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int64 1
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|---|---|---|---|
#!/usr/bin/env python
# -*- coding:utf-8 -*-
import urllib
import requests
from bs4 import BeautifulSoup
"""
https://wenku.baidu.com/browse/getrequest?doc_id=a1eec6289b6648d7c1c7468f&pn=22&rn=1&type=ppt&callback=bd__cbs__s5lw72
https://wenku.baidu.com/browse/getrequest?doc_id=a1eec6289b6648d7c1c7468f&pn=23&rn=1&type=ppt&callback=bd__cbs__coo5j5
https://wenku.baidu.com/browse/getrequest?doc_id=a1eec6289b6648d7c1c7468f&pn=21&rn=1&type=ppt&callback=bd__cbs__2hc9ds
https://wenku.baidu.com/browse/getrequest?doc_id=a1eec6289b6648d7c1c7468f&pn=5&rn=1&type=ppt&callback=bd__cbs__nh2gao
"""
linkfiles = "F:\\PythonProject\\python-scripts\\spider\\wenkubaidu\\odnimages\\"
class WK():
'''
百度文库
'''
if __name__=="__main__":
wk = WK()
for pn in range(1,26):
url = 'https://wenku.baidu.com/browse/getrequest?doc_id=a1eec6289b6648d7c1c7468f&pn={}&rn=1&type=ppt&callback=bd__cbs__nh2gao'.format(pn)
print(url,"下载完成")
wk.spyder(url)
"""
with open(linkfiles + "wenkulink.txt",'a+') as fw:
# fw.write(url) # 是统计的页数连接,可以从中获取到图片的链接
# fw.write("\n")
"""
# wk.spyder(wk.baseUrl)
"""
注意该网址粘贴到浏览器上访问是可以的,但是在代码中若不替换\该字符,会导致报错.
https:\/\/wkretype.bdimg.com\/retype\/zoom\/6a30bde2f8c75fbfc77db23c?pn=4&raww=1080&rawh=810&o=jpg_6&md5sum=f9ace759cd13bfd0f9ad186d77af05fa&sign=0756077547&png=41164-280359&jpg=227559-365825
""" | [
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] | 1.615472 | 879 |
from header_common import *
from header_presentations import *
from header_mission_templates import *
from ID_meshes import *
from header_operations import *
from header_triggers import *
from module_constants import *
#import string
from xgm_mod_options_header import *
############################################################################
## 0) overlay id (not used atm, but can allow searches in future. just put something unique)
## 1) overlay type (defined in xgm_mod_options_header)
## 2) overlay type specific parameters (e.g. for number box, it can be lower/upper range, for cbobox, it would be the cbo items etc)
## a) xgm_ov_numberbox : lower_bound(inclusive), upper_bound(exclusive). e.g. [0,101] for range of values from 0-100
## b) xgm_ov_combolabel/xgm_ov_combobutton : list of combo items. e.g. ["option1", "option2", "option3"]
## c) xgm_ov_slider : lower_bound(inclusive), upper_bound(exclusive). e.g. [0,101] for range of values from 0-100
## d) xgm_ov_checkbox : not used fttb. just leave empty. e.g. []
## 3) text label
## 4) reserved for text label flags
## 5) description (unused for now. may be used for stuff like tooltip in future)
## 6) reserved for description flags
## 7) initialization op block. Used for updating the overlay values from game values. Must assign the desired value to reg1.
## 8) update op block. Used for updating game values from overlay values. The overlay value is in reg1.
## 9) optional. reserved for option page id. unused for now. leave out for options using general page.
############################################################################
mod_options = [
("camp_fuck_setting", xgm_ov_combolabel, ["Disabled", "Consensual Only", "All Enabled"], "Sexual Content:", 0,
"Settings for sexual content in game.", 0,
[(try_begin),
(eq, "$g_sexual_content", 0),
(assign, reg1, 0),
(else_try),
(eq, "$g_sexual_content", 1),
(assign, reg1, 1),
(else_try),
(eq, "$g_sexual_content", 2),
(assign, reg1, 2),
(try_end),],
[(try_begin),
(eq, reg1, 0),
(assign, "$g_sexual_content", 0),
(else_try),
(eq, reg1, 1),
(assign, "$g_sexual_content", 1),
(else_try),
(eq, reg1, 2),
(assign, "$g_sexual_content", 2),
(try_end),
],
),
("dplmc_woman_prejudice", xgm_ov_combolabel, ["Historical", "Tolerant", "Utopian"], "Diplomacy - Prejudice:", 0,
"Setting for Diplomacy's prejudice changes.", 0,
[
(assign, reg1, "$g_disable_condescending_comments"),
],
[
(assign, "$g_disable_condescending_comments", reg1),
],
),
("camp_polygamy", xgm_ov_checkbox, [], "Polygamy:", 0,
"Toggles polygamy settings", 0,
[(try_begin),
(eq, "$g_polygamy", 0),
(assign, reg1, 0),
(else_try),
(eq, "$g_polygamy", 1),
(assign, reg1, 1),
(try_end),
],
[
(try_begin),
(eq, reg1, 0),
(assign, "$g_polygamy", 0),
(else_try),
(eq, reg1, 1),
(assign, "$g_polygamy", 1),
(try_end),
],
),
( "camp_nohomobro", xgm_ov_checkbox , [],
"Disable Gay:", 0,
"Disables gay scenes.", 0,
[ # initialization block (set value in reg1)
(assign, reg1, "$g_nohomo"),
],
[ # update block (value is in reg1)
(assign, "$g_nohomo", reg1),
],
),
( "camp_no_dancers", xgm_ov_checkbox , [],
"Feast Dancers:", 0,
"Toggles dancers during feasts.", 0,
[ # initialization block (set value in reg1)
(assign, reg1, "$g_feast_dancers"),
],
[ # update block (value is in reg1)
(assign, "$g_feast_dancers", reg1),
],
),
("camp_dark_hunters", xgm_ov_checkbox, [], "Black Khergits and Dark Hunters:", 0,
"Settings for Dark Hunters and Black Khergits.", 0,
[
(try_begin),
(eq, "$g_dark_hunters_enabled", 0),
(assign, reg1, 0),
(else_try),
(eq, "$g_dark_hunters_enabled", 1),
(assign, reg1, 1),
(try_end),
],
[
(try_begin),
(eq, reg1, 0),
(assign, "$g_dark_hunters_enabled", 0),
(assign, ":removed", 0),
(try_for_parties, ":party_no"),
(party_get_template_id, ":ptid", ":party_no"),
(this_or_next|eq, ":ptid", "pt_dark_hunters"),
(eq, ":ptid", "pt_black_khergit_raiders"),
(remove_party, ":party_no"),
(val_add, ":removed", 1),
(try_end),
(assign, reg0, ":removed"),
(display_message, "@{reg0} parties removed from the map."),
(else_try),
(eq, reg1, 1),
(assign, "$g_dark_hunters_enabled", 1),
(try_end),
],
),
( "keep_companions", xgm_ov_checkbox , [],
"Keep Companions:", 0,
"Setting for keeping companions after defeat", 0,
[ # initialization block (set value in reg1)
(assign, reg1, "$g_keep_companions"),
],
[ # update block (value is in reg1)
(assign, "$g_keep_companions", reg1),
],
),
( "disable_complaints", xgm_ov_checkbox , [],
"Disable Complaints:", 0,
"Setting for disabling companion complaints", 0,
[ # initialization block (set value in reg1)
(assign, reg1, "$disable_npc_complaints"),
],
[ # update block (value is in reg1)
(assign, "$disable_npc_complaints", reg1),
],
),
( "disable_bodyguard", xgm_ov_checkbox , [],
"Disable Bodyguards:", 0,
"Setting for disabling companions as bodyguards", 0,
[ # initialization block (set value in reg1)
(assign, reg1, "$disable_bodyguards"),
],
[ # update block (value is in reg1)
(assign, "$disable_bodyguards", reg1),
],
),
("camp_realistic_wounding", xgm_ov_checkbox, [], "Realistic Casualties:", 0,
"Toggles realistic wounding for other damage types", 0,
[(try_begin),
(eq, "$g_realistic_wounding", 0),
(assign, reg1, 0),
(else_try),
(eq, "$g_realistic_wounding", 1),
(assign, reg1, 1),
(try_end),
],
[
(try_begin),
(eq, reg1, 0),
(assign, "$g_realistic_wounding", 0),
(else_try),
(eq, reg1, 1),
(assign, "$g_realistic_wounding", 1),
(try_end),
],
),
("enable_shield_bash", xgm_ov_combolabel, ["Disabled", "Player Only", "All Combatants"], "Shield Bash:", 0,
"Setting for Diplomacy's prejudice changes.", 0,
[
(assign, reg1, "$g_enable_shield_bash"),
],
[
(assign, "$g_enable_shield_bash", reg1),
],
),
("horizontal_divide", xgm_ov_line, [], "", 0,"", 0,[],[],),
( "dplmc_horsespeed", xgm_ov_checkbox , [],
"Diplomacy - Horse Speed:", 0,
"Setting for Diplomacy's horse speed changes", 0,
[ # initialization block (set value in reg1)
(store_sub,reg1,1,"$g_dplmc_horse_speed"),
],
[ # update block (value is in reg1)
(store_sub,"$g_dplmc_horse_speed",1,reg1),
],
),
( "dplmc_battlecontinue", xgm_ov_checkbox , [],
"Diplomacy - Battle Continuation:", 0,
"Setting for Diplomacy's battle continuation", 0,
[ # initialization block (set value in reg1)
(store_sub,reg1,1,"$g_dplmc_battle_continuation"),
],
[ # update block (value is in reg1)
(store_sub,"$g_dplmc_battle_continuation",1,reg1),
],
),
( "dplmc_disguise", xgm_ov_checkbox , [],
"Diplomacy - Disguise System:", 0,
"Setting for Diplomacy's disguise system", 0,
[ # initialization block (set value in reg1)
(assign, reg1, "$g_dplmc_player_disguise"),
],
[ # update block (value is in reg1)
(assign, "$g_dplmc_player_disguise", reg1),
],
),
( "dplmc_terrain_advantage", xgm_ov_checkbox , [],
"Diplomacy - Autocalc Terrain Advantage:", 0,
"Setting for Diplomacy's terrain advantage.", 0,
[ # initialization block (set value in reg1)
(try_begin),
(eq, "$g_dplmc_terrain_advantage", DPLMC_TERRAIN_ADVANTAGE_DISABLE),
(assign, reg1, 0),
(else_try),
(eq, "$g_dplmc_terrain_advantage", DPLMC_TERRAIN_ADVANTAGE_ENABLE),
(assign, reg1, 1),
(try_end),
],
[ # update block (value is in reg1)
(try_begin),
(eq, reg1, 0),
(assign, "$g_dplmc_terrain_advantage", DPLMC_TERRAIN_ADVANTAGE_DISABLE),
(else_try),
(eq, reg1, 1),
(assign, "$g_dplmc_terrain_advantage", DPLMC_TERRAIN_ADVANTAGE_ENABLE),
(try_end),
],
),
( "dplmc_lord_recycling", xgm_ov_checkbox , [],
"Diplomacy - Returning From Exile:", 0,
"Setting for Diplomacy's terrain advantage.", 0,
[ # initialization block (set value in reg1)
(try_begin),
(eq, "$g_dplmc_lord_recycling", DPLMC_LORD_RECYCLING_DISABLE),
(assign, reg1, 0),
(else_try),
(eq, "$g_dplmc_lord_recycling", DPLMC_LORD_RECYCLING_ENABLE),
(assign, reg1, 1),
(try_end),
],
[ # update block (value is in reg1)
(try_begin),
(eq, reg1, 0),
(assign, "$g_dplmc_lord_recycling", DPLMC_LORD_RECYCLING_DISABLE),
(else_try),
(eq, reg1, 1),
(assign, "$g_dplmc_lord_recycling", DPLMC_LORD_RECYCLING_ENABLE),
(try_end),
],
),
("dplmc_ai_changes_a", xgm_ov_combolabel, ["Disabled", "Low", "Medium", "High"], "Diplomacy - AI Changes:", 0,
"Setting for Diplomacy's AI changes.", 0,
[
(try_begin),
(eq, "$g_dplmc_ai_changes", DPLMC_AI_CHANGES_DISABLE),
(assign, reg1, 0),
(else_try),
(eq, "$g_dplmc_ai_changes", DPLMC_AI_CHANGES_LOW),
(assign, reg1, 1),
(else_try),
(eq, "$g_dplmc_ai_changes", DPLMC_AI_CHANGES_MEDIUM),
(assign, reg1, 2),
(else_try),
(eq, "$g_dplmc_ai_changes", DPLMC_AI_CHANGES_HIGH),
(assign, reg1, 3),
(try_end),
],
[
(try_begin),
(eq, reg1, 0),
(assign, "$g_dplmc_ai_changes", DPLMC_AI_CHANGES_DISABLE),
(else_try),
(eq, reg1, 1),
(assign, "$g_dplmc_ai_changes", DPLMC_AI_CHANGES_LOW),
(else_try),
(eq, reg1, 2),
(assign, "$g_dplmc_ai_changes", DPLMC_AI_CHANGES_MEDIUM),
(else_try),
(eq, reg1, 3),
(assign, "$g_dplmc_ai_changes", DPLMC_AI_CHANGES_HIGH),
(try_end),
],
),
("dplmc_gold_changes", xgm_ov_combolabel, ["Disabled", "Low", "Medium", "High"], "Diplomacy - Economy Changes:", 0,
"Setting for Diplomacy's economy changes.", 0,
[
(try_begin),
(eq, "$g_dplmc_gold_changes", DPLMC_GOLD_CHANGES_DISABLE),
(assign, reg1, 0),
(else_try),
(eq, "$g_dplmc_gold_changes", DPLMC_GOLD_CHANGES_LOW),
(assign, reg1, 1),
(else_try),
(eq, "$g_dplmc_gold_changes", DPLMC_GOLD_CHANGES_MEDIUM),
(assign, reg1, 2),
(else_try),
(eq, "$g_dplmc_gold_changes", DPLMC_GOLD_CHANGES_HIGH),
(assign, reg1, 3),
(try_end),
],
[
(try_begin),
(eq, reg1, 0),
(assign, "$g_dplmc_gold_changes", DPLMC_GOLD_CHANGES_DISABLE),
(else_try),
(eq, reg1, 1),
(assign, "$g_dplmc_gold_changes", DPLMC_GOLD_CHANGES_LOW),
(else_try),
(eq, reg1, 2),
(assign, "$g_dplmc_gold_changes", DPLMC_GOLD_CHANGES_MEDIUM),
(else_try),
(eq, reg1, 3),
(assign, "$g_dplmc_gold_changes", DPLMC_GOLD_CHANGES_HIGH),
(try_end),
],
),
("horizontal_divide", xgm_ov_line, [], "", 0,"", 0,[],[],),
("minimap_setting", xgm_ov_combolabel, ["Compass Style", "Small Minimap", "Medium Minimap", "Large Minimap", "Disabled"], "Battle Minimap Overlay:", 0,
"Setting for the minimap.", 0,
[
(try_begin),
(eq, "$g_minimap_style", -1),
(assign, reg1, 4),
(else_try),
(assign, reg1, "$g_minimap_style"),
(try_end),
],
[
(try_begin),
(eq, reg1, 4),
(assign, "$g_minimap_style", -1),
(else_try),
(assign, "$g_minimap_style", reg1),
(try_end),
],
),
("minimap_setting", xgm_ov_combolabel, ["Disabled", "Only Allies", "Only Enemies", "All Troops"], "Troop HP Bars:", 0,
"Setting for troop HP bars.", 0,
[
(try_begin), # Ally
(eq, "$g_hp_bar_enemy", 0),
(eq, "$g_hp_bar_ally", 1),
(assign, reg1, 1),
(else_try), # Enemy
(eq, "$g_hp_bar_enemy", 1),
(eq, "$g_hp_bar_ally", 0),
(assign, reg1, 2),
(else_try), # Both
(eq, "$g_hp_bar_enemy", 1),
(eq, "$g_hp_bar_ally", 1),
(assign, reg1, 3),
(else_try), # None
(assign, reg1, 0),
(try_end),
],
[
(try_begin), # Ally
(eq, reg1, 1),
(assign, "$g_hp_bar_enemy", 0),
(assign, "$g_hp_bar_ally", 1),
(else_try), # Enemy
(eq, reg1, 2),
(assign, "$g_hp_bar_enemy", 1),
(assign, "$g_hp_bar_ally", 0),
(else_try), # Both
(eq, reg1, 3),
(assign, "$g_hp_bar_enemy", 1),
(assign, "$g_hp_bar_ally", 1),
(else_try), # None
(assign, "$g_hp_bar_enemy", 0),
(assign, "$g_hp_bar_ally", 0),
(try_end),
],
),
("minimap_setting", xgm_ov_numberbox, [3,81], "HP Bar Distance Limit:", 0,
"Setting for the HP Bars.", 0,
[
(assign, reg1, "$g_hp_bar_dis_limit"),
],
[
(assign, "$g_hp_bar_dis_limit", reg1),
],
),
("camp_troop_ratio_bar", xgm_ov_checkbox, [], "Troop ratio bar:", 0,
"Toggles troop ratio bar", 0,
[(try_begin),
(eq, "$g_troop_ratio_bar", 0),
(assign, reg1, 0),
(else_try),
(eq, "$g_troop_ratio_bar", 1),
(assign, reg1, 1),
(try_end),
],
[
(try_begin),
(eq, reg1, 0),
(assign, "$g_troop_ratio_bar", 0),
(else_try),
(eq, reg1, 1),
(assign, "$g_troop_ratio_bar", 1),
(try_end),
],
),
("camp_decapitation", xgm_ov_checkbox, [], "Decapitation:", 0,
"Toggles Decapitation", 0,
[(try_begin),
(eq, "$g_decapitation_enabled", 0),
(assign, reg1, 0),
(else_try),
(eq, "$g_decapitation_enabled", 1),
(assign, reg1, 1),
(try_end),
],
[
(try_begin),
(eq, reg1, 0),
(assign, "$g_decapitation_enabled", 0),
(else_try),
(eq, reg1, 1),
(assign, "$g_decapitation_enabled", 1),
(try_end),
],
),
("horizontal_divide", xgm_ov_line, [], "", 0,"", 0,[],[],),
( "op_cheatmode", xgm_ov_checkbox , [],
"Cheat mode:", 0,
"This sets the in-game cheat mode", 0,
[ # initialization block (set value in reg1)
(assign, reg1, "$cheat_mode"),
],
[ # update block (value is in reg1)
(assign, "$cheat_mode", reg1),
],
),
] # mod_options
# TODO: add option pages here
# collation of all *_mod_options.py from active mods
# import and merge related variables from all {active_mod}_mod_options.py for all active mods
#try:
# from modmerger_options import options, mods_active
# from modmerger import mod_get_process_order, mod_is_active
# from util_common import add_objects
# modcomp_name = "mod_options"
# var_list = ["mod_options",]
#from modmerger import modmerge
#modmerge(var_set)
# mod_process_order = mod_get_process_order(modcomp_name)
# vars_to_import= ["mod_options"]
# for x in mod_process_order:
# if(mod_is_active(x) and x <> "xgm_mod_options"): # must exclude this file since we are using this file as base
# try:
#mergefn_name = "modmerge_%s"%(modcomp_name)
# target_module_name = "%s_%s"%(x,modcomp_name)
# _temp = __import__( target_module_name , globals(), locals(), vars_to_import,-1)
# logger.info("Merging objects for component \"%s\" from mod \"%s\"..."%(modcomp_name,x))
#
# add_objects(mod_options, _temp.mod_options) # import from target module.
#
# # TODO: collect option pages
# except ImportError:
# errstring = "Failed importing for component \"%s\" for mod \"%s\"." % (modcomp_name, x)
# logger.debug(errstring)
# else:
# errstring = "Mod \"%s\" not active for Component \"%s\"." % (x, modcomp_name)
# logger.debug(errstring)
#except:
# raise
# collation end
# At this point, mod_options will contain the list of all mod_options specified.
## utility functions
from util_wrappers import *
# helper wrapper to access mod_options
## class ModOptionWrapper
# this function will compute the total height required for a list of mod_options.
## mod_options_get_total_height
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] | 2.016123 | 8,497 |
import torch
import numpy as np
import scipy.io
import h5py
import torch.nn as nn
#################################################
#
# Utilities
#
#################################################
device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
# reading data
# normalization, pointwise gaussian
# normalization, Gaussian
# normalization, scaling by range
#loss function with rel/abs Lp loss
# A simple feedforward neural network
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] | 3.786885 | 122 |
from io import StringIO
import re
from pathlib import Path
import logging
from django.db import models
from django.conf import settings
from bs4 import BeautifulSoup
from tika import parser
logger = logging.getLogger(__name__)
class TikaParseError(RuntimeError):
"""Raised when the conversion of a document into html by Tika fails."""
def extract_author_and_title(metadata: dict) -> (str, str):
"""Try to get the author and title from the metadata.
Return empty strings if not found."""
author, title = '', ''
for key in ('Author', 'author', 'dc:creator', 'creator', 'meta:author'):
if key in metadata:
author = metadata[key]
break
for key in ('Title', 'title', 'dc:title', 'meta:title'):
if key in metadata:
title = metadata[key]
break
return author, title
class ParentDocument(models.Model):
"""Each book/file is represented here.
"""
# source document's full path
filepath = models.CharField(unique=True, max_length=1024)
# try to get the author and title from the document metadata
# but it's not always there
author = models.CharField(max_length=512, blank=True, default='')
title = models.CharField(max_length=512, blank=True, default='')
def convert_to_html_child_pages(self, clean=True):
"""Convert book/file at filepath to html pages.
This constructs a ChildPage object for each page of the document.
Pages are determined by Tika's parsing.
Populates author and title if available in the metadata.
:param clean - if True clean non-ascii whitespace
"""
try_count, successful_parse = 0, False
while try_count < settings.TIKA_PARSE_MAX_RETRY:
if settings.TIKA_CONFIG_FILE:
data = parser.from_file(str(self.filepath), xmlContent=True, config_path=settings.TIKA_CONFIG_FILE)
else:
data = parser.from_file(str(self.filepath), xmlContent=True)
if data['status'] == 200:
successful_parse = True
break
if not successful_parse:
logger.error('Failed to parse file: %s', self.filepath)
author, title = extract_author_and_title(data['metadata'])
self.author, self.title = author, title
self.save()
soup = BeautifulSoup(data['content'], features='lxml')
# convert all pages successfully before creating children
pages = []
for i, content in enumerate(soup.find_all('div', attrs={'class': 'page'})):
_buffer = StringIO()
_buffer.write(str(content))
parsed_content = parser.from_buffer(_buffer.getvalue(), xmlContent=True)
text = parsed_content['content'].strip()
if clean:
text = re.sub(r' +\n', '\n', parsed_content['content'].strip().replace('\xa0', ' '))
# remove the html head from the doc so it doesn't cause any garbage in ES highlights
page_soup = BeautifulSoup(text, features='lxml')
page_soup.head.extract()
pages.append(page_soup.prettify())
for i, html in enumerate(pages):
child = ChildPage(parent=self, page_number=i+1, html_content=html,
author=self.author, title=self.title,
parent_doc_id=self.id)
if i == len(pages) - 1:
child.is_last_page = True
child.save()
class ChildPage(models.Model):
"""Each page of a book/file is represented by a ChildPage.
With the initial implementation, this model will also have the html_content
field filled with the full text of the page. This is very inefficient
space-wise as you are storing the full text in the database as well as in
Elasticsearch. But it allows reading the text online and being able to
navigate directly from the search to the location in the text.
The reason that it is mandatory now is due to using django-elasticsearch-dsl.
In the future, we can get rid of django-es-dsl and then allow an option to
not store the full text to save space.
"""
parent = models.ForeignKey(ParentDocument, on_delete=models.CASCADE)
page_number = models.IntegerField()
html_content = models.TextField()
is_last_page = models.BooleanField(default=False)
# need to duplicate keys from parent so django-elasticsearch-dsl can access them
author = models.CharField(max_length=512)
title = models.CharField(max_length=512)
parent_doc_id = models.IntegerField()
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] | 2.584641 | 1,784 |
from typing import List, Tuple, Type
from .tensor import Tensor
from .device import Device
from .allocator import Allocator
from cpm_kernels.library import cudart
import numpy as np
import logging
logger = logging.getLogger(__name__) | [
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] | 3.426471 | 68 |
if __name__ == "__main__":
dlinkedList = DoublyLinkedList(10)
dlinkedList.append(20)
dlinkedList.append(30)
dlinkedList.prepend(-5)
dlinkedList.prepend(-8)
dlinkedList.insert(value=12, index=2)
dlinkedList.print_list()
dlinkedList.remove(index=5)
dlinkedList.insert(value=30, index=4)
dlinkedList.append(55)
dlinkedList.print_list()
dlinkedList.print_head()
dlinkedList.print_tail()
| [
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] | 2.271357 | 199 |
import setuptools
import os
with open("README.md", "r") as fh:
long_description = fh.read()
if os.environ.get('CI_COMMIT_TAG'):
version = os.environ['CI_COMMIT_TAG']
else:
version = "0.0.4"
setuptools.setup(
name="abbreviator",
version=version,
author="Stephanie Wagenaar",
author_email="[email protected]",
description="Abbreviate Long Sentences/Names based on hyphenation",
long_description=long_description,
long_description_content_type="text/markdown",
url="https://github.com/BOLD-lab/abbreviator",
packages=setuptools.find_packages(),
classifiers=[
"Programming Language :: Python :: 3",
"License :: OSI Approved :: MIT License",
"Operating System :: OS Independent",
],
python_requires='>=3.6',
install_requires=['pyphen>=0.11.0']
)
| [
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] | 2.494083 | 338 |
'''
QUESTION:
561. Array Partition I
Given an array of 2n integers, your task is to group these integers into n pairs of integer, say (a1, b1), (a2, b2), ..., (an, bn) which makes sum of min(ai, bi) for all i from 1 to n as large as possible.
Example 1:
Input: [1,4,3,2]
Output: 4
Explanation: n is 2, and the maximum sum of pairs is 4 = min(1, 2) + min(3, 4).
Note:
n is a positive integer, which is in the range of [1, 10000].
All the integers in the array will be in the range of [-10000, 10000].
