESTPRED_PATH.format(prefix)).exists():
X_val, y_val = _get_valtest_mul_data(area_id)
X_mean = get_mul_mean_image(area_id)
# Load model weights
# Predict and Save prediction result
model = get_unet()
model.load_weights(FMT_VALMODEL_PATH.format(prefix))
y_pred = model.predict(X_val - X_mean, batch_size=8, verbose=1)
del model
# Save prediction result
fn = FMT_VALTESTPRED_PATH.format(prefix)
with tb.open_file(fn, 'w') as f:
atom = tb.Atom.from_dtype(y_pred.dtype)
filters = tb.Filters(complib='blosc', complevel=9)
ds = f.create_carray(f.root, 'pred', atom, y_pred.shape,
filters=filters)
ds[:] = y_pred
# Postprocessing phase
if not Path(FMT_VALTESTPOLY_PATH.format(prefix)).exists():
fn_test = FMT_VALTEST_IMAGELIST_PATH.format(prefix=prefix)
df_test = pd.read_csv(fn_test, index_col='ImageId')
fn = FMT_VALTESTPRED_PATH.format(prefix)
with tb.open_file(fn, 'r') as f:
y_pred = np.array(f.get_node('/pred'))
print(y_pred.shape)
fn_out = FMT_VALTESTPOLY_PATH.format(prefix)
with open(fn_out, 'w') as f:
f.write("ImageId,BuildingId,PolygonWKT_Pix,Confidence\n")
for idx, image_id in enumerate(df_test.index.tolist()):
df_poly = mask_to_poly(y_pred[idx][0])
if len(df_poly) > 0:
for i, row in df_poly.iterrows():
f.write("{},{},\"{}\",{:.6f}\n".format(
image_id,
row.bid,
row.wkt,
row.area_ratio))
else:
f.write("{},{},{},0\n".format(
image_id,
-1,
"POLYGON EMPTY"))
# update fn_out
with open(fn_out, 'r') as f:
lines = f.readlines()
with open(fn_out, 'w') as f:
f.write(lines[0])
for line in lines[1:]:
line = _remove_interiors(line)
f.write(line)
# Validation solution file
if not Path(FMT_VALTESTTRUTH_PATH.format(prefix)).exists():
fn_true = FMT_TRAIN_SUMMARY_PATH.format(prefix=prefix)
df_true = pd.read_csv(fn_true)
# # Remove prefix "PAN_"
# df_true.loc[:, 'ImageId'] = df_true.ImageId.str[4:]
fn_test = FMT_VALTEST_IMAGELIST_PATH.format(prefix=prefix)
df_test = pd.read_csv(fn_test)
df_test_image_ids = df_test.ImageId.unique()
fn_out = FMT_VALTESTTRUTH_PATH.format(prefix)
with open(fn_out, 'w') as f:
f.write("ImageId,BuildingId,PolygonWKT_Pix,Confidence\n")
df_true = df_true[df_true.ImageId.isin(df_test_image_ids)]
for idx, r in df_true.iterrows():
f.write("{},{},\"{}\",{:.6f}\n".format(
r.ImageId,
r.BuildingId,
r.PolygonWKT_Pix,
1.0))
def validate_all_score():
header_line = []
lines = []
for area_id in range(2, 6):
prefix = area_id_to_prefix(area_id)
assert Path(FMT_VALTESTTRUTH_PATH.format(prefix)).exists()
with open(FMT_VALTESTTRUTH_PATH.format(prefix), 'r') as f:
header_line = f.readline()
lines += f.readlines()
with open(FMT_VALTESTTRUTH_OVALL_PATH, 'w') as f:
f.write(header_line)
for line in lines:
f.write(line)
# Predicted polygons
header_line = []
lines = []
for area_id in range(2, 6):
prefix = area_id_to_prefix(area_id)
assert Path(FMT_VALTESTPOLY_PATH.format(prefix)).exists()
with open(FMT_VALTESTPOLY_PATH.format(prefix), 'r') as f:
header_line = f.readline()
lines += f.readlines()
with open(FMT_VALTESTPOLY_OVALL_PATH, 'w') as f:
f.write(header_line)
for line in lines:
f.write(line)
def generate_valtest_batch(area_id,
batch_size=8,
immean=None,
enable_tqdm=False):
prefix = area_id_to_prefix(area_id)
df_train = pd.read_csv(FMT_VALTEST_IMAGELIST_PATH.format(prefix=prefix))