'''
'''
Ideas/thoughts:
sort and return even nums
''' | [
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] | 2.818182 | 198 |
from . import account_payment
| [
6738,
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62,
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198
] | 4.285714 | 7 |
from time import sleep
import csv
from datetime import datetime
import mac_vendor_lookup
import cisco_service
if __name__ == "__main__":
# Cool banner ofc
print("""
╔═╗╦╔═╗╔═╗╔═╗ ╔╦╗╔╗╔╔═╗╔═╗ ╔╦╗╔═╗╔═╗ ╦ ╔═╗╔═╗╦╔═╦ ╦╔═╗
║ ║╚═╗║ ║ ║ ║║║║║╠═╣║ ║║║╠═╣║ ║ ║ ║║ ║╠╩╗║ ║╠═╝
╚═╝╩╚═╝╚═╝╚═╝ ═╩╝╝╚╝╩ ╩╚═╝ ╩ ╩╩ ╩╚═╝ ╩═╝╚═╝╚═╝╩ ╩╚═╝╩
MMMMMMMMMMMMMMMMMMMMMMMMMMMMMMMMMMMMMMMMMMMMMMMMMMMMMMMMMMMMMMMMMMMMMMMMMMMMMMMM
MMMMMMMMMMMMMMMMMMMMMMMMMMMMMMMMMMMMMMMMMMMMMMMMMMMMMMMMMMMMMMMMMMMMMMMMMMMMMMMM
MMMMMMMMMMMMMMMMMMMMMMMMMMMMMMMMMMMMMWMMMMMMMMMMMMMMMMMMMMMMMMMMMMMMMMMMMMMMMMMM
MMMMMMMMMMMMMMMMMMMMMMMMMMMMMMMMMMWKKNMMMMMMMMMMMMMMMMMMMMWWWMMMMMMMMMMMMMMMMMMM
MMMMMMMMMMMMMMMMMMMMMMMMMMMMMMMMMMXl,co0NWMMMMMMMMMMMMMMXxc:xWMMMMMMMMMMMMMMMMMM
MMMMMMMMMMMMMMMMMMMMMMMMMMMMMMMMMMNd''',;cdkKNNNNNNWNKko,...oWMMMMMMMMMMMMMMMMMM
MMMMMMMMMMMMMMMMMMMMMMMMMMMMMMMMMMMO;''.....';ccllc:,. ...'kMMMMMMMMMMMMMMMMMMM
MMMMMMMMMMMMWXOxdllllldxOXWMMMMMMMWNd'........ .... ..lNMMMMMMMMMMMMMMMMMMM
MMMMMMMMMN0o:,;;:clllc:;,';oONMMMMMWd'',,,'. ..... .dWMMMMMMMMMMMMMMMMMMM
MMMMMWWWO:,cdOO0K0O0K0K0klc:';dXMMMXl,'',;;. .'''''.lXMMMMMMMMMMMMMMMMMMM
MMMMMMXo;oKWM0dkkdddoo0xddkW0o',kWM0c...,lol;. . .ccoc..;cdXMMMMMMMMMMMMMMMMMMM
MMMMMXo:0MMMMWK0KXXKKKKX00NMMWK:'dWO,....';;' .. .;::,'',,lKMMMMMMMMMMMMMMMMMMM
MMMMWxc0MMMMWW0kOxxkKkk0OXWWWMMNl'kO:'........,:'........,,cKMMMMMMMMMMMMMMMMMMM
MMMMNdxWMMMMMWOxkdddxxdxkKNWWWWMK;cXd'........,,'''.....',,:kXMMMMMMMMMMMMMMMMMM
MMMMXokMMMMMMMNXXXNNXNX0KXWWWWWWNlcXXd,.'......'..'.','.'',;:oKWMMMMMMMMMMMMMMMM
MMMMXoxWMMMMMMM0olxkoxxkXWMMMMMMNloNWNd... ..................:0WMMMMMMMMMMMMMMM
MMMMNxcOWMMMMMMKkkkOOkOOXWMMMMMMO:kMMNl.. .. .l0WMMMMMMMMMMMMMM
MMMMM0:;kNWXXNKO0K0000KKXK0OONWKlcOWNd' .,oKWMMMMMMMMMMMMM
MMMMMWO;'lOxxOddooddlcdxxxlox0Oolo0W0,. .,;oKMMMMMMMMMMMMM
MMMMMMWKc..';dkOKX0KXXXK00Oxdl:;,,oOo. .'',oKWMMMMMMMMMMM
MMMMMMMMWOl,..';coddxxdol:,..,;:;..':;.. .. ..''';dKWWMMMMMMMM
MMMMMMMMMMMN0dl:;''.'',:cokO0KNWW0l..''. ... ..,,'':xXWMMMMMMM
MMMMMMMMMMMMMMMWWNXKKXXWMMMMMMMMMMNl... . ..,'',,:xNWMMMMM
MMMMMMMMMMMMMMMMMMMMMMMMMMMMMMMMMM0;.. .. .,;::,'cKMMMMM
MMMMMMMMMMMMMMMMMMMMMMMMMMMMMMMMMWx' .,;'. ....... ..','.lXMMMM
MMMMMMMMMMMMMMMMMMMMMMMMMMMMMMMMMK:. . .',. .. .. ....dWMMM
MMMMMMMMMMMMMMMMMMMMMMMMMMMMMMMMMk. .. ...cXMMM
""")
print("Starting script..")
CiscoDnacMacLookupRunner().main() | [
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] | 1.435154 | 2,105 |
from SCons.Script import *
| [
6738,
6374,
684,
13,
7391,
1330,
1635,
198
] | 3.375 | 8 |
power = {'BUSES': {'Area': 1.08752,
'Bus/Area': 1.08752,
'Bus/Gate Leakage': 0.00541455,
'Bus/Peak Dynamic': 0.0,
'Bus/Runtime Dynamic': 0.0,
'Bus/Subthreshold Leakage': 0.0564625,
'Bus/Subthreshold Leakage with power gating': 0.0211734,
'Gate Leakage': 0.00541455,
'Peak Dynamic': 0.0,
'Runtime Dynamic': 0.0,
'Subthreshold Leakage': 0.0564625,
'Subthreshold Leakage with power gating': 0.0211734},
'Core': [{'Area': 32.6082,
'Execution Unit/Area': 8.2042,
'Execution Unit/Complex ALUs/Area': 0.235435,
'Execution Unit/Complex ALUs/Gate Leakage': 0.0132646,
'Execution Unit/Complex ALUs/Peak Dynamic': 0.0955308,
'Execution Unit/Complex ALUs/Runtime Dynamic': 0.277723,
'Execution Unit/Complex ALUs/Subthreshold Leakage': 0.20111,
'Execution Unit/Complex ALUs/Subthreshold Leakage with power gating': 0.0754163,
'Execution Unit/Floating Point Units/Area': 4.6585,
'Execution Unit/Floating Point Units/Gate Leakage': 0.0656156,
'Execution Unit/Floating Point Units/Peak Dynamic': 0.852868,
'Execution Unit/Floating Point Units/Runtime Dynamic': 0.679223,
'Execution Unit/Floating Point Units/Subthreshold Leakage': 0.994829,
'Execution Unit/Floating Point Units/Subthreshold Leakage with power gating': 0.373061,
'Execution Unit/Gate Leakage': 0.122718,
'Execution Unit/Instruction Scheduler/Area': 2.17927,
'Execution Unit/Instruction Scheduler/FP Instruction Window/Area': 0.328073,
'Execution Unit/Instruction Scheduler/FP Instruction Window/Gate Leakage': 0.00115349,
'Execution Unit/Instruction Scheduler/FP Instruction Window/Peak Dynamic': 1.20978,
'Execution Unit/Instruction Scheduler/FP Instruction Window/Runtime Dynamic': 0.337297,
'Execution Unit/Instruction Scheduler/FP Instruction Window/Subthreshold Leakage': 0.017004,
'Execution Unit/Instruction Scheduler/FP Instruction Window/Subthreshold Leakage with power gating': 0.00962066,
'Execution Unit/Instruction Scheduler/Gate Leakage': 0.00730101,
'Execution Unit/Instruction Scheduler/Instruction Window/Area': 1.00996,
'Execution Unit/Instruction Scheduler/Instruction Window/Gate Leakage': 0.00529112,
'Execution Unit/Instruction Scheduler/Instruction Window/Peak Dynamic': 2.07911,
'Execution Unit/Instruction Scheduler/Instruction Window/Runtime Dynamic': 0.584077,
'Execution Unit/Instruction Scheduler/Instruction Window/Subthreshold Leakage': 0.0800117,
'Execution Unit/Instruction Scheduler/Instruction Window/Subthreshold Leakage with power gating': 0.0455351,
'Execution Unit/Instruction Scheduler/Peak Dynamic': 4.84781,
'Execution Unit/Instruction Scheduler/ROB/Area': 0.841232,
'Execution Unit/Instruction Scheduler/ROB/Gate Leakage': 0.000856399,
'Execution Unit/Instruction Scheduler/ROB/Peak Dynamic': 1.55892,
'Execution Unit/Instruction Scheduler/ROB/Runtime Dynamic': 0.377493,
'Execution Unit/Instruction Scheduler/ROB/Subthreshold Leakage': 0.0178624,
'Execution Unit/Instruction Scheduler/ROB/Subthreshold Leakage with power gating': 0.00897339,
'Execution Unit/Instruction Scheduler/Runtime Dynamic': 1.29887,
'Execution Unit/Instruction Scheduler/Subthreshold Leakage': 0.114878,
'Execution Unit/Instruction Scheduler/Subthreshold Leakage with power gating': 0.0641291,
'Execution Unit/Integer ALUs/Area': 0.47087,
'Execution Unit/Integer ALUs/Gate Leakage': 0.0265291,
'Execution Unit/Integer ALUs/Peak Dynamic': 0.202647,
'Execution Unit/Integer ALUs/Runtime Dynamic': 0.319665,
'Execution Unit/Integer ALUs/Subthreshold Leakage': 0.40222,
'Execution Unit/Integer ALUs/Subthreshold Leakage with power gating': 0.150833,
'Execution Unit/Peak Dynamic': 6.59121,
'Execution Unit/Register Files/Area': 0.570804,
'Execution Unit/Register Files/Floating Point RF/Area': 0.208131,
'Execution Unit/Register Files/Floating Point RF/Gate Leakage': 0.000232788,
'Execution Unit/Register Files/Floating Point RF/Peak Dynamic': 0.161125,
'Execution Unit/Register Files/Floating Point RF/Runtime Dynamic': 0.0122273,
'Execution Unit/Register Files/Floating Point RF/Subthreshold Leakage': 0.00399698,
'Execution Unit/Register Files/Floating Point RF/Subthreshold Leakage with power gating': 0.00176968,
'Execution Unit/Register Files/Gate Leakage': 0.000622708,
'Execution Unit/Register Files/Integer RF/Area': 0.362673,
'Execution Unit/Register Files/Integer RF/Gate Leakage': 0.00038992,
'Execution Unit/Register Files/Integer RF/Peak Dynamic': 0.110483,
'Execution Unit/Register Files/Integer RF/Runtime Dynamic': 0.0904283,
'Execution Unit/Register Files/Integer RF/Subthreshold Leakage': 0.00614175,
'Execution Unit/Register Files/Integer RF/Subthreshold Leakage with power gating': 0.00246675,
'Execution Unit/Register Files/Peak Dynamic': 0.271608,
'Execution Unit/Register Files/Runtime Dynamic': 0.102656,
'Execution Unit/Register Files/Subthreshold Leakage': 0.0101387,
'Execution Unit/Register Files/Subthreshold Leakage with power gating': 0.00423643,
'Execution Unit/Results Broadcast Bus/Area Overhead': 0.0442632,
'Execution Unit/Results Broadcast Bus/Gate Leakage': 0.00607074,
'Execution Unit/Results Broadcast Bus/Peak Dynamic': 0.293143,
'Execution Unit/Results Broadcast Bus/Runtime Dynamic': 0.835198,
'Execution Unit/Results Broadcast Bus/Subthreshold Leakage': 0.0920413,
'Execution Unit/Results Broadcast Bus/Subthreshold Leakage with power gating': 0.0345155,
'Execution Unit/Runtime Dynamic': 3.51333,
'Execution Unit/Subthreshold Leakage': 1.83518,
'Execution Unit/Subthreshold Leakage with power gating': 0.709678,
'Gate Leakage': 0.372997,
'Instruction Fetch Unit/Area': 5.86007,
'Instruction Fetch Unit/Branch Predictor/Area': 0.138516,
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'Instruction Fetch Unit/Branch Predictor/Chooser/Subthreshold Leakage with power gating': 0.0039236,
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'Instruction Fetch Unit/Branch Predictor/Global Predictor/Subthreshold Leakage with power gating': 0.0039236,
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'Instruction Fetch Unit/Branch Predictor/L1_Local Predictor/Gate Leakage': 0.000154548,
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'Instruction Fetch Unit/Branch Predictor/L1_Local Predictor/Subthreshold Leakage with power gating': 0.00198631,
'Instruction Fetch Unit/Branch Predictor/L2_Local Predictor/Area': 0.0151917,
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'Instruction Fetch Unit/Branch Predictor/L2_Local Predictor/Subthreshold Leakage with power gating': 0.000957045,
'Instruction Fetch Unit/Branch Predictor/Peak Dynamic': 0.0597838,
'Instruction Fetch Unit/Branch Predictor/RAS/Area': 0.0105732,
'Instruction Fetch Unit/Branch Predictor/RAS/Gate Leakage': 4.63858e-05,
'Instruction Fetch Unit/Branch Predictor/RAS/Peak Dynamic': 0.0117602,
'Instruction Fetch Unit/Branch Predictor/RAS/Runtime Dynamic': 0.00129901,
'Instruction Fetch Unit/Branch Predictor/RAS/Subthreshold Leakage': 0.000932505,
'Instruction Fetch Unit/Branch Predictor/RAS/Subthreshold Leakage with power gating': 0.000494733,
'Instruction Fetch Unit/Branch Predictor/Runtime Dynamic': 0.00353661,
'Instruction Fetch Unit/Branch Predictor/Subthreshold Leakage': 0.0199703,
'Instruction Fetch Unit/Branch Predictor/Subthreshold Leakage with power gating': 0.0103282,
'Instruction Fetch Unit/Branch Target Buffer/Area': 0.64954,
'Instruction Fetch Unit/Branch Target Buffer/Gate Leakage': 0.00272758,
'Instruction Fetch Unit/Branch Target Buffer/Peak Dynamic': 0.177867,
'Instruction Fetch Unit/Branch Target Buffer/Runtime Dynamic': 0.0076553,
'Instruction Fetch Unit/Branch Target Buffer/Subthreshold Leakage': 0.0811682,
'Instruction Fetch Unit/Branch Target Buffer/Subthreshold Leakage with power gating': 0.0435357,
'Instruction Fetch Unit/Gate Leakage': 0.0590479,
'Instruction Fetch Unit/Instruction Buffer/Area': 0.0226323,
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'Instruction Fetch Unit/Instruction Buffer/Peak Dynamic': 0.606827,
'Instruction Fetch Unit/Instruction Buffer/Runtime Dynamic': 0.0869311,
'Instruction Fetch Unit/Instruction Buffer/Subthreshold Leakage': 0.00151885,
'Instruction Fetch Unit/Instruction Buffer/Subthreshold Leakage with power gating': 0.000701682,
'Instruction Fetch Unit/Instruction Cache/Area': 3.14635,
'Instruction Fetch Unit/Instruction Cache/Gate Leakage': 0.029931,
'Instruction Fetch Unit/Instruction Cache/Peak Dynamic': 5.52956,
'Instruction Fetch Unit/Instruction Cache/Runtime Dynamic': 0.213019,
'Instruction Fetch Unit/Instruction Cache/Subthreshold Leakage': 0.367022,
'Instruction Fetch Unit/Instruction Cache/Subthreshold Leakage with power gating': 0.180386,
'Instruction Fetch Unit/Instruction Decoder/Area': 1.85799,
'Instruction Fetch Unit/Instruction Decoder/Gate Leakage': 0.0222493,
'Instruction Fetch Unit/Instruction Decoder/Peak Dynamic': 1.37404,
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'Instruction Fetch Unit/Instruction Decoder/Subthreshold Leakage': 0.442943,
'Instruction Fetch Unit/Instruction Decoder/Subthreshold Leakage with power gating': 0.166104,
'Instruction Fetch Unit/Peak Dynamic': 8.02054,
'Instruction Fetch Unit/Runtime Dynamic': 0.606399,
'Instruction Fetch Unit/Subthreshold Leakage': 0.932587,
'Instruction Fetch Unit/Subthreshold Leakage with power gating': 0.408542,
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'L2/Gate Leakage': 0.015464,
'L2/Peak Dynamic': 0.152811,
'L2/Runtime Dynamic': 0.0364529,
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'L2/Subthreshold Leakage with power gating': 0.401066,
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'Load Store Unit/Data Cache/Area': 6.84535,
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'Load Store Unit/Data Cache/Peak Dynamic': 3.72689,
'Load Store Unit/Data Cache/Runtime Dynamic': 1.24846,
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'Load Store Unit/Data Cache/Subthreshold Leakage with power gating': 0.25085,
'Load Store Unit/Gate Leakage': 0.0351387,
'Load Store Unit/LoadQ/Area': 0.0836782,
'Load Store Unit/LoadQ/Gate Leakage': 0.00059896,
'Load Store Unit/LoadQ/Peak Dynamic': 0.08055,
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'Load Store Unit/LoadQ/Subthreshold Leakage': 0.00941961,
'Load Store Unit/LoadQ/Subthreshold Leakage with power gating': 0.00536918,
'Load Store Unit/Peak Dynamic': 4.10881,
'Load Store Unit/Runtime Dynamic': 1.72626,
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'Load Store Unit/StoreQ/Peak Dynamic': 0.198623,
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'Memory Management Unit/Gate Leakage': 0.00813591,
'Memory Management Unit/Itlb/Area': 0.301552,
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'Renaming Unit/Runtime Dynamic': 0.750006,
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'Renaming Unit/Subthreshold Leakage with power gating': 0.0362779,
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'Execution Unit/Area': 8.2042,
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'Execution Unit/Floating Point Units/Area': 4.6585,
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'Execution Unit/Gate Leakage': 0.122718,
'Execution Unit/Instruction Scheduler/Area': 2.17927,
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'Execution Unit/Instruction Scheduler/Instruction Window/Area': 1.00996,
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'Execution Unit/Instruction Scheduler/Peak Dynamic': 4.84781,
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'Execution Unit/Peak Dynamic': 6.59314,
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'Execution Unit/Register Files/Floating Point RF/Area': 0.208131,
'Execution Unit/Register Files/Floating Point RF/Gate Leakage': 0.000232788,
'Execution Unit/Register Files/Floating Point RF/Peak Dynamic': 0.161317,
'Execution Unit/Register Files/Floating Point RF/Runtime Dynamic': 0.0122428,
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'Execution Unit/Register Files/Floating Point RF/Subthreshold Leakage with power gating': 0.00176968,
'Execution Unit/Register Files/Gate Leakage': 0.000622708,
'Execution Unit/Register Files/Integer RF/Area': 0.362673,
'Execution Unit/Register Files/Integer RF/Gate Leakage': 0.00038992,
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'Execution Unit/Register Files/Integer RF/Subthreshold Leakage with power gating': 0.00246675,
'Execution Unit/Register Files/Peak Dynamic': 0.271902,
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'Execution Unit/Register Files/Subthreshold Leakage': 0.0101387,
'Execution Unit/Register Files/Subthreshold Leakage with power gating': 0.00423643,
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'Execution Unit/Results Broadcast Bus/Subthreshold Leakage with power gating': 0.0345155,
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'Instruction Fetch Unit/Branch Predictor/L1_Local Predictor/Subthreshold Leakage with power gating': 0.00198631,
'Instruction Fetch Unit/Branch Predictor/L2_Local Predictor/Area': 0.0151917,
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'Instruction Fetch Unit/Branch Predictor/RAS/Runtime Dynamic': 0.00130065,
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'Instruction Fetch Unit/Branch Predictor/RAS/Subthreshold Leakage with power gating': 0.000494733,
'Instruction Fetch Unit/Branch Predictor/Runtime Dynamic': 0.00354144,
'Instruction Fetch Unit/Branch Predictor/Subthreshold Leakage': 0.0199703,
'Instruction Fetch Unit/Branch Predictor/Subthreshold Leakage with power gating': 0.0103282,
'Instruction Fetch Unit/Branch Target Buffer/Area': 0.64954,
'Instruction Fetch Unit/Branch Target Buffer/Gate Leakage': 0.00272758,
'Instruction Fetch Unit/Branch Target Buffer/Peak Dynamic': 0.177867,
'Instruction Fetch Unit/Branch Target Buffer/Runtime Dynamic': 0.0076668,
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'Instruction Fetch Unit/Branch Target Buffer/Subthreshold Leakage with power gating': 0.0435357,
'Instruction Fetch Unit/Gate Leakage': 0.0590479,
'Instruction Fetch Unit/Instruction Buffer/Area': 0.0226323,
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'Instruction Fetch Unit/Instruction Buffer/Peak Dynamic': 0.606827,
'Instruction Fetch Unit/Instruction Buffer/Runtime Dynamic': 0.0870411,
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'Instruction Fetch Unit/Instruction Cache/Area': 3.14635,
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'Instruction Fetch Unit/Instruction Cache/Subthreshold Leakage with power gating': 0.180386,
'Instruction Fetch Unit/Instruction Decoder/Area': 1.85799,
'Instruction Fetch Unit/Instruction Decoder/Gate Leakage': 0.0222493,
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'Instruction Fetch Unit/Instruction Decoder/Subthreshold Leakage': 0.442943,
'Instruction Fetch Unit/Instruction Decoder/Subthreshold Leakage with power gating': 0.166104,
'Instruction Fetch Unit/Peak Dynamic': 8.02789,
'Instruction Fetch Unit/Runtime Dynamic': 0.606744,
'Instruction Fetch Unit/Subthreshold Leakage': 0.932587,
'Instruction Fetch Unit/Subthreshold Leakage with power gating': 0.408542,
'L2/Area': 4.53318,
'L2/Gate Leakage': 0.015464,
'L2/Peak Dynamic': 0.152816,
'L2/Runtime Dynamic': 0.036542,
'L2/Subthreshold Leakage': 0.834142,
'L2/Subthreshold Leakage with power gating': 0.401066,
'Load Store Unit/Area': 8.80969,
'Load Store Unit/Data Cache/Area': 6.84535,
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'Load Store Unit/Data Cache/Peak Dynamic': 3.73104,
'Load Store Unit/Data Cache/Runtime Dynamic': 1.25059,
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'Load Store Unit/Gate Leakage': 0.0351387,
'Load Store Unit/LoadQ/Area': 0.0836782,
'Load Store Unit/LoadQ/Gate Leakage': 0.00059896,
'Load Store Unit/LoadQ/Peak Dynamic': 0.0806842,
'Load Store Unit/LoadQ/Runtime Dynamic': 0.0806842,
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'Load Store Unit/Peak Dynamic': 4.1136,
'Load Store Unit/Runtime Dynamic': 1.72918,
'Load Store Unit/StoreQ/Area': 0.322079,
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6435,
461,
26977,
10354,
657,
13,
2931,
40486,
2718,
11,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
705,
23002,
1009,
11801,
14,
5377,
11141,
8355,
5842,
14,
41006,
26977,
10354,
657,
13,
1983,
3324,
2414,
11,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
705,
23002,
1009,
11801,
14,
5377,
11141,
8355,
5842,
14,
7004,
400,
10126,
1004,
461,
496,
10354,
657,
13,
1264,
1157,
11,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
705,
23002,
1009,
11801,
14,
5377,
11141,
8355,
5842,
14,
7004,
400,
10126,
1004,
461,
496,
351,
1176,
308,
803,
10354,
657,
13,
15,
41874,
24136,
11,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
705,
23002,
1009,
11801,
14,
33574,
803,
6252,
27719,
14,
30547,
10354,
604,
13,
2996,
5332,
11,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
705,
23002,
1009,
11801,
14,
33574,
803,
6252,
27719,
14,
22628,
1004,
461,
496,
10354,
657,
13,
15,
37466,
21599,
11,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
705,
23002,
1009,
11801,
14,
33574,
803,
6252,
27719,
14,
6435,
461,
26977,
10354,
657,
13,
5332,
2548,
5332,
11,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
705,
23002,
1009,
11801,
14,
33574,
803,
6252,
27719,
14,
41006,
26977,
10354,
657,
13,
37601,
36657,
11,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
705,
23002,
1009,
11801,
14,
33574,
803,
6252,
27719,
14,
7004,
400,
10126,
1004,
461,
496,
10354,
657,
13,
2079,
2780,
1959,
11,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
705,
23002,
1009,
11801,
14,
33574,
803,
6252,
27719,
14,
7004,
400,
10126,
1004,
461,
496,
351,
1176,
308,
803,
10354,
657,
13,
2718,
1270,
5333,
11,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
705,
23002,
1009,
11801,
14,
22628,
1004,
461,
496,
10354,
657,
13,
1065,
1983,
1507,
11,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
705,
23002,
1009,
11801,
14,
6310,
2762,
27774,
18173,
14,
30547,
10354,
362,
13,
21738,
1983,
11,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
705,
23002,
1009,
11801,
14,
6310,
2762,
27774,
18173,
14,
5837,
46486,
26580,
14,
30547,
10354,
657,
13,
2624,
1795,
4790,
11,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
705,
23002,
1009,
11801,
14,
6310,
2762,
27774,
18173,
14,
5837,
46486,
26580,
14,
22628,
1004,
461,
496,
10354,
657,
13,
405,
15363,
27371,
11,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
705,
23002,
1009,
11801,
14,
6310,
2762,
27774,
18173,
14,
5837,
46486,
26580,
14,
6435,
461,
26977,
10354,
352,
13,
22567,
3695,
11,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
705,
23002,
1009,
11801,
14,
6310,
2762,
27774,
18173,
14,
5837,
46486,
26580,
14,
41006,
26977,
10354,
657,
13,
2091,
3324,
1731,
11,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
705,
23002,
1009,
11801,
14,
6310,
2762,
27774,
18173,
14,
5837,
46486,
26580,
14,
7004,
400,
10126,
1004,
461,
496,
10354,
657,
13,
486,
9879,
19,
11,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
705,
23002,
1009,
11801,
14,
6310,
2762,
27774,
18173,
14,
5837,
46486,
26580,
14,
7004,
400,
10126,
1004,
461,
496,
351,
1176,
308,
803,
10354,
657,
13,
405,
4846,
1238,
2791,
11,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
705,
23002,
1009,
11801,
14,
6310,
2762,
27774,
18173,
14,
22628,
1004,
461,
496,
10354,
657,
13,
405,
4790,
486,
486,
11,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
705,
23002,
1009,
11801,
14,
6310,
2762,
27774,
18173,
14,
6310,
2762,
26580,
14,
30547,
10354,
352,
13,
405,
38565,
11,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
705,
23002,
1009,
11801,
14,
6310,
2762,
27774,
18173,
14,
6310,
2762,
26580,
14,
22628,
1004,
461,
496,
10354,
657,
13,
22544,
1959,
14686,
11,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
705,
23002,
1009,
11801,
14,
6310,
2762,
27774,
18173,
14,
6310,
2762,
26580,
14,
6435,
461,
26977,
10354,
362,
13,
2998,
35549,
11,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
705,
23002,
1009,
11801,
14,
6310,
2762,
27774,
18173,
14,
6310,
2762,
26580,
14,
41006,
26977,
10354,
657,
13,
3365,
2780,
1433,
11,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
705,
23002,
1009,
11801,
14,
6310,
2762,
27774,
18173,
14,
6310,
2762,
26580,
14,
7004,
400,
10126,
1004,
461,
496,
10354,
657,
13,
2919,
405,
17657,
11,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
705,
23002,
1009,
11801,
14,
6310,
2762,
27774,
18173,
14,
6310,
2762,
26580,
14,
7004,
400,
10126,
1004,
461,
496,
351,
1176,
308,
803,
10354,
657,
13,
15,
30505,
35273,
11,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
705,
23002,
1009,
11801,
14,
6310,
2762,
27774,
18173,
14,
6435,
461,
26977,
10354,
604,
13,
23,
2857,
6659,
11,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
705,
23002,
1009,
11801,
14,
6310,
2762,
27774,
18173,
14,
49,
9864,
14,
30547,
10354,
657,
13,
5705,
1065,
2624,
11,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
705,
23002,
1009,
11801,
14,
6310,
2762,
27774,
18173,
14,
49,
9864,
14,
22628,
1004,
461,
496,
10354,
657,
13,
830,
23,
3980,
28771,
11,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
705,
23002,
1009,
11801,
14,
6310,
2762,
27774,
18173,
14,
49,
9864,
14,
6435,
461,
26977,
10354,
352,
13,
2816,
4531,
17,
11,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
705,
23002,
1009,
11801,
14,
6310,
2762,
27774,
18173,
14,
49,
9864,
14,
41006,
26977,
10354,
657,
13,
2718,
3720,
1983,
11,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
705,
23002,
1009,
11801,
14,
6310,
2762,
27774,
18173,
14,
49,
9864,
14,
7004,
400,
10126,
1004,
461,
496,
10354,
657,
13,
486,
46302,
1731,
11,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
705,
23002,
1009,
11801,
14,
6310,
2762,
27774,
18173,
14,
49,
9864,
14,
7004,
400,
10126,
1004,
461,
496,
351,
1176,
308,
803,
10354,
657,
13,
405,
4531,
22,
29626,
11,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
705,
23002,
1009,
11801,
14,
6310,
2762,
27774,
18173,
14,
41006,
26977,
10354,
352,
13,
6200,
2857,
11,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
705,
23002,
1009,
11801,
14,
6310,
2762,
27774,
18173,
14,
7004,
400,
10126,
1004,
461,
496,
10354,
657,
13,
1157,
2780,
3695,
11,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
705,
23002,
1009,
11801,
14,
6310,
2762,
27774,
18173,
14,
7004,
400,
10126,
1004,
461,
496,
351,
1176,
308,
803,
10354,
657,
13,
15,
2414,
1065,
6420,
11,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
705,
23002,
1009,
11801,
14,
46541,
8355,
5842,
14,
30547,
10354,
657,
13,
27790,
5774,
11,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
705,
23002,
1009,
11801,
14,
46541,
8355,
5842,
14,
22628,
1004,
461,
496,
10354,
657,
13,
15,
22980,
33551,
11,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
705,
23002,
1009,
11801,
14,
46541,
8355,
5842,
14,
6435,
461,
26977,
10354,
657,
13,
1238,
1959,
1415,
11,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
705,
23002,
1009,
11801,
14,
46541,
8355,
5842,
14,
41006,
26977,
10354,
657,
13,
18,
19104,
3312,
11,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
705,
23002,
1009,
11801,
14,
46541,
8355,
5842,
14,
7004,
400,
10126,
1004,
461,
496,
10354,
657,
13,
1821,
23148,
11,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
705,
23002,
1009,
11801,
14,
46541,
8355,
5842,
14,
7004,
400,
10126,
1004,
461,
496,
351,
1176,
308,
803,
10354,
657,
13,
8628,
48634,
11,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
705,
23002,
1009,
11801,
14,
6435,
461,
26977,
10354,
718,
13,
3270,
33638,
11,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
705,
23002,
1009,
11801,
14,
38804,
13283,
14,
30547,
10354,
657,
13,
39254,
36088,
11,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
705,
23002,
1009,
11801,
14,
38804,
13283,
14,
33574,
803,
6252,
20445,
14,
30547,
10354,
657,
13,
21315,
22042,
11,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
705,
23002,
1009,
11801,
14,
38804,
13283,
14,
33574,
803,
6252,
20445,
14,
22628,
1004,
461,
496,
10354,
657,
13,
830,
1954,
1983,
3459,
11,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
705,
23002,
1009,
11801,
14,
38804,
13283,
14,
33574,
803,
6252,
20445,
14,
6435,
461,
26977,
10354,
657,
13,
1433,
1485,
1558,
11,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
705,
23002,
1009,
11801,
14,
38804,
13283,
14,
33574,
803,
6252,
20445,
14,
41006,
26977,
10354,
657,
13,
486,
24137,
2078,
11,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
705,
23002,
1009,
11801,
14,
38804,
13283,
14,
33574,
803,
6252,
20445,
14,
7004,
400,
10126,
1004,
461,
496,
10354,
657,
13,
11245,
2079,
39357,
11,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
705,
23002,
1009,
11801,
14,
38804,
13283,
14,
33574,
803,
6252,
20445,
14,
7004,
400,
10126,
1004,
461,
496,
351,
1176,
308,
803,
10354,
657,
13,
405,
1558,
3388,
3104,
11,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
705,
23002,
1009,
11801,
14,
38804,
13283,
14,
22628,
1004,
461,
496,
10354,
657,
13,
830,
21,
1828,
32583,
11,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
705,
23002,
1009,
11801,
14,
38804,
13283,
14,
46541,
20445,
14,
30547,
10354,
657,
13,
2623,
2075,
4790,
11,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
705,
23002,
1009,
11801,
14,
38804,
13283,
14,
46541,
20445,
14,
22628,
1004,
461,
496,
10354,
657,
13,
830,
2548,
41561,
11,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
705,
23002,
1009,
11801,
14,
38804,
13283,
14,
46541,
20445,
14,
6435,
461,
26977,
10354,
657,
13,
11442,
38905,
11,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
705,
23002,
1009,
11801,
14,
38804,
13283,
14,
46541,
20445,
14,
41006,
26977,
10354,
657,
13,
2931,
2713,
40173,
11,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
705,
23002,
1009,
11801,
14,
38804,
13283,
14,
46541,
20445,
14,
7004,
400,
10126,
1004,
461,
496,
10354,
657,
13,
28041,
1415,
17430,
11,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
705,
23002,
1009,
11801,
14,
38804,
13283,
14,
46541,
20445,
14,
7004,
400,
10126,
1004,
461,
496,
351,
1176,
308,
803,
10354,
657,
13,
405,
1731,
2791,
2425,
11,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
705,
23002,
1009,
11801,
14,
38804,
13283,
14,
6435,
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14,
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14,
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1507,
11,
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13,
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220,
220,
220,
220,
705,
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1004,
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496,
10354,
657,
13,
2718,
1959,
5607,
11,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
705,
6310,
2762,
376,
7569,
11801,
14,
30547,
10354,
642,
13,
4521,
25816,
11,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
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705,
6310,
2762,
376,
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11801,
14,
33,
25642,
49461,
273,
14,
30547,
10354,
657,
13,
1485,
5332,
1433,
11,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
705,
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2762,
376,
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11801,
14,
33,
25642,
49461,
273,
14,
22164,
13416,
14,
30547,
10354,
657,
13,
3023,
2327,
26115,
11,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
705,
6310,
2762,
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11801,
14,
33,
25642,
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273,
14,
22164,
13416,
14,
22628,
1004,
461,
496,
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657,
13,
830,
25870,
35667,
11,
198,
220,
220,
220,
220,
220,
220,
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14,
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13416,
14,
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11,
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14,
33,
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14,
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13416,
14,
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26977,
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657,
13,
830,
46519,
19420,
11,
198,
220,
220,
220,
220,
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220,
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705,
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14,
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14,
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14,
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400,
10126,
1004,
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657,
13,
405,
2425,
5607,
1129,
11,
198,
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14,
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14,
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14,
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308,
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13,
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2670,
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13,
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22,
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11,
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11,
198,
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14,
33,
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273,
14,
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273,
14,
41006,
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657,
13,
830,
46519,
19420,
11,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
705,
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2762,
376,
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11801,
14,
33,
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273,
14,
22289,
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273,
14,
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400,
10126,
1004,
461,
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657,
13,
405,
2425,
5607,
1129,
11,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
705,
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2762,
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14,
33,
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273,
14,
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273,
14,
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400,
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1004,
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351,
1176,
308,
803,
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657,
13,
405,
2670,
24940,
11,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
705,
6310,
2762,
376,
7569,
11801,
14,
33,
25642,
49461,
273,
14,
43,
16,
62,
14565,
49461,
273,
14,
30547,
10354,
657,
13,
36629,
2154,
2414,
11,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
705,
6310,
2762,
376,
7569,
11801,
14,
33,
25642,
49461,
273,
14,
43,
16,
62,
14565,
49461,
273,
14,
22628,
1004,
461,
496,
10354,
657,
13,
18005,
45326,
2780,
11,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
705,
6310,
2762,
376,
7569,
11801,
14,
33,
25642,
49461,
273,
14,
43,
16,
62,
14565,
49461,
273,
14,
6435,
461,
26977,
10354,
657,
13,
28645,
1495,
2425,
11,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
705,
6310,
2762,
376,
7569,
11801,
14,
33,
25642,
49461,
273,
14,
43,
16,
62,
14565,
49461,
273,
14,
41006,
26977,
10354,
657,
13,
830,
3134,
2996,
2091,
11,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
705,
6310,
2762,
376,
7569,
11801,
14,
33,
25642,
49461,
273,
14,
43,
16,
62,
14565,
49461,
273,
14,
7004,
400,
10126,
1004,
461,
496,
10354,
657,
13,
405,
2548,
3559,
2598,
11,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
705,
6310,
2762,
376,
7569,
11801,
14,
33,
25642,
49461,
273,
14,
43,
16,
62,
14565,
49461,
273,
14,
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400,
10126,
1004,
461,
496,
351,
1176,
308,
803,
10354,
657,
13,
405,
28054,
3132,
11,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
705,
6310,
2762,
376,
7569,
11801,
14,
33,
25642,
49461,
273,
14,
43,
17,
62,
14565,
49461,
273,
14,
30547,
10354,
657,
13,
25150,
1129,
1558,
11,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
705,
6310,
2762,
376,
7569,
11801,
14,
33,
25642,
49461,
273,
14,
43,
17,
62,
14565,
49461,
273,
14,
22628,
1004,
461,
496,
10354,
807,
13,
405,
25272,
68,
12,
2713,
11,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
705,
6310,
2762,
376,
7569,
11801,
14,
33,
25642,
49461,
273,
14,
43,
17,
62,
14565,
49461,
273,
14,
6435,
461,
26977,
10354,
657,
13,
22544,
1983,
34825,
11,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
705,
6310,
2762,
376,
7569,
11801,
14,
33,
25642,
49461,
273,
14,
43,
17,
62,
14565,
49461,
273,
14,
41006,
26977,
10354,
657,
13,
830,
1495,
6052,
1983,
11,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
705,
6310,
2762,
376,
7569,
11801,
14,
33,
25642,
49461,
273,
14,
43,
17,
62,
14565,
49461,
273,
14,
7004,
400,
10126,
1004,
461,
496,
10354,
657,
13,
405,
1507,
1485,
2857,
11,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
705,
6310,
2762,
376,
7569,
11801,
14,
33,
25642,
49461,
273,
14,
43,
17,
62,
14565,
49461,
273,
14,
7004,
400,
10126,
1004,
461,
496,
351,
1176,
308,
803,
10354,
657,
13,
830,
3865,
2154,
2231,
11,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
705,
6310,
2762,
376,
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14,
33,
25642,
49461,
273,
14,
6435,
461,
26977,
10354,
657,
13,
2713,
32196,
2548,
11,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
705,
6310,
2762,
376,
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14,
33,
25642,
49461,
273,
14,
49,
1921,
14,
30547,
10354,
657,
13,
486,
43526,
2624,
11,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
705,
6310,
2762,
376,
7569,
11801,
14,
33,
25642,
49461,
273,
14,
49,
1921,
14,
22628,
1004,
461,
496,
10354,
604,
13,
21,
2548,
3365,
68,
12,
2713,
11,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
705,
6310,
2762,
376,
7569,
11801,
14,
33,
25642,
49461,
273,
14,
49,
1921,
14,
6435,
461,
26977,
10354,
657,
13,
486,
1558,
31418,
11,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
705,
6310,
2762,
376,
7569,
11801,
14,
33,
25642,
49461,
273,
14,
49,
1921,
14,
41006,
26977,
10354,
657,
13,
405,
1485,
405,
2996,
11,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
705,
6310,
2762,
376,
7569,
11801,
14,
33,
25642,
49461,
273,
14,
49,
1921,
14,
7004,
400,
10126,
1004,
461,
496,
10354,
657,
13,
830,
24,
2624,
31654,
11,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
705,
6310,
2762,
376,
7569,
11801,
14,
33,
25642,
49461,
273,
14,
49,
1921,
14,
7004,
400,
10126,
1004,
461,
496,
351,
1176,
308,
803,
10354,
657,
13,
830,
2920,
2857,
2091,
11,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
705,
6310,
2762,
376,
7569,
11801,
14,
33,
25642,
49461,
273,
14,
41006,
26977,
10354,
657,
13,
405,
32182,
18444,
11,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
705,
6310,
2762,
376,
7569,
11801,
14,
33,
25642,
49461,
273,
14,
7004,
400,
10126,
1004,
461,
496,
10354,
657,
13,
486,
2079,
36809,
11,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
705,
6310,
2762,
376,
7569,
11801,
14,
33,
25642,
49461,
273,
14,
7004,
400,
10126,
1004,
461,
496,
351,
1176,
308,
803,
10354,
657,
13,
486,
3070,
32568,
11,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
705,
6310,
2762,
376,
7569,
11801,
14,
33,
25642,
12744,
47017,
14,
30547,
10354,
657,
13,
2414,
48372,
11,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
705,
6310,
2762,
376,
7569,
11801,
14,
33,
25642,
12744,
47017,
14,
22628,
1004,
461,
496,
10354,
657,
13,
405,
1983,
1983,
3365,
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13,
23055,
2481,
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18,
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14,
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13,
3829,
2425,
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14,
22628,
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15,
34137,
19708,
11,
198,
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220,
220,
220,
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2999,
17657,
2682,
11709
] | 2.319168 | 15,296 |
#!/usr/bin/env python
#########
#LICENSE#
#########
'''
MIT License
Copyright (c) 2021 ItsMeAlfie0
Permission is hereby granted, free of charge, to any person obtaining a copy
of this software and associated documentation files (the "Software"), to deal
in the Software without restriction, including without limitation the rights
to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
copies of the Software, and to permit persons to whom the Software is
furnished to do so, subject to the following conditions:
The above copyright notice and this permission notice shall be included in all
copies or substantial portions of the Software.
THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
SOFTWARE.
'''
#########
#IMPORTS#
#########
import os
import sys
import urllib.request
import json
######
#CODE#
######
arg = sys.argv
if arg[1] == "--add-host":
with open("conf/hosts.json", "r") as f: data = json.load(f)
data[arg[2]] = arg[3]
with open("conf/hosts.json", "w") as e: json.dump(e)
print(f"Added host '{arg[2]}' '{arg[3]}'")
elif arg[1] == "install":
with open("conf/hosts.json", "r") as f: data = json.load(f)
host = data[arg[2]]
setup_sh = urllib.request.urlopen(f"{host}?repo={arg[3]}").read()
os.system(f"mkdir /etc/chum/{arg[3]}")
with open(f"/etc/chum/{arg[3]}/setup.sh", "w")as f:
f.write(setup_sh)
f.close()
os.system(f"sh /etc/chumj/{arg[3]}/setup.sh")
print("Package installed!")
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] | 2.816692 | 671 |
from pygame import *
#создай окно игры
window = display.set_mode((1000, 700))
display.set_caption('догонялки')
#задай фон сцены
background = transform.scale(image.load('ping.jpg'), (1000, 700))
#создай 2 спрайта и размести их на сцене
x1 = 0
y1 = 300
x2 = 900
y2 = 300
sprite1 = transform.scale(image.load('raketka1.png'), (100, 100)
)
sprite2 = transform.scale(image.load('raketka2.jpg'), (100, 100)
)
run = True
clock = time.Clock()
FPS = 60
while run:
window.blit(background,(0, 0))
window.blit(sprite1, (x1, y1))
window.blit(sprite2, (x2, y2))
for e in event.get():
if e.type == QUIT:
run = False
speed = 4
keys_pressed = key.get_pressed()
if keys_pressed[K_w] and y1 > 5:
y1 -= speed
if keys_pressed[K_s] and y1 < 600:
y1 += speed
if keys_pressed[K_UP] and y2 > 5:
y2 -= speed
if keys_pressed[K_DOWN] and y2 < 600:
y2 += speed
display.update()
clock.tick(FPS) | [
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8
] | 1.784148 | 593 |
import csv
import datetime
import matplotlib.pyplot as plt
import pandas as pd
import requests
import seaborn as sns
def measure_response_time(url, criteria, write=True):
'''
Measures and saves an API request's response time to a CSV file
:param url: The URL for API request
:param criteria: The criteria in effect
:return: Path to a CSV file with response time in seconds with its timestamp as columns
'''
response = requests.get(url)
response_time = response.elapsed.total_seconds()
date_time = datetime.datetime.now()
fieldnames = ['timestamp', 'responseTime', 'criteria'] # Headers of the CSV file
out_path = 'Response-Times.csv'
if write:
with open(out_path, 'a') as csvFile:
writer = csv.DictWriter(csvFile, fieldnames=fieldnames)
if csvFile.tell() == 0:
writer.writeheader()
writer.writerow({'timestamp': date_time, 'responseTime': response_time, 'criteria': criteria})
csvFile.close()
return out_path
def generate_histogram(path, title):
'''
Saves a histogram with average response time per number of requests
:param path: Path to a csv file
'''
response_times = pd.read_csv(path)
criteria_dict = response_times.groupby("criteria")["responseTime"].apply(list).to_dict()
critera_keys = list(criteria_dict.keys())
criteria_values = list(criteria_dict.values())
plt.title(title)
plt.style.use("seaborn-deep")
plt.hist(x=criteria_values, bins=30, label=critera_keys)
plt.legend(loc="upper right")
plt.xlabel("Response Time in Seconds")
plt.ylabel("Number of Requests")
plt.savefig(title + " Histogram")
plt.show()
def generate_density_plot(path, title):
'''
Saves a density plot with density of requests per second
:param path: Path to a csv file
'''
response_times = pd.read_csv(path)
criteria_dict = response_times.groupby("criteria")["responseTime"].apply(list).to_dict()
critera_keys = list(criteria_dict.keys())
# criteria_values = list(criteria_dict.values())
for criteria in critera_keys:
subset = response_times[response_times["criteria"] == criteria]
sns.distplot(subset["responseTime"], hist=False, kde=True, kde_kws={"linewidth": 3}, label=criteria)
plt.title(title)
plt.legend(loc="upper right")
plt.xlabel("Response Time in Seconds")
plt.ylabel("Density")
plt.savefig(title + " Density Plot")
plt.show()
local_simple_csv = "output/local/simple/Response-Times.csv"
local_complex_csv = "output/local/complex/Response-Times.csv"
cloud_simple_csv = "output/gcloud/simple/Response-Times.csv"
cloud_complex_csv = "output/gcloud/complex/Response-Times.csv"
generate_histogram(local_simple_csv, "Local Machine Simple Task")
generate_density_plot(local_complex_csv, "Local Machine Complex Task")
generate_density_plot(cloud_simple_csv, "Cloud Simple Task")
generate_histogram(cloud_complex_csv, "Cloud Complex Task")
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] | 2.716606 | 1,108 |
api_key = "AIzaSyAedPSTmyoW1ejPtwG_cSu7fEjLxOOUrXg"
# Uses the Geocode API
import requests
from urllib.parse import urlencode
#Input address here!
lat, lng = extract_lat_lng("1600 Amphitheatre Parkway, Mountain View, CA")
places_endpoint_2 = "https://maps.googleapis.com/maps/api/place/nearbysearch/json"
params_2 = {
"key": api_key,
"location": f"{lat},{lng}",
"radius": "1500",
"keyword": "pharmacy"
}
params_2_encoded = urlencode(params_2)
places_url=f"{places_endpoint_2}?{params_2_encoded}"
r2 = requests.get(places_url)
# Returns the first 3 closest locations and stores it in variables within a 1500 meter radius
try:
nameVicinity0 = r2.json()['results'][0]
name0 = nameVicinity0.get('name')
vicinity0 = nameVicinity0.get('vicinity')
except:
pass
try:
nameVicinity1 = r2.json()['results'][1]
name1 = nameVicinity1.get('name')
vicinity1 = nameVicinity1.get('vicinity')
except:
pass
try:
nameVicinity2 = r2.json()['results'][2]
name2 = nameVicinity2.get('name')
vicinity2 = nameVicinity2.get('vicinity')
except:
pass
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25,
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220,
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] | 2.39693 | 456 |
# Generated by Django 2.0.1 on 2018-01-28 19:30
from django.db import migrations
| [
2,
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515,
416,
37770,
362,
13,
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319,
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12,
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12,
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25,
1270,
198,
198,
6738,
42625,
14208,
13,
9945,
1330,
15720,
602,
628,
628
] | 2.741935 | 31 |
from ._interfaces import EventCallbackType, EventType, IDispatchEvents
__all__ = [
"IDispatchEvents",
"EventType",
"EventCallbackType",
]
| [
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] | 2.849057 | 53 |
# user defined exception
"""
Exceptions
|
--------------------------------------------
| |
Build-in Exceptions User defined Exceptions
"""
#
# >>> class CustomError(Exception):
# ... pass
# ...
#
# >>> raise CustomError
# Traceback (most recent call last):
# ...
# __main__.CustomError
#
# >>> raise CustomError("An error occurred")
# Traceback (most recent call last):
# ...
# __main__.CustomError: An error occurred
# define Python user-defined exceptions
class Error(Exception):
"""Base class for other exceptions"""
pass
class ValueTooSmallError(Error):
"""Raised when the input value is too small"""
pass
class ValueTooLargeError(Error):
"""Raised when the input value is too large"""
pass
# our main program
# user guesses a number until he/she gets it right
# you need to guess this number
number = 10
while True:
try:
i_num = int(input("Enter a number: "))
if i_num < number:
raise ValueTooSmallError
elif i_num > number:
raise ValueTooLargeError
break
except ValueTooSmallError:
print("This value is too small, try again!\n")
except ValueTooLargeError:
print("This value is too large, try again!\n")
print("Congratulations! You guessed it correctly.")
| [
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198,
4798,
7203,
45048,
0,
921,
25183,
340,
9380,
19570,
198
] | 2.492228 | 579 |
# APROVANDO EMPRÉSTIMO — Escreva um programa para aprovar o empréstimo bancário para a compra de uma casa.
# O programa vai perguntar o valor da casa, o salário do comprador e em quantos anos ele vai pagar.
#
# Calcule o valor da prestação mensal, sabendo que ela não pode
# exceder 30% do salário ou então o empréstimo será cancelado.
casa = float(input('\033[1mQual é o valor da casa a ser comprada, em reais?\033[m R$'))
sal = float(input('\033[1mQual é o valor do seu salário mensal, em reais?\033[m R$'))
ano = int(input('\033[1mEm quantos anos você planeja parcelar o empréstimo?\033[m '))
pres = casa / (ano * 12)
if pres > (0.3 * sal):
print('\n\033[1;31mEmpréstimo negado!\033[m')
else:
print('\n\033[1;32mEmpréstimo concedido!\033[m')
| [
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2634,
301,
25147,
15096,
17305,
0,
59,
44427,
58,
76,
11537,
198
] | 2.29878 | 328 |
import pytest
from elmo.settings.exceptions import ValidationError
from elmo.settings.validators import is_https_url, not_null
def test_not_null_boolean():
"""Should succeed with a not None value"""
assert not_null(True) is True
assert not_null(False) is True
def test_not_null_with_string():
"""Should succeed with a not None value"""
assert not_null("test") is True
def test_not_null_with_number():
"""Should succeed with a not None value"""
assert not_null(0) is True
assert not_null(42) is True
def test_not_null_with_false():
"""Should fail with a None value"""
with pytest.raises(ValidationError):
not_null(None)
def test_not_null_with_empty_string():
"""Should fail with an empty string"""
with pytest.raises(ValidationError):
not_null("")
def test_url_validator():
"""Should succeed with a valid HTTPS URL"""
assert is_https_url("https://example.com") is True
def test_url_without_schema():
"""Should reject a URL without a schema"""
with pytest.raises(ValidationError):
is_https_url("example.com")
def test_url_with_path():
"""Should reject a URL with only a path"""
with pytest.raises(ValidationError):
is_https_url("/example.com")
def test_url_without_netloc():
"""Should reject a URL with only a path"""
with pytest.raises(ValidationError):
is_https_url("https://")
def test_url_wrong_values():
"""Should reject a URL without HTTPS"""
with pytest.raises(ValidationError):
is_https_url("http://foo")
| [
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220,
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220,
220,
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220,
220,
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62,
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62,
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5450,
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4943,
628,
198,
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1332,
62,
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62,
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62,
27160,
33529,
198,
220,
220,
220,
37227,
19926,
4968,
257,
10289,
1231,
38288,
37811,
198,
220,
220,
220,
351,
12972,
9288,
13,
430,
2696,
7,
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24765,
12331,
2599,
198,
220,
220,
220,
220,
220,
220,
220,
318,
62,
5450,
62,
6371,
7203,
4023,
1378,
21943,
4943,
198
] | 2.795009 | 561 |
from pure_fb_openmetrics_exporter.flashblade_collector import collector
| [
6738,
5899,
62,
21855,
62,
9654,
4164,
10466,
62,
1069,
26634,
13,
34167,
22500,
62,
33327,
273,
1330,
22967,
198
] | 3.6 | 20 |
from django.urls import include, re_path
from oauth import views as oauth_views
urlpatterns = [
re_path(r"^$", oauth_views.index, name="evernote_index"),
re_path(r"^auth/$", oauth_views.auth, name="evernote_auth"),
re_path(r"^callback/$", oauth_views.callback, name="evernote_callback"),
re_path(r"^reset/$", oauth_views.reset, name="evernote_auth_reset"),
] | [
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# __all__ = ["DAQ_Move", "DAQ_Navigation_Visu", "DAQ_Utils","DAQ_1DViewer"]
# from .DAQ_move import *
# from .DAQ_Navigation_Visu import *
# from .DAQ_Utils import *
from .DAQ_Utils.plotting.QLED import QLED
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] | 2.535714 | 84 |
#!/usr/bin/env python3
# -*- coding: utf-8 -*-
import sys
sys.path.append("/home/jacobsin/Development/python/rifflib")
import riff
# contract = riff.make_contract("contract.yml")
riff.endpoint.walker(
[
{
"userId": 1,
"id": 1,
"title": "sunt aut facere repellat provident occaecati excepturi optio reprehenderit",
"body": {"grrr": ["test"]},
}
],
[
"dict",
"dict",
"dict",
"dict",
{
"userId": "int",
"id": "int",
"title": "str",
"body": {"str": "list"},
},
],
)
| [
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220,
220,
220,
220,
220,
220,
8964,
198,
220,
220,
220,
16589,
198,
8,
198
] | 1.803371 | 356 |
import FWCore.ParameterSet.Config as cms
from HLTriggerOffline.SMP.hltSMPValidator_cfi import *
SMPValidationSequence = cms.Sequence(
hltSMPValidator
)
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#!/usr/bin/env python
import sys
from os import path
import django
from django.conf import settings, global_settings
from django.core.management import execute_from_command_line
MIDDLEWARE = (
'django.middleware.common.CommonMiddleware',
'django.contrib.sessions.middleware.SessionMiddleware',
'django.middleware.csrf.CsrfViewMiddleware',
'django.contrib.auth.middleware.AuthenticationMiddleware',
)
if not settings.configured:
module_root = path.dirname(path.realpath(__file__))
settings.configure(
DEBUG = False,
DATABASES = {
'default': {
'ENGINE': 'django.db.backends.sqlite3',
'NAME': ':memory:'
}
},
TEMPLATES = [
{
'BACKEND': 'django.template.backends.django.DjangoTemplates',
'APP_DIRS': True,
'OPTIONS': {
'debug': True,
},
},
],
INSTALLED_APPS = (
'django.contrib.auth',
'django.contrib.contenttypes',
'django.contrib.sessions',
'django.contrib.sites',
'currencies',
),
# For django 1.8 to 2.1 compatibility
MIDDLEWARE = MIDDLEWARE,
MIDDLEWARE_CLASSES = MIDDLEWARE,
SITE_ID = 1,
ROOT_URLCONF = 'currencies.tests.test_urls',
)
if __name__ == '__main__':
runtests()
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220,
220,
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1057,
41989,
3419,
198
] | 1.963014 | 730 |
# coding: utf-8
# In[1]:
from __future__ import absolute_import, division
import os
import time
import numpy as np
import pandas as pd
import gensim
from tqdm import tqdm
from nltk.stem import PorterStemmer
ps = PorterStemmer()
from nltk.stem.lancaster import LancasterStemmer
lc = LancasterStemmer()
from nltk.stem import SnowballStemmer
sb = SnowballStemmer("english")
import gc
from nltk.stem import WordNetLemmatizer
lemmatizer = WordNetLemmatizer()
# In[2]:
spell_model = gensim.models.KeyedVectors.load_word2vec_format('wiki-news-300d-1M/wiki-news-300d-1M.vec')
words = spell_model.index2word
w_rank = {}
for i,word in enumerate(words):
w_rank[word] = i
WORDS = w_rank
# In[3]:
# Use fast text as vocabulary
def P(word):
"Probability of `word`."
# use inverse of rank as proxy
# returns 0 if the word isn't in the dictionary
return - WORDS.get(word, 0)
def correction(word):
"Most probable spelling correction for word."