fn_im = FMT_VALTEST_MUL_STORE.format(prefix)
fn_mask = FMT_VALTEST_MASK_STORE.format(prefix)
image_id_list = df_train.ImageId.tolist()
if enable_tqdm:
pbar = tqdm.tqdm(total=len(image_id_list))
while 1:
total_sz = len(image_id_list)
n_batch = int(math.floor(total_sz / batch_size) + 1)
with tb.open_file(fn_im, 'r') as f_im,\
tb.open_file(fn_mask, 'r') as f_mask:
for i_batch in range(n_batch):
target_image_ids = image_id_list[
i_batch*batch_size:(i_batch+1)*batch_size
]
if len(target_image_ids) == 0:
continue
X_train = []
y_train = []
for image_id in target_image_ids:
im = np.array(f_im.get_node('/' + image_id))
im = np.swapaxes(im, 0, 2)
im = np.swapaxes(im, 1, 2)
X_train.append(im)
mask = np.array(f_mask.get_node('/' + image_id))
mask = (mask > 0).astype(np.uint8)
y_train.append(mask)
X_train = np.array(X_train)
y_train = np.array(y_train)
y_train = y_train.reshape((-1, 1, INPUT_SIZE, INPUT_SIZE))
if immean is not None:
X_train = X_train - immean
if enable_tqdm:
pbar.update(y_train.shape[0])
yield (X_train, y_train)
if enable_tqdm:
pbar.close()
def generate_valtrain_batch(area_id, batch_size=8, immean=None):
prefix = area_id_to_prefix(area_id)
df_train = pd.read_csv(FMT_VALTRAIN_IMAGELIST_PATH.format(prefix=prefix))
fn_im = FMT_VALTRAIN_MUL_STORE.format(prefix)
fn_mask = FMT_VALTRAIN_MASK_STORE.format(prefix)
image_id_list = df_train.ImageId.tolist()
np.random.shuffle(image_id_list)
while 1:
total_sz = len(image_id_list)
n_batch = int(math.floor(total_sz / batch_size) + 1)
with tb.open_file(fn_im, 'r') as f_im,\
tb.open_file(fn_mask, 'r') as f_mask:
for i_batch in range(n_batch):
target_image_ids = image_id_list[
i_batch*batch_size:(i_batch+1)*batch_size
]
if len(target_image_ids) == 0:
continue
X_train = []
y_train = []
for image_id in target_image_ids:
im = np.array(f_im.get_node('/' + image_id))
im = np.swapaxes(im, 0, 2)
im = np.swapaxes(im, 1, 2)
X_train.append(im)
mask = np.array(f_mask.get_node('/' + image_id))
mask = (mask > 0).astype(np.uint8)
y_train.append(mask)
X_train = np.array(X_train)
y_train = np.array(y_train)
y_train = y_train.reshape((-1, 1, INPUT_SIZE, INPUT_SIZE))
if immean is not None:
X_train = X_train - immean
yield (X_train, y_train)
def _get_test_data(area_id):
prefix = area_id_to_prefix(area_id)
fn_test = FMT_TEST_IMAGELIST_PATH.format(prefix=prefix)
df_test = pd.read_csv(fn_test)
X_test = []
fn_im = FMT_TEST_IM_STORE.format(prefix)
with tb.open_file(fn_im, 'r') as f:
for idx, image_id in enumerate(df_test.ImageId.tolist()):
im = np.array(f.get_node('/' + image_id))
im = np.swapaxes(im, 0, 2)
im = np.swapaxes(im, 1, 2)
X_test.append(im)
X_test = np.array(X_test)
return X_test
def _get_valtest_data(area_id):
prefix = area_id_to_prefix(area_id)
fn_test = FMT_VALTEST_IMAGELIST_PATH.format(prefix=prefix)
df_test = pd.read_csv(fn_test)
X_val = []
fn_im = FMT_VALTEST_IM_STORE.format(prefix)
with tb.open_file(fn_im, 'r') as f:
for idx, image_id in enumerate(df_test.ImageId.tolist()):
im = np.array(f.get_node('/' + image_id))
im = np.swapaxes(im, 0, 2)
im = np.swapaxes(im, 1, 2)
X_val.append(im)
X_val = np.array(X_val)
y_val = []
fn_mask = FMT_VALTEST_MASK_STORE.format(prefix)
with tb.open_file(fn_mask, 'r') as f:
for idx, image_id in enumerate(df_test.ImageId.tolist()):
mask = np.array(f.get_node('/' + image_id))
mask = (mask > 0.5).astype(np.uint8)
y_val.append(mask)
y_val = np.array(y_val)
y_val = y_val.reshape((-1, 1, INPUT_SIZE, INPUT_SIZE))
return X_val, y_val
def _get_valtrain_data(area_id):
prefix = area_id_to_prefix(area_id)
fn_train = FMT_VALTRAIN_IMAGELIST_PATH.format(prefix=prefix)
df_train = pd.read_csv(fn_train)
X_val = []
fn_im = FMT_VALTRAIN_IM_STORE.format(prefix)
with tb.open_file(fn_im, 'r') as f:
for idx, image_id in enumerate(df_train.ImageId.tolist()):
im = np.array(f.get_node('/' + image_id))
im = np.swapaxes(im, 0, 2)
im = np.swapaxes(im, 1, 2)
X_val.append(im)
X_val = np.array(X_val)
y_val = []
fn_mask = FMT_VALTRAIN_MASK_STORE.format(prefix)
with tb.open_file(fn_mask, 'r') as f:
for idx, image_id in enumerate(df_train.ImageId.tolist()):
mask = np.array(f.get_node('/' + image_id))
mask = (mask > 0.5).astype(np.uint8)
y_val.append(mask)
y_val = np.array(y_val)
y_val = y_val.reshape((-1, 1, INPUT_SIZE, INPUT_SIZE))
return X_val, y_val
def predict(area_id):
prefix = area_id_to_prefix(area_id)
X_test = _get_test_mul_data(area_id)
X_mean = get_mul_mean_image(area_id)
# Load model weights
# Predict and Save prediction result
model = get_unet()
model.load_weights(FMT_VALMODEL_PATH.format(prefix))
y_pred = model.predict(X_test - X_mean, batch_size=8, verbose=1)
del model
# Save prediction result
fn = FMT_TESTPRED_PATH.format(prefix)
with tb.open_file(fn, 'w') as f:
atom = tb.Atom.from_dtype(y_pred.dtype)
filters = tb.Filters(complib='blosc', complevel=9)
ds = f.create_carray(f.root, 'pred', atom, y_pred.shape,
filters=filters)
ds[:] = y_pred
def _internal_validate_predict_best_param(area_id,
enable_tqdm=False):
param = _get_model_parameter(area_id)
epoch = param['fn_epoch']
y_pred = _internal_validate_predict(
area_id,
epoch=epoch,
save_pred=False,
enable_tqdm=enable_tqdm)
return y_pred
def _internal_validate_predict(area_id,
epoch=3,
save_pred=True,
enable_tqdm=False):
prefix = area_id_to_prefix(area_id)
X_mean = get_mul_mean_image(area_id)
# Load model weights
# Predict and Save prediction result
fn_model = FMT_VALMODEL_PATH.format(prefix + '_{epoch:02d}')
fn_model = fn_model.format(epoch=epoch)
model = get_unet()
model.load_weights(fn_model)
fn_test = FMT_VALTEST_IMAGELIST_PATH.format(prefix=prefix)
df_test = pd.read_csv(fn_test, index_col='ImageId')
y_pred = model.predict_generator(
generate_valtest_batch(
area_id,
batch_size=64,
immean=X_mean,
enable_tqdm=enable_tqdm,
),
val_samples=len(df_test),
)
del model
# Save prediction result
if save_pred:
fn = FMT_VALTESTPRED_PATH.format(prefix)
with tb.open_file(fn, 'w') as f:
atom = tb.Atom.from_dtype(y_pred.dtype)
filters = tb.Filters(complib='blosc', complevel=9)
ds = f.create_carray(f.root,
'pred',
atom,
y_pred.shape,
filters=filters)
ds[:] = y_pred
return y_pred
def _internal_validate_fscore_wo_pred_file(area_id,
epoch=3,
min_th=MIN_POLYGON_AREA,
enable_tqdm=False):
prefix = area_id_to_prefix(area_id)
# Prediction phase
logger.info("Prediction phase")
y_pred = _internal_validate_predict(
area_id,
epoch=epoch,
save_pred=False,
enable_tqdm=enable_tqdm)
# Postprocessing phase
logger.info("Postprocessing phase")
fn_test = FMT_VALTEST_IMAGELIST_PATH.format(prefix=prefix)