"correction('quikly') returns quickly correction('israil') returns israel"
return max(candidates(word), key=P)
def candidates(word):
"Generate possible spelling corrections for word."
return (known([word]) or known(edits1(word)) or [word])
def known(words):
"The subset of `words` that appear in the dictionary of WORDS."
return set(w for w in words if w in WORDS)
def edits1(word):
"All edits that are one edit away from `word`."
letters = 'abcdefghijklmnopqrstuvwxyz'
splits = [(word[:i], word[i:]) for i in range(len(word) + 1)]
deletes = [L + R[1:] for L, R in splits if R]
transposes = [L + R[1] + R[0] + R[2:] for L, R in splits if len(R)>1]
replaces = [L + c + R[1:] for L, R in splits if R for c in letters]
inserts = [L + c + R for L, R in splits for c in letters]
return set(deletes + transposes + replaces + inserts)
def edits2(word):
"All edits that are two edits away from `word`."
return (e2 for e1 in edits1(word) for e2 in edits1(e1))
# In[4]:
obscene_words = ['sex','fuck','shit','cunt','gay','lesbian','ass','pussy','dick','penis','vagina','asshole','fap','porn', 'masturbate','sperm','semen','pregnate','impregnate','boobs','getting laid','get laid','bitch','undress','castrate', 'castration','incest','sexual','rape','hooker','slut','prostitute','panty','bikini','underwear', 'dildo','breast','transgender','homosexual','anal','butt','bra','paedophilo','']
# In[9]:
# In[13]:
sent = "Can Aman pregnate a cow?"
print(chk_words(sent))
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] | 2.505736 | 1,046 |
# ---
# jupyter:
# jupytext:
# text_representation:
# extension: .py
# format_name: light
# format_version: '1.3'
# jupytext_version: 0.8.6
# kernelspec:
# display_name: Python [conda env:DL-wpython3]
# language: python
# name: conda-env-DL-wpython3-py
# ---
import time
import numpy as np
import pandas as pd
from re import finditer
import re
from nltk.tokenize import sent_tokenize, word_tokenize
import string
import nltk
import itertools
try:
nltk.data.find('tokenizers/punkt')
nltk.data.find('taggers/averaged_perceptron_tagger')
except LookupError:
nltk.download('punkt')
nltk.download('averaged_perceptron_tagger')
punct = set(string.punctuation)
stop_words = set(nltk.corpus.stopwords.words('english'))
contractions = {
"ain't": "am not",
"aren't": "are not",
"can't": "cannot",
"can't've": "cannot have",
"'cause": "because",
"could've": "could have",
"couldn't": "could not",
"couldn't've": "could not have",
"didn't": "did not",
"doesn't": "does not",
"don't": "do not",
"hadn't": "had not",
"hadn't've": "had not have",
"hasn't": "has not",
"haven't": "have not",
"he'd": "he would",
"he'd've": "he would have",
"he'll": "he will",
"he's": "he is",
"how'd": "how did",
"how'll": "how will",
"how's": "how is",
"i'd": "i would",
"i'll": "i will",
"i'm": "i am",
"i've": "i have",
"isn't": "is not",
"it'd": "it would",
"it'll": "it will",
"it's": "it is",
"let's": "let us",
"ma'am": "madam",
"mayn't": "may not",
"might've": "might have",
"mightn't": "might not",
"must've": "must have",
"mustn't": "must not",
"needn't": "need not",
"oughtn't": "ought not",
"shan't": "shall not",
"sha'n't": "shall not",
"she'd": "she would",
"she'll": "she will",
"she's": "she is",
"should've": "should have",
"shouldn't": "should not",
"that'd": "that would",
"that's": "that is",
"there'd": "there had",
"there's": "there is",
"they'd": "they would",
"they'll": "they will",
"they're": "they are",
"they've": "they have",
"wasn't": "was not",
"we'd": "we would",
"we'll": "we will",
"We'll": "We will",
"we're": "we are",
"we've": "we have",
"weren't": "were not",
"what'll": "what will",
"what're": "what are",
"what's": "what is",
"what've": "what have",
"where'd": "where did",
"where's": "where is",
"who'll": "who will",
"who's": "who is",
"won't": "will not",
"wouldn't": "would not",
"you'd": "you would",
"you'll": "you will",
"you're": "you are"
}
'''
def getCandidatePhrases(transcript):
input_ = replaceContractions(transcript)
Keywords_all = list (set (extract_candidate_chunk (transcript) + extract_candidate_words (transcript)))
return Keywords_all
'''
getCandidatePhrases("With a foundation in artificial intelligence and media analytics, Ether starts its course by enabling a smart call service on top of Slack, Stride, and Teams. Ether captures and analyzes the call (audio, video, shared content, etc) as the call happens and extracts key markers.")
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] | 2.508503 | 1,176 |
from datetime import datetime, timedelta
from os import listdir, system
import aiohttp
import discord
import json
from discord.ext import commands
from pretty_help import PrettyHelp
with open('./data.json') as f:
d1 = json.load(f)
with open('./market.json') as f:
d2 = json.load(f)
TOKEN = d1['token']
bot = Echo()
@bot.command(hidden=True)
@commands.is_owner()
@bot.command(hidden=True)
@commands.is_owner()
@bot.command(hidden=True)
@commands.is_owner()
for filename in listdir("./cogs"):
if filename.endswith(".py"):
bot.load_extension(f"cogs.{filename[:-3]}")
bot.load_extension("jishaku")
bot.loop.run_until_complete(bot.run(TOKEN)) | [
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62,
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7,
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7,
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] | 2.601563 | 256 |
# -*- coding: utf-8 -*-
"""
Created on Sat Apr 18 13:02:47 2020
@author: user
"""
# python David_4_7_2_index_features.py --dataset output/data/training --features-db output/training_features.hdf5
# python David_4_7_2_index_features.py
# import the necessary packages
from __future__ import print_function
from pyimagesearch.descriptors import DetectAndDescribe
from pyimagesearch.indexer import FeatureIndexer
from imutils.feature import FeatureDetector_create, DescriptorExtractor_create
from imutils import paths
import argparse
import imutils
import random
import cv2
import sys
# construct the argument parser and parse the arguments
ap = argparse.ArgumentParser()
ap.add_argument("-d", "--dataset", required=True,
help="Path to the directory that contains the images to be indexed")
ap.add_argument("-f", "--features-db", required=True,
help="Path to where the features database will be stored")
ap.add_argument("-a", "--approx-images", type=int, default=250,
help="Approximate # of images in the dataset")
ap.add_argument("-b", "--max-buffer-size", type=int, default=50000,
help="Maximum buffer size for # of features to be stored in memory")
sys.argv[1:] = '-d output/data/training -f output/training_features.hdf5'.split()
args = vars(ap.parse_args())
# initialize the keypoint detector, local invariant descriptor, and the descriptor
# pipeline
detector = FeatureDetector_create("GFTT")
descriptor = DescriptorExtractor_create("RootSIFT")
dad = DetectAndDescribe(detector, descriptor)
# initialize the feature indexer
fi = FeatureIndexer(args["features_db"], estNumImages=args["approx_images"],
maxBufferSize=args["max_buffer_size"], verbose=True)
# grab the image paths and randomly shuffle them
imagePaths = list(paths.list_images(args["dataset"]))
random.shuffle(imagePaths)
# loop over the images in the dataset
for (i, imagePath) in enumerate(imagePaths):
# check to see if progress should be displayed
if i > 0 and i % 10 == 0:
fi._debug("processed {} images".format(i), msgType="[PROGRESS]")
# load the image and pre-process it
image = cv2.imread(imagePath)
image = imutils.resize(image, width=320)
image = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
# describe the image
(kps, descs) = dad.describe(image)
# if either the keypoints or descriptors are None, then ignore the image
if kps is None or descs is None:
continue
# extract the image filename and label from the path, then index the features
(label, filename) = imagePath.split("/")[-2:]
k = "{}:{}".format(label, filename)
fi.add(k, image.shape, kps, descs)
# finish the indexing process
fi.finish()
| [
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] | 3.108205 | 841 |
""" Basic template file that you should fill in for Problem Set 3. Some util
functions are provided from the NAND notebooks online that implement some
of the NAND essentials. """
from util import EVAL
from util import TRUTH
from util import NANDProgram
# TODO: Implement this function and return a string representation of its NAND
# implementation. You don't have to use the class we supplied - you could use
# other methods of building up your NAND program from scratch.
def nandsquare(n):
'''Takes in an integer n. Outputs the string representation of a NAND prog
that takes in inputs x_0, ..., x_{n-1} and squares it mod 2^n. The output
will be y_0, ..., y_{n-1}. The first digit will be the least significant
digit (ex: 110001 --> 35)'''
# creates a blank NAND program with n inputs and n outputs.
prog = NANDProgram(n, n)
# now add lines to your NAND program by calling python functions like
# prog.NAND() or prog.OR() or other helper functions. For an example, take
# a look at the stuff after if __name__ == '__main__':
# "compiles" your completed program as a NAND program string.
return str(prog)
def rightshift(n):
'''Returns a program that takes [x_0,...x_n] as inputs and returns [0,...,x_n-1] '''
# TODO: Do this for bonus points and the leaderboard.
def nandsquare256():
'''Implement nandsquare for a specific input size, n=256. This result gets
placed on the leaderboard for extra credit. If you get close to the top
score on the leaderboard, you'll still recieve BONUS POINTS!!!'''
raise NotImplementedError
def badadder(N):
'''Should create a NAND adder that takes two n digits and outputs an n digit
because it's bad'''
return
# Examples of using the NANDProgram class to build NAND Programs. Please don't
# worry too much about the details of using this class - this is not a class
# about designing NAND programs.
def nandadder(N):
'''Creates a NAND adder that takes in two n-digit binary numbers and gets
the sum, returning a n+1-digit binary number. Returns the string repr. of
the NAND program created.'''
nand = NANDProgram(2 * N, N + 1, debug=False) #set debug=True to show debug lines
nand.ONE("ONE")
carry = nand.allocate()
nand.ADD_3(nand.output_var(0), carry,
nand.input_var(0), nand.input_var(N), nand.NAND("ZERO", "ONE", "ONE"), debug=True)
last_carry = ""
for i in range(1, N - 1):
last_carry = carry
carry = nand.allocate()
nand.ADD_3(nand.output_var(i), carry,
nand.input_var(i), nand.input_var(N + i), last_carry, debug=True)
nand.ADD_3(nand.output_var(N-1), nand.output_var(N),
nand.input_var(N-1), nand.input_var(2 * N - 1), carry, debug=True)
return str(nand)
if __name__ == '__main__':
# Generate the string representation of a NAND prog. that adds numbers
addfive = str(nandadder(10))
# Input Number 1: 11110 --> 15
# Input Number 2: 10110 --> 13 1111010110
# Expected Output: 28 --> 001110
#816 0000110011
#877 1011011011
# 10111001011
print(EVAL(addfive,'00001100111011011011'))
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8784,
16,
6,
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] | 2.679043 | 1,212 |
# Generated by Django 2.1.1 on 2018-09-16 08:39
from django.db import migrations, models
| [
2,
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] | 2.84375 | 32 |
# save snapshot
from evdev import UInput, ecodes
cfg = globals()["config"]
drv = globals()["drivers"]["keybd1"]
drv.press_keys(ecodes.KEY_LEFTALT, ecodes.KEY_S)
| [
2,
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] | 2.5 | 66 |
# Copyright 2009-2013 Nokia Siemens Networks Oyj
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from rfdoc.rfdocapp.utils.robot_htmlutils import html_escape
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62,
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] | 3.738636 | 176 |
import pytest
import sys
sys.path.append('../')
from search import *
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#!/usr/bin/env python
from ..exrpc.rpclib import *
from ..exrpc.server import *
from ..mllib.model_util import ModelID
class df_to_sparse_info:
'''A python container for holding information related to dataframe to sparse conversion'''
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# Generated by Django 3.1.1 on 2020-09-12 19:06
from django.db import migrations
import multiselectfield.db.fields
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import re
from ipaddress import ip_address
import pytest
from harness.runtime._validate import validate, ValidationError
@pytest.fixture()
@pytest.fixture()
@pytest.fixture()
@pytest.fixture()
def test_disabled(message_type):
"""
message Message {
message Inner {
option (validate.disabled) = true;
string value = 1 [(validate.rules).string.const = "valid"];
}
Inner field = 1;
}
"""
validate(message_type(field=dict(value="invalid")))
def test_oneof_required(message_type):
"""
message Message {
oneof type {
option (validate.required) = true;
string foo = 1;
int32 bar = 2;
}
}
"""
validate(message_type(foo="test"))
validate(message_type(bar=42))
with pytest.raises(ValidationError, match="Oneof type is required"):
validate(message_type())
def test_float_const(message_type):
"""
message Message {
float value = 1 [(validate.rules).float.const = 4.2];
}
"""
validate(message_type(value=4.2))
with pytest.raises(ValidationError, match="value not equal to"):
validate(message_type(value=2.4))
def test_timestamp_lt(message_type, timestamp_type):
"""
message Message {
google.protobuf.Timestamp value = 1 [
(validate.rules).timestamp.lt = {seconds: 1000}
];
}
"""
validate(message_type(value=timestamp_type(seconds=999)))
with pytest.raises(ValidationError, match="is not lesser than"):
validate(message_type(value=timestamp_type(seconds=1000)))
def test_timestamp_within(message_type, timestamp_type):
"""
message Message {
google.protobuf.Timestamp value = 1 [
(validate.rules).timestamp.within = {seconds: 60}
];
}
"""
value = timestamp_type()
value.GetCurrentTime()
validate(message_type(value=value))
valid_seconds = value.seconds
with pytest.raises(ValidationError, match="value is not within 60s from now"):
value.seconds = valid_seconds - 100
validate(message_type(value=value))
with pytest.raises(ValidationError, match="value is not within 60s from now"):
value.seconds = valid_seconds - 100
validate(message_type(value=value))
value.seconds = valid_seconds
validate(message_type(value=value))
def test_duration_in(message_type, duration_type):
"""
message Message {
google.protobuf.Duration value = 1 [
(validate.rules).duration.in = {seconds: 60},
(validate.rules).duration.in = {seconds: 30}
];
}
"""
validate(message_type(value=duration_type(seconds=60)))
with pytest.raises(ValidationError, match="value not in {60s, 30s}"):
validate(message_type(value=duration_type(seconds=120)))
def test_duration_lte(message_type, duration_type):
"""
message Message {
google.protobuf.Duration value = 1 [
(validate.rules).duration.lte = {seconds: 60}
];
}
"""
validate(message_type(value=duration_type(seconds=60)))
with pytest.raises(
ValidationError, match="value is not lesser than or equal to 60s"
):
validate(message_type(value=duration_type(seconds=60, nanos=1)))
def test_enum_defined_only(message_type):
"""
message Message {
enum Foo {
A = 0;
B = 1;
}
Foo value = 1 [(validate.rules).enum.defined_only = true];
}
"""
validate(message_type())
validate(message_type(value=1))
with pytest.raises(ValidationError, match="value is not defined"):
validate(message_type(value=2))
def test_repeated_unique(message_type):
"""
message Message {
repeated int32 value = 1 [(validate.rules).repeated.unique = true];
}
"""
validate(message_type(value=[1, 2, 3]))
with pytest.raises(
ValidationError,
match="value must contain unique items; repeated items: \\[2, 3\\]",
):
validate(message_type(value=[1, 2, 3, 2, 4, 3, 5]))
def test_repeated_items(message_type):
"""
message Message {
repeated int32 field = 1 [(validate.rules).repeated.items.int32.lt = 5];
}
"""
validate(message_type(field=[1, 2, 3, 4]))
with pytest.raises(ValidationError, match="field\\[\\] is not lesser than 5"):
validate(message_type(field=[1, 2, 3, 4, 5]))
def test_map_key(message_type):
"""
message Message {
map<string, int32> field = 1 [(validate.rules).map.keys.string.min_len = 3];
}
"""
validate(message_type(field={"test": 42}))
with pytest.raises(ValidationError, match="field<key> length is less than 3"):
validate(message_type(field={"t": 42}))
def test_map_values(message_type):
"""
message Message {
map<string, int32> field = 1 [(validate.rules).map.values.int32.const = 42];
}
"""
validate(message_type(field={"test": 42}))
with pytest.raises(ValidationError, match="field<value> not equal to 42"):
validate(message_type(field={"test": 43}))
def test_any_in(message_type, any_type, duration_type, timestamp_type):
"""
message Message {
google.protobuf.Any field = 1 [(validate.rules).any.in = "type.googleapis.com/google.protobuf.Duration"];
}
""" # noqa
any_1 = any_type()
any_1.Pack(duration_type(seconds=42))
validate(message_type(field=any_1))
with pytest.raises(ValidationError, match="field.type_url not in"):
any_2 = any_type()
any_2.Pack(timestamp_type(seconds=42))
validate(message_type(field=any_2))
def test_nested(message_type):
"""
message Message {
message Inner {
string value = 1 [(validate.rules).string.const = "valid"];
}
Inner field = 1;
}
"""
validate(message_type())
validate(message_type(field=dict(value="valid")))
with pytest.raises(ValidationError, match="value not equal to 'valid'"):
validate(message_type(field=dict(value="invalid")))
def test_message_skip(message_type):
"""
message Message {
message Inner {
string value = 1 [(validate.rules).string.const = "valid"];
}
Inner field = 1 [(validate.rules).message.skip = true];
}
"""
validate(message_type(field=dict(value="invalid")))
def test_message_required(message_type):
"""
message Message {
message Inner {
string value = 1;
}
Inner field = 1 [(validate.rules).message.required = true];
}
"""
validate(message_type(field=dict()))
validate(message_type(field=dict(value="test")))
with pytest.raises(ValidationError, match="field is required"):
validate(message_type())
def test_email(message_type):
"""
message Message {
string field = 1 [(validate.rules).string.email = true];
}
"""
validate(message_type(field="[email protected]"))
validate(
message_type(field="Jean-Luc Picard <[email protected]>")
)
with pytest.raises(ValidationError, match="field contains invalid email address"):
validate(message_type(field="example.com"))
with pytest.raises(
ValidationError, match="field contains more than one email address"
):
validate(message_type(field="[email protected], [email protected]"))
def test_hostname(message_type):
"""
message Message {
string field = 1 [(validate.rules).string.hostname = true];
}
"""
validate(message_type(field="example.com"))
validate(message_type(field="Example.com"))
with pytest.raises(ValidationError, match="field contains invalid hostname"):
validate(message_type(field="-example.com"))
def test_string_prefix(message_type):
"""
message Message {
string field = 1 [(validate.rules).string.prefix = "har"];
}
"""
validate(message_type(field="harness"))
with pytest.raises(ValidationError, match="field does not start with prefix 'har'"):
validate(message_type(field="bottle"))
def test_string_pattern(message_type):
"""
message Message {
string field = 1 [(validate.rules).string.pattern = "^(foo|bar)-app$"];
}
"""
validate(message_type(field="foo-app"))
validate(message_type(field="bar-app"))
with pytest.raises(
ValidationError,
match=re.escape("field does not match pattern '^(foo|bar)-app$'"),
):
validate(message_type(field="invalid"))
def test_string_ip(message_type):
"""
message Message {
string field = 1 [(validate.rules).string.ip = true];
}
"""
validate(message_type(field="0.0.0.0"))
validate(message_type(field="127.0.0.1"))
validate(message_type(field="::1"))
validate(message_type(field="2001:0db8:85a3:0000:0000:8a2e:0370:7334"))
with pytest.raises(ValidationError, match="field contains invalid IP address"):
validate(message_type(field="0.0.0"))
def test_string_ipv4(message_type):
"""
message Message {
string field = 1 [(validate.rules).string.ipv4 = true];
}
"""
validate(message_type(field="0.0.0.0"))
validate(message_type(field="127.0.0.1"))
with pytest.raises(ValidationError, match="field contains invalid IPv4 address"):
validate(message_type(field="0.0.0"))
with pytest.raises(ValidationError, match="field contains invalid IPv4 address"):
validate(message_type(field="2001:0db8:85a3:0000:0000:8a2e:0370:7334"))
def test_string_ipv6(message_type):
"""
message Message {
string field = 1 [(validate.rules).string.ipv6 = true];
}
"""
validate(message_type(field="::1"))
validate(message_type(field="2001:0db8:85a3:0000:0000:8a2e:0370:7334"))
with pytest.raises(ValidationError, match="field contains invalid IPv6 address"):
validate(message_type(field="2001:0db8:85a3:0000:0000:8a2e:0370:733."))
with pytest.raises(ValidationError, match="field contains invalid IPv6 address"):
validate(message_type(field="127.0.0.1"))
def test_bytes_ip(message_type):
"""
message Message {
bytes field = 1 [(validate.rules).bytes.ip = true];
}
"""
validate(message_type(field=ip_address("0.0.0.0").packed))
validate(message_type(field=ip_address("127.0.0.1").packed))
validate(message_type(field=ip_address("::1").packed))
validate(
message_type(field=ip_address("2001:0db8:85a3:0000:0000:8a2e:0370:7334").packed)
)
with pytest.raises(ValidationError, match="field contains invalid IP address"):
validate(message_type(field=ip_address("0.0.0.0").packed[:-1]))
def test_bytes_ipv4(message_type):
"""
message Message {
bytes field = 1 [(validate.rules).bytes.ipv4 = true];
}
"""
validate(message_type(field=ip_address("0.0.0.0").packed))
validate(message_type(field=ip_address("127.0.0.1").packed))
with pytest.raises(ValidationError, match="field contains invalid IPv4 address"):
validate(message_type(field=b"deadbeef"))
with pytest.raises(ValidationError, match="field contains invalid IPv4 address"):
validate(
message_type(
field=ip_address("2001:0db8:85a3:0000:0000:8a2e:0370:7334").packed
)
)
def test_bytes_ipv6(message_type):
"""
message Message {
bytes field = 1 [(validate.rules).bytes.ipv6 = true];
}
"""
validate(message_type(field=ip_address("::1").packed))
validate(
message_type(field=ip_address("2001:0db8:85a3:0000:0000:8a2e:0370:7334").packed)
)
with pytest.raises(ValidationError, match="field contains invalid IPv6 address"):
validate(message_type(field=b"deadbeef"))
with pytest.raises(ValidationError, match="field contains invalid IPv6 address"):
validate(message_type(field=ip_address("127.0.0.1").packed))
def test_address(message_type):
"""
message Message {
string field = 1 [(validate.rules).string.address = true];
}
"""
validate(message_type(field="::1"))
validate(message_type(field="127.0.0.1"))
validate(message_type(field="Example.com"))
with pytest.raises(ValidationError, match="field contains invalid address"):
validate(message_type(field="invalid"))
def test_uri(message_type):
"""
message Message {
string field = 1 [(validate.rules).string.uri = true];
}
"""
validate(message_type(field="http://google.com"))
validate(message_type(field="http://127.0.0.1/page.html#fragment"))
with pytest.raises(ValidationError, match="field contains invalid URI"):
validate(message_type(field="/local/path"))
def test_uri_ref(message_type):
"""
message Message {
string field = 1 [(validate.rules).string.uri_ref = true];
}
"""
validate(message_type(field="http://google.com"))
validate(message_type(field="/local/path"))
with pytest.raises(ValidationError, match="field contains invalid URI-reference"):
validate(message_type(field="\\invalid\\path"))
def test_uuid(message_type):
"""
message Message {
string field = 1 [(validate.rules).string.uuid = true];
}
"""
validate(message_type(field="adbf3fd4-6a41-41a8-b5c1-df09adc3a9b3"))
validate(message_type(field="ADBF3FD4-6A41-41A8-B5C1-DF09ADC3A9B3"))
with pytest.raises(ValidationError, match="field contains invalid UUID"):
validate(message_type(field="adbf3fd46a4141a8b5c1df09adc3a9b3"))
with pytest.raises(ValidationError, match="field contains invalid UUID"):
validate(message_type(field="adbf3fd4-6a41-41a8-b5c1-df09adc3a9b3-ext"))
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220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
357,
12102,
378,
13,
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737,
16514,
27823,
13,
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796,
1391,
43012,
25,
3126,
92,
198,
220,
220,
220,
220,
220,
220,
220,
28013,
198,
220,
220,
220,
1782,
198,
220,
220,
220,
37227,
198,
220,
220,
220,
1988,
796,
41033,
62,
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3419,
198,
220,
220,
220,
1988,
13,
3855,
11297,
7575,
3419,
198,
220,
220,
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7,
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28,
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4008,
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220,
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4938,
62,
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13,
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220,
220,
220,
351,
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9288,
13,
430,
2696,
7,
7762,
24765,
12331,
11,
2872,
2625,
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318,
407,
1626,
3126,
82,
422,
783,
1,
2599,
198,
220,
220,
220,
220,
220,
220,
220,
1988,
13,
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62,
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532,
1802,
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220,
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11,
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1,
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220,
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220,
1988,
13,
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220,
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220,
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7,
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220,
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62,
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7,
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62,
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28,
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198,
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62,
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62,
259,
7,
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62,
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11,
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62,
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2599,
198,
220,
220,
220,
37227,
198,
220,
220,
220,
3275,
16000,
1391,
198,
220,
220,
220,
220,
220,
220,
220,
23645,
13,
11235,
672,
3046,
13,
26054,
1988,
796,
352,
685,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
357,
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13,
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13,
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25,
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13,
259,
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25,
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92,
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220,
220,
220,
220,
220,
220,
220,
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198,
220,
220,
220,
1782,
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220,
220,
220,
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198,
220,
220,
220,
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7,
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62,
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7,
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28,
32257,
62,
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7,
43012,
28,
1899,
22305,
198,
220,
220,
220,
351,
12972,
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13,
430,
2696,
7,
7762,
24765,
12331,
11,
2872,
2625,
8367,
407,
287,
1391,
1899,
82,
11,
1542,
82,
36786,
2599,
198,
220,
220,
220,
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220,
220,
220,
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7,
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62,
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7,
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28,
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28,
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628,
198,
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62,
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62,
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660,
7,
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62,
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220,
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220,
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198,
220,
220,
220,
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1391,
198,
220,
220,
220,
220,
220,
220,
220,
23645,
13,
11235,
672,
3046,
13,
26054,
1988,
796,
352,
685,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
357,
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13,
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32257,
13,
75,
660,
796,
1391,
43012,
25,
3126,
92,
198,
220,
220,
220,
220,
220,
220,
220,
28013,
198,
220,
220,
220,
1782,
198,
220,
220,
220,
37227,
198,
220,
220,
220,
26571,
7,
20500,
62,
4906,
7,
8367,
28,
32257,
62,
4906,
7,
43012,
28,
1899,
22305,
198,
220,
220,
220,
351,
12972,
9288,
13,
430,
2696,
7,
198,
220,
220,
220,
220,
220,
220,
220,
3254,
24765,
12331,
11,
2872,
2625,
8367,
318,
407,
14494,
621,
393,
4961,
284,
3126,
82,
1,
198,
220,
220,
220,
15179,
198,
220,
220,
220,
220,
220,
220,
220,
26571,
7,
20500,
62,
4906,
7,
8367,
28,
32257,
62,
4906,
7,
43012,
28,
1899,
11,
15709,
418,
28,
16,
22305,
628,
198,
4299,
1332,
62,
44709,
62,
23211,
62,
8807,
7,
20500,
62,
4906,
2599,
198,
220,
220,
220,
37227,
198,
220,
220,
220,
3275,
16000,
1391,
198,
220,
220,
220,
220,
220,
220,
220,
33829,
36080,
1391,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
317,
796,
657,
26,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
347,
796,
352,
26,
198,
220,
220,
220,
220,
220,
220,
220,
1782,
198,
220,
220,
220,
220,
220,
220,
220,
36080,
1988,
796,
352,
47527,
12102,
378,
13,
38785,
737,
44709,
13,
23211,
62,
8807,
796,
2081,
11208,
198,
220,
220,
220,
1782,
198,
220,
220,
220,
37227,
198,
220,
220,
220,
26571,
7,
20500,
62,
4906,
28955,
198,
220,
220,
220,
26571,
7,
20500,
62,
4906,
7,
8367,
28,
16,
4008,
198,
220,
220,
220,
351,
12972,
9288,
13,
430,
2696,
7,
7762,
24765,
12331,
11,
2872,
2625,
8367,
318,
407,
5447,
1,
2599,
198,
220,
220,
220,
220,
220,
220,
220,
26571,
7,
20500,
62,
4906,
7,
8367,
28,
17,
4008,
628,
198,
4299,
1332,
62,
45956,
515,
62,
34642,
7,
20500,
62,
4906,
2599,
198,
220,
220,
220,
37227,
198,
220,
220,
220,
3275,
16000,
1391,
198,
220,
220,
220,
220,
220,
220,
220,
5100,
493,
2624,
1988,
796,
352,
47527,
12102,
378,
13,
38785,
737,
45956,
515,
13,
34642,
796,
2081,
11208,
198,
220,
220,
220,
1782,
198,
220,
220,
220,
37227,
198,
220,
220,
220,
26571,
7,
20500,
62,
4906,
7,
8367,
41888,
16,
11,
362,
11,
513,
60,
4008,
198,
220,
220,
220,
351,
12972,
9288,
13,
430,
2696,
7,
198,
220,
220,
220,
220,
220,
220,
220,
3254,
24765,
12331,
11,
198,
220,
220,
220,
220,
220,
220,
220,
2872,
2625,
8367,
1276,
3994,
3748,
3709,
26,
5100,
3709,
25,
26867,
58,
17,
11,
513,
6852,
60,
1600,
198,
220,
220,
220,
15179,
198,
220,
220,
220,
220,
220,
220,
220,
26571,
7,
20500,
62,
4906,
7,
8367,
41888,
16,
11,
362,
11,
513,
11,
362,
11,
604,
11,
513,
11,
642,
60,
4008,
628,
198,
4299,
1332,
62,
45956,
515,
62,
23814,
7,
20500,
62,
4906,
2599,
198,
220,
220,
220,
37227,
198,
220,
220,
220,
3275,
16000,
1391,
198,
220,
220,
220,
220,
220,
220,
220,
5100,
493,
2624,
2214,
796,
352,
47527,
12102,
378,
13,
38785,
737,
45956,
515,
13,
23814,
13,
600,
2624,
13,
2528,
796,
642,
11208,
198,
220,
220,
220,
1782,
198,
220,
220,
220,
37227,
198,
220,
220,
220,
26571,
7,
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62,
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7,
3245,
41888,
16,
11,
362,
11,
513,
11,
604,
60,
4008,
198,
220,
220,
220,
351,
12972,
9288,
13,
430,
2696,
7,
7762,
24765,
12331,
11,
2872,
2625,
3245,
6852,
58,
6852,
60,
318,
407,
14494,
621,
642,
1,
2599,
198,
220,
220,
220,
220,
220,
220,
220,
26571,
7,
20500,
62,
4906,
7,
3245,
41888,
16,
11,
362,
11,
513,
11,
604,
11,
642,
60,
4008,
628,
198,
4299,
1332,
62,
8899,
62,
2539,
7,
20500,
62,
4906,
2599,
198,
220,
220,
220,
37227,
198,
220,
220,
220,
3275,
16000,
1391,
198,
220,
220,
220,
220,
220,
220,
220,
3975,
27,
8841,
11,
493,
2624,
29,
2214,
796,
352,
47527,
12102,
378,
13,
38785,
737,
8899,
13,
13083,
13,
8841,
13,
1084,
62,
11925,
796,
513,
11208,
198,
220,
220,
220,
1782,
198,
220,
220,
220,
37227,
198,
220,
220,
220,
26571,
7,
20500,
62,
4906,
7,
3245,
28,
4895,
9288,
1298,
5433,
92,
4008,
198,
220,
220,
220,
351,
12972,
9288,
13,
430,
2696,
7,
7762,
24765,
12331,
11,
2872,
2625,
3245,
27,
2539,
29,
4129,
318,
1342,
621,
513,
1,
2599,
198,
220,
220,
220,
220,
220,
220,
220,
26571,
7,
20500,
62,
4906,
7,
3245,
28,
4895,
83,
1298,
5433,
92,
4008,
628,
198,
4299,
1332,
62,
8899,
62,
27160,
7,
20500,
62,
4906,
2599,
198,
220,
220,
220,
37227,
198,
220,
220,
220,
3275,
16000,
1391,
198,
220,
220,
220,
220,
220,
220,
220,
3975,
27,
8841,
11,
493,
2624,
29,
2214,
796,
352,
47527,
12102,
378,
13,
38785,
737,
8899,
13,
27160,
13,
600,
2624,
13,
9979,
796,
5433,
11208,
198,
220,
220,
220,
1782,
198,
220,
220,
220,
37227,
198,
220,
220,
220,
26571,
7,
20500,
62,
4906,
7,
3245,
28,
4895,
9288,
1298,
5433,
92,
4008,
198,
220,
220,
220,
351,
12972,
9288,
13,
430,
2696,
7,
7762,
24765,
12331,
11,
2872,
2625,
3245,
27,
8367,
29,
407,
4961,
284,
5433,
1,
2599,
198,
220,
220,
220,
220,
220,
220,
220,
26571,
7,
20500,
62,
4906,
7,
3245,
28,
4895,
9288,
1298,
5946,
92,
4008,
628,
198,
4299,
1332,
62,
1092,
62,
259,
7,
20500,
62,
4906,
11,
597,
62,
4906,
11,
9478,
62,
4906,
11,
41033,
62,
4906,
2599,
198,
220,
220,
220,
37227,
198,
220,
220,
220,
3275,
16000,
1391,
198,
220,
220,
220,
220,
220,
220,
220,
23645,
13,
11235,
672,
3046,
13,
7149,
2214,
796,
352,
47527,
12102,
378,
13,
38785,
737,
1092,
13,
259,
796,
366,
4906,
13,
13297,
499,
271,
13,
785,
14,
13297,
13,
11235,
672,
3046,
13,
26054,
8973,
26,
198,
220,
220,
220,
1782,
198,
220,
220,
220,
37227,
220,
1303,
645,
20402,
198,
220,
220,
220,
597,
62,
16,
796,
597,
62,
4906,
3419,
198,
220,
220,
220,
597,
62,
16,
13,
11869,
7,
32257,
62,
4906,
7,
43012,
28,
3682,
4008,
198,
220,
220,
220,
26571,
7,
20500,
62,
4906,
7,
3245,
28,
1092,
62,
16,
4008,
198,
220,
220,
220,
351,
12972,
9288,
13,
430,
2696,
7,
7762,
24765,
12331,
11,
2872,
2625,
3245,
13,
4906,
62,
6371,
407,
287,
1,
2599,
198,
220,
220,
220,
220,
220,
220,
220,
597,
62,
17,
796,
597,
62,
4906,
3419,
198,
220,
220,
220,
220,
220,
220,
220,
597,
62,
17,
13,
11869,
7,
16514,
27823,
62,
4906,
7,
43012,
28,
3682,
4008,
198,
220,
220,
220,
220,
220,
220,
220,
26571,
7,
20500,
62,
4906,
7,
3245,
28,
1092,
62,
17,
4008,
628,
198,
4299,
1332,
62,
77,
7287,
7,
20500,
62,
4906,
2599,
198,
220,
220,
220,
37227,
198,
220,
220,
220,
3275,
16000,
1391,
198,
220,
220,
220,
220,
220,
220,
220,
3275,
24877,
1391,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
4731,
1988,
796,
352,
47527,
12102,
378,
13,
38785,
737,
8841,
13,
9979,
796,
366,
12102,
8973,
26,
198,
220,
220,
220,
220,
220,
220,
220,
1782,
198,
220,
220,
220,
220,
220,
220,
220,
24877,
2214,
796,
352,
26,
198,
220,
220,
220,
1782,
198,
220,
220,
220,
37227,
198,
220,
220,
220,
26571,
7,
20500,
62,
4906,
28955,
198,
220,
220,
220,
26571,
7,
20500,
62,
4906,
7,
3245,
28,
11600,
7,
8367,
2625,
12102,
1,
22305,
198,
220,
220,
220,
351,
12972,
9288,
13,
430,
2696,
7,
7762,
24765,
12331,
11,
2872,
2625,
8367,
407,
4961,
284,
705,
12102,
29653,
2599,
198,
220,
220,
220,
220,
220,
220,
220,
26571,
7,
20500,
62,
4906,
7,
3245,
28,
11600,
7,
8367,
2625,
259,
12102,
1,
22305,
628,
198,
4299,
1332,
62,
20500,
62,
48267,
7,
20500,
62,
4906,
2599,
198,
220,
220,
220,
37227,
198,
220,
220,
220,
3275,
16000,
1391,
198,
220,
220,
220,
220,
220,
220,
220,
3275,
24877,
1391,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
4731,
1988,
796,
352,
47527,
12102,
378,
13,
38785,
737,
8841,
13,
9979,
796,
366,
12102,
8973,
26,
198,
220,
220,
220,
220,
220,
220,
220,
1782,
198,
220,
220,
220,
220,
220,
220,
220,
24877,
2214,
796,
352,
47527,
12102,
378,
13,
38785,
737,
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"""
StateIO
Container to store state object.