df_test = pd.read_csv(fn_test, index_col='ImageId')
fn = FMT_VALTESTPRED_PATH.format(prefix)
fn_out = FMT_VALTESTPOLY_PATH.format(prefix)
with open(fn_out, 'w') as f:
f.write("ImageId,BuildingId,PolygonWKT_Pix,Confidence\n")
test_list = df_test.index.tolist()
iterator = enumerate(test_list)
for idx, image_id in tqdm.tqdm(iterator, total=len(test_list)):
df_poly = mask_to_poly(y_pred[idx][0], min_polygon_area_th=min_th)
if len(df_poly) > 0:
for i, row in df_poly.iterrows():
line = "{},{},\"{}\",{:.6f}\n".format(
image_id,
row.bid,
row.wkt,
row.area_ratio)
line = _remove_interiors(line)
f.write(line)
else:
f.write("{},{},{},0\n".format(
image_id,
-1,
"POLYGON EMPTY"))
# ------------------------
# Validation solution file
logger.info("Validation solution file")
fn_true = FMT_TRAIN_SUMMARY_PATH.format(prefix=prefix)
df_true = pd.read_csv(fn_true)
# # Remove prefix "PAN_"
# df_true.loc[:, 'ImageId'] = df_true.ImageId.str[4:]
fn_test = FMT_VALTEST_IMAGELIST_PATH.format(prefix=prefix)
df_test = pd.read_csv(fn_test)
df_test_image_ids = df_test.ImageId.unique()
fn_out = FMT_VALTESTTRUTH_PATH.format(prefix)
with open(fn_out, 'w') as f:
f.write("ImageId,BuildingId,PolygonWKT_Pix,Confidence\n")
df_true = df_true[df_true.ImageId.isin(df_test_image_ids)]
for idx, r in df_true.iterrows():
f.write("{},{},\"{}\",{:.6f}\n".format(
r.ImageId,
r.BuildingId,
r.PolygonWKT_Pix,
1.0))
def _internal_validate_fscore(area_id,
epoch=3,
predict=True,
min_th=MIN_POLYGON_AREA,
enable_tqdm=False):
prefix = area_id_to_prefix(area_id)
# Prediction phase
logger.info("Prediction phase")
if predict:
_internal_validate_predict(
area_id,
epoch=epoch,
enable_tqdm=enable_tqdm)
# Postprocessing phase
logger.info("Postprocessing phase")
fn_test = FMT_VALTEST_IMAGELIST_PATH.format(prefix=prefix)
df_test = pd.read_csv(fn_test, index_col='ImageId')
fn = FMT_VALTESTPRED_PATH.format(prefix)
fn_out = FMT_VALTESTPOLY_PATH.format(prefix)
with open(fn_out, 'w') as f,\
tb.open_file(fn, 'r') as fr:
y_pred = np.array(fr.get_node('/pred'))
f.write("ImageId,BuildingId,PolygonWKT_Pix,Confidence\n")
test_list = df_test.index.tolist()
iterator = enumerate(test_list)
for idx, image_id in tqdm.tqdm(iterator, total=len(test_list)):
df_poly = mask_to_poly(y_pred[idx][0], min_polygon_area_th=min_th)
if len(df_poly) > 0:
for i, row in df_poly.iterrows():
line = "{},{},\"{}\",{:.6f}\n".format(
image_id,
row.bid,
row.wkt,
row.area_ratio)
line = _remove_interiors(line)
f.write(line)
else:
f.write("{},{},{},0\n".format(
image_id,
-1,
"POLYGON EMPTY"))
# ------------------------
# Validation solution file
logger.info("Validation solution file")
# if not Path(FMT_VALTESTTRUTH_PATH.format(prefix)).exists():
if True:
fn_true = FMT_TRAIN_SUMMARY_PATH.format(prefix=prefix)
df_true = pd.read_csv(fn_true)
# # Remove prefix "PAN_"
# df_true.loc[:, 'ImageId'] = df_true.ImageId.str[4:]
fn_test = FMT_VALTEST_IMAGELIST_PATH.format(prefix=prefix)
df_test = pd.read_csv(fn_test)
df_test_image_ids = df_test.ImageId.unique()
fn_out = FMT_VALTESTTRUTH_PATH.format(prefix)
with open(fn_out, 'w') as f:
f.write("ImageId,BuildingId,PolygonWKT_Pix,Confidence\n")
df_true = df_true[df_true.ImageId.isin(df_test_image_ids)]
for idx, r in df_true.iterrows():
f.write("{},{},\"{}\",{:.6f}\n".format(
r.ImageId,
r.BuildingId,
r.PolygonWKT_Pix,
1.0))
@click.group()
def cli():
pass
@cli.command()
@click.argument('datapath', type=str)
def validate(datapath):
area_id = directory_name_to_area_id(datapath)
prefix = area_id_to_prefix(area_id)
logger.info(">> validate sub-command: {}".format(prefix))
X_mean = get_mul_mean_image(area_id)
X_val, y_val = _get_valtest_mul_data(area_id)
X_val = X_val - X_mean
if not Path(MODEL_DIR).exists():
Path(MODEL_DIR).mkdir(parents=True)
logger.info("load valtrain")
X_trn, y_trn = _get_valtrain_mul_data(area_id)
X_trn = X_trn - X_mean
model = get_unet()
model_checkpoint = ModelCheckpoint(
FMT_VALMODEL_PATH.format(prefix + "_{epoch:02d}"),
monitor='val_jaccard_coef_int',
save_best_only=False)
model_earlystop = EarlyStopping(
monitor='val_jaccard_coef_int',
patience=10,
verbose=0,
mode='max')
model_history = History()
df_train = pd.read_csv(FMT_VALTRAIN_IMAGELIST_PATH.format(prefix=prefix))
logger.info("Fit")
model.fit(
X_trn, y_trn,
nb_epoch=200,
shuffle=True,
verbose=1,
validation_data=(X_val, y_val),
callbacks=[model_checkpoint, model_earlystop, model_history])
model.save_weights(FMT_VALMODEL_LAST_PATH.format(prefix))
# Save evaluation history
pd.DataFrame(model_history.history).to_csv(
FMT_VALMODEL_HIST.format(prefix), index=False)
logger.info(">> validate sub-command: {} ... Done".format(prefix))
@cli.command()
@click.argument('datapath', type=str)
def testproc(datapath):
area_id = directory_name_to_area_id(datapath)
prefix = area_id_to_prefix(area_id)
logger.info(">>>> Test proc for {}".format(prefix))
_internal_test(area_id)
logger.info(">>>> Test proc for {} ... done".format(prefix))
@cli.command()
@click.argument('datapath', type=str)
def evalfscore(datapath):
area_id = directory_name_to_area_id(datapath)
prefix = area_id_to_prefix(area_id)
logger.info("Evaluate fscore on validation set: {}".format(prefix))
# for each epoch
# if not Path(FMT_VALMODEL_EVALHIST.format(prefix)).exists():
if True:
df_hist = pd.read_csv(FMT_VALMODEL_HIST.format(prefix))
df_hist.loc[:, 'epoch'] = list(range(1, len(df_hist) + 1))
rows = []
for zero_base_epoch in range(0, len(df_hist)):
logger.info(">>> Epoch: {}".format(zero_base_epoch))
_internal_validate_fscore_wo_pred_file(
area_id,
epoch=zero_base_epoch,
enable_tqdm=True,
min_th=MIN_POLYGON_AREA)
evaluate_record = _calc_fscore_per_aoi(area_id)
evaluate_record['zero_base_epoch'] = zero_base_epoch
evaluate_record['min_area_th'] = MIN_POLYGON_AREA
evaluate_record['area_id'] = area_id
logger.info("\n" + json.dumps(evaluate_record, indent=4))
rows.append(evaluate_record)
pd.DataFrame(rows).to_csv(
FMT_VALMODEL_EVALHIST.format(prefix),
index=False)
# find best min-poly-threshold
df_evalhist = pd.read_csv(FMT_VALMODEL_EVALHIST.format(prefix))
best_row = df_evalhist.sort_values(by='fscore', ascending=False).iloc[0]
best_epoch = int(best_row.zero_base_epoch)
best_fscore = best_row.fscore
# optimize min area th
rows = []
for th in [30, 60, 90, 120, 150, 180, 210, 240]:
logger.info(">>> TH: {}".format(th))
predict_flag = False
if th == 30:
predict_flag = True
_internal_validate_fscore(
area_id,
epoch=best_epoch,
enable_tqdm=True,
min_th=th,
predict=predict_flag)