Several useful functions are implemented in this class:
1. Saving intermediate results to files.
2. Recover workspace at any iteration (label set and unlabel set).
3. Recover workspace from the intermediate result file in case the program exits unexpectedly.
4. Gathering and checking the information stored in State object.
5. Print active learning progress: current_iteration, current_mean_performance, current_cost, etc.
"""
# Authors: Ying-Peng Tang
# License: BSD 3 clause
from __future__ import division
import collections.abc
import copy
import os
import pickle
import sys
import numpy as np
import prettytable as pt
from .state import State
from ..index import IndexCollection, MultiLabelIndexCollection
from ..index.multi_label_tools import check_index_multilabel
from ..utils.interface import BaseCollection
__all__ = ['StateIO',
]
class StateIO:
"""
A class to store states.
Functions including:
1. Saving intermediate results to files.
2. Recover workspace at any iteration (label set and unlabel set).
3. Recover workspace from the intermediate result file in case the program exits unexpectedly.
4. Gathering and checking the information stored in State object.
5. Print active learning progress: current_iteration, current_mean_performance, current_cost, etc.
Parameters
----------
round: int
Number of k-fold experiments loop. 0 <= round < k
train_idx: array_like
Training index of one fold experiment.
test_idx: array_like
Testing index of one fold experiment.
init_L: array_like
Initial labeled index of one fold experiment.
init_U: array_like
Initial unlabeled index of one fold experiment.
initial_point: object, optional (default=None)
The performance before any querying.
If not specify, the initial point of different methods will be different.
saving_path: str, optional (default='.')
Path to save the intermediate files. If None is given, it will
not save the intermediate result.
check_flag: bool, optional (default=True)
Whether to check the validity of states.
verbose: bool, optional (default=True)
Whether to print query information during the AL process.
print_interval: int optional (default=1)
How many queries will trigger a print when verbose is True.
"""
@classmethod
def load(cls, path):
"""Load StateIO object from file.
Parameters
----------
path: str
The path should be a specific .pkl file.
Returns
-------
object: StateIO
The StateIO object in the file.
"""
f = open(os.path.abspath(path), 'rb')
saver_from_file = pickle.load(f)
f.close()
return saver_from_file
def set_initial_point(self, perf):
"""The initial point of performance before querying.
Parameters
----------
perf: float
The performance value.
"""
self.initial_point = perf
def save(self):
"""Saving intermediate results to file."""
if self._saving_dir is None:
return
f = open(os.path.join(self._saving_dir, self._saving_file_name), 'wb')
pickle.dump(self, f)
f.close()
def add_state(self, state):
"""Add a State object to the container.
Parameters
----------
state: {dict, State}
State object to be added. Or a dictionary with
the following keys: ['select_index', 'queried_info', 'performance']
"""
if not isinstance(state, State):
assert isinstance(state, dict), "state must be dict or State object."
assert 'select_index' in state and 'queried_info' in state and 'performance' in state, "The dict must contain the following keys: ['select_index', 'queried_info', 'performance']"
self.__state_list.append(copy.deepcopy(state))
self.__update_info()
if self.__verbose and len(self) % self.__print_interval == 0:
if self._first_print:
print('\n' + self.__repr__(), end='')
self._first_print = False
else:
print('\r' + self._refresh_dataline(), end='')
sys.stdout.flush()
def get_state(self, index):
"""Get a State object in the container.
Parameters
----------
index: int
The index of the State object. 0 <= index < len(self)
Returns
-------
st: State
The State object in the previous iteration.
"""
assert (0 <= index < len(self))
return copy.deepcopy(self.__state_list[index])
def check_batch_size(self):
"""Check if all queries have the same batch size.
Returns
-------
result: bool
Whether all the states have the same batch size.
"""
ind_uni = np.unique(
[self.__state_list[i].batch_size for i in range(len(self.__state_list) - 1)], axis=0)
if len(ind_uni) == 1:
self.batch_size = ind_uni[0]
return True
else:
return False
def pop(self, i=None):
"""remove and return item at index (default last)."""
return self.__state_list.pop(i)
def recover_workspace(self, iteration=None):
"""Recover workspace after $iteration$ querying.
For example, if 0 is given, the initial workspace without any querying will be recovered.
Note that, the object itself will be recovered, the information after the iteration will be discarded.
Parameters
----------
iteration: int, optional(default=None)
Number of iteration to recover, start from 0.
If nothing given, it will return the current workspace.
Returns
-------
train_idx: list
Index of training set, shape like [n_training_samples]
test_idx: list
Index of testing set, shape like [n_testing_samples]
label_idx: list
Index of labeling set, shape like [n_labeling_samples]
unlabel_idx: list
Index of unlabeling set, shape like [n_unlabeling_samples]
"""
if iteration is None:
iteration = len(self.__state_list)
assert (0 <= iteration <= len(self))
work_U = copy.deepcopy(self.init_U)
work_L = copy.deepcopy(self.init_L)
for i in range(iteration):
state = self.__state_list[i]
work_U.difference_update(state.get_value('select_index'))
work_L.update(state.get_value('select_index'))
self.__state_list = self.__state_list[0:iteration]
return copy.copy(self.train_idx), copy.copy(self.test_idx), copy.deepcopy(work_L), copy.deepcopy(work_U)
def get_workspace(self, iteration=None):
"""Get workspace after $iteration$ querying.
For example, if 0 is given, the initial workspace without any querying will be recovered.
Parameters
----------
iteration: int, optional(default=None)
Number of iteration, start from 0.
If nothing given, it will get the current workspace.
Returns
-------
train_idx: list
Index of training set, shape like [n_training_samples]
test_idx: list
Index of testing set, shape like [n_testing_samples]
label_idx: list
Index of labeling set, shape like [n_labeling_samples]
unlabel_idx: list
Index of unlabeling set, shape like [n_unlabeling_samples]
"""
if iteration is None:
iteration = len(self.__state_list)
assert (0 <= iteration <= len(self))
work_U = copy.deepcopy(self.init_U)
work_L = copy.deepcopy(self.init_L)
for i in range(iteration):
state = self.__state_list[i]
work_U.difference_update(state.get_value('select_index'))
work_L.update(state.get_value('select_index'))
return copy.copy(self.train_idx), copy.copy(self.test_idx), copy.deepcopy(work_L), copy.deepcopy(work_U)
def num_of_query(self):
"""Return the number of queries"""
return len(self.__state_list)
def get_current_performance(self):
"""Return the mean ± std performance of all existed states.
Only available when the performance of each state is a single float value.
Returns
-------
mean: float
Mean performance of the existing states.
std: float
Std performance of the existing states.
"""
if len(self) == 0:
return 0, 0
else:
tmp = [self[i].get_value('performance') for i in range(self.__len__())]
if isinstance(tmp[0], collections.Iterable):
return np.NaN, np.NaN
else:
return np.mean(tmp), np.std(tmp)
def refresh_info(self):
"""re-calculate current active learning progress."""
numqdata = 0
cost = 0.0
for state in self.__state_list:
numqdata += len(state.get_value('select_index'))
if 'cost' in state.keys():
cost += np.sum(state.get_value('cost'))
self.cost_inall = cost
self._numqdata = numqdata
return numqdata, cost
def __update_info(self):
"""Update current active learning progress"""
state = self.__state_list[len(self) - 1]
if 'cost' in state.keys():
self.cost_inall += np.sum(state.get_value('cost'))
self._numqdata += len(state.get_value('select_index'))
# class StateIO_all_labels(StateIO):
# """StateIO for all _labels querying"""
# def add_state(self, state):
# assert (isinstance(state, experiment_saver.state.State))
# self.__state_list.append(copy.deepcopy(state))
# if self.__check_flag:
# res, err_st, err_ind = self.check_select_index()
# if res == -1:
# warnings.warn(
# 'Checking validity fails, there is a queried instance not in set_U in '
# 'State:%d, index:%s.' % (err_st, str(err_ind)),
# category=ValidityWarning)
# if res == -2:
# warnings.warn('Checking validity fails, there are instances already queried '
# 'in previous iteration in State:%d, index:%s.' % (err_st, str(err_ind)),
# category=ValidityWarning)
# self.__update_info()
#
#
# if self.__verbose and len(self) % self.__print_interval == 0:
# if self._first_print:
# print('\n' + self.__repr__(), end='')
# self._first_print = False
# else:
# print('\r' + self._refresh_dataline(), end='')
# sys.stdout.flush()
#
# def check_select_index(self):
# """
# check:
# - Q has no repeating elements
# - Q in U
# Returns
# -------
# result: int
# check result
# - if -1 is returned, there is a queried instance not in U
# - if -2 is returned, there are repeated instances in Q
# - if 1 is returned, CHECK OK
#
# state_index: int
# the state index when checking fails (start from 0)
# if CHECK OK, None is returned.
#
# select_index: object
# the select_index when checking fails.
# if CHECK OK, None is returned.
# """
# repeat_dict = dict()
# ind = -1
# for st in self.__state_list:
# ind += 1
# for instance in st.get_value('select_index'):
# if instance not in self.init_U:
# return -1, ind, instance
# if instance not in repeat_dict.keys():
# repeat_dict[instance] = 1
# else:
# return -2, ind, instance
# return 1, None, None
#
# @property
# def queried_percentage(self):
# """return the queried percentage of unlabeled data"""
# return 100 * self._numqdata / len(self.init_U)
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] | 2.314179 | 5,360 |
#!/usr/bin/env python3
import re
import os
import torch
import torch.nn.functional as F
import struct
import glob
from PIL import Image
import torchvision.transforms.functional as TF
from shutil import copyfile
root_dir = ''
ori_dir = ''
if __name__ == '__main__':
sub_dir = [os.path.join(root_dir, name) for name in os.listdir(root_dir) if os.path.isdir(os.path.join(root_dir, name))]
rec_err = 0
rec_bg = 0
for d in sub_dir:
print(d)
input = glob.glob(os.path.join(d, '*input.png'))[0]
name = input[:-10] + '.jpg'
name = name.split('/')[-1]
ori = glob.glob(os.path.join(ori_dir, name))[0]
copyfile(ori, input[:-10] + '_tar.png')
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] | 2.374101 | 278 |
import torch
from vap_turn_taking.utils import find_island_idx_len
from vap_turn_taking.hold_shifts import get_dialog_states, get_last_speaker
def find_isolated_within(vad, prefix_frames, max_duration_frames, suffix_frames):
"""
... <= prefix_frames (silence) | <= max_duration_frames (active) | <= suffix_frames (silence) ...
"""
isolated = torch.zeros_like(vad)
for b, vad_tmp in enumerate(vad):
for speaker in [0, 1]:
starts, durs, vals = find_island_idx_len(vad_tmp[..., speaker])
for step in range(1, len(starts) - 1):
# Activity condition: current step is active
if vals[step] == 0:
continue
# Prefix condition:
# check that current active step comes after a certain amount of inactivity
if durs[step - 1] < prefix_frames:
continue
# Suffix condition
# check that current active step comes after a certain amount of inactivity
if durs[step + 1] < suffix_frames:
continue
current_dur = durs[step]
if current_dur <= max_duration_frames:
start = starts[step]
end = start + current_dur
isolated[b, start:end, speaker] = 1.0
return isolated
if __name__ == "__main__":
import matplotlib.pyplot as plt
from vap_turn_taking.plot_utils import plot_vad_oh
BS = Backhannel(**bs_dict)
tt_bc = BS(va)
(tt_bc["backchannel"] != bc).sum()
n_rows = 4
n_cols = 4
fig, ax = plt.subplots(n_rows, n_cols, sharey=True, sharex=True, figsize=(16, 4))
b = 0
for row in range(n_rows):
for col in range(n_cols):
_ = plot_vad_oh(vad[b], ax=ax[row, col])
_ = plot_vad_oh(
bc["backchannel"][b],
ax=ax[row, col],
colors=["purple", "purple"],
alpha=0.8,
)
b += 1
if b == vad.shape[0]:
break
if b == vad.shape[0]:
break
plt.pause(0.1)
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] | 1.960217 | 1,106 |
import datetime as dt
from flask_login import UserMixin
from SUIBE_DID_Data_Manager.database import (
Column,
Model,
SurrogatePK,
db,
reference_col,
relationship,
) | [
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220,
220,
220,
4941,
62,
4033,
11,
198,
220,
220,
220,
2776,
11,
198,
8
] | 2.5 | 76 |
import argparse
import signal
import time
import sys
import os
import logging
| [
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628
] | 4.157895 | 19 |
A = [4, 5, 6, 7, 0, 1, 2, 3]
A = [0,1,2,3,4,5,6,7]
B = 4
A = [ 101, 103, 106, 109, 158, 164, 182, 187, 202, 205, 2, 3, 32, 57, 69, 74, 81, 99, 100 ]
B = 202
a = Rotated_Search()
print(a.search(A, B))
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] | 1.80531 | 113 |
import numpy as np
from collections.abc import Mapping
from mesohops.util.exceptions import AuxError
from scipy.special import binom
__title__ = "AuxiliaryVector Class"
__author__ = "D. I. G. Bennett"
__version__ = "1.0"
class AuxiliaryVector(Mapping):
"""
This is a class that encodes a sparse representation of auxiliary vectors
with some extra helper functions to simplify some common actions, such as:
determining the absolute index, adding a unit vector, and calculating the sum.
The class is not mutable - which is to say, once an auxiliary vector is defined,
it cannot be changed.
"""
__slots__ = ('dict_aux_vec', 'tuple_aux_vec', 'array_aux_vec', '__abs_index', '__len'
, 'hash', 'index', '_sum', '_dict_aux_p1', '_dict_aux_m1')
def __init__(self, aux_array, nmodes):
"""
INPUTS
------
1. aux_array : iterable
list of (mode, value) pairs for all non-zero indices of the auxiliary
vector
2. nmodes : int
the number of modes in the hierarchy which is the length of the dense
auxiliary vector.
RETURNS
-------
None
"""
self.dict_aux_vec = {
index_mode: aux_value for (index_mode, aux_value) in aux_array
}
self.tuple_aux_vec = tuple(
[tuple([mode, value]) for (mode, value) in aux_array]
)
self.array_aux_vec = np.array(aux_array)
if len(self.array_aux_vec)>0 and not np.all(np.diff(self.array_aux_vec[:,0])>0):
raise AuxError("array_aux_vec not properly ordered")
self.__abs_index = None
self.__len = nmodes
self.hash = hash(self.tuple_aux_vec)
self.index = None
self._sum = np.sum(self.values())
self._dict_aux_p1 = {}
self._dict_aux_m1 = {}
# Dictionary-like methods overwriting Mutable Mapping
# ===================================================
def keys(self):
"""
This function returns an array of mode indices for the auxiliary vectors
Parameters
----------
None
Returns
-------
1. keys : array
an array of mode indices with nonzero auxiliary index
"""
if len(self.dict_aux_vec) > 0:
return self.array_aux_vec[:, 0]
else:
return np.array([])
def values(self):
"""
This function returns an array of the auxiliary vector values
Parameters
----------
None
Returns
-------
1. values : array
an array of nonzero auxiliary index values
"""
if len(self.dict_aux_vec) > 0:
return self.array_aux_vec[:, 1]
else:
return np.array([])
# Comparison Methods
# ==================
def _compare(self, other, comparison_function):
"""
This function compares two auxiliary vectors
Parameters
----------
1. other : array
the array you want to compare
2. comparison_function : function
a comparison function
Returns
-------
1. bool_compare : bool
a boolean for the comparison
"""
if isinstance(other, AuxiliaryVector) and len(self) == len(other):
return comparison_function(self.absolute_index, other.absolute_index)
else:
return False
# Special Methods
# ===============
def difference_by_mode(self, other):
"""
Compares the current HopsAux object to another HopsAux object. If they differ
by only 1 step, then it returns the mode along which they differ.
Parameters
----------
1. other: HopsAux object
The HopsAux object to which the current object is compared.
Returns
-------
1. diff_mode : int or False
The mode index along which they differ or False if they differ
by more than 1 step.
"""
set_key_self = set(self.keys())
set_key_other = set(other.keys())
# Check that the two HopsAux belong to the same hierarchy
assert self.__len == len(other)
if np.abs(self._sum - other._sum) == 1:
if set_key_self == set_key_other:
values = np.abs(self.array_aux_vec[:,1]- other.array_aux_vec[:,1])
if np.sum(values) == 1:
return self.array_aux_vec[np.where(values)[0][0],0]
elif (len(set_key_self | set_key_other)
- len(set_key_self & set_key_other)) == 1:
value = 0
for key in set_key_self | set_key_other:
value += np.abs(self[key] - other[key])
if value == 1:
index = list((set_key_self | set_key_other) - (set_key_self &
set_key_other))[0]
return index
return False
def dot(self, vec):
"""
This is a function that performs a sparse dot product between the
auxiliary index vector and another vector.
Parameters
----------
1. vec : np.array
a vector
Returns
-------
1. product : float
the dot product value
"""
if len(self.dict_aux_vec) == 0:
return 0
else:
return np.dot(self.array_aux_vec[:, 1], vec[self.array_aux_vec[:, 0]])
def sum(self, **unused_kwargs):
"""
This function returns the sum of the auxiliary vector values
Parameters
----------
None
Returns
-------
1. sum : float
the sum of the nonzero values of the auxiliary vectors
"""
try:
return self._sum
except:
return np.sum(self.values())
def todense(self):
"""
This function will take a sparse vector and make it dense
Parameters
----------
None
Returns
-------
1. output : array
the dense vector
"""
output = np.zeros(self.__len)
if len(self.dict_aux_vec) == 0:
return output
output[self.keys()] = self.values()
return output
def toarray(self):
"""
This function converts a dict to an array
Parameters
----------
None
Returns
-------
1. array : array
a dict in an array form
"""
return self.array_aux_vec
def get_values(self, index_slice):
"""
This function gets the dense auxiliary vector values from a sub-indexed list
Parameters
----------
1. index_slice : list
a list of indices
Returns
-------
1. values : array
an array of values at the given indices
"""
return np.array([self.__getitem__(key) for key in index_slice])
def get_values_nonzero(self, index_slice):
"""
This function gets the sparse auxiliary vector values from a sub-indexed list
NOTE: the values are returned in key order not the order
they are present in index_slice
Parameters
----------
1. index_slice : list
a list of indices
Returns
-------
1. values : array
a sparse array of the non-zero auxiliary vector values
"""
return np.array(
[self.dict_aux_vec[key] for key in self.keys() if key in index_slice]
)
def e_step(self, mode, step):
"""
This function returns a new Auxiliary Vector with the desired step in the given
mode
Parameters
----------
1. mode : int
The absolute mode index
2. step : int
The change in the aux value for the given mode
Returns
-------
1. aux_vec : tuple
the new sparse auxiliary vector
"""
return AuxiliaryVector(self.tuple_from_e_step(mode, step), nmodes=self.__len)
def hash_from_e_step(self, mode, step):
"""
This function returns the hash of a new Auxiliary Vector with the desired step
in the given mode
Parameters
----------
1. mode : int
The absolute mode index
2. step : int
The change in the aux value for the given mode
Returns
-------
1. hash : int
the hash of the tuple sparse auxiliary vector created from e_step
"""
return hash(self.tuple_from_e_step(mode, step))
def tuple_from_e_step(self, mode, step):
"""
Returns the sparse tuple representation of the auxiliary that is the given step
length along the given absolute mode index away from the current auxiliary.
Parameters
----------
1. mode : int
The absolute mode index
2. step : int
The change in the aux value for the given mode
Returns
-------
1. tuple_aux : tuple
The sparse representation of the auxiliary (sorted mode order)
"""
if step == 0:
return self.tuple_aux_vec
elif self.__getitem__(mode) + step < 0:
return ((0, -1),)
elif len(self.dict_aux_vec) == 0:
return tuple([(mode, step)])
elif mode in self.array_aux_vec[:, 0]:
if self.__getitem__(mode) + step == 0:
return tuple(
[
tuple([mode_i, value_i])
for (mode_i, value_i) in self.tuple_aux_vec
if mode_i != mode
]
)
else:
return tuple(
[
tuple([mode_i, value_i + step])
if mode_i == mode
else tuple([mode_i, value_i])
for (mode_i, value_i) in self.tuple_aux_vec
]
)
else:
list_keys = list(self.dict_aux_vec.keys())
list_keys.append(mode)
list_keys.sort()
list_values = [
step if key == mode else self.dict_aux_vec[key] for key in list_keys
]
return tuple(
[tuple([mode, value]) for (mode, value) in zip(list_keys, list_values)]
)
def index_analytic(self):
"""
This function provides an absolute index value for an auxiliary
vector using an analytic function of the indices. The basic idea
is that the indices are always ordered by increasing hierarchy
'level' (i.e. the sum of the indices). Within a level they are ordered
by first comparing the first values, then the second values, etc.
This gives the indexing a particularly simple form with a level:
L = sum(i_0,...,i_n)
(i_0, ... i_N) = sum_n<N sum_L>ln>i_n ((N-n-1 , L-sum(aux[:n])-ln)
where ((L,K)) denotes a L multichoose K.
The derivation of the following equations is given on p. 68 of
Quantum Notebook #1. The sums have been removed by making use of the
binomial sum property and the binomial symmetry property. The result is
a equation that only sums over a number of elements equal to the number
of non-zero terms in aux.
PARAMETERS
----------
None
RETURNS
-------
1. index : int
the absolute index for an auxiliary
"""
# Constants
# ---------
aux = self.toarray()
n_hmode = self.__len
L = self.sum()
if not aux.size:
return 0
else:
# Calculate number of aux at order less than L
# --------------------------------------------
n_aux_below_l = int(binom(n_hmode + L - 1, L - 1))
# Calculate N+ contribution
# -------------------------
list_np_boxes = [n_hmode]
list_np_boxes.extend(n_hmode - aux[:-1, 0] - 1)
list_np_boxes = np.array(list_np_boxes)
list_np_balls = [L]
list_np_balls.extend(L - np.cumsum(aux[:-1, 1]))
list_np_balls = np.array(list_np_balls)
n_plus = np.nansum(
binom(list_np_boxes + list_np_balls - 1, list_np_boxes - 1)
)
# Calculate N- contributions
# --------------------------
list_nm_boxes = n_hmode - aux[:, 0] - 1
n_minus = np.nansum(binom(list_nm_boxes + list_np_balls, list_nm_boxes))
# calculate M contributions
# -------------------------
list_m_balls = L - np.cumsum(aux[:, 1]) - 1
m = np.nansum(binom(list_nm_boxes + list_m_balls, list_m_balls))
return int(n_aux_below_l + m + n_plus - n_minus)
def add_aux_connect(self, index_mode, aux_other, type):
"""
The function that updates the HopsAux object to contain a pointer to the
other HopsAux objects it is connected to.