evaluate_record = _calc_fscore_per_aoi(area_id)
evaluate_record['zero_base_epoch'] = best_epoch
evaluate_record['min_area_th'] = th
evaluate_record['area_id'] = area_id
logger.info("\n" + json.dumps(evaluate_record, indent=4))
rows.append(evaluate_record)
pd.DataFrame(rows).to_csv(
FMT_VALMODEL_EVALTHHIST.format(prefix),
index=False)
logger.info("Evaluate fscore on validation set: {} .. done".format(prefix))
def mask_to_poly(mask, min_polygon_area_th=MIN_POLYGON_AREA):
"""
Convert from 256x256 mask to polygons on 650x650 image
"""
mask = (skimage.transform.resize(mask, (650, 650)) > 0.5).astype(np.uint8)
shapes = rasterio.features.shapes(mask.astype(np.int16), mask > 0)
poly_list = []
mp = shapely.ops.cascaded_union(
shapely.geometry.MultiPolygon([
shapely.geometry.shape(shape)
for shape, value in shapes
]))
if isinstance(mp, shapely.geometry.Polygon):
df = pd.DataFrame({
'area_size': [mp.area],
'poly': [mp],
})
else:
df = pd.DataFrame({
'area_size': [p.area for p in mp],
'poly': [p for p in mp],
})
df = df[df.area_size > min_polygon_area_th].sort_values(
by='area_size', ascending=False)
df.loc[:, 'wkt'] = df.poly.apply(lambda x: shapely.wkt.dumps(
x, rounding_precision=0))
df.loc[:, 'bid'] = list(range(1, len(df) + 1))
df.loc[:, 'area_ratio'] = df.area_size / df.area_size.max()
return df
def postproc(area_id):
# Mask to poly
print(area_id)
prefix = area_id_to_prefix(area_id)
fn_test = FMT_TEST_IMAGELIST_PATH.format(prefix=prefix)
df_test = pd.read_csv(fn_test, index_col='ImageId')
fn = FMT_TESTPRED_PATH.format(prefix)
with tb.open_file(fn, 'r') as f:
y_pred = np.array(f.get_node('/pred'))
print(y_pred.shape)
fn_out = FMT_TESTPOLY_PATH.format(prefix)
with open(fn_out, 'w') as f:
f.write("ImageId,BuildingId,PolygonWKT_Pix,Confidence\n")
for idx, image_id in enumerate(df_test.index.tolist()):
df_poly = mask_to_poly(y_pred[idx][0])
if len(df_poly) > 0:
for i, row in df_poly.iterrows():
f.write("{},{},\"{}\",{:.6f}\n".format(
image_id,
row.bid,
row.wkt,
row.area_ratio))
else:
f.write("{},{},{},0\n".format(
image_id,
-1,
"POLYGON EMPTY"))
def merge():
df_list = []
for area_id in range(2, 6):
prefix = area_id_to_prefix(area_id)
df_part = pd.read_csv(
FMT_TESTPOLY_PATH.format(prefix))
df_list.append(df_part)
df = pd.concat(df_list)
df.to_csv(FN_SOLUTION_CSV, index=False)
with open(FN_SOLUTION_CSV, 'r') as f:
lines = f.readlines()
with open(FN_SOLUTION_CSV, 'w') as f:
f.write(lines[0])
for line in lines[1:]:
line = _remove_interiors(line)
f.write(line)
if __name__ == '__main__':
cli()
| 35.034072
| 79
| 0.604495
| 9,038
| 64,778
| 4.019916
| 0.057203
| 0.030221
| 0.028074
| 0.026038
| 0.80458
| 0.770285
| 0.733898
| 0.693741
| 0.677337
| 0.656721
| 0
| 0.015977
| 0.272438
| 64,778
| 1,848
| 80
| 35.05303
| 0.754912
| 0.029887
| 0
| 0.645848
| 0
| 0
| 0.062755
| 0.016549
| 0
| 0
| 0
| 0
| 0.006863
| 1
| 0.039808
| false
| 0.001373
| 0.019218
| 0
| 0.085106
| 0.002745
| 0
| 0
| 0
| null | 0
| 0
| 0
| 1
| 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
| 4
|
67e3dc13a1b35006643aaa0824b10d5857fb6b8b
| 198
|
py
|
Python
|
hackerrank/python-sort-sort/solution.py
|
SamProkopchuk/coding-problems
|
fa0ca2c05ac90e41945de1a5751e5545a8459ac4
|
[
"MIT"
] | null | null | null |
hackerrank/python-sort-sort/solution.py