Parameters
----------
1. index_mode : int
the mode along which the two HopsAux objects are connected
2. aux_other : HopsAux
the HopsAux object it is connected to
3. type : int
+1 or -1 depending on if the other aux has a larger or smaller sum
Returns
-------
1. None
"""
if type == 1:
self._dict_aux_p1.update({index_mode: aux_other})
elif type == -1:
self._dict_aux_m1.update({index_mode: aux_other})
else:
raise AuxError('add_aux_connect does not support type={}'.format(type))
def remove_aux_connect(self, index_mode, type):
"""
The function that removes the connection between the HopsAux object and another
connected with type (+1/-1) along index mode.
Parameters
----------
1. index_mode : int
the mode along which the two HopsAux objects are connected
2. type : int
+1 or -1 depending on if the other aux has a larger or smaller sum
Returns
-------
1. None
"""
if type == 1:
self._dict_aux_p1.pop(index_mode)
elif type == -1:
self._dict_aux_m1.pop(index_mode)
else:
raise AuxError('add_aux_connect does not support type={}'.format(type))
def remove_pointers(self):
"""
The function that removes all pointers targeting the current HopsAux object
from the set of HopsAux objects it has connections to.
Parameters
----------
1. None
Returns
-------
1. None
"""
for (index_mode, aux) in self.dict_aux_p1.items():
aux.remove_aux_connect(index_mode, -1)
for (index_mode, aux) in self.dict_aux_m1.items():
aux.remove_aux_connect(index_mode, 1)
self._dict_aux_m1 = {}
self._dict_aux_p1 = {}
@property
@property
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220,
220,
220,
220,
220,
220,
220,
362,
13,
7208,
62,
8818,
1058,
2163,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
257,
7208,
2163,
628,
220,
220,
220,
220,
220,
220,
220,
16409,
198,
220,
220,
220,
220,
220,
220,
220,
35656,
198,
220,
220,
220,
220,
220,
220,
220,
352,
13,
20512,
62,
5589,
533,
1058,
20512,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
257,
25131,
329,
262,
7208,
198,
220,
220,
220,
220,
220,
220,
220,
37227,
198,
220,
220,
220,
220,
220,
220,
220,
611,
318,
39098,
7,
847,
11,
47105,
28129,
38469,
8,
290,
18896,
7,
944,
8,
6624,
18896,
7,
847,
2599,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
1441,
7208,
62,
8818,
7,
944,
13,
48546,
62,
9630,
11,
584,
13,
48546,
62,
9630,
8,
198,
220,
220,
220,
220,
220,
220,
220,
2073,
25,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
1441,
10352,
628,
220,
220,
220,
1303,
6093,
25458,
198,
220,
220,
220,
1303,
796,
25609,
855,
198,
220,
220,
220,
825,
3580,
62,
1525,
62,
14171,
7,
944,
11,
584,
2599,
198,
220,
220,
220,
220,
220,
220,
220,
37227,
198,
220,
220,
220,
220,
220,
220,
220,
3082,
3565,
262,
1459,
367,
2840,
32,
2821,
2134,
284,
1194,
367,
2840,
32,
2821,
2134,
13,
1002,
484,
13238,
198,
220,
220,
220,
220,
220,
220,
220,
416,
691,
352,
2239,
11,
788,
340,
5860,
262,
4235,
1863,
543,
484,
13238,
13,
628,
220,
220,
220,
220,
220,
220,
220,
40117,
198,
220,
220,
220,
220,
220,
220,
220,
24200,
438,
198,
220,
220,
220,
220,
220,
220,
220,
352,
13,
584,
25,
367,
2840,
32,
2821,
2134,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
383,
367,
2840,
32,
2821,
2134,
284,
543,
262,
1459,
2134,
318,
3688,
13,
628,
220,
220,
220,
220,
220,
220,
220,
16409,
198,
220,
220,
220,
220,
220,
220,
220,
35656,
198,
220,
220,
220,
220,
220,
220,
220,
352,
13,
814,
62,
14171,
1058,
493,
393,
10352,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
383,
4235,
6376,
1863,
543,
484,
13238,
393,
10352,
611,
484,
13238,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
416,
517,
621,
352,
2239,
13,
198,
220,
220,
220,
220,
220,
220,
220,
37227,
198,
220,
220,
220,
220,
220,
220,
220,
900,
62,
2539,
62,
944,
796,
900,
7,
944,
13,
13083,
28955,
198,
220,
220,
220,
220,
220,
220,
220,
900,
62,
2539,
62,
847,
796,
900,
7,
847,
13,
13083,
28955,
628,
220,
220,
220,
220,
220,
220,
220,
1303,
6822,
326,
262,
734,
367,
2840,
32,
2821,
5594,
284,
262,
976,
18911,
198,
220,
220,
220,
220,
220,
220,
220,
6818,
2116,
13,
834,
11925,
6624,
18896,
7,
847,
8,
628,
220,
220,
220,
220,
220,
220,
220,
611,
45941,
13,
8937,
7,
944,
13557,
16345,
532,
584,
13557,
16345,
8,
6624,
352,
25,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
611,
900,
62,
2539,
62,
944,
6624,
900,
62,
2539,
62,
847,
25,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
3815,
796,
45941,
13,
8937,
7,
944,
13,
18747,
62,
14644,
62,
35138,
58,
45299,
16,
45297,
584,
13,
18747,
62,
14644,
62,
35138,
58,
45299,
16,
12962,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
611,
45941,
13,
16345,
7,
27160,
8,
6624,
352,
25,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
1441,
2116,
13,
18747,
62,
14644,
62,
35138,
58,
37659,
13,
3003,
7,
27160,
38381,
15,
7131,
15,
4357,
15,
60,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
1288,
361,
357,
11925,
7,
2617,
62,
2539,
62,
944,
930,
900,
62,
2539,
62,
847,
8,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
532,
18896,
7,
2617,
62,
2539,
62,
944,
1222,
900,
62,
2539,
62,
847,
4008,
6624,
352,
25,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
1988,
796,
657,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
329,
1994,
287,
900,
62,
2539,
62,
944,
930,
900,
62,
2539,
62,
847,
25,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
1988,
15853,
45941,
13,
8937,
7,
944,
58,
2539,
60,
532,
584,
58,
2539,
12962,
628,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
611,
1988,
6624,
352,
25,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
6376,
796,
1351,
19510,
2617,
62,
2539,
62,
944,
930,
900,
62,
2539,
62,
847,
8,
532,
357,
2617,
62,
2539,
62,
944,
1222,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
900,
62,
2539,
62,
847,
4008,
58,
15,
60,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
1441,
6376,
628,
220,
220,
220,
220,
220,
220,
220,
1441,
10352,
628,
220,
220,
220,
825,
16605,
7,
944,
11,
43030,
2599,
198,
220,
220,
220,
220,
220,
220,
220,
37227,
198,
220,
220,
220,
220,
220,
220,
220,
770,
318,
257,
2163,
326,
17706,
257,
29877,
16605,
1720,
1022,
262,
198,
220,
220,
220,
220,
220,
220,
220,
37419,
6376,
15879,
290,
1194,
15879,
13,
628,
220,
220,
220,
220,
220,
220,
220,
40117,
198,
220,
220,
220,
220,
220,
220,
220,
24200,
438,
198,
220,
220,
220,
220,
220,
220,
220,
352,
13,
43030,
1058,
45941,
13,
18747,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
257,
15879,
628,
220,
220,
220,
220,
220,
220,
220,
16409,
198,
220,
220,
220,
220,
220,
220,
220,
35656,
198,
220,
220,
220,
220,
220,
220,
220,
352,
13,
1720,
1058,
12178,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
262,
16605,
1720,
1988,
628,
220,
220,
220,
220,
220,
220,
220,
37227,
198,
220,
220,
220,
220,
220,
220,
220,
611,
18896,
7,
944,
13,
11600,
62,
14644,
62,
35138,
8,
6624,
657,
25,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
1441,
657,
198,
220,
220,
220,
220,
220,
220,
220,
2073,
25,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
1441,
45941,
13,
26518,
7,
944,
13,
18747,
62,
14644,
62,
35138,
58,
45299,
352,
4357,
43030,
58,
944,
13,
18747,
62,
14644,
62,
35138,
58,
45299,
657,
11907,
8,
628,
220,
220,
220,
825,
2160,
7,
944,
11,
12429,
403,
1484,
62,
46265,
22046,
2599,
198,
220,
220,
220,
220,
220,
220,
220,
37227,
198,
220,
220,
220,
220,
220,
220,
220,
770,
2163,
5860,
262,
2160,
286,
262,
37419,
15879,
3815,
628,
220,
220,
220,
220,
220,
220,
220,
40117,
198,
220,
220,
220,
220,
220,
220,
220,
24200,
438,
198,
220,
220,
220,
220,
220,
220,
220,
6045,
628,
220,
220,
220,
220,
220,
220,
220,
16409,
198,
220,
220,
220,
220,
220,
220,
220,
35656,
198,
220,
220,
220,
220,
220,
220,
220,
352,
13,
2160,
1058,
12178,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
262,
2160,
286,
262,
1729,
22570,
3815,
286,
262,
37419,
30104,
198,
220,
220,
220,
220,
220,
220,
220,
37227,
198,
220,
220,
220,
220,
220,
220,
220,
1949,
25,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
1441,
2116,
13557,
16345,
198,
220,
220,
220,
220,
220,
220,
220,
2845,
25,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
1441,
45941,
13,
16345,
7,
944,
13,
27160,
28955,
628,
220,
220,
220,
825,
284,
67,
1072,
7,
944,
2599,
198,
220,
220,
220,
220,
220,
220,
220,
37227,
198,
220,
220,
220,
220,
220,
220,
220,
770,
2163,
481,
1011,
257,
29877,
15879,
290,
787,
340,
15715,
628,
220,
220,
220,
220,
220,
220,
220,
40117,
198,
220,
220,
220,
220,
220,
220,
220,
24200,
438,
198,
220,
220,
220,
220,
220,
220,
220,
6045,
628,
220,
220,
220,
220,
220,
220,
220,
16409,
198,
220,
220,
220,
220,
220,
220,
220,
35656,
198,
220,
220,
220,
220,
220,
220,
220,
352,
13,
5072,
1058,
7177,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
262,
15715,
15879,
198,
220,
220,
220,
220,
220,
220,
220,
37227,
198,
220,
220,
220,
220,
220,
220,
220,
5072,
796,
45941,
13,
9107,
418,
7,
944,
13,
834,
11925,
8,
198,
220,
220,
220,
220,
220,
220,
220,
611,
18896,
7,
944,
13,
11600,
62,
14644,
62,
35138,
8,
6624,
657,
25,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
1441,
5072,
198,
220,
220,
220,
220,
220,
220,
220,
5072,
58,
944,
13,
13083,
3419,
60,
796,
2116,
13,
27160,
3419,
198,
220,
220,
220,
220,
220,
220,
220,
1441,
5072,
628,
220,
220,
220,
825,
284,
18747,
7,
944,
2599,
198,
220,
220,
220,
220,
220,
220,
220,
37227,
198,
220,
220,
220,
220,
220,
220,
220,
770,
2163,
26161,
257,
8633,
284,
281,
7177,
628,
220,
220,
220,
220,
220,
220,
220,
40117,
198,
220,
220,
220,
220,
220,
220,
220,
24200,
438,
198,
220,
220,
220,
220,
220,
220,
220,
6045,
628,
220,
220,
220,
220,
220,
220,
220,
16409,
198,
220,
220,
220,
220,
220,
220,
220,
35656,
198,
220,
220,
220,
220,
220,
220,
220,
352,
13,
7177,
1058,
7177,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
257,
8633,
287,
281,
7177,
1296,
198,
220,
220,
220,
220,
220,
220,
220,
37227,
198,
220,
220,
220,
220,
220,
220,
220,
1441,
2116,
13,
18747,
62,
14644,
62,
35138,
628,
220,
220,
220,
825,
651,
62,
27160,
7,
944,
11,
6376,
62,
48369,
2599,
198,
220,
220,
220,
220,
220,
220,
220,
37227,
198,
220,
220,
220,
220,
220,
220,
220,
770,
2163,
3011,
262,
15715,
37419,
15879,
3815,
422,
257,
850,
12,
9630,
276,
1351,
628,
220,
220,
220,
220,
220,
220,
220,
40117,
198,
220,
220,
220,
220,
220,
220,
220,
24200,
438,
198,
220,
220,
220,
220,
220,
220,
220,
352,
13,
6376,
62,
48369,
1058,
1351,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
257,
1351,
286,
36525,
628,
220,
220,
220,
220,
220,
220,
220,
16409,
198,
220,
220,
220,
220,
220,
220,
220,
35656,
198,
220,
220,
220,
220,
220,
220,
220,
352,
13,
3815,
1058,
7177,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
281,
7177,
286,
3815,
379,
262,
1813,
36525,
198,
220,
220,
220,
220,
220,
220,
220,
37227,
198,
220,
220,
220,
220,
220,
220,
220,
1441,
45941,
13,
18747,
26933,
944,
13,
834,
1136,
9186,
834,
7,
2539,
8,
329,
1994,
287,
6376,
62,
48369,
12962,
628,
220,
220,
220,
825,
651,
62,
27160,
62,
13159,
22570,
7,
944,
11,
6376,
62,
48369,
2599,
198,
220,
220,
220,
220,
220,
220,
220,
37227,
198,
220,
220,
220,
220,
220,
220,
220,
770,
2163,
3011,
262,
29877,
37419,
15879,
3815,
422,
257,
850,
12,
9630,
276,
1351,
628,
220,
220,
220,
220,
220,
220,
220,
24550,
25,
262,
3815,
389,
4504,
287,
1994,
1502,
407,
262,
1502,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
484,
389,
1944,
287,
6376,
62,
48369,
628,
220,
220,
220,
220,
220,
220,
220,
40117,
198,
220,
220,
220,
220,
220,
220,
220,
24200,
438,
198,
220,
220,
220,
220,
220,
220,
220,
352,
13,
6376,
62,
48369,
1058,
1351,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
257,
1351,
286,
36525,
628,
220,
220,
220,
220,
220,
220,
220,
16409,
198,
220,
220,
220,
220,
220,
220,
220,
35656,
198,
220,
220,
220,
220,
220,
220,
220,
352,
13,
3815,
1058,
7177,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
257,
29877,
7177,
286,
262,
1729,
12,
22570,
37419,
15879,
3815,
198,
220,
220,
220,
220,
220,
220,
220,
37227,
198,
220,
220,
220,
220,
220,
220,
220,
1441,
45941,
13,
18747,
7,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
685,
944,
13,
11600,
62,
14644,
62,
35138,
58,
2539,
60,
329,
1994,
287,
2116,
13,
13083,
3419,
611,
1994,
287,
6376,
62,
48369,
60,
198,
220,
220,
220,
220,
220,
220,
220,
1267,
628,
220,
220,
220,
825,
304,
62,
9662,
7,
944,
11,
4235,
11,
2239,
2599,
198,
220,
220,
220,
220,
220,
220,
220,
37227,
198,
220,
220,
220,
220,
220,
220,
220,
770,
2163,
5860,
257,
649,
47105,
28129,
20650,
351,
262,
10348,
2239,
287,
262,
1813,
198,
220,
220,
220,
220,
220,
220,
220,
4235,
628,
220,
220,
220,
220,
220,
220,
220,
40117,
198,
220,
220,
220,
220,
220,
220,
220,
24200,
438,
198,
220,
220,
220,
220,
220,
220,
220,
352,
13,
4235,
1058,
493,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
383,
4112,
4235,
6376,
198,
220,
220,
220,
220,
220,
220,
220,
362,
13,
2239,
1058,
493,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
383,
1487,
287,
262,
27506,
1988,
329,
262,
1813,
4235,
628,
220,
220,
220,
220,
220,
220,
220,
16409,
198,
220,
220,
220,
220,
220,
220,
220,
35656,
198,
220,
220,
220,
220,
220,
220,
220,
352,
13,
27506,
62,
35138,
1058,
46545,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
262,
649,
29877,
37419,
15879,
198,
220,
220,
220,
220,
220,
220,
220,
37227,
198,
220,
220,
220,
220,
220,
220,
220,
1441,
47105,
28129,
38469,
7,
944,
13,
83,
29291,
62,
6738,
62,
68,
62,
9662,
7,
14171,
11,
2239,
828,
28642,
4147,
28,
944,
13,
834,
11925,
8,
628,
220,
220,
220,
825,
12234,
62,
6738,
62,
68,
62,
9662,
7,
944,
11,
4235,
11,
2239,
2599,
198,
220,
220,
220,
220,
220,
220,
220,
37227,
198,
220,
220,
220,
220,
220,
220,
220,
770,
2163,
5860,
262,
12234,
286,
257,
649,
47105,
28129,
20650,
351,
262,
10348,
2239,
198,
220,
220,
220,
220,
220,
220,
220,
287,
262,
1813,
4235,
628,
220,
220,
220,
220,
220,
220,
220,
40117,
198,
220,
220,
220,
220,
220,
220,
220,
24200,
438,
198,
220,
220,
220,
220,
220,
220,
220,
352,
13,
4235,
1058,
493,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
383,
4112,
4235,
6376,
198,
220,
220,
220,
220,
220,
220,
220,
362,
13,
2239,
1058,
493,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
383,
1487,
287,
262,
27506,
1988,
329,
262,
1813,
4235,
628,
220,
220,
220,
220,
220,
220,
220,
16409,
198,
220,
220,
220,
220,
220,
220,
220,
35656,
198,
220,
220,
220,
220,
220,
220,
220,
352,
13,
12234,
1058,
493,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
262,
12234,
286,
262,
46545,
29877,
37419,
15879,
2727,
422,
304,
62,
9662,
198,
220,
220,
220,
220,
220,
220,
220,
37227,
198,
220,
220,
220,
220,
220,
220,
220,
1441,
12234,
7,
944,
13,
83,
29291,
62,
6738,
62,
68,
62,
9662,
7,
14171,
11,
2239,
4008,
628,
220,
220,
220,
825,
46545,
62,
6738,
62,
68,
62,
9662,
7,
944,
11,
4235,
11,
2239,
2599,
198,
220,
220,
220,
220,
220,
220,
220,
37227,
198,
220,
220,
220,
220,
220,
220,
220,
16409,
262,
29877,
46545,
10552,
286,
262,
37419,
326,
318,
262,
1813,
2239,
198,
220,
220,
220,
220,
220,
220,
220,
4129,
1863,
262,
1813,
4112,
4235,
6376,
1497,
422,
262,
1459,
37419,
13,
628,
220,
220,
220,
220,
220,
220,
220,
40117,
198,
220,
220,
220,
220,
220,
220,
220,
24200,
438,
198,
220,
220,
220,
220,
220,
220,
220,
352,
13,
4235,
1058,
493,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
383,
4112,
4235,
6376,
198,
220,
220,
220,
220,
220,
220,
220,
362,
13,
2239,
1058,
493,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
383,
1487,
287,
262,
27506,
1988,
329,
262,
1813,
4235,
628,
220,
220,
220,
220,
220,
220,
220,
16409,
198,
220,
220,
220,
220,
220,
220,
220,
35656,
198,
220,
220,
220,
220,
220,
220,
220,
352,
13,
46545,
62,
14644,
1058,
46545,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
383,
29877,
10552,
286,
262,
37419,
357,
82,
9741,
4235,
1502,
8,
198,
220,
220,
220,
220,
220,
220,
220,
37227,
198,
220,
220,
220,
220,
220,
220,
220,
611,
2239,
6624,
657,
25,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
1441,
2116,
13,
83,
29291,
62,
14644,
62,
35138,
198,
220,
220,
220,
220,
220,
220,
220,
1288,
361,
2116,
13,
834,
1136,
9186,
834,
7,
14171,
8,
1343,
2239,
1279,
657,
25,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
1441,
14808,
15,
11,
532,
16,
828,
8,
198,
220,
220,
220,
220,
220,
220,
220,
1288,
361,
18896,
7,
944,
13,
11600,
62,
14644,
62,
35138,
8,
6624,
657,
25,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
1441,
46545,
26933,
7,
14171,
11,
2239,
8,
12962,
198,
220,
220,
220,
220,
220,
220,
220,
1288,
361,
4235,
287,
2116,
13,
18747,
62,
14644,
62,
35138,
58,
45299,
657,
5974,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
611,
2116,
13,
834,
1136,
9186,
834,
7,
14171,
8,
1343,
2239,
6624,
657,
25,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
1441,
46545,
7,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
685,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
46545,
26933,
14171,
62,
72,
11,
1988,
62,
72,
12962,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
329,
357,
14171,
62,
72,
11,
1988,
62,
72,
8,
287,
2116,
13,
83,
29291,
62,
14644,
62,
35138,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
611,
4235,
62,
72,
14512,
4235,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
2361,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
1267,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
2073,
25,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
1441,
46545,
7,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
685,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
46545,
26933,
14171,
62,
72,
11,
1988,
62,
72,
1343,
2239,
12962,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
611,
4235,
62,
72,
6624,
4235,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
2073,
46545,
26933,
14171,
62,
72,
11,
1988,
62,
72,
12962,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
329,
357,
14171,
62,
72,
11,
1988,
62,
72,
8,
287,
2116,
13,
83,
29291,
62,
14644,
62,
35138,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
2361,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
1267,
198,
220,
220,
220,
220,
220,
220,
220,
2073,
25,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
1351,
62,
13083,
796,
1351,
7,
944,
13,
11600,
62,
14644,
62,
35138,
13,
13083,
28955,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
1351,
62,
13083,
13,
33295,
7,
14171,
8,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
1351,
62,
13083,
13,
30619,
3419,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
1351,
62,
27160,
796,
685,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
2239,
611,
1994,
6624,
4235,
2073,
2116,
13,
11600,
62,
14644,
62,
35138,
58,
2539,
60,
329,
1994,
287,
1351,
62,
13083,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
2361,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
1441,
46545,
7,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
685,
83,
29291,
26933,
14171,
11,
1988,
12962,
329,
357,
14171,
11,
1988,
8,
287,
19974,
7,
4868,
62,
13083,
11,
1351,
62,
27160,
15437,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
1267,
628,
220,
220,
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] | 2.090631 | 7,525 |
import os
from app import create_app, db, cli
from flask import current_app
from flask_security import Security, SQLAlchemyUserDatastore, current_user
from flask_security.utils import encrypt_password
from app.auth.models import Role, User
# Setup Flask-Security
app = create_app()
with app.app_context():
user_datastore = SQLAlchemyUserDatastore(db, User, Role)
user_datastore.create_user(email=os.environ.get('ADMIN_USERNAME'), password=encrypt_password(os.environ.get('ADMIN_PASSWORD')))
db.session.commit()
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] | 3.094675 | 169 |
import pytest
import numpy as np
from bionumpy.position_weight_matrix import PositionWeightMatrix
@pytest.fixture
@pytest.fixture
@pytest.fixture
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] | 2.980769 | 52 |
import argparse
from track import *
parser = argparse.ArgumentParser()
parser.add_argument('-mp','--model_path',help='path to model',type=str)
parser.add_argument('-vp','--video_path',help='path to the video',type=str)
parser.add_argument('-od','--output_dir',help='path to save the video',type=str)
if __name__=='__main__':
args = parser.parse_args()
out_dir = args.output_dir
model_path = args.model_path
video_path = args.video_path
dl = datasets.LoadVideo(video_path, (1088,608))
opt = opts().init()
opt.load_model = model_path
show_image = False
output_dir = out_dir
eval_seq(opt, dl, 'mot',save_dir=output_dir, show_image=show_image)
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] | 2.603774 | 265 |
import numpy as np
from pyriemann.clustering import Kmeans,KmeansPerClassTransform
def generate_cov(Nt,Ne):
"""Generate a set of cavariances matrices for test purpose"""
diags = 1.0+0.1*np.random.randn(Nt,Ne)
covmats = np.empty((Nt,Ne,Ne))
for i in range(Nt):
covmats[i] = np.diag(diags[i])
return covmats
def test_Kmeans_init():
"""Test init of Kmeans"""
km = Kmeans(2)
def test_Kmeans_fit():
"""Test Fit of Kmeans"""
covset = generate_cov(20,3)
km = Kmeans(2)
km.fit(covset)
def test_Kmeans_fit_with_init():
"""Test Fit of Kmeans wit matric initialization"""
covset = generate_cov(20,3)
km = Kmeans(2,init=covset[0:2])
km.fit(covset)
def test_Kmeans_fit_with_y():
"""Test Fit of Kmeans with a given y"""
covset = generate_cov(20,3)
labels = np.array([0,1]).repeat(10)
km = Kmeans(2)
km.fit(covset,y=labels)
def test_Kmeans_fit_parallel():
"""Test Fit of Kmeans using paralell"""
covset = generate_cov(20,3)
km = Kmeans(2,n_jobs=2)
km.fit(covset)
def test_Kmeans_predict():
"""Test prediction of Kmeans"""
covset = generate_cov(20,3)
km = Kmeans(2)
km.fit(covset)
km.predict(covset)
def test_Kmeans_transform():
"""Test transform of Kmeans"""
covset = generate_cov(20,3)
km = Kmeans(2)
km.fit(covset)
km.transform(covset)
def test_KmeansPCT_init():
"""Test init of Kmeans PCT"""
km = KmeansPerClassTransform(2)
def test_KmeansPCT_fit():
"""Test Fit of Kmeans PCT"""
covset = generate_cov(20,3)
labels = np.array([0,1]).repeat(10)
km = KmeansPerClassTransform(2)
km.fit(covset,labels)
def test_KmeansPCT_transform():
"""Test Transform of Kmeans PCT"""
covset = generate_cov(20,3)
labels = np.array([0,1]).repeat(10)
km = KmeansPerClassTransform(2)
km.fit(covset,labels)
km.transform(covset) | [
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220,
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10571,
13,
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66,
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8
] | 2.110989 | 910 |
# encoding= utf-8
from __future__ import division, absolute_import, with_statement, print_function
import requests
def download_file(url, file_path, params=None, proxies=None, request_session=None, cookies=None):
"""
proxies = {
"http": "http://10.10.1.10:3128",
"https": "http://10.10.1.10:1080",
}
"""
if not request_session:
request_session = requests.session()
r = request_session.get(url, params=params, stream=True, proxies=proxies, cookies=cookies)
with open(file_path, 'wb') as f:
for chunk in r.iter_content(1024):
if chunk:
f.write(chunk)
f.flush()
| [
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] | 2.353357 | 283 |
from twisted_fate import __version__
| [
6738,
19074,
62,
69,
378,
1330,
11593,
9641,
834,
628
] | 3.8 | 10 |
import os
from wyriwyd.executor import ShellExecutor
| [
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] | 3.055556 | 18 |
import os
curdir = os.path.dirname(__file__)
datadir = os.path.join(curdir, 'data')
def load_telescope_parameters(version, **kwargs):
"""
Utility script to load the telescope parameters
version = 0: Pre-design version (to compare with Rick's stuff)
version = 1: 210 mm design
version = 2: 300 mm design
version = 3: 350 mm design
version = 4: 400 mm design
###
### Version 2:
Syntax:
diameter, qe, psf_fwhm, pixel_size, efficiency = load_telescope_parameters(version)
---
Note, going to depreicate versions < 4 eventually since those assume that
the pixels are 0.5 * pixel size
To be done: Remove QE from this method and put it somewhere else.
---
"""
import astropy.units as ur
from numpy import pi
diag = kwargs.pop('diag', True)
name = ''
# Eventually depricate these things
if version == 0:
qe = 0.8 # To be improved later.
diameter = 30*ur.cm
psf_fwhm = 10*ur.arcsec
pixel_size = psf_fwhm * 0.5
efficiency = 0.87 # Ultrasat spec
if version == 1:
qe = 0.8
efficiency = 0.54 # Reported from Mike
diameter = 21 * ur.cm
psf_fwhm = 4 * ur.arcsec
pixel_size = psf_fwhm * 0.5
if version == 2:
qe = 0.8
efficiency = 0.65 # Reported from Mike
diameter = 30 * ur.cm
psf_fwhm = 9*ur.arcsec
pixel_size = psf_fwhm * 0.5
if version == 3:
qe = 0.8
diameter = 35*ur.cm
efficiency = 0.67 # Reported from Mike
psf_fwhm = 18*ur.arcsec
pixel_size = psf_fwhm * 0.5
if version == 4:
qe = 0.8
diameter = 40*ur.cm
efficiency = 0.70 # Reported from Mike
psf_fwhm = 23*ur.arcsec
pixel_size = psf_fwhm * 0.5
# Versions below here allow the PSF and the pixel to be decoupled
# "Big Schmidt" w/ 6k x 6k array
if version == 5:
name = 'Big Schmidt'
qe = 0.7
diameter = 33.0*ur.cm
eff_diam = 29.1*ur.cm
efficiency = (eff_diam/diameter)**2
plate_scale = 0.43 # arcsec per micron
psf_fwhm_um = 21.6 # microns
psf_fwhm = plate_scale * psf_fwhm_um * ur.arcsec
pixel_size = plate_scale * 10 * ur.arcsec
# Smaller Schmidts (same focal length?) each with 6k x 6k focal plane array
if version == 6:
name = 'Two mini Big Schmidts'
qe = 0.7
diameter = 21.0*ur.cm
eff_diam = 15.1*ur.cm
efficiency = (eff_diam/diameter)**2
plate_scale = 0.43 # arcsec per micron
psf_fwhm_um = 6.7 # microns
psf_fwhm = plate_scale * psf_fwhm_um * ur.arcsec
pixel_size = plate_scale * 10 * ur.arcsec
# Medium Schmidt (same focal length?) each with 6k x 6k focal plane array
if version == 7:
name = 'Medium Schmidt'
qe = 0.7
diameter = 24.0*ur.cm
eff_diam = 19.1*ur.cm
efficiency = (eff_diam/diameter)**2
plate_scale = 0.43 # arcsec per micron
psf_fwhm_um = 7.6 # microns
psf_fwhm = plate_scale * psf_fwhm_um * ur.arcsec
pixel_size = plate_scale * 10 * ur.arcsec
# Smaller Medium Schmidts (same focal length?) each with 6k x 6k focal plane array
if version == 8:
name = 'Two Small "Medium" Schmidts'
qe = 0.7
diameter = 14.0*ur.cm
eff_diam = 6.3*ur.cm
efficiency = (eff_diam/diameter)**2
plate_scale = 0.43 # arcsec per micron
psf_fwhm_um = 8.6 # microns
psf_fwhm = plate_scale * psf_fwhm_um * ur.arcsec
pixel_size = plate_scale * 10 * ur.arcsec
# Fast Medium Schmidts (same focal length?) each with 6k x 6k focal plane array
if version == 9:
name = 'Fast Schmidt'
qe = 0.7
diameter = 32.0*ur.cm
eff_diam = 29.89*ur.cm
efficiency = (eff_diam/diameter)**2
plate_scale = 0.64 # arcsec per micron
psf_fwhm_um = 44.3 # microns
psf_fwhm = plate_scale * psf_fwhm_um * ur.arcsec
pixel_size = plate_scale * 10 * ur.arcsec
# Mini-fast Schmidts
if version == 10:
name="Mini Fast Schmidts"
qe = 0.7
diameter = 22.0*ur.cm
eff_diam = 19.2*ur.cm
efficiency = (eff_diam/diameter)**2
plate_scale = 0.64 # arcsec per micron
psf_fwhm_um = 14.1 # microns
psf_fwhm = plate_scale * psf_fwhm_um * ur.arcsec
pixel_size = plate_scale * 10 * ur.arcsec
##### Second round of telescope designs
if version == 11:
name="Small Focal Plane CMOS"
qe = 0.6
diameter = 26.0*ur.cm
eff_diam = 23.1*ur.cm
efficiency = (eff_diam/diameter)**2
plate_scale = 6.4/10. # arcsec per micron
psf_fwhm_um = 6.7 # microns
psf_fwhm = plate_scale * psf_fwhm_um * ur.arcsec
pixel_size = plate_scale * 10 * ur.arcsec
if version == 12:
name="Swiss Cross CMOS"
qe = 0.6
diameter = 30.*ur.cm
eff_diam = 21.7*ur.cm
eff_diam = 24.7*ur.cm
efficiency = (eff_diam/diameter)**2
plate_scale = 4.0/10. # arcsec per micron
psf_fwhm_um = 7.2 # microns
psf_fwhm = plate_scale * psf_fwhm_um * ur.arcsec
pixel_size = plate_scale * 10. * ur.arcsec
if version == 13:
name="Swiss Cross CCD"
qe = 0.6
diameter = 30.*ur.cm
eff_diam = 20.2*ur.cm
efficiency = (eff_diam/diameter)**2
plate_scale = 5.4/13. # arcsec per micron
psf_fwhm_um = 16.1 # microns
psf_fwhm = plate_scale * psf_fwhm_um * ur.arcsec
pixel_size = plate_scale * 13 * ur.arcsec
if version == 14:
name="Medium Focal Plane (CMOS 6k x 6k)"
qe = 0.6
diameter = 30.*ur.cm
eff_diam = 0.7*27.3*ur.cm
efficiency = (eff_diam/diameter)**2
plate_scale = 4.3/10. # arcsec per micron
psf_fwhm_um = 7.1 # microns
psf_fwhm = plate_scale * psf_fwhm_um * ur.arcsec
pixel_size = plate_scale * 10 * ur.arcsec
######
if version == 15:
name="25 cm primary"
qe = 0.6
diameter = 20.*ur.cm
eff_diam = 17*ur.cm
efficiency = (eff_diam/diameter)**2
plate_scale = 6.4/10. # arcsec per micron
psf_fwhm_um = 10.3 # microns
psf_fwhm = plate_scale * psf_fwhm_um * ur.arcsec
pixel_size = plate_scale * 10 * ur.arcsec
if diag:
print('Telescope Configuration {}'.format(version))
print('Name: {}'.format(name))
print('Entrance Pupil diameter {}'.format(diameter))
print('Optical Efficiency {}'.format(efficiency))
print('PSF FWHM {}'.format(psf_fwhm))
print('Pixel size {}'.format(pixel_size))
print('Effective Aperture {}'.format(diameter*(efficiency)**0.5))
print('Effective Area {}'.format( efficiency * pi * (0.5*diameter)**2))
return diameter, qe, psf_fwhm, pixel_size, efficiency
def load_qe(**kwargs):
"""
Loads the detector QE and returns the values.
band = 1 (default, 180-220 nm)
band = 2 (260-320 nm)
band = 3 (340-380 nm)
Syntax:
wave, qe = load_qe(band = 1)
"""
import astropy.units as ur
import numpy as np
band = kwargs.pop('band', 1)
diag = kwargs.pop('diag', False)
if band == 1:
infile = os.path.join(datadir, 'detector_180_220nm.csv')
if band == 2:
infile = os.path.join(datadir, 'detector_260_300nm.csv')
if band == 3:
infile = os.path.join(datadir, 'detector_340_380nm.csv')
f = open(infile, 'r')
header = True
qe = {}
set = False
for line in f:
if header:
header = False
continue
fields = line.split(',')
if not set:
wave = float(fields[0])
qe = float(fields[3])
set = True
else:
wave = np.append(wave, float(fields[0]))
qe = np.append(qe, float(fields[3]))
f.close()
# Give wavelength a unit
wave *= ur.nm
if diag:
print('Detector Q.E. loader')
print('Band {} has input file {}'.format(band, infile))
return wave, qe / 100.
def load_reflectivity(**kwargs):
"""
Loads the optics reflectivity and returns the values.
Syntax:
wave, reflectivity = load_reflectivity()
"""
import astropy.units as ur
import numpy as np
diag = kwargs.pop('diag', False)
infile = os.path.join(datadir, 'al_mgf2_mirror_coatings.csv')
f = open(infile, 'r')
header = True
qe = {}
set = False
for line in f:
if header:
header = False
continue
fields = line.split(',')
if not set:
wave = float(fields[0])
reflectivity = float(fields[1])
set = True
else:
wave = np.append(wave, float(fields[0]))
reflectivity = np.append(reflectivity, float(fields[1]))
f.close()
# Give wavelength a unit
wave *= ur.nm
if diag:
print('Optics reflectivity loader')
print('Input file {}'.format(infile))
return wave, reflectivity
def load_redfilter(**kwargs):
"""
Loads the detector QE and returns the values.
band = 1 (default, 180-220 nm)
band = 2 (260-320 nm)
Syntax:
wave, transmission = load_redfilter(band=1)
"""
import astropy.units as ur
import numpy as np
band = kwargs.pop('band', 1)
diag = kwargs.pop('diag', False)
light = kwargs.pop('light', True)
if light:
infile = os.path.join(datadir, 'duet{}_filter_light.csv'.format(band))
else:
infile = os.path.join(datadir, 'duet{}_filter.csv'.format(band))
f = open(infile, 'r')
header = True
qe = {}
set = False
for line in f:
if header:
if (line.startswith('Wavelength')) or ('%T' in line):
header = False
continue
fields = line.split(',')
if not set:
wave = float(fields[0])
transmission = float(fields[1])
set = True
else:
wave = np.append(wave, float(fields[0]))
transmission = np.append(transmission, float(fields[1]))
f.close()
# Give wavelength a unit
wave *= ur.nm
if diag:
print('Red filter loader')
print('Band {} has input file {}'.format(band, infile))
return wave, transmission / 100.
def apply_filters(wave, spec, **kwargs):
"""
Loads the detector QE and returns the values.
band = 1 (default, 180-220 nm)
band = 2 (260-320 nm)
Syntax:
wave, transmission = load_redfilter(band=1)
"""
from .apply_transmission import apply_trans
# Load filters
ref_wave, reflectivity = load_reflectivity(**kwargs)
qe_wave, qe = load_qe(**kwargs)
red_wave, red_trans = load_redfilter(**kwargs)
ref_flux = apply_trans(wave, spec, ref_wave, reflectivity/100.)
qe_flux = apply_trans(wave, ref_flux, qe_wave, qe)
band_flux = apply_trans(wave, qe_flux, red_wave, red_trans)
return band_flux
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7645,
8,
628,
220,
220,
220,
1441,
4097,
62,
69,
22564,
628,
628
] | 2.115247 | 5,293 |
from collada_wt.collada_wt import create_turbine | [
6738,
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62,
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13,
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62,
46569,
1330,
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62,
83,
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500
] | 3 | 16 |
from typing import Dict
import tensorflow as tf
from model.position_encoding import build_position_encoding
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62,
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201,
198,
201
] | 3 | 40 |
from django.urls import re_path
from . import views
urlpatterns = [
re_path(r'^$', views.Home.as_view(), name='geopost_home'),
re_path(r'^entry/$', views.Entry.as_view(), name='geopost_entry'),
re_path(r'^photo/(?P<entry_uuid>[0-9A-Fa-f-]+)$',
views.photo,
name="geopost_photo"),
re_path(r'^delete/$', views.delete, name='geopost_delete'),
re_path(r'^vantechy/$', views.vantechy, name='geopost_vantechy')
]
| [
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469,
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62,
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3055,
88,
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198,
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] | 2.034483 | 232 |
from depto import departamento
import psycopg2
#except ValueError:
# print('Valor não encontrado.')
#except psycopg2.Error as e:
# print(e.pgerror)
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7,
68,
13,
79,
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1472,
8,
198
] | 2.043956 | 91 |
__version__ = "0.51.1"
__version_info__ = tuple(__version__.split("."))
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7,
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9641,
834,
13,
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7203,
526,
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198
] | 2.482759 | 29 |
#!/usr/bin/env python
| [
2,
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] | 2.333333 | 12 |
from setuptools import setup
import fabrant
version = fabrant.__version__
setup(
name='fabrant',
version=version,
description="Easy handling of vagrant hosts within fabric",
long_description=open("README.rst").read(),
author='Fabian Schindler',
author_email='[email protected]',
license='MIT',
url='https://github.com/constantinius/fabrant',
py_modules=['fabrant'],
classifiers=[
"Programming Language :: Python :: 2",
"Programming Language :: Python :: 3",
"Intended Audience :: Developers",
"License :: OSI Approved :: MIT License",
"Topic :: Software Development :: Libraries",
"Topic :: Utilities",
]
)
| [
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220,
220,
220,
220,
220,
220,
220,
366,
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198,
220,
220,
220,
220,
220,
220,
220,
366,
33221,
7904,
41086,
1600,
198,
220,
220,
220,
2361,
198,
8,
198
] | 2.716475 | 261 |
# tests for lab06, UCSB, CMPSC 8, Summer 2017
# Instructor: P. Conrad
# Student(s): (insert name here)
# @@@ This next function has an error. Can you fix it?
# @@@ Hint: you might need to use the "and" or "or" keywords of python
# @@@ and modify the if test.
def notStringContainingE(word):
"""
return True when word is a string that contains no letter 'e' (or 'E')
It should work both for lower and upper case.
When word isn't a string, return True
(because it is not a string contaning an E)
"""
if not(type(word)==str):
return True
for letter in word:
if letter == 'e':
return False
return True
#@@@ Here is a function definition that doesn't pass one or more of its tests.
#@@@ Fix it! (Also try to understand why it is wrong)
def hasNoX(word):
"""
return True when word is a string that contains no letter 'x'
(and no letter 'X')
It should work both for lower and upper case.
When word isn't a string, return True (because it is not a string
with an x in that case!)
"""
if (type(word)!=str):
return True
for letter in word:
if letter != 'x' and letter != 'X':
return True
return False
# The following function is provided for you as an example
# of how to write a Python function that checks if EVERY element
# of a list has some property.
def isNumber(item):
" return True if item is of type int or type float otherwise False "
return "stub" # HINT You already did this on in a previous lab
def isListOfNumber(theList):
"""
indicates whether value of argument is a list of only simple numbers
(int or float)
Note: empty list should return True---it doesn't contain anything that
ISN'T a simple number
theList can be anything, and the function will return either True or False.
"""
if not type(theList)==list:
return False # it isn't really a list!
# Now we can assume that theList really is a list
# But is it a list of all numerics?
# If we find even a single item that isn't numeric, we can
# immediately return false.
for item in theList:
if not isNumber(item):
return False
# If we get here and didn't return yet, then we know everything
# in the list is a simple numeric!
# (i.e. there isn't anything in the list that is NOT simple numeric)
return True
### @@@ NOW, write a function called isListOfIntegers(x)
### @@@ The function should take anything as an argument, and produce True
### @@@ only if argument is a list consisting of only int values
### @@@ similar to the comments above the other function definitions in this file
### @@@ See previous function for a clue as to how to proceed
### @@@ Note that empty list should return True (for same reasoning as in the previous function)
def isListOfIntegers(theList):
"""
indicates whether value of argument is a list of only int
Note: empty list should return True
because it doesn't contain anything that ISN'T int
theList can be anything, and it will return either True or False.
"""
return "stub"
### @@@ NOW, write a function called isListOfEvenIntegers(x)
### @@@ The function should take anything as an argument, and produce True
### @@@ only if argument is a list consisting of only int values that
### @@@ are even. See previous function for a clue as to how to proceed
### @@@ Note that empty list should return True
### @@@ HINT: to avoid problems when using the % operator
### @@@ (that's another hint), use your isListOfIntegers function first.
### @@@ This is sort of like the way that isListOfSimpleNumeric
### @@@ checks first to see if theList is a list.
### @@@ That way, you kill two birds with one stone---you immediately
### @@@ know that you are working with a list of integers, and you
### @@@ only have to worry about whether all of them are even or not.
def isListOfEvenIntegers(theList):
"""
indicates whether value of argument is a list of only even integers
Note: empty list should return True---it doesn't contain anything that
ISN'T an even integer
theList can be anything, and it will return either True or False.
"""
return "stub"
### @@@ NOW, write a function called totalLength(x)
### @@@ Use the accumulator pattern to compute the total length
### @@@ of all the words in a string
### @@@ The accumulator will be an integer that starts at zero.
### @@@ You'll use a for loop to look at each item in the list
def totalLength(listOfStrings):
"""
returns total length of all the strings in a list of strings,
False if argument not a list, 0 for empty list
"""
return "stub"
### @@@ NOW, write a function called lengthOfEach
### @@@ Use the accumulator pattern to make a list of each of
### @@@ the lengths of the words
### @@@ You'll use a for loop, starting the list as an empty list
def lengthOfEach(listOfStrings):
"""
given list of strings, returns list of ints correponding
to length of each string, otherwise False.
empty list yields empty list.
"""
return "stub"
### @@@ NOW, write a function called countEvens
### @@@ Use the accumulator pattern, starting at zero
### @@@ and add one each time you find an even number
def countEvens(listOfInts):
"""
given a list of ints, counts even ints in list.
Otherwise, returns False.
returns 0 for empty list, or for a list of ints with no evens in it.
"""
return "stub"
### @@@ NOW, write a function called onlyEvens
### @@@ Use the accumulator pattern, starting with an empty list.
### @@@ Use a for loop to traverse the list. Each time you find an item
### @@@ if it isn't an int, return False---otherwise, if it is even, add
### @@@ it to your accumulated list.
def onlyEvens(listOfInts):
"""
given a list of ints, return new list with only the even ones.
Otherwise, return false.
empty list yields empty list
"""
return "stub"
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262,
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220,
220,
1441,
366,
301,
549,
1,
198,
220,
220,
220,
220,
628,
628,
198
] | 3.226859 | 1,869 |
from functools import partial
import json
from unittest import TestCase
from requests import HTTPError
import responses
from tamr_unify_client import Client
from tamr_unify_client.auth import UsernamePasswordAuth
from tamr_unify_client.categorization.category.collection import CategoryCollection
from tamr_unify_client.categorization.category.resource import Category, CategorySpec
from tamr_unify_client.categorization.taxonomy import Taxonomy
from tamr_unify_client.project.resource import Project
| [
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] | 3.847328 | 131 |
#==========================================================================
#
# Copyright Insight Software Consortium
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0.txt
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
#
#==========================================================================*/
import itk
from sys import argv, exit
itk.auto_progress(2)
if argv[2] == "Ball":
print "Ball"
strel = itk.FlatStructuringElement[2].Ball( int( argv[3] ) )
elif argv[2] == "Box":
print "Box"
strel = itk.FlatStructuringElement[2].Box( int( argv[3] ) )
elif argv[2] == "FromImage":
print "FromImage"
reader = itk.ImageFileReader.IUC2.New( FileName=argv[3] )
strel = itk.FlatStructuringElement[2].FromImageUC( reader.GetOutput() )
else:
print "invalid arguement: " + argv[2]
exit(1)
img = strel.GetImageUC()
size = itk.size( img )
for y in range(0, size.GetElement(1)):
for x in range(0, size.GetElement(0)):
if img.GetPixel( [x, y] ):
print "X",
else:
print " ",
print "\n",
itk.write( img, argv[1] )
# writer = itk.ImageFileWriter.IUC2.New(FileName=argv[1], Input=img )
# itk.echo(writer)
# writer.Update()
| [
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7,
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329,
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7,
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13,
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7,
15,
8,
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220,
220,
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220,
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220,
220,
220,
220,
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366,
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220,
3601,
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77,
1600,
198,
198,
270,
74,
13,
13564,
7,
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11,
1822,
85,
58,
16,
60,
1267,
198,
198,
2,
6260,
796,
340,
74,
13,
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8979,
34379,
13,
40,
9598,
17,
13,
3791,
7,
8979,
5376,
28,
853,
85,
58,
16,
4357,
23412,
28,
9600,
1267,
198,
2,
340,
74,
13,
30328,
7,
16002,
8,
198,
2,
6260,
13,
10260,
3419,
198
] | 2.911871 | 556 |
x = int(input())
y = int(input())
if x > y:
x, y = y, x
soma = 0
for i in range(x+1, y, 1):
if i%2 != 0:
soma += i
print(soma) | [
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8
] | 1.790123 | 81 |
from django.db import models
from cloudinary.models import CloudinaryField
from django.contrib.auth.models import User
# Create your models here.
| [
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2,
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534,
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994,
13,
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220,
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] | 3.477273 | 44 |
from fastapi import FastAPI
from fastapi.responses import FileResponse
app = FastAPI()
@app.get("/") | [
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31,
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13,
1136,
7203,
14,
4943
] | 3.21875 | 32 |
from rest_framework.serializers import ModelSerializer
from article_comment.models import ArticleComment
from myuser.serializers import TempUserListSerializer
| [
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628
] | 4.405405 | 37 |
'''
promt translate for free as in beer
'''
from typing import Any, Callable, Dict, Tuple
import sys
import logging
import json
from time import time
from random import randint
import pytest # type: ignore
# import mock
import urllib3
from ratelimit import limits, sleep_and_retry # type: ignore
import requests
from fuzzywuzzy import fuzz, process # type: ignore
import coloredlogs # type: ignore
from jmespath import search # type: ignore
urllib3.disable_warnings()
# logging.captureWarnings(True)
# logging.getLogger('requests.packages.urllib3.connectionpool').level = 30
LOGGER = logging.getLogger(__name__)
FMT = '%(filename)-14s[%(lineno)-3d] %(message)s [%(funcName)s]'
coloredlogs.install(level=20, logger=LOGGER, fmt=FMT)
# en-ar en-zhcn
LANG_CODES = (
"ar,ca,zhcn,nl,fi,fr,de,el,he,hi,it,ja,kk,ko,pt,ru,es,tr,uk"
).split(',') + ['auto']
URL = (
'https://www.online-translator.com/'
'services/soap.asmx/GetTranslation'
)
UA = 'Mozilla/5.0 (Macintosh; Intel Mac OS X 10_8_2) AppleWebKit/537.17 (KHTML, like Gecko) Chrome/24.0.1309.0 Safari/537.17' # noqa
# HEADERS = {"User-Agent": UA}
HEADERS = {
'Host': 'www.online-translator.com',
'User-Agent': 'Mozilla/5.0 (Windows NT 10.0; Win64; x64; rv:70.0) Gecko/20100101 Firefox/70.0',
'Accept': 'application/json, text/javascript, */*; q=0.01',
'Accept-Language': 'zh-CN,zh;q=0.8,zh-TW;q=0.7,zh-HK;q=0.5,en-US;q=0.3,en;q=0.2',
'Content-Type': 'application/json; charset=utf-8',
'X-Requested-With': 'XMLHttpRequest',
'Origin': 'https://www.online-translator.com',
# 'DNT': '1',
'Referer': 'https://www.online-translator.com/',
}
SESS = requests.Session()
SESS.get('https://www.online-translator.com/', verify=0)
def with_func_attrs(**attrs: Any) -> Callable:
''' with_func_attrs '''
return with_attrs
@with_func_attrs(text='')
def _promt_tr(
text: str,
from_lang: str = 'auto',
to_lang: str = 'zh',
timeout: Tuple[float, float] = (55, 66),
) -> Dict[str, str]:
''' promt_tr
text = 'test one two three'
from_lang = 'auto'
to_lang = 'zh'
timeout = (55, 66)
'''
try:
from_lang = from_lang.lower()
except Exception as exc: # pragma: no cover
LOGGER.warning("%s", exc)
from_lang = 'auto'
try:
to_lang = to_lang.lower()
except Exception as exc: # pragma: no cover
LOGGER.warning("%s", exc)
to_lang = 'zh'
if from_lang in ['zh', 'chinese', 'zhongwen']:
from_lang = 'zhcn'
if to_lang in ['zh', 'chinese', 'zhongwen']:
to_lang = 'zhcn'
try:
from_lang = process.extractOne(from_lang, LANG_CODES, scorer=fuzz.UWRatio)[0] # noqa
except Exception as exc: # pragma: no cover
LOGGER.warning("%s", exc)
from_lang = 'en'
try:
to_lang = process.extractOne(to_lang, LANG_CODES, scorer=fuzz.UWRatio)[0] # noqa
except Exception as exc: # pragma: no cover
LOGGER.warning("%s", exc)
to_lang = 'en'
if from_lang == 'auto':
from_lang = 'au'
if to_lang == 'auto': # pragma: no cover
to_lang = 'au'
dir_code = f'{from_lang}-{to_lang}'
data = {
'dirCode': dir_code,
# 'dirCode': 'de-en',
'template': 'General',
# 'text': 'Das sind drei Teste.',
'text': text,
'lang': 'en',
'limit': '3000',
'useAutoDetect': True,
'key': '123',
'ts': 'MainSite',
'tid': '',
'IsMobile': False
}
try:
resp = SESS.post( # type: ignore # data # expected "Union[None, bytes, MutableMapping[str, str], IO[Any]] # noqa
URL,
# data=data2,
data=json.dumps(data),
headers=HEADERS,
timeout=timeout,
)
resp.raise_for_status()
except Exception as exc: # pragma: no cover
LOGGER.error('%s', exc)
resp = requests.models.Response()
resp._content = f'{{"errorCode": "{exc}"}}'.encode()
resp.status_code = 499
try:
jdata = resp.json()
except Exception as exc: # pragma: no cover
LOGGER.error('%s', exc)
jdata = {'error': str(exc)}
promt_tr.text = resp.text
try:
# res = search('[0].translations[0].text', jdata)
res = search('d.result', jdata)
except Exception as exc: # pragma: no cover
LOGGER.error('%s', exc)
res = {'error': str(exc)}
return res
@sleep_and_retry
@limits(calls=30, period=20, raise_on_limit=True) # raise_on_limit probably superfluous
def _rl_promt_tr(*args, **kwargs):
''' be nice and throttle'''
LOGGER.info(' rate limiting 3 calls/2 secs... ')
return _promt_tr(*args, **kwargs)
@with_func_attrs(calls=0, call_tick=-1)
def promt_tr(*args, **kwargs):
''' exempt first 200 calls from rate limiting '''
# increase calls unto 210
if promt_tr.calls < 210:
promt_tr.calls += 1
# reset rate limit if the last call was 2 minutes ago
tick = time()
if tick - promt_tr.call_tick > 120:
promt_tr.calls = 1
promt_tr.call_tick = tick
if promt_tr.calls < 200:
return _promt_tr(*args, **kwargs)
return _rl_promt_tr(*args, **kwargs)
@pytest.mark.parametrize(
# 'to_lang', LANG_CODES
'to_lang', ['zh', 'de', 'fr', 'it', 'ko', 'ja', 'ru']
)
def test_sanity(to_lang):
'sanity test'
numb = str(randint(1, 10000))
text = 'test ' + numb
assert numb in promt_tr(text, to_lang=to_lang)
def test_calls():
''' test calls '''
_ = promt_tr('test ')
calls = promt_tr.calls
_ = promt_tr('test ')
assert promt_tr.calls == calls + 1
def main(): # pragma: no cover
''' main '''
text = sys.argv[1:]
text1 = ''
if not text:
print(' Provide something to translate, testing with some random text\n')
text = 'test tihs and that' + str(randint(1, 1000))
text1 = 'test tihs and that' + str(randint(1, 1000))
print(f'{text} translated to:')
for to_lang in ['zh', 'de', 'fr', ]:
print(f'{to_lang}: {promt_tr(text, to_lang=to_lang)}')
if not text1:
print(f'{to_lang}: {promt_tr(text1, to_lang=to_lang)}')
def init():
''' attempted to pytest __name__ == '__main__' '''
LOGGER.debug('__name__: %s', __name__)
if __name__ == '__main__':
sys.exit(main())
init()
# test_init()
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2,
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62,
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198
] | 2.186776 | 2,934 |
import argparse
import json
from collections import defaultdict
from typing import Dict, List
if __name__ == "__main__":
main()
| [
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] | 3.4 | 40 |
from os import path,makedirs
from urllib.request import urlretrieve
def static_download(model):
'''
Download static gravity modle from icgem.gfz-potsdam.de; if the file to be downloaded is already included in the download directory, the download is automatically skipped.
Usage:
static_download('GGM05C')
static_download('EIGEN-6C4')
Inputs:
model -> [str] Available options are 'GGM05C' and 'EIGEN-6C'.