|
SamProkopchuk/coding-problems
|
fa0ca2c05ac90e41945de1a5751e5545a8459ac4
|
[
"MIT"
] | null | null | null |
hackerrank/python-sort-sort/solution.py
|
SamProkopchuk/coding-problems
|
fa0ca2c05ac90e41945de1a5751e5545a8459ac4
|
[
"MIT"
] | null | null | null |
n, m = map(int, input().strip().split())
matrix = [list(map(int, input().strip().split())) for _ in range(n)]
k = int(input().strip())
for lst in sorted(matrix, key=lambda l: l[k]):
print(*lst)
| 33
| 68
| 0.606061
| 34
| 198
| 3.5
| 0.558824
| 0.201681
| 0.327731
| 0.268908
| 0.352941
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.141414
| 198
| 5
| 69
| 39.6
| 0.7
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| false
| 0
| 0
| 0
| 0
| 0.2
| 0
| 0
| 0
| null | 1
| 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
| 4
|
db0fc1f5c06cbc189e0b49852b5c6f2bd3629ed0
| 231
|
py
|
Python
|
typed_environment_configuration/__init__.py
|
springload/typed_environment_configuration
|
9d88f067fcbd1e4d896c15084aa35ccc020b43ac
|
[
"MIT"
] | 1
|
2019-12-02T03:42:12.000Z
|
2019-12-02T03:42:12.000Z
|
typed_environment_configuration/__init__.py
|
springload/typed_environment_configuration
|
9d88f067fcbd1e4d896c15084aa35ccc020b43ac
|
[
"MIT"
] | 3
|
2020-07-15T02:43:52.000Z
|
2020-07-21T02:41:47.000Z
|
typed_environment_configuration/__init__.py
|
springload/typed_environment_configuration
|
9d88f067fcbd1e4d896c15084aa35ccc020b43ac
|
[
"MIT"
] | 1
|
2022-03-08T20:55:02.000Z
|
2022-03-08T20:55:02.000Z
|
# -*- coding: utf-8 -*-
"""Top-level package for Typed Environment Configuration."""
__author__ = """Eugene Dementyev"""
__email__ = "eugene@springload.co.nz"
__version__ = '0.1.4'
from .typed_environment_configuration import *
| 23.1
| 60
| 0.718615
| 27
| 231
| 5.62963
| 0.851852
| 0.210526
| 0.381579
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.019704
| 0.121212
| 231
| 9
| 61
| 25.666667
| 0.729064
| 0.333333
| 0
| 0
| 0
| 0
| 0.297297
| 0.155405
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| false
| 0
| 0.25
| 0
| 0.25
| 0
| 1
| 0
| 0
| null | 1
| 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
| 4
|
db25cdd2c0e67e26adf5ea6063dfe30cf7097124
| 95
|
py
|
Python
|
python/34.find-first-and-last-position-of-element-in-sorted-array.py
|
stavanmehta/leetcode
|
1224e43ce29430c840e65daae3b343182e24709c
|
[
"Apache-2.0"
] | null | null | null |
python/34.find-first-and-last-position-of-element-in-sorted-array.py
|
stavanmehta/leetcode
|
1224e43ce29430c840e65daae3b343182e24709c
|
[
"Apache-2.0"
] | null | null | null |
python/34.find-first-and-last-position-of-element-in-sorted-array.py
|
stavanmehta/leetcode
|
1224e43ce29430c840e65daae3b343182e24709c
|
[
"Apache-2.0"
] | null | null | null |
class Solution:
def searchRange(self, nums: List[int], target: int) -> List[int]:
| 23.75
| 69
| 0.610526
| 12
| 95
| 4.833333
| 0.75
| 0.241379
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.242105
| 95
| 3
| 70
| 31.666667
| 0.805556
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| null | null | 0
| 0
| null | null | 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
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
|
0
| 4
|
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