Outputs: downloaded static gravity model
Examples:
>>> static_download('GGM05C')
Downloading the static gravity model GGM05C ... Finished
'static_models/GGM05C.gfc'
>>> static_download('EIGEN-6C4')
Downloading the static gravity model EIGEN-6C4 ... Finished
'static_models/EIGEN-6C4.gfc'
'''
direc = 'static_models/'
if not path.exists(direc): makedirs(direc)
if model == 'GGM05C':
gravity_file = direc + 'GGM05C.gfc'
url = 'http://icgem.gfz-potsdam.de/getmodel/gfc/778a683780a5b0ad3163f4772b97b9075a0a13c389d2bd8ea3f891b64cfa383d/GGM05C.gfc'
elif model == 'EIGEN-6C4':
gravity_file = direc + 'EIGEN-6C4.gfc'
url = 'http://icgem.gfz-potsdam.de/getmodel/gfc/7fd8fe44aa1518cd79ca84300aef4b41ddb2364aef9e82b7cdaabdb60a9053f1/EIGEN-6C4.gfc'
else:
raise Exception('Currently, available static gravity models are GGM05C and EIGEN-6C4.')
if not path.exists(gravity_file):
print('Downloading the static gravity model '+ model,end=' ... ')
urlretrieve(url, gravity_file)
print('Finished')
return gravity_file | [
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] | 2.254213 | 712 |
# -*- coding: utf-8 -*-
# Generated by Django 1.10.4 on 2017-01-31 04:19
from __future__ import unicode_literals
from django.db import migrations
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] | 2.690909 | 55 |
__author__ = 'justinarmstrong'
| [
834,
9800,
834,
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705,
3137,
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11576,
6,
198
] | 2.818182 | 11 |
from agent import Agent
| [
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1330,
15906,
628
] | 5 | 5 |
import sys
sys.path.append('/home/pavlis/src/mspass/python')
from mspasspy.ccore import CoreSeismogram
d=CoreSeismogram(200)
d.put_double('delta',1.0)
d.put_double('dt',1.0)
d.put('npts',200)
d.ns=200
d.t0=100.0
d.live=True
from mspasspy.ccore import Seismogram
d2=Seismogram(d,'invalid')
import pickle
x=pickle.dumps(d2)
print("pickle succeeded")
print("trying to restore")
d3=pickle.loads(x)
print("pickle loads completed")
print("data npts=",d.get_int('npts'))
print('same stored in struct of BasicTimeSeries=',d.ns)
print('data t0=',d.t0)
print('Now testing pickle for TimeSeries data')
from mspasspy.ccore import TimeSeries
from mspasspy.ccore import CoreTimeSeries
d0=CoreTimeSeries(500)
d0.live=True
d0.dt=1.0
d0.ns=500
d=TimeSeries(d0,'invalid')
s=pickle.dumps(d)
print('Pickle dumps succeeded')
print('size of string returned by pickle=',len(s))
print('Trying loads')
dr=pickle.loads(s)
print('Finished - BasicTimeSeries attributes in output')
print('ns=',dr.ns)
print('dt=',dr.dt)
print('t0=',dr.t0)
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] | 2.453883 | 412 |
from __future__ import division
#from sys import path
#path.append('modules/')
import os.path
import click
import h5py
from argparse import ArgumentParser
from math import pi, log10
import sys
from scidata.utils import locate
import scidata.carpet.hdf5 as h5
from scidata.carpet.interp import Interpolator
import numpy as np
from glob import glob
import sys
sys.path.append("..")
from plotting.plotting_methods import PLOT_MANY_TASKS
from uutils import Printcolor, REFLEVEL_LIMITS
import config as Paths
from module_slices.slices_methods import COMPUTE_STORE
from module_slices.add_q_r_t_to_prof_xyxz import add_q_r_t_to_prof_xyxz
from module_slices.slices_dens_modes import compute_density_modes
__movie__ = "ffmpeg -framerate 10 -pattern_type glob -i '{}*.png' -s:v 1280x720 " \
"-c:v libx264 -module_profile:v high -crf 20 -pix_fmt yuv420p {}"
__tasklist__ = ["plot", "movie", "addm0", "dm"]
__reflevels__ = [0, 1, 2, 3, 4, 5, 6]
__outdirname__ = "module_slices"
__planes__ = ["xy", "xz"]
if __name__ == '__main__':
parser = ArgumentParser(description="postprocessing pipeline")
parser.add_argument("-s", dest="sim", required=True, help="name of the simulation dir")
parser.add_argument("-t", dest="tasklist", nargs='+', required=False, default=[], help="tasks to perform")
#
parser.add_argument("--v_n", dest="v_ns", nargs='+', required=False, default=[], help="variable names to compute")
parser.add_argument("--time", dest="times", nargs='+', required=False, default=[], help="times to iterate over [ms]")
parser.add_argument("--it", dest="it", nargs='+', required=False, default=[],
help="iterations to use ")
parser.add_argument("--rl", dest="reflevels", nargs='+', required=False, default=[], help="reflevels to use")
parser.add_argument('--plane', dest="plane", required=False, nargs='+', default=[], help='Plane: xy,xz,yz for slice analysis')
#
parser.add_argument("-o", dest="outdir", required=False, default=None, help="path for output dir")
parser.add_argument("-i", dest="indir", required=False, default=None, help="path to simulation dir")
parser.add_argument("-p", dest="path_to_profs", required=False, default=None, help="path to 3D profiles")
parser.add_argument("--overwrite", dest="overwrite", required=False, default="no", help="overwrite if exists")
#
args = parser.parse_args()
glob_sim = args.sim
glob_indir = args.indir
glob_outdir = args.outdir
glob_tasklist = args.tasklist
glob_overwrite = args.overwrite
glob_v_ns = args.v_ns
glob_times =args.times
glob_it = args.it
glob_reflevels = args.reflevels
glob_planes = args.plane
#
glob_profxyxz_path = args.path_to_profs#Paths.ppr_sims+glob_sim+'/profiles/'
#
if glob_indir is None:
glob_indir = Paths.default_data_dir + glob_sim + '/'
if not os.path.isdir(glob_indir):
raise IOError("Default path to simulation data is not valid: {}".format(glob_indir))
if not os.path.isdir(glob_indir):
raise IOError("Path to simulation data is not valid: {}".format(glob_indir))
if glob_outdir is None:
glob_outdir = Paths.default_ppr_dir + glob_sim + '/'
if not os.path.isdir(glob_indir):
raise IOError("Default path to postprocessed data is not valid: {}".format(glob_outdir))
if not os.path.isdir(glob_indir):
raise IOError("Path to postprocessed data is not valid: {}".format(glob_outdir))
if len(glob_tasklist) == 0:
raise NameError("tasklist is empty. Set what tasks to perform with '-t' option")
else:
for task in glob_tasklist:
if task not in __tasklist__:
raise NameError("task: {} is not among available ones: {}"
.format(task, __tasklist__))
if glob_overwrite == "no":
glob_overwrite = False
elif glob_overwrite == "yes":
glob_overwrite = True
else:
raise NameError("for '--overwrite' option use 'yes' or 'no'. Given: {}"
.format(glob_overwrite))
# glob_outdir_sim = Paths.ppr_sims + glob_sim
# if not os.path.isdir(glob_outdir_sim):
# os.mkdir(glob_outdir_sim)
# check plane
if len(glob_planes) == 0:
raise IOError("Option --plane unfilled")
elif len(glob_planes) == 1 and "all" in glob_planes:
glob_planes = __planes__
elif len(glob_planes) > 1:
for plane in glob_planes:
if not plane in __planes__:
raise NameError("plane:{} is not in the list of the __d3slicesplanes__:{}"
.format(plane, __planes__))
# set globals
# Paths.gw170817 = glob_simdir
# Paths.ppr_sims = glob_outdir
if len(glob_tasklist) == 1 and "all" in glob_tasklist:
# do all tasksk
pass
o_slice = COMPUTE_STORE(glob_sim, indir=glob_indir, pprdir=glob_outdir)
# deal with iterations and timesteps -- available as well as required by user
do_all_iterations = False
if len(glob_it) == 0 and len(glob_times) == 0:
raise IOError("please specify timesteps to use '--time' or iterations '--it' ")
elif len(glob_it) != 0 and len(glob_times) != 0:
raise IOError("please specify Either timesteps to use '--time' or iterations '--it' (not both)")
elif len(glob_times) == 0 and len(glob_it) == 1 and "all" in glob_it:
do_all_iterations = True
glob_times = o_slice.times
glob_it = o_slice.iterations
elif len(glob_it) == 0 and len(glob_times) == 1 and "all" in glob_times:
do_all_iterations = True
glob_times = o_slice.times
glob_it = o_slice.iterations
elif len(glob_it) > 0 and not "all" in glob_it and len(glob_times) == 0:
glob_it = np.array(glob_it, dtype=int) # array of iterations
glob_times = []
for it in glob_it:
glob_times.append(o_slice.get_time_for_it(it, "overall", "d2"))
glob_times = np.array(glob_times, dtype=float)
elif len(glob_times) > 0 and not "all" in glob_times and len(glob_it) == 0:
glob_times = np.array(glob_times, dtype=float) / 1e3 # back to seconds
else:
raise IOError("input times and iterations are not recognized: --time {} --it {}"
.format(glob_times, glob_it))
# deal with reflevels -- availble as well as required by user
do_all_reflevels = False
if len(glob_reflevels) == 1 and "all" in glob_reflevels:
glob_reflevels = __reflevels__
do_all_reflevels = True
else:
glob_reflevels = np.array(glob_reflevels, dtype=int)
# deal with variable names -- available as well as required by user
do_all_v_ns = False
if len(glob_v_ns) == 1 and "all" in glob_v_ns:
glob_v_ns=o_slice.list_v_ns
do_all_v_ns = True
else:
pass
# summarize what is avaialble and what is requried
if do_all_v_ns or do_all_iterations or do_all_reflevels:
Printcolor.yellow("Selected all", comma=True)
if do_all_iterations:
Printcolor.print_colored_string(["timesteps", "({})".format(len(glob_times))],
["blue", "green"], comma=True)
if do_all_v_ns: Printcolor.print_colored_string(["v_ns", "({})".format(len(glob_v_ns))],
["blue", "green"], comma=True)
if do_all_reflevels: Printcolor.print_colored_string(["reflevels", "({})".format(len(glob_reflevels))],
["blue", "green"], comma=True)
Printcolor.yellow("this might take time.")
# if not click.confirm(text="Confirm?",default=True,show_default=True):
# exit(0)
# perform tasks
do_tasks(glob_v_ns) | [
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7,
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28,
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466,
62,
83,
6791,
7,
4743,
672,
62,
85,
62,
5907,
8
] | 2.319457 | 3,387 |
import numpy as np
import tensorflow as tf
import matplotlib.pyplot as plt
from torch import nn
import torch
| [
11748,
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198,
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83,
198,
198,
6738,
28034,
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299,
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] | 3.235294 | 34 |
from .exipicrename import *
| [
6738,
764,
1069,
541,
291,
918,
480,
1330,
1635,
198
] | 2.8 | 10 |
from Algorithims import Algorithms
import time
import threading
| [
6738,
978,
7727,
12078,
1330,
978,
7727,
907,
198,
11748,
640,
198,
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4704,
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628
] | 4.0625 | 16 |
#extract_ml_features.py
import emoji, re, os, time, sys
from gensim.models import LdaModel
from gensim.corpora import MmCorpus, Dictionary
from isc_tokenizer import Tokenizer
from isc_tagger import Tagger
from tqdm import tqdm
import vocab_helpers as helper
import utils
import pre_processing as dproc
from nltk import ngrams
#DONE
#DONE FOR WORD AND POS
#predict topic of unseen tweet using testing example based lda model built on train set
if __name__ == '__main__':
sample = 'मैं लगातार ट्विटर पर आर्सेनल के बारे में ट्वीट्स देखता हूं। दुनिया को अपडेट करने के लिए धन्यवाद @उपयोगकर्ता & @उपयोगकर्ता शॉनक्स। #'
tknzr = Tokenizer(lang='hin')
sys.stdout = open("toutput.txt", "a", encoding='utf-8')
tokens = tknzr.tokenize(sample)
tagger = Tagger(lang='hin')
tags = tagger.tag(tokens)
valid_tokens = []
for p in tags:
if p[1] != 'SYM' and p[0] !='#':
valid_tokens.append(p[0])
#for t in tokens:
#print("=>",tokens)
#ngram_list = [gram for gram in ngrams(tokens, 2)]
#print(get_ngrams(tokens, [1,2]))
print("Tokens ",tokens)
print("POS ", tags)
print("Filtered:", valid_tokens)
| [
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81,
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13,
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64,
1600,
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23,
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7,
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8,
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83,
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7,
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197,
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7621,
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13,
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8,
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83,
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58,
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197,
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62,
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13,
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7,
79,
58,
15,
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197,
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197,
2,
1640,
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16326,
25,
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197,
2,
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83,
482,
641,
8,
198,
197,
2,
782,
859,
62,
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796,
685,
4546,
329,
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287,
299,
4546,
82,
7,
83,
482,
641,
11,
362,
15437,
198,
197,
2,
4798,
7,
1136,
62,
782,
9474,
7,
83,
482,
641,
11,
685,
16,
11,
17,
60,
4008,
198,
197,
4798,
7203,
22906,
33172,
83,
482,
641,
8,
198,
197,
4798,
7203,
37997,
33172,
15940,
8,
198,
197,
4798,
7203,
11928,
4400,
25,
1600,
4938,
62,
83,
482,
641,
8,
198
] | 1.867227 | 595 |
from flask import Flask
| [
6738,
42903,
1330,
46947,
628
] | 5 | 5 |
# Chunk
# Write to chunk
# Add constant to chunk | [
2,
609,
2954,
198,
198,
2,
19430,
284,
16058,
198,
198,
2,
3060,
6937,
284,
16058
] | 3.125 | 16 |
#
# Copyright 2018-2022 Elyra Authors
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
#
import pytest
from elyra.pipeline.parser import PipelineParser
from elyra.pipeline.pipeline import GenericOperation
from elyra.tests.pipeline.util import _read_pipeline_resource
@pytest.fixture
| [
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] | 3.537778 | 225 |
import os
import pandas as pd
from collections import OrderedDict
import cv2
import numpy as np
import json
gt_data_path = "/home/priv-lab1/workspace/zxh/end2/csv/anno_box_train.csv"
hoi_list_path = "/home/priv-lab1/workspace/zxh/end2/origin_lists/hico_list_hoi.txt"
img_path = '/home/priv-lab1/workspace/zxh/My_Database/hico_20160224_det/images/train2015/' # image folder path
save_json_path = 'train_hico.json' # name for save json
_classes = ('person', 'bicycle', 'car', 'motorcycle', 'airplane',
'bus', 'train', 'truck', 'boat', 'traffic_light',
'fire_hydrant', 'stop_sign', 'parking_meter', 'bench', 'bird',
'cat', 'dog', 'horse', 'sheep', 'cow', 'elephant', 'bear',
'zebra', 'giraffe', 'backpack', 'umbrella', 'handbag', 'tie',
'suitcase', 'frisbee', 'skis', 'snowboard', 'sports_ball',
'kite', 'baseball_bat', 'baseball_glove', 'skateboard',
'surfboard', 'tennis_racket', 'bottle', 'wine_glass', 'cup',
'fork', 'knife', 'spoon', 'bowl', 'banana', 'apple',
'sandwich', 'orange', 'broccoli', 'carrot', 'hot_dog', 'pizza',
'donut', 'cake', 'chair', 'couch', 'potted_plant', 'bed',
'dining_table', 'toilet', 'tv', 'laptop', 'mouse', 'remote',
'keyboard', 'cell_phone', 'microwave', 'oven', 'toaster',
'sink', 'refrigerator', 'book', 'clock', 'vase', 'scissors',
'teddy_bear', 'hair_drier', 'toothbrush')
df_gt_data = pd.DataFrame.from_csv(gt_data_path)
# str to list
df_gt_data['human_bbox'] = df_gt_data['human_bbox'].apply(lambda x: list(map(int, x.strip('[]').split(','))))
df_gt_data['obj_bbox'] = df_gt_data['obj_bbox'].apply(lambda x: list(map(int, x.strip('[]').split(','))))
df_gt_data['img_size_w_h'] = df_gt_data['img_size_w_h'].apply(lambda x: list(map(int, x.strip('[]').split(','))))
human_bbox_dict=OrderedDict()
object_bbox_dict=OrderedDict()
filenames=[]
action_dict=OrderedDict()
list_action=[]
for index,row in df_gt_data.iterrows():
filenames.append(row['name'])
if row['name'] in human_bbox_dict:
human_bbox_dict[row['name']].append(row['human_bbox'])
object_bbox_dict[row['name']].append(row['obj_bbox'])
action_dict[row['name']].append(row['action_no'])
else:
human_bbox_dict[row['name']]=[row['human_bbox']]
object_bbox_dict[row['name']] = [row['obj_bbox']]
action_dict[row['name']] = [row['action_no']]
filenames=set(filenames)
with open(hoi_list_path,'r') as f :
lines=f.readlines()
for line in lines[2:]:
list_action.append(line.split())
print("data set done")
if __name__ == '__main__':
Convert_csv_to_coco('train')
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] | 2.179936 | 1,256 |
from multiprocessing import cpu_count
from multiprocessing.pool import Pool
from tqdm import tqdm
def pool_process(func, iterable, cpus=cpu_count(), return_vals=False, cpu_reduction=0, progress_bar=False):
"""
Multiprocessing helper function for performing looped operation using multiple processors.
:param func: Function to call
:param iterable: Iterable object to perform each function on
:param cpus: Number of cpu cores, defaults to system's cpu count
:param return_vals: Bool, returns output values when True
:param cpu_reduction: Number of cpu core's to not use
:param progress_bar: Display text based progress bar
:return:
"""
with Pool(cpus - abs(cpu_reduction)) as pool:
# Return values returned by 'func'
if return_vals:
# Show progress bar
if progress_bar:
vals = [v for v in tqdm(pool.imap_unordered(func, iterable), total=len(iterable))]
# No progress bar
else:
vals = pool.map(func, iterable)
# Close pool and return values
pool.close()
# pool.join()
return vals
# Don't capture values returned by 'func'
else:
pool.map(func, iterable)
pool.close()
return True
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] | 2.459108 | 538 |
import numpy as np
from laplace import laplace_mech
def noisy_max(answers: np.ndarray, epsilon: float, sensitivity: float):
""" Implementation of the noisy max mechanism with gap using Laplace noise
Given a set of queries, this mechanism will return the **index**, not the
value, of the query that is probably largest.
Args:
answers (float or numpy array): the set of queries
epsilon (float): the privacy budget
sensitivity (float): the global sensitivity of the query
"""
noisy_answers = laplace_mech(answers, epsilon/2.0, sensitivity)
return noisy_answers.argmax()
| [
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] | 3.100503 | 199 |
# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved.
import unittest
from dynamic_atss_core.utils.metric_logger import MetricLogger
if __name__ == "__main__":
unittest.main()
| [
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] | 2.970588 | 68 |
import argparse
import pygame
import sys
from nes import Controller
from screen import Screen
from fps import FPS
from tas import TAS
from emulator import Emulator
if __name__ == "__main__":
parser = argparse.ArgumentParser(
description = 'Dumps NES header information')
parser.add_argument('pathname', help='specify the NES file to open')
parser.add_argument('--tas', dest='tas', help='specify a TAS file to run', default=None)
parser.add_argument('--scale', dest='scale', help='specify the screen scale', default = 1)
args = parser.parse_args()
emulator = Emulator(args.pathname)
screen = Screen(args.scale)
fps = FPS()
if args.tas:
target_fps = 1000.0
tas = TAS(args.tas)
else:
target_fps = 60.0
tas = None
keep_going = True
while keep_going:
try:
# Get button presses
if tas == None:
pressed = pygame.key.get_pressed()
controller = emulator.controllers[0]
if pressed[pygame.K_RETURN]:
controller.set_key(Controller.BUTTON_START)
if pressed[pygame.K_RSHIFT]:
controller.set_key(Controller.BUTTON_SELECT)
if pressed[pygame.K_a]:
controller.set_key(Controller.BUTTON_LEFT)
if pressed[pygame.K_d]:
controller.set_key(Controller.BUTTON_RIGHT)
if pressed[pygame.K_w]:
controller.set_key(Controller.BUTTON_UP)
if pressed[pygame.K_s]:
controller.set_key(Controller.BUTTON_DOWN)
if pressed[pygame.K_j]:
controller.set_key(Controller.BUTTON_B)
if pressed[pygame.K_k]:
controller.set_key(Controller.BUTTON_A)
if tas != None:
tas.update_controller(emulator.controllers[0])
emulator.tick(screen)
keep_going = screen.render()
fps.update(target_fps)
except Exception, e:
print 'Exception occurred: ' + str(e)
keep_going = False | [
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198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
1394,
62,
5146,
796,
3159,
13,
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3419,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
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220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
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220,
220,
220,
220,
220,
220,
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220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
198,
220,
220,
220,
220,
220,
220,
220,
2845,
35528,
11,
304,
25,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
3601,
705,
16922,
5091,
25,
705,
1343,
965,
7,
68,
8,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
1394,
62,
5146,
796,
10352
] | 2 | 1,112 |
# Copyright (C) 2016 MediaMath <http://www.mediamath.com>
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import os
import datetime
from command import AwsCommand
| [
2,
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2,
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28686,
198,
11748,
4818,
8079,
198,
6738,
3141,
1330,
5851,
82,
21575,
628
] | 3.809249 | 173 |
# -*- coding: UTF-8 -*-
from pyramid.i18n import get_localizer, TranslationStringFactory
from pyramid.view import view_config
from pyramid.response import Response
from c2cgeoportal_commons.models import DBSessions, DBSession
import logging
import owncloud
import shutil
import os
import smtplib
import urllib.request
from email.mime.text import MIMEText
import time
import datetime
import sys
_ = TranslationStringFactory("geoportailv3_geoportal-server")
log = logging.getLogger(__name__)
| [
171,
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2,
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6404,
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18931,
13,
1136,
11187,
1362,
7,
834,
3672,
834,
8,
628
] | 3.222222 | 153 |
from urlScanner import UrlScanner | [
6738,
19016,
33351,
1008,
1330,
8799,
75,
33351,
1008
] | 3.666667 | 9 |
"""
Custom exceptions for the License Manager Agent.
"""
from buzz import Buzz
class LicenseManagerAuthTokenError(Buzz):
"""Exception for backend connection issues."""
class LicenseManagerBackendConnectionError(Buzz):
"""Exception for backend connection issues."""
class LicenseManagerBackendVersionError(Buzz):
"""Exception for backend/agent version mismatches."""
class LicenseManagerEmptyReportError(Buzz):
"""Exception for empty report when no licenses added in backend."""
class LicenseManagerNonSupportedServerTypeError(Buzz):
"""Exception for entry with non supported server type."""
class LicenseManagerBadServerOutput(Buzz):
"""Exception for license server bad output."""
| [
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220,
220,
220,
37227,
16922,
329,
5964,
4382,
2089,
5072,
526,
15931,
198
] | 4.150289 | 173 |
import dask as da
from raptor.momentum import (
KAMAIndicator,
ROCIndicator,
RSIIndicator,
StochasticOscillator,
StochRSIIndicator,
)
from raptor.trend import (
MACD,
ADXIndicator,
AroonIndicator,
EMAIndicator,
SMAIndicator,
TRIXIndicator,
)
from raptor.volatility import (
AverageTrueRange,
BollingerBands,
)
from raptor.volume import (
AccDistIndexIndicator,
ForceIndexIndicator,
VolumePriceTrendIndicator,
)
| [
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220,
220,
220,
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220,
220,
220,
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11,
198,
8,
198,
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13,
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1330,
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198,
220,
220,
220,
6366,
20344,
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5497,
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11,
198,
220,
220,
220,
220,
198,
220,
220,
220,
5221,
15732,
5497,
26407,
11,
198,
220,
220,
220,
220,
198,
220,
220,
220,
14701,
18124,
45461,
5497,
26407,
11,
198,
220,
220,
220,
220,
198,
8,
628,
628,
628,
628,
198
] | 2.072519 | 262 |
# %% [209. Minimum Size Subarray Sum](https://leetcode.com/problems/minimum-size-subarray-sum/)
| [
2,
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685,
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5450,
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22143,
14,
39504,
12,
7857,
12,
7266,
18747,
12,
16345,
34729,
198
] | 3 | 32 |
import argparse
import datetime
import glob
import os
from pathlib import Path
from test import repeat_eval_ckpt
import tqdm
import torch
import torch.distributed as dist
import torch.nn as nn
from tensorboardX import SummaryWriter
from pcdet.config import cfg, cfg_from_list, cfg_from_yaml_file, log_config_to_file
from pcdet.datasets import build_dataloader
from pcdet.models import build_network, model_fn_decorator
from pcdet.utils import common_utils
from train_utils.optimization import build_optimizer, build_scheduler
if __name__ == '__main__':
main()
| [
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62,
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13,
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1634,
1330,
1382,
62,
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7509,
11,
1382,
62,
1416,
704,
18173,
628,
628,
628,
628,
628,
628,
198,
220,
220,
220,
220,
628,
198,
361,
11593,
3672,
834,
6624,
705,
834,
12417,
834,
10354,
198,
220,
220,
220,
1388,
3419,
198
] | 3.015464 | 194 |
# -*-coding:utf-8 -*
# Copyright (c) 2011-2015, Intel Corporation
# All rights reserved.
#
# Redistribution and use in source and binary forms, with or without modification,
# are permitted provided that the following conditions are met:
#
# 1. Redistributions of source code must retain the above copyright notice, this
# list of conditions and the following disclaimer.
#
# 2. Redistributions in binary form must reproduce the above copyright notice,
# this list of conditions and the following disclaimer in the documentation and/or
# other materials provided with the distribution.
#
# 3. Neither the name of the copyright holder nor the names of its contributors
# may be used to endorse or promote products derived from this software without
# specific prior written permission.
#
# THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" AND
# ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED
# WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE
# DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE LIABLE FOR
# ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES
# (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES;
# LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON
# ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT
# (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS
# SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
import os
import commands, string, random
from Util.PfwUnitTestLib import PfwTestCase
from Util import ACTLogging
log=ACTLogging.Logger()
# Test of type UINT8_S - range [-100, 100]
| [
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] | 3.536437 | 494 |
import sys
import math
import numpy as np
if __name__ == "__main__":
# We're using 70% of the data for training
TRAIN_SPLIT = 0.8
GENDER_MALE = 1
GENDER_FEMALE = 0
alpha = 0.54
X_train = None
Y_train = None
X_validation = None
Y_validation = None
X_test = None
Y_test = None
Best_theta = None
fileName = "/var/www/html/training_dataset.txt"
g_feature_names = []
g_vocabulary = {}
names = np.genfromtxt(fileName, delimiter = ",", dtype = "U25",
autostrip = True)
np.random.shuffle(names)
features = np.vectorize(features)
X = features(names[:, 0])
Y = np.array([GENDER_MALE if x == "male" else GENDER_FEMALE for x in names[:, 1]] )
# creating testing and training set
X_train, X_validation = X[:int(TRAIN_SPLIT * len(X))], X[int(TRAIN_SPLIT * len(X)):]
Y_train, Y_validation = Y[:int(TRAIN_SPLIT * len(Y))], Y[int(TRAIN_SPLIT * len(Y)):]
(g_feature_names, g_vocabulary) = fit(X_train)
initial_theta = np.zeros((len(g_feature_names), 1), dtype=np.float64)
X_validation = transform(X_validation, g_vocabulary)
iterations = len(X_validation)
theta = logistic_regression_by_stochastic_gradient_descent(
transform(X_train, g_vocabulary), Y_train,
alpha,initial_theta)
do_test(g_vocabulary)
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220,
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70,
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220,
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220,
34820,
796,
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7,
55,
62,
12102,
341,
8,
220,
628,
198,
220,
220,
220,
262,
8326,
796,
2604,
2569,
62,
2301,
2234,
62,
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62,
301,
5374,
3477,
62,
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62,
8906,
1087,
7,
198,
220,
220,
220,
220,
220,
220,
220,
6121,
7,
55,
62,
27432,
11,
308,
62,
18893,
22528,
828,
575,
62,
27432,
11,
198,
220,
220,
220,
220,
220,
220,
220,
17130,
11,
36733,
62,
1169,
8326,
8,
198,
220,
220,
220,
220,
220,
220,
220,
220,
198,
220,
220,
220,
466,
62,
9288,
7,
70,
62,
18893,
22528,
8,
198
] | 2.165625 | 640 |
# Generated by Django 3.1.6 on 2021-02-15 14:56
from django.db import migrations, models
| [
2,
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42625,
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1330,
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11,
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628
] | 2.84375 | 32 |
import jinja2
import aiohttp_jinja2
import urllib.parse as up
import asyncpg
from aiohttp import web
from .routes import setup_routes
from .config import config_obj as config
| [
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628,
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] | 3 | 60 |
"""
This file has been auto-generated by pydantic.
TODO: Maybe some fields should be renamed or the architecture should be changed a bit
"""
from __future__ import annotations
from typing import List, Optional
from pydantic import BaseModel, Field
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] | 3.760563 | 71 |
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